Identification of biological signatures of cruciferous vegetable consumption utilizing machine learning-based global untargeted stable isotope traced metabolomics - Bouranis, John A.; Ren, Yijie; Beaver, Laura M.; Choi, Jaewoo; Wong, Carmen P.; He, Lily; Traber, Maret G.; Kelly, Jennifer; Booth, Sarah L.; Stevens, Jan F.; Fern, Xiaoli Z.; Ho, Emily (Frontiers in Nutrition, 2024)
In recent years there has been increased interest in identifying biological signatures of food consumption for use as biomarkers. Traditional metabolomics-based biomarker discovery approaches rely on multivariate statistics which cannot differentiate between host-and food-derived compounds, thus novel approaches to biomarker discovery are required to advance the field. To this aim, we have developed a new method that combines global untargeted stable isotope traced metabolomics and a machine learning approach to identify biological signatures of cruciferous vegetable consumption. Participants (n = 32) consumed a single serving of deuterium-labeled broccoli (n=16), alfalfa sprouts (n = 16) or collard greens (n = 26) which contained either control unlabeled metabolites, or that were grown in the presence of deuterium-labeled water to intrinsically label metabolites with deuterium isotopes. Mass spectrometry analysis indicated 133 metabolitesthat in the broccoli sprouts and 139 metabolites in the alfalfa sprouts 133 metabolites were labeled with deuterium isotopes, in the alfalfa sprouts 139 metabolites were labeled with deuterium. Urine and plasma were collected and analyzed using untargeted metabolomics on an AB SCIEX TripleTOF 5600 mass spectrometer. Global untargeted stable isotope tracing was completed using openly available software and a novel random forest machine learning based classifier. Among participants who consumed labeled broccoli sprouts or collard greens, 13Thirteen deuterium-incorporated metabolomicmetabolomics features were detected in urine representing 8 urine metabolites. and Plasma was analyzed among collard green consumers and 11 labeled features were detected in plasma representing 8 urine metabolites andrepresenting 5 plasma metabolites. , representing potential biological signatures of cruciferous vegetables consumption. TWhile these dese deuterium-labeled metabolites represent potential biological signatures of cruciferous vegetables consumption. Isoleucine, indole-3-acetic acid-N-Oglucuronide, dihydrosinapic acid were annotated as labeled compounds but other labeled metabolites could not be annotated to reference MS2 databases, de novo prediction of compound classes indicates they were most likely glucuronidated xenobiotics, fatty acids, and amino acids. This work furthers the field of precision nutrition and presents a novel framework for identifying biological signatures of food consumption for biomarker discovery. Additionally, this work presents novel applications of metabolomics and machine learning in the life sciences.