Enhanced killing of antibiotic-resistant bacteria enabled by massively parallel combinatorial genetics
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...
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| Other Authors: | |
| Format: | Article |
| Language: | en_US |
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National Academy of Sciences (U.S.)
2015
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| Online Access: | http://hdl.handle.net/1721.1/95764 https://orcid.org/0000-0002-9999-6690 |
| _version_ | 1826192217643941888 |
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| author | Cheng, Allen A. Ding, Huiming Lu, Timothy K. |
| author2 | Harvard University--MIT Division of Health Sciences and Technology |
| author_facet | Harvard University--MIT Division of Health Sciences and Technology Cheng, Allen A. Ding, Huiming Lu, Timothy K. |
| author_sort | Cheng, Allen A. |
| collection | MIT |
| description | 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 ~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[superscript 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. |
| first_indexed | 2024-09-23T09:07:43Z |
| format | Article |
| id | mit-1721.1/95764 |
| institution | Massachusetts Institute of Technology |
| language | en_US |
| last_indexed | 2024-09-23T09:07:43Z |
| publishDate | 2015 |
| publisher | National Academy of Sciences (U.S.) |
| record_format | dspace |
| spelling | mit-1721.1/957642022-09-30T13:36:54Z Enhanced killing of antibiotic-resistant bacteria enabled by massively parallel combinatorial genetics Cheng, Allen A. Ding, Huiming Lu, Timothy K. Harvard University--MIT Division of Health Sciences and Technology Massachusetts Institute of Technology. Department of Biological Engineering Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology. Research Laboratory of Electronics Massachusetts Institute of Technology. Synthetic Biology Center Ding, Huiming Lu, Timothy K. Cheng, Allen A. 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 ~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[superscript 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. National Institutes of Health (U.S.) (New Innovator Award DP2 OD008435) United States. Office of Naval Research Ellison Medical Foundation (New Scholar in Aging Award) Henry L. and Grace Doherty Charitable Foundation 2015-03-03T19:29:59Z 2015-03-03T19:29:59Z 2014-08 2014-01 Article http://purl.org/eprint/type/JournalArticle 0027-8424 1091-6490 http://hdl.handle.net/1721.1/95764 Cheng, A. A., H. Ding, and T. K. Lu. “Enhanced Killing of Antibiotic-Resistant Bacteria Enabled by Massively Parallel Combinatorial Genetics.” Proceedings of the National Academy of Sciences 111, no. 34 (August 11, 2014): 12462–12467. https://orcid.org/0000-0002-9999-6690 en_US http://dx.doi.org/10.1073/pnas.1400093111 Proceedings of the National Academy of Sciences of the United States of America Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use. application/pdf National Academy of Sciences (U.S.) National Academy of Sciences (U.S.) |
| spellingShingle | Cheng, Allen A. Ding, Huiming Lu, Timothy K. Enhanced killing of antibiotic-resistant bacteria enabled by massively parallel combinatorial genetics |
| title | Enhanced killing of antibiotic-resistant bacteria enabled by massively parallel combinatorial genetics |
| title_full | Enhanced killing of antibiotic-resistant bacteria enabled by massively parallel combinatorial genetics |
| title_fullStr | Enhanced killing of antibiotic-resistant bacteria enabled by massively parallel combinatorial genetics |
| title_full_unstemmed | Enhanced killing of antibiotic-resistant bacteria enabled by massively parallel combinatorial genetics |
| title_short | Enhanced killing of antibiotic-resistant bacteria enabled by massively parallel combinatorial genetics |
| title_sort | enhanced killing of antibiotic resistant bacteria enabled by massively parallel combinatorial genetics |
| url | http://hdl.handle.net/1721.1/95764 https://orcid.org/0000-0002-9999-6690 |
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