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Phage therapy is a promising alternative to chemotherapeutic antibiotics for the treatment of bacterial infections. However, despite recent clinical uses of combinations of phages to treat multidrug-resistant infections, a mechanistic understanding of how bacteria evolve resistance against multiple phages is lacking, limiting our ability to deploy phage combinations optimally. Here, we show, using Pseudomonas aeruginosa and pairs of phages targeting shared or distinct surface receptors, that the timing and order of phage exposure determine the strength, cost, and mutational basis of resistance. Whereas sequential exposure allowed bacteria to acquire multiple resistance mutations effective against both phages, this evolutionary trajectory was prevented by simultaneous exposure, resulting in quantitatively weaker resistance. The order of phage exposure determined the fitness costs of sequential resistance, such that certain sequential orders imposed much higher fitness costs than the same phage pair in the reverse order. Together, these data suggest that phage combinations can be optimized to limit the strength of evolved resistances while maximizing their associated fitness costs to promote the long-term efficacy of phage therapy.IMPORTANCE Globally rising rates of antibiotic resistance have renewed interest in phage therapy where combinations of phages have been successfully used to treat multidrug-resistant infections. To optimize phage therapy, we first need to understand how bacteria evolve resistance against combinations of multiple phages. Here, we use simple laboratory experiments and genome sequencing to show that the timing and order of phage exposure determine the strength, cost, and mutational basis of resistance evolution in the opportunistic pathogen Pseudomonas aeruginosa These findings suggest that phage combinations can be optimized to limit the emergence and persistence of resistance, thereby promoting the long-term usefulness of phage therapy.
Pubmed ID: 31551330
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NIH genetic sequence database that provides annotated collection of all publicly available DNA sequences for almost 280 000 formally described species (Jan 2014) .These sequences are obtained primarily through submissions from individual laboratories and batch submissions from large-scale sequencing projects, including whole-genome shotgun (WGS) and environmental sampling projects. Most submissions are made using web-based BankIt or standalone Sequin programs, and GenBank staff assigns accession numbers upon data receipt. It is part of International Nucleotide Sequence Database Collaboration and daily data exchange with European Nucleotide Archive (ENA) and DNA Data Bank of Japan (DDBJ) ensures worldwide coverage. GenBank is accessible through NCBI Entrez retrieval system, which integrates data from major DNA and protein sequence databases along with taxonomy, genome, mapping, protein structure and domain information, and biomedical journal literature via PubMed. BLAST provides sequence similarity searches of GenBank and other sequence databases. Complete bimonthly releases and daily updates of GenBank database are available by FTP.
View all literature mentionsGenetic variant annotation and effect prediction software toolbox that annotates and predicts effects of variants on genes (such as amino acid changes). By using standards, such as VCF, SnpEff makes it easy to integrate with other programs.
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