13C Stable Isotope Tracing-Based MFA Reveals the Contribution of Glucose to Glycolytic and TCA Fluxes and Its Application in Depression Research

Abstract

Metabolomics is widely applied to dissect metabolic pathways and their correlations with biological phenotypes. Unlike genomics and proteomics, metabolites exhibit substantial heterogeneity in chemical structure, physicochemical properties, and biological origin. Accordingly, pathway enrichment and annotation relying merely on alterations in metabolite abundance are prone to incomplete coverage, ionization bias, and ambiguous annotation, which inevitably impair the accuracy of pathway interpretation. Metabolic flux analysis (MFA) coupled with stable isotope-resolved metabolomics (SIRM) offers a powerful quantitative framework for tracing in vivo carbon flow and estimating reaction fluxes across key metabolic nodes. Glucose metabolism lies at the core of systemic energy homeostasis; however, most current investigations are confined to cell lines or in vitro systems, and a simple, easy-to-implement computational pipeline for in vivo glucose flux analysis in animal models is still lacking. Herein, we established an in vivo 13C-labeling-based MFA workflow to trace and resolve the systemic metabolic fate of glucose in rats. The pipeline covers tracer administration, sample preparation, LC-MS detection, isotopologue data acquisition and correction, construction of a glucose-metabolism-related metabolite database, MFA model establishment, and metabolic flux quantification. By infusing rats with [U–13C6]-glucose and [U–13C3]-sodium L-lactate, we precisely characterized the in vivo metabolic fates of circulating glucose and lactate and quantified their respective contributions to glycolytic flux and tricarboxylic acid (TCA) cycle flux. We further applied this workflow to profile energy metabolic reprogramming in depression. The results revealed a systemic shift toward aerobic glycolysis in rats exposed to chronic unpredictable mild stress (CUMS). Overall, the expanded application of this MFA strategy can provide mechanistic and quantitative insights into the regulation of metabolic pathways.