Mutations and binding sites of human transcription factors

Identification of h-TERT Promoter Mutations in Germline DNA from North Indian Lung Carcinoma Patients

Lung cancer is a severe and the leading cause of cancer related deaths worldwide. The recurrent h-TERT promoter mutations have been implicated in various cancer types. Thus, the present study is extended to analyze h-TERT promoter mutations from the North Indian lung carcinoma patients. Total 20 histopathologically and clinically confirmed cases of lung cancer were enrolled in this study. The genomic DNA was extracted from venous blood and subjected to amplification using appropriate h-TERT promoter primers. Amplified PCR products were subjected for DNA Sanger sequencing for the identification of novel h-TERT mutations. Further, these identified h-TERT promoter mutations were analysed for the prediction of pathophysiological consequences using bioinformatics tools such as Tfsitescan and CIIDER. The average age of patients was 45 ± 8 years which was categorized in early onset of lung cancer with predominance of male patients by 5.6 fold. Interestingly, h-TERT promoter mutations were observed highly frequent in lung cancer. Identified mutations include c. G272A, c. T122A, c. C150A, c. 123 del C, c. C123T, c. G105A, c. 107 Ins A, c. 276 del C corresponding to -168 G>A, -18 T>A, -46 C>A, -19 del C, -19 C>T, -1 G>A, -3 Ins A, -172 del C respectively from the translation start site in the promoter of the telomerase reverse transcriptase gene which are the first time reported in germline genome from lung cancer. Strikingly, c. -18 T>A [C.T122A] was found the most prevalent variant with 75% frequency. Notwithstanding, other mutations viz c. -G168A [c. G272A] and c. -1 G>A [c. G105A] were found to be at 35% and 15% frequency respectively whilst the rest of the mutations were present at 10% and 5% frequency. Additionally, bioinformatics analysis revealed that these mutations can lead to either loss or gain of various transcription factor binding sites in the h-TERT promoter region. Henceforth, these mutations may play a pivotal role in h-TERT gene expression. Taken together, these identified novel promoter mutations may alter the epigenetics and subsequently various transcription factor binding sites which are of great functional significance. Thereby, it is plausible that these germline mutations may involve either as predisposing factor or direct participation in the pathophysiology of lung cancer through entangled molecular mechanisms.

Interleukin 17A rs2275913 polymorphism is associated with susceptibility to systemic lupus erythematosus: A meta and trial sequential analysis

BACKGROUND

The role of cytokines in the development of systemic lupus erythematosus (SLE) has received much attention. Interleukin-17 A upregulates several inflammation-related genes and is thought to have a crucial role in SLE development. The susceptibility to SLE development has been linked to functional genetic variations of the IL-17A gene; nevertheless, the findings have been conflicting. We conducted a meta-analysis that included previously published reports to establish a definitive conclusion on the role of the IL-17A rs2275913 polymorphism in SLE propensity.

MATERIALS AND METHODS

The PubMed, Google Scholar, and Scopus databases were used to find eligible published articles. All analyses were conducted using Comprehensive Meta-analysis V3.1. Funnel plots and Egger's regression analysis were used to assess publication bias. Q statistics and I² test explored the heterogeneity among the included studies. Combined odds ratio, 95% confidence interval were calculated for each comparison model.

RESULTS

Based on the inclusion and exclusion criteria, a total of four reports, comprising of 608 SLE patients and 815 healthy controls, were considered for the present meta-analysis. The homozygous comparison (AA vs. GG: combined odds ratio= 2.046, p = 0.005) and recessive genetic model (AA vs. GG+GA: combined odds ratio=1.901, p = 0.010) analysis revealed a significant association of rs2275913 with susceptibility to the development of SLE. However, other genetic comparisons (A vs. G, GA vs. GG, AA+GA vs. GG) failed to demonstrate such association. Furthermore, trial sequential analysis revealed a sufficient number of studies, including enough cases and controls that have already been considered to conclude the role of IL17-A rs2275913 polymorphism in SLE.

CONCLUSIONS

IL-17A rs2275913 polymorphism is associated with susceptibility to SLE development.

Functionally Antagonistic Transcription Factors IRF1 and IRF2 Regulate the Transcription of the Dopamine Receptor D2 Gene Associated with Aggressive Behavior of Weaned Pigs

Aggressive behavior has negative effects on animal welfare and growth performance in pigs. The dopamine receptor D2 (DRD2) has a critical neuromodulator role in the dopamine signal pathway within the brain to control behavior. A functional single-nucleotide polymorphism (SNP), rs1110730503, in the promoter region of the porcine DRD2 gene was identified, which affects aggressive behavior in pigs. A chromatin immunoprecipitation (ChIP) assay was used to identify the interactions between interferon regulatory factor 1 (IRF1) and IRF2 with the DRD2 gene. The overexpression or knockdown of these two transcription factors in porcine kidney-15 (PK15) and porcine neuronal cells (PNCs) indicate that the binding of IRF1 to DRD2 promotes the transcription of the DRD2 gene, but the binding of IRF2 to the DRD2 gene inhibits its transcription. Furthermore, IRF1 and IRF2 are functionally antagonistic to each other. The downregulation of DRD2 or upregulation of IRF2 increased the apoptosis rate of porcine neuroglial cells. Taken together, we found that transcriptional factors IRF1 and IRF2 have vital roles in regulating the transcription of the DRD2 gene, and rs1110730503 (−915A/T) is a functional SNP that influences IRF2 binding to the promoter of the DRD2 gene. These findings will provide further insight towards controlling aggressive behavior in pigs.

Analysis of the androgen receptor (AR) gene in a cohort of Indonesian undermasculinized 46, XY DSD patients

Background

Pathogenic variants in the androgen receptor (AR) gene located on chromosome Xq11-12, are known to cause varying degrees of undermasculinization in 46, XY individuals. The aim of this study was to investigate the frequency of pathogenic variants in the AR gene in a cohort of 46, XY undermasculinized individuals from Indonesia who were suspected of having androgen insensitivity syndrome (AIS). All patients with 46, XY DSD referred to our center between 1994 and 2019 were collected from our clinical database. All 46, XY DSD patients without a prior molecular diagnosis with an external masculinization score (EMS) ≤ 9 were included in this study. All exons and intron–exon boundaries of AR gene were analyzed using Sanger sequencing to identify pathogenic variants of the AR gene.

Results

A cohort of 75 undermasculinized patients were selected for the study. Direct Sanger sequencing of all eight exons of the AR gene led to a genetic diagnosis in 11 patients (14.67%). All of the variants identified (p.Arg841His; p.Ile604Asn; p.Val731Met; p.Pro672Ser; p.Gln739Arg; p.Ser302Glufs*3) have been previously reported in patients with AIS.

Conclusions

This is the first study in Indonesia that highlights the significance of molecular analysis in providing a definitive diagnosis of AIS for patients with 46, XY DSD undermasculinization. This is an uncommon finding in the Indonesian population presenting with 46, XY DSD undermasculinization. A genetic diagnosis allows optimal clinical management and genetic counseling for patients and their families. As 46, XY DSD can be caused by pathogenic variants in other genes involved in gonadal development and differentiation, further genetic analysis, such as whole exome sequencing, should be carried out on those patients that did not carry an AR variant.

Incorporating Genome Annotation Into Genomic Prediction for Carcass Traits in Chinese Simmental Beef Cattle

Various methods have been proposed for genomic prediction (GP) in livestock. These methods have mainly focused on statistical considerations and did not include genome annotation information. In this study, to improve the predictive performance of carcass traits in Chinese Simmental beef cattle, we incorporated the genome annotation information into GP. Single nucleotide polymorphisms (SNPs) were annotated to five genomic classes: intergenic, gene, exon, protein coding sequences, and 3'/5' untranslated region. Haploblocks were constructed for all markers and these five genomic classes by defining a biologically functional unit, and haplotype effects were modeled in both numerical dosage and categorical coding strategies. The first-order epistatic effects among SNPs and haplotypes were modeled using a categorical epistasis model. For all makers, the extension from the SNP-based model to a haplotype-based model improved the accuracy by 5.4-9.8% for carcass weight (CW), live weight (LW), and striploin (SI). For the five genomic classes using the haplotype-based prediction model, the incorporation of gene class information into the model improved the accuracies by an average of 1.4, 2.1, and 1.3% for CW, LW, and SI, respectively, compared with their corresponding results for all markers. Including the first-order epistatic effects into the prediction models improved the accuracies in some traits and genomic classes. Therefore, for traits with moderate-to-high heritability, incorporating genome annotation information of gene class into haplotype-based prediction models could be considered as a promising tool for GP in Chinese Simmental beef cattle, and modeling epistasis in prediction can further increase the accuracy to some degree.

Structural And Computational Perspectives of Selectively Targeting Mutant Proteins

Diseases are often caused by mutant proteins. Many drugs have limited effectiveness and/or toxic side effects because of a failure to selectively target the disease-causing mutant variant, rather than the functional wild type protein. Otherwise, the drugs may even target different proteins with similar structural features. Designing drugs that successfully target mutant proteins selectively represents a major challenge. Decades of cancer research have led to an abundance of potential therapeutic targets, often touted to be "master regulators". For many of these proteins, there are no FDA-approved drugs available; for others, off-target effects result in dose-limiting toxicity. Cancer-related proteins are an excellent medium to carry the story of mutant-specific targeting, as the disease is both initiated and sustained by mutant proteins; furthermore, current chemotherapies generally fail at adequate selective distinction. This review discusses some of the challenges associated with selective targeting from a structural biology perspective, as well as some of the developments in algorithm approach and computational workflow that can be applied to address those issues. One of the most widely researched proteins in cancer biology is p53, a tumor suppressor. Here, p53 is discussed as a specific example of a challenging target, with contemporary drugs and methodologies used as examples of burgeoning successes. The oncogene KRAS, which has been described as "undruggable", is another extensively investigated protein in cancer biology. This review also examines KRAS to exemplify progress made towards selective targeting of diseasecausing mutant proteins. Finally, possible future directions relevant to the topic are discussed.

Variance explained by whole genome sequence variants in coding and regulatory genome annotations for six dairy traits

Background

There are an exceedingly large number of sequence variants discovered through whole genome sequencing in most populations, including cattle. Deciphering which of these affect complex traits is a major challenge. In this study we hypothesize that variants in some functional classes, such as splice site regions, coding regions, DNA methylated regions and long noncoding RNA will explain more variance in complex traits than others. Two variance component approaches were used to test this hypothesis – the first determines if variants in a functional class capture a greater proportion of the variance, than expected by chance, the second uses the proportion of variance explained when variants in all annotations are fitted simultaneously.

Results

Our data set consisted of 28.3 million imputed whole genome sequence variants in 16,581 dairy cattle with records for 6 complex trait phenotypes, including production and fertility. We found that sequence variants in splice site regions and synonymous classes captured the greatest proportion of the variance, explaining up to 50% of the variance across all traits. We also found sequence variants in target sites for DNA methylation (genomic regions that are found be highly methylated in bovine placentas), captured a significant proportion of the variance. Per sequence variant, splice site variants explain the highest proportion of variance in this study. The proportion of variance captured by the missense predicted deleterious (from SIFT) and missense tolerated classes was relatively small.

Conclusion

The results demonstrate using functional annotations to filter whole genome sequence variants into more informative subsets could be useful for prioritization of the variants that are more likely to be associated with complex traits. In addition to variants found in splice sites and protein coding genes regulatory variants and those found in DNA methylated regions, explained considerable variation in milk production and fertility traits. In our analysis synonymous variants captured a significant proportion of the variance, which raises the possible explanation that synonymous mutations might have some effects, or more likely that these variants are miss-annotated, or alternatively the results reflect imperfect imputation of the actual causative variants.

Electronic supplementary material

The online version of this article (10.1186/s12864-018-4617-x) contains supplementary material, which is available to authorized users.

Systems analysis of dilated cardiomyopathy in the next generation sequencing era

Dilated cardiomyopathy (DCM) is a form of severe failure of cardiac muscle caused by a long list of etiologies ranging from myocardial infarction, DNA mutations in cardiac genes, to toxics. Systems analysis integrating next-generation sequencing (NGS)-based omics approaches, such as the sequencing of DNA, RNA, and chromatin, provide valuable insights into DCM mechanisms. The outcome and interpretation of NGS methods can be affected by the localization of cardiac biopsy, level of tissue degradation, and variable ratios of different cell populations, especially in the presence of fibrosis. Heart tissue composition may even differ between sexes, or siblings carrying the same disease causing mutation. Therefore, before planning any experiments, it is important to fully appreciate the complexities of DCM, and the selection of samples suitable for given research question should be an interdisciplinary effort involving clinicians and biologists. The list of NGS omics datasets in DCM to date is short. More studies have to be performed to contribute to public data repositories and facilitate systems analysis. In addition, proper data integration is a difficult task requiring complex computational approaches. Despite these complications, there are multiple promising implications of systems analysis in DCM. By combining various types of datasets, for example, RNA-seq, ChIP-seq, or 4C, deep insights into cardiac biology, and possible biomarkers and treatment targets, can be gained. Systems analysis can also facilitate the annotation of noncoding mutations in cardiac-specific DNA regulatory regions that play a substantial role in maintaining the tissue- and cell-specific transcriptional programs in the heart. This article is categorized under: Physiology > Mammalian Physiology in Health and Disease Laboratory Methods and Technologies > Genetic/Genomic Methods Laboratory Methods and Technologies > RNA Methods.

Association of oligodendrocytes differentiation regulator gene DUSP15 with autism

OBJECTIVES

Autism is a pervasive neurodevelopmental disorder with high heritability. Genetic factors play crucial roles in the aetiology of autism. Dual specificity phosphatase 15 (DUSP15) has been recognised as a key regulator gene for oligodendrocytes differentiation. A previous study detected one de novo missense variant (p.Thr107Met) with probable deleterious function in exon 6 of DUSP15 among patients with autism. Therefore, we sequenced this mutation in autistic children and performed an association analysis between DUSP15 polymorphisms and autism.

METHODS

We performed a case-control study between 255 children affected with autism and 427 healthy controls. Four tag-single nucleotide polymorphisms (SNPs) were selected. These SNPs and the previously reported mutation in exon 6 of DUSP15 were genotyped via Sanger sequencing.

RESULTS

Our results showed that rs3746599 was significantly associated with autism under allelic, additive and dominant models, respectively (χ²=9.699, P = 0.0018; χ²=16.224, P = 0.001; χ²=7.198, P = 0.007). The association remained significant after Bonferroni correction and permutation tests (n = 10,000). We did not detect the missense variant p.Thr107Met reported in previous studies. However, a de novo missense variant of DUSP15 (p.Ala56Thr) with a probable disease-causing effect was detected in one autistic child while absent in healthy controls.

CONCLUSIONS

Our findings initially suggest that DUSP15 might be a susceptibility gene for autism in Chinese Han population.

HOCOMOCO: expansion and enhancement of the collection of transcription factor binding sites models

Models of transcription factor (TF) binding sites provide a basis for a wide spectrum of studies in regulatory genomics, from reconstruction of regulatory networks to functional annotation of transcripts and sequence variants. While TFs may recognize different sequence patterns in different conditions, it is pragmatic to have a single generic model for each particular TF as a baseline for practical applications. Here we present the expanded and enhanced version of HOCOMOCO (http://hocomoco.autosome.ru and http://www.cbrc.kaust.edu.sa/hocomoco10), the collection of models of DNA patterns, recognized by transcription factors. HOCOMOCO now provides position weight matrix (PWM) models for binding sites of 601 human TFs and, in addition, PWMs for 396 mouse TFs. Furthermore, we introduce the largest up to date collection of dinucleotide PWM models for 86 (52) human (mouse) TFs. The update is based on the analysis of massive ChIP-Seq and HT-SELEX datasets, with the validation of the resulting models on in vivo data. To facilitate a practical application, all HOCOMOCO models are linked to gene and protein databases (Entrez Gene, HGNC, UniProt) and accompanied by precomputed score thresholds. Finally, we provide command-line tools for PWM and diPWM threshold estimation and motif finding in nucleotide sequences.

Promoter analysis reveals globally differential regulation of human long non-coding RNA and protein-coding genes

Transcriptional regulation of protein-coding genes is increasingly well-understood on a global scale, yet no comparable information exists for long non-coding RNA (lncRNA) genes, which were recently recognized to be as numerous as protein-coding genes in mammalian genomes. We performed a genome-wide comparative analysis of the promoters of human lncRNA and protein-coding genes, finding global differences in specific genetic and epigenetic features relevant to transcriptional regulation. These two groups of genes are hence subject to separate transcriptional regulatory programs, including distinct transcription factor (TF) proteins that significantly favor lncRNA, rather than coding-gene, promoters. We report a specific signature of promoter-proximal transcriptional regulation of lncRNA genes, including several distinct transcription factor binding sites (TFBS). Experimental DNase I hypersensitive site profiles are consistent with active configurations of these lncRNA TFBS sets in diverse human cell types. TFBS ChIP-seq datasets confirm the binding events that we predicted using computational approaches for a subset of factors. For several TFs known to be directly regulated by lncRNAs, we find that their putative TFBSs are enriched at lncRNA promoters, suggesting that the TFs and the lncRNAs may participate in a bidirectional feedback loop regulatory network. Accordingly, cells may be able to modulate lncRNA expression levels independently of mRNA levels via distinct regulatory pathways. Our results also raise the possibility that, given the historical reliance on protein-coding gene catalogs to define the chromatin states of active promoters, a revision of these chromatin signature profiles to incorporate expressed lncRNA genes is warranted in the future.