title: Evaluation of "shotgun" proteomics for identification of biological threat agents in complex environmental matrixes: experimental simulations.
abstract: There is currently a great need for rapid detection and positive identification of biological threat agents, as well as microbial species in general, directly from complex environmental samples. This need is most urgent in the area of homeland security, but also extends into medical, environmental, and agricultural sciences. Mass-spectrometry-based analysis is one of the leading technologies in the field with a diversity of different methodologies for biothreat detection. Over the past few years, "shotgun"proteomics has become one method of choice for the rapid analysis of complex protein mixtures by mass spectrometry. Recently, it was demonstrated that this methodology is capable of distinguishing a target species against a large database of background species from a single-component sample or dual-component mixtures with relatively the same concentration. Here, we examine the potential of shotgun proteomics to analyze a target species in a background of four contaminant species. We tested the capability of a common commercial mass-spectrometry-based shotgun proteomics platform for the detection of the target species (Escherichia coli) at four different concentrations and four different time points of analysis. We also tested the effect of database size on positive identification of the four microbes used in this study by testing a small (13-species) database and a large (261-species) database. The results clearly indicated that this technology could easily identify the target species at 20% in the background mixture at a 60, 120, 180, or 240 min analysis time with the small database. The results also indicated that the target species could easily be identified at 20% or 6% but could not be identified at 0.6% or 0.06% in either a 240 min analysis or a 30 h analysis with the small database. The effects of the large database were severe on the target species where detection above the background at any concentration used in this study was impossible, though the three other microbes used in this study were clearly identified above the background when analyzed with the large database. This study points to the potential application of this technology for biological threat agent detection but highlights many areas of needed research before the technology will be useful in real world samples.
authors.affiliation: Graduate School of Genome Science and Technology, University of Tennessee--Oak Ridge National Laboratory, 1060 Commerce Park, Oak Ridge, TN 37830-8026, USA.
authors.forename: Nathan C
authors.initials: NC
authors.lastname: Verberkmoes
authors.forename: W Judson
authors.initials: WJ
authors.lastname: Hervey
authors.forename: Manesh
authors.initials: M
authors.lastname: Shah
authors.forename: Miriam
authors.initials: M
authors.lastname: Land
authors.forename: Loren
authors.initials: L
authors.lastname: Hauser
authors.forename: Frank W
authors.initials: FW
authors.lastname: Larimer
authors.forename: Gary J
authors.initials: GJ
authors.lastname: Van Berkel
authors.forename: Douglas E
authors.initials: DE
authors.lastname: Goeringer
mesh_terms: D001694:Biological Warfare; D003198:Computer Simulation; D016208:Databases, Factual; D004784:Environmental Monitoring; D004926:Escherichia coli / Q000737:chemistry / Q000235:genetics; D005069:Evaluation Studies as Topic; D013058:Mass Spectrometry / Q000379:methods; D020543:Proteome / Q000032:analysis / Q000737:chemistry*; D040901:Proteomics / Q000379:methods*; D013997:Time Factors
chemical_list: D020543:Proteome