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<record><TEI><teiHeader><fileDesc><titleStmt><title xml:lang="en">Host-pathogen coevolution increases genetic variation in susceptibility to infection</title><author><name sortKey="Duxbury, Elizabeth Ml" sort="Duxbury, Elizabeth Ml" uniqKey="Duxbury E" first="Elizabeth Ml" last="Duxbury">Elizabeth Ml Duxbury</name><affiliation><nlm:aff id="aff1"><institution content-type="dept">Department of Genetics</institution><institution>University of Cambridge</institution><addr-line>Cambridge</addr-line><country>United Kingdom</country></nlm:aff></affiliation><affiliation><nlm:aff id="aff2"><institution content-type="dept">School of Biological Sciences</institution><institution>University of East Anglia</institution><addr-line>Norwich</addr-line><country>United Kingdom</country></nlm:aff></affiliation></author><author><name sortKey="Day, Jonathan P" sort="Day, Jonathan P" uniqKey="Day J" first="Jonathan P" last="Day">Jonathan P. Day</name><affiliation><nlm:aff id="aff1"><institution content-type="dept">Department of Genetics</institution><institution>University of Cambridge</institution><addr-line>Cambridge</addr-line><country>United Kingdom</country></nlm:aff></affiliation></author><author><name sortKey="Maria Vespasiani, Davide" sort="Maria Vespasiani, Davide" uniqKey="Maria Vespasiani D" first="Davide" last="Maria Vespasiani">Davide Maria Vespasiani</name><affiliation><nlm:aff id="aff1"><institution content-type="dept">Department of Genetics</institution><institution>University of Cambridge</institution><addr-line>Cambridge</addr-line><country>United Kingdom</country></nlm:aff></affiliation></author><author><name sortKey="Thuringer, Yannik" sort="Thuringer, Yannik" uniqKey="Thuringer Y" first="Yannik" last="Thüringer">Yannik Thüringer</name><affiliation><nlm:aff id="aff1"><institution content-type="dept">Department of Genetics</institution><institution>University of Cambridge</institution><addr-line>Cambridge</addr-line><country>United Kingdom</country></nlm:aff></affiliation></author><author><name sortKey="Tolosana, Ignacio" sort="Tolosana, Ignacio" uniqKey="Tolosana I" first="Ignacio" last="Tolosana">Ignacio Tolosana</name><affiliation><nlm:aff id="aff1"><institution content-type="dept">Department of Genetics</institution><institution>University of Cambridge</institution><addr-line>Cambridge</addr-line><country>United Kingdom</country></nlm:aff></affiliation></author><author><name sortKey="Smith, Sophia Cl" sort="Smith, Sophia Cl" uniqKey="Smith S" first="Sophia Cl" last="Smith">Sophia Cl Smith</name><affiliation><nlm:aff id="aff1"><institution content-type="dept">Department of Genetics</institution><institution>University of Cambridge</institution><addr-line>Cambridge</addr-line><country>United Kingdom</country></nlm:aff></affiliation></author><author><name sortKey="Tagliaferri, Lucia" sort="Tagliaferri, Lucia" uniqKey="Tagliaferri L" first="Lucia" last="Tagliaferri">Lucia Tagliaferri</name><affiliation><nlm:aff id="aff1"><institution content-type="dept">Department of Genetics</institution><institution>University of Cambridge</institution><addr-line>Cambridge</addr-line><country>United Kingdom</country></nlm:aff></affiliation></author><author><name sortKey="Kamacioglu, Altug" sort="Kamacioglu, Altug" uniqKey="Kamacioglu A" first="Altug" last="Kamacioglu">Altug Kamacioglu</name><affiliation><nlm:aff id="aff1"><institution content-type="dept">Department of Genetics</institution><institution>University of Cambridge</institution><addr-line>Cambridge</addr-line><country>United Kingdom</country></nlm:aff></affiliation></author><author><name sortKey="Lindsley, Imogen" sort="Lindsley, Imogen" uniqKey="Lindsley I" first="Imogen" last="Lindsley">Imogen Lindsley</name><affiliation><nlm:aff id="aff1"><institution content-type="dept">Department of Genetics</institution><institution>University of Cambridge</institution><addr-line>Cambridge</addr-line><country>United Kingdom</country></nlm:aff></affiliation></author><author><name sortKey="Love, Luca" sort="Love, Luca" uniqKey="Love L" first="Luca" last="Love">Luca Love</name><affiliation><nlm:aff id="aff1"><institution content-type="dept">Department of Genetics</institution><institution>University of Cambridge</institution><addr-line>Cambridge</addr-line><country>United Kingdom</country></nlm:aff></affiliation></author><author><name sortKey="Unckless, Robert L" sort="Unckless, Robert L" uniqKey="Unckless R" first="Robert L" last="Unckless">Robert L. Unckless</name><affiliation><nlm:aff id="aff3"><institution content-type="dept">Department of Molecular Biosciences</institution><institution>University of Kansas</institution><addr-line>Lawrence</addr-line><country>United States</country></nlm:aff></affiliation></author><author><name sortKey="Jiggins, Francis M" sort="Jiggins, Francis M" uniqKey="Jiggins F" first="Francis M" last="Jiggins">Francis M. Jiggins</name><affiliation><nlm:aff id="aff1"><institution content-type="dept">Department of Genetics</institution><institution>University of Cambridge</institution><addr-line>Cambridge</addr-line><country>United Kingdom</country></nlm:aff></affiliation></author><author><name sortKey="Longdon, Ben" sort="Longdon, Ben" uniqKey="Longdon B" first="Ben" last="Longdon">Ben Longdon</name><affiliation><nlm:aff id="aff1"><institution content-type="dept">Department of Genetics</institution><institution>University of Cambridge</institution><addr-line>Cambridge</addr-line><country>United Kingdom</country></nlm:aff></affiliation><affiliation><nlm:aff id="aff4"><institution content-type="dept">Centre for Ecology and Conservation, Biosciences</institution><institution>University of Exeter (Penryn Campus)</institution><addr-line>Cornwall</addr-line><country>United Kingdom</country></nlm:aff></affiliation></author></titleStmt><publicationStmt><idno type="wicri:source">PMC</idno><idno type="pmid">31038124</idno><idno type="pmc">6491035</idno><idno type="url">http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6491035</idno><idno type="RBID">PMC:6491035</idno><idno type="doi">10.7554/eLife.46440</idno><date when="????">????</date><idno type="wicri:Area/Pmc/Corpus">000B52</idno><idno type="wicri:explorRef" wicri:stream="Pmc" wicri:step="Corpus" wicri:corpus="PMC">000B52</idno></publicationStmt><sourceDesc><biblStruct><analytic><title xml:lang="en" level="a" type="main">Host-pathogen coevolution increases genetic variation in susceptibility to infection</title><author><name sortKey="Duxbury, Elizabeth Ml" sort="Duxbury, Elizabeth Ml" uniqKey="Duxbury E" first="Elizabeth Ml" last="Duxbury">Elizabeth Ml Duxbury</name><affiliation><nlm:aff id="aff1"><institution content-type="dept">Department of Genetics</institution><institution>University of Cambridge</institution><addr-line>Cambridge</addr-line><country>United Kingdom</country></nlm:aff></affiliation><affiliation><nlm:aff id="aff2"><institution content-type="dept">School of Biological Sciences</institution><institution>University of East Anglia</institution><addr-line>Norwich</addr-line><country>United Kingdom</country></nlm:aff></affiliation></author><author><name sortKey="Day, Jonathan P" sort="Day, Jonathan P" uniqKey="Day J" first="Jonathan P" last="Day">Jonathan P. Day</name><affiliation><nlm:aff id="aff1"><institution content-type="dept">Department of Genetics</institution><institution>University of Cambridge</institution><addr-line>Cambridge</addr-line><country>United Kingdom</country></nlm:aff></affiliation></author><author><name sortKey="Maria Vespasiani, Davide" sort="Maria Vespasiani, Davide" uniqKey="Maria Vespasiani D" first="Davide" last="Maria Vespasiani">Davide Maria Vespasiani</name><affiliation><nlm:aff id="aff1"><institution content-type="dept">Department of Genetics</institution><institution>University of Cambridge</institution><addr-line>Cambridge</addr-line><country>United Kingdom</country></nlm:aff></affiliation></author><author><name sortKey="Thuringer, Yannik" sort="Thuringer, Yannik" uniqKey="Thuringer Y" first="Yannik" last="Thüringer">Yannik Thüringer</name><affiliation><nlm:aff id="aff1"><institution content-type="dept">Department of Genetics</institution><institution>University of Cambridge</institution><addr-line>Cambridge</addr-line><country>United Kingdom</country></nlm:aff></affiliation></author><author><name sortKey="Tolosana, Ignacio" sort="Tolosana, Ignacio" uniqKey="Tolosana I" first="Ignacio" last="Tolosana">Ignacio Tolosana</name><affiliation><nlm:aff id="aff1"><institution content-type="dept">Department of Genetics</institution><institution>University of Cambridge</institution><addr-line>Cambridge</addr-line><country>United Kingdom</country></nlm:aff></affiliation></author><author><name sortKey="Smith, Sophia Cl" sort="Smith, Sophia Cl" uniqKey="Smith S" first="Sophia Cl" last="Smith">Sophia Cl Smith</name><affiliation><nlm:aff id="aff1"><institution content-type="dept">Department of Genetics</institution><institution>University of Cambridge</institution><addr-line>Cambridge</addr-line><country>United Kingdom</country></nlm:aff></affiliation></author><author><name sortKey="Tagliaferri, Lucia" sort="Tagliaferri, Lucia" uniqKey="Tagliaferri L" first="Lucia" last="Tagliaferri">Lucia Tagliaferri</name><affiliation><nlm:aff id="aff1"><institution content-type="dept">Department of Genetics</institution><institution>University of Cambridge</institution><addr-line>Cambridge</addr-line><country>United Kingdom</country></nlm:aff></affiliation></author><author><name sortKey="Kamacioglu, Altug" sort="Kamacioglu, Altug" uniqKey="Kamacioglu A" first="Altug" last="Kamacioglu">Altug Kamacioglu</name><affiliation><nlm:aff id="aff1"><institution content-type="dept">Department of Genetics</institution><institution>University of Cambridge</institution><addr-line>Cambridge</addr-line><country>United Kingdom</country></nlm:aff></affiliation></author><author><name sortKey="Lindsley, Imogen" sort="Lindsley, Imogen" uniqKey="Lindsley I" first="Imogen" last="Lindsley">Imogen Lindsley</name><affiliation><nlm:aff id="aff1"><institution content-type="dept">Department of Genetics</institution><institution>University of Cambridge</institution><addr-line>Cambridge</addr-line><country>United Kingdom</country></nlm:aff></affiliation></author><author><name sortKey="Love, Luca" sort="Love, Luca" uniqKey="Love L" first="Luca" last="Love">Luca Love</name><affiliation><nlm:aff id="aff1"><institution content-type="dept">Department of Genetics</institution><institution>University of Cambridge</institution><addr-line>Cambridge</addr-line><country>United Kingdom</country></nlm:aff></affiliation></author><author><name sortKey="Unckless, Robert L" sort="Unckless, Robert L" uniqKey="Unckless R" first="Robert L" last="Unckless">Robert L. Unckless</name><affiliation><nlm:aff id="aff3"><institution content-type="dept">Department of Molecular Biosciences</institution><institution>University of Kansas</institution><addr-line>Lawrence</addr-line><country>United States</country></nlm:aff></affiliation></author><author><name sortKey="Jiggins, Francis M" sort="Jiggins, Francis M" uniqKey="Jiggins F" first="Francis M" last="Jiggins">Francis M. Jiggins</name><affiliation><nlm:aff id="aff1"><institution content-type="dept">Department of Genetics</institution><institution>University of Cambridge</institution><addr-line>Cambridge</addr-line><country>United Kingdom</country></nlm:aff></affiliation></author><author><name sortKey="Longdon, Ben" sort="Longdon, Ben" uniqKey="Longdon B" first="Ben" last="Longdon">Ben Longdon</name><affiliation><nlm:aff id="aff1"><institution content-type="dept">Department of Genetics</institution><institution>University of Cambridge</institution><addr-line>Cambridge</addr-line><country>United Kingdom</country></nlm:aff></affiliation><affiliation><nlm:aff id="aff4"><institution content-type="dept">Centre for Ecology and Conservation, Biosciences</institution><institution>University of Exeter (Penryn Campus)</institution><addr-line>Cornwall</addr-line><country>United Kingdom</country></nlm:aff></affiliation></author></analytic><series><title level="j">eLife</title><idno type="eISSN">2050-084X</idno><imprint><date when="????">????</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en"><p>It is common to find considerable genetic variation in susceptibility to infection in natural populations. We have investigated whether natural selection increases this variation by testing whether host populations show more genetic variation in susceptibility to pathogens that they naturally encounter than novel pathogens. In a large cross-infection experiment involving four species of <italic>Drosophila</italic> and four host-specific viruses, we always found greater genetic variation in susceptibility to viruses that had coevolved with their host. We went on to examine the genetic architecture of resistance in one host species, finding that there are more major-effect genetic variants in coevolved host-pathogen interactions. 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<front><journal-meta><journal-id journal-id-type="nlm-ta">eLife</journal-id><journal-id journal-id-type="iso-abbrev">Elife</journal-id><journal-id journal-id-type="publisher-id">eLife</journal-id><journal-title-group><journal-title>eLife</journal-title></journal-title-group><issn pub-type="epub">2050-084X</issn><publisher><publisher-name>eLife Sciences Publications, Ltd</publisher-name></publisher></journal-meta><article-meta><article-id pub-id-type="pmid">31038124</article-id><article-id pub-id-type="pmc">6491035</article-id><article-id pub-id-type="publisher-id">46440</article-id><article-id pub-id-type="doi">10.7554/eLife.46440</article-id><article-categories><subj-group subj-group-type="display-channel"><subject>Research Article</subject></subj-group><subj-group subj-group-type="heading"><subject>Evolutionary Biology</subject></subj-group></article-categories><title-group><article-title>Host-pathogen coevolution increases genetic variation in susceptibility to infection</article-title></title-group><contrib-group><contrib id="author-126042" contrib-type="author" equal-contrib="yes"><name><surname>Duxbury</surname><given-names>Elizabeth ML</given-names></name><contrib-id authenticated="true" contrib-id-type="orcid">http://orcid.org/0000-0002-5733-3645</contrib-id><xref ref-type="aff" rid="aff1">1</xref><xref ref-type="aff" rid="aff2">2</xref><xref ref-type="author-notes" rid="equal-contrib1">†</xref><xref ref-type="other" rid="fund1"></xref><xref ref-type="other" rid="fund3"></xref><xref ref-type="fn" rid="con1"></xref><xref ref-type="fn" rid="conf1"></xref><xref ref-type="other" rid="dataset1"></xref><xref ref-type="other" rid="dataset2"></xref><xref ref-type="other" rid="dataset3"></xref></contrib><contrib id="author-62984" contrib-type="author" equal-contrib="yes"><name><surname>Day</surname><given-names>Jonathan P</given-names></name><xref ref-type="aff" rid="aff1">1</xref><xref ref-type="author-notes" rid="equal-contrib1">†</xref><xref ref-type="other" rid="fund1"></xref><xref ref-type="other" rid="fund3"></xref><xref ref-type="fn" rid="con2"></xref><xref ref-type="fn" rid="conf1"></xref><xref ref-type="other" rid="dataset1"></xref><xref ref-type="other" rid="dataset2"></xref><xref ref-type="other" rid="dataset3"></xref></contrib><contrib id="author-137466" contrib-type="author"><name><surname>Maria Vespasiani</surname><given-names>Davide</given-names></name><xref ref-type="aff" rid="aff1">1</xref><xref ref-type="fn" rid="con3"></xref><xref ref-type="fn" rid="conf1"></xref><xref ref-type="other" rid="dataset1"></xref><xref ref-type="other" rid="dataset2"></xref><xref ref-type="other" rid="dataset3"></xref></contrib><contrib id="author-126052" contrib-type="author"><name><surname>Thüringer</surname><given-names>Yannik</given-names></name><xref ref-type="aff" rid="aff1">1</xref><xref ref-type="fn" rid="con4"></xref><xref ref-type="fn" rid="conf1"></xref><xref ref-type="other" rid="dataset1"></xref><xref ref-type="other" rid="dataset2"></xref><xref ref-type="other" rid="dataset3"></xref></contrib><contrib id="author-126053" contrib-type="author"><name><surname>Tolosana</surname><given-names>Ignacio</given-names></name><contrib-id authenticated="true" contrib-id-type="orcid">http://orcid.org/0000-0002-3766-8296</contrib-id><xref ref-type="aff" rid="aff1">1</xref><xref ref-type="fn" rid="con5"></xref><xref ref-type="fn" rid="conf1"></xref><xref ref-type="other" rid="dataset1"></xref><xref ref-type="other" rid="dataset2"></xref><xref ref-type="other" rid="dataset3"></xref></contrib><contrib id="author-138388" contrib-type="author"><name><surname>Smith</surname><given-names>Sophia CL</given-names></name><xref ref-type="aff" rid="aff1">1</xref><xref ref-type="fn" rid="con6"></xref><xref ref-type="fn" rid="conf1"></xref><xref ref-type="other" rid="dataset1"></xref><xref ref-type="other" rid="dataset2"></xref><xref ref-type="other" rid="dataset3"></xref></contrib><contrib id="author-126047" contrib-type="author"><name><surname>Tagliaferri</surname><given-names>Lucia</given-names></name><xref ref-type="aff" rid="aff1">1</xref><xref ref-type="fn" rid="con7"></xref><xref ref-type="fn" rid="conf1"></xref><xref ref-type="other" rid="dataset1"></xref><xref ref-type="other" rid="dataset2"></xref><xref ref-type="other" rid="dataset3"></xref></contrib><contrib id="author-126054" contrib-type="author"><name><surname>Kamacioglu</surname><given-names>Altug</given-names></name><xref ref-type="aff" rid="aff1">1</xref><xref ref-type="fn" rid="con8"></xref><xref ref-type="fn" rid="conf1"></xref><xref ref-type="other" rid="dataset1"></xref><xref ref-type="other" rid="dataset2"></xref><xref ref-type="other" rid="dataset3"></xref></contrib><contrib id="author-126055" contrib-type="author"><name><surname>Lindsley</surname><given-names>Imogen</given-names></name><xref ref-type="aff" rid="aff1">1</xref><xref ref-type="fn" rid="con9"></xref><xref ref-type="fn" rid="conf1"></xref><xref ref-type="other" rid="dataset1"></xref><xref ref-type="other" rid="dataset2"></xref><xref ref-type="other" rid="dataset3"></xref></contrib><contrib id="author-126056" contrib-type="author"><name><surname>Love</surname><given-names>Luca</given-names></name><xref ref-type="aff" rid="aff1">1</xref><xref ref-type="fn" rid="con10"></xref><xref ref-type="fn" rid="conf1"></xref><xref ref-type="other" rid="dataset1"></xref><xref ref-type="other" rid="dataset2"></xref><xref ref-type="other" rid="dataset3"></xref></contrib><contrib id="author-108407" contrib-type="author"><name><surname>Unckless</surname><given-names>Robert L</given-names></name><contrib-id authenticated="true" contrib-id-type="orcid">http://orcid.org/0000-0001-8586-7137</contrib-id><xref ref-type="aff" rid="aff3">3</xref><xref ref-type="fn" rid="con11"></xref><xref ref-type="fn" rid="conf1"></xref><xref ref-type="other" rid="dataset1"></xref><xref ref-type="other" rid="dataset2"></xref><xref ref-type="other" rid="dataset3"></xref></contrib><contrib id="author-62985" contrib-type="author" corresp="yes"><name><surname>Jiggins</surname><given-names>Francis M</given-names></name><contrib-id authenticated="true" contrib-id-type="orcid">https://orcid.org/0000-0001-7470-8157</contrib-id><email>fmj1001@cam.ac.uk</email><xref ref-type="aff" rid="aff1">1</xref><xref ref-type="other" rid="fund1"></xref><xref ref-type="other" rid="fund3"></xref><xref ref-type="fn" rid="con12"></xref><xref ref-type="fn" rid="conf1"></xref><xref ref-type="other" rid="dataset1"></xref><xref ref-type="other" rid="dataset2"></xref><xref ref-type="other" rid="dataset3"></xref></contrib><contrib id="author-113350" contrib-type="author" corresp="yes"><name><surname>Longdon</surname><given-names>Ben</given-names></name><contrib-id authenticated="true" contrib-id-type="orcid">http://orcid.org/0000-0001-6936-1697</contrib-id><email>b.longdon2@exeter.ac.uk</email><xref ref-type="aff" rid="aff1">1</xref><xref ref-type="aff" rid="aff4">4</xref><xref ref-type="other" rid="fund1"></xref><xref ref-type="other" rid="fund2"></xref><xref ref-type="other" rid="fund3"></xref><xref ref-type="other" rid="fund4"></xref><xref ref-type="fn" rid="con13"></xref><xref ref-type="fn" rid="conf1"></xref><xref ref-type="other" rid="dataset1"></xref><xref ref-type="other" rid="dataset2"></xref><xref ref-type="other" rid="dataset3"></xref></contrib><aff id="aff1"><label>1</label><institution content-type="dept">Department of Genetics</institution><institution>University of Cambridge</institution><addr-line>Cambridge</addr-line><country>United Kingdom</country></aff><aff id="aff2"><label>2</label><institution content-type="dept">School of Biological Sciences</institution><institution>University of East Anglia</institution><addr-line>Norwich</addr-line><country>United Kingdom</country></aff><aff id="aff3"><label>3</label><institution content-type="dept">Department of Molecular Biosciences</institution><institution>University of Kansas</institution><addr-line>Lawrence</addr-line><country>United States</country></aff><aff id="aff4"><label>4</label><institution content-type="dept">Centre for Ecology and Conservation, Biosciences</institution><institution>University of Exeter (Penryn Campus)</institution><addr-line>Cornwall</addr-line><country>United Kingdom</country></aff></contrib-group><contrib-group><contrib contrib-type="editor"><name><surname>Lemaître</surname><given-names>Bruno</given-names></name><role>Reviewing Editor</role><aff><institution>École Polytechnique Fédérale de Lausanne</institution><country>Switzerland</country></aff></contrib><contrib contrib-type="editor"><name><surname>Tautz</surname><given-names>Diethard</given-names></name><role>Senior Editor</role><aff><institution>Max-Planck Institute for Evolutionary Biology</institution><country>Germany</country></aff></contrib></contrib-group><author-notes><fn fn-type="con" id="equal-contrib1"><label>†</label><p>These authors contributed equally to this work.</p></fn></author-notes><pub-date date-type="pub" publication-format="electronic"><day>30</day><month>4</month><year>2019</year></pub-date><pub-date pub-type="collection"><year>2019</year></pub-date><volume>8</volume><elocation-id>e46440</elocation-id><history><date date-type="received" iso-8601-date="2019-03-03"><day>03</day><month>3</month><year>2019</year></date><date date-type="accepted" iso-8601-date="2019-04-07"><day>07</day><month>4</month><year>2019</year></date></history><permissions><copyright-statement>© 2019, Duxbury et al</copyright-statement><copyright-year>2019</copyright-year><copyright-holder>Duxbury et al</copyright-holder><ali:free_to_read></ali:free_to_read><license xlink:href="http://creativecommons.org/licenses/by/4.0/"><ali:license_ref>http://creativecommons.org/licenses/by/4.0/</ali:license_ref><license-p>This article is distributed under the terms of the <ext-link ext-link-type="uri" xlink:href="http://creativecommons.org/licenses/by/4.0/">Creative Commons Attribution License</ext-link>, which permits unrestricted use and redistribution provided that the original author and source are credited.</license-p></license></permissions><self-uri content-type="pdf" xlink:href="elife-46440.pdf"></self-uri><abstract><p>It is common to find considerable genetic variation in susceptibility to infection in natural populations. We have investigated whether natural selection increases this variation by testing whether host populations show more genetic variation in susceptibility to pathogens that they naturally encounter than novel pathogens. In a large cross-infection experiment involving four species of <italic>Drosophila</italic> and four host-specific viruses, we always found greater genetic variation in susceptibility to viruses that had coevolved with their host. We went on to examine the genetic architecture of resistance in one host species, finding that there are more major-effect genetic variants in coevolved host-pathogen interactions. We conclude that selection by pathogens has increased genetic variation in host susceptibility, and much of this effect is caused by the occurrence of major-effect resistance polymorphisms within populations.</p></abstract><kwd-group kwd-group-type="author-keywords"><kwd><italic>Drosophila</italic></kwd><kwd>viruses</kwd><kwd>sigma virus</kwd><kwd>coevolution</kwd></kwd-group><kwd-group kwd-group-type="research-organism"><title>Research organism</title><kwd><italic>D. melanogaster</italic></kwd><kwd>Virus</kwd></kwd-group><funding-group><award-group id="fund1"><funding-source><institution-wrap><institution-id institution-id-type="FundRef">http://dx.doi.org/10.13039/100008668</institution-id><institution>NERC Environmental Bioinformatics Centre</institution></institution-wrap></funding-source><award-id>NE/L004232/1</award-id><principal-award-recipient><name><surname>Duxbury</surname><given-names>Elizabeth ML</given-names></name><name><surname>Day</surname><given-names>Jonathan P</given-names></name><name><surname>Jiggins</surname><given-names>Francis M</given-names></name><name><surname>Longdon</surname><given-names>Ben</given-names></name></principal-award-recipient></award-group><award-group id="fund2"><funding-source><institution-wrap><institution-id institution-id-type="FundRef">http://dx.doi.org/10.13039/100004440</institution-id><institution>Wellcome</institution></institution-wrap></funding-source><award-id>Sir Henry Dale Fellowship (Grant Number 109356/Z/15/Z)</award-id><principal-award-recipient><name><surname>Longdon</surname><given-names>Ben</given-names></name></principal-award-recipient></award-group><award-group id="fund3"><funding-source><institution-wrap><institution-id institution-id-type="FundRef">http://dx.doi.org/10.13039/100010663</institution-id><institution>H2020 European Research Council</institution></institution-wrap></funding-source><award-id>281668</award-id><principal-award-recipient><name><surname>Duxbury</surname><given-names>Elizabeth ML</given-names></name></principal-award-recipient></award-group><award-group id="fund4"><funding-source><institution-wrap><institution-id institution-id-type="FundRef">http://dx.doi.org/10.13039/501100000288</institution-id><institution>Royal Society</institution></institution-wrap></funding-source><award-id>Sir Henry Dale Fellowship (Grant Number 109356/Z/15/Z)</award-id><principal-award-recipient><name><surname>Longdon</surname><given-names>Ben</given-names></name></principal-award-recipient></award-group><funding-statement>The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.</funding-statement></funding-group><custom-meta-group><custom-meta specific-use="meta-only"><meta-name>Author impact statement</meta-name><meta-value>A history of coevolution increases genetic variation in the susceptibility of <italic>Drosophila</italic> to viruses, largely by introducing major-effect resistance polymorphisms into populations.</meta-value></custom-meta></custom-meta-group></article-meta></front><body><sec sec-type="intro" id="s1"><title>Introduction</title><p>From bacteria to plants and insects to humans, it is common to find considerable genetic variation in susceptibility to infection in natural populations (<xref rid="bib20" ref-type="bibr">Chapman and Hill, 2012</xref>; <xref rid="bib6" ref-type="bibr">Bangham et al., 2008a</xref>; <xref rid="bib36" ref-type="bibr">Hammond-Kosack and Jones, 1997</xref>; <xref rid="bib45" ref-type="bibr">Lazzaro et al., 2004</xref>). This variation in susceptibility can determine the impact of disease on health and economic output (<xref rid="bib24" ref-type="bibr">Cooke and Hill, 2001</xref>; <xref rid="bib43" ref-type="bibr">King and Lively, 2012</xref>; <xref rid="bib1" ref-type="bibr">Alonso-Blanco and Méndez-Vigo, 2014</xref>; <xref rid="bib17" ref-type="bibr">Burgner et al., 2006</xref>). In nature and breeding programs, it determines the ability of populations to evolve resistance to infection. Insect populations, like those of other organisms, typically contain considerable genetic variation in susceptibility to infection (<xref rid="bib6" ref-type="bibr">Bangham et al., 2008a</xref>; <xref rid="bib45" ref-type="bibr">Lazzaro et al., 2004</xref>; <xref rid="bib75" ref-type="bibr">Tinsley et al., 2006</xref>; <xref rid="bib66" ref-type="bibr">Obbard and Dudas, 2014</xref>), and provide a convenient laboratory model in which to investigate basic questions about how this variation is maintained (<xref rid="bib60" ref-type="bibr">Magwire et al., 2012</xref>). Within vector species like mosquitoes, resistant genotypes are less likely to transmit pathogens, and this has the potential to reduce disease in vertebrate populations (<xref rid="bib9" ref-type="bibr">Beerntsen et al., 2000</xref>). Where pathogens are contributing the decline of beneficial species like pollinators, high levels of genetic variation may allow populations to recover (<xref rid="bib61" ref-type="bibr">Maori et al., 2007</xref>). Understanding the origins of genetic variation in susceptibility is therefore a fundamental question in infectious disease biology.</p><p>As pathogens are harmful, natural selection is expected to favour resistant host genotypes. Directional selection on standing genetic variation will drive alleles to fixation, removing variants from the population (<xref rid="bib25" ref-type="bibr">Falconer, 1960</xref>; <xref rid="bib26" ref-type="bibr">Falconer and Mackay, 1996</xref>; <xref rid="bib11" ref-type="bibr">Blows and Hoffmann, 2005</xref>). However, as directional selection also increases the frequency of new mutations that change the trait in the direction of selection, at equilibrium it is expected to have no effect on levels of standing genetic variation (relative to mutation-drift balance; <xref rid="bib37" ref-type="bibr">Hill, 1982</xref>). However, selection mediated by pathogens may be different. Coevolution with pathogens can result in the maintenance of both resistant and susceptible alleles by negative frequency dependent selection (<xref rid="bib84" ref-type="bibr">Woolhouse et al., 2002</xref>; <xref rid="bib35" ref-type="bibr">Haldane, 1949</xref>). Similarly, when infection prevalence exhibits geographical or temporal variation, selection can maintain genetic variation, especially if pleiotropic costs to resistance provide an advantage to susceptible individuals when infection is rare (<xref rid="bib64" ref-type="bibr">Nuismer et al., 2003</xref>; <xref rid="bib74" ref-type="bibr">Thompson, 1999</xref>; <xref rid="bib44" ref-type="bibr">Koskella, 2018</xref>). Even when there is simple directional selection on alleles that increase resistance, the direction of selection by pathogens may frequently change so populations may not be at equilibrium. If selection favours rare alleles – such as new mutations – directional selection can transiently increase genetic variation during their spread through the population (<xref rid="bib8" ref-type="bibr">Barton and Turelli, 1987</xref>; <xref rid="bib5" ref-type="bibr">Bangham et al., 2007</xref>; <xref rid="bib59" ref-type="bibr">Magwire et al., 2011</xref>).</p><p>As part of a whole genome association study, we have previously estimated levels of genetic variation in the susceptibility of <italic>D. melanogaster</italic> to four different viruses (<xref rid="bib60" ref-type="bibr">Magwire et al., 2012</xref>). We found that there was more genetic variation in susceptibility to the two viruses that were isolated from <italic>D. melanogaster</italic> than the two viruses from other insect species. Furthermore, in each of these naturally coevolved host-pathogen associations we detected a single major-effect polymorphism affecting resistance. This led us to propose that coevolution had increased genetic variation in susceptibility due to the presence of major-effect resistance polymorphisms. However, this conclusion remains anecdotal. First, aside from two sigma viruses, the viruses we used were mostly very distantly related, so their biology may differ for many reasons. Second, our association study had low statistical power, so conclusions about the genetics were based on just two genes. Finally, and most importantly, the link between coevolutionary history and genetic variation is based on a single host and four viruses, and so could arise by chance. For this reason, here we return to this question and formally test whether a history of coevolution alters the amount and nature of genetic variation.</p><p>To examine how selection by a pathogen affects levels of genetic variation we used a natural host-virus system; <italic>Drosophila</italic> and sigma viruses (<xref rid="bib57" ref-type="bibr">Longdon and Jiggins, 2012</xref>; <xref rid="bib52" ref-type="bibr">Longdon et al., 2012</xref>). Sigma viruses are a clade of insect RNA viruses with negative-sense genomes in the family Rhabdoviridae (<xref rid="bib56" ref-type="bibr">Longdon et al., 2017</xref>; <xref rid="bib54" ref-type="bibr">Longdon et al., 2015a</xref>; <xref rid="bib48" ref-type="bibr">Longdon et al., 2010</xref>; <xref rid="bib49" ref-type="bibr">Longdon et al., 2011a</xref>). They are vertically transmitted through eggs and sperm, and each sigma virus infects a single host species, simplifying studies of coevolution (<xref rid="bib56" ref-type="bibr">Longdon et al., 2017</xref>; <xref rid="bib49" ref-type="bibr">Longdon et al., 2011a</xref>). In <italic>Drosophila melanogaster</italic>, despite the virus causing little adult mortality, infection reduces host fitness by approximately 25% (<xref rid="bib82" ref-type="bibr">Wilfert and Jiggins, 2013</xref>; <xref rid="bib85" ref-type="bibr">Yampolsky et al., 1999</xref>). As prevalence in wild populations is typically around 10% (<xref rid="bib82" ref-type="bibr">Wilfert and Jiggins, 2013</xref>), there is the necessary selective pressure for resistance to evolve (<xref rid="bib59" ref-type="bibr">Magwire et al., 2011</xref>; <xref rid="bib18" ref-type="bibr">Cao et al., 2016</xref>). In <italic>D. melanogaster</italic>, three major-effect resistance alleles have been identified (<xref rid="bib6" ref-type="bibr">Bangham et al., 2008a</xref>; <xref rid="bib60" ref-type="bibr">Magwire et al., 2012</xref>; <xref rid="bib5" ref-type="bibr">Bangham et al., 2007</xref>; <xref rid="bib59" ref-type="bibr">Magwire et al., 2011</xref>; <xref rid="bib18" ref-type="bibr">Cao et al., 2016</xref>; <xref rid="bib7" ref-type="bibr">Bangham et al., 2008b</xref>; <xref rid="bib78" ref-type="bibr">Wayne et al., 1996</xref>). There has been a recent sweep of <italic>D. melanogaster</italic> sigma virus (DMelSV) genotypes that are able to overcome one of these host resistance genes (<xref rid="bib29" ref-type="bibr">Fleuriet, 1988</xref>; <xref rid="bib80" ref-type="bibr">Wilfert and Jiggins, 2010a</xref>; <xref rid="bib83" ref-type="bibr">Wilfert and Jiggins, 2014</xref>). Given the power of <italic>Drosophila</italic> genetics, this system is an excellent model of a coevolutionary arms race between hosts and pathogens.</p><p>Sigma viruses offer a novel way to test how coevolution with a pathogen alters the amount of genetic variation in host susceptibility. As sigma viruses are vertically transmitted, we can be certain of which hosts and viruses are naturally coevolving and which are not. In this study we have used four species of <italic>Drosophila</italic> (<italic>D. affinis, D. immigrans, D. melanogaster</italic> and <italic>D. obscura</italic>) that shared a common ancestor approximately 40 million years ago (<xref rid="bib65" ref-type="bibr">Obbard et al., 2012</xref>; <xref rid="bib73" ref-type="bibr">Tamura et al., 2004</xref>), and their natural sigma viruses (DAffSV, DImmSV, DMelSV and DObsSV, which have amino acid identities of <55% in the most conserved gene) (<xref rid="bib54" ref-type="bibr">Longdon et al., 2015a</xref>; <xref rid="bib48" ref-type="bibr">Longdon et al., 2010</xref>; <xref rid="bib50" ref-type="bibr">Longdon et al., 2011b</xref>). Despite their vertical mode of transmission, the phylogenies of the viruses and their hosts are incongruent, suggesting they have jumped between host species during their evolution (<xref rid="bib50" ref-type="bibr">Longdon et al., 2011b</xref>). To test whether selection by viruses increases the amount of genetic variation in host susceptibility we have compared the viral load of endemic viruses that naturally infect each of the four host species to non-endemic viruses. We then examined how selection by these viruses has altered the genetic architecture of resistance by mapping loci that confer resistance to endemic and non-endemic viruses in <italic>D. melanogaster</italic>.</p></sec><sec sec-type="results" id="s2"><title>Results</title><sec id="s2-1"><title>Genetic variation in susceptibility to infection is greatest in coevolved host-virus associations</title><p>To test whether selection by viruses has increased genetic variation in susceptibility to infection, we compared endemic host-virus associations with novel associations that have no history of coevolution. We used four different species of <italic>Drosophila,</italic> each of which is naturally host to a different sigma virus (<xref rid="bib57" ref-type="bibr">Longdon and Jiggins, 2012</xref>; <xref rid="bib52" ref-type="bibr">Longdon et al., 2012</xref>; <xref rid="bib56" ref-type="bibr">Longdon et al., 2017</xref>; <xref rid="bib48" ref-type="bibr">Longdon et al., 2010</xref>; <xref rid="bib49" ref-type="bibr">Longdon et al., 2011a</xref>). We collected four species from the wild and created genetically diverse populations in the laboratory. Using flies from these populations we crossed single males to single females to create full-sib families. The progeny of these crosses were then injected with either the virus isolated from that species, or a virus isolated from one of the other species (<xref ref-type="fig" rid="fig1">Figure 1</xref>). Studies on DMelSV have shown that loci that reduce loads when the virus is injected also reduce infection rates in both the lab and field (<xref rid="bib6" ref-type="bibr">Bangham et al., 2008a</xref>; <xref rid="bib52" ref-type="bibr">Longdon et al., 2012</xref>; <xref rid="bib15" ref-type="bibr">Bregliano, 1970</xref>; <xref rid="bib16" ref-type="bibr">Brun and Plus, 1980</xref>; <xref rid="bib67" ref-type="bibr">Ohanessian-Guillemain, 1963</xref>; <xref rid="bib81" ref-type="bibr">Wilfert and Jiggins, 2010b</xref>). As infection is costly, this is expected to increase host fitness. Fifteen days post infection we extracted RNA from the flies and measured viral load by quantitative RT-PCR. The differences between the viral load of different families allowed us to estimate the genetic variance (<italic>V<sub>G</sub></italic>) in viral load – a measure of how much viral resistance varies in the population due to genetic as opposed to environmental causes. In total we infected 52,592 flies and measured the viral load in 4295 biological replicates (a vial containing a mean of 12 flies) across 1436 full-sib families (details of sample sizes in Additional Methods).</p><fig id="fig1" orientation="portrait" position="float"><object-id pub-id-type="doi">10.7554/eLife.46440.002</object-id><label>Figure 1.</label><caption><title>Experimental design and phylogenies.</title><p>(<bold>A</bold>) Four species of <italic>Drosophila</italic> were independently infected both with a sigma virus with which they are naturally infected with in nature (red) and two viruses that naturally infect another species (black). (<bold>B</bold>) Phylogenies of the sigma viruses (inferred using the <italic>L</italic> gene) and their <italic>Drosophila</italic> hosts (inferred using <italic>COI, COII, 28S rDNA, Adh, SOD, Amyrel</italic> and <italic>RpL32</italic> genes), redrawn from <xref rid="bib54" ref-type="bibr">Longdon et al. (2015a)</xref> and <xref rid="bib55" ref-type="bibr">Longdon et al. (2015b)</xref>. Scale bars represent substitutions per site under a relaxed clock model. Posterior supports for nodes are all >0.99.</p></caption><graphic xlink:href="elife-46440-fig1"></graphic></fig><p>Within populations of all four species, we found significantly greater genetic variance in susceptibility to the sigma virus that naturally infects that species compared to viruses from other species (<xref ref-type="fig" rid="fig2">Figure 2</xref>; <xref ref-type="fig" rid="fig2s1">Figure 2—figure supplement 1</xref>; <xref ref-type="supplementary-material" rid="supp1">Supplementary file 1</xref> and <xref ref-type="supplementary-material" rid="supp2">2</xref>). These different variances reflect considerable differences in the mean viral load between families (<xref ref-type="fig" rid="fig2">Figure 2A</xref>). For example, when families of <italic>D. obscura</italic> in the 2<sup>nd</sup> and 98<sup>th</sup> percentile were compared, there was a 1294 fold difference between the viral loads of the coevolved virus in the families (<xref ref-type="fig" rid="fig2">Figure 2A</xref>). In contrast, for the non-coevolved viruses there was a 27 fold difference in DMelSV loads and a 19 fold difference in DAffSV loads (see <xref ref-type="fig" rid="fig2">Figure 2A</xref> for statistics). The data in <xref ref-type="fig" rid="fig2">Figure 2</xref> is zero-centred to allow comparison of the variances, but across the four species, there was no consistent difference between the mean viral load in coevolved versus non-coevolved associations (i.e. the coevolved virus does not always replicate to higher levels suggesting this is not an artefact of simply replicating poorly in a host – see <xref ref-type="fig" rid="fig2s1">Figure 2—figure supplement 1</xref>). Additionally, there was no correlation between the genetic variance in viral load and the mean viral load (<xref ref-type="fig" rid="fig2s1">Figure 2—figure supplement 1</xref>, Spearman’s correlation: <inline-formula><mml:math id="inf1"><mml:mi>ρ</mml:mi></mml:math></inline-formula>= -0.38, S=296, <italic>P</italic>=0.22).</p><fig id="fig2" position="float" orientation="portrait"><object-id pub-id-type="doi">10.7554/eLife.46440.003</object-id><label>Figure 2.</label><caption><title>Genetic variation in susceptibility to coevolved and non-coevolved viruses.</title><p>The viral load was measured 15 days post infection by quantitative RT-PCR relative to a <italic>Drosophila</italic> reference gene (<italic>RpL32</italic>). (<bold>A</bold>) The points show model prediction family means from our GLM and are centred on zero. The number of families in each panel was down-sampled so the same number of families is shown for each virus. Coevolved host-virus associations are in red. (<bold>B</bold>) The genetic variance in log<sub>2 </sub>viral load was estimated from the between family variance assuming that all genetic variance is additive. The bars are 95% credible intervals. Posterior probabilities for significantly different genetic variances are shown in grey (see <xref ref-type="supplementary-material" rid="supp1">Supplementary file 1</xref> and <xref ref-type="supplementary-material" rid="supp2">2</xref>).</p></caption><graphic xlink:href="elife-46440-fig2"></graphic><p content-type="supplemental-figure"><fig id="fig2s1" specific-use="child-fig" orientation="portrait" position="anchor"><object-id pub-id-type="doi">10.7554/eLife.46440.004</object-id><label>Figure 2—figure supplement 1.</label><caption><title>Estimates of genetic variance plotted against mean viral load for each species-virus combination.</title><p>Viral load is measured by qRT-PCR relative to a housekeeping gene (<italic>RpL32</italic>). All viral loads were significantly different from one another (<italic>P</italic><0.001 in all cases).</p></caption><graphic xlink:href="elife-46440-fig2-figsupp1"></graphic></fig></p></fig></sec><sec id="s2-2"><title>Major-effect genetic variants that are known to provide resistance to DMelSV do not protect against other viruses</title><p>To examine whether the genetic basis of resistance to coevolved and non-coevolved viruses was different we estimated the genetic correlations (<italic>r<sub>g</sub></italic>) in their viral loads. These represent the proportion of genetic variance in viral load between pairs of viruses that shares the same genetic causes. In <italic>D. melanogaster</italic> these were 0.40 for DMelSV-DAffSV, (95% CIs: 0.20, 0.61) and 0.25 for DMelSV-DObsSV (95% CIs: −0.01,0.47). Similar results were obtained using the <italic>D. melanogaster</italic> mapping population described below (<xref ref-type="supplementary-material" rid="supp3">Supplementary file 3</xref>). In the other species our estimates sometimes had wide credible intervals, but the genetic correlations between coevolved and non-coevolved viruses were mostly below 0.5 (<xref ref-type="supplementary-material" rid="supp3">Supplementary file 3</xref>; note correlations are frequently low between pairs of non-endemic viruses too). Therefore if natural selection increases genetic variation in susceptibility to a natural pathogen, there is expected to be a smaller effect on non-coevolved viruses.</p><p>In <italic>D. melanogaster</italic> a substantial proportion of the genetic variance in susceptibility to DMelSV is explained by major-effect variants in the genes <italic>CHKov1</italic> and <italic>p62</italic> (also known as <italic>Ref(2)P</italic>) (<xref rid="bib60" ref-type="bibr">Magwire et al., 2012</xref>; <xref rid="bib5" ref-type="bibr">Bangham et al., 2007</xref>; <xref rid="bib59" ref-type="bibr">Magwire et al., 2011</xref>; <xref rid="bib78" ref-type="bibr">Wayne et al., 1996</xref>; <xref rid="bib23" ref-type="bibr">Contamine et al., 1989</xref>). The resistant allele in each of these genes has arisen recently by mutation and been driven up in frequency by natural selection, presumably due to the presence of DMelSV in natural populations (<xref rid="bib5" ref-type="bibr">Bangham et al., 2007</xref>; <xref rid="bib59" ref-type="bibr">Magwire et al., 2011</xref>). Therefore, if these genetic variants confer resistance to DMelSV but not the other sigma viruses, then this may explain the differences in genetic variance that we observed.</p><p>To examine whether <italic>CHKov1</italic> or <italic>p62</italic> contributed to the differences in genetic variance we observed in <italic>D. melanogaster</italic>, we genotyped the parents of the full sib families for variants that confer resistance (<xref ref-type="supplementary-material" rid="supp4">Supplementary file 4</xref>). Assuming the effects of the resistant alleles are additive, we estimated that the load of the coevolved virus DMelSV was more than halved in homozygous <italic>CHKov1</italic> resistant flies compared to susceptible flies (reduction in log<sub>2 </sub>viral load = 1.2, 95% CI = 0.6, 1.8). In contrast we found no significant effect of this gene on loads of the non-coevolved viruses (DAffSV = −0.2, 95% CI = 0.2,–0.8; DObsSV = −0.4, 95% CI = −0.04, 1.0). The resistant allele of <italic>p62</italic> was present at such a low frequency (1.5%) in the population that we lacked statistical power to investigate its effects.</p><p>To confirm these results we infected 1869 flies from 32 inbred <italic>D. melanogaster</italic> lines (<xref rid="bib58" ref-type="bibr">Mackay et al., 2012</xref>) that had known <italic>CHKov1</italic> or <italic>p62</italic> genotypes. The effect of these genes was greater on the naturally occurring virus than the viruses from other species (<xref ref-type="fig" rid="fig3">Figure 3</xref>; effect of genotype on DMelSV load: <italic>F<sub>2,28</sub></italic> = 13.2, p=0.00001; DAffSV: <italic>F<sub>2,29</sub></italic> = 4.9, p=0.01; DObsSV: <italic>F<sub>2,29</sub></italic> = 5.7, p=0.01).</p><fig id="fig3" orientation="portrait" position="float"><object-id pub-id-type="doi">10.7554/eLife.46440.005</object-id><label>Figure 3.</label><caption><title>Viral load in <italic>D. melanogaster</italic> lines carrying different alleles of <italic>CHKov1</italic> and <italic>p62.</italic></title><p>Each point is the viral load of a separate inbred fly line carrying the resistant (Res) or susceptible (Sus) allele of <italic>P62</italic> or <italic>CHKov1.</italic> Horizontal bars are medians. Viral load was measured 15 days post infection by quantitative RT-PCR relative to a <italic>Drosophila</italic> reference gene (<italic>RpL32</italic>).</p></caption><graphic xlink:href="elife-46440-fig3"></graphic></fig></sec><sec id="s2-3"><title>There are a greater number of major-effect variants in coevolved host-virus associations</title><p>To investigate how coevolution shapes the genetics of resistance, we mapped loci controlling resistance using a <italic>D. melanogaster</italic> advanced intercross population (the DSPR panel [<xref rid="bib42" ref-type="bibr">King et al., 2012</xref>]). This population samples genetic variation in a small number of genotypes from around the world (the experiments above sampled many genotypes from a single location). It was founded by allowing two sets of 8 inbred founder lines to interbreed for 50 generations, then creating recombinant inbred lines (RILs) whose genomes are a fine-scale mosaic of the original founder genomes. We used 377 RILs from these populations, which have up to 15 alleles of each gene (one founder line is shared between the two populations). We infected 15,916 flies across 1362 biological replicates (a vial containing a mean of 12 flies) with DMelSV, DAffSV or DObsSV and measured viral load as above (see Materials and methods).</p><p>We first estimated the genetic variance in viral load within our mapping population. The results recapitulated what we had found above in a natural population of flies — there was considerably more genetic variation in susceptibility to the coevolved virus than the non-coevolved viruses (<xref ref-type="fig" rid="fig4">Figure 4A</xref>, filled circles). Therefore, our earlier result from a single population holds when sampling flies from across six continents, although the magnitude of the effect is considerably greater in this mapping population.</p><fig id="fig4" orientation="portrait" position="float"><object-id pub-id-type="doi">10.7554/eLife.46440.006</object-id><label>Figure 4.</label><caption><title>The genetic architecture of resistance to coevolved and non-coevolved viruses in <italic>D. melanogaster</italic>.</title><p>(<bold>A</bold>) The genetic variance in viral load within the mapping population (filled circles). The open circles are estimates of the genetic variance after accounting for the effects of the QTL in panel C. Error bars are 95% credible intervals. (<bold>B</bold>) QTL affecting viral load. The horizontal line shows a genome-wide significance threshold of p<0.05 that was obtained by permutation of Logarithm of odds (LOD) scores. (<bold>C</bold>) The effect of the seven QTL detected on the load of the three viruses. Only QTL that remained were significant following multiple regression with all the loci are shown. The coevolved virus is shown in red.</p></caption><graphic xlink:href="elife-46440-fig4"></graphic></fig><p>To examine the genetic basis of virus resistance, we looked for associations between genotype and viral load across the genome (<xref ref-type="fig" rid="fig4">Figure 4B</xref>). In the coevolved association (DMelSV) we identified seven QTL associated with resistance, compared to one that affects DObsSV and none affecting DAffSV (<xref ref-type="supplementary-material" rid="supp5">Supplementary file 5</xref>; this excludes one DMelSV QTL on the X chromosome that did not remain significant after accounting for the other QTL). The QTL affecting DObsSV also has a significant effect on DMelSV. One of the QTL corresponded to <italic>p62</italic> (2L 53 cM). The susceptible allele of <italic>CHKov1</italic> was not present in the fly lines assayed.</p><p>To examine the effect that the QTL have on viral load, we first split the founder alleles into a resistant class and a susceptible class (see Materials and methods) and then estimated the difference in viral load between the functionally distinct alleles. Six of the seven QTL resulted in greater reductions in the load of the coevolved virus (DMelSV) than the viruses isolated from other species (<xref ref-type="fig" rid="fig4">Figure 4C</xref>). There were only two cases where there was substantial cross-resistance to multiple viruses—3R 49 cM confers strong resistance to DMelSV and weak resistance to DObsSV, while 3L 46 cM confers weak resistance to DMelSV and strong resistance to DObsSV.</p><p>Together, this modest number of loci with substantial effects on resistance explains most of the high genetic variance in resistance to the coevolved virus (<xref ref-type="fig" rid="fig4">Figure 4A</xref>, filled versus open circles). Individually, resistant alleles cause an approximate 3–7 fold reduction in viral load (<xref ref-type="fig" rid="fig4">Figure 4C</xref>), and together they explain 59% of the genetic variance in susceptibility to DMelSV, 77% for DObsSV and 3% for DAffSV (<xref ref-type="fig" rid="fig4">Figure 4A</xref>, filled versus open circles). However, even after accounting for these genes there remains a significantly higher genetic variance in the viral load of the coevolved virus (<xref ref-type="fig" rid="fig4">Figure 4A</xref>, open circles, non-overlapping 95% CI).</p></sec></sec><sec sec-type="discussion" id="s3"><title>Discussion</title><p>We have found greater genetic variation in susceptibility to viruses that naturally infect <italic>Drosophila</italic> compared to viruses that do not, suggesting that selection by these pathogens has acted to increase the amount of genetic variation in susceptibility. This effect was largely caused by a modest number of major-effect genes that explain over half of the genetic variance in resistance.</p><p>As the genetic variants in the genes <italic>p62 (ref(2)P)</italic> and <italic>CHKov1</italic> that confer resistance to DMelSV have been identified, this has previously allowed us to use patterns of DNA sequence variation to infer how selection has acted on resistance in <italic>D. melanogaster</italic>. In both these genes the resistant alleles have arisen relatively recently by mutation and natural selection has pushed them rapidly up in frequency, leaving a characteristic signature of elevated linkage disequilibrium and low genetic diversity around the variant causing resistance (<xref rid="bib5" ref-type="bibr">Bangham et al., 2007</xref>; <xref rid="bib59" ref-type="bibr">Magwire et al., 2011</xref>; <xref rid="bib78" ref-type="bibr">Wayne et al., 1996</xref>). There is no indication of negative frequency dependent selection, and these polymorphisms appear to have arisen from partial selective sweeps (<xref rid="bib5" ref-type="bibr">Bangham et al., 2007</xref>; <xref rid="bib59" ref-type="bibr">Magwire et al., 2011</xref>).</p><p>The most parsimonious explanation of these observations is that there has been directional selection favouring resistance alleles (although this type of data cannot rule out negative frequency dependent selection as is predicted by models of coevolution). At equilibrium, directional selection on a trait is not expected to affect its genetic variance (relative to a population under mutation-drift balance; <xref rid="bib37" ref-type="bibr">Hill, 1982</xref>). However, the genetic variance will transiently increase if the variants under selection are initially at low frequency (<xref rid="bib8" ref-type="bibr">Barton and Turelli, 1987</xref>), as was the case for both <italic>p62</italic> and <italic>CHKov1</italic> (<xref rid="bib5" ref-type="bibr">Bangham et al., 2007</xref>; <xref rid="bib59" ref-type="bibr">Magwire et al., 2011</xref>). A particular feature of pathogens is that the direction of selection is likely to continually change as new pathogens appear in populations or existing pathogens evolve to overcome host defences. For example, in France and Germany in the 1980s, DMelSV evolved to largely overcome the effects of the resistant allele of <italic>p62</italic> (<xref rid="bib30" ref-type="bibr">Fleuriet and Periquet, 1993</xref>; <xref rid="bib31" ref-type="bibr">Fleuriet and Sperlich, 1992</xref>). Similarly, DImmSV has swept through European populations of <italic>D. immigrans</italic> in the last ~16 years and DObsSV through UK populations of <italic>D. obscura</italic> in the last ~11 years (<xref rid="bib56" ref-type="bibr">Longdon et al., 2017</xref>; <xref rid="bib49" ref-type="bibr">Longdon et al., 2011a</xref>). If selection by pathogens continually changes and resistance evolves from new mutations, then this may cause a sustained increase in genetic variance in susceptibility to infection.</p><p>A key question is whether the increased genetic variation that we see in coevolved <italic>Drosophila</italic>–sigma virus interactions will hold for coevolved pathogens more generally. Theory suggests that a critical factor determining levels of genetic variation is whether resistance is costly to evolve, as this can result in the maintenance of variation by negative frequency dependent selection (<xref rid="bib3" ref-type="bibr">Antonovics and Thrall, 1994</xref>; <xref rid="bib12" ref-type="bibr">Boots et al., 2014</xref>). In humans this has been proposed as an explanation of why there is less genetic variation in susceptibility to pathogens that are effectively controlled by the adaptive immune response, as these resistance mechanisms may be less costly (<xref rid="bib4" ref-type="bibr">Baker and Antonovics, 2012</xref>). However, it seems unlikely that virus resistance in <italic>Drosophila</italic> is costly, as experiments have failed to detect costs of DCV resistance (<xref rid="bib27" ref-type="bibr">Faria et al., 2015</xref>) despite costs of parasitoid and bacterial resistance being repeatedly detected (<xref rid="bib62" ref-type="bibr">McGonigle et al., 2017</xref>; <xref rid="bib63" ref-type="bibr">McKean et al., 2008</xref>; <xref rid="bib87" ref-type="bibr">Ye et al., 2009</xref>). Sigma viruses are also extreme host specialists, so evolutionary changes in resistance will tend to alter pathogen prevalence and so the strength of selection. These epidemiological feedbacks are predicted to frequently increase genetic diversity (<xref rid="bib12" ref-type="bibr">Boots et al., 2014</xref>; <xref rid="bib10" ref-type="bibr">Best et al., 2009</xref>; <xref rid="bib13" ref-type="bibr">Boots and Haraguchi, 1999</xref>). However, in <italic>D. melanogaster</italic> we see large amounts of genetic variation in susceptibility to viruses that have a broader host range than sigma viruses (DCV and Kallithea virus that infect <italic>D. melanogaster</italic> and <italic>D. simulans</italic> in the wild) (<xref rid="bib60" ref-type="bibr">Magwire et al., 2012</xref>; <xref rid="bib70" ref-type="bibr">Palmer et al., 2018</xref>; <xref rid="bib21" ref-type="bibr">Christian, 1987</xref>; <xref rid="bib79" ref-type="bibr">Webster et al., 2015</xref>). Therefore, it seems unlikely that our conclusions will be a quirk of the sigma virus system. In an analogous study of rust resistance in wild flax plants, sympatric (putatively coevolved) populations had fewer partial resistances than allopatric populations, suggesting more major gene effects, even though overall there was somewhat less genetic variation in susceptibility to sympatric fungal pathogens (<xref rid="bib2" ref-type="bibr">Antonovics et al., 2011</xref>).</p><p>Quantitative traits are typically controlled by a very large number of genetic variants, each of which tends to have a very small effect (<xref rid="bib72" ref-type="bibr">Shi et al., 2016</xref>; <xref rid="bib86" ref-type="bibr">Yang et al., 2010</xref>). However, susceptibility to sigma viruses has a simpler genetic basis, with seven polymorphisms explaining over half the genetic variance. This confirms our previous work in <italic>D. melanogaster</italic> showing a simple genetic basis of virus resistance (<xref rid="bib60" ref-type="bibr">Magwire et al., 2012</xref>; <xref rid="bib22" ref-type="bibr">Cogni et al., 2016</xref>). As these genetic variants mostly only affect the naturally occurring pathogen of <italic>D. melanogaster,</italic> our results suggest that not only is selection by pathogens increasing the genetic variance but it is also altering the genetic architecture of resistance by introducing major-effect variants into the population. One explanation for this observation is that most quantitative traits are under stabilising selection, so major effect variants will tend to be deleterious and removed by selection (<xref rid="bib32" ref-type="bibr">Gibson, 2018</xref>). In contrast, selection by pathogens likely changes through time and populations may be far from their optimal level of resistance. If this is the case, Fisher’s geometric model predicts that major effect variants will be favoured by directional selection (<xref rid="bib28" ref-type="bibr">Fisher, 1930</xref>). Alternatively, the coevolution of hosts and parasites can favour discrete susceptible and resistant hosts (<xref rid="bib12" ref-type="bibr">Boots et al., 2014</xref>), and at the genetic level this may result in major-effect variants (although this theory does not explicitly address the underlying genetics). The simple genetics may also be driven by mutation—for many traits major-effect mutations that increase fitness may be extremely rare, but this may not be the case for virus resistance. For example a single (loss of function) mutation may prevent a virus binding to a host receptor or utilising other parts of the host cellular machinery, and so confer strong resistance.</p><p>Regardless of its causes, it may be common that susceptibility to infectious disease has a simple genetic basis. In humans, <xref rid="bib39" ref-type="bibr">Hill (2012)</xref> advocated the view that susceptibility to infectious disease is qualitatively different from other traits and has a much simpler genetic basis (<xref rid="bib39" ref-type="bibr">Hill, 2012</xref>). In <italic>Drosophila,</italic> resistance to DCV and parasitoid wasps both have a simple genetic basis (<xref rid="bib60" ref-type="bibr">Magwire et al., 2012</xref>; <xref rid="bib19" ref-type="bibr">Cao et al., 2017</xref>; <xref rid="bib68" ref-type="bibr">Orr and Irving, 1997</xref>). In plants, major-effect polymorphisms in R genes are commonplace (<xref rid="bib36" ref-type="bibr">Hammond-Kosack and Jones, 1997</xref>) and in a plant-fungi system genotype-by-genotype interactions explain a larger proportion of the total variance in sympatric (more coevolved) associations (<xref rid="bib2" ref-type="bibr">Antonovics et al., 2011</xref>). In contrast, studies of bacterial resistance in <italic>Drosophila</italic> have typically used pathogens that are unlikely to have any history of coevolution, and have found a polygenic basis to resistance (<xref rid="bib14" ref-type="bibr">Bou Sleiman et al., 2015</xref>; <xref rid="bib40" ref-type="bibr">Hotson and Schneider, 2015</xref>; <xref rid="bib41" ref-type="bibr">Howick and Lazzaro, 2017</xref>; <xref rid="bib77" ref-type="bibr">Wang et al., 2017</xref>). In these studies the polymorphism with the largest effect was found against the only natural <italic>D. melanogaster</italic> pathogen tested (a polymorphism in <italic>Diptericin</italic> detected using <italic>Providencia rettgeri</italic> infection), anecdotally supporting the patterns seen here (<xref rid="bib41" ref-type="bibr">Howick and Lazzaro, 2017</xref>; <xref rid="bib76" ref-type="bibr">Unckless et al., 2015</xref>).</p><p>A major source of emerging infectious disease is pathogens jumping into novel hosts where they have no co-evolutionary history (<xref rid="bib53" ref-type="bibr">Longdon et al., 2014</xref>; <xref rid="bib71" ref-type="bibr">Parrish et al., 2008</xref>). Our results suggest that when a pathogen infects a novel host species, there may be far less genetic variation in susceptibility among individuals than is normally the case. This may create a ‘monoculture effect’ (<xref rid="bib43" ref-type="bibr">King and Lively, 2012</xref>; <xref rid="bib46" ref-type="bibr">Lively, 2010</xref>; <xref rid="bib69" ref-type="bibr">Ostfeld and Keesing, 2012</xref>), which could leave populations vulnerable to epidemics of pathogens that have previously circulated in other host species. Longer term, low levels of pre-standing genetic variation may slow down the rate at which the new host can evolve resistance to a new pathogen.</p><p>In conclusion, we have demonstrated that selection by pathogens has increased the amount of genetic variation in host susceptibility. We find resistance has a simple underlying genetic architecture and is largely controlled by major effect resistance loci.</p></sec><sec sec-type="materials|methods" id="s4"><title>Materials and methods</title><sec id="s4-1"><title>Virus extraction and infection</title><p>We extracted the sigma viruses DAffSV, DImmSV, DMelSV and DObsSV from infected stocks of <italic>D. affinis</italic> (line: NC10), <italic>D. immigrans</italic> (line: DA2), <italic>D. melanogaster</italic> (line: E320 Ex) and <italic>D. obscura</italic> (line: 10A) respectively (<xref rid="bib56" ref-type="bibr">Longdon et al., 2017</xref>; <xref rid="bib54" ref-type="bibr">Longdon et al., 2015a</xref>; <xref rid="bib48" ref-type="bibr">Longdon et al., 2010</xref>; <xref rid="bib49" ref-type="bibr">Longdon et al., 2011a</xref>; <xref rid="bib16" ref-type="bibr">Brun and Plus, 1980</xref>). These infected lines were collected from the wild between 2007–2012 (all from the UK, bar <italic>D.affinis</italic> which was collected in the USA). Infected fly stocks were checked for infection by exposing the flies to 100% CO<sub>2</sub> at 12°C for 15mins then paralysed flies were collected 30mins later. The DImmSV infected line does not show CO<sub>2</sub> sensitivity and so was confirmed to have a high level of infection using RT-PCR. Infected flies were frozen at −80°C and later homogenised in ringers solution (2.5 μl per fly) and centrifuged at 13,000 g for 10 min at 4°C. The supernatant was collected, 2% v/v FBS was added then virus solutions were aliquoted and stored at −80°C.</p></sec><sec id="s4-2"><title>Experimental design</title><p>We set up a common garden experiment to measure genetic variation in susceptibility to natural and non-natural viruses across four host species. In a fractional factorial experiment each species was infected it with its own virus, as well as two viruses that do not infect that host species (see <xref ref-type="fig" rid="fig1">Figure 1</xref>). All of the viruses replicate in all hosts (with the exception of DImmSV in <italic>D. melanogaster</italic> that showed limited evidence of replication – this combination was not used in the experiment). All fly stocks used (see additional methods for stock details) were tested for existing sigma virus infection using RT-PCR over two generations. For all species we collected flies from the wild and we used a full-sib mating design. The progeny of these crosses were infected by injecting them with 69 nl of the viruses intrathoracically and measuring viral RNA loads 15 days post infection, as in <xref rid="bib51" ref-type="bibr">Longdon et al. (2011c)</xref>. This time point was selected as RNA viral load tends to plateau from around day 15 post infection and there is no mortality from infection in this period. The specifics for each host species, including sample sizes, are detailed in the additional methods.</p></sec><sec id="s4-3"><title>Known resistance genes in <italic>D. melanogaster</italic></title><p>We genotyped parents of each <italic>D. melanogaster</italic> full sib family from the experiment above for two resistance alleles that are known to confer protection against DMelSV; <italic>p62</italic> (<italic>Ref(2)P)</italic> and <italic>CHKov1</italic>. We genotyped parental flies using PCR assays that produce different sized products depending on whether flies carry resistant or susceptible alleles. Information on these PCRs and primer sequences can be found in <xref ref-type="supplementary-material" rid="supp4">Supplementary file 4</xref>. We then calculated the number of resistance alleles in each family by summing the number of alleles from both mothers and fathers. We produced genotype information for 230 of the 255 families.</p><p>Another resistance allele has been identified in the gene <italic>Ge-1</italic> (<xref rid="bib18" ref-type="bibr">Cao et al., 2016</xref>). However, this allele has been found to occur at a low frequency in wild populations. We genotyped 184 parental flies from our experiment (parental flies for some families could not be collected) and found the resistant allele was not present, suggesting it is rare or absent.</p><p>We further examined the effect of alleles known to affect susceptibility to DMelSV on all three viruses. Firstly, we infected 32 lines from the Drosophila Genetic Reference Panel (DGRP) (<xref rid="bib58" ref-type="bibr">Mackay et al., 2012</xref>) that were susceptible for both <italic>p62</italic> and <italic>CHKov1</italic> (<italic>n</italic> = 11 lines), were resistant for <italic>CHKov1</italic> only (<italic>n</italic> = 13 lines), or were resistant for both genes (<italic>n</italic> = 8 lines) with DMelSV, DAffSV and DObsSV. No lines in the panel were resistant for <italic>p62</italic> and susceptible for <italic>CHKov1</italic>. We infected a mean of 18 flies per line (range = 3–22).</p></sec><sec id="s4-4"><title>Mapping resistance genes in <italic>D. melanogaster</italic></title><p>We used 377 DSPR lines (154 from panel A and 223 from panel B, <ext-link ext-link-type="uri" xlink:href="http://FlyRILs.org">http://FlyRILs.org</ext-link> [<xref rid="bib42" ref-type="bibr">King et al., 2012</xref>; <xref rid="bib47" ref-type="bibr">Long et al., 2014</xref>]), kindly provided by S.J. Macdonald, University of Kansas) to carry out a Quantitative Trait Locus (QTL) study to examine the genetic basis of resistance to DAffSV, DMelSV and DObsSV in <italic>D. melanogaster</italic>.</p><p>Three females and three males from each DSPR line were placed into yeasted cornmeal vials and allowed to lay for 3–4 days, at 25°C. Male offspring were collected at 0–4 days post-eclosion, and placed at 18°C for 4–6 days. Flies were then injected with DMelSV, DAffSV or DObsSV as described above. Injected males were maintained on unyeasted cornmeal at 18°C, and frozen on day 15 post-infection as above.</p><p>Injections were carried out over 13 weeks. Each day of injection a mean of 47 unique lines (range 20–60) and 51 replicate vials were injected with 1–3 different viruses. In total we assayed 377 DSPR lines (108 lines had two biological replicates). Each replicate vial contained a mean of 12 flies (range 1–22). In total, 15,916 flies were injected across both panels of DSPR lines. We injected 319 lines with all three viruses, 38 with 2 viruses and 20 with one virus. The order of injection of lines and of viruses, was randomised across injection days. Independent biological replicates were injected on different days. Panel A and Panel B lines were assayed in two overlapping blocks.</p></sec><sec id="s4-5"><title>Measuring viral load</title><p>We measured the change in RNA viral load using qRT-PCR. The viral RNA load was expressed relative to the endogenous control housekeeping gene <italic>RpL32</italic> (<italic>Rp49</italic>). RNA was extracted from flies homogenised in Trizol and reverse transcribed with GoScript reverse transcriptase (Promega) and random hexamer primers, and then diluted 1:10 with nuclease free water. The qRT-PCR was performed on an Applied Biosystems StepOnePlus system using Sensifast Hi-Rox Sybr kit (Bioline) with the following PCR cycle: 95°C for 2 min followed by 40 cycles of: 95°C for 5 s followed by 60°C for 30 s. Two qRT-PCR reactions (technical replicates) were carried out per sample with both the viral and endogenous control primers. Each qRT-PCR plate contained three standard samples, and all experimental samples were split across plates in a blocked design. A linear model was used to correct the cycle threshold (Ct) values for differences between qRT-PCR plates. Primer sequences are in <xref ref-type="supplementary-material" rid="supp6">Supplementary file 6</xref>.</p><p>To estimate viral load, we calculated ΔCt as the difference between the qRT-PCR cycle thresholds of the virus and the endogenous control. Viral load calculated without using the endogenous control is strongly correlated to ΔCt for all species.</p></sec><sec id="s4-6"><title>Statistical analysis full-sib experiments</title><p>We used a linear mixed model to examine the amount of genetic variation in susceptibility to the different viruses. We used a trivariate model with the load of the three viruses as the response variable. For each species the model was structured as:<disp-formula id="equ1"><label>(1)</label><mml:math id="m1"><mml:mstyle displaystyle="true" scriptlevel="0"><mml:mrow><mml:mstyle displaystyle="true" scriptlevel="0"><mml:msub><mml:mi>y</mml:mi><mml:mrow><mml:mi>v</mml:mi><mml:mi>f</mml:mi><mml:mi>i</mml:mi></mml:mrow></mml:msub><mml:mo>=</mml:mo><mml:msub><mml:mi>β</mml:mi><mml:mrow><mml:mn>1</mml:mn><mml:mo>:</mml:mo><mml:mi>v</mml:mi></mml:mrow></mml:msub><mml:mo>+</mml:mo><mml:msub><mml:mi>u</mml:mi><mml:mrow><mml:mi>v</mml:mi><mml:mo>:</mml:mo><mml:mi>f</mml:mi></mml:mrow></mml:msub><mml:mo>+</mml:mo><mml:msub><mml:mi>u</mml:mi><mml:mrow><mml:mi>v</mml:mi><mml:mo>:</mml:mo><mml:mi>d</mml:mi></mml:mrow></mml:msub><mml:mo>+</mml:mo><mml:msub><mml:mi>e</mml:mi><mml:mrow><mml:mi>v</mml:mi><mml:mi>f</mml:mi><mml:mi>i</mml:mi></mml:mrow></mml:msub></mml:mstyle></mml:mrow></mml:mstyle></mml:math></disp-formula></p><p>Where <inline-formula><mml:math id="inf2"><mml:mstyle displaystyle="true" scriptlevel="0"><mml:mrow><mml:msub><mml:mi>y</mml:mi><mml:mrow><mml:mi>v</mml:mi><mml:mi>f</mml:mi><mml:mi>i</mml:mi></mml:mrow></mml:msub></mml:mrow></mml:mstyle></mml:math></inline-formula> is the log<sub>2 </sub>viral load of the <italic>i<sup>th</sup></italic> biological replicate of full-sib family <italic>f</italic> infected with virus <italic>v.</italic><inline-formula><mml:math id="inf3"><mml:mstyle displaystyle="true" scriptlevel="0"><mml:mrow><mml:mi>β</mml:mi></mml:mrow></mml:mstyle></mml:math></inline-formula> are the fixed effects, with <inline-formula><mml:math id="inf4"><mml:mstyle displaystyle="true" scriptlevel="0"><mml:mrow><mml:msub><mml:mi>β</mml:mi><mml:mrow><mml:mn>1</mml:mn></mml:mrow></mml:msub></mml:mrow></mml:mstyle></mml:math></inline-formula> being the mean viral load of each virus. <italic>u</italic> are the random effects for full-sib families (<italic>f</italic>) and for the day of injection (<italic>d</italic>), <italic>e</italic> are the residuals. By assuming that all the genetic variation in the population is additive (<xref rid="bib38" ref-type="bibr">Hill et al., 2008</xref>), we estimated the genetic variance (<italic>V<sub>G</sub></italic>) of the viral load as twice the between-family variance (<xref rid="bib25" ref-type="bibr">Falconer, 1960</xref>; <xref rid="bib26" ref-type="bibr">Falconer and Mackay, 1996</xref>). Both empirical data and theory suggest additive genetic variation makes up large proportion of the total genetic variance (<xref rid="bib38" ref-type="bibr">Hill et al., 2008</xref>).</p><p>In addition, for <italic>D. melanogaster</italic> we ran a further model that included the additional fixed effects <inline-formula><mml:math id="inf5"><mml:mstyle displaystyle="true" scriptlevel="0"><mml:mrow><mml:msub><mml:mi>β</mml:mi><mml:mrow><mml:mn>2</mml:mn><mml:mo>:</mml:mo><mml:mi>v</mml:mi></mml:mrow></mml:msub></mml:mrow></mml:mstyle></mml:math></inline-formula> and <inline-formula><mml:math id="inf6"><mml:mstyle displaystyle="true" scriptlevel="0"><mml:mrow><mml:msub><mml:mi>β</mml:mi><mml:mrow><mml:mn>3</mml:mn><mml:mo>:</mml:mo><mml:mi>v</mml:mi></mml:mrow></mml:msub></mml:mrow></mml:mstyle></mml:math></inline-formula> that are the linear effects of the <italic>CHKov1</italic> and <italic>p62</italic> (<italic>Ref(2)P)</italic> resistance alleles. We assumed these genes had additive effects, and modelled their effects simply as the proportion of resistant alleles in a family (if one parent was heterozygous and the other homozygous susceptible, the value is 0.25).</p><p>The model was fitted using the MCMCglmm package in R (<xref rid="bib34" ref-type="bibr">Hadfield, 2010</xref>). The random effects (and residuals) are assumed to be multivariate normal with zero mean and covariance structure <bold>V</bold> ⊗ <bold>I. I</bold> is an identity matrix, and <bold>V</bold> a matrix of estimated variances and covariances. For the random effects <bold>V</bold> is a 3 × 3 covariance matrix describing the variances for each virus and the covariances between them. The off-diagonal elements of <bold>V</bold> for the residual were set to zero because the covariances between traits at these levels are not estimable by design.</p><p>Diffuse independent normal priors were placed on the fixed effects (means of zero and variances of 10<sup>8</sup>). Parameter expanded priors were placed on the covariance matrices resulting in scaled multivariate <italic>F</italic> distributions which have the property that the marginal distributions for the variances are scaled (by 1000) <italic>F</italic><sub>1, 1</sub>. The exceptions were the residual variances for which an inverse-gamma prior was used with shape and scale equal to 0.001. The MCMC chain was ran for 130 million iterations with a burn-in of 30 million iterations and a thinning interval of 100,000.</p><p>We confirmed the results were not sensitive to the choice of prior by also fitting models with inverse-Wishart and flat priors for the variance covariance matrices (described in <xref rid="bib51" ref-type="bibr">Longdon et al., 2011c</xref>), as well as fitting the models by REML in ASReml R (<xref rid="bib33" ref-type="bibr">Gilmour et al., 2002</xref>). These analyses all gave qualitatively similar results (data not shown).</p></sec><sec id="s4-7"><title>Statistical analysis of QTL experiment</title><p>Based on genotyping data, the probability that each Recombinant Inbred Line (RIL) in the DSPR panel was derived from each of the eight founder lines has been estimated at 10 kB intervals across the genome (<xref rid="bib42" ref-type="bibr">King et al., 2012</xref>). To identify QTL affecting viral load we first calculated the mean viral load (ΔCt) across the biological replicates of each RIL. We then regressed the mean viral load against the eight genotype probabilities and calculated logarithm of odds (LOD) scores using the DSPRqtl package in R (<xref rid="bib42" ref-type="bibr">King et al., 2012</xref>). These LOD scores were calculated separately for DSPR Panel A and Panel B, and then summed at each genomic location. To obtain a significance threshold, we permuted our mean viral load estimates across the RILs within each panel, repeated the analysis above and recorded the highest LOD score across the entire genome. This process was repeated 1000 times to obtain a null distribution of the maximum LOD score.</p><p>To estimate the effect of each QTL we assumed that there was a single genetic variant affecting viral load, so the founder alleles could be assigned to two functionally distinct allelic classes. First, we regressed the mean viral load against the genotype probabilities (as described above), resulting in estimates of the mean viral load of each founder allele in the dataset. The two DSPR panels had one founder line (line 8) in common. For QTL where the line eight allele was present in both panels, this analysis included data from both panels, and ‘panel’ was included as a fixed effect in the analysis. When this was not the case, we analysed only data from the panel where the QTL was most significant. We then ranked the founder alleles by viral load estimate, and split this ranked list into all possible groups of two alleles. For each split, the genotype probabilities in the first group of founder alleles were summed. We then regressed mean viral load against each of these combined genotype probabilities. The regression model with the highest likelihood was taken as the most likely classification into allelic classes. The effect size of the QTL was then estimated from this model.</p><p>To estimate the genetic variance in viral load within the DSPR panels we modified the model described in <xref ref-type="disp-formula" rid="equ1">Equation 1</xref> as follows. <inline-formula><mml:math id="inf7"><mml:mstyle displaystyle="true" scriptlevel="0"><mml:mrow><mml:msub><mml:mi>y</mml:mi><mml:mrow><mml:mi>v</mml:mi><mml:mi>f</mml:mi><mml:mi>i</mml:mi></mml:mrow></mml:msub></mml:mrow></mml:mstyle></mml:math></inline-formula> is the log<sub>2 </sub>viral load of the <italic>i<sup>th</sup></italic> biological replicate of each RIL <italic>f</italic> infected with virus <italic>v.</italic> There was a single fixed effect, <inline-formula><mml:math id="inf8"><mml:mstyle displaystyle="true" scriptlevel="0"><mml:mrow><mml:mi>β</mml:mi></mml:mrow></mml:mstyle></mml:math></inline-formula>, of the panel the line is from. <italic>u</italic> is the random effect for each RIL (<italic>f</italic>). The day of injection (<italic>d</italic>) was omitted. As all the RILs are homozygous, we estimated the genetic variance in viral load (<italic>V<sub>G</sub></italic>) as half the between-RIL variance. This assumes all the genetic variation is additive.</p><p>To estimate the proportion of the genetic variance that is explained by the QTL we identified, we repeated this analysis but included the 7 QTL we identified as fixed effects in the model. Each QTL was included by estimating the probability that each line carried the resistant allele of the QTL and adding this as a fixed effect to the model. The between-RIL variance then allowed us to estimate the genetic variance in viral load after removing the effects of the QTL.</p></sec><sec id="s4-8"><title>Additional methods</title><p>All lines were screened for their retrospective sigma virus over two generations by RT-PCR, and infected isofemale lines discarded prior to the experiment.</p></sec><sec id="s4-9"><title>Drosophila melanogaster</title><p>We created an outcrossed population by combining 150 isofemale lines of <italic>D. melanogaster</italic> (collected in Accra, Ghana (5.593,–0.188) in 2014) in a population cage. The population was maintained throughout the experiment with a large population size (~1500–2000 flies), with eggs collected from the population cage used to set up each subsequent generation. All rearing was carried out on cornmeal medium (recipe below) sprinkled with live yeast (‘yeasted’) at 25°C.</p><p>Virgin flies were collected daily from bottles set up at a controlled egg density. Full-sib families were set up using crosses of single male and female virgins placed in the same vial and aged for 3 days. Each of these families was tipped onto fresh food daily for 5 days to create replicate vials. After 5 days the adult flies were frozen for later genotyping. 12 days after laying, male offspring were collected from each replicate vial and split into two vials of cornmeal medium without any yeast on the surface (‘unyeasted’) and placed at 18°C.</p><p>After 5 days these flies were injected with 69 nl of virus extract intra-abdominally using a Nanoject II micro-injector (Drummond scientific). Injected flies were kept at 18°C and tipped onto fresh unyeasted cornmeal every 5 days, before being homogenised in Trizol (Invitrogen) and frozen at −80°C on day 15 post injection for later RNA extraction and qRT-PCR.</p><p>Injections were carried out over 25 overlapping blocks. Each block consisted of 10 families, and each day, two vials per family were injected with two different viruses. Each replicate vial contained a mean of 14 flies (range 3–28 flies). In total we measured 255 families over 1567 biological replicates. We aimed to carry out a minimum of 2 replicates of each virus per family, but where possible we carried out 3 or four replicates (92 virus-family combinations had one replicate, 555 had two replicates, 82 had 3 replicates and 25 had four replicates.). 248 families had replicates for all three viruses. Blocks were staggered to overlap with at least 20 families being infected on any one day. The order families were injected in was randomised, and the order the different viruses were injected was blocked across days.</p></sec><sec id="s4-10"><title>D. immigrans</title><p>92 <italic>D. immigrans</italic> lines were collected from Madingley, Cambridge, UK (52.225, 0.043) in 2012 and 2015. Full-sib families were set up using crosses between the 92 isofemale lines of <italic>Drosophila immigrans</italic>. Flies were reared on malt food (recipe below) at 18°C. Crosses were between different isofemale lines (i.e. excluding reciprocal crosses) and maximising the number of lines used. Families were established from 2 to 4 day old single female and male virgin flies placed in the same vial for 7 days. These crosses were tipped onto fresh food every 7 days to generate replicate vials of each family. Eclosed males were collected 27–34 days after initial egg laying and injected with DImmSV, DMelSV or DObsSV 1–3 days post-collection, then maintained and frozen on day 15 post-infection as above.</p><p>Injections were carried out over 18 overlapping blocks. Each block consisted of an average of 19 families and 46 replicate vials. Each replicate vial contained a mean of 14 flies (range: 4–26). In total we assayed 341 families over 812 biological replicates. We aimed to have a minimum of 2 replicates per virus per family (235 virus-family combinations had one replicate, 270 had two replicates, 11 had 3 replicates and 1 had four replicates). 140 families had replicates across two different viruses and 18 families had replicates for all three viruses. Blocks were staggered to overlap with a mean of 39 families being infected on any one day. The order families were injected in was randomised, and the order the different viruses were injected was blocked across days.</p></sec><sec id="s4-11"><title>D. affinis</title><p><table-wrap id="inlinetable1" orientation="portrait" position="anchor"><table frame="hsides" rules="groups"><thead><tr><th valign="top" rowspan="1" colspan="1">Site</th><th valign="top" rowspan="1" colspan="1">N</th></tr></thead><tbody><tr><td valign="top" rowspan="1" colspan="1">Athens, Georgia, USA, (33.946,–83.384) in 2012</td><td valign="top" rowspan="1" colspan="1">13</td></tr><tr><td valign="top" rowspan="1" colspan="1">Great Smokey Mountain National Park, Gatlinburg, USA (35.698,–83.613) in 2015</td><td valign="top" rowspan="1" colspan="1">23</td></tr><tr><td valign="top" rowspan="1" colspan="1">Rochester, New York, USA (43.135,–77.599) in 2012</td><td valign="top" rowspan="1" colspan="1">4</td></tr></tbody></table></table-wrap></p><p>Full-sib families were set up using crosses between 40 isofemale lines of <italic>Drosophila affinis</italic> (see above for collection details) collected in the U.S. Flies were reared on malt food (recipe below) at 18°C. Crosses were between different isofemale lines (i.e. excluding reciprocal crosses) and maximising the number of lines used. Families were established from 6 day old single female and male virgin flies placed in the same vial for 7 days. These crosses were tipped onto fresh food every 7 days to generate replicate vials of each family. Eclosed males were collected 35–42 days after initial egg laying and then injected with DAffSV, DImmSV or DMelSV 1–3 days post-collection, then maintained and frozen on day 15 post-infection as above.</p><p>Injections were carried out over 27 overlapping blocks. Each block consisted of an average of 19 families and 28 replicate vials. Each replicate vial contained a mean of 11 flies (range: 3–23). In total we assayed 520 families over 1003 biological replicates. We aimed to have a minimum of 2 replicates per virus per family (336 virus-family combinations had one replicate, 286 had 2 replicates and 30 had three replicates). 109 families had replicates across two different viruses and 12 families had replicates for all three viruses. Blocks were staggered to overlap with a mean of 23 families being infected on any one day. The order families were injected in was randomised, and the order the different viruses were injected was blocked across days.</p></sec><sec id="s4-12"><title>D. obscura</title><p><table-wrap id="inlinetable2" orientation="portrait" position="anchor"><table frame="hsides" rules="groups"><thead><tr><th valign="top" rowspan="1" colspan="1">Site</th><th valign="top" rowspan="1" colspan="1">N</th></tr></thead><tbody><tr><td valign="top" rowspan="1" colspan="1">Derbyshire Site A, UK (52.978,–1.440) in 2012</td><td valign="top" rowspan="1" colspan="1">4</td></tr><tr><td valign="top" rowspan="1" colspan="1">Derbyshire Site C, UK (52.903,–1.374), in 2012</td><td valign="top" rowspan="1" colspan="1">1</td></tr><tr><td valign="top" rowspan="1" colspan="1">Les Gorges du Chambon, France (45.622, 0.555) in 2012</td><td valign="top" rowspan="1" colspan="1">1</td></tr><tr><td valign="top" rowspan="1" colspan="1">Madingley, Cambridge, UK (52.226, 0.046) in 2014</td><td valign="top" rowspan="1" colspan="1">15</td></tr></tbody></table></table-wrap></p><p><italic>D. obscura</italic> were collected in the United Kingdom and France (see above). Males and females were separated, and females were placed in vials to establish isofemale lines. Full-sib families were set up using crosses between 21 isofemale lines of <italic>Drosophila obscura</italic> collected in the UK. Flies were reared on banana food (recipe below) at 18°C. Crosses were between different isofemale lines (i.e. excluding reciprocal crosses) and maximising the number of lines used. Families were established from 6 day old single female and male virgin flies placed in the same vial for 7 days. These crosses were tipped onto fresh food every 7 days to generate replicate vials of each family. Eclosed males were collected 35–42 days after initial egg laying and then injected with DAffSV, DMelSV, or DObsSV 1–3 days post-collection, then maintained and frozen on day 15 post-infection as above.</p><p>Injections were carried out over 25 overlapping blocks. Each block consisted of a mean of 16 families with a mean of 76 vials being injected each day for 12 days. Each replicate vial contained a mean of 8 flies (range: 1–15). In total we assayed 320 families over 913 biological replicates. We aimed to have a minimum of 2 replicates per virus per family (126 virus-family combinations had one replicate, 314 had 2 replicates and 49 had 3 replicates and 3 had four replicates). 94 families had replicates across two different viruses and 39 families had replicates for all three viruses. Blocks were staggered to overlap with at least 10 families being infected on any one day. The order families were injected in was randomised, and the order the different viruses were injected was blocked across days.</p></sec><sec id="s4-13"><title>Sample size estimation</title><p>The number of full-sib families required for estimating genetic variance in susceptibility was determined by simulation using previous estimates of genetic variation to DMelSV in <italic>D. melanogaster</italic> (<xref rid="bib60" ref-type="bibr">Magwire et al., 2012</xref>). After carrying out the full-sib experiment in <italic>D. melanogaster</italic>, we then down-sampled this data to calculate the minimum number of families required to provide accurate estimates for the other species. Sample sizes for the DSPR experiment were based on previous data (<xref rid="bib27" ref-type="bibr">Faria et al., 2015</xref>).</p></sec><sec id="s4-14"><title>Food recipes</title><p>Banana:</p><list list-type="simple"><list-item><p>Mixture 1</p><list list-type="simple"><list-item><p>1000 ml water</p></list-item><list-item><p>30 g yeast</p></list-item><list-item><p>10 g agar</p></list-item></list></list-item><list-item><p>Mixture 2</p><list list-type="simple"><list-item><p>20 ml Nipagin</p></list-item><list-item><p>150 g pureed banana</p></list-item><list-item><p>50 g corn syrup</p></list-item><list-item><p>30 g malt powder</p></list-item></list></list-item></list><p>Bring mixture one to the boil for 3–4 min, whisk constantly. Add to Mixture two to Mixture 1. Whisk constantly and simmer for 5 min.</p><p>Cornmeal:</p><list list-type="simple"><list-item><p>1200 ml water</p></list-item><list-item><p>13 g agar</p></list-item><list-item><p>105 g dextrose</p></list-item><list-item><p>105 g maize</p></list-item><list-item><p>23 g yeast</p></list-item></list><p>Combine and bring to a boil for 5mins, cool to 70°C before adding 35 ml Nipagin (10%)</p><p>Malt:</p><list list-type="simple"><list-item><p>1000 ml water</p></list-item><list-item><p>10 g agar</p></list-item><list-item><p>60 g semolina</p></list-item><list-item><p>20 g yeast</p></list-item><list-item><p>80 g malt extract</p></list-item></list><p>Combine and bring to a boil for 5mins, cool to 70°C and then add 14 ml Nipagin (10%) and 5 ml propionic acid.</p></sec><sec sec-type="data-availability" id="s4-15"><title>Data availability</title><p>Datasets and R code for estimating the amount of genetic variation in susceptibility <ext-link ext-link-type="uri" xlink:href="https://doi.org/10.6084/m9.figshare.6743339">https://doi.org/10.6084/m9.figshare.6743339</ext-link></p><p>DGRP dataset <ext-link ext-link-type="uri" xlink:href="https://doi.org/10.6084/m9.figshare.6743354">https://doi.org/10.6084/m9.figshare.6743354</ext-link></p><p>DSPR dataset and R code <ext-link ext-link-type="uri" xlink:href="https://doi.org/10.6084/m9.figshare.7195751">https://doi.org/10.6084/m9.figshare.7195751</ext-link></p></sec></sec></body><back><sec sec-type="funding-information"><title>Funding Information</title><p>This paper was supported by the following grants:</p><list list-type="bullet"><list-item><p><funding-source><institution-wrap><institution-id institution-id-type="FundRef">http://dx.doi.org/10.13039/100008668</institution-id><institution>NERC Environmental Bioinformatics Centre</institution></institution-wrap></funding-source><award-id>NE/L004232/1</award-id> to Elizabeth ML Duxbury, Jonathan P Day, Francis M Jiggins, Ben Longdon.</p></list-item><list-item><p><funding-source><institution-wrap><institution-id institution-id-type="FundRef">http://dx.doi.org/10.13039/100004440</institution-id><institution>Wellcome</institution></institution-wrap></funding-source><award-id>Sir Henry Dale Fellowship (Grant Number 109356/Z/15/Z)</award-id> to Ben Longdon.</p></list-item><list-item><p><funding-source><institution-wrap><institution-id institution-id-type="FundRef">http://dx.doi.org/10.13039/100010663</institution-id><institution>H2020 European Research Council</institution></institution-wrap></funding-source><award-id>281668</award-id> to Elizabeth ML Duxbury.</p></list-item><list-item><p><funding-source><institution-wrap><institution-id institution-id-type="FundRef">http://dx.doi.org/10.13039/501100000288</institution-id><institution>Royal Society</institution></institution-wrap></funding-source><award-id>Sir Henry Dale Fellowship (Grant Number 109356/Z/15/Z)</award-id> to Ben Longdon.</p></list-item></list></sec><ack id="ack"><title>Acknowledgements</title><p>Many thanks to: Alastair Wilson and Jarrod Hadfield for useful advice and discussion; Stuart Macdonald for providing DSPR lines, Trudy Mackay for providing the DGRP fly lines and Kelly Dyer with help collecting <italic>D. affinis</italic>; Darren Obbard for providing photographs of <italic>Drosophila</italic> species; Camille Bonneaud, Katherine Roberts, Ryan Imrie and the Unckless lab group for constructive comments on this work. Many thanks to Brian Lazzaro, Janis Antonovics, Bruno Lemaître and one anonymous reviewer for constructive comments that greatly improved the manuscript.</p></ack><sec id="s5" sec-type="additional-information"><title>Additional information</title><fn-group content-type="competing-interest"><title><bold>Competing interests</bold></title><fn fn-type="COI-statement" id="conf1"><p>No competing interests declared.</p></fn></fn-group><fn-group content-type="author-contribution"><title><bold>Author contributions</bold></title><fn fn-type="con" id="con1"><p>Data curation, Formal analysis, Supervision, Investigation, Writing—original draft, Writing—review and editing.</p></fn><fn fn-type="con" id="con2"><p>Data curation, Formal analysis, Validation, Investigation, Visualization, Methodology, Writing—original draft, Project administration, Writing—review and editing.</p></fn><fn fn-type="con" id="con3"><p>Investigation, Writing—original draft, Writing—review and editing.</p></fn><fn fn-type="con" id="con4"><p>Investigation, Writing—original draft, Writing—review and editing.</p></fn><fn fn-type="con" id="con5"><p>Investigation, Writing—original draft, Writing—review and editing.</p></fn><fn fn-type="con" id="con6"><p>Investigation, Writing—original draft, Writing—review and editing.</p></fn><fn fn-type="con" id="con7"><p>Investigation, Writing—original draft, Writing—review and editing.</p></fn><fn fn-type="con" id="con8"><p>Investigation, Writing—original draft, Writing—review and editing.</p></fn><fn fn-type="con" id="con9"><p>Investigation, Writing—original draft, Writing—review and editing.</p></fn><fn fn-type="con" id="con10"><p>Investigation, Writing—original draft, Writing—review and editing.</p></fn><fn fn-type="con" id="con11"><p>Resources, Writing—review and editing.</p></fn><fn fn-type="con" id="con12"><p>Conceptualization, Resources, Data curation, Formal analysis, Supervision, Funding acquisition, Validation, Investigation, Visualization, Methodology, Writing—original draft, Project administration, Writing—review and editing.</p></fn><fn fn-type="con" id="con13"><p>Conceptualization, Resources, Data curation, Formal analysis, Supervision, Funding acquisition, Validation, Investigation, Visualization, Methodology, Writing—original draft, Project administration, Writing—review and editing.</p></fn></fn-group></sec><sec id="s6" sec-type="supplementary-material"><title>Additional files</title><supplementary-material content-type="local-data" id="supp1"><object-id pub-id-type="doi">10.7554/eLife.46440.007</object-id><label>Supplementary file 1.</label><caption><title>Credible intervals of the differences between estimates of genetic variance in susceptibility across host species and viruses.</title><p>The natural virus for each host is in red and bold. Genetic variances are estimated from the among-family variances in viral load. 95% CIs show differences in estimates of genetic variation for different host-virus combinations, intervals that do not cross zero represent statistically significant differences.</p></caption><media mime-subtype="docx" mimetype="application" xlink:href="elife-46440-supp1.docx" orientation="portrait" id="d35e2135" position="anchor"></media></supplementary-material><supplementary-material content-type="local-data" id="supp2"><object-id pub-id-type="doi">10.7554/eLife.46440.008</object-id><label>Supplementary file 2.</label><caption><title>Estimates of genetic variation for each host virus combination.</title><p>The natural virus for each host is in red and bold. Genetic variances are estimated from the among-family variances in viral load.</p></caption><media mime-subtype="docx" mimetype="application" xlink:href="elife-46440-supp2.docx" orientation="portrait" id="d35e2146" position="anchor"></media></supplementary-material><supplementary-material content-type="local-data" id="supp3"><object-id pub-id-type="doi">10.7554/eLife.46440.009</object-id><label>Supplementary file 3.</label><caption><title>Table S3 Genetic correlations (<italic>r<sub>g</sub></italic>) in viral loads across host species after infection by coevolved and non-coevolved viruses.</title><p>The coevolved (natural) virus for each host is in red and bold. Models ran using REML gave similar estimates.</p></caption><media mime-subtype="docx" mimetype="application" xlink:href="elife-46440-supp3.docx" orientation="portrait" id="d35e2162" position="anchor"></media></supplementary-material><supplementary-material content-type="local-data" id="supp4"><object-id pub-id-type="doi">10.7554/eLife.46440.010</object-id><label>Supplementary file 4.</label><caption><title>Table S4 Primers for genotyping<italic>D. melanogaster</italic>resistance genes<italic>Ge-1, p62 (Ref(2)P) and CHKov-1.</italic></title><p>PCRs were carried out using a touchdown PCR cycle (95°C 30sec, 62°C (-1°C per cycle) 30sec, 72°C 1min; for 10x cycles followed by; 95°C 30sec, 52°C 30sec, 72°C 1min; for a further 25x cycles).</p></caption><media mime-subtype="docx" mimetype="application" xlink:href="elife-46440-supp4.docx" orientation="portrait" id="d35e2178" position="anchor"></media></supplementary-material><supplementary-material content-type="local-data" id="supp5"><object-id pub-id-type="doi">10.7554/eLife.46440.011</object-id><label>Supplementary file 5.</label><caption><title>Table S5 QTL and their locations.</title></caption><media mime-subtype="docx" mimetype="application" xlink:href="elife-46440-supp5.docx" orientation="portrait" id="d35e2187" position="anchor"></media></supplementary-material><supplementary-material content-type="local-data" id="supp6"><object-id pub-id-type="doi">10.7554/eLife.46440.012</object-id><label>Supplementary file 6.</label><caption><title>Table S6 Primers for qRT-PCR (5’-3’).</title><p>RpL32 primers overlap an intron-exon boundary. Sigma virus primers cross gene boundaries except for DImmSV that amplifies the L gene</p></caption><media mime-subtype="docx" mimetype="application" xlink:href="elife-46440-supp6.docx" orientation="portrait" id="d35e2198" position="anchor"></media></supplementary-material><supplementary-material content-type="local-data" id="transrepform"><object-id pub-id-type="doi">10.7554/eLife.46440.013</object-id><label>Transparent reporting form</label><media mime-subtype="pdf" mimetype="application" xlink:href="elife-46440-transrepform.pdf" orientation="portrait" id="d35e2204" position="anchor"></media></supplementary-material></sec><sec id="s7" sec-type="data-availability"><title>Data availability</title><p>All data generated or analysed during this study are available at Figshare: Datasets and R code for estimating the amount of genetic variation in susceptibility <ext-link ext-link-type="uri" xlink:href="https://doi.org/10.6084/m9.figshare.6743339">https://doi.org/10.6084/m9.figshare.6743339</ext-link>; DGRP dataset <ext-link ext-link-type="uri" xlink:href="https://doi.org/10.6084/m9.figshare.6743354">https://doi.org/10.6084/m9.figshare.6743354</ext-link>; DSPR dataset and R code <ext-link ext-link-type="uri" xlink:href="https://doi.org/10.6084/m9.figshare.7195751">https://doi.org/10.6084/m9.figshare.7195751</ext-link>.</p><p>The following datasets were generated:</p><p><element-citation publication-type="data" id="dataset1"><person-group person-group-type="author"><name><surname>Elizabeth</surname><given-names>ML Duxbury</given-names></name><name><surname>Jonathan</surname><given-names>P Day</given-names></name><name><surname>Davide</surname><given-names>Maria Vespasiani</given-names></name><name><surname>Yannik</surname><given-names>Thüringer</given-names></name><name><surname>Ignacio</surname><given-names>Tolosana</given-names></name><name><surname>Sophia</surname><given-names>CL Smith</given-names></name><name><surname>Lucia</surname><given-names>Tagliaferri</given-names></name><name><surname>Altug</surname><given-names>Kamacioglu</given-names></name><name><surname>Imogen</surname><given-names>Lindsley</given-names></name><name><surname>Luca</surname><given-names>Love</given-names></name><name><surname>Robert</surname><given-names>L Unckless</given-names></name><name><surname>Francis</surname><given-names>M Jiggins</given-names></name><name><surname>Ben</surname><given-names>Longdon</given-names></name></person-group><year iso-8601-date="2019">2019</year><data-title>Susceptibility of different Drosophila melanogaster DGRP lines to three sigma viruses</data-title><source>Figshare</source><pub-id pub-id-type="doi">10.6084/m9.figshare.6743354</pub-id></element-citation></p><p><element-citation publication-type="data" id="dataset2"><person-group person-group-type="author"><name><surname>Elizabeth</surname><given-names>ML Duxbury</given-names></name><name><surname>Jonathan</surname><given-names>P Day</given-names></name><name><surname>Davide</surname><given-names>Maria Vespasiani</given-names></name><name><surname>Yannik</surname><given-names>Thüringer</given-names></name><name><surname>Ignacio</surname><given-names>Tolosana</given-names></name><name><surname>Sophia</surname><given-names>CL Smith</given-names></name><name><surname>Lucia</surname><given-names>Tagliaferri</given-names></name><name><surname>Altug</surname><given-names>Kamacioglu</given-names></name><name><surname>Imogen</surname><given-names>Lindsley</given-names></name><name><surname>Luca</surname><given-names>Love</given-names></name><name><surname>Robert</surname><given-names>L Unckless</given-names></name><name><surname>Francis</surname><given-names>M Jiggins</given-names></name><name><surname>Ben</surname><given-names>Longdon</given-names></name></person-group><year iso-8601-date="2019">2019</year><data-title>Natural selection by pathogens increases genetic variation in susceptibility to infection</data-title><source>Figshare</source><pub-id pub-id-type="doi">10.6084/m9.figshare.6743339</pub-id></element-citation></p><p><element-citation publication-type="data" id="dataset3"><person-group person-group-type="author"><name><surname>Elizabeth</surname><given-names>ML Duxbury</given-names></name></person-group><year iso-8601-date="2019">2019</year><data-title>Susceptibility of Drosophila melanogaster DSPR lines to three sigma viruses</data-title><source>Figshare</source><pub-id pub-id-type="doi">10.6084/m9.figshare.7195751</pub-id></element-citation></p></sec><ref-list><title>References</title><ref id="bib1"><element-citation 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pub-id-type="doi">10.7554/eLife.46440.021</article-id><title-group><article-title>Decision letter</article-title></title-group><contrib-group><contrib contrib-type="editor"><name><surname>Lemaître</surname><given-names>Bruno</given-names></name><role>Reviewing Editor</role><aff><institution>École Polytechnique Fédérale de Lausanne</institution><country>Switzerland</country></aff></contrib><contrib contrib-type="reviewer"><name><surname>Lemaître</surname><given-names>Bruno</given-names></name><role>Reviewer</role><aff><institution>École Polytechnique Fédérale de Lausanne</institution><country>Switzerland</country></aff></contrib></contrib-group></front-stub><body><boxed-text position="float" orientation="portrait"><p>In the interests of transparency, eLife includes the editorial decision letter and accompanying author responses. A lightly edited version of the letter sent to the authors after peer review is shown, indicating the most substantive concerns; minor comments are not usually included.</p></boxed-text><p>[Editors’ note: a previous version of this study was rejected after peer review, but the authors submitted for reconsideration. The first decision letter after peer review is shown below.]</p><p>Thank you for submitting your work entitled "Host-pathogen coevolution increases genetic variation in susceptibility to infection" for consideration by <italic>eLife</italic>. Your article has been reviewed by three peer reviewers, and the evaluation has been overseen by a Reviewing Editor and a Senior Editor. The following individuals involved in review of your submission have agreed to reveal their identity: Brian P Lazzaro (Reviewer #1); Janis Antonovics (Reviewer #3).</p><p>Our decision has been reached after consultation between the reviewers. Based on these discussions and the individual reviews below, we regret to inform you that your work will not be considered further for publication in <italic>eLife</italic>.</p><p>As you will note, reviewer's comments were generally positive about your manuscript. Nevertheless, in the course of the discussion between reviewers, two issues arise that justify the decision:</p><p>1) The present paper was judged too similar to a previous study of the authors (see reviewer 2). It was felt that the authors have already shown this result with <italic>melanogaster</italic> and that the present study is expanding on that previous observation using more host species. Thus the novelty was questioned.</p><p>2) The generality of the result might be overstated. Reviewers felt that the authors over-claim the implication for general host-parasite evolution, and that the pattern they described might only apply to infections for which resistance has a simple genetic architecture and the pathogen is rapidly evolving, although that may capture most specialist viruses.</p><p>Based on these concerns, it was decided to reject the manuscript but allow re-submission. At this stage, you are allowed to re-submit a revised manuscript that will be treated as a new submission and try to convince the reviewers of the importance of your manuscript.</p><p><italic>Reviewer #1:</italic></p><p>Duxbury, Day and colleagues present a clever test of whether specialist coevolution between a virus and its host increases the genetic variance for resistance in the host population. The paper is conceptually and methodologically sound. It is interesting and well written and could be published without modification. I offer only a few suggestions for improvement, and these are largely discretionary.</p><p>I have one major point of interpretation that may be worth adding to the paper. The authors imply that the results from this study will be generally applicable (e.g., last paragraph of the Introduction), but I suspect they will be most applicable to systems where genetic architecture of resistance is similar to what is seen in this system: variation in resistance explained by few genes of large effect. I am less confident that systems where variation in resistance is determined by a large set of genes with individually small effects will fit the evolutionary model presented here. The authors interchangeably using the words pathogen and parasite to describe the infection, but viruses are a specialized class. This may collapse to being a model for rapidly evolving specialists with less application to generalists or more slowly evolving pathogens and parasites. The authors themselves emphasize the nearly Mendelian architecture of resistance (paragraph one) and the specialist nature of the coevolutionary interaction (paragraph four) in Discussion and at multiple places in Results. In the final paragraph of the Discussion, they conclude that selection by viruses increases genetic diversity and causes a simple genetic architecture of resistance, but I suspect the causality is the other way around: a simple architecture of resistance and rapid evolution of the pathogen facilitates the evolved increase in diversity.</p><p>Upon reading Figure 2, I wondered if there are some host families that are universally susceptible and others that are universally resistant to the non-coevolved viruses. This would be evident in the correlation among families in viral load of the two non-coevolved viruses for each species. Those correlations are given in Supplementary file 3 but they are never discussed in the main text. I would add a few sentences describing these correlations and their implication, perhaps in subsection “Major-effect genetic variants that are known to provide resistance to DMelSV do not protect against other viruses” where the correlation in resistance between coevolved and non-coevolved viruses is presented. This could also be evaluated by looking at the correlation in resistance to DAffSV and DObsSV across the DSPR, which is not presented. In Supplementary file 3, the correlation in resistance to non-coevolved viruses is stronger in <italic>D. immigrans</italic> and <italic>D. subobscura</italic> and is also fairly strong in <italic>D. melanogaster</italic>. This would appear to support variation for generalized resistance in the absence of specific coevolution.</p><p><italic>Reviewer #2:</italic></p><p>The major result of this study is that organisms harbor more genetic variation to deal with their native pathogens than with foreign ones. This is interesting and important because it tells us about the nature of genetic variation for pathogen resistance. It is also informative with respect to predicting how hosts might respond when infected with novel pathogens. The paper is very nicely written, interesting and easy to follow, and the authors have done a very thorough job and used a number of interesting approaches. They start by infecting four species of <italic>Drosophila</italic> with native and foreign Sigma viruses. All four show the same pattern – significantly more genetic variation in the titer of native virus. The rest of the paper focuses on <italic>D. melanogaster</italic>. The authors show that variation in two genes previously shown to confer resistance to native Sigma virus don't have a major effect on foreign Sigma viruses. They also perform an additional QTL analysis of resistance, identifying 7 QTL (only one of these had been previously shown to be imprortant in virus resistance, so these new QTLs will be interesting to follow up on). Most of the QTLs did not affect titers of the foreign viruses.</p><p>My main concern is that I am not sure that this study is so different from previous work. The authors performed a similar study, with a similar conclusion, published in PLoS Genetics (Magwire et al., 2012). In that study they assessed genetic variation in <italic>D. melanogaster</italic> resistance to 2 native and 2 foreign viruses (including two viruses used here – DMelSV and DAffSV). It would be useful for the authors to discuss this paper and how it compares with the present one more explicitly. The authors have also done extensive work identifying genetic variants that contribute to resistance to Sigma virus (and other viruses) in <italic>D. melanogaster</italic>.</p><p>It would also be useful for the authors to include some more information and details about Sigma virus. How does Sigma virus affect host fitness (the authors don't say much about this except that there was no mortality after 15 days)? It is also a bit hard to tell how successful novel infections are. What percent of individuals/families are not infected at all? (It looks like the infection rate of DAffSV in <italic>D. melanogaster</italic> is quite low in the Magwire et al. paper.) What is known about genetic variation in tolerance to Sigma viruses?</p><p><italic>Reviewer #3:</italic></p><p>This is a very nice paper showing that pathogens select for not just resistance per se but specifically that they can select for increased genetic variation in resistance in host populations. Moreover, they can select for genes/gene-action of major effect. The analysis is carried out using large cross inoculations studies, inoculations on lineages with known resistance genes in <italic>D. melanogaster</italic>, and by a QTL study of a composite inbred population…and the results are congruent among all approaches.</p><p>My only (somewhat apologetic) gripe is that one of our papers which showed essentially the same results (selection for genes of large effect, increased variance, and increased pathogen infectivity) based on a reanalysis of a large cross-inoculation experiment in the Linum-Melampsora system, is not cited (Antonovics et al., 2011). However, what we bemoaned in that paper (lack of knowledge of the genes involved) is very much made up for in this paper.</p><p>I also think it would be useful to have clear discussion of the previous theoretical work by the Boots group showing that there are conditions under which co-evolution would indeed result in increased variance in resistance, yet that there are equally other conditions under which the opposite result of reduced variance is expected. An application of this theory to levels of genetic variation in susceptibility to different human diseases is in Baker and Antonovics, 2012. Basically, it would be nice to strengthen the Discussion, paragraph two, and get a better sense of whether the authors think that the inference that changing selection pressures, paragraph four, result in increased variance is sound and parsimonious – or if it is just guessing.</p><p>The paper is focused on viral loads, but what the effect is of these loads on fitness is never clear, and some mention of this should be included in the discussion if not before.</p></body></sub-article><sub-article id="SA2" article-type="reply"><front-stub><article-id pub-id-type="doi">10.7554/eLife.46440.022</article-id><title-group><article-title>Author response</article-title></title-group></front-stub><body><p>[Editors’ note: the author responses to the first round of peer review follow.]</p><disp-quote content-type="editor-comment"><p>As you will note, reviewer's comments were generally positive about your manuscript. Nevertheless, in the course of the discussion between reviewers, two issues arise that justify the decision:</p><p>1) The present paper was judged too similar to a previous study of the authors (see reviewer 2). It was felt that the authors have already shown this result with melanogaster and that the present study is expanding on that previous observation using more host species. Thus the novelty was questioned.</p><p>2) The generality of the result might be overstated. Reviewers felt that the authors over-claim the implication for general host-parasite evolution, and that the pattern they described might only apply to infections for which resistance has a simple genetic architecture and the pathogen is rapidly evolving, although that may capture most specialist viruses.</p></disp-quote><p>We understand the reasons why it was rejected, but would ask you to reconsider the manuscript for two reasons.</p><p>The first reason for the manuscript being rejected was that the conclusions are similar to one of our earlier papers (Magwire et al., 2012). This was the inspiration for this study, but here we have rigorously tested what were anecdotal conclusions in that study. The number of host-virus associations in the Magwire paper was simply too few to draw robust associations between coevolution and levels of genetic variation. We now have a paragraph in the Introduction discussing the results of Magwire, 2012 and explaining how our new data advances on this. We argue that the Magwire paper dataset generated the hypothesis we are testing, and the ability to build on previous work underpins rigorous and reproducible science.</p><p>The second reason for rejection was that our conclusions might not be applicable to other classes of parasites. Rereading the manuscript it was clear our choice of words overstated the generality of our results, and we are now much more careful with our prose. We also discuss the extent to which our conclusions may be extrapolated to other viruses and pathogens by a more detailed review of the literature. All work on model organisms aims to shed light on biology more broadly, but whether it succeeds ultimately awaits research on other species.</p><disp-quote content-type="editor-comment"><p>Reviewer #1:</p><p>[…] I have one major point of interpretation that may be worth adding to the paper. The authors imply that the results from this study will be generally applicable (e.g., last paragraph of the Introduction), but I suspect they will be most applicable to systems where genetic architecture of resistance is similar to what is seen in this system: variation in resistance explained by few genes of large effect. I am less confident that systems where variation in resistance is determined by a large set of genes with individually small effects will fit the evolutionary model presented here. The authors interchangeably using the words pathogen and parasite to describe the infection, but viruses are a specialized class. This may collapse to being a model for rapidly evolving specialists with less application to generalists or more slowly evolving pathogens and parasites. The authors themselves emphasize the nearly Mendelian architecture of resistance (paragraph one) and the specialist nature of the coevolutionary interaction (paragraph four) in Discussion and at multiple places in Results. In the final paragraph of the Discussion, they conclude that selection by viruses increases genetic diversity and causes a simple genetic architecture of resistance, but I suspect the causality is the other way around: a simple architecture of resistance and rapid evolution of the pathogen facilitates the evolved increase in diversity.</p></disp-quote><p>Reading our manuscript again we agree our choice of words overstated the generality of our conclusions. Throughout the manuscript we have altered our language to make it clear our results relate to a group of <italic>Drosophila</italic> viruses, and have added three new paragraphs to the Discussion relating our results to the literature. These changes include:</p><p>1) We have changed the text throughout, including the Abstract, last paragraph of the Introduction, and first paragraph of the Discussion to refer to viruses/sigma viruses and avoid the impression our results necessarily apply to all parasites.</p><p>2) We have changed all instances of parasite to pathogen, virus or sigma virus.</p><p>3) We have added these paragraphs to the Discussion:</p><p>“A key question is whether the increased genetic variation that we see in coevolved <italic>Drosophila</italic>–sigma virus interactions will hold for coevolved pathogens more generally. […] Nonetheless, in an analogous study of wild flax plants, there was less genetic variation in susceptibility to sympatric (coevolved) fungal pathogens [Antonovics et al., 2011].”</p><p>“Regardless of its causes, it may be common that susceptibility to infectious disease has a simple genetic basis. […] In these studies the polymorphism with the largest effect was found against the only natural <italic>D. melanogaster</italic> pathogen tested (a polymorphism in Diptericin detected using Providencia rettgeri infection), anecdotally supporting the patterns seen here [Unckless, Rottschaefer and Lazzaro, 2015].”</p><disp-quote content-type="editor-comment"><p>Upon reading Figure 2, I wondered if there are some host families that are universally susceptible and others that are universally resistant to the non-coevolved viruses. This would be evident in the correlation among families in viral load of the two non-coevolved viruses for each species. Those correlations are given in Supplementary file 3 but they are never discussed in the main text. I would add a few sentences describing these correlations and their implication, perhaps in subsection “Major-effect genetic variants that are known to provide resistance to DMelSV do not protect against other viruses” where the correlation in resistance between coevolved and non-coevolved viruses is presented. This could also be evaluated by looking at the correlation in resistance to DAffSV and DObsSV across the DSPR, which is not presented. In Supplementary file 3, the correlation in resistance to non-coevolved viruses is stronger in <italic>D. immigrans</italic> and <italic>D. subobscura</italic> and is also fairly strong in <italic>D. melanogaster</italic>. This would appear to support variation for generalized resistance in the absence of specific coevolution.</p></disp-quote><p>We have added the DSPR genetic correlations, which are fairly well-estimated, to Supplementary file 3.</p><p>Testing whether resistance to the non-coevolved viruses tends to be ‘non-specific’ is good suggestion which our experimental design and analysis was designed to address. Unfortunately, it was frequently not possible to inject the same family with multiple viruses, so the correlations mostly have broad confidence intervals (Supplementary file 3). Especially now the DSPR results are added; this means we cannot reach any robust conclusion as to whether genes that confer resistance to the non-coevolved viruses tend to be less specific in their action. For this reason we restricted our results to highlighting the one very clear result – different genes control resistance to different viruses. We now draw the reader’s attention to the non-coevolved pairs:</p><p>“(Supplementary file 3; note correlations are frequently low between pairs of non-endemic viruses too).”</p><disp-quote content-type="editor-comment"><p>Reviewer #2:</p><p>The major result of this study is that organisms harbor more genetic variation to deal with their native pathogens than with foreign ones. This is interesting and important because it tells us about the nature of genetic variation for pathogen resistance. It is also informative with respect to predicting how hosts might respond when infected with novel pathogens. The paper is very nicely written, interesting and easy to follow, and the authors have done a very thorough job and used a number of interesting approaches. They start by infecting four species of Drosophila with native and foreign Sigma viruses. All four show the same pattern – significantly more genetic variation in the titer of native virus. The rest of the paper focuses on <italic>D. melanogaster</italic>. The authors show that variation in two genes previously shown to confer resistance to native Sigma virus don't have a major effect on foreign Sigma viruses. They also perform an additional QTL analysis of resistance, identifying 7 QTL (only one of these had been previously shown to be imprortant in virus resistance, so these new QTLs will be interesting to follow up on). Most of the QTLs did not affect titers of the foreign viruses.</p><p>My main concern is that I am not sure that this study is so different from previous work. The authors performed a similar study, with a similar conclusion, published in PLoS Genetics (Magwire et al., 2012). In that study they assessed genetic variation in <italic>D. melanogaster</italic> resistance to 2 native and 2 foreign viruses (including two viruses used here – DMelSV and DAffSV). It would be useful for the authors to discuss this paper and how it compares with the present one more explicitly. The authors have also done extensive work identifying genetic variants that contribute to resistance to Sigma virus (and other viruses) in <italic>D. melanogaster</italic>.</p></disp-quote><p>Our response to this criticism is in our covering letter. We have added a paragraph to the Introduction that explains how this dataset advances Magwire, 2012:</p><p>“As part of a whole genome association study, we have previously estimated levels of genetic variation in the susceptibility of <italic>D. melanogaster</italic> to four different viruses [Magwire et al., 2012]. […]For this reason, here we return to this question and formally test whether a history of coevolution alters the amount and nature of genetic variation.”</p><disp-quote content-type="editor-comment"><p>It would also be useful for the authors to include some more information and details about Sigma virus. How does Sigma virus affect host fitness (the authors don't say much about this except that there was no mortality after 15 days)?</p></disp-quote><p>The two most reliable and relevant studies of the effect of the virus on the host have estimated its effect on host fitness without estimating fitness components. Despite using entirely different methods these generated very similar results. Nonetheless, it seems it is unlikely the cost of infection is due to adult mortality. The Introduction now reads:</p><p>“In <italic>Drosophila melanogaster</italic>, despite the virus causing little adult mortality, infection reduces host fitness by approximately 25% [Fleuriet, 1988; Wilfert and Jiggins, 2010].”</p><disp-quote content-type="editor-comment"><p>It is also a bit hard to tell how successful novel infections are. What percent of individuals/families are not infected at all? (It looks like the infection rate of DAffSV in <italic>D. melanogaster</italic> is quite low in the Magwire et al. paper.)</p></disp-quote><p>We could detect viral RNA in all samples, and our previous data (Longdon et al 2011 PLOS Pathogens) suggest the virus must be replicating at some level to persist in the host for 15 days. Note Magwire et al. measured a symptom of sigma virus infection (CO2 sensitivity) that represents whether the infection is over a threshold viral load. The far greater dynamic range of the quantitative PCR assay we used is a considerable improvement over Magwire et al.’s approach.</p><disp-quote content-type="editor-comment"><p>What is known about genetic variation in tolerance to Sigma viruses?</p></disp-quote><p>Nothing is known. We have never measured tolerance, and the resistance genes studied in <italic>D. melanogaster</italic> all act to reduce viral load (see Figure 3 and Cao et al., 2015)</p><disp-quote content-type="editor-comment"><p>Reviewer #3:</p><p>This is a very nice paper showing that pathogens select for not just resistance per se but specifically that they can select for increased genetic variation in resistance in host populations. Moreover, they can select for genes/gene-action of major effect. The analysis is carried out using large cross inoculations studies, inoculations on lineages with known resistance genes in <italic>D. melanogaster</italic>, and by a QTL study of a composite inbred population…and the results are congruent among all approaches.</p><p>My only (somewhat apologetic) gripe is that one of our papers which showed essentially the same results (selection for genes of large effect, increased variance, and increased pathogen infectivity) based on a reanalysis of a large cross-inoculation experiment in the Linum-Melampsora system, is not cited (Antonovics et al., 2011). However, what we bemoaned in that paper (lack of knowledge of the genes involved) is very much made up for in this paper.</p></disp-quote><p>Sorry we missed this; we now discuss this at two places in the Discussion (paragraphs three - five).</p><disp-quote content-type="editor-comment"><p>I also think it would be useful to have clear discussion of the previous theoretical work by the Boots group showing that there are conditions under which co-evolution would indeed result in increased variance in resistance, yet that there are equally other conditions under which the opposite result of reduced variance is expected. An application of this theory to levels of genetic variation in susceptibility to different human diseases is in Baker and Antonovics, 2012. Basically, it would be nice to strengthen the Discussion, paragraph two, and get a better sense of whether the authors think that the inference that changing selection pressures, paragraph four, result in increased variance is sound and parsimonious – or if it is just guessing.</p></disp-quote><p>The sigma virus system is unusual for an animal pathogen in that we have a strong literature on its genetics and how selection has acted. We have therefore tried to keep our discussion strongly rooted in this. In particular there is a recurrent pattern if selective sweeps, and we have highlighted population genetics theory about how this can affect genetic variation (note that the adaptive dynamics models of Boots are not appropriate to model these processes). Alongside this we have now considerably increased our discussion of the coevolution literature, including the Boots models. The changes are in the two paragraphs quoted at the start of our response to Reviewer 1.</p><disp-quote content-type="editor-comment"><p>The paper is focused on viral loads, but what the effect is of these loads on fitness is never clear, and some mention of this should be included in the discussion if not before.</p></disp-quote><p>Unfortunately, it was not feasible to measure the fitness of >50,000 flies. Fortunately, there is good reason to believe viral load correlated with fitness.</p><p>1) The virus reduces fitness, and viral load is correlated with vertical transmission rates and infection rates in both in the lab and field</p><p>2) Alleles of two genes that reduce viral loads have been positively selected in nature.</p><p>We have added the text:</p><p>“Studies on DMelSV have shown that loci that reduce loads when the virus is injected also reduce infection rates in both the lab and field [2, 27, 43-46]. As infection is costly, this is expected to increase host fitness.”</p></body></sub-article></pmc></record>Pour manipuler ce document sous Unix (Dilib)
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