Identification of key gene pathways and coexpression networks of islets in human type 2 diabetes

Li, Lu; Pan, Zongfu; Yang, Shuanying; Shan, Wenya; Yang, Yanyan

doi:10.2147/dmso.s178894

Cited by 9 publications

(9 citation statements)

References 42 publications

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“…MEDAG is a well-conserved pro-inflammatory protein that promotes adipocyte differentiation and regulates adipocyte glucose uptake [ 55 ]. Islet expression of MEDAG is different between diabetic and healthy individuals [ 56 ], and copy number variation in the MEDAG loci is associated with obesity [ 57 ]. SPP1, also known as osteopontin, is a multifunctional protein involved in biomineralization and bone remodeling.…”

Section: Discussionmentioning

confidence: 99%

Divergent genes in gerbils: prevalence, relation to GC-biased substitution, and phenotypic relevance

2020

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Background Two gerbil species, sand rat (Psammomys obesus) and Mongolian jird (Meriones unguiculatus), can become obese and show signs of metabolic dysregulation when maintained on standard laboratory diets. The genetic basis of this phenotype is unknown. Recently, genome sequencing has uncovered very unusual regions of high guanine and cytosine (GC) content scattered across the sand rat genome, most likely generated by extreme and localized biased gene conversion. A key pancreatic transcription factor PDX1 is encoded by a gene in the most extreme GC-rich region, is remarkably divergent and exhibits altered biochemical properties. Here, we ask if gerbils have proteins in addition to PDX1 that are aberrantly divergent in amino acid sequence, whether they have also become divergent due to GC-biased nucleotide changes, and whether these proteins could plausibly be connected to metabolic dysfunction exhibited by gerbils. Results We analyzed ~ 10,000 proteins with 1-to-1 orthologues in human and rodents and identified 50 proteins that accumulated unusually high levels of amino acid change in the sand rat and 41 in Mongolian jird. We show that more than half of the aberrantly divergent proteins are associated with GC biased nucleotide change and many are in previously defined high GC regions. We highlight four aberrantly divergent gerbil proteins, PDX1, INSR, MEDAG and SPP1, that may plausibly be associated with dietary metabolism. Conclusions We show that through the course of gerbil evolution, many aberrantly divergent proteins have accumulated in the gerbil lineage, and GC-biased nucleotide substitution rather than positive selection is the likely cause of extreme divergence in more than half of these. Some proteins carry putatively deleterious changes that could be associated with metabolic and physiological phenotypes observed in some gerbil species. We propose that these animals provide a useful model to study the ‘tug-of-war’ between natural selection and the excessive accumulation of deleterious substitutions mutations through biased gene conversion.

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Section: Discussionmentioning

confidence: 99%

Divergent genes in gerbils: prevalence, relation to GC-biased substitution, and phenotypic relevance

2020

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“…Weighted gene correlation network analysis (WGCNA) is applied to reveal the correlation patterns among genes in microarray samples as previous described. We used the WGCNA package in R software (https://www.r-project.org/) to carry out weighted correlation network analysis of all genes in GSE15653.…”

Section: Methodsmentioning

confidence: 99%

Key genes and co‐expression network analysis in the livers of type 2 diabetes patients

Pan

Yang

2019

J of Diabetes Invest

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Aims/Introduction The incidence of type 2 diabetes is increasing worldwide. Hepatic insulin resistance and liver lipid accumulation contributes to type 2 diabetes development. The aim of the present study was to investigate the key gene pathways and co‐expression networks in the livers of type 2 diabetes patients. Materials and Methods Dataset GSE 15653 containing nine healthy individuals and nine type 2 diabetes patients was downloaded from the National Center for Biotechnology Information Gene Expression Omnibus database. Differentially expressed genes were obtained from the livers of type 2 diabetes patients, annotated pathway enrichment and protein–protein interaction network analysis. Next, functional modules and transcription factor networks were constructed. Gene co‐expression networks were analyzed by weighted correlation network analysis to identify key modules related to clinical traits, and the candidate key genes were validated in hepatic insulin resistance models in vitro . Results A total of 778 differentially expressed genes were filtered in the livers of type 2 diabetes patients, pathway enrichment analysis identified ke y pathways, such as the mitogen‐activated protein kinase signaling pathway, Hippo signaling pathway and hypoxia‐inducible factor ‐1 signaling pathway, that were associated with type 2 diabetes. Several transcription factors of three functional modules identified from protein–protein interaction networks are likely to be implicated in type 2 diabetes. Furthermore, weighted correlation network analysis identified five modules that were shown to be highly correlated with type 2 diabetes and other clinical traits. Functional annotation showed that these modules were mainly enriched in pathways such as metabolic pathways, phosphoinositide 3‐kinase‐protein kinase B signaling pathway and natural killer cell‐mediated cytotoxicity. UBE 2M and GPER were upregulated in L02 and HepG2 models, whereas P2 RY 11 only upregulated in L02 model, and UBE 2N only downregulated in HepG2 model at a significant level. Conclusions These results would offer new insights into hepatic insulin resistance, type 2 diabetes pathogenesis, development and drug discovery.

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“…WGCNA was applied to reveal the weighted correlations between genes and clinical features, as previously described 5,13. We used the WGCNA package in R to perform weighted correlation network analysis of all genes of healthy samples, and newly diagnosed T1DM and T2DM patients in GSE9006.…”

Section: Methodsmentioning

confidence: 99%

“…However, so far, there are no effective clinical immune-based therapies to prevent and reverse T1DM 4. Several studies have used gene expression proﬁle analysis to explore the pathological mechanisms and potential treatment of diabetes 5–7. Islet-infiltrating immune effectors and metabolic derangements in T1DM could be sampled in peripheral blood mononuclear cells (PBMCs) 8.…”

Section: Introductionmentioning

confidence: 99%

<p>Identification of dynamic molecular networks in peripheral blood mononuclear cells in type 1 diabetes mellitus</p>

Pan

Yang

2019

DMSO

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Background Type 1 diabetes mellitus (T1DM) is an autoimmune disease caused by the immune destruction of islet β cells. Gene expression in peripheral blood mononuclear cells (PBMCs) could offer new disease and treatment markers in T1DM. The objective of this study was to explore the coexpression and dynamic molecular networks in PBMCs of T1DM patients. Methods Dataset GSE9006 contains PBMC samples of healthy volunteers, newly diagnosed T1DM patients, T1DM patients after insulin treatment, and newly diagnosed type 2 diabetes mellitus (T2DM) patients. Weighted correlation network analysis (WGCNA) was used to generate coexpression networks in T1DM and T2DM. Functional pathways in highly correlated modules of T1DM were enriched by gene set enrichment analysis (GSEA). We next filtered the differentially expressed genes (DEGs) and revealed their dynamic expression profiles in T1DM with or without insulin treatment. Furthermore, dynamic clusters and dynamic protein–protein interaction networks were identified. Kyoto Encyclopedia of Genes and Genomes pathway enrichment analysis was developed in dynamic clusters. Results WGCNA disclosed 12 distinct gene modules, and distinguished between correlated networks in T1DM and T2DM. Two modules were closely associated with T1DM. GSEA showed that the immune response and response to cytokines were enriched in the T1DM highly correlated module. Next, we screened 44 DEGs in newly diagnosed T1DM compared with healthy donors, and 71 DEGs in 1-month and 97 DEGs in 4-month insulin treatment groups compared with newly diagnosed T1DM. Dynamic expression profiles of DEGs indicated the potential targets for T1DM treatment. Moreover, four molecular dynamic clusters were analyzed in newly diagnosed and insulin-treated T1DM. Functional annotation showed that these clusters were mainly enriched in the IL-17 signaling pathway, nuclear factor-ϰB signaling pathway, and tumor necrosis factor signaling pathway. Conclusion The results indicate potential drug targets or clinical efficacy markers, as well as demonstrating the underlying molecular mechanisms of T1DM treatment.

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Identification of key gene pathways and coexpression networks of islets in human type 2 diabetes

Cited by 9 publications

References 42 publications

Divergent genes in gerbils: prevalence, relation to GC-biased substitution, and phenotypic relevance

Divergent genes in gerbils: prevalence, relation to GC-biased substitution, and phenotypic relevance

Key genes and co‐expression network analysis in the livers of type 2 diabetes patients

<p>Identification of dynamic molecular networks in peripheral blood mononuclear cells in type 1 diabetes mellitus</p>

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