JMIR Publications

ABSTRACT

Background:

Environmentally-sensitive pathogens exhibit ecological and evolutionary responses to climate change, that result in the emergence and global expansion of well-adapted variants. It is imperative to understand the mechanisms that facilitate pathogen emergence and expansion, and the drivers behind these, to understand and prepare for future pandemic expansions.

Objective:

The unique, rapid global expansion of a clonal complex of Vibrio parahaemolyticus (VpST3), a marine bacteria causing gastroenteritis infections, provides an opportunity to explore eco-evolutionary drivers of pathogen expansion.

Methods:

The global expansion of VpST3 was reconstructed using VpST3 genomes which were then classified into metrics characterizing stages of this expansion process, indicative of stages of emergence and establishment. We used machine learning to test a range of ecological and evolutionary drivers for their potential in predicting these VpST3 expansion dynamics, using a random forest classifier.

Results:

We identified a range of evolutionary features, including mutations in the core genome and accessory gene presence, associated with expansion dynamics. A range of random forest classifier approaches were tested to predict expansion classification metrics for each genome. The highest predictive accuracy (ranging from 0.722 to 0.967) was achieved for models using a combined eco-evolutionary approach. While population structure and the difference between introduced and established isolates could be predicted to a high accuracy, our model reported multiple false positives when predicting the success of an introduced isolate, inferring potential limiting factors not represented in our eco-evolutionary features. Regional models produced for two countries reporting the most VpST3 genomes had varying success, reflecting impacts of class imbalance.

Conclusions:

These novel insights into evolutionary features and ecological conditions related to stages of VpST3 expansion showcase the potential of machine learning models using genomic data, and will contribute to future understanding of eco-evolutionary pathways of climate-sensitive pathogens.