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Metabolomic profiling of spontaneous macaque model for diabetes mellitus

ABSTRACT: The prevalence of diabetes mellitus has been increasing for decades worldwide. To develop safe and potent therapeutics, insights into the mechanisms underlying its pathogenesis are urgently needed. We reported the multi-omics profiling of the liver and sera of both peripheral blood and hepatic portal vein blood from Macaca fascicularis with spontaneous diabetes mellitus with a chow diet. The other two groups of the monkeys fed with chow diet and high-sugar high-fat (HSHF) diet, respectively, were included for comparison. These multi-omics datasets can provide a comprehensive picture of the molecular changes caused by diabetes in primates. Analyses of various omics datasets revealed the alterations of high consistency. Correlation between transcripts and proteins derived from the same genes was observed across individuals for most genes, especially the ones of differential expression. As a result, we found that distinct patterns of the metabolome, proteome, and transcriptome between spontaneous diabetes and HSHF diet-induced diabetes compared with healthy individuals.

ORGANISM(S): Macaca Fascicularis Macaque Monkey

TISSUE(S): Liver, Blood

DISEASE(S): Diabetes

SUBMITTER: Zhu Yang

PROVIDER: ST001935 | MetabolomicsWorkbench | Sat Sep 18 00:00:00 BST 2021

REPOSITORIES: MetabolomicsWorkbench

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Project description:Recent discovery reveals HFD insult can cause insulin resistance very rapidly, but the underlying mechanism is still not well understood. We performed a short term experiment in a Diet Induced Insulin resistance mouse model. Objective: Insulin resistance (IR) is one of the earliest predictors of type 2 diabetes. However, diagnosis of IR is limited. High fat fed mouse models provide key insights into IR. We hypothesized that early features of IR are associated with persistent changes in gene expression (GE) and endeavoured to (a) develop novel methods for improving signal:noise in analysis of human GE using mouse models; (b) identify a GE motif that accurately diagnoses IR in humans; and (c) identify novel biology associated with IR in humans. Methods: We integrated human muscle GE data with longitudinal mouse GE data and developed an unbiased three-level cross-species analysis platform (single-gene, gene-set and networks) to generate a gene expression motif (GEM) indicative of IR. A logistic regression classification model validated GEM in 3 independent human datasets (n =115). Results: This GEM of 93 genes substantially improved diagnosis of IR compared to routine clinical measures across multiple independent datasets. Individuals misclassified by GEM possessed other metabolic features raising the possibility that they represent a separate metabolic subclass. The GEM was enriched in pathways previously implicated in insulin action and revealed novel associations between Î²-catenin and Jak1 and IR. Functional analyses using small molecule inhibitors showed an important role for these proteins in insulin action. Conclusions: This study shows that systems approaches for identifying molecular signatures provides a powerful way to stratify individuals into discrete metabolic groups. Moreover, we speculate that the Î²-catenin pathway may represent a novel biomarker for IR in humans that warrant future investigation. Comparison of gene expression in muscle tissue during High Fat Diet (HFD) time course (day 5 and day 42). Chow diet will serve as control for HFD. 4 samples per group serve as experimental replicates.

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Metabolomic profiling of spontaneous macaque model for diabetes mellitus

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