Transomics analysis reveals allosteric and gene regulation axes for altered hepatic glucose-responsive metabolism in obesity

Abstract

Impaired glucose tolerance associated with obesity causes postprandial hyperglycemia and can lead to type 2 diabetes. To study the differences in liver metabolism in healthy and obese states, we constructed and analyzed transomics glucose-responsive metabolic networks with layers for metabolites, expression data for metabolic enzyme genes, transcription factors, and insulin signaling proteins from the livers of healthy and obese mice. We integrated multiomics time course data from wild-type and leptin-deficient obese (ob/ob) mice after orally administered glucose. In wild-type mice, metabolic reactions were rapidly regulated within 10 min of oral glucose administration by glucose-responsive metabolites, which functioned as allosteric regulators and substrates of metabolic enzymes, and by Akt-induced changes in the expression of glucose-responsive genes encoding metabolic enzymes. In ob/ob mice, the majority of rapid regulation by glucose-responsive metabolites was absent. Instead, glucose administration produced slow changes in the expression of carbohydrate, lipid, and amino acid metabolic enzyme–encoding genes to alter metabolic reactions on a time scale of hours. Few regulatory events occurred in both healthy and obese mice. Thus, our transomics network analysis revealed that regulation of glucose-responsive liver metabolism is mediated through different mechanisms in healthy and obese states. Rapid changes in allosteric regulators and substrates and in gene expression dominate the healthy state, whereas slow changes in gene expression dominate the obese state.

Metabolomic and transcriptomic profiling of adult mice and larval zebrafish leptin mutants reveal a common pattern of changes in metabolites and signaling pathways

“…7 A). A recent paper published by Kokaji et al reported the transcriptomes of mice liver from ten-week-old male ob/ob mutant and C57BL/6 wild type mice [ 13 ]. The comparison of the two mice liver groups showed 6693 genes significantly regulated at a p value < 0.05 (Additional file 1 : Figure S4).…”

“…Using a positional isotopomer NMR tracer analysis method, Perry et al showed that leptin mediates a glucose-fatty acid cycle to maintain glucose homeostasis in starvation in rats [ 12 ]. Using a combination of metabolomics and transcriptomics, a recent published paper demonstrates that the carbohydrate, lipid and amino acid metabolic liver responses to glucose administration are broadly different between wild type and ob/ob mice [ 13 ].…”

Multi-omics-based label-free metabolic flux inference reveals obesity-associated dysregulatory mechanisms in liver glucose metabolism

“…However, both groups reached a steady state four hours after oral glucose administration. The transcriptomic data were reported in our previous studies (Egami et al, 2021; Kokaji et al, 2020), and the metabolomic and proteomic data were newly obtained in this study (Materials and Methods). We selected 28 metabolites, 15 enzymes, and 17 transcripts relevant to glucose metabolism from the metabolomic, proteomic, and transcriptomic data, respectively (Table S2).…”

“…Liver-specific knockdown of CREB reduced fasting plasma glucose concentrations in ob / ob mice through downregulation of G6pc and Fbp1 (Erion et al, 2009). In addition to these key transcription factors identified by individual experiments, high-throughput measurements and multi-omic analyses have revealed many more transcription factors involved in metabolic alteration associated with obesity (Egami et al, 2021; Kokaji et al, 2020; Soltis et al, 2017). Although transcription factors contribute changes in metabolic flux in glucose metabolism associated with obesity, metabolic flux is also regulated by metabolites that include substrates, products, cofactors, and allosteric effectors.…”

“…The amounts of enzymes can be measured by mass spectrometry-based proteomics, transcripts for enzymes by RNA sequencing, and the amounts of substrates, products, cofactors, and allosteric effectors by mass spectrometry-based metabolomics. We developed a method of trans-omic analysis based on direct molecular interactions to construct a multilayered biochemical network using simultaneously obtained multi-omic data (Egami et al, 2021; Kawata et al, 2018; Kokaji et al, 2020; Yugi et al, 2014, 2016). This trans-omic approach was not used to infer metabolic fluxes.…”