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Study of a classification algorithm for AIEC identification in geographically distinct E. coli strains
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...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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Nature Publishing Group UK
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7229014/ https://www.ncbi.nlm.nih.gov/pubmed/32415168 http://dx.doi.org/10.1038/s41598-020-64894-5 |
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author | Camprubí-Font, Carla Bustamante, Paula Vidal, Roberto M. O’Brien, Claire L. Barnich, Nicolas Martinez-Medina, Margarita |
author_facet | Camprubí-Font, Carla Bustamante, Paula Vidal, Roberto M. O’Brien, Claire L. Barnich, Nicolas Martinez-Medina, Margarita |
author_sort | Camprubí-Font, Carla |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-7229014 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-72290142020-05-26 Study of a classification algorithm for AIEC identification in geographically distinct E. coli strains Camprubí-Font, Carla Bustamante, Paula Vidal, Roberto M. O’Brien, Claire L. Barnich, Nicolas Martinez-Medina, Margarita Sci Rep Article 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. Nature Publishing Group UK 2020-05-15 /pmc/articles/PMC7229014/ /pubmed/32415168 http://dx.doi.org/10.1038/s41598-020-64894-5 Text en © The Author(s) 2020 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/. |
spellingShingle | Article Camprubí-Font, Carla Bustamante, Paula Vidal, Roberto M. O’Brien, Claire L. Barnich, Nicolas Martinez-Medina, Margarita Study of a classification algorithm for AIEC identification in geographically distinct E. coli strains |
title | Study of a classification algorithm for AIEC identification in geographically distinct E. coli strains |
title_full | Study of a classification algorithm for AIEC identification in geographically distinct E. coli strains |
title_fullStr | Study of a classification algorithm for AIEC identification in geographically distinct E. coli strains |
title_full_unstemmed | Study of a classification algorithm for AIEC identification in geographically distinct E. coli strains |
title_short | Study of a classification algorithm for AIEC identification in geographically distinct E. coli strains |
title_sort | study of a classification algorithm for aiec identification in geographically distinct e. coli strains |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7229014/ https://www.ncbi.nlm.nih.gov/pubmed/32415168 http://dx.doi.org/10.1038/s41598-020-64894-5 |
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