<p><i>Vibrio (V.) parahaemolyticus</i> is a ubiquitous marine bacterium that persists in dynamic marine environments through adaptive behaviors including motility and biofilm formation. Gene expression data obtained under planktonic conditions are widely used to infer surface-associated phenotypes, implicitly assuming that such measurements are informative of these behaviors. However, the extent to which this assumption holds across genetically diverse strains remains unclear. This study systematically assessed whether baseline transcriptional profiles provide predictive value for surface-associated phenotypes across a diverse panel of <i>V. parahaemolyticus</i> strains. Phenotypic traits and gene expression patterns varied substantially across individual strains. Correlation analyses revealed no robust associations between transcriptional levels and phenotypes after multiple-testing correction, and predictive modeling further demonstrated that transcriptional profiles do not reliably predict surface-associated phenotypes across strains (leave-one-out cross-validation R<sup>2</sup> &lt; 0). Principal component analysis (PCA) further confirmed strong strain-dependent transcriptional variation, with no distinct clustering among strains (PC1 and PC2 explaining 44.5% of total variance). These findings demonstrate that strain-level heterogeneity dominates both phenotypic and transcriptional variation and limits the use of baseline planktonic transcription as a predictor of surface-associated traits, highlighting the need to account for strain-level heterogeneity and condition-specific regulation in predictive frameworks.</p>

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Strain-level heterogeneity in Vibrio parahaemolyticus limits the predictive value of baseline planktonic gene expression for surface-associated phenotypes

  • Xia Huang,
  • Thomas Alter,
  • Roswitha Merle,
  • Vanessa Szott

摘要

Vibrio (V.) parahaemolyticus is a ubiquitous marine bacterium that persists in dynamic marine environments through adaptive behaviors including motility and biofilm formation. Gene expression data obtained under planktonic conditions are widely used to infer surface-associated phenotypes, implicitly assuming that such measurements are informative of these behaviors. However, the extent to which this assumption holds across genetically diverse strains remains unclear. This study systematically assessed whether baseline transcriptional profiles provide predictive value for surface-associated phenotypes across a diverse panel of V. parahaemolyticus strains. Phenotypic traits and gene expression patterns varied substantially across individual strains. Correlation analyses revealed no robust associations between transcriptional levels and phenotypes after multiple-testing correction, and predictive modeling further demonstrated that transcriptional profiles do not reliably predict surface-associated phenotypes across strains (leave-one-out cross-validation R2 < 0). Principal component analysis (PCA) further confirmed strong strain-dependent transcriptional variation, with no distinct clustering among strains (PC1 and PC2 explaining 44.5% of total variance). These findings demonstrate that strain-level heterogeneity dominates both phenotypic and transcriptional variation and limits the use of baseline planktonic transcription as a predictor of surface-associated traits, highlighting the need to account for strain-level heterogeneity and condition-specific regulation in predictive frameworks.