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| Content Provider | World Health Organization (WHO)-Global Index Medicus |
|---|---|
| Author | Ding, Huiming Lu, Timothy K. Cheng, Allen A. |
| Description | Author Affiliation: Cheng AA ( Synthetic Biology Group, MIT Synthetic Biology Center, Research Lab of Electronics, Departments of Electrical Engineering and Computer Science and Biological Engineering, Medical Engineering and Medical Physics Program, Harvard-MIT Health Sciences and Technology, Harvard Biophysics Program and Harvard MD-PhD Program, Harvard University, Boston, MA 02115.); Ding H ( MIT BioMicro Center, Department of Biological Engineering, and.); Lu TK ( Synthetic Biology Group, MIT Synthetic Biology Center, Research Lab of Electronics, Departments of Electrical Engineering and Computer Science and Biological Engineering, Harvard Biophysics Program and MIT Microbiology Program, Massachusetts Institute of Technology, Cambridge, MA 02139); |
| Abstract | New therapeutic strategies are needed to treat infections caused by drug-resistant bacteria, which constitute a major growing threat to human health. Here, we use a high-throughput technology to identify combinatorial genetic perturbations that can enhance the killing of drug-resistant bacteria with antibiotic treatment. This strategy, Combinatorial Genetics En Masse (CombiGEM), enables the rapid generation of high-order barcoded combinations of genetic elements for high-throughput multiplexed characterization based on next-generation sequencing. We created â ¼ 34,000 pairwise combinations of Escherichia coli transcription factor (TF) overexpression constructs. Using Illumina sequencing, we identified diverse perturbations in antibiotic-resistance phenotypes against carbapenem-resistant Enterobacteriaceae. Specifically, we found multiple TF combinations that potentiated antibiotic killing by up to 10(6)-fold and delivered these combinations via phagemids to increase the killing of highly drug-resistant E. coli harboring New Delhi metallo-beta-lactamase-1. Moreover, we constructed libraries of three-wise combinations of transcription factors with >4 million unique members and demonstrated that these could be tracked via next-generation sequencing. We envision that CombiGEM could be extended to other model organisms, disease models, and phenotypes, where it could accelerate massively parallel combinatorial genetics studies for a broad range of biomedical and biotechnology applications, including the treatment of antibiotic-resistant infections. |
| ISSN | 00278424 |
| e-ISSN | 10916490 |
| Journal | Proceedings of the National Academy of Sciences of the United States of America |
| Issue Number | 34 |
| Volume Number | 111 |
| Language | English |
| Publisher | National Academy of Sciences |
| Publisher Date | 2014-08-01 |
| Publisher Place | United States |
| Access Restriction | Open |
| Subject Keyword | Drug Resistance, Bacterial Genetics Escherichia Coli Drug Effects Genetic Techniques Carbapenems Pharmacology DNA Barcoding, Taxonomic Enzymology Escherichia Coli Proteins Gene Library Genes, Bacterial High-Throughput Nucleotide Sequencing High-Throughput Screening Assays Synthetic Biology Systems Biology Transcription Factors Beta-Lactam Resistance Beta-Lactamases Research Support, N.I.H., Extramural Research Support, Non-U.S. Gov't Research Support, U.S. Gov't, Non-P.H.S. Multidisciplinary |
| Content Type | Text |
| Resource Type | Article |
| Subject | Multidisciplinary |
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