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A Gene Expression Network Model of Type 2 Diabetes Links Cell Cycle

ABSTRACT: Insulin resistance is necessary but not sufficient for the development of type 2 diabetes. Diabetes results when pancreatic beta-cells fail to compensate for insulin resistance by increasing insulin production through an expansion of beta-cell mass or increased insulin secretion. Communication between insulin target tissues and beta-cells may initiate this compensatory response. Correlated changes in gene expression between tissues can provide evidence for such intercellular communication. We profiled gene expression in six tissues of mice from an obesity-induced diabetes-resistant and a diabetes-susceptible strain before and after the onset of diabetes. We studied the correlation structure of mRNA abundance and identified 105 co-expression gene modules. We provide an interactive gene network model showing the correlation structure between the expression modules within and among the six tissues. This resource also provides a searchable database of gene expression profiles for all genes in six tissues in lean and obese diabetes-resistant and diabetes-susceptible mice, at 4 and 10 weeks of age. A cell cycle regulatory module in islets predicts diabetes susceptibility. The module predicts islet replication; we found a strong correlation between ^2 H_2 O incorporation into islet DNA /in vivo/ and the expression pattern of the cell cycle module. This pattern is highly correlated with that of several individual genes in insulin target tissues, including IGF2, which has been shown to promote beta-cell proliferation, suggesting that these genes may provide a link between insulin resistance and beta-cell proliferation. Keywords: time course, mouse strain comparison, effect of obesity,

ORGANISM(S): Mus musculus

PROVIDER: GSE10785 | GEO | 2008/03/15

SECONDARY ACCESSION(S): PRJNA107473

REPOSITORIES: GEO

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Project description:Insulin resistance is necessary but not sufficient for the development of type 2 diabetes. Diabetes results when pancreatic beta-cells fail to compensate for insulin resistance by increasing insulin production through an expansion of beta-cell mass or increased insulin secretion. Communication between insulin target tissues and beta-cells may initiate this compensatory response. Correlated changes in gene expression between tissues can provide evidence for such intercellular communication. We profiled gene expression in six tissues of mice from an obesity-induced diabetes-resistant and a diabetes-susceptible strain before and after the onset of diabetes. We studied the correlation structure of mRNA abundance and identified 105 co-expression gene modules. We provide an interactive gene network model showing the correlation structure between the expression modules within and among the six tissues. This resource also provides a searchable database of gene expression profiles for all genes in six tissues in lean and obese diabetes-resistant and diabetes-susceptible mice, at 4 and 10 weeks of age. A cell cycle regulatory module in islets predicts diabetes susceptibility. The module predicts islet replication; we found a strong correlation between ^2 H_2 O incorporation into islet DNA /in vivo/ and the expression pattern of the cell cycle module. This pattern is highly correlated with that of several individual genes in insulin target tissues, including IGF2, which has been shown to promote beta-cell proliferation, suggesting that these genes may provide a link between insulin resistance and beta-cell proliferation. Keywords: time course, mouse strain comparison, effect of obesity, Type 2 diabetes is a disorder that involves an increased demand for insulin brought about by insulin resistance, together with a failure to compensate with sufficient insulin production. Although Insulin resistance occurs in most obese individuals, diabetes is generally forestalled through compensation with increased insulin. This increase in insulin occurs through an expansion of beta-cell mass and/or increased insulin secretion by individual beta-cells. Failure to compensate for insulin resistance leads to type 2 diabetes. One way to understand the pathophysiology of diabetes is to examine the coordinate changes in gene expression that occur in insulin-responsive tissues and pancreatic islets in obese animals that either compensate for insulin resistance or progress to type 2 diabetes. In each case, there are groups of genes that undergo changes in expression in a highly correlated fashion. By identifying groups of correlated transcripts (gene expression modules) during the compensation and development of diabetes, we can gain insight into potential pathways and regulatory networks in obesity-induced diabetes. We study two strains of mice that differ in obesity-induced diabetes susceptibility. In this study, we surveyed gene expression in six tissues of lean and obese C57BL/6 (B6) and BTBR mice aged 4 wks and 10 wks. B6 mice remain essentially non-diabetic at all ages, irrespective of obesity. When obese, BTBR mice become severely diabetic by 10 weeks of age. By analyzing the correlation structure of the genes under three contrast conditions, obesity, strain, and age, we identified gene expression modules associated with the onset of diabetes and provide an interactive co-expression network model of type 2 diabetes. We found a key module that is comprised of cell cycle regulatory genes. In the islet, the expression profile of these transcripts accurately predicts diabetes and is highly correlated with islet cell proliferation.

Project description:Purpose: Hyperinsulinemia and insulin resistance are co-existing characteristics of type 2 diabetes, whereas the forerunner initiating the deleterious cycle remains elusive. The temporal transcriptomic landscape of islets (responsible for hyperinsulinemia) and liver (involved in insulin resistance) could provide new insights. Methods: C57BL/6N mice were fed a 60% high-fat diet (HFD) or control diet (CD) for 24 weeks. RNA-sequencing of islet and liver samples were respectively performed in quadruplicates at six consecutive time points of diet treatments (week 4, 8, 12, 16, 20 and 24), using BGISEQ-500 sequencing platform by the Beijing Genomics Institute (Shenzhen, China).The sequencing raw reads were filtered for low-quality, adaptor-polluted, high content of unknown base reads by SOAPnuke (v1.5.2). We used Trinity (v2.0.6) to perform de novo assembly, and Tgicl (v2.0.6) on cluster transcripts to remove redundancy and get unigenes. The high-quality clean reads were then mapped to the mouse reference genome (GRCm38) via HISAT2 (v2.0.4) and full-length transcriptome database via Bowtie2 (v2.2.5). The gene expression levels were then quantified by RSEM (v1.1.12) and were normalized by the method of fragments per kilobase of exon model per million reads mapped (FPKM). To interpret the functional significance of differentially expressed genes (DEGs), pathway analyses was conducted to determine enriched canonical pathways. Results: Combined analyses of all 96 samples yielded the identification of 21990 annotated genes. Differentially expressed genes (DEGs) between the two groups (HFD vs. CD) at each time point were identified using the criteria of fold change ≥2 and adjusted P-value ≤0.05. In total, 3844 DEGs were found in islets, of which 33 were shared among all six time points. With regard to liver, 4101 DEGs were discovered throughout 24 weeks of feeding, of which 39 were overlapped. Our islet and liver RNA-sequencing datasets outlined the impact of HFD on dynamics of molecular network at different stages. Correlation analyses of islet and liver modules with metabolic phenotypes illustrated that these two tissues jointly program β-cell adaption to irreversible impairment. Top scored networks combining islet and liver transcriptomes showed potential interactions of genes implicated in cell cycle during week 4, organismal development around week 12, and immune cell trafficking at week 24. Conclusions: Our data provide a comprehensive landscape of crosstalk between islets and liver in diet-induced diabetes, linking to the development of islet dysfunction and insulin resistance.

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A Gene Expression Network Model of Type 2 Diabetes Links Cell Cycle

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