Controlling Antibiotic Resistance by Vaccinating Bacterial Populations

Controlling Antibiotic Resistance by Vaccinating Bacterial Populations

Award Information
Agency: Department of Defense
Branch: Defense Advanced Research Projects Agency
Contract: W31P4Q14C0035
Agency Tracking Number: D2-1232
Amount: $1,750,000.00
Phase: Phase II
Program: SBIR
Awards Year: 2014
Solicitation Year: 2013
Solicitation Topic Code: SB122-001
Solicitation Number: 2012.2
Small Business Information
27 Drydock Ave 8th Floor, Boston, MA, 02210-2413
DUNS: 827811626
HUBZone Owned: N
Woman Owned: N
Socially and Economically Disadvantaged: N
Principal Investigator
 Reshma Shetty
 President
 (814) 422-5362
 rshetty@ginkgobioworks.com
Business Contact
 Reshma Shetty
Title: Dr.
Phone: (814) 422-5362
Email: rshetty@ginkgobioworks.com
Research Institution
N/A
Abstract
Next generation sequencing technologies are allowing researchers to rapidly and accurately interrogate the genomic content of microbiomes. Advances in our understanding of the human microbiome are likely to lead to the use of next generation sequencing as a diagnostic tool to identify the existence and precise genotype of pathogens and virulence genes. Likewise, our ability to engineer microbes using synthetic biology tools and technologies has advanced to the point where we can begin to consider applying engineered microbes to restore healthy microbiome states. Engineered organisms are already used widely in human health for therapeutic production, biomedical research, and more recently as products themselves. This SBIR Phase II proposal outlines the opportunity to merge the fields of microbiome discovery and organism engineering to create new strategies for modulating human health and disease: via engineered probiotics. We describe engineered microbes with the potential to 1) target specific pathogens based on sequence data, 2) destroy targeted gene sequences, 3) and avoid the use of traditional antibiotics. The performance of this system will be demonstrated in an in vivo mouse model.

* Information listed above is at the time of submission. *

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