Thinking about the microbiome as a causal factor in human health and disease: philosophical and experimental considerations

Best Practices and Considerations for Conducting Research on Diet-Gut Microbiome Interactions and Their Impact on Health in Adult Populations: An Umbrella Review

Diet modulates gut microbiome composition and function. However, determining causal links between diet-gut microbiome interactions and human health is complicated by inconsistencies in the evidence, arising partially from variability in research methods and reporting. Widespread adoption of standardized best practices would advance the field but require those practices to be identified, consolidated, and discussed. This umbrella review aimed to identify recommended best practices, define existing gaps, and collate considerations for conducting research on diet-gut microbiome interactions and their impact on human health outcomes. Reviews meeting inclusion criteria and published after 2013 were identified using a systematic search. Recommendations, considerations, and gaps relating to the best practices associated with study design, participant selection, dietary intervention/assessment, biological sample collection, and data analysis and reporting were extracted and consolidated. Eight narrative reviews were included. Several general points of agreement were identified, and a recurring theme was that best practices are dependent upon the research aims, outcomes, and feasibility. Multiple gaps were also identified. Some, such as suboptimal diet assessment methods and lack of validated dietary intake biomarkers, are particularly relevant to nutrition science. Others, including defining a "healthy" gut microbiome and the absence of standardized sample and data collection/analysis protocols, were relevant specifically to gut microbiome research. Gaps specific to diet-gut microbiome research include the underrepresentation of microbiome-modulating dietary components in food databases, lack of knowledge regarding interventions eliciting changes in the gut microbiome to confer health benefits, lack of in situ measurement methods, and the need to further develop and refine statistical approaches for integrating diet and gut microbiome data. Future research and cross-disciplinary exchange will address these gaps and evolve the best practices. In the interim, the best practices and considerations discussed herein, and the publications from which that information was extracted provide a roadmap for conducting diet-gut microbiome research. This trial was registered at PROSPERO as CRD42023437645.

The Role of Gut Microbiota Dysbiosis in Erectile Dysfunction: From Pathophysiology to Treatment Strategies

Erectile dysfunction (ED) is a prevalent male sexual disorder characterized by the persistent inability to achieve or maintain an erection sufficient for satisfactory sexual performance. While its etiology is multifactorial, encompassing vascular, neurological, hormonal, and psychological components, emerging evidence suggests a significant role for gut microbiota dysbiosis in its development. The gut microbiota influences various metabolic, inflammatory, and neuropsychological processes critical to erectile function. Dysbiosis can lead to systemic inflammation, endothelial dysfunction, hormonal imbalances, and altered neurotransmitter production, all of which are key factors in ED pathogenesis. This narrative review synthesizes current research on the association between gut microbiota alterations and ED, highlighting specific bacterial taxa implicated in ED through mechanisms involving inflammation, metabolic disturbances, and hormonal regulation. This review explores potential mechanisms linking gut microbiota and ED, including pro-inflammatory cytokines, gut barrier integrity disruption, metabolic disorders, psychological factors via the gut-brain axis, and hormonal regulation. Furthermore, the gut microbiota offers promising avenues for developing non-invasive biomarkers and therapeutic interventions such as probiotics, prebiotics, dietary modifications, and fecal microbiota transplantation. Future research should focus on longitudinal studies, mechanistic explorations, and clinical trials to validate these findings and translate them into clinical practice. Understanding the interplay between the gut microbiota and erectile function could unveil novel diagnostic biomarkers and pave the way for innovative treatments targeting the microbiota, ultimately improving men's sexual and overall health.

Unfolding and de-confounding: biologically meaningful causal inference from longitudinal multi-omic networks using METALICA

A key challenge in the analysis of microbiome data is the integration of multi-omic datasets and the discovery of interactions between microbial taxa, their expressed genes, and the metabolites they consume and/or produce. In an effort to improve the state of the art in inferring biologically meaningful multi-omic interactions, we sought to address some of the most fundamental issues in causal inference from longitudinal multi-omics microbiome data sets. We developed METALICA, a suite of tools and techniques that can infer interactions between microbiome entities. METALICA introduces novel unrolling and de-confounding techniques used to uncover multi-omic entities that are believed to act as confounders for some of the relationships that may be inferred using standard causal inferencing tools. The results lend support to predictions about biological models and processes by which microbial taxa interact with each other in a microbiome. The unrolling process helps identify putative intermediaries (genes and/or metabolites) to explain the interactions between microbes; the de-confounding process identifies putative common causes that may lead to spurious relationships to be inferred. METALICA was applied to the networks inferred by existing causal discovery, and network inference algorithms were applied to a multi-omics data set resulting from a longitudinal study of IBD microbiomes. The most significant unrollings and de-confoundings were manually validated using the existing literature and databases.

IMPORTANCE

We have developed a suite of tools and techniques capable of inferring interactions between microbiome entities. METALICA introduces novel techniques called unrolling and de-confounding that are employed to uncover multi-omic entities considered to be confounders for some of the relationships that may be inferred using standard causal inferencing tools. To evaluate our method, we conducted tests on the inflammatory bowel disease (IBD) dataset from the iHMP longitudinal study, which we pre-processed in accordance with our previous work. From this dataset, we generated various subsets, encompassing different combinations of metagenomics, metabolomics, and metatranscriptomics datasets. Using these multi-omics datasets, we demonstrate how the unrolling process aids in the identification of putative intermediaries (genes and/or metabolites) to explain the interactions between microbes. Additionally, the de-confounding process identifies potential common causes that may give rise to spurious relationships to be inferred. The most significant unrollings and de-confoundings were manually validated using the existing literature and databases.

Unfolding and De-confounding: Biologically meaningful causal inference from longitudinal multi-omic networks using METALICA

A key challenge in the analysis of microbiome data is the integration of multi-omic datasets and the discovery of interactions between microbial taxa, their expressed genes, and the metabolites they consume and/or produce. In an effort to improve the state-of-the-art in inferring biologically meaningful multi-omic interactions, we sought to address some of the most fundamental issues in causal inference from longitudinal multi-omics microbiome data sets. We developed METALICA, a suite of tools and techniques that can infer interactions between microbiome entities. METALICA introduces novel unrolling and de-confounding techniques used to uncover multi-omic entities that are believed to act as confounders for some of the relationships that may be inferred using standard causal inferencing tools. The results lend support to predictions about biological models and processes by which microbial taxa interact with each other in a microbiome. The unrolling process helps to identify putative intermediaries (genes and/or metabolites) to explain the interactions between microbes; the de-confounding process identifies putative common causes that may lead to spurious relationships to be inferred. METALICA was applied to the networks inferred by existing causal discovery and network inference algorithms applied to a multi-omics data set resulting from a longitudinal study of IBD microbiomes. The most significant unrollings and de-confoundings were manually validated using the existing literature and databases.

Diet leaves a genetic signature in a keystone member of the gut microbiota

Switching from a low-fat and high-fiber diet to a Western-style high-fat and high-sugar diet causes microbiota imbalances that underlay many pathological conditions (i.e., dysbiosis). Although the effects of dietary changes on microbiota composition and functions are well documented, their impact in gut bacterial evolution remains unexplored. We followed the emergence of mutations in Bacteroides thetaiotaomicron, a prevalent fiber-degrading microbiota member, upon colonization of the murine gut under different dietary regimens. B. thetaiotaomicron evolved rapidly in the gut and Western-style diet selected for mutations that promote degradation of mucin-derived glycans. Periodic dietary changes caused fluctuations in the frequency of such mutations and were associated with metabolic shifts, resulting in the maintenance of higher intraspecies genetic diversity compared to constant dietary regimens. These results show that dietary changes leave a genetic signature in microbiome members and suggest that B. thetaiotaomicron genetic diversity could be a biomarker for dietary differences among individuals.

Natural experiments and long-term monitoring are critical to understand and predict marine host-microbe ecology and evolution

Marine multicellular organisms host a diverse collection of bacteria, archaea, microbial eukaryotes, and viruses that form their microbiome. Such host-associated microbes can significantly influence the host’s physiological capacities; however, the identity and functional role(s) of key members of the microbiome (“core microbiome”) in most marine hosts coexisting in natural settings remain obscure. Also unclear is how dynamic interactions between hosts and the immense standing pool of microbial genetic variation will affect marine ecosystems’ capacity to adjust to environmental changes. Here, we argue that significantly advancing our understanding of how host-associated microbes shape marine hosts’ plastic and adaptive responses to environmental change requires (i) recognizing that individual host–microbe systems do not exist in an ecological or evolutionary vacuum and (ii) expanding the field toward long-term, multidisciplinary research on entire communities of hosts and microbes. Natural experiments, such as time-calibrated geological events associated with well-characterized environmental gradients, provide unique ecological and evolutionary contexts to address this challenge. We focus here particularly on mutualistic interactions between hosts and microbes, but note that many of the same lessons and approaches would apply to other types of interactions.

This Essay argues that in order to truly understand how marine hosts benefit from the immense diversity of microbes, we need to expand towards long-term, multi-disciplinary research focussing on few areas of the world’s ocean that we refer to as “natural experiments,” where processes can be studied at scales that far exceed those captured in laboratory experiments.

Gut Microbiota: Influence on Carcinogenesis and Modulation Strategies by Drug Delivery Systems to Improve Cancer Therapy

Gut microbiota have close interactions with the host. It can affect cancer progression and the outcomes of cancer therapy, including chemotherapy, immunotherapy, and radiotherapy. Therefore, approaches toward the modulation of gut microbiota will enhance cancer prevention and treatment. Modern drug delivery systems (DDS) are emerging as rational and promising tools for microbiota intervention. These delivery systems have compensated for the obstacles associated with traditional treatments. In this review, the essential roles of gut microbiota in carcinogenesis, cancer progression, and various cancer therapies are first introduced. Next, advances in DDS that are aimed at enhancing the efficacy of cancer therapy by modulating or engineering gut microbiota are highlighted. Finally, the challenges and opportunities associated with the application of DDS targeting gut microbiota for cancer prevention and treatment are briefly discussed.