Genome-wide analysis of transcription factor binding sites based on ChIP-Seq data

From Gene to Whole Cell: Modeling, Visualization, and Analysis

Proteogenomics combines proteomic and genetic data to gain new insights in molecular mechanisms. Here, we extend this approach toward structural biology from a tool perspective. The chapter starts with tools which can be used to explore genetic information and then enrich those with proteomic data. Based on the corresponding identifiers, three-dimensional structures of proteins are identified and used to embed them in their molecular environment, here the surrounding membrane. This membrane is then mapped onto the surface of an interpretative three-dimensional cell model. Then, the embedded protein and the cell environment are associated with a metabolic pathway, again based on the identifiers provided by biomedical databases. Accompanying the different chapters, related work is discussed which can alternatively be used. Finally, an outlook toward immersive analytics is given.

Homeodomain-only protein suppresses proliferation and contributes to differentiation- and age-related reduced CD8+ T cell expansion

T cell activation is a tightly controlled process involving both positive and negative regulators. The precise mechanisms governing the negative regulators in T cell proliferation remain incompletely understood. Here, we report that homeodomain-only protein (HOPX), a homeodomain-containing protein, and its most abundant isoform HOPXb, negatively regulate activation-induced proliferation of human T cells. We found that HOPX expression progressively increased from naïve (TN) to central memory (TCM) to effector memory (TEM) cells, with a notable upregulation following in vitro stimulation. Overexpression of HOPXb leads to a reduction in TN cell proliferation while HOPX knockdown promotes proliferation of TN and TEM cells. Furthermore, we demonstrated that HOPX binds to promoters and exerts repressive effects on the expression of MYC and NR4A1, two positive regulators known to promote T cell proliferation. Importantly, our findings suggest aging is associated with increased HOPX expression, and that knockdown of HOPX enhances the proliferation of CD8+ T cells in older adults. Our findings provide compelling evidence that HOPX serves as a negative regulator of T cell activation and plays a pivotal role in T cell differentiation and in age-related-reduction in T cell proliferation.

REST Is Not Resting: REST/NRSF in Health and Disease

Chromatin modifications play a crucial role in the regulation of gene expression. The repressor element-1 (RE1) silencing transcription factor (REST), also known as neuron-restrictive silencer factor (NRSF) and X2 box repressor (XBR), was found to regulate gene transcription by binding to chromatin and recruiting chromatin-modifying enzymes. Earlier studies revealed that REST plays an important role in the development and disease of the nervous system, mainly by repressing the transcription of neuron-specific genes. Subsequently, REST was found to be critical in other tissues, such as the heart, pancreas, skin, eye, and vascular. Dysregulation of REST was also found in nervous and non-nervous system cancers. In parallel, multiple strategies to target REST have been developed. In this paper, we provide a comprehensive summary of the research progress made over the past 28 years since the discovery of REST, encompassing both physiological and pathological aspects. These insights into the effects and mechanisms of REST contribute to an in-depth understanding of the transcriptional regulatory mechanisms of genes and their roles in the development and progression of disease, with a view to discovering potential therapeutic targets and intervention strategies for various related diseases.

Proteomics screening uncovers HMGA1 as a promising negative regulator for γ-globin expression in response to decreased β-globin levels

Reactivation of fetal hemoglobin (HbF) is a critical goal for the treatment of patients with hemoglobinopathies. β-globin disorders can trigger stress erythropoiesis in red blood cells (RBCs). Cell-intrinsic erythroid stress signals promote erythroid precursors to express high levels of fetal hemoglobin, which is also known as γ-globin. However, the molecular mechanism underlying γ-globin production during cell-intrinsic erythroid stress remains to be elucidated. Here, we utilized CRISPR-Cas9 to model a stressed state caused by reduced levels of adult β-globin in HUDEP2 human erythroid progenitor cells. We found that a decrease in β-globin expression correlates with the upregulation of γ-globin expression. We also identified transcription factor high-mobility group A1 (HMGA1; formerly HMG-I/Y) as a potential γ-globin regulator that responds to reduced β-globin levels. Upon erythroid stress, there is a downregulation of HMGA1, which normally binds -626 to -610 base pairs upstream from the STAT3 promoter, to downregulate STAT3 expression. STAT3 is a known γ-globin repressor, so the downregulation of HMGA1 ultimately upregulates γ-globin expression. SIGNIFICANCE: This study demonstrated HMGA1 as a potential regulator in the poorly understood phenomenon of stress-induced globin compensation, and after further validation these results might inform new strategies to treat patients with sickle cell disease and β-thalassemia.

BIND&MODIFY: a long-range method for single-molecule mapping of chromatin modifications in eukaryotes

Epigenetic modifications of histones are associated with development and pathogenesis of disease. Existing approaches cannot provide insights into long-range interactions and represent the average chromatin state. Here we describe BIND&MODIFY, a method using long-read sequencing for profiling histone modifications and transcription factors on individual DNA fibers. We use recombinant fused protein A-M.EcoGII to tether methyltransferase M.EcoGII to protein binding sites to label neighboring regions by methylation. Aggregated BIND&MODIFY signal matches bulk ChIP-seq and CUT&TAG. BIND&MODIFY can simultaneously measure histone modification status, transcription factor binding, and CpG 5mC methylation at single-molecule resolution and also quantifies correlation between local and distal elements.

Regulatory mechanism of MeGI on sexuality in Diospyros oleifera

Dioecy system is an important strategy for maintaining genetic diversity. The transcription factor MeGI, contributes to dioecy by promoting gynoecium development in Diospyros lotus and D. kaki. However, the function of MeGI in D. oleifera has not been identified. In this study, we confirmed that MeGI, cloned from D. oleifera, repressed the androecium development in Arabidopsis thaliana. Subsequently, chromatin immunoprecipitation-sequencing (ChIP-seq), DNA affinity purification-sequencing (DAP-seq), and RNA-seq were used to uncover the gene expression response to MeGI. The results showed that the genes upregulated and downregulated in response to MeGI were mainly enriched in the circadian rhythm-related and flavonoid biosynthetic pathways, respectively. Additionally, the WRKY DNA-binding protein 28 (WRKY28) gene, which was detected by ChIP-seq, DAP-seq, and RNA-seq, was emphasized. WRKY28 has been reported to inhibit salicylic acid (SA) biosynthesis and was upregulated in MeGI-overexpressing A. thaliana flowers, suggesting that MeGI represses the SA level by increasing the expression level of WRKY28. This was confirmed that SA level was lower in D. oleifera female floral buds than male. Overall, our findings indicate that the MeGI mediates its sex control function in D. oleifera mainly by regulating genes in the circadian rhythm, SA biosynthetic, and flavonoid biosynthetic pathways.

Bone marrow stromal cell antigen-1 (CD157) regulated by sphingosine kinase 2 mediates kidney fibrosis

Chronic kidney disease is a progressive disease that may lead to end-stage renal disease. Interstitial fibrosis develops as the disease progresses. Therapies that focus on fibrosis to delay or reverse progressive renal failure are limited. We and others showed that sphingosine kinase 2-deficient mice (Sphk2 ^-/-) develop less fibrosis in mouse models of kidney fibrosis. Sphingosine kinase2 (SphK2), one of two sphingosine kinases that produce sphingosine 1-phosphate (S1P), is primarily located in the nucleus. S1P produced by SphK2 inhibits histone deacetylase (HDAC) and changes histone acetylation status, which can lead to altered target gene expression. We hypothesized that Sphk2 epigenetically regulates downstream genes to induce fibrosis, and we performed a comprehensive analysis using the combination of RNA-seq and ChIP-seq. Bst1/CD157 was identified as a gene that is regulated by SphK2 through a change in histone acetylation level, and Bst1 ^-/- mice were found to develop less renal fibrosis after unilateral ischemia-reperfusion injury, a mouse model of kidney fibrosis. Although Bst1 is a cell-surface molecule that has a wide variety of functions through its varied enzymatic activities and downstream intracellular signaling pathways, no studies on the role of Bst1 in kidney diseases have been reported previously. In the current study, we demonstrated that Bst1 is a gene that is regulated by SphK2 through epigenetic change and is critical in kidney fibrosis.

The essential Rhodobacter sphaeroides CenKR two-component system regulates cell division and envelope biosynthesis

Bacterial two-component systems (TCSs) often function through the detection of an extracytoplasmic stimulus and the transduction of a signal by a transmembrane sensory histidine kinase. This kinase then initiates a series of reversible phosphorylation modifications to regulate the activity of a cognate, cytoplasmic response regulator as a transcription factor. Several TCSs have been implicated in the regulation of cell cycle dynamics, cell envelope integrity, or cell wall development in Escherichia coli and other well-studied Gram-negative model organisms. However, many α-proteobacteria lack homologs to these regulators, so an understanding of how α-proteobacteria orchestrate extracytoplasmic events is lacking. In this work we identify an essential TCS, CenKR (Cell envelope Kinase and Regulator), in the α-proteobacterium Rhodobacter sphaeroides and show that modulation of its activity results in major morphological changes. Using genetic and biochemical approaches, we dissect the requirements for the phosphotransfer event between CenK and CenR, use this information to manipulate the activity of this TCS in vivo, and identify genes that are directly and indirectly controlled by CenKR in Rb. sphaeroides. Combining ChIP-seq and RNA-seq, we show that the CenKR TCS plays a direct role in maintenance of the cell envelope, regulates the expression of subunits of the Tol-Pal outer membrane division complex, and indirectly modulates the expression of peptidoglycan biosynthetic genes. CenKR represents the first TCS reported to directly control the expression of Tol-Pal machinery genes in Gram-negative bacteria, and we predict that homologs of this TCS serve a similar function in other closely related organisms. We propose that Rb. sphaeroides genes of unknown function that are directly regulated by CenKR play unknown roles in cell envelope biosynthesis, assembly, and/or remodeling in this and other α-proteobacteria.

Genome-wide protein-DNA interaction site mapping in bacteria using a double-stranded DNA-specific cytosine deaminase

DNA-protein interactions are central to fundamental cellular processes, yet widely implemented technologies for measuring these interactions on a genome scale in bacteria are laborious and capture only a snapshot of binding events. We devised a facile method for mapping DNA-protein interaction sites in vivo using the double-stranded DNA-specific cytosine deaminase toxin DddA. In 3D-seq (DddA-sequencing), strains containing DddA fused to a DNA-binding protein of interest accumulate characteristic mutations in DNA sequence adjacent to sites occupied by the DNA-bound fusion protein. High-depth sequencing enables detection of sites of increased mutation frequency in these strains, yielding genome-wide maps of DNA-protein interaction sites. We validated 3D-seq for four transcription regulators in two bacterial species, Pseudomonas aeruginosa and Escherichia coli. We show that 3D-seq offers ease of implementation, the ability to record binding event signatures over time and the capacity for single-cell resolution.

Roles of the Neuron-Restrictive Silencer Factor in the Pathophysiological Process of the Central Nervous System

The neuron-restrictive silencer factor (NRSF), also known as repressor element 1 (RE-1) silencing transcription factor (REST) or X2 box repressor (XBR), is a zinc finger transcription factor that is widely expressed in neuronal and non-neuronal cells. It is a master regulator of the nervous system, and the function of NRSF is the basis of neuronal differentiation, diversity, plasticity, and survival. NRSF can bind to the neuron-restrictive silencer element (NRSE), recruit some co-repressors, and then inhibit transcription of NRSE downstream genes through epigenetic mechanisms. In neurogenesis, NRSF functions not only as a transcriptional silencer that can mediate the transcriptional inhibition of neuron-specific genes in non-neuronal cells and thus give neuron cells specificity, but also as a transcriptional activator to induce neuronal differentiation. Many studies have confirmed the association between NRSF and brain disorders, such as brain injury and neurodegenerative diseases. Overexpression, underexpression, or mutation may lead to neurological disorders. In tumorigenesis, NRSF functions as an oncogene in neuronal tumors, such as neuroblastomas, medulloblastomas, and pheochromocytomas, stimulating their proliferation, which results in poor prognosis. Additionally, NRSF-mediated selective targets gene repression plays an important role in the development and maintenance of neuropathic pain caused by nerve injury, cancer, and diabetes. At present, several compounds that target NRSF or its co-repressors, such as REST-VP16 and X5050, have been shown to be clinically effective against many brain diseases, such as seizures, implying that NRSF and its co-repressors may be potential and promising therapeutic targets for neural disorders. In the present review, we introduced the biological characteristics of NRSF; reviewed the progress to date in understanding the roles of NRSF in the pathophysiological processes of the nervous system, such as neurogenesis, brain disorders, neural tumorigenesis, and neuropathic pain; and suggested new therapeutic approaches to such brain diseases.

Refactoring transcription factors for metabolic engineering

Due to the ability to regulate target metabolic pathways globally and dynamically, metabolic regulation systems composed of transcription factors have been widely used in metabolic engineering and synthetic biology. This review introduced the categories, action principles, prediction strategies, and related databases of transcription factors. Then, the application of global transcription machinery engineering technology and the transcription factor-based biosensors and quorum sensing systems are overviewed. In addition, strategies for optimizing the transcriptional regulatory tools' performance by refactoring transcription factors are summarized. Finally, the current limitations and prospects of constructing various regulatory tools based on transcription factors are discussed. This review will provide theoretical guidance for the rational design and construction of transcription factor-based metabolic regulation systems.

Machine learning for multi-omics data integration in cancer

Multi-omics data analysis is an important aspect of cancer molecular biology studies and has led to ground-breaking discoveries. Many efforts have been made to develop machine learning methods that automatically integrate omics data. Here, we review machine learning tools categorized as either general-purpose or task-specific, covering both supervised and unsupervised learning for integrative analysis of multi-omics data. We benchmark the performance of five machine learning approaches using data from the Cancer Cell Line Encyclopedia, reporting accuracy on cancer type classification and mean absolute error on drug response prediction, and evaluating runtime efficiency. This review provides recommendations to researchers regarding suitable machine learning method selection for their specific applications. It should also promote the development of novel machine learning methodologies for data integration, which will be essential for drug discovery, clinical trial design, and personalized treatments.

CASCADE: high-throughput characterization of regulatory complex binding altered by non-coding variants

Non-coding DNA variants (NCVs) impact gene expression by altering binding sites for regulatory complexes. New high-throughput methods are needed to characterize the impact of NCVs on regulatory complexes. We developed CASCADE (Customizable Approach to Survey Complex Assembly at DNA Elements), an array-based high-throughput method to profile cofactor (COF) recruitment. CASCADE identifies DNA-bound transcription factor-cofactor (TF-COF) complexes in nuclear extracts and quantifies the impact of NCVs on their binding. We demonstrate CASCADE sensitivity in characterizing condition-specific recruitment of COFs p300 and RBBP5 (MLL subunit) to the CXCL10 promoter in lipopolysaccharide (LPS)-stimulated human macrophages and quantify the impact of all possible NCVs. To demonstrate applicability to NCV screens, we profile TF-COF binding to ~1,700 single-nucleotide polymorphism quantitative trait loci (SNP-QTLs) in human macrophages and identify perturbed ETS domain-containing complexes. CASCADE will facilitate high-throughput testing of molecular mechanisms of NCVs for diverse biological applications.

Bray et al. develop CASCADE, a method to profile transcription factor (TF)-cofactor (COF) complexes binding to DNA. They demonstrate the approach by profiling complex binding across the CXCL10 cytokine promoter and to ~1,700 single-nucleotide polymorphisms (SNPs). They anticipate that CASCADE can be applied to diverse biological systems to examine regulatory complex binding to DNA.

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.

ANANSE: an enhancer network-based computational approach for predicting key transcription factors in cell fate determination

Proper cell fate determination is largely orchestrated by complex gene regulatory networks centered around transcription factors. However, experimental elucidation of key transcription factors that drive cellular identity is currently often intractable. Here, we present ANANSE (ANalysis Algorithm for Networks Specified by Enhancers), a network-based method that exploits enhancer-encoded regulatory information to identify the key transcription factors in cell fate determination. As cell type-specific transcription factors predominantly bind to enhancers, we use regulatory networks based on enhancer properties to prioritize transcription factors. First, we predict genome-wide binding profiles of transcription factors in various cell types using enhancer activity and transcription factor binding motifs. Subsequently, applying these inferred binding profiles, we construct cell type-specific gene regulatory networks, and then predict key transcription factors controlling cell fate transitions using differential networks between cell types. This method outperforms existing approaches in correctly predicting major transcription factors previously identified to be sufficient for trans-differentiation. Finally, we apply ANANSE to define an atlas of key transcription factors in 18 normal human tissues. In conclusion, we present a ready-to-implement computational tool for efficient prediction of transcription factors in cell fate determination and to study transcription factor-mediated regulatory mechanisms. ANANSE is freely available at https://github.com/vanheeringen-lab/ANANSE.

Identification of Group A Streptococcus Genes Directly Regulated by CsrRS and Novel Intermediate Regulators

Adaptation of group A Streptococcus (GAS) to its human host is mediated by two-component systems that transduce external stimuli to regulate bacterial physiology. Among such systems, CsrRS (also known as CovRS) is the most extensively characterized for its role in regulating ∼10% of the GAS genome, including several virulence genes. Here, we show that extracellular magnesium and the human antimicrobial peptide LL-37 have opposing effects on the phosphorylation of the response regulator CsrR by the receptor kinase CsrS. Genetic inactivation of CsrS phosphatase or kinase activity, respectively, had similar but more pronounced effects on CsrR phosphorylation compared to growth in magnesium or LL-37. These changes in CsrR phosphorylation were correlated with the repression or activation of CsrR-regulated genes as assessed by NanoString analysis. Chromatin immunoprecipitation and DNA sequencing (ChIP-seq) revealed CsrR occupancy at CsrRS-regulated promoters and lower-affinity associations at many other locations on the GAS chromosome. Because ChIP-seq did not detect CsrR occupancy at promoters associated with some CsrR-regulated genes, we investigated whether these genes might be controlled indirectly by intermediate regulators whose expression is modulated by CsrR. Transcriptional profiling of mutant strains deficient in the expression of either of two previously uncharacterized transcription regulators in the CsrR regulon indicated that one or both proteins participated in the regulation of 22 of the 42 CsrR-regulated promoters for which no CsrR association was detected by ChIP-seq. Taken together, these results illuminate CsrRS-mediated regulation of GAS gene expression through modulation of CsrR phosphorylation, CsrR association with regulated promoters, and the control of intermediate transcription regulators. IMPORTANCE Group A Streptococcus (GAS) is an important public health threat as a cause of sore throat, skin infections, life-threatening invasive infections, and the postinfectious complications of acute rheumatic fever, a leading cause of acquired heart disease. This work characterizes CsrRS, a GAS system for the detection of environmental signals that enables adaptation of the bacteria for survival in the human throat by regulating the production of products that allow the bacteria to resist clearance by the human immune system. CsrRS consists of two proteins: CsrS, which is on the bacterial surface to detect specific stimuli, and CsrR, which receives signals from CsrS and, in response, represses or activates the expression of genes coding for proteins that enhance bacterial survival. Some of the genes regulated by CsrR encode proteins that are themselves regulators of gene expression, thereby creating a regulatory cascade.

Increased peak detection accuracy in over-dispersed ChIP-seq data with supervised segmentation models

BACKGROUND

Histone modification constitutes a basic mechanism for the genetic regulation of gene expression. In early 2000s, a powerful technique has emerged that couples chromatin immunoprecipitation with high-throughput sequencing (ChIP-seq). This technique provides a direct survey of the DNA regions associated to these modifications. In order to realize the full potential of this technique, increasingly sophisticated statistical algorithms have been developed or adapted to analyze the massive amount of data it generates. Many of these algorithms were built around natural assumptions such as the Poisson distribution to model the noise in the count data. In this work we start from these natural assumptions and show that it is possible to improve upon them.

RESULTS

Our comparisons on seven reference datasets of histone modifications (H3K36me3 & H3K4me3) suggest that natural assumptions are not always realistic under application conditions. We show that the unconstrained multiple changepoint detection model with alternative noise assumptions and supervised learning of the penalty parameter reduces the over-dispersion exhibited by count data. These models, implemented in the R package CROCS ( https://github.com/aLiehrmann/CROCS ), detect the peaks more accurately than algorithms which rely on natural assumptions.

CONCLUSION

The segmentation models we propose can benefit researchers in the field of epigenetics by providing new high-quality peak prediction tracks for H3K36me3 and H3K4me3 histone modifications.

NoPeak: k-mer-based motif discovery in ChIP-Seq data without peak calling

MOTIVATION

The discovery of sequence motifs mediating DNA-protein binding usually implies the determination of binding sites using high-throughput sequencing and peak calling. The determination of peaks, however, depends strongly on data quality and is susceptible to noise.

RESULTS

Here, we present a novel approach to reliably identify transcription factor-binding motifs from ChIP-Seq data without peak detection. By evaluating the distributions of sequencing reads around the different k-mers in the genome, we are able to identify binding motifs in ChIP-Seq data that yield no results in traditional pipelines.

AVAILABILITY AND IMPLEMENTATION

NoPeak is published under the GNU General Public License and available as a standalone console-based Java application at https://github.com/menzel/nopeak.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Genome Scale Analysis Reveals IscR Directly and Indirectly Regulates Virulence Factor Genes in Pathogenic Yersinia

The iron-sulfur cluster coordinating transcription factor IscR is important for the virulence of Yersinia pseudotuberculosis and a number of other bacterial pathogens. However, the IscR regulon has not yet been defined in any organism. To determine the Yersinia IscR regulon and identify IscR-dependent functions important for virulence, we employed chromatin immunoprecipitation sequencing (ChIP-Seq) and RNA sequencing (RNA-Seq) of Y. pseudotuberculosis expressing or lacking iscR following iron starvation conditions, such as those encountered during infection. We found that IscR binds to the promoters of genes involved in iron homeostasis, reactive oxygen species metabolism, and cell envelope remodeling and regulates expression of these genes in response to iron depletion. Consistent with our previous work, we also found that IscR binds in vivo to the promoter of the Ysc type III secretion system (T3SS) master regulator LcrF, leading to regulation of T3SS genes. Interestingly, comparative genomic analysis suggested over 93% of IscR binding sites were conserved between Y. pseudotuberculosis and the related plague agent Yersinia pestis. Surprisingly, we found that the IscR positively regulated sufABCDSE Fe-S cluster biogenesis pathway was required for T3SS activity. These data suggest that IscR regulates the T3SS in Yersinia through maturation of an Fe-S cluster protein critical for type III secretion, in addition to its known role in activating T3SS genes through LcrF. Altogether, our study shows that iron starvation triggers IscR to coregulate multiple, distinct pathways relevant to promoting bacterial survival during infection.

Sequence deeper without sequencing more: Bayesian resolution of ambiguously mapped reads

Next-generation sequencing (NGS) has transformed molecular biology and contributed to many seminal insights into genomic regulation and function. Apart from whole-genome sequencing, an NGS workflow involves alignment of the sequencing reads to the genome of study, after which the resulting alignments can be used for downstream analyses. However, alignment is complicated by the repetitive sequences; many reads align to more than one genomic locus, with 15-30% of the genome not being uniquely mappable by short-read NGS. This problem is typically addressed by discarding reads that do not uniquely map to the genome, but this practice can lead to systematic distortion of the data. Previous studies that developed methods for handling ambiguously mapped reads were often of limited applicability or were computationally intensive, hindering their broader usage. In this work, we present SmartMap: an algorithm that augments industry-standard aligners to enable usage of ambiguously mapped reads by assigning weights to each alignment with Bayesian analysis of the read distribution and alignment quality. SmartMap is computationally efficient, utilizing far fewer weighting iterations than previously thought necessary to process alignments and, as such, analyzing more than a billion alignments of NGS reads in approximately one hour on a desktop PC. By applying SmartMap to peak-type NGS data, including MNase-seq, ChIP-seq, and ATAC-seq in three organisms, we can increase read depth by up to 53% and increase the mapped proportion of the genome by up to 18% compared to analyses utilizing only uniquely mapped reads. We further show that SmartMap enables the analysis of more than 140,000 repetitive elements that could not be analyzed by traditional ChIP-seq workflows, and we utilize this method to gain insight into the epigenetic regulation of different classes of repetitive elements. These data emphasize both the dangers of discarding ambiguously mapped reads and their power for driving biological discovery.

DNA methylation patterns expose variations in enhancer-chromatin modifications during embryonic stem cell differentiation

In mammals, cellular identity is defined through strict regulation of chromatin modifications and DNA methylation that control gene expression. Methylation of cytosines at CpG sites in the genome is mainly associated with suppression; however, the reason for enhancer-specific methylation is not fully understood. We used sequential ChIP-bisulfite-sequencing for H3K4me1 and H3K27ac histone marks. By collecting data from the same genomic region, we identified enhancers differentially methylated between these two marks. We observed a global gain of CpG methylation primarily in H3K4me1-marked nucleosomes during mouse embryonic stem cell differentiation. This gain occurred largely in enhancer regions that regulate genes critical for differentiation. The higher levels of DNA methylation in H3K4me1- versus H3K27ac-marked enhancers, despite it being the same genomic region, indicates cellular heterogeneity of enhancer states. Analysis of single-cell RNA-seq profiles demonstrated that this heterogeneity correlates with gene expression during differentiation. Furthermore, heterogeneity of enhancer methylation correlates with transcription start site methylation. Our results provide insights into enhancer-based functional variation in complex biological systems.

Cellular dynamics are underlined by numerous regulatory layers. The regulatory mechanism of interest in this work are enhancers. Enhancers are regulatory regions responsible, mainly, for increasing the possibility of transcription of a certain gene. Enhancers are marked by two distinct chemical groups-H3K4me1 and H3K27ac on the tail of histones. Histones are the proteins responsible for DNA packaging into condensed chromatin structure. In contrast, DNA methylation is a chemical modification often found on enhancers, and is traditionally associated with repression. A long-debated question revolves around the functional relevance of DNA methylation in the context of enhancers. Here, we combined the two regulatory layers, histone marks and DNA methylation, to a single measurement that can highlight DNA methylation separately on each histone mark but at the same genomic region. When isolated with H3K4me1, enhancers showed higher levels of methylation compared to H3K27ac. As we measured the same genomic locations, we show that differences of DNA methylation between these marks can only be explained by cellular heterogeneity. We also demonstrated that these enhancers tend to play roles in stem cell differentiation and expression levels of the genes they control correlate with cell-to-cell variation.

Global Analyses to Identify Direct Transcriptional Targets of p53

The transcription factor p53 controls a gene expression program with pleiotropic effects on cell biology including cell cycle arrest and apoptosis. Identifying direct p53 target genes within this network and determining how they influence cell fate decisions downstream of p53 activation is a prerequisite for designing therapeutic approaches that target p53 to effectively kill cancer cells. Here we describe a comprehensive multi-omics approach for identifying genes that are direct transcriptional targets of p53. We provide detailed procedures for measuring global RNA polymerase activity, defining p53 binding sites across the genome, and quantifying changes in steady-state mRNA in response to p53 activation.

Control of a programmed cell death pathway in Pseudomonas aeruginosa by an antiterminator

In Pseudomonas aeruginosa the alp system encodes a programmed cell death pathway that is switched on in a subset of cells in response to DNA damage and is linked to the virulence of the organism. Here we show that the central regulator of this pathway, AlpA, exerts its effects by acting as an antiterminator rather than a transcription activator. In particular, we present evidence that AlpA positively regulates the alpBCDE cell lysis genes, as well as genes in a second newly identified target locus, by recognizing specific DNA sites within the promoter, then binding RNA polymerase directly and allowing it to bypass intrinsic terminators positioned downstream. AlpA thus functions in a mechanistically unusual manner to control the expression of virulence genes in this opportunistic pathogen.

F-Seq2: improving the feature density based peak caller with dynamic statistics

Genomic and epigenomic features are captured at a genome-wide level by using high-throughput sequencing (HTS) technologies. Peak calling delineates features identified in HTS experiments, such as open chromatin regions and transcription factor binding sites, by comparing the observed read distributions to a random expectation. Since its introduction, F-Seq has been widely used and shown to be the most sensitive and accurate peak caller for DNase I hypersensitive site (DNase-seq) data. However, the first release (F-Seq1) has two key limitations: lack of support for user-input control datasets, and poor test statistic reporting. These constrain its ability to capture systematic and experimental biases inherent to the background distributions in peak prediction, and to subsequently rank predicted peaks by confidence. To address these limitations, we present F-Seq2, which combines kernel density estimation and a dynamic 'continuous' Poisson test to account for local biases and accurately rank candidate peaks. The output of F-Seq2 is suitable for irreproducible discovery rate analysis as test statistics are calculated for individual candidate summits, allowing direct comparison of predictions across replicates. These improvements significantly boost the performance of F-Seq2 for ATAC-seq and ChIP-seq datasets, outperforming competing peak callers used by the ENCODE Consortium in terms of precision and recall.

A β-catenin-driven switch in TCF/LEF transcription factor binding to DNA target sites promotes commitment of mammalian nephron progenitor cells

The canonical Wnt pathway transcriptional co-activator β-catenin regulates self-renewal and differentiation of mammalian nephron progenitor cells (NPCs). We modulated β-catenin levels in NPC cultures using the GSK3 inhibitor CHIR99021 (CHIR) to examine opposing developmental actions of β-catenin. Low CHIR-mediated maintenance and expansion of NPCs are independent of direct engagement of TCF/LEF/β-catenin transcriptional complexes at low CHIR-dependent cell-cycle targets. In contrast, in high CHIR, TCF7/LEF1/β-catenin complexes replaced TCF7L1/TCF7L2 binding on enhancers of differentiation-promoting target genes. Chromosome confirmation studies showed pre-established promoter–enhancer connections to these target genes in NPCs. High CHIR-associated de novo looping was observed in positive transcriptional feedback regulation to the canonical Wnt pathway. Thus, β-catenin’s direct transcriptional role is restricted to the induction of NPCs, where rising β-catenin levels switch inhibitory TCF7L1/TCF7L2 complexes to activating LEF1/TCF7 complexes at primed gene targets poised for rapid initiation of a nephrogenic program.

Evidence of widespread, independent sequence signature for transcription factor cobinding

Transcription factors (TFs) are the vocabulary that genomes use to regulate gene expression and phenotypes. The interactions among TFs enrich this vocabulary and orchestrate diverse biological processes. Although simple models identify open chromatin and the presence of TF motifs as the two major contributors to TF binding patterns, it remains elusive what contributes to the in vivo TF cobinding landscape. In this study, we developed a machine learning algorithm to explore the contributors of the cobinding patterns. The algorithm substantially outperforms the state-of-the-field models for TF cobinding prediction. Game theory-based feature importance analysis reveals that, for most of the TF pairs we studied, independent motif sequences contribute one or more of the two TFs under investigation to their cobinding patterns. Such independent motif sequences include, but are not limited to, transcription initiation-related proteins and known TF complexes. We found the motif sequence signatures and the TFs are rarely mutual, corroborating a hierarchical and directional organization of the regulatory network and refuting the possibility of artifacts caused by shared sequence similarity with the TFs under investigation. We modeled such regulatory language with directed graphs, which reveal shared, global factors that are related to many binding and cobinding patterns.

Using GRO-Seq to Measure Circadian Transcription and Discover Circadian Enhancers

Circadian gene transcription transmits timing information and drives cyclic physiological processes across various tissues. Recent studies indicate that oscillating enhancer activity is a major driving force of rhythmic gene transcription. Functional circadian enhancers can be identified in an unbiased manner by correlation with the rhythms of nearby gene transcription.Global run-on sequencing (GRO-seq) measures nascent transcription of both pre-mRNAs and enhancer RNAs (eRNAs) at a genome-wide level, making it a unique tool for unraveling complex gene regulation mechanisms in vivo. Here, we describe a comprehensive protocol, ranging from wet lab to in silico analysis, for detecting and quantifying circadian transcription of genes and eRNAs. Moreover, using gene-eRNA correlation, we detail the steps necessary to identify functional enhancers and transcription factors (TFs) that control circadian gene expression in vivo. While we use mouse liver as an example, this protocol is applicable for multiple tissues.

Single-Cell RNA Sequencing Efficiently Predicts Transcription Factor Targets in Plants

Discovering transcription factor (TF) targets is necessary for the study of regulatory pathways, but it is hampered in plants by the lack of highly efficient predictive technology. This study is the first to establish a simple system for predicting TF targets in rice (Oryza sativa) leaf cells based on 10 × Genomics' single-cell RNA sequencing method. We effectively utilized the transient expression system to create the differential expression of a TF (OsNAC78) in each cell and sequenced all single cell transcriptomes. In total, 35 candidate targets having strong correlations with OsNAC78 expression were captured using expression profiles. Likewise, 78 potential differentially expressed genes were identified between clusters having the lowest and highest expression levels of OsNAC78. A gene overlapping analysis identified 19 genes as final candidate targets, and various assays indicated that Os01g0934800 and Os01g0949900 were OsNAC78 targets. Additionally, the cell profiles showed extremely similar expression trajectories between OsNAC78 and the two targets. The data presented here provide a high-resolution insight into predicting TF targets and offer a new application for single-cell RNA sequencing in plants.

In Silico Estimation of the Abundance and Phylogenetic Significance of the Composite Oct4-Sox2 Binding Motifs within a Wide Range of Species

High-throughput sequencing technologies have greatly accelerated the progress of genomics, transcriptomics, and metagenomics. Currently, a large amount of genomic data from various organisms is being generated, the volume of which is increasing every year. Therefore, the development of methods that allow the rapid search and analysis of DNA sequences is urgent. Here, we present a novel motif-based high-throughput sequence scoring method that generates genome information. We found and identified Utf1-like, Fgf4-like, and Hoxb1-like motifs, which are cis-regulatory elements for the pluripotency transcription factors Sox2 and Oct4 within the genomes of different eukaryotic organisms. The genome-wide analysis of these motifs was performed to understand the impact of their diversification on mammalian genome evolution. Utf1-like, Fgf4-like, and Hoxb1-like motif diversity was evaluated across genomes from multiple species.

Proteomic profiling and genome-wide mapping of O-GlcNAc chromatin-associated proteins reveal an O-GlcNAc-regulated genotoxic stress response

O-GlcNAc modification plays critical roles in regulating the stress response program and cellular homeostasis. However, systematic and multi-omics studies on the O-GlcNAc regulated mechanism have been limited. Here, comprehensive data are obtained by a chemical reporter-based method to survey O-GlcNAc function in human breast cancer cells stimulated with the genotoxic agent adriamycin. We identify 875 genotoxic stress-induced O-GlcNAc chromatin-associated proteins (OCPs), including 88 O-GlcNAc chromatin-associated transcription factors and cofactors (OCTFs), subsequently map their genomic loci, and construct a comprehensive transcriptional reprogramming network. Notably, genotoxicity-induced O-GlcNAc enhances the genome-wide interactions of OCPs with chromatin. The dynamic binding switch of hundreds of OCPs from enhancers to promoters is identified as a crucial feature in the specific transcriptional activation of genes involved in the adaptation of cancer cells to genotoxic stress. The OCTF nuclear respiratory factor 1 (NRF1) is found to be a key response regulator in O-GlcNAc-modulated cellular homeostasis. These results provide a valuable clue suggesting that OCPs act as stress sensors by regulating the expression of various genes to protect cancer cells from genotoxic stress.

Protein O-GlcNAcylation is involved in regulating gene expression and maintaining cellular homeostasis. Here, the authors develop a chemical reporter-based strategy for the proteomic profiling and genome-wide mapping of genotoxic stress-induced O-GlcNAcylated chromatin-associated proteins.

Robust inference of positive selection on regulatory sequences in the human brain

A longstanding hypothesis is that divergence between humans and chimpanzees might have been driven more by regulatory level adaptations than by protein sequence adaptations. This has especially been suggested for regulatory adaptations in the evolution of the human brain. We present a new method to detect positive selection on transcription factor binding sites on the basis of measuring predicted affinity change with a machine learning model of binding. Unlike other methods, this approach requires neither defining a priori neutral sites nor detecting accelerated evolution, thus removing major sources of bias. We scanned the signals of positive selection for CTCF binding sites in 29 human and 11 mouse tissues or cell types. We found that human brain-related cell types have the highest proportion of positive selection. This result is consistent with the view that adaptive evolution to gene regulation has played an important role in evolution of the human brain.

Genome-wide analysis of PmTCP4 transcription factor binding sites by ChIP-Seq during pistil abortion in Japanese apricot

The TCP4 transcription factor plays an important role in plant growth and development, especially in flower development. PmTCP4 is involved in the process of pistil abortion in Japanese apricot, but its molecular mechanism, particularly the DNA binding sites and co-regulatory genes, are quite unknown. Therefore, to identify the genome-wide binding sites of PmTCP4 transcription factors and their co-regulatory genes, chromatin immunoprecipitation sequencing (ChIP-Seq) was carried out. ChIP-Seq data produced the maximum enriched peaks in two Japanese apricot cultivars 'Daqiandi' (DQD) and 'Longyan' (LY), which showed that the majority of DNA-protein interactions are relevant and have a significant function in binding sites. Moreover, 720 and 251 peak-associated genes regulated by PmTCP4 were identified in DQD and LY, respectively, and most of them were involved in the flower and pistil development process. Furthermore, Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis showed that photosynthesis and oxidative phosphorylation were the most enriched pathways in both cultivars and all identified genes related to these pathways were down-regulated. This study will provide a reference for a better understanding of the PmTCP4 regulatory mechanism during pistil abortion in Japanese apricot.

Single-cell ATAC sequencing analysis: From data preprocessing to hypothesis generation

Most genetic variations associated with human complex traits are located in non-coding genomic regions. Therefore, understanding the genotype-to-phenotype axis requires a comprehensive catalog of functional non-coding genomic elements, most of which are involved in epigenetic regulation of gene expression. Genome-wide maps of open chromatin regions can facilitate functional analysis of cis- and trans-regulatory elements via their connections with trait-associated sequence variants. Currently, Assay for Transposase Accessible Chromatin with high-throughput sequencing (ATAC-seq) is considered the most accessible and cost-effective strategy for genome-wide profiling of chromatin accessibility. Single-cell ATAC-seq (scATAC-seq) technology has also been developed to study cell type-specific chromatin accessibility in tissue samples containing a heterogeneous cellular population. However, due to the intrinsic nature of scATAC-seq data, which are highly noisy and sparse, accurate extraction of biological signals and devising effective biological hypothesis are difficult. To overcome such limitations in scATAC-seq data analysis, new methods and software tools have been developed over the past few years. Nevertheless, there is no consensus for the best practice of scATAC-seq data analysis yet. In this review, we discuss scATAC-seq technology and data analysis methods, ranging from preprocessing to downstream analysis, along with an up-to-date list of published studies that involved the application of this method. We expect this review will provide a guideline for successful data generation and analysis methods using appropriate software tools and databases for the study of chromatin accessibility at single-cell resolution.

RECAP reveals the true statistical significance of ChIP-seq peak calls

Motivation

Chromatin Immunopreciptation (ChIP)-seq is used extensively to identify sites of transcription factor binding or regions of epigenetic modifications to the genome. A key step in ChIP-seq analysis is peak calling, where genomic regions enriched for ChIP versus control reads are identified. Many programs have been designed to solve this task, but nearly all fall into the statistical trap of using the data twice—once to determine candidate enriched regions, and again to assess enrichment by classical statistical hypothesis testing. This double use of the data invalidates the statistical significance assigned to enriched regions, thus the true significance or reliability of peak calls remains unknown.

Results

Using simulated and real ChIP-seq data, we show that three well-known peak callers, MACS, SICER and diffReps, output biased P-values and false discovery rate estimates that can be many orders of magnitude too optimistic. We propose a wrapper algorithm, RECAP, that uses resampling of ChIP-seq and control data to estimate a monotone transform correcting for biases built into peak calling algorithms. When applied to null hypothesis data, where there is no enrichment between ChIP-seq and control, P-values recalibrated by RECAP are approximately uniformly distributed. On data where there is genuine enrichment, RECAP P-values give a better estimate of the true statistical significance of candidate peaks and better false discovery rate estimates, which correlate better with empirical reproducibility. RECAP is a powerful new tool for assessing the true statistical significance of ChIP-seq peak calls.

Availability and implementation

The RECAP software is available through www.perkinslab.ca or on github at https://github.com/theodorejperkins/RECAP.

Supplementary information

Supplementary data are available at Bioinformatics online.

CNN-Peaks: ChIP-Seq peak detection pipeline using convolutional neural networks that imitate human visual inspection

ChIP-seq is one of the core experimental resources available to understand genome-wide epigenetic interactions and identify the functional elements associated with diseases. The analysis of ChIP-seq data is important but poses a difficult computational challenge, due to the presence of irregular noise and bias on various levels. Although many peak-calling methods have been developed, the current computational tools still require, in some cases, human manual inspection using data visualization. However, the huge volumes of ChIP-seq data make it almost impossible for human researchers to manually uncover all the peaks. Recently developed convolutional neural networks (CNN), which are capable of achieving human-like classification accuracy, can be applied to this challenging problem. In this study, we design a novel supervised learning approach for identifying ChIP-seq peaks using CNNs, and integrate it into a software pipeline called CNN-Peaks. We use data labeled by human researchers who annotate the presence or absence of peaks in some genomic segments, as training data for our model. The trained model is then applied to predict peaks in previously unseen genomic segments from multiple ChIP-seq datasets including benchmark datasets commonly used for validation of peak calling methods. We observe a performance superior to that of previous methods.

Widespread targeting of nascent transcripts by RsmA in Pseudomonas aeruginosa

In the opportunistic pathogen Pseudomonas aeruginosa, RsmA is an RNA-binding protein that plays critical roles in the control of virulence, interbacterial interactions, and biofilm formation. Although RsmA is thought to exert its regulatory effects by binding full-length transcripts, the extent to which RsmA binds nascent transcripts has not been addressed. Moreover, which transcripts are direct targets of this key posttranscriptional regulator is largely unknown. Using chromatin immunoprecipitation coupled with high-throughput DNA sequencing, with cells grown in the presence and absence of the RNA polymerase inhibitor rifampicin, we identify hundreds of nascent transcripts that RsmA associates with in P. aeruginosa We also find that the RNA chaperone Hfq targets a subset of those nascent transcripts that RsmA associates with and that the two RNA-binding proteins can exert regulatory effects on common targets. Our findings establish that RsmA associates with many transcripts as they are being synthesized in P. aeruginosa, identify the transcripts targeted by RsmA, and suggest that RsmA and Hfq may act in a combinatorial fashion on certain transcripts. The binding of posttranscriptional regulators to nascent transcripts may be commonplace in bacteria where distinct regulators can function alone or in concert to achieve control over the translation of transcripts as soon as they emerge from RNA polymerase.

Tailoring a Global Iron Regulon to a Uropathogen

Host iron restriction is a common mechanism for limiting the growth of pathogens. We compared the regulatory network controlled by Fur in uropathogenic E. coli (UPEC) to that of nonpathogenic E. coli K-12 to uncover strategies that pathogenic bacteria use to overcome iron limitation. Although iron homeostasis functions were regulated by Fur in the uropathogen as expected, a surprising finding was the activation of the stringent and general stress responses in the uropathogen fur mutant, which was rescued by amino acid addition. This coordinated global response could be important in controlling growth and survival under nutrient-limiting conditions and during transitions from the nutrient-rich environment of the lower gastrointestinal (GI) tract to the more restrictive environment of the urinary tract. The coupling of the response of iron limitation to increased demand for amino acids could be a critical attribute that sets UPEC apart from other E. coli pathotypes.

Pathogenicity islands and plasmids bear genes for pathogenesis of various Escherichia coli pathotypes. Although there is a basic understanding of the contribution of these virulence factors to disease, less is known about variation in regulatory networks in determining disease phenotypes. Here, we dissected a regulatory network directed by the conserved iron homeostasis regulator, ferric uptake regulator (Fur), in uropathogenic E. coli (UPEC) strain CFT073. Comparing anaerobic genome-scale Fur DNA binding with Fur-dependent transcript expression and protein levels of the uropathogen to that of commensal E. coli K-12 strain MG1655 showed that the Fur regulon of the core genome is conserved but also includes genes within the pathogenicity/genetic islands. Unexpectedly, regulons indicative of amino acid limitation and the general stress response were also indirectly activated in the uropathogen fur mutant, suggesting that induction of the Fur regulon increases amino acid demand. Using RpoS levels as a proxy, addition of amino acids mitigated the stress. In addition, iron chelation increased RpoS to the same levels as in the fur mutant. The increased amino acid demand of the fur mutant or iron chelated cells was exacerbated by aerobic conditions, which could be partly explained by the O₂-dependent synthesis of the siderophore aerobactin, encoded by an operon within a pathogenicity island. Taken together, these data suggest that in the iron-poor environment of the urinary tract, amino acid availability could play a role in the proliferation of this uropathogen, particularly if there is sufficient O₂ to produce aerobactin.

Functional genomic approaches to elucidate the role of enhancers during development

Successful development depends on the precise tissue-specific regulation of genes by enhancers, genetic elements that act as switches to control when and where genes are expressed. Because enhancers are critical for development, and the majority of disease-associated mutations reside within enhancers, it is essential to understand which sequences within enhancers are important for function. Advances in sequencing technology have enabled the rapid generation of genomic data that predict putative active enhancers, but functionally validating these sequences at scale remains a fundamental challenge. Herein, we discuss the power of genome-wide strategies used to identify candidate enhancers, and also highlight limitations and misconceptions that have arisen from these data. We discuss the use of massively parallel reporter assays to test enhancers for function at scale. We also review recent advances in our ability to study gene regulation during development, including CRISPR-based tools to manipulate genomes and single-cell transcriptomics to finely map gene expression. Finally, we look ahead to a synthesis of complementary genomic approaches that will advance our understanding of enhancer function during development. This article is categorized under: Physiology > Mammalian Physiology in Health and Disease Developmental Biology > Developmental Processes in Health and Disease Laboratory Methods and Technologies > Genetic/Genomic Methods.

The assessment of histone acetylation marks in the vicinity of transcription factor binding sites in human CD4 + T cells using information theory methods

The genetic information encoded in structural genes is decoded by an intracellular process called gene expression. This mechanism is regulated by epigenetic processes such as histone acetylation. Histone acetylation, which happens in nucleosomes, exposes DNA (genome) to transcription factors. Therefore, the correlation between histone acetylation and gene expression has been assessed as a fundamental issue in many previous studies. In the proposed research, we investigate which marks of histone acetylation are informative and which ones are redundant in the vicinity of SP1 transcription factor binding sites, in human CD4 + T cell. To achieve this, we use information theory methods. Subsequently, we apply a multilayer perceptron neural network to show that the selected histone acetylation marks by information theory methods are sufficiently informative. Finally, we use the neural network to predict binding sites of 17 other transcription factors on chromosomes 1 and 2. The results suggest that information conveyed by the selected histone acetylation marks are equivalent to that of all 18 marks associated with SP1 transcription factor binding sites on chromosome 1. Furthermore, almost 91.75 % of SP1 binding sites of chromosome 2 are predicted by the selected histone acetylation marks while all 18 marks predict 90.56 % correctly. Moreover, the selected histone acetylation marks are efficient at predicting 17 other types of transcription factor binding sites.

Genome Wide Approaches to Identify Protein-DNA Interactions

BACKGROUND

Transcription factors are DNA-binding proteins that play key roles in many fundamental biological processes. Unraveling their interactions with DNA is essential to identify their target genes and understand the regulatory network. Genome-wide identification of their binding sites became feasible thanks to recent progress in experimental and computational approaches. ChIP-chip, ChIP-seq, and ChIP-exo are three widely used techniques to demarcate genome-wide transcription factor binding sites.

OBJECTIVE

This review aims to provide an overview of these three techniques including their experiment procedures, computational approaches, and popular analytic tools.

CONCLUSION

ChIP-chip, ChIP-seq, and ChIP-exo have been the major techniques to study genome- wide in vivo protein-DNA interaction. Due to the rapid development of next-generation sequencing technology, array-based ChIP-chip is deprecated and ChIP-seq has become the most widely used technique to identify transcription factor binding sites in genome-wide. The newly developed ChIP-exo further improves the spatial resolution to single nucleotide. Numerous tools have been developed to analyze ChIP-chip, ChIP-seq and ChIP-exo data. However, different programs may employ different mechanisms or underlying algorithms thus each will inherently include its own set of statistical assumption and bias. So choosing the most appropriate analytic program for a given experiment needs careful considerations. Moreover, most programs only have command line interface so their installation and usage will require basic computation expertise in Unix/Linux.

Regulation of embryonic and adult neurogenesis by Ars2

Neural development is controlled at multiple levels to orchestrate appropriate choices of cell fate and differentiation. Although more attention has been paid to the roles of neural-restricted factors, broadly expressed factors can have compelling impacts on tissue-specific development. Here, we describe in vivo conditional knockout analyses of murine Ars2, which has mostly been studied as a general RNA-processing factor in yeast and cultured cells. Ars2 protein expression is regulated during neural lineage progression, and is required for embryonic neural stem cell (NSC) proliferation. In addition, Ars2 null NSCs can still transition into post-mitotic neurons, but fail to undergo terminal differentiation. Similarly, adult-specific deletion of Ars2 compromises hippocampal neurogenesis and results in specific behavioral defects. To broaden evidence for Ars2 as a chromatin regulator in neural development, we generated Ars2 ChIP-seq data. Notably, Ars2 preferentially occupies DNA enhancers in NSCs, where it colocalizes broadly with NSC regulator SOX2. Ars2 association with chromatin is markedly reduced following NSC differentiation. Altogether, Ars2 is an essential neural regulator that interacts dynamically with DNA and controls neural lineage development.

WITMSG: Large-scale Prediction of Human Intronic m6A RNA Methylation Sites from Sequence and Genomic Features

INTRODUCTION

N 6-methyladenosine (m6A) is one of the most widely studied epigenetic modifications. It plays important roles in various biological processes, such as splicing, RNA localization and degradation, many of which are related to the functions of introns. Although a number of computational approaches have been proposed to predict the m6A sites in different species, none of them were optimized for intronic m6A sites. As existing experimental data overwhelmingly relied on polyA selection in sample preparation and the intronic RNAs are usually underrepresented in the captured RNA library, the accuracy of general m6A sites prediction approaches is limited for intronic m6A sites prediction task.

METHODOLOGY

A computational framework, WITMSG, dedicated to the large-scale prediction of intronic m6A RNA methylation sites in humans has been proposed here for the first time. Based on the random forest algorithm and using only known intronic m6A sites as the training data, WITMSG takes advantage of both conventional sequence features and a variety of genomic characteristics for improved prediction performance of intron-specific m6A sites.

RESULTS AND CONCLUSION

It has been observed that WITMSG outperformed competing approaches (trained with all the m6A sites or intronic m6A sites only) in 10-fold cross-validation (AUC: 0.940) and when tested on independent datasets (AUC: 0.946). WITMSG was also applied intronome-wide in humans to predict all possible intronic m6A sites, and the prediction results are freely accessible at http://rnamd.com/intron/.

The Bdnf and Npas4 genes are targets of HDAC3-mediated transcriptional repression

Background

Histone deacetylase-3 (HDAC3) promotes neurodegeneration in various cell culture and in vivo models of neurodegeneration but the mechanism by which HDAC3 exerts neurotoxicity is not known. HDAC3 is known to be a transcriptional co-repressor. The goal of this study was to identify transcriptional targets of HDAC3 in an attempt to understand how it promotes neurodegeneration.

Results

We used chromatin immunoprecipitation analysis coupled with deep sequencing (ChIP-Seq) to identify potential targets of HDAC3 in cerebellar granule neurons. One of the genes identified was the activity-dependent and neuroprotective transcription factor, Neuronal PAS Domain Protein 4 (Npas4). We confirmed using ChIP that in healthy neurons HDAC3 associates weakly with the Npas4 promoter, however, this association is robustly increased in neurons primed to die. We find that HDAC3 also associates differentially with the brain-derived neurotrophic factor (Bdnf) gene promoter, with higher association in dying neurons. In contrast, association of HDAC3 with the promoters of other neuroprotective genes, including those encoding c-Fos, FoxP1 and Stat3, was barely detectable in both healthy and dying neurons. Overexpression of HDAC3 leads to a suppression of Npas4 and Bdnf expression in cortical neurons and treatment with RGFP966, a chemical inhibitor of HDAC3, resulted in upregulation of their expression. Expression of HDAC3 also repressed Npas4 and Bdnf promoter activity.

Conclusion

Our results suggest that Bdnf and Npas4 are transcriptional targets of Hdac3-mediated repression. HDAC3 inhibitors have been shown to protect against behavioral deficits and neuronal loss in mouse models of neurodegeneration and it is possible that these inhibitors work by upregulating neuroprotective genes like Bdnf and Npas4.

iProDNA-CapsNet: identifying protein-DNA binding residues using capsule neural networks

BACKGROUND

Since protein-DNA interactions are highly essential to diverse biological events, accurately positioning the location of the DNA-binding residues is necessary. This biological issue, however, is currently a challenging task in the age of post-genomic where data on protein sequences have expanded very fast. In this study, we propose iProDNA-CapsNet - a new prediction model identifying protein-DNA binding residues using an ensemble of capsule neural networks (CapsNets) on position specific scoring matrix (PSMM) profiles. The use of CapsNets promises an innovative approach to determine the location of DNA-binding residues. In this study, the benchmark datasets introduced by Hu et al. (2017), i.e., PDNA-543 and PDNA-TEST, were used to train and evaluate the model, respectively. To fairly assess the model performance, comparative analysis between iProDNA-CapsNet and existing state-of-the-art methods was done.

RESULTS

Under the decision threshold corresponding to false positive rate (FPR) ≈ 5%, the accuracy, sensitivity, precision, and Matthews's correlation coefficient (MCC) of our model is increased by about 2.0%, 2.0%, 14.0%, and 5.0% with respect to TargetDNA (Hu et al., 2017) and 1.0%, 75.0%, 45.0%, and 77.0% with respect to BindN+ (Wang et al., 2010), respectively. With regards to other methods not reporting their threshold settings, iProDNA-CapsNet also shows a significant improvement in performance based on most of the evaluation metrics. Even with different patterns of change among the models, iProDNA-CapsNets remains to be the best model having top performance in most of the metrics, especially MCC which is boosted from about 8.0% to 220.0%.

CONCLUSIONS

According to all evaluation metrics under various decision thresholds, iProDNA-CapsNet shows better performance compared to the two current best models (BindN and TargetDNA). Our proposed approach also shows that CapsNet can potentially be used and adopted in other biological applications.

Improved detection of epigenomic marks with mixed-effects hidden Markov models

Chromatin immunoprecipitation followed by next-generation sequencing (ChIP-seq) is a technique to detect genomic regions containing protein-DNA interaction, such as transcription factor binding sites or regions containing histone modifications. One goal of the analysis of ChIP-seq experiments is to identify genomic loci enriched for sequencing reads pertaining to DNA bound to the factor of interest. The accurate identification of such regions aids in the understanding of epigenomic marks and gene regulatory mechanisms. Given the reduction of massively parallel sequencing costs, methods to detect consensus regions of enrichment across multiple samples are of interest. Here, we present a statistical model to detect broad consensus regions of enrichment from ChIP-seq technical or biological replicates through a class of zero-inflated mixed-effects hidden Markov models. We show that the proposed model outperforms existing methods for consensus peak calling in common epigenomic marks by accounting for the excess zeros and sample-specific biases. We apply our method to data from the Encyclopedia of DNA Elements and Roadmap Epigenomics projects and also from an extensive simulation study.

Condition-Specific Modeling of Biophysical Parameters Advances Inference of Regulatory Networks

Large-scale inference of eukaryotic transcription-regulatory networks remains challenging. One underlying reason is that existing algorithms typically ignore crucial regulatory mechanisms, such as RNA degradation and post-transcriptional processing. Here, we describe InfereCLaDR, which incorporates such elements and advances prediction in Saccharomyces cerevisiae. First, InfereCLaDR employs a high-quality Gold Standard dataset that we use separately as prior information and for model validation. Second, InfereCLaDR explicitly models transcription factor activity and RNA half-lives. Third, it introduces expression subspaces to derive condition-responsive regulatory networks for every gene. InfereCLaDR’s final network is validated by known data and trends and results in multiple insights. For example, it predicts long half-lives for transcripts of the nucleic acid metabolism genes and members of the cytosolic chaperonin complex as targets of the proteasome regulator Rpn4p. InfereCLaDR demonstrates that more biophysically realistic modeling of regulatory networks advances prediction accuracy both in eukaryotes and prokaryotes.

aFARP-ChIP-seq, a convenient and reliable method for genome profiling in as few as 100 cells with a capability for multiplexing ChIP-seq

Much effort has been devoted to understand how chromatin modification regulates development and disease. Despite recent progress, however, it remains difficult to obtain high-quality epigenomic maps using chromatin-immunoprecipitation-coupled deep sequencing (ChIP-seq) in samples with low-cell numbers. Here, we present an Atlantis dsDNase-based technology, aFARP-ChIP-seq, that provides accurate profiling of genome-wide histone modifications in as few as 100 cells. By mapping histone lysine trimethylation (H3K4me3) and acetylation (H3K27Ac) in group I innate lymphoid cells (ILC1) sorted from different tissues in parallel, aFARP-ChIP-seq uncovers putative active promoter and enhancer landscapes of several tissue-specific Natural Killer cells (NK) and ILC1. aFARP-ChIP-seq is also highly effective in mapping transcription factor binding sites in small number of cells. Thus, aFARP-ChIP-seq offers multiplexing mapping of both epigenome and transcription factor binding sites using a small number of cells.