Trans-omic analysis reveals obesity-associated dysregulation of inter-organ metabolic cycles between the liver and skeletal muscle

Abstract

Summary Systemic metabolic homeostasis is regulated by inter-organ metabolic cycles involving multiple organs. Obesity impairs inter-organ metabolic cycles, resulting in metabolic diseases. The systemic landscape of dysregulated inter-organ metabolic cycles in obesity has yet to be explored. Here, we measured the transcriptome, proteome, and metabolome in the liver and skeletal muscle and the metabolome in blood of fasted wild-type and leptin-deficient obese ( ob / ob ) mice, identifying components with differential abundance and differential regulation in ob / ob mice. By constructing and evaluating the trans-omic network controlling the differences in metabolic reactions between fasted wild-type and ob / ob mice, we provided potential mechanisms of the obesity-associated dysfunctions of metabolic cycles between liver and skeletal muscle involving glucose-alanine, glucose-lactate, and ketone bodies. Our study revealed obesity-associated systemic pathological mechanisms of dysfunction of inter-organ metabolic cycles.

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).…”

“…We obtained metabolomic, proteomic and transcriptomic data from livers of WT and ob/ob mice at fasting state and four hours after oral glucose administration (Figure 2), where the blood glucose and insulin remained constant (Figures S1A and S1B). 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 proteins and 17 transcripts in the glucose metabolism from the metabolomic, proteomic and transcriptomic data (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 simultaneously obtained multi-omic data can be used to analyze the regulation of metabolic flux. We have proposed a trans-omic analysis for the construction of a global biochemical network using the simultaneously obtained multi-omic data based on direct molecular interaction (Egami et al, 2021;Kawata et al, 2018;Kokaji et al, 2020;Yugi et al, 2014Yugi et al, , 2016, but metabolic fluxes have not been inferred. To analyze the regulation of metabolic flux, we developed a kinetic trans-omic analysis using isotopic labeling experiments, and inferred metabolic flux together with contributions of regulators to changes in metabolic flux across multiple omic layers (Ohno et al, 2020).…”

“…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.…”