CKO) caused lethal dilated cardiomyopathy. Surprisingly, gene upregulation in EedCKO was not coupled with loss of H3K27me3. Rather, the activating histone mark H3K27ac increased. EED interacted with histone deacetylases (HDACs) and enhanced their catalytic activity. HDAC overexpression normalized EedCKO heart function and expression of derepressed genes. Our results uncovered a non-canonical, H3K27me3-independent EED repressive mechanism that is essential for normal heart function. Our results further illustrate that organ dysfunction due to epigenetic dysregulation can be corrected by epigenetic rewiring.In proliferating cells, where most Polycomb repressive complex 2 (PRC2) studies have been performed, gene repression is associated with PRC2 trimethylation of H3K27 (H3K27me3). However, it is uncertain whether PRC2 writing of H3K27me3 is mechanistically required for gene silencing. Here, we studied PRC2 function in postnatal mouse cardiomyocytes, where the paucity of cell division obviates bulk H3K27me3 rewriting after each cell cycle. EED (embryonic ectoderm development) inactivation in the postnatal heart (EedDOI:http://dx.doi.org/10.7554/eLife.24570.001 Normal developmental maturation of organ function requires precise transcriptional regulation of gene expression. This transcriptional regulation depends upon the interplay of an array of epigenetic regulators, which shape the epigenetic landscape by repositioning nucleosomes and depositing covalent modifications on histones. Since the chromatin landscape is established through a series of steps, each contingent upon normal completion of prior steps, transient disruption, through environmental mishaps or genetic mutations, might be anticipated to break the normal sequence and irreversibly impact organ development and function. Whether or not this is the case is presently unknown, and the answer has clear therapeutic implications for diseases that involve epigenetic changes.Development and function of the heart is vulnerable to epigenetic insults, as mutation of epigenetic regulators causes both structural heart disease and cardiomyopathy in humans and in experimental model systems . Recent Another class of epigenetic repressors is polycomb repressive complex 2 (PRC2), comprising the core subunits EED, SUZ12, and either EZH1 or EZH2. Canonically, PRC2 represses gene transcription by catalyzing trimethylation of histone H3 on lysine 27 (H3K27me3) . HoweverThe role of PRC2 in the heart has been studied by cardiac-specific inactivation of EZH2 or EED during early cardiac development. This resulted in disruption of H3K27me3 deposition, derepression of non-cardiomyocyte gene programs, and lethal heart malformations . Here, wEed in regulating cardiac gene expression during heart maturation, we generated Eedfl/fl; Myh6Cre (EedCKO) mice, in which Myh6Cre specifically inactivates the conditional Eedfl allele in CMs. Western blotting and qRTPCR demonstrated effective cardiac EED protein depletion that occurred between postnatal day (P) 0 and 5  in purified, 2-month-old CMs. CMs were over 95% pure by immunofluorescence microscopy . Markedltivation . PCR of M purity . ChIP-seH3K27me3 . On the H3K27me3 .About half of the upregulated genes (49.71%), including 7 of 14 upregulated skeletal muscle genes, were occupied by EED at their TSSs in WT. EED occupancy at a subset of the upregulated genes was confirmed by ChIP-qPCR . Unlike CKO. First, we quantitatively measured H3K27me3 signal within ±500 bp of TSS on EED target genes in control and EedCKO, stratified by H3K27me3 signal in wild type  had reduced promoter H3K27me3, indicating lack of enrichment of H3K27me3 loss among upregulated genes. Again, the expected relationship between gene upregulation and H3K27me3 loss did not hold for the majority of upregulated genes, as those in Clusters 2 (303 genes) and 3 (308 genes) were not linked to loss of H3K27me3  is linked to gene activation and active enhancers . Increas and K27 ; H3K14acp=0.074; . This reell line .10.7554/CKO purified CMs showed that Eed inactivation led to acquisition of 8161 new H3K27ac sites and loss of 5950 sites, with 20799 sites in common  associated with these upregulated genes   . We deve (Tnnt2) . We eval (Tnnt2) . Interes mutants .10.7554/CKO  were significantly downregulated  . HDACs g changed . Otte an changed , suggest changed . This wa changed . We furt changed .10.7554/To further approach the molecular function of this interaction on a genome-wide level, we examined the genomic binding distribution of EED and HDAC2 in adult CMs. Nearly half of HDAC2 peaks  overlapped with EED peaks in WT, in line with their biochemical interaction . HDAC2, CKO compared to control, revealing no significant difference between HDAC2 or HDAC5 occupancy  to direct CM-selective overexpression of HDAC1/2 (CKO mice (CKO-HDAC1/2) normalized systolic dysfunction and cardiomegaly, compared to control treatment with luciferase  were used as unaffected controls. AAV-HDAC1/2 also restored repression of slow twitch sarcomere genes Acta1 and Myh7, and reduced expression of the heart failure marker Nppa  >1.5; Both class I and class II HDACs regulate cardiac gene expression, hypertrophy, and function . Notably HDAC1/2 . RemarkaCKO-luc; . Heterozker Nppa . RNA-seqCKO was normalized by AAV-HDAC1/2: the ratio of H3K27ac signal in CKO-luc to CKO-HDAC1/2 was ≥1.5 for 460 genes and 1–1.5 for 293 genes, while only 110 were not changed (Eed loss of function (CKO-luc/het) compared to its change with AAV-HDAC1/2 rescue  or vehicle control. Het mice transduced with AAV9-luc and then SAHA or vehicle served as additional controls. Consistent with previous reports  that were aberrantly upregulated in EedCKO. Interestingly, SAHA treatment appeared to block heart function rescue by AAV9-EED re-expression. Two out of five mice died prematurely in the EedCKO+EED+SAHA group and were lost to the study. Despite this potential survivor bias, heart function rescue by AAV9-EED tended to impair AAV9-EED rescue in the remaining three mice, although this did not reach statistical significance  by intraperitoneal or intravascular injection into pups and adults, respectively. Mice were intraperitoneally injected with SAHA  for the period of time as indicated. Echocardiography was conducted in either conscious or lightly anesthetized mice (isoflurane) using a Vevo 2100 imaging system . Adult cardiomyocytes were isolated using type II collagenase in the Langendorff retrograde perfusion mode  approved by the Institutional Animal Care and Use Committees of Peking University and Boston Children's Hospital. Eedfl  and Myh6, 1 mM EDTA, and fresh 1 mM PMSF and protease inhibitor cocktail (PIC)) for 30 mins, and 1% SDS was added before homogenization on QIAshredder columns. Equal amounts of proteins were resolved on 7.5% or 10% SDS-polyacrylamide gels and immunoblotted with primary antibodies listed . EED antCo-immunoprecipitation was performed as described  with minIn vitro deacetylation assay was performed using a colorimetric immunoassay for deacetylated histones . Fifty nanogram recombinant HDAC2 (Active Motif 31343) and 0–100 ng recombinant EED  or bovine serum albumin were incubated at 37°C for 1 hr, and determined from the absorbance at 450 nm.Antibody-based ChIP was performed as described  using anIllumina ChIP-seq libraries were prepared as described  from purPeaks were identified using MACS2  version. BigWig CKO vs WT, we firstly implemented a normalizing factor quantified through ChIP experiments (the relative levels of ChIP derived DNA) to the reads intensities in EedCKO and WT . Primer sequences are listed here . ChIP-qPt-test was used to determine the significance of differences between two groups in all qPCR assays. For bar charts, data are presented as mean ± SEM. For violin plots, the center line indicates the median. Wilcoxon-Mann-Whitney test was used to compare aggregate curves and violin-bar plots. Statistical significance was indicated with: *p<0.05; **p<0.01; ***p<0.001.Student’s two-tailed https://b2b.hci.utah.edu/gnomex/. Sign in as guest and go to experiment #410R. The data have also been deposited at GEO (accession number GSE73771).The high-throughput data used in this study are available through the Cardiovascular Development Consortium Server at  In the interests of transparency, eLife includes the editorial decision letter and accompanying author responses. A lightly edited version of the letter sent to the authors after peer review is shown, indicating the most substantive concerns; minor comments are not usually included.eLife. Your article has been favorably evaluated by Kevin Struhl (Senior Editor) and three reviewers, one of whom is a member of our Board of Reviewing Editors. The following individuals involved in review of your submission have agreed to reveal their identity: Mauro Calabrese (Reviewer #3).Thank you for submitting your article "EED orchestration of heart maturation through interaction with HDACs is H3K27me3-independent" for consideration by The reviewers have discussed the reviews with one another and the Reviewing Editor has drafted this decision to help you prepare a revised submission.eLife. However, the surprising and significant conclusions rest heavily on there being no cardiac cell division after birth to dilute away the H3K27 mark. Recent papers suggest that heart cells can and do divide after birth. The authors must test this to see if cell division is taking place. Current models are that non-dividing cells should be able to maintain H3K27me3 without PRC2, which is why the paper concludes that PRC2 must have another function independent of H3K27me3 in light of the cardiomyopathy seen after EED deletion.First: There is general agreement that your manuscript is a potentially important study and that the findings you report are surprising. There is enthusiasm for publishing this paper in Second: The reviewers also commented that the Text understated the loss of the histone mark. One third of derepressed genes did show loss of H3K27me3. How did this take place? This could have been due to cell division and passive loss of the histone mark. Quantitative measurements with statistical testing should be performed for known polycomb targets in heart muscle.Third: there is general concern that the EED KO may not be a true null. Peptides can still be seen in Western. It would be important to determine whether the mutant protein truly has no activity.Finally: your paper proposes a direct mechanistic link between EED and HDAC. One reviewer suggested the excellent idea of treating cardiomyocytes with HDAC-inhibitors to see if the same target genes are upregulated as in the EED CKO. If so, the results would speak for a common mechanism.For publication, we would like to see these points and related points addressed in a revised manuscript. If you choose to revise your manuscript, we would also require a reviewer-link to the GEO data for this study. We hope to see a revised manuscript within 2-3 months, although the nature of the requested experiments may require you to take more time.Reviewer #1:This is a potentially important study that proposes that depleting EED (and therefore PRC2) in postnatal cardiomyocytes results in a lethal dilated cardiomyopathy with loss of silencing of myocardial gene program. This finding is surprising because non-dividing cells are thought to be able to maintain H3K27me3 without PRC2. The authors conclude that EED has a non-canonical function that is independent of its H3K27 methylation activity. The paper reports exciting findings. However, there are two concerns that must be addressed for publication.1) Postnatal cardiomyocytes have been shown in recent studies to proliferate. Please consult (as one example): Naqvi N, Li M, Calvert JW, Tejada T, Lambert JP, Wu J, Kesteven SH, Holman SR, Matsuda T, Lovelock JD, et al. 2014. A proliferative burst during preadolescence establishes the final cardiomyocyte number. Cell 157: 795-807. The authors' findings relating to an EED deletion could be explained if there is proliferation in the postnatal cardiomyocytes. In this case, there would not be a "non-canonical" function of PRC2, and the authors would have to tone down their conclusions considerably. The paper may still be publishable without this dramatic conclusion.2) The authors' data suggest that there is in fact some loss of H3K27me3. This would be consistent with continued (though limited) proliferation of cardiomyocytes after birth. I think the authors underestimated the depletion of the H3K27me3 mark.Reviewer #2:In this manuscript, the authors describe the interesting observation that EED is critical for heart maturation and propose a novel mechanism, in which EED does not repress its target genes via its chanonical role in PRC2 through applying the H3K27me3 mark, but rather through affecting histone acetylation by modulating HDAC activity. The authors present clear evidence for a striking phenotype, and provide a comprehensive analysis with convincing rescue experiments showing a clear role for EED and HDACs. However, some discrepancies in the data, like for example opposing results for recruitment of HDACs by EED depending on the assay (EED loss of function – no effect vs. sufficiency via EED tethering), need to be addressed before firm mechanistic claims linking EED with HDAC activity can be made.CKO mice. The Western Blot is cut, so it is not visible, if there is a remaining Eed protein of reduced size. The authors should show the entire Western Blot and quantify the remaining RNA-levels based on qPCR (both for wt and CKO transcript levels). It is possible that a truncated Eed protein could still have a remaining function in the CKO allele, or incomplete deletion by the Cre-driver (some remaining protein visible in 1) 2) 3) Reviewer #3:This is an impressive study that demonstrates the functions and mechanisms of Eed-mediated gene regulation in the developing heart. The authors find that deletion of Eed during heart development results in cardiomyopathy concomitant with upregulation of several slow-twitch myofiber genes. A battery of genomic profiling experiments performed in wild-type, mutant, and Eed or HDAC rescued cardiomyocytes, demonstrate that Eed recruits and/or stimulates histone deacetylase activity at key target genes to acutely repress their expression during heart maturation. The major surprise is that this repressive effect is caused by recruitment and/or stimulation of HDAC activity, and is independent of major changes in H3K27me3 at two-thirds of target genes. The experiments presented are thorough and extensive. The results corroborate prior works that have linked PRC2 complex to HDACs, and should be of broad interest to many researchers. I think the manuscript could almost be published as is.1) From a mechanistic standpoint, the incomplete loss of H3K27me3 upon EED knockout left me scratching my head, because loss of Eed causes a global loss of H3K27me3 in just about every other cell type in which an Eed deletion has been performed. The authors suggest that the lack of complete H3K27me3 loss upon Eed deletion is due to the fact that the modification is very stable in cells that do not divide, like cardiomyocytes.An alternate explanation for the persistence of H3K27me3 upon Eed deletion is that the knockout is incomplete and/or there remains some functional PRC2 in the cells. For example, the Cre-driver may not KO Eed in 100% of cells. Or (less likely), maybe Ezh2 can be recruited to some sites independently of Eed.The authors should do a few simple characterization experiments in WT vs. CKO cardiomyocytes to clear up the uncertainty. A PCR-based assay to detect the floxed vs. deleted allele, and an immunostain to Eed would be informative. Also, Ezh2 is known to be destabilized in the absence of Eed. Thus, a western blot and immunostain to Ezh2 would also be informative.Also, does the Eed deletion generate a premature truncation of the protein? Or is the deletion in frame?2) Along the lines of 1, the authors should better explain the Eed western blot in  First: There is general agreement that your manuscript is a potentially important study and that the findings you report are surprising. There is enthusiasm for publishing this paper in eLife. However, the surprising and significant conclusions rest heavily on there being no cardiac cell division after birth to dilute away the H3K27 mark. Recent papers suggest that heart cells can and do divide after birth. The authors must test this to see if cell division is taking place. Current models are that non-dividing cells should be able to maintain H3K27me3 without PRC2, which is why the paper concludes that PRC2 must have another function independent of H3K27me3 in light of the cardiomyopathy seen after EED deletion.Our main finding is that upregulation of the majority of genes in postnatal cardiomyocyte EED knockout was not linked to loss of H3K27me3. This observation is independent of whether cardiomyocyte proliferate or not. However, the paucity of postnatal cardiomyocyte proliferation is likely an important reason that our data differs from studies of EED in cell lines or proliferating cell types.Although Naqvi et al. (PMID:24813607) suggested a burst of cardiomyocyte proliferation in mouse preadolescence (2 weeks after birth), this finding has not been reproduced by the work of four independent groups using multiple independent methods . The general consensus currently is that there is detectable but limited cardiomyocyte proliferation in the postnatal heart . The large majority of murine cardiomyocytes undergo a final round of nuclear division within the first postnatal week of life, and then terminally exit the cell cycle. In the mature adult heart, there is general agreement that the rate of cardiomyocyte turnover is low (CM turnover of 0.7-1% per year in adult mouse and human). We performed immunofluorescent staining for phosphohistone H3  of heart sections at three different postnatal ages and confirmed that there is little cardiomyocyte cell cycle activity after the first postnatal week . Given tSecond: The reviewers also commented that the Text understated the loss of the histone mark. One third of derepressed genes did show loss of H3K27me3. How did this take place? This could have been due to cell division and passive loss of the histone mark. Quantitative measurements with statistical testing should be performed for known polycomb targets in heart muscle.As noted by the reviewers, 1/3 of upregulated genes had H3K27me3 loss, whereas 2/3 of upregulated genes had retained H3K27me3. This variation in H3K27me3 levels strongly argues that loss of H3K27me3 is not due to passive dilution during proliferation. We have done separate experiments that measure locus-specific histone turnover in cardiomyocytes, which is possible because of their low turnover rate. We observed that cardiomyocytes display finely-regulated locus-specific histone turnover. This separate manuscript is currently in preparation.CKO  in cardiomyocytes as suggested by the editor and reviewers . We founThird: there is general concern that the EED KO may not be a true null. Peptides can still be seen in Western. It would be important to determine whether the mutant protein truly has no activity.In In the EED conditional allele, the floxed exons are 3-6, which contain the critical WD-repeat domain of EED that binds to nucleosomes and EZH2. This same floxed allele was tested by Stuart Orkin’s lab and demonstrated to be functionally null . In RNA-seq of purified cardiomyocytes, exons 3-6 were not detectably expressed, confirming high efficiency gene ablation . We furtFinally: your paper proposes a direct mechanistic link between EED and HDAC. One reviewer suggested the excellent idea of treating cardiomyocytes with HDAC-inhibitors to see if the same target genes are upregulated as in the EED CKO. If so, the results would speak for a common mechanism.Our model is that EED stimulates HDAC activity, and therefore HDAC inhibition might mimic some of the features of EED-CKO. As suggested by the editor and reviewers, we treated control (Het + AAV-luc) mice with the broad spectrum HDAC inhibitor SAHA . SAHA diFor publication, we would like to see these points and related points addressed in a revised manuscript. If you choose to revise your manuscript, we would also require a reviewer-link to the GEO data for this study. We hope to see a revised manuscript within 2-3 months, although the nature of the requested experiments may require you to take more time.We have deposited the data to GEO and have supplied a reviewer link in the revised manuscript.Reviewer #1:This is a potentially important study that proposes that depleting EED (and therefore PRC2) in postnatal cardiomyocytes results in a lethal dilated cardiomyopathy with loss of silencing of myocardial gene program. This finding is surprising because non-dividing cells are thought to be able to maintain H3K27me3 without PRC2. The authors conclude that EED has a non-canonical function that is independent of its H3K27 methylation activity. The paper reports exciting findings. However, there are two concerns that must be addressed for publication.1) Postnatal cardiomyocytes have been shown in recent studies to proliferate. Please consult (as one example): Naqvi N, Li M, Calvert JW, Tejada T, Lambert JP, Wu J, Kesteven SH, Holman SR, Matsuda T, Lovelock JD, et al. 2014. A proliferative burst during preadolescence establishes the final cardiomyocyte number. Cell 157: 795-807. The authors' findings relating to an EED deletion could be explained if there is proliferation in the postnatal cardiomyocytes. In this case, there would not be a "non-canonical" function of PRC2, and the authors would have to tone down their conclusions considerably. The paper may still be publishable without this dramatic conclusion.We addressed this comment in detail in the summary critique (point #1). The main observation that gene upregulation was not associated with H3K27me3 loss at 2/3 of genes is not dependent on whether or not cardiomyocytes proliferate. The terminal differentiation of postnatal CMs is relevant to the study because it provides a potential explanation for why this non-canonical mechanism is not observed in intensively studied cell types such as ESCs and cancer cell lines, where cell proliferation demands PRC2 deposition of new H3K27me3 and hence emphasizes this aspect of PRC2 function.Although Naqvi et al. (PMID:24813607) showed a burst of cardiomyocyte proliferation in mouse preadolescence (2 weeks after birth), this result has not been reproduced by four independent groups using multiple independent approaches . The general consensus currently is that there is detectable but limited cardiomyocyte proliferation in the postnatal heart. The large majority of murine cardiomyocytes undergo a final round of nuclear division within the first postnatal week of life, and then terminally exit the cell cycle. In the mature adult heart, there is general agreement that the rate of cardiomyocyte turnover is low (CM turnover of 0.7-1% per year in adult mouse and human). We observed that H3K27me3 is retained at some sites and lost at others. This cannot be accounted for simply by proliferation-related dilution, and further indicates that other mechanisms are responsible for H3K27me3 loss. We have studied histone dynamics in postnatal cardiomyocytes extensively and are preparing a separate manuscript on this topic. However, it is outside of the scope of the present study.2) The authors' data suggest that there is in fact some loss of H3K27me3. This would be consistent with continued (though limited) proliferation of cardiomyocytes after birth. I think the authors underestimated the depletion of the H3K27me3 mark.We agree that about 1/3 of derepressed genes have reduced H3K27me3. On the other hand, 2/3 of these genes retain H3K27me3, which was quantified in The major focus of this manuscript is understanding the mechanism of derepression of the 2/3 of genes that retained H3K27me3.Reviewer #2: In this manuscript, the authors describe the interesting observation that EED is critical for heart maturation and propose a novel mechanism, in which EED does not repress its target genes via its chanonical role in PRC2 through applying the H3K27me3 mark, but rather through affecting histone acetylation by modulating HDAC activity. The authors present clear evidence for a striking phenotype, and provide a comprehensive analysis with convincing rescue experiments showing a clear role for EED and HDACs. However, some discrepancies in the data, like for example opposing results for recruitment of HDACs by EED depending on the assay (EED loss of function – no effect vs. sufficiency via EED tethering), need to be addressed before firm mechanistic claims linking EED with HDAC activity can be made. 1) In 2) We thank the reviewer for this critical point. Although the tethering experiment demonstrated that EED can stimulate local histone deacetylation, it is confusing because it appears to contradict the in vivo data that show that EED is dispensable for recruiting HDAC2 to chromatin. The cellular and chromatin contexts of these experiments are quite different and we do not believe the results are incompatible. For instance, the tested genes do not naturally have HDAC2 or EED occupancy, whereas the in vivo experiment was a loci that do have endogenous HDAC2 and EED occupancy. Nevertheless, because this experiment is confusing, we removed it from the revised manuscript.3) We thank the reviewer for this excellent suggestion. We treated control (Het + AAV-luc) mice with the broad spectrum HDAC inhibitor SAHA . SAHA diReviewer #3:[…] 1) From a mechanistic standpoint, the incomplete loss of H3K27me3 upon EED knockout left me scratching my head, because loss of Eed causes a global loss of H3K27me3 in just about every other cell type in which an Eed deletion has been performed. The authors suggest that the lack of complete H3K27me3 loss upon Eed deletion is due to the fact that the modification is very stable in cells that do not divide, like cardiomyocytes.An alternate explanation for the persistence of H3K27me3 upon Eed deletion is that the knockout is incomplete and/or there remains some functional PRC2 in the cells. For example, the Cre-driver may not KO Eed in 100% of cells. Or (less likely), maybe Ezh2 can be recruited to some sites independently of Eed.Ezh2 is below detection level by Western blot in adult cardiomyocytes . We believe that H3K27me3 persists largely because of low histone turnover rates, i.e. H3K27me3 that was deposited prior to EED deletion persists for weeks in the absence of de novo deposition. This view is supported by a separate study in which we have studied histone turnover in terminally differentiated cardiomyocytes (in preparation). In the revised manuscript, we provide several additional lines of evidence to show that Cre-mediated EED recombination was highly efficient in CMs . Their data suggests that it does not generate a truncated protein but rather leads to loss of protein.2) Along the lines of 1, the authors should better explain the Eed western blot in The largest band marked by "*" is a non-specific band. We made this more clear in the figure legend. The size of this band is larger than the size expected for full length EED. There are weak bands of remaining EED in the EED CKO that are likely due to signals from non-cardiomyocytes of P0 and P5 heart apex used for Western blot and RT-qPCR . PCR froEED has multiple different isoforms: the Reinberg group, the Magnuson group, and our group showed that four EED isoforms exist and each varies during ESCs differentiation and heart development . These isoforms account for the multiple bands of EED on the western blot.


Caenorhabditis elegans is affected by absence of the sole C. elegans pocket protein LIN-35. Using a LIN-35 protein null mutant, we analyzed the assembly of E2F-DP and MuvB at promoters that are bound by DREAM and the level of expression of those "DREAM target genes" in embryos. We report that LIN-35 indeed mediates the association of E2F-DP and MuvB, a function that stabilizes DREAM subunit occupancy at target genes. In the absence of LIN-35, the occupancy of E2F-DP and MuvB at most DREAM target genes decreases dramatically and many of those genes become upregulated. The retention of E2F-DP and MuvB at some target gene promoters in lin-35 null embryos allowed us to test their contribution to DREAM target gene repression. Depletion of MuvB, but not E2F-DP, in the sensitized lin-35 null background caused further upregulation of DREAM target genes. We conclude that the pocket protein functions primarily to support MuvB-mediated repression of DREAM targets and that transcriptional repression is the innate function of the evolutionarily conserved MuvB complex. Our findings provide important insights into how mammalian DREAM assembly and disassembly may regulate gene expression and the cell cycle.The DREAM (Dp/Retinoblastoma(Rb)-like/E2F/MuvB) transcriptional repressor complex acts as a gatekeeper of the mammalian cell cycle by establishing and maintaining cellular quiescence. How DREAM’s three functional components, the E2F-DP heterodimer, the Rb-like pocket protein, and the MuvB subcomplex, form and function at target gene promoters remains unknown. The current model invokes that the pocket protein links E2F-DP and MuvB and is essential for gene repression. We tested this model by assessing how the conserved yet less redundant DREAM system in  C. elegans pocket protein LIN-35, we demonstrate that the pocket protein indeed links E2F-DP and MuvB, which aids in the stable occupancy of DREAM components near target genes. Depletion of additional DREAM components in lin-35 null worms revealed that the remaining chromatin-bound MuvB represses target genes. We conclude that the MuvB subcomplex mediates DREAM’s critical repressive function. Our functional genomics approach in the simplified C. elegans system reveals that the ancestral function of the pocket protein is to stabilize the innate repressive activity of MuvB, ensuring proper regulation of DREAM target genes through development.The 8-subunit DREAM transcriptional repressor complex contains 3 functional components that together control expression of cell cycle and developmental genes. How the E2F-DP transcription factor heterodimer, the pocket protein, and the highly conserved MuvB complex coalesce on chromatin and repress DREAM target genes has yet to be determined. We directly tested the prevailing model that the DREAM pocket protein links E2F-DP to MuvB and is required for gene repression. Using a protein null mutant of the sole  The highly conserved DREAM -like, E2F, and MuvB) transcriptional repressor complex mediates this cell cycle quiescence program . To. To11]. seq data , we perfseq data . We appl embryos . We obseC. elegans, we performed co-immunoprecipitation (co-IP) experiments from WT and lin-35(n745) late embryo protein extracts  , 6 showed a significant increase in chromatin occupancy of at least one subunit; this corresponds to 0.4% of all high-confidence DRM peaks. Our data demonstrate that in the absence of LIN-35, the chromatin association of E2F-DP and MuvB is significantly reduced  and MuvB  on 3 biological replicates of extracts , S3A Figanalysis . Of the  < 0.05) . 71% of  reduced , S3A Figlin-35 null compared to WT, these peaks appeared reduced in lin-35  of a transcriptional start site  mutant to WT in mixed-stage embryos , 3938, 3lin-35 null and lin-54(n2231) mutant late embryos to match our ChIP-seq analysis. The lin-54(n2231) allele contains the same point mutation as lin-54(n2990), which impairs its DNA binding . E2. E2lin-3vere EMO . Similarvere EMO . We concC. elegans lacking the sole pocket protein, we report evidence that MuvB mediates DRM target gene repression and that MuvB does not require LIN-35 to act as a transcriptional repressor. We demonstrate that loss of the sole C. elegans pocket protein LIN-35, which abolishes E2F-DP and MuvB association, results in a dramatic decrease in chromatin occupancy by both E2F-DP and MuvB and upregulation of many DRM target genes in late embryos. To determine how LIN-35 contributes to DRM gene repression, we depleted E2F-DP or MuvB subunits in lin-35 null animals. Depletion of MuvB, but not E2F-DP, causes further target gene upregulation and enhances the lin-35 null phenotype, indicating that MuvB directs repression of DRM target genes. Our study demonstrates that LIN-35 bridges E2F-DP and MuvB association, supporting their chromatin occupancy at target genes, and that MuvB functions innately as a transcriptional repressor of DRM target genes.Using genomic, genetic, and cell biological experiments on 0/G1 and activated in the later cell cycle contain a CDE, the binding motif for repressive E2F-DP, and a CHR, the binding motif for the LIN-54 subunit of MuvB [Studies of the mammalian DREAM DNA-binding motifs support the notion that MuvB mediates target gene repression. Most mammalian genes that are repressed in G of MuvB . Promote of MuvB . In cont of MuvB . Althouglin-35 null background may provide a clue as to how C. elegans E2F-DP contributes to DRM activity. EMO can result from germline dysfunction, e.g. defects in meiosis or fertilization, or somatic dysfunction, e.g. defects in somatic sheath cell formation or function [dpl-1, efl-1, lin-9, and lin-54 [dpl-1 and efl-1 null phenotype results from loss of E2F-DP’s transcriptional activation role in the germline, although some unknown somatic component is also involved [lin-9 null phenotype results from defects in somatic sheath development [lin-35 null worms, efl-1 knockdown resulted in significant reduction in fertility but no enhancement of the EMO phenotype. In E2F-DP null worms, we suspect that E2F-DP loss affects MuvB function in somatic cells but only when LIN-35 is present, and that effect is overshadowed by loss of E2F-DP activating function in the germline. Thus, we speculate that E2F-DP function in DRM is to support the association of MuvB with gene promoters, with the pocket protein acting as the intermediary.Our assessment of the endomitotic oocytes (EMO) phenotype in the sensitized function , 45. EMOd lin-54 , 42, 43.involved , 43. In C. elegans, loss of DRM activity leads to ectopic expression of germline genes in somatic cells [C. elegans E2F-DP occupying new genomic sites in lin-35 null embryos that are normally only observed in the adult germline. Perhaps an additional event following loss of LIN-35 must occur before E2F-DP can localize to and activate a germline program. Our study further suggests that some DRM target genes require secondary regulatory factors in addition to loss of DRM for their activation. As an example, E2F-DP was recently shown to co-regulate some developmental genes with heat-shock factor (HSF) [In ic cells , 28. Intic cells . Additioic cells , and by ic cells . Howeveric cells . Our anaor (HSF) . It is lC. elegans DRM provides insight into how the mammalian DREAM complex components maintain cellular quiescence and promote the transition into the cell cycle. Upon exit from the cell cycle, p130/p107 promotes E2F-DP and MuvB association on chromatin, stabilizing MuvB-mediated transcriptional repression of important cell cycle gene targets , a WD-repeat family protein known to bind histones when present in other complexes [Drosophila has been shown to be required for dREAM-mediated gene repression [C. elegans, LIN-53’s role may include facilitating deposition of the histone variant HTZ-1/H2A.Z within the body of target genes [The MuvB complex’s dual transcriptional role in mammalian cells has obscured how it may contribute to mammalian DREAM-mediated gene repression. Recently, it was hypothesized that DREAM stably positions a nucleosome at the transcriptional start site of mammalian DREAM target gene promoters to prevent transcriptional activation . A likelomplexes . The RBApression . Additioet genes . If MuvBC. elegans indicate that MuvB mediates the repressive functions of the worm DREAM complex, and can perform this activity in the absence of p130/p107 and a link to E2F-DP. Thus, if DREAM-associated gene repression represents the ancestral function of E2F-DP and pocket proteins, MuvB may have emerged prior to or coincident with E2F-DP and a pocket protein. Moreover, since C. elegans does not have a B-Myb homolog, MuvB’s repressive mechanism likely represents its ancestral function, with its activating role emerging more recently [Our demonstration of MuvB’s innate repressive role may provide clues as to how the DREAM complex components evolved to meet diverse biological needs. Phylogenetic analyses suggest that the activating E2F-DPs and pRb coevolved, diverging from their respective common ancestors, which are more similar to the repressive E2F-DPs and p130/p107 DREAM components , 50. Ourrecently .Compared to the strong tumor suppressor activity of pRb, functional redundancy in p130/p107 has obscured if and how they act as tumor suppressors . InteresAll strains were cultured at 20°C using standard methods, unless otherwise noted. N2 (Bristol) was used as wild type (WT). Mutant alleles that were used in this study are listed in Following embryo collection, extracts were prepared and CoIP performed based on . Frozen Following embryo collection, extracts were prepared and ChIP performed based on . Frozen www.bioconductor.org) and the R statistical programming language [de novo using the MEME suite [Sequencing libraries were prepared from genomic DNA fragments (input) or those obtained after ChIP using the TruSeq ChIP Sample Prep Kit . Amplified libraries were size selected to obtain 200–500 bp fragments using Agencourt AMPure XP beads . After verifying library fragment sizes using a 2100 Bioanalyzer , sequencing was performed using the Illumina HiSeq 2000/4000 platforms. Libraries were sequenced at the Vincent J. Coates Genomics Sequencing Laboratory at the University of California, Berkeley. Sequencing reads were mapped to the ce10 reference genome using bowtie-1.1.2 , allowinlanguage . Motif aME suite , or overME suite . Motif sME suite . Importaefl-1, lin-9, and lin-54 were sequence-verified and fed to lin-35(n745) worms. Progeny from gravid worms grown in liquid media were synchronized as L1 larvae and fed RNAi on NGM plates containing 1 mM IPTG and ampicillin. RNAi feeding was administered for 2 generations at 24°C, after which late embryos from 2nd generation gravid adults were collected in Trizol for RNA isolation and transcript analysis. A total of 1.5 μg RNA was DNAse treated and reverse transcribed using the High Capacity cDNA Kit . qPCR was performed using SYBR green reagents on a LightCycler 480 (Roche) or an Applied Biosystems ViiA 7 Real-Time PCR System (ThermoFisher) using primers specific to a select gene set, which are provided in act-2 as the control gene using the ΔCt method with efficiency correction. Statistical analysis of genome-wide expression data was performed using R, using the Quantile normalization and Robust Multichip Average (RMA) algorithm in the affy package from Bioconductor [q-value < 0.05 and fold change > 1.5 as the significance thresholds [nd generation RNAi-fed L4 larvae were aged overnight and scored.Bacteria from the Ahringer RNAi feeding library  expressionductor , 69. To resholds . For EMOFor endomitotic oocyte (EMO) scoring, L4 larvae were aged overnight, fixed using Carnoy’s solution, stained with the DNA intercalator DAPI, and scored blind. The definitions of severe EMO, moderate EMO, and no Emo are in the S1 Fig(A) UpSet visualization of DRM subunit peak overlaps. Black filled circles indicate the factor(s) present within an intersection category. Intersection categories consisting of less than 20 represented peaks were omitted. (B) Heatmap of normalized ChIP-seq profiles of pooled replicates for each DRM subunit across all identified peaks. High confidence DRM peaks (identified in (TIF)Click here for additional data file.S2 FigFull western blots from 2 co-immunoprecipitation experiments performed on biological replicate late embryo lysates. Proteins bound (B) and unbound (UB) by EFL-1, LIN-37, or control IgG immunoprecipitation were separated by SDS/PAGE, and western blot analysis was performed using the antibodies indicated in the bottom right corner. 5% of Input was included. Arrows indicate protein bands presented in main (TIF)Click here for additional data file.S3 Figlin-35 vs. wild type (WT) for each DRM subunit for Class I (dark) and Class II (light) peaks, as described in the lin-35 null (y-axis) read density of pooled replicates within a single wild-type DRM peak from E2F/DP (blue), LIN-35 (purple), and MuvB (green) subunit ChIP-seq. Dotted lines indicate the slope expected if DRM occupancy is equivalent in WT and lin-35 ChIP-seq. (B) ChIP-qPCR of 5 DRM subunits at 8 Class I peaks and 4 Class II peaks. H3K4me3 ChIP was included as a positive control. Signals are presented as the log2 fold enrichment of the ChIP signal for each region vs. a negative control region (a non-coding region of chromosome IV). Error bars indicate standard error of the mean. Significance was determined by a student’s T test between subunit ChIP values in WT versus lin-35  or between subunit versus IgG ChIP values in lin-35 . The chromatin occupancy of the majority of DRM subunits significantly decreased at all sites tested. Additionally, the majority of DRM subunits retained significant chromatin enrichment over IgG in lin-35 null embryos at 9 of the 12 sites tested.(A) Scatter plot of normalized ChIP-seq reads in (TIF)Click here for additional data file.S4 Figlin-35 null replicates for each DRM subunit. Box plots show the median (black bar) with each box extending from the 25th to 75th percentile. Extended whiskers indicate the 2.5th and 97.5th percentile, and wedges indicate the 95% confidence interval for the medians. Outliers were removed from the graphs. Signal from the 2ndlin-35 null ChIP-seq replicate appears to be abnormally elevated compared to the 1st and 3rd replicates.Box plots of normalized read counts in DRM peaks in wild-type (WT) versus (TIF)Click here for additional data file.S5 Figlin-35 late embryos near DRM subunit genes. E2F-DP subunits (blues), LIN-35 (purple), and MuvB subunits (greens) were overlaid on the same track. Normalized ChIP-seq enrichment values for each data track are indicated on the y-axis, and the chromosomal coordinates of the areas shown are indicated below the x-axis. Gene transcriptional start sites are indicated by black boxes, coding strand directions are indicated by white arrowheads, and gene transcriptional termination sites are indicated by black arrowheads. Other genes in the region are similarly indicated in grey. Each DRM peak location is indicated by a black rectangle with the peak center highlighted in red. Of the 8 DRM genes, only lin-37 and lin-52 are not targeted by a promoter-bound DRM peak. (B) RT-qPCR analysis comparing transcript levels of DRM subunit genes in lin-35(n745) (purple) and lin-54(n2231) (green) late embryos to the level in WT (white) late embryos, presented as the relative quantity (Rq) compared to act-2. Error bars indicate standard error of the mean, and significance was determined by a student’s T test between transcript levels in mutant vs WT . 7 of the 8 DRM genes were upregulated in lin-35 and/or lin-54 mutants. lin-35 transcripts in lin-35 null embryos were significantly decreased compared to WT, consistent with the W151Stop* mutation leading to nonsense-mediated decay of the transcript. (C) Western blot analysis of 2 biological replicates of WT and lin-35 lysates performed using the antibodies indicated on the right. Alpha-tubulin served as a loading control. (D) ChIP-qPCR of DRM subunits at the mes-2, cdk-1, and pcn-1 gene promoters in L1 larvae. Signals are presented as the log2 fold enrichment of the ChIP signal for each region vs. a negative control region (a non-coding region of chromosome IV). Error bars indicate standard error of the mean. Significance was determined by a student’s T test between subunit ChIP values in WT versus lin-35  or between subunit versus IgG ChIP values in lin-35 .(A) Genomic profiles of each DRM component in wild-type (WT) and (TIF)Click here for additional data file.S6 Figde novo motif in Class I peaks compared to Class II peaks, the enrichment was lost when we tested conserved CDE and/or CHR motifs. This result suggests that the presence of a DRM motif does not distinguish the two peak classes. Conserved CDE and CHR motifs were observed in HOT DRM peaks, suggesting that many DRM peaks in HOT regions are true DRM binding sites. Enrichment of phylogenetically conserved (phasCons > 0.7) CDE-like sequences  and CHR-like sequences (IUPAC code: TTYRAA) in DRM peak regions (1418 regions) compared to background promoters of all non-overlapping and non-DRM target genes . Black bars and bolded sequences indicate significant enrichment over background using a hypergeometric distribution . Not all potential CDE sequences are shown.  Motif enrichment in Class I versus Class II DRM peaks and in HOT versus not HOT DRM peaks using a chi-squared test (p < 0.05). Although we observed enrichment of the (TIF)Click here for additional data file.S7 Figlin-35 compared to wild-type (WT) ChIP-seq. The number of subunit peaks observed only in WT (i), in both WT and lin-35 (ii), and only in lin-35 (iii) are indicated. Total overlaps of subunit peaks observed only in lin-35 (iii) for E2F-DP (A) or MuvB (B) are indicated in the 2nd venn diagram (grey). (C) Heatmap of normalized ChIP-seq profiles of pooled replicates for each DRM subunit. Plotted regions for each subunit are separated based on whether the peak was observed only in wild type (WT) (i), observed in both WT and lin-35 (ii), or observed only in lin-35 (iii). Many "lin-35 only" peaks reside near DRM binding sites already observed in WT. Venn diagrams showing total peak overlaps for each E2F-DP subunit  and each MuvB subunit  in (TIF)Click here for additional data file.S8 Figlin-35 early embryo (EE) and L1, light and dark purple) and [lin-54 mixed-stage embryo (ME), green) (Group A genes). (B) Box plots of wild-type (WT) or mutant (lin-35 or lin-54) log2 average expression values of Upregulated DRM targets (Group A), Not Upregulated DRM Targets (Group B), and All Genes. Staging of the samples for each experimental group is indicated: early embryos (EE), mixed-stage embryos (ME), and L1 larvae (L1). Box plots show the median (black bar) with each box extending from the 25th to 75th percentile. Extended whiskers indicate the 2.5th and 97.5th percentile, and wedges indicate the 95% confidence interval for the medians. Outliers were removed from the graphs. Significance  was determined by a Wilcoxon-Mann-Whitney test. These results indicate that detectable upregulation reflects a drop in the average expression of DRM target genes in WT over developmental time, not an increase in the average expression of DRM target genes in mutants. This effect is also observed in Group B genes but the repression is not as dramatic. We speculate that Group A genes are progressively more repressed by DRM over developmental time; DRM dysfunction manifests as a failure in this repression and hence gene upregulation as development progresses. In contrast, Group B genes may include genes that 1) DRM does not fully repress or 2) are activated intermittently as development progresses. These findings suggest that detection of DRM target gene upregulation is dependent on developmental stage because wild-type DRM requires developmental time to establish repression of gene targets.(A) Venn diagrams showing DRM target genes that are upregulated based on microarray analysis from  (lin-35 ple) and  (lin-54 (TIF)Click here for additional data file.S9 Fig10 Benjamini adjusted p-value of selected GO term enrichment observed in (B) DRM targets marked by Class I peaks vs. Class II peaks, and (C) DRM targets marked by HOT regions vs. not HOT regions.(A) REViGO gene ontology (GO) visualization of terms, which helped identify informative GO terms, enriched in Group A targets (left) vs. Group B targets (right), as described in (TIF)Click here for additional data file.S10 Figefl-1 (blue), lin-9 (light green), and lin-54 (dark green) transcript levels in lin-35 null (lin-35(n745)) late embryos compared to empty-vector  RNAi. (B) Genomic profiles of each DRM component in wild-type (WT) and lin-35 null late embryos at rme-2 and mex-5. E2F/DP subunits (blues), LIN-35 (purple) and MuvB subunits (greens) were overlaid on the same track. Normalized ChIP-seq enrichment values for each data track are indicated on the y-axis, and the chromosomal coordinates of the areas shown are indicated below the x-axis. Gene transcriptional start sites are indicated by black boxes, and coding strand directions are indicated by white arrowheads. (C) RT-qPCR analysis of rme-2 and mex-5 following efl-1 (blue), lin-9 (light green), and lin-54 (dark green) RNAi in lin-35 null late embryos as compared to empty-vector . Expression values from 2 independent experiments each consisting of 4 biological replicates for each RNAi condition were averaged and presented as the relative quantity (Rq) compared to act-2. Error bars indicate standard error of the mean.(A) RT-qPCR quantification of the effectiveness of RNAi depletion of (TIF)Click here for additional data file.S1 Table(XLSX)Click here for additional data file.S2 Table(XLSX)Click here for additional data file.S3 Table(XLSX)Click here for additional data file.S4 Table(XLSX)Click here for additional data file.S5 Table(TIF)Click here for additional data file.S6 Table(TIF)Click here for additional data file.S7 Table(TIF)Click here for additional data file.S8 Table(TIF)Click here for additional data file.S9 Table(TIF)Click here for additional data file.S10 Table(TIF)Click here for additional data file.


Escherichia coli MalI transcription factor, fused to mCherry fluorescent protein. We have used this in combination with a LacI repressor::GFP protein based FROS probe to assess the cellular location of commonly regulated transcription units that are distal on the Escherichia coli genome.In bacteria, many transcription activator and repressor proteins regulate multiple transcription units that are often distally distributed on the bacterial genome. To investigate the subcellular location of DNA bound proteins in the folded bacterial nucleoid, fluorescent reporters have been developed which can be targeted to specific DNA operator sites. Such Fluorescent Reporter-Operator System (FROS) probes consist of a fluorescent protein fused to a DNA binding protein, which binds to an array of DNA operator sites located within the genome. Here we have developed a new FROS probe using the Escherichia coli MalI protein fused to the mCherry fluorescent protein. This was used in combination with a Lac repressor:green fluorescent protein fusion to examine the spatial positioning and possible co-localisation of target genes, regulated by the Escherichia coli AraC protein. We report that induction of gene expression with arabinose does not result in co-localisation of AraC-regulated transcription units. However, measurable repositioning was observed when gene expression was induced at the AraC-regulated promoter controlling expression of the araFGH genes, located close to the DNA replication terminus on the chromosome. Moreover, in dividing cells, arabinose-induced expression at the araFGH locus enhanced chromosome segregation after replication.We developed a new DNA binding fluorescent reporter, consisting of the Regions of the chromosome regulated by AraC do not colocalise, but transcription events can induce movement of chromosome loci in bacteria and our observations suggest a role for gene expression in chromosome segregation.The online version of this article (doi:10.1186/s12866-017-1079-2) contains supplementary material, which is available to authorized users. Escherichia coli K-12 nucleoid is folded into a structure consisting of four independently folded macrodomains, and two non-structured regions [E. coli GalR transcription repressor protein, associated with DNA target sites in different macrodomains, could co-localise. Also, a plasmid-encoded transcription unit can re-locate to particular cellular positions when being actively expressed [Bacterial nucleoids are highly compacted structures composed of chromosomal DNA, nucleoid structuring proteins and RNA . The DNA regions . Each do regions  exploitixpressed .E. coli AraC regulon. AraC is a transcription activator that regulates genes involved in the uptake and metabolism of arabinose. AraC binds to its DNA target in the absence of arabinose, and activates transcription of four transcription units, located in three different macrodomains, only in the presence of arabinose [E. coli MalI DNA binding protein [E. coli strain MG1655, adjacent to AraC regulated genes, and determined the relative cellular locations by fluorescence microscopy. We show that AraC-regulated genes, within different macrodomains, do not co-localise in the cell. However, we show that the araFGH operon, which is near to the replication terminus, is spatially repositioned upon induction of transcription. This was particularly evident in dividing cells, where it was observed that induction of transcription facilitated separation of newly-replicated sister chromatids.To investigate these points, we have exploited the rabinose . Thus, irabinose –12, adja protein , 14, fus650 reached approximately 0.1. For cultures supplemented with sugars, a final concentration of 0.3% of the required sugar was added to the culture for 1 min before slides were prepared [All bacterial strains and plasmids used in this study are listed in Additional file prepared . For culmalI::mcherry fusion, is a derivative of pACYC184 and carries resistance to chloramphenicol and contains the p15A origin of replication. The malI promoter and gene were amplified from the plasmid pACYCMalI using oligo’s D63433 and D71192  or MalO array and a kanamycin cassette, flanked by 500 bp regions of homology from both sides of the insertion site, adjacent to either the araBAD, araJ or araFGH for MalO, or adjacent to either araBAD, araJ or dps for LacO. Oligonucleotides were designed to amplify 500 bp upstream of each insertion site,  and Nikon Plan Apo VC 100× Oil immersion lens , with a final optical magnification of 100×. A DAPI filter set was used for visualising the Hoechst 33,258 stained nucleoid, FITC filter set for GFP and TxRed filter set for mCherry. Cells were also imaged using brightfield. Microscopy was carried out at room temperature, within 30 min of slides being prepared.Bacterial cultures were grown for 24 h at 23 °C  with aerMicroscope images were analysed using Image J software. To determine the position of foci within cells, the measuring function was used to measure both the length of the cell and the distance from the focus to the nearest pole. The position of the focus within the cell was then calculated and is presented relative to the length of the cell, which was set at an arbitrary value of 1. For cells containing two foci, the focus nearest to a pole was designated as the ‘1st of 2 foci’, and the distance from the focus to the nearest pole was measured. The distance from the ‘2nd of 2 foci’ to the same pole was then measured. Measurements were taken from at least 300 cells. Where the data are presented on a scatter plot Fig. , the relE. coli K-12 MalI protein is a transcription repressor associated with the Mal operon, and is a member of the GalR/LacI family of DNA binding proteins [malI gene is located on its chromosome, convergent to the malXY operon. When expressed, MalI binds to a 16 bp target site at both the malI and malXY promoters to repress transcription. To generate an array of MalI binding sites, we used an iterative PCR procedure, followed by a cloning approach to build the required number of DNA binding sites in a plasmid. Hence, 20 DNA sites for MalI were incorporated into the MalI operator array , which was then targeted to specific positions in the chromosome of E. coli strain MG1655 using the gene doctoring recombineering method [araBAD, araJ and araFGH promoter regions  –12. Typiproteins , 14. Theg method . The arrmalI:mCherry gene fusion was cloned downstream of the melR promoter in plasmid pACYC184, creating plasmid pLER108. This resulted in constitutive, low level expression of MalI:mCherry. To examine the DNA binding efficiency and fluorescence derived from the fusion protein, wildtype MG1655 cells, and cells carrying the MalO array situated at the araBAD, araJ and araFGH loci were transformed with pLER108. Fluorescence derived from cells in the mid-logarithmic phase of growth was examined using epifluorescence microscopy. In the absence of a MalO array, there are no visible foci and the background fluorescence in the cell was negligible  [lacI binding sites, which we introduced at the araBAD and araJ loci in MG1655 cells harbouring a lacI:gfp chromosome fusion at the natural lacI loci  was less than between the araBAD and araFGH probes (average 0.64 μm). This was expected since araBAD and araJ are located within the same macrodomain, whereas the araBAD and araFGH are in different domains. Thus, unlike previously reported with GalR regulated promoters [To assess whether AraC regulated promoters co-localised, strains of opy Fig. . Cells cose Fig.  and c. TE. coli strains containing LacI:GFP FROS probes at the araBAD and araJ loci, and the MalI:mCherry FROS probe at the araFGH locus, were grown in the presence or absence of arabinose. Cells were analysed by fluorescence microscopy, and individual, non-dividing cells containing a single fluorescent focus were analysed. The distance from each focus to the nearest cell pole was measured, and this value was divided by the total cell length, thereby providing a position relative to total cell length , with 98% of cells containing at least 2 foci. In conditions of growth supplemented with glucose or arabinose, no change in the number of araBAD foci in each individual cell was observed. Similarly, no change in the number of fluorescent foci in each individual cell was observed when the FROS probe was located adjacent to the dps promoter that was used as a control region of the chromosome, unaffected by arabinose. However, at the araFGH, there was a clear reduction in the number of cells containing only one foci locus in the presence of arabinose, but not glucose. The observed shift from 37% of the population containing a single focus to 13% upon induction suggests that expression of the araFGH operon assists separation of newly replicated sister chromatids. To test this, cultures were supplemented with arabinose, to induce expression of the araFGH operon, and either rifampicin: to inhibit transcription, or erythromycin: to inhibit translation. In both cases, the addition of the inhibitors prevented the separation of foci Additional file 2:Plasmids used in this study. (DOCX 23 kb)Additional file 3:DNA oligonucleotides used in this study. (DOCX 20 kb)Additional file 4:araBAD, (b) araFGH and (c) mntH. (DOCX 74 kb)Schematic diagram to show the insertion sites of FROS operators adjacent to (a) Additional file 5:MalI:mCherry expressed from plasmid pLER108 in MG1655. (DOCX 325 kb)


There is an enormous need to make better use of the ever increasing wealth of publicly available genomic information and to utilize the tremendous progress in computational approaches in the life sciences. Transcriptional regulation of protein-coding genes is a major mechanism of controlling cellular functions. However, the myriad of transcription factors potentially controlling transcription of any given gene makes it often difficult to quickly identify the biological relevant transcription factors. Here, we report on the identification of Hnf4a as a major transcription factor of the so far unstudied DnaJ heat shock protein family (Hsp40) member C22 (Dnajc22). We propose an approach utilizing recent advances in computational biology and the wealth of publicly available genomic information guiding the identification of potential transcription factor candidates together with wet-lab experiments validating computational models. More specifically, the combined use of co-expression analyses based on self-organizing maps with sequence-based transcription factor binding prediction led to the identification of Hnf4a as the potential transcriptional regulator for Dnajc22 which was further corroborated using publicly available datasets on Hnf4a. Following this procedure, we determined its functional binding site in the murine Dnajc22 locus using ChIP-qPCR and luciferase assays and verified this regulatory loop in fruitfly, zebrafish, and humans. Transcription is the first step of decoding the information in the genes of our genome. It is the basis for the proteins that the cells in our body can produce and shape their fate during development, their reaction to changes in their surroundings, or their contribution in pathogenesis. Regulation of gene expression is therefore a time-, cell type-, and stimulus-specific tightly controlled process.1. Classical experimental approaches included cloning of site-specific genomic sequences, followed by intensive testing of individual sections of a genomic locus by reporter assays1. However, these assays only allowed the identification of functional sequences, but did not yet reveal the TF responsible for a specific gene regulation. Prior to computational prediction of TF binding motifs, TFs could only be postulated and assessed by electro mobility shift assays2, or chromatin immunoprecipitation followed by PCR or microarray (ChIP-PCR/ChIP-chip)3.Identifying regulating transcription factors (TFs) for a particular gene of interest (GOI) can reveal information about the context of that gene’s function as well as be useful in designing strategies to alter its expression level. Originally only addressed experimentally, computational approaches using large datasets have been introduced to identify TFs for GOIs in recent years4 or Transfac5 and principles for motifs of a given TF were established computationally6. With whole genome information, such motifs were used to predict genome wide binding sites, although it also became quickly clear that this approach results in a large number of false positive results7. More recent tools improved this drawback considerably by including conservation information, p-value thresholds and by evaluating the quality of the motif8.As a next step, experimentally determined binding sites for individual TFs were collected in databases such as JASPAR9, NOMe-seq10 (Nucleosome Occupancy and Methylome sequencing) or ATAC-seq11 (Assay for Transposase-Accessible Chromatin using sequencing), and global TF binding maps by ChIP-seq12 allow the global assessment of TF expression, chromatin accessibility and the assessment of global binding maps for individual TFs. Consortia such as ENCODE (www.encodeproject.org) or IHEC (www.ihec-epigenomes.org) have compiled enormous publicly available datasets of global TF binding maps, which are now available for further mining. These approaches have broadened from a sole TF-GOI context to the attempt to compute complete TF networks13.The advent of next generation sequencing again accelerated the development of technologies to study genomic regulation on a global level. RNA sequencing (RNA-seq) to assess global transcriptomes, the assessment of open chromatin by DNase-Seq14 and for many TFs, ChIP-seq grade antibodies do not yet exist. Global transcriptome data provide information about expression levels of TFs; however, the mere expression level of TFs cannot necessarily predict its transcriptional activity. TFs are often subject to post-translational modifications and controlled by signalling cascades within the cell. As a consequence, experimental validation of the functionality of a particular TF at a given gene locus is a complementary requirement to the assessment of global binding maps when studying TF-GOI interactions. While many of the necessary steps were introduced individually, comprehensive workflows are not available. Moreover, many of these separate approaches are still tailored towards the domain expert, particularly computational biologists and bioinformaticians. Yet, linking different computational approaches is still a daunting task for a scientist focusing on the experimental assessment of transcriptional regulation. What is required is an easy-to-follow approach combining the existing genome-wide datasets, computational prediction and concise experimental validation of the TF-GOI interaction.However, even in light of this very rich resource of available data, there are still limitations, when searching for TFs regulating a particular GOI. Moreover, it is probably not feasible to generate TF maps for any given tissue. While improving significantly during the last years, ChIP-seq data are still hampered by the required quality of the antibodies utilized for immunoprecipitationDrosophila melanogaster Wurst – a gene, we previously identified as an essential factor for the functional development of the tracheal system – the respiratory organ of the fly15. It is conserved with single orthologs across the species16, yet there is neither functional data nor information about the regulation for its vertebrate counterparts.DnaJ heat shock protein family (Hsp40) member C22 (Dnajc22) is the vertebrate ortholog of in silico and experimental approach of how to harness the wealth of already published transcriptomic and ChIP-seq data complemented with selective wet-lab validation to functionally identify a transcription factor for a particular gene of interest. Following this approach and using Dnajc22 as an example, we identified Hnf4a as a major transcriptional regulator.Here, we show a step-by-step combined Drosophila melanogaster15. According to the Ensembl database, wurst is evolutionary conserved, bearing single orthologs in at least 49 species including human and mouse17  of any given gene of interest (GOI), we first postulated as a major requirement a substantial co-expression of TFs and the GOI across tissues and even species Fig. . As a moMus musculus, for which large publicly available data sources concerning tissue, organ and cell-type specific gene expression are available. For this purpose, we utilized the murine gene atlas dataset (GSE10246)18 containing 182 transcriptomes of which we acquired 104 transcriptomes, pre-processed the data, and determined co-expression of genes across 22 organs, 14 tissues, and 16 cell types -clustering was performed to determine those transcripts being co-expressed with Dnajc22 across organs, tissues, and individual cell types Fig. . The SOM8 determined Hnf4a, Hnf1b, Gata1, and Cdx2, whereas the top results of pcaGoPromoter20 disclosed Hnf4a, Hnf1b, Nr1h2, as well as Hnf1a , identifying five potential HNF4 binding sites within the murine Dnajc22 locus  consisting of 25 transcriptomes composed of six organs, five tissues, and two cell types. As expected, SOM-clustering revealed a strong transcriptional relationship between DNAJC22 and HNF4A since both genes were found within the same cluster 31 and analysed the expression of Dnajc22 in these datasets. Microarray analysis of livers from a liver-specific Hnf4a knockout mouse model26 showed a reduction of Dnajc22 transcript to background levels  in the cloned sequence  regulating a given gene of interest (GOI) is a major research area. Classical approaches including gene cloning strategies, reporter assays, and protein-DNA binding studies are labour-intensive, highly dependent on availability of respective reagents such as specific antibodies and often do not lead to the identification of the major transcriptional regulators. Here, we describe an integrated methodology that makes use of recent technological advances in genomics, transcriptomics, and epigenomics and is based on the hypothesis that TFs and their regulated GOIs are co-expressed across tissues and species. In a first step, applying clustering approaches to large transcriptome datasets such as human and murine tissue atlases, we identified co-expressed TF candidates for our GOI Dnajc22. Prediction for binding of these candidates to the Dnajc22 locus further decimated the number of true candidate TFs leaving Hnf4a as the most likely candidate regulating Dnajc22. An important next step was the As the primary input data source for co-expression analysis, we used the murine tissue array. Based on the assumption of similar regulation of Dnajc22 orthologs, an important step was also the confirmation of similar co-expression patterns between potential TFs and GOI in a second species, e.g. RNA-seq data from various human samples provided by the Roadmap Epigenomics Consortium -clustering, which is among hierarchical clustering and K-means one of the most popular clustering methods in the field of gene expression analysis8. Other TF prediction tools such as pcaGoPromoter rely on smaller databases and thus are likely to produce less comprehensive results compared to e.g. iRegulon. However, in case of pcaGoPromoter, the great advantage is the implementation in R which e.g. permits easier integration in our R-based analysis workflow.Global gene expression analyses, as well as the accumulating knowledge about chromatin structure and DNA accessibility, are currently advancing the algorithms for TF binding site predictions. In addition, the recent progress in optimizing TF prediction tools has considerably reduced the rate of false positive results. For example, iRegulon accesses large databases to combine information from several species and experiments to substantiate the predicted motifs and hence reduces the number of biologically less relevant motifs43. However, in context of Dnajc22, Hnf1b did not show similar expression patterns across all cell types and tissues arguing against a global regulator for this gene. Collectively, although our presented example focussed on the prediction of the major transcription factor for our GOI, in principle, the approach can be utilized to find additional transcription factors for a GOI when using less stringent parameter thresholds.Overall, the strength of our approach lies in the combination of various data sources, approaches and tools, which allows us to focus on TFs fulfilling several criteria including co-expression of the TF in the subcluster of our GOI and binding prediction to the locus of our GOI. Although we focused on Hnf4a in the present study based on the high expression correlation to Dnajc22  will certainly show differences in the basal expression of the potential regulators or gene of interest, yet collectively enables to demonstrate concurrent changes in transcript levels. Once proteome data will be available to the same extent, one will be able to broaden the in silico validation step in our approach also on protein level.Depending on the availability of transcriptomic and ChIP-seq data in the public domain, the resulting potential candidates can be further validated in vivo co-expression analysis of Dnajc22 with potential transcription factors, followed by in silico prediction of TF binding, evaluation of transcriptome data studying regulation of the potential TFs and ChIP-data addressing natural binding of the TF to the Dnajc22 locus, followed by functional analysis of the identified binding sites, and lastly experimental validation of regulation of Dnajc22 by the TF Hnf4a, we establish a straightforward workflow of identifying potential transcriptional regulators for any gene of interest. A major focus in designing the described strategy was that only freely available algorithms were used for analysis, opening the way for researchers to easily apply this strategy to their biological questions.Alongside discovering one of the major regulators of Dnajc22, we established a workflow that can be applied to any gene of interest. This overview is depicted in Fig. This study makes use of publicly available data sets which were combined and analysed in a new context. These studies, gene accession numbers and names in different species as well as the utilized databases and tools are listed below.Drosophila melanogaster wurst/CG9089/FBgn0030805Danio rerio dnajc22/ENSDARG00000037067/si:dkey-166n8.8Rattus norvegicus Dnajc22/ENSRNOG00000053498/RGD1311098Mus musculus Dnajc22/ENSMUSG00000038009/2810451A06RikHomo sapiens DNAJC22/ENSG00000178401/FLJ1323644 from the Comparative Genomics section by choosing Gene Tree. GeneTree ID is ENSGT00390000012136.The phylogenetic tree for Dnajc22 was extracted from the Ensembl databaseThe R-scripts used for analysing transcriptomes in this study are available on our GitHub page (https://github.com/LIMES-immunogenomics/Dnajc22-regulation).45. In detail, the dataset was loaded into R and subsequently normalized using the robust multi-array average (RMA) expression measure function of the Bioconductor package ‘affy’46. Genes were defined as expressed and kept for further analyses if the log2-transformed mean expression values were higher than the background value, which was set to 6. Afterwards, multi-probes were filtered to retain only a single probe with the highest variance across the dataset as representative for the corresponding gene. For subsequent clustering analysis, the mean gene expression values of the tissues were used. In the present study, 104 samples were used. However, we also performed the analysis with both all primary samples of the GSE10246 dataset (160 transcriptomes) and a reduced dataset (72 transcriptomes). Using these datasets and our provided workflow, we also identified Hnf4a as potential regulator of Dnajc22 expression  was downloaded from NCBI GEO database. All pre-processing steps were performed using R version 3.3.047 using default parameters. Quantification of aligned reads and extraction of gene and transcript information was performed using htseq-count (HTSeq version 0.6.1p1)48. Next, annotated data was imported into R and normalized using the Bioconductor package DESeq249. For further analysis, only genes were used which exhibited a normalized RNA-seq group mean value higher than the 33th percentile. Here, the cut-off value at the 33th percentile was approximately 200; 16,141 genes had a group mean value higher than 200 and thus were kept for further analysis.Human RNA-seq samples from 6 organs, 5 tissues, and 2 cell-types were downloaded from NCBI GEO online database  and converted from sra format to fastq using the fastq-dump function of sratoolkit 2.6.3 (https://github.com/ncbi/sra-tools). Reads were aligned to human reference genome hg19 from UCSC by HISAT2 version 2.0.4Organs:spleen 1: SRR1220413 SRR1220414 SRR1220415 SRR1220416 SRR1220417 SRR1220418 SRR1220419 SRR1220420spleen 2: SRR1220493 SRR1220494 SRR1220495 SRR1220496 SRR1220497 SRR1220498 SRR1220499 SRR1220500small intestine 1: SRR651668 SRR651667 SRR651666 SRR651665small intestine 2: SRR1220477 SRR1220478 SRR1220479 SRR1220480 SRR1220481 SRR1220482 SRR1220483 SRR1220484sigmoid colon 1: SRR651669 SRR651670 SRR651671 SRR651672sigmoid colon 2: SRR1220485 SRR1220486 SRR1220487 SRR1220488 SRR1220489 SRR1220490 SRR1220491 SRR1220492pancreas 1: SRR651695 SRR651696 SRR651697 SRR651698pancreas 2: SRR1220461 SRR1220462 SRR1220463 SRR1220464 SRR1220465 SRR1220466 SRR1220467 SRR1220468lung 1: SRR577579 SRR577580 SRR577581 SRR577582lung 2: SRR1220389 SRR1220390 SRR1220391 SRR1220392 SRR1220393 SRR1220394 SRR1220395 SRR1220396liver: SRR651663 SRR651664Tissues:adipose 1: SRR1220373 SRR1220374 SRR1220375 SRR1220376 SRR1220377 SRR1220378 SRR1220379 SRR1220380adipose 2: SRR1220445 SRR1220446 SRR1220447 SRR1220448 SRR1220449 SRR1220450 SRR1220451 SRR1220452adrenal gland 1: SRR651679 SRR651680 SRR651681 SRR651682adrenal gland 2: SRR1220421 SRR1220422 SRR1220423 SRR1220424 SRR1220425 SRR1220426 SRR1220427 SRR1220428kidney cortex 1: SRR643771kidney cortex 2: SRR643770kidney pelvis 1: SRR643789kidney pelvis 2: SRR643787psoas muscle 1: SRR1220397 SRR1220398 SRR1220399 SRR1220400 SRR1220401 SRR1220402 SRR1220403 SRR1220404psoas muscle 2: SRR1220469 SRR1220470 SRR1220471 SRR1220472 SRR1220473 SRR1220474 SRR1220475 SRR1220476Cell-types:CD4 T cells: SRR644513 SRR643766CD8 T cells: SRR644512 SRR64451450. For this purpose, we utilized the ‘som’-function of the Bioconductor package ‘kohonen’51. Therefore, a training dataset was generated by randomly picking 1.000 genes from the transcriptome dataset. A tissue-wise z-transformation was computed on the basis of the training dataset and subsequently applied to the test dataset which was composed of the complete transcriptome data. Next, the self-organizing maps were trained using 100 iterations and a 10 × 10 grid. Finally, the trained model was applied to the test dataset. All genes which were predicted by SOM to be co-expressed with Dnajc22 were used as input for hierarchical clustering, which was performed using the ‘hclust’-function of R.The complexity of the pre-processed transcriptome data was initially reduced by taking advantage of Kohonen’s self-organizing maps24). We performed WGCNA clustering using the 1-TOMsimilarityFromExpr function, a power parameter of 6, a cut height of 0. 5, and a minimum module size of 20 dissecting the data into 29 modules.To determine gene clusters associated with the 52 cell types/tissues of the mouse dataset, we have used the 18,556 present genes and applied the R implementation of the Weighted Gene Correlation Network Analysis . Pearson’s correlation was calculated using BioLayout Express53.Setting the correlation cut-off to 0.8 resulted in a network consisting of 464 nodes with 5,834 edges. Next, the gene-to-gene relationships were visualized in a force-directed layout in Cytoscape53 plug-in iRegulon8 was used to predict TFs potentially regulating the expression of genes which were predicted to be co-expressed to Dnajc22. The genomic region for TF-prediction was limited to 10kbp centred around the respective transcriptional start sites. The statistical threshold settings were left unaltered. We confirmed the robustness of the iRegulon results against parameter changes . Peaks between control and treatment samples were called by MACS2 (version 2.1.0.20140616) using default parameters. Visualization of ChIP-seq peaks was performed with the Integrative Genomics Viewer (IGV)55.ChIP-seq samples from human , mouse , and rat  were downloaded from NCBI GEO online database and converted from sra format to fastq using the fastq-dump function of sratoolkit 2.6.3. Reads from all three species were aligned to their reference genomes  by Bowtie1 version v1.1.126.Murine renal cortex tissue was cut into small pieces, digested with 0.05% trypsin and 1 mg/ml collagenase for 1 h at 37 °C, fixed with 1% formaldehyde and quenched with 125 mM glycine. Cells were resuspended in sonication buffer including protease inhibitors. Cell lysis and chromatin fragmentation were performed in two subsequent steps using a Covaris sonicator. Chromatin/protein complexes were incubated with anti-HNF4A antibody  to capture endogenous Hnf4a or mouse IgG  as a control, followed by addition of protein G-conjugated magnetic beads (Life Technologies). Chromatin/protein crosslinking was reversed and the DNA was purified with the NucleoSpin Gel and PCR Clean-up kit (Macherey Nagel). Binding enrichment was quantified by real-time PCR using the Apoc3 promoter as a positive and a Hprt1 coding region as a negative control34. Various sites were mutated using the Q5 Site-Directed Mutagenesis Kit (NEB) following the manufacturer’s instructions.A 2 kb fragment of the murine Dnajc22 locus was subcloned into pGL4.24 (Promega/Dnajc22>luc). Potential HNF4a binding sites in the cloned sequence were identified using the Support Vector Machine (SVM) of the HNF4 Binding Site Scanner (http://nrmotif.ucr.edu)M-1 cells (ECACC #95092201) were maintained following ECACC recommendations. Co-transfections were performed with Lipofectamine2000 (Life Technologies) containing Renilla luciferase bearing pGL4.74 (Promega) for normalization of transfection efficiency, empty pGL4.24 or Dnajc22 > luc as well as HNF4A or an empty control vector. Luciferase activity was assessed 22–25 h after transfection using the Dual-Glo Luciferase Assay System (Promega).17.Alignment of the four identified potential HNF4A binding sites was extracted from the genomic alignments in the comparative genomics section on the Ensembl page of murine Dnajc22 35 and RNA was extracted from L2 larvae.Hnf4 mutant flies were a kind gift of Carls S. Thummel . Transheterozygous crossings for precise excisions of the EP2449 and KG08976 P-elements, and Hnf4Δ33/Hnf4Δ17 deletion alleles were established as described30 was subcloned into pCS2+ and in vitro transcribed . Zebrafish embryos were obtained from WT TL crossings and injected with 25 ng HNF4A mRNA each. Samples were processed for RNA extraction after 24 h.For heterologous expression studies in zebrafish, human HNF4A 2. Human HNF4A or empty control vector were transfected using Lipofectamine2000 (Life Technologies) and RNA was prepared after 24 h.HEK293 cells  were cultured in DMEM (high glucose) including 10% FCS, GlutaMAX and penicillin/streptomycin (Life Technologies) at 37 °C/5% CODrosophila melanogaster), eif1a and rpl13 (Danio rerio) and Hprt (Mus musculus and Homo sapiens).RNA was isolated , cDNA synthetized , and quantitative real-time PCR was performed using the iQ SYBR Green Supermix (Bio-Rad). Expression was normalized to rp49 and actin . Differences were assessed with t-tests and considered significant when p-value was p < 0.05. Marginal significance, also referred to as trends, was defined as p < 0.1.Supplementary FilesSupplementary Table 1


Gametogenesis represents the most dramatic cellular differentiation pathways in both female and male flies. At the genome level, meiosis ensures that diploid germ cells become haploid gametes. At the epigenome level, extensive changes are required to turn on and shut off gene expression in a precise spatiotemporally controlled manner. Research applying conventional molecular genetics and cell biology, in combination with rapidly advancing genomic tools have helped us to investigate (1) how germ cells maintain lineage specificity throughout their adult reproductive lifetime; (2) what molecular mechanisms ensure proper oogenesis and spermatogenesis, as well as protect genome integrity of the germline; (3) how signaling pathways contribute to germline-soma communication; and (4) if such communication is important. In this chapter, we highlight recent discoveries that have improved our understanding of these questions. On the other hand, restarting a new life cycle upon fertilization is a unique challenge faced by gametes, raising questions that involve intergenerational and transgenerational epigenetic inheritance. Therefore, we also discuss new developments that link changes during gametogenesis to early embryonic development—a rapidly growing field that promises to bring more understanding to some fundamental questions regarding metazoan development. Studying gametogenesis in Drosophila allows us to follow the linear organization of germ cells in adult ovaries and testes. Such organization ensures that all stages of oogenesis and spermatogenesis can be recognized in a highly orchestrated manner  first interact with escort cells, followed by the follicle stem cell (FSC) lineage. FSC homeostasis depends on an epithelial niche structure that involves migration of FSCs across the ovariole , 2010 F. In male drivers  for perfe.g., methylation, acetylation, phosphorylation, and ubiquitination, etc.); (4) incorporation of histone variants; and (5) noncoding RNA-mediated chromatin regulation, including piRNA- and microRNA-mediated mechanisms.It has been demonstrated that transcriptional changes at both local and global levels are robust throughout gametogenesis. Epigenetic mechanisms that modify chromatin state without altering primary DNA sequences have profound influence on regulating dynamic transcriptome changes in germ cells. Epigenetic regulation could act through modifications of DNA-associated proteins and/or RNAs, resulting in structural changes of chromatin or recruitment of effector proteins or RNAs, and leading, in turn, to activation or repression of target gene(s). The basic unit of chromatin is called a nucleosome, which contains 147 bp of DNA wrapped around a histone octamer composed of two copies each of H3, H4, H2A, and H2B—the core histones. The major epigenetic mechanisms known to orchestrate cell fate and function include (1) DNA methylation; (2) nucleosome repositioning driven by chromatin remodeling factors; (3) post-translational modifications (PTMs) of histones , and retention of the DNMT2 homolog DNMT2/Mt2. In addition, 5mC levels in Drosophila have been found to be very low compared to other organisms , during embryogenesis and oogenesis. During oogenesis, loss of Dmad results in an increase of 6mA in the ovaries and accumulation of GSC-like cells. On the other hand, overexpression of Dmad leads to a significant loss of germ cells, including GSCs. Furthermore, Dmad-mediated 6mA demethylation correlates with transposon suppression, indicating that Dmad actively removes 6mA to suppress transposon expression  was thought to be restricted to bacteria, archaea, protists, and fungi . HoweverThe chromatin structure of GSCs and somatic stem cells (SSCs) is regulated by ATP-dependent chromatin remodeling enzymes in both males and females in order to maintain self-renewal and prevent differentiation. These enzymes utilize the energy of ATP hydrolysis to establish and maintain a particular chromatin state during development. The different subfamilies of chromatin-remodeling enzymes catalyze a remarkable range of chromatin modifications that include histone exchange, translocating the histone octamer and changing the conformation of nucleosomal DNA .Drosophila, ISWI serves as an ATP-dependent motor that governs transcriptional regulation through catalyzing changes in nucleosomal assembly and composition  signal transduction is essential for GSC maintenance. Upon signal transduction, the BMP signaling cascade is mediated by phosphorylated MAD (pMAD), which activates the target Daughters against dpp (Dad) transcription, and results in transcriptional repression of the differentiation marker bag of marbles (bam) , ACF (ATP-utilizing Chromatin assembly and remodeling Factor), and CHRAC (CHRomatin Accessibility Complex). In males, GSC self-renewal is specifically regulated by the NURF complex , or the signal transducer and activator of transcription (STAT), encoded by Stat92E, in the germline leads to rapid loss of GSCs and early germ cells  is a conserved target of the JAK/STAT pathway in CySCs that functions in a negative feedback loop by downregulating JAK/STAT activity  to support stem cell self-renewal of both GSCs and CySCs, as well as adhesion of GSCs to the hub cells , 2010. Lrm cells . ConsistE74, E75, and broad (br) as targets , a bromodomain protein, is the sole member of the no child left behind (nclb) specifically regulates male, but not female, GSC maintenance  histone H3 segregating to the stem cell and newly synthesized (new) H3 localizing to the differentiating daughter cell during in situ hybridization) probes, which allow strand-specific hybridization of sister chromatids, it has been demonstrated that both X and Y sister chromatids exhibit an ∼85:15 bias during male GSC asymmetric division. Autosomes, specifically chromosome 2 and 3, display a random segregation pattern, but do show a remarkable cosegregation mode   gene acts in the CySC lineage to regulate multiple signaling pathways, including both EGF and JAK-STAT pathways, in order to promote both CySC and GSC differentiation. In addition, consistent with biochemical data showing E(Pc) as a component of the NuA4 (nucleosome acetyltransferase of H4) histone acetyltransferase (HAT) complex  loss-of-function phenotype, suggesting that they may act together in vivo  signaling pathway plays an important role in the regulation of the mitosis-to-meiosis switch. The Egfr (Egf receptor) ligand Spitz is processed by Stet—a transmembrane protease—in germ cells  transposons  protein complex licenses transcription from piRNA clusters . RDC is Recently, it was reported that Tho5 and other THO subunits of the Transcription/Export (TREX) complex are recruited to piRNA clusters by Cuff, and are loaded onto cluster transcripts . Tho proflamenco, located near the pericentric region of X-chromosome, is particularly active in the ovarian follicle cells. The flamenco locus has fragmented copies of transposons expressed in somatic cells, such as ZAM and gypsy (flamenco-derived transcripts are in antisense orientation to the active transposons. Transcription from the flamenco locus is reported to be dependent on the transcription factor Cubitus interruptus (Ci). The flamenco-derived transcripts are alternatively spliced, probably for diversity . Most nuage components are Tudor domain proteins, such as Tudor (Tud), Qin/Kumo, Tejas (Tej), Tapas (Tap), Krimp (Krimp), and SpnE. Other proteins include HMG box protein Mael and nucleases, such as Zuc and Squash (Squ) .The details of function of many nuage proteins have been revealed by a series of recent studies. For example, Vasa interacts with UAP56 and likely functions for transport of piRNA precursors to the nuage . The nuaDrosophila female germline and somatic cells , has been shown as a downstream Piwi-piRISC partner in both germline and somatic cells in the ovary  and Twine (Cdc25 homolog), all transcribed in spermatocytes , cookie monster (comr), matotopetli (topi), tombola (tomb), achintya/vismay (achi/vis), and Caf1  to form the testis meiotic arrest complex tMAC, which also contains Topi, Comr, and CAF1 . TFIID is one of the general transcription factors that constitute the RNA Pol II preinitiation complex composed of TATA-binding protein (TBP) and 13–14 TBP-associated factors (TAFs) , meiosis I arrest , no hitter , ryan express , and spermatocyte arrest . Among the five TAF homologs, four, including Mia, Nht, Rye, and Sa, share similar structural domains called histone folding motifs for protein–protein interaction, while Can is a WD40-repeat-containing protein (in vitro (fuzzy onions (fzo), which encodes a protein required for mitochondrial fusion in early spermatids , which encodes a sperm-specific DNA-binding protein that also localizes to mitochondria  and tTAFs (can-class) is not fully understood, the transcription coactivator Mediator likely acts to coordinate tMAC and tTAFs. The tMAC recruits Mediator components to spermatocyte chromatin, and Mediator subsequently helps proper tTAF localization. Together, tMAC, tTAFs and Mediator coregulate a cohort of spermatid differentiation gene expression  was identified by its physical interaction with Aly in a yeast-two-hybrid screen , the Ubi-p63E gene encoding polyubiquitin has been shown to regulate proper spermatocyte chromatin structure, meiotic cell cycle progression, and spermiogenesis. However, the different phenotypes caused by loss-of-function of proteasome subunits suggest that Ubi-p63E acts in a protein degradation-independent manner in spermatocytes (kumgang (kmg), which encodes a zinc finger-containing protein. The kmg gene is specifically turned on in early spermatocytes, independent of either tMAC or tTAF. Interestingly, Kmg is required to maintain germline identity by suppressing the expression of hundreds of somatic genes. Genetic, genomic, and biochemical analyses reveal that Kmg acts with the chromatin remodeler dMi-2 to restrict the tMAC component Aly from helping to fire transcription from cryptic promoters of a cohort of somatic genes, which are normally turned on in somatic tissues, such as gut and brain  and aub, DSBs persist longer and are accumulated in the oocyte during later stages of development (gurken (grk) mRNA is localized posteriorly , as polarity determinants, and define the anterior and posterior sides of the developing oocyte, respectively  and Su(z)12 were knocked down in the Drosophila female germline. This cell fate change results from derepression of Cyclin E and cyclin-dependent kinase inhibitor Dacapo upon loss of the repressive H3K27me3 mark , during embryogenesis was sufficient to induce activation of the mini-white gene throughout development, resulting in adult flies with red eyes  demonstrated that this temperature-sensitive hypermorphic allele is able to antagonize a cellular program that erases DNA methylation of parental origin, allowing the epigenetic alterations to be maintained in the absence of the original mutation , enhances tum-1hop tumorigenicity. When tum-1hop heterozygotic females were mated with male heterozygotes for Kr, tumorigenesis associated with tum-1hop was enhanced in the F1 generation, irrespective of the inheritance of the modifier mutation itself. This enhancement persisted into the F2 generation, but diminished by the F3 generation. Further studies indicate that the Kr mutation establishes DNA methylation at promoters during early embryogenesis. Furthermore, the epigenetic alterations induced by Kr are normally erased in the next generation. However, in the presence of the tum-1hop allele, the increased DNA methylation induced by Kr was transmitted to the next generation.Little is known about epigenetic reprogramming of DNA methylation during gametogenesis and early embryogenesis in Drosophila spermatogenesis  is also present in mature sperm  paralog, HP1E. The hromatin . HP1D anhromatin . HP1D isThe oocyte provides transcripts and proteins to embryos for their early development. Transcripts of many genes, including those required for early development, are maternally deposited through the ring canals from nurse cells to the oocyte during oogenesis (reviewed by nanos (nos) transcripts  experiments by two groups further revealed the functions of piRNAs in anchoring and enriching maternally deposited transcripts at the posterior pole , polar granule component (pgc), hsp83, and nos, depend on Aub for their degradation in the bulk of embryos , or transposons, which are DNA sequences that can alter the genetic identity of a cell by changing their position in the genome. Charged with this task is the piRNA pathway, which is directly involved with the silencing of transposons. Both the steady state of TE repression, and the dynamics of the piRNA pathway during germline development are better understood today. Newly introduced transposons initially escape from repression via the piRNA pathway, but germ cells quickly acquire adaptation to new invasion of transposons by producing piRNAs in a single generation . CertainIn the future, we can expect a better understanding of the unique features of germ cells, which will greatly facilitate applying them for the treatment of diseases and regenerative medicine. For example, piRNA pathway proteins are reported for transposon repression of embryonic stem cells in mammals (Drosophila.However, it remains challenging to study epigenetic regulation at the individual gene in germ cells at particular stages, such as PGCs in embryos and GSCs in adults. A major technical hurdle involves obtaining a sufficient number of homogeneous cells to investigate their chromatin structure. However, technological advances have significantly reduced the required cell number for such studies, thus providing unprecedented opportunities to understand germ cell identity and activity. This step will be invaluable for treating diseases associated with defects in germ cell differentiation, such as infertility and germ cell tumors, as well as applying germ cells in regenerative medicine. Also, new imaging techniques, such as live cell imaging and superresolution imaging, in combination with genomic engineering, will allow us to trace distinct molecules, such as mRNAs and proteins, as well as organelles and subcellular structures in order to gain new insights into germ cell differentiation at individual developmental stages . Further