Machine-learning to predict and understand the zoonotic threat of E. coli O157 isolates

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Enterohemorrhagic Escherichia coli (EHEC) O157 are bacteria that have their main reservoir in food production animals, predominately cattle, and can be responsible for serious and life-threatening infections in humans. There are specific factors that define EHEC O157, including a micro-injection (type 3 secretion) system and production of specific Shiga toxins. However, we have known for nearly twenty years that not all subtypes represent the same threat to human health and significant effort has gone into understanding why this is the case. On key reason is that there are different Shiga toxin types, some potentially more toxic than others, and their production levels differ between isolates. This variability comes from the fact that Shiga toxins are introduced into the bacteria by infection with bacterial viruses, known as bacteriophages. These integrate their DNA into the bacterial genome in a 'prophage' state. When the bacterial cell is threatened this can activate the prophage to produce copies of itself and new bacteriophages. From whole genome sequencing of E. coli we are now aware that multiple prophages are present in E. coli genomes, some in different states of decay, but they can impact on each other and recombine to produce new variants. Much of the differences between E. coli O157 isolates are down to their prophage content yet sequence identification methods generally use only 'core' genes for epidemiological studies. We have recently applied machine-learning approaches to examine whole genome sequences of E. coli O157 from cattle and humans. We use these as training sets and then ask it to predict which group other E. coli O157 isolates should be assigned to. Surprisingly it only assigns a small proportion (<10%) of isolates from cattle to the human grouping, indicating that only this small subset may be more of a threat to human health. This grant is to investigate the biological basis of this selection process. We know that the machine-learning assignment is based on discriminatory protein variants predicted to be expressed from mainly prophage genes, so this fits with our understanding of the variation present in these isolates. The proposed work will be a combination of bioinformatics research and 'wet' infection biology research. For the bioinformatics we can use subjective and objective approaches to swap gene variants, including whole prophage, between isolate sequences and re-calculate their host prediction scores. This will allow us to define the most important combinations of genes being used for the prediction of zoonotic potential. It may also highlight specific genes to simplify the identification process. In the laboratory we will initially compare isolates that are very similar at the core genome level but differ markedly in their prediction scores. We will examine their gene expression profiles, metabolic profiles and key phenotypes such as Shiga toxin production, cellular interactions and pathology in a mouse model. Then we will swap or mutate genes identified by the bioinformatics and test these strain variants in the same laboratory assays. The research should help validate this exciting new approach to understanding bacterial virulence and identify genes involved in the zoonotic threat of this dangerous pathogen. We should then be able to develop simpler approaches to identifying these specific variants on farms and intervene with, for example a vaccine, to reduce the threat to human health. The approach may also work to predict differences in virulence between human isolates and this could have repercussions for how specific outbreaks are managed. This research is timely as it builds on our recent and unique application of machine learning to predict zoonotic potential and access to fully annotated PacBio sequences of UK cattle and human E. coli O157 isolates generated in partnership with Dr James Bono (USDA, Nebraska).