An Untargeted Metabolomics Analysis in Feces and Brain of Orthoflaviviruses-infected Mice

Annually, millions of people are affected by mosquito-borne Orthoflavivirus infections. These include diseases caused by the Dengue virus (DENV), Japanese encephalitis virus (JEV), and Zika virus (ZIKV), posing a formidable challenge to global public health. Rapid and accurate diagnosis of Orthoflavivirus infections and prediction of disease progression are of significant clinical importance. Metabolomics technology offers unique advantages in the identification of biomarkers for clinical diagnosis. Therefore, we employed an untargeted Liquid Chromatography-Mass Spectrometry (LC-MS) metabolomics platform to examine alterations in metabolite concentrations within the feces and brain tissues of mice infected with DENV, JEV, or ZIKV, as well as uninfected controls. The results showed that 225, 240, and 252 differential metabolites were identified in the fecal metabolome of DENV, JEV, and ZIKV infections, respectively, with amino acid metabolism and lipid metabolism being significantly disrupted. In the brain metabolome, 37, 81, and 18 differential metabolites were identified for DENV, JEV, and ZIKV infections, respectively, with lipid metabolism and purine metabolism being significantly disrupted. Amino acids with low abundance in viral proteins are significantly disrupted in the amino acid metabolism pathway, suggesting that Orthoflaviviruses adapt to its needs for synthesizing viral proteins by regulating the host's amino acid composition. The disruption of purine metabolism also implies the viral genome replication process occurring in the brain. Moreover, the disturbance of lipid metabolism is highly correlated with the biological function of the Orthoflavivirus envelope, where Sphingosine 1-phosphate (S1P) may be the key for Orthoflaviviruses to enter the human central nervous system via the gut-brain axis (GBA). This investigation conducted the first exploration of the potential role of the GBA in Orthoflavivirus infection using fecal and brain samples for metabolomics research, providing a theoretical basis for predicting cerebral infection status through fecal metabolomics. While elucidating viral infection characteristics from the complementary perspectives of fecal and brain samples, it offers preliminary insights into infection-related metabolic changes, thereby facilitating the search for biomarkers to diagnose disease states in subsequent research.