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Updated: Jul 16, 2026

A Practical Guide to Phage- and Robotics-Assisted Near-Continuous Evolution

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Combining Evolutionary Steering and Coevolutionary Phage Training to Generate Predictable Phage-Antibiotic Synergy.

Nehme Lahoud¹, Sweetzel D Labador¹, Mrudula Sane¹

¹Ecology, Behavior and Evolution Department, University of California San Diego, San Diego, CA, 92093, USA.

Npj Antimicrobials and Resistance

|July 14, 2026

Summary

Bacteriophage therapy is promising against resistant bacteria. Training phages can make bacteria sensitive to antibiotics, enhancing treatment effectiveness through predictable coevolutionary strategies.

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Creating Highly Specific Chemically Induced Protein Dimerization Systems by Stepwise Phage Selection of a Combinatorial Single-Domain Antibody Library

Creating Highly Specific Chemically Induced Protein Dimerization Systems by Stepwise Phage Selection of a Combinatorial Single-Domain Antibody Library

Published on: January 14, 2020

Area of Science:

Microbiology
Evolutionary Biology
Drug Discovery

Background:

Multidrug-resistant bacterial pathogens pose a significant global health threat.
Bacteriophage therapy offers an alternative but faces challenges due to rapidly evolving bacterial resistance.
Two strategies, phage training and phage-antibiotic synergy, aim to overcome resistance.

Purpose of the Study:

To investigate the intersection of phage training and phage-antibiotic synergy.
To determine if phage training can induce collateral sensitivity to antibiotics.
To explore how mechanistic insights can guide predictive therapeutic design.

Main Methods:

Utilized Escherichia coli and bacteriophage λ model system.
Applied coevolutionary phage training and assessed bacterial resistance/sensitivity profiles.
Investigated genetic mechanisms underlying observed sensitivities, focusing on lipopolysaccharide biosynthesis genes.
Predicted and validated collateral sensitivity with additional antibiotics based on mechanistic understanding.

Main Results:

Resistance to trained phage, but not untrained phage, induced collateral sensitivity to erythromycin and rifampicin in E. coli.
This training-induced synergy was mechanistically linked to disruption of the lpcA gene.
Successfully predicted collateral sensitivity to novobiocin and rifapentine.
Incorporating sub-lethal erythromycin during phage training enhanced bacterial suppression by promoting phage host receptor switching.

Conclusions:

Phage-bacteria coevolutionary outcomes can be anticipated and directed.
Evolution-informed strategies integrating phage training and antibiotic synergy show promise for novel therapeutic design.
This research highlights the potential of predictive, evolution-guided approaches for combating bacterial resistance.