PRAGMA foreign_keys=OFF;
BEGIN TRANSACTION;
CREATE TABLE alembic_version (
	version_num VARCHAR(32) NOT NULL, 
	CONSTRAINT alembic_version_pkc PRIMARY KEY (version_num)
);
INSERT INTO alembic_version VALUES('c9c8c82c25eb');
CREATE TABLE annotation_configs (
	id INTEGER NOT NULL, 
	name VARCHAR NOT NULL, 
	description TEXT, 
	required BOOLEAN NOT NULL, 
	annotation_type VARCHAR NOT NULL, 
	config_json JSON NOT NULL, 
	custom_fields_json JSON, 
	created_at DATETIME NOT NULL, 
	updated_at DATETIME NOT NULL, 
	is_deleted BOOLEAN NOT NULL, 
	deleted_at DATETIME, show_reason BOOLEAN DEFAULT 'false' NOT NULL, show_confidence BOOLEAN DEFAULT 'false' NOT NULL, 
	PRIMARY KEY (id)
);
INSERT INTO annotation_configs VALUES(1,'test1111',NULL,1,'score','{"min_score": 1, "max_score": 5, "score_step": 1.0}','null','2026-03-23 15:24:06.818192','2026-03-23 15:24:06.818202',0,NULL,1,1);
INSERT INTO annotation_configs VALUES(2,'领域相关性与价值',NULL,1,'single_choice','{"options": [{"option_id": "5\u5206\uff1a", "label": "\u9ad8\u5ea6\u76f8\u5173\u4e14\u6781\u5177\u4ef7\u503c", "description": null, "value": 5, "order": 0, "enabled": true}, {"option_id": "4\u5206\uff1a", "label": "\u76f8\u5173\u6027\u5f3a\uff0c\u6709\u5b9e\u7528\u4ef7\u503c\u4f46\u9700\u7ed3\u5408\u5177\u4f53\u573a\u666f", "description": null, "value": 4, "order": 1, "enabled": true}, {"option_id": "3\u5206\uff1a", "label": "\u76f8\u5173\u4f46\u4ef7\u503c\u5e73\u5eb8", "description": null, "value": 3, "order": 2, "enabled": true}, {"option_id": "2\u5206\uff1a", "label": "\u90e8\u5206\u76f8\u5173\uff0c\u4ef7\u503c\u6709\u9650", "description": null, "value": 2, "order": 3, "enabled": true}, {"option_id": "1\u5206\uff1a", "label": "\u76f8\u5173\u6027\u5f31\u4e14\u65e0\u4ef7\u503c", "description": null, "value": 1, "order": 4, "enabled": true}, {"option_id": "0\u5206\uff1a", "label": "\u5b8c\u5168\u65e0\u5173\u6216\u8d1f\u9762\u4ef7\u503c", "description": null, "value": 0, "order": 5, "enabled": true}]}','null','2026-03-23 17:20:34.341708','2026-03-23 17:20:34.341733',0,NULL,1,0);
CREATE TABLE question_types (
	id INTEGER NOT NULL, 
	type VARCHAR NOT NULL, 
	subtype VARCHAR NOT NULL, 
	"order" INTEGER NOT NULL, 
	created_at DATETIME NOT NULL, 
	updated_at DATETIME NOT NULL, 
	PRIMARY KEY (id)
);
CREATE TABLE system_configs (
	id INTEGER NOT NULL, 
	"key" VARCHAR NOT NULL, 
	value TEXT NOT NULL, 
	description TEXT, 
	created_at DATETIME NOT NULL, 
	updated_at DATETIME NOT NULL, 
	PRIMARY KEY (id)
);
INSERT INTO system_configs VALUES(1,'allow_registration','true','是否允许用户注册（true/false）','2026-03-23 14:09:27.427149','2026-03-23 14:09:27.427163');
INSERT INTO system_configs VALUES(2,'llm_base_url','http://43.159.131.233:3001/v1','LLM API Base URL','2026-04-09 09:32:11.544387','2026-04-09 09:32:11.544400');
INSERT INTO system_configs VALUES(3,'llm_model_name','gpt-5.1','LLM Model Name','2026-04-09 09:32:11.677416','2026-04-09 09:32:11.677429');
CREATE TABLE users (
	id INTEGER NOT NULL, 
	username VARCHAR NOT NULL, 
	full_name VARCHAR, 
	organization VARCHAR, 
	team VARCHAR, 
	species VARCHAR, 
	hashed_password VARCHAR NOT NULL, 
	is_active BOOLEAN NOT NULL, 
	is_superuser BOOLEAN NOT NULL, 
	created_at DATETIME NOT NULL, 
	updated_at DATETIME NOT NULL, 
	PRIMARY KEY (id)
);
INSERT INTO users VALUES(1,'admin','管理员',NULL,NULL,NULL,'8d969eef6ecad3c29a3a629280e686cf0c3f5d5a86aff3ca12020c923adc6c92',1,1,'2026-03-23 11:13:36.887666','2026-04-09 09:17:33.887672');
INSERT INTO users VALUES(2,'jack',NULL,'AAA','xxxx','yyy','8d969eef6ecad3c29a3a629280e686cf0c3f5d5a86aff3ca12020c923adc6c92',1,0,'2026-03-23 14:10:00.007117','2026-04-08 14:35:23.320591');
CREATE TABLE projects (
	id INTEGER NOT NULL, 
	name VARCHAR NOT NULL, 
	description TEXT, 
	version VARCHAR, 
	status VARCHAR, 
	tags_json JSON, 
	category VARCHAR, 
	creator VARCHAR, 
	creator_id INTEGER, 
	source VARCHAR, 
	source_url TEXT, 
	metadata_json JSON, 
	display_extra_fields_json JSON, 
	created_at DATETIME NOT NULL, 
	updated_at DATETIME NOT NULL, 
	PRIMARY KEY (id), 
	FOREIGN KEY(creator_id) REFERENCES users (id) ON DELETE SET NULL
);
INSERT INTO projects VALUES(1,'测试','QA评估',NULL,'active','null',NULL,'admin',1,NULL,NULL,'{"evaluation_purpose": "\u8bc4\u4f30QA\u7684\u8d28\u91cf\u662f\u5426\u53ef\u4ee5\u7528\u4e8e\u6a21\u578b\u8bad\u7ec3", "deadline": "2026-03-24T14:40"}','null','2026-03-23 14:40:47.629113','2026-03-23 14:40:47.629123');
CREATE TABLE seed_questions (
	id INTEGER NOT NULL, 
	question TEXT NOT NULL, 
	type VARCHAR NOT NULL, 
	subtype VARCHAR NOT NULL, 
	species_or_domain VARCHAR, 
	model VARCHAR, 
	date DATE, 
	is_verified BOOLEAN NOT NULL, 
	creator_id INTEGER NOT NULL, 
	created_at DATETIME NOT NULL, 
	updated_at DATETIME NOT NULL, 
	PRIMARY KEY (id), 
	FOREIGN KEY(creator_id) REFERENCES users (id) ON DELETE SET NULL
);
CREATE TABLE datasets (
	id INTEGER NOT NULL, 
	name VARCHAR NOT NULL, 
	description TEXT, 
	version VARCHAR, 
	status VARCHAR, 
	tags_json JSON, 
	category VARCHAR, 
	creator VARCHAR, 
	creator_id INTEGER, 
	annotator_id INTEGER, 
	annotator_name VARCHAR, 
	source VARCHAR, 
	source_url TEXT, 
	metadata_json JSON, 
	display_extra_fields_json JSON, 
	project_id INTEGER, 
	created_at DATETIME NOT NULL, 
	updated_at DATETIME NOT NULL, 
	PRIMARY KEY (id), 
	FOREIGN KEY(annotator_id) REFERENCES users (id) ON DELETE SET NULL, 
	FOREIGN KEY(creator_id) REFERENCES users (id) ON DELETE SET NULL, 
	FOREIGN KEY(project_id) REFERENCES projects (id) ON DELETE SET NULL
);
INSERT INTO datasets VALUES(1,'xxx','xxx',NULL,'active','null',NULL,'admin',1,NULL,NULL,NULL,NULL,'null','null',NULL,'2026-03-23 14:10:12.926571','2026-03-23 14:10:12.926584');
INSERT INTO datasets VALUES(2,'测试_全文QA_1',NULL,NULL,'active','null',NULL,'admin',1,1,'admin','imported',NULL,'{}','null',1,'2026-03-23 14:42:29.806546','2026-03-23 14:47:13.277423');
INSERT INTO datasets VALUES(3,'测试_全文QA_2',NULL,NULL,'active','null',NULL,NULL,NULL,2,'jack',NULL,NULL,'null','null',1,'2026-03-23 15:33:28.215888','2026-03-23 15:33:42.646162');
CREATE TABLE project_annotation_config_association (
	project_id INTEGER NOT NULL, 
	annotation_config_id INTEGER NOT NULL, 
	"order" INTEGER NOT NULL, 
	created_at DATETIME NOT NULL, 
	PRIMARY KEY (project_id, annotation_config_id), 
	FOREIGN KEY(annotation_config_id) REFERENCES annotation_configs (id) ON DELETE CASCADE, 
	FOREIGN KEY(project_id) REFERENCES projects (id) ON DELETE CASCADE
);
INSERT INTO project_annotation_config_association VALUES(1,1,0,'2026-03-23 15:24:28.527654');
INSERT INTO project_annotation_config_association VALUES(1,2,1,'2026-03-23 17:21:00.673835');
CREATE TABLE dataset_annotation_config_association (
	dataset_id INTEGER NOT NULL, 
	annotation_config_id INTEGER NOT NULL, 
	created_at DATETIME NOT NULL, 
	PRIMARY KEY (dataset_id, annotation_config_id), 
	FOREIGN KEY(annotation_config_id) REFERENCES annotation_configs (id) ON DELETE CASCADE, 
	FOREIGN KEY(dataset_id) REFERENCES datasets (id) ON DELETE CASCADE
);
CREATE TABLE qa_pairs (
	id INTEGER NOT NULL, 
	dataset_id INTEGER NOT NULL, 
	question TEXT NOT NULL, 
	answer TEXT NOT NULL, 
	extra_fields_json JSON, 
	created_at DATETIME NOT NULL, 
	updated_at DATETIME NOT NULL, 
	PRIMARY KEY (id), 
	FOREIGN KEY(dataset_id) REFERENCES datasets (id) ON DELETE CASCADE
);
INSERT INTO qa_pairs VALUES(1,2,'在玉米籽粒的胚轴、胚乳盾和糊粉层中分离油体时，检测到三种油体结构蛋白 OLE16、OLE17 和 OLE18 在不同组织中的相对丰度模式始终相似，且在油体中维持稳定的比例关系。如果这三种油质体蛋白分别由不同的基因 ole16、ole17 和 ole18 编码，应如何推断这些基因在籽粒发育过程中在不同含油组织间的表达调控特点？','可以推断 ole16、ole17 和 ole18 在玉米籽粒发育过程中在胚轴、胚乳盾和糊粉层等不同含油组织之间受到协调表达调控，使得三种对应的油体蛋白 OLE16、OLE17 和 OLE18 在不同组织形成的油体中始终维持相对稳定、相似的比例，而不是各自独立、随组织而大幅变化地表达。','{"species": "Zea mays L.", "paper_id": "1724822", "reasoning_steps": ["Step 1: The three oleosin genes ole16, ole17, and ole18 encode the proteins OLE16, OLE17, and OLE18, respectively.", "Step 2: In isolated oil bodies from maize kernels, these three oleosins are present in proportional amounts of approximately 2:1:1 (OLE16:OLE17:OLE18).", "Step 3: The same ~2:1:1 proportion of OLE16, OLE17, and OLE18 is observed in oil bodies isolated from each of the three oil\u2011containing parts of the kernel: embryonic axis, scutellum, and aleurone layer.", "Step 4: When different tissues show the same stable ratio of several proteins that are encoded by distinct genes, this indicates that the expression of those genes is coordinated across those tissues.", "Step 5: Therefore, the genes ole16, ole17, and ole18 are coordinately expressed in maize kernel tissues such that the relative abundances of their oleosin products remain similar across the embryonic axis, scutellum, and aleurone layer."], "question_cot": "", "final_conclusion": "In maize kernels, the three oleosins OLE16, OLE17, and OLE18 are coordinately expressed so that their relative abundance (~2:1:1) in oil bodies is maintained across different oil\u2011containing kernel tissues (embryonic axis, scutellum, and aleurone layer).", "difficulty": "medium", "tags": ["mechanism", "result", "concept"], "created_at": "2025-12-16T21:17:20", "token_est_question": 11, "token_est_answer": 11, "section": "Abstract", "context": "=== Abstract ===\nWe have investigated all three oleosin genes which are expressed in the kernel of maize (Zea mays L., Mo17). Oleosin genes, ole16, ole17, and ole18, encode OLE16, OLE17, and OLE18, respectively, in proportional amounts of approximately 2:1:1 in isolated oil bodies. None of the three genes has an intron or a sequence encoding an N-terminal signal peptide. The three genes are expressed coordinately during seed maturation, and their encoded oleosins are present in similar proportional amounts in oil bodies isolated from the embryonic axis, scutellum, and aleurone layer. OLE16 represents one oleosin isoform, whereas OLE17 and OLE18 are close members of another oleosin isoform, olel6 and olel8 have been mapped to single loci on chromosomes 2 (near gene) and 5S (near phya2), respectively. We predict that ole17 is located on chromosome 1 (near phyal), in a chromosomal segment duplicated on chromosome 5.\n\nMost seeds contain storage oils in the form of triacylglycerols (TAG). The oils are packaged in spherical intracellular oil bodies, which are in diameter. Each oil body has a TAG matrix surrounded by a layer of phospholipids embedded with unique and abundant proteins termed oleosins [5]. Oleosins have apparent molecular weights of this range depends upon the particular isoforms and species in which they occur [ 14]. Each oleosin molecule has three structural domains [5]. The N-terminal domain is amphipathic, and its length and sequence do not have extensive similarities among species. The central hydrophobic domain of antiparallel #-structures contains 72 residues, and its length and sequence are highly conserved among species. The C-terminal domain is largely an amphipathic 0~-helix, and its length and sequence\n\nhave some similarities among species. The three domains enable the oleosin to reside stably on the surface of the oil body. Oleosins stabilize the oil bodies [13] and possibly act as recognition signals for the binding of lipase during germination [5]. The sequences of about 20 oleosin genes of diverse species have been reported [5], and at least two oleosin isoforms exist based on immunological cross-reactivity [ 14] and amino acid sequence comparison. The genes from maize [10] and carrot [2] do not have introns, whereas those from Brassica [6] and Arabidopsis [15] contain one intron. Two motifs in the 5''-upstream region of the carrot gene have been shown to be the binding sites for regulatory proteins [2]. Overall, the characterization of oleosin genes in diverse species, as well as their encoded isoforms, has been cursory. This report reveals the organiza-\n \ntion, origin, and expression of all the oleosin genes expressed in the seed of an individual species.\n\nThree oleosins are present in defined proportions in the embryonic axis, the scutellum, and the aleurone layer of mature maize kernel\n\nThe proteins in the oil body fraction and the crude extract of maize (Mo17) embryos were separated into numerous protein fractions (bands) by SDSPAGE (Fig. 1). Three oleosins, termed OLE16, OLE17, and OLE18 according to their in the oil body fraction were resolved into distinct fractions (OLE16 and OLE18 were designated earlier as KD16 [16] and KD18 [9], respectively). In our laboratory, we used a SDS-PAGE system to separate the three oleosins into distinct protein fractions [ 14] (Fig. 1). While this gel system was adequate in the separation, it had two disadvantages. First, the three oleosin fractions (bands) were in close proximity to one another. Second, the protein pattern could be modified if the mature kernel or the frozen oil body fraction was\n\nstored for a long period of time. These disadvantages were eliminated by using a gel system containing urea (Fig. 1). In the subsequent portion of the current study, the urea SDS-PAGE system was used.\n\nOLE16, OLE17, and OLE18 were present in similar proportions of roughly 2:1:1 in the isolated oil body fraction from each of the three oil-containing parts of the kernel: the axis, the scutellum, and the aleurone layer. This proportional value is a simplified approximation, since OLE18 appeared to be present at a slightly higher level than OLE17. The oil body fraction also contained several minor proteins of about (Fig. 1). Their amounts proportional to the oleosins were reduced if the oil body fraction was washed extensively, and presumably, they were contaminants.", "Thinking\u6a21\u5f0f": "high", "generation_type": "\u63a8\u7406\u578b"}','2026-03-23 14:42:29.910009','2026-03-23 14:42:29.910020');
INSERT INTO qa_pairs VALUES(2,2,'在玉米中，oleosin 被描述为一种由特定基因编码的蛋白质，这些 oleosin 蛋白具有不同同工型，并被归类为油体相关蛋白；同时，有关玉米籽粒自交系 Mo17 和 B73 的研究标题指出其籽粒中存在编码 oleosin 的基因。对于关注籽粒储藏结构的育种或分子生物学研究者，应如何推断这两个自交系籽粒中油体结构蛋白的存在形式及其与油体的关系？','可以推断，在玉米自交系 Mo17 和 B73 的籽粒中，存在一组由 oleosin 基因编码的 oleosin 蛋白，这些蛋白以不同同工型形式存在，并定位于籽粒油体表面或与油体膜紧密结合，作为油体结构蛋白参与油体的形成和稳定，因此 Mo17 和 B73 籽粒中的油体都携带各自基因组编码的 oleosin 蛋白。','{"species": "Zea mays L.", "paper_id": "1724822", "reasoning_steps": ["Step 1: \u6807\u9898\u6307\u51fa\uff0c\u5728\u7389\u7c73\u7c7d\u7c92\u4e2d\u5b58\u5728\u7f16\u7801 oleosin \u7684\u57fa\u56e0\uff0c\u7814\u7a76\u5bf9\u8c61\u4e3a\u81ea\u4ea4\u7cfb Mo17 \u548c B73\u3002", "Step 2: \u5173\u952e\u8bcd\u4e2d\u5305\u542b\u201cMaize oleosin\u201d\u548c\u201coleosin gene\u201d\uff0c\u8868\u660e oleosin \u662f\u7389\u7c73\u4e2d\u7684\u4e00\u79cd\u86cb\u767d\u8d28\uff0c\u5e76\u7531\u7279\u5b9a\u57fa\u56e0\u7f16\u7801\u3002", "Step 3: \u5173\u952e\u8bcd\u4e2d\u8fd8\u5305\u542b\u201coil body\u201d\uff0c\u8868\u660e oleosin \u4e0e\u6cb9\u4f53\u6709\u5173\uff0c\u662f\u6cb9\u4f53\u76f8\u5173\u86cb\u767d\u3002", "Step 4: \u5173\u952e\u8bcd\u201coleosin isoform\u201d\u8868\u660e oleosin \u53ef\u80fd\u6709\u4e0d\u540c\u7684\u540c\u5de5\u578b\uff0c\u4f46\u90fd\u5f52\u5c5e\u4e8e\u7531 oleosin \u57fa\u56e0\u7f16\u7801\u7684\u540c\u4e00\u7c7b\u6cb9\u4f53\u86cb\u767d\u3002", "Step 5: \u7531\u6b64\u53ef\u63a8\u65ad\uff0cMo17 \u548c B73 \u7684\u7c7d\u7c92\u4e2d\u542b\u6709\u6cb9\u4f53\uff0c\u800c\u8fd9\u4e9b\u6cb9\u4f53\u643a\u5e26\u7531\u5404\u81ea oleosin \u57fa\u56e0\u7f16\u7801\u7684 oleosin \u86cb\u767d\u3002"], "question_cot": "", "final_conclusion": "\u7389\u7c73\u81ea\u4ea4\u7cfb Mo17 \u548c B73 \u7684\u7c7d\u7c92\u4e2d\u5b58\u5728\u7f16\u7801\u6cb9\u4f53\u7ed3\u6784\u86cb\u767d oleosin \u7684\u57fa\u56e0\uff0c\u8fd9\u4e9b\u57fa\u56e0\u4ea7\u7269\u4e0e\u7c7d\u7c92\u6cb9\u4f53\u76f8\u5173\u8054\u3002", "difficulty": "medium", "tags": ["concept", "mechanism", "result"], "created_at": "2025-12-16T21:18:00", "token_est_question": 13, "token_est_answer": 19, "section": "Genes encoding oleosins in maize kernel of inbreds Mo17 and B73", "context": "=== Genes encoding oleosins in maize kernel of inbreds Mo17 and B73 ===\nKeunmyoung Lee and Anthony H.C. Huang*\nDepartment of Botany and Plant Sciences, University of California, Riverside, CA 92521-0124, USA (* author for correspondence)\n\nReceived 13 June 1994; accepted in revised form 29 August 1994\n\nKey words: Maize oleosin, oil body, oleosin gene, oleosin isoform", "Thinking\u6a21\u5f0f": "high", "generation_type": "\u63a8\u7406\u578b"}','2026-03-23 14:42:29.993341','2026-03-23 14:42:29.993352');
INSERT INTO qa_pairs VALUES(3,2,'在玉米胚乳成熟过程中，ole16、ole17 和 ole18 的转录本在同一发育时段达到高峰，三种对应油体蛋白的积累模式与各自 mRNA 的发育变化高度一致，并且在多个发育阶段和完全成熟时三种蛋白的相对含量始终维持在近似恒定的 OLE16:OLE17:OLE18 ≈ 2:1:1 比例，同时这三种转录本和蛋白仅在胚和糊粉层等特定组织中可检测而在其他器官中缺失。研究者应如何从这些时序表达、定量比例和组织特异性的证据，推断这三个油体蛋白基因在调控方式和功能协同上的主要特点？','这些证据表明 ole16、ole17 和 ole18 受到共同的时间程序和组织特异性调控，在胚乳成熟过程中其转录和翻译过程高度同步，产物在不同发育阶段保持稳定的定量比例，从而形成一个协同工作的功能模块，专一参与胚和糊粉层中油体形成与脂质储存等相关过程。','{"species": "Zea mays L.", "paper_id": "1724822", "reasoning_steps": ["Step 1: \u5317\u65b9\u6742\u4ea4\u7ed3\u679c\u8868\u660e\uff0cole16\u3001ole17 \u548c ole18 \u7684\u8f6c\u5f55\u672c\u5728\u80da\u53d1\u80b2 20 \u4e0e 25 \u5929\u65f6\u8fbe\u5230\u6700\u9ad8\u6c34\u5e73\uff0c\u5728 15 \u4e0e 30 \u5929\u65f6\u7ea6\u4e3a\u5cf0\u503c\u7684\u4e00\u534a\u3002", "Step 2: \u540c\u4e00\u65f6\u671f\uff0cSDS-PAGE \u663e\u793a OLE16\u3001OLE17 \u548c OLE18 \u86cb\u767d\u7684\u79ef\u7d2f\u6a21\u5f0f\u4e0e\u5176 mRNA \u6c34\u5e73\u7684\u53d1\u80b2\u53d8\u5316\u9ad8\u5ea6\u543b\u5408\uff0c\u6700\u5927\u79ef\u7d2f\u91cf\u51fa\u73b0\u5728\u7ea6 25\u201330 \u5929\u3002", "Step 3: \u5728\u80da\u6210\u719f\u7684\u591a\u4e2a\u65f6\u95f4\u70b9\uff0820\u300125\u300130 \u5929\u53ca\u5b8c\u5168\u6210\u719f\u65f6\uff09\uff0c\u4e09\u79cd\u6cb9\u4f53\u86cb\u767d\u7684\u76f8\u5bf9\u542b\u91cf\u59cb\u7ec8\u7ef4\u6301\u5728\u7ea6 OLE16:OLE17:OLE18 = 2:1:1 \u7684\u6bd4\u4f8b\u3002", "Step 4: \u6ca1\u6709\u5728\u80da\u548c\u7cca\u7c89\u5c42\u4e4b\u5916\u7684\u5176\u4ed6\u7389\u7c73\u5668\u5b98\u6216\u7ec4\u7ec7\u4e2d\u68c0\u6d4b\u5230\u8fd9\u4e09\u79cd\u6cb9\u4f53\u86cb\u767d\u53ca\u5176\u8f6c\u5f55\u672c\uff0c\u8bf4\u660e\u5b83\u4eec\u7684\u8868\u8fbe\u5177\u6709\u660e\u786e\u7684\u7ec4\u7ec7\u7279\u5f02\u6027\u3002", "Step 5: mRNA \u7684\u5cf0\u503c\u65f6\u95f4\u3001\u86cb\u767d\u79ef\u7d2f\u5cf0\u503c\u65f6\u95f4\u4ee5\u53ca\u4e09\u8005\u4e4b\u95f4\u76f8\u5bf9\u6bd4\u4f8b\u5728\u6574\u4e2a\u6210\u719f\u8fc7\u7a0b\u4e2d\u90fd\u4fdd\u6301\u4e00\u81f4\uff0c\u4e14\u4ec5\u9650\u4e8e\u7279\u5b9a\u7ec4\u7ec7\u51fa\u73b0\uff0c\u8868\u660e\u8fd9\u4e09\u4e2a\u57fa\u56e0\u53d7\u540c\u4e00\u65f6\u95f4\u7a0b\u5e8f\u548c\u7ec4\u7ec7\u7279\u5f02\u6027\u7684\u5171\u540c\u8c03\u63a7\u3002", "Step 6: \u56e0\u6b64\uff0c\u53ef\u4ee5\u63a8\u65ad ole16\u3001ole17 \u548c ole18 \u7684\u8868\u8fbe\u4e0d\u4ec5\u5728\u65f6\u95f4\u4e0a\u540c\u6b65\uff0c\u800c\u4e14\u5728\u4ea7\u7269\u95f4\u7ef4\u6301\u7a33\u5b9a\u7684\u5b9a\u91cf\u6bd4\u4f8b\u5173\u7cfb\u3002"], "question_cot": "", "final_conclusion": "\u7389\u7c73\u4e09\u79cd\u6cb9\u4f53\u86cb\u767d\u57fa\u56e0 ole16\u3001ole17 \u548c ole18 \u5728\u80da\u4e73\u6210\u719f\u8fc7\u7a0b\u4e2d\u5177\u6709\u9ad8\u5ea6\u534f\u8c03\u7684\u4e00\u81f4\u65f6\u5e8f\u8868\u8fbe\u6a21\u5f0f\uff0c\u5e76\u5728\u6574\u4e2a\u6210\u719f\u9636\u6bb5\u4fdd\u6301\u7ea6 2:1:1 \u7684\u86cb\u767d\u8d28\u6bd4\u4f8b\u3002", "difficulty": "medium", "tags": ["mechanism", "result"], "created_at": "2025-12-16T21:18:59", "token_est_question": 13, "token_est_answer": 6, "section": "The genes encoding all three oleosins have been cloned and sequenced", "context": "=== The genes encoding all three oleosins have been cloned and sequenced ===\nEarlier, we reported an incomplete cDNA sequence ofolel6 [ 16] and the complete cDNA and\n \nFig. 1. SDS-PAGE of proteins from embryos, scutella, embryonic axes, mad aleurone layers of maize kernels. Left and middle panels: embryos from kernels of inbred Mo17 were homogenized (total), and the homogenate was subjected to floatation centrifugation to yield a crude oil body fraction (OB crude) [14]. The crude oil body fraction was washed two times wash) or four times wash) by floatation centrifugations. Oil body fractions washed four times were also isolated from the scutella, embryonic axes, and Neurone layers. Right panel: Oil body fractions washed four times were obtained from embryos of inbreds Mo17 and B73. The gel was run with [1] or without [14] urea. The proteins on the gels were stained with Coomassie Blue. The of marker proteins, and the three oleosins and putative contaminants are indicated.\n \ngenomic sequences of olel8 [9]. We have now obtained the genomic sequences of olel6 and olel 7. A maize (inbred B73) genomic DNA library (2-GEM-11) of DNA obtained from 5-day old seedlings (provided by Dr John C. Walker, University of Missouri, Columbia, MO) was screened with 32p-labeled cDNA ofolel6 [ 16] or ole18 [9]. Four clones hybridizing to the olel6 probe and 17 clones hybridizing to the oleI8 probe were obtained; none of these clones hybridized to both probes. One of the former clones contained the complete coding sequence of ole16 plus the and 3''-flanking sequences. Of the 17 clones hybridizing to ole18 probe, one clone contained the complete coding sequence plus the and 3''-flanking sequences of ole18 [9], and another clone had the complete coding sequence plus the and 3''-flanking sequences of olel 7. olel 7 and ole18 encode OLE17 and OLE18, respectively, as judged from microsequencing of the isolated oleosins [9]. A 2.7 kb Eco RI fragment ofolel6 and a 2.2 kb Eco RI fragment ofolel 7 were subcloned and sequenced by the dideoxynucleotide chain termination method with the Sequenase kit (US Biochemical, Cleveland, OH).\n\nA comparison of the genomic sequences of the three oleosin genes (ole16, olel7 and oleIS, available with EMBL accessions UI3701, UI3702, and J05212, respectively) and the deduced amino acid sequences (Fig. 2) is made. None of the three oleosin genes contains an intron or a sequence\n\nencoding an N-terminal signal peptide. The latter finding is consistent with experimental results showing that in vitro and in vivo synthesized oleosins have the same apparent [4, 7, 11]. olel 7 and olel8 share a much higher degree of nucleotide identity than either with olel6 in both the coding region and the immediate and noncoding regions. The difference in between OLE17 and OLE18 is due to the absence of several short nucleotide segments, in multiples of three nucleotides each, in the coding region of olel7. As a consequence, OLE17 has 12 amino acid residues fewer than OLE18 in the less conserved N-terminal and C-terminal domains (Fig. 2).\n\nEarlier, we obtained the olel8 genomic sequence, and noted the presence of specific motifs at the 5''-upstream region [9]. In the current report, we have found that of these motifs, only the RY repeats are present in all the three oleosin genes. This finding casts doubts on the putative role of CACA and CATC motifs in specific regulation of oleosin gene expression, since the expression of all three genes is coordinated (described below). In the 5''-upstream regions (around of the three oleosin genes, there are AT-rich regions. However, their sequences are not similar to those of the putative regulatory AT-rich motifs at the 5''-upstream region of a carrot oleosin gene [2].\n\nNo closely related genes or pseudogenes of\n \nFig. 2. Alignment of amino acid sequences of OLE16, OLEI7, and OLE18. Each amino acid sequence is continuous from the upper, to the middle, and to the lower panels. The upper, middle, and lower panels correspond to the N-terminal, central, and C-terminal domains, respectively. The sequences are best aligned using PILEUP program in GCG package, and the alignment was then adjusted after consideration of the nucleotide sequences. Residues having two or three identities among the three sequences are shaded. The loop region of the antiparallel fi-strands of the central domains are indicated as bold letters, and the e-helical regions of the C-terminal domains are underlined. The total residue number of each oleosin is indicated at the end of the C-terminus.\n \nFig. 3. Restriction maps and Southern blot results of maize olel6, olel7, and ole18 genomic DNA. Upper panel of restriction map: the nucleotide number starts at the first nucleotide of the coding sequence. The TATA box (CTATA), the polyA motif, the start (ATG) and stop (TAA or TGA) codons of the coding sequence (thick line), and the restriction sites are indicated. The sequence between the triangles along each gene is the probe used for Southern blotting. The dotted lines represent the sequences used as gene-specific probes in RNA hybridization in Fig. 4. The inbreds from which the genomic DNA clones were isolated are indicated. Lower panel of southern blot: genomic DNA was isolated from 5-day old shoots of inbreds Mo17 and B73, digested with Sst I or Eco RI, and subjected to Southern blotting [10]. The blot was hybridized with probe at in buffer containing \n\nolel6, olel7, and olel8 were found in Mo17 or B73 by Southern blot analyses under moderate hybridization conditions and S SC) (Fig. 3). In the analyses, we used genomic DNA from both Mo17 and B73 inbreds because the genomic clones of olel6 and olel 7 were obtained from B73, whereas that of ole18 was prepared from Mo17. Mo17 and B73 had the same oleosins of similar proportional amounts in their oil bodies (Fig. 1).\n\nA restriction fragment length polymorphism was identified for olel6 in Mo17 and B73 using Eco RI but not Sst I (Fig. 3). A 2.8 kb Eco RI fragment was observed in B73, whereas a 6.5 kb Eco RI fragment was observed in Mo17. Apparently, the two inbreds differ in one or both EcoRI sites bordering oIe16. Genomic DNA of both inbreds yielded the expected 1.1 kb fragment of ole16 after digestion (Fig. 3).\n\nGenomic DNA digestion with Eco RI and hybridization to the coding sequence of olel 7 revealed a major fragment of and a minor fragment of in both Mo 17 and B73 (Fig. 3). The major fragment corresponds to the expected Eco RI fragment ofoleI 7, and the minor fragment is in agreement with the expected Eco RI fragment of olel8. The finding that the olel 7-coding sequence recognized oleI8 is not surprising since olel7 and olel8 share nucleotide sequence identities in their coding regions. Similarly, but in the reverse way, after Eco RI digestion and hybridization to the coding sequence of olel8, a major fragment of and a minor fragment of 2.2 kb were found in both Mo17 and B73 (Fig. 3).\n\nExpression of the three oleosin genes are under the same temporal control in a tissue-specific manner\n\nThe three genes are expressed under the same temporal control in the maturing embryo. North-\n \nFig. 4. Levels of oleosin mRNA and proteins in maize embryos during seed maturation. Upper panel: the relative hybridizations of gene-specific probes to the mRNA from the embryos determined by RNA slot-blot hybridization [ 10] are plotted against the time of maturation (DAP). The hybridization conditions followed those described for Southern blotting (Fig. 3). The gene-specific, 32p-labeled, probes shown as dotted lines in the restriction maps of Fig. 3 were generated by Eco RI and Ava I (322 bp ole16 [16]) or PCR (150 bp ole17 and 189 bp olel8). Lower panel: SDS-PAGE gel of oil body fractions washed twice prepared from embryos at the indicated maturation stage (DAP) is shown. The oil body fraction corresponding to one-fifth of an embryo from each maturation stage was applied to each lane. The three oleosins and putative contaminants are indicated.\n\nern blot analyses revealed that the amounts of ole16, olel 7, and olel8 transcripts were highest at 20 and 25 DAP, and were roughly half of the peak value at 15 and 30 DAP (Fig. 4). This developmental pattern coincided with the accumulation\n\nof OLE16, OLE17, and OLE18 (Fig. 4). The oleosins reached their maximal values at about 25-30 DAP. At each of the maturation periods of 20, 25, and as well as at full maturity, the amounts ofOLE16, OLE17, and OLE18 were always present at proportions of about 2:1:1, respectively (Fig. 4). None of the oleosins or their gene transcripts was detected in maize organs and tissues other than the embryo and the aleutone layer [ 10].", "Thinking\u6a21\u5f0f": "high", "generation_type": "\u63a8\u7406\u578b"}','2026-03-23 14:42:30.074632','2026-03-23 14:42:30.074642');
INSERT INTO qa_pairs VALUES(4,2,'在玉米中，三个籽粒油体蛋白基因 OLE16、OLE17 和 OLE18 都具有典型油体蛋白的三段结构域，其中 OLE17 和 OLE18 在氨基酸序列以及 C 端两性 α 螺旋与中央结构域的相对位置上彼此更为接近，而与 OLE16 存在明显差异；抗 OLE16 抗体能够识别玉米 OLE16 和大豆 L 型油体蛋白，却不能识别玉米 OLE17、OLE18 及大豆 H 型油体蛋白，而抗 OLE17 抗体则能识别玉米 OLE17、OLE18 及大豆 H 型油体蛋白，却不识别玉米 OLE16 及大豆 L 型油体蛋白；同时，在玉米籽粒中检测到的活跃油体蛋白基因仅有 OLE16、OLE17 和 OLE18。结合这些序列、结构及免疫学特征，从油体蛋白家族的亚型划分和代表基因角度，应如何判断玉米籽粒油体蛋白的主要亚型构成？','可以判断玉米籽粒油体蛋白家族中仅存在两种主要亚型：一类为与大豆 L 型油体蛋白在抗原表位上相似的 L 型亚型，由结构和免疫学特征均独立于另外两基因的 OLE16 代表；另一类为与大豆 H 型油体蛋白在序列结构和抗原表位上均相似的 H 型亚型，由彼此高度相似且共同被抗 OLE17 抗体识别的 OLE17 和 OLE18 共同代表，未见第三种结构或免疫学上可区分的主要亚型。','{"species": "Zea mays L.", "paper_id": "1724822", "reasoning_steps": ["Step 1: \u4e09\u4e2a\u7389\u7c73\u6cb9\u4f53\u86cb\u767d\u57fa\u56e0 OLE16\u3001OLE17 \u548c OLE18 \u90fd\u7f16\u7801\u5177\u6709\u4e09\u6bb5\u7ed3\u6784\u57df\u7684\u6cb9\u4f53\u86cb\u767d\uff0c\u4f46 OLE17 \u4e0e OLE18 \u5728\u6c28\u57fa\u9178\u5e8f\u5217\u548c\u7ed3\u6784\u7279\u5f81\u4e0a\u5f7c\u6b64\u66f4\u76f8\u4f3c\uff0c\u800c\u4e0e OLE16 \u5dee\u5f02\u8f83\u5927\u3002", "Step 2: \u5728 C \u7aef\u7ed3\u6784\u4e0a\uff0cOLE17 \u4e0e OLE18 \u7684\u4e24\u6027 \u03b1 \u87ba\u65cb\u7d27\u63a5\u4e2d\u592e\u7ed3\u6784\u57df\uff0c\u800c OLE16 \u7684\u5bf9\u5e94 \u03b1 \u87ba\u65cb\u5219\u4e0e\u4e2d\u592e\u7ed3\u6784\u57df\u76f8\u9694\u7ea6 20 \u4e2a\u6c28\u57fa\u9178\u6b8b\u57fa\uff0c\u663e\u793a\u51fa\u4e00\u7c7b\u7ed3\u6784\u7279\u6027\u4e0e\u53e6\u4e00\u7c7b\u660e\u663e\u4e0d\u540c\u3002", "Step 3: \u514d\u75ab\u5b66\u4e0a\uff0c\u9488\u5bf9\u7389\u7c73 OLE16 \u4ea7\u751f\u7684\u6297\u4f53\u80fd\u8bc6\u522b\u7389\u7c73 OLE16 \u548c\u5927\u8c46 L \u4e9a\u578b\u6cb9\u4f53\u86cb\u767d\uff0c\u4f46\u4e0d\u80fd\u8bc6\u522b\u7389\u7c73 OLE17\u3001OLE18 \u4ee5\u53ca\u5927\u8c46 H \u4e9a\u578b\u6cb9\u4f53\u86cb\u767d\uff0c\u8bf4\u660e OLE16 \u5c5e\u4e8e\u4e0e L \u578b\u6cb9\u4f53\u86cb\u767d\u6297\u539f\u8868\u4f4d\u76f8\u540c\u7684\u4e00\u7c7b\u3002", "Step 4: \u76f8\u53cd\uff0c\u9488\u5bf9\u7389\u7c73 OLE17 \u4ea7\u751f\u7684\u6297\u4f53\u80fd\u8bc6\u522b\u7389\u7c73 OLE17\u3001OLE18 \u4ee5\u53ca\u5927\u8c46 H \u4e9a\u578b\u6cb9\u4f53\u86cb\u767d\uff0c\u4f46\u4e0d\u80fd\u8bc6\u522b\u7389\u7c73 OLE16 \u53ca\u5927\u8c46 L \u4e9a\u578b\u6cb9\u4f53\u86cb\u767d\uff0c\u8bf4\u660e OLE17 \u4e0e OLE18 \u5c5e\u4e8e\u4e0e H \u578b\u6cb9\u4f53\u86cb\u767d\u6297\u539f\u8868\u4f4d\u4e00\u81f4\u7684\u53e6\u4e00\u7c7b\u3002", "Step 5: \u7cfb\u7edf\u6bd4\u8f83\u5404\u7c7b\u7269\u79cd\u7684 L \u578b\u548c H \u578b\u6cb9\u4f53\u86cb\u767d\u53d1\u73b0\uff0cOLE17 \u5728\u5e8f\u5217\u4e0e\u7ed3\u6784\u7279\u5f81\u4e0a\u7c7b\u4f3c OLE18 \u53ca H \u578b\uff0c\u800c\u4e0e OLE16 \u53ca L \u578b\u6709\u660e\u786e\u533a\u5206\uff0c\u914d\u5408\u514d\u75ab\u8bc6\u522b\u6a21\u5f0f\uff0c\u5c06 OLE16 \u5f52\u5165 L \u578b\u4e9a\u578b\uff0c\u5c06 OLE17 \u548c OLE18 \u5f52\u5165 H \u578b\u4e9a\u578b\u3002", "Step 6: \u7389\u7c73\u7c7d\u7c92\u4e2d\u6240\u6709\u6d3b\u8dc3\u7684\u6cb9\u4f53\u86cb\u767d\u57fa\u56e0\u5df2\u88ab\u8bc6\u522b\u5e76\u5f52\u7c7b\u4e3a OLE16\uff08L \u578b\uff09\u4e0e OLE17/OLE18\uff08H \u578b\uff09\u4e24\u5927\u7c7b\uff0c\u672a\u53d1\u73b0\u989d\u5916\u7ed3\u6784\u6216\u514d\u75ab\u5b66\u4e0a\u53ef\u533a\u5206\u7684\u4e3b\u4e9a\u578b\u3002", "Step 7: \u56e0\u6b64\uff0c\u53ef\u4ee5\u5224\u65ad\uff0c\u5728\u7389\u7c73\u7c7d\u7c92\u6cb9\u4f53\u86cb\u767d\u4e2d\uff0c\u4ec5\u5b58\u5728\u4ee5 OLE16 \u4e3a\u4ee3\u8868\u7684 L \u578b\u548c\u4ee5 OLE17/OLE18 \u4e3a\u4ee3\u8868\u7684 H \u578b\u8fd9\u4e24\u79cd\u4e3b\u8981\u4e9a\u578b\u3002"], "question_cot": "", "final_conclusion": "\u5728\u7389\u7c73\u4e2d\u4ec5\u5b58\u5728\u4e24\u79cd\u4e3b\u8981\u7684\u7c7d\u7c92\u6cb9\u4f53\u86cb\u767d\u4e9a\u578b\uff1a\u4e00\u7c7b\u7531 OLE16 \u4ee3\u8868\u7684 L \u578b\u4e9a\u578b\uff0c\u53e6\u4e00\u7c7b\u7531 OLE17 \u4e0e OLE18 \u4ee3\u8868\u7684 H \u578b\u4e9a\u578b\u3002", "difficulty": "medium", "tags": ["concept", "classification", "result", "protein_family"], "created_at": "2025-12-16T21:20:07", "token_est_question": 49, "token_est_answer": 22, "section": "OLE16 is the L isoform and OLEI7 and OLE18 are the H isoforms", "context": "=== OLE16 is the L isoform and OLEI7 and OLE18 are the H isoforms ===\nWe identified OLE17 to be similar to OLE18 and the H isoforms and distinct from the OLE16 and the L isoforms of various species [14]. Each of the three maize oleosins has the three predicted structural domains, and OLE17 and OLE18 share substantially more amino acid similarities than each with OLE16 (Fig. 2). These similarities and differences are reflected in the fine structures of the N-terminal domain and the C-terminal domain. The fine structures of the N-terminal amphipathic domains are unknown, but they should be much more similar between OLE17 and OLE18. The amphipathic e-helices of the Cterminal domains of OLE17 and OLE18 follow immediately the central domains, whereas that of OLE16 is located about 20 amino acid residues away from the central domain (Fig. 2). Chicken antibodies raised against maize OLE16 recognized maize OLE16 and soybean L oleosin isoform [ 14], but did not recognize maize OLE17 and OLE18 (H isoforms) and soybean H oleosin isoform (Fig. 5). Conversely, chicken antibodies raised against maize OLE17 recognized maize OLE17 and OLE18 as well as soybean H oleosin isoform but did not recognize maize OLE16 and soybean L oleosin isoform .\n\nEarlier, when we identified the isoform and the H isoform in the seeds of diverse species, we wondered if there were only two seed oleosin isoforms [14]. Since we have accounted for all the oleosin genes active in maize kernel, we can con-\n \nFig. 5. Immunoblot assays of the two isoforms of maize and soybean oleosins. Left panel: proteins in the oil body fractions of maize (Mo17) and soybean [14] were resolved by SDSPAGE, and stained with Coomassie Blue. The soybean sample was separated into oleosins of r 17 and the latter oleosin and a contaminating protein (resolved as 34 000 in a non-urea gel [14]) co-migrated in the current urea gel system. Right panel: the proteins on the gel were subjected to immunoblotting with anti-OLE16 or anti-OLE17 antibodies [ 14]. Because the antibodies against the maize oleosins were much less reactive toward the soybean oleosins, the gel for immunoassay was loaded with 30 times less maize oleosins than soybean oleosins, as compared to the proportion shown on the left panel. The is indicated.\n\nclude that there are only two major seed oleosin isoforms in maize. This conclusion should be extended to other angiosperms, since the oleosin gene apparently had duplicated once to produce the and isoforms before the divergence of mono- and dicotyledonous species [5].\n\nole16 and ole18 are mapped at single loci on chromosomes 2 and 5, respectively, and the locus of ole l 7 is predicted to be on chromosome I\n\nUsing RFLP mapping with a set of monosomic strains, ole16 was mapped at a single locus offthe centromere, near bl gene on chromosome 2 (courtesy of Dr Tim Helentjaris, then at Native Plants Inc., Salt Lake City, UT). Similarly, ole18 was mapped at a single locus on chromosome 5S near phya2. In our laboratory, we do not have the\n\nsetup to map the newly cloned oleI 7. Nevertheless, we predict a single locus of ole17 on chromosome 1, near phyal. This prediction is based on the following: (1) the coding and the and non-coding regions of ole18 and ole17 are highly similar in nucleotide sequences, suggesting that the two genes are recent descendants of a common ancestral gene; and (2) the mapped locus of ole18 on chromosome 5 near phya2 and the predicted locus ofolel 7 on chromosome 1 near phyal represent highly duplicated regions of the maize genome, where several dozens of RFLP loci in a defined order have been observed [3]. Apparently, olel 7 and olel8 are descendants of a common ancestral oleosin gene as a result of chromosomal segment duplication when the putative maize ancestor of 5 chromosomal pairs became one with 10 chromosomal pairs [3].\n\nThe mapped loci of the oleosin genes coincide with the quantitative trait loci linked to high oil contents [12]: olel6 and olel8 coincide with, or are at the vicinity of, Umc134 and Npi213, respectively. Whether or not the coincidence is fortuitous or is related to the metabolism of oils remains to be determined.", "Thinking\u6a21\u5f0f": "high", "generation_type": "\u63a8\u7406\u578b"}','2026-03-23 14:42:30.149849','2026-03-23 14:42:30.149860');
INSERT INTO qa_pairs VALUES(5,2,'在玉米中，β-葡萄糖苷酶广泛存在于多种组织中，可以通过电泳分离出多个具有不同条带型的同工酶，这些同工酶型式由特定核基因控制，在同一遗传背景内代际相对稳定，却在不同遗传背景的自交系或种族之间表现出明显差异；同时，玉米的不同种族本身在基因组成上存在系统性的差别。如何从这些现象出发，论证β-葡萄糖苷酶同工酶条带型适合作为分析和评价不同玉米种族亲缘关系的分子标记，而不仅仅是一个生理生化性状？','β-葡萄糖苷酶同工酶条带型是由特定基因座决定的可遗传蛋白质变异，在同一遗传背景内表现出代际稳定性，而在不同遗传背景的玉米种族间呈现出可重复、可识别的型式差异。这说明同工酶谱实际上是群体基因组成的一个可检测表征。由于玉米种族之间的亲缘远近本质上体现在基因组相似性上，比较各种族β-葡萄糖苷酶同工酶条带型的相似程度，就可以间接衡量其遗传距离和系统发育关系，因此这种同工酶变异可以用作评价玉米种族亲缘关系的分子标记，而不是单纯的生理生化表型。','{"species": "Zea mays L.", "paper_id": "1856762", "reasoning_steps": ["Step 1: \u03b2-\u8461\u8404\u7cd6\u82f7\u9176\u662f\u4e00\u79cd\u5728\u52a8\u690d\u7269\u7ec4\u7ec7\u4e2d\u5e7f\u6cdb\u5b58\u5728\u7684\u9176\uff0c\u5728\u7389\u7c73\u4e2d\u5b58\u5728\u591a\u79cd\u7535\u6cf3\u53d8\u4f53\uff08\u540c\u5de5\u9176\uff09\u3002", "Step 2: \u901a\u8fc7\u7535\u6cf3\u7b49\u5206\u79bb\u6280\u672f\uff0c\u53ef\u4ee5\u5728\u7389\u7c73\u6750\u6599\u4e2d\u5206\u79bb\u548c\u9274\u5b9a\u51fa\u591a\u4e2a\u03b2-\u8461\u8404\u7cd6\u82f7\u9176\u540c\u5de5\u9176\u53d8\u4f53\u3002", "Step 3: \u8fd9\u4e9b\u540c\u5de5\u9176\u53d8\u4f53\u5177\u6709\u9057\u4f20\u57fa\u7840\uff0c\u5373\u4e0d\u540c\u9057\u4f20\u80cc\u666f\u4f1a\u5bf9\u5e94\u4e0d\u540c\u7684\u9176\u578b\u7ec4\u5408\u3002", "Step 4: \u4e0d\u540c\u7389\u7c73\u201c\u79cd\u65cf\u201d\uff08\u5373\u5177\u6709\u4e0d\u540c\u9057\u4f20\u80cc\u666f\u7684\u7389\u7c73\u7fa4\u4f53\uff09\u4e4b\u95f4\uff0c\u5176\u9057\u4f20\u7ec4\u6210\u5b58\u5728\u5dee\u5f02\u3002", "Step 5: \u82e5\u67d0\u4e00\u9176\u7684\u540c\u5de5\u9176\u578b\u5f0f\u80fd\u5728\u4e0d\u540c\u9057\u4f20\u80cc\u666f\u95f4\u8868\u73b0\u7a33\u5b9a\u5dee\u5f02\uff0c\u5c31\u53ef\u4ee5\u53cd\u6620\u8fd9\u4e9b\u7fa4\u4f53\u4e4b\u95f4\u7684\u9057\u4f20\u5dee\u5f02\u7a0b\u5ea6\u3002", "Step 6: \u56e0\u6b64\uff0c\u4e0d\u540c\u7389\u7c73\u79cd\u65cf\u4e2d\u03b2-\u8461\u8404\u7cd6\u82f7\u9176\u540c\u5de5\u9176\u7684\u53d8\u5f02\u6a21\u5f0f\u53ef\u7528\u4e8e\u6bd4\u8f83\u548c\u8bc4\u4ef7\u8fd9\u4e9b\u79cd\u65cf\u95f4\u7684\u4eb2\u7f18\u5173\u7cfb\u3002"], "question_cot": "", "final_conclusion": "\u03b2-\u8461\u8404\u7cd6\u82f7\u9176\u7684\u540c\u5de5\u9176\u53d8\u5f02\u53ef\u4ee5\u4f5c\u4e3a\u8bc4\u4ef7\u7389\u7c73\u79cd\u65cf\u4eb2\u7f18\u5173\u7cfb\u7684\u4e00\u79cd\u5206\u5b50\u6807\u8bb0\u3002", "difficulty": "medium", "tags": ["concept", "application", "method", "molecular_marker", "genetic_diversity"], "created_at": "2025-12-16T21:15:50", "token_est_question": 1, "token_est_answer": 1, "section": "Introduction", "context": "=== INTRODUCTION ===\n fl-Glucosidase (fl-GLU) or fl-D-glucoside glucohydrolase (E.C. 3.2.1.21) is an enzyme found widely in both plant and animal tissues. In an earlier brief report, we had identified 20 electrophoretic variants of this enzyme in maize (Stuber and Goodman, 1976). Since that report, we have identified two additional fl-GLU isozymes. Pryor (1976) has also reported the existence of\n \nglucosidase isozymes in maize. However, he has identified only three variants in his studies. The objectives of the study reported herein were to (1) develop techniques for separation of fi-glucosidase isozymes in maize, (2) establish the genetic basis for these isozymes, and (3) assess the variation of these isozymes in a number of races of maize for use in evaluation of racial relationships.", "Thinking\u6a21\u5f0f": "high", "generation_type": "\u63a8\u7406\u578b"}','2026-03-23 14:42:30.225383','2026-03-23 14:42:30.225394');
INSERT INTO qa_pairs VALUES(6,2,'在玉米幼苗的同工酶电泳分析中，根、胚轴和胚芽鞘在电泳中可以呈现相同的同工酶带型，但根部的酶活性水平偏低、条带较弱且评分困难，胚轴和胚芽鞘的染色强度相似、更易判读；同时已有实验操作可以在不牺牲植株、并允许植株继续在营养钵中生长和移栽用于田间杂交与遗传分析的前提下切除胚芽鞘。如果研究者既希望获得具有代表性的清晰同工酶谱，又希望保留植株供后续遗传研究，应优先选择哪一种幼苗组织作为取样材料？为什么？','应优先选择胚芽鞘。胚芽鞘与根和胚轴具有相同的同工酶带型，保证结果具有代表性，其酶活性和染色强度与胚轴相当、条带清晰，便于判读和评分；根虽有相同带型，但酶活性低、条带模糊，不利于可靠分析。同时，已有成熟技术可以在不杀死植株的情况下切取胚芽鞘，使植株仍能继续生长并用于田间杂交和后续遗传分析，因此综合酶谱质量和植株后续利用，胚芽鞘是最合适的取样组织。','{"species": "Zea mays L.", "paper_id": "1856762", "reasoning_steps": ["Step 1: \u5e7c\u82d7\u7684\u6839\u3001\u80da\u8f74\uff08mesocotyl\uff09\u548c\u80da\u82bd\u9798\uff08coleoptile\uff09\u5728\u7535\u6cf3\u4e2d\u8868\u73b0\u51fa\u76f8\u540c\u7684\u540c\u5de5\u9176\u5e26\u578b\uff0c\u8fd9\u662f\u7ec4\u7ec7\u95f4\u9176\u8c31\u4e00\u81f4\u6027\u7684\u76f4\u63a5\u8bc1\u636e\u3002", "Step 2: \u80da\u8f74\u548c\u80da\u82bd\u9798\u7684\u67d3\u8272\u5f3a\u5ea6\u76f8\u4f3c\uff0c\u8bf4\u660e\u8fd9\u4e24\u79cd\u7ec4\u7ec7\u7684\u9176\u6d3b\u6027\u6c34\u5e73\u548c\u7535\u6cf3\u6761\u5e26\u6e05\u6670\u5ea6\u76f8\u5f53\uff0c\u53ef\u4ee5\u540c\u7b49\u7528\u4e8e\u5224\u8bfb\u9176\u8c31\u3002", "Step 3: \u6839\u7ec4\u7ec7\u7684\u9176\u6d3b\u6027\u6c34\u5e73\u666e\u904d\u8f83\u4f4e\uff0c\u6761\u5e26\u8f83\u5f31\u4e14\u96be\u4ee5\u8bc4\u5206\uff0c\u8fd9\u4f7f\u5f97\u6839\u4f5c\u4e3a\u6837\u672c\u5728\u5b9e\u9645\u8bc4\u5206\u548c\u5224\u8bfb\u4e2d\u4e0d\u591f\u53ef\u9760\u3002", "Step 4: \u6280\u672f\u4e0a\u5df2\u7ecf\u5efa\u7acb\u4e86\u5728\u4e0d\u727a\u7272\u690d\u682a\u7684\u60c5\u51b5\u4e0b\u5207\u53d6\u80da\u82bd\u9798\u7684\u65b9\u6cd5\uff0c\u53d6\u6837\u540e\u690d\u682a\u4ecd\u53ef\u5728\u8425\u517b\u94b5\u4e2d\u7ee7\u7eed\u751f\u957f\uff0c\u5e76\u4e14\u53ef\u4ee5\u79fb\u683d\u5230\u7530\u95f4\u7528\u4e8e\u6742\u4ea4\u548c\u9057\u4f20\u5206\u6790\u3002", "Step 5: \u7531\u4e8e\u80da\u82bd\u9798\u4e0e\u6839\u3001\u80da\u8f74\u5448\u73b0\u76f8\u540c\u7684\u5e26\u578b\uff08\u4fdd\u8bc1\u7ed3\u679c\u4ee3\u8868\u6027\uff09\uff0c\u53c8\u5177\u6709\u4e0e\u80da\u8f74\u76f8\u4f3c\u3001\u660e\u663e\u9ad8\u4e8e\u6839\u7684\u53ef\u5224\u8bfb\u9176\u6d3b\u6027\uff0c\u540c\u65f6\u8fd8\u80fd\u5728\u4e0d\u6740\u6b7b\u690d\u682a\u7684\u6761\u4ef6\u4e0b\u53d6\u6837\uff0c\u56e0\u6b64\u7efc\u5408\u6bd4\u8f83\uff0c\u80da\u82bd\u9798\u6bd4\u6839\u66f4\u9002\u5408\u4f5c\u4e3a\u8be5\u7c7b\u7535\u6cf3\u5206\u6790\u7684\u53d6\u6837\u7ec4\u7ec7\u3002"], "question_cot": "", "final_conclusion": "\u5728\u8be5\u7c7b\u7389\u7c73\u5e7c\u82d7\u7684\u9176\u8c31\u7535\u6cf3\u5206\u6790\u4e2d\uff0c\u9009\u7528\u80da\u82bd\u9798\u7ec4\u7ec7\u53ef\u4ee5\u5728\u4e0d\u727a\u7272\u690d\u682a\u7684\u524d\u63d0\u4e0b\u83b7\u5f97\u4e0e\u6839\u3001\u80da\u8f74\u76f8\u540c\u7684\u540c\u5de5\u9176\u5e26\u578b\uff0c\u800c\u4e14\u5177\u6709\u66f4\u6613\u5224\u8bfb\u7684\u9176\u6d3b\u6027\u6c34\u5e73\uff0c\u56e0\u6b64\u662f\u6bd4\u6839\u66f4\u5408\u9002\u7684\u53d6\u6837\u7ec4\u7ec7\u3002", "difficulty": "medium", "tags": ["method", "application", "result"], "created_at": "2025-12-16T21:16:56", "token_est_question": 1, "token_est_answer": 1, "section": "Materials & Methods", "context": "=== MATERIALS AND METHODS ===\nPlant materials studied included a large number of inbred lines from two North Carolina maize varieties (Jarvis Golden Prolific and Indian Chief), a number of crosses among these lines and their correspondipg (selfs), 51 racial collections of maize from Mexico, 38 racial collections from Guatemala, and one racial composite from E1 Salvador. Twenty-four plants were sampled from each of these 90 collections for analyses. In addition, a number of individual plants from the racial collections were crossed to three single-cross hybrids and to several inbred lines for genetic analyses.\n\nPreliminary electrophoretic studies of seedlings showed that roots, mesocotyls, and coleoptiles produced identical isozyme banding patterns. Staining intensities were similar in mesocotyls and coleoptiles. However, levels of enzyme activity in root sections were generally quite low and the bands were faint and difficult to score. Techniques were developed whereby coleoptiles could be removed without sacrificing the plants. These plants could then be grown in peat pots and later transplanted to the field for crosses and subsequent genetic analyses. Consequently, coleoptiles were used in all of the work reported here.\n\nColeoptilar sections (1.2 cm long) were taken from 5-day-old plants, grown in darkness at Each section was homogenized with of a solution containing sucrose and sodium ascorbate in a disposable microcentrifuge tube using a power-driven acetal pestle. The homogenized material was then centrifuged for 4 rain in a refrigerated Beckman/Spinco No. 152 Microfuge s and stored at for later electrophoretic analyses. Filter paper wicks (2 by were used to absorb samples of the thawed supernatant for insertion into a slit in a starch gel.\n\nGels were made by pouring of a buffer solution containing starch lot 371, Electrostarch Company, Madison, Wisconsin; and lot 323-3, Connaught Laboratories Limited, Willowdale, Ontario, Canada) into an acrylic mold. An L-histidine-citric acid buffer was\n \nused, in the gels and in the electrode trays. Seventeen watts of constant power (Schaffer and Johnson, 1973) was delivered to the horizontal gels during a period at \n\nIt should be noted that several other types of buffers varying frompH to were tried and all produced the same banding patterns for glucosidase. The buffer was chosen because it provided excellent resolution variants as well as for several other enzyme systems.\n\nAfter electrophoresis, zones of/%glucosidase activity were revealed by immersing a slice of the gel in a filtered mixture containing 6-bromo-2naphthyl-r dissolved in acetone, phosphate buffer with polyvinylpyrrolidone, and fast blue BB salt. The staining reaction was complete in 2-3 hr at however, the gels were normally left in the staining mixture overnight at room temperature.", "Thinking\u6a21\u5f0f": "high", "generation_type": "\u63a8\u7406\u578b"}','2026-03-23 14:42:30.299346','2026-03-23 14:42:30.299356');
INSERT INTO qa_pairs VALUES(7,2,'在利用 fl-GLU1 β-葡萄糖苷酶同工酶位点评估玉米地方品种的群体结构时，观察到该位点存在大量等位基因，因此适合作为分析亲缘关系的遗传标记。同时发现，从墨西哥采集的多数材料在 fl-GLU1 上各自具有独特的等位基因构型，而从危地马拉采集的材料在该位点上的等位基因构型彼此相似、仅凭该位点难以区分不同材料。应如何据此判断两地材料在群体内部的遗传差异大小及亲缘远近关系？','fl-GLU1 位点拥有较多等位变异，可反映材料间的遗传差异。墨西哥材料中，大多数样本在该位点表现出彼此独特的等位基因构型，说明同一地区内不同材料在这一位点上的遗传差异较大、亲缘关系相对较疏远。相反，危地马拉材料在 fl-GLU1 位点上较难区分，等位基因构型相似，表明这些材料在该位点上的遗传差异较小，群体内部彼此更为接近、亲缘关系更紧密。','{"species": "Zea mays L.", "paper_id": "1856762", "reasoning_steps": ["Step 1: fl-GLU1 \u4f4d\u70b9\u5b58\u5728\u5927\u91cf\u7b49\u4f4d\u57fa\u56e0\u53d8\u5f02\uff08\u7b49\u4f4d\u57fa\u56e0\u6570\u8f83\u591a\uff09\uff0c\u56e0\u6b64\u53ef\u7528\u5176\u7b49\u4f4d\u57fa\u56e0\u6784\u578b\u6765\u533a\u5206\u4e0d\u540c\u7389\u7c73\u6750\u6599\u5e76\u8bc4\u4f30\u5b83\u4eec\u4e4b\u95f4\u7684\u4eb2\u7f18\u5173\u7cfb\u3002", "Step 2: \u5728\u58a8\u897f\u54e5\u91c7\u96c6\u7684\u7389\u7c73\u6750\u6599\u4e2d\uff0c\u5927\u591a\u6570\u6750\u6599\u5177\u6709\u5f7c\u6b64\u72ec\u7279\u7684 fl-GLU1 \u7b49\u4f4d\u57fa\u56e0\u6784\u578b\uff0c\u5373\u4e0d\u540c\u6750\u6599\u4e4b\u95f4\u5728\u8be5\u4f4d\u70b9\u7684\u7ec4\u5408\u591a\u6837\u6027\u8f83\u9ad8\u3002", "Step 3: \u5728\u5371\u5730\u9a6c\u62c9\u91c7\u96c6\u7684\u7389\u7c73\u6750\u6599\u4e2d\uff0c\u4ec5\u51ed fl-GLU1 \u7b49\u4f4d\u53d8\u5f02\u7684\u6570\u636e\uff0c\u5f88\u96be\u5c06\u4e0d\u540c\u6750\u6599\u6e05\u6670\u533a\u5206\u5f00\uff0c\u8bf4\u660e\u8fd9\u4e9b\u6750\u6599\u5728\u8be5\u4f4d\u70b9\u4e0a\u7684\u7b49\u4f4d\u57fa\u56e0\u6784\u578b\u8f83\u4e3a\u76f8\u4f3c\u3002", "Step 4: \u5982\u679c\u4e00\u7ec4\u6750\u6599\u4e2d\u5927\u591a\u6570\u6837\u672c\u5728\u540c\u4e00\u4f4d\u70b9\u7684\u7b49\u4f4d\u57fa\u56e0\u6784\u578b\u72ec\u7279\u4e14\u5dee\u5f02\u660e\u663e\uff0c\u5219\u8fd9\u7ec4\u6750\u6599\u4e4b\u95f4\u7684\u9057\u4f20\u5dee\u5f02\u76f8\u5bf9\u66f4\u5927\uff1b\u53cd\u4e4b\uff0c\u82e5\u96be\u4ee5\u901a\u8fc7\u8be5\u4f4d\u70b9\u533a\u5206\u4e0d\u540c\u6750\u6599\uff0c\u5219\u8fd9\u7ec4\u6750\u6599\u5728\u8be5\u4f4d\u70b9\u4e0a\u7684\u9057\u4f20\u5dee\u5f02\u76f8\u5bf9\u66f4\u5c0f\u3002", "Step 5: \u56e0\u6b64\uff0c\u57fa\u4e8e fl-GLU1 \u7b49\u4f4d\u57fa\u56e0\u6784\u578b\uff0c\u53ef\u4ee5\u63a8\u65ad\u5371\u5730\u9a6c\u62c9\u7389\u7c73\u6750\u6599\u5f7c\u6b64\u4e4b\u95f4\u66f4\u4e3a\u63a5\u8fd1\uff0c\u800c\u58a8\u897f\u54e5\u7389\u7c73\u6750\u6599\u4e4b\u95f4\u7684\u5dee\u5f02\u66f4\u5927\u3002"], "question_cot": "", "final_conclusion": "\u6839\u636e fl-GLU1 \u7b49\u4f4d\u53d8\u5f02\u7684\u7b49\u4f4d\u57fa\u56e0\u6784\u578b\uff0c\u5371\u5730\u9a6c\u62c9\u7389\u7c73\u6750\u6599\u4e4b\u95f4\u5f7c\u6b64\u66f4\u4e3a\u63a5\u8fd1\uff0c\u800c\u58a8\u897f\u54e5\u7389\u7c73\u6750\u6599\u4e4b\u95f4\u7684\u5dee\u5f02\u66f4\u5927\u3002", "difficulty": "medium", "tags": ["concept", "method", "result", "application"], "created_at": "2025-12-16T21:17:51", "token_est_question": 6, "token_est_answer": 5, "section": "Discussion", "context": "=== DISCUSSION ===\nThe pattern of Mendelian inheritance demonstrated for fi-glucosidase variants in maize indicates that they are under nuclear gene control. Because of the large number of alleles (22) identified for the fl-gtul locus, it appears to be a very useful system for the evaluation of racial relationships in maize. It should also prove to be quite useful, in conjunction with other isozyme systems, for discrimination among specific inbred lines or hybrids.\n\nA comparison of the variation in the Mexican collections with that from the Guatemalan collections suggests that the Guatemalan materials may be more closely related than those from Mexico. Although sampling errors are quite high from the evaluations of only 24 plants per collection, allelic configurations (Table II) are unique for most collections from Mexico. Discrimination among the Guatemalan collections (Table III) would be difficult with data from only fl-GLU1 isozyme variants.\n \n 0 = + 5 9 02 00 0 1 0 80 \u2212 0 \u2212 00 \u2212 0 00 R 0 00 0 0 0000 0 \u2248 \u2212 E 55 0 0 0 \u2212 0 0 0 8 0 0 \u4e00 0 0 5064 0 0 4 0 00 0301 31 0 0 0 0 0 0 0 0 00 0 0 0 \u2212 ) 09 0 4825 0 0 2 082540 + 0 0.6 00 0 3.4 0625 40 4825 0 0 0 \u03c9 \u2212 09 0 04 04 2 8 0 04 83 0 00 78 2 2 2 Z2 G 8 o (c) ) \u2212 (c R 20 \\ 20 20 0 \n \n o O o ~ o 0 00 000 0 \n\no\n \n = 1 0 0 8 \u2212 \u2212 \u2212 5 9 0 Q. 0 8 0 0 0 0 0 1 0 0 \u2212 \u2212 0 8 50 p 0 0 0 0 0 0 0 0 0 0 4 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 04 \u2212 Q 0 0 0 0 0 0 0 0 0 0 0 0 0 0 G 0 0 0 0 0 0 0 0 0 0 0 0 0 0 4 \u00b7 \\f [\\beta] \\beta \\beta$ the/~-glucosidases in maize are in the process of evolving new but related enzymes.", "Thinking\u6a21\u5f0f": "high", "generation_type": "\u63a8\u7406\u578b"}','2026-03-23 14:42:30.374717','2026-03-23 14:42:30.374727');
INSERT INTO qa_pairs VALUES(8,2,'在玉米 β-葡萄糖苷酶同工酶分析中，电泳表型是由电泳条带的迁移距离和条带型式来刻画的，而这些电泳特征又取决于酶蛋白的结构和带电性质。长期冷冻保存的玉米组织样品在电泳图谱上与新鲜匀浆材料表现出完全相同的迁移距离和条带模式。当实验只能获取长期冷冻的玉米样品时，是否仍可以将这些材料用于可靠的 β-葡萄糖苷酶同工酶电泳分型？请说明你的判断及其推理依据。','可以使用长期冷冻的玉米材料进行可靠的 β-葡萄糖苷酶同工酶电泳分型，因为电泳条带的迁移距离和条带型式完全由酶的结构和带电性质决定，若冷冻保存破坏或改变了酶，则电泳图谱应发生变化；而观察到长期冷冻样品与新鲜材料的电泳模式完全相同，说明冷冻并未改变酶的相关性质，其电泳表型与新鲜材料等效，因此可安心用于表型鉴定。','{"species": "Zea mays L.", "paper_id": "1856762", "reasoning_steps": ["Step 1: \u03b2-\u8461\u8404\u7cd6\u82f7\u9176\u7684\u7535\u6cf3\u8868\u578b\u662f\u901a\u8fc7\u7535\u6cf3\u6761\u5e26\u7684\u8fc1\u79fb\u8ddd\u79bb\u548c\u6761\u5e26\u578b\u5f0f\u6765\u9274\u5b9a\u7684\u3002", "Step 2: \u7535\u6cf3\u6761\u5e26\u7684\u8fc1\u79fb\u8ddd\u79bb\u548c\u6761\u5e26\u578b\u5f0f\u4f9d\u8d56\u4e8e\u9176\u86cb\u767d\u7684\u7ed3\u6784\u53ca\u5176\u5e26\u7535\u6027\u8d28\uff0c\u5982\u679c\u51b7\u51bb\u4fdd\u5b58\u7834\u574f\u9176\u6216\u6539\u53d8\u5176\u6027\u8d28\uff0c\u5219\u7535\u6cf3\u56fe\u8c31\u4f1a\u53d1\u751f\u53d8\u5316\u3002", "Step 3: \u5b9e\u9a8c\u4e2d\u5bf9\u6bd4\u4e86\u51b7\u51bb\u8d85\u8fc7 6 \u4e2a\u6708\u7684\u6837\u54c1\u4e0e\u65b0\u9c9c\u5300\u6d46\u7684\u690d\u7269\u6750\u6599\u7684\u7535\u6cf3\u7ed3\u679c\u3002", "Step 4: \u89c2\u6d4b\u7ed3\u679c\u8868\u660e\uff0c\u51b7\u51bb\u8d85\u8fc7 6 \u4e2a\u6708\u7684\u6837\u54c1\u4e0e\u65b0\u9c9c\u6750\u6599\u7684\u7535\u6cf3\u6a21\u5f0f\u5b8c\u5168\u76f8\u540c\u3002", "Step 5: \u7531\u4e8e\u4e0d\u5b58\u5728\u7535\u6cf3\u8fc1\u79fb\u8ddd\u79bb\u6216\u6761\u5e26\u6570\u91cf/\u4f4d\u7f6e\u7684\u5dee\u5f02\uff0c\u53ef\u4ee5\u63a8\u65ad\u51b7\u51bb\u81f3\u5c11 6 \u4e2a\u6708\u4e0d\u4f1a\u6539\u53d8 \u03b2-\u8461\u8404\u7cd6\u82f7\u9176\u7684\u7535\u6cf3\u8868\u578b\u3002", "Step 6: \u56e0\u6b64\uff0c\u4f7f\u7528\u957f\u671f\u51b7\u51bb\u4fdd\u5b58\u7684\u7389\u7c73\u6750\u6599\u8fdb\u884c \u03b2-\u8461\u8404\u7cd6\u82f7\u9176\u540c\u5de5\u9176\u7535\u6cf3\u5206\u6790\uff0c\u5728\u8868\u578b\u5224\u5b9a\u4e0a\u4e0e\u4f7f\u7528\u65b0\u9c9c\u6750\u6599\u540c\u6837\u53ef\u9760\u3002"], "question_cot": "", "final_conclusion": "\u957f\u65f6\u95f4\u51b7\u51bb\uff08\u81f3\u5c11 6 \u4e2a\u6708\uff09\u4fdd\u5b58\u7684\u7389\u7c73\u6750\u6599\u5728 \u03b2-\u8461\u8404\u7cd6\u82f7\u9176\u540c\u5de5\u9176\u7535\u6cf3\u8868\u578b\u4e0a\u4e0e\u65b0\u9c9c\u6750\u6599\u6ca1\u6709\u5dee\u5f02\uff0c\u56e0\u6b64\u7535\u6cf3\u8868\u578b\u9274\u5b9a\u53ef\u4ee5\u53ef\u9760\u5730\u5728\u957f\u671f\u51b7\u51bb\u6837\u54c1\u4e0a\u8fdb\u884c\u3002", "difficulty": "medium", "tags": ["method", "application", "result", "mechanism"], "created_at": "2025-12-16T21:18:43", "token_est_question": 3, "token_est_answer": 2, "section": "Electrophoretic Phenotypes of $\\beta_{1}$ p-Glucosidase", "context": "=== Electrophoretic Phenotypes of p-Glucosidase ===\nTwenty-one different homozygote isozyme bands of activity, as well as an apparent null phenotype, have been identified for ]~-glucosidase in the inbred lines and exotic races of maize examined. However, individual plants show either one or three distinct, well-stained bands. In addition, light-staining (shadow) bands are present which migrate about ahead of each wellstained band. (These shadow bands are not considered further in this article.) The 21 homozygote isozyme bands have been designated to in order of decreasing anodal mobility (Fig. I). All of isozymes migrated toward the anode for the buffers evaluated Distances of migration from the origin are shown (Fig. l) for each band with system described in the previous section. Samples frozen for more than 6 months showed electrophoretic patterns identical to those from freshly homogenized plant material.", "Thinking\u6a21\u5f0f": "high", "generation_type": "\u63a8\u7406\u578b"}','2026-03-23 14:42:30.449923','2026-03-23 14:42:30.449933');
INSERT INTO qa_pairs VALUES(9,2,'在玉米中，对只带单一主β-葡萄糖苷酶（β-GLU）条带的不同亲本进行互交时，杂合后代表现出两个亲本电泳条带的同时，还出现一个迁移率介于两者之间的第三条带，而任一亲本与空等位基因杂合时，后代仅表现单一条带；进一步对多种杂交和自交后代进行遗传分析时，电泳表型的分离比稳定符合典型单基因孟德尔比例。结合这些现象，应如何推断玉米β-GLU同工酶在分子亚基结构和遗传控制方式上的基本特征？','玉米β-GLU同工酶是由两个亚基组成的功能性二聚体，控制这些同工酶差异的遗传基础是一个具有多个等位基因的单一基因座。每个等位基因在纯合体中产生一种亚基并形成对应的同源二聚体，在电泳中表现为单一主条带；在杂合体中，两种等位基因各自产生不同亚基，这些亚基随机组合形成两种同源二聚体（对应两个亲本条带）和一种杂合二聚体（表现为中间迁移率条带）。当其中一方为空等位基因时，只能形成一种同源二聚体，因此仅见单一条带。杂交后代表型分离比符合单基因多等位孟德尔遗传，说明这些结构与同工酶型变化由一个位点的多等位基因控制。','{"species": "Zea mays L.", "paper_id": "1856762", "reasoning_steps": ["Step 1: \u4eb2\u672c\u5404\u81ea\u4ec5\u8868\u73b0\u4e00\u4e2a\u4e3b\u8981\u03b2-GLU\u6761\u5e26\uff08\u4e0d\u540c\u540c\u5de5\u9176\u578b\uff09\uff0c\u8bf4\u660e\u6bcf\u79cd\u7b49\u4f4d\u57fa\u56e0\u53ef\u4ee5\u5728\u7eaf\u5408\u72b6\u6001\u4e0b\u4ea7\u751f\u4e00\u4e2a\u7a33\u5b9a\u7684\u540c\u5de5\u9176\u6761\u5e26\u3002", "Step 2: \u8fd9\u7c7b\u4e0d\u540c\u5355\u4e00\u6761\u5e26\u578b\u4eb2\u672c\u6742\u4ea4\u540e\uff0c\u5176\u540e\u4ee3\u7535\u6cf3\u8868\u73b0\u4e3a\u4e24\u4e2a\u4eb2\u672c\u6761\u5e26\u52a0\u4e0a\u4e00\u4e2a\u5177\u6709\u4e2d\u95f4\u7535\u6cf3\u8fc1\u79fb\u7387\u7684\u7b2c\u4e09\u6761\u5e26\uff0c\u663e\u793a\u6742\u5408\u4f53\u4e2d\u5b58\u5728\u4eb2\u672c\u578b\u548c\u4e2d\u95f4\u578b\u5206\u5b50\u3002", "Step 3: \u5728\u9664\u4e0e\u7a7a\u7b49\u4f4d\u57fa\u56e0\uff08null\uff09\u6742\u5408\u7684\u60c5\u51b5\u5916\uff0c\u6240\u6709\u5df2\u89c2\u5bdf\u5230\u7684\u6742\u5408\u57fa\u56e0\u578b\u90fd\u51fa\u73b0\u4e86\u8fd9\u79cd\u201c\u4e24\u4e2a\u4eb2\u672c\u6761\u5e26 + \u4e00\u4e2a\u4e2d\u95f4\u6761\u5e26\u201d\u7684\u4e09\u6761\u5e26\u6a21\u5f0f\uff0c\u800c\u7a7a\u7b49\u4f4d\u57fa\u56e0\u6742\u5408\u4f53\u4ec5\u8868\u73b0\u5355\u4e00\u6761\u5e26\uff0c\u8bf4\u660e\u4e2d\u95f4\u6761\u5e26\u4f9d\u8d56\u4e24\u4e2a\u529f\u80fd\u7b49\u4f4d\u57fa\u56e0\u5171\u540c\u5b58\u5728\u3002", "Step 4: \u5404\u79cd\u6742\u4ea4\u548c\u81ea\u4ea4\u540e\u4ee3\u7684\u5206\u79bb\u6bd4\u4f8b\u7b26\u54081:1\u30011:2:1\u62161:1:1:1\u7b49\u5178\u578b\u5b5f\u5fb7\u5c14\u5206\u79bb\u6bd4\uff0c\u4e14\u65e0\u663e\u8457\u504f\u79bb\uff0c\u8868\u660e\u8fd9\u4e9b\u03b2-GLU\u540c\u5de5\u9176\u53d8\u5f02\u7531\u5355\u4e00\u9057\u4f20\u4f4d\u70b9\u7684\u591a\u7b49\u4f4d\u57fa\u56e0\u63a7\u5236\uff08\u5355\u57fa\u56e0\u591a\u7b49\u4f4d\u578b\uff09\u3002", "Step 5: \u5728\u6742\u5408\u4e2a\u4f53\u4e2d\u53ef\u4ee5\u540c\u65f6\u68c0\u6d4b\u5230\u4eb2\u672c\u578b\u548c\u4e2d\u95f4\u578b\u6761\u5e26\uff0c\u4e14\u9057\u4f20\u5206\u79bb\u6a21\u5f0f\u4e0e\u4e00\u4e2a\u4f4d\u70b9\u4e0a\u4e24\u79cd\u4e9a\u57fa\u968f\u673a\u7ec4\u5408\u76f8\u4e00\u81f4\uff0c\u8bf4\u660e\u8be5\u9176\u5206\u5b50\u7531\u4e24\u4e2a\u4e9a\u57fa\u7ec4\u6210\uff1b\u4e0d\u540c\u7b49\u4f4d\u57fa\u56e0\u5206\u522b\u4ea7\u751f\u4e0d\u540c\u7684\u4e9a\u57fa\u80bd\u94fe\uff0c\u8fd9\u4e9b\u4e9a\u57fa\u968f\u673a\u914d\u5bf9\u5f62\u6210\u540c\u6e90\u4e8c\u805a\u4f53\uff08\u4ea7\u751f\u4eb2\u672c\u6761\u5e26\uff09\u548c\u6742\u5408\u4e8c\u805a\u4f53\uff08\u4ea7\u751f\u4e2d\u95f4\u8fc1\u79fb\u7387\u6761\u5e26\uff09\u3002", "Step 6: \u56e0\u6b64\uff0c\u53ef\u4ee5\u63a8\u65ad\u03b2-GLU\u540c\u5de5\u9176\u4e3a\u529f\u80fd\u6027\u4e8c\u805a\u4f53\uff0c\u4e14\u7531\u4e00\u4e2a\u5177\u6709\u591a\u4e2a\u7b49\u4f4d\u57fa\u56e0\u7684\u5355\u4e00\u57fa\u56e0\u5ea7\uff08\u03b2-glu1\uff09\u7f16\u7801\uff0c\u6bcf\u4e2a\u7b49\u4f4d\u57fa\u56e0\u5728\u6742\u5408\u4f53\u4e2d\u8d21\u732e\u4e00\u4e2a\u4e9a\u57fa\uff0c\u901a\u8fc7\u968f\u673a\u4e8c\u805a\u4ea7\u751f\u4eb2\u672c\u548c\u6742\u5408\u578b\u540c\u5de5\u9176\u6761\u5e26\u3002"], "question_cot": "", "final_conclusion": "\u7389\u7c73\u03b2-\u8461\u8404\u7cd6\u82f7\u9176\uff08\u03b2-GLU\uff09\u540c\u5de5\u9176\u662f\u7531\u5355\u4e00\u4f4d\u70b9\u7684\u7b49\u4f4d\u57fa\u56e0\u7f16\u7801\u7684\u529f\u80fd\u6027\u4e8c\u805a\u4f53\uff0c\u6bcf\u4e2a\u7b49\u4f4d\u57fa\u56e0\u5728\u6742\u5408\u4f53\u4e2d\u4ea7\u751f\u5176\u5bf9\u5e94\u4e9a\u57fa\uff0c\u591a\u79cd\u4e8c\u805a\u4f53\u7ec4\u5408\u5f62\u6210\u4eb2\u672c\u548c\u4e2d\u95f4\u7535\u6cf3\u8fc1\u79fb\u7387\u7684\u6742\u5408\u540c\u5de5\u9176\u6761\u5e26\u3002", "difficulty": "medium", "tags": ["mechanism", "concept", "result", "genetics", "enzyme_structure", "maize"], "created_at": "2025-12-16T21:19:36", "token_est_question": 1, "token_est_answer": 1, "section": "Genetic Analyses of $\\beta$ ~-GLU Isozymes", "context": "=== Genetic Analyses of ~-GLU Isozymes ===\nCrosses were made among a number of ]~-GLU phenotypes and the resulting progenies were analyzed electrophoretically. Crosses between parents, in which each exhibited a different single primary band, always produced progeny exhibiting the two parental isozymes and a third isozyme with intermediate electrophoretic mobility.\n\nResults of a number of crosses and self-fertilizations to demonstrate genetic control of fl-GLU variants are summarized in Table I. Zymograms of two crosses depicting segregation patterns are shown in Figs. 2 and 3.\n \nFig. 1. Composite schematic diagram showing relative positions of fl-glucosidase isozyme bands (homozygotes) found in maize coleoptiles. Migration is anodal.\n\nCrosses and selfs with expected ratios of 1:1, 1:2:1, and 1:1:1:1 were evaluated. None deviated significantly from the expected ratio at the level of probability. Thus the data indicate a monogenic pattern of inheritance with 22 alleles at a single locus. The gene symbol ~-glui was assigned to this locus.\n\nBecause it would have required 210 different crosses just to produce all possible pairwise matings among the 21 PoSsible homozygotes (excluding the null), some of the possible heterozygotes haze not been observed. However, all of the variants have been observed as heterozygotes with one or more other variants. A third band with intermediate mobility was present in all cases except for heterozygotes with the null which exhibit only a single band.\n\nSegregation patterns observed in the genetic crosses (Table I) and in the 90 racial collections from Mexico and Central America indicate that each allele in a heterozygote produces its corresponding subunit polypeptide and that random assortment of the two subunits results in the parental and hybrid dimeric molecules. Thus the genetic data support the hypothesis that ]3-GLU isozymes in maize are functional dimers.\n \nTable I. Results of Crosses and Self-fertilizations to Demonstrate Genetic Control of Several -Glucosidase Variants in Maize\n\n Cross Phenotypes in offspring Expected ratio P (1) \u03b2-glu1-d|\u03b2-glu1-d\u00d7\u03b2-glu1-j|\u03b2-glu1-k d|j [Tb] d|k 10 13 1:1 0.391 0.75 -0.50 (2) \u03b2-glu1-d|\u03b2-glu1-j\u00d7\u03b2-glu1-k|\u03b2glu1-k d|k j/k 13 10 1:1 0.391 0.75 -0.50 (3) \u03b2-glu1-d|\u03b2-glu1-k\u00d7\u03b2-glu1-k|\u03b2-glu1-k d|k \u2212 k|k 11 13 1:1 0.167 0.75 -0.50 (4) \u03b2-glu1-d|\u03b2-glu1-s\u00d7\u03b2-glu1-k|\u03b2-glu1-k d|k k|s 7 5 1:1 0.333 0.75 -0.50 (5) (\u03b2-glu1-b/\u03b2-glu1-k) selfed b/b b/k k|k 9 31 12 1:2:1 2.269 0.50 -0.25 12 32 6 1:2:1 5.360 0.10 -0.05 21 63 18 1:2:1 5.823 0.10 -0.05 (7) (\u03b2-glu1-d|\u03b2-glu1-k) selfed d|d d|k k|k 6 17 9 1:2:1 0.688 0.75 -0.50 (8) (\u03b2-glu1-d|\u03b2-glu1-k) selfed 9 33 18 1:2:1 3.300 0.25 -0.10 17 28 16 1:2:1 0.443 0.90 -0.75 32 78 43 1:2:1 1.640 0.50 -0.25 e/e e|j j|j (10) (\u03b2-glu1-e|\u03b2-glu1-j) selfed 16 17 6 1:2:1 5.769 0.10 -0.05 b|j b/k j/q k|q (11) \u03b2-glu1-b/\u03b2-glu1-q\u00d7\u03b2-glu1-j|\u03b2-glu1-k15 14 4 1:1:1:1 6.953 0 10 -0.05 a|j a|k d|j d|k (12) \u03b2-glu1-a|\u03b2-glu1-d\u00d7\u03b2-glu1-j|\u03b2-glu1-k 5 2 3 1:1:1:1 2.000 0.75 -0.50 d/j d/k e|k (13) \u03b2-glu1-d|\u03b2-glu1-e\u00d7\u03b2-glu1-j|\u03b2-glu1-k 4 1 1:1:1:1 3.333 0.50 -0.25 5 j|q (14) \u03b2-glu1-j|\u03b2-glu1-k\u00d7\u03b2-glu1-j|\u03b2-glu1-q j|j j|k k|q 2 3 1:1:1:1 0.667 0.90 -0.75", "Thinking\u6a21\u5f0f": "high", "generation_type": "\u63a8\u7406\u578b"}','2026-03-23 14:42:30.532749','2026-03-23 14:42:30.532759');
INSERT INTO qa_pairs VALUES(10,2,'在对危地马拉不同海拔玉米群体的β-glu1位点进行分型时发现，高海拔与中海拔群体中可以检测到某些特定等位基因，而这些等位基因在低海拔群体中完全缺失；同时，另一些等位基因如e和k在各海拔群体中均可检测到，但其频率随海拔降低呈持续下降趋势。对于希望通过分子标记选择来培育适应高海拔环境的玉米材料的育种家，应如何判断哪些β-glu1等位基因更可能与高海拔适应性紧密相关，从而优先作为选择和聚合的目标？','更可能与高海拔适应性紧密相关的，是那些在中高海拔群体中出现而在低海拔群体中缺失的等位基因，以及在所有海拔均存在但随海拔降低而频率系统性下降的等位基因。前一类等位基因表现为明显的海拔特异性分布，提示其可能在中高海拔环境中受到正向选择或在低海拔被强烈淘汰；后一类等位基因在整个海拔梯度上维持存在，却在高海拔群体中相对富集，其频率随海拔降低逐步下降，符合与高海拔环境适应性相关、但在低海拔仍有一定保留的模式。因此，在分子标记辅助选择中，中高海拔特有等位基因以及像e、k这类高海拔富集且低海拔频率下降的等位基因，应被优先视为候选标记，用于筛选和聚合有利于高海拔适应的基因型。','{"species": "Zea mays L.", "paper_id": "1856762", "reasoning_steps": ["Step 1: \u5371\u5730\u9a6c\u62c9\u7389\u7c73\u6750\u6599\u88ab\u6309\u6d77\u62d4\u68af\u5ea6\u5206\u7ec4\uff0c\u9ad8\u6d77\u62d4\u6750\u6599\u6392\u5217\u5728\u8868\u683c\u4e0a\u65b9\uff0c\u4f4e\u6d77\u62d4\u6750\u6599\u5728\u4e0b\u65b9\u3002", "Step 2: \u5728\u8fd9\u4e9b\u6309\u6d77\u62d4\u5206\u7ec4\u7684\u6750\u6599\u4e2d\uff0c\u67d0\u4e9b \u03b2-glu1 \u7b49\u4f4d\u57fa\u56e0\uff08\u6587\u4e2d\u4ee5\u7279\u5b9a\u5b57\u6bcd\u6807\u8bb0\uff09\u53ea\u5728\u4e2d\u3001\u9ad8\u6d77\u62d4\u7ec4\u4e2d\u88ab\u68c0\u6d4b\u5230\uff0c\u800c\u5728\u4f4e\u6d77\u62d4\u7ec4\u4e2d\u672a\u51fa\u73b0\u3002", "Step 3: \u540c\u65f6\uff0c\u53e6\u4e00\u4e9b\u7b49\u4f4d\u57fa\u56e0\uff08\u4f8b\u5982 e \u548c k\uff09\u5728\u6240\u6709\u6d77\u62d4\u7ec4\u4e2d\u90fd\u53ef\u4ee5\u68c0\u6d4b\u5230\uff0c\u4f46\u5176\u9891\u7387\u968f\u6d77\u62d4\u964d\u4f4e\u5448\u7cfb\u7edf\u6027\u53d8\u5316\u3002", "Step 4: \u660e\u786e\u89c2\u5bdf\u5230\u7684\u6a21\u5f0f\u662f\uff1a\u968f\u7740\u6d77\u62d4\u964d\u4f4e\uff0c\u67d0\u4e9b\u7b49\u4f4d\u57fa\u56e0\uff08\u5982 a\u3001f\uff09\u4e0d\u518d\u51fa\u73b0\uff0c\u800c e\u3001k \u7b49\u4f4d\u57fa\u56e0\u7684\u9891\u7387\u6574\u4f53\u5448\u4e0b\u964d\u8d8b\u52bf\u3002", "Step 5: \u7531\u4e8e\u8fd9\u4e9b\u9891\u7387\u53d8\u5316\u662f\u76f8\u5bf9\u4e8e\u6d77\u62d4\u68af\u5ea6\u7cfb\u7edf\u5730\u53d1\u751f\uff0c\u5e76\u5728\u591a\u79cd\u7fa4\uff08\u591a\u4e2a\u79cd\u8d28\u96c6\u5408\uff09\u4e2d\u91cd\u590d\u89c2\u5bdf\u5230\uff0c\u8fd9\u8bf4\u660e \u03b2-glu1 \u7b49\u4f4d\u57fa\u56e0\u7ec4\u6210\u4e0e\u6d77\u62d4\u73af\u5883\u4e4b\u95f4\u5b58\u5728\u5173\u8054\u3002", "Step 6: \u56e0\u6b64\uff0c\u53ef\u4ee5\u63a8\u65ad\u5371\u5730\u9a6c\u62c9\u7389\u7c73\u4e2d \u03b2-glu1 \u7b49\u4f4d\u57fa\u56e0\u7684\u5206\u5e03\u4e0e\u9002\u5e94\u7684\u6d77\u62d4\u73af\u5883\u5bc6\u5207\u76f8\u5173\uff0c\u4e2d\u9ad8\u6d77\u62d4\u66f4\u503e\u5411\u4e8e\u4fdd\u7559\u6216\u5bcc\u96c6\u67d0\u4e9b\u7279\u5b9a\u7b49\u4f4d\u57fa\u56e0\uff0c\u800c\u4f4e\u6d77\u62d4\u5219\u8868\u73b0\u51fa\u8fd9\u4e9b\u7b49\u4f4d\u57fa\u56e0\u7684\u7f3a\u5931\u6216\u9891\u7387\u4e0b\u964d\u3002"], "question_cot": "", "final_conclusion": "\u5728\u5371\u5730\u9a6c\u62c9\u7389\u7c73\u79cd\u8d28\u4e2d\uff0c\u03b2-glu1 \u7b49\u4f4d\u57fa\u56e0\u7684\u9891\u7387\u968f\u6d77\u62d4\u5b58\u5728\u660e\u663e\u7684\u89c4\u5f8b\uff1aa\u3001f \u7b49\u4f4d\u57fa\u56e0\u53ea\u51fa\u73b0\u5728\u4e2d\u9ad8\u6d77\u62d4\u6750\u6599\u4e2d\uff0c\u800c\u67d0\u4e9b\u7b49\u4f4d\u57fa\u56e0\uff08\u5982 e\u3001k\uff09\u7684\u9891\u7387\u5728\u6d77\u62d4\u964d\u4f4e\u65f6\u603b\u4f53\u5448\u4e0b\u964d\u8d8b\u52bf\u3002", "difficulty": "medium", "tags": ["result", "application", "population_genetics", "environmental_adaptation", "molecular_breeding"], "created_at": "2025-12-16T21:20:16", "token_est_question": 1, "token_est_answer": 1, "section": "Variation of $\\beta$ -GLU Isozymes in Exotic Racest", "context": "=== Variation of -GLU Isozymes in Exotic Racest ===\nFrequencies of glu1 alleles in 51 racial collections of maize from Mexico and in 39 collections from Central America are presented in Tables II and III, respectively. Overall frequencies in each of the two countries are also shown.r It should be noted that frequencies of \"nulls'' were probably underestimated because heterozygotes with the null allele would be classified as homozygotes.ra Isozymes from 24 plants were observed in each collection. Grouping of the races in the tables is based primarily on morphological and/or geographical similarities, as proposed by Goodman and Bird (1976)..\n\nIn the Mexican races evaluated,s gIu1-a was the only allele absent of those that have been identified.. -glu1-d, and glu1- alleles were much\n \nOrigin\n\nFig. 2. Zymogram showing segregation pattern in the offspring of a self-fertilized plant with the genotype and designate the two types of homozygotes, d/k designates the heterozygotes. The control is .\n \nFig. 3. Zymogram showing segregation pattern in the offspring of the cross j/q, and designate the four types of heterozygotes. The control is .\n \nmore frequent than any of the others and had a combined frequency of about 0.65. fi-glul-q was prevalent in more than one-third of the races, having an overall frequency of 0.08.\n\nAlthough it was difficult to detect any correlation of fl-gIul allelic frequencies with morphological data in the Mexican races, alleles designated and were primarily associated with the Cdnico group. Only one collection, from the race Reventador (Nay 15), was fixed for a single allele in the sample of 24 plants evaluated. Several races segregated for as many as five to seven alleles.\n\nThe groups of Central American races (Table III) are arranged according to altitude, with the races at the top of the table being those adapted to high altitudes, fi-glul alleles and were not represented in the Guatemalan races evaluated. As in the Mexican collections, fi-gIul-d, and fl-glul-k were more frequent than any of the others, and had a combined frequency of 0.75. In addition, fi-glul-e and were each present in about of the collections. The variation pattern appeared to have some association with altitude in the Guatemalan collections. For example, fl-glul alleles and were found only in the mid- to high-altitude collections. Also, with a few exceptions, frequencies of and tended to increase and frequencies of fi-glul-e and fl-glul-k tended to decrease as altitude decreased.\n\nEach of the two source populations for the inbred lines used in this study showed segregation for six alleles, and Null. However, predominated in Jarvis Golden Prolific, having a frequency of 0.57, and fi-glul-b predominated in Indian Chief, with a frequency of 0.55.", "Thinking\u6a21\u5f0f": "high", "generation_type": "\u63a8\u7406\u578b"}','2026-03-23 14:42:30.620087','2026-03-23 14:42:30.620098');
INSERT INTO qa_pairs VALUES(11,2,'在研究玉米植物是否会因水稻黑条矮缩病毒（RBSDV）感染而在激素含量和组织结构上产生特异变化时，幼苗阶段分别让携带RBSDV的飞虱和不携带RBSDV的飞虱在相同时间内取食，随后将植株移栽到田间，在相同生育时期采集相同部位组织并测定GA、IAA、CK等激素及观察细胞学特征。面对这种试验设计，科研人员应如何利用两类飞虱处理之间的比较，将病毒感染效应与飞虱取食本身的效应区分开，并据此判断观察到的性状或激素差异是否可以归因于RBSDV？','通过让携带RBSDV和不携带RBSDV的飞虱在相同条件下取食玉米幼苗，两组植株在昆虫取食方式和强度上基本一致，系统性差异仅在于是否发生病毒感染。随后在相同时间点、相同组织部位测定内源激素和观察组织结构时，若“毒虱处理组”与“非毒虱处理组”之间出现稳定的激素水平改变或细胞学/形态学差异，即可将这些差异主要归因于RBSDV感染引起的特异效应，而非飞虱取食造成的普遍应激效应，因为飞虱本身的取食影响在两组之间是对照并被抵消的。','{"species": "Zea mays", "paper_id": "PMC11365956", "reasoning_steps": ["Step 1: \u7389\u7c73\u5e7c\u82d7\u5728\u6e29\u5ba4\u4e2d\u4e8e\u51fa\u82d7\u671f\u88ab\u4eba\u5de5\u63a5\u79cd\uff0c\u5904\u7406\u65b9\u5f0f\u5305\u62ec\uff1a\u7531\u643a\u5e26RBSDV\u7684\u6bd2\u8671\u53d6\u98df\u548c\u7531\u4e0d\u643a\u5e26RBSDV\u7684\u975e\u6bd2\u8671\u53d6\u98df\uff0c\u53d6\u98df\u65f6\u95f4\u76f8\u540c\uff083\u5929\uff09\u3002", "Step 2: \u8fd9\u4e24\u7c7b\u98de\u8671\u5728\u53d6\u98df\u65b9\u5f0f\u548c\u53d6\u98df\u65f6\u95f4\u4e0a\u662f\u4e00\u81f4\u7684\uff0c\u552f\u4e00\u7cfb\u7edf\u6027\u5dee\u5f02\u5728\u4e8e\u662f\u5426\u643a\u5e26RBSDV\uff0c\u56e0\u6b64\u4e24\u7ec4\u690d\u682a\u627f\u53d7\u7684\u6606\u866b\u53d6\u98df\u538b\u529b\u662f\u53ef\u6bd4\u7684\u3002", "Step 3: \u63a5\u79cd\u540e\uff0c\u690d\u682a\u88ab\u79fb\u683d\u5230\u7530\u95f4\u7ee7\u7eed\u751f\u957f\uff0c\u540e\u7eed\u5728\u76f8\u540c\u65f6\u95f4\u70b9\uff08\u598260 dpi\uff09\u5206\u522b\u91c7\u96c6\u4e0a\u90e8\u8282\u95f4\u548c\u53f6\u7247\u7b49\u7ec4\u7ec7\uff0c\u7528\u4e8e\u6d4b\u5b9a\u5185\u6e90\u6fc0\u7d20\uff08GA\u3001IAA\u3001CK\u7b49\uff09\u6216\u89c2\u5bdf\u7ec4\u7ec7\u5b66\u7279\u5f81\u3002", "Step 4: \u5bf9\u6bd4\u201c\u6bd2\u8671\u5904\u7406\u7ec4\u201d\u548c\u201c\u975e\u6bd2\u8671\u5904\u7406\u7ec4\u201d\u690d\u682a\u7684\u6fc0\u7d20\u542b\u91cf\u3001\u7ec6\u80de\u5b66\u7ed3\u6784\u6216\u5176\u4ed6\u6027\u72b6\u65f6\uff0c\u7531\u4e8e\u4e8c\u8005\u5728\u98de\u8671\u53d6\u98df\u672c\u8eab\u4e0a\u7684\u6761\u4ef6\u76f8\u540c\uff0c\u5dee\u5f02\u4e3b\u8981\u53ef\u5f52\u56e0\u4e8e\u662f\u5426\u53d1\u751fRBSDV\u611f\u67d3\u3002", "Step 5: \u56e0\u6b64\uff0c\u82e5\u5728\u4e24\u7ec4\u4e4b\u95f4\u89c2\u5bdf\u5230\u7cfb\u7edf\u6027\u5dee\u5f02\uff08\u5982GA\u6c34\u5e73\u6539\u53d8\u6216\u89e3\u5256\u7ed3\u6784\u4e0d\u540c\uff09\uff0c\u5373\u53ef\u5c06\u8fd9\u4e9b\u5dee\u5f02\u89e3\u91ca\u4e3aRBSDV\u611f\u67d3\u5f15\u8d77\u7684\u7279\u5f02\u6548\u5e94\uff0c\u800c\u975e\u5355\u7eaf\u98de\u8671\u53d6\u98df\u9020\u6210\u7684\u6548\u5e94\u3002"], "question_cot": "", "final_conclusion": "\u5229\u7528\u540c\u65f6\u653e\u517b\u643a\u5e26RBSDV\u548c\u4e0d\u643a\u5e26RBSDV\u7684\u98de\u8671\u8fdb\u884c\u63a5\u79cd\uff0c\u53ef\u4ee5\u5c06\u75c5\u6bd2\u611f\u67d3\u6548\u5e94\u4e0e\u98de\u8671\u53d6\u98df\u672c\u8eab\u7684\u6548\u5e94\u533a\u5206\u5f00\u6765\uff0c\u4ece\u800c\u5224\u5b9aRBSDV\u662f\u5426\u5bfc\u81f4\u7389\u7c73\u690d\u682a\u6fc0\u7d20\u542b\u91cf\u6216\u6027\u72b6\u7684\u7279\u5f02\u53d8\u5316\u3002", "difficulty": "medium", "tags": ["method", "mechanism", "experiment_design", "plant_pathology", "hormone"], "created_at": "2025-12-16T21:15:53", "token_est_question": 1, "token_est_answer": 1, "section": "Methods", "context": "=== Plant materials ===\nA pair of near-isogenic lines (NILs), namely NIL-S and NIL-R, were derived from our previous study5. NIL-R carries the resistance gene _ZmGDI\u03b1-hel_ , while NIL-S carries the susceptibility gene _ZmGDI\u03b1_. Transgenic plants (_ZmGA2ox7.3-GFP_ -OE and _P7-1-GFP_ -OE) were generated in the Maize Functional Genomic Project of China Agricultural University, using the susceptible maize inbred line B73 as the recipient.\n=== Artificial inoculation of RBSDV ===\nMaize kernels were germinated in vermiculite soil in pots in a greenhouse. The seedlings were artificially inoculated by either viruliferous planthoppers carrying RBSDV or non-viruliferous planthoppers without RBSDV during the emergence stage for a duration of 3 days35. After inoculation, the seedlings were transplanted into the field. The detailed methodology was described in our previous study5.\n=== Cytological and histological examination ===\nSeedlings of the NILs were artificially infested with viruliferous planthoppers. At 60 days post-inoculation (dpi), the uppermost internodes of both NIL-S and NIL-R were sampled by cutting discs with a thickness of less than 1 mm. These samples were then immersed in a 2.5% glutaraldehyde stationary solution (Huayueyang Biotechnology, China) overnight at 4 \u00b0C. The samples then underwent a series of five processing steps, including fixation, post-fixation, dehydration, embedding, and sectioning, following the methodology outlined in the previous study36. Eventually, images of the samples were captured using Tecnai G2 spirit BioTwin (FEI, USA).\nThe transgenic ZmGA2ox7.3-GFP-OE and the recipient B73 plants were grown until anthesis, at which point the uppermost internodes were sampled and immobilized using the same procedures as described above. The samples were washed three times with PBS buffer, followed by a 2-h treatment with 1% OsO4 in PBS buffer (136.8 mM NaCl, 2.7 mM KCl, 4 mM Na2HPO4, 1.8 mM KH2PO4). They were then washed an additional three times with PBS. The samples underwent a series of ethanol dehydration steps at concentrations of 30%, 50%, 70%, 80%, 90% and 100%, with two subsequent rounds using 100% ethanol. Afterwards, the samples were further dehydrated in an automatic critical point dryer (Leica EM CPD 00, Germany). Ultimately, the samples were coated with a layer of gold and photographed using a S-3400N Hitachi scanning electron microscope (Hitachi Hi-Tech, Tokyo, Japan).\n=== RNA extraction, RT-qPCR and RNA-seq ===\nTotal RNA was extracted from maize leaves or internodes with a PLANTpure plant RNA Kit (Aidlab, China), following the manufacturer\u2019s protocol. Reverse transcription was performed using _TransScript_ \u00ae RT/RI reverse transcriptase with random primers (Transgen Biotech, China), according to the manufacturer\u2019s protocol. The full-length cDNA sequence of _ZmGA2ox7.3_ was amplified from the two NILs. RT-qPCR assays were performed on a CFX Connect real-time RT-PCR system (BIO-RAD, USA) using a SYBR Green qRT-PCR kit (Takara, Japan) to detect the expression levels of specific genes. The primer pairs qZmGA2ox, qP7-1, qZmDAO, qZmPP1 and qZmB1B2 were used to quantify the transcript levels of _ZmGA2ox7.3_ , _RBSDV-S7-1_ , _ZmDAO1_ , _ZmPP1_ and _ZmB1B2_ respectively (Supplementary Data 1). The expression level of _ZmTubulin_ was used as an internal control to calculate the relative expression levels of other genes using the 2\u2212\u0394\u0394Ct method.\nRNA-seq was performed by Novogene Bioinformatics Technology (Beijing, China). The purity and integrity of the extracted RNA samples were evaluated using a Nanodrop spectrophotometer and an Agilent 2100 Bioanalyzer, respectively. The libraries were constructed and sequenced on an Illumina Hiseq platform, generating 125 bp/150 bp paired-end reads. The raw data (raw reads) in FASTQ format were subjected to quality control, with the removal of adaptor sequences and low-quality reads to get clean reads. These clean reads were then aligned to the Maize genome (B73 RefGen_v4, AGPv4) using HISAT (2.0.4) with default parameters. FPKM (fragments per kilobase of transcript per million mapped reads) values of each gene were calculated using HTSeq (version 0.6.1) software. Differentially expressed genes (DEGs) were analyzed using DESeq (1.12.0) software, with significant DEGs defined based on a _P_ -value threshold (log2 fold change > 1, _P_ ) and the maize database ( ), respectively. The phylogenetic tree was constructed using the neighbor-joining method with MEGA7.0 software ( ).\n=== Split luciferase complementation (SLC) assay ===\nThe resulting constructs were transformed into _Agrobacterium_ EHA105 and co-infiltrated into _N. benthamiana_ leaves as previously described. 72 h after transformation, the initial infiltration sites were injected with 1 mmol/L beetle luciferase (Promega, USA). The chemiluminescent signal was then captured using the Chemiluminescent Imaging System (Tanon, China). Additionally, to further assess the chemiluminescent signal, the infiltrated sites were injected with 0.5 mM GA4.\nTotal protein was extracted from the injected leaves using laemmli sample buffer (0.125 M Tris-HCl, 4% SDS, 20% glycerol, 10% 2-mercaptoethanol, 0.004% bromophenol blue, pH 6.8). The resulting supernatants were subjected to immunoblotting with an \u03b1-LUC antibody (Abcam, UK) and a plant-specific actin antibody (ABclonal Technology, China). The fluorescence signals and immunoblot bands were quantified for their intensities using ImageJ Launcher software (National Institutes of Health), and the measurements were expressed as mean gray value.\n=== Co-immunoprecipitation (Co-IP) assay ===\nThe relevant constructs were co-expressed in _N. benthamiana_ leaves through co-infiltration for 72 h. Total protein was then extracted from the infiltrated leaves using IP buffer (50 mM Tris-MES, 10 mM EDTA, 0.5 M sucrose, 1 mM MgCl2, 5 mM DTT, 1 mM PMSF, pH 8.0). The extracted supernatants were incubated with anti-GFP magnetic beads (MBL, China) for 2 h, after which the beads were washed three times with IP buffer. The immunoprecipitated complexes were heated in SDS loading buffer (Genstar, China) at 100 \u00b0C for 5 min and subsequently subjected to immunoblotting using an anti-Myc antibody (MBL, China).\n=== Pull-down assays ===\nThe relevant fusion constructs were transformed into _Escherichia coli_ strain BL21 (DE3) (TransGen Biotech, China). The recombinant GST-ZmGA2ox7.3, His-ZmGDI\u03b1, His-ZmGDI\u03b1-hel, and His-P7-1 proteins were subsequently purified using affinity chromatography. GST-fused proteins were purified using glutathione sepharose (Yeasen, China), while His-fused proteins were purified using Ni Sepharose (GE Healthcare, USA), all according to the manufacturers\u2019 instructions. GST or GST-ZmGA2ox7.3 was immobilized on glutathione agarose beads and incubated at 4 \u00b0C for 1 h. These immobilized proteins were then incubated with His-ZmGDI\u03b1, His-ZmGDI\u03b1-hel, and His-P7-1 for an additional 2 h. Following elution from the beads, the proteins were subjected to immunoblotting using an anti-His antibody (Easybio, China).\n=== Bimolecular fluorescence complementation (BiFC) assay ===\nThe relevant constructs were transformed into _Agrobacterium_ EHA105 and were co-infiltrated into _N. benthamiana_ leaves and allowed to express for 72 h. GFP fluorescence was visualized using confocal microscopy (ZEISS880, Carl Zeiss) with an excitation wavelength set at 488 nm. For the combinations involving _nYFP-ZmGA2ox7.3_ and _cYFP-ZmGA2ox7.3_ , the infiltrated sites were treated with either 0.1% Tween 20 or 0.1% Tween 20 with 0.5 mM GA4. Subsequently, the GFP fluorescence signals were assessed. The fluorescence intensities were quantified using ZEN software.\n=== Microscale Thermophoresis (MST) Assay ===\nThe purified proteins, GST-ZmGA2ox7.3, His-P7-1, His-ZmGDI\u03b1, and His-ZmGDI\u03b1-hel, were concentrated and dialyzed in PBS buffer (pH 7.4) using an Amicon Ultra-4 concentrator unit with a 10 kDa molecular weight cutoff (Millipore, USA). To investigate the strength of interaction between ZmGA2ox7.3 and P7-1, ZmGDI\u03b1 or ZmGDI\u03b1-hel, the MST assays were subjected to performed according to the manufacturer\u2019s instructions (Nano Temper Technologies, Cat# MO-L011). The binding reactions were measured by using a microscale thermophoresis instrument (Nano Temper Technologies) at 25 \u00b0C, 40% MST power and 20% LED power. The Nano Temper Analysis Software MO affinity Analysis was used to calculate the value of the dissociation constant (Kd).\n=== Transient expression assays in maize protoplasts ===\nFor dual-luciferase assays, the _pZmGA2ox7.3:LUC_ construct served as the reporter gene, while the _62SK-RBSDVs_ and _35S:S7-1-GFP_ constructs, driven by the 35S promoter, acted as effectors. The REN gene, under the control of the 35S promoter, was included in the _pGreenII0800-LUC_ vector as an internal control. Empty effectors _pGreenII 62-SK_ and _35:GFP_ were employed as controls. Appropriate combinations of reporter and effector constructs were co-transformed into maize protoplasts and incubated at 28 \u00b0C for 12 h. Luciferase signals were detected according to the instructions of the Dual-Luciferase Reporter Assay System (Promega, USA). The ratio of REN activity to LUC activity was taken as the relative LUC activity. Each experiment was performed with at least three biological replicates.\n=== Gel filtration chromatography analysis ===\nStandard curves correlating elution volume to molecular weight were generated according to the manufacturer\u2019s instructions (GE Healthcare, USA). The mixture, containing ~3 mg of each standard protein, was injected onto a SuperdexTM 200 Increase 10/300 GL column (GE Healthcare, USA). The standard proteins were added in the following order: thyroglobulin (669 kDa), ferritin (450 kDa), aldolase (158 kDa), conalbumin (75 kDa), and ovalbumin (42 kDa). Blue Dextran 2000 was used to define the column void volume (Vo). The mobile phase consisted of 0.01 M phosphate buffer saline at pH 7.4, and the flow rate was set to 0.3 ml/min. A standard curve for Kav and Mr was constructed based on the formula Kav = (Ve-Vo)/(Vc-Vo) = a-b*log10(Mr), where Vc represents the column volume, Ve is the elution volume, and Mr denotes the protein molecular weight, using data obtained from the standard proteins.\nThe purified proteins, GST-ZmGA2ox7.3, GST-ZmGA2ox7.33A, MBP-P7-1, His-ZmGDI\u03b1, and His-ZmGDI\u03b1-hel, were concentrated and dialyzed in PBS buffer (pH 7.4) using an Amicon Ultra-4 concentrator unit with a 10 kDa molecular weight cutoff (Millipore, USA). These proteins were then incubated at 25 \u00b0C for 12 h in PBS buffer containing 10 mM GA4, using ethanol as the control. After incubation, the proteins were centrifuged at high speed for 10 min and fractionated through size exclusion chromatography. The chromatography was executed using an AKTA Purifier System (Cytiva, USA) equipped with a Superdex 200 Increase 10/300 GL column. The system, equilibrated with PBS buffer, eluted samples with the same buffer at a flow rate of 0.3 ml/min at 4 \u00b0C. Eluted fractions (0.5 ml each) were continuously collected, and peak fractions were subjected to 4\u201313% Blue Native-PAGE and 10% SDS-PAGE, followed by immunoblotting with an anti-GST antibody.\n=== Blue native polyacrylamide gel electrophoresis (BN-PAGE) ===\nTo investigate the oligomerization of ZmGA2ox7.3, the peak fractions were subjected to 4\u201313% Blue Native PAGE (WSHTBio, China) according to the manufacturer\u2019s instructions. The resolved samples were then transferred onto polyvinylidene difluoride (PVDF) membranes using a methanol-free transfer buffer (48 mM Tris-base, 39 mM glycine), followed by immunoblotting using the appropriate antibodies.\n=== Enzyme activity assay ===\nFor the enzymatic activity assay, we adopted specific procedures from a prior study27. The purified proteins GST-ZmGA2ox7.3, MBP-P7-1, His-ZmGDI\u03b1, and His-ZmGDI\u03b1-hel were each incubated in 1 ml of enzymatic activity buffer (100 mM Tris-HCl, 4 mM ascorbic acid, 4 mM 2-oxoglutaric acid, 0.5 mM FeSO4, 4 mM DTT, 2 mg/ml BSA, and 1 mg/ml catalase, pH 7.9). The mixture was then incubated with 0.5 mM GA4 at 30 \u00b0C for 12 h. To terminate the reaction, 125 \u03bcl of acetic acid was added to each solution, and the mixture was kept at 30 \u00b0C for an additional hour. The level of the enzyme reaction product, GA34, was subsequently quantified by Wuhan Greensword Creation Technology Co. Ltd. ( ). Each experiment was conducted with three biological replicates.\n=== Mass Spectrometry (MS) ===\nThe elution fractions were analyzed by mass spectrometry (MS) at the Biological Mass Spectrum Laboratory, College of Biological Sciences, China Agricultural University. The elution fractions from the two \u201cMu\u201d peaks were collected and separated by BN-PAGE as described. The gel lanes were excised, sliced, subjected to in-gel digestion with trypsin, and redissolved in trifluoroacetic acid, following the methodology outlined in a previous study37. The extracted peptides were then analyzed using an LTQ Orbitrap Velos mass spectrometer (Thermo Fisher Scientific). Protein identification was performed using the Mascot search engine (Mascot Server 2.3, Matrix Science) by blasting with the UniProt protein database ( ). In the previous study, the false discovery rate (FDR) and significance threshold have been set37.\n=== Transgenic tests in Arabidopsis thaliana ===\nThe _pSuper1300_ :_ZmGA29X7.3-GFP_ and _pSuper_ :_ZmGA2ox7.3_ 3A _-GFP_ constructs were transformed into Col-0 to obtain OE-ZmGA2ox7.33A (OE3A\u22121 and OE3A\u22122), and OE-ZmGA2ox7.3 (OE-1 and OE-2) transgenic plants, respectively. Stable T2 generation of four above lines were used for experiments.\n=== Measurement of plant hormones ===\nNIL-S and NIL-R were both artificially infested with planthoppers, either carrying RBSDV or not. Subsequently, the uppermost internodes and upper leaves were sampled at 60 dpi. The collected samples were used to measure the content of various endogenous phytohormones, including gibberellins (Gas), indole-3-acetic acid (IAA), and cytokinins (CKs). The quantification of these phytohormones was executed using UHPLC-MS/MS analysis (Thermo Scientific Ultimate 3000 UHPLC coupled with TSQ Quantiva). This analysis was conducted by Wuhan Greensword Creation Technology Co. Ltd. ( ) according to established methods38. The experiments were conducted with three biological replicates.\n=== Application of exogenous GAs ===\nExogenous GA3 and GA4 treatments were performed following previously described methods39. Seeds from both the recipient B73 and _ZmGA2ox7.3-GFP_ -OE transgenic lines were germinated in pots filled with vermiculite soil and placed in a greenhouse. They were kept under conditions of 26 \u00b0C with a 16-h white light/8-h dark cycle for 6 days. Subsequently, the entire plants were uniformly sprayed with either GA1 (100 \u03bcg\u00b7ml\u22121, 0.1% Tween-20), GA3 (1 mg\u00b7ml\u22121, 0.1% Tween-20), GA4 (100 \u03bcg\u00b7ml\u22121, 0.1% Tween-20) or 0.1% Tween-20 (as a control). Plants heights were measured 8 days post-treatment. Both the transgenic lines and the control plants were represented by at least three biological replicates.\n=== Quantification and statistics analysis ===\nStatistical parameters are reported in the figures and figure legends. Statistical analyses were preformed using GraphPad Prism 8.3.0. Data were tested for normality using the Shapiro-Wilk normality test in GraphPad Prism 8.3.0. For normally distributed data, statistical significane between two groups was assessed using a two-tailed student\u2019s _t_ test or LSD\u2019s _t_ test. For comparisons among more than two groups, one-way ANOVA with Tukey\u2019s test or LSD\u2019s _t_ -test was used, and different lowercase letters indicate a significant difference (_P_ < 0.05). For data that were not normally distributed, the Mann-Whitney U test or Wilcoxon matched-pairs signed-rank test were used for two-group comparisons, while the Kruskal-Wallis test was employed for comparisons among more than two groups. Different lowercase letters indicate a significant difference (_P_ < 0.05). The Pearson\u2019s correlation coefficient (R) and _P_ values were calculated using GraphPad Prism 8.3.0. All statistics analyses in this study were conducted using GraphPad Prism 8.3.0.\n=== Reporting summary ===\nFurther information on research design is available in the Nature Portfolio Reporting Summary linked to this article.\n=== Supplementary information ===\nSupplementary Information (9.4MB, pdf) \nPeer Review File (6.4MB, pdf) \n41467_2024_51726_MOESM3_ESM.pdf (71KB, pdf) \nDescription of Additional Supplementary Files\nSupplementary Data 1 (16.1KB, xlsx) \nReporting Summary (4.1MB, pdf)\n=== Source data ===\nSource Data (38.1MB, zip)", "Thinking\u6a21\u5f0f": "high", "generation_type": "\u63a8\u7406\u578b"}','2026-03-23 14:42:30.729278','2026-03-23 14:42:30.729289');
INSERT INTO qa_pairs VALUES(12,2,'在玉米中，GA2 氧化酶 ZmGA2ox7.3 在 GA4 存在时更易形成具有更高酶活的寡聚体，可高效将活性赤霉素转化为失活形式。RBSDV 侵染感病材料时会强烈诱导 ZmGA2ox7.3 表达，同时病毒效应子 P7‑1 大量积累并招募该酶，从而显著促进寡聚体形成；而抗病材料携带的 ZmGDIα‑hel 等位变异能够抑制 RBSDV 诱导的 ZmGA2ox7.3 表达并阻止其寡聚化，使活性 GA 的失活程度明显降低。围绕激素平衡与病害发生机制，说明这两类材料在 MRDD 症状严重程度上的差异成因，并推断玉米抗病育种中应如何选择 ZmGDIα 相关等位基因？','感病材料中，RBSDV 既上调 ZmGA2ox7.3 表达，又通过 P7‑1 促进其寡聚化，使该酶以高活性形式大量存在，大幅度失活 GA1/GA4，打破赤霉素与生长素、细胞分裂素之间的平衡，从而有利于系统性侵染并诱导严重 MRDD 症状。抗病材料中，ZmGDIα‑hel 抑制 ZmGA2ox7.3 的诱导表达并阻止其寡聚化，活性 GA 仅被有限失活，整体激素平衡基本维持，因此 MRDD 症状轻微或难以显现。由此推断，抗病育种应优先选择类似 ZmGDIα‑hel 的等位基因，使植株在 RBSDV 侵染下仍能限制 ZmGA2ox7.3 过度活化并维持激素稳态，从而提高对 MRDD 的抗性。','{"species": "Zea mays", "paper_id": "PMC11365956", "reasoning_steps": ["Step 1: ZmGA2ox7.3 \u5728\u5b58\u5728 GA4 \u65f6\u80fd\u591f\u5f62\u6210\u5be1\u805a\u4f53\uff08\u56db\u805a\u4f53\u548c\u4e8c\u805a\u4f53\uff09\uff0c\u8fd9\u4e9b\u5be1\u805a\u4f53\u7684\u9176\u6d3b\u6027\u9ad8\u4e8e\u5355\u4f53\uff0c\u80fd\u5c06\u6d3b\u6027 GA4 \u8f6c\u5316\u4e3a\u5931\u6d3b\u7684 GA34\uff0c\u56e0\u6b64\u5be1\u805a\u5316\u662f\u5176\u9ad8\u6548\u5931\u6d3b\u8d64\u9709\u7d20\u7684\u524d\u63d0\u3002", "Step 2: \u5728\u611f\u75c5\u7389\u7c73\u4e2d\uff0cRBSDV \u4fb5\u67d3\u663e\u8457\u4e0a\u8c03 ZmGA2ox7.3 \u7684\u57fa\u56e0\u8868\u8fbe\uff0c\u540c\u65f6\u75c5\u6bd2\u6548\u5e94\u5b50 P7-1 \u5927\u91cf\u79ef\u7d2f\uff0c\u5e76\u80fd\u62db\u52df ZmGA2ox7.3\u3002", "Step 3: ZmGA2ox7.3 \u7684\u4e0a\u8c03\u53e0\u52a0 P7-1 \u7684\u62db\u52df\u4f5c\u7528\uff0c\u5bfc\u81f4\u5f62\u6210\u5927\u91cf ZmGA2ox7.3 \u5be1\u805a\u4f53\uff0c\u8fd9\u4e9b\u5be1\u805a\u4f53\u81ea\u8eab\u6216\u4e0e ZmGDI\u03b1/P7-1 \u590d\u5408\u4f53\u4e00\u8d77\uff0c\u53ef\u9ad8\u6548\u5c06\u6d3b\u6027 GA \u8f6c\u5316\u4e3a\u5931\u6d3b\u5f62\u5f0f\uff0c\u4f7f GA1 \u548c GA4 \u6c34\u5e73\u663e\u8457\u4e0b\u964d\u3002", "Step 4: \u6d3b\u6027\u8d64\u9709\u7d20\u542b\u91cf\u7684\u663e\u8457\u964d\u4f4e\u4f1a\u7834\u574f\u8d64\u9709\u7d20\u4e0e\u5176\u4ed6\u6fc0\u7d20\uff08\u5982\u751f\u957f\u7d20\u548c\u7ec6\u80de\u5206\u88c2\u7d20\uff09\u4e4b\u95f4\u7684\u5e73\u8861\uff0c\u4ece\u800c\u5bfc\u81f4\u690d\u7269\u4f53\u5185\u6fc0\u7d20\u5931\u8861\u3002", "Step 5: \u6fc0\u7d20\u5e73\u8861\u5bf9\u7cfb\u7edf\u6027\u75c5\u6bd2\u4fb5\u67d3\u53ca\u75c5\u75c7\u5f62\u6210\u81f3\u5173\u91cd\u8981\uff0c\u5f53\u8d64\u9709\u7d20-\u751f\u957f\u7d20-\u7ec6\u80de\u5206\u88c2\u7d20\u5e73\u8861\u88ab\u4e25\u91cd\u6270\u4e71\u65f6\uff0c\u5c31\u4f1a\u51fa\u73b0\u5178\u578b\u7684 MRDD \u75c7\u72b6\u3002", "Step 6: \u5728\u6297\u75c5\u7389\u7c73\u4e2d\uff0c\u5b58\u5728 ZmGDI\u03b1-hel \u7b49\u4f4d\u53d8\u5f02\uff0c\u8be5\u5206\u5b50\u663e\u8457\u6291\u5236 RBSDV \u8bf1\u5bfc\u7684 ZmGA2ox7.3 \u8868\u8fbe\uff0c\u5e76\u963b\u6b62 ZmGA2ox7.3 \u7684\u5be1\u805a\u5316\uff0c\u4f7f\u5176\u96be\u4ee5\u5f62\u6210\u5177\u6709\u9ad8\u9176\u6d3b\u7684\u5be1\u805a\u4f53\u3002", "Step 7: \u7531\u4e8e ZmGA2ox7.3 \u5728\u6297\u75c5\u6750\u6599\u4e2d\u96be\u4ee5\u9ad8\u8868\u8fbe\u548c\u5be1\u805a\u5316\uff0c\u6d3b\u6027 GA \u7684\u5931\u6d3b\u7a0b\u5ea6\u6709\u9650\uff0c\u6fc0\u7d20\u5e73\u8861\u53d7\u5230\u7684\u5e72\u6270\u8f83\u5c0f\uff0c\u56e0\u6b64\u51e0\u4e4e\u4e0d\u51fa\u73b0\u6216\u4ec5\u51fa\u73b0\u8f7b\u5fae\u7684 MRDD \u75c7\u72b6\u3002"], "question_cot": "", "final_conclusion": "\u5728\u611f\u75c5\u7389\u7c73\u4e2d\uff0cRBSDV \u901a\u8fc7 P7-1 \u4e0a\u8c03\u5e76\u4fc3\u6d3b ZmGA2ox7.3\uff0c\u4f7f\u5176\u5f62\u6210\u9ad8\u6d3b\u6027\u7684\u5be1\u805a\u4f53\uff0c\u5927\u91cf\u5c06\u6d3b\u6027\u8d64\u9709\u7d20\u8f6c\u5316\u4e3a\u5931\u6d3b\u5f62\u5f0f\uff0c\u4ece\u800c\u6253\u7834\u6fc0\u7d20\u5e73\u8861\u5e76\u5bfc\u81f4 MRDD \u75c7\u72b6\uff1b\u800c\u5728\u6297\u75c5\u7389\u7c73\u4e2d\uff0cZmGDI\u03b1-hel \u6291\u5236 ZmGA2ox7.3 \u7684\u8868\u8fbe\u548c\u5be1\u805a\u5316\uff0c\u56e0\u6b64\u51e0\u4e4e\u4e0d\u8868\u73b0\u51fa MRDD \u75c7\u72b6\u3002", "difficulty": "hard", "tags": ["mechanism", "application", "plant pathology", "hormone signaling", "molecular breeding"], "created_at": "2025-12-16T21:17:16", "token_est_question": 29, "token_est_answer": 31, "section": "Discussion", "context": "=== Discussion ===\nViral proteins can hijack host target proteins to disrupt the balance of plant hormones, which are crucial for facilitating viral systemic infection, ultimately leading to the appearance of viral symptoms24,30,31. However, little is known about the molecular mechanisms underlying the manifestation of MRDD symptoms. Our previous study demonstrated that the viral P7-1 effector binds more tightly to ZmGDI\u03b1 than to ZmGDI\u03b1-hel, which underpins the differential resistance to RBSDV. In this study, we discovered that P7-1 can recruit another host target, ZmGA2ox7.3, which also exhibits a tighter binding affinity to ZmGDI\u03b1 than to ZmGDI\u03b1-hel. During RBSDV infection, the allelic variation at the _ZmGDI\u03b1_ locus, caused by _helitron_ insertion, greatly influences both the quantities and interactions among ZmGA2ox7.3, ZmGDI\u03b1/ZmGDI\u03b1-hel, and the viral P7-1. All these factors play crucial roles in reducing active gibberellins and, consequently, disrupting the balance of auxin and cytokinins in maize, thereby determining the outcome of MRDD. Our discovery thus elucidates the intricate molecular mechanisms that underlie the development of MRDD symptoms.\nIn susceptible maize, RBSDV infection could significantly enhance the gene expression of _ZmGA2ox7.3_. Given that no RBSDV protein serves as a transcription factor, the viral proteins likely recruit host transcription factors to promote _ZmGA2ox7.3_ expression. Previous researches indicated that GA-2 oxidase family genes are regulated by multiple transcription factors32,33. Intriguingly, our previous Co-IP/MS assay revealed numerous transcription factors in immunoprecipitates associated with RBSDV infection, such as growth-regulating factor (GRF), AP2-EREBP, basic helix-loop-helix (bHLH), MADS-box, and GRAS (Supplemental Table 2). These findings suggest that, upon RBSDV invasion, the viral P7-1 effector may recruit certain maize transcription factors to promote the expression of _ZmGA2ox7.3_.\nOsGA2ox3 forms GA4-dependent multimers, displaying higher enzymatic activity than monomer27. Similarly, when exposed to GA4, ZmGA2ox7.3 can form oligomers (tetramers and dimers) with enhanced catalytic activity, converting the bioactive GA4 into its inactive GA34. In the ZmGA2ox7.33A variant, the three mutated residues are solely associated with the formation of oligomers, but not with enzymatic activity27. The failure to form ZmGA2ox7.33A oligomers leads to the absence of enzymatic activity, indicating that oligomer formation is a prerequisite for the enzymatic activity. Given that ZmGA2ox7.3 tetramers and dimers exhibited simultaneous increases in the presence of GA4, we thus cannot distinguish between them in their enzymatic activities. Therefore, we generally conclude that ZmGA2ox7.3 oligomers enhance enzymatic activity. In susceptible maize, RBSDV-triggered upregulation of ZmGA2ox7.3, combined with a massive accumulation of the viral P7-1 effector, leads to the formation of abundant ZmGA2ox7.3 oligomers. These oligomers, whether acting alone or in conjunction with ZmGDI\u03b1/P7-1, can efficiently convert active GAs into their inactive forms, thereby contributing to the manifestation of MRDD syndrome. Conversely, in resistant maize, the presence of ZmGDI\u03b1-hel dramatically inhibits the RBSDV-induced _ZmGA2ox7.3_ expression and prevents ZmGA2ox7.3 oligomerization, resulting in minimal or no MRDD symptoms.\nViral effectors such as TMV replicase and RDV-P2 disrupt plant hormone balance19,34. In the current study, the RBSDV P7-1 effector not only stimulates _ZmGA2ox7.3_ expression but also amplifies its enzymatic activity when combined with ZmGDI\u03b1. Therefore, P7-1 plays a vital role in promoting ZmGA2ox7.3 to reduce GA1 and GA4 levels. Notably, in a previous Co-IP/MS assay, we identified seven auxin response factors (ARFs) (Supplemental Table 2). Given that ARFs act as transcriptional activators in the auxin signaling pathway, our findings hint at the possibility that the ZmGDI\u03b1/P7-1 complex might also interface with these ARFs, thereby potentially disrupting the auxin balance.", "Thinking\u6a21\u5f0f": "high", "generation_type": "\u63a8\u7406\u578b"}','2026-03-23 14:42:30.828914','2026-03-23 14:42:30.828924');
INSERT INTO qa_pairs VALUES(13,2,'在构建针对特定转基因玉米事件的分子检测体系时，外源 T-DNA 被证明以单拷贝形式插入玉米基因组，随后利用纳米孔单分子测序明确了插入位点及其两侧的基因组侧翼序列，并与非转基因对照基因组比对确认插入发生在特定染色体的非编码区域，研究者进一步根据这一插入—侧翼连接区域设计了只在该事件中能扩增出目标条带的特异性 PCR 引物。应如何利用这些信息建立一种既具有事件特异性又可实现精确定量的检测方法，并说明这一方法在转基因安全性评价和监管中的意义？','首先，利用纳米孔测序获得的插入位点及其两侧基因组侧翼序列，确定仅存在于该转基因事件中的插入—基因组连接区序列，这一连接区在非转基因材料或其他事件中不存在。以该连接序列为靶标，可设计一对引物（一端位于外源片段，一端位于宿主侧翼序列），构建只在该事件中产生扩增产物的事件特异 PCR 体系，实现对该事件的高特异性鉴定。在南方杂交已证实外源片段为单拷贝插入的前提下，再将这一事件特异扩增体系与实时荧光定量 PCR 或数字 PCR 等平台结合，并配合一个拷贝数已知的内源基因作为参照，通过标准曲线或绝对定量即可将扩增信号直接换算为样品中该事件的基因组拷贝数和质量百分含量，从而建立既定性又定量的检测方法。由于检测靶标是唯一的插入位点，结果具有高度特异性和可重复性，可以在复杂样品中精确区分目标事件与非转基因材料及其他转基因事件，为后续对该事件的安全性评价（如环境释放监测、产业链中含量追踪、标签合规性核查等）提供可靠的分子工具，支撑监管部门对特定事件进行精确识别和长期监控。','{"species": "Zea mays", "paper_id": "PMC11685737", "reasoning_steps": ["Step 1: \u5c06\u7389\u7c73\u9ad8\u6548\u5229\u7528\u785d\u9178\u76d0\u57fa\u56e0 ZmNRT1.1 A \u8f6c\u5165\u7389\u7c73\u54c1\u79cd y822\uff0c\u83b7\u5f97\u5177\u6709\u589e\u5f3a\u4f4e\u6c2e\u80c1\u8feb\u8010\u53d7\u6027\u7684\u8f6c\u57fa\u56e0\u7389\u7c73\u4e8b\u4ef6 ND4401 \u548c ND4403\u3002", "Step 2: \u901a\u8fc7 Southern \u6742\u4ea4\u786e\u8ba4 ND4401 \u548c ND4403 \u4e2d\u5916\u6e90 T-DNA \u4e3a\u5355\u4e00\u63d2\u5165\uff0c\u8fd9\u8868\u660e\u6bcf\u4e2a\u4e8b\u4ef6\u4e2d\u4ec5\u5b58\u5728\u4e00\u4e2a\u63d2\u5165\u4f4d\u70b9\uff0c\u4fbf\u4e8e\u660e\u786e\u754c\u5b9a\u4e8b\u4ef6\u7279\u5f02\u7684\u5206\u5b50\u6807\u8bb0\u3002", "Step 3: \u5229\u7528\u7b2c\u4e09\u4ee3\u6d4b\u5e8f\u6280\u672f\u2014\u2014\u7eb3\u7c73\u5b54\u5355\u5206\u5b50\u6d4b\u5e8f\uff0c\u5bf9 ND4401 \u548c ND4403 \u7684\u57fa\u56e0\u7ec4\u8fdb\u884c\u5206\u5b50\u8868\u5f81\uff0c\u786e\u5b9a\u5916\u6e90\u57fa\u56e0\u7684\u63d2\u5165\u4f4d\u70b9\u53ca\u5176\u4e24\u4fa7\u7684\u4fa7\u7ffc\u5e8f\u5217\u3002", "Step 4: \u5c06\u83b7\u5f97\u7684\u63d2\u5165\u4f4d\u70b9\u548c\u4fa7\u7ffc\u5e8f\u5217\u4e0e\u5bf9\u7167\u7ec4\u57fa\u56e0\u7ec4\u8fdb\u884c\u6bd4\u8f83\uff0c\u521d\u6b65\u5224\u5b9a\u5916\u6e90 T-DNA \u5728 ND4401 \u63d2\u5165\u4e8e\u7b2c5\u67d3\u8272\u4f53\u975e\u7f16\u7801\u533a\uff0c\u5728 ND4403 \u63d2\u5165\u4e8e\u7b2c3\u67d3\u8272\u4f53\u975e\u7f16\u7801\u533a\uff0c\u5e76\u53ef\u533a\u5206\u4e0d\u540c\u4e8b\u4ef6\u7684\u63d2\u5165\u7279\u5f81\u3002", "Step 5: \u57fa\u4e8e\u5df2\u786e\u5b9a\u7684\u4fa7\u7ffc\u5e8f\u5217\uff0c\u4e3a\u4e0d\u540c\u8f6c\u5316\u4e8b\u4ef6\u8bbe\u8ba1\u4e86\u7279\u5f02\u6027\u7684 PCR \u5f15\u7269\uff0c\u8fd9\u4e9b\u5f15\u7269\u53ea\u5728\u542b\u6709\u7279\u5b9a\u63d2\u5165\u4f4d\u70b9\u7684\u4e8b\u4ef6\u4e2d\u80fd\u591f\u6269\u589e\u51fa\u76ee\u6807\u7247\u6bb5\uff0c\u4ece\u800c\u5b9e\u73b0\u4e8b\u4ef6\u7279\u5f02\u7684\u5206\u5b50\u68c0\u6d4b\u3002", "Step 6: \u5355\u62f7\u8d1d\u660e\u786e\u63d2\u5165\u4f4d\u70b9 + \u4e8b\u4ef6\u7279\u5f02\u4fa7\u7ffc\u5e8f\u5217 + \u4e8b\u4ef6\u7279\u5f02 PCR \u5f15\u7269\u7684\u7ec4\u5408\uff0c\u4f7f\u5f97\u68c0\u6d4b\u65b9\u6cd5\u5177\u6709\u9ad8\u5ea6\u7279\u5f02\u6027\u548c\u53ef\u91cd\u590d\u6027\uff0c\u53ef\u4ee5\u7cbe\u51c6\u533a\u5206 ND4401\u3001ND4403 \u4e0e\u975e\u8f6c\u57fa\u56e0\u5bf9\u7167\u6216\u5176\u4ed6\u4e8b\u4ef6\u3002", "Step 7: \u7531\u4e8e\u8f6c\u57fa\u56e0\u5b89\u5168\u8bc4\u4ef7\u9700\u8981\u5bf9\u5177\u4f53\u8f6c\u57fa\u56e0\u4e8b\u4ef6\u8fdb\u884c\u51c6\u786e\u8bc6\u522b\u548c\u76d1\u6d4b\uff0c\u8fd9\u79cd\u57fa\u4e8e\u5df2\u77e5\u63d2\u5165\u4f4d\u70b9\u548c\u7279\u5f02\u6027\u5f15\u7269\u7684\u7cbe\u786e\u5b9a\u91cf\u68c0\u6d4b\u65b9\u6cd5\u53ef\u76f4\u63a5\u7528\u4e8e\u540e\u7eed\u7684\u5b89\u5168\u6027\u68c0\u6d4b\u548c\u76d1\u7ba1\uff0c\u4e3a\u5b89\u5168\u8bc4\u4ef7\u63d0\u4f9b\u53ef\u9760\u7684\u5206\u5b50\u8bc1\u636e\u3002"], "question_cot": "", "final_conclusion": "\u5229\u7528\u5357\u65b9\u6742\u4ea4\u548c\u7eb3\u7c73\u5b54\u5355\u5206\u5b50\u6d4b\u5e8f\u786e\u5b9a\u8f6c\u57fa\u56e0\u7389\u7c73\u4e8b\u4ef6\u7684\u5916\u6e90\u57fa\u56e0\u5355\u62f7\u8d1d\u63d2\u5165\u4f4d\u70b9\u53ca\u5176\u4fa7\u7ffc\u5e8f\u5217\uff0c\u5e76\u636e\u6b64\u8bbe\u8ba1\u7279\u5f02\u6027\u5f15\u7269\uff0c\u53ef\u4ee5\u5efa\u7acb\u4e00\u79cd\u5bf9\u7279\u5b9a\u65b0\u8f6c\u57fa\u56e0\u7389\u7c73\u4e8b\u4ef6\u8fdb\u884c\u7cbe\u786e\u5b9a\u91cf\u68c0\u6d4b\u7684\u65b9\u6cd5\uff0c\u4ece\u800c\u4e3a\u5176\u540e\u7eed\u5b89\u5168\u6027\u8bc4\u4ef7\u63d0\u4f9b\u53ef\u9760\u5206\u5b50\u68c0\u6d4b\u5de5\u5177\u3002", "difficulty": "medium", "tags": ["method", "application", "mechanism", "biosafety", "molecular_detection"], "created_at": "2025-12-16T21:15:26", "token_est_question": 6, "token_est_answer": 9, "section": "Abstract", "context": "=== Abstract ===\nThe increasing development of new genetically modified organisms underscores the critical need for comprehensive safety assessments, emphasizing the significance of molecular evidence such as gene integration, copy numbers, and adjacent sequences. In this study, the maize nitrate-efficient utilization gene _ZmNRT1.1 A_ was introduced into maize variety y822 using transgenic technology, producing transgenic maize events ND4401 and ND4403 with enhanced tolerance to low nitrogen stress. Southern hybridization confirmed that the exogenous T-DNA was singly inserted in both maize transformation events, ND4401 and ND4403. This study utilized third-generation sequencing technology\u2014nanopore single-molecule sequencing\u2014to perform molecular characterization of the integration events. It successfully determined the exogenous gene insertion sites and flanking sequences in ND4401 and ND4403. Comparative analysis with the control group facilitated the preliminary identification of the integration sites of the exogenous T-DNA fragments in these transgenic maize events. Based on the obtained flanking sequences, specific PCR primers were designed for different transformation events. The insertion site for ND4401 was pinpointed in the non-coding region of chromosome 5, and for ND4403, in the non-coding region of chromosome 3. Utilizing the sequencing results, the study developed specific detection primers for the maize transformation events, establishing a precise method for detecting newly created transgenic maize events, which will contribute to subsequent safety assessments.\n=== Supplementary Information ===\nThe online version contains supplementary material available at 10.1038/s41598-024-83403-6.\n**Keywords:** Maize (_Zea mays_ L.), Transgenic, Nanopore single-molecule sequencing, Exogenous gene insertion sites\n**Subject terms:** Agricultural genetics, Molecular engineering in plants, Plant biotechnology", "Thinking\u6a21\u5f0f": "high", "generation_type": "\u63a8\u7406\u578b"}','2026-03-23 14:42:30.927925','2026-03-23 14:42:30.927936');
INSERT INTO qa_pairs VALUES(14,2,'在某转基因作物中，外源片段插入区域发生了复杂的基因组重排，受体基因组本身重复序列丰富，并且部分转基因元件序列与内源基因组序列高度相似，而安全评价又要求得到外源基因拷贝数、插入位点及其周边序列等较为完整和准确的分子信息。当常规的Genome Walking、TAIL-PCR等依赖PCR扩增的方法难以稳定放大并精确定位插入位点时，应优先选择哪一类技术来进行插入位点解析，这类技术通过哪些关键机制在这种复杂背景下优于传统PCR策略？','在这类具有复杂重排和高重复背景的转基因作物中，应优先选择以PacBio SMRT或Oxford Nanopore为代表的第三代长读长单分子测序技术。其关键优势在于能够直接获得跨越插入位点及大片上下游区域的长读长，从而一次性覆盖复杂或重复的基因组片段，并精确描绘外源片段与受体基因组的拼接关系；同时测序过程不依赖PCR扩增，避免了引物设计不当、扩增偏好和与内源同源序列误扩增带来的偏倚，因此在插入拷贝数统计、插入位点定位以及周边结构变异解析方面，比传统PCR类方法更加全面、标准化且准确。','{"species": "Zea mays", "paper_id": "PMC11685737", "reasoning_steps": ["Step 1: \u8f6c\u57fa\u56e0\u4f5c\u7269\u5728\u5b89\u5168\u8bc4\u4ef7\u4e2d\u5fc5\u987b\u63d0\u4ea4\u5916\u6e90\u57fa\u56e0\u6574\u5408\u7684\u5206\u5b50\u8bc1\u636e\uff0c\u5305\u62ec\u63d2\u5165\u7247\u6bb5\u5e8f\u5217\u3001\u62f7\u8d1d\u6570\u3001\u63d2\u5165\u4f4d\u70b9\u53ca\u5176\u90bb\u8fd1\u5e8f\u5217\u7b49\u4fe1\u606f\u3002", "Step 2: \u4f20\u7edf\u7528\u4e8e\u89e3\u6790\u63d2\u5165\u4f4d\u70b9\u7684\u5206\u5b50\u65b9\u6cd5\u4e3b\u8981\u4f9d\u8d56PCR\uff08\u5982Genome Walking\u3001TAIL-PCR\u3001I-PCR\u3001T-linker PCR\uff09\uff0c\u8fd9\u4e9b\u65b9\u6cd5\u5728\u591a\u79cd\u8f6c\u57fa\u56e0\u4f5c\u7269\u4e2d\u88ab\u5e7f\u6cdb\u4f7f\u7528\u3002", "Step 3: \u8fd9\u4e9bPCR\u65b9\u6cd5\u7684\u51c6\u786e\u6027\u4f1a\u53d7\u5230\u5f15\u7269\u8bbe\u8ba1\u7b49\u56e0\u7d20\u5f71\u54cd\uff0c\u5c24\u5176\u662f\u5728\u63d2\u5165\u4f4d\u70b9\u53d1\u751f\u590d\u6742\u57fa\u56e0\u7ec4\u91cd\u6392\u65f6\uff0c\u5bb9\u6613\u51fa\u73b0\u8bc6\u522b\u56f0\u96be\u6216\u7ed3\u679c\u4e0d\u5b8c\u6574\u3002", "Step 4: \u5f53\u8f6c\u57fa\u56e0\u5143\u4ef6\u4e0e\u53d7\u4f53\u57fa\u56e0\u7ec4\u5185\u7684\u5e8f\u5217\u76f8\u4f3c\u65f6\uff0cPCR\u7c7b\u65b9\u6cd5\u4e5f\u96be\u4ee5\u6709\u6548\u533a\u5206\u5e76\u51c6\u786e\u9501\u5b9a\u63d2\u5165\u4f4d\u70b9\u53ca\u90bb\u8fd1\u5e8f\u5217\uff0c\u4ece\u800c\u9650\u5236\u4e86\u5176\u5728\u590d\u6742\u60c5\u51b5\u4e0b\u7684\u5e94\u7528\u3002", "Step 5: \u7b2c\u4e09\u4ee3\u6d4b\u5e8f\u6280\u672f\uff08\u5982PacBio SMRT\u548cOxford Nanopore\uff09\u53ef\u4ee5\u4ea7\u751f\u957f\u8bfb\u957f\u5e8f\u5217\uff0c\u5e76\u4e14\u4ee5\u5355\u5206\u5b50\u4e3a\u6d4b\u5e8f\u5bf9\u8c61\uff0c\u4e0d\u9700\u8981\u5728\u6d4b\u5e8f\u8fc7\u7a0b\u4e2d\u8fdb\u884cPCR\u6269\u589e\u3002", "Step 6: \u957f\u8bfb\u957f\u6709\u5229\u4e8e\u8de8\u8d8a\u590d\u6742\u6216\u91cd\u590d\u7684\u57fa\u56e0\u7ec4\u533a\u57df\uff0c\u4ece\u800c\u5728\u5168\u57fa\u56e0\u7ec4\u8303\u56f4\u5185\u66f4\u5168\u9762\u3001\u51c6\u786e\u5730\u8986\u76d6\u63d2\u5165\u4f4d\u70b9\u53ca\u5176\u5468\u8fb9\u5e8f\u5217\uff1b\u5355\u5206\u5b50\u6d4b\u5e8f\u907f\u514dPCR\u6269\u589e\u504f\u597d\uff0c\u63d0\u9ad8\u7ed3\u679c\u7684\u6807\u51c6\u5316\u3001\u91cd\u590d\u6027\u548c\u51c6\u786e\u6027\u3002", "Step 7: \u56e0\u6b64\uff0c\u5728\u5b58\u5728\u590d\u6742\u57fa\u56e0\u7ec4\u91cd\u6392\u3001\u91cd\u590d\u5e8f\u5217\u6216\u8f6c\u57fa\u56e0\u5143\u4ef6\u4e0e\u57fa\u56e0\u7ec4\u5185\u6e90\u5e8f\u5217\u76f8\u4f3c\u7b49\u60c5\u5f62\u4e0b\uff0c\u7b2c\u4e09\u4ee3\u957f\u8bfb\u957f\u5355\u5206\u5b50\u6d4b\u5e8f\u6280\u672f\u6bd4\u4f9d\u8d56PCR\u7684\u4f20\u7edf\u65b9\u6cd5\u66f4\u9002\u5408\u7cbe\u786e\u89e3\u6790\u5916\u6e90\u57fa\u56e0\u63d2\u5165\u4f4d\u70b9\u53ca\u5176\u5468\u8fb9\u57fa\u56e0\u7ec4\u53d8\u5316\u3002"], "question_cot": "", "final_conclusion": "\u7b2c\u4e09\u4ee3\u957f\u8bfb\u957f\u5355\u5206\u5b50\u6d4b\u5e8f\u6280\u672f\u6bd4\u4f20\u7edfPCR\u7c7b\u65b9\u6cd5\u66f4\u9002\u5408\u51c6\u786e\u89e3\u6790\u8f6c\u57fa\u56e0\u690d\u7269\u4e2d\u5916\u6e90\u57fa\u56e0\u63d2\u5165\u4f4d\u70b9\u53ca\u5176\u5468\u8fb9\u590d\u6742\u57fa\u56e0\u7ec4\u7ed3\u6784\u3002", "difficulty": "medium", "tags": ["method", "application", "mechanism"], "created_at": "2025-12-16T21:16:37", "token_est_question": 2, "token_est_answer": 3, "section": "Introduction", "context": "=== Introduction ===\nAmidst the escalating challenges posed by global climate degradation and an ever-increasing population, a primary concern for nations worldwide is ensuring food security (Santos et al.20). Since commercial cultivation of genetically modified crops began in 1996, the area dedicated to their cultivation has shown a consistent increase. From 1996 to 2016, the cultivation area evolved from 1.7 million hectares to an impressive 190.4 million hectares, providing substantial economic, societal, and ecological benefits (James13). In recent years, the strategic incorporation of superior exogenous genes into diverse crops through genetic engineering has proven to be an efficient method for breeding superior cultivars (Qaim18).\nThe introduction of genetically modified crops has invariably placed their safety under extensive scrutiny and discussion (Domingo et al.6). In response, numerous countries worldwide have implemented strict legal frameworks and established dedicated regulatory bodies to oversee the deployment of genetically modified crops (Falkner et al.7). The careful selection of genetically modified varieties for commercial cultivation comes from a rigorous selection process among many transformation events, subjecting them to thorough scrutiny and safety assessments from production to regulatory approval (Bradford et al3.). In the context of genetically modified crop screening and safety evaluation, molecular evidence that proves the integration of exogenous genes into the recipient genome is essential. Submission of comprehensive molecular evidence and information, including sequence details of inserted exogenous genes, copy numbers, insertion sites, and adjacent sequences, is imperative for compliance with established standards (Li et al.15).\nDuring the integration of exogenous genes into the recipient genome, the insertion of plasmid DNA can cause mutations or breaks in the native genes of the recipient plant genome (Wilson et al28.). This may lead to gene silencing or activation, potentially forming new proteins or deactivating existing ones, resulting in unexpected changes that affect the quality and safety of genetically modified crops (Latham et al14.). These changes require the submission of detailed evidence to safety assessment authorities for thorough examination and approval (Cellini et al5.). Currently, traditional methods to characterize exogenous gene integration sites mainly use PCR technology, including techniques such as Genome Walking (GW), Thermal Asymmetric Interlaced PCR (TAIL-PCR), Inverse PCR (I-PCR), and T-linker PCR (Shu et al22.). These methods have effectively been used to examine the molecular features of integration sites in various genetically modified crops like Arabidopsis (Tan et al.25), maize (Spalinskas et al24.; Liu et al16.), rice (Fraiture et al8.), and tomato (Yang et al.32). While these techniques can identify the number of copies of inserted exogenous genes and the presence of plasmid backbones, they have limitations. The accuracy of these methods can be affected by factors such as primer selection, particularly in cases of complex genome rearrangements at the insertion site (Holst-Jensen et al12.). Furthermore, when transgenic elements are similar to sequences in the recipient genome, the effectiveness of traditional methods can be reduced, making it difficult to identify insertion sites and nearby sequences using PCR-based methods (Yang et al.31).\nIn recent years, DNA sequencing technology has evolved rapidly, marked by significant increases in sequencing capacity along with a decrease in costs. This evolution has made sequencing technology more accessible and widespread (Satam et al.21). A significant advancement in this field is represented by third-generation sequencing technologies, such as PacBio\u2019s SMRT and Oxford Nanopore Technologies\u2019 nanopore single-molecule sequencing. The primary advantage of these technologies is their ability to produce long reads, which are crucial for the comprehensive mapping and characterization of complex genomic regions (Lu et al.17). These long reads not only enhance the depth and breadth of genome sequencing but also improve the accuracy of detecting and characterizing the insertion sites of exogenous genes in transgenic plants (Heather and Chain11). Studies show that deep sequencing can comprehensively cover complex genomes (Wang et al.27), laying the groundwork for precise whole-genome research (Ajay et al1.). Different from Southern hybridization, PCR technology, and previous generations of sequencing, the key feature of third-generation sequencing is its ability to sequence single molecules, removing the need for PCR amplification during sequencing (Van Dijk et al.26). This method ensures high standardization, reliable repeatability, and superior accuracy, effectively and accurately detailing the insertion of exogenous genes, changes in the recipient genome, and the detection of plasmid scaffolds (Goodwin et al.10). Despite the vast, complex, and highly repetitive nature of the maize genome, the use of this technology in detailing the molecular characteristics of transgenic maize is still limited, presenting significant challenges (Cade et al.4).\nIn a previous study, two separate transgenic maize transformation events, ND4401 and ND4403, were created using the _ZmNRT1.1 A_ nitrate gene, which is recognized for its ability to withstand low nitrogen conditions. The goal was to provide strong molecular evidence to deeply investigate these two events, aiming to develop new transgenic maize varieties that can tolerate low nitrogen levels. To enhance the safety evaluation of these events, third-generation nanopore single-molecule sequencing technology was used to accurately determine the insertion sites and surrounding sequences of the exogenous genes in the ND4401 and ND4403 events. From the sequences obtained near the exogenous genes, specific PCR primers were crafted for each transformation event and used to detect these transgenic maize events. This study highlights the effectiveness of nanopore single-molecule sequencing technology in identifying molecular characteristics in transgenic plants.", "Thinking\u6a21\u5f0f": "high", "generation_type": "\u63a8\u7406\u578b"}','2026-03-23 14:42:31.037247','2026-03-23 14:42:31.037258');
INSERT INTO qa_pairs VALUES(15,2,'在利用农杆菌介导转化获得带有CaMV35S驱动的bar选择标记基因的玉米转化事件后，通过Southern blot仅能在真正整合了bar基因的材料中检测到特异杂交条带，进一步又利用长读长测序精确定位T-DNA在玉米基因组中的插入位置并获得两侧插入边界序列。如果在此基础上围绕插入位点设计PCR引物，使一端落在特定玉米基因组侧翼序列、另一端落在特定T-DNA序列，并用凝胶电泳观察扩增条带，应如何解释这种事件特异性PCR能够同时实现对ND4401、ND4403等特定转化事件的准确鉴定，并将其与其他转化事件或非转基因对照材料可靠区分？','事件特异性PCR之所以能特异鉴定ND4401和ND4403，并区分其他转化事件或非转基因材料，关键在于引物跨越了“宿主基因组侧翼序列—特定T-DNA片段”的连接区域。Southern blot首先证明bar基因真实稳定整合，长读长测序进一步给出每个事件唯一的插入位点和两侧边界序列，在此基础上设计的引物一端只能与该事件特有的玉米基因组侧翼序列互补，另一端只能与特定T-DNA序列互补。只有在基因组中同时存在这两段序列并按该事件特有的方式相邻连接时，两条引物才会形成成对结合位点并产生扩增条带；在非转基因材料中缺少T-DNA，在其他插入事件中虽然有T-DNA但侧翼序列或连接方式不同，都无法形成完整的引物结合组合，因此不产生特异条带。通过观察预期大小条带是否出现即可准确判断样本是否携带ND4401或ND4403等目标转化事件，从而实现对不同事件与非转基因材料的可靠分辨。','{"species": "Zea mays", "paper_id": "PMC11685737", "reasoning_steps": ["Step 1: \u91cd\u7ec4\u8868\u8fbe\u8f7d\u4f53\u4ee5pCAMBIA1301\u4e3a\u9aa8\u67b6\uff0c\u542b\u6709\u5728pCaMV35S\u542f\u52a8\u5b50\u9a71\u52a8\u4e0b\u8868\u8fbe\u7684bar\u9009\u62e9\u6807\u8bb0\u57fa\u56e0\uff0c\u5e76\u7528\u4e8e\u83b7\u5f97\u7389\u7c73\u8f6c\u5316\u4e8b\u4ef6ND4401\u548cND4403\u3002", "Step 2: Southern blot\u5b9e\u9a8c\u4e2d\uff0c\u5229\u7528PCR\u6269\u589e\u83b7\u5f97\u7684bar\u57fa\u56e0\u7247\u6bb5\u4f5c\u4e3a\u63a2\u9488\uff0c\u4e0e\u7ecf\u9650\u5236\u6027\u5185\u5207\u9176\u5b8c\u5168\u6d88\u5316\u7684\u7389\u7c73\u57fa\u56e0\u7ec4DNA\u6742\u4ea4\uff0c\u53ea\u80fd\u5728\u771f\u5b9e\u6574\u5408\u4e86bar\u57fa\u56e0\u7684\u57fa\u56e0\u7ec4\u4e2d\u5f62\u6210\u7279\u5f02\u6742\u4ea4\u6761\u5e26\uff0c\u56e0\u6b64\u53ef\u786e\u8ba4ND4401\u548cND4403\u4e2dbar\u7684\u7a33\u5b9a\u6574\u5408\uff0c\u800c\u5bf9\u7167\u54c1\u79cdY822\u4e0d\u663e\u793a\u8be5\u6761\u5e26\u3002", "Step 3: \u5bf9ND4401\u548cND4403\u8fdb\u884c\u5355\u5206\u5b50\u7eb3\u7c73\u5b54\u6d4b\u5e8f\uff0c\u5e76\u5c06\u6d4b\u5e8f\u6570\u636e\u4e0eNos\u57fa\u56e0\u53ca\u8f7d\u4f53\u5e8f\u5217\u8fdb\u884c\u6bd4\u5bf9\uff0c\u53ef\u4ee5\u7cbe\u786e\u5b9a\u4f4dT-DNA\u5728\u7389\u7c73\u57fa\u56e0\u7ec4\u4e2d\u7684\u63d2\u5165\u4f4d\u7f6e\u53ca\u5176\u4e24\u4fa7\u7684\u8fb9\u754c\u5e8f\u5217\u3002", "Step 4: \u5728\u83b7\u5f97\u63d2\u5165\u4f4d\u70b9\u4e24\u4fa7\u8fb9\u754c\u5e8f\u5217\u540e\uff0c\u8bbe\u8ba1\u4e00\u7ec4\u5f15\u7269\uff0c\u4f7fPCR\u6269\u589e\u7247\u6bb5\u540c\u65f6\u5305\u542b\u7389\u7c73\u672c\u5e95\u57fa\u56e0\u7ec4\u5e8f\u5217\u548c\u76f8\u90bb\u7684T-DNA\u5143\u4ef6\uff0c\u901a\u8fc7\u6d4b\u5e8f\u6bd4\u5bf9\u9a8c\u8bc1\u8be5\u6269\u589e\u7247\u6bb5\u786e\u4e3a\u201c\u7389\u7c73\u57fa\u56e0\u7ec4\u5e8f\u5217\u2014T-DNA\u201d\u8fde\u63a5\u533a\u3002", "Step 5: \u7531\u4e8e\u8fd9\u79cd\u5f15\u7269\u4e00\u7aef\u4f4d\u4e8e\u7279\u5b9a\u7684\u7389\u7c73\u57fa\u56e0\u7ec4\u4fa7\u7ffc\u5e8f\u5217\uff0c\u53e6\u4e00\u7aef\u4f4d\u4e8e\u7279\u5b9a\u7684T-DNA\u5e8f\u5217\uff0c\u8be5\u7ec4\u5408\u4ec5\u5728\u5b58\u5728\u8be5\u7279\u5b9a\u63d2\u5165\u4e8b\u4ef6\uff08\u5982ND4401\u6216ND4403\uff09\u65f6\u624d\u80fd\u5f62\u6210\u6b63\u786e\u7684\u5f15\u7269\u7ed3\u5408\u4f4d\u70b9\u5e76\u4ea7\u751f\u6269\u589e\u4ea7\u7269\uff0c\u800c\u5728\u975e\u8f6c\u57fa\u56e0\u6750\u6599\u6216\u5176\u4ed6\u63d2\u5165\u4f4d\u70b9\u4e2d\u4e0d\u4f1a\u4ea7\u751f\u6761\u5e26\u3002", "Step 6: \u5c06\u4e0a\u8ff0\u4e8b\u4ef6\u7279\u5f02\u6027\u5f15\u7269\u7528\u4e8ePCR\u5e76\u8fdb\u884c\u743c\u8102\u7cd6\u51dd\u80f6\u7535\u6cf3\u5206\u6790\uff0c\u5373\u53ef\u901a\u8fc7\u662f\u5426\u51fa\u73b0\u9884\u671f\u6761\u5e26\u6765\u7279\u5f02\u6027\u9274\u5b9aND4401\u6216ND4403\uff0c\u5e76\u53ef\u9760\u5730\u533a\u5206\u4e0d\u542b\u8be5\u63d2\u5165\u4e8b\u4ef6\u7684\u5bf9\u7167\u7389\u7c73\u6750\u6599\u3002"], "question_cot": "", "final_conclusion": "\u57fa\u4e8e\u8f6c\u57fa\u56e0\u63d2\u5165\u8fb9\u754c\u5e8f\u5217\u8bbe\u8ba1\u7684\u4e8b\u4ef6\u7279\u5f02\u6027PCR\u5f15\u7269\uff0c\u53ea\u4f1a\u5728\u643a\u5e26\u76f8\u5e94\u8f6c\u5316\u4e8b\u4ef6\u63d2\u5165\u4f4d\u70b9\u7684\u7389\u7c73\u57fa\u56e0\u7ec4\u4e2d\u6269\u589e\u51fa\u6761\u5e26\uff0c\u4ece\u800c\u53ef\u7279\u5f02\u9274\u5b9aND4401\u548cND4403\u7b49\u7279\u5b9a\u8f6c\u5316\u4e8b\u4ef6\u5e76\u533a\u5206\u975e\u8f6c\u57fa\u56e0\u6750\u6599\u3002", "difficulty": "medium", "tags": ["method", "mechanism", "application"], "created_at": "2025-12-16T21:17:33", "token_est_question": 2, "token_est_answer": 2, "section": "Materials & Methods", "context": "=== Vector and plant materials ===\nThe recombinant expression vector pCAMBIA1301-_ZmNRT1.1 A_ -_bar_ was engineered using the pCAMBIA1301 vector as a backbone. This construct was engineered to express the _ZmNRT1.1 A_ gene under the regulation of the maize ubiquitin (_pZmUbi_) promoter, while the bar selection marker was under the control of the cauliflower mosaic virus 35 S (_pCaMV35S_) promoter. Transformation events for maize ND4401 and ND4403 were developed and maintained by our research team. The maize variety y822, soybean variety Williams82, and rice variety JiGeng88 were provided by Hongxiang Seed Company, the Soybean Research Institute and the Rice Research Institute of the Jilin Academy of Agricultural Sciences, respectively.\n=== Plant DNA extraction ===\nCultivated maize, soybean, and rice were grown in controlled pot conditions. Upon full leaf expansion, approximately 100 mg of leaf tissue was harvested and pulverized under liquid nitrogen. Genomic DNA was extracted utilizing the cetyltrimethylammonium bromide (CTAB) method. Concentration and purity assessments of the isolated DNA were performed using a Nanodrop UV spectrophotometer, and the DNA integrity was verified through 1% agarose gel electrophoresis.\n=== Southern blot assay ===\nFor Southern blotting, leaf samples from stable transgenic maize lines ND4401 and ND4403, alongside non-transgenic control Y822, were processed. Roughly 30 \u00b5g of genomic DNA was thoroughly digested with the restriction endonucleases _Sac_ I and _Xma_ I (New England Biolabs Inc., USA). The DNA fragments were then blotted onto a HybondTM-N + nylon membrane (GE Amersham, RPN303B, USA). A PCR-amplified bar gene segment served as the probe, with primers detailed in Table 2. PCR reactions were optimized in a 25 \u00b5L mixture containing 2\u00d7 PCR Mix (AS111, TransGen Biotech, Beijing), primers, genomic template, and nuclease-free water, followed by a standard amplification protocol. Post-PCR, the products were resolved on a 1% agarose gel, and the specific bands were excised and purified. Probe labeling and hybridization followed the kit\u2019s protocol (Labeling and Detection Starter Kit I, Roche Applied Science, USA), with hybridization at 42 \u2103 for 12\u201316 h. Post-hybridization washes were performed, and the membrane developed using BCIP/NBT substrate until distinct bands were observed.\n=== Table 2. ===\nPrediction of hybridization fragment size between transformant restriction endonuclease fragment and different probes.\nProbes | Prediction of hybridization fragment size \n \nSac I | Xma I \n_bar_ probes | 7469 bp | 7347 bp \nOpen in a new tab\n=== Single-molecule nanopore sequencing ===\nThe resequencing of the maize transgenic events ND4401 and ND4403 was contracted to the Jiangsu Academy of Agricultural Sciences. Nanopore sequencing, a method developed by Oxford Nanopore Technologies (ONT), was utilized. This technology is based on monitoring changes in electrical resistance as individual nucleotides pass through a nanoscale pore. Each nucleotide (A, C, G, T) induces a unique change in electrical resistance due to its specific chemical properties, enabling the accurate determination of the sequence of bases. The sequencing process includes three main steps: preparation of the DNA library, the sequencing itself, and data interpretation. Sequencing data underwent a BLAST search against the Nos gene sequence, and contigs were aligned to the vector sequence to pinpoint the precise insertion loci and flanking boundary sequences.\n=== Boundary sequence PCR validation ===\nPrimers flanking the transgene insertion sites were designed, producing PCR amplicons that included native maize genomic sequences and adjacent T-DNA elements. Sequencing services were procured from Kumei company, and the resulting sequences were aligned with both the inserted T-DNA and the reference maize genome.\n=== Specific PCR detection ===\nFollowing the PCR confirmation of the lateral sequences for the transformation events ND4401 and ND4403, a pair of event-specific PCR detection primers for maize were devised. The amplified products were subjected to analysis via 1% agarose gel electrophoresis.", "Thinking\u6a21\u5f0f": "high", "generation_type": "\u63a8\u7406\u578b"}','2026-03-23 14:42:31.145198','2026-03-23 14:42:31.145209');
INSERT INTO qa_pairs VALUES(16,2,'在一个转基因玉米事件中，Southern blot 在多个世代中始终只检测到一条杂交条带，长读长测序和基因组比对显示外源 T‑DNA 仅插入到一条染色体上的单一位置，插入位点两侧的侧翼序列与受体玉米基因组连续拼接，仅在插入处造成一小段位于非编码区的基因组序列缺失，并且这段缺失序列不位于任何蛋白质编码基因的编码区内。面对这种插入结构特征，应如何评价该转化事件在外源片段拷贝数和受体基因组编码功能完整性方面的安全性？','该转化事件的外源 T‑DNA 以单拷贝形式稳定整合到玉米基因组的非编码区，插入仅伴随一小段非编码序列缺失，没有落在任何蛋白质编码基因的编码区内，因此在结构层面既满足单拷贝、位点清晰的分子特征要求，又基本不直接破坏受体玉米基因组的蛋白质编码功能，可被视为在拷贝数和编码区完整性方面较为安全的插入事件。','{"species": "Zea mays", "paper_id": "PMC11685737", "reasoning_steps": ["Step 1: Southern blot \u68c0\u6d4b\u4e2d\uff0cND4401 \u548c ND4403 \u5728\u8fde\u7eed\u4e09\u4ee3\u6837\u672c\u4e2d\u4ec5\u51fa\u73b0\u5355\u4e00\u5927\u4e8e 7.5 kb \u7684\u6742\u4ea4\u6761\u5e26\uff0c\u800c\u9634\u6027\u5bf9\u7167 y822 \u65e0\u6761\u5e26\uff0c\u8bf4\u660e\u6bcf\u4e2a\u4e8b\u4ef6\u7684\u5916\u6e90\u7247\u6bb5\u4ee5\u5355\u62f7\u8d1d\u5f62\u5f0f\u6574\u5408\u3002", "Step 2: \u7b2c\u4e09\u4ee3\u6d4b\u5e8f\uff08\u7eb3\u7c73\u5b54\u6d4b\u5e8f\uff09\u5f97\u5230\u5305\u542b\u8f7d\u4f53\u5e8f\u5217\u7684\u957f\u7247\u6bb5\uff0c\u901a\u8fc7\u4e0e\u516c\u5f00\u7389\u7c73\u57fa\u56e0\u7ec4\u6bd4\u5bf9\uff0c\u786e\u8ba4 ND4401 \u63d2\u5165\u4e8e\u67d3\u8272\u4f53 5\u3001ND4403 \u63d2\u5165\u4e8e\u67d3\u8272\u4f53 3\uff0c\u4e14\u4e3a\u5355\u62f7\u8d1d\u63d2\u5165\uff0c\u8fd9\u4e0e Southern blot \u7684\u5355\u6761\u5e26\u7ed3\u679c\u76f8\u543b\u5408\u3002", "Step 3: \u5bf9 ND4401 \u7684\u63d2\u5165\u4f4d\u70b9\u8fdb\u884c PCR \u6269\u589e\u548c\u6d4b\u5e8f\u8868\u660e\uff0c\u5176\u5de6\u4fa7 1\u2013331 bp \u548c\u53f3\u4fa7 586\u2013689 bp \u4e3a\u7389\u7c73\u57fa\u56e0\u7ec4\u5e8f\u5217\uff0c\u4e2d\u95f4\u4e3a T\u2011DNA\uff0c\u63d2\u5165\u5bfc\u81f4\u67d3\u8272\u4f53 5 \u4e0a\u4ec5\u6709 27 bp \u57fa\u56e0\u7ec4\u5e8f\u5217\u7f3a\u5931\u3002", "Step 4: \u5bf9 ND4403 \u7684\u63d2\u5165\u4f4d\u70b9\u8fdb\u884c PCR \u6269\u589e\u548c\u6d4b\u5e8f\u8868\u660e\uff0c\u5176\u5de6\u4fa7 1\u2013507 bp \u548c\u53f3\u4fa7 459\u2013804 bp \u4e3a\u7389\u7c73\u57fa\u56e0\u7ec4\u5e8f\u5217\uff0c\u4e2d\u95f4\u4e3a T\u2011DNA\uff0c\u63d2\u5165\u5bfc\u81f4\u67d3\u8272\u4f53 3 \u4e0a\u4ec5\u6709 75 bp \u57fa\u56e0\u7ec4\u5e8f\u5217\u7f3a\u5931\u3002", "Step 5: \u4e0a\u8ff0\u7f3a\u5931\u533a\u6bb5\u7ecf\u4e0e\u516c\u5f00\u7389\u7c73\u57fa\u56e0\u7ec4\u6570\u636e\u5e93\u6bd4\u5bf9\uff0c\u5747\u4f4d\u4e8e\u76f8\u5e94\u67d3\u8272\u4f53\u7684\u975e\u7f16\u7801\u533a\uff0c\u4e14\u672a\u843d\u5728\u4efb\u4f55\u57fa\u56e0\u7f16\u7801\u533a\u5185\u3002", "Step 6: \u975e\u7f16\u7801\u533a\u5e8f\u5217\u7684\u7f3a\u5931\u548c\u63d2\u5165\u4e0d\u4f1a\u76f4\u63a5\u6539\u53d8\u86cb\u767d\u8d28\u7f16\u7801\u5e8f\u5217\u672c\u8eab\uff0c\u56e0\u6b64\u5728\u8fd9\u4e24\u4e2a\u4e8b\u4ef6\u4e2d\uff0cT\u2011DNA \u5355\u62f7\u8d1d\u6574\u5408\u672a\u7834\u574f\u57fa\u56e0\u7ec4\u4e2d\u7684\u57fa\u56e0\u7f16\u7801\u533a\u3002", "Step 7: \u7531\u6b64\u53ef\u63a8\u65ad\uff0c\u8fd9\u4e24\u79cd\u8f6c\u5316\u4e8b\u4ef6\u5728\u7ed3\u6784\u4e0a\u65e2\u5b9e\u73b0\u4e86\u5355\u62f7\u8d1d\u7a33\u5b9a\u63d2\u5165\uff0c\u53c8\u4fdd\u6301\u4e86\u53d7\u4f53\u7389\u7c73\u57fa\u56e0\u7ec4\u7f16\u7801\u529f\u80fd\u7684\u5b8c\u6574\u6027\u3002"], "question_cot": "", "final_conclusion": "\u5728\u8f6c\u5316\u4e8b\u4ef6 ND4401 \u548c ND4403 \u4e2d\uff0c\u5916\u6e90 T\u2011DNA \u4ee5\u5355\u62f7\u8d1d\u5f62\u5f0f\u7a33\u5b9a\u6574\u5408\u4e8e\u7389\u7c73\u975e\u7f16\u7801\u533a\u4e14\u4e0d\u7834\u574f\u57fa\u56e0\u7f16\u7801\u533a\uff0c\u56e0\u6b64\u4e0d\u4f1a\u76f4\u63a5\u7834\u574f\u53d7\u4f53\u7389\u7c73\u57fa\u56e0\u7ec4\u7684\u7f16\u7801\u529f\u80fd\u3002", "difficulty": "medium", "tags": ["result", "mechanism", "method", "application"], "created_at": "2025-12-16T21:18:28", "token_est_question": 6, "token_est_answer": 3, "section": "Results", "context": "=== Genomic southern blot analysis of transgenic maize events ===\nIn the genomic Southern blot analysis, single bands exceeding 7.5 kb were detected across three successive generations for transformation events ND4401 and ND4403, indicating the integration of a single copy of the transgene into the maize genome. No hybridization bands were observed in the negative control (y822), corroborating the specificity of the transgene insertion (Figs. 1 and 2).\n=== Fig. 1. ===\n \nOpen in a new tab\nSchematic diagram of the probe and the cleavage site on the inserted fragment.1: Target genes: maize ubi promoter, ZmNRT1.1 A coding region, T-NOS terminator; 2: Screening marker genes: P35S promoter, bar gene coding region, T-35 S terminator.\n=== Fig. 2. ===\n \nOpen in a new tab\nSouthern blot detection of the bar gene in transgenic maize events ND4401 and ND4403. (**A)** M represents Trans15K DNA Marker; P represents positive plasmid (pCAMBIA1301-_ZmNRT1.1 A_ -bar); 1 represents genomic DNA of T4 transgenic maize event ND4401 completely digested with _Sac_ I; 2 represents genomic DNA of T5 transgenic maize event ND4401 completely digested with _Sac_ I; 3 represents genomic DNA of T6 transgenic maize event ND4401 completely digested with _Sac_ I; 4 represents WT. (**B)** M represents Trans15K DNA Marker; P represents positive plasmid (pCAMBIA1301-_ZmNRT1.1 A_ -bar); 1 represents genomic DNA of T4 transgenic maize event ND4403 completely digested with _Xma_ I; 2 represents genomic DNA of T5 transgenic maize event ND4403 completely digested with _Xma_ I; 3 represents genomic DNA of T6 transgenic maize event ND4403 completely digested with _Xma_ I; 4 represents WT. Note: T4, T5, and T6 represent the fourth, fifth, and sixth generations, respectively, of self-pollinated transgenic plants. WT refers to the wild-type maize line y822.\n=== Resequencing and integration site analysis of transgenic maize events ===\nTo delve deeper into the precise full-length DNA sequence of the T-DNA inserted into the maize genome and the flanking sequences at the integration sites, we utilized Oxford Nanopore Technologies\u2019 third-generation sequencing. The sequencing depth was 10-fold with an average read length of 20 kb, peaking at 60 kb, and an accuracy rate of 85%. Through BLAST alignment of Nos gene sequences from the sequencing output, contigs of approximately 14 kb and 10 kb were identified containing the target vector sequences. This enabled us to pinpoint the genomic integration sites and their flanking sequences for both ND4401 and ND4403 events. The acquired sequences were then aligned against the publicly available maize genome database, confirming the single-copy insertion of the transgenes at chromosomes 5 and 3, respectively. The alignment results were consistent with the single-copy insertion deduced from the Southern blot analysis (Fig. 3).\n=== Fig. 3. ===\n \nOpen in a new tab\nPrimer binding sites for the T-DNA integration sites and flanking sequences of the two transgenic maize events. AB Integration sites and primer binding sites for the flanking sequences of the T-DNA inserted into chromosome 5 of transgenic maize ND4401; CD Integration sites and primer binding sites for the flanking sequences of the T-DNA inserted into chromosome 3 of transgenic maize ND4403; * denotes omitted bases; underlined regions represent genomic sequences on both sides; unmarked regions indicate T-DNA sequences; arrows indicate primer binding sites. Blue in AC represents insert vector sequences, pink represents genomic sequences, gray represents lost sequences, and the same colors in BD are highlighted as upstream and downstream of the same primer.\nIn ND4401 and ND4403, we aligned the contig with the target vector sequence to determine the gene insertion sites and the sequences of the flanking regions at both ends of the insert. For the two transformation events, sequencing yielded 625 bp from the 5\u2019 end of the insert and 132 bp from the 3\u2019 end, as well as 647 bp from the 5\u2019 end and 356 bp from the 3\u2019 end. These two types of genomic flanking sequences were then subjected to BLAST nucleotide-nucleotide alignment against the publicly available maize genome database ( ). This analysis confirmed that the insertion sequences of the two transformation events were integrated into the maize chromosome 5 and chromosome 3, respectively. The feedback maps from the alignment are shown in Fig. 3.\n=== PCR amplification of flanking sequences adjacent to T-DNA integration sites ===\nIn light of the sequencing outcomes, primers were designed for the PCR detection of flanking sequences on both the left and right sides of the transformation events ND4401 and ND4403 (Table 1). These primers aimed to amplify products encompassing both maize genomic sequences and T-DNA sequences. The electropherograms clearly exhibit the target bands solely within the transformation events ND4401 and ND4403, conspicuously absent in the non-transgenic maize line y822 (Fig. 4).\n=== Table 1. ===\nPrimer sequences and purpose used in the research.\nPrimer ID | Primer sequences(5\u2019\u22123\u2019) | Primer purpose \n \nBar428-F(428 bp) | AGAAACCCACGTCATGCCAGTT | Southern hybridization probe primer \nBar428-R(428 bp) | CTGCACCATCGTCAACCACTACAT | Southern hybridization probe primer \nND4401LB-F(1069 bp) | TCTCAGCCCCTTACGGCA | Side sequence PCR primers \nND4401LB-R(1069 bp) | GCACCATCGTCAACCACT | Side sequence PCR primers \nND4401RB-F(689 bp) | CAGAAGTAACACCAAACA | Side sequence PCR primers \nND4401RB-R(689 bp) | TGGACCGACGAGTGAGAT | Side sequence PCR primers \nND4403LB-F(897 bp) | CTATGGCGCTGTGTTGTCG | Side sequence PCR primers \nND4403LB-R(897 bp) | AACTGGCATGACGTGGGT | Side sequence PCR primers \nND4403RB-F(804 bp) | ACACAAACACGGATCTAACACA | Side sequence PCR primers \nND4403RB-R(804 bp) | TTTTGGGATAGTTTAGGGCTTCT | Side sequence PCR primers \nND4401LB-SF(242 bp) | GAAGGAGTGGACGAACATGC | Specific PCR primers \nND4401LB-SR(242 bp) | GGGTTTCGCTCATGTGTTGA | Specific PCR primers \nND4401RB-SF(239 bp) | CACACCACATCATCACAACCA | Specific PCR primers \nND4401RB-SR(239 bp) | GTTGGCGCGGGATGGAAC | Specific PCR primers \nND4403LB-SF(244 bp) | CAAGTGAGGTGACGATCGAC | Specific PCR primers \nND4403LB-SR(244 bp) | AAACGTCCGCAATGTGTTATT | Specific PCR primers \nND4403RB-SF(287 bp) | TGTTGCCGGTCTTGCGAT | Specific PCR primers \nND4403RB-SR(287 bp) | ATGCCGTCCATCCTGTTCC | Specific PCR primers \nOpen in a new tab\n=== Fig. 4. ===\n \nOpen in a new tab\nPCR amplification of the T-DNA flanking sequences of transgenic events ND4401 and ND4403. LB represents the left border flanking sequence isolation; RB represents the right border flanking sequence; M represents the DNA marker DL2000; BK represents the blank control; y822 represents the negative control.\nSubsequent sequencing of the fragments derived from Fig. 3A discerns that the left flanking sequence of ND4401 spans a length of 1069 bp (Fig. 3A). Commencing from the 5\u2019 terminus, positions 1\u2013331 correspond to maize genomic sequences (Chr5: 38326912\u201338327242 bp), whereas positions 332\u20131069 encapsulate the T-DNA sequence. The right flanking sequence of ND4401 extends to 689 bp, with positions 1\u2013585 representing the T-DNA sequence, and positions 586\u2013689 depicting maize genomic sequences (Chr05: 38327270-3832773 bp). The integration of exogenous T-DNA induces a 27 bp deletion within the recipient maize genome (Chr05: 38327243\u201338327269 bp). Substantiating this, BLAST nucleotide-nucleotide alignment of the obtained 5\u2019 end 625 bp and 3\u2019 end 132 bp genomic flanking sequences against the maize public genome database unequivocally confirms the insertion site in the non-coding region of chromosome 5 in maize. Crucially, this insertion does not disrupt gene-coding regions, affirming the integrity of maize genome functionality, with the excised 27 bp constituting non-coding region sequences.\nSimilarly, sequencing of the fragments sourced from Fig. 3B unveils that the left flanking sequence of ND4403 encompasses 897 bp (Fig. 3B). Starting from the 5\u2019 end, positions 1\u2013507 correspond to maize genomic sequences (Chr03: 7773225\u20137773731 bp), while positions 508\u2013897 encapsulate the T-DNA sequence. The right flanking sequence of ND4403 extends to 804 bp, with positions 1\u2013458 representing the T-DNA sequence, and positions 459\u2013804 depicting maize genomic sequences (Chr03: 7773807\u20137774152 bp). The introduction of exogenous T-DNA manifests an 75 bp deletion within the recipient maize genome (Chr03: 7773732\u20137773806 bp). Validation through BLAST nucleotide-nucleotide alignment of the obtained 5\u2019 end 647 bp and 3\u2019 end 356 bp genomic flanking sequences against the maize public genome database definitively establishes the insertion site in the non-coding region of chromosome 3 in maize. Importantly, this insertion has no impact on gene-coding regions, thus preserving the normal biological functionality of the maize genome. The omitted 75 bp sequences represent non-coding region sequences.\n=== Specific PCR detection of transgenic maize transformation events ===\nSpecific primers were carefully designed for both the left and right integration sites of the T-DNA within the genomes of ND4401 and ND4403. A variety of control materials were selected for thorough specificity testing, including maize varieties y822 and KN5585, soybean variety Williams 82, rice variety Jigeng 88, and cotton variety Lumenyan 15. DNA was extracted from five different tissues/organs of ND4401 and ND4403\u2014roots, stems, leaves, male spikes, and seeds\u2014and PCR amplification was conducted using two sets of maize transformation event-specific primers. The PCR results were clear, showing distinct bands only in the transgenic tissues of maize ND4401 and ND4403, and not in the control materials. This confirmed the exceptional specificity of the primer pairs in identifying the transgenic maize events ND4401 and ND4403 (Fig. 5).\n=== Fig. 5. ===\n \nOpen in a new tab\nSpecific primer PCR detection of transgenic events ND4401 and ND4403. LB represents the left border flanking sequence isolation; RB represents the right border flanking sequence; M represents the DNA marker DL2000; 1 represents transgenic maize roots; 2 represents transgenic maize stems; 3 represents transgenic maize leaves; 4 represents transgenic maize male spikes; 5 represents transgenic maize seeds; 6 represents maize variety y822; 7 represents maize variety KN5585; 8 represents cultivated soybean variety Williams 82; 9 represents rice variety Jigeng 88; 10 represents cotton variety Lumianyan 15.", "Thinking\u6a21\u5f0f": "high", "generation_type": "\u63a8\u7406\u578b"}','2026-03-23 14:42:31.243643','2026-03-23 14:42:31.243649');
INSERT INTO qa_pairs VALUES(17,2,'在对转基因作物进行分子水平安全性评价和事件追踪时，研究者利用第三代纳米孔单分子测序解析外源基因在基因组中的整合位点及其侧翼序列，可以同时获得插入所在染色体位置、是否位于非编码区、是否伴随局部序列缺失或意外小片段插入等信息，并在此基础上设计针对特定转化事件的特异性引物。应如何说明这一技术路径既能提高安全性评价的全面性，又能提升不同转化事件快速鉴别与追踪的效率？','第三代纳米孔单分子测序能够在全基因组尺度上直接读出外源片段与受体基因组的完整连接序列，明确插入的精确染色体位置、所在区域是否为非编码区，以及是否伴随局部缺失或小片段意外插入，从而判断外源整合是否可能破坏重要基因或调控元件，并对潜在结构变异进行风险排查。同时，精确的插入位点及两侧侧翼序列为设计高度特异的事件特异性引物提供了模板，这类引物只在特定转化事件中扩增，从而实现不同转基因事件的快速分型、鉴别和长期追踪。因此，该技术路径一方面满足了安全性评价中对插入位置及结构变异的精细解析需求，另一方面又为转化事件的高效检测提供了分子标记基础。','{"species": "Zea mays", "paper_id": "PMC11685737", "reasoning_steps": ["Step 1: \u5bf9\u8f6c\u57fa\u56e0\u4f5c\u7269\u8fdb\u884c\u5b89\u5168\u6027\u8bc4\u4ef7\u65f6\uff0c\u9700\u8981\u786e\u5b9a\u5916\u6e90\u57fa\u56e0\u5728\u53d7\u4f53\u57fa\u56e0\u7ec4\u4e2d\u7684\u6574\u5408\u4f4d\u70b9\u548c\u4fa7\u7ffc\u5e8f\u5217\uff0c\u4ee5\u5224\u65ad\u662f\u5426\u5f71\u54cd\u53d7\u4f53\u539f\u6709\u6027\u72b6\u53ca\u5916\u6e90\u57fa\u56e0\u7684\u6709\u6548\u8868\u8fbe\u3002", "Step 2: \u7b2c\u4e09\u4ee3\u6d4b\u5e8f\u6280\u672f\uff08\u5982Oxford Nanopore\u7684\u7eb3\u7c73\u5b54\u5355\u5206\u5b50\u6d4b\u5e8f\uff09\u53ef\u4ee5\u7528\u4e8e\u89e3\u6790\u57fa\u56e0\u7ec4\u4e2d\u5916\u6e90\u57fa\u56e0\u7684\u6574\u5408\u4f4d\u70b9\u53ca\u5176\u4fa7\u7ffc\u5e8f\u5217\u3002", "Step 3: \u5229\u7528\u8be5\u6280\u672f\uff0c\u53ef\u4ee5\u8bc6\u522b\u51fa\u5916\u6e90\u57fa\u56e0\u63d2\u5165\u5728\u57fa\u56e0\u7ec4\u7684\u5177\u4f53\u67d3\u8272\u4f53\u4f4d\u7f6e\uff0c\u5e76\u5224\u65ad\u63d2\u5165\u533a\u57df\u662f\u5426\u4e3a\u975e\u7f16\u7801\u533a\uff0c\u4ee5\u53ca\u662f\u5426\u4f34\u968f\u5c40\u90e8\u57fa\u56e0\u7ec4\u5e8f\u5217\u7f3a\u5931\u6216\u5c0f\u7247\u6bb5\u610f\u5916\u63d2\u5165\u3002", "Step 4: \u5728\u83b7\u53d6\u7cbe\u786e\u63d2\u5165\u4f4d\u70b9\u548c\u4fa7\u7ffc\u5e8f\u5217\u4fe1\u606f\u540e\uff0c\u53ef\u4ee5\u636e\u6b64\u8bbe\u8ba1\u9488\u5bf9\u7279\u5b9a\u8f6c\u5316\u4e8b\u4ef6\u7684\u7279\u5f02\u6027\u68c0\u6d4b\u5f15\u7269\uff0c\u5b9e\u73b0\u5bf9\u4e0d\u540c\u8f6c\u57fa\u56e0\u4e8b\u4ef6\u7684\u5feb\u901f\u8bc6\u522b\u4e0e\u533a\u5206\u3002", "Step 5: \u80fd\u591f\u68c0\u6d4b\u5230\u9884\u671f\u63d2\u5165\u3001\u610f\u5916\u5c0f\u63d2\u5165\u4ee5\u53ca\u63d2\u5165\u4f4d\u7f6e\uff08\u5982\u975e\u7f16\u7801\u533a\uff09\uff0c\u5e76\u914d\u5408\u7279\u5f02\u5f15\u7269\u8fdb\u884c\u5206\u578b\uff0c\u6709\u52a9\u4e8e\u66f4\u5168\u9762\u5730\u8fdb\u884c\u8f6c\u57fa\u56e0\u751f\u7269\u7684\u5206\u5b50\u8868\u5f81\u548c\u98ce\u9669\u6392\u67e5\u3002", "Step 6: \u56e0\u6b64\uff0c\u5229\u7528\u7b2c\u4e09\u4ee3\u7eb3\u7c73\u5b54\u5355\u5206\u5b50\u6d4b\u5e8f\u5f00\u5c55\u63d2\u5165\u4f4d\u70b9\u548c\u4fa7\u7ffc\u5e8f\u5217\u5206\u6790\uff0c\u53ef\u540c\u65f6\u6ee1\u8db3\u5b89\u5168\u6027\u8bc4\u4ef7\u5bf9\u7cbe\u786e\u4fe1\u606f\u7684\u9700\u6c42\uff0c\u5e76\u63d0\u4f9b\u6709\u6548\u7684\u6280\u672f\u624b\u6bb5\u7528\u4e8e\u8f6c\u5316\u4e8b\u4ef6\u7684\u9274\u522b\u4e0e\u8ffd\u8e2a\u3002"], "question_cot": "", "final_conclusion": "\u91c7\u7528\u7b2c\u4e09\u4ee3\u7eb3\u7c73\u5b54\u5355\u5206\u5b50\u6d4b\u5e8f\u6280\u672f\u5206\u6790\u8f6c\u57fa\u56e0\u4f5c\u7269\u5916\u6e90\u57fa\u56e0\u63d2\u5165\u4f4d\u70b9\u53ca\u5176\u4fa7\u7ffc\u5e8f\u5217\uff0c\u6709\u5229\u4e8e\u8fdb\u884c\u5168\u9762\u7684\u5b89\u5168\u6027\u8bc4\u4ef7\u5e76\u5b9e\u73b0\u5bf9\u8f6c\u5316\u4e8b\u4ef6\u7684\u5feb\u901f\u9274\u522b\u4e0e\u8ffd\u8e2a\u3002", "difficulty": "medium", "tags": ["application", "method", "mechanism", "biosafety", "GMO_detection"], "created_at": "2025-12-16T21:19:06", "token_est_question": 1, "token_est_answer": 1, "section": "Discussion", "context": "=== Discussion ===\nWhile creating genetically modified varieties with breeding value, it is paramount to ensure that the integration of exogenous genes neither compromises the original traits of recipient crops nor hinders the efficient expression of desired traits (Li et al.15). Thus, determining the integration sites and flanking sequences of exogenous genes in the recipient genome holds profound significance for the safety assessment of genetically modified crops. Since the advent of the first-generation DNA sequencing technology in 1977, substantial progress has occurred in gene sequencing technologies (Xu et al.29). Although the current landscape favors second-generation short-read sequencing technology, the rapid development and recent applications of third-generation sequencing technology underscore its potential across diverse fields. While genomic resequencing techniques have found application in various crops, the analysis of flanking sequences in transgenic events remains underexplored (Zeng et al.33).\nIn this study, we leveraged Oxford Nanopore Technologies\u2019 third-generation sequencing, a nanopore single-molecule sequencing technique, to discern integration sites and flanking sequences of exogenous genes in two independent transgenic maize events. This methodological choice is pivotal for robust safety assessments of genetically modified crops and effective tracking of transformation events. the insertion sites of transgenic maize events ND4401 and ND4403 were determined as Chr05:38327236\u201338,327,270 bp and Chr03:7866622\u20137,866,705 bp, respectively. Despite genomic sequence deletions at both insertion sites, ND4401 and ND4403 were strategically inserted into non-coding regions of the maize genome. Capitalizing on these sequencing results, we devised specific detection primers for these maize transformation events, facilitating swift identification and discrimination.\nAs the area dedicated to genetically modified crops grows, the need for detailed research and the ongoing improvement of regulatory frameworks has become essential (Rozas et al19.). Safety assessments of transformation events, which result from the integration of exogenous genes, are now more thorough (Xu and Zhang30; Atherton2). It is not only important to confirm the effectiveness of transgenic crops\u2019 target genes, but also crucial to accurately identify where genes integrate into the genome and to gather comprehensive information about the inserted sequences (Singh et al23.). Consistent with findings from Giraldo et al. (9), our methodology also facilitates the detection of unexpected small insertions, a critical aspect of thorough safety assessments for genetically modified organisms. The methodology used in this study provides a new strategy for identifying the validity of transgenic plants in terms of molecular characterization.", "Thinking\u6a21\u5f0f": "high", "generation_type": "\u63a8\u7406\u578b"}','2026-03-23 14:42:31.353427','2026-03-23 14:42:31.353437');
INSERT INTO qa_pairs VALUES(18,2,'色胺N-乙酰转移酶是褪黑素生物合成途径中的关键酶，在玉米中，ZmSNAT1和ZmSNAT3的重组蛋白在体外均能将色胺转化为N-乙酰色胺。酶学测定显示，ZmSNAT1催化色胺的表观Km为8.6 mM、Vmax为4050 pmol/min/mg蛋白，而ZmSNAT3的Km为11.51 mM、Vmax为142 pmol/min/mg蛋白。若希望通过在玉米中过表达单一SNAT基因以最大程度提高色胺向N-乙酰色胺的转化通量，更适合优先选择ZmSNAT1还是ZmSNAT3？请结合两种酶的Km和Vmax参数，从底物亲和力与最大催化能力两个方面进行分析并给出判断。','更适合优先选择ZmSNAT1。因为与ZmSNAT3相比，ZmSNAT1对底物色胺的Km更低，说明其底物亲和力更高；同时其Vmax更高，表明在底物饱和时单位时间内生成产物的能力更强。在比较同一底物的酶时，同时具有较低Km和较高Vmax通常意味着整体催化效率更高，因此ZmSNAT1更有利于提高色胺向N-乙酰色胺的转化通量。','{"species": "Zea mays", "paper_id": "PMC11481039", "reasoning_steps": ["Step 1: Serotonin N-\u4e59\u9170\u8f6c\u79fb\u9176\uff08SNAT\uff09\u53ef\u4ee5\u5c06\u8272\u80fa\u8f6c\u5316\u4e3aN-\u4e59\u9170\u8272\u80fa\uff0c\u662f\u892a\u9ed1\u7d20\u751f\u7269\u5408\u6210\u9014\u5f84\u4e2d\u7684\u5173\u952e\u9176\u3002", "Step 2: \u5728\u7389\u7c73\u4e2d\uff0cZmSNAT1\u548cZmSNAT3\u7684\u91cd\u7ec4\u86cb\u767d\u4f53\u5916\u9176\u5b66\u6d4b\u5b9a\u8868\u660e\uff0c\u4e24\u8005\u90fd\u80fd\u5c06\u8272\u80fa\u8f6c\u5316\u4e3aN-\u4e59\u9170\u8272\u80fa\u3002", "Step 3: ZmSNAT1\u5728\u50ac\u5316\u8272\u80fa\u65f6\u7684\u8868\u89c2Km\u503c\u4e3a8.6 mM\uff0c\u800cZmSNAT3\u7684Km\u503c\u4e3a11.51 mM\uff0c\u8f83\u4f4e\u7684Km\u8868\u793aZmSNAT1\u5bf9\u5e95\u7269\u8272\u80fa\u7684\u4eb2\u548c\u529b\u66f4\u9ad8\u3002", "Step 4: ZmSNAT1\u7684Vmax\u4e3a4050 pmol/min/mg\u86cb\u767d\uff0c\u800cZmSNAT3\u7684Vmax\u4e3a142 pmol/min/mg\u86cb\u767d\uff0c\u66f4\u9ad8\u7684Vmax\u8868\u793aZmSNAT1\u5728\u9971\u548c\u5e95\u7269\u6761\u4ef6\u4e0b\u5355\u4f4d\u65f6\u95f4\u5185\u53ef\u751f\u6210\u66f4\u591a\u4ea7\u7269\u3002", "Step 5: \u5728\u6bd4\u8f83\u540c\u4e00\u5e95\u7269\u7684\u9176\u65f6\uff0c\u540c\u65f6\u5177\u5907\u66f4\u4f4eKm\u548c\u66f4\u9ad8Vmax\u901a\u5e38\u610f\u5473\u7740\u6574\u4f53\u50ac\u5316\u6548\u7387\u66f4\u9ad8\u3002", "Step 6: \u56e0\u6b64\uff0c\u76f8\u5bf9\u4e8eZmSNAT3\uff0cZmSNAT1\u5728\u50ac\u5316\u8272\u80fa\u751f\u6210N-\u4e59\u9170\u8272\u80fa\u7684\u53cd\u5e94\u4e2d\u8868\u73b0\u51fa\u66f4\u9ad8\u7684\u50ac\u5316\u6548\u7387\u3002"], "question_cot": "", "final_conclusion": "\u5728\u5c06\u8272\u80fa\uff08serotonin\uff09\u50ac\u5316\u4e3aN-\u4e59\u9170\u8272\u80fa\uff08NAS\uff09\u7684\u53cd\u5e94\u4e2d\uff0cZmSNAT1\u6bd4ZmSNAT3\u5177\u6709\u66f4\u9ad8\u7684\u50ac\u5316\u6548\u7387\u3002", "difficulty": "medium", "tags": ["concept", "mechanism", "application", "result"], "created_at": "2025-12-16T21:13:55", "token_est_question": 6, "token_est_answer": 1, "section": "Abstract", "context": "=== Introduction ===\nMelatonin, a tryptophan-derived indoleamine metabolite with important roles in plant growth and defense, has recently been regarded as a new plant hormone. Maize is one of the most important cereal crops in the world. Although the melatonin receptor gene, _ZmPMTR1_ , has already been identified, the genetic basis of melatonin biosynthesis in maize has still not been elucidated. _Serotonin N-acetyltransferase_ (SNAT) is the enzyme that converts serotonin to N-acetylserotonin (NAS) or 5-methoxytryptamine (5MT) to melatonin in Arabidopsis and rice, but no SNAT encoding gene has been identified yet in maize.\n=== Methods ===\nThe bioinformatics analysis was used to identify maize SNAT genes and the enzyme activity of the recombinant proteins was determined through in vitro assay. The expression levels of _ZmSNAT1_ and _ZmSNAT3_ under drought and heat stresses were revealed by public RNA-seq datasets and qRT-PCR analysis.\n=== Results ===\nWe first identified three maize SNAT genes, ZmSNAT1, ZmSNAT2, and ZmSNAT3, through bioinformatics analysis, and demonstrated that ZmSNAT2 was present in only eight of the 26 cultivars analyzed. We then determined the enzyme activity of ZmSNAT1 and ZmSNAT3 using their recombinant proteins through in vitro assay. The results showed that both ZmSNAT1 and ZmSNAT3 could convert serotonin to NAS and 5-MT to melatonin. Recombinant ZmSNAT1 catalyzed serotonin into NAS with a higher catalytic activity (_K_ m, 8.6 mM; _V_ max, 4050 pmol/min/mg protein) than ZmSNAT3 (_K_ m, 11.51 mM; _V_ max, 142 pmol/min/mg protein). We further demonstrated that the 228th amino acid Tyr (Y228) was essential for the enzymatic activity of ZmSNAT1. Finally, we revealed that the expression of ZmSNAT1 and ZmSNAT3 varied among different maize cultivars and different tissues of a plant, and was responsive to drought and heat stresses.\n=== Discussion ===\nIn summary, the present study identified and characterized the first two functional SNAT genes in maize, laying the foundation for further research on melatonin biosynthesis and its regulatory role in plant growth and response to abiotic stresses.\n**Keywords:** maize, serotonin N-acetyltransferase, melatonin, drought stress, heat stress", "Thinking\u6a21\u5f0f": "high", "generation_type": "\u63a8\u7406\u578b"}','2026-03-23 14:42:31.461571','2026-03-23 14:42:31.461581');
INSERT INTO qa_pairs VALUES(19,2,'在玉米中，褪黑素的生物合成起始于色氨酸，经多种酶促反应最终生成褪黑素，其中由 SNAT 催化的 5-羟色胺向 N-乙酰色胺的转化被认为是该途径的限速步骤；另一方面，玉米中已经鉴定出褪黑素受体基因 ZmPMTR1，表明玉米细胞可以特异感受并转导褪黑素信号，同时外源施用褪黑素被反复观察到能够缓解玉米干旱胁迫、提高光合作用并减少氧化损伤。若希望通过代谢工程策略提升玉米在干旱条件下的耐受性，应优先将哪类褪黑素合成相关基因视为关键分子靶点，并说明这一判断在代谢通量调控和抗旱机理上的逻辑依据？','在玉米中应优先将 SNAT（serotonin N-acetyltransferase）这一限速酶对应的基因视为提高抗旱性的关键分子靶点。SNAT 催化褪黑素合成途径中的限速步骤，其活性通常决定该途径的整体通量，因此通过提高 SNAT 的表达量或酶活能够有效提升内源褪黑素水平。玉米细胞中存在功能性褪黑素受体 ZmPMTR1，并且外源褪黑素已被证明能显著缓解干旱造成的生长抑制和氧化伤害，说明褪黑素本身是调控抗旱反应的有效信号分子。在此背景下，增强限速酶 SNAT 的功能有望在不依赖外源施用的前提下增加内源褪黑素积累，从而更充分激活褪黑素受体介导的抗旱防御途径，是代谢工程改良玉米抗旱性的优先选择环节。','{"species": "Zea mays", "paper_id": "PMC11481039", "reasoning_steps": ["Step 1: \u690d\u7269\u892a\u9ed1\u7d20\u7684\u751f\u7269\u5408\u6210\u9014\u5f84\u7531\u8272\u6c28\u9178\u51fa\u53d1\uff0c\u7ecf\u8fc7\u591a\u6b65\u9176\u4fc3\u53cd\u5e94\u751f\u6210\u892a\u9ed1\u7d20\uff0c\u5176\u4e2d\u5305\u62ec\u4ece 5-\u7f9f\u8272\u80fa\u751f\u6210 N-\u4e59\u9170\u8272\u80fa\uff08NAS\uff09\u8fd9\u4e00\u6b65\u3002", "Step 2: SNAT\uff08serotonin N-acetyltransferase\uff09\u50ac\u5316 5-\u7f9f\u8272\u80fa\u5411 NAS \u7684\u8f6c\u5316\uff0c\u5e76\u88ab\u660e\u786e\u4e3a\u892a\u9ed1\u7d20\u751f\u7269\u5408\u6210\u9014\u5f84\u4e2d\u7684\u9650\u901f\u9176\u3002", "Step 3: \u9650\u901f\u9176\u901a\u5e38\u51b3\u5b9a\u6574\u4e2a\u4ee3\u8c22\u9014\u5f84\u7684\u901a\u91cf\uff0c\u56e0\u6b64 SNAT \u6d3b\u6027\u7684\u9ad8\u4f4e\u4f1a\u663e\u8457\u5f71\u54cd\u892a\u9ed1\u7d20\u7684\u5408\u6210\u91cf\u3002", "Step 4: \u5728\u7389\u7c73\u4e2d\u5df2\u9274\u5b9a\u51fa\u892a\u9ed1\u7d20\u53d7\u4f53\u57fa\u56e0 ZmPMTR1\uff0c\u8bf4\u660e\u7389\u7c73\u7ec6\u80de\u80fd\u7279\u5f02\u8bc6\u522b\u5e76\u54cd\u5e94\u892a\u9ed1\u7d20\u4fe1\u53f7\u3002", "Step 5: \u5916\u6e90\u65bd\u7528\u892a\u9ed1\u7d20\u5df2\u88ab\u8bc1\u660e\u53ef\u4ee5\u7f13\u89e3\u7389\u7c73\u7684\u5e72\u65f1\u80c1\u8feb\uff0c\u8868\u73b0\u4e3a\u51cf\u8f7b\u751f\u957f\u6291\u5236\u3001\u63d0\u9ad8\u5149\u5408\u4f5c\u7528\u6d3b\u6027\u5e76\u964d\u4f4e\u6c27\u5316\u635f\u4f24\u3002", "Step 6: \u65e2\u7136\u892a\u9ed1\u7d20\u901a\u8fc7\u53d7\u4f53\u4ecb\u5bfc\u7684\u65b9\u5f0f\u80fd\u589e\u5f3a\u7389\u7c73\u7684\u6297\u65f1\u8868\u73b0\uff0c\u90a3\u4e48\u5185\u6e90\u892a\u9ed1\u7d20\u6c34\u5e73\u7684\u5347\u9ad8\u5728\u673a\u7406\u4e0a\u6709\u671b\u4ea7\u751f\u7c7b\u4f3c\u7684\u6709\u76ca\u6548\u5e94\u3002", "Step 7: \u7ed3\u5408 SNAT \u5728\u892a\u9ed1\u7d20\u5408\u6210\u4e2d\u7684\u9650\u901f\u4f5c\u7528\uff0c\u53ef\u4ee5\u63a8\u7406\u51fa\uff1a\u901a\u8fc7\u63d0\u9ad8\u7389\u7c73 SNAT \u9176\u7684\u6d3b\u6027\u6216\u8868\u8fbe\u91cf\uff0c\u6709\u53ef\u80fd\u63d0\u5347\u5185\u6e90\u892a\u9ed1\u7d20\u79ef\u7d2f\uff0c\u4ece\u800c\u6210\u4e3a\u589e\u5f3a\u7389\u7c73\u6297\u65f1\u80fd\u529b\u7684\u5173\u952e\u5206\u5b50\u8c03\u63a7\u70b9\u3002"], "question_cot": "", "final_conclusion": "\u5728\u7389\u7c73\u4e2d\uff0cSNAT \u9176\u6d3b\u6027\u7684\u589e\u5f3a\u53ef\u4ee5\u88ab\u89c6\u4e3a\u63d0\u9ad8\u5185\u6e90\u892a\u9ed1\u7d20\u6c34\u5e73\u3001\u4ece\u800c\u6f5c\u5728\u589e\u5f3a\u6297\u65f1\u80fd\u529b\u7684\u5173\u952e\u5206\u5b50\u6760\u6746\u70b9\u3002", "difficulty": "medium", "tags": ["mechanism", "application", "concept", "crop_breeding", "stress_resistance", "signaling"], "created_at": "2025-12-16T21:16:21", "token_est_question": 7, "token_est_answer": 11, "section": "Introduction", "context": "=== Introduction ===\nMelatonin is a tryptophan-derived indoleamine metabolite that has been extensively studied for its important physiological roles in animals such as circadian rhythm, sleep, mood and immune response (Pieri et al., 1994; Rodriguez et al., 2004; Jan et al., 2009; Tan et al., 2015). Since its discovery in plants, melatonin has been proven to be involved in a wide range of plant developmental processes, stress responses, as well as in post-harvest storage, which has led to the recognition of melatonin as a novel phytohormone (Arnao and Hern\u00e1ndez-Ruiz, 2020; Tan et al., 2012). The melatonin biosynthesis pathway is thought to be conserved among plants, consisting of four steps, (i) decarboxylation of tryptophan to form tryptamine by tryptophan decarboxylase (TDC); (ii) synthesis of serotonin (5-hydroxytryptamine) from tryptamine by tryptamine 5-hydroxylase (T5H), (iii) conversion of serotonin to N-acetyl-serotonin (NAS) by serotonin N-acetyltransferase (SNAT); and (iv) NAS converted to melatonin catalyzed by NAS methyltransferase (ASMT) and caffeic acid O-methyltransferase (COMT) (Back, 2021). Serotonin can also be converted first to 5-methoxytryptamine (5-MT) and then to melatonin by SNAT.\nSNAT is the rate-limiting enzyme in the melatonin biosynthesis pathway. It belongs to the family of General Control Non-repressible 5 (GCN5)-related N-acetyltransferases (GNAT). Two rice genes encoding SNAT, _OsSNAT1_ (Kang et al., 2012) and _OsSNAT2_ (Byeon et al., 2016) have been cloned and their catalytic activity was validated using recombinant proteins through _in vitro_ assays. The amino acid sequences of OsSNAT1 and OsSNAT2 are quite divergent, with only 39% identity and 60% similarity. Phylogenetic analysis indicates that OsSNAT1 and OsSNAT2 are distantly related, suggesting their independent evolution from cyanobacteria before the endosymbiotic event (Byeon et al., 2016). In addition, two SNAT-encoding genes (_AtSNAT1_ and _AtSNAT2_) have also been identified in _Arabidopsis thaliana_ (Lee et al., 2014, 2019). Based on sequence alignments with rice and/or Arabidopsis SNATs, one or two SNAT genes each have been identified in the following plant species: _Hypericum perforatum_ (Zhou et al., 2021), _Pinus taeda_ (Park et al., 2014), apple (Wang et al., 2017), grape (Yu et al., 2019), raspberry (Zheng et al., 2021), _Nicotiana benthamiana_ (Lee et al., 2021), soybean (Kumar et al., 2022), and cotton (Zhang et al., 2022). Recently, Wang et al. (2022) identified a total of nine homologous genes (named _AtSNAT1_ -_AtSNAT9_) in Arabidopsis by searching the apple (_Malus zumi_) SNAT amino acid sequences (Wang et al., 2017), with _AtSNAT8_ being _SNAT1_ from Lee et al. (2014), and _AtSNAT9_ being _SNAT2_ from Lee et al. (2019). They found that _AtSNAT6_ was highly expressed in the dark and its recombinant protein had a high SNAT activity _in vitro_ assay.\nMaize (_Zea mays_ L.) is one of the most important crops worldwide. The melatonin receptor gene in maize, _ZmPMTR1_ , has recently been identified (Khan et al., 2023), and the application of exogenous melatonin has been proven to be beneficial for ameliorating drought stress in maize via alleviation of growth inhibition (Guo et al., 2021), increase of photosynthetic activity and reduction of oxidative damage (Huang et al., 2019). However, not a single gene in the melatonin biosynthesis pathway has been identified in maize. In this study, we first identified SNAT homologous genes and investigated their presence across different cultivars of maize; we then investigated the enzymatic activity of two maize SNATs and the amino acid critical for substrate recognition by using their recombinant proteins; and finally, we investigated their transcriptional response to drought and heat stresses.", "Thinking\u6a21\u5f0f": "high", "generation_type": "\u63a8\u7406\u578b"}','2026-03-23 14:42:31.572554','2026-03-23 14:42:31.572564');
INSERT INTO qa_pairs VALUES(20,2,'在体外测定一种植物SNAT酶活时，研究者在所有反应中统一加入相同浓度的色胺底物和乙酰辅酶A，并在同一缓冲体系和温度下孵育，随后用相同的HPLC荧光检测条件测定NAS或褪黑素含量；其中一组反应额外加入纯化的重组ZmSNAT蛋白，另一组则不加蛋白仅作为非酶促对照，并用“含酶反应的产物量减去无酶对照的产物量”来计算酶活并拟合米氏方程。应如何从这一整套设计与数据处理思路，论证在加入重组ZmSNAT时检测到的额外NAS或褪黑素主要来源于该酶的催化作用，而不是非酶促反应或检测差异？','对照体系与含酶体系在底物、辅酶、缓冲液、温度和检测条件等方面完全相同，唯一差异是是否加入重组ZmSNAT，因此两组中共同存在的NAS或褪黑素代表在该理化条件下的非酶促背景生成。通过在相同HPLC条件下测量产物并用含酶反应的产物量减去无酶对照的产物量，可以将这一背景系统性扣除，得到仅由ZmSNAT催化贡献的净产物生成速率。只有当这一净生成量在时间或底物浓度梯度上明显高于背景并呈现随底物浓度变化的饱和动力学，才能被米氏方程良好拟合并得到合理的Km和Vmax。由此可推断，在加入重组ZmSNAT的体系中，新增的NAS或褪黑素信号主要来源于ZmSNAT介导的酶促反应，而非非酶促过程或检测方法差异。','{"species": "Zea mays", "paper_id": "PMC11481039", "reasoning_steps": ["Step 1: \u53cd\u5e94\u4f53\u7cfb\u4e2d\u7edf\u4e00\u542b\u6709\u5e95\u7269\uff080.5 mM 5-\u7f9f\u8272\u80fa\u62165-MT\uff09\u548c0.5 mM\u4e59\u9170\u8f85\u9176A\uff0c\u5e76\u5728PBS\uff08pH 8.0\uff09\u4e2d\u4e8e45\u00b0C\u5b75\u80b2\u3002", "Step 2: \u4f5c\u4e3a\u5bf9\u7167\uff0c\u8bbe\u7f6e\u4e86\u53ea\u542b\u5e95\u7269\u548c\u4e59\u9170\u8f85\u9176A\u3001\u4f46\u4e0d\u542bZmSNAT\u91cd\u7ec4\u86cb\u767d\u7684\u53cd\u5e94\uff0c\u7528\u4e8e\u68c0\u6d4b\u975e\u9176\u4fc3\u53cd\u5e94\u4ea7\u751f\u7684NAS\u6216\u892a\u9ed1\u7d20\u80cc\u666f\u6c34\u5e73\u3002", "Step 3: \u5b9e\u9a8c\u7ec4\u5728\u4e0a\u8ff0\u76f8\u540c\u4f53\u7cfb\u4e2d\u52a0\u5165\u7eaf\u5316\u7684\u91cd\u7ec4ZmSNAT\u86cb\u767d\uff0c\u5e76\u5728\u76f8\u540c\u6761\u4ef6\u4e0b\u5b75\u80b2\u3002", "Step 4: \u53cd\u5e94\u7ed3\u675f\u540e\uff0c\u7ecf\u79bb\u5fc3\u548c\u8fc7\u6ee4\u540e\uff0c\u4f7f\u7528\u540c\u4e00HPLC\u8367\u5149\u68c0\u6d4b\u65b9\u6cd5\u548c\u8272\u8c31\u6761\u4ef6\u6d4b\u5b9aNAS\u6216\u892a\u9ed1\u7d20\u542b\u91cf\uff0c\u56e0\u800c\u4e0d\u540c\u5904\u7406\u95f4\u6d4b\u5b9a\u65b9\u6cd5\u4e0e\u7075\u654f\u5ea6\u4e00\u81f4\uff0c\u53ef\u4ee5\u76f4\u63a5\u6bd4\u8f83\u4fe1\u53f7\u5f3a\u5ea6\u3002", "Step 5: \u9176\u6d3b\u7ed3\u679c\u901a\u8fc7\u7528\u201c\u542b\u9176\u53cd\u5e94\u6d4b\u5f97\u7684NAS\u6216\u892a\u9ed1\u7d20\u91cf\u201d\u51cf\u53bb\u201c\u65e0\u9176\u5bf9\u7167\u4e2d\u975e\u9176\u4fc3\u53cd\u5e94\u7684NAS\u6216\u892a\u9ed1\u7d20\u91cf\u201d\u6765\u8ba1\u7b97\uff0c\u610f\u5473\u7740\u5982\u679c\u542b\u9176\u53cd\u5e94\u4e0e\u5bf9\u7167\u65e0\u5dee\u5f02\uff0c\u5219\u8ba1\u7b97\u51fa\u7684\u9176\u6d3b\u4e3a\u96f6\u6216\u63a5\u8fd1\u96f6\u3002", "Step 6: \u80fd\u591f\u62df\u5408\u7c73\u6c0f\u65b9\u7a0b\u5e76\u83b7\u5f97ZmSNAT\u7684Km\u548cVmax\uff0c\u524d\u63d0\u662f\u968f\u5e95\u7269\u6216\u65f6\u95f4\u53d8\u5316\u7684\u4ea7\u7269\u91cf\u5728\u542b\u9176\u53cd\u5e94\u4e2d\u660e\u663e\u9ad8\u4e8e\u975e\u9176\u4fc3\u5bf9\u7167\u5e76\u5448\u7cfb\u7edf\u6027\u53d8\u5316\u3002", "Step 7: \u56e0\u6b64\uff0c\u5f53\u5728\u8be5\u4f53\u7cfb\u4e2d\u52a0\u5165\u91cd\u7ec4ZmSNAT\u86cb\u767d\u65f6\uff0c\u53ef\u4ee5\u68c0\u6d4b\u5230\u9ad8\u4e8e\u975e\u9176\u4fc3\u6c34\u5e73\u7684NAS\u6216\u892a\u9ed1\u7d20\u4ea7\u7269\u4fe1\u53f7\uff0c\u4ece\u800c\u8bc1\u660e\u4ea7\u7269\u4e3b\u8981\u6765\u6e90\u4e8eZmSNAT\u50ac\u5316\u7684\u9176\u4fc3\u53cd\u5e94\u3002"], "question_cot": "", "final_conclusion": "\u5728\u4f53\u5916SNAT\u9176\u6d3b\u6d4b\u5b9a\u4f53\u7cfb\u4e2d\uff0c\u53ea\u6709\u5728\u52a0\u5165\u91cd\u7ec4ZmSNAT\u86cb\u767d\u65f6\uff0c\u624d\u80fd\u68c0\u6d4b\u5230\u7531\u5e95\u7269\u548c\u4e59\u9170\u8f85\u9176A\u751f\u6210\u7684NAS\u6216\u892a\u9ed1\u7d20\u7684\u9176\u4fc3\u4ea7\u7269\u4fe1\u53f7\u3002", "difficulty": "medium", "tags": ["method", "mechanism", "concept", "result"], "created_at": "2025-12-16T21:18:07", "token_est_question": 1, "token_est_answer": 1, "section": "Materials & Methods", "context": "=== Materials and methods ===\nThe genomes of 26 maize cultivars sequenced and assembled by Hufford et al. (2021) were downloaded from maizeGDB (Welcome to MaizeGDB). The hidden Markov model of GNAT domain (PF00583) was used as a query to search the proteins in the genomes of maize cultivars (Yu et al., 2019), with a threshold of e < 1e-5 and the length of protein < 500 aa (amino acid).\n=== Materials and methods ===\nThe protein sequences of ZmGNAT identified above and verified OsSNAT1 (Kang et al., 2012), OsSNAT2 of rice (Byeon et al., 2016) and AtSNAT1 (Kang et al., 2012), AtSNAT2, AtSNAT6 of Arabidopsis (Lee et al., 2014, 2019; Wang et al., 2022) were used for phylogenetic analysis. Multiple sequence alignments were performed using MAFFT version 7 [MAFFT alignment and NJ/UPGMA phylogeny (cbrc.jp)] and the phylogenetic tree was constructed by IQ-TREE web server [IQTREE Web Server: Fast and accurate phylogenetic trees under maximum likelihood (univie.ac.at)] with the default parameter. The presence/absence of ZmGNATs across 26 maize cultivars was generated by TBtools v2.112 (Chen et al., 2023). The heatmap was generated by TBtools v2.112 (Chen et al., 2023).\n=== Production of ZmSNAT recombinant proteins in _Escherichia coli_ ===\nBoth ZmSNAT1 and ZmSNAT3 contain a short chloroplast transit peptide (cTP) sequence as predicted by ChloroP (Emanuelsson et al., 1999). To exclude this cTP sequence in recombinant proteins, PCR primers were designed to amplify _ZmSNAT1_ or _ZmSNAT3_ coding sequences without the cTP portion from maize cultivar _CML322_ ( **Supplementary Table S1** ). The PCR amplicons thus amplified from _ZmSNAT1_ and _ZmSNAT3_ cDNA were then inserted into the pMAL-C6T vector linearized by _Alwn_ I or pET-45b vector linearized by _Kpn_ I. The recombinant vectors were then transformed into _E. coli_ Transetta (DE3) (TransGen Biotech, Beijing, China). To produce recombinant ZmSNATs proteins, the transformed _E.coli_ were cultured on the Luria-Bertani (LB) broth agar (10 g/L Tryptone, 5 g/L yeast extract, 10 g/L NaCl and 15 g/L agar) containing 100 mg/L ampicillin and picked up one colony into 5 mL of Luria-Bertani (LB) broth (10 g/L Tryptone, 5 g/L yeast extract, 10 g/L NaCl) containing 100 mg/L ampicillin and 34 mg/L chloramphenicol and incubated at 37\u00b0C overnight. 3 mL overnight culture was inoculated into 300 mL LB broth containing 100 mg/L ampicillin and 34 mg/L chloramphenicol until the optical density of the _E.coli_ culture at 600 nm (OD600) reached 0.6-0.8. after the addition of 100 \u00b5M isopropyl-\u03b2-D-thiogalactopyranoside (IPTG; Sigma, St.Louis, MO, USA), the culture was grown at 23\u00b0C and shaken at 160 rpm for 14 hours. After breaking up the cells of _E.coli_ by the high-pressure homogenizer, the following purification steps using Ni-NTA or amylose resin were performed according to the manufacturer\u2019s instructions (Vazyme, Nanjing, China; NEB, Beijing, China). Purified protein was concentrated through an Amicon ultra-4 centrifugal filter (Merck Millipore, Carrigtwohill, Ireland) and dissolved in PBS (pH 8.0) ( **Supplementary Figure S3** ).\n=== Measurement of SNAT enzyme activity ===\nPurified recombinant SNATs were used for enzymatic activity assay. The reaction was performed in a total volume of 100 \u00b5L containing 0.5 mM of serotonin or 5-MT as substrate, and 0.5 mM of acetyl-CoA in a PBS buffer (pH 8.0), the mixture was then incubated at 45\u00b0C for 3 hours (or various other time periods for the determination of _K_ m and _V_ max) and stopped by the addition of 200 \u00b5L methanol (MeOH). The reaction buffers were then centrifuged at 10,000 rpm for 2 min and the supernatants were passed through a PTFE membrane (0.22 \u00b5m). A 5 \u00b5L aliquot was used for the determination of NAS or melatonin on an HPLC with the fluorescence detector system (LC-20, Shimadzu). All chemicals\u2019 separations were performed at a flow rate of 1 mL/min and separated on the Ultimate XB-C18 column (4.6 x 150 mm; Welch, Shanghai, China) with an isocratic elution with 30% MeOH in 0.1% formic acid for 20 min. NAS and melatonin were detected at 280 nm (excitation) and 348 nm (emission). All measurements were reproduced in triplicate. The enzymatic activities were calculated by subtracting the value of nonenzymatic reaction controls, i.e., reactions with only substrate and 0.5 mM of acetyl-CoA but without ZmSNAT recombinant proteins. The data were fit to the Michaelis-Menten equation using Prism 9 (GraphPad) to obtain the kinetic parameters. All assays were run in triplicate. The concentration of recombinant proteins was determined by the Bradford method using a protein assay dye (Bio-Rad, Hercules, CA, USA).\n=== Comparison and visualization of protein tertiary structures ===\nThe crystal structure of OsSNAT1 (PDBe ID: 7DAI) was downloaded from PDBe [Homepage | Protein Data Bank in Europe (ebi.ac.uk)]. The tertiary structures of ZmSNATs were downloaded from AlphaFold2 (AlphaFold Protein Structure Database (ebi.ac.uk)). The tertiary structure alignment of OsSNAT1 and ZmSNAT1 was performed and the RMSD was calculated by Pymol 2.5.2.\n=== Source of transcriptomes and visualization of transcription profile ===\nFor analysis of the expression profile of _ZmSNAT1_ and _ZmSNAT3_ , the revelation of tissue-specific expression profile was performed in ZEAMAP [ZEAMAP: a comprehensive database adapted to the maize multi-omics era. (cngb.org)], using the RNA-seq data of _B73_. The dataset of drought stress was downloaded from GSE137780 and the dataset of heat stress from GSE268429. The FPKM values were averaged per treatment per cultivar for _ZmSNAT1_ and _ZmSNAT3_. The expression datasets of 26 maize cultivars were obtained from Hufford et al. (2021).\n=== Plant growth conditions and stress treatment ===\nThe maize _W22_ cultivar was used in this experiment. Seeds were germinated and grown in Hoagland solution at 28\u00b0C for 14 days. Then the plants were grown in pots with the nutritional soil (Peilei, Jiangsu, China) with a soil moisture of ~30% in a greenhouse with an average temperature of 28\u00b0C. For drought treatment, three-leaf seedlings were grown in two groups, one group with normal watering (control) and another without watering for 10 days. For heat treatment, three-leaf seedlings were incubated in a phytotron under 45\u00b0C and leaves were collected at 0, 1, 3, 6,12 and 24 h, respectively. The plants grown under 28\u00b0C were used as the control of each time point. The leaf samples were collected in due time of treatment and were immediately frozen in liquid nitrogen and stored at \u221280\u00b0C until use for RNA extraction.\n=== Quantitative real-time PCR ===\nFor quantitative real-time PCR (qRT-PCR), total RNAs were extracted from maize leaves with the RNAprep Pure Plant Kit (Tiangen, Beijing, China). According to supplier instructions, cDNAs were synthesized using PrimeScriptTM RT Reagent Kit with gDNA Eraser (Tiangen, Beijing, China). The Primer Premier 5.0 was used to design the primers for qRT-PCR ( **Supplementary Table S1** ), and the maize TUB-ribosylation factor gene was used as an internal control. The reaction was performed on Bio-Rad CFX ConnectTM using SYBR-Green to detect gene expression levels. For all qRT-PCR analyses, triplicate biological samples were collected. Data were analyzed using Bio-Rad CFX Manager software.\n=== Statistical analysis ===\nStudent\u2019s _t_ -test and one-way ANOVA were used for the statistical evaluations.", "Thinking\u6a21\u5f0f": "high", "generation_type": "\u63a8\u7406\u578b"}','2026-03-23 14:42:31.686338','2026-03-23 14:42:31.686348');
INSERT INTO qa_pairs VALUES(21,2,'在玉米中有两个具有SNAT活性的基因ZmSNAT1和ZmSNAT3，它们都能催化5-羟基色胺向NAS以及5-MT向褪黑素的转化，但体外测定显示ZmSNAT1的催化效率远高于ZmSNAT3，同时转录组分析表明在叶片尤其是成熟叶中，ZmSNAT1的表达量普遍高于ZmSNAT3，而两者在根和成熟花粉中几乎不表达。对于希望解析叶片褪黑素生物合成的关键限速位点或设计通过基因工程提高叶片褪黑素含量的策略，哪一个基因更可能是在叶片中对褪黑素合成贡献更大的SNAT酶？请阐明推理依据。','在玉米叶片中，ZmSNAT1更可能是对褪黑素合成贡献更大的SNAT酶。理由是，两种酶虽然都能催化褪黑素合成通路中的相同步骤，但ZmSNAT1的体外催化效率显著高于ZmSNAT3，且在叶片中的转录水平也普遍更高。更强的酶学活性叠加更高的表达量，意味着在叶片中单位时间内由ZmSNAT1催化完成的底物转化量远大于ZmSNAT3，因此ZmSNAT1在叶片褪黑素生物合成中的总体通量贡献更大，更适合作为调控叶片褪黑素含量的优先靶基因。','{"species": "Zea mays", "paper_id": "PMC11481039", "reasoning_steps": ["Step 1: ZmSNAT1\u548cZmSNAT3\u90fd\u80fd\u4f53\u5916\u50ac\u53165-\u7f9f\u57fa\u8272\u80fa\u751f\u6210NAS\u4ee5\u53ca5-MT\u751f\u6210\u892a\u9ed1\u7d20\uff0c\u8bf4\u660e\u4e24\u8005\u90fd\u5177\u5907SNAT\u9176\u6d3b\u6027\u5e76\u53c2\u4e0e\u892a\u9ed1\u7d20\u5408\u6210\u901a\u8def\u3002", "Step 2: \u5728\u76f8\u540c\u4f53\u5916\u6761\u4ef6\u4e0b\uff0cZmSNAT1\u75315-\u7f9f\u57fa\u8272\u80fa\u751f\u6210NAS\u4ee5\u53ca\u75315-MT\u751f\u6210\u892a\u9ed1\u7d20\u7684\u9176\u6d3b\u5206\u522b\u662fZmSNAT3\u768483\u500d\u548c68\u500d\uff0c\u4e14\u9176\u52a8\u529b\u5b66\u53c2\u6570\u663e\u793aZmSNAT1\u7684Vmax\u8fdc\u9ad8\u4e8eZmSNAT3\uff0c\u8868\u660eZmSNAT1\u7684\u50ac\u5316\u80fd\u529b\u66f4\u5f3a\u3002", "Step 3: \u572826\u4e2a\u7389\u7c73\u54c1\u79cd\u7684RNA-seq\u6570\u636e\u4e2d\uff0cZmSNAT1\u5728\u7b2c11\u53f6\u7684FPKM\u7ea6\u4e3aZmSNAT3\u76841.7\u20136.9\u500d\uff0c\u8bf4\u660e\u5728\u53f6\u7247\u4e2dZmSNAT1\u7684\u8f6c\u5f55\u6c34\u5e73\u666e\u904d\u9ad8\u4e8eZmSNAT3\u3002", "Step 4: \u5728\u591a\u4e2a\u7ec4\u7ec7\u4e2d\uff0c\u5c24\u5176\u662f\u6210\u719f\u53f6\u7247\u4e2d\uff0cZmSNAT1\u7684\u8868\u8fbe\u91cf\u5728\u5927\u591a\u6570\u7ec4\u7ec7\u4e2d\u4e5f\u9ad8\u4e8eZmSNAT3\uff0c\u4e14\u4e24\u57fa\u56e0\u90fd\u5728\u53f6\u7247\u4e2d\u9ad8\u8868\u8fbe\u800c\u5728\u6839\u548c\u6210\u719f\u82b1\u7c89\u4e2d\u51e0\u4e4e\u4e0d\u8868\u8fbe\uff0c\u8bf4\u660e\u5b83\u4eec\u4e3b\u8981\u5728\u53f6\u7247\u53d1\u6325\u4ee3\u8c22\u529f\u80fd\u3002", "Step 5: \u7531\u4e8e\u4e24\u79cd\u9176\u90fd\u80fd\u50ac\u5316\u892a\u9ed1\u7d20\u5408\u6210\u5173\u952e\u6b65\u9aa4\uff0c\u800cZmSNAT1\u5728\u53f6\u7247\u4e2d\u65e2\u5177\u6709\u66f4\u9ad8\u7684\u9176\u5b66\u6d3b\u6027\u53c8\u5177\u6709\u66f4\u9ad8\u7684\u8f6c\u5f55\u6c34\u5e73\uff0c\u56e0\u6b64\u5728\u53f6\u7247\u4e2d\u7684\u603b\u4f53\u50ac\u5316\u901a\u91cf\u66f4\u5927\u3002", "Step 6: \u7531\u6b64\u53ef\u63a8\u65ad\uff0c\u5728\u53f6\u7247\u7684\u892a\u9ed1\u7d20\u751f\u7269\u5408\u6210\u8fc7\u7a0b\u4e2d\uff0cZmSNAT1\u76f8\u5bf9\u4e8eZmSNAT3\u8d21\u732e\u66f4\u5927\u3002"], "question_cot": "", "final_conclusion": "\u5728\u53f6\u7247\u4e2d\uff0cZmSNAT1\u76f8\u5bf9\u4e8eZmSNAT3\u66f4\u53ef\u80fd\u662f\u892a\u9ed1\u7d20\u5408\u6210\u4e2d\u8d21\u732e\u66f4\u5927\u7684SNAT\u9176\u3002", "difficulty": "medium", "tags": ["mechanism", "result", "application"], "created_at": "2025-12-16T21:18:45", "token_est_question": 1, "token_est_answer": 1, "section": "Results", "context": "=== Results ===\nBecause SNAT belongs to the GNAT family, we hence first searched the GNAT members in the genome of maize cultivar _Ki3_. We then constructed a phylogenetic tree for the ZmGNAT genes together with OsSNAT1, OsSNAT2, AtSNAT1, AtSNAT2 and AtSNAT6 ( **Figure 1A** ).\n=== Figure 1. ===\n \nOpen in a new tab\nPhylogenetic tree of ZmGNATs and other cloned SNATs. **(A)** ZmGNATs of the _Ki3_ genome and SNATs in Arabidopsis and rice; **(B)** Phylogenetic tree of ZmSNATs and SNATs in other species.\nA total of 37 GNATs were identified in the _Ki3_ genome. Among them, ZmGNAT15, ZmGNAT34, and ZmGNAT16 are closely aligned to OsSNAT1/AtSNAT1, OsSNAT2/AtSNAT2, and AtSNAT6, respectively ( **Figure 1A** ). Hence, we designated them as ZmSNAT1, ZmSNAT2, and ZmSNAT3 hereafter.\nTo determine the evolution of maize SNAT genes, we constructed a phylogenetic tree with a cyanobacteria SNAT as the outgroup ( **Figure 1B** ). Based on this phylogenetic tree, we reason that _ZmSNAT1_ probably evolved earlier than _ZmSNAT2_ , and _ZmSNAT3_ was the latest evolved one ( **Figure 1B** ).\n=== Structural characteristics of maize SNAT genes ===\nThe three maize SNAT genes have very different structures in terms of the number and length of exons, as well as the length of encoded proteins ( **Figure 2A** ). _ZmSNAT1_ harbors 8 exons while _ZmSNAT2_ contains only one. The proteins encoded by all three genes contain an acetyltransferase domain of different lengths ( **Figure 2A** , _right_). Prediction of protein tertiary structure using AlphaFold2 (AlphaFold Protein Structure Database (ebi.ac.uk) showed the three maize SNATs had strikingly different structures, especially the orientation and helix-sheet-helix architecture ( **Figure 2B** ).\n=== Figure 2. ===\n \nOpen in a new tab\nGene and protein structures of ZmSNATs. **(A)** Gene structures (_left_) and domains (_right_) of ZmSNATs. Untranslated region (UTR) and exon are indicated by the green rectangle and yellow rectangle, respectively. The purple rectangle indicates the predicted chloroplast transit peptide; **(B)** Tertiary structures of ZmSNAT proteins. The structures were constructed on AlphaFold2 with ID in parentheses.\n=== Pan-genome-wide presence of ZmSNATs ===\nWith the sequencing of more and more maize genomes, the maize pan-genome has been composed of genomes up to 26 maize cultivars (Hufford et al., 2021). To figure out the presence of ZmSNAT genes in different maize cultivars, we first performed a search of the ZmGNATs in each of the 26 cultivars ( **Figure 3** ; **Supplementary File 1** ). The results show that there was a total of 71 ZmGNAT homologous genes across the 26 cultivars, each with 30 (_B73_) to 38 (_CML322_ , _P39_) genes ( **Figure 3** ). Thirty GNAT genes are highly conserved across the 26 cultivars, they are present at least in 24 cultivars; Twenty GNAT genes (ZmGNAT52-71) are present only in one cultivar, while others are present in two or more cultivars. While _ZmSNAT1_ and _ZmSNAT3_ are present in all the cultivars, _ZmSNAT2_ (_ZmGNAT34_) is present in only eight cultivars ( **Figure 3** ).\n=== Figure 3. ===\n \nOpen in a new tab\nZmGNAT genes present in 26 maize cultivars. ZmGNAT2, 3, 9-11, 13-16, 19-22,24, 26 are present in all cultivars, hence are not listed. The number in parenthesis of each cultivar is the number of GNAT genes identified in that cultivar.\n=== Enzymatic activity of ZmSNAT1 and ZmSNAT3 ===\nBecause ZmSNAT2 is present in merely eight cultivars, enzymatic activity assay was performed only for ZmSNAT1 and ZmSNAT3.\nRecombinant ZmSNAT1 and ZmSNAT3 were tested _in vitro_ using either serotonin or 5-MT as substrate. As shown in **Figure 4** , both proteins catalyzed the production of NAS from serotonin or melatonin from 5-MT. The activity of ZmSNAT1 was 83 (serotonin to NAS) and 68 (5-MT to melatonin) fold higher than that of ZmSNAT3.\n=== Figure 4. ===\n \nOpen in a new tab\nEnzymatic activity of the recombinant ZmSNAT1 and ZmSNAT3, with the substrate of serotonin **(A)** and 5-MT **(B)**. All assays were performed in a total volume of 100 \u00b5L PBS (pH = 8.0) with 50 \u00b5g the recombinant ZmSNAT1 or ZmSNAT3, and 0.5 mM serotonin **(A)** or 5-MT **(B)** , the reaction was performed at 45\u00b0C for 3 hours. Data are shown as mean \u00b1 SD, n=3.\nThe recombinant ZmSNAT1 with the MBP tag had a low enzymatic activity, with a _K_ m value of 4.90 mM and a _V_ max value of 173.8 pmol/min/mg, for catalyzing the conversion of serotonin to NAS ( **Supplementary Figure S1** ). To exclude the MBP tag effect, ZmSNAT1 and ZmSNAT3 were fused with a smaller His-tag for testing for their enzyme kinetics. The results showed that His-ZmSNAT1 had a _K_ m of 8.6 mM and a _V_ max of 4.05 nmol/min/mg protein ( **Figure 5A** ). Correspondingly, His-ZmSNAT3 had a _K_ m of 11.51 mM) and a _V_ max of 142 pmol/min/mg protein ( **Figure 5B** ), in consistent with the above observation that ZmSNAT3 had a weaker catalytic activity of converting serotonin to NAS than ZmSNAT1.\n=== Figure 5. ===\n \nOpen in a new tab\nKinetic parameters of the recombinant ZmSNAT1 and ZmSNAT3. The reaction was performed in PBS buffer (pH = 8.8) with 0.5 mmol/L acetyl-CoA and a series of the concentrations of serotonin for 30 minutes at 45\u00b0C. The _K_ m and the _V_ max were determined using Michaelis-Menten kinetics.\n=== The amino acid essential for the enzymatic activity of ZmSNAT1 ===\nIt was reported that the amino acid Y233 of OsSNAT1 is essential for its function during acetyl-transfer reaction because the replacement of Y233 with Ala or Phe could result in the abolishment of enzymatic activity (Liao et al., 2021). To identify the amino acids in ZmSNAT1 essential for its enzymatic activity, we first performed a tertiary structure comparison between OsSNAT1 and ZmSNAT1, the results showed the tertiary structures of the two proteins are almost identical with an RMSD of 0.312 ( **Figure 6A** ). Further analysis revealed that Y228 of ZmSNAT1 corresponds to Y233 of OsSNAT1 in the _CML228_ cultivar ( **Figure 6B** ).\n=== Figure 6. ===\n \nOpen in a new tab\nTertiary structure alignment of OsSNAT1 and ZmSNAT1. **(A)** The overall alignment of OsSNAT1 (cyan) and ZmSNAT1 (white) proteins; **(B)** The enlarged view of 233 TYR (in red) of OsSNAT1 overlapping 228 TYR (in black) of ZmSNAT1. The structure of OsSNAT1 was downloaded from PDBe with ID code 7DAI. The alignment was performed in Pymol.\nTo test whether Y228 is truly important, site-directed mutagenesis was applied to generate a mutated ZmSNAT1-Y228A protein. _In vitro_ analysis showed that the Y228A mutation almost abolished its enzymatic activity both for converting serotonin to NAS and for 5-MT to melatonin ( **Figure 7** ). These results indicate that the Y228 is essential for ZmSNAT1.\n=== Figure 7. ===\n \nOpen in a new tab\nRelative catalytic activities of wild-type ZmSNAT1 and its variant Y228A converting serotonin (dark) to NAS and 5-MT (gray) to melatonin. The activities of the ZmSNAT1 are set to 100%. Each bar represents the mean value of triplicate experiments \u00b1 SD (n=3).\n=== Transcription of _ZmSNAT1_ and _ZmSNAT3_ varied among maize cultivars and in various tissues ===\nTo have a general view of the transcription of _ZmSNAT1_ and _ZmSNAT3_ in maize, we first investigated the RNA-seq data of 26 maize cultivars generated by Hufford et al. (2021). Overall, _ZmSNAT1_ seemed to have a greater transcription level than _ZmSNAT3_. For instance, the FPKM of _ZmSNAT1_ was ~1.7-6.9 fold greater than that of _ZmSNAT3_ in the eleventh leaves ( **Figure 8** ). Furthermore, there were also variations of transcription of both _ZmSNAT1_ and _ZmSNAT3_ among the 26 cultivars tested, with the FPKM value varied from 23.41 to (_CML277_) to 95.12 (_CML232_) for _ZmSNAT1_ , and from 6.35 (_B73_) to 31.45 (_Oh43_) for _ZmSNAT3_ ( **Figure 8** ).\n=== Figure 8. ===\n \nOpen in a new tab\nThe expression levels of _ZmSNAT1_ and _ZmSNAT3_ in 26 maize cultivars. Data were extracted from ENA AaaryExpress E-MTAB-8633 and E-MTAB-8628 generated by Hufford et al. (2021).\nWe also determined the expression pattern of _ZmSNAT1_ and _ZmSNAT3_ across various tissues using the RNA-seq data of the cultivar _B73_ collected in the ZEAMAP database. Overall, both genes were highly expressed in leaves, particularly in mature leaves, and to a lesser extent in other tissues (embryos, silk, developing and mature seeds, etc.). They are barely expressed in mature pollen and roots ( **Figure 9** ). Comparatively, _ZmSNAT1_ had greater expression than _ZmSNAT3_ in most tissues except roots.\n=== Figure 9. ===\n \nOpen in a new tab\nTranscription of _ZmSNAT1_ and _ZmSNAT3_ in different tissues of a maize plant (cultivar _B73_ , unit: FPKM). Data were extracted from the ZEAMAP database.\n=== Transcription of _ZmSNAT1_ and _ZmSNAT3_ was responsive to drought and heat stress ===\nIt is known that melatonin content is increased upon certain stresses in maize (Colombage et al., 2023). Therefore, we investigated the expression levels of _ZmSNAT1_ and _ZmSNAT3_ in the leaves of plants subjected to drought and heat stresses. First, we analyzed the RNA-seq data of three cultivars available in the GEO database. The results showed that _ZmSNAT1_ was significantly upregulated in the cultivars _DH4866_ and _W22_ , but downregulated in _B104_ when subjected to a 5-day drought stress ( **Figure 10A** ). _ZmSNAT3_ was also significantly upregulated in _DH4866_ and downregulated in _B104_ , but no significant changes were observed in _W22_ ( **Figure 10B** ). After being subjected to heat stress (39\u00b0C, 1 hour), both _ZmSNAT1_ and _ZmSNAT3_ were significantly down-regulated in leaves of the _W22_ cultivar ( **Figures 10C, D** ).\n=== Figure 10. ===\n \nOpen in a new tab\nTranscription of _ZmSNAT1_ and _ZmSNAT3_ in maize plants subjected to drought and heat stress. The FPKM values of _ZmSNAT1_ **(A)** and _ZmSNAT3_ **(B)** in response to drought stress in the cultivars _B104_ , _DH4866_ and _W22_ ; **(C, D)** The FPKM values of _ZmSNAT1_ and _ZmSNAT3_ in response to hear stress in the cultivar _W22_. The datasets analyzed for drought and heat stress were downloaded from the GEO database with the accession GSE137780 and GSE268429, respectively. Each bar represents the mean value of triplicate experiments \u00b1 SD (n=3).\nWe further performed indoor experiments to investigate the transcriptional responses to drought and heat stresses, using three-leaf seedlings of _W22_. QRT-PCR analysis showed that, after being subjected to drought stress, _ZmSNAT1_ was significantly up-regulated, with a 5.5-folder increase of transcript abundance, while _ZmSNAT3_ was not ( **Figure 11A** ). After being subject to heat stress (45\u00b0C), _ZmSNAT1_ was first significantly down-regulated (1, 3 h), then significantly upregulated (6 h), and again downregulated (12, 24 h) ( **Figure 11B** ). _ZmSNAT3_ was significantly up-regulated soon after heat stress (1 h, +110%), and significantly downregulated afterwards, with mRNA abundance being reduced to 40.2%, 24.6%, 4. 7% and 8.7% that of the control at 3, 6, 12 and 24 h, respectively ( **Figure 11C** ).\n=== Figure 11. ===\n \nOpen in a new tab\nThe relative expression levels of _ZmSNAT1_ and _ZmSNAT3_ in plants of maize cultivar _W22_ subjected to drought stress **(A)** and heat stress **(B, C)**. Each bar represents the mean value of triplicate experiments \u00b1 SD (n=3). The maize TUB-ribosylation factor gene was used as an internal control. The relative expression levels were calculated using the comparative 2-\u0394\u0394Ct method (Livak and Schmittgen, 2001).", "Thinking\u6a21\u5f0f": "high", "generation_type": "\u63a8\u7406\u578b"}','2026-03-23 14:42:31.810985','2026-03-23 14:42:31.811012');
INSERT INTO qa_pairs VALUES(22,2,'在玉米中，ZmSNAT1和ZmSNAT3编码能够利用5-羟色胺或5-MT催化生成NAS或褪黑素的SNAT酶，其表达水平会直接影响褪黑素合成；转录组分析和qRT-PCR结果显示，在不同品种和处理条件下，这两个基因在干旱和高温胁迫下出现显著的上调或下调变化，有的情况下仅ZmSNAT1被干旱诱导，而高温则对二者产生随时间变化的影响。面对这些在不同胁迫和品种间并不完全一致的表达模式，应如何推理得出玉米在干旱和热胁迫下是通过对ZmSNAT1和ZmSNAT3转录水平的调节来精细控制褪黑素生物合成的？','可以认为玉米在干旱和热胁迫下通过调节ZmSNAT1和ZmSNAT3的转录来精细控制褪黑素合成。原因在于，这两个基因编码褪黑素生物合成途径中的关键SNAT酶，其表达量变化会直接影响褪黑素产量；多种独立的转录组和qRT-PCR数据一致表明，在干旱和高温条件下，这两个基因的转录水平均发生显著且具有胁迫特异性和品种特异性的动态变化，而不是在胁迫下保持稳定不变。由于这种调节紧密伴随环境胁迫发生，且作用于限速的合成酶基因，因此可以推断褪黑素生物合成在玉米中是通过ZmSNAT1和ZmSNAT3的转录调控实现对干旱和热胁迫的精细响应。','{"species": "Zea mays", "paper_id": "PMC11481039", "reasoning_steps": ["Step 1: ZmSNAT1 and ZmSNAT3 encode SNAT enzymes that can use serotonin or 5-MT as substrates to produce NAS or melatonin, so their expression levels directly affect melatonin biosynthesis.", "Step 2: Transcriptome analysis shows that the transcription of ZmSNAT1 and ZmSNAT3 is altered by drought and heat stresses, with both genes being significantly up- or downregulated depending on the cultivar and stress condition.", "Step 3: qRT-PCR in the W22 cultivar confirms that drought stress activates the transcription of ZmSNAT1 (but not ZmSNAT3), and heat stress causes time-dependent changes in the transcription of both ZmSNAT1 and ZmSNAT3.", "Step 4: These consistent observations from independent datasets indicate that the transcription of ZmSNAT1 and ZmSNAT3 is responsive to heat and drought rather than remaining constitutive under stress.", "Step 5: Because these genes encode key melatonin biosynthetic enzymes and their transcription changes systematically under drought and heat, melatonin biosynthesis in maize is finely regulated at the transcriptional level in response to these abiotic stresses."], "question_cot": "", "final_conclusion": "In maize, melatonin biosynthesis is finely regulated in response to drought and heat stress through stress\u2011induced transcriptional changes of ZmSNAT1 and ZmSNAT3.", "difficulty": "medium", "tags": ["mechanism", "result", "stress_response", "gene_expression", "maize", "melatonin_biosynthesis"], "created_at": "2025-12-16T21:19:29", "token_est_question": 1, "token_est_answer": 1, "section": "Discussion", "context": "=== Discussion ===\nPrevious studies have already shown that the application of exogenous melatonin could promote plant growth and development, and ameliorate damages caused by abiotic stresses. However, little is known about melatonin biosynthesis, and its regulation and response to abiotic stresses in maize plants. The present study first identified three maize SNAT genes that are homologous to cloned SNAT genes of either rice or Arabidopsis or both. Two of them, _ZmSNAT1_ and _ZmSNAT3_ , are present in all 26 maize cultivars, while _ZmSNAT2_ is present only in eight cultivars. This study further demonstrated that while both ZmSNAT1 and ZmSNAT3 could catalyze melatonin biosynthesis, ZmSNAT1 had an enzymatic activity far greater than ZmSNAT3. Furthermore, this study determined an amino acid in ZmSNAT1 essential for its enzymatic activity. At last, this study demonstrated that transcription of _ZmSNAT1_ and _ZmSNAT3_ varied among different maize cultivars and different tissues, and was responsive to drought and heat stresses. The above findings hence laid a solid foundation for further research on the genetics, biology and evolution of melatonin in maize.\n=== More SNAT genes in maize ===\nThrough the homolog search of rice and Arabidopsis SNAT genes, we identified 3 SNAT homologs in maize in this study. However, we believe that there might be more SNAT genes in maize to be identified. The plant SNAT gene was first identified in rice (Kang et al., 2012), which was later used directly for the search for homologs in other plant species including Arabidopsis (Lee et al., 2014, 2019). The Arabidopsis SNATs were also later used for search of homologs in dicots (Wang et al., 2017). Because the two rice SNATs had a low identity and similarity, suggesting that there might be more SNATs to be identified in rice and other plant species. Indeed, Wang et al. (2022) identified 9 SNAT genes in Arabidopsis (including two that had already been functionally validated) through a homolog search of an apple SNAT, though they only validated the anticipated enzymatic function for one of them.\nThe results of the present study also suggest there might be more SNAT genes in maize based on the following evidence. Firstly, only ZmSNAT1 and ZmSNAT2 were aligned to rice SNATs, while ZmSNAT3 was aligned to a newly identified Arabidopsis SNAT6 ( **Figure 1** ), suggesting that it is likely to identify the new SNATs in maize when new SNAT genes identified in other plants are used for search. Secondly, the presence of ZmSNAT2 in only 8 of the 26 maize cultivars, together with the presence of various GNATs only in a single cultivar ( **Figure 3** ), also suggests that more cultivar-specific SNATs might exist. Thirdly, it is known that melatonin is in mitochondria in animals (Reiter et al., 2022). However, this study only identified SNATs with a chloroplast transit peptide ( **Figure 2B** ), and hence presumably to be located in chloroplast (see Materials and Methods). Because mitochondrion is known to be an important organelle, particularly in stress response, where melatonin plays an important role, it is quite plausible that there are new genes encoding mitochondrial SNATs in maize yet to be identified.\n=== Enzymatic characteristics of ZmSNATs ===\nThe present study demonstrated that both ZmSNAT1 and ZmSNAT3 could use serotonin or 5-MT as the substrate to produce NAS or melatonin, which is consistent with the characteristics of other plant SNATs (Lee et al., 2014). It was revealed that ZmSNAT1 had an enzymatic activity far higher than ZmSNAT3, although the underlying mechanism remains to be investigated.\nThe kinetic parameters of the recombinant proteins showed that ZmSNAT1 and ZmSNAT3 had the enzymatic activity similar to those of rice and Arabidopsis. For instance, ZmSNAT1 had a _K_ m value (8.60 mM), which is ~ 20 folder greater than that of AtSNAT1 (309 \u00b5M) (Lee et al., 2014) and OsSNAT1 (385 \u00b5M) (Kang et al., 2012), but its _V_ max value (4.05 nmol/min/mg) ( **Figure 5A** ) is also greater than that of AtSNAT1 (1.4 nmol/min/mg) (Lee et al., 2014) and OsSNAT1 (282 pmol/min/mg) (Kang et al., 2012). Based on the _K_ m value ZmSNAT3 (_K_ m 11.51 mM) seemed to have weaker substrate binding ability than AtSNAT6 (_K_ m = 5 mM), however, its _V_ max value (142 pmol/min/mg) is very similar to that of AtSNAT6 (147 pmol/min/mg) ( **Figure 5B** ), indicating ZmSNAT3 also had a catalytic rate similar to AtSNAT6. The reasons why the maize SNATs had higher _K_ m values than rice and Arabidopsis homologs but still had greater or similar _V_ max values remain to be investigated.\nWe further determined Y228 in ZmSNAT1 to be the amino acid essential for its enzymatic activity. This was achieved by the alignment of the protein tertiary structures between ZmSNAT1 and OsSNAT1, of which the crystal structure and the essential amino acids were already reported (Liao et al., 2021). Further analysis revealed that Y228 is well conserved in all 26 maize cultivars analyzed ( **Supplementary Figure S2** ), which also corroborates the importance of this key residue.\n=== Responsive transcription of _ZmSNATs_ to abiotic stresses ===\nDue to climate change, plants are experiencing more and ever-increasing stresses such as heat and drought (Li et al., 2023; Wang et al., 2023). It has already been well proven that melatonin could alleviate growth inhibition and damage caused by drought and other abiotic stresses (Huang et al., 2019). Application of exogenous melatonin can reduce ROS accumulation and improve the efficiency of photosystem and stomatal conductance to enhance drought tolerance (Ye et al., 2016; Fleta-Soriano et al., 2017; Huang et al., 2019; Su et al., 2019) and thermotolerance (Ahammed et al., 2019). By analyzing the public available maize transcriptome datasets, we revealed that the transcription of _ZmSNAT1_ and _ZmSNAT3_ was responsive to drought and heat stresses ( **Figure 11** ). When subject to drought stress, the transcription of _ZmSNAT1_ and _ZmSNAT3_ was significantly downregulated in one cultivar but upregulated in the other two cultivars. A similar observation was reported in _Hypericum perforatum_ , where the expression level of _HpSNAT1_ reached the highest at 8 h after drought stress (Zhou et al., 2021). In one cultivar tested for heat stress, both genes were significantly downregulated ( **Figure 10** ).\nWe further determined the transcription of _ZmSNAT1_ and _ZmSNAT3_ in the _W22_ cultivar subjected to heat or drought stresses by qRT-PCR analysis. Drought stress activated the transcription of _ZmSNAT1_ but not _ZmSNAT3_ ( **Figure 11A** ), which is consistent with the results of transcriptome analysis. On the other hand, we observed time-dependent effect of heat stress on the transcription of _ZmSNAT1_ and _ZmSNAT3_ ( **Figures 11B, C** ). All these observations suggest the transcription of _ZmSNAT1_ and _ZmSNAT3_ is responsive to heat and drought stresses, suggesting melatonin biosynthesis is finely regulated through the transcription of _ZmSNAT1_ and _ZmSNAT3_. However, the mechanism(s) underlying this phenomenon and the key players remain to be investigated.\nIn a few plants, it is already known that the crosstalk between melatonin and other hormones such as SA and ABA is involved in the melatonin-mediated tolerance to drought stress (Li et al., 2015; Wei et al., 2015; Prakash et al., 2019; Arnao and Hern\u00e1ndez-Ruiz, 2019; Moustafa-Farag et al., 2020; Tiwari et al., 2021). In tomato seedlings, it was observed that melatonin enhances drought tolerance by stimulating sugar metabolism and negatively regulating ABA synthesis (Jahan et al., 2024). Melatonin biosynthesis under stress could also be regulated through protein interactions. In cotton, for instance, GhSNAT3D might interact with GhASMT and GhSNAT25D to regulate the biosynthesis of melatonin when subjected to different stresses (Zhang et al., 2022). In tomato, a chaperone HSP40 in chloroplasts could stabilize SlSNAT against heat shock-induced degradation (Wang et al., 2020). These findings of melatonin biosynthesis regulation provide useful clues to uncover the mechanisms of melatonin biosynthesis regulation in response to heat and drought stress in maize.\nMaize is a worldwide crop and has spread across the world. We observed that there is a great variation of the _ZmSNAT1_ and _ZmSNAT3_ transcription level among the 26 cultivars ( **Figure 8** ). It is worthwhile to further investigate whether it is a result of their local adaptation. It is also very intriguing to identify the genetic basis underlying the transcriptional variation for enhancing the resilience of maize cultivars to environmental stresses. Since melatonin has recently been considered as a new plant hormone, it will be worthwhile to dissect the pathways of its biosynthesis, regulation and signaling in maize. The identification and characterization of ZmSNAT1 and ZmSNAT3, though very preliminary, lay the foundation for further studies on identifying new SNATs in maize and other plants as Wang et al. (2022) did in Arabidopsis. The functions of melatonin in maize growth, development, environmental stresses and other biological processes could be investigated by producing knockout/edited/silenced/overexpressed plants, such as in rice (Byeon and Back, 2016; Hwang and Back, 2019, 2020).", "Thinking\u6a21\u5f0f": "high", "generation_type": "\u63a8\u7406\u578b"}','2026-03-23 14:42:31.936209','2026-03-23 14:42:31.936219');
INSERT INTO qa_pairs VALUES(23,2,'在玉米中鉴定出两个 SNAT 基因 ZmSNAT1 和 ZmSNAT3，它们都能催化 5-甲氧基色胺向褪黑素或 5-羟色胺向 NAS 的转化。表达分析显示，在相同条件下 ZmSNAT1 的转录水平整体高于 ZmSNAT3，酶学分析表明 ZmSNAT1 编码蛋白的催化活性也更强；氨基酸序列比对进一步发现，决定 ZmSNAT1 酶活性的关键位点 Y228 在不同玉米品种间高度保守。面对这类分子和功能证据，在判断玉米褪黑素合成途径中哪一个 SNAT 基因更可能承担主要功能时，应得出怎样的结论，并给出推理依据？','玉米褪黑素合成途径中更可能承担主要功能的是 ZmSNAT1 而非 ZmSNAT3。因为两者都具备 SNAT 的催化功能时，ZmSNAT1 在转录水平上表达更高，可产生更多酶分子；其编码蛋白本身的酶活性也更强，单位酶分子催化效率更高；同时，决定其酶活性的关键位点 Y228 在不同玉米品种中高度保守，表明该位点以及相应基因功能在种群中受到强烈选择并被长期维持。高表达量、更强酶活性以及关键功能残基的保守性共同指向 ZmSNAT1 在玉米 SNAT 家族中承担更核心、主要的功能角色。','{"species": "Zea mays", "paper_id": "PMC11481039", "reasoning_steps": ["Step 1: ZmSNAT1 \u548c ZmSNAT3 \u90fd\u7f16\u7801\u5177\u6709\u5c06 5-MT \u8f6c\u5316\u4e3a\u892a\u9ed1\u7d20\u6216\u5c06 5-\u7f9f\u8272\u80fa\u8f6c\u5316\u4e3a NAS \u529f\u80fd\u7684 SNAT \u86cb\u767d\u3002", "Step 2: ZmSNAT1 \u7684\u8f6c\u5f55\u6c34\u5e73\u9ad8\u4e8e ZmSNAT3\uff0c\u8bf4\u660e\u5728\u76f8\u540c\u6761\u4ef6\u4e0b ZmSNAT1 \u4ea7\u7269\u7684\u6f5c\u5728\u8868\u8fbe\u91cf\u66f4\u9ad8\u3002", "Step 3: ZmSNAT1 \u7f16\u7801\u7684\u86cb\u767d\u9176\u6d3b\u6027\u9ad8\u4e8e ZmSNAT3 \u7f16\u7801\u7684\u86cb\u767d\uff0c\u8868\u660e\u5355\u4f4d\u9176\u5206\u5b50\u50ac\u5316\u6548\u7387\u66f4\u9ad8\u3002", "Step 4: ZmSNAT1 \u4e2d\u7684 Y228 \u4f4d\u70b9\u5bf9\u5176\u9176\u6d3b\u6027\u81f3\u5173\u91cd\u8981\uff0c\u662f\u51b3\u5b9a\u5176\u529f\u80fd\u7684\u5173\u952e\u6c28\u57fa\u9178\u6b8b\u57fa\u3002", "Step 5: Y228 \u4f4d\u70b9\u5728\u4e0d\u540c\u7389\u7c73\u54c1\u79cd\u95f4\u5747\u4e3a\u4fdd\u5b88\uff0c\u610f\u5473\u7740\u8fd9\u4e00\u5173\u952e\u529f\u80fd\u4f4d\u70b9\u5728\u79cd\u5185\u5f97\u5230\u4e86\u5e7f\u6cdb\u7ef4\u6301\u3002", "Step 6: \u5173\u952e\u529f\u80fd\u4f4d\u70b9\u7684\u4fdd\u5b88\u6027\u7ed3\u5408\u66f4\u9ad8\u7684\u8868\u8fbe\u91cf\u548c\u66f4\u5f3a\u7684\u9176\u6d3b\u6027\uff0c\u6307\u5411\u8be5\u57fa\u56e0\u5728\u79cd\u7fa4\u4e2d\u5177\u6709\u91cd\u8981\u4e14\u7a33\u5b9a\u7684\u529f\u80fd\u3002", "Step 7: \u56e0\u6b64\uff0c\u5728\u7389\u7c73 SNAT \u5bb6\u65cf\u4e2d\uff0cZmSNAT1 \u76f8\u5bf9\u4e8e ZmSNAT3 \u5145\u5f53\u4e86\u66f4\u4e3b\u8981\u3001\u66f4\u6838\u5fc3\u7684\u529f\u80fd\u57fa\u56e0\u3002"], "question_cot": "", "final_conclusion": "\u5728\u7389\u7c73\u4e2d\uff0cZmSNAT1 \u76f8\u5bf9\u4e8e ZmSNAT3 \u662f\u66f4\u4e3b\u8981\u7684 SNAT \u529f\u80fd\u57fa\u56e0\uff0c\u56e0\u4e3a\u5b83\u8f6c\u5f55\u6c34\u5e73\u66f4\u9ad8\u3001\u9176\u6d3b\u6027\u66f4\u5f3a\u4e14\u5173\u952e\u4f4d\u70b9 Y228 \u5728\u5404\u54c1\u79cd\u4e2d\u9ad8\u5ea6\u4fdd\u5b88\u3002", "difficulty": "medium", "tags": ["mechanism", "result", "application"], "created_at": "2025-12-16T21:20:22", "token_est_question": 27, "token_est_answer": 16, "section": "Conclusion", "context": "=== Conclusions ===\nIn this study, we identified and validated two maize SNAT genes, _ZmSNAT1_ and _ZmSNAT3_ , that encode proteins with the function of converting serotonin to NAS or 5-MT to melatonin. _ZmSNAT1_ appears to have a greater transcription level and to encode a protein having the enzyme activity greater than that encoded by _ZmSNAT3_. The Y228 residue in ZmSNAT1 is essential for its enzymatic activity and is conserved across maize cultivars. Another maize SNAT gene, ZmSNAT2 is only present in 8 of 26 cultivars analyzed. We also determined the transcription of both _ZmSNAT1_ and _ZmSNAT3_ is responsive to drought and heat stresses.", "Thinking\u6a21\u5f0f": "high", "generation_type": "\u63a8\u7406\u578b"}','2026-03-23 14:42:32.059764','2026-03-23 14:42:32.059770');
INSERT INTO qa_pairs VALUES(24,2,'在玉米中对若干单个转录因子获得功能缺失突变后，形态学观察几乎不见明显表型差异，但对同一材料进行转录组测定可以发现每种基因型中都有一定数量的差异表达基因，针对酚类代谢的靶向代谢组检测也显示部分突变体在酚类化合物积累上存在变化；同时，将这些转录因子通过酵母单杂交或共表达网络预测得到的靶基因集合与突变体的表达数据进行比对时，仅有少数预测靶基因的表达发生改变。面对这种“整体表型效应弱、分子层面改变有限而非普遍、预测靶基因只被少量实验证实”的情况，科研人员在设计利用单基因敲除来验证转录因子调控网络时，应如何评价这种策略在功能验证中的有效性与局限性？','单个转录因子的功能缺失在玉米中往往不会引起显著的形态学改变，但仍能在转录组和特定代谢通路中揭示一部分真实的调控效应，因此单基因敲除可用于验证预测网络中部分功能相关靶基因的存在。然而，由于表型冗余、调控网络互补以及环境和组织特异性等原因，大多数预测靶基因在该背景下并不表现出显著表达变化，导致无法全面反映转录因子的全部调控范围，也难以通过整体形态表型捕捉强烈效应。因此，这种策略在功能层面的有效性是有限且偏保守的，更适合作为验证预测网络中部分关键靶点和局部通路的工具，而不适合作为评价转录因子全局调控作用或系统性检验大规模网络预测准确性的唯一手段，需要与多基因突变、时空特异取样和其他功能分析方法结合使用。','{"species": "Zea mays", "paper_id": "PMC11979765", "reasoning_steps": ["Step 1: \u8f6c\u5f55\u56e0\u5b50\u5728\u57fa\u56e0\u8868\u8fbe\u8c03\u63a7\u548c\u8868\u578b\u5f62\u6210\u4e2d\u8d77\u91cd\u8981\u4f5c\u7528\uff0c\u56e0\u6b64\u7406\u8bba\u4e0a\u5176\u529f\u80fd\u7f3a\u5931\u53ef\u80fd\u5f71\u54cd\u5f62\u6001\u3001\u8f6c\u5f55\u7ec4\u548c\u4ee3\u8c22\u7ec4\u3002", "Step 2: \u5bf9\u591a\u4e2a\u7389\u7c73\u8f6c\u5f55\u56e0\u5b50\u83b7\u5f97\u4e86\u529f\u80fd\u7f3a\u5931\u7b49\u4f4d\u57fa\u56e0\uff0c\u5e76\u5bf9\u8fd9\u4e9b\u7a81\u53d8\u4f53\u8fdb\u884c\u4e86\u8868\u578b\u89c2\u5bdf\uff0c\u672a\u53d1\u73b0\u660e\u663e\u7684\u5f62\u6001\u5b66\u5dee\u5f02\u3002", "Step 3: \u5c3d\u7ba1\u6ca1\u6709\u660e\u663e\u5f62\u6001\u8868\u578b\uff0c\u8fd9\u4e9b\u7a81\u53d8\u4f53\u7684\u8f6c\u5f55\u7ec4\u5206\u6790\u4ecd\u68c0\u6d4b\u5230\u5728\u6bcf\u4e00\u79cd\u57fa\u56e0\u578b\u4e2d\u90fd\u5b58\u5728\u5dee\u5f02\u8868\u8fbe\u57fa\u56e0\uff0c\u8bf4\u660e\u8f6c\u5f55\u56e0\u5b50\u7f3a\u5931\u4ecd\u7136\u5f71\u54cd\u57fa\u56e0\u8868\u8fbe\u3002", "Step 4: \u9488\u5bf9\u915a\u7c7b\u4ee3\u8c22\u7684\u9776\u5411\u4ee3\u8c22\u7ec4\u5206\u6790\u8868\u660e\uff0c\u90e8\u5206\u7a81\u53d8\u4f53\u4e2d\u915a\u7c7b\u5316\u5408\u7269\u79ef\u7d2f\u53d1\u751f\u53d8\u5316\uff0c\u8fdb\u4e00\u6b65\u8868\u660e\u5728\u4ee3\u8c22\u6c34\u5e73\u5b58\u5728\u6548\u5e94\uff0c\u4f46\u5e76\u975e\u666e\u904d\u548c\u5267\u70c8\u3002", "Step 5: \u5c06\u8f6c\u5f55\u56e0\u5b50\u9776\u57fa\u56e0\u7684\u5148\u524d\u9884\u6d4b\u7ed3\u679c\u4e0e\u7a81\u53d8\u4f53\u7684\u8868\u8fbe\u6570\u636e\u6bd4\u5bf9\u65f6\uff0c\u65e0\u8bba\u662f\u57fa\u4e8e\u9175\u6bcd\u5355\u6742\u4ea4\u7b5b\u9009\u8fd8\u662f\u57fa\u4e8e\u5171\u8868\u8fbe\u7f51\u7edc\u7684\u65b9\u6cd5\uff0c\u53ea\u5728\u9884\u6d4b\u9776\u57fa\u56e0\u96c6\u5408\u4e2d\u7684\u4e00\u5c0f\u90e8\u5206\u57fa\u56e0\u4e0a\u89c2\u5bdf\u5230\u8868\u8fbe\u91cf\u53d1\u751f\u6539\u53d8\uff0c\u800c\u4e14\u5171\u8868\u8fbe\u9884\u6d4b\u4e2d\u5927\u591a\u6570\u9776\u57fa\u56e0\u5e76\u672a\u53d1\u751f\u663e\u8457\u53d8\u5316\u3002", "Step 6: \u8fd9\u4e9b\u7ed3\u679c\u5171\u540c\u6307\u5411\uff1a\u5355\u4e2a\u8f6c\u5f55\u56e0\u5b50\u7684\u7f3a\u5931\u5728\u6574\u4f53\u8868\u578b\u4e0a\u5f71\u54cd\u6709\u9650\uff0c\u4f46\u5728\u5206\u5b50\u5c42\u9762\u53ef\u4ee5\u9a8c\u8bc1\u5e76\u652f\u6301\u90e8\u5206\u7f51\u7edc\u9884\u6d4b\u9776\u6807\uff0c\u800c\u5927\u90e8\u5206\u9884\u6d4b\u9776\u6807\u672a\u88ab\u5b9e\u9a8c\u8bc1\u5b9e\uff0c\u8868\u660e\u9884\u6d4b\u9776\u6807\u7684\u603b\u4f53\u529f\u80fd\u76f8\u5173\u6027\u6709\u9650\u3002", "Step 7: \u56e0\u6b64\uff0c\u53ef\u4ee5\u63a8\u65ad\uff1a\u5229\u7528\u5355\u57fa\u56e0\u6572\u9664\u6765\u6d4b\u8bd5\u8f6c\u5f55\u56e0\u5b50\u8c03\u63a7\u7f51\u7edc\u65f6\uff0c\u5f80\u5f80\u53ea\u80fd\u6355\u6349\u5230\u90e8\u5206\u771f\u5b9e\u8c03\u63a7\u5173\u7cfb\uff0c\u5e76\u4e0d\u5bb9\u6613\u5728\u6574\u4f53\u8868\u578b\u6216\u5927\u8303\u56f4\u57fa\u56e0\u8868\u8fbe\u53d8\u5316\u4e0a\u4f53\u73b0\u51fa\u5f3a\u70c8\u6548\u5e94\u3002"], "question_cot": "", "final_conclusion": "\u5728\u7389\u7c73\u4e2d\uff0c\u5355\u4e2a\u672a\u8868\u5f81\u8f6c\u5f55\u56e0\u5b50\u7684\u529f\u80fd\u7f3a\u5931\u5f80\u5f80\u4e0d\u4f1a\u4ea7\u751f\u660e\u663e\u5f62\u6001\u53d8\u5316\uff0c\u4f46\u53ef\u4ee5\u63ed\u793a\u57fa\u56e0\u8c03\u63a7\u7f51\u7edc\u9884\u6d4b\u9776\u57fa\u56e0\u4e2d\u4e00\u5c0f\u90e8\u5206\u7684\u8868\u8fbe\u6539\u53d8\uff0c\u4ece\u800c\u663e\u793a\u8fd9\u4e9b\u9884\u6d4b\u5177\u6709\u6709\u9650\u4f46\u5b58\u5728\u7684\u529f\u80fd\u76f8\u5173\u6027\u3002", "difficulty": "medium", "tags": ["mechanism", "method", "result", "application"], "created_at": "2025-12-16T21:13:56", "token_est_question": 1, "token_est_answer": 1, "section": "Abstract", "context": "=== Abstract ===\nTranscription factors play important roles in regulation of gene expression and phenotype. A variety of approaches have been utilized to develop gene regulatory networks to predict the regulatory targets for each transcription factor, such as yeast-1-hybrid screens and gene co-expression network analysis. Here we identified potential transcription factor targets and used a reverse genetics approach to test the predictions of several gene regulatory networks in maize. Loss-of-function mutant alleles were isolated for 22 maize transcription factors. These mutants did not exhibit obvious morphological phenotypes. However, transcriptomic profiling identified differentially expressed genes in each of the mutant genotypes, and targeted metabolic profiling indicated variable phenolic accumulation in some mutants. An analysis of expression levels for predicted target genes based on yeast-1-hybrid screens identified a small subset of predicted targets that exhibit altered expression levels. The analysis of predicted targets from gene co-expression network-based methods found significant enrichments for prediction sets of some transcription factors, but most predicted targets did not exhibit altered expression. This could result from false-positive gene co-expression network predictions, a transcription factor with a secondary regulatory role resulting in minor effects on gene regulation, or redundant gene regulation by other transcription factors. Collectively, these findings suggest that loss-of-function for single uncharacterized transcription factors might have limited phenotypic impacts but can reveal subsets of gene regulatory network predicted targets with altered expression.\n**Keywords:** gene regulatory networks, maize, transcription factors\nLoss-of-function mutant alleles were characterized for 22 maize transcription factors to test the functional relevance of prior gene regulatory network predictions. The transcriptome and phenolic metabolome of these mutants were profiled to document the functional roles of these transcription factors. A subset of the prior gene regulatory network predictions were supported but had limited overall power in predicting changes in gene expression or phenolic compounds. The limitations of testing transcription factor-target predictions with single TF knockouts are discussed.", "Thinking\u6a21\u5f0f": "high", "generation_type": "\u63a8\u7406\u578b"}','2026-03-23 14:42:32.185781','2026-03-23 14:42:32.185791');
INSERT INTO qa_pairs VALUES(25,2,'在玉米中，针对一批在基因调控网络中具有预测靶基因的转录因子获得了缺失等位，研究发现这些材料在常规生长条件下与对应自交系相比并未出现明显的宏观表型差异，但转录组分析却检测到一定数量的差异表达基因，其中部分通过酵母单杂交或共表达网络预测的靶基因确实发生了表达变化，而大多数预测靶基因的表达保持稳定，且功能富集分析只能看到若干功能类群的变化，却难以明确界定这些转录因子的具体生物学功能。考虑到玉米经历过全基因组加倍并保留了大量同源基因，这样的表型与转录组结果共同说明了单个转录因子敲除在扰动基因调控网络方式上的什么特征，以及在功能验证设计上为何往往需要联合敲除多个相关转录因子？','这些结果表明，单个转录因子敲除通常只对基因调控网络造成有限扰动：虽然能引起部分下游基因表达变化，但大多数预测靶基因保持稳定，宏观表型也不明显，说明网络具有较强的稳健性和高度功能冗余。玉米的全基因组加倍为许多转录因子提供了可代偿的同源或功能相近成员，因此往往需要联合敲除多个相关转录因子，才能显著破坏冗余调控模块，产生大范围的表达重编程和清晰的表型效应。','{"species": "Zea mays", "paper_id": "PMC11979765", "reasoning_steps": ["Step 1: \u8f6c\u5f55\u56e0\u5b50\u901a\u8fc7\u7279\u5f02\u6027\u7ed3\u5408 DNA \u6765\u8c03\u63a7\u5927\u91cf\u4e0b\u6e38\u57fa\u56e0\u7684\u8f6c\u5f55\uff0c\u56e0\u6b64\u7406\u8bba\u4e0a\u6572\u9664\u4e00\u4e2a\u8f6c\u5f55\u56e0\u5b50\u53ef\u80fd\u5f71\u54cd\u5176\u76ee\u6807\u57fa\u56e0\u7684\u8868\u8fbe\u4e0e\u76f8\u5173\u6027\u72b6\u3002", "Step 2: \u4e3a\u591a\u4e2a\u5177\u6709\u57fa\u56e0\u8c03\u63a7\u7f51\u7edc\u9884\u6d4b\u9776\u6807\u7684\u7389\u7c73\u8f6c\u5f55\u56e0\u5b50\u83b7\u5f97\u4e86\u7f3a\u5931\u7b49\u4f4d\u57fa\u56e0\uff0c\u5e76\u5728\u5e38\u89c4\u751f\u957f\u6761\u4ef6\u4e0b\u8fdb\u884c\u8868\u578b\u89c2\u5bdf\u3002", "Step 3: \u5728\u8fd9\u4e9b\u8f6c\u5f55\u56e0\u5b50\u7f3a\u5931\u6750\u6599\u4e2d\uff0c\u672a\u89c2\u5bdf\u5230\u660e\u663e\u7684\u5b8f\u89c2\u8868\u578b\u5dee\u5f02\uff0c\u8bf4\u660e\u5728\u6574\u4f53\u6027\u72b6\u5c42\u9762\u8fd9\u4e9b\u6572\u9664\u5bf9\u690d\u682a\u7684\u5f71\u54cd\u6709\u9650\u3002", "Step 4: \u8f6c\u5f55\u7ec4\u6d4b\u5e8f\u663e\u793a\uff0c\u8fd9\u4e9b\u7f3a\u5931\u6750\u6599\u4e0e\u5bf9\u7167\u76f8\u6bd4\u5b58\u5728\u4e0d\u540c\u6570\u91cf\u7684\u5dee\u5f02\u8868\u8fbe\u57fa\u56e0\uff0c\u4e14\u90e8\u5206\u901a\u8fc7 Y1H \u6216\u5171\u8868\u8fbe\u7f51\u7edc\u9884\u6d4b\u7684\u9776\u57fa\u56e0\u786e\u5b9e\u8868\u8fbe\u53d1\u751f\u4e86\u53d8\u5316\uff0c\u5e76\u5728\u4e2a\u522b\u60c5\u51b5\u4e0b\u5728\u5dee\u5f02\u8868\u8fbe\u57fa\u56e0\u4e2d\u663e\u8457\u5bcc\u96c6\u3002", "Step 5: \u7136\u800c\uff0c\u8d85\u8fc7 75% \u7684\u9884\u6d4b\u9776\u57fa\u56e0\u5728\u8fd9\u4e9b\u8f6c\u5f55\u56e0\u5b50\u6572\u9664\u80cc\u666f\u4e0b\u5e76\u672a\u8868\u73b0\u51fa\u8868\u8fbe\u53d8\u5316\uff0c\u540c\u65f6 GO \u5bcc\u96c6\u5206\u6790\u867d\u80fd\u68c0\u51fa\u67d0\u4e9b\u529f\u80fd\u7c7b\u522b\u7684\u8868\u8fbe\u6539\u53d8\uff0c\u4f46\u96be\u4ee5\u89e3\u6790\u8fd9\u4e9b\u8f6c\u5f55\u56e0\u5b50\u7684\u660e\u786e\u751f\u7269\u5b66\u529f\u80fd\u3002", "Step 6: \u7efc\u4e0a\uff0c\u5355\u4e2a\u8f6c\u5f55\u56e0\u5b50\u7f3a\u5931\u4ec5\u80fd\u5f15\u8d77\u6709\u9650\u8303\u56f4\u7684\u8868\u8fbe\u4e0e\u529f\u80fd\u53d8\u5316\uff0c\u800c\u5927\u90e8\u5206\u9884\u6d4b\u9776\u6807\u672a\u53d7\u663e\u8457\u5f71\u54cd\uff0c\u8fd9\u4e0e\u7389\u7c73\u57fa\u56e0\u7ec4\u4e2d\u7531\u4e8e\u5168\u57fa\u56e0\u7ec4\u52a0\u500d\u5bfc\u81f4\u7684\u529f\u80fd\u5197\u4f59\u73b0\u8c61\u76f8\u543b\u5408\uff0c\u56e0\u6b64\u53ef\u4ee5\u63a8\u65ad\u5355\u57fa\u56e0\u6572\u9664\u5f80\u5f80\u53ea\u80fd\u5bf9\u57fa\u56e0\u8c03\u63a7\u7f51\u7edc\u4ea7\u751f\u6709\u9650\u6270\u52a8\uff0c\u53cd\u6620\u4e86\u8f83\u9ad8\u7a0b\u5ea6\u7684\u529f\u80fd\u5197\u4f59\u3002"], "question_cot": "", "final_conclusion": "\u5355\u4e2a\u8f6c\u5f55\u56e0\u5b50\u57fa\u56e0\u7684\u6572\u9664\u5f80\u5f80\u53ea\u80fd\u5bf9\u57fa\u56e0\u8c03\u63a7\u7f51\u7edc\u4ea7\u751f\u6709\u9650\u6270\u52a8\uff0c\u8fd9\u53ef\u80fd\u53cd\u6620\u4e86\u57fa\u56e0\u8c03\u63a7\u4e2d\u5b58\u5728\u8f83\u9ad8\u7a0b\u5ea6\u7684\u529f\u80fd\u5197\u4f59\u3002", "difficulty": "medium", "tags": ["mechanism", "result", "concept", "application", "gene_regulatory_network", "functional_redundancy", "transcription_factor"], "created_at": "2025-12-16T21:15:08", "token_est_question": 1, "token_est_answer": 1, "section": "Introduction", "context": "=== Introduction ===\nTranscription factors (TFs) transcriptionally regulate gene expression by recognizing and binding to DNA in a sequence-specific fashion. In eukaryotic genomes, \u223c5\u201310% of the genes encode TFs that regulate transcription of all genes (Riechmann 2002; Sperling 2007; Brkljacic and Grotewold 2017; Lambert _et al_. 2018). Gene regulatory networks (GRNs) represent the interactions between TFs and target genes that regulate spatial and temporal expression of genes (Macneil and Walhout 2011; Mejia-Guerra _et al_. 2012; Badia-I-Mompel _et al_. 2023). The data to match the discrete number of TFs to the larger number of target genes they regulate in GRNs remains limited. However, it is important to identify and understand how GRNs regulate endogenous metabolic pathways as this may provide key insights for modulating whole pathways or branch points of pathways (Farr\u00e9 _et al_. 2014). Likewise, GRN inference can be used to select for existing variants or introduce novel TF alleles as a potential strategy to generate novel phenotypes for crop improvement (Springer _et al_. 2019).\nSeveral methods have been used to predict TF\u2013target gene interactions to generate GRNs in maize. These methods can include gene-centered approaches; a gene is known but regulators of the gene are not, or TF-centered approaches; the TF is known but the target genes it regulates are not (Yang _et al_. 2016). Gene-centered approach methods can identify interactions where TFs directly bind to promoters or _cis_ -regulatory elements (CREs) of a particular gene. Yeast-1-hybrid (Y1H) is a gene-centered approach that involves screening for interactions between the DNA sequence of interest (DNA bait) and a TF (protein prey) by activation of a reporter gene in yeast (Arda and Walhout 2010; Yang _et al_. 2016). Studies in _Arabidopsis_ (Brady _et al_. 2011; Taylor-Teeples _et al_. 2015; Ikeuchi _et al_. 2018) and maize (Yang _et al_. 2017; Abnave _et al_. 2024) have successfully used Y1H to predict TF\u2013target gene interactions. However, there are numerous limitations of the Y1H approach, including interactions are tested in yeast outside of genome tissue-specific or chromatin landscapes, cloned promoter sequences (DNA bait) are usually short (\u223c1 kb) and may not capture the TF binding site, and interactions that require multiple TFs or post-translational modifications will be missed (Arda and Walhout 2010; Walhout 2011). TF-centered approaches, such as chromatin-immunoprecipitation coupled with DNA sequencing (ChIP-seq), can identify potential TF targets genome-wide. ChIP-seq is a technique that uses a TF-specific antibody to selectively recover bound DNA from cross-linked DNA-protein complexes (Kuo and Allis 1999; Johnson _et al_. 2007). In maize, direct targets for several TFs have been identified by ChIP-seq (Bolduc _et al_. 2012; Morohashi _et al_. 2012; Eveland _et al_. 2014; Li _et al_. 2015; Pautler _et al_. 2015; Dong _et al_. 2019; Tu _et al_. 2020; Hartwig _et al_. 2023). Unlike Y1H, ChIP-seq captures in vivo TF\u2013target interactions within accessible chromatin regions and identifies target sequences that are both directly and indirectly bound (if the TF of interest forms a complex with another TF that binds the target directly). ChIP-seq is limited by the availability of antibodies and loss-of-function alleles to test antibody specificity. Another approach to GRN inference is to build gene co-expression networks (GCNs) by statistically measuring the relationship between TF and target gene expression profiles. If the expression pattern of the TF and target gene is similar, these genes are considered co-expressed and may have shared regulation (Eisen _et al_. 1998). GCNs require large sets of quantitative data, usually RNA-seq, to capture gene expression and the statistical methods used for correlation can have a significant impact on the results (Huang _et al_. 2017). Each approach to predict GRNs could identify portions of true GRNs, but comparisons between these predictions reveal only limited overlap, suggesting many false positives and false negatives from each approach.\nOne method to test GRN predictions is to isolate loss-of-function mutants in TFs and test expression of predicted targets (Scherens and Goffeau 2004; Thompson _et al_. 2015). In maize, there are limited methods for moderate scale reverse genetics studies to assess if absence of the TF results in target genes with altered expression. Current maize mutant libraries only provide functional knockouts for a subset of genes in the genome (Lu _et al_. 2018). Further, the ability to test GRN predictions with TF loss-of-function alleles will vary based on the GRN prediction method. For example, the sample size of putative TF regulators identified by GCN-based methods is usually much larger than those identified by Y1H screens. The number of Y1H predicted TF\u2013target interactions is limited by the size and number of promoter regions that are cloned and tested, usually from a single putative pathway or functional type. GCN-based methods allow for construction of much larger networks and the likelihood of isolating loss-of-function alleles for some of these predicted TF regulators increases by the size of the network alone. Due to the recent whole-genome duplication in maize (Gaut _et al_. 2000), testing functional impacts of GRN predictions in vivo with TF knockouts may be limited by genetic redundancy, which requires loss-of-function mutants for multiple related TF genes.\nA set of putative loss-of-function alleles were recovered for 22 maize TFs with predicted GRN targets based on either Y1H and/or GCNs. We did not observe major phenotypic differences in these mutants under normal growth conditions. However, transcriptome profiling revealed variable numbers of differentially expressed (DE) genes in transposon-insertion mutants relative to the corresponding inbred line plants. The analysis of transcript abundance for GRN targets predicted by Y1H or GCN methods does reveal examples of predicted targets with altered expression. In some cases, the predicted targets are significantly over-represented in the DE genes. However, the majority (>75%) of predicted targets did not exhibit altered expression. Gene ontology (GO) enrichment analyses identified functional groups of genes with altered expression in some of these mutants but did not point to clear biological functions for these TFs. These findings suggest limited perturbation of GRNs in these single gene TF knockouts and could reflect high degrees of functional redundancy in gene regulation.", "Thinking\u6a21\u5f0f": "high", "generation_type": "\u63a8\u7406\u578b"}','2026-03-23 14:42:32.310758','2026-03-23 14:42:32.310768');
INSERT INTO qa_pairs VALUES(26,3,'在利用转座子插入突变体分析转录因子功能时，某一等位基因的转录组装结果显示：插入位点上游只能检测到5′端转录本，而且该区域原本的起始密码子AUG已不再处于正确阅读框；插入位点下游则存在3′端转录本，但翻译后仅能得到长度不足正常蛋白一半的多肽。面对这种既缺乏正常起始位点又只能产生明显截短产物的情况，应怎样判断该转录因子等位基因是否还能产生全长功能蛋白，并在功能性质上如何进行客观分类？','应判断该等位基因已不能产生全长、结构完整的转录因子蛋白，可客观界定为转录因子功能缺失（loss-of-function）等位基因，因为所有可见转录本要么起始移码、要么严重截短，均无法编码完整蛋白。','{"species": "Zea mays", "paper_id": "PMC11979765", "reasoning_steps": ["Step 1: \u529f\u80fd\u6b63\u5e38\u7684\u8f6c\u5f55\u56e0\u5b50\u86cb\u767d\u9700\u8981\u4e00\u4e2a\u5728\u6b63\u786e\u9605\u8bfb\u6846\u4e2d\u7684\u8d77\u59cb\u5bc6\u7801\u5b50\uff08AUG\uff09\u4ee5\u53ca\u8db3\u591f\u957f\u7684\u5f00\u653e\u9605\u8bfb\u6846\uff0c\u624d\u80fd\u5408\u6210\u5168\u957f\u86cb\u767d\u3002", "Step 2: \u8f6c\u5ea7\u5b50\uff08Mu\uff09\u63d2\u5165\u53ef\u6253\u65ad\u57fa\u56e0\u7684\u7f16\u7801\u5e8f\u5217\uff0c\u5bfc\u81f4\u8f6c\u5f55\u672c\u5728\u63d2\u5165\u4f4d\u70b9\u4e0a\u4e0b\u6e38\u53d1\u751f\u7ed3\u6784\u548c\u5e8f\u5217\u6539\u53d8\u3002", "Step 3: \u5728\u8fd9\u4e9b\u7b49\u4f4d\u57fa\u56e0\u4e2d\uff0c5\u2032 \u7aef\u7684\u8f6c\u5f55\u672c\u51fa\u73b0\u5728 Mu \u63d2\u5165\u4e0a\u6e38\uff0c\u4f46\u5176\u6700\u521d\u7684 AUG \u53d1\u751f\u79fb\u7801\uff08\u8d77\u59cb\u5bc6\u7801\u5b50\u4e0d\u518d\u5904\u4e8e\u6b63\u786e\u9605\u8bfb\u6846\uff09\uff0c\u4ece\u800c\u65e0\u6cd5\u4ece\u6b63\u5e38\u8d77\u59cb\u4f4d\u70b9\u7ffb\u8bd1\u51fa\u6b63\u786e\u7684\u86cb\u767d\u5e8f\u5217\u3002", "Step 4: \u540c\u65f6\u6216\u53e6\u5916\u4e00\u7c7b\u8f6c\u5f55\u672c\u6765\u81ea Mu \u63d2\u5165\u4e0b\u6e38\uff083\u2032 \u7aef\uff09\uff0c\u8fd9\u4e9b\u8f6c\u5f55\u672c\u53ea\u80fd\u7f16\u7801\u51fa\u957f\u5ea6\u5c0f\u4e8e\u6b63\u5e38\u86cb\u767d\u4e00\u534a\u7684\u591a\u80bd\u7247\u6bb5\u3002", "Step 5: \u8d77\u59cb\u5bc6\u7801\u5b50\u79fb\u51fa\u9605\u8bfb\u6846\u4f1a\u963b\u6b62\u5408\u6210\u6b63\u786e\u8d77\u59cb\u7684\u86cb\u767d\uff0c\u800c\u86cb\u767d\u957f\u5ea6\u4e0d\u8db3\u6b63\u5e38\u7684\u4e00\u534a\u901a\u5e38\u610f\u5473\u7740\u7f3a\u5931\u5927\u91cf\u7ed3\u6784\u548c\u529f\u80fd\u57df\uff0c\u4e24\u79cd\u60c5\u51b5\u90fd\u65e0\u6cd5\u4ea7\u751f\u5177\u6709\u5b8c\u6574\u7ed3\u6784\u548c\u529f\u80fd\u7684\u5168\u957f\u8f6c\u5f55\u56e0\u5b50\u86cb\u767d\u3002", "Step 6: \u56e0\u6b64\uff0c\u8fd9\u7c7b\u5728 Mu \u63d2\u5165\u4f4d\u70b9\u4e0a\u4e0b\u6e38\u4ec5\u751f\u6210\u79fb\u7801\u6216\u4e25\u91cd\u622a\u77ed\u8f6c\u5f55\u672c\u7684\u7b49\u4f4d\u57fa\u56e0\uff0c\u88ab\u5408\u7406\u5f52\u7c7b\u4e3a\u4e0d\u80fd\u4ea7\u751f\u5168\u957f\u86cb\u767d\u7684\u8f6c\u5f55\u56e0\u5b50\u529f\u80fd\u7f3a\u5931\u7b49\u4f4d\u57fa\u56e0\u3002"], "question_cot": "", "final_conclusion": "\u82e5\u8f6c\u5f55\u672c\u5728\u8f6c\u5ea7\u5b50\u63d2\u5165\u4f4d\u70b9\u4e0a\u6e38\u7684\u8d77\u59cb\u5bc6\u7801\u5b50\u79fb\u51fa\u9605\u8bfb\u6846\uff0c\u4e14/\u6216\u5728\u63d2\u5165\u4f4d\u70b9\u4e0b\u6e38\u4ec5\u80fd\u7f16\u7801\u5c0f\u4e8e\u6b63\u5e38\u86cb\u767d\u4e00\u534a\u957f\u5ea6\u7684\u591a\u80bd\uff0c\u5219\u8be5\u7b49\u4f4d\u57fa\u56e0\u4e0d\u80fd\u4ea7\u751f\u5168\u957f\u86cb\u767d\uff0c\u53ef\u88ab\u5ba2\u89c2\u754c\u5b9a\u4e3a\u8f6c\u5f55\u56e0\u5b50\u529f\u80fd\u7f3a\u5931\u7b49\u4f4d\u57fa\u56e0\u3002", "difficulty": "medium", "tags": ["concept", "mechanism", "method"], "created_at": "2025-12-16T21:16:12", "token_est_question": 1, "token_est_answer": 1, "section": "Materials & Methods", "context": "=== Isolation of TF mutant alleles from the UniformMu population in maize ===\nMethods for isolating 32 mutant alleles representing 22 TFs are detailed in Ellison _et al_. (2023).\n=== RNA-seq plant samples, data processing, and transcriptome profiling ===\nEllison _et al_. (2023) methods detail how: plant material from the 32 TF mutant alleles and W22 r-g control was sampled for RNA-seq, RNA-seq data was processed, transcriptome assembly was performed, and transcriptome profiling was used to identify TF loss-of-function alleles. Our experimental design focused on collecting mutant and wild type transcriptome data from tissues with moderate\u2013high expression (>6 CPM) of the TF relative to other tissues. In total, we surveyed 5 different tissues: coleoptile tip, imbibed embryo, seedling leaf, tassel, and tassel stem, with variable numbers of mutant alleles assessed (Supplementary Table 1 and Supplementary Table 2 in Ellison _et al_. (2023)). PCAtools (Blighe and Lun 2019) was used to conduct a principal component analysis of the CPM values of expressed genes and explore sample cluster patterns among and between biological replicates of mutant alleles and wild type W22 control. TF mutant alleles were characterized as potential loss-of-function alleles by determining that variation in the de novo assembled transcript structure and sequence resulted in an allele that could not produce the full-length protein. The 32 TF loss-of-function alleles produced transcripts 5\u2032 of the _Mu_ insertion with the initial AUG out-of-frame and/or transcripts 3\u2032 of _Mu_ that would only produce a protein less than half the length of the normal protein.\n=== Identification of TF mutant allele DE genes ===\nRaw read counts of expressed genes (CPM \u2265 1 in at least 1 sample per tissue) from all replicates of each TF mutant allele and W22 control from the same tissue were used to call differentially expressed (DE) genes: false discovery rate (FDR) adjusted _P_ 1 and _P_ < 0.05 (Kim _et al._ 2001), where the expected number of genes = (number of genes in allele A \u00d7 number of genes in allele B)/number of expressed genes. The number of possible shared DEGs between 2 independent mutant alleles per TF is the minimum number of either up- or down-regulated DEGs between both alleles. The number of shared DEGs was calculated as a proportion out of the possible shared DEGs between 2 independent alleles per TF (Supplementary Fig. 2).\n=== Enrichment for TF predicted targets ===\nAll statistical analyses for enrichment of DEGs utilized a hypergeometric probability. Testing for over-representation or enrichment of mutant allele DEGs for GO terms (Supplementary File 4) or GCN predicted targets was calculated with R stats v4.0.2 hypergeometric phyper(q = x - 1, m, n = N - m, k, lower.tail = FALSE) function, _P_ < 0.05 for significance (R Core Team 2020). For GO term enrichment: N = number of expressed genes associated with any GO term, m = number of genes with a specific GO term, k = number of DEGs with any GO term, x = number of DEGs with a specific GO term. For GCN (n1 and n3) predicted target enrichment: N = number of expressed nonredundant predicted target genes in the genome, m = number of expressed predicted targets per TF, k = number of DEGs, and x = number of DEGs that are predicted targets. The fold-enrichment of GCN predicted targets that were DE was calculated by [x/(k/N) \u00d7 m].", "Thinking\u6a21\u5f0f": "high", "generation_type": "\u63a8\u7406\u578b"}','2026-03-23 15:33:28.307406','2026-03-23 15:33:28.307417');
INSERT INTO qa_pairs VALUES(27,3,'在某植物中，对一批转录因子突变等位基因分别与同组织的野生型材料进行了转录组比较，统计了每个突变体中相对于野生型的上调和下调差异表达基因。分析发现，大多数转录因子突变体中下调基因的数目明显多于上调基因。已知主要作为转录激活子的转录因子在失活时往往导致其正调控靶基因在突变体中下调，而主要作为转录抑制子的转录因子失活时更常见的是靶基因在突变体中上调。研究者应如何据此判断这批转录因子在正常植株中整体上更可能是哪一类调控因子（激活子或抑制子），并说明判断的逻辑依据？','这批转录因子整体上更可能主要作为转录激活子而非抑制子。因为在它们的突变体中，差异表达基因以下调为主，说明许多在野生型中依赖这些转录因子正向调控的靶基因在功能丧失后表达降低，这正是激活子失活时通常出现的全局转录特征，而与抑制子失活时应以上调基因为主的预期模式不符。','{"species": "Zea mays", "paper_id": "PMC11979765", "reasoning_steps": ["Step 1: \u5dee\u5f02\u8868\u8fbe\u5206\u6790\u5bf9\u6bcf\u4e2a\u8f6c\u5f55\u56e0\u5b50\u7a81\u53d8\u7b49\u4f4d\u57fa\u56e0\u5206\u522b\u9274\u5b9a\u4e86\u76f8\u5bf9\u4e8e\u540c\u7ec4\u7ec7\u91ce\u751f\u578b\u7684\u4e0a\u8c03\u548c\u4e0b\u8c03\u57fa\u56e0\u3002", "Step 2: \u5728\u8fd9\u4e9b\u8f6c\u5f55\u56e0\u5b50\u4e2d\uff0c\u4e0d\u9884\u5148\u77e5\u9053\u5b83\u4eec\u5728\u4f53\u5185\u4e3b\u8981\u662f\u6fc0\u6d3b\u5b50\u8fd8\u662f\u6291\u5236\u5b50\uff0c\u56e0\u6b64\u540c\u65f6\u7edf\u8ba1\u4e86\u4e0a\u8c03\u548c\u4e0b\u8c03\u5dee\u5f02\u8868\u8fbe\u57fa\u56e0\u3002", "Step 3: \u7edf\u8ba1\u7ed3\u679c\u663e\u793a\uff0c\u572832\u4e2a\u7a81\u53d8\u7b49\u4f4d\u57fa\u56e0\u4e2d\uff0c\u670926\u4e2a\u7a81\u53d8\u4f53\u7684\u4e0b\u8c03\u57fa\u56e0\u6570\u91cf\u591a\u4e8e\u4e0a\u8c03\u57fa\u56e0\u6570\u91cf\u3002", "Step 4: \u5982\u679c\u4e00\u4e2a\u8f6c\u5f55\u56e0\u5b50\u5728\u91ce\u751f\u578b\u80cc\u666f\u4e0b\u4e3b\u8981\u4f5c\u4e3a\u8f6c\u5f55\u6fc0\u6d3b\u5b50\uff0c\u90a3\u4e48\u5176\u529f\u80fd\u4e27\u5931\u6216\u663e\u8457\u524a\u5f31\u65f6\uff0c\u9884\u671f\u5176\u6b63\u8c03\u63a7\u7684\u9776\u57fa\u56e0\u5728\u7a81\u53d8\u4f53\u4e2d\u4f1a\u503e\u5411\u4e8e\u4e0b\u8c03\u3002", "Step 5: \u5728\u5927\u591a\u6570\u7a81\u53d8\u4f53\u4e2d\u89c2\u5bdf\u5230\u7684\u201c\u4e0b\u8c03\u57fa\u56e0\u591a\u4e8e\u4e0a\u8c03\u57fa\u56e0\u201d\u7684\u603b\u4f53\u8d8b\u52bf\uff0c\u4e0e\u8fd9\u4e9b\u8f6c\u5f55\u56e0\u5b50\u4e3b\u8981\u627f\u62c5\u6fc0\u6d3b\u9776\u57fa\u56e0\u8868\u8fbe\u7684\u529f\u80fd\u662f\u4e00\u81f4\u7684\u3002", "Step 6: \u56e0\u6b64\uff0c\u53ef\u4ee5\u636e\u6b64\u63a8\u65ad\uff0c\u8fd9\u6279\u88ab\u6d4b\u8bd5\u7684\u8f6c\u5f55\u56e0\u5b50\u6574\u4f53\u4e0a\u66f4\u53ef\u80fd\u4e3b\u8981\u4f5c\u4e3a\u8f6c\u5f55\u6fc0\u6d3b\u5b50\u800c\u975e\u6291\u5236\u5b50\u3002"], "question_cot": "", "final_conclusion": "\u5bf9\u4e8e\u591a\u6570\u88ab\u6d4b\u8bd5\u7684\u8f6c\u5f55\u56e0\u5b50\u7a81\u53d8\u4f53\uff0c\u5dee\u5f02\u8868\u8fbe\u57fa\u56e0\u4e2d\u4e0b\u8c03\u57fa\u56e0\u6570\u591a\u4e8e\u4e0a\u8c03\u57fa\u56e0\u6570\uff0c\u7b26\u5408\u8fd9\u4e9b\u8f6c\u5f55\u56e0\u5b50\u4e3b\u8981\u4f5c\u4e3a\u8f6c\u5f55\u6fc0\u6d3b\u5b50\u7684\u9884\u671f\u4f5c\u7528\u65b9\u5f0f\u3002", "difficulty": "medium", "tags": ["concept", "mechanism", "method", "result"], "created_at": "2025-12-16T21:19:13", "token_est_question": 1, "token_est_answer": 1, "section": "Results", "context": "=== Results ===\nTo test the functional relevance of GRN predictions in maize, we obtained stocks containing putative loss-of-function alleles for a series of TFs. Two primary sources of GRN predictions were utilized to select TFs for testing. The first source of GRN predictions was from a Y1H screen that identified putative TF regulators of maize phenolic biosynthesis (Yang _et al_. 2017). This Y1H screen identified 45 TFs that exhibit interactions with at least 4 of the 54 phenolic biosynthesis gene promoters tested (Burdo _et al_. 2014; Yang _et al_. 2017). The second source of GRN predictions was a meta-analysis of TF\u2013target gene co-expression from 45 GCNs (Zhou _et al_. 2020). To test the GRN predictions generated from these 45 GCNs, we identified 64 TFs that had \u2265400 predicted targets and at least 1 coding sequence insertion indexed in the UniformMu mutant collection (McCarty _et al_. 2005). GRNs constructed from both sources, Y1H and GCNs, identified predictions for genes annotated in the B73v4 genome (Schnable _et al_. 2009; Jiao _et al_. 2017). Before moving forward with network perturbation analyses, all TFs selected for testing (45 from Y1H and 64 from GCNs) were confirmed to be single copy syntenic orthologs in the B73v4 and W22 (considered the wild type in this study) genomes (Monnahan _et al_. 2020).\n=== Isolation of TF mutant alleles from the UniformMu population ===\n_Mutator_ (_Mu_) transposon insertions located within the set of TFs predicted from the Y1H or GCNs were identified using the sequence-indexed UniformMu population created in a W22 inbred genetic background (McCarty _et al_. 2005). While most maize genes are associated with _Mu_ insertions in this population, only 27.1% of annotated W22 genes have an insertion within the coding sequence (Springer _et al_. 2018). We identified all available insertions for the 45 TFs identified as candidates in the Y1H screen, including insertions in UTRs, introns, and proximal promoter regions (up to 1 kb upstream of the annotated TSS). Given the prior evidence that some insertions within UTRs, introns, or 5\u2032 regions have minimal or no effect on the transcript produced by the allele (Kidwell and Lisch 1997; Dietrich _et al_. 2002; Liu _et al_. 2009), we focused primarily on coding region insertions for GCN predicted TFs. We initially obtained stocks representing 150 alleles (82 TFs) but some of these were subsequently eliminated from our study because we could not confirm the presence of the insertion or there was lack of evidence for loss-of-function. In total, we isolated 32 putative loss-of-function alleles for 22 TFs, including 12 alleles for 8 TFs selected from initial Y1H predictions (Yang _et al_. 2017) and 20 alleles for 14 TFs selected from GCN predictions (Fig. 1, Supplementary Table 1). After isolating the mutant alleles for the GCN selected TFs, another Y1H screen was performed using the promoters of the top 19 predominantly expressed phenolic genes from Gomez-Cano _et al_. (2020) as bait, 7 of which were not included in the initial Y1H screen with 54 phenolic promoters (Abnave _et al_. 2024). This new Y1H screen identified phenolic targets for 6 of the TFs with GCN predicted targets (Abnave _et al_. 2024). In total, 13 of the 22 TFs in this study were predicted to regulate phenolic biosynthesis genes by Y1H (Fig. 1, Supplementary Table 1).\n=== Fig. 1. ===\n \nOpen in a new tab\nExperimental design to test transcription factor\u2013\u2013target gene predictions in maize. Maize transcription factors (TFs) were predicted to be transcriptional regulators of a set of target genes based on a) a maize phenolic biosynthesis Y1H screen (Yang _et al_. 2017) or b) construction of 45 maize gene co-expression networks (GCNs) (Zhou _et al_. 2020). To test these TF\u2013target gene predictions in vivo, we focused on a) 45 TFs that bound \u22654 phenolic biosynthesis promoters in the Y1H screen or b) 64 TFs that were co-expressed with \u2265400 target genes across the 45 GCNs and had an available UniformMu insertion in the TF gene coding sequence. c) The UniformMu population was used to isolate 12 mutant alleles for 7/45 Y1H predicted TFs and 20 mutant alleles for 15/64 GCN predicted TFs (Supplementary Table 1). Seven of the 15 GCN predicted TFs with mutant alleles were also predicted to be phenolic regulators in an additional Y1H screen (Abnave _et al_. 2024). d) Transcriptome profiling was completed on wild-type plants with a functional TF and mutant plants to quantify expression of predicted target genes.\n=== Identifying TF loss-of-function alleles and DE genes by transcriptome profiling ===\nTranscriptome profiling by RNA-seq was performed for each TF mutant allele to characterize genome-wide perturbations of expression. We sampled a single tissue for each TF mutant in which the TE gene exhibited moderate expression. Across the 22 TFs, a total of 5 different tissues were sampled (Supplementary Fig. 1, Supplementary Table 1). To confirm the potential functional impact of the _Mu_ insertion on the gene product of the TF, we assessed the expression level and transcript structure for each mutant allele. The change in mRNA accumulation for each TF gene in the mutant allele harboring the _Mu_ insertion relative to the wild type allele was estimated from RNA-seq reads of all biological replicates mapped to the W22 reference genome (which lacks the _Mu_ insertion). This expression analysis revealed that 8 of the 32 mutant alleles had significantly reduced expression levels and 2 (_wrky8-m1_ and _hsf18-m1_) exhibited significant increases in total transcript abundance (Fig. 2, Supplementary Table 1) (Ellison _et al_. 2023). However, the lack of a difference in mRNA accumulation level does not necessarily mean that a functional product is produced. To determine if the _Mu_ insertion resulted in potential loss-of-function alleles, we generated a de novo transcript assembly and identified variation in transcript structure and sequence for each mutant. Our prior work found that most mutant alleles have either altered transcript structure or sequence that is predicted to result in the production of truncated or altered protein sequences (Ellison _et al_. 2023). In this study, we only retained mutant alleles that could not produce the full-length protein by selecting mutants with either transcripts 5\u2032 of the _Mu_ insertion with the initial AUG out-of-frame and/or transcripts 3\u2032 of _Mu_ that would only produce a protein less than half the length of the normal protein. Based on the assembled transcripts, it is unlikely that functional proteins are produced for most of these mutant alleles, although it is possible that partial fragments could be generated in some cases.\n=== Fig. 2. ===\n \nOpen in a new tab\nChanges in transcript abundance for TF mutant alleles. The fold change in expression of each TF gene in the mutant allele harboring the _Mu_ insertion relative to wild type plants was estimated from RNA-seq reads of all biological replicates mapped to the W22 reference genome (log2 fold change TF mutant allele/WT). The standard error of the log2 fold change (log2fc) estimate is represented as error bars for each allele. Significant differential expression between the TF mutant and W22 control is indicated by an asterisk (*). Alleles are rank ordered by the difference in expression relative to wild type, from positive log2fc to negative log2fc.\nThe overall changes to the transcriptome were assessed through principal component analysis for each tissue (Supplementary Fig. 1). In general, the samples tended to cluster by mutant allele, but some samples were more like W22 wild type while others were more distinct (Supplementary Fig. 1). Genes that were DE were identified for each mutant allele relative to W22 replicates from the same tissue (Fig. 3). The number of DE genes (DEGs) was quite variable with some mutants only exhibiting \u223c100 and others having >1,500 (Fig. 3, Supplementary File 1). For these putative TFs, it is not known whether they function as primarily activators or repressors; therefore, both up- and down-regulated genes were identified for each mutant allele. Most of the mutants (26/32) have more down-regulated genes than up-regulated genes, which would be expected for TFs that have a primary role as transcriptional activators. We examined the 10 TFs that are represented by 2 independent mutant alleles and found significant overlap in both the up- and down-regulated DEGs for all 10 pairs of mutant alleles. While the overlap of DEGs between the 2 mutant alleles was highly significant, the proportion of the up- or down-regulated genes that are significant in both mutant alleles was highly variable (Supplementary Fig. 2). In several cases, >50% of the DEGs are identified in both mutant alleles, but in other cases, the overlap only accounted for 5\u201310% of the DEGs (Supplementary Fig. 2).\n=== Fig. 3. ===\n \nOpen in a new tab\nIdentification of differentially expressed genes in transcription factor mutant genotypes. Genes with significant differences in expression were determined for each mutant based on comparison to W22 samples of the same tissue type using DEseq2 (FDR adjusted _P_ 2). The number of significantly up- (red) or down-regulated (blue) differentially expressed genes (DEGs) in the TF mutant allele relative to the W22 control is shown.\n=== Morphological and metabolic phenotypic characterization of TF mutant lines ===\nEach mutant line was grown for at least 2 field seasons with replicated plots. No obvious morphological differences were observed in segregating rows comparing mutant and wild type siblings or in plots of homozygous mutants compared to standard W22 for any of the TF mutant lines. To determine if these lines exhibited any quantitative variation in overall plant vigor, we measured 2 plant architecture traits\u2014plant height and ear height\u2014and recorded flowering time for a subset (12/32) of mutant alleles during 1 field season. We focused on whole plant traits that were easy to measure in the field and could reveal subtle phenotypic changes that may not be apparent by visual observation. Traits were measured in 2 environments that represented distinct planting dates in the same growing season. While there were some examples in which specific mutant alleles exhibited a significant difference in plant or ear height relative to the W22 control in 1 environment, there were no mutants with a significant difference in both environments (Supplementary File 2). This suggests that these mutants have a limited effect on plant or ear height. The analysis of relative flowering time in both environments revealed that one of the mutant alleles, _c3h42-m1_ , shows a delay in tassel shedding by 7 days and ear silking by 3 days (Supplementary File 2). The other mutants did not exhibit significant changes in flowering time in these environments. Overall, these mutants may have subtle phenotypic effects, but major morphological differences were not noticeable.\nAs some of the TFs were selected from the initial Y1H screen as putative phenolic biosynthesis regulators, we performed targeted LC-MS for 24 phenolic compounds on all 32 mutant alleles. For all mutants, we sampled shoots 21 DAS for 2 reasons: to obtain the quantity of tissue required for LC-MS and prior W22 RNA-seq data revealed that 21/22 TFs are expressed (CPM > 2.4) in shoots 6 DAS, excluding lowly expressed HSF6 (0.55 CPM) (Monnahan _et al_. 2020). Although we back-crossed these mutant alleles to the W22 _r-g_ inbred, the UniformMu W22 lines include introgression of several loci providing anthocyanin expression. To control for potential segregating variation from these introgression regions, we compared the mutant metabolite profiles to controls from both the W22 _r-g_ colorless and UniformMu color-converted W22 lines (McCarty _et al_. 2005).\nIn total, we found that at least 1 mutant allele for 7 (_BAF60.21_ , _BZIP76_ , _E2F19_ , _HSF13_ , _HSF24_ , _JMJ13_ , and _MYB40_) of the 13 TFs predicted to regulate phenolic genes by Y1H exhibited statistically significant variation in at least 1 of the 24 phenolic compounds analyzed compared to both W22 controls (Fig. 4, Supplementary File 3). There were 5 phenolic compounds (caffeic acid, maysin, rhamnosylisoorientin, syringic acid, and vanillic acid) that exhibit significant down-regulation in the mutant genotype compared to W22. For 2 Y1H predicted TFs with 2 independent mutant alleles, _E2F19_ and _MYB40_ , the same phenolic compound is significantly down-regulated in both alleles per TF: syringic acid in _EF19_ alleles, _e2f19-m1_ and _e2f19-m2_ , and vanillic acid in _MYB40_ mutant alleles, _myb40-m1_ and _myb40-m2_ (Fig. 4, Supplementary File 3). In the initial Y1H screen, _E2F19_ showed Y1H interactions with 3 lignin biosynthesis genes (_COMT1_ , _CCR1_ , and _HCT11_) upstream of syringic acid production and MYB40 exhibits Y1H interactions with 6 lignin biosynthesis genes (ALDH1, _CCR-like7_ , _CCR1_ , _COMT1_ , _FAH1_ , and _HCT6_) upstream of vanillic acid production (Supplementary Fig. 5) (Yang _et al_. 2017). In addition, _OMT1_ , another lignin biosynthesis gene, was significantly down-regulated in both E2F19 mutant alleles, _e2f19-m1_ and _e2f19-m2_ , but was not a Y1H target (Supplementary Fig. 5).\n=== Fig. 4. ===\n \nOpen in a new tab\nMetabolic profiles of 24 phenolic compounds in TF mutant genotypes. Targeted LC-MS was used to quantify arbitrary units of area (AUA) for 24 phenolic compounds in 32 TF mutant genotypes and 2 W22 control lines: W22 r-g colorless and UniformMu color-converted. Mutant phenolic profiles are shown as the fold change of log2 transformed (log2fc) mutant AUA compared to the AUA of each W22 control, W22 r-g or UniformMu W22 (depicted in figure columns as \u201crg\u201d for W22 r-g and \u201cuf\u201d for UniformMu W22). Phenolic compounds that are statistically significant (unpaired _t_ -test, FDR adjusted _P_ 80%) of the GCN predicted targets did not exhibit differential expression in the mutant relative to wild type (Fig. 7b). A similar set of analyses were performed after restricting the predicted targets to genes identified in at least 3 of the GCNs, n3 (Supplementary Fig. 7). For 3 TFs (_HSF18_ , _HSF20_ , and _WRKY82_), the targets predicted in at least 3 GCNs, n3, exhibit higher enrichment for differential expression than those predicted in at least 1 GCN, n1 (Supplementary Fig. 7a, Fig. 7a), but most predicted targets did not exhibit altered expression (Supplementary Fig. 7b).\n=== Fig. 7. ===\n \nOpen in a new tab\nEnrichments of gene co-expression network (GCN) n1 predictions for some TF mutants. The predicted targets for each mutant were identified based on co-expression interactions detected in at least 1 of the 45 GCNs (GCN n1) (Supplementary File 2). a) For each mutant allele, we calculated the fold-enrichment of DE genes that were predicted targets (the observed number of GCN n1 predicted target genes that were significantly DE divided by the expected number of DE predicted targets) and the enrichments are shown for any mutants that exhibit significant enrichment (*_P_ < 0.05). b) The proportion of GCN predicted targets that are up- (red) or down-regulated (blue) in each mutant is shown. For each allele, significant hypergeometric enrichment for up- and/or down-regulated DE target genes were marked (*).", "Thinking\u6a21\u5f0f": "high", "generation_type": "\u63a8\u7406\u578b"}','2026-03-23 15:33:28.390821','2026-03-23 15:33:28.390831');
INSERT INTO qa_pairs VALUES(28,3,'在利用酵母单杂交和共表达网络预测作物转录因子调控网络时，往往观察到单个转录因子敲除突变只在少数预测靶基因上产生显著差异表达，而大部分预测靶标并未通过差异表达得到验证，同时突变体中又能检测到大量差异表达基因。面对这种“预测靶基因总体验证率偏低”的现象，应如何从预测网络的假阳性、转录因子的次级调控作用以及调控功能冗余三个层面进行综合分析，而不是简单将原因归结为预测质量差？','预测靶基因的大量未验证不能简单理解为预测网络质量很差，而是多种机制共同造成的结果。酵母单杂交和共表达网络本身确实会产生一定比例的假阳性互作，这会稀释真实靶基因的比例，但同时还必须考虑调控生物学本身的复杂性。许多基因往往由庞大的转录因子复合体协同调控，其中部分成员只承担微调或次级调控角色，敲除单个这类因子只会带来幅度很小的表达改变，容易低于差异表达检测阈值而被“看不见”。另外，很多靶基因受多个相似或不同类型转录因子的共同调控，存在显著的功能冗余，当敲除其中一个转录因子时，其家族成员或其他调控因子可以部分或完全补偿，从而使相当一部分真实靶基因在整体表达水平上并不表现为显著差异。与此同时，在单基因突变材料中仍然可以检测到成批差异表达基因，说明该转录因子又具备实质的非冗余调控作用，因此既不能说预测网络主要是“假阳性”，也不能认为调控完全被冗余掩盖。更合理的解释是：有限的预测准确性、次级调控效应难以被统计学检出以及冗余补偿共同作用，导致单基因突变条件下预测靶标的表型验证率偏低。','{"species": "Zea mays", "paper_id": "PMC11979765", "reasoning_steps": ["Step 1: \u5229\u7528 Y1H \u548c\u5171\u8868\u8fbe\u7f51\u7edc\uff08GCN\uff09\u53ef\u4ee5\u9884\u6d4b\u8bb8\u591a\u8f6c\u5f55\u56e0\u5b50\u4e0e\u5176\u6f5c\u5728\u9776\u57fa\u56e0\u4e4b\u95f4\u7684\u8c03\u63a7\u5173\u7cfb\uff0c\u4f46\u8fd9\u4e9b\u6570\u636e\u7c7b\u578b\u672c\u8eab\u5df2\u77e5\u4f1a\u4ea7\u751f\u4e00\u5b9a\u6bd4\u4f8b\u7684\u5047\u9633\u6027\u76f8\u4e92\u4f5c\u7528\u3002", "Step 2: \u5728\u5bf9\u5355\u4e2a\u8f6c\u5f55\u56e0\u5b50\u7f3a\u5931\u7a81\u53d8\u6750\u6599\u8fdb\u884c\u8f6c\u5f55\u7ec4\u5206\u6790\u65f6\uff0c\u53ea\u6709\u5927\u7ea6\u4e09\u5206\u4e4b\u4e00\u5177\u6709 GCN \u9884\u6d4b\u7684\u8f6c\u5f55\u56e0\u5b50\uff0c\u5176\u9884\u6d4b\u9776\u57fa\u56e0\u5728\u5dee\u5f02\u8868\u8fbe\u57fa\u56e0\u4e2d\u51fa\u73b0\u4e86\u663e\u8457\u5bcc\u96c6\uff0c\u800c\u5bf9\u4efb\u4f55\u4e00\u4e2a\u7a81\u53d8\u6750\u6599\u800c\u8a00\uff0c\u5927\u591a\u6570\u5dee\u5f02\u8868\u8fbe\u57fa\u56e0\u5e76\u975e\u9884\u6d4b\u9776\u6807\uff0c\u540c\u65f6\u5927\u591a\u6570\u9884\u6d4b\u9776\u6807\u4e5f\u5e76\u672a\u6210\u4e3a\u5dee\u5f02\u8868\u8fbe\u57fa\u56e0\u3002", "Step 3: \u4e00\u79cd\u89e3\u91ca\u662f\u9884\u6d4b\u7684 GRN \u4e2d\u5b58\u5728\u9ad8\u5047\u9633\u6027\u7387\uff0cY1H \u5728\u975e\u5929\u7136\u67d3\u8272\u8d28\u73af\u5883\u4e0b\u8fdb\u884c\uff0cGCN \u91c7\u7528\u201c\u6309\u5171\u8868\u8fbe\u63a8\u6d4b\u529f\u80fd\u201d\u7684\u7b80\u5355\u76f8\u5173\u65b9\u6cd5\uff0c\u4e24\u8005\u90fd\u6709\u53ef\u80fd\u4ea7\u751f\u5047\u9633\u6027\uff1b\u6b64\u5916\uff0cGCN \u4e3b\u8981\u57fa\u4e8e B73 \u6216\u591a\u6837\u6027\u7fa4\u4f53\u7684\u6570\u636e\uff0c\u53ef\u80fd\u4e0d\u9002\u5408\u7528\u4e8e W22 \u80cc\u666f\u7684\u76f8\u4e92\u4f5c\u7528\u9884\u6d4b\u3002", "Step 4: \u4f46\u7814\u7a76\u8005\u5bf9\u201c\u5047\u9633\u6027\u5f88\u591a\u201d\u8fd9\u4e00\u89e3\u91ca\u5e76\u4e0d\u5341\u5206\u8ba4\u53ef\uff0c\u63d0\u51fa\u7b2c\u4e8c\u79cd\u89e3\u91ca\uff1a\u5728\u591a\u8f6c\u5f55\u56e0\u5b50\u590d\u5408\u4f53\u5171\u540c\u8c03\u63a7\u57fa\u56e0\u8868\u8fbe\u7684\u60c5\u51b5\u4e0b\uff0c\u6709\u4e9b\u8f6c\u5f55\u56e0\u5b50\u53ea\u8d77\u5230\u6b21\u7ea7\u6216\u5fae\u8c03\u4f5c\u7528\uff0c\u5bfc\u81f4\u5176\u7f3a\u5931\u4ec5\u5f15\u8d77\u8f7b\u5fae\u8868\u8fbe\u53d8\u5316\uff0c\u8fd9\u7c7b\u7ec6\u5c0f\u53d8\u5316\u5728\u5f53\u524d\u5dee\u5f02\u8868\u8fbe\u7b5b\u9009\u9608\u503c\u4e0b\u5bb9\u6613\u88ab\u6f0f\u68c0\u3002", "Step 5: \u7b2c\u4e09\u79cd\u89e3\u91ca\u662f\u8c03\u63a7\u529f\u80fd\u5b58\u5728\u5197\u4f59\uff1a\u9884\u6d4b\u7684\u9776\u57fa\u56e0\u53ef\u80fd\u540c\u65f6\u88ab\u591a\u4e2a\u76f8\u4f3c\u6216\u4e0d\u76f8\u4f3c\u7684\u8f6c\u5f55\u56e0\u5b50\u8c03\u63a7\uff0c\u5176\u4e2d\u5305\u62ec\u5168\u57fa\u56e0\u7ec4\u590d\u5236\u540e\u4fdd\u7559\u7684\u91cd\u590d\u57fa\u56e0\u6216\u540c\u4e00\u5bb6\u65cf\u5176\u4ed6\u6210\u5458\uff0c\u56e0\u6b64\u6572\u9664\u5355\u4e2a\u8f6c\u5f55\u56e0\u5b50\u65f6\uff0c\u5176\u4ed6\u56e0\u5b50\u53ef\u4ee5\u5728\u4e00\u5b9a\u7a0b\u5ea6\u4e0a\u8865\u507f\uff0c\u4f7f\u8bb8\u591a\u9884\u6d4b\u9776\u6807\u4e0d\u8868\u73b0\u4e3a\u5dee\u5f02\u8868\u8fbe\u3002", "Step 6: \u540c\u65f6\uff0c\u5728\u5355\u57fa\u56e0\u6572\u9664\u6750\u6599\u4e2d\u4ecd\u7136\u89c2\u5bdf\u5230\u5927\u91cf\u5dee\u5f02\u8868\u8fbe\u57fa\u56e0\uff0c\u8bf4\u660e\u8fd9\u4e9b\u8f6c\u5f55\u56e0\u5b50\u53c8\u786e\u5b9e\u5177\u6709\u975e\u5197\u4f59\u7684\u8c03\u63a7\u529f\u80fd\uff0c\u4ec5\u4ec5\u4ee5\u201c\u5168\u662f\u5197\u4f59\u6216\u5168\u662f\u5047\u9633\u6027\u201d\u65e0\u6cd5\u89e3\u91ca\u7ed3\u679c\u3002", "Step 7: \u7efc\u5408\u5047\u9633\u6027\u3001\u6b21\u7ea7\u8c03\u63a7\u4f5c\u7528\u548c\u529f\u80fd\u5197\u4f59\u4e09\u4e2a\u65b9\u9762\u7684\u8bc1\u636e\uff0c\u53ef\u4ee5\u63a8\u65ad\uff1a\u5bfc\u81f4\u9884\u6d4b\u9776\u57fa\u56e0\u9a8c\u8bc1\u7387\u504f\u4f4e\u7684\u539f\u56e0\u5e76\u975e\u5355\u4e00\u7684\u9ad8\u5047\u9633\u6027\u7387\uff0c\u800c\u662f\u591a\u79cd\u673a\u5236\u5171\u540c\u4f5c\u7528\uff0c\u56e0\u6b64\u4e0d\u80fd\u5c06\u201c\u591a\u6570\u9884\u6d4b\u9776\u6807\u672a\u5dee\u5f02\u8868\u8fbe\u201d\u7b80\u5355\u5f52\u56e0\u4e8e GRN \u9884\u6d4b\u8d28\u91cf\u5dee\u3002"], "question_cot": "", "final_conclusion": "\u5728\u5229\u7528 Y1H \u548c\u5171\u8868\u8fbe\u7f51\u7edc\u9884\u6d4b\u7684\u8f6c\u5f55\u56e0\u5b50\u8c03\u63a7\u7f51\u7edc\u4e2d\uff0c\u5355\u4e2a\u8f6c\u5f55\u56e0\u5b50\u7a81\u53d8\u540e\u591a\u6570\u9884\u6d4b\u9776\u57fa\u56e0\u5e76\u672a\u8868\u73b0\u4e3a\u5dee\u5f02\u8868\u8fbe\uff0c\u5176\u539f\u56e0\u4e0d\u80fd\u5355\u7eaf\u5f52\u7ed3\u4e3a\u9884\u6d4b\u7f51\u7edc\u7684\u9ad8\u5047\u9633\u6027\u7387\uff0c\u8fd8\u5fc5\u987b\u8003\u8651\u8f6c\u5f55\u56e0\u5b50\u7684\u6b21\u7ea7\u8c03\u63a7\u4f5c\u7528\u548c\u529f\u80fd\u5197\u4f59\u7b49\u56e0\u7d20\u3002", "difficulty": "medium", "tags": ["mechanism", "concept", "method", "result"], "created_at": "2025-12-16T21:20:29", "token_est_question": 1, "token_est_answer": 1, "section": "Discussion", "context": "=== Discussion ===\nPerturbing GRNs is considered a potential mechanism to influence traits in many species. Substantial investments have been made to develop GRNs that predict the functional targets for many TFs and to generate mutant collections for maize genes. In this study, we monitored the consequences of mutant alleles for 22 maize TFs within predicted GRNs. These TFs were selected based on prior Y1H work to identify TFs that bind to promoters of multiple genes in the phenylpropanoid pathway (Yang _et al_. 2017) or based on GCN analyses (Zhou _et al_. 2020). All the mutant lines that were analyzed are derived from the UniformMu population and are in a W22 genetic background (McCarty _et al_. 2018). Loss-of-function for the single TFs in this study did not result in major phenotypes in the field environments. In contrast, targeted metabolic profiling of 24 phenolic compounds did reveal that the absence of a single functional TF may significantly change phenolic accumulation. This was most evident for 2 TFs, _E2F19_ and _MYB40_ , that were predicted to be regulators of phenolic biosynthesis in the Y1H screen and exhibited significant phenolic compound down-regulation in both independent mutant alleles isolated per TF.\nTranscriptome profiling was utilized for 3 distinct purposes in this study: evaluation of the mutant allele transcripts, GO enrichment analyses of genes with altered expression, and assessment of expression changes for GRN predicted targets. Although we initially performed transcriptome profiling on a larger set of UniformMu mutant alleles (Ellison _et al_. 2023), for this study, we focused on the subset of alleles that most likely represent loss-of-function mutations. Evaluation of TF gene expression levels in the mutant alleles revealed some cases of reduced expression, but most genes did not exhibit a significant change in transcript abundance. However, the mutants often had reduced coverage in regions flanking the _Mu_ insertion site, which is expected if the mutant transcripts include sequences that are novel relative to the W22 reference genome (Ellison _et al_. 2023). To identify potential loss-of-function alleles, we performed de novo transcriptome assemblies and found that for most of the mutant alleles with a _Mu_ insertion in the coding sequence there are often multiple transcripts generated (Ellison _et al_. 2023). Mutant alleles were selected as loss-of-function mutations if there was transcript assembly evidence that the original full-length protein could not be encoded.\nInvestigating enrichment of GO terms in genes that are DE can be useful in understanding functional consequences of the mutant alleles. Many of the mutants exhibited significant enrichments for some GO terms in the up- or down-regulated DE genes. However, a specific biological function of the TFs could not be determined based on the significantly enriched GO terms.\nThe evaluation of transcript abundance for genes that are predicted as targets for each of the TFs revealed some examples of altered expression. In about a third of the TFs with GCN predictions, there was a significant enrichment of the predicted targets within the DEGs. However, the majority of the DEGs for any specific mutant line are not predicted targets and most of the predicted targets are not DE. We considered 3 main explanations for this observation. One potential explanation is that the GRN predictions have a high rate of false positives. The types of data used to generate the GRN predictions in this study are both known to have false positives. Y1H assays are conducted in the absence of the normal chromatin environment of the endogenous promoters and can generate false positives. Co-expression analyses are simple guilt-by-association approaches and can suggest functional interactions for sets of genes that are co-expressed in similar patterns, even if these genes have independent regulation. In addition, the GCNs that were used to make predictions were largely based on RNA-seq data from B73 or large panels of maize diversity and these may be less effective at predicting interactions in W22. While these false positives can occur in both types of GRN predictions, Y1H and GCN, we are not confident that this is a primary explanation for the low validation rates we observed. A second explanation is that the TF plays a secondary role in regulation of the predicted target gene resulting in minor changes to gene expression, at least under some conditions. Transcriptional regulation of gene expression can be controlled by large complexes of >50 TFs with TFs directly or indirectly tethered to the target gene promoter (Nakagawa _et al_. 2018; G\u00f6\u00f6s _et al_. 2022). Some of the TFs within the complex will play a primary role in either bringing the complex to DNA or keeping the complex together, but many of the TFs have secondary roles that may involve fine-tuning of gene expression (Mouchiroud _et al_. 2014; Parab _et al_. 2022). These minor changes in gene expression would have been missed with the criteria used to identify DEGs between the TF loss-of-function alleles and wild type in this study. A third explanation is the potential for functional redundancy in the regulation of these predicted targets. This redundancy could be due to highly similar TFs (either retained duplicated genes from the recent whole-genome duplication event in maize or other members of the same TF family) or other TFs that independently regulate the same target gene. It is worth noting that, in many cases, there are substantial numbers of DEGs in the single TF knockouts, so along with the possibility that there is partially redundant regulation of target genes, there must be some nonredundant function of these TFs. The explanation of redundancy could explain why some of the predicted targets are not DE, but we often did not find evidence from Y1H or GCN data that would have implicated other highly similar TFs. This might suggest that the GRN predictions tend to highlight specific potential TF\u2013target interactions when the biological reality might be much more complex. In most cases, we were not able to recover loss-of-function alleles for multiple related TFs, which reduces our ability to perform targeted analyses of redundant regulation. Future studies that utilize genome editing or other approaches to create loss-of-function alleles in multiple TF family members could better explore the potential redundancy of regulation in these pathways.", "Thinking\u6a21\u5f0f": "high", "generation_type": "\u63a8\u7406\u578b"}','2026-03-23 15:33:28.465301','2026-03-23 15:33:28.465311');
INSERT INTO qa_pairs VALUES(29,3,'在玉米杂交种及其亲本中，干旱阶段一类保守的超显性干旱响应基因富集于L-苯丙氨酸代谢、碳水化合物代谢和热应答等抗逆相关过程，而另一类保守的低显性干旱响应基因与葡萄糖代谢、磷酸戊糖途径和淀粉代谢等碳流重分配过程相关；复水阶段又有一组保守的超显性复水响应基因促进细胞增殖和光合作用上调，另一些保守的低显性复水响应基因则抑制逆境应答过程，使植株由“抗逆模式”转向“生长模式”。从杂种优势形成机制的角度，如何推理这些在干旱与复水阶段表现为超显性或低显性的保守响应基因共同对杂种优势的贡献方式？','干旱阶段，超显性干旱响应基因通过在杂交种中将抗逆相关代谢和热应答过程的表达水平提升到高于双亲中值甚至超出双亲的水平，使其能更快、更强地启动防御和保护机制；同时，低显性干旱响应基因在杂交种中打破亲本中值表达，重塑葡萄糖代谢、磷酸戊糖途径和淀粉代谢等碳流分配，有利于在胁迫下节约和合理调配能量与碳源，从而形成高效、稳健的抗旱表型。复水阶段，另一组超显性复水响应基因在杂交种中把细胞增殖和光合作用等促进生长的过程强力上调，使其比双亲更快恢复生长和光合能力；与此同时，低显性复水响应基因通过在杂交种中更强地抑制逆境应答过程，加速从“防御状态”退出，降低恢复期逆境反应带来的资源消耗。干旱与复水两个阶段中，这些保守的超显性和低显性基因在不同功能通路上以“增强抗逆–优化代谢–加速恢复–强化生长”的接力式协同方式，使杂交种在环境波动下始终保持比亲本更高的存活率和生长势，从分子层面构成杂种优势在干旱及复水交替条件下的重要基础。','{"species": "Zea mays", "paper_id": "PMC11494150", "reasoning_steps": ["Step 1: \u5728\u5e72\u65f1\u6761\u4ef6\u4e0b\uff0c\u4e24\u79cd\u8868\u73b0\u51fa\u5f3a\u6742\u79cd\u4f18\u52bf\u7684\u7389\u7c73\u6742\u4ea4\u79cd\u53ca\u5176\u4eb2\u672c\u4e2d\u88ab\u9274\u5b9a\u51fa\u4e00\u6279\u4fdd\u5b88\u7684\u5e72\u65f1\u54cd\u5e94\u57fa\u56e0\u548c\u4fdd\u5b88\u7684\u590d\u6c34\u54cd\u5e94\u57fa\u56e0\u3002", "Step 2: \u8fd9\u4e9b\u5e72\u65f1\u548c\u590d\u6c34\u54cd\u5e94\u57fa\u56e0\u4e2d\uff0c\u6709\u4e00\u90e8\u5206\u5728\u6742\u4ea4\u79cd\u4e2d\u7684\u8868\u8fbe\u6a21\u5f0f\u5c5e\u4e8e\u8d85\u663e\u6027\uff08overdominance\uff09\u548c\u4f4e\u663e\u6027\uff08underdominance\uff09\uff0c\u5373\u5176\u8868\u8fbe\u6c34\u5e73\u660e\u663e\u504f\u79bb\u53cc\u4eb2\u7684\u4e2d\u95f4\u6c34\u5e73\u3002", "Step 3: \u5728\u5e72\u65f1\u9636\u6bb5\uff0c\u4fdd\u5b88\u7684\u5e72\u65f1\u54cd\u5e94\u8d85\u663e\u6027\u57fa\u56e0\u4e3b\u8981\u5bcc\u96c6\u4e8eL-\u82ef\u4e19\u6c28\u9178\u4ee3\u8c22\u3001\u78b3\u6c34\u5316\u5408\u7269\u4ee3\u8c22\u548c\u70ed\u5e94\u7b54\u7b49\u6297\u9006\u76f8\u5173\u8fc7\u7a0b\uff0c\u800c\u4fdd\u5b88\u7684\u5e72\u65f1\u54cd\u5e94\u4f4e\u663e\u6027\u57fa\u56e0\u5219\u4e0e\u8461\u8404\u7cd6\u4ee3\u8c22\u3001\u78f7\u9178\u620a\u7cd6\u9014\u5f84\u548c\u6dc0\u7c89\u4ee3\u8c22\u7b49\u78b3\u6d41\u91cd\u5206\u914d\u8fc7\u7a0b\u76f8\u5173\u3002", "Step 4: \u5728\u590d\u6c34\u9636\u6bb5\uff0c\u4fdd\u5b88\u7684\u590d\u6c34\u54cd\u5e94\u8d85\u663e\u6027\u57fa\u56e0\u4fc3\u8fdb\u7ec6\u80de\u589e\u6b96\u548c\u5149\u5408\u4f5c\u7528\u7b49\u8fc7\u7a0b\u4e0a\u8c03\uff0c\u800c\u4fdd\u5b88\u7684\u590d\u6c34\u54cd\u5e94\u4f4e\u663e\u6027\u57fa\u56e0\u5219\u6291\u5236\u4e0e\u9006\u5883\u5e94\u7b54\u76f8\u5173\u7684\u8fc7\u7a0b\uff0c\u4ece\u800c\u6709\u5229\u4e8e\u4ece\u201c\u6297\u9006\u6a21\u5f0f\u201d\u5411\u201c\u751f\u957f\u6a21\u5f0f\u201d\u8f6c\u6362\u3002", "Step 5: \u6297\u65f1\u9636\u6bb5\u901a\u8fc7\u8d85\u663e\u6027\u4e0e\u4f4e\u663e\u6027\u57fa\u56e0\u534f\u540c\u5b9e\u73b0\u5feb\u901f\u6709\u6548\u7684\u6297\u9006\u53cd\u5e94\uff0c\u800c\u590d\u6c34\u9636\u6bb5\u901a\u8fc7\u53e6\u4e00\u7ec4\u8d85\u663e\u6027\u4e0e\u4f4e\u663e\u6027\u57fa\u56e0\u534f\u540c\u5b9e\u73b0\u9ad8\u6548\u6062\u590d\u751f\u957f\u548c\u5149\u5408\u529f\u80fd\uff0c\u8fd9\u79cd\u5728\u4e0d\u540c\u73af\u5883\u9636\u6bb5\u5747\u8868\u73b0\u51fa\u7684\u4fdd\u5b88\u57fa\u56e0\u8868\u8fbe\u504f\u79bb\u53cc\u4eb2\u4e2d\u503c\u7684\u6a21\u5f0f\uff0c\u6709\u52a9\u4e8e\u6742\u4ea4\u79cd\u5728\u5e72\u65f1\u4e0e\u590d\u6c34\u4ea4\u66ff\u6761\u4ef6\u4e0b\u83b7\u5f97\u6bd4\u53cc\u4eb2\u66f4\u4f18\u7684\u751f\u957f\u548c\u9002\u5e94\u6027\u3002", "Step 6: \u56e0\u6b64\uff0c\u53ef\u4ee5\u63a8\u65ad\u8fd9\u4e9b\u4fdd\u5b88\u7684\u8d85\u663e\u6027\u548c\u4f4e\u663e\u6027\u54cd\u5e94\u57fa\u56e0\uff0c\u5728\u5e72\u65f1\u5e94\u7b54\u548c\u590d\u6c34\u6062\u590d\u4e24\u65b9\u9762\u5171\u540c\u6784\u6210\u4e86\u6742\u4ea4\u79cd\u5728\u9006\u5883\u4e0b\u8868\u73b0\u6742\u79cd\u4f18\u52bf\u7684\u91cd\u8981\u5206\u5b50\u57fa\u7840\u3002"], "question_cot": "", "final_conclusion": "\u5728\u7389\u7c73\u5e72\u65f1\u53ca\u590d\u6c34\u8fc7\u7a0b\u4e2d\uff0c\u4fdd\u5b88\u7684\u8d85\u663e\u6027\u548c\u4f4e\u663e\u6027\u5dee\u5f02\u8868\u8fbe\u57fa\u56e0\u901a\u8fc7\u5206\u522b\u589e\u5f3a\u6297\u9006\u53cd\u5e94\u4e0e\u6062\u590d\u751f\u957f\u76f8\u5173\u8fc7\u7a0b\uff0c\u5ba2\u89c2\u4e0a\u5171\u540c\u4fc3\u8fdb\u4e86\u6742\u79cd\u4f18\u52bf\u8868\u73b0\u3002", "difficulty": "hard", "tags": ["mechanism", "result", "concept", "stress response", "heterosis", "maize"], "created_at": "2025-12-16T21:13:44", "token_est_question": 1, "token_est_answer": 1, "section": "Abstract", "context": "=== Abstract ===\nMaize is one of the most successful crops in utilizing heterosis which significantly improves maize stresses resistance and yield. Drought is a destructive abiotic stress that significantly reduces crop yield, particularly in maize. Drought stress and re-watering frequently occur during the growth and development of maize; however, the molecular mechanisms of heterosis under drought stress and re-watering have rarely been systematically investigated. Zhengdan7137 and Zhengdan7153 are two maize hybrid varieties with robust heterosis, and separately belongs to the SS\u00d7NSS and Reid\u00d7Tangsipingtou heterotic groups. 54 transcriptomes of these two hybrids and their parental inbred lines were analyzed under well-watering (WW), water-deficit (WD), and re-watering (RW) conditions using RNA-Seq. In this study, we identified 3,411 conserved drought response genes (CDRGs) and 3,133 conserved re-watering response genes (CRRGs) between Zhengdan7137 and Zhengdan7153. When comparing CDRGs and CRRGs to overdominance and underdominance genes, we identified 303 and 252 conservative drought response overdominance genes (DODGs) and underdominance genes (DUDGs), respectively, and 165 and 267 conservative re-watering response overdominance genes (RODGs) and underdominance genes (RUDGs), respectively. DODGs are involved in stress response-related processes, such as L-phenylalanine metabolism, carbohydrate metabolism, and heat response, whereas DUDGs are associated with glucose metabolism, pentose-phosphate shunt, and starch metabolism. RODGs and RUDGs contribute to the recovery of hybrids from drought stress by upregulating cell propagation and photosynthesis processes, and repressing stress response processes, respectively. It indicated overdominant and underdominant genes conservatively contributed to hybrid heterosis under drought stress. These results deepen our understanding of the molecular mechanisms of drought resistance, uncover conservative molecular mechanisms of heterosis under drought stress and re-watering, and provide potential targets for improving drought resistance in maize.\n**Keywords:** drought, re-watering, heterosis, maize, RNA-Seq, overdominant genes, underdominant genes", "Thinking\u6a21\u5f0f": "high", "generation_type": "\u63a8\u7406\u578b"}','2026-03-23 15:33:28.539156','2026-03-23 15:33:28.539161');
INSERT INTO qa_pairs VALUES(30,3,'在研究作物杂交种的转录组时，通常可以将相对于亲本的差异表达基因划分为加性和非加性两大类，其中非加性又包含显性、超显性和低显性表达模式。转录组分析表明，一方面，加性基因被认为是玉米等作物在正常发育阶段杂种优势的重要基础；另一方面，在热、干旱或盐等环境胁迫条件下，与杂种优势关联更紧密的是非加性基因而不是加性基因。面对这一现象，如何判断在正常生长条件与环境胁迫条件下，分别应将哪一类差异表达基因视为支撑杂种优势的关键对象，并说明其背后的生物学逻辑？','在正常生长条件下，应将加性差异表达基因视为支撑杂种优势的关键对象；在热、干旱或盐胁迫等逆境条件下，则应将非加性差异表达基因视为更关键的对象。原因在于：杂种优势本质上来源于杂交后基因表达水平及其调控网络的重组。高通量转录组研究显示，杂交种中存在大量相对亲本发生改变的差异表达基因，这些基因可按表达模式分为加性和非加性两类。加性基因的表达水平通常接近双亲的中间值，更稳定地支撑生长发育、器官形成和产量形成等基础过程，因此在无明显胁迫的正常发育阶段，它们构成了发育期杂种优势的主要分子基础。相反，在高温、干旱或盐胁迫等逆境下，杂交种往往通过一批在表达上明显偏离双亲中间水平、呈显性或超显性等非加性模式的基因来实现对胁迫信号的强化感知和高效响应，这类基因富集于逆境应答相关通路，与杂交种在逆境中表现出的超亲抗性和生存优势联系更紧密。因此，解析正常发育优势时应重点关注加性表达基因，而解释环境胁迫下的杂种优势则需要优先分析非加性表达基因。','{"species": "Zea mays", "paper_id": "PMC11494150", "reasoning_steps": ["Step 1: \u6742\u79cd\u4f18\u52bf\u662f\u6307\u6742\u4ea4\u79cd\u5728\u751f\u7269\u91cf\u3001\u53d1\u80b2\u901f\u5ea6\u3001\u4ea7\u91cf\u548c\u9006\u5883\u6297\u6027\u7b49\u65b9\u9762\u4f18\u4e8e\u4eb2\u672c\u7eaf\u5408\u54c1\u7cfb\u7684\u73b0\u8c61\u3002", "Step 2: \u9ad8\u901a\u91cf\u8f6c\u5f55\u7ec4\u7814\u7a76\u8868\u660e\uff0c\u76f8\u6bd4\u4eb2\u672c\uff0c\u6742\u4ea4\u79cd\u4e2d\u5b58\u5728\u4e00\u6279\u5dee\u5f02\u8868\u8fbe\u57fa\u56e0\uff08DEGs\uff09\uff0c\u8fd9\u4e9b\u57fa\u56e0\u88ab\u8ba4\u4e3a\u5bf9\u6742\u79cd\u4f18\u52bf\u8d1f\u6709\u8d23\u4efb\u3002", "Step 3: \u5dee\u5f02\u8868\u8fbe\u57fa\u56e0\u53ef\u6309\u8868\u8fbe\u6a21\u5f0f\u5206\u4e3a\u52a0\u6027\u57fa\u56e0\u548c\u975e\u52a0\u6027\u57fa\u56e0\uff0c\u5176\u4e2d\u975e\u52a0\u6027\u57fa\u56e0\u53c8\u5305\u62ec\u663e\u6027\u3001\u8d85\u663e\u6027\u548c\u4f4e\u663e\u6027\u57fa\u56e0\u3002", "Step 4: \u6709\u7814\u7a76\u6307\u51fa\uff0c\u52a0\u6027\u57fa\u56e0\u88ab\u8ba4\u4e3a\u662f\u7389\u7c73\u53d1\u80b2\u671f\u6742\u79cd\u4f18\u52bf\u7684\u57fa\u7840\uff0c\u5373\u5728\u6b63\u5e38\u53d1\u80b2\u8fc7\u7a0b\u4e2d\u7684\u4f18\u52bf\u4e3b\u8981\u7531\u52a0\u6027\u8868\u8fbe\u6a21\u5f0f\u8d21\u732e\u3002", "Step 5: \u591a\u9879\u7814\u7a76\u53d1\u73b0\uff0c\u5728\u70ed\u3001\u5e72\u65f1\u548c\u76d0\u7b49\u73af\u5883\u80c1\u8feb\u6761\u4ef6\u4e0b\uff0c\u4e0e\u6742\u79cd\u4f18\u52bf\u5173\u8054\u66f4\u663e\u8457\u7684\u662f\u975e\u52a0\u6027\u57fa\u56e0\uff0c\u800c\u4e0d\u662f\u52a0\u6027\u57fa\u56e0\u3002", "Step 6: \u7531\u6b64\u53ef\u4ee5\u63a8\u7406\uff0c\u5728\u73af\u5883\u80c1\u8feb\u5b58\u5728\u65f6\uff0c\u6742\u4ea4\u79cd\u8868\u73b0\u51fa\u7684\u66f4\u9ad8\u6297\u9006\u6027\u6216\u7efc\u5408\u4f18\u52bf\uff0c\u66f4\u591a\u4f9d\u8d56\u4e8e\u975e\u52a0\u6027\u8868\u8fbe\u7684\u57fa\u56e0\u5bf9\u80c1\u8feb\u7684\u7279\u5f02\u6027\u54cd\u5e94\uff0c\u800c\u5728\u65e0\u80c1\u8feb\u6216\u6b63\u5e38\u53d1\u80b2\u6761\u4ef6\u4e0b\uff0c\u6742\u79cd\u4f18\u52bf\u5219\u4e3b\u8981\u7531\u52a0\u6027\u57fa\u56e0\u7ef4\u6301\u3002", "Step 7: \u56e0\u6b64\uff0c\u53ef\u4ee5\u5f97\u51fa\u7ed3\u8bba\uff1a\u975e\u52a0\u6027\u57fa\u56e0\u4e3b\u8981\u4e0e\u6742\u79cd\u5728\u9006\u5883\u6761\u4ef6\u4e0b\u7684\u4f18\u52bf\u76f8\u5173\uff0c\u800c\u52a0\u6027\u57fa\u56e0\u5bf9\u6b63\u5e38\u53d1\u80b2\u6761\u4ef6\u4e0b\u7684\u6742\u79cd\u4f18\u52bf\u66f4\u4e3a\u5173\u952e\u3002"], "question_cot": "", "final_conclusion": "\u5728\u73af\u5883\u80c1\u8feb\uff08\u5982\u5e72\u65f1\u3001\u9ad8\u6e29\u3001\u76d0\u80c1\u8feb\uff09\u6761\u4ef6\u4e0b\uff0c\u6742\u4ea4\u79cd\u7684\u4f18\u52bf\u4e0e\u57fa\u56e0\u8868\u8fbe\u4e2d\u7684\u975e\u52a0\u6027\u57fa\u56e0\uff08\u5305\u62ec\u663e\u6027\u3001\u8d85\u663e\u6027\u548c\u4f4e\u663e\u6027\u8868\u8fbe\u6a21\u5f0f\uff09\u5173\u7cfb\u66f4\u4e3a\u7d27\u5bc6\uff0c\u800c\u52a0\u6027\u57fa\u56e0\u5219\u4e3b\u8981\u652f\u6491\u4f5c\u7269\u5728\u6b63\u5e38\u53d1\u80b2\u6761\u4ef6\u4e0b\u7684\u6742\u79cd\u4f18\u52bf\u3002", "difficulty": "medium", "tags": ["concept", "mechanism", "application"], "created_at": "2025-12-16T21:15:26", "token_est_question": 1, "token_est_answer": 1, "section": "Introduction", "context": "=== Introduction ===\nMaize (_Zea mays L._) is the main cereal crop planted worldwide and used as food, livestock forage, and industrial material. Drought is a typical destructive abiotic stress that systematically damages maize organs and physiological functions, leading to significant yield losses (Tari and Fathi, 2016). Moreover, the risk of drought has increased significantly with global warming. In recent years, extreme long-term droughts accompanied by high temperatures have frequently occurred worldwide, threatening maize agricultural production (Sheoran et al., 2022). Therefore, improving the drought resistance of maize is an urgent task to ensure global food safety and satisfy industrial demands.\nDrought, a water-deficit condition, primarily causes plant cellular dehydration and an increase in osmotic pressure, resulting in the induction of a series of physiological and biochemical reactions such as plasma membrane disruption, photosynthesis reaction inhibition, and excessive reactive oxygen species (ROS) accumulation which promote lipid peroxidation and macromolecule (e.g., protein, lipid, and nucleic acid) degradation (Singh et al., 2023; Liu et al., 2023). Ultimately, drought can lead to the impairment of plant cell vitality and even cell death. And plants exhibit various drought stress phenotypes depending on the developmental stage, such as stunted seedling growth, rolling leaves, pollen abortion, enlarged anthesis-silking interval (ASI), seed-filling suppression, and grain yield loss (Liu et al., 2023; Hu et al., 2020; Zenda et al., 2018; Bheemanahalli et al., 2022). The grain yield is the primary trait used to assess drought impairment in crops. Secondary traits, e.g., 100-kernel weight (KW), are also used as criteria for the evaluation of drought impairment (Monneveux et al., 2008). To cope with drought stress, plants activate a range of morphological, physiological, and biochemical mechanisms known as drought resistance, to preserve their viability and reproduction (Liu et al., 2023). Drought resistance is involved in maintaining cellular homeostasis to adapt to increased osmotic pressure through the synthesis of osmoprotectants, activating ROS scavenging, adjusting regulatory pathways of phytohormones [e.g., abscisic acid (ABA), auxin and ethylene], and expressing more molecular chaperones (Liu et al., 2023; Singh et al., 2023; Sheoran et al., 2022; Waititu et al., 2021). Damage caused by drought stress can be restored by re-watering (Wang et al., 2022b). Plants can then undergo extraordinarily rapid growth to compensate or overcompensate for the losses incurred during drought stress. Therefore, the capacity of plants to recover after re-watering is a criterion for evaluating drought resistance.\nHeterosis is a phenomenon widely used in crop breeding, in which hybrids exhibit superior performance compared to their homozygous parents (e.g. higher biomass, developmental speed, yield, and resistance to environmental stresses such as drought) (Luo et al., 2021). Therefore, elucidating the mechanisms of heterosis is important (Lippman and Zamir, 2007). Multiple hypotheses have been formed to explain the genetic basis of heterosis (including dominance, overdominance, epistasis). Recently, homo-insufficiency under insufficient background (HoIIB) has also been proposed (Hochholdinger and Baldauf, 2018; Xie et al., 2022). However, these genetic hypotheses have not revealed the molecular mechanisms underlying heterosis. With advancements in high-throughput transcriptome profiling technology, differentially expressed genes (DEGs) between hybrids and their parental lines have been identified and found to be responsible for heterosis (Li et al., 2015). The DEGs can be classified into additive genes and nonadditive genes consisting of dominant, overdominant, and underdominant genes. These different expression patterns genes contribute differently to heterosis depending on the species, genetic background, and environment. For examples, additive genes have been suggested to be fundamental for maize development heterosis (Zhao et al., 2019). Nonadditive genes are associated with heterosis, particularly under environmental stresses such as heat, drought, and salt (Zhao et al., 2019; Marcon et al., 2017; Zhang et al., 2022a; Liu et al., 2024). In rubber tree seedlings, over-dominant genes play an important role in growth heterosis (Yang et al., 2018).\nUnveiling the molecular mechanisms that underlie heterosis in maize hybrids under drought and re-watering stresses will offer novel insights and genes for drought resistance, which can significantly contribute to drought resistance improvement. However, such investigations have been sparingly undertaken. Zhengdan7137 (Zheng1110\u00d7Zheng1117) and Zhengdan7153 (Zheng1121\u00d7Zheng641) are two hybrid varieties of maize with significant heterosis under normal and drought conditions, and separately belongs to the SS\u00d7NSS and Reid\u00d7Tangsipingtou heterotic groups. The SS\u00d7NSS and Reid\u00d7Tangsipingtou are two typical and widely used heterosis patterns in China. To explore the molecular mechanisms of heterosis under drought and re-watering stresses, we analyzed the transcriptomes of Zhengdan7137, Zhengdan7153, and their parental inbred lines to explore the molecular mechanisms of heterosis under well-watered (WW), water-deficit (WD), and re-watering (RW) conditions. For convenience, Zhengdan7137 and its parental inbred lines Zheng1110 and Zheng1117 were designated as AB, AA, and BB, respectively, whereas Zhengdan7153 and its parental inbred lines Zheng1121 and Zheng641 were designated as CD, CC, and DD, respectively. Our results revealed the conserved molecular mechanisms of drought resistance and heterosis under drought stress.", "Thinking\u6a21\u5f0f": "high", "generation_type": "\u63a8\u7406\u578b"}','2026-03-23 15:33:28.615012','2026-03-23 15:33:28.615023');
INSERT INTO qa_pairs VALUES(31,3,'在利用qRT‑PCR评估某胁迫处理对目标基因表达影响时，研究者在同一反应体系中扩增目标基因和GAPDH，并将各样本目标基因的Ct值以GAPDH的Ct值进行标准化，得到胁迫处理与未处理对照之间目标基因的相对表达量，当统计分析表明两者的标准化相对表达量存在显著差异时，研究者应如何根据这一结果客观判断该基因在胁迫处理下是上调还是下调表达？','在以GAPDH为内参的qRT‑PCR结果中，应先比较胁迫处理与对照之间目标基因经GAPDH标准化后的相对表达量：若处理组的标准化相对表达量在统计学上显著高于对照，判定该基因在胁迫下表达上调；若显著低于对照，则判定该基因在胁迫下表达下调。','{"species": "Zea mays", "paper_id": "PMC11494150", "reasoning_steps": ["Step 1: qRT\u2011PCR\u662f\u4e00\u79cd\u53ef\u4ee5\u5b9a\u91cf\u6d4b\u5b9a\u57fa\u56e0\u8f6c\u5f55\u672c\u4e30\u5ea6\u7684\u6280\u672f\uff0c\u901a\u8fc7\u6269\u589e\u8fc7\u7a0b\u4e2d\u8367\u5149\u4fe1\u53f7\u7684\u53d8\u5316\u6765\u53cd\u6620\u57fa\u56e0\u8868\u8fbe\u6c34\u5e73\u3002", "Step 2: \u5728\u8be5qRT\u2011PCR\u4f53\u7cfb\u4e2d\uff0cGAPDH\u88ab\u7528\u4f5c\u5185\u53c2\u57fa\u56e0\uff0c\u5373\u7528\u6765\u5bf9\u4e0d\u540c\u6837\u672c\u95f4\u7684\u603b\u4f53cDNA\u91cf\u548c\u6280\u672f\u5dee\u5f02\u8fdb\u884c\u6807\u51c6\u5316\u3002", "Step 3: \u5c06\u76ee\u6807\u57fa\u56e0\u7684Ct\u503c\u4e0eGAPDH\u7684Ct\u503c\u6bd4\u8f83\uff0c\u53ef\u8ba1\u7b97\u51fa\u76f8\u5bf9\u8868\u8fbe\u91cf\uff0c\u4ece\u800c\u6d88\u9664RNA\u7528\u91cf\u548c\u6269\u589e\u6548\u7387\u5dee\u5f02\u7684\u5f71\u54cd\u3002", "Step 4: \u82e5\u67d0\u4e00\u5904\u7406\u6761\u4ef6\u4e0b\uff0c\u76ee\u6807\u57fa\u56e0\u76f8\u5bf9\u8868\u8fbe\u91cf\u5728\u7edf\u8ba1\u5b66\u4e0a\u663e\u8457\u9ad8\u4e8e\u53c2\u8003\u5904\u7406\uff08\u5982\u5bf9\u7167\u6216\u53e6\u4e00\u5904\u7406\uff09\uff0c\u8bf4\u660e\u8be5\u5904\u7406\u4e0b\u8be5\u57fa\u56e0\u8868\u8fbe\u4e0a\u8c03\uff1b\u82e5\u663e\u8457\u4f4e\u4e8e\uff0c\u5219\u8bf4\u660e\u8868\u8fbe\u4e0b\u8c03\u3002", "Step 5: \u56e0\u6b64\uff0c\u5728\u4f7f\u7528GAPDH\u4f5c\u4e3a\u5185\u53c2\u5e76\u91c7\u7528qRT\u2011PCR\u6d4b\u5b9a\u8f6c\u5f55\u672c\u4e30\u5ea6\u7684\u524d\u63d0\u4e0b\uff0c\u6bd4\u8f83\u4e0d\u540c\u5904\u7406\u95f4\u6807\u51c6\u5316\u540e\u7684\u76f8\u5bf9\u8868\u8fbe\u91cf\uff0c\u5c31\u80fd\u5ba2\u89c2\u5224\u65ad\u76ee\u6807\u57fa\u56e0\u5728\u67d0\u5904\u7406\u4e0b\u662f\u4e0a\u8c03\u8fd8\u662f\u4e0b\u8c03\u3002"], "question_cot": "", "final_conclusion": "\u5728\u5229\u7528qRT\u2011PCR\u9a8c\u8bc1\u57fa\u56e0\u8868\u8fbe\u65f6\uff0c\u53ea\u8981\u76ee\u6807\u57fa\u56e0\u7684\u6d4b\u5f97\u8f6c\u5f55\u672c\u6c34\u5e73\u5728GAPDH\u8fd9\u4e00\u5185\u53c2\u57fa\u56e0\u6807\u51c6\u5316\u540e\u663e\u8457\u9ad8\u4e8e\u6216\u4f4e\u4e8e\u53c2\u8003\u5904\u7406\uff0c\u5c31\u53ef\u4ee5\u5ba2\u89c2\u5224\u65ad\u8be5\u57fa\u56e0\u5728\u8be5\u5904\u7406\u4e0b\u4e3a\u4e0a\u8c03\u6216\u4e0b\u8c03\u8868\u8fbe\u3002", "difficulty": "medium", "tags": ["method", "application", "concept"], "created_at": "2025-12-16T21:16:21", "token_est_question": 1, "token_est_answer": 1, "section": "Methods", "context": "=== Material planting and drought treatments ===\nThe hybrids Zhengdan7137(AB), Zhengdan7153(CD), and their parental inbred lines [Zheng1110(AA), Zheng1117(BB), Zheng1121(CC), and Zheng641(DD)] were sown on June 3th in two parallel blocks at 5m\u00d70.6m\u00d70.15m in the experimental fields of the Henan Academy of Agricultural Sciences, located in Yuanyang, China (113.7\u00b0 E, 35\u00b0 N). Standard irrigation and fertilizer management were performed to ensure seed germination. After seed germination, one block was not irrigated until the relative humidity of soil at the depth of 10-20cm (R) \u2266 30%, i.e. circularly suffered water-deficit (WD, 30% 2 and FDR 0&F1 = 0), one specific parental inbred line expressing genes (type II, P1>0&P2, F1 = 0 or P2>0&P1, F1 = 0), the hybrid specifically expressing genes (type III, F1>0&P1, P2 = 0), the hybrid and one specific parental inbred line expressing genes (type IV, P1, F1>0&P2 = 0 or P2, F1>0&P1 = 0) and both parental inbred lines and the hybrid co-expressing genes (type V, P1, P2, F1>0) (Zhao et al., 2019, 2019). Subsequently, type V genes were further divided into additive genes (F1=MPV, MPV=(P1+P2)/2) and nonadditive genes (F1\u2260MPV). Nonadditive contained over-dominant (F1>P1, P2), under-dominant genes (F1 P2 or F1=P2&P2>P1), and low dominance genes (F1=P1&P1<P2 or F1=P2&P2<P1).\n=== Quantitative real-time fluorescence polymerase chain reaction ===\nQuantitative real-time fluorescence polymerase chain reaction (qRT-PCR) was used to validate the expression of six dominant genes. qRT-PCR was performed using a CFX96 Real-Time PCR Detection System. The transcript levels of each gene were measured using a 25 \u00b5L volume of SYBR (Accurate Biology, Changsha, China) on a CFX96 real-time PCR assay system (Bio-Rad, Hercules, CA, USA). qRT-PCR thermal cycling treatments included a single pre-incubation at 95\u00b0C for 30 s, followed by 5 s at 95\u00b0C and 56\u00b0C for 30 s for 40 amplification cycles, a 95\u00b0C ramp-up and a melt curve of 65 to 95\u00b0C with an increment of 0.5\u00b0C. GAPDH was used as an internal control. Primers used are listed in **Supplementary Table 3** .\n=== Statistical analysis ===\nWe measured the grain weight, ear length, and ear thickness of every 5 years with 3 repeats. The grain moisture content was measured in two replicates using a PM-8188-A moisture meter (Kett Electric Laboratory, Tokyo, Japan). Phenotypic data were analyzed using SPSS (version 26; SPSS Inc., Chicago, IL, USA) and Excel (version 2021; Excel Inc., Chicago, IL, USA). To assess significant differences between means, the Student\u2019s t-test and Duncan\u2019s multiple comparison test were employed. Additionally, we calculated mid-parent heterosis (MPH) and high-parent heterosis (HPH) to evaluate heterosis between hybrids and parents. The MPH was determined using the formula: MPH (%) = (F1 - MP)/MP, where MP represented the parents\u2019 mean. Additionally, HPH was calculated using the formula: HPH (%) = (F1 - HP)/HP, where HP was the high parent.", "Thinking\u6a21\u5f0f": "high", "generation_type": "\u63a8\u7406\u578b"}','2026-03-23 15:33:28.689631','2026-03-23 15:33:28.689636');
INSERT INTO qa_pairs VALUES(32,3,'在玉米等作物的抗旱评价中，某两份杂交种在正常供水和水分亏缺条件下，其穗长、穗粗和穗粒重基本保持稳定，而其各自亲本自交系在相同干旱强度下穗粒重显著下降且部分穗部性状也明显降低，同时穗粒重被视为衡量干旱损伤程度的主要指标，并且这两份杂交种在干旱前后始终对亲本表现出显著的中亲优势和超亲优势。面对这一类表型结果，应如何判断这两份杂交种与亲本在抗旱性上的差异，并据此评价它们是否具有抗旱杂种优势？','由于在相同干旱强度下，杂交种的穗粒重和关键穗部性状基本稳定，而亲本自交系穗粒重显著下降且降幅更大，在穗粒重被视为主要抗旱评价指标的前提下，说明杂交种对干旱造成的产量损失更小，抗旱性更强；再结合其在干旱前后均保持显著的中亲和超亲杂种优势，可判断这两份杂交种相对于亲本表现出明显的抗旱杂种优势。','{"species": "Zea mays", "paper_id": "PMC11494150", "reasoning_steps": ["Step 1: \u6c34\u5206\u4e8f\u7f3a\uff08WD\uff09\u5904\u7406\u4f1a\u663e\u8457\u964d\u4f4e\u6240\u6709\u6750\u6599\u7c7d\u7c92\u542b\u6c34\u91cf KWC\uff0c\u8bf4\u660e WD \u5904\u7406\u786e\u5b9e\u5f62\u6210\u4e86\u5e72\u65f1\u80c1\u8feb\u73af\u5883\u3002", "Step 2: \u5728\u6b63\u5e38\u4f9b\u6c34\uff08WW\uff09\u548c\u5e72\u65f1\uff08WD\uff09\u6761\u4ef6\u4e0b\uff0c\u6742\u4ea4\u79cd AB \u548c CD \u5728\u7a57\u957f(EL)\u3001\u7a57\u7c97(ET)\u3001\u767e\u7c92\u91cd(KW)\u3001\u7a57\u7c92\u91cd(GY)\u3001\u7c7d\u7c92\u542b\u6c34\u91cf(KWC)\u7b49\u6027\u72b6\u6574\u4f53\u8868\u73b0\u663e\u8457\u4f18\u4e8e\u5404\u81ea\u7684\u4eb2\u672c\u81ea\u4ea4\u7cfb\uff0c\u4e14\u5728\u5927\u591a\u6570\u6027\u72b6\u4e0a\u5177\u6709\u660e\u663e\u7684\u4e2d\u4eb2\u4f18\u52bf\u548c\u8d85\u4eb2\u4f18\u52bf\u3002", "Step 3: \u4e0e WW \u76f8\u6bd4\uff0c\u5728 WD \u6761\u4ef6\u4e0b\uff0cAB \u548c CD \u7684\u7a57\u957f\u3001\u7a57\u7c97\u548c\u7a57\u7c92\u91cd GY \u5e76\u672a\u51fa\u73b0\u663e\u8457\u53d8\u5316\uff0c\u8bf4\u660e\u6742\u4ea4\u79cd\u6574\u4f53\u4ea7\u91cf\u6027\u72b6\u5bf9\u5e72\u65f1\u7684\u654f\u611f\u6027\u8f83\u4f4e\u3002", "Step 4: \u540c\u6837\u4e0e WW \u76f8\u6bd4\uff0c\u5728 WD \u6761\u4ef6\u4e0b\uff0c\u5404\u4eb2\u672c\u81ea\u4ea4\u7cfb\u7684\u7a57\u7c92\u91cd GY \u663e\u8457\u4e0b\u964d\uff0c\u540c\u65f6\u7a57\u957f\u3001\u7a57\u7c97\u6216\u767e\u7c92\u91cd\u7b49\u4e5f\u5728\u4e0d\u540c\u4eb2\u672c\u4e2d\u660e\u663e\u964d\u4f4e\uff0c\u8868\u660e\u4eb2\u672c\u81ea\u4ea4\u7cfb\u7684\u7a57\u90e8\u4ea7\u91cf\u6027\u72b6\u5bf9\u5e72\u65f1\u8f83\u4e3a\u654f\u611f\u3002", "Step 5: \u5728 WD \u6761\u4ef6\u4e0b\uff0c\u5404\u4eb2\u672c\u81ea\u4ea4\u7cfb\u7684\u7a57\u7c92\u91cd GY \u964d\u5e45\u8fbe\u5230\u7ea6 11%\u2013\u8fd1 50%\uff0c\u800c\u6742\u4ea4\u79cd AB \u548c CD \u7684\u7a57\u7c92\u91cd\u964d\u5e45\u663e\u8457\u5c0f\u4e8e\u5176\u4eb2\u672c\u81ea\u4ea4\u7cfb\u3002", "Step 6: \u7a57\u7c92\u91cd GY \u88ab\u89c6\u4e3a\u8861\u91cf\u5e72\u65f1\u635f\u4f24\u7684\u4e3b\u8981\u8bc4\u4ef7\u6307\u6807\uff0c\u56e0\u6b64\u540c\u7b49\u5e72\u65f1\u6761\u4ef6\u4e0b\u7a57\u7c92\u91cd\u4e0b\u964d\u8d8a\u5c0f\uff0c\u4ee3\u8868\u6750\u6599\u7684\u6297\u65f1\u6027\u8d8a\u5f3a\u3002", "Step 7: \u7531\u4e8e\u5728\u76f8\u540c\u5e72\u65f1\u5f3a\u5ea6\u4e0b\uff0cAB \u548c CD \u7684\u7a57\u7c92\u91cd\u57fa\u672c\u7a33\u5b9a\u800c\u4eb2\u672c\u663e\u8457\u4e0b\u964d\uff0c\u7ed3\u5408\u5176\u6301\u7eed\u5b58\u5728\u7684\u663e\u8457\u6742\u79cd\u4f18\u52bf\uff0c\u53ef\u5f97\u51fa AB \u548c CD \u76f8\u6bd4\u4eb2\u672c\u5177\u6709\u66f4\u5f3a\u6297\u65f1\u6027\uff0c\u5373\u8868\u73b0\u51fa\u6297\u65f1\u6742\u79cd\u4f18\u52bf\u3002"], "question_cot": "", "final_conclusion": "\u5728\u6c34\u5206\u4e8f\u7f3a\u6761\u4ef6\u4e0b\uff0c\u6742\u4ea4\u79cd AB \u548c CD \u7684\u7a57\u90e8\u4ea7\u91cf\u51e0\u4e4e\u4e0d\u53d7\u663e\u8457\u5f71\u54cd\uff0c\u800c\u4eb2\u672c\u81ea\u4ea4\u7cfb\u7a57\u4ea7\u91cf\u663e\u8457\u4e0b\u964d\uff0c\u56e0\u6b64 AB \u548c CD \u76f8\u6bd4\u5176\u4eb2\u672c\u5177\u6709\u66f4\u9ad8\u7684\u6297\u65f1\u6027\uff08\u8868\u73b0\u4e3a\u6297\u65f1\u6742\u79cd\u4f18\u52bf\uff09\u3002", "difficulty": "medium", "tags": ["result", "application", "concept", "breeding", "stress_tolerance"], "created_at": "2025-12-16T21:18:36", "token_est_question": 1, "token_est_answer": 1, "section": "Results", "context": "=== Heterosis of hybrids Zhengdan7137 and Zhengdan7153 under drought stress ===\nTo ascertain the impact of drought on heterosis, we cultivated the hybrids AB, CD, and their parental inbred lines under WW and WD conditions and investigated traits including length, thickness, and grain yield of each ear (EL, ET, and GY, respectively), 100-kernel weight (KW), and kernel water content (KWC) to evaluate heterosis. Compared with WW conditions, the KWC of AB, CD, and their parental inbred lines significantly decreased under WD conditions, which corresponded to drought stress ( **Figure 1A** ). Under WW and WD conditions, AB and CD consistently displayed significantly superior performance than their parents in EL, ET, KW, GY, and KWC with varied mid-parent heterosis (MPH, 6.9\u2013198.1%) and high-parent heterosis (HPH, 10.0\u2013155.5%), except for KWC under WW conditions of AB and CD, KW of AB under WW conditions, and KW of CD under WD conditions ( **Supplementary Table S1** ). In addition, the GY of AB and CD displayed the highest MPH (94.1\u2013198.1%) and HPH (85.5\u2013155.5%) ( **Supplementary Table S1** ). Hence, hybrids AB and CD exhibited reliable heterosis under both normal and drought conditions.\n=== Figure 1. ===\n \nOpen in a new tab\nKernels water content **(A)** and grain yield per ear **(B)** of AB, CD and their parental inbred lines under well-watering (WW) and water-deficit (WD) conditions. KWC, kernels water content; GY, grain yield per ear; \u201c*\u201d, \u201c**\u201d and ns, p-value 0.05 in student\u2019s t test, respectively. AB, hybrid Zhengdan7137; AA, inbred line Zheng1110; BB, inbred line Zheng1117. AA and BB are parental inbred lines of AB. CD, hybrid Zhengdan7153; CC, inbred line Zheng1121; DD, inbred line Zheng641. CC and DD are parental inbred lines of CD.\nCompared to WW conditions, the EL, ET, and GY of hybrids AB and CD were not significantly altered under WD conditions, whereas the GY of the parental inbred lines, EL, ET, and KW of AA, ET of BB, EL of CC, and ET of DD were significantly decreased ( **Figure 1B** ; **Supplementary Table S1** ). In contrast, the KW of AB, CD, and CC increased under drought conditions owing to the lower seed-setting rate ( **Supplementary Table S1** ). Moreover, under WD condition, the GY in AA, BB, CC, and DD decreased by 48.6%, 27.6%, 11.3%, and 27.5%, respectively, which were significantly higher than hybrids AB and CD ( **Figure 1B** ). The GY is the primary criterion used for evaluating drought impairment (Monneveux et al., 2008). Therefore, these results indicated that hybrids AB and CD possessed higher drought resistance than their parent inbred lines; in other words, AB and CD exhibited drought-resistant heterosis.\n=== RNA-Seq of hybrids Zhengdan7137, Zhengdan7153 and their parental inbred lines ===\nTo investigate the molecular mechanism of heterosis under drought and re-watering stress, we analyzed the transcriptomes of the leaves of hybrids AB, CD, and their parental inbred lines with three biological repeats under WW, WD, and RW conditions. Fifty-four RNA-seq libraries were constructed and a total of 349.32 G clean data were obtained after quality control, in which each clean library was 5.84\u20137.46G. The Q30, Validbases, and GC contents of clean reads from each library were 91.8\u201393.5%, 93.9\u201399.1%, and 51.9\u201355.8%, respectively. On average, 85.6% of the clean reads were uniquely mapped to the reference genome B73 ( **Supplementary Table S2** ). In addition, the biological replicates were highly reproducible (Pearson\u2019s correlation rate > 0.88) ( **Figure 2A** ). These results indicate the high quality of our transcriptome data for AB, CD, and their parental inbred lines. The Pearson correlation rates between the WW, WD, and RW conditions of hybrids AB and CD were significantly lower than those of their parental inbred lines, suggesting that the transcriptomes of the hybrids might be more sensitive to drought and re-watering stresses than their parental inbred lines ( **Figure 2B** ). Hybrids AB and CD were highly correlated (R > 0.83) under WW, WD, and RW conditions ( **Figure 2C** ), suggesting that there might be a large similarity between hybrids AB and CD in dealing with drought and re-watering stresses. Overall, our transcriptome data were ideal for investigating the molecular mechanisms underlying drought resistance and heterosis.\n=== Figure 2. ===\n \nOpen in a new tab\nThe Pearson correlation rate analysis of RNA-Seq libraries of AB, CD and their parental inbred lines under WW, WD and RW conditions. **(A)** the biological repeates of RNA-Seq libraries were high related in the Pearson correlation rate ananlysis; **(B)** Pearson correlation rates between WW, WD and RW conditions of hybrid AB (middle) were significant lower than its parental inbred lines AA (left) and BB (right); **(C)** RNA-Seq librarys of hybrids AB and CD were highly correlated under WW, WD and RW conditions. AB, hybrid Zhengdan7137; AA, inbred line Zheng1110; BB, inbred line Zheng1117. AA and BB are parental inbred lines of AB. CD, hybrid Zhengdan7153; CC, inbred line Zheng1121; DD, inbred line Zheng641. CC and DD are parental inbred lines of CD.\n=== Drought response genes of hybrids Zhengdan7137, Zhengdan7153, and their parental inbred lines ===\nWe identified differentially expressed genes (|fold change| > 2 and FDR 2 and FDR 81%) with few changes (18338_WW, 18456_WD, and 18307_RW in AB; 18456_WW, 18414_WD and 18541 RW in CD). Conversely, the number of type I genes was the lowest (264_WW, 242_WD, and 243_RW in AB; 324_WW, 339_WD, and 231_WD in CD). Type II and type IV genes slightly decreased and increased, respectively, when WD and RW conditions were compared with WW conditions. Interestingly, type III genes significantly increased under WD conditions (154_WW vs. 866_WD in AB and 137_WW vs. 740_WD in CD) and decreased under RW conditions (866_WD vs. 498_RW in AB and 740_WD vs. 483_RW in CD) ( **Figures 4A, B** ), which was consistent with the change in water potential.\n=== Figure 4. ===\n \nOpen in a new tab\nGenes expression patterns of hybrids Zhengdan7137 (AB) and Zhengdan7153 (CD) under well-watering (WW), water-deficited (WD) and re-watering (RW) conditions. **(A, B)** genes in PAV related expression patterns of AB **(A)** and CD **(B)**. AB and CD, Zhengdan7137 and Zhengdan7153. WW, WD and RW, well-watering, water-deficited and re-watering conditions; I, II, III, IV, and V were co-expression in both parents but not expression in hybrids genes (type I), only one parent specifically expressed genes (type II), hybrids specifically expressed genes (type III), hybrids and one specific parent expressed genes (type IV), and both parent and hybrid co-expression genes (type V); **(C, D)** nonadditive expression genes of AB **(C)** and CD **(D)**. AB, hybrid Zhengdan7137; AA, inbred line Zheng1110; BB, inbred line Zheng1117. AA and BB are parental inbred lines of AB. CD, hybrid Zhengdan7153; CC, inbred line Zheng1121; DD, inbred line Zheng641. CC and DD are parental inbred lines of CD.\nType V genes are further divided into additive and nonadditive genes. The additive genes were more abundant than nonadditive genes, except for AB under WD condition. The number of nonadditive genes increased under WD condition (9,529 in AB and 8,309 in CD) and RW condition (8,425 in AB and 8,367 in CD) when compared to WW condition (1,377 in AB and 6,764 in CD), especially for hybrid AB, in which nonadditive genes increased 6.9 and 6.1 times, respectively ( **Figures 4C, D** ). Nonadditive genes included overdominant, dominant, and underdominant genes. The quantity of overdominant and underdominant genes in AB and CD was less than the dominant expression genes under all WW, WD, and RW conditions ( **Figures 4C, D** ). Compared to WW condition, the numbers of overdominant, underdominant, and dominant genes were significantly increased under WD conditions, particularly for overdominant and underdominant genes which increased 4.6\u201322.7 times. After re-watering, the overdominant, underdominant, and dominant genes decreased by 61.4%, 48.7%, and 22.6% in AB and increased by 141.9%, 169.5%, and 106.8% in CD, respectively ( **Figures 4C, D** ). These results suggest that nonadditive genes responded to drought and re-watering stresses, especially the overdominant and underdominant genes, which was consistent with the drought-resistant heterosis of hybrids AB and CD.\n=== GO and KEGG analysis of overdominant and underdominant genes ===\nType III genes were essentially overdominant. Therefore, we combined them with the overdominant genes in type V and generated 286 (AB_WW), 355 (CD_WW), 2,489 (AB_WD), 1,744 (CD_WD), 1,124 (AB_RW), and 1,908 (CD_RW) overdominant genes. Under WW conditions, these overdominant genes were mainly enriched in photosynthesis-related terms in the GO and KEGG analyses ( **Supplementary Figures S2A\u2013D** ). However, under WD conditions, the overdominant genes were significantly enriched in stress response-related terms such as response to heat, cell wall organization, plant hormone signal transduction, and phenylalanine, tyrosine, and tryptophan biosynthesis ( **Supplementary Figures S3A\u2013D** ). Under RW conditions, the overdominant genes were recovered to enriched in the photosynthesis-related pathway. There were still many overdominant genes enriched in stress response-related terms, such as carotenoid biosynthesis in KEGG ( **Supplementary Figures S4A\u2013D** ). We also identified 65 (AB_WW), 90 (CD_WW), 1,477 (AB_WD), 784 (CD_WD), 757 (AB_RW), and 1,329 (CD_RW) underdominant genes. Under WW conditions, different from overdominant genes, underdominant genes were enriched in stress-related terms in GO and KEGG analysis (e.g., flavonoid biosynthesis, phenylpropanoid biosynthesis, and phenylalanine metabolism in AB; response to heat, protein folding, and flavonoid biosynthesis in CD) ( **Supplementary Figures S5A\u2013D** ). Under WD conditions, the underdominant genes were enriched during photosynthesis (e.g., photosynthesis in GO), antioxidant capacity (e.g., glutathione metabolism), and energy metabolism (e.g., pentose phosphate pathway and glycolysis/gluconeogenesis) ( **Supplementary Figures S6A\u2013D** ). Under RW conditions, GO and KEGG analyses showed underdominant genes enriched in stress response-related processes and photosynthesis-related processes (e.g., response to heat, protein folding, response to stress and photosynthesis, and light harvesting in photosystem I in GO; photosynthesis-antenna proteins in KEGG) ( **Supplementary Figures S7A\u2013D** ), which suggested that the physiological state of the hybrids was recovering. The variation in overdominance and underdominance genes under WD and RW conditions was similar to the upregulation and downregulation genes of CDRGs and CRRGs, which suggested that overdominant and underdominant genes might play important roles in dealing with drought and re-watering stresses.\n=== Conservative drought and re-watering response overdominance and underdominance genes ===\nWe identified common overdominant genes (714 and 722 under WD and RW conditions, respectively) and underdominant genes (584 and 492 under WD and RW conditions, respectively) between AB and CD. To gain further insight into the roles of over- and under-dominant genes in drought and re-watering stress resistance, we compared CDRGs and CRRGs with the common over- and under-dominant genes under WD and RW conditions, respectively. We found that the overdominant genes mainly overlapped with the upregulated CDRGs or CRRGs, whereas the underdominant genes mainly overlapped with the downregulated CDRGs or CRRGs. Under WD conditions, there were 303 overlapping genes between the upregulated CDRGs with common over-dominance genes and 252 overlapping genes between the downregulated CDRGs with common underdominance genes, designated as conservative drought response overdominance genes (DODGs) and underdominance genes (DUDGs). In addition, 165 and 267 common genes were identified by comparing unregulated and downregulated CRRGs to common overdominance and underdominance genes under RW conditions, respectively, and designated as conservative re-watering response overdominance genes (RODGs) and under-dominance expression genes (RUDGs).\nThe DODGs enriched at multiple stresses response processes and metabolism in GO and KEGG analysis, such as cinnamic acid biosynthetic process, carbohydrate metabolic process, L-phenylalanine catabolic process, S-adenosylmethionine biosynthetic process, and response to heat in GO; and phenylalanine metabolism, amino sugar and nucleotide sugar metabolism, and arginine and proline metabolism in KEGG ( **Figures 5A, C** ). DUDGs were enriched in the glucose metabolic process, pentose-phosphate shunt, starch metabolic process, pentose phosphate pathway, glutathione metabolism, and starch and sucrose metabolism ( **Figures 5B, D** ). In the RODGs, there were some cell propagation- and growth-related genes, as evidenced by their enrichment in the circadian rhythm, regulation of auxin biosynthetic processes, regulation of cyclin-dependent protein serine/threonine kinase activity, and photosynthesis in GO ( **Supplementary Figures S8A, C** ). In the RUDGs, stress-related terms such as response to heat, protein folding, and chaperone-mediated protein complex assembly were significantly enriched ( **Supplementary Figures S8B, D** ).\n=== Figure 5. ===\n \nOpen in a new tab\nGO and KEGG analysis of DODGs and DUDGs. **(A, B)** the top 30 GO enrichment terms for DODGs and DUDGs; **(C, D)** the top 20 KEGG enrichment terms for DODGs and DUDGs.\n=== qRT-PCR validation of overdominant genes ===\nTo validate the results of the RNA-Seq analysis, six overdominantly expressed DRGs or RRGs (Zm00001d020717, Zm00001d028219, Zm00001d048709, LOC100384645, LOC109939524, and Zm00001d031662) were randomly selected for quantitative real-time fluorescence polymerase chain reaction (qRT-PCR). Among these genes, Zm00001d028219 and Zm00001d020717 were DRG and RRG in AB, respectively, whereas Zm00001d048709 and the remaining genes (LOC100384645, LOC109939524, and Zm00001d031662) were DRG and RRGs in CD, respectively. The leaves of AB, CD, and their parental inbred lines were harvested under WD and RW conditions for qRT-PCR analysis. In qRT-PCR, the relative expression levels of these six genes were higher in the hybrids than in the parental inbred lines ( **Figure 6** ), which was consistent with the FPKM values in RNA-Seq and validated the reliability of our RNA-Seq data.\n=== Figure 6. ===\n \nOpen in a new tab\nqRT-PCR for overdominant genes. _ZmGAPDH_ was used as the internal reference. AB, hybrid Zhengdan7137; AA, inbred line Zheng1110; BB, inbred line Zheng1117. AA and BB are parental inbred lines of AB. CD, hybrid Zhengdan7153; CC, inbred line Zheng1121; DD, inbred line Zheng641. CC and DD are parental inbred lines of CD.", "Thinking\u6a21\u5f0f": "high", "generation_type": "\u63a8\u7406\u578b"}','2026-03-23 15:33:28.765256','2026-03-23 15:33:28.765266');
INSERT INTO qa_pairs VALUES(33,3,'在某玉米杂交种中，干旱胁迫时保守上调基因主要富集于细胞壁生物合成、热响应、L-苯丙氨酸和碳水化合物代谢等应激相关过程，而保守下调基因多与光合作用有关；恢复供水后，与光合作用相关的保守基因被显著上调，应激相关过程中的许多基因则转为下调并趋于原始水平，同时仍有一部分在干旱期被上调或下调的基因在复水后继续保持改变的表达状态。如何从这一套可逆与不可逆的转录调控特征，推断出该杂交种在水分亏缺与复水循环中兼顾增强抗旱性、快速恢复生长以及形成干旱记忆的适应策略？','干旱期间，杂交种通过上调细胞壁改造、抗氧化代谢和渗透调节等应激相关保守基因，同时压制光合作用相关基因，将资源优先投入到维持细胞结构稳定和抗胁迫防御上，从而增强抗旱存活能力。复水后，原本被抑制的光合作用相关保守基因被强劲上调，而许多应激相关基因则下调并基本恢复到常态，使植株迅速恢复甚至过度补偿光合和生长，减小干旱造成的产量损失。与此同时，仍有一部分在干旱中被激活或抑制的保守基因在复水后保持改变状态，构成分子层面的“干旱记忆”，使植株在再次遭遇水分亏缺时能更快、更强烈地启动防御。这种“多数基因可逆、少数基因保持改变”的表达组合，使杂交种在干旱-复水循环中既能提高抗旱性，又能迅速恢复生长，并在此基础上形成对后续干旱的预适应能力。','{"species": "Zea mays", "paper_id": "PMC11494150", "reasoning_steps": ["Step 1: \u5728\u5e72\u65f1\u6761\u4ef6\u4e0b\uff0c\u6742\u4ea4\u79cd\u4e2d\u4e00\u7c7b\u4fdd\u5b88\u7684\u4e0a\u8c03\u57fa\u56e0\uff08CDRGs \u7684\u4e0a\u8c03\u90e8\u5206\uff09\u663e\u8457\u5bcc\u96c6\u4e8e\u5e94\u6fc0\u54cd\u5e94\u76f8\u5173\u8fc7\u7a0b\uff0c\u5982\u7ec6\u80de\u58c1\u751f\u7269\u5408\u6210\u3001\u70ed\u54cd\u5e94\u3001L-\u82ef\u4e19\u6c28\u9178\u4ee3\u8c22\u548c\u78b3\u6c34\u5316\u5408\u7269\u4ee3\u8c22\u3002", "Step 2: \u8fd9\u4e9b\u8fc7\u7a0b\u5728\u5df2\u6709\u7814\u7a76\u4e2d\u88ab\u8bc1\u660e\u6709\u5229\u4e8e\u6297\u65f1\uff1a\u7ec6\u80de\u58c1\u52a0\u539a\u548c\u53d8\u786c\u6709\u52a9\u4e8e\u62b5\u6297\u6c34\u5206\u4e8f\u7f3a\u9020\u6210\u7684\u9ad8\u5f20\u529b\u5e76\u51cf\u5c11\u5931\u6c34\uff1bL-\u82ef\u4e19\u6c28\u9178\u4ee3\u8c22\u589e\u5f3a\u53ef\u4ea7\u751f\u66f4\u591a\u9ec4\u916e\u548c\u82b1\u9752\u7d20\u7b49\u6297\u6c27\u5316\u7269\uff1b\u78b3\u6c34\u5316\u5408\u7269\u4ee3\u8c22\u589e\u52a0\u53ef\u6eb6\u6027\u7cd6\u4f5c\u4e3a\u6e17\u900f\u8c03\u8282\u7269\u3002", "Step 3: \u540c\u65f6\uff0c\u5e72\u65f1\u4e0b\u4fdd\u5b88\u4e0b\u8c03\u57fa\u56e0\u663e\u8457\u5bcc\u96c6\u4e8e\u5149\u5408\u4f5c\u7528\u76f8\u5173\u8fc7\u7a0b\uff0c\u8868\u660e\u5149\u5408\u4f5c\u7528\u53d7\u6291\u662f\u4e00\u79cd\u53d7\u635f\u8868\u73b0\uff0c\u800c\u5e94\u6fc0\u76f8\u5173\u4e0a\u8c03\u57fa\u56e0\u5728\u6297\u65f1\u4e2d\u8d77\u4e3b\u5bfc\u4f5c\u7528\u3002", "Step 4: \u590d\u6c34\u540e\uff0c\u4fdd\u5b88\u4e0a\u8c03\u590d\u6c34\u54cd\u5e94\u57fa\u56e0\uff08CRRGs \u7684\u4e0a\u8c03\u90e8\u5206\uff09\u5728 GO/KEGG \u4e2d\u663e\u8457\u5bcc\u96c6\u4e8e\u5149\u5408\u4f5c\u7528\u76f8\u5173\u8fc7\u7a0b\uff0c\u663e\u793a\u5149\u5408\u4f5c\u7528\u5728\u590d\u6c34\u540e\u88ab\u5f3a\u52b2\u6062\u590d\uff0c\u4e3a\u690d\u682a\u8865\u507f\u6216\u8fc7\u5ea6\u8865\u507f\u5e72\u65f1\u9020\u6210\u7684\u751f\u957f\u635f\u5931\u63d0\u4f9b\u57fa\u7840\u3002", "Step 5: \u590d\u6c34\u540e\uff0c\u8bb8\u591a\u5728\u5e72\u65f1\u4e2d\u4e0a\u8c03\u7684\u5e94\u6fc0\u76f8\u5173\u8fc7\u7a0b\uff08\u5982\u70ed\u54cd\u5e94\u3001\u7ec6\u80de\u58c1\u751f\u7269\u5408\u6210\u3001\u6c28\u57fa\u7cd6\u548c\u6838\u82f7\u9178\u7cd6\u4ee3\u8c22\u3001\u7cbe\u6c28\u9178\u548c\u812f\u6c28\u9178\u4ee3\u8c22\uff09\u5bf9\u5e94\u7684 CRRGs \u51fa\u73b0\u4e0b\u8c03\uff0c\u6709\u5229\u4e8e\u9002\u5e94\u6c34\u52bf\u6062\u590d\u5e76\u51cf\u8f7b\u751f\u957f\u969c\u788d\u3002", "Step 6: \u5728\u5e72\u65f1\u4e2d\u4e0a\u8c03\u6216\u4e0b\u8c03\u7684\u4fdd\u5b88\u57fa\u56e0\u4e2d\uff0c\u7ea6 70% \u4e0a\u8c03 CDRGs \u548c\u7ea6 64% \u4e0b\u8c03 CDRGs \u5728\u590d\u6c34\u540e\u8868\u8fbe\u6062\u590d\uff0c\u8bf4\u660e\u8fd9\u90e8\u5206\u57fa\u56e0\u5bf9\u6c34\u52bf\u53d8\u5316\u4f5c\u51fa\u53ef\u9006\u8c03\u8282\uff0c\u662f\u6297\u65f1\u4e0e\u590d\u6c34\u54cd\u5e94\u7684\u5173\u952e\u7ec4\u6210\u3002", "Step 7: \u4ecd\u6709\u7ea6 26%\u201334% \u5728\u5e72\u65f1\u4e2d\u88ab\u4e0a\u8c03\u6216\u4e0b\u8c03\u7684\u4fdd\u5b88\u57fa\u56e0\u5728\u590d\u6c34\u540e\u4e0d\u6062\u590d\u8868\u8fbe\uff0c\u88ab\u8ba4\u4e3a\u4e0e\u5e72\u65f1\u8bb0\u5fc6\u76f8\u5173\uff0c\u53ef\u4f7f\u690d\u682a\u5728\u518d\u6b21\u906d\u9047\u6c34\u5206\u80c1\u8feb\u65f6\u5177\u6709\u9884\u9002\u5e94\u4f18\u52bf\uff1b\u56e0\u6b64\uff0c\u901a\u8fc7\u53ef\u9006\u6062\u590d\u7684\u5927\u90e8\u5206\u57fa\u56e0\u548c\u4fdd\u6301\u6539\u53d8\u7684\u4e00\u90e8\u5206\u57fa\u56e0\u7684\u7ec4\u5408\uff0c\u6742\u4ea4\u79cd\u5728\u5e72\u65f1\u4e0e\u590d\u6c34\u5faa\u73af\u4e2d\u65e2\u589e\u5f3a\u6297\u65f1\uff0c\u53c8\u5feb\u901f\u6062\u590d\u5149\u5408\u4f5c\u7528\u5e76\u5f62\u6210\u5e72\u65f1\u8bb0\u5fc6\u3002"], "question_cot": "", "final_conclusion": "\u5728\u7389\u7c73\u6742\u4ea4\u79cd\u4e2d\uff0c\u6c34\u5206\u4e8f\u7f3a\u548c\u590d\u6c34\u8fc7\u7a0b\u4e2d\u901a\u8fc7\u534f\u8c03\u4fdd\u5b88\u5dee\u5f02\u8868\u8fbe\u57fa\u56e0\u7684\u4e0a\u8c03\u4e0e\u6062\u590d\uff0c\u4e0b\u8c03\u4e0e\u6062\u590d\uff0c\u53ef\u4ee5\u65e2\u589e\u5f3a\u6297\u65f1\u6027\u53c8\u5728\u590d\u6c34\u540e\u5feb\u901f\u6062\u590d\u5149\u5408\u4f5c\u7528\u5e76\u5f62\u6210\u5e72\u65f1\u8bb0\u5fc6\u3002", "difficulty": "medium", "tags": ["mechanism", "result", "application"], "created_at": "2025-12-16T21:19:57", "token_est_question": 1, "token_est_answer": 1, "section": "Discussion", "context": "=== Conservative mechanism of hybrids in response to drought and re-watering stresses ===\nIn natural agricultural environments, drought and re-watering often occur circularly for maize. When subjected to drought stress, plants trigger a series of physiological, biochemical, metabolic, and cellular responses to resist dehydration, including stress perception, signal transduction, activation or suppression of transcription factors, accumulation of osmoprotectants, protective proteins, and antioxidants, and stomatal closure, thereby reducing transpiration and photosynthesis (Sheoran et al., 2022). After re-watering, these responses can be restored to accelerate plant growth, depending on the plant species and intensity and duration of drought stress (Wang et al., 2022b). Therefore, drought resistance is not only related to the water scarcity response, but also to the re-watering response. However, systematic research focusing on the molecular mechanisms of drought and re-watering responses in maize is rare. In this study, we identified drought and re-watering response genes (DRGs and RRGs, respectively) in hybrids AB and CD and their parental inbred lines using transcriptomic analysis. Although there were large differences in DRGs and RRGs between the hybrids and their parental inbred lines, we found 3,411 (49.8% and 60.9%) and 3,133 (51.4% and 58.1%) conservative DRGs and RRGs (CDRGs and CRRGs) between AB and CD, respectively, indicating that there were conservative mechanisms of hybrids in response to drought and re-watering.\nIn CDRGs, the upregulation CDRGs was significantly enriched in stress response-related terms in GO and KEGG analyses, whereas the downregulation CDRGs was significantly enriched in photosynthesis-related terms exhibiting the injury of drought stress, which suggested that the upregulation of CDRGs played a predominant role in drought resistance. The most significantly enriched processes of up-regulation CDRGs in GO or KEGG analysis included cell wall biogenesis (plant-type primary and secondary cell wall biogenesis, cell wall organization, and cellulose biosynthesis), response to heat, trichome morphogenesis, L-phenylalanine metabolism (cinnamic acid biosynthesis and L-phenylalanine catabolic process), carbohydrate metabolism, and snRNA pseudouridine synthesis. Cell wall biogenesis, response to heat, L-phenylalanine metabolism, and carbohydrate metabolism have been recognized to contribute to drought resistance in many studies (Li et al., 2024; Waititu et al., 2021; Zhang et al., 2022b; Amara et al., 2013; Wang et al., 2023). Stiffening and thickening of the cell wall increase the strength of plant cells to resist high tension caused by water deficiency, reduce water loss, and enhance drought resistance (Alvarez et al., 2008). The heat response involves heat shock proteins (HSPs) and late embryogenesis abundant proteins (LEAs), which are two important types of protective proteins that respond to diverse abiotic stresses (Wang et al., 2023; Ristic et al., 1991). Overexpression of the LEA proteins _Rab28_ and _ZmNHL1_ improves drought resistance in transgenic maize plants (Amara et al., 2013; Wang et al., 2023). Augmentation of the L-phenylalanine metabolism pathway is a universal strategy for plants to resist abiotic stress by producing more antioxidants such as flavonoids and anthocyanins (Yuan et al., 2022; Adams et al., 2019). Carbohydrate metabolism increases the concentration of soluble sugars (e.g., sucrose, amino sugars, and nucleotide sugars) as osmoprotectants to resist drought stress (Fang et al., 2024). Although trichomes may reduce transpiration under water scarcity conditions and pseudouridine modification of snRNA is involved in the response to stress (Xuan et al., 2020; Lippman and Zamir, 2007), their function in drought resistance in plants has not been well investigated.\nAfter re-watering, GO and KEGG analyses of upregulated CRRGs showed that photosynthesis was robustly recovered, which was the basis for hybrids to compensate or overcompensate for the losses caused by drought stress. Downregulation of CRRGs is involved in many processes that are upregulated under drought stress, such as responses to heat, cell wall biogenesis (plant-type secondary cell wall biogenesis and cell wall organization), benzoxazinoid biosynthesis, amino sugar and nucleotide sugar metabolism, and arginine and proline metabolism. Downregulation of these drought response processes contributes to the adaptation of maize to the recovered water potential and reduced growth obstacles. In addition, 1,225\u20131,260 (70.2\u201372.2%) upregulated CDRGs and 1,054\u20131,059 (64.0\u201364.3%) downregulated CDRGs in AB or CD were recovered after re-watering, indicating that these genes were adjusted with water potential and might play key roles in resisting drought and re-watering stress. Yet, there were still 498\u2013462 (28.5\u201326.5%) upregulated CDRGs and 555\u2013519 (33.7\u201331.5%) in AB or CD that did not change after re-watering, which might be the reason for pre-exposure to drought enhancing drought resistance as drought stress memory (Ding et al., 2012).\n=== Conservative mechanism of hybrids in response to drought and re-watering stresses ===\nIn previous studies, nonadditive genes in hybrids that were significantly different from the average of their parents were deemed responsible for heterosis (Zhao et al., 2019). In this study, we investigated the gene expression patterns of the maize hybrids Zhengdan7137(AB) and Zhengdan7153(CD) under well-watered, water-deficit, and recover-watered conditions. AB and CD were obtained from SS\u00d7NSS and Reid\u00d7Tangsipingtou heterotic patterns, respectively. These two heterotic patterns are widely used in China, especially in the major production areas Huanghuaihai. Therefore, the molecular mechanisms of heterosis in AB and CD are representative in China. Under well-watering conditions, there were 1,377 (7.5%) and 6,764 (36.7%) nonadditive genes in AD, and CD, respectively. Under water-deficit conditions, the number of nonadditive genes increased to 9,529 (51.6%) and 8,390 (45.4%) in AB and CD, respectively. The phenomenon that the percentage of nonadditive genes increased under abiotic stress was also observed in previous studies. For examples, Zheng et al. identified 46.5\u201352.6% and 57.6\u201362.08% nonadditive genes in maize reciprocal hybrids Zhengdan538 under control and water-deficit conditions, respectively (Zhang et al., 2022a). There are 2,326 (10.5%) and 5,817 (26.2%) nonadditive genes in the hybrid An\u2019nong591 under controlled conditions and heat stress, respectively (Zhao et al., 2019). Therefore, it is usual for hybrids to increase nonadditive genes under abiotic stresses, which may be favorable for hybrids to resist environmental stresses. Under re-watering conditions, nonadditive gene slightly decreased in hybrids AB (8,425, 46%) and CD (8,367, 45.1%) compared to water-deficit conditions. This suggests that re-watering was incapable of completely eliminating the influence of drought on the gene expression patterns of the hybrids, which might be a mechanism by which hybrids maintain drought memory.\nNonadditive genes included overdominant, dominant, and underdominant genes. Compared to the dominant genes, the numbers of overdominant and underdominant genes were more sensitive to water deficit and re-watering conditions in hybrids AB and CD. Under well-watering conditions, over- and under-dominant genes were significantly enriched in photosynthesis-related and stress response-related processes, respectively. Overdominant expression of photosynthesis-related genes might provide a higher photosynthetic capacity for hybrids, which, in turn, provides more photosynthetic products to promote growth than their parental inbred lines. The elevated expression of stress-response genes represses plant growth and reduces grain yield (Sun et al., 2023). The under-dominant expression of stress response genes in hybrids may minimize their obstacle effects on growth. Therefore, these over- and under-dominant genes may contribute to the rapid growth of hybrids, which are important for heterosis under WW conditions. In contrast to well-watered conditions, overdominant genes were significantly enriched in stress response-related processes under water-deficit conditions, whereas underdominant genes were significantly enriched in photosynthesis-related processes. In addition, we identified 714 and 584 common over-dominant and under-dominant genes, respectively, between hybrids AB and CD. Approximately 43% of these overdominant and underdominant genes were upregulated and downregulated CDRGs, respectively, and were designated as DODGs and DUDGs, respectively. DODGs and DUDGs are enriched in multiple resistance processes, such as l-phenylalanine metabolism, carbohydrate metabolism, and response to heat and glutathione metabolism. These results indicate that increased over-dominance and under-dominance of genes under WD conditions enhanced the drought resistance of hybrids. After re-watering, overdominant genes and RODGs were significantly enriched in cell propagation processes, photosynthesis-related processes, and stress response-related processes, and under-dominant genes and RUDGs were enriched in stress response-related processes, which accelerated the recovery of hybrids from drought stress and retained drought stress memory. Therefore, it was a conservative mechanism by which hybrids adjusted their overdominance and underdominance genes to deal with drought and re-watering stresses, which contributed to hybrid heterosis under drought stress.\n=== Conservative mechanism of hybrids in response to drought and re-watering stresses ===\nDrought is a destructive abiotic stress that leads to significant maize yield losses. Although many drought-resistance genes have been cloned in maize, novel and excellent gene resources are required to improve maize drought resistance without yield losses. Considering that upregulated drought response genes play a dominant role in drought resistance and that overdominant genes are important for heterosis under drought stress, we suggest that DODGs involved in multiple pathways are valuable potential targets for improving drought resistance in maize. Drought stress signals are detected in the plasma membrane and activate successive signal transduction pathways. Ca2+ is a secondary messenger that responds to drought stress (Jiang et al., 2013). In DODGs, there are several genes involved in Ca2+ signaling pathways; for example, LOC100273477 and LOC100141385 each encode a calcium-binding protein annexin and a CBL-interacting protein kinase (CIPK), which are involved in Ca2+ signal perception and activation of downstream drought response proteins (Ma et al., 2020; Saad et al., 2019). In addition, protein kinases (e.g., MAP kinase protein LOC100278992, calcium-dependent protein kinase LOC100383301, and serine/threonine protein kinase LOC100384302) may be involved in drought signal transduction (Chen et al., 2021). The drought signal is then transduced into the nucleus to regulate the expression of transcription factors. We identified 27 transcription factors from 15 families in DODGs, such as five MYB, three NAC, three bHLH, three trihelix, two ERF, and one bZIP, which fine-tune the expression of downstream genes to deal with drought stress (Singh et al., 2023; Mao et al., 2015; Wei et al., 2021; Cao et al., 2021; Xie et al., 2019; Zhao et al., 2023). Polyamines (PAs) consisting of putrescine, spermidine, and spermine promote reactive oxygen species scavenging and stabilize macromolecules and membrane structures to resist abiotic stress. Arginine and methionine are precursors for PA biosynthesis (Shi and Chan, 2014). Among the DODGs, arginine decarboxylase (AY110562) and S-adenosylmethionine synthase (c12757_5c) are the two key rate-limiting enzymes that catalyze the conversion of L-arginine and methionine into putrescine (Shi and Chan, 2014). Additionally, polyamine oxidase (PAO1 and LOC103632539) might enhance PA content (Liu et al., 2022). Shikimate kinase phosphorylates shikimate into 3-phosphate shikimate in the shikimate pathway and promoted L-phenylalanine, anthocyanin and flavonoid synthesis (Yuan et al., 2022). Phenylalanine ammonia lyase (PAL) catalyzes the conversion of l-phenylalanine to trans-cinnamic acid, which is the key rate-limiting step in phenylpropanoid biosynthesis (Zhang et al., 2022b). Phenylpropanoids are further catalyzed into flavonoids and anthocyanins, which function as antioxidants (Yuan et al., 2022). There was one shikimate kinase (LOC10019326) and three PALs (PAL13, LOC100285115, and LOC103653804) in DODGs. Moreover, anthocyanidin 3-O-glucosyltransferase (LOC100282556) catalyzes the formation of the first stable anthocyanin (Wang et al., 2022a). Thus, antioxidant (e.g., flavonoid and anthocyanin) production chain centered on L-phenylalanine metabolism was enhanced under water-deficit conditions. Lignin is another downstream product of l-phenylalanine metabolism and is important for cell wall biogenesis (Zhan et al., 2022; Pesquet et al., 2024). Furthermore, cellulose synthase CESA8, Brittle stalk-2-like proteins BK2L3/7, tubulin Tua5/Tub3/TUBB5, and microtubule-associated proteins (MAP65/70) in DODGs may participate in the remodeling of cell wall properties under water scarcity (Julius et al., 2021; Wang et al., 2022c; Sasaki et al., 2023). Increased plant tissue temperature and cellular viscosity under drought stress cause protein denaturation, which is detrimental to plant physiological and biochemical reactions. HSPs and LEAs are two important types of protective proteins in response to diverse abiotic stresses (Wang et al., 2023; Ristic et al., 1991). We identified nine HSPs (e.g., LOC100286044, LOC103653979, and LOC100282976) and three LEAs (DHN1, LOC100279027, and LOC103643070) in the DODGs. DODGs are also involved in sucrose and hexosamine biosynthesis, glycolysis, lysine catabolism, and amino sugar metabolism. Hence, verifying the functions of these DODGs in drought resistance is urgently required.", "Thinking\u6a21\u5f0f": "high", "generation_type": "\u63a8\u7406\u578b"}','2026-03-23 15:33:28.840375','2026-03-23 15:33:28.840385');
INSERT INTO qa_pairs VALUES(34,3,'在杂交作物中，干旱和复水处理可以鉴定出一批具有保守响应模式的应激反应基因，其中一部分在杂交种中的表达偏离双亲加性水平，表现为超显性或劣显性等非加性表达类型，同时发现这类非加性基因在干旱胁迫下对杂种优势具有保守贡献，并且其中在干旱条件下显著差异表达的基因被归类为干旱反应差异表达基因（DODGs）。从这些信息出发，如何推理出DODGs不仅是干旱条件下杂种优势的重要分子基础，而且适合作为改良作物抗旱性的分子育种靶标？','在干旱与复水处理中首先能够筛选出一批在不同材料间具有相似、稳定响应模式的应激反应基因，说明这些基因参与了普遍且保守的干旱应答过程。在这批基因中，有一部分在杂交种中的表达不再简单等于或介于双亲，而是呈现超显性或劣显性等非加性模式，这类基因被证明对杂交种在干旱条件下表现出的杂种优势有稳定贡献，意味着它们的表达状态与杂交种在胁迫下的优异表现存在因果或紧密功能关联。进一步地，从这些非加性基因中筛选出在干旱条件下显著差异表达的那一部分，即DODGs，则可以锁定那些既参与干旱应答，又通过非加性表达增强或削弱性状表现的关键节点。由于能保守地贡献于干旱条件下杂种优势的基因往往直接参与或调控抗旱相关生理和分子过程，对其进行定向改良有望增强作物在干旱胁迫下的适应性和产量稳定性。因此，DODGs既构成了干旱杂种优势形成的重要分子基础，又因其功能和表达特点成为有潜力的抗旱分子育种靶标。','{"species": "Zea mays", "paper_id": "PMC11494150", "reasoning_steps": ["Step 1: \u5728\u5e72\u65f1\u4e0e\u590d\u6c34\u6761\u4ef6\u4e0b\uff0c\u53ef\u4ee5\u9274\u5b9a\u51fa\u4e00\u6279\u5177\u6709\u4fdd\u5b88\u54cd\u5e94\u6a21\u5f0f\u7684\u5e94\u6fc0\u53cd\u5e94\u57fa\u56e0\u3002", "Step 2: \u8fd9\u4e9b\u5e94\u6fc0\u53cd\u5e94\u57fa\u56e0\u4e2d\u5b58\u5728\u975e\u52a0\u6027\u8868\u8fbe\u7c7b\u578b\u7684\u57fa\u56e0\uff0c\u5305\u62ec\u8d85\u663e\u6027\u548c\u52a3\u663e\u6027\u57fa\u56e0\u3002", "Step 3: \u975e\u52a0\u6027\u57fa\u56e0\uff0c\u5c24\u5176\u662f\u8d85\u663e\u6027\u548c\u52a3\u663e\u6027\u57fa\u56e0\uff0c\u88ab\u53d1\u73b0\u5bf9\u6742\u4ea4\u79cd\u5728\u5e72\u65f1\u80c1\u8feb\u4e0b\u7684\u6742\u79cd\u4f18\u52bf\u5177\u6709\u4fdd\u5b88\u8d21\u732e\u4f5c\u7528\u3002", "Step 4: \u8fd9\u7c7b\u5728\u5e72\u65f1\u6761\u4ef6\u4e0b\u8868\u73b0\u51fa\u5dee\u5f02\u8868\u8fbe\u7684\u975e\u52a0\u6027\u57fa\u56e0\u88ab\u5b9a\u4e49\u4e3a\u5e72\u65f1\u53cd\u5e94\u5dee\u5f02\u8868\u8fbe\u57fa\u56e0\uff08DODGs\uff09\u3002", "Step 5: \u80fd\u591f\u4fdd\u5b88\u5730\u8d21\u732e\u4e8e\u5e72\u65f1\u6761\u4ef6\u4e0b\u6742\u79cd\u4f18\u52bf\u7684\u57fa\u56e0\uff0c\u5f80\u5f80\u76f4\u63a5\u53c2\u4e0e\u6216\u8c03\u63a7\u6297\u65f1\u76f8\u5173\u7684\u5206\u5b50\u673a\u5236\u3002", "Step 6: \u56e0\u6b64\uff0c\u5c06\u8fd9\u4e9bDODGs\u4f5c\u4e3a\u5206\u5b50\u80b2\u79cd\u4e2d\u7684\u6539\u826f\u9776\u6807\uff0c\u6709\u671b\u63d0\u5347\u4f5c\u7269\u7684\u6297\u65f1\u6027\u5e76\u95f4\u63a5\u4fc3\u8fdb\u4ea7\u91cf\u7a33\u5b9a\u3002", "Step 7: \u7531\u6b64\u53ef\u4ee5\u5f97\u51fa\uff0c\u975e\u52a0\u6027\u57fa\u56e0\u5c24\u5176\u662fDODGs\u65e2\u662f\u5e72\u65f1\u6742\u79cd\u4f18\u52bf\u5f62\u6210\u7684\u91cd\u8981\u5206\u5b50\u57fa\u7840\uff0c\u53c8\u662f\u9002\u5408\u4f5c\u4e3a\u6297\u65f1\u6539\u826f\u76ee\u6807\u7684\u57fa\u56e0\u96c6\u5408\u3002"], "question_cot": "", "final_conclusion": "\u5728\u5e72\u65f1\u80c1\u8feb\u6761\u4ef6\u4e0b\uff0c\u975e\u52a0\u6027\u8868\u8fbe\u57fa\u56e0\uff08\u7279\u522b\u662f\u8d85\u663e\u6027\u548c\u52a3\u663e\u6027\u57fa\u56e0\uff09\u662f\u6742\u4ea4\u4f18\u52bf\u7684\u91cd\u8981\u4fdd\u5b88\u8d21\u732e\u8005\uff0c\u56e0\u6b64\u5176\u4e2d\u7684\u5e72\u65f1\u53cd\u5e94\u5dee\u5f02\u8868\u8fbe\u57fa\u56e0\uff08DODGs\uff09\u53ef\u4ee5\u4f5c\u4e3a\u6539\u826f\u4f5c\u7269\u6297\u65f1\u6027\u7684\u6f5c\u5728\u5206\u5b50\u80b2\u79cd\u9776\u6807\u3002", "difficulty": "medium", "tags": ["mechanism", "application", "result", "concept", "molecular_breeding", "drought_tolerance", "heterosis"], "created_at": "2025-12-16T21:20:37", "token_est_question": 1, "token_est_answer": 1, "section": "Conclusion", "context": "=== Conclusions ===\nUnderstanding the inheritance and molecular mechanisms of drought-resistance heterosis will advance the improvement and breeding of maize plants with high drought resistance and yield. Our findings identified conservative drought and re-watering stress response genes, indicating that nonadditive genes, especially overdominant and underdominant genes, conservatively contributed to hybrid heterosis under drought stress and suggested that DODGs are potential targets for drought resistance improvement. These results revealed the conserved molecular mechanisms of drought resistance and heterosis of hybrids, which were helpful for drought resistance improvement.", "Thinking\u6a21\u5f0f": "high", "generation_type": "\u63a8\u7406\u578b"}','2026-03-23 15:33:28.926738','2026-03-23 15:33:28.926744');
INSERT INTO qa_pairs VALUES(35,3,'在玉米籽粒的非光合胚组织中，黑暗处理会使胚中叶绿素含量大幅降低，同时生育酚含量也明显下降；利用CRISPR/Cas9构建的por1 por2双敲除材料中，同样观察到胚中叶绿素和生育酚同时显著减少。结合生育酚生物合成需要植醇前体且POR1/POR2属于叶绿素生物合成关键酶这一事实，应如何推理：胚中生育酚所需植醇主要来源于哪条代谢途径，以及在玉米籽粒维生素E生物强化中为何POR1和POR2是优先的改良靶点？','玉米非光合籽粒胚组织中的生育酚，其植醇前体主要来自叶绿素生物合成途径，而不是来自其他独立的植醇供应途径。无论是通过黑暗处理抑制POR酶活，还是通过por1 por2双敲除在遗传上阻断POR功能，只要叶绿素合成被严重抑制，胚中叶绿素和生育酚就同步大幅下降，这表明生育酚合成几乎完全依赖由POR介导的叶绿素途径提供植醇。因此，在玉米籽粒维生素E生物强化中，优先提升POR1和POR2等位基因的表达量或酶活性，可以增强叶绿素途径向生育酚通路的植醇供应，从而有效提高籽粒中生育酚水平。','{"species": "Zea mays", "paper_id": "PMC11905637", "reasoning_steps": ["Step 1: \u751f\u80b2\u915a\u662f\u4e00\u7c7b\u8102\u6eb6\u6027\u5316\u5408\u7269\uff0c\u5177\u6709\u7ef4\u751f\u7d20E\u6d3b\u6027\uff0c\u5176\u751f\u7269\u5408\u6210\u9014\u5f84\u5728\u690d\u7269\u754c\u9ad8\u5ea6\u4fdd\u5b88\uff0c\u5e76\u9700\u8981\u524d\u4f53\u5206\u5b50\u690d\u9187\u3002", "Step 2: POR1\u548cPOR2\u662f\u539f\u53f6\u7eff\u7d20ide\u8fd8\u539f\u9176\uff0c\u5c5e\u4e8e\u53f6\u7eff\u7d20\u751f\u7269\u5408\u6210\u9014\u5f84\u4e2d\u7684\u5173\u952e\u57fa\u56e0\uff0c\u4e14\u5176\u9176\u6d3b\u6027\u9700\u8981\u5149\u7167\u624d\u80fd\u53d1\u6325\u3002", "Step 3: \u5728\u53d1\u80b2\u4e2d\u7684\u7389\u7c73\u7c7d\u7c92\u4e0a\u65bd\u52a0\u9ed1\u6697\u5904\u7406\u4f1a\u663e\u8457\u6291\u5236POR\u9176\u6d3b\uff0c\u5bfc\u81f4\u80da\u7ec4\u7ec7\u4e2d\u53f6\u7eff\u7d20a\u542b\u91cf\u964d\u4f4e92\u201399%\uff0c\u540c\u65f6\u751f\u80b2\u915a\u542b\u91cf\u4e5f\u76f8\u5e94\u964d\u4f4e87\u201390%\uff0c\u8868\u660e\u53f6\u7eff\u7d20\u5408\u6210\u53d7\u963b\u4f1a\u5927\u5e45\u964d\u4f4e\u751f\u80b2\u915a\u6c34\u5e73\u3002", "Step 4: \u901a\u8fc7CRISPR/Cas9\u6784\u5efapor1 por2\u53cc\u6572\u9664\u7a81\u53d8\u4f53\uff0c\u540c\u6837\u5728\u80da\u4e2d\u89c2\u5bdf\u5230\u53f6\u7eff\u7d20a\u964d\u4f4e98\u2013100%\uff0c\u751f\u80b2\u915a\u964d\u4f4e76\u201383%\uff0c\u5373\u5728\u9057\u4f20\u4e0a\u963b\u65adPOR\u529f\u80fd\u4e5f\u4f1a\u540c\u6b65\u5927\u5e45\u964d\u4f4e\u53f6\u7eff\u7d20\u548c\u751f\u80b2\u915a\u542b\u91cf\u3002", "Step 5: \u751f\u7406\uff08\u9ed1\u6697\uff09\u548c\u9057\u4f20\uff08por1 por2\u53cc\u7a81\u53d8\uff09\u4e24\u79cd\u5b8c\u5168\u4e0d\u540c\u5e72\u9884\u65b9\u5f0f\u90fd\u6307\u5411\u540c\u4e00\u7ed3\u679c\uff1a\u53ea\u8981\u53f6\u7eff\u7d20\u751f\u7269\u5408\u6210\u88ab\u4e25\u91cd\u6291\u5236\uff0c\u80da\u4e2d\u7684\u751f\u80b2\u915a\u5c31\u4f1a\u5927\u5e45\u4e0b\u964d\uff0c\u8bf4\u660e\u751f\u80b2\u915a\u5408\u6210\u4f9d\u8d56\u53f6\u7eff\u7d20\u9014\u5f84\u63d0\u4f9b\u7684\u524d\u4f53\u690d\u9187\u3002", "Step 6: \u7531\u6b64\u63a8\u65ad\uff0c\u5728\u7389\u7c73\u975e\u5149\u5408\u7c7d\u7c92\u80da\u7ec4\u7ec7\u4e2d\uff0c\u751f\u80b2\u915a\u5408\u6210\u7684\u690d\u9187\u6765\u6e90\u51e0\u4e4e\u5b8c\u5168\u6765\u81ea\u53f6\u7eff\u7d20\u751f\u7269\u5408\u6210\uff0c\u800cPOR1\u548cPOR2\u662f\u8be5\u9014\u5f84\u4e2d\u63a7\u5236\u690d\u9187\u4f9b\u5e94\u7684\u5173\u952e\u8282\u70b9\u3002", "Step 7: \u56e0\u6b64\uff0c\u901a\u8fc7\u6539\u826f\u6216\u589e\u5f3aPOR1\u548cPOR2\u7b49\u7b49\u4f4d\u57fa\u56e0\u548c\u6d3b\u6027\uff0c\u6709\u671b\u63d0\u9ad8\u975e\u5149\u5408\u7389\u7c73\u7c7d\u7c92\u4e2d\u7684\u7ef4\u751f\u7d20E\uff08\u751f\u80b2\u915a\uff09\u542b\u91cf\uff0c\u5b9e\u73b0\u751f\u7269\u5f3a\u5316\u3002"], "question_cot": "", "final_conclusion": "\u7389\u7c73\u7c7d\u7c92\u80da\u7ec4\u7ec7\u4e2d\u7ef4\u751f\u7d20E\u7c7b\u751f\u80b2\u915a\u7684\u5408\u6210\u51e0\u4e4e\u5b8c\u5168\u4f9d\u8d56\u4e8e\u7531\u53f6\u7eff\u7d20\u751f\u7269\u5408\u6210\u9014\u5f84\u63d0\u4f9b\u7684\u690d\u9187\uff0c\u800c\u8fd9\u4e00\u9014\u5f84\u4e2d\u7684POR1\u548cPOR2\u6d3b\u6027\u5bf9\u63d0\u9ad8\u7c7d\u7c92\u751f\u80b2\u915a\u542b\u91cf\u5177\u6709\u5173\u952e\u4f5c\u7528\u3002", "difficulty": "medium", "tags": ["mechanism", "result", "application", "metabolism", "biofortification"], "created_at": "2025-12-16T21:14:54", "token_est_question": 2, "token_est_answer": 2, "section": "Abstract", "context": "=== Background ===\nTocopherols are a class of lipid-soluble compounds that have multiple functional roles in plants and exhibit vitamin E activity, an essential nutrient for human and animal health. The tocopherol biosynthetic pathway is conserved across the plant kingdom, but source of the key tocopherol pathway precursor, phytol, is unclear. Two protochlorophyllide reductases (_POR1_ and _POR2_) were previously identified as loci controlling the natural variation of total tocopherols in maize grain, a non-photosynthetic tissue. _POR1_ and _POR2_ are key genes in chlorophyll biosynthesis yet the contribution of the chlorophyll biosynthetic pathway to tocopherol biosynthesis is still not understood.\n=== Results ===\nWe took two approaches to alter the activity of these two _POR_ genes within kernel tissue, physiological treatments and CRISPR/Cas9-mediated knockouts, to determine the role of chlorophyll biosynthesis for tocopherol content. Since light is required for POR enzymatic activity, we imposed a dark treatment on developing kernels, which reduced chlorophyll _a_ and tocopherols levels in embryo tissue by 92\u201399% and 87\u201390%, respectively, compared to the light treatment. In CRISPR/Cas9-mediated knockouts, the levels of chlorophyll _a_ and tocopherols in embryos of the _por1 por2_ double homozygous mutant were reduced by 98\u2013100% and 76\u201383%, respectively, compared to WT.\n=== Conclusion ===\nThese findings demonstrate that tocopherol synthesis in maize grain depends almost entirely on phytol derived from chlorophyll biosynthesis within the embryo. _POR1_ and _POR2_ activity play crucial roles in chlorophyll biosynthesis, underscoring the importance of _POR_ alleles and their activity in the biofortification of vitamin E levels in non-photosynthetic grain of maize.\n=== Supplementary Information ===\nThe online version contains supplementary material available at 10.1186/s12870-025-06267-6.\n**Keywords:** Biofortification, Chlorophyll, Maize, CRISPR/Cas9, Tocopherol, Vitamin E", "Thinking\u6a21\u5f0f": "high", "generation_type": "\u63a8\u7406\u578b"}','2026-03-23 15:33:29.014415','2026-03-23 15:33:29.014425');
INSERT INTO qa_pairs VALUES(36,3,'在玉米中，生育酚合成需要由游离 phytol 磷酸化生成的 phytyl 二磷酸（PDP）作为关键前体；在某些模式植物中，破坏叶绿素降解相关酶（如 PPH 或叶绿素回收相关的 CLD1）时，叶片和种子中的生育酚含量变化不大，而失去叶绿素生物合成过程中定位于质体的水解酶 VTE7 功能会显著降低种子生育酚并导致带部分还原侧链的叶绿素合成中间体积累；在玉米材料中，vte7 等位变异以及 POR1/POR2、LIL3、GGR、CHS 等叶绿素生物合成相关基因的突变往往同时造成叶绿素和籽粒生育酚显著下降，且玉米籽粒本身是非绿色、非光合组织。研究者应如何从这些现象出发，推理出玉米籽粒中生育酚合成所需 phytol 主要来源于叶绿素生物合成循环而不是大规模叶绿素降解？','生育酚合成直接依赖 PDP，而 PDP 又来源于游离 phytol，因此首先需要确定 phytol 的主要供给途径。若大规模叶绿素降解是主要来源，那么破坏典型叶绿素降解或回收通路应显著改变叶和种子中的生育酚水平，但相关突变体中生育酚含量基本保持稳定，说明整体叶绿素降解并非关键供体。相反，质体水解酶 VTE7 缺失会同时引起种子生育酚显著下降并积累特定叶绿素生物合成中间体，表明 VTE7 直接从叶绿素合成中间体上水解释放 phytol 供 PDP 和生育酚合成。在玉米中，vte7 等位变异以及 POR1/POR2、LIL3、GGR、CHS 等叶绿素生物合成酶的缺失或活性降低，会同步削弱叶绿素和籽粒生育酚的积累，且控制籽粒生育酚含量的主效位点由光依赖的原叶绿素ide还原酶 POR1/POR2 决定，这表明籽粒生育酚水平与叶绿素生物合成通量高度正相关。考虑到玉米籽粒为非绿色、几乎不存在可供大规模降解的叶绿素贮库，但仍然对这些合成酶和 VTE7 的功能高度敏感，只能解释为叶绿素生物合成过程中存在一个不断生成并水解释放 phytol 的“合成循环”，该循环提供了大部分用于 PDP 和生育酚合成的 phytol，而不是依靠终末叶绿素的大量降解。','{"species": "Zea mays", "paper_id": "PMC11905637", "reasoning_steps": ["Step 1: Tocopherol biosynthesis requires the precursor phytyl diphosphate (PDP), which in Arabidopsis is produced by phosphorylation of free phytol; PDP availability is a primary determinant of tocopherol levels in a tissue.", "Step 2: Classical chlorophyll degradation pathways (via PPH or the chlorophyll-salvage enzyme CLD1) do not significantly affect tocopherol levels when disrupted, indicating that bulk chlorophyll breakdown is not the main phytol source for tocopherol synthesis.", "Step 3: Genetic evidence in Arabidopsis shows that null alleles of the plastid-localized alpha/beta hydrolase VTE7 cause a strong reduction in seed tocopherol content and concomitant accumulation of specific chlorophyll biosynthetic intermediates with partially reduced tails, implying that phytol is hydrolyzed from chlorophyll biosynthetic intermediates.", "Step 4: In maize, a leaky vte7 allele reduces both kernel and leaf tocopherol levels, and multiple chlorophyll biosynthetic components (POR1, POR2, LIL3, GGR, CHS) are genetically or functionally associated with strong reductions in both chlorophylls and tocopherols when disrupted, linking chlorophyll biosynthesis to tocopherol accumulation in both photosynthetic and non-photosynthetic tissues.", "Step 5: The largest effect quantitative trait locus for total tocopherols in maize grain is underlain by two protochlorophyllide reductases (POR1 and POR2), key chlorophyll biosynthetic genes whose activity is light-dependent, further tying kernel tocopherol levels to chlorophyll biosynthesis rather than chlorophyll degradation.", "Step 6: Because tocopherol levels scale with the functioning of chlorophyll biosynthetic enzymes and with a hydrolase (VTE7) that acts on chlorophyll biosynthetic intermediates, and are not altered by disabling bulk chlorophyll degradation pathways, phytol used for tocopherol synthesis must primarily come from a chlorophyll-based biosynthetic cycle that repeatedly removes phytol during chlorophyll a synthesis.", "Step 7: Therefore, in maize kernels, which are non-green and non-photosynthetic, tocopherol synthesis is nonetheless strongly dependent on chlorophyll biosynthesis as the major provider of phytol via a biosynthetic cycle, rather than on the degradation of bulk chlorophyll."], "question_cot": "", "final_conclusion": "In maize, tocopherol synthesis in kernels depends on chlorophyll biosynthesis, which provides phytol for tocopherol production via a chlorophyll-based biosynthetic cycle rather than bulk chlorophyll degradation.", "difficulty": "hard", "tags": ["concept", "mechanism", "result"], "created_at": "2025-12-16T21:16:31", "token_est_question": 19, "token_est_answer": 35, "section": "Background", "context": "=== Background ===\nSuboptimal dietary intake of vitamin E, an essential micronutrient, can be prevalent in countries where maize grain significantly contributes to the caloric requirements of human populations [8, 15]. Increased dietary vitamin E intake has been associated with reduced mortality from cardiovascular disease and cancer [22, 44]. Improved crop nutritional quality through agronomic, conventional breeding, or bioengineering approaches, collectively known as biofortification, represents a potential avenue to enhance dietary vitamin E by increasing tocochromanols, a group of biosynthetically related compounds with varying levels of vitamin E activity [37]. Biofortification efforts can be maximized through a comprehensive understanding of the genetic and molecular basis underlying tocochromanol synthesis and accumulation in maize grain. \nTocochromanols, lipid-soluble compounds synthesized in plants, algae, and some cyanobacteria, protect lipids by quenching both reactive oxygen species and lipid peroxyl radicals [4, 30, 42]. They are structurally separated into two classes: tocotrienols and tocopherols. Each class has four types: \u03b1, \u03b2, \u03b4, and \u03b3 (Fig. 1). Tocotrienols, predominantly produced in the endosperm in monocots and a few dicots [16, 21, 57], are associated with numerous health benefits due to their potent antioxidant properties [45]. Tocopherols, primarily produced in the embryo [16], generally have higher vitamin E activity, with \u03b1-tocopherol providing the highest vitamin E activity on a molar basis [26].\n=== Fig. 1. ===\n \nOpen in a new tab\nTocochromanol biosynthesis pathway. The two precursor pathways and the carotenoid pathway are represented as black boxes. The six quantified tocochromanols and chlorophyll a are indicated in black nonitalicized text. The names of key a priori genes are bolded and italicized at the pathway step(s) catalyzed by their encoded enzymes, with the _POR_ genes in red text. The key tocopherol precursor phytol is bolded in red. Compound abbreviations: DMGGBQ, 2,3-dimethyl-5-geranylgeranyl-1,4-benzoquinol; DMPBQ, 2,3-dimethyl-6-phytyl-1,4-benzoquinol; GGDP, geranylgeranyl diphosphate; GG-Chlorophyll, geranylgeranyl-chlorophyll a; HGA, homogentisic acid; HPP, p-hydroxyphenylpyruvate; MGGBQ, 2-methyl-6-geranylgeranyl-1,4-benzoquinol; MPBQ, 2-methyl-6-phytyl-1,4-benzoquinol; Phytyl-DP, phytyl diphosphate; Phytyl-P, phytyl monophosphate. Gene abbreviations: chlorophyll synthase (_CHS_); chlorophyll dephytylase1 (_CLD1_); farnesol kinase (_FOLK_); geranylgeranyl reductase (_GGR_); homogentisate geranylgeranyl transferase (_HGGT_); light harvesting protein-like 3 (_LIL3_); protochlorophyllide reductase (_POR1_ , _POR2_ , and _POR3_); tocopherol cyclase (_VTE1_); homogentisate phytyltransferase (_VTE2_); MPBQ/MGGBQ methyltransferase (_VTE3_); \u03b3-tocopherol methyltransferase (_VTE4_); phytol kinase (_VTE5_); phytol phosphate kinase (_VTE6_); \u03b1-/\u03b2-hydrolase (_VTE7_). Figure updated from [55]\nThe core tocopherol biosynthesis pathway has been well-characterized in _Arabidopsis thaliana_ [34] and is conserved across the plant kingdom (Fig. 1). The committed step of tocopherol synthesis is catalyzed by VTE2 (homogentisate phytyltransferase), which condenses homogentisic acid (HGA) and phytyl diphosphate (PDP) to produce 2-methyl-6-phytyl-1,4-benzoquinol (MPBQ), the immediate precursor of the four tocopherol types (Fig. 1). This condensation reaction and the availability of its two substrates, HGA and PDP, primarily determine the level of tocopherol produced within a tissue [4, 37]. HGA is derived from the aromatic amino acid pathway, which is tightly controlled by feedback inhibition [50]. In Arabidopsis, the PDP required for tocopherol synthesis is derived from a two-step phosphorylation of free phytol. Phytyl-P synthesis depends on two kinases, VTE5 and FOLK _,_ which when knocked out results in tocopherol deficiency in 4-week-old Arabidopsis seed and leaf tissues [40]. Phytyl-P is subsequently phosphorylated to PDP by VTE6. _vte6_ null alleles result in non-detectable levels of tocopherol [51]. While the synthesis of PDP from phytol is well understood, the mechanism of phytol production for tocopherol synthesis remains unclear.\nIn photosynthetic tissues such as Arabidopsis leaf and seed, the phytol supply for tocopherol synthesis was thought to depend on chlorophyll degradation, during which chlorophyll _a_ is converted to pheophytin _a_ and then hydrolyzed by pheophytin pheophorbide hydrolase (PPH) to produce pheophorbide and phytol. However, in an Arabidopsis _pph_ null mutant, tocopherol levels did not differ from the wild type (WT) in leaf and seed tissue [60]. An alternative pathway for phytol could be the chlorophyll-salvage cycle, which releases both chlorophyllide _a_ and phytol through the dephytylation of chlorophyll _a_. Yet when a key gene in the chlorophyll-salvage cycle, _CHLOROPHYLL DEPHYTYLASE1_ (_CLD1_), was silenced there was no significant difference in tocopherol levels compared to the WT [27].\nA recent study of natural variation in total seed tocopherols in Arabidopsis identified a strong dependence of tocopherol content on chlorophyll biosynthesis, with phytol being hydrolyzed from chlorophyll biosynthetic intermediates rather than from the degradation of the bulk chlorophyll pool [1]. Specifically, Albert et al. [1] found that null alleles of a seed-specific, plastid-localized alpha/beta hydrolase (_VTE7_) decreased seed tocopherol content by 55% with concomitant increases in specific chlorophyll biosynthetic intermediates with partially reduced tails. There was no impact on tocopherol content of Arabidopsis leaves, but in maize a leaky _vte7_ Mu-allele decreased kernel and leaf tocopherol content by 38% and 50%, respectively, indicating differences in phytol provision for tocopherol synthesis between monocot and dicot leaves [1].\nDespite maize grain being a non-green, non-photosynthetic tissue, tocopherol synthesis in grain is also strongly dependent on chlorophyll synthesis. Two protochlorophyllide reductases (_POR1_ and _POR2_), key chlorophyll biosynthetic genes, underlie the largest effect QTL for total tocopherols in maize grain [5]. Their involvement is supported by near-isogenic and single knockout lines in maize, further highlighting the role of chlorophyll in tocopherol synthesis in non-photosynthetic tissue [29, 56]. In addition to its expression being regulated by light, POR also requires light as a co-substrate to catalyze the reduction of protochlorophyllide to chlorophyllide [14, 47]. POR is part of a complex that includes geranylgeranyl reductase (GGR), chlorophyll synthase (CHS), and a non-enzymatic protein, light harvesting protein-like 3 (LIL3), which catalyzes the final steps of chlorophyll _a_ synthesis [19, 49]. The dependence of tocopherol synthesis on chlorophyll synthesis is further supported by knockouts of LIL3, GGR, and CHS, which result in severely reduced levels of chlorophylls and tocopherols in leaf tissue [18, 19, 48, 49, 58]. Given the large molar ratio of tocopherol to chlorophyll in developing maize embryos, Diepenbrock et al. [5] hypothesized that instead of chlorophyll degradation, a chlorophyll-based biosynthetic cycle provides phytol for tocopherol synthesis through the repeated removal of phytol during chlorophyll _a_ synthesis. The identification of _VTE7_ and its impact on tocopherol abundance and chlorophyll biosynthetic intermediates in both leaf and grain tissue in maize support this model [1].\nIn this study, we took two complementary experimental approaches to assess the role(s) of POR1 and POR2 and therefore, the contribution of chlorophyll biosynthesis, in tocopherol biosynthesis within maize kernels. In physiological experiments, we imposed light and dark treatments on developing kernels to alter the in vivo light-dependent activities of all PORs [17] and measured the impacts on tocochromanols, chlorophyll _a,_ and the transcriptome within embryo tissue. In CRISPR/Cas9 knockout experiments, we generated null alleles of _POR1_ and _POR2_ and evaluated the metabolomic consequences in embryos of the single and double mutants. From these experiments, we demonstrated the central role of chlorophyll biosynthesis in tocopherol synthesis within the maize embryo.", "Thinking\u6a21\u5f0f": "high", "generation_type": "\u63a8\u7406\u578b"}','2026-03-23 15:33:29.114246','2026-03-23 15:33:29.114257');
INSERT INTO qa_pairs VALUES(37,3,'在玉米籽粒中，发育中的胚胎在遮光处理时叶绿素和生育酚含量都会明显降低，而具有 POR1 和 POR2 双敲除突变的胚胎几乎不再积累叶绿素且生育酚含量同步下降，但类胡萝卜素水平与野生型相近；不同基因型之间的胚乳叶绿素几乎检测不到且整体代谢物水平差异很小，而在同一果穗上剖取籽粒时可以观察到生育酚含量随着胚胎基因型变化而改变。应如何据此判断：哪一种组织的基因型在决定籽粒生育酚含量方面起主导作用，以及生育酚合成主要依赖哪一类由哪种酶介导的代谢途径？','玉米籽粒的生育酚水平主要由胚胎的基因型控制，而不是由代谢相对稳定、几乎不合成叶绿素的胚乳决定；胚胎中通过 POR1/POR2 介导的叶绿素生物合成途径为生育酚合成提供关键前提，因此籽粒生育酚积累依赖胚胎叶绿素的合成。','{"species": "Zea mays", "paper_id": "PMC11905637", "reasoning_steps": ["Step 1: POR \u9176\u6d3b\u6027\u9700\u8981\u5149\u4f5c\u4e3a\u5171\u5e95\u7269\uff0c\u4e14\u5728\u53d1\u80b2\u4e2d\u7684\u7c7d\u7c92\u4e0a\u8fdb\u884c\u906e\u5149\uff08\u94dd\u7b94\u5305\u88f9\uff09\u5904\u7406\uff0c\u4f1a\u5927\u5e45\u964d\u4f4e\u53f6\u7eff\u7d20 a \u548c\u751f\u80b2\u915a\u542b\u91cf\uff1b\u5728 24 DAP \u80da\u80ce\u4e2d\uff0c\u9ed1\u6697\u5904\u7406\u76f8\u5bf9\u4e8e\u5149\u5904\u7406\u4f7f\u53f6\u7eff\u7d20 a \u51cf\u5c11\u7ea6 96%\uff0c\u751f\u80b2\u915a\u51cf\u5c11\u7ea6 88%\u3002", "Step 2: \u5728 24 DAP \u80da\u80ce\u6837\u672c\u4e2d\uff0c\u53f6\u7eff\u7d20 a \u542b\u91cf\u4e0e\u603b\u751f\u80b2\u915a\u542b\u91cf\u4e4b\u95f4\u5b58\u5728\u663e\u8457\u6b63\u76f8\u5173\uff08r = 0.6\uff09\uff0c\u8bf4\u660e\u53f6\u7eff\u7d20\u6c34\u5e73\u8d8a\u9ad8\u65f6\uff0c\u603b\u751f\u80b2\u915a\u6c34\u5e73\u5f80\u5f80\u4e5f\u8d8a\u9ad8\u3002", "Step 3: POR1 \u548c POR2 \u7684\u53cc\u91cd\u6572\u9664\u7a81\u53d8\u4f53\u5728\u80da\u80ce\u4e2d\u51e0\u4e4e\u5b8c\u5168\u7f3a\u5931\u53f6\u7eff\u7d20 a\uff08>98% \u964d\u4f4e\uff09\uff0c\u540c\u65f6\u5176\u603b\u751f\u80b2\u915a\u6c34\u5e73\u4e5f\u663e\u8457\u964d\u4f4e\uff0c\u800c\u7c7b\u80e1\u841d\u535c\u7d20\u542b\u91cf\u4e0e\u91ce\u751f\u578b\u76f8\u5f53\uff0c\u8868\u660e Por \u9014\u5f84\u4e3b\u8981\u5f71\u54cd\u53f6\u7eff\u7d20\u53ca\u4e0e\u5176\u76f8\u5173\u7684\u751f\u80b2\u915a\u5408\u6210\u800c\u975e\u5168\u90e8\u8272\u7d20\u4ee3\u8c22\u3002", "Step 4: \u4e0e\u80da\u80ce\u7ec4\u7ec7\u76f8\u6bd4\uff0c\u80da\u4e73\u7ec4\u7ec7\u7684\u4ee3\u8c22\u7269\u6c34\u5e73\u5728\u4e0d\u540c\u57fa\u56e0\u578b\u95f4\u57fa\u672c\u4e00\u81f4\uff0c\u4e14\u80da\u4e73\u4e2d\u53f6\u7eff\u7d20 a \u542b\u91cf\u4f4e\u4e8e\u68c0\u6d4b\u9650\uff0c\u81f3\u5c11\u6bd4\u80da\u80ce\u7ec4\u7ec7\u4f4e 100 \u500d\uff0c\u8bf4\u660e\u80da\u4e73\u51e0\u4e4e\u4e0d\u8fdb\u884c\u53f6\u7eff\u7d20\u5408\u6210\uff0c\u4e5f\u96be\u4ee5\u901a\u8fc7\u53f6\u7eff\u7d20\u9014\u5f84\u9a71\u52a8\u672c\u8eab\u7684\u751f\u80b2\u915a\u5408\u6210\u3002", "Step 5: \u901a\u8fc7\u5728\u540c\u4e00\u679c\u7a57\u4e0a\u5256\u89e3\u6765\u81ea\u4e0d\u540c\u80da\u80ce\u57fa\u56e0\u578b\u7684\u80da\u4e73\u548c\u80da\u80ce\uff0c\u89c2\u5bdf\u5230\u7c7d\u7c92\u4e2d\u7684\u751f\u80b2\u915a\u6c34\u5e73\u968f\u80da\u80ce\u57fa\u56e0\u578b\u53d8\u5316\uff0c\u800c\u80da\u4e73\u4ee3\u8c22\u6c34\u5e73\u5728\u8fd9\u4e9b\u57fa\u56e0\u578b\u95f4\u4fdd\u6301\u7a33\u5b9a\uff0c\u8bf4\u660e\u7c7d\u7c92\u603b\u751f\u80b2\u915a\u542b\u91cf\u4e3b\u8981\u53d7\u5230\u80da\u80ce\u57fa\u56e0\u578b\u63a7\u5236\uff0c\u800c\u4e0d\u662f\u80da\u4e73\u672c\u8eab\u4ee3\u8c22\u5dee\u5f02\u3002", "Step 6: \u7efc\u5408\u9ed1\u6697\u5904\u7406\u524a\u5f31\u53f6\u7eff\u7d20\u4e0e\u751f\u80b2\u915a\u3001POR1/POR2 \u529f\u80fd\u7f3a\u5931\u5bfc\u81f4\u80da\u80ce\u53f6\u7eff\u7d20\u548c\u751f\u80b2\u915a\u540c\u65f6\u4e0b\u964d\u3001\u80da\u4e73\u51e0\u4e4e\u4e0d\u5408\u6210\u53f6\u7eff\u7d20\u4e14\u5728\u4e0d\u540c\u57fa\u56e0\u578b\u95f4\u76f8\u5bf9\u7a33\u5b9a\u7b49\u73b0\u8c61\uff0c\u53ef\u4ee5\u63a8\u65ad\u80da\u80ce\u4e2d\u901a\u8fc7 POR \u4ecb\u5bfc\u7684\u53f6\u7eff\u7d20\u751f\u7269\u5408\u6210\u662f\u751f\u80b2\u915a\u751f\u7269\u5408\u6210\u7684\u5173\u952e\u524d\u63d0\u3002", "Step 7: \u56e0\u6b64\uff0c\u53ef\u4ee5\u5f97\u51fa\u7ed3\u8bba\uff1a\u5728\u7389\u7c73\u7c7d\u7c92\u4e2d\uff0c\u80da\u80ce\u7684\u57fa\u56e0\u578b\u901a\u8fc7\u8c03\u63a7\u53f6\u7eff\u7d20\uff08\u4f9d\u8d56\u5149\u548c POR1/POR2\uff09\u7684\u5408\u6210\u6765\u51b3\u5b9a\u7c7d\u7c92\u7684\u751f\u80b2\u915a\u6c34\u5e73\uff0c\u800c\u53f6\u7eff\u7d20\u542b\u91cf\u6781\u4f4e\u4e14\u4ee3\u8c22\u6c34\u5e73\u7a33\u5b9a\u7684\u80da\u4e73\u5bf9\u7c7d\u7c92\u751f\u80b2\u915a\u7684\u8d21\u732e\u6709\u9650\u3002"], "question_cot": "", "final_conclusion": "\u5728\u7389\u7c73\u7c7d\u7c92\u4e2d\uff0c\u80da\u4e73\u51e0\u4e4e\u4e0d\u5408\u6210\u53f6\u7eff\u7d20\u4e14\u4ee3\u8c22\u6c34\u5e73\u5728\u4e0d\u540c\u57fa\u56e0\u578b\u95f4\u76f8\u5bf9\u7a33\u5b9a\uff0c\u7c7d\u7c92\u4e2d\u751f\u80b2\u915a\u542b\u91cf\u4e3b\u8981\u53d6\u51b3\u4e8e\u80da\u80ce\u7684\u57fa\u56e0\u578b\uff0c\u5e76\u4e14\u80da\u80ce\u7684\u751f\u80b2\u915a\u5408\u6210\u4f9d\u8d56\u4e8e\u53f6\u7eff\u7d20\uff08\u7531 POR1/POR2 \u4ecb\u5bfc\uff09\u7684\u5408\u6210\u3002", "difficulty": "medium", "tags": ["mechanism", "concept", "result"], "created_at": "2025-12-16T21:19:13", "token_est_question": 6, "token_est_answer": 3, "section": "Results", "context": "=== Tocopherol synthesis is light-dependent within developing embryo tissue ===\nPOR enzyme activity requires light as a co-substrate [17] and to understand the relationship between chlorophyll synthesis and tocopherol levels in non-photosynthetic kernel tissue, we conducted a physiological experiment on developing grain by exposing the ear to light and dark treatments (Fig. S1). At 12 days after pollination (DAP), we applied both treatments to a subset of maize NAM founder lines by covering the ears with aluminum foil for the dark treatment or removing 3\u20134 outer husks for the light treatment. In 2018, we tested seven NAM founders with contrasting allelic effects [5] of one or both _POR_ loci, while a further subset of three NAM founders was evaluated in 2019. Metabolite analysis of mature kernels revealed that in both 2018 and 2019, the dark treatment reduced total tocopherols compared to light-treated samples by a mean of 82% and control samples by 78% across genotypes (Figs. S4, S5). Similar trends were observed for individual tocopherols (\u03b1-tocopherol, \u03b4-tocopherol, \u03b3-tocopherol) (Figs. S4-S6). We dissected embryos from 24 DAP developing kernels (from 2018) and found both chlorophyll _a_ and tocopherols were reduced by a mean of 96% and 88%, respectively, in dark-treated samples compared to light-treated samples (Fig. 2a, b). There was a strong positive correlation (_r_ = 0.6) between the concentrations of chlorophyll _a_ and total tocopherol (Fig. S7). In 24 DAP embryos, total tocopherol levels were 40- to 200-fold higher than chlorophyll _a_ levels across all genotypes in both the control and light treatments (Fig. 2). While the reduction in tocopherol abundance between dark-treated and control samples was consistent across genotypes, years, and tissues, the changes in total tocopherols between light-treated and control samples were inconsistent. For example, in 2018, six of the seven genotypes had a mean increase of 54% in embryo tocopherol levels in response to light treatment compared to control treatment, but in only two lines (B97 and MS71) did the significant increase in tocopherols persist in mature kernels (Figs. 2, S4).\n=== Fig. 2. ===\n \nOpen in a new tab\nUntransformed best linear unbiased estimators (BLUEs) of metabolites from developing embryo tissue. **a** Total tocopherol concentration. **b** Chlorophyll a concentration. **c** Total tocotrienol concentration. **d** Total carotenoid concentration. Different letters denote _P_ -value 98% reduction in chlorophyll _a_ compared to the WT (Fig. 6a). Comparable levels of total carotenoids were found in double mutant embryos compared to WT. In contrast to embryo tissue, the metabolite levels in endosperm tissue were consistent across genotypes (Fig. 6b). Endosperm tissue accumulated chlorophyll _a_ below our limit of detection, at least 100-fold lower than that observed in embryo tissue (Fig. 6b). By dissecting endosperm and embryo tissue from genotypically distinct kernels from the same ear, we show that tocopherol levels in maize grain are dependent on the genotype of the embryo. Overall, the functional knockout of _POR1_ and _POR2_ supports the reliance of tocopherol biosynthesis on chlorophyll biosynthesis within the embryo.\n=== Fig. 6. ===\n \nOpen in a new tab\nEffects of _por1_ and/or _por2_ knockout mutations on tocochromanols, carotenoids, and chlorophyll _a_ in developing kernel tissues. The metabolomic results from family CR9.3\u201341 (indicated by red dots) and CR9.3\u20135 (indicated by black dots) from 24 DAP embryo (**a**) and endosperm (**b**). Different letters denote _P_ -value < 0.05 in Tukey\u2019s HSD for genotype comparisons. The boxplot color reflects the leaf color of the genotypes grown in the 2019 field experiment (Fig. 4)", "Thinking\u6a21\u5f0f": "high", "generation_type": "\u63a8\u7406\u578b"}','2026-03-23 15:33:29.215693','2026-03-23 15:33:29.215704');
INSERT INTO qa_pairs VALUES(38,3,'在玉米中，有研究通过黑暗处理抑制籽粒发育期间胚内光依赖型原卟啉还原酶活性，发现虽然母体叶片光合状态基本不受影响，但胚和成熟籽粒中的叶绿素和生育酚含量大幅下降；另有分离群体中，不同胚基因型的籽粒在同一雌穗上发育、共享完全相同的母体组织和环境时，携带por1/por1;por2/por2双突变胚的籽粒其叶绿素和生育酚含量远低于同穗上的其他基因型。如何利用这两类证据判断，胚生育酚合成所需的植醇主要来源于胚自身合成而不是由母体叶片运输？','黑暗处理只抑制胚内光依赖型POR活性而不影响叶片，但仍导致胚和成熟籽粒生育酚显著下降，说明母体叶片即使正常合成植醇，也不足以维持胚内生育酚水平。分离群体中，不同胚POR基因型在完全相同的母体环境下表现出截然不同的胚和籽粒生育酚含量，表明生育酚水平主要由胚自身的POR功能决定，而不是由统一的母体供给所主导。两类结果共同指向：用于生育酚合成的植醇主要在胚内通过叶绿素途径局部合成，而非依赖母体叶片合成后长距离运输。','{"species": "Zea mays", "paper_id": "PMC11905637", "reasoning_steps": ["Step 1: Tocopherol biosynthesis in maize embryos depends on POR1 and POR2 activity in the chlorophyll biosynthesis pathway, as genetic or physiological inhibition of these enzymes causes up to ~93% reduction in kernel tocopherol content.", "Step 2: In a physiological approach, light deprivation during kernel development inhibits the light-dependent POR activity in embryos without affecting leaves, yet embryo and mature kernel tocopherol levels are still severely decreased.", "Step 3: If phytol for embryo tocopherol synthesis were mainly produced in leaves and transported to the embryo, an embryo-specific light treatment that does not affect leaves would be expected to have little or no effect on embryo tocopherol levels.", "Step 4: In a genetic approach, ears were produced that segregate for different embryo genotypes (_por1/por1;por2/por2, por1/+;por2/por2,_ and _por2/por2_), while all kernels on the same ear share identical maternal tissues and environmental conditions.", "Step 5: Under these identical maternal and environmental conditions, embryos with the double knockout genotype _por1/por1;por2/por2_ show a large reduction in embryo and mature kernel tocopherol and chlorophyll levels compared with the other genotypes on the same ear.", "Step 6: If phytol for embryo tocopherol synthesis were primarily transported from maternal tissues (e.g., leaves), embryo genotype differences would not be expected to cause large differences in embryo tocopherol content under shared maternal conditions.", "Step 7: Therefore, the strong dependence of embryo tocopherol levels on embryo-local POR1/POR2 function, together with severe effects of embryo-targeted light deprivation, indicates that the phytol used for tocopherol synthesis is generated within the embryo rather than imported from maternal leaf tissue."], "question_cot": "", "final_conclusion": "Maize embryo tocopherol levels are determined by chlorophyll-derived phytol synthesized within the embryo itself, rather than by phytol transported from maternal leaf tissue.", "difficulty": "medium", "tags": ["mechanism", "result", "plant_metabolism", "seed_biology", "maize"], "created_at": "2025-12-16T21:20:03", "token_est_question": 1, "token_est_answer": 1, "section": "Discussion", "context": "=== Discussion ===\nThe core tocopherol biosynthesis pathway is well known, but the mechanism(s) supplying the phytol group, a key precursor for tocopherol synthesis, has yet to be understood in both photosynthetic and non-photosynthetic tissues, such as maize grain. Despite maize embryos having extremely low chlorophyll levels (~ 500-fold lower than leaves) and tocopherol levels up to ~ 1000-fold higher than chlorophyll _a_ , association studies in maize indicated that _POR1_ and _POR2_ , which encode a key activity in chlorophyll biosynthesis, underlie major QTL for natural variation in total tocopherol levels in mature grain [5, 55]. To understand the contribution that chlorophyll biosynthesis makes to tocopherol biosynthesis within the maize embryo, we altered POR1 and POR2 activity by two complementary approaches: physiological inhibition of POR activity by darkness and single and double CRISPR/Cas9-mediated knockouts of the two genes. This resulted in up to a 93% reduction in tocopherol content in mature kernels, indicating tocopherol biosynthesis is almost entirely dependent on chlorophyll biosynthesis within the non-photosynthetic maize embryo.\nIn the first approach, we took advantage of the light-dependent enzyme activity of POR1/POR2 to inhibit chlorophyll synthesis in vivo [39]. By withholding light during kernel development, we could assess the dependence of tocopherol synthesis on chlorophyll synthesis. Using a diverse subset of the NAM founders with differing _POR1_ and/or _POR2_ allelic effects [5], we found that the abundance of chlorophyll _a_ and total tocopherols in dark-treated embryo tissue decreased by 92\u201399% and 87\u201390%, respectively, compared to light-treated embryo tissue (Fig. 2a and b). Because light levels strongly impact _POR_ gene expression in vegetative tissues [47], we assessed if light deprivation impacts _POR1_ and _POR2_ gene expression in embryo tissue. Surprisingly, compared to control, the dark treatment did not significantly reduce the mRNA levels of _POR1, POR2_ , or other a priori genes for IPP synthesis, chlorophyll metabolism, and tocopherol biosynthesis (Table S8) within embryos across all genotypes (Fig. 3). This is consistent with the impact of the dark treatment on tocopherol and chlorophyll _a_ abundance being caused by translational or posttranslational impacts on POR activity, or the stability/activity of other members of the POR/CHS/GGR/LIL3 complex [18, 48, 49, 58], as well as potential interactions with VTE biosynthetic enzymes. The low tocopherol levels remaining in dark-treated grain samples could arise from multiple sources including a trace amount of light leakage into the ear, the accumulation of tocopherols or their precursors prior to initiating dark treatment at 12 DAP, and/or from the availability of small amounts of GGDP-derived PDP that might not rely on chlorophyll-derived phytol.\nIn the second approach, we targeted the third exon of both _POR1_ and _POR2_ to create homozygous single and double knockout mutants with severely truncated, nonfunctional enzymes. Compared to the WT, the _por1 por2_ double homozygous mutant (_por1/por1;por2/por2_) had a metabolomic phenotype similar to that of dark-treated ears: a ~ 93% reduction of tocopherols in mature kernels (Fig. 5b) and a 76\u201383% and 98\u2013100% reduction in tocopherols and chlorophyll _a_ , respectively, in 24 DAP developing embryos (Fig. 6a). The low levels of tocopherol in mature grain of the _por1/por1;por2/por2_ double mutant could be due to the activity of POR3, the third POR homolog in maize that is mainly expressed during the first several days of grain development [7]. The similarity of reductions in grain tocopherol levels between the single _por_ mutants (_por1/por1_ or _por2/por2_) and between the monoallelic double mutants (_por1/_ + _;por2/por2_ or _por1/por1;por2/_ +) indicate that _POR1_ and _POR2_ play complementary roles in tocopherol synthesis within maize grain. In contrast, leaves of _por1/por1;por2/_ + mutant seedlings were visibly paler than the _por1/_ + _;por2/por2_ mutant (Fig. 4), consistent with POR2 making a greater contribution to chlorophyll biosynthesis in leaf tissue than POR1 [56]. Both the genetic and physiological inhibition of POR provide conclusive evidence that nearly all tocopherol produced in maize kernels depends on chlorophyll biosynthesis within the embryo.\nTocopherols and tocotrienols are closely related chemically and biosynthetically with only a single gene differing in their pathways (Fig. 1); tocopherols require VTE2 for the condensation of HGA and PDP, while tocotrienols require HGGT for condensation of HGA and GGDP. Unlike the stark reduction in tocopherols, _por_ mutations and dark treatments increased the tocotrienol content in developing embryos, suggesting that the availability of HGA was not decreased by the enzymatic activity of POR (Figs. 2d, and 6a). Similarly, GGDP levels also appear to be unaffected, as carotenoids\u2014which depend on GGDP for their synthesis\u2014remain unchanged in both experiments (Figs. 2c, and 6a). The increase in embryo tocotrienols is likely due to the ability of VTE2 to utilize its less preferred substrate, GGDP, when the levels of GGDP greatly exceed that of PDP [57]. The opposite response of tocotrienol and tocopherol synthesis to light deprivation is consistent with decreased availability of chlorophyll-derived PDP specifically for tocopherol synthesis being impacted rather than a reduction in other tocochromanol pathway substrates or _VTE2_ expression (Fig. 3). Given the large stoichiometric differences between chlorophyll _a_ and tocopherol levels in developing maize kernels (Figs. 2a,b, and 6a), our data supports the hypothesis [5] that chlorophyll participates in a biosynthetic cycle within embryos. This biosynthetic cycle generates many molecules of phytol per molecule of chlorophyll\u2014unlike chlorophyll degradation, an irreversible process that produces only a single molecule of phytol per molecule of chlorophyll [20].\nA prior study [56] used tocopherol data from reciprocal hybrids with differing _POR2_ alleles, along with in vitro feeding of phytol to isolated, developing kernels, to hypothesize that the phytol for tocopherol synthesis in embryos is generated in leaf tissue and transported to the embryo by an unknown mechanism. Our data do not support this hypothesis, instead, they indicate the source of phytol for tocopherol synthesis in embryos is the embryo itself. The physiological experiment did not affect leaves, and if phytol was transported from leaves for synthesis in embryos, one would expect no impact on embryo tocopherol synthesis. Embryo and mature kernel tocopherol levels were instead severely decreased (Figs. 2, S4 and S5). In a second independent approach, we self-pollinated the phenotypically robust _por1/_ + _;por2/por2_ mutant in the greenhouse to generate ears segregating for _por1/por1;por2/por2, por1/_ + _;por2/por2,_ and _por2/por2_ genotypes, which were identified by single kernel genotyping. In this experiment, kernel genotypes on each ear experience identical maternal effects and environmental conditions during development and, if phytol for tocopherol synthesis in embryos were transported from other tissue, one would again expect no impact on embryo tocopherol levels. Instead, a large reduction in tocopherol and chlorophyll levels was observed in _por1/por1;por2/por2_ embryos and mature kernels compared to _por2/por2_ and _por1/_ + _;por2/por2_. These two experiments conclusively demonstrate that the phytol produced within the embryo, rather than the transport of phytol from maternal leaf tissue, determines embryo tocopherol levels (Fig. 6a).\nIn summary, we can now conclude that ~ 93% of total tocopherol in mature maize kernels is dependent on chlorophyll-derived phytol synthesized within embryo tissue. Defining the contribution of chlorophyll biosynthesis to tocopherol synthesis in the maize embryo indicates that _POR1_ and _POR2_ are primary breeding targets for elevating the total tocopherol content in maize, and likely other cereal grains. Selecting superior _POR1_ and _POR2_ alleles for total tocopherols, along with other loci that control the types of tocopherols accumulated (_VTE3_ and _VTE4_), should result in an optimized grain profile that has both increased tocopherol content and enhanced vitamin E activity to benefit agriculture and human health.", "Thinking\u6a21\u5f0f": "high", "generation_type": "\u63a8\u7406\u578b"}','2026-03-23 15:33:29.356619','2026-03-23 15:33:29.356629');
INSERT INTO qa_pairs VALUES(39,3,'FAR1/FHY3 转录因子家族源自转座酶，在植物中被证明可通过调控下游基因参与光信号转导、生长发育和多种胁迫响应。在玉米中，这一家族成员的启动子区域富含非生物胁迫和激素相关顺式作用元件，其中干旱响应元件尤为集中；这些基因在正常条件下可在初生根、次生根、侧根和鞘节等根系组织中表达，但在自然水分亏缺以及 PEG 处理模拟干旱时，其在根系相关组织中的表达水平大多降低。结合上述特征，如何判断 FAR1/FHY3 家族在玉米根系干旱胁迫信号通路中的功能角色以及在干旱条件下的总体表达趋势？','可以判断 FAR1/FHY3 家族成员作为具有调控能力的转录因子，被纳入玉米根系的干旱信号调控网络，在干旱与模拟干旱胁迫过程中参与根系干旱响应，其在根系中的整体表达模式以胁迫诱导的下调为主。','{"species": "Zea mays", "paper_id": "PMC11216215", "reasoning_steps": ["Step 1: FAR1/FHY3 \u88ab\u63cf\u8ff0\u4e3a\u4e00\u79cd\u6765\u6e90\u4e8e\u8f6c\u5ea7\u9176\u7684\u8f6c\u5f55\u56e0\u5b50\u5bb6\u65cf\uff0c\u80fd\u591f\u901a\u8fc7\u8c03\u63a7\u4e0b\u6e38\u57fa\u56e0\u8868\u8fbe\u5728\u5149\u4fe1\u53f7\u8f6c\u5bfc\u3001\u751f\u957f\u53d1\u80b2\u548c\u80c1\u8feb\u54cd\u5e94\u4e2d\u53d1\u6325\u91cd\u8981\u4f5c\u7528\u3002", "Step 2: \u5728\u7389\u7c73\u57fa\u56e0\u7ec4\u4e2d\u9274\u5b9a\u51fa\u7684 ZmFAR \u57fa\u56e0\u542f\u52a8\u5b50\u533a\u57df\u4e2d\uff0c\u542b\u6709\u5927\u91cf\u4e0e\u975e\u751f\u7269\u80c1\u8feb\u548c\u6fc0\u7d20\u76f8\u5173\u7684\u987a\u5f0f\u4f5c\u7528\u5143\u4ef6\uff0c\u5176\u4e2d\u4e0e\u5e72\u65f1\u54cd\u5e94\u76f8\u5173\u7684\u987a\u5f0f\u5143\u4ef6\u975e\u5e38\u4e30\u5bcc\u3002", "Step 3: \u542f\u52a8\u5b50\u4e2d\u5bcc\u96c6\u67d0\u7c7b\u80c1\u8feb\u76f8\u5173\u987a\u5f0f\u5143\u4ef6\uff0c\u901a\u5e38\u610f\u5473\u7740\u8be5\u57fa\u56e0\u6613\u53d7\u76f8\u5e94\u80c1\u8feb\u4fe1\u53f7\u901a\u8def\u8c03\u63a7\uff0c\u4ece\u800c\u5728\u8be5\u7c7b\u80c1\u8feb\u6761\u4ef6\u4e0b\u53d1\u751f\u8f6c\u5f55\u6c34\u5e73\u53d8\u5316\u3002", "Step 4: \u5b9e\u9a8c\u68c0\u6d4b\u8868\u660e\uff0c\u5728\u6c34\u5206\u4e8f\u7f3a\u6761\u4ef6\u4e0b\uff0cZmFAR \u57fa\u56e0\u5728\u521d\u751f\u6839\u3001\u6b21\u751f\u6839\u3001\u4fa7\u6839\u548c\u9798\u8282\u7b49\u6839\u7cfb\u76f8\u5173\u7ec4\u7ec7\u4e2d\u7684\u8868\u8fbe\u5927\u591a\u88ab\u4e0b\u8c03\u3002", "Step 5: \u5728 PEG \u6a21\u62df\u5e72\u65f1\u5904\u7406\u540e\uff0c\u5927\u591a\u6570 ZmFAR \u5728\u6839\u4e2d\u7684\u8868\u8fbe\u540c\u6837\u5448\u4e0b\u8c03\u8d8b\u52bf\uff0c\u8bf4\u660e\u8fd9\u79cd\u8868\u8fbe\u53d8\u5316\u4e0d\u4ec5\u51fa\u73b0\u5728\u81ea\u7136\u6c34\u5206\u4e8f\u7f3a\u60c5\u666f\uff0c\u8fd8\u53ef\u7531\u6e17\u900f\u80c1\u8feb\u6a21\u62df\u6761\u4ef6\u8bf1\u5bfc\u3002", "Step 6: \u7531\u4e8e ZmFAR \u5c5e\u4e8e\u5177\u6709\u8c03\u63a7\u4e0b\u6e38\u57fa\u56e0\u80fd\u529b\u7684\u8f6c\u5f55\u56e0\u5b50\uff0c\u5176\u5728\u5e72\u65f1\u6216\u6a21\u62df\u5e72\u65f1\u4e0b\u7684\u4e00\u81f4\u6027\u4e0b\u8c03\u6a21\u5f0f\uff0c\u8868\u660e\u5176\u8f6c\u5f55\u6d3b\u6027\u672c\u8eab\u662f\u5e72\u65f1\u4fe1\u53f7\u8c03\u63a7\u7f51\u7edc\u7684\u4e00\u90e8\u5206\u3002", "Step 7: \u7efc\u4e0a\uff0c\u7ed3\u5408\u5176\u8f6c\u5f55\u56e0\u5b50\u5c5e\u6027\u3001\u542f\u52a8\u5b50\u4e2d\u4e30\u5bcc\u7684\u5e72\u65f1\u76f8\u5173\u987a\u5f0f\u5143\u4ef6\u4ee5\u53ca\u5728\u5e72\u65f1\u548c\u6a21\u62df\u5e72\u65f1\u6761\u4ef6\u4e0b\u6839\u7cfb\u4e2d\u666e\u904d\u4e0b\u8c03\u7684\u8868\u8fbe\u6a21\u5f0f\uff0c\u53ef\u4ee5\u63a8\u65ad FAR1/FHY3 \u5bb6\u65cf\u6210\u5458\u5728\u7389\u7c73\u6839\u7cfb\u7684\u5e72\u65f1\u80c1\u8feb\u54cd\u5e94\u8fc7\u7a0b\u4e2d\u53d1\u6325\u91cd\u8981\u4f5c\u7528\uff0c\u5e76\u4e3b\u8981\u901a\u8fc7\u8868\u8fbe\u4e0b\u8c03\u7684\u65b9\u5f0f\u53c2\u4e0e\u8be5\u8fc7\u7a0b\u3002"], "question_cot": "", "final_conclusion": "FAR1/FHY3 \u8f6c\u5f55\u56e0\u5b50\u5bb6\u65cf\u6210\u5458\u5728\u7389\u7c73\u6839\u7cfb\u4e2d\u53c2\u4e0e\u5e72\u65f1\u80c1\u8feb\u54cd\u5e94\uff0c\u5e76\u5728\u5e72\u65f1\u6761\u4ef6\u4e0b\u603b\u4f53\u8868\u73b0\u4e3a\u4e0b\u8c03\u8868\u8fbe\u3002", "difficulty": "medium", "tags": ["mechanism", "result", "application"], "created_at": "2025-12-16T21:12:05", "token_est_question": 7, "token_est_answer": 3, "section": "Abstract", "context": "=== Background ===\nFAR1/FHY3 transcription factors are derived from transposase, which play important roles in light signal transduction, growth and development, and response to stress by regulating downstream gene expression. Although many FAR1/FHY3 members have been identified in various species, the _FAR1/FHY3_ genes in maize are not well characterized and their function in drought are unknown.\n=== Method ===\nThe FAR1/FHY3 family in the maize genome was identified using PlantTFDB, Pfam, Smart, and NCBI-CDD websites. In order to investigate the evolution and functions of FAR1 genes in maize, the information of protein sequences, chromosome localization, subcellular localization, conserved motifs, evolutionary relationships and tissue expression patterns were analyzed by bioinformatics, and the expression patterns under drought stress were detected by quantitative real-time polymerase chain reaction (qRT-PCR).\n=== Results ===\nA total of 24 ZmFAR members in maize genome, which can be divided into five subfamilies, with large differences in protein and gene structures among subfamilies. The promoter regions of _ZmFARs_ contain abundant abiotic stress-responsive and hormone-respovensive _cis_ -elements. Among them, drought-responsive _cis_ -elements are quite abundant. _ZmFARs_ were expressed in all tissues detected, but the expression level varies widely. The expression of _ZmFARs_ were mostly down-regulated in primary roots, seminal roots, lateral roots, and mesocotyls under water deficit. Most _ZmFARs_ were down-regulated in root after PEG-simulated drought stress.\n=== Conclusions ===\nWe performed a genome-wide and systematic identification of _FAR1/FHY3_ genes in maize. And most _ZmFARs_ were down-regulated in root after drought stress. These results indicate that FAR1/FHY3 transcription factors have important roles in drought stress response, which can lay a foundation for further analysis of the functions of _ZmFARs_ in response to drought stress.\n**Keywords:** ZmFHY3/FAR1 transcription factors, Bioinformatics, Genome-wide analysis, Spatiotemporal expression pattern, Drought stress", "Thinking\u6a21\u5f0f": "high", "generation_type": "\u63a8\u7406\u578b"}','2026-03-23 15:33:29.456616','2026-03-23 15:33:29.456627');
INSERT INTO qa_pairs VALUES(40,3,'在多种植物中，FHY3/FAR1类转录因子不仅参与光信号和发育调控，还在盐、干旱、渗透、高温和低温等非生物胁迫下表现出明显的诱导或下调，一些成员的过表达能够通过提高POD、SOD、CAT等清除活性增强干旱耐性，而缺失突变体在干旱下则更敏感。另一方面，玉米是重要粮食作物，其幼苗期对干旱高度敏感，且根和胚轴节间是早期感知水分胁迫并反映抗性的重要器官。科研人员如果希望围绕FHY3/FAR1基因开展玉米抗旱改良研究，应优先设计哪一类系统性研究思路，并说明该思路在理论依据和应用前景上的合理性？','在玉米中应优先开展FHY3/FAR1家族成员的系统鉴定，并结合幼苗根系及相关组织在干旱胁迫下的表达分析与功能验证，构建其在玉米抗旱调控中的潜在调控网络。这一思路首先通过全基因组水平识别玉米FHY3/FAR1家族，解析其序列特征和保守结构域，再在幼苗期根和胚轴节间等对水分最敏感、最能表型化干旱耐性的组织中，比较正常与干旱条件下的表达模式，筛选对干旱响应敏感的候选基因，随后通过过表达或基因编辑等手段评估其对抗氧化酶活性和干旱存活性的影响。由于在茶树、花生、阿拉伯芥和水稻等物种中，FHY3/FAR1成员已被证明普遍参与非生物胁迫响应并在多种情形下正向调节逆境耐受性，而玉米幼苗对干旱格外敏感且根系是关键感受器官，因此将玉米FHY3/FAR1的全家族鉴定与根系干旱表达特征及功能测试相结合，既有明确的跨物种机理支撑，又紧密对接改良玉米抗旱性的育种目标，具有清晰的理论依据和应用潜力。','{"species": "Zea mays", "paper_id": "PMC11216215", "reasoning_steps": ["Step 1: FHY3/FAR1\u662f\u690d\u7269\u7279\u5f02\u7684\u8f6c\u5f55\u56e0\u5b50\uff0c\u53c2\u4e0e\u5149\u4fe1\u53f7\u4f20\u5bfc\u3001\u53d1\u80b2\u8fc7\u7a0b\u4ee5\u53ca\u5bf9\u975e\u751f\u7269\u80c1\u8feb\u7684\u54cd\u5e94\uff0c\u5728\u591a\u79cd\u690d\u7269\u4e2d\u88ab\u8bc1\u660e\u5177\u6709\u591a\u91cd\u8c03\u63a7\u4f5c\u7528\u3002", "Step 2: \u5728\u8336\u6811\u3001\u82b1\u751f\u3001\u963f\u62c9\u4f2f\u82a5\u3001\u6c34\u7a3b\u548c\u9a6c\u94c3\u85af\u7b49\u4f5c\u7269\u4e2d\uff0cFHY3/FAR1\u5bb6\u65cf\u6210\u5458\u5728\u76d0\u3001\u5e72\u65f1\u3001\u6e17\u900f\u3001\u9ad8\u6e29\u3001\u4f4e\u6e29\u7b49\u80c1\u8feb\u6761\u4ef6\u4e0b\u53d1\u751f\u8bf1\u5bfc\u6216\u4e0b\u8c03\uff0c\u5e76\u4e14\u67d0\u4e9b\u6210\u5458\uff08\u5982\u82b1\u751f\u4e2dAhJ11-FAR1-5\u7684\u8fc7\u8868\u8fbe\uff09\u80fd\u591f\u901a\u8fc7\u63d0\u9ad8POD\u3001SOD\u3001CAT\u7b49\u6e05\u9664\u80fd\u529b\u589e\u5f3a\u5e72\u65f1\u8010\u6027\uff0c\u800c\u963f\u62c9\u4f2f\u82a5fhy3/far1\u7a81\u53d8\u4f53\u5728\u5e72\u65f1\u4e0b\u6bd4\u91ce\u751f\u578b\u66f4\u654f\u611f\u3002", "Step 3: \u8fd9\u4e9b\u8de8\u7269\u79cd\u7684\u9057\u4f20\u548c\u8868\u8fbe\u8bc1\u636e\u8868\u660e\uff0cFHY3/FAR1\u5bb6\u65cf\u6210\u5458\u666e\u904d\u53c2\u4e0e\u975e\u751f\u7269\u80c1\u8feb\u54cd\u5e94\uff0c\u5e76\u5728\u591a\u79cd\u60c5\u51b5\u4e0b\u5bf9\u9006\u5883\u8010\u53d7\u6027\u8d77\u6b63\u5411\u8c03\u8282\u4f5c\u7528\u3002", "Step 4: \u7389\u7c73\u662f\u5168\u7403\u91cd\u8981\u7cae\u98df\u4f5c\u7269\uff0c\u4f46\u7389\u7c73\u5e7c\u82d7\u5bf9\u5e72\u65f1\u9ad8\u5ea6\u654f\u611f\uff0c\u4e14\u5e72\u65f1\u4e25\u91cd\u5f71\u54cd\u4ea7\u91cf\uff1b\u6839\u548c\u80da\u8f74\u8282\u95f4\uff08mesocotyl\uff09\u662f\u611f\u77e5\u73af\u5883\u6c34\u5206\u80c1\u8feb\u7684\u91cd\u8981\u5668\u5b98\uff0c\u5728\u5e7c\u82d7\u671f\u5bf9\u6fc0\u7d20\u548c\u73af\u5883\u80c1\u8feb\u53cd\u5e94\u654f\u611f\uff0c\u662f\u8bc4\u4ef7\u80c1\u8feb\u8010\u53d7\u6027\u7684\u91cd\u8981\u7ec4\u7ec7\u3002", "Step 5: \u7531\u4e8eFHY3/FAR1\u5bb6\u65cf\u5728\u5176\u4ed6\u690d\u7269\u4e2d\u5df2\u88ab\u8bc1\u660e\u4e0e\u5e72\u65f1\u7b49\u9006\u5883\u54cd\u5e94\u5bc6\u5207\u76f8\u5173\uff0c\u800c\u7389\u7c73\u5e7c\u82d7\u5e72\u65f1\u654f\u611f\u6027\u4e3b\u8981\u901a\u8fc7\u6839\u7cfb\u7b49\u5668\u5b98\u4f53\u73b0\uff0c\u63a8\u7406\u53ef\u5f97\uff1a\u5728\u7389\u7c73\u4e2d\u7cfb\u7edf\u9274\u5b9aFHY3/FAR1\u5bb6\u65cf\u5e76\u5206\u6790\u5176\u5728\u5e7c\u82d7\u6839\u7cfb\u5e72\u65f1\u80c1\u8feb\u4e0b\u7684\u8868\u8fbe\u7279\u5f81\uff0c\u6709\u52a9\u4e8e\u63ed\u793a\u5176\u6f5c\u5728\u6297\u65f1\u8c03\u63a7\u529f\u80fd\u3002", "Step 6: \u56e0\u6b64\uff0c\u4ece\u6539\u826f\u7389\u7c73\u6297\u65f1\u6027\u8fd9\u4e00\u5ba2\u89c2\u80b2\u79cd\u76ee\u6807\u51fa\u53d1\uff0c\u56f4\u7ed5\u7389\u7c73FHY3/FAR1\u5bb6\u65cf\u7684\u6210\u5458\u9274\u5b9a\u548c\u5e72\u65f1\u80c1\u8feb\u8868\u8fbe\u89e3\u6790\uff0c\u662f\u4e00\u4e2a\u5177\u6709\u660e\u786e\u7406\u8bba\u4f9d\u636e\u548c\u5e94\u7528\u524d\u666f\u7684\u5173\u952e\u7814\u7a76\u8def\u5f84\u3002"], "question_cot": "", "final_conclusion": "\u9274\u4e8eFHY3/FAR1\u7c7b\u57fa\u56e0\u5728\u591a\u79cd\u690d\u7269\u4e2d\u4e0e\u6297\u9006\u6027\u5bc6\u5207\u76f8\u5173\u4e14\u7389\u7c73\u5e7c\u82d7\u9636\u6bb5\u5bf9\u5e72\u65f1\u9ad8\u5ea6\u654f\u611f\uff0c\u5728\u7389\u7c73\u4e2d\u9274\u5b9aFHY3/FAR1\u5bb6\u65cf\u5e76\u89e3\u6790\u5176\u5728\u5e7c\u82d7\u6839\u7cfb\u5e72\u65f1\u80c1\u8feb\u4e0b\u7684\u8868\u8fbe\u6a21\u5f0f\uff0c\u662f\u6539\u826f\u7389\u7c73\u6297\u65f1\u6027\u7684\u91cd\u8981\u548c\u5408\u7406\u7814\u7a76\u65b9\u5411\u3002", "difficulty": "medium", "tags": ["application", "mechanism", "method", "breeding", "stress_resistance"], "created_at": "2025-12-16T21:13:04", "token_est_question": 1, "token_est_answer": 1, "section": "Introduction", "context": "=== Introduction ===\nAs sessile organisms, plants are frequently challenged by abiotic stresses, such as drought, salt, temperature, heavy metal ions and ultraviolet radiation, which severely affect their growth and reduce their yield (Zhang et al., 2022). Meanwhile, plants are autotrophic organisms and light is the basis of photosynthesis, therefore, studying the expression patterns and functions of light-related genes in plants can improve yield-related agronomic traits. FHY3 (far-red elongated hypocotyls 3) and FAR1 (far-red-impaired response) are two homologous proteins derived from transposases, which were initially identified in Arabidopsis, as an important component of the phytochrome A (phyA)-mediated far-red light signaling pathway (Hudson et al., 1999; Wang & Deng, 2002). FHY3 and FAR1 are plant-specific transcription factors, with separable DNA-binding domain, namely N-terminal C2H2 zinc finger domain, and transcriptional activation domains, including a central putative core transposase domain and a C-terminal SWIM motif (named after SWI2/SNF and MuDR transposases), which are essential for forming homo- or heterodimers to regulate and modulate downstream gene expression (Lin et al., 2008; Makarova, Aravind & Koonin, 2002). Studies have shown that FHY3 and FAR1 have multifaceted roles in light signaling, physiological and developmental processes, as well as in response to abiotic stresses (Lin et al., 2007; Ma & Li, 2018; Tang et al., 2013; Zheng, Sun & Liu, 2023).\nIn Arabidopsis, _FHY3_ and _FAR1_ encode two proteins related to mutator-like transposases that co-regulate phyA nuclear accumulation, and participate in the plant light signalling response by activating the transcription of _FHY1_ and _FHL_ through direct binding to the FBS motif \u201cCACGCGC\u201d in the promoter (Lin et al., 2007). _FHY3_ and _FAR1_ act as positive regulators of _ELF4_ and _CCA1_ , which are involved in the regulation of plant biological rhythms (Li et al., 2011; Liu et al., 2020). _FHY3_ is the epistatic regulator of WUSCHEL (WUS) and CLAVAT3 (CLV3), two central players in the establishment and maintenance of meristems, which in turn regulate flowering time (Li et al., 2016). Besides, FHY3 binds directly to the promoter and activates the expression of _ACCUMULATION AND REPLICATION OF CHLOROPLASTS5 (ARC5)_ and _HEMB1_ , which regulates chloroplast development and chlorophyll biosynthesis, respectively (Ouyang et al., 2011; Tang et al., 2012). FHY3 negatively regulate age- and light-mediated leaf senescence by repressing the transcription of _WRKY28_ (Tian et al., 2020). In addition, FHY3/FAR1 binds to the promoter of _MIPS1_ to activate its expression directly, thereby promoting inositol biosynthesis to prevent light-induced oxidative stress and SA-dependent cell death (Ma et al., 2016). Moreover, FHY3 and FAR1 promotes branching and stress tolerance in _Arabidopsis thaliana_ by integrating auxin and strigolactone signalling (Liu et al., 2020; Stirnberg et al., 2012). Furthermore, FHY3 and FAR1 can also responds to ABA signaling to regulate seed germination, seedling development, and primary root growth (Tang et al., 2013), and through the transcriptional activation of starch-debranching enzyme ISOAMYLASE2 (ISA2) affects starch synthesis and starch granule formation (Ma et al., 2017).\nFHY3/FAR1 family has been identified in many plant species, including Arabidopsis, tea, cucumber, peanut, walnut, and potato (Chen et al., 2023b, 2023c; Li et al., 2023; Lin & Wang, 2004; Liu et al., 2021; Lu et al., 2022). FHY3/FAR1 family has been reported to be associated with plant development in Arabidopsis, peanut, and walnut. However, in tea, potato and cucumber the researchers were mainly focused on stresses. The role of _FAR1_ in abiotic stresses has attracted attention in recent years. In tea, _CsFHY3/FAR1s_ were strongly expressed in leaves, and the expression of most genes were induced under salt stress, and negatively expressed under low temperature stress (Liu et al., 2021). In _Arachis hypogaea_ , overexpression of the _AhJ11-FAR1-5_ can enhance tolerance to drought stress by increasing POD, SOD, and CAT scavenging (Yan et al., 2020). In Arabidopsis, _fhy3_ and _far1_ mutants are less sensitive to salt, osmotic, while more sensitive to drought than the wild type (Tang et al., 2013). In rice, FHY3/FAR1 family member _TSD1_ is induced by heat and highly expressed in spikelets, and specifically enhances its thermotolerance during spikelet morphogenesis (Cai et al., 2023). In potato, most _StFRS_ genes were down-regulated by low temperature and polyethyleneglycol (PEG) treatment (Chen et al., 2023b). These results suggested that FHY3/FAR1 genes play important roles in the response to abiotic stress.\nMaize (_Zea mays_ L.) is one of the most important cereal crops in the world, which is grown over a very wide area, between 58\u00b0N and 40\u00b0S latitude. Maize seedlings are sensitive to drought stress, especially in the early growth stage, and drought severely influence maize production. Plant roots and mesocotyl play important roles in sensing environmental water stress (Saenz Rodriguez & Cassab, 2021). Maize has a complex root system that consists of primary, secondary and aerial roots. The formation of these root types is characterized by temporal and spatial developmental variability, implying that they have specific functions during maize development (de Dorlodot et al., 2007). At the early seedling stage, roots and mesocotyls can respond sensitively to phytohormones and environmental stresses, and they are important organs as evaluators of stress tolerance (Niu et al., 2020; Zhang et al., 2023). Previous studies have shown that _FHY3/FAR1_ genes participate in response to drought stress, but their functions in maize are still unknown. It is valuable to identify the FHY3/FAR1 family members, and to clarify their functions under drought stress. In this study, we used bioinformatics analyzed the number and classification, gene structure, chromosomal localization, and tissue expression patterns of the maize FAR1/FHY3 family members. In addition, qRT-PCR was used to detect the expression patterns of the _ZmFAR1s_ in the roots of B73 seedlings at one-leaf and three-leaf stage under drought stress, revealing the molecular characteristics of the FAR1/FHY3 genes in maize. This study will provide the basis for further research on the biological functions of the FAR1/FHY3 gene family in maize, and also has important reference value for the genetic improvement of drought-tolerant maize lines.", "Thinking\u6a21\u5f0f": "high", "generation_type": "\u63a8\u7406\u578b"}','2026-03-23 15:33:29.555359','2026-03-23 15:33:29.555369');
INSERT INTO qa_pairs VALUES(41,3,'在玉米三叶期幼苗根系中，用不同浓度的PEG-6000溶液处理不同时间，以正常供水植株根为对照，随后迅速冻存根组织、提取总RNA并逆转录为cDNA，再用针对 ZmFHY3/FAR1 等 ZmFAR 基因的特异引物进行qRT-PCR扩增，同时检测Actin1的Ct值并采用统一反应体系与技术重复，最终用2−ΔΔCt方法计算各处理相对表达量。研究者如果想客观判断在不同干旱强度和持续时间下这些基因在根中的表达高低及变化趋势，应怎样利用这一整套实验设计和数据分析流程得出可靠结论？','先通过可控的PEG-6000处理设置不同干旱强度和时间，使各处理之间只有胁迫条件不同，再在严格保护RNA完整性的前提下提取根部总RNA并逆转录为cDNA，以特异引物在统一条件下进行qRT-PCR扩增，同时测定内参基因Actin1的Ct值用于归一化。随后利用2−ΔΔCt方法将各处理条件下目标基因的表达量折算为相对于正常供水对照的相对表达量，并比较不同PEG浓度和处理时间之间的这些相对表达值。由于处理条件是人为可控且可区分的自变量，而相对表达量是统一标准下获得的量化指标，通过这一比较即可客观判断目标基因在不同干旱强度和持续时间下表达量的高低及其动态变化趋势。','{"species": "Zea mays", "paper_id": "PMC11216215", "reasoning_steps": ["Step 1: \u7389\u7c73\u81ea\u4ea4\u7cfbB73\u79cd\u5b50\u5728\u53ef\u63a7\u751f\u957f\u5ba4\u6761\u4ef6\u4e0b\u57f9\u517b\uff0c\u5230\u4e09\u53f6\u671f\u65f6\u9009\u53d6\u5e7c\u82d7\u6839\u4f5c\u4e3a\u5f85\u6d4b\u7ec4\u7ec7\u3002", "Step 2: \u4e09\u53f6\u671f\u5e7c\u82d7\u6839\u7cfb\u5206\u522b\u7ed9\u4e8810%\u548c25% PEG-6000\u6c34\u6eb6\u6db2\u5904\u74066\u300124\u548c48\u5c0f\u65f6\uff0c\u5e76\u8bbe\u7f6e\u6b63\u5e38\u4f9b\u6c34\u5904\u7406\u4f5c\u4e3a\u5bf9\u7167\uff0c\u4ece\u800c\u5f62\u6210\u4e0d\u540c\u5e72\u65f1\u80c1\u8feb\u5f3a\u5ea6\u4e0e\u6301\u7eed\u65f6\u95f4\u7684\u5904\u7406\u7ec4\u5408\u3002", "Step 3: \u5404\u5904\u7406\u4e0b\u6839\u7ec4\u7ec7\u88ab\u91c7\u96c6\u540e\u8fc5\u901f\u7528\u6db2\u6c2e\u51b7\u51bb\u5e76\u5728\u221280 \u00b0C\u4fdd\u5b58\uff0c\u4fdd\u8bc1RNA\u4e0d\u964d\u89e3\u4ee5\u53cd\u6620\u5f53\u65f6\u771f\u5b9e\u7684\u57fa\u56e0\u8868\u8fbe\u72b6\u6001\u3002", "Step 4: \u4ece\u8fd9\u4e9b\u6839\u6837\u54c1\u4e2d\u63d0\u53d6\u603bRNA\uff0c\u7ecf\u9006\u8f6c\u5f55\u83b7\u5f97cDNA\uff0c\u5e76\u5c06cDNA\u7a00\u91ca\u540e\u4f5c\u4e3aqRT-PCR\u6a21\u677f\uff0c\u4f7f\u7528\u4e13\u95e8\u8bbe\u8ba1\u7684ZmFAR\uff08\u5305\u62ecZmFHY3/FAR1\uff09\u7279\u5f02\u6027\u5f15\u7269\u8fdb\u884c\u6269\u589e\u3002", "Step 5: qRT-PCR\u91c7\u7528\u7edf\u4e00\u7684\u53cd\u5e94\u4f53\u7cfb\u548c\u5faa\u73af\u7a0b\u5e8f\uff0c\u5e76\u5bf9\u6bcf\u4e2a\u6837\u54c1\u8fdb\u884c\u6280\u672f\u91cd\u590d\uff0c\u540c\u65f6\u6d4b\u5b9a\u5185\u53c2\u57fa\u56e0Actin1\u7684Ct\u503c\u4ee5\u8fdb\u884c\u8868\u8fbe\u91cf\u5f52\u4e00\u5316\u3002", "Step 6: \u901a\u8fc72\u2212\u0394\u0394Ct\u65b9\u6cd5\uff0c\u5c06\u5404PEG\u5904\u7406\u53ca\u65f6\u95f4\u70b9\u7684\u76ee\u6807\u57fa\u56e0\u8868\u8fbe\u91cf\u76f8\u5bf9\u4e8e\u5bf9\u7167\u5904\u7406\u8fdb\u884c\u8ba1\u7b97\uff0c\u83b7\u5f97\u5404\u5904\u7406\u7684\u76f8\u5bf9\u8868\u8fbe\u91cf\u3002", "Step 7: \u7531\u4e8ePEG\u6d53\u5ea6\u548c\u5904\u7406\u65f6\u95f4\u662f\u4eba\u4e3a\u63a7\u5236\u4e14\u53ef\u533a\u5206\u7684\u53d8\u91cf\uff0c\u800c\u76f8\u5bf9\u8868\u8fbe\u91cf\u662f\u5b9a\u91cf\u6307\u6807\uff0c\u56e0\u6b64\u53ef\u4ee5\u636e\u6b64\u6bd4\u8f83\u5728\u4e0d\u540cPEG\u6d53\u5ea6\u548c\u5904\u7406\u65f6\u95f4\u4e0b\u76ee\u6807\u57fa\u56e0\u5728\u6839\u4e2d\u7684\u8868\u8fbe\u5f3a\u5f31\u53ca\u53d8\u5316\u8d8b\u52bf\u3002"], "question_cot": "", "final_conclusion": "\u5728\u7389\u7c73\u5e7c\u82d7\u4e2d\uff0c\u5229\u7528PEG-6000\u6a21\u62df\u5e72\u65f1\u5e76\u7ed3\u5408qRT-PCR\u548c2\u2212\u0394\u0394Ct\u65b9\u6cd5\uff0c\u53ef\u4ee5\u5ba2\u89c2\u6bd4\u8f83\u4e0d\u540cPEG\u6d53\u5ea6\u548c\u5904\u7406\u65f6\u95f4\u4e0b\u6839\u90e8 ZmFHY3/FAR1\uff08\u6216\u5176\u4ed6 ZmFAR \u57fa\u56e0\uff09\u7684\u76f8\u5bf9\u8868\u8fbe\u6c34\u5e73\u53d8\u5316\u8d8b\u52bf\u3002", "difficulty": "medium", "tags": ["method", "application", "result"], "created_at": "2025-12-16T21:13:42", "token_est_question": 6, "token_est_answer": 1, "section": "Materials & Methods", "context": "=== Plant materials and treatments ===\nThe seeds of maize inbred line B73, were cultivated in a growth chamber under long day conditions (16 h of light, 25 \u00b0C, and 8 h of darkness, 22 \u00b0C). In order to study the expression patterns of _ZmFHY3/FAR1_ in roots and mesocotyls under drought stress, the control was watered normally, while the experimental group without watering. The roots and mesocotyls were harvested at one-leaf (V1) stage (Tai et al., 2016). For study the expression patterns of _ZmFHY3/FAR1_ at three-leaf (V3) stage in root, seedlings were treated with 10% Polyethylene glycol (PEG)-6000 (w/v) and 25% PEG-6000 aqueous solution (w/v) for 6, 24 and 48 h, respectively. Six seedlings were treated per sample, and three biological replicates were conducted for each sample. The sampling parts of maize seedlings are shown in Fig. S1. All samples were frozen in liquid nitrogen and stored at \u221280 \u00b0C for later use.\n=== Plant materials and treatments ===\nFAR1/FHY3 protein sequences of maize were obtained from Plant TFDB ( ). Using the Pfam ( ), Smart ( ), and NCBI online tools Conservative Domain Database (CDD) ( ) to verify FAR/FHY3 members. Protein sequence comparison was performed with MegAlign software in DNASTAR package (Lasergene, Madison, WI, USA). Protein sequences of sugarcane, and Arabidopsis were also downloaded, and phylogenetic trees were constructed using the neighbor-joining method (NJ, bootstrap = 1,000) in MEGA 7.0 software (Kumar et al., 2018). The collinearity relationship between different gene pairs was performed using MCScanX (Wang et al., 2012). Finally, the results were visualized using the Dual Systeny Plot for MCScanX package in the TBtools (Chen et al., 2023a).\n=== The characterization of ZmFAR proteins and subcellular localization analysis ===\nThe .gff file was downloaded from Maize GDB ( ), which can query the chromosome location and structure information of FAR1/FHY3. Based on the localization in chromosome, the _ZmFAR1/FHY3_ genes were renamed. Protein characterization information was obtained from the ProtParam ( ) tool in Expasy. Plant-mPLoc ( ) was used for subcellular localization analysis, and SOMPA (Institut de Biologie et Chimie de Proteines, Lyon, France), was used to predict ZmFARs protein secondary structures.\n=== Conserved motifs and promoter analysis of _ZmFAR_ s ===\nMotif analysis was performed using the online tool MEME ( ). Firstly, the promoter sequences (2,000 bp upstream of CDS) were obtained using the .gff3 sequence extraction tool in TBtool, and then submitted to the PlantCARE website ( ) for _cis_ -acting element scan. And integrated for mapping by using the Gene Structure View tool in TBtools software (Chen et al., 2023a).\n=== Expression patterns analysis of _ZmFARs_ ===\nThe transcriptome dataset of maize genes was downloaded from the NCBI database (GSE50191) (Walley et al., 2016) and visualized using TBtools software, for tissues expression patterns analysis.\nTotal RNA was extracted using RNAiso Plus (TaKaRa, Shiga, Japan). The concentration and purity of nucleic acids are determined by NanoDrop2000. cDNA was obtained by reverse transcription reaction using PrimeScriptTM RT reagent Kit with gDNA Eraser (TaKaRa, Shiga, Japan). The cDNA template was diluted for 30-fold and then stored at \u221220 \u00b0C for later use. _ZmFAR_ -specific primers were designed using Beacon Designer software (Table S1), and the expression level of _ZmFARs_ was detected by CFX96 PCR instrument (Bio-Rad, Hercules, CA, USA). The qRT-PCR reaction system (15 \u03bcL) consisted of 7.5 \u03bcL of 2 \u00d7 TB Green Premix Ex TaqTM II (TaKaRa, Shiga, Japan), 0.45 \u03bcL upstream and downstream specific primers, 1.6 \u03bcL of ddH2O and 5 \u03bcL of cDNA template. The reaction program was 95 \u00b0C pre-denaturation for 30 s; 95 \u00b0C denaturation for 5 s, 60 \u00b0C for 30 s, 72 \u00b0C extension for 10 s, and 40 cycles. Melt curve 65 \u00b0C to 95 \u00b0C, increment 0.5 \u00b0C. Three replications were performed for each sample, and the corresponding Ct values were obtained for different samples. After homogenization of the internal reference gene _Actin 1_ , the relative expression of genes was calculated by the 2\u2212\u0394\u0394Ct method (Livak & Schmittgen, 2001). Finally, the data is visualized using GraphPad prism.", "Thinking\u6a21\u5f0f": "high", "generation_type": "\u63a8\u7406\u578b"}','2026-03-23 15:33:29.656170','2026-03-23 15:33:29.656180');
INSERT INTO qa_pairs VALUES(42,3,'在玉米中，ZmFAR 家族基因的启动子普遍富含与干旱、厌氧、损伤以及 ABA 等逆境相关的顺式作用元件，显示其具有被干旱及相关激素信号调控的潜力；同时，在幼苗自然失水干旱和三叶期 PEG 诱导的低水势条件下，根系中大多数 ZmFAR 的转录水平均表现为下调，部分成员在两个体系中都呈持续下降，而另一些成员只在短时间或低胁迫水平下略有上调、整体仍以下调或变化不显著为主。如何从这些启动子特征和表达模式推断，ZmFAR 家族在玉米根系干旱响应过程中更可能扮演正调控因子，还是负调控/冗余调控因子？','ZmFAR 启动子富含干旱和 ABA 等逆境相关顺式元件，说明这些基因确实处在干旱信号通路的调控网络中；但从表达模式看，它们在自然干旱和 PEG 诱导的低水势条件下，根系中多表现为由相对较高的基础表达被持续压低，而不是在胁迫下被诱导升高。典型的正调控应激基因往往在胁迫到来时上调以激活下游防御反应，而那些在胁迫时被下调的基因更符合负调控因子或抑制因子的特征，即在正常条件下维持对下游干旱响应通路的抑制作用，胁迫信号到来时通过自身被抑制来“解除刹车”。再加上同一家族中部分成员变化幅度较小或方向不完全一致，提示存在功能冗余和精细分工。综合启动子元件信息与在两个独立低水势体系中普遍下调的表达模式，可以判断 ZmFAR 家族整体更可能以负调控或冗余调控方式参与根系干旱响应，而不是作为典型的正向应激诱导基因。','{"species": "Zea mays", "paper_id": "PMC11216215", "reasoning_steps": ["Step 1: \u591a\u6570 ZmFAR \u542f\u52a8\u5b50\u533a\u57df\u4e2d\u542b\u6709\u591a\u79cd\u4e0e\u5e72\u65f1\u3001\u6e29\u5ea6\u3001\u538c\u6c27\u548c\u635f\u4f24\u7b49\u9006\u5883\u76f8\u5173\u7684\u987a\u5f0f\u5143\u4ef6\uff0c\u5e76\u4e14 ABA \u7b49\u5e72\u65f1\u76f8\u5173\u6fc0\u7d20\u7684\u54cd\u5e94\u5143\u4ef6\u5728\u7c7b\u578b\u548c\u6570\u91cf\u4e0a\u90fd\u8f83\u4e3a\u4e30\u5bcc\uff0c\u8fd9\u8868\u660e ZmFAR \u57fa\u56e0\u5177\u6709\u88ab\u5e72\u65f1\u548c\u76f8\u5173\u6fc0\u7d20\u8c03\u63a7\u8868\u8fbe\u7684\u6f5c\u529b\u3002", "Step 2: \u5728\u5e7c\u82d7\u65e9\u671f\uff0c\u6b63\u5e38\u4f9b\u6c34\u4e0e\u5e72\u65f1\u5904\u7406\u6bd4\u8f83\u663e\u793a\uff0cB73 \u7684\u4e3b\u6839\u3001\u6b21\u751f\u6839\u3001\u4fa7\u6839\u548c\u80da\u8f74\u4e2d\uff0c\u5927\u591a\u6570 ZmFAR \u7684\u8868\u8fbe\u5728\u5e72\u65f1\u6761\u4ef6\u4e0b\u51fa\u73b0\u4e0b\u8c03\uff0c\u5176\u4e2d ZmFAR01\u3001ZmFAR03\u3001ZmFAR07\u3001ZmFAR08\u3001ZmFAR09\u3001ZmFAR10 \u7b49\u5728\u4e0d\u540c\u6839\u578b\u4e2d\u5747\u663e\u8457\u4e0b\u964d\uff0c\u8bf4\u660e\u8fd9\u4e9b\u57fa\u56e0\u5bf9\u6c34\u5206\u4e8f\u7f3a\u5177\u6709\u654f\u611f\u7684\u8f6c\u5f55\u54cd\u5e94\u3002", "Step 3: \u5728\u4e09\u53f6\u671f\uff0c\u7528 10% \u548c 25% PEG-6000 \u5236\u9020\u4f4e\u6c34\u52bf\u6761\u4ef6\u65f6\uff0c\u6839\u4e2d ZmFAR \u8868\u8fbe\u5927\u591a\u4ecd\u5448\u4e0b\u8c03\u8d8b\u52bf\uff1b\u5c24\u5176\u662f ZmFAR01\u3001ZmFAR03\u3001ZmFAR05\u3001ZmFAR06\u3001ZmFAR09\u3001ZmFAR12\u3001ZmFAR13 \u548c ZmFAR14 \u5728 6\u201348 h \u5904\u7406\u8fc7\u7a0b\u4e2d\u8868\u73b0\u4e3a\u6301\u7eed\u4e0b\u964d\u5e76\u663e\u8457\u4e0b\u8c03\uff0c\u5f3a\u5316\u4e86\u201c\u5e72\u65f1\u6216\u6e17\u900f\u80c1\u8feb\u4f1a\u538b\u4f4e\u8fd9\u4e9b\u57fa\u56e0\u8f6c\u5f55\u6c34\u5e73\u201d\u7684\u89c2\u5bdf\u3002", "Step 4: \u5176\u4f59\u90e8\u5206 ZmFAR\uff08\u5982 ZmFAR02\u3001ZmFAR04\u3001ZmFAR10 \u7b49\uff09\u5728\u4e0d\u540c PEG \u6d53\u5ea6\u548c\u5904\u7406\u65f6\u95f4\u4e0b\u7684\u53d8\u5316\u8d8b\u52bf\u5e76\u4e0d\u5b8c\u5168\u4e00\u81f4\uff0c\u6709\u7684\u77ed\u65f6\u95f4\u6216\u4f4e\u6d53\u5ea6\u65f6\u7565\u6709\u4e0a\u8c03\uff0c\u4f46\u6574\u4f53\u4ecd\u4ee5\u4e0b\u8c03\u6216\u4e0d\u663e\u8457\u53d8\u5316\u4e3a\u4e3b\uff0c\u63d0\u793a\u540c\u4e00\u5bb6\u65cf\u6210\u5458\u4e4b\u95f4\u5b58\u5728\u4e00\u5b9a\u7684\u529f\u80fd\u5197\u4f59\u548c\u8c03\u63a7\u5dee\u5f02\u3002", "Step 5: \u9006\u5883\u76f8\u5173\u987a\u5f0f\u5143\u4ef6\u7684\u5b58\u5728\u8bf4\u660e ZmFAR \u53ef\u80fd\u53c2\u4e0e\u8c03\u63a7\u5e72\u65f1\u76f8\u5173\u901a\u8def\uff1b\u5728\u591a\u6570\u57fa\u56e0\u4e2d\uff0c\u5e72\u65f1\u548c PEG \u5904\u7406\u5bfc\u81f4\u8868\u8fbe\u4e0b\u8c03\u800c\u975e\u4e0a\u8c03\uff0c\u8bf4\u660e\u8fd9\u4e9b\u57fa\u56e0\u5728\u6b63\u5e38\u6761\u4ef6\u4e0b\u5904\u4e8e\u76f8\u5bf9\u8f83\u9ad8\u8868\u8fbe\u72b6\u6001\uff0c\u800c\u5e72\u65f1\u4fe1\u53f7\u5bfc\u81f4\u5b83\u4eec\u88ab\u6291\u5236\uff0c\u7b26\u5408\u201c\u8d1f\u8c03\u63a7\u56e0\u5b50\u5728\u80c1\u8feb\u65f6\u88ab\u4e0b\u8c03\u4ee5\u91ca\u653e\u4e0b\u6e38\u54cd\u5e94\u201d\u7684\u4e00\u79cd\u5e38\u89c1\u6a21\u5f0f\u3002", "Step 6: \u7ed3\u5408\u542f\u52a8\u5b50\u4e2d\u5e72\u65f1\u548c ABA \u7b49\u4fe1\u53f7\u5143\u4ef6\u7684\u5bcc\u96c6\uff0c\u4ee5\u53ca\u5728\u4e24\u4e2a\u72ec\u7acb\u5e72\u65f1/\u4f4e\u6c34\u52bf\u4f53\u7cfb\uff08\u81ea\u7136\u5931\u6c34\u4e0e PEG \u5904\u7406\uff09\u4e2d\u6839\u90e8\u8868\u8fbe\u666e\u904d\u4e0b\u8c03\u4e14\u90e8\u5206\u5448\u6301\u7eed\u4e0b\u964d\u8d8b\u52bf\uff0c\u53ef\u4ee5\u63a8\u65ad ZmFAR \u5bb6\u65cf\u6574\u4f53\u66f4\u504f\u5411\u5728\u6839\u7cfb\u5e72\u65f1\u54cd\u5e94\u4e2d\u53d1\u6325\u8d1f\u5411\u6216\u5197\u4f59\u8c03\u63a7\u4f5c\u7528\uff0c\u800c\u4e0d\u662f\u4f5c\u4e3a\u5178\u578b\u7684\u6b63\u8c03\u63a7\u5e94\u6fc0\u8bf1\u5bfc\u57fa\u56e0\u3002"], "question_cot": "", "final_conclusion": "\u5728\u5e72\u65f1\u6216PEG\u8bf1\u5bfc\u7684\u4f4e\u6c34\u52bf\u80c1\u8feb\u6761\u4ef6\u4e0b\uff0c\u5927\u591a\u6570 ZmFAR \u57fa\u56e0\u5728\u7389\u7c73\u6839\u7cfb\u4e2d\u88ab\u6301\u7eed\u4e0b\u8c03\uff0c\u7ed3\u5408\u5176\u542f\u52a8\u5b50\u4e2d\u5bcc\u96c6\u7684\u5e72\u65f1/ABA \u7b49\u9006\u5883\u76f8\u5173\u987a\u5f0f\u5143\u4ef6\uff0c\u8fd9\u4e9b\u57fa\u56e0\u6574\u4f53\u4e0a\u66f4\u53ef\u80fd\u4f5c\u4e3a\u5bf9\u5e72\u65f1\u8d1f\u8c03\u63a7\u6216\u5197\u4f59\u8c03\u63a7\u7684\u56e0\u5b50\u53c2\u4e0e\u54cd\u5e94\u3002", "difficulty": "medium", "tags": ["mechanism", "result"], "created_at": "2025-12-16T21:18:26", "token_est_question": 11, "token_est_answer": 10, "section": "Results", "context": "=== Screening and identification of ZmFAR family members ===\nA total of 24 putative members with typical FAR1/FHY3 structural domains were identified from maize genome, and they were encoded by 14 genes (Fig. 1). Their CDS and protein sequences are listed in Table S2. They all have N-terminal WRKY-GCM1 zinc finger domain with the conserved cysteines and histidines of the CCHH motif (Fig. S2). And a putative \u201cDDE\u201d catalytic triad motif (E323 is not conserved, while G305 is conserved in FHY3) that is critical for transposase/integrase function, and C-terminal SWIM zinc-finger domain of a CxCxnCXH motif were found in all ZmFARs, expect ZmFAR01.1 (Fig. S2). Based on the chromosomal distribution, they were renamed ZmFAR01.1 to ZmFAR14.1. And they were unevenly distributed on seven chromosomes of maize, mainly on Chr1, Chr5, and Chr7. Among them, Chr7 has the highest distribution with 9 members. However, no ZmFAR was present in Chr2, Chr6, and Chr8 (Fig. 1). According to the CDD website, all members contained FHY3 conserved structural domains, among which eight members contained 1\u20132 FAR1 structural domains, while the rest of them did not contain FAR1 domain (Fig. 2C). These results indicated that the identification of 24 FHY3/FAR1 family members in maize was accurate.\n=== Figure 1. The location of ZmFAR family members on chromosome. ===\n \nOpen in a new tab\nChromosome numbers are on the left and ZmFARs are on the right of chromosomes. Scale bar on the left indicates chromosome length.\n=== Figure 1. The location of ZmFAR family members on chromosome. ===\n \nOpen in a new tab\n(A) The unrooted phylogenetic tree constructed using ZmFAR protein sequences. The subtree branch lines are colored indicate the different subfamilies. (B) Motifs analysis of ZmFARs. The top eight motifs identified by the MEME are represented by the number. (C) Conservative domains analyzed by NCBI-CDD. (D) Exon-intron structures of ZmFARs. The exons are marked as blue boxes, and the introns are represented by black lines; UTRs are shown as green boxes.\n=== Figure 1. The location of ZmFAR family members on chromosome. ===\nIn order to understand the evolutionary history and phylogenetic relationship of the FAR1/FHY3 genes family, 54 FAR1/FHY3 protein sequences (24 for maize, four for sugarcane, and 26 for Arabidopsis) were compared, and a phylogenetic tree was constructed using MEGA 7.0 software. The results showed that the FAR1/FHY3 can be divided into six subfamilies based on their sequence similarity (Fig. 3). The ZmFAR family members were most distributed in three subfamilies, mainly Group2, Group4 and Group5, with a total of 21 members; Group1 and Group3 had three members (ZmFAR01.1, ZmFAR05.1 and ZmFAR13.1); In contrast, Group6 had no ZmFAR family members. Notably, _FAR1/FHY3_ genes from maize and sugarcane showed close pairwise relationships based on genetic distance, compared with other proteins from different species, implying that the relationship between maize and sugarcane is closer than that between maize and other species.\n=== Figure 3. Evolutionary relationships of FAR1/FHY3 transcription factors in maize, sugarcane, and Arabidopsis. ===\n \nOpen in a new tab\nZm indicates _Zea maize_ (red dots), Sof indicates _Saccharum officinarum_ (black dots), At indicates _Arabidopsis thaliana_. These FAR1/FHY3 proteins were divided into six groups, and were represented by different colors, respectively.\n=== Collinearity analysis of ZmFARs ===\nCollinearity analysis can elucidate the evolutionary history of genomes and gene families (Wang et al., 2012). To investigate the molecular mechanism of ZmFARs evolution, we analyzed the co-linearity of ZmFAR members among maize and other species by using MCScanX. Five monocots (_Sorghum bicolor_ , _Oryza sativa_ , _Oryza indica_ , _Saccharum spontaneum_ , and _Hordeum vulgare_), and four dicots (_Arabidopsis thaliana_ , _Solanum tuberosum_ , _Solanum lycopersicum_ , and _Glycine max_) were applied for the co-linearity analyses. Surprisingly, there was no orthologous genes were identified in maize (Fig. S3), and dicots (Fig. S4). However, a total of 14, 12, 11, 16, and eight FAR1 paralogous gene pairs were identified in _Sorghum bicolor_ , _Oryza sativa_ , _Oryza indica_ , _Saccharum spontaneum_ , and _Hordeum vulgare_ , respectively (Fig. 4 and Table S3). Notably, six ZmFAR members (ZmFAR03.2, ZmFAR02.2, ZmFAR01.1, ZmFAR07.2, ZmFAR12.1, and ZmFAR09.2) had syntenic pairs throughout all five monocots, which were further indicated in bold in Table S4. While, ZmFAR08.1 had one syntenic pair with sugarcane, indicating that they have a common ancestor (Abrouk et al., 2010). These results suggest that FAR1 genes evolved after the differentiation of monocots and dicots plants.\n=== Figure 4. Synteny analysis of ZmFARs between maize and other five monocots, including _Sorghum bicolor_ , _Oryza sativa_ , _Oryza indica_ , _Saccharum spontaneum_ , and _Hordeum vulgare_. ===\n \nOpen in a new tab\nGray lines represent the collinear blocks between two plants in their genome. Syntenic _FAR1/FHY3_ gene pairs are represented by the marked red lines.\n=== Protein characterization and subcellular localization analysis of the ZmFARs ===\nThe individual characteristics- including their physiological and biochemical properties, and cellular location are summarized in Table 1. The protein sequences and physicochemical properties of different ZmFAR transcription factors varied greatly, with amino acid lengths ranged from 648aa (ZmFAR12.2) to 1202aa (ZmFAR02.2), molecular weights ranged from 73.27 kDa (ZmFAR01.1) to 135.849 kDa (ZmFAR02.2), theoretical isoelectric points ranged from 5.31 (ZmFAR04.1) to 8.99 (ZmFAR08.1). There are seventeen basic proteins and seven acidic proteins, respectively. The grand average of hydropathicity of all ZmFAR proteins were less than 0, indicating that they all belonged to hydrophilic proteins. The instability index of ZmFAR06.1 was 39.68, which was the only stable protein in the ZmFAR family, while the rest members with instability indexes greater than 40, which belonged to unstable proteins. Plant-mPLoc localization analysis revealed that ZmFAR proteins were not only found in the nucleus, but also distributed in chloroplast, mitochondrion, vacuole, and cell wall, suggesting the evolution of potentially new functions in these locations for these proteins.\n=== Table 1. Basic information of maize FAR1/FHY3 transcription factor family members. ===\nID | Gene name | Number of amino acids | Molecular weight | Theoretical pI | Instability index | Grand average of hydropathicity | Subcellular localization \n \nZmFAR01.1 | GRMZM2G001663 | 666 | 73,270.02 | 7.83 | 53.37 | \u22120.477 | Nucleus \nZmFAR02.1 | GRMZM2G463730 | 1,198 | 135,289.47 | 6.37 | 53.39 | \u22120.653 | Chloroplast. Mitochondrion. Nucleus \nZmFAR02.2 | GRMZM2G463730 | 1,202 | 135,849.03 | 6.14 | 52.73 | \u22120.664 | Chloroplast. Mitochondrion \nZmFAR03.1 | GRMZM2G155980 | 863 | 97,236.72 | 5.76 | 41.19 | \u22120.356 | Nucleus \nZmFAR03.2 | GRMZM2G155980 | 870 | 98,009.58 | 5.81 | 40.98 | \u22120.365 | Chloroplast. Nucleus. \nZmFAR04.1 | GRMZM2G034868 | 817 | 94,179.37 | 5.31 | 47.83 | \u22120.665 | Nucleus \nZmFAR04.2 | GRMZM2G034868 | 772 | 89,257.18 | 5.46 | 47.97 | \u22120.614 | Nucleus \nZmFAR05.1 | GRMZM2G302323 | 850 | 96,714.61 | 6.77 | 51.62 | \u22120.427 | Chloroplast. Nucleus. \nZmFAR06.1 | GRMZM2G043250 | 687 | 78,325.27 | 6.26 | 39.68 | \u22120.572 | Nucleus \nZmFAR07.1 | GRMZM2G117108 | 879 | 100,616.89 | 6.08 | 45.16 | \u22120.573 | Chloroplast \nZmFAR07.2 | GRMZM2G117108 | 883 | 101,135.51 | 6.08 | 44.66 | \u22120.567 | Chloroplast \nZmFAR08.1 | GRMZM2G106653 | 805 | 91,773.36 | 8.99 | 55.30 | \u22120.383 | Chloroplast \nZmFAR09.1 | GRMZM2G406651 | 855 | 94,584.69 | 7.02 | 52.79 | \u22120.378 | Chloroplast \nZmFAR09.2 | GRMZM2G406651 | 855 | 94,584.69 | 7.02 | 52.79 | \u22120.378 | Chloroplast \nZmFAR10.1 | GRMZM2G048987 | 690 | 79,156.85 | 6.29 | 40.42 | \u22120.634 | Chloroplast. Nucleus. Vacuole \nZmFAR10.2 | GRMZM2G048987 | 779 | 88,945.87 | 6.47 | 44.02 | \u22120.631 | Cell wall \nZmFAR10.3 | GRMZM2G048987 | 779 | 88,945.87 | 6.47 | 44.02 | \u22120.631 | Cell wall \nZmFAR11.1 | GRMZM2G129311 | 956 | 107,796.18 | 6.15 | 42.91 | \u22120.431 | Cell wall. Nucleus \nZmFAR11.2 | GRMZM2G129311 | 899 | 101,760.36 | 6.35 | 44.47 | \u22120.455 | Cell wall. Nucleus \nZmFAR12.1 | GRMZM2G114461 | 704 | 80,279.66 | 7.79 | 48.05 | \u22120.434 | Nucleus \nZmFAR12.2 | GRMZM2G114461 | 648 | 74,258.86 | 8.50 | 47.52 | \u22120.435 | Nucleus \nZmFAR13.1 | GRMZM2G148940 | 673 | 76,583.46 | 8.89 | 43.22 | \u22120.329 | Chloroplast. Nucleus \nZmFAR14.1 | GRMZM2G104268 | 1,023 | 115,820.99 | 6.03 | 48.53 | \u22120.408 | Nucleus \nZmFAR14.2 | GRMZM2G104268 | 1,047 | 118,413.75 | 5.94 | 48.49 | \u22120.414 | Nucleus \nOpen in a new tab\n**Note:**\nProtein characterization information (Number of amino acids, Molecular weight, Theoretical pI, Instability index, Grand average of hydropathicity) was obtained from the ProtParam ( ). Plant-mPLoc ( ) was used for subcellular localization analysis.\n=== The secondary structure and conserved structural domains analysis of ZmFARs proteins ===\nThe secondary structure of ZmFARs were analyzed by SOMPA online software. The results showed that all ZmFAR members contained four conformations, with the highest proportion of \u03b1-helical and random coil structures, accounting for more than 80%. The second highest proportion is extended strand, accounting for 11.46\u201315.32%. And the lowest proportion is \u03b2-turned structures, accounting for only 3.00\u20136.84%. All proteins do not contain a beta sheet structure (Table S5).\nThe conserved motifs analysis of ZmFAR members were performed using MEME online software. Eight conserved motifs were set for testing. The sequence of conserved motifs was shown in Fig. S5. Noticeably, motifs composition and arrangement were in good agreement with the phylogenetic tree (Fig. 2B). The ZmFAR01.1 protein of Group1 differed from the other ZmFAR family proteins greatly, and it contained only Motif4 and Motif7, indicating that the C-terminal of ZmFAR01.1 is very different from other proteins, which is consistent with the result of MegAlign comparison of protein sequences (Fig. S2). The motifs position and number of the remaining ZmFAR proteins were highly conserved and similar, indicating these genes might have similar biological functions. However, ZmFAR09.1 and ZmFAR09.2 in Group2 did not contain Motif7. Members in Group4 did not contain Motif7 except for ZmFAR14.1 and ZmFAR14.2 (Fig. 2B), suggesting _ZmFAR14_ may have different biological functions from _ZmFAR3_ , _ZmFAR8_ , and _ZmFAR7_.\n=== The secondary structure and conserved structural domains analysis of ZmFARs proteins ===\nAnalyzing gene structure, especially the distribution and number of introns and exons, is very important for studying gene\u2019s function. Therefore, we investigated the genes structures of ZmFHY3/FAR1 members. The results showed that _ZmFAR01.1_ in Group1 contained 4 introns; members in Group2 contained a large variation in the number of introns, containing 1\u20137 introns; Group3 had no introns; members in Group4 contained 4\u201310 introns; and members in Group5 contained 0\u20133 introns (Fig. 2D). UTR plays important roles in gene regulation and mRNA stability (Barrett, Fletcher & Wilton, 2012). The _ZmFAR08.1_ gene did not contain UTR, and the rest of the _ZmFARs_ contained either 5\u2019UTR or 3\u2019UTR (Fig. 2D). It can be seen that the gene structures of the subfamilies are significantly different. Interestingly, ZmFAR family members in the same group revealed a high degree of similarity in the arrangement and distribution of exons, indicating that they might have similar biological functions. It is worth noting that nine of the fourteen genes have alternative splicing forms, and they encode at least two variations, indicating _ZmFAR1s_ may function through variable splicing to increase the functional complexity of genes under certain conditions.\n_Cis_ -acting elements in the promoter often determine the expression and function of genes (Hernandez-Garcia & Finer, 2014). In order to explore the _ZmFARs_ expression patterns, we analyzed its promoter by PlantCare website. There are abundant tissue-specific expression response elements, stress response elements, and hormone response elements in the promoter regions (Fig. 5). The stress response elements included high temperature (STRE), low temperature (LTR), temperature (TCA), drought (CCAAT-box, DRE core, DRE1, MBS, MRE, Myb, MYB recognition site, Myb-binding site, MYB-like sequence), anaerobic (ARE and GC-motif), damage (WRE3, W box and WUN-motif), defense and stress response (TC-rich repeats), _etc_. Noticeably, the drought stress response elements contained nine kinds. Hormone response elements included gibberellins (P-box, TATC-box, and GARE-motif), jasmonic acid (MYC, Myc, and JERE), methyl jasmonate (CGTCA-motif, TGACG-motif, and as-1), auxin (TGA-element, TGA-box, and AuxRR-core) salicylic acid (TCA-element, and SARE), abscisic acid (AAGAA-motif, ABRE, ABRE2, ABRE3a, and ABRE4), ethylene (ERE), and others. Especially, ABA response elements are abundant in type and number. The presence of these elements on the promoter implies that the _ZmFARs_ may be involved in abiotic stress response in maize.\n=== Figure 5. _Cis_ -acting elements in promoters of _ZmFARs_. ===\n \nOpen in a new tab\n(A) The unrooted phylogenetic tree of ZmFARs. (B) _Cis_ -elements on promoters associated with stress response. (C) _Cis_ -elements on promoters associated with hormone response. Boxes with different colors represent different _cis_ -element identified by the PlantCARE, and each colored box representing a different motif, shown in the right.\n=== Analysis of tissue expression patterns of _ZmFARs_ ===\nIn order to elucidate the expression patterns of _ZmFARs_ in various tissue during maize development, we analyzed the transcriptome data downloaded from the NCBI. As shown in Fig. 6, _ZmFARs_ displayed different expression patterns in different tissues. Most _ZmFARs_ were highly expressed in the primordium, germinatin kernel, embryo, meristem, leaf, and internode. However, the expression levels in mature pollen, silk, root, endosperm and pericarp/aleurone were low. The expression of _ZmFARs_ varied in different tissues, with _ZmFAR01_ in Group1 being the most different from the other members. The highest expression of _ZmFAR01_ was found in mature leaf 8, while all other _ZmFARs_ had lower expression. This result indicated that _ZmFAR01_ played more important roles in mature leaf than the other genes. The expression levels of ZmFAR members in Group5 were high at all tissues, and especially _ZmFAR04_ was most highly expressed in the primordium. The above results suggested that _ZmFARs_ may play vital roles in different tissues during maize development.\n=== Figure 6. The expression profiles of _ZmFARs_ in various tissues of maize growth stage. ===\n \nOpen in a new tab\n=== Figure 6. The expression profiles of _ZmFARs_ in various tissues of maize growth stage. ===\nThe _ZmFARs_ promoter contains many drought stress response elements, so we suppose that the _ZmFARs_ might be involved in drought stress response in maize. At the early seedling stage, root system and mesocotyls are critical for the early vigour of maize seedlings (Peter et al., 2009; Saenz Rodriguez & Cassab, 2021). The expression level of _ZmFARs_ in primary roots, seminal roots, lateral roots and mesocotyls of B73 seedlings before and after drought stress treatment were detect by qRT-PCR. The results showed that most _ZmFARs_ were down-regulated in root and mesocotyls after drought stress (Fig. 7). _ZmFAR01_ was significantly down-regulated in primary roots, lateral roots and mesocotyls, and its expression level decrease about 2/3 under drought stress than under normal conditions (Fig. 7A). _ZmFAR04_ , _ZmFAR13_ , and _ZmFAR14_ were significantly down-regulated in primary root, seminal root, and mesocotyl, but the fold change was not large (Figs. 7D, 7M, and 7N). _ZmFAR02_ , and _ZmFAR05_ were significantly down-regulated in mesocotyl (Figs. 7B, and 7E). _ZmFAR03_ , _ZmFAR08_ , and _ZmFAR09_ were significantly down-regulated in primary root (Figs. 7C, 7H, and 7I). _ZmFAR07_ were significantly down-regulated in primary root and seminal root (Fig. 7G), while _ZmFAR10_ down-regulated in primary root and lateral root (Fig. 7J). _ZmFAR06_ , _ZmFAR11_ , and _ZmFAR12_ were down-regulated in mesocotyl after drought stress (Figs. 7F, 7K, and 7L).\n=== Figure 7. At the early seedling stage, the expression patterns of _ZmFARs_ in root system and mesocotyls under water deficit. ===\n \nOpen in a new tab\nThe bars indicate the mean \u00b1 SD of three replicates (Student\u2019s _t_ test, *_p_ < 0.05).\nAs a non-permeable, non-ionic osmoticum, polyethyleneglycol (PEG) 6000 cannot enter the pores of plant cell wall space (Oertli, 1985), and many early studies also used PEG-6000 solution to induce drought stress (Liu et al., 2024; Suslov, Daminova & Egorov, 2024). It is a better choice for imposing low water potential, causing a drought stress. To further investigate the expression of _ZmFARs_ in maize roots at the three-leaf stage under drought conditions, 10% (w/v) and 25% (w/v) PEG-6000 aqueous solution were used to treat B73 seedlings. qRT-PCR was performed to detect the expression of _ZmFARs_ after 6, 24, and 48 h of treatment, respectively. The results showed that the expression of _ZmFARs_ were mostly down-regulated in root after PEG-6000 treatment (Fig. 8), which was consistent with the expression patterns in root at the early seedling stage. Compared with control, the expression patterns of _ZmFAR01_ , _ZmFAR03_ , _ZmFAR05_ , _ZmFAR06_ , _ZmFAR09_ , _ZmFAR12_ , _ZmFAR13_ , and _ZmFAR14_ showed a continuous decrease tendency from 6 to 48 h treatments. All of them were significantly down-regulated, but the fold changes were slightly different. The remaining _ZmFARs_ showed inconsistent expression trends under different concentrations of PEG-6000 treatments. For example, after 10% PEG treatment, the expression of _ZmFAR02_ showed a decreasing trend after 6 h, an increasing trend after 24 h, and a decreasing trend after 48 h. Whereas, _ZmFAR02_ was significantly down-regulated after 25% PEG treatment for all time detected (Fig. 8B). The expression of _ZmFAR04_ and _ZmFAR10_ showed up-regulation after 10% PEG-6000 treatment, but not significantly (Figs. 8D and 8J). These results suggested that _ZmFAR_ s may be functional redundancy and play negatively roles in the regulation of target genes expression by altering its own expression in root of maize under drought stress conditions.\n=== Figure 8. The expression patterns of _ZmFARs_ in root under different concentrations of PEG-6000 treatment. ===\n \nOpen in a new tab\nSeedlings with no treatment as control. The bars indicate the mean \u00b1 SD of three replicates (Student\u2019s _t_ test, *_p_ < 0.05).", "Thinking\u6a21\u5f0f": "high", "generation_type": "\u63a8\u7406\u578b"}','2026-03-23 15:33:29.756585','2026-03-23 15:33:29.756596');
INSERT INTO qa_pairs VALUES(43,3,'在玉米中，一类ZmFAR蛋白同时具有典型FAR1/FHY3转录因子的结构域（包括C2H2型锌指DNA结合结构域、含DDE基序的转座酶核心区以及带CxCxnCXH基序的SWIM锌指激活结构域），并且相关基因在分生组织、胚、萌发种子、叶片和节间等生长旺盛的器官中表达量较高。面对这样的结构特征和组织特异性表达格局，如何推断这类ZmFAR基因在玉米生长发育中的功能定位及其在调控网络中的角色？','ZmFAR基因最有可能作为FAR1/FHY3型转录因子家族成员，参与调控与细胞增殖、器官分化和代谢相关的大量下游基因表达，因此在分生组织、胚、幼叶和节间等生长旺盛部位发挥重要的生长发育调控作用，而不是仅作为结构或代谢酶类基因被动表达。','{"species": "Zea mays", "paper_id": "PMC11216215", "reasoning_steps": ["Step 1: FAR1/FHY3 proteins contain DNA-binding and transcriptional activation domains (C2H2-type zinc finger and SWIM zinc finger), so members of this family function as transcription factors that regulate gene expression.", "Step 2: ZmFAR proteins all contain the conserved FHY3 domain and key motifs (CCHH in N-terminus, DDE catalytic triad, CxCxnCXH in SWIM domain), indicating they have typical FAR1/FHY3 structural features and can form homo- or heterodimers, consistent with transcription factor activity.", "Step 3: FAR1/FHY3-type transcription factors in plants are known to participate in many growth- and development-related processes, including photomorphogenesis, stem and flower development, chloroplast division, chlorophyll biosynthesis, starch synthesis, and responses to hormones and stresses.", "Step 4: In maize, most ZmFAR genes show high expression in tissues with vigorous growth, such as primordium, germinating kernel, embryo, meristem, leaf, and internode.", "Step 5: High expression of regulatory transcription factors in vigorously growing tissues usually indicates that these factors participate in controlling cellular processes required for growth and development in those tissues.", "Step 6: Therefore, combining their structural identity as FAR1/FHY3-type transcription factors with their expression enrichment in rapidly growing organs, most ZmFAR genes are inferred to play important roles in regulating maize growth and development."], "question_cot": "", "final_conclusion": "Most ZmFAR genes are important regulators of maize growth and development because they are highly expressed in vigorously growing tissues and belong to a transcription factor family known to control multiple developmental processes.", "difficulty": "medium", "tags": ["mechanism", "concept", "result", "application"], "created_at": "2025-12-16T21:19:53", "token_est_question": 1, "token_est_answer": 1, "section": "Discussion", "context": "=== Discussion ===\nIn this study, we identified 24 ZmFAR members encoded by 14 genes (Fig. 1), which all contain the FHY3 structural domain (Fig. 2B). FAR1/FHY3 proteins contain three functional structural domains, including the C2H2-type zinc finger domain with DNA-binding activity, the putative core transposase domain, and the SWIM zinc finger domain with transcriptional activation activity (Lin et al., 2008). All ZmFAR members have conserved CCHH motif in N-terminal (Fig. S2). The \u201cDDE\u201d catalytic triad motif in the middle position, as well as the SWIM zinc-finger domain at the C-terminal with conserved CxCxnCXH motif, are essential for their homodimerization or heterodimerization with other FAR1 (Lin et al., 2008). Evolutionary relationships and conserved motifs analyses revealed that the protein sequence of ZmFAR01 differs significantly from the other ZmFARs (Figs. 2 and 3), indicating ZmFAR01 may function differently from the other proteins, although its expression pattern is similar to that of the other genes after drought treatment in root (Figs. 7 and 8). Compared to other tissues, _ZmFAR01_ is highly expressed in mature leaves, suggesting that _ZmFAR01_ functions mainly in mature leaves. But what it functions? To regulate leaf response to light, to regulate leaf growth and development (leaf senescence), to regulate chloroplast synthesis in leaves, or to regulate leaf response to drought stress similar to the function in roots, all of which need to be verified.\nFAR1/FHY3 play multiple roles in a wide range of cellular processes, including light signal transduction, photomorphogenesis, regulation of the biological clock and flowering time, stem meristem and flower development, chloroplast division, chlorophyll biosynthesis, starch synthesis, abscisic acid response, oxidative stress response, plant immunity, and low-phosphorus response, _etc_ (Ma & Li, 2018). Many transcription factors, proteins involved in cell wall extension, and related to redox balance control reduced responsiveness in _fhy3_ (Hudson, Lisch & Quail, 2003). Subcellular localization analysis showed that ZmFARs were distributed in nucleus, chloroplast, mitochondrion, vacuole, and cell wall (Table 1). In Arabidopsis, _FRS1_ , _FRS8_ , and _FRS9_ are targeted into the nucleus despite their lack of predicted NLSs (Lin & Wang, 2004). In tea plants, _CsFRS1_ , _CsFRS7_ , _CsFRS14_ , _CsFRS16_ , _CsFRS18_ , and _CsFRS23_ are predicted to target other cellular components besides the nucleus (Liu et al., 2021). This suggested that ZmFAR1s might enter the nucleus by interacting with other members of ZmFAR1s to form a homologous or heterodimers to regulate target genes expression under certain specific conditions. But the subcellular localization of ZmFAR1 needs further experimental verification.\nPrevious reports FAR1 family members exhibit different tissue-specific expression patterns in species. For example, in Arabidopsis, the _AtFAR1s_ were expressed in leaves, stems, and flowers (Lin & Wang, 2004). In cotton, most genes were highly expressed in leaves and only a few genes were expressed in the stem, petal, and torus (Yuan et al., 2018). About 36.2% of _AhFAR1s_ specific expression in flower, peg, leaf, root, and stem, 34.1% of genes were specifically expressed in shells and seeds (Lu et al., 2022). We found that most of the _ZmFARs_ were expressed in high levels at sites of vigorous growth, such as primordium, germinatin kernel, embryo, meristem, leaf, and internode (Fig. 6), suggesting that _ZmFARs_ plays an important role in regulating the growth and development of maize.\nImprovement of root system architecture has been the goal of modern breeding programs to produce drought-tolerant varieties (Ranjan et al., 2022). Many transcription factors families have been identified participating in gene expression regulation or having an impact on root development under drought stress conditions (Janiak, Kwasniewski & Szarejko, 2016). The enrichment of the _ZmFAR_ s promoter regions with drought stress-responsive _cis_ -acting elements predicts that the ZmFAR family members may be involved in drought stress response in maize. At early growth and development of B73 seedlings, most of the _ZmFARs_ were down-regulated in primary roots, lateral roots and mesocotyls (Fig. 7). This is consistent with the results of different concentration of PEG simulated drought treatment of B73 seedlings in root at the three-leaf stage (Fig. 8). However, some _ZmFARs_ were also up-regulated, such as _ZmFAR04_ and _ZmFAR10_. In barley, some _HvFRF_ genes were significantly upregulated in response to drought stress, and _HvFRF9_ overexpression could enhance drought resistance in _Arabidopsis_ (He et al., 2024). _MYB96_ reduces lateral root growth and enhances drought tolerance in plants by integrating ABA and auxin pathways (Seo et al., 2009). It has been found that root growth was not or only slightly affected at \u22120.2 MPa for short-term (Sharp et al., 2004), and lower water potentials \u22120.8 MPa can significantly reduce primary root elongation (Opitz et al., 2014; Sharp et al., 2004). Both concentrations of 10% (about \u22120.15 MPa) and 25% PEG-6000 (about \u22120.15 MPa) taken in this study to simulate drought (Michel & Kaufmann, 1973), root growth was affected and root hairs were significantly reduced. After 48 h of treatment, partial necrosis had appeared at the root tip site (Fig. S1). There is an epistatic role of FAR1/FHY3 family genes, which regulate the expression of other transcription factors and essential for plant growth and development (Li et al., 2016; Lin et al., 2007). We speculate that ZmFARs regulates plant drought stress resistance, may integrate ABA signalling and may regulate ABI5. It is also possible that ROS signalling may be integrated. This conjecture needs to be subsequently verified. In addition to involvement in drought, the ZmFAR1 family also has potential applications in inflorescence development (Tang et al., 2024).", "Thinking\u6a21\u5f0f": "high", "generation_type": "\u63a8\u7406\u578b"}','2026-03-23 15:33:29.860907','2026-03-23 15:33:29.860917');
INSERT INTO qa_pairs VALUES(44,3,'在玉米中鉴定的 FAR1/FHY3 家族基因（ZmFARs）启动子区域富含胁迫和激素应答元件，尤其是干旱应答相关的 cis 元件，同时这些基因在正常条件下在多种组织中广泛表达，但在干旱胁迫时初生根、胚根、侧根和中胚轴中的大多数成员转录水平均明显降低。结合植物在胁迫过程中通常会上调有利于提高抗性的基因、而下调削弱抗性的基因这一一般规律，应如何判断 ZmFARs 对玉米干旱胁迫抗性的调控方向，并说明理由？','ZmFARs 更可能作为玉米干旱胁迫抗性的负调控因子，其表达在干旱条件下被普遍下调，有利于解除对抗旱反应的不利影响，从而提高植株整体抗性。','{"species": "Zea mays", "paper_id": "PMC11216215", "reasoning_steps": ["Step 1: \u7389\u7c73\u4e2d\u9274\u5b9a\u5230\u4e00\u7c7b FAR1/FHY3 \u5bb6\u65cf\u57fa\u56e0\uff08ZmFARs\uff09\uff0c\u5176\u542f\u52a8\u5b50\u533a\u542b\u6709\u4e30\u5bcc\u7684\u80c1\u8feb\u5e94\u7b54\u548c\u6fc0\u7d20\u5e94\u7b54\u5143\u4ef6\uff0c\u5c24\u5176\u662f\u4e0e\u5e72\u65f1\u5e94\u7b54\u76f8\u5173\u7684 cis \u5143\u4ef6\uff0c\u8868\u660e\u8fd9\u4e9b\u57fa\u56e0\u5177\u6709\u53c2\u4e0e\u5e72\u65f1\u54cd\u5e94\u8c03\u63a7\u7684\u6f5c\u529b\u3002", "Step 2: ZmFARs \u5728\u7389\u7c73\u7684\u6240\u6709\u7ec4\u7ec7\u4e2d\u5747\u6709\u8868\u8fbe\uff0c\u8bf4\u660e\u5b83\u4eec\u4e0e\u690d\u682a\u7684\u751f\u957f\u53d1\u80b2\u8fc7\u7a0b\u5e7f\u6cdb\u76f8\u5173\u3002", "Step 3: \u5728\u5e72\u65f1\u80c1\u8feb\u6761\u4ef6\u4e0b\uff0c\u7389\u7c73\u7684\u521d\u751f\u6839\u3001\u80da\u6839\u3001\u4fa7\u6839\u548c\u4e2d\u80da\u8f74\u4e2d\uff0c\u5927\u591a\u6570 ZmFARs \u8868\u8fbe\u91cf\u88ab\u4e0b\u8c03\uff0c\u8bf4\u660e\u690d\u7269\u5728\u5e72\u65f1\u65f6\u4e3b\u52a8\u964d\u4f4e\u8fd9\u4e9b\u57fa\u56e0\u7684\u8868\u8fbe\u3002", "Step 4: \u4e00\u822c\u800c\u8a00\uff0c\u5982\u679c\u67d0\u57fa\u56e0\u6709\u52a9\u4e8e\u63d0\u9ad8\u6297\u9006\u6027\uff0c\u690d\u7269\u5728\u80c1\u8feb\u4e0b\u5f80\u5f80\u4f1a\u4e0a\u8c03\u5176\u8868\u8fbe\uff1b\u76f8\u53cd\uff0c\u82e5\u67d0\u57fa\u56e0\u524a\u5f31\u6297\u6027\uff0c\u5219\u5728\u80c1\u8feb\u65f6\u4e0b\u8c03\u5176\u8868\u8fbe\u6709\u5229\u4e8e\u63d0\u9ad8\u6574\u4f53\u6297\u6027\u3002", "Step 5: \u7ed3\u5408 ZmFARs \u7684\u5e72\u65f1\u76f8\u5173 cis \u5143\u4ef6\u7279\u5f81\u4e0e\u5e72\u65f1\u6761\u4ef6\u4e0b\u8868\u8fbe\u88ab\u666e\u904d\u4e0b\u8c03\u7684\u6a21\u5f0f\uff0c\u53ef\u4ee5\u63a8\u65ad\u8fd9\u4e9b\u57fa\u56e0\u5728\u5e72\u65f1\u54cd\u5e94\u901a\u8def\u4e2d\u66f4\u53ef\u80fd\u53d1\u6325\u8d1f\u8c03\u63a7\u4f5c\u7528\u3002", "Step 6: \u56e0\u6b64\uff0cFAR1/FHY3 \u5bb6\u65cf\u57fa\u56e0\u88ab\u8ba4\u4e3a\u53ef\u80fd\u8d1f\u5411\u8c03\u63a7\u7389\u7c73\u7684\u5e72\u65f1\u80c1\u8feb\u6297\u6027\u3002"], "question_cot": "", "final_conclusion": "FAR1/FHY3 \u5bb6\u65cf\u57fa\u56e0\u5728\u7389\u7c73\u4e2d\u5f88\u53ef\u80fd\u4f5c\u4e3a\u5e72\u65f1\u80c1\u8feb\u6297\u6027\u7684\u4e00\u7c7b\u8d1f\u8c03\u63a7\u56e0\u5b50\u3002", "difficulty": "medium", "tags": ["mechanism", "result"], "created_at": "2025-12-16T21:20:41", "token_est_question": 9, "token_est_answer": 2, "section": "Conclusion", "context": "=== Conclusions ===\nIn short, we performed a systematic identification and analysis of the FAR1/FHY3 family genes in maize. A total of twenty-four ZmFAR members, named ZmFAR01-14, were identified in the maize through a genome-wide study. ZmFARs can be divided into five subgroups based on their phylogenic relationships, and the protein and gene structures of each subfamily varied greatly. The promoter regions of _ZmFARs_ contained abundant stress-responsive and hormone-responsive _cis_ -elements, especially drought-responsive _cis_ -elements. _ZmFARs_ were expressed in all tissues of maize, but the expression level varies greatly. Most _ZmFARs_ were down-regulated in primary roots, seminal roots, lateral roots and mesocotyls of maize under drought stress, implying that the FAR1/FHY3 family has important roles in plant growth and development, and drought stress response. FAR1/FHY3 family may negatively regulate drought stress resistance in maize. These results lay the foundation for analysis of the functions of _ZmFARs_ in response to abiotic stresses, and also provide potential genetic resources for the genetic improvement of drought-tolerant maize lines.", "Thinking\u6a21\u5f0f": "high", "generation_type": "\u63a8\u7406\u578b"}','2026-03-23 15:33:29.965045','2026-03-23 15:33:29.965055');
INSERT INTO qa_pairs VALUES(45,3,'Annexin 蛋白在植物中被认为是依赖钙的磷脂结合蛋白，参与多种生长发育过程并在胁迫响应中具有重要功能。同一物种内的不同 annexin 基因成员往往在不同组织以及不同非生物或生物胁迫下呈现差异化表达。对玉米多种组织在冷胁迫条件下获得的转录组数据进行加权基因共表达网络分析时，发现在冷胁迫相关的共表达模块中同时包含 ZmAnn2、ZmAnn6、ZmAnn7 和 ZmAnn9，而此类共表达模块通常由在特定条件下表达变化高度相关、共同参与相近生物学过程或调控网络的基因组成。面对这些信息，一名育种或分子生物学研究者应如何推断这四个 ZmAnn 基因在玉米冷胁迫响应中的潜在作用及其在调控网络中的地位？','可以推断 ZmAnn2、ZmAnn6、ZmAnn7 和 ZmAnn9 是玉米冷胁迫响应调控网络中的候选关键成员，很可能参与冷胁迫相关的基因共表达与信号转导过程。理由是：首先，annexin 家族在植物多种胁迫响应中具有保守而重要的功能，这提示家族成员本身就倾向于参与胁迫相关通路；其次，同一物种内不同 annexin 基因在组织和胁迫条件下呈现特异性表达，意味着不同成员在不同环境刺激下有功能分工；再次，这四个基因在冷胁迫条件下与一组其他基因共同形成高度相关的共表达模块，而此类模块一般对应某一特定生物学过程或调控通路；因此，它们在冷胁迫特异的共表达模块中被同时检出，结合家族已知的胁迫功能，说明这四个基因极有可能直接或间接参与冷胁迫响应相关的基因调控网络，可作为功能验证与抗冷育种的优先候选基因。','{"species": "Zea mays", "paper_id": "PMC11832922", "reasoning_steps": ["Step 1: Annexin \u86cb\u767d\u662f\u4e00\u7c7b\u8fdb\u5316\u4e0a\u4fdd\u5b88\u3001\u4f9d\u8d56\u9499\u5e76\u4e0e\u78f7\u8102\u7ed3\u5408\u7684\u86cb\u767d\uff0c\u5728\u690d\u7269\u7684\u751f\u957f\u53d1\u80b2\u4ee5\u53ca\u5404\u79cd\u80c1\u8feb\u54cd\u5e94\u4e2d\u53d1\u6325\u5173\u952e\u4f5c\u7528\uff0c\u56e0\u6b64\u7279\u5b9a\u7684 Ann \u57fa\u56e0\u901a\u5e38\u4e0e\u80c1\u8feb\u53cd\u5e94\u529f\u80fd\u76f8\u5173\u3002", "Step 2: \u5728\u7389\u7c73\u4e2d\u5b58\u5728\u4e00\u7ec4 Ann \u57fa\u56e0\u5bb6\u65cf\u6210\u5458\uff08ZmAnn \u57fa\u56e0\uff09\uff0c\u5176\u4e2d\u4e0d\u540c\u6210\u5458\u5728\u4e0d\u540c\u7ec4\u7ec7\u4ee5\u53ca\u5728\u5404\u79cd\u975e\u751f\u7269\u548c\u751f\u7269\u80c1\u8feb\u5904\u7406\u4e0b\u5448\u73b0\u51fa\u4e0d\u540c\u7684\u8868\u8fbe\u6a21\u5f0f\uff0c\u8fd9\u8868\u660e\u5404\u4e2a\u6210\u5458\u53ef\u80fd\u5728\u4e0d\u540c\u7c7b\u578b\u7684\u80c1\u8feb\u54cd\u5e94\u4e2d\u5206\u5de5\u4e0d\u4e00\u3002", "Step 3: \u5229\u7528\u591a\u79cd\u7389\u7c73\u7ec4\u7ec7\u5728\u51b7\u80c1\u8feb\u6761\u4ef6\u4e0b\u7684\u8f6c\u5f55\u7ec4\u6570\u636e\u8fdb\u884c\u52a0\u6743\u57fa\u56e0\u5171\u8868\u8fbe\u7f51\u7edc\u5206\u6790\u65f6\uff0c\u53d1\u73b0\u6709\u56db\u4e2a Ann \u57fa\u56e0\uff08ZmAnn2\u3001ZmAnn6\u3001ZmAnn7\u3001ZmAnn9\uff09\u51fa\u73b0\u5728\u5171\u8868\u8fbe\u6a21\u5757\u4e2d\u3002", "Step 4: \u5171\u8868\u8fbe\u6a21\u5757\u662f\u7531\u5728\u7279\u5b9a\u6761\u4ef6\uff08\u6b64\u5904\u4e3a\u51b7\u80c1\u8feb\uff09\u4e0b\u8868\u8fbe\u91cf\u53d8\u5316\u9ad8\u5ea6\u76f8\u5173\u7684\u57fa\u56e0\u7ec4\u6210\u7684\uff0c\u8fd9\u4e9b\u57fa\u56e0\u901a\u5e38\u5171\u540c\u53c2\u4e0e\u76f8\u540c\u6216\u5bc6\u5207\u76f8\u5173\u7684\u751f\u7269\u5b66\u8fc7\u7a0b\u6216\u8c03\u63a7\u7f51\u7edc\u3002", "Step 5: \u7531\u4e8e\u4e0a\u8ff0\u56db\u4e2a ZmAnn \u57fa\u56e0\u5728\u51b7\u80c1\u8feb\u6761\u4ef6\u4e0b\u4e0e\u5176\u4ed6\u57fa\u56e0\u4e00\u8d77\u5f62\u6210\u5171\u8868\u8fbe\u6a21\u5757\uff0c\u5e76\u4e14 annexin \u5bb6\u65cf\u88ab\u8ba4\u4e3a\u5728\u80c1\u8feb\u54cd\u5e94\u4e2d\u5177\u6709\u5173\u952e\u4f5c\u7528\uff0c\u53ef\u4ee5\u63a8\u65ad\u8fd9\u4e9b\u57fa\u56e0\u4e0e\u51b7\u80c1\u8feb\u54cd\u5e94\u8fc7\u7a0b\u5bc6\u5207\u76f8\u5173\u3002", "Step 6: \u56e0\u6b64\uff0c\u53ef\u4ee5\u5f97\u51fa\u7ed3\u8bba\uff1aZmAnn2\u3001ZmAnn6\u3001ZmAnn7 \u548c ZmAnn9 \u5f88\u53ef\u80fd\u53c2\u4e0e\u51b7\u80c1\u8feb\u76f8\u5173\u7684\u57fa\u56e0\u5171\u8868\u8fbe\u7f51\u7edc\uff0c\u5e76\u5728\u51b7\u80c1\u8feb\u54cd\u5e94\u4e2d\u53d1\u6325\u529f\u80fd\u3002"], "question_cot": "", "final_conclusion": "\u5728\u7389\u7c73\u4e2d\uff0cZmAnn2\u3001ZmAnn6\u3001ZmAnn7 \u548c ZmAnn9 \u5f88\u53ef\u80fd\u53c2\u4e0e\u51b7\u80c1\u8feb\u54cd\u5e94\u76f8\u5173\u7684\u57fa\u56e0\u8c03\u63a7\u7f51\u7edc\u3002", "difficulty": "medium", "tags": ["result", "mechanism", "method", "application"], "created_at": "2025-12-16T21:14:24", "token_est_question": 11, "token_est_answer": 10, "section": "Abstract", "context": "=== Abstract ===\nAnnexins (Anns) are a family of evolutionarily conserved, calcium-dependent, phospholipid-binding proteins that play critical roles in plant growth, development, and stress responses. Utilizing the pan-genome of 26 high-quality maize genomes, we identified 12 Ann genes, comprising 9 core genes (present in all 26 lines) and 3 near-core genes (present in 24\u201325 lines). This highlights the limitations of studying _ZmAnn_ genes based on a single reference genome. Evaluating the Ka/Ks values of Ann genes in 26 varieties revealed that _ZmAnn10_ was under positive selection in certain varieties, while the remaining genes had Ka/Ks values less than 1, indicating purifying selection. Phylogenetic analysis divided _ZmAnn_ proteins into six groups, with group VI containing only _ZmAnn12_. Structural variation in certain varieties altered the conserved domains, generating many atypical genes. Transcriptome analysis showed that different Ann members have distinct expression patterns in various tissues and under different abiotic and biotic stress treatments. Weighted gene co-expression network analysis of transcriptome data from various maize tissues under cold stress identified four Ann genes (_ZmAnn2_ , _ZmAnn6_ , _ZmAnn7_ , _ZmAnn9_) involved in co-expression modules. Overall, this study utilized high-quality maize pangenomes to perform a bioinformatic analysis of _ZmAnn_ genes, providing a foundation for further research on _ZmAnn_ genes.\n=== Supplementary Information ===\nThe online version contains supplementary material available at 10.1038/s41598-025-89119-5.\n**Keywords:** Maize pan-genome, Annexin, Expression profiles, Cold stress\n**Subject terms:** Genetics, Plant sciences", "Thinking\u6a21\u5f0f": "high", "generation_type": "\u63a8\u7406\u578b"}','2026-03-23 15:33:30.076793','2026-03-23 15:33:30.076804');
INSERT INTO qa_pairs VALUES(46,3,'在拟南芥中，低温激活的蛋白激酶 OST1 能磷酸化 annexin 成员 AtAnn1，增强其 Ca2+ 转运活性并放大细胞内 Ca2+ 信号，从而启动冷响应、提高抗冻性；在水稻中，CRISPR/Cas9 敲除 annexin 家族成员 OsAnn3 和 OsAnn5 会使幼苗对冷处理更敏感；在玉米中，将 ZmAnn33/35 异源表达到拟南芥中有助于幼苗从冷胁迫中恢复，并增强质膜完整性，这一效应与活性氧清除和胞吐作用有关。结合这些跨物种的遗传和功能证据，应如何推理出 annexin 在冷胁迫响应中主要承担哪几类关键功能，以及为何可以将其视为提高作物耐寒性的有前景育种靶标？','跨物种的遗传和功能证据表明，annexin 普遍作为植物耐寒性的正调控因子，其关键作用可以概括为两大类：一类是通过被冷信号激活的蛋白激酶（如 OST1）磷酸化后，提高自身 Ca2+ 转运或通道活性，从而放大冷诱导的 Ca2+ 信号并高效启动下游冷响应基因表达；另一类是通过参与活性氧清除、胞吐调节和质膜稳定性维持，减轻低温造成的膜损伤和氧化胁迫。拟南芥中 AtAnn1 被磷酸化后增强 Ca2+ 信号并提高抗冻性、水稻中 OsAnn3/OsAnn5 缺失导致冷敏感、玉米 ZmAnn33/35 在拟南芥中的异源表达改善冷后恢复和膜完整性，这些结果在不同物种和不同基因背景下一致指向 annexin 功能缺失会削弱耐寒性，而功能增强则提高耐寒性。因此，在分子育种中可将 annexin 视为优先考虑的靶标，通过过表达、增强其被激活能力或利用高效等位变异，有望同时强化冷信号转导和细胞保护两方面，从而系统性提高作物对低温和冷冻胁迫的适应能力。','{"species": "Zea mays", "paper_id": "PMC11832922", "reasoning_steps": ["Step 1: Annexin\u662f\u4e00\u7c7bCa2+\u4f9d\u8d56\u4e14\u80fd\u7ed3\u5408\u78f7\u8102\u7684\u86cb\u767d\uff0c\u53c2\u4e0eCa2+\u611f\u77e5\u548c\u4fe1\u53f7\u4f20\u5bfc\uff0c\u4ece\u800c\u5c06Ca2+\u52a8\u6001\u53d8\u5316\u4e0e\u7ec6\u80de\u9aa8\u67b6\u548c\u591a\u79cd\u751f\u7406\u54cd\u5e94\u8054\u7cfb\u8d77\u6765\u3002", "Step 2: \u5728\u51b7\u80c1\u8feb\u6761\u4ef6\u4e0b\uff0c\u86cb\u767d\u6fc0\u9176OST1\u88ab\u51b7\u4fe1\u53f7\u6fc0\u6d3b\uff0cOST1\u53ef\u78f7\u9178\u5316AtAnn1\u5e76\u589e\u5f3aAtAnn1\u7684Ca2+\u8f6c\u8fd0\u6d3b\u6027\uff0c\u4ece\u800c\u653e\u5927Ca2+\u4fe1\u53f7\uff0c\u8fd9\u6761\u4fe1\u53f7\u9014\u5f84\u6fc0\u6d3b\u51b7\u54cd\u5e94\u5e76\u63d0\u9ad8Arabidopsis\u7684\u6297\u51bb\u6027\u3002", "Step 3: \u5728\u6c34\u7a3b\u4e2d\uff0c\u5229\u7528CRISPR/Cas9\u6572\u9664Annexin\u5bb6\u65cf\u6210\u5458OsAnn5\u548cOsAnn3\uff0c\u4f1a\u4f7f\u690d\u682a\u5728\u5e7c\u82d7\u671f\u5bf9\u51b7\u5904\u7406\u66f4\u654f\u611f\uff0c\u8bf4\u660e\u8fd9\u4e9bannexin\u57fa\u56e0\u5728\u6c34\u7a3b\u7684\u51b7\u80c1\u8feb\u8010\u53d7\u4e2d\u53d1\u6325\u6b63\u5411\u4f5c\u7528\u3002", "Step 4: \u5728\u7389\u7c73\u4e2d\uff0c\u5c06ZmAnn33/35\u5f02\u6e90\u8868\u8fbe\u5230Arabidopsis\u4e2d\uff0c\u53ef\u4ee5\u4fc3\u8fdb\u5e7c\u82d7\u4ece\u51b7\u80c1\u8feb\u4e2d\u6062\u590d\uff0c\u5e76\u589e\u5f3a\u8d28\u819c\u5b8c\u6574\u6027\uff0c\u8fd9\u4e00\u6548\u5e94\u4e0e\u5176\u5728ROS\u6e05\u9664\u548c\u80de\u5410\u4f5c\u7528\u4e2d\u7684\u529f\u80fd\u6709\u5173\u3002", "Step 5: \u7efc\u4e0a\uff0c\u4e0d\u540c\u7269\u79cd\uff08Arabidopsis\u3001\u6c34\u7a3b\u3001\u7389\u7c73\uff09\u7684\u591a\u4e2aannexin\u86cb\u767d\uff0c\u8981\u4e48\u88ab\u6fc0\u6d3b\u540e\u589e\u5f3aCa2+\u4fe1\u53f7\u3001\u542f\u52a8\u51b7\u54cd\u5e94\uff0c\u8981\u4e48\u901a\u8fc7\u6e05\u9664ROS\u548c\u4fdd\u62a4\u8d28\u819c\uff0c\u4ece\u800c\u5728\u4f4e\u6e29\u6216\u51b7\u51bb\u80c1\u8feb\u4e0b\u63d0\u9ad8\u690d\u7269\u5b58\u6d3b\u548c\u6062\u590d\u80fd\u529b\u3002", "Step 6: \u56e0\u6b64\uff0c\u53ef\u4ee5\u63a8\u65ad\u690d\u7269annexin\u86cb\u767d\u5728\u4f4e\u6e29\u76f8\u5173\u9006\u5883\u4e2d\u666e\u904d\u8d77\u5230\u63d0\u9ad8\u8010\u5bd2/\u6297\u51bb\u6027\u7684\u6b63\u8c03\u63a7\u4f5c\u7528\uff0c\u5176\u5173\u952e\u673a\u5236\u5305\u62ecCa2+\u4fe1\u53f7\u653e\u5927\u4e0e\u819c\u548c\u6c27\u5316\u7a33\u6001\u7684\u7ef4\u62a4\u3002"], "question_cot": "", "final_conclusion": "\u591a\u79cd\u690d\u7269annexin\u86cb\u767d\u6574\u4f53\u4e0a\u4f5c\u4e3a\u6b63\u8c03\u63a7\u56e0\u5b50\uff0c\u63d0\u9ad8\u690d\u7269\u5bf9\u4f4e\u6e29/\u51b7\u51bb\u80c1\u8feb\u7684\u8010\u53d7\u6027\uff0c\u4e3b\u8981\u901a\u8fc7\u589e\u5f3aCa2+\u4fe1\u53f7\u4e0e\u7ef4\u6301\u819c\u5b8c\u6574\u6027\u7b49\u673a\u5236\u5b9e\u73b0\u3002", "difficulty": "medium", "tags": ["concept", "mechanism", "application"], "created_at": "2025-12-16T21:16:14", "token_est_question": 27, "token_est_answer": 24, "section": "Introduction", "context": "=== Introduction ===\nAnnexin (Ann), comprising a multigene family classified as a subfamily of calcium (Ca2+)-dependent and phospholipid-binding proteins, is evolutionarily conserved and widely distributed across various taxonomic groups, encompassing prokaryotes, fungi, protists, plants, and vertebrates1. Annexins are proteins with multiple functions, featuring the distinctive annexin repeat and possessing residues associated with peroxidase, ATPase/GTPase activity, and Ca2+ channel regulatory activity. They serve to mediate Ca2+ sensing and signaling, thereby linking Ca2+ dynamics to cytoskeletal responses. This functional versatility results in numerous regulatory effects on plant growth, development, and stress response2,3. Typical mammalian membrane annexins possess a C-terminal core comprising four annexin repeats, with each repeat consisting of approximately 70 amino acids and containing a characteristic type II motif for calcium ion binding. Additionally, these annexins feature a highly variable N-terminal region, which serves as a secondary modification site that dictates interactions with other proteins. This variability in the N-terminal region is responsible for the functional diversity observed among animal annexins1,4. The diverse functions of annexins are attributed to their ability to interact with various ligands, including lipids, proteins, and ions, and to their subcellular localization in different compartments, such as the cytosol, membranes, and organelles.\nInvestigating the genetic mechanisms underlying crop resistance to drought and tolerance to salt, cold, heat, and other adverse conditions represents one of the most cost-effective and sustainable approaches to enhancing crop productivity and yield stability, thereby significantly contributing to the global food security agenda5\u20137. Therefore, understanding the molecular mechanisms that confer stress tolerance in crops is essential for developing new varieties that can withstand these challenges and ensure food security for a growing population. Recent investigations have demonstrated the significant role of plant annexin proteins in both plant growth and development, as well as in response to abiotic and biotic stresses. During the elongation phases of cotton fiber development, the highly expressed cotton annexin gene, _GhAnn2_ , assumes a crucial regulatory function by modulating the flux of Ca2+ signaling in fiber development8. Oxylipin treatment upregulates _Arabidopsis AtAnn7_ expression, leading to callose deposition, wavy root development, and lateral root inhibition9. The expression of _AtAnn5_ is specifically observed in mature pollen, and silencing this gene leads to the production of abnormal pollen grains and severe male sterility10. Under long day conditions, _Arabidopsis_ single-mutant _AtAnn1_ and _AtAnn4_ plants exhibit tolerance to drought and salt stress11. Overexpression of _OsANN3_ enhances sensitivity to exogenous ABA, and when plants are subjected to drought stress, may activate the ABA signaling pathway by mediating Ca\u00b2\u207a influx through interaction with phospholipids12. Overexpression of _RsAnn1a_ in _Raphanus sativus_ can improve plant heat tolerance13. It was demonstrated that cold activation of _OST1_ leads to the phosphorylation of _AtAnn1_ , thereby enhancing its Ca2+ transport activity and further potentiating Ca2+ signaling. This unraveled a cascade linking OST1-AtAnn1 to cold-induced Ca2+ signal generation, which activates the cold response and consequently enhances freezing tolerance in _Arabidopsis_14. Compared to wild-type rice, CRISPR/Cas9-mediated genome editing to knock out _OsAnn5_ and _OsAnn3_ renders the plants sensitive to cold treatment, suggesting the involvement of _OsAnn5_ and _OsAnn3_ in cold stress tolerance during the seedling stage15,16. The ectopic expression of _ZmAnn33/35_ in _Arabidopsis_ facilitates seedling recovery from cold stress and reinforces PM (Plasma Membrane) integrity, potentially ascribed to their roles in ROS (Reactive Oxygen Species) elimination and exocytosis17.\nThe recent surge in functional genomics research has revealed a high degree of genomic variation within species. This highlights the limitations of a single reference genome, which typically represents a single individual and might not capture the full range of genetic diversity18. Pan-genomics, which encompasses most of the genetic diversity of species or populations, has been proven to be a highly effective tool for studying genome evolution, species origin and domestication, and providing valuable insights for plant improvement19. Leveraging the maize pan-genome, comprising 26 high-quality genomes as publish by Hufford et al.20, this study comprehensively characterizes the maize annexin gene family. It delves into the gene structure, conserved domains, cis-regulatory elements, and phylogenetic classification of annexin genes across the pan-genome. Transcriptome data is employed to investigate gene expression patterns under diverse stress conditions. Furthermore, co-expression network analysis and GO enrichment analysis are utilized to unravel the regulatory network and biological processes involving annexin proteins under cold stress.", "Thinking\u6a21\u5f0f": "high", "generation_type": "\u63a8\u7406\u578b"}','2026-03-23 15:33:30.197188','2026-03-23 15:33:30.197199');
INSERT INTO qa_pairs VALUES(47,3,'在对一批玉米自交系开展群体层面的结构变异功能分析时，研究者同时掌握了每份材料的基因组结构变异定位信息和同一批材料在多种组织中的转录组表达数据。若希望系统评估某个基因的表达水平在不同材料之间是否与该基因上是否存在结构变异状态显著相关，而不仅是做简单的分组均值比较，应该如何设计完整的数据处理与统计分析流程，从原始表达矩阵和SV注释出发，得到可以客观判定“该基因表达是否显著受SV影响”的结果？','需要先将多组织的表达量在每个材料内对同一基因进行汇总，得到该基因在每份材料中的总体表达水平；然后利用SV注释信息判断该基因在各材料中是否与结构变异发生重叠，并据此为每个材料的该基因赋予“有SV/无SV”的二元状态；在所有材料中，以“是否与SV重叠”作为一个变量、该基因的总体表达水平作为另一个变量，计算二者的Pearson相关系数，并对相关性进行显著性检验；最终，仅在相关系数通过显著性检验的情况下，才认定该基因的表达水平与其是否发生结构变异在群体中存在显著相关关系，从而判定其表达显著受SV影响。','{"species": "Zea mays", "paper_id": "PMC11832922", "reasoning_steps": ["Step 1: 26\u4efd\u7389\u7c73\u6750\u6599\u5747\u6709\u57fa\u56e0\u7ec4SV\u4f4d\u70b9\u4fe1\u606f\u548c\u572810\u79cd\u7ec4\u7ec7\u4e2d\u7684\u57fa\u56e0\u8868\u8fbe\u6570\u636e\u3002", "Step 2: \u5bf9\u6bcf\u4e2a\u57fa\u56e0\uff0c\u5728\u6bcf\u4e2a\u6750\u6599\u4e2d\u7528\u5404\u7ec4\u7ec7\u7684\u8868\u8fbe\u91cf\u6c42\u548c\uff0c\u5f97\u5230\u8be5\u57fa\u56e0\u5728\u8be5\u6750\u6599\u4e2d\u7684\u603b\u4f53\u8868\u8fbe\u6c34\u5e73\u3002", "Step 3: \u5229\u7528Perl\u811a\u672c\u5224\u65ad\u6bcf\u4e2a\u6750\u6599\u4e2d\u67d0\u4e2a\u57fa\u56e0\u662f\u5426\u4e0eSV\u53d1\u751f\u91cd\u53e0\uff0c\u4ece\u800c\u5c06\u8be5\u57fa\u56e0\u5728\u8be5\u6750\u6599\u4e2d\u7684\u8868\u8fbe\u6570\u636e\u5206\u4e3a\u201c\u5e26SV\u201d\u4e0e\u201c\u4e0d\u5e26SV\u201d\u4e24\u7c7b\u3002", "Step 4: \u5728\u6240\u6709\u6750\u6599\u4e2d\uff0c\u4ee5\u201c\u8be5\u57fa\u56e0\u662f\u5426\u4e0eSV\u91cd\u53e0\u201d\u4e3a\u4e00\u4e2a\u53d8\u91cf\uff0c\u4ee5\u8be5\u57fa\u56e0\u5728\u5404\u6750\u6599\u4e2d\u7684\u603b\u4f53\u8868\u8fbe\u6c34\u5e73\u4e3a\u53e6\u4e00\u4e2a\u53d8\u91cf\uff0c\u8ba1\u7b97Pearson\u76f8\u5173\u7cfb\u6570\u3002", "Step 5: \u5bf9\u76f8\u5173\u7cfb\u6570\u8fdb\u884c\u663e\u8457\u6027\u68c0\u9a8c\uff0c\u53ea\u4fdd\u7559\u5728\u7edf\u8ba1\u5b66\u4e0a\u663e\u8457\u7684\u76f8\u5173\u7ed3\u679c\u3002", "Step 6: \u82e5\u67d0\u57fa\u56e0\u7684SV\u5b58\u5728\u72b6\u6001\u4e0e\u5176\u8868\u8fbe\u91cf\u4e4b\u95f4\u7684Pearson\u76f8\u5173\u8fbe\u5230\u663e\u8457\u6c34\u5e73\uff0c\u5219\u8bf4\u660e\u8be5\u57fa\u56e0\u7684\u8868\u8fbe\u6c34\u5e73\u4e0e\u5176\u4e0a\u662f\u5426\u5b58\u5728\u7ed3\u6784\u53d8\u5f02\u5728\u4e0d\u540c\u6750\u6599\u95f4\u5448\u663e\u8457\u76f8\u5173\u5173\u7cfb\u3002", "Step 7: \u56e0\u6b64\uff0c\u901a\u8fc7\u8fd9\u4e00\u6d41\u7a0b\u53ef\u4ee5\u5ba2\u89c2\u3001\u7cfb\u7edf\u5730\u5224\u5b9a\u54ea\u4e9b\u57fa\u56e0\u7684\u8868\u8fbe\u4e0e\u5176\u91cd\u53e0\u7684\u7ed3\u6784\u53d8\u5f02\u72b6\u6001\u5728\u7fa4\u4f53\u4e2d\u663e\u8457\u76f8\u5173\u3002"], "question_cot": "", "final_conclusion": "\u901a\u8fc7\u5728\u591a\u4e2a\u81ea\u4ea4\u7cfb\u4e2d\u6bd4\u8f83\u662f\u5426\u4e0e\u7ed3\u6784\u53d8\u5f02\u91cd\u53e0\u7684\u57fa\u56e0\u53ca\u5176\u8868\u8fbe\u91cf\uff0c\u5e76\u8ba1\u7b97Pearson\u76f8\u5173\u7cfb\u6570\uff0c\u53ef\u4ee5\u5ba2\u89c2\u5224\u5b9a\u67d0\u4e9b\u57fa\u56e0\u7684\u8868\u8fbe\u6c34\u5e73\u662f\u5426\u4e0e\u5176\u4e0a\u662f\u5426\u5b58\u5728\u7ed3\u6784\u53d8\u5f02\u663e\u8457\u76f8\u5173\u3002", "difficulty": "medium", "tags": ["method", "application", "statistical_analysis", "genomics", "expression_SV_association"], "created_at": "2025-12-16T21:17:14", "token_est_question": 1, "token_est_answer": 1, "section": "Materials & Methods", "context": "=== Materials and methods ===\nThe 26 maize genomes were obtained from a study by Hufford et al.20. The hidden Markov model (HMM) profiles of the primary annexin domain (PF00191) was retrieved from the Pfam database ( ). The hidden Markov model (HMM) profiles corresponding to the primary annexin domain (PF00191) were obtained from the Pfam database ( ). Subsequently, these profiles were employed to search against the protein sequences derived from the 26 maize genomes using HMMER v3.3.242, with a threshold of e o confirm the existence of annexin domains. Utilizing the online tool ExPASy ( ), we investigated the fundamental molecular characteristics of proteins, including molecular weight (MW), theoretical isoelectric point (pI), and overall average hydrophilicity (GRAVY).\n=== Materials and methods ===\nMultiple sequence alignments were performed using the ClustalW tool to analyze the evolutionary relationships of the maize and _Arabidopsis_ annexin genes, and the tree was constructed by the Maximum Likelihood (ML) method with MEGA X ( ), based on the Bayesian Information Criterion (BIC) for model selection. A total of 1000 bootstrap replications were performed to evaluate node support. The final graphics were generated using iTOL v6 ( ). The presence or absence of each Ann in the 26 accessions was visualized using a heatmap generated with the ComplexHeatmap package43.\n=== Ka/Ks calculation ===\nThe protein and coding sequence (CDS) sequences of _ZmAnn_ genes in 26 maize genomes were compared, and Ka/Ks values were calculated using the KaKs Calculator 2.044. The R packages ggridges and ggplot2 were used to create the Ridgeline plot of ka/ks values.\n=== Gene structure and cis-acting elements of _ZmAnns_ ===\nThe conserved motifs were identified using MEME V5.4.1 ( ), with the number of motifs set to 10. Genome annotation files were download from to identified the gene structure. The 2-kb sequences upstream of Ann genes were analyzed by PlantCARE ( ).\n=== Analysis of the expression of _ZmAnns_ that overlap with structural variants (SVs) ===\nThe SVs locations in each variety and the gene expression data from 26 accessions were obtained from Hufford et al.14. The expression data included the gene expression levels across 10 tissue types in each genome: (1) primary root at six days after planting; (2) shoot and coleoptile at six days after planting; (3) base of the 10th leaf at the Vegetative 11 (V11) growth stage; (4) middle of the 10th leaf at the V11 growth stage; (5) tip of the 10th leaf at the V11 growth stage; (6) meiotic tassel at the Vegetative 18 (V18) growth stage; (7) immature ear at the V18 growth stage; (8) anthers at the Reproductive 1 (R1) growth stage; (9) endosperm at 16 days after pollination; and (10) embryo at 16 days after pollination. For the subsequent calculations, the sum of the expression levels across all tissues in each accession was used. An in-house Perl script was employed to assess whether the _ZmAnns_ overlapped with SVs in each variety. If a _ZmAnn_ was found to overlap with SVs, its expression data in that variety were classified as the expression data of genes associated with SVs; otherwise, they were categorized as the expression data of genes without SVs. Pearson correlation coefficients were computed between the presence of SVs overlapped with genes and gene expression levels. _ZmAnns_ with _p_ ) such as PRJNA294848, PRJNA295410, PRJNA308155, PRJNA343268, PRJNA395908 and PRJNA506720. Raw data (raw reads) in FASTQ format were first processed with Trimmomatic v0.3945 to filter adapter sequences. These clean reads were then mapped to the reference genome sequence. Only reads with a perfect match or one mismatch were further analyzed and annotated based on the reference genome. Kallisto v0.50.146 to align reads to the B73 reference genome ( ) and count them, obtain expression data. The resulting FPKM (Fragments Per Kilobase of transcript per Million mapped reads) values were used to quantify gene expression, which accounts for both gene length and sequencing depth. To prepare the data for downstream analysis, log2 normalization was applied to the FPKM values, adding 1 to avoid undefined values for genes with zero expression. Additionally, the data were standardized by applying z-score normalization, which centers and scales the data to account for differences in sequencing depth and expression across samples. The normalized and log2-transformed expression data were visualized using the ComplexHeatmap package in R.\n=== Co-expression network analysis of _ZmAnn_ s ===\nThe co-expression network was constructed using a set of PRJNA705456 for expression profiling analysis, which encompassed 26 samples from various tissues, including leaves, roots, and top-crown. These samples were subjected to three distinct low-temperature treatments: 4\u2103 shoot + 4\u2103 root, 4\u2103 shoot + 26\u2103 root, 26\u2103 shoot + 0\u2103 root. The Weighted Gene Co-expression Network Analysis (WGCNA) was conducted using the TBtools software (Version 2.085)47. Subsequently, the main modules of _ZmAnn_ s were identified and subjected to a Gene Ontology (GO) enrichment analysis. The GO dataset was retrieved from the KOBAS database ( ), and R packages were employed for data visualization.\n=== Growth conditions and cold stress treatment ===\nIn this study, we used the maize inbred line B73. Forty B73 seeds were sterilized in a 1% sodium hypochlorite solution for 5 min and then placed on moist germination paper, with an additional sheet of moist paper used as a cover. The two sheets of germination paper were rolled together and positioned vertically in a sealed plastic bag, where they were incubated at 22 \u00b0C for 7 days. After germination, the seeds were transferred to two pots containing a substrate composed of peat, pine needles, and yellow clay, and cultured at 22 \u00b0C for an additional 5 days. One of the pots was then moved to 4 \u00b0C for 1 day. Leaves from three plants in each pot were collected, frozen in liquid nitrogen, and stored at -80 \u00b0C for RNA extraction. Total RNA was extracted from the leaves using the Plant RNA Extraction Kit-V1.5 (Chengdu Biofit Biotechnologies Co., Ltd., Chengdu, China). The concentration and purity of the RNA were assessed using a NanoDrop 2000 (Thermo Fisher Scientific, Wilmington, DE).\n=== Validation of gene expression by RT-qPCR ===\nActin was utilized as the reference gene to assess relative expression values, and quantitative real-time PCR (qPCR) was conducted using the CFX96 PCR System. The 10 \u00b5l reaction mixture consisted of 5 \u00b5l of 2\u00d7 TB Green Premix Ex Taq II (Tli RNaseH Plus), 0.4 \u00b5l of each 10 \u00b5M primer, 1 \u00b5l of cDNA, and sterile double-distilled water to achieve the final volume. The qRT-PCR cycling conditions included an initial step at 95 \u00b0C for 3 min, followed by 39 cycles of 95 \u00b0C for 10 s and 58 \u00b0C for 30 s. After the final qRT-PCR cycle, a melt curve was generated by heating to 95 \u00b0C for 5 s and then gradually increasing the temperature from 65 \u00b0C to 95 \u00b0C at 0.5 \u00b0C increments. RT-PCR primers were designed using Primer Premier Software (version 5.0) and are provided in Table S11. Statistical analysis of the qPCR results was performed using a t-test with GraphPad Prism (v7.04), and a bar graph was generated to display the expression levels.", "Thinking\u6a21\u5f0f": "high", "generation_type": "\u63a8\u7406\u578b"}','2026-03-23 15:33:30.322893','2026-03-23 15:33:30.322904');
INSERT INTO qa_pairs VALUES(48,3,'在玉米中，有两类含有低温响应顺式元件LTR的Annexin基因ZmAnn6和ZmAnn9。启动子分析显示二者都具备潜在的低温应答能力，共表达网络分析发现ZmAnn6所在的表达模块富集于对非生物刺激响应等胁迫相关过程，而ZmAnn9所在模块主要富集于含氮化合物的生物合成等代谢过程；在幼苗经低温处理后的RT-qPCR中，ZmAnn6表达量显著上调，而ZmAnn9表达量无显著变化。对于想筛选更有可能直接参与幼苗期冷胁迫应答的候选基因，应优先考虑哪一个，并说明判断依据？','应优先考虑ZmAnn6。尽管ZmAnn6和ZmAnn9的启动子都含有LTR，具备潜在的低温应答能力，但ZmAnn6所在的共表达模块显著富集于对非生物刺激等胁迫相关的生物过程，提示其在全基因组表达网络中与冷等逆境响应基因协同变化；同时，低温处理后ZmAnn6在幼苗中的转录水平被显著诱导，提供了直接的表达证据。相比之下，ZmAnn9所在模块更偏向一般代谢与生物合成功能，且在相同低温条件下未出现显著诱导表达，缺乏与胁迫响应直接相关的功能和表达支持。因此，在两者均含低温响应元件的前提下，ZmAnn6具有更一致的功能富集和表达证据，更适合作为直接参与幼苗期冷胁迫应答的候选基因。','{"species": "Zea mays", "paper_id": "PMC11832922", "reasoning_steps": ["Step 1: \u6240\u6709ZmAnn9\u4ee5\u53ca\u5927\u591a\u6570ZmAnn6\u7684\u542f\u52a8\u5b50\u533a\u57df\u90fd\u542b\u6709\u4f4e\u6e29\u54cd\u5e94\u987a\u5f0f\u5143\u4ef6LTR\uff0c\u8fd9\u63d0\u793a\u8fd9\u4e24\u7c7b\u57fa\u56e0\u90fd\u5177\u6709\u6f5c\u5728\u7684\u4f4e\u6e29\u5e94\u7b54\u80fd\u529b\u3002", "Step 2: LTR\u8fd9\u7c7b\u987a\u5f0f\u5143\u4ef6\u901a\u5e38\u901a\u8fc7\u8c03\u63a7\u90bb\u8fd1\u57fa\u56e0\u7684\u8f6c\u5f55\uff0c\u4f7f\u57fa\u56e0\u5728\u4f4e\u6e29\u80c1\u8feb\u4e0b\u88ab\u8bf1\u5bfc\u8868\u8fbe\uff0c\u4ece\u800c\u53c2\u4e0e\u4f4e\u6e29\u5e94\u7b54\u6216\u8010\u5bd2\u8fc7\u7a0b\u3002", "Step 3: \u5171\u8868\u8fbe\u7f51\u7edc\u5206\u6790\u8868\u660e\uff0cZmAnn6\u6240\u5728\u7684brown\u6a21\u5757\u5bcc\u96c6\u4e8e\u201c\u5bf9\u975e\u751f\u7269\u523a\u6fc0\u7684\u54cd\u5e94\u201d\u7b49\u4e0e\u80c1\u8feb\u76f8\u5173\u7684\u751f\u7269\u8fc7\u7a0b\uff0c\u8bf4\u660eZmAnn6\u5728\u5168\u57fa\u56e0\u7ec4\u8868\u8fbe\u6a21\u5f0f\u4e2d\u4e0e\u51b7\u7b49\u975e\u751f\u7269\u80c1\u8feb\u76f8\u5173\u57fa\u56e0\u534f\u540c\u53d8\u5316\u3002", "Step 4: \u540c\u4e00\u51b7\u80c1\u8feb\u8f6c\u5f55\u7ec4\u5206\u6790\u4e2d\uff0cZmAnn9\u6240\u5728\u7684blue\u6a21\u5757\u4e3b\u8981\u5bcc\u96c6\u4e8e\u201c\u9170\u80fa/\u80bd/\u6709\u673a\u6c2e\u5316\u5408\u7269\u7684\u751f\u7269\u5408\u6210\u201d\u7b49\u4ee3\u8c22\u548c\u751f\u7269\u5408\u6210\u8fc7\u7a0b\uff0c\u800c\u4e0d\u662f\u5178\u578b\u7684\u80c1\u8feb\u54cd\u5e94\u8fc7\u7a0b\uff0c\u8fd9\u6697\u793aZmAnn9\u4e0e\u57fa\u7840\u4ee3\u8c22\u76f8\u5173\u6027\u66f4\u5f3a\u3002", "Step 5: \u57284 \u2103\u51b7\u80c1\u8feb24 h\u6761\u4ef6\u4e0b\u5bf9\u5e7c\u82d7\u8fdb\u884cRT-qPCR\u9a8c\u8bc1\u65f6\uff0cZmAnn6\u7684\u8868\u8fbe\u91cf\u76f8\u5bf9\u4e8e\u5bf9\u7167\u663e\u8457\u4e0a\u5347\uff0c\u800cZmAnn9\u7684\u8868\u8fbe\u91cf\u4e0e\u5bf9\u7167\u65e0\u663e\u8457\u5dee\u5f02\u3002", "Step 6: \u5c06\u542f\u52a8\u5b50LTR\u4fe1\u606f\u3001\u51b7\u80c1\u8feb\u8f6c\u5f55\u7ec4\u5171\u8868\u8fbe\u529f\u80fd\u5bcc\u96c6\u7ed3\u679c\u4ee5\u53caRT-qPCR\u7684\u76f4\u63a5\u8868\u8fbe\u53d8\u5316\u7ed3\u679c\u7efc\u5408\u8003\u8651\uff0c\u53ef\u89c1ZmAnn6\u65e2\u5177\u6709\u51b7\u76f8\u5173\u987a\u5f0f\u5143\u4ef6\uff0c\u53c8\u5728\u51b7\u80c1\u8feb\u6761\u4ef6\u4e0b\u663e\u8457\u8bf1\u5bfc\u4e14\u4f4d\u4e8e\u4e0e\u975e\u751f\u7269\u80c1\u8feb\u5bc6\u5207\u76f8\u5173\u7684\u5171\u8868\u8fbe\u6a21\u5757\u4e2d\uff0c\u800cZmAnn9\u867d\u7136\u5177\u6709LTR\u5e76\u53c2\u4e0e\u67d0\u5171\u8868\u8fbe\u6a21\u5757\uff0c\u4f46\u5728\u5e7c\u82d7\u671f\u51b7\u5904\u7406\u4e0b\u672a\u8868\u73b0\u51fa\u663e\u8457\u8bf1\u5bfc\u4e14\u6240\u5728\u6a21\u5757\u66f4\u504f\u5411\u4e00\u822c\u4ee3\u8c22\u8fc7\u7a0b\u3002", "Step 7: \u56e0\u6b64\uff0c\u5728\u540c\u6837\u5177\u6709\u4f4e\u6e29\u54cd\u5e94\u987a\u5f0f\u5143\u4ef6\u7684\u524d\u63d0\u4e0b\uff0cZmAnn6\u76f8\u6bd4ZmAnn9\u62e5\u6709\u66f4\u76f4\u63a5\u4e14\u4e00\u81f4\u7684\u591a\u5c42\u8bc1\u636e\u652f\u6301\u5176\u53c2\u4e0e\u5e7c\u82d7\u671f\u7684\u4f4e\u6e29\u80c1\u8feb\u54cd\u5e94\u3002"], "question_cot": "", "final_conclusion": "\u4e0e\u542b\u76f8\u540c\u4f4e\u6e29\u54cd\u5e94\u5143\u4ef6\u7684ZmAnn9\u76f8\u6bd4\uff0cZmAnn6\u5728\u4f4e\u6e29\u80c1\u8feb\u4e0b\u66f4\u6709\u76f4\u63a5\u8bc1\u636e\u53c2\u4e0e\u5e7c\u82d7\u671f\u7684\u6297\u51b7\u54cd\u5e94\u3002", "difficulty": "medium", "tags": ["result", "mechanism", "application"], "created_at": "2025-12-16T21:18:15", "token_est_question": 1, "token_est_answer": 1, "section": "Results", "context": "=== Results ===\nBoth an HMMER search and the Blastp program were used for identification. Twelve _ZmAnn_ genes were identified in the maize pan-genome, of which 9 were core genes, 3 were near-core genes (Table. S1). Although _ZmAnn12_ was not found in the B73 reference genome, a collinear segment with the gene was identified, a similar situation was observed in the M162W and NC350 genomes. The CML52 genome lacks the _ZmAnn7_ and _ZmAnn8_ gene, and the Ki11 genome lacks the _ZmAnn4_ gene. However, the other 24 genomes analyzed in this study contain all 12 _ZmAnn_ genes or their corresponding collinear segments. This suggests that the absence of these genes in the CML52 and Ki11 genomes may be due to genetic variation or deletion events.\nAccording to this analysis, the physicochemical properties of the annexin genes are shown in Table S2, The annexin genes encoded proteins ranging from 85 (B97_Ann3) to 446 aa (NC350_Ann9) in length, with isoelectric points (pIs) ranging from 5.4 (HP301_Ann10, Ki11_Ann10, Mo18W_Ann10, M126W_Ann10) to 9.6 (NC350_Ann9) and molecular weights (MWs) varying from 9.1 (B97_Ann3) to 48.6 kDa (NC350_Ann9). The instability index ranging from 30.57 (CML69_Ann5) to 84.91 (B97_Ann3) and the aliphatic index ranging from 76 (CML103_Ann9) to 104.71 (B97_Ann3). The GRAVY values ranged from \u2212 0.534 (M126W_Ann1) to 0.232 (B97_Ann3). Except for B97_Ann3 and M126W_Ann1, all other annexin proteins are hydrophobic proteins. Furthermore, subcellular localization results revealed that the majority of the genes are located in mitochondria (58.3%), while the remaining genes are distributed in the cytoplasm (18.2%), chloroplasts (22.4%), and nucleus (0.6%) (Table S2.). Specifically, only two genes, CML247-Ann9 and M126W-Ann1, were found to be located in the nucleus. Four _ZmAnns_ have the same localization results in 26 genomes, such as _ZmAnn2_ , _ZmAnn4_ , _ZmAnn5_ , and _ZmAnn12._\n=== Results ===\nIn order to investigate the phylogenetic relationships among maize annexin genes within maize genomes, a neighbor-joining (NJ) model tree was constructed. This tree was generated utilizing protein sequences derived from 12 distinctly identified maize annexin genes, in conjunction with 8 annexin genes sourced from _Arabidopsis_. The _ZmAnn_ genes were categorized into six distinct subgroups (Fig. 1a). With the exception of group VI, each subgroup contains Ann proteins derived from Arabidopsis. Group I comprises three _ZmAnn_ genes, while group VI consists of a single _ZmAnn_ gene. The remaining four groups each contain two _ZmAnn_ genes. Figure 1b illustrates the presence or absence of _ZmAnn_ genes, across 26 maize varieties. Notably, _ZmAnn7_ and _ZmAnn8_ are absent in CML52, while _ZmAnn4_ is missing in Ki11. All other genes are either present or have collinear segments in all 26 genomes. This observation suggests that the Ann gene family is relatively conserved across different maize varieties, indicating a high degree of genetic stability and functional importance within the gene family.\n=== Fig. 1. ===\n \nOpen in a new tab\n(**a**) Phylogenetic tree of Ann genes in maize and Arabidopsis. (**b**) Heatmap illustrating the presence and absence of the 12 _ZmAnn_ genes in 26 maize varieties. The tree was constructed by the neighbor-joining (NJ) method with 1000 bootstrap replications.\n=== _ZmAnn_ is subjected to different selection pressures among maize varieties ===\nVariations between the aligned sequences may lead to amino acid changes (nonsynonymous substitutions) or maintain the same amino acids (synonymous substitutions). Quantifying these changes provides insight into the extent of sequence alteration and the underlying evolutionary processes. The ratio of nonsynonymous substitutions per nonsynonymous site (Ka) to synonymous substitutions per synonymous site (Ks) serves as an indicator of the selective pressures acting on the protein. The Ka/Ks values of the majority of Ann genes are less than 1, suggesting that these genes have undergone purifying selection during the evolutionary process, thereby preserving their function. However, some genes in _ZmAnn10_ exhibited values greater than 1, indicating that they underwent positive selection in some materials, possibly contributing to adaptive evolution in certain maize varieties. (Fig. 2).\n=== Fig. 2. ===\n \nOpen in a new tab\nDistribution of Ka/Ks values of _ZmAnn_ genes in 26 maize varieties.\n=== Cis-element analysis of _ZmAnn_ ===\nIn the analysis of the 2 kb upstream sequences of the genes, the online tool PlantCARE database was utilized for cis-element prediction. This analysis identified in addition to the core cis elements, a total of 64 distinct cis-elements within the 2000 bp upstream region from the transcription start sites of the _ZmAnn_. These cis-elements play a significant role in various biological processes, including stress responses, hormone responses, metabolic regulation, as well as growth and development. All Ann genes contain varying numbers of light response elements, with the G-box being the most prevalent element, present in each gene. Among hormone-responsive cis-elements, the abscisic acid responsive element (ABRE) is the most abundant, with certain genes (B97-Ann2, Mo18W-Ann2, Oh43-Ann2 and Tx303-Ann2) containing as many as 15 ABRE elements (Fig. 3, Table. S3, S4). In addition, it contains various types of stress response components, such as ARE (anaerobic induction), GC-motif (anoxic specific inducibility), TC-rich repeats (defense and stress responsiveness), MBS (drought inducibility), LTR (low-temperature responsive). TCA-element (salicylic acid responsiveness). All _ZmAnn9_ and the majority of _ZmAnn6_ in the 26 genomes contain the LTR (Table. S4), suggesting that these two genes may associated with maize low-temperature stress tolerance. In the context of metabolic response processes, the O2-site, a cis-acting regulatory element involved in zein metabolism regulation, was the only element successfully identified. Notably, meristem expression elements (CAT-box) were identified in all _ZmAnn8_ , while involved in seed-specific regulation were present in _ZmAnn11_ , indicating potential roles in tissue-specific expression and developmental processe _s_.\n=== Fig. 3. ===\n \nOpen in a new tab\nThe number and functional classification of cis-acting elements in the genomes of B97, B73, and CML103.\nMultiple MYB binding sites (MBS, MBSI, MRE) were identified in the promoter regions of the 26 genomes (Table S3), suggesting that the Ann gene can interact with MYB transcription factors and participate in various processes, including photoresponse, drought induction, and regulation of flavonoid biosynthesis genes.\n=== Fig. 3. ===\nAbundant SVs (structural variations)were identified by aligning 26 high-quality maize genomes with reference genome (B73) in the study by Hufford et al. (2021)20. Compared with the reference genome, the main types of SVs are deletion (59), followed by insertion (14) and translocation (3), overlapped with gene regions of _ZmAnn_ as well as upstream and downstream 2-kb regions (Fig. 4a Table. S5). SVs can impact the expression of genes by altering the composition or position of adjacent cis-regulatory sequences. We analyzed the correlation between expression values of genes containing and not containing SVs, and the results revealed a significant difference (_p_ < 0.05) only in _ZmAnn2_ and _ZmAnn11_ (Fig. 4b). This finding suggests that the expression of _ZmAnn2_ and _ZmAnn11_ is influenced by the presence of SVs.\n=== Fig. 4. ===\n \nOpen in a new tab\nSVs overlapping with the Ann gene family. (**a**) Display of SV insertions or deletions 2 kb upstream or downstream of Ann genes. (**b**) The expression of _ZmAnn2 and ZmAnn11_ were significantly affected by SVs (*_p_ < 0.05).\nTo investigate the impact of SVs on the gene structure within the Ann gene family, we examined the gene structures of Ann genes across 26 accessions using TBtools. The results indicate that the gene structure of most Ann is consistent with the reference genome B73. However, some Ann genes that overlap with SVs have undergone structural changes. For instance, in _ZmAnn3_ , most genes contain 5 exons, whereas in the B97 accession, there is only one exon (Fig. 5a). A similar situation is observed in _ZmAnn10_ , where the accessions M37W, P39, CML33, and CML103 contain two exons, with only 14 materials harboring all 10 motifs (Fig. 5b). Other genes exhibit varying degrees of structural variation, which may potentially result in functional changes.\n=== Fig. 5. ===\n \nOpen in a new tab\nGene structure of _ZmAnn3_ (**a**) and _ZmAnn10_ (**b**) in 26 maize genomes.\n=== Atypical Ann genes were widely expressed in maize ===\nSVs are a significant factor contributing to changes in the protein spatial folding structure. To further investigate the impact of SVs on the Ann gene family, we quantified the number of typical (containing 4 annexin repeats) and atypical (containing 1\u20133 annexin repeats) Ann genes in the 26 genomes. In most of the materials, _ZmAnn2_ , _ZmAnn8_ , _ZmAnn9_ , and _ZmAnn11_ were found to be typical genes, while _ZmAnn1_ , _ZmAnn6_ , _ZmAnn7_ , and _ZmAnn10_ were identified as atypical genes (Fig. 6a). The remaining genes exhibited a coexistence of both types, a few genes with only collinear fragments cannot be counted.\n=== Fig. 6. ===\n \nOpen in a new tab\n(**a**) Heatmap showing the typical or atypical nature of each Ann gene in each variety. (**b**) Number of _ZmAnn_ and total expressed dose in 26 maize genomes.\nThe majority of the materials contain 6\u20138 typical genes, with only a few materials (CML103, M162W and Oh7B) having as few as 5 typical genes (Table. S6). To determine if there is a relationship between the number of Ann genes and their total expression levels among different varieties, we quantified the number and total expression of Ann genes in 26 materials (Fig. 6b). The number of Ann genes in each material is relatively consistent, ranging from 10 to 12, but the total expression levels vary significantly, with the highest RPKM at 374.14 (Ms71) and the lowest at 122.30 (Mo18W). Correlation analysis was conducted to examine the relationship between the number of typical genes, the number of atypical genes, and the total number of Ann genes and log2RPKM values. The results showed correlation coefficients (r) and significance test p-values of -0.048 and 0.818, respectively, for typical genes, 0.002 and 0.990 for atypical genes, and 0.003 and 0.985 for the total number of Ann genes (Fig.S1). These findings indicate that there is no significant correlation between these genes and their total expression levels.\n=== Expression profiles of _ZmAnn_ under stress conditions based on RNA-Seq data ===\nAnalysis of cis-acting regulatory elements in the promoter regions of Ann genes suggests their potential involvement in various stress response pathways. To further investigate the expression patterns of Ann genes under various biotic and abiotic stress conditions, we obtained transcriptome data for biotic and non-stress treatments from public databases (Table S7). The stress conditions analyzed include _Aspergillus flavus_ infection (Fig. 7a), aphid infestation (Fig. 7b), salt and mannitol treatments (Fig. 7c), drought treatment (Fig. 7d), cold stress (Fig. 7e) and heat stress (Fig. 7f). The expression levels of _ZmAnn6_ and _ZmAnn8_ were upregulated to different extents following _Aspergillus_ flavus infection (Fig. 7a). Additionally, after two hours of aphid infestation, elevated expression levels were observed for _ZmAnn5_ , _ZmAnn6_ , _ZmAnn7_ , and _ZmAnn8_ (Fig. 7b). Under salt and mannitol treatment, the expression levels of some Ann genes change in the roots and leaves. _ZmAnn7_ and _ZmAnn10_ are upregulated in the shoots under salt stress, while _ZmAnn1_ , _ZmAnn3_ , and _ZmAnn6_ are upregulated in the roots. Additionally, _ZmAnn4_ and _ZmAnn9_ are upregulated in the shoots of the knockout line R101 and the wild type under mannitol treatment (Fig. 7c). During drought treatment, the expression levels of some Ann genes are upregulated, such as _ZmAnn2_ , _ZmAnn5_ , _ZmAnn8_ , and _ZmAnn11_ (Fig. 7d). _ZmAnn4_ exhibited higher expression levels during the early stages of cold treatment, specifically at 0.5 h, 1 h, and 2 h, while _ZmAnn8_ showed increased expression at later time points, namely 16 h and 24 h (Fig. 7e). These findings suggest that the Ann gene family plays a role in cold stress response at different time intervals. Under heat stress conditions, the expression levels of certain Ann genes in the thermosensitive maternal plants of An\u2019nong 591 were found to be upregulated. Collectively, these results underscore the multifunctional role of the _ZmAnn_ gene family in various stress response pathways, highlighting their importance in plant defense mechanisms.\n=== Fig. 7. ===\n \nOpen in a new tab\nExpression profiles of _ZmAnns_ under diverse stresses. (**a**) Transcriptome analysis of 13 stages of _Aspergillus_ flavus infection from PRJNA395908. (**b**) Transcriptome analysis of six stages of aphid infection from PRJNA295410. (**c**) Expression patterns of maize _ZmCHB101_ gene knockout under simulated salt and mannitol treatments from PRJNA308155. Both _ZmCHB101_ knockout lines and wild-type plants were subjected to salt and mannitol treatments in leaves and roots. (**d**) Transcriptome analysis of the drought-tolerant maize inbred line AC7643, the drought-sensitive inbred line AC7729/TZSRW, and their two recombinant inbred lines (RIL208 and RIL64) under well-watered and drought stress conditions from PRJNA506720. (**e**) Transcriptome analysis of maize under cold stress from PRJNA344653. (**f**) Transcriptome analysis of maize hybrid An\u2019nong 591 and its parental lines under heat stress from PRJNA308155.\n=== Fig. 7. ===\nCold reduces both the seed germination rate and seedling vigor, as exposure to low temperatures during the water absorption phase (imbibition) impairs cell membrane permeability, resulting in the loss of cellular components21. To further elucidate the role of _ZmAnn_ in response to cold stress, we obtained expression data from Xue et al. (2021)22 on various maize tissues subjected to cold stress treatment (Table.S8) for Weighted Gene Co-Expression Network Analysis(WGCNA). This analysis revealed 19 co-expression modules, with gene counts ranging from 157 to 2810 (Fig.S2, Table.S9). Subsequently, correlation analysis was conducted on co-expressed genes utilizing diverse treatment information. Modules exhibiting higher correlation coefficients were identified in three distinct tissues: blue, brown, and turquoise (Fig.S3). The genes in these modules have similar expression patterns. A total of 4 Ann genes were found to be involved in these three co-expression modules. Specifically, _ZmAnn2_ and _ZmAnn7_ were clustered in the turquoise module, which is primarily associated with stress response processes, such as cellular protein modification, carboxylic acid metabolism, ubiquitin-like protein transferase activity, and organic acid metabolism (Fig. 8a). In addition, _ZmAnn9_ was also found to be clustered in the blue module, which is mainly related to various biosynthetic processes, such as amide biosynthesis, peptide biosynthesis, and organic nitrogen compound biosynthesis (Fig. 8b). _ZmAnn6_ is located in the brown module related to carboxylic acid metabolism, response to abiotic stimuli, and cofactor metabolism processes (Fig. 8c). Notably, _ZmAnn2_ and _ZmAnn7_ are co-expressed with three genes (Table.S10), among which LRR-RLKs (LRR-like receptor kinases) _FEI1_ is associated with cellulose. The loss-of-function mutant of _FEI1_ exhibits increased sensitivity to humidity gradients and decreased tolerance to osmotic stress23. Among the other co expressed genes, SR34A targets all alternative splicing event types, including in RNAs encoding known determinants of ABA sensitivity, to prevent ABA-responsive splicing in germinated seeds24. Expression of _JMJ25_ was induced significantly by darkness, suggesting that _JMJ25_ might be involved in stress responses25. _FtsH2_ may be involved in cold stress response processes by affecting the ABA-dependent signaling pathways26.\n=== Fig. 8. ===\n \nOpen in a new tab\nCo-expression networks based on transcriptome data under different cold stress conditions, constructed using the WGCNA method. (**a**) Co-expression network of _ZmAnn2_ and _ZmAnn7_ and Gene Ontology (GO) analysis of genes in the turquoise module; (**b**) Co-expression network of _ZmAnn9_ and GO analysis of genes in the blue module; (**c**) Co-expression network of _ZmAnn6_ and GO analysis of genes in the brown modul.\n=== Validation of gene expression by RT-qPCR ===\nTo further confirm the response of _ZmAnn_ genes to cold stress during maize seedling development, B73 seedlings were subjected to 24 h of cold stress at 4 \u00b0C, and the expression changes of six Ann genes (_ZmAnn2_ , _ZmAnn4_ , _ZmAnn6_ , _ZmAnn7_ , _ZmAnn8_ and _ZmAnn9_) were observed. The expression levels of _ZmAnn2_ , _ZmAnn4_ , _ZmAnn6_ and _ZmAnn8_ under cold stress were significantly higher than the control, whereas _ZmAnn7_ and _ZmAnn9_ showed no significant difference (Fig. 9). These results suggest that these four genes may play a role in the response of maize seedlings to cold stress.\n=== Fig. 9. ===\n \nOpen in a new tab\nQuantitative RT-PCR analysis of six _ZmAnn_ genes under 24 h of cold stress during the seedling stage.", "Thinking\u6a21\u5f0f": "high", "generation_type": "\u63a8\u7406\u578b"}','2026-03-23 15:33:30.447945','2026-03-23 15:33:30.447956');
INSERT INTO qa_pairs VALUES(49,3,'在玉米中，低温处理构建的转录组共表达网络显示某些 ZmAnn 基因位于在茎叶、根和生长点中都与冷胁迫高度相关、且在功能富集分析中主要关联应激反应和次生代谢生物合成的模块内，同时这些 ZmAnn 又与 GDSL 脂肪酶、SE1 和 RZ 蛋白等已知冷胁迫应答基因处于同一互作网络并在幼苗期冷处理时自身表达显著上调。应如何从共表达关系和表达模式两方面推断这些 ZmAnn 基因在玉米低温胁迫响应与耐受调节中的潜在功能？','这些 ZmAnn 基因应被视为玉米冷胁迫响应与耐受调节网络中的潜在功能成员：一方面，它们位于对冷胁迫高度敏感且在功能上富集应激反应的共表达模块中，并与多种已知冷相关基因共同构成互作网络，说明其功能背景与冷应答过程高度一致；另一方面，它们在冷处理下转录水平显著上调，具有典型的应激响应基因表达特征。二者结合表明，这些 ZmAnn 很可能直接或间接参与玉米的低温信号传导和耐性调控，而不只是对冷胁迫的被动伴随表达。','{"species": "Zea mays", "paper_id": "PMC11832922", "reasoning_steps": ["Step 1: \u7389\u7c73\u6765\u6e90\u4e8e\u70ed\u5e26/\u4e9a\u70ed\u5e26\uff0c\u662f\u559c\u6e29\u4f5c\u7269\uff0c\u5bf9\u4f4e\u6e29\u9ad8\u5ea6\u654f\u611f\uff0c\u51b7\u5bb3\u4f1a\u663e\u8457\u5f71\u54cd\u840c\u53d1\u3001\u5e7c\u82d7\u751f\u957f\u5e76\u964d\u4f4e\u4ea7\u91cf\uff0c\u56e0\u6b64\u7389\u7c73\u9700\u8981\u4e00\u5957\u5e94\u5bf9\u51b7\u80c1\u8feb\u7684\u5206\u5b50\u8c03\u63a7\u4f53\u7cfb\u3002", "Step 2: \u5728\u4e0d\u540c\u7ec4\u7ec7\u7684\u4f4e\u6e29\u8f6c\u5f55\u7ec4\u6570\u636e\u4e2d\u6784\u5efa\u5171\u8868\u8fbe\u7f51\u7edc\u65f6\uff0c\u53d1\u73b0\u4e0e\u51b7\u80c1\u8feb\u9ad8\u5ea6\u76f8\u5173\u7684\u6a21\u5757\u5728\u830e\u53f6\u3001\u6839\u548c\u751f\u957f\u70b9\u4e2d\u88ab\u9274\u5b9a\u51fa\u6765\uff0c\u8fd9\u4e9b\u6a21\u5757\u5728 GO \u5bcc\u96c6\u5206\u6790\u4e2d\u4e3b\u8981\u4e0e\u5e94\u6fc0\u53cd\u5e94\u548c\u6b21\u751f\u4ee3\u8c22\u751f\u7269\u5408\u6210\u8fc7\u7a0b\u76f8\u5173\u3002", "Step 3: \u5728\u8fd9\u4e9b\u4e0e\u51b7\u80c1\u8feb\u76f8\u5173\u7684\u6a21\u5757\u4e2d\uff0c\u68c0\u6d4b\u5230 4 \u4e2a Ann \u57fa\u56e0\u53c2\u4e0e\u5176\u4e2d\uff0c\u4e14\u8fd9\u4e9b\u6a21\u5757\u7684\u4e92\u4f5c\u7f51\u7edc\u4e2d\u5305\u542b\u591a\u4e2a\u5df2\u77e5\u4e0e\u51b7\u80c1\u8feb\u6709\u5173\u7684\u57fa\u56e0\uff0c\u5982\u5728\u975e\u751f\u7269\u80c1\u8feb\u4e0b\u88ab\u8bf1\u5bfc\u7684 GDSL \u8102\u80aa\u9176\u3001\u5728\u51b7\u80c1\u8feb\u4e0b\u8c03\u63a7\u4eba\u53c2\u7682\u82f7\u5408\u6210\u7684 SE1\u3001\u4ee5\u53ca\u5728\u51b7\u5904\u7406\u4e0b\u8f6c\u5f55\u6c34\u5e73\u663e\u8457\u5347\u9ad8\u7684 Arabidopsis RZ \u86cb\u767d\u540c\u7c7b\u57fa\u56e0\u3002", "Step 4: \u4e0e\u5df2\u77e5\u51b7\u80c1\u8feb\u5e94\u7b54\u57fa\u56e0\u5728\u540c\u4e00\u5171\u8868\u8fbe\u6a21\u5757\u4e2d\u5e76\u5f62\u6210\u4e92\u4f5c\u7f51\u7edc\uff0c\u901a\u5e38\u8bf4\u660e\u8fd9\u4e9b\u57fa\u56e0\u5728\u540c\u4e00\u6216\u76f8\u8fd1\u7684\u751f\u7406\u8fc7\u7a0b\u6216\u4fe1\u53f7\u901a\u8def\u4e2d\u534f\u540c\u53d1\u6325\u4f5c\u7528\uff0c\u56e0\u6b64\u53c2\u4e0e\u6a21\u5757\u7684 Ann \u57fa\u56e0\u5177\u6709\u53c2\u4e0e\u51b7\u80c1\u8feb\u5e94\u7b54\u7684\u53ef\u80fd\u6027\u3002", "Step 5: \u5728\u5e7c\u82d7\u671f\u51b7\u80c1\u8feb\u5904\u7406\u4e0b\u5bf9 6 \u4e2a Ann \u57fa\u56e0\u505a\u8868\u8fbe\u9a8c\u8bc1\u65f6\uff0cZmAnn2\u3001ZmAnn4\u3001ZmAnn6 \u548c ZmAnn8 \u7684\u8868\u8fbe\u91cf\u51fa\u73b0\u663e\u8457\u4e0a\u8c03\uff0c\u800c\u51b7\u80c1\u8feb\u8bf1\u5bfc\u8868\u8fbe\u589e\u5f3a\u662f\u5178\u578b\u7684\u5e94\u6fc0\u54cd\u5e94\u57fa\u56e0\u7279\u5f81\u4e4b\u4e00\u3002", "Step 6: \u7531\u4e8e\u8fd9\u4e9b Ann \u57fa\u56e0\u4e00\u65b9\u9762\u4e0e\u5df2\u77e5\u51b7\u80c1\u8feb\u76f8\u5173\u57fa\u56e0\u5171\u8868\u8fbe\u5e76\u5904\u4e8e\u5e94\u6fc0\u5bcc\u96c6\u6a21\u5757\u4e2d\uff0c\u53e6\u4e00\u65b9\u9762\u5728\u51b7\u5904\u7406\u4e0b\u88ab\u663e\u8457\u8bf1\u5bfc\uff0c\u4e8c\u8005\u5171\u540c\u6307\u5411\u5b83\u4eec\u5728\u51b7\u80c1\u8feb\u54cd\u5e94\u7f51\u7edc\u4e2d\u53d1\u6325\u529f\u80fd\u7684\u9ad8\u53ef\u80fd\u6027\u3002", "Step 7: \u56e0\u6b64\uff0c\u53ef\u4ee5\u63a8\u65ad ZmAnn2\u3001ZmAnn4\u3001ZmAnn6 \u548c ZmAnn8 \u7b49 Ann \u57fa\u56e0\u5f88\u53ef\u80fd\u53c2\u4e0e\u7389\u7c73\u5bf9\u4f4e\u6e29\u80c1\u8feb\u7684\u54cd\u5e94\u4e0e\u8010\u53d7\u8c03\u8282\u3002"], "question_cot": "", "final_conclusion": "\u90e8\u5206 ZmAnn \u57fa\u56e0\uff08\u5982 ZmAnn2\u3001ZmAnn4\u3001ZmAnn6 \u548c ZmAnn8\uff09\u5f88\u53ef\u80fd\u53c2\u4e0e\u7389\u7c73\u5bf9\u4f4e\u6e29\u80c1\u8feb\u7684\u54cd\u5e94\u4e0e\u8010\u53d7\u8c03\u8282\u3002", "difficulty": "medium", "tags": ["mechanism", "result"], "created_at": "2025-12-16T21:19:47", "token_est_question": 15, "token_est_answer": 6, "section": "Discussion", "context": "=== Discussion ===\nThe annexin gene family in maize was first identified through the B73 reference genome (B73 RefGen_v3)27. Recently, a pan-genome consisting of 26 high-quality maize genomes has been published, which provides a more precise assembly and annotation of the B73 reference genome20. Research has demonstrated that a single reference genome is inadequate to capture the full extent of genetic diversity within a species18,28. Consequently, it is essential to identify and characterize Ann genes, based on the maize pan-genome. We utilized the maize pan-genome and identified 12 _ZmAnn_ s, including 9 core genes and 3 near-core genes. In the reference genome B73, we identified 11 Ann genes and one collinear fragment. Previous studies had identified 12 Ann genes in the B73 reference genome. Upon comparison, we found that GRMZM2G134502, which was present in the previous studies27, does not have convertible gene model IDs in subsequent versions of the B73 reference genome. This may be due to issues with early genome assembly. Regarding _ZmAnn12_ , only collinear fragments were found in the three genomes (B73, NC350, M126W). This lack of clear annotation makes it more likely for _ZmAnn12_ to be overlooked when using B73 as the reference genome. Despite the variations in the number of _ZmAnns_ , the total gene expression did not exhibit correlation. This could be attributed to the fact that certain members within the gene family have similar functions, which can compensate for each other to maintain normal physiological processes29.\nSV is a common type of genetic variation, and changes in conserved domains caused by SV can potentially lead to alterations in gene structure, expression, and conserved domains. In the CSSL population derived from _japonica_ rice NIP and _indica_ rice 9311, a 42 kb insertion/deletion SV was identified around the _qPH8.2_ locus, which has an impact on plant growth30. Studies in peach trees have demonstrated that SV downstream of the _OVATE_ gene can regulate fruit characteristics and leaf development31. This study found that SVs affects the expression of _ZmAnn2_ and _ZmAnn11_ , which can be further studied. SVs can also alter the number of exons, such as _ZmAnn3_ and _ZmAnn10_ , with the reference genome identifying five exons, but 1\u20134 exons appearing in some materials, leading to the emergence of atypical Ann genes. However, these atypical genes may also have certain functions. For example, the atypical E2F gene _AtE2Ff_ is part of a critical regulatory network required for the differentiation of certain cell types during post-embryonic growth and development in _Arabidopsis_32. The atypical CC-NBS-LRR protein _Pb1_ in rice is known as a gene for durable resistance to rice blast, but its NBS domain lacks the P-loop essential for the function of R proteins33. Therefore, this study identified several atypical Ann genes in maize, which enhances our understanding of the functional roles of Ann in maize.\nThe cis-elements present in plant annexins play a crucial role in responding to environmental stress and signaling pathways during plant growth and development, and have been widely studied in various plants2\u20134. A total of 64 different cis-elements were also identified within the 2000 bp upstream region of the _ZmAnn_ transcription start site. These cis-elements play important roles in various biological processes, including stress response, hormone response, metabolic regulation, and growth and development. All _ZmAnns_ harbor ABRE elements, with B97-Ann2, Mo18W-Ann2, Oh43-Ann2, and Tx303-Ann2 containing up to 15 ABRE elements. Under abiotic stress conditions, ABA can trigger the activation of ABFs, which subsequently bind to ABRE elements, initiating the transcription of downstream genes to aid plants in withstanding stress34,35. _AtbZIP1_ is involved in the regulation of ABA signaling by binding to ABRE, thereby modulating the expression of ABA-responsive genes and contributing to the stress response in plants36. All _ZmAnn6_ and _ZmAnn9_ in 26 genomes contain LTR elements related to cold stress, indicating that these two genes may be involved in maize\u2019s response to cold. Transcription factors (TFs) are involved in regulating various target genes responsible for plant adaptation and tolerance37. This study identified several transcription factor-related elements, including DRE, MYB, MYC, MBS, and MRE, with all _ZmAnn_ members harboring MYB and MYC elements. DRE elements are present in all _ZmAnn9_ ; the drought-inducible MBS is found in _ZmAnn8_ ; the MBSI involved in the regulation of flavonoid biosynthesis genes is located in _ZmAnn3_ and _ZmAnn6_ in most materials. This suggests that Ann genes may bind to specific elements to participate in various life processes.\nMaize is highly susceptible to cold damage, as it is a crop that thrives in high-temperature environments due to its tropical/subtropical origin. Cold stress can negatively impact seed germination, seedling development, and growth during the seedling stage, ultimately resulting in decreased grain yield38. To further elucidate the role of _ZmAnns_ in response to cold stress, this study constructed co-expression networks using transcriptome data from different maize tissues under cold stress. Highly correlated modules were found in the shoot, root, and top-crown, specifically brown, turquoise, and blue. GO enrichment analysis found that these three modules are mainly related to stress response and secondary metabolite biosynthesis processes. A total of 4 Ann genes were found to participate in these three modules, and the interaction network of these genes was identified. The network contains multiple genes related to cold stress, such as GDSL lipases, squalene monooxygenase (SE), glycine-rich RNA-binding protein (RZ). Abiotic stress in certain plant species can induce the expression of GDSL lipases, suggesting that they play a protective role against abiotic stress39. _SE1_ is one of key regulators of ginsenoside biosynthesis in response to cold stress40. The transcription levels of _AtRZ-1a_ , _AtRZ-1b_ , and _AtRZ-1c_ in Arabidopsis significantly increase due to cold stress treatment41. Subsequently, the expression levels of the six Ann genes under cold stress during the seedling stage were validated. The expression of _ZmAnn2_ , _ZmAnn4_ , _ZmAnn6_ and _ZmAnn8_ showed a significant increase. This comprehensive analysis helps to identify potential regulatory networks and key genes involved in the cold stress response.", "Thinking\u6a21\u5f0f": "high", "generation_type": "\u63a8\u7406\u578b"}','2026-03-23 15:33:30.571311','2026-03-23 15:33:30.571316');
INSERT INTO qa_pairs VALUES(50,3,'研究者通过抗 CAT-2 抗体对幼苗胚乳来源的多聚核糖体进行免疫吸附，洗脱并经寡聚 dT 进一步纯化得到与 CAT-2 相关的 mRNA，将其在兔网织红细胞无细胞体系中以放射性标记甲硫氨酸进行体外翻译，然后用 SDS-PAGE 分离翻译产物，并在同一电泳条件下同时跑分子量标准和纯化的未标记 CAT-2 酶，最终在氟显影图上观察到主要放射性条带与纯化 CAT-2 的迁移位置重合。在这种实验设计和结果下，应如何判断该 mRNA 编码产物的性质，并说明判断所依据的关键逻辑？','可以判断该 mRNA 编码的蛋白就是 CAT-2。原因在于：首先，多聚核糖体是通过抗 CAT-2 抗体特异性免疫吸附获得的，因此其所携带的 mRNA 预期与 CAT-2 翻译相关；其次，提取并纯化出的这部分 mRNA 在无细胞体系中被翻译成放射性标记蛋白；再次，在严格相同的 SDS-PAGE 条件下，翻译产物中的主要放射性条带与纯化的天然 CAT-2 酶具有完全相同的迁移位置，表明两者具有相同的分子量和电泳行为；在 SDS-PAGE 中迁移位置高度重合通常代表是同一种或极为相近的蛋白。因此，将免疫富集来源与电泳一致性结合，可以得出该 mRNA 编码的即为 CAT-2 蛋白。','{"species": "Zea mays L.", "paper_id": "29564865", "reasoning_steps": ["Step 1: \u591a\u805a\u6838\u7cd6\u4f53\u4ece\u5e7c\u82d7\u80da\u4e73\uff08scutella\uff09\u4e2d\u5206\u79bb\uff0c\u5e76\u901a\u8fc7\u6297 CAT-2 \u6297\u4f53\u4e0e\u86cb\u767d A-Sepharose \u8fdb\u884c\u591a\u8f6e\u514d\u75ab\u5438\u9644\uff0c\u83b7\u5f97\u4e0e CAT-2 \u6297\u539f\u7279\u5f02\u7ed3\u5408\u7684\u591a\u805a\u6838\u7cd6\u4f53\u7fa4\u4f53\u3002", "Step 2: \u8fd9\u4e9b\u88ab\u6297\u4f53\u5bcc\u96c6\u7684\u591a\u805a\u6838\u7cd6\u4f53\u4e2d\u6240\u643a\u5e26\u7684 mRNA \u88ab\u6d17\u8131\u5e76\u7ecf oligo(dT)-\u7ea4\u7ef4\u7d20\u8fdb\u4e00\u6b65\u7eaf\u5316\uff0c\u5f97\u5230\u4e0e CAT-2 \u76f8\u5173\u8054\u7684 mRNA \u7ec4\u5206\u3002", "Step 3: \u7eaf\u5316\u7684 mRNA \u5728\u5154\u7f51\u7ec7\u7ea2\u7ec6\u80de\u65e0\u7ec6\u80de\u7ffb\u8bd1\u4f53\u7cfb\u4e2d\uff0c\u4ee5 [35S]\u7532\u786b\u6c28\u9178\u4e3a\u653e\u5c04\u6027\u6807\u8bb0\u6c28\u57fa\u9178\u8fdb\u884c\u4f53\u5916\u7ffb\u8bd1\uff0c\u4ea7\u751f\u653e\u5c04\u6027\u6807\u8bb0\u7684\u591a\u80bd\u4ea7\u7269\u3002", "Step 4: \u7ffb\u8bd1\u4ea7\u7269\u901a\u8fc7 SDS-PAGE \u5206\u79bb\uff0c\u5e76\u901a\u8fc7\u6c1f\u663e\u5f71\u68c0\u6d4b\u653e\u5c04\u6027\u86cb\u767d\u6761\u5e26\uff0c\u540c\u65f6\u5728\u540c\u4e00\u80f6\u6216\u5e73\u884c\u80f6\u4e0a\u7535\u6cf3\u5df2\u77e5\u5206\u5b50\u91cf\u6807\u51c6\u548c\u7eaf\u5316\u7684\u672a\u6807\u8bb0 CAT-2 \u9176\u4f5c\u4e3a\u4f4d\u7f6e\u5bf9\u7167\u3002", "Step 5: \u5728\u6c1f\u663e\u5f71\u56fe\u4e2d\uff0c\u6765\u81ea\u7eaf\u5316 mRNA \u7684\u4e00\u4e2a\u4e3b\u8981\u653e\u5c04\u6027\u6761\u5e26\u4e0e\u672a\u6807\u8bb0\u7684\u7eaf\u5316 CAT-2 \u9176\u5728 SDS-PAGE \u4e0a\u5177\u6709\u76f8\u540c\u7684\u8fc1\u79fb\u4f4d\u7f6e\uff08\u7531\u7bad\u5934\u6807\u51fa\uff09\uff0c\u8bf4\u660e\u4e24\u8005\u5177\u6709\u76f8\u540c\u6216\u9ad8\u5ea6\u76f8\u8fd1\u7684\u5206\u5b50\u91cf\u548c\u7535\u6cf3\u884c\u4e3a\u3002", "Step 6: \u5728 SDS-PAGE \u6761\u4ef6\u4e0b\uff0c\u86cb\u767d\u7684\u8fc1\u79fb\u4e3b\u8981\u7531\u5176\u5206\u5b50\u91cf\u51b3\u5b9a\uff0c\u540c\u4e00\u7535\u6cf3\u6761\u4ef6\u548c\u51dd\u80f6\u7cfb\u7edf\u4e2d\u8fc1\u79fb\u4f4d\u7f6e\u91cd\u5408\u901a\u5e38\u8868\u793a\u4e3a\u540c\u4e00\u79cd\u6216\u9ad8\u5ea6\u76f8\u540c\u7684\u86cb\u767d\u79cd\u7c7b\u3002", "Step 7: \u56e0\u6b64\uff0c\u53ef\u4ee5\u63a8\u65ad\uff1a\u6240\u5206\u79bb\u5e76\u4f53\u5916\u7ffb\u8bd1\u7684 mRNA \u7f16\u7801\u7684\u5373\u662f CAT-2 \u86cb\u767d\u3002"], "question_cot": "", "final_conclusion": "\u514d\u75ab\u5438\u9644\u83b7\u5f97\u7684\u591a\u805a\u6838\u7cd6\u4f53\u4e0a\u6240\u643a\u5e26\u7684 mRNA \u80fd\u5728\u65e0\u7ec6\u80de\u7ffb\u8bd1\u4f53\u7cfb\u4e2d\u5408\u6210\u7684\u653e\u5c04\u6027\u6807\u8bb0\u86cb\u767d\uff0c\u4e0e\u5185\u6e90\u5929\u7136 CAT-2 \u9176\u5728 SDS-PAGE \u4e0a\u5177\u6709\u76f8\u540c\u7684\u8fc1\u79fb\u4f4d\u7f6e\uff0c\u56e0\u6b64\u8be5 mRNA \u6240\u7f16\u7801\u7684\u86cb\u767d\u4e3a CAT-2\u3002", "difficulty": "medium", "tags": ["method", "mechanism", "result", "protein_expression", "mRNA_identification"], "created_at": "2025-12-16T21:16:47", "token_est_question": 20, "token_est_answer": 28, "section": "Materials & Methods", "context": "=== MATERIALS AND METHODS ===\nMaterials. RNase-free sucrose and reticulocyte lysates were purchased from Bethesda Research Laboratories, protein A-Sepharose was from Pharmacia, low melting point agarose was from Marine Colloids, oligo(dT)-cellulose was from Collaborative Research, and nitrocellulose filters were from Schleicher and Schuell. All other chemicals were reagentgrade, purchased from Sigma Chemical Co.\n\nPolysome Immunoadsorption. Seeds of the high CAT-2 activity line R6-67 (Scandalios et al., 1980b) were surface-\n \nsterilized with sodium hypochlorite and grown in the dark at for days. Scutella were excised, frozen immediately in liquid and stored at Polysomes were prepared by a modification of the procedure of Davies and Knight (1972). Scutella were ground in a mortar in liquid and transferred to an ice-cold mortar for further grinding in Tris-HC1 , KCI, (w/v) PVP-40, 0.25 M RNase-free sucrose, and 5.8 0-mercaptoethanol. The sample was always maintained at Six milliliters of grinding buffer per gram of scutella was used. The homogenate was filtered through four layers of cheesecloth and centrifuged at for in a fixed-angle rotor. The supernatant was stirred for with Triton X-100 at a final concentration of and centrifuged at for The supernatant was layered over 8-mL pads of sucrose in Tris-HC1 at ), and in 32-mL polycarbonate bottles and centrifuged in a Ti 70 rotor at (average) for After the supernazant above the pads was aspirated off and the sides were washed with sterile distilled the pad was removed, and polysome suspension buffer Hepes-KOH , DTT, 0.625 mM and KOAc] was added. The polysomes were frozen as droplets in liquid and stored at Antibodies against purified CAT-2 (Chandlee et al., 1983) were prepared from New Zealand white rabbits. The antibodies were bound to protein A-Sepharose (Goudswaard et al., 1978; Pharmacia Fine Chemicals Handbook, 1979) and washed with NaSCN to remove possible RNase (Gough Adams, 1978). Immunoadsorption of polysomes was conducted essentially according to the method of Shapiro and Young (1 98 1). Antibody was added to units of polysomes in of immunoadsorption buffer Tris-HCI at , Nonidet P-40, and 20 units/mL heparin] (Shapiro & Young, 1981). After incubation for 2.5 h at the immunoconjugates were batch-adsorbed for 30 min to (packed volume) of protein A-Sepharose, which had been washed and charged (Phartnacia Fine Chemicals Handbook. 1979). The mixture was poured through a sialinized Pasteur pipet column and washed overnight with 700 mL of immunoadsorption buffer. CAT-2 antibody was added to the unbound polysomal fraction, and the immunoadsorption procedure was repeated. This was again repeated an additional 2 times. After washing, approximateiy of packed beads was removed from the column and, later, washed several times with Hepes-KOH (pH 7.6), KOAc, and DTT. The bound polysomes were translated in situ (see below). The was eluted from the column with EDTA, Tris-HCI and heparin (Shapiro & Young, 198 1) and further purified on oligo(dT)-cellulose (Aviv & Leder, 1972). The mRNA was precipitated overnight at after addition of of yeast tRNA, KOAc (pH 5.5) to and 2.5 volumes of ethanol. The mRNA was pelleted by centrifugation in an SW27 rotor at for and taken up in of sterile distilled \n\nPurification and Size Fractionation of Total mRNA. The mRNA was purified from frozen 6- and 7day-old scutella by homogenization in sodium borate buffer, digestion with proteinase md precipitation in LiCl (Hall et al., 1978). The mRNA was purified on oligo(dT)-cellulose and precipitated as above (without carrier tRNA). Ninety-six micrograms of the mRNA was size-fractionated on a methylmercury-agarose gel (Bailey & Davidson, 1976); low\n\nmelting point (Sea Plaque) agarose was used. The sides of the gel were removed, stained in ethidium bromide, and then returned in order to mark the position of the 18s and 26s rRNA Contaminants. The gel was sliced horizontally, and the was extracted with phenol, followed by chloroform extraction of the aqveous phase. The mRNA was precipitated as above and solubilized in distilled \n\nTranslation of Polysomes and mRNA and Immunoprecipitation of Products. Polysomes were translated in both the wheat germ and reticulocyte lysate systems. Wheat germ was extracted (Marcu Dudock, 1974) and stored at Reactions were conducted in of Hepes-KOH , DTT, ATP, GTP. KOAc, each of 19 unlabeled amino acids, creatine phosphate, 6520 units/mL creatine phosphokinase, 55 pmol of . methionine per unit of polysomes, and of wheat germ extract. Reactions were at Reticulocyte lysate reactions (Pelham & Jackson, 1976) in volumes consisted of Hepes-KOH ), creatine phosphate, each of 19 unlabeled amino acids, 55 pmol of [35S]methionine, cyclic AMP, KCI, and of rabbit reticulocyte lysate. When polysomes were translated, one unit, KOAc, and were used. When mRNA was translated, KOAc and no were used. One milliliter of translation immunoadsorption buffer Tris-NaOH 150 mM NaCI, 10 mM Na,EDTA, Triton X-100, sodium deoxycholate, and methionine] was added following each reaction. Thirty micrograms of CAT-2 antibody and PMSF were then added. The samples were incubated for 1 h at followed by at The immunoconjugates were batch-adsorbed to protein A-Sepharose beads for 1 h. Products were eluted by boiling the beads for in Laemmli SDS sample buffer (Laemmli, 1970).\n\nIn Vivo Labeling. Two scutella from 7-day-old R6-67 seedlings were isolated and partially submerged in of labeling solution: sodium phosphate (dibasic, mannitol, and 55 pmol of methionine. Labeling was conducted for at Each scutellum was rinsed for at in sodium phosphate and unlabeled methionine and then ground in of immunoadsorption buffer (above) with a small amount of sand and insoluble PVP-40, unlabeled methionine, /3-mercaptoethanol, and PMSF. The homogenate was centrifuged at for after which time of CAT-2 antibody was added per scutellum. After incubation at for the labeled CAT-2-antibody complex was bound to a protein A-Sepharose column, washed with of polysome immunoadsorption buffer, and eluted with glycine The sample was neutralized with Tris and precipitated with acetone. The precipitate was dissolved in either Laemmli SDS sample buffer for SDS-PAGE or O''Farrell lysis buffer and urea for 2-D PAGE (O''Farrell, 1975).\n\nElectrophoresis and Detection. SDS-PAGE was conducted according to the method of Laemmli (1970). Eleven percent running gels beneath stacking gels were used. Molecular weight standards were phosphorylase bovine serum albumin ovalbumin (45 carbonic anhydrase soybean trypsin inhibitor and @-lactalbumin Two-dimensional PAGE was conducted according to the method of O''Farrell (1975). using a stacking gel and an running gel in\n \nFIGURE 1 : Fluorogram of polyacrylamide gel of mRNA translation products from purified mRNA: lane 1, total scutellar mRNA translation products; lane 2, purified mRNA products (same prep aration as in Figure 5); lane 3, control translation without exogenous mRNA, showing endogenous reticulocyte lysate mRNA products; lane 4, position of purified unlabeled CAT-2 enzyme (radioactive ink dot); lane 5, positions of molecular weight markers. The large arrow indicates the position of the in vitro labeled CAT-2, while the small arrow indicates the position of the main form of endogenous native CAT-2 seen by Coomassie staining.\n\nthe SDS-PAGE dimension, as above. Gels were stained with Coomassie brilliant blue, impregnated with PPO, dried, and fluorographed (Laskey & Mills, 1975). When proteins were to be electrophoretically transferred to nitrocellulose filters (Towbin et al., 1979; Burnette, 1981), the gels were run for 3 h after the bromophenol blue dye front ran off the gel. Native CAT-2 was detected on the nitrocellulose filter immunologically by horseradish peroxidase conjugated goatanti-rabbit IgG (Towbin et al., 1979; Avrameas Guilbert, 1971).", "Thinking\u6a21\u5f0f": "high", "generation_type": "\u63a8\u7406\u578b"}','2026-03-23 15:33:30.697699','2026-03-23 15:33:30.697709');
CREATE TABLE annotation_results (
	id INTEGER NOT NULL, 
	dataset_id INTEGER NOT NULL, 
	dataset_item_id INTEGER NOT NULL, 
	annotation_config_id INTEGER NOT NULL, 
	value_json JSON NOT NULL, 
	annotator_id INTEGER, 
	annotator_name VARCHAR, 
	created_at DATETIME NOT NULL, 
	updated_at DATETIME NOT NULL, 
	duration_seconds FLOAT, 
	confidence FLOAT, 
	notes TEXT, 
	custom_fields_json JSON, 
	PRIMARY KEY (id), 
	FOREIGN KEY(annotation_config_id) REFERENCES annotation_configs (id) ON DELETE CASCADE, 
	FOREIGN KEY(annotator_id) REFERENCES users (id) ON DELETE SET NULL, 
	FOREIGN KEY(dataset_id) REFERENCES datasets (id) ON DELETE CASCADE, 
	FOREIGN KEY(dataset_item_id) REFERENCES qa_pairs (id) ON DELETE CASCADE
);
INSERT INTO annotation_results VALUES(1,2,1,1,'{"score": {"score": 3}}',1,'admin','2026-03-23 15:24:56.312532','2026-03-24 09:34:15.314943',NULL,NULL,NULL,'null');
INSERT INTO annotation_results VALUES(2,2,2,1,'{"score": {"score": 4}}',1,'admin','2026-03-23 15:25:04.350381','2026-03-24 09:34:19.706828',NULL,NULL,NULL,'null');
INSERT INTO annotation_results VALUES(3,2,3,1,'{"score": {"score": 3}}',1,'admin','2026-03-23 15:25:08.567639','2026-03-24 09:34:21.927506',NULL,NULL,NULL,'null');
INSERT INTO annotation_results VALUES(4,2,4,1,'{"score": {"score": 2}}',1,'admin','2026-03-23 15:25:10.999490','2026-03-23 15:25:10.999500',NULL,NULL,NULL,'null');
INSERT INTO annotation_results VALUES(5,2,5,1,'{"score": {"score": 1}}',1,'admin','2026-03-23 15:25:13.634369','2026-03-23 15:25:13.634379',NULL,NULL,NULL,'null');
INSERT INTO annotation_results VALUES(6,2,6,1,'{"score": {"score": 3}}',1,'admin','2026-03-23 15:25:17.349607','2026-03-23 15:25:17.349616',NULL,NULL,NULL,'null');
INSERT INTO annotation_results VALUES(7,2,7,1,'{"score": {"score": 4}}',1,'admin','2026-03-23 15:25:23.967749','2026-03-23 15:25:23.967759',NULL,NULL,NULL,'null');
INSERT INTO annotation_results VALUES(8,3,28,1,'{"score": {"score": 2}}',1,'admin','2026-03-23 15:59:57.482109','2026-03-23 17:21:37.839431',NULL,NULL,NULL,'null');
INSERT INTO annotation_results VALUES(9,3,31,1,'{"score": {"score": 3}}',1,'admin','2026-03-23 16:00:07.024492','2026-03-23 17:21:55.119119',NULL,NULL,NULL,'null');
INSERT INTO annotation_results VALUES(10,3,32,1,'{"score": {"score": 2}}',1,'admin','2026-03-23 16:00:09.757182','2026-03-23 16:00:09.757191',NULL,NULL,NULL,'null');
INSERT INTO annotation_results VALUES(11,3,33,1,'{"score": {"score": 4}}',1,'admin','2026-03-23 16:00:12.275292','2026-03-23 16:00:12.275303',NULL,NULL,NULL,'null');
INSERT INTO annotation_results VALUES(12,3,34,1,'{"score": {"score": 2}}',1,'admin','2026-03-23 16:00:14.306883','2026-03-23 16:00:14.306892',NULL,NULL,NULL,'null');
INSERT INTO annotation_results VALUES(13,2,8,1,'{"score": {"score": 3}}',1,'admin','2026-03-23 17:07:14.893522','2026-03-23 17:07:14.893535',NULL,NULL,NULL,'null');
INSERT INTO annotation_results VALUES(14,2,9,1,'{"score": {"score": 4}}',1,'admin','2026-03-23 17:07:16.958038','2026-03-23 17:07:16.958048',NULL,NULL,NULL,'null');
INSERT INTO annotation_results VALUES(15,2,10,1,'{"score": {"score": 2}}',1,'admin','2026-03-23 17:07:18.677244','2026-03-23 17:07:18.677253',NULL,NULL,NULL,'null');
INSERT INTO annotation_results VALUES(16,2,11,1,'{"score": {"score": 3}}',1,'admin','2026-03-23 17:07:26.053955','2026-03-23 17:07:26.053966',NULL,NULL,'可以的','null');
INSERT INTO annotation_results VALUES(17,2,12,1,'{"score": {"score": 3}}',1,'admin','2026-03-23 17:07:29.174360','2026-03-23 17:07:29.174369',NULL,NULL,NULL,'null');
INSERT INTO annotation_results VALUES(18,2,13,1,'{"score": {"score": 4}}',1,'admin','2026-03-23 17:07:31.338156','2026-03-23 17:07:31.338164',NULL,NULL,NULL,'null');
INSERT INTO annotation_results VALUES(19,2,14,1,'{"score": {"score": 2}}',1,'admin','2026-03-23 17:07:33.222629','2026-03-23 17:07:33.222637',NULL,NULL,NULL,'null');
INSERT INTO annotation_results VALUES(20,2,15,1,'{"score": {"score": 3}}',1,'admin','2026-03-23 17:07:35.010631','2026-03-23 17:07:35.010640',NULL,NULL,NULL,'null');
INSERT INTO annotation_results VALUES(21,2,16,1,'{"score": {"score": 4}}',1,'admin','2026-03-23 17:07:36.737356','2026-03-23 17:07:36.737364',NULL,NULL,NULL,'null');
INSERT INTO annotation_results VALUES(22,2,17,1,'{"score": {"score": 2}}',1,'admin','2026-03-23 17:07:38.660269','2026-03-23 17:07:38.660278',NULL,NULL,NULL,'null');
INSERT INTO annotation_results VALUES(23,2,18,1,'{"score": {"score": 3}}',1,'admin','2026-03-23 17:07:40.236747','2026-03-23 17:07:40.236756',NULL,NULL,NULL,'null');
INSERT INTO annotation_results VALUES(24,2,19,1,'{"score": {"score": 4}}',1,'admin','2026-03-23 17:07:42.084098','2026-03-23 17:07:42.084107',NULL,NULL,NULL,'null');
INSERT INTO annotation_results VALUES(25,2,20,1,'{"score": {"score": 1}}',1,'admin','2026-03-23 17:07:43.817847','2026-03-23 17:07:43.817856',NULL,NULL,NULL,'null');
INSERT INTO annotation_results VALUES(26,2,21,1,'{"score": {"score": 1}}',1,'admin','2026-03-23 17:07:45.628201','2026-03-23 17:07:45.628211',NULL,NULL,NULL,'null');
INSERT INTO annotation_results VALUES(27,2,22,1,'{"score": {"score": 2}}',1,'admin','2026-03-23 17:07:47.337652','2026-03-23 17:07:47.337661',NULL,NULL,NULL,'null');
INSERT INTO annotation_results VALUES(28,2,23,1,'{"score": {"score": 3}}',1,'admin','2026-03-23 17:07:49.055568','2026-03-23 17:07:49.055578',NULL,NULL,NULL,'null');
INSERT INTO annotation_results VALUES(29,2,24,1,'{"score": {"score": 3}}',1,'admin','2026-03-23 17:07:55.821446','2026-03-23 17:07:55.821457',NULL,NULL,'发发','null');
INSERT INTO annotation_results VALUES(30,2,25,1,'{"score": {"score": 3}}',1,'admin','2026-03-23 17:08:02.537354','2026-03-23 17:08:05.602892',NULL,NULL,'答复','null');
INSERT INTO annotation_results VALUES(31,3,26,1,'{"score": {"score": 2}}',1,'admin','2026-03-23 17:08:22.589885','2026-03-23 17:21:32.556767',NULL,NULL,NULL,'null');
INSERT INTO annotation_results VALUES(32,3,27,1,'{"score": {"score": 2}}',1,'admin','2026-03-23 17:08:24.523663','2026-03-23 17:21:34.739702',NULL,NULL,NULL,'null');
INSERT INTO annotation_results VALUES(33,3,29,1,'{"score": {"score": 3}}',1,'admin','2026-03-23 17:08:26.609927','2026-03-23 17:21:39.470520',NULL,NULL,NULL,'null');
INSERT INTO annotation_results VALUES(34,3,30,1,'{"score": {"score": 2}}',1,'admin','2026-03-23 17:08:34.740539','2026-03-23 17:21:41.120078',NULL,0.4000000000000000222,NULL,'null');
INSERT INTO annotation_results VALUES(35,3,35,1,'{"score": {"score": 2}}',1,'admin','2026-03-23 17:08:38.332103','2026-03-23 17:22:08.870085',NULL,NULL,NULL,'null');
INSERT INTO annotation_results VALUES(36,3,36,1,'{"score": {"score": 3}}',1,'admin','2026-03-23 17:08:40.128983','2026-03-23 17:08:40.129007',NULL,NULL,NULL,'null');
INSERT INTO annotation_results VALUES(37,3,37,1,'{"score": {"score": 1}}',1,'admin','2026-03-23 17:08:41.774012','2026-03-23 17:08:41.774021',NULL,NULL,NULL,'null');
INSERT INTO annotation_results VALUES(38,3,38,1,'{"score": {"score": 4}}',1,'admin','2026-03-23 17:08:44.524347','2026-03-23 17:08:44.524357',NULL,NULL,NULL,'null');
INSERT INTO annotation_results VALUES(39,3,39,1,'{"score": {"score": 2}}',1,'admin','2026-03-23 17:08:46.324967','2026-03-23 17:08:46.324977',NULL,NULL,NULL,'null');
INSERT INTO annotation_results VALUES(40,3,40,1,'{"score": {"score": 3}}',1,'admin','2026-03-23 17:08:48.156701','2026-03-23 17:08:48.156710',NULL,NULL,NULL,'null');
INSERT INTO annotation_results VALUES(41,3,41,1,'{"score": {"score": 2}}',1,'admin','2026-03-23 17:08:49.784809','2026-03-23 17:08:49.784818',NULL,NULL,NULL,'null');
INSERT INTO annotation_results VALUES(42,3,42,1,'{"score": {"score": 1}}',1,'admin','2026-03-23 17:08:51.424343','2026-03-23 17:08:51.424352',NULL,NULL,NULL,'null');
INSERT INTO annotation_results VALUES(43,3,43,1,'{"score": {"score": 2}}',1,'admin','2026-03-23 17:08:53.042899','2026-03-23 17:08:53.042908',NULL,NULL,NULL,'null');
INSERT INTO annotation_results VALUES(44,3,44,1,'{"score": {"score": 3}}',1,'admin','2026-03-23 17:08:54.608294','2026-03-23 17:08:54.608303',NULL,NULL,NULL,'null');
INSERT INTO annotation_results VALUES(45,3,45,1,'{"score": {"score": 3}}',1,'admin','2026-03-23 17:08:56.333801','2026-03-23 17:08:56.333810',NULL,NULL,NULL,'null');
INSERT INTO annotation_results VALUES(46,3,46,1,'{"score": {"score": 4}}',1,'admin','2026-03-23 17:08:58.008343','2026-03-23 17:08:58.008353',NULL,NULL,NULL,'null');
INSERT INTO annotation_results VALUES(47,3,47,1,'{"score": {"score": 5}}',1,'admin','2026-03-23 17:09:01.241531','2026-03-23 17:09:01.241540',NULL,NULL,NULL,'null');
INSERT INTO annotation_results VALUES(48,3,48,1,'{"score": {"score": 1}}',1,'admin','2026-03-23 17:09:05.559254','2026-03-23 17:09:05.559263',NULL,NULL,NULL,'null');
INSERT INTO annotation_results VALUES(49,3,49,1,'{"score": {"score": 2}}',1,'admin','2026-03-23 17:09:07.127331','2026-03-23 17:09:07.127340',NULL,NULL,NULL,'null');
INSERT INTO annotation_results VALUES(50,3,50,1,'{"score": {"score": 3}}',1,'admin','2026-03-23 17:09:08.625292','2026-03-23 17:09:10.206025',NULL,NULL,NULL,'null');
INSERT INTO annotation_results VALUES(51,3,26,2,'{"choice": {"selected_options": ["4\u5206\uff1a"]}}',1,'admin','2026-03-23 17:21:32.663097','2026-03-23 17:21:32.663106',NULL,NULL,NULL,'null');
INSERT INTO annotation_results VALUES(52,3,27,2,'{"choice": {"selected_options": ["2\u5206\uff1a"]}}',1,'admin','2026-03-23 17:21:34.849023','2026-03-23 17:21:34.849031',NULL,NULL,NULL,'null');
INSERT INTO annotation_results VALUES(53,3,28,2,'{"choice": {"selected_options": ["4\u5206\uff1a"]}}',1,'admin','2026-03-23 17:21:38.086455','2026-03-23 17:21:38.086464',NULL,NULL,NULL,'null');
INSERT INTO annotation_results VALUES(54,3,29,2,'{"choice": {"selected_options": ["2\u5206\uff1a"]}}',1,'admin','2026-03-23 17:21:39.576011','2026-03-23 17:21:39.576020',NULL,NULL,NULL,'null');
INSERT INTO annotation_results VALUES(55,3,30,2,'{"choice": {"selected_options": ["3\u5206\uff1a"]}}',1,'admin','2026-03-23 17:21:41.210600','2026-03-23 17:21:41.210609',NULL,NULL,NULL,'null');
INSERT INTO annotation_results VALUES(56,3,31,2,'{"choice": {"selected_options": ["3\u5206\uff1a"]}}',1,'admin','2026-03-23 17:21:55.238925','2026-03-23 17:21:55.238934',NULL,NULL,'不太确定','null');
INSERT INTO annotation_results VALUES(57,3,35,2,'{"choice": {"selected_options": ["2\u5206\uff1a"]}}',1,'admin','2026-03-23 17:22:08.993525','2026-03-23 17:22:08.993534',NULL,NULL,NULL,'null');
INSERT INTO annotation_results VALUES(58,2,1,2,'{"choice": {"selected_options": ["2\u5206\uff1a"]}}',1,'admin','2026-03-24 09:34:15.452148','2026-03-24 09:34:15.452157',NULL,NULL,NULL,'null');
INSERT INTO annotation_results VALUES(59,2,2,2,'{"choice": {"selected_options": ["3\u5206\uff1a"]}}',1,'admin','2026-03-24 09:34:19.844374','2026-03-24 09:34:19.844383',NULL,NULL,NULL,'null');
INSERT INTO annotation_results VALUES(60,2,3,2,'{"choice": {"selected_options": ["3\u5206\uff1a"]}}',1,'admin','2026-03-24 09:34:22.068739','2026-03-24 09:34:22.068748',NULL,NULL,NULL,'null');
CREATE TABLE llm_analysis_cache (
	id INTEGER NOT NULL, 
	project_id INTEGER NOT NULL, 
	analysis_text TEXT NOT NULL, 
	model_name VARCHAR NOT NULL, 
	notes_count INTEGER NOT NULL, 
	language VARCHAR NOT NULL, 
	created_at DATETIME NOT NULL, 
	updated_at DATETIME NOT NULL, 
	PRIMARY KEY (id)
);
CREATE INDEX ix_annotation_configs_annotation_type ON annotation_configs (annotation_type);
CREATE INDEX ix_annotation_configs_id ON annotation_configs (id);
CREATE INDEX ix_annotation_configs_is_deleted ON annotation_configs (is_deleted);
CREATE INDEX ix_annotation_configs_name ON annotation_configs (name);
CREATE INDEX ix_question_types_id ON question_types (id);
CREATE INDEX ix_question_types_subtype ON question_types (subtype);
CREATE INDEX ix_question_types_type ON question_types (type);
CREATE INDEX ix_system_configs_id ON system_configs (id);
CREATE UNIQUE INDEX ix_system_configs_key ON system_configs ("key");
CREATE INDEX ix_users_id ON users (id);
CREATE INDEX ix_users_is_active ON users (is_active);
CREATE UNIQUE INDEX ix_users_username ON users (username);
CREATE INDEX ix_projects_category ON projects (category);
CREATE INDEX ix_projects_creator_id ON projects (creator_id);
CREATE INDEX ix_projects_id ON projects (id);
CREATE INDEX ix_projects_name ON projects (name);
CREATE INDEX ix_projects_status ON projects (status);
CREATE INDEX ix_projects_version ON projects (version);
CREATE INDEX ix_seed_questions_creator_id ON seed_questions (creator_id);
CREATE INDEX ix_seed_questions_id ON seed_questions (id);
CREATE INDEX ix_seed_questions_subtype ON seed_questions (subtype);
CREATE INDEX ix_seed_questions_type ON seed_questions (type);
CREATE INDEX ix_datasets_annotator_id ON datasets (annotator_id);
CREATE INDEX ix_datasets_category ON datasets (category);
CREATE INDEX ix_datasets_creator_id ON datasets (creator_id);
CREATE INDEX ix_datasets_id ON datasets (id);
CREATE INDEX ix_datasets_name ON datasets (name);
CREATE INDEX ix_datasets_project_id ON datasets (project_id);
CREATE INDEX ix_datasets_status ON datasets (status);
CREATE INDEX ix_datasets_version ON datasets (version);
CREATE INDEX ix_qa_pairs_dataset_id ON qa_pairs (dataset_id);
CREATE INDEX ix_qa_pairs_id ON qa_pairs (id);
CREATE INDEX ix_annotation_results_annotation_config_id ON annotation_results (annotation_config_id);
CREATE INDEX ix_annotation_results_annotator_id ON annotation_results (annotator_id);
CREATE INDEX ix_annotation_results_dataset_id ON annotation_results (dataset_id);
CREATE INDEX ix_annotation_results_dataset_item_id ON annotation_results (dataset_item_id);
CREATE INDEX ix_annotation_results_id ON annotation_results (id);
CREATE INDEX ix_llm_analysis_cache_id ON llm_analysis_cache (id);
CREATE INDEX ix_llm_analysis_cache_project_id ON llm_analysis_cache (project_id);
COMMIT;
