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MiMeNet: Exploring microbiome-metabolome relationships using neural networks.

ABSTRACT: The advance in microbiome and metabolome studies has generated rich omics data revealing the involvement of the microbial community in host disease pathogenesis through interactions with their host at a metabolic level. However, the computational tools to uncover these relationships are just emerging. Here, we present MiMeNet, a neural network framework for modeling microbe-metabolite relationships. Using ten iterations of 10-fold cross-validation on three paired microbiome-metabolome datasets, we show that MiMeNet more accurately predicts metabolite abundances (mean Spearman correlation coefficients increase from 0.108 to 0.309, 0.276 to 0.457, and -0.272 to 0.264) and identifies more well-predicted metabolites (increase in the number of well-predicted metabolites from 198 to 366, 104 to 143, and 4 to 29) compared to state-of-art linear models for individual metabolite predictions. Additionally, we demonstrate that MiMeNet can group microbes and metabolites with similar interaction patterns and functions to illuminate the underlying structure of the microbe-metabolite interaction network, which could potentially shed light on uncharacterized metabolites through "Guilt by Association". Our results demonstrated that MiMeNet is a powerful tool to provide insights into the causes of metabolic dysregulation in disease, facilitating future hypothesis generation at the interface of the microbiome and metabolomics.

SUBMITTER: Reiman D

PROVIDER: S-EPMC8158931 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:Pathogenic fungal infections in plants may, in some cases, lead to downstream systematic impacts on the plant metabolome and microbiome that may either alleviate or exacerbate the effects of the fungal pathogen. While Sphaerulina musiva is a well-characterized fungal pathogen which infects Populus tree species, an important wood fiber and biofuel feedstock, little is known about its systematic effects on the metabolome and microbiome of Populus. Here, we investigated the metabolome of Populus trichocarpa and Populus deltoides leaves and roots and the microbiome of the leaf and root endospheres, phylloplane, and rhizosphere to understand the systematic impacts of S. musiva abundance and infection on Populus species in a common garden field setting. We found that S. musiva is indeed present in both P. deltoides and P. trichocarpa, but S. musiva abundance was not statistically related to stem canker onset. We also found that the leaf and root metabolomes significantly differ between the two Populus species and that certain leaf metabolites, particularly the phenolic glycosides salirepin and salireposide, are diminished in canker-infected P. trichocarpa trees compared to their uninfected counterparts. Furthermore, we found significant associations between the metabolome, S. musiva abundance, and microbiome composition and α-diversity, particularly in P. trichocarpa leaves. Our results show that S. musiva colonizes both resistant and susceptible hosts and that the effects of S. musiva on susceptible trees are not confined to the site of canker infection. IMPORTANCE Poplar (Populus spp.) trees are ecologically and economically important trees throughout North America. However, many western North American poplar plantations are at risk due to the introduction of the nonnative fungal pathogen Sphaerulina musiva, which causes leaf spot and cankers, limiting their production. To better understand the interactions among the pathogen S. musiva, the poplar metabolome, and the poplar microbiome, we collected leaf, root, and rhizosphere samples from poplar trees consisting of 10 genotypes and two species with differential resistance to S. musiva in a common garden experiment. Here, we outline the nuanced relationships between the poplar metabolome, microbiome, and S. musiva, showing that S. musiva may affect poplar trees in tissues distal to the site of infection (i.e., stem). Our research contributes to improving the fundamental understanding of S. musiva and Populus sp. ecology and the utility of a holobiont approach in understanding plant disease.

Project description:MotivationRecent technological developments have facilitated an expansion of microbiome-metabolome studies, in which samples are assayed using both genomic and metabolomic technologies to characterize the abundances of microbial taxa and metabolites. A common goal of these studies is to identify microbial species or genes that contribute to differences in metabolite levels across samples. Previous work indicated that integrating these datasets with reference knowledge on microbial metabolic capacities may enable more precise and confident inference of microbe-metabolite links.ResultsWe present MIMOSA2, an R package and web application for model-based integrative analysis of microbiome-metabolome datasets. MIMOSA2 uses genomic and metabolic reference databases to construct a community metabolic model based on microbiome data and uses this model to predict differences in metabolite levels across samples. These predictions are compared with metabolomics data to identify putative microbiome-governed metabolites and taxonomic contributors to metabolite variation. MIMOSA2 supports various input data types and customization with user-defined metabolic pathways. We establish MIMOSA2's ability to identify ground truth microbial mechanisms in simulation datasets, compare its results with experimentally inferred mechanisms in honeybee microbiota, and demonstrate its application in two human studies of inflammatory bowel disease. Overall, MIMOSA2 combines reference databases, a validated statistical framework, and a user-friendly interface to facilitate modeling and evaluating relationships between members of the microbiota and their metabolic products.Availability and implementationMIMOSA2 is implemented in R under the GNU General Public License v3.0 and is freely available as a web server at http://elbo-spice.cs.tau.ac.il/shiny/MIMOSA2shiny/ and as an R package from http://www.borensteinlab.com/software_MIMOSA2.html.Supplementary informationSupplementary data are available at Bioinformatics online.

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MiMeNet: Exploring microbiome-metabolome relationships using neural networks.

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