A Comparative Evaluation of Tools to Predict Metabolite Profiles From Microbiome Sequencing Data

Area of Science:

• Microbiome research
• Metabolomics
• Computational biology

Background:

• Metabolomic analysis of the human gut microbiome reveals metabolic potential and aids disease diagnosis.
• Large-scale metabolomic profiling is expensive and logistically challenging.
• Predicting metabolites from microbiome data could overcome these limitations.

Purpose of the Study:

• To assess the feasibility of predicting microbial community metabolites using only microbiome sequencing data.
• To compare the performance of machine learning (ML) and reference-based pipelines for metabolite prediction.
• To evaluate prediction accuracy for identifying differential metabolites between disease states.

Main Methods:

• Utilized paired microbiome sequencing (16S rRNA, shotgun metagenomics, metatranscriptomics) and metabolome data from six independent studies.
• Evaluated two reference-based gene-to-metabolite prediction pipelines.
• Developed and tested a machine-learning based metabolic profile prediction approach trained on over 900 samples.

Main Results:

• The ML approach achieved the highest accuracy (F1 scores) in predicting gut metabolite occurrences.
• The ML model outperformed reference-based pipelines in predicting differential metabolites between case and control groups.
• The study identified limitations in detecting differential metabolites using this predictive approach.

Conclusions:

• Predicting microbial metabolites from microbiome sequencing data is possible, offering a scalable alternative to direct metabolomics.
• The developed ML framework provides a valuable tool for evaluating prediction pipelines.
• This approach can facilitate future research on microbial metabolites and their impact on human health.