Machine learning identifies signatures of host adaptation in the bacterial pathogen Salmonella enterica

Author summary Researchers are now collecting a wealth of genomic data from bacterial pathogens, and this will continue to grow with the introduction of routine sequencing for disease surveillance. However, our ability to use this data to predict how changes in genome sequence lead to differences in disease is limited. Here, we have used machine learning to detect an enrichment in functionally significant mutations in genes associated with a shift in pathogenic niche. This approach captures convergence in functional outcomes that does not necessarily result in a convergence in sequence, facilitating the inclusion of rare variants of large effect in an analysis, and allowing for complex interactions between genes. We apply this approach to Salmonella, showing that we can detect changes associated with disease phenotype in emerging lineages associated with the HIV epidemic. This approach should be applicable to other bacterial species with lineages independently adapting to similar niches. We provide open-source implementations of both the predictive model, and the workflow used to build it.