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Eco-Evolutionary Drivers of Vibrio parahaemolyticus Sequence Type 3 Expansion: Retrospective Machine Learning

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JMIR Bioinformatics and Biotechnology
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Climate change drives pathogen expansion. Machine learning identified evolutionary and ecological factors predicting Vibrio parahaemolyticus sequence type 3 (VpST3) expansion, aiding future pandemic preparedness.

Keywords:
VpST3climate changeecologyevolutiongenomicsmachine learningpathogen expansionsequence type 3sequencingvibrio parahaemolyticus

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Area of Science:

  • Microbiology and Evolutionary Biology
  • Genomics and Bioinformatics
  • Climate Change and Public Health

Background:

  • Environmentally sensitive pathogens adapt to climate change, leading to global expansion of variants.
  • Understanding pathogen emergence mechanisms and drivers is crucial for pandemic preparedness.
  • The rapid global expansion of Vibrio parahaemolyticus sequence type 3 (VpST3) offers a model for studying pathogen eco-evolution.

Purpose of the Study:

  • To investigate the eco-evolutionary drivers behind the rapid global expansion of Vibrio parahaemolyticus sequence type 3 (VpST3).
  • To utilize genomic data and machine learning to predict pathogen expansion dynamics.

Main Methods:

  • Reconstructed the global expansion of VpST3 using whole-genome sequencing data.
  • Classified VpST3 genomes to define stages of emergence and establishment.
  • Employed random forest machine learning models to identify predictive ecological and evolutionary drivers of expansion.

Main Results:

  • Identified key evolutionary features, including core genome mutations and accessory gene presence, associated with VpST3 expansion.
  • Achieved high predictive accuracies (0.722–0.967) using combined eco-evolutionary models.
  • Successfully predicted population structure and isolate establishment, but noted limitations in predicting introduction success due to unrepresented factors.

Conclusions:

  • Machine learning models integrating genomic and eco-evolutionary data offer powerful insights into pathogen expansion.
  • These findings enhance understanding of the eco-evolutionary pathways of climate-sensitive pathogens like VpST3.
  • The study highlights the potential for genomic surveillance and predictive modeling in anticipating future pandemic threats.