Sample preparation optimization in fecal metabolic profiling

Optimization of the quantitative protocol for organic acid in fecal samples using gas chromatography-mass spectrometry

Organic acids (OAs) play important roles in a variety of intracellular metabolic pathways, such as the tricarboxylic acid cycle, fatty acid oxidation, glycolysis. The accurate detection of OAs in fecal samples was crucial for comprehending the metabolic changes associated with various metabolic disease. However, the analytical protocol detecting OAs profiling in feces have received scant attention. In this work, an optimized protocol based on chromatography-mass spectrometry for simultaneous quantification of 23 OAs in rat feces was developed. The optimal conditions involved using a 40-mg fecal sample mixed with isopropyl alcohol, acetonitrile, and deionized water (3:2:2 vol ratio) with a total volume of 1500 μL, followed by ultrasonic extraction and a derivatization reaction with an 80 μL derivative agent. The protocol showed an acceptable linearity (R2 ≥ 0.9906), the satisfactory precision (RSD% ≤ 14.87%), the low limits of detection (0.001 to 1 μg/mL) and the limit of quantification (0.005 to 1.5 μg/mL). Moreover, the dried residues of the extracted solution showed the better stability of OAs at -20 °C, which was more suitable for a large-scale sample analysis. Finally, the developed protocol was successfully applied to compare the difference of OAs profiling in fecal samples harvested from normal and nonalcoholic fatty liver disease rats, which was beneficial to find out the metabolic change of OAs profiling and explain the related mechanism of the disease.

Colonization of chickens with competitive exclusion products results in extensive differences in metabolite composition in cecal digesta

The concept of competitive exclusion is well established in poultry and different products are used to suppress the multiplication of enteric pathogens in the chicken intestinal tract. While the effect has been repeatedly confirmed, the specific principles of competitive exclusion are less clear. The aim of the study was to compare metabolites in the cecal digesta of differently colonized chickens. Metabolites in the cecal contents of chickens treated with a commercial competitive exclusion product or with an experimental product consisting of 23 gut anaerobes or in control untreated chickens were determined by mass spectrometry. Extensive differences in metabolite composition among the digesta of all 3 groups of chickens were recorded. Out of 1,706 detected compounds, 495 and 279 were differently abundant in the chicks treated with a commercial or experimental competitive exclusion product in comparison to the control group, respectively. Soyasaponins, betaine, carnitine, glutamate, tyramine, phenylacetaldehyde, or 3-methyladenine were more abundant in the digesta of control chicks while 4-oxododecanedioic acid, nucleotides, dipeptides, amino acids (except for glutamate), and vitamins were enriched in the digesta of chickens colonized by competitive exclusion products. Metabolites enriched in the digesta of control chicks can be classified as of plant feed origin released in the digesta by degradative activities of the chicken. Some of these molecules disappeared from the digesta of chicks colonized by complex microbiota due to them being metabolized. Instead, nucleotides, amino acids, and vitamins increased in the digesta of colonized chicks as a consequence of the additional digestive potential brought to the cecum by microbiota from competitive exclusion products. It is therefore possible to affect metabolite profiles in the chicken cecum by its colonization with selected bacterial species.

Optimization of Carob Products Preparation for Targeted LC-MS/MS Metabolomics Analysis

Carob (Ceratonia siliqua) is an exceptional source of significant bioactive compounds with great economic importance in the Mediterranean region, where it is widely cultivated. Carob fruit is used for the production of a variety of products and commodities such as powder, syrup, coffee, flour, cakes, and beverages. There is growing evidence of the beneficial effects of carob and the products made from it on a range of health problems. Therefore, metabolomics could be used to explore the nutrient-rich compounds of carob. Sample preparation is a crucial step in metabolomics-based analysis and has a great impact on the quality of the data obtained. Herein, sample preparation of carob syrup and powder was optimized, to enable highly efficient metabolomics-based HILIC-MS/MS analysis. Pooled powder and syrup samples were extracted under different conditions by adjusting pH, solvent type, and sample weight to solvent volume ratio (Wc/Vs). The metabolomics profiles obtained were evaluated using the established criteria of total area and number of maxima. It was observed that the Wc/Vs ratio of 1:2 resulted in the highest number of metabolites, regardless of solvent type or pH. Aqueous acetonitrile with a Wc/Vs ratio of 1:2 satisfied all established criteria for both carob syrup and powder samples. However, when the pH was adjusted, basic aqueous propanol 1:2 Wc/Vs and acidic aqueous acetonitrile 1:2 Wc/Vs provided the best results for syrup and powder, respectively. We strongly believe that the current study could support the standardization of the metabolomics sample preparation process to enable more efficient LC-MS/MS carob analysis.

Molecular characterization of gut microbiome in weaning pigs supplemented with multi-strain probiotics using metagenomic, culturomic, and metabolomic approaches

BACKGROUND

Probiotics have been reported to exhibit positive effects on host health, including improved intestinal barrier function, preventing pathogenic infection, and promoting nutrient digestion efficiency. These internal changes are reflected to the fecal microbiota composition and, bacterial metabolites production. In accordance, the application of probiotics has been broadened to industrial animals, including swine, which makes people to pursue better knowledge of the correlation between changes in the fecal microbiota and metabolites. Therefore, this study evaluated the effect of multi-strain probiotics (MSP) supplementation to piglets utilizing multiomics analytical approaches including metagenomics, culturomics, and metabolomics.

RESULTS

Six-week-old piglets were supplemented with MSP composed of Lactobacillus isolated from the feces of healthy piglets. To examine the effect of MSP supplement, piglets of the same age were selected and divided into two groups; one with MSP supplement (MSP group) and the other one without MSP supplement (Control group). MSP feeding altered the composition of the fecal microbiota, as demonstrated by metagenomics analysis. The abundance of commensal Lactobacillus was increased by 2.39%, while Clostridium was decreased, which revealed the similar pattern to the culturomic approach. Next, we investigated the microbial metabolite profiles, specifically SCFAs using HPLC-MS/MS and others using GC-MS, respectively. MSP supplement elevated the abundance of amino acids, including valine, isoleucine and proline as well as the concentration of acetic acid. According to the correlation analyses, these alterations were found out to be crucial in energy synthesizing metabolism, such as branched-chain amino acid (BCAA) metabolism and coenzyme A biosynthesis. Furthermore, we isolated commensal Lactobacillus strains enriched by MSP supplement, and analyzed the metabolites and evaluated the functional improvement, related to tight junction from intestinal porcine enterocyte cell line (IPEC-J2).

CONCLUSIONS

In conclusion, MSP administration to piglets altered their fecal microbiota, by enriching commensal Lactobacillus strains. This change contributed amino acid, acetic acid, and BCAA concentrations to be increased, and energy metabolism pathway was also increased at in vivo and in vitro. These changes produced by MSP supplement suggests the correlation between the various physiological energy metabolism functions induced by health-promoting Lactobacillus and the growth performance of piglets.

Reference materials for MS-based untargeted metabolomics and lipidomics: a review by the metabolomics quality assurance and quality control consortium (mQACC)

INTRODUCTION

The metabolomics quality assurance and quality control consortium (mQACC) is enabling the identification, development, prioritization, and promotion of suitable reference materials (RMs) to be used in quality assurance (QA) and quality control (QC) for untargeted metabolomics research.

OBJECTIVES

This review aims to highlight current RMs, and methodologies used within untargeted metabolomics and lipidomics communities to ensure standardization of results obtained from data analysis, interpretation and cross-study, and cross-laboratory comparisons. The essence of the aims is also applicable to other 'omics areas that generate high dimensional data.

RESULTS

The potential for game-changing biochemical discoveries through mass spectrometry-based (MS) untargeted metabolomics and lipidomics are predicated on the evolution of more confident qualitative (and eventually quantitative) results from research laboratories. RMs are thus critical QC tools to be able to assure standardization, comparability, repeatability and reproducibility for untargeted data analysis, interpretation, to compare data within and across studies and across multiple laboratories. Standard operating procedures (SOPs) that promote, describe and exemplify the use of RMs will also improve QC for the metabolomics and lipidomics communities.

CONCLUSIONS

The application of RMs described in this review may significantly improve data quality to support metabolomics and lipidomics research. The continued development and deployment of new RMs, together with interlaboratory studies and educational outreach and training, will further promote sound QA practices in the community.

Urinary and faecal metabolic characteristics in APP/PS1 transgenic mouse model of Alzheimer's disease with and without cognitive decline

Alzheimer's disease (AD) has been considered to be a systematic metabolic disorder, but little information is available about metabolic changes in the urine and feces. In this study, we investigated urinary and faecal metabolic profiles in amyloid precursor protein/presenilin 1 (APP/PS1) mice at 3 and 9 months of age (3 M and 9 M) and age-matched wild-type (WT) mice by using ¹H NMR-based metabolomics, and aimed to explore changes in metabolic pathways during amyloid pathology progression and identify potential metabolite biomarkers at earlier stage of AD. The results show that learning and memory abilities were impaired in APP/PS1 mice relative to WT mice at 9 M, but not at 3 M. However, metabolomics analysis demonstrates that AD disrupted metabolic phenotypes in the urine and feces of APP/PS1 mice at both 3 M and 9 M, including amino acid metabolism, microbial metabolism and energy metabolism. In addition, several potential metabolite biomarkers were identified for discriminating AD and WT mice prior to cognitive decline with the AUC values from 0.755 to 0.971, such as taurine, hippurate, urea and methylamine in the urine as well as alanine, leucine and valine in the feces. Therefore, our results not only confirmed AD as a metabolic disorder, but also contributed to the identification of potential biomarkers at earlier stage of AD.

Fecal 1H-NMR Metabolomics: A Comparison of Sample Preparation Methods for NMR and Novel in Silico Baseline Correction

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.

Evaluation of different stool extraction methods for metabolomics measurements in human faecal samples

Background

Metabolomics analysis of human stool samples is of great interest for a broad range of applications in biomedical research including early detection of colorectal neoplasms. However, due to the complexity of metabolites there is no consensus on how to process samples for stool metabolomics measurements to obtain a broad coverage of hydrophilic and hydrophobic substances.

Methods

We used frozen stool samples (50 mg) from healthy study participants. Stool samples were processed after thawing using eight different processing protocols and different solvents (solvents such as phosphate-buffered saline, isopropanol, methanol, ethanol, acetonitrile and solvent mixtures with or without following evaporation and concentration steps). Metabolites were measured afterwards using the MxP Quant 500 kit (Biocrates). The best performing protocol was subsequently applied to compare stool samples of participants with different dietary habits.

Results

In this study, we were able to determine up to 340 metabolites of various chemical classes extracted from stool samples of healthy study participants with eight different protocols. Polar metabolites such as amino acids could be measured with each method while other metabolite classes, particular lipid species (better with isopropanol and ethanol or methanol following a drying step), are more dependent on the solvent or combination of solvents used. Only a small number of triglycerides or acylcarnitines were detected in human faeces. Extraction efficiency was higher for protocols using isopropanol (131 metabolites>limit of detection (LOD)) or those using ethanol or methanol and methyl tert-butyl ether (MTBE) including an evaporation and concentration step (303 and 342 metabolites>LOD, respectively) than for other protocols. We detected significant faecal metabolite differences between vegetarians, semivegetarians and non-vegetarians.

Conclusion

For the evaluation of metabolites in faecal samples, we found protocols using solvents like isopropanol and those using ethanol or methanol, and MTBE including an evaporation and concentration step to be superior regarding the number of detected metabolites of different chemical classes over others tested in this study.

Multispecies probiotics alter fecal short-chain fatty acids and lactate levels in weaned pigs by modulating gut microbiota

Short-chain fatty acids (SCFAs) are metabolic products produced during the microbial fermentation of non-digestible fibers and play an important role in metabolic homeostasis and overall gut health. In this study, we investigated the effects of supplementation with multispecies probiotics (MSPs) containing Bacillus amyloliquefaciens, Limosilactobacillus reuteri, and Levilactobacillus brevis on the gut microbiota, and fecal SCFAs and lactate levels of weaned pigs. A total of 38 pigs weaned at 4 weeks of age were fed either a basal diet or a diet supplemented with MSPs for 6 weeks. MSP administration significantly increased the fecal concentrations of lactate (2.3-fold; p < 0.01), acetate (1.8-fold; p < 0.05), and formate (1.4-fold; p < 0.05). Moreover, MSP supplementation altered the gut microbiota of the pigs by significantly increasing the population of potentially beneficial bacteria such as Olsenella, Catonella, Catenibacterium, Acidaminococcus, and Ruminococcaceae. MSP supplementation also decreased the abundance of pathogenic bacteria such as Escherichia and Chlamydia. The modulation of the gut microbiota was observed to be strongly correlated with the changes in fecal SCFAs and lactate levels. Furthermore, we found changes in the functional pathways present within the gut, which supports our findings that MSP modulates the gut microbiota and SCFAs levels in pigs. The results support the potential use of MSPs to improve the gut health of animals by modulating SCFAs production.

Liquid Chromatography-Mass Spectrometry for Clinical Metabolomics: An Overview

Metabolomics is a discipline that offers a comprehensive analysis of metabolites in biological samples. In the last decades, the notable evolution in liquid chromatography and mass spectrometry technologies has driven an exponential progress in LC-MS-based metabolomics. Targeted and untargeted metabolomics strategies are important tools in health and medical science, especially in the study of disease-related biomarkers, drug discovery and development, toxicology, diet, physical exercise, and precision medicine. Clinical and biological problems can now be understood in terms of metabolic phenotyping. This overview highlights the current approaches to LC-MS-based metabolomics analysis and its applications in the clinical research.

Assessment of Biphasic Extraction Methods of Mouse Fecal Metabolites for Liquid Chromatography-Mass Spectrometry-Based Metabolomic Studies

With the increasing knowledge about the important roles of gut microbiota on the biological system, a systematic strategy to profile the fecal metabolome is urgently needed. Thus, an unbiased, efficient, and reproducible fecal metabolite extraction protocol needs to be established; however, the effect of biphasic extraction methods for the fecal samples remains unclear. In this study, five different methods were assessed in the extraction of polar and non-polar metabolites for the liquid chromatography-mass spectrometry (LC-MS)-based mouse fecal metabolomic study. First, the detection coverage of two extraction systems, the Bligh and Dyer extraction method (M1, chloroform/methanol/water, 2/2/1.8) and Matyash method (M2, methyl tert-butyl ether (MTBE)/methanol/water, 10/3/2.5), was compared; then, MTBE/methanol/water system with different solvent ratios (M3, 2.6/2.0/2.4; M4, 4.5/1/2.5; and M5, 3/2.5/2.5) were further evaluated. The results showed that M2 showed higher detection coverage than M1. For the MTBE/methanol/water system with different solvent ratios, M3 showed the largest detection coverage based on peak numbers and numbers of putatively annotated metabolites, while M4 presented the least overlap between two phases, higher peak intensities of metabolites, and superior reproducibility. Based on the above evidence, M4 was recommended for the biphasic extraction of fecal metabolites in the LC-MS-based mouse fecal metabolomic study.

Towards Standards for Human Fecal Sample Preparation in Targeted and Untargeted LC-HRMS Studies

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.

Metabolomics Monitoring of Treatment Response to Brain Tumor Immunotherapy

Immunotherapy has revolutionized care for many solid tissue malignancies, and is being investigated for efficacy in the treatment of malignant brain tumors. Identifying a non-invasive monitoring technique such as metabolomics monitoring to predict patient response to immunotherapy has the potential to simplify treatment decision-making and to ensure therapy is tailored based on early patient response. Metabolomic analysis of peripheral immune response is feasible due to large metabolic shifts that immune cells undergo when activated. The utility of this approach is under investigation. In this review, we discuss the metabolic changes induced during activation of an immune response, and the role of metabolic profiling to monitor immune responses in the context of immunotherapy for malignant brain tumors. This review provides original insights into how metabolomics monitoring could have an important impact in the field of tumor immunotherapy if achievable.

Multi-Solvent Extraction Procedure for the Pioneer Fecal Metabolomic Analysis-Identification of Potential Biomarkers in Stable Kidney Transplant Patients

Metabolic alteration plays a functional role in kidney allograft complications. Metabolomics is a promising high-throughput approach in nephrology but is still limited by the lack of overlap in metabolite coverage. We performed an untargeted fecal metabolomic analysis of forty stable kidney allograft recipients and twenty non-transplant controls. First, we applied the ultra-high performance liquid chromatography (UHPLC) analysis coupled with the Diod Array detector. The potential biomarkers were then collected and identified by gas chromatography-mass spectrometry (GCMS). In order to allow for complete coverage of the fecal polar and non-polar metabolites, the performance of five organic solvents with increasing polarity was investigated successively. UHPLC analysis revealed that the fecal metabolite profiles following the five extractions were significantly different between controls and kidney allografts. GC-MS analysis showed that the best predictors’ metabolites belonged mainly to long-chain fatty acids, phenolic compounds, and amino acids. Collectively, our results showed the efficiency of our pioneer method to successfully discriminate stable kidney-transplant recipients from controls. These findings suggest that distinct metabolic profiles mainly affect fatty acid biosynthesis and amino acid metabolism. In such a context, the novel insights into metabolomic investigation may be a valuable tool that could provide useful new relevant biomarkers for preventing kidney transplant complications.

Extraction strategies for tackling complete hair metabolome using LC-HRMS-based analysis

Over recent years, metabolomics has been featured as the state-of-the-art technology that successfully opens the paths to understanding biological mechanisms and facilitating biomarker discovery. However, the inherent dynamic and sensitive nature of the metabolome have been challenging the accuracy of capturing the timepoints of interest while using biofluids such as urine and blood. Hair has thus emerged as a valuable analytical specimen for the long-term and retrospective determinations. Unfortunately, notwithstanding the apparent interest on global hair metabolomics, very few studies have engaged in the optimisation of the extraction strategy. In this study, we systemically investigated the extraction procedures for hair metabolome using a single factor experimental design. Three pH values (acidic, neutral, and basic) in aqueous solution, six extraction solvents (methanol, acetonitrile, acetone, phosphate-buffered saline, deionised water, and dichloromethane), different compositions of selected solvent mixtures and their sequential extraction, and a series of extraction times (15, 45, 60, 120, 240, and 480 min) were evaluated. The ideal condition for hair extraction is ultrasonic-assisted extraction with methanol:phosphate-buffered saline 50:50 (v/v) under +55 °C for 240 min. This strategy may secure the true composition of the metabolome, maximise the signal abundance, and guarantee a high coverage of wide-range metabolites in a straightforward approach. The optimised extraction strategy was then coupled with structure annotation tools for hair metabolome profiling. After a single RPLC-HRMS run, hair metabolite identification was achieved as the annotations of 171 probable structures and 853 tentative structures as well as the assignments of 414 unequivocal molecular formulae. In conclusion, we established an efficient extraction strategy for untargeted hair metabolomics, which the method is deliverable to any analytical laboratories and the sample can be directly profiled by means of a conventional RPLC-HRMS gradient.

Exploration of metabolite profiles in the biofluids of dairy cows by proton nuclear magnetic resonance analysis

Studies that screen for metabolites produced in ruminants are actively underway. We aimed to evaluate the metabolic profiles of five biofluids (ruminal fluid, serum, milk, urine, and feces) in dairy cow by using proton nuclear magnetic resonance (1H-NMR) and provide a list of metabolites in each biofluid for the benefit of future research. We analyzed the metabolites in five biofluids from lactating cows using proton nuclear magnetic resonance imaging; 96, 73, 88, 118, and 128 metabolites were identified in the five biofluids, respectively. In addition, 8, 6, 9, and 17 metabolites were unique to ruminal fluid, serum, milk, and urine, respectively. The metabolites present at high concentrations were: acetate, propionate, and butyrate in ruminal fluid; lactate, glucose, and acetate in serum; and lactose, guanidoacetate, and glucitol in milk. In addition, the following metabolites were present at high concentrations: hippurate, urea, and trimethylamine N-oxide in urine and acetate, propionate, and butyrate in feces. The score plots of the principal component analysis did not show clear distinctions among the five biofluid samples. The purpose of this study was to verify the ability of our metabolomics approaches to identify metabolites in the biofluids of dairy cows.

FoodOmicsGR_RI. A Consortium for Comprehensive Molecular Characterisation of Food Products

The national infrastructure FoodOmicsGR_RI coordinates research efforts from eight Greek Universities and Research Centers in a network aiming to support research and development (R&D) in the agri-food sector. The goals of FoodOmicsGR_RI are the comprehensive in-depth characterization of foods using cutting-edge omics technologies and the support of dietary/nutrition studies. The network combines strong omics expertise with expert field/application scientists (food/nutrition sciences, plant protection/plant growth, animal husbandry, apiculture and 10 other fields). Human resources involve more than 60 staff scientists and more than 30 recruits. State-of-the-art technologies and instrumentation is available for the comprehensive mapping of the food composition and available genetic resources, the assessment of the distinct value of foods, and the effect of nutritional intervention on the metabolic profile of biological samples of consumers and animal models. The consortium has the know-how and expertise that covers the breadth of the Greek agri-food sector. Metabolomics teams have developed and implemented a variety of methods for profiling and quantitative analysis. The implementation plan includes the following research axes: development of a detailed database of Greek food constituents; exploitation of "omics" technologies to assess domestic agricultural biodiversity aiding authenticity-traceability control/certification of geographical/genetic origin; highlighting unique characteristics of Greek products with an emphasis on quality, sustainability and food safety; assessment of diet's effect on health and well-being; creating added value from agri-food waste. FoodOmicsGR_RI develops new tools to evaluate the nutritional value of Greek foods, study the role of traditional foods and Greek functional foods in the prevention of chronic diseases and support health claims of Greek traditional products. FoodOmicsGR_RI provides access to state-of-the-art facilities, unique, well-characterised sample sets, obtained from precision/experimental farming/breeding (milk, honey, meat, olive oil and so forth) along with more than 20 complementary scientific disciplines. FoodOmicsGR_RI is open for collaboration with national and international stakeholders.

Circulating exosomes and gut microbiome induced insulin resistance in mice exposed to intermittent hypoxia: Effects of physical activity

Background

Gut microbiota (GM) contribute to obesity and insulin resistance (IR). Obstructive sleep apnea (OSA), characterized by intermittent hypoxia (IH), promotes IR and alters GM. Since circulating exosomes are implicated in IR, we examined the effects of IH and physical activity (PA) in mice on GM, colonic epithelium permeability, systemic IR, and plasma exosome cargo, and exosome effects on visceral white adipose tissues (vWAT) IR.

Methods

C57BL/6 mice were exposed to IH or room air (RA) for 6 weeks with and without PA (n = 12/group), and GM and systemic IR changes were assessed, as well as the effects of plasma exosomes on naïve adipocyte insulin sensitivity. Fecal microbiota transfers (FMT) were performed in naïve mice (n = 5/group), followed by fecal 16S rRNA sequencing, and systemic IR and exosome-induced effects on adipocyte insulin sensitivity were evaluated.

Findings

Principal coordinate analysis (PCoA) ordinates revealed B-diversity among IH and FMT recipients that accounted for 64% principal component 1 (PC1) and 12.5% (PC2) of total variance. Dominant microbiota families and genera in IH-exposed and FMT-treated were preserved, and IH-exposed GM and IH-FMT induced increased gut permeability. Plasma exosomes from IH-exposed and IH-FMT mice decreased pAKT/AKT responses to exogenous insulin in adipocytes vs. IH+PA or RA FMT-treated mice (p = 0.001).

Interpretation

IH exposures mimicking OSA induce changes in GM, increase gut permeability, and alter plasma exosome cargo, the latter inducing adipocyte dysfunction (increased IR). Furthermore, these alterations improved with PA. Thus, IH leads to perturbations of a singular GM-circulating exosome pathway that disrupts adipocyte homeostasis resulting in metabolic dysfunction, as reflected by IR.

Funding

This study was supported by grants from the National Institutes of Health grants HL130984 and HL140548 and University of Missouri Tier 2 grant. The study has not received any funding or grants from pharmaceutical or other industrial corporations.

Optimization of metabolite extraction and analytical methods from shrimp intestine for metabolomics profile analysis using LC-HRMS/MS

INTRODUCTION

Intestinal microbiota and metabolites play important roles for further improvement of animal production. Metabolomics of shrimp intestine to understand roles and their relationship to the host is hampered by the lack of metabolome profiling method.

OBJECTIVES

This study aims to develop extraction and analytical methods to allow accurate metabolic analysis in shrimp intestine.

METHODS

Conditions for extraction and LC-HRMS/MS analysis were optimized.

RESULTS

Extraction with ethyl acetate:acetone (15:2 v/v) acidified with 0.5% acetic acid, elution with acetonitrile:water acidified with 0.01% acetic acid for 25 min, and mass fragmentation at 15% HCD were the optimal conditions, yielding the highest signal intensity and numbers of putative metabolites.

CONCLUSION

Our method enabled in-depth study for shrimp-microbial interaction at metabolite level.

Reviewing the metabolome coverage provided by LC-MS: Focus on sample preparation and chromatography-A tutorial

Metabolomics has become an invaluable tool for both studying metabolism and biomarker discovery. The great technical advances in analytical chemistry and bioinformatics have considerably increased the number of measurable metabolites, yet an important part of the human metabolome remains uncovered. Among the various MS hyphenated techniques available, LC-MS stands out as the most used. Here, we aimed to show the capabilities of LC-MS to uncover part of the metabolome and how to best proceed with sample preparation and LC to maximise metabolite detection. The analyses of various open metabolite databases served us to estimate the size of the already detected human metabolome, the expected metabolite composition of most used human biospecimens and which part of the metabolome can be detected when LC-MS is used. Based on an extensive review and on our experience, we have outlined standard procedures for LC-MS analysis of urine, cells, serum/plasma, tissues and faeces, to guide in the selection of the sample preparation method that best matches with one or more LC techniques in order to get the widest metabolome coverage. These standard procedures may be a useful tool to explore, at a glance, the wide spectrum of possibilities available, which can be a good starting point for most of the LC-MS metabolomic studies.

A Multi-Omics Protocol for Swine Feces to Elucidate Longitudinal Dynamics in Microbiome Structure and Function

Swine are regarded as promising biomedical models, but the dynamics of their gastrointestinal microbiome have been much less investigated than that of humans or mice. The aim of this study was to establish an integrated multi-omics protocol to investigate the fecal microbiome of healthy swine. To this end, a preparation and analysis protocol including integrated sample preparation for meta-omics analyses of deep-frozen feces was developed. Subsequent data integration linked microbiome composition with function, and metabolic activity with protein inventories, i.e., 16S rRNA data and expressed proteins, and identified proteins with corresponding metabolites. 16S rRNA gene amplicon and metaproteomics analyses revealed a fecal microbiome dominated by Prevotellaceae, Lactobacillaceae, Lachnospiraceae, Ruminococcaceae and Clostridiaceae. Similar microbiome compositions in feces and colon, but not ileum samples, were observed, showing that feces can serve as minimal-invasive proxy for porcine colon microbiomes. Longitudinal dynamics in composition, e.g., temporal decreased abundance of Lactobacillaceae and Streptococcaceae during the experiment, were not reflected in microbiome function. Instead, metaproteomics and metabolomics showed a rather stable functional state, as evident from short-chain fatty acids (SCFA) profiles and associated metaproteome functions, pointing towards functional redundancy among microbiome constituents. In conclusion, our pipeline generates congruent data from different omics approaches on the taxonomy and functionality of the intestinal microbiome of swine.

Analytical Tools for Disease Diagnosis in Animals via Fecal Volatilome

Volatilome analysis is growing in attention for the diagnosis of diseases in animals and humans. In particular, volatilome analysis in fecal samples is starting to be proposed as a fast, easy and noninvasive method for disease diagnosis. Volatilome comprises volatile organic compounds (VOCs), which are produced during both physiological and patho-physiological processes. Thus, VOCs from a pathological condition often differ from those of a healthy state and therefore the VOCs profile can be used in the detection of some diseases. Due to their strengths and advantages, feces are currently being used to obtain information related to health status in animals. However, they are complex samples, that can present problems for some analytical techniques and require special consideration in their use and preparation before analysis. This situation demands an effort to clarify which analytic options are currently being used in the research context to analyze the possibilities these offer, with the final objectives of contributing to develop a standardized methodology and to exploit feces potential as a diagnostic matrix. The current work reviews the studies focused on the diagnosis of animal diseases through fecal volatilome in order to evaluate the analytical methods used and their advantages and limitations. The alternatives found in the literature for sampling, storage, sample pretreatment, measurement and data treatment have been summarized, considering all the steps involved in the analytical process.

1H NMR-Based Fecal Metabolomics Reveals Changes in Gastrointestinal Function of Aging Rats Induced by d-Galactose

d-galactose (d-gal) is widely used to induce aging. However, it is still unclear whether long-term injection of d-gal affects the gastrointestinal functions of aging rats, and how. In this study, we investigated the effects of d-gal on the gastrointestinal functions of aging rats, especially from the perspective of fecal metabolomics. Biochemical and behavioral analyses were performed. Besides, a ¹H NMR-based metabolomics approach was built and applied in combination with multivariate data analysis including principal components analysis (PCA) and orthogonal partial least squares-discriminate analysis (OPLS-DA). Regarding gastrointestinal functions, d-gal significantly decreased the small intestine propulsion rates and prolonged gastrointestinal transit time. In addition, d-gal significantly increased the oxidative damages. PCA results showed that d-gal interrupted the metabolic profiles of endogenous small molecules in aging rats. Furthermore, OPLS-DA showed that 40 metabolites were screened and identified to be involved in the disruption of gastrointestinal functions in aging rats. Accordingly, seven metabolic pathways were recognized as the most influenced pathways associated with gastrointestinal functions of aging rats induced by d-gal, including amino acid metabolism, energy metabolism, intestinal flora metabolism, and metabolism of short chain fatty acids. It is the first report to investigate the effects and underlying mechanisms of d-gal on gastrointestinal functions of aging rats from the perspective of fecal metabolomics. The current results are conducive to further comprehensively understand d-gal-induced aging and will expand the applications of d-gal in pharmacological researches.

The role of gut microbiota (GM) and GM-related metabolites in diabetes and obesity. A review of analytical methods used to measure GM-related metabolites in fecal samples with a focus on metabolites' derivatization step

Disruption of gut microbiota (GM) composition is increasingly related to the pathogenesis of various metabolic diseases. Additionally, GM is responsible for the production and transformation of metabolites involved in the development of metabolic disorders, such as obesity and type 2 diabetes mellitus (T2DM). The current state of knowledge regarding the composition of GM and GM-related metabolites in relation to the progress and development of obesity and T2DM is presented in this review. To understand the relationships between GM-related metabolites and the development of metabolic disorders, their accurate qualitative and quantitative measurement in biological samples is needed. Feces represent a valuable biological matrix which composition may reflect the health status of the lower gastrointestinal tract and the whole organism. Mass spectrometry (MS), mainly in combination with gas chromatography (GC) or liquid chromatography (LC), is commonly used to measure fecal metabolites. However, profiling metabolites in such a complex matrix as feces is challenging from both analytical chemistry and biochemistry standpoints. Chemical derivatization is one of the most effective methods used to overcome these problems. In this review, we provide a comprehensive summary of the derivatization methods of GM-related metabolites prior to GC-MS or LC-MS analysis, which have been published in the last five years (2015-2020). Additionally, analytical methods used for the analysis of GM-related metabolites without the derivatization step are also presented.

1D 1 H NMR as a Tool for Fecal Metabolomics

Metabolomic studies allow a deeper understanding of the processes of a given ecological community than nucleic acid-based surveys alone. In the case of the gut microbiota, a metabolic profile of, for example, a fecal sample provides details about the function and interactions within the distal region of the gastrointestinal tract, and such a profile can be generated in a number of different ways. This unit elaborates on the use of 1D ¹ H NMR spectroscopy as a commonly used method to characterize small-molecule metabolites of the fecal metabonome (meta-metabolome). We describe a set of protocols for the preparation of fecal water extraction, storage, scanning, measurement of pH, and spectral processing and analysis. We also compare the effects of various sample storage conditions for processed and unprocessed samples to provide a framework for comprehensive analysis of small molecules from stool-derived samples. © 2020 Wiley Periodicals LLC Basic Protocol 1: Extracting fecal water from crude fecal samples Alternate Protocol 1: Extracting fecal water from small crude fecal samples Basic Protocol 2: Acquiring NMR spectra of metabolite samples Alternate Protocol 2: Acquiring NMR spectra of metabolite samples using Bruker spectrometer running TopSpin 3.x Alternate Protocol 3: Acquiring NMR spectra of metabolite samples by semiautomated process Basic Protocol 3: Measuring sample pH Support Protocol 1: Cleaning NMR tubes Basic Protocol 4: Processing raw spectra data Basic Protocol 5: Profiling spectra Support Protocol 2: Spectral profiling of sugars and other complex metabolites.

Metabolic profiling of organic and fatty acids in chronic and autoimmune diseases

Metabolomics is a powerful tool of omics that permits the simultaneous identification of metabolic perturbations in several autoimmune and chronic diseases. Several parameters can affect a metabolic profile, from the population characteristics to the selection of the analytical method. In the current chapter, we summarize the main analytical methods and results of the metabolic profiling of fatty and organic acids performed in human metabolomic studies for asthma, COPD, psoriasis and Hashimoto's thyroiditis. We discuss the most significant metabolic alterations associated with these diseases, after comparison of either a single patient's group with healthy controls or several patient's subgroups of different disease severity and phenotype with healthy controls or of a patient's group before and after treatment. Finally, we present critical metabolic patterns that are associated with each disease and their potency for the unraveling of disease pathogenesis, prediction, diagnosis, patient stratification and treatment selection.

An LC-QToF MS based method for untargeted metabolomics of human fecal samples

INTRODUCTION

Consensus in sample preparation for untargeted human fecal metabolomics is lacking.

OBJECTIVES

To obtain sample preparation with broad metabolite coverage for high-throughput LC-MS.

METHODS

Extraction solvent, solvent ratio and fresh frozen-vs-lyophilized samples were evaluated by metabolite feature quality.

RESULTS

Methanol at 5 mL per g wet feces provided a wide metabolite coverage with optimal balance between signal intensity and saturation for both fresh frozen and lyophilized samples. Lyophilization did not affect SCFA and is recommended because of convenience in normalizing to dry matter.

CONCLUSION

The suggested sample preparation is simple, efficient and suitable for large-scale human fecal metabolomics.

From Samples to Insights into Metabolism: Uncovering Biologically Relevant Information in LC-HRMS Metabolomics Data

Untargeted metabolomics (including lipidomics) is a holistic approach to biomarker discovery and mechanistic insights into disease onset and progression, and response to intervention. Each step of the analytical and statistical pipeline is crucial for the generation of high-quality, robust data. Metabolite identification remains the bottleneck in these studies; therefore, confidence in the data produced is paramount in order to maximize the biological output. Here, we outline the key steps of the metabolomics workflow and provide details on important parameters and considerations. Studies should be designed carefully to ensure appropriate statistical power and adequate controls. Subsequent sample handling and preparation should avoid the introduction of bias, which can significantly affect downstream data interpretation. It is not possible to cover the entire metabolome with a single platform; therefore, the analytical platform should reflect the biological sample under investigation and the question(s) under consideration. The large, complex datasets produced need to be pre-processed in order to extract meaningful information. Finally, the most time-consuming steps are metabolite identification, as well as metabolic pathway and network analysis. Here we discuss some widely used tools and the pitfalls of each step of the workflow, with the ultimate aim of guiding the reader towards the most efficient pipeline for their metabolomics studies.

Evaluating different extraction solvents for GC-MS based metabolomic analysis of the fecal metabolome of adult and baby giant pandas

The gut microbiome plays a fundamental role in host health and the fecal metabolome can be analysed to assess microbial activity and can be used as an intermediate phenotype monitoring the host-microbiome relationship. However, there is no established extraction protocol to study the fecal metabolome of giant pandas. The aim of this research is to optimize extraction of the fecal metabolome from adult and baby pandas for high throughput metabolomics analysis using gas chromatography-mass spectrometry (GC-MS). Fecal samples were collected from eight adult pandas and a pair of twin baby pandas. Six different extraction solvents were investigated and evaluated for their reproducibility, metabolite coverage, and extraction efficiency, particularly in relation to the biochemical compound classes such as amino acids, tricarboxylic acid (TCA) cycle intermediates, fatty acids, secondary metabolites, and vitamin and cofactors. Our GC-MS results demonstrated that the extraction solvents with isopropanol: acetonitrile: water (3:2:2 ratio) and 80% methanol were the most appropriate for studying the fecal metabolome of adult and baby giant pandas respectively. These extraction solvents can be used in future study protocols for the analysis of the fecal metabolome in giant pandas.

Early genistein exposure of California mice and effects on the gut microbiota-brain axis

Human offspring encounter high amounts of phytoestrogens, such as genistein (GEN), through maternal diet and soy-based formulas. Such chemicals can exert estrogenic activity and thereby disrupt neurobehavioral programming. Besides inducing direct host effects, GEN might cause gut dysbiosis and alter gut metabolites. To determine whether exposure to GEN affects these parameters, California mice (Peromyscus californicus) dams were placed 2 weeks prior to breeding and throughout gestation and lactation on a diet supplemented with GEN (250 mg/kg feed weight) or AIN93G phytoestrogen-free control diet (AIN). At weaning, offspring socio-communicative behaviors, gut microbiota and metabolite profiles were assayed. Exposure of offspring to GEN-induced sex-dependent changes in gut microbiota and metabolites. GEN exposed females were less likely to investigate a novel female mouse when tested in a three-chamber social test. When isolated, GEN males and females exhibited increased latency to elicit their first call, suggestive of reduced motivation to communicate with other individuals. Correlation analyses revealed interactions between GEN-induced microbiome, metabolome and socio-communicative behaviors. Comparison of GEN males with AIN males revealed the fraction of calls above 20 kHz was associated with daidzein, α-tocopherol, Flexispira spp. and Odoribacter spp. Results suggest early GEN exposure disrupts normal socio-communicative behaviors in California mice, which are otherwise evident in these social rodents. Such effects may be due to GEN disruptions on neural programming but might also be attributed to GEN-induced microbiota shifts and resultant changes in gut metabolites. Findings indicate cause for concern that perinatal exposure to GEN may detrimentally affect the offspring microbiome-gut-brain axis.

Metaproteomic and Metabolomic Approaches for Characterizing the Gut Microbiome

The gut microbiome has been shown to play a significant role in human healthy and diseased states. The dynamic signaling that occurs between the host and microbiome is critical for the maintenance of host homeostasis. Analyzing the human microbiome with metaproteomics, metabolomics, and integrative multi-omics analyses can provide significant information on markers for healthy and diseased states, allowing for the eventual creation of microbiome-targeted treatments for diseases associated with dysbiosis. Metaproteomics enables functional activity information to be gained from the microbiome samples, while metabolomics provides insight into the overall metabolic states affecting/representing the host-microbiome interactions. Combining these functional -omic platforms together with microbiome composition profiling allows for a holistic overview on the functional and metabolic state of the microbiome and its influence on human health. Here the benefits of metaproteomics, metabolomics, and the integrative multi-omic approaches to investigating the gut microbiome in the context of human health and diseases are reviewed.

The potential of nanoflow liquid chromatography-nano electrospray ionisation-mass spectrometry for global profiling the faecal metabolome

Faeces are comprised of a wide array of metabolites arising from the circulatory system as well as the human microbiome. A global metabolite analysis (metabolomics) of faecal extracts offers the potential to uncover new compounds which may be indicative of the onset of bowel diseases such as colorectal cancer (CRC). To date, faecal metabolomics is still in its infancy and the compounds of low abundance present in faecal extracts poorly characterised. In this study, extracts of faeces from healthy subjects were profiled using a sensitive nanoflow-nanospray LC-MS platform which resulted in highly repeatable peak retention times (<2% CV) and intensities (<15% CV). Analysis of the extracts revealed wide coverage of the faecal metabolome including detection of low abundant signalling compounds such as sex steroids and eicosanoids, alongside highly abundant pharmaceuticals and tetrapyrrole metabolites. A small pilot study investigating differences in metabolomics profiles of faecal samples obtained from 7 CRC, 25 adenomatous polyp and 26 healthy groups revealed that secondary bile acids, conjugated androgens, eicosanoids, phospholipids and an unidentified haem metabolite were potential classes of metabolites that discriminated between the CRC and control sample groups. However, much larger follow up studies are needed to confirm which components of the faecal metabolome are associated with actual CRC disease rather than dietary influences. This study reveals the potential of nanospray-nanoflow LC-MS profiling of faecal samples from large scale cohort studies for uncovering the role of the faecal metabolome in colorectal disease formation.

A non-targeted LC-HRMS approach for detecting exposure to illegal veterinary treatments: The case of cephalosporins in commercial laying Hens

Cephalosporins are of particular importance in human medicine and should be reserved for second-line curative treatment in the veterinary field to avoid any emerging antimicrobial resistance. Due to misuse of ceftiofur in the poultry sector in France, it is now recommended to completely stop using cephalosporins in this sector. Methods currently used for the control of veterinary practices are mostly based on liquid chromatography coupled to mass spectrometry in a targeted mode, including parent compounds and any major metabolites. The aim of the present study was to evaluate the relevance of untargeted metabolomic approaches to highlight a possible exposure of laying hens to cephalosporins using a predictive model including selected treatment biomarkers. An experimentation carried out on living animals involved the administration of cefquinome and ceftiofur. Three biological matrices-droppings, eggs and liver-were investigated. Metabolites were extracted and analysed by liquid chromatography coupled to high resolution mass spectrometry in a full scan mode. Metabolites impacted by the treatment were selected by using univariate and multivariate statistical analyses. Predictive models built from the potential biomarkers selected in the "droppings" matrix were validated and able to classify "treated" and "control" hens. PLS-DA and logistic regression models were compared and both models gave satisfactory results in terms of prediction. Results were of less interest for other matrices in which only biomarkers of exposure to cefquinome were detected.

Untargeted LC/MS-based metabolic phenotyping (metabonomics/metabolomics): The state of the art

Liquid chromatography (LC) hyphenated to mass spectrometry is currently the most widely used means of determining metabolic phenotypes via both untargeted and targeted analysis. At present a range of analytical separations, including reversed-phase, hydrophilic interaction and ion-pair LC are employed to maximise metabolome coverage with ultra (high) performance liquid chromatography (UHPLC) increasingly displacing conventional high performance liquid chromatography because of the need for short analysis times and high peak capacity in such applications. However, it is widely recognized that these methodologies do not entirely solve the problems facing researchers trying to perform comprehensive metabolic phenotyping and in addition to these "routine" approaches there are continuing investigations of alternative separation methods including 2-dimensional/multi column approaches. These involve either new stationary phases or multidimensional combinations of the more conventional materials currently used, as well as application of miniaturization or "new" approaches such as supercritical HP and UHP- chromatographic separations. There is also a considerable amount of interest in the combination of chromatographic and ion mobility separations, with the latter providing both an increase in resolution and the potential to provide additional structural information via the determination of molecular collision cross section data. However, key problems remain to be solved including ensuring quality, comparability across different laboratories and the ever present difficulty of identifying unknowns.

Efficient Extraction from Mice Feces for NMR Metabolomics Measurements with Special Emphasis on SCFAs

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.

Comprehensive analysis of pig feces metabolome by chromatographic techniques coupled to mass spectrometry in high resolution mode: Influence of sample preparation on the identification coverage

Pig feces is an interesting biological sample to be implemented in metabolomics experiments by virtue of the information that can be deduced from the interaction between host and microbiome. However, pig fecal samples have received scant attention, especially in untargeted metabolomic studies. In this research, an analytical strategy was planned to maximize the identification coverage of metabolites found in pig fecal samples. For this purpose, two complementary platforms such as LC-QTOF MS/MS and GC-TOF/MS were used. Concerning sample preparation six extractant solvents with different polarity grade were tested to evaluate the extraction performance and, in the particular case of GC-MS, two derivatization protocols were compared. A total number of 303 compounds by combination of all the extractants and analytical platforms were tentatively identified. The main identified families were amino acids, fatty acids and derivatives, carbohydrates and carboxylic acids. For GC-TOF/MS analysis, the recommended extractant is methanol, while methoxymation was required in the derivatization protocol since this step allows detecting the α-keto acids, which are direct markers of the microbiome status. Concerning LC-QTOF MS/MS analysis, a dual extraction approach with methanol (MeOH) or MeOH/water and ethyl acetate is proposed to enhance the detection of polar and non-polar metabolites.

Development and application of a HILIC UHPLC-MS method for polar fecal metabolome profiling

The fecal metabolome is a complex mixture of endogenous, microbial metabolites, and food derived compounds. Hydrophilic interaction liquid chromatography (HILIC) enables the analysis of polar compounds, which is a valuable alternative to reversed-phase liquid chromatography in the field of metabolomics due to its ability to retain a greater portion of the polar metabolome. The objective of the study was to find the optimal chromatographic solution to perform non-targeted metabolomics of feces by means of HILIC ultra-high-pressure liquid chromatography mass spectrometry (UHPLC-Q-TOF-MS). The performance was systematically investigated analyzing a pooled fecal sample, and mixtures of 150 metabolites from different families, including for example amino acids, amines, indole derivatives, fatty acids and carbohydrates. Three different stationary phases (zwitterionic, amide and unbonded silica) were operated at three pH values (4.6, 6.8 and 9.0), and three salt gradient conditions (5-5, 5-10 and 5-25 mM ammonium acetate). Amide and zwitterionic stationary phases performed similarly at low pH, with highest number of detected standards, which increased by increasing the salt gradient. The amide column showed slightly better performance in terms of separation of isomers and peak widths and remarkably good performance at basic pH, with highest number of metabolite features in the non-targeted analysis. The zwitterionic column operated best in terms of number of detected standards, retention time distribution of standards and metabolite feature across whole chromatographic run. Thus, the zwitterionic column was proven to suit for non-targeted analysis of fecal samples, resulting in good coverage of especially amino acids and carbohydrates.

Soy-Induced Fecal Metabolome Changes in Ovariectomized and Intact Female Rats: Relationship with Cardiometabolic Health

Phytoestrogens are plant-derived compounds found in a variety of foods, most notably, soy. These compounds have been shown to improve immuno-metabolic health, yet mechanisms remain uncertain. We demonstrated previously that dietary phytoestrogen-rich soy (SOY) rescued metabolic dysfunction/inflammation following ovariectomy (OVX) in female rats; we also noted remarkable shifts in gut microbiota in SOY vs control diet-fed rats. Importantly, specific bacteria that significantly increased in those fed the SOY correlated positively with several favorable host metabolic parameters. One mechanism by which gut microbes might lead to such host effects is through production of bacterial metabolites. To test this possibility, we utilized non-targeted gas chromatography-mass spectrometry (GCMS) to assess the fecal metabolome in those previously studied animals. Partial least square discriminant analysis (PLSDA) revealed clear separation of fecal metabolomes based on diet and ovarian state. In particular, SOY-fed animals had greater fecal concentrations of the beneficial bacterial metabolite, S-equol, which was positively associated with several of the bacteria upregulated in the SOY group. S-equol was inversely correlated with important indicators of metabolic dysfunction and inflammation, suggesting that this metabolite might be a key mediator between SOY and gut microbiome-positive host health outcomes.

Simultaneous quantification of straight-chain and branched-chain short chain fatty acids by gas chromatography mass spectrometry

Biomedical research in areas such as metabolic disorders, neuromodulatory, and immunomodulatory conditions involves lipid metabolism and demands a reliable and inexpensive method for quantification of short chain fatty acids (SCFAs). We report a GC-MS method for analysis of all straight-chain and branched-chain SCFAs using pentafluorobenzyl bromide (PFBBr) as derivatization reagent. We optimized the derivatization and GC-MS conditions using a mixture containing all eight SCFA standards, i.e., five straight-chain and three branched-chain SCFAs. The optimal derivatization conditions were derivatization time 90 min, temperature 60 °C, pH 7, and (CH3)2CO:H2O ratio 2:1 (v:v). Comparing the performance of different GC column configurations, a 30 m DB-225ms hyphenated with a 30 m DB-5ms column in tandem showed the best separation of SCFAs. Using the optimized experiment conditions, we simultaneously detected all SCFAs with much improved detection limit, 0.244-0.977 μM. We further applied the developed method to measure the SCFAs in mouse feces and all SCFAs were successfully quantified. The recovery rates of the eight SCFAs ranged from 55.7% to 97.9%.

Integrated Omics: Tools, Advances, and Future Approaches

With the rapid adoption of high-throughput omic approaches to analyze biological samples such as genomics, transcriptomics, proteomics, and metabolomics, each analysis can generate tera- to peta-byte sized data files on a daily basis. These data file sizes, together with differences in nomenclature among these data types, make the integration of these multi-dimensional omics data into biologically meaningful context challenging. Variously named as integrated omics, multi-omics, poly-omics, trans-omics, pan-omics, or shortened to just 'omics', the challenges include differences in data cleaning, normalization, biomolecule identification, data dimensionality reduction, biological contextualization, statistical validation, data storage and handling, sharing, and data archiving. The ultimate goal is towards the holistic realization of a 'systems biology' understanding of the biological question in hand. Commonly used approaches in these efforts are currently limited by the 3 i's - integration, interpretation, and insights. Post integration, these very large datasets aim to yield unprecedented views of cellular systems at exquisite resolution for transformative insights into processes, events, and diseases through various computational and informatics frameworks. With the continued reduction in costs and processing time for sample analyses, and increasing types of omics datasets generated such as glycomics, lipidomics, microbiomics, and phenomics, an increasing number of scientists in this interdisciplinary domain of bioinformatics face these challenges. We discuss recent approaches, existing tools, and potential caveats in the integration of omics datasets for development of standardized analytical pipelines that could be adopted by the global omics research community.

Optimized Sampling Conditions for Fecal Volatile Organic Compound Analysis by Means of Field Asymmetric Ion Mobility Spectrometry

Fecal volatile organic compounds (VOCs) are increasingly considered to be potential noninvasive, diagnostic biomarkers for various gastrointestinal diseases. Knowledge of the influence of sampling conditions on VOC outcomes is limited. We aimed to evaluate the effects of sampling conditions on fecal VOC profiles and to assess under which conditions an optimal diagnostic accuracy in the discrimination between pediatric inflammatory bowel disease (IBD) and controls could be obtained. Fecal samples from de novo treatment-naïve pediatric IBD patients and healthy controls (HC) were used to assess the effects of sampling conditions compared to the standard operating procedure (reference standard), defined as 500 mg of sample mass diluted with 10 mL tap water, using field asymmetric ion mobility spectrometry (FAIMS). A total of 17 IBD (15 CD (Crohn's disease) and 2 UC (ulcerative colitis)) and 25 HC were included. IBD and HC could be discriminated with high accuracy (accuracy = 0.93, AUC = 0.99, p < 0.0001). A smaller fecal sample mass resulted in a decreased diagnostic accuracy (300 mg accuracy = 0.77, AUC = 0.69, p = 0.02; 100 mg accuracy = 0.70, AUC = 0.74, p = 0.003). A loss of diagnostic accuracy was seen toward increased numbers of thaw–freeze cycles (one cycle, accuracy = 0.61, AUC = 0.80, p = 0.0004; two cycles, accuracy = 0.64, AUC = 0.56, p = 0.753; and three cycles, accuracy = 0.57, AUC = 0.50, p = 0.5101) and when samples were kept at room temperature for 180 min prior to analysis (accuracy = 0.60, AUC = 0.51, p = 0.46). Diagnostic accuracy of VOC profiles was not significantly influenced by storage duration differences of 20 months. The application of a 500 mg sample mass analyzed after one thaw–freeze cycle showed the best discriminative accuracy for the differentiation of IBD and HC. VOC profiles and diagnostic accuracy were significantly affected by sampling conditions, underlining the need for the implementation of standardized protocols in fecal VOC analysis.

Gut metabolome meets microbiome: A methodological perspective to understand the relationship between host and microbe

It is well established that gut microbes and their metabolic products regulate host metabolism. The interactions between the host and its gut microbiota are highly dynamic and complex. In this review we present and discuss the metabolomic strategies to study the gut microbial ecosystem. We highlight the metabolic profiling approaches to study faecal samples aimed at deciphering the metabolic product derived from gut microbiota. We also discuss how metabolomics data can be integrated with metagenomics data derived from gut microbiota and how such approaches may lead to better understanding of the microbial functions. Finally, the emerging approaches of genome-scale metabolic modelling to study microbial co-metabolism and host-microbe interactions are highlighted.