MIMOSA2: a metabolic network-based tool for inferring mechanism-supported relationships in microbiome-metabolome data

Moving from genome-scale to community-scale metabolic models for the human gut microbiome

Metabolic models of individual microorganisms or small microbial consortia have become standard research tools in the bioengineering and systems biology fields. However, extending metabolic modelling to diverse microbial communities, such as those in the human gut, remains a practical challenge from both modelling and experimental validation perspectives. In complex communities, metabolic models accounting for community dynamics, or those that consider multiple objectives, may provide optimal predictions over simpler steady-state models, but require a much higher computational cost. Here we describe some of the strengths and limitations of microbial community-scale metabolic models and argue for a robust validation framework for developing personalized, mechanistic and accurate predictions of microbial community metabolic behaviours across environmental contexts. Ultimately, quantitatively accurate microbial community-scale metabolic models could aid in the design and testing of personalized prebiotic, probiotic and dietary interventions that optimize for translationally relevant outcomes.

Cooling redistributed endotoxin across different biofluids via modulating the ruminal microbiota and metabolome without altering quorum sensing signal levels in heat-stressed beef bulls

BACKGROUND

Cooling is one of the most common and economical methods to ameliorate heat stress (HS), and it has been discovered to alter the lipopolysaccharide (LPS) endotoxin level in ruminants. However, whether the endotoxin variation induced by cooling relates to the quorum sensing (QS) within the ruminal microflora remains unknown. The current study was consequently performed to examine whether cooling could influence the endotoxin distribution across different biofluids, ruminal microbiota, and ruminal metabolisms through affecting the QS of rumen microorganisms in beef cattle exposed to HS. Thirty-two Simmental bulls were used as experimental animals and randomly assigned to either the control (CON) group, or the mechanical ventilation and water spray (MVWS) treatment. The temperature-humidity index (THI) was recorded throughout this trial, and samples of the rumen liquid, blood, and urine were collected.

RESULTS

Cooling significantly lowered (P < 0.05) the temperature-humidity index (THI), ruminal endotoxin, and endotoxin concentration and excretion in urine, and significantly raised endotoxin level in blood (P < 0.05), but did not change the ruminal concentrations of QS signals including 3-OXO-C6-HSL and the AI-2 (P > 0.05). The linear discriminant analysis effect size (LEfSe) analysis revealed that Prevotellaceae, Rikenellaceae, Monoglobales and their affiliated members, as well as other bacterial taxa were significantly differently (P < 0.05) enriched between the two treatments. The Tax4Fun2 prediction suggested that QS function was upregulated in MVWS compared to CON. The metabolomic analysis indicated that cooling altered the ruminal metabolism profile and downregulated the pathways of lysine degradation, phenylalanine, tyrosine and tryptophan biosynthesis, and ubiquinone and other terpenoid-quinone biosynthesis. The significant (P < 0.05) correlations of the differential bacteria and metabolites with endotoxin and QS molecules were also demonstrated through Spearman analysis.

CONCLUSIONS

Based on the results of this trial, it could be speculated that the cooling reshaped the endotoxin distribution across different biofluids through manipulating ruminal microbiota and metabolome, which might involve the participation of QS. Further investigations are warranted to disclose and verify the mechanisms for those correlations found in this study.

Lactobacillus Johnsonii YH1136 alleviates schizophrenia-like behavior in mice: a gut-microbiota-brain axis hypothesis study

Based on the microbiota-gut-brain axis (MGBA) hypothesis, probiotics play an increasingly important role in treating various psychiatric disorders. Schizophrenia (SCZ) is a common mental disease with a complex pathogenesis and is challenging to treat. Although studies have elucidated the mechanisms associated with the interactions between the microbiota-gut-brain axis and SCZ, few have specifically used probiotics as a therapeutic intervention for SCZ. Accordingly, the current study determines whether L. johnsonii YH1136 effectively prevents SCZ-like behavior in mice and identifies the associated key microbes and metabolites. An SCZ mouse model was established by intraperitoneal injection of MK-801; L. johnsonii YH1136 was administered via oral gavage. L. johnsonii YH1136 significantly improves abnormal behaviors, including psychomotor hyperactivity and sociability and alleviates aberrant enzyme expression associated with tryptophan metabolism in SCZ mice. Additionally, L. johnsonii YH1136 upregulates hippocampal brain-derived neurotrophic factor (BDNF) levels while downregulating tryptophan 2,3-dioxygenase (TDO2), indoleamine-pyrrole 2,3-dioxygenase 1 (IDO1), kynurenine aminotransferase 1 (KAT1). Subsequent 16S rRNA sequencing of intestinal contents suggests that L. johnsonii YH1136 modulates the gut flora structure and composition by increasing the relative abundance of Lactobacillus and decreasing Dubosiella in SCZ mice. N-acetylneuraminic acid and hypoxanthine are the key serum metabolites mediating the interaction between the MGBA and SCZ. These results partially reveal the mechanism underlying the effects of L. johnsonii YH1136 on SCZ-like behavior in mice, supporting the development of therapeutic L. johnsonii probiotic formulations against SCZ.

Advancing Gut Microbiome Research: The Shift from Metagenomics to Multi-Omics and Future Perspectives

The gut microbiome, a dynamic and integral component of human health, has co-evolved with its host, playing essential roles in metabolism, immunity, and disease prevention. Traditional microbiome studies, primarily focused on microbial composition, have provided limited insights into the functional and mechanistic interactions between microbiota and their host. The advent of multi-omics technologies has transformed microbiome research by integrating genomics, transcriptomics, proteomics, and metabolomics, offering a comprehensive, systems-level understanding of microbial ecology and host-microbiome interactions. These advances have propelled innovations in personalized medicine, enabling more precise diagnostics and targeted therapeutic strategies. This review highlights recent breakthroughs in microbiome research, demonstrating how these approaches have elucidated microbial functions and their implications for health and disease. Additionally, it underscores the necessity of standardizing multi-omics methodologies, conducting large-scale cohort studies, and developing novel platforms for mechanistic studies, which are critical steps toward translating microbiome research into clinical applications and advancing precision medicine.

Unravelling metabolite-microbiome interactions in inflammatory bowel disease through AI and interaction-based modelling

Inflammatory Bowel Diseases (IBDs) are chronic inflammatory disorders of the gastrointestinal tract and colon affecting approximately 7 million individuals worldwide. The knowledge of specific pathology and aetiological mechanisms leading to IBD is limited, however a reduced immune system, antibiotic use and reserved diet may initiate symptoms. Dysbiosis of the gut microbiome, and consequently a varied composition of the metabolome, has been extensively linked to these risk factors and IBD. Metagenomic sequencing and liquid-chromatography mass spectrometry (LC-MS) of N = 220 fecal samples by Fransoza et al., provided abundance data on microbial genera and metabolites for use in this study. Identification of differentially abundant microbes and metabolites was performed using a Wilcoxon test, followed by feature selection of random forest (RF), gradient-boosting (XGBoost) and least absolute shrinkage operator (LASSO) models. The performance of these features was then validated using RF models on the Human Microbiome Project 2 (HMP2) dataset and a microbial community (MICOM) model was utilised to predict and interpret the interactions between key microbes and metabolites. The Flavronifractor genus and microbes of the families Lachnospiraceae and Oscillospiraceae were found differential by all models. Metabolic pathways commonly influenced by such microbes in IBD were CoA biosynthesis, bile acid metabolism and amino acid production and degradation. This study highlights distinct interactive microbiome and metabolome profiles within IBD and the highly potential pathways causing disease pathology. It therefore paves way for future investigation into new therapeutic targets and non-invasive diagnostic tools for IBD.

Early-life gut mycobiome core species modulate metabolic health in mice

The gut microbiome causally contributes to obesity; however, the role of fungi remains understudied. We previously identified three core species of the infant gut mycobiome (Rhodotorula mucilaginosa, Malassezia restricta and Candida albicans) that correlated with body mass index, however their causal contributions to obesity development are unknown. Here we show the effects of early-life colonization by these fungal species on metabolic health in gnotobiotic mice fed standard (SD) or high-fat-high-sucrose (HFHS) diets. Each species resulted in bacterial microbiome compositional and functional differences. R. mucilaginosa and M. restricta increased adiposity in mice fed SD, while only R. mucilaginosa exacerbated metabolic disease. In contrast, C. albicans resulted in leanness and resistance to diet-induced obesity. Intestinal nutrient transporter expression was unaffected by the presence of fungi in jejunal enteroids, yet the immune landscape in white adipose tissue was distinctly impacted by each fungal species, suggesting that these phenotypes may be a result of fungal immune regulation. This work revealed that three common fungal colonizers have distinct causal influences on obesity and metabolic inflammation and justifies the consideration of fungi in microbiome research on host metabolism.

Determining the incidence, risk factors and biological drivers of irritable bowel syndrome (IBS) as part of the constellation of postacute sequelae of SARS-CoV-2 infection (PASC) outcomes in the Arizona CoVHORT-GI: a longitudinal cohort study

INTRODUCTION

Postacute sequelae of SARS-CoV-2 infection (PASC) are extensive. Also known as long COVID, primary outcomes reported are neurologic, cardiac and respiratory in nature. However, several studies have also reported an increase in gastrointestinal (GI) symptoms and syndromes following COVID-19. This study of PASC will include extensive analyses of GI symptoms, determine if people with pre-existing irritable bowel syndrome (IBS) are at higher risk of developing PASC generally or PASC-GI, and which biomarkers are impacted and to what degree. This R01 study is being funded by the National Institute of Diabetes and Digestive and Kidney Diseases (1R01DK135483-01) from 2023 to 2028.

METHODS AND ANALYSES

This study combines a longitudinal epidemiologic cohort study and in-depth, novel biologic analyses. In collaboration with a pre-existing study, the Arizona CoVID-19 Cohort (CoVHORT)-GI will recruit participants based on the history of COVID infection(s), new or ongoing GI symptoms 3-6 months postinfection, and pre-existing or incident IBS diagnosis to represent five study groups for comparison and analyses. A subset (n=1000) of those recruited will submit both stool and blood samples. Both samples will undergo a novel method to quantitate humoral and mucosal immune responses to host-derived faecal communities in conjunction with magnetic bead-based separation and high-depth shotgun microbial sequencing. Stool samples will also undergo traditional microbiome analyses (diversity and abundance) and faecal calprotectin assays. Additional serum analyses will aim to determine if a proteomics-based signature exists that differentiates a unique biomarker compositional signature discriminating PASC-GI versus no PASC. All laboratory data will be linked with in-depth epidemiologic data on demographics, symptoms and chronic conditions.

ETHICS AND DISSEMINATION

This study involves human participants and was approved by the University of Arizona Institutional Review Board (IRB (#00002332) and has been deemed minimal risk. Participants gave informed consent to participate in the study before taking part. All publications from the study will be shared back to participants along with alternative lay summaries and webinars to communicate key findings. The data management plan has been published and is publicly available online, including protocols for data requests.

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.

Effective data visualization strategies in untargeted metabolomics

Covering: 2014 to 2023 for metabolomics, 2002 to 2023 for information visualizationLC-MS/MS-based untargeted metabolomics is a rapidly developing research field spawning increasing numbers of computational metabolomics tools assisting researchers with their complex data processing, analysis, and interpretation tasks. In this article, we review the entire untargeted metabolomics workflow from the perspective of information visualization, visual analytics and visual data integration. Data visualization is a crucial step at every stage of the metabolomics workflow, where it provides core components of data inspection, evaluation, and sharing capabilities. However, due to the large number of available data analysis tools and corresponding visualization components, it is hard for both users and developers to get an overview of what is already available and which tools are suitable for their analysis. In addition, there is little cross-pollination between the fields of data visualization and metabolomics, leaving visual tools to be designed in a secondary and mostly ad hoc fashion. With this review, we aim to bridge the gap between the fields of untargeted metabolomics and data visualization. First, we introduce data visualization to the untargeted metabolomics field as a topic worthy of its own dedicated research, and provide a primer on cutting-edge visualization research into data visualization for both researchers as well as developers active in metabolomics. We extend this primer with a discussion of best practices for data visualization as they have emerged from data visualization studies. Second, we provide a practical roadmap to the visual tool landscape and its use within the untargeted metabolomics field. Here, for several computational analysis stages within the untargeted metabolomics workflow, we provide an overview of commonly used visual strategies with practical examples. In this context, we will also outline promising areas for further research and development. We end the review with a set of recommendations for developers and users on how to make the best use of visualizations for more effective and transparent communication of results.

Cat-E: A comprehensive web tool for exploring cancer targeting strategies

Identifying potential cancer-associated genes and drug targets from omics data is challenging due to its diverse sources and analyses, requiring advanced skills and large amounts of time. To facilitate such analysis, we developed Cat-E (Cancer Target Explorer), a novel R/Shiny web tool designed for comprehensive analysis with evaluation according to cancer-related omics data. Cat-E is accessible at https://cat-e.bioinfo-wuerz.eu/. Cat-E compiles information on oncolytic viruses, cell lines, gene markers, and clinical studies by integrating molecular datasets from key databases such as OvirusTB, TCGA, DrugBANK, and PubChem. Users can use all datasets and upload their data to perform multiple analyses, such as differential gene expression analysis, metabolic pathway exploration, metabolic flux analysis, GO and KEGG enrichment analysis, survival analysis, immune signature analysis, single nucleotide variation analysis, dynamic analysis of gene expression changes and gene regulatory network changes, and protein structure prediction. Cancer target evaluation by Cat-E is demonstrated here on lung adenocarcinoma (LUAD) datasets. By offering a user-friendly interface and detailed user manual, Cat-E eliminates the need for advanced computational expertise, making it accessible to experimental biologists, undergraduate and graduate students, and oncology clinicians. It serves as a valuable tool for investigating genetic variations across diverse cancer types, facilitating the identification of novel diagnostic markers and potential therapeutic targets.

The Microbiome-Genetics Axis in Autism Spectrum Disorders: A Probiotic Perspective

Autism spectrum disorder (commonly known as autism) is a complex and prevalent neurodevelopmental condition characterized by challenges in social behavior, restricted interests, and repetitive behaviors. It is projected that the annual cost of autism spectrum disorder in the US will reach USD 461 billion by 2025. However, despite being a major public health problem, effective treatment for the underlying symptoms remains elusive. As numerous literature data indicate the role of gut microbiota in autism prognosis, particularly in terms of alleviating gastrointestinal (GI) symptoms, high hopes have been placed on probiotics for autism treatment. Approximately twenty clinical studies have been conducted using single or mixed probiotic cultures. However, unequivocal results on the effect of probiotics on people with autism have not been obtained. The small sample sizes, differences in age of participants, choice of probiotics, dose and duration of treatment, outcome measures, and analytical methods used are largely inconsistent, making it challenging to draw distinctive conclusions. Here, we discuss the experimental evidence for specific gut bacteria and their metabolites and how they affect autism in light of the phenotypic and etiological complexity and heterogeneity. We propose a personalized medicine approach for using probiotics to increase the quality of life of individuals with autism by selecting specific probiotics to improve particular features of the condition.

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.

Untargeted faecal metabolomics for the discovery of biomarkers and treatment targets for inflammatory bowel diseases

The gut microbiome has been recognised as a key component in the pathogenesis of inflammatory bowel diseases (IBD), and the wide range of metabolites produced by gut bacteria are an important mechanism by which the human microbiome interacts with host immunity or host metabolism. High-throughput metabolomic profiling and novel computational approaches now allow for comprehensive assessment of thousands of metabolites in diverse biomaterials, including faecal samples. Several groups of metabolites, including short-chain fatty acids, tryptophan metabolites and bile acids, have been associated with IBD. In this Recent Advances article, we describe the contribution of metabolomics research to the field of IBD, with a focus on faecal metabolomics. We discuss the latest findings on the significance of these metabolites for IBD prognosis and therapeutic interventions and offer insights into the future directions of metabolomics research.

Integrated Microbiome and Metabolome Analysis Reveals Hypothalamic-Comorbidities Related Signatures in Craniopharyngioma

Craniopharyngioma (CP) is an intracranial tumor with high mortality and morbidity. Though biologically benign, CP will damage the hypothalamus, inducing comorbidities such as obesity, metabolic syndrome, and cognitive impairments. The roles of gut microbiome and serum metabolome in CP-associated hypothalamic comorbidities are aimed to be explored. Patients with CP are characterized by increased Shannon diversity, Eubacterium, Clostridium, and Roseburia, alongside decreased Alistipes and Bacteroides. CP-enriched taxa are positively correlated with dyslipidemia and cognitive decline, while CP-depleted taxa are negatively associated with fatty liver. Subsequent serum metabolomics identified notably up-regulated purine metabolism, and integrative analysis indicated an association between altered microbiota and elevated hypoxanthine. Phenotypic study and multi-omics analysis in the Rax-CreERT2::BrafV600E/+::PtenFlox/+ mouse model validated potential involvement of increased Clostridium and dysregulated purine metabolism in hypothalamic comorbidities. To further consolidate this, intervention experiments are performed and it is found that hypoxanthine co-variated with the severity of hypothalamic comorbidities and abundance of Clostridium, and induced dysregulated purine metabolism along with redox imbalance in target organs (liver and brain cortex). Overall, the study demonstrated the potential of increased Clostridium and up-regulated purine metabolism as signatures of CP-associated hypothalamic-comorbidities, and unveiled that elevated Clostridium, dysregulated purine metabolism, and redox imbalance may mediate the development and progression of CP-associated hypothalamic-comorbidities.

Sustained mucosal colonization and fecal metabolic dysfunction by Bacteroides associates with fecal microbial transplant failure in ulcerative colitis patients

Fecal microbial transplantation (FMT) offers promise for treating ulcerative colitis (UC), though the mechanisms underlying treatment failure are unknown. This study harnessed longitudinally collected colonic biopsies (n = 38) and fecal samples (n = 179) from 19 adults with mild-to-moderate UC undergoing serial FMT in which antimicrobial pre-treatment and delivery mode (capsules versus enema) were assessed for clinical response (≥ 3 points decrease from the pre-treatment Mayo score). Colonic biopsies underwent dual RNA-Seq; fecal samples underwent parallel 16S rRNA and shotgun metagenomic sequencing as well as untargeted metabolomic analyses. Pre-FMT, the colonic mucosa of non-responsive (NR) patients harbored an increased burden of bacteria, including Bacteroides, that expressed more antimicrobial resistance genes compared to responsive (R) patients. NR patients also exhibited muted mucosal expression of innate immune antimicrobial response genes. Post-FMT, NR and R fecal microbiomes and metabolomes exhibited significant divergence. NR metabolomes had elevated concentrations of immunostimulatory compounds including sphingomyelins, lysophospholipids and taurine. NR fecal microbiomes were enriched for Bacteroides fragilis and Bacteroides salyersiae strains that encoded genes capable of taurine production. These findings suggest that both effective mucosal microbial clearance and reintroduction of bacteria that reshape luminal metabolism associate with FMT success and that persistent mucosal and fecal colonization by antimicrobial-resistant Bacteroides species may contribute to FMT failure.

Majorbio Cloud 2024: Update single-cell and multiomics workflows

Majorbio Cloud (https://cloud.majorbio.com/) is a one-stop online analytic platform aiming at promoting the development of bioinformatics services, narrowing the gap between wet and dry experiments, and accelerating the discoveries for the life sciences community. In 2024, three single-omics workflows, two multiomics workflows, and extensions were newly released to facilitate omics data mining and interpretation.

Konjac supplementation can alleviate obesity induced by high-fat diet in mice by modulating gut microbiota and its metabolites

As a multi-factorial disease, obesity has become one of the major health problems in the world, and it is still increasing rapidly. Konjac supplementation, as a convenient dietary therapy, has been shown to be able to regulate gut microbiota and improve obesity. However, the specific mechanism by which konjac improves obesity through gut microbiota remains to be studied. In this study, a high-fat diet (HFD) was used to induce a mouse obesity model, and 16S rDNA sequencing and an untargeted metabolomics were used to investigate the impact of konjac on gut microbiota and gut metabolites in HFD-induced obese mice. The results show that konjac can reduce the body weight, adipose tissue weight, and lipid level of high-fat diet induced obese mice by changing the gut microbiota structure and gut metabolic profile. Association analysis revealed that konjac supplementation induced changes in gut microbiota, resulting in the up-regulation of 7-dehydrocholesterol and trehalose 6-phosphate, as well as the down-regulation of glycocholic acid and ursocholic acid within the Secondary bile acid biosynthesis pathway, ultimately leading to improvements in obesity. Among them, g_Acinetobacter (Greengene ID: 911888) can promote the synthesis of 7-dehydrocholesterol by synthesizing ERG3. g_Allobaculum (Greengene ID: 271516) and g_Allobaculum (Greengene ID: 259370) can promote the breakdown of trehalose 6-phosphate by synthesizing glvA. Additionally, the down-regulation of glycocholic acid and ursocholic acid may be influenced by the up-regulation of Lachnospiraceae_NK4A136_group. In conclusion, konjac exerts an influence on gut metabolites through the regulation of gut microbiota, thereby playing a pivotal role in alleviating obesity induced by a high-fat diet.

Emerging tools and best practices for studying gut microbial community metabolism

The human gut microbiome vastly extends the set of metabolic reactions catalysed by our own cells, with far-reaching consequences for host health and disease. However, our knowledge of gut microbial metabolism relies on a handful of model organisms, limiting our ability to interpret and predict the metabolism of complex microbial communities. In this Perspective, we discuss emerging tools for analysing and modelling the metabolism of gut microorganisms and for linking microorganisms, pathways and metabolites at the ecosystem level, highlighting promising best practices for researchers. Continued progress in this area will also require infrastructure development to facilitate cross-disciplinary synthesis of scientific findings. Collectively, these efforts can enable a broader and deeper understanding of the workings of the gut ecosystem and open new possibilities for microbiome manipulation and therapy.

Ectomycorrhizal fungi alter soil food webs and the functional potential of bacterial communities

Most of Earth's trees rely on critical soil nutrients that ectomycorrhizal fungi (EcMF) liberate and provide, and all of Earth's land plants associate with bacteria that help them survive in nature. Yet, our understanding of how the presence of EcMF modifies soil bacterial communities, soil food webs, and root chemistry requires direct experimental evidence to comprehend the effects that EcMF may generate in the belowground plant microbiome. To this end, we grew Pinus muricata plants in soils that were either inoculated with EcMF and native forest bacterial communities or only native bacterial communities. We then profiled the soil bacterial communities, applied metabolomics and lipidomics, and linked omics data sets to understand how the presence of EcMF modifies belowground biogeochemistry, bacterial community structure, and their functional potential. We found that the presence of EcMF (i) enriches soil bacteria linked to enhanced plant growth in nature, (ii) alters the quantity and composition of lipid and non-lipid soil metabolites, and (iii) modifies plant root chemistry toward pathogen suppression, enzymatic conservation, and reactive oxygen species scavenging. Using this multi-omic approach, we therefore show that this widespread fungal symbiosis may be a common factor for structuring soil food webs.IMPORTANCEUnderstanding how soil microbes interact with one another and their host plant will help us combat the negative effects that climate change has on terrestrial ecosystems. Unfortunately, we lack a clear understanding of how the presence of ectomycorrhizal fungi (EcMF)-one of the most dominant soil microbial groups on Earth-shapes belowground organic resources and the composition of bacterial communities. To address this knowledge gap, we profiled lipid and non-lipid metabolites in soils and plant roots, characterized soil bacterial communities, and compared soils amended either with or without EcMF. Our results show that the presence of EcMF changes soil organic resource availability, impacts the proliferation of different bacterial communities (in terms of both type and potential function), and primes plant root chemistry for pathogen suppression and energy conservation. Our findings therefore provide much-needed insight into how two of the most dominant soil microbial groups interact with one another and with their host plant.

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

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

Comparison of canine colostrum and milk using a multi-omics approach

BACKGROUND

A mother's milk is considered the gold standard of nutrition in neonates and is a source of cytokines, immunoglobulins, growth factors, and other important components, yet little is known about the components of canine milk, specifically colostrum, and the knowledge related to its microbial and metabolic profiles is particularly underwhelming. In this study, we characterized canine colostrum and milk microbiota and metabolome for several breeds of dogs and examined profile shifts as milk matures in the first 8 days post-whelping.

RESULTS

Through untargeted metabolomics, we identified 63 named metabolites that were significantly differentially abundant between days 1 and 8 of lactation. Surprisingly, the microbial compositions of the colostrum and milk, characterized using 16S rRNA gene sequencing, were largely similar, with only two differentiating genera. The shifts observed, mainly increases in several sugars and amino sugars over time and shifts in amino acid metabolites, align with shifts observed in human milk samples and track with puppy development.

CONCLUSION

Like human milk, canine milk composition is dynamic, and shifts are well correlated with developing puppies' needs. Such a study of the metabolic profile of canine milk, and its relation to the microbial community, provides insights into the changing needs of the neonate, as well as the ideal nutrition profile for optimal functionality. This information will add to the existing knowledge base of canine milk composition with the prospect of creating a quality, tailored milk substitute or supplement for puppies.

Correlation between the gut microbiome and neurodegenerative diseases: a review of metagenomics evidence

A growing body of evidence suggests that the gut microbiota contributes to the development of neurodegenerative diseases via the microbiota-gut-brain axis. As a contributing factor, microbiota dysbiosis always occurs in pathological changes of neurodegenerative diseases, such as Alzheimer's disease, Parkinson's disease, and amyotrophic lateral sclerosis. High-throughput sequencing technology has helped to reveal that the bidirectional communication between the central nervous system and the enteric nervous system is facilitated by the microbiota's diverse microorganisms, and for both neuroimmune and neuroendocrine systems. Here, we summarize the bioinformatics analysis and wet-biology validation for the gut metagenomics in neurodegenerative diseases, with an emphasis on multi-omics studies and the gut virome. The pathogen-associated signaling biomarkers for identifying brain disorders and potential therapeutic targets are also elucidated. Finally, we discuss the role of diet, prebiotics, probiotics, postbiotics and exercise interventions in remodeling the microbiome and reducing the symptoms of neurodegenerative diseases.

Gut microbiota affects obesity susceptibility in mice through gut metabolites

Introduction

It is well-known that different populations and animals, even experimental animals with the same rearing conditions, differ in their susceptibility to obesity. The disparity in gut microbiota could potentially account for the variation in susceptibility to obesity. However, the precise impact of gut microbiota on gut metabolites and its subsequent influence on susceptibility to obesity remains uncertain.

Methods

In this study, we established obesity-prone (OP) and obesity-resistant (OR) mouse models by High Fat Diet (HFD). Fecal contents of cecum were examined using 16S rDNA sequencing and untargeted metabolomics. Correlation analysis and MIMOSA2 analysis were used to explore the association between gut microbiota and intestinal metabolites.

Results

After a HFD, gut microbiota and gut metabolic profiles were significantly different between OP and OR mice. Gut microbiota after a HFD may lead to changes in eicosapentaenoic acid (EPA), docosahexaenoic acid (DHA), a variety of branched fatty acid esters of hydroxy fatty acids (FAHFAs) and a variety of phospholipids to promote obesity. The bacteria g_Akkermansia (Greengene ID: 175696) may contribute to the difference in obesity susceptibility through the synthesis of glycerophosphoryl diester phosphodiesterase (glpQ) to promote choline production and the synthesis of valyl-tRNA synthetase (VARS) which promotes L-Valine degradation. In addition, gut microbiota may affect obesity and obesity susceptibility through histidine metabolism, linoleic acid metabolism and protein digestion and absorption pathways.

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.

Multiomics Analyses Reveal Microbiome-Gut-Brain Crosstalk Centered on Aberrant Gamma-Aminobutyric Acid and Tryptophan Metabolism in Drug-Naïve Patients with First-Episode Schizophrenia

BACKGROUND AND HYPOTHESIS

Schizophrenia (SCZ) is associated with complex crosstalk between the gut microbiota and host metabolism, but the underlying mechanism remains elusive. Investigating the aberrant neurotransmitter processes reflected by alterations identified using multiomics analysis is valuable to fully explain the pathogenesis of SCZ.

STUDY DESIGN

We conducted an integrative analysis of multiomics data, including the serum metabolome, fecal metagenome, single nucleotide polymorphism data, and neuroimaging data obtained from a cohort of 127 drug-naïve, first-episode SCZ patients and 92 healthy controls to characterize the microbiome-gut-brain axis in SCZ patients. We used pathway-based polygenic risk score (PRS) analyses to determine the biological pathways contributing to genetic risk and mediation effect analyses to determine the important neuroimaging features. Additionally, a random forest model was generated for effective SCZ diagnosis.

STUDY RESULTS

We found that the altered metabolome and dysregulated microbiome were associated with neuroactive metabolites, including gamma-aminobutyric acid (GABA), tryptophan, and short-chain fatty acids. Further structural and functional magnetic resonance imaging analyses highlighted that gray matter volume and functional connectivity disturbances mediate the relationships between Ruminococcus_torgues and Collinsella_aerofaciens and symptom severity and the relationships between species Lactobacillus_ruminis and differential metabolites l-2,4-diaminobutyric acid and N-acetylserotonin and cognitive function. Moreover, analyses of the Polygenic Risk Score (PRS) support that alterations in GABA and tryptophan neurotransmitter pathways are associated with SCZ risk, and GABA might be a more dominant contributor.

CONCLUSIONS

This study provides new insights into systematic relationships among genes, metabolism, and the gut microbiota that affect brain functional connectivity, thereby affecting SCZ pathogenesis.

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.

Lipid complexation reduces rice starch digestibility and boosts short-chain fatty acid production via gut microbiota

In this study, two rice varieties (RS4 and GZ93) with different amylose and lipid contents were studied, and their starch was used to prepare starch-palmitic acid complexes. The RS4 samples showed a significantly higher lipid content in their flour, starch, and complex samples compared to GZ93. The static in vitro digestion highlighted that RS4 samples had significantly lower digestibility than the GZ93 samples. The C∞ of the starch-lipid complex samples was found to be 17.7% and 18.5% lower than that of the starch samples in GZ93 and RS4, respectively. The INFOGEST undigested fractions were subsequently used for in vitro colonic fermentation. Short-chain fatty acids (SCFAs) concentrations, mainly acetate, and propionate were significantly higher in starch-lipid complexes compared to native flour or starch samples. Starch-lipid complexes produced a distinctive microbial composition, which resulted in different gene functions, mainly related to pyruvate, fructose, and mannose metabolism. Using Model-based Integration of Metabolite Observations and Species Abundances 2 (MIMOSA2), SCFA production was predicted and associated with the gut microbiota. These results indicated that incorporating lipids into rice starch promotes SCFA production by modulating the gut microbiota selectively.

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.

Recent advances in data- and knowledge-driven approaches to explore primary microbial metabolism

With the rapid progress in metabolomics and sequencing technologies, more data on the metabolome of single microbes and their communities become available, revealing the potential of microorganisms to metabolize a broad range of chemical compounds. The analysis of microbial metabolomics datasets remains challenging since it inherits the technical challenges of metabolomics analysis, such as compound identification and annotation, while harboring challenges in data interpretation, such as distinguishing metabolite sources in mixed samples. This review outlines the recent advances in computational methods to analyze primary microbial metabolism: knowledge-based approaches that take advantage of metabolic and molecular networks and data-driven approaches that employ machine/deep learning algorithms in combination with large-scale datasets. These methods aim at improving metabolite identification and disentangling reciprocal interactions between microbes and metabolites. We also discuss the perspective of combining these approaches and further developments required to advance the investigation of primary metabolism in mixed microbial samples.

Multi-omics profiles of the intestinal microbiome in irritable bowel syndrome and its bowel habit subtypes

BACKGROUND

Irritable bowel syndrome (IBS) is a common gastrointestinal disorder that is thought to involve alterations in the gut microbiome, but robust microbial signatures have been challenging to identify. As prior studies have primarily focused on composition, we hypothesized that multi-omics assessment of microbial function incorporating both metatranscriptomics and metabolomics would further delineate microbial profiles of IBS and its subtypes.

METHODS

Fecal samples were collected from a racially/ethnically diverse cohort of 495 subjects, including 318 IBS patients and 177 healthy controls, for analysis by 16S rRNA gene sequencing (n = 486), metatranscriptomics (n = 327), and untargeted metabolomics (n = 368). Differentially abundant microbes, predicted genes, transcripts, and metabolites in IBS were identified by multivariate models incorporating age, sex, race/ethnicity, BMI, diet, and HAD-Anxiety. Inter-omic functional relationships were assessed by transcript/gene ratios and microbial metabolic modeling. Differential features were used to construct random forests classifiers.

RESULTS

IBS was associated with global alterations in microbiome composition by 16S rRNA sequencing and metatranscriptomics, and in microbiome function by predicted metagenomics, metatranscriptomics, and metabolomics. After adjusting for age, sex, race/ethnicity, BMI, diet, and anxiety, IBS was associated with differential abundance of bacterial taxa such as Bacteroides dorei; metabolites including increased tyramine and decreased gentisate and hydrocinnamate; and transcripts related to fructooligosaccharide and polyol utilization. IBS further showed transcriptional upregulation of enzymes involved in fructose and glucan metabolism as well as the succinate pathway of carbohydrate fermentation. A multi-omics classifier for IBS had significantly higher accuracy (AUC 0.82) than classifiers using individual datasets. Diarrhea-predominant IBS (IBS-D) demonstrated shifts in the metatranscriptome and metabolome including increased bile acids, polyamines, succinate pathway intermediates (malate, fumarate), and transcripts involved in fructose, mannose, and polyol metabolism compared to constipation-predominant IBS (IBS-C). A classifier incorporating metabolites and gene-normalized transcripts differentiated IBS-D from IBS-C with high accuracy (AUC 0.86).

CONCLUSIONS

IBS is characterized by a multi-omics microbial signature indicating increased capacity to utilize fermentable carbohydrates-consistent with the clinical benefit of diets restricting this energy source-that also includes multiple previously unrecognized metabolites and metabolic pathways. These findings support the need for integrative assessment of microbial function to investigate the microbiome in IBS and identify novel microbiome-related therapeutic targets. Video Abstract.

Computational approaches for network-based integrative multi-omics analysis

Advances in omics technologies allow for holistic studies into biological systems. These studies rely on integrative data analysis techniques to obtain a comprehensive view of the dynamics of cellular processes, and molecular mechanisms. Network-based integrative approaches have revolutionized multi-omics analysis by providing the framework to represent interactions between multiple different omics-layers in a graph, which may faithfully reflect the molecular wiring in a cell. Here we review network-based multi-omics/multi-modal integrative analytical approaches. We classify these approaches according to the type of omics data supported, the methods and/or algorithms implemented, their node and/or edge weighting components, and their ability to identify key nodes and subnetworks. We show how these approaches can be used to identify biomarkers, disease subtypes, crosstalk, causality, and molecular drivers of physiological and pathological mechanisms. We provide insight into the most appropriate methods and tools for research questions as showcased around the aetiology and treatment of COVID-19 that can be informed by multi-omics data integration. We conclude with an overview of challenges associated with multi-omics network-based analysis, such as reproducibility, heterogeneity, (biological) interpretability of the results, and we highlight some future directions for network-based integration.

The Integration of Proteomics and Metabolomics Data Paving the Way for a Better Understanding of the Mechanisms Underlying Microbial Acquired Drug Resistance

Due to an increase in the overuse of antimicrobials and accelerated incidence of drug resistant pathogens, antimicrobial resistance has become a global health threat. In particular, bacterial antimicrobial resistance, in both hospital and community acquired transmission, have been found to be the leading cause of death due to infectious diseases. Understanding the mechanisms of bacterial drug resistance is of clinical significance irrespective of hospital or community acquired since it plays an important role in the treatment strategy and controlling infectious diseases. Here we highlight the advances in mass spectrometry-based proteomics impact in bacterial proteomics and metabolomics analysis- focus on bacterial drug resistance. Advances in omics technologies over the last few decades now allows multi-omics studies in order to obtain a comprehensive understanding of the biochemical alterations of pathogenic bacteria in the context of antibiotic exposure, identify novel biomarkers to develop new drug targets, develop time-effectively screen for drug susceptibility or resistance using proteomics and metabolomics.

Omics-based ecosurveillance for the assessment of ecosystem function, health, and resilience

Current environmental monitoring efforts often focus on known, regulated contaminants ignoring the potential effects of unmeasured compounds and/or environmental factors. These specific, targeted approaches lack broader environmental information and understanding, hindering effective environmental management and policy. Switching to comprehensive, untargeted monitoring of contaminants, organism health, and environmental factors, such as nutrients, temperature, and pH, would provide more effective monitoring with a likely concomitant increase in environmental health. However, even this method would not capture subtle biochemical changes in organisms induced by chronic toxicant exposure. Ecosurveillance is the systematic collection, analysis, and interpretation of ecosystem health-related data that can address this knowledge gap and provide much-needed additional lines of evidence to environmental monitoring programs. Its use would therefore be of great benefit to environmental management and assessment. Unfortunately, the science of 'ecosurveillance', especially omics-based ecosurveillance is not well known. Here, we give an overview of this emerging area and show how it has been beneficially applied in a range of systems. We anticipate this review to be a starting point for further efforts to improve environmental monitoring via the integration of comprehensive chemical assessments and molecular biology-based approaches. Bringing multiple levels of omics technology-based assessment together into a systems-wide ecosurveillance approach will bring a greater understanding of the environment, particularly the microbial communities upon which we ultimately rely to remediate perturbed ecosystems.