UVM Theses and Dissertations
Format:
Print
Author:
Zhu, Jiangjiang
Dept./Program:
Civil and Environmental Engineering
Year:
2013
Degree:
PhD
Abstract:
Bacterial infection is the root cause and/or the complicating factor ofmany human diseases. Typical bacterial identification methods (e.g., culture, serological, and genetic methods) may require invasive techniques for diagnosing diseases such as sputum induction or bronchoalveolar lavage collection for isolating samples from patients, and could be time-consuming (from hours to days), for instance in the conventional detection methods for pathogenic bacteria contamination in food. Therefore, an in situ test that is non-invasive, rapid, and sensitive, and that will reveal timely and effective bacterial identification is desired.
Bacteria produce unique combinations of volatiles that can be used to identify the genus and species, and in many cases the strain or serovar. The ability to identify bacteria by their volatilomes has generated great expectations for rapid and sensitive tests that can be used to (1) detect bacterial contamination in food; (2) diagnose infections in situ, particularly lung infections via breath analysis. The dissertation presented here demonstrates a step-by-step approach for using SESI-MS for rapid, sensitive and reliable bacterial detection through their volatile profiles, first in vitro and then in vivo. In this dissertation work I was able to apply SESI-MS to detect and characterize volatiles produced by Gram positive and negative bacteria in vitro, culminating in the separation of a group of 11 E. coli Serotypes (including 0157:H7) from S. aureus and S. Typhimurium in three food modeling media.
After the proof of concept studies in vitro, I established murine lung infection models using seven clinically important lung pathogenic bacteria, and I was able to identify the infectious species based on the breath volatile profiles from infected mice. In addition, breath volatiles were monitored via SESI-MS for P. aeruginosa and S. aureus acute lung infections through a period of 120 h. With supervised statistical data analyses, I was able to identify groups of peaks that are unique to each infection and are consistently detected at all tested time points. The studies presented in this dissertation have demonstrated the utility of applying SESI-MS to the rapid detection of bacteria in vitro and in vivo.
Bacteria produce unique combinations of volatiles that can be used to identify the genus and species, and in many cases the strain or serovar. The ability to identify bacteria by their volatilomes has generated great expectations for rapid and sensitive tests that can be used to (1) detect bacterial contamination in food; (2) diagnose infections in situ, particularly lung infections via breath analysis. The dissertation presented here demonstrates a step-by-step approach for using SESI-MS for rapid, sensitive and reliable bacterial detection through their volatile profiles, first in vitro and then in vivo. In this dissertation work I was able to apply SESI-MS to detect and characterize volatiles produced by Gram positive and negative bacteria in vitro, culminating in the separation of a group of 11 E. coli Serotypes (including 0157:H7) from S. aureus and S. Typhimurium in three food modeling media.
After the proof of concept studies in vitro, I established murine lung infection models using seven clinically important lung pathogenic bacteria, and I was able to identify the infectious species based on the breath volatile profiles from infected mice. In addition, breath volatiles were monitored via SESI-MS for P. aeruginosa and S. aureus acute lung infections through a period of 120 h. With supervised statistical data analyses, I was able to identify groups of peaks that are unique to each infection and are consistently detected at all tested time points. The studies presented in this dissertation have demonstrated the utility of applying SESI-MS to the rapid detection of bacteria in vitro and in vivo.