Understanding variation in transcription factor binding by modeling transcription factor genome-epigenome interactions

Ten Years of EWAS

Epigenome-wide association study (EWAS) has been applied to analyze DNA methylation variation in complex diseases for a decade, and epigenome as a research target has gradually become a hot topic of current studies. The DNA methylation microarrays, next-generation, and third-generation sequencing technologies have prepared a high-quality platform for EWAS. Here, the progress of EWAS research is reviewed, its contributions to clinical applications, and mainly describe the achievements of four typical diseases. Finally, the challenges encountered by EWAS and make bold predictions for its future development are presented.

Stochastic epigenetic dynamics of gene switching

Epigenetic modifications of histones crucially affect eukaryotic gene activity, while the epigenetic histone state is largely determined by the binding of specific factors such as the transcription factors (TFs) to DNA. Here, the way in which the TFs and the histone state are dynamically correlated is not obvious when the TF synthesis is regulated by the histone state. This type of feedback regulatory relation is ubiquitous in gene networks to determine cell fate in differentiation and other cell transformations. To gain insights into such dynamical feedback regulations, we theoretically analyze a model of epigenetic gene switching by extending the Doi-Peliti operator formalism of reaction kinetics to the problem of coupled molecular processes. Spin-1 and spin-1/2 coherent-state representations are introduced to describe stochastic reactions of histones and binding or unbinding of TFs in a unified way, which provides a concise view of the effects of timescale difference among these molecular processes; even in the case that binding or unbinding of TFs to or from DNA is adiabatically fast, the slow nonadiabatic histone dynamics gives rise to a distinct circular flow of the probability flux around basins in the landscape of the gene state distribution, which leads to hysteresis in gene switching. In contrast to the general belief that the change in the amount of TF precedes the histone state change, flux drives histones to be modified prior to the change in the amount of TF in self-regulating circuits. Flux-landscape analyses shed light on the nonlinear nonadiabatic mechanism of epigenetic cell fate decision making.

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

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

Sequence-based 5-mers highly correlated to epigenetic modifications in genes interactions

One of the main concerns in biology is extracting sophisticated features from DNA sequence for gene interaction determination, receiving a great deal of researchers' attention. The epigenetic modifications along with their patterns have been intensely recognized as dominant features affecting on gene expression. However, studying sequenced-based features highly correlated to this key element has remained limited. The main objective in this research was to propose a new feature highly correlated to epigenetic modifications capable of classification of genes. In this paper, classification of 34 genes in PPAR signaling pathway associated with muscle fat tissue in human was performed. Using different statistical outlier detection methods, we proposed that 5-mers highly correlated to epigenetic modifications can correctly categorize the genes involved in the same biological pathway or process. Thirty-four genes in PPAR signaling pathway were classified via applying a proposed feature, 5-mers strongly associated to 17 different epigenetic modifications. For this, diverse statistical outlier detection methods were applied to specify the group of thoroughly correlated genes. The results indicated that these 5-mers can appropriately identify correlated genes. In addition, our results corresponded to GeneMania interaction information, leading to support the suggested method. The appealing findings imply that not only epigenetic modifications but also their highly correlated 5-mers can be applied for reconstructing gene regulatory networks as supplementary data as well as other applications like physical interaction, genes prioritization, indicating some sort of data fusion in this analysis.

TALE factors use two distinct functional modes to control an essential zebrafish gene expression program

TALE factors are broadly expressed embryonically and known to function in complexes with transcription factors (TFs) like Hox proteins at gastrula/segmentation stages, but it is unclear if such generally expressed factors act by the same mechanism throughout embryogenesis. We identify a TALE-dependent gene regulatory network (GRN) required for anterior development and detect TALE occupancy associated with this GRN throughout embryogenesis. At blastula stages, we uncover a novel functional mode for TALE factors, where they occupy genomic DECA motifs with nearby NF-Y sites. We demonstrate that TALE and NF-Y form complexes and regulate chromatin state at genes of this GRN. At segmentation stages, GRN-associated TALE occupancy expands to include HEXA motifs near PBX:HOX sites. Hence, TALE factors control a key GRN, but utilize distinct DNA motifs and protein partners at different stages – a strategy that may also explain their oncogenic potential and may be employed by other broadly expressed TFs.

An efficient method to transcription factor binding sites imputation via simultaneous completion of multiple matrices with positional consistency

Transcription factors (TFs) are DNA-binding proteins that have a central role in regulating gene expression. Identification of DNA-binding sites of TFs is a key task in understanding transcriptional regulation, cellular processes and disease. Chromatin immunoprecipitation followed by high-throughput sequencing (ChIP-seq) enables genome-wide identification of in vivo TF binding sites. However, it is still difficult to map every TF in every cell line owing to cost and biological material availability, which poses an enormous obstacle for integrated analysis of gene regulation. To address this problem, we propose a novel computational approach, TFBSImpute, for predicting additional TF binding profiles by leveraging information from available ChIP-seq TF binding data. TFBSImpute fuses the dataset to a 3-mode tensor and imputes missing TF binding signals via simultaneous completion of multiple TF binding matrices with positional consistency. We show that signals predicted by our method achieve overall similarity with experimental data and that TFBSImpute significantly outperforms baseline approaches, by assessing the performance of imputation methods against observed ChIP-seq TF binding profiles. Besides, motif analysis shows that TFBSImpute preforms better in capturing binding motifs enriched in observed data compared with baselines, indicating that the higher performance of TFBSImpute is not simply due to averaging related samples. We anticipate that our approach will constitute a useful complement to experimental mapping of TF binding, which is beneficial for further study of regulation mechanisms and disease.

Candidate SNP markers of reproductive potential are predicted by a significant change in the affinity of TATA-binding protein for human gene promoters

BACKGROUND

The progress of medicine, science, technology, education, and culture improves, year by year, quality of life and life expectancy of the populace. The modern human has a chance to further improve the quality and duration of his/her life and the lives of his/her loved ones by bringing their lifestyle in line with their sequenced individual genomes. With this in mind, one of genome-based developments at the junction of personalized medicine and bioinformatics will be considered in this work, where we used two Web services: (i) SNP_TATA_Comparator to search for alleles with a single nucleotide polymorphism (SNP) that alters the affinity of TATA-binding protein (TBP) for the TATA boxes of human gene promoters and (ii) PubMed to look for retrospective clinical reviews on changes in physiological indicators of reproductive potential in carriers of these alleles.

RESULTS

A total of 126 SNP markers of female reproductive potential, capable of altering the affinity of TBP for gene promoters, were found using the two above-mentioned Web services. For example, 10 candidate SNP markers of thrombosis (e.g., rs563763767) can cause overproduction of coagulation inducers. In pregnant women, Hughes syndrome provokes thrombosis with a fatal outcome although this syndrome can be diagnosed and eliminated even at the earliest stages of its development. Thus, in women carrying any of the above SNPs, preventive treatment of this syndrome before a planned pregnancy can reduce the risk of death. Similarly, seven SNP markers predicted here (e.g., rs774688955) can elevate the risk of myocardial infarction. In line with Bowles' lifespan theory, women carrying any of these SNPs may modify their lifestyle to improve their longevity if they can take under advisement that risks of myocardial infarction increase with age of the mother, total number of pregnancies, in multiple pregnancies, pregnancies under the age of 20, hypertension, preeclampsia, menstrual cycle irregularity, and in women smokers.

CONCLUSIONS

According to Bowles' lifespan theory-which links reproductive potential, quality of life, and life expectancy-the above information was compiled for those who would like to reduce risks of diseases corresponding to alleles in own sequenced genomes. Candidate SNP markers can focus the clinical analysis of unannotated SNPs, after which they may become useful for people who would like to bring their lifestyle in line with their sequenced individual genomes.

Candidate SNP markers of aggressiveness-related complications and comorbidities of genetic diseases are predicted by a significant change in the affinity of TATA-binding protein for human gene promoters

BACKGROUND

Aggressiveness in humans is a hereditary behavioral trait that mobilizes all systems of the body-first of all, the nervous and endocrine systems, and then the respiratory, vascular, muscular, and others-e.g., for the defense of oneself, children, family, shelter, territory, and other possessions as well as personal interests. The level of aggressiveness of a person determines many other characteristics of quality of life and lifespan, acting as a stress factor. Aggressive behavior depends on many parameters such as age, gender, diseases and treatment, diet, and environmental conditions. Among them, genetic factors are believed to be the main parameters that are well-studied at the factual level, but in actuality, genome-wide studies of aggressive behavior appeared relatively recently. One of the biggest projects of the modern science-1000 Genomes-involves identification of single nucleotide polymorphisms (SNPs), i.e., differences of individual genomes from the reference genome. SNPs can be associated with hereditary diseases, their complications, comorbidities, and responses to stress or a drug. Clinical comparisons between cohorts of patients and healthy volunteers (as a control) allow for identifying SNPs whose allele frequencies significantly separate them from one another as markers of the above conditions. Computer-based preliminary analysis of millions of SNPs detected by the 1000 Genomes project can accelerate clinical search for SNP markers due to preliminary whole-genome search for the most meaningful candidate SNP markers and discarding of neutral and poorly substantiated SNPs.

RESULTS

Here, we combine two computer-based search methods for SNPs (that alter gene expression) {i} Web service SNP_TATA_Comparator (DNA sequence analysis) and {ii} PubMed-based manual search for articles on aggressiveness using heuristic keywords. Near the known binding sites for TATA-binding protein (TBP) in human gene promoters, we found aggressiveness-related candidate SNP markers, including rs1143627 (associated with higher aggressiveness in patients undergoing cytokine immunotherapy), rs544850971 (higher aggressiveness in old women taking lipid-lowering medication), and rs10895068 (childhood aggressiveness-related obesity in adolescence with cardiovascular complications in adulthood).

CONCLUSIONS

After validation of these candidate markers by clinical protocols, these SNPs may become useful for physicians (may help to improve treatment of patients) and for the general population (a lifestyle choice preventing aggressiveness-related complications).

Candidate SNP Markers of Chronopathologies Are Predicted by a Significant Change in the Affinity of TATA-Binding Protein for Human Gene Promoters

Variations in human genome (e.g., single nucleotide polymorphisms, SNPs) may be associated with hereditary diseases, their complications, comorbidities, and drug responses. Using Web service SNP_TATA_Comparator presented in our previous paper, here we analyzed immediate surroundings of known SNP markers of diseases and identified several candidate SNP markers that can significantly change the affinity of TATA-binding protein for human gene promoters, with circadian consequences. For example, rs572527200 may be related to asthma, where symptoms are circadian (worse at night), and rs367732974 may be associated with heart attacks that are characterized by a circadian preference (early morning). By the same method, we analyzed the 90 bp proximal promoter region of each protein-coding transcript of each human gene of the circadian clock core. This analysis yielded 53 candidate SNP markers, such as rs181985043 (susceptibility to acute Q fever in male patients), rs192518038 (higher risk of a heart attack in patients with diabetes), and rs374778785 (emphysema and lung cancer in smokers). If they are properly validated according to clinical standards, these candidate SNP markers may turn out to be useful for physicians (to select optimal treatment for each patient) and for the general population (to choose a lifestyle preventing possible circadian complications of diseases).

Candidate SNP Markers of Gender-Biased Autoimmune Complications of Monogenic Diseases Are Predicted by a Significant Change in the Affinity of TATA-Binding Protein for Human Gene Promoters

Some variations of human genome [for example, single nucleotide polymorphisms (SNPs)] are markers of hereditary diseases and drug responses. Analysis of them can help to improve treatment. Computer-based analysis of millions of SNPs in the 1000 Genomes project makes a search for SNP markers more targeted. Here, we combined two computer-based approaches: DNA sequence analysis and keyword search in databases. In the binding sites for TATA-binding protein (TBP) in human gene promoters, we found candidate SNP markers of gender-biased autoimmune diseases, including rs1143627 [cachexia in rheumatoid arthritis (double prevalence among women)]; rs11557611 [demyelinating diseases (thrice more prevalent among young white women than among non-white individuals)]; rs17231520 and rs569033466 [both: atherosclerosis comorbid with related diseases (double prevalence among women)]; rs563763767 [Hughes syndrome-related thrombosis (lethal during pregnancy)]; rs2814778 [autoimmune diseases (excluding multiple sclerosis and rheumatoid arthritis) underlying hypergammaglobulinemia in women]; rs72661131 and rs562962093 (both: preterm delivery in pregnant diabetic women); and rs35518301, rs34166473, rs34500389, rs33981098, rs33980857, rs397509430, rs34598529, rs33931746, rs281864525, and rs63750953 (all: autoimmune diseases underlying hypergammaglobulinemia in women). Validation of these predicted candidate SNP markers using the clinical standards may advance personalized medicine.

Obesity-related known and candidate SNP markers can significantly change affinity of TATA-binding protein for human gene promoters

BACKGROUND

Obesity affects quality of life and life expectancy and is associated with cardiovascular disorders, cancer, diabetes, reproductive disorders in women, prostate diseases in men, and congenital anomalies in children. The use of single nucleotide polymorphism (SNP) markers of diseases and drug responses (i.e., significant differences of personal genomes of patients from the reference human genome) can help physicians to improve treatment. Clinical research can validate SNP markers via genotyping of patients and demonstration that SNP alleles are significantly more frequent in patients than in healthy people. The search for biomedical SNP markers of interest can be accelerated by computer-based analysis of hundreds of millions of SNPs in the 1000 Genomes project because of selection of the most meaningful candidate SNP markers and elimination of neutral SNPs.

RESULTS

We cross-validated the output of two computer-based methods: DNA sequence analysis using Web service SNP_TATA_Comparator and keyword search for articles on comorbidities of obesity. Near the sites binding to TATA-binding protein (TBP) in human gene promoters, we found 22 obesity-related candidate SNP markers, including rs10895068 (male breast cancer in obesity); rs35036378 (reduced risk of obesity after ovariectomy); rs201739205 (reduced risk of obesity-related cancers due to weight loss by diet/exercise in obese postmenopausal women); rs183433761 (obesity resistance during a high-fat diet); rs367732974 and rs549591993 (both: cardiovascular complications in obese patients with type 2 diabetes mellitus); rs200487063 and rs34104384 (both: obesity-caused hypertension); rs35518301, rs72661131, and rs562962093 (all: obesity); and rs397509430, rs33980857, rs34598529, rs33931746, rs33981098, rs34500389, rs63750953, rs281864525, rs35518301, and rs34166473 (all: chronic inflammation in comorbidities of obesity). Using an electrophoretic mobility shift assay under nonequilibrium conditions, we empirically validated the statistical significance (α < 0.00025) of the differences in TBP affinity values between the minor and ancestral alleles of 4 out of the 22 SNPs: rs200487063, rs201381696, rs34104384, and rs183433761. We also measured half-life (t1/2), Gibbs free energy change (ΔG), and the association and dissociation rate constants, ka and kd, of the TBP-DNA complex for these SNPs.

CONCLUSIONS

Validation of the 22 candidate SNP markers by proper clinical protocols appears to have a strong rationale and may advance postgenomic predictive preventive personalized medicine.

Modeling co-occupancy of transcription factors using chromatin features

Regulation of gene expression requires both transcription factor (TFs) and epigenetic modifications, and interplays between the two types of factors have been discovered. However study of relationships between chromatin features and TF–TF co-occupancy remains limited. Here, we revealed the relationship by first illustrating distinct profile patterns of chromatin features related to different binding events, including single TF binding and TF–TF co-occupancy of 71 TFs from five human cell lines. We further implemented statistical analyses to demonstrate the relationship by accurately predicting co-occupancy genome-widely using chromatin features including DNase I hypersensitivity, 11 histone modifications (HMs) and GC content. Remarkably, our results showed that the combination of chromatin features enables accurate predictions across the five cells. For individual chromatin features, DNase I enables high and consistent predictions. H3K27ac, H3K4me 2, H3K4me3 and H3K9ac are more reliable predictors than other HMs. Although the combination of 11 HMs achieves accurate predictions, their predictive ability varies considerably when a model obtained from one cell is applied to others, indicating relationship between HMs and TF–TF co-occupancy is cell type dependent. GC content is not a reliable predictor, but the addition of GC content to any other features enhances their predictive ability. Together, our results elucidate a strong relationship between TF–TF co-occupancy and chromatin features.

How to Use SNP_TATA_Comparator to Find a Significant Change in Gene Expression Caused by the Regulatory SNP of This Gene's Promoter via a Change in Affinity of the TATA-Binding Protein for This Promoter

The use of biomedical SNP markers of diseases can improve effectiveness of treatment. Genotyping of patients with subsequent searching for SNPs more frequent than in norm is the only commonly accepted method for identification of SNP markers within the framework of translational research. The bioinformatics applications aimed at millions of unannotated SNPs of the "1000 Genomes" can make this search for SNP markers more focused and less expensive. We used our Web service involving Fisher's Z-score for candidate SNP markers to find a significant change in a gene's expression. Here we analyzed the change caused by SNPs in the gene's promoter via a change in affinity of the TATA-binding protein for this promoter. We provide examples and discuss how to use this bioinformatics application in the course of practical analysis of unannotated SNPs from the "1000 Genomes" project. Using known biomedical SNP markers, we identified 17 novel candidate SNP markers nearby: rs549858786 (rheumatoid arthritis); rs72661131 (cardiovascular events in rheumatoid arthritis); rs562962093 (stroke); rs563558831 (cyclophosphamide bioactivation); rs55878706 (malaria resistance, leukopenia), rs572527200 (asthma, systemic sclerosis, and psoriasis), rs371045754 (hemophilia B), rs587745372 (cardiovascular events); rs372329931, rs200209906, rs367732974, and rs549591993 (all four: cancer); rs17231520 and rs569033466 (both: atherosclerosis); rs63750953, rs281864525, and rs34166473 (all three: malaria resistance, thalassemia).

Genome-wide DNA methylation analysis identifies a metabolic memory profile in patient-derived diabetic foot ulcer fibroblasts

Diabetic foot ulcers (DFUs) are a serious complication of diabetes. Previous exposure to hyperglycemic conditions accelerates a decline in cellular function through metabolic memory despite normalization of glycemic control. Persistent, hyperglycemia-induced epigenetic patterns are considered a central mechanism that activates metabolic memory; however, this has not been investigated in patient-derived fibroblasts from DFUs. We generated a cohort of patient-derived lines from DFU fibroblasts (DFUF), and site- and age-matched diabetic foot fibroblasts (DFF) and non-diabetic foot fibroblasts (NFF) to investigate global and genome-wide DNA methylation patterns using liquid chromatography/mass spectrometry and the Illumina Infinium HumanMethylation450K array. DFFs and DFUFs demonstrated significantly lower global DNA methylation compared to NFFs (p = 0.03). Hierarchical clustering of differentially methylated probes (DMPs, p = 0.05) showed that DFFs and DFUFs cluster together and separately from NFFs. Twenty-five percent of the same probes were identified as DMPs when individually comparing DFF and DFUF to NFF. Functional annotation identified enrichment of DMPs associated with genes critical to wound repair, including angiogenesis (p = 0.07) and extracellular matrix assembly (p = 0.035). Identification of sustained DNA methylation patterns in patient-derived fibroblasts after prolonged passage in normoglycemic conditions demonstrates persistent metabolic memory. These findings suggest that epigenetic-related metabolic memory may also underlie differences in wound healing phenotypes and can potentially identify therapeutic targets.

Cis-regulatory somatic mutations and gene-expression alteration in B-cell lymphomas

BACKGROUND

With the rapid increase of whole-genome sequencing of human cancers, an important opportunity to analyze and characterize somatic mutations lying within cis-regulatory regions has emerged. A focus on protein-coding regions to identify nonsense or missense mutations disruptive to protein structure and/or function has led to important insights; however, the impact on gene expression of mutations lying within cis-regulatory regions remains under-explored. We analyzed somatic mutations from 84 matched tumor-normal whole genomes from B-cell lymphomas with accompanying gene expression measurements to elucidate the extent to which these cancers are disrupted by cis-regulatory mutations.

RESULTS

We characterize mutations overlapping a high quality set of well-annotated transcription factor binding sites (TFBSs), covering a similar portion of the genome as protein-coding exons. Our results indicate that cis-regulatory mutations overlapping predicted TFBSs are enriched in promoter regions of genes involved in apoptosis or growth/proliferation. By integrating gene expression data with mutation data, our computational approach culminates with identification of cis-regulatory mutations most likely to participate in dysregulation of the gene expression program. The impact can be measured along with protein-coding mutations to highlight key mutations disrupting gene expression and pathways in cancer.

CONCLUSIONS

Our study yields specific genes with disrupted expression triggered by genomic mutations in either the coding or the regulatory space. It implies that mutated regulatory components of the genome contribute substantially to cancer pathways. Our analyses demonstrate that identifying genomically altered cis-regulatory elements coupled with analysis of gene expression data will augment biological interpretation of mutational landscapes of cancers.

Global DNA methylation profiling technologies and the ovarian cancer methylome

Cytosine methylation in DNA constitutes an important epigenetic layer of transcriptional and regulatory control in many eukaryotes. Profiling DNA methylation across the genome is critical to understanding the influence of epigenetics in normal biology and disease, such as cancer. Genome-wide analyses such as arrays and next-generation sequencing (NGS) technologies have been used to assess large fractions of the methylome at a single-base-pair resolution. However, the range of DNA methylation profiling techniques can make selecting the appropriate protocol a challenge. This chapter discusses the advantages and disadvantages of various methylome detection approaches to assess which is appropriate for the question at hand. Here, we focus on four prominent genome-wide approaches: whole-genome bisulfite sequencing (WGBS); methyl-binding domain capture sequencing (MBDCap-Seq); reduced-representation-bisulfite-sequencing (RRBS); and Infinium Methylation450 BeadChips (450 K, Illumina). We discuss some of the requirements, merits, and challenges that should be considered when choosing a methylome technology to ensure that it will be informative. In addition, we show how genome-wide methylation detection arrays and high-throughput sequencing have provided immense insight into ovarian cancer-specific methylation signatures that may serve as diagnostic biomarkers or predict patient response to epigenetic therapy.

Comparative epigenomics: defining and utilizing epigenomic variations across species, time-course, and individuals

Epigenomic profiling, by revealing genome-wide distributions of epigenetic modifications, generated a large amount of structural information about the chromosomes. Epigenomic analysis has quickly become a big data science, posing tremendous challenges on its translation into knowledge. To meet this challenge, comparative analysis of epigenomes, dubbed comparative epigenomics, has emerged as an active research area. Here, we summarize the recent developments in comparative epigenomic analyses into three major directions, namely the comparisons across species, the time-course of a biological process, and individuals. We review the main ideas, methods, and findings in each direction, and discuss the implications to understanding the regulatory functions of the genomes.

CONFLICT OF INTEREST

The authors have declared no conflicts of interest for this article.

From genomes to societies: a holistic view of determinants of human health

Both biological and social sciences have identified contributing factors to human health. However, health outcomes are unlikely to equal a simple sum of these identified factors. This article makes an attempt to put together the information, methods, and technologies that relate to health outcomes from biological, behavioral, and social disciplines. Much of this information was obtained by controlling for the variations of the factors in 'other' disciplines. For example, genetic factors were controlled for in identifying the behavioral determinants of health. Looking forward, better understandings of health outcomes may require exploiting the interactions of health determinants that were identified from different disciplines. We propose the concept of 'systems health' studies, which take health outcomes as the outputs of a system, where the inputs and their interactions from multiple disciplines are considered.