Generation of a database of mass spectra patterns of selected Mycobacterium species using MALDI-ToF mass spectrometry
Thesis (MScMedSc (Pathology. Medical Microbiology))--Stellenbosch University, 2008.
The genus Mycobacterium is a group of acid–fast, aerobic, slow- growing organisms which include more than 90 different species. A member of this genus, Mycobacterium tuberculosis, belonging to the Mycobacterium tuberculosis complex (MTB), is the causative agent of tuberculosis (TB). This disease is currently considered a global emergency, with more than 2 million deaths and over 8 million new cases annually. TB is the world’s second most common cause of death after HIV/AIDS. About one-third of the world’s population is estimated to be infected with TB. This catastrophic situation is further compounded by the emergence of Multi Drug Resistant tuberculosis (MDR-TB) and in more recent times, Extensive Drug Resistant tuberculosis (XDR-TB). Early diagnosis is critical to the successful management of patients as it allows informed use of chemotherapy. Also, early diagnosis is also of great importance if the menace of MDR-TB and XDR-TB is to be curbed and controlled. As MTB is highly infectious for humans, it is of paramount importance that TB be diagnosed as early as possible to stop the spread of the disease. Traditional conventional laboratory procedures involving microscopy, culture and sensitivity tests may require turnaround times of 3-4 weeks or longer. Tremendous technological advancement over the years such as the advent of automated liquid culture systems like the BACTEC® 960 and the MGITTM Tube system, and the development of a myriad of molecular techniques most of which involves nucleic acid amplification (NAA) for the rapid identification of mycobacterial isolates from cultures or even directly from clinical specimens have contributed immensely to the early diagnosis of tuberculosis. Most of these NAA tests are nevertheless fraught with various limitations, thus the search for a rapid, sensitive and specific way of diagnosing tuberculosis is still an active area of research. The search has expanded to areas that would otherwise not have been considered ‘conventional’ in diagnostic mycobacteriology. One of such areas is mass spectrometry. This study joins the relatively few studies of its kind encountered in available literature to establish the ground work for the application of mass spectrometry, specifically Matrix Assisted Laser Desorption/Ionization Time-of-Flight Mass Spectrometry (MALDI-ToF MS) in the field of diagnostic mycobacteriology. This is an area which is in need of the speed, sensitivity and specificity that MALDI-ToF technique promises to offer. Since this technology is still in its infancy, the use of utmost care in the preparation of reagents, and the handling and storage of the organisms used to generate reference mass spectra for the database cannot be overemphasized. Similarly, the optimization of certain crucial experimental factors such as inactivating method and choice of matrix is of paramount importance. The main aim of this thesis was to generate a database of reference mass spectra fingerprints of selected (repository) Mycobacterium species. This necessitated the standardization of an experimental protocol which ensured that experimental factors and the various instrument parameters were optimized for maximum spectra generation and reproducibility. A standard operating procedure (SOP) for generating the database of reference mass spectra finger print of selected Mycobacterium species was developed and used to investigate the ability of the database to differentiate between species belonging to the same clinical disease complex as well as the nontuberculosis complex. The findings of this study imply that if the defined protocol is followed, the database generated has the potential to routinely identify and differentiate (under experimental conditions) more species of Mycobacterium than is currently practical using PCR and its related techniques. It is therefore a realistic expectation that when the database is clinically validated and tested in the next phase of the study, it will contribute immensely to the diagnosis of tuberculosis and other mycobacterioses. It will also aid in the identification of emerging pathogens particularly amongst the non-tuberculous mycobacteria.