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

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.