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.

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.

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.

Transforming Big Data into AI-ready data for nutrition and obesity research.

OBJECTIVE: Big Data are increasingly used in obesity and nutrition research to gain new insights and derive personalized guidance; however, this data in raw form are often not usable. Substantial preprocessing, which requires machine learning (ML), human judgment, and specialized software, is required to transform Big Data into artificial intelligence (AI)- and ML-ready data. These preprocessing steps are the most complex part of the entire modeling pipeline. Understanding the complexity of these steps by the end user is critical for reducing misunderstanding, faulty interpretation, and erroneous downstream conclusions. METHODS: We reviewed three popular obesity/nutrition Big Data sources: microbiome, metabolomics, and accelerometry. The preprocessing pipelines, specialized software, challenges, and how decisions impact final AI- and ML-ready products were detailed. RESULTS: Opportunities for advances to improve quality control, speed of preprocessing, and intelligent end user consumption were presented. CONCLUSIONS: Big Data have the exciting potential for identifying new modifiable factors that impact obesity research. However, to ensure accurate interpretation of conclusions arising from Big Data, the choices involved in preparing AI- and ML-ready data need to be transparent to investigators and clinicians relying on the conclusions.

Perspective: use and reuse of NMR-based metabolomics data: what works and what remains challenging.

BACKGROUND: The National Cancer Institute issued a Request for Information (RFI; NOT-CA-23-007) in October 2022, soliciting input on using and reusing metabolomics data. This RFI aimed to gather input on best practices for metabolomics data storage, management, and use/reuse. AIM OF REVIEW: The nuclear magnetic resonance (NMR) Interest Group within the Metabolomics Association of North America (MANA) prepared a set of recommendations regarding the deposition, archiving, use, and reuse of NMR-based and, to a lesser extent, mass spectrometry (MS)-based metabolomics datasets. These recommendations were built on the collective experiences of metabolomics researchers within MANA who are generating, handling, and analyzing diverse metabolomics datasets spanning experimental (sample handling and preparation, NMR/MS metabolomics data acquisition, processing, and spectral analyses) to computational (automation of spectral processing, univariate and multivariate statistical analysis, metabolite prediction and identification, multi-omics data integration, etc.) studies. KEY SCIENTIFIC CONCEPTS OF REVIEW: We provide a synopsis of our collective view regarding the use and reuse of metabolomics data and articulate several recommendations regarding best practices, which are aimed at encouraging researchers to strengthen efforts toward maximizing the utility of metabolomics data, multi-omics data integration, and enhancing the overall scientific impact of metabolomics studies.

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.

Fatty acid metabolism promotes TRPV4 activity in lung microvascular endothelial cells in pulmonary arterial hypertension.

Pulmonary arterial hypertension (PAH) is a morbid disease characterized by significant lung endothelial cell (EC) dysfunction. Prior work has shown that microvascular endothelial cells (MVECs) isolated from animals with experimental PAH and patients with PAH exhibit significant abnormalities in metabolism and calcium signaling. With regards to metabolism, we and others have shown evidence of increased aerobic glycolysis and evidence of increased utilization of alternate fuel sources (such as fatty acids) in PAH EC. In the realm of calcium signaling, our prior work linked increased activity of the transient receptor potential vanilloid-4 (TRPV4) channel to increased proliferation of MVECs isolated from the Sugen/Hypoxia rat model of PAH (SuHx-MVECs). However, the relationship between metabolic shifts and calcium abnormalities was not clear. Specifically, whether shifts in metabolism were responsible for increasing TRPV4 channel activity in SuHx-MVECs was not known. In this study, using human data, serum samples from SuHx rats, and SuHx-MVECs, we describe the consequences of increased MVEC fatty acid oxidation in PAH. In human samples, we observed an increase in long-chain fatty acid levels that was associated with PAH severity. Next, using SuHx rats and SuHx-MVECs, we observed increased intracellular levels of lipids. We also show that increasing intracellular lipid content increases TRPV4 activity, whereas inhibiting fatty acid oxidation normalizes basal calcium levels in SuHx-MVECs. By exploring the fate of fatty acid-derived carbons, we observed that the metabolite linking increased intracellular lipids to TRPV4 activity was ß-hydroxybutyrate (BOHB), a product of fatty acid oxidation. Finally, we show that BOHB supplementation alone is sufficient to sensitize the TRPV4 channel in rat and mouse MVECs. Returning to humans, we observe a transpulmonary BOHB gradient in human patients with PAH. Thus, we establish a link between fatty acid oxidation, BOHB production, and TRPV4 activity in MVECs in PAH. These data provide new insight into metabolic regulation of calcium signaling in lung MVECs in PAH.NEW & NOTEWORTHY In this paper, we explore the link between metabolism and intracellular calcium levels in microvascular endothelial cells (MVECs) in pulmonary arterial hypertension (PAH). We show that fatty acid oxidation promotes sensitivity of the transient receptor potential vanilloid-4 (TRPV4) calcium channel in MVECs isolated from a rodent model of PAH.

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.

Multi-omics analyses reveal ClpP activators disrupt essential mitochondrial pathways in triple-negative breast cancer.

ClpP activators ONC201 and related small molecules (TR compounds, Madera Therapeutics), have demonstrated significant anti-cancer potential in vitro and in vivo studies, including clinical trials for refractory solid tumors. Though progress has been made in identifying specific phenotypic outcomes following ClpP activation, the exact mechanism by which ClpP activation leads to broad anti-cancer activity has yet to be fully elucidated. In this study, we utilized a multi-omics approach to identify the ClpP-dependent proteomic, transcriptomic, and metabolomic changes resulting from ONC201 or the TR compound TR-57 in triple-negative breast cancer cells. Applying mass spectrometry-based methods of proteomics and metabolomics, we identified ∼8,000 proteins and 588 metabolites, respectively. From proteomics data, 113 (ONC201) and 191 (TR-57) proteins significantly increased and 572 (ONC201) and 686 (TR-57) proteins significantly decreased in this study. Gene ontological (GO) analysis revealed strong similarities between proteins up- or downregulated by ONC201 or TR-57 treatment. Notably, this included the downregulation of many mitochondrial processes and proteins, including mitochondrial translation and mitochondrial matrix proteins. We performed a large-scale transcriptomic analysis of WT SUM159 cells, identifying ∼7,700 transcripts (746 and 1,100 significantly increasing, 795 and 1,013 significantly decreasing in ONC201 and TR-57 treated cells, respectively). Less than 21% of these genes were affected by these compounds in ClpP null cells. GO analysis of these data demonstrated additional similarity of response to ONC201 and TR-57, including a decrease in transcripts related to the mitochondrial inner membrane and matrix, cell cycle, and nucleus, and increases in other nuclear transcripts and transcripts related to metal-ion binding. Comparison of response between both compounds demonstrated a highly similar response in all -omics datasets. Analysis of metabolites also revealed significant similarities between ONC201 and TR-57 with increases in α-ketoglutarate and 2-hydroxyglutaric acid and decreased ureidosuccinic acid, L-ascorbic acid, L-serine, and cytidine observed following ClpP activation in TNBC cells. Further analysis identified multiple pathways that were specifically impacted by ClpP activation, including ATF4 activation, heme biosynthesis, and the citrulline/urea cycle. In summary the results of our studies demonstrate that ONC201 and TR-57 induce highly similar and broad effects against multiple mitochondrial processes required for cell proliferation.

NMR and Metabolomics-A Roadmap for the Future.

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.

Oral administration of TiO2 nanoparticles during early life impacts cardiac and neurobehavioral performance and metabolite profile in an age- and sex-related manner.

BACKGROUND: Nanoparticles (NPs) are increasingly incorporated in everyday products. To investigate the effects of early life exposure to orally ingested TiO2 NP, male and female Sprague-Dawley rat pups received four consecutive daily doses of 10 mg/kg body weight TiO2 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 TiO2 NP administration for all dose groups. Metabolomic pathways perturbed by TiO2 NP administration included pathways involved in amino acid and lipid metabolism. CONCLUSION: Oral administration of TiO2 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 TiO2 NP than male pups. Collectively the data from this exploratory study suggest oral administration of TiO2 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 TiO2 NP.

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.

Intravenous administration of three multiwalled carbon nanotubes to female rats and their effect on urinary biochemical profile.

This study was conducted to investigate the influence of outer diameter (OD) and length (L) of multiwalled carbon nanotubes (MWCNTs) on biodistribution and the perturbation of endogenous metabolite profiles. Three different-sized carboxylated MWCNTs (NIEHS-12-2: L 0.5-2 µm, OD 10-20 nm, NIEHS-13-2: L 0.5-2 µm, OD 30-50 nm, and NIEHS-14-2: L 10-30 µm, OD 10-20 nm) in water were administered to female Sprague-Dawley rats as a single intravenous dose of 1 mg/kg MWCNTs. Biodistribution in liver, lung, spleen, and lymph nodes was evaluated in tissue sections at 1 and 7 days' post-dosing using enhanced darkfield microscopy and hyperspectral imaging. Nuclear magnetic resonance (NMR) analysis was used for biochemical profiling and pathway mapping of endogenous metabolites in urine collected at 24-h intervals prior to dosing, at Day 1 and Day 7. At Day 1 and Day 7, all three MWCNTs were observed in liver. NIEHS-12-2 was observed in spleen, whereas NIEHS-13-2 and NIEHS-14-2 were not. All three MWCNTs were observed in lymph nodes and lung at Day 7. The urinary biochemical profile showed the highest positive fold change (FC) at Day 7 for the metabolites acetate, alanine, and lactate, whereas 1-methylnicotinamide, 2-oxoglutarate, and hippurate had some of the lowest FCs for all three MWCNTs. This study demonstrates that the observed tissue location of MWCNTs is size dependent. Overlaps in the perturbation of endogenous metabolite profiles were found regardless of their size, and the biochemical responses were more profound at Day 7 compared with Day 1, indicating a delayed biological response to MWCNTs.

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.

Bridging the Gap between Analytical and Microbial Sciences in Microbiome Research.

Metabolites from the microbiome influence human, animal, and environmental health, but the diversity and functional roles of these compounds have only begun to be elucidated. Comprehensively characterizing these molecules are significant challenges, as it requires expertise in analytical methods, such as mass spectrometry and nuclear magnetic resonance spectroscopy, skills that not many traditional microbiologists or microbial ecologists possess. This creates a gap between microbiome scientists that want to understand the role of microbial metabolites in microbiome systems and the skills required to generate and interpret complex metabolomics data sets. To bridge this gap, microbiome scientists should engage analytical chemists to best understand the underlying chemical principles of the data. Conversely, analytical scientists are encouraged to engage with microbiome scientists to better understand the biological questions being asked with metabolomics and to best communicate its intricacies. Better communication across the chemistry/biology disciplines will further reveal the "dark matter" within microbiomes that maintain healthy humans and environments.