Eco-Evolutionary Drivers of Vibrio parahaemolyticus Sequence Type 3 Expansion: Retrospective Machine Learning Approach
BackgroundEnvironmentally 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 expan...
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
JMIR Publications
2024-11-01
|
| Series: | JMIR Bioinformatics and Biotechnology |
| Online Access: | https://bioinform.jmir.org/2024/1/e62747 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1846150487339958272 |
|---|---|
| author | Amy Marie Campbell Chris Hauton Ronny van Aerle Jaime Martinez-Urtaza |
| author_facet | Amy Marie Campbell Chris Hauton Ronny van Aerle Jaime Martinez-Urtaza |
| author_sort | Amy Marie Campbell |
| collection | DOAJ |
| description |
BackgroundEnvironmentally 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, as well as the drivers behind the mechanisms, to understand and prepare for future pandemic expansions.
ObjectiveThe unique, rapid, global expansion of a clonal complex of Vibrio parahaemolyticus (a marine bacterium causing gastroenteritis infections) named Vibrio parahaemolyticus sequence type 3 (VpST3) provides an opportunity to explore the eco-evolutionary drivers of pathogen expansion.
MethodsThe global expansion of VpST3 was reconstructed using VpST3 genomes, which were then classified into metrics characterizing the stages of this expansion process, indicative of the stages of emergence and establishment. We used machine learning, specifically a random forest classifier, to test a range of ecological and evolutionary drivers for their potential in predicting VpST3 expansion dynamics.
ResultsWe 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 accuracies (ranging from 0.722 to 0.967) were 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, suggesting potential limiting factors not represented in our eco-evolutionary features. Regional models produced for 2 countries reporting the most VpST3 genomes had varying success, reflecting the impacts of class imbalance.
ConclusionsThese novel insights into evolutionary features and ecological conditions related to the stages of VpST3 expansion showcase the potential of machine learning models using genomic data and will contribute to the future understanding of the eco-evolutionary pathways of climate-sensitive pathogens. |
| format | Article |
| id | doaj-art-bd15d37380b74f1e8c2aeb0ca1fa1f22 |
| institution | Kabale University |
| issn | 2563-3570 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | JMIR Publications |
| record_format | Article |
| series | JMIR Bioinformatics and Biotechnology |
| spelling | doaj-art-bd15d37380b74f1e8c2aeb0ca1fa1f222024-11-28T18:45:33ZengJMIR PublicationsJMIR Bioinformatics and Biotechnology2563-35702024-11-015e6274710.2196/62747Eco-Evolutionary Drivers of Vibrio parahaemolyticus Sequence Type 3 Expansion: Retrospective Machine Learning ApproachAmy Marie Campbellhttps://orcid.org/0000-0003-4111-8286Chris Hautonhttps://orcid.org/0000-0002-2313-4226Ronny van Aerlehttps://orcid.org/0000-0002-2605-6518Jaime Martinez-Urtazahttps://orcid.org/0000-0001-6219-0418 BackgroundEnvironmentally 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, as well as the drivers behind the mechanisms, to understand and prepare for future pandemic expansions. ObjectiveThe unique, rapid, global expansion of a clonal complex of Vibrio parahaemolyticus (a marine bacterium causing gastroenteritis infections) named Vibrio parahaemolyticus sequence type 3 (VpST3) provides an opportunity to explore the eco-evolutionary drivers of pathogen expansion. MethodsThe global expansion of VpST3 was reconstructed using VpST3 genomes, which were then classified into metrics characterizing the stages of this expansion process, indicative of the stages of emergence and establishment. We used machine learning, specifically a random forest classifier, to test a range of ecological and evolutionary drivers for their potential in predicting VpST3 expansion dynamics. ResultsWe 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 accuracies (ranging from 0.722 to 0.967) were 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, suggesting potential limiting factors not represented in our eco-evolutionary features. Regional models produced for 2 countries reporting the most VpST3 genomes had varying success, reflecting the impacts of class imbalance. ConclusionsThese novel insights into evolutionary features and ecological conditions related to the stages of VpST3 expansion showcase the potential of machine learning models using genomic data and will contribute to the future understanding of the eco-evolutionary pathways of climate-sensitive pathogens.https://bioinform.jmir.org/2024/1/e62747 |
| spellingShingle | Amy Marie Campbell Chris Hauton Ronny van Aerle Jaime Martinez-Urtaza Eco-Evolutionary Drivers of Vibrio parahaemolyticus Sequence Type 3 Expansion: Retrospective Machine Learning Approach JMIR Bioinformatics and Biotechnology |
| title | Eco-Evolutionary Drivers of Vibrio parahaemolyticus Sequence Type 3 Expansion: Retrospective Machine Learning Approach |
| title_full | Eco-Evolutionary Drivers of Vibrio parahaemolyticus Sequence Type 3 Expansion: Retrospective Machine Learning Approach |
| title_fullStr | Eco-Evolutionary Drivers of Vibrio parahaemolyticus Sequence Type 3 Expansion: Retrospective Machine Learning Approach |
| title_full_unstemmed | Eco-Evolutionary Drivers of Vibrio parahaemolyticus Sequence Type 3 Expansion: Retrospective Machine Learning Approach |
| title_short | Eco-Evolutionary Drivers of Vibrio parahaemolyticus Sequence Type 3 Expansion: Retrospective Machine Learning Approach |
| title_sort | eco evolutionary drivers of vibrio parahaemolyticus sequence type 3 expansion retrospective machine learning approach |
| url | https://bioinform.jmir.org/2024/1/e62747 |
| work_keys_str_mv | AT amymariecampbell ecoevolutionarydriversofvibrioparahaemolyticussequencetype3expansionretrospectivemachinelearningapproach AT chrishauton ecoevolutionarydriversofvibrioparahaemolyticussequencetype3expansionretrospectivemachinelearningapproach AT ronnyvanaerle ecoevolutionarydriversofvibrioparahaemolyticussequencetype3expansionretrospectivemachinelearningapproach AT jaimemartinezurtaza ecoevolutionarydriversofvibrioparahaemolyticussequencetype3expansionretrospectivemachinelearningapproach |