Predictive metabolomic profiling of microbial communities using amplicon or metagenomic sequences

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.

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.

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.

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.

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.

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.

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.

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.

Multi-omics in Crohn's disease: New insights from inside

Crohn's disease (CD) is an inflammatory bowel disease (IBD) with complex clinical manifestations such as chronic diarrhea, weight loss and hematochezia. Despite the increasing incidence worldwide, cure of CD remains extremely difficult. The rapid development of high-throughput sequencing technology with integrated-omics analyses in recent years has provided a new means for exploring the pathogenesis, mining the biomarkers and designing targeted personalized therapeutics of CD. Host genomics and epigenomics unveil heredity-related mechanisms of susceptible individuals, while microbiome and metabolomics map host-microbe interactions in CD patients. Proteomics shows great potential in searching for promising biomarkers. Nonetheless, single omics technology cannot holistically connect the mechanisms with heterogeneity of pathological behavior in CD. The rise of multi-omics analysis integrates genetic/epigenetic profiles with protein/microbial metabolite functionality, providing new hope for comprehensive and in-depth exploration of CD. Herein, we emphasized the different omics features and applications of CD and discussed the current research and limitations of multi-omics in CD. This review will update and deepen our understanding of CD from integration of broad omics spectra and will provide new evidence for targeted individualized therapeutics.

Association of distinct microbial signatures with premalignant colorectal adenomas

Environmental exposures are a major risk factor for developing colorectal cancer, and the gut microbiome may serve as an integrator of such environmental risk. To study the microbiome associated with premalignant colon lesions, such as tubular adenomas (TAs) and sessile serrated adenomas (SSAs), we profiled stool samples from 971 participants undergoing colonoscopy and paired these data with dietary and medication history. The microbial signatures associated with either SSA or TA are distinct. SSA associates with multiple microbial antioxidant defense systems, whereas TA associates with a depletion of microbial methanogenesis and mevalonate metabolism. Environmental factors, such as diet and medications, link with the majority of identified microbial species. Mediation analyses found that Flavonifractor plautii and Bacteroides stercoris transmit the protective or carcinogenic effects of these factors to early carcinogenesis. Our findings suggest that the unique dependencies of each premalignant lesion may be exploited therapeutically or through dietary intervention.

Modulators of a robust and efficient metabolism: Perspective and insights from the Rid superfamily of proteins

Metabolism is an integrated network of biochemical pathways that assemble to generate the robust, responsive physiologies of microorganisms. Despite decades of fundamental studies on metabolic processes and pathways, our understanding of the nuance and complexity of metabolism remains incomplete. The ability to predict and model metabolic network structure, and its influence on cellular fitness, is complicated by the persistence of genes of unknown function, even in the best-studied model organisms. This review describes the definition and continuing study of the Rid superfamily of proteins. These studies are presented with a perspective that illustrates how metabolic complexity can complicate the assignment of function to uncharacterized genes. The Rid superfamily of proteins has been divided into eight subfamilies, including the well-studied RidA subfamily. Aside from the RidA proteins, which are present in all domains of life and prevent metabolic stress, most members of the Rid superfamily have no demonstrated physiological role. Recent progress on functional assignment supports the hypothesis that, overall, proteins in the Rid superfamily modulate metabolic processes to ensure optimal organismal fitness.

Small molecule metabolites: discovery of biomarkers and therapeutic targets

Metabolic abnormalities lead to the dysfunction of metabolic pathways and metabolite accumulation or deficiency which is well-recognized hallmarks of diseases. Metabolite signatures that have close proximity to subject's phenotypic informative dimension, are useful for predicting diagnosis and prognosis of diseases as well as monitoring treatments. The lack of early biomarkers could lead to poor diagnosis and serious outcomes. Therefore, noninvasive diagnosis and monitoring methods with high specificity and selectivity are desperately needed. Small molecule metabolites-based metabolomics has become a specialized tool for metabolic biomarker and pathway analysis, for revealing possible mechanisms of human various diseases and deciphering therapeutic potentials. It could help identify functional biomarkers related to phenotypic variation and delineate biochemical pathways changes as early indicators of pathological dysfunction and damage prior to disease development. Recently, scientists have established a large number of metabolic profiles to reveal the underlying mechanisms and metabolic networks for therapeutic target exploration in biomedicine. This review summarized the metabolic analysis on the potential value of small-molecule candidate metabolites as biomarkers with clinical events, which may lead to better diagnosis, prognosis, drug screening and treatment. We also discuss challenges that need to be addressed to fuel the next wave of breakthroughs.

[Bacterial gut microbiota-key player in sepsis]

The gut microbiota is comprised of over 1200 different bacteria and forms a symbiotic community with the human organism, the holobiont. It plays an important role in the maintenance of homeostasis, e.g., of the immune system and essential metabolic processes. Disturbances in the balance of this reciprocal relationship are called dysbiosis and, in the field of sepsis, are associated with incidence of disease, extent of the systemic inflammatory response, severity of organ dysfunction, and mortality. In addition to providing guiding principles in the fascinating relationship between "human and microbe," this article summarizes recent findings regarding the role of the bacterial gut microbiota in sepsis, which is one a very relevant in intensive care medicine.

Predicting metabolomic profiles from microbial composition through neural ordinary differential equations

Characterizing the metabolic profile of a microbial community is crucial for understanding its biological function and its impact on the host or environment. Metabolomics experiments directly measuring these profiles are difficult and expensive, while sequencing methods quantifying the species composition of microbial communities are well-developed and relatively cost-effective. Computational methods that are capable of predicting metabolomic profiles from microbial compositions can save considerable efforts needed for metabolomic profiling experimentally. Yet, despite existing efforts, we still lack a computational method with high prediction power, general applicability, and great interpretability. Here we develop a method - mNODE (Metabolomic profile predictor using Neural Ordinary Differential Equations), based on a state-of-the-art family of deep neural network models. We show compelling evidence that mNODE outperforms existing methods in predicting the metabolomic profiles of human microbiomes and several environmental microbiomes. Moreover, in the case of human gut microbiomes, mNODE can naturally incorporate dietary information to further enhance the prediction of metabolomic profiles. Besides, susceptibility analysis of mNODE enables us to reveal microbe-metabolite interactions, which can be validated using both synthetic and real data. The presented results demonstrate that mNODE is a powerful tool to investigate the microbiome-diet-metabolome relationship, facilitating future research on precision nutrition.

Changes in the Urine Metabolomic Profile in Patients Recovering from Severe COVID-19

Metabolomics is a relatively new research area that focuses mostly on the profiling of selected molecules and metabolites within the organism. A SARS-CoV-2 infection itself can lead to major disturbances in the metabolite profile of the infected individuals. The aim of this study was to analyze metabolomic changes in the urine of patients during the acute phase of COVID-19 and approximately one month after infection in the recovery period. We discuss the observed changes in relation to the alterations resulting from changes in the blood plasma metabolome, as described in our previous study. The metabolome analysis was performed using NMR spectroscopy from the urine of patients and controls. The urine samples were collected at three timepoints, namely upon hospital admission, during hospitalization, and after discharge from the hospital. The acute COVID-19 phase induced massive alterations in the metabolic composition of urine was linked with various changes taking place in the organism. Discriminatory analyses showed the feasibility of successful discrimination of COVID-19 patients from healthy controls based on urinary metabolite levels, with the highest significance assigned to citrate, Hippurate, and pyruvate. Our results show that the metabolomic changes persist one month after the acute phase and that the organism is not fully recovered.

Metabolic models of human gut microbiota: Advances and challenges

The human gut is a complex ecosystem consisting of hundreds of microbial species interacting with each other and with the human host. Mathematical models of the gut microbiome integrate our knowledge of this system and help to formulate hypotheses to explain observations. The generalized Lotka-Volterra model has been widely used for this purpose, but it does not describe interaction mechanisms and thus does not account for metabolic flexibility. Recently, models that explicitly describe gut microbial metabolite production and consumption have become popular. These models have been used to investigate the factors that shape gut microbial composition and to link specific gut microorganisms to changes in metabolite concentrations found in diseases. Here, we review how such models are built and what we have learned so far from their application to human gut microbiome data. In addition, we discuss current challenges of these models and how these can be addressed in the future.

Comparison of fecal and blood metabolome reveals inconsistent associations of the gut microbiota with cardiometabolic diseases

Blood metabolome is commonly used in human studies to explore the associations of gut microbiota-derived metabolites with cardiometabolic diseases. Here, in a cohort of 1007 middle-aged and elderly adults with matched fecal metagenomic (149 species and 214 pathways) and paired fecal and blood targeted metabolomics data (132 metabolites), we find disparate associations with taxonomic composition and microbial pathways when using fecal or blood metabolites. For example, we observe that fecal, but not blood butyric acid significantly associates with both gut microbiota and prevalent type 2 diabetes. These findings are replicated in an independent validation cohort involving 103 adults. Our results suggest that caution should be taken when inferring microbiome-cardiometabolic disease associations from either blood or fecal metabolome data.

Microbiome epidemiology and association studies in human health

Studies of the human microbiome share both technical and conceptual similarities with genome-wide association studies and genetic epidemiology. However, the microbiome has many features that differ from genomes, such as its temporal and spatial variability, highly distinct genetic architecture and person-to-person variation. Moreover, there are various potential mechanisms by which distinct aspects of the human microbiome can relate to health outcomes. Recent advances, including next-generation sequencing and the proliferation of multi-omic data types, have enabled the exploration of the mechanisms that connect microbial communities to human health. Here, we review the ways in which features of the microbiome at various body sites can influence health outcomes, and we describe emerging opportunities and future directions for advanced microbiome epidemiology.

Understanding the bacterial compositional network associations between oral and gut microbiome within healthy Koreans

Oral microbial ecosystem could influence intestinal diseases, but there have been insufficient studies demonstrating the association of microbial composition between the oral cavity and the intestinal system. Thus, we aimed to investigate the compositional network within the oral microbiome related to gut enterotype from saliva and stool samples collected from 112 healthy Korean subjects. Here, we performed bacterial 16S amplicon sequencing from clinical samples. Then, we determined oral microbiome type related to individual's gut enterotype for healthy Korean. The co-occurrence analysis was performed to interactivity prediction of microbiome within saliva samples. As a result, it could be classified into two Korean oral microbiome types (KO) and four oral-gut-associated microbiome types (KOGA) according to distribution and significant differences of oral microflora. The co-occurrence analysis showed various bacterial compositional networks linked around Streptococcus and Haemophilus within healthy subjects. The present study was first approach in healthy Koreans to identify the oral microbiome types related to the gut microbiome and investigate their characteristics. Hence, we suggest that our results could be potential healthy control data for identifying differences in microbial composition between healthy people and oral disease patients and studying microbial association with the gut microbial environment (oral-gut microbiome axis).

Quantitative analysis of disease-related metabolic dysregulation of human microbiota

The metabolic activity of all the micro-organism composing the human microbiome interacts with the host metabolism contributing to human health and disease in a way that is not fully understood. Here, we introduce STELLA, a computational method to derive the spectrum of metabolites associated with the microbiome of an individual. STELLA integrates known information on metabolic pathways associated with each bacterial species and extracts from these the list of metabolic products of each singular reaction by means of automatic text analysis. By comparing the result obtained on a single subject with the metabolic profile data of a control set of healthy subjects, we are able to identify individual metabolic alterations. To illustrate the method, we present applications to autism spectrum disorder and multiple sclerosis.

Spraying compound probiotics improves growth performance and immunity and modulates gut microbiota and blood metabolites of suckling piglets

One factor that shapes the establishment of early neonatal intestinal microbiota is environmental microbial exposure, and probiotic application has been shown to promote health and growth of piglets. Thus, this study hypothesized that environmental probiotic application in early days of life would be beneficial to newborn piglets. This study aimed to investigate the effect of spraying a compound probiotic fermented liquid (CPFL) into the living environment of piglets on their early growth performance and immunity. This work included 68 piglets, which were randomized into probiotic and control groups. Blood and fecal samples were collected at 0, 3, 7, 14, and 21 days of age. Spraying CPFL significantly reshaped the microbiota composition of the delivery room environment, increased piglets' daily weight gain and weaning weight (P<0.001), and modulated piglets' serum cytokine levels (increases in IgA, IgG, and IL-10; decrease in IFN-γ; P<0.05 in each case) in piglets. Additionally, spraying CPFL during early days of life modified piglets' gut microbiota structure and diversity, increased the abundance of some potentially beneficial bacteria (such as Bacteroides uniformis, Butyricimonas virosa, Parabacteroides distasonis, and Phascolarctobacterium succinatutens) and decreased the abundance of Escherichia coli (P<0.05). Interestingly, CPFL application also significantly enhanced the gut microbial bioactive potential and levels of several serum metabolites involved in the metabolism of vitamins (B2, B3, B6, and E), medium/long-chain fatty acids (caproic, tetradecanoic, and peptadecanoic acids), and dicarboxylic acids (azelaic and sebacic acids). Our study demonstrated that spraying CPFL significantly could improve piglets' growth performance and immunity, and the beneficial effects are associated with changes in the gut microbiota and host metabolism. Our study has provided novel data for future development of probiotic-based health-promoting strategies and expanded our knowledge of probiotic application in animal husbandry.