Fangous Marie-Sarah, Mougari Faiza, Gouriou Stéphanie, Calvez Elodie, Raskine Laurent, Cambau Emmanuelle, Payan Christopher, Héry-Arnaud Geneviève
CHRU Brest, Hôpital Cavale Blanche, Pôle de Biologie-Pathologie, Unité de Bactériologie, Brest, France.
Centre National de Référence des Mycobactéries et de la Résistance des Mycobactéries aux Antituberculeux, Laboratoire Associé and APHP, GH Saint-Louis-Lariboisière, Paris, France INSERM, IAME, UMR 1137 and Université Paris Diderot, Sorbonne Paris Cité, Paris, France.
J Clin Microbiol. 2014 Sep;52(9):3362-9. doi: 10.1128/JCM.00788-14. Epub 2014 Jul 9.
Mycobacterium abscessus, as a species, has been increasingly implicated in respiratory infections, notably in cystic fibrosis patients. The species comprises 3 subspecies, which can be difficult to identify. Since they differ in antibiotic susceptibility and clinical relevance, developing a routine diagnostic tool discriminating Mycobacterium abscessus at the subspecies level is a real challenge. Forty-three Mycobacterium abscessus species isolates, previously identified by multilocus sequence typing, were analyzed by matrix-assisted laser desorption ionization-time of flight mass spectrometry (MALDI-TOF MS). A subspecies identification algorithm, based on five discriminating peaks, was drawn up and validated by blind identification of a further 49 strains, 94% of which (n = 46) were correctly identified. Two M. abscessus subsp. massiliense strains were misidentified as M. abscessus subsp. abscessus, and for 1 other strain identification failed. Inter- and intralaboratory reproducibility tests were conclusive. This study presents, for the first time, a classification algorithm for MALDI-TOF MS identification of the 3 M. abscessus subspecies. MALDI-TOF MS proved effective in discriminating within the M. abscessus species and might be easily integrated into the workflow of microbiology labs.
脓肿分枝杆菌作为一个菌种,越来越多地与呼吸道感染有关,尤其是在囊性纤维化患者中。该菌种包括3个亚种,可能难以鉴别。由于它们在抗生素敏感性和临床相关性方面存在差异,开发一种在亚种水平上区分脓肿分枝杆菌的常规诊断工具是一项真正的挑战。对43株先前通过多位点序列分型鉴定的脓肿分枝杆菌菌株进行了基质辅助激光解吸电离飞行时间质谱(MALDI-TOF MS)分析。基于5个鉴别峰制定了一个亚种鉴定算法,并通过对另外49株菌株的盲法鉴定进行了验证,其中94%(n = 46)被正确鉴定。2株马赛亚种菌株被误鉴定为脓肿亚种菌株,另有1株菌株鉴定失败。实验室间和实验室内的重复性测试结果是确定的。本研究首次提出了一种用于MALDI-TOF MS鉴定3种脓肿分枝杆菌亚种的分类算法。MALDI-TOF MS被证明在区分脓肿分枝杆菌菌种内有效,并且可能很容易整合到微生物实验室的工作流程中。