Computational methodology for ChIP-seq analysis

Potential multiple disease progression pathways in female patients with Alzheimer's disease inferred from transcriptome and epigenome data of the dorsolateral prefrontal cortex

Late-onset Alzheimer's disease (AD) is a typical type of dementia for which therapeutic strategies have not yet been established. The database of the Rush Alzheimer's Disease study by the ENCODE consortium contains transcriptome and various epigenome data. Although the Rush AD database may contain a satisfactory amount of data for women, the amount of data for men remains insufficient. Here, based on an analysis of publicly available data from female patients, this study found that AD pathology appears to be nonuniform; AD patients were divided into several groups with differential gene expression patterns, including those related to cognitive function. First, cluster analysis was performed on individuals diagnosed with "No Cognitive Impairment (NCI)," "Mild Cognitive Impairment (MCI)," and "Alzheimer's Disease (AD)" stages in clinical trials using gene expression, and multiple substages were identified across AD progression. The epigenome data, in particular genome-wide H3k4me3 distribution data, also supported the existence of multiple AD substages. However, APOE gene polymorphisms of individuals seemed to not correlate with disease stage. An inference of adjacency networks among substages, evaluated via partition-based graph abstraction using the gene expression profiles of individuals, suggested the possibility of multiple typical disease progression pathways from NCI to different AD substages through various MCI substages. These findings could refine biomarker discovery or inform personalized therapeutic approaches.

Statistical Analysis in ChIP-seq-Related Applications

Chromatin immunoprecipitation sequencing (ChIP-seq) has been widely performed to identify protein binding information along the genome. The sequencing protocol is quite flexible and mature to measure different types of protein binding as long as sequencing parameters are properly tailored to accommodate protein features. Two distinct types of protein binding are point-source-like binding by transcription factors and diffused-distribution binding by histone modifications. Consequently, statistical approaches have been proposed to address ChIP-seq-related questions according to different protein features. In this chapter, we briefly summarize statistical principles, approaches, and tools that are widely implemented in modeling ChIP-seq data, from raw data quality control to final result reporting. We discuss the key solutions in addressing eight routine questions in ChIP-seq applications. We also include discussion on approaches fitting unique data features in different ChIP-seq types. We hope this chapter will serve as a brief guide, especially for ChIP-seq beginners, to provide them with a high-level overview to understand and design processing plans for their ChIP-seq experiments.

2,3,7,8-Tetrachlorodibenzo-p-dioxin induces multigenerational alterations in the expression of microRNA in the thymus through epigenetic modifications

2,3,7,8-Tetrachlorodibenzo-p-dioxin (TCDD), a potent AhR ligand, is an environmental contaminant that is known for mediating toxicity across generations. However, whether TCDD can induce multigenerational changes in the expression of microRNAs (miRs) has not been previously studied. In the current study, we investigated the effect of administration of TCDD in pregnant mice (F0) on gestational day 14, on the expression of miRs in the thymus of F0 and subsequent generations (F1 and F2). Of the 3200 miRs screened, 160 miRs were dysregulated similarly in F0, F1, and F2 generations, while 46 miRs were differentially altered in F0 to F2 generations. Pathway analysis revealed that the changes in miR signature profile mediated by TCDD affected the genes that regulate cell signaling, apoptosis, thymic atrophy, cancer, immunosuppression, and other physiological pathways. A significant number of miRs that showed altered expression exhibited dioxin response elements (DRE) on their promoters. Focusing on one such miR, namely miR-203 that expressed DREs and was induced across F0 to F2 by TCDD, promoter analysis showed that one of the DREs expressed by miR-203 was functional to TCDD-mediated upregulation. Also, the histone methylation status of H3K4me3 in the miR-203 promoter was significantly increased near the transcriptional start site in TCDD-treated thymocytes across F0 to F2 generations. Genome-wide chromatin immunoprecipitation sequencing study suggested that TCDD may cause alterations in histone methylation in certain genes across the three generations. Together, the current study demonstrates that gestational exposure to TCDD can alter the expression of miRs in F0 through direct activation of DREs as well as across F0, F1, and F2 generations through epigenetic pathways.

Meta-analysis and Consolidation of Farnesoid X Receptor Chromatin Immunoprecipitation Sequencing Data Across Different Species and Conditions

Farnesoid X receptor (FXR) is a nuclear receptor that controls gene regulation of different metabolic pathways and represents an upcoming drug target for various liver diseases. Several data sets on genome-wide FXR binding in different species and conditions exist. We have previously reported that these data sets are heterogeneous and do not cover the full spectrum of potential FXR binding sites. Here, we report the first meta-analysis of all publicly available FXR chromatin immunoprecipitation sequencing (ChIP-seq) data sets from mouse, rat, and human across different conditions using a newly generated analysis pipeline. All publicly available single data sets were biocurated in a standardized manner and compared on every relevant level from raw reads to affected functional pathways. Individual murine data sets were then virtually merged into a single unique "FXR binding atlas" spanning all potential binding sites across various conditions. Comparison of the single biocurated data sets showed that the overlap of FXR binding sites between different species is modest and ranges from 48% (mouse-human) to 55% (mouse-rat). Moreover, in vivo data among different species are more similar than human in vivo data compared to human in vitro data. The consolidated murine global FXR binding atlas virtually increases sequencing depth and allows recovering more and novel potential binding sites and signaling pathways that were missed in the individual data sets. The FXR binding atlas is publicly searchable (https://fxratlas.tugraz.at). Conclusion: Published single FXR ChIP-seq data sets and large-scale integrated omics data sets do not cover the full spectrum of FXR binding. Combining different individual data sets and creating an "FXR super-binding atlas" enhances understanding of FXR signaling capacities across different conditions. This is important when considering the potential wide spectrum for drugs targeting FXR in liver diseases.

CpG content-dependent associations between transcription factors and histone modifications

Understanding the factors that underlie the epigenetic regulation of genes is crucial to understand the gene regulatory machinery as a whole. Several experimental and computational studies examined the relationship between different factors involved. Here we investigate the relationship between transcription factors (TFs) and histone modifications (HMs), based on ChIP-seq data in cell lines. As it was shown that gene regulation by TFs differs depending on the CpG class of a promoter, we study the impact of the CpG content in promoters on the associations between TFs and HMs. We suggest an approach based on sparse linear regression models to infer associations between TFs and HMs with respect to CpG content. A study of the partial correlation of HMs for the two classes of high and low CpG content reveals possible CpG dependence and potential candidates for confounding factors in our models. We show that the models are accurate, inferred associations reflect known biological relationships, and we give new insight into associations with respect to CpG content. Moreover, analysis of a ChIP-seq dataset in HepG2 cells of the HM H3K122ac, an HM about little is known, reveals novel TF associations and supports a previously established link to active transcription.

RECOGNICER: A coarse-graining approach for identifying broad domains from ChIP-seq data

Background

Histone modifications are major factors that define chromatin states and have functions in regulating gene expression in eukaryotic cells. Chromatin immunoprecipitation coupled with high-throughput sequencing (ChIP-seq) technique has been widely used for profiling the genome-wide distribution of chromatin-associating protein factors. Some histone modifications, such as H3K27me3 and H3K9me3, usually mark broad domains in the genome ranging from kilobases (kb) to megabases (Mb) long, resulting in diffuse patterns in the ChIP-seq data that are challenging for signal separation. While most existing ChIP-seq peak-calling algorithms are based on local statistical models without account of multi-scale features, a principled method to identify scale-free board domains has been lacking.

Methods

Here we present RECOGNICER (Recursive coarse-graining identification for ChIP-seq enriched regions), a computational method for identifying ChIP-seq enriched domains on a large range of scales. The algorithm is based on a coarse-graining approach, which uses recursive block transformations to determine spatial clustering of local enriched elements across multiple length scales.

Results

We apply RECOGNICER to call H3K27me3 domains from ChIP-seq data, and validate the results based on H3K27me3's association with repressive gene expression. We show that RECOGNICER outperforms existing ChIP-seq broad domain calling tools in identifying more whole domains than separated pieces.

Conclusion

RECOGNICER can be a useful bioinformatics tool for next-generation sequencing data analysis in epigenomics research.

Occupancy maps of 208 chromatin-associated proteins in one human cell type

Transcription factors are DNA-binding proteins that have key roles in gene regulation1,2. Genome-wide occupancy maps of transcriptional regulators are important for understanding gene regulation and its effects on diverse biological processes3-6. However, only a minority of the more than 1,600 transcription factors encoded in the human genome has been assayed. Here we present, as part of the ENCODE (Encyclopedia of DNA Elements) project, data and analyses from chromatin immunoprecipitation followed by high-throughput sequencing (ChIP-seq) experiments using the human HepG2 cell line for 208 chromatin-associated proteins (CAPs). These comprise 171 transcription factors and 37 transcriptional cofactors and chromatin regulator proteins, and represent nearly one-quarter of CAPs expressed in HepG2 cells. The binding profiles of these CAPs form major groups associated predominantly with promoters or enhancers, or with both. We confirm and expand the current catalogue of DNA sequence motifs for transcription factors, and describe motifs that correspond to other transcription factors that are co-enriched with the primary ChIP target. For example, FOX family motifs are enriched in ChIP-seq peaks of 37 other CAPs. We show that motif content and occupancy patterns can distinguish between promoters and enhancers. This catalogue reveals high-occupancy target regions at which many CAPs associate, although each contains motifs for only a minority of the numerous associated transcription factors. These analyses provide a more complete overview of the gene regulatory networks that define this cell type, and demonstrate the usefulness of the large-scale production efforts of the ENCODE Consortium.

FiTAc-seq: fixed-tissue ChIP-seq for H3K27ac profiling and super-enhancer analysis of FFPE tissues

Fixed-tissue ChIP-seq for H3K27 acetylation (H3K27ac) profiling (FiTAc-seq) is an epigenetic method for profiling active enhancers and promoters in formalin-fixed, paraffin-embedded (FFPE) tissues. We previously developed a modified ChIP-seq protocol (FiT-seq) for chromatin profiling in FFPE. FiT-seq produces high-quality chromatin profiles particularly for methylated histone marks but is not optimized for H3K27ac profiling. FiTAc-seq is a modified protocol that replaces the proteinase K digestion applied in FiT-seq with extended heating at 65 °C in a higher concentration of detergent and a minimized sonication step, to produce robust genome-wide H3K27ac maps from clinical samples. FiTAc-seq generates high-quality enhancer landscapes and super-enhancer (SE) annotation in numerous archived FFPE samples from distinct tumor types. This approach will be of great interest for both basic and clinical researchers. The entire protocol from FFPE blocks to sequence-ready library can be accomplished within 4 d.

Facilitating Analysis of Publicly Available ChIP-Seq Data for Integrative Studies

ChIP-Seq, a technique that allows for quantification of DNA sequences bound by transcription factors or histones, has been widely used to characterize genome-wide DNA-protein binding at baseline and induced by specific exposures. Integrating results of multiple ChIP-Seq datasets is a convenient approach to identify robust DNA- protein binding sites and determine their cell-type specificity. We developed brocade, a computational pipeline for reproducible analysis of publicly available ChIP-Seq data that creates R markdown reports containing information on datasets downloaded, quality control metrics, and differential binding results. Glucocorticoids are commonly used anti-inflammatory drugs with tissue-specific effects that are not fully understood. We demonstrate the utility of brocade via the analysis of five ChIP-Seq datasets involving glucocorticoid receptor (GR), a transcription factor that mediates glucocorticoid response, to identify cell type-specific and shared GR binding sites across the five cell types. Our results show that brocade facilitates analysis of individual ChIP-Seq datasets and comparative studies involving multiple datasets.

HMMRATAC: a Hidden Markov ModeleR for ATAC-seq

ATAC-seq has been widely adopted to identify accessible chromatin regions across the genome. However, current data analysis still utilizes approaches initially designed for ChIP-seq or DNase-seq, without considering the transposase digested DNA fragments that contain additional nucleosome positioning information. We present the first dedicated ATAC-seq analysis tool, a semi-supervised machine learning approach named HMMRATAC. HMMRATAC splits a single ATAC-seq dataset into nucleosome-free and nucleosome-enriched signals, learns the unique chromatin structure around accessible regions, and then predicts accessible regions across the entire genome. We show that HMMRATAC outperforms the popular peak-calling algorithms on published human ATAC-seq datasets. We find that single-end sequenced or size-selected ATAC-seq datasets result in a loss of sensitivity compared to paired-end datasets without size-selection.

Epigenetic control of gene regulation during development and disease: A view from the retina

Complex biological processes, such as organogenesis and homeostasis, are stringently regulated by genetic programs that are fine-tuned by epigenetic factors to establish cell fates and/or to respond to the microenvironment. Gene regulatory networks that guide cell differentiation and function are modulated and stabilized by modifications to DNA, RNA and proteins. In this review, we focus on two key epigenetic changes - DNA methylation and histone modifications - and discuss their contribution to retinal development, aging and disease, especially in the context of age-related macular degeneration (AMD) and diabetic retinopathy. We highlight less-studied roles of DNA methylation and provide the RNA expression profiles of epigenetic enzymes in human and mouse retina in comparison to other tissues. We also review computational tools and emergent technologies to profile, analyze and integrate epigenetic information. We suggest implementation of editing tools and single-cell technologies to trace and perturb the epigenome for delineating its role in transcriptional regulation. Finally, we present our thoughts on exciting avenues for exploring epigenome in retinal metabolism, disease modeling, and regeneration.

Loss of the Hematopoietic Stem Cell Factor GATA2 in the Osteogenic Lineage Impairs Trabecularization and Mechanical Strength of Bone

The transcription factor GATA2 is required for expansion and differentiation of hematopoietic stem cells (HSCs). In mesenchymal stem cells (MSCs), GATA2 blocks adipogenesis, but its biological relevance and underlying genomic events are unknown. We report a dual function of GATA2 in bone homeostasis. GATA2 in MSCs binds near genes involved in skeletal system development and colocalizes with motifs for FOX and HOX transcription factors, known regulators of skeletal development. Ectopic GATA2 blocks osteoblastogenesis by interfering with SMAD1/5/8 activation. MSC-specific deletion of GATA2 in mice increases the numbers and differentiation capacity of bone-derived precursors, resulting in elevated bone formation. Surprisingly, MSC-specific GATA2 deficiency impairs the trabecularization and mechanical strength of bone, involving reduced MSC expression of the osteoclast inhibitor osteoprotegerin and increased osteoclast numbers. Thus, GATA2 affects bone turnover via MSC-autonomous and indirect effects. By regulating bone trabecularization, GATA2 expression in the osteogenic lineage may contribute to the anatomical and cellular microenvironment of the HSC niche required for hematopoiesis.

MicroScope: ChIP-seq and RNA-seq software analysis suite for gene expression heatmaps

Background

Heatmaps are an indispensible visualization tool for examining large-scale snapshots of genomic activity across various types of next-generation sequencing datasets. However, traditional heatmap software do not typically offer multi-scale insight across multiple layers of genomic analysis (e.g., differential expression analysis, principal component analysis, gene ontology analysis, and network analysis) or multiple types of next-generation sequencing datasets (e.g., ChIP-seq and RNA-seq). As such, it is natural to want to interact with a heatmap’s contents using an extensive set of integrated analysis tools applicable to a broad array of genomic data types.

Results

We propose a user-friendly ChIP-seq and RNA-seq software suite for the interactive visualization and analysis of genomic data, including integrated features to support differential expression analysis, interactive heatmap production, principal component analysis, gene ontology analysis, and dynamic network analysis.

Conclusions

MicroScope is hosted online as an R Shiny web application based on the D3 JavaScript library: http://microscopebioinformatics.org/. The methods are implemented in R, and are available as part of the MicroScope project at: https://github.com/Bohdan-Khomtchouk/Microscope.

Genome-wide analysis of HIF-2α chromatin binding sites under normoxia in human bronchial epithelial cells (BEAS-2B) suggests its diverse functions

Constitutive functional HIF-2α was recently identified in cancer and stem cell lines under normoxia. In this study, BEAS-2B, a bronchial epithelial cell line, was shown to constitutively express active HIF-2α under normoxia and exhibit markers of pluripotency including Oct-4, Nanog, and sphere formation. Oct-4 expression was reduced after knockdown of HIF-2α under normoxia. Global enrichment analysis of HIF-2α demonstrated the diverse functions of HIF-2α under normoxia. Bioinformatics analysis of the enriched loci revealed an enhancer role of HIF-2α binding sites, involvement of HIF-2α interacting proteins, and enriched de novo motifs which suggest the diverse role of HIF-2α in pseudohypoxia. The low ratio of the discovered loci overlapping with those revealed in cancer cell lines 786-O (16.1%) and MCF-7 (15.9%) under hypoxia indicated a prevailing non-canonical mechanism. Hypoxia had positive, marginal or adverse effects on the enrichment of the selected loci in ChIP-PCR assays. Deletion of the N-terminal activation domain (N-TAD) of HIF-2α disrupted the reporting activity of two of the loci annotated to ELN and ANKRD31. Hypoxia incurring abundance variation of HIF-2α may misrepresent the N-TAD functions as canonical hypoxia inducible features via C-TAD activation. Elucidation of the pseudohypoxia functions of constitutive HIF-2α is useful for resolving its role in malignancy and pluripotency.

Chromatin immunoprecipitation from fixed clinical tissues reveals tumor-specific enhancer profiles

Extensive cross-linking introduced during routine tissue fixation of clinical pathology specimens severely hampers chromatin immunoprecipitation followed by next-generation sequencing (ChIP-seq) analysis from archived tissue samples. This limits the ability to study the epigenomes of valuable, clinically annotated tissue resources. Here we describe fixed-tissue chromatin immunoprecipitation sequencing (FiT-seq), a method that enables reliable extraction of soluble chromatin from formalin-fixed paraffin-embedded (FFPE) tissue samples for accurate detection of histone marks. We demonstrate that FiT-seq data from FFPE specimens are concordant with ChIP-seq data from fresh-frozen samples of the same tumors. By using multiple histone marks, we generate chromatin-state maps and identify cis-regulatory elements in clinical samples from various tumor types that can readily allow us to distinguish between cancers by the tissue of origin. Tumor-specific enhancers and superenhancers that are elucidated by FiT-seq analysis correlate with known oncogenic drivers in different tissues and can assist in the understanding of how chromatin states affect gene regulation.

Analysis of Combinatorial Epigenomic States

Hundreds of distinct chemical modifications to DNA and histone amino acids have been described. Regulation exerted by these so-called epigenetic marks is vital to normal development, stability of cell identity through mitosis, and nongenetic transmission of traits between generations through meiosis. Loss of this regulation contributes to many diseases. Evidence indicates epigenetic marks function in combinations, whereby a given modification has distinct effects on local genome control, depending on which additional modifications are locally present. This review summarizes emerging methods for assessing combinatorial epigenomic states, as well as challenges and opportunities for their refinement.

Role of ChIP-seq in the discovery of transcription factor binding sites, differential gene regulation mechanism, epigenetic marks and beyond

Many biologically significant processes, such as cell differentiation and cell cycle progression, gene transcription and DNA replication, chromosome stability and epigenetic silencing etc. depend on the crucial interactions between cellular proteins and DNA. Chromatin immunoprecipitation (ChIP) is an important experimental technique for studying interactions between specific proteins and DNA in the cell and determining their localization on a specific genomic locus. In recent years, the combination of ChIP with second generation DNA-sequencing technology (ChIP-seq) allows precise genomic functional assay. This review addresses the important applications of ChIP-seq with an emphasis on its role in genome-wide mapping of transcription factor binding sites, the revelation of underlying molecular mechanisms of differential gene regulation that are governed by specific transcription factors, and the identification of epigenetic marks. Furthermore, we also describe the ChIP-seq data analysis workflow and a perspective for the exciting potential advancement of ChIP-seq technology in the future.

Single molecule and single cell epigenomics

Dynamically regulated changes in chromatin states are vital for normal development and can produce disease when they go awry. Accordingly, much effort has been devoted to characterizing these states under normal and pathological conditions. Chromatin immunoprecipitation followed by sequencing (ChIP-seq) is the most widely used method to characterize where in the genome transcription factors, modified histones, modified nucleotides and chromatin binding proteins are found; bisulfite sequencing (BS-seq) and its variants are commonly used to characterize the locations of DNA modifications. Though very powerful, these methods are not without limitations. Notably, they are best at characterizing one chromatin feature at a time, yet chromatin features arise and function in combination. Investigators commonly superimpose separate ChIP-seq or BS-seq datasets, and then infer where chromatin features are found together. While these inferences might be correct, they can be misleading when the chromatin source has distinct cell types, or when a given cell type exhibits any cell to cell variation in chromatin state. These ambiguities can be eliminated by robust methods that directly characterize the existence and genomic locations of combinations of chromatin features in very small inputs of cells or ideally, single cells. Here we review single molecule epigenomic methods under development to overcome these limitations, the technical challenges associated with single molecule methods and their potential application to single cells.