Highly perturbed genes and hub genes associated with type 2 diabetes in different tissues of adult humans: a bioinformatics analytic ... View Full Text


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Article Info

DATE

2022-07-05

AUTHORS

Kushan De Silva, Ryan T. Demmer, Daniel Jönsson, Aya Mousa, Andrew Forbes, Joanne Enticott

ABSTRACT

Type 2 diabetes (T2D) has a complex etiology which is not yet fully elucidated. The identification of gene perturbations and hub genes of T2D may deepen our understanding of its genetic basis. We aimed to identify highly perturbed genes and hub genes associated with T2D via an extensive bioinformatics analytic workflow consisting of five steps: systematic review of Gene Expression Omnibus and associated literature; identification and classification of differentially expressed genes (DEGs); identification of highly perturbed genes via meta-analysis; identification of hub genes via network analysis; and downstream analysis of highly perturbed genes and hub genes. Three meta-analytic strategies, random effects model, vote-counting approach, and p value combining approach, were applied. Hub genes were defined as those nodes having above-average betweenness, closeness, and degree in the network. Downstream analyses included gene ontologies, Kyoto Encyclopedia of Genes and Genomes pathways, metabolomics, COVID-19-related gene sets, and Genotype-Tissue Expression profiles. Analysis of 27 eligible microarrays identified 6284 DEGs (4592 downregulated and 1692 upregulated) in four tissue types. Tissue-specific gene expression was significantly greater than tissue non-specific (shared) gene expression. Analyses revealed 79 highly perturbed genes and 28 hub genes. Downstream analyses identified enrichments of shared genes with certain other diabetes phenotypes; insulin synthesis and action-related pathways and metabolomics; mechanistic associations with apoptosis and immunity-related pathways; COVID-19-related gene sets; and cell types demonstrating over- and under-expression of marker genes of T2D. Our approach provided valuable insights on T2D pathogenesis and pathophysiological manifestations. Broader utility of this pipeline beyond T2D is envisaged. More... »

PAGES

1-27

References to SciGraph publications

  • 2020-10-12. Pathogenic variants in actionable MODY genes are associated with type 2 diabetes in NATURE METABOLISM
  • 2017-10-05. Human amylin induces CD4+Foxp3+ regulatory T cells in the protection from autoimmune diabetes in IMMUNOLOGIC RESEARCH
  • 2019-10-09. Identifying significantly impacted pathways: a comprehensive review and assessment in GENOME BIOLOGY
  • 2020-05-18. Circulating lymphocytes and monocytes transcriptomic analysis of patients with type 2 diabetes mellitus, dyslipidemia and periodontitis in SCIENTIFIC REPORTS
  • 2017-11-28. Systems biology of the IMIDIA biobank from organ donors and pancreatectomised patients defines a novel transcriptomic signature of islets from individuals with type 2 diabetes in DIABETOLOGIA
  • 2019-03-28. Topconfects: a package for confident effect sizes in differential expression analysis provides a more biologically useful ranked gene list in GENOME BIOLOGY
  • 2011-08-04. High glucose-induced apoptosis in human coronary artery endothelial cells involves up-regulation of death receptors in CARDIOVASCULAR DIABETOLOGY
  • 2020-06-16. A novel gene in early childhood diabetes: EDEM2 silencing decreases SLC2A2 and PXD1 expression, leading to impaired insulin secretion in MOLECULAR GENETICS AND GENOMICS
  • 2008-06-27. GeneMANIA: a real-time multiple association network integration algorithm for predicting gene function in GENOME BIOLOGY
  • 2012-06-02. SLC2A2 mutations can cause neonatal diabetes, suggesting GLUT2 may have a role in human insulin secretion in DIABETOLOGIA
  • 2017-12-08. Global aetiology and epidemiology of type 2 diabetes mellitus and its complications in NATURE REVIEWS ENDOCRINOLOGY
  • 2008-04-29. Gene Ontology annotations: what they mean and where they come from in BMC BIOINFORMATICS
  • 2013-05-29. The Genotype-Tissue Expression (GTEx) project in NATURE GENETICS
  • 2008-06-27. A critical assessment of Mus musculusgene function prediction using integrated genomic evidence in GENOME BIOLOGY
  • 2014-09-05. Overlap of Genetic Susceptibility to Type 1 Diabetes, Type 2 Diabetes, and Latent Autoimmune Diabetes in Adults in CURRENT DIABETES REPORTS
  • 2013-04-15. Enrichr: interactive and collaborative HTML5 gene list enrichment analysis tool in BMC BIOINFORMATICS
  • 2021-03-17. Defining the underlying defect in insulin action in type 2 diabetes in DIABETOLOGIA
  • 2016-04-15. Common and rare forms of diabetes mellitus: towards a continuum of diabetes subtypes in NATURE REVIEWS ENDOCRINOLOGY
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    74 marker genes
    75 mechanistic association
    76 meta-analytic strategy
    77 metabolomics
    78 microarray
    79 model
    80 network
    81 network analysis
    82 nodes
    83 ontology
    84 p-value
    85 pathogenesis
    86 pathophysiological manifestations
    87 pathway
    88 perturbations
    89 phenotype
    90 pipeline
    91 profile
    92 random-effects model
    93 review
    94 set
    95 step
    96 strategies
    97 synthesis
    98 systematic review
    99 tissue
    100 tissue types
    101 tissue-specific gene expression
    102 type 2 diabetes
    103 types
    104 understanding
    105 utility
    106 valuable insights
    107 values
    108 vote-counting approach
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