Quantifying the Impact of Non-coding Variants on Transcription Factor-DNA Binding

Functional insights into PTGS2 rs689466 polymorphism associated to asthma in West Bengal, India

BACKGROUND

Asthma is characterized by bronchoconstriction and airway hyperresponsiveness. Interplay of environmental and genetic factors led to significant rise in asthma prevalence in India during past decades.

OBJECTIVE

This study investigated functional insights of prostaglandin-endoperoxide synthase 2 (PTGS2) rs689466 polymorphism among 155 pollen-induced patients and 155 controls in West Bengal population, India.

METHODS

Genotyping was performed using Polymerase chain reaction-Restriction fragment length polymorphism. Transcription Factors (TFs) for promoter region corresponding to the polymorphism location were searched by SNP2TFBS. DNA structures were docked with TFs in HDOCK. Protein-DNA interface was analysed by DNAproDB. Relative abundance of PTGS2 mRNA in controls and different polymorphic genotypes was obtained using Real time PCR. Protein expression was analyzed by western blot.

RESULTS

Genotype and allele frequencies differed significantly between study groups (P = 0.01 and 0.03 respectively). Frequency of GG and AG genotype was significantly higher in patients (P = 0.02 and 0.04 respectively). Significant differences in FEV1/FVC were obtained in different polymorphic genotypes of patients (P < 0.0001) whereas no difference was found in controls (P = 0.08). It was predicted PTGS2 expression decreased due to altered interactions between DNA and TFs in promoter region harbouring the polymorphism. PTGS2 mRNA expression was upregulated in patients bearing AA genotype whereas downregulated in patients with AG and GG genotypes. Protein expression was reduced in AG and GG carrying patients compared to patients bearing AA genotype.

CONCLUSION

This study is the first one to report association of PTGS2 rs689466 polymorphism in Indian population. This will help in gene-based therapy for improved asthma management in future.

A statistical approach for identifying single nucleotide variants that affect transcription factor binding

Non-coding variants located within regulatory elements may alter gene expression by modifying transcription factor (TF) binding sites, thereby leading to functional consequences. Different TF models are being used to assess the effect of DNA sequence variants, such as single nucleotide variants (SNVs). Often existing methods are slow and do not assess statistical significance of results. We investigated the distribution of absolute maximal differential TF binding scores for general computational models that affect TF binding. We find that a modified Laplace distribution can adequately approximate the empirical distributions. A benchmark on in vitro and in vivo datasets showed that our approach improves upon an existing method in terms of performance and speed. Applications on eQTLs and on a genome-wide association study illustrate the usefulness of our statistics by highlighting cell type-specific regulators and target genes. An implementation of our approach is freely available on GitHub and as bioconda package.

Regulatory landscape enrichment analysis (RLEA): a computational toolkit for non-coding variant enrichment and cell type prioritization

BACKGROUND

As genomic studies continue to implicate non-coding sequences in disease, testing the roles of these variants requires insights into the cell type(s) in which they are likely to be mediating their effects. Prior methods for associating non-coding variants with cell types have involved approaches using linkage disequilibrium or ontological associations, incurring significant processing requirements. GaiaAssociation is a freely available, open-source software that enables thousands of genomic loci implicated in a phenotype to be tested for enrichment at regulatory loci of multiple cell types in minutes, permitting insights into the cell type(s) mediating the studied phenotype.

RESULTS

In this work, we present Regulatory Landscape Enrichment Analysis (RLEA) by GaiaAssociation and demonstrate its capability to test the enrichment of 12,133 variants across the cis-regulatory regions of 44 cell types. This analysis was completed in 134.0 ± 2.3 s, highlighting the efficient processing provided by GaiaAssociation. The intuitive interface requires only four inputs, offers a collection of customizable functions, and visualizes variant enrichment in cell-type regulatory regions through a heatmap matrix. GaiaAssociation is available on PyPi for download as a command line tool or Python package and the source code can also be installed from GitHub at https://github.com/GreallyLab/gaiaAssociation .

CONCLUSIONS

GaiaAssociation is a novel package that provides an intuitive and efficient resource to understand the enrichment of non-coding variants across the cis-regulatory regions of different cells, empowering studies seeking to identify disease-mediating cell types.

Evolutionary dynamics and adaptive benefits of deleterious mutations in crop gene pools

Mutations with deleterious consequences in nature may be conditionally deleterious in crop plants. That is, while some genetic variants may reduce fitness under wild conditions and be subject to purifying selection, they can be under positive selection in domesticates. Such deleterious alleles can be plant breeding targets, particularly for complex traits. The difficulty of distinguishing favorable from unfavorable variants reduces the power of selection, while favorable trait variation and heterosis may be attributable to deleterious alleles. Here, we review the roles of deleterious mutations in crop breeding and discuss how they can be used as a new avenue for crop improvement with emerging genomic tools, including HapMaps and pangenome analysis, aiding the identification, removal, or exploitation of deleterious mutations.

Regulatory de novo mutations underlying intellectual disability

The genetic aetiology of a major fraction of patients with intellectual disability (ID) remains unknown. De novo mutations (DNMs) in protein-coding genes explain up to 40% of cases, but the potential role of regulatory DNMs is still poorly understood. We sequenced 63 whole genomes from 21 ID probands and their unaffected parents. In addition, we analysed 30 previously sequenced genomes from exome-negative ID probands. We found that regulatory DNMs were selectively enriched in fetal brain-specific enhancers as compared with adult brain enhancers. DNM-containing enhancers were associated with genes that show preferential expression in the prefrontal cortex. Furthermore, we identified recurrently mutated enhancer clusters that regulate genes involved in nervous system development (CSMD1, OLFM1, and POU3F3). Most of the DNMs from ID probands showed allele-specific enhancer activity when tested using luciferase assay. Using CRISPR-mediated mutation and editing of epigenomic marks, we show that DNMs at regulatory elements affect the expression of putative target genes. Our results, therefore, provide new evidence to indicate that DNMs in fetal brain-specific enhancers play an essential role in the aetiology of ID.

PSnpBind-ML: predicting the effect of binding site mutations on protein-ligand binding affinity

Protein mutations, especially those which occur in the binding site, play an important role in inter-individual drug response and may alter binding affinity and thus impact the drug's efficacy and side effects. Unfortunately, large-scale experimental screening of ligand-binding against protein variants is still time-consuming and expensive. Alternatively, in silico approaches can play a role in guiding those experiments. Methods ranging from computationally cheaper machine learning (ML) to the more expensive molecular dynamics have been applied to accurately predict the mutation effects. However, these effects have been mostly studied on limited and small datasets, while ideally a large dataset of binding affinity changes due to binding site mutations is needed. In this work, we used the PSnpBind database with six hundred thousand docking experiments to train a machine learning model predicting protein-ligand binding affinity for both wild-type proteins and their variants with a single-point mutation in the binding site. A numerical representation of the protein, binding site, mutation, and ligand information was encoded using 256 features, half of them were manually selected based on domain knowledge. A machine learning approach composed of two regression models is proposed, the first predicting wild-type protein-ligand binding affinity while the second predicting the mutated protein-ligand binding affinity. The best performing models reported an RMSE value within 0.5 [Formula: see text] 0.6 kcal/mol^-1 on an independent test set with an R² value of 0.87 [Formula: see text] 0.90. We report an improvement in the prediction performance compared to several reported models developed for protein-ligand binding affinity prediction. The obtained models can be used as a complementary method in early-stage drug discovery. They can be applied to rapidly obtain a better overview of the ligand binding affinity changes across protein variants carried by people in the population and narrow down the search space where more time-demanding methods can be used to identify potential leads that achieve a better affinity for all protein variants.

Transcription Factor-Centric Approach to Identify Non-Recurring Putative Regulatory Drivers in Cancer

Recent efforts to sequence the genomes of thousands of matched normal-tumor samples have led to the identification of millions of somatic mutations, the majority of which are non-coding. Most of these mutations are believed to be passengers, but a small number of non-coding mutations could contribute to tumor initiation or progression, e.g. by leading to dysregulation of gene expression. Efforts to identify putative regulatory drivers rely primarily on information about the recurrence of mutations across tumor samples. However, in regulatory regions of the genome, individual mutations are rarely seen in more than one donor. Instead of using recurrence information, here we present a method to identify putative regulatory driver mutations based on the magnitude of their effects on transcription factor-DNA binding. For each gene, we integrate the effects of mutations across all its regulatory regions, and we ask whether these effects are larger than expected by chance, given the mutation spectra observed in regulatory DNA in the cohort of interest. We applied our approach to analyze mutations in a liver cancer data set with ample somatic mutation and gene expression data available. By combining the effects of mutations across all regulatory regions of each gene, we identified dozens of genes whose regulation in tumor cells is likely to be significantly perturbed by non-coding mutations. Overall, our results show that focusing on the functional effects of non-coding mutations, rather than their recurrence, has the potential to identify putative regulatory drivers and the genes they dysregulate in tumor cells.

Genome-wide analysis of somatic noncoding mutation patterns in cancer

We established a genome-wide compendium of somatic mutation events in 3949 whole cancer genomes representing 19 tumor types. Protein-coding events captured well-established drivers. Noncoding events near tissue-specific genes, such as ALB in the liver or KLK3 in the prostate, characterized localized passenger mutation patterns and may reflect tumor-cell-of-origin imprinting. Noncoding events in regulatory promoter and enhancer regions frequently involved cancer-relevant genes such as BCL6, FGFR2, RAD51B, SMC6, TERT, and XBP1 and represent possible drivers. Unlike most noncoding regulatory events, XBP1 mutations primarily accumulated outside the gene's promoter, and we validated their effect on gene expression using CRISPR-interference screening and luciferase reporter assays. Broadly, our study provides a blueprint for capturing mutation events across the entire genome to guide advances in biological discovery, therapies, and diagnostics.

Coding and noncoding variants in EBF3 are involved in HADDS and simplex autism

BACKGROUND

Previous research in autism and other neurodevelopmental disorders (NDDs) has indicated an important contribution of protein-coding (coding) de novo variants (DNVs) within specific genes. The role of de novo noncoding variation has been observable as a general increase in genetic burden but has yet to be resolved to individual functional elements. In this study, we assessed whole-genome sequencing data in 2671 families with autism (discovery cohort of 516 families, replication cohort of 2155 families). We focused on DNVs in enhancers with characterized in vivo activity in the brain and identified an excess of DNVs in an enhancer named hs737.

RESULTS

We adapted the fitDNM statistical model to work in noncoding regions and tested enhancers for excess of DNVs in families with autism. We found only one enhancer (hs737) with nominal significance in the discovery (p = 0.0172), replication (p = 2.5 × 10-3), and combined dataset (p = 1.1 × 10-4). Each individual with a DNV in hs737 had shared phenotypes including being male, intact cognitive function, and hypotonia or motor delay. Our in vitro assessment of the DNVs showed they all reduce enhancer activity in a neuronal cell line. By epigenomic analyses, we found that hs737 is brain-specific and targets the transcription factor gene EBF3 in human fetal brain. EBF3 is genome-wide significant for coding DNVs in NDDs (missense p = 8.12 × 10-35, loss-of-function p = 2.26 × 10-13) and is widely expressed in the body. Through characterization of promoters bound by EBF3 in neuronal cells, we saw enrichment for binding to NDD genes (p = 7.43 × 10-6, OR = 1.87) involved in gene regulation. Individuals with coding DNVs have greater phenotypic severity (hypotonia, ataxia, and delayed development syndrome [HADDS]) in comparison to individuals with noncoding DNVs that have autism and hypotonia.

CONCLUSIONS

In this study, we identify DNVs in the hs737 enhancer in individuals with autism. Through multiple approaches, we find hs737 targets the gene EBF3 that is genome-wide significant in NDDs. By assessment of noncoding variation and the genes they affect, we are beginning to understand their impact on gene regulatory networks in NDDs.

Competition for DNA binding between paralogous transcription factors determines their genomic occupancy and regulatory functions

Most eukaryotic transcription factors (TFs) are part of large protein families, with members of the same family (i.e., paralogous TFs) recognizing similar DNA-binding motifs but performing different regulatory functions. Many TF paralogs are coexpressed in the cell and thus can compete for target sites across the genome. However, this competition is rarely taken into account when studying the in vivo binding patterns of eukaryotic TFs. Here, we show that direct competition for DNA binding between TF paralogs is a major determinant of their genomic binding patterns. Using yeast proteins Cbf1 and Pho4 as our model system, we designed a high-throughput quantitative assay to capture the genomic binding profiles of competing TFs in a cell-free system. Our data show that Cbf1 and Pho4 greatly influence each other's occupancy by competing for their common putative genomic binding sites. The competition is different at different genomic sites, as dictated by the TFs' expression levels and their divergence in DNA-binding specificity and affinity. Analyses of ChIP-seq data show that the biophysical rules that dictate the competitive TF binding patterns in vitro are also followed in vivo, in the complex cellular environment. Furthermore, the Cbf1-Pho4 competition for genomic sites, as characterized in vitro using our new assay, plays a critical role in the specific activation of their target genes in the cell. Overall, our study highlights the importance of direct TF-TF competition for genomic binding and gene regulation by TF paralogs, and proposes an approach for studying this competition in a quantitative and high-throughput manner.

The Genome in a Three-Dimensional Context: Deciphering the Contribution of Noncoding Mutations at Enhancers to Blood Cancer

Associations between blood cancer and genetic predisposition, including both inherited variants and acquired mutations and epimutations, have been well characterized. However, the majority of these variants affect noncoding regions, making their mechanisms difficult to hypothesize and hindering the translation of these insights into patient benefits. Fueled by unprecedented progress in next-generation sequencing and computational integrative analysis, studies have started applying combinations of epigenetic, genome architecture, and functional assays to bridge the gap between noncoding variants and blood cancer. These complementary tools have not only allowed us to understand the potential malignant role of these variants but also to differentiate key variants, cell-types, and conditions from misleading ones. Here, we briefly review recent studies that have provided fundamental insights into our understanding of how noncoding mutations at enhancers predispose and promote blood malignancies in the context of spatial genome architecture.

Computational Approaches for Unraveling the Effects of Variation in the Human Genome and Microbiome

The past two decades of analytical efforts have highlighted how much more remains to be learned about the human genome and, particularly, its complex involvement in promoting disease development and progression. While numerous computational tools exist for the assessment of the functional and pathogenic effects of genome variants, their precision is far from satisfactory, particularly for clinical use. Accumulating evidence also suggests that the human microbiome's interaction with the human genome plays a critical role in determining health and disease states. While numerous microbial taxonomic groups and molecular functions of the human microbiome have been associated with disease, the reproducibility of these findings is lacking. The human microbiome–genome interaction in healthy individuals is even less well understood. This review summarizes the available computational methods built to analyze the effect of variation in the human genome and microbiome. We address the applicability and precision of these methods across their possible uses. We also briefly discuss the exciting, necessary, and now possible integration of the two types of data to improve the understanding of pathogenicity mechanisms.

QBiC-Pred: quantitative predictions of transcription factor binding changes due to sequence variants

Non-coding genetic variants/mutations can play functional roles in the cell by disrupting regulatory interactions between transcription factors (TFs) and their genomic target sites. For most human TFs, a myriad of DNA-binding models are available and could be used to predict the effects of DNA mutations on TF binding. However, information on the quality of these models is scarce, making it hard to evaluate the statistical significance of predicted binding changes. Here, we present QBiC-Pred, a web server for predicting quantitative TF binding changes due to nucleotide variants. QBiC-Pred uses regression models of TF binding specificity trained on high-throughput in vitro data. The training is done using ordinary least squares (OLS), and we leverage distributional results associated with OLS estimation to compute, for each predicted change in TF binding, a P-value reflecting our confidence in the predicted effect. We show that OLS models are accurate in predicting the effects of mutations on TF binding in vitro and in vivo, outperforming widely-used PWM models as well as recently developed deep learning models of specificity. QBiC-Pred takes as input mutation datasets in several formats, and it allows post-processing of the results through a user-friendly web interface. QBiC-Pred is freely available at http://qbic.genome.duke.edu.

Multiple selective sweeps of ancient polymorphisms in and around LTα located in the MHC class III region on chromosome 6

BACKGROUND

Lymphotoxin-α (LTα), located in the Major Histocompatibility Complex (MHC) class III region on chromosome 6, encodes a cytotoxic protein that mediates a variety of antiviral responses among other biological functions. Furthermore, several genotypes at this gene have been implicated in the onset of a number of complex diseases, including myocardial infarction, autoimmunity, and various types of cancer. However, little is known about levels of nucleotide variation and linkage disequilibrium (LD) in and near LTα, which could also influence phenotypic variance. To address this gap in knowledge, we examined sequence variation across ~ 10 kilobases (kbs), encompassing LTα and the upstream region, in 2039 individuals from the 1000 Genomes Project originating from 21 global populations.

RESULTS

Here, we observed striking patterns of diversity, including an excess of intermediate-frequency alleles, the maintenance of multiple common haplotypes and a deep coalescence time for variation (dating > 1.0 million years ago), in global populations. While these results are generally consistent with a model of balancing selection, we also uncovered a signature of positive selection in the form of long-range LD on chromosomes with derived alleles primarily in Eurasian populations. To reconcile these findings, which appear to support different models of selection, we argue that selective sweeps (particularly, soft sweeps) of multiple derived alleles in and/or near LTα occurred in non-Africans after their ancestors left Africa. Furthermore, these targets of selection were predicted to alter transcription factor binding site affinity and protein stability, suggesting they play a role in gene function. Additionally, our data also showed that a subset of these functional adaptive variants are present in archaic hominin genomes.

CONCLUSIONS

Overall, this study identified candidate functional alleles in a biologically-relevant genomic region, and offers new insights into the evolutionary origins of these loci in modern human populations.

Genomic structural variations lead to dysregulation of important coding and non-coding RNA species in dilated cardiomyopathy

The transcriptome needs to be tightly regulated by mechanisms that include transcription factors, enhancers, and repressors as well as non‐coding RNAs. Besides this dynamic regulation, a large part of phenotypic variability of eukaryotes is expressed through changes in gene transcription caused by genetic variation. In this study, we evaluate genome‐wide structural genomic variants (SVs) and their association with gene expression in the human heart. We detected 3,898 individual SVs affecting all classes of gene transcripts (e.g., mRNA, miRNA, lncRNA) and regulatory genomic regions (e.g., enhancer or TFBS). In a cohort of patients (n = 50) with dilated cardiomyopathy (DCM), 80,635 non‐protein‐coding elements of the genome are deleted or duplicated by SVs, containing 3,758 long non‐coding RNAs and 1,756 protein‐coding transcripts. 65.3% of the SV‐eQTLs do not harbor a significant SNV‐eQTL, and for the regions with both classes of association, we find similar effect sizes. In case of deleted protein‐coding exons, we find downregulation of the associated transcripts, duplication events, however, do not show significant changes over all events. In summary, we are first to describe the genomic variability associated with SVs in heart failure due to DCM and dissect their impact on the transcriptome. Overall, SVs explain up to 7.5% of the variation of cardiac gene expression, underlining the importance to study human myocardial gene expression in the context of the individual genome. This has immediate implications for studies on basic mechanisms of cardiac maladaptation, biomarkers, and (gene) therapeutic studies alike.