Uneven genotypic diversity of Escherichia coli in fecal sources limits the performance of a library-dependent method of microbial source tracking on the southwestern French Atlantic coast

Abstract

To develop a library-dependent method of fecal sources tracking, a reference library of 6,368 Escherichia coli isolates was constructed based on fecal samples collected around the Arcachon Bay, in 2010, and in French Basque Country, Landes and Béarn, between 2017 and 2018. Different schemes of source identification were tested: use of the complete or filtered library; characterization of the isolates based on ERIC – PCR or MALDI-TOF mass spectrometry; assignment using either similarity-based classifiers or SVM. With the exception of one scheme which was discarded since it turned out to use self-assignment, all tested schemes resulted in low rates of correct classifications (<35%), and significant rates of incorrect classifications (> 15%). The E. coli genotypic diversity of the library likely explained it. Shannon diversity index ranged from 0 to 3.03 for several wildlife species and for sewage treatment plant effluents, respectively, showing discrepancies among sources. The uneven genotypic composition of the library was attested by the Pielou index value (0.54), the high proportion of non-discriminatory genotypes (> 91% of isolates) and the very low proportion of discriminatory genotypes (< 3%). The results question the relevance of developing such a method for identifying sources of fecal contamination on such a coastline.

Microbial source tracking of fecal contamination in Laguna Lake, Philippines using the library-dependent method, rep-PCR

“…Lyautey et al (2010) reported an accuracy of 77% using BOX and ERIC libraries, while Mohapatra et al (2007) achieved 79.89% in GTG(5) library. Other gel fingerprinting studies have used different classifiers such as discriminant analysis (Dombek et al 2000;Mohapatra et al 2007;Somarelli et al 2007), support vector machines (Garabetian et al 2020), kNN and neural networks (Carlos et al 2012) with lower or similar accuracy rates. Robinson et al (2007) suggested kNN as a compromise between the strengths of maximum similarity (MS) and discriminant analysis in terms of accuracy and prediction bias when dealing with disproportionate libraries.…”

An Overview of Microbial Source Tracking Using Host-Specific Genetic Markers to Identify Origins of Fecal Contamination in Different Water Environments

“…For instance, Dela Peña et al [24] established a fecal source library with rep-PCR fingerprints of a variety of hosts to study fecal contamination sources in Laguna Lake (Philippines). Similarly, an E. coli-based fecal library was created for source tracking on the French Atlantic coast [25]. However, the authors indicated that the uneven genotypic composition of the library with a high proportion of nondiscriminatory genotypes hampered its performance in MST analysis.…”

“…The inventory of host-specific/associated genetic markers is constantly expanded and is increasingly exploited for qualitative and quantitative MST analyses in different water environments across the world [3,5,9,14,21,[23][24][25][26][27][28]. The majority of genetic markers are developed to target the 16S rRNA gene of Bacteroides spp.…”