Person:

Pinello, Luca

Background: Genome-wide mapping of protein-DNA interactions has been widely used to investigate biological functions of the genome. An important question is to what extent such interactions are regulated at the DNA sequence level. However, current investigation is hampered by the lack of computational methods for systematic evaluating sequence specificity. Results: We present a simple, unbiased quantitative measure for DNA sequence specificity called the Motif Independent Measure (MIM). By analyzing both simulated and real experimental data, we found that the MIM measure can be used to detect sequence specificity independent of presence of transcription factor (TF) binding motifs. We also found that the level of specificity associated with H3K4me1 target sequences is highly cell-type specific and highest in embryonic stem (ES) cells. We predicted H3K4me1 target sequences by using the N- score model and found that the prediction accuracy is indeed high in ES cells.The software to compute the MIM is freely available at: https://github.com/lucapinello/mim. Conclusions: Our method provides a unified framework for quantifying DNA sequence specificity and serves as a guide for development of sequence-based prediction models.

Background: Precise nucleosome positioning is an increasingly recognized feature of promoters and enhancers, reflecting complex contributions of DNA sequence, nucleosome positioning, histone modification and transcription factor binding to enhancer activity and regulation of gene expression. Changes in nucleosome position and occupancy, histone variants and modifications, and chromatin remodeling are also critical elements of dynamic transcriptional regulation, but poorly understood at enhancers. We investigated glucocorticoid receptor-associated (GR) nucleosome dynamics at enhancers in acute lymphoblastic leukemia. Results: For the first time, we demonstrate functionally distinct modes of nucleosome remodeling upon chromatin binding by GR, which we term central, non-central, phased, and minimal. Central and non-central remodeling reflect nucleosome eviction by GR and cofactors, respectively. Phased remodeling involves nucleosome repositioning and is associated with rapidly activated enhancers and induction of gene expression. Minimal remodeling sites initially have low levels of enhancer-associated histone modification, but the majority of these regions gain H3K4me2 or H3K27Ac to become de novo enhancers. Minimal remodeling regions are associated with gene ontologies specific to decreased B cell number and mTOR inhibition and may make unique contributions to glucocorticoid-induced leukemia cell death. Conclusions: Our findings form a novel framework for understanding the dynamic interplay between transcription factor binding, nucleosome remodeling, enhancer function, and gene expression in the leukemia response to glucocorticoids. Electronic supplementary material The online version of this article (doi:10.1186/s13072-015-0046-0) contains supplementary material, which is available to authorized users.

Genome-wide mapping of three dimensional chromatin organization is an important yet technically challenging task. To aid experimental effort and to understand the determinants of long-range chromatin interactions, we have developed a computational model integrating Hi-C and histone mark ChIP-seq data to predict two important features of chromatin organization: chromatin interaction hubs and topologically associated domain (TAD) boundaries. Our model accurately and robustly predicts these features across datasets and cell types. Cell-type specific histone mark information is required for prediction of chromatin interaction hubs but not for TAD boundaries. Our predictions provide a useful guide for the exploration of chromatin organization. Electronic supplementary material The online version of this article (doi:10.1186/s13059-015-0740-z) contains supplementary material, which is available to authorized users.

High-throughput single-cell technologies have great potential to discover new cell types; however, it remains challenging to detect rare cell types that are distinct from a large population. We present a novel computational method, called GiniClust, to overcome this challenge. Validation against a benchmark dataset indicates that GiniClust achieves high sensitivity and specificity. Application of GiniClust to public single-cell RNA-seq datasets uncovers previously unrecognized rare cell types, including Zscan4-expressing cells within mouse embryonic stem cells and hemoglobin-expressing cells in the mouse cortex and hippocampus. GiniClust also correctly detects a small number of normal cells that are mixed in a cancer cell population. Electronic supplementary material The online version of this article (doi:10.1186/s13059-016-1010-4) contains supplementary material, which is available to authorized users.

Background: Clustering is one of the most well known activities in scientific investigation and the object of research in many disciplines, ranging from statistics to computer science. Following Handl et al., it can be summarized as a three step process: (1) choice of a distance function; (2) choice of a clustering algorithm; (3) choice of a validation method. Although such a purist approach to clustering is hardly seen in many areas of science, genomic data require that level of attention, if inferences made from cluster analysis have to be of some relevance to biomedical research. Results: A procedure is proposed for the assessment of the discriminative ability of a distance function. That is, the evaluation of the ability of a distance function to capture structure in a dataset. It is based on the introduction of a new external validation index, referred to as Balanced Misclassification Index (BMI, for short) and of a nontrivial modification of the well known Receiver Operating Curve (ROC, for short), which we refer to as Corrected ROC (CROC, for short). The main results are: (a) a quantitative and qualitative method to describe the intrinsic separation ability of a distance; (b) a quantitative method to assess the performance of a clustering algorithm in conjunction with the intrinsic separation ability of a distance function. The proposed procedure is more informative than the ones available in the literature due to the adopted tools. Indeed, the first one allows to map distances and clustering solutions as graphical objects on a plane, and gives information about the bias of the clustering algorithm with respect to a distance. The second tool is a new external validity index which shows similar performances with respect to the state of the art, but with more flexibility, allowing for a broader spectrum of applications. In fact, it allows not only to quantify the merit of each clustering solution but also to quantify the agglomerative or divisive errors due to the algorithm. Conclusions: The new methodology has been used to experimentally study three popular distance functions, namely, Euclidean distance d2, Pearson correlation dr and mutual information dMI. Based on the results of the experiments, we have that the Euclidean and Pearson correlation distances have a good intrinsic discrimination ability. Conversely, the mutual information distance does not seem to offer the same flexibility and versatility as the other two distances. Apparently, that is due to well known problems in its estimation. since it requires that a dataset must have a substantial number of features to be reliable. Nevertheless, taking into account such a fact, together with results presented in Priness et al., one receives an indication that dMI may be superior to the other distances considered in this study only in conjunction with clustering algorithms specifically designed for its use. In addition, it results that K-means, Average Link, and Complete link clustering algorithms are in most cases able to improve the discriminative ability of the distances considered in this study with respect to clustering. The methodology has a range of applicability that goes well beyond microarray data since it is independent of the nature of the input data. The only requirement is that the input data must have the same format of a "feature matrix". In particular it can be used to cluster ChIP-seq data.

Inappropriate activation of the transcription factors STAT3 and STAT5 has been shown to drive cancer pathogenesis through dysregulation of genes involved in cell survival, growth, and differentiation. Although STAT3 and STAT5 are structurally related, they can have opposite effects on key genes, including BCL6. BCL6, a transcriptional repressor, has been shown to be oncogenic in diffuse large B cell lymphoma. BCL6 also plays an important role in breast cancer pathogenesis, a disease in which STAT3 and STAT5 can be activated individually or concomitantly. To determine the mechanism by which these oncogenic transcription factors regulate BCL6 transcription, we analyzed their effects at the levels of chromatin and gene expression. We found that STAT3 increases expression of BCL6 and enhances recruitment of RNA polymerase II phosphorylated at a site associated with transcriptional initiation. STAT5, in contrast, represses BCL6 expression below basal levels and decreases the association of RNA polymerase II at the gene. Furthermore, the repression mediated by STAT5 is dominant over STAT3-mediated induction. STAT5 exerts this effect by displacing STAT3 from one of the two regulatory regions to which it binds. These findings may underlie the divergent biology of breast cancers containing activated STAT3 alone or in conjunction with activated STAT5.

Background: The cohesin complex consists of multiple core subunits that play critical roles in mitosis and transcriptional regulation. The cohesin-associated protein Wapal plays a central role in off-loading cohesin to facilitate sister chromatid separation, but its role in regulating mammalian gene expression is not understood. We used embryonic stem cells as a model, given that the well-defined transcriptional regulatory circuits were established through master transcription factors and epigenetic pathways that regulate their ability to maintain a pluripotent state. Results: RNAi-mediated depletion of Wapal causes a loss of pluripotency, phenocopying loss of core cohesin subunits. Using chromatin immunoprecipitation coupled with next-generation sequencing (ChIP-seq), we determine that Wapal occupies genomic sites distal to genes in combination with CTCF and core cohesin subunits such as Rad21. Interestingly, genomic sites occupied by Wapal appear enriched for cohesin, implying that Wapal does not off-load cohesin at regions it occupies. Wapal depletion induces derepression of Polycomb group (PcG) target genes without altering total levels of Polycomb-mediated histone modifications, implying that PcG enzymatic activity is preserved. By integrating ChIP-seq and gene expression changes data, we identify that Wapal binding is enriched at the promoters of PcG-silenced genes and is required for proper Polycomb repressive complex 2 (PRC2) recruitment. Lastly, we demonstrate that Wapal is required for the interaction of a distal cis-regulatory element (CRE) with the c-Fos promoter. Conclusions: Collectively, this work indicates that Wapal plays a critical role in silencing of PcG target genes through the interaction of distal CREs with promoters. Electronic supplementary material The online version of this article (doi:10.1186/s13072-016-0063-7) contains supplementary material, which is available to authorized users.

Little is known about how pro-obesity diets regulate tissue stem and progenitor cell function. Here we find that high fat diet (HFD)-induced obesity augments the numbers and function of Lgr5+ intestinal stem-cells (ISCs) of the mammalian intestine. Mechanistically, HFD induces a robust peroxisome proliferator-activated receptor delta (PPAR-d) signature in intestinal stem and (non-ISC) progenitor cells, and pharmacologic activation of PPAR-d recapitulates the effects of a HFD on these cells. Like a HFD, ex vivo treatment of intestinal organoid cultures with fatty acid constituents of the HFD enhances the self-renewal potential of these organoid bodies in a PPAR-d dependent manner. Interestingly, HFD- and agonist-activated PPAR-d signaling endow organoid-initiating capacity to progenitors, and enforced PPAR-d signaling permits these progenitors to form in vivo tumors upon loss of the tumor suppressor Apc. These findings highlight how diet-modulated PPAR-d activation alters not only the function of intestinal stem and progenitor cells, but also their capacity to initiate tumors.

Chromatin-state analysis is widely applied in the studies of development and diseases. However, existing methods operate at a single length scale, and therefore cannot distinguish large domains from isolated elements of the same type. To overcome this limitation, we present a hierarchical hidden Markov model, diHMM, to systematically annotate chromatin states at multiple length scales. We apply diHMM to analyse a public ChIP-seq data set. diHMM not only accurately captures nucleosome-level information, but identifies domain-level states that vary in nucleosome-level state composition, spatial distribution and functionality. The domain-level states recapitulate known patterns such as super-enhancers, bivalent promoters and Polycomb repressed regions, and identify additional patterns whose biological functions are not yet characterized. By integrating chromatin-state information with gene expression and Hi-C data, we identify context-dependent functions of nucleosome-level states. Thus, diHMM provides a powerful tool for investigating the role of higher-order chromatin structure in gene regulation.

Summary The mechanisms contributing to transcription-associated genomic instability are both complex and incompletely understood. Although R-loops are normal transcriptional intermediates, they are also associated with genomic instability. Here, we show that BRCA1 is recruited to R-loops that form normally over a subset of transcription termination regions. There it mediates the recruitment of a specific, physiological binding partner, senataxin (SETX). Disruption of this complex led to R-loop-driven DNA damage at those loci as reflected by adjacent γ-H2AX accumulation and ssDNA breaks within the untranscribed strand of relevant R-loop structures. Genome-wide analysis revealed widespread BRCA1 binding enrichment at R-loop-rich termination regions (TRs) of actively transcribed genes. Strikingly, within some of these genes in BRCA1 null breast tumors, there are specific insertion/deletion mutations located close to R-loop-mediated BRCA1 binding sites within TRs. Thus, BRCA1/SETX complexes support a DNA repair mechanism that addresses R-loop-based DNA damage at transcriptional pause sites.