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BookmarkScienceDaily (May 5, 2005) — Researchers at the Johns Hopkins Bloomberg School of Public Health have developed a new method for identifying specific proteins in whole cell extracts of microorganisms using traditional peptide mass fingerprinting (PMF). The key to the new method, according to the researchers, is a “shortcut” for preparing samples that makes PMF faster and more economical. By reducing the cost of protein identification, they believe PMF can become an economical tool for monitoring and evaluating the effectiveness of microorganisms used in environmental cleanup. The researchers used a dioxin-eating organism to demonstrate the capabilities of their methodology, which they described in an article published in the May 2005 edition of Applied and Environmental Microbiology.
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PMF typically involves elaborate sample preparation. A protein mixture is spread across a gel and separated into individual proteins, which are scooped out of the gel and cut with protein scissors into predictable, small pieces called peptides. The samples are then analyzed using matrix-assisted laser desorption ionization-time of flight mass spectrometry (MALDI-TOF MS), which identifies protein fragments based on the time they need to travel a defined distance when being accelerated in a vacuum.
In their study, Rolf U. Halden, PhD, PE, assistant professor in the Department of Environmental Health Sciences Bloomberg School of Public Health and his colleagues demonstrate how PMF and mass spectrometry are used to identify a unique dioxin-degrading enzyme in a soup of hundreds of cell proteins. The technique avoids elaborate conventional sample preparation steps by coaxing the cells into mass production of the protein the researchers wish to analyze.
“Finding a specific target in a mixture of hundreds of proteins can be likened to finding the proverbial needle in the haystack; this task can be performed much faster and more economically if you have more needles—and that’s exactly what our method is based on,” explained Dr. Halden. “Instead of spending a lot of time and resources on eliminating the background noise to find the signal, our method increases the signal upfront so that it stands out above the background noise. By forcing an up-regulation of enzyme expression in the bacterium of interest, our target can be identified amidst all of the other cell components,” he said.
Halden and his colleagues tested their technique using Sphingomonas wittichii strain RW1, the only bacterium known to consume the backbone of toxic polychlorinated dibenzo-p-dioxins and dibenzofurans as a food source. The researchers already knew that feeding dioxins to RW1 would cause an increased enzyme level as the bacterium consumed the model pollutant. Their study shows that this increase can be easily identified by PMF using mass spectrometry.
“Our procedure simplifies the entire identification process,” said David Colquhoun, MS, a doctoral fellow with the Johns Hopkins Center for a Livable Future, “With the new tool, we can conveniently and rapidly identify both pollutant-degrading bacteria and their characteristic proteins that effect pollutant transformation.”
“This method represents a new investigative tool in bioremediation, which is the science of using biological organisms as a means of decontaminating polluted soils and water,” said Dr. Halden.
Johns Hopkins University is seeking partners who would like to license this patent-pending methodology. Inquiries may be directed to Deborah Alper at the Johns Hopkins Bloomberg School of Public Health at dalper@jhsph.edu or 443-287-0402.
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“Identification and Phenotypic Characterization of Sphingomonas wittichii Strain RW1 by Peptide Mass Fingerprinting Using Matrix-Assisted Laser Desorption Ionization-Time of Flight Mass Spectrometry” was written by Rolf U. Halden, David R. Colquhoun and E.S. Wisniewski.
Funding was provided by grants from the Johns Hopkins Bloomberg School of Public Health Technology Transfer Committee, the National Institutes of Health Training Grant and the Johns Hopkins Center for a Livable Future.
