0
views
0
recommends
+1 Recommend
0 collections
    0
    shares
      • Record: found
      • Abstract: found
      • Article: found
      Is Open Access

      Bacteriophage specificity is impacted by interactions between bacteria

      research-article

      Read this article at

      Bookmark
          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          ABSTRACT

          Predators play a central role in shaping community structure, function, and stability. The degree to which bacteriophage predators (viruses that infect bacteria) evolve to be specialists with a single bacterial prey species versus generalists able to consume multiple types of prey has implications for their effect on microbial communities. The presence and abundance of multiple bacterial prey types can alter selection for phage generalists, but less is known about how interactions between prey shape predator specificity in microbial systems. Using a phenomenological mathematical model of phage and bacterial populations, we find that the dominant phage strategy depends on prey ecology. Given a fitness cost for generalism, generalist predators maintain an advantage when prey species compete, while specialists dominate when prey are obligately engaged in cross-feeding interactions. We test these predictions in a synthetic microbial community with interacting strains of Escherichia coli and Salmonella enterica by competing a generalist T5-like phage able to infect both prey against P22 vir, an S. enterica-specific phage. Our experimental data conform to our modeling expectations when prey species are competing or obligately mutualistic, although our results suggest that the in vitro cost of generalism is caused by a combination of biological mechanisms not anticipated in our model. Our work demonstrates that interactions between bacteria play a role in shaping ecological selection on predator specificity in obligately lytic bacteriophages and emphasizes the diversity of ways in which fitness trade-offs can manifest.

          IMPORTANCE

          There is significant natural diversity in how many different types of bacteria a bacteriophage can infect, but the mechanisms driving this diversity are unclear. This study uses a combination of mathematical modeling and an in vitro system consisting of Escherichia coli, Salmonella enterica, a T5-like generalist phage, and the specialist phage P22 vir to highlight the connection between bacteriophage specificity and interactions between their potential microbial prey. Mathematical modeling suggests that competing bacteria tend to favor generalist bacteriophage, while bacteria that benefit each other tend to favor specialist bacteriophage. Experimental results support this general finding. The experiments also show that the optimal phage strategy is impacted by phage degradation and bacterial physiology. These findings enhance our understanding of how complex microbial communities shape selection on bacteriophage specificity, which may improve our ability to use phage to manage antibiotic-resistant microbial infections.

          Related collections

          Most cited references76

          • Record: found
          • Abstract: found
          • Article: found
          Is Open Access

          Unicycler: Resolving bacterial genome assemblies from short and long sequencing reads

          The Illumina DNA sequencing platform generates accurate but short reads, which can be used to produce accurate but fragmented genome assemblies. Pacific Biosciences and Oxford Nanopore Technologies DNA sequencing platforms generate long reads that can produce complete genome assemblies, but the sequencing is more expensive and error-prone. There is significant interest in combining data from these complementary sequencing technologies to generate more accurate “hybrid” assemblies. However, few tools exist that truly leverage the benefits of both types of data, namely the accuracy of short reads and the structural resolving power of long reads. Here we present Unicycler, a new tool for assembling bacterial genomes from a combination of short and long reads, which produces assemblies that are accurate, complete and cost-effective. Unicycler builds an initial assembly graph from short reads using the de novo assembler SPAdes and then simplifies the graph using information from short and long reads. Unicycler uses a novel semi-global aligner to align long reads to the assembly graph. Tests on both synthetic and real reads show Unicycler can assemble larger contigs with fewer misassemblies than other hybrid assemblers, even when long-read depth and accuracy are low. Unicycler is open source (GPLv3) and available at github.com/rrwick/Unicycler.
            Bookmark
            • Record: found
            • Abstract: found
            • Article: not found

            Construction of Escherichia coli K-12 in-frame, single-gene knockout mutants: the Keio collection

            We have systematically made a set of precisely defined, single-gene deletions of all nonessential genes in Escherichia coli K-12. Open-reading frame coding regions were replaced with a kanamycin cassette flanked by FLP recognition target sites by using a one-step method for inactivation of chromosomal genes and primers designed to create in-frame deletions upon excision of the resistance cassette. Of 4288 genes targeted, mutants were obtained for 3985. To alleviate problems encountered in high-throughput studies, two independent mutants were saved for every deleted gene. These mutants—the ‘Keio collection'—provide a new resource not only for systematic analyses of unknown gene functions and gene regulatory networks but also for genome-wide testing of mutational effects in a common strain background, E. coli K-12 BW25113. We were unable to disrupt 303 genes, including 37 of unknown function, which are candidates for essential genes. Distribution is being handled via GenoBase (http://ecoli.aist-nara.ac.jp/).
              Bookmark
              • Record: found
              • Abstract: found
              • Article: not found

              Identification of mutations in laboratory-evolved microbes from next-generation sequencing data using breseq.

              Next-generation DNA sequencing (NGS) can be used to reconstruct eco-evolutionary population dynamics and to identify the genetic basis of adaptation in laboratory evolution experiments. Here, we describe how to run the open-source breseq computational pipeline to identify and annotate genetic differences found in whole-genome and whole-population NGS data from haploid microbes where a high-quality reference genome is available. These methods can also be used to analyze mutants isolated in genetic screens and to detect unintended mutations that may occur during strain construction and genome editing.
                Bookmark

                Author and article information

                Contributors
                Role: ConceptualizationRole: Data curationRole: Formal analysisRole: InvestigationRole: MethodologyRole: Project administrationRole: VisualizationRole: Writing – original draftRole: Writing – review and editing
                Role: ConceptualizationRole: Funding acquisitionRole: Writing – review and editing
                Role: ConceptualizationRole: Funding acquisitionRole: Writing – review and editing
                Role: ResourcesRole: Writing – review and editing
                Role: ResourcesRole: Writing – review and editing
                Role: ConceptualizationRole: Funding acquisitionRole: SupervisionRole: Writing – original draftRole: Writing – review and editing
                Role: Editor
                Role: Ad Hoc Peer Reviewer
                Journal
                mSystems
                mSystems
                msystems
                mSystems
                American Society for Microbiology (1752 N St., N.W., Washington, DC )
                2379-5077
                March 2024
                20 February 2024
                20 February 2024
                : 9
                : 3
                : e01177-23
                Affiliations
                [1 ]Department of Ecology, Evolution and Behavior, University of Minnesota; , St. Paul, Minnesota, USA
                [2 ]Living Systems Institute, University of Exeter; , Exeter, United Kingdom
                [3 ]Department of Physics and Astronomy, University of Exeter; , Exeter, United Kingdom
                [4 ]Department of Biological Sciences, Dartmouth College; , Hanover, New Hampshire, USA
                [5 ]Department of Food Science and Nutrition, University of Minnesota; , St. Paul, Minnesota, USA
                [6 ]BioTechnology Institute, University of Minnesota; , St. Paul, Minnesota, USA
                London School of Hygiene & Tropical Medicine; , London, United Kingdom
                Institute of Biochemistry and Biophysics, Polish Academy of Sciences; , Warsaw, Poland
                Author notes
                Address correspondence to William R. Harcombe, harcombe@ 123456umn.edu

                The authors declare no conflict of interest.

                Author information
                https://orcid.org/0000-0001-9076-7384
                https://orcid.org/0000-0003-0926-4590
                https://orcid.org/0000-0003-1751-4895
                https://orcid.org/0000-0001-5438-2532
                https://orcid.org/0000-0001-8445-2052
                Article
                01177-23 msystems.01177-23
                10.1128/msystems.01177-23
                11237722
                38376179
                715c6cff-a4b6-4325-b935-2760790c803a
                Copyright © 2024 Bisesi et al.

                This is an open-access article distributed under the terms of the Creative Commons Attribution 4.0 International license.

                History
                : 07 November 2023
                : 20 January 2024
                Page count
                supplementary-material: 8, authors: 6, Figures: 6, Tables: 2, Equations: 6, References: 76, Pages: 22, Words: 13103
                Funding
                Funded by: National Science Foundation (NSF);
                Award ID: IOS-2019304
                Award Recipient :
                Funded by: National Science Foundation (NSF);
                Award ID: IOS-2017879
                Award Recipient :
                Funded by: UKRI | Biotechnology and Biological Sciences Research Council (BBSRC);
                Award ID: BB/V011464/1
                Award Recipient :
                Categories
                Research Article
                open-peer-review, Open Peer Review
                bacteriophages, Bacteriophages
                Custom metadata
                March 2024

                bacteriophages,virus-host interactions,microbial communities,microbial ecology,competition,mutualism

                Comments

                Comment on this article