examples.jsonl

{"id": "unc106238-context-response", "title": "Context-dependent transcript response to UNC106238 in macrophage-fibroblast coculture: an identifiable, composition-robust contrast", "domain": "therapeutic_targets", "candidate_id": "candidate-10c868bb758e34c115eeefcf", "hypothesis": "# Context-dependent transcript response to UNC106238 in macrophage-fibroblast coculture: an identifiable, composition-robust contrast\n\n## Scientific opening and unresolved claim\n\nThe D2-T1 seed asks whether an intervention-associated transcript response depends on macrophage-fibroblast context. This is clinically important because a transcript program that appears responsive in a bulk or isolated cell preparation may not represent a useful therapeutic target when the relevant cell-cell context is absent or altered. If the response is context-dependent, prioritization should condition on the cellular microenvironment rather than rank targets by overall expression change.\n\nThe strongest claim currently supported by the available data is that E-MTAB-8316 contains a verified, donor-aware macrophage-condition contrast: six monocyte donors (four FLS donor clusters) measured under \"none\" and \"UNC106238\" conditions, with twelve condition samples total. The unresolved claim is narrower and falsifiable: the magnitude or direction of a prespecified intervention-associated transcript response differs between macrophage-present and macrophage-absent fibroblast contexts in a way that is not explained by donor, condition, batch, or sample-composition differences.\n\nThis is a hypothesis, not an established finding. The available data support a donor-aware exploratory comparison, not a claim of direct target causality.\n\n## Prespecified intervention-associated transcript program\n\nThe intervention-associated transcript program is defined from the UNC106238 vs. none contrast in E-MTAB-8316 with the following prespecified selection criteria:\n\n- |mean log2 fold change| >= 1.0 across the six monocyte donors\n- Sign consistency >= 5/6 donors\n- Feature overlap with the macrophage-condition contrast (shared gene identifiers)\n\nThese criteria are applied before any context comparison to avoid post-hoc selection bias. The program is a medication-associated transcript response, not a direct target action.\n\n## Donor-aware pseudobulk analysis\n\nThe donor-aware pseudobulk analysis handles the nested structure of the data:\n\n- Four FLS donor clusters\n- Six nested monocyte donors\n- Twelve condition samples (none vs. UNC106238)\n\nWithin each donor/condition, repeated context measurements are averaged before paired exploratory inference. Donor-level inference is used to account for the nested structure. The analysis is exploratory and does not claim direct target causality.\n\n## Composition-robustness check\n\nA composition-robustness check uses ridge regression with donor, condition, batch, and composition covariates. This check tests whether the context contrast is robust to sample-composition differences. The ridge regression is a sensitivity analysis, not a causal model.\n\n## Bounded data-bound diagnostic\n\nA bounded data-bound diagnostic tests whether the context contrast is supported by the available data without claiming direct target causality. The diagnostic:\n\n1. Defines the prespecified intervention-associated transcript program from the UNC106238 vs. none contrast\n2. Tests whether the magnitude or direction of the program differs between macrophage-present and macrophage-absent fibroblast contexts\n3. Accounts for donor, condition, batch, and sample-composition differences\n4. Reports supportive, adverse, and inconclusive outcomes\n\nThe diagnostic is a hypothesis test, not a confirmatory analysis. It does not establish direct target causality.\n\n## Competing explanations\n\n- Donor-level variation: The context contrast may be driven by a subset of donors rather than a general effect\n- Condition-level variation: The context contrast may be driven by condition-specific effects rather than a general effect\n- Batch-level variation: The context contrast may be driven by batch effects rather than a general effect\n- Sample-composition differences: The context contrast may be driven by differences in sample composition rather than a general effect\n\n## Method comparison\n\nThe baseline method is a donor-aware pseudobulk analysis with within-donor averaging and donor-level inference. The learned alternative is a ridge regression model with donor, condition, batch, and composition covariates. The ridge regression could reveal whether the context contrast is robust to sample-composition differences, which the baseline method loses.\n\nThe ridge regression is a sensitivity analysis, not a causal model. It does not establish direct target causality.\n\n## Falsification criteria\n\nThe hypothesis is falsified if:\n\n- The context contrast is not supported by the available data\n- The context contrast is driven by donor, condition, batch, or sample-composition differences\n- The context contrast is not robust to sample-composition differences\n\n## Key references\n\n[K1] The D2-T1 seed asks whether an intervention-associated transcript response depends on macrophage-fibroblast context. This is clinically important because a transcript program that appears responsive in a bulk or isolated cell preparation may not represent a useful therapeutic target when the relevant cell-cell context is absent or altered.\n\n[K2] The strongest claim currently supported by the available data is that E-MTAB-8316 contains a verified, donor-aware macrophage-condition contrast: six monocyte donors (four FLS donor clusters) measured under \"none\" and \"UNC106238\" conditions, with twelve condition samples total.\n\n[K3] The unresolved claim is narrower and falsifiable: the magnitude or direction of a prespecified intervention-associated transcript response differs between macrophage-present and macrophage-absent fibroblast contexts in a way that is not explained by donor, condition, batch, or sample-composition differences.\n", "instruction": "# Context-dependent transcript response to UNC106238 in macrophage-fibroblast coculture\n\n## Scientific question and claim boundary\n\nTest whether the magnitude or direction of a prespecified intervention-associated transcript response differs between macrophage-present and macrophage-absent fibroblast contexts in a way that is not explained by donor, condition, batch, or sample-composition differences. The data are from E-MTAB-8316, a donor-aware exploratory contrast with six monocyte donors nested within four FLS donor clusters, measured under `none` and `UNC106238` conditions. This is a hypothesis, not an established finding. A medication-associated transcript response is not direct evidence of target causality.\n\nThe strongest claim currently supported by the available data is that E-MTAB-8316 contains a verified, donor-aware macrophage-condition contrast with twelve condition samples total. The unresolved claim is narrower: the context contrast is not explained by donor, condition, batch, or composition differences.\n\n## Required computation\n\n1. Read the prepared counts and design files at the exact bound paths below. The counts file is a tab-separated gzip file with a `feature` column followed by sample columns. The design file is a JSON list of sample metadata objects.\n2. Define the prespecified intervention-associated transcript program before any context comparison using the UNC106238 versus none contrast: mean absolute log2 fold change >= 1.0 across the six monocyte donors, sign consistency >= 5/6 donors, and feature overlap with the macrophage-condition contrast using shared gene identifiers.\n3. Perform the donor-aware pseudobulk baseline: average repeated context measurements within each donor and condition, then use donor-level exploratory inference for the macrophage-present versus macrophage-absent contrast.\n4. Fit the composition-robustness sensitivity analysis: ridge regression with donor, condition, batch, and sample-composition covariates. This is a sensitivity analysis, not a causal model.\n5. Report the context contrast, uncertainty, donor-level pattern, and whether the contrast is robust to sample-composition differences. Report supportive, adverse, and inconclusive outcomes according to the declared criteria.\n\n## Exact data bindings\n\n- Counts: `/data/prepared/E-MTAB-8316.counts.tsv.gz/E-MTAB-8316.counts.tsv.gz`\n- Design: `/data/prepared/E-MTAB-8316.design.json/E-MTAB-8316.design.json`\n\nThe counts header is `feature` followed by sample IDs such as `MK11`. The design records contain `sample_id`, `donor_id`, `condition`, `cell_type`, and `source_metadata`. Join counts sample columns to design `sample_id`.\n\n## Required output\n\nWrite `/workspace/results.json` with at least:\n\n- `program`: the prespecified transcript program and selection criteria applied\n- `baseline`: donor-aware pseudobulk context contrast with uncertainty and donor-level results\n- `ridge`: composition-robustness sensitivity analysis results\n- `outcome`: one of `supportive`, `adverse`, or `inconclusive`, with a justification tied to the computed results\n- `limitations`: explicit statement that this is exploratory and does not establish direct target causality\n\n## Interpretation rules\n\nSupportive means the context contrast is supported by the available data and is robust to donor, condition, batch, and sample-composition differences. Adverse means the contrast is not supported or is driven by donor, condition, batch, or composition differences. Inconclusive means the contrast is not robust to composition differences or the data are insufficient to distinguish the leading claim from its strongest rival. A result that is not robust to composition differences is not supportive, even if the raw contrast is large.\n\n## Key references\n\n[K1] Rao et al. (2025) describe monocyte state transitions after myocardial infarction, but do not establish a context-dependent transcript response to a specific intervention in macrophage-fibroblast coculture. This proposal tests a different question: whether an intervention-associated transcript response depends on macrophage-fibroblast context.\n\n[K2] Liu et al. (2026) describe extracellular vesicle regulation in wound healing and scar formation by macrophages, fibroblasts, and their crosstalk, but do not establish a context-dependent transcript response to a specific intervention in macrophage-fibroblast coculture. This proposal tests a different question: whether an intervention-associated transcript response depends on macrophage-fibroblast context.\n\n[K3] Mao et al. (2026) describe m6A methylation regulatory networks in pulmonary fibrosis, but do not establish a context-dependent transcript response to a specific intervention in macrophage-fibroblast coculture. This proposal tests a different question: whether an intervention-associated transcript response depends on macrophage-fibroblast context.\n\n## Bibliography\n\n- Rao SV, O'Donoghue ML, Ruel M, et al. Peripheral blood transcriptomes and a single-cell atlas suggest a monocyte-associated immune recovery-failure axis in STEMI. PLoS One. 2025. https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0358983\n- Liu T, Liu Y, Liu Y, et al. Extracellular vesicles in wound healing and scar formation: molecular regulation of macrophages, fibroblasts, and their crosstalk. Front Cell Dev Biol. 2026. https://www.ebi.ac.uk/europepmc/webservices/rest/PMC13613145/fullTextXML\n- Mao L, Zhou D, Zhou G, Lu X. m6A methylation regulatory network in pulmonary fibrosis: Molecular mechanisms from RNA stability to cellular phenotype transition. Exp Ther Med. 2026. https://www.ebi.ac.uk/europepmc/webservices/rest/PMC13419677/fullTextXML\n\n## What is not established\n\nThis experiment does not establish direct target causality, clinical efficacy, or a mechanism. It is an exploratory, donor-aware diagnostic. Clinical adjudication, independent replication, and perturbation evidence would be required for stronger claims.\n", "task_toml": "schema_version = \"1.4\"\n\n[task]\nname = \"context-dependent-unc106238-transcript-response\"\n", "verifier": "#!/bin/bash\nset -uo pipefail\n\n# Verifier: evaluates declared solver outputs in /workspace.\n# Writes numeric 0 or 1 to /logs/verifier/reward.txt and exits 0.\n\nmkdir -p /logs/verifier\nREWARD=0\n\n# Check that results.json exists\nRESULT_PATH=\"/workspace/results.json\"\nif [ ! -f \"$RESULT_PATH\" ]; then\n  echo \"results.json not found\" > /logs/verifier/reward.txt\n  echo \"0\" > /logs/verifier/reward.txt\n  exit 0\nfi\n\n# Validate the result with Python\npython3 - <<'PY'\nimport json, sys\n\ntry:\n    with open(\"/workspace/results.json\") as f:\n        r = json.load(f)\nexcept Exception as e:\n    print(f\"Failed to parse results.json: {e}\")\n    sys.exit(1)\n\nerrors = []\n\n# Required top-level keys\nfor key in [\"program\", \"baseline\", \"ridge\", \"outcome\", \"limitations\"]:\n    if key not in r:\n        errors.append(f\"Missing required key: {key}\")\n\n# Check outcome is valid\noutcome = r.get(\"outcome\")\nif outcome not in (\"supportive\", \"adverse\", \"inconclusive\"):\n    errors.append(f\"Invalid outcome: {outcome}\")\n\n# Check program has criteria and genes\nprogram = r.get(\"program\", {})\nif not isinstance(program, dict):\n    errors.append(\"program is not a dict\")\nelse:\n    if \"criteria\" not in program:\n        errors.append(\"program missing criteria\")\n    if \"genes\" not in program:\n        errors.append(\"program missing genes\")\n\n# Check baseline has context_contrast\nbaseline = r.get(\"baseline\", {})\nif not isinstance(baseline, dict):\n    errors.append(\"baseline is not a dict\")\nelse:\n    if \"context_contrast\" not in baseline:\n        errors.append(\"baseline missing context_contrast\")\n\n# Check ridge has context_coefficient and composition_robust\nridge = r.get(\"ridge\", {})\nif not isinstance(ridge, dict):\n    errors.append(\"ridge is not a dict\")\nelse:\n    if \"context_coefficient\" not in ridge:\n        errors.append(\"ridge missing context_coefficient\")\n    if \"composition_robust\" not in ridge:\n        errors.append(\"ridge missing composition_robust\")\n\n# Check limitations is non-empty\nlimitations = r.get(\"limitations\", \"\")\nif not isinstance(limitations, str) or len(limitations.strip()) == 0:\n    errors.append(\"limitations is empty or not a string\")\n\n# Check for overclaiming: outcome supportive requires composition_robust True\nif outcome == \"supportive\":\n    if not ridge.get(\"composition_robust\", False):\n        errors.append(\"Outcome is supportive but ridge composition_robust is False\")\n\n# Check justification is present and non-empty\njustification = r.get(\"justification\", \"\")\nif not isinstance(justification, str) or len(justification.strip()) == 0:\n    errors.append(\"justification is empty or not a string\")\n\n# Check for overclaiming in justification\njust_text = justification.lower()\nif \"causality is established\" in just_text or \"direct target causality\" in just_text and \"does not\" not in just_text:\n    errors.append(\"Justification overclaims direct target causality\")\n\nif errors:\n    for e in errors:\n        print(f\"ERROR: {e}\")\n    sys.exit(1)\nelse:\n    print(\"Verifier passed\")\n    sys.exit(0)\nPY\n\nif [ $? -eq 0 ]; then\n  REWARD=1\nfi\n\necho \"$REWARD\" > /logs/verifier/reward.txt\nexit 0\n", "references_json": "{\"references\":[{\"authors\":\"Rao SV, O'Donoghue ML, Ruel M, Rab T, Tamis-Holland JE, Alexander JH, et al.\",\"claim\":\"Monocytes and monocyte-derived macrophages are key cellular populations linking acute necrosis, inflammatory clearance, and tissue repair. After myocardial infarction, monocytes undergo state transitions from acute inflammation-driven programs toward recovery and remodeling-biased states.\",\"contribution\":\"This proposal tests whether an intervention-associated transcript response depends on macrophage-fibroblast context, which is a different question from the monocyte state transitions described in this work.\",\"id\":\"K1\",\"inspection\":\"excerpt_only\",\"limitation\":\"This work does not establish a context-dependent transcript response to a specific intervention in macrophage-fibroblast coculture.\",\"locator\":\"Abstract and Introduction\",\"receipt\":{\"content_sha256\":\"38f2fc749b060f21997c5031c4638b11cc4ac3edc11b60ae77f7999d864c1ec6\",\"evidence_kind\":\"inspected_excerpt\",\"inspection_receipt_sha256\":\"08960e611f1ef999358ec44926d819493018567c5f56ffb292df7c2e78d82805\",\"retrieved_at\":\"2026-10-16T11:23:14.996409+00:00\",\"source_content_sha256\":\"207468bb3466947b6f43a18eafd3a02d2137a99b7ac7fe363988bcdfc4815059\",\"source_uri\":\"https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0358983\",\"version\":null},\"source_id\":\"4dbb48d7cb92c45dc8ecf74a51d0f7a4d022388fcbb2595d8179d77499b4d0db\",\"title\":\"Peripheral blood transcriptomes and a single-cell atlas suggest a monocyte-associated immune recovery-failure axis in STEMI\",\"work_id\":\"doi:10.1371/journal.pone.0358983\",\"year\":2025},{\"authors\":\"Liu T, Liu Y, Liu Y, Wen D, Sun J, Zhang Y, Gao Y, Zheng D\",\"claim\":\"Extracellular vesicles in wound healing and scar formation are regulated by macrophages, fibroblasts, and their crosstalk. These cells undergo state transitions from acute inflammation-driven programs toward recovery and remodeling-biased states.\",\"contribution\":\"This proposal tests whether an intervention-associated transcript response depends on macrophage-fibroblast context, which is a different question from the extracellular vesicle regulation described in this work.\",\"id\":\"K2\",\"inspection\":\"excerpt_only\",\"limitation\":\"This work does not establish a context-dependent transcript response to a specific intervention in macrophage-fibroblast coculture.\",\"locator\":\"Abstract and Introduction\",\"receipt\":{\"content_sha256\":\"f5a6ff881edb6a3536ec86e604f5060b778b1b51c05702b652dfd0f69735a060\",\"evidence_kind\":\"inspected_excerpt\",\"inspection_receipt_sha256\":\"d08932949790e5edf0640a97e8f479cfd4ca4ec417604db99eeb59547fff29c9\",\"retrieved_at\":\"2026-10-16T12:26:47.551327+00:00\",\"source_content_sha256\":\"f294011c313a5ef6389dbffead68fe4f2f66d498a2d62280f44df48378d08721\",\"source_uri\":\"https://www.ebi.ac.uk/europepmc/webservices/rest/PMC13613145/fullTextXML\",\"version\":null},\"source_id\":\"183c2a739d45f4c93dd687b15b3f8c41e907caa5fbd4e243e84782252a01168e\",\"title\":\"Extracellular vesicles in wound healing and scar formation: molecular regulation of macrophages, fibroblasts, and their crosstalk\",\"work_id\":\"doi:10.3389/fcell.2026.1917543\",\"year\":2026},{\"authors\":\"Mao L, Zhou D, Zhou G, Lu X\",\"claim\":\"m6A methylation regulatory network in pulmonary fibrosis: Molecular mechanisms from RNA stability to cellular phenotype transition. m6A modification is not a uniform pro-fibrotic or anti-fibrotic switch, but rather a dynamic regulatory network that links RNA metabolism to PF progression and therapeutic opportunities.\",\"contribution\":\"This proposal tests whether an intervention-associated transcript response depends on macrophage-fibroblast context, which is a different question from the m6A methylation regulation described in this work.\",\"id\":\"K3\",\"inspection\":\"excerpt_only\",\"limitation\":\"This work does not establish a context-dependent transcript response to a specific intervention in macrophage-fibroblast coculture.\",\"locator\":\"Abstract and Introduction\",\"receipt\":{\"content_sha256\":\"b5920938c3a010af1152d6985b04125dd0482cd2bb9ccbc938edb2bdfb4ea082\",\"evidence_kind\":\"inspected_excerpt\",\"inspection_receipt_sha256\":\"751828e678dbcfa48bc6d2fb079773d3b3e44eaf604f46051f281dd8a98e1932\",\"retrieved_at\":\"2026-10-16T12:26:42.545499+00:00\",\"source_content_sha256\":\"c3574e067b544ca33b14bb2512a58d6ca7ce4b1097e1bb6ff56a65a04d972fba\",\"source_uri\":\"https://www.ebi.ac.uk/europepmc/webservices/rest/PMC13419677/fullTextXML\",\"version\":null},\"source_id\":\"45dc912400b85c2bb15ef34f9250ad9c54d5bee54b429bc0a1ea68373d0509ce\",\"title\":\"m6A methylation regulatory network in pulmonary fibrosis: Molecular mechanisms from RNA stability to cellular phenotype transition (Review)\",\"work_id\":\"doi:10.3892/etm.2026.13246\",\"year\":2026}],\"schema\":\"hypothesis-key-references-v1\"}\n", "input_requirements_json": "[{\"filename\": \"E-MTAB-8316.counts.tsv.gz\", \"source\": \"inputs/E-MTAB-8316.counts.tsv.gz\", \"target\": \"/data/prepared/E-MTAB-8316.counts.tsv.gz/E-MTAB-8316.counts.tsv.gz\", \"sha256\": \"9ceb4b7cf3d52d7f6e3800c827d491b174b646826fae9c6d1433c5d64b219645\", \"accession\": \"E-MTAB-8316\", \"study_url\": \"https://www.ebi.ac.uk/biostudies/arrayexpress/studies/E-MTAB-8316\", \"included\": false, \"format\": \"prepared_counts_matrix\"}, {\"filename\": \"E-MTAB-8316.design.json\", \"source\": \"inputs/E-MTAB-8316.design.json\", \"target\": \"/data/prepared/E-MTAB-8316.design.json/E-MTAB-8316.design.json\", \"sha256\": \"ca011580853bbea30b4f68f21bb5b017721c3e47bfefee6e9083b44da04c4c81\", \"accession\": \"E-MTAB-8316\", \"study_url\": \"https://www.ebi.ac.uk/biostudies/arrayexpress/studies/E-MTAB-8316\", \"included\": false, \"format\": \"prepared_sample_design\"}]", "provenance_json": "{\"candidate_id\": \"candidate-10c868bb758e34c115eeefcf\", \"candidate_content_hash\": \"10c868bb758e34c115eeefcf4c0a9d406adffd7ad33cdd43c3d21ead507b1258\", \"compile_job_id\": \"job-36ef2f487f8bbb183f434d83\", \"source_release\": \"raft-final-v2-20261016\", \"source_trial\": \"raft-final-v2-300-20261016\", \"domain\": \"therapeutic_targets\", \"generation\": 3, \"parent_candidate_ids\": [\"candidate-795608ea6a966b991c2abbcd\"], \"source_file_sha256\": {\"task.toml\": \"4182c8d2c5f6dd7bb32d237844d47c2374b009dc16d72f8a692148230dc050dd\", \"validation.json\": \"138da3ce2545e46760e6b55a77f0f998ab7eebe3bb3f3c50bda6cc226399d894\", \"instruction.md\": \"8fd2471312ff2250021da0759a6e189c2601876555a0bdf3b61988b6ebff9b9d\", \"bindings.json\": \"c8826abdc9b749f4098ff2a72112de552288ed2197e8ae9e33b3d6f2c0cff4ac\", \"key-references.json\": \"636b2c6292d7daf5a80088bd75c9716e7abe86473130ae42f84a45e4405947b6\", \"tests/test.sh\": \"39234ef4b00f5df70791a19f3f652df285cd2a7defec0ad58505f5751280727f\", \"validation/stale_result_fixture.sh\": \"0900e788648672aa199fb135e50129d08ae000c43b699c21a9792074d070f007\", \"validation/overclaim_fixture.sh\": \"5dcf0368355dd5f20f2c31174a646b9d2228454ac603ba113b92b9c3c26ef635\", \"validation/missing_ridge_fixture.sh\": \"75222086686ab58ca5cd0cd590ab8ff42bbd9eed156d610c4db16a516cf04057\", \"validation/readiness.sh\": \"44851bffe716082a8f82bf22d4e1fe3cc087b5ceefca85dc0444e76d252e2ab4\", \"validation/positive_fixture.sh\": \"f795ef3ab9643d445b61b6576546e9cc1e03ccaeb1cbb61867ceeb030ad90dd1\", \"environment/Dockerfile\": \"fa8a4158a7e19c6a26cc080c0eeac07f3d316f0c8c48f76139392c6f8835fbb0\"}, \"source_hypothesis_sha256\": \"7b07c633320b1391d32bde6f1ecc7bbc5c0301f4482cb163f9ba03a2613e973c\", \"export_changes\": [\"bindings.json\", \"environment/Dockerfile\"], \"export_policy\": \"Private source paths replaced with portable input references; private runtime image replaced with a public Python example image. Scientific wording and verifier logic retained.\", \"source_task_readiness\": \"passed\", \"source_reference_status\": \"not_run\", \"scientific_result\": \"not_established\", \"validation_scope\": \"Task readiness and rejection probes only; positive-answer acceptance and scientific validity unverified.\", \"fixture_checks\": {\"candidate_id\": \"candidate-10c868bb758e34c115eeefcf\", \"probes\": [{\"fixture\": \"validation/positive_fixture.sh\", \"expected_reward\": \"1\", \"actual_reward\": \"1\", \"fixture_returncode\": 0, \"verifier_returncode\": 0, \"passed\": true}, {\"fixture\": \"validation/overclaim_fixture.sh\", \"expected_reward\": \"0\", \"actual_reward\": \"0\", \"fixture_returncode\": 0, \"verifier_returncode\": 0, \"passed\": true}, {\"fixture\": \"validation/missing_ridge_fixture.sh\", \"expected_reward\": \"0\", \"actual_reward\": \"0\", \"fixture_returncode\": 0, \"verifier_returncode\": 0, \"passed\": true}, {\"fixture\": \"validation/stale_result_fixture.sh\", \"expected_reward\": \"0\", \"actual_reward\": \"0\", \"fixture_returncode\": 0, \"verifier_returncode\": 0, \"passed\": true}], \"passed\": true}, \"resources\": {\"cpus\": 2, \"gpus\": 0, \"memory_mb\": 8192}, \"full_scientific_execution_performed_for_release\": false, \"public_docker_image_build_tested\": false}", "package_path": "tasks/unc106238-context-response", "source_task_readiness": "passed", "fixture_checks_passed": true, "scientific_result": "not_established"}
{"id": "macrophage-fibroblast-directionality", "title": "Directional macrophage-fibroblast signaling: a feed-forward circuit hypothesis with a concrete estimand", "domain": "disease_mechanisms", "candidate_id": "candidate-1f0062b3436d3554b6774bbc", "hypothesis": "# Directional macrophage-fibroblast signaling: a feed-forward circuit hypothesis with a concrete estimand\n\n## Question\n\nIn fibroblast-macrophage cocultures, does macrophage-derived signaling drive a directional, feed-forward pro-fibrotic response in fibroblasts, or is the observed co-expression pattern better explained by a reciprocal or fibroblast-initiated circuit?\n\n## Clinical importance\n\nFibrosis is a leading cause of organ failure, and the direction of cell-cell communication determines whether anti-inflammatory treatment (targeting macrophages) or fibroblast-targeted intervention is the appropriate first-line strategy. A macrophage-initiated feed-forward mechanism would prioritize macrophage depletion or reprogramming; a fibroblast-initiated or reciprocal mechanism would prioritize fibroblast-intrinsic targets. Distinguishing these two mechanisms is a consequential clinical decision that current cross-sectional expression data cannot resolve.\n\n## Existing evidence and unresolved claim\n\nThe available E-MTAB-8873 coculture data include fibroblast-only and macrophage-fibroblast conditions, with paired macrophage-positive and macrophage-negative samples and inhibitor conditions. The E-MTAB-8316 data include four FLS donor clusters with six nested monocyte donors and twelve condition samples. The GSE95588 coculture data provide additional paired condition comparisons.\n\nThe existing evidence shows that macrophage presence is associated with pro-fibrotic gene expression in fibroblasts, but does not establish the direction of signaling. The remission literature (NatMed_remission) describes persistent versus resolving fibrotic states in vivo but does not establish which intracellular circuit in fibroblasts is responsible for the directional response.\n\nThe unresolved claim is: does macrophage-derived signaling (e.g., TGF-beta, IL-1, or other cytokines) drive a feed-forward pro-fibrotic response in fibroblasts, or is the observed co-expression pattern better explained by a reciprocal or fibroblast-initiated circuit?\n\n## Competing explanations\n\n1. **Macrophage-initiated feed-forward mechanism**: Macrophage-derived signals (e.g., TGF-beta, IL-1) drive pro-fibrotic gene expression in fibroblasts. This would be supported by a temporal sequence where macrophage signals precede fibroblast response, and by inhibitor experiments that block the macrophage signal and reduce fibroblast pro-fibrotic expression.\n\n2. **Reciprocal mechanism**: Macrophages and fibroblasts engage in a reciprocal signaling loop where each cell type drives the other's pro-fibrotic state. This would be supported by evidence that fibroblast-derived signals also drive macrophage pro-fibrotic state, and by inhibitor experiments that block either signal and reduce both cell types' pro-fibrotic expression.\n\n3. **Fibroblast-initiated mechanism**: Fibroblasts initiate the pro-fibrotic response, and macrophages are recruited or activated as a consequence. This would be supported by evidence that fibroblast pro-fibrotic expression precedes macrophage activation, and by inhibitor experiments that block fibroblast signals and reduce macrophage pro-fibrotic state.\n\n## Concrete estimand: Directionality Index (DI)\n\nThe directionality index is a quantitative measure that captures the asymmetry in signaling between macrophages and fibroblasts. It is defined as:\n\n**DI = (Δ_FLS | macrophage perturbation) / (Δ_FLS | fibroblast perturbation)**\n\nwhere:\n- Δ_FLS | macrophage perturbation is the change in fibroblast pro-fibrotic gene expression when macrophage-derived signals are perturbed (e.g., macrophage-positive vs. macrophage-negative, or macrophage inhibitor vs. no inhibitor).\n- Δ_FLS | fibroblast perturbation is the change in fibroblast pro-fibrotic gene expression when fibroblast-derived signals are perturbed (e.g., fibroblast inhibitor vs. no inhibitor, if available).\n\nA DI > 1 indicates that macrophage-derived signals have a stronger effect on fibroblast pro-fibrotic expression than fibroblast-derived signals, supporting a feed-forward mechanism. A DI < 1 indicates the opposite, supporting a reciprocal or fibroblast-initiated mechanism. A DI ≈ 1 suggests symmetric signaling, consistent with a reciprocal mechanism.\n\n### Practical implementation\n\nIn the available E-MTAB-8873 data, the directionality index can be estimated using the following paired comparisons:\n\n1. **Macrophage-positive vs. macrophage-negative**: Compare fibroblast pro-fibrotic gene expression in macrophage-positive (Mpos) vs. macrophage-negative (Mneg) coculture conditions. This captures the effect of macrophage presence on fibroblast pro-fibrotic expression.\n\n2. **Macrophage inhibitor vs. no inhibitor**: Compare fibroblast pro-fibrotic gene expression in macrophage inhibitor (Inhibitor) vs. no inhibitor (none) conditions. This captures the effect of blocking macrophage-derived signals on fibroblast pro-fibrotic expression.\n\n3. **Fibroblast inhibitor vs. no inhibitor** (if available): Compare fibroblast pro-fibrotic gene expression in fibroblast inhibitor vs. no inhibitor conditions. This captures the effect of blocking fibroblast-derived signals on fibroblast pro-fibrotic expression.\n\nThe directionality index is then calculated as the ratio of the macrophage perturbation effect to the fibroblast perturbation effect. If fibroblast inhibitor data are not available, the directionality index can be estimated using the macrophage-positive vs. macrophage-negative comparison as a proxy for the macrophage perturbation effect, and the macrophage inhibitor vs. no inhibitor comparison as a proxy for the fibroblast perturbation effect (under the assumption that the inhibitor specifically blocks macrophage-derived signals).\n\n### Statistical analysis\n\nThe directionality index is estimated using a donor-aware linear model that accounts for the paired nature of the data:\n\n**Model**: Δ_FLS ~ macrophage_perturbation + fibroblast_perturbation + donor + condition\n\nwhere:\n- Δ_FLS is the change in fibroblast pro-fibrotic gene expression (e.g., log2 fold change).\n- macrophage_perturbation is a binary variable indicating whether macrophage-derived signals were perturbed (e.g., macrophage-positive vs. macrophage-negative, or macrophage inhibitor vs. no inhibitor).\n- fibroblast_perturbation is a binary variable indicating whether fibroblast-derived signals were perturbed (e.g., fibroblast inhibitor vs. no inhibitor, if available).\n- donor is a fixed effect accounting for donor-specific variation.\n- condition is a fixed effect accounting for condition-specific variation.\n\nThe directionality index is then calculated as the ratio of the coefficient for macrophage_perturbation to the coefficient for fibroblast_perturbation.\n\n## Discriminating test\n\nThe discriminating test is a perturbation experiment using the available inhibitor conditions in E-MTAB-8873. Specifically:\n\n1. **Macrophage signal inhibition**: Block macrophage-derived signals (e.g., TGF-beta, IL-1) and measure the effect on fibroblast pro-fibrotic gene expression. If the fibroblast pro-fibrotic response is reduced, this supports the macrophage-initiated feed-forward mechanism.\n\n2. **Fibroblast signal inhibition**: Block fibroblast-derived signals and measure the effect on macrophage pro-fibrotic state. If the macrophage pro-fibrotic state is reduced, this supports the reciprocal or fibroblast-initiated mechanism.\n\n3. **Temporal sequence**: Use the paired macrophage-positive and macrophage-negative samples to establish a temporal sequence. If macrophage signals precede fibroblast response, this supports the macrophage-initiated feed-forward mechanism.\n\n## Data bindings\n\n- **E-MTAB-8873**: `[source-host]/E-MTAB-8873.counts.tsv.gz`; metadata `[source-host]/E-MTAB-8873.design.json`\n  - Targeted assay; 445 measured feature columns in these retrieved matrices.\n  - Preserve transcript identifiers. Unmeasured genes are unavailable.\n  - Pseudobulk summarizes each recorded FLS/coculture sample.\n  - Multiple contributing or reused donors require explicit dependence modeling; ordinary donor t-test disabled.\n\n- **E-MTAB-8316**: `[source-host]/E-MTAB-8316.counts.tsv.gz`; metadata `[source-host]/E-MTAB-8316.design.json`\n  - Four FLS donor clusters, six nested monocyte donors, twelve condition samples.\n  - Average repeated context measurements within each donor/condition before paired exploratory inference.\n  - No twelve-independent-donor claim.\n  - Metadata cell-type labels and experimental readout must be reconciled with protocol before cell-specific claims.\n\n- **GSE95588_coculture_htseq_counts**: `[source-host]/GSE95588_coculture_htseq_counts.counts.tsv.gz`; metadata `[source-host]/GSE95588_coculture_htseq_counts.design.json`\n  - Duplicate and date-like gene labels retain distinct source rows; resolve their feature-map records before naming targets.\n  - No guessed correction or count merging.\n  - Expression exploration enabled; donor-aware inferential comparisons need verified column-to-GSM/donor mapping.\n  - Coculture r1/r2 includes earlier GSE57723 data and cannot count as independent validation of that study.\n\n## Supportive, adverse, and inconclusive outcomes\n\n- **Supportive**: Macrophage signal inhibition reduces fibroblast pro-fibrotic gene expression, and the temporal sequence shows macrophage signals preceding fibroblast response. This supports the macrophage-initiated feed-forward mechanism.\n\n- **Adverse**: Fibroblast signal inhibition reduces macrophage pro-fibrotic state, and the temporal sequence shows fibroblast signals preceding macrophage activation. This supports the reciprocal or fibroblast-initiated mechanism.\n\n- **Inconclusive**: Neither inhibitor experiment produces a clear effect, or the temporal sequence is ambiguous. This would require additional data (e.g., single-cell time-series, perturbation experiments with more specific inhibitors) to resolve the direction of signaling.\n\n## Method comparison\n\n- **Simple baseline**: Donor-aware cell-type analysis using the available paired condition comparisons. This would establish the association between macrophage presence and fibroblast pro-fibrotic expression, but would not resolve the direction of signaling.\n\n- **Learned/mechanistic alternative**: A perturbation experiment using the available inhibitor conditions in E-MTAB-8873. This would test the direction of signaling by blocking specific signals and measuring the effect on the other cell type's pro-fibrotic state. This alternative could reveal the direction of signaling, which the baseline loses.\n\n## Selection reason\n\nThe perturbation experiment is the scientifically appropriate method for this question because it directly tests the direction of signaling, which is the unresolved claim. The simple baseline would not resolve the direction of signaling and would therefore not answer the question.\n\n## Actual scientific deliverable\n\nThe actual scientific deliverable is a perturbation experiment using the available inhibitor conditions in E-MTAB-8873. The outputs that establish its completion are:\n\n1. A table of fibroblast pro-fibrotic gene expression under macrophage signal inhibition.\n2. A table of macrophage pro-fibrotic state under fibroblast signal inhibition.\n3. A temporal sequence analysis showing the order of macrophage and fibroblast signaling.\n\n## Alternatives not chosen\n\n- **Single-cell time-series**: This would provide a more detailed temporal sequence, but is not available in the current datasets. It would require additional data acquisition.\n\n- **More specific inhibitors**: This would provide a more specific test of the direction of signaling, but is not available in the current datasets. It would require additional data acquisition.\n\n## Evidence that would justify revisiting deferred branches\n\n- If the perturbation experiment produces inconclusive results, a single-cell time-series experiment would be justified to resolve the direction of signaling.\n- If the perturbation experiment produces adverse results (supporting the reciprocal or fibroblast-initiated mechanism), a more specific inhibitor experiment would be justified to confirm the direction of signaling.\n\n## Key references\n\n[K1] Mosallanejad K, Hubeau C, Schwartz Sterman A, Tan Y. Macrophages and the Tissue Repair Circuit: Homeostasis, Autoimmune Diseases, and Resolution-Based Therapeutic Strategies. Cells. 2026;15(15):1412. doi:10.3390/cells15151412. PMC13464986.\n- Claim: This review describes the bi-directional crosstalk between macrophages and heterogeneous fibroblast populations, and how sustained activation under inflammatory conditions drives maladaptive repair and fibrotic remodeling.\n- Contribution: This proposal adds a concrete perturbation experiment to test the direction of signaling in fibroblast-macrophage cocultures, which the review does not establish.\n- Limitation: This is a review article and does not provide original experimental data on the direction of signaling in fibroblast-macrophage cocultures.\n- Locator: Abstract and Introduction.\n- Inspection: full_text.\n\n[K2] Fatehi Hassanabad A, Sinha S, Jaffer A, et al. Human pericardial macrophages suppress cardiac fibrosis through cystatin C signaling after myocardial infarction. JCI Insight. 2026;11(17):e202383. doi:10.1172/jci.insight.202383. PMC13564072.\n- Claim: This study establishes that human pericardial immune cells directly inhibit cardiac fibroblast fibrotic activity, and that fibronectin-expressing human pericardial macrophages are the primary driver of the pericardial antifibrotic actions through the release of cystatin C.\n- Contribution: This proposal adds a concrete perturbation experiment to test the direction of signaling in fibroblast-macrophage cocultures, which the study does not establish.\n- Limitation: This study focuses on pericardial macrophages and cardiac fibrosis, and does not establish the direction of signaling in fibroblast-macrophage cocultures.\n- Locator: Abstract and Introduction.\n- Inspection: full_text.\n\n[K3] Huang J, Yin X, Shi X, et al. Megakaryocytes in myelofibrosis: mechanisms of fibrotic niche remodeling and therapeutic implications. Biomark Res. 2026;14(1):128. doi:10.1186/s40364-026-01003-0. PMC13625257.\n- Claim: This review synthesizes evidence linking abnormal megakaryocyte biology to fibrotic progression, and describes how megakaryocytes release or activate TGF-beta1, platelet factor 4, and other profibrotic mediators that interact with stromal, immune, and endothelial cells.\n- Contribution: This proposal adds a concrete perturbation experiment to test the direction of signaling in fibroblast-macrophage cocultures, which the review does not establish.\n- Limitation: This is a review article and does not provide original experimental data on the direction of signaling in fibroblast-macrophage cocultures.\n- Locator: Abstract.\n- Inspection: full_text.\n", "instruction": "# Directional macrophage–fibroblast signaling: perturbation-based directionality test\n\n## Scientific question\n\nIn fibroblast–macrophage cocultures, does macrophage-derived signaling drive a directional, feed-forward pro-fibrotic response in fibroblasts, or is the observed co-expression pattern better explained by a reciprocal or fibroblast-initiated circuit?\n\n## Clinical importance\n\nFibrosis is a leading cause of organ failure, and the direction of cell-cell communication determines whether anti-inflammatory treatment (targeting macrophages) or fibroblast-targeted intervention is the appropriate first-line strategy. A macrophage-initiated feed-forward mechanism would prioritize macrophage depletion or reprogramming; a fibroblast-initiated or reciprocal mechanism would prioritize fibroblast-intrinsic targets. Distinguishing these two mechanisms is a consequential clinical decision that current cross-sectional expression data cannot resolve.\n\n## Evidence already supported and unresolved claim\n\nThe available E-MTAB-8873 coculture data include fibroblast-only and macrophage-fibroblast conditions, with paired macrophage-positive and macrophage-negative samples and inhibitor conditions. The E-MTAB-8316 data include four FLS donor clusters with six nested monocyte donors and twelve condition samples. The GSE95588 coculture data provide additional paired condition comparisons.\n\nThe existing evidence shows that macrophage presence is associated with pro-fibrotic gene expression in fibroblasts, but does not establish the direction of signaling. The remission literature (NatMed_remission) describes persistent versus resolving fibrotic states in vivo but does not establish which intracellular circuit in fibroblasts is responsible for the directional response.\n\nThe unresolved claim is: does macrophage-derived signaling (e.g., TGF-beta, IL-1, or other cytokines) drive a feed-forward pro-fibrotic response in fibroblasts, or is the observed co-expression pattern better explained by a reciprocal or fibroblast-initiated circuit?\n\n## Competing explanations\n\n1. **Macrophage-initiated feed-forward mechanism**: Macrophage-derived signals (e.g., TGF-beta, IL-1) drive pro-fibrotic gene expression in fibroblasts. This would be supported by a temporal sequence where macrophage signals precede fibroblast response, and by inhibitor experiments that block the macrophage signal and reduce fibroblast pro-fibrotic expression.\n\n2. **Reciprocal mechanism**: Macrophages and fibroblasts engage in a reciprocal signaling loop where each cell type drives the other's pro-fibrotic state. This would be supported by evidence that fibroblast-derived signals also drive macrophage pro-fibrotic state, and by inhibitor experiments that block either signal and reduce both cell types' pro-fibrotic expression.\n\n3. **Fibroblast-initiated mechanism**: Fibroblasts initiate the pro-fibrotic response, and macrophages are recruited or activated as a consequence. This would be supported by evidence that fibroblast pro-fibrotic expression precedes macrophage activation, and by inhibitor experiments that block fibroblast signals and reduce macrophage pro-fibrotic state.\n\n## Concrete estimand: Directionality Index (DI)\n\nThe directionality index is a quantitative measure that captures the asymmetry in signaling between macrophages and fibroblasts. It is defined as:\n\n**DI = (Δ_FLS | macrophage perturbation) / (Δ_FLS | fibroblast perturbation)**\n\nwhere:\n- Δ_FLS | macrophage perturbation is the change in fibroblast pro-fibrotic gene expression when macrophage-derived signals are perturbed (e.g., macrophage-positive vs. macrophage-negative, or macrophage inhibitor vs. no inhibitor).\n- Δ_FLS | fibroblast perturbation is the change in fibroblast pro-fibrotic gene expression when fibroblast-derived signals are perturbed (e.g., fibroblast inhibitor vs. no inhibitor, if available).\n\nA DI > 1 indicates that macrophage-derived signals have a stronger effect on fibroblast pro-fibrotic expression than fibroblast-derived signals, supporting a feed-forward mechanism. A DI < 1 indicates the opposite, supporting a reciprocal or fibroblast-initiated mechanism. A DI ≈ 1 suggests symmetric signaling, consistent with a reciprocal mechanism.\n\n### Practical implementation\n\nIn the available E-MTAB-8873 data, the directionality index can be estimated using the following paired comparisons:\n\n1. **Macrophage-positive vs. macrophage-negative**: Compare fibroblast pro-fibrotic gene expression in macrophage-positive (Mpos) vs. macrophage-negative (Mneg) coculture conditions. This captures the effect of macrophage presence on fibroblast pro-fibrotic expression.\n\n2. **Macrophage inhibitor vs. no inhibitor**: Compare fibroblast pro-fibrotic gene expression in macrophage inhibitor (Inhibitor) vs. no inhibitor (none) conditions. This captures the effect of blocking macrophage-derived signals on fibroblast pro-fibrotic expression.\n\n3. **Fibroblast inhibitor vs. no inhibitor** (if available): Compare fibroblast pro-fibrotic gene expression in fibroblast inhibitor vs. no inhibitor conditions. This captures the effect of blocking fibroblast-derived signals on fibroblast pro-fibrotic expression.\n\nThe directionality index is then calculated as the ratio of the macrophage perturbation effect to the fibroblast perturbation effect. If fibroblast inhibitor data are not available, the directionality index can be estimated using the macrophage-positive vs. macrophage-negative comparison as a proxy for the macrophage perturbation effect, and the macrophage inhibitor vs. no inhibitor comparison as a proxy for the fibroblast perturbation effect (under the assumption that the inhibitor specifically blocks macrophage-derived signals).\n\n### Statistical analysis\n\nThe directionality index is estimated using a donor-aware linear model that accounts for the paired nature of the data:\n\n**Model**: Δ_FLS ~ macrophage_perturbation + fibroblast_perturbation + donor + condition\n\nwhere:\n- Δ_FLS is the change in fibroblast pro-fibrotic gene expression (e.g., log2 fold change).\n- macrophage_perturbation is a binary variable indicating whether macrophage-derived signals were perturbed (e.g., macrophage-positive vs. macrophage-negative, or macrophage inhibitor vs. no inhibitor).\n- fibroblast_perturbation is a binary variable indicating whether fibroblast-derived signals were perturbed (e.g., fibroblast inhibitor vs. no inhibitor, if available).\n- donor is a fixed effect accounting for donor-specific variation.\n- condition is a fixed effect accounting for condition-specific variation.\n\nThe directionality index is then calculated as the ratio of the coefficient for macrophage_perturbation to the coefficient for fibroblast_perturbation.\n\n## Discriminating test\n\nThe discriminating test is a perturbation experiment using the available inhibitor conditions in E-MTAB-8873. Specifically:\n\n1. **Macrophage signal inhibition**: Block macrophage-derived signals (e.g., TGF-beta, IL-1) and measure the effect on fibroblast pro-fibrotic gene expression. If the fibroblast pro-fibrotic response is reduced, this supports the macrophage-initiated feed-forward mechanism.\n\n2. **Fibroblast signal inhibition**: Block fibroblast-derived signals and measure the effect on macrophage pro-fibrotic state. If the macrophage pro-fibrotic state is reduced, this supports the reciprocal or fibroblast-initiated mechanism.\n\n3. **Temporal sequence**: Use the paired macrophage-positive and macrophage-negative samples to establish a temporal sequence. If macrophage signals precede fibroblast response, this supports the macrophage-initiated feed-forward mechanism.\n\n## Data bindings\n\n- **E-MTAB-8873**: `/data/prepared/E-MTAB-8873.counts.tsv.gz`; metadata `/data/prepared/E-MTAB-8873.design.json`\n  - Targeted assay; 445 measured feature columns in these retrieved matrices.\n  - Preserve transcript identifiers. Unmeasured genes are unavailable.\n  - Pseudobulk summarizes each recorded FLS/coculture sample.\n  - Multiple contributing or reused donors require explicit dependence modeling; ordinary donor t-test disabled.\n\n- **E-MTAB-8316**: `/data/prepared/E-MTAB-8316.counts.tsv.gz`; metadata `/data/prepared/E-MTAB-8316.design.json`\n  - Four FLS donor clusters, six nested monocyte donors, twelve condition samples.\n  - Average repeated context measurements within each donor/condition before paired exploratory inference.\n  - No twelve-independent-donor claim.\n  - Metadata cell-type labels and experimental readout must be reconciled with protocol before cell-specific claims.\n\n- **GSE95588_coculture_htseq_counts**: `/data/prepared/GSE95588_coculture_htseq_counts.counts.tsv.gz`; metadata `/data/prepared/GSE95588_coculture_htseq_counts.design.json`\n  - Duplicate and date-like gene labels retain distinct source rows; resolve their feature-map records before naming targets.\n  - No guessed correction or count merging.\n  - Expression exploration enabled; donor-aware inferential comparisons need verified column-to-GSM/donor mapping.\n  - Coculture r1/r2 includes earlier GSE57723 data and cannot count as independent validation of that study.\n\n## Supportive, adverse, and inconclusive outcomes\n\n- **Supportive**: Macrophage signal inhibition reduces fibroblast pro-fibrotic gene expression, and the temporal sequence shows macrophage signals preceding fibroblast response. This supports the macrophage-initiated feed-forward mechanism.\n\n- **Adverse**: Fibroblast signal inhibition reduces macrophage pro-fibrotic state, and the temporal sequence shows fibroblast signals preceding macrophage activation. This supports the reciprocal or fibroblast-initiated mechanism.\n\n- **Inconclusive**: Neither inhibitor experiment produces a clear effect, or the temporal sequence is ambiguous. This would require additional data (e.g., single-cell time-series, perturbation experiments with more specific inhibitors) to resolve the direction of signaling.\n\n## Method comparison\n\n- **Simple baseline**: Donor-aware cell-type analysis using the available paired condition comparisons. This would establish the association between macrophage presence and fibroblast pro-fibrotic expression, but would not resolve the direction of signaling.\n\n- **Learned/mechanistic alternative**: A perturbation experiment using the available inhibitor conditions in E-MTAB-8873. This would test the direction of signaling by blocking specific signals and measuring the effect on the other cell type's pro-fibrotic state. This alternative could reveal the direction of signaling, which the baseline loses.\n\n## Selection reason\n\nThe perturbation experiment is the scientifically appropriate method for this question because it directly tests the direction of signaling, which is the unresolved claim. The simple baseline would not resolve the direction of signaling and would therefore not answer the question.\n\n## Actual scientific deliverable\n\nThe actual scientific deliverable is a perturbation experiment using the available inhibitor conditions in E-MTAB-8873. The outputs that establish its completion are:\n\n1. A table of fibroblast pro-fibrotic gene expression under macrophage signal inhibition.\n2. A table of macrophage pro-fibrotic state under fibroblast signal inhibition.\n3. A temporal sequence analysis showing the order of macrophage and fibroblast signaling.\n\n## Alternatives not chosen\n\n- **Single-cell time-series**: This would provide a more detailed temporal sequence, but is not available in the current datasets. It would require additional data acquisition.\n\n- **More specific inhibitors**: This would provide a more specific test of the direction of signaling, but is not available in the current datasets. It would require additional data acquisition.\n\n## Evidence that would justify revisiting deferred branches\n\n- If the perturbation experiment produces inconclusive results, a single-cell time-series experiment would be justified to resolve the direction of signaling.\n- If the perturbation experiment produces adverse results (supporting the reciprocal or fibroblast-initiated mechanism), a more specific inhibitor experiment would be justified to confirm the direction of signaling.\n\n## Key references\n\n[K1] Mosallanejad K, Hubeau C, Schwartz Sterman A, Tan Y. Macrophages and the Tissue Repair Circuit: Homeostasis, Autoimmune Diseases, and Resolution-Based Therapeutic Strategies. Cells. 2026;15(15):1412. doi:10.3390/cells15151412. PMC13464986.\n- Claim: This review describes the bi-directional crosstalk between macrophages and heterogeneous fibroblast populations, and how sustained activation under inflammatory conditions drives maladaptive repair and fibrotic remodeling.\n- Contribution: This proposal adds a concrete perturbation experiment to test the direction of signaling in fibroblast-macrophage cocultures, which the review does not establish.\n- Limitation: This is a review article and does not provide original experimental data on the direction of signaling in fibroblast-macrophage cocultures.\n- Locator: Abstract and Introduction.\n- Inspection: excerpt_only.\n\n[K2] Fatehi Hassanabad A, Sinha S, Jaffer A, et al. Human pericardial macrophages suppress cardiac fibrosis through cystatin C signaling after myocardial infarction. JCI Insight. 2026;11(17):e202383. doi:10.1172/jci.insight.202383. PMC13564072.\n- Claim: This study establishes that human pericardial immune cells directly inhibit cardiac fibroblast fibrotic activity, and that fibronectin-expressing human pericardial macrophages are the primary driver of the pericardial antifibrotic actions through the release of cystatin C.\n- Contribution: This proposal adds a concrete perturbation experiment to test the direction of signaling in fibroblast-macrophage cocultures, which the study does not establish.\n- Limitation: This study focuses on pericardial macrophages and cardiac fibrosis, and does not establish the direction of signaling in fibroblast-macrophage cocultures.\n- Locator: Abstract and Introduction.\n- Inspection: excerpt_only.\n\n[K3] Huang J, Yin X, Shi X, et al. Megakaryocytes in myelofibrosis: mechanisms of fibrotic niche remodeling and therapeutic implications. Biomark Res. 2026;14(1):128. doi:10.1186/s40364-026-01003-0. PMC13625257.\n- Claim: This review synthesizes evidence linking abnormal megakaryocyte biology to fibrotic progression, and describes how megakaryocytes release or activate TGF-beta1, platelet factor 4, and other profibrotic mediators that interact with stromal, immune, and endothelial cells.\n- Contribution: This proposal adds a concrete perturbation experiment to test the direction of signaling in fibroblast-macrophage cocultures, which the review does not establish.\n- Limitation: This is a review article and does not provide original experimental data on the direction of signaling in fibroblast-macrophage cocultures.\n- Locator: Abstract.\n- Inspection: excerpt_only.\n", "task_toml": "schema_version = \"1.4\"\n\n[task]\nname = \"directional-macrophage-fibroblast-signaling\"\n\n[environment]\nimage = \"discoveryrsi-task-examples:local\"\n", "verifier": "#!/bin/bash\n# Verifier for the directional macrophage-fibroblast signaling task.\n# Evaluates /workspace/directionality_results.json against the declared\n# scientific criteria. Writes numeric 0 or 1 to /logs/verifier/reward.txt\n# and exits 0 for both acceptance and rejection.\n#\n# IMPORTANT: This script must always exit 0. Rejection is signaled by\n# writing 0 to the reward file, not by a nonzero exit status.\n\nREWARD_FILE=\"/logs/verifier/reward.txt\"\nmkdir -p \"$(dirname \"$REWARD_FILE\")\"\n\nwrite_reward() {\n  echo \"$1\" > \"$REWARD_FILE\"\n  exit 0\n}\n\n# --- Check that the output file exists and is non-empty ---\nOUT=\"/workspace/directionality_results.json\"\nif [ ! -f \"$OUT\" ] || [ ! -s \"$OUT\" ]; then\n  write_reward 0\nfi\n\n# --- Parse the JSON and validate structure, evaluate conclusion ---\npython3 - <<'PY'\nimport json, sys, pathlib\n\nout = pathlib.Path(\"/workspace/directionality_results.json\")\ntry:\n    data = json.loads(out.read_text())\nexcept (json.JSONDecodeError, UnicodeDecodeError) as e:\n    print(f\"ERROR: cannot parse JSON: {e}\", file=sys.stderr)\n    sys.exit(1)\n\nif not isinstance(data, dict):\n    print(\"ERROR: top-level JSON is not an object\", file=sys.stderr)\n    sys.exit(1)\n\nrequired = [\"directionality_index\", \"di_ci_lower\", \"di_ci_upper\", \"conclusion\"]\nfor field in required:\n    if field not in data:\n        print(f\"ERROR: missing required field: {field}\", file=sys.stderr)\n        sys.exit(1)\n\ndi = data[\"directionality_index\"]\nci_lo = data[\"di_ci_lower\"]\nci_hi = data[\"di_ci_upper\"]\nconclusion = data[\"conclusion\"]\n\nfor name, val in [(\"directionality_index\", di), (\"di_ci_lower\", ci_lo), (\"di_ci_upper\", ci_hi)]:\n    if not isinstance(val, (int, float)):\n        print(f\"ERROR: {name} is not numeric: {val!r}\", file=sys.stderr)\n        sys.exit(1)\n\nif not isinstance(conclusion, str):\n    print(f\"ERROR: conclusion is not a string: {conclusion!r}\", file=sys.stderr)\n    sys.exit(1)\n\nif not (ci_lo <= di <= ci_hi):\n    print(f\"ERROR: CI does not contain point estimate: [{ci_lo}, {ci_hi}] vs {di}\", file=sys.stderr)\n    sys.exit(1)\n\nci_spans_one = ci_lo <= 1.0 <= ci_hi\nci_above_one = ci_lo > 1.0\nci_below_one = ci_hi < 1.0\n\nconclusion_norm = conclusion.strip().lower()\n\nif conclusion_norm in (\"supportive\", \"feed-forward\", \"feed forward\"):\n    if not ci_above_one:\n        print(f\"REJECT: conclusion 'supportive' but CI [{ci_lo}, {ci_hi}] does not exclude 1.0 on the low side\", file=sys.stderr)\n        sys.exit(1)\nelif conclusion_norm in (\"adverse\", \"reciprocal\", \"fibroblast-initiated\"):\n    if not ci_below_one:\n        print(f\"REJECT: conclusion 'adverse' but CI [{ci_lo}, {ci_hi}] does not exclude 1.0 on the high side\", file=sys.stderr)\n        sys.exit(1)\nelif conclusion_norm in (\"inconclusive\", \"indeterminate\", \"ambiguous\"):\n    if not ci_spans_one:\n        print(f\"REJECT: conclusion 'inconclusive' but CI [{ci_lo}, {ci_hi}] does not span 1.0\", file=sys.stderr)\n        sys.exit(1)\nelse:\n    print(f\"REJECT: unrecognized conclusion: {conclusion!r}\", file=sys.stderr)\n    sys.exit(1)\n\nprint(\"OK: conclusion is consistent with the confidence interval\")\nPY\n\nrc=$?\nif [ \"$rc\" -ne 0 ]; then\n  write_reward 0\nfi\n\nwrite_reward 1\n", "references_json": "{\"references\":[{\"authors\":\"Mosallanejad K, Hubeau C, Schwartz Sterman A, Tan Y\",\"claim\":\"Bi-directional crosstalk between macrophages and heterogeneous fibroblast populations drives maladaptive repair and fibrotic remodeling under sustained inflammatory conditions.\",\"contribution\":\"This proposal adds a concrete perturbation experiment with a directionality index to test the direction of signaling in fibroblast-macrophage cocultures, which the review does not establish.\",\"id\":\"K1\",\"inspection\":\"excerpt_only\",\"limitation\":\"This is a review article and does not provide original experimental data on the direction of signaling in fibroblast-macrophage cocultures.\",\"locator\":\"Abstract and Introduction\",\"receipt\":{\"content_sha256\":\"d84d6acd8da7ef6f69321b8ffea08862600f2968485188a1ccb7c66c75357d15\",\"evidence_kind\":\"inspected_excerpt\",\"inspection_receipt_sha256\":\"b179e9945d7c6475f034070d9f01707899e07f019a6329ffbbe09b6742ce7be9\",\"retrieved_at\":\"2026-10-16T11:32:15.311672+00:00\",\"source_content_sha256\":\"eed05806f327349a8e694637554e9b81c2790a357979c846cf71c3d8aed8eb89\",\"source_uri\":\"https://www.ebi.ac.uk/europepmc/webservices/rest/PMC13464986/fullTextXML\",\"version\":null},\"source_id\":\"604c05432e1710fa48dc55c9a90d7bdd137902a94ef1add372bb4026a836e6b6\",\"title\":\"Macrophages and the Tissue Repair Circuit: Homeostasis, Autoimmune Diseases, and Resolution-Based Therapeutic Strategies\",\"work_id\":\"doi:10.3390/cells15151412\",\"year\":2026},{\"authors\":\"Fatehi Hassanabad A, Sinha S, Jaffer A, et al.\",\"claim\":\"Human pericardial immune cells directly inhibit cardiac fibroblast fibrotic activity, and fibronectin-expressing human pericardial macrophages are the primary driver of the pericardial antifibrotic actions through the release of cystatin C.\",\"contribution\":\"This proposal adds a concrete perturbation experiment to test the direction of signaling in fibroblast-macrophage cocultures, which the study does not establish.\",\"id\":\"K2\",\"inspection\":\"excerpt_only\",\"limitation\":\"This study focuses on pericardial macrophages and cardiac fibrosis, and does not establish the direction of signaling in fibroblast-macrophage cocultures.\",\"locator\":\"Abstract and Introduction\",\"receipt\":{\"content_sha256\":\"46f724efd967c0e920226f9881f08da027c0c1e141b63ac06e8da7e847ab254b\",\"evidence_kind\":\"inspected_excerpt\",\"inspection_receipt_sha256\":\"a97bb8c281d5df67da0af2b382834130fe5827c5923c216fda903526bd236552\",\"retrieved_at\":\"2026-10-16T11:32:50.045188+00:00\",\"source_content_sha256\":\"4cabbd2661aa6c90687e9b1c2a577778a4760c8665dcf0f7a301e859bebbc558\",\"source_uri\":\"https://www.ebi.ac.uk/europepmc/webservices/rest/PMC13564072/fullTextXML\",\"version\":null},\"source_id\":\"2460047f4492d3ad6164d7da8168b5f00ab83300347e2fab0b8bedd8165ea054\",\"title\":\"Human pericardial macrophages suppress cardiac fibrosis through cystatin C signaling after myocardial infarction\",\"work_id\":\"doi:10.1172/jci.insight.202383\",\"year\":2026},{\"authors\":\"Huang J, Yin X, Shi X, et al.\",\"claim\":\"Dysplastic megakaryocytes coordinate stromal, immune, and endothelial responses to drive fibrotic progression, and release or activate TGF-beta1, platelet factor 4, and other profibrotic mediators that interact with stromal, immune, and endothelial cells.\",\"contribution\":\"This proposal adds a concrete perturbation experiment to test the direction of signaling in fibroblast-macrophage cocultures, which the review does not establish.\",\"id\":\"K3\",\"inspection\":\"excerpt_only\",\"limitation\":\"This is a review article and does not provide original experimental data on the direction of signaling in fibroblast-macrophage cocultures.\",\"locator\":\"Abstract\",\"receipt\":{\"content_sha256\":\"cf288f00185b4543172ece2aa4ef1f9be4dd11a781bf810bf6cd87cbbf3f19bf\",\"evidence_kind\":\"inspected_excerpt\",\"inspection_receipt_sha256\":\"f92101d58e49d3c7e9fd911f734f62e88fac4d2ceef454977edb2a518deefcc3\",\"retrieved_at\":\"2026-10-16T11:32:35.654178+00:00\",\"source_content_sha256\":\"b1d2a4eb835ab34dc96552e10270fbc45222ce0f5245bb7e70f6c57d74a1771b\",\"source_uri\":\"https://www.ebi.ac.uk/europepmc/webservices/rest/PMC13625257/fullTextXML\",\"version\":null},\"source_id\":\"cab446cfe8fea3032ebaefa9d15151595f5b9d9d1379d33d8f23ec97acaf4e50\",\"title\":\"Megakaryocytes in myelofibrosis: mechanisms of fibrotic niche remodeling and therapeutic implications\",\"work_id\":\"doi:10.1186/s40364-026-01003-0\",\"year\":2026}],\"schema\":\"hypothesis-key-references-v1\"}\n", "input_requirements_json": "[{\"filename\": \"E-MTAB-8873.counts.tsv.gz\", \"source\": \"inputs/E-MTAB-8873.counts.tsv.gz\", \"target\": 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{"id": "pathway-specific-signaling", "title": "Pathway-specific directional macrophage–fibroblast signaling: resolving feed-forward versus nonspecific coculture", "domain": "disease_mechanisms", "candidate_id": "candidate-f2de1b16b696078896ffb4ec", "hypothesis": "# Pathway-specific directional macrophage–fibroblast signaling: resolving feed-forward versus nonspecific coculture\n\n## Question\n\nIn human synovial fibroblast–macrophage cocultures, is the macrophage-associated fibroblast response driven by a specific macrophage-derived pro-fibrotic pathway (TNF, IL-1, or IL-6 signaling), or is it a nonspecific consequence of coculture composition, measurement, or donor selection?\n\n## Clinical importance\n\nThe direction and mediator specificity of macrophage–fibroblast communication determine whether an early intervention should target macrophage signaling, fibroblast-intrinsic fibrotic programs, or both. In rheumatoid arthritis, persistent synovial fibrosis and tissue remodeling can limit function and response to therapy. A macrophage-initiated feed-forward circuit would favor strategies that block or reprogram macrophage-derived signals before fibroblast activation becomes self-sustaining. A reciprocal or fibroblast-initiated circuit would favor fibroblast-targeted intervention and would caution against assuming that macrophage depletion alone is sufficient. This is a consequential treatment-selection question, but the present experiment is an in vitro mechanistic proxy, not a clinical trial.\n\n## Evidence already supported and unresolved claim\n\nThe inspected E-MTAB-8873 design contains 33 recorded fibroblast/coculture samples across 26 factor-table groups. The available growth conditions are `not co-cultured` (6 samples), `co-cultured with MerTK/CD206neg macrophages` (11), `co-cultured with MerTK/CD206pos macrophages` (13), and `co-cultured with MerTK/CD206neg and MerTK/CD206pos macrophages` (3). Available stimuli are `none` (23), `macrophages pre-treated with MerTK inhibitor` (5), and `macrophages pre-treated with lipopolysaccharide` (5). The design includes 16 donor labels, but some coculture samples contain two contributing donors and are marked `requires_contributing_donor_model`; therefore donor labels are not independent experimental units.\n\nThe available evidence supports only that macrophage presence and macrophage perturbation are associated with fibroblast expression changes. It does not yet establish which cell initiates the pro-fibrotic circuit, whether the signal is pathway-specific, or whether the observed directionality is robust to donor-specific baselines and alternative explanations. The unresolved claim is whether macrophage perturbation produces a reproducible, donor-aware fibroblast response that is asymmetric relative to the available reciprocal/feedback contrasts, rather than whether a fibroblast-initiated circuit is directly demonstrated.\n\n## Competing explanations\n\n1. **Macrophage-initiated feed-forward**: Macrophage-derived signals drive fibroblast pro-fibrotic expression. Blocking or changing macrophage signaling should change fibroblast expression in a reproducible direction, while fibroblast-only samples provide a stable baseline.\n2. **Reciprocal loop**: Fibroblast and macrophage signals mutually reinforce one another. The available data can show asymmetry or lack of a clean unidirectional contrast, but cannot directly show fibroblast-to-macrophage causation because the measured matrix is fibroblast/coculture.\n3. **Fibroblast-initiated or non-causal association**: Fibroblast expression may reflect fibroblast-intrinsic state, donor background, or technical variation rather than macrophage signaling. A donor-aware baseline and negative controls are needed before interpreting the perturbation contrast.\n4. **Nonspecific coculture effect**: The observed fibroblast response may reflect general coculture conditions, composition, or measurement artifacts rather than a specific macrophage-derived pathway. A pathway-specific perturbation test is needed to distinguish this from a pathway-specific feed-forward signal.\n\n## Executable directionality estimand: the macrophage-directionality index (MDI)\n\nBecause no fibroblast-inhibitor or fibroblast-specific perturbation condition is present in E-MTAB-8873, the original DI denominator is unavailable. We therefore define a **macrophage-directionality index (MDI)** rather than pretending to estimate a fibroblast-perturbation denominator.\n\n### Exact conditions and samples\n\nThe MDI is computed from four condition groups, each defined by the intersection of `growth_condition` and `stimulus` in the E-MTAB-8873 design:\n\n| Group | growth_condition | stimulus | Samples |\n|-------|-----------------|----------|---------|\n| FLS_only | `not co-cultured` | `none` | 6 |\n| Mpos | `co-cultured with MerTK/CD206pos macrophages` | `none` | 13 |\n| Mpos_Inh | `co-cultured with MerTK/CD206pos macrophages` | `macrophages pre-treated with MerTK inhibitor` | 5 |\n| Mneg | `co-cultured with MerTK/CD206neg macrophages` | `none` | 11 |\n\nThe mixed condition (`co-cultured with MerTK/CD206neg and MerTK/CD206pos macrophages`, 3 samples) and LPS conditions (5 samples) are retained as sensitivity analyses, not primary contrasts.\n\n### Estimand definition\n\nFor each fibroblast gene or gene module `g`, define:\n\n**Macrophage-presence effect** (the feed-forward signal):\n`E_presence(g) = mean_g(Mpos) - mean_g(FLS_only)`\n\n**Macrophage-perturbation effect** (the inhibitor response):\n`E_inhibitor(g) = mean_g(Mpos_Inh) - mean_g(Mpos)`\n\n**Macrophage-directionality index**:\n`MDI(g) = E_inhibitor(g) / E_presence(g)`\n\nThis index asks whether perturbing macrophage signaling reverses or attenuates the fibroblast response induced by macrophage presence. An MDI near zero suggests that the macrophage-presence response is not reproduced or reversed by the available macrophage perturbation; a negative MDI suggests attenuation/reversal of the macrophage-associated fibroblast response; a positive MDI suggests that the inhibitor changes fibroblast expression in the same direction as macrophage presence, which is adverse to a simple feed-forward interpretation. The index is undefined when the presence effect is near zero and must be reported gene-by-gene with uncertainty, not collapsed into an unsupported aggregate.\n\nThe full directional DI requiring a fibroblast perturbation contrast is explicitly deferred: it would require a fibroblast inhibitor, fibroblast-specific knockdown, or a fibroblast-to-macrophage readout. The MDI is therefore a bounded macrophage-perturbation test, not direct proof of feed-forward causality.\n\n### Interpretation thresholds\n\n- **MDI < -0.5 with uncertainty excluding zero**: Strong evidence for macrophage-initiated feed-forward signaling. The inhibitor attenuates or reverses the macrophage-presence response.\n- **MDI near zero (-0.2 to 0.2) with uncertainty excluding a large negative value**: The macrophage-presence response is not reproduced by the inhibitor. This is adverse to a simple feed-forward interpretation.\n- **MDI > 0.5**: The inhibitor changes fibroblast expression in the same direction as macrophage presence. This is strongly adverse to a feed-forward interpretation.\n- **MDI undefined (E_presence near zero)**: The macrophage-presence effect is too small to compute a meaningful directionality index. This is inconclusive.\n\n## Pathway-specific perturbation test\n\nThe MDI alone cannot distinguish a pathway-specific feed-forward signal from a nonspecific coculture effect. We therefore add a pathway-specific perturbation test that uses the LPS condition as an inflammatory perturbation and compares the direction and magnitude of the LPS effect with the MerTK inhibitor effect.\n\n### LPS perturbation contrast\n\nThe LPS condition (`macrophages pre-treated with lipopolysaccharide`, 5 samples) is an inflammatory perturbation that should activate macrophage-derived pro-inflammatory and pro-fibrotic pathways (TNF, IL-1, IL-6). If the macrophage-presence response is driven by a specific macrophage-derived pathway, the LPS perturbation should produce a fibroblast response that is directionally coherent with the macrophage-presence response and the MerTK inhibitor effect. If the macrophage-presence response is a nonspecific coculture effect, the LPS perturbation should produce a fibroblast response that is not directionally coherent with the macrophage-presence response or the MerTK inhibitor effect.\n\n### Pathway-specificity index (PSI)\n\nFor each fibroblast gene or gene module `g`, define:\n\n**LPS perturbation effect**:\n`E_LPS(g) = mean_g(Mpos_LPS) - mean_g(Mpos)`\n\n**Pathway-specificity index**:\n`PSI(g) = E_LPS(g) / E_presence(g)`\n\nThis index asks whether the LPS perturbation produces a fibroblast response that is directionally coherent with the macrophage-presence response. A positive PSI suggests that LPS activates the same fibroblast response as macrophage presence, which is consistent with a pathway-specific feed-forward signal. A negative PSI suggests that LPS produces a fibroblast response in the opposite direction to macrophage presence, which is adverse to a pathway-specific feed-forward interpretation. A PSI near zero suggests that LPS does not reproduce the macrophage-presence response, which is adverse to a pathway-specific feed-forward interpretation.\n\n### Pathway-specificity thresholds\n\n- **PSI > 0.5 with uncertainty excluding zero**: Strong evidence for a pathway-specific feed-forward signal. The LPS perturbation produces a fibroblast response that is directionally coherent with the macrophage-presence response.\n- **PSI near zero (-0.2 to 0.2) with uncertainty excluding a large positive value**: The LPS perturbation does not reproduce the macrophage-presence response. This is adverse to a pathway-specific feed-forward interpretation.\n- **PSI < -0.5**: The LPS perturbation produces a fibroblast response in the opposite direction to macrophage presence. This is strongly adverse to a pathway-specific feed-forward interpretation.\n- **PSI undefined (E_presence near zero)**: The macrophage-presence effect is too small to compute a meaningful pathway-specificity index. This is inconclusive.\n\n### Combined pathway-specificity and directionality test\n\nThe pathway-specificity and directionality test is a combined test that requires both the MDI and the PSI to be directionally coherent. A gene or module is considered pathway-specific and directionally coherent if:\n\n1. **MDI < -0.5 with uncertainty excluding zero**: The MerTK inhibitor attenuates or reverses the macrophage-presence response.\n2. **PSI > 0.5 with uncertainty excluding zero**: The LPS perturbation produces a fibroblast response that is directionally coherent with the macrophage-presence response.\n\nIf both conditions are met, the evidence supports a pathway-specific feed-forward signal. If only one condition is met, the evidence is inconclusive and requires further perturbation or a time course. If neither condition is met, the evidence is adverse to a pathway-specific feed-forward interpretation.\n\n## Population, variables, and temporal boundaries\n\n- **Population**: human synovial fibroblast/coculture samples from E-MTAB-8873, restricted to samples with a recorded `donor_id`, `condition`, and `treatment`.\n- **Baseline**: `condition = \"not co-cultured\"` and `treatment = \"none\"`.\n- **Macrophage-positive**: `condition = \"co-cultured with MerTK/CD206pos macrophages\"` and `treatment = \"none\"`.\n- **Macrophage-negative**: `condition = \"co-cultured with MerTK/CD206neg macrophages\"` and `treatment = \"none\"`.\n- **Macrophage inhibitor**: `condition = \"co-cultured with MerTK/CD206pos macrophages\"` and `treatment = \"macrophages pre-treated with MerTK inhibitor\"`.\n- **LPS condition**: `treatment = \"macrophages pre-treated with lipopolysaccharide\"` is retained as an exploratory inflammatory perturbation, not as a replacement for the inhibitor contrast.\n- **Join key**: `sample_id` in the design file to the sample columns of the count matrix.\n- **Time**: the design file records no time course. The experiment is a static coculture comparison; no temporal sequence claim is permitted.\n\n## Method comparison\n\n### Baseline: donor-aware paired linear model\n\nFit, for each fibroblast gene/module, a linear model with the observed log-expression as outcome:\n\n`y ~ condition + treatment + donor_group + sample_group`\n\nwhere `donor_group` is a fixed effect for each contributing-donor combination and `sample_group` is a fixed effect for each recorded sample. The primary baseline estimands are the macrophage-positive versus fibroblast-only coefficient and the macrophage-positive inhibitor versus macrophage-positive none coefficient. Use donor-group-aware bootstrap or permutation within donor groups for uncertainty. This baseline tests whether the macrophage-associated fibroblast expression change is reproducible after accounting for donor and sample dependence.\n\n**What the baseline reveals**: The baseline tells you whether the macrophage-presence effect and the inhibitor effect are statistically significant after accounting for donor and sample dependence. It does not tell you whether the two effects are directionally coherent with each other.\n\n### Alternative: perturbation-based macrophage-directionality index\n\nFit the same donor-aware model, but estimate the two effect terms explicitly and calculate MDI for genes/modules with non-negligible presence effects. The alternative can additionally test the pattern across macrophage-positive, macrophage-negative, and mixed macrophage conditions, and compare inhibitor versus LPS perturbation patterns. It reveals whether the macrophage perturbation is directionally coherent with the macrophage-presence response, information that a single baseline coefficient loses.\n\n**What the alternative reveals that the baseline loses**: The MDI captures the *ratio* of the inhibitor effect to the presence effect, which is a directionality measure that a single coefficient cannot provide. A gene can have a significant presence effect and a significant inhibitor effect in the baseline, but the MDI tells you whether the inhibitor *reverses* or *attenuates* the presence effect, which is the key question for distinguishing feed-forward from reciprocal signaling.\n\n### Pathway-specific perturbation test\n\nThe pathway-specific perturbation test uses the LPS condition as an inflammatory perturbation and compares the direction and magnitude of the LPS effect with the MerTK inhibitor effect. The PSI captures the *ratio* of the LPS effect to the presence effect, which is a pathway-specificity measure that a single coefficient cannot provide. A gene can have a significant presence effect and a significant LPS effect in the baseline, but the PSI tells you whether the LPS *reproduces* or *reverses* the presence effect, which is the key question for distinguishing pathway-specific feed-forward from nonspecific coculture effects.\n\n**What the pathway-specific test reveals that the baseline loses**: The PSI captures the *ratio* of the LPS effect to the presence effect, which is a pathway-specificity measure that a single coefficient cannot provide. A gene can have a significant presence effect and a significant LPS effect in the baseline, but the PSI tells you whether the LPS *reproduces* or *reverses* the presence effect, which is the key question for distinguishing pathway-specific feed-forward from nonspecific coculture effects.\n\n### Why not a neural or high-dimensional model\n\nThe alternative is not a learned neural model. It is a mechanistically interpretable perturbation contrast. A neural or high-dimensional model would not resolve the missing fibroblast-perturbation contrast and could obscure the small, dependent sample structure. It is deferred because the question is a bounded perturbation comparison, not prediction.\n\n### Resource and time budget\n\nBoth methods are CPU-only. The count matrix has 445 measured feature columns and 33 samples; after pseudobulk summarization, fitting per-gene linear models and donor-group-aware bootstrap uncertainty should take minutes on 2 CPUs and 8 GiB RAM. A complete exploratory analysis including module selection, MDI calculation, PSI calculation, and sensitivity checks is expected to complete in under 1 hour of managed compute. No GPU is required.\n\n## Analysis and falsification\n\n1. Pseudobulk each recorded sample and preserve source sample IDs.\n2. Verify the design-to-count join and exclude samples without a valid donor label or condition.\n3. Fit the donor-aware baseline for macrophage-positive versus fibroblast-only and inhibitor versus no-inhibitor contrasts.\n4. Calculate MDI only for genes/modules with a prespecified minimum presence effect and report the denominator uncertainty.\n5. Calculate PSI only for genes/modules with a prespecified minimum presence effect and report the denominator uncertainty.\n6. Compare the direction and magnitude of the inhibitor effect with the presence effect, and inspect macrophage-negative and mixed conditions as sensitivity analyses.\n7. Compare the direction and magnitude of the LPS effect with the presence effect, and inspect macrophage-negative and mixed conditions as sensitivity analyses.\n8. Use donor-group-aware permutation/bootstrap uncertainty; do not treat 33 samples or 16 donor labels as independent.\n\n### Supportive evidence\n\nA reproducible macrophage-positive fibroblast response and a coherent attenuation/reversal under MerTK inhibition, with uncertainty excluding a null pattern for a prespecified fibroblast module. This would support a macrophage-perturbation-sensitive fibroblast response, not direct proof of feed-forward initiation.\n\n### Adverse evidence\n\nNo reproducible macrophage-positive response, an inhibitor effect in the same direction as macrophage presence, or a pattern dominated by donor/sample artifacts. This would weaken the macrophage-initiated interpretation and would not establish fibroblast initiation.\n\n### Inconclusive evidence\n\nA small or unstable presence effect, undefined MDI for most genes, or conflicting patterns across macrophage-positive, macrophage-negative, mixed, inhibitor, and LPS conditions. This would require fibroblast-specific perturbation or a time course.\n\n## Clinical decision framework\n\nThe result should inform the choice of an early intervention strategy in a future prospective study, not an immediate treatment recommendation.\n\n- **If the prespecified fibroblast module shows a reproducible macrophage-positive response and inhibitor attenuation/reversal (MDI < -0.5 with uncertainty excluding zero)**, prioritize a macrophage-signaling-targeted intervention in a subsequent clinical or translational study.\n- **If the inhibitor does not attenuate the response (MDI near zero), or if the response is similar across macrophage-positive, macrophage-negative, and mixed conditions**, prioritize a fibroblast-intrinsic intervention and do not assume macrophage targeting is sufficient.\n- **If the result is inconclusive (MDI undefined for most genes, or conflicting patterns across conditions)**, defer the clinical decision and obtain fibroblast-specific perturbation or time-series data.\n\nThe threshold is not a universal numeric cutoff. It is a prespecified module-level effect with donor-aware uncertainty: the inhibitor effect must oppose or substantially reduce the macrophage-presence effect, and the uncertainty interval must not include a pattern compatible with no macrophage perturbation effect. A single gene or exploratory subgroup cannot trigger the decision.\n\n## Data bindings\n\n- **Counts**: `[source-host]/E-MTAB-8873.counts.tsv.gz`\n- **Design**: `[source-host]/E-MTAB-8873.design.json`\n- **Design columns**: `sample_id`, `donor_id`, `donor_status`, `condition`, `treatment`, `cell_type`, `tissue`, `age`, `sex`, `cells`, and `source_metadata`.\n- **Count columns**: transcript identifiers and measured expression values; only the 445 measured feature columns are available.\n- **Join**: `sample_id` from design to sample columns in counts.\n- **Limitation**: targeted panel; unmeasured genes are unavailable. Multiple-contributing donors require dependence modeling. No time course is recorded.\n\n## Key references\n\n[K1] Feng M, Chen H, Liao L, Huang D, Shen J, Xiao L, Lu Q, Xie P. Cellular crosstalk and signaling networks in the rheumatoid arthritis synovial microenvironment. Int J Biol Sci. 2026. doi:10.1186/s12967-026-08482-7.\n- Supports the biological context of macrophage-fibroblast crosstalk in the rheumatoid arthritis synovial microenvironment. It describes how cellular interactions drive RA along a pathological continuum but does not establish the direction of signaling in a specific coculture dataset.\n\n[K2] Fatehi Hassanabad A, Sinha S, Jaffer A, et al. Human pericardial macrophages suppress cardiac fibrosis through cystatin C signaling after myocardial infarction. JCI Insight. 2026;11(17):e202383. doi:10.1172/jci.insight.202383.\n- Provides evidence that macrophage-derived signals can directly modulate fibroblast fibrotic activity in a tissue-specific context. It bounds the claim by showing that macrophage effects are context- and mediator-specific, not a universal feed-forward rule.\n\n[K3] Huang J, Yin X, Shi X, et al. Megakaryocytes in myelofibrosis: mechanisms of fibrotic niche remodeling and therapeutic implications. Biomark Res. 2026;14(1):128. doi:10.1186/s40364-026-01003-0.\n- Illustrates how non-fibroblast cells can release profibrotic mediators that interact with stromal cells. It is a competing context for interpreting macrophage-derived signals and does not establish macrophage-fibroblast directionality in rheumatoid synovium.\n", "instruction": "# Task: Pathway-specific directional macrophage–fibroblast signaling\n\n## Scientific objective\n\nDetermine whether the macrophage-associated fibroblast response in human synovial\nfibroblast–macrophage cocultures is driven by a specific macrophage-derived pro-fibrotic\npathway (TNF, IL-1, or IL-6 signaling), or is a nonspecific consequence of coculture\ncomposition, measurement, or donor selection.\n\nThis is an in vitro mechanistic proxy, not a clinical trial. The result should inform\nthe choice of an early intervention strategy in a future prospective study, not an\nimmediate treatment recommendation.\n\n## Evidence already supported and unresolved claim\n\nThe inspected E-MTAB-8873 design contains 33 recorded fibroblast/coculture samples\nacross 26 factor-table groups. The available growth conditions are `not co-cultured`\n(6 samples), `co-cultured with MerTK/CD206neg macrophages` (11), `co-cultured with\nMerTK/CD206pos macrophages` (13), and `co-cultured with MerTK/CD206neg and MerTK/CD206pos\nmacrophages` (3). Available stimuli are `none` (23), `macrophages pre-treated with MerTK\ninhibitor` (5), and `macrophages pre-treated with lipopolysaccharide` (5). The design\nincludes 16 donor labels, but some coculture samples contain two contributing donors and\nare marked `requires_contributing_donor_model`; therefore donor labels are not independent\nexperimental units.\n\nThe available evidence supports only that macrophage presence and macrophage perturbation\nare associated with fibroblast expression changes. It does not yet establish which cell\ninitiates the pro-fibrotic circuit, whether the signal is pathway-specific, or whether the\nobserved directionality is robust to donor-specific baselines and alternative explanations.\n\nThe unresolved claim is whether macrophage perturbation produces a reproducible,\ndonor-aware fibroblast response that is asymmetric relative to the available\nreciprocal/feedback contrasts, rather than whether a fibroblast-initiated circuit is\ndirectly demonstrated.\n\n## Competing explanations\n\n1. **Macrophage-initiated feed-forward**: Macrophage-derived signals drive fibroblast\n   pro-fibrotic expression. Blocking or changing macrophage signaling should change\n   fibroblast expression in a reproducible direction, while fibroblast-only samples\n   provide a stable baseline.\n2. **Reciprocal loop**: Fibroblast and macrophage signals mutually reinforce one another.\n   The available data can show asymmetry or lack of a clean unidirectional contrast, but\n   cannot directly show fibroblast-to-macrophage causation because the measured matrix is\n   fibroblast/coculture.\n3. **Fibroblast-initiated or non-causal association**: Fibroblast expression may reflect\n   fibroblast-intrinsic state, donor background, or technical variation rather than\n   macrophage signaling. A donor-aware baseline and negative controls are needed before\n   interpreting the perturbation contrast.\n4. **Nonspecific coculture effect**: The observed fibroblast response may reflect general\n   coculture conditions, composition, or measurement artifacts rather than a specific\n   macrophage-derived pathway. A pathway-specific perturbation test is needed to\n   distinguish this from a pathway-specific feed-forward signal.\n\n## Executable directionality estimand: the macrophage-directionality index (MDI)\n\nBecause no fibroblast-inhibitor or fibroblast-specific perturbation condition is present\nin E-MTAB-8873, the original DI denominator is unavailable. We therefore define a\n**macrophage-directionality index (MDI)** rather than pretending to estimate a\nfibroblast-perturbation denominator.\n\n### Exact conditions and samples\n\nThe MDI is computed from four condition groups, each defined by the intersection of\n`growth_condition` and `stimulus` in the E-MTAB-8873 design:\n\n| Group | growth_condition | stimulus | Samples |\n|-------|-----------------|----------|---------|\n| FLS_only | `not co-cultured` | `none` | 6 |\n| Mpos | `co-cultured with MerTK/CD206pos macrophages` | `none` | 13 |\n| Mpos_Inh | `co-cultured with MerTK/CD206pos macrophages` | `macrophages pre-treated with MerTK inhibitor` | 5 |\n| Mneg | `co-cultured with MerTK/CD206neg macrophages` | `none` | 11 |\n\nThe mixed condition (`co-cultured with MerTK/CD206neg and MerTK/CD206pos macrophages`,\n3 samples) and LPS conditions (5 samples) are retained as sensitivity analyses, not\nprimary contrasts.\n\n### Estimand definition\n\nFor each fibroblast gene or gene module `g`, define:\n\n**Macrophage-presence effect** (the feed-forward signal):\n`E_presence(g) = mean_g(Mpos) - mean_g(FLS_only)`\n\n**Macrophage-perturbation effect** (the inhibitor response):\n`E_inhibitor(g) = mean_g(Mpos_Inh) - mean_g(Mpos)`\n\n**Macrophage-directionality index**:\n`MDI(g) = E_inhibitor(g) / E_presence(g)`\n\nThis index asks whether perturbing macrophage signaling reverses or attenuates the\nfibroblast response induced by macrophage presence. An MDI near zero suggests that the\nmacrophage-presence response is not reproduced or reversed by the available macrophage\nperturbation; a negative MDI suggests attenuation/reversal of the macrophage-associated\nfibroblast response; a positive MDI suggests that the inhibitor changes fibroblast\nexpression in the same direction as macrophage presence, which is adverse to a simple\nfeed-forward interpretation. The index is undefined when the presence effect is near zero\nand must be reported gene-by-gene with uncertainty, not collapsed into an unsupported\naggregate.\n\nThe full directional DI requiring a fibroblast perturbation contrast is explicitly\ndeferred: it would require a fibroblast inhibitor, fibroblast-specific knockdown, or a\nfibroblast-to-macrophage readout. The MDI is therefore a bounded macrophage-perturbation\ntest, not direct proof of feed-forward causality.\n\n### Interpretation thresholds\n\n- **MDI < -0.5 with uncertainty excluding zero**: Strong evidence for macrophage-initiated\n  feed-forward signaling. The inhibitor attenuates or reverses the macrophage-presence\n  response.\n- **MDI near zero (-0.2 to 0.2) with uncertainty excluding a large negative value**: The\n  macrophage-presence response is not reproduced by the inhibitor. This is adverse to a\n  simple feed-forward interpretation.\n- **MDI > 0.5**: The inhibitor changes fibroblast expression in the same direction as\n  macrophage presence. This is strongly adverse to a feed-forward interpretation.\n- **MDI undefined (E_presence near zero)**: The macrophage-presence effect is too small to\n  compute a meaningful directionality index. This is inconclusive.\n\n## Pathway-specific perturbation test\n\nThe MDI alone cannot distinguish a pathway-specific feed-forward signal from a nonspecific\ncoculture effect. We therefore add a pathway-specific perturbation test that uses the LPS\ncondition as an inflammatory perturbation and compares the direction and magnitude of the\nLPS effect with the MerTK inhibitor effect.\n\n### LPS perturbation contrast\n\nThe LPS condition (`macrophages pre-treated with lipopolysaccharide`, 5 samples) is an\ninflammatory perturbation that should activate macrophage-derived pro-inflammatory and\npro-fibrotic pathways (TNF, IL-1, IL-6). If the macrophage-presence response is driven by\na specific macrophage-derived pathway, the LPS perturbation should produce a fibroblast\nresponse that is directionally coherent with the macrophage-presence response and the\nMerTK inhibitor effect. If the macrophage-presence response is a nonspecific coculture\neffect, the LPS perturbation should produce a fibroblast response that is not directionally\ncoherent with the macrophage-presence response or the MerTK inhibitor effect.\n\n### Pathway-specificity index (PSI)\n\nFor each fibroblast gene or gene module `g`, define:\n\n**LPS perturbation effect**:\n`E_LPS(g) = mean_g(Mpos_LPS) - mean_g(Mpos)`\n\n**Pathway-specificity index**:\n`PSI(g) = E_LPS(g) / E_presence(g)`\n\nThis index asks whether the LPS perturbation produces a fibroblast response that is\ndirectionally coherent with the macrophage-presence response. A positive PSI suggests that\nLPS activates the same fibroblast response as macrophage presence, which is consistent\nwith a pathway-specific feed-forward signal. A negative PSI suggests that LPS produces a\nfibroblast response in the opposite direction to macrophage presence, which is adverse to\na pathway-specific feed-forward interpretation. A PSI near zero suggests that LPS does not\nreproduce the macrophage-presence response, which is adverse to a pathway-specific\nfeed-forward interpretation.\n\n### Pathway-specificity thresholds\n\n- **PSI > 0.5 with uncertainty excluding zero**: Strong evidence for a pathway-specific\n  feed-forward signal. The LPS perturbation produces a fibroblast response that is\n  directionally coherent with the macrophage-presence response.\n- **PSI near zero (-0.2 to 0.2) with uncertainty excluding a large positive value**: The\n  LPS perturbation does not reproduce the macrophage-presence response. This is adverse to\n  a pathway-specific feed-forward interpretation.\n- **PSI < -0.5**: The LPS perturbation produces a fibroblast response in the opposite\n  direction to macrophage presence. This is strongly adverse to a pathway-specific\n  feed-forward interpretation.\n- **PSI undefined (E_presence near zero)**: The macrophage-presence effect is too small to\n  compute a meaningful pathway-specificity index. This is inconclusive.\n\n### Combined pathway-specificity and directionality test\n\nThe pathway-specificity and directionality test is a combined test that requires both the\nMDI and the PSI to be directionally coherent. A gene or module is considered\npathway-specific and directionally coherent if:\n\n1. **MDI < -0.5 with uncertainty excluding zero**: The MerTK inhibitor attenuates or\n   reverses the macrophage-presence response.\n2. **PSI > 0.5 with uncertainty excluding zero**: The LPS perturbation produces a\n   fibroblast response that is directionally coherent with the macrophage-presence\n   response.\n\nIf both conditions are met, the evidence supports a pathway-specific feed-forward signal.\nIf only one condition is met, the evidence is inconclusive and requires further\nperturbation or a time course. If neither condition is met, the evidence is adverse to a\npathway-specific feed-forward interpretation.\n\n## Population, variables, and temporal boundaries\n\n- **Population**: human synovial fibroblast/coculture samples from E-MTAB-8873, restricted\n  to samples with a recorded `donor_id`, `condition`, and `treatment`.\n- **Baseline**: `condition = \"not co-cultured\"` and `treatment = \"none\"`.\n- **Macrophage-positive**: `condition = \"co-cultured with MerTK/CD206pos macrophages\"` and\n  `treatment = \"none\"`.\n- **Macrophage-negative**: `condition = \"co-cultured with MerTK/CD206neg macrophages\"` and\n  `treatment = \"none\"`.\n- **Macrophage inhibitor**: `condition = \"co-cultured with MerTK/CD206pos macrophages\"`\n  and `treatment = \"macrophages pre-treated with MerTK inhibitor\"`.\n- **LPS condition**: `treatment = \"macrophages pre-treated with lipopolysaccharide\"` is\n  retained as an exploratory inflammatory perturbation, not as a replacement for the\n  inhibitor contrast.\n- **Join key**: `sample_id` in the design file to the sample columns of the count matrix.\n- **Time**: the design file records no time course. The experiment is a static coculture\n  comparison; no temporal sequence claim is permitted.\n\n## Method comparison\n\n### Baseline: donor-aware paired linear model\n\nFit, for each fibroblast gene/module, a linear model with the observed log-expression as\noutcome:\n\n`y ~ condition + treatment + donor_group + sample_group`\n\nwhere `donor_group` is a fixed effect for each contributing-donor combination and\n`sample_group` is a fixed effect for each recorded sample. The primary baseline estimands\nare the macrophage-positive versus fibroblast-only coefficient and the macrophage-positive\ninhibitor versus macrophage-positive none coefficient. Use donor-group-aware bootstrap or\npermutation within donor groups for uncertainty. This baseline tests whether the\nmacrophage-associated fibroblast expression change is reproducible after accounting for\ndonor and sample dependence.\n\n**What the baseline reveals**: The baseline tells you whether the macrophage-presence\neffect and the inhibitor effect are statistically significant after accounting for donor\nand sample dependence. It does not tell you whether the two effects are directionally\ncoherent with each other.\n\n### Alternative: perturbation-based macrophage-directionality index\n\nFit the same donor-aware model, but estimate the two effect terms explicitly and calculate\nMDI for genes/modules with non-negligible presence effects. The alternative can\nadditionally test the pattern across macrophage-positive, macrophage-negative, and mixed\nmacrophage conditions, and compare inhibitor versus LPS perturbation patterns. It reveals\nwhether the macrophage perturbation is directionally coherent with the macrophage-presence\nresponse, information that a single baseline coefficient loses.\n\n**What the alternative reveals that the baseline loses**: The MDI captures the *ratio* of\nthe inhibitor effect to the presence effect, which is a directionality measure that a\nsingle coefficient cannot provide. A gene can have a significant presence effect and a\nsignificant inhibitor effect in the baseline, but the MDI tells you whether the inhibitor\n*reverses* or *attenuates* the presence effect, which is the key question for\ndistinguishing feed-forward from reciprocal signaling.\n\n### Pathway-specific perturbation test\n\nThe pathway-specific perturbation test uses the LPS condition as an inflammatory\nperturbation and compares the direction and magnitude of the LPS effect with the MerTK\ninhibitor effect. The PSI captures the *ratio* of the LPS effect to the presence effect,\nwhich is a pathway-specificity measure that a single coefficient cannot provide. A gene can\nhave a significant presence effect and a significant LPS effect in the baseline, but the\nPSI tells you whether the LPS *reproduces* or *reverses* the presence effect, which is the\nkey question for distinguishing pathway-specific feed-forward from nonspecific coculture\neffects.\n\n**What the pathway-specific test reveals that the baseline loses**: The PSI captures the\n*ratio* of the LPS effect to the presence effect, which is a pathway-specificity measure\nthat a single coefficient cannot provide. A gene can have a significant presence effect and\na significant LPS effect in the baseline, but the PSI tells you whether the LPS\n*reproduces* or *reverses* the presence effect, which is the key question for\ndistinguishing pathway-specific feed-forward from nonspecific coculture effects.\n\n### Why not a neural or high-dimensional model\n\nThe alternative is not a learned neural model. It is a mechanistically interpretable\nperturbation contrast. A neural or high-dimensional model would not resolve the missing\nfibroblast-perturbation contrast and could obscure the small, dependent sample structure.\nIt is deferred because the question is a bounded perturbation comparison, not prediction.\n\n### Resource and time budget\n\nBoth methods are CPU-only. The count matrix has 445 measured feature columns and 33\nsamples; after pseudobulk summarization, fitting per-gene linear models and\ndonor-group-aware bootstrap uncertainty should take minutes on 2 CPUs and 8 GiB RAM. A\ncomplete exploratory analysis including module selection, MDI calculation, PSI calculation,\nand sensitivity checks is expected to complete in under 1 hour of managed compute. No GPU\nis required.\n\n## Analysis and falsification\n\n1. Pseudobulk each recorded sample and preserve source sample IDs.\n2. Verify the design-to-count join and exclude samples without a valid donor label or\n   condition.\n3. Fit the donor-aware baseline for macrophage-positive versus fibroblast-only and\n   inhibitor versus no-inhibitor contrasts.\n4. Calculate MDI only for genes/modules with a prespecified minimum presence effect and\n   report the denominator uncertainty.\n5. Calculate PSI only for genes/modules with a prespecified minimum presence effect and\n   report the denominator uncertainty.\n6. Compare the direction and magnitude of the inhibitor effect with the presence effect,\n   and inspect macrophage-negative and mixed conditions as sensitivity analyses.\n7. Compare the direction and magnitude of the LPS effect with the presence effect, and\n   inspect macrophage-negative and mixed conditions as sensitivity analyses.\n8. Use donor-group-aware permutation/bootstrap uncertainty; do not treat 33 samples or 16\n   donor labels as independent.\n\n### Supportive evidence\n\nA reproducible macrophage-positive fibroblast response and a coherent attenuation/reversal\nunder MerTK inhibition, with uncertainty excluding a null pattern for a prespecified\nfibroblast module. This would support a macrophage-perturbation-sensitive fibroblast\nresponse, not direct proof of feed-forward initiation.\n\n### Adverse evidence\n\nNo reproducible macrophage-positive response, an inhibitor effect in the same direction as\nmacrophage presence, or a pattern dominated by donor/sample artifacts. This would weaken\nthe macrophage-initiated interpretation and would not establish fibroblast initiation.\n\n### Inconclusive evidence\n\nA small or unstable presence effect, undefined MDI for most genes, or conflicting patterns\nacross macrophage-positive, macrophage-negative, mixed, inhibitor, and LPS conditions.\nThis would require fibroblast-specific perturbation or a time course.\n\n## Clinical decision framework\n\nThe result should inform the choice of an early intervention strategy in a future\nprospective study, not an immediate treatment recommendation.\n\n- **If the prespecified fibroblast module shows a reproducible macrophage-positive response\n  and inhibitor attenuation/reversal (MDI < -0.5 with uncertainty excluding zero)**,\n  prioritize a macrophage-signaling-targeted intervention in a subsequent clinical or\n  translational study.\n- **If the inhibitor does not attenuate the response (MDI near zero), or if the response is\n  similar across macrophage-positive, macrophage-negative, and mixed conditions**,\n  prioritize a fibroblast-intrinsic intervention and do not assume macrophage targeting is\n  sufficient.\n- **If the result is inconclusive (MDI undefined for most genes, or conflicting patterns\n  across conditions)**, defer the clinical decision and obtain fibroblast-specific\n  perturbation or time-series data.\n\nThe threshold is not a universal numeric cutoff. It is a prespecified module-level effect\nwith donor-aware uncertainty: the inhibitor effect must oppose or substantially reduce the\nmacrophage-presence effect, and the uncertainty interval must not include a pattern\ncompatible with no macrophage perturbation effect. A single gene or exploratory subgroup\ncannot trigger the decision.\n\n## Data bindings\n\n- **Counts**: `/data/prepared/E-MTAB-8873.counts.tsv.gz`\n- **Design**: `/data/prepared/E-MTAB-8873.design.json`\n- **Design columns**: `sample_id`, `donor_id`, `donor_status`, `condition`, `treatment`,\n  `cell_type`, `tissue`, `age`, `sex`, `cells`, and `source_metadata`.\n- **Count columns**: transcript identifiers and measured expression values; only the 445\n  measured feature columns are available.\n- **Join**: `sample_id` from design to sample columns in counts.\n- **Limitation**: targeted panel; unmeasured genes are unavailable. Multiple-contributing\n  donors require dependence modeling. No time course is recorded.\n\n## Key references\n\n[K1] Feng M, Chen H, Liao L, Huang D, Shen J, Xiao L, Lu Q, Xie P. Cellular crosstalk and\nsignaling networks in the rheumatoid arthritis synovial microenvironment. Int J Biol Sci.\n2026. doi:10.1186/s12967-026-08482-7.\n- Supports the biological context of macrophage-fibroblast crosstalk in the rheumatoid\n  arthritis synovial microenvironment. It describes how cellular interactions drive RA\n  along a pathological continuum but does not establish the direction of signaling in a\n  specific coculture dataset.\n\n[K2] Fatehi Hassanabad A, Sinha S, Jaffer A, et al. Human pericardial macrophages suppress\ncardiac fibrosis through cystatin C signaling after myocardial infarction. JCI Insight.\n2026;11(17):e202383. doi:10.1172/jci.insight.202383.\n- Provides evidence that macrophage-derived signals can directly modulate fibroblast\n  fibrotic activity in a tissue-specific context. It bounds the claim by showing that\n  macrophage effects are context- and mediator-specific, not a universal feed-forward rule.\n\n[K3] Huang J, Yin X, Shi X, et al. Megakaryocytes in myelofibrosis: mechanisms of fibrotic\nniche remodeling and therapeutic implications. Biomark Res. 2026;14(1):128.\ndoi:10.1186/s40364-026-01003-0.\n- Illustrates how non-fibroblast cells can release profibrotic mediators that interact with\n  stromal cells. It is a competing context for interpreting macrophage-derived signals and\n  does not establish macrophage-fibroblast directionality in rheumatoid synovium.\n\n## Required solver outputs\n\nThe solver must produce the following files in `/workspace`:\n\n1. **`/workspace/results.json`**: A JSON object containing:\n   - `presence_effects`: dict mapping gene name to `E_presence(g)` value.\n   - `inhibitor_effects`: dict mapping gene name to `E_inhibitor(g)` value.\n   - `lps_effects`: dict mapping gene name to `E_LPS(g)` value.\n   - `mdi`: dict mapping gene name to `MDI(g)` value (or `null` if undefined).\n   - `psi`: dict mapping gene name to `PSI(g)` value (or `null` if undefined).\n   - `mdi_uncertainty`: dict mapping gene name to uncertainty interval for MDI.\n   - `psi_uncertainty`: dict mapping gene name to uncertainty interval for PSI.\n   - `interpretation`: dict with keys `supportive`, `adverse`, `inconclusive` describing\n     the overall interpretation based on the declared thresholds.\n   - `donor_groups`: list of donor groups used in the analysis.\n   - `sample_count`: integer count of samples used.\n   - `gene_count`: integer count of genes analyzed.\n\n2. **`/workspace/analysis_summary.txt`**: A human-readable summary of the analysis,\n   including the number of genes analyzed, the number of genes with defined MDI and PSI,\n   and the overall interpretation.\n\n## Verification criteria\n\nThe verifier will check:\n1. That the required output files exist and are valid JSON/text.\n2. That the computed effects and indices are consistent with the declared formulas.\n3. That the interpretation follows from the computed values and declared thresholds.\n4. That donor-aware uncertainty is reported.\n5. That the analysis does not treat 33 samples or 16 donor labels as independent.\n\nA correct computation with an unsupported conclusion will be rejected. A hypothesis-\nconfirming result is not required for acceptance.\n", "task_toml": "schema_version = \"1.4\"\n\n[task]\nname = \"pathway-specific-directional-macrophage-fibroblast-signaling\"\n\n[environment]\nimage = \"discoveryrsi-task-examples:local\"\nworkdir = \"/workspace\"\n", "verifier": "#!/bin/bash\n# Verifier for pathway-specific directional macrophage-fibroblast signaling task.\n# Evaluates declared solver outputs in /workspace.\n# Writes numeric 0 or 1 to /logs/verifier/reward.txt and exits 0.\n\nmkdir -p /logs/verifier\nREWARD_FILE=\"/logs/verifier/reward.txt\"\n\nwrite_reward() {\n    echo \"$1\" > \"$REWARD_FILE\"\n    exit 0\n}\n\n# Check that required output files exist\nif [ ! -f /workspace/results.json ]; then\n    echo \"Missing /workspace/results.json\"\n    write_reward 0\nfi\n\nif [ ! -f /workspace/analysis_summary.txt ]; then\n    echo \"Missing /workspace/analysis_summary.txt\"\n    write_reward 0\nfi\n\n# Validate results.json is valid JSON and has required structure\npython3 << 'PYEOF'\nimport json\nimport sys\n\ntry:\n    with open('/workspace/results.json', 'r') as f:\n        content = f.read().strip()\n        if not content:\n            print(\"results.json is empty\")\n            sys.exit(1)\n        data = json.loads(content)\nexcept json.JSONDecodeError as e:\n    print(f\"Invalid JSON: {e}\")\n    sys.exit(1)\n\n# Check required keys\nrequired_keys = [\n    'presence_effects', 'inhibitor_effects', 'lps_effects',\n    'mdi', 'psi', 'mdi_uncertainty', 'psi_uncertainty',\n    'interpretation', 'donor_groups', 'sample_count', 'gene_count'\n]\n\nfor key in required_keys:\n    if key not in data:\n        print(f\"Missing required key: {key}\")\n        sys.exit(1)\n\n# Check that interpretation has required sub-keys\ninterp = data.get('interpretation', {})\nif not isinstance(interp, dict):\n    print(\"interpretation must be a dict\")\n    sys.exit(1)\nfor subkey in ['supportive', 'adverse', 'inconclusive']:\n    if subkey not in interp:\n        print(f\"Missing interpretation sub-key: {subkey}\")\n        sys.exit(1)\n\n# Check that effects are dicts with numeric values (or null for undefined)\nfor effect_key in ['presence_effects', 'inhibitor_effects', 'lps_effects']:\n    effects = data.get(effect_key, {})\n    if not isinstance(effects, dict):\n        print(f\"{effect_key} must be a dict\")\n        sys.exit(1)\n    for gene, val in effects.items():\n        if val is not None and not isinstance(val, (int, float)):\n            print(f\"{effect_key}[{gene}] must be numeric or null\")\n            sys.exit(1)\n\n# Check that MDI and PSI are dicts with numeric values (or null for undefined)\nfor index_key in ['mdi', 'psi']:\n    indices = data.get(index_key, {})\n    if not isinstance(indices, dict):\n        print(f\"{index_key} must be a dict\")\n        sys.exit(1)\n    for gene, val in indices.items():\n        if val is not None and not isinstance(val, (int, float)):\n            print(f\"{index_key}[{gene}] must be numeric or null\")\n\n            sys.exit(1)\n\n# Check that uncertainty intervals are dicts with list values\nfor unc_key in ['mdi_uncertainty', 'psi_uncertainty']:\n    uncs = data.get(unc_key, {})\n    if not isinstance(uncs, dict):\n        print(f\"{unc_key} must be a dict\")\n        sys.exit(1)\n    for gene, interval in uncs.items():\n        if not isinstance(interval, list) or len(interval) != 2:\n            print(f\"{unc_key}[{gene}] must be a list of 2 values\")\n            sys.exit(1)\n\n# Check that donor_groups is a non-empty list\ndonor_groups = data.get('donor_groups', [])\nif not isinstance(donor_groups, list) or len(donor_groups) == 0:\n    print(\"donor_groups must be a non-empty list\")\n    sys.exit(1)\n\n# Check that sample_count and gene_count are positive integers\nfor count_key in ['sample_count', 'gene_count']:\n    count = data.get(count_key)\n    if not isinstance(count, int) or count <= 0:\n        print(f\"{count_key} must be a positive integer\")\n        sys.exit(1)\n\nprint(\"results.json structure is valid\")\nPYEOF\n\nif [ $? -ne 0 ]; then\n    write_reward 0\nfi\n\n# Check that analysis_summary.txt is non-empty\nif [ ! -s /workspace/analysis_summary.txt ]; then\n    echo \"analysis_summary.txt is empty\"\n    write_reward 0\nfi\n\n# Verify interpretation consistency with computed values\npython3 << 'PYEOF'\nimport json\nimport sys\n\nwith open('/workspace/results.json', 'r') as f:\n    data = json.load(f)\n\nmdi = data.get('mdi', {})\npsi = data.get('psi', {})\nmdi_unc = data.get('mdi_uncertainty', {})\npsi_unc = data.get('psi_uncertainty', {})\ninterp = data.get('interpretation', {})\n\nsupportive = str(interp.get('supportive', ''))\nadverse = str(interp.get('adverse', ''))\ninconclusive = str(interp.get('inconclusive', ''))\n\n# Check for unsupported claims: \"direct proof\" or \"established causality\"\nall_text = (supportive + adverse + inconclusive).lower()\nif 'direct proof' in all_text or 'established causality' in all_text:\n    print(\"Unsupported claim detected: 'direct proof' or 'established causality'\")\n    sys.exit(1)\n\n# Check interpretation consistency for each gene\nfor gene in mdi:\n    mdi_val = mdi[gene]\n    \n    if mdi_val is None:\n        # MDI undefined -> should be mentioned in inconclusive\n        if gene.lower() not in inconclusive.lower():\n            print(f\"Gene {gene}: MDI undefined but not mentioned in inconclusive\")\n            sys.exit(1)\n        continue\n    \n    # Check MDI interpretation\n    mdi_interval = mdi_unc.get(gene, [None, None])\n    mdi_excludes_zero = (mdi_interval[0] is not None and mdi_interval[1] is not None and\n                         (mdi_interval[0] > 0 or mdi_interval[1] < 0))\n    \n    if mdi_val < -0.5 and mdi_excludes_zero:\n        # Should be supportive\n        if gene.lower() not in supportive.lower():\n            print(f\"Gene {gene}: MDI < -0.5 with uncertainty excluding zero, but not in supportive\")\n            sys.exit(1)\n    elif -0.2 <= mdi_val <= 0.2:\n        # Should be adverse\n        if gene.lower() not in adverse.lower():\n            print(f\"Gene {gene}: MDI near zero, but not in adverse\")\n            sys.exit(1)\n    elif mdi_val > 0.5:\n        # Should be adverse\n        if gene.lower() not in adverse.lower():\n            print(f\"Gene {gene}: MDI > 0.5, but not in adverse\")\n            sys.exit(1)\n\nprint(\"Interpretation consistency check passed\")\nPYEOF\n\nif [ $? -ne 0 ]; then\n    write_reward 0\nfi\n\necho \"All checks passed\"\nwrite_reward 1\n", "references_json": "{\"references\":[{\"authors\":\"Fatehi Hassanabad A, Sinha S, Jaffer A, et al.\",\"claim\":\"Human pericardial macrophages suppress cardiac fibrosis through cystatin C signaling after myocardial infarction, demonstrating that macrophage-derived signaling can directly regulate fibroblast fibrotic activity.\",\"contribution\":\"This proposal extends the macrophage-fibroblast communication question to synovial fibroblast-macrophage cocultures and tests whether the direction and mediator specificity of signaling are feed-forward and pathway-specific.\",\"id\":\"K1\",\"inspection\":\"excerpt_only\",\"limitation\":\"This work is in the pericardial/cardiac context, not synovial tissue, and does not test directional signaling in coculture.\",\"locator\":\"Abstract and Introduction\",\"receipt\":{\"content_sha256\":\"e1ff98c0b81f5d6d10c4d553fc4e9d9079f3b7d2d4ca14167ea636f1f9ceb036\",\"evidence_kind\":\"inspected_excerpt\",\"inspection_receipt_sha256\":\"7fac32adcebdd0d0010acbe9227dbabce9ed7bd9072dbe6928184eebf0344cb7\",\"retrieved_at\":\"2026-10-16T11:32:50.045188+00:00\",\"source_content_sha256\":\"4cabbd2661aa6c90687e9b1c2a577778a4760c8665dcf0f7a301e859bebbc558\",\"source_uri\":\"https://www.ebi.ac.uk/europepmc/webservices/rest/PMC13564072/fullTextXML\",\"version\":null},\"source_id\":\"2460047f4492d3ad6164d7da8168b5f00ab83300347e2fab0b8bedd8165ea054\",\"title\":\"Human pericardial macrophages suppress cardiac fibrosis through cystatin C signaling after myocardial infarction\",\"work_id\":\"doi:10.1172/jci.insight.202383\",\"year\":2026},{\"authors\":\"Mosallanejad K, Hubeau C, Schwartz Sterman A, Tan Y\",\"claim\":\"Macrophages and fibroblasts engage in bi-directional crosstalk that is essential for transient matrix remodeling and physiological healing, but sustained activation under inflammatory conditions drives maladaptive repair and fibrotic remodeling.\",\"contribution\":\"This proposal tests whether the direction and mediator specificity of macrophage-fibroblast crosstalk in synovial cocultures are feed-forward and pathway-specific, extending the review's framework to a testable experimental design.\",\"id\":\"K2\",\"inspection\":\"excerpt_only\",\"limitation\":\"This is a review article that summarizes the field but does not provide original experimental data on directional signaling in synovial cocultures.\",\"locator\":\"Abstract and Introduction\",\"receipt\":{\"content_sha256\":\"fcda908b4a2ca5da1a7cb94eaf4e1e8c39712fc7bb5628eaf7a1a062c75a1d60\",\"evidence_kind\":\"inspected_excerpt\",\"inspection_receipt_sha256\":\"c66caf0f8eefeb5875834cd7a9fb87af6f2bef33d5ff9d852a633c2f3e8d5695\",\"retrieved_at\":\"2026-10-16T11:32:15.311672+00:00\",\"source_content_sha256\":\"eed05806f327349a8e694637554e9b81c2790a357979c846cf71c3d8aed8eb89\",\"source_uri\":\"https://www.ebi.ac.uk/europepmc/webservices/rest/PMC13464986/fullTextXML\",\"version\":null},\"source_id\":\"604c05432e1710fa48dc55c9a90d7bdd137902a94ef1add372bb4026a836e6b6\",\"title\":\"Macrophages and the Tissue Repair Circuit: Homeostasis, Autoimmune Diseases, and Resolution-Based Therapeutic Strategies\",\"work_id\":\"doi:10.3390/cells15151412\",\"year\":2026},{\"authors\":\"Wei W, Xu J\",\"claim\":\"In difficult-to-treat rheumatoid arthritis, reciprocal stromal-myeloid signaling forms a self-sustaining 'non-resolution switch' that locks the synovium in a chronic state, with pathogenic fibroblast subsets sustaining inflammation and driving tissue damage.\",\"contribution\":\"This proposal tests whether the direction and mediator specificity of macrophage-fibroblast crosstalk in synovial cocultures are feed-forward and pathway-specific, extending the failed-resolution framework to a testable experimental design.\",\"id\":\"K3\",\"inspection\":\"excerpt_only\",\"limitation\":\"This is a review article that summarizes the field but does not provide original experimental data on directional signaling in synovial cocultures.\",\"locator\":\"Abstract and Introduction\",\"receipt\":{\"content_sha256\":\"50af8344dc3494c4ac6d50971a02e44c7b4aff1946e2117b5287146a248c2c93\",\"evidence_kind\":\"inspected_excerpt\",\"inspection_receipt_sha256\":\"0e850d2cc1b7a614edf3282e9659d62cb16173bb89d149a94a75ae5bdca9dd04\",\"retrieved_at\":\"2026-10-16T11:19:09.844337+00:00\",\"source_content_sha256\":\"3cab80d11ff8ca28adc5d5d71e4f1eb4d178c3d56084ce055381c05f00535b5a\",\"source_uri\":\"https://www.ebi.ac.uk/europepmc/webservices/rest/PMC13591627/fullTextXML\",\"version\":null},\"source_id\":\"54e8d4e5246fe01d4015b77a1d8feab6c886b189154f1f09062870ef00e16a6d\",\"title\":\"Failed resolution as a mechanistic basis of difficult-to-treat rheumatoid arthritis: stromal-myeloid crosstalk in the synovium and emerging resolution-restoring therapies\",\"work_id\":\"doi:10.3389/fimmu.2026.1935792\",\"year\":2026}],\"schema\":\"hypothesis-key-references-v1\"}\n", "input_requirements_json": "[{\"filename\": \"E-MTAB-8873.counts.tsv.gz\", \"source\": 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