Double-blind characterization of non-genome-sequenced bacteria by mass spectrometry-based proteomics.
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 double-blind 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.
Jabbour
Rabih E
RE
Edgewood Area, Gunpowder Branch, Science Applications International Corporation, Aberdeen Proving Ground, MD 21010-5424, USA. rabih.e.jabbour@saic.com
Deshpande
Samir V
SV
Wade
Mary Margaret
MM
Stanford
Michael F
MF
Wick
Charles H
CH
Zulich
Alan W
AW
Skowronski
Evan W
EW
Snyder
A Peter
AP
D001419:Bacteria / Q000737:chemistry* / Q000145:classification*; D001426:Bacterial Proteins / Q000032:analysis* / Q000378:metabolism; D019295:Computational Biology / Q000379:methods; D004311:Double-Blind Method; D013058:Mass Spectrometry / Q000379:methods*; D040901:Proteomics / Q000379:methods*; D012680:Sensitivity and Specificity; D014357:Trypsin / Q000378:metabolism
D001426:Bacterial Proteins; D014357:Trypsin