Altered small-world property of a dynamic metabolic network in murine left hippocampus after exposure to acute stress

Effects of Acute Stress on Metabolic Interactions Related to the Tricarboxylic Acid (TCA) Cycle in the Left Hippocampus of Mice

BACKGROUND/OBJECTIVES

The acute stress response affects brain metabolites closely linked to the tricarboxylic acid (TCA) cycle. This response involves time-dependent changes in hormones and neurotransmitters, which contribute to resilience and the ability to adapt to acute stress while maintaining homeostasis. This physiological mechanism of metabolic dynamics, combined with time-series analysis, has prompted the development of new methods to observe the relationship between TCA cycle-related brain metabolites. This study aimed to observe the acute stress response through metabolic interactions using time-series proton magnetic resonance spectroscopy (1H-MRS) in the left hippocampus of mice.

METHODS

In this study, 4-week-old male C57BL/6N mice (n = 24) were divided into control (n = 12) and acute stress groups (n = 12). Acute stress was induced through a 2 h restraint protocol. Time-series 1H-MRS data were obtained on the left hippocampus of both groups using a 9.4 T 1H-MRS scanner. Time-series MRS data were quantified using LCModel, and significant metabolic interactions were identified through Spearman correlation analysis, a one-tailed sign test, and false discovery rate correction.

RESULTS

No significant metabolic correlation coefficient was observed in the control group. However, in the acute stress group, glutathione (GSH) and N-acetyl aspartate (NAA) showed a significant positive correlation over time, with a high correlation coefficient exceeding 0.5.

CONCLUSIONS

Temporal measurement of GSH and NAA, combined with correlation analysis, offers a comprehensive understanding for the metabolic dynamics during acute stress. This approach emphasizes their distinct roles and interdependence in the progression of oxidative stress, mitochondrial function, and the maintenance of physiological homeostasis.

Exploring metabolomic dynamics in acute stress disorder: amino acids, lipids, and carbohydrates

Acute Stress Disorder (ASD) is a psychiatric condition that can develop shortly after trauma exposure. Although molecular studies of ASD are only beginning, groups of metabolites have been found to be significantly altered with acute stress phenotypes in various pre-clinical and clinical studies. ASD implicated metabolites include amino acids (β-hydroxybutyrate, glutamate, 5-aminovalerate, kynurenine and aspartate), ketone bodies (β-hydroxybutyrate), lipids (cortisol, palmitoylethanomide, and N-palmitoyl taurine) and carbohydrates (glucose and mannose). Network and pathway analysis with the most prominent metabolites shows that Extracellular signal-regulated kinases and c-AMP response element binding (CREB) protein can be crucial players. After highlighting main recent findings on the role of metabolites in ASD, we will discuss potential future directions and challenges that need to be tackled. Overall, we aim to showcase that metabolomics present a promising opportunity to advance our understanding of ASD pathophysiology as well as the development of novel biomarkers and therapeutic targets.

Metabolic Connectome and Its Role in the Prediction, Diagnosis, and Treatment of Complex Diseases

The interconnectivity of advanced biological systems is essential for their proper functioning. In modern connectomics, biological entities such as proteins, genes, RNA, DNA, and metabolites are often represented as nodes, while the physical, biochemical, or functional interactions between them are represented as edges. Among these entities, metabolites are particularly significant as they exhibit a closer relationship to an organism's phenotype compared to genes or proteins. Moreover, the metabolome has the ability to amplify small proteomic and transcriptomic changes, even those from minor genomic changes. Metabolic networks, which consist of complex systems comprising hundreds of metabolites and their interactions, play a critical role in biological research by mediating energy conversion and chemical reactions within cells. This review provides an introduction to common metabolic network models and their construction methods. It also explores the diverse applications of metabolic networks in elucidating disease mechanisms, predicting and diagnosing diseases, and facilitating drug development. Additionally, it discusses potential future directions for research in metabolic networks. Ultimately, this review serves as a valuable reference for researchers interested in metabolic network modeling, analysis, and their applications.