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Michel Batista, Ph.D., has worked in proteomics and phosphoproteomics
since 2009. Along with his lab, he's employed different techniques to
study the protozoans Trypanosoma cruzi and Trypanosoma brucei.
Besides, he also collaborates in studies of cancer proteomics,
evaluating the profile of cancer tumors and plasma. Since 2018, he's
been involved in a project aiming a proof of concept for sepsis
diagnosis by LC-MS/MS. Because of this project, he started to use the
Skyline platform to analyze LC-MS/MS targeted data. In addition, he's
used Skyline in other projects, such as the evaluation of system
complement proteins in patients infected with Leishmania and the
quantification of therapeutic peptides in serum of rats after
intravenous or oral administration. Since 2011, he's worked in a MS
core facility in Fiocruz-Brazil, and has been manager since 2017. |
We aimed to develop a method based on LC-MS/MS that targets specific peptides of sepsis-causing microorganisms. We described an assay to detect S. aureus, P. aeruginosa, and C. albicans directly from whole-blood samples as a diagnostic alternative for bloodstream infection. This method combines differential basic pH and acidic cell lysis, fast protein digestion, LC/ESI-MS/MS, and data analysis on Skyline.
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After selecting in Skyline peptides combining both high similarity to the spectral library (high dotp) and high fragment area, we established a proof of concept for detecting specific pathogens from whole blood samples within seven hours. The workflow presented allowed the screening of specific peptides for microorganism identification. Using LC/ESI-MS/MS peptide identification, P. aeruginosa, S. aureus, and C. albicans were correctly identified in 100% of the tested samples, with a sensitivity of 87.5%. The standardized LC-MS/MS method coupled with a fast extraction/digestion permits the identification of one or more etiological agents directly from blood samples within seven hours without the need for prior enrichment in microculture. In addition, the method has potential future applications, such as identifying filamentous fungi and antimicrobial resistance mechanisms and contributing to epidemiological data.
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