ArticleMolecular biology and evolution2023
Stepwise Evolution of E. coli C and ΦX174 Reveals Unexpected Lipopolysaccharide (LPS) Diversity.
Article in Molecular biology and evolution, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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Who cites it
15 citing papers in PubMed, 22 citations in OpenAlex.
- Unmasking pathogen traits for chronic colonization in neurogenic bladder.Cell reports · 2026Article
- Phase variation inEngineering microbiology · 2026Article
- Bacterial defense mechanisms against bacteriophages: an evolutionary arms race.Archives of microbiology · 2026Review
- Phage receptor specificity drives cross-resistance patterns and governs fitness trade-offs during sequential resistance acquisition in Salmonella.The ISME journal · 2026Article
- Spatial structure: shaping the ecology and evolution of microbial communities.FEMS microbiology reviews · 2026Review
- Single capsid mutations modulating phage adsorption, persistence, and plaque morphology shape evolutionary trajectories in ΦX174.Virus evolution · 2026Article
- Rational design of a hospital-specific phage cocktail to treat Enterobacter cloacae complex infections.Nature microbiology · 2025Article
- ImprovedMicrobial genomics · 2025Article
- Bacteriophage resistance evolution in a honey bee pathogen.Microbiology (Reading, England) · 2025Article
- A more significant role for insertion sequences in large-scale rearrangements in bacterial genomes.mBio · 2025Article
- Integrating Subacute Ruminal Acidosis, Lipopolysaccharide, and Trained Immunity: A Comprehensive Review.International journal of biological sciences · 2025Review
- Chloramphenicol and gentamicin reduce the evolution of resistance to phage ΦX174 by suppressing a subset of E. coli LPS mutants.PLoS biology · 2025Article
- Study of Endogenous Viruses in the Strawberry Plants.Viruses · 2024Article
- Diversity, Complexity, and Specificity of Bacterial Lipopolysaccharide (LPS) Structures Impacting Their Detection and Quantification.International journal of molecular sciences · 2024Article
- The untapped potential of phage model systems as therapeutic agents.Virus evolution · 2024Article
Corrections and comments
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Authors and funding
4 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Phage therapy is a promising method for the treatment of multidrug-resistant bacterial infections. However, its long-term efficacy depends on understanding the evolutionary effects of the treatment. Current knowledge of such evolutionary effects is lacking, even in well-studied systems. We used the bacterium Escherichia coli C and its bacteriophage ΦX174, which infects cells using host lipopolysaccharide (LPS) molecules. We first generated 31 bacterial mutants resistant to ΦX174 infection. Based on the genes disrupted by these mutations, we predicted that these E. coli C mutants collectively produce eight unique LPS structures. We then developed a series of evolution experiments to select for ΦX174 mutants capable of infecting the resistant strains. During phage adaptation, we distinguished two types of phage resistance: one that was easily overcome by ΦX174 with few mutational steps ("easy" resistance) and one that was more difficult to overcome ("hard" resistance). We found that increasing the diversity of the host and phage populations could accelerate the adaptation of phage ΦX174 to overcome the hard resistance phenotype. From these experiments, we isolated 16 ΦX174 mutants that, together, can infect all 31 initially resistant E. coli C mutants. Upon determining the infectivity profiles of these 16 evolved phages, we uncovered 14 distinct profiles. Given that only eight profiles are anticipated if the LPS predictions are correct, our findings highlight that the current understanding of LPS biology is insufficient to accurately forecast the evolutionary outcomes of bacterial populations infected by phage.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.