Genetic and Phenotypic Features to Screen for Putative Adherent-Invasive Escherichia coli

To date no molecular tools are available to identify the adherent-invasive Escherichia coli (AIEC) pathotype, which has been associated with Crohn’s disease and colonizes the intestine of different hosts. Current techniques based on phenotypic screening of isolates are extremely time-consuming. The aim of this work was to search for signature traits to assist in rapid AIEC identification. The occurrence of at least 54 virulence genes (VGs), the resistance to 30 antibiotics and the distribution of FimH and ChiA amino acid substitutions was studied in a collection of 48 AIEC and 56 non-AIEC isolated from the intestine of humans and animals. χ(2) test was used to find frequency differences according to origin of isolation, AIEC phenotype and phylogroup. Mann–Whitney test was applied to test association with adhesion and invasion indices. Binary logistic regression was performed to search for variables of predictive value. Animal strains (N = 45) were enriched in 12 VGs while 7 VGs were more predominant in human strains (N = 59). The prevalence of 15 VGs was higher in AIEC (N = 49) than in non-AIEC (N = 56) strains, but only pic gene was still differentially distributed when analyzing human and animal strains separately. Among human strains, three additional VGs presented higher frequency in AIEC strains (papGII/III, iss and vat; N = 22) than in non-AIEC strains (N = 37). No differences between AIEC/non-AIEC were found in FimH variants. In contrast, the ChiA sequence of LF82 was shared with the 35.5% of AIEC studied (N = 31) and only with the 7.4% of non-AIEC strains (N = 27; p = 0.027). Binary logistic regression analysis, using as input variables all the VGs and antibiotic resistances tested, revealed that typifying E. coli isolates using pic gene and ampicillin resistance was useful to correctly classify strains according to the phenotype with a 75.5% of accuracy. Although there is not a molecular signature fully specific and sensitive to identify the AIEC pathotype, we propose two features easy to be tested that could assist in AIEC screening. Future work using additional strain collections would be required to assess the applicability of this method.