Wieme Anneleen D, Spitaels Freek, Aerts Maarten, De Bruyne Katrien, Van Landschoot Anita, Vandamme Peter
Laboratory of Biochemistry and Brewing, Faculty of Bioscience Engineering, Ghent University, Valentin Vaerwyckweg 1, B-9000 Ghent, Belgium; Laboratory of Microbiology, Faculty of Sciences, Ghent University, K.L. Ledeganckstraat 35, B-9000 Ghent, Belgium.
Laboratory of Microbiology, Faculty of Sciences, Ghent University, K.L. Ledeganckstraat 35, B-9000 Ghent, Belgium.
Int J Food Microbiol. 2014 Aug 18;185:41-50. doi: 10.1016/j.ijfoodmicro.2014.05.003. Epub 2014 May 29.
Applicability of matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS) for identification of beer-spoilage bacteria was examined. To achieve this, an extensive identification database was constructed comprising more than 4200 mass spectra, including biological and technical replicates derived from 273 acetic acid bacteria (AAB) and lactic acid bacteria (LAB), covering a total of 52 species, grown on at least three growth media. Sequence analysis of protein coding genes was used to verify aberrant MALDI-TOF MS identification results and confirmed the earlier misidentification of 34 AAB and LAB strains. In total, 348 isolates were collected from culture media inoculated with 14 spoiled beer and brewery samples. Peak-based numerical analysis of MALDI-TOF MS spectra allowed a straightforward species identification of 327 (94.0%) isolates. The remaining isolates clustered separately and were assigned through sequence analysis of protein coding genes either to species not known as beer-spoilage bacteria, and thus not present in the database, or to novel AAB species. An alternative, classifier-based approach for the identification of spoilage bacteria was evaluated by combining the identification results obtained through peak-based cluster analysis and sequence analysis of protein coding genes as a standard. In total, 263 out of 348 isolates (75.6%) were correctly identified at species level and 24 isolates (6.9%) were misidentified. In addition, the identification results of 50 isolates (14.4%) were considered unreliable, and 11 isolates (3.2%) could not be identified. The present study demonstrated that MALDI-TOF MS is well-suited for the rapid, high-throughput and accurate identification of bacteria isolated from spoiled beer and brewery samples, which makes the technique appropriate for routine microbial quality control in the brewing industry.
考察了基质辅助激光解吸/电离飞行时间质谱（MALDI-TOF MS）用于鉴定啤酒腐败菌的适用性。为此，构建了一个广泛的鉴定数据库，包含4200多个质谱图，包括来自273株醋酸菌（AAB）和乳酸菌（LAB）的生物学和技术重复样本，涵盖总共52个物种，这些菌株在至少三种生长培养基上生长。利用蛋白质编码基因的序列分析来验证MALDI-TOF MS异常的鉴定结果，并证实了之前对34株AAB和LAB菌株的错误鉴定。总共从接种了14个变质啤酒和啤酒厂样品的培养基中收集了348株分离株。基于峰的MALDI-TOF MS谱图数值分析能够直接鉴定327株（94.0%）分离株。其余分离株单独聚类，并通过蛋白质编码基因的序列分析，要么归类为非啤酒腐败菌（因此不在数据库中），要么归类为新的AAB物种。通过将基于峰的聚类分析和蛋白质编码基因序列分析获得的鉴定结果作为标准，评估了一种用于鉴定腐败菌的基于分类器的替代方法。在348株分离株中，共有263株（75.6%）在物种水平上被正确鉴定，24株（6.9%）被错误鉴定。此外，50株（14.4%）分离株的鉴定结果被认为不可靠，11株（3.2%）无法鉴定。本研究表明，MALDI-TOF MS非常适合从变质啤酒和啤酒厂样品中分离出的细菌的快速、高通量和准确鉴定，这使得该技术适用于酿造行业的常规微生物质量控制。
