Mechanistic interpretation of non-coding variants for discovering transcriptional regulators of drug response

Mechanistic analysis of enhancer sequences in the estrogen receptor transcriptional program

Estrogen Receptor α (ERα) is a major lineage determining transcription factor (TF) in mammary gland development. Dysregulation of ERα-mediated transcriptional program results in cancer. Transcriptomic and epigenomic profiling of breast cancer cell lines has revealed large numbers of enhancers involved in this regulatory program, but how these enhancers encode function in their sequence remains poorly understood. A subset of ERα-bound enhancers are transcribed into short bidirectional RNA (enhancer RNA or eRNA), and this property is believed to be a reliable marker of active enhancers. We therefore analyze thousands of ERα-bound enhancers and build quantitative, mechanism-aware models to discriminate eRNAs from non-transcribing enhancers based on their sequence. Our thermodynamics-based models provide insights into the roles of specific TFs in ERα-mediated transcriptional program, many of which are supported by the literature. We use in silico perturbations to predict TF-enhancer regulatory relationships and integrate these findings with experimentally determined enhancer-promoter interactions to construct a gene regulatory network. We also demonstrate that the model can prioritize breast cancer-related sequence variants while providing mechanistic explanations for their function. Finally, we experimentally validate the model-proposed mechanisms underlying three such variants.

Comparison of salivary MicroRNA-6734, microRNA-3123 and microRNA-4483 expression in smoker and nonsmoker patients: a case control study

BACKGROUND

Tobacco is a major risk factor in oral diseases. Considering the important role of expression miRNA molecules in different diseases, the present study aimed to compare the expression of salivary miRNA-6734, miRNA-3123 and miRNA-4483 in smoker and non-smoker peoples.

METHODS

In this case-control study, salivary samples were obtained from 30 smoker's patients and 27 healthy nonsmokers and matched in term of age and sex. RNA was extracted in salivary samples and gene expression was evaluated in all samples. Statistical analysis of data was performed using T and chi-square tests by SPSS (Ver. 16) software at a significant level of less than 0.05.

RESULTS

The results of this study showed that the expression level of miRNA-3123 and miRNA-4483 in smokers group was 2.8 and 3.2, respectively, which was increased compared to non-smokers with expression level of 1 (P<0.01). There was a significant decrease in the expression rate of miRNA-6734 in smokers (0.6%) compared to non-smokers (P<0.05).

CONCLUSIONS

Based on the results of this study, considering the increasing miRNA-4483 and miRNA-3123 level, as well as the reducing miRNA-6734 level in smokers compared to non-smokers, it seems that evaluation of these two miRNAs as indicator for diagnosis and determination of prognosis of oral diseases associated with smoking can be used.

Thermodynamics-based modeling reveals regulatory effects of indirect transcription factor-DNA binding

Transcription factors (TFs) influence gene expression by binding to DNA, yet experimental data suggests that they also frequently bind regulatory DNA indirectly by interacting with other DNA-bound proteins. Here, we used a data modeling approach to test if such indirect binding by TFs plays a significant role in gene regulation. We first incorporated regulatory function of indirectly bound TFs into a thermodynamics-based model for predicting enhancer-driven expression from its sequence. We then fit the new model to a rich data set comprising hundreds of enhancers and their regulatory activities during mesoderm specification in Drosophila embryogenesis and showed that the newly incorporated mechanism results in significantly better agreement with data. In the process, we derived the first sequence-level model of this extensively characterized regulatory program. We further showed that allowing indirect binding of a TF explains its localization at enhancers more accurately than with direct binding only. Our model also provided a simple explanation of how a TF may switch between activating and repressive roles depending on context.

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Inclusion of indirect DNA binding of transcription factor improves enhancer function prediction

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Context specific activating or repressive roles of TFs

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Indirect binding improves fits to experimental TF-DNA binding data

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Role of Tinman depends on its DNA-binding mode (direct or indirect)

Dysregulation of Transglutaminase type 2 through GATA3 defines aggressiveness and Doxorubicin sensitivity in breast cancer

The role of transglutaminase type 2 in cell physiology is related to protein transamidation and signal transduction (affecting extracellular, intracellular and nuclear processes) aided by the expression of truncated isoforms and of two lncRNAs with regulatory functions. In breast cancer TG2 is associated with disease progression supporting motility, epithelial-mesenchymal transition, invasion and drug resistance. The aim of his work is to clarify these issues by emphasizing the interconnections among TGM2 variants and transcription factors associated with an aggressive phenotype, in which the truncated TGH isoform correlates with malignancy. TGM2 transcripts are upregulated by several drugs in MCF-7, but only Doxorubicin is effective in MDA-MB-231 cells. These differences reflect the expression of GATA3, as demonstrated by silencing, suggesting a link between this transcription factor and gene dysregulation. Of note, NC9, an irreversible inhibitor of enzymatic TG2 activities, emerges to control NF-ĸB and apoptosis in breast cancer cell lines, showing potential for combination therapies with Doxorubicin.

VarSAn: associating pathways with a set of genomic variants using network analysis

There is a pressing need today to mechanistically interpret sets of genomic variants associated with diseases. Here we present a tool called ‘VarSAn’ that uses a network analysis algorithm to identify pathways relevant to a given set of variants. VarSAn analyzes a configurable network whose nodes represent variants, genes and pathways, using a Random Walk with Restarts algorithm to rank pathways for relevance to the given variants, and reports P-values for pathway relevance. It treats non-coding and coding variants differently, properly accounts for the number of pathways impacted by each variant and identifies relevant pathways even if many variants do not directly impact genes of the pathway. We use VarSAn to identify pathways relevant to variants related to cancer and several other diseases, as well as drug response variation. We find VarSAn's pathway ranking to be complementary to the standard approach of enrichment tests on genes related to the query set. We adopt a novel benchmarking strategy to quantify its advantage over this baseline approach. Finally, we use VarSAn to discover key pathways, including the VEGFA-VEGFR2 pathway, related to de novo variants in patients of Hypoplastic Left Heart Syndrome, a rare and severe congenital heart defect.

Computational Tools to Assess the Functional Consequences of Rare and Noncoding Pharmacogenetic Variability

Interindividual differences in drug response are a common concern in both drug development and across layers of care. While genetics clearly influences drug response and toxicity of many drugs, a substantial fraction of the heritable pharmacological and toxicological variability remains unexplained by known genetic polymorphisms. In recent years, population-scale sequencing projects have unveiled tens of thousands of coding and noncoding pharmacogenetic variants with unclear functional effects that might explain at least part of this missing heritability. However, translating these personalized variant signatures into drug response predictions and actionable advice remains challenging and constitutes one of the most important frontiers of contemporary pharmacogenomics. Conventional prediction methods are primarily based on evolutionary conservation, which drastically reduces their predictive accuracy when applied to poorly conserved pharmacogenes. Here, we review the current state-of-the-art of computational variant effect predictors across variant classes and critically discuss their utility for pharmacogenomics. Besides missense variants, we discuss recent progress in the evaluation of synonymous, splice, and noncoding variations. Furthermore, we discuss emerging possibilities to assess haplotypes and structural variations. We advocate for the development of algorithms trained on pharmacogenomic instead of pathogenic data sets to improve the predictive accuracy in order to facilitate the utilization of next-generation sequencing data for personalized clinical decision support and precision pharmacogenomics.

Imprints of selection in peripheral and ecologically marginal central-eastern European Scots pine populations

Knowledge of the molecular mechanisms underlying the stress response in plants is essential to understand evolutionary processes that result in long-term persistence of populations. Populations inhabiting marginal ecological conditions at the distribution range periphery may have preserved imprints of natural selection that have shaped functional genetic variation of the species. Our aim was to evaluate the extent of selection processes in the extremely fragmented, peripheral and isolated populations of Scots pine in central-eastern Europe. Autochthonous populations of the Carpathian Mts. and the Pannonian Basin were sampled and drought stress-related candidate genes were re-sequenced. Neutrality tests and outlier detection approaches were applied to infer the effect and direction of selection. Populations retained high genetic diversity by preserving a high number of alleles and haplotypes, many of them being population specific. Neutrality tests and outlier detection highlighted nucleotide positions that are under divergent selection and may be involved in local adaptation. The detected genetic pattern confirms that natural selection has played an important role in shaping modern-day genetic variation in marginal Scots pine populations, allowing for the long-term persistence of populations. Selection detected at functional regions possibly acts to maintain diversity and counteract the effect of genetic erosion.

Integrative genomics analysis of various omics data and networks identify risk genes and variants vulnerable to childhood-onset asthma

BACKGROUND

Childhood-onset asthma is highly affected by genetic components. In recent years, many genome-wide association studies (GWAS) have reported a large group of genetic variants and susceptible genes associated with asthma-related phenotypes including childhood-onset asthma. However, the regulatory mechanisms of these genetic variants for childhood-onset asthma susceptibility remain largely unknown.

METHODS

In the current investigation, we conducted a two-stage designed Sherlock-based integrative genomics analysis to explore the cis- and/or trans-regulatory effects of genome-wide SNPs on gene expression as well as childhood-onset asthma risk through incorporating a large-scale GWAS data (N = 314,633) and two independent expression quantitative trait loci (eQTL) datasets (N = 1890). Furthermore, we applied various bioinformatics analyses, including MAGMA gene-based analysis, pathway enrichment analysis, drug/disease-based enrichment analysis, computer-based permutation analysis, PPI network analysis, gene co-expression analysis and differential gene expression analysis, to prioritize susceptible genes associated with childhood-onset asthma.

RESULTS

Based on comprehensive genomics analyses, we found 31 genes with multiple eSNPs to be convincing candidates for childhood-onset asthma risk; such as, PSMB9 (cis-rs4148882 and cis-rs2071534) and TAP2 (cis-rs9267798, cis-rs4148882, cis-rs241456, and trans-10,447,456). These 31 genes were functionally interacted with each other in our PPI network analysis. Our pathway enrichment analysis showed that numerous KEGG pathways including antigen processing and presentation, type I diabetes mellitus, and asthma were significantly enriched to involve in childhood-onset asthma risk. The co-expression patterns among 31 genes were remarkably altered according to asthma status, and 25 of 31 genes (25/31 = 80.65%) showed significantly or suggestively differential expression between asthma group and control group.

CONCLUSIONS

We provide strong evidence to highlight 31 candidate genes for childhood-onset asthma risk, and offer a new insight into the genetic pathogenesis of childhood-onset asthma.

Pharmacogenetics in the treatment of gastrointestinal stromal tumors - an updated review

INTRODUCTION

Gastrointestinal stromal tumors (GIST) are the best example of a targeted therapy in solid tumors. The introduction of tyrosine kinase inhibitors (TKIs) deeply improved the prognosis of this tumor. However, a degree of inter-patient variability is still reported in response rates and pharmacogenetics may play an important role in the final clinical outcome.

AREAS COVERED

In this review, the authors provide an updated overview of the pharmacogenetic literature analyzing the role of polymorphisms in both GIST treatment efficacy and toxicity.

EXPERT OPINION

Besides the primary role of somatic DNA in dictating the clinical response to TKIs, several polymorphisms influencing their pharmacokinetics and pharmacodynamics have been identified as being potentially involved. In the last 10 years, many potential biomarkers have been proposed to predict clinical response and toxicity after TKI administration. However, the evidence is still too limited to promote a clinical translation. To date, the somatic mutational status represents the main player in clinical response to TKIs in GIST treatment; however, pharmacogenetics could still explain the degree of inter-patient variability observed in GIST patients. A combination of different theoretical approaches, experimental model systems, and statistical methods is clearly needed, in order to translate pharmacogenetics to clinical practice in the near future.

Roles of OCT4 in pathways of embryonic development and cancer progression

Somatic cells may be reprogrammed to pluripotent state by ectopic expression of certain transcription factors; namely, OCT4, SOX2, KLF4 and c-MYC. However, the molecular and cellular mechanisms are not adequately understood, especially for human embryonic development. Studies during the last five years implicated importance of OCT4 in human zygotic genome activation (ZGA), patterns of OCT4 protein folding and role of specialized sequences in binding to DNA for modulation of gene expression during development. Epigenetic modulation of OCT4 gene and post translational modifications of OCT4 protein activity in the context of multiple cancers are important issues. A consensus is emerging that chromatin organization and epigenetic landscape play crucial roles for the interactions of transcription factors, including OCT4 with the promoters and/or regulatory sequences of genes associated with human embryonic development (ZGA through lineage specification) and that when the epigenome niche is deregulated OCT4 helps in cancer progression, and how OCT4 silencing in somatic cells of adult organisms may impact ageing.

Gene-Wise Burden of Coding Variants Correlates to Noncoding Pharmacogenetic Risk Variants

Genetic variability can modulate individual drug responses. A significant portion of pharmacogenetic variants reside in the noncoding genome yet it is unclear if the noncoding variants directly influence protein function and expression or are present on a haplotype including a functionally relevant genetic variation (synthetic association). Gene-wise variant burden (GVB) is a gene-level measure of deleteriousness, reflecting the cumulative effects of deleterious coding variants, predicted in silico. To test potential associations between noncoding and coding pharmacogenetic variants, we computed a drug-level GVB for 5099 drugs from DrugBank for 2504 genomes of the 1000 Genomes Project and evaluated the correlation between the long-known noncoding variant-drug associations in PharmGKB, with functionally relevant rare and common coding variants aggregated into GVBs. We obtained the area under the receiver operating characteristics curve (AUC) by comparing the drug-level GVB ranks against the corresponding pharmacogenetic variants-drug associations in PharmGKB. We obtained high overall AUCs (0.710 ± 0.022-0.734 ± 0.018) for six different methods (i.e., SIFT, MutationTaster, Polyphen-2 HVAR, Polyphen-2 HDIV, phyloP, and GERP⁺⁺), and further improved the ethnicity-specific validations (0.759 ± 0.066-0.791 ± 0.078). These results suggest that a significant portion of the long-known noncoding variant-drug associations can be explained as synthetic associations with rare and common coding variants burden of the corresponding pharmacogenes.