The ETO2–IRF2BP2 axis, interacting with the NCOR1/SMRT co-repressor complex, suppresses the expression of the vast majority of archetypical erythroid genes and pathways until its decommissioning at the onset of terminal erythroid differentiation. Our experiments demonstrate that multimeric regulatory complexes feature a dynamic interplay between activating and repressing components that determines lineage-specific gene expression and cellular differentiation.How transcription factors (TFs) cooperate within large protein complexes to allow rapid modulation of gene expression during development is still largely unknown. Here we show that the key haematopoietic LIM-domain-binding protein-1 (LDB1) TF complex contains several activator and repressor components that together maintain an erythroid-specific gene expression programme primed for rapid activation until differentiation is induced. A combination of proteomics, functional genomics and  Conserved sets of transcription factors (TFs) regulate hematopoiesis. Here, Stadhouders et al. show that IRF2BP2 is a component of the LDB1 TF complex and together with its co-repressor ETO2, enhances transcriptional repression, which plays a crucial role at the erythroid progenitor stage. Haematopoietic development relies on the stepwise activation and repression of lineage-specific gene expression programmes. This process is regulated by sets of conserved transcription factors (TFs) acting in a combinatorial and/or antagonistic pattern to establish cellular identity through tight control of gene regulatory networks345634571213315in vivo relevance of the newly identified IRF2BP2 co-repressor by using an IRF2BP2-deficient mouse model. Animals homozygous for the genetrap Irf2bp2 allele display an ineffective fetal liver (FL) erythropoiesis during gestation and die around birth. Thus, our data reveal a complex collaborative action of multiple co-repressor proteins within the LDB1 complex at the erythroid progenitor stage. As a result, late erythroid-specific genes are maintained in a primed state before their rapid activation upon terminal differentiation.In this study, to begin addressing these questions, we performed a proteomics screen for novel ETO2-binding partners. This screen identifies the interferon regulatory factor 2-binding protein 2 (IRF2BP2), growth factor-independent 1B (GFI1B) and lysine-specific demethylase 1 (LSD1) transcriptional repressors as ETO2-interacting proteins. We show here that IRF2BP2 is a novel component of the LDB1 complex able to strongly enhance ETO2-mediated transcriptional repression. Chromatin immunoprecipitation-sequencing (ChIP-Seq) analysis and loss-of-function studies reveal that ETO2 and IRF2BP2 chromatin occupancy significantly overlap at a genome-wide scale, and that both factors regulate a common set of key erythroid target genes and regulatory pathways. Subsequent analysis of IRF2BP2 protein partners shows that IRF2BP2 is able to recruit the well-known NCOR1 co-repressor, which is able to bind ETO2/IRF2BP2 erythroid target genes to potentially mediate their repression. We finally confirm the ‘Primed' developmentally regulated genes have been previously defined as being already expressed at low levels before full activation at the onset of differentiationWe conclude that primed archetypical erythroid genes in progenitor cells are uniformly defined by: (1) low expression levels that are strongly induced upon differentiation; (2) occupancy of the TFs responsible for their later induction; (3) an active chromatin environment at the regulatory elements controlling their expression and (4) not having substantial amounts of paused RNAPII at their promoters. Thus, primed erythroid genes are subjected to active transcriptional repression involving ETO2 that prevents their premature activationWe next employed a proteomics approach to characterize the molecular determinants of ETO2's repressive activity. An epitope-tagged form of ETO2 ETO2-V5-Bio) was stably expressed in MEL cells-Bio was in vitro reporter expressionVEGFA expression has been described as wellIRF2BP2 is a highly conserved zinc-finger/RING-finger protein belonging to a family of three evolutionary conserved factors  sharing high sequence homology. IRF2BP1 and IRF2BP2 were originally identified as interacting partners of IRF2, mediating its ability to repress in vitro using luciferase reporter assays. ETO2 was fused to a Gal4 DNA-binding domain and co-expressed in HEK 293T cells together with a luciferase reporter plasmid containing Gal4-responsive elements. As previously reported, ETO2 induces a 20–30-fold repression of luciferase activity /ETS-related gene (ERG) TFs) and G/C-rich (early growth response (EGR) family) TF motifs were detected in all three categories, while Runt-related TF (RUNX) and TCF motifs show enrichment at sites occupied only by IRF2BP2 (5A  IRF2BP2 . StrikinIRF2BP2 5. This shCbfa2t3 (encoding ETO2) was repressed upon GATA1-driven erythroid maturation33Cbfa2t3 expression is regulated by the ETO2-containing LDB1 complex, which involves an ETO2 negative autoregulatory loop. To gain more insight into the regulation of Irf2bp2 during erythropoiesis, we examined its expression levels during mouse FL erythropoiesis. RNA-Seq analysis of fluorescence-activated cell sorting (FACS)-purified populations of developing erythroid cells  or decreased expression (Epb4.2) is observed. This result, together with the data derived from the reporter assays (Cbfa2t3 (ETO2) and Kdm1a (LSD1) KD shows an inverse trend, as genes repressed by ETO2 are activated by LSD1 and vice versa (Gfi1b (encoding GFI1B), which is known to interact with both ETO2 and LSD1, results in a very similar profile of differentially expressed genes when compared with the Cbfa2t3 and Irf2bp2 KD results -mediated knockdowns (KDs) of r assays  suggest r assays , showingce versa . In addi results . This su results . The eme results , while tCbfa2t3/Irf2bp2 or loss-of-interaction mutant complementary DNAs (cDNAs) were transfected into ETO2/IRF2BP2-depleted MEL cells  are also found affected in the IRF2BP2 data set . In general, 30–40% of the genes misregulated upon either Cbfa2t3 or Irf2bp2 KD are also bound by the corresponding factor  by ETO2 and IRF2BP2 , with a g factor , despiteg factor . TogetheETO2 and IRF2BP2 also modulate a set of 186 genes that are downregulated upon factor depletion , of whicNcor1−/− mice die in utero due to abnormal erythropoiesisAlthough our data strongly suggest a repressor function for IRF2BP2 in erythroid gene regulation, how IRF2BP2 achieves gene repression is still unclear. We therefore purified endogenous IRF2BP2-containing protein complexes from MEL cells and identified the interacting proteins by IP-MS. As shown in n=3–4 for each, Ncor1 isoforms expressed in MEL cells   transfections) failed to provide consistent effects on erythroid gene expression between individual shRNAs/siRNAs  production  are rarely obtained and did not survive past 4 weeks of age, displaying severe growth retardation. In fact, although Irf2bp2trp/trp embryos appear to develop normally up to E18.5 and are obtained at the expected Mendelian ratio, live births are very rare (<5% of the expected number). This indicates that Irf2bp2trp/trp mice die either late during gestation or immediately after birth. Given that Irf2bp2 expression is particularly high in the developing mouse lungs, skeletal muscle and heart at E17.5  are more abundant in Irf2bp2trp/trp FLs. Furthermore, the relative number of mature Ter119+CD71− erythrocytes is significantly reduced in the absence of IRF2BP2. These data indicate that IRF2BP2 is important for effective FL erythropoiesis, as the output of mature erythrocytes is impaired in the absence of a functional Irf2bp2 allele.Next, we interrogated IRF2BP2 function strategy . The genoduction . Animals7.5 ref. , defectslularity . When st defects . We obseIrf2bp2trp/trp FLs by separating the Ter119+ population based on its forward scatter (FSC) profileWe further characterized terminal differentiation in Irf2bp2trp/trp embryos—in particular, within the mature Ter119+ compartment  in sorted FL erythroid populations reveals an upregulation of these late erythroid markers in Irf2bp2trp/trp red cell precursors, as well as a downregulation of Myb expression—a marker for early erythroid progenitors  also shows altered proportions of the different developing erythroid populations in partment . Defectsgenitors . Derepreecursors . This phin vivo.Combined, these observations point at disturbed erythroid differentiation kinetics and transcriptional regulation in the absence of IRF2BP2, confirming the notion that IRF2BP2 is important for effective erythropoiesis Developmental processes are coordinated by spatio-temporal changes in gene expression laid down by the combinatorial actions of TFs and the cofactors they recruit. Exactly how TFs in large multimeric complexes cooperate to create a regulatory environment that allows for rapid modulation of gene expression programmes is under intense investigation. Here we address the observation of a master haematopoietic TF complex, containing key factors required for the activation of a tissue-specific gene expression programme, that binds its target genes but maintains them in a developmental stage-specific primed state. Previous studies have shown that the activating LDB1 TF complex is already recruited to genes of the late erythroid-specific transcriptome in erythroid progenitors, before their full activation3456345330in vitro, which is fully dependent on the ETO2–IRF2BP2 interaction (Cbfa2t3 (ETO2), Irf2bp2 expression is reduced upon erythroid differentiation, concomitant with the upregulation of its erythroid target genes  ETOeraction ; (3) simet genes ; (4) depet genes ; (5) ETOet genes ; (6) ETOet genes .Our biochemical analyses of IRF2BP2 protein complexes in MEL cells reveal the presence of NCOR/SMRT co-repressor complex members . In accoet al.Epb4.2 gene  through the insertion of a genetrap construct in the first intron of the Irf2bp2 gene (clone IST11591C1). Genetrap location was verified using standard PCR and sequencing methods. Mouse ES cells were injected into blastocysts and implanted into pseudopregnant albino fosters according to standard methods. Chimeric animals were further crossed to obtain heterozygous founders and mice were further crossed on a mixed-FVB/N-C57BL/6 background. Mice were genotyped using a standard 3-primer PCR method. Up to E18.5 of embryonic development, wild-type, heterozygous and homozygous genetrap animals were obtained at the expected Mendelian ratio and with no visible signs of developmental defects. Although Irf2bp2trp/wt animals were born in normal numbers, Irf2bp2trp/trp mice were rarely born (<5% of the expected number). Homozygous animals showed severe growth retardation and all died the first weeks after birth. Ethical approval was obtained from the Committee on the Ethics of Animal Experiments (DEC) of the Erasmus MC as well as the French Ministry of Agriculture regulations . All animal experiments were carried out according to institutional and national guidelines.MEL C88 clone and HEK 54HCO3), after which to each 50 μl of beads 100 μl of trypsin digestion buffer and 10 μl of trypsin solution (10 μg ml−1) were added. Samples were incubated overnight at 37 °C with shaking. Digested samples were analysed by nanoflow LC–MS/MS on a LTQ-Orbitrap (Thermo) mass spectrometer coupled to an 1100 series LC pump and autosampler (Agilent), operating in positive mode and equipped with a nanospray source. Peptide mixtures were trapped on a ReproSil C18 reversed-phase column  with a flow rate of 8 μl min−1. Peptide separation was performed using a ReproSil C18 reversed-phase column  using a linear gradient from 0 to 80% B  acetonitrile, 0.1 M formic acid) over 70 min with a constant flow rate of 200 nl min−1 using a splitter. The column eluent was directly sprayed into the electrospray ionization source of the mass spectrometer. Mass spectra were acquired in continuum mode; while fragmentation of the peptides was performed in a data-dependent mode. Peak lists were automatically created from raw data files using the Mascot Distiller software . The Mascot search algorithm  was used for searching against the NCBInr database . The peptide tolerance was typically set to 10 p.p.m. and the fragment ion tolerance to 0.8 Da. A maximum number of two missed cleavages by trypsin were allowed and carbamidomethylated cysteine and oxidized methionine were set as fixed and variable modifications, respectively. The Mascot score cutoff value for a positive protein hit was set to 60. Individual peptide MS/MS spectra with Mowse scores below 40 were checked manually and either interpreted as valid identifications or discarded. Identified proteins were filtered against a database of background hits obtained from BirA-expressing MEL cells. Proteins with no peptides identified in the BirA control experiment or showing Mascot scores at least threefold higher than the control were considered as positive hits, and were further validated by conventional co-IP experiments (−1 insulin (Sigma), 0.2 mg ml−1 chicken egg albumin (Sigma) and 1% fish skin gelatin (Sigma) for 1 h at room temperature and directly added to 1.5 ml of MEL nuclear extracts containing benzonase. After 3 h incubation at 4 °C, antibody–bead complexes were washed five times in C-100 buffer  and boiled in Laemmli buffer. Proteins were loaded on a 4–12% acrylamide gel and lanes were cut for LC–MS/MS analysis as described above. For MS analysis of ETO2- and IRF2BP2-interacting proteins, two independent biological replicates  were analysed to ensure reproducible and specific binding partner identification. The following antibodies were used: ETO2 G-20 , an IRF2BP2 rat monoclonal KT139 , GFI1B B-7 , LDB1 N-18 , LSD1 , RUNX1 H-65 , E2A V-18 (sc-349), HEB A-20 (sc-357), SSBP3 , V5 , Flag M2  and haemagglutinin (HA) .Protocols for the preparation of nuclear extracts, streptavidin-mediated protein capture and LC–MS/MS in MEL cells have been described previouslyeriments  For endoKdm1a (LSD1) cDNA was cloned in pcDNA3.1 for reporter assay experiments. The Gal4–ETO2 fusion protein was generated by fusing full-length Cbfa2t3 cDNA sequence to a Gal4 DNA-binding domain in pcDNA3.1. The Gal4-responsive firefly luciferase plasmid was a kind gift from Dr Jan van der Knaap (Erasmus MC). A Renilla luciferase expressing vector  was co-transfected and used for normalization. Luciferase assays were performed using the Dual-Luciferase Reporter Assay System (Promega) according to the manufacturer's instructionscbfa2t3 (ETO2) or Irf2bp2 were transfected with expression vectors containing shRNA-immune cDNAs encoding ETO2, IRF2BP2 or mutant versions of these proteins using GeneCellin . RNA extractions and gene expression analysis by qPCR were carried out 48 h after transfection.HEK 293T cells were transfected with Lipofectamine 2000 (Invitrogen) according to the manufacturer's instructions. For ETO2–IRF2BP2 interaction domain mapping, we constructed a series of Flag-tagged ETO2-deletion mutants, V5-IRF2BP2 and the HA-IRF2BP2deltaRING-deletion mutant see  in the p8 MEL cells were crosslinked with 2 mM disuccinimidyl glutarate (Thermo Fisher Scientific) and 1% formaldehyde as previously describedhttps://github.com/supatt/rChIPSeqTools.git) with default parameters were used to comprehensively identify binding sites. We combined binding sites identified by all three methods to define consensus-binding regions using GenomicRanges. Binding regions predicted by each program were profiled on each genomic location. We assigned value ‘1' to the genomic locations that overlapped with the predicted binding sites, and we assigned ‘0' to the non-overlapping locations. We then generated the whole-genome coverage vectors from all binding regions of all methods and summed up these coverage vectors. We selected only genomic regions that had a summed up coverage value ⩾2 and a minimum region length ⩾150 bp as candidate consensus-binding regions. Consensus-binding regions were given P values based on a negative binomial distribution as modified from PeakSeqP values were adjusted using the Benjamini–Hochberg method. Candidate binding sites were then selected for the downstream analysis based on the following criteria: read counts ⩾10 reads, fold changes (FCs) ⩾2 compared with immunoglobulin (Ig)G control and adjusted P values ≤0.01. To classify co-binding patterns, ETO2- and IRF2BP2-binding sites were combined using GenomicRanges (using the ‘findOverlaps' function with the minimum overlap regions=250 bp from the peak centre). Binding signal coverage for each site was then normalized to obtain equal levels of background signal in both antibody and IgG control experiments . The matrix of standard z-scores per individual binding site was then subjected to K-means (K=3) clustering. Clustering analysis results were visualized with Java TreeviewK-means clustering, we selected representative binding sites for each co-binding pattern . We retrieved repeat-masked 200-bp DNA sequences centred on each binding site and removed the binding sites that contain ⩾150 bp repeat-masked DNA. We next performed de novo motif discovery using MEMEhttps://github.com/supatt/rChIPSeqTools.git) to generate motif logos and to calculate the proportion of motif containing sites. Derived motifs were then compared with known motifs in the JASPAR databaseProtocols for the preparation of chromatin from MEL cells, IP and sample preparation for Illumina sequencing have been previously described in great detail5Rnh1 mRNA levelsFor gene expression analysis, RNA extractions were performed using TRIPure (Sigma) and cDNA synthesized using SuperScript II reverse transcriptase and Oligo(dT) primers (Invitrogen). ChIP DNA or cDNA were used as template in triplicate qPCR reactions  and analysed on a CFX96 system (Bio-Rad). SYBR Green (Invitrogen) was used for quantification. Gene expression values were normalized to Kdm1a and Ncor1), designed manually and cloned into pLKO.1  or described before  using HiPerfect transfection reagent (Qiagen) according to the manufacturer's instructions. Non-targeting shRNAs/siRNAs were used as controls. Cells were harvested 48 or 72 h after transduction/transfection and processed for RNA/protein extraction as described above. The generation of knockout cell lines by CRISPR/Cas9 technology was carried out as previously describedCbfa2t3 and Irf2bp2 in MEL cells were designed using the CRISPOR webtool (http://crispor.tefor.net/crispor.cgi). Combinations of two different guide RNAs were used for each gene to delete critical exons . MEL cells were transfected using Genecellin , single clones were isolated and targeted genomic deletions were verified by PCR and sequencing.Lentivirus particles were produced as describedrpkmforgenes script. The non-adjusted read counts for each gene were used for statistical calculation of global differential expression using DESeq2 . We selected differentially expressed genes with log2 FCs ⩾0.5 and log2 FC ≤−0.5 in each KD data set to generate the correlation plots shown in Cbfa2t3 KD data set for the clustering analysis. We then retrieved all log2 FC values for the 142 genes in the other KD data sets. Hierarchical clustering and visualization were performed using MeV  was used to extract Gene Ontology terms that were associated with a gene set in a statistically significant fashion.Total RNA was extracted from MEL or E13.5 sorted FL populations using the RNeasy mini kit (Qiagen). After qPCR validation, RNA was used for mRNA-sequencing on an Illumina HiSeq 2000 (standard TruSeq RNA-sequencing protocol). At least two independent biological replicate samples were sequenced and used for downstream analysis. Raw reads were mapped with Tophat2 (version 2.0.10)eq2 ref. . DiffereMeV ref. . For Ing−1 CD71-FITC (553266) and Ter119-PE (553673) antibodies (BD Pharmingen). Hoechst was used as a viability dye (Sigma), and cells positive for Hoechst staining were excluded from further analysis . Flowcytometric analysis was performed using a BD LSRFortessa flowcytometer (BD Biosciences), collecting a minimum of 10,000 events per sample. FACS sorting was performed with a BD FACSAria III (BD Biosciences) using the above described staining protocol. At least 1 million cells were sorted for RNA extraction.E11.5–13.5 embryos were harvested and dissected to collect the FL. Hundred microlitre of PBS containing 1 million single cells obtained from whole E13.5 FLs were stained with 5 ng μlMEL cells were fixed on poly-prep glass slides (Sigma) and fixed in 4% paraformaldehyde for 15 min at room temperature. Cells were permeabilized with 0.1% Triton X-100, blocked with 0.5% bovine serum albumin/0.15% glycin (in PBS) and incubated overnight with ETO2 or V5 antibodies at 4 °C. After a 2-h incubation with appropriate secondary antibodies at room temperature, coverslips were mounted on glass slides with Vectashield .The following publicly available data sets were used: LDB1, GATA1 and ETO2 ChIP-Seq data ; RNA-SeAccession codes: Nucleotide sequences for ChIP-Seq and RNA-Seq data sets have been deposited in the Gene Expression Omnibus (GEO) database under accession code GSE59859. The mass spectrometry proteomics data have been deposited in the ProteomeXchange Consortium/PRIDE partner repository under accession code PXD001892.How to cite this article: Stadhouders, R. et al. Control of developmentally primed erythroid genes by combinatorial corepressor actions. Nat. Commun. 6:8893 doi: 10.1038/ncomms9893 (2015).Supplementary Figures 1-11, Supplementary Table 1 and Supplementary References


We describe the genome-wide distributions and temporal dynamics of nucleosomes and post-translational histone modifications throughout the maternal-to-zygotic transition in embryos of Drosophila melanogaster. At mitotic cycle 8, when few zygotic genes are being transcribed, embryonic chromatin is in a relatively simple state: there are few nucleosome free regions, undetectable levels of the histone methylation marks characteristic of mature chromatin, and low levels of histone acetylation at a relatively small number of loci. Histone acetylation increases by cycle 12, but it is not until cycle 14 that nucleosome free regions and domains of histone methylation become widespread. Early histone acetylation is strongly associated with regions that we have previously shown to be bound in early embryos by the maternally deposited transcription factor Zelda, suggesting that Zelda triggers a cascade of events, including the accumulation of specific histone modifications, that plays a role in the subsequent activation of these sequences.DOI:http://dx.doi.org/10.7554/eLife.03737.001 Drosophila melanogaster begins with the nuclei that contain this genetic material repeatedly dividing for the first 2 hr. These nuclear divisions are initially controlled by molecules that the mother deposits into the egg cell. However, as these molecules degrade, the zygote's genome is activated and its own genes take control of embryonic development, in a process referred to as the ‘maternal-to-zygotic transition’.For a fertilized egg to develop into an embryo, many genes must be switched on and off at specific times. A fertilized egg (or zygote) contains genetic material from both parents; and the life of the fruit fly In the fruit fly zygote, this burst of regulated gene activation is likely to be accompanied by changes to the way that the DNA is packed inside the nuclei. Most DNA in a cell is packaged into a structure called chromatin, which can be marked at specific sites by chemical modifications. For example, chromatin can be acetylated or methylated, which alters its physical structure, helping the underlying genes to be either activated or repressed.In the fruit fly, the first genes to be switched on  have a DNA motif that is recognized, and is bound by, a protein called Zelda. The Zelda protein plays a major role in activating the genome of the early fruit fly embryo, by marking thousands of genes and regulatory regions for activation. This is somewhat similar to the activity of so-called ‘pioneer’ factors that alter chromatin structure to allow particular genes to be switched on or off, and to trigger the formation and development of specific tissues.Here, Li et al. have investigated whether the Zelda protein—like known pioneer factors—also affects chromatin during the maternal-to-zygotic transition. Different chromatin modifications across the whole fruit fly genome were characterized at specific time-points during the maternal-to-zygotic transition, and the information gathered was then analyzed along with previous data on gene activity.In the early stages of the maternal-to-zygotic transition, Li et al. found very few of the chromatin features that characterize more mature cells. This indicates that the chromatin is in a so-called ‘naïve’ state. As the transition progresses, Li et al. observed that the chromatin becomes acetylated before it is methylated, and that marks associated with activation appear before those associated with repression. Chromatin acetylation was strongly associated with the early binding of the Zelda protein to its target genes.Drosophila zygotes. A future challenge will be to identify whether Zelda directly recruits the proteins that cause chromatin acetylation, or whether it blocks the changes to chromatin that repress gene expression.Li et al.'s findings show when, and in what order, the different features of mature chromatin appear in DOI:http://dx.doi.org/10.7554/eLife.03737.002 In most animals, the first phase of embryonic development depends solely on maternally deposited proteins and RNAs and is often accompanied by very low or undetectable transcription . After sDrosophila melanogaster, sustained zygotic transcription begins around mitotic cycle 7, about an hour into development, although there is growing evidence that very low levels of transcription occur even earlier  embryos from population cages at 25°C for 30 min, and aged them for 55, 85, 120 and 160 min to target mitotic cycles 8, 12, 14a and 14c respectively, prior to fixing them with formaldehyde  using commercial antibodies against nine post-translation modifications , as well as histone H3 .10.7554/Drosophila pseudoobscura embryos (mitotic cycle 14), and ‘spiked in’ a fixed amount of this common reference to each D. melanogaster chromatin sample prior to ChIP and sequencing. We chose D. pseudoobscura since it is sufficiently diverged from D. melanogaster that there is very little ambiguity in the assignment of reads to the correct species . Indeed, we found that both the number of peaks  emerged around the transcription start sites of zygotically transcribed genes at roughly the same time that their transcripts were evident in our transcription data . SeveralIn cycle 14 embryos, regions both upstream and downstream of the promoters of zygotically expressed genes were enriched for the Polycomb-associated mark H3K27me3 , while tWe also observed that maternally deposited genes, both those transcribed in the early embryo and those that are not, had fairly strong NFRs upstream of the promoter at all time points . A stronMany of the genes transcribed by cycle 14 are expressed in clear spatial patterns  driven bThe chromatin state we observed associated with these putative enhancers during cycle 14, when blastoderm enhancers are active and bound by multiple transcription factors were as expected based on previous studies of transcriptionally active mammalian and insect cells and tissues . As showWhile the chromatin status of enhancers at cycle 14 has been intensively investigated, their status earlier during the MZT has received much less attention. Our set of likely blastoderm enhancers had, as a class, relatively high nucleosome densities at mitotic cycle 8 . Thus these ChIP data reflect the depletion, rather than complete elimination, of ZLD.ChIP with an anti-ZLD antibody on these nominally  embryos . This reIntergenic ZLD-bound regions  had a maDrosophila melanogaster, occurs by transitioning through a naïve state in which many histone marks commonly present in somatic cells are absent or at comparably low levels. We further demonstrate that histone acetylation of H3K18, H3K27, and H4K8 precedes most histone methylation. Thus we suggest that the establishment of the totipotent chromatin architecture proceeds in an ordered process with acetyl marks being deposited prior to methyl marks.During the first stage of embryonic development, the genome must be reprogrammed from the differentiated states associated with the egg or sperm to create a set of totipotent cells capable of generating a new organism. By combining high-resolution gene expression analysis with precise mapping of nine histone marks throughout this early stage of development, our data suggest that this reprogramming, at least in Studies in other organisms have similarly suggested that this early reprogramming is characterized by a transition through a relatively unmodified chromatin state. There is a loss and then reestablishment of DNA methylation following fertilization in mouse embryos . ImmunosOne important lingering question is how this naïve state is established; whether there is a specific system that removes gametic marks associated with sperm and egg at fertilization, or whether the rapid replication cycles of early development are simply incompatible with active and differentiated chromatin. The lack of some histone marks in developing mouse and bovine embryos, which do not undergo rapid cell cycles early in development, suggests that while the mechanisms may vary between species, the removal of parental histone modifications may be a general feature of reprogramming. In the future, it will be important to understand how this transition is regulated to allow for the generation of a totipotent cell population.The presence of several post-translational modifications (e.g. H3K4me3 and H3K36me3) is correlated with transcription and with different states of gene expression in a variety of eukaryotic cells and tissues (c.f. ). By comDrosophila wing disc, methylation of H3K4me3 is not required for transcriptional activity  they bind to DNA prior to activation and prior to the binding of other factors, and 2) they bind their target sites in nucleosomes and in condensed chromatin .ZLD clearly has the first characteristic. But it is not clear that is has the second. Our data suggest that there is essentially no condensed chromatin in the early embryo, as nucleosome density is relatively low, nucleosomes are relatively evenly distributed across the genome, and hallmarks of repressed chromatin are absent. This is consistent with the unusually broad binding of ZLD to its target sequences: ZLD binds to more than fifty percent of its target sites in the genome, far more than what is typical for other factors later in development . We and Nonetheless, it is clear, if our model is correct, that ZLD is fulfilling the same general role that pioneer factors carry out—getting to the genome first and facilitating the subsequent binding of other factors.While there are no clear ZLD homologs outside of insects, it has recently been shown that in zebrafish the transcription factor Pou5f1 (Oct4), in combination with Nanog and SoxB1, drives zygotic genome activation and may share with ZLD a pioneer-like activity . TogetheDrosophila genome contains a large number of binding sites for the factors active in the cellular blastoderm (or any other stage of development)  , why is Drosophila enhancers is highly flexible . Embryos were collected for 30 min, and then allowed to develop for 55, 85, 120 or 160 additional minutes before being harvested and fixed with formaldehyde. The fixed embryos were hand sorted in small batches using an inverted microscope to remove embryos younger or older than the targeted age range based on morphology of the embryos as previous described  for each chromatin immunoprecipitation. The chromatin immunoprecipitation reactions were carried out as described previously  for a total of processing time of 140 min , with power setting at ‘H’. Prior to carrying out chromatin immunoprecipitation, we mixed the chromatin from each sample with a roughly equivalent amount of chromatin isolated from stage 5 (mitotic cycle 14) eviously  with 0.5D. melanogaster genome [Flybase Release 5] and the November 2004 assembly of the D. pseudoobscura genome [Flybase Release 1.0] using Bowtie . Similarly, the author response typically shows only responses to the major concerns raised by the reviewers.eLife posts the editorial decision letter and author response on a selection of the published articles . An edited version of the letter sent to the authors after peer review is shown, indicating the substantive concerns or comments; minor concerns are not usually shown. Reviewers have the opportunity to discuss the decision before the letter is sent , a Reviewing editor, and 2 reviewers.Thank you for sending your work entitled “Establishment of regions of genomic activity during the The Reviewing editor and the reviewers discussed their comments before we reached this decision, and the Reviewing editor has assembled the following comments to help you prepare a revised submission.eLife. However, two major concerns on the manuscript need to be addressed before further consideration.The manuscript presents interesting data that provide a unique view into how histone modifications accumulate at enhancers that are activated during the maternal-to-zygotic transition. The reviewers agree in principle that this is worthy of publication in 1) The first is a concern raised by reviewer 2 on the normalization of datasets across different samples (here embryonic stages and wildtype versus Zelda-depleted embryos), when the samples are expected to strongly differ in the strengths and relevant features.2) The second is a shared general concern of both reviewers that key aspects of existing literature are not cited and integrated into the text. Several conclusions drawn are already well established in the literature. These omissions detract from the balance and quality of the work and must be addressed. Specific examples are noted in the reviewers' opinions, which are included below to aid the authors in revising the manuscript.Reviewer #1:Drosophila embryos. Their major finding is that histone acetylation at many residues is present before the mid-blastula transition and that the presence correlates with sites of Zelda motifs and Zelda binding. In contrast, histone methylation, including H3K4me1, which is thought to be a general mark of enhancers, is not present until after widespread transcription begins during the mid-blastula transition.Li et al. perform a careful genome-wide analysis of various histone modifications during the maternal-to-zygotic transition in eLife.Overall, the data and the analysis seemed to be of high quality and provide a unique view into how histone modifications accumulate at enhancers that are activated during the maternal-to-zygotic transition. Unfortunately, the interpretation of the data frequently does not take into account the current knowledge of the literature and thus many observations that the authors describe are either already published or are very speculative and not well supported. While the novelty of the manuscript lies in the enhancer analysis before transcription begins, the authors spend quite a deal on well-studied areas such as the role of H3K27me3 or the differences in nucleosomes and histone modifications at different gene classes (see below). These oversights unfortunately detract from the manuscript and leave me wondering what the novelty and significance of the presented findings is. Looking at the data, I do think that the manuscript has value but I would have expected a much more balanced paper (with the inclusion of appropriate citations and better emphasis on what's novel) for publication in Findings that were already known and not acknowledged:1) The authors write “Despite the generally assumed correlation between H3K4me3 at promoters and H3K36me36 at gene bodies, our data demonstrate that at early expressed genes transcription proceeds in the absence of these marks .”Drosophila do not show H3K4me3. The latter paper is cited in the manuscript with regard to the Pol II data so I am surprised that the authors do not cite the paper in this context.It has become clear in the last few years that this general assumption is incorrect. Rach et al. (PLOS Genetics 2011) showed that epigenetic features such as histone modifications differ between promoter classes and this difference has also been observed between the five chromatin states observed by Bas van Steensel'd group in Kc cells. They show that H3K36me3 is specific for yellow chromatin (housekeeping genes) versus red . Finally, Chen et al. (eLife 2013) showed that genes expressed before the midblatula transition in 2) The authors write “Surprisingly, we observed NFRs at the promoters of maternally deposited genes at all time points , even thDrosophila and that regulated promoters tend to have a strong disposition for promoter nucleosomes. For example, it has been shown by Gilchrist et al. (Cell 2010) and Gaertner et al. (Cell reports 2012) that promoters with paused Pol II  tend to have a strong promoter nucleosome when Pol II is not present. Thus, the fact that maternal genes and developmental genes have a different natural nucleosome configuration at the promoter was already known.Actually, the traditional view is usually that promoters are naturally nucleosome-free. However, it is known from yeast and Drosophila and mammals has been known for a long time  and the relationship between H3K27me3 and Pol II  is fairly complex and not accurately described in the manuscript.3) The authors find that H3K27me3 was completely absent from maternal genes and they speculate in quite some detail about the “balanced state” of Polycomb-repressed genes as if nothing had been said about this before. The fact that H3K27me3 is specifically found at developmental genes both in Recommended reading (to name a few): Dellino (Mol Cell 2004); Stock et al. (Nat Cell Biol 2007); Enderle et al. (Genome Res 2011); Gaertner et al. (Cell reports 2012); Bonn et al. (Nat Genet 2012); Lehmann et al. (JBC 2012)4) The authors propose that inconsistent results from reporter assays on Zelda enhancers stem from the fact that reporter lines usually have insulator sites and thus are not under the influence of Polycomb repression. This is certainly a good point but has previously been reported by Arnold at el. (Science 2013). In fact, Arnold et al. found this in S2 cells, suggesting that this is a general phenomenon and not specifically linked to Zelda.Other major points:The authors describe “within the broad H3k27me3 domains, the signal was almost completely absent in regions corresponding to ZLD-binding peaks” and speculate a fair bit about the significance. However, the simplest explanation for this is that ZLD-bound regions are nucleosome-depleted and thus do not have H3K27me3 signal. Looking at the average plots of other histone modifications on The authors propose that some enhancers that are bound by Zelda and have H3K27me3 are poised for future activation later in development. Unfortunately, this is only based on one gene and its published expression pattern. At the very least, one would like to see that the ZLD-bound region is indeed a late enhancer  and that this pattern can be observed for multiple enhancers. To my knowledge, early ZLD-bound regions are generally not active late so I would need better evidence to be convinced otherwise.Reviewer #2:How zygotic genomes are transcriptionally activated during early embryogenesis is an important open question. The authors determine the genomic distribution of histone H3 and different histone marks at 4 stages of embryogenesis using ChIP from tightly staged embryos. They integrate these datasets with previously published data on transcription and the binding of Pol II and the transcription factor Zelda. This allows the authors to investigate the dynamics of these chromatin features in general, at the transcription starting sites of different sets of genes , and around the locations of putative blastoderm embryo enhancers. Overall, this reveals that the genome appears relatively uniformly at mitotic cycle 8 and acquires the different histone modifications and nucleosome free regions only later between cycles 12 and 14. Correlations between histone modification patterns and Zelda binding strengthens the previous notion that Zelda is involved in zygotic genome activation and in defining embryonic enhancers. The authors repeat some of their experiments and analyses in embryos in which Zelda had been genetically depleted and found that the chromatin structure at intergenic position that bind Zelda in wildtype embryos was strongly affected while little effect was observed at TSS.I find this an interesting and important paper that presents a time-course of how prominent chromatin features associated with transcription and transcriptional regulation are formed during the activation of the zygotic genome in early embryogenesis. I have only 1 major technical comment and a few suggestions that might help the authors to clarify a few sections of the text and figures.Major technical comment:D. melanogaster chromatin samples with a constant spike-in sample, D. pseudoobscura chromatin from stage 5 (mitotic cycle 14) embryos. As the associated DNA molecules differ characteristically in their sequences, the origin of the sequencing reads can be determined after deep sequencing, allowing the normalization of the different D. melanogaster samples to the constant spike-in D. pseudoobscura sample.The normalization of datasets across different samples (here embryonic stages and wildtype versus Zelda-depleted embryos) is an important issue, especially when the samples are expected to strongly differ in the strengths or even the presence of the studied features. The authors present an elegant solution: prior to the chromatin immunoprecipication, they mix their stage-specific Judging from the description of the data normalization, this spike-in control might however have been used sub-optimally; the authors state that they normalize their data based on the number of reads, which can only ensure that the same number of reads (i.e. the same 'sequencing power') goes into the analysis of each sample. While this makes sense for RNA-seq data, it does not account for possible experimental variability in a ChIP experiment and the results are in my opinion difficult to interpret. In other words, the number of reads is unrelated and uninformative regarding the quality of a ChIP enrichment, i.e. if the IP step enriched for the respective antibody epitope comparably strongly or not. Similarly, it cannot control for potential differences in library complexity, i.e. the number of identical reads that can arise during PCR amplification.D. melanogaster data). This is particularly relevant when no signal is seen in the D. melanogaster samples, e.g. for early stages in To demonstrate that the different experiments worked comparably, the authors should determine peaks in the D. pseudoobscura spike-in sample and show that they are of similar heights across the different experiments  The authors write “Despite the generally assumed correlation between H3K4me3 at promoters and H3K36me36 at gene bodies, our data demonstrate that at early expressed genes transcription proceeds in the absence of these marks .”It has become clear in the last few years that this general assumption is incorrect. Rach et al. (PLOS Genetics 2011) showed that epigenetic features such as histone modifications differ between promoter classes and this difference has also been observed between the five chromatin states observed by Bas van Steensel's group in Kc cells. They show that H3K36me3 is specific for yellow chromatin (housekeeping genes) versus red . Finally, Chen et al. (eLife 2013) showed that genes expressed before the midblatula transition in Drosophila do not show H3K4me3. The latter paper is cited in the manuscript with regard to the Pol II data so I am surprised that the authors do not cite the paper in this context.We have modified the text referred to here (which is from the Results section) to simply note the absence of these methyl marks at transcribed genes prior to the cellular blastoderm. We have expanded the discussion to properly situate it with respect to earlier observations.2) The authors write “Surprisingly, we observed NFRs at the promoters of maternally deposited genes at all time points , even though there is no evidence that genes in this set are transcribed either from expression data or ChIP with RNA polymerase.”Actually, the traditional view is usually that promoters are naturally nucleosome-free. However, it is known from yeast and Drosophila and that regulated promoters tend to have a strong disposition for promoter nucleosomes. For example, it has been shown by Gilchrist et al. (Cell 2010) and Gaertner et al. (Cell reports 2012) that promoters with paused Pol II  tend to have a strong promoter nucleosome when Pol II is not present. Thus, the fact that maternal genes and developmental genes have a different natural nucleosome configuration at the promoter was already known.Here too we have modified the text in the Results to simply point out that the non-transcribed maternal genes have a clear nucleosome-free region, and to highlight the earlier work from Gaertner (which we think is more relevant to our point than Gilchrist). We point out the new contribution from our data, which is the developmental stability of the maternal-gene associated NFR, and interpret it as consistent with the observation from Gaertner.3) The authors find that H3K27me3 was completely absent from maternal genes and they speculate in quite some detail about the “balanced state” of Polycomb-repressed genes as if nothing had been said about this before. The fact that H3K27me3 is specifically found at developmental genes both in Drosophila and mammals has been known for a long time  and the relationship between H3K27me3 and Pol II  is fairly complex and not accurately described in the manuscript.Recommended reading (to name a few):Dellino (Mol Cell 2004); Stock et al. (Nat Cell Biol 2007); Enderle et al. (Genome Res 2011); Gaertner et al. (Cell reports 2012); Bonn et al. (Nat Genet 2012); Lehmann et al. (JBC 2012)We have removed this discussion of the balanced state from the Results. It was superfluous to our main point and, as the reviewer points out, our data do not significantly enhance our understanding of this state. We thank the reviewer for the guidance and added relevant references.4) The authors propose that inconsistent results from reporter assays on Zelda enhancers stem from the fact that reporter lines usually have insulator sites and thus are not under the influence of Polycomb repression. This is certainly a good point but has previously been reported by Arnold at el. (Science 2013). In fact, Arnold et al. found this in S2 cells, suggesting that this is a general phenomenon and not specifically linked to Zelda.We did not mean to suggest that it was a new idea that enhancers can be silenced in their endogenous location but active in a transgene assay. Rather, we were suggesting a specific mechanism to explain the observed effect. However, this is really just speculation that could be relatively easily tested, so we have removed this section of the manuscript and will investigate this question experimentally and publish it elsewhere.Other major points:The authors describe “within the broad H3k27me3 domains, the signal was almost completely absent in regions corresponding to ZLD-binding peaks” and speculate a fair bit about the significance. However, the simplest explanation for this is that ZLD-bound regions are nucleosome-depleted and thus do not have H3K27me3 signal. Looking at the average plots of other histone modifications on, this dip is certainly observed for all other histone modifications, too .We agree that the simplest explanation for the drop in H3K27me3 levels in the immediate vicinity of ZLD is nucleosome depletion, although the regions of H3K27me3 depletion tend to be larger than that of nucleosome depletion. However, our primary point was that ZLD binding tends to interrupt regions H3K27me3 – it is this spatial relationship, not the specific mechanism of depletion, to which we were referring. We have rewritten the sections referring to H3K27me3 to make this clear.The overlap between ZLD-bound regions and H3K27me3 regions as shown inis actually fairly low (<50%) and could be explained by the fact that both Zelda and H3K27me3 are preferentially found at developmental genes. I would predict that the same relationship would be found between any other early transcription factor and H3K27me3 regions.We have dropped this section from the paper. We agree with the reviewer, it was not compelling.I do find it very conceivable, however, that the H327ac that accumulates at ZLD enhancers would prevent H3K27me3  but the data shown in the manuscript have not convinced me yet that this is visible in the genome-wide H3K27me3 data .We have added a discussion of Tie’s results to the Discussion section, and agree that this is a tantalizing possibility, although our current data do not really speak to it.The authors propose that some enhancers that are bound by Zelda and have H3K27me3 are poised for future activation later in development. Unfortunately, this is only based on one gene and its published expression pattern. At the very least, one would like to see that the ZLD-bound region is indeed a late enhancer  and that this pattern can be observed for multiple enhancers. To my knowledge, early ZLD-bound regions are generally not active late so I would need better evidence to be convinced otherwise.There are several additional examples of this, but we feel that additional data are needed to understand their functional relevance, so we have removed any discussion of poised enhancers from the paper.Reviewer #2:[…] Judging from the description of the data normalization, this spike-in control might however have been used sub-optimally; the authors state that they normalize their data based on the number of reads, which can only ensure that the same number of reads (i.e. the same 'sequencing power') goes into the analysis of each sample. While this makes sense for RNA-seq data, it does not account for possible experimental variability in a ChIP experiment and the results are in my opinion difficult to interpret. In other words, the number of reads is unrelated and uninformative regarding the quality of a ChIP enrichment, i.e. if the IP step enriched for the respective antibody epitope comparably strongly or not. Similarly, it cannot control for potential differences in library complexity, i.e. the number of identical reads that can arise during PCR amplification.To demonstrate that the different experiments worked comparably, the authors should determine peaks in the D. pseudoobscura spike-in sample and show that they are of similar heights across the different experiments (e.g. next to the respective D. melanogaster data). This is particularly relevant when no signal is seen in the D. melanogaster samples, e.g. for early stages in, and the current normalization cannot exclude that the ChIP failed.D. pseudoobscura IPs and discuss the use of peak-based normalization in the text.We agree with everything the reviewer says here. Indeed we had done all of the analyses they suggest in the early stages of this paper, and chose to use the simple read-based normalization because the results were the same as when we used peak heights. We note that while it is certainly true that there is a lot of variation between antibodies in the efficacy of the immunoprecipitation, here we are only normalizing between parallel IPs using the same antibody (and carried out at the same time). While it is obviously possible for there to be differences in IP efficiency between experiments with the same antibody, our results here, and more generally, suggest that this is relatively uncommon, meaning that read- based normalization will work in most cases. Nonetheless, we have added an additional figure addressing consistency in the


Saccharomyces cerevisiae contributes both to terminate transcription of some RNA Pol II transcripts and to antagonize the formation of DNA:RNA hybrids in the genome. Using two independent mapping techniques, we show that DNA:RNA hybrids form in abundance at Pol III-transcribed genes in fission yeast but we demonstrate that they are unlikely to faciliate the recruitment of condensin. Instead, we show that Sen1 forms a stable and abundant complex with RNA Pol III and that Swd2.2 and Sen1 antagonize both the interaction of RNA Pol III with chromatin and RNA Pol III-dependent transcription. When Swd2.2 and Sen1 are lacking, the increased concentration of RNA Pol III and condensin at Pol III-transcribed genes is accompanied by the accumulation of topoisomerase I and II and by local nucleosome depletion, suggesting that Pol III-transcribed genes suffer topological stress. We provide evidence that this topological stress contributes to recruit and/or stabilize condensin at Pol III-transcribed genes in the absence of Swd2.2 and Sen1. Our data challenge the idea that a processive RNA polymerase hinders the binding of condensin and suggest that transcription-associated topological stress could in some circumstances facilitate the association of condensin.Condensin-mediated chromosome condensation is essential for genome stability upon cell division. Genetic studies have indicated that the association of condensin with chromatin is intimately linked to gene transcription, but what transcription-associated feature(s) direct(s) the accumulation of condensin remains unclear. Here we show in fission yeast that condensin becomes strikingly enriched at RNA Pol III-transcribed genes when Swd2.2 and Sen1, two factors involved in the transcription process, are simultaneously deleted. Sen1 is an ATP-dependent helicase whose orthologue in  Failure to condense chromosomes prior to anaphase onset can lead to genome instability. The evolutionary-conserved condensin complex drives chromosome condensation, probably by changing the topology of chromatin around its binding sites. Condensin localizes to regions of high transcription, suggesting that some transcription-associated feature(s) direct its association with chromatin. Here we considered that transcription-dependent DNA:RNA hybrids or topological stress could be involved in recruiting condensin. Our data show that condensin is indeed enriched at regions accumulating DNA:RNA hybrids but that they are not involved in its recruitment. Rather, we identify a mutant combination where increased transcription by RNA Pol III is associated locally with stronger topological stress. Strikingly the localization of condensin is dramatically enhanced at the same loci and we show that topological stress contributes to this enhanced association. Our data strengthen the idea that transcription creates the environment necessary to recruit condensin in mitosis. Cut14, SMC4Cut3, CAP-D2Cnd1, CAP-GCnd3 and CAP-HCnd2, name of the human protein followed by its name in fission yeast) and it is one of the main components of mitotic chromosomes In vitro, purified condensin can introduce positive supercoils into a relaxed plasmid in the presence of topoisomerase I Mitotic chromosome condensation is essential for genome integrity. When defective, chromosomes often remain entangled and fail to segregate properly in anaphase. A key driver of chromosome condensation is the highly conserved condensin complex. Condensin is made of five sub-units  and sen1 (sen1Δ) in vitroIn order to clarify the functional relationships between transcription and chromosome condensation, we recently carried out a genetic screen in fission yeast to identify deletions of transcription-associated factors that would rescue a condensin deficiency swd2.2 (swd2.2Δ) and sen1 (sen1Δ) partly restored growth of cut3-477 cells at the restrictive temperature (sen1Δswd2.2Δ) resulted in a stronger suppressor effect  were comparable , consistent with the observations reported previously that lack of Top1 triggers the accumulation of R-Loops at rDNA in budding yeast To establish whether or not R-Loops form at Pol III-transcribed genes in fission yeast, we first monitored by ChIP the chromatin association of RNase H1, one of the endogenous enzymes known to disassemble R-Loops. More specifically, we introduced at the endogenous locus a point mutation (D129N) in the fission yeast RNase H1 (Rnh1), because the same mutation was shown to weaken the catalytic activity of human RNase H1 m E.coli . Upon ovm E.coli , showingin vitro was greatly diminished both by formaldehyde cross-linking and by sonication (rnh1Δrnh201Δ cells) and disappeared almost entirely upon treatment of the genomic DNA with commercial RNase H  and in cells synchronized in early mitotis . ConsistFinally, lack of Swd2.2 and Sen1 resulted in a small but significant increase in the formation of R-Loops at some but not all Pol III-transcribed genes . Note hoswd2.2Δsen1Δ cells  at most loci  as a suppressor of cut3-477swd2.2Δsen1Δtop1Δ was a better suppressor of cut3-477 than the double deletion swd2.2Δsen1Δ. This genetic evidence suggested that failure to monitor topological stress in top1Δ cells might facilitate the association/function of condensin. In support of this, ChIP analysis showed that there was a small but significant increase in the association of condensin at most Pol III-transcribed genes in cells deleted for Swd2.2, Sen1 and Top1 (swd2.2Δsen1Δtop1Δ cells)  . Taken tΔ cells) .top2-191  for which condensin has been shown to display high affinity in vitro with positively supercoiled plasmids How could topological stress create a better binding site for condensin at Pol III-transcribed genes in the absence of Swd2.2 and Sen1? First, condensin might simply have a higher affinity for supercoiled chromatin, as suggested by the observation that condensin associates preferentially in vivo. We presented evidence that the commonly used S9.6 ChIP method to map R-Loop forming regions in yeast is challenged by the fact that R-Loops, or at least their recognition by the S9.6 antibody, are partly sensitive to formaldehyde cross-linking and sonication. To circumvent this problem, we have developed two reliable alternatives to map R-Loop forming regions in fission yeast. Both of our methods concur to demonstrate that RNA-Pol III transcribed genes are major R-Loop forming regions in fission yeast. R-Loops have also been detected at Pol III-transcribed genes in budding yeast , whilst the DRIP signals at the rDNA (18S) are significant in wild-type cells, when RNase H1 and RNase H2 are fully active. At Pol III-transcribed genes, DRIP signals increase 10-20 fold in rnh1Δrnh201Δ cells, whilst they only increase ∼3-fold at the rDNA (18S). Our interpretation of these data is that R-Loops formed at 18S are stable and a relatively poor substrate for RNase H1, whilst R-Loops formed at Pol III-transcribed genes are unstable and a good substrate for RNase H1. A corollary to these observations is that DRIP is probably better suited to detect long-lived, stable R-Loops. This might explain why DRIP did not detect significant R-Loop formation at Pol III-transcribed genes in human cells  was not sufficient to significantly improve chromosome segregation when condensin was deficient R-Loop formation has been shown to be associated with increased phosphorylation of histone H3 on Serine 10 and reduced chromatin accessibility Our data have highlighted unexpected ways by which proteins involved in the metabolism of RNA can affect chromosome segregation and genome integrity. Published data demonstrated conclusively that mutations in such factors in general and in Sen1 in particular resulted in chromosome instability (CIN) in yeast, in a mechanism involving R-Loop formation antagonizing replication fork progression . Here onIn addition, our data show that Swd2.2 and Sen1 keep topological stress under control at Pol III-transcribed genes. We speculate that the enhanced transcription at Pol III-transcription associated with lack of Swd2.2 and Sen1 could contribute to such stress. However, we cannot exclude the possibility that RNA Pol III-dependent transcription is also defective in other ways that could explain the accumulation of topological stress when Swd2.2 and Sen1 are missing. The answer to this question will require further studies.in vitro approaches demonstrated unequivocally that budding yeast Sen1 contributes to transcription termination of some RNA Pol II transcripts . In order to stably integrate the plasmid in the genome, it was linearized by digestion with MluI and then transformed in to yeast according to standard procedures.A complete list of all of the strains used in this study is given in 8 cells were treated with 1% formaldehyde (Sigma) at 17°C for 30′. After extensive washes with cold PBS, cells were frozen in liquid Nitrogen. Frozen cells were then broken open using a RETSCH MM400 Mill and then resuspended in cold lysis buffer . The lysats were then sonicated at 4°C using a Diagenode sonicator. Immuno-precipitation was done overnight at 4°C using Protein A-coupled Dynabeads previously incubated with the anti-GFP A11122 antibody (Invitrogen) or using Protein G-coupled Dynabeads previously incubated with the anti-myc 9E10 antibody (Sigma) according to the manufacturer's instructions. Beads were washed successively with (5′ incubation on rotating wheel): Wash I buffer , Wash II buffer  and Wash III buffer . After two additional washes in TE pH 8, the beads were resuspended in 10% Chelex resin (Biorad) and incubated at 98°C for 10′. After addition of 2 µL of 10 mg/mL of proteinase K, the mixture was incubated at 43°C for 1 hour, then for another 10 mn at 98°C. After centrifugation, the supernatant was collected and analyzed by qPCR.1,5.108 cells were frozen in liquid nitrogen, broken open using a RETSCH MM400 Mill and then resuspended in cold lysis buffer . After phenol/chloroform purification and ethanol precipitation, the DNA was resuspended in TE pH 8 and split into two samples. Both samples were digested with BsrGI, EcoRI, HindIII, SspI and XbaI according to the manufacturer's instructions and RNase H was added to one of the two samples. After digestion, each sample was divided into two and incubated overnight at 4°C in IP buffer  in the presence of either Protein A-coupled Dynabeads or Protein A-coupled Dynabeads previously incubated with the S9.6 antibody according to the manufacturer's instructions. The beads were then washed three times in IP buffer. After two additional washes in TE pH 8, the beads were resuspended in 10% Chelex resin (Biorad) and incubated at 98°C for 5′. After addition of 2 µL of 10 mg/mL of proteinase K, the mixture was incubated at 43°C for 30′, then for another 5′ at 98°C. After centrifugation, the supernatant was collected and analyzed by qPCR.8.109 cells expressing GFP-tagged Sen1 from the endogenous locus. After immuno-precipitation with 15 µL of magnetic beads, the beads were washed three times with 1 mL of lysis buffer and twice with 1 mL of PBS containing 0,02% Tween. The beads samples were then subjected to in-solution reduction, carbamidomethylation and tryptic digestion. After acidification with 10%Trifluoroacetic Acid the samples were centrifuged 3 times to eliminate the beads.Immunoprecipitation was carried out as described previously Peptide sequences were determined by mass spectrometry performed using a LTQ Velos instrument  equipped with a nanospray source (Thermo Fisher Scientific) and coupled to a U3000 nanoLC system (Thermo Fisher Scientific). A MS survey scan was acquired over the m/z range 400–1600 in Enhanced resolution mode. The MS/MS scans were acquired in Normal resolution mode over the m/z range 65–2000 for the 20 most intense MS ions with a charge of 2 or more and with a collision energy set to 35eV. The spectra were recorded using dynamic exclusion of previously analyzed ions for 0.5 min with 50 millimass units (mmu) of mass tolerance. The peptide separation was obtained on a C18 PepMap micro-precolumn  and a C18 PepMap nanocolumn  using a linear 90 min gradient from 0 to 40%, where solvent A was 0.1% HCOOH in H2O/CH3CN (95/5) and solvent B was 0.1% HCOOH in H2O/CH3CN (20/80) at 300 nL/min flow rate.Schizosaccharomyces pombe species [UniProt release 2013_12].Protein identification was performed using the MASCOT Algorithm from the Proteome Discoverer software v1.1 (Thermo Fisher Scientific) against the UniProtKB database reduced to These were performed as previously described Figure S1cut14-208 mutant of condensin. Serial dilutions of the indicated strains were plated on rich media at the indicated temperaturesLack of Swd2.2 and Sen1 suppresses the growth defect of the (TIF)Click here for additional data file.Figure S2Sen1 associates with RNA Pol III but not with TFIIIC. Flag-tagged Sen1 co-immunoprecipitates with myc-tagged RNA Pol III component Rpc25 but not with myc-tagged TFIIIC component Sfc6. Whole cell extracts (WCE) and the immuno-precipitated material (Flag IP) of the indicated strains were analyzed by western blot.(TIF)Click here for additional data file.Figure S3A. ChIP-qPCR of the indicated strains grown in cycling conditions, at the indicated loci . B. Western blot analysis of the stability of Rpc25-13myc.The RNA Pol III subunit Rpc25 becomes more abundant at Pol III-transcribed genes when Sen1 is missing. (TIF)Click here for additional data file.Figure S4nmt1 promoter . After cross-linking and sonication, the whole cell extract was divided in two. In one half, the endogenous GFP-tagged Rnh1D129N was immuno-precipitated with a GFP antibody. In the other half, the over-expressed Flag-tagged RnhA was immuno-precipitated with a Flag antibody. For comparison, values were normalized to the enrichment obtained at 18S.Over-expressed RnhA does not replace the endogenous Rnh1 at Pol III-transcribed genes. ChIP qPCR analysis of the indicated strains grown in minimum medium to drive the over-expression of RnhA by the (TIF)Click here for additional data file.Figure S5R-Loops accumulate at 18S in the absence of Top1. ChIP qPCR of the indicated strains grown in cycling conditions at the indicated loci .(TIF)Click here for additional data file.Figure S6AIRN gene were generated after transcription in vitro with the T3 polymerase as described previously in vitro transcription were then digested with RNase A, sonicated for 10′ and/or cross-linked with 1% formaldehyde for 10′. The DNA was purified and either (A) run on an agarose gel without Ethidium bromide or (B) analyzed by dot-blot using the S9.6 antibody.The amount of R-Loops detected by the S9.6 antibody is reduced after sonication and formaldehyde cross-linking. R-Loops derived from the mouse (TIF)Click here for additional data file.Figure S7nda3KM311 (mitotic) cells were grown at the restrictive temperature of 17°C for 6 hours to synchronize cells prior to anaphase onset. ChIP-qPCR was performed to monitor the association of (A) Rnh1D129N-GFP or (B) Rpc25-13myc at the indicated loci .R-Loop form at Pol III-transcribed genes in early mitotic cells. nda3+ (cycling) and (TIF)Click here for additional data file.Figure S8Lack of Swd2.2 and Sen1 results in a small but significant accumulation of R-Loops at Pol III-transcribed genes. ChIP qPCR of the indicated strains grown in cycling conditions at the indicated loci .(TIF)Click here for additional data file.Figure S9A. Cells carrying the cold-sensitive nda3-KM311 mutation were grown in minimum medium lacking thiamine for a minimum of 18 hours to drive the over-expression of RnhA by the nmt1 promoter and then shifted at 17°C for 6 hours to synchronize them prior to anaphase onset. ChIP-qPCR was then performed to analyze the association of GFP-tagged Cut3 with chromatin . B. Mitotic indexes of the cell populations in A as established by scoring the number of Cut3-GFP-positive nuclei.RnhA over-expression does not displace condensin from chromatin in mitotic cells. (TIF)Click here for additional data file.Figure S10A. ChIP qPCR of the indicated strains grown in cycling conditions in minimum medium at the indicated loci . B. ChIP qPCR of the indicated strains grown in cycling conditions in minimum medium at the indicated loci .RnhA over-expression does not impact the association of RNA Pol III or Sen1 at Pol III-transcribed genes. (TIF)Click here for additional data file.Figure S11RnhA over-expression impacts the association of Top2 with chromatin differently at the rDNA and at Pol III-transcribed genes. ChIP qPCR of the indicated strains grown in cycling conditions in minimum medium at the indicated loci .(TIF)Click here for additional data file.Figure S12top2-191 and top1Δ. A ChIP qPCR of the indicated strains grown in cycling conditions at the semi-restrictive temperature of 28°C at the indicated loci . B. ChIP qPCR of the indicated strains grown in cycling conditions at the semi-restrictive temperature of 30°C at the indicated loci .The association of condensin with chromatin is not significantly altered in the topoisomerase mutants (TIF)Click here for additional data file.Table S1Primers used in this study.(XLSX)Click here for additional data file.Table S2Identification by mass-spectrometry analysis of the proteins associated with Sen1 in fission yeast.(XLSX)Click here for additional data file.Table S3Yeast strains used in this study.(XLSX)Click here for additional data file.


The clear connection between ribosome biogenesis dysfunction and specific hematopoiesis-related disorders prompted us to examine the role of critical lineage-specific transcription factors in the transcriptional regulation of ribosomal protein (RP) genes during terminal erythroid differentiation. By applying EMSA and ChIP methodologies in mouse erythroleukemia cells we show that GATA1 and PU.1 bind in vitro and in vivo the proximal promoter region of the RPS19 gene which is frequently mutated in Diamond-Blackfan Anemia. Moreover, ChIPseq data analysis also demonstrates that several RP genes are enriched as potential GATA1 and PU.1 gene targets in mouse and human erythroid cells, with GATA1 binding showing an association with higher ribosomal protein gene expression levels during terminal erythroid differentiation in human and mouse. Our results suggest that RP gene expression and hence balanced ribosome biosynthesis may be specifically and selectively regulated by lineage specific transcription factors during hematopoiesis, a finding which may be clinically relevant to ribosomopathies. Ribosome biogenesis is a highly coordinated process leading to the stoichiometric assembly of all ribosomal components. In eukaryotes, 4 rRNAs and 80 different ribosomal proteins (RPs) are produced, processed and assembled into functional ribosomes , 2. RP b-) syndrome, an acquired hematological disorder [Despite ubiquitous RP gene expression and functions across all tissues, RP gene haploinsufficiency leading to perturbation of balanced ribosome assembly results in clinical syndromes with highly specific phenotypes in man, including bone marrow aplasia and cancer susceptibility . For exadisorder , 9. In adisorder –12. DBA disorder –15. It idisorder , 17.A number of non-mutually exclusive mechanisms have been proposed to account for the hematopoietic specificity of DBA, including an increased sensitivity of erythroid precursors to apoptosis and the high demands imposed on protein synthesis by hemoglobin accumulation , 18, 19.Here, we describe the binding of several RP genes, including genes mutated in DBA, by the GATA1 and PU.1 TFs in murine erythroleukemic (MEL) cells, a well characterized cellular model of erythropoiesis. GATA1 and PU.1 are considered master regulators of the erythroid and myeloid-lymphoid lineage transcription programs, respectively, and are known to be functionally cross-antagonistic . We alsoMouse erythroleukemic (MEL) cells (clone 745-PC–4) were derived from the MEL 745 cell line, originally isolated by Friend et al.  and wereTotal MEL cell extracts were prepared by lysis with RIPA buffer with protease inhibitors, followed by sonication of 5cycles/20seconds each and collection of the supernatant by centrifugation. Nuclear extracts were prepared using the Dignam method . ProteinWestern blot analysis was performed using 10–20μg of protein/sample, as previously described . PrimaryEMSA reactions using 10μg of MEL nuclear extracts were carried out as previously indicated . Supersh7 MEL cells and 4μg each of anti-GATA1  and anti-PU.1 . ChIP assays were carried out in biological replicates.ChIP assays were essentially performed as previously described  using chT values for all samples were normalized to GAPDH and absolute enrichments were calculated versus input, as previously described [Real time PCR was carried out in triplicate with ChIPed DNA and 400nM specific primers using a SYBR Green kit (Kappa Biosystems). ChIP primer sequences are shown in escribed . Gene ex5’- AGGTGGTGGTGGCCACATGTCAT–3’ and Reverse: 5’- GTGCTCGCGAGAGCGGCTAAA -3’ (-959bp to -559bp relative to the ATG). The amplified product was sequenced (accession # LN651201) and scanned for GATA1 and PU.1 binding sites using bioinformatics tools available at http://snpper.chip.org/mapper, http://labmom.com/link/alibaba_2_1_tf_binding_prediction.The RPS19 proximal promoter region was amplified form MEL genomic DNA using primers Forward: GATA1 genome wide occupancies in mouse fetal liver derived erythroid cells have been previously published by our group . All othWe initially investigated the mouse RPS19 proximal promoter region experimentally verified from MEL genomic DNA for the presence of consensus binding motifs using standard TF binding prediction tools. This region has been previously investigated by Da Costa and colleagues and shown to be highly conserved between human and mouse . We idenWe next tested whether GATA1 and PU.1 could bind to the RPS19 proximal promoter region by EMSA using MEL nuclear extracts. MEL cells are spleen virus-transformed hematopoietic cells blocked at the proerythroblast stage of erythroid differentiation which, upon exposure to inducers such as HMBA, undergo the erythroid differentiation program  were employed to test TF binding  by EMSA , lane P  by EMSA , lane Ps by EMSA , lane Pm by EMSA , PU.1 bivice versa, suggesting independent binding of the two TFs to the probe. Furthermore, when using probes mutated for the binding of either factor  and in differentiating (6–96 hours) MEL cells , by inspecting GATA1 and PU.1 genome occupancy profiles in mouse fetal liver [Our observations on GATA1 and PU.1 binding to the murine RPS19 gene promoter are significant in light of the RPS19 gene being mutated in ~25% of DBA cases. We thus assessed al liver  and mEs-al liver , respectDue to their clinical relevance, we also surveyed GATA1 occupancies of DBA-associated RP genes in human fetal and adult-derived erythroid cells Fig 3C)Fig 3C). - syndrome   . The ass RPL35a) . Interes RPL35a) , also co RPL35a) . RPS14 a RPL35a) . Overall RPL35a) . Express RPL35a) . In concin silico analysis for GATA1 occupancies to include all RP genes in mouse fetal liver-derived [We next expanded our -derived  and huma-derived  erythroi-derived , however-derived .In human erythroid cells, several RP genes also appear to be GATA1 targets . Again, We also interrogated ChIPseq data for PU.1 binding profiles in hematopoietic lineages where PU.1 is physiologically active, for example, in macrophages and in lymphoid cells . It is eWe next sought to identify the relationship between GATA1 occupancy and RP gene expression profiles during erythroid differentiation by analyzing RNAseq data of sequential erythroid differentiation stages in mouse and human cells . We findLastly, we quantified the absolute expression levels of GATA1-occupied RP genes. This revealed a clear association of GATA1 binding with the higher expressing RP genes. This is maintained throughout erythroid differentiation and is evident in both mouse  and huma- syndrome, isolated congenital asplenia and DBA [Haploinsufficiency in specific RP genes perturbing balanced ribosome biogenesis, invariably results in specific hematopoietic disorders such as the 5q and DBA , 47. The and DBA . Recent  and DBA , 49. Fir and DBA , 23. In  and DBA , which i and DBA , 52. Howin vitro binding of GATA1 and PU.1 to these motifs in nuclear extracts from MEL cells. Furthermore, we confirmed these findings in vivo by ChIP assays using chromatin from MEL cells, further supported by ChIPseq data in mouse fetal liver erythropoiesis. We next used publically available ChIPseq data to extend our observations of GATA1 and/or PU.1 erythroid-specific occupancies to many additional RP genes and, moreover, we confirmed by ChIP the GATA1 and/or PU.1 binding in MEL chromatin for a subset of RP genes that are mutated in DBA. Importantly, our observations were also extended to human erythropoiesis where we observe higher GATA1 occupancies of RP genes in the adult compared to the fetal stage of erythropoiesis. We further describe a difference in the timing of RP gene transcriptional decline in mouse versus human erythroid differentiation, in that this appears to take place at a later differentiation stage in human cells. Lastly, we find that despite the overall decline in RP gene expression levels, GATA1 binding is associated with higher expressed RP genes throughout erythroid terminal differentiation stages in human and in mouse.Here, we asked whether lineage-specific hematopoietic TFs might be involved in RP gene regulation in erythroid cells. We initially used a computational approach to identify consensus binding motifs for the key hematopoietic TFs GATA1 and PU.1 in a region upstream of the start codon of the murine RPS19, at positions -694bp and -709bp. We verified by EMSA The GATA1 and PU.1 binding sites upstream of the RPS19 promoter that we describe here coincide with a region of high sequence conservation between the mouse and human Rps19 genes previously described by Da Costa et al.. The cluBy expanding our studies to include all RP genes using publicly available ChIPseq data in erythroid cells , we founHow might PU.1 and GATA1 binding serve to regulate RP gene expression in erythroid cells? Recent observations suggest that PU.1 acts primarily as an activator of non-erythroid genes in proliferating erythroid progenitors , 40. ThuOur data on the dynamic GATA1 occupancies of RP genes in erythroid cells raise the prospect of GATA1 directly regulating RP gene expression in the erythroid lineage, thus providing an additional mechanism for GATA1’s implication in DBA. It also raises the prospect that mutations in TF binding motifs in RP gene regulatory regions, which are not routinely screened in diagnosis, may be an alternative underlying cause of DBA. According to our model, mutations in GATA1 (or PU.1) binding motifs in RP gene regulatory elements resulting in reduced RP gene expression and/or mutations resulting in the production of GATA1s which may be deficient in properly regulating RP gene expression in erythroid differentiation, would lead to RP imbalance and disease . This moS1 Figmajor globin, GATA1, PU.1, and RPS19 genes in HMBA-induced MEL cell differentiation were analyzed by qRT-PCR analysis. (E) Western blot analysis for GATA1 and PU.1 protein levels in nuclear extracts isolated from either untreated or HMBA-treated MEL cells. Nucleophosmin was used as protein loading control.MEL cells grown in culture for 96 hr in the presence of HMBA (5mM) were assessed for their proliferation potential (A), cell viability (B), and differentiation capacity (accumulation of benzidine-positive stained hemoglobin-containing cells) (C) as described in Materials and Methods. (D) The expression profiles of β(PPT)Click here for additional data file.S2 Fig(A) GATA1 binding profiles in the RPS19 gene locus in proliferating and DMSO-induced MEL cells as well as in fetal liver derived Ter119- and Ter119+ immature and mature erythroid cells, respectively. (B) PU.1 binding profiles by ChIPseq to the RPS19 gene locus in two independent experiments (A or B) using differentiated MEL cells or mES-EPs .(PPT)Click here for additional data file.S3 Fig(PPT)Click here for additional data file.S4 Fig(A) mouse homologues of DBA associated or (B) all RP genes in mouse fetal liver derived Ter119- erythroblasts and Ter119+ mature erythroid cells. P value was calculated using the one sided Wilcoxon rank sum test.(PPT)Click here for additional data file.S1 TableOnly the forward single stranded oligonucleotide is depicted. TF binding sites appear underlined. Mutations introduced to abolish TF binding appear as bold letters.(DOC)Click here for additional data file.S2 Table(DOC)Click here for additional data file.S3 Table(DOC)Click here for additional data file.S4 Table(DOC)Click here for additional data file.S5 TableIndicated by asterisk are the RP genes that have been found mutated in DBA.(DOC)Click here for additional data file.


Most mammalian genes have mRNA variants due to alternative promoter usage, alternative splicing, and alternative cleavage and polyadenylation. Expression of alternative RNA isoforms has been found to be associated with tumorigenesis, proliferation and differentiation. Detection of condition-associated transcription variation requires association methods. Traditional association methods such as Pearson chi-square test and Fisher Exact test are single test methods and do not work on count data with replicates. Although the Cochran Mantel Haenszel (CMH) approach can handle replicated count data, our simulations showed that multiple CMH tests still had very low power. To identify condition-associated variation of transcription, we here proposed a ranking analysis of chi-squares (RAX2) for large-scale association analysis. RAX2 is a nonparametric method and has accurate and conservative estimation of FDR profile. Simulations demonstrated that RAX2 performs well in finding condition-associated transcription variants. We applied RAX2 to primary T-cell transcriptomic data and identified 1610 (16.3%) tags associated in transcription with immune stimulation at FDR < 0.05. Most of these tags also had differential expression. Analysis of two and three tags within genes revealed that under immune stimulation short RNA isoforms were preferably used. It has recently been revealed that alternative splicing and alternative cleavage and polyadenylation (ACP) is not only a universal post-transcription processing step in eukaryotic gene expression but also a versatile mechanism for post-transcriptional regulation of genes –3 . AfteHigh-expression switches from site A in condition 1 to site B in condition 2. This is a case that high expression of sites is associated with change in condition. If one just looks at the difference in expression of site A or site B between two conditions, then this is another case that one concerns differential expression of single sites between two conditions. In the first case only an association method can be used to test if sites A and B are associated with conditions 1 and 2 in expression but for the second case one can use differential methods to test for differential expression of each site between two conditions. So the use of differential analysis methods to identify usage and splicing switches from site to site on the same RNA unit is not ideal. To our best knowledge, no existing large-scale association analysis methodologies have been proposed. The traditional association methods such as Pearson chi-square test and Fisher exact test are single-test methods and work on the count data without replicates, they hence could not directly be applied to high-throughput transcriptomic count data of RNA reads with replicate libraries. The Cochran Mantel Haenszel (CMH) chi-square test approach may handle the data with replication, but it is a single test method, not a large-scale association test approach. This is why we need to develop a large-scale association statistical method for identifying tissue-associated or condition-associated isoform transcriptions. This method is named ranking analysis of chi-squares (RAX2). RAX2, which extends the traditional chi-square test to replicated and high-throughput transcriptomic count data of RNA reads derived from NGS, is based on comparison between a set of ranked chi-square statistics for association effects and a set of ranked null chi-square values across a set of given thresholds and estimation of a false discovery rate (FDR) profile.Human primary CD4 T cells were obtained from buffy coats derived from the Gulf Coast Regional Blood Bank via the EasySep Negative Selection Kit (Stem Cell Technologies) as per manufacturer's instructions. Purity (>90%) was assessed via flow cytometry for CD3 and CD4 . Cells were maintained in RPMI (ATCC) with 10% fetal bovine serum supplemented with 10 mM HEPES pH 7.4, nonessential amino acids, 2 mM L-glutamate, 50 μM β-mercaptoethanol, 100 units/ml penicillin and 100 μg/ml streptomycin . T-cells were stimulated with plate-bound antibodies (1 μg/ml anti-CD3 (OKT3 – eBiosciences), 5 μg/ml anti-CD28 (CD28.2 – BD Pharmingen). Activation of T-lymphocytes was verified via flow cytometric detection of CD69 expression (FN-50) 16 h after stimulation, and cells were harvested at 48 h.Total RNA was harvested from resting and stimulated cells with Trizol reagent (Life Technologies) as per manufacturer instructions. Polyadenylated RNA was isolated with the Poly(A)-Purist MAG (Ambion/Life Technologies) kit as per manufacturer instructions. High-throughput sequencing libraries were generated essentially as described , with thPaired end reads were mapped to the hg18 build of the human genome using the paired-end mapping module of BWA , defaultThe methods described in this paper, including estimations of null chi-square distribution and FDR, are implemented in software package RAX2. RAX2 was written in R. The current version of RAX2 is designed to analyze count data in two distinct states and the results are output in MS DOS csv format. Performance of RAX2 and the other statistical analysis process are given in Supplementary User Guidance for RAX2. The RAX2 package can be found in Supplementary Package or Bioconductor.g , gZ alternative poly(A) sites (gZ is number of poly(A) sites within gene g) correspondingly have gZ alternative tags. For example, gene AARS2 on chromosome 6 has four poly(A) sites(gZ = 4) and hence its mRNA transcription unit  has four isoforms or tags (see columns gene name and pos (position) in Supplementary Table S1). Supplementary Table S1 lists such count data of reads of tags within genes where columns 1–7 list information of tags or poly(A) sites within genes: tagid, geneid, name, DNA strand, position on transcription unit and annotation; columns 8–10 list count data of cells in normal and stimulated states. The data were normalized so that they look decimal. Here, we let z  in cell state j in replicate v . Our concern here is if change in count of a tag z at a special poly(A) site within a gene of interest is associated with change in condition (cell state) in the context of a discontinuous process. For the convenience, we focus on two states: treatment state (TS) and normal state (NS). We set j = 1 for NS and j = 2 for TS. To find if the transcription variation of an individual tag is associated with change in cell state, we need to compare the count of tag z to that of all the other tags  site has two states: z and i = 1 for z and i = 2 for z within gene g in tag state i in condition j in replicate v  and there are g. Since our interest is in regard to tag z (or poly(A) site z) within gene g instead of gene g itself, totally we have g and z into s where s = 1, …, S. Thus, each tag s has two states  site s and cell state j and i of poly(A) site s and cell state j in replicate observation v. Since sijn and sijve, respectively. As s in state i and in cell state j, s within g across two cell states and two tag states over all replicate libraries. Thus, model  site s in tag state i and cell state j equals expectation of association effect ((See Supplementary Note S1 for detail), that is, expectation of mean count . From Equation , we can s and g, and r replicates in cell state j. Set i = 1 for s and i = 2 for s in cell state j. Thus the null chi-square is easily estimated by Similarly to using within-group variance to estimate null variance, we also employ within-group chi-square to estimate null chi-square. To this end, we need to construct another contingent table in which we have site states s* in the ranking linear space(*), the chi-square values are smallest at s* = 1 and largest at s* = S*. Then we can compare s* in a one-to-one fashion. Given threshold Δ, expression change of tag s* is declared to be associated with change in cell state if and only ifSince NΔ) of tags whose expression changes are declared to be uniquely associated with change in cell state may consist of the number of truly positive tags, TΔ, and number of falsely positive tags, FΔ:In large-scale statistical analysis, we do not need to be concerned with so-called type 1 error but we must consider how to control false discovery rate (FDR) because in, for example, 10 000 hypotheses to be tested, at least 500 hypotheses would commit type I error if α = 0.05 is chosen as significance level . ObviousFΔ is unknown in experimental datasets, FDR must be estimated.Therefore, FDR is expected as P-values obtained from chi-square distribution are also biased. For this reason, we here propose a novel method. The principle of the method is shown in Figure The existing methods for estimation of FDR are not suitable to our current chi-square statistics because, as seen above, chi-square statistics are remarkably biased against standard chi-square statistics such that a in the ranking linear space  would not alone fit to changeable false-positive probability from practical data; while t* is chosen as false positive. Additionally, in a single hypothesis test the P-value of statistic is negatively related to sample size, similarly, the probability that the null chi-square at position t* is chosen as false-positive chi-square is also negatively related to sample size because increment of sample size would let space of t* is chosen as a false-positive tag is given byIn order to avoid confusion, x value that causes a smaller The number of false positives in S tags with r replicates based on the real data in a group:We use the following steps to generate the null data of r replicates for each tag within a gene.Step1: Calculate four variances and four means from two-by-two tables over Step2: Choose randomly one variance and one mean with equal probability for each tag within a gene.r replicates using negative binomial pseudorandom generator in R environment with the mean as mu and the variance as dispersion (size).Step 3: Generate a two-by-two random null or baseline count data with g = G.Step 4: Repeat steps 1–3 until σ = 50 and mean = 100  within gene g, i = 1 and 2, j = 1 and 2, z = 1, …, Zg. The association effect We then adopted uniform and normal pseudorandom generators to generate count data with association effects:r = 6, x = 0 and x = 0 , FDR was significantly overestimated in all three given scenarios such that many tags with true association effects would be missed at FDR <0.05 level. In contrast, with r = 3 and 10, 20 and 30% of tags having association effect, respectively. The fact that these FDR curves are very similar to those in Supplementary Figure S1B1, B2 and B3 indicates that our estimate of FDR profile with Supplementary Figure S1 summarizes these results in the simulated scenarios where 10% (A1 and B1), 20%, (A2 and B2) and 30% (A3 and B3) of tags have association effects on transcription with cell states and r replicate libraries to construct a two-by-two table for each tag. These association test methods were performed in R packages , the BH procedure was conducted in R package p.adjust with method = BH. Package qvalue . As indicated above, Pearson chi-square based on mean counts over replicates is not unbiased and the P-value for such chi-square obtained from a chi-square distribution is greatly biased against its true value (See Supplementary Table S2). So BH and q-value procedures are not available to adjust such biased P-value profiles.In order to show strong advantages of our method, we applied Fisher exact test, Pearson chi-square test and CMH chi-square test approaches to the simulated data and used Benjamini-Hochberg (BH) multiple-testing procedure ,35 and qe qvalue ,37 was d+ T cells (Supplementary Table S1). After normalizing and filtering, the data contain 16247 tags scattered in 10160 transcription units or genes. Transcription units with a single tag were filtered and the remaindered 9899 tags scattered in 3812 genes were available for RAX2 analysis. We performed classical chi-square and the BH- procedure on our primary T-cell transcriptomic data. As expected in Supplementary Figures S2, only 747 tags were found to be associated with cell states at FDR < 0.05 (see Supplementary Table S4). We therefore applied RAX2 to the real data. The estimated null chi-square value falls in a range of 0 to 6.7  tags were found to be associated in transcription with antigen receptor stimulation (Supplementary Table S3). Among 1610 tags, 1279 have fold change >1.4 or < 0.715. Heatmap shows that these 1279 associated tags also displayed strong differential expression between rest and stimulation Figure  where foet al.  found thet al.  is complIn our result, 162 genes were found to have three poly(A) sites whose usages were associated with stimulation (Supplementary Table S8). Supplementary Figure S3 displays three scatter plots of these genes with proximal tags versus middle tags , proximal tags versus distal tags  and middle tags versus distal tags . In plot of proximal versus middle tags , accordantly changed tags were many more than switched tags . Among accordantly changed tags, proximal and middle tags positively responding to stimulation are more than those negatively responding to stimulation . This result suggests that stimulation indeed raised transcription of tags but did not significantly trigger transcription switch between proximal and middle poly(A) sites. However, Supplementary Figures S3A2 and S3A3 definitely show that stimulation remarkably increased transcription switch from distal poly(A) sites backward to middle poly(A) site  or backward to proximal poly(A) sites  while positively and negatively accordant transcriptions between proximal and distal poly(A) sites and between middle and distal poly(A) sites became very weak. In genes with proximal and middle poly(A) sites, positive tags were more than negative tags but in genes with distal poly(A) sites, negative tags were more than positive tags . These results strongly indicate that, as seen in genes with two tags, in genes with three tags, CD3/28 cosimulation resulted in more usage of shorter tags.Transcriptional profiling via NGS technologies has underscored the complexity of transcript isoform variation in mammalian cells. The extent of this variation, whether the identity and function of a protein product  or the visibility of the gene product is altered due to the posttranscriptional regulatory machinery (e.g. ACP), has been broadly appreciated. It is of great interest to leverage newer technologies to globally assess the impact of these processes on human disease, particularly since several individual examples in which dysregulation of either process contributes to human disease exist .The identification of relative expression changes of individual mRNA isoforms derived from a single transcription unit can in theory be performed by considering each of these isoforms from the transcription unit as a single entity and applying statistical approaches DESeq , baySeq P-values of chi-square statistics. To avoid this puzzle issue, a nonparametric approach is preferably chosen. This approach requires that an observed Pearson chi-square profile be compared to the null Pearson chi-square profile given a threshold. Since null chi-square profile does not exist, it must be estimated. In our methodology, we adopt a manner similar to estimation of null variance from replicate within-cell data to estimate the null chi-square profile. To choose appropriate threshold, we need to estimate FDR cutoff within which the findings declared by comparison between observed and null chi-square profiles are believed to be reliable.Therefore, we need to develop a novel chi-square test methodology. Within the context of NGS datasets where individual transcription units have a potential to be characterized by the production of multiple mRNA isoforms, it is not clear what the relationship is between replication and the P-value profile and are not suitable to our nonparametric method. The permutation-based estimator developed by Tusher et al. (P-values (k. As seen in Figure While there are several alternative approaches for estimation of FDR, for example, the Benjamini-Hochberg procedure ,35, the r et al.  has beenr et al. ,42. Storr et al.  proposedP-values ,37. The P-values –43 are dSupplementary Data are available at NAR Online.