METHODIZATION OF AN ATP BIOLUMINESCENCE ASSAY FOR DETECTING THE VIABILITY OF Mycobacterium tuberculosis IN CULTURE AND FOR ITS POTENTIAL USE IN SENSITIVITY TESTS TO ANTIMICROBIALS
Name: MARIANA ABOU MOURAD FERREIRA
Publication date: 29/02/2024
Examining board:
Name |
Role |
|---|---|
| CARLOS GRAEFF TEIXEIRA | Examinador Interno |
| ERICA CHIMARA SILVA | Examinador Externo |
| KENIA VALERIA DOS SANTOS | Examinador Interno |
| MOISES PALACI | Presidente |
| PEDRO EDUARDO ALMEIDA DA SILVA | Examinador Externo |
Summary: Tuberculosis (TB), caused by Mycobacterium tuberculosis, remains a global health threat, requiring faster and more accessible diagnostic methods. This study explores the application of ATP bioluminescence as a phenotypic method for detection of M.tuberculosis. Our objective was to optimize the ATP bioluminescence protocol using the BacTiter-GloTM for M. tuberculosis, investigating the impact of varying volumes of bacterial suspension and reagent on assay sensitivity, evaluating ATP extraction methods, establishing calibration curves, and elucidating responses. strain-specific antimicrobial agents. The study systematically varied bacterial suspension and reagent volumes, identifying an optimal combination for sensitivity. ATP extraction methods showed no significant improvement over controls, with freezing and thawing exhibiting promising trends. Calibration curves revealed a linear correlation between relative light units (RLU) and colony forming units (CFU/mL), establishing low detection limits. Antimicrobial testing demonstrated strain-specific responses aligned with sensitivity and resistance patterns. In conclusion, this study optimized the ATP bioluminescence protocol, highlighting its potential as a rapid and cost-effective method for M. tuberculosis detection and antimicrobial susceptibility testing. The findings contribute to the advancement of TB diagnostics and understanding the mechanisms of antimicrobial resistance. Additional refinement and validation efforts are needed, promising more efficient diagnostic platforms in the future.
