mAtg9 affects autophagy at an early omegasome stage. (A) Domain structure of yeast and human Atg9. Red and blue boxes represent conserved transmembrane domains. (B) HEK293 cells were treated with either control siRNA (ctrl) or siRNA against mAtg9 (Atg9 KD) and incubated in full (F) or starvation medium (S) for 2 h. Cell lysates were analyzed by Western blot using antibodies against mAtg9, β-tubulin, and LC3. (C) Confocal microscopy of HEK293/GFP-LC3 and HEK293/GFP-DFCP1 cells treated with RISC-Free (ctrl) or mAtg9 (Atg9 KD) siRNA and starved for 2 h. ULK1, WIPI2, and Atg16 were detected by indirect immunofluorescence using anti-ULK1, -WIPI2, and -Atg16 antibodies. Bars, 10 μm. (D–H) Quantification of ULK1, GFP-DFCP1, WIPI2, Atg16, or GFP-LC3 spots from B expressed as spots/cell area (ULK1, GFP-DFCP1) or spots/cell (WIPI2, Atg16, GFP-LC3). Error bars, SEM; **p < 0.01 ***p < 0.001 (n = 3, two-tailed unpaired t test). (I) Colocalization of GFP-LC3 with WIPI2 and Atg16 in HEK293/GFP-LC3 cells treated with mAtg9 siRNA, starved, and detected in C. (J) Quantification of colocalization of WIPI2 and Atg16 with GFP-LC3 as shown in (I). Error bars, SEM, n = 3.

Knockdown of mAtg9 by small interfering RNA (siRNA) depletion in HEK293 cells was nearly complete as revealed by Western blot (Figure 1B). As expected, depletion of mAtg9 significantly reduced lipidation of endogenous LC3 (Figure 1B) and the number of autophagic structures positive for GFP-LC3 (Figure 1C and I) observed upon starvation. All other autophagic structures, that is, those positive for DFCP1, WIPI2, and Atg16, were also significantly reduced (Figure 1, C and E–H), suggesting that mAtg9 may act very early, at the same step as or even before DFCP1. ULK1-positive puncta were reduced after depletion of Atg9 but not significantly (Figure 1, C and D). These results are in agreement with results in yeast, in which Atg9 is one of the first Atg proteins to be recruited to the PAS (Suzuki et al., 2007). We next investigated whether mAtg9 was required for the correct recruitment of Atg proteins to the forming autophagosome, and, in particular, we looked at the recruitment of LC3 to WIPI2- or Atg16-positive structures. In control starved cells, ∼72 and 85% of WIPI2 and Atg16 spots, respectively, were also positive for LC3 (Figure 1, I and J). In cells depleted of mAtg9, although the total number of these structures was slightly reduced (Figure 1, D–H), the percentage of WIPI2 and Atg16 spots containing LC3 was unaltered (Figure 1J). This indicates that in the absence of mAtg9, these phagophores and autophagosome membranes were able to recruit Atg proteins upstream of LC3 lipidation and conjugation. Thus we hypothesized that mAtg9 may be delivering membrane and/or other components to speed up the process of autophagosome formation.


Fig. 1. Cavin-1 altered EV proteins and EV miR-148a with no effect on total cellular levels. (A) EVs and whole cell lysates (WCL) were collected from GFP-PC3 and cavin-1-PC3 cells and analysed by Western blot with the indicated antibodies. EV preparations were free from ER protein calnexin. Equal loading was shown by Coomassie staining. Images are representative of 3 biological replicates. Raw images of the western blots are available in figures in Supplementary file. (B) Cavin-1 alters the miRNA content of EVs. Real-time qPCR was performed on cDNA synthesized from equal amounts of RNA extracted from GFP-PC3-EVs and cavin-1-PC3-EVs or total cell lysates. Bar graphs show the mean relative expression±SEM of miR-148a was significantly decreased in cavin-1-PC3-EVs compared to GFP-PC3-EVs (p ≤ 0.02, n = 3) but expression of miR-148a was unaffected in cells. (C) In contrast, miR-125a was not significantly different between GFP-PC3-EVs and cavin-1-PC3-EVs (n = 3).

To confirm the cavin-1 effect on the EV-mediated release of these proteins, and to determine whether cavin-1 affected total protein expression or only the release as we previously reported for cytokines (18), we compared the levels of selected proteins in the WCL and EVs from GFP-PC3 and cavin-1-PC3 cells by immunoblotting. As shown in Fig. 1A, caveolin-1 WCL and EV levels were not altered by cavin-1 expression. Cavin-1 itself was not detected in EVs indicating its effects are indirect (Fig. 1A). Calnexin was used as an intracellular membrane marker, which was not detected in EVs (Fig. 1A). We also probed for cofilin and CD63 as known exosome cargo and marker protein, respectively. Interestingly, both cofilin and CD63 were increased in the EVs from cavin-1-PC3 cells compared to GFP-PC3 cells (Fig. 1A). Additionally, CD63 total cellular levels were also elevated in cavin-1-PC3 cells (Fig. 1A), suggesting potential increase in multi-vesicular body formation. In contrast, cavin-1 expression reduced the abundance of 4F2 and EphA2 in EVs without affecting total cellular levels (WCL, Fig. 1A) similar to our previous report for cytokines and proteases (18). This suggests that cavin-1 did not cause a decrease of key bone remodelling molecules on the cellular level but instead altered their secretion through EVs. Recent studies show that EVs mediate the transfer of functional microRNA (miRNA) (4,6), and miRNA changes are implicated in osteolytic bone metastasis (33). Therefore, we were interested to investigate if, in addition to proteins, cavin-1 expression altered miRNA secretion via EVs. Based on a review of recent literature, we chose to measure 2 miRNA species, miRNA-148a and miR-125a. Overexpression of miR-148a was recently reported to induce osteoclastogenesis and the reverse was observed when miR-148a was inhibited (34). In contrast, miR-125a was reported to be decreased during osteoclastogenesis (34). qPCR performed revealed that PC3-EV miRNA-148a levels were significantly reduced by 8.68±0.01-fold (p ≤ 0.02) upon cavin-1 expression without altering total cellular miR-148a levels (Fig. 1B). In contrast, no significant difference was found for miR-125a in either EVs or total cell (Fig. 1C). These results suggest that cavin-1 expression can selectively modulate EV-mediated transfer of miRNA. A reduction in 4F2 EV levels upon cavin-1 expression is consistent with our observation that cavin-1-PC3-EVs caused TRAP expression but did not lead to formation of multi-nucleated cells (Fig. 1A). Our data confirms previous reports that PC3 conditioned media can induce osteoclastogenesis from murine RAW264.7 cells (25–27), and further show that PC3-EVs alone are sufficient to enhance osteoclastogenesis in vitro (Fig. 1). However, cavin-1-PC3-EVs were unable to induce full osteoclastogenesis without the soluble secretome (Fig. 1B), indicating the requirement of SF not released via EVs. We also report for the first time that PC3 conditioned media and EVs increase proliferation of primary hOBs (Fig. 1D), although the magnitude of PC3-EV osteoblastic activity was much lower than its osteoclastic activity. In contrast to our data, PC3 conditioned media has been reported to suppress primary murine osteoblast proliferation (64,65). The discrepancy may be due to the use of human versus murine osteoblasts.


Detection of heroin and fentanyl using aptamer-based dye-displacement assays. (A) Secondary structures of HM20 and F17 based on NUPACK predictions. (B) Scheme of the dye-displacement assay using HM20, F17, and MTC (purple bars indicate monomeric/dimeric forms of the dye; blue bars indicate J-aggregates). (C) Photographs taken 5 min after addition of various concentrations of heroin to solutions of MTC alone (−HM20) or HM20-MTC complexes (+HM20). (D) Signal gain and cross-reactivity of the displacement assay to various interferents relative to the response to heroin. Photographs of samples were taken 5 min after adding mixtures of 25 μM heroin and 50 μM interferent to HM20-MTC complexes. (E) Detection of heroin in binary mixtures of 25 μM heroin (∼33%) with 50 μM interferent (∼67%). (F) Absorbance spectra of HM20-MTC complexes in the presence of a 33:8:59 mixture of heroin, fentanyl, and an interferent (caffeine, lidocaine, lactose, or mannitol) as well as each interferent alone. These spectra overlap for all four drug mixtures and all four interferent samples. (G) Photographs of the dye-displacement assays analyzed in panel F (top) as well as equivalent assays performed with the fentanyl-specific aptamer F17 (bottom). (H) Photographs of Marquis test results for interferent alone, binary mixtures containing heroin and interferent (33:67) or fentanyl and interferent (8:92), or ternary mixtures containing heroin, fentanyl, and interferent (33:8:59). Error bars represent the standard deviations of three independent experiments.

Notably, the presence of heroin can be clearly identified at concentrations as low as 16 μM with the naked eye via a purple-to-blue-green color change (Figure 4C), which is sufficiently sensitive for detecting opioids in seized drug samples. We then determined the specificity of the assay against a variety of ligands commonly found in illicit drug samples. Since the purity of heroin is often 30–50%, (41,42) we assessed the specificity of our assay by challenging aptamer-dye complexes with 25 μM heroin or 50 μM interferent. The assay did not have any meaningful cross-reactivity to any of these interferents, and only the heroin sample induced a purple-to-blue-green color change that was visible to the naked eye (Figure 4D). We then challenged the assay with binary mixtures of 25 μM heroin (33%) and 50 μM interferent (67%) and did not observe that any of these interferents altered the signal produced by heroin, demonstrating the excellent specificity of the assay (Figure 4E).


Figure 2. Confirmation of SYNJ2 interaction with select binding partners. (A) SYNJ2 interacts with Lyn. HEK293 cells were transiently co-transfected with expression plasmids for Lyn or SYNJ2 or transfected with a SYNJ2 plasmid alone. Cells were lysed 24 h post-transfection. Cell lysates were incubated with an anti-Lyn antibody, followed by protein A-agarose. Expression of Lyn and SYNJ2 in lysates and immunoprecipitated SYNJ2 were visualized by western blotting. (B) SYNJ2 interacts with Fyn. The same methodology was used as in (A), except that Lyn was replaced by Fyn. (C) SYNJ2 interacts with intersectin. HEK293 cells were transiently transfected with the indicated combinations of expression plasmids for FLAG-tagged intersectin-1 (ITSN) or SYNJ2. Cells were lysed 24 h post-transfection and lysates were incubated with an anti-FLAG antibody, followed by protein G-agarose. Expression of intersectin and SYNJ2 in lysates and immunoprecipitated SYNJ2 were visualized by western blotting. (D and E) SYNJ2 interacts with cortactin. (D) Coimmunoprecipitation experiment. HEK293 cells were transiently co-transfected with cortactin and SYNJ2 expression plasmids. Cells were lysed 24 h posttransfection and lysates were incubated either with an anti-cortactin antibody followed by protein G-sepharose or with control IgG followed by protein G-sepharose. Expression of cortactin and SYNJ2 in lysates and immunoprecipitated SYNJ2 were visualized by western blotting. Immunoglobulin heavy chain (IgG-HC) was visualized by anti-mouse HRP. (E) GST-pulldown experiment. SNB19 cell lysates were incubated with immobilized GST or GST-SJ2 (GST-SYNJ2-C). Precipitated cortactin was visualized by western blotting using an anti-cortactin antibody (upper panel) and bound GST proteins were visualized by Ponceau staining (lower panel). Data shown are representative of at least 2 experiments.

To confirm the interaction of SYNJ2 with Src family kinases in a cellular setting, we performed co-expression of full length SYNJ2 together with full length Lyn or Fyn in HEK293 cells. Immunoprecipitation of Lyn or Fyn from lysates of co-expressing cells, but not from cells in which SYNJ2 was overexpressed on its own, showed specific interaction between SYNJ2 and these kinases (Fig. 2A and B). We obtained opposite results for the interaction of SYNJ2 with intersectin (Fig. 2C). We also confirmed the interaction between SYNJ2 and cortactin, using two different approaches. First, endogenous cortactin could be precipitated from SNB19 glioblastoma cell lysates with an immobilized SYNJ2 C-terminal domain GST-fusion protein, but not GST itself (Fig. 2D). Second, SYNJ2 non-immunoprecipitated with cortactin from HEK293 cells co-expressing full length versions of both SYNJ2 and cortactin (Fig. 2E). We also identified intersectin and cortactin using mass spectrometry, as endogenous proteins associated with SYNJ2 that was overexpressed in HEK293 cells (data not shown).


Figure 1. Epigenetic marks that are influenced by EBNA3A and EBNA3C during the repression of BIM by EBV. (A) The levels of BIM protein (whose predominant isoform is the extra-large BIMEL) were assayed by western blotting protein extracts from BL31 cell lines. BIM levels in uninfected BL31 or BL31 infected with the wild-type (B95.8-BAC) EBV (WT), or viruses with each individual EBNA3 knocked out (KO) and the revertant viruses (REV) are shown. In all the EBNA3A and EBNA3C knockouts BIM levels are similar to the levels observed in the uninfected cells. The expression of g-tubulin is shown as a control for the amount of protein loaded. (B) Schematic of the BIM locus showing the approximate locations of the CpG island, TSS, BIM start codon and the PCR primer pairs used in this study (A–J). (C) Methylated DNA precipitation was used to assess whether DNA methylation at the BIM promoter was responsible for repression of BIM expression. The histogram bars represent fold enrichment for co-precipitated DNA relative to input DNA, as determined by Q-PCR. The height of the bars represents the abundance of the epigenetic mark at the locus assessed by each primer set. Uninfected BL31 cells, BL31 infected with WT EBV, or EBNA3 knockouts (KO) or the revertant viruses were tested. DNA from peripheral blood mononuclear cells (PBMC) and in vitro methylated PBMC DNA were used as negative and positive controls respectively. The error bars represent the standard deviation from triplicate Q-PCR assays. (D) The same panel of BL31 cell

In order to investigate the roles of EBNA3A and EBNA3C in the regulation of cellular gene expression, a panel of cell lines based on the EBV-negative BL-derived line BL31 were established and characterized (50,53). Here a selection of these were subjected to western blot analysis to confirm our previous observation that BL31 expressed high levels of BIM protein and that this was substantially reduced in BL31 latently infected in vitro with wild-type (WT) EBV-BAC-derived virus (Figure 1A). This analysis also confirmed that in BL31 infected with recombinant viruses from which EBNA3A or EBNA3C genes were deleted, BIM expression was not repressed and remained at a similar level to that in BL31 (Figure 1A). Revertant (REV) viruses reconstituted with the genes that had previously been deleted behaved—as expected—like the WT EBV and repressed BIM expression. Flanking the BIM transcription start site (TSS) is a remarkably large CGI that extends for 13 kb (http://genome.ucsc.edu/cgi-bin/hgGateway; Figure 1B). We previously showed that in various EBV-positive B cell lines and primary BL samples significant DNA methylation across this region could be detected, but this was absent in EBV-negative equivalents (52). However, analysis of several BL cell lines, including BL31, that were newly infected with EBV and also recently established LCLs revealed that although BIM transcription was repressed, there was little or no CpG methylation but rather the epigenetic repressive histone modification H3K27me3 was evident. Because of the apparently progressive nature of DNA methylation across the BIM CGI, a panel of low passage BL31-derived cell lines was subjected to methylated-DNA precipitation (MeDP) coupled with quantitative real-time PCR analysis using primer-pairs (A–F) as indicated (Figure 1B). Primary peripheral blood mononuclear cells and in vitro methylated genomic DNA were used as negative and positive controls respectively. All the lines analysed, irrespective of their EBV status, showed no detectable DNA methylation, as judged by MeDP, across a region extending from 4 kb upstream of the BIM TSS to the start codon—indicating that here histone modification was the most likely mode of repression (Figure 1C). Having shown in these EBV-infected BL31 cells that CpG methylation was not established at BIM, ChIP with an antibody directed against H3K27me3 and quantitative PCR analyses across the CGI was performed using the same primer-pairs as for MeDP. This showed that repression correlated with deposition of the H3K27me3 mark and expression of EBNA3A and EBNA3C (in the WT-, EBNA3BKO- and the revertant-infected cell lines in Figure 1D). In contrast, when either EBNA3A or EBNA3C were deleted from EBV (EBNA3AKO and EBNA3CKO) BIM expression remained high and, significantly, the amount of H3K27me3 detected across the BIM locus was below the background in uninfected BL31 cells. Occupancy of H3K27me3 on promoters of genes encoding Myoglobin and GAPDH were analysed in a similar manner as positive and negative controls respectively (Supplementary Figure S1A). ChIP for total histone H3 at BIM, Myoglobin and GAPDH revealed little difference between the cell lines used (Figure 1E and Supplementary Figure S1B). This indicates that any changes in the distribution of covalent histone modifications detected are not due to alterations in nucleosome density. The effects on H3K27me3 distribution at the BIM promoter induced by EBNA3A and EBNA3C are specific, since the overall levels of H3K27me3 were unaffected by EBV, EBNA3A or EBNA3C (Figure 1F). The protein complex responsible for establishing the H3K27me3 mark is PRC2. In mammals this is generally composed of a catalytic core made up of the histone methyltransferase EZH2 and subunits SUZ12, EED and RbAp46/48 (see ‘Introduction’ section). ChIPs for SUZ12, EZH2 and RbAp48 were performed on the panel of BL31 cell lines and analysed using the same primer-pairs as in Figure 1. Figure 2. PRC2 occupancy across the BIM promoter region. The presence and abundance of PRC2 proteins was assayed by ChIP analysis of a BL31 panel of cells, as indicated. Presentation is the same as for ChIP analysis in Figure 1. The region of amplification for each primer set (A–F) can be seen in Figure 1B. The error bars represent the standard deviation from triplicate PCR assays. ChIP was performed using an antibody specific for (A) PRC2 core subunit SUZ12, (B) PRC2 catalytic subunit EZH2, (C) core subunit RbAp48, (D) ancillary factor JARID2.


Figure 1. C-trapping of FACT. (A) Immunoblotting of soluble protein extracts and chromatin pellets of HT1080 cells treated with CBL0137 for 1 h, probed with the indicated antibodies. (B) Immunofluorescence staining of HT1080 cells with antibodies to SSRP1. (C) Fluorescent imaging of two live cells expressing GFP-tagged SSRP1 before and after treatment with CBL0137. Top row––HT1080 cell in interphase, 3 μM CBL0137, bottom row––DLD1 cell in mitosis, 5 μM CBL0137. (D) Fluorescent imaging of two live HT1080 cells, interphase and mitotic, expressing GFP-tagged SPT16 before and after treatment with CBL0137. Bars are 10 μm.

RESULTS FACT binds to unfolding chromatin in CBL0137-treated cells Using biochemical fractionation and fluorescent microscopy we previously demonstrated that curaxin treatment causes a rapid transition of FACT from the nucleoplasm to a state of tight association with chromatin (13). We named this phenomenon ‘chromatin trapping’ of FACT or c-trapping. To investigate the mechanism of c-trapping, we first examined the dose and time dependence as well as specificity of this phenomenon. To do this we used two methods, (i) immunoblotting of soluble, representing nucleoplasm, and pelleted, representing chromatin, fractions of total cell lysates ((Figure 1A) see details in Materials and Methods), and (ii) ectopic expression of GFP or mCherry tagged SSRP1 and SPT16 subunits of FACT, which undergo c-trapping in CBL0137-treated cells similarly to endogenous FACT subunits (Figure 1B–D and Supplemental Figure S1). C-trapping was observed in all tested cell lines (>20 cell lines, data not shown). Importantly, c-trapping and toxicity of CBL0137 occur at the same concentrations (∼0.3–0.5 μM depending on cell line, Figures 1A, 2 and (13)), however, c-trapping is observed within minutes after start of treatment (Figure 1C and D), while cell death after 48 h (13). Thus, these two effects of CBL0137 are probably connected, but c-trapping cannot be viewed as a sign of cell death. In untreated cells, FACT is diffusely spread throughout the nucleoplasm with some accumulation in nucleoli ((13,42) and Figure 2A and B). Changes in the distribution of FACT subunits begin 30–60 s after the addition of CBL0137 to cell culture medium (Figure 1C and D). In the presence of 0.3 μM CBL0137, both subunits become associated with thin fibers of chromatin, which is illustrated by their overlap with fluorescently labeled histone H2B (Figure 2A and B). At higher concentrations of CBL0137 (1–3 μM), FACT subunits are still associated with chromatin, however, the chromatin itself remains such that it appears as thicker fibers with bead like structures contouring nucleoli (Figure 2A and B). At concentrations of ≥5 μM CBL0137, the cellular distribution of SSRP1, SPT16, and H2B is dramatically changed. SSRP1 appears as thin fibers situated along the nuclear periphery and absent in and near nucleoli (Figure 2A). SPT16 becomes more diffuse (Figure 2B). Histone H2B loses its fiber-like appearance and accumulates in nucleoli (Figure 2A and B). Full transition takes 20–30 min (Figure 3A). Behavior similar to H2B was observed with fluorescently tagged histones H3 and H4 (Supplemental Figure S2). Similar to our results presented here, Musinova et al previously demonstrated that histone H2B, which is not incorporated into chromatin, accumulates in nucleoli due to the presence of a nucleoli localization signal within its nuclear localization signal (43). Therefore, we hypothesized that CBL0137 treatment leads to accumulation of histones in nucleoli due to their liberation from chromatin, which is consistent with the loss of visible chromatin fibers (Figure 2A and B, 10 μM). C-trapping not only occurs in interphase cells but also in mitotic cells. As shown in Figure 1 C and D, diffuse GFP-SSRP1 or GFP-SPT16 signals in mitotic cells became overlapped with mitotic chromosomes within a few minutes following the addition of CBL0137, which is indicative of a rapid CBL0137-induced binding of FACT to chromatin (Figure 1C and D). However, later on and in the presence of ≥3 μM of CBL0137, histone H2B was lost from chromosomes and appeared more like a diffuse cloud (Figure 3B). FACT subunits followed histones into the cloud-like shape, however, some SSRP1 was still associated with loops in the periphery, which were most likely leftover of decondensed and disassembled chromatin (Figure 3B).


Figure 2. XRK3F2 plus bortezomib combination activates multiple death pathways and overcomes apoptosis resistance in mul-tiple myeloma. MM.1S cells treated with XRK3F2 (5 μM), bortezomib (Btz) (3 nM), or combined XRK3F2-Btz (5 μM/3 nM) for 24 hours in the presence or absence of autophagy inhibitor BafilomycinA1 (Baf, 40 nM) (A, D), Pan caspase OPH inhibitor Q-VD (QVD, 20 μM) (B, E), or caspase-3 inhibitor Z-DEVD-FMK (Z-DEV, 20 μM) (C, F). Cell viability was evaluated using alamarBlue assays and is reported as percent versus dimethyl sulfoxide (DMSO) vehicle control (A-C). Analysis of autophagic flux and apoptosis were assessed by immunoblotting by LC3I-LC3II conversion (D) and for cleavage of caspase 8 and 3 (E, F). Data are presented as bars, means ± standard deviation (N=4-6/group). *P<0.05 versusvehicle, ^P<0.05 versus XRK3F2 or Btz alone and ^P<0.05 versus control versus Baf/QVD/ZDEV culture by two-way ANOVA with post hoc Bonferroni’s correction.

Btz decrease p62 levels by inducing de novo p62 expressionand preventing its degradation.In agreement with the pre-vious studies,:: we found that Btz increased levels of p62mRNA 8-fold and protein (Figure 2D; Online SupplementaryFigure S2D)independent of autophagy since changes inLC3l-LC3ll conversion were not found in Btz-treated cells.Btz-induced p62 mRNA expression was reduced by XRK3F2Ligands binding to p62-2Z stimulate p62 oligomerizationand autophagy.34 XRK3F2 alone or in combination with Btz decreased LC3l-LC3ll conversion in MM1S,indicating induction ofautophagy (Figure 2D, left panel). Pretreatment of MMcells with bafilomycin A1 (Baf), which disrupts autophagicflux, further increased LC3ll/LC3l ratio and p62 protein levels compared to XRK3F2 alone or XRK3F2-Btz combination(Figure 2D, right panel). Furthermore, autophagy blockadeby Baf treatment further amplified the negative effects oXRK3F2 and Btz on MM cell viability (Figure 2A, Online sup.plementary Figure S4A), indicating that the autophagy-induced by targeting p62-ZZ domain in MM cells was not thepathway responsible for MM cell death.We previously reported that high doses ofXRK3F2 triggeredcaspase 3 cleavage in MM.1S cells.7 Here, we found that inMM.1S cells, co-administration of 5uM XRK3F2 and 3 nMBtz strongly activated caspase-8 and caspase-3 (Figure 2Eleft panel).In contrast, we only detected partial activationof caspases by Btz alone and no activation by XRK3F2 atthe concentration used. To test if the increased cell deathseen with the combination therapy was due to apoptosiswe used the pan-caspase inhibitor O-VD-OPh (OvD) and thecaspase-3 inhibitor Z-DEVD-FMK (Z-DEV). OVD and Z-DEVcompletely blocked Btz-induced cleavage of caspase-8 and3 (Figure 2E, F, right panels). In addition, QvD or Z-DEV fullyprevented the decrease in cell viabilityinduced by Btz (Figure2B,C;Online Supplementary Figure S4B, C)but enhancedthe anti-MM effects of XRK3F2. QVD and Z-DEV only partially blocked caspase 8 and 3 cleavage by XRK3F2-Btz andpartially reduced the effects ofthe combination on MM celviability. These findings suggest that XRK3F2-Btz combinationinhibits MM viability by activating both caspase-dependentand independent-cell death pathways.


Figure 5 (A) Immunofluorescence staining of GIG on expression of ABCB4 and BSEP in ANIT-treated rats’ liver tissues. (B) The relative percentage of ABCB4 expression. (C) The relative percentage of BSEP expression. Red represents ABCB4, and green represents BSEP. **P<0.01, ****P<0.0001.

Effect of GIG on ABCB4 and BSEP Protein Expression in ANIT-Treated Rats. Compared with the normal control group, there was almost the high expression of ABCB4 and BSEP in the model group’s liver tissues (Figure 5). Compared with the model group, the expression of BSEP and ABCB4 decreased significantly in the GIG middle and high dose groups, and the changes of BSEP and ABCB4 expression showed a negative correlation with the dose of GIG (Figure 5). The expression trends of ABCB4 and BSEP in each group can be seen in Figures 5B and C.


Figure 1 The expression of miR-29c-3p and LIF in UC tissues and IECs. (A) The mRNA levels of LIF in UC and normal tissues. (B) Representative Western blots and densitometric quantitative analysis of LIF protein levels in UC and normal tissues. (C) The mRNA levels of LIF in primary IECs of UC and normal colon. (D) Representative Western blots and densitometric quantitative analysis of LIF protein levels in IECs of UC and normal colon. (E) The expression of miR-29c-3p in UC and normal tissues. (F) The expression of miR-29c-3p in primary IECs of UC and normal colon. (G) Pearson’s correlation analysis of the expression levels of miR-29c-3p and LIF protein. (H) Pearson’s correlation analysis of the expression levels of miR-29c-3p and LIF mRNA. **P<0.01 versus normal group.

ResultsThe Expression of miR-29c-3p and LIF in UC Tissues and Primary IECsColonic biopsies were obtained during endoscopic examinations. We first assessed LIF expression in colon tissues using qRT-PCR and found it to be upregulated in UC patients compared with healthy controls (Figure 1A). The weakened LIF protein expression was then verified by Western blotting (Figure 1B). To further explore cellular level of LIF, primary IECs were isolated and quantified for LIF expression. As expected, the same change trends were observed in IECs (Figure 1C and D). Next, we adopted five miRNA databases (TargetScan, miRDB, starBase, mirDIP and miRmap) to predict the upstream miRNA of LIF. As a result, miR-29c-3p was the only miRNA with the highest possibility to interact with LIF predicted simultaneously by all databases. Therefore, we explored miR-29c-3p expression by qRT-PCR. As illustrated in Figure 1E and F, miR-29c-3p was upregulated both in colonic tissues and IECs from UC patients relative to normal controls. Subsequently, as illustrated by Pearson correlation scatter plots (Figure 1G and H), miR-29c-3p and LIF protein expression in UC tissues were inversely correlated, whereas disparity was found between miR-29c-3p and LIF mRNA expression. Accordingly, we hypothesized that miR-29c-3p is involved in the post-transcriptional regulation of LIF expression.


R-Ras2/TC21 regulates the timely development of the pubertal mammary gland. (A) qRT-PCR analysis of total RNA obtained from mammary glands extracted from the indicated stages. Prepubertal, pubertal, and adult virgins correspond to samples obtained from 3-, 8-, and 14-wk-old RRas2+/+ female mice, respectively. Early and late pregnancy correspond to female mice within weeks 1 and 2.5 of pregnancy, respectively. The early and late lactation samples were taken 1 and 2 wk after labor, respectively. The involution phase samples were taken 3.5 d after offspring weaning. Data are given relative to the expression levels obtained in virgin adult state (which was given an arbitrary value of 1). All values were normalized taking into consideration the expression levels of the housekeeping Gapdh mRNA in each sample. *p ≤ 0.05; ***p ≤ 0.001 (n = 3). (B) Representative images of carmine alum–stained mammary fat pads obtained from female mice of the indicated ages (top) and genotypes (left). Scale bars, 500 μm. One of the inguinal lymph nodes is indicated by a white asterisk. (C–F) Quantification of the total area (C), maximal length (D), number of TEBs (E), and branching points (F) of mammary glands obtained from animals of the indicated genotypes and ages. *p ≤ 0.05, **p ≤ 0.01, ***p ≤ 0.001 when compared with values obtained in the appropriate wild-type control (n = 5–10 animals per genotype and developmental stage).

We found no major differences in any of those parameters in the mammary glands of 4-wk-old R-Ras2/TC21-deficient and control mice (Figure 3B, 3C, 3E, and 3F). However, when we analyzed 5-wk-old mice, we observed that the mammary glands extracted from RRas2−/− mice were significantly smaller than those obtained from control animals (Figure 3B–3D). Those glands also displayed significantly higher numbers of TEBs (Figure 3B and 3E) and branching points (Figure 3B and 3F). Most of those differences disappeared in the mammary glands of 12-wk-old mice (Figure 3B–3D). However, unlike the case of control mice, we found that ∼50% of the mammary glands obtained from RRas2−/− mice still contained a insignificant number of TEBs (Figure 3E), further suggesting that the R-Ras2/TC21 deficiency induces a delay in the development of this gland. This phenotype was highly penetrant, since it was observed in mice of homogenized (C57BL/6J, Figure 3) and mixed (129Sv/J × C57BL/6J, Supplemental Figure S2) genetic backgrounds. In the latter strain, R-Ras2/TC21 became specifically upregulated during those two highly morphogenetic periods of the mammary gland (Figure 3A). As previously seen with RRas2−/− mice (Figure 3), this observation, coupled with the upregulation of the RRas2 gene found in specific developmental stages of the gland (Figure 3A), supports a role for R-Ras2/TC21 in mammary gland maturation.


Tissue penetration of Humabodies with and without albumin in spheroids. Spheroids show cell nuclei (blue) and fluorescent drug (green). Incubations included 20 nM J591 antibody, monovalent-HLE Humabody, PSMA-CD137 with MSA binding HLE (mHLE) incubated with and without MSA, PSMA-CD137 with HSA binding HLE (hHLE) incubated with and without HSA (A). Euclidean distance mapping shows the fluorescent intensity versus distance from the spheroid edge in μm (B-D), indicating that specific albumin binding is not impacting tissue penetration to the spheroid center. The MSA binders (Fig. 2B) had lower maximum signal with the presence of MSA (p=0.002), but HSA binders did not have differences in the maximum signal (p = 0.85). *, p < 0.05.

Given the importance of tissue penetration, we next investigated the impact of albumin binding on diffusion. In theory, the diffusion of a Humabody-albumin complex (~110 kDa) would be slower than the Humabody alone (~45 kDa), which could reduce tissue penetration. The impact of albumin binding on tissue distribution was therefore tested in DU145T spheroids. Humabodies with HLE domains were incubated with and without their corresponding albumin type (HSA or MSA), which have 8 nM and 50 nM affinity (Kd), respectively. In both cases, the addition of albumin did not result in changes in tissue penetration compared to Humabody alone (Fig. 3A). Despite the concentration of albumin well above the Kd (~ 200 µM), where most of the Humabody could be in complex with albumin, the presence of albumin had a negligible effect on tissue penetration. These findings were confirmed by quantitative image analysis to map the fluorescence distribution as a function of distance from the spheroid edge (Fig. 3B–D). The fluorescent signal in the center of the spheroids did not show any statistically significant differences with and without albumin. In contrast, an equal molar amounts of J591 antibody (~150 kDa) resulted in homogeneous distribution, which is typical for large biologics. A control monovalent-HLE (that had no CD137 binding domain) with the lowest molecular weight (~30 kDa) distributed the most evenly.


Figure 7 (A and B) The BAL fluids and lungs were harvested 24 h after last rmIL-33 challenge to measure the protein expression of IL-1α, IL-5, IL-13, CCL11, CCL17, CCL22 and CCL24. (C) Cell differentials from BAL fluids harvested 24 h after the last rmIL-33 challenge. The results are shown as mean ± SEM of four mice in PBS-challenged groups and 11 mice in rmIL-33-challenged groups. *p < 0.05.

We assessed cell differentials and cytokine and chemokine expression levels in the BAL fluid and lung homogenates 24 h after rmIL-33 challenge for four consecutive days (Figure 6A). IL-1α protein expression was not changed by exogenous rmIL-33 challenge in lung homogenate (Figure 7A). This result is consistent with a previous report that IL-33 neutralisation by soluble ST2 administration had no effect in house dust mite extract-induced lung IL-1α protein expression in a mouse model. On the other hand, protein expression of IL-5 and IL-13 in both BAL fluids and lung homogenates was significantly increased in rmIL-33-challenged mice compared with vehicle-challenged mice (p < 0.05); and TSA treatment significantly increased the rmIL-33-induced IL-5 and IL-13 protein expression compared with TSA–vehicle treatment (p < 0.05) (Figure 7B). CCL11 and CCL24 were also increased in lung homogenates from rmIL-33-challenged mice compared with vehicle-challenged mice (p < 0.05); and TSA treatment significantly decreased the rmIL-33-induced CCL11 and CCL24 expression in lung homogenates compared with treatment with the vehicle for TSA (p < 0.05) (Figure 7B). rmIL-33 challenge significantly induced CCL17 and CCL22 expression in lung homogenates (p < 0.05), but TSA treatment did not decrease these chemokines compared with treatment with the vehicle for TSA (Figure 7B). Further, there was a statistically significant decrease in the number of total BAL cells and eosinophils in rmIL-33-challenged mice treated with TSA compared with rmIL-33-challenged mice treated with vehicle (p < 0.05) (Figure 7C). These results suggest that TSA treatment suppresses IL-33-induced Th2 cytokine and eosinophil chemotactic chemokine expression, and subsequent eosinophil accumulation in the airway.


Figure 4. Binding of a2NTD and intracellular cytokine production in monocyte subpopulations. (A–D) Peripheral blood mononuclear cells (PBMCs) were incubated with 10 μg/mL a2NTD conjugated to Alexa Fluor 488 (a2NTD-AF488) for 1 h and then surface stained with anti-CD14-Pacific Blue and anti-CD16-PE antibodies. (A) Percentage of monocyte populations positive for a2NTD-AF488 (means ± SEM, n = 10, # p < 0.005, CD14+CD16+ vs. CD14+CD16− or CD14+CD16+ cells). (B–D) PBMCs were kept in control conditions or stimulated with 10 ng/mL a2NTD for 18 h and sequentially stained for surface expression of CD14/CD16 and the intracellular expression of interleukin (IL)-1α: (B), IL-1β (C) and tumor necrosis factor α (TNFα) (D). The bar graph reports the percentage of a2NTD-positive cells (means ± SEM, n = 5, *p < 0.05, ** p < 0.001, compared with unstimulated cells of the same subpopulation).

Uptake of a2NTD by monocyte subsets. To determine if different monocyte subsets preferentially internalize a2NTD, monocytes were stained for the surface markers CD14 and CD16 after incubation with a2NTD. CD14++CD16− cells constitute the majority of all monocytes, while CD14++CD16+ and CD14+CD16++ represent two minor subpopulations.25 We were able to retrieve these three populations by flow cytometry (Table 1). Of note, nearly 80% of CD14++CD16+ monocytes were positive for a2NTD endocytosis, as compared with approximately 90% of CD14++CD16− monocytes. The CD14+CD16++ population showed a a2NTD uptake profile similar to that of CD14++CD16− cells (Figure 4A). a2NTD stimulates IL-1α and IL-1β production. It has previously been shown that a2NTD stimulates monocytes to produce the pro-inflammatory cytokines IL-1α and IL-1β.17,18 We examined whether a particular subset of monocytes is responsible for the production of inflammatory cytokines following a2NTD stimulation. To this aim, monocytes were incubated with a2NTD and then stained for surface CD14 and CD16 expression, followed by staining for the intracellular cytokines IL-1α, IL-1β and TNF-α. Only CD14++CD16+ monocytes showed a statistically significant increase in IL-1α production when compared with cells maintained in control conditions (Figure 4B). Both CD14++CD16− and CD14++CD16+ monocytes produced significantly higher amounts of IL-1β (Figure 4C). There was no significant difference in the production of TNFα by cells treated with a2NTD and control cells (Figure 4D). Of note, the CD14++CD16+ subpopulation invariably produced higher amounts of IL-1α, IL-1β and TNFα than the other two subpopulations. In addition, the intracellular levels of IL-1α and IL-1β were greater than those of TNFα. This finding is consistent with our previous data demonstrating that monocytes produce more IL-1 than TNF.16 LPS contamination of the recombinant peptide is always a concern when investigating inflammatory cytokines. A multiexperimental approach was used to investigate the presence of LPS contamination, including the Limulus assay and polymyxin B neutralization tests. The results (which have previously been published) demonstrated negligible levels of LPS and that cytokine secretion could not be inhibited by pre-treatment with polymyxin B.16


Reduced GRHL2 level triggers expression of EDC proteins in differentiating keratinocytes. (a) Rapidly proliferating NHEK were serially subcultured until senescence, and western blotting was performed at varying PD levels for GRHL2, IVL, Jmjd3, and p16INK4A.(b) NHEK were exposed to 1.5mM Caþþfor up to 3 days and collected for western blotting for GRHL2, Jmjd3, IVL, and GRHL3. (c) HaCaT cells were infected with LV-EGFP or LV-GRHL2i to knock down endogenous GRHL2. The cells were then exposed to 2.0mM Caþþ for 3 days and harvested for western blotting for GRHL2, Jmjd3, IVL, FLG, KRT1, GRHL1, GRHL3, and Jmjd3. Arrow indicates the expected size of GRHL3 (B 60kDa). For all experiments, GAPDH (glyceraldehyde 3-phosphate dehydrogenase) was used as a loading control

To understand the mechanism by which GRHL2 inhibits EDC gene expression and keratinocyte differentiation, we explored the functional relationship between GRHL2 and epigenetic gene regulation by Jmjd3. In NHEK with increasing PDs, GRHL2 levels progressively decreased while the levels of IVL and p16INK4A decreased (Figure 5a). Caþþ-induced terminal differentiation in NHEK led to induction of IVL and decreased GRHL2 expression (Figure 5b). In both serially subcultured cells and those exposed to Caþþ, changes in Jmjd3 level were remote. In another experiment, expression of endogenous GRHL2 was knocked down in HaCaT cells by lentiviral vector expressing shRNA against GRHL2 (LV-GRHL2i). HaCaT cells were used herein because these cells express high level of endogenous GRHL2 and undergo normal keratinocyte differentiation when exposed to physiological Caþþ level (Figure 4b). As a control, HaCaT cells were infected with an empty viral vector (LV-EGFP). GRHL2 knockdown in HaCaT led to induction of IVL, FLG, and KRT1 even in the absence of Caþþ treatment to the levels similar to Caþþ-exposed control cells (Figure 5c).


Figure2 Roles of ALK5 receptor and ROS in TGF-b-induced apoptosis in HUVECs. (a) Effects of the ALK5 inhibitor SB431542 (2mM) on TGF-b (10ng/ml for 24h)induced caspase 3/7 activation. (b) Effects of SB431542 on TGF-b-induced caspase 3 cleavage. (c) Effects of SB431542 on TGF-b-induced DCm depression (reduced red fluorescence) as detected with JC-1 staining (representative images of three independent experiments). The red fluorescence specifically labeled the mitochondria. (d) Effects of TGF-b and SB431542 on intracellular ROS production detected with DCFH-DA (green fluorescence). (e) Effects of antioxidant compounds NAC (1mM) and EUK-134 (100mM) on TGF-b-induced caspase 3/7 activation. Data are mean±S.E.M. *Po0.05 versus control (Con), **Po0.05 versus TGF alone, one-way ANOVA (n¼3–5)

TGF-b-induced apoptosis is dependent on ALK5 and ROS. To examine whether the ALK5 receptor was involved in TGF-b-induced apoptosis, HUVECs were pre-treated with the ALK5 inhibitor SB431542. Treatment with SB431542 at 2mM enhanced the apoptotic response induced by TGF-b as indicated by caspase 3/7 activation and caspase 3 cleavage (Figures 2a and b). Moreover, we showed that SB431542 also prevented the collapse of mitochondrial membrane potential (DCm) in response to TGF-b as measured with JC-1 staining (Figure 2c and Supplementary Figure S1). Using 20,70-dichlorofluorescin diacetate (DCFH-DA) fluorescence, we found that TGF-b treatment enhanced intracellular ROSproduction in HUVECs, and this effect could be blocked by SB431542 (Figure 2d and Supplementary Figure S1). To elucidate the role of ROS in TGF-b-induced apoptosis, we preincubated the cells with antioxidant compounds N-acetyl-L-cysteine (NAC) or EUK-134. We showed that both NAC and EUK-134 abrogated TGF-b-induced caspase 3/7 activation (Figure 2e).


Morphological and growth characterization of hBMCs and hPDCs. (A) Growth curves of hBMCs (human bone marrow derived cells) and hPDCs (human periosteum-derived cells) over 2 weeks in culture. (B) Phase contrast images of hBMCs and hPDCs at one day after seeding at 5 k/cm2, 25 k/cm2, and 85 k/cm2 (scale bar 5 100 mm). White arrowheads indicate flattened morphology whereas black arrowheads indicate spindle-shaped morphology. (C) Growth kinetics of hBMCs (2 donors/2 experiments) and hPDCs (4 donors) seeded at different densities from passage 4. All error bars indicate standard error of the mean (n53 hBMC donors or 4 hPDC donors).

Independently of the cell culture vessel used, hPDCs proliferated faster than hBMCs and reached full confluence after 7 days (Fig. 1A) when initially seeded at low seeding density (5000 cells/cm2). In addition, the cell density of hPDCs obtained at confluence was consistently higher as compared to hBMCs, which suggested that hPDCs are smaller than hBMCs. Indeed, side-by-side comparison of adherent hBMC and hPDC populations under phase contrast microscopy suggested that hBMCs were more spread on tissue culture plastic with a broader cytoplasmic body than hPDCs (Fig. 1B, white arrows), whereas hPDCs displayed a more spindle-shaped phenotype (Fig. 1B, black arrows). To examine how initial seeding density impacted hBMC and hPDC populations, we empirically chose three seeding densities, 5000 (5 k/cm2), 25000 (25 k/cm2), and 85000 (85 k/cm2). When seeded at 5 k/cm2, both hPDCs and hBMCs attached efficiently as mostly isolated cells with few if any cell–cell contacts. At 25 k/cm2, both cell types arranged in a nearly confluent, but not densely packed, monolayer of cells in which cells remained well spread against the substrate but also displayed augmented cell–cell contact. At 85 k/cm2, both cell types appeared smaller, highly packed, and extended processes over each other (Fig. 1B), but a significant number of hBMCs did not attach after seeding at 85 k/cm2. Because differentiation experiments last one to two weeks, we quantified the extent to which cell proliferation affected cell density over time at the different seeding densities. hBMCs seeded at 5 k/cm2 or 25 k/cm2 reached a cell density of 35 k/cm2 (62.6 k/cm2) and 41 k/cm2 (64.1 k/cm2), respectively, whereas hPDCs at those seeding densities expanded to 55 k/cm2 (65k/cm2)and 64 k/cm2 (64.6 k/cm2). Cell density did not change over time for both cell types when seeded at 85 k/cm2, though fewer hBMCs than hPDCs attached at this density (Fig. 1C).


FIGURE 3. Co-i-cIDPRE evokes Ca2+ increase in human Jurkat cells after UV photolysis. A, Co-i-cIDPRE (200 µM) incited much weaker Ca2+ increases after UV photolysis than that by cIDPRE (200 µM) in Jurkat cells. Fluo-4-loaded cells were incubated in regular HBSS containing extracellular Ca2+ during the experiment. B, after Fluo-4-loaded Jurkat cells were incubated with Co-i-cIDPRE (120 µM) in regular HBSS containing extracellular Ca2+, washing away Co-i-cIDPRE in the medium did not have any effect on Ca2+ changes triggered by uncaging of Co-i-cIDPRE. C and D, in Fluo-4-loaded Jurkat cells, uncaging of Co-i-cIDPRE triggered Ca2+ increases in a dose-dependent manner in both the presence (C) and the absence of extracellular Ca2+ (D). E, in Fluo-4-loaded Jurkat cells, thapsigargin (10 µM) pretreatment promoted Co-i-cIDPRE (120 µM)-induced Ca2+ increases in the absence of extracellular Ca2+. Data quantifications of [Ca2+]i peak induced by drug treatment in A, C, D, and E were expressed as mean ± S.E., n = 30 – 40 cells, *, p ≤ 0.05. In A, C, D, and E, cells were all continuously incubated with Co-i-cIDPRE throughout the experiments.

It has been shown that cADPR targets the RyR, but which specific isoform it targets remains to be determined (1–3). Here we showed that both RyR2 and RyR3 are expressed in Jurkat cells. Individual knockdown of these isoforms inhibited cIDPRE- or cADPR-induced Ca2+ release to a similar extent, whereas double knockdown of both isoforms abolished cADPR-induced Ca2+ release in Jurkat cells (Fig. 4E). These data indicate that cADPR does not distinguish either isoform. However, it still remains to be determined whether cADPR binds directly to the RyRs or through some unknown proteins. Recently, the long sought after store-operated Ca2+ entry proteins were identified using a genome-wide RNAi screen by several groups (36–38). Co-i-cIDPRE could be a valuable tool to be used in a similar cell-based screening for cADPR-interacting proteins or regulators.


Figure 4 ‘Late’ Hsp27 phosphorylation induced by apigenin is modulated by p38-MAPK and PKCd. Lysates from THP-1 cells treated with 50 μM apigenin for 6 h and diluent or following 1 h pretreatment with 10 μM SB203580, 15 μM rottlerin, or both. Treatments marked with (-) denote addition of diluent. (a) Immunoblots with anti-phospho-Hsp27-pS78 and anti-total-Hsp27 antibodies. (b) Immunoblots with anti-phospho-Hsp27-pS82 and anti-total-Hsp27 antibodies. (c) Immunoblots with antiphospho-Hsp27-pS15 and anti-total-Hsp27 antibodies. Densitometry data normalized by the loading control are represented as mean±S.E.M. (N ¼ 5, *Pp0.05 and **Pp0.001, NS denotes non-statistical significance)

Next, we investigated the role of p38 and PKCσ on the ‘late’ Hsp27 phosphorylation using pharmacological inhibitors. THP-1 cells were pretreated with the p38 inhibitor SB203580, or the PKCσ inhibitor rottlerin, or both inhibitors, or with diluent for 1 h before the addition of 50 μM apigenin for 6 h. We found that apigenin-induced phosphorylation of S78 was significantly stimulated in the presence of the p38 inhibitor SB203580 (Figure 4a, lane 3 versus 2, **P<0.001), reaching levels similar to that in control cells (Figure 4a, lane 3 versus 1). In contrast, the PKCσ inhibitor rottlerin had no effect on the phosphorylation of S78 (Figure 4a, lane 4 versus 2, NS ¼ nonstatistically significant). Addition of both SB203580 and rottlerin showed decreased S78 phosphorylation to levels similarly observed in the presence of the p38 inhibitor alone (Figure 4a, lane 5 versus 3). However, we found no additive or synergistic effects when cells were treated with both inhibitors simultaneously (Figure 4a, lane 5 versus 2, **P<0.001). The phosphorylation of S82 induced by apigenin was significantly inhibited by pretreatment with the p38 inhibitor SB203580 (Figure 4b, lane 3 versus 2, *P<0.05). In contrast to the lack of effect observed on S78, the PKCσ inhibitor rottlerin, significantly inhibited apigenin-induced phosphorylationof S82 (Figure 4b, lane 4 versus 2, *P<0.05). Similar to S82 phosphorylation, we observed that the pretreatment with the p38 inhibitor SB203580 inhibited apigenin-induced phosphorylation of S15 (Figure 4c, lane 3 versus 2, *P<0.05). In addition, rottlerin decreased apigenin-induced phosphorylation of S15 (Figure 4c, lane 4 versus 2, *P<0.05). The addition of both inhibitors, however, showed no additive or synergistic effects (Figure 4c, lanes 5, 4 versus 2, **P<0.001).


Figure 3. Expression of histone mRNAs in mouse fibroblast cells and mouse liver. (A) Schematic of generalized S1 nuclease protection assay performed on mouse histone mRNA. A generalized histone mRNA is depicted indicating possible 5’ splice sites (5’ss), stem-loop cleavage sites (S-L cleavage) or poly(A) cleavage sites. S1 nuclease protection probes will protect a given mRNA to the point that cleavage or splicing occurs (indicated below). Note that due to the high degree of conservation in mice, all of the mRNAs for a specific histone class (i.e. H2B.1 or H2A.2) will be protected up to the stop codon. (B–F) Total cell RNA from NIH3T3 cells and mouse liver was fractionated into polyA+ and polyA− fractions. Total RNA (whole cell (WC)) and poly(A)+ and poly(A−) RNA were probed in an S1 nuclease protection assay. Five micrograms of total 3T3 cell RNA and 50 μg of liver RNA was analyzed using the S1 nuclease protection assay of a 32P labeled fragment at the indicated restriction enzyme site for (B) Hist2H2aa1, (C) Hist1H2bc, (D) Hist1H2ac, (E) Hist2H3c or (F) Hist2H4H. The probe used for histone H4H was labeled at the 5’ end of the indicated restriction site. For each experiment, below the autoradiogram, possible mRNA isoforms are indicated as well as protected fragments and sizes for S1 nuclease probes.

Validation of polyadenylated histone mRNA expression in adult mouse liver. To confirm the expression of the mouse histone mRNAs detected by high-throughput sequencing, we analysed histone gene expression in actively growing cultured fibroblasts (NIH3T3 cells) and liver tissue from 72-week-old mice. The liver is a relatively homogenous tissue with hepatocytes making up more than 70% of the cells. In healthy adult livers, the vast majority of these hepatocytes are quiescent, only re-entering the cell cycle after injury. Unlike the human histone genes, the coding regions of the mouse histone genes are highly conserved (>95%) at the nucleotide level (37). Because of the conserved ORF sequences, we can use an S1 nuclease mapping assay to simultaneously analyse both the mRNA expressed from a specific gene, as well as the mRNAs from all the other histone genes encoding that histone protein (26,42). A probe labelled in the coding region will protect all the histone mRNAs to a region close to the stop codon or initiation codon, as well as a band from the specific gene extending to the 3' end or the 5' end of the transcript, depending on whether one is probing the 3' or the 5' end of the mRNA (Figure 3A). This highly sensitive method allows for mapping of multiple isoforms of mRNA from a single sample. Further, since all of the radioactivity is at the end of the probe, the relative intensity of the bands gives the relative amounts of the transcripts. A generalized schematic is shown in Figure 3A. In each experiment below, the different protected fragments for a given probe are diagrammed below the autoradiogram. Total RNA from NIH3T3 cells and mouse liver was fractionated into polyA− and polyA+ RNA using oligo-d(T) cellulose. Orthologues of human histone HIST1H2BC (Hist1H2bc), HIST2H2AA3 (Hist2H2aa1) and HIST1H4H (Hist1H4h), which were expressed in human tissues, and the HIST2H3C (Hist2H3c) gene, adjacent to the Hist2H2AA3 gene, which was not expressed in human tissues, were analysed by S1 nuclease mapping, using 5 µg of total RNA from exponentially growing NIH3T3 cells and 50 µg of total liver RNA (Figure 3). In actively proliferating NIH3T3 cells, most of the 3' S1 probes map three protected fragments: one extending to the 3' end of the mRNA ending in the stem-loop and a second fragment, mapping near the stop codon, due to the high level of conservation between the open reading frames of all mouse histone genes of a given class (26). This band is indicated by the name of that class of histone protein (e.g. H3.1 or H2a.1). If the histone mRNA is spliced or polyadenylated then the probe is protected up to the point where the mRNA sequence diverges from the probe (Figure 3A; e.g. at the 5' splice site in Hist1H2bc, before the 3' end of the mRNA or at the polyA addition site in Hist2H2aa1). The S1 nuclease protection assays confirmed the low levels of histone mRNAs in liver and there were easily detectable amounts of the polyadenylated forms of Hist2H2aa1 (Figure 3B, lane 7) and Hist1H2bc (Figure 3C, lane 7) mRNAs. There were no polyadenylated mRNAs detected in the NIH3T3 cells. There is some non-polyadenylated histone mRNA detected in the liver that gives the same pattern of S1 nuclease protected fragments as the NIH3T3 cell mRNA. This likely arises from a low level of expression of the histone mRNAs expressed in growing cells. Note that the non-polyA mRNA expressed in mouse liver is greatly reduced compared to NIH3T3 cells, and we used 10 times more RNA (50 µg versus 5 µg) for assay of liver RNA, compared to neonatal mice or growing 3T3 cells. We assayed the same RNAs with probes for Hist1H2ac (Figure 3D), Hist2H3c (Figure 3F) and Hist1H4h (Figure 3E) genes. There was no polyadenylated mRNAs detected from these genes in mouse, consistent with the sequencing data.


Production of Th2- and Th1-type cytokines from wild-type and Runt-transgenic CD4⁺ T cells. (A) Naive CD4⁺ T cells were isolated from the spleens of wild-type and Runt–transgenic mice and stimulated with anti-CD3/anti-CD28 antibodies, and the culture supernatants were collected at the indicated times after stimulation. (B) The TCR-stimulated cells were cultured in the presence of IL-2 for 5 d, washed with fresh media, and restimulated via TCR. The culture supernatants were collected after 24 h. (C) The cells were cultured for four days in the Th2- or Th1-inducing condition. The cells were washed with fresh media and restimulated via TCR. The culture supernatants were collected after 24 h. (D) Same as C, but note the difference of scale used in C and here. The amounts of cytokines secreted into the supernatants were measured by ELISA and their averages and standard deviations are shown. Data are representative of four independent experiments.

Respectively, compared with the wild-type cells. A similar result as that shown in Figure 1A was obtained when the TCR-stimulated cells were incubated in the presence of IL-2 for 5 d, washed with fresh media, and restimulated via TCR (Figure 1B). Under this neutral culture condition where neither IL-4 nor IL-12 was added to media, the Runt-transgenic cells secreted several fold more amount of Th2-type cytokines and less amount of Th1-type cytokines than the wild-type cells. We next examined the cytokine production from the Runt-transgenic cells under culture conditions that favor either Th1 or Th2 differentiation. CD4⁺ T cells were stimulated with anti-CD3/anti-CD28 antibodies and incubated in the presence of either IL-4 and an anti-IFN-γ antibody (Th2 condition) or IL-12 and an anti-IL-4 antibody (Th1 condition) for 4 d. Cells were washed with fresh media and restimulated via TCR, and cytokine secretion was measured (Figure 1C). In the Th2 condition, the Runt-transgenic cells produced three times as much IL-4 as the wild-type cells. In contrast, in the Th1 condition, although IFN-γ production from the Runt-transgenic cells was reduced compared with the wild-type cells, IL-4 production from the Runt-transgenic cells did not reach the level of 10 to 100 ng/ml concentration. Thus, the Runt-expressing cells effectively differentiate into the Th2 lineage in a Th2- but not a Th1-favorable environment. It must be pointed here that, even in the Th1 condition, the Runt-transgenic cells actually produced 10 times as much IL-4 as the wild-type cells (Figure 1D). The absolute amount of IL-4 secreted from the Runt-transgenic cells was, however, 5 ng/ml in the Th1 condition (Figure 1D) instead of 120 ng/ml in the Th2 condition (Figure 1C). Therefore, the Runt-transgenic cells, though showing a Th2 tendency to some degree, could not have differentiated effectively into the Th2 lineage under the Th1 condition. Overexpression of Runx1 Prevents Naive Cells from Differentiating Into the Th2 Lineage. If reducing the effect of Runx1 with the Runt transgene induces Th2 differentiation, as shown in Figure 1, we wondered whether the artificial overexpression of Runx1 protein in naive T cells would cause a decrease in Th2 differentiation.


Figure 5. Reporter assay tests for the repression of NL4-3 by miR-223 and miR-29. For all transfections, the values represent the average of at least three independent experiments and the error bar represents the standard deviation. (a) Reporter assays show miR-223/29 repression is very weak when Nef serves as the 30 -UTR. (b) Reporter assays show miR-223/29 repression of Nef when it serves as a coding region. (c) Reporter assays  to measure the miR-223/29 repression using the 39 nt target sequence. Co-transfection of the reporter and miRNA genes in HEK-293 cells show  strong repression by miR-223 and strong repression by miR-29. Mutated seed binding site for miR-223 or miR-29 abrogated the repressions mediated by either miRNA. (d) Reporter assays to measure the miR-223/29 repression using a 56 nt target sequence. Co-transfection of the reporter and miRNA expression constructs in HEK-293 cells show strong repression mediated by miR-223 and reduced repression mediated by the miR-29 miRNAs.


Figure 9. Activity and selectivity of ASOs in the rodent CNS. (A) Allele selective knockdown of muHTT protein with ASO A30 in neuronal cells derived from cortical and striatal tissues of Hu97/18 mouse embryos under free-uptake conditions. (B–D) Hu97/18 mice (n = 4/group) were injected ICV with a single dose of 300 µg of ASO (B and D) or the indicated dose (C) in PBS. Mice were sacrificed after 28 days, the brain was harvested, and a 2 mm coronal slab from each hemisphere (R, L) was analyzed by allelic separation immunoblotting for muHTT and wtHTT protein, and the results were normalized to calnexin protein. (B) Optimized ASOs A25, A26, A30, and A31 show similar activity but worsened allele selectivity relative to control ASO A1. (C) Dose response for allele-selective knockdown of muHTT protein following ICV injection of ASO A30 in Hu97/18 mice. (D) Immunohistochemical staining for ASO (red) illustrates distribution to all parts of the brain following a single ICV bolus injection.

Improved allele selectivity in cell culture translates to brains of transgenic mice. We evaluated selected ASOs in Hu97/18 mice (45), a completely humanized mouse model of HD (Figure 9). This mouse model contains both the mutant human HTT allele, with the associated SNPs, and the wt human HTT allele. We first evaluated the ability of ASO A30 to reduce muHTT protein in primary neuronal cells derived from Hu97/18 mouse embryos in the absence of any transfection agent to facilitate delivery of the ASO across the cell membrane (Figure 9A). The ASO reduced muHTT expression in a dose-dependent manner (IC50 = 101 nM). No reduction of wtHTT was observed even at the highest dose of ASO A30 (1000 nM) tested. Next, mice (n = 4/group) were injected in the right lateral ventricle (ICV) with a single bolus injection (300 µg) of ASO (A1, A25, A26, A30, or A31) or vehicle control (PBS), and tissue was collected 28 days later. Brains were harvested, and HTT protein was quantified in a 2 mm coronal slab from each hemisphere by allelic separation immunoblotting for mu and wtHTT protein; the results were normalized to calnexin (Figure 9B). The posterior portions of treated brains were then post-fixed for immunohistochemical assessment of ASO distribution.As observed in the cell culture experiment, all the ASOs showed 60–80% reduction of muHTT protein. However, although ASO A1 showed a 52% reduction of the wtHTT protein, we observed essentially no reduction of the wtHTT protein for the mice treated with the allele-selective ASOs A25, A26, A30, and A31. We also measured the ability of ASO A30 to reduce muHTT protein in a dose-response experiment (Figure 9C). Mice (n = 4/group) were injected (ICV bolus) with increasing doses (75, 150, 300, and 500 µg) of ASO A30 or vehicle control, and brain tissue was harvested after 28 days using the procedures described earlier in the text. The ASOs produced a dose-dependent reduction in muHTT (ED50 = 110 µg) with no reduction in wtHTT even at the highest dose tested (500 µg). All ASOs also showed similar broad and even bilateral localization after ICV bolus injection, indicating that the modifications had no effect on distribution (Figure 9D). Thus, the animal model recapitulated the potency and selectivity observations from the cell culture experiments. Collectively, these studies illustrate the potency, selectivity, and tolerability of these compounds and support their development as potential therapeutics for HD.


Figure 4. Reversion of Splicing Abnormalities in DM1 Patient-Derived Muscle Cells by CRISPR-SaCas9 Deletion of Expanded CTG Repeats Splicing profiles and quantification of LDB3 exon 11-, ATP2A1 exon 22-, MBNL1 exon 7-, DMD exon 78-, INSR exon 11-, and BIN1 exon 11-containing transcripts in differentiated myoblasts from DM1-Delta clones 10, 3, 17, and 22 compared to control (Ctrl), DM1, and DM1-sgRNA clones. Graphs show average values from independent biological replicates ± SD (n = 6 for Ctrl and DM1, n = 3 for the other samples). Statistical analysis by two-tailed Student’s t test. *p < 0.05, **p < 0.01, ***p < 0.001; ns, not significant; ˂DL, below detection limit. Error bars represent SD.

Aggravation of Alternative Splicing Defects in DM1-Edited Muscle Cells Sequestration of MBNL-splicing factors in nuclear foci of DM1 cells leads to alterations in the alternative splicing of numerous pre-mRNAs, some of which are eliminated in differentiated muscle cells in culture.45 We therefore assessed the splicing pattern of LIM domain binding 3 (LDB3) exon 11, Sarco-endoplasmic reticulum calcium ATPase 1 (ATP2A1) exon 22, Muscleblind-like splicing regulator 1 (MBNL1) exon 7, Duchenne muscular dystrophy (DMD) exon 78, insulin receptor (INSR) exon 11, and Bridging integrator 1 (BIN1) exon 11 in edited DM1-Delta clones compared to DM1, DM1-sgRNA, and Ctrl cells (Figure 4).


Figure 2. Overexpression of Ahi-1 in Sca-1 +lin _x0004_ mouse stem/progenitor BM cells perturbs their in vitro proliferative activity and enhances the effects of BCR-ABL. (A) The levels of Ahi-1 transcripts relative to GAPDH from FACS-purified MIY (Sca-1 + lin _x0004_ YFP +), Ahi-1 (Sca-1 + lin _x0004_ YFP +), BCR-ABL (Sca-1 + lin _x0004_ GFP +), and Ahi-1 plus BCR-ABL (Sca-1 + lin _x0004_ YFP + GFP +) transduced primary mouse BM cells. (B) Growth of each FACS-purified population with GFs. Viable cell numbers were determined by hematocytometer counts of trypan blue – excluding cells. (C) Number of CFC colonies produced in semisolid cultures ± GF from the same FACS-purified mouse BM cells. (D) The appearance of GF-independent CFC colonies is shown. Bar, 250 μm. (E) The numbers of LTC-IC-derived CFCs produced from the same cells ± GF. Values shown are the mean ± SEM of triplicate measurements. * indicates significantly different from MIY control cells.

Elevated endogenous Ahi-1 expression was also observed in cells transduced with BCR-ABL alone as compared with control cells (8-fold; P < 0.01; Fig. 2 A). 5 d after transduction, the rate of expansion of the Sca-1 +lin _x0004_YFP + (Ahi-1 +) cells in liquid cultures was approximately twofold lower than in cultures initiated with control cells (Fig. 2 B). Ahi-1– transduced Sca-1 +lin _x0004_ cells produced a slightly fewer number of colony-forming cells (CFCs) than MIY-transduced control cells in semisolid cultures in the presence of growth factors (GFs), and some of the Ahi-1 + CFCs (15%) were already GF independent (Fig. 2, C and D). After 4 wk in long-term culture-initiating cell (LTC-IC) assays, which are used to measure stem cell activities in vitro, the Sca-1 +lin _x0004_Ahi-1 + cells produced 3-fold more CFCs than control cells (P < 0.01; Fig. 2 E). All of these endpoints (proliferative activity, GF dependence, and CFC output in LTC-IC assays) are also perturbed by BCR-ABL transduction. In cells cotransduced with Ahi-1 and BCR-ABL, all of these effects were further enhanced compared with cells transduced with either BCR-ABL alone (2–4-fold) or Ahi-1 alone (3–6-fold; P < 0.01; Fig. 2). Thus, overexpression of Ahi-1 alone deregulates proliferative control of primitive mouse hematopoietic cells, and these effects can be enhanced by BCR-ABL.


ECM softness upregulates MMP secretion and promotes MMP activity. (A) Primary human fibroblasts (2 × 104 cells per well) were seeded onto gels of varying stiffness in 12-well plates and cultured for 48 h. Cell culture medium from each condition was collected and subject to the RayBio Human MMP Array to determine the total secreted MMP protein quantity in each medium. (B) Three independent experiments (n = 3, ±SD) as in A were performed and quantified. Note that proliferation rates vary on gels of different stiffness. Total MMP quantities were normalized to total cell numbers on gels of varying stiffness at 48 h. Student’s test was used for the comparison of each mean with the mean of MMP protein quantity of the same MMP type on 25.6-kPa gels. *p < 0.05 considered significant. (C) Cell culture conditioned media were collected as in A, and the total MMP activity in each medium was measured and quantified (n = 3, ±SD) and normalized to cell number. Student’s t test was used for the comparison of each mean with the mean of “0.4 kPa.” *p < 0.05 considered significant.

Matrix metalloproteinases (MMPs) are critical proteinases in ECM degradation and include two major subfamilies of secreted MMPs (MMP-2, -3, etc.) and integral membrane MMPs (MMP-14 [MT1-MMP], -15, etc.; Kessenbrock et al., 2010). For all types of MMPs that were measured, increased ECM stiffness significantly inhibited MMP secretion from these cells (Figure 2A). Previous studies reported that increased ECM stiffness significantly promotes primary cell proliferation (Wang et al., 2000; Liu et al., 2010). Thus, by normalizing to total cell numbers at 48 h after cell seeding, the relative amount of MMPs secreted per cell were even lower on stiffer substrates (Figure 2B). Further, the total MMP activities of these secreted MMPs also were higher on softer substrates and lower on stiffer substrates (Figure 2C). Of interest, we also detected small differences of MMP secretion on substrates with varying stiffness as early as 4 h after cell seeding (Supplemental Figure S1B), suggesting that cells begin responding to stiffness during their initial adhesion to ECM.


Figure 4. Quantitative analysis of CTCF and BORIS expression in human PCa. (A) Nuclear CTCF significantly decreases in metastases cores (P < .001). (B) BORIS nuclear expression significantly increased in cancer cores (P = .002); nuclear and cytoplasmic expression increased in HGPIN cores compared to benign (both P < .001). (C) The ratio of BORIS to CTCF expression was analyzed for differences among cores. Significant increases in the BORIS/CTCF ratio are seen among cancer and metastases cores compared to benign (P = .031 and P = .006 respectively). (D) Comparing the BORIS/CTCF ratio among cancer cores, high Gleason grade cancers have significantly lower BORIS/CTCF ratio than both benign and intermediate Gleason cores (P = .006 and P = .024 respectively).

CTCF is known to impact gene regulation through the management of chromatin organization and the maintenance of epigenetic marks [26]. CTCF expression levels measured by VECTRA were insignificantly decreased in metastatic PCa tumors (P < .001), but not localized PCa when compared to benign prostate tissues (P = .17) (Figure 4A). When CTCF expression within primary tumors was compared against clinicopathologic patient variables, decreased nuclear and total CTCF expression was associated with higher Gleason’s Score (P = .01 and P = .001 respectively) and positive margins (P = .03 and P = .043 respectively) (Data not shown). The CTCF paralogue, BORIS, shares a common zinc-finger DNA-binding domain with divergent N- and C- termini. BORIS and CTCF have competing effects on gene expression and epigenetic regulation [5,27]. In contrast to CTCF, BORIS expression was increased in HGPIN and primary cancer cores compared to benign (P < .001 and P = .002 respectively) (Figure 4B). No clinicopathologic correlates were noted. The competing role for BORIS/CTCF suggests the ratio of their expression may contain functional significance and greater predictive power than the expression of these proteins individually as seen in ovarian cancer [28]. Analyzing all prostate tissues, a weak correlation was seen between BORIS and CTCF (Pearson’s r = .224, P < .001). BORIS/CTCF levels increased significantly in cancer and metastases compared to benign (P = .03 and P = .006 respectively) (Figure 4C). High Gleason cancers have a significantly greater BORIS/CTCF ratio than both benign tissues and intermediate Gleason cancers (P = .006 and P = .024 respectively) (Figure 4D). The ratio of nuclear and total expression was calculated and found to be associated with Gleason’s grade, positive surgical margins, and tumor volume (nuclear P = .024, P = .027, P = .020, respectively) (Table 2, for full table see supplementary data, Table S2).


Effects of TRAF3 deficiency on effector functions of CD40 and LMP1. (A) A20.2J cells stably transfected with hCD40LMP1 were stimulated with 2 µg/ml anti-mCD40 Ab (CD40), anti-hCD40 Ab to trigger signaling through hCD40LMP1 (LMP1), or isotype control Abs for each (iso1 and iso2) for 48 h. Expression of CD23, CD80, and CD95 was determined by immunofluorescence staining and analyzed by flow cytometry. MCF, mean channel fluorescence. Values presented are the mean ± SEM of three independent experiments. (B) CH12.LX cells stably transfected with hCD40LMP1 were stimulated with Abs as in A for 72 h and assayed for IgM secretion. Plaque-forming cells (Pfcs, IgM-secreting cells) per 10⁶ viable recovered cells are shown. Values presented are the mean ± SE of replicate samples. Data are representative of three independent experiments. (C) TRAF3⁻/⁻ CH12.LX cells stably transfected with hCD40LMP1 were stably supertransfected with an IPTG-inducible TRAF3 (Reconstituted TRAF3⁻/⁻). Reconstituted cells were preincubated in the absence (−IPTG) or presence of IPTG (+IPTG) for 24 h and stimulated with Abs as in B in the absence or presence of IPTG, whereas TRAF3⁻/⁻ cells were stimulated with Abs as in B in the absence of IPTG. IgM-secreting cells were enumerated as in B. Values presented are the mean ± SE of replicate samples. Data are representative of three independent experiments. (D) Expression of TRAF3 in the reconstituted TRAF3⁻/⁻ CH12.LX cells (Recon.) in the absence (−) or presence of IPTG (+IPTG), compared with that of WT (+/+) and TRAF3⁻/⁻ (−/−) cells, was analyzed by immunoblotting. An immunoblot for actin served as loading control.

Effects of TRAF3 Deficiency on Effector Functions. Next, we determined if the effects of TRAF3 deficiency on early signaling pathways of CD40 and LMP1 are predictive of downstream B cell effector functions, including upregulation of surface molecules and secretion of cytokines and antibodies. After CD40 engagement, TRAF3-deficient A20.2J cells exhibited upregulation of CD23 and CD95 similar to that of WT cells (summarized in Figure 3A; representative histograms shown in Figure S7, available at http://www.jem.org/cgi/content/full/jem.20031255/DC1). Although we consistently observed a partial increase in CD40-mediated upregulation of CD80 in TRAF3⁻/⁻ B cells (Figure 3A), it may be due to clonal variation because different A20.2J subclones vary in this response (not depicted). Using the mCD40-elicited response as an internal control for each subclone, we found that LMP1-induced upregulation of CD23 and CD80 was reduced approximately two- and threefold, respectively, whereas upregulation of CD95 and MHC class II was not affected by TRAF3 deficiency in A20.2J cells (Figure 3A and Figure S7).For detection of cytokine secretion, the membrane ligands for hCD40 and mCD40 (hCD154, mCD154, respectively) were also used, as they are more potent activators of this specific function of CD40. Although mCD154 is capable of activating hCD40 to a certain extent (39), cross-reaction between hCD154 and mCD40 is negligible (21). Neither CD40- nor LMP1-mediated secretion of TNF and IL-6 was affected by TRAF3 deficiency in CH12.LX cells (Figure S8, available at http://www.jem.org/cgi/content/full/jem.20031255/DC1). Interestingly, TRAF3⁻/⁻ CH12.LX cells exhibited enhanced IgM secretion after CD40 engagement, but profoundly suppressed IgM secretion in response to LMP1 signaling as compared with TRAF3⁺/⁺ cells (Figure 3B). We further verified this observation by examining TRAF3⁻/⁻ CH12.LX cells reconstituted with an IPTG-inducible TRAF3. Induction of TRAF3 expression with IPTG completely restored IgM secretion in response to LMP1 signaling, while decreasing IgM secretion mediated by CD40 signaling (Figure 3C and D). These results extend our previous finding that overexpression of TRAF3 decreases CD40-mediated IgM secretion (29), and in contrast, highlight the importance and indispensability of TRAF3 in LMP1-induced B cell activation. Furthermore, we also stably transfected an IPTG-inducible full-length LMP1 into WT and TRAF3⁻/⁻ B cells. A similar requirement for TRAF3 was observed when the signal was delivered by inducible full-length LMP1 (unpublished data).


Figure 4 The effects of hydrogels on skin wound healing in type 2 diabetes mellitus (T2D) by topical treatment. (A) Representative images of wounds in the normal control (NC) group, T2D group, CAH (T2D+CAH) group, and CAVBPH (T2D+CAVBPH) group on days 0, 3, 9, and 15. (B) Wound healing rate. *p < 0.05, **p < 0.01, and ***p < 0.001.

As shown in Figure 4A, the wound size in all groups decreased gradually over time. Compared with the NC group, the T2D group displayed a slower wound shrinkage due to the influence of diabetes, and exudate was still found on the wound surface on day 9. Topical application of CAH and CAVBPH significantly accelerated diabetic wound recovery compared to the T2D group. Quantitative analysis of wound healing confirmed the above results (Figure 4B). The healing rate in the T2D group was more optimistic than that in the NC group (p < 0.01 or p < 0.001). Compared to the T2D group, the CAH group showed a better healing effect on days 9 and 15 (p < 0.001), while the CAVBPH group had the best wound shrinkage than the T2D and CAH groups on all observed days (p < 0.01 or p < 0.001). At day 15, the wounds of the CAVBPH group (96.55% of healing rate) and the NC group (91.77% of healing rate) were basically healed and covered with hair, while the healing rates of the CAH and T2D groups were 84.23% and 63.81%, respectively. Compared to the T2D and CAH groups, the CAVBPH group had the best curative effect, with a 96.55% wound healing rate on day 15 (Figure 4).


Figure 1. Telomeric element HeT-A expression is downregulated in ovaries by Trf2, Woc and Ars2. (A) RT-qPCR analysis of TE expression in ovaries of Trf2KDnos, wocKDnos and Ars2KDnos. (B) HeT-A transcript abundance in ovaries of Trf2/Trf2, spnE/spnE and Trf2,spnE double mutants. The fold change in the HeT-A RNA level is shown. (C) NRO analysis of HeT-A transcription in ovaries of Trf2KDnos, wocKDnos, Ars2KDnos and piwiKDnos flies. The RNA level was normalized to rp49. Mean values (±SD) for three biological samples normalized to control knockdown (white(w)KDnos) are shown. (D) In situ RNA hybridization of HeT-A, TART or TAHRE antisense probes with ovaries of the indicated genotypes (GAL4-nos driver was used). (E) RNA FISH of HeT-A antisense probe with ovaries of the indicated genotypes. The accumulation of HeT-A transcripts (green) in nuclei of nurse cells was observed. DNA was stained with DAPI (blue). GAL4-nos driver was used.

RESULTS Trancription factors Woc and Trf2 suppress telomeric repeat HeT-A transcription in ovaries In order to detect germline components contributing to telomeric homeostasis, we performed a selective RNAi screen for factors that downregulate HeT-A expression in Drosophila ovaries. This approach was based on the existing data that in somatic tissues certain factors of the telomeric complex are required for the silencing of telomeric repeats (34–36). As reported earlier, HP1, encoded by Su(var)205 gene, a component of the telomere protection complex (37,38), elicits transcriptional repression of telomeric retrotransposons both in soma (34) and in the germline (39,40). At the same time, transcription of telomeric and non-telomeric transposons in the germline is controlled by the piRNA system, supporting the idea that the telomeric complex in the germline consists of both germline-specific and ubiquitous components. To investigate the structure of telomeric chromatin in the germline, we examined HeT-A expression in the ovaries of flies depleted for known components of the somatic telomeric complex as well as factors involved in RNA surveillance. Here, we describe genes exerting the greatest effect on telomeric repeat expression in ovaries. Germline knockdown of several genes, previously implicated in telomere functioning in soma, did not noticeably affect HeT-A expression in our screen and were not included in further analysis (Supplementary Table S2). In parallel to an independent study that describes Trf2 as a negative regulator of telomeric transposon expression in somatic tissues (41), we demonstrated that Trf2 is associated with the polytene chromosome telomeres of salivary glands (Supplementary Figure S1) and discovered a role of Trf2 in telomere functioning in the germline. RT-qPCR analysis of telomeric and non-telomeric retrotransposon expression demonstrated decreased levels of HeT-A and TAHRE transcripts in ovaries upon germline knockdown of Trf2 (Trf2KDnos; nos designates the promoter of the germline-specific gene nanos that was used to express the Gal4 driver) (Figure 1A). Given that the HeT-A and TAHRE untranslated regions (UTR) are highly homologous (7), we suggest that Trf2 recognizes a similar sequence motif in the promoter regions of both elements. In order to determine if Trf2 functions in the piRNA pathway together with spindle-E (encoding DEAD box RNA helicase SpnE) to silence HeT-A, we applied a double mutant analysis. We used a viable Trf2p1 mutation (26) to generate Trf2/Trf2; spnE/spnE double mutants and compared HeT-A transcript abundance in their ovaries to that in ovaries of single Trf2/Trf2 or spnE/spnE mutants. HeT-A expression in the double mutants was higher than in Trf2 single mutants, but not in spnE single mutants (Figure 1B, Supplementary Figure S2). This could mean that in the absence of piRNAs, inhibitory complex containing Trf2 cannot be recruited to the HeT-A promoter. Northern blotting of short RNA did not reveal changes in the abundance of HeT-A-specific piRNA in ovaries of Trf2 mutants and Trf2KDnos flies (Supplementary Figure S3A). These data suggest that Trf2 may act downstream of piRNA processing to establish transcriptional silencing of HeT-A. The zinc finger transcription factor Woc has been shown to bind Drosophila telomeres, exerting telomere-capping functions (42). wocKDnos causes a strong derepression of HeT-A and TAHRE but not TART and other TEs (Figure 1A), suggesting sequence-specific binding of this transcription factor to telomeric repeats in ovaries. We extended the analysis of the telomeric protein complex by including an RNA binding protein, Ars2, involved in mi- and siRNA biogenesis (43), which was identified in an RNAi screen as a negative regulator of HeT-A expression (44). Particular interest in this protein was due to the fact that Ars2 downregulates the expression of human TERRA (45). RT-qPCR analysis revealed a strong derepression of HeT-A/TAHRE in ovaries of Ars2KDnos flies (Figure 1A). To address the question about the involvement of Trf2, Woc and Ars2 in the transcriptional regulation of HeT-A, we conducted an NRO assay, which allows estimating the level of nascent HeT-A transcripts (46). NRO on ovaries with Trf2, woc and Ars2 knockdowns demonstrates that all these factors provide transcriptional silencing of the telomeric retrotransposon HeT-A (Figure 1C). In situ RNA hybridization analysis was performed to examine the effect of Trf2, Woc and Ars2 depletion on telomeric retroelement transcript distribution in ovaries (Figure 1D, Supplementary Figure S4). Knockdowns of Trf2, Woc and Ars2 exerted the most pronounced effect on HeT-A and TAHRE expression, resulting in the accumulation of their transcripts in nurse cells. The RNA FISH analysis on ovaries depleted for Trf2, Woc and Ars2 revealed HeT-A RNA signals not only in the cytoplasm, but also in the nuclei of nurse cells, where HeT-A transcripts were accumulated in distinct foci (Figure 1E). Similar nuclear foci were observed in spnEKD ovaries (Figure 1E). Previously, using combined DNA/RNA FISH analysis, it was shown that both HeT-A RNA and telomeric DNA were co-localized in nurse cell nuclei in spnE mutants (19). Most likely, we observed the accumulation of nascent transcripts at sites of transcription upon Trf2, Woc and Ars2 knockdown, in accordance with the result of NRO. Taking into account the HeT-A/TAHRE-specific effects of Woc and Trf2, it is unlikely that these factors participate in piRNA biogenesis. However, it is possible that DNA-binding factors Woc and Trf2 are involved in the piRNA-mediated transcriptional silencing mechanism that is specific for telomeric repeats. Thus, HeT-A transcripts accumulated upon Ccr4-Not knockdown avoid piRNA-mediated degradation. In situ RNA hybridization on Ccr4, Pop2 and Not1 KD ovaries revealed HeT-A transcripts in nurse cells (Figure 2C). Importantly, RNA FISH revealed HeT-A RNA foci in the nuclei of twinKDnos nurse cells (Figure 1E) as well as upon Trf2, Woc, Ars2 and SpnE knockdown, suggesting that all these components regulate the telomeric transcript level at the site of transcription. Given the accumulation of HeT-A transcripts in the nuclei upon Ccr4 depletion (Figure 1E), we investigated the poly(A) tail length of the HeT-A RNA in the nuclear fraction of ovarian RNA upon germline knockdown of Ccr4. The PAT assay demonstrated that in the nuclei of control ovaries, HeT-A transcripts were not adenylated, while Ccr4 KD led to an increase in HeT-A poly(A) length in the nucleus (Figure 3C).


Agrin-mediated decrease in gap junction–dependent intercellular communication between chromaffin cells in neonates. (A) Reduced number of electrically and LY-coupled chromaffin cells in neonatal agrin-treated slices. *, P ≤ 0.01 when compared with control slices. (B) Monitoring of electrical coupling between chromaffin cell pairs in control and agrin-containing saline showing examples of robust and weak coupling. Cells were current-clamped at −65 mV. A weak coupling was less frequently observed in agrin-treated slices. (C) Histograms illustrating the wide distribution range of the coupling ratio calculated in 26 control and 18 agrin-treated cells from voltage-clamp measurements of Ij (holding potential −60 mV, transjunctional potential from −120 to −60 mV, 150 ms duration). Insets: pooled data of Gj calculated in 11 control cell pairs and 4 agrin-treated cell pairs.

Studies were next undertaken to examine the effect of agrin on both gap junction–mediated electrical and metabolic coupling between neonate chromaffin cells. Metabolic coupling was assessed using Lucifer yellow (LY) to label coupled cells, whereas electrical coupling was evidenced by dual whole-cell patch-clamp recording of intercellular macroscopic junctional currents (Ij). The probability of LY spreading between chromaffin cells was significantly reduced in agrin-treated slices compared with untreated slices (31% vs. 57%, P ≤ 0.01; Figure 5A). In the presence of the anti–rat agrin-neutralizing pAb, agrin failed to decrease LY diffusion between neonate chromaffin cells (58.3%, n = 24; unpublished data). Similarly, the dye coupling was not modified by inactive agrin0,0 (61.5%, n = 13; unpublished data). In adults, agrin did not change the incidence of LY diffusion (37.9% in treated slices, n = 29 vs. 44% under control conditions, P > 0.01; unpublished data). Agrin decreased the percentage of electrically coupled cell pairs (53.8%, n = 26 pairs in control slices vs. 22.2%, n = 18 pairs in agrin-treated slices, P ≤ 0.01; Figure 5A). In addition, agrin reduced the incidence of robustly coupled cells (Figure 5B). The voltage changes in response to depolarizing current injected into the stimulated cell were robustly reflected in the unstepped cell in 64.3% (n = 9/14 cell pairs) and 50% (n = 4 cell pairs) in control and agrin-treated slices, respectively (P ≤ 0.01). The resulting coupling ratios exhibited a wide distribution range from 0.01 to 0.97 (Figure 5C), strongly suggesting the presence of two chromaffin cell populations—a weakly coupled cell population and a highly coupled cell population. Recordings of Ij in chromaffin cell pairs voltage clamped at −60 mV confirmed these findings (unpublished data). Delivering voltage steps with command pulses of both polarities triggered Ij in the unstepped cell that exhibited variable degrees of attenuation. In control slices, 81.8% of recorded pairs (n = 11) exhibited Ij amplitudes that could reach 1–2 nA, whereas in the remaining pairs Ij did not exceed several picoamperes. In agrin-treated slices, the percentage of coupled cell pairs exhibiting a high Ij amplitude dropped to 50%. When plotted as a function of the transjunctional potential, the I/V curve of Ij displayed a linear relationship within the membrane potential range of −60 to −120 mV. The curve used to fit the data was derived from a linear regression, giving a macroscopic junctional conductance (Gj) of 43 and 60 pS for the weak coupling and 12 and 13 nS for the robust coupling (Figure 5C, insets).


FIGURE 2. Fold enrichment of selenoprotein mRNAs on UPF1 in low versus high Se.(A) Levels of selenoprotein mRNAs that coprecipitated with UPF1, following culturing HEK293T cells in 3 nM Se or 60 nM Se. Transcript levels are normalized to a synthetic in vitro transcribed RNA and then to the value of the corresponding sample from the total RNA extraction (see Methods; Supplemental Fig. S3).(B) UPF1 protein levels in 3 nM Se or 60 nM Se.(C) Quantitation of (B). Error bars represent standard deviation of the mean; n = 4 for (A), and n = 6 for (B) and (C); (*) P < 0.05.

We reasoned that if the transcripts that decreased in low Se were undergoing NMD, then they should similarly be enriched on UPF1. The same experiment modeling conditions of low Se was repeated, and RNA immunoprecipitation was performed with UPF1. The cytoplasmic nucleoprotein complexes were coimmunoprecipitated with an anti-UPF1 antibody, followed by quantification of coimmunoprecipitated RNAs by qRT-PCR. A representative set of RNAs from each category of NMD susceptibility was analyzed, including those most responsive to Se status (SelW, SelP, GPx1), as well as GPx4, which has been consistently unresponsive to Se status in past research. Interestingly, all of the selenoprotein mRNAs analyzed coimmunoprecipitated with UPF1 to some degree in agreement with UPF1’s general role in translation (Ghosh et al., 2010). A strikingly similar pattern to the profile in Figure 1A was observed in response to Se status. In this case, the RNAs predicted resistant to NMD showed a difference in their abundance on UPF1 with respect to Se. The RNAs that were the most responsive to Se status, however, were also the less abundantly bound to UPF1 under conditions of low Se (Fig. 2A). Importantly, UPF1 did not change with Se, ruling out the possibility that changes in immunoprecipitated RNAs could have been due to changes in total UPF1 protein (Fig. 2B, C). Overall, the degree of mRNA responsiveness to Se status directly correlated with the degree of mRNA abundance on UPF1, suggesting the involvement of NMD in the regulation of selenoprotein mRNAs during Se deficiency.


3D fibronectin matrix reduces both Rac activity and random migration. (A) Primary human fibroblasts were plated on 2D substrates coated with fibronectin or 3D matrices rich in fibronectin and assayed for activity as in Figure 1. The reduction of active Rac in cells plated in a 3D matrix compared with 2D was 31% (P ≤ 0.017), whereas Cdc42 and Rho changes were not significant (Cdc42 4.5% reduction, P ≤ 0.66; Rho 25% increase, P ≤ 0.65). Error bars indicate SEM. (B) Cells in 2D versus 3D environments have different morphologies and locations of lamellae (arrowheads), as shown after staining for F-actin with phalloidin-Alexa 594 (left and red color in the right) and anti-fibronectin antibody (green). Bar, 20 μm. (C) Migration of cells in 2D versus 3D environments was recorded, and directionality (D/T) and velocity were calculated. (C and D) Error bars represent SEM. (D) Decreased random motility of cells within three-dimensional (3D) matrix. Primary human fibroblasts were cultured overnight within intact 3D matrices, on mechanically flattened 3D matrix (2D matrix), or on surfaces coated with solubilized 3D matrix (2D mix). Cell movements were recorded for 10 h, and the D/T ratio and velocity were calculated as described above. (E) Activated Rac in cells in 3D matrix restores random motility. Primary human fibroblasts were cotransfected with Rac Q61L VSV and Rac Q61L GFP or GFP alone (Control), sorted for low levels of GFP expression (1–50% from the peak of positive cells), and plated on 3D matrices. Cell movements were recorded for 10 h, and the D/T ratio and velocity were quantified. The bars represent the means of data pooled from two experiments, and error bars indicate SEM (n = 32–34 cells).

Cells migrating in 3D cell-derived matrices have different types of cell adhesions, morphology, and signaling when compared with cells in standard 2D tissue culture (Cukierman et al., 2001; Walpita and Hay, 2002). Human fibroblasts in such 3D matrices were found to have a partial, but highly reproducible, 10–50% reduction in active Rac, but no reduction or increase in Cdc42 or Rho activities (Figure 7A). This reduction in Rac activity was accompanied by decreased random migration (Figure 7C and Video 3), with directional persistence increasing from D/T₂ᴰ = 0.25 ± 0.03 to D/T₃ᴰ = 0.87 ± 0.02 (P < 0.0001). Individual cells in the 3D matrix became spindle shaped as previously described (Cukierman et al., 2001), but in addition, staining for F-actin revealed a substantial reduction in peripheral lamellae in 3D compared with 2D (Figure 7B). In contrast to the results in cells cultured on 2D substrates, there was a significant increase in overall cell migration velocity by 34% associated with the reductions in active Rac and random migration in a 3D environment (Figure 7C; P < 0.0001). These findings further indicate the separate regulation of speed and directionality. Because the differences between 2D fibronectin and 3D substrates could have been the result of either three dimensionality or of differences in molecular composition, the 3D matrix was converted to 2D as a direct test of the role of dimensionality. Physically flattening 3D cell-derived matrices by compression still retained the increased overall velocity, but it was accompanied by a restoration of random motility (Figure 7D; P < 0.0001). Moreover, solubilizing the matrix and spreading its mixed components on a 2D substrate showed the same restoration of random motility (Figure 7D; P < 0.0001), with migration rates similar to those on 3D matrix (no statistically significant difference). These analyses indicate that the increased directionality in 3D is related to the three dimensionality of the matrix, and that it is unrelated to cell migration speed.