MALDI-TOF MS与16SrDNA方法对肠杆菌科微生物鉴定比较分析

Analysis and Comparison of MALDI-TOF MS and 16SrDNA for the Identification of Enterobacteriaceae

  • 摘要: 为了研究基质辅助激光解吸电离飞行时间质谱(MALDI-TOF MS)法鉴定肠杆菌科微生物及其对微生物系统分类学相关分析的准确性,采用MALDI-TOF MS对金桔表面分离的10种肠杆菌科微生物进行蛋白质图谱收集,通过比对图谱特征峰实现微生物的鉴定与系统进化学分析;同时对10种微生物进行16SrDNA提取与测序,得到分子生物化学水平鉴定,并采用邻近连接法对16SrDNA序列做系统进化树分析。结果表明:MALDI-TOF MS与16SrDNA测序对10种肠杆菌科微生物鉴定结果中,克雷伯菌属2株菌鉴定种级有偏差,其他8种一致;MALDI-TOF MS与16SrDNA测得的数据都可计算微生物相关性,从而得到微生物系统发育树,两株系统发育树中同属级微生物归类的亲缘位置与方向一致,但不同属肠杆菌科微生物的亲缘距离与位置差异较大。MALDI-TOF MS法鉴定农产品表面微生物具有快速、准确的特点,但准确建立系统发育相关性还需要扩大数据库和优化算法。

     

    Abstract: To study the accuracy of identification and correlation analysis of microbial taxonomy for enterobacteriaceae by matrix assisted laser desorption ionization-time of flight mass spectrometry (MALDI-TOF MS) method, it was adopted to conduct protein mass spectrum collection on 10 species of enterobacteriaceae isolated from the surface of kumquat, and microorganism identification and phylogenetic analysis were achieved by comparing the characteristic peaks of mass spectrum. And in the meanwhile, 10 species of enterobacteriaceae were extracted and sequenced by 16SrDNA to get the identification on the level of molecular biochemistry, in addition, phylogenetic tree analysis of 16SrDNA sequences was realized by Neighbor-joining. The identification results of enterobacteriaceae based on MALDI-TOF MS and 16SrDNA were identical for 8 of 10 species of enterobacteriaceae, but the identification of two Klebsiella spp. differed on species level. The data resulted from MALDI-TOF MS and 16SrDNA can be used to evaluate the correlation of microorganisms and obtain the phylogenetic tree, on which the positions and directions of microorganisms that belong to one genus were the same, but enterobacteriaceae that belong to different genus had comparatively large disparity on the related distances and positions. MALDI-TOF MS is rapid and accurate for the identification of microorganisms on the surface of agricultural products, however, to establish phylogenetic correlation, it is necessary to expand the database and optimize algorithms.

     

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