The Promise and Limitations of Metabolic Trait Prediction Across Diverse Bacteria

ABSTRACT

Metabolic traits mediate microbial interactions and underpin mechanistic models of community assembly. With the rapid expansion of genomic data and bioinformatic tools, it is widely assumed that metabolic traits such as substrate utilization can be reliably inferred from genomes alone. Here, we systematically evaluate this assumption by comparing genome‐based predictions from four commonly used tools to high‐throughput growth phenotypes of 186 marine bacteria. For simple substrates, prediction accuracy was limited and highly variable across taxa and substrates, with many strains predicted at or near chance levels. For three of four tools, most errors were false positives, indicating that apparent pathway completeness frequently fails to translate into growth under laboratory conditions. In contrast, genomic prediction of polysaccharide utilization based on carbohydrate‐active enzyme content performed substantially better, suggesting that traits that depend on a few highly specific enzymes are easier to predict. While genome‐based approaches can recover broad statistical patterns in metabolic strategies, their limited strain‐level reliability constrains ecological inference and modelling. For the foreseeable future, large‐scale experimental phenotypic characterization will remain an indispensable component of microbial ecology, both to reveal trait distributions and to ground‐truth and improve genomic predictions.