Metabolomics is the scientific study of the changes in all metabolites regulated by genes and proteins in biological organisms, directly exploring the patterns and functions of these metabolic changes. It serves as a scientific inquiry into the outcomes of biological phenomena. Metabolomics research can uncover new molecular biomarkers and, through integrated analysis with proteomics and genomics data, provide insights into the intrinsic changes within organisms from both causal and resultant perspectives, thereby advancing systems biology research to a higher level. LC-MS untargeted metabolomics technology involves the qualitative and quantitative analysis of all metabolites in specific biological samples under defined conditions using LC-MS platforms.
Targeted metabolomics is an essential component of metabolomics research and extends and expands upon whole metabolome studies. Compared to untargeted metabolome analysis, targeted metabolomics offers strong specificity, high detection sensitivity, and accurate quantification. By enriching and accurately quantifying specific metabolites in blood, urine, or other body fluids and tissues, targeted metabolomics enables the elucidation of relevant molecular biological mechanisms in conjunction with other experimental data. Furthermore, it provides robust support for the in-depth research and development of subsequent metabolite biomarkers.
Case. Metabolic Insights into COVID-19 Infection(1)
The COVID-19 pandemic has infected over 160 million people and resulted in millions of deaths worldwide. At least partially due to the unclear pathophysiology of this disease, identifying the potential molecular mechanisms of COVID-19 is crucial for overcoming this global pandemic. Comprehensive observations of the metabolic characteristics of serum from COVID-19 patients at all stages were conducted using both untargeted and targeted metabolomics analyses. Comparisons with healthy control groups revealed different patterns of circulating metabolites in mild, severe, and recovery stages in both discovery and validation cohorts, suggesting that metabolic rearrangements in glucose metabolism and the urea cycle are potential pathological mechanisms in the progression of COVID-19. The study suggests that targeting glucose metabolism and the urea cycle may be a feasible approach to combat COVID-19 at different stages of the disease.
Results
Untargeted metabolomics study of serum from COVID-19 patients at different stages
Untargeted metabolomics analysis was conducted on a discovery cohort, including 13 healthy individuals as controls, 18 mild patients, 12 severe patients, and 20 severe COVID-19 recovery patients. Clinical symptoms and laboratory indicators of COVID-19-infected individuals were collected and analyzed.
Figure 1. Untargeted metabolomics analysis of serum from healthy control (Normal) and mild, severe, and recovery group patients.
(A) Schematic representation of the study design;
(B) Principal component analysis (PCA) of untargeted metabolomics among the four groups;
(C) Heatmap of 193 selected metabolites with false discovery rate (FDR) < 0.05;
(D) Volcano plot highlighting serum metabolites increased (in red) or decreased (in blue) in mild, severe, and recovery groups compared to the Normal group.
Targeted metabolomics reveals alterations in metabolic products at various stages of COVID-19 infection.
Figure 2. Changes in major metabolites in serum at different stages
(A) Heatmap of quantified metabolites in targeted metabolomics analysis with false discovery rate (FDR) < 0.05;
(B) Pathway analysis reveals significant differences in amino acid metabolism, particularly arginine, ornithine, and glutamine, as well as energy metabolism, including the TCA cycle and glycolysis.
Dysregulation of the urea cycle and tricarboxylic acid cycle during disease progression.
The box plots illustrate the levels of these metabolites in discovery and validation samples at different stages. Metabolomic pathway analysis in both discovery and validation cohorts suggests that the urea cycle and tricarboxylic acid cycle are the two most affected pathways in COVID-19 patients.
The combination of serum metabolites may serve as a potential predictive biomarker.
To investigate whether these nine metabolites could serve as biomarkers for risk stratification of COVID-19 patients, several classical models were trained on the discovery cohort and validated on the validation cohort, including decision trees, random forests, support vector machines, and logistic regression. To achieve better performance, logistic regression was employed by the authors.
Figure 4. Area under the curve (AUC) of models constructed using combinations of nine significantly altered metabolites from the urea cycle and tricarboxylic acid cycle. Logistic regression models were built using the nine metabolites as features.
(A-F) Scatter plots of AUCs for distinguishing different stages using 1-9 metabolites. The x-axis indicates the number of metabolites used in the model. Models were constructed for all possible combinations, and each point represents the AUC of a single combination. The curve illustrates that as the number of metabolites in the model increases, the performance of the model improves.
Discussion
Metabolomics data offer a comprehensive view of circulating metabolite characteristics at all stages of COVID-19 infection and identify metabolic reprogramming of glucose metabolism and the urea cycle as potential pathological mechanisms of COVID-19. Targeting host metabolism may be a feasible approach to combating COVID-19 at different stages of the disease.
Reference
- Jia, Hongling, et al. "Metabolomic analyses reveal new stage-specific features of COVID-19." European Respiratory Journal 59.2 (2022).