Eukaryotic DNA replication initiates during S phase from origins that have been licensed in the preceding G1 phase. Here, we compare ChIP-seq profiles of the licensing factors Orc2, Orc3, Mcm3, and Mcm7 with gene expression, replication timing, and fork directionality profiles obtained by RNA-seq, Repli-seq, and OK-seq. Both, the origin recognition complex (ORC) and the minichromosome maintenance complex (MCM) are significantly and homogeneously depleted from transcribed genes, enriched at gene promoters, and more abundant in early- than in late-replicating domains. Surprisingly, after controlling these variables, no difference in ORC/MCM density is detected between initiation zones, termination zones, unidirectionally replicating regions, and randomly replicating regions. Therefore, ORC/MCM density correlates with replication timing but does not solely regulate the probability of replication initiation. Interestingly, H4K20me3, a histone modification proposed to facilitate late origin licensing, was enriched in late-replicating initiation zones and gene deserts of stochastic replication fork direction. We discuss potential mechanisms specifying when and where replication initiates in human cells. In human cells, DNA replication initiates from 20,000 to 50,000 replication origins selected from a five- to tenfold excess of potential or ‘licensed’ origins . Origin Saccharomyces cerevisiae, origins are genetically defined by specific DNA sequences  (Chromatin immunoprecipitation followed by sequencing (ChIP-seq) was used to map ORC and MCM chromatin binding. In s (TSSs) , but MCMs (TSSs) . In humas (TSSs) . These ss (TSSs) . We and s (TSSs) . A five-s (TSSs) . These fHere, we present the first comparative survey of four different pre-RC components, replication initiation events, transcription activity, and RT in the genome of the human lymphoblastoid Raji cell line by combining ChIP-seq, OK-seq, RNA-seq, and previously published Repli-seq data . We findThese findings support the cascade model for replication initiation: the entire genome (except transcribed genes) is licensed for replication initiation. Additional process and factors like adjacent active transcription and epigenetic marks are required to specify master zones of higher replication initiation efficiency. The distributed licensing pattern allows the stochastic activation of secondary origins, possibly triggered by approaching replication forks.oriP as reference  and of three replicates for MCM proteins  resulted in reproducible, but dispersed, ChIP-seq signals as exemplified by the well-characterized replication origin Mcm4/PRKDC . We emplConsequently, we summed up the reads of the ChIP replicates at different binning sizes and normalized the signals against the mean read frequencies of each ChIP sample and against input, as is standard in most ChIP-seq analyses. We computed the Pearson correlation coefficients between ORC/MCM ChIPs and obtained good correlations at 1 kb bin size and only marginal improvement at larger sizes . When woIn line with a previous report , the inpThe reliability and reproducibility of our ChIP experiments is reflected by the high Pearson correlation coefficients of the relative read frequencies of Orc2/Orc3  well correlated with RT. Thus, potential origins are widespread through the genome, but additional genetic or epigenetic factors are regulating whether and when they fire.2(Early/Late) > 1.6 for early RTDs and <−2.0 for late RTDs, which resulted in 302 early RTDs covering 642.8 Mb and 287 late RTDs covering 617.4 Mb of the genome. Restricting the analysis to intergenic regions, we calculated the mean ORC/MCM relative read frequencies of pre-replicative G1-phased chromatin in early compared to late RTDs. ORC was 1.4 times enriched in early RTDs compared to late RTDs  or termination (DSs), or random replication (NRRs), or unidirectional, passive replication (URRs). We consequently propose that potential origins, defined by loaded MCM-DHs, are widespread through the genome and that preferential initiation sites are selected for activation in S phase based on additional genetic and/or epigenetic factors. We further show that subsets of non-genic ASs and randomly replicating gene deserts are enriched in H4K20me3, which helps recruiting ORC/MCM to these late-replicating segments.Drosophila  typical of active or poised enhancers , which cH4K20 methylation has multiple functions in ensuring genome integrity, such as DNA replication , DNA repIn higher eukaryotes, it has been proposed that RT could simply result from the spatial distribution of potential origins upon S phase entry. The latter distribution has been derived from ORC  or MCM-Dis not a distinguishing feature of IZs from the rest of the genome, IZ specification cannot solely occur at the origin-licensing step. Open and dynamic chromatin structures found at Ma-IZs and ERCEs  supplemented with 8% FCS , 100 Units/ml penicillin, 100 µg/ml streptomycin , 1× MEM non-essential amino acids , 2 mM L-glutamine , and 1 mM sodium pyruvate .Raji cells  were directly obtained from DZMS and tested mycoplasma negative. Raji cells were cultured at 37°C and 5% CO5 Raji cells using Direct-zolTM RNA MiniPrep kit (Zymo Research) according to manufacturer’s instructions. RNA quality was confirmed by Bioanalyzer RNA integrity numbers between 9.8 and 10 followed by library preparation (Encore Complete RNA-Seq Library Systems kit [NuGEN]). Single-end 100 bp sequencing was performed by Illumina HiSeq 1500 to a sequencing depth of 25 million reads. The reads were mapped to hg19 genome using Tophat2 and assigned to annotated genes (HTSeq-count)  as previously described (RNA-seq was performed in three independent replicates. RNA was extracted from 3 × 10q-count) . TPM valescribed .Raji OK-seq was recently published and is available from the European Nucleotide Archive under accession number PRJEB25180 (see Data access section) . Reads >pos_5’ and pos_3’ the location of the start and end position of an AS or DS segment, each segment was associated to its size pos_3’-pos_5’ and the RFD shift across its length: ΔRFD = RFD (pos_3’) – RFD (pos_5’). DS segments were less numerous (2477 versus 4891) and on average larger (126 kb versus 38.8 kb) than AS segments, as expected, and presented a smaller average RFD shift (|ΔRFD| = 0.69 versus 0.83).RFD profiling of two human cell lines revealed that replication primarily initiates stochastically within broad (up to 150 kb) zones and terminates dispersedly between them . These IInitial RFD profiling in human also revealed regions of unidirectional fork progression and regions of null RFD where replication is bidirectional. URRs were delineated as regions where |ΔRFD| > 0.8 homogeneously over at least 300 kb (401 regions of mean length 442 kb covering 177 Mb). NRRs were delineated as regions where |ΔRFD| < 0.15 homogeneously over at least 500 kb (127 regions of mean length 862 kb covering 110 Mb). Thresholds were set in a conservative manner to avoid false positive, particularly not to confuse RFD zero-crossing segments with NRR.9 exponentially growing Raji cells were harvested, washed with PBS, and resuspended in 50 ml RPMI 1680, 8% FCS, 1 mM EDTA, 0.25 U/ml DNaseI . Concentrated cell suspension was passed through 40 µm cell strainer and injected in a Beckman JE-5.0 rotor with a large separation chamber turning at 1500 rpm and a flow rate of 30 ml/min controlled by a Cole-Parmer Masterflex pump. While rotor speed was kept constant, 400 ml fractions were collected at increasing flow rates . Individual fractions were quantified, 5 × 106 cells washed in PBS, ethanol fixed, RNase treated and stained with 0.5 mg/ml Propidium Iodide. DNA content was measured using the FL2 channel of FACSCalibur . Remaining cells were subjected to chromatin cross-linking.For centrifugal elutriation, 5 × 10Rat monoclonal antibody GAPDH3 10F4 were generated by immunization with a peptide comprising amino acids RLEKPAKYDDIKKVVK of human GAPDH (aa246-263) coupled to OVA. Animals were injected subcutaneously and intraperitoneally with a mixture of 50 μg peptide, 5 nmol CpG , and an equal volume of incomplete Freund’s adjuvant. Six weeks later a booster injection was performed without Freund’s adjuvant. Three days later, spleen cells were fused with P3X63Ag8.653 myeloma cells using standard procedures. Hybridoma supernatants were screened in a solid-phase enzyme-linked immunosorbent assay for binding to GAPDH antigen. Positive supernatants were further assayed for western blotting. Hybridoma cells were subcloned twice by limiting dilution to obtain the monoclonal cell line stably producing antibody GAPDH3 10F4 (rat IgG2c).7 cells/ml, and passed through 100 µm cell strainer . Fixation for 5 min at room temperature was performed by adding an equal volume of PBS 2% methanol-free formaldehyde  and stopped by the addition of glycine . After washing once with PBS and once with PBS 0.5% NP-40, cells were resuspended in PBS containing 10% glycerol, pelleted, and snap frozen in liquid nitrogen.Raji cells were washed twice with PBS, resuspended in PBS to a concentration of 2 × 10Cross-linked samples were thawed on ice, then resuspended in LB3+ sonication buffer (see below) containing protease inhibitor and 10 mM MG132. After sonicating 3 × 5 min  using Bioruptor in the presence of 212–300 µm glass beads, samples were treated with 50 U Benzonase for 15 min at room temperature and centrifuged 15 min at maximum speed. Also, 50 µg protein lysates were loaded on 10% SDS-polyacrylamide gel , or 12.5–15% gradient gel (H3S10P). Cyclin A1/A2 , cyclin B1 , and H3S10P  antibodies were used in 1:1000 dilutions, and GAPDH  was diluted 1:50. HRP-coupled secondary antibodies were used in 1:10,000 dilutions. Detection was done using ECL on CEA Blue Sensitive X-ray films.7 cells/ml. Sonication was performed in AFA Fiber and Cap tubes  at an average temperature of 5°C at 100 W, 150 cycles/burst, 10% duty cycle, 20 min (S-G2-M fraction: 17 min) using the Covaris S220 , resulting in DNA fragments of 100–300 bp on average.Cross-linked cell pellets were thawed on ice, then resuspended in LB3(+) buffer  to a final concentration of 2 × 10oriP Dyad Symmetry element (for pre-RC ChIP) or H4K20me1 and -me3 positive loci were performed using the SYBR Green I Master Mix (Roche) and the Roche LightCycler 480 System. Oligo sequences for qPCR were DS_fw: AGTTCACTGCCCGCTCCT, DS_rv: CAGGATTCCACGAGGGTAGT, H4K20me1positive_fw: ATGCCTTCTTGCCTCTTGTC, H4K20me1positive_rv: AGTTAAAAGCAGCCCTGGTG, H4K20me3positive_fw: TCTGAGCAGGGTTGCAAGTAC, H4K20me3positive_rv: AAGGAAATGATGCCCAGCTG. Chromatin fragment sizes were verified by loading 1–2 µg chromatin on a 1.5% agarose gel. Samples were quantified using Qubit HS dsDNA assay.Sheared chromatin was pre-cleared with 50 µl protein A Sepharose 4 Fast Flow beads  per 500 µg chromatin for 2 hr. Then, 500 µg chromatin (or 250 µg for histone methylation) were incubated with rabbit anti-Orc2, anti-Orc3, anti-Mcm3, anti-Mcm7 , mouse ahttp://genome.ucsc.edu).ChIP sample library preparations from >4 ng of ChIP-DNA was performed using Accel-NGS 1S Plus DNA Library Kit for Illumina (Swift Biosciences). A 50 bp single-end sequencing was done with the Illumina HiSEQ 1500 sequencer to a sequencing depth of ~70 million reads. Fastq-files were mapped against the human genome , extended for the EBV genome (NC007605) using bowtie (v1.1.1) . SequencFor H4K20me1 and -me3 ChIP-seq data, MACS2 peak-calling  was perfAll data processing and analysis steps were performed in R (v.3.2.3) and numpy (v.1.18.5) python library, and visualizations were done using the ggplot2 (v3.1.0) package  and matpOrc2 ChIP-seq data in asynchronously cycling K562 cells was retrieved from GSE70165 . Peak caThe ENCODE ‘DNase clusters’ track wgEncodeRegDnaseClusteredV3.bed.gz  containing DNase hypersensitive sites from 125 cell lines were retrieved from ASs were aligned on their left (5') and right (3') borders. Mean and standard error of the mean (SEM) of relative read frequencies of aligned 1 kb bins were then computed to assess the average ChIP signal around the considered AS borders 50 kb away from the AS to 10 kb within the AS as this was sufficient to visualize the full increase of ORC/MCM coverage when entering ASs – the ORC/MCM relative read frequency plateaus inside ASs in In order to interrogate the relationship between ASs and transcription, we compared the results obtained for different AS groups: 506 ASs were classified as non-genic AS when the AS locus extended 20 kb at both ends did not overlap any annotated gene; the remaining 2451 ASs were classified as genic ASs. From the latter group, 673 ASs were classified as type 1 ASs when both AS borders were flanked by at least one actively transcribed gene (distance of both AS borders to the closest transcribed [TPM >3] gene body was <20 kb), and 1026 ASs were classified as type 2 ASs when only one AS border was associated to a transcribed gene .In order to assess the role of H4H20me3 mark on AS specification, we also classified non-genic ASs depending on their input-normed H4K20me3 relative read frequency. We grouped the non-genic ASs where the H4K20me3 relative read frequency was above the genome mean value by more than 1.5 standard deviation (estimated over the whole genome) and the non-genic ASs where the H4K20me3 relative read frequency was below the genome mean value. This resulted in 154 non-genic ASs with H4K20me3 signal significantly higher than genome average and 242 non-genic ASs with H4K20me3 signal lower than genome average.A similar selection was performed on fully intergenic 10 kb windows within NRRs . This resulted in 504 and 3986 windows with high and low H4K20me3 signal, respectively.Gene-containing bins were determined and overlapping genes removed from the analysis. For cumulative analysis, we only worked with genes larger 30 kb and assigned the gene expression levels in TPM accordingly. Genes were either aligned at their TSS or their TTS, and the corresponding average ChIP read frequency windows were calculated in a 30 kb window centered on the alignment site.2(Early/Late) ratio within the surrounding 100 kb window. Early RTDs were defined as regions where the average log ratio >1.6 and late RTDs as regions where the average log ratio <−2.0. These thresholds resulted in 1648 early RTDs, ranging from 10 to 8940 kb in size, with a mean size of 591 kb, while we detected 2046 late RTDs in sizes from 10 to 8860 kb, averaging at 470 kb. These RTDs were used to classify ChIP read relative frequencies calculated in 10 kb bins as early or late RT. Bins overlapping any gene extended by 10 kb on both sides were removed from the analysis to avoid effects of gene activity on ChIP signals.For identification of RTDs in Raji cells, we used the early- to late-RT ratio determined by Repli-seq . We direAll non-overlapping 10 kb windows were classified as intergenic if closest genes were more than 5 kb away, as belonging to a silent (resp. expressed) gene body if the window was inside a gene with TPM <3 (resp. TPM >3) and at more than 3 kb of gene borders, otherwise windows were disregarded. This made sure that specific ChIP signal at gene TSS and TTS was not considered in the analysis. Using the three window categories, we computed the 2D histograms of ChIP relative read frequencies versus RT in intergenic, silent and expressed gene bodies. We used 10 timing bins corresponding to the deciles of the whole genome timing distribution. For each timing bin, the histogram counts were normalized so as to obtain an estimate of the probability distribution function of the ChIP signal at the considered RT. The analysis was reproduced after restricting for windows fully in (i) AS segments , (ii) DS segments , (iii) URRs, and (iv) NRRs.t-test with Welch correction and 95% confidence interval or one-way ANOVA followed by Tukey’s multiple comparisons of means with 95% family-wise confidence level, if appropriate. Comparison between ChIP signal distribution observed in two situations was performed computing the two-sample Kolmogorov–Smirnov statistics KSD using SciPy (v.1.5.0) statistical library and correcting for sample sizes by reporting n and m are the sizes of the two samples, respectively.Statistical analyses were performed in R using one-sided The positions of the three genetically identified ERCEs in the mESC Dppa2/4 locus and of the 1835 predicted mESC ERCEs were downloaded from  In the interests of transparency, eLife publishes the most substantive revision requests and the accompanying author responses.Acceptance summary:The manuscript characterizes the chromatin binding of two ORC subunits and two subunits of the MCM2-7 hexamer that are required for the initiation of DNA replication in different domains of the genome that are defined by the timing of DNA replication during S phase, early versus late. The binding of ORC and MCM subunits was compared with replication fork direction, transcription and replication timing profiles in human cells and subtle changes in the densities of these proteins in the different chromatin regions were observed. The distribution of these subunits, however, does not determine replication timing. The distribution in the genome of the histone H4K20me3 modification was also examined, indicating that it facilitates origin licensing in late-replicating regions. The authors suggest that factors other than the density and distribution of pre-Replicative Complexes determine the timing of the initiation of DNA replication during S phase.Decision letter after peer review:eLife. Your article has been reviewed by three peer reviewers, including Bruce Stillman as the Reviewing Editor and Reviewer #1, and the evaluation has been overseen by Kevin Struhl as the Senior Editor.Thank you for submitting your article "Human ORC/MCM density is low in active genes and correlates with replication time but does not delimit initiation zones" 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.As the editors have judged that your manuscript is of interest, but as described below that additional experiments and analysis are required before it will be re-considered for publication.Summary:The authors have performed an extensive analysis of the binding or ORC2 and ORC3 subunits of the Origin Recognition Complex (ORC) and the MCM3 and MCM7 subunits of the MCM2-7 helicase subunits by chromatin immunoprecipitation and then compared the data to replicating timing patterns throughout the genome of human Raji cells. Based on previous studies, some of which derive from the authors' labs, they have identified different regions of the genome that replicate at different times and these are associated or not with transcription start sites , with non-transcribed DNA and regions that replicate unidirectionally or have no preference in a population of cells. Now the authors correlate the MCM2-7 and ORC binding with the replication timing and the various categories of chromatin.They conclude that ORC and MCM binding correlates with each other in G1 phase, which is not surprising, and then that early replicating regions are more associated with domains enriched in ORC/MCM binding, and uniquely, that there are large regions of homogeneous distributions of ORC/MCM. They conclude that ORC/MCM correlates with replication timing but not the probability of replication initiation. They also demonstrate that histone H4K20me3 location is localized with ORC/MCM and that a certain late replicating DNA has higher H4K20me3 binding.The paper contains a great deal of work and is of interest to those in the DNA replication field, adding to what is already known. There are some papers that are either not discussed well or not even mentioned and this should be corrected. However, before the paper can be re-considered for publication, the authors need to address some major concerns:Essential revisions1) One major issue relates to the small enrichments of ORC/MCM they observe in early versus late replicating DNA and the possibility that this represents a DNA isolation bias rather than a real correlation or cause . The paper does not address this issue which, given the small differences in ORC/MCM between early and late replicating regions (1.4 fold), this needs to be addressed. It is understood that genome-wide mapping of pre-RC components in mammalian cells is challenging. In all of the studies to date, the ChIP enrichment is very modest and not confined to tight peaks that are typical of transcription factor binding. The weak and broad patterns of localization and their enrichment at hypersensitive and transcription start sites may be a technical artifact or is reflective of the underlying biology. While the signals they are observing are likely biological, it is still very difficult  to disentangle causation and correlation with the observed patterns and enrichment at transcription start sites and DNase HS sites. What would make this story much stronger is to demonstrate that the MCM2-7 signal is dynamic – that is that the enrichment patterns they observe in G1 should be very different from the patterns in late S-phase or G2 when replication forks have displaced most of the MCMs. The authors need to perform ChIP-seq on the cells elutriated at 80 ml/min. The ORC profile may also change due to the dynamic nature of ORC in human cells, but the MCM definitely should only be enriched in late replicating regions of the genome in late S phase and this comparison is needed.2) As shown in Figure 1A, stochastic variation can be observed for MCM3/7 and ORC2/3 ChIP-seq replicates, it's reasonable to speculate that the input signal can also fluctuate randomly among replicates. Moreover, most of the conclusions in this manuscript are based on the input normalized signals. However, as shown by Figure 1A and the record for ENA PRJEB32855, no replication is performed for the input. Thus, we suggest that the authors provide replicates for the input, and normalize the ChIP signal to pooled input signals.3) As shown in Figure 1—figure supplement 2A, the input signals at "DNase hypersensitive (HS)" is lower than those at "no HS"; and in Figure 1—figure supplement 3E, the ChIP signals of MCM3/7 and ORC2/3 at "HS" are higher than those in "no HS". Thus, when the ChIP signals of MCM3/7 and ORC2/3 are normalized to the input, will the difference of ChIP signals of McCM3/7 or ORC2/3 at "HS" and "no HS" be amplified artificially?Additional comments related to the above major comments described above:1) In Figure 1, the authors show a convincing correlation between the ChIP-seq profiles of ORC2 and ORC3, as well as between MCM3 and MCM7. Miotto et al. had published ORC2 ChIP-seq data using asynchronous K562 human erythroid cells. The data in the Kirstein et al. paper reports ChIP data of ORC2 form Raji lymphoblastoid cells. Although the cell types and cell cycle stage are different, it would be valuable to show a Pearson correlation between the two different ORC2 sets. This should be shown as a Supplement to Figure 1. The authors could also comment on the data of ORC1 ChIP from Dellino et al., 2013 and Long et al., 2019 (see point 8 below) whether these ORC patterns correlate with the other ORC ChIP data.2) Figure 5A and Figure 4—figure supplement 2B. The results show that ORC is 1.4 times more frequently found in early versus late replicating regions. It is possible that the chromatin in early replicating regions is more accessible to the ChIP procedure than late replicating regions, which are likely more compact and hence difficult to access using antibodies. How have the authors excluded the possibility that extraction of DNA fragments in early versus late replicating regions could explain the difference in ORC binding? It should be noted that the authors previous papers and the Discussion in this paper claims accessibility to chromatin by replication factors may explain replication timing, yet they have assumed that the sonication and antibodies used for ChIP analysis are equally accessible. It is known that heterochromatic regions of the genome form phase transitions that may behave completely differently than actively transcribed and "accessible" regions of the genome.3) Figure 5E. The same concern outlined in comment 2 above could explain the small, albeit statistically significant difference between H4K20me3 high and low regions of the genome.4) Figure 6A and related text. The very slight differences in H4K20me3 levels could also be explained by extraction artIfact.5) “However, potential origins are defined by assembled MCM-DHs, not by ORC”. This statement that potential origins are determined by the MCM2-7 DH and not by ORC is not logical because MCM2-7 DH is loaded by ORC and other factors. The idea that it correlates with ORC is dismissed a few lines later, but none of these statements are justified. What is the evidence that access of firing factors to MCM2-7 DH are regulated by chromatin access?6) In a significant paper describing ORC ChIP and replication initiation, it was shown that ORC binding correlates with histone H2AZ and this could explain early replication origin activity. This paper is not even cited, much less discussed, and it should be  The authors have dismissed the replication timing model proposed by Miottto et al., 2016, but it is not clear why. This model should be discussed in relationship to the model in Figure 7.eLife. Your article has been reviewed by three peer reviewers, including Bruce Stillman as the Reviewing Editor and Reviewer #1, and the evaluation has been overseen by Kevin Struhl as the Senior Editor.Thank you for submitting your article "Human ORC/MCM density is low in active genes and correlates with replication time but does not delimit initiation zones" for consideration by The reviewers have discussed your response to the reviews with one another and the Reviewing Editor has drafted this decision to help you prepare a revised submission that addresses one issue.eLife papers without additional data, even if they feel that they would make the manuscript stronger. Thus the revisions requested below only address clarity and presentation.We would like to draw your attention to changes in our revision policy that we have made in response to COVID-19 (https://elifesciences.org/articles/57162). Specifically, we are asking editors to accept without delay manuscripts, like yours, that they judge can stand as The revised paper has incorporated new data that compares the abundance of ORC2, ORC3, MCM3 and MCM7 protein at two difference stages of the cell division cycle and raises some interesting observations. The authors have extensively addressed all of the original reviewer comments and provide new analysis. The differences in MCM and ORC levels at the different classes of gene expression, except for the gene bodies, are very modest but nonetheless statistically significant.The general conclusion is that ORC is wide spread on the genome in G1 phase and MCM localizes with sites of initiation of DNA replication, and that histone H3K4me3 is correlated with late origin firing. At all locations, the exact site of initiation of DNA replication is stochastic.One paradox needs explaining that arises from the new data presented in the revised paper compared to data in the literature.1) The data in Figure 4—figure supplement 2C show that MCM3 and MCM7 levels are reduced in S-G2-M cells compared to G1 cells, but there remains a difference between early versus late replication timing domains in both cell cycle stages. In contrast, ORC is high in early RFDs and low in late RFDs at both stages. Perhaps the authors should discuss the significance of this result, in light of the fact that ORC1 is degraded in human cells at the G1-S transition and should not be present later in the cell cycle util it is re-synthesized. Does this mean ORC2 and ORC3 remain chromatin bound during the cell cycle and what does this mean.We suggest that the authors address this issue in the Discussion of a revised manuscript which can then proceed. Essential revisions1) One major issue relates to the small enrichments of ORC/MCM they observe in early versus late replicating DNA and the possibility that this represents a DNA isolation bias rather than a real correlation or cause . The paper does not address this issue which, given the small differences in ORC/MCM between early and late replicating regions (1.4 fold), this needs to be addressed. It is understood that genome-wide mapping of pre-RC components in mammalian cells is challenging. In all of the studies to date, the ChIP enrichment is very modest and not confined to tight peaks that are typical of transcription factor binding. The weak and broad patterns of localization and their enrichment at hypersensitive and transcription start sites may be a technical artifact or is reflective of the underlying biology. While the signals they are observing are likely biological, it is still very difficult  to disentangle causation and correlation with the observed patterns and enrichment at transcription start sites and DNase HS sites. What would make this story much stronger is to demonstrate that the MCM2-7 signal is dynamic – that is that the enrichment patterns they observe in G1 should be very different from the patterns in late S-phase or G2 when replication forks have displaced most of the MCMs. The authors need to perform ChIP-seq on the cells elutriated at 80 ml/min. The ORC profile may also change due to the dynamic nature of ORC in human cells, but the MCM definitely should only be enriched in late replicating regions of the genome in late S phase and this comparison is needed.We thank the reviewers for this well-argued comment. We performed ORC and MCM ChIP-seq from elutriation fraction 80 ml/min, hereafter referred to as late S-G2-M  and analyzed their distribution with respect to replication timing.In G1-derived chromatin, we previously observed both higher ORC and MCM binding in early RTDs compared to late RTDs . In S-G2-M samples, ORC was still enriched in early compared to late RTDs. However, MCM signals were reduced in early compared to late RTDs, especially Mcm3. The early/late and G1/S-G2-M ratios ORC/MCM ChIP relative read frequencies are presented in Table 2. Early RTDs bind more ORC and MCM than late RTDs in G1, but this tendency is reduced or abolished in S-G2-M. Statistical testing validates that both ORC and MCM signals are dynamic between G1 and S-G2-M as predicted by the reviewers.The cell-cycle dependent dynamic binding of ORC and MCM clearly suggests that the ORC/MCM enrichments observed in G1 in early compared late RTDs are of biological nature and not a technical artifact. We included the S-G2-M chromatin analyses in updated Figure 5B and the data without input normalization in updated Figure 4—figure supplement 2C.2) As shown in Figure 1A, stochastic variation can be observed for MCM3/7 and ORC2/3 ChIP-seq replicates, it's reasonable to speculate that the input signal can also fluctuate randomly among replicates. Moreover, most of the conclusions in this manuscript are based on the input normalized signals. However, as shown by Figure 1A and the record for ENA PRJEB32855, no replication is performed for the input. Thus, we suggest that the authors provide replicates for the input, and normalize the ChIP signal to pooled input signals.The reviewers are correct, we indeed did not show the input in replicates, as we were working with pooled input to obtain a higher read coverage. However, we performed input sequencing in three replicates and now show input replicates in Figure 1A. We uploaded the according input files to ENA PRJEB32855.3) As shown in Figure 1—figure supplement 2A, the input signals at "DNase hypersensitive (HS)" is lower than those at "no HS"; and in Figure 1—figure supplement 3E, the ChIP signals of MCM3/7 and ORC2/3 at "HS" are higher than those in "no HS". Thus, when the ChIP signals of MCM3/7 and ORC2/3 are normalized to the input, will the difference of ChIP signals of McCM3/7 or ORC2/3 at "HS" and "no HS" be amplified artificially?We indeed observed differential normalized read frequencies for the input when comparing “HS” to “no HS” .When analyzing ORC and MCM normalized read frequencies without input division, we still observe a significant enrichment of ORC and MCM at HS sites. However, the enrichment is clearly more prominent for ORC, which is in line with previously reported Orc2 ChIP . We do not necessarily expect the same observation for MCM due to their more disperse distribution.We added DNase HS data without input normalization as new Figure 1—figure supplement 3G to the manuscript.Input division is a widely used approach for ChIP-seq data analysis, because it also corrects for chromatin solubilization efficiencies between experiments. However, in our manuscript, we were aware of a potential bias introduced by the input. This is of relevance if the signal heights are low, as is the case for ORC and MCM. To visualize the impact of input normalization on the ORC/MCM profiles, we additionally included all analyses without input normalization in the figure supplements. When comparing both analysis approaches, our conclusions do not change qualitatively, although we do observe minor quantitative differences. We explain these important controls in the Results and mention figures comparing data with or without input normalization throughout the text.Additional comments related to the above major comments described above:1) In Figure 1, the authors show a convincing correlation between the ChIP-seq profiles of ORC2 and ORC3, as well as between MCM3 and MCM7. Miotto et al. had published ORC2 ChIP-seq data using asynchronous K562 human erythroid cells. The data in the Kirstein et al. paper reports ChIP data of ORC2 form Raji lymphoblastoid cells. Although the cell types and cell cycle stage are different, it would be valuable to show a Pearson correlation between the two different ORC2 sets. This should be shown as a Supplement to Figure 1. The authors could also comment on the data of ORC1 ChIP from Dellino et al., 2013 and Long et al., 2019 (see point 8 below) whether these ORC patterns correlate with the other ORC ChIP data.Our ORC/MCM ChIP-seq was established using optimized cross-linking time and mild sonication settings to avoid disruption of complexes on chromatin. The Covaris ultrasonicator S220 focuses the energy on the sample, allowing a considerably reduced applied power. These carefully established settings and the use of different antibodies to IP ORC- and MCM-proteins were presumably also the reason why we observed the dispersed ORC/MCM profile described in the manuscript. The homogenous ORC/MCM distribution over the genome was addressed by our binning approach at 1 kb resolution.Dellino et al. performed Orc1 ChIP from low-density chromatin of HeLa cells . Thereby, they introduced a bias towards early replicating and transcriptionally active euchromatic regions. Indeed, when binning Orc1 data at 1kb, Orc1 was also enriched at TSS . InteresOrc2 ChIPs were performed by Miotto et al. in asynchronously cycling K562 cells using unfractionated chromatin and found to majorly depend on chromatin accessibility . They detected Orc2 in promoters and regions enriched for active chromatin marks, hence also reporting a link between Orc2 and transcription. This was confirmed by binning their data at 1kb and analyzing their distribution across genes .Both Orc1 and Orc2 were found at TSS, in line with our data . However, both approaches did not detect the depletion from the gene body. This observation may either result from G1 cell cycle enrichment, or technical differences, or a combination of both. TSS also seem to be robust “storage sites” of ORC, independent of the cell cycle. We also assume that rigorous sonication reduces “weak” and disperse binding throughout the genome, thus mainly detecting majorly robust ORC binding at accessible sites. Technical differences, as well as the different cell lines and cell cycle stages may be the reason why we observe a poor correlation between Orc1 (Dellino), Orc2 (Miotto) and our ORC data at 1 kb resolution .However, when we performed MACS2 peak calling on Orc2 in K562  and calculated the average profile of our Orc2 or Orc3 at those peaks, we observe substantial co-enrichment .We included 2) Figure 5A and Figure 4—figure supplement 2B. The results show that ORC is 1.4 times more frequently found in early versus late replicating regions. It is possible that the chromatin in early replicating regions is more accessible to the ChIP procedure than late replicating regions, which are likely more compact and hence difficult to access using antibodies. How have the authors excluded the possibility that extraction of DNA fragments in early versus late replicating regions could explain the difference in ORC binding? It should be noted that the authors previous papers and the Discussion in this paper claims accessibility to chromatin by replication factors may explain replication timing, yet they have assumed that the sonication and antibodies used for ChIP analysis are equally accessible. It is known that heterochromatic regions of the genome form phase transitions that may behave completely differently than actively transcribed and "accessible" regions of the genome.The reviewers are addressing a very important issue that is often neglected when discussing ChIP results and is also often leading to misunderstanding. Late replicating regions of the genome are mainly heterochromatic and therefore often described as more compact and less accessible. This describes the situation within a cell. As a consequence, this chromatin is often more difficult to solubilize by sonification and/or MNase digest and therefore underrepresented in ChIP samples. In the ChIP test tube, however, the solubilized heterochromatin is equally accessible to antibodies as euchromatin. Furthermore, in our experiments, early replicating, “accessible” (eu)chromatin is in fact underrepresented in the input compared to late replicating (hetero)chromatin . This is a consequence of the ultrasonication step: Less compact euchromatin requires less power to be fragmented to smaller fragments than compact heterochromatin. These euchromatic fragments are often too small to be captured resulting in an underrepresentation of euchromatin in ChIP samples. Therefore, input normalization enhances, rather than negates, the observed ORC enrichment in early replicating regions. In conclusion, neither an easier chromatin sonication nor an easier access of solubilized chromatin to antibodies can explain the stronger binding of ORC to early replicating regions.3) Figure 5E. The same concern outlined in comment 2 above could explain the small, albeit statistically significant difference between H4K20me3 high and low regions of the genome.We do not have any information about the relative accessibility of H4K20me3-high/low regions to provide a direct answer to this point. However, both H4K20me3-low and -high data sets are extracted from late replicating domains, as we are exclusively investigating late-replicating, non-genic AS and flat, null RFD (NRRs) that are only found in late-replicating DNA. Therefore, we do not expect a differential cellular accessibility and solubility unless uncorrrelated to replication timing.Furthermore, analyses of ORC/MCM enrichments at H4K20me3-low and H4K20me3-high sites in G1 and S-G2-M chromatin clearly show the dynamics of ORC/MCM before and after replication. In particular ORC/MCM binding is reduced in S-G2-M at the high-H4K20me3 windows, confirming the expected dynamic behavior at potential replication origins.We have integrated this argument in our manuscript and the S-G2-M data set as Fiugre 5F and Figure 5—figure supplement 1F.4) Figure 6A and related text. The very slight differences in H4K20me3 levels could also be explained by extraction artIfact.We are working with up to three biological replicates and target 6 different proteins . Extraction artefacts specific to early versus late replicating chromatin would appear in all samples, disregarding the targeted protein. In the case of H4K20me3, we observe a specific enrichment of high H4K20me3 levels in late replicating NRRs  that is not observed for any ORC/MCM, hence we can almost certainly exclude that our observation relies on extraction artefacts.5) “However, potential origins are defined by assembled MCM-DHs, not by ORC”. This statement that potential origins are determined by the MCM2-7 DH and not by ORC is not logical because MCM2-7 DH is loaded by ORC and other factors. The idea that it correlates with ORC is dismissed a few lines later, but none of these statements are justified.We agree that MCM-DH is loaded by ORC. However, it is the MCM-DH helicase that eventually nucleates replisome assembly following duplex melting . Therefore, the sites of replication initiation are ultimately dictated by the location of activated MCM-DH, not ORC. Accordingly, it has been shown several times that ORC is dispensable for replication initiation once MCM-DH have been loaded . Furthermore, once loaded onto DNA by ORC, the MCM-DH helicase is free to either diffuse away from ORC  or be actively displaced by transcription or other chromatin-related processes  Therefore, we expect that the location of initiation sites is defined by activated MCM-DH, whose location can differ from ORC.What is the evidence that access of firing factors to MCM2-7 DH are regulated by chromatin access?A large number of studies correlate genome-wide DNA accessibility  with early replication and/or sites of replication initiation . We assume that the reviewers do not question this correlation. The question is therefore whether this correlation reflects causality.On the one hand, we see no reason to assume that limiting origin firing factors would diffuse in chromatin very differently from the proteins used in DNA accessibility assays. We therefore expect them to encounter MCM-DH in accessible chromatin faster than in less accessible chromatin, so that even a genome with a theoretical strictly uniform density of MCM-DH would still show early- and late-replicating regions. Thus, replication timing would be at least in part the inevitable consequence of the uneven accessibility of the genome.On the other hand, it has been proposed that budding yeast replication timing profiles can simply be accounted by a "multiple initiator model" in which all MCM-DH are equally accessible to limiting firing factors but individual origins differ by the number of bound MCM-DH so that origins with more MCM-DH fire earlier on average than those with less . However, Das et al. have reported that the correlation between MCM ChIP-seq signal and timing, although significant, is < 0.5 and that other factors must contribute as well . For example, large numbers of MCM-DHs are loaded at late-replicating telomeres, suggesting that heterochromatin can somehow delay the firing of loaded MCM-DHs.Histone modifications are known to modulate chromatin accessibility with effects on origin firing time. In budding yeast, moving a given origin from a late to an early replicating region of the chromosome can advance its firing time . Targeting histone acetylases or deacetylases to specific chromosomal sites can increase or decrease origin efficiency both in yeast  and human cells . Deleting the yeast Sin3-Rpd3 deacetylase causes origins that normally fire late to fire earlier in S phase, coincident with increased acetylation and accelerated loading of Cdc45, a limiting origin firing factor . Thus, the local chromatin environment has the ability to potentially permit or restrict an origin from firing.One potentially important difference between protein samples used in accessibility assays and diffusible origin firing factors is that the latter may experience specific physical interactions that modulate the effect of chromatin accessibility. For example Swi6, a fission yeast HP1 homologue, interacts with DDK, a rate-limiting origin firing kinase, to activate origins in early S phase, specifically at pericentromeric and silent mating-type heterochromatin loci . Mutations that diminish this interaction but do not affect Swi6/HP1 localization result in retardation of replication specifically at these heterochromatin loci. Therefore, this specific interaction can overcome the suppressive effect of “closed” chromatin created by Swi6/HP1. Similarly, a prevailing hypothesis is that chromatin acetylation stimulates DNA replication by opening chromatin structure . However, recent results suggest that the acetyl-histone binding proteins BRD2 and BRD4 physically interact with the rate limiting origin firing factor TICRR/TRESLIN and that abrogation of this interaction disrupts the normal replication program .In summary, it is clear that the replication program reflects both, heterogeneity in MCM-DH loading and regulation of MCM-DH activation by chromatin structure. Whether specific histone modifications act by facilitating diffusion of firing factors through chromatin, by providing specific docking platforms for such factors, or by a combination of both mechanisms is not completely elucidated. At any rate, however, heterogeneity in MCM-DH loading alone appears insufficient to explain our results. An additional, significant role for chromatin structure in regulating MCM activation after MCM loading by ORC therefore appears likely. We have restructured the corresponding paragraphs in the Discussion, cited selected relevant studies, and tried to integrate these complex mechanisms in a rephrased Conclusion and the graphical model .6) In a significant paper describing ORC ChIP and replication initiation, it was shown that ORC binding correlates with histone H2AZ and this could explain early replication origin activity. This paper is not even cited, much less discussed, and it should be .7) The authors have dismissed the replication timing model proposed by Miottto et al. 2016, but it is not clear why. This model should be discussed in relationship to the model in Figure 7.We had no intention of dismissing the replication timing model proposed by Miotto et al. . The authors simulated in this study replication timing based on their detected ~52,000 Orc2 peaks and assuming stochastic origin firing. They obtained a good correlation between simulated and Repli-seq determined replication timing profiles, and the correlation improved when they also assumed dispersed origin firing from “non-specific” Orc2 sites, that escaped experimental detection in their setup. Consistent with Miotto et al., we observed global differences of ORC levels when comparing early against late RTDs . In addition, we detected ORC in regions where Miotto et al. suggested its presence but did not detect it.However, in an independent modelling approach , all chromatin marks associated to open chromatin also allowed very good predictions of replication timing based on a large diversity of chromatin marks associated to open chromatin. Hence, the question remains of what is causally responsible for early replication / replication initiation. Furthermore, the current resolution of RT profiles (50-100 kb) is not sufficient to map replication IZs, whereas the resolution of RFD profiles (1-5 kb) does. Whether the models of Miotto et al. or Gindin et al. remain valid up to replication initiation sites also remains to be addressed.et al., but with an additional layer of regulation. We added a paragraph in the Discussion to explicit these points.Our work, which for the first time compares the location of ORC and MCM to high-resolution RFD profiles, suggests that neither ORC nor MCM density suffices to predict replication initiation at this resolution. We therefore conclude that the replication program reflects not only heterogeneity in ORC/MCM-DH density but also regulation of MCM-DH activation by chromatin structure . In other words, our model is consistent with the model of Miotto The revised paper has incorporated new data that compares the abundance of ORC2, ORC3, MCM3 and MCM7 protein at two difference stages of the cell division cycle and raises some interesting observations. The authors have extensively addressed all of the original reviewer comments and provide new analysis. The differences in MCM and ORC levels at the different classes of gene expression, except for the gene bodies, are very modest but nonetheless statistically significant.The general conclusion is that ORC is wide spread on the genome in G1 phase and MCM localizes with sites of initiation of DNA replication, and that histone H3K4me3 is correlated with late origin firing. At all locations, the exact site of initiation of DNA replication is stochastic.One paradox needs explaining that arises from the new data presented in the revised paper compared to data in the literature.1) The data in Figure 4—figure supplement 2C show that MCM3 and MCM7 levels are reduced in S-G2-M cells compared to G1 cells, but there remains a difference between early versus late replication timing domains in both cell cycle stages. In contrast, ORC is high in early RFDs and low in late RFDs at both stages. Perhaps the authors should discuss the significance of this result, in light of the fact that ORC1 is degraded in human cells at the G1-S transition and should not be present later in the cell cycle util it is re-synthesized. Does this mean ORC2 and ORC3 remain chromatin bound during the cell cycle and what does this mean.We suggest that the authors address this issue in the Discussion of a revised manuscript which can then proceed.We thank the reviewer for raising this interesting question. It is well described that Orc1 in human cells is degraded at the G1-S transition and in early S phase and is re-synthesized at in mitosis. However, the chromatin binding of the remaining ORC subunits is controversially discussed. In our study, we detect Orc2 and Orc3 binding to S-G2-M chromatin. However, ChIP-seq, which we used to detect chromatin-bound ORC only allows monitoring the relative distribution of chromatin-bound proteins along the genome and not their absolute levels. Therefore, we can only be speculative and do not exclude that Orc2 and Orc3 binding to chromatin is globally decreased after replication. The binding of Orc2 and Orc3 we detect in S-G2-M may either occur independently of Orc1 or reflect the binding of the entire complex in late mitotic cells. We have added a corresponding discussion in our manuscript.


The formation of adipocytes during embryogenesis has been largely understudied. However, preadipocytes appear to originate from multipotent mesenchymal stromal/stem cells which migrate from the mesoderm to their anatomical localization. Most studies on adipocyte formation (adipogenesis) have used preadipocytes derived from adult stem/stromal cells. Adipogenesis consists of two phases, namely commitment and terminal differentiation. This review discusses the role of signalling pathways, epigenetic modifiers, and transcription factors in preadipocyte commitment and differentiation into mature adipocytes, as well as limitations in our understanding of these processes. To date, a limited number of transcription factors, genes and signalling pathways have been described to regulate preadipocyte commitment. One reason could be that most studies on adipogenesis have used preadipocytes already committed to the adipogenic lineage, which are therefore not suitable for studying preadipocyte commitment. Conversely, over a dozen molecular players including transcription factors, genes, signalling pathways, epigenetic regulators, and microRNAs have been described to be involved in the differentiation of preadipocytes to adipocytes; however, only peroxisome proliferator-activated receptor gamma has proven to be clinically relevant. A detailed understanding of how the molecular players underpinning adipogenesis relate to adipose tissue function could provide new therapeutic approaches for addressing obesity without compromising adipose tissue function. A 2020 World Health Organization report states that globally, 39% and 13% of adults 18 years and older are overweight and obese, respectively. It also lists obesity as a chronic disease that has nearly tripled since 1975 ,5. EnvirAdipose tissue is a loose connective tissue found beneath the skin (subcutaneous) and/or surrounding organs and tissues, and it contains a collection of mature adipocytes, preadipocytes, mesenchymal stromal/stem cells (MSCs), vascular endothelial and contractile cells (pericytes and smooth muscle cells), nerves, and an array of immune cells . Adipose18fluoro-2-deoxy-d-glucose position emission tomography-computed tomography (18FDG PET-CT) showed that the location of BAT is not limited to the carotid artery, aorta and subscapular region, but that it is widespread and inversely proportional to body mass index (BMI) [Adipocytes that constitute BAT have distinctive features such as the presence of many intracellular lipid droplets and numerous mitochondria, as well as high levels of expression of mitochondrial uncoupling protein 1 (UCP1), that distinguish it from WAT. The main function of BAT is non-shivering thermogenesis in response to cold stress or β-adrenergic stimulus . Activatex (BMI) ,16.White adipose tissue is the most abundant type of adipose tissue composed of adipocytes characterized by a large unilocular lipid droplet whose main function is energy storage. White adipose tissue is also secretes adipokines like leptin and adiponectin for energy homeostasis . AnatomiA third class of adipose tissue is located within WAT and is known as brite/beige, containing adipocytes with characteristic features of BAT, being induced by cold stress . ResearcAdipose tissue develops during embryogenesis in week 14–24 of gestation in humans . AdipocySignalling pathways such as the canonical Wnt/β-catenin, Hedgehog and transforming growth factor beta (TGF-β) 1 and 2, as well as Sirtuin (Sirt) 1, microRNA (MiR)-27a and MiR-93, inhibit PPARγ and C/EBPα expression. Conversely, the glucocorticoid, cAMP, and bone morphogenetic proteins (BMPs) signalling as well as methyltransferase mixed lineage leukaemia protein 3/4 (MLL3/4) polycomb repressive complex 2 (PRC2) and enhancer of zeste homolog 2 (Ezh2), MiR-210 (MiR-210), bromodomain-containing protein 4 (BRD4), Sirt 7 and MiR-146 promote PPARγ and C/EBPα expression. H3K9me2 and HDAC9 directly inhibit C/EBPα expression while H3K27me3 and H3K4me2 directly promote it. LSD1 promotes and inhibits C/EBPα expression indirectly through H3K4me2 and H3K9me2, respectively. H3K9, H3K18, H3K27, coactivator-associated arginine methyltransferase 1 (CARM1), ten eleven translocation (Tet) 2, C/EBPβ, C/EBPδ, signal transducers and activators of transcription (STAT)5, early B-cell factor 1 (EBF1), sex determining region y-box (SOX) 6, kruppel-like factors (KLF) 5, 6 and 9, MiR125p-5p, switch/sucrose nonfermenting family (SWI/SNF), and protein arginine methyltransferases (PRMT) 5 directly promote PPARγ expression, while general control non-depressible 5 (Gcn5) and p300/CREB-binding protein (CBP)-associated factor (PCAF), CBP/p300, H3K4me3, H3K9/K14, Krox20, zinc finger factor 638 (ZNF638), KLF4, and KLF9 are the indirect promoters of PPARγ expression. Forkhead box protein O1 (FOXO1), DNA methyl transferase (Dnmt) 1, GATA2, KLF2 and MiR-130 directly inhibit PPARγ expression while Sirt 2, G9a and H3K9me2 and SOX9 are the indirect inhibitors of PPARγ expression. PPARγ or C/EBPα transactivate each other.Myf5) and myoblast determination protein 1 (MyoD). This BAT had a gene expression profile that was similar to that of skeletal muscle rather than preadipocytes of perigonadal WAT [Myf5 while inguinal and perigonadal WAT are negative for Myf5 [Myf5+ and Pax7+ precursor cell, while WAT arises from a different lineage. More recently, it has been shown using Myf5 labelling to distinguish between BAT and WAT lineages, that interscapular, anterior, and retroperitoneal WAT was labelled with the same Myf5-Cre knock-in allele used in the BAT studies, suggesting that the situation is more complex than previously demonstrated [En1+, Pax3+ and Myf5+ mesenchymal precursors [A panel of cell surface markers which includes positive expression of platelet-derived growth factor receptor α (PDGFRα), cluster of differentiation 34 (CD34), CD24, CD29, and spinocerebellar ataxia type 1 (Sca1), and negative expression of CD31, Ter119 (lymphocyte antigen 76) and CD45, have been established to isolate and study adipocyte progenitor cells. However, these markers are not known to have a functional role in adipocyte development nor do they provide information on the developmental origins of adipocytes . In vivoadal WAT . Anotherfor Myf5 , confirmnstrated ,27. Furtnstrated , confirmMyf5 give rise to BAT and skeletal muscle, since some Myf5 promoter expressing precursor cells also give rise to white/brite adipocytes. Furthermore, it is not known if brite adipocytes in subcutaneous tissue arise as a result of trans-differentiation or interconversion of pre-existing mature UCP1 negative white adipocytes [It is evident from lineage tracing studies that adipocytes arise from multiple lineages that are dynamic and heterogeneously distributed. Also, not all precursor cells that express ipocytes ,31, or wipocytes ; there iAdipogenesis is a complex multi-step process that involves the differentiation of MSCs into mature, lipid containing adipocytes ,33,34. TSeveral signalling pathways have been described to play a role in adipocyte differentiation , that subsequently regulates transcription factors . DexametCyclic AMP signalling is primarily mediated through cAMP-responsive element-binding protein (CREB). cAMP, through its cellular target protein kinase A (PKA), phosphorylates and activates CREB, which binds to the Cyclin D1 promoter to activate transcription in the early stages of adipogenesis, thereby promoting 3T3-L1 differentiation . EctopicPetersen et al. (2003) showed that an exchange protein directly activated by cAMP (Epac) is required for cAMP dependent activation of adipocyte differentiation . Epac, iBoth GC and cAMP signalling pathways positively regulate preadipocyte commitment and differentiation ,50. HoweTgfbr2) in MSCs resulted in a marked increase in adipocyte expansion in murine bone marrow and this was accompanied by an increase in PPARγ expression [SERPINB2) was significantly downregulated in TGF-β1 treated cells. Silencing of SERPINB2 in untreated cells enhanced both their adipogenic and osteogenic differentiation capacity. This suggests that TGF-β signalling plays a role in both adipogenic and osteogenic differentiation, and SERPINB2 was identified as the TGF-β1 responsive gene through which it negatively regulates human BM-MSCs differentiation [Transforming growth factor beta inhibits preadipocyte commitment through mothers against decapentaplegic 3 (SMAD3) signalling, by phosphorylating and suppressing PPARγ expression as well as the expression of C/EBPs ,53. Delepression . Transfopression . Global ntiation . Skeletantiation . Furtherntiation . Bone morphogenetic protein 4 signalling is important in the preadipocyte commitment process, and has been shown to commit C3H10T1/2 pluripotent cells to the adipocyte lineage . BMP4 biThe role of BMP2 signalling in preadipocyte commitment is not fully understood, but several studies have implicated it in the commitment of C3H10T1/2 cells to this lineage ,59. BMP2BMP7 predominantly plays a role in brown adipocyte lineage commitment . This, hWingless-type MMTV integration site family members are glycoproteins that play an essential role in various cellular processes, including embryogenesis, cell proliferation, and cell fate determination ,66. TherThe Wnt signalling pathway has been extensively studied and is a negative regulator of adipogenesis. Several in vitro studies have found that the Wnt signalling pathway inhibits the terminal differentiation of preadipocytes into mature adipocytes ,72. FollThe Hedgehog (Hh) signalling pathway plays a role in embryogenesis and cell differentiation . ActivatThe intracellular MAPK signalling pathway is important for cell proliferation and differentiation. It is subdivided into three pathways: extracellular signal-regulated kinases (ERKs), c-Jun amino-terminal kinases (JNKs) and p38 MAPK. The ERK and p38 MAPK pathways have been implicated in the regulation of adipogenesis . ExtraceThe Ras signalling pathway induces 3T3-L1 preadipocyte differentiation; ectopic expression of the Ras oncogene induces preadipocyte differentiation in the absence of insulin and IGF-1 . FurtherEpigenetic mechanisms play a crucial role in regulating gene expression and chromatin structure and result in heritable changes in gene expression without altering DNA sequences. A broadened definition of epigenetics includes any alteration of chromatin or DNA that effects gene expression, and this includes post translational modifications to proteins such as acetylation, methylation and phosphorylation, histone modifications, RNA silencing, non-coding RNA regulation including miRNAs, short interfering RNAs (siRNAs), piwi-interacting RNAs (piRNAs) and long non-coding RNAs (lncRNAs), protein phosphorylation, genomic imprinting, cell differentiation, and environmental factors. This review will focus on those epigenetic modifiers acting at the genomic level with a well described role in adipogenesis, such as chromatin remodelling complexes, epigenomic readers, histone methyltransferases/demethylases, histone acetylases/deacetylases, DNA methylases/demethylases, and miRNAs .Remodelling of chromatin architecture renders DNA accessible during replication, transcription, and DNA repair, thereby allowing for changes in gene expression. Remodelling occurs through covalently modified histones and ATP-dependent chromatin remodelling factors . The swiHistones are a family of alkaline proteins and are of five major types: H1, H2A, H2B, H3, and H4. Their main function is to package DNA to form structural units called nucleosomes . HistoneMyf5+ progenitor cells in which LSD1 had been knocked out, and found an increase in H3K9 di-methylation (H3K9me2) on the UCP1 promoter. This inhibited UCP1 expression, resulting in defective BAT function and development. The authors then undertook a study in UCP1-Cre mediated LSD1 knockout mice, and found an increase in adipose tissue as well as cold intolerance [Lysine specific demethylase 1 (LSD1) plays a role in both the activation and repression of transcription, depending on the biological processes and associated protein complex . A studyolerance . During olerance .Nuclear receptor binding SET domain protein 2 (Nsd2) is an H3K36 di-methyltransferase (H3K36me2) and is involved in the positive regulation of adipogenesis. The depletion of Nsd2 has no effect on H3K36me2 but increases H3K27 tri-methyltransferase (H3K27me3), which in turn represses adipogenesis . In vivoZhang and colleagues (2012) stated that “global levels of histone modifications remain stable during adipogenesis” . Using 3In addition to lysine methylation, arginine methylation is also implicated in adipogenesis. In mammals, 9 PRMTs mediate arginine methylation, of which PRMT5 and CARM1 play a role. CARM1 is recruited to promoters and is a transcriptional coactivator for PPARγ, hence promoting adipogenesis . Yadav aHistone acetylation plays an important role in gene expression. It involves the addition of acetyl groups on the histone N-terminal tail by histone acetyl transferases (HAT). There are two types of HAT: Type A contains a bromodomain, that is found in the nucleus and acetylates chromatin and nucleosomal histones, for example Gcn5/PCAF and CBP/p300; Type B is found in the cytoplasm and acetylates newly transcribed histones . AcetylaUsing ChiP analysis and 3T3-L1 cells, Zhang et al. (2012) analysed H3K9/K14 and H4K12 acetylation in relation to adipogenesis. Like H3K4 tri-methylation, C/EBPβ exon and 3′UTR were highly acetylated during H3K9/K14 acetylation, but not in H4K12 acetylation. In addition, the aP2 gene showed the highest amount of histone acetylation. PPARγ2 and aP2 showed increased levels of acetylation on both H3 and H4 tails . Xu, AndBromodomain-containing protein 4 is a member of the bromodomain and extraterminal domain (BET) protein family, and acts as an epigenetic reader by binding to acetylated histones and transcription factors that promote the expression of PPARγ. The disruption of BRD4 in BRD4 knockout cell lines inhibits PPARγ expression and suppresses adipogenesis . Lee andHistone deacetylases (HDACs) are involved in chromatin modification. HDACs deacetylate histones , compacting the chromatin structure and preventing the binding of transcription factors. There are 4 classes of HDACs. Class I consists of HDACs 1, 2, 3, and 8. Class II consists of HDACs 4, 5, 6, 7, 9 and 10. Class III consists of sirtuins 1–7, a group of nicotinamide adenine dinucleotide (NAD)-dependent enzymes. Class IV consists of HDAC 11 ,117.HDAC 1 negatively regulates adipogenesis. Eung and colleagues (2006) observed that levels of HDAC 1 expression were reduced during adipogenesis. Further investigation identified HDAC 1 as a possible regulator of PPARγ, C/EBPα, sterol regulatory element binding transcription factor 1c (SREBP-1c) and aP2 expression . AlthougHistone deacetylases class III consists of 7 Sirt enzymes that regulate various cellular processes by deacetylating lysine residues. A few of the Sirt enzymes have been implicated in adipogenesis ,122. SirDNA methylation is a key element in the regulation of gene expression and cell differentiation. It involves the transfer of a methyl group onto the fifth carbon (C5) of cytosine, forming 5-methylcytosine (5mCs). This is facilitated by Dnmts ,128. DnmTen eleven translocation enzymes are a group of enzymes implicated in the reversal of DNA methylation through the oxidation of 5mCs . Yoo andMicroRNAs are small non-coding 20–22 nucleotide RNA sequences, that are critical posttranscriptional regulators modulating the expression of transcription factors and genes in various cellular processes . SeveralEsau and colleagues (2004) investigated miRNA expression in preadipocytes and mature adipocytes. Levels of miR-143 expression were found to increase in human white preadipocytes induced to undergo adipogenic differentiation , and theThe miR-30 family (miR-30 a–d) has been identified as being pro-adipogenic. A study by Zaragosi et al. (2011) showed miR-30 a–d was upregulated in mature adipocytes. It was further reported that miR-30 a and d specifically target RUNX2, a major pro-osteogenic transcription factor, thereby inhibiting osteogenesis and stimulating adipogenesis . AnotherThe process of adipogenesis has been extensively studied and decades of research have reported over a dozen transcription factors to be involved in regulating this process, both in vitro and in vivo ,147 Fig. The traA large body of evidence indicates that PPARγ and C/EBPα are the master regulators of adipocyte development and adipogenesis. PPARγ and C/EBPα knockout in murine adipocytes during embryonic and adult development showed that PPARγ was important for adipocyte development in both embryonic and adult stages, while C/EBPα was crucial for adult murine WAT adipogenesis but not in embryonic WAT adipogenesis . The exp+/− mice show a significant reduction in WAT [The expression of STAT 1, 3 5A, and 5B has been shown to increase during adipogenic differentiation in 3T3-L1 cells ,154; conn in WAT . KLF15 wn in WAT ; howevern in WAT . Nonethen in WAT ,176. Then in WAT . Overexpn in WAT . In vivon in WAT . This man in WAT . Sex Detn in WAT . SOX6 men in WAT . Conversn in WAT . The tran in WAT . Other tn in WAT . In silin in WAT  has idenn in WAT . It is evident that many studies over the past few decades have reported many new transcription factors with roles in adipogenesis in vitro; however, for some, their functions in vivo remain to be fully investigated. For those that have been shown to play a role in adipocyte formation in vivo, their clinical translation has been very limited. Hence, more studies are needed to carefully understand the mechanistic role of each transcription factor in order to advance our knowledge on how best to interfere with adipocyte formation for potential health benefits.Adipogenesis in WAT is a complex molecular process in which the expression of PPARγ and C/EBPα is key to the formation of a mature white adipocyte. Several molecular factors including signalling pathways, epigenetic modifiers and other transcription factors regulate the expression of these two transcription factors during adipogenesis , which tTo date, PPARγ is the only modulator of adipogenesis that has shown clinical relevance in addressing an obesity associated comorbid condition like diabetes ,185. Thi


In this study from Yague-Sanz et al., the authors investigated the physiological relevance of variations in RNAPII elongation kinetics, and show in yeast that a RNAPII mutant that reduces the transcription elongation rate causes widespread changes in alternative polyadenylation (APA). Their findings indicate that RNAPII is a sensor of nucleotide availability and that genes important for nucleotide pool maintenance have adopted regulatory mechanisms responsive to reduced rates of transcription elongation. Transcription by RNA polymerase II (RNAPII) is a dynamic process with frequent variations in the elongation rate. However, the physiological relevance of variations in RNAPII elongation kinetics has remained unclear. Here we show in yeast that a RNAPII mutant that reduces the transcription elongation rate causes widespread changes in alternative polyadenylation (APA). We unveil two mechanisms by which APA affects gene expression in the slow mutant: 3′ UTR shortening and gene derepression by premature transcription termination of upstream interfering noncoding RNAs. Strikingly, the genes affected by these mechanisms are enriched for functions involved in phosphate uptake and purine synthesis, processes essential for maintenance of the intracellular nucleotide pool. As nucleotide concentration regulates transcription elongation, our findings argue that RNAPII is a sensor of nucleotide availability and that genes important for nucleotide pool maintenance have adopted regulatory mechanisms responsive to reduced rates of transcription elongation. During this transcription cycle, transcription units are generally characterized by a conserved 5′-to-3′ profile of phosphorylation on the carboxy-terminal domain (CTD) of Rpb1, the catalytic subunit of RNAPII. Rpb1 CTD heptad repeats, which consist of the consensus amino acid sequence Ye 3′ end . These Ce 3′ end . In addie 3′ end . This coe 3′ end . TerminaTranscription elongation is normally viewed as a continuous process during which RNAPII traverses the coding region after initiating at a promoter. However, genome-wide studies indicate that transcription elongation by RNAPII is a rather intermittent process, with frequent occurrences of pausing, backtracking, and arrest . FurtherAnother cotranscriptional process potentially controlled by RNAPII elongation rate is poly(A) site selection in alternative polyadenylation (APA). APA occurs when genes harbor multiple poly(A) sites, which give rise to mRNA isoforms with 3′ untranslated regions (UTRs) of different lengths . AccordiAlthough evidence strongly supports the view that the rate of RNAPII elongation can influence cotranscriptional RNA processing, how kinetics of RNAPII passage through chromatin globally affects gene expression remains poorly understood. Furthermore, whether variations in transcription elongation rate are used in a natural context to control the expression of a specific set of genes is largely unknown. In this work, we analyzed the genome-wide expression changes imposed by a Rpb1 catalytic mutant with a reduced elongation rate in fission yeast. We found that a conserved core regulon sensitive to phosphate starvation (PHO regulon) is activated by a slowly transcribing RNAPII via a mechanism that involves APA and premature transcription termination of interfering upstream long noncoding (lnc) RNAs. Importantly, we show that APA and premature termination are naturally used to regulate this core PHO regulon upon phosphate starvation. Collectively, our data reveal that a reduction in the transcription elongation rate causes a distal-to-proximal shift in APA, resulting in increased expression of a specific set of genes that includes the core PHO regulon and genes involved in the purine biosynthesis pathway. Our findings support a model in which phosphate limitation results in reduced elongation rates as a consequence of nucleotide depletion and that phosphate-responsive genes have evolved activation mechanisms sensitive to suboptimal transcription elongation kinetics.S. cerevisiae . This mutation substitutes an evolutionarily conserved asparagine at position 494 of S. pombe Rpb1 to an aspartic acid residue (N494D) , in the vicinity of the magnesium ions and the incoming nucleoside triphosphates  , drugs known to deplete intracellular NTP pools and stress transcription elongation, as previously shown for the analogous S. cerevisiae mutant  in revisiae  in the g94D) see A and corin vitro  and in vin vitro  as well in vitro . Asparagosphates . To gene editing  and isole mutant .rpb1-N494D clones as well as the parental control strain by strand-specific RNA-seq. Expression data from the two N494D mutants clustered together , showing strong reproducibility between the independent clones. In total, 323 genes were down-regulated and 457 were up-regulated in the slow mutant . To further validate our RNA-seq experiment, we measured the change of expression of 10 differentially expressed genes by RT-qPCR and observed good overall correlation (R2= 0.92) with the RNA-seq data . Down-regulated genes were enriched for gene ontology categories related to transmembrane transport, ribosome biogenesis, and iron assimilation , whereas up-regulated genes were significantly enriched for genes involved in purine  biosynthesis as well as sexual reproduction . Many of the up-regulated genes in the slow mutant are direct targets of Ste11, which is induced in the slow mutant and functions as the master transcription factor for sexual development in fission yeast. Thus, up-regulation of genes such as mei2, mfm1, mfm2, and mfm3 is likely to be an indirect consequence of ste11 induction.To measure the impact of the RNAPII slow mutant on gene expression, we analyzed total RNA prepared from two independent rpb1 slow mutant, as previous studies indicate that RNAPII elongation rate can influence splicing efficiency . Moreover, intron-containing genes were neither enriched in the set of genes that were significantly up-regulated nor in the set of genes significantly down-regulated in the slow mutant. We therefore conclude that the rpb1 N494D slow mutant affects the expression of a specific set of genes but does not largely influence RNA splicing.We also examined for splicing deficiencies in the ficiency . Two indficiency . Howeverpho1, pho84, and tgp1 genes, which encode phosphate harvesting and transport proteins  site (PAS) mapping in S. pombe using 3′READS . Reduction of the nc-tgp1 lncRNA in the RNAPII slow mutant was also confirmed by RNase protection assays . Importantly, by segmenting the nc-tgp1 locus into different regions based on PAS identification , consistent with up-regulation of tgp1 mRNA in rpb1-N494D cells. In contrast, RNAPII levels markedly declined over the proximal nc-tgp1 poly(A) site in the slow mutant (regions 2–5), consistent with the idea that the slower elongation rate in rpb1-N494D cells promotes premature transcription termination at the nc-tgp1 locus. Our data argue that the induction of tgp1 expression in the RNAPII slow mutant results from premature termination of nc-tgp1 transcription.To begin to elucidate the molecular mechanisms by which the slow RNAPII mutant results in gene activation, we focused on on yeast  and showp1 locus A. Howeve 3′READS  indicates motifs A,B, 3D. loci see A, confirw mutant C. Similafication , we notefication B, suggesocessing , this wonc-tgp1 locus, we used ChIP analysis to monitor the binding profile of Rna14, a conserved component of the cleavage and polyadenylation factor I complex  , whereas the prt2 and nc-pho1 lncRNAs were both down-regulated . Similar to the mechanism of tgp1 derepression, RNAPII and Rna14 ChIP data support that the prt2 lncRNA undergoes premature transcription termination in the rpb1 slow mutant, leading to the activation of pho84 . However, it remains unclear whether such a mechanism also applies to the nc-pho1/pho1 locus. Indeed, the proximity between the 3′ end of the pho84 gene and the nc-pho1 promoter makes the RNAPII and Rna14 ChIP data difficult to interpret . One possibility is that the derepression of pho84 as a consequence of premature termination of the prt2 lncRNA might result in transcriptional interference at the nc-pho1 promoter, thereby repressing nc-pho1 transcription and ultimately coupling pho84 and pho1 derepression.RT-qPCR and ChIP assays were also used to assess whether APA-dependent premature termination of lncRNA transcription was responsible for derepression of the other core PHO regulon genes, namely, nc-tgp1 locus. Since CTD Ser2 phosphorylation (Ser2P) coincides with the recruitment of the 3′ end processing machinery , decreased levels of CTD Ser2P were detected at the distal PAS (regions 5–6) of nc-tgp1 together with a concomitant increase at the proximal PAS  where we found increased levels of premature transcription termination in the slow mutant , which could be a consequence of tgp1 transcriptional activation.Next, we wondered which molecular feature of the Rpb1 N494D protein might lead to an increase in premature termination at the achinery , we analTD Ser2P  and normon yeast . Specifiw mutant , 3. The nc-tgp1 transcription was sufficient to induce tgp1 expression, we designed a strategy to strengthen the proximal PAS of nc-tgp1. Upon examination of the sequences surrounding the two major nc-tgp1 poly(A) signals, we noticed that whereas consensus 5′-GUA-3′ Seb1-binding motifs . Notably, the insertion of Seb1-binding motifs downstream from the proximal nc-tgp1 PAS resulted in increased tgp1 expression together with reduced levels of nc-tgp1 lncRNA  and phosphate-deprived (−PO4) conditions using wild-type cells. As expected, the levels of tgp1 mRNA increased after phosphate starvation  site selection from our transcriptomic data of the slow mutant and control strain for genes with at least two independent PASs (n = 2208). Specifically, we calculated a distal/proximal ratio from the RNA-seq read coverage for genomic regions based on proximal and distal PAS usage . Notably, of the 516 APA-sensitive genes identified in the RNAPII slow mutant, 441 (85%) showed a shift toward increased usage of the proximal PAS, including the nc-tgp1 lncRNA  downstream from the cleavage sites of affected genes. Analysis of nucleotide frequencies also revealed that the regions immediately upstream of and downstream from the A-rich cleavage sites of proximal PASs of genes affected by the slow mutant were significantly enriched in uridine (P = 3.084 × 10−8) compared with proximal PAS of unaffected genes . These observations support the notion that specific genes tend to share certain sequence motifs surrounding the proximal PAS, which may allow them to be particularly sensitive to a reduction in transcription elongation rate.Altogether, we found that ∼20% (441 out of 2208) of genes with at least two independent PASs showed a shift toward the preferential use of the proximal PAS in the w mutant A,B. To ew mutant  was usedw mutant F was fourpb1-N494D cells . An example of this is shown for tim11, a gene encoding a subunit of the F1-FO ATP synthase, for which we noted decreased read coverage downstream from the proximal poly(A) site together with a twofold increase in read coverage inside the gene body in the slow mutant (tim11 poly(A) site selection was confirmed by Northern blotting  and genes involved in the purine biosynthesis pathway (via 3′ UTR shortening).We next examined the connection between APA-sensitive events and changes in gene expression. We observed a significant overlap between genes that showed distal-to-proximal APA and those that were up-regulated in 4D cells G. Within4D cells  to examiw mutant D. The efblotting H. Our datgp1, pho1, and pho84 genes may have evolved to be positively regulated by a reduced rate of transcription elongation, which is anticipated in conditions of phosphate starvation as a consequence of limiting NTP concentration. To test whether limiting NTP concentration results in the activation of the core PHO regulon, we treated wild-type cells with 6AU and MPA. As shown in pho84 expression was minimal. NTP depletion using 6AU or MPA also resulted in increased levels of the short 3′ UTR version of tim11 relative to the long 3′ UTR isoform  and premature transcription termination of an upstream interfering lncRNA  site selection  sites and a growing number of mechanisms have been shown to control APA, including core 3′ end processing factors and specific RNA-binding proteins . Previouelection . MechaniAS usage . It therAS usage , poly(A)AS usage , and traAS usage .pho1, pho84, and tgp1  provides the initial response to phosphate starvation by rapidly inducing and tgp1 , whose pand tgp1 . In receand tgp1 . Transcrand tgp1 , how the1 lncRNA , increaspromoter , and indgp1 mRNA ,2. Secon1 lncRNA . Third, nditions D,E. Finanduction .nc-tgp1  relative to protein-coding genes (n = 406) showed premature 3′ end processing. Although nc-tgp1/tgp1 and prt2/pho84 provide strong cases where premature termination of an upstream interfering ncRNA affects the expression of a downstream gene, manual inspection of the remaining ncRNAs affected in the RNAPII slow mutant did not provide clear evidence for activation of a neighboring protein-coding gene. However, it remains possible that regulation of transcription elongation rate at ncRNA genes may modulate the expression of adjacent protein-coding genes by other mechanisms than premature termination. For instance, the RNA-seq analysis of the RNAPII slow mutant revealed the induction of ste11, which encodes a master transcription factor for sexual development that activates a number of genes required for mating and meiosis in fission yeast. Interestingly, similar to the core PHO regulon genes, ste11 is induced by nutrient starvation and is negatively regulated by a lncRNA. In the case of ste11, gene repression is mediated in cis by a divergently transcribed lncRNA, rse1, that recruits a protein complex that promotes deacetylation of the ste11 promoter  site selection to regulate specific expression programs. Given the primordial role of alternative splicing and alternative polyadenylation in mammalian cells, it will be interesting to see whether variations in the RNAPII elongation rate in responses to changes in the nutritional status or the during cellular differentiation can also activate specific expression programs in higher eukaryotes.S. pombe strains used in this study is provided in Supplemental Table S3. Unless stated otherwise, cells were grown to mid-log phase (OD600 0.5–0.7) at 30°C in YES or EMM medium supplemented with adenine, histidine, leucine, and uracil. CRISPR/Cas9-mediated mutagenesis was used to generate the rpb1-N494D and +5seb1 mutants by applying the “fluoride-selection” . Exponentially growing cultures of those ura4+ strains were concentrated to an OD600:1 and serially diluted fivefold in the appropriate liquid media. Each dilution was spotted on freshly prepared plates containing the indicated concentration of drug or an equal volume of solvent.Mycophenolic acid  and 6-azauracil  were resuspended in methanol and DMSO, respectively, to a stock concentration of 50 mg/mL. As the large quantity of uracil contained in uracil-supplemented media neutralizes the effect of 6AU , the tesTotal cell extracts were prepared by harvesting cells in mid-log phase in ice-cold lysis buffer  containing a cocktail of protease inhibitors  with half of a phosphor STOP tablet, prior to lysis with glass beads using a FastPrep instrument . Clarified lysates were normalized for total protein concentration using the Bradford protein assay. Thirty micrograms of total proteins was separated by SDS-PAGE, transferred to nitrocellulose membranes, and analyzed by immunoblotting using a rabbit polyclonal antibody against protein A . Membranes were then probed with goat anti-rabbit secondary antibodies conjugated to IRDye 800CW . Detection of the proteins was performed using an Odyssey infrared imaging system (LI-COR).For each IP, 50 mL of cells grown in EMM were incubated for 20 min at room temperature with 1% formaldehyde. After quenching the reaction with glycine  for 5 min at room temperature, cells were washed twice with cold Tris-buffered saline . Cell pellets from 50-mL cultures were resuspended in 500 μL of lysis buffer  containing protease inhibitors and disrupted using a FastPrep instrument. Samples were then sonicated 10 times for 10 sec at 20% intensity using a Branson digital sonifier. Whole-cell extract  was incubated overnight at 4°C with 50 µL of magnetic beads only  in the case of the immunoprecipitation of HTP/TAP-tagged proteins, or of magnetic beads coupled with 2 µg of one of the following antibodies: 8WG16 (unphosphorylated CTD), clone 3E10 , clone 3E8 , clone 3D4A12 , and clone 6D7 . Beads were washed twice with 1 mL of lysis buffer, twice with 1 mL of lysis buffer plus 500 mM NaCl, twice with 1 mL of wash buffer , and once with 1 mL of Tris-EDTA . Bound material was eluted by resuspending beads in 50 μL of elution buffer  after 15-min incubation at 65°C. After overnight incubation at 65°C for reverse-crosslinking, the samples were treated with proteinase K and cleaned up by phenol-chloroform extraction before undergoing a RNase treatment and a final purification on PCR purification column (Qiagen). DNA from the inputs and IPs were analyzed on a LightCycler 96 Instrument using perfecta SYBR supermix in the presence of a 150 nM concentration (each) of gene-specific primers in 15-μL reaction mixtures. Protein occupancy was then calculated using the percent input method . For the32P-labeled riboprobes. For RT-qPCR analyses, RNA samples  were treated with DNase RQ1  and reverse transcribed using the Omniscript RT (Quiagen). Gene expression relative to the appropriate control strain was measured on a LightCycler 96 system (Roche) with the ΔΔCT method using the gene nda2 as internal reference  according to the manufacturer's protocol. RNA integrity was assessed on a 2100 Bioanalyzer machine (Agilent Technologies). Ribo-depleted RNA was isolated from 1.25 µg of total RNA using Illumina RiboZero (yeast), as per manufacturer's protocol. Stranded RNA-seq libraries were then built using the SSV21106 kit (Epicentre) from the ribo-depleted RNA with 13 cycles of PCR amplification. After assessment of libraries quality and molarity on a DNA high-sensitivity chip (Agilent), the libraries were pooled and sequenced in paired-end (2 × 100 bp) on a HiSeq 4000 (Illumina) at the McGill University and Génome Québec Innovation Centre (MUGQIC) Sequencing Service with at least 250× genome depth (>35 millions reads) for each sample.S. pombe genome assembly ASM294v2 with STAR v2.5.1 . Briefly, low-quality reads and adaptor sequences were removed with Trimmomatic v0.36 with parameters ILLUMINACLIP:adapters-truseq.fa:2:30:15:8:true TRAILING:30 MINLEN:32 . The remR v2.5.1  and summR v2.5.1 . DiffereR v2.5.1 . The AnGR v2.5.1  was used2[(slow/wt)distal/slow/wt)proximal]log. Statistical significance of the difference in ratios between the slow mutants and the wild-type strain was assessed with a generalized linear model of the binomial family using the glm function in R. This method allows one to take into account not only the change in PAS usage ratio, but also the normalized read counts of the numerator and denominator of the ratio, therefore accounting for the magnitude of count values in the calculation of statistical significance.To estimate the change in poly(A) site selection from transcriptomic data, we first segmented genes in smaller windows delimited by peaks of 3′READS read counts , represen = 441) were defined as having an APA score <1 and an associated adjusted P-value (FDR) <0.01. Proximal PAS whose APA score is close to 0, within thresholds (between −0.2215 and 0.02215) allowing the same number of PAS to be selected (441), served as the unaffected control group. Differential motif analysis was performed using DREME (Affected proximal PAS (ng DREME  and aver