From genotype to antibiotic susceptibility phenotype in the order Enterobacterales: a clinical perspective. (May 2020)

• Record Type: Journal Article
• Authors: Ruppé, E.; Cherkaoui, A.; Charretier, Y.; Girard, M.; Schicklin, S.; Lazarevic, V.; Schrenzel, J.
• Is Part Of: Clinical microbiology and infection. Volume 26:Number 5(2020)
• Page Start: 643.e1
• Page End: 643.e7
• Publication Date: 2020-05
• Subjects: Antibiotic resistance -- Antibiotic resistance genes -- Antibiotic susceptibility testing -- Bioinformatics -- Inference -- Whole-genome sequencing; Medical microbiology -- Periodicals; Diagnostic microbiology -- Periodicals; Communicable diseases -- Periodicals; Infection -- Periodicals; 616.01
• DOI: 10.1016/j.cmi.2019.09.018
• Languages: English
• ISSNs: 1198-743X

Abstract

Abstract: Objectives: Predicting the antibiotic susceptibility phenotype from genomic data is challenging, especially for some specific antibiotics in the order Enterobacterales. Here we aimed to assess the performance of whole genomic sequencing (WGS) for predicting the antibiotic susceptibility in various Enterobacterales species using the detection of antibiotic resistance genes (ARGs), specific mutations and a knowledge-based decision algorithm. Methods: We sequenced (Illumina MiSeq, 2×250 bp) 187 clinical isolates from species possessing ( n = 98) or not ( n = 89) an intrinsic AmpC-type cephalosporinase. Phenotypic antibiotic susceptibility was performed by the disc diffusion method. Reads were assembled by A5-miseq and ARGs were identified from the ResFinder database using Diamond. Mutations on GyrA and ParC topoisomerases were studied. Piperacillin, piperacillin-tazobactam, ceftazidime, cefepime, meropenem, amikacin, gentamicin and ciprofloxacin were considered for prediction. Results: A total of 1496 isolate/antibiotic combinations (187 isolates × 8 antibiotics) were considered. In 230 cases (15.4%), no attempt of prediction was made because it could not be supported by current knowledge. Among the 1266 attempts, 1220 (96.4%) were correct (963 for predicting susceptibility and 257 for predicting resistance), 24 (1.9%) were major errors (MEs) and 22 (1.7%) were very major errors (VMEs). Concordance were similar between non-AmpC and AmpC-producing Enterobacterales (754/784 (96.2%) vs 466/482 (96.7%), chi-square test p 0.15), but more VMEs were observed in non-AmpC producing strains than in those producing an AmpC (19/784 (2.4%) vs 3/466 (0.6%), chi-square test p 0.02). The majority of VMEs were putatively due to the overexpression of chromosomal genes. Conclusions: In conclusion, the inference of antibiotic susceptibility from genomic data showed good performances for non-AmpC and AmpC-producing Enterobacterales species. However, more knowledge about the mechanisms underlying the derepression of AmpC are needed.