Predictive metabolomic profiling of microbial communities using amplicon or metagenomic sequences

Metagenomic Analysis Identifies Sex-Related Gut Microbial Functions and Bacterial Taxa Associated With Skeletal Muscle Mass

BACKGROUND

This study aimed to explore the association between gut microbiota functional profiles and skeletal muscle mass, focusing on sex-specific differences in a population under 65 years of age.

METHODS

Stool samples from participants were analysed using metagenomic shotgun sequencing. Skeletal muscle mass and skeletal muscle mass index (SMI) were quantified (SMI [%] = total appendage muscle mass [kg]/body weight [kg] × 100) using bioelectrical impedance analysis. Participants were categorized into SMI quartiles, and associations between gut microbiota, functional profiling and SMI were assessed by sex, adjusting for age, BMI and physical activity.

RESULTS

The cohort included 1027 participants (651 men, 376 women). In men, Escherichia coli (log2 fold change 3.08, q = 0.001), Ruminococcus_B gnavus (log2 fold change 2.89, q = 0.014) and Enterocloster sp001517625 (log2 fold change 2.47, q = 0.026) were more abundant in the lowest SMI compared to the highest SMI group. In contrast, Bifidobacterium bifidum (log2 fold change 3.13, q = 0.025) showed higher levels in the second lowest SMI group in women. Microbial pathways associated with amino acid synthesis (MET-SAM-PWY: log2 fold change 0.42; METSYN-PWY: log2 fold change 0.44; SER-GLYSYN-PWY: log2 fold change 0.20; PWY-5347: log2 fold change 0.41; P4-PWY: log2 fold change 0.53), N-acetylneuraminate degradation (log2 fold change 0.43), isoprene biosynthesis (log2 fold change 0.20) and purine nucleotide degradation and salvage (PWY-6353: log2 fold change 0.42; PWY-6608: log2 fold change 0.38; PWY66-409: log2 fold change 0.52; SALVADEHYPOX-PWY: log2 fold change 0.43) were enriched in the lowest SMI in men (q < 0.10). In women, the second lowest SMI group showed enrichment in energy-related pathways, including lactic acid fermentation (ANAEROFRUCAT-PWY: log2 fold change 0.19), pentose phosphate pathway (PENTOSE-P-PWY: log2 fold change 0.30) and carbohydrate degradation (PWY-5484: log2 fold change 0.31; GLYCOLYSIS: log2 fold change 0.29; PWY-6901: log2 fold change 0.27) (q < 0.05).

CONCLUSIONS

This study highlights sex-specific differences in gut microbiota and functional pathways associated with SMI. These findings suggest that gut microbiota may play a role in muscle health and point toward microbiota-targeted strategies for maintaining muscle mass.

A shotgun metagenomic study identified short-chain fatty acid-producing species and their functions in the gut microbiome of adults with depressive symptoms: Large-scale shotgun sequencing data of the gut microbiota using a cross-sectional design

BACKGROUND

The gut-brain axis is emerging as a novel mechanism to explain depressive disorders.

METHODS

We performed shotgun metagenomic sequencing of stool samples obtained from 133 individuals with depression and 532 without depression. This study examined the taxonomy, functional pathways, and predicted metabolites profiles associated with depressive symptoms, using generalized linear models. To explore links between the taxonomic and functional pathway results, we compared the relative abundance of specific species contributing to pathways significantly associated with depressive symptoms.

RESULTS

Taxonomic composition suggested a disruption in short-chain fatty acid (SCFA)-producing capacity of the gut microbiome in the depressed group. Pathways related to SCFA biosynthesis were also depleted in this group. Faecalibacterium prausnitzii, a well-known SCFA-producing bacterium, was significantly decreased in the depressed group and was identified as a major contributor to the depleted pathways. When inferring the metabolites related to depression from metagenomic data, higher levels of docosapentaenoic acid, stearoyl ethanolamide, putrescine, and bilirubin were more likely to be found in the depressed group.

CONCLUSION

The present findings highlight the altered gut microbiota and associated SCFA-related pathways in individuals with depression. The depletion of F. prausnitzii and its contribution to SCFA production suggest that it is a potential therapeutic target for depression.

Predicting metabolite response to dietary intervention using deep learning

Due to highly personalized biological and lifestyle characteristics, different individuals may have different metabolite responses to specific foods and nutrients. In particular, the gut microbiota, a collection of trillions of microorganisms living in the gastrointestinal tract, is highly personalized and plays a key role in the metabolite responses to foods and nutrients. Accurately predicting metabolite responses to dietary interventions based on individuals' gut microbial compositions holds great promise for precision nutrition. Existing prediction methods are typically limited to traditional machine learning models. Deep learning methods dedicated to such tasks are still lacking. Here we develop a method McMLP (Metabolite response predictor using coupled Multilayer Perceptrons) to fill in this gap. We provide clear evidence that McMLP outperforms existing methods on both synthetic data generated by the microbial consumer-resource model and real data obtained from six dietary intervention studies. Furthermore, we perform sensitivity analysis of McMLP to infer the tripartite food-microbe-metabolite interactions, which are then validated using the ground-truth (or literature evidence) for synthetic (or real) data, respectively. The presented tool has the potential to inform the design of microbiota-based personalized dietary strategies to achieve precision nutrition.

Advances in functional analysis of the microbiome: Integrating metabolic modeling, metabolite prediction, and pathway inference with Next-Generation Sequencing data

This review explores current advancements in microbiome functional analysis enabled by next-generation sequencing technologies, which have transformed our understanding of microbial communities from mere taxonomic composition to their functional potential. We examine approaches that move beyond species identification to characterize microbial activities, interactions, and their roles in host health and disease. Genome-scale metabolic models allow for in-depth simulations of metabolic networks, enabling researchers to predict microbial metabolism, growth, and interspecies interactions in diverse environments. Additionally, computational methods for predicting metabolite profiles offer indirect insights into microbial metabolic outputs, which is crucial for identifying biomarkers and potential therapeutic targets. Functional pathway analysis tools further reveal microbial contributions to metabolic pathways, highlighting alterations in response to environmental changes and disease states. Together, these methods offer a powerful framework for understanding the complex metabolic interactions within microbial communities and their impact on host physiology. While significant progress has been made, challenges remain in the accuracy of predictive models and the completeness of reference databases, which limit the applicability of these methods in under-characterized ecosystems. The integration of these computational tools with multi-omic data holds promise for personalized approaches in precision medicine, allowing for targeted interventions that modulate the microbiome to improve health outcomes. This review highlights recent advances in microbiome functional analysis, providing a roadmap for future research and translational applications in human health and environmental microbiology.

PredCMB: predicting changes in microbial metabolites based on the gene-metabolite network analysis of shotgun metagenome data

MOTIVATION

Microbiota-derived metabolites significantly impact host biology, prompting extensive research on metabolic shifts linked to the microbiota. Recent studies have explored both direct metabolite analyses and computational tools for inferring metabolic functions from microbial shotgun metagenome data. However, no existing tool specifically focuses on predicting changes in individual metabolite levels, as opposed to metabolic pathway activities, based on shotgun metagenome data. Understanding these changes is crucial for directly estimating the metabolic potential associated with microbial genomic content.

RESULTS

We introduce Predicting Changes in Microbial metaBolites (PredCMB), a novel method designed to predict alterations in individual metabolites between conditions using shotgun metagenome data and enzymatic gene-metabolite networks. PredCMB evaluates differential enzymatic gene abundance between conditions and estimates its influence on metabolite changes. To validate this approach, we applied it to two publicly available datasets comprising paired shotgun metagenomics and metabolomics data from inflammatory bowel disease cohorts and the cohort of gastrectomy for gastric cancer. Benchmark evaluations revealed that PredCMB outperformed a previous method by demonstrating higher correlations between predicted metabolite changes and experimentally measured changes. Notably, it identified metabolite classes exhibiting major alterations between conditions. By enabling the prediction of metabolite changes directly from shotgun metagenome data, PredCMB provides deeper insights into microbial metabolic dynamics than existing methods focused on pathway activity evaluation. Its potential applications include refining target metabolite selection in microbial metabolomic studies and assessing the contributions of microbial metabolites to disease pathogenesis.

AVAILABILITY AND IMPLEMENTATION

Freely available to non-commercial users at https://www.sysbiolab.org/predcmb.

MetOrigin 2.0: Advancing the discovery of microbial metabolites and their origins

First introduced in 2021, MetOrigin has quickly established itself as a powerful web server to distinguish microbial metabolites and identify the bacteria responsible for specific metabolic processes. Building on the growing understanding of the interplay between the microbiome and metabolome, and in response to user feedback, MetOrigin has undergone a significant upgrade to version 2.0. This enhanced version incorporates three new modules: (1) Quick search module that facilitates the rapid identification of bacteria associated with a particular metabolite; (2) Orthology analysis module that links metabolic enzyme genes with their corresponding bacteria; (3) Mediation analysis module that investigates potential causal relationships among bacteria, metabolites, and phenotypes, highlighting the mediating role of metabolites. Additionally, the backend MetOrigin database has been updated with the latest data from seven public databases (KEGG, HMDB, BIGG, ChEBI, FoodDB, Drugbank, and T3DB), with expanded coverage of 210,732 metabolites, each linked to its source organism. MetOrigin 2.0 is freely accessible at http://metorigin.met-bioinformatics.cn.

Review on computer-assisted biosynthetic capacities elucidation to assess metabolic interactions and communication within microbial communities

Microbial communities thrive through interactions and communication, which are challenging to study as most microorganisms are not cultivable. To address this challenge, researchers focus on the extracellular space where communication events occur. Exometabolomics and interactome analysis provide insights into the molecules involved in communication and the dynamics of their interactions. Advances in sequencing technologies and computational methods enable the reconstruction of taxonomic and functional profiles of microbial communities using high-throughput multi-omics data. Network-based approaches, including community flux balance analysis, aim to model molecular interactions within and between communities. Despite these advances, challenges remain in computer-assisted biosynthetic capacities elucidation, requiring continued innovation and collaboration among diverse scientists. This review provides insights into the current state and future directions of computer-assisted biosynthetic capacities elucidation in studying microbial communities.

Gut microbiome metabolites, molecular mimicry, and species-level variation drive long-term efficacy and adverse event outcomes in lung cancer survivors

BACKGROUND

The influence of the gut microbiota on long-term immune checkpoint inhibitor (ICI) efficacy and immune-related adverse events (irAEs) is poorly understood, as are the underlying mechanisms.

METHODS

We performed gut metagenome and metabolome sequencing of gut microbiotas from patients with lung cancer initially treated with anti-PD-1/PD-L1 therapy and explored the underlying mechanisms mediating long-term (median follow-up 1167 days) ICI responses and immune-related adverse events (irAEs). Results were validated in external, publicly-available datasets (Routy, Lee, and McCulloch cohorts).

FINDINGS

The ICI benefit group was enriched for propionate (P = 0.01) and butyrate/isobutyrate (P = 0.12) compared with the resistance group, which was validated in the McCulloch cohort (propionate P < 0.001, butyrate/isobutyrate P = 0.002). The acetyl-CoA pathway (P = 0.02) in beneficial species mainly mediated butyrate production. Microbiota sequences from irAE patients aligned with antigenic epitopes found in autoimmune diseases. Microbiotas of responsive patients contained more lung cancer-related antigens (P = 0.07), which was validated in the Routy cohort (P = 0.02). Escherichia coli and SGB15342 of Faecalibacterium prausnitzii showed strain-level variations corresponding to clinical phenotypes. Metabolome validation reviewed more abundant acetic acid (P = 0.03), propionic acid (P = 0.09), and butyric acid (P = 0.02) in the benefit group than the resistance group, and patients with higher acetic, propionic, and butyric acid levels had a longer progression-free survival and lower risk of tumor progression after adjusting for histopathological subtype and stage (P < 0.05).

INTERPRETATION

Long-term ICI survivors have coevolved a compact microbial community with high butyrate production, and molecular mimicry of autoimmune and tumor antigens by microbiota contribute to outcomes. These results not only characterize the gut microbiotas of patients who benefit long term from ICIs but pave the way for "smart" fecal microbiota transplantation. Registered in the Chinese Clinical Trial Registry (ChiCTR2000032088).

FUNDING

This work was supported by Beijing Natural Science Foundation (7232110), National High Level Hospital Clinical Research Funding (2022-PUMCH-A-072, 2023-PUMCH-C-054), CAMS Innovation Fund for Medical Sciences (CIFMS) (2022-I2M-C&T-B-010).

Secondary analysis reveals gut microbiota differences in patients with Parkinson's disease and/or cognitive impairment

Background: Parkinson's disease (PD) is a neurodegenerative disorder, and the main clinical characteristics are bradykinesia and muscle stiffness. Cognitive impairment (CI) is a prevalent non-motor manifestation observed in individuals with PD. According to disease severity, it can be divided into PD with mild cognitive impairment (MCI) and PD dementia. CI in PD patients may precede motor symptoms, and the gut microbiota plays an important role in PD pathogenesis. Therefore, gut microbiota may be one of the diagnostic targets for PD-CI. Methods: This study compared the gut microbiota of 43 PD-CI patients [Montreal Cognitive Assessment (MoCA) score < 26] and 38 PD patients without CI (MoCA ≥ 26). Patients' neuropsychological conditions, depression scale, and brain structure scanned by magnetic resonance imaging (MRI) were also recorded. The fecal metagenomic datasets of patients with PD, PD-CI, and CI only were retrieved from public databases for reanalysis to explore the relationship between PD, CI, and gut microbiota. Results: We found that the cortical thickness and the volume of the hippocampus, gray matter, and thalamus were significantly reduced among patients with PD-CI compared to PD without CI (P < 0.05). Moreover, the gut microbiome in patients with PD-CI had fewer short-chain fatty acid (SCFA) producing bacteria and more pathogenic bacteria. There were also alterations in patterns of metabolic pathway-encoding genes. Additionally, PD affected gut microbiota more than CI. Conclusion: CI may aggravate the severity of PD, but it did not drastically alter subjects' gut microbiota. This study reveals the relationship between gut microbiota, PD, and CI.

Predictive modeling of colorectal cancer using exhaustive analysis of microbiome information layers available from public metagenomic data

This study aimed to compare the microbiome profiles of patients with colorectal cancer (CRC, n = 380) and colorectal adenomas (CRA, n = 110) against generally healthy participants (n = 2,461) from various studies. The overarching objective was to conduct a real-life experiment and develop a robust machine learning model applicable to the general population. A total of 2,951 stool samples underwent a comprehensive analysis using the in-house MetaBakery pipeline. This included various data matrices such as microbial taxonomy, functional genes, enzymatic reactions, metabolic pathways, and predicted metabolites. The study found no statistically significant difference in microbial diversity among individuals. However, distinct clusters were identified for healthy, CRC, and CRA groups through linear discriminant analysis (LDA). Machine learning analysis demonstrated consistent model performance, indicating the potential of microbiome layers (microbial taxa, functional genes, enzymatic reactions, and metabolic pathways) as prediagnostic indicators for CRC and CRA. Notable biomarkers on the taxonomy level and microbial functionality (gene families, enzymatic reactions, and metabolic pathways) associated with CRC were identified. The research presents promising avenues for practical clinical applications, with potential validation on external clinical datasets in future studies.

Bioenergetic mapping of 'healthy microbiomes' via compound processing potential imprinted in gut and soil metagenomes

Despite mounting evidence of their importance in human health and ecosystem functioning, the definition and measurement of 'healthy microbiomes' remain unclear. More advanced knowledge exists on health associations for compounds used or produced by microbes. Environmental microbiome exposures (especially via soils) also help shape, and may supplement, the functional capacity of human microbiomes. Given the synchronous interaction between microbes, their feedstocks, and micro-environments, with functional genes facilitating chemical transformations, our objective was to examine microbiomes in terms of their capacity to process compounds relevant to human health. Here we integrate functional genomics and biochemistry frameworks to derive new quantitative measures of in silico potential for human gut and environmental soil metagenomes to process a panel of major compound classes (e.g., lipids, carbohydrates) and selected biomolecules (e.g., vitamins, short-chain fatty acids) linked to human health. Metagenome functional potential profile data were translated into a universal compound mapping 'landscape' based on bioenergetic van Krevelen mapping of function-level meta-compounds and corresponding functional relative abundances, reflecting imprinted genetic capacity of microbiomes to metabolize an array of different compounds. We show that measures of 'compound processing potential' associated with human health and disease (examining atherosclerotic cardiovascular disease, colorectal cancer, type 2 diabetes and anxious-depressive behavior case studies), and displayed seemingly predictable shifts along gradients of ecological disturbance in plant-soil ecosystems (three case studies). Ecosystem quality explained 60-92 % of variation in soil metagenome compound processing potential measures in a post-mining restoration case study dataset. With growing knowledge of the varying proficiency of environmental microbiota to process human health associated compounds, we might design environmental interventions or nature prescriptions to modulate our exposures, thereby advancing microbiota-oriented approaches to human health. Compound processing potential offers a simplified, integrative approach for applying metagenomics in ongoing efforts to understand and quantify the role of microbiota in environmental- and human-health.

Modeling Microbial Communities: Perspective and Challenges

Microbial communities are immensely important due to their widespread presence and profound impact on various facets of life. Understanding these complex systems necessitates mathematical modeling, a powerful tool for simulating and predicting microbial community behavior. This review offers a critical analysis of metabolic modeling and highlights key areas that would greatly benefit from broader discussion and collaboration. Moreover, we explore the challenges and opportunities linked to the intricate nature of these communities, spanning data generation, modeling, and validation. We are confident that ongoing advancements in modeling techniques, such as machine learning, coupled with interdisciplinary collaborations, will unlock the full potential of microbial communities across diverse applications.

Metabolomics-based estimation of activated sludge microbial composition and prediction of filamentous bulking

Understanding the structure and activity of activated sludge (AS) microbiome is key to ensuring optimal operation of wastewater treatment processes. While high-throughput metagenomics offers a comprehensive view of AS microbiome, its cost and time demands warrant alternative approaches. This study employed machine learning methods to integrate metabolomic and metagenomic data, enabling predictions of selected microbial abundances from metabolite profiling. Model training relied on rich microbial and metabolite abundance data collected in an intensively sampled AS system, including a period of filamentous bulking, as well as a few other AS systems. Multiple linear regression out-competed other three algorithms in achieving relatively high prediction accuracy (R2 = 0.70±0.02) for the abundances of 10 selected, either keystone or core metagenome-assembled genomes (MAGs). The model predicted the abundances of filamentous Microtrichaceae and Thiotrichaceae during bulking with an error range of 14-17.8 %. This predictive power extends beyond the specific system studied, showcasing potentials for broader applications across other AS systems. Aspartate, glycine, and folate were the most influential metabolite features contributing to model performance, which were also effective indicators for filamentous bulking, with up to one week of early warning potential. This study pioneers the application of metabolomics for fast, relatively accurate and cost-effective prediction of AS community composition, enabling proactive management of AS systems towards improved efficiency and stability.

The Y-ome Conundrum: Insights into Uncharacterized Genes and Approaches for Functional Annotation

The ever-increasing availability of genome sequencing data has revealed a substantial number of uncharacterized genes without known functions across various organisms. The first comprehensive genome sequencing of E. coli K12 revealed that more than 50% of its open reading frames corresponded to transcripts with no known functions. The group of protein-coding genes without a functional description and/or a recognized pathway, beginning with the letter "Y", is classified as the "y-ome". Several efforts have been made to elucidate the functions of these genes and to recognize their role in biological processes. This review provides a brief update on various strategies employed when studying the y-ome, such as high-throughput experimental approaches, comparative omics, metabolic engineering, gene expression analysis, and data integration techniques. Additionally, we highlight recent advancements in functional annotation methods, including the use of machine learning, network analysis, and functional genomics approaches. Novel approaches are required to produce more precise functional annotations across the genome to reduce the number of genes with unknown functions.

MetaBakery: a Singularity implementation of bioBakery tools as a skeleton application for efficient HPC deconvolution of microbiome metagenomic sequencing data to machine learning ready information

In this study, we present MetaBakery (http://metabakery.fe.uni-lj.si), an integrated application designed as a framework for synergistically executing the bioBakery workflow and associated utilities. MetaBakery streamlines the processing of any number of paired or unpaired fastq files, or a mixture of both, with optional compression (gzip, zip, bzip2, xz, or mixed) within a single run. MetaBakery uses programs such as KneadData (https://github.com/bioBakery/kneaddata), MetaPhlAn, HUMAnN and StrainPhlAn as well as integrated utilities and extends the original functionality of bioBakery. In particular, it includes MelonnPan for the prediction of metabolites and Mothur for calculation of microbial alpha diversity. Written in Python 3 and C++ the whole pipeline was encapsulated as Singularity container for efficient execution on various computing infrastructures, including large High-Performance Computing clusters. MetaBakery facilitates crash recovery, efficient re-execution upon parameter changes, and processing of large data sets through subset handling and is offered in three editions with bioBakery ingredients versions 4, 3 and 2 as versatile, transparent and well documented within the MetaBakery Users' Manual (http://metabakery.fe.uni-lj.si/metabakery_manual.pdf). It provides automatic handling of command line parameters, file formats and comprehensive hierarchical storage of output to simplify navigation and debugging. MetaBakery filters out potential human contamination and excludes samples with low read counts. It calculates estimates of alpha diversity and represents a comprehensive and augmented re-implementation of the bioBakery workflow. The robustness and flexibility of the system enables efficient exploration of changing parameters and input datasets, increasing its utility for microbiome analysis. Furthermore, we have shown that the MetaBakery tool can be used in modern biostatistical and machine learning approaches including large-scale microbiome studies.

The Response of the Gut Physiological Function and Microbiome of a Wild Freshwater Fish (Megalobrama terminalis) to Alterations in Reproductive Behavior

The fish gut microbiome is well known for its role in degrading nutrients to improve the host's digestion and absorption efficiency. In this study, we focused on the core physiological adaptability during the various reproductive stages of the black Amur bream (Megalobrama terminalis) to explore the interaction mechanisms among the fish host gut mucosal structure, gut enzyme activity, and gut microbial metabolism in the course of the host's reproductive cycle. Our findings showed that M. terminalis exhibited locomotion metabolic type (aids in sporting) in the reproductive stage, and a change to visceral metabolic type (aids in digestion) during non-reproductive and post-reproductive stage phases. The impact of metabolic type selection and energy demand during various reproductive stages on fish nutrition strategy and digestive function was substantial. Our resulted showed that mitochondria in intestinal epithelial cells of reproductive M. terminalis appeared autophagy phenomenon, and the digestive enzyme activities in the intestines of reproductive M. terminalis were lower than those in the non-reproductive and post-reproductive individuals. Moreover, these differences in nutrition strategy have a prominent impact on the gut microbiome of reproductive M. terminalis, compared to non-reproductive and post-reproductive samples. Our findings showed that reproductive females had lower levels of alpha diversity compared to non-reproductive and post-reproductive females. Our results also showed a greater functional variety and an increase in functional genes related to carbohydrate, lipid, amino acid, cofactors, and vitamin metabolic pathways in the NRS and PRS group. It is noteworthy that an enrichment of genes encoding putative enzymes implicated in the metabolism of taurine and hypotaurine was observed in the RS samples. Our findings illustrated that the stability and resilience of the gut bacterial community could be shaped in the wild fish host-microbiome interactions during reproductive life history.

Novel strategies for modulating the gut microbiome for cancer therapy

Recent advancements in genomics, transcriptomics, and metabolomics have significantly advanced our understanding of the human gut microbiome and its impact on the efficacy and toxicity of anti-cancer therapeutics, including chemotherapy, immunotherapy, and radiotherapy. In particular, prebiotics, probiotics, and postbiotics are recognized for their unique properties in modulating the gut microbiota, maintaining the intestinal barrier, and regulating immune cells, thus emerging as new cancer treatment modalities. However, clinical translation of microbiome-based therapy is still in its early stages, facing challenges to overcome physicochemical and biological barriers of the gastrointestinal tract, enhance target-specific delivery, and improve drug bioavailability. This review aims to highlight the impact of prebiotics, probiotics, and postbiotics on the gut microbiome and their efficacy as cancer treatment modalities. Additionally, we summarize recent innovative engineering strategies designed to overcome challenges associated with oral administration of anti-cancer treatments. Moreover, we will explore the potential benefits of engineered gut microbiome-modulating approaches in ameliorating the side effects of immunotherapy and chemotherapy.

Campylobacter jejuni and casein hydrolysate addition: Impact on poultry in vitro cecal microbiota and metabolome

This study investigates the impact of casein hydrolysates on the poultry ceca inoculated with Campylobacter focusing on microbial molecular preferences for different protein sources in the presence of Campylobacter jejuni. Three casein sources (intact casein (IN), casein enzyme hydrolysate (EH), and casein acid hydrolysate (AH)) were introduced to cecal contents in combination with inoculated C. jejuni in an in vitro model system incubated for 48 h at 42°C under microaerophilic conditions. Samples were collected at 0, 24, and 48 h. Genomic DNA was extracted and amplified using custom dual-indexed primers, followed by sequencing on an Illumina MiSeq platform. The obtained sequencing data were then analyzed via QIIME2-2021.11. Metabolite extracts were analyzed with ultra-high-performance liquid orbitrap chromatography-mass spectrometry (UHPLC-MS). Statistical analysis of metabolites was conducted using MetaboAnalyst 5.0, while functional analysis was performed using Mummichog 2.0 with a significance threshold set at P < 0.00001. DNA sequencing and metabolomic analyses revealed that C. jejuni was most abundant in the EH group. Microbial diversity and richness improved in casein supplemented groups, with core microbial differences observed, compared to non-supplemented groups. Vitamin B-associated metabolites significantly increased in the supplemented groups, displaying distinct patterns in vitamin B6 and B9 metabolism between EH and AH groups (P < 0.05). Faecalibacterium and Phascolarctobacterium were associated with AH and EH groups, respectively. These findings suggest microbial interactions in the presence of C. jejuni and casein supplementation are influenced by microbial community preferences for casein hydrolysates impacting B vitamin production and shaping competitive dynamics within the cecal microbial community. These findings underscore the potential of nutritional interventions to modulate the poultry GIT microbiota for improved health outcomes.

Multi-omic analysis tools for microbial metabolites prediction

How to resolve the metabolic dark matter of microorganisms has long been a challenging problem in discovering active molecules. Diverse omics tools have been developed to guide the discovery and characterization of various microbial metabolites, which make it gradually possible to predict the overall metabolites for individual strains. The combinations of multi-omic analysis tools effectively compensates for the shortcomings of current studies that focus only on single omics or a broad class of metabolites. In this review, we systematically update, categorize and sort out different analysis tools for microbial metabolites prediction in the last five years to appeal for the multi-omic combination on the understanding of the metabolic nature of microbes. First, we provide the general survey on different updated prediction databases, webservers, or software that based on genomics, transcriptomics, proteomics, and metabolomics, respectively. Then, we discuss the essentiality on the integration of multi-omics data to predict metabolites of different microbial strains and communities, as well as stressing the combination of other techniques, such as systems biology methods and data-driven algorithms. Finally, we identify key challenges and trends in developing multi-omic analysis tools for more comprehensive prediction on diverse microbial metabolites that contribute to human health and disease treatment.

Challenges in IBD Research 2024: Preclinical Human IBD Mechanisms

Preclinical human inflammatory bowel disease (IBD) mechanisms is one of 5 focus areas of the Challenges in IBD Research 2024 document, which also includes environmental triggers, novel technologies, precision medicine, and pragmatic clinical research. Herein, we provide a comprehensive overview of current gaps in inflammatory bowel diseases research that relate to preclinical research and deliver actionable approaches to address them with a focus on how these gaps can lead to advancements in IBD interception, remission, and restoration. The document is the result of multidisciplinary input from scientists, clinicians, patients, and funders and represents a valuable resource for patient-centric research prioritization. This preclinical human IBD mechanisms section identifies major research gaps whose investigation will elucidate pathways and mechanisms that can be targeted to address unmet medical needs in IBD. Research gaps were identified in the following areas: genetics, risk alleles, and epigenetics; the microbiome; cell states and interactions; barrier function; IBD complications (specifically fibrosis and stricturing); and extraintestinal manifestations. To address these gaps, we share specific opportunities for investigation for basic and translational scientists and identify priority actions.

Gut Microbiota Improves Prognostic Prediction in Critically Ill COVID-19 Patients Alongside Immunological and Hematological Indicators

The gut microbiota undergoes substantial changes in COVID-19 patients; yet, the utility of these alterations as prognostic biomarkers at the time of hospital admission, and its correlation with immunological and hematological parameters, remains unclear. The objective of this study is to investigate the gut microbiota's dynamic change in critically ill patients with COVID-19 and evaluate its predictive capability for clinical outcomes alongside immunological and hematological parameters. In this study, anal swabs were consecutively collected from 192 COVID-19 patients (583 samples) upon hospital admission for metagenome sequencing. Simultaneously, blood samples were obtained to measure the concentrations of 27 cytokines and chemokines, along with hematological and biochemical indicators. Our findings indicate a significant correlation between the composition and dynamics of gut microbiota with disease severity and mortality in COVID-19 patients. Recovered patients exhibited a higher abundance of Veillonella and denser interactions among gut commensal bacteria compared to deceased patients. Furthermore, the abundance of gut commensal bacteria exhibited a negative correlation with the concentration of proinflammatory cytokines and organ damage markers. The gut microbiota upon admission showed moderate prognostic prediction ability with an AUC of 0.78, which was less effective compared to predictions based on immunological and hematological parameters (AUC 0.80 and 0.88, respectively). Noteworthy, the integration of these three datasets yielded a higher predictive accuracy (AUC 0.93). Our findings suggest the gut microbiota as an informative biomarker for COVID-19 prognosis, augmenting existing immune and hematological indicators.

Actinomycetota bioprospecting from ore-forming environments

Natural products from Actinomycetota have served as inspiration for many clinically relevant therapeutics. Despite early triumphs in natural product discovery, the rate of unearthing new compounds has decreased, necessitating inventive approaches. One promising strategy is to explore environments where survival is challenging. These harsh environments are hypothesized to lead to bacteria developing chemical adaptations (e.g. natural products) to enable their survival. This investigation focuses on ore-forming environments, particularly fluoride mines, which typically have extreme pH, salinity and nutrient scarcity. Herein, we have utilized metagenomics, metabolomics and evolutionary genome mining to dissect the biodiversity and metabolism in these harsh environments. This work has unveiled the promising biosynthetic potential of these bacteria and has demonstrated their ability to produce bioactive secondary metabolites. This research constitutes a pioneering endeavour in bioprospection within fluoride mining regions, providing insights into uncharted microbial ecosystems and their previously unexplored natural products.

Clostridioides difficile infection is associated with differences in transcriptionally active microbial communities

Clostridioides difficile infection (CDI) is responsible for around 300,000 hospitalizations yearly in the United States, with the associated monetary cost being billions of dollars. Gut microbiome dysbiosis is known to be important to CDI. To the best of our knowledge, metatranscriptomics (MT) has only been used to characterize gut microbiome composition and function in one prior study involving CDI patients. Therefore, we utilized MT to investigate differences in active community diversity and composition between CDI+ (n = 20) and CDI- (n = 19) samples with respect to microbial taxa and expressed genes. No significant (Kruskal-Wallis, p > 0.05) differences were detected for richness or evenness based on CDI status. However, clustering based on CDI status was significant for both active microbial taxa and expressed genes datasets (PERMANOVA, p ≤ 0.05). Furthermore, differential feature analysis revealed greater expression of the opportunistic pathogens Enterocloster bolteae and Ruminococcus gnavus in CDI+ compared to CDI- samples. When only fungal sequences were considered, the family Saccharomycetaceae expressed more genes in CDI-, while 31 other fungal taxa were identified as significantly (Kruskal-Wallis p ≤ 0.05, log(LDA) ≥ 2) associated with CDI+. We also detected a variety of genes and pathways that differed significantly (Kruskal-Wallis p ≤ 0.05, log(LDA) ≥ 2) based on CDI status. Notably, differential genes associated with biofilm formation were expressed by C. difficile. This provides evidence of another possible contributor to C. difficile's resistance to antibiotics and frequent recurrence in vivo. Furthermore, the greater number of CDI+ associated fungal taxa constitute additional evidence that the mycobiome is important to CDI pathogenesis. Future work will focus on establishing if C. difficile is actively producing biofilms during infection and if any specific fungal taxa are particularly influential in CDI.

Longitudinal profiling of the microbiome at four body sites reveals core stability and individualized dynamics during health and disease

To understand the dynamic interplay between the human microbiome and host during health and disease, we analyzed the microbial composition, temporal dynamics, and associations with host multi-omics, immune, and clinical markers of microbiomes from four body sites in 86 participants over 6 years. We found that microbiome stability and individuality are body-site specific and heavily influenced by the host. The stool and oral microbiome are more stable than the skin and nasal microbiomes, possibly due to their interaction with the host and environment. We identify individual-specific and commonly shared bacterial taxa, with individualized taxa showing greater stability. Interestingly, microbiome dynamics correlate across body sites, suggesting systemic dynamics influenced by host-microbial-environment interactions. Notably, insulin-resistant individuals show altered microbial stability and associations among microbiome, molecular markers, and clinical features, suggesting their disrupted interaction in metabolic disease. Our study offers comprehensive views of multi-site microbial dynamics and their relationship with host health and disease.

High microbiome and metabolome diversification in coexisting sponges with different bio-ecological traits

Marine Porifera host diverse microbial communities, which influence host metabolism and fitness. However, functional relationships between sponge microbiomes and metabolic signatures are poorly understood. We integrate microbiome characterization, metabolomics and microbial predicted functions of four coexisting Mediterranean sponges -Petrosia ficiformis, Chondrosia reniformis, Crambe crambe and Chondrilla nucula. Microscopy observations reveal anatomical differences in microbial densities. Microbiomes exhibit strong species-specific trends. C. crambe shares many rare amplicon sequence variants (ASV) with the surrounding seawater. This suggests important inputs of microbial diversity acquired by selective horizontal acquisition. Phylum Cyanobacteria is mainly represented in C. nucula and C. crambe. According to putative functions, the microbiome of P. ficiformis and C. reniformis are functionally heterotrophic, while C. crambe and C. nucula are autotrophic. The four species display distinct metabolic profiles at single compound level. However, at molecular class level they share a "core metabolome". Concurrently, we find global microbiome-metabolome association when considering all four sponge species. Within each species still, sets of microbe/metabolites are identified driving multi-omics congruence. Our findings suggest that diverse microbial players and metabolic profiles may promote niche diversification, but also, analogous phenotypic patterns of "symbiont evolutionary convergence" in sponge assemblages where holobionts co-exist in the same area.

Short-chain fatty acids: linking diet, the microbiome and immunity

The short-chain fatty acids (SCFAs) butyrate, propionate and acetate are microbial metabolites and their availability in the gut and other organs is determined by environmental factors, such as diet and use of antibiotics, that shape the diversity and metabolism of the microbiota. SCFAs regulate epithelial barrier function as well as mucosal and systemic immunity via evolutionary conserved processes that involve G protein-coupled receptor signalling or histone deacetylase activity. Indicatively, the anti-inflammatory role of butyrate is mediated through direct effects on the differentiation of intestinal epithelial cells, phagocytes, B cells and plasma cells, and regulatory and effector T cells. Intestinally derived SCFAs also directly and indirectly affect immunity at extra-intestinal sites, such as the liver, the lungs, the reproductive tract and the brain, and have been implicated in a range of disorders, including infections, intestinal inflammation, autoimmunity, food allergies, asthma and responses to cancer therapies. An ecological understanding of microbial communities and their interrelated metabolic states, as well as the engineering of butyrogenic bacteria may support SCFA-focused interventions for the prevention and treatment of immune-mediated diseases.

Predicting the final metabolic profile based on the succession-related microbiota during spontaneous fermentation of the starter for Chinese liquor making

Microbial inoculation is an effective way to improve the quality of fermented foods via affecting the microbiota structure. However, it is unclear how the inoculation regulates the microbiota structure, and it is still difficult to directionally control the microbiota function via the inoculation. In this work, using the spontaneous fermentation of the starter (Daqu) for Chinese liquor fermentation as a case, we inoculated different microbiota groups at different time points in Daqu fermentation, and analyzed the effect of the inoculation on the final metabolic profile of Daqu. The inoculated microbiota and inoculated time points both significantly affected the final metabolites via regulating the microbial succession (P < 0.001), and multiple inoculations can promote deterministic assembly. Twenty-seven genera were identified to be related to microbial succession, and drove the variation of 121 metabolites. We then constructed an elastic network model to predict the profile of these 121 metabolites based on the abundances of 27 succession-related genera in Daqu fermentation. Procrustes analysis showed that the model could accurately predict the metabolic abundances (average Spearman correlation coefficients >0.3). This work revealed the effect of inoculation on the microbiota succession and the metabolic profile. The established predicted model of metabolic profile would be beneficial for directionally improving the food quality.IMPORTANCEThis work revealed the importance of microbial succession to microbiota structure and metabolites. Multi-inoculations would promote deterministic assembly. It would facilitate the regulation of microbiota structure and metabolic profile. In addition, we established a model to predict final metabolites based on microbial genera related to microbial succession. This model was beneficial for optimizing the inoculation of the microbiota. This work would be helpful for controlling the spontaneous food fermentation and directionally improving the food quality.

A comprehensive overview of microbiome data in the light of machine learning applications: categorization, accessibility, and future directions

Metagenomics, Metabolomics, and Metaproteomics have significantly advanced our knowledge of microbial communities by providing culture-independent insights into their composition and functional potential. However, a critical challenge in this field is the lack of standard and comprehensive metadata associated with raw data, hindering the ability to perform robust data stratifications and consider confounding factors. In this comprehensive review, we categorize publicly available microbiome data into five types: shotgun sequencing, amplicon sequencing, metatranscriptomic, metabolomic, and metaproteomic data. We explore the importance of metadata for data reuse and address the challenges in collecting standardized metadata. We also, assess the limitations in metadata collection of existing public repositories collecting metagenomic data. This review emphasizes the vital role of metadata in interpreting and comparing datasets and highlights the need for standardized metadata protocols to fully leverage metagenomic data's potential. Furthermore, we explore future directions of implementation of Machine Learning (ML) in metadata retrieval, offering promising avenues for a deeper understanding of microbial communities and their ecological roles. Leveraging these tools will enhance our insights into microbial functional capabilities and ecological dynamics in diverse ecosystems. Finally, we emphasize the crucial metadata role in ML models development.

Gut microbiome-metabolome interactions predict host condition

BACKGROUND

The effect of microbes on their human host is often mediated through changes in metabolite concentrations. As such, multiple tools have been proposed to predict metabolite concentrations from microbial taxa frequencies. Such tools typically fail to capture the dependence of the microbiome-metabolite relation on the environment.

RESULTS

We propose to treat the microbiome-metabolome relation as the equilibrium of a complex interaction and to relate the host condition to a latent representation of the interaction between the log concentration of the metabolome and the log frequencies of the microbiome. We develop LOCATE (Latent variables Of miCrobiome And meTabolites rElations), a machine learning tool to predict the metabolite concentration from the microbiome composition and produce a latent representation of the interaction. This representation is then used to predict the host condition. LOCATE's accuracy in predicting the metabolome is higher than all current predictors. The metabolite concentration prediction accuracy significantly decreases cross datasets, and cross conditions, especially in 16S data. LOCATE's latent representation predicts the host condition better than either the microbiome or the metabolome. This representation is strongly correlated with host demographics. A significant improvement in accuracy (0.793 vs. 0.724 average accuracy) is obtained even with a small number of metabolite samples ([Formula: see text]).

CONCLUSION

These results suggest that a latent representation of the microbiome-metabolome interaction leads to a better association with the host condition than any of the two separated or the simple combination of the two. Video Abstract.

Longitudinal profiling of the microbiome at four body sites reveals core stability and individualized dynamics during health and disease

To understand dynamic interplay between the human microbiome and host during health and disease, we analyzed the microbial composition, temporal dynamics, and associations with host multi-omics, immune and clinical markers of microbiomes from four body sites in 86 participants over six years. We found that microbiome stability and individuality are body-site-specific and heavily influenced by the host. The stool and oral microbiome were more stable than the skin and nasal microbiomes, possibly due to their interaction with the host and environment. Also, we identified individual-specific and commonly shared bacterial taxa, with individualized taxa showing greater stability. Interestingly, microbiome dynamics correlated across body sites, suggesting systemic coordination influenced by host-microbial-environment interactions. Notably, insulin-resistant individuals showed altered microbial stability and associations between microbiome, molecular markers, and clinical features, suggesting their disrupted interaction in metabolic disease. Our study offers comprehensive views of multi-site microbial dynamics and their relationship with host health and disease.

Study Highlights

The stability of the human microbiome varies among individuals and body sites.Highly individualized microbial genera are more stable over time.At each of the four body sites, systematic interactions between the environment, the host and bacteria can be detected.Individuals with insulin resistance have lower microbiome stability, a more diversified skin microbiome, and significantly altered host-microbiome interactions.

Bifidobacterium animalis subsp. lactis Probio-M8 alleviates abnormal behavior and regulates gut microbiota in a mouse model suffering from autism

Probiotics can effectively improve a variety of neurological diseases, but there is little research on autism, and the specific mechanism is unclear. In this study, shotgun metagenomics analysis was used to investigate the preventive and therapeutic effects of Bifidobacterium animalis subsp. lactis Probio-M8 on autism. The results showed that Probio-M8 treatment significantly alleviated valproate (VPA)-induced autism in mice, with autistic symptoms characterized by increased stereotyped behaviors such as grooming, reduced learning ability, and decreased desire to socialize. Further studies have found that Probio-M8 can alleviate autism by optimizing gut microbiota diversity and regulating metabolic levels. Probio-M8 regulates gut microbiota structure by increasing the abundance of beneficial bacteria such as Bifidobacterium globosum and Akkermansia muciniphila. In addition, Probio-M8 regulates metabolic activity by increasing levels of choline, which corrects CAZy disorders. In conclusion, Probio-M8 is therapeutic in the VPA-induced autism mouse model by regulating the gut microbiome and metabolic levels.IMPORTANCEIndividuals with autism often exhibit symptoms of social invariance, obsessive-compulsive tendencies, and repetitive behaviors. However, early intervention and treatment can be effective in improving social skills and mitigating autism symptoms, including behaviors related to irritability. Although taking medication for autism may lead to side effects such as weight gain, probiotics can be an ideal intervention for alleviating these symptoms. In this study, we investigated the effects of Probio-M8 intervention on the behavior of autistic mice using an open-field test, a three-chamber sociability test, and a novel object recognition test. Metagenomic analysis revealed differences in gut microbiota diversity among groups, predicted changes in metabolite levels, and functionally annotated CAZy. Additionally, we analyzed serum neurotransmitter levels and found that probiotics were beneficial in mitigating neurotransmitter imbalances in mice with autism.

Community-scale models of microbiomes: Articulating metabolic modelling and metagenome sequencing

Building models is essential for understanding the functions and dynamics of microbial communities. Metabolic models built on genome-scale metabolic network reconstructions (GENREs) are especially relevant as a means to decipher the complex interactions occurring among species. Model reconstruction increasingly relies on metagenomics, which permits direct characterisation of naturally occurring communities that may contain organisms that cannot be isolated or cultured. In this review, we provide an overview of the field of metabolic modelling and its increasing reliance on and synergy with metagenomics and bioinformatics. We survey the means of assigning functions and reconstructing metabolic networks from (meta-)genomes, and present the variety and mathematical fundamentals of metabolic models that foster the understanding of microbial dynamics. We emphasise the characterisation of interactions and the scaling of model construction to large communities, two important bottlenecks in the applicability of these models. We give an overview of the current state of the art in metagenome sequencing and bioinformatics analysis, focusing on the reconstruction of genomes in microbial communities. Metagenomics benefits tremendously from third-generation sequencing, and we discuss the opportunities of long-read sequencing, strain-level characterisation and eukaryotic metagenomics. We aim at providing algorithmic and mathematical support, together with tool and application resources, that permit bridging the gap between metagenomics and metabolic modelling.

Abnormalities in gut virome signatures linked with cognitive impairment in older adults

Multiple emerging lines of evidence indicate that the microbiome contributes to aging and cognitive health. However, the roles of distinct microbial components, such as viruses (virome) and their interactions with bacteria (bacteriome), as well as their metabolic pathways (metabolome) in relation to aging and cognitive function, remain poorly understood. Here, we present proof-of-concept results from a pilot study using datasets (n = 176) from the Microbiome in Aging Gut and Brain (MiaGB) consortium, demonstrating that the human virome signature significantly differs across the aging continuum (60s vs. 70s vs. 80+ years of age) in older adults. We observed that the predominant virome signature was enriched with bacteriophages, which change considerably with aging continuum. Analyses of interactions between phages and the host bacteriome suggest that lytic or temperate relationships change distinctly across the aging continuum, as well as cognitive impairment. Interestingly, the phage-bacteriome-metabolome interactions develop unique patterns that are distinctly linked to aging and cognitive dysfunction in older adults. The phage-bacteriome interactions affect bacterial metabolic pathways, potentially impacting older adults' health, including the risk of cognitive decline and dementia. Further comprehension of these studies could provide opportunities to target the microbiome by developing phage therapies to improve aging and brain health in older adults.

Application of artificial intelligence approaches to predict the metabolism of xenobiotic molecules by human gut microbiome

A highly complex, diverse, and dense community of more than 1,000 different gut bacterial species constitutes the human gut microbiome that harbours vast metabolic capabilities encoded by more than 300,000 bacterial enzymes to metabolise complex polysaccharides, orally administered drugs/xenobiotics, nutraceuticals, or prebiotics. One of the implications of gut microbiome mediated biotransformation is the metabolism of xenobiotics such as medicinal drugs, which lead to alteration in their pharmacological properties, loss of drug efficacy, bioavailability, may generate toxic byproducts and sometimes also help in conversion of a prodrug into its active metabolite. Given the diversity of gut microbiome and the complex interplay of the metabolic enzymes and their diverse substrates, the traditional experimental methods have limited ability to identify the gut bacterial species involved in such biotransformation, and to study the bacterial species-metabolite interactions in gut. In this scenario, computational approaches such as machine learning-based tools presents unprecedented opportunities and ability to predict the gut bacteria and enzymes that can potentially metabolise a candidate drug. Here, we have reviewed the need to identify the gut microbiome-based metabolism of xenobiotics and have provided comprehensive information on the available methods, tools, and databases to address it along with their scope and limitations.

How is Big Data reshaping preclinical aging research?

The exponential scientific and technological progress during the past 30 years has favored the comprehensive characterization of aging processes with their multivariate nature, leading to the advent of Big Data in preclinical aging research. Spanning from molecular omics to organism-level deep phenotyping, Big Data demands large computational resources for storage and analysis, as well as new analytical tools and conceptual frameworks to gain novel insights leading to discovery. Systems biology has emerged as a paradigm that utilizes Big Data to gain insightful information enabling a better understanding of living organisms, visualized as multilayered networks of interacting molecules, cells, tissues and organs at different spatiotemporal scales. In this framework, where aging, health and disease represent emergent states from an evolving dynamic complex system, context given by, for example, strain, sex and feeding times, becomes paramount for defining the biological trajectory of an organism. Using bioinformatics and artificial intelligence, the systems biology approach is leading to remarkable advances in our understanding of the underlying mechanism of aging biology and assisting in creative experimental study designs in animal models. Future in-depth knowledge acquisition will depend on the ability to fully integrate information from different spatiotemporal scales in organisms, which will probably require the adoption of theories and methods from the field of complex systems. Here we review state-of-the-art approaches in preclinical research, with a focus on rodent models, that are leading to conceptual and/or technical advances in leveraging Big Data to understand basic aging biology and its full translational potential.

Management of Biofilm with Breast Implant Surgery

LEARNING OBJECTIVES

After studying this article, the participant should be able to: 1. Understand how bacteria negatively impact aesthetic and reconstructive breast implants. 2. Understand how bacteria infect breast implants. 3. Understand the evidence associated with common implant infection-prevention strategies, and their limitations. 4. Understand why implementation of bacteria-mitigation strategies such as antibiotic administration or "no-touch" techniques may not indefinitely prevent breast implant infection.

SUMMARY

Bacterial infection of aesthetic and reconstructive breast implants is a common and expensive problem. Subacute infections or chronic capsular contractures leading to device explantation are the most commonly documented sequelae. Although bench and translational research underscores the complexities of implant-associated infection, high-quality studies with adequate power, control groups, and duration of follow-up are lacking. Common strategies to minimize infections use antibiotics-administered systemically, in the breast implant pocket, or by directly bathing the implant before insertion-to limit bacterial contamination. Limiting contact between the implant and skin or breast parenchyma represents an additional common strategy. The clinical prevention of breast implant infection is challenged by the clean-contaminated nature of breast parenchyma, and the variable behavior of not only specific bacterial species but also their strains. These factors impact bacterial virulence and antibiotic resistance.

The volatilome reveals microcystin concentration, microbial composition, and oxidative stress in a critical Oregon freshwater lake

IMPORTANCE

Harmful algal blooms are among the most significant threats to drinking water safety. Blooms dominated by cyanobacteria can produce potentially harmful toxins and, despite intensive research, toxin production remains unpredictable. We measured gaseous molecules in Upper Klamath Lake, Oregon, over 2 years and used them to predict the presence and concentration of the cyanotoxin, microcystin, and microbial community composition. Subsets of gaseous compounds were identified that are associated with microcystin production during oxidative stress, pointing to ecosystem-level interactions leading to microcystin contamination. Our approach shows potential for gaseous molecules to be harnessed in monitoring critical waterways.

Metabolite diversity among representatives of divergent Prochlorococcus ecotypes

IMPORTANCE

Approximately half of the annual carbon fixation on Earth occurs in the surface ocean through the photosynthetic activities of phytoplankton such as the ubiquitous picocyanobacterium Prochlorococcus. Ecologically distinct subpopulations (or ecotypes) of Prochlorococcus are central conduits of organic substrates into the ocean microbiome, thus playing important roles in surface ocean production. We measured the chemical profile of three cultured ecotype strains, observing striking differences among them that have implications for the likely chemical impact of Prochlorococcus subpopulations on their surroundings in the wild. Subpopulations differ in abundance along gradients of temperature, light, and nutrient concentrations, suggesting that these chemical differences could affect carbon cycling in different ocean strata and should be considered in models of Prochlorococcus physiology and marine carbon dynamics.

Analysis of the milk kefir pan-metagenome reveals four community types, core species, and associated metabolic pathways

A comprehensive metagenomics-based investigation of the microorganisms present within milk kefir communities from across the globe was carried out with a view to defining the milk kefir pan-metagenome, including details relating to core and non-core components. Milk kefir samples, generated by inoculating full fat, pasteurized cow's milk with 64 kefir grains sourced from 25 different countries, were analyzed. We identified core features, including a consistent pattern of domination by representatives from the species Lactobacillus helveticus or the sub-species Lactobacillus kefiranofaciens subsp. kefiranofaciens, Lactococcus lactis subsp. lactis or Lla. cremoris subsp. cremoris in each kefir. Notably, even in kefirs where the lactococci did not dominate, they and 51 associated metabolic pathways were identified across all metagenomes. These insights can contribute to future efforts to create tailored kefir-based microbial communities for different applications and assist regulators and producers to ensure that kefir products have a microbial composition that reflects the artisanal beverage.

gutSMASH predicts specialized primary metabolic pathways from the human gut microbiota

The gut microbiota produce hundreds of small molecules, many of which modulate host physiology. Although efforts have been made to identify biosynthetic genes for secondary metabolites, the chemical output of the gut microbiome consists predominantly of primary metabolites. Here we introduce the gutSMASH algorithm for identification of primary metabolic gene clusters, and we used it to systematically profile gut microbiome metabolism, identifying 19,890 gene clusters in 4,240 high-quality microbial genomes. We found marked differences in pathway distribution among phyla, reflecting distinct strategies for energy capture. These data explain taxonomic differences in short-chain fatty acid production and suggest a characteristic metabolic niche for each taxon. Analysis of 1,135 individuals from a Dutch population-based cohort shows that the level of microbiome-derived metabolites in plasma and feces is almost completely uncorrelated with the metagenomic abundance of corresponding metabolic genes, indicating a crucial role for pathway-specific gene regulation and metabolite flux. This work is a starting point for understanding differences in how bacterial taxa contribute to the chemistry of the microbiome.

Microbiome Metabolome Integration Platform (MMIP): a web-based platform for microbiome and metabolome data integration and feature identification

A microbial community maintains its ecological dynamics via metabolite crosstalk. Hence, knowledge of the metabolome, alongside its populace, would help us understand the functionality of a community and also predict how it will change in atypical conditions. Methods that employ low-cost metagenomic sequencing data can predict the metabolic potential of a community, that is, its ability to produce or utilize specific metabolites. These, in turn, can potentially serve as markers of biochemical pathways that are associated with different communities. We developed MMIP (Microbiome Metabolome Integration Platform), a web-based analytical and predictive tool that can be used to compare the taxonomic content, diversity variation and the metabolic potential between two sets of microbial communities from targeted amplicon sequencing data. MMIP is capable of highlighting statistically significant taxonomic, enzymatic and metabolic attributes as well as learning-based features associated with one group in comparison with another. Furthermore, MMIP can predict linkages among species or groups of microbes in the community, specific enzyme profiles, compounds or metabolites associated with such a group of organisms. With MMIP, we aim to provide a user-friendly, online web server for performing key microbiome-associated analyses of targeted amplicon sequencing data, predicting metabolite signature, and using learning-based linkage analysis, without the need for initial metabolomic analysis, and thereby helping in hypothesis generation.

Gut Microbiome Composition Is Associated With Future Onset of Crohn's Disease in Healthy First-Degree Relatives

BACKGROUND & AIMS

The cause of Crohn's disease (CD) is unknown, but the current hypothesis is that microbial or environmental factors induce gut inflammation in genetically susceptible individuals, leading to chronic intestinal inflammation. Case-control studies of patients with CD have cataloged alterations in the gut microbiome composition; however, these studies fail to distinguish whether the altered gut microbiome composition is associated with initiation of CD or is the result of inflammation or drug treatment.

METHODS

In this prospective cohort study, 3483 healthy first-degree relatives (FDRs) of patients with CD were recruited to identify the gut microbiome composition that precedes the onset of CD and to what extent this composition predicts the risk of developing CD. We applied a machine learning approach to the analysis of the gut microbiome composition (based on 16S ribosomal RNA sequencing) to define a microbial signature that associates with future development of CD. The performance of the model was assessed in an independent validation cohort.

RESULTS

In the validation cohort, the microbiome risk score (MRS) model yielded a hazard ratio of 2.24 (95% confidence interval, 1.03-4.84; P = .04), using the median of the MRS from the discovery cohort as the threshold. The MRS demonstrated a temporal validity by capturing individuals that developed CD up to 5 years before disease onset (area under the curve > 0.65). The 5 most important taxa contributing to the MRS included Ruminococcus torques, Blautia, Colidextribacter, an uncultured genus-level group from Oscillospiraceae, and Roseburia.

CONCLUSION

This study is the first to demonstrate that gut microbiome composition is associated with future onset of CD and suggests that gut microbiome is a contributor in the pathogenesis of CD.

Faecal metabolome and its determinants in inflammatory bowel disease

OBJECTIVE

Inflammatory bowel disease (IBD) is a multifactorial immune-mediated inflammatory disease of the intestine, comprising Crohn's disease and ulcerative colitis. By characterising metabolites in faeces, combined with faecal metagenomics, host genetics and clinical characteristics, we aimed to unravel metabolic alterations in IBD.

DESIGN

We measured 1684 different faecal metabolites and 8 short-chain and branched-chain fatty acids in stool samples of 424 patients with IBD and 255 non-IBD controls. Regression analyses were used to compare concentrations of metabolites between cases and controls and determine the relationship between metabolites and each participant's lifestyle, clinical characteristics and gut microbiota composition. Moreover, genome-wide association analysis was conducted on faecal metabolite levels.

RESULTS

We identified over 300 molecules that were differentially abundant in the faeces of patients with IBD. The ratio between a sphingolipid and L-urobilin could discriminate between IBD and non-IBD samples (AUC=0.85). We found changes in the bile acid pool in patients with dysbiotic microbial communities and a strong association between faecal metabolome and gut microbiota. For example, the abundance of Ruminococcus gnavus was positively associated with tryptamine levels. In addition, we found 158 associations between metabolites and dietary patterns, and polymorphisms near NAT2 strongly associated with coffee metabolism.

CONCLUSION

In this large-scale analysis, we identified alterations in the metabolome of patients with IBD that are independent of commonly overlooked confounders such as diet and surgical history. Considering the influence of the microbiome on faecal metabolites, our results pave the way for future interventions targeting intestinal inflammation.

Fish Gut Microbiome Analysis Provides Insight into Differences in Physiology and Behavior of Invasive Nile Tilapia and Indigenous Fish in a Large Subtropical River in China

The gut microbiome is thought to play vital roles in host fitness and local adaptation to new environments, thereby facilitating the invasion of the host species. The Nile tilapia (Oreochromis niloticus) (NT) is an aggressive and omnivorous species that competes with native fishes for food resources, and it has successfully invaded much of the Pearl River basin in China. Here, we investigated the gut microbiomes of invasive Nile tilapia and indigenous black Amur bream (BA) in the same river section using high-throughput 16S rRNA gene sequencing. The results indicated that the gut microbiome of NT had several special characteristics, e.g., higher alpha diversity and greater niche breadth, compared with the bream. The gut microbiota of the small size of Nile tilapia (NTS) and small size of black Amur bream (BAS) groups were dominated by Proteobacteria, while those of the NTS and large size of Nile tilapia (NTL) and BAS and large size of black Amur bream (BAL). BAL and NTL were characterized by Firmicutes and Fusobacteriota, respectively. We found that Pseudomonas, Cetobacterium, Ralstonia, and Romboutsia were biomarkers of the NTS, NTL, BAS, and BAL groups, respectively. Moreover, the results collectively suggested that the clustering coefficients of BAL and NTL networks were greater than those of BAS and NTS networks, and BAS had the smallest network among the four groups. Positive interactions between two ASVs dominated the BAS, NTS, and NTL networks, while the proportion of negative interactions between two ASVs in the BAL network was remarkably increased. Low levels of interspecies competition in the NT gut microbiome would contribute to high diversity in the dietary niches and would also benefit the survival and local adaptation of the host. Our results identified specific biomarkers of gut microbial species in invasive Nile tilapia and provided useful information concerning how to monitor and manage invasive Nile tilapia populations.

Metagenomic assessment of gut microbial communities and risk of severe COVID-19

BACKGROUND

The gut microbiome is a critical modulator of host immunity and is linked to the immune response to respiratory viral infections. However, few studies have gone beyond describing broad compositional alterations in severe COVID-19, defined as acute respiratory or other organ failure.

METHODS

We profiled 127 hospitalized patients with COVID-19 (n = 79 with severe COVID-19 and 48 with moderate) who collectively provided 241 stool samples from April 2020 to May 2021 to identify links between COVID-19 severity and gut microbial taxa, their biochemical pathways, and stool metabolites.

RESULTS

Forty-eight species were associated with severe disease after accounting for antibiotic use, age, sex, and various comorbidities. These included significant in-hospital depletions of Fusicatenibacter saccharivorans and Roseburia hominis, each previously linked to post-acute COVID syndrome or "long COVID," suggesting these microbes may serve as early biomarkers for the eventual development of long COVID. A random forest classifier achieved excellent performance when tasked with classifying whether stool was obtained from patients with severe vs. moderate COVID-19, a finding that was externally validated in an independent cohort. Dedicated network analyses demonstrated fragile microbial ecology in severe disease, characterized by fracturing of clusters and reduced negative selection. We also observed shifts in predicted stool metabolite pools, implicating perturbed bile acid metabolism in severe disease.

CONCLUSIONS

Here, we show that the gut microbiome differentiates individuals with a more severe disease course after infection with COVID-19 and offer several tractable and biologically plausible mechanisms through which gut microbial communities may influence COVID-19 disease course. Further studies are needed to expand upon these observations to better leverage the gut microbiome as a potential biomarker for disease severity and as a target for therapeutic intervention.

Gut microbiota short-chain fatty acids and their impact on the host thyroid function and diseases

Thyroid disorders are clinically characterized by alterations of L-3,5,3',5'-tetraiodothyronine (T4), L-3,5,3'-triiodothyronine (T3), and/or thyroid-stimulating hormone (TSH) levels in the blood. The most frequent thyroid disorders are hypothyroidism, hyperthyroidism, and hypothyroxinemia. These conditions affect cell differentiation, function, and metabolism. It has been reported that 40% of the world's population suffers from some type of thyroid disorder and that several factors increase susceptibility to these diseases. Among them are iodine intake, environmental contamination, smoking, certain drugs, and genetic factors. Recently, the intestinal microbiota, composed of more than trillions of microbes, has emerged as a critical player in human health, and dysbiosis has been linked to thyroid diseases. The intestinal microbiota can affect host physiology by producing metabolites derived from dietary fiber, such as short-chain fatty acids (SCFAs). SCFAs have local actions in the intestine and can affect the central nervous system and immune system. Modulation of SCFAs-producing bacteria has also been connected to metabolic diseases, such as obesity and diabetes. In this review, we discuss how alterations in the production of SCFAs due to dysbiosis in patients could be related to thyroid disorders. The studies reviewed here may be of significant interest to endocrinology researchers and medical practitioners.

Effects of microbial-derived biotics (meta/pharma/post-biotics) on the modulation of gut microbiome and metabolome; general aspects and emerging trends

Potential effects of metabiotics (probiotics effector molecules or signaling factors), pharmabiotics (pro-functional metabolites produced by gut microbiota (GMB)) and postbiotics (multifunctional metabolites and structural compounds of food-grade microorganisms) on GMB have been rarely reviewed. These multifunctional components have several promising capabilities for prevention, alleviation and treatment of some diseases or disorders. Correlations between these essential biotics and GMB are also very interesting and important in human health and nutrition. Furthermore, these natural bioactives are involved in modulation of the immune function, control of metabolic dysbiosis and regulation of the signaling pathways. This review discusses the potential of meta/pharma/post-biotics as new classes of pharmaceutical agents and their effective mechanisms associated with GMB-host cell to cell communications with therapeutic benefits which are important in balance and the integrity of the host microbiome. In addition, cutting-edge findings about bioinformatics /metabolomics analyses related to GMB and these essential biotics are reviewed.

Moving beyond DNA: towards functional analysis of the vaginal microbiome by non-sequencing-based methods

Over the last two decades, sequencing-based methods have revolutionised our understanding of niche-specific microbial complexity. In the lower female reproductive tract, these approaches have enabled identification of bacterial compositional structures associated with health and disease. Application of metagenomics and metatranscriptomics strategies have provided insight into the putative function of these communities but it is increasingly clear that direct measures of microbial and host cell function are required to understand the contribution of microbe-host interactions to pathophysiology. Here we explore and discuss current methods and approaches, many of which rely upon mass-spectrometry, being used to capture functional insight into the vaginal mucosal interface. In addition to improving mechanistic understanding, these methods offer innovative solutions for the development of diagnostic and therapeutic strategies designed to improve women's health.