Integrative analysis of histone ChIP-seq and transcription data using Bayesian mixture models

Histone deacetylase 9 interacts with SiHAT3.1 and SiHDA19 to repress dehydration responses through H3K9 deacetylation in foxtail millet

Climate change inflicts several stresses on plants, of which dehydration stress severely affects growth and productivity. C4 plants possess better adaptability to dehydration stress; however, the role of epigenetic modifications underlying this trait is unclear. In particular, the molecular links between histone modifiers and their regulation remain elusive. In this study, genome-wide H3K9 acetylation (H3K9ac) enrichment using ChIP-sequencing was performed in two foxtail millet cultivars with contrasting dehydration tolerances (IC403579, cv. IC4-tolerant, and IC480117, cv. IC41-sensitive). It revealed that a histone deacetylase, SiHDA9, was significantly up-regulated in the sensitive cultivar. Further characterization indicated that SiHDA9 interacts with SiHAT3.1 and SiHDA19 to form a repressor complex. SiHDA9 might be recruited through the SiHAT3.1 recognition sequence onto the upstream of dehydration-responsive genes to decrease H3K9 acetylation levels. The silencing of SiHDA9 resulted in the up-regulation of crucial genes, namely, SiRAB18, SiRAP2.4, SiP5CS2, SiRD22, SiPIP1;4, and SiLHCB2.3, which imparted dehydration tolerance in the sensitive cultivar (IC41). Overall, the study provides mechanistic insights into SiHDA9-mediated regulation of dehydration stress response in foxtail millet.

Euo is a developmental regulator that represses late genes and activates midcycle genes in Chlamydia trachomatis

IMPORTANCE

In this study, we developed a correlative approach that combined DNA immunoprecipitation-seq and RNA-seq analyses to define the regulon of the Chlamydia trachomatis transcription factor Euo. We confirmed the proposed role of Euo as a transcriptional repressor of late chlamydial genes but also showed that Euo activates transcription of a subset of midcycle genes and autoregulates its own expression via negative feedback. This study validates and expands the role of Euo as an important developmental regulator in C. trachomatis. In addition, this genome-wide correlative approach can be applied to study transcription factors in other pathogenic bacteria.

Dynamicity of histone H3K27ac and H3K27me3 modifications regulate the cold-responsive gene expression in Oryza sativa L. ssp. indica

Cultivated in tropical and subtropical regions, Oryza sativa L. ssp. indica is largely affected by cold-stress, especially at the seedling stage. The present model of the stress-responsive regulatory network in plants entails the role of genetic and epigenetic factors in stress-responsive gene expression. Despite extensive transcriptomic studies, the regulation of various epigenetic factors in plants cold-stress response is less explored. The present study addresses the effect of genome-wide changes of H3K27 modifications on gene expression in IR64 rice, during cold-stress. Our results suggest a positive correlation between the changes in H3K27 modifications and stress-responsive gene activation in indica rice. Cold-induced enrichment of H3K27 acetylation promotes nucleosomal rearrangement, thereby facilitating the accessibility of the transcriptional machinery at the stress-responsive loci for transcription activation. Although H3K27ac exhibits uniform distribution throughout the loci of enriched genes; occupancy of H3K27me3 is biased to intergenic regions. Integration of the ChIP-seq data with transcriptome indicated that upregulation of stress-responsive TFs, photosynthesis-TCA-related, water-deficit genes, redox and JA signalling components, was associated with differential changes of H3K27ac and H3K27me3 levels. Furthermore, cold-induced upregulation of histone acetyltransferases and downregulation of DNA methyltransferases was noted through the antagonistic switch of H3K27ac and H3K27me3. Moreover, motif analysis of H3K27ac and H3K27me3 enriched regions are associated with putative stress responsive transcription factors binding sites, GAGA element and histone H3K27demethylase. Collectively our analysis suggests that differential expression of various chromatin and DNA modifiers coupled with increased H3K27ac and depleted H3K27me3 increases DNA accessibility, thereby promoting transcription of the cold-responsive genes in indica rice.

Joint changes in RNA, RNA polymerase II, and promoter activity through the cell cycle identify non-coding RNAs involved in proliferation

Proper regulation of the cell cycle is necessary for normal growth and development of all organisms. Conversely, altered cell cycle regulation often underlies proliferative diseases such as cancer. Long non-coding RNAs (lncRNAs) are recognized as important regulators of gene expression and are often found dysregulated in diseases, including cancers. However, identifying lncRNAs with cell cycle functions is challenging due to their often low and cell-type specific expression. We present a highly effective method that analyses changes in promoter activity, transcription, and RNA levels for identifying genes enriched for cell cycle functions. Specifically, by combining RNA sequencing with ChIP sequencing through the cell cycle of synchronized human keratinocytes, we identified 1009 genes with cell cycle-dependent expression and correlated changes in RNA polymerase II occupancy or promoter activity as measured by histone 3 lysine 4 trimethylation (H3K4me3). These genes were highly enriched for genes with known cell cycle functions and included 57 lncRNAs. We selected four of these lncRNAs-SNHG26, EMSLR, ZFAS1, and EPB41L4A-AS1-for further experimental validation and found that knockdown of each of the four lncRNAs affected cell cycle phase distributions and reduced proliferation in multiple cell lines. These results show that many genes with cell cycle functions have concomitant cell-cycle dependent changes in promoter activity, transcription, and RNA levels and support that our multi-omics method is well suited for identifying lncRNAs involved in the cell cycle.

Histone methyltransferase EZH2 epigenetically affects CCNA1 expression in acute myeloid leukemia

Cyclin A1 (CCNA1) is an alternative A-type cyclin that is expressed in acute myeloid leukemia (AML). However, its functions in AML cell chemoresistance, an important cause for mortality, are incompletely understood. The purpose of this study was to expound the role and potential mechanism of CCNA1 in AML cell chemoresistance. Upregulation of CCNA1 promoted resistance of AML cells to PKC412, AC220, and AraC. Mechanistically, it was confirmed that CCNA1 transcription was negatively regulated by forkhead box A2 (FOXA2), and the downregulation of FOXA2 promoted chemoresistance in AML cells. Moreover, the promoter sequence of CCNA1 has a significant H3K27me3 modification. Enhancer of zeste homolog 2 (EZH2) enhanced H3K27me3 modification of CCNA1 promoter to inhibit CCNA1 expression, thus promoting sensitivity of AML cells to drugs. Taken together, these findings lead to deeper insights into the mechanism of AML cell chemo-sensitivity by inhibiting CCNA1 at the transcriptional level.

intePareto: an R package for integrative analyses of RNA-Seq and ChIP-Seq data

BACKGROUND

RNA-Seq, the high-throughput sequencing (HT-Seq) of mRNAs, has become an essential tool for characterizing gene expression differences between different cell types and conditions. Gene expression is regulated by several mechanisms, including epigenetically by post-translational histone modifications which can be assessed by ChIP-Seq (Chromatin Immuno-Precipitation Sequencing). As more and more biological samples are analyzed by the combination of ChIP-Seq and RNA-Seq, the integrated analysis of the corresponding data sets becomes, theoretically, a unique option to study gene regulation. However, technically such analyses are still in their infancy.

RESULTS

Here we introduce intePareto, a computational tool for the integrative analysis of RNA-Seq and ChIP-Seq data. With intePareto we match RNA-Seq and ChIP-Seq data at the level of genes, perform differential expression analysis between biological conditions, and prioritize genes with consistent changes in RNA-Seq and ChIP-Seq data using Pareto optimization.

CONCLUSION

intePareto facilitates comprehensive understanding of high dimensional transcriptomic and epigenomic data. Its superiority to a naive differential gene expression analysis with RNA-Seq and available integrative approach is demonstrated by analyzing a public dataset.

Computational Oncology in the Multi-Omics Era: State of the Art

Cancer is the quintessential complex disease. As technologies evolve faster each day, we are able to quantify the different layers of biological elements that contribute to the emergence and development of malignancies. In this multi-omics context, the use of integrative approaches is mandatory in order to gain further insights on oncological phenomena, and to move forward toward the precision medicine paradigm. In this review, we will focus on computational oncology as an integrative discipline that incorporates knowledge from the mathematical, physical, and computational fields to further the biomedical understanding of cancer. We will discuss the current roles of computation in oncology in the context of multi-omic technologies, which include: data acquisition and processing; data management in the clinical and research settings; classification, diagnosis, and prognosis; and the development of models in the research setting, including their use for therapeutic target identification. We will discuss the machine learning and network approaches as two of the most promising emerging paradigms, in computational oncology. These approaches provide a foundation on how to integrate different layers of biological description into coherent frameworks that allow advances both in the basic and clinical settings.

Bayesian integrative analysis of epigenomic and transcriptomic data identifies Alzheimer's disease candidate genes and networks

Biomedical research studies have generated large multi-omic datasets to study complex diseases like Alzheimer’s disease (AD). An important aim of these studies is the identification of candidate genes that demonstrate congruent disease-related alterations across the different data types measured by the study. We developed a new method to detect such candidate genes in large multi-omic case-control studies that measure multiple data types in the same set of samples. The method is based on a gene-centric integrative coefficient quantifying to what degree consistent differences are observed in the different data types. For statistical inference, a Bayesian hierarchical model is used to study the distribution of the integrative coefficient. The model employs a conditional autoregressive prior to integrate a functional gene network and to share information between genes known to be functionally related. We applied the method to an AD dataset consisting of histone acetylation, DNA methylation, and RNA transcription data from human cortical tissue samples of 233 subjects, and we detected 816 genes with consistent differences between persons with AD and controls. The findings were validated in protein data and in RNA transcription data from two independent AD studies. Finally, we found three subnetworks of jointly dysregulated genes within the functional gene network which capture three distinct biological processes: myeloid cell differentiation, protein phosphorylation and synaptic signaling. Further investigation of the myeloid network indicated an upregulation of this network in early stages of AD prior to accumulation of hyperphosphorylated tau and suggested that increased CSF1 transcription in astrocytes may contribute to microglial activation in AD. Thus, we developed a method that integrates multiple data types and external knowledge of gene function to detect candidate genes, applied the method to an AD dataset, and identified several disease-related genes and processes demonstrating the usefulness of the integrative approach.

Recent technological advances have led to a new generation of studies that interrogate multiple molecular levels in the same target tissue of a set of subjects, generating complex multi-omic datasets with which to study disease mechanism. These datasets of genetic, epigenomic, transcriptomic, and other data have the potential to reveal novel biological insights; however, integrative analyses remain challenging and require new computational methods. We developed an integrative Bayesian approach to detect genes with consistent differences between case and control samples across multiple data types. The method further integrates prior knowledge about gene function in the form of a gene functional similarity network to improve statistical inference by sharing information between related genes. We applied our method to an Alzheimer’s disease dataset of epigenomic and transcriptomic data and detected and then validated several novel and known candidate genes as well as three major disease-related biological processes. One of these processes reflected microglial activation and included the cytokine CSF1. Single-nucleus data revealed that CSF1 was primarily upregulated in astrocytes, implicating the involvement of this cell type in microglial activation. Hence, we demonstrated that integrative analysis approaches to multi-omic datasets can improve candidate gene detection and thereby generate new insights into complex diseases.

Integrative analysis of multiple genomic variables using a hierarchical Bayesian model

Motivation

Genes showing congruent differences in several genomic variables between two biological conditions are crucial to unravel causalities behind phenotypes of interest. Detecting such genes is important in biomedical research, e.g. when identifying genes responsible for cancer development. Small sample sizes common in next-generation sequencing studies are a key challenge, and there are still only very few statistical methods to analyze more than two genomic variables in an integrative, model-based way. Here, we present a novel bioinformatics approach to detect congruent differences between two biological conditions in a larger number of different measurements such as various epigenetic marks or mRNA transcript levels.

Results

We propose a coefficient quantifying the degree to which genes present consistent alterations in multiple (more than two) genomic variables when comparing samples presenting a condition of interest (e.g. cancer) to a reference group. A hierarchical Bayesian model is employed to assess uncertainty on a gene level, incorporating information on functional relationships between genes. We demonstrate the approach on different data sets containing RNA-seq gene transcripton and up to four ChIP-seq histone modification measurements. Both the coefficient-based ranking and the inference based on the model lead to a plausible prioritizing of candidate genes when analyzing multiple genomic variables.

Availability and implementation

BUGS code in the Supplement.

Contact

m.schaefer@uni-duesseldorf.de.

Supplementary information

Supplementary data are available at Bioinformatics online.

H2A.Z Represses Gene Expression by Modulating Promoter Nucleosome Structure and Enhancer Histone Modifications in Arabidopsis

Deposition of the histone variant H2A.Z at gene bodies regulates transcription by modifying chromatin accessibility in plants. However, the role of H2A.Z enrichment at the promoter and enhancer regions is unclear, and how H2A.Z interacts with other mechanisms of chromatin modification to regulate gene expression remains obscure. Here, we mapped genome-wide H2A.Z, H3K4me3, H3K27me3, Pol II, and nucleosome occupancy in Arabidopsis inflorescence. We showed that H2A.Z preferentially associated with H3K4me3 at promoters, while it was found with H3K27me3 at enhancers, and that H2A.Z deposition negatively correlated with gene expression. In addition, we demonstrated that H2A.Z represses gene expression by establishing low gene accessibility at +1 nucleosome and maintaining high gene accessibility at -1 nucleosome. We further showed that the high measures of gene responsiveness correlate with the H2A.Z-associated closed +1 nucleosome structure. Moreover, we found that H2A.Z represses enhancer activity by promoting H3K27me3 and preventing H3K4me3 histone modifications. This study provides a framework for future studies of H2A.Z functions and opens up new aspects for decoding the interplay between chromatin modification and histone variants in transcriptional control.

Integrated genomic analysis of biological gene sets with applications in lung cancer prognosis

Background

Burgeoning interest in integrative analyses has produced a rise in studies which incorporate data from multiple genomic platforms. Literature for conducting formal hypothesis testing on an integrative gene set level is considerably sparse. This paper is biologically motivated by our interest in the joint effects of epigenetic methylation loci and their associated mRNA gene expressions on lung cancer survival status.

Results

We provide an efficient screening approach across multiplatform genomic data on the level of biologically related sets of genes, and our methods are applicable to various disease models regardless whether the underlying true model is known (iTEGS) or unknown (iNOTE). Our proposed testing procedure dominated two competing methods. Using our methods, we identified a total of 28 gene sets with significant joint epigenomic and transcriptomic effects on one-year lung cancer survival.

Conclusions

We propose efficient variance component-based testing procedures to facilitate the joint testing of multiplatform genomic data across an entire gene set. The testing procedure for the gene set is self-contained, and can easily be extended to include more or different genetic platforms. iTEGS and iNOTE implemented in R are freely available through the inote package at https://cran.r-project.org//.

Electronic supplementary material

The online version of this article (doi:10.1186/s12859-017-1737-2) contains supplementary material, which is available to authorized users.

Omics Informatics: From Scattered Individual Software Tools to Integrated Workflow Management Systems

Omic data analyses pose great informatics challenges. As an emerging subfield of bioinformatics, omics informatics focuses on analyzing multi-omic data efficiently and effectively, and is gaining momentum. There are two underlying trends in the expansion of omics informatics landscape: the explosion of scattered individual omics informatics tools with each of which focuses on a specific task in both single- and multi- omic settings, and the fast-evolving integrated software platforms such as workflow management systems that can assemble multiple tools into pipelines and streamline integrative analysis for complicated tasks. In this survey, we give a holistic view of omics informatics, from scattered individual informatics tools to integrated workflow management systems. We not only outline the landscape and challenges of omics informatics, but also sample a number of widely used and cutting-edge algorithms in omics data analysis to give readers a fine-grained view. We survey various workflow management systems (WMSs), classify them into three levels of WMSs from simple software toolkits to integrated multi-omic analytical platforms, and point out the emerging needs for developing intelligent workflow management systems. We also discuss the challenges, strategies and some existing work in systematic evaluation of omics informatics tools. We conclude by providing future perspectives of emerging fields and new frontiers in omics informatics.

Integrated analysis and transcript abundance modelling of H3K4me3 and H3K27me3 in developing secondary xylem

Despite the considerable contribution of xylem development (xylogenesis) to plant biomass accumulation, its epigenetic regulation is poorly understood. Furthermore, the relative contributions of histone modifications to transcriptional regulation is not well studied in plants. We investigated the biological relevance of H3K4me3 and H3K27me3 in secondary xylem development using ChIP-seq and their association with transcript levels among other histone modifications in woody and herbaceous models. In developing secondary xylem of the woody model Eucalyptus grandis, H3K4me3 and H3K27me3 genomic spans were distinctly associated with xylogenesis-related processes, with (late) lignification pathways enriched for putative bivalent domains, but not early secondary cell wall polysaccharide deposition. H3K27me3-occupied genes, of which 753 (~31%) are novel targets, were enriched for transcriptional regulation and flower development and had significant preferential expression in roots. Linear regression models of the ChIP-seq profiles predicted ~50% of transcript abundance measured with strand-specific RNA-seq, confirmed in a parallel analysis in Arabidopsis where integration of seven additional histone modifications each contributed smaller proportions of unique information to the predictive models. This study uncovers the biological importance of histone modification antagonism and genomic span in xylogenesis and quantifies for the first time the relative correlations of histone modifications with transcript abundance in plants.

Epimetheus - a multi-profile normalizer for epigenomic sequencing data

Background

Exponentially increasing numbers of NGS-based epigenomic datasets in public repositories like GEO constitute an enormous source of information that is invaluable for integrative and comparative studies of gene regulatory mechanisms. One of today’s challenges for such studies is to identify functionally informative local and global patterns of chromatin states in order to describe the regulatory impact of the epigenome in normal cell physiology and in case of pathological aberrations. Critically, the most preferred Chromatin ImmunoPrecipitation-Sequencing (ChIP-Seq) is inherently prone to significant variability between assays, which poses significant challenge on comparative studies. One challenge concerns data normalization to adjust sequencing depth variation.

Results

Currently existing tools either apply linear scaling corrections and/or are restricted to specific genomic regions, which can be prone to biases. To overcome these restrictions without any external biases, we developed Epimetheus, a genome-wide quantile-based multi-profile normalization tool for histone modification data and related datasets.

Conclusions

Epimetheus has been successfully used to normalize epigenomics data in previous studies on X inactivation in breast cancer and in integrative studies of neuronal cell fate acquisition and tumorigenic transformation; Epimetheus is freely available to the scientific community.

Electronic supplementary material

The online version of this article (doi:10.1186/s12859-017-1655-3) contains supplementary material, which is available to authorized users.

Loss of the histone methyltransferase EZH2 induces resistance to multiple drugs in acute myeloid leukemia

In acute myeloid leukemia (AML), therapy resistance frequently occurs, leading to high mortality among patients. However, the mechanisms that render leukemic cells drug resistant remain largely undefined. Here, we identified loss of the histone methyltransferase EZH2 and subsequent reduction of histone H3K27 trimethylation as a novel pathway of acquired resistance to tyrosine kinase inhibitors (TKIs) and cytotoxic drugs in AML. Low EZH2 protein levels correlated with poor prognosis in AML patients. Suppression of EZH2 protein expression induced chemoresistance of AML cell lines and primary cells in vitro and in vivo. Low EZH2 levels resulted in derepression of HOX genes, and knockdown of HOXB7 and HOXA9 in the resistant cells was sufficient to improve sensitivity to TKIs and cytotoxic drugs. The endogenous loss of EZH2 expression in resistant cells and primary blasts from a subset of relapsed AML patients resulted from enhanced CDK1-dependent phosphorylation of EZH2 at Thr487. This interaction was stabilized by heat shock protein 90 (HSP90) and followed by proteasomal degradation of EZH2 in drug-resistant cells. Accordingly, inhibitors of HSP90, CDK1 and the proteasome prevented EZH2 degradation, decreased HOX gene expression and restored drug sensitivity. Finally, patients with reduced EZH2 levels at progression to standard therapy responded to the combination of bortezomib and cytarabine, concomitant with the re-establishment of EZH2 expression and blast clearance. These data suggest restoration of EZH2 protein as a viable approach to overcome treatment resistance in this AML patient population.

Quantitative analysis of ChIP-seq data uncovers dynamic and sustained H3K4me3 and H3K27me3 modulation in cancer cells under hypoxia

Background

A comprehensive assessment of the epigenetic dynamics in cancer cells is the key to understanding the molecular mechanisms underlying cancer and to improving cancer diagnostics, prognostics and treatment. By combining genome-wide ChIP-seq epigenomics and microarray transcriptomics, we studied the effects of oxygen deprivation and subsequent reoxygenation on histone 3 trimethylation of lysine 4 (H3K4me3) and lysine 27 (H3K27me3) in a breast cancer cell line, serving as a model for abnormal oxygenation in solid tumors. A priori, epigenetic markings and gene expression levels not only are expected to vary greatly between hypoxic and normoxic conditions, but also display a large degree of heterogeneity across the cell population. Where traditionally ChIP-seq data are often treated as dichotomous data, the model and experiment here necessitate a quantitative, data-driven analysis of both datasets.

Results

We first identified genomic regions with sustained epigenetic markings, which provided a sample-specific reference enabling quantitative ChIP-seq data analysis. Sustained H3K27me3 marking was located around centromeres and intergenic regions, while sustained H3K4me3 marking is associated with genes involved in RNA binding, translation and protein transport and localization. Dynamic marking with both H3K4me3 and H3K27me3 (hypoxia-induced bivalency) was found in CpG-rich regions at loci encoding factors that control developmental processes, congruent with observations in embryonic stem cells.

Conclusions

In silico-identified epigenetically sustained and dynamic genomic regions were confirmed through ChIP-PCR in vitro, and obtained results are corroborated by published data and current insights regarding epigenetic regulation.

Electronic supplementary material

The online version of this article (doi:10.1186/s13072-016-0090-4) contains supplementary material, which is available to authorized users.

Rebalancing gene haploinsufficiency in vivo by targeting chromatin

Congenital heart disease (CHD) affects eight out of 1,000 live births and is a major social and health-care burden. A common genetic cause of CHD is the 22q11.2 deletion, which is the basis of the homonymous deletion syndrome (22q11.2DS), also known as DiGeorge syndrome. Most of its clinical spectrum is caused by haploinsufficiency of Tbx1, a gene encoding a T-box transcription factor. Here we show that Tbx1 positively regulates monomethylation of histone 3 lysine 4 (H3K4me1) through interaction with and recruitment of histone methyltransferases. Treatment of cells with tranylcypromine (TCP), an inhibitor of histone demethylases, rebalances the loss of H3K4me1 and rescues the expression of approximately one-third of the genes dysregulated by Tbx1 suppression. In Tbx1 mouse mutants, TCP treatment ameliorates substantially the cardiovascular phenotype. These data suggest that epigenetic drugs may represent a potential therapeutic strategy for rescue of gene haploinsufficiency phenotypes, including structural defects.

Deficit in transcription factor Tbx1 causes heart defects in humans and mice. Here the authors show that Tbx1 regulates gene expression by recruiting histone methyltransferases that affect chromatin marks, and that a drug inhibiting histone demethylation ameliorates the cardiovascular phenotype in Tbx1 haploinsufficient or hypomorphic mice.

Understanding gene regulatory mechanisms by integrating ChIP-seq and RNA-seq data: statistical solutions to biological problems

The availability of omic data produced from international consortia, as well as from worldwide laboratories, is offering the possibility both to answer long-standing questions in biomedicine/molecular biology and to formulate novel hypotheses to test. However, the impact of such data is not fully exploited due to a limited availability of multi-omic data integration tools and methods. In this paper, we discuss the interplay between gene expression and epigenetic markers/transcription factors. We show how integrating ChIP-seq and RNA-seq data can help to elucidate gene regulatory mechanisms. In particular, we discuss the two following questions: (i) Can transcription factor occupancies or histone modification data predict gene expression? (ii) Can ChIP-seq and RNA-seq data be used to infer gene regulatory networks? We propose potential directions for statistical data integration. We discuss the importance of incorporating underestimated aspects (such as alternative splicing and long-range chromatin interactions). We also highlight the lack of data benchmarks and the need to develop tools for data integration from a statistical viewpoint, designed in the spirit of reproducible research.