Double-Blind Characterization of Non-Genome-Sequenced Bacteria by Mass Spectrometry-Based Proteomics

Abstract

Due to the possibility of a biothreat attack on civilian or military installations, a need exists for technologies that can detect and accurately identify pathogens in a near-real-time approach. One technology potentially capable of meeting these needs is a high-throughput mass spectrometry (MS)-based proteomic approach. This approach utilizes the knowledge of amino acid sequences of peptides derived from the proteolysis of proteins as a basis for reliable bacterial identification. To evaluate this approach, the tryptic digest peptides generated from double-blind biological samples containing either a single bacterium or a mixture of bacteria were analyzed using liquid chromatography-tandem mass spectrometry. Bioinformatic tools that provide bacterial classification were used to evaluate the proteomic approach. Results showed that bacteria in all of the double-blind samples were accurately identified with no false-positive assignment. The MS proteomic approach showed strain-level discrimination for the various bacteria employed. The approach also characterized doubleblind bacterial samples to the respective genus, species, and strain levels when the experimental organism was not in the database due to its genome not having been sequenced. One experimental sample did not have its genome sequenced, and the peptide experimental record was added to the virtual bacterial proteome database. A replicate analysis identified the sample to the peptide experimental record stored in the database. The MS proteomic approach proved capable of identifying and classifying organisms within a microbial mixture.The detection and accurate identification of pathogens of biological origin are of great importance to the armed forces and civilian sectors. Achieving these tasks is vital in the response to manmade or natural biothreat attacks in a proper and efficient manner to minimize the outbreak of epidemic cases. Several approaches reported in the literature have addressed the detection and identification of microorganisms based on the characterization of metabolites (1, 17) and genomic contents of bacterial cells (16). In these studies, the genomic sequence similarities generated from PCR were used to group bacteria at the genus/species level (27). Prior knowledge of the sample, or the targeting of one or a group of biological substances, is required in PCR techniques for proper primer utilization. However, proteins constitute greater than 60% of the dry weight of microorganism cellular components (4,8,12,13,22) and could provide in-depth information for the bacterial differentiation of species and their strains. Moreover, advancements in mass spectrometry (MS) ionization, detection methods, and data processing make MS a suitable analytical technique for the differentiation of microorganisms (5-7).Using MS techniques for bacterial differentiation relies on the comparison of the proteomic information generated from the analysis of either intact protein profiles (top down) or the product ion mass spectra of digested peptide sequences ...

Comparative Proteomics of Tandem Mass Spectrometry Analyses for Bacterial Strains Identification and Differentiation

“…Proteomic analyses by LC-MS have been used in the characterization of bacteria (Castanha,2006;Dworzanski, 2004Dworzanski, , 2006Lambert, 2005;). Given the degree of success for tandem MS-based proteomics in bacterial characterization, a comparative proteomic study was reported about the potential of the outer membrane protein (OMP) and whole cell protein extracts, independently, can distinguish between strains of the same species (Jabbour et al, 2010). Typically, whole cell protein extracts are usually investigated or select portions of the bacterium, such as the outer membrane, are isolated and the proteins extracted there from.…”

“…Alternatively, high-throughput tandem mass spectrometry-based proteomics was applied as a means for characterizing cellular proteins and producing amino acid sequence information for peptides derived from these proteins for E. coli and Y. pestis. Whole cell protein and cell membrane OMP extracts were compared and contrasted with the in-house BACid bioinformatics modeling tools for species and strain level discrimination (Jabbour, 2010).…”

Mass spectrometry and tandem mass spectrometry characterization of protein patterns, protein markers and whole proteomes for pathogenic bacteria

“…Strain level identification was obtained for S. aureus ATCC 3359, a strain whose genome has yet to be sequenced. All of the unsequenced bacteria in the blind sample were correctly speciated, further demonstrating that comparing the experimentally obtained peptides to the virtual digests in the proteome could yield statistically significant identification (Jabbour et al, 2010). It is apparent that the utility and precision of MADLI-TOF MS, MALDI-TOF-TOF MS/MS and LC-ESI-MS-MS are all reliant on the development and maintenance of accurate and representative databases.…”

“…It is also necessary to have some prior knowledge of the gene of interest in order to create operational primers. Additionally, effective primer selection requires there be two conserved regions for genetic recognition and amplification, thereby limiting the number of useful genes (Jabbour et al, 2010;Fox et al, 2011).…”

Identification of Microorganisms Grown in Blood Culture Flasks using Liquid Chromatography–Tandem Mass Spectrometry

“…Using this LC-MS/MS method, proteins of pure cultures of a microorganism are digested with trypsin, generating peptides of which the amino acid sequence is determined based on their measured masses, the masses of fragmentation product ions and their comparison with in silico trypsin digests and corresponding predicted fragments of a protein database. From these peptide sequences, not only proteins of interest can be identified, also the micro organism itself can be specified to, at least, species level [16,17]. Recent developments in proteomic analysis using LC-MS/MS reduced the time to result, making it possible to analyze a sample within 6 h [18].…”