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Introduction: Bifidobacterium longum subspecies infantis ( B. infantis ) may play a key role in infant gut development. This trial evaluated safety, tolerability, and efficacy of B. infantis LMG11588 supplementation., Methods: This randomized, placebo-controlled, double-blind study conducted in the Philippines included healthy breastfed and/or formula-fed infants (14-21 days old) randomized for 8 weeks to a control group (CG; n  = 77), or any of two B. infantis experimental groups (EGs): low (Lo-EG; 1*10 8 CFU/day; n  = 75) or high dose (Hi-EG; 1.8*10 10 CFU/day; n  = 76). Primary endpoint was weight gain; secondary endpoints included stooling patterns, gastrointestinal symptoms, adverse events, fecal microbiome, biomarkers, pH, and organic acids., Results: Non-inferiority in weight gain was demonstrated for Hi-EG and Lo-EG vs. CG. Overall, probiotic supplementation promoted mushy-soft stools, fewer regurgitation episodes, and increased fecal acetate production, which was more pronounced in the exclusively breastfed infants (EBF) and positively correlated with B. infantis abundance. In EBF, fecal pro-inflammatory cytokines (IL-1 beta, IL-8) were reduced. Strain-level metagenomic analysis allowed attributing the increased abundance of B. infantis in EGs versus CG, to LMG11588 probiotic colonization. Colonization by autochthonous B. infantis strains was similar between groups., Discussion: B. infantis LMG11588 supplementation was associated with normal infant growth, was safe and well-tolerated and promoted a Bifidobacterium -rich microbiota driven by B. infantis LMG11588 colonization without disturbing the natural dispersal of autochthonous B. infantis strains. In EBF, supplementation stimulated microbial metabolic activity and beneficially modulated enteric inflammation., Competing Interests: Authors employed by Nestlé Nutrition and Nestlé Research, Société des Produits Nestlé SA, Switzerland were involved in the study design (CIC), data analysis (CM, MN, KV, GLC, AF, BB, CB, CIC), as well as preparation and decision to submit the manuscript (TS, MN, KV, GLC, AF, BB, CIC). In addition, JMM, JKV, PM, and HBN, employees of Clinical Microbiomics, were involved in data analysis and preparation of the manuscript. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2023 Capeding, Phee, Ming, Noti, Vidal, Le Carrou, Frézal, Moll, Vogt, Myers, Nielsen, Boulangé, Samuel, Berger and Cercamondi.)

Cow's milk protein allergy (CMPA) is a prevalent food allergy among infants and young children. We conducted a randomized, multicenter intervention study involving 194 non-breastfed infants with CMPA until 12 months of age (clinical trial registration: NCT03085134). One exploratory objective was to assess the effects of a whey-based extensively hydrolyzed formula (EHF) supplemented with 2'-fucosyllactose (2'-FL) and lacto- N -neotetraose (LNnT) on the fecal microbiome and metabolome in this population. Thus, fecal samples were collected at baseline, 1 and 3 months from enrollment, as well as at 12 months of age. Human milk oligosaccharides (HMO) supplementation led to the enrichment of bifidobacteria in the gut microbiome and delayed the shift of the microbiome composition toward an adult-like pattern. We identified specific HMO-mediated changes in fecal amino acid degradation and bile acid conjugation, particularly in infants commencing the HMO-supplemented formula before the age of three months. Thus, HMO supplementation partially corrected the dysbiosis commonly observed in infants with CMPA. Further investigation is necessary to determine the clinical significance of these findings in terms of a reduced incidence of respiratory infections and other potential health benefits.

The evaluation of joint disease using synovial fluid is an emerging field of metabolic profiling. The analysis is challenged by multiple macromolecules which can obscure the small molecule chemistry. The use of protein precipitation and extraction has been evaluated previously, but not in synovial fluid. We systematically review the published NMR spectroscopy methods of synovial fluid analysis and investigated the efficacy of three different protein precipitation techniques: methanol, acetonitrile and trichloroacetic acid. The trichloroacetic wash removed the most protein. However, metabolite recoveries were universally very poor. Acetonitrile liquid/liquid extraction gave metabolite gains from four unknown compounds with spectral peaks at δ = 1.91 ppm, 3.64 ppm, 3.95 ppm & 4.05 ppm. The metabolite recoveries for acetonitrile were between 1.5 and 7 times higher than the methanol method, across all classes of metabolite. The methanol method was more effective in removing protein as reported by the free GAG undefined peak (44 % vs 125 %). However, qualitative evaluation showed that acetonitrile and methanol provided good restoration of the spectra to baseline. The methanol extraction has issues of a gelatinous substrate in the samples. All metabolite recoveries had a CV of > 15 %. A recommendation of acetonitrile liquid/liquid extraction was made for human synovial fluid (HSF) analysis. This is due to consistency, effective protein precipitation, recovery of metabolites and additional compounds not previously visible., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 Elsevier B.V. All rights reserved.)

The gut microbiome has an important role in infant health and development. We characterized the fecal microbiome and metabolome of 222 young children in Dhaka, Bangladesh during the first two years of life. A distinct Bifidobacterium longum clade expanded with introduction of solid foods and harbored enzymes for utilizing both breast milk and solid food substrates. The clade was highly prevalent in Bangladesh, present globally (at lower prevalence), and correlated with many other gut taxa and metabolites, indicating an important role in gut ecology. We also found that the B. longum clades and associated metabolites were implicated in childhood diarrhea and early growth, including positive associations between growth measures and B. longum subsp. infantis, indolelactate and N-acetylglutamate. Our data demonstrate geographic, cultural, seasonal, and ecological heterogeneity that should be accounted for when identifying microbiome factors implicated in and potentially benefiting infant development., Competing Interests: Declaration of interests L.S., C.L.B., C.I.L.R., S.D., O.D-B., C.N-B., and O.S. are employees of Société des Produits Nestlé (SPN). R.J.X. is a member of the Scientific Advisory Board at Nestlé, founder of Jnana and Celsius Therapeutics and board member of MoonLake Immunotherapeutics. We filed a patent related to findings described in this study. R.J.X., H.V., T.V., O.S., L.S., S.D., and C.N.-B. are listed as inventors (Bifidobacterium Longum Transitional Microorganisms, Compositions and Uses Thereof, PCT/US2022/035,310)., (Copyright © 2022 Elsevier Inc. All rights reserved.)

Fermentation is an ancient food preservation process, and fermented products have been traditionally consumed in different cultures worldwide over the years. The interplay between human gut microbiota, diet and host health is widely recognized. Diet is one of the main factors modulating gut microbiota potentially with beneficial effects on human health. Fermented dairy products have received much attention, but other sources of probiotic delivery through food received far less attention. In this research, a combination of in vitro tools mimicking colonic fermentation and the intestinal epithelium have been applied to study the effect of different pasteurized and non-pasteurized water kefir products on gut microbiota, epithelial barrier function and immunomodulation. Water kefir increased beneficial short-chain fatty acid production at the microbial level, reduced detrimental proteolytic fermentation compounds and increased Bifidobacterium genus abundance. The observed benefits are enhanced by pasteurization. Pasteurized products also had a significant effect at the host level, improving inflammation-induced intestinal epithelial barrier disruption and increasing IL-10 and IL-1β compared to the control condition. Our data support the potential health benefits of water kefir and demonstrate that pasteurization, performed to prolong shelf life and stability of the product, also enhanced these benefits.

The impact of metabolism upon the altered pathology of joint disease is rapidly becoming recognized as an important area of study. Synovial joint fluid is an attractive and representative biofluid of joint disease. A systemic review revealed little evidence of the metabolic stability of synovial joint fluid collection, handling or storage, despite recent reports characterizing the metabolic phenotype in joint disease. We aim to report the changes in small molecule detection within human synovial fluid (HSF) using nuclear magnetic resonance (NMR) spectroscopy at varying storage temperatures, durations and conditions. HSF was harvested by arthrocentesis from patients with isolated monoarthropathy or undergoing joint replacement (n = 30). Short-term storage (0-12 h, 4°C & 18°C) and the effect of repeated freeze-thaw cycles (-80°C to 18°C) was assessed. Long-term storage was evaluated by early (-80°C, <21days) and late analysis (-80°C, 10-12 months). 1D NMR spectroscopy experiments, NOESYGPPR1D and CPMG identified metabolites and semi-quantification was performed. Samples demonstrated broad stability to freeze-thaw cycling and refrigeration of <4 h. Short-term room temperature or refrigerated storage showed significant variation in 2-ketoisovalerate, valine, dimethylamine, succinate, 2-hydroxybutyrate, and acetaminophen glucuronide. Lipid and macromolecule detection was variable. Long-term storage demonstrated significant changes in: acetate, acetoacetate, creatine, N,N-dimethylglycine, dimethylsulfone, 3-hydroxybutyrate and succinate. Changeable metabolites during short-term storage appeared to be energy-synthesis intermediates. Most metabolites were stable for the first four hours at room temperature or refrigeration, with notable exceptions. We therefore recommend that HSF samples should be kept refrigerated for no more than 4 hours prior to freezing at -80°C. Furthermore, storage of HSF samples for 10-12 months before analysis can affect the detection of selected metabolites., Competing Interests: Declaration of Competing Interest The authors report no declarations of interest., (Crown Copyright © 2021. Published by Elsevier B.V. All rights reserved.)

Aims: The diagnosis of joint infections is an inexact science using combinations of blood inflammatory markers and microscopy, culture, and sensitivity of synovial fluid (SF). There is potential for small molecule metabolites in infected SF to act as infection markers that could improve accuracy and speed of detection. The objective of this study was to use nuclear magnetic resonance (NMR) spectroscopy to identify small molecule differences between infected and noninfected human SF., Methods: In all, 16 SF samples (eight infected native and prosthetic joints plus eight noninfected joints requiring arthroplasty for end-stage osteoarthritis) were collected from patients. NMR spectroscopy was used to analyze the metabolites present in each sample. Principal component analysis and univariate statistical analysis were undertaken to investigate metabolic differences between the two groups., Results: A total of 16 metabolites were found in significantly different concentrations between the groups. Three were in higher relative concentrations (lipids, cholesterol, and N-acetylated molecules) and 13 in lower relative concentrations in the infected group (citrate, glycine, glycosaminoglycans, creatinine, histidine, lysine, formate, glucose, proline, valine, dimethylsulfone, mannose, and glutamine)., Conclusion: Metabolites found in significantly greater concentrations in the infected cohort are markers of inflammation and infection. They play a role in lipid metabolism and the inflammatory response. Those found in significantly reduced concentrations were involved in carbohydrate metabolism, nucleoside metabolism, the glutamate metabolic pathway, increased oxidative stress in the diseased state, and reduced articular cartilage breakdown. This is the first study to demonstrate differences in the metabolic profile of infected and noninfected human SF, using a noninfected matched cohort, and may represent putative biomarkers that form the basis of new diagnostic tests for infected SF. Cite this article: Bone Joint Res  2021;10(1):85-95.

Osteoarthritis (OA) is a multifactorial disease contributing to significant disability and economic burden in Western populations. The aetiology of OA remains poorly understood, but is thought to involve genetic, mechanical and environmental factors. Currently, the diagnosis of OA relies predominantly on clinical assessment and plain radiographic changes long after the disease has been initiated. Recent advances suggest that there are changes in joint fluid metabolites that are associated with OA development. If this is the case, biochemical and metabolic biomarkers of OA could help determine prognosis, monitor disease progression and identify potential therapeutic targets. Moreover, for focussed management and personalised medicine, novel biomarkers could sub-stratify patients into OA phenotypes, differentiating metabolic OA from post-traumatic, age-related and genetic OA. To date, OA biomarkers have concentrated on cytokine action and protein signalling with some progress. However, these remain to be adopted into routine clinical practice. In this review, we outline the emerging metabolic links to OA pathogenesis and how an elucidation of the metabolic changes in this condition may provide future, more descriptive biomarkers to differentiate OA subtypes.

Metabolic profiling of biological samples provides important insights into multiple physiological and pathological processes but is hindered by a lack of automated annotation and standardized methods for structure elucidation of candidate disease biomarkers. Here we describe a system for identifying molecular species derived from nuclear magnetic resonance (NMR) spectroscopy-based metabolic phenotyping studies, with detailed information on sample preparation, data acquisition and data modeling. We provide eight different modular workflows to be followed in a recommended sequential order according to their level of difficulty. This multi-platform system involves the use of statistical spectroscopic tools such as Statistical Total Correlation Spectroscopy (STOCSY), Subset Optimization by Reference Matching (STORM) and Resolution-Enhanced (RED)-STORM to identify other signals in the NMR spectra relating to the same molecule. It also uses two-dimensional NMR spectroscopic analysis, separation and pre-concentration techniques, multiple hyphenated analytical platforms and data extraction from existing databases. The complete system, using all eight workflows, would take up to a month, as it includes multi-dimensional NMR experiments that require prolonged experiment times. However, easier identification cases using fewer steps would take 2 or 3 days. This approach to biomarker discovery is efficient and cost-effective and offers increased chemical space coverage of the metabolome, resulting in faster and more accurate assignment of NMR-generated biomarkers arising from metabolic phenotyping studies. It requires a basic understanding of MATLAB to use the statistical spectroscopic tools and analytical skills to perform solid phase extraction (SPE), liquid chromatography (LC) fraction collection, LC-NMR-mass spectroscopy and one-dimensional and two-dimensional NMR experiments.

Objective: The hip and knee joints differ biomechanically in terms of contact stresses, fluid lubrication and wear patterns. These differences may be reflected in the synovial fluid (SF) composition of the two joints, but the nature of these differences remains unknown. The objective was to identify differences in osteoarthritic hip and knee SF metabolites using metabolic profiling with Nuclear Magnetic Resonance (NMR) spectroscopy., Design: Twenty-four SF samples (12 hip, 12 knee) were collected from patients with end-stage osteoarthritis (ESOA) undergoing hip/knee arthroplasty. Samples were matched for age, gender, ethnicity and had similar medical comorbidities. NMR spectroscopy was used to analyse the metabolites present in each sample. Principal Component Analysis and Orthogonal Partial Least Squares Discriminant Analysis were undertaken to investigate metabolic differences between the groups. Metabolites were identified using 2D NMR spectra, statistical spectroscopy and by comparison to entries in published databases., Results: There were significant differences in the metabolic profile between the groups. Four metabolites were found in significantly greater quantities in the knee group compared to the hip group (N-acetylated molecules, glycosaminoglycans, citrate and glutamine)., Conclusions: This is the first study to indicate differences in the metabolic profile of hip and knee SF in ESOA. The identified metabolites can broadly be grouped into those involved in collagen degradation, the tricarboxylic acid cycle and oxidative metabolism in diseased joints. These findings may represent a combination of intra and extra-articular factors., (Copyright © 2019 Osteoarthritis Research Society International. Published by Elsevier Ltd. All rights reserved.)

Aims: To characterize serum metabolic signatures associated with atherosclerosis in the coronary or carotid arteries and subsequently their association with incident cardiovascular disease (CVD)., Methods and Results: We used untargeted one-dimensional (1D) serum metabolic profiling by proton nuclear magnetic resonance spectroscopy (1H NMR) among 3867 participants from the Multi-Ethnic Study of Atherosclerosis (MESA), with replication among 3569 participants from the Rotterdam and LOLIPOP studies. Atherosclerosis was assessed by coronary artery calcium (CAC) and carotid intima-media thickness (IMT). We used multivariable linear regression to evaluate associations between NMR features and atherosclerosis accounting for multiplicity of comparisons. We then examined associations between metabolites associated with atherosclerosis and incident CVD available in MESA and Rotterdam and explored molecular networks through bioinformatics analyses. Overall, 30 1H NMR measured metabolites were associated with CAC and/or IMT, P = 1.3 × 10-14 to 1.0 × 10-6 (discovery) and P = 5.6 × 10-10 to 1.1 × 10-2 (replication). These associations were substantially attenuated after adjustment for conventional cardiovascular risk factors. Metabolites associated with atherosclerosis revealed disturbances in lipid and carbohydrate metabolism, branched chain, and aromatic amino acid metabolism, as well as oxidative stress and inflammatory pathways. Analyses of incident CVD events showed inverse associations with creatine, creatinine, and phenylalanine, and direct associations with mannose, acetaminophen-glucuronide, and lactate as well as apolipoprotein B (P < 0.05)., Conclusion: Metabolites associated with atherosclerosis were largely consistent between the two vascular beds (coronary and carotid arteries) and predominantly tag pathways that overlap with the known cardiovascular risk factors. We present an integrated systems network that highlights a series of inter-connected pathways underlying atherosclerosis., (© The Author(s) 2019. Published by Oxford University Press on behalf of the European Society of Cardiology.)

Importance: Chinese women have the highest rate of lung cancer among female never-smokers in the world, and the etiology is poorly understood., Objective: To assess the association between metabolomics and lung cancer risk among never-smoking women., Design, Setting, and Participants: This nested case-control study included 275 never-smoking female patients with lung cancer and 289 never-smoking cancer-free control participants from the prospective Shanghai Women's Health Study recruited from December 28, 1996, to May 23, 2000. Validated food frequency questionnaires were used for the collection of dietary information. Metabolomic analysis was conducted from November 13, 2015, to January 6, 2016. Data analysis was conducted from January 6, 2016, to November 29, 2018., Exposures: Untargeted ultra-high-performance liquid chromatography-tandem mass spectrometry and nuclear magnetic resonance metabolomic profiles were characterized using prediagnosis urine samples. A total of 39 416 metabolites were measured., Main Outcomes and Measures: Incident lung cancer., Results: Among the 564 women, those who developed lung cancer (275 participants; median [interquartile range] age, 61.0 [52-65] years) and those who did not develop lung cancer (289 participants; median [interquartile range] age, 62.0 [53-66] years) at follow-up (median [interquartile range] follow-up, 10.9 [9.0-11.7] years) were similar in terms of their secondhand smoke exposure, history of respiratory diseases, and body mass index. A peak metabolite, identified as 5-methyl-2-furoic acid, was significantly associated with lower lung cancer risk (odds ratio, 0.57 [95% CI, 0.46-0.72]; P < .001; false discovery rate = 0.039). Furthermore, this peak was weakly correlated with self-reported dietary soy intake (ρ = 0.21; P < .001). Increasing tertiles of this metabolite were associated with lower lung cancer risk (in comparison with first tertile, odds ratio for second tertile, 0.52 [95% CI, 0.34-0.80]; and odds ratio for third tertile, 0.46 [95% CI, 0.30-0.70]), and the association was consistent across different histological subtypes and follow-up times. Additionally, metabolic pathway analysis found several systemic biological alterations that were associated with lung cancer risk, including 1-carbon metabolism, nucleotide metabolism, oxidative stress, and inflammation., Conclusions and Relevance: This prospective study of the untargeted urinary metabolome and lung cancer among never-smoking women in China provides support for the hypothesis that soy-based metabolites are associated with lower lung cancer risk in never-smoking women and suggests that biological processes linked to air pollution may be associated with higher lung cancer risk in this population.

Objective: To perform a systematic review of the small molecule metabolism studies of osteoarthritis utilising nuclear magnetic resonance (NMR) or mass spectroscopy (MS) analysis (viz., metabolomics or metabonomics), thereby providing coherent conclusions and reference material for future study., Method: We applied PRISMA guidelines (PROSPERO 95068) with the following MESH terms: 1. "osteoarthritis" AND ("metabolic" OR "metabonomic" OR "metabolomic" OR "metabolism") 2. ("synovial fluid" OR "cartilage" OR "synovium" OR "serum" OR "plasma" OR "urine") AND ("NMR" or "Mass Spectroscopy"). Databases searched were "Medline" and "Embase". Studies were searched in English and excluded review articles not containing original research. Study outcomes were significant or notable metabolites, species (human or animal) and the Newcastle-Ottawa Score., Results: In the 27 studies meeting the inclusion criteria, there was a shift towards anaerobic and fatty acid metabolism in OA disease, although whether this represents the inflammatory state remains unclear. Lipid structure and composition was altered within disease subclasses including phosphatidyl choline (PC) and the sphingomyelins. Macromolecular proteoglycan destruction was described, but the correlation to disease factors was not demonstrated. Collated results suggested arachidonate signalling pathways and androgen sex hormones as future metabolic pathways for investigation., Conclusion: Our meta-analysis demonstrates significant small molecule differences between sample types, between species (such as human and bovine), with potential OA biomarkers and targets for local or systemic therapies. Studies were limited by numbers and a lack of disease correlation. Future studies should use NMR and MS analysis to further investigate large population subgroups including inflammatory arthropathy, OA subclasses, age and joint differences., (Crown Copyright © 2018. Published by Elsevier Ltd. All rights reserved.)

Nephrotic syndrome with idiopathic membranous nephropathy as a major contributor, is characterized by proteinuria, hypoalbuminemia and oedema. Diagnosis is based on renal biopsy and the condition is treated using immunosuppressive drugs; however nephrotic syndrome treatment efficacy varies among patients. Multi-omic urine analyses can discover new markers of nephrotic syndrome that can be used to develop personalized treatments. For proteomics, a protease inhibitor (PI) is sometimes added at sample collection to conserve proteins but its impact on urine metabolic phenotyping needs to be evaluated. Urine from controls (n = 4) and idiopathic membranous nephropathy (iMN) patients (n = 6) were collected with and without PI addition and analysed using 1H NMR spectroscopy and UPLC-MS. PI-related data features were observed in the 1H NMR spectra but their removal followed by a median fold change normalisation, eliminated the PI contribution. PI-related metabolites in UPLC-MS data had limited effect on metabolic patterns specific to iMN. When using an appropriate data processing pipeline, PI-containing urine samples are appropriate for 1H NMR and MS metabolic profiling of patients with nephrotic syndrome.

Introduction: Differences in the metabolite profiles between serum and plasma are incompletely understood., Objectives: To evaluate metabolic profile differences between serum and plasma and among plasma sample subtypes., Methods: We analyzed serum, platelet rich plasma (PRP), platelet poor plasma (PPP), and platelet free plasma (PFP), collected from 8 non-fasting apparently healthy women, using untargeted standard 1D and CPMG 1 H NMR and reverse phase and hydrophilic (HILIC) UPLC-MS. Differences between metabolic profiles were evaluated using validated principal component and orthogonal partial least squares discriminant analysis., Results: Explorative analysis showed the main source of variation among samples was due to inter-individual differences with no grouping by sample type. After correcting for inter-individual differences, lipoproteins, lipids in VLDL/LDL, lactate, glutamine, and glucose were found to discriminate serum from plasma in NMR analyses. In UPLC-MS analyses, lysophosphatidylethanolamine (lysoPE)(18:0) and lysophosphatidic acid(20:0) were higher in serum, and phosphatidylcholines (PC)(16:1/18:2, 20:3/18:0, O-20:0/22:4), lysoPC(16:0), PE(O-18:2/20:4), sphingomyelin(18:0/22:0), and linoleic acid were lower. In plasma subtype analyses, isoleucine, leucine, valine, phenylalanine, glutamate, and pyruvate were higher among PRP samples compared with PPP and PFP by NMR while lipids in VLDL/LDL, citrate, and glutamine were lower. By UPLC-MS, PE(18:0/18:2) and PC(P-16:0/20:4) were higher in PRP compared with PFP samples., Conclusions: Correction for inter-individual variation was required to detect metabolite differences between serum and plasma. Our results suggest the potential importance of inter-individual effects and sample type on the results from serum and plasma metabolic phenotyping studies.

1 H NMR spectroscopy of biofluids generates reproducible data allowing detection and quantification of small molecules in large population cohorts. Statistical models to analyze such data are now well-established, and the use of univariate metabolome wide association studies (MWAS) investigating the spectral features separately has emerged as a computationally efficient and interpretable alternative to multivariate models. The MWAS rely on the accurate estimation of a metabolome wide significance level (MWSL) to be applied to control the family wise error rate. Subsequent interpretation requires efficient visualization and formal feature annotation, which, in-turn, call for efficient prioritization of spectral variables of interest. Using human serum 1 H NMR spectroscopic profiles from 3948 participants from the Multi-Ethnic Study of Atherosclerosis (MESA), we have performed a series of MWAS for serum levels of glucose. We first propose an extension of the conventional MWSL that yields stable estimates of the MWSL across the different model parameterizations and distributional features of the outcome. We propose both efficient visualization methods and a strategy based on subsampling and internal validation to prioritize the associations. Our work proposes and illustrates practical and scalable solutions to facilitate the implementation of the MWAS approach and improve interpretation in large cohort studies.

The influence of the gut microbiome on metabolic and behavioral traits is widely accepted, though the microbiome-derived metabolites involved remain unclear. We carried out untargeted urine 1 H-NMR spectroscopy-based metabolic phenotyping in an isogenic C57BL/6J mouse population (n = 50) and show that microbial-host co-metabolites are prodromal (i.e., early) markers predicting future divergence in metabolic (obesity and glucose homeostasis) and behavioral (anxiety and activity) outcomes with 94%-100% accuracy. Some of these metabolites also modulate disease phenotypes, best illustrated by trimethylamine-N-oxide (TMAO), a product of microbial-host co-metabolism predicting future obesity, impaired glucose tolerance (IGT), and behavior while reducing endoplasmic reticulum stress and lipogenesis in 3T3-L1 adipocytes. Chronic in vivo TMAO treatment limits IGT in HFD-fed mice and isolated pancreatic islets by increasing insulin secretion. We highlight the prodromal potential of microbial metabolites to predict disease outcomes and their potential in shaping mammalian phenotypic heterogeneity., (Copyright © 2017 The Authors. Published by Elsevier Inc. All rights reserved.)

Large-scale metabolomics studies involving thousands of samples present multiple challenges in data analysis, particularly when an untargeted platform is used. Studies with multiple cohorts and analysis platforms exacerbate existing problems such as peak alignment and normalization. Therefore, there is a need for robust processing pipelines that can ensure reliable data for statistical analysis. The COMBI-BIO project incorporates serum from ∼8000 individuals, in three cohorts, profiled by six assays in two phases using both 1 H NMR and UPLC-MS. Here we present the COMBI-BIO NMR analysis pipeline and demonstrate its fitness for purpose using representative quality control (QC) samples. NMR spectra were first aligned and normalized. After eliminating interfering signals, outliers identified using Hotelling's T 2 were removed and a cohort/phase adjustment was applied, resulting in two NMR data sets (CPMG and NOESY). Alignment of the NMR data was shown to increase the correlation-based alignment quality measure from 0.319 to 0.391 for CPMG and from 0.536 to 0.586 for NOESY, showing that the improvement was present across both large and small peaks. End-to-end quality assessment of the pipeline was achieved using Hotelling's T 2 distributions. For CPMG spectra, the interquartile range decreased from 1.425 in raw QC data to 0.679 in processed spectra, while the corresponding change for NOESY spectra was from 0.795 to 0.636, indicating an improvement in precision following processing. PCA indicated that gross phase and cohort differences were no longer present. These results illustrate that the pipeline produces robust and reproducible data, successfully addressing the methodological challenges of this large multifaceted study.

Aim: Determining perturbed biochemical functions associated with tobacco smoking should be helpful for establishing causal relationships between exposure and adverse events., Results: A multiplatform comparison of serum of smokers (n = 55) and never-smokers (n = 57) using nuclear magnetic resonance spectroscopy, UPLC-MS and statistical modeling revealed clustering of the classes, distinguished by metabolic biomarkers. The identified metabolites were subjected to metabolic pathway enrichment, modeling adverse biological events using available databases. Perturbation of metabolites involved in chronic obstructive pulmonary disease, cardiovascular diseases and cancer were identified and discussed., Conclusion: Combining multiplatform metabolic phenotyping with knowledge-based mapping gives mechanistic insights into disease development, which can be applied to next-generation tobacco and nicotine products for comparative risk assessment.

Longitudinal studies aim typically at following populations of subjects over time and are important to understand the global evolution of biological processes. When it comes to longitudinal omics data, it will often depend on the overall objective of the study, and constraints imposed by the data, to define the appropriate modeling tools. Here, we report the use of multilevel simultaneous component analysis (MSCA), orthogonal projection on latent structures (OPLS), and regularized canonical correlation analysis (rCCA) to study associations between specific longitudinal urine metabonomics data and microbiome data in a diet-induced obesity model using C57BL/6 mice. (1)H NMR urine metabolic profiling was performed on samples collected weekly over a period of 13 weeks, and stool microbial composition was assessed using 16S rRNA gene sequencing at three specific time periods (baseline, first week response, end of study). MSCA and OPLS allowed us to explore longitudinal urine metabonomics data in relation to the dietary groups, as well as dietary effects on body weight. In addition, we report a data integration strategy based on regularized CCA and correlation analyses of urine metabonomics data and 16S rRNA gene sequencing data to investigate the functional relationships between metabolites and gut microbial composition. Thanks to this workflow enabling the breakdown of this data set complexity, the most relevant patterns could be extracted to further explore physiological processes at an anthropometric, cellular, and molecular level.

Purpose of Review: The microbial-mammalian symbiosis plays a critical role in metabolic health. Microbial metabolites emerge as key messengers in the complex communication between the gut microbiota and their host. These chemical signals are mainly derived from nutritional precursors, which in turn are also able to modify gut microbiota population. Recent advances in the characterization of the gut microbiome and the mechanisms involved in this symbiosis allow the development of nutritional interventions. This review covers the latest findings on the microbial-mammalian metabolic axis as a critical symbiotic relationship particularly relevant to clinical nutrition., Recent Findings: The modulation of host metabolism by metabolites derived from the gut microbiota highlights the importance of gut microbiota in disease prevention and causation. The composition of microbial populations in our gut ecosystem is a critical pathophysiological factor, mainly regulated by diet, but also by the host's characteristics (e.g. genetics, circadian clock, immune system, age). Tailored interventions, including dietary changes, the use of antibiotics, prebiotic and probiotic supplementation and faecal transplantation are promising strategies to manipulate microbial ecology., Summary: The microbiome is now considered as an easily reachable target to prevent and treat related diseases. Recent findings in both mechanisms of its interactions with host metabolism and in strategies to modify gut microbiota will allow us to develop more effective treatments especially in metabolic diseases.

The human gut harbors more than 100 trillion microbial cells, which have an essential role in human metabolic regulation via their symbiotic interactions with the host. Altered gut microbial ecosystems have been associated with increased metabolic and immune disorders in animals and humans. Molecular interactions linking the gut microbiota with host energy metabolism, lipid accumulation, and immunity have also been identified. However, the exact mechanisms that link specific variations in the composition of the gut microbiota with the development of obesity and metabolic diseases in humans remain obscure owing to the complex etiology of these pathologies. In this review, we discuss current knowledge about the mechanistic interactions between the gut microbiota, host energy metabolism, and the host immune system in the context of obesity and metabolic disease, with a focus on the importance of the axis that links gut microbes and host metabolic inflammation. Finally, we discuss therapeutic approaches aimed at reshaping the gut microbial ecosystem to regulate obesity and related pathologies, as well as the challenges that remain in this area.

Consisting of trillions of non-pathogenic bacteria living in a symbiotic relationship with their mammalian host, the gut microbiota has emerged in the past decades as one of the key drivers for cardiometabolic diseases (CMD). By degrading dietary substrates, the gut microbiota produces several metabolites that bind human pharmacological targets, impact subsequent signalling networks and in fine modulate host's metabolism. In this review, we revisit the pharmacological relevance of four classes of gut microbial metabolites in CMD: short-chain fatty acids (SCFA), bile acids, methylamines and indoles. Unravelling the signalling mechanisms of the microbial-mammalian metabolic axis adds one more layer of complexity to the physiopathology of CMD and opens new avenues for the development of microbiota-based pharmacological therapies., (Copyright © 2015 Elsevier Ltd. All rights reserved.)

We investigated the short-term (7 days) and long-term (60 days) metabolic effect of high fat diet induced obesity (DIO) and weight gain in isogenic C57BL/6 mice and examined the specific metabolic differentiation between mice that were either strong-responders (SR), or non-responders (NR) to weight gain. Mice (n = 80) were fed a standard chow diet for 7 days prior to randomization into a high-fat (HF) (n = 56) or a low-fat (LF) (n = 24) diet group. The (1)H NMR urinary metabolic profiles of LF and HF mice were recorded 7 and 60 days after the diet switch. On the basis of the body weight gain (BWG) distribution of HF group, we identified NR mice (n = 10) and SR mice (n = 14) to DIO. Compared with LF, HF feeding increased urinary excretion of glycine conjugates of β-oxidation intermediate (hexanoylglycine), branched chain amino acid (BCAA) catabolism intermediates (isovalerylglycine, α-keto-β-methylvalerate and α-ketoisovalerate) and end-products of nicotinamide adenine dinucleotide (NAD) metabolism (N1-methyl-2-pyridone-5-carboxamide, N1-methyl-4-pyridone-3-carboxamide) suggesting up-regulation of mitochondrial oxidative pathways. In the HF group, NR mice excreted relatively more hexanoylglycine, isovalerylglycine, and fewer tricarboxylic acid (TCA) cycle intermediate (succinate) in comparison to SR mice. Thus, subtle regulation of ketogenic pathways in DIO may alleviate the saturation of the TCA cycle and mitochondrial oxidative metabolism.