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Modern Molecular Taxonomy01:29

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Predicting Host Association for Shiga Toxin-Producing E. coli Serogroups by Machine Learning.

Nadejda Lupolova1, Antonia Chalka1, David L Gally2

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Predicting Escherichia coli host origins is possible using whole genome sequences. Machine learning models can identify Shiga toxin-producing E. coli (STEC) strains with high zoonotic potential, aiding in public health risk assessment.

Keywords:
Host attributionMachine learningSTECWhole genome sequence (WGS)Zoonotic threat

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Area of Science:

  • Microbiology and Genomics
  • Bioinformatics and Machine Learning
  • Zoonotic Disease Epidemiology

Background:

  • Escherichia coli (E. coli) exhibits significant genomic diversity across various hosts, including mammals and soil environments.
  • Shiga toxin-producing E. coli (STEC) strains, primarily hosted by ruminants, pose a significant threat due to their potential to cause severe human infections.
  • Understanding host association and transmission dynamics of E. coli is crucial for public health.

Purpose of the Study:

  • To develop and apply a machine learning classifier for predicting the host association (human vs. cattle) of E. coli isolates using whole genome sequences (WGS).
  • To evaluate the zoonotic potential of key STEC serotypes by scoring their likelihood of human association.
  • To lay the groundwork for predicting the zoonotic threat of E. coli isolates based on WGS data.

Main Methods:

  • Utilized whole genome sequences (WGS) of E. coli isolates from cattle and human hosts.
  • Developed a machine learning classifier to predict host association based on genomic content.
  • Applied the classifier to score STEC serotypes for their predicted association with human hosts.

Main Results:

  • The developed classifier successfully predicted host association for E. coli isolates.
  • Serogroups O157, O26, and O111 demonstrated the highest predicted human association, while O103 and O145 showed the lowest.
  • The study identified specific STEC serotypes with a higher likelihood of zoonotic transmission.

Conclusions:

  • Genomic content of E. coli isolates can accurately predict their host association.
  • Machine learning applied to WGS data is a powerful tool for assessing zoonotic risks posed by STEC.
  • Future integration with phylogenetic analysis will enhance the prediction of zoonotic threats from E. coli.