MTHFR Allele and One-Carbon Metabolic Profile Predict Severity of COVID-19 Disease 2260350

Abstract Introduction Alterations in the methionine cycle, a key component of the one-carbon metabolism pathway that is critical for viral propagation and disease progression, may serve as early indicators of severe SARS-CoV-2 infection. Methods As part of the Immunophenotyping Assessment in a COVID-19 Cohort (IMPACC) study, we performed a longitudinal analysis of untargeted plasma metabolic profiles of hospitalized adult COVID-19 patients followed from admission to 1-year post-discharge. Using a 7-point ordinal scale, patients were clustered into 5 disease trajectory groups based on respiratory illness during hospitalization and targeted metabolomics was assessed using liquid chromatography-mass spectrometry. Results Metabolic profiling revealed that patients in more severe disease trajectory groups (TG4-TG5) showed alterations in the methionine cycle. Specifically, S-adenosylmethionine (SAM), a methyl group donor important for gene expression and methylation of DNA, RNA and proteins, was highly abundant among the more severe trajectory groups (TG4-TG5) compared to the less severe trajectory groups (TG1-TG3). In addition, we identified the C766T allele mutation of the MTHFR gene, a common polymorphism, as a genetic contributor to the methionine pathway and predictor of disease trajectory. Conclusion These results highlight the potential of metabolic characterization of the methionine pathway in combination with screening for the common genetic MTHFR variant as a practical approach for precision COVID-19 management. Funding Source NIH (3U01AI167892-03S2, 3U01AI167892-01S2, 5R01AI135803-03, 5U19AI118608-04, 5U19AI128910-04, 4U19AI090023-11, 4U19AI118610-06, R01AI145835-01A1S1, 5U19AI062629-17, 5U19AI057229-17, 5U19AI057229-18, 5U19AI125357-05, 5U19AI128913-03, 3U19AI077439-13, 5U54AI142766-03, 5R01AI104870-07, 3U19AI089992-09, and 5T32DA018926-18, 3U19AI1289130, U19AI128913-04S1, and R01AI122220, UM1TR004528); NSF DMS2310836 Topic Categories Computational and Systems Immunology (COMP)