Genome-wide analysis of chromatin features identifies histone modification sensitive and insensitive yeast transcription factors

Optogenetic Control Reveals Differential Promoter Interpretation of Transcription Factor Nuclear Translocation Dynamics

Gene expression is thought to be affected not only by the concentration of transcription factors (TFs) but also the dynamics of their nuclear translocation. Testing this hypothesis requires direct control of TF dynamics. Here, we engineer CLASP, an optogenetic tool for rapid and tunable translocation of a TF of interest. Using CLASP fused to Crz1, we observe that, for the same integrated concentration of nuclear TF over time, changing input dynamics changes target gene expression: pulsatile inputs yield higher expression than continuous inputs, or vice versa, depending on the target gene. Computational modeling reveals that a dose-response saturating at low TF input can yield higher gene expression for pulsatile versus continuous input, and that multi-state promoter activation can yield the opposite behavior. Our integrated tool development and modeling approach characterize promoter responses to Crz1 nuclear translocation dynamics, extracting quantitative features that may help explain the differential expression of target genes.

CLASP is a modular optogenetic strategy to control the nuclear localization of transcription factors (TFs) and elicit gene expression from their cognate promoters. CLASP control of Crz1 nuclear localization, coupled with computational modeling, revealed how promoters can differentially decode dynamic transcription factor signals. The integrated strategy of CLASP development and modeling presents a generalized approach to causally investigate the transcriptional consequences of dynamic TF nuclear shuttling.

Dynamic motif occupancy (DynaMO) analysis identifies transcription factors and their binding sites driving dynamic biological processes

Biological processes are usually associated with genome-wide remodeling of transcription driven by transcription factors (TFs). Identifying key TFs and their spatiotemporal binding patterns are indispensable to understanding how dynamic processes are programmed. However, most methods are designed to predict TF binding sites only. We present a computational method, dynamic motif occupancy analysis (DynaMO), to infer important TFs and their spatiotemporal binding activities in dynamic biological processes using chromatin profiling data from multiple biological conditions such as time-course histone modification ChIP-seq data. In the first step, DynaMO predicts TF binding sites with a random forests approach. Next and uniquely, DynaMO infers dynamic TF binding activities at predicted binding sites using their local chromatin profiles from multiple biological conditions. Another landmark of DynaMO is to identify key TFs in a dynamic process using a clustering and enrichment analysis of dynamic TF binding patterns. Application of DynaMO to the yeast ultradian cycle, mouse circadian clock and human neural differentiation exhibits its accuracy and versatility. We anticipate DynaMO will be generally useful for elucidating transcriptional programs in dynamic processes.

HebbPlot: an intelligent tool for learning and visualizing chromatin mark signatures

BACKGROUND

Histone modifications play important roles in gene regulation, heredity, imprinting, and many human diseases. The histone code is complex and consists of more than 100 marks. Therefore, biologists need computational tools to characterize general signatures representing the distributions of tens of chromatin marks around thousands of regions.

RESULTS

To this end, we developed a software tool, HebbPlot, which utilizes a Hebbian neural network in learning a general chromatin signature from regions with a common function. Hebbian networks can learn the associations between tens of marks and thousands of regions. HebbPlot presents a signature as a digital image, which can be easily interpreted. Moreover, signatures produced by HebbPlot can be compared quantitatively. We validated HebbPlot in six case studies. The results of these case studies are novel or validating results already reported in the literature, indicating the accuracy of HebbPlot. Our results indicate that promoters have a directional chromatin signature; several marks tend to stretch downstream or upstream. H3K4me3 and H3K79me2 have clear directional distributions around active promoters. In addition, the signatures of high- and low-CpG promoters are different; H3K4me3, H3K9ac, and H3K27ac are the most different marks. When we studied the signatures of enhancers active in eight tissues, we observed that these signatures are similar, but not identical. Further, we identified some histone modifications - H3K36me3, H3K79me1, H3K79me2, and H4K8ac - that are associated with coding regions of active genes. Other marks - H4K12ac, H3K14ac, H3K27me3, and H2AK5ac - were found to be weakly associated with coding regions of inactive genes.

CONCLUSIONS

This study resulted in a novel software tool, HebbPlot, for learning and visualizing the chromatin signature of a genetic element. Using HebbPlot, we produced a visual catalog of the signatures of multiple genetic elements in 57 cell types available through the Roadmap Epigenomics Project. Furthermore, we made a progress toward a functional catalog consisting of 22 histone marks. In sum, HebbPlot is applicable to a wide array of studies, facilitating the deciphering of the histone code.

Transcription factor HIF1A: downstream targets, associated pathways, polymorphic hypoxia response element (HRE) sites, and initiative for standardization of reporting in scientific literature

Hypoxia-inducible factor-1α (HIF-1α) has crucial role in adapting cells to hypoxia through expression regulation of many genes. Identification of HIF-1α target genes (HIF-1α-TGs) is important for understanding the adapting mechanism. The aim of the present study was to collect known HIF-1α-TGs and identify their associated pathways. Targets and associated genomics data were retrieved using PubMed, WoS ( http://apps.webofknowledge.com/ ), HGNC ( http://www.genenames.org/ ), NCBI ( http://www.ncbi.nlm.nih.gov/ ), Ensemblv.84 ( http://www.ensembl.org/index.html ), DAVID Bioinformatics Resources ( https://david.ncifcrf.gov /), and Disease Ontology database ( http://disease-ontology.org/ ). From 51 papers, we collected 98 HIF-1α TGs found to be associated with 20 pathways, including metabolism of carbohydrates and pathways in cancer. Reanalysis of genomic coordinates of published HREs (hypoxia response elements) revealed six polymorphisms within HRE sites (HRE-SNPs): ABCG2, ACE, CA9, and CP. Due to large heterogeneity of results presentation in scientific literature, we also propose a first step towards reporting standardization of HIF-1α-target interactions consisting of ten relevant data types. Suggested minimal checklist for reporting will enable faster development of a complete catalog of HIF-1α-TGs, data sharing, bioinformatics analyses, and setting novel more targeted hypotheses. The proposed format for data standardization is not yet complete but presents a baseline for further optimization of the protocol with additional details, for example, regarding the experimental validation.

Identifying novel protein phenotype annotations by hybridizing protein-protein interactions and protein sequence similarities

Studies of protein phenotypes represent a central challenge of modern genetics in the post-genome era because effective and accurate investigation of protein phenotypes is one of the most critical procedures to identify functional biological processes in microscale, which involves the analysis of multifactorial traits and has greatly contributed to the development of modern biology in the post genome era. Therefore, we have developed a novel computational method that identifies novel proteins associated with certain phenotypes in yeast based on the protein-protein interaction network. Unlike some existing network-based computational methods that identify the phenotype of a query protein based on its direct neighbors in the local network, the proposed method identifies novel candidate proteins for a certain phenotype by considering all annotated proteins with this phenotype on the global network using a shortest path (SP) algorithm. The identified proteins are further filtered using both a permutation test and their interactions and sequence similarities to annotated proteins. We compared our method with another widely used method called random walk with restart (RWR). The biological functions of proteins for each phenotype identified by our SP method and the RWR method were analyzed and compared. The results confirmed a large proportion of our novel protein phenotype annotation, and the RWR method showed a higher false positive rate than the SP method. Our method is equally effective for the prediction of proteins involving in all the eleven clustered yeast phenotypes with a quite low false positive rate. Considering the universality and generalizability of our supporting materials and computing strategies, our method can further be applied to study other organisms and the new functions we predicted can provide pertinent instructions for the further experimental verifications.

Contribution of Sequence Motif, Chromatin State, and DNA Structure Features to Predictive Models of Transcription Factor Binding in Yeast

Transcription factor (TF) binding is determined by the presence of specific sequence motifs (SM) and chromatin accessibility, where the latter is influenced by both chromatin state (CS) and DNA structure (DS) properties. Although SM, CS, and DS have been used to predict TF binding sites, a predictive model that jointly considers CS and DS has not been developed to predict either TF-specific binding or general binding properties of TFs. Using budding yeast as model, we found that machine learning classifiers trained with either CS or DS features alone perform better in predicting TF-specific binding compared to SM-based classifiers. In addition, simultaneously considering CS and DS further improves the accuracy of the TF binding predictions, indicating the highly complementary nature of these two properties. The contributions of SM, CS, and DS features to binding site predictions differ greatly between TFs, allowing TF-specific predictions and potentially reflecting different TF binding mechanisms. In addition, a "TF-agnostic" predictive model based on three DNA “intrinsic properties” (in silico predicted nucleosome occupancy, major groove geometry, and dinucleotide free energy) that can be calculated from genomic sequences alone has performance that rivals the model incorporating experiment-derived data. This intrinsic property model allows prediction of binding regions not only across TFs, but also across DNA-binding domain families with distinct structural folds. Furthermore, these predicted binding regions can help identify TF binding sites that have a significant impact on target gene expression. Because the intrinsic property model allows prediction of binding regions across DNA-binding domain families, it is TF agnostic and likely describes general binding potential of TFs. Thus, our findings suggest that it is feasible to establish a TF agnostic model for identifying functional regulatory regions in potentially any sequenced genome.

Identification of transcription factor binding sites based on sequence motifs is typically accompanied by a high false positive rate. Increasing evidence suggests that there are many other factors besides DNA sequence that may affect the binding and interaction of TFs with DNA. Through the integration of sequence motif, chromatin state, and DNA structure properties, we show that TF binding can be better predicted. Moreover, considering chromatin state and DNA structure properties simultaneously yields a significant improvement. While the binding of some TFs can be readily predicted using either chromatin state information or DNA structure, other TFs need both. Thus, our findings provide insights on how different histone modifications and DNA structure properties may influence the binding of a particular TF and thus how TFs regulate gene expression. These features are referred to as sequence “intrinsic properties” because they can be predicted from sequences alone. These intrinsic properties can be used to build a TF binding prediction model that has a similar performance to considering all features. Moreover, the intrinsic property model allows TFBS predictions not only across TFs, but also across DNA-binding domain families that are present in most eukaryotes, suggesting that the model likely can be used across species.

Long non-coding RNA identification over mouse brain development by integrative modeling of chromatin and genomic features

In silico prediction of genomic long non-coding RNAs (lncRNAs) is prerequisite to the construction and elucidation of non-coding regulatory network. Chromatin modifications marked by chromatin regulators are important epigenetic features, which can be captured by prevailing high-throughput approaches such as ChIP sequencing. We demonstrate that the accuracy of lncRNA predictions can be greatly improved when incorporating high-throughput chromatin modifications over mouse embryonic stem differentiation toward adult Cerebellum by logistic regression with LASSO regularization. The discriminating features include H3K9me3, H3K27ac, H3K4me1, open reading frames and several repeat elements. Importantly, chromatin information is suggested to be complementary to genomic sequence information, highlighting the importance of an integrated model. Applying integrated model, we obtain a list of putative lncRNAs based on uncharacterized fragments from transcriptome assembly. We demonstrate that the putative lncRNAs have regulatory roles in vicinity of known gene loci by expression and Gene Ontology enrichment analysis. We also show that the lncRNA expression specificity can be efficiently modeled by the chromatin data with same developmental stage. The study not only supports the biological hypothesis that chromatin can regulate expression of tissue-specific or developmental stage-specific lncRNAs but also reveals the discriminating features between lncRNA and coding genes, which would guide further lncRNA identifications and characterizations.

Machine learning and genome annotation: a match meant to be?

By its very nature, genomics produces large, high-dimensional datasets that are well suited to analysis by machine learning approaches. Here, we explain some key aspects of machine learning that make it useful for genome annotation, with illustrative examples from ENCODE.

Epigenetic regulation of cardiac fibrosis

Cardiac fibrosis is characterized by excessive extracellular matrix accumulation that ultimately destroys tissue architecture and eventually abolishes normal function. In recent years, despite the underlying mechanisms of cardiac fibrosis are still unknown, numerous studies suggest that epigenetic modifications impact on the development of cardiac fibrosis. Epigenetic modifications control cell proliferation, differentiation, migration, and so on. Epigenetic modifications contain three main processes: DNA methylation, histone modifications, and silencing by microRNAs. We here outline the recent work pertaining to epigenetic changes in cardiac fibrosis. This review focuses on the epigenetic regulation of cardiac fibrosis.

Decoupling epigenetic and genetic effects through systematic analysis of gene position

Classic "position-effect" experiments repositioned genes near telomeres to demonstrate that the epigenetic landscape can dramatically alter gene expression. Here, we show that systematic gene knockout collections provide an exceptional resource for interrogating position effects, not only near telomeres but at every genetic locus. Because a single reporter gene replaces each deleted gene, interrogating this reporter provides a sensitive probe into different chromatin environments while controlling for genetic context. Using this approach, we find that, whereas systematic replacement of yeast genes with the kanMX marker does not perturb the chromatin landscape, chromatin differences associated with gene position account for 35% of kanMX activity. We observe distinct chromatin influences, including a Set2/Rpd3-mediated antagonistic interaction between histone H3 lysine 36 trimethylation and the Rap1 transcriptional activation site in kanMX. This interaction explains why some yeast genes have been resistant to deletion and allows successful generation of these deletion strains through the use of a modified transformation procedure. These findings demonstrate that chromatin regulation is not governed by a uniform "histone code" but by specific interactions between chromatin and genetic factors.

Chromatin acetylation at transcription start sites and vitamin D receptor binding regions relates to effects of 1α,25-dihydroxyvitamin D3 and histone deacetylase inhibitors on gene expression

The nuclear hormone 1α,25-dihydroxyvitamin D₃ (1α,25(OH)₂D₃ or 1,25D) regulates its target genes via activation of the transcription factor vitamin D receptor (VDR) far more specifically than the chromatin modifier trichostatin A (TsA) via its inhibitory action on histone deacetylases. We selected the thrombomodulin gene locus with its complex pattern of five VDR binding sites and multiple histone acetylation and open chromatin regions as an example to investigate together with a number of reference genes, the primary transcriptional responses to 1α,25(OH)₂D₃ and TsA. Transcriptome-wide, 18.4% of all expressed genes are either up-or down-regulated already after a 90 min TsA treatment; their response pattern to 1α,25(OH)₂D₃ and TsA sorts them into at least six classes. TsA stimulates a far higher number of genes than 1α,25(OH)₂D₃ and dominates the outcome of combined treatments. However, 200 TsA target genes can be modulated by 1α,25(OH)₂D₃ and more than 1000 genes respond only when treated with both compounds. The genomic view on the genes suggests that the degree of acetylation at transcription start sites and VDR binding regions may determine the effect of TsA on mRNA expression and its interference with 1α,25(OH)₂D₃. Our findings hold true also for other HDAC inhibitors and may have implications on dual therapies using chromatin modifiers and nuclear receptor ligands.

Global Mapping of Transcription Factor Binding Sites by Sequencing Chromatin Surrogates: a Perspective on Experimental Design, Data Analysis, and Open Problems

Mapping genome-wide binding sites of all transcription factors (TFs) in all biological contexts is a critical step toward understanding gene regulation. The state-of-the-art technologies for mapping transcription factor binding sites (TFBSs) couple chromatin immunoprecipitation (ChIP) with high-throughput sequencing (ChIP-seq) or tiling array hybridization (ChIP-chip). These technologies have limitations: they are low-throughput with respect to surveying many TFs. Recent advances in genome-wide chromatin profiling, including development of technologies such as DNase-seq, FAIRE-seq and ChIP-seq for histone modifications, make it possible to predict in vivo TFBSs by analyzing chromatin features at computationally determined DNA motif sites. This promising new approach may allow researchers to monitor the genome-wide binding sites of many TFs simultaneously. In this article, we discuss various experimental design and data analysis issues that arise when applying this approach. Through a systematic analysis of the data from the Encyclopedia Of DNA Elements (ENCODE) project, we compare the predictive power of individual and combinations of chromatin marks using supervised and unsupervised learning methods, and evaluate the value of integrating information from public ChIP and gene expression data. We also highlight the challenges and opportunities for developing novel analytical methods, such as resolving the one-motif-multiple-TF ambiguity and distinguishing functional and non-functional TF binding targets from the predicted binding sites.

ELECTRONIC SUPPLEMENTARY MATERIAL

The online version of this article (doi:10.1007/s12561-012-9066-5) contains supplementary material, which is available to authorized users.