Untargeted metabolomics reveal signatures of a healthy lifestyle.

This cross-sectional study investigated differences in the plasma metabolome in two groups of adults that were of similar age but varied markedly in body composition and dietary and physical activity patterns. Study participants included 52 adults in the lifestyle group (LIFE) (28 males, 24 females) and 52 in the control group (CON) (27 males, 25 females). The results using an extensive untargeted ultra high-performance liquid chromatography-high resolution mass spectrometry (UHPLC-HRMS) metabolomics analysis with 10,535 metabolite peaks identified 486 important metabolites (variable influence on projections scores of VIP ≥ 1) and 16 significantly enriched metabolic pathways that differentiated LIFE and CON groups. A novel metabolite signature of positive lifestyle habits emerged from this analysis highlighted by lower plasma levels of numerous bile acids, an amino acid profile characterized by higher histidine and lower glutamic acid, glutamine, ß-alanine, phenylalanine, tyrosine, and proline, an elevated vitamin D status, higher levels of beneficial fatty acids and gut microbiome catabolism metabolites from plant substrates, and reduced levels of N-glycan degradation metabolites and environmental contaminants. This study established that the plasma metabolome is strongly associated with body composition and lifestyle habits. The robust lifestyle metabolite signature identified in this study is consistent with an improved life expectancy and a reduced risk for chronic disease.

Macronutrient-differential dietary pattern impacts on body weight, hepatic inflammation, and metabolism.

Introduction: Obesity is a multi-factorial disease frequently associated with poor nutritional habits and linked to many detrimental health outcomes. Individuals with obesity are more likely to have increased levels of persistent inflammatory and metabolic dysregulation. The goal of this study was to compare four dietary patterns differentiated by macronutrient content in a postmenopausal model. Dietary patterns were high carbohydrate (HC), high fat (HF), high carbohydrate plus high fat (HCHF), and high protein (HP) with higher fiber. Methods: Changes in body weight and glucose levels were measured in female, ovariectomized C57BL/6 mice after 15 weeks of feeding. One group of five mice fed the HCHF diet was crossed over to the HP diet on day 84, modeling a 21-day intervention. In a follow-up study comparing the HCHF versus HP dietary patterns, systemic changes in inflammation, using an 80-cytokine array and metabolism, by untargeted liquid chromatography-mass spectrometry (LCMS)-based metabolomics were evaluated. Results: Only the HF and HCHF diets resulted in obesity, shown by significant differences in body weights compared to the HP diet. Body weight gains during the two-diet follow-up study were consistent with the four-diet study. On Day 105 of the 4-diet study, glucose levels were significantly lower for mice fed the HP diet than for those fed the HC and HF diets. Mice switched from the HCHF to the HP diet lost an average of 3.7 grams by the end of the 21-day intervention, but this corresponded with decreased food consumption. The HCHF pattern resulted in dramatic inflammatory dysregulation, as all 80 cytokines were elevated significantly in the livers of these mice after 15 weeks of HCHF diet exposure. Comparatively, only 32 markers changed significantly on the HP diet (24 up, 8 down). Metabolic perturbations in several endogenous biological pathways were also observed based on macronutrient differences and revealed dysfunction in several nutritionally relevant biosynthetic pathways. Conclusion: Overall, the HCHF diet promoted detrimental impacts and changes linked to several diseases, including arthritis or breast neoplasms. Identification of dietary pattern-specific impacts in this model provides a means to monitor the effects of disease risk and test interventions to prevent poor health outcomes through nutritional modification.

Early life exposure to vitamin D deficiency impairs molecular mechanisms that regulate liver cholesterol biosynthesis, energy metabolism, inflammation, and detoxification.

Introduction: Emerging data suggests liver disease may be initiated during development when there is high genome plasticity and the molecular pathways supporting liver function are being developed. Methods: Here, we leveraged our Collaborative Cross mouse model of developmental vitamin D deficiency (DVD) to investigate the role of DVD in dysregulating the molecular mechanisms underlying liver disease. We defined the effects on the adult liver transcriptome and metabolome and examined the role of epigenetic dysregulation. Given that the parental origin of the genome (POG) influences response to DVD, we used our established POG model [POG1-(CC011xCC001)F1 and POG2-(CC001xCC011)F1] to identify interindividual differences. Results: We found that DVD altered the adult liver transcriptome, primarily downregulating genes controlling liver development, response to injury/infection (detoxification & inflammation), cholesterol biosynthesis, and energy production. In concordance with these transcriptional changes, we found that DVD decreased liver cell membrane-associated lipids (including cholesterol) and pentose phosphate pathway metabolites. Each POG also exhibited distinct responses. POG1 exhibited almost 2X more differentially expressed genes (DEGs) with effects indicative of increased energy utilization. This included upregulation of lipid and amino acid metabolism genes and increased intermediate lipid and amino acid metabolites, increased energy cofactors, and decreased energy substrates. POG2 exhibited broader downregulation of cholesterol biosynthesis genes with a metabolomics profile indicative of decreased energy utilization. Although DVD primarily caused loss of liver DNA methylation for both POGs, only one epimutation was shared, and POG2 had 6.5X more differentially methylated genes. Differential methylation was detected at DEGs regulating developmental processes such as amino acid transport (POG1) and cell growth & differentiation (e.g., Wnt & cadherin signaling, POG2). Conclusions: These findings implicate a novel role for maternal vitamin D in programming essential offspring liver functions that are dysregulated in liver disease. Importantly, impairment of these processes was not rescued by vitamin D treatment at weaning, suggesting these effects require preventative measures. Substantial differences in POG response to DVD demonstrate that the parental genomic context of exposure determines offspring susceptibility.

Prospective association of the infant gut microbiome with social behaviors in the ECHO consortium.

BACKGROUND: Identifying modifiable risk factors of autism spectrum disorders (ASDs) may inform interventions to reduce financial burden. The infant/toddler gut microbiome is one such feature that has been associated with social behaviors, but results vary between cohorts. We aimed to identify consistent overall and sex-specific associations between the early-life gut microbiome and autism-related behaviors. METHODS: Utilizing the Environmental influences on Children Health Outcomes (ECHO) consortium of United States (U.S.) pediatric cohorts, we gathered data on 304 participants with fecal metagenomic sequencing between 6-weeks to 2-years postpartum (481 samples). ASD-related social development was assessed with the Social Responsiveness Scale (SRS-2). Linear regression, PERMANOVA, and Microbiome Multivariable Association with Linear Models (MaAsLin2) were adjusted for sociodemographic factors. Stratified models estimated sex-specific effects. RESULTS: Genes encoding pathways for synthesis of short-chain fatty acids were associated with higher SRS-2 scores, indicative of ASDs. Fecal concentrations of butyrate were also positively associated with ASD-related SRS-2 scores, some of which may be explained by formula use. LIMITATIONS: The distribution of age at outcome assessment differed in the cohorts included, potentially limiting comparability between cohorts. Stool sample collection methods also differed between cohorts. Our study population reflects the general U.S. population, and thus includes few participants who met the criteria for being at high risk of developing ASD. CONCLUSIONS: Our study is among the first multicenter studies in the U.S. to describe prospective microbiome development from infancy in relation to neurodevelopment associated with ASDs. Our work contributes to clarifying which microbial features associate with subsequent diagnosis of neuropsychiatric outcomes. This will allow for future interventional research targeting the microbiome to change neurodevelopmental trajectories.

Patterns of infant fecal metabolite concentrations and social behavioral development in toddlers.

BACKGROUND: Gut-derived metabolites, products of microbial and host co-metabolism, may inform mechanisms underlying children's neurodevelopment. We investigated whether infant fecal metabolites were related to toddler social behavior. METHODS: Stool samples collected from 6-week-olds (n = 86) and 1-year-olds (n = 209) in the New Hampshire Birth Cohort Study (NHBCS) were analyzed using nuclear magnetic resonance spectroscopy metabolomics. Autism-related behavior in 3-year-olds was assessed by caregivers using the Social Responsiveness Scale (SRS-2). To assess the association between metabolites and SRS-2 scores, we used a traditional single-metabolite approach, quantitative metabolite set enrichment (QEA), and self-organizing maps (SOMs). RESULTS: Using a single-metabolite approach and QEA, no individual fecal metabolite or metabolite set at either age was associated with SRS-2 scores. Using the SOM method, fecal metabolites of six-week-olds organized into four profiles, which were unrelated to SRS-2 scores. In 1-year-olds, one of twelve fecal metabolite profiles was associated with fewer autism-related behaviors, with SRS-2 scores 3.4 (95%CI: -7, 0.2) points lower than the referent group. This profile had higher concentrations of lactate and lower concentrations of short chain fatty acids than the reference. CONCLUSIONS: We uncovered metabolic profiles in infant stool associated with subsequent social behavior, highlighting one potential mechanism by which gut bacteria may influence neurobehavior. IMPACT: Differences in host and microbial metabolism may explain variability in neurobehavioral phenotypes, but prior studies do not have consistent results. We applied three statistical techniques to explore fecal metabolite differences related to social behavior, including self-organizing maps (SOMs), a novel machine learning algorithm. A 1-year-old fecal metabolite pattern characterized by high lactate and low short-chain fatty acid concentrations, identified using SOMs, was associated with social behavior less indicative of autism spectrum disorder. Our findings suggest that social behavior may be related to metabolite profiles and that future studies may uncover novel findings by applying the SOM algorithm.

Metabolomic data presents challenges for epidemiological meta-analysis: a case study of childhood body mass index from the ECHO consortium.

INTRODUCTION: Meta-analyses across diverse independent studies provide improved confidence in results. However, within the context of metabolomic epidemiology, meta-analysis investigations are complicated by differences in study design, data acquisition, and other factors that may impact reproducibility. OBJECTIVE: The objective of this study was to identify maternal blood metabolites during pregnancy (> 24 gestational weeks) related to offspring body mass index (BMI) at age two years through a meta-analysis framework. METHODS: We used adjusted linear regression summary statistics from three cohorts (total N = 1012 mother-child pairs) participating in the NIH Environmental influences on Child Health Outcomes (ECHO) Program. We applied a random-effects meta-analysis framework to regression results and adjusted by false discovery rate (FDR) using the Benjamini-Hochberg procedure. RESULTS: Only 20 metabolites were detected in all three cohorts, with an additional 127 metabolites detected in two of three cohorts. Of these 147, 6 maternal metabolites were nominally associated (P < 0.05) with offspring BMI z-scores at age 2 years in a meta-analytic framework including at least two studies: arabinose (Coefmeta = 0.40 [95% CI 0.10,0.70], Pmeta = 9.7 × 10-3), guanidinoacetate (Coefmeta = - 0.28 [- 0.54, - 0.02], Pmeta = 0.033), 3-ureidopropionate (Coefmeta = 0.22 [0.017,0.41], Pmeta = 0.033), 1-methylhistidine (Coefmeta = - 0.18 [- 0.33, - 0.04], Pmeta = 0.011), serine (Coefmeta = - 0.18 [- 0.36, - 0.01], Pmeta = 0.034), and lysine (Coefmeta = - 0.16 [- 0.32, - 0.01], Pmeta = 0.044). No associations were robust to multiple testing correction. CONCLUSIONS: Despite including three cohorts with large sample sizes (N > 100), we failed to identify significant metabolite associations after FDR correction. Our investigation demonstrates difficulties in applying epidemiological meta-analysis to clinical metabolomics, emphasizes challenges to reproducibility, and highlights the need for standardized best practices in metabolomic epidemiology.

Untargeted metabolomics on first trimester serum implicates metabolic perturbations associated with BMI in development of hypertensive disorders: a discovery study.

Goal: Body mass index (BMI) in early pregnancy is a critical risk factor for hypertensive disorders of pregnancy (HDP). The pathobiology of the interplay between BMI and HDP is not fully understood and represents the focus of this investigation. Methods: BMI and 1st-trimester serum samples were obtained from the Global Alliance to Prevent Prematurity and Stillbirth repository for 154 women (105 without HDP and 49 with HDP). Metabotyping was conducted using ultra-high-performance liquid-chromatography high-resolution mass spectrometry (UHPLC HR-MS). Multivariable linear regression and logistic models were used to determine metabolites and pathway perturbations associated with BMI in women with and without HDP, and to determine metabolites and pathway perturbations associated with HDP for women in categories of obese, overweight, and normal weight based on the 1st trimester BMI. These outcome-associated signals were identified or annotated by matching against an in-house physical standards library and public database. Pathway analysis was conducted by the Mummichog algorithm in MetaboAnalyst. Result: Vitamin D3 and lysine metabolism were enriched to associate with BMI for women with and without HDP. Tryptophan metabolism enrichment was associated with HDP in all the BMI categories. Pregnant women who developed HDP showed more metabolic perturbations with BMI (continuous) than those without HDP in their 1st-trimester serum. The HDP-associated pathways for women with normal weight indicated inflammation and immune responses. In contrast, the HDP-associated pathways for women of overweight and obese BMI indicated metabolic syndromes with disorders in glucose, protein, and amino acid, lipid and bile acid metabolism, and oxidative and inflammatory stress. Conclusion: High first-trimester BMI indicates underlying metabolic syndromes, which play critical roles in HDP development. Vitamin D3 and tryptophan metabolism may be the targets to guide nutritional interventions to mitigate metabolic and inflammatory stress in pregnancy and reduce the onset of HDP.

Baseline Serum Biomarkers Predict Response to a Weight Loss Intervention in Older Adults with Obesity: A Pilot Study.

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.

Multiomics Analysis of Plasma Proteomics and Metabolomics of Steroid Resistance in Childhood Nephrotic Syndrome Using a "Patient-Specific" Approach.

Introduction: Nephrotic syndrome (NS) occurs commonly in children with glomerular disease and glucocorticoids (GCs) are the mainstay treatment. Steroid resistant NS (SRNS) develops in 15% to 20% of children, increasing the risk of chronic kidney disease compared to steroid sensitive NS (SSNS). NS pathogenesis is unclear in most children, and no biomarkers exist that predict the development of pediatric SRNS. Methods: We studied a unique patient cohort with plasma specimens collected before GC treatment, yielding a disease-only sample not confounded by steroid-induced gene expression changes (SSNS n = 8; SRNS n = 7). A novel "patient-specific" bioinformatic approach merged paired pretreatment and posttreatment proteomic and metabolomic data and identified candidate SRNS biomarkers and altered molecular pathways in SRNS versus SSNS. Results: Joint pathway analyses revealed perturbations in nicotinate or nicotinamide and butanoate metabolic pathways in patients with SRNS. Patients with SSNS had perturbations of lysine degradation, mucin type O-glycan biosynthesis, and glycolysis or gluconeogenesis pathways. Molecular analyses revealed frequent alteration of molecules within these pathways that had not been observed by separate proteomic and metabolomic studies. We observed upregulation of NAMPT, NMNAT1, and SETMAR in patients with SRNS, in contrast to upregulation of ALDH1B1, ACAT1, AASS, ENPP1, and pyruvate in patients with SSNS. Pyruvate regulation was the change seen in our previous analysis; all other targets were novel. Immunoblotting confirmed increased NAMPT expression in SRNS and increased ALDH1B1 and ACAT1 expression in SSNS, following GC treatment. Conclusion: These studies confirmed that a novel "patient-specific" bioinformatic approach can integrate disparate omics datasets and identify candidate SRNS biomarkers not observed by separate proteomic or metabolomic analysis.

Exploring the internal exposome of seminal plasma with semen quality and live birth: A Pilot Study.

Infertility is clinically defined as the inability to achieve pregnancy within 12 months of regular unprotected sexual intercourse and affects 15% of couples worldwide. Therefore, the identification of novel biomarkers that can accurately predict male reproductive health and couples' reproductive success is of major public health significance. The objective of this pilot study is to test whether untargeted metabolomics is capable of discriminating reproductive outcomes and understand associations between the internal exposome of seminal plasma and the reproductive outcomes of semen quality and live birth among ten participants undergoing assisted reproductive technology (ART) in Springfield, MA. We hypothesize that seminal plasma offers a novel biological matrix by which untargeted metabolomics is able to discern male reproductive status and predict reproductive success. The internal exposome data was acquired using UHPLC-HR-MS on randomized seminal plasma samples at UNC at Chapel Hill. Unsupervised and supervised multivariate analyses were used to visualize the differentiation of phenotypic groups classified by men with normal or low semen quality based on World Health Organization guidelines as well as by successful ART: live birth or no live birth. Over 100 exogenous metabolites, including environmentally relevant metabolites, ingested food components, drugs and medications, and metabolites relevant to microbiome-xenobiotic interaction, were identified and annotated from the seminal plasma samples, through matching against the NC HHEAR hub in-house experimental standard library. Pathway enrichment analysis indicated that fatty acid biosynthesis and metabolism, vitamin A metabolism, and histidine metabolism were associated sperm quality; while pathways involving vitamin A metabolism, C21-steroid hormone biosynthesis and metabolism, arachidonic acid metabolism, and Omega-3 fatty acid metabolism distinguished live birth groups. Taken together, these pilot results suggest that seminal plasma is a novel matrix to study the influence of the internal exposome on reproductive health outcomes. Future research aims to increase the sample size to validate these findings.

Associations of maternal plasma and umbilical cord plasma metabolomics profiles with birth anthropometric measures.

BACKGROUND: The metabolomics profiles of maternal plasma during pregnancy and cord plasma at birth might influence fetal growth and birth anthropometry. The objective was to examine how maternal plasma and umbilical cord plasma metabolites are associated with newborn anthropometric measures, a known predictor of future health outcomes. METHODS: Pregnant women between 24 and 28 weeks of gestation were recruited as part of a prospective cohort study. Blood samples from 413 women at enrollment and 787 infant cord blood samples were analyzed using the Biocrates AbsoluteIDQ® p180 kit. Multivariable linear regression models were used to examine associations of cord and maternal metabolites with infant anthropometry at birth. RESULTS: In cord blood samples from this rural cohort from New Hampshire of largely white residents, 13 metabolites showed negative associations, and 10 metabolites showed positive associations with birth weight Z-score. Acylcarnitine C5 showed negative association, and 4 lysophosphatidylcholines showed positive associations with birth length Z-score. Maternal blood metabolites did not significantly correlate with birth weight and length Z-scores. CONCLUSIONS: Consistent findings were observed for several acylcarnitines that play a role in utilization of energy sources, and a lysophosphatidylcholine that is part of oxidative stress and inflammatory response pathways in cord plasma samples. IMPACT: The metabolomics profiles of maternal plasma during pregnancy and cord plasma at birth may influence fetal growth and birth anthropometry. This study examines the independent effects of maternal gestational and infant cord blood metabolomes across different classes of metabolites on birth anthropometry. Acylcarnitine species were negatively associated and glycerophospholipids species were positively associated with weight and length Z-scores at birth in the cord plasma samples, but not in the maternal plasma samples. This study identifies lipid metabolites in infants that possibly may affect early growth.

Salivary Metabolomics of Well and Poorly Controlled Type 1 and Type 2 Diabetes.

Objective: The concentrations of endogenous metabolites in saliva can be altered based on the systemic condition of the hosts and may, in theory, serve as a reflection of systemic disease progression. Hemoglobin A1C is used clinically to measure long-term average glycemic control. The aim of the study was to demonstrate if there were differences in the salivary metabolic profiles between well and poorly controlled type 1 and type 2 subjects with diabetes. Subjects and Methods. Subjects with type 1 and type 2 diabetes were enrolled (n = 40). The subjects were assigned to phenotypic groups based on their current level of A1C: <7 = well-controlled and >7 = poorly controlled. Demographic data, age, gender, and ethnicity, were used to match the two phenotypic groups. Whole saliva samples were collected and immediately stored at -80°C. Samples were spiked using an isotopically labeled internal standard and analyzed by UPLC-TOF-MS using a Waters SYNAPT G2-Si mass spectrometer. Results: Unsupervised principal components analysis (PCA) and orthogonal partial least squares regression discrimination analysis (OPLS-DA) were used to define unique metabolomic profiles associated with well and poorly controlled diabetes based on A1C levels. Conclusion: OPLS-DA demonstrates good separation of well and poorly controlled in both type 1 and type 2 diabetes. This provides evidence for developing saliva-based monitoring tools for diabetes.

Sex-Specific Metabolic Effects of Dietary Folate Withdrawal in Wild-Type and Aldh1l1 Knockout Mice.

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.

Longitudinal associations of pre-pregnancy BMI and gestational weight gain with maternal urinary metabolites: an NYU CHES study.

BACKGROUND/OBJECTIVES: Excessive gestational weight gain (GWG) and pre-pregnancy obesity affect a significant portion of the US pregnant population and are linked with negative maternal and child health outcomes. The objective of this study was to explore associations of pre-pregnancy body mass index (pBMI) and GWG with longitudinally measured maternal urinary metabolites throughout pregnancy. SUBJECTS/METHODS: Among 652 participants in the New York University Children's Health and Environment Study, a longitudinal pregnancy cohort, targeted metabolomics were measured in serially collected urine samples throughout pregnancy. Metabolites were measured at median 10 (T1), 21 (T2), and 29 (T3) weeks gestation using the Biocrates AbsoluteIDQ® p180 Urine Extension kit. Acylcarnitine, amino acid, biogenic amine, phosphatidylcholine, lysophosphatidylcholine, sphingolipid, and sugar levels were quantified. Pregnant people 18 years or older, without type 1 or 2 diabetes and with singleton live births and valid pBMI and metabolomics data were included. GWG and pBMI were calculated using weight and height data obtained from electronic health records. Linear mixed effects models with interactions with time were fit to determine the gestational age-specific associations of categorical pBMI and continuous interval-specific GWG with urinary metabolites. All analyses were corrected for false discovery rate. RESULTS: Participants with obesity had lower long-chain acylcarnitine levels throughout pregnancy and lower phosphatidylcholine and glucogenic amino acids and higher phenylethylamine concentrations in T2 and T3 compared with participants with normal/underweight pBMI. GWG was associated with taurine in T2 and T3 and C5 acylcarnitine species, C5:1, C5-DC, and C5-M-DC, in T2. CONCLUSIONS: pBMI and GWG were associated with the metabolic environment of pregnant individuals, particularly in relation to mid-pregnancy. These results highlight the importance of both preconception and prenatal maternal health.

Multi-Omics Analysis of Multiple Glucose-Sensing Receptor Systems in Yeast.

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.

Information enhanced model selection for Gaussian graphical model with application to metabolomic data.

In light of the low signal-to-noise nature of many large biological data sets, we propose a novel method to learn the structure of association networks using Gaussian graphical models combined with prior knowledge. Our strategy includes two parts. In the first part, we propose a model selection criterion called structural Bayesian information criterion, in which the prior structure is modeled and incorporated into Bayesian information criterion. It is shown that the popular extended Bayesian information criterion is a special case of structural Bayesian information criterion. In the second part, we propose a two-step algorithm to construct the candidate model pool. The algorithm is data-driven and the prior structure is embedded into the candidate model automatically. Theoretical investigation shows that under some mild conditions structural Bayesian information criterion is a consistent model selection criterion for high-dimensional Gaussian graphical model. Simulation studies validate the superiority of the proposed algorithm over the existing ones and show the robustness to the model misspecification. Application to relative concentration data from infant feces collected from subjects enrolled in a large molecular epidemiological cohort study validates that metabolic pathway involvement is a statistically significant factor for the conditional dependence between metabolites. Furthermore, new relationships among metabolites are discovered which can not be identified by the conventional methods of pathway analysis. Some of them have been widely recognized in biological literature.

Chemical exposures assessed via silicone wristbands and endogenous plasma metabolomics during pregnancy.

BACKGROUND: Metabolomics 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. OBJECTIVE: Investigate the influence of chemical exposures on the maternal plasma metabolome during pregnancy. METHODS: Data 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. RESULTS: Chemical 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. SIGNIFICANCE: This study contributes evidence to the potential effects of chemical exposures during pregnancy upon the endogenous maternal plasma metabolome.

Alterations in Microbial-Associated Fecal Metabolites in Relation to Arsenic Exposure Among Infants.

In utero and early life exposure to inorganic arsenic (iAs) alters immune response in experimental animals and is associated with an increased risk of infant infections. iAs exposure is related to differences in the gut microbiota diversity, community structure, and the relative abundance of individual microbial taxa both in laboratory and human studies. Metabolomics permits a direct measure of molecular products of microbial and host metabolic processes. We conducted NMR metabolomics analysis on infant stool samples and quantified the relative concentrations of 34 known microbial-related metabolites. We examined these metabolites in relation to both in utero and infant log2 urinary total arsenic concentrations (utAs, the sum of iAs and iAs metabolites) collected at approximately 6 weeks of age using linear regression models, adjusted for infant sex, age at sample collection, type of delivery (vaginal vs. cesarean section), feeding mode (breast milk vs. any formula), and specific gravity. Increased fecal butyrate (b = 214.24), propionate (b = 518.33), cholate (b = 8.79), tryptophan (b= 14.23), asparagine (b = 28.80), isoleucine (b = 65.58), leucine (b = 95.91), malonate (b = 50.43), and uracil (b = 36.13), concentrations were associated with a doubling of infant utAs concentrations (p< 0.05). These associations were largely among infants who were formula fed. No clear associations were observed with maternal utAs and infant fecal metabolites. Metabolomic analyses of infant stool samples lend further evidence that the infant gut microbiota is sensitive to As exposure, and these effects may have functional consequences.

Association of Cesarean Delivery and Formula Supplementation with the Stool Metabolome of 6-Week-Old Infants.

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.

Multi-omics analysis of glucose-mediated signaling by a moonlighting Gß protein Asc1/RACK1.

Heterotrimeric G proteins were originally discovered through efforts to understand the effects of hormones, such as glucagon and epinephrine, on glucose metabolism. On the other hand, many cellular metabolites, including glucose, serve as ligands for G protein-coupled receptors. Here we investigate the consequences of glucose-mediated receptor signaling, and in particular the role of a Gα subunit Gpa2 and a non-canonical Gß subunit, known as Asc1 in yeast and RACK1 in animals. Asc1/RACK1 is of particular interest because it has multiple, seemingly unrelated, functions in the cell. The existence of such "moonlighting" operations has complicated the determination of phenotype from genotype. Through a comparative analysis of individual gene deletion mutants, and by integrating transcriptomics and metabolomics measurements, we have determined the relative contributions of the Gα and Gß protein subunits to glucose-initiated processes in yeast. We determined that Gpa2 is primarily involved in regulating carbohydrate metabolism while Asc1 is primarily involved in amino acid metabolism. Both proteins are involved in regulating purine metabolism. Of the two subunits, Gpa2 regulates a greater number of gene transcripts and was particularly important in determining the amplitude of response to glucose addition. We conclude that the two G protein subunits regulate distinct but complementary processes downstream of the glucose-sensing receptor, as well as processes that lead ultimately to changes in cell growth and metabolism.