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Refining microbial community metabolic models derived from metagenomics using reference-based taxonomic profiling.

Marwan E Majzoub1, Laurence D W Luu1, Craig Haifer2,3

  • 1School of Biomedical Sciences, Faculty of Medicine and Health, UNSW Sydney, Sydney, New South Wales, Australia.

Msystems
|August 13, 2024
PubMed
Summary
This summary is machine-generated.

Comparing two methods for predicting microbial metabolite production from metagenomics data, this study found that a reference-guided approach generally predicts more metabolites than a MAG-guided approach, with significant overlap but unique predictions from each. Caution is advised when interpreting results due to approach-dependent differences.

Keywords:
environmentalhumanmetabolic modellingmetagenomicsmicrobiome

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Area of Science:

  • Microbial Ecology
  • Metagenomics
  • Systems Biology

Background:

  • Characterizing microbial community metabolic output is vital for understanding their functions.
  • Genome-scale metabolic models (GEMs) constructed from metagenome-assembled genomes (MAGs) predict metabolite production, but their accuracy is not well-established.
  • Assessing the performance of different approaches for metabolite prediction from metagenomic data is crucial.

Purpose of the Study:

  • To evaluate the accuracy of two distinct approaches for predicting microbial metabolite production from metagenomic data: a MAG-guided approach and a taxonomic reference-guided approach.
  • To compare the performance of these approaches across human and environmental microbial communities.
  • To validate predictions in human samples using untargeted metabolomics.

Main Methods:

  • Applied both MAG-guided and taxonomic reference-guided approaches to shotgun metagenomics data from human and environmental samples.
  • Normalized the number of input taxa for both approaches to ensure fair comparison.
  • Validated metabolite predictions in human samples using untargeted metabolomics.

Main Results:

  • In human samples, the reference-guided approach predicted more metabolites than the MAG-guided approach after normalizing input taxa.
  • Both approaches showed substantial overlap in predictions but also identified unique metabolites.
  • Pathway enrichment analyses revealed significant differences in biological inferences depending on the approach used.
  • In environmental samples, the reference-guided approach also predicted more metabolites than the MAG-guided approach.

Conclusions:

  • The taxonomic reference-guided approach offers a computationally less intensive alternative to MAG assembly for metabolite prediction, especially useful for shallow sequencing data.
  • Findings highlight the utility of complementary inputs for GEM construction.
  • Significant differences in biological inferences underscore the need for careful interpretation of GEM-based predictions and suggest using multiple approaches.