Highly perturbed genes and hub genes associated with type 2 diabetes in different tissues of adult humans : a bioinformatics analytic workflow
(2022) In Functional & integrative genomics 22(5). p.1003-1029- 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... (More)
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.
(Less)
- author
- De Silva, Kushan ; Demmer, Ryan T ; Jönsson, Daniel LU ; Mousa, Aya ; Forbes, Andrew and Enticott, Joanne
- organization
- publishing date
- 2022-10
- type
- Contribution to journal
- publication status
- published
- subject
- keywords
- COVID-19, Computational Biology, Diabetes Mellitus, Type 2/genetics, Humans, Insulin, Meta-Analysis as Topic, Systematic Reviews as Topic, Workflow
- in
- Functional & integrative genomics
- volume
- 22
- issue
- 5
- pages
- 1003 - 1029
- publisher
- Springer Nature
- external identifiers
-
- pmid:35788821
- scopus:85133495332
- ISSN
- 1438-793X
- DOI
- 10.1007/s10142-022-00881-5
- language
- English
- LU publication?
- yes
- additional info
- © 2022. The Author(s).
- id
- d9dfd059-d3e9-4b24-a7b2-c108ec24f4a0
- date added to LUP
- 2024-07-04 10:29:28
- date last changed
- 2024-07-05 04:02:15
@article{d9dfd059-d3e9-4b24-a7b2-c108ec24f4a0,
abstract = {{<p>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.</p>}},
author = {{De Silva, Kushan and Demmer, Ryan T and Jönsson, Daniel and Mousa, Aya and Forbes, Andrew and Enticott, Joanne}},
issn = {{1438-793X}},
keywords = {{COVID-19; Computational Biology; Diabetes Mellitus, Type 2/genetics; Humans; Insulin; Meta-Analysis as Topic; Systematic Reviews as Topic; Workflow}},
language = {{eng}},
number = {{5}},
pages = {{1003--1029}},
publisher = {{Springer Nature}},
series = {{Functional & integrative genomics}},
title = {{Highly perturbed genes and hub genes associated with type 2 diabetes in different tissues of adult humans : a bioinformatics analytic workflow}},
url = {{http://dx.doi.org/10.1007/s10142-022-00881-5}},
doi = {{10.1007/s10142-022-00881-5}},
volume = {{22}},
year = {{2022}},
}