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Identifying genes associated with invasive disease in S. pneumoniae by applying a machine learning approach to whole

Uri Obolski1, Andrea Gori2, José Lourenço3

  • 1University of Oxford, Department of Zoology, Oxford, UK. UriObolski@gmail.com.

Scientific Reports
|March 13, 2019
PubMed

Insights

This study identifies 43 genes linked to invasive pneumococcal disease (IPD) using machine learning on whole genome sequences. The findings include known and novel uncharacterized genes potentially driving IPD development.

Area of Science:

  • Microbiology
  • Genomics
  • Infectious Diseases

Background:

  • Streptococcus pneumoniae causes significant global morbidity and mortality from pneumonia, meningitis, and sepsis.
  • The mechanisms driving invasive pneumococcal disease (IPD) remain unclear.
  • Identifying genetic factors associated with IPD is crucial for understanding pathogenesis.

Purpose of the Study:

  • To identify specific genes associated with invasive pneumococcal disease (IPD).
  • To leverage whole genome sequence (WGS) data and machine learning for genetic association studies.
  • To discover novel genetic targets for IPD prevention and treatment.

Main Methods:

  • Transformed whole genome sequence (WGS) data into a sequence typing scheme using a constructed pangenome.
  • Employed a random forest machine-learning algorithm to analyze genetic associations.
  • Validated findings across three geographically distinct WGS datasets of pneumococcal carriage isolates.

Main Results:

  • Identified 43 genes consistently associated with IPD across diverse datasets.
  • Confirmed 23 previously known IPD-associated genes.
  • Discovered 18 uncharacterized genes also linked to IPD.

Conclusions:

  • The study successfully identified a set of genes associated with IPD.
  • The identified uncharacterized genes represent potential novel targets for understanding and combating IPD.
  • This approach provides a robust method for genetic association studies in bacterial pathogens.

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