Bartek Wilczynski

Tissue-specific analysis of chromatin state identifies temporal signatures of enhancer activity during embryonic development

Chromatin modifications are associated with many aspects of gene expression, yet their role in cellular transitions during development remains elusive. Here, we use a new approach to obtain cell type–specific information on chromatin state and RNA polymerase II (Pol II) occupancy within the multicellular Drosophila melanogaster embryo. We directly assessed the relationship between chromatin modifications and the spatio-temporal activity of enhancers. Rather than having a unique chromatin state, active developmental enhancers show heterogeneous histone modifications and Pol II occupancy. Despite this complexity, combined chromatin signatures and Pol II presence are sufficient to predict enhancer activity de novo. Pol II recruitment is highly predictive of the timing of enhancer activity and seems dependent on the timing and location of transcription factor binding. Chromatin modifications typically demarcate large regulatory regions encompassing multiple enhancers, whereas local changes in nucleosome positioning and Pol II occupancy delineate single active enhancers. This cell type–specific view identifies dynamic enhancer usage, an essential step in deciphering developmental networks.

Applying dynamic Bayesian networks to perturbed gene expression data

BackgroundA central goal of molecular biology is to understand the regulatory mechanisms of gene transcription and protein synthesis. Because of their solid basis in statistics, allowing to deal with the stochastic aspects of gene expressions and noisy measurements in a natural way, Bayesian networks appear attractive in the field of inferring gene interactions structure from microarray experiments data. However, the basic formalism has some disadvantages, e.g. it is sometimes hard to distinguish between the origin and the target of an interaction. Two kinds of microarray experiments yield data particularly rich in information regarding the direction of interactions: time series and perturbation experiments. In order to correctly handle them, the basic formalism must be modified. For example, dynamic Bayesian networks (DBN) apply to time series microarray data. To our knowledge the DBN technique has not been applied in the context of perturbation experiments.ResultsWe extend the framework of dynamic Bayesian networks in order to incorporate perturbations. Moreover, an exact algorithm for inferring an optimal network is proposed and a discretization method specialized for time series data from perturbation experiments is introduced. We apply our procedure to realistic simulations data. The results are compared with those obtained by standard DBN learning techniques. Moreover, the advantages of using exact learning algorithm instead of heuristic methods are analyzed.ConclusionWe show that the quality of inferred networks dramatically improves when using data from perturbation experiments. We also conclude that the exact algorithm should be used when it is possible, i.e. when considered set of genes is small enough.

A Specialized Histone H1 Variant Is Required for Adaptive Responses to Complex Abiotic Stress and Related DNA Methylation in Arabidopsis

Stress-inducible linker histone variant is required for adaptive response of Arabidopsis to complex environmental stress. Linker (H1) histones play critical roles in chromatin compaction in higher eukaryotes. They are also the most variable of the histones, with numerous nonallelic variants cooccurring in the same cell. Plants contain a distinct subclass of minor H1 variants that are induced by drought and abscisic acid and have been implicated in mediating adaptive responses to stress. However, how these variants facilitate adaptation remains poorly understood. Here, we show that the single Arabidopsis (Arabidopsis thaliana) stress-inducible variant H1.3 occurs in plants in two separate and most likely autonomous pools: a constitutive guard cell-specific pool and a facultative environmentally controlled pool localized in other tissues. Physiological and transcriptomic analyses of h1.3 null mutants demonstrate that H1.3 is required for both proper stomatal functioning under normal growth conditions and adaptive developmental responses to combined light and water deficiency. Using fluorescence recovery after photobleaching analysis, we show that H1.3 has superfast chromatin dynamics, and in contrast to the main Arabidopsis H1 variants H1.1 and H1.2, it has no stable bound fraction. The results of global occupancy studies demonstrate that, while H1.3 has the same overall binding properties as the main H1 variants, including predominant heterochromatin localization, it differs from them in its preferences for chromatin regions with epigenetic signatures of active and repressed transcription. We also show that H1.3 is required for a substantial part of DNA methylation associated with environmental stress, suggesting that the likely mechanism underlying H1.3 function may be the facilitation of chromatin accessibility by direct competition with the main H1 variants.

Predicting spatial and temporal gene expression using an integrative model of transcription factor occupancy and chromatin state.

Precise patterns of spatial and temporal gene expression are central to metazoan complexity and act as a driving force for embryonic development. While there has been substantial progress in dissecting and predicting cis-regulatory activity, our understanding of how information from multiple enhancer elements converge to regulate a genes expression remains elusive. This is in large part due to the number of different biological processes involved in mediating regulation as well as limited availability of experimental measurements for many of them. Here, we used a Bayesian approach to model diverse experimental regulatory data, leading to accurate predictions of both spatial and temporal aspects of gene expression. We integrated whole-embryo information on transcription factor recruitment to multiple cis-regulatory modules, insulator binding and histone modification status in the vicinity of individual gene loci, at a genome-wide scale during Drosophila development. The model uses Bayesian networks to represent the relation between transcription factor occupancy and enhancer activity in specific tissues and stages. All parameters are optimized in an Expectation Maximization procedure providing a model capable of predicting tissue- and stage-specific activity of new, previously unassayed genes. Performing the optimization with subsets of input data demonstrated that neither enhancer occupancy nor chromatin state alone can explain all gene expression patterns, but taken together allow for accurate predictions of spatio-temporal activity. Model predictions were validated using the expression patterns of more than 600 genes recently made available by the BDGP consortium, demonstrating an average 15-fold enrichment of genes expressed in the predicted tissue over a naïve model. We further validated the model by experimentally testing the expression of 20 predicted target genes of unknown expression, resulting in an accuracy of 95% for temporal predictions and 50% for spatial. While this is, to our knowledge, the first genome-wide approach to predict tissue-specific gene expression in metazoan development, our results suggest that integrative models of this type will become more prevalent in the future.

Dynamic CRM occupancy reflects a temporal map of developmental progression.

Development is driven by tightly coordinated spatio‐temporal patterns of gene expression, which are initiated through the action of transcription factors (TFs) binding to cis‐regulatory modules (CRMs). Although many studies have investigated how spatial patterns arise, precise temporal control of gene expression is less well understood. Here, we show that dynamic changes in the timing of CRM occupancy is a prevalent feature common to all TFs examined in a developmental ChIP time course to date. CRMs exhibit complex binding patterns that cannot be explained by the sequence motifs or expression of the TFs themselves. The temporal changes in TF binding are highly correlated with dynamic patterns of target gene expression, which in turn reflect transitions in cellular function during different stages of development. Thus, it is not only the timing of a TFs expression, but also its temporal occupancy in refined time windows, which determines temporal gene expression. Systematic measurement of dynamic CRM occupancy may therefore serve as a powerful method to decode dynamic changes in gene expression driving developmental progression.

Active enhancer positions can be accurately predicted from chromatin marks and collective sequence motif data

BackgroundTranscriptional regulation in multi-cellular organisms is a complex process involving multiple modular regulatory elements for each gene. Building whole-genome models of transcriptional networks requires mapping all relevant enhancers and then linking them to target genes. Previous methods of enhancer identification based either on sequence information or on epigenetic marks have different limitations stemming from incompleteness of each of these datasets taken separately.ResultsIn this work we present a new approach for discovery of regulatory elements based on the combination of sequence motifs and epigenetic marks measured with ChIP-Seq. Our method uses supervised learning approaches to train a model describing the dependence of enhancer activity on sequence features and histone marks. Our results indicate that using combination of features provides superior results to previous approaches based on either one of the datasets. While histone modifications remain the dominant feature for accurate predictions, the models based on sequence motifs have advantages in their general applicability to different tissues. Additionally, we assess the relevance of different sequence motifs in prediction accuracy showing that even tissue-specific enhancer activity depends on multiple motifs.ConclusionsBased on our results, we conclude that it is worthwhile to include sequence motif data into computational approaches to active enhancer prediction and also that classifiers trained on a specific set ofenhancers can generalize with significant accuracy beyond the training set.

Using local gene expression similarities to discover regulatory binding site modules

BackgroundWe present an approach designed to identify gene regulation patterns using sequence and expression data collected for Saccharomyces cerevisae. Our main goal is to relate the combinations of transcription factor binding sites (also referred to as binding site modules) identified in gene promoters to the expression of these genes. The novel aspects include local expression similarity clustering and an exact IF-THEN rule inference algorithm. We also provide a method of rule generalization to include genes with unknown expression profiles.ResultsWe have implemented the proposed framework and tested it on publicly available datasets from yeast S. cerevisae. The testing procedure consists of thorough statistical analyses of the groups of genes matching the rules we infer from expression data against known sets of co-regulated genes. For this purpose we have used published ChIP-Chip data and Gene Ontology annotations. In order to make these tests more objective we compare our results with recently published similar studies.ConclusionResults we obtain show that local expression similarity clustering greatly enhances overall quality of the derived rules, both in terms of enrichment of Gene Ontology functional annotation and coherence with ChIP-Chip binding data. Our approach thus provides reliable hypotheses on co-regulation that can be experimentally verified. An important feature of the method is its reliance only on widely accessible sequence and expression data. The same procedure can be easily applied to other microbial organisms.

BNFinder2: Faster Bayesian network learning and Bayesian classification

Summary: Bayesian Networks (BNs) are versatile probabilistic models applicable to many different biological phenomena. In biological applications the structure of the network is usually unknown and needs to be inferred from experimental data. BNFinder is a fast software implementation of an exact algorithm for finding the optimal structure of the network given a number of experimental observations. Its second version, presented in this article, represents a major improvement over the previous version. The improvements include (i) a parallelized learning algorithm leading to an order of magnitude speed-ups in BN structure learning time; (ii) inclusion of an additional scoring function based on mutual information criteria; (iii) possibility of choosing the resulting network specificity based on statistical criteria and (iv) a new module for classification by BNs, including cross-validation scheme and classifier quality measurements with receiver operator characteristic scores. Availability and implementation: BNFinder2 is implemented in python and freely available under the GNU general public license at the project Web site https://launchpad.net/bnfinder, together with a user’s manual, introductory tutorial and supplementary methods. Contact: dojer@mimuw.edu.pl or bartek@mimuw.edu.pl Supplementary information: Supplementary data are available at Bioinformatics online.

Arabidopsis SWI/SNF chromatin remodeling complex binds both promoters and terminators to regulate gene expression

Abstract ATP-dependent chromatin remodeling complexes are important regulators of gene expression in Eukaryotes. In plants, SWI/SNF-type complexes have been shown critical for transcriptional control of key developmental processes, growth and stress responses. To gain insight into mechanisms underlying these roles, we performed whole genome mapping of the SWI/SNF catalytic subunit BRM in Arabidopsis thaliana, combined with transcript profiling experiments. Our data show that BRM occupies thousands of sites in Arabidopsis genome, most of which located within or close to genes. Among identified direct BRM transcriptional targets almost equal numbers were up- and downregulated upon BRM depletion, suggesting that BRM can act as both activator and repressor of gene expression. Interestingly, in addition to genes showing canonical pattern of BRM enrichment near transcription start site, many other genes showed a transcription termination site-centred BRM occupancy profile. We found that BRM-bound 3΄ gene regions have promoter-like features, including presence of TATA boxes and high H3K4me3 levels, and possess high antisense transcriptional activity which is subjected to both activation and repression by SWI/SNF complex. Our data suggest that binding to gene terminators and controlling transcription of non-coding RNAs is another way through which SWI/SNF complex regulates expression of its targets.

Genome-Wide Analysis of Drosophila RBf2 Protein Highlights the Diversity of RB Family Targets and Possible Role in Regulation of Ribosome Biosynthesis

RBf2 is a recently evolved retinoblastoma family member in Drosophila that differs from RBf1, especially in the C-terminus. To investigate whether the unique features of RBf2 contribute to diverse roles in gene regulation, we performed chromatin immunoprecipitation sequencing for both RBf2 and RBf1 in embryos. A previous model for RB−E2F interactions suggested that RBf1 binds dE2F1 or dE2F2, whereas RBf2 is restricted to binding to dE2F2; however, we found that RBf2 targets approximately twice as many genes as RBf1. Highly enriched among the RBf2 targets were ribosomal protein genes. We tested the functional significance of this finding by assessing RBf activity on ribosomal protein promoters and the endogenous genes. RBf1 and RBf2 significantly repressed expression of some ribosomal protein genes, although not all bound genes showed transcriptional effects. Interestingly, many ribosomal protein genes are similarly targeted in human cells, indicating that these interactions may be relevant for control of ribosome biosynthesis and growth. We carried out bioinformatic analysis to investigate the basis for differential targeting by these two proteins and found that RBf2-specific promoters have distinct sequence motifs, suggesting unique targeting mechanisms. Association of RBf2 with these promoters appears to be independent of dE2F2/dDP, although promoters bound by both RBf1 and RBf2 require dE2F2/dDP. The presence of unique RBf2 targets suggest that evolutionary appearance of this corepressor represents the acquisition of potentially novel roles in gene regulation for the RB family.