The Genetics of Transcription Factor DNA Binding Variation

Mechanistic insights into genetic susceptibility to prostate cancer

Prostate cancer (PCa) is the second most common cancer in men and is a highly heritable disease that affects millions of individuals worldwide. Genome-wide association studies have to date discovered nearly 270 genetic loci harboring hundreds of single nucleotide polymorphisms (SNPs) that are associated with PCa susceptibility. In contrast, the functional characterization of the mechanisms underlying PCa risk association is still growing. Given that PCa risk-associated SNPs are highly enriched in noncoding cis-regulatory genomic regions, accumulating evidence suggests a widespread modulation of transcription factor chromatin binding and allelic enhancer activity by these noncoding SNPs, thereby dysregulating gene expression. Emerging studies have shown that a proportion of noncoding variants can modulate the formation of transcription factor complexes at enhancers and CTCF-mediated 3D genome architecture. Interestingly, DNA methylation-regulated CTCF binding could orchestrate a long-range chromatin interaction between PCa risk enhancer and causative genes. Additionally, one-causal-variant-two-risk genes or multiple-risk-variant-multiple-genes are prevalent in some PCa risk-associated loci. In this review, we will discuss the current understanding of the general principles of SNP-mediated gene regulation, experimental advances, and functional evidence supporting the mechanistic roles of several PCa genetic loci with potential clinical impact on disease prevention and treatment.

COVID-19 genetic risk variants are associated with expression of multiple genes in diverse immune cell types

Common genetic polymorphisms associated with COVID-19 illness can be utilized for discovering molecular pathways and cell types driving disease pathogenesis. Given the importance of immune cells in the pathogenesis of COVID-19 illness, here we assessed the effects of COVID-19-risk variants on gene expression in a wide range of immune cell types. Transcriptome-wide association study and colocalization analysis revealed putative causal genes and the specific immune cell types where gene expression is most influenced by COVID-19-risk variants. Notable examples include OAS1 in non-classical monocytes, DTX1 in B cells, IL10RB in NK cells, CXCR6 in follicular helper T cells, CCR9 in regulatory T cells and ARL17A in TH2 cells. By analysis of transposase accessible chromatin and H3K27ac-based chromatin-interaction maps of immune cell types, we prioritized potentially functional COVID-19-risk variants. Our study highlights the potential of COVID-19 genetic risk variants to impact the function of diverse immune cell types and influence severe disease manifestations.

Machine learning meets omics: applications and perspectives

The innovation of biotechnologies has allowed the accumulation of omics data at an alarming rate, thus introducing the era of 'big data'. Extracting inherent valuable knowledge from various omics data remains a daunting problem in bioinformatics. Better solutions often need some kind of more innovative methods for efficient handlings and effective results. Recent advancements in integrated analysis and computational modeling of multi-omics data helped address such needs in an increasingly harmonious manner. The development and application of machine learning have largely advanced our insights into biology and biomedicine and greatly promoted the development of therapeutic strategies, especially for precision medicine. Here, we propose a comprehensive survey and discussion on what happened, is happening and will happen when machine learning meets omics. Specifically, we describe how artificial intelligence can be applied to omics studies and review recent advancements at the interface between machine learning and the ever-widest range of omics including genomics, transcriptomics, proteomics, metabolomics, radiomics, as well as those at the single-cell resolution. We also discuss and provide a synthesis of ideas, new insights, current challenges and perspectives of machine learning in omics.

Single molecule characterization of the binding kinetics of a transcription factor and its modulation by DNA sequence and methylation

The interaction of transcription factors with their response elements in DNA is emerging as a highly complex process, whose characterization requires measuring the full distribution of binding and dissociation times in a well-controlled assay. Here, we present a single-molecule assay that exploits the thermal fluctuations of a DNA hairpin to detect the association and dissociation of individual, unlabeled transcription factors. We demonstrate this new approach by following the binding of Egr1 to its consensus motif and the three binding sites found in the promoter of the Lhb gene, and find that both association and dissociation are modulated by the 9 bp core motif and the sequences around it. In addition, CpG methylation modulates the dissociation kinetics in a sequence and position-dependent manner, which can both stabilize or destabilize the complex. Together, our findings show how variations in sequence and methylation patterns synergistically extend the spectrum of a protein's binding properties, and demonstrate how the proposed approach can provide new insights on the function of transcription factors.

Deciphering enhancer sequence using thermodynamics-based models and convolutional neural networks

Deciphering the sequence-function relationship encoded in enhancers holds the key to interpreting non-coding variants and understanding mechanisms of transcriptomic variation. Several quantitative models exist for predicting enhancer function and underlying mechanisms; however, there has been no systematic comparison of these models characterizing their relative strengths and shortcomings. Here, we interrogated a rich data set of neuroectodermal enhancers in Drosophila, representing cis- and trans- sources of expression variation, with a suite of biophysical and machine learning models. We performed rigorous comparisons of thermodynamics-based models implementing different mechanisms of activation, repression and cooperativity. Moreover, we developed a convolutional neural network (CNN) model, called CoNSEPT, that learns enhancer 'grammar' in an unbiased manner. CoNSEPT is the first general-purpose CNN tool for predicting enhancer function in varying conditions, such as different cell types and experimental conditions, and we show that such complex models can suggest interpretable mechanisms. We found model-based evidence for mechanisms previously established for the studied system, including cooperative activation and short-range repression. The data also favored one hypothesized activation mechanism over another and suggested an intriguing role for a direct, distance-independent repression mechanism. Our modeling shows that while fundamentally different models can yield similar fits to data, they vary in their utility for mechanistic inference. CoNSEPT is freely available at: https://github.com/PayamDiba/CoNSEPT.

Reciprocal stabilization of transcription factor binding integrates two signaling pathways to regulate fission yeast fbp1 transcription

Transcriptional regulation, a pivotal biological process by which cells adapt to environmental fluctuations, is achieved by the binding of transcription factors to target sequences in a sequence-specific manner. However, how transcription factors recognize the correct target from amongst the numerous candidates in a genome has not been fully elucidated. We here show that, in the fission-yeast fbp1 gene, when transcription factors bind to target sequences in close proximity, their binding is reciprocally stabilized, thereby integrating distinct signal transduction pathways. The fbp1 gene is massively induced upon glucose starvation by the activation of two transcription factors, Atf1 and Rst2, mediated via distinct signal transduction pathways. Atf1 and Rst2 bind to the upstream-activating sequence 1 region, carrying two binding sites located 45 bp apart. Their binding is reciprocally stabilized due to the close proximity of the two target sites, which destabilizes the independent binding of Atf1 or Rst2. Tup11/12 (Tup-family co-repressors) suppress independent binding. These data demonstrate a previously unappreciated mechanism by which two transcription-factor binding sites, in close proximity, integrate two independent-signal pathways, thereby behaving as a hub for signal integration.

Single-nuclei chromatin profiling of ventral midbrain reveals cell identity transcription factors and cell-type-specific gene regulatory variation

BACKGROUND

Cell types in ventral midbrain are involved in diseases with variable genetic susceptibility, such as Parkinson's disease and schizophrenia. Many genetic variants affect regulatory regions and alter gene expression in a cell-type-specific manner depending on the chromatin structure and accessibility.

RESULTS

We report 20,658 single-nuclei chromatin accessibility profiles of ventral midbrain from two genetically and phenotypically distinct mouse strains. We distinguish ten cell types based on chromatin profiles and analysis of accessible regions controlling cell identity genes highlights cell-type-specific key transcription factors. Regulatory variation segregating the mouse strains manifests more on transcriptome than chromatin level. However, cell-type-level data reveals changes not captured at tissue level. To discover the scope and cell-type specificity of cis-acting variation in midbrain gene expression, we identify putative regulatory variants and show them to be enriched at differentially expressed loci. Finally, we find TCF7L2 to mediate trans-acting variation selectively in midbrain neurons.

CONCLUSIONS

Our data set provides an extensive resource to study gene regulation in mesencephalon and provides insights into control of cell identity in the midbrain and identifies cell-type-specific regulatory variation possibly underlying phenotypic and behavioural differences between mouse strains.

Motif-Raptor: a cell type-specific and transcription factor centric approach for post-GWAS prioritization of causal regulators

MOTIVATION

Genome-wide association studies (GWASs) have identified thousands of common trait-associated genetic variants but interpretation of their function remains challenging. These genetic variants can overlap the binding sites of transcription factors (TFs) and therefore could alter gene expression. However, we currently lack a systematic understanding on how this mechanism contributes to phenotype.

RESULTS

We present Motif-Raptor, a TF-centric computational tool that integrates sequence-based predictive models, chromatin accessibility, gene expression datasets and GWAS summary statistics to systematically investigate how TF function is affected by genetic variants. Given trait-associated non-coding variants, Motif-Raptor can recover relevant cell types and critical TFs to drive hypotheses regarding their mechanism of action. We tested Motif-Raptor on complex traits such as rheumatoid arthritis and red blood cell count and demonstrated its ability to prioritize relevant cell types, potential regulatory TFs and non-coding SNPs which have been previously characterized and validated.

AVAILABILITY AND IMPLEMENTATION

Motif-Raptor is freely available as a Python package at: https://github.com/pinellolab/MotifRaptor.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Individual-specific functional epigenomics reveals genetic determinants of adverse metabolic effects of glucocorticoids

Glucocorticoids (GCs) are widely used as anti-inflammatory drugs, but their long-term use has severe metabolic side effects. Here, by treating multiple individual adipose stem cell-derived adipocytes and induced pluripotent stem cell-derived hepatocytes with the potent GC dexamethasone (Dex), we uncovered cell-type-specific and individual-specific GC-dependent transcriptomes and glucocorticoid receptor (GR) cistromes. Individual-specific GR binding could be traced to single-nucleotide polymorphisms (SNPs) that altered the binding motifs of GR or its cooperating factors. We also discovered another set of genetic variants that modulated Dex response through affecting chromatin accessibility or chromatin architecture. Several SNPs that altered Dex-regulated GR binding and gene expression controlled Dex-driven metabolic perturbations. Remarkably, these genetic variations were highly associated with increases in serum glucose, lipids, and body mass in subjects on GC therapy. Knowledge of the genetic variants that predispose individuals to metabolic side effects allows for a precision medicine approach to the use of clinically relevant GCs.

The genomic basis of evolutionary differentiation among honey bees

In contrast to the western honey bee, Apis mellifera, other honey bee species have been largely neglected despite their importance and diversity. The genetic basis of the evolutionary diversification of honey bees remains largely unknown. Here, we provide a genome-wide comparison of three honey bee species, each representing one of the three subgenera of honey bees, namely the dwarf (Apis florea), giant (A. dorsata), and cavity-nesting (A. mellifera) honey bees with bumblebees as an outgroup. Our analyses resolve the phylogeny of honey bees with the dwarf honey bees diverging first. We find that evolution of increased eusocial complexity in Apis proceeds via increases in the complexity of gene regulation, which is in agreement with previous studies. However, this process seems to be related to pathways other than transcriptional control. Positive selection patterns across Apis reveal a trade-off between maintaining genome stability and generating genetic diversity, with a rapidly evolving piRNA pathway leading to genomes depleted of transposable elements, and a rapidly evolving DNA repair pathway associated with high recombination rates in all Apis species. Diversification within Apis is accompanied by positive selection in several genes whose putative functions present candidate mechanisms for lineage-specific adaptations, such as migration, immunity, and nesting behavior.

Disruptive Selection of Human Immunostimulatory and Immunosuppressive Genes Both Provokes and Prevents Rheumatoid Arthritis, Respectively, as a Self-Domestication Syndrome

Using our previously published Web service SNP_TATA_Comparator, we conducted a genome-wide study of single-nucleotide polymorphisms (SNPs) within core promoters of 68 human rheumatoid arthritis (RA)-related genes. Using 603 SNPs within 25 genes clinically associated with RA-comorbid disorders, we predicted 84 and 70 candidate SNP markers for overexpression and underexpression of these genes, respectively, among which 58 and 96 candidate SNP markers, respectively, can relieve and worsen RA as if there is a neutral drift toward susceptibility to RA. Similarly, we predicted natural selection toward susceptibility to RA for 8 immunostimulatory genes (e.g., IL9R) and 10 genes most often associated with RA (e.g., NPY). On the contrary, using 25 immunosuppressive genes, we predicted 70 and 109 candidate SNP markers aggravating and relieving RA, respectively (e.g., IL1R2 and TGFB2), suggesting that natural selection can simultaneously additionally yield resistance to RA. We concluded that disruptive natural selection of human immunostimulatory and immunosuppressive genes is concurrently elevating and reducing the risk of RA, respectively. So, we hypothesize that RA in human could be a self-domestication syndrome referring to evolution patterns in domestic animals. We tested this hypothesis by means of public RNA-Seq data on 1740 differentially expressed genes (DEGs) of pets vs. wild animals (e.g., dogs vs. wolves). The number of DEGs in the domestic animals corresponding to worsened RA condition in humans was significantly larger than that in the related wild animals (10 vs. 3). Moreover, much less DEGs in the domestic animals were accordant to relieved RA condition in humans than those in the wild animals (1 vs. 8 genes). This indicates that the anthropogenic environment, in contrast to a natural one, affects gene expression across the whole genome (e.g., immunostimulatory and immunosuppressive genes) in a manner that likely contributes to RA. The difference in gene numbers is statistically significant as confirmed by binomial distribution (p < 0.01), Pearson's χ² (p < 0.01), and Fisher's exact test (p < 0.05). This allows us to propose RA as a candidate symptom within a self-domestication syndrome. Such syndrome might be considered as a human's payment with health for the benefits received during evolution.

Regulatory SNPs: Altered Transcription Factor Binding Sites Implicated in Complex Traits and Diseases

The vast majority of the genetic variants (mainly SNPs) associated with various human traits and diseases map to a noncoding part of the genome and are enriched in its regulatory compartment, suggesting that many causal variants may affect gene expression. The leading mechanism of action of these SNPs consists in the alterations in the transcription factor binding via creation or disruption of transcription factor binding sites (TFBSs) or some change in the affinity of these regulatory proteins to their cognate sites. In this review, we first focus on the history of the discovery of regulatory SNPs (rSNPs) and systematized description of the existing methodical approaches to their study. Then, we brief the recent comprehensive examples of rSNPs studied from the discovery of the changes in the TFBS sequence as a result of a nucleotide substitution to identification of its effect on the target gene expression and, eventually, to phenotype. We also describe state-of-the-art genome-wide approaches to identification of regulatory variants, including both making molecular sense of genome-wide association studies (GWAS) and the alternative approaches the primary goal of which is to determine the functionality of genetic variants. Among these approaches, special attention is paid to expression quantitative trait loci (eQTLs) analysis and the search for allele-specific events in RNA-seq (ASE events) as well as in ChIP-seq, DNase-seq, and ATAC-seq (ASB events) data.

A Variant Noncoding Region Regulates Prrx1 and Predisposes to Atrial Arrhythmias

[Figure: see text].

Technologies for profiling the impact of genomic variants on transcription factor binding

Transcription factors (TFs) bind DNA in a sequence-specific manner and thereby regulate target gene expression. TF binding and its regulatory activity is highly context dependent, and is not only determined by specific cell types or differentiation stages but also relies on other regulatory mechanisms, such as DNA and chromatin modifications. Interactions between TFs and their DNA binding sites are critical mediators of phenotypic variation and play important roles in the onset of disease. A continuously growing number of studies therefore attempts to elucidate TF:DNA interactions to gain knowledge about regulatory mechanisms and disease-causing variants. Here we summarize how TF-binding characteristics and the impact of variants can be investigated, how bioinformatic tools can be used to analyze and predict TF:DNA binding, and what additional information can be obtained from the TF protein structure.

Enhanced C/EBP binding to G·T mismatches facilitates fixation of CpG mutations in cancer and adult stem cells

Somatic mutations in regulatory sites of human stem cells affect cell identity or cause malignant transformation. By mining the human genome for co-occurrence of mutations and transcription factor binding sites, we show that C/EBP binding sites are strongly enriched with [C > T]G mutations in cancer and adult stem cells, which is of special interest because C/EBPs regulate cell fate and differentiation. In vitro protein-DNA binding assay and structural modeling of the CEBPB-DNA complex show that the G·T mismatch in the core CG dinucleotide strongly enhances affinity of the binding site. We conclude that enhanced binding of C/EBPs shields CpG·TpG mismatches from DNA repair, leading to selective accumulation of [C > T]G mutations and consequent deterioration of the binding sites. This mechanism of targeted mutagenesis highlights the effect of a mutational process on certain regulatory sites and reveals the molecular basis of putative regulatory alterations in stem cells.

Mechanisms underlying divergent responses of genetically distinct macrophages to IL-4

Mechanisms by which noncoding genetic variation influences gene expression remain only partially understood but are considered to be major determinants of phenotypic diversity and disease risk. Here, we evaluated effects of >50 million single-nucleotide polymorphisms and short insertions/deletions provided by five inbred strains of mice on the responses of macrophages to interleukin-4 (IL-4), a cytokine that plays pleiotropic roles in immunity and tissue homeostasis. Of >600 genes induced >2-fold by IL-4 across the five strains, only 26 genes reached this threshold in all strains. By applying deep learning and motif mutation analyses to epigenetic data for macrophages from each strain, we identified the dominant combinations of lineage-determining and signal-dependent transcription factors driving IL-4 enhancer activation. These studies further revealed mechanisms by which noncoding genetic variation influences absolute levels of enhancer activity and their dynamic responses to IL-4, thereby contributing to strain-differential patterns of gene expression and phenotypic diversity.

Landscape of allele-specific transcription factor binding in the human genome

Sequence variants in gene regulatory regions alter gene expression and contribute to phenotypes of individual cells and the whole organism, including disease susceptibility and progression. Single-nucleotide variants in enhancers or promoters may affect gene transcription by altering transcription factor binding sites. Differential transcription factor binding in heterozygous genomic loci provides a natural source of information on such regulatory variants. We present a novel approach to call the allele-specific transcription factor binding events at single-nucleotide variants in ChIP-Seq data, taking into account the joint contribution of aneuploidy and local copy number variation, that is estimated directly from variant calls. We have conducted a meta-analysis of more than 7 thousand ChIP-Seq experiments and assembled the database of allele-specific binding events listing more than half a million entries at nearly 270 thousand single-nucleotide polymorphisms for several hundred human transcription factors and cell types. These polymorphisms are enriched for associations with phenotypes of medical relevance and often overlap eQTLs, making candidates for causality by linking variants with molecular mechanisms. Specifically, there is a special class of switching sites, where different transcription factors preferably bind alternative alleles, thus revealing allele-specific rewiring of molecular circuitry.

Immune Regulation in Time and Space: The Role of Local- and Long-Range Genomic Interactions in Regulating Immune Responses

Mammals face and overcome an onslaught of endogenous and exogenous challenges in order to survive. Typical immune cells and barrier cells, such as epithelia, must respond rapidly and effectively to encountered pathogens and aberrant cells to prevent invasion and eliminate pathogenic species before they become overgrown and cause harm. On the other hand, inappropriate initiation and failed termination of immune cell effector function in the absence of pathogens or aberrant tissue gives rise to a number of chronic, auto-immune, and neoplastic diseases. Therefore, the fine control of immune effector functions to provide for a rapid, robust response to challenge is essential. Importantly, immune cells are heterogeneous due to various factors relating to cytokine exposure and cell-cell interaction. For instance, tissue-resident macrophages and T cells are phenotypically, transcriptionally, and functionally distinct from their circulating counterparts. Indeed, even the same cell types in the same environment show distinct transcription patterns at the single cell level due to cellular noise, despite being robust in concert. Additionally, immune cells must remain quiescent in a naive state to avoid autoimmunity or chronic inflammatory states but must respond robustly upon activation regardless of their microenvironment or cellular noise. In recent years, accruing evidence from next-generation sequencing, chromatin capture techniques, and high-resolution imaging has shown that local- and long-range genome architecture plays an important role in coordinating rapid and robust transcriptional responses. Here, we discuss the local- and long-range genome architecture of immune cells and the resultant changes upon pathogen or antigen exposure. Furthermore, we argue that genome structures contribute functionally to rapid and robust responses under noisy and distinct cellular environments and propose a model to explain this phenomenon.

Interpretation of allele-specific chromatin accessibility using cell state-aware deep learning

Genomic sequence variation within enhancers and promoters can have a significant impact on the cellular state and phenotype. However, sifting through the millions of candidate variants in a personal genome or a cancer genome, to identify those that impact cis-regulatory function, remains a major challenge. Interpretation of noncoding genome variation benefits from explainable artificial intelligence to predict and interpret the impact of a mutation on gene regulation. Here we generate phased whole genomes with matched chromatin accessibility, histone modifications, and gene expression for 10 melanoma cell lines. We find that training a specialized deep learning model, called DeepMEL2, on melanoma chromatin accessibility data can capture the various regulatory programs of the melanocytic and mesenchymal-like melanoma cell states. This model outperforms motif-based variant scoring, as well as more generic deep learning models. We detect hundreds to thousands of allele-specific chromatin accessibility variants (ASCAVs) in each melanoma genome, of which 15%-20% can be explained by gains or losses of transcription factor binding sites. A considerable fraction of ASCAVs are caused by changes in AP-1 binding, as confirmed by matched ChIP-seq data to identify allele-specific binding of JUN and FOSL1. Finally, by augmenting the DeepMEL2 model with ChIP-seq data for GABPA, the TERT promoter mutation, as well as additional ETS motif gains, can be identified with high confidence. In conclusion, we present a new integrative genomics approach and a deep learning model to identify and interpret functional enhancer mutations with allelic imbalance of chromatin accessibility and gene expression.

BAX -248 G>A and BCL2 -938 C>A Variant Lowers the Survival in Patients with Nasopharyngeal Carcinoma and Could be Associated with Tissue-Specific Malignancies: A Multi-Method Approach

BACKGROUND

The association of BAX -248 G>A and BCL2 -938 C>A with different cancers created conflicts.  We studied the correlation and the effect of these polymorphisms in patients with Nasopharyngeal Carcinoma (NPC).  Methods: PCR-RFLP and Sanger sequencing were used to detect polymorphisms. Statistical analysis including forest plot and Kaplan-Meier Log-rank test was conducted to investigate the association and effect of these SNPs on the NPC patients' survival. The computational study was performed to investigate the possible regulatory role between these polymorphisms and the poor survival of NPC patients. Meta-analysis was executed to check the tissue-specific association of these polymorphisms in the context of global cancer prognosis.

RESULTS

We observed an increased and significant association of BAX -248 G>A [GA:OR=5.29, 95%CI=1.67,16.67, P=0.004; GA+AA:OR=5.71, 95%CI=1.82,17.90, P =0.002; A:OR=5.33, 95%CI=1.76,16.13, P=0.003], and BCL2 -938 C>A [CA:OR=2.26, 95%CI=1.03,4.96, P=0.04; AA:OR=3.56, 95%CI=0.97,13.05, P=0.05; CA+AA:OR=3.10, 95%CI=1.51,6.35, P=0.002; A:OR=2.90, 95% CI=1.59,5.29, P=0.0005] with the risk of NPC. Also, these SNPs were strongly correlated with poor survival in NPC patients (lower estimated survival mean, lower estimated proportion surviving at 5 years with p <0.05). The computational study showed that these SNPs altered the binding affinity of transcription factors HIF1, SP1, PAX3, PAX9 and CREB towards promoter (Lower p indicates strong affinity). The meta-analysis revealed the tissue-specific association of these polymorphisms. BAX -248 G>A showed a significant correlation with carcinomas [A vs G:OR=1.60, 95%CI=1.09,2.34, P=0.01; AA vs GG:OR=2.61, 95%CI=1.68,4.06, p <0.001; AA+GA vs GG:OR=1.53,95%CI=1.04,2.25, P=0.02); AA vs GG+GA:OR=2.53, 95%CI=1.65,3.87, p <0.001], and BCL2 -938 C>A with other malignancies [A vs C:OR=1.45, 95%CI=1.26,1.66, p <0.001; AA vs CC:OR=2.07, 95%CI: 1.15,3.72, P=0.01; AA+CA vs CC:OR=1.42, 95%CI=1.18,1.72, p <0.001; AA vs CC+CA:OR=1.89, 95%CI=1.02,3.50, P=0.04].

CONCLUSIONS

BAX -248 G>A and BCL2 -938 C>A was associated with poor survival in NPC patients. It may increase cancer susceptibility through transcriptional regulation. Moreover, these SNPs' effects could be tissue-specific.
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Transcriptional circuitry atlas of genetic diverse unstimulated murine and human macrophages define disparity in population-wide innate immunity

Macrophages are ubiquitous custodians of tissues, which play decisive role in maintaining cellular homeostasis through regulatory immune responses. Within tissues, macrophage exhibit extremely heterogeneous population with varying functions orchestrated through regulatory response, which can be further exacerbated in diverse genetic backgrounds. Gene regulatory networks (GRNs) offer comprehensive understanding of cellular regulatory behavior by unfolding the transcription factors (TFs) and regulated target genes. RNA-Seq coupled with ATAC-Seq has revolutionized the regulome landscape influenced by gene expression modeling. Here, we employ an integrative multi-omics systems biology-based analysis and generated GRNs derived from the unstimulated bone marrow-derived macrophages of five inbred genetically defined murine strains, which are reported to be linked with most of the population-wide human genetic variants. Our probabilistic modeling of a basal hemostasis pan regulatory repertoire in diverse macrophages discovered 96 TFs targeting 6279 genes representing 468,291 interactions across five inbred murine strains. Subsequently, we identify core and distinctive GRN sub-networks in unstimulated macrophages to describe the system-wide conservation and dissimilarities, respectively across five murine strains. Our study concludes that discrepancies in unstimulated macrophage-specific regulatory networks not only drives the basal functional plasticity within genetic backgrounds, additionally aid in understanding the complexity of racial disparity among the human population during stress.

A non-coding genetic variant associated with abdominal aortic aneurysm alters ERG gene regulation

Abdominal aortic aneurysm (AAA) is a major cause of sudden death in the elderly. While AAA has some overlapping genetic and environmental risk factors with atherosclerosis, there are substantial differences, and AAA-specific medication is lacking. A recent meta-analysis of genome-wide association studies has identified four novel single-nucleotide polymorphisms (SNPs) specifically associated with AAA. Here, we investigated the gene regulatory function for one of four non-coding SNPs associated with AAA, rs2836411, which is located in an intron of the ERG gene. Rs2836411 resides within a >70 kb super-enhancer that has high levels of H3K27ac and H3K4me1 in vascular endothelial and haematopoietic cell types. Enhancer luciferase assays in cell lines showed that the risk allele significantly alters enhancer activity. The risk allele also correlates with reduced ERG expression in aortic and other vascular tissues. To identify whether rs2836411 directly contacts the promoters of ERG and/or of genes further away, we performed allele-specific circular chromosome conformation capture sequencing. In vascular endothelial cells, which express ERG, the SNP region interacts highly within the super-enhancer, while in vascular smooth muscle cells, which do not express ERG, the interactions are distributed across a wider region that includes neighbouring genes. Furthermore, the risk allele has fewer interactions within the super-enhancer compared to the protective allele. In conclusion, our results indicate that rs2836411 likely affects ERG expression by altering enhancer activity and changing local chromatin interactions. ERG is involved in vascular development, angiogenesis, and inflammation in atherosclerosis; therefore mechanistically, rs2836411 could contribute to AAA by modulating ERG levels.

Global discovery of lupus genetic risk variant allelic enhancer activity

Genome-wide association studies of Systemic Lupus Erythematosus (SLE) nominate 3073 genetic variants at 91 risk loci. To systematically screen these variants for allelic transcriptional enhancer activity, we construct a massively parallel reporter assay (MPRA) library comprising 12,396 DNA oligonucleotides containing the genomic context around every allele of each SLE variant. Transfection into the Epstein-Barr virus-transformed B cell line GM12878 reveals 482 variants with enhancer activity, with 51 variants showing genotype-dependent (allelic) enhancer activity at 27 risk loci. Comparison of MPRA results in GM12878 and Jurkat T cell lines highlights shared and unique allelic transcriptional regulatory mechanisms at SLE risk loci. In-depth analysis of allelic transcription factor (TF) binding at and around allelic variants identifies one class of TFs whose DNA-binding motif tends to be directly altered by the risk variant and a second class of TFs that bind allelically without direct alteration of their motif by the variant. Collectively, our approach provides a blueprint for the discovery of allelic gene regulation at risk loci for any disease and offers insight into the transcriptional regulatory mechanisms underlying SLE.

Thousands of genetic variants have been associated with lupus, but causal variants and mechanisms are unknown. Here, the authors combine a massively parallel reporter assay with genome-wide ChIP experiments to identify risk variants with allelic enhancer activity mediated through transcription factor binding.

Editing GWAS: experimental approaches to dissect and exploit disease-associated genetic variation

Genome-wide association studies (GWAS) have uncovered thousands of genetic variants that influence risk for human diseases and traits. Yet understanding the mechanisms by which these genetic variants, mainly noncoding, have an impact on associated diseases and traits remains a significant hurdle. In this review, we discuss emerging experimental approaches that are being applied for functional studies of causal variants and translational advances from GWAS findings to disease prevention and treatment. We highlight the use of genome editing technologies in GWAS functional studies to modify genomic sequences, with proof-of-principle examples. We discuss the challenges in interrogating causal variants, points for consideration in experimental design and interpretation of GWAS locus mechanisms, and the potential for novel therapeutic opportunities. With the accumulation of knowledge of functional genetics, therapeutic genome editing based on GWAS discoveries will become increasingly feasible.

MAGGIE: leveraging genetic variation to identify DNA sequence motifs mediating transcription factor binding and function

MOTIVATION

Genetic variation in regulatory elements can alter transcription factor (TF) binding by mutating a TF binding motif, which in turn may affect the activity of the regulatory elements. However, it is unclear which motifs are prone to impact transcriptional regulation if mutated. Current motif analysis tools either prioritize TFs based on motif enrichment without linking to a function or are limited in their applications due to the assumption of linearity between motifs and their functional effects.

RESULTS

We present MAGGIE (Motif Alteration Genome-wide to Globally Investigate Elements), a novel method for identifying motifs mediating TF binding and function. By leveraging measurements from diverse genotypes, MAGGIE uses a statistical approach to link mutations of a motif to changes of an epigenomic feature without assuming a linear relationship. We benchmark MAGGIE across various applications using both simulated and biological datasets and demonstrate its improvement in sensitivity and specificity compared with the state-of-the-art motif analysis approaches. We use MAGGIE to gain novel insights into the divergent functions of distinct NF-κB factors in pro-inflammatory macrophages, revealing the association of p65-p50 co-binding with transcriptional activation and the association of p50 binding lacking p65 with transcriptional repression.

AVAILABILITY AND IMPLEMENTATION

The Python package for MAGGIE is freely available at https://github.com/zeyang-shen/maggie. The accession number for the NF-κB ChIP-seq data generated for this study is Gene Expression Omnibus: GSE144070.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Integrative analysis identifies bHLH transcription factors as contributors to Parkinson's disease risk mechanisms

Genome-wide association studies (GWAS) have identified multiple genetic risk signals for Parkinson’s disease (PD), however translation into underlying biological mechanisms remains scarce. Genomic functional annotations of neurons provide new resources that may be integrated into analyses of GWAS findings. Altered transcription factor binding plays an important role in human diseases. Insight into transcriptional networks involved in PD risk mechanisms may thus improve our understanding of pathogenesis. We analysed overlap between genome-wide association signals in PD and open chromatin in neurons across multiple brain regions, finding a significant enrichment in the superior temporal cortex. The involvement of transcriptional networks was explored in neurons of the superior temporal cortex based on the location of candidate transcription factor motifs identified by two de novo motif discovery methods. Analyses were performed in parallel, both finding that PD risk variants significantly overlap with open chromatin regions harboring motifs of basic Helix-Loop-Helix (bHLH) transcription factors. Our findings show that cortical neurons are likely mediators of genetic risk for PD. The concentration of PD risk variants at sites of open chromatin targeted by members of the bHLH transcription factor family points to an involvement of these transcriptional networks in PD risk mechanisms.

Battle for supremacy: nucleic acid interactions between viruses and cells

Since the COVID-19 pandemic swept across the globe, researchers have been trying to understand its origin, life cycle, and pathogenesis. There is a striking variability in the phenotypic response to infection with SARS-CoV-2 that may reflect differences in host genetics and/or immune response. It is known that the human epigenome is influenced by ethnicity, age, lifestyle, and environmental factors, including previous viral infections. This Review examines the influence of viruses on the host epigenome. We describe general lessons and methodologies that can be used to understand how the virus evades the host immune response. We consider how variation in the epigenome may contribute to heterogeneity in the response to SARS-CoV-2 and may identify a precision medicine approach to treatment.

Extensive tissue-specific expression variation and novel regulators underlying circadian behavior

Natural genetic variation affects circadian rhythms across the evolutionary tree, but the underlying molecular mechanisms are poorly understood. We investigated population-level, molecular circadian clock variation by generating >700 tissue-specific transcriptomes of Drosophila melanogaster (w1118 ) and 141 Drosophila Genetic Reference Panel (DGRP) lines. This comprehensive circadian gene expression atlas contains >1700 cycling genes including previously unknown central circadian clock components and tissue-specific regulators. Furthermore, >30% of DGRP lines exhibited aberrant circadian gene expression, revealing abundant genetic variation-mediated, intertissue circadian expression desynchrony. Genetic analysis of one line with the strongest deviating circadian expression uncovered a novel cry mutation that, as shown by protein structural modeling and brain immunohistochemistry, disrupts the light-driven flavin adenine dinucleotide cofactor photoreduction, providing in vivo support for the importance of this conserved photoentrainment mechanism. Together, our study revealed pervasive tissue-specific circadian expression variation with genetic variants acting upon tissue-specific regulatory networks to generate local gene expression oscillations.

Insights into gene regulation: From regulatory genomic elements to DNA-protein and protein-protein interactions

Transcription is orchestrated by non-coding regulatory elements embedded in chromatin, which exist within the larger context of chromosome topology. Here, we review recent insights into the functions of non-coding regulatory elements and their protein interactors during transcription control. A picture emerges in which the topological environment constraints enhancer-promoter interactions and specific enhancer-bound proteins with distinct promoter-compatibilities refine target promoter choice. Such compatibilities are encoded within the sequences of enhancers and promoters and realized by diverse transcription factors and cofactors with distinct biochemical activities. An emerging property of transcription factors and cofactors is the formation of nuclear microenvironments or membraneless compartments that can have properties of phase-separated liquids. These environments are able to selectively enrich certain proteins and small molecules over others. Further investigation into the interaction of transcriptional regulators with themselves and regulatory DNA elements will help reveal the complexities of gene regulation within the context of the nucleus.

Molecular Co-occupancy Identifies Transcription Factor Binding Cooperativity In Vivo

Gene activation requires the cooperative activity of multiple transcription factors at cis-regulatory elements (CREs). Yet, most transcription factors have short residence time, questioning the requirement of their physical co-occupancy on DNA to achieve cooperativity. Here, we present a DNA footprinting method that detects individual molecular interactions of transcription factors and nucleosomes with DNA in vivo. We apply this strategy to quantify the simultaneous binding of multiple transcription factors on single DNA molecules at mouse CREs. Analysis of the binary occupancy patterns at thousands of motif combinations reveals that high DNA co-occupancy occurs for most types of transcription factors, in the absence of direct physical interaction, at sites of competition with nucleosomes. Perturbation of pairwise interactions demonstrates the function of molecular co-occupancy in binding cooperativity. Our results reveal the interactions regulating CREs at molecular resolution and identify DNA co-occupancy as a widespread cooperativity mechanism used by transcription factors to remodel chromatin.

Rational design of minimal synthetic promoters for plants

Promoters serve a critical role in establishing baseline transcriptional capacity through the recruitment of proteins, including transcription factors. Previously, a paucity of data for cis-regulatory elements in plants meant that it was challenging to determine which sequence elements in plant promoter sequences contributed to transcriptional function. In this study, we have identified functional elements in the promoters of plant genes and plant pathogens that utilize plant transcriptional machinery for gene expression. We have established a quantitative experimental system to investigate transcriptional function, investigating how identity, density and position contribute to regulatory function. We then identified permissive architectures for minimal synthetic plant promoters enabling the computational design of a suite of synthetic promoters of different strengths. These have been used to regulate the relative expression of output genes in simple genetic devices.

Rational design of minimal synthetic promoters for plants

Promoters serve a critical role in establishing baseline transcriptional capacity through the recruitment of proteins, including transcription factors. Previously, a paucity of data for cis-regulatory elements in plants meant that it was challenging to determine which sequence elements in plant promoter sequences contributed to transcriptional function. In this study, we have identified functional elements in the promoters of plant genes and plant pathogens that utilize plant transcriptional machinery for gene expression. We have established a quantitative experimental system to investigate transcriptional function, investigating how identity, density and position contribute to regulatory function. We then identified permissive architectures for minimal synthetic plant promoters enabling the computational design of a suite of synthetic promoters of different strengths. These have been used to regulate the relative expression of output genes in simple genetic devices.

Vascular homeostasis at high-altitude: role of genetic variants and transcription factors

High-altitude pulmonary edema occurs most frequently in non-acclimatized low landers on exposure to altitude ≥2500 m. High-altitude pulmonary edema is a complex condition that involves perturbation of signaling pathways in vasoconstrictors, vasodilators, anti-diuretics, and vascular growth factors. Genetic variations are instrumental in regulating these pathways and evidence is accumulating for a role of epigenetic modification in hypoxic responses. This review focuses on the crosstalk between high-altitude pulmonary edema-associated genetic variants and transcription factors, comparing high-altitude adapted and high-altitude pulmonary edema-afflicted subjects. This approach might ultimately yield biomarker information both to understand and to design therapies for high-altitude adaptation.

Rare genetic variation at transcription factor binding sites modulates local DNA methylation profiles

Although DNA methylation is the best characterized epigenetic mark, the mechanism by which it is targeted to specific regions in the genome remains unclear. Recent studies have revealed that local DNA methylation profiles might be dictated by cis-regulatory DNA sequences that mainly operate via DNA-binding factors. Consistent with this finding, we have recently shown that disruption of CTCF-binding sites by rare single nucleotide variants (SNVs) can underlie cis-linked DNA methylation changes in patients with congenital anomalies. These data raise the hypothesis that rare genetic variation at transcription factor binding sites (TFBSs) might contribute to local DNA methylation patterning. In this work, by combining blood genome-wide DNA methylation profiles, whole genome sequencing-derived SNVs from 247 unrelated individuals along with 133 predicted TFBS motifs derived from ENCODE ChIP-Seq data, we observed an association between the disruption of binding sites for multiple TFs by rare SNVs and extreme DNA methylation values at both local and, to a lesser extent, distant CpGs. While the majority of these changes affected only single CpGs, 24% were associated with multiple outlier CpGs within ±1kb of the disrupted TFBS. Interestingly, disruption of functionally constrained sites within TF motifs lead to larger DNA methylation changes at nearby CpG sites. Altogether, these findings suggest that rare SNVs at TFBS negatively influence TF-DNA binding, which can lead to an altered local DNA methylation profile. Furthermore, subsequent integration of DNA methylation and RNA-Seq profiles from cardiac tissues enabled us to observe an association between rare SNV-directed DNA methylation and outlier expression of nearby genes. In conclusion, our findings not only provide insights into the effect of rare genetic variation at TFBS on shaping local DNA methylation and its consequences on genome regulation, but also provide a rationale to incorporate DNA methylation data to interpret the functional role of rare variants.

Mapping Regulatory Determinants in Plants

The domestication and improvement of many plant species have frequently involved modulation of transcriptional outputs and continue to offer much promise for targeted trait engineering. The cis-regulatory elements (CREs) controlling these trait-associated transcriptional variants however reside within non-coding regions that are currently poorly annotated in most plant species. This is particularly true in large crop genomes where regulatory regions constitute only a small fraction of the total genomic space. Furthermore, relatively little is known about how CREs function to modulate transcription in plants. Therefore understanding where regulatory regions are located within a genome, what genes they control, and how they are structured are important factors that could be used to guide both traditional and synthetic plant breeding efforts. Here, we describe classic examples of regulatory instances as well as recent advances in plant regulatory genomics. We highlight valuable molecular tools that are enabling large-scale identification of CREs and offering unprecedented insight into how genes are regulated in diverse plant species. We focus on chromatin environment, transcription factor (TF) binding, the role of transposable elements, and the association between regulatory regions and target genes.

Seven myths of how transcription factors read the cis-regulatory code

Genomics data are now being generated at large quantities, of exquisite high resolution and from single cells. They offer a unique opportunity to develop powerful machine learning algorithms, including neural networks, to uncover the rules of the cis-regulatory code. However, current modeling assumptions are often not based on state-of-the-art knowledge of the cis-regulatory code from transcription, developmental genetics, imaging and structural studies. Here I aim to fill this gap by giving a brief historical overview of the field, describing common misconceptions and providing knowledge that might help to guide computational approaches. I will describe the principles and mechanisms involved in the combinatorial requirement of transcription factor binding motifs for enhancer activity, including the role of chromatin accessibility, repressors and low-affinity motifs in the cis-regulatory code. Deciphering the cis-regulatory code would unlock an enormous amount of regulatory information in the genome and would allow us to locate cis-regulatory genetic variants involved in development and disease.

Sex differences in human adipose tissue gene expression and genetic regulation involve adipogenesis

Sex differences in adipose tissue distribution and function are associated with sex differences in cardiometabolic disease. While many studies have revealed sex differences in adipocyte cell signaling and physiology, there is a relative dearth of information regarding sex differences in transcript abundance and regulation. We investigated sex differences in subcutaneous adipose tissue transcriptional regulation using omic-scale data from ∼3000 geographically and ethnically diverse human samples. We identified 162 genes with robust sex differences in expression. Differentially expressed genes were implicated in oxidative phosphorylation and adipogenesis. We further determined that sex differences in gene expression levels could be related to sex differences in the genetics of gene expression regulation. Our analyses revealed sex-specific genetic associations, and this finding was replicated in a study of 98 inbred mouse strains. The genes under genetic regulation in human and mouse were enriched for oxidative phosphorylation and adipogenesis. Enrichment analysis showed that the associated genetic loci resided within binding motifs for adipogenic transcription factors (e.g., PPARG and EGR1). We demonstrated that sex differences in gene expression could be influenced by sex differences in genetic regulation for six genes (e.g., FADS1 and MAP1B). These genes exhibited dynamic expression patterns during adipogenesis and robust expression in mature human adipocytes. Our results support a role for adipogenesis-related genes in subcutaneous adipose tissue sex differences in the genetic and environmental regulation of gene expression.

The Interchromatin Compartment Participates in the Structural and Functional Organization of the Cell Nucleus

This article focuses on the role of the interchromatin compartment (IC) in shaping nuclear landscapes. The IC is connected with nuclear pore complexes (NPCs) and harbors splicing speckles and nuclear bodies. It is postulated that the IC provides routes for imported transcription factors to target sites, for export routes of mRNA as ribonucleoproteins toward NPCs, as well as for the intranuclear passage of regulatory RNAs from sites of transcription to remote functional sites (IC hypothesis). IC channels are lined by less-compacted euchromatin, called the perichromatin region (PR). The PR and IC together form the active nuclear compartment (ANC). The ANC is co-aligned with the inactive nuclear compartment (INC), comprising more compacted heterochromatin. It is postulated that the INC is accessible for individual transcription factors, but inaccessible for larger macromolecular aggregates (limited accessibility hypothesis). This functional nuclear organization depends on still unexplored movements of genes and regulatory sequences between the two compartments.

Systems-Genetics-Based Inference of a Core Regulatory Network Underlying White Fat Browning

Recruitment of brite/beige cells, known as browning of white adipose tissue (WAT), is an efficient way to turn an energy-storing organ into an energy-dissipating one and may therefore be of therapeutic value in combating obesity. However, a comprehensive understanding of the regulatory mechanisms mediating WAT browning is still lacking. Here, we exploit the large natural variation in WAT browning propensity between inbred mouse strains to gain an inclusive view of the core regulatory network coordinating this cellular process. Combining comparative transcriptomics, perturbation-based validations, and gene network analyses, we present a comprehensive gene regulatory network of inguinal WAT browning, revealing up to four distinct regulatory modules with key roles for uncovered transcriptional factors, while also providing deep insights into the genetic architecture of brite adipogenesis. The presented findings therefore greatly increase our understanding of the molecular drivers mediating the intriguing cellular heterogeneity and plasticity of adipose tissue.

Asymmetric Conservation within Pairs of Co-Occurred Motifs Mediates Weak Direct Binding of Transcription Factors in ChIP-Seq Data

(1) Background: Transcription factors (TFs) are main regulators of eukaryotic gene expression. The cooperative binding to genomic DNA of at least two TFs is the widespread mechanism of transcription regulation. Cooperating TFs can be revealed through the analysis of co-occurrence of their motifs. (2) Methods: We applied the motifs co-occurrence tool (MCOT) that predicted pairs of spaced or overlapped motifs (composite elements, CEs) for a single ChIP-seq dataset. We improved MCOT capability for the prediction of asymmetric CEs with one of the participating motifs possessing higher conservation than another does. (3) Results: Analysis of 119 ChIP-seq datasets for 45 human TFs revealed that almost for all families of TFs the co-occurrence with an overlap between motifs of target TFs and more conserved partner motifs was significantly higher than that for less conserved partner motifs. The asymmetry toward partner TFs was the most clear for partner motifs of TFs from the ETS (E26 Transformation Specific) family. (4) Conclusion: Co-occurrence with an overlap of less conserved motif of a target TF and more conserved motifs of partner TFs explained a substantial portion of ChIP-seq data lacking conserved motifs of target TFs. Among other TF families, conservative motifs of TFs from ETS family were the most prone to mediate interaction of target TFs with its weak motifs in ChIP-seq.

Asymmetric Conservation within Pairs of Co-Occurred Motifs Mediates Weak Direct Binding of Transcription Factors in ChIP-Seq Data

(1) Background: Transcription factors (TFs) are main regulators of eukaryotic gene expression. The cooperative binding to genomic DNA of at least two TFs is the widespread mechanism of transcription regulation. Cooperating TFs can be revealed through the analysis of co-occurrence of their motifs. (2) Methods: We applied the motifs co-occurrence tool (MCOT) that predicted pairs of spaced or overlapped motifs (composite elements, CEs) for a single ChIP-seq dataset. We improved MCOT capability for the prediction of asymmetric CEs with one of the participating motifs possessing higher conservation than another does. (3) Results: Analysis of 119 ChIP-seq datasets for 45 human TFs revealed that almost for all families of TFs the co-occurrence with an overlap between motifs of target TFs and more conserved partner motifs was significantly higher than that for less conserved partner motifs. The asymmetry toward partner TFs was the most clear for partner motifs of TFs from the ETS (E26 Transformation Specific) family. (4) Conclusion: Co-occurrence with an overlap of less conserved motif of a target TF and more conserved motifs of partner TFs explained a substantial portion of ChIP-seq data lacking conserved motifs of target TFs. Among other TF families, conservative motifs of TFs from ETS family were the most prone to mediate interaction of target TFs with its weak motifs in ChIP-seq.

How affinity of the ELT-2 GATA factor binding to cis-acting regulatory sites controls Caenorhabditis elegans intestinal gene transcription

We define a quantitative relationship between the affinity with which the intestine-specific GATA factor ELT-2 binds to cis-acting regulatory motifs and the resulting transcription of asp-1, a target gene representative of genes involved in Caenorhabditis elegans intestine differentiation. By establishing an experimental system that allows unknown parameters (e.g. the influence of chromatin) to effectively cancel out, we show that levels of asp-1 transcripts increase monotonically with increasing binding affinity of ELT-2 to variant promoter TGATAA sites. The shape of the response curve reveals that the product of the unbound ELT-2 concentration in vivo [i.e. (ELT-2free) or ELT-2 'activity'] and the largest ELT-XXTGATAAXX association constant (Kmax) lies between five and ten. We suggest that this (unitless) product [Kmax×(ELT-2free) or the equivalent product for any other transcription factor] provides an important quantitative descriptor of transcription-factor/regulatory-motif interaction in development, evolution and genetic disease. A more complicated model than simple binding affinity is necessary to explain the fact that ELT-2 appears to discriminate in vivo against equal-affinity binding sites that contain AGATAA instead of TGATAA.

Trait-associated noncoding variant regions affect TBX3 regulation and cardiac conduction

Genome-wide association studies have implicated common genomic variants in the gene desert upstream of TBX3 in cardiac conduction velocity. Whether these noncoding variants affect expression of TBX3 or neighboring genes and how they affect cardiac conduction is not understood. Here, we use high-throughput STARR-seq to test the entire 1.3 Mb human and mouse TBX3 locus, including two cardiac conduction-associated variant regions, for regulatory function. We identified multiple accessible and functional regulatory DNA elements that harbor variants affecting their activity. Both variant regions drove gene expression in the cardiac conduction tissue in transgenic reporter mice. Genomic deletion from the mouse genome of one of the regions caused increased cardiac expression of only Tbx3, PR interval shortening and increased QRS duration. Combined, our findings address the mechanistic link between trait-associated variants in the gene desert, TBX3 regulation and cardiac conduction.

ProSampler: an ultrafast and accurate motif finder in large ChIP-seq datasets for combinatory motif discovery

MOTIVATION

The availability of numerous ChIP-seq datasets for transcription factors (TF) has provided an unprecedented opportunity to identify all TF binding sites in genomes. However, the progress has been hindered by the lack of a highly efficient and accurate tool to find not only the target motifs, but also cooperative motifs in very big datasets.

RESULTS

We herein present an ultrafast and accurate motif-finding algorithm, ProSampler, based on a novel numeration method and Gibbs sampler. ProSampler runs orders of magnitude faster than the fastest existing tools while often more accurately identifying motifs of both the target TFs and cooperators. Thus, ProSampler can greatly facilitate the efforts to identify the entire cis-regulatory code in genomes.

AVAILABILITY AND IMPLEMENTATION

Source code and binaries are freely available for download at https://github.com/zhengchangsulab/prosampler. It was implemented in C++ and supported on Linux, macOS and MS Windows platforms.

SUPPLEMENTARY INFORMATION

Supplementary materials are available at Bioinformatics online.

Epigenetic and Transcriptional Networks Underlying Atrial Fibrillation

Genome-wide association studies have uncovered over a 100 genetic loci associated with atrial fibrillation (AF), the most common arrhythmia. Many of the top AF-associated loci harbor key cardiac transcription factors, including PITX2, TBX5, PRRX1, and ZFHX3. Moreover, the vast majority of the AF-associated variants lie within noncoding regions of the genome where causal variants affect gene expression by altering the activity of transcription factors and the epigenetic state of chromatin. In this review, we discuss a transcriptional regulatory network model for AF defined by effector genes in Genome-wide association studies loci. We describe the current state of the field regarding the identification and function of AF-relevant gene regulatory networks, including variant regulatory elements, dose-sensitive transcription factor functionality, target genes, and epigenetic states. We illustrate how altered transcriptional networks may impact cardiomyocyte function and ionic currents that impact AF risk. Last, we identify the need for improved tools to identify and functionally test transcriptional components to define the links between genetic variation, epigenetic gene regulation, and atrial function.

A Comprehensive Drosophila melanogaster Transcription Factor Interactome

Combinatorial interactions among transcription factors (TFs) play essential roles in generating gene expression specificity and diversity in metazoans. Using yeast 2-hybrid (Y2H) assays on nearly all sequence-specific Drosophila TFs, we identified 1,983 protein-protein interactions (PPIs), more than doubling the number of currently known PPIs among Drosophila TFs. For quality assessment, we validated a subset of our interactions using MITOMI and bimolecular fluorescence complementation assays. We combined our interactome with prior PPI data to generate an integrated Drosophila TF-TF binary interaction network. Our analysis of ChIP-seq data, integrating PPI and gene expression information, uncovered different modes by which interacting TFs are recruited to DNA. We further demonstrate the utility of our Drosophila interactome in shedding light on human TF-TF interactions. This study reveals how TFs interact to bind regulatory elements in vivo and serves as a resource of Drosophila TF-TF binary PPIs for understanding tissue-specific gene regulation.

Combinatorial regulation by transcription factors (TFs) is one mechanism for achieving condition and tissue-specific gene regulation. Shokri et al. mapped TF-TF interactions between most Drosophila TFs, reporting a comprehensive TF-TF network integrated with previously known interactions. They used this network to discern distinct TF-DNA binding modes.

Large-scale genome-wide association study in a Japanese population identifies novel susceptibility loci across different diseases

The overwhelming majority of participants in current genetic studies are of European ancestry. To elucidate disease biology in the East Asian population, we conducted a genome-wide association study (GWAS) with 212,453 Japanese individuals across 42 diseases. We detected 320 independent signals in 276 loci for 27 diseases, with 25 novel loci (P < 9.58 × 10^-9). East Asian-specific missense variants were identified as candidate causal variants for three novel loci, and we successfully replicated two of them by analyzing independent Japanese cohorts; p.R220W of ATG16L2 (associated with coronary artery disease) and p.V326A of POT1 (associated with lung cancer). We further investigated enrichment of heritability within 2,868 annotations of genome-wide transcription factor occupancy, and identified 378 significant enrichments across nine diseases (false discovery rate < 0.05) (for example, NKX3-1 for prostate cancer). This large-scale GWAS in a Japanese population provides insights into the etiology of complex diseases and highlights the importance of performing GWAS in non-European populations.

A parallelized, automated platform enabling individual or sequential ChIP of histone marks and transcription factors

Despite its popularity, chromatin immunoprecipitation followed by sequencing (ChIP-seq) remains a tedious (>2 d), manually intensive, low-sensitivity and low-throughput approach. Here, we combine principles of microengineering, surface chemistry, and molecular biology to address the major limitations of standard ChIP-seq. The resulting technology, FloChIP, automates and miniaturizes ChIP in a beadless fashion while facilitating the downstream library preparation process through on-chip chromatin tagmentation. FloChIP is fast (<2 h), has a wide dynamic range (from 10⁶ to 500 cells), is scalable and parallelized, and supports antibody- or sample-multiplexed ChIP on both histone marks and transcription factors. In addition, FloChIP's interconnected design allows for straightforward chromatin reimmunoprecipitation, which allows this technology to also act as a microfluidic sequential ChIP-seq system. Finally, we ran FloChIP for the transcription factor MEF2A in 32 distinct human lymphoblastoid cell lines, providing insights into the main factors driving collaborative DNA binding of MEF2A and into its role in B cell-specific gene regulation. Together, our results validate FloChIP as a flexible and reproducible automated solution for individual or sequential ChIP-seq.