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Discrimination of the microbial subspecies using the ribosomal protein spectra coupled with the metabolite high resolution mass spectra

Xu, Xiaoyan, Xiao, Nong, Yang, Minghua, Su, Yue, Guo, Yinlong
Talanta 2020 v.208 pp. 120361
Acremonium, Aspergillus niger, cost effectiveness, desorption, matrix-assisted laser desorption-ionization mass spectrometry, metabolites, mildews, molecular weight, ribosomal proteins
Routine microbial identification by matrix-assisted laser desorption/ionization time of flight mass spectrometry (MALDI-TOF MS) has been achieved based on the spectra of ribosomal proteins with molecular masses between 2000 and 20000Da. It is a rapid, cost-effective, and simple method to characterize different species of microorganisms. But for some subspecies of molds, there are high similarities between their spectra in 2000–20000Da, it makes them indistinguishable in this mass range. Based on the specialized metabolite production, there are obvious differences between the high resolution spectra of the same samples in 600–2000Da. It allows the rapid discrimination of these microbial subspecies. The ability of the method to discriminate microbial subspecies was demonstrated by characterizing three different Aspergillus niger strains. Furthermore, this approach has been applied to discriminate two different Acremonium alternatum strains which were collected from mildew plants. It demonstrated the applicability of the method to the actual samples. The high resolution MS in the range of 600–2000Da was presented as a complementary approach for the routine method in 2000–20000Da. The molds could be identified into species-level group by the spectra between 2000 and 20000Da and the strains within each group could be further discriminated based on differences in metabolites. The spectra between 2000 and 20000Da and the spectra between 600 and 2000Da were obtained from the same samples, which extracted with the same method. There is no need of additional pre-processing to obtain the high resolution spectra. It potentially provides a powerful tool for the fast and accurate identification of microbial subspecies.