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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 R 2 values 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.

Due to the surge in type 2 diabetes mellitus (T2DM), treatments for chronic metabolic dysregulations with fewer side-effects are sought. Lycii Cortex (LyC), a traditional Chinese Medicine (TCM) herb has a long history of being widely prescribed to treat T2DM as alternative medicine; however, the bioactive molecules and working mechanism remained unknown. Previous studies revealed kukoamine B (KB) as a major and featured compound for LyC with bioactivities for anti-oxidation and acute inflammation, which may be related to anti-diabetes properties. This study aims to understand the efficacy and the mode of action of KB in the diabetic (db/db) mouse model using a metabolomics approach. Parallel comparison was conducted using the first-line anti-diabetic drugs, metformin and rosligtazone, as positive controls. The db/db mice were treated with KB (50 mg kg day ) for 9 weeks. Bodyweight and fasting blood glucose were monitored every 5 and 7 days, respectively. Metabolomics and high-throughput molecular approaches, including lipidomics, targeted metabolomics (Biocrates p180), and cytokine profiling were applied to measure the alteration of serum metabolites and inflammatory biomarkers between different treatments vs. control (db/db mice treated with vehicle). After 9 weeks of treatment, KB lowered blood glucose, without the adverse effects of bodyweight gain and hepatomegaly shown after rosiglitazone treatment. Lipidomics analysis revealed that KB reduced levels of circulating triglycerides, cholesterol, phosphatidylethanolamine, and increased levels of phosphatidylcholines. KB also increased acylcarnitines, and reduced systemic inflammation (cytokine array). Pathway analysis suggested that KB may regulate nuclear transcription factors (e.g., NF-κB and/or PPAR) to reduce inflammation and facilitate a shift toward metabolic and inflammatory homeostasis. Comparison of KB with first-line drugs suggests that rosiglitazone may over-regulate lipid metabolism and anti-inflammatory responses, which may be associated with adverse side effects, while metformin had less impact on lipid and anti-inflammation profiles. Our research from holistic and systemic views supports the conclusion that KB is the bioactive compound of LyC for managing T2DM, and suggests KB as a nutraceutical or a pharmaceutical candidate for T2D treatment. In addition, our research provides insights related to metformin and rosiglitazone action, beyond lowering blood glucose.

Breast carcinogenesis is a multistep process accompanied by widespread molecular and genomic alterations, both in tumor and in surrounding microenvironment. It is known that tumors have altered metabolism, but the metabolic changes in normal or cancer-adjacent, nonmalignant normal tissues and how these changes relate to alterations in gene expression and histological composition are not well understood. Normal or cancer-adjacent normal breast tissues from 99 women of the Normal Breast Study (NBS) were evaluated. Data of metabolomics, gene expression and histological composition was collected by mass spectrometry, whole genome microarray, and digital image, respectively. Unsupervised clustering analysis determined metabolomics-derived subtypes. Their association with genomic and histological features, as well as other breast cancer risk factors, genomic and histological features were evaluated using logistic regression. Unsupervised clustering of metabolites resulted in two main clusters. The metabolite differences between the two clusters suggested enrichment of pathways involved in lipid metabolism, cell growth and proliferation, and migration. Compared with Cluster 1, subjects in Cluster 2 were more likely to be obese (body mass index ≥30 kg/m2, p<0.05), have increased adipose proportion (p<0.01) and associated with a previously defined Active genomic subtype (p<0.01). By the integrated analyses of histological, metabolomics and transcriptional data, we characterized two distinct subtypes of non-malignant breast tissue. Further research is needed to validate our findings, and understand the potential role of these alternations in breast cancer initiation, progression and recurrence.

Purpose. To conduct an exploratory study to identify mechanisms that differentiate Luminal A (BT474 and MCF-7) and triple-negative (MDA-MB-231 and MDA-MB-468) breast cancer (BCa) cell lines to potentially provide novel therapeutic targets based on differences in energy utilization. Methods. Cells were cultured in media containing either [U-13C]-glucose or [U-13C]-glutamine for 48 hours. Conditioned media and cellular extracts were analyzed by 1H and 13C NMR spectroscopy. Results. MCF-7 cells consumed the most glucose, producing the most lactate, demonstrating the greatest Warburg effect-associated energy utilization. BT474 cells had the highest tricarboxylic acid cycle (TCA) activity. The majority of energy utilization patterns in MCF-7 cells were more similar to MDA-MB-468 cells, while the patterns for BT474 cells were more similar to MDA-MB-231 cells. Compared to the Luminal A cell lines, TNBC cell lines consumed more glutamine and less glucose. BT474 and MDA-MB-468 cells produced high amounts of 13C-glycine from media [U-13C]-glucose which was integrated into glutathione, indicating de novo synthesis. Conclusions. Stable isotopic resolved metabolomics using 13C substrates provided mechanistic information about energy utilization that was difficult to interpret using 1H data alone. Overall, cell lines that have different hormone receptor status have different energy utilization requirements, even if they are classified by the same clinical BCa subtype; and these differences offer clues about optimizing treatment strategies.

BackgroundMetabolomics is a promising method to investigate physiological effects of chemical exposures during pregnancy, with the potential to clarify toxicological mechanisms, suggest sensitive endpoints, and identify novel biomarkers of exposures.ObjectiveInvestigate the influence of chemical exposures on the maternal plasma metabolome during pregnancy.MethodsData were obtained from participants (n = 177) in the New Hampshire Birth Cohort Study, a prospective pregnancy cohort. Chemical exposures were assessed via silicone wristbands worn for one week at ~13 gestational weeks. Metabolomic features were assessed in plasma samples obtained at ~24–28 gestational weeks via the Biocrates AbsoluteIDQ® p180 kit and nuclear magnetic resonance (NMR) spectroscopy. Associations between chemical exposures and plasma metabolomics were investigated using multivariate modeling.ResultsChemical exposures predicted 11 (of 226) and 23 (of 125) metabolomic features in Biocrates and NMR, respectively. The joint chemical exposures did not significantly predict pathway enrichment, though some individual chemicals were associated with certain amino acids and related metabolic pathways. For example, N,N-diethyl-m-toluamide was associated with the amino acids glycine, L-glutamic acid, L-asparagine, and L-aspartic acid and enrichment of the ammonia recycling pathway.SignificanceThis study contributes evidence to the potential effects of chemical exposures during pregnancy upon the endogenous maternal plasma metabolome.

•Metabolomics is the study of the small-molecule metabolite profile of an organism.•Metabolomics may help identify individuals predisposed to developing adverse events after vaccines.•Our study is first to describe pre- and post-vaccination metabolic profiles.•Profiles differ substantially when an adverse event after vaccination develops. An Adverse Event Following Immunization (AEFI) is an adverse reaction to a vaccination that goes above and beyond the usual side effects associated with vaccinations. One serious AEFI related to the smallpox vaccine is myopericarditis. Metabolomics involves the study of the low molecular weight metabolite profile of cells, tissues, and biological fluids, and provides a functional readout of the phenotype. Metabolomics may help identify a particular metabolic signature in serum of subjects who are predisposed to developing AEFIs. The goal of this study was to identify metabolic markers that may predict the development of adverse events following smallpox vaccination. Serum samples were collected from military personnel prior to and following receipt of smallpox vaccine. The study population included five subjects who were clinically diagnosed with myopericarditis, 30 subjects with asymptomatic elevation of troponins, and 31 subjects with systemic symptoms following immunization, and 34 subjects with no AEFI, serving as controls. Two-hundred pre- and post-smallpox vaccination sera were analyzed by untargeted metabolomics using 1H nuclear magnetic resonance (NMR) spectroscopy. Baseline (pre-) and post-vaccination samples from individuals who experienced clinically verified myocarditis or asymptomatic elevation of troponins were more metabolically distinguishable pre- and post-vaccination compared to individuals who only experienced systemic symptoms, or controls. Metabolomics profiles pre- and post-receipt of vaccine differed substantially when an AEFI resulted. This study is the first to describe pre- and post-vaccination metabolic profiles of subjects who developed an adverse event following immunization. The study demonstrates the promise of metabolites for determining mechanisms associated with subjects who develop AEFI and the potential to develop predictive biomarkers.

Metabolomics investigates global metabolic alterations associated with chemical, biological, physiological, or pathological processes. These metabolic changes are measured with various analytical platforms including liquid chromatography-mass spectrometry (LC-MS), gas chromatography-mass spectrometry (GC-MS) and nuclear magnetic resonance spectroscopy (NMR). While LC-MS methods are becoming increasingly popular in the field of metabolomics (accounting for more than 70% of published metabolomics studies to date), there are considerable benefits and advantages to NMR-based methods for metabolomic studies. In fact, according to PubMed, more than 926 papers on NMR-based metabolomics were published in 2021—the most ever published in a given year. This suggests that NMR-based metabolomics continues to grow and has plenty to offer to the scientific community. This perspective outlines the growing applications of NMR in metabolomics, highlights several recent advances in NMR technologies for metabolomics, and provides a roadmap for future advancements.

The early life microbiome plays important roles in host immunological and metabolic development. Because the incidence of type 1 diabetes (T1D) has been increasing substantially in recent decades, we hypothesized that early-life antibiotic use alters gut microbiota, which predisposes to disease. Using non-obese diabetic mice that are genetically susceptible to T1D, we examined the effects of exposure to either continuous low-dose antibiotics or pulsed therapeutic antibiotics (PAT) early in life, mimicking childhood exposures. We found that in mice receiving PAT, T1D incidence was significantly higher, and microbial community composition and structure differed compared with controls. In pre-diabetic male PAT mice, the intestinal lamina propria had lower Th17 and T reg proportions and intestinal SAA expression than in controls, suggesting key roles in transducing the altered microbiota signals. PAT affected microbial lipid metabolism and host cholesterol biosynthetic gene expression. These findings show that early-life antibiotic treatments alter the gut microbiota and its metabolic capacities, intestinal gene expression and T-cell populations, accelerating T1D onset in non-obese diabetic mice. Pulsed doses of antibiotics increased the incidence of type 1 diabetes in non-obese diabetic (NOD) mice. This was associated with altered gut microbial composition, lipid metabolism, host cholesterol biosynthetic gene expression and Th17 and Treg cell proportions.

Objectives: This study investigated the effects of 2-week ingestion of hemp fiber (high and low doses) versus placebo bars on gut permeability and plasma metabolite shifts during recovery from 2.25 h intensive cycling. Hemp hull powder is a rich source of two bioactive compounds, N-trans-caffeoyl tyramine (NCT) and N-trans-feruloyl tyramine (NFT), with potential gut health benefits. Methods: The study participants included 23 male and female cyclists. A three-arm randomized, placebo-controlled, double-blind, crossover design was used with two 2-week supplementation periods and 2-week washout periods. Supplement bars provided 20, 5, or 0 g/d of hemp hull powder. Participants engaged in an intensive 2.25 h cycling bout at the end of each of the three supplementation periods. Five blood samples were collected before and after supplementation (overnight fasted state), and at 0 h-, 1.5 h-, and 3 h-post-exercise. Five-hour urine samples were collected pre-supplementation and post-2.25 h cycling after ingesting a sugar solution containing 5 g of lactulose, 100 mg of 13C mannitol, and 1.9 g of mannitol in 450 mL of water. An increase in the post-exercise lactulose/13C mannitol ratio (L:13CM) was used as the primary indicator of altered gut permeability. Other outcome measures included muscle damage biomarkers (serum creatine kinase, myoglobin), serum cortisol, complete blood cell counts, and shifts in plasma metabolites using untargeted metabolomics. Results: No trial differences were found for L:13CM, cortisol, blood cell counts, and muscle damage biomarkers. Orthogonal partial least-squares discriminant analysis (OPLSDA) showed distinct trial differences when comparing high- and low-dose hemp fiber compared to placebo supplementation (R2Y = 0.987 and 0.995, respectively). Variable Importance in Projection (VIP) scores identified several relevant metabolites, including 3-hydroxy-4-methoxybenzoic acid (VIP = 1.9), serotonin (VIP = 1.5), 5-hydroxytryptophan (VIP = 1.4), and 4-methoxycinnamic acid (VIP = 1.4). Mummichog analysis showed significant effects of hemp fiber intake on multiple metabolic pathways, including alpha-linolenic acid, porphyrin, sphingolipid, arginine and proline, tryptophan, and primary bile acid metabolism. Conclusions: Hemp fiber intake during a 2-week supplementation period did not have a significant effect on post-exercise gut permeability in cyclists (2.25 h cycling bout) using urine sugar data. On the contrary, untargeted metabolomics showed that the combination of consuming nutrient-rich hemp fiber bars and exercising for 135 min increased levels of beneficial metabolites, including those derived from the gut in healthy cyclists.

Background Nanoparticles (NPs) are increasingly incorporated in everyday products. To investigate the effects of early life exposure to orally ingested TiO 2 NP, male and female Sprague–Dawley rat pups received four consecutive daily doses of 10 mg/kg body weight TiO 2 NP (diameter: 21 ± 5 nm) or vehicle control (water) by gavage at three different pre-weaning ages: postnatal day (PND) 2–5, PND 7–10, or PND 17–20. Cardiac assessment and basic neurobehavioral tests (locomotor activity, rotarod, and acoustic startle) were conducted on PND 20. Pups were sacrificed at PND 21. Select tissues were collected, weighed, processed for neurotransmitter and metabolomics analyses. Results Heart rate was found to be significantly decreased in female pups when dosed between PND 7–10 and PND 17–20. Females dosed between PND 2–5 showed decrease acoustic startle response and when dosed between PND 7–10 showed decreased performance in the rotarod test and increased locomotor activity. Male pups dosed between PND 17–20 showed decreased locomotor activity. The concentrations of neurotransmitters and related metabolites in brain tissue and the metabolomic profile of plasma were impacted by TiO 2 NP administration for all dose groups. Metabolomic pathways perturbed by TiO 2 NP administration included pathways involved in amino acid and lipid metabolism. Conclusion Oral administration of TiO 2 NP to rat pups impacted basic cardiac and neurobehavioral performance, neurotransmitters and related metabolites concentrations in brain tissue, and the biochemical profiles of plasma. The findings suggested that female pups were more likely to experience adverse outcome following early life exposure to oral TiO 2 NP than male pups. Collectively the data from this exploratory study suggest oral administration of TiO 2 NP cause adverse biological effects in an age- and sex-related manner, emphasizing the need to understand the short- and long-term effects of early life exposure to TiO 2 NP.