Exploring Acylcarnitine Metabolism Using Reverse Metabolomics

Untargeted mass spectrometry (MS) is a valuable tool for studying human metabolism and identifying small molecule disease biomarkers. However, annotation of chemical structures and validation of findings across numerous cohorts remains challenging. Reverse metabolomics employs a structure-driven approach to overcome these issues by searching spectra of known structures against an entire repository of untargeted LC-MS/MS data to see where metabolites of interest are found. This work uses reverse metabolomics to study acylcarnitine (AC) metabolism in humans and other animals. Here, a library of 76 ACs was chemically synthesized then searched against public metabolomics data to explore where metabolites of interest are detected. From this analysis, it was determined that acylcarnitines are most frequently observed in human and mouse samples, with about 90% of all searched AC structures present in both blood and fecal samples from these species. This work identified positive associations between certain AC structures and disease, indicating their capacity as health biomarkers. Machine learning was applied, determining that AC presence and absence data can accurately predict healthy versus unhealthy individuals with good precision and recall, albeit the models lack disease specificity. Overall, our findings suggest that AC profiles can serve as valuable biomarkers for disease detection throughout the entire lifespan and should be examined for their potential beyond current clinical screening protocols.