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We recently reported a novel, heterozygous, and non-synonymous ACTC1 mutation (p.Gly247Asp or G247D) in a large, multi-generational family, causing atrial-septal defect followed by late-onset dilated cardiomyopathy (DCM). We also found that the G247D ACTC1 mutation negatively regulated serum response (SRF)-signaling thereby contributing to the late-onset DCM observed in human patients carrying this mutation (“A cardiac α-actin (ACTC1) p. Gly247Asp mutation inhibits SRF-signaling in vitro in neonatal rat cardiomyocytes” [1]). There are some ACTC1 mutations known to date, majority of which, though, have not been investigated for their functional consequence. We thus aimed at determining the functional impact of various ACTC1 gene mutations on SRF-signaling using SM22-response element driven firefly luciferase activity assays in C2C12 cells. © 2019 The Author(s)

World University root node

Department of Bioscience & Bioengineering

Fulltext

Data in Brief

Data on the role of cardiac α-actin (ACTC1) gene mutations on SRF-signaling

Inherited genetic susceptibility to multiple myeloma has been investigated in a number of studies. Although 23 individual risk loci have been identified, much of the genetic heritability remains unknown. Here we carried out genome-wide interaction analyses on two European cohorts accounting for 3,999 cases and 7,266 controls and characterized genetic susceptibility to multiple myeloma with subsequent meta-analysis that discovered 16 unique interacting loci. These risk loci along with previously known variants explain 17% of the heritability in liability scale. The genes associated with the interacting loci were found to be enriched in transforming growth factor beta signaling and circadian rhythm regulation pathways suggesting immunoglobulin trait modulation, TH17 cell differentiation and bone morphogenesis as mechanistic links between the predisposition markers and intrinsic multiple myeloma biology. Further tissue/cell-type enrichment analysis associated the discovered genes with hemic-immune system tissue types and immune-related cell types indicating overall involvement in immune response. © 2019, The Author(s).

Communications Biology

Genome-wide interaction and pathway-based identification of key regulators in multiple myeloma

MicroRNA (miRNA) is a small non-coding molecule that is involved in gene regulation and RNA silencing by complementary on their targets. Experimental methods for target prediction can be time-consuming and expensive. Thus, the application of the computational approach is implicated to enlighten these complications with experimental studies. However, there is still a need for an optimized approach in miRNA biology. Therefore, machine learning (ML) would initiate a new era of research in miRNA biology towards potential diseases biomarker. In this article, we described the application of ML approaches in miRNA discovery and target prediction with functions and future prospective. The implementation of a new era of computational methodologies in this direction would initiate further advanced levels of discoveries in miRNA. © 2019 Bentham Science Publishers.

Current Genomics

Applications of machine learning in miRNA discovery and target prediction

BACKGROUND: Familial atrial septal defect (ASD) has previously been attributed primarily to mutations in cardiac transcription factors. Here, we report a large, multi-generational family (78 members) with ASD combined with a late-onset dilated cardiomyopathy and further characterize the consequences of mutant α-actin. METHODS: We combined a genome-wide linkage analysis with cell biology, microscopy, and molecular biology tools to characterize a novel ACTC1 (cardiac α-actin) mutation identified in association with ASD and late-onset dilated cardiomyopathy in a large, multi-generational family. RESULTS: Using a genome-wide linkage analysis, the ASD disease locus was mapped to chromosome 15q14 harboring the ACTC1 gene. In 15 affected family members, a heterozygous, nonsynonymous, and fully penetrant mutation (p. Gly247Asp) was identified in exon 5 of ACTC1 that was absent in all healthy family members (n=63). In silico tools predicted deleterious consequences of this variant that was found absent in control databases. Ultrastructural analysis of myocardial tissue of one of the mutation carriers showed sarcomeric disarray, myofibrillar degeneration, and increased apoptosis, while cardiac proteomics revealed a significant increase in extracellular matrix proteins. Consistently, structural defects and increased apoptosis were also observed in neonatal rat ventricular cardiomyocytes overexpressing the mutant, but not native human ACTC1. Molecular dynamics studies and additional mechanistic analyses in cardiomyocytes confirmed actin polymerization/turnover defects, thereby affecting contractility. CONCLUSIONS: A combined phenotype of ASD and late-onset heart failure was caused by a heterozygous, nonsynonymous ACTC1 mutation. Mechanistically, we found a shared molecular mechanism of defective actin signaling and polymerization in both cardiac development and contractile function. Detection of ACTC1 mutations in patients with ASD may thus have further clinical implications with regard to monitoring for (late-onset) dilated cardiomyopathy. © 2019 American Heart Association, Inc.

Circulation: Genomic and Precision Medicine

Cardiac α-actin (ACTC1) gene mutation causes atrial-septal defects associated with late-onset dilated cardiomyopathy

Haematologica

Cytogenetic aberrations in multiple myeloma are associated with shifts in serum immunoglobulin isotypes distribution and levels

Background &amp; Aims The role of tobacco smoke in the etiology of inflammatory bowel disease (IBD) is unclear. We investigated interactions between genes and smoking (gene−smoking interactions) that affect risk for Crohn's disease (CD) and ulcerative colitis (UC) in a case-only study of patients and in mouse models of IBD. Methods We used 55 Immunochip-wide datasets that included 19,735 IBD cases (10,856 CD cases and 8879 UC cases) of known smoking status. We performed 3 meta-analyses each for CD, UC, and IBD (CD and UC combined), comparing data for never vs ever smokers, never vs current smokers, and never vs former smokers. We studied the effects of exposure to cigarette smoke in Il10−/− and Nod2−/− mice, as well as in Balb/c mice without disruption of these genes (wild-type mice). Mice were exposed to the smoke of 5 cigarettes per day, 5 days a week, for 8 weeks, in a ventilated smoking chamber, or ambient air (controls). Intestines were collected and analyzed histologically and by reverse transcription polymerase chain reaction. Results We identified 64 single nucleotide polymorphisms (SNPs) for which the association between the SNP and IBD were modified by smoking behavior (meta-analysis Wald test P &lt; 5.0 × 10−5; heterogeneity Cochrane Q test P &gt;.05). Twenty of these variants were located within the HLA region at 6p21. Analysis of classical HLA alleles (imputed from SNP genotypes) revealed an interaction with smoking. We replicated the interaction of a variant in NOD2 with current smoking in relation to the risk for CD (frameshift variant fs1007insC; rs5743293). We identified 2 variants in the same genomic region (rs2270368 and rs17221417) that interact with smoking in relation to CD risk. Approximately 45% of the SNPs that interact with smoking were in close vicinity (≤1 Mb) to SNPs previously associated with IBD; many were located near or within genes that regulate mucosal barrier function and immune tolerance. Smoking modified the disease risk of some variants in opposite directions for CD vs UC. Exposure of Interleukin 10 (il10)-deficient mice to cigarette smoke accelerated development of colitis and increased expression of interferon gamma in the small intestine compared to wild-type mice exposed to smoke. NOD2-deficient mice exposed to cigarette smoke developed ileitis, characterized by increased expression of interferon gamma, compared to wild-type mice exposed to smoke. Conclusions In an analysis of 55 Immunochip-wide datasets, we identified 64 SNPs whose association with risk for IBD is modified by tobacco smoking. Gene−smoking interactions were confirmed in mice with disruption of Il10 and Nod2—variants of these genes have been associated with risk for IBD. Our findings from mice and humans revealed that the effects of smoking on risk for IBD depend on genetic variants. © 2017 AGA Institute

Gastroenterology

Genetic Factors Interact With Tobacco Smoke to Modify Risk for Inflammatory Bowel Disease in Humans and Mice

Gene–environment (G × E) interactions have been invoked to account, at least in part, for the gap between the known heritability of common human diseases and the phenotypic variation hitherto explained by genetic variants. Noteworthy in this context, a case-only (CO) design has been proposed in the past as a means to detect G × E interactions possibly more efficiently than by using classical case–control and cohort designs. So far, however, most CO studies have followed a candidate (or single) gene approach, and the genome-wide utility of the CO design is still more or less unknown. In particular, the way in which linkage disequilibrium (LD) impacts upon the chance to detect G × E interaction through the analysis of proxy markers has not been studied in much detail before. Therefore, we systematically assessed the power to indirectly detect a given G × E interaction through exploiting LD in a CO design. Our simulations revealed a strong relationship between LD and detection power that was subsequently validated in a real colorectal cancer data set. © 2014, Springer-Verlag Berlin Heidelberg.

Human Genetics

The role of linkage disequilibrium in case-only studies of gene–environment interactions

Gene–environment interactions (G × E) have attracted considerable research interest in the past owing to their scientific and public health implications, but powerful statistical methods are required to successfully track down G × E, particularly at a genome-wide level. Previously, a case-only (CO) design has been proposed as a means to identify G × E with greater efficiency than traditional case–control or cohort studies. However, as with genotype–phenotype association studies themselves, hidden population stratification (PS) can impact the validity of G × E studies using a CO design. Since this problem has been subject to little research to date, we used comprehensive simulation to systematically assess the type I error rate, power and effect size bias of CO studies of G × E in the presence of PS. Three types of PS were considered, namely genetic-only (PSG), environment-only (PSE), and joint genetic and environmental stratification (PSGE). Our results reveal that the type I error rate of an unadjusted Wald test, appropriate for the CO design, would be close to its nominal level (0.05 in our study) as long as PS involves only one interaction partner (i.e., either PSG or PSE). In contrast, if the study population is stratified with respect to both G and E (i.e., if there is PSGE), then the type I error rate is seriously inflated and estimates of the underlying G × E interaction are biased. Comparison of CO to a family-based case–parents design confirmed that the latter is more robust against PSGE, as expected. However, case–parent trios may be particularly unsuitable for G × E studies in view of the fact that they require genotype data from parents and that many diseases with an environmental component are likely to be of late onset. An alternative approach to adjusting for PS is principal component analysis (PCA), which has been widely used for this very purpose in past genome-wide association studies (GWAS). However, resolving genetic PS properly by PCA requires genetic data at the population level, the availability of which would conflict with the basic idea of the CO design. Therefore, we explored three modified Wald test statistics, inspired by the genomic control (GC) approach to GWAS, as an alternative means to allow for PSGE. The modified statistics were benchmarked against a stratified Wald test assuming known population affiliation, which should provide maximum power under PS. Our results confirm that GC is capable of successfully and efficiently correcting the PS-induced inflation of the type I error rate in CO studies of G × E. © 2015, Springer-Verlag Berlin Heidelberg.

Allowing for population stratification in case-only studies of gene–environment interaction, using genomic control

India became one of the most COVID-19 affected countries with more than 4 million infected cases and 71,000 deaths by September 2020. We studied the temporal dynamics and geographic distribution of SARS-CoV-2 subtypes in India. Moreover, we analysed the RGD motif and D614G mutation in the spike protein of SARS-CoV-2. We used a previously proposed viral subtyping method based upon informative subtype markers (ISMs). The ISMs were identified on the basis of information entropy using 94,515 genome sequences of SARS-CoV-2 available publicly at the Global Initiative on Sharing All Influenza Data (GISAID). We identified 11 distinct positions in the SARS-CoV-2 genomes for defining ISMs resulting in 798 unique ISMs. The most abundant ISM in India was transferred from European countries. In contrast, the second most abundant ISM in India was found to be transferred via Australia. Moreover, the eastern regions in India were infected by the ISM most abundant in China due to geographical linkage. Our analysis confirmed higher rates of new cases in the countries abundant with S-G614 strain compared to countries with abundant S-D614 strain. In India, overall S-G614 was most prevalent compared to S-D614, except a few regions including New Delhi, Bihar, and Rajasthan.