Classification and Identification of Bacteria by Mass Spectrometry and Computational Analysis

TLDR

Bacterial species identification is traditionally tedious and labor‑intensive, and Erwinia species are notorious for causing destructive plant diseases such as fire blight. The authors aim to provide a robust, standardized automated bacterial analysis workflow based on MALDI‑MS protein mass patterns, supported by an expanding database. They applied this workflow to Erwinia strains, combining MALDI‑MS protein profiling with SNP genotyping, and developed comprehensive software tools to enable accurate identification of fire blight pathogens from diverse biological samples. The approach produced a 2,800‑entry mass spectra database, accurately identified fire blight pathogens and other bacterial genera, and integrated genomic and proteomic data for enhanced analytical reliability.

Abstract

In general, the definite determination of bacterial species is a tedious process and requires extensive manual labour. Novel technologies for bacterial detection and analysis can therefore help microbiologists in minimising their efforts in developing a number of microbiological applications.We present a robust, standardized procedure for automated bacterial analysis that is based on the detection of patterns of protein masses by MALDI mass spectrometry. We particularly applied the approach for classifying and identifying strains in species of the genus Erwinia. Many species of this genus are associated with disastrous plant diseases such as fire blight. Using our experimental procedure, we created a general bacterial mass spectra database that currently contains 2800 entries of bacteria of different genera. This database will be steadily expanded. To support users with a feasible analytical method, we developed and tested comprehensive software tools that are demonstrated herein. Furthermore, to gain additional analytical accuracy and reliability in the analysis we used genotyping of single nucleotide polymorphisms by mass spectrometry to unambiguously determine closely related strains that are difficult to distinguish by only relying on protein mass pattern detection.With the method for bacterial analysis, we could identify fire blight pathogens from a variety of biological sources. The method can be used for a number of additional bacterial genera. Moreover, the mass spectrometry approach presented allows the integration of data from different biological levels such as the genome and the proteome.