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Area of Science:

  • Microbiology
  • Gastroenterology
  • Molecular Biology

Background:

  • Adherent-invasive Escherichia coli (AIEC) are linked to Crohn's disease, but a specific molecular marker is lacking.
  • Current identification of AIEC is phenotypic, necessitating molecular tools for accurate classification.
  • A previously developed algorithm using single nucleotide polymorphisms (SNPs) showed promise for AIEC/non-AIEC classification.

Purpose of the Study:

  • To evaluate the accuracy of an SNP-based algorithm for classifying AIEC/non-AIEC strains.
  • To test the algorithm's performance with isolates from diverse geographical origins and extraintestinal pathogenic E. coli (ExPEC) strains.
  • To assess the need for broader strain inclusion in validating diagnostic tools.

Main Methods:

  • An existing SNP-based algorithm was tested on a new set of AIEC/non-AIEC and ExPEC strains.
  • Isolates were sourced from various international locations including France, Chile, Australia, and different regions of Spain.
  • The algorithm's accuracy was calculated for different strain subsets based on origin and pathotype.

Main Results:

  • The algorithm's accuracy decreased significantly (61%) when applied to strains from diverse geographical origins.
  • The tool showed higher accuracy (84.6% sensitivity, 79% accuracy) in differentiating AIEC from ExPEC strains.
  • Accuracy remained high (81%) for AIEC/non-AIEC strains from geographically proximate regions (Girona and Mallorca).

Conclusions:

  • The SNP-based tool for AIEC identification is not universally applicable and shows geographic bias.
  • Validation of such molecular tools requires a comprehensive inclusion of strains from varied geographical locations.
  • Further research is needed to develop robust molecular markers for AIEC detection in Crohn's disease.