Rapid diagnostics for infectious diseases that both identify the pathogen and provide drug susceptibility data in real-time would transform patient management and critical public health issues such as the current antibiotic resistance crisis. Their application would reach broadly from clinics and primary care offices to tertiary care hospitals, providing immediate guidance for therapeutic intervention thereby resulting in more prudent and appropriate use of antibiotics, in some cases with mortality benefit. The need to generate real- time drug susceptibility patterns is particularly acute in the face of escalating antibiotic resistance that is challenging empiric antibiotic decision-making. We propose to develop a rapid, universal RNA hybridization-based diagnostic platform that would provide both real-time identification of microbial pathogens and reveal their susceptibility to diverse antibiotics. Pathogen identification will be achieved by the detection of species-specific sequence variations in highly abundant ribosomal RNAs. Drug susceptibility will be determined by detection of transcriptional signatures that are extremely rapidly induced (within minutes) by antibiotic exposure in susceptible but not resistant strains. Importantly, in contrast o existing DNA-based diagnostics, this RNA-based approach will enable pathogen identification without nucleic acid amplification and drug susceptibility profiling without a priori knowledge of the genes or mutations that confer drug resistance. Moreover, since transcriptional responses to antibiotics occur much earlier than measurable changes in growth, this platform would provide a phenotypic readout much earlier than culture-based assays. Significantly, this work is coupled with microfluidic technology to obviate the need for culture. Proof of principle studies have demonstrated the feasibility of this approach. We now propose to expand upon and optimize our RNA-signatures for pathogen identification and drug susceptibility for the high priority antibiotic resistant ESKAPE pathogens (Enterococcus faecium, Staphylococcus aureus, Klebsiella pneumoniae, Acinetobacter baumanii, Pseudomonas aeruginosa, and Enterobacter species), to solve individual engineering challenges required for development of a diagnostic device, and to integrate these elements into a microfluidic prototype for rapid pathogen identification and antibiotic susceptibility. To that ed, we have formed a multidisciplinary team of scientists, engineers, and industry partners that will work collaboratively to carry out the proposed research.

Public Health Relevance

In the face of increasing antibiotic resistance, a rapid diagnostic platform to identify in real-time infectious pathogens and their drug susceptibility patterns will be critical to guide immediate therapeutic decisions that are life-saving. Taking advantage of the advances in genomics, sequencing, nanotechnology, and engineering, we will develop a single platform for diagnosing all infectious pathogens with drug resistance testing in real-time based on detecting RNA expression signatures of the different pathogens.

Agency
National Institute of Health (NIH)
Institute
National Institute of Allergy and Infectious Diseases (NIAID)
Type
Research Project (R01)
Project #
5R01AI117043-04
Application #
9416072
Study Section
Special Emphasis Panel (ZAI1)
Program Officer
Ritchie, Alec
Project Start
2015-02-15
Project End
2020-01-31
Budget Start
2018-02-01
Budget End
2019-01-31
Support Year
4
Fiscal Year
2018
Total Cost
Indirect Cost
Name
Broad Institute, Inc.
Department
Type
DUNS #
623544785
City
Cambridge
State
MA
Country
United States
Zip Code
02142
Abudayyeh, Omar O; Gootenberg, Jonathan S; Essletzbichler, Patrick et al. (2017) RNA targeting with CRISPR-Cas13. Nature 550:280-284
Gootenberg, Jonathan S; Abudayyeh, Omar O; Lee, Jeong Wook et al. (2017) Nucleic acid detection with CRISPR-Cas13a/C2c2. Science 356:438-442
Hou, Han Wei; Bhattacharyya, Roby P; Hung, Deborah T et al. (2015) Direct detection and drug-resistance profiling of bacteremias using inertial microfluidics. Lab Chip 15:2297-307