A new method for phylogenetic classification of bacterial strains using matrix-assisted laser desorption/ionization-mass spectrometry (MALDI-MS) is proposed. This method was developed using a bioinformatics-based approach to the rapid identification of bacteria as previously proposed by Demirev and co-workers, which uses ribosomal proteins composed of approximately 50 subunit proteins as biomarkers. Although the amino acid sequences of ribosomal proteins are highly conserved, slight sequence variations can occur at the strain level. Since ribosomal subunit proteins are a complex of housekeeping proteins that have different phylogenetic evolution rates, sequence variation detected as mass differences by MALDI-MS may be useful for the phylogenetic classification of bacteria at strain level. In our proposed method, the first step is the selection of reliable biomarkers through characterization of the expressed ribosomal subunit proteins of a reference strain (usually a genome-sequenced strain) by MALDI-MS. The observed masses in the MALDI mass spectra of cell lysates of sample strains are then compared with the biomarker masses of the reference strain. The biomarkers for each sample strain were designated as present or absent at the reference masses, indicated by 1 or 0, respectively, which were summarized in a table. This table is processed by cluster analysis, generating a phylogenetic tree. In this study, the success of this approach was confirmed by classification of Pseudomonas putida strains because its classification is much more complicated than that of other bacterial strains. Forty-three reliable biomarkers were selected from ribosomal sub-unit proteins of a genome-sequenced strain, P. putida KT2440. The numbers and kinds of biomarkers observed for 16 strains of P. putida, including different biovars, were markedly different, reflecting the variety of the strains. The classification results by the proposed method were highly comparable to those based on the DNA gyrase subunit B gene (gyrB) sequence analysis, suggesting our proposed method would be a useful high-throughput method for phylogenetic classification of newly isolated bacteria.
We have proposed a rapid phylogenetic classification at the strain level by MALDI-TOF MS using ribosomal protein matching profiling. In this study, the S10-spc-alpha operon, encoding half of the ribosomal subunit proteins and highly conserved in eubacterial genomes, was selected for construction of the ribosomal protein database as biomarkers for bacterial identification by MALDI-TOF MS analysis to establish a more reliable phylogenetic classification. Our method revealed that the 14 reliable and reproducible ribosomal subunit proteins with less than m/z 15,000, except for L14, coded in the S10-spc-alpha operon were significantly useful biomarkers for bacterial classification at species and strain levels by MALDI-TOF MS analysis of genus Pseudomonas strains. The obtained phylogenetic tree was consisted with that based on genetic sequence (gyrB). Since S10-spc-alpha operons of genus Pseudomonas strains were sequenced using specific primers designed based on nucleotide sequences of genome-sequenced strains, the ribosomal subunit proteins encoded in S10-spc-alpha operon were suitable biomarkers for construction and correction of the database. MALDI-TOF MS analysis using these 14 selected ribosomal proteins is a rapid, efficient, and versatile bacterial identification method with the validation procedure for the obtained results.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
customersupport@researchsolutions.com
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.