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Groupwise antibiotic treatments are common in broiler chicken production. They induce selection for antibiotic resistance in commensal Escherichia coli. This study aimed to investigate antibiotic resistance after individual (I, drenching) or groupwise treatment (G, by water) with amoxicillin, and after contact with I or G (KI or KG), compared with untreated broilers without contact with treated broilers (C), and pretreatment values. Finally, we compared antibiotic resistance from broilers (G) after a second treatment, with a treatment in the contact animals (KG), and a first treatment in the control animals (C). Resistance to ampicillin and other antibiotics was significantly increased in groups G and I within 2 days, suggesting (co-)selection of resistance. The increase was lower in groups KI, KG, and C during the first treatment (days 1-5). The increased resistance in group C was interpreted as a change in the microbiota after initial moving and first feeding. After treatment, resistance rates decreased to initial or lower values in all groups. During the second treatment period (days 34-38), all three groups' (G, KG, and C) resistance levels increased to equally high levels. Cephalosporin resistance was low, and did not change over the experimental period. On days 3 and 38, resistance rates of E. coli from duodenum, jejunum, and cecum did not differ between segments and treatment routes. Overall, the baseline levels of antibiotic resistance in E. coli were high. Amoxicillin triggered an increase in resistance levels, irrespective of the mode of treatment. Substantial resistance dynamics in untreated controls warrant further investigation.

We compared the performance of four open-source in silico Salmonella typing tools (SeqSero, SeqSero2, Salmonella In Silico Typing Resource [SISTR], and Metric Oriented Sequence Typer [MOST]) to assess their potential for replacing laboratory serological testing with serovar predictions from whole-genome sequencing data. We conducted a retrospective analysis of 1,624 Salmonella isolates of 72 serovars submitted to the German National Salmonella Reference Laboratory between 1999 and 2019. All isolates are derived from animal and foodstuff origins. We conducted Illumina short-read sequencing and compared the in silico serovar prediction results with the results of routine laboratory serotyping. We found the best-performing in silico serovar prediction tool to be SISTR, with 94% correctly typed isolates, followed by SeqSero2 (87%), SeqSero (81%), and MOST (79%). Furthermore, we found that mapping-based tools like SeqSero and SeqSero2 (allele mode) were more reliable for the prediction of monophasic variants, while sequence type and cluster-based methods like MOST and SISTR (core-genome multilocus sequence type [cgMLST]), showed greater resilience when confronted with GC-biased sequencing data. We showed that the choice of library preparation kit could substantially affect O antigen detection, due to the low GC content of the wzx and wzy genes. Although the accuracy of computational serovar predictions is still not quite on par with traditional serotyping by Salmonella reference laboratories, the command-line tools investigated in this study perform a rapid, efficient, inexpensive, and reproducible analysis, which can be integrated into in-house characterization pipelines. Based on our results, we find SISTR most suitable for automated, routine serotyping for public health surveillance of Salmonella IMPORTANCE Salmonella spp. are important foodborne pathogens. To reduce the number of infected patients, it is essential to understand which subtypes of the bacteria cause disease outbreaks. Traditionally, characterization of Salmonella requires serological testing, a laboratory method by which Salmonella isolates can be classified into over 2,600 distinct subtypes, called serovars. Due to recent advances in whole-genome sequencing, many tools have been developed to replace traditional testing methods with computational analysis of genome sequences. It is crucial to validate that these tools, many already in use for routine surveillance, deliver accurate and reliable serovar information. In this study, we set out to compare which of the currently available open-source command-line tools is most suitable to replace serological testing. A thorough evaluation of the differing computational approaches is highly important to ensure the backward compatibility of serotyping data and to maintain comparability between laboratories., (Copyright © 2020 Uelze et al.)