MMINP: A computational framework of microbe-metabolite interactions-based metabolic profiles predictor based on the...

Area of Science:

• Microbiome research
• Metabolomics
• Bioinformatics

Background:

• The gut metabolome links gut microbiota and host physiology, offering diagnostic and therapeutic potential.
• Existing bioinformatic tools primarily focus on microbial gene impact on metabolites, neglecting metabolite influence on microbial genes.

Purpose of the Study:

• To develop a novel computational framework, Microbe-Metabolite INteractions-based metabolic profiles Predictor (MMINP), for predicting gut metabolic profiles.
• To investigate the predictive performance of MMINP compared to existing methods.
• To identify key factors influencing the accuracy of data-driven prediction methods.

Main Methods:

• Construction of the MMINP framework using the Two-Way Orthogonal Partial Least Squares (O2-PLS) algorithm.
• Comparative analysis of MMINP against other methods like MelonnPan and ENVIM.
• Evaluation of factors such as training sample size, host disease state, and data preprocessing on prediction accuracy.

Main Results:

• MMINP demonstrated significant predictive value for gut metabolic profiles.
• The study identified training sample size, host disease state, and upstream data processing as critical features impacting prediction performance.
• MMINP showed improved or comparable performance to existing methods.

Conclusions:

• MMINP provides a robust method for predicting metabolic profiles from gut microbiota data.
• Accurate predictions using data-driven methods necessitate standardized host disease states, consistent preprocessing, and adequate training sample sizes.
• This framework enhances understanding of microbe-metabolite interactions and their role in host health.