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

A transcription network underlies the dual genomic coordination of mitochondrial biogenesis

Mitochondrial biogenesis requires the expression of genes encoded by both the nuclear and mitochondrial genomes. However, aside from a handful transcriptional factors regulating specific subsets of mitochondrial genes, the overall architecture of the transcriptional control of mitochondrial biogenesis remains to be elucidated. The mechanisms coordinating these two genomes are largely unknown. We performed a targeted RNAi screen in developing eyes with reduced mitochondrial DNA content, anticipating a synergistic disruption of tissue development due to impaired mitochondrial biogenesis and mtDNA deficiency. Among 638 transcription factors annotated in Drosophila genome, 77 were identified as potential regulators of mitochondrial biogenesis. Utilizing published ChIP-seq data of positive hits, we constructed a regulatory network revealing the logic of the transcription regulation of mitochondrial biogenesis. Multiple transcription factors in core layers had extensive connections, collectively governing the expression of nearly all mitochondrial genes, whereas factors sitting on the top layer may respond to cellular cues to modulate mitochondrial biogenesis through the underlying network. CG1603, a core component of the network, was found to be indispensable for the expression of most nuclear mitochondrial genes, including those required for mtDNA maintenance and gene expression, thus coordinating nuclear genome and mtDNA activities in mitochondrial biogenies. Additional genetics analyses validated YL-1, a transcription factor upstream of CG1603 in the network, as a regulator controlling CG1603 expression and mitochondrial biogenesis.

Application of data science and bioinformatics in RNA therapeutics

Nowadays, information technology (IT) has been holding a significant role in daily life worldwide. The trajectory of data science and bioinformatics promises pioneering personalized therapies, reshaping medical landscapes and patient care. For RNA therapy to reach more patients, a comprehensive understanding of the application of data science and bioinformatics to this therapy is essential. Thus, this chapter has summarized the application of data science and bioinformatics in RNA therapeutics. Data science applications in RNA therapy, such as data integration and analytics, machine learning, and drug development, have been discussed. In addition, aspects of bioinformatics such as RNA design and evaluation, drug delivery system simulation, and databases for personalized medicine have also been covered in this chapter. These insights have shed light on existing evidence and opened potential future directions. From there, scientists can elevate RNA-based therapeutics into an era of tailored treatments and revolutionary healthcare.

The regulatory landscape of neurite development in Caenorhabditis elegans

Neuronal communication requires precise connectivity of neurite projections (axons and dendrites). Developing neurites express cell-surface receptors that interpret extracellular cues to enable correct guidance toward, and connection with, target cells. Spatiotemporal regulation of neurite guidance molecule expression by transcription factors (TFs) is critical for nervous system development and function. Here, we review how neurite development is regulated by TFs in the Caenorhabditis elegans nervous system. By collecting publicly available transcriptome and ChIP-sequencing data, we reveal gene expression dynamics during neurite development, providing insight into transcriptional mechanisms governing construction of the nervous system architecture.

Patterns of selection across gene regulatory networks

Gene regulatory networks (GRNs) are the core engine of organismal development. If we would like to understand the origin and diversification of phenotypes, it is necessary to consider the structure of GRNs in order to reconstruct the links between genetic mutations and phenotypic change. Much of the progress in evolutionary developmental biology, however, has occurred without a nuanced consideration of the evolution of functional relationships between genes, especially in the context of their broader network interactions. Characterizing and comparing GRNs across traits and species in a more detailed way will allow us to determine how network position influences what genes drive adaptive evolution. In this perspective paper, we consider the architecture of developmental GRNs and how positive selection strength may vary across a GRN. We then propose several testable models for these patterns of selection and experimental approaches to test these models.

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.

Genome-Wide Histone Modifications and CTCF Enrichment Predict Gene Expression in Sheep Macrophages

Alveolar macrophages function in innate and adaptive immunity, wound healing, and homeostasis in the lungs dependent on tissue-specific gene expression under epigenetic regulation. The functional diversity of tissue resident macrophages, despite their common myeloid lineage, highlights the need to study tissue-specific regulatory elements that control gene expression. Increasing evidence supports the hypothesis that subtle genetic changes alter sheep macrophage response to important production pathogens and zoonoses, for example, viruses like small ruminant lentiviruses and bacteria like Coxiella burnetii. Annotation of transcriptional regulatory elements will aid researchers in identifying genetic mutations of immunological consequence. Here we report the first genome-wide survey of regulatory elements in any sheep immune cell, utilizing alveolar macrophages. We assayed histone modifications and CTCF enrichment by chromatin immunoprecipitation with deep sequencing (ChIP-seq) in two sheep to determine cis-regulatory DNA elements and chromatin domain boundaries that control immunity-related gene expression. Histone modifications included H3K4me3 (denoting active promoters), H3K27ac (active enhancers), H3K4me1 (primed and distal enhancers), and H3K27me3 (broad silencers). In total, we identified 248,674 reproducible regulatory elements, which allowed assignment of putative biological function in macrophages to 12% of the sheep genome. Data exceeded the FAANG and ENCODE standards of 20 million and 45 million useable fragments for narrow and broad marks, respectively. Active elements showed consensus with RNA-seq data and were predictive of gene expression in alveolar macrophages from the publicly available Sheep Gene Expression Atlas. Silencer elements were not enriched for expressed genes, but rather for repressed developmental genes. CTCF enrichment enabled identification of 11,000 chromatin domains with mean size of 258 kb. To our knowledge, this is the first report to use immunoprecipitated CTCF to determine putative topological domains in sheep immune cells. Furthermore, these data will empower phenotype-associated mutation discovery since most causal variants are within regulatory elements.

Assessment of transcriptional importance of cell line-specific features based on GTRD and FANTOM5 data

Creating a complete picture of the regulation of transcription seems to be an urgent task of modern biology. Regulation of transcription is a complex process carried out by transcription factors (TFs) and auxiliary proteins. Over the past decade, ChIP-Seq has become the most common experimental technology studying genome-wide interactions between TFs and DNA. We assessed the transcriptional significance of cell line-specific features using regression analysis of ChIP-Seq datasets from the GTRD database and transcriptional start site (TSS) activities from the FANTOM5 expression atlas. For this purpose, we initially generated a large number of features that were defined as the presence or absence of TFs in different promoter regions around TSSs. Using feature selection and regression analysis, we identified sets of the most important TFs that affect expression activity of TSSs in human cell lines such as HepG2, K562 and HEK293. We demonstrated that some TFs can be classified as repressors and activators depending on their location relative to TSS.

Epigenetic principles underlying epileptogenesis and epilepsy syndromes

Epilepsy is a network disorder driven by fundamental changes in the function of the cells which compose these networks. Driving this aberrant cellular function are large scale changes in gene expression and gene expression regulation. Recent studies have revealed rapid and persistent changes in epigenetic control of gene expression as a critical regulator of the epileptic transcriptome. Epigenetic-mediated gene output regulates many aspects of cellular physiology including neuronal structure, neurotransmitter assembly and abundance, protein abundance of ion channels and other critical neuronal processes. Thus, understanding the contribution of epigenetic-mediated gene regulation could illuminate novel regulatory mechanisms which may form the basis of novel therapeutic approaches to treat epilepsy. In this review we discuss the effects of epileptogenic brain insults on epigenetic regulation of gene expression, recent efforts to target epigenetic processes to block epileptogenesis and the prospects of an epigenetic-based therapy for epilepsy, and finally we discuss technological advancements which have facilitated the interrogation of the epigenome.

Supervised learning of gene-regulatory networks based on graph distance profiles of transcriptomics data

Characterisation of gene-regulatory network (GRN) interactions provides a stepping stone to understanding how genes affect cellular phenotypes. Yet, despite advances in profiling technologies, GRN reconstruction from gene expression data remains a pressing problem in systems biology. Here, we devise a supervised learning approach, GRADIS, which utilises support vector machine to reconstruct GRNs based on distance profiles obtained from a graph representation of transcriptomics data. By employing the data from Escherichia coli and Saccharomyces cerevisiae as well as synthetic networks from the DREAM4 and five network inference challenges, we demonstrate that our GRADIS approach outperforms the state-of-the-art supervised and unsupervided approaches. This holds when predictions about target genes for individual transcription factors as well as for the entire network are considered. We employ experimentally verified GRNs from E. coli and S. cerevisiae to validate the predictions and obtain further insights in the performance of the proposed approach. Our GRADIS approach offers the possibility for usage of other network-based representations of large-scale data, and can be readily extended to help the characterisation of other cellular networks, including protein–protein and protein–metabolite interactions.

Complex impact of DNA methylation on transcriptional dysregulation across 22 human cancer types

Accumulating evidence has demonstrated that transcriptional regulation is affected by DNA methylation. Understanding the perturbation of DNA methylation-mediated regulation between transcriptional factors (TFs) and targets is crucial for human diseases. However, the global landscape of DNA methylation-mediated transcriptional dysregulation (DMTD) across cancers has not been portrayed. Here, we systematically identified DMTD by integrative analysis of transcriptome, methylome and regulatome across 22 human cancer types. Our results revealed that transcriptional regulation was affected by DNA methylation, involving hundreds of methylation-sensitive TFs (MethTFs). In addition, pan-cancer MethTFs, the regulatory activity of which is generally affected by DNA methylation across cancers, exhibit dominant functional characteristics and regulate several cancer hallmarks. Moreover, pan-cancer MethTFs were found to be affected by DNA methylation in a complex pattern. Finally, we investigated the cooperation among MethTFs and identified a network module that consisted of 43 MethTFs with prognostic potential. In summary, we systematically dissected the transcriptional dysregulation mediated by DNA methylation across cancer types, and our results provide a valuable resource for both epigenetic and transcriptional regulation communities.

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.

The single-cell eQTLGen consortium

In recent years, functional genomics approaches combining genetic information with bulk RNA-sequencing data have identified the downstream expression effects of disease-associated genetic risk factors through so-called expression quantitative trait locus (eQTL) analysis. Single-cell RNA-sequencing creates enormous opportunities for mapping eQTLs across different cell types and in dynamic processes, many of which are obscured when using bulk methods. Rapid increase in throughput and reduction in cost per cell now allow this technology to be applied to large-scale population genetics studies. To fully leverage these emerging data resources, we have founded the single-cell eQTLGen consortium (sc-eQTLGen), aimed at pinpointing the cellular contexts in which disease-causing genetic variants affect gene expression. Here, we outline the goals, approach and potential utility of the sc-eQTLGen consortium. We also provide a set of study design considerations for future single-cell eQTL studies.

BINDER: computationally inferring a gene regulatory network for Mycobacterium abscessus

BACKGROUND

Although many of the genic features in Mycobacterium abscessus have been fully validated, a comprehensive understanding of the regulatory elements remains lacking. Moreover, there is little understanding of how the organism regulates its transcriptomic profile, enabling cells to survive in hostile environments. Here, to computationally infer the gene regulatory network for Mycobacterium abscessus we propose a novel statistical computational modelling approach: BayesIan gene regulatory Networks inferreD via gene coExpression and compaRative genomics (BINDER). In tandem with derived experimental coexpression data, the property of genomic conservation is exploited to probabilistically infer a gene regulatory network in Mycobacterium abscessus.Inference on regulatory interactions is conducted by combining 'primary' and 'auxiliary' data strata. The data forming the primary and auxiliary strata are derived from RNA-seq experiments and sequence information in the primary organism Mycobacterium abscessus as well as ChIP-seq data extracted from a related proxy organism Mycobacterium tuberculosis. The primary and auxiliary data are combined in a hierarchical Bayesian framework, informing the apposite bivariate likelihood function and prior distributions respectively. The inferred relationships provide insight to regulon groupings in Mycobacterium abscessus.

RESULTS

We implement BINDER on data relating to a collection of 167,280 regulator-target pairs resulting in the identification of 54 regulator-target pairs, across 5 transcription factors, for which there is strong probability of regulatory interaction.

CONCLUSIONS

The inferred regulatory interactions provide insight to, and a valuable resource for further studies of, transcriptional control in Mycobacterium abscessus, and in the family of Mycobacteriaceae more generally. Further, the developed BINDER framework has broad applicability, useable in settings where computational inference of a gene regulatory network requires integration of data sources derived from both the primary organism of interest and from related proxy organisms.

Co-expression networks for plant biology: why and how

Co-expression network analysis is one of the most powerful approaches for interpretation of large transcriptomic datasets. It enables characterization of modules of co-expressed genes that may share biological functional linkages. Such networks provide an initial way to explore functional associations from gene expression profiling and can be applied to various aspects of plant biology. This review presents the applications of co-expression network analysis in plant biology and addresses optimized strategies from the recent literature for performing co-expression analysis on plant biological systems. Additionally, we describe the combined interpretation of co-expression analysis with other genomic data to enhance the generation of biologically relevant information.

CRNET: an efficient sampling approach to infer functional regulatory networks by integrating large-scale ChIP-seq and time-course RNA-seq data

Motivation

NGS techniques have been widely applied in genetic and epigenetic studies. Multiple ChIP-seq and RNA-seq profiles can now be jointly used to infer functional regulatory networks (FRNs). However, existing methods suffer from either oversimplified assumption on transcription factor (TF) regulation or slow convergence of sampling for FRN inference from large-scale ChIP-seq and time-course RNA-seq data.

Results

We developed an efficient Bayesian integration method (CRNET) for FRN inference using a two-stage Gibbs sampler to estimate iteratively hidden TF activities and the posterior probabilities of binding events. A novel statistic measure that jointly considers regulation strength and regression error enables the sampling process of CRNET to converge quickly, thus making CRNET very efficient for large-scale FRN inference. Experiments on synthetic and benchmark data showed a significantly improved performance of CRNET when compared with existing methods. CRNET was applied to breast cancer data to identify FRNs functional at promoter or enhancer regions in breast cancer MCF-7 cells. Transcription factor MYC is predicted as a key functional factor in both promoter and enhancer FRNs. We experimentally validated the regulation effects of MYC on CRNET-predicted target genes using appropriate RNAi approaches in MCF-7 cells.

Availability and implementation

R scripts of CRNET are available at http://www.cbil.ece.vt.edu/software.htm.

Contact

xuan@vt.edu.

Supplementary information

Supplementary data are available at Bioinformatics online.

Integrating Bacterial ChIP-seq and RNA-seq Data With SnakeChunks

Next-generation sequencing (NGS) is becoming a routine approach in most domains of the life sciences. To ensure reproducibility of results, there is a crucial need to improve the automation of NGS data processing and enable forthcoming studies relying on big datasets. Although user-friendly interfaces now exist, there remains a strong need for accessible solutions that allow experimental biologists to analyze and explore their results in an autonomous and flexible way. The protocols here describe a modular system that enable a user to compose and fine-tune workflows based on SnakeChunks, a library of rules for the Snakemake workflow engine. They are illustrated using a study combining ChIP-seq and RNA-seq to identify target genes of the global transcription factor FNR in Escherichia coli, which has the advantage that results can be compared with the most up-to-date collection of existing knowledge about transcriptional regulation in this model organism, extracted from the RegulonDB database. © 2019 by John Wiley & Sons, Inc.

Dual RNA-Seq of Chlamydia and Host Cells

During the infection of a host cell by a bacterial pathogen, a cascading series of gene expression changes occurs as each organism manipulates or responds to the other via defense or survival strategies. Unraveling this complex interplay is key for our understanding of bacterial virulence and host response pathways for the development of novel therapeutics. Dual RNA sequencing (dual RNA-Seq) has recently been developed to simultaneously capture host and bacterial transcriptomes from an infected cell. Leveraging the sensitivity and resolution allowed by RNA-seq, dual RNA-Seq can be applied to any bacteria-eukaryotic host interaction. We pioneered dual RNA-Seq to simultaneously capture Chlamydia and host expression profiles during an in vitro infection as proof of principle. Here we provide a detailed laboratory protocol and bioinformatics analysis guidelines for dual RNA-seq experiments focusing on Chlamydia as the organism of interest.

Combining Pathway Identification and Breast Cancer Survival Prediction via Screening-Network Methods

Breast cancer is one of the most common invasive tumors causing high mortality among women. It is characterized by high heterogeneity regarding its biological and clinical characteristics. Several high-throughput assays have been used to collect genome-wide information for many patients in large collaborative studies. This knowledge has improved our understanding of its biology and led to new methods of diagnosing and treating the disease. In particular, system biology has become a valid approach to obtain better insights into breast cancer biological mechanisms. A crucial component of current research lies in identifying novel biomarkers that can be predictive for breast cancer patient prognosis on the basis of the molecular signature of the tumor sample. However, the high dimension and low sample size of data greatly increase the difficulty of cancer survival analysis demanding for the development of ad-hoc statistical methods. In this work, we propose novel screening-network methods that predict patient survival outcome by screening key survival-related genes and we assess the capability of the proposed approaches using METABRIC dataset. In particular, we first identify a subset of genes by using variable screening techniques on gene expression data. Then, we perform Cox regression analysis by incorporating network information associated with the selected subset of genes. The novelty of this work consists in the improved prediction of survival responses due to the different types of screenings (i.e., a biomedical-driven, data-driven and a combination of the two) before building the network-penalized model. Indeed, the combination of the two screening approaches allows us to use the available biological knowledge on breast cancer and complement it with additional information emerging from the data used for the analysis. Moreover, we also illustrate how to extend the proposed approaches to integrate an additional omic layer, such as copy number aberrations, and we show that such strategies can further improve our prediction capabilities. In conclusion, our approaches allow to discriminate patients in high-and low-risk groups using few potential biomarkers and therefore, can help clinicians to provide more precise prognoses and to facilitate the subsequent clinical management of patients at risk of disease.

Integrating ChIP-seq with other functional genomics data

Transcription is regulated by transcription factor (TF) binding at promoters and distal regulatory elements and histone modifications that control the accessibility of these elements. Chromatin immunoprecipitation followed by sequencing (ChIP-seq) has become the standard assay for identifying genome-wide protein-DNA interactions in vitro and in vivo. As large-scale ChIP-seq data sets have been collected for different TFs and histone modifications, their potential to predict gene expression can be used to test hypotheses about the mechanisms of gene regulation. In addition, complementary functional genomics assays provide a global view of chromatin accessibility and long-range cis-regulatory interactions that are being combined with TF binding and histone remodeling to study the regulation of gene expression. Thus, ChIP-seq analysis is now widely integrated with other functional genomics assays to better understand gene regulatory mechanisms. In this review, we discuss advances and challenges in integrating ChIP-seq data to identify context-specific chromatin states associated with gene activity. We describe the overall computational design of integrating ChIP-seq data with other functional genomics assays. We also discuss the challenges of extending these methods to low-input ChIP-seq assays and related single-cell assays.

Genome-wide analyses of direct target genes of four rice NAC-domain transcription factors involved in drought tolerance

BACKGROUND

Plant stress responses and mechanisms determining tolerance are controlled by diverse sets of genes. Transcription factors (TFs) have been implicated in conferring drought tolerance under drought stress conditions, and the identification of their target genes can elucidate molecular regulatory networks that orchestrate tolerance mechanisms.

RESULTS

We generated transgenic rice plants overexpressing the 4 rice TFs, OsNAC5, 6, 9, and 10, under the control of the root-specific RCc3 promoter. We showed that they were tolerant to drought stress with reduced loss of grain yield under drought conditions compared with wild type plants. To understand the molecular mechanisms underlying this tolerance, we here performed chromatin immunoprecipitation (ChIP)-Seq and RNA-Seq analyses to identify the direct target genes of the OsNAC proteins using the RCc3:6MYC-OsNAC expressing roots. A total of 475 binding loci for the 4 OsNAC proteins were identified by cross-referencing their binding to promoter regions and the expression levels of the corresponding genes. The binding loci were distributed among the promoter regions of 391 target genes that were directly up-regulated by one of the OsNAC proteins in four RCc3:6MYC-OsNAC transgenic lines. Based on gene ontology (GO) analysis, the direct target genes were related to transmembrane/transporter activity, vesicle, plant hormones, carbohydrate metabolism, and TFs. The direct targets of each OsNAC range from 4.0-8.7% of the total number of up-regulated genes found in the RNA-Seq data sets. Thus, each OsNAC up-regulates a set of direct target genes that alter root system architecture in the RCc3:OsNAC plants to confer drought tolerance. Our results provide a valuable resource for functional dissection of the molecular mechanisms of drought tolerance.

CONCLUSIONS

Many of the target genes, including transmembrane/transporter, vesicle related, auxin/hormone related, carbohydrate metabolic processes, and transcription factor genes, that are up-regulated by OsNACs act as the cellular components which would alter the root architectures of RCc3:OsNACs for drought tolerance.

Considerations on Experimental Design and Data Analysis of Chromatin Immunoprecipitation Experiments

Arguably one of the most valuable techniques to study chromatin organization, ChIP is the method of choice to map the contacts established between proteins and genomic DNA. Ever since its inception, more than 30 years ago, ChIP has been constantly evolving, improving, and expanding its capabilities and reach. Despite its widespread use by many laboratories across a wide variety of disciplines, ChIP assays can be sometimes challenging to design, and are often sensitive to variations in practical implementation.In this chapter, we provide a general overview of the ChIP method and its most common variations, with a special focus on ChIP-seq. We try to address some of the most important aspects that need to be taken into account in order to design and perform experiments that generate the most reproducible, high-quality data. Some of the main topics covered include the use of properly characterized antibodies, alternatives to chromatin preparation, the need for proper controls, and some recommendations about ChIP-seq data analysis.

Pan-Cancer Mutational and Transcriptional Analysis of the Integrator Complex

The integrator complex has been recently identified as a key regulator of RNA Polymerase II-mediated transcription, with many functions including the processing of small nuclear RNAs, the pause-release and elongation of polymerase during the transcription of protein coding genes, and the biogenesis of enhancer derived transcripts. Moreover, some of its components also play a role in genome maintenance. Thus, it is reasonable to hypothesize that their functional impairment or altered expression can contribute to malignancies. Indeed, several studies have described the mutations or transcriptional alteration of some Integrator genes in different cancers. Here, to draw a comprehensive pan-cancer picture of the genomic and transcriptomic alterations for the members of the complex, we reanalyzed public data from The Cancer Genome Atlas. Somatic mutations affecting Integrator subunit genes and their transcriptional profiles have been investigated in about 11,000 patients and 31 tumor types. A general heterogeneity in the mutation frequencies was observed, mostly depending on tumor type. Despite the fact that we could not establish them as cancer drivers, INTS7 and INTS8 genes were highly mutated in specific cancers. A transcriptome analysis of paired (normal and tumor) samples revealed that the transcription of INTS7, INTS8, and INTS13 is significantly altered in several cancers. Experimental validation performed on primary tumors confirmed these findings.

MapReduce Algorithms for Inferring Gene Regulatory Networks from Time-Series Microarray Data Using an Information-Theoretic Approach

Gene regulation is a series of processes that control gene expression and its extent. The connections among genes and their regulatory molecules, usually transcription factors, and a descriptive model of such connections are known as gene regulatory networks (GRNs). Elucidating GRNs is crucial to understand the inner workings of the cell and the complexity of gene interactions. To date, numerous algorithms have been developed to infer gene regulatory networks. However, as the number of identified genes increases and the complexity of their interactions is uncovered, networks and their regulatory mechanisms become cumbersome to test. Furthermore, prodding through experimental results requires an enormous amount of computation, resulting in slow data processing. Therefore, new approaches are needed to expeditiously analyze copious amounts of experimental data resulting from cellular GRNs. To meet this need, cloud computing is promising as reported in the literature. Here, we propose new MapReduce algorithms for inferring gene regulatory networks on a Hadoop cluster in a cloud environment. These algorithms employ an information-theoretic approach to infer GRNs using time-series microarray data. Experimental results show that our MapReduce program is much faster than an existing tool while achieving slightly better prediction accuracy than the existing tool.

Analysis of Co-Associated Transcription Factors via Ordered Adjacency Differences on Motif Distribution

Transcription factors (TFs) binding to specific DNA sequences or motifs, are elementary to the regulation of transcription. The gene is regulated by a combination of TFs in close proximity. Analysis of co-TFs is an important problem in understanding the mechanism of transcriptional regulation. Recently, ChIP-seq in mapping TF provides a large amount of experimental data to analyze co-TFs. Several studies show that if two TFs are co-associated, the relative distance between TFs exhibits a peak-like distribution. In order to analyze co-TFs, we develop a novel method to evaluate the associated situation between TFs. We design an adjacency score based on ordered differences, which can illustrate co-TF binding affinities for motif analysis. For all candidate motifs, we calculate corresponding adjacency scores, and then list descending-order motifs. From these lists, we can find co-TFs for candidate motifs. On ChIP-seq datasets, our method obtains best AUC results on five datasets, 0.9432 for NMYC, 0.9109 for KLF4, 0.9006 for ZFX, 0.8892 for ESRRB, 0.8920 for E2F1. Our method has great stability on large sample datasets. AUC results of our method on all datasets are above 0.8.

Epigenetic Landscape during Coronavirus Infection

Coronaviruses (CoV) comprise a large group of emerging human and animal pathogens, including the highly pathogenic severe acute respiratory syndrome coronavirus (SARS-CoV) and Middle East respiratory syndrome coronavirus (MERS-CoV) strains. The molecular mechanisms regulating emerging coronavirus pathogenesis are complex and include virus–host interactions associated with entry, replication, egress and innate immune control. Epigenetics research investigates the genetic and non-genetic factors that regulate phenotypic variation, usually caused by external and environmental factors that alter host expression patterns and performance without any change in the underlying genotype. Epigenetic modifications, such as histone modifications, DNA methylation, chromatin remodeling, and non-coding RNAs, function as important regulators that remodel host chromatin, altering host expression patterns and networks in a highly flexible manner. For most of the past two and a half decades, research has focused on the molecular mechanisms by which RNA viruses antagonize the signaling and sensing components that regulate induction of the host innate immune and antiviral defense programs upon infection. More recently, a growing body of evidence supports the hypothesis that viruses, even lytic RNA viruses that replicate in the cytoplasm, have developed intricate, highly evolved, and well-coordinated processes that are designed to regulate the host epigenome, and control host innate immune antiviral defense processes, thereby promoting robust virus replication and pathogenesis. In this article, we discuss the strategies that are used to evaluate the mechanisms by which viruses regulate the host epigenome, especially focusing on highly pathogenic respiratory RNA virus infections as a model. By combining measures of epigenome reorganization with RNA and proteomic datasets, we articulate a spatial-temporal data integration approach to identify regulatory genomic clusters and regions that play a crucial role in the host’s innate immune response, thereby defining a new viral antagonism mechanism following emerging coronavirus infection.

Insights into the Ecology and Evolution of Polyploid Plants through Network Analysis

Polyploidy is a widespread phenomenon throughout eukaryotes, with important ecological and evolutionary consequences. Although genes operate as components of complex pathways and networks, polyploid changes in genes and gene expression have typically been evaluated as either individual genes or as a part of broad-scale analyses. Network analysis has been fruitful in associating genomic and other 'omic'-based changes with phenotype for many systems. In polyploid species, network analysis has the potential not only to facilitate a better understanding of the complex 'omic' underpinnings of phenotypic and ecological traits common to polyploidy, but also to provide novel insight into the interaction among duplicated genes and genomes. This adds perspective to the global patterns of expression (and other 'omic') change that accompany polyploidy and to the patterns of recruitment and/or loss of genes following polyploidization. While network analysis in polyploid species faces challenges common to other analyses of duplicated genomes, present technologies combined with thoughtful experimental design provide a powerful system to explore polyploid evolution. Here, we demonstrate the utility and potential of network analysis to questions pertaining to polyploidy with an example involving evolution of the transgressively superior cotton fibres found in polyploid Gossypium hirsutum. By combining network analysis with prior knowledge, we provide further insights into the role of profilins in fibre domestication and exemplify the potential for network analysis in polyploid species.

Single-cell transcriptome sequencing: recent advances and remaining challenges

Single-cell RNA-sequencing methods are now robust and economically practical and are becoming a powerful tool for high-throughput, high-resolution transcriptomic analysis of cell states and dynamics. Single-cell approaches circumvent the averaging artifacts associated with traditional bulk population data, yielding new insights into the cellular diversity underlying superficially homogeneous populations. Thus far, single-cell RNA-sequencing has already shown great effectiveness in unraveling complex cell populations, reconstructing developmental trajectories, and modeling transcriptional dynamics. Ongoing technical improvements to single-cell RNA-sequencing throughput and sensitivity, the development of more sophisticated analytical frameworks for single-cell data, and an increasing array of complementary single-cell assays all promise to expand the usefulness and potential applications of single-cell transcriptomic profiling.

A survey of best practices for RNA-seq data analysis

RNA-sequencing (RNA-seq) has a wide variety of applications, but no single analysis pipeline can be used in all cases. We review all of the major steps in RNA-seq data analysis, including experimental design, quality control, read alignment, quantification of gene and transcript levels, visualization, differential gene expression, alternative splicing, functional analysis, gene fusion detection and eQTL mapping. We highlight the challenges associated with each step. We discuss the analysis of small RNAs and the integration of RNA-seq with other functional genomics techniques. Finally, we discuss the outlook for novel technologies that are changing the state of the art in transcriptomics.

Mechanisms underlying epigenetic and transcriptional heterogeneity in Chinese hamster ovary (CHO) cell lines

BACKGROUND

Recombinant cell lines developed for therapeutic antibody production often suffer instability or lose recombinant protein expression during long-term culture. Heterogeneous gene expression among cell line subclones may result from epigenetic modifications of DNA or histones, the protein component of chromatin. We thus investigated in such cell lines, DNA methylation and the chromatin environment along the human eukaryotic translation elongation factor 1 alpha 1 (EEF1A1) promoter in an antibody protein-expression vector which was integrated into the Chinese hamster ovary (CHO) cell line genome.

RESULTS

We analyzed four PT1-CHO cell lines which exhibited losses of protein expression at advanced passage number (>P35) growing in adherent conditions and in culture medium with 10 % FCS. These cell lines exhibited different integration sites and transgene copy numbers as determined by fluorescence in situ hybridization (FISH) and quantitative PCR (qPCR), respectively. By qRT-PCR, we analyzed the recombinant mRNA expression and correlated it with DNA methylation and with results from various approaches interrogating the chromatin landscape along the EEF1A1 promoter region. Each PT1-CHO cell line displayed specific epigenetic signatures or chromatin marks correlating with recombinant mRNA expression. The cell line with the lowest recombinant mRNA expression (PT1-1) was characterized by the highest nucleosome occupancy and displayed the lowest enrichment for histone marks associated with active transcription. In contrast, the cell line with the highest recombinant mRNA expression (PT1-55) exhibited the highest numbers of formaldehyde-assisted isolation of regulatory elements (FAIRE)-enriched regions, and was marked by enrichment for histone modifications H3K9ac and H3K9me3. Another cell line with the second highest recombinant mRNA transcription and the most stable protein expression (PT1-7) had the highest enrichments of the histone variants H3.3 and H2A.Z, and the histone modification H3K9ac. A further cell line (PT1-30) scored the highest enrichments for the bivalent marks H3K4me3 and H3K27me3. Finally, DNA methylation made a contribution, but only in the culture medium with reduced FCS or in a different expression vector.

CONCLUSIONS

Our results suggest that the chromatin state along the EEF1A1 promoter region can help predict recombinant mRNA expression, and thus may assist in selecting desirable clones during cell line development for protein production.

Inference of transcriptional regulation in cancers

Despite the rapid accumulation of tumor-profiling data and transcription factor (TF) ChIP-seq profiles, efforts integrating TF binding with the tumor-profiling data to understand how TFs regulate tumor gene expression are still limited. To systematically search for cancer-associated TFs, we comprehensively integrated 686 ENCODE ChIP-seq profiles representing 150 TFs with 7484 TCGA tumor data in 18 cancer types. For efficient and accurate inference on gene regulatory rules across a large number and variety of datasets, we developed an algorithm, RABIT (regression analysis with background integration). In each tumor sample, RABIT tests whether the TF target genes from ChIP-seq show strong differential regulation after controlling for background effect from copy number alteration and DNA methylation. When multiple ChIP-seq profiles are available for a TF, RABIT prioritizes the most relevant ChIP-seq profile in each tumor. In each cancer type, RABIT further tests whether the TF expression and somatic mutation variations are correlated with differential expression patterns of its target genes across tumors. Our predicted TF impact on tumor gene expression is highly consistent with the knowledge from cancer-related gene databases and reveals many previously unidentified aspects of transcriptional regulation in tumor progression. We also applied RABIT on RNA-binding protein motifs and found that some alternative splicing factors could affect tumor-specific gene expression by binding to target gene 3'UTR regions. Thus, RABIT (rabit.dfci.harvard.edu) is a general platform for predicting the oncogenic role of gene expression regulators.