Background: The infant intestinal microbiome plays an important role in metabolism and immune development with impacts on lifelong health. The linkage between the taxonomic composition of the microbiome and its metabolic phenotype is undefined and complicated by redundancies in the taxon-function relationship within microbial communities. To inform a more mechanistic understanding of the relationship between the microbiome and health, we performed an integrative statistical and machine learning-based analysis of microbe taxonomic structure and metabolic function in order to characterize the taxa-function relationship in early life.Results: Stool samples collected from infants enrolled in the New Hampshire Birth Cohort Study (NHBCS) at approximately 6-weeks (n = 158) and 12-months (n = 282) of age were profiled using targeted and untargeted nuclear magnetic resonance (NMR) spectroscopy as well as DNA sequencing of the V4-V5 hypervariable region from the bacterial 16S rRNA gene. There was significant inter-omic concordance based on Procrustes analysis (6 weeks:p = 0.056; 12 months:p = 0.001), however this association was no longer significant when accounting for phylogenetic relationships using generalized UniFrac distance metric (6 weeks:p = 0.376; 12 months:p = 0.069). Sparse canonical correlation analysis showed significant correlation, as well as identifying sets of microbe/metabolites driving microbiome-metabolome relatedness. Performance of machine learning models varied across different metabolites, with support vector machines (radial basis function kernel) being the consistently top ranked model. However, predictive R²values demonstrated poor predictive performance across all models assessed (avg: − 5.06% -- 6 weeks; − 3.7% -- 12 months). Conversely, the Spearman correlation metric was higher (avg: 0.344–6 weeks; 0.265–12 months). This demonstrated that taxonomic relative abundance was not predictive of metabolite concentrations.Conclusions: Our results suggest a degree of overall association between taxonomic profiles and metabolite concentrations. However, lack of predictive capacity for stool metabolic signatures reflects, in part, the possible role of functional redundancy in defining the taxa-function relationship in early life as well as the bidirectional nature of the microbiome-metabolome association. Our results provide evidence in favor of a multi-omic approach for microbiome studies, especially those focused on health outcomes.

Caloric restriction and aerobic and resistance exercise are safe and effective lifestyle interventions for achieving weight loss in the obese older population (>65 years) and may improve physical function and quality of life. However, individual responses are heterogeneous. Our goal was to explore the use of untargeted metabolomics to identify metabolic phenotypes associated with achieving weight loss after a multi-component weight loss intervention. Forty-two older adults with obesity (body mass index, BMI, ≥30 kg/m²) participated in a six-month telehealth-based weight loss intervention. Each received weekly dietitian visits and twice-weekly physical therapist-led group strength training classes with a prescription for aerobic exercise. We categorized responders’ weight loss using a 5% loss of initial body weight as a cutoff. Baseline serum samples were analyzed to determine the variable importance to the projection (VIP) of signals that differentiated the responder status of metabolic profiles. Pathway enrichment analysis was conducted in Metaboanalyst. Baseline data did not differ significantly. Weight loss was 7.2 ± 2.5 kg for the 22 responders, and 2.0 ± 2.0 kg for the 20 non-responders. Mummichog pathway enrichment analysis revealed that perturbations were most significant for caffeine and caffeine-related metabolism (p = 0.00028). Caffeine and related metabolites, which were all increased in responders, included 1,3,7-trimethylxanthine (VIP = 2.0, p = 0.033, fold change (FC) = 1.9), theophylline (VIP = 2.0, p = 0.024, FC = 1.8), paraxanthine (VIP = 2.0, p = 0.028, FC = 1.8), 1-methylxanthine (VIP = 1.9, p = 0.023, FC = 2.2), 5-acetylamino-6-amino-3-methyluracil (VIP = 2.2, p = 0.025, FC = 2.2), 1,3-dimethyl uric acid (VIP = 2.1, p = 0.023, FC = 2.3), and 1,7-dimethyl uric acid (VIP = 2.0, p = 0.035, FC = 2.2). Increased levels of phytochemicals and microbiome-related metabolites were also found in responders compared to non-responders. In this pilot weight loss intervention, older adults with obesity and evidence of significant enrichment for caffeine metabolism were more likely to achieve ≥5% weight loss. Further studies are needed to examine these associations in prospective cohorts and larger randomized trials.

Meprin metalloproteases are abundantly expressed in the brush border membranes of kidney proximal tubules and small intestines. Meprins are also expressed in podocytes and leukocytes (monocytes and macrophages). Meprins are implicated in the pathophysiology of diabetic nephropathy (DN) but underlying mechanisms are not fully understood. Single nucleotide polymophisms (SNPs) in the meprin β gene were associated with DKD in human subjects. Furthermore, meprin α and β double deficiency resulted in more severe kidney injury and higher mortality rates in mice with Streptozotocin (STZ)-induced type 1 diabetes. Identification of meprin substrates has provided insights on how meprins could modulate kidney injury. Meprin targets in the kidney include extracellular matrix (ECM) proteins, modulators of inflammation, and proteins involved in the protein kinase A (PKA) and PKC signaling pathways. The current study used a global metabolomics approach to determine how meprin β expression impacts the metabolite milieu in diabetes and DKD. Low dose STZ was used to induce type 1 diabetes in 8-week old wild-type (WT) and meprin β knockout (βKO) mice. Blood and urine samples were obtained at 4 and 8 weeks post-STZ injection. Assays for albumin, creatinine, neutrophil gelatinase-associated lipocalin (NGAL), kidney injury molecule − 1 (KIM-1), and cystatin C were used for biochemical assessment of kidney injury. Data for biomarkers of kidney injury utilized two-way ANOVA. Metabolomics data analysis utilized UPLC-QTOF MS and multivariate statistics. The number of metabolites with diabetes-associated changes in levels were significantly higher in the WT mice when compared to meprin βKO counterparts. Annotated meprin β expression-associated metabolites with strong variable importance in projection (VIP) scores play roles in lipid metabolism (LysoPC(16:1(9Z)), taurocholic acid), amino acid metabolism (indoxyl sulfate, hippuric acid), and neurotransmitter/stress hormone synthesis (cortisol, 3-methoxy-4-hydroxyphenylethylene glycolsulfate, homovanillic acid sulfate). Metabolites that associated with meprin β deficiency include; 3,5-dihydroxy-3′,4′-dimethoxy-6,7-methylenedioxyflavone 3-glucuronide, pantothenic acid, and indoxyl glucuronide (all decreased in plasma). Taken together, the annotated metabolites suggest that meprin β impacts complications of diabetes such as DKD by altering distinct metabolite profiles.

The yeast Saccharomyces cerevisiae has long been used to produce alcohol from glucose and other sugars. While much is known about glucose metabolism, relatively little is known about the receptors and signaling pathways that indicate glucose availability. Here, we compare the two glucose receptor systems in S. cerevisiae. The first is a heterodimer of transporter-like proteins (transceptors), while the second is a seven-transmembrane receptor coupled to a large G protein (Gpa2) that acts in coordination with two small G proteins (Ras1 and Ras2). Through comprehensive measurements of glucose-dependent transcription and metabolism, we demonstrate that the two receptor systems have distinct roles in glucose signaling: the G-protein-coupled receptor directs carbohydrate and energy metabolism, while the transceptors regulate ancillary processes such as ribosome, amino acids, cofactor and vitamin metabolism. The large G-protein transmits the signal from its cognate receptor, while the small G-protein Ras2 (but not Ras1) integrates responses from both receptor pathways. Collectively, our analysis reveals the molecular basis for glucose detection and the earliest events of glucose-dependent signal transduction in yeast.

Cesarean delivery and formula feeding have both been implicated as important factors associated with perturbations to the infant gut microbiome. To investigate the functional metabolic response of the infant gut microbial milieu to these factors, we profiled the stool metabolomes of 121 infants from a US pregnancy cohort study at approximately 6 weeks of life and evaluated associations with delivery mode and feeding method. Multivariate analysis of six-week stool metabolomic profiles indicated discrimination by both delivery mode and diet. For diet, exclusively breast-fed infants exhibited metabolomic profiles that were distinct from both exclusively formula-fed and combination-fed infants, which were relatively more similar to each other in metabolomic profile. We also identified individual metabolites that were important for differentiating delivery mode groups and feeding groups and metabolic pathways related to delivery mode and feeding type. We conclude based on previous work and this current study that the microbial communities colonizing the gastrointestinal tracts of infants are not only taxonomically, but also functionally distinct when compared according to delivery mode and feeding groups. Further, different sets of metabolites and metabolic pathways define delivery mode and diet metabotypes.

ALDH1L1 (10-formyltetrahydrofolate dehydrogenase), an enzyme of folate metabolism, is highly expressed in the liver. It regulates the overall flux of folate-bound one-carbon groups by converting 10-formyltetrahydrofolate to tetrahydrofolate and CO₂ in a NADP⁺-dependent reaction. Our previous study revealed that Aldh1l1 knockout (KO) mice have an altered liver metabotype with metabolic symptoms of folate deficiency when fed a standard chow diet containing 2 ppm folic acid. Here we performed untargeted metabolomic analysis of liver and plasma of KO and wild-type (WT) male and female mice fed for 16 weeks either standard or folate-deficient diet. OPLS-DA, a supervised multivariate technique that was applied to 6595 and 10,678 features for the liver and plasma datasets, respectively, indicated that genotype and diet, alone or in combination, gave distinct metabolic profiles in both types of biospecimens. A more detailed analysis of affected metabolic pathways based on most confidently identified metabolites in the liver and plasma (OL1 and OL2a ontology level) indicated that the dietary folate restriction itself does not fully recapitulate the metabolic effect of the KO. Of note, dietary folate withdrawal enhanced the metabolic perturbations linked to the ALDH1L1 loss only for a subset of metabolites. Importantly, both the ALDH1L1 loss and dietary folate deficiency produced sex-specific metabolic effects.