Gram-negative bacteria have a well-known impact on the disease state of neonatal calves and their mortality. This study was the first to implement untargeted metabolomics on calves' fecal samples to unravel the effect of Gram-negative bacterial endotoxin lipopolysaccharide (LPS). In this context, calves were challenged with LPS and administered with fish oil, nanocurcumin, or dexamethasone to evaluate treatment effects. Ultra-high-performance liquid-chromatography high-resolution mass spectrometry (UHPLC-HRMS) was employed to map fecal metabolic fingerprints from the various groups before and after LPS challenge. Based on the generated fingerprints, including 9650 unique feature ions, significant separation according to LPS group was achieved through orthogonal partial least squares discriminant analysis (Q2 of 0.57 and p-value of 0.022), which allowed the selection of 37 metabolites as bacterial endotoxin markers. Tentative identification of these markers suggested that the majority belonged to the subclass of the carboxylic acid derivatives-amino acids, peptides, and analogs-and fatty amides, with these subclasses playing a role in the metabolism of steroids, histidine, glutamate, and folate. Biological interpretations supported the revealed markers' potential to aid in disease diagnosis, whereas beneficial effects were observed following dexamethasone, fish oil, and nanocurcumin treatment.

Quantitative analysis of bile acids in human feces can potentially help to better understand the influence of the gut microbiome and diet on human health. Feces is a highly heterogeneous sample matrix, mainly consisting of water and indigestible solid material (as plant fibers) that show high inter-individual variability. To compare bile acid concentrations among different individuals, a reliable normalization approach is needed. Here, we compared the impact of three normalization approaches, namely sample wet weight, dry weight, and protein concentration, on the absolute concentrations of fecal bile acids. Bile acid concentrations were determined in 70 feces samples from healthy humans. Our data show that bile acid concentrations normalized by the three different approaches are substantially different for each individual sample. Fecal bile acid concentrations normalized by wet weight show the narrowest distribution. Therefore, our analysis will provide the basis for the selection of a suitable normalization approach for the quantitative analysis of bile acids in feces.

Feces are the product of our diets and have been linked to diseases of the gut, including Chron's disease and metabolic diseases such as diabetes. For screening metabolites in heterogeneous samples such as feces, it is necessary to use fast and reproducible analytical methods that maximize metabolite detection. As sample preparation is crucial to obtain high quality data in MS-based clinical metabolomics, we developed a novel, efficient and robust method for preparing fecal samples for analysis with a focus in reducing aliquoting and detecting both polar and non-polar metabolites. Fecal samples (n = 475) from patients with alcohol-related liver disease and healthy controls were prepared according to the proposed method and analyzed in an UHPLC-QQQ targeted platform in order to obtain a quantitative profile of compounds that impact liver-gut axis metabolism. MS analyses of the prepared fecal samples have shown reproducibility and coverage of n = 28 metabolites, mostly comprising bile acids and amino acids. We report metabolite-wise relative standard deviation (RSD) in quality control samples, inter-day repeatability, LOD (limit of detection), LOQ (limit of quantification), range of linearity and method recovery. The average concentrations for 135 healthy participants are reported here for clinical applications. Our high-throughput method provides a novel tool for investigating gut-liver axis metabolism in liver-related diseases using a noninvasive collected sample.

Nuclear magnetic resonance (NMR) spectroscopy is one of the most promising methods for use in metabolomics studies as it is able to perform non targeted measurement of metabolites in a quantitative and non-destructive way. Sample preparation of liquid samples like urine or blood serum is comparatively easy in NMR metabolomics, because mainly buffer and chemical shift reference substance are added. For solid samples like feces suitable extraction protocols need to be defined as initial step, where the exact protocol depends on sample type and features. Focusing on short chain fatty acids (SCFAs) in mice feces, we describe here a set of extraction protocols developed with the aim to suppress changes in metabolite composition within 24 h after extraction. Feces are obtained from mice fed on either standard rodent diet or high fat diet. The protocols presented in this manuscript are straightforward for application, and successfully minimize residual bacterial and enzymatic activities. Additionally, they are able to minimize the lipid background originating from the high fat diet.

Nuclear magnetic resonance (NMR)-based metabolomics, which comprehensively measures metabolites in biological systems and investigates their response to various perturbations, is widely used in research to identify biomarkers and investigate the pathogenesis of underlying diseases. However, further applications of high-field superconducting NMR for medical purposes and field research are restricted by its high cost and low accessibility. In this study, we applied a low-field, benchtop NMR spectrometer (60 MHz) employing a permanent magnet to characterize the alterations in the metabolic profile of fecal extracts obtained from dextran sodium sulfate (DSS)-induced ulcerative colitis model mice and compared them with the data acquired from high-field NMR (800 MHz). Nineteen metabolites were assigned to the 60 MHz 1H NMR spectra. Non-targeted multivariate analysis successfully discriminated the DSS-induced group from the healthy control group and showed high comparability with high-field NMR. In addition, the concentration of acetate, identified as a metabolite with characteristic behavior, could be accurately quantified using a generalized Lorentzian curve fitting method based on the 60 MHz NMR spectra.

The pattern of metabolites produced by the gut microbiome comprises a phenotype indicative of the means by which that microbiome affects the gut. We characterized that phenotype in mice by conducting metabolomic analyses of the colonic-cecal contents, comparing that to the metabolite patterns of feces in order to determine the suitability of fecal specimens as proxies for assessing the metabolic impact of the gut microbiome. We detected a total of 270 low molecular weight metabolites in colonic-cecal contents and feces by gas chromatograph, time-of-flight mass spectrometry (GC-TOF) and ultra-high performance liquid chromatography, quadrapole time-of-flight mass spectrometry (UPLC-Q-TOF). Of that number, 251 (93%) were present in both types of specimen, representing almost all known biochemical pathways related to the amino acid, carbohydrate, energy, lipid, membrane transport, nucleotide, genetic information processing, and cancer-related metabolism. A total of 115 metabolites differed significantly in relative abundance between both colonic-cecal contents and feces. These data comprise the first characterization of relationships among metabolites present in the colonic-cecal contents and feces in a healthy mouse model, and shows that feces can be a useful proxy for assessing the pattern of metabolites to which the colonic mucosum is exposed.

Metabolomics has been increasingly used in animal and food sciences. Animal health is one of the most important factor that can also alter animal integrity and welfare. Some studies have already investigated the link between health and metabolic profile of dairy animals. These studies in metabolomics often consider a single type of sample using a single analytical platform (nuclear magnetic resonance or mass spectrometry). Only few studies with multi-platform approaches are also used with a single or a multi type of sample, but they mainly consider dairy cows' metabolome although dairy goats present similar diseases, that it could be interesting to detect early to preserve animal health and milk production. This study aims to create a metabolic atlas of goat plasma, milk and feces, based on healthy animals. Our study describes a standard operating procedure for three goat matrices: blood plasma, milk, and feces using multiple platforms (NMR (1H), UHPLC (RP)-MS and UHPLC (HILIC)-MS) that follows a unique sample preparation procedure for each sample type to be analyzed on multi-platforms basis. Our method was evaluated for its robustness and allowed a better characterization of goat metabolic profile in healthy conditions.

A simple, sensitive, and reliable quantification and identification method was developed and validated for simultaneous analysis of 58 bile acids (BAs) in human and rodent (mouse and rat) fecal samples. The method involves an extraction step with a 5% ammonium-ethanol aqueous solution; the BAs were quantified by high-resolution mass spectrometry (ultra-high-performance liquid chromatography coupled with quadrupole-time-of-flight mass spectrometry, UPLC-Q-TOF). The recoveries were 80.05-120.83%, with coefficient variations (CVs) of 0.01-9.82% for three biological species. The limits of detection (LODs) were in the range of 0.01-0.24 μg/kg, and the limits of quantification (LOQs) ranged from 0.03 to 0.81 μg/kg. In addition, the analytical method was used to identify and quantify BAs in end-stage renal disease (ESRD) patients, C57BL/6 mice, and Sprague-Dawley (SD) rats. The fecal BA profile and analysis of BA indices in these samples provide valuable information for further BA metabolic disorder research.

The metabolic profiles of human feces are influenced by various genetic and environmental factors, which makes feces an attractive biosample for numerous applications, including the early detection of gut diseases. However, feces is complex, heterogeneous, and dynamic with a significant live bacterial biomass. With such challenges, stool metabolomics has been understudied compared to other biospecimens, and there is a current lack of consensus on methods to collect, prepare, and analyze feces. One of the critical steps required to accelerate the field is having a metabolomics stool reference material available. Fecal samples are generally presented in two major forms: fecal water and lyophilized feces. In this study, two-dimensional gas chromatography coupled with time-of-flight mass spectrometry (GC×GC-TOFMS) was used as an analytical platform to characterize pooled human feces, provided by the National Institute of Standards and Technology (NIST) as Research-Grade Test Materials. The collected fecal samples were derived from eight healthy individuals with two different diets: vegans and omnivores, matched by age, sex, and body mass index (BMI), and stored as fecal water and lyophilized feces. Various data analysis strategies were presented to determine the differences in the fecal metabolomic profiles. The results indicate that the sample storage condition has a major influence on the metabolic profiles of feces such that the impact from storage surpasses the metabolic differences from the diet types. The findings of the current study would contribute towards the development of a stool reference material.

How gut immunity in early life is shaped by birth in relation to delivery mode, intrapartum antibiotic prophylaxis (IAP) and labor remains undetermined. We aimed to address this gap with a study of secretory Immunoglobulin A (SIgA) in the infant gut that also tested SIgA-stimulating pathways mediated by gut microbiota and metabolites. Among 1017 Canadian full-term infants, gut microbiota of fecal samples collected at 3 and 12 months were profiled using 16S rRNA sequencing; C. difficile was quantified by qPCR; fecal metabolites and SIgA levels were measured by NMR and SIgA enzyme-linked immunosorbent assay, respectively. We assessed the putative causal relationships from birth events to gut microbiota and metabolites, and ultimately to SIgA, in statistical sequential mediation models, adjusted for maternal gravida status in 551 infants. As birth mode influences the ability to breastfeed, the statistical mediating role of breastfeeding status and milk metabolites was also evaluated. Relative to vaginal birth without maternal IAP, cesarean section (CS) after labor was associated with reduced infant gut SIgA levels at 3 months (6.27 vs. 4.85 mg/g feces, p < 0.05); this association was sequentially mediated through gut microbiota and metabolites of microbial or milk origin. Mediating gut microbiota included Enterobacteriaceae, C. difficile, and Streptococcus. The milk or microbial metabolites in CS-SIgA mediating pathways were galactose, fucose, GABA, choline, lactate, pyruvate and 1,2-propanediol. This cohort study documented the impact of birth on infant gut mucosal SIgA. It is the first to characterize gut microbe-metabolite mediated pathways for early-life SIgA maturation, pathways that require experimental verification.

Sanguisorba officinalis L. (SO), a well-known herbal medicine, has been proven to show effect against thrombocytopenia. However, metabolites of SO in vivo are still unclear, and the underlying mechanism of SO against thrombocytopenia from the aspect of metabolites have not been well elucidated. In this study, an improved analytical method combined with UHPLC-QTOF MS and a molecular network was developed for the rapid characterization of metabolites in vivo based on fragmentation patterns. Then, network pharmacology (NP) was used to elucidate the potential mechanism of SO against thrombocytopenia. As a result, a total of 1678 exogenous metabolites were detected in urine, feces, plasma, and bone marrow, in which 104 metabolites were tentatively characterized. These characterized metabolites that originated from plasma, urine, and feces were then imported to the NP analysis. The results showed that the metabolites from plasma, urine, and feces could be responsible for the pharmacological activity against thrombocytopenia by regulating the PI3K-Akt, MAPK, JAK-STAT, VEGF, chemokine, actin cytoskeleton, HIF-1, and pluripotency of stem cells. This study provides a rapid method for metabolite characterization and a new perspective of underlying mechanism study from the aspect of active metabolites in vivo.

Gut microbial metabolites, short-chain fatty acids (SCFAs), are found at multiple locations in the host body and are identified as important metabolites in gut microbiome-associated diseases. Quantifying SCFAs in diverse biological samples is important to understand their roles in host health. This study developed an accurate SCFA quantification method by performing gas chromatography-mass spectrometry (GC/MS) in human plasma, serum, feces, and mouse cecum tissue. The samples were acidified with hydrochloric acid, and the SCFAs were extracted using methyl tert-butyl ether. In this method, distilled water was selected as a surrogate matrix for the quantification of SCFAs in target biological samples. The method was validated in terms of linearity, parallelism, precision, recovery, and matrix effect. The developed method was further applied in target biological samples. In conclusion, this optimized method can be used as a simultaneous SCFA quantification method in diverse biological samples.

Metabolomics has found numerous applications in the study of liver metabolism in health and disease. Metabolomics studies can be conducted in a variety of biological matrices ranging from easily accessible biofluids such as urine, blood or feces, to organs, tissues or even cells. Sample collection and storage are critical steps for which standard operating procedures must be followed. Inappropriate sample collection or storage can indeed result in high variability, interferences with instrumentation or degradation of metabolites. In this review, we will first highlight important general factors that should be considered when planning sample collection in the study design of metabolomic studies, such as nutritional status and circadian rhythm. Then, we will discuss in more detail the specific procedures that have been described for optimal pre-analytical handling of the most commonly used matrices (urine, blood, feces, tissues and cells).

The aim of this study was to evaluate the metabolic profiles of yak (Bos grunniens) serum, feces, and urine by using proton nuclear magnetic resonance (¹H-NMR), to serve as a reference guide for the healthy yak milieu. A total of 108 metabolites, giving information about diet, protein digestion, and energy generation or gut-microbial co-metabolism, were assigned across the three biological matrices. A core metabolome of 15 metabolites was ubiquitous across all biofluids. Lactate, acetate, and creatinine could be regarded as the most abundant metabolites in the metabolome of serum, feces, and urine, respectively. Metabolic pathway analysis showed that the molecules identified could be able to give thorough information about four main metabolic pathways, namely valine, leucine, and isoleucine biosynthesis; phenylalanine, tyrosine, and tryptophan biosynthesis; glutamine and glutamate metabolism; and taurine and hypotaurine metabolism.

The bioactivities and potential health benefits of green tea polyphenols (GTP) have been extensively investigated, but the metabolic impact of chronic GTP intake on humans is not well defined. In this study, fecal and urine samples from postmenopausal female subjects taking a GTP supplement or placebo for 12 months were compared by liquid chromatography-mass spectrometry-based metabolomic analysis. The GTP-derived and GTP-responsive metabolites were identified and characterized by structural elucidation and quantitative analysis of the metabolites contributing to the separation of control and treatment samples in the multivariate models. Major GTP and their direct sulfate and glucuronide metabolites were absent in feces and urine. In contrast, GTP-derived phenyl-γ-valerlactone and phenylvaleric acid metabolites were identified as the most abundant GTP-derived metabolites in feces and urine, suggesting extensive microbial biotransformation of GTP in humans. Interestingly, GTP decreased the levels of microbial metabolites of aromatic amino acids (AAA), including indoxyl sulfate, phenylacetylglutamine, and hippuric acid, in urine. However, it did not affect the levels of AAA, as well as other microbial metabolites, including short-chain fatty acids and secondary bile acids, in feces. 16S rRNA gene sequencing indicated that the fecal microbiome was not significantly affected by chronic consumption of GTP. Overall, microbial metabolism is responsible for the formation of GTP metabolites while GTP metabolism may inhibit the formation of AAA metabolites from microbial metabolism. Because these GTP-derived and GTP-responsive metabolites have diverse bioactivities, microbial metabolism of GTP and AAA may play important roles in the beneficial health effects of green tea consumption in humans.

This prospective observational study aimed to evaluate the association of metabolomic alterations with weight loss outcomes following sleeve gastrectomy (SG). We evaluated the metabolomic profile of serum and feces prior to SG and three months post-SG, along with weight loss outcomes in 45 adults with obesity. The percent total weight loss for the highest versus the lowest weight loss tertiles (T3 vs. T1) was 17.0 ± 1.3% and 11.1 ± 0.8%, p < 0.001. Serum metabolite alterations specific to T3 at three months included a decrease in methionine sulfoxide concentration as well as alterations to tryptophan and methionine metabolism (p < 0.03). Fecal metabolite changes specific to T3 included a decrease in taurine concentration and perturbations to arachidonic acid metabolism, and taurine and hypotaurine metabolism (p < 0.002). Preoperative metabolites were found to be highly predictive of weight loss outcomes in machine learning algorithms, with an average area under the curve of 94.6% for serum and 93.4% for feces. This comprehensive metabolomics analysis of weight loss outcome differences post-SG highlights specific metabolic alterations as well as machine learning algorithms predictive of weight loss. These findings could contribute to the development of novel therapeutic targets to enhance weight loss outcomes after SG.

The interaction between gut microbiota and the health of the host has gained increasing attention. Chitosan is a natural alkaline polysaccharide with a wide range of beneficial effects. However, rare studies have been observed on the effects of dietary chitosan supplementation on intestinal health in cats. A total of 30 cats with mild diarrhea were divided into three groups, receiving a basic diet with 0 (CON), 500 (L-CS) or 2000 (H-CS) mg/kg chitosan. Samples of blood and feces were collected and analyzed for serology and gut microbiota composition. The results demonstrated that chitosan alleviated symptoms of diarrhea, with enhanced antioxidant capability and decreased inflammatory biomarker levels in serum. Chitosan reshaped the composition of gut microbiota in cats that the beneficial bacteria Allobaculum was significantly increased in the H-CS group. Acetate and butyrate contents in feces were significantly higher in the H-CS group in comparison to the CON group (p < 0.05). In conclusion, the addition of dietary chitosan in cats enhanced intestinal health by modulating their intestinal microbes and improved microbiota-derived SCFA production. Our results provided insights into the role of chitosan in the gut microbiota of felines.

Butyric acid produced in the intestine by butyric acid-producing bacteria (BAPB) is known to suppress excessive inflammatory response and may prevent chronic disease development. We evaluated whether fiber-rich barley intake increases BAPB in the gut and concomitantly butyric acid in feces. Eighteen healthy adults received granola containing functional barley (BARLEYmax®) once daily for four weeks. Fecal DNA before intake, after intake, and one month after intake was analyzed using 16S rRNA gene sequencing to assess microbial diversity, microbial composition at the order level, and the proportion of BAPB. Fecal butyric acid concentration was also measured. There were no significant differences in diversities and microbial composition between samples. The proportion of BAPB increased significantly after the intake (from 5.9% to 8.2%). However, one month after stopping the intake, the proportion of BAPB returned to the original value (5.4%). Fecal butyric acid concentration increased significantly from 0.99 mg/g feces before intake to 1.43 mg/g after intake (p = 0.028), which decreased significantly to 0.87 mg/g after stopping intake (p = 0.008). As BAPB produce butyric acid by degrading dietary fiber, functional barley may act as a prebiotic, increasing BAPB and consequently butyric acid in the intestine.

Analysis of enteric microbiota function indirectly through the fecal metabolome has the potential to be an informative diagnostic tool. However, metabolomic analysis of feces is hampered by high concentrations of macromolecules such as proteins, fats, and fiber in samples. Three methods-ultrafiltration (UF), Bligh-Dyer (BD), and no extraction (samples added directly to buffer, vortexed, and centrifuged)-were tested on multiple rat (n = 10) and chicken (n = 8) fecal samples to ascertain whether the methods worked equally well across species and individuals. An in silico baseline correction method was evaluated to determine if an algorithm could produce spectra similar to those obtained via UF. For both rat and chicken feces, UF removed all macromolecules and produced no baseline distortion among samples. By contrast, the BD and no extraction methods did not remove all the macromolecules and produced baseline distortions. The application of in silico baseline correction produced spectra comparable to UF spectra. In the case of no extraction, more intense peaks were produced. This suggests that baseline correction may be a cost-effective method for metabolomic analyses of fecal samples and an alternative to UF. UF was the most versatile and efficient extraction method; however, BD and no extraction followed by baseline correction can produce comparable results.

Gut microbiota and their metabolic products are increasingly being recognized as important modulators of human health. The fecal metabolome provides a functional readout of the interactions between human metabolism and the gut microbiota in health and disease. Due to the high complexity of the fecal matrix, sample preparation often introduces technical variation, which must be minimized to accurately detect and quantify gut bacterial metabolites. Here, we tested six different representative extraction methods (single-phase and liquid-liquid extractions) and compared differences due to fecal amount, extraction solvent type and solvent pH. Our results indicate that a minimum fecal (wet) amount of 0.50 g is needed to accurately represent the complex texture of feces. The MTBE method (MTBE/methanol/water, 3.6/2.8/3.5, v/v/v) outperformed the other extraction methods, reflected by the highest extraction efficiency for 11 different classes of compounds, the highest number of extracted features (97% of the total identified features in different extracts), repeatability (CV < 35%) and extraction recovery (≥70%). Importantly, optimization of the solvent volume of each step to the initial dried fecal material (µL/mg feces) offers a major step towards standardization, which enables confident assessment of the contributions of gut bacterial metabolites to human health.