Arabidopsis thaliana and referred to as cryptic TSSs. Many have not been identified in previous studies, of which up to 65% are contributed by transposons. They possess similar genetic features to regular TSSs and their activation is strongly associated with the ectopic recruitment of RNAPII machinery. The activation of cryptic TSSs significantly alters transcription of nearby TSSs, including those of genes important for development and stress responses. Our study, therefore, sheds light on the role of epigenetic regulation in maintaining proper gene functions in plants by suppressing transcription from cryptic TSSs.In plants, epigenetic regulation is critical for silencing transposons and maintaining proper gene expression. However, its impact on the genome-wide transcription initiation landscape remains elusive. By conducting a genome-wide analysis of transcription start sites (TSSs) using cap analysis of gene expression (CAGE) sequencing, we show that thousands of TSSs are exclusively activated in various epigenetic mutants of  Arabidopsis mutants defective in epigenetic regulation and show ectopic activation of thousands of cryptic TSSs and altered expression of nearby genes demonstrating the importance of suppressing spurious transcription.Epigenetic regulation can silence transposons and maintain gene expression. Here the authors survey  Due to their mobility, TEs induce various alterations to the host genome, ranging from genetic mutations to large-scale genomic rearrangements, such as inversions and translocations3. Genetic variations caused by TEs can introduce novel regulatory elements and therefore be a major driving force underlying genome evolution2. On the other hand, uncontrolled activities of TEs can severely damage gene expression and the integrity of the host genomes3.Eukaryotic genomes are comprised a large part of mobile genetic sequences, so-called transposable elements (TEs)5. Compared to mammals, plants are equipped with a different set of epigenetic mechanisms for greater adaptability to dynamic environmental changes, partly due to their sessile nature. For example, in mammalian genomes, DNA sequences are mainly methylated at the cytosine in the CG dinucleotides, while in plants cytosine methylation exists in both CG and non-CG contexts, which has different functional impacts on gene and TE regulation6.To suppress negative impacts without losing potential benefits brought in by TEs, both plants and animals have evolved numerous epigenetic mechanisms involving DNA methylation, histone modifications, and small non-coding RNAs, allowing TEs remain silenced in their genomesArabidopsis thaliana , DNA methylation is established de novo by the RNA-directed DNA methylation (RdDM) pathway, which requires the functional activity of PolIV and PolV, two plant-specific RNA polymerases5. After establishment, methylation patterns can be maintained by different factors depending on cytosine contexts. CG methylation is maintained by METHYLTRANSFERASE 1 (MET1), a plant homologue of the mammalian DNA (cytosine-5)-methyltransferase 1 (DNMT1). Maintenance of DNA methylation in CHG context, on the other hand, is facilitated by CHROMOMETHYLASE 3 (CMT3) in a positive feedback loop with the histone H3K9 methylase KRYPTONITE (KYP) (or SUPPRESSOR OF VARIEGATION 3-9 HOMOLOGUE 4 (SUVH4))7. Together with two of its paralogues, SUVH5 and SUVH6, KYP regulates the genome-wide accumulation of H3K9me2 and consequently, CHG methylation6. CHH methylation can be maintained by either CMT2 or DOMAINS REARRANGED METHYLASE 2 (DRM2) depending on the features of their targets, in which DRM2 often methylates short, euchromatic TEs, while CMT2 targets long TEs located in histone H1-containing heterochromatic regions with the help of chromatin remodeler DECREASED DNA METHYLATION 1 (DDM1)8. These epigenetic pathways in plants, however, are highly interwoven. For example, MET1 and CMT3 are involved in maintaining asymmetric methylation, while DMR2 and CMT2 may also affect DNA methylation in other contexts9.In the plant model 4. In plants, repressive modifications triggered by TE insertions within introns or promoter regions can attenuate or even turn off the expression of the associated genes12. At a global scale, genes harboring, or located close to, silenced TEs exhibit lower expression than their counterparts14. Due to such unfavorable impacts, plants have evolved specific pathways to keep transcription units clear of repressive modifications, or to tolerate the presence of such modifications when necessary. For example, in A. thaliana the Jumonji C (jmjC) domain-containing histone demethylase INCREASE IN BONSAI METHYLATION 1 (IBM1) prevents repressive H3K9 methylation and consequently, CHG methylation, from accumulating at actively transcribed genes15. On the other hand, host factors, such as INCREASE IN BONSAI METHYLATION 2 (IBM2) and Enhanced Downy Mildew 2 (EDM2) are required for proper transcription of genes containing heterochromatic domains17, likely due to the functional importance of these domains14.Epigenetic silencing of TEs inevitably confers regulatory impacts on gene expression, especially when TEs are located close to transcription units18 or cap analysis of gene expression (CAGE)19, has greatly advanced our understanding of gene regulation at a transcription initiation level. Studies employing these techniques have revealed both common and distinct features of the core promoters and their origin and regulation, in many organisms22. In mammals, for example, CAGE sequencing (CAGE-seq) analyses revealed that a large fraction of cell-type specific transcripts in stem and cancer cells originate from long terminal repeats (LTRs) of retroelements24. The loss of DNA methylation also causes spurious transcription within thousands of genes in mouse embryonic stem cells25. In addition, modulating DNA methylation and histone deacetylation pathways pervasively activates cryptic transcription start sites (TSSs) normally silenced in human cells26. These examples demonstrate the importance of epigenetic mechanisms in regulating transcription initiation in mammalian genomes.The development of high resolution 28. Previous studies have also revealed core promoter structures and sequence elements associated with plant TSSs31. However, these studies mainly focus on active TSSs in the wild-type background. The contribution of epigenetic regulation to shaping the genome-wide transcription initiation landscape and its functional significance in plants, therefore, remains largely unexplored.In plants, large-scale analyses have determined thousands of TSSs, providing fundamental information about genetic structure and regulatory elements important for transcription in plant genomesA. thaliana that compromise epigenetic control. Our analysis identifies thousands of TSSs exclusively activated in the mutant backgrounds, demonstrating that epigenetic regulation profoundly affects transcription initiation in Arabidopsis. These so-called cryptic TSSs are mainly located at heterochromatic regions, which hinder their accessibility to RNA Polymerase II (RNAPII) transcription machinery. The alteration of DNA methylation maintenance in met1 activates the largest number of cryptic TSSs, which significantly overlap with the targets regulated by other epigenetic pathways. A large fraction of cryptic TSSs originate from TEs of both retro and DNA-transposon families, suggesting that TEs are reservoirs of putative TSSs in the A. thaliana genome. Strikingly, the activation of cryptic TSSs significantly alters the regular transcription of nearby TSSs, which includes those of genes important for development and stress responses in Arabidopsis. This study, therefore, sheds light on the role of epigenetic regulation in maintaining proper gene functions in plants by suppressing transcription initiated from cryptic TSSs. In addition, the accompanying data are a valuable resource for studying the epigenetic control of the transcription of genes and TEs in plants.To dissect the functional impacts of epigenetic regulation in shaping the plant transcription initiation landscape, we employ CAGE-seq to generate the genome-wide profiles of TSSs at a high resolution for various mutants of A. thaliana mutants, where epigenetic control is compromised, including mutants of maintenance DNA methyltransferase met1, the chromatin remodeler ddm1, RdDM pathway components nrpd1 and nrpe1, histone H3K9 methyltransferases suvh456, histone H3K9 demethylase ibm1, and intragenic heterochromatin regulatory factors, ibm2 and edm2. A total of 1,250,203,294 CAGE-seq reads were mapped to the A. thaliana Col reference genome, achieving an average mapping efficiency of 97.53%. Of which, 402,814,394 reads were mapped uniquely, compiling a large collection of CAGE-seq data for this model plant  was highly correlated between replicates (the median of Pearson correlation coefficients was 0.95)  were located in promoters and 31. They were indeed consistent even though the samples were prepared from different tissues  and sAPX (AT4G08390), was also reexamined. The former has three functional TSSs within its promoter and the latter has one upstream and one intragenic TSS21. Our data recapitulated these structures  methodT1 AT1G0830 and sAFLOWERING WAGENINGEN (FWA), causing a late flowering phenotype of Arabidopsis36. CAGE-seq analysis identified a TSS encoded within the SINE repeat, which was highly activated in met1 and ddm1 backgrounds , whose promoter contains a tandem repeat co-regulated by H3K9 methylation and the RdDM pathway8, was also detected by our data  induced stronger activation of the EPICATs than met1 . Moreover, the expression levels of EPICATs were not significantly different from those of the annotated TSSs activated de novo in epigenetic mutants  motifs around the TSSs , which is known to recruit development-associated repressive modification H3K27me3 in A. thaliana42, was also found at the EPICATs in met1, ddm1, and suvh456. The presence of the Telobox sequence around EPICATs may partially explain the accumulation of H3K27me3 at the heterochromatic regions upon the loss of DNA methylation and H3K9 methylation43.Ectopic transcription initiation in mutants and the convergence of various epigenetic pathways on a large number of EPICATs Fig. b, togethSSs Fig. . TATA-boA. thaliana genome harbors hundreds of potential TSSs equipped with functional core promoter architecture similar to that of regular TSSs. Their activities, however, are suppressed by repressive chromatin restricting their accessibility to transcription machinery.Taken together, we conclude that the A. thaliana, about 20% of protein coding genes accumulate CG methylation in their bodies44. Moreover, gene body methylation (gbM) is largely conserved across plant species, especially in angiosperms45, suggesting its functional importance. Although many hypotheses have been proposed regarding the biological functions of gbM, such as suppressing spurious intragenic transcription25, impeding transcriptional elongation46, or reducing transcription noise47, so far its role in plants has been largely elusive48. By exploiting the high resolution CAGE-seq data of genome-wide TSSs, we reexamined the relationship between gbM and intragenic transcription initiation in A. thaliana. Our data showed that, in wild-type plants, a similar fraction of both body methylated (BM) and non body methylated (non-BM) genes harbored intragenic TSSs, suggesting that the methylation state of gene body is not significantly associated with the occurrence of intragenic TSSs  feature PolII-like TSSs, PolIV and PolII target distinct genomic territoriesA. thaliana, has raised important questions regarding their origin. Investigations involving mammalian genomes have shown that TEs are a major genetic element that can be exapted as TSSs in the host genomes52. Although less prevalent, several lines of study have demonstrated a similar function of TEs in plant genomes54. Together with the evidence that EPICATs are mainly located at intergenic regions decorated with repressive chromatin modifications , while members of the LTR/Copia family were highly represented among the TE targets of ddm1 and suvh456 . In addition, the DNA/En-Spm family was highly associated with the EPICATs in met1, ddm1, and suvh456 . Due to the minor numbers of TE instances associated with the EPICATs in ibm1, pol4, and pol5, they were skipped from enrichment analysis. The data suggest that both retro- and DNA transposons are genetic suppliers of cryptic TSSs in the A. thaliana genome.There are numerous types of TEs with different origins and mobility strategiesies Fig. a, comparddm1 affected the largest number of TEs harboring EPICATs, and these elements largely overlapped with TEs activated in other mutants  shared splicing junctions with 75 reference gene units , a gene critical for vegetative shoot maturation57, COQ3 (AT2G30920), a gene encoding a mitochondria-localized methyltransferase important for ubiquinone biosynthesis and embryo development59, and a gene of unknown function (AT2G16050)  Fig. a, b. To 14, direct consequences of TE-encoded TSS activation on the surrounding transcriptional environment remain obscure. Inspection of the loci producing cryptic fusion transcripts revealed that some of them concurrently exhibited reduced transcription from their regular TSSs in the mutant backgrounds , and PRB1 (AT2G14580), a gene involved in response to abiotic stress in Arabidopsis60.Although it has been suggested that repressive chromatin associated with TE insertions potentially imposes negative impacts on the transcription of nearby genesnds Fig. d, e. Nevnds Fig. . Of the A. thaliana, both qualitatively and quantitatively.Taken together, these data demonstrate that the activation of cryptic TSSs has critical impacts on the transcriptome of A. thaliana using CAGE-seq. Compared to mammals, epigenetic mechanisms regulating transcription initiation in plants are much less clear, mainly due to a lack of suitable resources which allow the investigation of the alteration of transcription initiation under different conditions56. This study, therefore, provides valuable reference data for research communities to enlighten the impact of epigenetic regulation on transcription initiation landscapes in plants.To understand how transcription initiation in plants is epigenetically regulated, we have generated a comprehensive maps of TSSs in various epigenetic mutants of met1 regulates the largest number of targets  mediated by SET2 suppresses cryptic intragenic transcription initiation63. In plants, however, concurrent loss of both gbM and H3K36me3 does not show significant difference in transcription between (BM) and unmethylated (UM) loci48. On the other hand, regulation of cryptic transcription from intronic heterochromatin by the RdDM pathway64, and the suppression of intragenic antisense transcripts by histone H1 and DNA methylation65 have also recently been reported. These results suggest that plants may employ additional layers of epigenetic regulation to prevent spurious transcription initiation, especially in intragenic regions.In mammals, the loss of gene-body DNA methylation caused by ome Fig. a, b. RecAT2G16050 and SQN loci , a gene important for vegetative shoot maturation in Arabidopsis57, leads to ectopic production of aberrant transcripts and a decreased accumulation of the normal one  was obtained from the Arabidopsis Biological Resource Center (https://abrc.osu.edu). All the mutants are in Columbia (Col) background. The second generation of homozygous met1, ddm1, ibm1, ibm2, and edm2 were used for the RNA experiments described below. nrpd1a, nrpe1, and suvh456 were maintained as homozygous for at least three generations before the experiments. The seeds were germinated and grown on 1/2 Murashige and Skoog (MS) plate under long-day conditions  at 22 ∘C.met1, and two technical replicates of other samples were prepared for CAGE. Single end 75bp CAGE libraries were prepared and sequenced in DNAFORM . RNA quality was assessed by Bioanalyzer (Agilent) to ensure that the RIN (RNA integrity number) was over 7.0, and A260/280 and 260/230 ratios were over 1.7.For CAGE analysis, 10-to-12-day-old whole seedlings of wild-type Col and mutant plants were pooled for RNA extraction. Total RNA was extracted using RNAiso (TAKARA), and DNA was digested with TURBO DNase (Thermo Fisher Scientific), followed by purification by RNeasy Plant Minikit (QIAGEN). Four technical replicates of WT Col and 76 with the following parameters: HEADCROP:1, TRAILING:20, to remove nonspecific guanines38 and low quality bases at the read ends. These were then mapped to the Arabidopsis Col reference genome by HISAT2 (v2.0.0-beta)77, allowing up to ten alignments for a single read. Due to low mapping coverage, met1.4 replicate was excluded from further analysis. met1.3 was also discarded due to its low correlations with two other replicates (met1.1 and met1.2). Then, uniquely mapped reads were used to identify TSSs at a single base resolution (CTSSs) by CAGEr (v1.20.0)78 with the following parameters: sequencingQualityThreshold = 20, mappingQualityThreshold = 20. After being normalized to Tags Per Million (TPM), CTSSs in each sample were grouped into tag clusters by the paraclu method, with threshold = 0.1, nrPassThreshold = 2, removeSingletons = TRUE, keepSingletonAbove = 0.3, minStability = 2, maxLength = 100. Finally, tag clusters from individual samples were merged into a common set of consensus tag clusters by the aggregateTagCluster function, with threshold = 0.3, qLow = NULL, qUP = NULL, maxDist = 100, excludeSignalBelowThreshold = TRUE. Each consensus tag cluster was then considered a single reliable TSS, represented by its dominant CTSS, to distinguish from the TSSs annotated by TAIR10. Promoter width was defined by the distance between the 10th (qLow = 0.1) and 90th (qUp = 0.9) quantiles of the cummulative distribution of CAGE signal along each tag cluster, as described in ref. 78. Raw tag counts were used to identify differentially expressed TSSs in the mutants compared to wild-type plants by DESeq2 (v1.22.2)79, with significance cut-off threshold padj ≤ 0.1.The CAGE sequencing (CAGE-seq) data were processed as follows: sequencing reads were trimmed using Trimmomatic (v0.30)A. thaliana were obtained from ref. 14. Araport11 version of genome annotations were also downloaded from The Arabidopsis Information Resource (TAIR) (https://www.arabidopsis.org/). Promoters were defined as the regions of 1 kb upstream of the TAIR-annotated TSSs. A TSS identified by CAGE-seq was annotated based on genomic location of its dominant CTSS, in the following order: promoter, TAIR10 genome annotations of 19,891 TEs and 27,600 protein coding genes and non coding RNAs in 31. Then, the nearest distance between the dominant CTSS of each CAGE-seq tag cluster and the mode locations of PEAT TSSs in the same direction was calculated. PEAT TSSs, which exactly matched with CAGE-seq TSSs (distance = 0 nt), were used as the proxy to estimate interquantile widths for each shape category defined in ref. 31, including NP, broad with peak (BP), and weak peak (WP).TSSs identified by PEAT method were obtained from ref. 14 and processed as follows: reads were trimmed by Trimmomatic to remove sequencing bias and adapter sequences, then mapped to the Arabidopsis Col reference genome by HISAT2, allowing up to ten alignments for a read pair. The featureCounts function in the package Rsubread (v1.14.2)80 was used to identify the number of read pairs uniquely mapped to genes and TEs.Paired-end mRNA sequencing (mRNA-seq) data were prepared following the method described in ref. 81. Low-expressed transcripts  were then removed. The remaining transcripts from all samples were merged to create a unified set of transcripts. They were then compared to reference transcripts in TAIR10 by the cuffcompare function to identify splicing patterns. Differential promoter usage was assessed by the cuffdiff function.The outputs of mRNA-seq mapping were also used for transcript assembly as follows: first, transcripts of each individual sample were assembled by Cufflinks (v2.2.1)To identify assembled transcripts associated with EPICATs, overlap tests were conducted between the transcripts and genomic regions centering around the EPICATs’ dominant CTSSs , 50 mM NaCl, 0.1 M sucrose, 1% formaldehyde) for 20-min, followed by quenching by 125 mM Glycine. Nuclei isolation was performed as previously described83. PolII ChIP was performed for two replicates for each genotype (about 1 g tissue/IP) by SimpleChIP Plus Kit  according to the manufacturer’s instructions. Anti-RNA polymerase II CTD repeat YSPTSPS (phospho S2) (Abcam ab5095) and Anti-RNA polymerase II CTD repeat YSPTSPS (phospho S5) (Abcam ab5408) antibodies were used for IPs (4 μg/IP). Precipitated DNA samples were sequenced by Hiseq 4000 in the 150 bp paired-end mode in OIST SQC. Due to the large overlap between two reads, only one read (read 1) in each pair was used for downstream analysis. Reads were trimmed to remove sequencing bias and adapter sequences using Trimmomatic, then mapped to the Arabidopsis Col reference genome by Bowtie (v1.0.0)84. Reads mapped to an identical position were collapsed into a single read, and only the best alignment was kept for a read mapped to multiple locations. Mapping results were given in Supplementary Data ChIP sequencing (ChIP-seq) data of histone modifications, including H3K27me1/3, H3K9me2, H3K36me3, and H3K4me3, in wild-type plants were retrieved from a previous study85. Genomic locations of NRPD1 binding loci were then converted from TAIR8 to TAIR9 coordinates using the update_coordinates.pl script provided by TAIR. ChIP-seq data of RNAPII in pol4 and corresponding wild-type plants were obtained from ref. 50. These data were processed as described above. Preprocessed RNAPII Ser5P ChIP-seq data (in bigwig format) in pol5 were downloaded from ref. 64 and directly used for visualization.ChIP-seq data of PolIV (NRPD1) and the list of NRPD1 binding loci were obtained from ref. 9. High quality reads (q ≥ 28), trimmed to remove adapter effects and sequencing bias, were mapped to the Arabidopsis Col reference genome using Bismark (v0.12.1)86 allowing up to two mismatches. Bases covered by fewer than 3 reads were excluded, and only uniquely mapped reads were used for further analysis. Methylation levels were calculated using MethylKit (v0.5.7)87. The list of BM, intermediate methylated (IM), and unmethylated (UM) genes were obtained from ref. 44. To exclude the potential impacts of non-CG methylation on the activation of intragenic EPICATs, only met1-activated intragenic EPICATs with low (less than 10%) CHG methylation in the 101 bp regions centering around their dominant CTSSs were examined (Supplementary Data Whole-genome bisulfite sequencing (WGBS) MethylC-Seq data of wild-type plants and epigenetic mutants were retrieved from ref. nrpd1 mutant plants were obtained from ref. 85 and trimmed by TrimGalore (v0.4.5)88 with Cutadapt (v1.8.3)89, using the following parameters: stringency:4, quality:20, length:15, max_length:30. PolIV-dependent small RNAs (P4RNAs) longer than 27 nt in dcl2/3/4 and corresponding wild-type plants were obtained from ref. 50 and trimmed by Trimmomatic. These data were then mapped to the Arabidopsis Col reference genome by Bowtie (v1.0.0), allowing up to two mismatches. Only uniquely mapped reads were used for further analysis.Sequencing data of 24 nt small interference RNAs (siRNAs) in wild-type and 90.De novo motif analysis and search of motif instances were conducted using MEME suite (v4.11.2) with default parameters91. Several copies from each family were used to obtain consensus sequences of LTRs (Supplementary Data Arabidopsis genome (TAIR10) using BLAST (v2.0)92. BLAST hits shorter than 100 bp were discarded. LTR sequences were then aligned using ClustalW (v2.1)93, and edited using Jalview (v2.11.0)94.Gypsy family sequences were retrieved from the TAIR database and aligned to obtain the full-length sequence for each family. LTR regions were then determined by comparing 95, Integrated Genome Browser (IGB) (v9.1.2)96 with the Araport11 version of genome annotations, Excel, and the R package ggplot2 (v2.3.1)97. DNA methylation files were firstly converted from bedGraph into bigWig format by the bedGraphToBigWig function (http://hgdownload.soe.ucsc.edu/admin/exe/linux.x86_64/), then used to generate heatmap and metaplot figures using deepTools. mRNA-seq data were normalized to reads per million (RPM), and a single replicate was used to create IGB track. ChIP-seq signals were normalized to log2(ChIP/input), and a single replicate of RNAPII (both Ser5P and Ser2P) were used for visualization in IGB. Small RNA sequencing data and RNAPII ChIP-seq data with no input samples were normalized to counts per million (CPM).Figures were created using deepTools (v3.3.0)75. All primers used in this study are listed in Supplementary Data Further information on research design is available in the Supplementary InformationPeer Review FileDescription of Additional Supplementary FilesSupplementary Data 1Supplementary Data 2Supplementary Data 3Supplementary Data 4Supplementary Data 5Supplementary Data 6Supplementary Data 7Supplementary Data 8Supplementary Data 9Supplementary Data 10Supplementary Data 11Reporting Summary


However, while pipelines and best practices have been established for the analysis of a single experiment, there is still no consensus on the best way to perform an integrated analysis of multiple datasets in the same condition, in order to identify the most relevant and widespread regulatory modules composed by different transcription factors and cofactors. We present here a computational pipeline for this task, that integrates peak summit colocalization, a novel statistical framework for the evaluation of its significance, and motif enrichment analysis. We show examples of its application to ENCODE data, that led to the identification of relevant regulatory modules composed of different factors, as well as the organization on DNA of the binding motifs responsible for their recruitment.Chromatin immunoprecipitation followed by next-generation sequencing (ChIP-Seq) has opened new avenues of research in the genome-wide characterization of regulatory DNA-protein interactions at the genetic and epigenetic level. As a consequence, it has become the  Next-generation sequencing based assays have opened novel avenues of investigation in every aspect of research in genomics. In particular, they have become the standard in the genome-wide characterization of all the elements concurring to the regulation of gene transcription, like nucleosome positioning , DNA accAs a consequence, literally thousands of experiments have been published on one or more of the above aspects in different species and conditions, and large scale projects like ENCODE  and Roadad hoc for the protein or condition studied.Genome-wide NGS-based assays usually produce as a main result a list of genomic regions, where base pairs included in these regions satisfy the condition being tested, e.g., they are nucleosome-free, bound by a transcription factor, occupied by a nucleosome carrying a given histone modification, and so on. Further information can be associated with each region, as for example, its enrichment in the sequenced sample, that can be expressed according to different measures. Key factors for the reliability of the results produced are both wet lab protocols and the downstream bioinformatic analysis of the data, and indeed, as of today, a consensus has been reached for which are to be considered the best practices for both . HoweverIn this work we present a simple pipeline for the integrative analysis of any number of ChIP-Seq experiments for transcription factors (TFs) or cofactors. Each ChIP-Seq experiment returns a genome-wide map of the binding locations on DNA of the protein studied. While this phenomenon is usually represented as a single protein interacting with DNA, in reality different factors and cofactors form large protein complexes, binding DNA at distal and proximal regions, that recruit RNA polymerase and initiate transcription. Thus, it is of the utmost importance for obtaining a complete understanding of the mechanisms behind the regulation of transcription not to treat each factor as a separate entity, but to identify combinations of different factors binding DNA as a complex at the same loci of the genome, and evaluate if these coassociations can constitute widespread regulatory modules.Given the results of ChIP-Seq experiments for any number of different TFs or cofactors, our pipeline has been designed to answer the following questions: (1) Which are the combinations of factors and cofactors that are found with higher frequency on the genome? (2) Are the combinations found actually significant, that is, not resulting from random associations between different proteins but indeed found with high frequency on the genome, thus signaling a higher level of organization in transcriptional regulation? (3) Which are the recruiting rules on DNA, that is, which are the factors actually bound to DNA, and are there specific combinations  for their DNA-binding sites?These questions have become more and more relevant over the years, once large datasets, like the assays performed by the ENCODE project, have been released. Indeed, curated databases containing thousands of ChIP-Seq datasets for TFs in different species and conditions are now publicly available, like ChIP-Atlas  or ReMapTF colocalization on the genome has been already defined and tacked with different approaches, ever since the introduction of the first NGS-based assays, as for example in . Severalad hoc to assess both.An orthogonal approach is to analyze regions resulting from a single ChIP-Seq experiment for enrichment of sequence motifs known to represent sites be bound by other TFs, as for example in . CandidaThe overlap among two or more genome-wide datasets can formalized in different ways, both in the definition of common features and in the evaluation of the significance of the overlap found, as reviewed for example in .ChIP-Seq experiments for DNA binding proteins like TFs produce as output enriched regions usually called “peaks.” This is due to the experimental protocol preparing the DNA to be sequenced Figure 1Algorithms for “peak calling” thus return regions where the observed enrichment and respective signal is not considered to be due to random experimental noise . A typicsd base pairs from one another, with an approach similar to  TFs or cofactors as “colocalizing” on the genome is based instead on peak summits coordinates. In other words, we do not require two peak regions just to overlap, but we consider the location of the respective summits. We then define two peaks as “overlapping,” and the TFs to bind DNA in close proximity, if the respective summits are within milar to , where tmilar to , howeverhis kind , which acobinding peaks. This, in turn, usually corresponds to having the binding sites on DNA of the two factors located within a number of base pairs (bsd) not too much different from the sd distance. Or, alternatively, summit proximity could be due to only one of the two factors in contact with DNA, with the other one being anyway part of the same complex , given n and m.Let TF1 and TF2 be two TFs on which the cobinding analysis is performed; let N, denoting the number of regions across the genome available for TF binding, whose size equals our “cobinding” region size of 150 bps. A straightforward way to estimate N would be to count the overall number of regions bound by all the TFs active in the condition studied. This approach, however, would have the effect of underestimating N if binding data were available only for a limited number of TFs, i.e. several regions would not be included in the count simply because the ChIP-Seq experiments for the TFs binding them had not been performed.In our approach, we also define a constant N can be obtained from the number of nucleosome-free regions, and their respective size. Thus, we took advantage of maps of accessible DNA produced in several different cell lines in the framework of the ENCODE project, through digital genomic footprinting . Once again, the sole exception are stem cell lines, where the percentage is lower (70% in ENCODE H7-ESC cells), also because less TF ChIP-Seq experiments are available for this condition. The number of regions actually bound  is anyway larger, nearly twice as much as the other cell lines.Similar estimates can be derived for other species and taxa, since nucleosome occupancy and DNA accessibility data are available for all the most widely studied species, as for example in Drosophila , or ArabN can be thus derived by the number of regions of size sd found annotated as active promoter or enhancer. Also, the overlap between two TFs can be assessed in either subset of regions, thus identifying promoter- or enhancer-specific modules.An alternative, if data are available, could be to focus on regions annotated as active promoters or enhancers, as revealed by presence of specific histone marks or resulting from a genome segmentation approach like ChromHMM . An estiN has been produced, the probability of finding k cobinding peaks for two TFs by chance given n and m  can be computed with different approaches, for example with the hypergeometric distribution:Once an estimate for or the Poisson distribution:k higher than the expected value), or vice versa if k is lower than the expected value than the two TFs considered tend to avoid one another on the genome.where 2 = 108,241.This analysis is performed on every pair of experiments available. If several pairwise comparisons are performed, then the p-values should also be corrected for multiple testing. For example, in the results we present here we analyzed 329 ENCODE datasets for TFs in the K562 cell line, thus with 329 × 329 pairwise comparisons. We employed once again the most conservative choice, the Bonferroni correction, multiplying the p-values by 329The results of the pairwise comparisons described at the previous step can be further extended to modules composed by more than two TFs or cofactors.i and j, starting from the corresponding number of cobinding peaks k, and the respective p-value pij, as -log10pij if the observed overlap is higher than the expected value, log10pij (and hence a negative number) otherwise. The resulting values can be employed to represent the results as a heatmap, and clustering the heatmap can in turn highlight groups of TFs with significant pairwise overlaps, hence likely to be found together in the same regulatory module.An initial explorative analysis (see Results) is to define a colocalization score for each pair of experiments b, and determine whether other TFs tend to colocalize within its peaks. For every pair of TFs (TFi and TFj) different from TFb, this step is formalized as follows:Another approach we introduce is to choose a “base” TFbk the number of peaks for the base TFb;- Let ik and jk the number of cobinding regions with TFb of the two other TFs (TFi and TFj);- Let ijk be the number of cobinding regions for both TFi and TFj with TFb- Let i and TFj in correspondence to TFb binding sites can be assessed again with a statistical test as in normal pairwise comparison. That is, we can compute the probability of finding ijk cobinding regions for TFi, TFj and TFb, given ki, kj, and bk. The p-value can be computed again with a Poisson distribution:At this point, the significance of the cobinding of TFwhere b as “tether” on DNA. Once the base TFb has been chosen, this step can be performed by selecting only TFs that had a significant overlap with TFb at the previous step in their pairwise comparison with it.The resulting p-values can in turn be converted again into scores, with the respective clustering highlighting groups of TFs colocalizing, but this time with TFb, TFi, TFj, and so on.If necessary, this step can be iterated any number of times, e.g. assessing the significance of the overlap of a fourth TF given the cobinding regions of TFOnce a list of genomic regions bound simultaneously by two (or more) TFs has been produced, the next step is to determine if the respective binding sites are actually present on DNA, and if so if they present any arrangement, e.g., are found at a precise distance, further hinting at co-operative binding and interactions between the respective proteins.position specific frequency matrix, or profile, obtained by the alignment of a collection of binding sites for the TF , or binding sites of other TFs which show a clear genome-wide tendency to bind in association with it.- Local enrichment: the enrichment of the motif in the peak summit regions is compared to the regions immediately upstream and downstream of the summit regions themselves;- Positional bias: the localization of the most likely instance of the motif in each summit regions is identified, and the resulting distribution is compared to a theoretical uniform distribution.in vivo by one or more TFs, then the corresponding motifs should be the ones with the lowest global p-values among those employed in the analysis. Also, given the centrality of ChIP-Seq peaks, they should present a positional bias towards the middle of the regions. Otherwise, TFs for which no significant motif enrichment is found can be considered not to be directly binding DNA, although part of a complex in contact with DNA  as for example in recent works , which aPscanChIP also permits to perform a “motif centered” analysis. Once the first round of motif enrichment analysis has been completed in the neighborhood of peak summits, users can select one of the motifs resulting to be significantly enriched, and rerun the analysis centered this time on the most likely instance of the motif in each of the input regions. Regions containing a low quality instance for the motif chosen are automatically discarded. The idea is to replace the peak summit for the TF of interest with the most likely location of its actual binding site on DNA. Thus, if two or more TFs have their respective binding sites enriched in the regions, then the motif centered analysis is meant to highlight if there is also a preferential arrangement of their sites in the regions, signaled by the “positional bias” p-value output by PscanChIP. This fact is in turn a strong indicator that the corresponding TFs are likely to interact, require a precise arrangement for their binding sites on DNA, and thus influence the respective recruitment on DNA. Thus, by submitting to PscanChIP cobinding peak regions for two of more TFs, we can assess the enrichment and relative positions of the respective binding sites.Given a set of ChIP-Seq experiments for TFs and cofactors, and the corresponding peaks and summits, our pipeline can be thus summarized in the following steps:Compute the summit neighborhood overlap for each pair of TFs, and the corresponding p-values;convert the p-values into scores, and cluster the experiments according to the scores; this step is optional, but provides a quick overview of the results obtained;for selected pairs of TFs, define the recruitment and binding rules on DNA by submitting the list of peak summits of either one falling in cobinding regions to PscanChIP:If motifs for both TFs are found to be significantly enriched according to the global p-value, assess whether there is a preferential arrangement or spacing of the corresponding binding sites through motif centered analysis on either one, by checking whether PscanChIP reports a significant positional bias p-value (< 0.01) for the other; if so, the distribution of the distances between the two motifs can be further analyzed, starting from the relative motif position in each of the input regions reported by PscanChIP.If only one motif is found to be enriched, then the corresponding TF can be considered to be recruiting the other on DNA.If neither motif is enriched, then either the motifs employed are not correct for the TFs studied, or there might exist a third factor responsible for the recruitment of the two factors considered.The previous steps can be iterated in order to find significant triplets, quadruplets, and so on, of TFs, and the corresponding binding sites on DNA.Peak cobinding analysis can be easily implemented with in-house scripts, or with utilities like bedtools . A shellPscanChIP is available both through a dedicated web interface, or as a standalone software package. Both already contain the latest release of the JASPAR database. Users can anyway add to the already present collection their own profiles, e.g., the result of a motif discovery analysis on the regions of ChIP-Seq experiments with tools like MEME , HOMER  or Weedewww.encodeproject.org) as of 31st December of 2018. Each experiment has been performed in at least two replicates, whose consistency has been checked according to different metrics. Only experiments with consistent replicates have been released by ENCODE, with replicates merged into a single list of consensus peak and summit coordinates  for 492 experiments of transcription factors of cofactors in the K562 cell line were retrieved from the ENCODE data repository  , we filtered the datasets as follows: (1) Experiments on stimulated cells were not considered. (2) In case for the same TF experiments were performed with antibodies against both the wild-type protein and a tagged protein , only the former was kept. Finally, in case of redundant experiments for the same TF not satisfying any of the above conditions we proceeded as follows: (a) if an experiment contained less than 10,000 peaks, and less than half of the peaks of the other(s), it was discarded; (b) if the overlap among the remaining experiments was above 66% we kept the one with the highest number of peaks; otherwise all the experiments for the TF were discarded. Peak overlap was defined as for cobinding peaks, that is, the respective summits had to be located within 150 bps.After filtering, we obtained non redundant experiments for 329 TFs and other DNA binding proteins. The resulting list, with the respective ENCODE identifiers, is available as Sequence analysis was performed with PscanChIP version 1.3  using thA preliminary version of the pipeline we present had been applied to a comprehensive analysis of ENCODE ChIP-Seq data for transcription factors and cofactors in three different cell lines, focused on modules containing transcription factor NF-Y . NF-Y isWe consider NF-Y an excellent case study for several reasons. Its binding sites are functionally very well characterized from the genetic point of view, are in general important, and in some cases their presence in promoters is outright essential for the transcription of the corresponding genes. The binding site motif has a high information content, spanning 5 base pairs flanking the central CCAAT, for a total of 10 discriminatory base pairs. The motif is specific for only one complex, hence avoiding the daunting task of disentangling subtle differences in binding preferences among members of large TFs families.ENCODE data contain experiments for two of the subunits of the complex (NF-YA and NF-YB) in three cell lines. In each cell line, the number of peaks for NF-YA is always lower than NF-YB, and virtually all peaks for NF-YA overlap a peak for NF-YB. A more detailed analysis revealed that the peaks identified for NF-YB only indeed correspond to “quasi-peaks” for NF-YA, that present enrichment levels below detection thresholds for the bioinformatic tools employed. The conclusion was thus that the NF-YB antibody is more efficient than the one for NF-YA, and that the two subunits can be assumed to be found together bound on DNA, as further discussed in . We thusSince the original release, new datasets have considerably expanded the ENCODE repository, for new TFs or new cell lines. Furthermore, while the initial ENCODE release contained datasets processed with different tools and strategies, all ChIP-Seq datasets have been reprocessed with a unique bioinformatic pipeline, applying also more stringent quality controls for experiments to be included in the official release. The result is that some of the TFs originally included in the early ENCODE releases -and in our study- have been removed, or the original peak lists changed, both in number of peaks, peak size, or genomic coordinates of peak regions. We thus reprocessed the new datasets, focusing on the K562 cell line, with our pipeline .An updated version of the results is summarized in -10). It can be observed how several small clusters emerge, clearly identifiable along the main diagonal, each corresponding to a potential genome-wide regulatory module composed by NF-Y and other factors and cofactors.The already identified module containing NF-Y, FOS, and other factors  was confTo further substantiate these results we repeated the analysis of step 2 computing the significance of cobinding peaks between pairs of TFs within FOS peaks, shown in Another cluster  and the accession numbers are listed in Publicly available datasets were analyzed in this study. This data can be found at the Encode Project (GP and RM devised the original pipeline, that has been fine tuned and modified with input from DD, MR, and FZ. MR and FZ implemented different parts of the pipeline, supervised by GP. MR run the analyses presented in the article and collected the results. All authors examined and discussed the results.Consiglio Nazionale delle Ricerche, Flagship Research Project “EPIGEN.”The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


It occurs in bursts, which results in vastly different numbers of an mRNA species in isogenic cell populations. Several factors contributing to transcriptional bursting have been identified, usually classified as intrinsic, in other words local to single genes, or extrinsic, relating to the macroscopic state of the cell. However, some possible contributors have not been explored yet. Here, we focus on processes at the 3 Using Bayesian methodology, we measure the transcriptional bursting in inducible transgenes, showing that perturbation of polymerase shuttling typically reduces burst size, increases burst frequency, and thus limits transcriptional noise. Analysis based on paired-end tag sequencing (PolII ChIA-PET) suggests that this effect is genome wide. The observed noise patterns are also reproduced by a generative model that captures major characteristics of the polymerase flux between the ends of a gene and a phase-separated compartment.′ and 5 ′ ends of a gene, which facilitate polymerase recycling, are major contributors to transcriptional noise.Interactions between the 3 The online version contains supplementary material available at (10.1186/s13059-020-02227-5). In many cellular systems, mRNAs appear to be produced in burst-like fashion. This is directly observed in real-time experimental studies –3 and alThe microscopic dynamics underlying transcription are not yet well understood. Various factors have been found to influence transcriptional dynamics, mostly by modulating bursting parameters such as the size or frequency of bursts , 5. ThesIt is unclear how these phenomena relate to the local environment at transcribing genes. These are associated to clusters of RNA polymerase II (PolII), which have been interpreted as “transcription factories”  and sugg′ processes, and protein interactions that have been suggested to juxtapose the promoter and the terminator DNA, forming a structure that has been referred to as a “gene loop” [′-end processing favours transcription initiation; the presence of such 3 ′- 5′ crosstalk in a gene increases its mean expression level [′-end transcriptional processes [′ to 3 ′, potentially passing through the LLPS compartment, as the recycling. It has been suggested that a repetitive cycle of reinitiation and termination due to these mechanisms is likely to produce a rapid succession of mRNA creation events, thus potentially contributing to the transcriptional bursts [While a comprehensive description of the interactions between PolIIs, other factors, and the chromatin within these niches is missing, several observations suggest that termination is linked to reinitiation; these include the presence of the same factor species at both ends of a gene, the reduction of initiation upon perturbation of 3 ne loop” , 32. Impon level . The conon level . In linerocesses . We genel bursts , but to ′- 5′ interactions using an interdisciplinary approach. We first consider two integrated genes that permit studying transcription upon perturbation of their 3 ′- 5′ processes at different induction levels; we demonstrate that these interactions strikingly influence the transcription kinetics and typically elicit the transcriptional noise, by decreasing burst frequency and increasing burst size. We then focus on genome-wide 3 ′- 5′ interactions involved in transcription by means of PolII ChIA-PET sequencing data, showing that they are related to the gene-expression parameters similarly to the transgenes’ results. This scenario is well described by a microscopic stochastic model of gene expression, where tuning a single parameter—corresponding to the probability of local polymerase recycling—naturally yields the observed expression patterns, without involving extrinsic-noise contributors or alternative intrinsic mechanisms.In this paper, we investigate the interplay between bursty expression and 3 β-globin (HBB) [env [We utilised two HEK293 cell lines which contain on their genomes copies of the genes in (HBB)  and a moBB) [env , respectβ-globin-like genes at the locus, likely involving LLPS [′ mRNA processing contributes to reinitiation of transcription [′ and 5 ′ transcriptional activities [This transgene approach allowed us to exploit very well-characterised model systems for recycling perturbation, which achieve mono-allelic expression and, most importantly, allow precise control of expression levels with inducers . The firing LLPS . A recening LLPS . Our celcription . This notivities , while ptivities  and appetivities . A furthtivities .env, was previously studied in similar fashion to the HBB constructs. Results using a mutated version of the pA site  promoter exhibits bursting dynamics [The second cell line, containing a Tet-inducible version of HIV-1-lymerase , 45. Thedynamics .′- 5′ crosstalk. We confirmed by total RNA-seq that HBB and env mRNAs are expressed inducibly in all cell lines , the fold changes over the un-induced samples were ≈16 and ≈26 for HBB and env, respectively. The mutants were expressed at lower levels and featured read-through transcription as described, with intact transcript sequences, i.e. not subject to splicing defects  and confirmed detection of large transcript numbers upon Tet stimulation of the cells, while the expression of a control gene, AKT1, remained constant  rather than in absolute counts. We thus used microscope imaging and nCounterrithm of –58 to thenv, indicating specific detection of transcripts above background noise. The stationary expression levels appeared to reach saturation at 80 ng mL −1 Tet  of the mRNA population, due to the cell cycle and size was minor . Note that flow-FISH and its analysis/interpretation are unaffected by nuclear export issues.Flow-FISH data demonstrate Tet-dose dependent expression of HBB and ith Cas9  and perfenv, we employed a Markov chain Monte Carlo (MCMC) sampling approach to fit statistical models to the flow-FISH data  as proxies of the cell cycle progression and the cell size, respectively, we heuristically selected populations corresponding to G1, S, and G2 phases of three different sizes each from 40 ng mL env gene but appear to support the conclusions from the HBB gene  [′- 5′ interaction of a gene, we first aggregated the reads corresponding to the interaction between the bins that include its transcription start site (TSS) and transcription end site (TES). The resulting metrics depend on the gene length, which we addressed by dividing the number of reads for each gene by the average read number from 104 genomic intervals of the same length as the gene, randomly sampled across the chromosome. We then applied the ′ to 3 ′ interaction scores correlate with those for 5 ′ to gene body interactions; this appears unsurprising, given that spatial proximity at one location will favour interaction signals at neighbouring regions, and is tangential to our analyses. We also discarded genes that are shorter than the resolution of our interaction matrices.To jointly study the expression of a gene and its 3 hIA-PET)  and singhIA-PET) . We choshIA-PET) . We filton score . Note thkon, and the average burst size α/koff . The interaction score of the high-noise genes is significantly higher than the score of the intermediate group, which in turn is higher than the low-noise genes’ .Fitting a negative-binomial distribution to the scRNA-seq UMI counts data of  allows umentclass2pt{minimν and the interaction score , thus showing that the noise level of genes with high interaction score is typically higher than the mean predicts; we also observe a significant negative correlation between the interaction score and the burst frequency kon  and a significant positive correlation between the interaction score and the burst size , consistent with the results on the transgenes. Filtering out zero-count genes, for which there is little statistical information, increases the P values above to 2.0·10−7,2.0·10−3, and 1.68·10−5, respectively, due to smaller sample sizes, and yields the scatter plots of Fig. There is a significant positive correlation between the distance e P <2.2·0−16, lm,γ and δ, respectively. In addition to this, by transcribing at rate β, the PolIIs leave the compartment with probability 1−l or are re-injected otherwise. This latter reaction represents the crosstalk between the 3 ′-end processing and the transcription initiation and helps to sustain the compartment population despite the presence of initiation, which on average contributes to depleting it. Consistently with the two genes integrated in our cell lines, the model encodes a Tet-repressor binding site downstream of the TSS which binds to the TetR factor, present at concentration n. Such a binding event interrupts the transcription; therefore, tuning n allows us to control the blocking rate λoff. The model parameters l and n are akin to the pA mutation and the Tet concentration, respectively, in the experimental settings. We assume that the pA mutation hinders but does not completely block PolII flux back to the compartment  and theinstance , 24); thrvations , 73); th [l Fig. . This bel and the factor abundance n. Notably, our results demonstrate a minor role for extrinsic contributions to noise . The presence of the termination defect increases the transcript degradation rate, which lowers the UMI counts; we found indeed that μX,inf.<μX,uninf. for the overwhelming majority of genes. We also found an overall increase in noise with infection, i.e. νinf.>νuninf. for many genes, as illustrated in Fig. d and with recycling rate l held fixed  and an observational study of ChIA-PET sequencing data (which provide a genome-wide view of chromatin interactions involved in transcription), we present results to suggest that PolII-mediated 3 ′- 5′ interactions and transcription kinetics. Modelling transcription requires abstraction and simplification due to the complexity of the molecular processes involved and the inadequacy of current experimental methodologies to dynamically resolve structural interactions at individual loci. Furthermore, the Bayesian estimates of the kinetic parameters reflect the incomplete quantitative information available on the experimental device. Also note that our transgenes might not exactly represent the average endogenous gene. Nevertheless, our setting is sufficient to resolve specific patterns, which can be reproduced by an ab initio mechanistic model, thus supporting our conclusions.Building on standard phenomenological models, transcription parameters, such as average burst size and frequency, are consistently inferred across the different conditions using a Bayesian methodology, to demonstrate the presence of association between 3 The analysis suggests that recycling of the polymerase typically increases noise at a given expression level, while an alternative symmetric interpretation is possible, viz., that recycling permits higher expression at a given noise level. These relations either are a byproduct of the construction of the transcriptional machinery or were selected for. It will be interesting to further explore our findings from an evolutionary perspective. In particular, many studies show how selection of noisy expression can be critical by contributing to cell fate diversity , 84 and Yi of cell i is proportional to the true mRNA abundance Xi and therefore can be expressed as k) indexes the replicate, κk)( are point estimates from the control datasets. Prior distributions for κk) fPoi(x|α)=αxeα−/x! and The transcriptional bursting is fully characterised by the rates μX=αkon/(koff+kon), to get It is convenient to reparametrise the Poisson-beta PDF in terms of its mean μX in the form of informative priors and infer the dimensionless rates α,koff, and kon. These are converted to min−1 by using koff→∞,α→∞, with their ratio α/koff held finite, the population mean satisfies μX=konα/koff, while the PDF of X approaches the negative-binomial distribution fGamma is the density of a Gamma RV with mean μX and variance μXkoff/α; when this RV concentrates near the mean as kon→∞ and koff/α→0, X is Poisson with PDF fPoi(x|μX).In fact, this allows us to exploit knowledge on The microscopic model is defined by means of the following chemical reaction scheme: λoff=nKλ,λon=Kλ, where Kλ and n represent the chemical affinity and concentration of TetR homodimers that bind to the TetO 2 operators downstream of the TSS, respectively. When such a binding event occurs, the transcription is inhibited as elongation is impeded and the resulting locked DNA configuration is represented by the species DNAoff. The switch to DNAon corresponds to the release of the lock. A variant of this model that does not allow PolII to accumulate before transcription is obtained with γ>0 when the PolII compartment is empty and γ=0 otherwise.By the law of mass action, Additional file 1 “Supporting Information to: “3’-5’ crosstalk contributes to transcriptional bursting”.Additional file 2 Review history.