Transcription Factor Binding Affinity Research Articles

Abstract 6146: Characterizing underlying molecular mechanisms of prostate cancer risk variants identified from multi-ethnic GWAS

Abstract Prostate cancer (PCa) is a complex disease with the second leading cause of cancer death in US males. Multi-ethnic genome-wide association studies (GWAS) identified a thousand genetic variants associated with PCa risk in different races. However, the majority of risk variants are located in non-coding regions, making it challenging to understand their contribution to PCa risk. Non-coding regions include functional regulatory elements (REs) such as promoters and enhancers, whose activity can be evaluated by histone modification H3K4me3 and H3K27ac, respectively. In this study, we integrated PCa risk variants with hundreds of H3K4me3 and H3K27ac ChIP-seq data from normal and cancer prostate cell lines and tissues, finding 122 variants in promoters and 301 variants in enhancers. Enhancers are distal to promoters, and they form loops to regulate gene expression, mediated by transcription factors (TFs). Enhancer activities are tightly associated with cell fate. Genetic variants can affect the activity of enhancers by changing the binding preference of TFs and such changes can affect chromatin interactions and dysregulate gene expression. We hypothesize that allele changes of enhancer variants contribute to PCa risk differently among individuals from different ancestries, leading to the development of heterogeneous PCa molecular subgroups, characterized by genetic alterations including TMPRSS2-ERG fusion. By integrating H3K27ac ChIP-seq data from prostate cell lines derived from European (EUR) and African (AFR) ancestries, we found 185 risk variants in enhancers in EUR cell lines and 69 risk variants in enhancers in AFR cell lines. Next, we integrated patient tissue-derived H3K27ac ChIP-seq data and performed chromatin quantitative trait loci (cQTL) and allelic imbalance (AI) analyses to determine risk variants associated with enhancer activity. Among the 185 risk enhancer variants in EUR and 69 in AFR cell lines, we found 130 and 46 with significant association, respectively. To determine TFs that change binding upon allele change of enhancer risk variants, we integrated hundreds of TF ChIP-seq data from prostate cell lines and tissues. Among the 185 risk enhancer variants in EUR and 69 in AFR cell lines, we found 142 and 44 variants bound by TFs, respectively. When we further performed cQTL and AI analyses for FOXA1, HOXB13, and AR, we found 87 and 29 variants significantly associated with TF binding affinity, respectively. Moreover, we found that these associations differ between TMPRSS2-ERG fusion positive and negative tumors. To identify the target genes of enhancer risk variants, we are currently integrating chromatin interaction data including Hi-C and Micro-C. Overall, this study provides valuable insights into the underlying molecular mechanisms of risk variants in various populations and individuals which lead to the development of heterogeneous PCa subgroups. Citation Format: Zexun Wu, Andrew Vu, Ethan S. Nelson-Moore, Huan Cao, Suhn K. Rhie. Characterizing underlying molecular mechanisms of prostate cancer risk variants identified from multi-ethnic GWAS [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 6146.

Abstract 2850: Noncoding regulatory mutations as driving event for the oncogenic core regulatory circuitries of neuroblastoma

Abstract Background and Aim: Neuroblastoma (NB) is the most aggressive solid tumor of infancy and arises from neural-crest-derived progenitor cells. It is composed by cell types reflecting developmental stages of the adrenergic lineage: mesenchymal (MES) and adrenergic (ADRN) cell types. These cell identities result from the activity of NB specific Core Regulatory Circuitries (CRCs), transcription factor (TF) sets that co-occupy regulatory regions together impacting the expression of their own genes and those of lineage-specific ones governing cell state transitions and cellular identities. Our hypothesis is that noncoding somatic single nucleotide variants (SNVs) located in NB CRCs TF binding sites (TFBSs) underlie NB CRCs activity, affecting cellular differentiation and promoting tumor onset. We aim to investigate such pattern of regulatory elements and identify putative driver SNVs, exploring their role and characterizing their action mechanism. Methods: Transcriptionally active TFBSs (aTFBSs) bound by ADRN CRC TFs (GATA3, HAND2, ISL1, TBX2, MYCN, PHOX2B, ASCL1 and LMO1) were identified integrating 27 ChIP-seq and 11 ATAC-seq experiments in 6 NB ADRN cell lines (SKNBE2C, Kelly, NGP, COGN415, LAN5 and NB1643). In particular, aTFBSs were defined as ChIP-seq peaks overlapping ATAC-seq signals in the same cell line. SNVs from 317 NB whole genomes were mapped on aTFBSs and the enrichment of mutations in core regions respect to flanking ones was tested using Fisher test. Observed mutation rate in aTFBSs was compared with background mutation rate due to local sequence context. SNVs in mutated aTFBSs were selected and their impact on TF binding affinity was evaluated with FABIAN-variant and motifbreakR tools. aTFBSs target-genes were identified through promoter capture HiC (CHiC) in ADRN NB cells (SKNBE2C and SHSY5Y). Results: We found a significant (FDR < 0.05) mutation enrichment in aTFBSs of ISL1, PHOX2B, TBX2, GATA3 and MYCN, suggesting a common somatic mutation burden for a subset of ADRN CRC TFBSs in ADRN NB cells. Starting from 839 SNVs falling in significantly mutated aTFBSs, we selected 269 mutations impacting the binding of ADRN CRC TFs. This mutations subset mapped in aTFBSs interacting with genes that significantly enriched pathways involved in neuronal development and migration (FDR < 0.05), thus suggesting their possible role in affecting cell identity definition. As an example, chr6:137753475:G>T mapped in aTFBSs interacting with the promoter of OLIG3, which takes part in neuronal differentiation. Interestingly, chr6:137753475:G>T was predicted to break the binding to ISL1 (FABIAN score: -0.24), a key regulator of ADRN CRC. Conclusions: These results demonstrate that somatic noncoding SNVs can act synergistically to promote NB onset, affecting CRCs activity and, in turn, the normal cell identity definition. Citation Format: Mario Capasso, Vincenzo Aievola, Vito Alessandro Lasorsa, Annalaura Montella, Ferdinando Bonfiglio, Marianna Avitabile, Teresa Maiorino, Matilde Tirelli, Giuseppe D'Alterio, Matthias Fischer, Frank Westermann, Achille Iolascon. Noncoding regulatory mutations as driving event for the oncogenic core regulatory circuitries of neuroblastoma [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 2850.

Placental expression quantitative trait loci in an East Asian population

Expression quantitative trait loci (eQTL) analysis measures the contribution of genetic variation in gene expression on complex traits. Although this methodology has been used to examine gene regulation in numerous human tissues, eQTL research in solid tissues is relatively lacking. We conducted eQTL analysis on placentas collected from an East Asian population in an effort to identify gene regulatory mechanisms in this tissue. Placentas (n=102) were collected at the time of cesarean delivery. mRNA was extracted, sequenced with NGS, and compared to matched maternal and fetal DNA arrays performed using maternal and neonatal cord blood. Linear regression modeling was performed using tensorQTL. Fine-mapping along with epigenomic annotation was used to select putative functional variants. We identified 2,703 coding genes that contained at least one eQTL with statistical significance (false discovery rate <0.05). After fine-mapping, we found 108 previously unreported eQTL variants with posterior inclusion probability >0.1. Of these, 19% were located in genomic regions with evidence from public placental epigenome suggesting that they may be functionally relevant. For example, variant rs28379289 located in the placenta-specific regulatory region changes the binding affinity of transcription factor leading to higher expression of LGALS3, which is known to affect placental function. This study expands the knowledge base of regulatory elements within the human placenta and identifies 108 previously unreported placenta eQTL signals, which are listed in our publicly available GMI eQTL database. Further studies are needed to identify and characterize genetic regulatory mechanisms that affect placental function in normal pregnancy and placenta-related diseases.

Modeling methyl-sensitive transcription factor motifs with an expanded epigenetic alphabet

BackgroundTranscription factors bind DNA in specific sequence contexts. In addition to distinguishing one nucleobase from another, some transcription factors can distinguish between unmodified and modified bases. Current models of transcription factor binding tend not to take DNA modifications into account, while the recent few that do often have limitations. This makes a comprehensive and accurate profiling of transcription factor affinities difficult.ResultsHere, we develop methods to identify transcription factor binding sites in modified DNA. Our models expand the standard A/C/G/T DNA alphabet to include cytosine modifications. We develop Cytomod to create modified genomic sequences and we also enhance the MEME Suite, adding the capacity to handle custom alphabets. We adapt the well-established position weight matrix (PWM) model of transcription factor binding affinity to this expanded DNA alphabet. Using these methods, we identify modification-sensitive transcription factor binding motifs. We confirm established binding preferences, such as the preference of ZFP57 and C/EBPβ for methylated motifs and the preference of c-Myc for unmethylated E-box motifs.ConclusionsUsing known binding preferences to tune model parameters, we discover novel modified motifs for a wide array of transcription factors. Finally, we validate our binding preference predictions for OCT4 using cleavage under targets and release using nuclease (CUT&RUN) experiments across conventional, methylation-, and hydroxymethylation-enriched sequences. Our approach readily extends to other DNA modifications. As more genome-wide single-base resolution modification data becomes available, we expect that our method will yield insights into altered transcription factor binding affinities across many different modifications.

Ascertainment Bias in the Genomic Test of Positive Selection on Regulatory Sequences.

Evolution of gene expression mediated by cis-regulatory changes is thought to be an important contributor to organismal adaptation, but identifying adaptive cis-regulatory changes is challenging due to the difficulty in knowing the expectation under no positive selection. A new approach for detecting positive selection on transcription factor binding sites (TFBSs) was recently developed, thanks to the application of machine learning in predicting transcription factor (TF) binding affinities of DNA sequences. Given a TFBS sequence from a focal species and the corresponding inferred ancestral sequence that differs from the former at n sites, one can predict the TF-binding affinities of many n-step mutational neighbors of the ancestral sequence and obtain a null distribution of the derived binding affinity, which allows testing whether the binding affinity of the real derived sequence deviates significantly from the null distribution. Applying this test genomically to all experimentally identified binding sites of 3 TFs in humans, a recent study reported positive selection for elevated binding affinities of TFBSs. Here, we show that this genomic test suffers from an ascertainment bias because, even in the absence of positive selection for strengthened binding, the binding affinities of known human TFBSs are more likely to have increased than decreased in evolution. We demonstrate by computer simulation that this bias inflates the false positive rate of the selection test. We propose several methods to mitigate the ascertainment bias and show that almost all previously reported positive selection signals disappear when these methods are applied.

Molecular Genetic Study on HAND2 Gene Promoter in Ventricular Septal Defect.

Ventricular septal defect (VSD), the most common type of congenital heart disease (CHD), is primarily caused by cardiac dysplasia. Heart and neural crest derivatives expressed 2 (HAND2) participates in developing the right heart. The loss of HAND2 expression in humans is closely connected with ventricular septal defects. We used a case-control study to analyze the genetic variations in the HAND2 promoter region in VSD patients and controls. Some statistical analysis methods were used to analyze the association of single nucleotide polymorphisms (SNPs) with VSD. The dual-luciferase reporter assay and electrophoretic mobility shift assay (EMSA) were used to conduct functional analysis and molecular mechanism study of genetic variations. Through sequencing, we identified nine genetic variants in patients with VSD. The SNP rs2276940 G>T and rs2276941 G>A were associated with an increased risk of VSD. The dual-luciferase reporter assay showed that SNP rs2276940 G>T and rs138531627 C>G decreased the transcriptional activity of the HAND2 promoter. Transcription factors (TFs) predicting suggested that all three SNPs may change the binding of TFs. The result of EMSA showed that rs138531627 C>G may create a new binding site for TFs while rs2276940 G>T enhanced the binding affinity for TFs. These results indicated that genetic variants of the HAND2 promoter may increase the risk of VSD, and the molecular mechanism may be the change of the binding affinity of TFs.

DeepSATA: A Deep Learning-Based Sequence Analyzer Incorporating the Transcription Factor Binding Affinity to Dissect the Effects of Non-Coding Genetic Variants.

Utilizing large-scale epigenomics data, deep learning tools can predict the regulatory activity of genomic sequences, annotate non-coding genetic variants, and uncover mechanisms behind complex traits. However, these tools primarily rely on human or mouse data for training, limiting their performance when applied to other species. Furthermore, the limited exploration of many species, particularly in the case of livestock, has led to a scarcity of comprehensive and high-quality epigenetic data, posing challenges in developing reliable deep learning models for decoding their non-coding genomes. The cross-species prediction of the regulatory genome can be achieved by leveraging publicly available data from extensively studied organisms and making use of the conserved DNA binding preferences of transcription factors within the same tissue. In this study, we introduced DeepSATA, a novel deep learning-based sequence analyzer that incorporates the transcription factor binding affinity for the cross-species prediction of chromatin accessibility. By applying DeepSATA to analyze the genomes of pigs, chickens, cattle, humans, and mice, we demonstrated its ability to improve the prediction accuracy of chromatin accessibility and achieve reliable cross-species predictions in animals. Additionally, we showcased its effectiveness in analyzing pig genetic variants associated with economic traits and in increasing the accuracy of genomic predictions. Overall, our study presents a valuable tool to explore the epigenomic landscape of various species and pinpoint regulatory deoxyribonucleic acid (DNA) variants associated with complex traits.

Identifying functional regulatory mutation blocks by integrating genome sequencing and transcriptome data

Millions of single nucleotide variants (SNVs) exist in the human genome; however, it remains challenging to identify functional SNVs associated with diseases. We propose a non-encoding SNVs analysis tool bpb3, BayesPI-BAR version 3, aiming to identify the functional mutation blocks (FMBs) by integrating genome sequencing and transcriptome data. The identified FMBs display high frequency SNVs, significant changes in transcription factors (TFs) binding affinity and are nearby the regulatory regions of differentially expressed genes. A two-level Bayesian approach with a biophysical model for protein-DNA interactions is implemented, to compute TF-DNA binding affinity changes based on clustered position weight matrices (PWMs) from over 1700 TF-motifs. The epigenetic data, such as the DNA methylome can also be integrated to scan FMBs. By testing the datasets from follicular lymphoma and melanoma, bpb3 automatically and robustly identifies FMBs, demonstrating that bpb3 can provide insight into patho-mechanisms, and therapeutic targets from transcriptomic and genomic data.

Integrating functional scoring and regulatory data to predict the effect of non-coding SNPs in a complex neurological disease.

Most SNPs associated with complex diseases seem to lie in non-coding regions of the genome; however, their contribution to gene expression and disease phenotype remains poorly understood. Here, we established a workflow to provide assistance in prioritising the functional relevance of non-coding SNPs of candidate genes as susceptibility loci in polygenic neurological disorders. To illustrate the applicability of our workflow, we considered the multifactorial disorder migraine as a model to follow our step-by-step approach. We annotated the overlap of selected SNPs with regulatory elements and assessed their potential impact on gene expression based on publicly available prediction algorithms and functional genomics information. Some migraine risk loci have been hypothesised to reside in non-coding regions and to be implicated in the neurotransmission pathway. In this study, we used a set of 22 non-coding SNPs from neurotransmission and synaptic machinery-related genes previously suggested to be involved in migraine susceptibility based on our candidate gene association studies. After prioritising these SNPs, we focused on non-reported ones that demonstrated high regulatory potential: (1) VAMP2_rs1150 (3' UTR) was predicted as a target of hsa-mir-5010-3p miRNA, possibly disrupting its own gene expression; (2) STX1A_rs6951030 (proximal enhancer) may affect the binding affinity of zinc-finger transcription factors (namely ZNF423) and disturb TBL2 gene expression; and (3) SNAP25_rs2327264 (distal enhancer) expected to be in a binding site of ONECUT2 transcription factor. This study demonstrated the applicability of our practical workflow to facilitate the prioritisation of potentially relevant non-coding SNPs and predict their functional impact in multifactorial neurological diseases.

Quantitative model for genome-wide cyclic AMP receptor protein binding site identification and characteristic analysis

Cyclic AMP receptor proteins (CRPs) are important transcription regulators in many species. The prediction of CRP-binding sites was mainly based on position-weighted matrixes (PWMs). Traditional prediction methods only considered known binding motifs, and their ability to discover inflexible binding patterns was limited. Thus, a novel CRP-binding site prediction model called CRPBSFinder was developed in this research, which combined the hidden Markov model, knowledge-based PWMs and structure-based binding affinity matrixes. We trained this model using validated CRP-binding data from Escherichia coli and evaluated it with computational and experimental methods. The result shows that the model not only can provide higher prediction performance than a classic method but also quantitatively indicates the binding affinity of transcription factor binding sites by prediction scores. The prediction result included not only the most knowns regulated genes but also 1089 novel CRP-regulated genes. The major regulatory roles of CRPs were divided into four classes: carbohydrate metabolism, organic acid metabolism, nitrogen compound metabolism and cellular transport. Several novel functions were also discovered, including heterocycle metabolic and response to stimulus. Based on the functional similarity of homologous CRPs, we applied the model to 35 other species. The prediction tool and the prediction results are online and are available at: https://awi.cuhk.edu.cn/∼CRPBSFinder.

A regulatory variant at 19p13.3 is associated with primary biliary cholangitis risk and ARID3A expression

Genome-wide association studies have identified 19p13.3 locus associated with primary biliary cholangitis (PBC). Here we aim to identify causative variant(s) and initiate efforts to define the mechanism by which the 19p13.3 locus variant(s) contributes to the pathogenesis of PBC. A genome-wide meta-analysis of 1931 PBC subjects and 7852 controls in two Han Chinese cohorts confirms the strong association between 19p13.3 locus and PBC. By integrating functional annotations, luciferase reporter assay and allele-specific chromatin immunoprecipitation, we prioritize rs2238574, an AT-Rich Interaction Domain 3A (ARID3A) intronic variant, as a potential causal variant at 19p13.3 locus. The risk allele of rs2238574 shows higher binding affinity of transcription factors, leading to an increased enhancer activity in myeloid cells. Genome-editing demonstrates the regulatory effect of rs2238574 on ARID3A expression through allele-specific enhancer activity. Furthermore, knock-down of ARID3A inhibits myeloid differentiation and activation pathway, and overexpression of the gene has the opposite effect. Finally, we find ARID3A expression and rs2238574 genotypes linked to disease severity in PBC. Our work provides several lines of evidence that a non-coding variant regulates ARID3A expression, presenting a mechanistic basis for association of 19p13.3 locus with the susceptibility to PBC.

Quantification of absolute transcription factor binding affinities in the native chromatin context using BANC-seq.

Transcription factor binding across the genome is regulated by DNA sequence and chromatin features. However, it is not yet possible to quantify the impact of chromatin context on transcription factor binding affinities. Here, we report a method called binding affinities to native chromatin by sequencing (BANC-seq) to determine absolute apparent binding affinities of transcription factors to native DNA across the genome. In BANC-seq, a concentration range of a tagged transcription factor is added to isolated nuclei. Concentration-dependent binding is then measured per sample to quantify apparent binding affinities across the genome. BANC-seq adds a quantitative dimension to transcription factor biology, which enables stratification of genomic targets based on transcription factor concentration and prediction of transcription factor binding sites under non-physiological conditions, such as disease-associated overexpression of (onco)genes. Notably, whereas consensus DNA binding motifs for transcription factors are important to establish high-affinity binding sites, these motifs are not always strictly required to generate nanomolar-affinity interactions in the genome.

Measuring the impact of chromatin context on transcription factor binding affinities.

Measuring the impact of chromatin context on transcription factor binding affinities.

Functional genomics identify causal variant underlying the protective CTSH locus for Alzheimer's disease.

Alzheimer's disease (AD) is the most prevalent age-related neurodegenerative disease, which has a high heritability of up to 79%. Exploring the genetic basis is essential for understanding the pathogenic mechanisms underlying AD development. Recent genome-wide association studies (GWASs) reported an AD-associated signal in the Cathepsin H (CTSH) gene in European populations. However, the exact functional/causal variant(s), and the genetic regulating mechanism of CTSH in AD remain to be determined. In this study, we carried out a comprehensive study to characterize the role of CTSH variants in the pathogenesis of AD. We identified rs2289702 in CTSH as the most significant functional variant that is associated with a protective effect against AD. The genetic association between rs2289702 and AD was validated in independent cohorts of the Han Chinese population. The CTSH mRNA expression level was significantly increased in AD patients and AD animal models, and the protective allele T of rs2289702 was associated with a decreased expression level of CTSH through the disruption of the binding affinity of transcription factors. Human microglia cells with CTSH knockout showed a significantly increased phagocytosis of Aβ peptides. Our study identified CTSH as being involved in AD genetic susceptibility and uncovered the genetic regulating mechanism of CTSH in pathogenesis of AD.

Regulatory Potential of SNP Markers in Genes of DNA Repair Systems

A broad range of SNP markers associated with diseases and pathogenically significant features were identified in noncoding regions of the human genome. The mechanisms that underlie their associations are a pressing problem. A number of associations was previously observed between polymorphic variants of DNA repair proteins genes and common diseases. To clarify the possible mechanisms of the associations, a detailed annotation of the regulatory potential of the markers was carried out using online resources (GTX-Portal, VannoPortal, Ensemble, RegulomeDB, Polympact, UCSC, GnomAD, ENCODE, GeneHancer, EpiMap Epigenomics 2021, HaploReg, GWAS4D, JASPAR, ORegAnno, DisGeNet, and OMIM). The review characterizes the regulatory potential for the polymorphisms rs560191 (of the TP53BP1 gene), rs1805800, rs709816 (NBN), rs473297 (MRE11), rs189037, rs1801516 (ATM), rs1799977 (MLH1), rs1805321 (PMS2), and rs20579 (LIG1). General characteristics of the markers are considered, and data are summarized to describe their influence on expression of their own and co-regulated genes and binding affinity of transcription factors. The review additionally considers the data on adaptogenic and pathogenic potentials of the SNPs and co-localized histone modifications. A possible involvement in regulating the functions of both their own and nearby genes may explain the associations of the SNPs with diseases and their clinical phenotypes.

Insight into the intrinsically disordered transactivation domain of NFkB as a regulator of DNA binding using UV-induced unnatural amino acid crosslinking.

Within our vast genome, specific transcription factors like NFkB manage to find target sequences amongst overwhelming nonspecific options and help elicit timely cellular responses. A transcription factor's binding affinity for specific vs. nonspecific DNA must therefore be in a delicate balance to facilitate this search. Key in vitro experiments that described these binding affinities for the dominant NFkB dimer RelA/p50 were carried out with a truncated version of RelA - excluding a large intrinsically disordered transactivation domain (TAD) that was believed to be important for transcription activation through cofactor interactions but not impactful for DNA binding. This fit with a traditional view of protein domains acting as modular units. More recently, however, we discovered a novel function of the TAD: binding experiments revealed that the TAD's presence increased RelA/p50's affinity for DNA, with the strongest increase in binding affinity observed for non-specific DNA sequences lacking kB consensus sites. Here we follow up these findings with an investigation of the TAD's mechanism of action. We present preliminary results using an unnatural amino acid crosslinker (p-benzoyl-l-phenylalanine or pBpF) in combination with mass spectrometry to probe TAD interactions that lead to the observed changes in DNA binding affinity and specificity. pBpF is incorporated into specific positions in the TAD using AMBER codon suppression, and crosslinking is initiated by UV light. Interactions are analyzed by mass spectrometry to identify key sites of contact between the TAD and the natively folded Rel-Homology Domain.

Genetic variant in a BaP-activated super-enhancer increases prostate cancer risk by promoting AhR-mediated FAM227A expression.

Genetic variants in super-enhancers (SEs) are increasingly implicated as a disease risk-driving mechanism. Previous studies have reported an associations between benzo[a]pyrene (BaP) exposure and some malignant tumor risk. Currently, it is unclear whether BaP is involved in the effect of genetic variants in SEs on prostate cancer risk, nor the associated intrinsic molecular mechanisms. In the current study, by using logistic regression analysis, we found that rs5750581T>C in 22q-SE was significantly associated with prostate cancer risk (odds ratio = 1.26, P = 7.61 × 10 -5). We also have found that the rs6001092T>G, in a high linkage disequilibrium with rs5750581T>C ( r 2 = 0.98), is located in a regulatory aryl hydrocarbon receptor (AhR) motif and may interact with the FAM227A promoter in further bioinformatics analysis. We then performed a series of functional and BaP acute exposure experiments to assess biological function of the genetic variant and the target gene. Biologically, the rs6001092-G allele strengthened the transcription factor binding affinity to AhR, thereby upregulating FAM227A, especially upon exposure to BaP, which induced the malignant phenotypes of prostate cancer. The current study highlights that AhR acts as an environmental sensor of BaP and is involved in the SE-mediated prostate cancer risk, which may provide new insights into the etiology of prostate cancer associated with the inherited SE variants under environmental carcinogen stressors.

Contribution of TEX15 genetic variants to the risk of developing severe non-obstructive oligozoospermia.

Background: Severe spermatogenic failure (SPGF) represents one of the most relevant causes of male infertility. This pathological condition can lead to extreme abnormalities in the seminal sperm count, such as severe oligozoospermia (SO) or non-obstructive azoospermia (NOA). Most cases of SPGF have an unknown aetiology, and it is known that this idiopathic form of male infertility represents a complex condition. In this study, we aimed to evaluate whether common genetic variation in TEX15, which encodes a key player in spermatogenesis, is involved in the susceptibility to idiopathic SPGF. Materials and Methods: We designed a genetic association study comprising a total of 727 SPGF cases (including 527 NOA and 200 SO) and 1,058 unaffected men from the Iberian Peninsula. Following a tagging strategy, three tag single-nucleotide polymorphisms (SNPs) of TEX15 (rs1362912, rs323342, and rs323346) were selected for genotyping using TaqMan probes. Case-control association tests were then performed by logistic regression models. In silico analyses were also carried out to shed light into the putative functional implications of the studied variants. Results: A significant increase in TEX15-rs1362912 minor allele frequency (MAF) was observed in the group of SO patients (MAF = 0.0842) compared to either the control cohort (MAF = 0.0468, OR = 1.90, p = 7.47E-03) or the NOA group (MAF = 0.0472, OR = 1.83, p = 1.23E-02). The genotype distribution of the SO population was also different from those of both control (p = 1.14E-02) and NOA groups (p = 4.33-02). The analysis of functional annotations of the human genome suggested that the effect of the SO-associated TEX15 variants is likely exerted by alteration of the binding affinity of crucial transcription factors for spermatogenesis. Conclusion: Our results suggest that common variation in TEX15 is involved in the genetic predisposition to SO, thus supporting the notion of idiopathic SPGF as a complex trait.

Targeted deep sequencing analyses of long QT syndrome in a Japanese population.

Long QT syndrome (LQTS) is one of the most common inherited arrhythmias and multiple genes have been reported as causative. Presently, genetic diagnosis for LQTS patients is becoming widespread and contributing to implementation of therapies. However, causative genetic mutations cannot be detected in about 20% of patients. To elucidate additional genetic mutations in LQTS, we performed deep-sequencing of previously reported 15 causative and 85 candidate genes for this disorder in 556 Japanese LQTS patients. We performed in-silico filtering of the sequencing data and found 48 novel variants in 33 genes of 53 cases. These variants were predicted to be damaging to coding proteins or to alter the binding affinity of several transcription factors. Notably, we found that most of the LQTS-related variants in the RYR2 gene were in the large cytoplasmic domain of the N-terminus side. They might be useful for screening of LQTS patients who had no known genetic factors. In addition, when the mechanisms of these variants in the development of LQTS are revealed, it will be useful for early diagnosis, risk stratification, and selection of treatment.

Systematic analysis of the effects of genetic variants on chromatin accessibility to decipher functional variants in non-coding regions.

Genome-wide association study (GWAS) has identified thousands of single nucleotide polymorphisms (SNPs) associated with complex diseases and traits. However, deciphering the functions of these SNPs still faces challenges. Recent studies have shown that SNPs could alter chromatin accessibility and result in differences in tumor susceptibility between individuals. Therefore, systematically analyzing the effects of SNPs on chromatin accessibility could help decipher the functions of SNPs, especially those in non-coding regions. Using data from The Cancer Genome Atlas (TCGA), chromatin accessibility quantitative trait locus (caQTL) analysis was conducted to estimate the associations between genetic variants and chromatin accessibility. We analyzed caQTLs in 23 human cancer types and identified 9,478 caQTLs in breast carcinoma (BRCA). In BRCA, these caQTLs tend to alter the binding affinity of transcription factors, and open chromatin regions regulated by these caQTLs are enriched in regulatory elements. By integrating with eQTL data, we identified 141 caQTLs showing a strong signal for colocalization with eQTLs. We also identified 173 caQTLs in genome-wide association studies (GWAS) loci and inferred several possible target genes of these caQTLs. By performing survival analysis, we found that ~10% caQTLs potentially influence the prognosis of patients. To facilitate access to relevant data, we developed a user-friendly data portal, BCaQTL (http://gong_lab.hzau.edu.cn/caqtl_database), for data searching and downloading. Our work may facilitate fine-map regulatory mechanisms underlying risk loci of cancer and discover the biomarkers or therapeutic targets for cancer prognosis. The BCaQTL database will be an important resource for genetic and epigenetic studies.