With the maturation of metabolomics science and proliferation of biobanks, clinical metabolic profiling is an increasingly opportunistic frontier for advancing translational clinical research. Automated Machine Learning (AutoML) approaches provide exciting opportunity to guide feature selection in agnostic metabolic profiling endeavors, where potentially thousands of independent data points must be evaluated. In previous research, AutoML using high-dimensional data of varying types has been demonstrably robust, outperforming traditional approaches. However, considerations for application in clinical metabolic profiling remain to be evaluated. Particularly, regarding the robustness of AutoML to identify and adjust for common clinical confounders. In this study, we present a focused case study regarding AutoML considerations for using the Tree-Based Optimization Tool (TPOT) in metabolic profiling of exposure to metformin in a biobank cohort. First, we propose a tandem rank-accuracy measure to guide agnostic feature selection and corresponding threshold determination in clinical metabolic profiling endeavors. Second, while AutoML, using default parameters, demonstrated potential to lack sensitivity to low-effect confounding clinical covariates, we demonstrated residual training and adjustment of metabolite features as an easily applicable approach to ensure AutoML adjustment for potential confounding characteristics. Finally, we present increased homocysteine with long-term exposure to metformin as a potentially novel, non-replicated metabolite association suggested by TPOT; an association not identified in parallel clinical metabolic profiling endeavors. While considerations are recommended, including adjustment approaches for clinical confounders, AutoML presents an exciting tool to enhance clinical metabolic profiling and advance translational research endeavors.
How does the gut microbiota shape the composition and function of distal host organs, despite being segregated in the gut? Uchimura et al. used stable isotope tracing to show that microbial metabolites penetrate host tissues and fluids to influence host immunological and metabolic signaling networks. However, metabolite impact is modulated by a high rate of urinary excretion of microbial products. Furthermore, secretory immunoglobulin A antibodies limit bacterial dwell times in the small intestine, which also ameliorates host exposure to microbial metabolites. The joint effect contributes to resolving gut function as both nutrient gateway and barrier.
We explored metabolic pathways related to early-stage BCa (Galactose metabolism and Starch and sucrose metabolism) and to late-stage BCa (Glycine, serine, and threonine metabolism, Arginine and proline metabolism, Glycerophospholipid metabolism, and Galactose metabolism) as well as those common to both stages pathways. The central metabolite impacting the most cancerogenic genes (AKT, EGFR, MAPK3) in early stage is d-glucose, while late-stage BCa is characterized by significant fold changes in several metabolites: glycerol, choline, 13(S)-hydroxyoctadecadienoic acid, 2′-fucosyllactose. Insulin was also seen to play an important role in late stages of BCa. The best performing model was able to predict metabolite class with an accuracy of 82.54% and the area under precision-recall curve (PRC) of 0.84 on the training set. The same model was applied to three separate sets of metabolites obtained from public sources, one set of the late-stage metabolites and two sets of the early-stage metabolites.
To survive in highly complex environments, plants universally rely on specialized, or secondary, metabolites to withstand abiotic challenges (for example, wax to limit transpiration) and biotic challenges (for example, glucosinolates to deter herbivores). These metabolites are lineage-specific, and functional studies usually consider them to have a singular function. However, the complexity of the environment is much larger than the number of secondary metabolites within a plant, indicating that individual specialized metabolites may need to have multiple roles. As the number of functions of a single metabolite increases, so does the number of proteins and processes affected, and there is no guarantee that all of these interactions are positive. On page 694 of this issue, Hu et al. (1) show that benzoxazinoids, a textbook example of specialized metabolites in maize, have a functional duality centered around iron acquisition.
The lysosome degrades and recycles macromolecules, signals to the cytosol and nucleus, and is implicated in many diseases. Here, we describe a method for the rapid isolation of mammalian lysosomes and use it to quantitatively profile lysosomal metabolites under various cell states. Under nutrient-replete conditions, many lysosomal amino acids are in rapid exchange with those in the cytosol. Loss of lysosomal acidification through inhibition of the vacuolar H –adenosine triphosphatase (V-ATPase) increased the luminal concentrations of most metabolites but had no effect on those of the majority of essential amino acids. Instead, nutrient starvation regulates the lysosomal concentrations of these amino acids, an effect we traced to regulation of the mechanistic target of rapamycin (mTOR) pathway.