The gut microbiome-metabolome dataset collection: a curated resource for integrative meta-analysis

Advancing Gastrointestinal Microbiota Research in Systemic Sclerosis: Lessons Learned from Prior Research and Opportunities to Accelerate Discovery

Dysbiosis is a feature of patients with systemic sclerosis (SSc). While a causal relationship between the gastrointestinal (GI) microbiota and SSc pathogenesis has not been established, alterations in the GI microbiota are appreciated early in the SSc disease course. Moreover, recent research has illuminated specific microbial signatures that define SSc phenotypes. This review summarizes new research on the GI microbiome in SSc with a focus on technical advancements and the emerging study of the GI metabolome. This review also addresses diverse modalities for manipulating the GI microbiome with the hope of developing preventative treatment strategies to avert progression of SSc.

A cost and community perspective on the barriers to microbiome data reuse

Microbiome research is becoming a mature field with a wealth of data amassed from diverse ecosystems, yet the ability to fully leverage multi-omics data for reuse remains challenging. To provide a view into researchers' behavior and attitudes towards data reuse, we surveyed over 700 microbiome researchers to evaluate data sharing and reuse challenges. We found that many researchers are impeded by difficulties with metadata records, challenges with processing and bioinformatics, and problems with data repository submissions. We also explored the cost constraints of data reuse at each step of the data reuse process to better understand "pain points" and to provide a more quantitative perspective from sixteen active researchers. The bioinformatics and data processing step was estimated to be the most time consuming, which aligns with some of the most frequently reported challenges from the community survey. From these two approaches, we present evidence-based recommendations for how to address data sharing and reuse challenges with concrete actions for future work.

DMoVGPE: predicting gut microbial associated metabolites profiles with deep mixture of variational Gaussian Process experts

BACKGROUND

Understanding the metabolic activities of the gut microbiome is vital for deciphering its impact on human health. While direct measurement of these metabolites through metabolomics is effective, it is often expensive and time-consuming. In contrast, microbial composition data obtained through sequencing is more accessible, making it a promising resource for predicting metabolite profiles. However, current computational models frequently face challenges related to limited prediction accuracy, generalizability, and interpretability.

METHOD

Here, we present the Deep Mixture of Variational Gaussian Process Experts (DMoVGPE) model, designed to overcome these issues. DMoVGPE utilizes a dynamic gating mechanism, implemented through a neural network with fully connected layers and dropout for regularization, to select the most relevant Gaussian Process experts. During training, the gating network refines expert selection, dynamically adjusting their contribution based on the input features. The model also incorporates an Automatic Relevance Determination (ARD) mechanism, which assigns relevance scores to microbial features by evaluating their predictive power. Features linked to metabolite profiles are given smaller length scales to increase their influence, while irrelevant features are down-weighted through larger length scales, improving both prediction accuracy and interpretability.

CONCLUSIONS

Through extensive evaluations on various datasets, DMoVGPE consistently achieves higher prediction performance than existing models. Furthermore, our model reveals significant associations between specific microbial taxa and metabolites, aligning well with findings from existing studies. These results highlight DMoVGPE's potential to provide accurate predictions and to uncover biologically meaningful relationships, paving the way for its application in disease research and personalized healthcare strategies.

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.

Microbiota in inflammatory bowel disease: mechanisms of disease and therapeutic opportunities

Perturbations in the intestinal microbiome are strongly linked to the pathogenesis of inflammatory bowel disease (IBD). Bacteria, fungi and viruses all make up part of a complex multi-kingdom community colonizing the gastrointestinal tract, often referred to as the gut microbiome. They can exert various effects on the host that can contribute to an inflammatory state. Advances in screening, multiomics and experimental approaches have revealed insights into host-microbiota interactions in IBD and have identified numerous mechanisms through which the microbiota and its metabolites can exert a major influence on the gastrointestinal tract. Looking into the future, the microbiome and microbiota-associated processes will be likely to provide unparalleled opportunities for novel diagnostic, therapeutic and diet-inspired solutions for the management of IBD through harnessing rationally designed microbial communities, powerful bacterial and fungal metabolites, individually or in combination, to foster intestinal health. In this Review, we examine the current understanding of the cross-kingdom gut microbiome in IBD, focusing on bacterial and fungal components and metabolites. We examine therapeutic and diagnostic opportunities, the microbial metabolism, immunity, neuroimmunology and microbiome-inspired interventions to link mechanisms of disease and identify novel research and therapeutic opportunities for IBD.

Understanding dysbiosis and resilience in the human gut microbiome: biomarkers, interventions, and challenges

The healthy gut microbiome is important in maintaining health and preventing various chronic and metabolic diseases through interactions with the host via different gut-organ axes, such as the gut-brain, gut-liver, gut-immune, and gut-lung axes. The human gut microbiome is relatively stable, yet can be influenced by numerous factors, such as diet, infections, chronic diseases, and medications which may disrupt its composition and function. Therefore, microbial resilience is suggested as one of the key characteristics of a healthy gut microbiome in humans. However, our understanding of its definition and indicators remains unclear due to insufficient experimental data. Here, we review the impact of key drivers including intrinsic and extrinsic factors such as diet and antibiotics on the human gut microbiome. Additionally, we discuss the concept of a resilient gut microbiome and highlight potential biomarkers including diversity indices and some bacterial taxa as recovery-associated bacteria, resistance genes, antimicrobial peptides, and functional flexibility. These biomarkers can facilitate the identification and prediction of healthy and resilient microbiomes, particularly in precision medicine, through diagnostic tools or machine learning approaches especially after antimicrobial medications that may cause stable dysbiosis. Furthermore, we review current nutrition intervention strategies to maximize microbial resilience, the challenges in investigating microbiome resilience, and future directions in this field of research.

Microbiota-derived metabolites in inflammatory bowel disease

Understanding the role of the gut microbiota in the pathogenesis of inflammatory bowel diseases (IBD) has been an area of intense research over the past decades. Patients with IBD exhibit alterations in their microbial composition compared to healthy controls. However, studies focusing solely on taxonomic analyses have struggled to deliver replicable findings across cohorts regarding which microbial species drive the distinct patterns in IBD. The focus of research has therefore shifted to studying the functionality of gut microbes, especially by investigating their effector molecules involved in the immunomodulatory functions of the microbiota, namely metabolites. Metabolic profiles are altered in IBD, and several metabolites have been shown to play a causative role in shaping immune functions in animal models. Therefore, understanding the complex communication between the microbiota, metabolites, and the host bears great potential to unlock new biomarkers for diagnosis, disease course and therapy response as well as novel therapeutic options in the treatment of IBD. In this review, we primarily focus on promising classes of metabolites which are thought to exert beneficial effects and are generally decreased in IBD. Though results from human trials are promising, they have not so far provided a large-scale break-through in IBD-therapy improvement. We therefore propose tailored personalized supplementation of microbiota and metabolites based on multi-omics analysis which accounts for the individual microbial and metabolic profiles in IBD patients rather than one-size-fits-all approaches.

Microbiota-Gut-Brain Axis: Mass-Spectrometry-Based Metabolomics in the Study of Microbiome Mediators-Stress Relationship

The microbiota-gut-brain axis is a complex bidirectional communication system that involves multiple interactions between intestinal functions and the emotional and cognitive centers of the brain. These interactions are mediated by molecules (metabolites) produced in both areas, which are considered mediators. To shed light on this complex mechanism, which is still largely unknown, a reliable characterization of the mediators is essential. Here, we review the most studied metabolites in the microbiota-gut-brain axis, the metabolic pathways in which they are involved, and their functions. This review focuses mainly on the use of mass spectrometry for their determination, reporting on the latest analytical methods, their limitations, and future perspectives. The analytical strategy for the qualitative-quantitative characterization of mediators must be reliable in order to elucidate the molecular mechanisms underlying the influence of the above-mentioned axis on stress resilience or vulnerability.

Multimedia: multimodal mediation analysis of microbiome data

Mediation analysis has emerged as a versatile tool for answering mechanistic questions in microbiome research because it provides a statistical framework for attributing treatment effects to alternative causal pathways. Using a series of linked regressions, this analysis quantifies how complementary data relate to one another and respond to treatments. Despite these advances, existing software's rigid assumptions often result in users viewing mediation analysis as a black box. We designed the multimedia R package to make advanced mediation analysis techniques accessible, ensuring that statistical components are interpretable and adaptable. The package provides a uniform interface to direct and indirect effect estimation, synthetic null hypothesis testing, bootstrap confidence interval construction, and sensitivity analysis, enabling experimentation with various mediator and outcome models while maintaining a simple overall workflow. The software includes modules for regularized linear, compositional, random forest, hierarchical, and hurdle modeling, making it well-suited to microbiome data. We illustrate the package through two case studies. The first re-analyzes a study of the microbiome and metabolome of Inflammatory Bowel Disease patients, uncovering potential mechanistic interactions between the microbiome and disease-associated metabolites, not found in the original study. The second analyzes new data about the influence of mindfulness practice on the microbiome. The mediation analysis highlights shifts in taxa previously associated with depression that cannot be explained indirectly by diet or sleep behaviors alone. A gallery of examples and further documentation can be found at https://go.wisc.edu/830110.

IMPORTANCE

Microbiome studies routinely gather complementary data to capture different aspects of a microbiome's response to a change, such as the introduction of a therapeutic. Mediation analysis clarifies the extent to which responses occur sequentially via mediators, thereby supporting causal, rather than purely descriptive, interpretation. Multimedia is a modular R package with close ties to the wider microbiome software ecosystem that makes statistically rigorous, flexible mediation analysis easily accessible, setting the stage for precise and causally informed microbiome engineering.

Characterizing the fecal microbiome in patients on the ketogenic diet for drug resistant epilepsy

Background

The ketogenic diet is a dietary therapy with anti-seizure effects. The efficacy of the diet is variable, with initial animal studies suggesting the intestinal microbiome may have a modulating effect. Initial research on the role of the human microbiome in pediatric epilepsy management has been inconclusive.

Methods

In this single-center prospective cohort study, stool samples were collected from 4 patients with drug resistant epilepsy on the ketogenic diet and 9 with drug resistant epilepsy as controls. The samples were analyzed by 16S RNA sequencing.

Results

A trend towards increased alpha diversity was noted among patients on the ketogenic diet compared to the control group. Patients on the ketogenic diet also trended towards a higher relative abundance of Bacteroidaceae, Ruminococcaceae, and Prevotellaceae species. A subset of the control group had a high relative abundance of Bifidobacterium, which may make them a candidate for a trial of the ketogenic diet as a therapeutic option.

Conclusion

These findings add to the growing field of research of how the ketogenic diet modulates the intestinal microbiome in pediatric epilepsy patients. Future emphasis on multi-centre trials, consistent stool collection practices and the establishment of standardized stool biobanking protocols are needed further to validate these novel findings in a pediatric population.

Utilizing Lactic Acid Bacteria to Improve Hyperlipidemia: A Comprehensive Analysis from Gut Microbiota to Metabolic Pathways

Hyperlipidemia poses significant risks for cardiovascular diseases, with emerging evidence underscoring the critical role of gut microbiota in metabolic regulation. This study explores Lactobacillus casei CAAS36, a probiotic strain with promising cholesterol-lowering capabilities, assessing its impact on hyperlipidemic hamsters. Utilizing 1H NMR-based metabolomics and 16S rRNA gene sequencing, we observed that L. casei CAAS36 treatment not only altered metabolic pathways but also reshaped gut microbiota composition. Notably, the treatment restored the balance between Firmicutes and Bacteroidetes and significantly increased the abundance of propionate-producing Muribaculaceae. Metabolically, L. casei CAAS36 administration led to the normalization of key lipid markers, including reductions in total cholesterol, LDL-C, and triglycerides (29.9%, 29.4% and 32.6%), while enhancing the protective HDL-C levels. These effects were accompanied by significant increases in beneficial metabolites such as propionate and succinate, which are known for their roles in preventing metabolic disorders. These findings highlight the dual regulatory effects of L. casei CAAS36 on the metabolic profile and gut microbiota, suggesting a substantial potential for this probiotic in the management of hyperlipidemia and possibly other metabolic diseases. Future applications may include its use as a natural therapeutic agent in clinical settings, aiming to reduce reliance on conventional pharmaceuticals and their associated side effects.

MMETHANE: interpretable AI for predicting host status from microbial composition and metabolomics data

Metabolite production, consumption, and exchange are intimately involved with host health and disease, as well as being key drivers of host-microbiome interactions. Despite the increasing prevalence of datasets that jointly measure microbiome composition and metabolites, computational tools for linking these data to the status of the host remain limited. To address these limitations, we developed MMETHANE, an open-source software package that implements a purpose-built deep learning model for predicting host status from paired microbial sequencing and metabolomic data. MMETHANE incorporates prior biological knowledge, including phylogenetic and chemical relationships, and is intrinsically interpretable, outputting an English-language set of rules that explains its decisions. Using a compendium of six datasets with paired microbial composition and metabolomics measurements, we showed that MMETHANE always performed at least on par with existing methods, including blackbox machine learning techniques, and outperformed other methods on >80% of the datasets evaluated. We additionally demonstrated through two cases studies analyzing inflammatory bowel disease gut microbiome datasets that MMETHANE uncovers biologically meaningful links between microbes, metabolites, and disease status.

Gut microbiota in health and disease: advances and future prospects

The gut microbiota plays a critical role in maintaining human health, influencing a wide range of physiological processes, including immune regulation, metabolism, and neurological function. Recent studies have shown that imbalances in gut microbiota composition can contribute to the onset and progression of various diseases, such as metabolic disorders (e.g., obesity and diabetes) and neurodegenerative conditions (e.g., Alzheimer's and Parkinson's). These conditions are often accompanied by chronic inflammation and dysregulated immune responses, which are closely linked to specific forms of cell death, including pyroptosis and ferroptosis. Pathogenic bacteria in the gut can trigger these cell death pathways through toxin release, while probiotics have been found to mitigate these effects by modulating immune responses. Despite these insights, the precise mechanisms through which the gut microbiota influences these diseases remain insufficiently understood. This review consolidates recent findings on the impact of gut microbiota in these immune-mediated and inflammation-associated conditions. It also identifies gaps in current research and explores the potential of advanced technologies, such as organ-on-chip models and the microbiome-gut-organ axis, for deepening our understanding. Emerging tools, including single-bacterium omics and spatial metabolomics, are discussed for their promise in elucidating the microbiota's role in disease development.

NMFGOT: a multi-view learning framework for the microbiome and metabolome integrative analysis with optimal transport plan

The rapid development of high-throughput sequencing techniques provides an unprecedented opportunity to generate biological insights into microbiome-related diseases. However, the relationships among microbes, metabolites and human microenvironment are extremely complex, making data analysis challenging. Here, we present NMFGOT, which is a versatile toolkit for the integrative analysis of microbiome and metabolome data from the same samples. NMFGOT is an unsupervised learning framework based on nonnegative matrix factorization with graph regularized optimal transport, where it utilizes the optimal transport plan to measure the probability distance between microbiome samples, which better dealt with the nonlinear high-order interactions among microbial taxa and metabolites. Moreover, it also includes a spatial regularization term to preserve the spatial consistency of samples in the embedding space across different data modalities. We implemented NMFGOT in several multi-omics microbiome datasets from multiple cohorts. The experimental results showed that NMFGOT consistently performed well compared with several recently published multi-omics integrating methods. Moreover, NMFGOT also facilitates downstream biological analysis, including pathway enrichment analysis and disease-specific metabolite-microbe association analysis. Using NMFGOT, we identified the significantly and stable metabolite-microbe associations in GC and ESRD diseases, which improves our understanding for the mechanisms of human complex diseases.

Semisynthetic simulation for microbiome data analysis

High-throughput sequencing data lie at the heart of modern microbiome research. Effective analysis of these data requires careful preprocessing, modeling, and interpretation to detect subtle signals and avoid spurious associations. In this review, we discuss how simulation can serve as a sandbox to test candidate approaches, creating a setting that mimics real data while providing ground truth. This is particularly valuable for power analysis, methods benchmarking, and reliability analysis. We explain the probability, multivariate analysis, and regression concepts behind modern simulators and how different implementations make trade-offs between generality, faithfulness, and controllability. Recognizing that all simulators only approximate reality, we review methods to evaluate how accurately they reflect key properties. We also present case studies demonstrating the value of simulation in differential abundance testing, dimensionality reduction, network analysis, and data integration. Code for these examples is available in an online tutorial (https://go.wisc.edu/8994yz) that can be easily adapted to new problem settings.

multimedia: Multimodal Mediation Analysis of Microbiome Data

Mediation analysis has emerged as a versatile tool for answering mechanistic questions in microbiome research because it provides a statistical framework for attributing treatment effects to alternative causal pathways. Using a series of linked regressions, this analysis quantifies how complementary data relate to one another and respond to treatments. Despite these advances, existing software's rigid assumptions often result in users viewing mediation analysis as a black box. We designed the multimedia R package to make advanced mediation analysis techniques accessible, ensuring that statistical components are interpretable and adaptable. The package provides a uniform interface to direct and indirect effect estimation, synthetic null hypothesis testing, bootstrap confidence interval construction, and sensitivity analysis, enabling experimentation with various mediator and outcome models while maintaining a simple overall workflow. The software includes modules for regularized linear, compositional, random forest, hierarchical, and hurdle modeling, making it well-suited to microbiome data. We illustrate the package through two case studies. The first re-analyzes a study of the microbiome and metabolome of Inflammatory Bowel Disease patients, uncovering potential mechanistic interactions between the microbiome and disease-associated metabolites, not found in the original study. The second analyzes new data about the influence of mindfulness practice on the microbiome. The mediation analysis highlights shifts in taxa previously associated with depression that cannot be explained indirectly by diet or sleep behaviors alone. A gallery of examples and further documentation can be found at https://go.wisc.edu/830110.

Unexplored microbial diversity from 2,500 food metagenomes and links with the human microbiome

Complex microbiomes are part of the food we eat and influence our own microbiome, but their diversity remains largely unexplored. Here, we generated the open access curatedFoodMetagenomicData (cFMD) resource by integrating 1,950 newly sequenced and 583 public food metagenomes. We produced 10,899 metagenome-assembled genomes spanning 1,036 prokaryotic and 108 eukaryotic species-level genome bins (SGBs), including 320 previously undescribed taxa. Food SGBs displayed significant microbial diversity within and between food categories. Extension to >20,000 human metagenomes revealed that food SGBs accounted on average for 3% of the adult gut microbiome. Strain-level analysis highlighted potential instances of food-to-gut transmission and intestinal colonization (e.g., Lacticaseibacillus paracasei) as well as SGBs with divergent genomic structures in food and humans (e.g., Streptococcus gallolyticus and Limosilactobabillus mucosae). The cFMD expands our knowledge on food microbiomes, their role in shaping the human microbiome, and supports future uses of metagenomics for food quality, safety, and authentication.

Stratification of human gut microbiomes by succinotype is associated with inflammatory bowel disease status

BACKGROUND

The human gut microbiome produces and consumes a variety of compounds that interact with the host and impact health. Succinate is of particular interest as it intersects with both host and microbiome metabolism. However, which gut bacteria are most responsible for the consumption of intestinal succinate is poorly understood.

RESULTS

We build upon an enrichment-based whole fecal sample culturing approach and identify two main bacterial taxa that are responsible for succinate consumption in the human intestinal microbiome, Phascolarctobacterium and Dialister. These two taxa have the hallmark of a functional guild and are strongly mutual exclusive across 21,459 fecal samples in 94 cohorts and can thus be used to assign a robust "succinotype" to an individual. We show that they differ with respect to their rate of succinate consumption in vitro and that this is associated with higher concentrations of fecal succinate. Finally, individuals suffering from inflammatory bowel disease (IBD) are more likely to have the Dialister succinotype compared to healthy subjects.

CONCLUSIONS

We identified that only two bacterial genera are the key succinate consumers in human gut microbiome, despite the fact that many more intestinal bacteria encode for the succinate pathway. This highlights the importance of phenotypic assays in functionally profiling intestinal microbiota. A stratification based on "succinotype" is to our knowledge the first function-based classification of human intestinal microbiota. The association of succinotype with IBD thus builds a bridge between microbiome function and IBD pathophysiology related to succinate homeostasis. Video Abstract.

Cutting-Edge iPSC-Based Approaches in Studying Host-Microbe Interactions in Neuropsychiatric Disorders

Gut microbiota (GM), together with its metabolites (such as SCFA, tryptophan, dopamine, GABA, etc.), plays an important role in the functioning of the central nervous system. Various neurological and psychiatric disorders are associated with changes in the composition of GM and their metabolites, which puts them in the foreground as a potential adjuvant therapy. However, the molecular mechanisms behind this relationship are not clear enough. Therefore, before considering beneficial microbes and/or their metabolites as potential therapeutics for brain disorders, the mechanisms underlying microbiota-host interactions must be identified and characterized in detail. In this review, we summarize the current knowledge of GM alterations observed in prevalent neurological and psychiatric disorders, multiple sclerosis, major depressive disorder, Alzheimer's disease, and autism spectrum disorders, together with experimental evidence of their potential to improve patients' quality of life. We further discuss the main obstacles in the study of GM-host interactions and describe the state-of-the-art solution and trends in this field, namely "culturomics" which enables the culture and identification of novel bacteria that inhabit the human gut, and models of the gut and blood-brain barrier as well as the gut-brain axis based on induced pluripotent stem cells (iPSCs) and iPSC derivatives, thus pursuing a personalized medicine agenda for neuropsychiatric disorders.

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.

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.

TARO: tree-aggregated factor regression for microbiome data integration

MOTIVATION

Although the human microbiome plays a key role in health and disease, the biological mechanisms underlying the interaction between the microbiome and its host are incompletely understood. Integration with other molecular profiling data offers an opportunity to characterize the role of the microbiome and elucidate therapeutic targets. However, this remains challenging to the high dimensionality, compositionality, and rare features found in microbiome profiling data. These challenges necessitate the use of methods that can achieve structured sparsity in learning cross-platform association patterns.

RESULTS

We propose Tree-Aggregated factor RegressiOn (TARO) for the integration of microbiome and metabolomic data. We leverage information on the taxonomic tree structure to flexibly aggregate rare features. We demonstrate through simulation studies that TARO accurately recovers a low-rank coefficient matrix and identifies relevant features. We applied TARO to microbiome and metabolomic profiles gathered from subjects being screened for colorectal cancer to understand how gut microrganisms shape intestinal metabolite abundances.

AVAILABILITY AND IMPLEMENTATION

The R package TARO implementing the proposed methods is available online at https://github.com/amishra-stats/taro-package.

Recommendations for the management of gastrointestinal comorbidities with or without trofinetide use in Rett syndrome

INTRODUCTION

Although gastrointestinal (GI) comorbidities are experienced by over 90% of individuals with Rett syndrome (RTT), a neurodevelopmental disorder associated with mutations in the MECP2 gene, many neurologists and pediatricians do not rank the management of these comorbidities among the most important treatment goals for RTT. Trofinetide, the first approved pharmacologic treatment for RTT, confers improvements in RTT symptoms but is associated with adverse GI events, primarily diarrhea and vomiting. Treatment strategies for GI comorbidities and drug-associated symptoms in RTT represent an unmet clinical need.

AREAS COVERED

This perspective covers GI comorbidities experienced by those with RTT, either with or without trofinetide treatment. PubMed literature searches were undertaken on treatment recommendations for the following conditions: constipation, diarrhea, vomiting, aspiration, dysphagia, gastroesophageal reflux, nausea, gastroparesis, gastritis, and abdominal bloating.

EXPERT OPINION

The authors recommend a proactive approach to management of symptomatic GI comorbidities and drug-associated symptoms in RTT to enhance drug tolerance and improve the quality of life of affected individuals. Management strategies for common GI comorbidities associated with RTT are reviewed based on authors' clinical experience and augmented by recommendations from the literature.

Multi-omic integration of microbiome data for identifying disease-associated modules

Multi-omic studies of the human gut microbiome are crucial for understanding its role in disease across multiple functional layers. Nevertheless, integrating and analyzing such complex datasets poses significant challenges. Most notably, current analysis methods often yield extensive lists of disease-associated features (e.g., species, pathways, or metabolites), without capturing the multi-layered structure of the data. Here, we address this challenge by introducing "MintTea", an intermediate integration-based approach combining canonical correlation analysis extensions, consensus analysis, and an evaluation protocol. MintTea identifies "disease-associated multi-omic modules", comprising features from multiple omics that shift in concord and that collectively associate with the disease. Applied to diverse cohorts, MintTea captures modules with high predictive power, significant cross-omic correlations, and alignment with known microbiome-disease associations. For example, analyzing samples from a metabolic syndrome study, MintTea identifies a module with serum glutamate- and TCA cycle-related metabolites, along with bacterial species linked to insulin resistance. In another dataset, MintTea identifies a module associated with late-stage colorectal cancer, including Peptostreptococcus and Gemella species and fecal amino acids, in line with these species' metabolic activity and their coordinated gradual increase with cancer development. This work demonstrates the potential of advanced integration methods in generating systems-level, multifaceted hypotheses underlying microbiome-disease interactions.

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.

TaxaHFE: a machine learning approach to collapse microbiome datasets using taxonomic structure

Motivation

Biologists increasingly turn to machine learning models not just to predict, but to explain. Feature reduction is a common approach to improve both the performance and interpretability of models. However, some biological datasets, such as microbiome data, are inherently organized in a taxonomy, but these hierarchical relationships are not leveraged during feature reduction. We sought to design a feature engineering algorithm to exploit relationships in hierarchically organized biological data.

Results

We designed an algorithm, called TaxaHFE, to collapse information-poor features into their higher taxonomic levels. We applied TaxaHFE to six previously published datasets and found, on average, a 90% reduction in the number of features (SD = 5.1%) compared to using the most complete taxonomy. Using machine learning to compare the most resolved taxonomic level (i.e. species) against TaxaHFE-preprocessed features, models based on TaxaHFE features achieved an average increase of 3.47% in receiver operator curve area under the curve. Compared to other hierarchical feature engineering implementations, TaxaHFE introduces the novel ability to consider both categorical and continuous response variables to inform the feature set collapse. Importantly, we find TaxaHFE's ability to reduce hierarchically organized features to a more information-rich subset increases the interpretability of models.

Availability and implementation

TaxaHFE is available as a Docker image and as R code at https://github.com/aoliver44/taxaHFE.

TARO: tree-aggregated factor regression for microbiome data integration

Motivation

Although the human microbiome plays a key role in health and disease, the biological mechanisms underlying the interaction between the microbiome and its host are incompletely understood. Integration with other molecular profiling data offers an opportunity to characterize the role of the microbiome and elucidate therapeutic targets. However, this remains challenging to the high dimensionality, compositionality, and rare features found in microbiome profiling data. These challenges necessitate the use of methods that can achieve structured sparsity in learning cross-platform association patterns.

Results

We propose Tree-Aggregated factor RegressiOn (TARO) for the integration of microbiome and metabolomic data. We leverage information on the phylogenetic tree structure to flexibly aggregate rare features. We demonstrate through simulation studies that TARO accurately recovers a low-rank coefficient matrix and identifies relevant features. We applied TARO to microbiome and metabolomic profiles gathered from subjects being screened for colorectal cancer to understand how gut microrganisms shape intestinal metabolite abundances.

Availability and implementation

The R package TARO implementing the proposed methods is available online at https://github.com/amishra-stats/taro-package .

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.

Uterine Tissue Metabonomics Combined with 16S rRNA Gene Sequencing To Analyze the Changes of Gut Microbiota in Mice with Endometritis and the Intervention Effect of Tau Interferon

Endometritis is a common cow disease characterized by inflammation of endometrium, which leads to infertility or low fertility of cows and brings huge economic losses to the dairy industry. Tau interferon (IFN-τ) has many important biological functions, including an anti-inflammatory effect. The present study aimed to survey the effects of IFN-τ administration on gut microflora and body metabolism in mice with endometritis and to explore the potential relationship. The results indicated that IFN-τ obviously alleviated the damage and ultrastructural changes of mouse endometrium induced by Escherichia coli and enhanced tight junction protein's expression level. Through analysis by 16S rRNA gene sequencing, we found that IFN-τ, especially at 12 h, could regulate the composition of gut microbiota associated with Pediococcus, Staphylococcus, and Enterorhabdus in E. coli-induced mouse endometritis. Through histometabonomics, it was found that endometritis was related to 11 different metabolites and 4 potential metabolic pathways. These metabolites and metabolic pathways were major participants in metabolic pathways, cysteine and methionine metabolism, arachidonic acid metabolism, and pyrimidine metabolism. Correlation analysis of gut microbiota with uterine tissue metabolomics showed that changes in metabolic pathways might be affected by gut microbiota, such as Enterorhabdus in mouse endometritis. The above results indicated that the anti-inflammatory mechanism of IFN-τ might be reduction of the abundance of Enterorhabdus in the gut microbiota, affecting the expression level of important metabolites in uterine tissue and thus playing an anti-inflammatory role. IMPORTANCE The change in intestinal flora has been the focus of many disease studies in recent years, but the pathogenetic effect of interferon on endometritis is still unclear. The results of this study showed that IFN-τ alleviated the damage in mouse endometritis induced by E. coli and improved the endometrial tissue barrier. Its functional mechanism may be reduction of the abundance of Enterorhabdus in the intestinal microbiota, affecting the expression level of important metabolites in uterine tissue and thus playing an anti-inflammatory role.

Modern Trends in Natural Antibiotic Discovery

Natural scaffolds remain an important basis for drug development. Therefore, approaches to natural bioactive compound discovery attract significant attention. In this account, we summarize modern and emerging trends in the screening and identification of natural antibiotics. The methods are divided into three large groups: approaches based on microbiology, chemistry, and molecular biology. The scientific potential of the methods is illustrated with the most prominent and recent results.

A systematic review of associations between gut microbiota composition and growth failure in preterm neonates

Growth failure is among the most prevalent and devastating consequences of prematurity. Up to half of all extremely preterm neonates struggle to grow despite modern nutrition practices. Although elegant preclinical models suggest causal roles for the gut microbiome, these insights have not yet translated into biomarkers that identify at-risk neonates or therapies that prevent or treat growth failure. This systematic review aims to identify features of the neonatal gut microbiota that are positively or negatively associated with early postnatal growth. We identified 860 articles, of which 14 were eligible for inclusion. No two studies used the same definitions of growth, ages at stool collection, and statistical methods linking microbiota to metadata. In all, 58 different taxa were associated with growth, with little consensus among studies. Two or more studies reported positive associations with Enterobacteriaceae, Bacteroides, Bifidobacterium, Enterococcus, and Veillonella, and negative associations with Citrobacter, Klebsiella, and Staphylococcus. Streptococcus was positively associated with growth in five studies and negatively associated with growth in three studies. To gain insight into how the various definitions of growth could impact results, we performed an exploratory secondary analysis of 245 longitudinally sampled preterm infant stools, linking microbiota composition to multiple clinically relevant definitions of neonatal growth. Within this cohort, every definition of growth was associated with a different combination of microbiota features. Together, these results suggest that the lack of consensus in defining neonatal growth may limit our capacity to detect consistent, meaningful clinical associations that could be leveraged into improved care for preterm neonates.