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Journal Articles Scientific Reports Year : 2020

Study of a classification algorithm for AIEC identification in geographically distinct E. coli strains

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Abstract

Adherent-invasive Escherichia coli (AIEC) have been extensively implicated in Crohn's disease pathogenesis. Currently, AIEC is identified phenotypically, since no molecular marker specific for AIEC exists. An algorithm based on single nucleotide polymorphisms was previously presented as a potential molecular tool to classify AIEC/non-AIEC, with 84% accuracy on a collection of 50 strains isolated in Girona (Spain). Herein, our aim was to determine the accuracy of the tool using AIEC/non-AIEC isolates from different geographical origins and extraintestinal pathogenic E. coli (ExPEC) strains. The accuracy of the tool was significantly reduced (61%) when external AIEC/non-AIEC strains from France, Chile, Mallorca (Spain) and Australia (82 AIEC, 57 non-AIEC and 45 ExPEC strains in total) were included. However, the inclusion of only the ExPEC strains showed that the tool was fairly accurate at differentiating these two close pathotypes (84.6% sensitivity; 79% accuracy). Moreover, the accuracy was still high (81%) for those AIEC/non-AIEC strains isolated from Girona and Mallorca (N = 63); two collections obtained from independent studies but geographically close. Our findings indicate that the presented tool is not universal since it would be only applicable for strains from similar geographic origin and demonstrates the need to include strains from different origins to validate such tools.
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Dates and versions

inserm-02865390 , version 1 (11-06-2020)

Licence

Attribution - CC BY 4.0

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Cite

Carla Camprubí-Font, Paula Bustamante, Roberto M Vidal, Claire L O'Brien, Nicolas Barnich, et al.. Study of a classification algorithm for AIEC identification in geographically distinct E. coli strains. Scientific Reports, 2020, 10 (1), pp.8094. ⟨10.1038/s41598-020-64894-5⟩. ⟨inserm-02865390⟩
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