In most C4 species, photosynthesis gene expression is compartmented between mesophyll and bundle-sheath cells. We performed DNaseI sequencing to identify genome-wide profiles of transcription factor binding in leaves of the C4 grasses Zea mays, Sorghum bicolor, and Setaria italica as well as C3Brachypodium distachyon. In C4 species, while bundle-sheath strands and whole leaves shared similarity in the broad regions of DNA accessible to transcription factors, the short sequences bound varied. Transcription factor binding was prevalent in gene bodies as well as promoters, and many of these sites could represent duons that influence gene regulation in addition to amino acid sequence. Although globally there was little correlation between any individual DNaseI footprint and cell-specific gene expression, within individual species transcription factor binding to the same motifs in multiple genes provided evidence for shared mechanisms governing C4 photosynthesis gene expression. Furthermore, interspecific comparisons identified a small number of highly conserved transcription factor binding sites associated with leaves from species that diverged around 60 million years ago. These data therefore provide insight into the architecture associated with C4 photosynthesis gene expression in particular and characteristics of transcription factor binding in cereal crops in general.The majority of plants use C Triticum aestivum), rice (Oryza sativa), and potato (Solanum tuberosum), use the C3 photosynthesis pathway in which Ribulose Bisphosphate Carboxylase Oxygenase (Rubisco) catalyzes the primary fixation of CO2. However, carboxylation by Rubisco is competitively inhibited by oxygen binding the active site , sorghum (Sorghum bicolor), and sugarcane (Saccharum officinarum), and they represent the most productive crops on the planet , S. bicolor (C4), Z. mays (C4), and B. distachyon (C3) leaves were treated with DNaseI  and subjected to deep sequencing. A total of 806,663,951 reads could be uniquely mapped to the respective genome sequences of these species . From all four genomes, 159,396 DNaseI-hypersensitive sites (DHSs) of between 150 and 15,060 bp representing broad regulatory regions accessible to transcription factor binding were identified . For subsequent analysis, only DHSs that were consistent between replicates as determined by the irreproducible discovery rate framework . For all species, analysis of the DGFs derived from naked DNA showed that treatment with DNaseI led to similar sequences being preferentially digested . However, because false-positive DGFs predicted from this approach will be influenced by the number of reads that map to each genome, and in the case of maize fewer reads mapped in total, the number of false-positive DGFs varied between species . To overcome this issue, we implemented a more conservative pipeline that, rather than defining false positives at specific locations within the genome, calculates DNaseI cutting bias for all hexamers across each genome. By employing a mixture model framework, these data are then used to generate a background signal to estimate footprint likelihood scores for each putative DGF  and left a total of 430,205 DGFs corresponding to individual transcription factor binding sites between 11 and 25 bp being identified . At least one transcription factor footprint was identified in >75% of the broader regions defined by DHSs .DNaseI footprinting is a well-established technique for detecting DNA-protein interactions at base-pair resolution and as such has been used to generate digital genomic footprints (DGFs) to predict transcription factor binding sites. DGFs are obtained by pooling all replicates to maximize the number of reads that map within each DHS and then modeling the differential accumulation of reads mapping to positive or negative strands around transcription factor binding sites within the DHSs . HoweverSupplemental Data Set 1). In silico subsampling of these data indicated that for S. bicolor, S. italica, and B. distachyon, the total number of DGFs was close to saturation, but for maize, despite obtaining 251,955,063 reads from whole leaves, this was not the case . Consequently, fewer DGFs were predicted in maize compared with the other species . According to the Encyclopedia of DNA Elements (ENCODE), the large number of reads from low-complexity libraries decreases the chances of identifying the majority of transcription factor binding sites. However, despite these differences in coverage and in certain quality metrics, for all four species, DHSs and DGFs were primarily located in gene-rich regions and depleted around centromeres  was compared with our data. Differences between specific binding sites are likely because in all cases growth conditions will have varied from ours, and in some cases different tissues were sampled. Despite this, 66 and 29% of the ChIP-seq peaks overlapped with our DHSs and DGFs, respectively. Although only 29% of DGFs overlapped with motifs defined by ChIP-seq, permutation tests performed using the regioneR package . In summary, despite detecting fewer DGFs in maize than in the other species, the DGFs we found are supported by publicly available ChIP-seq, electrophoretic mobility shift assay, and Systematic Evolution of Ligands by EXponential (Selex) data sets.To further test whether DGFs identified in our analysis derive from protein-DNA interactions, they were compared with previously identified motifs from maize. Maize is the most appropriate choice for this analysis, as there are more data on transcription factor binding sites than in lowering , meristelowering , gibberelowering , sugar slowering , and lealowering , but in lowering . In addi package  indicateSupplemental Figure 5). A similar pattern was observed for the ChIP-seq signal peaks . Transcription factor binding sites located in exons have been termed duons because they could influence the regulation of both transcription and amino acid sequence. While in general synonymous mutations not affecting amino acid sequence should be under relaxed purifying selection, because of transcription factor recognition, all nucleotides in duons should be under purifying selection and thus show lower mutation rates. We therefore investigated the nucleotide substitution rate at fourfold degenerate sites (FFDS) using variation data from 1218 maize lines . Taken together, we conclude that in these cereals a significant proportion of transcription factor binding likely takes place within genes.Consistent with the distribution of DHSs , annotatze lines  and founze lines . This coze lines . Althougze lines , we founcis-regulatory architecture associated with leaves of grasses. However, as with other complex multicellular systems, leaves are composed of many specialized cell types. Because DGFs are defined by the differential DNA cleavage between protected and unprotected regions of DNA within a DHS, a negative distribution compared with the larger DHS is produced  containing 244,554 DGFs . Of these, 138,075 were statistically enriched in the bundle-sheath samples compared with whole leaves . The number of these statistically enriched DGFs in bundle-sheath strands of C4 species was large and ranged from 14,250 to 73,057 in maize and S. italica, respectively . The lower number in maize is likely due to the reduced sequencing depth achieved. Genome-wide, the number of broad regulatory regions defined by DHSs in the bundle sheath that overlapped with those present in whole leaves ranged from 71% to 84% in S. italica and S. bicolor, respectively . However, only 6% to 20% of the narrower DGFs found in the bundle sheath were also identified in whole leaves . Taken together, these findings indicate that specific cell types of cereal leaves share similarity in the broad regions of DNA that are accessible to transcription factors (DHSs) but that the short sequences actually bound by transcription factors (DGFs) vary dramatically.The above analysis provides a genome-wide overview of the produced . Thus, tproduced . Since bproduced , C4 specf leaves . After b4 grasses, we found no such examples in S. bicolor or S. italica, but in the RbcS gene from maize, which is preferentially expressed in bundle-sheath cells, an I-box (GATAAG) is essential for light-mediated activation , both elements were detected in our pipeline. Interestingly, a signal suggesting transcription factor binding to the HOMO motif was enriched in the bundle-sheath strands  encodes the phosphoenolpyruvate carboxylase responsible for producing C4 acids used in the C4 pathway and is preferentially expressed in mesophyll cells. Previous reports showed that a region of 600 nucleotides upstream of the transcription start site carrying repeated C-rich sequences was sufficient to drive expression in mesophyll cells of maize  gene, which is highly expressed in mesophyll cells and encodes a protein of the core C4 cycle .To investigate the relationship between cell-specific gene expression and the positions of DHSs and DGFs, the DNaseI data were interrogated using RNA-seq data sets from mesophyll and bundle-sheath cells of C4 leaves . At leasC4 cycle , a broadC4 cycle . While tSupplemental Data Set 8).Second, in the next contiguous gene that encodes an additional isoform of MDH also preferentially expressed in mesophyll cells , a DHS w4 cycle in all three species . Overall, we conclude that differences in transcription factor binding between cells of C4 leaves are associated with both DNA accessibility defined by broad DHSs as well as fine-scale alterations to transcription factor binding defined by DGFs. Moreover, bundle-sheath strands possessed a distinct regulatory landscape compared with the whole leaf, and in genes encoding enzymes of the C4 pathway, multiple transcription factor binding sites differed between bundle-sheath and whole-leaf samples. This finding implies that cell-specific gene expression in C4 leaves is mediated by combinatorial effects derived from alterations to gene accessibility as defined by DHSs as well as changes to binding of multiple transcription within these regions.Lastly, in the third gene in this region, which encodes a NAC domain transcription factor preferentially expressed in bundle-sheath strands , differeArabidopsis thaliana; Z. mays, S. bicolor, S. italica, and B. distachyon .To provide an overview of the transcription factors most likely associated with DGF, ChIP-seq data from maize  togetherstachyon . To incrstachyon , but in stachyon . As woulstachyon . By comb3 and C4 photosynthesis and to investigate whether C4 photosynthesis is controlled by an increase in binding of sets of transcription factors, individual motifs were ranked by frequency and the Kendall rank correlation coefficient used to compare species , indicating that cell specificity is not associated with large-scale changes in the abundance of many transcription factor families ; 66 were only enriched in bundle-sheath strands and 91 in whole-leaf tissue . Some of these conserved and cell-specific motifs have been previously described to have a relevant role in photosynthesis. For instance, in whole leaves of maize and S. italica, we found significant enrichment of the bHLH129 motif  that has been proposed to act as a negative regulator of NADP-ME . While no single cis-element was found in all genes that are preferentially expressed in mesophyll or bundle-sheath cells, the number of genes possessing the same occupied motif ranged from nine in S. bicolor and S. italica to four in S. bicolor and Z. mays whole leaves . These data support a model where the combinatorial action of multiple transcription factors controls groups of C4 genes to produce the gene expression patterns required for C4 photosynthesis.To investigate whether genes involved in the Ccis-elements found in C4 genes of each species were common or whether C4 genes are regulated differently in each species. In pairwise comparisons, DGFs fell into three categories: conserved and occupied by a transcription factor, conserved but only occupied in one species, and not conserved . Consistent with this, the majority of C4 gene orthologs did not share DGFs. Due to the lack of DGF saturation in maize, these estimates likely set lower bounds for the extent of conservation. However, in several cases, patterning of C4 gene expression correlated with a set of motifs shared across species  gene contains several conserved DGFs that are present in the bundle sheath of the C4 species but also in whole leaves of C3B. distachyon . Twenty-seven genes associated with the C4 and Calvin-Benson-Bassham cycles contained a total of 379 DGFs. Although many of these transcription factor footprints were conserved in sequence within orthologous genes, only nine were both conserved and bound by a transcription factor . For example, Z. mays and S. bicolor shared 5775 DGFs that were both conserved and occupied. S. italica shared only 670 DGFs with Z. mays and S. bicolor . In whole leaves, this set of ancient and highly conserved DGFs were located predominantly in 5ʹ untranslated regions and coding sequences, but in bundle-sheath strands, over 50% of these hyperconserved DGFs were in coding sequences , which is consistent with previous analyses that identified shared cis-elements in PPDK and CA or NAD-ME1 and NAD-ME2 in C4Gynandropsis gynandra  M3 compost:medium vermiculite mixture, with a thin covering of soil. Seedlings were hand watered.Brachypodium distachyon plants were grown in a separate growth facility under controlled conditions optimized for its growth at the Sainsbury Laboratory at Cambridge University, first under short-day conditions (14 h/10 h light/dark for 2 weeks) and then shifted to long-day conditions (20 h/4 h light/dark for 1 week), and harvested at Zeitgeber time 20. Temperature was set at 20°C, humidity at 65%, and light intensity at 350 µmol m−2 s−1. All tissue was harvested from August to October 2015.S. bicolor, Z. mays, and S. italica, mature third and fourth leaves with a fully developed ligule were harvested 4 to 6 h into the light cycle 18 d after germination. Bundle-sheath cells were mechanically isolated as described previously by B. distachyon, plants were flash-frozen and material was pulverized in a coffee grinder. Three grams of plant material was added to 45 mL of nuclei isolation buffer  and incubated at 4°C on a rotating wheel for 5 min, after which debris was removed by sieving through two layers of Miracloth (Millipore) into precooled flasks. Nuclei were spun down at 2862g at 4°C for 20 min. Plastids were lysed by adding Triton X-100 to a final concentration of 0.3% (v/v) and incubated for 15 min on ice. Nuclei were pelleted by centrifugation at 4500g at 4°C for 15 min. Pellets were washed three times with chilled nuclei isolation buffer.To isolate nuclei from S. bicolor, Z. mays, B. distachyon, and S. italica, mature third and fourth leaves with a fully developed ligule were harvested 4 h into the light cycle 18 d after germination. A total of 100 mg of tissue was used for each extraction. Deproteinated DNA was extracted using a DNeasy Plant Mini Kit (Qiagen) according to the manufacturer’s instructions.For isolation of deproteinated DNA from 8 freshly extracted nuclei were resuspended at 4°C in digestion buffer . DNaseI (Fermentas) at 7.5 units was added to each tube and incubated at 37°C for 3 min. Digestion was arrested with the addition of a 1:1 volume of stop buffer  and incubated at 55°C for 15 min. Fifty units of proteinase K was added, and samples were incubated at 55°C for 1 h. DNA was isolated with 25:24:1 phenol:chloroform:isoamyl alcohol (Ambion) followed by ethanol precipitation. Fragments from 50 to 550 bp were selected using agarose gel electrophoresis. The extracted DNA samples were quantified fluorometrically with a Qubit 3.0 Fluorometer (Life Technologies), and a total of 10 ng of digested DNA (200 pg/L) was used for library construction.To obtain sufficient DNA, each biological replicate consisted of leaves from tens of individuals, and to conform to standards set by the Human Genome Project, at least two biological replicates were sequenced for each sample. A total of 2 × 102, 0.5 mM spermidine, 1 mM EDTA, and 0.5 mM EGTA, pH 8.0). DNaseI (Fermentas) at 2.5 units was added to each tube and incubated at 37°C for 2 min. Digestion was arrested with the addition of a 1:1 volume of stop buffer  and incubated at 55°C for 15 min. Fifty units of proteinase K was added, and samples were incubated at 55°C for 1 h. DNA was isolated by mixing with 1 mL of 25:24:1 phenol:chloroform:isoamyl alcohol (Ambion) and spun for 5 min at 15,700g followed by ethanol precipitation of the aqueous phase. Samples were then size-selected (50–400 bp) using agarose gel electrophoresis. The extracted DNA samples were quantified fluorometrically using a Qubit 3.0 Fluorometer (Life Technologies), and a total of 1 ng of digested DNA was used for library construction. Sequencing-ready libraries were prepared using a KAPA Hyper Prep Kit (KAPA Biosystems) according to the manufacturer’s instructions. To reduce bias due to amplification of DNA fragments by the PCR, as recommended by the manufacturers, 17 cycles were used. The quality of the libraries was checked using a Bioanalyzer High Sensitivity DNA Chip (Agilent Technologies). Libraries were quantified with a Qubit 3.0 Fluorometer (Life Technologies) and qPCR using an Next Generation Sequencing Library Quantification Kit (KAPA Biosystems) and then normalized, pooled, diluted, and denatured for paired end sequencing using high-output 150-cycle run (2× 75-bp reads). Sequencing was performed using NextSeq 500 (Illumina) in the Sainsbury Laboratory at the University of Cambridge with 2× 75 cycles of sequencing.Initial sample quality control of prefragmented DNA was assessed using a Tapestation DNA 1000 High Sensitivity Screen Tape (Agilent). Sequencing-ready libraries were prepared using the Hyper Prep DNA Library preparation kit (Kapa Biosystems) selecting fragments from 70 to 350 bp for optimization  and indehttps://github.com/modENCODE-DCC/Galaxy/blob/master/modENCODE_DCC_tools/idr/batch-consistency-analysis.r). The irreproducible discovery rate framework adapted from the ENCODE3 pipeline (https://sites.google.com/site/anshulkundaje/projects/idr) aims to measure the reproducibility of findings by identifying the point (threshold) at which peaks are no longer consistent across replicates.Genome sequences were downloaded from Phytozome v10; . The folThe SPOT score .After sequencing, the number of DNA 6-mers centered at each DNase cleavage site (between third and fourth bases) was counted and normalized by the total number of counts. Next, DNA 6-mer frequencies were normalized by the frequencies of each DNA 6-mer in the genome. The resulting background signal profile was used as input in the FootprintMixture.R package (http://www.epigenome.cuhk.edu.hk/C3C4.html). Permutation tests between ChIP peaks and DHSs or DGFs were performed using regioneR  with the B73_RefGen_v4 genome assembly specifically to allow identification of nonsynonymous sites. A custom script was used to identify all FFDS in the Z. mays genome. This bed file in turn was used to identify which of the synonymous polymorphic sites were FFDS. Each polymorphic site had its allele frequencies calculated. Putative Z. mays duons were identified by intersecting (with BEDTools intersect) the final DGFs identified with exonic regions. These duons were then used to extract only those exons within which a duon was found. These exons in turn had the duon regions themselves subtracted to leave the exon region except the duon. This provided the surrounding exonic sequences with which to compare the duons. These two regions were then intersected with the polymorphism data to identify both the number of occurrences and allelic frequencies of polymorphic sites (FFDS and nonsynonymous) within both the duons and their surrounding exonic sequences.To cross-map genomic features between species, mapping files were generated according to chainNet , and LASchainNet . BrieflychainNet . For theant data  was down10.17504/protocols.io.hdfb23n). Raw sequencing data and processed files are deposited in the Gene Expression Omnibus (GSE97369) and The National Center for Biotechnology Information (PRJNA381532). For full methods, commands, and scripts, as well as processed data to be loaded into a genome browser, see github (https://github.com/hibberd-lab/Burgess-Reyna_llorens-monocot-DNase) and Figshare 10.6084/m9.figshare.7649450.Methods for DNaseI digestion are on protocols.io  from the transcription start site (TSS) of S. bicolor, Z. mays, S. italica and B. distachyon whole leaves.Supplemental Figure 6. Nucleotide proportion of duons and surrounding exons used in the substitution analysis for Z. mays.Supplemental Figure 7. Transcript abundance for genes in mesophyll and bundle-sheath cells associated with DHSs and DGFs in S. bicolor.Supplemental Figure 8. Differential accessibility of broad regulatory regions in S. bicolor is not sufficient for cell preferential gene expression.Supplemental Figure 9. Representation of the C4 pathway showing differentially accessible DHSs, DGFs and cell-specific DGFs between whole-leaf (blue) and bundle-sheath (orange) samples in S. bicolor.Supplemental Figure 10. Representation of the C4 pathway showing differentially accessible DHSs, DGFs and cell-specific DGFs between whole-leaf (blue) and bundle-sheath (orange) samples in S. italica.Supplemental Figure 11. Representation of the C4 pathway showing differentially accessible DHSs, DGFs and cell-specific DGFs between whole-leaf (blue) and bundle-sheath (orange) samples in Z. mays.Supplemental Data Set 1. Summary of DNaseI-SEQ quality metrics.Supplemental Data Set 2. Summary statistics for genomic features identified in whole-leaf samples.Supplemental Data Set 3. DNaseI cutting bias calculation summary for whole-leaf and bundle-sheath data.Supplemental Data Set 4. Transcription factors included in the ChIP-SEQ data analysis.Supplemental Data Set 5. Summary statistics of DNaseI-SEQ analysis of bundle-sheath samplesSupplemental Data Set 6. Summary statistics of overlap between DHSs in whole-leaf and bundle-sheath samples.Supplemental Data Set 7. Summary statistics of overlap between DGFs in whole-leaf and bundle-sheath samples.Supplemental Data Set 8. Summary statistics for differential digital genomic footprint calling.Supplemental Data Set 9. Motifs mapped to genes of the C4, CBB and C2 cycles in Z. mays, S. bicolor, S. italica for whole-leaf and bundle-sheath samples and in B. distachyon for whole-leaf samples.Supplemental Data Set 10. Hypergeometric tests for enrichment of individual motifs in Z. mays, S. bicolor, S. italica for whole-leaf and bundle-sheath samples.Supplemental Data Set 11. Hypergeometric tests for enrichment of cell-specific individual motifs in Z. mays, S. bicolor, S. italica for whole-leaf and bundle-sheath samples.Supplemental Data Set 12. Number of genes in C4, CBB and C2 cycles annotated with a given motif in Z. mays, S. italica, S. bicolor and B. distachyon.Supplemental Data Set 12. Statistics for cross mapping of genomic features between S. bicolor, S. italica, Z. mays and B. distachyon.Supplemental Data Set 13. DGFs conserved and occupied in Z. mays, S. bicolor, S. italica for whole-leaf and bundle-sheath samples and in B. distachyon for whole-leaf samples.Supplemental Data Set 14. DGFs in C4 genes that are conserved between Z. mays and S. bicolor.Supplemental Data Set 15. DGFs conserved in all four species.Supplemental Data Set 16. Gene Ontology analysis on hyper-conserved DGFs in whole-leaf samples of S. italica, S. bicolor, Z. mays and B. distachyon.DHS Gramene: LOC_Os02g45780DHS Araport: LOC_Os02g45780SDS Gramene: AT1G14750SDS Araport: AT1G14750dna Gramene: EO:0007532dna Araport: EO:0007532The following phenotypic, genotypic, and functional terms are of significance to the work described in this paper:


Pseudomonas aeruginosa are often challenged by rapidly changing nutritional environments. In order to adapt to these shifts in nutrient availability, bacteria exert tight transcriptional control over the enzymes of central metabolism. This transcriptional control is orchestrated by a series of transcriptional repressors and activators. Although a number of these transcription factors have been identified, many others remain uncharacterized. Here, we present a simple pipeline to uncover and validate the targets of uncharacterized transcriptional regulators in P. aeruginosa. We use this approach to identify and confirm that an orthologue of the Pseudomonas fluorescens transcriptional regulator (RccR) binds to the upstream region of isocitrate lyase (aceA) in P. aeruginosa, thereby repressing flux through the glyoxylate shunt during growth on non‐C2 carbon sources.Microbes such as  Pseudomonas aeruginosa. We use this approach to identify and confirm that an orthologue of the Pseudomonas fluorescens transcriptional regulator (RccR) binds to the upstream region of isocitrate lyase (aceA) in P. aeruginosa, thereby repressing flux through the glyoxylate shunt during growth on non‐C2 carbon sources.Transcriptional control in bacteria is orchestrated by a series of transcriptional repressors and activators. Although a number of these transcription factors have been identified, many others remain uncharacterized. We have developed a robust pipeline to identify and functionally annotate new transcriptional regulators in  Pseudomonas aeruginosa (PA) is a metabolically versatile, Gram‐negative opportunistic pathogen capable of causing chronic lung infections in patients with cystic fibrosis (CF). The remarkable metabolic plasticity of the organism can be attributed to its large repertoire of encoded regulatory proteins – around 9% of the PA genome is dedicated to transcriptional regulation  and glcB  are known to be highly transcribed when the organism is grown on acetate as a sole carbon source, but are tightly repressed during growth on glycolytic carbon sources, such as glucose . Using this method, we uncover a transcriptional regulator which represses the GS in P. aeruginosa during growth on non‐C2 carbon sources, allowing for the rapid and reversible transcriptional activation of this key pathway in response to changes in carbon skeleton availability.The affinity of transcriptional regulators for their target operator DNA may be dependent on the presence of metabolic cues (ligands) which modify the structure of the regulator. This allows even subtle changes in the cellular environment to elicit rapid transcriptional responses Bateman, . CrucialThe workflow for this pipeline is illustrated in Fig. First, culture conditions where transcription of the gene of interest (GOI) is maximally activated or repressed  are confirmed using a plasmid‐borne promoter:luciferase reporter assay Fig. .The region upstream of the GOI (incorporating the promoter and associated operator sites) is PCR‐amplified using a non‐modified forward primer and a biotinylated reverse primer. This amplified segment of DNA is then bound at high immobilization density onto streptavidin‐coated magnetic beads (Dynabeads). In parallel, cells from cultures grown in the ‘max ON’ and ‘max OFF’ conditions identified in step 1 are harvested, washed and lysed to generate concentrated protein lysates.Key point: for best results, it is important to maximize the density of immobilized DNA fragment on the beads.The immobilized upstream region of the GOI is used to capture protein(s) from the concentrated cell lysates in step 2. The beads are washed to remove non‐specifically bound proteins, and the DNA‐bound regulators are released by treatment with a high‐salt buffer (containing 1M NaCl).Key point: transcription factors are often present at very low concentration in the cell, so the lysate should contain a high concentration of protein.Key point: best results were obtained when the pulldowns were carried out in the presence of non‐specific competitor DNA .The eluted proteins are concentrated by acetone precipitation and then analysed by SDS‐PAGE (we routinely use 4–20% polyacrylamide gradient gels for these purposes). Following staining to reveal the resolved proteins, bands of interest are excised and sent for MS‐MS fingerprinting.P. aeruginosa genome using established methods, and the promoter:luciferase reporter from step 1 is then introduced into the mutant(s) enabling comparison of the transcriptional profile of the GOI in the deletion mutant with the wild‐type progenitor.The identified transcriptional regulator(s) are then deleted from the lux reporter constructs means that the method is also tolerant of plasmid‐based complementation, if required.The pipeline above, which fuses biochemical and genetic approaches, can be readily extended to include, for example, site‐directed mutagenesis of the regulator itself, or of its binding site on the DNA. The use of stable, chromosomally integrated in vitro; crucially, our approach does not rely on this. Furthermore, the method is easily adapted to multiplexing, enabling several DNA‐binding sites to be probed simultaneously.Our approach is rapid, easy, inexpensive and internally validated. Due to solubility and expression issues, bacterial transcription factors are notoriously difficult to overexpress in soluble form and purify for characterization The detailed experimental procedure is described in Supplementary Information.aceA and glcB, respectively) have been shown previously to be transcriptionally activated during growth on acetate and repressed during growth on glycolytic carbon sources . These data confirm that the transcription of these genes is strongly stimulated during growth on acetate, but remains low in glucose, succinate or tryptone.To investigate the regulation of the GS genes further, we first cloned the upstream region of glcB and aceA upstream regions  were PCR‐amplified, incorporating a biotin tag on the 3′ end. These amplicons were then attached to magnetic streptavidin‐coated Dynabeads. We found that it was crucially important to ensure that the streptavidin sites on the beads were saturated with biotinylated probe. The beads were then mixed with freshly prepared protein lysates obtained from P. aeruginosa grown on glucose or acetate as a sole carbon source. Following 30‐min incubation at room temperature, the beads were washed, and the bound putative regulator(s) were eluted using a high‐salt buffer. The eluted proteins were concentrated and analysed by SDS‐PAGE. A protein (approx. 30 kDa in mass) from lysates obtained from cells grown on glucose, but not from cells grown on acetate, was found to bind to the aceA upstream region  showed comparable expression levels in the ΔPA5438 mutant and the PAO1 wild‐type  and glcB (encoding MS) are unlinked in P. aeruginosa, suggesting that PA5438 may act independently to repress both genes. By contrast, in E. coli and other enterics, ICL (aceA) and MS (denoted aceB in E. coli) are encoded in a single operon.RpiR‐family transcription factors are widespread in bacteria and are known to regulate sugar catabolism, such as maltose, glucose and ribose metabolism and the pentose phosphate pathway. RpiR‐family regulators can act as either activators or repressors of transcription and are composed of a characteristic N‐terminal DNA‐binding helix–turn–helix (HTH) domain and a C‐terminal sugar phosphate binding domain  located upstream of its target ORF(s). A similar sequence motif (GTGTAG‐X14‐CACTACAA) is centred 447 nucleotides upstream of the aceA translational start site and around 55 nucleotides upstream of the transcriptional start site. Subsequent truncation analyses of the long upstream region of aceA confirmed that this segment of DNA was essential for both acetate inducibility and transcription per se (data not shown).Interestingly, the ΔPA5438 mutant grew at a rate comparable with that of PAO1 on acetate, glucose, succinate or tryptone Fig. . This suCurrent efforts are aimed at characterizing a suite of novel transcriptional regulators captured using pulldowns with the upstream regions of other genes of interest.The authors confirm that they have no conflict of interest.Appendix S1. Supplementary Materials and Methods.Table S1. Oligonucleotide primers used in the study.Table S2: Bacterial strains and plasmids used in this study.Fig. S1. Capture of putative regulatory proteins on immobilized DNA.Fig. S2. Mass spectrometric identification of protein bands (A‐C) which bound to the aceA/glcB promoter regions.Fig. S3. Growth curves of P. aeruginosa PAO1 wild‐type (red) compared with the ΔPA5348 mutant (blue) grown on various carbon sources.Fig. S4. Quantitation of aceA transcriptional activity in wild‐type P. aeruginosa  compared with aceA transcription in a ΔPA5348 mutant (red line).Fig. S5.glcB:lux expression for the wild‐type and the ΔPA5438 mutant grown in MOPS minimal medium containing tryptone, acetate, glucose or succinate, as indicated.Fig. S6.cco1:lux expression for the wild‐type and the ΔPA5438 mutant grown in MOPS minimal medium containing tryptone, acetate, glucose or succinate, as indicated.Fig. S7. Western blot showing deregulated expression of AceA (59 kDa) and GlcB (79 kDa) in a ΔPA5348 mutant compared with the wild‐type during growth on the indicated carbon sources.Click here for additional data file.


Trypanosoma brucei, the causative agent of both human and animal African trypanosomiasis. In this mSphere of Influence article, she reflects on how “A CRISPR-based approach for proteomic analysis of a single genomic locus” by Z. J. Waldrip, S. D. Byrum, A. J. Storey, J.Lucy Glover’s research focuses on the role of DNA repair and recombination in antigenic variation in the parasite  Trypanosoma brucei, the causative agent of both human and animal African trypanosomiasis. In this mSphere of Influence article, she reflects on how “A CRISPR-based approach for proteomic analysis of a single genomic locus” by Z. J. Waldrip, S. D. Byrum, A. J. Storey, J. Gao, et al.  made an impact on her research by taking the precision of CRISPR-Cas9 and repurposing it to look at single-locus proteomics. By using this technology in trypanosomes, Dr. Glover and her colleagues could study the dynamic accumulation of repair proteins after specific damage and gain insight into how the location of a double-strand break (DSB) dictates repair pathway choice and how this may influence immune evasion in these parasites.Lucy Glover’s research focuses on the role of DNA repair and recombination in antigenic variation in the parasite  Streptococcus pyogenes that is the basis for prokaryote adaptive immunity  and how this influences the ability of Trypanosoma brucei to exchange genes, specifically the variant surface glycoprotein (VSG) genes, required for immune evasion. Of the kinetoplastid parasites, T. brucei is the most well studied and genetically tractable −CRISPR-associated protein 9 (Cas9)-mediated genome modification is a type II CRISPR system from immunity  and thatimmunity  with a mimmunity . This reimmunity  or live-immunity  and to iimmunity . In the immunity , the autractable , making GAL1 gene promoter region. The ability to activate the GAL1 gene promoter in a synchronous manner by supplying Saccharomyces cerevisiae with galactose instead of glucose allowed the authors to target transcriptionally active chromatin for analysis. Formaldehyde-fixed, sheared chromatin was affinity purified using the PrA tag and subjected to label-free mass spectrometry. The CRISPR-ChAP-MS method was shown to have two benefits: (i) the improved unbiased target chromatin isolation at 1-kb resolution and (ii) the ease with which gRNAs can be designed for multiplexing, making this approach cost-effective. One disadvantage is that both CRISPR-ChAP-MS and TAL-ChAP-MS, which require binding to the DNA, are unlikely to be able to target repressed regions of chromatin.Chromatin immunoprecipitation (ChIP)-MS has long been established as a method to define chromatin composition at specific regions in the cell , but it VSG gene conversion (GC) events, which are dependent on HR. We know that DSBs can trigger a VSG gene switch and that several DNA damage proteins are important for this process, but what constitutes this dynamic repair factory that forms and leads to switching is unknown  is a dynamic process that is essential for maintaining genomic integrity and for immune evasion in trypanosomes. Antigenic variation in trypanosomes is mainly via  unknown . There a unknown  in trypanuclease , which a


In order to pass on an intact copy of the genome during cell division, complete and faithful DNA replication is crucial. Yet, certain areas of the genome are intrinsically challenging to replicate, which manifests as high local mutation propensity. Such regions include trinucleotide repeat sequences, common chromosomal fragile sites (CFSs), and early replicating fragile sites (ERFSs). Despite their genomic instability CFSs are conserved, suggesting that they have a biological function. To shed light on the potential function of CFSs, this review summarizes the similarities and differences of the regions that challenge DNA replication with main focus on CFSs. Moreover, we review the mechanisms that operate when CFSs fail to complete replication before entry into mitosis. Finally, evolutionary perspectives and potential physiological roles of CFSs are discussed with emphasis on their potential role in neurogenesis. Wppressor . Though ppressor .Due to the shared features of CFSs with trinucleotide repeats , initialHydroxyurea (HU) is an inhibitor of the ribonucleotide reductase, thus HU treatment leads to the depletion of dNTPs. High doses of HU were found to result in DNA breaks at specific sites in the genome, called ERFSs . As the The transcription machinery may hinder the progression of the replication machinery since they work on the same template. Specifically, so-called R loops , where nTranscription at ERFSs is certainly contributing to their fragility , suggestR loops often form at expanded trinucleotide repeats due to secondary structure formation on one strand, leaving the other strand susceptible to annealing with nascent RNA. Such R loops at the trinucleotide repeats contribute to repeat instability ,46. One Drosophila melanogaster, active genes are replicated predominantly in early S phase, indicating a regulation of transcription timing in higher eukaryotes [Transcription activity is evidently regulated to ensure that transcription and replication are kept separate ,49. In Dkaryotes . Similarkaryotes . In factkaryotes . The spakaryotes . By perfkaryotes . Such a karyotes . Replicakaryotes ,51,52. Tkaryotes . After tkaryotes . Intereskaryotes . R loopskaryotes . It is tCollectively, it seems that transcription at ERFSs and CFSs, either directly or indirectly, increases the risk of cells entering mitosis prematurely before replication has been completed at these sites ,33,34,42Research from the last decade has given important insight into the cell biology of CFSs by deciphering cellular consequences of replication problems at CFSs. It is now more clear how underreplicated CFSs manifest and how they are processed in mitosis and beyond. The DNA damage bypass mechanism called translesion synthesis may have a role in completing replication of CFSs ,61. ThisFANCD2 foci in the M phase are thought to mark regions that have not been fully replicated before cells enter mitosis and chromatin immunoprecipitation sequencing (ChIP-seq) of FANCD2 in APH-treated cells consistently identifies CFSs ,66. In aWWOX gene [The recruitment mechanism as well as the exact role of FANCD2 at CFSs in mitosis is somewhat obscure. FANCD2 does not seem to be required for recruitment of endonucleases (described below) to underreplicated CFSs in mitosis . HoweverWOX gene . MoreoveWOX gene . FANCD2 WOX gene . InteresWOX gene .A number of structure selective endonucleases, including MUS81 and its binding partners EME1 and XPF-ERCC1, as well as the nuclease scaffold SLX4, localize to CFSs in mitosis where they are squeezed in between sister FANCD2 foci ,71. HerePreliminary work that was published on BioRxiv describes a process that may work as an alternative to MiDAS for processing of underreplicated regions in mitosis . This woUnderreplicated regions that persist in anaphase can lead to non-disjunction manifesting as either UFBs or chromatin bridges. UFBs may still be resolved in anaphase or telophase but they can leave a scar marked by large 53BP1 foci in subsequent G1 . These aPRKN, MARCOD2, GRID1, CCSER1, SETBP1, IMMP2L, and DACH1) have to some extent resisted size reduction [Despite being highly unstable genomic regions, increasing evidence suggests that CFSs may hold conserved biological functions. Molecular mapping revealed that CFSs are conserved between mammals and birds and coincide with orthologous genes  and compeduction , supportThe role of introns in creating diverse and complex transcriptomes through alternative splicing is well established. In addition, a recent study by Bonnet et al. proposes a role of introns in preventing R-loop accumulation and the associated genomic instability . Genome-Altogether, while this recent study proposes a role of small introns in maintaining genome integrity of transcribed loci, the role of long introns in CFS genes seems to be associated with the coinciding fragility. In line with this, the extreme length of fragile site genes may generate extensive topological problems because negative supercoils build up in the DNA behind RNA pol II . This phThe fragility of CFSs may carry a functional significance . As prevPRKN, CCSER1, WWOX, and FHIT, where two of these, PRKN and CCSER1, were confirmed as conserved CFSs by their fragility in chicken DT40 cells [PRKN, CCSER1, WWOX, and FHIT are poorly annotated in amphibian but based on the sizes of WWOX and CCSER1, which are represented it seems that they gain extra length in amphibians and certainly in reptiles, where the crocodile and alligator genomes present with PRKN and CCSER1 genes that exceed 1.3 megabases in length. Birds have undergone a reduction in total genome size during evolution, which reflects an adaption to flight [PRKN and CCSER1 to some degree have escaped the reduction in intron sizes, suggesting that the extreme size of the genes may provide fitness to the organisms [To understand at which point during the evolution the vast size of CFS genes arose, we performed a simple analysis of CFS orthologue length in their phylogenetic context . We here40 cells . When too flight . Thus, irganisms . MammalsSince CFSs seem to be detrimental, or at best neutral for the cells, why are they retained throughout evolution? A proposed explanation suggests that CFSs may have a role in producing the immense diversity ,97 that In a recent study by Wei et al. recurrent DSB clusters (RDCs) occurring in NSPCs of mice were identified using high throughput genome-wide translocation sequencing (HTGTS) . Using sCFSs might also have a supportive role in retrotransposon LINE1-dependent diversification of the neuronal genomes ,110. LINGenetic diversity is not the only potential explanation of the mosaicism observed in the brain. Such diversity could also stem from epigenetic diversification of the neurons. In particular, replication stress in primary fibroblasts can cause the accumulation of the histone variant macroH2A1.2 at CFSs in a FACT-dependent manner . MacroH2CFSs are regions prone to TRCs, replication stress and breakage as well as sources of increased genomic instability. Nevertheless, they, along with their characteristics, seem to be conserved among vertebrates. The large size of CFS genes somehow has resisted shortening throughout the evolutionary timeline, despite the fact that it contributes to their instability. Their conservation seems even more surprising when considering that their large size is owed to large introns and not to the coding regions of the genes. This suggests that CFSs might have a physiological role in the organisms. So far there are indications pointing to a possible role of CFS-associated replication stress in neuronal diversification, however further investigation is needed to establish whether that is true or not. Future studies focusing on the role of the large introns of CFS genes both on cellular and organismal level will be necessary. Identifying the potential physiological role that replication stress might have in an organism is key to understand why CFSs are conserved and it will pave the way for a better understanding of cancer, brain development, and neurodegenerative diseases.


R-loops are three-stranded nucleic acid structures that form during transcription, especially over unmethylated CpG-rich promoters of active genes. In mouse embryonic stem cells (mESCs), CpG-rich developmental regulator genes are repressed by the Polycomb complexes PRC1 and PRC2. Here, we show that R-loops form at a subset of Polycomb target genes, and we investigate their contribution to Polycomb repression. At R-loop-positive genes, R-loop removal leads to decreased PRC1 and PRC2 recruitment and Pol II activation into a productive elongation state, accompanied by gene derepression at nascent and processed transcript levels. Stable removal of PRC2 derepresses R-loop-negative genes, as expected, but does not affect R-loops, PRC1 recruitment, or transcriptional repression of R-loop-positive genes. Our results highlight that Polycomb repression does not occur via one mechanism but consists of different layers of repression, some of which are gene specific. We uncover that one such mechanism is mediated by an interplay between R-loops and RING1B recruitment.  •R-loops form at a subset of PcG target genes•R-loops contribute to PcG recruitment genome-wide•Loss of R-loops leads to transcriptional activation of R-loop-positive PcG targets•R-loops and PRC1 contribute to transcriptional repression of PcG targets Skourti-Stathaki and colleagues demonstrate that a subset of Polycomb-repressed genes form R-loops genome-wide. At these genes, R-loop loss leads to reduced Polycomb (PRC1 and PRC2) recruitment, enhanced polymerase II activation, and transcriptional derepression. Derepression increases in conditions of reduced PRC1 recruitment, suggesting that R-loops and PRC1 both contribute to Polycomb repression. During transcription, nascent RNA can hybridize with the DNA template strand, leaving the non-template DNA strand single stranded. These structures are called R-loops, and their persistent formation can cause deleterious effects on genome integrity, possibly due to the unpaired single-stranded DNA (ssDNA) .Even though R-loops have the potential to form over a large proportion of the genome, they are not a simple consequence of transcription. They occur at specific, conserved loci as a result of a complex interplay of the DNA sequence, transcription, topology, and chromatin environment . At actiThe Polycomb group (PcG) proteins are major epigenetic regulators of transcriptional repression, and they are required to silence CpG-rich developmental regulator genes in embryonic stem cells (ESCs) and maintain patterns of gene expression established during cell commitment . They asThe mechanisms of Polycomb-mediated transcriptional repression are not fully understood. Despite their roles in repression, PcG-target genes in mouse embryonic stem cells (mESCs) display the active chromatin mark H3K4me3 , RNA polPcG recruitment to its target genes remains a complex pathway, as different mechanisms and factors have been invoked  knockout alone is not sufficient to affect R-loops or RING1B (PRC1) recruitment and does not lead to transcriptional derepression of R-loop-positive genes. In contrast R-loop removal in these conditions causes gene activation and reduced RING1B recruitment. Upon inhibition of EZH2 catalytic activity, R-loops and RING1B can repress PcG targets. Our results uncover an unanticipated interplay between R-loops and PRC1 recruitment that contributes to PcG repression.Msx1, Math1, Nkx2.2, Nkx2.9, and Gata4. These genes have well-annotated CpG island promoters, are GC-rich throughout their promoters and coding regions, and in mESCs are co-occupied by PRC1, PRC2, and poised Ser5P Pol II  analysis  in mESCsP Pol II . Native P Pol II , and the regions . As nega R-loops  and the in mESCs . As expeed genes A.Figure in vivo, an enzyme that specifically degrades RNA in RNA-DNA hybrids without cleaving the single-stranded RNA (ssRNA). We transfected mESCs for 48 h with a construct expressing GFP-tagged RNase H1  and an active gene (β-actin) at specific time points after DRB treatment and post-wash conditions. As shown in β-actin after 10 min of DRB treatment and they reappeared 30 min post-wash. This argues for a dynamic resolution and formation of R-loops over active genes, as previously reported , a CDK9 inhibitor that interferes with transcriptional elongation by Pol II. R-loops were then measured over a PcG-repressed  transcripts by synthesizing cDNA from total RNA using reverse primers positioned over the first intron. Low levels of nascent transcripts could be detected over PcG-repressed genes B, as precent RNA . The oppβ-actin gene, whereas CyclinB1 mRNA levels were unchanged.We then tested whether R-loop removal is sufficient for transcript maturation and increased mRNA expression. To probe for poly-adenylated transcripts, we reverse-transcribed RNA using oligo-dT primers and amplified the cDNA using primers spanning the spliced junction between exon 1 and exon 2 for each gene C. We conTo further explore the mechanisms by which R-loop formation at PcG targets promote transcriptional repression, we asked whether R-loops and PcG enzymes simultaneously co-occupy chromatin using sequential native chromatin immunoprecipitation (ChIP) A. First,β-actin, as expected, confirming that there was no detectable carryover from the first ChIP with the S9.6 antibody. Negative control genes (CyclinB1 and Myf5) showed no enrichment. We also confirmed co-association of EZH2 with Ser5P Pol II in native conditions  on mock-transfected cells and cells overexpressing RNase H1 A and 3B.To explore the extent to which R-loops form at PcG-repressed genes genome-wide, we re-analyzed published genome-wide DRIP-seq dataset of R-loops in mESCs . R-loopsHoxa7 is a gene that shows nascent RNA in the GRO-seq, and Pax3 is a gene with nascent RNA as detected previously by northern blot  mESCs  for el genes A–S6C. Reel genes .Ezh2 KO cells over R-loop-forming PcG-repressed genes, and control gene β-actin also remained unaffected, as expected. The negative control genes Pax3, Mogat1, Hoxa7, and CyclinB1 showed no or very little enrichment, as expected  mESC clone. Importantly, R-loops were unaffected in expected A. This rexpected D and impEzh2 causes transcriptional derepression of R-loop-positive PcG-repressed genes. Analysis of spliced transcripts revealed that R-loop-positive PcG-repressed genes do not show prevalent upregulation of their mature transcripts upon Ezh2 KO. Interestingly, R-loop-negative PcG-repressed genes exhibited slight (Mogat1 and Hoxa7) or higher (Pax3) derepression upon constitutive Ezh2 loss , an inhibitor that prevents H3K27me3 deposition at PcG-repressed genes without disrupting EZH1 and EZH2 chromatin binding  unWe then tested whether the reduced recruitment of RING1B could account for the enhanced gene derepression observed in the combined conditions of R-loops loss and inhibition of PRC2 activity. After performing RING1B ChIP C as abovEzh2 KO system and tested whether removal of R-loops could induce gene activation in the absence of EZH2. We performed 8WG16 Pol II ChIP as a proxy for gene activation in the following conditions: (1) WT mock-transfected cells (Ezh2 KO cells (orange bars), and (4) Ezh2 KO cells overexpressing RNase H1 (green bars). Removal of R-loops led to an increase in 8WG16 Pol II levels in WT mESCs  over R-loop-positive PcG-repressed genes, indicative of gene activation. Pax3, Mogat1, and Hoxa7, PcG-repressed genes without R-loops, remained repressed. These results reveal that R-loop structures can act as transcriptional repressors in the PcG system independently of EZH2 (PRC2).We next sought to investigate whether EZH2 (PRC2) occupancy on chromatin is important for gene repression in R-loop-forming PcG targets. We went back to the WT mESCs C, Ezh2 KEzh2 KO conditions RING1B was unaffected at R-loop-positive genes (Ezh2 KO cells without R-loops could be due to changes in RING1B occupancy. We performed RING1B ChIP in Ezh2 KO cells overexpressing RNase H1 and used WT and Ezh2 KO cells as controls (Ezh2 KO (orange bars) and WT (gray bars) cells. Pax3, Mogat1, and Hoxa7 genes exhibited no change in RING1B occupancy upon R-loop resolution in Ezh2 KO cells. The above results suggested that R-loop formation is important for RING1B recruitment specifically at the subset of PcG-repressed genes that form R-loops.Given that in ve genes  and thatve genes C, we woncontrols B. RemarkOur results have collectively uncovered several unanticipated aspects of PcG-repression mechanisms at developmental regulator genes in mESCs, which consist of a synergistic interplay between R-loops and RING1B, the catalytic subunit of PRC1. We have identified a novel repression mechanism of PcG targets where R-loops are sufficient to repress, independently of EZH2 and of H3K27me3 and H2Aub1 chromatin marks.Ezh2 KO and upon chemical inhibition of EZH2, suggesting that R-loop formation may be a primary step in the repression pathway of R-loop-positive PcG targets.Experiments targeting the catalytic activity of EZH1-2 allowed us to further dissect the role of RING1B in PcG regulation. EZH2 chemical inhibition resulted in reduction of RING1B and transcriptional activation. These results are in line with previous studies showing that the canonical PRC1 can be recruited to chromatin via prior H3K27me3 deposition mediated by PRC2 . We now in vitro .Further information and requests for resources and reagents should be directed to and will be fulfilled by the Lead Contact, Konstantina Skourti-Stathaki (Ezh2 KO) were grown on 0.1% gelatin-coated surfaces in GMEM BHK21 supplemented with 10% Fetal Calf Serum (FCS), 2mM L-glutamine, 1% MEM non-essential amino acids (NEAA), 1mM sodium pyruvate , 50 μM 2-mercaptoethanol, 100 U/ml of human recombinant leukemia inhibitory factor .Mouse ESC cells , along with a single-stranded DNA oligonucleotide for homology-directed repair to introduce three consecutive stop codons at the 5′ end of exon 7 . After fluorescence-based sorting for GFP-positive transfected single cells, single cell colonies were expanded, genotyped, and analyzed for EZH2 expression by western blot. Correct genotypes were confirmed by Sanger sequencing on PCR-amplified genomic material.E14 mESCs were transfected with pX458 plasmid  encodingDNA immuno-precipitation (DIP/DRIP) analysis was carried out largely as described previously  and was free, Ambion kit), and reverse transcribed with SuperScript III Reverse Transcriptase (ThermoFisher Scientific), either using oligo-dT primer or a gene-specific primer from the Cells were washed with PBS and were harvested by adding 1 mL of Trizol reagent (ThermoFisher Scientific). RNA was then isolated following the manufacturer’s instructions, DNase I treated for a total of 2 h  and incubated for 10 mins on ice. The lysed cells were then centrifuged to pellet nuclei. The nuclear pellets were resuspended in Nuclear lysis buffer  and fragmented by sonication. 35-50 μg of chromatin was pre-cleared with A/G magnetic beads (ThermoFisher Scientific) for 1 h at 4°C and then immuno-precipitated in IP dilution buffer  with 3.5-5 μg of antibody overnight. Washes were performed using low salt wash buffer , high salt wash buffer  and LiCl wash buffer  and eluted samples were reverse crosslinked for 4 hr to O/N hr at 65°C with 0.3 M NaCl and 3 μg/ml RNase A (Roche). Proteinase K treatment was performed for 2 hr at 45°C with 10 mM EDTA, 40 mM Tris-HCl pH 6.5 and 20 μg proteinase K. The chromatin was purified. The immuno-precipitated, non-precipitated, and input DNAs were used as templates for qPCR. The PCR mixture contained QuantiTect SYBR green PCR master mix, 2 μl of the template DNA and primers from the The following antibodies were used for ChIP: anti-EZH2 , anti-RING1B , anti-H3K27me3 , anti-H2Aub1 , anti-8WG16 , anti-Ser5P  and anti-Ser7P .ChIP protocol as described above was followed. The chromatin was purified using the MinElute PCR Purification kit  and DNA concentration for library preparation was determined using Qubit fluorometric quantitation (ThermoFisher Scientific). Libraries were prepared from 8ng of DNA using the NEBNext Ultra II DNA library kit for Illumina , following the manufacturers’ instructions. Size selection was performed prior to PCR amplification using RNA clean XP beads . Adaptors, PCR amplification and Index Primers were used to multiplex libraries . Libraries were purified using RNA clean XP beads  and library size was assessed before high-throughput sequencing by Bioanalyzer (Agilent) using the High Sensitivity DNA analysis kit . ChIP-seq libraries were sequenced paired-end on an Illumina HiSeq2500 sequencer at the Wellcome Trust Sanger Institute . The following antibodies were used for ChIP-seq: anti-EZH2  and anti-SUZ12 .Nascent ChIP analysis was carried out using the Chromatrap kit for native ChIP (Chromatrap) following the manufacturers’ protocol. All buffers used were provided. In brief, non-crosslinked cells were harvested in ice-cold PBS and lysed in Hypotonic buffer. Nuclei were pelleted by nuclear separation. Chromatin was sheared using enzymatic shearing cocktail and smaller fragments were collected via centrifugation. Dialysis was performed O/N to remove unwanted contaminants and to obtain larger chromatin fragments. Small and large chromatin fragments were combined and immunoprecipitation was performed in a 35 μg:14 μg chromatin: antibody ratio, for all antibodies used in this study. Antibodies used were the same as indicated above.Washes and elution was performed using the columns provided. Chromatin samples were digested with Proteinase K and DNA purification was performed. The immuno-precipitated, non-precipitated, and input DNAs were used as templates for qPCR. The PCR mixture contained QuantiTect SYBR green PCR master mix, 2 μl of the template DNA and primers from the Native chromatin was prepared and the first immunoprecipitation was performed as in single native ChIP using the Chromatrap kit (see above). After the elution of native chromatin in 50 μL total volume, the eluate was diluted 10-fold to obtain final concentration of 0.1% SDS for optimal second immunoprecipitation. The second immunoprecipitation, washes, and elution were then carried out following the single native ChIP protocol (see above). A no-antibody control was included in the second round of immunoprecipitation as a negative control, to test for contamination of antibody remaining from the first immunoprecipitation. All antibodies used are indicated above.Cells were washed with ice-cold PBS. Cell pellets were resuspended in RIPA lysis buffer . Cell lysis was performed for 20 mins on ice. Protein lysate was recovered by centrifugation and protein-containing supernatant was kept. Protein concentration was measured by Bradford assay (BIORAD). Western blotting was performed on 40 μg of total 46C mES cell protein extracts with antibodies raised against EZH2 (Diagenode), RING1B , OCT4 (Abcam), NANOG (Abcam) and γ-tubulin (Sigma), all at 1:1000 dilutions. Western blotting was performed with ECL kit (PerkinElmer).Drosophila melanogaster genome dm6 with Bowtie v2.0.5  were aligned to the mouse genome mm9 and e v2.0.5 , with deDrosophila Spike-Ins as described in Active Motive catalog and as described in Drosophila in that dataset, then multiplied by 106 for convenience.To allow comparison between datasets, the amount of signal was normalized using Average ChIP-seq profiles were generated as previously , by plotR-loop DRIP peaks in E14 ESCs from PRC repressed genes from GSE48895). RPKM values represent the number of reads mapped in the sense of the gene from TSS to TES per kilobase (TSS to TES length) per million of reads mapped. Genes whose expression was >0.1 RPKM were considered positive for nascent RNA. As a technical note, GRO-seq relies on an in vitro transcription step by active transcribing polymerases which for Polycomb-repressed could be a technical challenge and therefore it is possible that not all nascent RNAs generated from Polycomb-repressed genes can be captured.Nascent RNA RPKMs (reads per kilobase per million of reads mapped) were calculated based on the bedgraph files from http://genome.ucsc.edu), using the following datasets:•R-loop peaks, from •R-loops from the same paper, raw data downloaded from GEO and remapped as described below;•GRO-seq for plus and minus strands from •H3K27me3, from •H2AK119ub1, from •GSM327669), raw data downloaded from GEO and remapped as described below;EZH2 and RING1B from •GSM850469, GSM850468, GSM850470), raw data downloaded from GEO and remapped as described below;RNAPII-S5p, RNAPII-8WG16, RNAPII-S7p and RNAPII-S2p from Brookes et al., 2012 (GSM850467, •mRNA-seq from Single gene profiles are taken from UCSC genome browser (ChIP-seq sequenced reads were aligned to the mouse genome mm9 with Bowtie v2.0.5 , with de∗p < 0.05, ∗∗p < 0.01, ∗∗∗p < 0.001. p value in Statistical tests in all figures, except https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE118115. Original images of western blot assays are available at Mendeley Data https://doi.org/10.17632/55f4vg9ww4.1.Datasets produced in this study have been deposited in GEO in the following link:


Transcription regulation is a major controller of gene expression dynamics during development and disease, where transcription factors (TFs) modulate expression of genes through direct or indirect DNA interaction. ChIP sequencing has become the most widely used technique to get a genome wide view of TF occupancy in a cell type of interest, mainly due to established standard protocols and a rapid decrease in the cost of sequencing. The number of available ChIP sequencing data sets in public domain is therefore ever increasing, including data generated by individual labs together with consortia such as the ENCODE project.A total of 1735 ChIP-sequencing datasets in mouse and human cell types and tissues were used to perform bioinformatic analyses to unravel diverse features of transcription control. 1- We used the Heat*seq webtool to investigate global relations across the ChIP-seq samples. 2- We demonstrated that factors have a specific genomic location preferences that are, for most factors, conserved across species. 3- Promoter proximal binding of factors was more conserved across cell types while the distal binding sites are more cell type specific. 4- We identified combinations of factors preferentially acting together in a cellular context. 5- Finally, by integrating the data with disease-associated gene loci from GWAS studies, we highlight the value of this data to associate novel regulators to disease.In summary, we demonstrate how ChIP sequencing data integration and analysis is powerful to get new insights into mammalian transcription control and demonstrate the utility of various bioinformatic tools to generate novel testable hypothesis using this public resource. The diversity of mammalian organs and tissues is manifested through differences in the gene expression across cell types with the same DNA sequence. To achieve this, specific sets of genes are activated or silenced during development using instructions which include epigenetic and transcription control mechanisms . ThroughThe high throughput sequencing data generation is now no more a barrier, but this data is not yet used to its full potential by analysis and integration. The explosion of data has therefore opened new avenues of research. New methods and tools have been developed to facilitate this data-driven biology. The ENCODE consortium has provided in-depth analyses of the TF ChIP-seq generated –10. DespWe therefore collected ChIP sequencing data from diverse compendia including  and consWe analysed four large resources of ChIP-seq datasets, with between 156 and 690 transcription Factor (TF) ChIP-seq experiments from mainly the ENCODE datasets in human (hg19)  and mousIn each datasets we noted similar groupings Fig. . A large2The Heat*seq tool allows easy sub-selection of samples in each dataset based on factor or cell type. Figure 2HeatChIPSeq can also be used to explore the fraction of cell-specific binding sites for different factors. Figure 2In Fig. 2The cluster analysis in the previous section indicated that some factors had a majority of their peaks close to gene transcription start sites (TSS) while others were mostly found at promoter-distal regions. We investigated this further by annotating each transcription factor peak (from the human an mouse ENCODE datasets) by its closest GENCODE gene TSS , 17 seea and b. 33333Most factors showed a conserved fraction of promoter proximal peaks across experiments and or cell types. Nonetheless, we noted a few inconsistent cases. For example, six of eight CFOS ChIP-seq in the human ENCODE datasets had less than 10% of the peaks overlapping a TSS Fig. f, while P-value <10−10). ETS1 and ZKSCAN1 had a greater fraction of peaks near TSS in human than in mouse, but the lack of replicates hindered assessing whether this is true biological difference or a technical variation. Overall, the linear regression slope was less than one, i.e. most factors had a higher promoter-bound fraction of peaks in human compared to mouse. It is unclear whether this reflects gene annotation differences between the species, gene density differences, or different affinity for promoters in different species.For each TF, we compared the fraction of peaks overlapping a TSS in mouse and human Fig. h. For moWe further clustered ChIP-seq experiments using only the promoter proximal or the promoter distal peaks separately, in the six human and the two mouse for cell lines with more than 30 ChIP-seq experiments. In the human ENCODE dataset, six cell lines , with a total of 474 experiments were selected Fig. 4. From th444Transcription factor hotspots are defined as genomic regions occupied by many factors in a given cellular context. They overlap with regulatory regions that are highly important for cell identity . We char4P-value < 10−256) [P-value < 10−256) genomic loci [P-value < 10−256) and in human GM12878, ETS1 co-occupied 1090 binding sites only with EGR1  and 1249 binding sites only with P300 .Mammalian transcription factors are known to work together by binding at the promoter or enhancer regions to activate or repress downstream target genes –24. Impo 10−256) . In mousmic loci . InteresP-value < 10−256) which were not occupied by any other factor. Similarly, E2A, FOxO1 and Pax5 co-bound 1087 genomic regions in mouse B cells  which were not occupied by any other factor studied. In human ES cells, 957 genomic loci were co-occupied only by RAD21 and TEAD4 . This postulates site specific role for these combinatorial interactions shadowed by the global analysis.Interestingly, there were a number of cases of combinatorial control where the two factors did not share most of the binding sites, therefore did not cluster together in the global clustering tree Fig.  but signTo investigate the sequence motif preference in genome wide biding patterns for each factor defined by ChIP sequencing experiment in a cellular context, we computed the enrichment of known motifs for each sample using HOMER . As expeTal1/Scl experiments had mostly ETS or RUNX or GATA as the top enriched motif, depending upon the cell type in which the factor was studied in both human and mouse. Similarly, the majority of RNAPII experiments across multiple cell types identified ETS as a top enriched motif, with only a handful of cases with a cell type motifs enriched such as GATA motif enriched in K562 RNAPII sample, or BZIP motif enriched in HeLa-S3 RNAPII sample. In embryonic stem cells (ESCs), OCT4-SOX2-TCF-NANOG motif was enriched for the ChIP sequencing of OCT4 and NANOG as expected; this motif was top enriched motif in SMAD3, P300, BCL11A, HDAC2 and CTBP2 samples as well. Interestingly, CTCF ChIP sequencing in different cell types did not result in enrichment of sequence motifs of cell type specific factors. This demonstrates that CTCF acts mainly as an insulator as well as defining gene regulatory boundaries which are largely independent of cell type.Genetic variations can be one of the causal factors of complex diseases. Genome-wide analysis (GWAS) studies have revealed genetic loci significantly associated with a disease risk, but the majority of the identified loci lie within the non-coding regions, specifically in the regulatory regions defined by chromatin modifications and DNase I hypersensitive sites across cell types . We calcAs the next generation sequencing is becoming a preferred tool of the experimental groups world-wide, the genome wide data will be ever increasing in public domain. Understanding the potential of this data for novel discoveries, huge efforts are ongoing in storing and assembling data in public repositories and databases –34. One In this paper, we explored multiple resources and tools Fig. , includiIn summary, we demonstrate potential ways to develop new hypotheses about transcription control mechanisms. Importantly, the above results form only the tip of the iceberg of the potential insights from these resources. Our analysis has allowed us to reproduce and expand some observations previously made using these data. This shows that the in depth analysis of such data is still far from complete and must continue. Importantly, the analysis performed in this study can easily be extended to exploit ChIP-seq datasets in other species or in other cellular contexts, and therefore has the potential to significantly advance our understanding of a wide range of both normal and pathological cellular processes.http://hgdownload.cse.ucsc.edu/goldenPath/hg19/encodeDCC/wgEncodeAwgTfbsUniform/) or from the ENCODE data portal  for a total of 690 experiments in human and 156 experiments in mouse. Human and mouse peak files from ChIP-seq experiments were also downloaded from CODEX [codex.stemcells.cam.ac.uk ), for a total of 238 experiments in human and 651 experiments in mouse.Peaks bed files where downloaded from ENCODE at UCSC  or the 2 cell lines (mouse) with the most experiments (ENCODE data). The binary matrices were used to compute the TF density of each merged peaks for each cell line, defined as the number of experiment with a peak at a given location divided by the total number of experiments from the cell line in a dataset. TF density matrices of merged peaks x cell line were clustered using k-mean clustering to identify clusters of high TF density that were either ubiquitous or cell-line specific.From each matrix of non-overlapping regions x experiments (ENCODE data for human and mouse), we computed Transcription Factor (TF) density by dividing the number of factors with a peak in that region by the total number of ChIP sequencing experiments done in that cell line, to obtain numbers between 0 and 1. The promoter proximal regions were defined as within 1kb of GENCODE TSS for both human and mouse, while promoter distal were further than 1kb from the nearest TSS. TF density matrices were then clustered using k-means clustering.P-value. The random binding sets maintained the same number of targets for each TF.TO study the combinatorial control of transcription factors in a cell type, the cell types with more than 6 factors studied were chosen and over-representation of each combination was calculated using random binding sets and estimating Bonferroni corrected www.ebi.ac.uk/gwas/. P values were calculated using hypergeometric test, and were adjusted using Bonferroni correction. The enriched combinatorial patterns were calculated by generating random binding data keeping the same number of peaks for each factor, and the significance was estimated compared to 100 randomizations.The sequence motif enrichment for each ChIP-seq sample was performed using HOMER  with all


Histone deacetylases (HDACs) are the proteins responsible for removing the acetyl group from lysine residues of core histones in chromosomes, a crucial component of gene regulation. Eleven known HDACs exist in humans and most other vertebrates. While the basic function of HDACs has been well characterized and new discoveries are still being made, the transcriptional regulation of their corresponding genes is still poorly understood.Here, we conducted a computational analysis of the eleven HDAC promoter sequences in 25 vertebrate species to determine whether transcription factor binding sites (TFBSs) are conserved in HDAC evolution, and if so, whether they provide useful information about HDAC expression and function. Furthermore, we used tissue-specific information of transcription factors to investigate the potential expression patterns of HDACs in different human tissues based on their transcription factor binding sites. We found that the TFBS profiles of most of the HDACs were well conserved in closely related species for all HDAC promoters except HDAC7 and HDAC10. HDAC5 had particularly strong conservation across over half of the species studied, with nearly identical profiles in the primate species. Our comparisons of TFBSs with the tissue specific gene expression profiles of their corresponding TFs showed that most HDACs had the ability to be ubiquitously expressed. A few HDAC promoters exhibited the potential for preferential expression in certain tissues, most notably HDAC11 in gall bladder, while HDAC9 seemed to have less propensity for expression in the nervous system.In general, we found evolutionary conservation in HDAC promoters that seems to be more prominent for the ubiquitously expressed HDACs. In turn, when conservation did not follow usual phylogeny, human TFBS patterns indicated possible functional relevance. While we found that HDACs appear to uniformly expressed, we confirm that the functional differences in HDACs may be less a matter of location of activity than a question of which proteins and which acetyl groups they may be acting on.The online version of this article (10.1186/s12864-019-5973-x) contains supplementary material, which is available to authorized users. Histone deacetylases (HDACs) remove the acetyl group from lysine residues of the N-terminal tail of core histones, allowing the repression of transcription. These metal binding proteins are mostly active in large multiprotein complexes, and can also act on non-histone proteins. Human histone deacetylases require zinc, and have been grouped into different classes based on their sequence similarity to homologues they have evolved from in yeast. Class I HDACs 1, 2, 3, 8) are most similar to yeast RPD3 protein, while Class II HDACs  are homologues of yeast HDA1 [, 2, 3, 8The HDAC proteins have been well characterized, and the position of their active site(s), their genomic position and cellular localization are well established Table . Their mEarly studies on HDAC evolution found evidence of an ancient family of proteins with de-acetylase activity . At the HDAC8, while also considered a Class I HDAC, seems to have evolved from a separate, equally ancient lineage that works on multiple substrates and is involved in several pathways , 14. ThiTraditionally considered as recruiters for Class I HDACs due to their low catalytic activity when compared to other histone deacetylases , Class IHDACs 6 and 10 seem to have an interesting relationship and are often classified separately as Class IIB, as both have two highly similar catalytic domains, although the second domain of HDAC10 is considered inactive , and ideThe only Class IV member, HDAC11, is arguably also the least well understood of the HDAC family. Recent reviews focus on its role in the immune system , 30, to the histone deacetylases, are necessarily involved in [N-acetyleserotonin [Given their vital and extensive roles in the regulation of gene expression and protein activity in eukaryotic genomes both in and out of the nucleus, relatively little is known about the regulation of HDAC expression. We expect that the transcription of HDACs does not differ markedly from other genes whose promoters are regulated by histone phosphorylation and acetylation , which tolved in . Regulatolved in , and theolved in , 35. Furolved in . Increaserotonin .Despite our expanding knowledge, there seems to be an urgent need to elucidate their separate functions and intersections of function, and to better understand how their own expression is regulated. Turning to computational methods as potential guides to bench experimentation, we conducted an in-depth analysis of HDAC promoter sequences with two questions in mind: Are transcription factor binding sites (TFBSs) conserved in HDAC evolution, and if so, do they provide useful information about HDAC transcriptional regulation and HDAC function?These questions were fueled by recent literature on the slow evolution of TFBSs , 39 and H. sapiens, G. gorilla and P. troglodytes) and rodents (R. norvegicus and M. musculus) forming clades. We observed similar patterns in HDACs 2, 3, 4, 6, 8, 9 and in HDAC11  clustering close to two old world monkeys, while promoters of HDAC5 homologues from rat and mouse occupied different ends of the dendrogram. Promoter sequences of HDACs 7 and 10 also showed transcription factor binding site patterns where the classic phylogenetic lineages did not hold true. The TFBS profile of human HDAC7  as designated in the National Center for Biotechnology Information’s (NCBI) Nucleotide database. Research in yeast found that conservation of TFBSs is highest within 200 bp upstream of the TSS . Furtherwww.genomatix.com), as its transcription factor database has a taxonomically relevant classification system that was applicable to all the considered species. The computational detection of TFBS motifs was based on scanning these promoter sequences through position weight matrices of corresponding transcription factors with MatInspector as implemented in the Genomatix software suite [We extracted known and annotated HDAC sequences in 25 different species from the NCBI’s Nucleotide database. To check their similarity and possible kinship we aligned organism-specific HDACs with their human counterparts. In particular, we established 11 HDAC groups of sequences that are annotated according to their corresponding human HDAC. Although we only considered known organism-specific HDACs, we reviewed their annotations and assigned a given organism-specific HDAC to the corresponding group, if the corresponding sequence was most similar to the underlying human HDAC. We then extracted their promoter sequences for our analysis using the Genomatix software suite  of TFBSs by m of TFBSs asIn a promoter sequence we determined the occurrence of a 3-mer As a consequence, each promoter sequence was represented as a profile of trimers.M and N as the cosine distance between profiles of transcription factor binding sites asComparing pairs of trimer profiles of TFBSs between species, we defined a distance between promoter sequences http://tree.bio.ed.ac.uk/software/figtree/).This similarity measure was used to determine all pairwise distances between promoter profiles of TFBSs between species. Distance matrices were used to reconstruct the trees using the neighbor-joining algorithm as implemented in the DendroPy Phylogenetic Computing Library . The resThe Genomatix database , 58 was p and are expressed in a tissue t, Ep,t, we defined xt is the number of transcription factors that are expressed in tissue t, while np is the number of transcription factor binding sites in the underlying promoters sequence p. We utilized this background distribution to determine the enrichment of a promoter sequence p in a tissue t, defined as Op,t is the observed number of transcription factor binding sites that appear in promoter sequence p and are expressed in tissue t. Given the difficulty of assessing significance in this domain, and the lack of direct activity data of TFs on HDAC promoters [2-fold change of observed and expected TFBS frequencies fcpt > 1 and diluted if fcpt < − 1.As a general expected value for transcription factors that appear on a given promoter romoters , we consAdditional file 1Table S1 Tissue to System classification. (DOCX 17 kb): Additional file 2Figure S1 Evolutionary conservation of transcription factor binding sites in HDAC2 promoter sequences in different organisms. (DOCX 3802 kb): Additional file 3Figure S2 Evolutionary conservation of transcription factor binding sites in HDAC3 promoter sequences in different organisms. (DOCX 3534 kb): Additional file 4Figure S3 Evolutionary conservation of transcription factor binding sites in HDAC4 promoter sequences in different organisms. (DOCX 3746 kb): Additional file 5Figure S4 Evolutionary conservation of transcription factor binding sites in HDAC6 promoter sequences in different organisms. (DOCX 4280 kb): Additional file 6Figure S5 Evolutionary conservation of transcription factor binding sites in HDAC8 promoter sequences in different organisms. (DOCX 3673 kb): Additional file 7Figure S6 Evolutionary conservation of transcription factor binding sites in HDAC9 promoter sequences in different organisms. (DOCX 3052 kb): Additional file 8Figure S7 Evolutionary conservation of transcription factor binding sites in HDAC11 promoter sequences in different organisms. (DOCX 3544 kb):


Drosophila melanogaster. In this article, we will review our understanding of the events that drive the MZT in Drosophila embryos and highlight parallel mechanisms driving this transition in other animals.The onset of metazoan development requires that two terminally differentiated germ cells, a sperm and an oocyte, become reprogrammed to the totipotent embryo, which can subsequently give rise to all the cell types of the adult organism. In nearly all animals, maternal gene products regulate the initial events of embryogenesis while the zygotic genome remains transcriptionally silent. Developmental control is then passed from mother to zygote through a process known as the maternal-to-zygotic transition (MZT). The MZT comprises an intimately connected set of molecular events that mediate degradation of maternally deposited mRNAs and transcriptional activation of the zygotic genome. This essential developmental transition is conserved among metazoans but is perhaps best understood in the fruit fly,  Maternally provided products play an essential role in driving this developmental transition and help to reprogramme the early embryonic genome. Subsequent activation of the zygotic genome and degradation of these maternally deposited products allows for the control of embryonic development to transition from the mother to the zygote. This maternal-to-zygotic transition (MZT) requires the coordination of multiple events, including remodelling of the mitotic division cycle, morphological changes, widespread transcriptional activation of the zygotic genome and degradation of a subset of maternal transcripts and proteins –5. RecenDrosophila melanogaster, the early nuclear divisions occur at an unprecedented rate of every 8–10 min. The MZT spans 13 rapid nuclear divisions that occur without cytokinesis, giving rise to a syncytium. At the 14th nuclear cycle, the division cycle slows dramatically, and the approximately 6000 nuclei become cellularized . T. TDrosop MZT (a) . The con MZT (a) .cis-acting elements within the open reading frames or 3′-untranslated regions of maternal transcripts, including SMG, Brain tumour (BRAT) and Pumillio (PUM) . T. TmiR-30al mRNAs . Likewis cyclins . Thus, m cyclins . Togethe3.Drosophila embryogenesis, the first 2 h of development comprise 13 rapid nuclear division cycles within a shared cytoplasm (a). These mitotic cycles lack gap phases and are therefore a series of repeating cycles of DNA synthesis and mitosis with each cycle occurring approximately every 10 min ..a). Two Drosophila and humans, do not change in volume during the initial stages of development. Thus, while each round of DNA replication and division amplifies the number of nuclei exponentially, the volume of the cytoplasm remains unchanged leading to a progressive change in the ratio of nuclear DNA to cytoplasmic components. Manipulation of the N : C ratio through changes in zygotic ploidy can uncover the relative contribution of the two mechanisms: N : C ratio verses ‘maternal clock’. For example, haploid embryos require an additional round of nuclear replication to achieve the same N : C ratio as a diploid . . d) [145d) ,149. The5.3.The eukaryotic genome is partitioned into topological and functionally distinct active and repressed domains . ConsistDrosophila embryogenesis, hierarchical compartmentalization of the genome occurs in multiple waves (e). Active chromatin loops and early TAD boundaries are first evident during the minor wave of ZGA. Nonetheless, these chromosomal interactions are weak and are rapidly followed by another structural transition correlating with the major wave of ZGA that further demarcates active and repressive chromatin domains [Drosophila embryos occurs independent of transcription, as higher-order structural TADs remain intact even when transcription is pharmacologically inhibited [In le waves e. Active domains ,10. Althnhibited . Moreovenhibited . Conversnhibited . This siDespite recent advances in our understanding of TAD formation during early embryogenesis, it remains unclear what initially nucleates and guides chromatin folding during early development. Topological boundaries established during the MZT are enriched in histone acetylation marks, chromatin accessibility and ZLD occupancy . During TADs appear to be common within eukaryotic genomes and are often conserved among cell types and even across closely related species –162, sug6.Drosophila melanogaster embryogenesis have paved the way towards a better understanding of the MZT and the dramatic transcriptional and cellular changes that govern this developmental transition. As the embryo transitions from fertilized egg to totipotent zygote, the embryonic genome and transcriptome need to be rapidly reprogrammed. This rapid and efficient in vivo embryonic transformation has a number of similarities with the much less efficient reprogramming of specified cell types to induced pluripotent stem cells in culture [Drosophila at the sequence level, they function in a similar manner to ZLD during early embryogenesis. They all share the capacity to facilitate chromatin accessibility and reprogramme cells to a naive developmental state, and misexpression of these factors can be detrimental to the cell or organism. Thus, in many species, a network of maternal transcription factors acts through conserved mechanisms to reprogramme the early embryonic genome. How additional factors and mechanisms cooperate with essential transcription factors to collectively reprogramme the genome during the MZT remains an important area for future research.Decades of insights from  culture . This pa culture ,78, and  culture ,117. AltGiven that division-cycle regulation, DNA replication, transcriptional activation, maternal RNA clearance and chromatin remodelling are all tightly coupled during the MZT, an exciting future challenge will be elucidating the mechanisms that coordinate these essential developmental processes. The advancements of single-cell sequencing technologies and high-resolution microscopy have allowed the critical events driving this developmental transition to be understood in ever greater detail. Through our elucidation of these conserved developmental mechanisms, we will provide important insights into the unifying principles that drive genome activation.