Background Antimicrobial resistance (AMR) is a rising health threat with 10 million annual casualties estimated by 2050. Appropriate treatment of infectious diseases with the right antibiotics reduces the spread of antibiotic resistance. Today, clinical practice relies on molecular and PCR techniques for pathogen identification and culture-based antibiotic susceptibility testing (AST). Recently, WGS has started to transform clinical microbiology, enabling prediction of resistance phenotypes from genotypes and allowing for more informed treatment decisions. WGS-based AST (WGS-AST) depends on the detection of AMR markers in sequenced isolates and therefore requires AMR reference databases. The completeness and quality of these databases are material to increase WGS-AST performance. Methods We present a systematic evaluation of the performance of publicly available AMR marker databases for resistance prediction on clinical isolates. We used the public databases CARD and ResFinder with a final dataset of 2587 isolates across five clinically relevant pathogens from PATRIC and NDARO, public repositories of antibiotic-resistant bacterial isolates. Results CARD and ResFinder WGS-AST performance had an overall balanced accuracy of 0.52 (±0.12) and 0.66 (±0.18), respectively. Major error rates were higher in CARD (42.68%) than ResFinder (25.06%). However, CARD showed almost no very major errors (1.17%) compared with ResFinder (4.42%). Conclusions We show that AMR databases need further expansion, improved marker annotations per antibiotic rather than per antibiotic class and validated multivariate marker panels to achieve clinical utility, e.g. in order to meet performance requirements such as provided by the FDA for clinical microbiology diagnostic testing.

Wastewater treatment plants play an important role in the emergence of antibiotic resistance. They provide a hot spot for exchange of resistance within and between species. Here, we analyse and quantify the genomic diversity of the indicator Escherichia coli in a German wastewater treatment plant and we relate it to isolates’ antibiotic resistance. Our results show a surprisingly large pan-genome, which mirrors how rich an environment a treatment plant is. We link the genomic analysis to a phenotypic resistance screen and pinpoint genomic hot spots, which correlate with a resistance phenotype. Besides well-known resistance genes, this forward genomics approach generates many novel genes, which correlated with resistance and which are partly completely unknown. A surprising overall finding of our analyses is that we do not see any difference in resistance and pan genome size between isolates taken from the inflow of the treatment plant and from the outflow. This means that while treatment plants reduce the amount of bacteria released into the environment, they do not reduce the potential for antibiotic resistance of these bacteria.

19Wastewater treatment plants play an important role in the release of antibiotic resistance into the 20 environment. It has been shown that wastewater contains multi-drug resistant Escherichia coli, 21 but information on strain diversity is surprisingly scarce. Here we present an exceptionally large 22 dataset on multidrug resistant Escherichia coli, originating from wastewater, over a thousand 23 isolates were phenotypically characterized for twenty antibiotics and for 103 isolates whole 24 genomes were sequenced. To our knowledge this is the first study documenting such a 25 comprehensive diversity of multi-drug resistant Escherichia coli in wastewater. The genomic 26 diversity of the isolates was unexpectedly high and contained a high number of resistance and 27 virulence genes. To illustrate the genomic diversity of the isolates we calculated the pan genome 28 of the wastewater Escherichia coli and found it to contain over sixteen thousand genes. To 29 analyse this diverse dataset, we devised a computational approach correlating genotypic variation 30 and resistance phenotype, this way we were able to identify not only known, but also candidate 31 resistance genes. Finally, we could verify that the effluent of a wastewater treatment plant will 32