Antibiotic resistance of Riemerella anatipestifer and comparative analysis of antibiotic-resistance gene detection methods

Riemerella anatipestifer is an important pathogen in waterfowl, and is generally multidrug resistant. This study assessed the current status of Riemerella anatipestifer antibiotic resistance and antibiotic-resistance genes (ARGs), compared the results of different detection methods, and evaluated a new method of studying the association between antibiotic resistance and ARGs in Riemerella anatipestifer. In this study, 51 strains of Riemerella anatipestifer were isolated from ducks on several farms, their resistance to 28 antibiotics was assessed, and the isolates were subjected to whole-genome sequencing. The number of ARGs carried by Riemerella anatipestifer was predicted, compared, and analyzed, and the consistency between ARGs and antibiotic-resistance phenotypes was assessed. The potential for loss of resistance genes during the sequencing and assembly of genome-wide framework map was assessed, and a new ARG detection method was pilot tested. The 51 strains of Riemerella anatipestifer were multidrug resistant (MDR) and had high level of resistance to aminoglycosides, trimethoprim, lincosamides, polypeptides, and macrolides. Based on the genome-wide framework map of the 51 strains, 3 local databases of ABRicate software and 1 online database of CARD website were used to detect ARGs, and a mean of 4 to 5 ARGs were identified per isolate. Although the detection results differed according to the database used, the general performance was consistent. The online website detected more types of ARGs than the ABRicate software. The association between ARGs and antibiotic-resistance phenotypes was assessed, and the ermF gene was identified as a possible key ARGs regulating macrolide resistance of Riemerella anatipestifer. The method used to investigate and detect Riemerella anatipestifer ARGs was convenient and rapid, and had strong accuracy and pertinence. The ARGs detection method reported here combined the advantages of PCR and genome detection, and could greatly reduce workload and detect ARGs more precisely.