Integrating ChIP-seq with other functional genomics data

Assessing next-generation sequencing-based computational methods for predicting transcriptional regulators with query gene sets

This article provides an in-depth review of computational methods for predicting transcriptional regulators (TRs) with query gene sets. Identification of TRs is of utmost importance in many biological applications, including but not limited to elucidating biological development mechanisms, identifying key disease genes, and predicting therapeutic targets. Various computational methods based on next-generation sequencing (NGS) data have been developed in the past decade, yet no systematic evaluation of NGS-based methods has been offered. We classified these methods into two categories based on shared characteristics, namely library-based and region-based methods. We further conducted benchmark studies to evaluate the accuracy, sensitivity, coverage, and usability of NGS-based methods with molecular experimental datasets. Results show that BART, ChIP-Atlas, and Lisa have relatively better performance. Besides, we point out the limitations of NGS-based methods and explore potential directions for further improvement.

Next-generation sequencing strategies in venous thromboembolism: in whom and for what purpose?

This invited review follows the oral presentation "To Sequence or Not to Sequence, That Is Not the Question; But 'When, Who, Which and What For?' Is" given during the State of the Art session "Translational Genomics in Thrombosis: From OMICs to Clinics" of the International Society on Thrombosis and Haemostasis 2023 Congress. Emphasizing the power of next-generation sequencing technologies and the diverse strategies associated with DNA variant analysis, this review highlights the unresolved questions and challenges in their implementation both for the clinical diagnosis of venous thromboembolism and in translational research.

From powerhouse to regulator: The role of mitoepigenetics in mitochondrion-related cellular functions and human diseases

Beyond their crucial role in energy production, mitochondria harbor a distinct genome subject to epigenetic regulation akin to that of nuclear DNA. This paper delves into the nascent but rapidly evolving fields of mitoepigenetics and mitoepigenomics, exploring the sophisticated regulatory mechanisms governing mitochondrial DNA (mtDNA). These mechanisms encompass mtDNA methylation, the influence of non-coding RNAs (ncRNAs), and post-translational modifications of mitochondrial proteins. Together, these epigenetic modifications meticulously coordinate mitochondrial gene transcription, replication, and metabolism, thereby calibrating mitochondrial function in response to the dynamic interplay of intracellular needs and environmental stimuli. Notably, the dysregulation of mitoepigenetic pathways is increasingly implicated in mitochondrial dysfunction and a spectrum of human pathologies, including neurodegenerative diseases, cancer, metabolic disorders, and cardiovascular conditions. This comprehensive review synthesizes the current state of knowledge, emphasizing recent breakthroughs and innovations in the field. It discusses the potential of high-resolution mitochondrial epigenome mapping, the diagnostic and prognostic utility of blood or tissue mtDNA epigenetic markers, and the promising horizon of mitochondrial epigenetic drugs. Furthermore, it explores the transformative potential of mitoepigenetics and mitoepigenomics in precision medicine. Exploiting a theragnostic approach to maintaining mitochondrial allostasis, this paper underscores the pivotal role of mitochondrial epigenetics in charting new frontiers in medical science.

A review of the role of epigenetic studies for intramuscular fat deposition in beef cattle

Intramuscular fat (IMF) deposition profoundly influences meat quality and economic value in beef cattle production. Meanwhile, contemporary developments in epigenetics have opened new outlooks for understanding the molecular basics of IMF regulation, and it has become a key area of research for world scholars. Therefore, the aim of this paper was to provide insight and synthesis into the intricate relationship between epigenetic mechanisms and IMF deposition in beef cattle. The methodology involves a thorough analysis of existing literature, including pertinent books, academic journals, and online resources, to provide a comprehensive overview of the role of epigenetic studies in IMF deposition in beef cattle. This review summarizes the contemporary studies in epigenetic mechanisms in IMF regulation, high-resolution epigenomic mapping, single-cell epigenomics, multi-omics integration, epigenome editing approaches, longitudinal studies in cattle growth, environmental epigenetics, machine learning in epigenetics, ethical and regulatory considerations, and translation to industry practices from perspectives of IMF deposition in beef cattle. Moreover, this paper highlights DNA methylation, histone modifications, acetylation, phosphorylation, ubiquitylation, non-coding RNAs, DNA hydroxymethylation, epigenetic readers, writers, and erasers, chromatin immunoprecipitation followed by sequencing, whole genome bisulfite sequencing, epigenome-wide association studies, and their profound impact on the expression of crucial genes governing adipogenesis and lipid metabolism. Nutrition and stress also have significant influences on epigenetic modifications and IMF deposition. The key findings underscore the pivotal role of epigenetic studies in understanding and enhancing IMF deposition in beef cattle, with implications for precision livestock farming and ethical livestock management. In conclusion, this review highlights the crucial significance of epigenetic pathways and environmental factors in affecting IMF deposition in beef cattle, providing insightful information for improving the economics and meat quality of cattle production.

Emerging Approaches to Profile Accessible Chromatin from Formalin-Fixed Paraffin-Embedded Sections

Nucleosomes are non-uniformly distributed across eukaryotic genomes, with stretches of 'open' chromatin strongly associated with transcriptionally active promoters and enhancers. Understanding chromatin accessibility patterns in normal tissue and how they are altered in pathologies can provide critical insights to development and disease. With the advent of high-throughput sequencing, a variety of strategies have been devised to identify open regions across the genome, including DNase-seq, MNase-seq, FAIRE-seq, ATAC-seq, and NicE-seq. However, the broad application of such methods to FFPE (formalin-fixed paraffin-embedded) tissues has been curtailed by the major technical challenges imposed by highly fixed and often damaged genomic material. Here, we review the most common approaches for mapping open chromatin regions, recent optimizations to overcome the challenges of working with FFPE tissue, and a brief overview of a typical data pipeline with analysis considerations.

Assessing NGS-based computational methods for predicting transcriptional regulators with query gene sets

This article provides an in-depth review of computational methods for predicting transcriptional regulators with query gene sets. Identification of transcriptional regulators is of utmost importance in many biological applications, including but not limited to elucidating biological development mechanisms, identifying key disease genes, and predicting therapeutic targets. Various computational methods based on next-generation sequencing (NGS) data have been developed in the past decade, yet no systematic evaluation of NGS-based methods has been offered. We classified these methods into two categories based on shared characteristics, namely library-based and region-based methods. We further conducted benchmark studies to evaluate the accuracy, sensitivity, coverage, and usability of NGS-based methods with molecular experimental datasets. Results show that BART, ChIP-Atlas, and Lisa have relatively better performance. Besides, we point out the limitations of NGS-based methods and explore potential directions for further improvement.

Key points

An introduction to available computational methods for predicting functional TRs from a query gene set.A detailed walk-through along with practical concerns and limitations.A systematic benchmark of NGS-based methods in terms of accuracy, sensitivity, coverage, and usability, using 570 TR perturbation-derived gene sets.NGS-based methods outperform motif-based methods. Among NGS methods, those utilizing larger databases and adopting region-centric approaches demonstrate favorable performance. BART, ChIP-Atlas, and Lisa are recommended as these methods have overall better performance in evaluated scenarios.

Genetic and bioinformatic analyses reveal transcriptional networks underlying dual genomic coordination of mitochondrial biogenesis

Mitochondrial genome encodes handful genes of respiratory chain complexes, whereas all the remaining mitochondrial proteins are encoded on the nuclear genome. However, the mechanisms coordinating these two genomes to control mitochondrial biogenesis remain largely unknown. To identify transcription circuits involved in these processes, we performed a candidate RNAi screen in developing eyes that had reduced mitochondrial DNA contents. We reasoned that impaired mitochondrial biogenesis would synergistically interact with mtDNA deficiency in disrupting tissue development. Over 638 transcription factors annotated in the fly genome, we identified 77 transcription factors that may be involved in mitochondrial genome maintenance and gene expression. Additional genetic and genomic analyses revealed that a novel transcription factor, CG1603, and its upstream factor YL-1 are essential for mitochondrial biogenesis. We constructed a regulator network among positive hits using the published CHIP-seq data. The network analysis revealed extensive connections, and complex hierarchical organization underlying the transcription regulation of mitochondrial biogenesis.

epidecodeR: a functional exploration tool for epigenetic and epitranscriptomic regulation

Recent technological advances in sequencing DNA and RNA modifications using high-throughput platforms have generated vast epigenomic and epitranscriptomic datasets whose power in transforming life science is yet fully unleashed. Currently available in silico methods have facilitated the identification, positioning and quantitative comparisons of individual modification sites. However, the essential challenge to link specific 'epi-marks' to gene expression in the particular context of cellular and biological processes is unmet. To fast-track exploration, we generated epidecodeR implemented in R, which allows biologists to quickly survey whether an epigenomic or epitranscriptomic status of their interest potentially influences gene expression responses. The evaluation is based on the cumulative distribution function and the statistical significance in differential expression of genes grouped by the number of 'epi-marks'. This tool proves useful in predicting the role of H3K9ac and H3K27ac in associated gene expression after knocking down deacetylases FAM60A and SDS3 and N6-methyl-adenosine-associated gene expression after knocking out the reader proteins. We further used epidecodeR to explore the effectiveness of demethylase FTO inhibitors and histone-associated modifications in drug abuse in animals. epidecodeR is available for downloading as an R package at https://bioconductor.riken.jp/packages/3.13/bioc/html/epidecodeR.html.

Probe-free optical chromatin deformation and measurement of differential mechanical properties in the nucleus

The nucleus is highly organized to facilitate coordinated gene transcription. Measuring the rheological properties of the nucleus and its sub-compartments will be crucial to understand the principles underlying nuclear organization. Here, we show that strongly localized temperature gradients (approaching 1°C/µm) can lead to substantial intra-nuclear chromatin displacements (>1 µm), while nuclear area and lamina shape remain unaffected. Using particle image velocimetry (PIV), intra-nuclear displacement fields can be calculated and converted into spatio-temporally resolved maps of various strain components. Using this approach, we show that chromatin displacements are highly reversible, indicating that elastic contributions are dominant in maintaining nuclear organization on the time scale of seconds. In genetically inverted nuclei, centrally compacted heterochromatin displays high resistance to deformation, giving a rigid, solid-like appearance. Correlating spatially resolved strain maps with fluorescent reporters in conventional interphase nuclei reveals that various nuclear compartments possess distinct mechanical identities. Surprisingly, both densely and loosely packed chromatin showed high resistance to deformation, compared to medium dense chromatin. Equally, nucleoli display particularly high resistance and strong local anchoring to heterochromatin. Our results establish how localized temperature gradients can be used to drive nuclear compartments out of mechanical equilibrium to obtain spatial maps of their material responses.

Analyzing histone ChIP-seq data with a bin-based probability of being signal

Histone ChIP-seq is one of the primary methods for charting the cellular epigenomic landscape, the components of which play a critical regulatory role in gene expression. Analyzing the activity of regulatory elements across datasets and cell types can be challenging due to shifting peak positions and normalization artifacts resulting from, for example, differing read depths, ChIP efficiencies, and target sizes. Moreover, broad regions of enrichment seen in repressive histone marks often evade detection by commonly used peak callers. Here, we present a simple and versatile method for identifying enriched regions in ChIP-seq data that relies on estimating a gamma distribution fit to non-overlapping 5kB genomic bins to establish a global background. We use this distribution to assign a probability of being signal (PBS) between zero and one to each 5 kB bin. This approach, while lower in resolution than typical peak-calling methods, provides a straightforward way to identify enriched regions and compare enrichments among multiple datasets, by transforming the data to values that are universally normalized and can be readily visualized and integrated with downstream analysis methods. We demonstrate applications of PBS for both broad and narrow histone marks, and provide several illustrations of biological insights which can be gleaned by integrating PBS scores with downstream data types.

Epstein-Barr virus evades restrictive host chromatin closure by subverting B cell activation and germinal center regulatory loci

Chromatin accessibility fundamentally governs gene expression and biological response programs that can be manipulated by pathogens. Here we capture dynamic chromatin landscapes of individual B cells during Epstein-Barr virus (EBV) infection. EBV+ cells that exhibit arrest via antiviral sensing and proliferation-linked DNA damage experience global accessibility reduction. Proliferative EBV+ cells develop expression-linked architectures and motif accessibility profiles resembling in vivo germinal center (GC) phenotypes. Remarkably, EBV elicits dark zone (DZ), light zone (LZ), and post-GC B cell chromatin features despite BCL6 downregulation. Integration of single-cell assay for transposase-accessible chromatin sequencing (scATAC-seq), single-cell RNA sequencing (scRNA-seq), and chromatin immunoprecipitation sequencing (ChIP-seq) data enables genome-wide cis-regulatory predictions implicating EBV nuclear antigens (EBNAs) in phenotype-specific control of GC B cell activation, survival, and immune evasion. Knockouts validate bioinformatically identified regulators (MEF2C and NFE2L2) of EBV-induced GC phenotypes and EBNA-associated loci that regulate gene expression (CD274/PD-L1). These data and methods can inform high-resolution investigations of EBV-host interactions, B cell fates, and virus-mediated lymphomagenesis.

Bioinformatics for wet-lab scientists: practical application in sequencing analysis

BACKGROUND

Genomics data is available to the scientific community after publication of research projects and can be investigated for a multitude of research questions. However, in many cases deposited data is only assessed and used for the initial publication, resulting in valuable resources not being exploited to their full depth. MAIN: A likely reason for this is that many wetlab-based researchers are not formally trained to apply bioinformatic tools and may therefore assume that they lack the necessary experience to do so themselves. In this article, we present a series of freely available, predominantly web-based platforms and bioinformatic tools that can be combined in analysis pipelines to interrogate different types of next-generation sequencing data. Additionally to the presented exemplary route, we also list a number of alternative tools that can be combined in a mix-and-match fashion. We place special emphasis on tools that can be followed and used correctly without extensive prior knowledge in programming. Such analysis pipelines can be applied to existing data downloaded from the public domain or be compared to the results of own experiments.

CONCLUSION

Integrating transcription factor binding to chromatin (ChIP-seq) with transcriptional output (RNA-seq) and chromatin accessibility (ATAC-seq) can not only assist to form a deeper understanding of the molecular interactions underlying transcriptional regulation but will also help establishing new hypotheses and pre-testing them in silico.

Detailed molecular and epigenetic characterization of the pig IPEC-J2 and chicken SL-29 cell lines

The pig IPEC-J2 and chicken SL-29 cell lines are of interest because of their untransformed nature and wide use in functional studies. Molecular characterization of these cell lines is important to gain insight into possible molecular aberrations. The aim of this paper is to provide a molecular and epigenetic characterization of the IPEC-J2 and SL-29 cell lines, a cell-line reference for the FAANG community, and future biomedical research. Whole genome sequencing, gene expression, DNA methylation, chromatin accessibility, and ChIP-seq of four histone marks (H3K4me1, H3K4me3, H3K27ac, H3K27me3) and an insulator (CTCF) are used to achieve these aims. Heteroploidy (aneuploidy) of various chromosomes was observed from whole genome sequencing analysis in both cell lines. Furthermore, higher gene expression for genes located on chromosomes with aneuploidy in comparison to diploid chromosomes was observed. Regulatory complexity of gene expression, DNA methylation, and chromatin accessibility was investigated through an integrative approach.

The GRN concept as a guide for evolutionary developmental biology

Organismal phenotypes result largely from inherited developmental programs, usually executed during embryonic and juvenile life stages. These programs are not blank slates onto which natural selection can draw arbitrary forms. Rather, the mechanisms of development play an integral role in shaping phenotypic diversity and help determine the evolutionary trajectories of species. Modern evolutionary biology must, therefore, account for these mechanisms in both theory and in practice. The gene regulatory network (GRN) concept represents a potent tool for achieving this goal whose utility has grown in tandem with advances in "omic" technologies and experimental techniques. However, while the GRN concept is widely utilized, it is often less clear what practical implications it has for conducting research in evolutionary developmental biology. In this Perspective, we attempt to provide clarity by discussing how experiments and projects can be designed in light of the GRN concept. We first map familiar biological notions onto the more abstract components of GRN models. We then review how diverse functional genomic approaches can be directed toward the goal of constructing such models and discuss current methods for functionally testing evolutionary hypotheses that arise from them. Finally, we show how the major steps of GRN model construction and experimental validation suggest generalizable workflows that can serve as a scaffold for project design. Taken together, the practical implications that we draw from the GRN concept provide a set of guideposts for studies aiming at unraveling the molecular basis of phenotypic diversity.

DiffChIPL: a differential peak analysis method for high-throughput sequencing data with biological replicates based on limma

MOTIVATION

ChIP-seq detects protein-DNA interactions within chromatin, such as that of chromatin structural components and transcription machinery. ChIP-seq profiles are often noisy and variable across replicates, posing a challenge to the development of effective algorithms to accurately detect differential peaks. Methods have recently been designed for this purpose but sometimes yield conflicting results that are inconsistent with the underlying biology. Most existing algorithms perform well on limited datasets. To improve differential analysis of ChIP-seq, we present a novel Differential analysis method for ChIP-seq based on Limma (DiffChIPL).

RESULTS

DiffChIPL is adaptive to asymmetrical or symmetrical data and can accurately report global differences. We used simulated and real datasets for transcription factors (TFs) and histone modification marks to validate and benchmark our algorithm. DiffChIPL shows superior performance in sensitivity and false positive rate in different simulations and control datasets. DiffChIPL also performs well on real ChIP-seq, CUT&RUN, CUT&Tag and ATAC-seq datasets. DiffChIPL is an accurate and robust method, exhibiting better performance in differential analysis for a variety of applications including TF binding, histone modifications and chromatin accessibility.

AVAILABILITY AND IMPLEMENTATION

https://github.com/yancychy/DiffChIPL.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Mitogen-activating protein kinase kinase kinase kinase-3, inhibited by Astragaloside IV through H3 lysine 4 monomethylation, promotes the progression of diabetic nephropathy by inducing apoptosis

Astragaloside IV (AS-IV) is a bioactive saponin extracted from the Astragalus root and has been reported to exert a protective effect on diabetic nephropathy (DN). However, the underlying mechanism remains unclear. Herein, we found that AS-IV treatment alleviated DN symptoms in DN mice accompanied by reduced metabolic parameters (body weight, urine microalbumin and creatinine, creatinine clearance, and serum urea nitrogen and creatinine), pathological changes, and apoptosis. Epigenetic histone modifications are closely related to diabetes and its complications, including H3 lysine 4 monomethylation (H3K4me1, a promoter of gene transcription). A ChIP-seq assay was conducted to identify the genes regulated by H3K4me1 in DN mice after AS-IV treatment and followed by a Kyoto Encyclopedia of Genes and Genomes pathway enrichment analysis. The results showed that there were 16 common genes targeted by H3K4me1 in normal and AS-IV-treated DN mice, 1148 genes were targeted by H3K4me1 only in DN mice. From the 1148 genes, we screened mitogen-activating protein kinase kinase kinase kinase-3 (MAP4K3) for the verification of gene expression and functional study. The results showed that MAP4K3 was significantly increased in DN mice and high glucose (HG)-treated NRK-52E cells, which was reversed by AS-IV. MAP4K3 silencing reduced the apoptosis of NRK-52E cells under HG condition, as evidenced by decreased cleaved caspase 3 and Bax (pro-apoptotic factors), and increased Bcl-2 and Bcl-xl (anti-apoptotic factors). Collectively, AS-IV may downregulate MAP4K3 expression by regulating H3K4me1 binding and further reducing apoptosis, which may be one of the potential mechanisms that AS-IV plays a protective effect on DN.

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.

Chromatin Structure and Dynamics: Focus on Neuronal Differentiation and Pathological Implication

Chromatin structure is an essential regulator of gene expression. Its state of compaction contributes to the regulation of genetic programs, in particular during differentiation. Epigenetic processes, which include post-translational modifications of histones, DNA methylation and implication of non-coding RNA, are powerful regulators of gene expression. Neurogenesis and neuronal differentiation are spatio-temporally regulated events that allow the formation of the central nervous system components. Here, we review the chromatin structure and post-translational histone modifications associated with neuronal differentiation. Studying the impact of histone modifications on neuronal differentiation improves our understanding of the pathophysiological mechanisms of chromatinopathies and opens up new therapeutic avenues. In addition, we will discuss techniques for the analysis of histone modifications on a genome-wide scale and the pathologies associated with the dysregulation of the epigenetic machinery.

Automation of high-throughput mRNA-seq library preparation: a robust, hands-free and time efficient methodology

Over the last decade, whole transcriptome profiling, also known as RNA-sequencing (RNA-seq), has quickly gained traction as a reliable method for unbiased assessment of gene expression. Integration of RNA-seq expression data into other omics datasets (e.g., proteomics, metabolomics, or epigenetics) solidifies our understanding of cell-specific regulatory patterns, yielding pathways to investigate the key rules of gene regulation. A limitation to efficient, at-scale utilization of RNA-seq is the time-demanding library preparation workflows, which is a 2-day or longer endeavor per cohort/sample size. To tackle this bottleneck, we designed an automated workflow that increases throughput capacity, while minimizing human error to enhance reproducibility. To this end, we converted the manual protocol of the NEBNext Directional Ultra II RNA Library Prep Kit for Illumina on the Beckman Coulter liquid handler, Biomek i7 Hybrid workstation. A total of 84 RNA samples were isolated from two human cell lines and subjected to comparative manual and automated library preparation methods. Qualitative and quantitative results indicated a high degree of similarity between libraries generated manually or through automation. Yet, there was a significant reduction in both hands-on and assay time from a 2-day manual to a 9-hour automated workflow. Using linear regression analysis, we found the Pearson correlation coefficient between libraries generated manually or by automation to be almost identical to a sample being sequenced twice (R²= 0.985 vs 0.983). This demonstrates that high-throughput automated workflows can be of great benefit to genomic laboratories by enhancing efficiency of library preparation, reducing hands-on time and increasing throughput potential.

Endothelial Notch signaling directly regulates the small GTPase RND1 to facilitate Notch suppression of endothelial migration

To control sprouting angiogenesis, endothelial Notch signaling suppresses tip cell formation, migration, and proliferation while promoting barrier formation. Each of these responses may be regulated by distinct Notch-regulated effectors. Notch activity is highly dynamic in sprouting endothelial cells, while constitutive Notch signaling drives homeostatic endothelial polarization, indicating the need for both rapid and constitutive Notch targets. In contrast to previous screens that focus on genes regulated by constitutively active Notch, we characterized the dynamic response to Notch. We examined transcriptional changes from 1.5 to 6 h after Notch signal activation via ligand-specific or EGTA induction in cultured primary human endothelial cells and neonatal mouse brain. In each combination of endothelial type and Notch manipulation, transcriptomic analysis identified distinct but overlapping sets of rapidly regulated genes and revealed many novel Notch target genes. Among the novel Notch-regulated signaling pathways identified were effectors in GPCR signaling, notably, the constitutively active GTPase RND1. In endothelial cells, RND1 was shown to be a novel direct Notch transcriptional target and required for Notch control of sprouting angiogenesis, endothelial migration, and Ras activity. We conclude that RND1 is directly regulated by endothelial Notch signaling in a rapid fashion in order to suppress endothelial migration.

Single Cell Transcriptomic and Chromatin Profiles Suggest Layer Vb Is the Only Layer With Shared Excitatory Cell Types in the Medial and Lateral Entorhinal Cortex

All brain functionality arises from the activity in neural circuits in different anatomical regions. These regions contain different circuits comprising unique cell types. An integral part to understanding neural circuits is a full census of the constituent parts, i.e., the neural cell types. This census can be based on different characteristics. Previously combinations of morphology and physiology, gene expression, and chromatin accessibility have been used in various cortical and subcortical regions. This has given an extensive yet incomplete overview of neural cell types. However, these techniques have not been applied to all brain regions. Here we apply single cell analysis of accessible chromatin on two similar but different cortical regions, the medial and the lateral entorhinal cortices. Even though these two regions are anatomically similar, their intrinsic and extrinsic connectivity are different. In 4,136 cells we identify 20 different clusters representing different cell types. As expected, excitatory cells show regionally specific clusters, whereas inhibitory neurons are shared between regions. We find that several deep layer excitatory neuronal cell types as defined by chromatin profile are also shared between the two different regions. Integration with a larger scRNA-seq dataset maintains this shared characteristic for cells in Layer Vb. Interestingly, this layer contains three clusters, two specific to either subregion and one shared between the two. These clusters can be putatively associated with particular functional and anatomical cell types found in this layer. This information is a step forwards into elucidating the cell types within the entorhinal circuit and by extension its functional underpinnings.

The transcriptomic revolution and radiation biology

PURPOSE

This article will briefly review the origins and evolution of functional genomics, first describing the experimental technology, and then some of the approaches applied to data analysis and visualization. It will emphasize application of functional genomics to radiation biology, using examples from the author's work to illustrate several key types of analysis. It concludes with a look at non-coding RNA, alternative reading of the genome, and single-cell transcriptomics, some of the innovative areas that may help to shape future research in radiation biology and oncology.

CONCLUSIONS

Transcriptomic approaches have provided insight into many areas of radiation biology and medicine, and innovations in technology and data analysis approaches promise continued contributions to radiation science in the future.

An Optimized ChIP-Seq Protocol to Determine Chromatin Binding of Estrogen Receptor Beta

Estrogen regulates transcription through two nuclear receptors, ERα and ERβ, in a tissue and cellular-dependent manner. Both the receptors bind estrogen and activate transcription through direct or indirect interactions with DNA. Revealing their interactions with the chromatin is key to understanding their transcriptional activities and their biological functions. Chromatin-immunoprecipitation followed by sequencing (ChIP-Seq) is a powerful technique to map protein-DNA interactions at precise genomic locations. The genome-wide binding of ERα has been extensively studied. Similar studies of ERβ, however, have been more difficult, in part due to a lack of endogenous expression in cell lines and lack of specific antibodies. In this chapter, we provide an optimized stepwise ChIP protocol for a well-validated ERβ antibody, which is applicable for ChIP-Seq analysis of cell lines with exogenous expression of ERβ.

Transcriptional regulation by a RecQ helicase

RecQ helicases participate in a variety of DNA metabolic processes through their multiple biochemical activities. In vitro characterization and cellular studies have suggested that RECQ1 (also known as RECQL or RECQL1) performs its diverse functions through specific interactions with DNA and protein partners. We have taken an unbiased approach to determine the contribution of RECQ1 in genome maintenance and as a putative susceptibility factor in breast cancer. Here, we provide methodology to map the genome-wide binding sites of RECQ1 together with the profiling of RECQ1-dependent transcriptome to investigate its role in gene regulation. The described approach will be helpful to develop a mechanistic framework for elucidating critical functions of RECQ1 and other RecQ homologs in distinct chromatin and biological contexts.

Bioinformatics Methods for ChIP-seq Histone Analysis

The field of genomics and genome-wide analysis has exploded since around 2008 with the development of high-throughput omics approaches, largely driven by the emergence of the next-generation sequencing technologies. Among the different biological applications supported by recent sequencing technologies, ChIP-seq (Chromatin ImmunoPrecipitation followed by Sequencing) is one of the most powerful techniques which has dramatically changed our view of the epigenetics landscape of cells.In this chapter, I will present and discuss the main steps of bioinformatic and biostatistical analysis of ChIP-seq data (Fig. 1). While this technique has been widely used to study transcription factor binding sites, I will focus here on the analysis of histone modifications.

Reconstitution and Purification of Nucleosomes with Recombinant Histones and Purified DNA

Nucleosomes are substrates for a broad range of factors, including those involved in transcription or chromosome maintenance/reorganization and enzymes that covalently modify histones. Given the heterogeneous nature of nucleosomes in vivo (i.e., varying histone composition, post-translational modifications, DNA sequence register), understanding the specificity and activities of chromatin-interacting factors has required in vitro studies using well-defined nucleosome substrates. Here, we provide detailed methods for large-scale PCR preparation of DNA, assembly of nucleosomes from purified DNA and histones, and purification of DNA and mononucleosomes. Such production of well-defined nucleosomes for biochemical and biophysical studies is key for studying numerous proteins and protein complexes that bind and/or alter nucleosomes and for revealing inherent characteristics of nucleosomes. © 2020 Wiley Periodicals LLC. Basic Protocol 1: Large-scale PCR amplification of DNA Basic Protocol 2: DNA and nucleosome purification using a Bio-Rad Mini Prep Cell/Prep Cell Basic Protocol 3: Nucleosome reconstitution via linear gradient salt dialysis.

The upstream 5' splice site remains associated to the transcription machinery during intron synthesis

In the earliest step of spliceosome assembly, the two splice sites flanking an intron are brought into proximity by U1 snRNP and U2AF along with other proteins. The mechanism that facilitates this intron looping is poorly understood. Using a CRISPR interference-based approach to halt RNA polymerase II transcription in the middle of introns in human cells, we discovered that the nascent 5′ splice site base pairs with a U1 snRNA that is tethered to RNA polymerase II during intron synthesis. This association functionally corresponds with splicing outcome, involves bona fide 5′ splice sites and cryptic intronic sites, and occurs transcriptome-wide. Overall, our findings reveal that the upstream 5′ splice sites remain attached to the transcriptional machinery during intron synthesis and are thus brought into proximity of the 3′ splice sites; potentially mediating the rapid splicing of long introns.

We know that most splicing reactions take place co-transcriptionally, but how the transcription machinery facilitate splicing of introns is unknown. Here the authors show that the 5′ splice site remains associated with the transcription machinery during intron synthesis through U1 snRNP, providing a basis for the rapid splicing reaction of introns.

Transcription factor enrichment analysis (TFEA) quantifies the activity of multiple transcription factors from a single experiment

Detecting changes in the activity of a transcription factor (TF) in response to a perturbation provides insights into the underlying cellular process. Transcription Factor Enrichment Analysis (TFEA) is a robust and reliable computational method that detects positional motif enrichment associated with changes in transcription observed in response to a perturbation. TFEA detects positional motif enrichment within a list of ranked regions of interest (ROIs), typically sites of RNA polymerase initiation inferred from regulatory data such as nascent transcription. Therefore, we also introduce muMerge, a statistically principled method of generating a consensus list of ROIs from multiple replicates and conditions. TFEA is broadly applicable to data that informs on transcriptional regulation including nascent transcription (eg. PRO-Seq), CAGE, histone ChIP-Seq, and accessibility data (e.g., ATAC-Seq). TFEA not only identifies the key regulators responding to a perturbation, but also temporally unravels regulatory networks with time series data. Consequently, TFEA serves as a hypothesis-generating tool that provides an easy, rigorous, and cost-effective means to broadly assess TF activity yielding new biological insights.

Sexual Dimorphism through the Lens of Genome Manipulation, Forward Genetics, and Spatiotemporal Sequencing

Sexual reproduction often leads to selection that favors the evolution of sex-limited traits or sex-specific variation for shared traits. These sexual dimorphisms manifest due to sex-specific genetic architectures and sex-biased gene expression across development, yet the molecular mechanisms underlying these patterns are largely unknown. The first step is to understand how sexual dimorphisms arise across the genotype-phenotype-fitness map. The emergence of "4D genome technologies" allows for efficient, high-throughput, and cost-effective manipulation and observations of this process. Studies of sexual dimorphism will benefit from combining these technological advances (e.g., precision genome editing, inducible transgenic systems, and single-cell RNA sequencing) with clever experiments inspired by classic designs (e.g., bulked segregant analysis, experimental evolution, and pedigree tracing). This perspective poses a synthetic view of how manipulative approaches coupled with cutting-edge observational methods and evolutionary theory are poised to uncover the molecular genetic basis of sexual dimorphism with unprecedented resolution. We outline hypothesis-driven experimental paradigms for identifying genetic mechanisms of sexual dimorphism among tissues, across development, and over evolutionary time.

Genome-in-a-Box: Building a Chromosome from the Bottom Up

Chromosome structure and dynamics are essential for life, as the way that our genomes are spatially organized within cells is crucial for gene expression, differentiation, and genome transfer to daughter cells. There is a wide variety of methods available to study chromosomes, ranging from live-cell studies to single-molecule biophysics, which we briefly review. While these technologies have yielded a wealth of data, such studies still leave a significant gap between top-down experiments on live cells and bottom-up in vitro single-molecule studies of DNA-protein interactions. Here, we introduce "genome-in-a-box" (GenBox) as an alternative in vitro approach to build and study chromosomes, which bridges this gap. The concept is to assemble a chromosome from the bottom up by taking deproteinated genome-sized DNA isolated from live cells and subsequently add purified DNA-organizing elements, followed by encapsulation in cell-sized containers using microfluidics. Grounded in the rationale of synthetic cell research, the approach would enable to experimentally study emergent effects at the global genome level that arise from the collective action of local DNA-structuring elements. We review the various DNA-structuring elements present in nature, from nucleoid-associated proteins and SMC complexes to phase separation and macromolecular crowders. Finally, we discuss how GenBox can contribute to several open questions on chromosome structure and dynamics.

Unified Analysis of Multiple ChIP-Seq Datasets

High-throughput sequencing technologies are increasingly used in molecular cell biology to assess genome-wide chromatin dynamics of proteins bound to DNA, through techniques such as chromatin immunoprecipitation sequencing (ChIP-seq). These techniques often rely on an analysis strategy based on identifying genomic regions with increased sequencing signal to infer the binding location or chemical modifications of proteins bound to DNA. Peak calling within individual samples has been well described, however relatively little attention has been devoted to the merging of replicate samples, and the cross-comparison of many samples. Here, we present a generalized strategy to enable the unification of ChIP-seq datasets, enabling enhanced cross-comparison of binding patterns. The strategy works by merging peak data between different (even unrelated) samples, and then using a local background to recalculate enrichment. This strategy redefines the peaks within each experiment, allowing for more accurate cross-comparison of datasets.

OneStopRNAseq: A Web Application for Comprehensive and Efficient Analyses of RNA-Seq Data

Over the past decade, a large amount of RNA sequencing (RNA-seq) data were deposited in public repositories, and more are being produced at an unprecedented rate. However, there are few open source tools with point-and-click interfaces that are versatile and offer streamlined comprehensive analysis of RNA-seq datasets. To maximize the capitalization of these vast public resources and facilitate the analysis of RNA-seq data by biologists, we developed a web application called OneStopRNAseq for the one-stop analysis of RNA-seq data. OneStopRNAseq has user-friendly interfaces and offers workflows for common types of RNA-seq data analyses, such as comprehensive data-quality control, differential analysis of gene expression, exon usage, alternative splicing, transposable element expression, allele-specific gene expression quantification, and gene set enrichment analysis. Users only need to select the desired analyses and genome build, and provide a Gene Expression Omnibus (GEO) accession number or Dropbox links to sequence files, alignment files, gene-expression-count tables, or rank files with the corresponding metadata. Our pipeline facilitates the comprehensive and efficient analysis of private and public RNA-seq data.

Identification of universal and cell-type specific p53 DNA binding

Background

The tumor suppressor p53 is a major regulator of the DNA damage response and has been suggested to selectively bind and activate cell-type specific gene expression programs. However recent studies and meta-analyses of genomic data propose largely uniform, and condition independent p53 binding and thus question the selective and cell-type dependent function of p53.

Results

To systematically assess the cell-type specificity of p53, we measured its association with DNA in 12 p53 wild-type cancer cell lines, from a range of epithelial linages, in response to ionizing radiation. We found that the majority of bound sites were occupied across all cell lines, however we also identified a subset of binding sites that were specific to one or a few cell lines. Unlike the shared p53-bound genome, which was not dependent on chromatin accessibility, the association of p53 with these atypical binding sites was well explained by chromatin accessibility and could be modulated by forcing cell state changes such as the epithelial-to-mesenchymal transition.

Conclusions

Our study reconciles previous conflicting views in the p53 field, by demonstrating that although the majority of p53 DNA binding is conserved across cell types, there is a small set of cell line specific binding sites that depend on cell state.

From reads to insight: a hitchhiker's guide to ATAC-seq data analysis

Assay of Transposase Accessible Chromatin sequencing (ATAC-seq) is widely used in studying chromatin biology, but a comprehensive review of the analysis tools has not been completed yet. Here, we discuss the major steps in ATAC-seq data analysis, including pre-analysis (quality check and alignment), core analysis (peak calling), and advanced analysis (peak differential analysis and annotation, motif enrichment, footprinting, and nucleosome position analysis). We also review the reconstruction of transcriptional regulatory networks with multiomics data and highlight the current challenges of each step. Finally, we describe the potential of single-cell ATAC-seq and highlight the necessity of developing ATAC-seq specific analysis tools to obtain biologically meaningful insights.

Challenges of Translating Gene Regulatory Information into Agronomic Improvements

Improvement of agricultural species has exploited the genetic variation responsible for complex quantitative traits. Much of the functional variation is regulatory, in cis-regulatory elements and trans-acting factors that ultimately contribute to gene expression differences. However, the identification of gene regulatory network components that, when modulated, will increase plant productivity or resilience, is challenging, yet essential to provide increased predictive power for genome engineering approaches that are likely to benefit useful traits. Here, we discuss the opportunities and limitations of using data obtained from gene coexpression, transcription factor binding, and genome-wide association mapping analyses to predict regulatory interactions that impact crop improvement. It is apparent that a combination of information from these data types is necessary for the reliable identification and utilization of important regulatory interactions that underlie complex agronomic traits.

Epigenetic dysregulation in pituitary tumors

Pituitary tumors are common intracranial neoplasms associated with significant morbidity due to hormonal dysregulation and neurologic symptoms. Somatic mutations are uncommon in sporadic pituitary adenomas, and only few monogenic conditions are associated with pituitary tumors. However, increasing evidence suggests that aberrant epigenetic modifications are found in pituitary tumors. In this review, we describe these mechanisms, including DNA methylation, histone modification and microRNA expression, and the evidence supporting their dysregulation in pituitary tumors, as well as their regulation of pro-tumorigenic genes. In addition, we provide an overview of findings from preclinical studies investigating the use of histone deacetylase inhibitors to treat pituitary adenomas and the need for further studies involving epigenetic drugs and functional characterization of epigenetic dysregulation.

The Role of Aberrant DNA Methylation in Misregulation of Gene Expression in Gonadotroph Nonfunctioning Pituitary Tumors

Gonadotroph nonfunctioning pituitary adenomas (NFPAs) are common intracranial tumors, but the role of aberrant epigenetic regulation in their development remains poorly understood. In this study, we investigated the effect of impaired CpG methylation in NFPAs. We determined DNA methylation and transcriptomic profiles in 32 NFPAs and normal pituitary sections using methylation arrays and sequencing, respectively. Ten percent of differentially methylated CpGs were correlated with gene expression, and the affected genes are involved in a variety of tumorigenesis-related pathways. Different proportions of gene body and promoter region localization were observed in CpGs with negative and positive correlations between methylation and gene expression, and different proportions of CpGs were located in 'open sea' and 'shelf/shore' regions. The expression of ~8% of genes differentially expressed in NFPAs was related to aberrant methylation. Methylation levels of seven CpGs located in the regulatory regions of FAM163A, HIF3A and PRSS8 were determined by pyrosequencing, and gene expression was measured by qRT-PCR and immunohistochemistry in 83 independent NFPAs. The results clearly confirmed the negative correlation between methylation and gene expression for these genes. By identifying which aberrantly methylated CpGs affect gene expression in gonadotrophinomas, our data confirm the role of aberrant methylation in pathogenesis of gonadotroph NFPAs.

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.

From Genotype to Phenotype: Through Chromatin

Advances in sequencing technologies have enabled the exploration of the genetic basis for several clinical disorders by allowing identification of causal mutations in rare genetic diseases. Sequencing technology has also facilitated genome-wide association studies to gather single nucleotide polymorphisms in common diseases including cancer and diabetes. Sequencing has therefore become common in the clinic for both prognostics and diagnostics. The success in follow-up steps, i.e., mapping mutations to causal genes and therapeutic targets to further the development of novel therapies, has nevertheless been very limited. This is because most mutations associated with diseases lie in inter-genic regions including the so-called regulatory genome. Additionally, no genetic causes are apparent for many diseases including neurodegenerative disorders. A complementary approach is therefore gaining interest, namely to focus on epigenetic control of the disease to generate more complete functional genomic maps. To this end, several recent studies have generated large-scale epigenetic datasets in a disease context to form a link between genotype and phenotype. We focus DNA methylation and important histone marks, where recent advances have been made thanks to technology improvements, cost effectiveness, and large meta-scale epigenome consortia efforts. We summarize recent studies unravelling the mechanistic understanding of epigenetic processes in disease development and progression. Moreover, we show how methodology advancements enable causal relationships to be established, and we pinpoint the most important issues to be addressed by future research.

AMPK: An Epigenetic Landscape Modulator

Activated by AMP-dependent and -independent mechanisms, AMP-activated protein kinase (AMPK) plays a central role in the regulation of cellular bioenergetics and cellular survival. AMPK regulates a diverse set of signaling networks that converge to epigenetically mediate transcriptional events. Reversible histone and DNA modifications, such as acetylation and methylation, result in structural chromatin alterations that influence transcriptional machinery access to genomic regulatory elements. The orchestration of these epigenetic events differentiates physiological from pathophysiological phenotypes. AMPK phosphorylation of histones, DNA methyltransferases and histone post-translational modifiers establish AMPK as a key player in epigenetic regulation. This review focuses on the role of AMPK as a mediator of cellular survival through its regulation of chromatin remodeling and the implications this has for health and disease.