The gut metabolome acts as an intermediary between the gut microbiota and host, and has tremendous diagnostic and therapeutic potential. Several studies have utilized bioinformatic tools to predict metabolites based on the different aspects of the gut microbiome. Although these tools have contributed to a better understanding of the relationship between the gut microbiota and various diseases, most of them have focused on the impact of microbial genes on the metabolites and the relationship between microbial genes. In contrast, relatively little is known regarding the effect of metabolites on the microbial genes or the relationship between these metabolites. In this study, we constructed a computational framework of Microbe-Metabolite INteractions-based metabolic profiles Predictor (MMINP), based on the Two-Way Orthogonal Partial Least Squares (O2-PLS) algorithm to predict the metabolic profiles associated with gut microbiota. We demonstrated the predictive value of MMINP relative to that of similar methods. Additionally, we identified the features that would profoundly impact the prediction performance of data-driven methods (O2-PLS, MMINP, MelonnPan, and ENVIM), including the training sample size, host disease state, and the upstream data processing methods of the different technical platforms. We suggest that when using data-driven methods, similar host disease states and preprocessing methods, and a sufficient number of training samples are necessary to achieve accurate prediction.
Keywords: data-driven method; metabolome; metagenome; prediction.
MMINP fully considers internal and mutual correlations in metabolites and microbial genes and infers metabolite information through their real joint parts.The feasibility of predicting metabolic profiles using gut microbiome data should be based on the premise of similar host disease states, similar preprocessing methods, and a sufficient number of training samples.Although the accuracy of predicted specific metabolites is affected by multiple factors, the systematic conclusions presented for predicted metabolites at higher levels (e.g., class level) are accurate, allowing metabolite prediction to be applied to the discovery of potential metabolite markers.