A Rapid Growth-Independent Antibiotic Resistance Detection Test by SYBR Green/Propidium Iodide Viability Assay

Frontiers in Medicine

Lili Tian¹  Rebecca Yee²  Shuo Zhang²  Wanliang Shi²  Jie Feng²  Ying Zhang²  Wen-Hong Zhang³ 

[1] Beijing Research Institute for Tuberculosis Control, Beijing, China;Department of Molecular Microbiology and Immunology, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD, United States;Key Laboratory of Medical Molecular Virology, Department of Infectious Diseases, Huashan Hospital, Shanghai Medical College, Fudan University, Shanghai, China;

关键词: antibiotic resistance;    antimicrobial susceptibility testing;    SYBR Green/PI availability assay;    Mycobacterium tuberculosis;    Staphylococcus aureus;    CRE bacteria;   

DOI  :  10.3389/fmed.2018.00127

来源: DOAJ

【 摘 要 】

Antibiotic-resistant bacteria have caused huge concerns and demand innovative approaches for their prompt detection. Current antimicrobial susceptibility tests (AST) rely on the growth of the organisms which takes 1–2 days for fast-growing organisms and several weeks for slow growing organisms. Here, we show for the first time the utility of the SYBR Green I/propidium iodide (PI) viability assay for rapidly identifying antibiotic resistance in less than 30 min for major, antibiotic-resistant, fast-growing bacteria, such as Staphylococcus aureus, Escherichia coli, Klebsiella pneumoniae, and Acinetobacter baumannii for bactericidal and bacteriostatic agents and in 16 h for extremely rapid detection of drug resistance for isoniazid and pyrazinamide in slow-growing Mycobacterium tuberculosis. The SYBR Green I/PI assay generated rapid and robust results in concordance with traditional AST methods. This novel growth-independent methodology changes the concept of the current growth-based AST and may revolutionize current drug susceptibility testing for all cells of prokaryotic and eukaryotic origin and, subject to further clinical validation, may play a major role in saving lives and improving patient outcomes.

【 授权许可】

Unknown