Background & 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 < 5.0 × 10⁻⁵; heterogeneity Cochrane Q test P > .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.

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.

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.

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, T_H17 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.

Lung cancer is the second most prevalent cancer worldwide and a leading cause of cancer-related deaths. Recent technological advancements have revealed that the lung microbiome, previously thought to be sterile, is host to various microorganisms. The association between the lung microbiome and lung cancer initiation, progression, and metastasis is complex and contradictory. However, disruption in the homeostasis of microbiome compositions correlated with the increased risk of lung cancer. This review summarizes current knowledge about the most recent developments and trends in lung cancer–related microbiota or microbial components. This article aims to provide information on this rapidly evolving field while giving context to the general role of the lung microbiome in lung cancer. In addition, this review briefly discussed the causative association of lung microbiome with lung cancer. We will review the mechanisms by which lung microbiota influence carcinogenesis, focusing on microbiota dysbiosis. Moreover, we will also discuss the host–microbiome interaction as it plays a crucial role in stimulating and regulating the immune response. Finally, we will provide information on the diagnostic role of the microbiome in lung cancer. This article aims to offer an overview of the lung microbiome as a predictive and diagnostic biomarker in lung cancer.

Efficiently mapping of cell types in situ remains a major challenge in spatial transcriptomics. Most spot deconvolution tools ignore spatial coordinate information and perform extremely slow on large datasets. Here, we introduce SpatialPrompt, a spatially aware and scalable tool for spot deconvolution and domain identification. SpatialPrompt integrates gene expression, spatial location, and single-cell RNA sequencing (scRNA-seq) dataset as reference to accurately infer cell-type proportions of spatial spots. SpatialPrompt uses non-negative ridge regression and graph neural network to efficiently capture local microenvironment information. Our extensive benchmarking analysis on Visium, Slide-seq, and MERFISH datasets demonstrated superior performance of SpatialPrompt over 15 existing tools. On mouse hippocampus dataset, SpatialPrompt achieves spot deconvolution and domain identification within 2 minutes for 50,000 spots. Overall, domain identification using SpatialPrompt was 44 to 150 times faster than existing methods. We build a database housing 40 plus curated scRNA-seq datasets for seamless integration with SpatialPrompt for spot deconvolution.