We hypothesize that our methodology will enable the efficient identification of IRF-specific and pan-IRF inhibitors that can be used for the treatment of IRF-dependent disorders and malignancies.Interferon regulatory factors (IRFs) are a family of homologous proteins that regulate the transcription of interferons (IFNs) and IFN-induced gene expression. As such they are important modulating proteins in the Toll-like receptor (TLR) and IFN signaling pathways, which are vital elements of the innate immune system. IRFs have a multi-domain structure, with the N-terminal part acting as a DNA binding domain (DBD) that recognizes a DNA-binding motif similar to the IFN-stimulated response element (ISRE). The C-terminal part contains the IRF-association domain (IAD), with which they can self-associate, bind to IRF family members or interact with other transcription factors. This complex formation is crucial for DNA binding and the commencing of target-gene expression. IRFs bind DNA and exert their activating potential as homo or heterodimers with other IRFs. Moreover, they can form complexes  and collaborate with other co-acting transcription factors such as Nuclear factor-κB (NF-κB) and PU.1. In time, more of these IRF co-activating mechanisms have been discovered, which may play a key role in the pathogenesis of many diseases, such as acute and chronic inflammation, autoimmune diseases, and cancer. Detailed knowledge of IRFs structure and activating mechanisms predisposes IRFs as potential targets for inhibition in therapeutic strategies connected to numerous immune system-originated diseases. Until now only indirect IRF modulation has been studied in terms of antiviral response regulation and cancer treatment, using mainly antisense oligonucleotides and siRNA knockdown strategies. However, none of these approaches so far entered clinical trials. Moreover, no direct IRF-inhibitory strategies have been reported. In this review, we summarize current knowledge of the different IRF-mediated transcriptional regulatory mechanisms and how they reflect the diverse functions of IRFs in homeostasis and in TLR and IFN signaling. Moreover, we present IRFs as promising inhibitory targets and propose a novel direct IRF-modulating strategy employing a pipeline approach that combines comparative  Homo sapiens IRFs are key mediators of signal transduction associated with host immune response, immunomodulation and hematopoietic differentiation. Accordingly, five functional subgroups can be distinguished: IRF1&2, IRF3&7, IRF4&8, IRF5&6, and IRF9 as a part of the Interferon stimulated gene factor 3 (ISGF3) complex. IRF1 and IRF2 promote the response of Th1 immune cells, whereas IRF3 and IRF7 are engaged in antibacterial and antiviral innate immunity. Expression of IRF4 and IRF8 is restricted to the lymphoid and myeloid lineages of the immune system (In 1988 the first interferon regulatory factor (IRF) was identified and named IRF1 . Since te system . This anAll IRF family members are characterized by a multi-domain structure, which consists of: N-terminal DNA binding domain (DBD), a peptide Linker (LK) and IRF-association domain (IAD)1 or IAD2 within the C terminal activation domain (AD) . A linkeAs mentioned above, IRFs closely control transcriptional activation of IFN-Is and ISGs. As such they are crucial modulators of Toll-like receptor (TLR) and IFN signaling, key pathways of the innate immune system. Upon binding of Pathogen-associated molecular patterns (PAMPs) to the TLRs, or IFNs to the IFN receptors, a signaling cascade causes IRF activation and re-localization to the nucleus where they activate gene expression. IRFs exert the ability to interact with numerous transcriptional partners, including IRF family members, STATs as well as other co-acting transcription factors such as NF-κB  and PU.1 (with IRF4 and IRF8). These interactions allow IRFs to activate a broad spectrum of genes and control diverse transcriptional programs. Despite the clear similarity between IRF-DBD structures and the fact that they recognize the same consensus DNA-binding site, there are major differences in DNA binding affinities between family members. Moreover, depending on the binding partner, IRFs exhibit various DNA binding modes . The ISRin silico docking to the IRF-DBD with in vitro validation of IRF inhibition  that combines comparative hibition .With this review, we summarize the current knowledge of the different IRF-mediated transcriptional regulatory mechanisms and their role in health disorders. We postulate that specific target genes activated by the different IRF dependent transcriptional mechanisms have potential as promising novel disease markers. Going a step further, we hypothesize that the presented IRF-specific and pan-IRF inhibition strategies might represent the future for treating numerous immunological diseases. Hence, better understanding of IRF-dependent transcriptional programs and development of direct IRF inhibition approaches, could provide novel insight in the therapeutic, diagnostic and prognostic space occupied by IRFs.IRFs are crucial modulators of production and IFN signaling. IFNs are a group of cytokines which regulate inflammation, cell proliferation, and apoptosis. IFNs are part of the first line of defense of the body against viral infections , 15. IFNISG15, OAS1-3, IFIT1-3, or MX1 and 2, which all are crucial in mediating antiviral activity (All types of IFN activate pathways based on Janus kinases (JAKs) and STAT signaling. While the signaling of IFNAR and IFNLR relies on juxta positioning and phosphorylation of JAK1 and tyrosine kinase 2 TYK2 , the IFNactivity . In a siactivity –25.CIITA, Gbp1, and Gp19 genes were shown to depend on both STAT1 and IRF1 . GAF translocates into the nucleus to activate genes containing the IFN gamma activating site (GAS) DNA element . GAand IRF1 –31 , a component of many bacteria. Besides LPS, other TLR4 ligands include several viral proteins and a variety of endogenous proteins such as low-density lipoprotein, beta-defensins, and heat shock proteins . TLR4 ispathways .IFNβ, which in an autocrine fashion through the IFNAR complex further stimulates ISGF3-dependent ISG expression , where they are responsible for a high level of IFN-I expression in response to viral infection , 51. TLRbspecies . In an ebspecies . The IRFbspecies . Using ie IFN-Is . Indeed,e IFN-Is . TogetheIFNα and IFNβ genes have IRE containing PRD-I, making them susceptible to IRF-induced upregulation -like receptors (RLRs) also utilize IRFs to induce the expression of cytokines, or exert gene expression independent effects. These pattern recognition receptors are also of great importance for antiviral responses and are addressed in more detail elsewhere –67.Mus musculus IRF1-DBD in complex with a 13nt DNA fragment from a PRD1 element containing GAAA core sequence was solved in 1998 (PDB Id 1IF1). Topologically the IRF1 DNA-binding region is similar to a helix-turn-helix DNA-binding domain and includes a four-stranded antiparallel β-sheet and three large loops (L1-L3) connecting the different secondary structure elements, but its mode of DNA interaction is distinct. Thus, four amino acids mediate contact with DNA in the major groove . Additionally, three tryptophan residues  are involved in hydrogen bonds and van der Waals contacts with the sugar-phosphate backbone , IRF4- (Homo sapiens–2DLL), and IRF7-DBDs (Mus musculus–3QU3). Despite the significant similarity between DBD structures of different IRFs and the fact that they all recognize the same consensus DNA binding site, there are major differences in DNA binding affinities between family members. Analysis of the DBDs from IRF3 and IRF7 reveals that this phenomenon can be explained by differences in flexibility and conformational changes in the loops, in particular L1. In IRF3 this loop is disordered in the apo-form and becomes ordered, when DNA is contacted. In contrast to IRF3, IRF7 L1 is ordered and stabilized by two hydrophobic residues (Phe45 and Leu50) that fold back into the core of the protein in the apo-form and during DNA-binding a 2Å rigid body transition is observed  solved by Panne‘s group revealed several important adaptations that explain the selective interaction between STAT2 and IRF9. Indeed, the IRF9-IAD is missing the regulatory apparatus that is used for IRF auto-inhibition in the latent form, and in the activated state enables IRF dimerization and interaction with the transcriptional co-activators CBP/p300. Accordingly, IRF9 interacts with the tip of the STAT2-CCD using the convex surface of the β-sandwich core of the IAD domain. While the same surface is available in other IRFs, amino acid substitutions at the key anchoring points account for the preferential IRF9-STAT2 interaction. Taken together, these adaptations explain why IRF9 binds constitutively and selectively to STAT2 and demonstrate that the observed interface is required for ISGF3 function in many cells , recognizing the ISRE element, is an example of a cooperativity between IRF and STAT transcription factors. The direct interaction between the STAT2 coiled-coil domain (STAT2-CCD) and the IRF9-IAD is critical for the function of ISGF3 and the antiviral response. Studies by Rengachari et al. showed that the overall architecture of these domains is similar to that of other STATs and IRFs . Crystalny cells , 3.MHC I locus, crucial for antigen presentation. Also, dual binding of IRF1 and STAT1 vs. single binding of IRF1 distinguished IFNγ induced ISGs vs. cell-specific IFNγ resistant ISGs , which in absence of STAT1 restores IFN-I responses. STAT2/IRF9, like ISGF3 also recognizes the ISRE sequence –25 and uant ISGs .In an unstimulated state, some ISG expression is present and known to be crucial for controlling cellular susceptibility to viral infection . Under tIL-1β. However, when IRF1 or IRF2 were co-expressed with IRF4-PU.1 or IRF8-PU.1, the IL-1β promoter activity was increased over 100-fold as compared to that observed in cells with IRF4-PU.1 or IRF8-PU.1 alone  is a protein of the ETS family, and has an ETS domain with which it can bind DNA at a sequence known as the PU box . PU.1 alone . These s.1 alone   on the DNA , 113. AfIn macrophage and DC differentiation, in which IRF4 and IRF8 are known to play crucial regulatory roles. A chromatin immunoprecipitation study revealed that IRF4 together with The Basic leucine zipper transcription factor (BATF) bound DNA in close proximity of DNA sequences that recognize AP-1 family members , 120. BAIFNβ gene. It has been observed that binding-induced changes in DNA conformation and not the surface of protein-protein interactions is crucial for cooperative binding and transcriptional activation. Detailed analysis of this enhanceosome assembly was conducted on crystal structures of the DNA-binding domains of human IRF3, IRF7, and NF-κB bound to the IFNβ enhancer   assemble together to form an enhanceosome , 77. Coo1, 2O6G) , 125. AdIFNβ promoter. NF-κB upregulates IFNβ gene expression by binding two recognition sites in its promoter. These recognition sites flank the PRD-I motif on which IRF1 binds  of the F1 binds , 127. IRF1 binds , 3. IRF1Cxcl10 and Ccl5, activator of inflammasome Gbp5, Immune-Responsive Gene 1, and IFNβ1. Detailed activation kinetics analysis suggested that individual genes within this small cluster use distinct regulatory mechanisms  mice  mRNA expression and significantly increased CD4/CD8 ratio as compared to WT mice  mice . IRF1 DNlunteers . Further WT mice , 138. Mo WT mice , 140.IRF2 originally identified as an IRF1 antagonist acts as an oncogene, promoting cellular transformation. Its role in suppression of IFN-I signals has also been well-documented  in regulation of their expression in different cell types present in human atherosclerotic plaques  is a common systemic autoimmune disease which affects a variety of organs, including skin, joints, lungs, kidneys, and nervous system . Many ofIRF5 polymorphisms associated with disease risk (IRF5 (rs2004640 GG and rs10954213 GG) revealed a protective effect against the risk of atherosclerosis and cardiovascular disease risk (Recent studies in SLE and Rheumatoid Arthritis (RA), concluded that disease-associated atherosclerosis is mediated through IRF5. Likewise, mice deficient in IRF5 presented increased atherosclerosis and also exhibited hyperlipidemia, increased adiposity, and insulin resistance compared to WT controls . Moreovease risk –174. In ase risk . Moreovease risk .IRF6 has recently been connected to TLR3 signaling in keratinocytes . TLR3 acIRF4 gene contribute to elevated IRF4 expression in cells from patients with multiple myeloma  to be associated with susceptibility to tuberculosis , which has been tested as a potent therapeutic agent for asthma , a major pharmacologically active compound in fruiting bodies of diseases . Thymoqudiseases . iNOS, adiseases , 204. NSdiseases .Francisella tularensis uses its components to block NF-κB p65 activity, IRF1 translocation and binding of IRF1 and IRF8 to the Ets2 element in the promoter region of the IL-12 gene -125b in multiple melanoma patients. Positive inhibitory effects of this synthetic microRNA have been confirmed in vitro and in vivo, leaving IRF4 as an interesting multiple myeloma therapeutic target . Applying genome-wide SNP association studies, it was demonstrated that IRF4 is strongly associated with susceptibility to Chronic lymphocytic leukemia (CLL), with risk loci identified at 6p25.3  . IRF5 anChanges in IRF expression could be a prognostic factor in several human diseases. For instance, IRF1, IRF4, and IRF8 are significantly downregulated in failing human hearts compared with healthy controls , 230, whNot only IRF expression itself, but downstream ISG expression provides interesting markers for diagnostic use. With the demonstrated variety in IRF-mediated transcriptional regulatory mechanisms implicated in diseases, leading to the upregulation of specific subsets of ISGs, several applications can be envisioned . Indeed,Dysregulation of IRF function is critical in the development of immune system-originated diseases. Therefore, investigating the regulatory mechanisms mediated by IRFs and modulating IRFs expression might be crucial for disease treatment .So far, IRFs have not been pursued as drug targets in terms of direct and selective inhibition. Current IRF inhibitory strategies are mainly limited to indirect modulation of their expression and function. Only direct inhibition strategies, which target IRFs transcription by siRNA or miRNA have been employed. Preventing IRF binding to DNA could serve as another potential therapeutically advantageous way to inhibit IRFs. An in-depth understanding of the IRF protein structure and the mechanisms involved in the binding of these transcription factors to DNA will allow the development of effective inhibition strategies. Moreover, the fact that many IRFs require a binding partner, such as PU.1, to effectively contact DNA can be used to develop a potent inhibitory system for IRFs. In addition, formation of homo- and heterodimers or cooperative DNA binding with co-activators, both promoted by the IAD in the C-terminal region can be directly blocked by inhibitory compounds.We postulate that successful targeting of IRF-DBDs using small-molecule inhibitors provides hope that IRFs can be “attacked” directly and used for the treatment of IRF-dependent disorders and malignancies. Considering the similarities and differences between the individual IRFs, in particular two directly modulating IRF DNA binding strategies can be proposed . The firIn this review, we have summarized the current knowledge of the different IRF-mediated transcriptional regulatory mechanisms and how they reflect the diverse functions of IRFs in homeostasis and in TLR and IFN signaling. IRFs orchestrate expression of distinct subsets of ISGs via dimer formation, their involvement in transcriptional complexes, and co-binding with other transcription factors. ISG subset expression, as well as the expression of IRFs or the SNPs they contain might be exploited in future diagnostic arrays for the assessment of disease progression of a wide variety of (auto)immune diseases and cancer.Several STAT inhibitors, including synthetic small compounds, natural products and oligonucleotide decoys, in recent (pre)clinical trials prove that strategies targeting transcription factors might find their way to the clinic in the near future. Therefore, we postulate that successful targeting of IRF-DBDs using small-molecule inhibitors provides hope that IRFs can be “attacked” directly and used for the treatment of IRF-dependent disorders and malignancies .In vitro and in vivo validation of IRF inhibitory compounds has to prove their hypothesized effectiveness, as well as assess potential cytotoxicity before these products can move to clinical studies. Another challenge for the use of inhibitory strategies in therapeutics is the administration of such compounds. Systemic administration of IRF inhibitors is undesirable, because of possible unforeseen side effects. Therefore, either local injection/release or targeted administration with labeled compounds will be more effective as inhibition therapy. Nanotechnology might offer novel ways of drug administration (stration . Antibodstration , and alrstration , 237. Thin silico analysis and described the results. AM took part in figures preparation and concept development. AP-B, MP-G, and JW participated in development of the concept and critically evaluated and edited the manuscript. HB developed the concept and was involved in writing and editing the manuscript and coordinated input from all co-authors.AA and BK were equally involved in concept development, writing and editing the manuscript. MS generated 3D models of IRFs DBD domains and performed 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.


Prediction of cell type-specific, in vivo transcription factor binding sites is one of the central challenges in regulatory genomics. Here, we present our approach that earned a shared first rank in the “ENCODE-DREAM in vivo Transcription Factor Binding Site Prediction Challenge” in 2017. In post-challenge analyses, we benchmark the influence of different feature sets and find that chromatin accessibility and binding motifs are sufficient to yield state-of-the-art performance. Finally, we provide 682 lists of predicted peaks for a total of 31 transcription factors in 22 primary cell types and tissues and a user-friendly version of our approach, Catchitt, for download.The online version of this article (10.1186/s13059-018-1614-y) contains supplementary material, which is available to authorized users. Activation or repression of transcription is one of the fundamental levels of gene regulation. Transcriptional gene regulation depends on transcription factors (TFs), which specifically bind directly to sites in promoters or enhancers of regulated genes or bind indirectly via other, sequence-specific TFs. Modeling binding specificities, typically represented as sequence motifs, has been an important topic of bioinformatics since its infancy , 2. HoweThe emergence of high-throughput techniques like ChIP-chip  or ChIP-High-throughput sequencing may also be used to obtain genome-wide assays of chromatin accessibility  aimed at assessing the performance of tools for predicting cell type-specific TF binding in human using a minimal set of experimental data in a fair and unbiased manner. The challenge setting has advantages over typical benchmark studies, because approaches are typically applied to the challenge data by their authors, ground truth is known only by the challenge organizers, and participants are typically required to provide code and documentation for their method such that predictions can be reproduced.Each of these approaches has its benefits and downsides, and the results of benchmark studies in the respective original publications are ambiguous with regard to their prediction performance. Against this background, the “ENCODE-DREAM in vivo Transcription Factor Binding Site Prediction Challenge”  were provided for training cell types and training chromosomes. Predictions had then to be made for combinations of TF and cell type not present in the training data on held-out chromosomes. Predictions were evaluated against labels derived from TF ChIP-seq data for that specific TF and test cell type.Here, we present our approach for predicting cell type-specific TF binding regions earning a shared first rank among 40 international teams, including developers of several established methods  models , which mhttps://www.synapse.org/#!Synapse:syn6131484/wiki/405275), we focus on the analysis for the contributions of different aspects of this approach on the final prediction performance in this paper. Specifically, we evaluate the contribution of different features, we compare the performance achieved by standard training with that achieved by the iterative training procedure, and we assess the performance of individual classifiers compared with their ensemble prediction. Based on these analyses, we define and benchmark a simplified variant of the proposed approach. Finally, we provide a large collection of predicted, cell type-specific tracks of binding regions for 31 TFs in 22 primary cell types and tissues to make predictions by this approach readily accessible.As this novel approach has already been benchmarked against a large number of competing approaches as part of the ENCODE-DREAM challenge (https://www.synapse.org/#!Synapse:syn6131484/wiki/405275).During the ENCODE-DREAM challenge, a large number of approaches created by 40 international teams have been benchmarked on 13 cell type-specific ChIP-seq assays for 12 different TFs in human  earned a shared first rank together with the approach created by team “Yuanfang Guan.”In the following, we investigate the influence of different aspects of the proposed approach on the final prediction performance. First, we inspect the impact of different sets of related features  on prediction performance. Second, we study the importance of the iterative training approach as opposed to a training on initial training data. Third, we compare the performance of the predictions gained by classifiers trained on training data for individual cell types with the performance of the aggregated prediction obtained by averaging over these cell types. Finally, we apply the proposed method for predicting cell type-specific TF binding for 31 TFs in 22 additional primary cell types yielding a total of 682 prediction tracks.We use the prediction performance obtained by the proposed approach using all sets of features “” sectionWe find that prediction performance as measured by AUC-PR varies greatly among the different transcription factors Fig.  with a mTo analyze the contribution of selected features on the final prediction performance, we systematically exclude sets of related features from the input data in training and prediction. As a baseline, we measure AUC-PR for the classifier using all feature sets. In addition, we measure AUC-PR when excluding each individual feature set, where the difference of these two AUC-PR values quantifies the improvement gained by including the feature set Fig. a.Fig. 2We observe the greatest impact for the set of features derived from DNase-seq data. The improvement in AUC-PR gained by including DNase-seq data varies between 0.087 for E2F1 and 0.440 for HNF4A with a median of 0.252.Features based on motif scores (including de novo discovered motifs and those from databases) also contribute substantially to the final prediction performance. Here, we observe large improvements for some TFs, namely 0.231 for CTCF in IPSC cells, 0.175 for CTCF in PC-3 cells, and 0.167 for FOXA1. By contrast, we observe a decrease in prediction performance in the case of JUND (− 0.080) when including motif-based features. For the remaining TFs, we find improvements of AUC-PR between 0.008 and 0.079. We further consider two subsets of motifs, namely all motifs obtained by de novo motif discovery on the challenge data and all Slim/LSlim models capturing intra-motif dependencies. For motifs from de novo motif discovery, we find an improvement for 9 of the 13 data sets, and for Slim/LSlim model, we find an improvement for 10 of the 13 data sets. However, the absolute improvements  are rather small, possibly because (i) motifs obtained by de novo motif discovery might be redundant to those found in databases and (ii) intra-motif dependencies and heterogeneities captured by Slim/LSlim models  might beNotably, RNA-seq-based features (median 0.001), annotation-based features (0.000), and sequence-based features (0.001) have almost no influence on prediction performance.As the set of DNase-seq-based features is rather diverse, including features derived from fold-enrichment tracks, peak lists, or variation among cell types, we aim at further dissecting the influence of related groups of those features. To this end, we further test how prediction performance is affected by removing specific groups of DNase-seq features  or from RNA-seq data  showed to be non-informative with regard to the similarity of TF binding regions in preliminary studies on the training cell types.Hence, we consider the choice of the training cell type a latent variable, and average over the predictions generated by the respective classifiers see the “” sectionFor all test data sets with multiple training cell types available, we find that the averaged prediction yields AUC-PR values above the median of the AUC-PR values achieved for individual training cell types. This improvement is especially pronounced for REST, GABPA, and MAX.To further investigate if averaging over classifiers for individual cell types favors conserved binding regions  over cell type-specific binding regions, we also assess prediction performance on such regions separately  that the best prediction performance achieved for one of the individual training cell types would have gained, in some cases considerable, improvements over the proposed averaging procedure. Notably, the variance of AUC-PR between the different training cell types is especially pronounced for JUND, which supports the previous hypothesis that some features, for instance binding motifs or co-binding of TFs, are highly cell type-specific for JUND. In general, deriving informative measures of TF-specific cell type similarity based on cell type-specific assays and preliminary binding site predictions, would likely lead to a further boost of the performance of computational approaches for predicting cell type-specific TF binding.Having established that a single type of experimental assay, namely DNase-seq, is sufficient for predicting cell type-specific TF binding with state-of-the-art accuracy, we may now use the classifiers obtained on the training cell types and TFs for predictions on further cell types. For this purpose, we use the classifiers considering only DNase-seq-based and motif-based features, but neither RNA-seq-based features, annotation-based features, nor features based on raw sequence, which showed to achieve a prediction performance comparable to the full model before cf. Fig. ” sectionFor the selected cell types .The 682 genome-wide prediction tracks are still rather large (approx. 880 MB per track) and, hence, demand for substantial storage space that might not be available to the typical user, while the majority of regions are likely not bound by the TF of interest. Hence, we further condense these predictions into predicted peak lists in narrowPeak format by joining contiguous stretches with high binding probability and applying a threshold of 0.6 (relaxed) and 0.8 (conservative) on the maximum probability observed in a predicted “peak.” We provide these peak files for download at To get an impression of the quality of the predicted peaks, we further compute Jaccard coefficients based on peak overlaps (computed using the GenomicRanges R-package ) betweenFinally, the data for CTCF allow for comparing the overlap between predicted peak lists and experimentally determined peak lists to the overlaps observed for (i) technical replicates .Predicting in vivo binding sites of a TF of interest in silico is still one of the central challenges in regulatory genomics. A variety of tools and approaches for this purpose have been created over the last years and, among these, the approach presented here is not exceptional in many of its aspects. Specifically, it works on hand-crafted features derived from genomic and experimental data, it considers TF binding motifs and chromatin accessibility as its major sources of information, and it uses supervised learning related to logistic regression. Yet, this approach gained the best performance in the ENCODE-DREAM challenge. Notably, the second approach gaining the first rank in the challenge is based on a similar rationale and uses a set of DNase-seq-based and motif-based features in a logistic regression, where parameters are learned iteratively from systematically chosen sub-sets of the training cell types and chromosomes  and DNase-seq-based features leads to high demands with regard to disk space but also runtime : http://www.gencodegenes.org/releases/19.html) [The raw sequence of the human genome (hg19) and gene annotations according to the gencode v19 annotation (19.html) Cell type-specific DNase-seq “fold-enrichment coverage” tracks, which represent DNase-seq signal relative to a pseudo control, smoothed in a 150-bp windowCell type-specific DNase-seq peak files in “conservative” (IDR threshold of 10% in pseudo replicates) and “relaxed” (no IDR threshold) flavorsCell type-specific TPM values from RNA-seq experiments in two bio-replicates for all gencode v19 genes as estimated by RSEM Cell type-specific and TF-specific ChIP-seq peak files in “conservative” (IDR threshold of 10% in pseudo replicates) and “relaxed” (no IDR threshold) flavorsCell type-specific and TF-specific label files classifying genome-wide 200-bp regions every 50 bp into B = “bound,” A = “ambiguous,” and U = “unbound” according to the respective conservative and relaxed ChIP-seq peak files; an overview of the combinations of TF and cell type in the training data, the leaderboard data, and the test data used for evaluation in the final challenge round is given in Additional file We use the following types of input data sets as provided by the challenge organizers . Specifically, we select all DNase-seq experiments that (i) are flagged as “released,” (ii) have FastQ files available, (iii) are not from immortalized cell lines, (iv) have no entry in one of the “Audit error” categories, and (v) are not in the “insufficient replicate concordance” category of “Audit not compliant.” A list of the corresponding experiments is obtained from the ENCODE project, and experiments are filtered for the existence of at least two replicates, yielding 23 experiments in total. One of these experiments had to be excluded later, because a different DNase protocol with much shorter reads had been used. For the remaining 22 experiments ). The data sets are analyzed using the following parameters: -species hg19 -type dnase-seq -subsample 50M -se. For further analyses, the relaxed (./out/peak/idr/pseudo_reps/rep1/*.filt.narrowPeak.gz) and conservative peaks  as well as the DNase coverage  are used.For predicting cell type-specific binding of TFs in additional cell types beyond those considered in the challenge, we download DNase-seq data (FastQ format) from the ENCODE project  or first computed with base-pair resolution and afterwards summarized as aggregate values  for each 50-bp bin. An odd number of several, adjacent bins, i.e., the respective feature values (see below), is then considered as input of the classifier composed of statistical models for the training process as well as for making predictions. Conceptually, the classifier uses the information from those bins to compute a posteriori probabilities The set of features considered may be roughly classified by the source of information: DNase-seq data, motif profiles, raw sequence, RNA-seq data. Here, we give a brief overview of these features, while we provide a complete list of definitions of all features in Additional file The most informative features with regard to the challenge task are likely motif-based and chromatin accessibility-based features. For obtaining a broad set of binding motifs for each TF at hand, we combine motifs from databases with motifs obtained by de novo motif discovery from the challenge data. We retrieve PWM models of the TF at hand from the databases HOCOMOCO  and DBcoAll of these motifs are then used in a sliding window approach to obtain base-pair resolution score profiles, which are summarized by aggregate statistics representing the binding affinity to the strongest binding site  as well as general affinity to broader regions . The set of motifs may comprise models of general binding affinity of the TF at hand but may also capture cell type-specific differences in the binding regions, which could be caused by interaction with other TFs including competition for similar binding sites.DNase-seq-based features are computed from the “fold-enrichment coverage” tracks and DNase-seq peak files provided with the challenge data. These features quantify short and long range chromatin accessibility, stability of the DNase signal in the region of interest and across different cell types, and overlaps with DNase-seq peak regions.The set of sequence-based features comprises the raw sequence  around the center bin and several measures computed from this sequence, for instance G/C-content, the frequency of CG di-nucleotides, or the length of homo-polymer tracts. Based on the gencode v19 genome annotation, we additionally define features based on overlapping annotation elements like CDS, UTRs, or TSS annotations and based on the distance to the closest TSS annotation in either strand orientation. All of these features are neither cell type-specific nor TF-specific. However, they may represent general features of genomic regions bound by TFs , which might be helpful to rule out false positive predictions based on TF-specific features like motif scores. In addition, the model parameters referring to those features may be adapted in a TF-specific and cell type-specific manner, which may yield auxiliary information for cell type-specific prediction of TF binding as well.Finally, RNA-seq data are represented by the TPM value of the gene closest to the bin of interest as well as measures of stability within biological replicates and across different cell types.DNase-seq and RNA-seq-based features are cell type-specific but not TF-specific by design. However, model parameters may adapt to situations where one TF preferentially binds to open chromatin, whereas another TF may also bind in nucleosomal regions.Feature values are computed using a combination of Perl scripts and Java classes implemented using the Java library Jstacs . Genome-We model the joint distribution of these different features by a simple product of independent densities or discrete distributions  regions and a set of negative (unbound) regions. In general, a training region is represented by a vector of all feature values described in the “Since we face a highly imbalanced classification problem with rather few ChIP-seq peaks compared with the large number of bins not covered by a peak, and since the inclusion of all such negative regions into the training set would lead to an inacceptable runtime, we decided to derive representative negative regions by three different sampling strategies. All sampling steps are performed stratified by chromosome.First, we sample on each training chromosome 10 times as many negative regions (spanning an odd number of consecutive bins) as we find positive regions on that chromosome. Center bins are sampled uniformly over all bins not covered by a “relaxed” peak for the same cell type and TF.Second, we over-sample negative regions with large DNase-seq median values similar to those of positive examples to yield a representative set of negative regions. This is especially important as these will be regions that are hard to classify using DNase-seq based features but are only lowly represented by the uniform sampling schema. The over-sampling is adjusted for by down-weighting the drawn negative examples to the corresponding frequency among all negative regions , but do not overlap a “relaxed peak” in the current cell type. These negative regions are weighted such that the sum of their weights matches the rate of such regions among all putative negative regions. This sampling schema is intended to foster learning cell type-specific properties as opposed to general properties of the binding regions of the current TF. In this case, we sample four times as many negative regions as we have positives.Together, these three sampling schemas yield an initial set of negative regions, which serve as input of the discriminative maximum conditional likelihood principle in addition to the positive regions. However, in preliminary tests during the leaderboard round of the challenge, we observed that even this non-trivial sampling schema is not fully satisfactory. As testing (a large number of) other sampling schemas seemed futile, we designed an iterative training schema Fig.  that is Pi for each bin i on training chromosomes. To limit the runtime required for this prediction step, we restrict the prediction to chromosomes chr10 to chr14. These probabilities are then used as input of the prediction schema  are averaged per region. Again, regions labeled U with large prediction scores are identified and added to the set of negative regions, which then serve as input of the following iteration. After five rounds of training yielding five classifiers, the iterative training procedure is terminated.K training cell types with ChIP-seq data for the TF of interest, which yields a total of 5·K classifiers. For the final prediction, the prediction schema  is consolidated into a single runnable JAR file to limit system requirements to a current Java installation only, (ii) has an extensible code base eliminating much of the experimental code of the challenge implementation, (iii) is applicable to data from individual cell types to reduce data interdependencies, and (iv) may be executed on a standard compute server in acceptable runtime.To achieve these aims, some parts of the methods have been simplified and streamlined. First, we consider only the most important chromatin accessibility and motif-based features, which reduces runtime and memory consumption. Second, we implement an accelerated motif scanning module that computes whole-genome score profiles even for the complex LSlim models within a few hours. Third, we skip steps that jointly evaluate data and/or feature files from multiple cell types. Specifically, we skip quantile normalization of chromatin accessibility features , and we omit the sampling step depending on ChIP-seq data for other cell types for determining initial negative regions. We call this implementation “Catchitt” comprising five modules for (i) computing chromatin accessiblity features from DNase-seq or ATAC-seq data, (ii) computing motif-based features, (iii) deriving labels from ChIP-seq peak lists, (iv) performing iterative training given feature files and labels, and (v) predicting binding probabilities for genomic regions.The models and basic conditional likelihood training including numerical optimization are implemented by core classes of the Java library Jstacs . The itet into a common peak region. For each region, we record the maximum probability p and discard bordering regions with a probability below 0.8·p. The resulting regions are then annotated according to the narrowPeak format with a “peak summit” at the center of the region yielding p, a “score” of −100·log10(1−p), and a “signal value” equal to p. We generate “relaxed” peak predictions using t=0.6 and “conservative” peak prediction using t=0.8.For the additional primary cell types and tissues beyond those considered in the challenge, we further process final predictions to yield peak lists in narrowPeak format, which are smaller and easier to handle than the genome-wide probability tracks with 50-bp resolution. To this end, we join contiguous stretches of regions with predicted binding probability above a pre-defined threshold Additional file 1Supplementary Tables and Figures. Figures S1 to S9. Tables S1 to S14. Text S1—Tools for predicting in vivo binding regions. Text S2—Features. Text S3—Model and learning principle. Text S4—Sampling of DNase-matched negative regions. Text S5—Implementation notes. (PDF 436 kb)


Our results reveal interplay between A- and B-type lamins on radial locus positioning, suggesting complementary contributions to large-scale genome architecture. The data also unveil a hitherto unsuspected impact of cytotoxic drugs on genome conformation.Abbreviations: ChIP-seq: chromatin immunoprecipitation sequencing; CsA: cyclosporin A; FISH; fluorescence in situ hybridization; ICMT: isoprenylcysteine methyltransferase; LAD: lamina-associated domain; TAD: topologically-associated domainImmunosuppressive drugs such as cyclosporin A (CsA) can elicit hepatotoxicity by affecting gene expression. Here, we address the link between CsA and large-scale chromatin organization in HepG2 hepatocarcinoma cells. We show the existence of lamina-associated domains (LADs) interacting with lamin A, lamin B, or both. These ‘A-B’, ‘A-only’ and ‘B-only’ LADs display distinct fates after CsA treatment: A-B LADs remain constitutive or lose A, A-only LADs mainly lose A or switch to B, and B-only LADs remain B-only or acquire A. LAD rearrangement is overall uncoupled from changes in gene expression. Three-dimensional (3D) genome modeling predicts changes in radial positioning of LADs as LADs switch identities, which are corroborated by fluorescence  Drug-induced hepatotoxicity, a common cause of clinical trial failure, has led to the use of cellular models such as HepG2 hepatocarcinoma cells for drug testing ,2. At miThe mammalian genome is organized into compartments of active and inactive chromatin  and withLamin-chromatin interactions can be altered during differentiation ,15,18–20in situ hybridization (FISH). The data suggest an A- and B-type lamin interplay in radial genome conformation and reveal unsuspected effects of cytotoxic compounds such as CsA on nuclear organization.In light of these observations, to address the relationship between CsA exposure and genome organization, we examined here the effect of CsA on the association of chromatin with nuclear lamina. We report an impact of CsA on the dynamics of interactions of lamins A and B with the genome. Such interactions can be gained, lost or interchanged in a manner uncoupled from gene expression changes. They also correlate with distinct patterns of radial repositioning of loci predicted from 3D genome models and confirmed by fluorescence −5], paired t-tests relative to controls; LMNA or LMNB1 transcript levels  and lamin A LADs (‘A-LADs’) by chromatin immunoprecipitation-sequencing (ChIP-seq) of lamin B1 and lamin A/C, respectively. Of note, the anti-lamin A/C ChIP antibody (Santa-Cruz sc7292x) immunoprecipitated not only lamin A/C but also pre-lamin A ), rulingde novo A-LADs with pre-existing B-LADs.We therefore examined the overlap of A- and B-LADs in control and CsA-treated cells by computing Jaccard indices (Ji) of their genome coverage ). We finTo further investigate the relationship between A-LADs and B-LADs, we divided these into LAD classes associated with lamin A only, lamin B only and both lamins A and B  and analyzed the properties of these LADs in control and CsA-treated cells (Supplementary Table S3). We find that (i) genome coverage by B-only LADs slightly increases ) due to de novo acquisition of lamin A on B LADs inferred from Jaccard indices . These −3; unpaired t-tests; –8 respectively, unpaired t-tests; We next determined whether LAD dynamics was associated with changes in gene expression. Genes in A-B, A-only and B-only LADs are not significantly differently expressed after CsA treatment , P > 0.01, unpaired t-tests) although trends are detectable. We thus asked whether genes in each LAD class in control cells showed altered expression as a function of LAD class fate after CsA treatment. Due to the low number of genes in LAD classes of CsA-treated cells, many LAD class changes were not associated with significant expression changes ). Howeve−16, unpaired t-tests; To assess whether a gain or loss of LAD or a change in LAD identity would coincide with changes in the radial positioning of these domains, we generated 3-dimensional (3D) models of the HepG2 genome and analyzed properties of the models. We used Chrom3D, a 3D genome modeling platform that integrates TAD-TAD interactions from Hi-C data, and genome interactions with the nuclear periphery from lamin ChIP-seq data ,28. Our −16). Moreover, retention of a LAD class does not result in significant repositioning. Significantly however, loss of lamin B from an A-B LAD (pair 2) or a switch from a B-only to an A-only LAD (pair 10) correlates with repositioning towards the center (P < 2.2 × 10−16). In contrast, gain of lamin B resulting from an A-only to B-only switch (pair 7), a de novo gain of A-B (pair 13) or a de novo gain of B-only (pair 15), correlates with repositioning towards the periphery (P < 2.2 × 10−16). Lastly, gain of lamin A does not correlate with significant repositioning ; however the impact of lamin A loss on radial placement depends on whether lamin B is bound in the region . Analysis of 3D genome models therefore predicts a dominant impact of lamin B over lamin A on radial positioning of the genome.We next estimated the radial repositioning of each LAD class according to their fate in CsA-treated cells ). Loss o−16, t-tests) . Thus, FISH data validate predictions from the 3D models and confirm that lamin B association influences the radial distribution of loci more prominently than lamin A.To independently validate Chrom3D model predictions on locus repositioning, we designed FISH probes to loci found in specific LAD classes in the ChIP-seq data (Supplementary Table S5). We also positioned in Chrom3D models the loci targeted by each FISH probe and measured their distance from the nucleus center ), consisper se. To address this, we expressed a Flag-tag wild-type (wt) pre-lamin A construct. Since, as we have shown earlier, wt pre-lamin A is fully processed into mature lamin A27, we also expressed a lamin A mutant (L647R) exclusively localized at the nuclear periphery as unprocessed pre- lamin A27 by retention of its pre-lamin A farnesyl group [Pre-lamin A accumulation following CsA exposure see ) could iyl group . We thenper se. Repositioning is probably independent of pre-lamin A interaction with chromatin; it is however likely dependent on lamin B, as discussed above.The data strikingly reveal no significant repositioning of the three loci in lamin A L647R vs. wt cells . Thus, overexpression of pre-lamin A (as lamin A L647R) does not recapitulate locus repositioning detected in CsA-treated cells (which show some pre-lamin A expression). This suggests that relocalization observed after CsA treatment is a CsA-linked phenotype and not a pre-lamin A expression phenotype We report a marked effect of CsA on the association of chromatin with A- and/or B-type lamins. We notably reveal distinct patterns of lamin A and B interaction with chromatin, resulting in variable LAD identities and correlated with a radial repositioning of loci concordant with predictions from 3D genome models. An account of redistribution of lamin A and B (A-B) LADs, A-only and B-only LADs between two cellular states provides biochemical evidence for previous microscopy-based observations of distinct lamin A and B micro-domains . Our resStudies of drug hepatotoxicity have revealed an accumulation of pre-lamin A in HepG2 cells exposed to an inhibitor of isoprenylcysteine carboxylmethyltransferases (ICMTs) . Pre-lamat the nuclear periphery at the single-cell level. In addition, lamin B-chromatin interactions detected by ChIP from millions of cells do not imply peripheral association of loci in all cells. Our results extend previous findings [12 [A key question in understanding spatial genome architecture is whether changes in lamin-chromatin interactions reported here occur at the nuclear periphery or in the nuclear interior. Pre-lamin A is detected at the nuclear envelope but could also be nucleoplasmic (if not farnesylated ), suggesfindings ,32,33 anfindings . They alings [12 ,18,35,36ings [12  or euchrings [12 .Lamins A and B are important regulators of spatial genome organization ,22,38, a21 but also a more local depletion promotes a restricted conformation change manifested by increased chromatin mobility. It will be relevant to investigate the impact of a local loss of LAD on the biophysical properties of chromatin in the vicinity.Global loss of lamin A results in increased chromatin mobility in the nuclear interior . A loss de novo, we predict and observe locus repositioning towards the periphery. In contrast, in the local absence of lamin B, A-LAD loss correlates with repositioning towards the center. This suggests that lamin B is an important factor in the directional radial organization of chromatin. The surrounding LAD neighborhood could also influence LAD behavior: LAD repositioning may not only depend on the LAD class per se, but also on LADs in the vicinity. For instance, absence of repositioning of a B-only LAD towards the nuclear center after losing lamin B, as seen by FISH, could be due to an A-B LAD in the vicinity. Thus, radial genome positioning may be influenced by a combination of both local and medium-range contributions of the linearly nearest region. This idea extends our previous observations that a lamin A neighborhood affects expression of genes in that neighborhood even though they are not bound by lamin A18.Notwithstanding, repositioning of A-LADs upon loss of lamin A depends on whether lamin B is in the neighborhood. When a lamin B LAD appears These findings raise the question of which factors may regulate the radial positioning of chromatin. Nuclear envelope proteins , histoneIt is becoming increasingly clear that patterns and mechanisms of nuclear lamin association with the genome are more complex and dynamic than previously understood. Functional and live-cell imaging studies are needed in order to fully understand the mechanisms by which lamins contribute to organizing the 3D conformation of chromatin in the nucleus.HepG2 cells were cultured in DMEM/F12 (Life Technologies) with 10% fetal calf serum and 1% penicillin-streptomycin. Cells were also cultured with 10 µM CsA  for 72 h before harvest.HepG2 cells were transiently transfected with pCMV-Flag-preLA-WT (lamin A wt) and pCMV-Flag-preLA-L647R (pre-lamin A L647R) plasmids  using LiProteins were separated by 10% SDS-PAGE, transferred onto an Immobilon-FL membrane (Millipore) and membranes blocked with Odyssey blocking buffer (LI-COR). Membranes were incubated in Odyssey blocking buffer with antibodies against lamin A/C Cells cultured on coverslips were fixed with 3% paraformaldehyde and permeabilized for 10 min in PBS/0.1% Triton X-100. Cells were blocked with 2% BSA in PBS/0.1% Tween (PBST-BSA) for 30 min and incubated with primary antibodies in PBST-BSA overnight at 4°C. Cells were incubated with secondary antibodies in PBST-BSA for 45 min and washed in PBST-BSA before mounting with DAPI . Antibodies used were mouse anti-lamin A/C , goat anti-pre-lamin A , anti-mouse Alexa Fluor® 594 and anti-goat Alexa Fluor® 488 .oC with Avidin-Alexa Fluor 488 . Slides were washed in 4x SSC/0.1% Tween 20 and incubated with Biotinylated Anti-Avidin D  for 45 min at 37°C. Slides were washed and incubated with Avidin-Alexa Fluor 488 as above and mounted with DAPI in Dako Fluorescent Mounting Medium.FISH was done as described . Cells wBased on ChIP-seq profiles, FISH probes were designed for regions changing LAD class after CsA (Supplementary Table S5). Since HepG2 cells harbor copy number variations, probes were designed outside these areas. 100 FISH images  were analyzed using FISHfinder  to calcuImmuno-FISH was done as described . Transfe6 cells per ChIP) were cross-linked with 1% formaldehyde and lysed in RIPA buffer . To generate 200–500 bp DNA fragments, cells were sonicated 4 × 10 min in a Bioruptor (Diagenode). After sedimentation, the supernatant was diluted 10x in RIPA and incubated with anti-lamin A/C  or lamin B1  antibodies coupled to Dynabeads Protein G (Invitrogen). ChIP samples were washed 3x in ice-cold-RIPA. Samples were incubated for 6 h at 68°C in elution buffer  and 40 ng proteinase K for crosslink reversal and DNA elution. DNA was extracted and libraries prepared and sequenced on an Illumina HiSeq2500.ChIP was done as described . Cells (ENCODE ChIP input data for HepG2 cells were downloaded from NCBI GEO accession No. GSE29611. Lamin A and B ChIP-seq reads and input reads were mapped to hg19 using Bowtie v2.25.0  with defHi-C data of HepG2 cells were downloaded from ENCODE (GEO accession No. GSM2825569)  and procWe applied Chrom3D to generate 3D genome models from Hi-C and lamin B1 ChIP-seq data ,28. We gTotal RNA was isolated from two CsA exposure experiments using the RNeasy Mini Kit (Qiagen). Libraries were sequenced on an Illumina HiSeq2500. RNA-seq reads were processed with Tuxedo . TopHat FPKM levels from RNA-seq data were compared using unpaired t-tests. For LAD size comparisons, a 2-way ANOVA was first applied to determine the global significance of CsA treatment vs. control. A Tukey honest significant difference (HSD) test was then applied to determine the significance of any difference between LAD classes. Comparisons of normalized LAD distances to the nuclear center in Chrom3D models and in FISH analyses were done using unpaired t-tests.Our ChIP-seq and RNA-seq data are available under GEO accession No. GSE119631. ChIP input data are available under GEO accession No. GSE29611. Hi-C data for HepG2 cells  are avai


Dear Editor,Similar to higher-order folding of polypeptide chains into functional proteins, linear DNA molecules are spatially folded in a hierarchical and dynamic manner into three-dimensional (3D) functional chromatin structures in eukaryotic nuclei  and Tcr (T-cell receptor) gene clusters in the immune system  gene clusters in the nervous system   of the Pcdh α, β, and γ gene clusters, there is a “CGCT” box-containing conserved sequence element (CSE)   whose methylation on the “CGCT” box precludes the binding of CTCF proteins  within each variable-exon coding region of each member (except αc1 and αc2) of the Pcdhα gene cluster . The downstream regulatory regions of the Pcdhα cluster were initially identified as clusters of DNase I hypersensitive sites (HS1-15) and function as strong enhancers by a luciferase reporter in transgenic mice and by targeted deletion in ES cells  are flanked by two reverse-oriented CBSs (HS5-1a and HS5-1b)   is not associated with any CBS . In situ CRISPR inversion of the HS5-1 enhancer demonstrated that its activity is not orientation-independent in the native chromosomal context in vivo . Remarkably, inversion of the CBS HS5-1b only or with the middle enhancer region results in a significant decrease of long-distance DNA-looping interactions between the HS5-1 enhancer and Pcdhα promoters, and a corresponding increase of long-distance DNA-looping interactions with the Pcdhβγ promoters  switch chromatin-looping directions . We conclude that single CBSs at domain boundaries determine chromatin looping orientation and function as insulators.To assess functional consequences of the altered chromatin looping, we performed RNA-seq experiments and found that each CRISPR inversion cell line with disrupted architecture of the Pcdh βγ clusters, which is located downstream of Pcdhγ cluster, contains tandem reverse CBSs and one forward CBS   experiments and found that HS7L, HS5-1bL, and HS18-20 form specific long-distance chromatin interactions with the promoter regions of the expressed Pcdhβγ genes in Neuro2A cells . To investigate the roles of these variable-region CBSs in the regulation of higher-order chromatin structure of the clustered Pcdh genes, we generated large deletions of forward-oriented tandem-arrayed CBSs by Cre/LoxP-mediated site-specific trans-allelic recombination in mice in vivo . Specifically, we bred mice to generate delα and delαβ mouse lines with the deletion of 24 or 45 forward-oriented tandem CBSs, respectively, but with the Pcdhα1 CSE intact . Conformation capture 4C experiments with HS5-1, γa3, or HS5-1bL as a viewpoint (VP) revealed that there is a significant increase of long-distance chromatin interactions between the viewpoint and Pcdhα1 CSE , suggesting that these large tandem-arrayed promoter CBSs function as insulators for the Pcdhα1 promoter. By contrast, there appears no significant alteration of long-distance chromatin interactions between the viewpoint and CBSs located downstream of the large deletions . These data demonstrated that the tandem-arrayed promoter CBSs function as insulators and that these insulators alter long-distance chromatin interactions of upstream but not downstream CBSs, thus functioning in an asymmetric manner in vivo.The variable regions of the three in vitro, we generated a series of luciferase reporter constructs . Interestingly, the pair of tandem CBSs in the forward-reverse orientation flanking the luciferase reporter has the strongest enhancer-blocking activity . This demonstrates that CBSs function as enhancer-blocking insulators in an orientation-dependent manner in the luciferase reporter assay in vitro, similar to functioning in the none-orientation-independent manner in vivo.To investigate whether the insulation activity of CBSs is orientation-dependent Pcdh and β-globin CBSs determine the directional looping between the distal enhancer and its target promoters . Finally, our CBSs b-e inversion in mice in vivo is consistent with previous deletion experiments that this region mainly enhances promoter activities of members of the Pcdhβ cluster Below is the link to the electronic supplementary material.


Flowering is a key process in the life cycle of plants. The transition from vegetative to reproductive growth is thus under sophisticated regulation by endogenous and environmental signals. The plant-specific Teosinte Branched 1/Cycloidea/Proliferating Cell Factors (TCP) family transcription factors are involved in many biological processes, but their roles in regulating flowering have not been totally elucidated.TCP8 in plant development and, especially, in flowering control. Overexpression of TCP8 significantly delayed flowering under both long-day and short-day conditions and dominant repression by TCP8 led to various growth defects. The upregulation of TCP8 led to more accumulated mRNA level of FLOWERING LOCUS C (FLC), a central floral repressor of Arabidopsis. TCP8 functions in an FLC-dependent manner, as TCP8 overexpression in the flc-6 loss-of-function mutant failed to delay flowering. The vernalization treatment could reverse the late flowering phenotype caused by TCP8 overexpression.We explored the role of Arabidopsis TCP8 in flowering control and add to our knowledge of the molecular basis of TCP8 function.Our results provide evidence for a role of  Arabidopsis thaliana  have identified major flowering-related genes, these genes were found to be involved in photoperiod, vernalization, gibberellin, aging, temperature, and the autonomous pathways [FLOWERING LOCUS C (FLC) is involved in both the vernalization and the autonomous pathways. During vernalization, FRIGIDA (FRI) activates FLC expression while prolonged cold repressed FLC expression [FLOWERING CONTROL LOCUS A (FCA), FLOWERING LOCUS PA (FPA), FLOWERING LOCUS KH DOMAIN (FLK), FLOWERING LOCUS Y (FY), LUMINIDEPENDENS (LD), FLOWERING LOCUS VE (FVE), and FLOWERING LOCUS D (FLD) repress FLC expression under both long-day (LD) and short-day (SD) conditions [FLOWERING LOCUS T (FT) and SUPPRESSOR OF OVEREXPRESSION OF CONSTANS 1 (SOC1) to repress flowering [FLC acts as a central floral repressor by converging the signal from different pathways in a dose-dependent manner [FLC locus not only shows the delicate regulation involving flowering control, but also provides an important platform for discovering epigenetic regulation of gene expression [The transition from vegetative to reproductive growth is one of the most important processes in the life cycle of flowering plants. As a result, flowering is under strict and sophisticated regulation by multiple endogenous cues and environmental signals . Many stpathways –4. For epression , 6. In tnditions , 8. The lowering , 10. FLCt manner . Its expt manner –14. ThroTCP family consists of 24 genes and can be further divided into two clades, class I and class II, based on their sequence features [SOC1 expression [CONSTANS (CO) promoter to induce flowering [CIRCADIAN CLOCK ASSOCIATED 1 (CCA1), while TCP21 represses CCA1 expression [Members of the Teosinte Branched 1/Cycloidea/Proliferating Cell Factors (TCP) family are plant-specific transcription factors involved in many biological processes including flowering . The Arafeatures . Involvepression , and TCPlowering , 19. Sevlowering , 21. TCPpression , 23. DesISOCHORISMATE SYNTHASE 1 promoter to activate plant immune response [EF-TU RECEPTOR in planta during pathogenesis [TCP8 in flowering control by overexpression. We found that TCP8 overexpression delays plant flowering in an FLC-dependent manner. Furthermore, TCP8 and its homologs are indispensable for plant development.Our previous work demonstrated TCP8, a member of class I TCPs, directly binds to the response . Besidesogenesis . In addiogenesis , 27. Theogenesis –33, but TCP8 at different developmental stages in detail, a 1.5 kb promoter region upstream of the start codon of TCP8 was fused with the GUS gene and the construct was transformed into Columbia-0 (Col-0). Several independent transgenic lines exhibited similar patterns of GUS expression. TCP8 showed a ubiquitous expression pattern in the transgenic plants—GUS signal was mainly detected in the vascular bundles in cotyledons, primary roots, hypocotyls and rosette leaves throughout development . Consistently, TCP8 expression was detected in all the tissues tested—including rosette leaves, cauline leaves, stem, inflorescence and root  in Col-0 and homozygous T3 progeny were analyzed. The 35S::TCP8 plants showed a clear late-flowering phenotype compared with wild-type (WT) Col-0 under the long-day (LD) condition, and the extent of delay well correlated with the relative transcription levels of TCP8  condition  was obtained from ABRC to investigate the role of TCP8 in flowering. However, we observed no visible defect in flowering and development , which directly interacts with Suppressor of FRIGIDA 4—a transcriptional activator of FLC [FLC mRNA level through interaction with MOS1. Alternatively, TCP8 may compete the interaction between TCP14, TCP15 with MOS1, as protein-protein interactions between TCPs are prevalent [FT [TEM1 and TEM2 in TCP8 overexpression lines under LD condition  was obtained from the Arabidopsis Biological Resource Center  and genotyped as previously reported [flc-6 (SALK_041126) by floral dipping and the transgenic plants were selected by half-strength Murashige and Skoog (MS) medium supplemented with proper antibiotic. For TCP8 overexpression lines, homozygous T3 transgenic plants were used for further analysis. For the co-segregation analysis, 35S::TCP8 or pTCP8::TCP8 plants crossed with flc-6, and the resulting F2 progenies in each population were genotyped. All primers used for genotyping are listed in Additional file All mutants and transgenic lines used in this study were in the reported . For tra− 2 s− 1, with a 16 h light/8 h dark photoperiod for LD and 8 h light/16 h dark photoperiod for SD. The number of rosette leaves was counted after the main stem has bolted to 2 cm. For the vernalization treatment, the imbibed seeds were grown on half-strength MS medium at 4 °C for 4 weeks under dim light and then transferred to 22 ± 1 °C and grown under LD condition.Imbibed seeds were sowed and stratified in a cold room at 4 °C for 3 days to break dormancy and then transferred to 22 ± 1 °C at a light intensity of approximately 120 μmol mTCP8 translation start site was amplified by PCR using primers pTCP8–1582-S/A, and then cloned into the pCAMBIA1301 (GeneBank accession number AF234297) vector through KpnI/BglII double digestion to generate the pTCP8::GUS construct (the TCP8 promoter-driven GUS expression construct). For TCP8 overexpression, the coding sequence (CDS) of TCP8 was amplified using primers OE-TCP8-S/A and cloned into the pCHF3 vector by SacI/BamHI double digestion to generate the 35S::TCP8 vector. For pTCP8::TCP8 vector construction, a DNA fragment containing the TCP8 coding region as well as the 1582 bp promoter region was amplified from Col-0 genomic DNA using primers pTCP8-TCP8-S/A and cloned into the pCAMBIA1301 through PstI/BstpI double digestion. To construct the 35S::TCP8-EAR vector, DNA fragment containing the EAR motif was synthesized using primers EAR-S/A and cloned into the 35S::TCP8 vector via BamHI/PstI double digestion. To construct the pTCP8::TCP8-EAR vector, a 1351 bp promoter fragment upstream of the TCP8 translation start site was amplified using primers pTCP8–1351-S/A and cloned into the 35S::TCP8-EAR vector through EcoRI/SacI double digestion. Primers used for vector construction are summarized in Additional file A 1582 bp promoter fragment upstream of the − 1 X-Gluc, 0.2 mM K3Fe(CN)6, 0.2 mM K4Fe(CN)6 and 0.2% Triton X-100, pH = 7.2) in the vacuum for 15 min at room temperature and incubated at 37 °C overnight. The samples were then washed with 70% ethanol several times until transparent before examined under the microscope.For the histochemical detection of GUS activity, plant tissues were immersed in the 5-Bromo-4-chloro-3-indolyl β-D-glucuronic acid (X-Gluc) solution , DNase I (TaKaRa) was used to wipe out genomic DNA. Two micrograms of total RNA of each sample was used for first-strand cDNA synthesis with M-MLV reverse transcriptase (TaKaRa).To investigate the transcript profile of ACTIN2 was used as the endogenous control for normalizing the transcript levels of the tested genes. All experiments were performed in three independent biological replicates and three technical replicates. Data are represented as mean ± standard deviation (S.D.). Asterisks indicate significant differences relative to control . Primers used for RT-qPCR are listed in Additional file RT-qPCR was performed as previously described . BrieflyAdditional file 1: Figure S1.TCP8 overexpression hinders plant growth. a. Twenty-day-old wild-type Col-0 and 35S::TCP8 transgenic plants grown in LD condition. b. Sixty-day-old wild-type Col-0 and 35S::TCP8 transgenic plants grown in LD condition. (LD: 16 h of light / 8 h of dark).Additional file 2: Figure S2.pTCP8::TCP8 delays flowering. a. Relative transcription levels of TCP8 in different pTCP8::TCP8 transgenic lines detected by RT-qPCR. Data are represented as mean ± SD of three biological replicates. ACTIN2 was used as the endogenous control for normalizing the transcription levels of TCP8. The transcription level of TCP8 in Col-0 was arbitrarily set to 1. b. Forty-day-old wild-type Col-0 and pTCP8::TCP8 transgenic plants grown in LD condition. c. The number of rosette leaves in wild-type Col-0 and pTCP8::TCP8 plants before bolting .Additional file 3: Figure S3. TCP8 failed to bind the FLC promoter in vitro and in vivo. a. EMSA detection of TCP8 binding to FLC promoter fragment. The ICS1 promoter containing a TCP binding site was used as a positive control. b. TCP8 failed to co-precipitated DNA fragments around FLC locus in the chromatin immunoprecipitation (ChIP) assay. The ICS1 promoter was used as a positive control. The 18S rRNA gene was used to normalize the quantitative PCR results for each of the ChIP samples. Values are means ± SD of three quantitative PCR measurements.Additional file 4: Figure S4. Detection of autonomous pathway genes and COOLAIR levels in TCP8 overexpression plants. a. Overexpression of TCP8 down-regulates a set of autonomous pathway genes expression. Relative transcription levels of autonomous pathway genes in TCP8 overexpression transgenic lines detected by RT-qPCR. Data are represented as mean ± SD of three biological replicates. ACTIN2 was used as the endogenous control for normalizing the transcription levels of genes detected. The transcription level of each gene in Col-0 was arbitrarily set to 1. (Student’s t test: * P < 0.05) b. Relative transcription levels of different COOLAIR isoforms in TCP8 overexpression transgenic lines detected by RT-qPCR. Data are represented as mean ± SD of three biological replicates. ACTIN2 was used as the endogenous control for normalizing the transcription levels of different COOLAIR isoforms. The transcription level of COOLAIR in Col-0 was arbitrarily set to 1.Additional file 5: Figure S5. The flowering phenotypes of pTCP8::TCP8 and flc-6 crossed F2 progenies.Additional file 6: Figure S6. The flowering phenotypes of tcp8–1 in LD condition. a. Forty-day-old plants of different genotypes grown in LD condition. b. The number of rosette leaves in Col-0 and tcp8–1 plants before bolting.Additional file 7: Figure S7. Detection of TEM1 and TEM2 expression levels in TCP8 overexpression plants in LD condition. Forty-day-old plants of different genotypes grown in LD condition. Relative transcription levels of TEM1 and TEM2 in TCP8 overexpression transgenic lines detected by RT-qPCR. Data are represented as mean ± SD of three biological replicates. ACTIN2 was used as the endogenous control for normalizing the transcription levels of genes detected. The transcription level of each gene in Col-0 was arbitrarily set to 1. (Student’s t test: * P < 0.05).Additional file 8: Table S1. Primers used in this paper.


Common fragile sites (CFSs) are chromosome regions prone to breakage upon replication stress known to drive chromosome rearrangements during oncogenesis. Most CFSs nest in large expressed genes, suggesting that transcription could elicit their instability; however, the underlying mechanisms remain elusive. Genome-wide replication timing analyses here show that stress-induced delayed/under-replication is the hallmark of CFSs. Extensive genome-wide analyses of nascent transcripts, replication origin positioning and fork directionality reveal that 80% of CFSs nest in large transcribed domains poor in initiation events, replicated by long-travelling forks. Forks that travel long in late S phase explains CFS replication features, whereas formation of sequence-dependent fork barriers or head-on transcription–replication conflicts do not. We further show that transcription inhibition during S phase, which suppresses transcription–replication encounters and prevents origin resetting, could not rescue CFS stability. Altogether, our results show that transcription-dependent suppression of initiation events delays replication of large gene bodies, committing them to instability. Common Fragile Sites (CFSs) are chromosome regions prone to breakage upon replication stress known to drive chromosome rearrangements during oncogenesis. Here the authors use genome-wide and single cell techniques to assess how replication timing and transcriptional activity correlate with genome stability. Nevertheless, conventional cytogenetic analyses have shown that in stressed cells, some regions of the genome, notably common fragile sites (CFSs), display breaks in metaphase chromosomes, suggesting that replication is recurrently not completed at these regions2. Therefore, CFSs are major drivers of genome instability and subsequent chromosome alterations associated with human diseases, notably cancer3. CFSs have long been associated with very large, mega-base (Mb)-sized genes4, some of which behave as tumour suppressors6. In addition, many very large genes have been associated with inherited diseases, such as neurodevelopmental and neuropsychiatric disorders7.The entire genome should be duplicated once and only once during each cell cycle to maintain genome integrity. Under normal growth conditions, the firing of tens of thousands of adequately distributed replication origins is needed to ensure the proper duplication of the human genome before mitotic onset. In cells exposed to mild replication stress, the overall replication rate is supported by the recruitment of a large pool of extra-origins, an adaptation process called compensation8. Strikingly, these genes and the associated CFSs are conserved in mouse8 and chicken cells9. A pioneer work focussing on five large genes has shown that CFSs are instable only in cells where the corresponding genes are expressed10. A recent chromatin immunoprecipitation-sequencing (ChIP-Seq) analysis of FANCD2, a factor that binds preferentially to CFSs from S-phase to mitosis upon replication stress11, has confirmed genomewide that CFSs colocalize with large transcribed genes in human13 and in chicken cells9 grown in vitro. Remarkably, this correlation between CFSs and large transcribed genes has been detected also by extensive mapping of copy number variations in a large series of tumours14. Therefore, it is now clear that transcription plays a major role in CFS setting, thus explaining why different subsets of large genes are fragile in different cell types.Molecular mapping of CFSs in different human cell types has further extended the correlation between large genes and CFSs to genes more than 300 kb long and, importantly, pointed out that CFSs display tissue-specific instability10. When replication and transcription interfere, the most deleterious situation arises upon head-on encounters that might favour the formation and stabilization of R-loops, namely DNA/RNA hybrids resulting from annealing of the nascent transcript with the template DNA strand16. It was therefore proposed that R-loops frequently form in the body of large genes, which delays fork progression and leads to CFS under-replication upon replication stress17. A variant of this model proposes that stress-induced uncoupling of the replicative helicase from DNA polymerases gives rise to single-stranded DNA, which elicits the formation of DNA secondary structures at particular sequences, notably at stretches of AT dinucleotides. These structures would further delay polymerase progression, increasing the frequency of replication–transcription encounters19.Two main models have been proposed to explain the susceptibility of large expressed genes to replication stress. The first one relies on the hypothesis that transcription of such genes takes more than one cell cycle, and that, consequently, the transcription and replication machineries will necessarily encounter during S phase11. The strong delay of replication completion that specifically occurs upon fork slowdown might elicit CFS instability. In this scenario, the role of transcription could be to remove origins from the gene body, as previously shown in various models24. A direct support to this view was recently provided experimentally by replacing the endogenous promoter of three large genes with promoters of various strengths, which has shown that the transcription level dictates the density of initiation events across the gene body25. However, many large transcribed genes escape fragility, indicating that transcription per se is not sufficient to commit them to instability8.The second model is based on molecular combing analyses of the distribution of initiation events along three CFSs, showing that the body of the hosting genes is origin-poor under normal growth conditions and/or upon stress. Consequently, these genes are replicated by long-travelling forks emanating from the flanking regions11. Nevertheless, it is not clear whether and how the replication timing affects CFS stability. Here we report Repli-Seq analysis of the replication programme in human lymphoblasts grown in the absence or in the presence of aphidicolin (Aph), an inhibitor of replicative DNA polymerases used in vitro to destabilize CFSs2. We identified regions that displayed specific replication delay upon Aph treatment, resulting in under-replication. More than 80% of these delayed/under-replicated regions nest in chromosome domains that are transcribed continuously across at least 300 kb and poor in replication initiation events, and thus are replicated by long-travelling forks. Strikingly, the orientation of these forks relative to transcription is neutral for establishment of delayed/under-replication. We further showed that these regions correspond to a major class of CFSs. Noteworthy, inhibition of transcription in cells already engaged in the S phase, a condition that suppresses transcription–replication encounters and prevents origin resetting26, did not alleviate CFS instability. Altogether, our results demonstrate genome-wide that transcription-dependent segregation of initiation events out of the gene body generates long-travelling forks in large transcribed domains, which elicits the replication timing delay responsible for CFS instability upon replication stress.Replication timing is another parameter often evoked to explain CFS susceptibility to replication stress. Indeed, the risk of being under-replicated at mitotic entry might be higher for late-replicating regionsR > 0.97, P < 10−15)    or not (NT) with Aph . Negative URI (ΔAph-NT) identified regions delayed/under-replicated upon stress.Using the Repli-Seq technique Fig. , we firsP < 0.05), called below significantly delayed windows (SDWs), among which 314 reside in domains with a S50 ≥ 0.5  , which is the moment in S phase when a sequence has been replicated in 50% of cells (Methods). We found that approximately half of the genome, essentially domains with S50 ≥ 0.5 (mid-late and late), displayed URI ≤ 0, whereas the rest of the genome showed URI ≥ 0 Fig. . Importa0.5 Fig. . Genome-n = 62) and late  replicating domains and are further delayed upon stress  expressed genes   from untreated GM06990 lymphoblastses Figs. . The humes Figs. . Noticea−5) Fig. .SEMA5 in Fig. We also found that four SDRs nested within large regions displaying strong GRO-Seq signal coming either from still non-annotated genes or from non-coding sequences and 11 SDRs were within long-transcribed domains (>300 kb) harbouring 2–3 adjacent genes, the individual size of which could be smaller than 300 kb , were poor in initiation events when compared with the genome-wide distribution (KS test P < 10−15)  Fig. . This fi15) Fig.  determinnd Figs.  and 3a. FHIT). A previous analysis of unstressed cells by molecular combing has shown that the FHIT gene displays an initiation poor core extending for about 800 kb, and that replication forks travel along the gene at ≈1.8 kb/min, like in the bulk genome11. In these conditions, convergent forks would need 8–9 h to complete FHIT replication, in agreement with the replication kinetics observed here , independently of the orientation and size of the genes 19 allowed us to precisely compare the position of instable regions with that of T-SDRs/SDWs. We found a very good concordance between the position of T-SDRs/SDWs and the most instable region of all eight fine-mapped CFSs with a break frequency ≥ 1% in primary lymphocytes  from JEFF cells treated for 16 h with 600 nM Aph. Scoring of 300 metaphase plates yielded a total of 320 breaks in 59 loci  contain T-SDRs and/or T-SDWs , are poor in initiation events and are replicated in the second half of S phase in untreated cells, suggesting that a few T-SDRs/SDWs may have escaped detection . Because of the large size of cytobands and in the absence of guides such as T-SDRs/SDWs or FISH mapping, the remaining sites cannot be further studied.Twelve CFSs mapped in JEFF cells by conventional cytogenetics remain free of T-SDRs/SDWs. Among them, FRA1E and FRA6C, each containing a large active gene  agrees with the genome-wide RNA polymerase II elongation rates previously measured in the body of genes poor in G/C and in exons34, two features common to large genes hosting CFSs. We chose to pre-treat the cells with Tpl for 5 h, the shorter time required to clear the FHIT body from most nascent RNAs. We also reduced the time of Aph treatment from 16 h to 7 h, which is sufficient to induce chromosome breaks at CFSs ion Fig. . We founion Fig.  but longene Fig. . NoticeaFSs Fig. . AlthougFHIT-specific probes showing that Tpl does not suppress Aph-induced breaks at FHIT/FRA3B -labelled metaphase plates recovered from cell populations pulse labelled at the beginning of the experiment was then determined Fig. . We founA3B Fig. . We conc11 and a previous genome-wide analysis has failed to establish a link between CFS instability and replication features35. Therefore, it is unclear to what extent under-replication is common and specific to CFSs. Here we searched blind for regions delayed/under-replicated in lymphoblasts treated with Aph and studied the properties of identified loci. The main category of delayed loci corresponds to the so-called T-SDRs/SDWs that nest in large genes or adjacent genes behaving similar to a single large transcribed domain, the body of which replicates in the second half of S phase. These genes display significantly higher transcription levels than the bulk genes with similar replication timing. We then showed that almost all T-SDRs and part of the T-SDWs overlap with the most fragile region of fine-mapped CFSs or nest in cytogenetic bands hosting CFSs in primary lymphocytes for those not mapped by FISH. Noticeably, our method proved to be highly sensitive and efficient for genome-wide mapping of CFSs, as 32 out of 39 (82%) CFSs displaying a break frequency higher than 1% in conventional cytogenetic experiments in JEFF lymphoblasts were identified here at the sequence level, including 24 not yet fine-mapped.Several mechanisms have been proposed to explain CFS instability, most of them postulating that CFS replication occurs in late S phase and is further delayed upon stress. CFSs therefore tend to remain under-replicated up to mitotic onset. However, this hypothesis stems from the study of only three sites36. At least four types of observations argue against this model: (i) analysis of the nucleotide sequence of several FISH-mapped CFSs has provided contrasting results regarding the presence of such roadblocks at a substantial proportion of the sites37. In addition, recent genome-wide mapping of CFSs has failed to identify sequence features that are specific and common to CFSs9. (ii) The fact that CFS setting is tissue-dependent11 strongly questions this hypothesis, at least in this simple form (see below). (iii) We did not find abrupt drops of URI in the vicinity of specific sequences. On the other hand, global analyses revealed that the URI decreases progressively and regularly across 150–250 kb and shows a near-symmetrical slope on each side of the T-SDRs/SDWs. To account for these results, sequences forming fork barriers should distribute in a very particular manner, similar in all large fragile genes, a feature that has never been reported. For example, stretches of AT-dinucleotide repeats, a type of sequence proposed to create fork barriers, do lie in the FHIT and WWOX genes19 but the distribution of these sequences does not account for the properties mentioned above. (iv) Molecular combing analyses of fork progressing across the FHIT gene in lymphoblasts treated or not with Aph did not detect any site-specific fork slowing or stalling38. We conclude that sequence-dependent fork barriers do not account for CFS instability, except in genetic contexts that impair resolution of secondary structures41. In striking contrast, our data clearly indicate that the slopes of URI decrease, which culminates at T-SDRs/SDWs, correlate with the relative efficiency, firing time and localization of the initiation zones flanking the large fragile genes. The present work focuses on T-SDRs/SDWs, so that our conclusions are specific to CFSs. Poly dA:dT tracks have been involved in the instability of early-replicating fragile sites (ERFS), another type of instable sequences associated with short, highly transcribed and early-replicating genes43. In addition, a recent report has shown that a 3.4 kb dA:dT dinucleotide sequence targeted to the 40 kb long early-replicating HGPRT gene triggers instability of this house-keeping gene44. Therefore, sequences able to form secondary structures may become instable in some chromosome contexts, notably ERFS, but not in the context of modestly transcribed large late-replicating genes.It has been proposed that CFS instability results from fork barriers raised by sequences prone to form secondary structures24. However, as mentioned above, the URI decreases symmetrically across hundreds of kb on each side of the T-SDRs/SDWs, showing that the delay of replication completion is independent of fork direction relative to transcription direction. The finding that R-loops have a short half-life45 also argues against a major role of these structures in CFS instability. Cells indeed contain many factors, such as FANCD246, to prevent harmful R-loop accumulation and subsequent fork stalling. Together, our results strongly suggest that cells can cope with dynamic R-loops that may form in the body of large genes except in particular genetic contexts, such as deficiencies in proteins of the FANC pathway that lead to increased CFS instability49 when R-loops abnormally accumulate50.Other models rely on the association of CFSs with large expressed genes. One model proposes that CFS instability is due to head-on encounter of the transcription and replication machineries34. The time course we chose allowed us to analyse metaphase plates coming from cells in which large genes were cleared while already engaged in the S phase, a period during which the setting of new origins is prevented by redundant pathways26. We found that Aph-induced breaks at CFSs were not suppressed under these conditions, again showing that transcription–replication encounters, whether head-on or co-directional, are not a significant source of CFS instability. Our results also strongly argue against the model proposing that CFS instability results from a combination of R-loops and secondary DNA structures19.To strengthen this conclusion, we directly checked the impact of transcription–replication encounters on CFS instability. To this aim, we treated the cells with Tpl to clear the genes from the transcription machinery and nascent RNA51 and ORC2-52 ChIP-Seq experiments have revealed an under-representation of these components of the pre-replication complex26 across genes associated with CFSs. Transcription-mediated chase of initiation events has been reported in the bulk genes24 and in large fragile genes25 of various organisms. Transcription therefore shapes the replication initiation profile along transcribed sequences independently of their size and replication timing. However, fork slowing perturbs more drastically replication completion of loci replicated by long-travelling forks, a feature determined both by the size of the transcribed domain and by the degree of initiation paucity of the gene body, the latter property being controlled by the level of transcription25.The results presented here point to initiation paucity as the major cause of CFS instability. Consistently, MCM7-12. We also showed here that the flanking initiation zones often fire in the first half of S phase, sometimes very early, indicating that long-travelling forks are strongly involved in late replication completion of the large gene bodies in unchallenged cells and in their under-replication upon slowing. Therefore, transcription-dependent modulation of the initiation programme dictates the tissue-dependent landscape of CFSs.In contrast to other models, the specific delay of replication completion elicited upon slowing of long-travelling forks together with the relative properties of the initiation zones flanking the large genes readily account for the slope of URI decrease on both sides of the T-SDRs/SDWs. Moreover, the size of the regions along which the URI decreases on each size of the T-SDRs/SDWs  is consistent with the 300 kb threshold repeatedly reported for genes hosting CFSs38. Aph and Tpl were obtained from Merck .JEFF cells  were grown as previously described27. Exponentially growing lymphoblastoid cells (~200 × 106) were pulse-labelled with 50 μM BrdU  prior to recovery. Untreated cells were pulse-labelled for 1 h and Aph-treated cells  were labelled during the last 3 h  and the paired-end reads that were mapped properly to a unique position of the genome were kept for downstream analysis. The sequence reads located within the regions likely resulting from the sequencing hotspots (defined as the 0.5% windows with the highest amount of reads within 200 bp windows) were also removed. The read density  was then computed for each 50 kb non-overlapping windows (w) for each sample Si corresponding to the different S phase fractions (i = 1–6) as well as for the control sample of cells within entire S phase named S0 . The background levels were then estimated as previously described:53 a background window in an Si fraction was defined as a window that is not enriched compared to the control window in the adjacent fraction(s) and enriched in the nonadjacent fraction(s). The replication timing, S50, defined as the moment in S phase, on a scale from 0 (Early) to 1 (Late), at which a given sequence has been replicated in 50% of the cells, was computed by linear interpolation of the enrichment values in the six compartments of S phase. When a region was not significantly enriched in all six Si periods, no S50 value was attributed . The S50 values of biological replicates were strongly correlated to each other   and the S50 were computed.The Repli-Seq data were demultiplexed using the distribution of CASAVA software (CASAVA-1.8.2 bcl2fastq2 v2.18.12). Illumina adapters were removed using Cutadapt-1.15, keeping only reads with a minimal length of ten nucleotides. The reads were mapped on the human genome (Hg19), the chicken genome  and the salmon genome  using bwa-0.6.2-r126. The mapped data were then processed as previous describedZ-score computed on the Δ(Aph − NT) by using all 50 kb window along the human genome, where the Z-scorei = σ were computed by all n = 330, 0.54% of genome) were defined as windows with a significantly delay (P < 0.05), called SDWs. To limit the false positive results, the windows with too low or too high amounts of reads were removed and only the windows with average coverage, i.e., mean, between 20 and 40 after normalization, were retained. The SDWs were frequently close to each other and formed clusters  passing the filtering process and separated by a distance < 250 kb (maximum distance between adjacent SDWs located within fine-mapped CFSs) were then merged and defined as an SDR.To identify the genomic loci, the replication of which is specifically delayed upon Aph treatment, we computed the difference between the amount of newly replicated DNA measured by Repli-Seq in Aph-treated cells (Aph) and in non-treated cells (NT). A URI was defined as the mentclass2pt{minimhttps://www.ncbi.nlm.nih.gov/geo/download/?acc=GSM758559&format=file&file=GSM758559%5Fhg19%5FwgEncodeCshlLongRnaSeqGm12878CellPapGeneGencV7%2Egtf%2Egz]) were used. Annotation of genes was retrieved according to gencode V7. The level of transcription was calculated in RPKM (reads per kilobase per mmillion mapped reads) for each protein coding gene. The genes with RPKM > 0.0001 for both biological replicates were kept and the mean values of the two replicates were used. Raw GRO-Seq data of GM12878 cells generated in ref. 29 (GEO: GSM1480326) were used for measuring the transcriptional level of each gene. GRO-seq read densities on the corresponding strand were calculated for all 1 kb non-overlapping windows along each gene (gencode v7) and the median value was then computed for each gene. Inter-origin distances in GM06990 lymphoblastoid cells were calculated by using replication origins identified by Bubble-Seq30. Only the Bubble-Seq origins identified in at least two biological replicates were retained in the analysis. The RFD data of GM06990 cells determined by sequencing of Okazaki fragments (OK-Seq)31 as well as the replication initiation zones, termination zones and regions replicated by unidirectional replication forks were used (SRA: SRP065949 [https://www.ncbi.nlm.nih.gov/sra/?term = SRP065949]). The Repli-Seq, Ok-Seq, Bubble-Seq and GRO-Seq data were analysed using custom scripts written in Python (v2.7.9) and R (v3.4.4), and the data were visualized with the Integrative Genomics Viewer54.The RNA-Seq data of GM12878 cells from the ENCOE project .The populations were enriched in metaphasic cells by 2 h of treatment with 200 nM nocodazole  prior to cell recovery. Total breaks were counted on metaphase plates stained with Giemsa (Prolabo) without pre-treatments to obtain a homogeneous staining of the chromosomes. Preparations were then de-stained in 70% ethanol and treated to reveal R-bands as previously describedFive-ethynyl-uridine (EU) ) was added to the cell culture medium  during the last 30 min of treatments Fig. . Total RFurther information on research design is available in the Supplementary InformationPeer Review FileDescription of Additional Supplementary FilesSuplementary Data 1Suplementary Data 2Suplementary Data 3Reporting Summary


Dietary restriction influences longevity through changes in gene expression on the level of transcription and translation. Our lab is investigating the changes occurring under dietary restriction in C. elegans on the level of individual ribosomal proteins (RPs) and changes in RP phosphorylation and how they shape gene expression and longevity. To look at the impact of changes in RP expression we used RNAi knockdowns starting at adulthood and assayed lifespan and health span. Two RPs knockdowns that increased longevity, rpl-7A and rpl-22, were then subjected to transcriptome and translatome analysis using polysome profiling and mRNA-seq. Both knockdowns resulted in similar transcriptomic changes with an up-regulation of genes related to translational fidelity and mitochondrion organization. In contrast, the translatome analysis showed minimal changes upon loss of rpl-7A and rpl-22 suggesting that they are not responsible for increase in selective translation previously observed under DR. To look at impact of changes in phosphorylation of rps-6 on DR, we generated mutant lines incapable of being phosphorylated by TOR signaling. These mutants were assayed for changes in lifespan, health span and selective translation. While phenotypically similar to controls under well fed conditions, the mutants were longer lived under DR. Furthermore, preliminary results indicated that the selective translation of the anti-longevity microchondrial electron transport genes cytochrome c oxidase and ubiquinol-cytochrome c reductase binding protein was repressed when rps-6 is not phosphorylated. In summary, our preliminary results that ribosomal protein phosphorylation but not protein composition is responsible for guiding selective translation pf longevity genes under DR.