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Model-driven characterization of functional diversity of Pseudomonas aeruginosa clinical isolates with broadly

Mohammad Mazharul Islam1, Glynis L Kolling1, Emma M Glass1

  • 1Department of Biomedical Engineering, University of Virginia, Charlottesville, VA 22903, USA.

Microbial Genomics
|June 5, 2024
PubMed

Insights

This study explores the metabolic diversity of Pseudomonas aeruginosa clinical isolates. Understanding these metabolic capabilities is crucial for combating infections in vulnerable patients.

Area of Science:

  • Microbiology
  • Metabolic Engineering
  • Genomics

Background:

  • Pseudomonas aeruginosa is a significant cause of infections, particularly in immunocompromised individuals and healthcare settings.
  • Understanding the metabolic diversity of P. aeruginosa is essential for developing effective treatment strategies.

Purpose of the Study:

  • To investigate the relationship between phenotypic variations and the metabolic functions of clinical P. aeruginosa isolates.
  • To characterize the metabolic repertoire of P. aeruginosa within the context of clinical infections.

Main Methods:

  • Deep profiling of 971 clinical P. aeruginosa isolates, including whole-genome sequencing and multi-parametric phenotypic analysis.
  • Comparison of genotypic and phenotypic clustering to assess genotype-phenotype correlations.
  • Development of genome-scale metabolic network reconstructions for each isolate.

Main Results:

  • Genotypic and phenotypic clustering revealed distinct correlations, highlighting the diversity within P. aeruginosa clinical isolates.
  • Metabolic network reconstructions demonstrated a wide range of metabolic functionalities, expanding the known P. aeruginosa metabolic pangenome.
  • The study characterized a rich dataset of clinical isolates, providing insights into their genotypic and metabolic landscape.

Conclusions:

  • The findings offer a deeper understanding of the genotypic and metabolic diversity of P. aeruginosa in clinical settings.
  • This research lays the groundwork for future investigations into the pathogen's metabolic landscape and host-pathogen metabolic interactions during infection.
Keywords:
Pseudomonas aeruginosametabolic modellingphenotypic diversity

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