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Bioinformatics-driven identification and examination of candidate genes for non-alcoholic fatty liver disease.

ABSTRACT: Candidate genes for non-alcoholic fatty liver disease (NAFLD) identified by a bioinformatics approach were examined for variant associations to quantitative traits of NAFLD-related phenotypes.By integrating public database text mining, trans-organism protein-protein interaction transferal, and information on liver protein expression a protein-protein interaction network was constructed and from this a smaller isolated interactome was identified. Five genes from this interactome were selected for genetic analysis. Twenty-one tag single-nucleotide polymorphisms (SNPs) which captured all common variation in these genes were genotyped in 10,196 Danes, and analyzed for association with NAFLD-related quantitative traits, type 2 diabetes (T2D), central obesity, and WHO-defined metabolic syndrome (MetS).273 genes were included in the protein-protein interaction analysis and EHHADH, ECHS1, HADHA, HADHB, and ACADL were selected for further examination. A total of 10 nominal statistical significant associations (P<0.05) to quantitative metabolic traits were identified. Also, the case-control study showed associations between variation in the five genes and T2D, central obesity, and MetS, respectively. Bonferroni adjustments for multiple testing negated all associations.Using a bioinformatics approach we identified five candidate genes for NAFLD. However, we failed to provide evidence of associations with major effects between SNPs in these five genes and NAFLD-related quantitative traits, T2D, central obesity, and MetS.

SUBMITTER: Banasik K

PROVIDER: S-EPMC3029374 | biostudies-literature | 2011 Jan

REPOSITORIES: biostudies-literature

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Bioinformatics-driven identification and examination of candidate genes for non-alcoholic fatty liver disease.

Banasik Karina K Justesen Johanne M JM Hornbak Malene M Krarup Nikolaj T NT Krarup Nikolaj T NT Gjesing Anette P AP Sandholt Camilla H CH Jensen Thomas S TS Grarup Niels N Andersson Asa A Jørgensen Torben T Witte Daniel R DR Sandbæk Annelli A Lauritzen Torsten T Thorens Bernard B Brunak Søren S Sørensen Thorkild I A TI Pedersen Oluf O Hansen Torben T

PloS one 20110127 1

<h4>Objective</h4>Candidate genes for non-alcoholic fatty liver disease (NAFLD) identified by a bioinformatics approach were examined for variant associations to quantitative traits of NAFLD-related phenotypes.<h4>Research design and methods</h4>By integrating public database text mining, trans-organism protein-protein interaction transferal, and information on liver protein expression a protein-protein interaction network was constructed and from this a smaller isolated interactome was identifi ...[more]

PMID: 21339799

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Project description:BackgroundStudies had shown that autophagy was closely related to nonalcoholic fat liver disease (NAFLD), while N6-methyladenosine (m6A) was involved in the regulation of autophagy. However, the mechanism of m6A related autophagy in NAFLD was unclear.MethodsThe NAFLD related datasets were gained via the Gene Expression Omnibus (GEO) database, and we also extracted 232 autophagy-related genes (ARGs) and 37 m6A. First, differentially expressed ARGs (DE-ARGs) and differentially expressed m6A (DE-m6A) were screened out by differential expression analysis. DE-ARGs associated with m6A were sifted out by Pearson correlation analysis, and the m6A-ARGs relationship pairs were acquired. Then, autophagic genes in m6A-ARGs pairs were analyzed for machine learning algorithms to obtain feature genes. Further, we validated the relationship between feature genes and NAFLD through quantitative real-time polymerase chain reaction (qRT-PCR), Western blot (WB). Finally, the immuno-infiltration analysis was implement, and we also constructed the TF-mRNA and drug-gene networks.ResultsThere were 19 DE-ARGs and four DE-m6A between NAFLD and normal samples. The three m6A genes and five AGRs formed the m6A-ARGs relationship pairs. Afterwards, genes obtained from machine learning algorithms were intersected to yield three feature genes (TBK1, RAB1A, and GOPC), which showed significant positive correlation with astrocytes, macrophages, smooth muscle, and showed significant negative correlation with epithelial cells, and endothelial cells. Besides, qRT-PCR and WB indicate that TBK1, RAB1A and GOPC significantly upregulated in NAFLD. Ultimately, we found that the TF-mRNA network included FOXP1-GOPC, ATF1-RAB1A and other relationship pairs, and eight therapeutic agents such as R-406 and adavosertib were predicted based on the TBK1.ConclusionThe study investigated the potential molecular mechanisms of m6A related autophagy feature genes (TBK1, RAB1A, and GOPC) in NAFLD through bioinformatic analyses and animal model validation. However, it is critical to note that these findings, although consequential, demonstrate correlations rather than cause-and-effect relationships. As such, more research is required to fully elucidate the underlying mechanisms and validate the clinical relevance of these feature genes.

Project description:BackgroundNon-alcoholic fatty liver disease (NAFLD) is the most common cause of chronic liver disease worldwide. Autophagy plays a vital role in NAFLD development and progression. We aimed to establish a novel autophagy-related gene (ARG) signature as a therapeutic target in NAFLD patients based on high-throughput sequencing data.MethodsARGs obtained from the HAMdb and high-sequencing data obtained from the Gene Expression Omnibus (GEO) database were analyzed to identify differentially expressed ARGs (DEARGs) between normal and NASH tissues. Then, gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis were performed to explore potential biological and pathological functions of DEARGs. The protein-protein interaction (PPI) network of the DEARGs was established through the STRING website, and visualized by Cytoscape. In addition, hub genes were validated by an independent dataset GSE89632. Finally, we performed Gene Set Variation Analysis (GSVA) pathway-related analysis to identify the pivotal signaling pathways and genes for the progression of non-alcoholic fatty liver (NAFL) to non-alcoholic steatohepatitis (NASH).ResultsA total of 76 DEARGs were identified in the GSE126848 dataset, of which 45 genes were upregulated and 31 genes were downregulated. GO analysis showed that the biological functions of DEARGs focused primarily on autophagy, cellular response to external stimulus, fibroblast proliferation, late endosome, and ubiquitin protein ligase binding. KEGG pathway analysis showed that these DEARGs were mainly involved in the apoptosis, PI3K-Akt signaling pathway, and estrogen signaling pathway. Among DEGs, 9 most closely related genes were identified from the PPI network. Furthermore, NOS3, IGF1, VAMP8, FOS, and HMOX1 were verified in the GSE89632 dataset. At last, the MAPK signal pathway was identified as important pathway, and JUN was identified as a key gene involved in the progression from NAFL to NASH.ConclusionThis study may provide credible molecular biomarkers in terms of screening and diagnosis for NAFLD. Meanwhile, it also serves as a basis for exploring the molecular mechanisms underlying the progression of NAFL to NASH.

Project description:BackgroundThe morbidity and mortality rates of patients with non-alcoholic fatty liver disease (NAFLD) have been steadily increasing in recent years. Previous studies have confirmed the important role of ferroptosis in NAFLD development; however, the precise mechanism through which ferroptosis influences NAFLD occurrence remains unclear. The present study aimed to identify and validate ferroptosis-related genes involved in NAFLD pathogenesis and to investigate the underlying molecular mechanisms of NAFLD.MethodsWe downloaded microarray datasets GSE72756 and GSE24807 to identify differentially expressed genes (DEGs) between samples from healthy individuals and patients with NAFLD. From these DEGs, we extracted ferroptosis-related DEGs. GSE89632, another microarray dataset, was used to validate the expression of ferroptosis-related genes. A protein-protein interaction (PPI) network of ferroptosis-related genes was then constructed. The target genes were also subjected to Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses. Finally, competing endogenous RNA networks were constructed. We used the CIBERSORT package to evaluate the infiltration of immune cells infiltration in NAFLD.ResultsFive ferroptosis-related genes (SCP2, MUC1, DPP4, SLC1A4, and TF) were identified as promising diagnostic biomarkers for NAFLD. Enrichment analyses revealed that these genes are mainly involved in metabolic processes. NEAT1-miR-1224-5p-SCP2, NEAT1-miR-485-5p-MUC1, MALAT1-miR-485-5p-MUC1, and CNOT6-miR-145-5p-SLC1A4 are likely to be the potential RNA regulatory pathways that affect NAFLD development. Principal component analysis indicated significant differences in immune cell infiltration between the two groups.ConclusionsThis study identified five ferroptosis-related genes as potential biomarkers for diagnosing NAFLD. The correlations between the expression of ferroptosis-related genes and immune cell infiltration might shed light on the study of the molecular mechanism underlying NAFLD development.

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Bioinformatics-driven identification and examination of candidate genes for non-alcoholic fatty liver disease.

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Bioinformatics-driven identification and examination of candidate genes for non-alcoholic fatty liver disease.

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