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Following the Dynamics of Structural Variants in Experimentally Evolved Populations
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Inferring strain-level mutational drivers of phage-bacteria interaction phenotypes arising during coevolutionary
Adriana Lucia-Sanz1, Shengyun Peng2, Chung Yin Joey Leung3
1School of Biological Sciences, Georgia Institute of Technology, Atlanta, Georgia, USA.
Biorxiv : the Preprint Server for Biology
|January 23, 2024
Summary
Predicting phage-bacteria interactions is challenging due to genetic diversity. This study uses machine learning on genome data to accurately predict infection outcomes and identify key mutations influencing phage and Escherichia coli interactions.
Area of Science:
- Microbiology and Genomics
- Computational Biology and Machine Learning
Background:
- Predicting bacteriophage (phage) and bacterial host interactions is complex due to vast genetic diversity and uncharacterized infection mechanisms (adsorption, injection, lysis).
- Understanding these interactions is crucial for fields like phage therapy and microbial ecology.
Purpose of the Study:
- To develop and evaluate a machine learning framework for predicting phage-bacteria interactions and infection strengths.
- To identify key genetic mutations in both phages and bacteria that drive interaction outcomes.
Main Methods:
- Trained a machine learning model on genome sequences and phenotypic interaction data from 51 Escherichia coli strains and 45 phage lambda strains.
- Utilized multiple inference strategies without prior knowledge of driver mutations to predict interactions.
- Analyzed feature importance to identify influential mutations.
Main Results:
- The most effective model accurately predicted 86% of phage-bacteria interactions.
- The model reduced the relative error in estimating infection strength by 40%.
- Identified known and novel mutations in phage lambda and E. coli influencing infection outcomes, including mutations in genes of unknown function.
Conclusions:
- Machine learning can effectively predict phage-bacteria infection outcomes and strengths using genomic and phenotypic data.
- The approach successfully identified key genetic drivers of these interactions, aiding in understanding bacterial resistance and phage infectivity.
- This framework has potential applications for imputing genetic drivers in complex microbial communities.
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