Evaluation of “Shotgun” Proteomics for Identification of Biological Threat Agents in Complex Environmental Matrixes: Experimental Simulations Nathan C. VerBerkmoes, ,‡ W. Judson Hervey, ,‡ Manesh Shah, § Miriam Land, ,§ Loren Hauser, ,§ Frank W. Larimer, ,§ Gary J. Van Berkel, ,‡ and Douglas E. Goeringer* ,‡ Graduate School of Genome Science and Technology, University of Tennessee-Oak Ridge National Laboratory, 1060 Commerce Park, Oak Ridge, Tennessee 37830-8026, and Organic and Biological Mass Spectrometry, Chemical Sciences Division, and Genome Analysis and Systems Modeling, Life Sciences Division, Oak Ridge National Laboratory, P.O. Box 2008, Oak Ridge, Tennessee 37831-6131 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-spectrom- etry-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 (Dworzanski, J. P.; Snyder, A. P.; Chen, R.; Zhang, H.; Wishart, D.; Li, L. Anal. Chem. 2004, 76, 2355-2366). 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 detec- tion above the background at any concentration used in this study was impossible, though the three other mi- crobes 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. There is currently a great need for rapid detection and positive identification of biological threat agents, including bacteria, toxins, and viruses, directly from complex environmental samples due to the recent increased threat of terrorism. This need also exists in the medical, environmental, and agricultural sciences. The detection/identification of microbial pathogens can be based on the presence of unique biomarkers from at least one of the major classes of macromolecules: DNA/RNA, lipids, and proteins. The selective detection of viruses can be centered on either DNA/ RNA or protein biomarkers, whereas for protein toxins the detection methods are limited to proteins. Many different technologies currently exist or are under development for the positive identification of potential bioweapons directly from environmental samples. While PCR-based methods rely on recognition of unique stretches of DNA or RNA 2,3 and antibody-based methods 4-7 depend mainly on detection of cell surface proteins and lipids, analysis of all three major macromol- * To whom correspondence should be addressed. Phone: (865) 574-3469. Fax: (865) 576-8559. E-mail: goeringerde@ornl.gov. University of Tennessee-Oak Ridge National Laboratory. ‡ Chemical Sciences Division, Oak Ridge National Laboratory. § Life Sciences Division, Oak Ridge National Laboratory. (1) Dworzanski, J. P.; Snyder, A. P.; Chen, R.; Zhang, H.; Wishart, D.; Li, L. Anal. Chem. 2004, 76, 2355-2366. (2) Broussard, L. A. Mol. Diagn. 2001, 6, 323. (3) Ivnitski, D.; O’Neil, D. J.; Gattuso, A.; Schlicht, R.; Calidonna, M.; Fisher, R. Biotechniques 2003, 35, 862. (4) Long, G. W.; O’Brien, T. J. Appl. Microbiol. 1999, 87, 214. (5) De, B. K.; Bragg, S. L.; Sanden, G. N.; Wilson, K. E.; Diem, L. A.; Marston, C. K.; Hoffmaster, A. R.; Barnett, G. A.; Weyant, R. S.; Abshire, T. G.; Ezzell, J. W.; Popovic, T. Emerging Infect. Dis. 2002, 8, 1060. (6) McBride, M. T.; Gammon, S.; Pitesky, M.; O’Brien, T. W.; Smith, T.; Aldrich, J.; Langlois, R. G.; Colston, B.; Venkateswaran, K. S. Anal. Chem. 2003, 75, 1924. Anal. Chem. 2005, 77, 923-932 10.1021/ac049127n CCC: $30.25 © 2005 American Chemical Society Analytical Chemistry, Vol. 77, No. 3, February 1, 2005 923 Published on Web 01/04/2005