Workshop report: Toward the development of a human whole stool reference material for metabolomic and metagenomic gut microbiome measurements

The microbiota-gut-brain-immune interface in the pathogenesis of neuroinflammatory diseases: a narrative review of the emerging literature

Importance

Research is beginning to elucidate the sophisticated mechanisms underlying the microbiota-gut-brain-immune interface, moving from primarily animal models to human studies. Findings support the dynamic relationships between the gut microbiota as an ecosystem (microbiome) within an ecosystem (host) and its intersection with the host immune and nervous systems. Adding this to the effects on epigenetic regulation of gene expression further complicates and strengthens the response. At the heart is inflammation, which manifests in a variety of pathologies including neurodegenerative diseases such as Alzheimer's disease, Parkinson's disease, and Multiple Sclerosis (MS).

Observations

Generally, the research to date is limited and has focused on bacteria, likely due to the simplicity and cost-effectiveness of 16s rRNA sequencing, despite its lower resolution and inability to determine functional ability/alterations. However, this omits all other microbiota including fungi, viruses, and phages, which are emerging as key members of the human microbiome. Much of the research has been done in pre-clinical models and/or in small human studies in more developed parts of the world. The relationships observed are promising but cannot be considered reliable or generalizable at this time. Specifically, causal relationships cannot be determined currently. More research has been done in Alzheimer's disease, followed by Parkinson's disease, and then little in MS. The data for MS is encouraging despite this.

Conclusions and relevance

While the research is still nascent, the microbiota-gut-brain-immune interface may be a missing link, which has hampered our progress on understanding, let alone preventing, managing, or putting into remission neurodegenerative diseases. Relationships must first be established in humans, as animal models have been shown to poorly translate to complex human physiology and environments, especially when investigating the human gut microbiome and its relationships where animal models are often overly simplistic. Only then can robust research be conducted in humans and using mechanistic model systems.

Microbiome function and neurodevelopment in Black infants: vitamin B12 emerges as a key factor

The early life gut microbiome affects the developing brain, and therefore may serve as a target to support neurodevelopment of children living in stressful and under-resourced environments, such as Black youth living on the South Side of Chicago, for whom we observe racial disparities in health. Microbiome compositions/functions key to multiple neurodevelopmental facets have not been studied in Black children, a vulnerable population due to racial disparities in health; thus, a subsample of Black infants living in urban, low-income neighborhoods whose mothers participated in a prenatal nutrition study were recruited for testing associations between composition and function of the gut microbiome (16S rRNA gene sequencing, shotgun metagenomics, and targeted metabolomics of fecal samples) and neurodevelopment (developmental testing, maternal report of temperament, and observed stress regulation). Two microbiome community types, defined by high Lachnospiraceae or Enterobacteriaceae abundance, were discovered in this cohort from 16S rRNA gene sequencing analysis; the Enterobacteriaceae-dominant community type was significantly negatively associated with cognition and language scores, specifically in male children. Vitamin B12 biosynthesis emerged as a key microbiome function from shotgun metagenomics sequencing analysis, showing positive associations with all measured developmental skills (i.e., cognition, language, motor, surgency, effortful control, and observed stress regulation). Blautia spp. also were identified as substantial contributors of important microbiome functions, including vitamin B12 biosynthesis and related vitamin B12-dependent microbiome functions, anti-inflammatory microbial surface antigens, competitive mechanisms against pathobionts, and production of antioxidants. The results are promising with respect to the potential for exploring therapeutic candidates, such as vitamin B12 nutritional or Blautia spp. probiotic supplementation, to support the neurodevelopment of infants at risk for experiencing racial disparities in health.

Fecal Metagenomics to Identify Biomarkers of Food Intake in Healthy Adults: Findings from Randomized, Controlled, Nutrition Trials

BACKGROUND

Undigested components of the human diet affect the composition and function of the microorganisms present in the gastrointestinal tract. Techniques like metagenomic analyses allow researchers to study functional capacity, thus revealing the potential of using metagenomic data for developing objective biomarkers of food intake.

OBJECTIVES

As a continuation of our previous work using 16S and metabolomic datasets, we aimed to utilize a computationally intensive, multivariate, machine-learning approach to identify fecal KEGG (Kyoto encyclopedia of genes and genomes) Orthology (KO) categories as biomarkers that accurately classify food intake.

METHODS

Data were aggregated from 5 controlled feeding studies that studied the individual impact of almonds, avocados, broccoli, walnuts, barley, and oats on the adult gastrointestinal microbiota. Deoxyribonucleic acid from preintervention and postintervention fecal samples underwent shotgun genomic sequencing. After preprocessing, sequences were aligned and functionally annotated with Double Index AlignMent Of Next-generation sequencing Data v2.0.11.149 and MEtaGenome ANalyzer v6.12.2, respectively. After the count normalization, the log of the fold change ratio for resulting KOs between pre- and postintervention of the treatment group against its corresponding control was utilized to conduct differential abundance analysis. Differentially abundant KOs were used to train machine-learning models examining potential biomarkers in both single-food and multi-food models.

RESULTS

We identified differentially abundant KOs in the almond (n = 54), broccoli (n = 2474), and walnut (n = 732) groups (q < 0.20), which demonstrated classification accuracies of 80%, 87%, and 86% for the almond, broccoli, and walnut groups using a random forest model to classify food intake into each food group's respective treatment and control arms, respectively. The mixed-food random forest achieved 81% accuracy.

CONCLUSIONS

Our findings reveal promise in utilizing fecal metagenomics to objectively complement self-reported measures of food intake. Future research on various foods and dietary patterns will expand these exploratory analyses for eventual use in feeding study compliance and clinical settings.

Differences in the Stool Metabolome between Vegans and Omnivores: Analyzing the NIST Stool Reference Material

To gain confidence in results of omic-data acquisitions, methods must be benchmarked using validated quality control materials. We report data combining both untargeted and targeted metabolomics assays for the analysis of four new human fecal reference materials developed by the U.S. National Institute of Standards and Technologies (NIST) for metagenomics and metabolomics measurements. These reference grade test materials (RGTM) were established by NIST based on two different diets and two different samples treatments, as follows: firstly, homogenized fecal matter from subjects eating vegan diets, stored and submitted in either lyophilized (RGTM 10162) or aqueous form (RGTM 10171); secondly, homogenized fecal matter from subjects eating omnivore diets, stored and submitted in either lyophilized (RGTM 10172) or aqueous form (RGTM 10173). We used four untargeted metabolomics assays (lipidomics, primary metabolites, biogenic amines and polyphenols) and one targeted assay on bile acids. A total of 3563 compounds were annotated by mass spectrometry, including 353 compounds that were annotated in more than one assay. Almost half of all compounds were annotated using hydrophilic interaction chromatography/accurate mass spectrometry, followed by the lipidomics and the polyphenol assays. In total, 910 metabolites were found in at least 4-fold different levels in fecal matter from vegans versus omnivores, specifically for peptides, amino acids and lipids. In comparison, only 251 compounds showed 4-fold differences between lyophilized and aqueous fecal samples, including DG O-34:0 and methionine sulfoxide. A range of diet-specific metabolites were identified to be significantly different between vegans and omnivores, exemplified by citrinin and C17:0-acylcarnitine for omnivores, and curcumin and lenticin for vegans. Bioactive molecules like acyl alpha-hydroxy-fatty acids (AAHFA) were differentially regulated in vegan versus omnivore fecal materials, highlighting the importance of diet-specific reference materials for dietary biomarker studies.

Methods in Nutrition & Gut Microbiome Research: An American Society for Nutrition Satellite Session [13 October 2022]

The microbial cells colonizing the human body form an ecosystem that is integral to the regulation and maintenance of human health. Elucidation of specific associations between the human microbiome and health outcomes is facilitating the development of microbiome-targeted recommendations and treatments (e.g., fecal microbiota transplant; pre-, pro-, and post-biotics) to help prevent and treat disease. However, the potential of such recommendations and treatments to improve human health has yet to be fully realized. Technological advances have led to the development and proliferation of a wide range of tools and methods to collect, store, sequence, and analyze microbiome samples. However, differences in methodology at each step in these analytic processes can lead to variability in results due to the unique biases and limitations of each component. This technical variability hampers the detection and validation of associations with small to medium effect sizes. Therefore, the American Society for Nutrition (ASN) Nutritional Microbiology Group Engaging Members (GEM), sponsored by the Institute for the Advancement of Food and Nutrition Sciences (IAFNS), hosted a satellite session on methods in nutrition and gut microbiome research to review currently available methods for microbiome research, best practices, as well as tools and standards to aid in comparability of methods and results. This manuscript summarizes the topics and research discussed at the session. Consideration of the guidelines and principles reviewed in this session will increase the accuracy, precision, and comparability of microbiome research and ultimately the understanding of the associations between the human microbiome and health.

Fecal Metabolites as Biomarkers for Predicting Food Intake by Healthy Adults

BACKGROUND

The fecal metabolome is affected by diet and includes metabolites generated by human and microbial metabolism. Advances in -omics technologies and analytic approaches have allowed researchers to identify metabolites and better utilize large data sets to generate usable information. One promising aspect of these advancements is the ability to determine objective biomarkers of food intake.

OBJECTIVES

We aimed to utilize a multivariate, machine learning approach to identify metabolite biomarkers that accurately predict food intake.

METHODS

Data were aggregated from 5 controlled feeding studies in adults that tested the impact of specific foods (almonds, avocados, broccoli, walnuts, barley, and oats) on the gastrointestinal microbiota. Fecal samples underwent GC-MS metabolomic analysis; 344 metabolites were detected in preintervention samples, whereas 307 metabolites were detected postintervention. After removing metabolites that were only detected in either pre- or postintervention and those undetectable in ≥80% of samples in all study groups, changes in 96 metabolites relative concentrations (treatment postintervention minus preintervention) were utilized in random forest models to 1) examine the relation between food consumption and fecal metabolome changes and 2) rank the fecal metabolites by their predictive power (i.e., feature importance score).

RESULTS

Using the change in relative concentration of 96 fecal metabolites, 6 single-food random forest models for almond, avocado, broccoli, walnuts, whole-grain barley, and whole-grain oats revealed prediction accuracies between 47% and 89%. When comparing foods with one another, almond intake was differentiated from walnut intake with 91% classification accuracy.

CONCLUSIONS

Our findings reveal promise in utilizing fecal metabolites as objective complements to certain self-reported food intake estimates. Future research on other foods at different doses and dietary patterns is needed to identify biomarkers that can be applied in feeding study compliance and clinical settings.

Diet-Related and Gut-Derived Metabolites and Health Outcomes: A Scoping Review

We conducted a scoping review to map available evidence about the health impact of gut microbiota-derived metabolites. We searched PubMed and Embase for studies that assessed the health impact of ten metabolites on any health condition: deoxycholate or deoxycholic acid (DCA), lithocholate or lithocholic acid (LCA), glycolithocholate or glycolithocholic acid, glycodeoxycholate or glycodeoxycholic acid, tryptamine, putrescine, d-alanine, urolithins, N-acetylmannosamine, and phenylacetylglutamine. We identified 352 eligible studies with 168,072 participants. Most (326, 92.6%) were case-control studies, followed by cohort studies (14, 4.0%), clinical trials (8, 2.3%), and cross-sectional studies (6, 1.7%). Most studies assessed the following associations: DCA on hepatobiliary disorders (64 studies, 7976 participants), colorectal cancer (19 studies, 7461 participants), and other digestive disorders (27 studies, 2463 participants); LCA on hepatobiliary disorders (34 studies, 4297 participants), colorectal cancers (14 studies, 4955 participants), and other digestive disorders (26 studies, 2117 participants); putrescine on colorectal cancers (16 studies, 94,399 participants) and cancers excluding colorectal and hepatobiliary cancers (42 studies, 4250 participants). There is a need to conduct more prospective studies, including clinical trials. Moreover, we identified metabolites and conditions for which systemic reviews are warranted to characterize the direction and magnitude of metabolite-disease associations.

Identification of diagnostic markers related to fecal and plasma metabolism in primary Sjögren's syndrome

BACKGROUND

Accurate diagnostic techniques for patients with primary Sjögren's syndrome (pSS) are needed. This study aimed to investigate new biomarkers related to fecal and plasma metabolism from pSS patients.

METHODS

The feces and plasma of 21 pSS patients and 18 controls admitted to the Second Hospital of Shanxi Medical University were collected for analysis. Metabolites in feces and plasma were quantified using liquid chromatography-mass spectrometry. The metabolic pathway alterations caused by pSS were studied and the expression of metabolites in the intersecting pathway was analyzed in the feces and plasma of pSS patients. Metabolites that showed the same alterations in feces and plasma in pSS patients were considered as diagnostic markers and receiver operating characteristic curves were generated to analyze the sensitivity of these markers in diagnosing pSS.

RESULTS

There were 114 and 92 upregulated metabolites and 54 and 125 downregulated metabolites in the feces and plasma of pSS patients, respectively. These metabolites were enriched in 8 pathways for feces and 12 pathways for plasma. Arginine biosynthesis, Linoleic acid metabolism, Tyrosine metabolism, Taurine and hypotaurine metabolism were pathways enriched by metabolites in both samples. Twelves metabolites were enriched in the above four pathways, while only 9,10-12,13-Diepoxyoctadecanoate, Tyramine, 9-OxoODE and 2-Hydroxyethanesulfonate showed the same trend. The candidate diagnostic markers were all predictive, with better diagnostic sensitivity in plasma samples.

CONCLUSIONS

9,10-12,13-Diepoxyoctadecanoate, Tyramine, 9-OxoODE, 2-Hydroxyethanesulfonate were metabolism-related diagnostic markers for pSS feces and plasma.

A Commentary on the Potential Use of Oral Microbiome in Prediction, Diagnosis or Prognostics of a Distant Pathology

In health, the oral microbiome is in balance with its host. If this balance is lost, this symbiosis is replaced by dysbiotic microbial communities, which are thought to affect the rest of the body either directly or via metabolites or pro-inflammatory molecules. The association of oral microbiome with general health has led to attempts to use oral microbial biomarkers for the prediction, diagnosis or prognosis of distant pathologies such as colorectal carcinoma or pancreatic cancer. These attempts however have no chance to succeed if the complexity of the oral ecosystem and the interplay of environmental, behavioral and biological factors is not taken into account. Standardized, well-documented oral sample collection procedures together with detailed clinical oral examination and behavioral data are the prerequisites for the successful evaluation of the oral microbiome as a potential biomarker for distant pathologies.

What if using certified reference materials (CRMs) was a requirement to publish in analytical/bioanalytical chemistry journals?

Certified reference materials (CRMs) are routinely used by analytical chemists to validate new analytical methods and to demonstrate the quality of their quantitative measurements. Even though CRMs for trace element and trace organic analysis have been available and widely used for over 50 years, the majority of papers published in analytical chemistry journals do not mention the use of CRMs. What if analytical/bioanalytical chemistry journals required the use of CRMs to publish a paper? This feature article attempts to address this question by providing examples of recent papers that have made exceptional use of CRMs to validate new analytical methods and to describe novel, alternative uses of CRMs that provide new characterization of the CRM. The potential benefits of using a CRM even when it does not have certified values for the analytes of interest are presented.

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.

Characterization and Demonstration of Mock Communities as Control Reagents for Accurate Human Microbiome Community Measurements

Standardization and quality assurance of microbiome community analysis by high-throughput DNA sequencing require widely accessible and well-characterized reference materials. Here, we report on newly developed DNA and whole-cell mock communities to serve as control reagents for human gut microbiota measurements by shotgun metagenomics and 16S rRNA gene amplicon sequencing. The mock communities were formulated as near-even blends of up to 20 bacterial species prevalent in the human gut, span a wide range of genomic guanine-cytosine (GC) contents, and include multiple strains with Gram-positive type cell walls. Through a collaborative study, we carefully characterized the mock communities by shotgun metagenomics, using previously developed standardized protocols for DNA extraction and sequencing library construction. Further, we validated fitness of the mock communities for revealing technically meaningful differences among protocols for DNA extraction and metagenome/16S rRNA gene amplicon library construction. Finally, we used the mock communities to reveal varying performance of metagenome-based taxonomic profilers and the impact of trimming and filtering of sequencing reads on observed species profiles. The latter showed that aggressive preprocessing of reads may result in substantial GC-dependent bias and should thus be carefully evaluated to minimize unintended effects on species abundances. Taken together, the mock communities are expected to support a myriad of applications that rely on well-characterized control reagents, ranging from evaluation and optimization of methods to assessment of reproducibility in interlaboratory studies and routine quality control.

IMPORTANCE Application of high-throughput DNA sequencing has greatly accelerated human microbiome research and its translation into new therapeutic and diagnostic capabilities. Microbiome community analyses results can, however, vary considerably across studies or laboratories, and establishment of measurement standards to improve accuracy and reproducibility has become a priority. The here-developed mock communities, which are available from the NITE Biological Resource Center (NBRC) at the National Institute of Technology and Evaluation (NITE, Japan), provide well-characterized control reagents that allow users to judge the accuracy of their measurement results. Widespread and consistent adoption of the mock communities will improve reproducibility and comparability of microbiome community analyses, thereby supporting and accelerating human microbiome research and development.

Metabolomics for personalized medicine: the input of analytical chemistry from biomarker discovery to point-of-care tests

Metabolomics refers to the large-scale detection, quantification, and analysis of small molecules (metabolites) in biological media. Although metabolomics, alone or combined with other omics data, has already demonstrated its relevance for patient stratification in the frame of research projects and clinical studies, much remains to be done to move this approach to the clinical practice. This is especially true in the perspective of being applied to personalized/precision medicine, which aims at stratifying patients according to their risk of developing diseases, and tailoring medical treatments of patients according to individual characteristics in order to improve their efficacy and limit their toxicity. In this review article, we discuss the main challenges linked to analytical chemistry that need to be addressed to foster the implementation of metabolomics in the clinics and the use of the data produced by this approach in personalized medicine. First of all, there are already well-known issues related to untargeted metabolomics workflows at the levels of data production (lack of standardization), metabolite identification (small proportion of annotated features and identified metabolites), and data processing (from automatic detection of features to multi-omic data integration) that hamper the inter-operability and reusability of metabolomics data. Furthermore, the outputs of metabolomics workflows are complex molecular signatures of few tens of metabolites, often with small abundance variations, and obtained with expensive laboratory equipment. It is thus necessary to simplify these molecular signatures so that they can be produced and used in the field. This last point, which is still poorly addressed by the metabolomics community, may be crucial in a near future with the increased availability of molecular signatures of medical relevance and the increased societal demand for participatory medicine.