Relative dispersion ratios following fecal microbiota transplant elucidate principles governing microbial migration dynamics.

Microorganisms frequently migrate from one ecosystem to another. Yet, despite the potential importance of this process in modulating the environment and the microbial ecosystem, our understanding of the fundamental forces that govern microbial dispersion is still lacking. Moreover, while theoretical models and in-vitro experiments have highlighted the contribution of species interactions to community assembly, identifying such interactions in vivo, specifically in communities as complex as the human gut, remains challenging. To address this gap, here we introduce a robust and rigorous computational framework, termed Relative Dispersion Ratio (RDR) analysis, and leverage data from well-characterized fecal microbiota transplant trials, to rigorously pinpoint dependencies between taxa during the colonization of human gastrointestinal tract. Our analysis identifies numerous pairwise dependencies between co-colonizing microbes during migration between gastrointestinal environments. We further demonstrate that identified dependencies agree with previously reported findings from in-vitro experiments and population-wide distribution patterns. Finally, we explore metabolic dependencies between these taxa and characterize the functional properties that facilitate effective dispersion. Collectively, our findings provide insights into the principles and determinants of community dynamics following ecological translocation, informing potential opportunities for precise community design.

Diet-omics in the Study of Urban and Rural Crohn disease Evolution (SOURCE) cohort.

Crohn disease (CD) burden has increased with globalization/urbanization, and the rapid rise is attributed to environmental changes rather than genetic drift. The Study Of Urban and Rural CD Evolution (SOURCE, n = 380) has considered diet-omics domains simultaneously to detect complex interactions and identify potential beneficial and pathogenic factors linked with rural-urban transition and CD. We characterize exposures, diet, ileal transcriptomics, metabolomics, and microbiome in newly diagnosed CD patients and controls in rural and urban China and Israel. We show that time spent by rural residents in urban environments is linked with changes in gut microbial composition and metabolomics, which mirror those seen in CD. Ileal transcriptomics highlights personal metabolic and immune gene expression modules, that are directly linked to potential protective dietary exposures (coffee, manganese, vitamin D), fecal metabolites, and the microbiome. Bacteria-associated metabolites are primarily linked with host immune modules, whereas diet-linked metabolites are associated with host epithelial metabolic functions.

A data-driven approach for predicting the impact of drugs on the human microbiome.

Many medications can negatively impact the bacteria residing in our gut, depleting beneficial species, and causing adverse effects. To guide personalized pharmaceutical treatment, a comprehensive understanding of the impact of various drugs on the gut microbiome is needed, yet, to date, experimentally challenging to obtain. Towards this end, we develop a data-driven approach, integrating information about the chemical properties of each drug and the genomic content of each microbe, to systematically predict drug-microbiome interactions. We show that this framework successfully predicts outcomes of in-vitro pairwise drug-microbe experiments, as well as drug-induced microbiome dysbiosis in both animal models and clinical trials. Applying this methodology, we systematically map a large array of interactions between pharmaceuticals and human gut bacteria and demonstrate that medications' anti-microbial properties are tightly linked to their adverse effects. This computational framework has the potential to unlock the development of personalized medicine and microbiome-based therapeutic approaches, improving outcomes and minimizing side effects.

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

Integrative analysis of microbiome and metabolome data obtained from human fecal samples is a promising avenue for better understanding the interplay between bacteria and metabolites in the human gut, in both health and disease. However, acquiring, processing, and unifying such datasets from multiple sources is a daunting and challenging task. Here we present a publicly available, simple-to-use, curated dataset collection of paired fecal microbiome-metabolome data from multiple cohorts. This data resource allows researchers to easily obtain multiple fully processed and integrated microbiome-metabolome datasets, facilitating the discovery of universal microbe-metabolite links, benchmark various microbiome-metabolome integration tools, and compare newly identified microbe-metabolite findings to other published datasets.

A meta-analysis study of the robustness and universality of gut microbiome-metabolome associations.

BACKGROUND: Microbiome-metabolome studies of the human gut have been gaining popularity in recent years, mostly due to accumulating evidence of the interplay between gut microbes, metabolites, and host health. Statistical and machine learning-based methods have been widely applied to analyze such paired microbiome-metabolome data, in the hope of identifying metabolites that are governed by the composition of the microbiome. Such metabolites can be likely modulated by microbiome-based interventions, offering a route for promoting gut metabolic health. Yet, to date, it remains unclear whether findings of microbially associated metabolites in any single study carry over to other studies or cohorts, and how robust and universal are microbiome-metabolites links. RESULTS: In this study, we addressed this challenge by performing a comprehensive meta-analysis to identify human gut metabolites that can be predicted based on the composition of the gut microbiome across multiple studies. We term such metabolites "robustly well-predicted". To this end, we processed data from 1733 samples from 10 independent human gut microbiome-metabolome studies, focusing initially on healthy subjects, and implemented a machine learning pipeline to predict metabolite levels in each dataset based on the composition of the microbiome. Comparing the predictability of each metabolite across datasets, we found 97 robustly well-predicted metabolites. These include metabolites involved in important microbial pathways such as bile acid transformations and polyamines metabolism. Importantly, however, other metabolites exhibited large variation in predictability across datasets, suggesting a cohort- or study-specific relationship between the microbiome and the metabolite. Comparing taxonomic contributors to different models, we found that some robustly well-predicted metabolites were predicted by markedly different sets of taxa across datasets, suggesting that some microbially associated metabolites may be governed by different members of the microbiome in different cohorts. We finally examined whether models trained on a control group of a given study successfully predicted the metabolite's level in the disease group of the same study, identifying several metabolites where the model was not transferable, indicating a shift in microbial metabolism in disease-associated dysbiosis. CONCLUSIONS: Combined, our findings provide a better understanding of the link between the microbiome and metabolites and allow researchers to put identified microbially associated metabolites within the context of other studies. Video abstract.

Light-Triggered Release of Trapped Charges in Molecular Assemblies.

We demonstrate the mediation of charge transport and release in thin films and devices by shifting the redox properties of layers of metal complexes by light. The nanoscale surface arrangement of both photo- and electrochemically-active components is essential for the function of the thin films. Layers of well-defined ruthenium complexes on indium-tin-oxide electrodes provide electron-transport channels that allow the electrochemical addressing of layers of isostructural cobalt complexes. These cobalt complexes are electrochemically inactive when assembled directly on transparent metal-oxide electrodes. The interlayer of ruthenium complexes on such electrodes allows irreversible oxidation of the cobalt complexes. However, shifting the redox properties of the ruthenium complexes by excitation with light opens up an electron-transport channel to reduce the cobalt complexes; hence releasing the trapped positive charges.