Combatting antibiotic resistance with synthetic biology technologies

利用合成生物学技术对抗抗生素耐药性

• 批准号: 9167953
• 负责人: Ahmad Samir Khalil
• 金额: $ 247.24万
• 依托单位: BOSTON UNIVERSITY (CHARLES RIVER CAMPUS)
• 依托单位国家: 美国
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-09-30 至 2021-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9167953
• 关键词: Accounting; Antibiotic Resistance; Antibiotic susceptibility; Antibiotics; Antimicrobial Resistance; Bacterial Infections; Cells; Centers for Disease Control and Prevention (U.S.); Cessation of life; Characteristics; Clinic; Clinical; Detection; Diagnosis; Diagnostic; Diagnostic Procedure; Drug resistance; Engineering; Freeze Drying; Genetic; Gold; Gonorrhea; Health; Hour; Infection; Messenger RNA; Methods; Microbe; Molecular; Molecular Profiling; Neisseria gonorrhoeae; Organism; Paper; Performance; Phenotype; Population; Predisposition; RNA; Resistance; Risk; Sampling; System; Techniques; Technology; Testing; Time; Treatment Failure; Work; bacterial resistance; base; cell growth; clinically relevant; combat; cost; disorder prevention; drug resistant bacteria; effective therapy; innovation; microorganism; new technology; novel diagnostics; pathogen; prevent; sensor; solid state; synthetic biology

PROJECT SUMMARY / ABSTRACT The emergence of antimicrobial resistance is one of the most serious health threats to our entire population. Infections from resistant bacteria are now too common, and some pathogens have become resistant to multiple antibiotic classes. The Centers for Disease Control and Prevention (CDC) recently estimated that drug-resistant bacteria account for more than 2 million illnesses and over 23,000 deaths every year in the U.S. With rising rates of drug-resistant infections, there is pressing need for new diagnostic methods that can rapidly determine the most effective therapy for an infection. Unfortunately, the current method for performing antibiotic susceptibility testing (AST) involves growing microorganisms from clinical samples and determining their sensitivity to antibiotics through cell growth. This “gold standard” technique is extremely time-consuming (minimum 48-72 hours) and can result in significant delays in appropriate therapy, prolonged illness, greater risk of death, inappropriate antibiotic use, and increased spread of resistance. For some infections like gonorrhea, AST is not even performed in the clinic and instead inferred based on treatment failure. In short, it is imperative that new strategies are developed to rapidly diagnose and prevent the amplification of drug resistance. Antibiotic exposure can trigger the expression of a signature set of mRNAs in susceptible microbes in as rapidly as a few minutes, raising the exciting possibility of using RNA detection – not cell growth – as a new means for rapid, phenotype-based AST. We will develop innovative RNA sensor technology that evaluates these molecular signatures within a clinically-relevant, low-cost, and easy-to-use diagnostic platform. To achieve this, we will use synthetic biology approaches to engineer highly-sensitive genetic sensors of mRNA. These sensors will be deployed in cell-free expression systems that can be arrayed and freeze-dried onto low-cost, solid-state substrates like paper. The result will be a new class of antibiotic diagnostics with ideal performance, storage, and distribution characteristics. The RNA sensor technology will be developed and validated with high priority bacterial organisms. Notably, we will, for the first time, define RNA signatures of susceptibility for N. gonorrhoeae, which the CDC recently elevated as a major cause for concern in the U.S. and for which AST capabilities do not currently existing in the clinical setting. This work will usher in a new technology for rapidly diagnosing antibiotic resistance, with the potential to transform the management of today's growing antimicrobial resistance problem.

项目摘要/摘要