DOE BRaVE Foundry

A High-Throughput Platform for Rapid Design and Development of
Countermeasures to Combat Emerging Drug- Resistant Pathogens

At its current rate, the rise of antimicrobial-resistant (AMR) infections is predicted to paralyze our industries and healthcare facilities while becoming the leading global cause of loss of human life. With limited new antibiotics on the horizon, we are ill-equipped to respond to the inevitable AMR pandemic. As noted in American Pandemic Preparedness Plan and DOE’s Biopreparedness report, to be prepared for any natural or human-made infectious disease outbreaks we urgently need to invest in foundational knowledge necessary to develop alternative therapies that can be scaled rapidly as new infections emerge. Bacteriophages (phages)—viruses targeting bacteria—offer a powerful alternative approach to combat AMR bacterial infections. Despite recent advances in using phages to treat recalcitrant AMR infections, the field lacks broad-scale mechanistic understanding of phage-host interactions in clinically and agriculturally relevant bacteria. The ability to rationally design therapeutic phage formulations to overcome AMR pathogens quickly and with seamless adaptability to new pathogens, can revolutionize our approach to combat AMR. With this goal, we have brought together multi-disciplinary and multi-institutional expertise to develop a foundational Phage Foundry platform that integrates in-depth multi-scale characterization of phage-host molecular interactions with high-throughput isolation, phage-host coevolution, machine-learning, and engineering design principles to enable rapid development of targeted phage-based therapeutics against AMR pathogens. We envision this Phage Foundry platform to serve as an open andintegrative knowledge-base available to researchers, clinicians, and industries in a fair and equitable manner, and serves to power biobased economy by developing other phage-based biotechnologies including diagnostics and vaccination strategies to treat emerging viral threats in future.

NSF EDGE CMT: Predicting bacteriophage susceptibility from Escherichia coli genotype

Machine-learning supported prediction of genetic basis of phage/host interactions

Microbial communities drive and are driven by significant environmental processes, affect agricultural output, and impact human and animal health. Complex interactions among themselves, their hosts and environments are important for these effects. The virome — the collection of viruses that parasitize these microbial communities– are a critical feature of microbial community dynamics, activity and adaptation. Bacterial viruses (bacteriophage or phages), represent the most abundant biological entities on earth attack exceptionally specific bacterial hosts. However, the mechanisms underlying this specificity are deeply under-characterized and studies have largely focused on handful of individual bacterium-phage systems. The lack of insights into phage specificity and the breadth of bacterial responses to different phages has limited our ability to build models that can predict which phages have the potential to infect specific bacterial strains. This has hampered their use in diverse biotechnologies. Here, we propose to create a powerful machine-learning-driven experimental workflow that exploits a natural genetic variation in bacterial strains and associated phages, scalable susceptibility assays and high throughput genetics to create a predictive model connecting bacterial genotype to phage susceptibility phenotype. We will leverage an extensive collection of non-model Escherichia coli strains originating from hundreds of reservoirs and geographic locations, a collection of diverse group of dsDNA lytic phages to gain mechanistic understanding of thousands of phage-host interactions necessary to build training datasets for predictive modeling. This research will provide a foundational advance in predicting phage susceptibility based on bacterial genome sequence alone, and has applications to precision microbiome manipulations and therapeutic approaches to treat antibiotic resistant infections.

Selected Publications

Kothari, Ankita; Roux, Simon; Zhang, Hanqiao; Prieto, Anatori; Soneja, Drishti; Chandonia, John-Marc; Spencer, Sarah; Wu, Xiaoqin; Altenburg, Sara; Fields, Matthew W.; Deutschbauer, Adam M.; Arkin, Adam P.; Alm, Eric J.; Chakraborty, Romy; Mukhopadhyay, Aindrila

Ecogenomics of Groundwater Phages Suggests Niche Differentiation Linked to Specific Environmental Tolerance Journal Article

In: mSystems, vol. 6, no. 3, 2021, ISSN: 2379-5077.

Abstract | Links | BibTeX

Ashley L. Azadeh Sean Carim, Alexey E. Kazakov

Systematic Discovery of Pseudomonad Genetic Factors Involved in Sensitivity to Tailocins Journal Article

In: 2020.


Crystal Zhong Benjamin A. Adler, Hualan Liu

Systematic Discovery of Salmonella Phage-Host Interactions via High-Throughput Genome-Wide Screens Journal Article

In: bioRxiv, 2020.


Benjamin A. Adler Vivek K. Mutalik, Harneet S. Rishi

High-throughput mapping of the phage resistance landscape in E. coli Journal Article

In: 2020.


Egbert, R G; Rishi, H S; Adler, B A; McCormick, D M; Toro, E; Gill, R T; Arkin, A P

A versatile platform strain for high-fidelity multiplex genome editing Journal Article

In: Nucleic Acids Res., vol. 47, no. 6, pp. 3244–3256, 2019.

Abstract | BibTeX

Price, M N; Zane, G M; Kuehl, J V; Melnyk, R A; Wall, J D; Deutschbauer, A M; Arkin, A P

Correction: Filling gaps in bacterial amino acid biosynthesis pathways with high-throughput genetics Journal Article

In: PLoS Genet., vol. 15, no. 4, pp. e1008106, 2019.

Abstract | BibTeX

Tei, M; Perkins, M L; Hsia, J; Arcak, M; Arkin, A P

Đesigning Spatially Đistributed Gene Regulatory Networks Ŧo Elicit Contrasting Patterns Journal Article

In: vol. 8, no. 1, pp. 119–126, 2019.

Abstract | BibTeX

Mutalik, V K; Novichkov, P S; Price, M N; Owens, T K; Callaghan, M; Carim, S; Deutschbauer, A M; Arkin, A P

Đual-barcoded shotgun expression library sequencing for high-throughput characterization of functional traits in bacteria Journal Article

In: Nat Commun, vol. 10, no. 1, pp. 308, 2019.

Abstract | BibTeX

Thompson, M G; Blake-Hedges, J M; Cruz-Morales, P; Barajas, J F; Curran, S C; Eiben, C B; Harris, N C; Benites, V T; Gin, J W; Sharpless, W A; Twigg, F F; Skyrud, W; Krishna, R N; Pereira, J H; Baidoo, E E K; Petzold, C J; Adams, P D; Arkin, A P; Deutschbauer, A M; Keasling, J D

Massively Parallel Fitness Profiling Reveals Multiple Novel Enzymes in Pseudomonas putida Lysine Metabolism Journal Article

In: mBio, vol. 10, no. 3, 2019.

Abstract | BibTeX

Price, M N; Ray, J; Iavarone, A T; Carlson, H K; Ryan, E M; Malmstrom, R R; Arkin, A P; Deutschbauer, A M

Oxidative Pathways of Đeoxyribose and Đeoxyribonate Catabolism Journal Article

In: mSystems, vol. 4, no. 1, 2019.

Abstract | BibTeX

Venturelli, O S; Carr, A C; Fisher, G; Hsu, R H; Lau, R; Bowen, B P; Hromada, S; Northen, T; Arkin, A P

Đeciphering microbial interactions in synthetic human gut microbiome communities Journal Article

In: Mol. Syst. Biol., vol. 14, no. 6, pp. e8157, 2018.

Abstract | BibTeX

Price, M N; Zane, G M; Kuehl, J V; Melnyk, R A; Wall, J D; Deutschbauer, A M; Arkin, A P

Filling gaps in bacterial amino acid biosynthesis pathways with high-throughput genetics Journal Article

In: PLoS Genet., vol. 14, no. 1, pp. e1007147, 2018.

Abstract | BibTeX

Coradetti, S T; Pinel, D; Geiselman, G M; Ito, M; Mondo, S J; Reilly, M C; Cheng, Y F; Bauer, S; Grigoriev, I V; Gladden, J M; Simmons, B A; Brem, R B; Arkin, A P; Skerker, J M

Functional genomics of lipid metabolism in the oleaginous yeast Rhodosporidium toruloides Journal Article

In: Elife, vol. 7, 2018.

Abstract | BibTeX