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What Do We See in Spectra?: Assignment of High-Intensity Peaks of Cutibacterium and Staphylococcus Spectra of MALDI-TOF Mass Spectrometry by Interspecies Comparative Proteogenomics.

Itaru DekioYuki SugiuraSusumu Hamada-TsutsumiYoshiyuki MurakamiHiroto TamuraMakoto Suematsu
Published in: Microorganisms (2021)
Matrix-assisted laser-desorption/ionization time-of-flight (MALDI-TOF) mass spectrometry is a widely used and reliable technology to identify microbial species and subspecies. The current methodology is based on spectral fingerprinting, analyzing protein peaks, most of which are yet to be characterized. In order to deepen the understanding of these peaks and to develop a more reasonable identification workflow, we applied proteogenomic approaches to assign the high-intensity peaks of MALDI-TOF spectra of two bacterial genera. First, the 3-22 kD proteomes of 5 Cutibacterium strains were profiled by UPLC-MS/MS, and the amino acid sequences were refined by referring to their genome in the public database. Then, the sequences were converted to m/z (x-axis) values based on their molecular masses. When the interspecies comparison of calculated m/z values was well-fitted to the observed peaks, the peak assignments for the five Cutibacterium species were confirmed. Second, the peak assignments for six Staphylococcus species were performed by using the above result for Cutibacterium and referring to ribosomal subunit proteins coded on the S10-spc-alpha operon (the S10-GERMS method), a previous proteomics report by Becher et al., and comprehensive genome analysis. We successfully assigned 13 out of 15 peaks for the Cutibacterium species and 11 out of 13 peaks for the Staphylococcus species. DNA-binding protein HU, the CsbD-like protein, and 50S ribosomal protein L7/L12 were observed in common. The commonality suggests they consist of high-intensity peaks in the MALDI spectra of other bacterial species. Our workflow may lead to the development of a more accurate species identification database of MALDI-TOF mass spectrometry based on genome data.
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