Cargando…
Computational Basis for On-Demand Production of Diversified Therapeutic Phage Cocktails
New therapies are necessary to combat increasingly antibiotic-resistant bacterial pathogens. We have developed a technology platform of computational, molecular biology, and microbiology tools which together enable on-demand production of phages that target virtually any given bacterial isolate. Two...
Autores principales: | , , , , , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
American Society for Microbiology
2020
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7426155/ https://www.ncbi.nlm.nih.gov/pubmed/32788409 http://dx.doi.org/10.1128/mSystems.00659-20 |
_version_ | 1783570626927132672 |
---|---|
author | Mageeney, Catherine M. Sinha, Anupama Mosesso, Richard A. Medlin, Douglas L. Lau, Britney Y. Rokes, Alecia B. Lane, Todd W. Branda, Steven S. Williams, Kelly P. |
author_facet | Mageeney, Catherine M. Sinha, Anupama Mosesso, Richard A. Medlin, Douglas L. Lau, Britney Y. Rokes, Alecia B. Lane, Todd W. Branda, Steven S. Williams, Kelly P. |
author_sort | Mageeney, Catherine M. |
collection | PubMed |
description | New therapies are necessary to combat increasingly antibiotic-resistant bacterial pathogens. We have developed a technology platform of computational, molecular biology, and microbiology tools which together enable on-demand production of phages that target virtually any given bacterial isolate. Two complementary computational tools that identify and precisely map prophages and other integrative genetic elements in bacterial genomes are used to identify prophage-laden bacteria that are close relatives of the target strain. Phage genomes are engineered to disable lysogeny, through use of long amplicon PCR and Gibson assembly. Finally, the engineered phage genomes are introduced into host bacteria for phage production. As an initial demonstration, we used this approach to produce a phage cocktail against the opportunistic pathogen Pseudomonas aeruginosa PAO1. Two prophage-laden P. aeruginosa strains closely related to PAO1 were identified, ATCC 39324 and ATCC 27853. Deep sequencing revealed that mitomycin C treatment of these strains induced seven phages that grow on P. aeruginosa PAO1. The most diverse five phages were engineered for nonlysogeny by deleting the integrase gene (int), which is readily identifiable and typically conveniently located at one end of the prophage. The Δint phages, individually and in cocktails, killed P. aeruginosa PAO1 in liquid culture as well as in a waxworm (Galleria mellonella) model of infection. IMPORTANCE The antibiotic resistance crisis has led to renewed interest in phage therapy as an alternative means of treating infection. However, conventional methods for isolating pathogen-specific phage are slow, labor-intensive, and frequently unsuccessful. We have demonstrated that computationally identified prophages carried by near-neighbor bacteria can serve as starting material for production of engineered phages that kill the target pathogen. Our approach and technology platform offer new opportunity for rapid development of phage therapies against most, if not all, bacterial pathogens, a foundational advance for use of phage in treating infectious disease. |
format | Online Article Text |
id | pubmed-7426155 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | American Society for Microbiology |
record_format | MEDLINE/PubMed |
spelling | pubmed-74261552020-08-24 Computational Basis for On-Demand Production of Diversified Therapeutic Phage Cocktails Mageeney, Catherine M. Sinha, Anupama Mosesso, Richard A. Medlin, Douglas L. Lau, Britney Y. Rokes, Alecia B. Lane, Todd W. Branda, Steven S. Williams, Kelly P. mSystems Research Article New therapies are necessary to combat increasingly antibiotic-resistant bacterial pathogens. We have developed a technology platform of computational, molecular biology, and microbiology tools which together enable on-demand production of phages that target virtually any given bacterial isolate. Two complementary computational tools that identify and precisely map prophages and other integrative genetic elements in bacterial genomes are used to identify prophage-laden bacteria that are close relatives of the target strain. Phage genomes are engineered to disable lysogeny, through use of long amplicon PCR and Gibson assembly. Finally, the engineered phage genomes are introduced into host bacteria for phage production. As an initial demonstration, we used this approach to produce a phage cocktail against the opportunistic pathogen Pseudomonas aeruginosa PAO1. Two prophage-laden P. aeruginosa strains closely related to PAO1 were identified, ATCC 39324 and ATCC 27853. Deep sequencing revealed that mitomycin C treatment of these strains induced seven phages that grow on P. aeruginosa PAO1. The most diverse five phages were engineered for nonlysogeny by deleting the integrase gene (int), which is readily identifiable and typically conveniently located at one end of the prophage. The Δint phages, individually and in cocktails, killed P. aeruginosa PAO1 in liquid culture as well as in a waxworm (Galleria mellonella) model of infection. IMPORTANCE The antibiotic resistance crisis has led to renewed interest in phage therapy as an alternative means of treating infection. However, conventional methods for isolating pathogen-specific phage are slow, labor-intensive, and frequently unsuccessful. We have demonstrated that computationally identified prophages carried by near-neighbor bacteria can serve as starting material for production of engineered phages that kill the target pathogen. Our approach and technology platform offer new opportunity for rapid development of phage therapies against most, if not all, bacterial pathogens, a foundational advance for use of phage in treating infectious disease. American Society for Microbiology 2020-08-11 /pmc/articles/PMC7426155/ /pubmed/32788409 http://dx.doi.org/10.1128/mSystems.00659-20 Text en Copyright © 2020 Mageeney et al. https://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution 4.0 International license (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Research Article Mageeney, Catherine M. Sinha, Anupama Mosesso, Richard A. Medlin, Douglas L. Lau, Britney Y. Rokes, Alecia B. Lane, Todd W. Branda, Steven S. Williams, Kelly P. Computational Basis for On-Demand Production of Diversified Therapeutic Phage Cocktails |
title | Computational Basis for On-Demand Production of Diversified Therapeutic Phage Cocktails |
title_full | Computational Basis for On-Demand Production of Diversified Therapeutic Phage Cocktails |
title_fullStr | Computational Basis for On-Demand Production of Diversified Therapeutic Phage Cocktails |
title_full_unstemmed | Computational Basis for On-Demand Production of Diversified Therapeutic Phage Cocktails |
title_short | Computational Basis for On-Demand Production of Diversified Therapeutic Phage Cocktails |
title_sort | computational basis for on-demand production of diversified therapeutic phage cocktails |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7426155/ https://www.ncbi.nlm.nih.gov/pubmed/32788409 http://dx.doi.org/10.1128/mSystems.00659-20 |
work_keys_str_mv | AT mageeneycatherinem computationalbasisforondemandproductionofdiversifiedtherapeuticphagecocktails AT sinhaanupama computationalbasisforondemandproductionofdiversifiedtherapeuticphagecocktails AT mosessoricharda computationalbasisforondemandproductionofdiversifiedtherapeuticphagecocktails AT medlindouglasl computationalbasisforondemandproductionofdiversifiedtherapeuticphagecocktails AT laubritneyy computationalbasisforondemandproductionofdiversifiedtherapeuticphagecocktails AT rokesaleciab computationalbasisforondemandproductionofdiversifiedtherapeuticphagecocktails AT lanetoddw computationalbasisforondemandproductionofdiversifiedtherapeuticphagecocktails AT brandastevens computationalbasisforondemandproductionofdiversifiedtherapeuticphagecocktails AT williamskellyp computationalbasisforondemandproductionofdiversifiedtherapeuticphagecocktails |