Coupling Targeted and Untargeted Mass Spectrometry for Metabolome-Microbiome-Wide Association Studies of Human Fecal Samples

Metabolomic Applications in Gut Microbiota-Host Interactions in Human Diseases

The human gut microbiota, consisting of trillions of microorganisms, encodes diverse metabolic pathways that impact numerous aspects of host physiology. One key way in which gut bacteria interact with the host is through the production of small metabolites. Several of these microbiota-dependent metabolites, such as short-chain fatty acids, have been shown to modulate host diseases. In this review, we examine how disease-associated metabolic signatures are identified using metabolomic platforms, and where metabolomics is applied in gut microbiota-disease interactions. We further explore how integration of metagenomic and metabolomic data in human studies can facilitate biomarkers discoveries in precision medicine.

Regulation of microbial gene expression: the key to understanding our gut microbiome

During the past two decades, gut microbiome studies have established the significant impact of the gut microbiota and its metabolites on host health. However, the molecular mechanisms governing the production of microbial metabolites in the gut environment remain insufficiently investigated and thus are poorly understood. Here, we propose that an enhanced understanding of gut microbial gene regulation, which is responsive to dietary components and gut environmental conditions, is needed in the research field and essential for our ability to effectively promote host health and prevent diseases through interventions targeting the gut microbiome.

High-throughput identification of gut microbiome-dependent metabolites

A significant hurdle that has limited progress in microbiome science has been identifying and studying the diverse set of metabolites produced by gut microbes. Gut microbial metabolism produces thousands of difficult-to-identify metabolites, which present a challenge to study their roles in host biology. In recent years, mass spectrometry-based metabolomics has become one of the core technologies for identifying small metabolites. However, metabolomics expertise, ranging from sample preparation to instrument use and data analysis, is often lacking in academic labs. Most targeted metabolomics methods provide high levels of sensitivity and quantification, while they are limited to a panel of predefined molecules that may not be informative to microbiome-focused studies. Here we have developed a gut microbe-focused and wide-spectrum metabolomic protocol using liquid chromatography-mass spectrometry and bioinformatic analysis. This protocol enables users to carry out experiments from sample collection to data analysis, only requiring access to a liquid chromatography-mass spectrometry instrument, which is often available at local core facilities. By applying this protocol to samples containing human gut microbial metabolites, spanning from culture supernatant to human biospecimens, our approach enables high-confidence identification of >800 metabolites that can serve as candidate mediators of microbe-host interactions. We expect this protocol will lower the barrier to tracking gut bacterial metabolism in vitro and in mammalian hosts, propelling hypothesis-driven mechanistic studies and accelerating our understanding of the gut microbiome at the chemical level.

A Prebiotic Diet Containing Galactooligosaccharides and Polydextrose Produces Dynamic and Reproducible Changes in the Gut Microbial Ecosystem in Male Rats

Despite substantial evidence supporting the efficacy of prebiotics for promoting host health and stress resilience, few experiments present evidence documenting the dynamic changes in microbial ecology and fecal microbially modified metabolites over time. Furthermore, the literature reports a lack of reproducible effects of prebiotics on specific bacteria and bacterial-modified metabolites. The current experiments examined whether consumption of diets enriched in prebiotics (galactooligosaccharides (GOS) and polydextrose (PDX)), compared to a control diet, would consistently impact the gut microbiome and microbially modified bile acids over time and between two research sites. Male Sprague Dawley rats were fed control or prebiotic diets for several weeks, and their gut microbiomes and metabolomes were examined using 16S rRNA gene sequencing and untargeted LC-MS/MS analysis. Dietary prebiotics altered the beta diversity, relative abundance of bacterial genera, and microbially modified bile acids over time. PICRUSt2 analyses identified four inferred functional metabolic pathways modified by the prebiotic diet. Correlational network analyses between inferred metabolic pathways and microbially modified bile acids revealed deoxycholic acid as a potential network hub. All these reported effects were consistent between the two research sites, supporting the conclusion that dietary prebiotics robustly changed the gut microbial ecosystem. Consistent with our previous work demonstrating that GOS/PDX reduces the negative impacts of stressor exposure, we propose that ingesting a diet enriched in prebiotics facilitates the development of a health-promoting gut microbial ecosystem.

Statistical and computational methods for integrating microbiome, host genomics, and metabolomics data

Large-scale microbiome studies are progressively utilizing multiomics designs, which include the collection of microbiome samples together with host genomics and metabolomics data. Despite the increasing number of data sources, there remains a bottleneck in understanding the relationships between different data modalities due to the limited number of statistical and computational methods for analyzing such data. Furthermore, little is known about the portability of general methods to the metagenomic setting and few specialized techniques have been developed. In this review, we summarize and implement some of the commonly used methods. We apply these methods to real data sets where shotgun metagenomic sequencing and metabolomics data are available for microbiome multiomics data integration analysis. We compare results across methods, highlight strengths and limitations of each, and discuss areas where statistical and computational innovation is needed.

Gut microbiome and metabolome to discover pathogenic bacteria and probiotics in ankylosing spondylitis

Objective

Previous research has partially revealed distinct gut microbiota in ankylosing spondylitis (AS). In this study, we performed non-targeted fecal metabolomics in AS in order to discover the microbiome-metabolome interface in AS. Based on prospective cohort studies, we further explored the impact of the tumor necrosis factor inhibitor (TNFi) on the gut microbiota and metabolites in AS.

Methods

To further understand the gut microbiota and metabolites in AS, along with the influence of TNFi, we initiated a prospective cohort study. Fecal samples were collected from 29 patients with AS before and after TNFi therapy and 31 healthy controls. Metagenomic and metabolomic experiments were performed on the fecal samples; moreover, validation experiments were conducted based on the association between the microbiota and metabolites.

Results

A total of 7,703 species were annotated using the metagenomic sequencing system and by profiling the microbial community taxonomic composition, while 50,046 metabolites were identified using metabolite profiling. Differential microbials and metabolites were discovered between patients with AS and healthy controls. Moreover, TNFi was confirmed to partially restore the gut microbiota and the metabolites. Multi-omics analysis of the microbiota and metabolites was performed to determine the associations between the differential microbes and metabolites, identifying compounds such as oxypurinol and biotin, which were correlated with the inhibition of the pathogenic bacteria Ruminococcus gnavus and the promotion of the probiotic bacteria Bacteroides uniformis. Through experimental studies, the relationship between microbes and metabolites was further confirmed, and the impact of these two types of microbes on the enterocytes and the inflammatory cytokine interleukin-18 (IL-18) was explored.

Conclusion

In summary, multi-omics exploration elucidated the impact of TNFi on the gut microbiota and metabolites and proposed a novel therapeutic perspective: supplementation of compounds to inhibit potential pathogenic bacteria and to promote potential probiotics, therefore controlling inflammation in AS.

Microbial and metabolic profiles unveil mutualistic microbe-microbe interaction in obesity-related colorectal cancer

Obesity is a risk factor for colorectal cancer (CRC), and the involvement of gut microbiota in the pathogenesis of obesity and CRC is widely recognized. However, the landscape of fecal microbiome and metabolome distinguishing patients with obesity-related CRC from obesity remains unknown. Here, we utilize metagenomic sequencing and metabolomics from 522 patients with CRC and healthy controls to identify the characteristics of obese CRC. Our integrated analysis reveals that obesity-related CRC is characterized by elevated Peptostreptococcus stomatis, dysregulated fatty acids and phospholipids, and altered Kyoto Encyclopedia of Genes and Genomes pathways involving glycerophospholipid metabolism and lipopolysaccharide synthesis. Correlation analysis unveils microbial interactions in obesity, where the probiotic Faecalibacterium prausnitzii and the tumor-promoting species P. stomatis may engage in cross-feeding, thereby promoting tumorigenesis. In vitro experiments affirm enhanced growth under cross-feeding conditions. The mutualistic microbe-microbe interaction may contribute to the association between obesity and elevated CRC risk. Additionally, diagnostic models incorporating BMI-specific microbial biomarkers display promise for precise CRC screening.

Butyrate ameliorated ferroptosis in ulcerative colitis through modulating Nrf2/GPX4 signal pathway and improving intestinal barrier

Oxidative stress and intestinal inflammation are main pathological features of ulcerative colitis (UC). Ferroptosis, characterized by iron accumulation and lipid peroxidation, is closely related to the pathologic process of UC. 16S rRNA sequencing for intestinal microbiota analysis and gas chromatography-mass spectrometry (GC-MS) for short-chain fatty acid (SCFA) contents clearly demonstrated lower amounts of butyrate-producing bacteria and butyrate in colitis mice. However, the precise mechanisms of sodium butyrate (NaB) in treating UC remain largely unclear. We found that ferroptosis occurred in colitis models, as evidenced by the inflammatory response, intracellular iron level, mitochondria ultrastructural observations and associated protein expression. NaB inhibited ferroptosis in colitis, significantly rescued weight loss and colon shortening in mice and reduced inflammatory lesions and mitochondrial damage. Furthermore, NaB improved intestinal barrier integrity and markedly suppressed the expression of pro-ferroptosis proteins. Conversely, the protein expression of anti-ferroptosis markers including nuclear factor erythroid-related Factor 2 (Nrf2) and glutathione peroxidase 4 (GPX4), was significantly upregulated with NaB treatment. Moreover, the knockdown of Nrf2 reversed the anti-colitis effect of NaB. Taken together, NaB exhibited a protective effect by ameliorating ferroptosis in experimental colitis through Nrf2/GPX4 signaling and improving intestinal barrier integrity, which provides a novel mechanism for NaB prevention of UC.

Mathematical modeling of fluid dynamics in in vitro gut fermentation systems: A new tool to improve the interpretation of microbial metabolism

In vitro systems are widely employed to assess the impact of dietary compounds on the gut microbiota and their conversion into beneficial bacterial metabolites. However, the complex fluid dynamics and multi-segmented nature of these systems can complicate the comprehensive analysis of dietary compound fate, potentially confounding physical dilution or washout with microbial catabolism. In this study, we developed fluid dynamics models based on sets of ordinary differential equations to simulate the behavior of an inert compound within two commonly used in vitro systems: the continuous two-stage PolyFermS system and the semi-continuous multi-segmented SHIME® system as well as into various declinations of those systems. The models were validated by investigating the fate of blue dextran, demonstrating excellent agreement between experimental and modeling data (with r2 values ranging from 0.996 to 0.86 for different approaches). As a proof of concept for the utility of fluid dynamics models in in vitro system, we applied generated models to interpret metabolomic data of procyanidin A2 (ProA2) generated from the addition of proanthocyanidin (PAC)-rich cranberry extract to both the PolyFermS and SHIME® systems. The results suggested ProA2 degradation by the gut microbiota when compared to the modeling of an inert compound. Models of fluid dynamics developed in this study provide a foundation for comprehensive analysis of gut metabolic data in commonly utilized in vitro PolyFermS and SHIME® bioreactor systems and can enable a more accurate understanding of the contribution of bacterial metabolism to the variability in the concentration of target metabolites.

Protective Effects of Lacticaseibacillus rhamnosus IDCC3201 on Motor Functions and Anxiety Levels in a Chronic Stress Mouse Model

Growing evidence indicates a crucial role of the gut microbiota in physiological functions. Gut-brain axis imbalance has also been associated with neuropsychiatric and neurodegenerative disorders. Studies have suggested that probiotics regulate the stress response and alleviate mood-related symptoms. In this study, we investigated the effects of the probiotic Lacticaseibacillus rhamnosus IDCC3201 (L3201) on the behavioral response and fecal metabolite content in an unpredictable chronic mild stress (UCMS) mouse model. Our study shows that chronic stress in mice for three weeks resulted in significant changes in behavior, including lower locomotor activity, higher levels of anxiety, and depressive-like symptoms, compared to the control group. Metabolomic analysis demonstrated that disrupted fecal metabolites associated with aminoacyl-tRNA biosynthesis and valine, leucine, and isoleucine biosynthesis by UCMS were restored with the administration of L3201. Oral administration of the L3201 ameliorated the observed changes and improved the behavioral alterations along with fecal metabolites, suggesting that probiotics play a neuroprotective role.

Prediction of Feed Efficiency and Performance-Based Traits in Fish via Integration of Multiple Omics and Clinical Covariates

Fish aquaculture is a rapidly expanding global industry, set to support growing demands for sources of marine protein. Enhancing feed efficiency (FE) in farmed fish is required to reduce production costs and improve sector sustainability. Recognising that organisms are complex systems whose emerging phenotypes are the product of multiple interacting molecular processes, systems-based approaches are expected to deliver new biological insights into FE and growth performance. Here, we establish 14 diverse layers of multi-omics and clinical covariates to assess their capacities to predict FE and associated performance traits in a fish model (Oncorhynchus tshawytscha) and uncover the influential variables. Inter-omic relatedness between the different layers revealed several significant concordances, particularly between datasets originating from similar material/tissue and between blood indicators and some of the proteomic (liver), metabolomic (liver), and microbiomic layers. Single- and multi-layer random forest (RF) regression models showed that integration of all data layers provide greater FE prediction power than any single-layer model alone. Although FE was among the most challenging of the traits we attempted to predict, the mean accuracy of 40 different FE models in terms of root-mean square errors normalized to percentage was 30.4%, supporting RF as a feature selection tool and approach for complex trait prediction. Major contributions to the integrated FE models were derived from layers of proteomic and metabolomic data, with substantial influence also provided by the lipid composition layer. A correlation matrix of the top 27 variables in the models highlighted FE trait-associations with faecal bacteria (Serratia spp.), palmitic and nervonic acid moieties in whole body lipids, levels of free glycerol in muscle, and N-acetylglutamic acid content in liver. In summary, we identified subsets of molecular characteristics for the assessment of commercially relevant performance-based metrics in farmed Chinook salmon.

Simultaneous Quantitation and Discovery (SQUAD) Analysis: Combining the Best of Targeted and Untargeted Mass Spectrometry-Based Metabolomics

Untargeted and targeted approaches are the traditional metabolomics workflows acquired for a wider understanding of the metabolome under focus. Both approaches have their strengths and weaknesses. The untargeted, for example, is maximizing the detection and accurate identification of thousands of metabolites, while the targeted is maximizing the linear dynamic range and quantification sensitivity. These workflows, however, are acquired separately, so researchers compromise either a low-accuracy overview of total molecular changes (i.e., untargeted analysis) or a detailed yet blinkered snapshot of a selected group of metabolites (i.e., targeted analysis) by selecting one of the workflows over the other. In this review, we present a novel single injection simultaneous quantitation and discovery (SQUAD) metabolomics that combines targeted and untargeted workflows. It is used to identify and accurately quantify a targeted set of metabolites. It also allows data retro-mining to look for global metabolic changes that were not part of the original focus. This offers a way to strike the balance between targeted and untargeted approaches in one single experiment and address the two approaches' limitations. This simultaneous acquisition of hypothesis-led and discovery-led datasets allows scientists to gain more knowledge about biological systems in a single experiment.

Multi-omics insights into the interplay between gut microbiota and colorectal cancer in the "microworld" age

Colorectal cancer (CRC) is a multifactorial heterogeneous disease largely due to both genetic predisposition and environmental factors including the gut microbiota, a dynamic microbial ecosystem inhabiting the gastrointestinal tract. Elucidation of the molecular mechanisms by which the gut microbiota interacts with the host may contribute to the pathogenesis, diagnosis, and promotion of CRC. However, deciphering the influence of genetic variants and interactions with the gut microbial ecosystem is rather challenging. Despite recent advancements in single omics analysis, the application of multi-omics approaches to integrate multiple layers of information in the microbiome and host to introduce effective prevention, diagnosis, and treatment strategies is still in its infancy. Here, we integrate host- and microbe-based multi-omics studies, respectively, to provide a strategy to explore potential causal relationships between gut microbiota and colorectal cancer. Specifically, we summarize the recent multi-omics studies such as metagenomics combined with metabolomics and metagenomics combined with genomics. Meanwhile, the sample size and sample types commonly used in multi-omics research, as well as the methods of data analysis, were also generalized. We highlight multiple layers of information from multi-omics that need to be verified by different types of models. Together, this review provides new insights into the clinical diagnosis and treatment of colorectal cancer patients.

Novel technologies to characterize and engineer the microbiome in inflammatory bowel disease

We present an overview of recent experimental and computational advances in technology used to characterize the microbiome, with a focus on how these developments improve our understanding of inflammatory bowel disease (IBD). Specifically, we present studies that make use of flow cytometry and metabolomics assays to provide a functional characterization of microbial communities. We also describe computational methods for strain-level resolution, temporal series, mycobiome and virome data, co-occurrence networks, and compositional data analysis. In addition, we review novel techniques to therapeutically manipulate the microbiome in IBD. We discuss the benefits and drawbacks of these technologies to increase awareness of specific biases, and to facilitate a more rigorous interpretation of results and their potential clinical application. Finally, we present future lines of research to better characterize the relation between microbial communities and IBD pathogenesis and progression.

Moving beyond descriptive studies: harnessing metabolomics to elucidate the molecular mechanisms underpinning host-microbiome phenotypes

Advances in technology and software have radically expanded the scope of metabolomics studies and allow us to monitor a broad transect of central carbon metabolism in routine studies. These increasingly sophisticated tools have shown that many human diseases are modulated by microbial metabolism. Despite this, it remains surprisingly difficult to move beyond these statistical associations and identify the specific molecular mechanisms that link dysbiosis to the progression of human disease. This difficulty stems from both the biological intricacies of host-microbiome dynamics as well as the analytical complexities inherent to microbiome metabolism research. The primary objective of this review is to examine the experimental and computational tools that can provide insights into the molecular mechanisms at work in host-microbiome interactions and to highlight the undeveloped frontiers that are currently holding back microbiome research from fully leveraging the benefits of modern metabolomics.

Recent Trends of Microbiota-Based Microbial Metabolites Metabolism in Liver Disease

The gut microbiome and microbial metabolomic influences on liver diseases and their diagnosis, prognosis, and treatment are still controversial. Research studies have provocatively claimed that the gut microbiome, metabolomics understanding, and microbial metabolite screening are key approaches to understanding liver cancer and liver diseases. An advance of logical innovations in metabolomics profiling, the metabolome inclusion, challenges, and the reproducibility of the investigations at every stage are devoted to this domain to link the common molecules across multiple liver diseases, such as fatty liver, hepatitis, and cirrhosis. These molecules are not immediately recognizable because of the huge underlying and synthetic variety present inside the liver cellular metabolome. This review focuses on microenvironmental metabolic stimuli in the gut-liver axis. Microbial small-molecule profiling (i.e., semiquantitative monitoring, metabolic discrimination, target profiling, and untargeted profiling) in biological fluids has been incompletely addressed. Here, we have reviewed the differential expression of the metabolome of short-chain fatty acids (SCFAs), tryptophan, one-carbon metabolism and bile acid, and the gut microbiota effects are summarized and discussed. We further present proof-of-evidence for gut microbiota-based metabolomics that manipulates the host's gut or liver microbes, mechanosensitive metabolite reactions and potential metabolic pathways. We conclude with a forward-looking perspective on future attention to the “dark matter” of the gut microbiota and microbial metabolomics.

Mammalian gut metabolomes mirror microbiome composition and host phylogeny

In the past decade, studies on the mammalian gut microbiome have revealed that different animal species have distinct gut microbial compositions. The functional ramifications of this variation in microbial composition remain unclear: do these taxonomic differences indicate microbial adaptations to host-specific functionality, or are these diverse microbial communities essentially functionally redundant, as has been indicated by previous metagenomics studies? Here, we examine the metabolic content of mammalian gut microbiomes as a direct window into ecosystem function, using an untargeted metabolomics platform to analyze 101 fecal samples from a range of 25 exotic mammalian species in collaboration with a zoological center. We find that mammalian metabolomes are chemically diverse and strongly linked to microbiome composition, and that metabolome composition is further correlated to the phylogeny of the mammalian host. Specific metabolites enriched in different animal species included modified and degraded host and dietary compounds such as bile acids and triterpenoids, as well as fermentation products such as lactate and short-chain fatty acids. Our results suggest that differences in microbial taxonomic composition are indeed translated to host-specific metabolism, indicating that taxonomically distant microbiomes are more functionally diverse than redundant.

Taxifolin Alleviates DSS-Induced Ulcerative Colitis by Acting on Gut Microbiome to Produce Butyric Acid

Taxifolin is a bioflavonoid which has been used to treat Inflammatory Bowel Disease. However, taxifolin on DSS-induced colitis and gut health is still unclear. Here, we studied the effect of taxifolin on DSS-induced intestinal mucositis in mice. We measured the degree of intestinal mucosal injury and inflammatory response in DSS treated mice with or without taxifolin administration and studied the changes of fecal metabolites and intestinal microflora using 16S rRNA. The mechanism was further explored by fecal microbiota transplantation. The results showed that the weight loss and diarrhea score of the mice treated with taxifolin decreased in DSS-induced mice and longer colon length was displayed after taxifolin supplementation. Meanwhile, the expression of GPR41 and GPR43 in the colon was significantly increased by taxifolin treatment. Moreover, the expression of TNF-α, IL-1β, and IL-6 in colon tissue was inhibited by taxifolin treatment. The fecal metabolism pattern changed significantly after DSS treatment, which was reversed by taxifolin treatment. Importantly, taxifolin significantly increased the levels of butyric acid and isobutyric acid in the feces of DSS-treated mice. In terms of gut flora, taxifolin reversed the changes of Akkermansia, and further decreased uncultured_bacterium_f_Muribaculaceae. Fecal transplantation from taxifolin-treated mice showed a lower diarrhea score, reduced inflammatory response in the colon, and reduced intestinal mucosal damage, which may be related to the increased level of butyric acid in fecal metabolites. In conclusion, this study provides evidence that taxifolin can ameliorate DSS-induced colitis by altering gut microbiota to increase the production of SCFAs.

Pivotal Dominant Bacteria Ratio and Metabolites Related to Healthy Body Index Revealed by Intestinal Microbiome and Metabolomics

Various body indexes, especially body fat percentage (BFP), are widely used as effective indicators to measure our health. BFP is used in medicine to assess obesity, which is a body fat mass disorder accompanied with changes of the gut microbiota. However, the relationship between BFP and the gut microbiota has not been studied so far. To address this problem, we examined how gut microbiota and metabolome associated with body indices in healthy people. Microbial and metabolomics data based on 16S rDNA sequencing and LC-MS were obtained from stool samples of 20 healthy adults. Bioinformatics analysis was performed to explore the correlations between the body indices and gut microbial characteristics. Significantly different microbes were further validated via qPCR. Differential characteristics were filtered by building machine learning models to predict body status. Our data showed that abundance of Prevotella and the Prevotella/Bacteroides (P/B) ratio in the gut were markedly higher in high-BFP individuals than in low-BFP individuals. Microbial and metabolomics data consistently suggested significant differences in fatty acid metabolism in stool samples from the two groups. The P/B ratio and fatty acids are discriminative for people with different index levels by cross validation tests with machine learning models. These results suggest using Prevotella and fecal fatty acids as predictors may offer an alternative method for evaluating health status or weight loss.

SUPPLEMENTARY INFORMATION

The online version contains supplementary material available at 10.1007/s12088-021-00989-5.

Mass spectrometry-based metabolomics in microbiome investigations

Microbiotas are a malleable part of ecosystems, including the human ecosystem. Microorganisms affect not only the chemistry of their specific niche, such as the human gut, but also the chemistry of distant environments, such as other parts of the body. Mass spectrometry-based metabolomics is one of the key technologies to detect and identify the small molecules produced by the human microbiota, and to understand the functional role of these microbial metabolites. This Review provides a foundational introduction to common forms of untargeted mass spectrometry and the types of data that can be obtained in the context of microbiome analysis. Data analysis remains an obstacle; therefore, the emphasis is placed on data analysis approaches and integrative analysis, including the integration of microbiome sequencing data.

TransDiscovery: Discovering Biotransformation from Human Microbiota by Integrating Metagenomic and Metabolomic Data

The human microbiome is a complex community of microorganisms, their enzymes, and the molecules they produce or modify. Recent studies show that imbalances in human microbial ecosystems can cause disease. Our microbiome affects our health through the products of biochemical reactions catalyzed by microbial enzymes (microbial biotransformations). Despite their significance, currently, there are no systematic strategies for identifying these chemical reactions, their substrates and molecular products, and their effects on health and disease. We present TransDiscovery, a computational algorithm that integrates molecular networks (connecting related molecules with similar mass spectra), association networks (connecting co-occurring molecules and microbes) and knowledge bases of microbial enzymes to discover microbial biotransformations, their substrates, and their products. After searching the metabolomics and metagenomics data from the American Gut Project and the Global Foodomic Project, TranDiscovery identified 17 potentially novel biotransformations from the human gut microbiome, along with the corresponding microbial species, substrates, and products.

The state of the art in plant lipidomics

Lipids are a group of compounds with diverse structures that perform several important functions in plants. To unravel and better understand their in vivo functions, plant biologists have been using various lipidomic technologies including liquid-chromatography (LC)-mass spectrometry (MS). However, there are still significant challenges in LC-MS based plant lipidomics, which need to be addressed. In this review, we provide an overview of the key developments in LC-MS based lipidomic approaches to detect and identify plant lipids with emphasis on areas that can be further improved. Given that the cellular lipidome is estimated to contain hundreds of thousands of lipids,^1,2 many of the lipid structures remain to be discovered. Furthermore, the plant lipidome is considered to be significantly more complex compared to that of mammals. Recent technical developments in mass spectrometry have made the detection of novel lipids possible; hence, approaches that can be used for plant lipid discovery are also discussed.

A meta-analysis study of the robustness and universality of gut microbiome-metabolome associations

BACKGROUND

Microbiome-metabolome studies of the human gut have been gaining popularity in recent years, mostly due to accumulating evidence of the interplay between gut microbes, metabolites, and host health. Statistical and machine learning-based methods have been widely applied to analyze such paired microbiome-metabolome data, in the hope of identifying metabolites that are governed by the composition of the microbiome. Such metabolites can be likely modulated by microbiome-based interventions, offering a route for promoting gut metabolic health. Yet, to date, it remains unclear whether findings of microbially associated metabolites in any single study carry over to other studies or cohorts, and how robust and universal are microbiome-metabolites links.

RESULTS

In this study, we addressed this challenge by performing a comprehensive meta-analysis to identify human gut metabolites that can be predicted based on the composition of the gut microbiome across multiple studies. We term such metabolites "robustly well-predicted". To this end, we processed data from 1733 samples from 10 independent human gut microbiome-metabolome studies, focusing initially on healthy subjects, and implemented a machine learning pipeline to predict metabolite levels in each dataset based on the composition of the microbiome. Comparing the predictability of each metabolite across datasets, we found 97 robustly well-predicted metabolites. These include metabolites involved in important microbial pathways such as bile acid transformations and polyamines metabolism. Importantly, however, other metabolites exhibited large variation in predictability across datasets, suggesting a cohort- or study-specific relationship between the microbiome and the metabolite. Comparing taxonomic contributors to different models, we found that some robustly well-predicted metabolites were predicted by markedly different sets of taxa across datasets, suggesting that some microbially associated metabolites may be governed by different members of the microbiome in different cohorts. We finally examined whether models trained on a control group of a given study successfully predicted the metabolite's level in the disease group of the same study, identifying several metabolites where the model was not transferable, indicating a shift in microbial metabolism in disease-associated dysbiosis.

CONCLUSIONS

Combined, our findings provide a better understanding of the link between the microbiome and metabolites and allow researchers to put identified microbially associated metabolites within the context of other studies. Video abstract.

Ruminiclostridium 5, Parabacteroides distasonis, and bile acid profile are modulated by prebiotic diet and associate with facilitated sleep/clock realignment after chronic disruption of rhythms

Chronic disruption of rhythms (CDR) impacts sleep and can result in circadian misalignment of physiological systems which, in turn, is associated with increased disease risk. Exposure to repeated or severe stressors also disturbs sleep and diurnal rhythms. Prebiotic nutrients produce favorable changes in gut microbial ecology, the gut metabolome, and reduce several negative impacts of acute severe stressor exposure, including disturbed sleep, core body temperature rhythmicity, and gut microbial dysbiosis. In light of previous compelling evidence that prebiotic diet broadly reduces negative impacts of acute, severe stressors, we hypothesize that prebiotic diet will also effectively mitigate the negative impacts of chronic disruption of circadian rhythms on physiology and sleep/wake behavior. Male, Sprague Dawley rats were fed diets enriched in prebiotic substrates or calorically matched control chow. After 5 weeks on diet, rats were exposed to CDR (12 h light/dark reversal, weekly for 8 weeks) or remained on undisturbed normal light/dark cycles (NLD). Sleep EEG, core body temperature, and locomotor activity were recorded via biotelemetry in freely moving rats. Fecal samples were collected on experimental days -33, 0 (day of onset of CDR), and 42. Taxonomic identification and relative abundances of gut microbes were measured in fecal samples using 16S rRNA gene sequencing and shotgun metagenomics. Fecal primary, bacterially modified secondary, and conjugated bile acids were measured using liquid chromatography with tandem mass spectrometry (LC-MS/MS). Prebiotic diet produced rapid and stable increases in the relative abundances of Parabacteroides distasonis and Ruminiclostridium 5. Shotgun metagenomics analyses confirmed reliable increases in relative abundances of Parabacteroides distasonis and Clostridium leptum, a member of the Ruminiclostridium genus. Prebiotic diet also modified fecal bile acid profiles; and based on correlational and step-wise regression analyses, Parabacteroides distasonis and Ruminiclostridium 5 were positively associated with each other and negatively associated with secondary and conjugated bile acids. Prebiotic diet, but not CDR, impacted beta diversity. Measures of alpha diversity evenness were decreased by CDR and prebiotic diet prevented that effect. Rats exposed to CDR while eating prebiotic, compared to control diet, more quickly realigned NREM sleep and core body temperature (ClockLab) diurnal rhythms to the altered light/dark cycle. Finally, both cholic acid and Ruminiclostridium 5 prior to CDR were associated with time to realign CBT rhythms to the new light/dark cycle after CDR; whereas both Ruminiclostridium 5 and taurocholic acid prior to CDR were associated with NREM sleep recovery after CDR. These results support our hypothesis and suggest that ingestion of prebiotic substrates is an effective strategy to increase the relative abundance of health promoting microbes, alter the fecal bile acid profile, and facilitate the recovery and realignment of sleep and diurnal rhythms after circadian disruption.

A Cutibacterium acnes antibiotic modulates human skin microbiota composition in hair follicles

The composition of the skin microbiota varies widely among individuals when sampled at the same body site. A key question is which molecular factors determine strain-level variability within sub-ecosystems of the skin microbiota. Here, we used a genomics-guided approach to identify an antibacterial biosynthetic gene cluster in Cutibacterium acnes (formerly Propionibacterium acnes), a human skin commensal bacterium that is widely distributed across individuals and skin sites. Experimental characterization of this biosynthetic gene cluster resulted in identification of a new thiopeptide antibiotic, cutimycin. Analysis of individual human skin hair follicles revealed that cutimycin contributed to the ecology of the skin hair follicle microbiota and helped to reduce colonization of skin hair follicles by Staphylococcus species.

Influence of Age, Sex, and Diet on the Human Fecal Metabolome Investigated by 1H NMR Spectroscopy

The human fecal metabolome is increasingly studied to explore the impact of diet and lifestyle on health and the gut microbiome. However, systematic differences and confounding factors related to age, sex, and diet remain largely unknown. In this study, absolute concentrations of fecal metabolites from 205 healthy Danes (105 males and 100 females, 49 ± 31 years old) were quantified using ¹H NMR spectroscopy and the newly developed SigMa software. The largest systemic variation was found to be highly related to age. Fecal concentrations of short-chain fatty acids (SCFA) were higher in the 18 years old group, while amino acids (AA) were higher in the elderly. Sex-related metabolic differences were weak but significant and mainly related to changes in SCFA. The concentrations of butyric, valeric, propionic, and isovaleric acids were found to be higher in males compared to females. Sex differences were associated with a stronger, possibly masking, effect from differential intake of macronutrients. Dietary fat intake decreased levels of SCFA and AA of both sexes, while carbohydrate intake showed weak correlations with valeric and isovaleric acids in females. This study highlights some possible demographic confounders linked to diet, disease, lifestyle, and microbiota that have to be taken into account when analyzing fecal metabolome data.

A strategy combining solid-phase extraction, multiple mass defect filtering and molecular networking for rapid structural classification and annotation of natural products: characterization of chemical diversity in Citrus aurantium as a case study

Medicinal plants are complex chemical systems containing thousands of secondary metabolites. The rapid classification and characterization of the components in medicinal plants using mass spectrometry (MS) remains an immense challenge. Herein, a novel strategy is presented for MS through the combination of solid-phase extraction (SPE), multiple mass defect filtering (MMDF) and molecular networking (MN). This strategy enables efficient classification and annotation of natural products. When combined with SPE and MMDF, the improved analytical method of MN can perform the rapid annotation of diverse natural products in Citrus aurantium according to the tandem mass spectrometry (MS/MS) fragments. In MN, MS2LDA can be initially applied to recognize substructures of natural products, according to the common fragmentation patterns and neutral losses in multiple MS/MS spectra. MolNetEnhancer was adopted here to obtain chemical classifications provided by ClassyFire. The results suggest that the integrated SPE-MMDF-MN method was capable of rapidly annotating a greater number of natural products from Citrus aurantium than the classical MN strategy alone. Moreover, SPE and MMDF enhanced the effectiveness of MN for annotating, classifying and distinguishing different types of natural products. Our workflow provides the foundation for the automated, high-throughput structural classification and annotation of secondary metabolites with various chemical structures. The developed approach can be widely applied in the analysis of constituents in natural products.

Linking Plant Secondary Metabolites and Plant Microbiomes: A Review

Plant secondary metabolites (PSMs) play many roles including defense against pathogens, pests, and herbivores; response to environmental stresses, and mediating organismal interactions. Similarly, plant microbiomes participate in many of the above-mentioned processes directly or indirectly by regulating plant metabolism. Studies have shown that plants can influence their microbiome by secreting various metabolites and, in turn, the microbiome may also impact the metabolome of the host plant. However, not much is known about the communications between the interacting partners to impact their phenotypic changes. In this article, we review the patterns and potential underlying mechanisms of interactions between PSMs and plant microbiomes. We describe the recent developments in analytical approaches and methods in this field. The applications of these new methods and approaches have increased our understanding of the relationships between PSMs and plant microbiomes. Though the current studies have primarily focused on model organisms, the methods and results obtained so far should help future studies of agriculturally important plants and facilitate the development of methods to manipulate PSMs-microbiome interactions with predictive outcomes for sustainable crop productions.

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.

Evaluation of Sample Preparation Methods for Inter-Laboratory Metabolomics Investigation of Streptomyces lividans TK24

In the past two decades, metabolomics has proved to be a valuable tool with many potential applications in different areas of science. However, there are still some challenges that need to be addressed, particularly for multicenter studies. These challenges are mainly attributed to various sources of fluctuation and unwanted variations that can be introduced at pre-analytical, analytical, and/or post-analytical steps of any metabolomics experiment. Thus, this study aimed at using Streptomyces lividans TK24 as the model organism in a cross-laboratory experiment in Manchester and Leuven to evaluate the reproducibility of a standard sample preparation method, and determine the optimal sample format (cell extract or quenched biomass) required to preserve the metabolic profile of the cells during cross-lab sample transportation and storage. Principal component analysis (PCA) scores plot of the gas chromatography-mass spectrometry (GC-MS) data from both laboratories displayed clear growth-dependent clustering patterns which was in agreement with the Procrustes analysis findings. In addition, the data generated in Manchester displayed tight clustering of cell pellets (quenched biomass) and metabolite extracts, confirming the stability of both sample formats during the transportation and storage period.

Microbiome Understanding in Maternity Study (MUMS), an Australian prospective longitudinal cohort study of maternal and infant microbiota: study protocol

INTRODUCTION

Pregnancy induces significant physiological and cardiometabolic changes, and is associated with alterations in the maternal microbiota. Increasing rates of prepregnancy obesity, metabolic abnormalities and reduced physical activity, all impact negatively on the microbiota causing an imbalance between the commensal microorganisms (termed dysbiosis), which may drive complications, such as gestational diabetes or hypertensive disorders. Considerable work is needed to define the inter-relationships between the microbiome, nutrition, physical activity and pregnancy outcomes. The role of the microbiota during pregnancy remains unclear. The aim of the study is to define microbiota signatures longitudinally throughout pregnancy and the first year post birth, and to identify key clinical and environmental variables that shape the female microbiota profile during and following pregnancy.

METHODS AND ANALYSIS

The Microbiome Understanding in Maternity Study (MUMS) is an Australian prospective longitudinal cohort study involving 100 mother-infant pairs. Women are enrolled in their first trimester and followed longitudinally. Assessment occurs at <13+0, 20+0-24+6 and 32+0-36+6 weeks gestation, birth and 6 weeks, 6 months and 12 months postpartum. At each assessment, self-collected oral, vaginal and faecal samples are collected with an additional postpartum skin swab and breastmilk sample. Each infant will have oral, faecal and skin swab samples collected. Measurements include anthropometrics, body composition, blood pressure, serum hormonal and metabolic parameters and vaginal pH. Dietary intake, physical activity and psychological state will be assessed using validated self-report questionnaires, and pregnancy and infant outcomes recorded. Parametric and non-parametric hypothesis tests will be used to test the association between high-risk and low-risk pregnancies and their outcomes.

ETHICS AND DISSEMINATION

The study received the following approval: South Eastern Sydney Local Health District Research Ethics Committee (17/293 (HREC/17/POWH/605). Results will be made available to the participants of MUMS, their families and the funding bodies; in the form of a summary document. Results for the greater maternity care community and other researchers will be disseminated through conferences, local, national and international presentations and peer-reviewed publications.

TRIAL REGISTRATION NUMBER

ACTRN12618000471280 (prospectively registered).

Integration of genomics, metagenomics, and metabolomics to identify interplay between susceptibility alleles and microbiota in adenoma initiation

Background

Colorectal cancer (CRC) is a multifactorial disease resulting from both genetic predisposition and environmental factors including the gut microbiota (GM), but deciphering the influence of genetic variants, environmental variables, and interactions with the GM is exceedingly difficult. We previously observed significant differences in intestinal adenoma multiplicity between C57BL/6 J-Apc^Min (B6-Min/J) from The Jackson Laboratory (JAX), and original founder strain C57BL/6JD-Apc^Min (B6-Min/D) from the University of Wisconsin.

Methods

To resolve genetic and environmental interactions and determine their contributions we utilized two genetically inbred, independently isolated Apc^Min mouse colonies that have been separated for over 20 generations. Whole genome sequencing was used to identify genetic variants unique to the two substrains. To determine the influence of genetic variants and the impact of differences in the GM on phenotypic variability, we used complex microbiota targeted rederivation to generate two Apc mutant mouse colonies harboring complex GMs from two different sources (GMJAX originally from JAX or GMHSD originally from Envigo), creating four Apc^Min groups. Untargeted metabolomics were used to characterize shifts in the fecal metabolite profile based on genetic variation and differences in the GM.

Results

WGS revealed several thousand high quality variants unique to the two substrains. No homozygous variants were present in coding regions, with the vast majority of variants residing in noncoding regions. Host genetic divergence between Min/J and Min/D and the complex GM additively determined differential adenoma susceptibility. Untargeted metabolomics revealed that both genetic lineage and the GM collectively determined the fecal metabolite profile, and that each differentially regulates bile acid (BA) metabolism. Metabolomics pathway analysis facilitated identification of a functionally relevant private noncoding variant associated with the bile acid transporter Fatty acid binding protein 6 (Fabp6). Expression studies demonstrated differential expression of Fabp6 between Min/J and Min/D, and the variant correlates with adenoma multiplicity in backcrossed mice.

Conclusions

We found that both genetic variation and differences in microbiota influences the quantitiative adenoma phenotype in Apc^Min mice. These findings demonstrate how the use of metabolomics datasets can aid as a functional genomic tool, and furthermore illustrate the power of a multi-omics approach to dissect complex disease susceptibility of noncoding variants.

Analysis of Serotonin in Human Feces Using Solid Phase Extraction and Column-Switching LC-MS/MS

Serotonin, an important neurotransmitter, is produced mainly in intestines, and serotonin levels in feces can be an indicator of the intestinal environment. Human feces, however, contain a large amount of contaminants, which vary widely owing to food contents and the intestinal environment, and these contaminants would be expected to interfere with the determination of serotonin levels in human feces. To remove these contaminants and determine serotonin levels, we developed a new method using solid phase extraction (SPE) and column-switching LC-MS/MS. Serotonin, labeled with a stable isotope, was added to human feces samples prior to SPE as an internal standard to correct for individual differences in matrix effects. The recovery rate for SPE was 55.9–81.0% (intraday) and 56.5–78.1% (interday) for feces from two subjects. We analyzed 220 fecal samples from 96 subjects including 76 pregnant and post-delivery women. The endogenous serotonin content per unit weight of dried feces was 0.09–14.13 ng/mg for pregnant and post-delivery women and 0.30–9.93 ng/mg for the remaining subjects.

Regulation of endocrine systems by the microbiome: Perspectives from comparative animal models

The microbiome regulates endocrine systems and influences many aspects of hormone signaling. Using examples from different animal taxa, we highlight the state of the science in microbiome research as it relates to endocrinology and endocrine disruption research. Using a comparative approach discussing fish, birds, and mammals, we demonstrate the bidirectional interaction between microbiota and hormone systems, presenting concepts that include (1) gastrointestinal microbiome regulation of the neuroendocrine feeding axis; (2) stress hormones and microbial communities; (3) the role of site-specific microbiota in animal reproduction; (4) microbiome effects on the neuroendocrine systems and behavior; and (5) novel mechanisms of endocrine disruption through the microbiome. This mini-review demonstrates that hormones can directly affect the richness and diversity of microbiota and conversely, microbiota can influence hormone production and mediate their functions in animals. In addition, microbiota can influence the action of a diverse range of neurotransmitters and neuropeptides in the central nervous system, which can lead to behavioral disruptions. As many animals have species-specific reproductive behaviors, it is important to understand how shifts in the microbiota relate to these complex interactions between sexes. This is especially important for captive animals on specialized diets, and there are significant implications for microbiome research in conservation and reproductive biology. For example, microbial metabolites may modify motility of gametes or modulate hormone-receptor interactions in reproductive tissues. Thus, efforts to incorporate metabolomics into the science of microbiome-endocrine relationships, both those produced by the host and those generated from microbial metabolism, are increasingly needed. These concepts have fostered an exciting emerging era in comparative endocrinology.

Dual mass spectrometry as a tool to improve annotation and quantification in targeted plasma lipidomics

INTRODUCTION

High quality data, based on reliable quantification and clear identification of the reported lipid species, are required for the clinical translation of human plasma lipidomic studies.

OBJECTIVE

Lipid quantification can be efficiently performed on triple quadrupole (QqQ) mass spectrometers in targeted multiple reaction monitoring (MRM) mode. However, a series of issues can be encountered when aiming at unambiguous identification and accurate quantification, including (i) resolving peaks of polyunsaturated species, (ii) discriminating between plasmanyl-, plasmenyl- and odd chain species and (iii) resolving the isotopic overlap between co-eluting lipid species.

METHODS

As a practical tool to improve the quality of targeted lipidomics studies, we applied a Dual MS platform by simultaneously coupling a reversed-phase liquid chromatography separation to a QqQ and a quadrupole-time of flight (Q-ToF) mass spectrometers. In one single experiment, this platform allows to correctly identify, by high-resolution MS and MS/MS, the peaks that are quantified by MRM.

RESULTS

As proof of concept, we applied the platform on glycerophosphocholines (GPCs) and sphingomyelins (SMs), which are highly abundant in human plasma and play crucial roles in various physiological functions. Our results demonstrated that Dual MS could provide a higher level of confidence in the identification and quantification of GPCs and SMs in human plasma. The same approach can also be applied to improve the study of other lipid classes and expanded for the identification of novel lipid molecular species.

CONCLUSIONS

This methodology might have a great potential to achieve a better specificity in the quantification of lipids by targeted lipidomics in high-throughput studies.

Fecal Metabolites As Non-Invasive Biomarkers of Gut Diseases

Recent studies have shown the importance of the human intestinal microbiome in maintaining a healthy gastrointestinal tract, as well as in the development of pathological processes. The intestinal microbiome manifests itself primarily as fecal metabolites. In the past decade, there has been growing interest in studying its composition, which for the most part had to do with the possibility of using the metabolomic analysis in clinical diagnosis. In contrast to the comprehensive description of blood serum, urine, saliva, and cerebrospinal fluid metabolites, data on fecal metabolites is sparse. Despite the instrumental and methodological achievements in the metabolomic analysis in general, the analysis of fecal metabolome remains less well developed, mainly because of the inhomogeneity of its composition and the lack of standardized methods for collecting, processing, and analyzing fecal samples. This review summarizes data on methods for studying and describing various groups of fecal metabolites. It also assesses their potential as tools in the diagnosis of gastrointestinal diseases.

Gut microbiota-derived metabolites as key actors in inflammatory bowel disease

A key role of the gut microbiota in the establishment and maintenance of health, as well as in the pathogenesis of disease, has been identified over the past two decades. One of the primary modes by which the gut microbiota interacts with the host is by means of metabolites, which are small molecules that are produced as intermediate or end products of microbial metabolism. These metabolites can derive from bacterial metabolism of dietary substrates, modification of host molecules, such as bile acids, or directly from bacteria. Signals from microbial metabolites influence immune maturation, immune homeostasis, host energy metabolism and maintenance of mucosal integrity. Alterations in the composition and function of the microbiota have been described in many studies on IBD. Alterations have also been described in the metabolite profiles of patients with IBD. Furthermore, specific classes of metabolites, notably bile acids, short-chain fatty acids and tryptophan metabolites, have been implicated in the pathogenesis of IBD. This Review aims to define the key classes of microbial-derived metabolites that are altered in IBD, describe the pathophysiological basis of these associations and identify future targets for precision therapeutic modulation.

Dietary prebiotics alter novel microbial dependent fecal metabolites that improve sleep

Dietary prebiotics produce favorable changes in the commensal gut microbiome and reduce host vulnerability to stress-induced disruptions in complex behaviors such as sleep. The mechanisms for how prebiotics modulate stress physiology remain unclear; however, emerging evidence suggests that gut microbes and their metabolites may play a role. This study tested if stress and/or dietary prebiotics (Test diet) alter the fecal metabolome; and explored if these changes were related to sleep and/or gut microbial alpha diversity. Male F344 rats on either Test or Control diet were instrumented for electroencephalography biotelemetry measures of sleep/wake. After 5 weeks on diet, rats were either stressed or remained in home cages. Based on untargeted mass spectrometry and 16S rRNA gene sequencing, both stress and Test diet altered the fecal metabolome/microbiome. In addition, Test diet prevented the stress-induced reduction in microbial alpha diversity based on PD_Whole_Tree, which has been previously published. Network propagation analysis revealed that stress increased members of the neuroactive steroidal pregnane molecular family; and that Test diet reduced this effect. We also discovered links between sleep, alpha diversity, and pyrimidine, secondary bile acid, and neuroactive glucocorticoid/pregnanolone-type steroidal metabolites. These results reveal novel microbial-dependent metabolites that may modulate stress physiology and sleep.

Emerging Priorities for Microbiome Research

Microbiome research has increased dramatically in recent years, driven by advances in technology and significant reductions in the cost of analysis. Such research has unlocked a wealth of data, which has yielded tremendous insight into the nature of the microbial communities, including their interactions and effects, both within a host and in an external environment as part of an ecological community. Understanding the role of microbiota, including their dynamic interactions with their hosts and other microbes, can enable the engineering of new diagnostic techniques and interventional strategies that can be used in a diverse spectrum of fields, spanning from ecology and agriculture to medicine and from forensics to exobiology. From June 19-23 in 2017, the NIH and NSF jointly held an Innovation Lab on Quantitative Approaches to Biomedical Data Science Challenges in our Understanding of the Microbiome. This review is inspired by some of the topics that arose as priority areas from this unique, interactive workshop. The goal of this review is to summarize the Innovation Lab's findings by introducing the reader to emerging challenges, exciting potential, and current directions in microbiome research. The review is broken into five key topic areas: (1) interactions between microbes and the human body, (2) evolution and ecology of microbes, including the role played by the environment and microbe-microbe interactions, (3) analytical and mathematical methods currently used in microbiome research, (4) leveraging knowledge of microbial composition and interactions to develop engineering solutions, and (5) interventional approaches and engineered microbiota that may be enabled by selectively altering microbial composition. As such, this review seeks to arm the reader with a broad understanding of the priorities and challenges in microbiome research today and provide inspiration for future investigation and multi-disciplinary collaboration.

Fecal Metabolomic Signatures in Colorectal Adenoma Patients Are Associated with Gut Microbiota and Early Events of Colorectal Cancer Pathogenesis

Colorectal adenomas are precancerous lesions of colorectal cancer (CRC) that offer a means of viewing the events key to early CRC development. A number of studies have investigated the changes and roles of gut microbiota in adenoma and carcinoma development, highlighting its impact on carcinogenesis. However, there has been less of a focus on the gut metabolome, which mediates interactions between the host and gut microbes. Here, we investigated metabolomic profiles of stool samples from patients with advanced adenoma (n = 102), matched controls (n = 102), and patients with CRC (n = 36). We found that several classes of bioactive lipids, including polyunsaturated fatty acids, secondary bile acids, and sphingolipids, were elevated in the adenoma patients compared to the controls. Most such metabolites showed directionally consistent changes in the CRC patients, suggesting that those changes may represent early events of carcinogenesis. We also examined gut microbiome-metabolome associations using gut microbiota profiles in these patients. We found remarkably strong overall associations between the microbiome and metabolome data and catalogued a list of robustly correlated pairs of bacterial taxa and metabolomic features which included signatures of adenoma. Our findings highlight the importance of gut metabolites, and potentially their interplay with gut microbes, in the early events of CRC pathogenesis.IMPORTANCE Colorectal adenomas are precursors of CRC. Recently, the gut microbiota, i.e., the collection of microbes residing in our gut, has been recognized as a key player in CRC development. There have been a number of gut microbiota profiling studies for colorectal adenoma and CRC; however, fewer studies have considered the gut metabolome, which serves as the chemical interface between the host and gut microbiota. Here, we conducted a gut metabolome profiling study of colorectal adenoma and CRC and analyzed the metabolomic profiles together with paired microbiota composition profiles. We found several chemical signatures of colorectal adenoma that were associated with some gut microbes and potentially indicative of future CRC. This study highlights potential early-driver metabolites in CRC pathogenesis and guides further targeted experiments and thus provides an important stepping stone toward developing better CRC prevention strategies.

Finding intestinal fortitude: Integrating the microbiome into a holistic view of depression mechanisms, treatment, and resilience

Depression affects at least 322 million people globally, or approximately 4.4% of the world's population. While the earnestness of researchers and clinicians to understand and treat depression is not waning, the number of individuals suffering from depression continues to increase over and above the rate of global population growth. There is a sincere need for a paradigm shift. Research in the past decade is beginning to take a more holistic approach to understanding depression etiology and treatment, integrating multiple body systems into whole-body conceptualizations of this mental health affliction. Evidence supports the hypothesis that the gut microbiome, or the collective trillions of microbes inhabiting the gastrointestinal tract, is an important factor determining both the risk of development of depression and persistence of depressive symptoms. This review discusses recent advances in both rodent and human research that explore bidirectional communication between the gut microbiome and the immune, endocrine, and central nervous systems implicated in the etiology and pathophysiology of depression. Through interactions with circulating inflammatory markers and hormones, afferent and efferent neural systems, and other, more niche, pathways, the gut microbiome can affect behavior to facilitate the development of depression, exacerbate current symptoms, or contribute to treatment and resilience. While the challenge of depression may be the direst mental health crisis of our age, new discoveries in the gut microbiome, when integrated into a holistic perspective, hold great promise for the future of positive mental health.

Describing the fecal metabolome in cryogenically collected samples from healthy participants

The chemical composition of feces plays an important role in human metabolism. Metabolomics and lipidomics are valuable tools for screening the metabolite composition in feces. Here we set out to describe fecal metabolite composition in healthy participants in frozen stools. Frozen stool samples were collected from 10 healthy volunteers and cryogenically drilled in four areas along the specimen. Polar metabolites were analyzed using derivatization followed by two-dimensional gas chromatography and time of flight mass spectrometry. Lipids were detected using ultra high-performance liquid chromatography coupled with quadruple time-of-flight mass spectrometry. 2326 metabolic features were detected. Out of a total of 298 metabolites that were annotated we report here 185 that showed a technical variation of x < 30%. These metabolites included amino acids, fatty acid derivatives, carboxylic acids and phenolic compounds. Lipids predominantly belonged to the groups of diacylglycerols, triacylglycerols and ceramides. Metabolites varied between sampling areas, some were broadly homogeneous, others varied 80%. A LASSO-computed network using metabolites present in all areas showed two main clusters describing the system, DAG lipids and phenyllactic acid. In feces from healthy participants, the main groups detected were phenolic compounds, ceramides, diacylglycerols and triacylglycerols.

Defining and Evaluating Microbial Contributions to Metabolite Variation in Microbiome-Metabolome Association Studies

Correlation-based analysis of paired microbiome-metabolome data sets is becoming a widespread research approach, aiming to comprehensively identify microbial drivers of metabolic variation. To date, however, the limitations of this approach and other microbiome-metabolome analysis methods have not been comprehensively evaluated. To address this challenge, we have introduced a mathematical framework to quantify the contribution of each taxon to metabolite variation based on uptake and secretion fluxes. We additionally used a multispecies metabolic model to simulate simplified gut communities, generating idealized microbiome-metabolome data sets. We then compared observed taxon-metabolite correlations in these data sets to calculated ground truth taxonomic contribution values. We found that in simulations of both a representative simple 10-species community and complex human gut microbiota, correlation-based analysis poorly identified key contributors, with an extremely low predictive value despite the idealized setting. We further demonstrate that the predictive value of correlation analysis is strongly influenced by both metabolite and taxon properties, as well as by exogenous environmental variation. We finally discuss the practical implications of our findings for interpreting microbiome-metabolome studies.IMPORTANCE Identifying the key microbial taxa responsible for metabolic differences between microbiomes is an important step toward understanding and manipulating microbiome metabolism. To achieve this goal, researchers commonly conduct microbiome-metabolome association studies, comprehensively measuring both the composition of species and the concentration of metabolites across a set of microbial community samples and then testing for correlations between microbes and metabolites. Here, we evaluated the utility of this general approach by first developing a rigorous mathematical definition of the contribution of each microbial taxon to metabolite variation and then examining these contributions in simulated data sets of microbial community metabolism. We found that standard correlation-based analysis of our simulated microbiome-metabolome data sets can identify true contributions with very low predictive value and that its performance depends strongly on specific properties of both metabolites and microbes, as well as on those of the surrounding environment. Combined, our findings can guide future interpretation and validation of microbiome-metabolome studies.

A Metabolome- and Metagenome-Wide Association Network Reveals Microbial Natural Products and Microbial Biotransformation Products from the Human Microbiota

The human microbiome consists of thousands of different microbial species, and tens of thousands of bioactive small molecules are associated with them. These associated molecules include the biosynthetic products of microbiota and the products of microbial transformation of host molecules, dietary components, and pharmaceuticals. The existing methods for characterization of these small molecules are currently time consuming and expensive, and they are limited to the cultivable bacteria. Here, we propose a method for detecting microbiota-associated small molecules based on the patterns of cooccurrence of molecular and microbial features across multiple microbiomes. We further map each molecule to the clade in a phylogenetic tree that is responsible for its production/transformation. We applied our proposed method to the tandem mass spectrometry and metagenomics data sets collected by the American Gut Project and to microbiome isolates from cystic fibrosis patients and discovered the genes in the human microbiome responsible for the production of corynomycolenic acid, which serves as a ligand for human T cells and induces a specific immune response against infection. Moreover, our method correctly associated pseudomonas quinolone signals, tyrvalin, and phevalin with their known biosynthetic gene clusters.IMPORTANCE Experimental advances have enabled the acquisition of tandem mass spectrometry and metagenomics sequencing data from tens of thousands of environmental/host-oriented microbial communities. Each of these communities contains hundreds of microbial features (corresponding to microbial species) and thousands of molecular features (corresponding to microbial natural products). However, with the current technology, it is very difficult to identify the microbial species responsible for the production/biotransformation of each molecular feature. Here, we develop association networks, a new approach for identifying the microbial producer/biotransformer of natural products through cooccurrence analysis of metagenomics and mass spectrometry data collected on multiple microbiomes.

The impact of skin care products on skin chemistry and microbiome dynamics

BACKGROUND

Use of skin personal care products on a regular basis is nearly ubiquitous, but their effects on molecular and microbial diversity of the skin are unknown. We evaluated the impact of four beauty products (a facial lotion, a moisturizer, a foot powder, and a deodorant) on 11 volunteers over 9 weeks.

RESULTS

Mass spectrometry and 16S rRNA inventories of the skin revealed decreases in chemical as well as in bacterial and archaeal diversity on halting deodorant use. Specific compounds from beauty products used before the study remain detectable with half-lives of 0.5-1.9 weeks. The deodorant and foot powder increased molecular, bacterial, and archaeal diversity, while arm and face lotions had little effect on bacterial and archaeal but increased chemical diversity. Personal care product effects last for weeks and produce highly individualized responses, including alterations in steroid and pheromone levels and in bacterial and archaeal ecosystem structure and dynamics.

CONCLUSIONS

These findings may lead to next-generation precision beauty products and therapies for skin disorders.

Human Gut Microbiota from Autism Spectrum Disorder Promote Behavioral Symptoms in Mice

Autism spectrum disorder (ASD) manifests as alterations in complex human behaviors including social communication and stereotypies. In addition to genetic risks, the gut microbiome differs between typically developing (TD) and ASD individuals, though it remains unclear whether the microbiome contributes to symptoms. We transplanted gut microbiota from human donors with ASD or TD controls into germ-free mice and reveal that colonization with ASD microbiota is sufficient to induce hallmark autistic behaviors. The brains of mice colonized with ASD microbiota display alternative splicing of ASD-relevant genes. Microbiome and metabolome profiles of mice harboring human microbiota predict that specific bacterial taxa and their metabolites modulate ASD behaviors. Indeed, treatment of an ASD mouse model with candidate microbial metabolites improves behavioral abnormalities and modulates neuronal excitability in the brain. We propose that the gut microbiota regulates behaviors in mice via production of neuroactive metabolites, suggesting that gut-brain connections contribute to the pathophysiology of ASD.

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.

Gut microbiome structure and metabolic activity in inflammatory bowel disease

The inflammatory bowel diseases (IBDs), which include Crohn's disease (CD) and ulcerative colitis (UC), are multifactorial chronic conditions of the gastrointestinal tract. While IBD has been associated with dramatic changes in the gut microbiota, changes in the gut metabolome-the molecular interface between host and microbiota-are less well understood. To address this gap, we performed untargeted metabolomic and shotgun metagenomic profiling of cross-sectional stool samples from discovery (n = 155) and validation (n = 65) cohorts of CD, UC and non-IBD control patients. Metabolomic and metagenomic profiles were broadly correlated with faecal calprotectin levels (a measure of gut inflammation). Across >8,000 measured metabolite features, we identified chemicals and chemical classes that were differentially abundant in IBD, including enrichments for sphingolipids and bile acids, and depletions for triacylglycerols and tetrapyrroles. While > 50% of differentially abundant metabolite features were uncharacterized, many could be assigned putative roles through metabolomic 'guilt by association' (covariation with known metabolites). Differentially abundant species and functions from the metagenomic profiles reflected adaptation to oxidative stress in the IBD gut, and were individually consistent with previous findings. Integrating these data, however, we identified 122 robust associations between differentially abundant species and well-characterized differentially abundant metabolites, indicating possible mechanistic relationships that are perturbed in IBD. Finally, we found that metabolome- and metagenome-based classifiers of IBD status were highly accurate and, like the vast majority of individual trends, generalized well to the independent validation cohort. Our findings thus provide an improved understanding of perturbations of the microbiome-metabolome interface in IBD, including identification of many potential diagnostic and therapeutic targets.

Microbiome 101: Studying, Analyzing, and Interpreting Gut Microbiome Data for Clinicians

Advances in technical capabilities for reading complex human microbiomes are leading to an explosion of microbiome research, leading in turn to intense interest among clinicians in applying these techniques to their patients. In this review, we discuss the content of the human microbiome, including intersubject and intrasubject variability, considerations of study design including important confounding factors, and different methods in the laboratory and on the computer to read the microbiome and its resulting gene products and metabolites. We highlight several common pitfalls for clinicians, including the expectation that an individual's microbiome will be stable, that diet can induce rapid changes that are large compared with the differences among subjects, that everyone has essentially the same core stool microbiome, and that different laboratory and computational methods will yield essentially the same results. We also highlight the current limitations and future promise of these techniques, with the expectation that an understanding of these considerations will help accelerate the path toward routine clinical application of these techniques developed in research settings.