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Network analysis reveals crosstalk between autophagy genes and disease genes.

ABSTRACT: Autophagy is a protective and life-sustaining process in which cytoplasmic components are packaged into double-membrane vesicles and targeted to lysosomes for degradation. Accumulating evidence supports that autophagy is associated with several pathological conditions. However, research on the functional cross-links between autophagy and disease genes remains in its early stages. In this study, we constructed a disease-autophagy network (DAN) by integrating known disease genes, known autophagy genes and protein-protein interactions (PPI). Dissecting the topological properties of the DAN suggested that nodes that both autophagy and disease genes (inter-genes), are topologically important in the DAN structure. Next, a core network from the DAN was extracted to analyze the functional links between disease and autophagy genes. The genes in the core network were significantly enriched in multiple disease-related pathways, suggesting that autophagy genes may function in various disease processes. Of 17 disease classes, 11 significantly overlapped with autophagy genes, including cancer diseases, metabolic diseases and hematological diseases, a finding that is supported by the literatures. We also found that autophagy genes have a bridging role in the connections between pairs of disease classes. Altogether, our study provides a better understanding of the molecular mechanisms underlying human diseases and the autophagy process.

SUBMITTER: Wang JY

PROVIDER: S-EPMC5353691 | biostudies-literature | 2017 Mar

REPOSITORIES: biostudies-literature

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Network analysis reveals crosstalk between autophagy genes and disease genes.

Wang Ji-Ye JY Yao Wei-Xuan WX Wang Yun Y Fan Yi-Lei YL Wu Jian-Bing JB

Scientific reports 20170315

Autophagy is a protective and life-sustaining process in which cytoplasmic components are packaged into double-membrane vesicles and targeted to lysosomes for degradation. Accumulating evidence supports that autophagy is associated with several pathological conditions. However, research on the functional cross-links between autophagy and disease genes remains in its early stages. In this study, we constructed a disease-autophagy network (DAN) by integrating known disease genes, known autophagy g ...[more]

PMID: 28295050

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Project description:BackgroundAt present, there is a paucity of research on the link between Crohn's disease (CD) and atrial fibrillation (AF). Nevertheless, both ailments are thought to entail inflammatory and autoimmune processes, and emerging evidence indicates that individuals with CD may face an elevated risk of AF. To shed light on this issue, our study seeks to explore the possibility of shared genes, pathways, and immune cells between these two conditions.MethodsWe retrieved the gene expression profiles of both CD and AF from the Gene Expression Omnibus (GEO) database and subjected them to analysis. Afterward, we utilized the weighted gene co-expression network analysis (WGCNA) to identify shared genes, which were then subjected to further Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses. Furthermore, we employed a rigorous analytical approach by screening hub genes through both least absolute shrinkage and selection operator (LASSO) regression and support vector machine (SVM), and subsequently constructing a receiver operating characteristic (ROC) curve based on the screening outcomes. Finally, we utilized single-sample gene set enrichment analysis (ssGSEA) to comprehensively evaluate the levels of infiltration of 28 immune cells within the expression profile and their potential association with the shared hub genes.ResultsUsing the WGCNA method, we identified 30 genes that appear to be involved in the pathological progression of both AF and CD. Through GO enrichment analysis on the key gene modules derived from WGCNA, we observed a significant enrichment of pathways related to major histocompatibility complex (MHC) and antigen processing. By leveraging the intersection of LASSO and SVM algorithms, we were able to pinpoint two overlapping genes, namely CXCL16 and HLA-DPB1. Additionally, we evaluated the infiltration of immune cells and observed the upregulation of CD4+ and CD8+ T cells, as well as dendritic cells in patients with AF and CD.ConclusionsBy employing bioinformatics tools, we conducted an investigation with the objective of elucidating the genetic foundations that connect AF and CD. This study culminated in the identification of CXCL16 and HLA-DPB1 as the most substantial genes implicated in the development of both disorders. Our findings suggest that the immune responses mediated by CD4+ and CD8+ T cells, along with dendritic cells, may hold a crucial role in the intricate interplay between AF and CD.

Project description:MicroRNAs (miRNAs/miRs) are small non‑coding RNAs (ncRNAs) that regulate gene expression. Emerging knowledge has suggested that miRNAs have a role in the pathogenesis of metabolic disorders, supporting the hypothesis that miRNAs may represent potential biomarkers or targets for this set of diseases. However, the current evidence is often controversial. Therefore, the aim of the present study was to determine the associations between miRNAs‑target genes, miRNA‑long ncRNAs (lncRNAs), and miRNAs‑small molecules in human metabolic diseases, including obesity, type 2 diabetes and non‑alcoholic fatty liver disease. The metabolic disease‑related miRNAs were obtained from the Human MicroRNA Disease Database (HMDD) and miR2Disease database. A search on the databases Matrix Decomposition and Heterogeneous Graph Inference (MDHGI) and DisGeNET were also performed. miRNAs target genes were obtained from three independent sources: Microcosm, TargetScan and miRTarBase. The interactions between miRNAs‑lncRNA and miRNA‑small molecules were performed using the miRNet web tool. The network analyses were performed using Cytoscape software. As a result, a total of 20 miRNAs were revealed to be associated with metabolic disorders in the present study. Notably, 6 miRNAs (miR‑17‑5p, miR‑29c‑3p, miR‑34a‑5p, miR‑103a‑3p, miR‑107 and miR‑132‑3p) were found in the four resources (HMDD, miR2Disease, MDHGI, and DisGeNET) used for these analyses, presenting a stronger association with the diseases. Furthermore, the target genes of these miRNAs participate in several pathways previously associated with metabolic diseases. In addition, interactions between miRNA‑lncRNA and miRNA‑small molecules were also found, suggesting that some molecules can modulate gene expression via such an indirect way. Thus, the results of this data mining and integration analysis provide further information on the possible molecular basis of the metabolic disease pathogenesis as well as provide a path to search for potential biomarkers and therapeutic targets concerning metabolic diseases.

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Network analysis reveals crosstalk between autophagy genes and disease genes.

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Network analysis reveals crosstalk between autophagy genes and disease genes.

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