Biomedical subjects

A Peter Snyder

Publications and source records attributed to A Peter Snyder.

5 recordsLinked to original sources

PubMed2006

Mass spectrometry-based proteomics combined with bioinformatic tools for bacterial classification.

Timely classification and identification of bacteria is of vital importance in many areas of public health. We present a mass spectrometry (MS)-based proteomics approach for bacterial classification. In this method, a bacterial proteome database is derived from all potential protein coding open reading frames (ORFs) found in 170 fully sequenced bacterial genomes. Amino acid sequences of tryptic peptides obtained by LC-ESI MS/MS analysis of the digest of bacterial cell extracts are assigned to individual bacterial proteomes in the database. Phylogenetic profiles of these peptides are used to create a matrix of sequence-to-bacterium assignments. These matrixes, viewed as specific assignment bitmaps, are analyzed using statistical tools to reveal the relatedness between a test bacterial sample and the microorganism database. It is shown that, if a sufficient amount of sequence information is obtained from the MS/MS experiments, a bacterial sample can be classified to a strain level by using this proteomics method, leading to its positive identification.

Amino Acid Sequence↗

PubMed2005

Classification and identification of bacteria using mass spectrometry-based proteomics.

Timely classification and identification of bacteria is of vital importance in many areas of public health. Mass spectrometry-based methods provide an attractive alternative to well-established microbiologic procedures. Mass spectrometry methods can be characterized by the relatively high speed of acquiring taxonomically relevant information. Gel-free mass spectrometry proteomics techniques allow for rapid fingerprinting of bacterial proteins using matrix-assisted laser desorption/ionization time-of-flight mass spectrometry or, for high-throughput sequencing of peptides from protease-digested cellular proteins, using mass analysis of fragments from collision-induced dissociation of peptide ions. The latter technique uses database searching of product ion mass spectra. A database contains a comprehensive list of protein sequences translated from protein-encoding open reading frames found in bacterial genomes. The results of such searches allow the assignment of experimental peptide sequences to matching theoretical bacterial proteomes. Phylogenetic profiles of sequenced peptides are then used to create a matrix of sequence-to-bacterium assignments, which are analyzed using numerical taxonomy tools. The results thereof reveal the relatedness between bacteria, and allow the taxonomic position of an investigated strain to be inferred.

Animals↗

PubMed2004

Bacterial identification by protein mass mapping combined with an experimentally derived protein mass database.

A protein mass mapping approach using mass spectrometry (MS) combined with an experimentally derived protein mass database is presented for rapid and effective identification of bacterial species. A prototype mass database from the protein extracts of nine bacterial species has been created by off-line high-performance liquid chromatography (HPLC) matrix-assisted laser desorption/ionization (MALDI) MS, in which the microbiological parameter of bacterial growth time is considered. A numerical method using a statistical weight factor algorithm is devised for matching the protein masses of an unknown bacterial sample against the database. The sum of these weight factors produces a corresponding summed weight factor score for each bacterial species listed in the database, and the database species producing the highest score represents the identity of the respective unknown bacterium. The applicability and reliability of this protein mass mapping approach has been tested with seven bacterial species in a single-blind study by both direct MALDI MS and HPLC electrospray ionization MS methods, and identification results with 100% accuracy are obtained. Our studies have demonstrated that the protein mass database can be rapidly established and readily adopted with relatively less dependency on experimental factors. Furthermore, it is shown that a number of proteins can be detected using a protein sample amount equivalent to an extract of less than 1000 cells, demonstrating that this protein mass mapping approach can potentially be highly sensitive for rapid bacterial identification.

Bacteria↗

PubMed2004

Correlation of mass spectrometry identified bacterial biomarkers from a fielded pyrolysis-gas chromatography-ion mobility spectrometry biodetector with the microbiological gram stain classification scheme.

A pyrolysis-gas chromatography-ion mobility spectrometry (Py-GC-IMS) briefcase system has been shown to detect and classify deliberately released bioaerosols in outdoor field scenarios. The bioaerosols included Gram-positive and Gram-negative bacteria, MS-2 coliphage virus, and ovalbumin protein species. However, the origin and structural identities of the pyrolysate peaks in the GC-IMS data space, their microbiological information content, and taxonomic importance with respect to biodetection have not been determined. The present work interrogates the identities of the peaks by inserting a time-of-flight mass spectrometry system in parallel with the IMS detector through a Tee connection in the GC module. Biological substances producing ion mobility peaks from the pyrolysis of microorganisms were identified by their GC retention time, matching of their electron ionization mass spectra with authentic standards, and the National Institutes for Standards and Technology mass spectral database. Strong signals from 2-pyridinecarboxamide were identified in Bacillus samples including Bacillus anthracis, and its origin was traced to the cell wall peptidoglycan macromolecule. 3-Hydroxymyristic acid is a component of lipopolysaccharides in the cell walls of Gram-negative organisms. The Gram-negative Escherichia coli organism showed significant amounts of 3-hydroxymyristic acid derivatives and degradation products in Py-GC-MS analyses. Some of the fatty acid derivatives were observed in very low abundance in the ion mobility spectra, and the higher boiling lipid species were absent. Evidence is presented that the Py-GC-ambient temperature and pressure-IMS system generates and detects bacterial biochemical information that can serve as components of a biological classification scheme directly correlated to the Gram stain reaction in microorganism taxonomy.

Biomarkers↗

PubMed2004

Identification of bacteria using tandem mass spectrometry combined with a proteome database and statistical scoring.

Detection and identification of pathogenic bacteria and their protein toxins play a crucial role in a proper response to natural or terrorist-caused outbreaks of infectious diseases. The recent availability of whole genome sequences of priority bacterial pathogens opens new diagnostic possibilities for identification of bacteria by retrieving their genomic or proteomic information. We describe a method for identification of bacteria based on tandem mass spectrometric (MS/MS) analysis of peptides derived from bacterial proteins. This method involves bacterial cell protein extraction, trypsin digestion, liquid chromatography MS/MS analysis of the resulting peptides, and a statistical scoring algorithm to rank MS/MS spectral matching results for bacterial identification. To facilitate spectral data searching, a proteome database was constructed by translating genomes of bacteria of interest with fully or partially determined sequences. In this work, a prototype database was constructed by the automated analysis of 87 publicly available, fully sequenced bacterial genomes with the GLIMMER gene finding software. MS/MS peptide spectral matching for peptide sequence assignment against this proteome database was done by SEQUEST. To gauge the relative significance of the SEQUEST-generated matching parameters for correct peptide assignment, discriminant function (DF) analysis of these parameters was applied and DF scores were used to calculate probabilities of correct MS/MS spectra assignment to peptide sequences in the database. The peptides with DF scores exceeding a threshold value determined by the probability of correct peptide assignment were accepted and matched to the bacterial proteomes represented in the database. Sequence filtering or removal of degenerate peptides matched with multiple bacteria was then performed to further improve identification. It is demonstrated that using a preset criterion with known distributions of discriminant function scores and probabilities of correct peptide sequence assignments, a test bacterium within the 87 database microorganisms can be unambiguously identified.

Algorithms↗