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.

Do amino and fatty acid profiles of pollen provisions correlate with bacterial microbiomes in the mason beeOsmia bicornis?

“…We were further interested in links between the relative abundance of individual bacterial community members and the centred log-ratio transformed abundance of individual amino acids and fatty acids in pollen provisions. For this, we used multi-layer perceptron neural networks using MiMeNet [91]. This method has been specifically designed to address microbe-metabolite interactions and can identify significantly correlated nutrient-bacteria pairs by testing against null-model background distributions.…”

“…This method has been specifically designed to address microbe-metabolite interactions and can identify significantly correlated nutrient-bacteria pairs by testing against null-model background distributions. It has been evaluated to be robust with respect to dataset size [91], which was important for our comparisons ( provision bacterial communities versus provision nutrients: 21 AA and 19 FA samples from the same nest cells).…”

Machine Learning Prediction of Nitrification From Ammonia- and Nitrite-Oxidizer Community Structure

“…Sampling more frequently, more extensively, and/or at a finer granular scale may provide a more robust classification and improve accuracy. Additionally, deep learning methods (Oh and Zhang, 2020 ; Reiman et al, 2021 ) could be explored, but our dataset may suffer from too few samples for such analyses. In our case, few-shot learning (FSL) may be more appropriate, as it relies less on large sample sizes (Sung et al, 2018 ).…”

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“…In both these studies, inclusion of microbe-derived metabolites was essential for observing associations between the microbiome and disease states. Promising results produced from leveraging both these data types have led to the development of further studies and software tools for effective combination of metabolite and microbiome information as input data [70,[119][120][121][122].…”