Arendowski Adrian
Department of Inorganic and Analytical Chemistry, Faculty of Chemistry, Rzeszów University of Technology, Powstańców Warszawy 6, 35-959 Rzeszów, Poland.
Centre for Modern Interdisciplinary Technologies, Nicolaus Copernicus University, Wileńska 4, 87-100 Toruń, Poland.
Pathogens. 2024 Dec 30;14(1):19. doi: 10.3390/pathogens14010019.
and are challenging to differentiate using methods such as phenotyping, 16S rRNA sequencing, or protein profiling through matrix-assisted laser desorption/ionization mass spectrometry (MALDI MS) due to their close relatedness. This study explores the potential for identifying and by incorporating reference spectra of metabolite profiles, obtained via surface-assisted laser desorption/ionization mass spectrometry (SALDI MS) employing gold nanoparticles (AuNPs), into the Bruker Biotyper database. Metabolite extracts from and cells were prepared using liquid-liquid extraction in a chloroform-methanol-water system. The extracts were analyzed using Au-SALDI MS in positive ion mode, and reference spectra, compiled from 30 spectra for each bacterium, were added to the database. Identification of bacteria based on metabolite fingerprints in the Biotyper database produced correct results with scores exceeding 2.75. The results of Partial Least Squares-Discriminant Analysis (PLS-DA) demonstrated that the metabolomic approach could accurately differentiate the microorganisms under study. A panel of nine / values was also identified, each with an area under the ROC curve of above 0.8, enabling accurate identification of and . A search of metabolite databases allowed the following compounds to be assigned to the selected / values: -acetylputrescine, arginine, 2-maleylacetate, benzoyl phosphate, 8-acetylspermidine, alanyl-glutamate, 4-hydroxy-2,3,4,5-tetrahydrodipicolinate, and sucrose. The analyses showed that identification of bacteria based on metabolite profiles obtained by the Au-SALDI MS method is feasible and can be useful for distinguishing closely related microorganisms that are difficult to differentiate by other techniques.
由于它们的亲缘关系很近，使用表型分析、16S rRNA测序或通过基质辅助激光解吸/电离质谱（MALDI MS）进行蛋白质谱分析等方法来区分[具体细菌名称1]和[具体细菌名称2]具有挑战性。本研究探索了通过将利用金纳米颗粒（AuNPs）的表面辅助激光解吸/电离质谱（SALDI MS）获得的代谢物谱参考光谱纳入布鲁克微生物鉴定系统数据库，来鉴定[具体细菌名称1]和[具体细菌名称2]的潜力。使用氯仿 - 甲醇 - 水系统中的液 - 液萃取法制备了[具体细菌名称1]和[具体细菌名称2]细胞的代谢物提取物。提取物在正离子模式下使用金 - SALDI MS进行分析，并将每种细菌的30个光谱汇编而成的参考光谱添加到数据库中。基于微生物鉴定系统数据库中的代谢物指纹对细菌进行鉴定，得到了得分超过2.75的正确结果。偏最小二乘判别分析（PLS - DA）的结果表明，代谢组学方法可以准确区分所研究的微生物。还确定了一组九个[具体指标名称]值，每个值的ROC曲线下面积均大于0.8，能够准确鉴定[具体细菌名称1]和[具体细菌名称2]。对代谢物数据库的搜索允许将以下化合物分配给选定的[具体指标名称]值：N - 乙酰腐胺、精氨酸、2 - 马来酰乙酸、苯甲酰磷酸、8 - 乙酰亚精胺、丙氨酰 - 谷氨酸、4 - 羟基 - 2,3,4,5 - 四氢二吡啶甲酸盐和蔗糖。分析表明，基于金 - SALDI MS方法获得的代谢物谱对细菌进行鉴定是可行的，并且可用于区分其他技术难以区分的亲缘关系密切的微生物。
