Wastewater based genomic surveillance key to population level monitoring of AmpC/ESBL producing Escherichia coli

Abstract

Antimicrobial resistance (AMR) is a serious threat to global public health, but obtaining representative data on AMR for healthy human populations is difficult. Here, we leverage the power of whole genome sequencing (WGS) to screen AmpC- and extended-spectrum beta-lactamase (ESBL)-producing Escherichia coli from 77 composite samples obtained from 10 wastewater treatment plants across Finland. We found a high abundance of multidrug-resistant AmpC/ESBL-producing E. coli and significant differences in the diversity of AMR genes between the sampled cities. The in silico analysis of 73 short-read genome sequences shows the clonally diverse isolates consisting of 30 sequence types (STs), including the globally distributed pandemic ST131 clone. The CTX-M ESBL genes were detected in 86.3% (63/73) of the isolates concurrently with the blaTEM-1 (31.5%, 23/73) and blaOXA-1 (9.6%, 7/73) genes. The most prevalent ESBL genes were blaCTX-M-15 (46.6%, 34/73), blaCTX-M-27 (16.4%, 12/73), blaCTX-M-14 (4.1%, 3/73), and blaCTX-M-55 (4.1%, 3/73). Two isolates harboured the carbapenemase resistance gene, blaKPC-2 and blaNDM-1, respectively. In addition, WGS predicted phenotypic resistance against aminoglycosides, beta-lactams, cephalosporins, quinolones, sulfonamides, carbapenems, and polymyxins. The diversity of antibiotic- and stress-resistance genes correlated with the clinical incidence reported in the Finnish AMR report. Core-genome MLST revealed two wastewater genomic clusters but no genomic clusters among human and wastewater ST131 isolates. Our findings suggest the circulation of distinct clonal lineages of AmpC/ESBL-producing E. coli across Finland, with variations in AMR gene diversity and abundance by wellbeing service county. Also, our findings underscore the fact that wastewater surveillance could be key to population-level monitoring of AmpC/ESBL-producing Escherichia coli and can serve as complementary data to guide public health decisions. We propose longitudinal WGS-based epidemiology as an economically feasible approach for global AMR surveillance, pathogen evolution, and prediction of AMR.