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Understanding the evolution of immune genes in jawed vertebrates

Vinkler, Michal, Fiddaman, Steven R., Těšický, Martin, O'Connor, Emily A., Savage, Anna E., Lenz, Tobias L., Smith, Adrian L., Kaufman, Jim, Bolnick, Daniel I., Davies, Charli S., Dedić, Neira, Flies, Andrew S., Samblás, M. Mercedes Gómez, Henschen, Amberleigh E., Novák, Karel, Palomar, Gemma, Raven, Nynke, Samaké, Kalifa, Slade, Joel, Veetil, Nithya Kuttiyarthu, Voukali, Eleni, Höglund, Jacob, Richardson, David S., Westerdahl, Helena·2023·Journal of Evolutionary Biology
DOI 10.1111/jeb.14181PMC PMC10247546
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Abstract

Driven by co-evolution with pathogens, host immunity continuously adapts to optimize defence against pathogens within a given environment. Recent advances in genetics, genomics and transcriptomics have enabled a more detailed investigation into how immunogenetic variation shapes the diversity of immune responses seen across domestic and wild animal species. However, a deeper understanding of the diverse molecular mechanisms that shape immunity within and among species is still needed to gain insight into—and generate evolutionary hypotheses on—the ultimate drivers of immunological differences. Here, we discuss current advances in our understanding of molecular evolution underpinning jawed vertebrate immunity. First, we introduce the immunome concept, a framework for characterizing genes involved in immune defence from a comparative perspective, then we outline how immune genes of interest can be identified. Second, we focus on how different selection modes are observed acting across groups of immune genes and propose hypotheses to explain these differences. We then provide an overview of the approaches used so far to study the evolutionary heterogeneity of immune genes on macro and microevolutionary scales. Finally, we discuss some of the current evidence as to how specific pathogens affect the evolution of different groups of immune genes. This review results from the collective discussion on the current key challenges in evolutionary immunology conducted at the ESEB 2021 Online Satellite Symposium: Molecular evolution of the vertebrate immune system, from the lab to natural populations. Abstract Reviewing current advances in our understanding of molecular evolution underpinning vertebrate immunity, we propose hypotheses to explain differences in selection modes across immune genes and discuss supporting evidence.

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FIGURE 1 A proposed system for the hierarchical classification of immune genes. A gene considered relevant to the immune system will fall into one of three categories: (i) the core immunome, (ii) the peripheral immunome, and (iii) non‐immune resistance genes. Core immunome components are unequivocally (and in many cases, exclusively) related to immune function. Peripheral immune genes contribute to aspects of the immune response, despite also having additional roles in the physiological functioning of other body systems. Non‐immune resistance genes (NIRGs) are genes that do not form part of the immune system but can functionally confer resistance to a pathogen and thus are important to consider as part of an immune phenotype. Examples of such genes (or gene classes/families) are given in each compartment. The dashed line between core and peripheral immune components indicates that there is likely a continuum of genes whose function ranges from exclusively immune to almost entirely for another physiological function, and that some genes may lie ambiguously between the two categories. Abbreviations: ACE‐2, angiotensin converting enzyme‐2; MHC, major histocompatibility complex; AMPs, anti‐microbial peptides; TCR, T‐cell receptor; BCR, B‐cell receptor; IFNs, interferons; PRRs, pattern‐recognition receptors.
FIGURE 1 A proposed system for the hierarchical classification of immune genes. A gene considered relevant to the immune system will fall into one of three categories: (i) the core immunome, (ii) the peripheral immunome, and (iii) non‐immune resistance genes. Core immunome components are unequivocally (and in many cases, exclusively) related to immune function. Peripheral immune genes contribute to aspects of the immune response, despite also having additional roles in the physiological functioning of other body systems. Non‐immune resistance genes (NIRGs) are genes that do not form part of the immune system but can functionally confer resistance to a pathogen and thus are important to consider as part of an immune phenotype. Examples of such genes (or gene classes/families) are given in each compartment. The dashed line between core and peripheral immune components indicates that there is likely a continuum of genes whose function ranges from exclusively immune to almost entirely for another physiological function, and that some genes may lie ambiguously between the two categories. Abbreviations: ACE‐2, angiotensin converting enzyme‐2; MHC, major histocompatibility complex; AMPs, anti‐microbial peptides; TCR, T‐cell receptor; BCR, B‐cell receptor; IFNs, interferons; PRRs, pattern‐recognition receptors.CC BY · PubMed Central
FIGURE 2 Differences in selective pressures in different immune gene sets. Current evidence suggests that groups of immune genes tend to have differing levels of genetic variation as well as strength of selection acting on them. We predict increased pathogen‐mediated natural selection (range from yellow = low to red = high) that drives increased interspecific genetic variation and intraspecific population polymorphism especially in the genes that encode proteins: (I) directly involved with pathogen structures rather than host‐self molecules; (II) involved with a high number of structurally distinct ligands rather than with structurally limited interactions; (III) expressed on the cell‐surface rather than to cytoplasm and (IV) with inducible rather than constitutive expression.
FIGURE 2 Differences in selective pressures in different immune gene sets. Current evidence suggests that groups of immune genes tend to have differing levels of genetic variation as well as strength of selection acting on them. We predict increased pathogen‐mediated natural selection (range from yellow = low to red = high) that drives increased interspecific genetic variation and intraspecific population polymorphism especially in the genes that encode proteins: (I) directly involved with pathogen structures rather than host‐self molecules; (II) involved with a high number of structurally distinct ligands rather than with structurally limited interactions; (III) expressed on the cell‐surface rather than to cytoplasm and (IV) with inducible rather than constitutive expression.CC BY · PubMed Central

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Research works printed but not linked: 332

  1. Acevedo-Whitehouse, 2006, Is MHC enough for understanding wildlife immunogenetics?, Trends in Ecology Evolution, 21, 433, 10.1016/j.tree.2006.05.010 · DOI
  2. Adamo, 2004, How should behavioural ecologists interpret measurements of immunity?, Animal Behaviour, 68, 1443, 10.1016/j.anbehav.2004.05.005 · DOI
  3. Adelman, 2013, House finch populations differ in early inflammatory signaling and pathogen tolerance at the peak of mycoplasma gallisepticum infection, The American Naturalist, 181, 674, 10.1086/670024 · DOI
  4. Afrache, 2020, A potential nomenclature for the Immuno polymorphism database (IPD) of chicken MHC genes: Progress and problems, Immunogenetics, 72, 9, 10.1007/s00251-019-01145-6 · DOI
  5. Albalat, 2016, Evolution by gene loss, Nature Reviews Genetics, 17, 379, 10.1038/nrg.2016.39 · DOI · Paper page
  6. Alcaide, 2011, Molecular evolution of the toll-like receptor multigene family in birds, Molecular Biology and Evolution, 28, 1703, 10.1093/molbev/msq351 · DOI
  7. Alizon, 2008, Multiple infections, immune dynamics, and the evolution of virulence, The American Naturalist, 172, E150, 10.1086/590958 · DOI
  8. Alves, 2019, Parallel adaptation of rabbit populations to myxoma virus, Science, 363, 1319, 10.1126/science.aau7285 · DOI
  9. Apanius, 1997, The nature of selection on the major histocompatibility complex, Critical Reviews in Immunology, 17, 179, 10.1615/CritRevImmunol.v17.i2.40 · DOI
  10. Areal, 2011, Signatures of positive selection in Tolllike receptor (TLR) genes in mammals, BMC Evolutionary Biology, 11, 368, 10.1186/1471-2148-11-368 · DOI
  11. Arora, 2020, HLA heterozygote advantage against HIV-1 is driven by quantitative and qualitative differences in HLA allele-specific peptide presentation, Molecular Biology and Evolution, 37, 639, 10.1093/molbev/msz249 · DOI
  12. Ashley, 2012, Inflammation: Mechanisms, costs, and natural variation, Annual Review of Ecology, Evolution, and Systematics, 43, 385, 10.1146/annurev-ecolsys-040212-092530 · DOI
  13. Backstrom, 2013, Cis-regulatory sequence variation and association with mycoplasma load in natural populations of the house finch (Carpodacus mexicanus), Ecology and Evolution, 3, 655, 10.1002/ece3.484 · DOI
  14. Bagheri, 2018, Evolution and species-specific conservation of toll-like receptors in terrestrial vertebrates, International Reviews of Immunology, 37, 217, 10.1080/08830185.2018.1506780 · DOI
  15. Bainová, 2014, First evidence of independent pseudogenization of toll-like receptor 5 in passerine birds, Developmental and Comparative Immunology, 45, 151, 10.1016/j.dci.2014.02.010 · DOI
  16. Banerjee, 2020, Novel insights into immune Systems of Bats, Frontiers in Immunology, 11, 26, 10.3389/fimmu.2020.00026 · DOI
  17. Barber, 2008, Growth and energetics in the stickleback–Schistocephalus host–parasite system: A review of experimental infection studies, Behaviour, 145, 647, 10.1163/156853908792451403 · DOI
  18. Barrow, 2008, The extended human leukocyte receptor complex: Diverse ways of modulating immune responses, Immunological Reviews, 224, 98, 10.1111/j.1600-065X.2008.00653.x · DOI
  19. Bashirova, 2020, HLA tapasin independence: Broader peptide repertoire and HIV control, Proceedings of the National Academy of Sciences of the United States of America, 117, 28232, 10.1073/pnas.2013554117 · DOI
  20. Bataille, 2015, Susceptibility of amphibians to chytridiomycosis is associated with MHC class II conformation, Proceedings of the Biological Sciences, 282, 20143127
  21. Bean, 2022, Avian Immunology, 249, 10.1016/B978-0-12-818708-1.00024-5 · DOI
  22. Berger, 2021, The parasite Schistocephalus solidus secretes proteins with putative host manipulation functions, Parasites & Vectors, 14, 436, 10.1186/s13071-021-04933-w · DOI
  23. Bernatchez, 2003, MHC studies in nonmodel vertebrates: What have we learned about natural selection in 15 years?, Journal of Evolutionary Biology, 16, 363, 10.1046/j.1420-9101.2003.00531.x · DOI
  24. Beutler, 2005, Genetic dissection of innate immunity to infection: The mouse cytomegalovirus model, Current Opinion in Immunology, 17, 36, 10.1016/j.coi.2004.11.004 · DOI
  25. Bhattacharya, 2018, ImmPort, toward repurposing of open access immunological assay data for translational and clinical research, Scientific Data, 5, 180015, 10.1038/sdata.2018.15 · DOI
  26. Bilham, 2017, Badger macrophages fail to produce nitric oxide, a key anti-mycobacterial effector molecule, Scientific Reports, 7, 45470, 10.1038/srep45470 · DOI
  27. Bolnick, 2014, Major histocompatibility complex class IIb polymorphism influences gut microbiota composition and diversity, Molecular Ecology, 23, 4831, 10.1111/mec.12846 · DOI
  28. Bonneaud, 2011, Rapid evolution of disease resistance is accompanied by functional changes in gene expression in a wild bird, Proceedings of the National Academy of Sciences of the United States of America, 108, 7866, 10.1073/pnas.1018580108 · DOI
  29. Boonyanuwat, 2006, Influences of major histocompatibility complex class I haplotypes on avian influenza virus disease traits in Thai indigenous chickens, Animal Science Journal, 77, 285, 10.1111/j.1740-0929.2006.00350.x · DOI
  30. Borg, 2011, Variation in MHC genotypes in two populations of house sparrow (Passer domesticus) with different population histories, Ecology and Evolution, 1, 145, 10.1002/ece3.13 · DOI
  31. Breuer, 2013, InnateDB: Systems biology of innate immunity and beyond--recent updates and continuing curation, Nucleic Acids Research, 41, D1228, 10.1093/nar/gks1147 · DOI
  32. Briles, 1977, Marek's disease: Effects of B histocompatibility alloalleles in resistant and susceptible chicken lines, Science, 195, 193, 10.1126/science.831269 · DOI
  33. Brouwer, 2010, MHC-dependent survival in a wild population: Evidence for hidden genetic benefits gained through extra-pair fertilizations, Molecular Ecology, 19, 3444, 10.1111/j.1365-294X.2010.04750.x · DOI
  34. Brunner, 2021, Global patterns of ranavirus detections, FACETS, 6, 912, 10.1139/facets-2020-0013 · DOI
  35. Buchmann, 2014, Evolution of innate immunity: clues from invertebrates via fish to mammals, Frontiers in Immunology, 5, 459, 10.3389/fimmu.2014.00459 · DOI
  36. Buckley, 2012, Dynamic evolution of toll-like receptor multigene families in echinoderms, Frontiers in Immunology, 3, 136, 10.3389/fimmu.2012.00136 · DOI
  37. Burri, 2010, Adaptive divergence of ancient gene duplicates in the avian MHC class II beta, Molecular Biology and Evolution, 27, 2360, 10.1093/molbev/msq120 · DOI
  38. Bustamante, 2005, Natural selection on protein-coding genes in the human genome, Nature, 437, 1153, 10.1038/nature04240 · DOI
  39. Campbell, 2018, A novel approach to wildlife transcriptomics provides evidence of disease-mediated differential expression and changes to the microbiome of amphibian populations, Molecular Ecology, 27, 1413, 10.1111/mec.14528 · DOI
  40. Carbon, 2009, AmiGO: Online access to ontology and annotation data, Bioinformatics, 25, 288, 10.1093/bioinformatics/btn615 · DOI
  41. Carrington, 1999, HLA and HIV-1: Heterozygote advantage and B*35-Cw*04 disadvantage, Science, 283, 1748, 10.1126/science.283.5408.1748 · DOI
  42. Casey, 2010, Sensors and regulators of intracellular pH, Nature Reviews Molecular Cell Biology, 11, 50, 10.1038/nrm2820 · DOI · Paper page
  43. Cattadori, 2008, Parasite co-infection and interaction as drivers of host heterogeneity, International Journal for Parasitology, 38, 371, 10.1016/j.ijpara.2007.08.004 · DOI
  44. Chakrabarti, 2010, Structural and functional roles of coevolved sites in proteins, PLoS One, 5, e8591, 10.1371/journal.pone.0008591 · DOI
  45. Chapman, 2016, The evolution of innate immune genes: Purifying and balancing selection on beta-defensins in waterfowl, Molecular Biology and Evolution, 33, 3075, 10.1093/molbev/msw167 · DOI
  46. Chappell, 2015, Expression levels of MHC class I molecules are inversely correlated with promiscuity of peptide binding, eLife, 4, e05345, 10.7554/eLife.05345 · DOI
  47. Charbonnel, 2005, A long-term genetic survey of an ungulate population reveals balancing selection acting on MHC through spatial and temporal fluctuations in selection, Heredity, 95, 377, 10.1038/sj.hdy.6800735 · DOI
  48. Chattopadhyay, 2020, Novel de novo genome of Cynopterus brachyotis reveals evolutionarily abrupt shifts in gene family composition across fruit bats, Genome Biology and Evolution, 12, 259, 10.1093/gbe/evaa030 · DOI
  49. Chebii, 2021, Genomics of Adaptations in Ungulates, Animals (Basel), 11, 1617, 10.3390/ani11061617 · DOI
  50. Chen, 2015, Genomic organization of the crested ibis MHC provides new insight into ancestral avian MHC structure, Scientific Reports, 5, 7963, 10.1038/srep07963 · DOI
  51. Clark, 2022, VLDLR and ApoER2 are receptors for multiple alphaviruses, Nature, 602, 475, 10.1038/s41586-021-04326-0 · DOI
  52. Colgan, 2021, Evolution and expression of the immune system of a Facultatively anadromous salmonid, Frontiers in Immunology, 12, 1, 10.3389/fimmu.2021.568729 · DOI
  53. Cortázar-Chinarro, 2022, Major histocompatibility complex variation and haplotype associated survival in response to experimental infection of two Bd-GPL strains along a latitudinal gradient, Frontiers in Ecology and Evolution, 10, 915271, 10.3389/fevo.2022.915271 · DOI
  54. Cortázar-Chinarro, 2018, Signatures of historical selection on MHC reveal different selection patterns in the moor frog (Rana arvalis), Immunogenetics, 70, 477, 10.1007/s00251-017-1051-1 · DOI
  55. Cunningham, 2022, Ensembl 2022, Nucleic Acids Research, 50, D988, 10.1093/nar/gkab1049 · DOI · Paper page
  56. Danilova, 2006, The evolution of immune mechanisms, Journal of Experimental Zoology Part B-Molecular and Developmental Evolution, 306B, 496, 10.1002/jez.b.21102 · DOI
  57. Dascher, 2003, Evolutionary constraints on CD1 structure: Insights from comparative genomic analysis, Trends in Immunology, 24, 412, 10.1016/S1471-4906(03)00179-0 · DOI
  58. Davies, 2021, Contemporary evolution of the innate immune receptor gene TLR3 in an isolated vertebrate population, Molecular Ecology, 30, 2528, 10.1111/mec.15914 · DOI
  59. Dawson, 2013, Structural and functional annotation of the porcine immunome, BMC Genomics, 14, 332, 10.1186/1471-2164-14-332 · DOI
  60. Dinkel, 2014, The eukaryotic linear motif resource ELM: 10 years and counting, Nucleic Acids Research, 42, D259, 10.1093/nar/gkt1047 · DOI
  61. Dionne, 2009, MHC standing genetic variation and pathogen resistance in wild Atlantic salmon, Philosophical Transactions of the Royal Society of London. Series B, Biological Sciences, 364, 1555, 10.1098/rstb.2009.0011 · DOI
  62. Dionne, 2007, Clinal variation in MHC diversity with temperature: Evidence for the role of host-pathogen interaction on local adaptation in Atlantic salmon, Evolution, 61, 2154, 10.1111/j.1558-5646.2007.00178.x · DOI
  63. Divín, 2022, Cannabinoid receptor 2 evolutionary gene loss makes parrots more susceptible to neuroinflammation, Proceedings of the Royal Society B: Biological Sciences, 289, 20221941, 10.1098/rspb.2022.1941 · DOI
  64. Djaoud, 2020, HLAs, TCRs, and KIRs, a triumvirate of human cell-mediated immunity, Annual Review of Biochemistry, 89, 717, 10.1146/annurev-biochem-011520-102754 · DOI
  65. Downing, 2010, The differential evolutionary dynamics of avian cytokine and TLR gene classes, Journal of Immunology, 184, 6993, 10.4049/jimmunol.0903092 · DOI
  66. Dubé, 2021, Underwhelming or misunderstood? Genetic variability of pattern recognition receptors in immune responses and resistance to mycobacterium tuberculosis, Frontiers in Immunology, 12, 714808, 10.3389/fimmu.2021.714808 · DOI
  67. Ebert, 2020, Host-parasite co-evolution and its genomic signature, Nature Reviews Genetics, 21, 754, 10.1038/s41576-020-0269-1 · DOI
  68. Eisen, 2010, Evolving concepts of specificity in immune reactions, PNAS, 107, 22373, 10.1073/pnas.1012051108 · DOI
  69. Eizaguirre, 2010, Major histocompatibility complex polymorphism: Dynamics and consequences of parasite-mediated local adaptation in fishes, Journal of Fish Biology, 77, 2023, 10.1111/j.1095-8649.2010.02819.x · DOI
  70. Eizaguirre, 2012, Rapid and adaptive evolution of MHC genes under parasite selection in experimental vertebrate populations, Nature Communications, 3, 621, 10.1038/ncomms1632 · DOI
  71. Ejsmond, 2014, Sexual selection and the evolutionary dynamics of the major histocompatibility complex, Proceedings of the Royal Society B: Biological Sciences, 281, 20141662, 10.1098/rspb.2014.1662 · DOI
  72. Ekblom, 2010, Evolutionary analysis and expression profiling of zebra finch immune genes, Genome Biology and Evolution, 2, 781, 10.1093/gbe/evq061 · DOI
  73. Elliott, 2011, Unifying concepts of MHC-dependent natural killer cell education, Trends in Immunology, 32, 364, 10.1016/j.it.2011.06.001 · DOI
  74. Ellison, 2014, Fighting a losing battle: Vigorous immune response countered by pathogen suppression of host defenses in the chytridiomycosis-susceptible frog Atelopus zeteki, G3: Genes Genomes, Genetics, 4, 1275, 10.1534/g3.114.010744 · DOI
  75. Ellison, 2014, More than skin deep: Functional genomic basis for resistance to amphibian chytridiomycosis, Genome Biology and Evolution, 7, 286, 10.1093/gbe/evu285 · DOI
  76. Enard, 2014, Genome-wide signals of positive selection in human evolution, Genome Research, 24, 885, 10.1101/gr.164822.113 · DOI
  77. Epstein, 2016, Rapid evolutionary response to a transmissible cancer in Tasmanian devils, Nature Communications, 7, 12684, 10.1038/ncomms12684 · DOI
  78. Erra Díaz, 2018, Unravelling the interplay between extracellular acidosis and immune cells, Mediators of Inflammation, 2018, e1218297, 10.1155/2018/1218297 · DOI
  79. Eskew, 2018, Gene expression differs in susceptible and resistant amphibians exposed to Batrachochytrium dendrobatidis, Royal Society Open Science, 5, 170910, 10.1098/rsos.170910 · DOI
  80. Farquharson, 2022, Restoring faith in conservation action: Maintaining wild genetic diversity through the Tasmanian devil insurance program, iScience, 25, 104474, 10.1016/j.isci.2022.104474 · DOI
  81. Feigin, 2018, Genome of the Tasmanian tiger provides insights into the evolution and demography of an extinct marsupial carnivore, Nature Ecology and Evolution, 2, 182, 10.1038/s41559-017-0417-y · DOI
  82. Fiddaman, 2022, Adaptation and cryptic Pseudogenization in penguin toll-like receptors, Molecular Biology and Evolution, 39, msab354, 10.1093/molbev/msab354 · DOI
  83. Figueiró, 2017, Genome-wide signatures of complex introgression and adaptive evolution in the big cats, Science Advances, 3, e1700299, 10.1126/sciadv.1700299 · DOI
  84. Figueroa, 1988, MHC polymorphism pre-dating speciation, Nature, 335, 265, 10.1038/335265a0 · DOI
  85. Fijarczyk, 2018, Balancing selection and introgression of newt immune-response genes, Proceedings of the Royal Society B: Biological Sciences, 285, 20180819, 10.1098/rspb.2018.0819 · DOI
  86. Fisher, 2011, Evolution of the bovine TLR gene family and member associations with Mycobacterium avium subspecies paratuberculosis infection, PLoS One, 6, e27744, 10.1371/journal.pone.0027744 · DOI
  87. Flies, 2017, Comparative analysis of immune checkpoint molecules and their potential role in the transmissible Tasmanian devil facial tumor disease, Frontiers in Immunology, 8, 513, 10.3389/fimmu.2017.00513 · DOI
  88. Flies, 2020, A novel system to map protein interactions reveals evolutionarily conserved immune evasion pathways on transmissible cancers, Science Advances, 6, eaba5031, 10.1126/sciadv.aba5031 · DOI
  89. Flies, 2020, Rewilding immunology, Science, 369, 37, 10.1126/science.abb8664 · DOI
  90. Follin, 2013, In silico peptide-binding predictions of passerine MHC class I reveal similarities across distantly related species, suggesting convergence on the level of protein function, Immunogenetics, 65, 299, 10.1007/s00251-012-0676-3 · DOI
  91. Fraik, 2020, Disease swamps molecular signatures of genetic-environmental associations to abiotic factors in Tasmanian devil (Sarcophilus harrisii) populations, Evolution, 74, 1392, 10.1111/evo.14023 · DOI
  92. Freud, 2017, The broad Spectrum of human natural killer cell diversity, Immunity, 47, 820, 10.1016/j.immuni.2017.10.008 · DOI
  93. Fuess, 2021, Between-population differences in constitutive and infection-induced gene expression in threespine stickleback, Molecular Ecology, 30, 6791, 10.1111/mec.16197 · DOI
  94. Garcia-Longoria, 2022, Reciprocal positive effects on parasitemia between coinfecting haemosporidian parasites in house sparrows, BMC Ecology and Evolution, 22, 73, 10.1186/s12862-022-02026-5 · DOI
  95. Gautier, 2009, A whole genome Bayesian scan for adaptive genetic divergence in west African cattle, BMC Genomics, 10, 550, 10.1186/1471-2164-10-550 · DOI
  96. Geng, 2010, PCR detection and electron microscopic observation of bred Chinese giant salamander infected with ranavirus associated with mass mortality, Chinese Veterinary Science/Zhongguo Shouyi Kexue, 40, 817
  97. Gibson, 2009, Enzymatic assembly of DNA molecules up to several hundred kilobases, Nature Methods, 6, 343, 10.1038/nmeth.1318 · DOI
  98. Gillespie, 2022, The reactome pathway knowledgebase 2022, Nucleic Acids Research, 50, D687, 10.1093/nar/gkab1028 · DOI
  99. Gong, 2020, DAMP-sensing receptors in sterile inflammation and inflammatory diseases, Nature Reviews Immunology, 20, 95, 10.1038/s41577-019-0215-7 · DOI · Paper page
  100. Gonzalez-Quevedo, 2015, Drift, not selection, shapes toll-like receptor variation among oceanic Island populations, Molecular Ecology, 24, 5852, 10.1111/mec.13437 · DOI
  101. Goulder, 2012, HIV and HLA class I: An evolving relationship, Immunity, 37, 426, 10.1016/j.immuni.2012.09.005 · DOI
  102. Graham, 2005, Evolutionary causes and consequences of immunopathology, Annual Review of Ecology, Evolution, and Systematics, 36, 373, 10.1146/annurev.ecolsys.36.102003.152622 · DOI
  103. Grandchamp, 2018, Synchronous birth is a dominant pattern in receptor-ligand evolution, BMC Genomics, 19, 611, 10.1186/s12864-018-4977-2 · DOI
  104. Grayfer, 2015, Ranaviruses, 141
  105. Griffin, 2014, Farmed deer: A veterinary model for chronic mycobacterial diseases that is accessible, appropriate and cost-effective, Journal of Pharmacy & Bioallied Sciences, 6, 10, 10.4103/0975-7406.124302 · DOI
  106. Grimholt, 2003, MHC polymorphism and disease resistance in Atlantic salmon (Salmo salar); facing pathogens with single expressed major histocompatibility class I and class II loci, Immunogenetics, 55, 210, 10.1007/s00251-003-0567-8 · DOI
  107. Grogan, 2018, Evolution of resistance to chytridiomycosis is associated with a robust early immune response, Molecular Ecology, 27, 919, 10.1111/mec.14493 · DOI
  108. Grossen, 2014, Introgression from domestic goat generated variation at the major histocompatibility complex of alpine ibex, PLoS Genetics, 10, e1004438, 10.1371/journal.pgen.1004438 · DOI
  109. Guethlein, 2015, Co-evolution of MHC class I and variable NK cell receptors in placental mammals, Immunological Reviews, 267, 259, 10.1111/imr.12326 · DOI
  110. Halabi, 2021, The dominantly expressed class II molecule from a resistant MHC haplotype presents only a few Marek's disease virus peptides by using an unprecedented binding motif, PLoS Biology, 19, e3001057, 10.1371/journal.pbio.3001057 · DOI
  111. Halabi, 2022, New vistas unfold: Chicken MHC molecules reveal unexpected ways to present peptides to the immune system, Frontiers in Immunology, 13, 886672, 10.3389/fimmu.2022.886672 · DOI
  112. Hammond, 2009, Evolution and survival of marine carnivores did not require a diversity of killer cell Ig-like receptors or Ly49 NK cell receptors, The Journal of Immunology, 182, 3618, 10.4049/jimmunol.0803026 · DOI
  113. Hanotte, 2003, Mapping of quantitative trait loci controlling trypanotolerance in a cross of tolerant west African N'Dama and susceptible east African Boran cattle, Proceedings. National Academy of Sciences. United States of America, 100, 7443, 10.1073/pnas.1232392100 · DOI
  114. Hawley, 2012, Contrasting epidemic histories reveal pathogen-mediated balancing selection on class II MHC diversity in a wild songbird, PLoS One, 7, e30222, 10.1371/journal.pone.0030222 · DOI
  115. Hedrick, 2013, Adaptive introgression in animals: Examples and comparison to new mutation and standing variation as sources of adaptive variation, Molecular Ecology, 22, 4606, 10.1111/mec.12415 · DOI
  116. Heins, 2008, The stickleback-Schistocephalus host-parasite system as a model for understanding the effect of a macroparasite on host reproduction, Behaviour, 145, 625, 10.1163/156853908792451412 · DOI
  117. Hilton, 2019, Single-cell transcriptomics of the naked mole-rat reveals unexpected features of mammalian immunity, PLoS Biology, 17, e3000528, 10.1371/journal.pbio.3000528 · DOI
  118. Hochachka, 2000, Density-dependent decline of host abundance resulting from a new infectious disease, Proceedings of the National Academy of Sciences of the United States of America, 97, 5303, 10.1073/pnas.080551197 · DOI
  119. Hochachka, 2013, Multiple host transfers, but only one successful lineage in a continent-spanning emergent pathogen, Proceedings Biological Sciences, 280, 20131068
  120. Hofmann, 2003, v-src oncogene-specific carboxy-terminal peptide is immunoprotective against Rous sarcoma growth in chickens with MHC class I allele B-F12, Vaccine, 21, 4694, 10.1016/S0264-410X(03)00516-4 · DOI
  121. Högstrand, 1999, Gene conversion can create new MHC alleles, Immunological Reviews, 167, 305, 10.1111/j.1600-065X.1999.tb01400.x · DOI
  122. Holt, 2015, Immunogenomics: A foundation for intelligent immune design, Genome Medicine, 7, 116, 10.1186/s13073-015-0247-y · DOI
  123. Hölzer, 2021, PoSeiDon: A Nextflow pipeline for the detection of evolutionary recombination events and positive selection, Bioinformatics, 37, 1018, 10.1093/bioinformatics/btaa695 · DOI
  124. Horrocks, 2011, Pathogen pressure puts immune defense into perspective, Integrative and Comparative Biology, 51, 563, 10.1093/icb/icr011 · DOI
  125. Howe, 2016, Structure and evolutionary history of a large family of NLR proteins in the zebrafish, Open Biology, 6, 160009, 10.1098/rsob.160009 · DOI · Paper page
  126. Huang, 2022, Contemporary selection on MHC genes in a free-living ruminant population, Ecology Letters, 25, 828, 10.1111/ele.13957 · DOI
  127. Huang, 2017, Global transcriptome analysis identifies differentially expressed genes related to lipid metabolism in wagyu and Holstein cattle, Scientific Reports, 7, 5278, 10.1038/s41598-017-05702-5 · DOI
  128. Huang, 2013, The duck genome and transcriptome provide insight into an avian influenza virus reservoir species, Nature Genetics, 45, 776, 10.1038/ng.2657 · DOI
  129. Huang, 2011, Molecular evolution of the vertebrate TLR1 gene family - a complex history of gene duplication, gene conversion, positive selection and co-evolution, BMC Evolutionary Biology, 11, 149, 10.1186/1471-2148-11-149 · DOI
  130. Hubert, 2018, Cancer- and behavior-related genes are targeted by selection in the Tasmanian devil (Sarcophilus harrisii), PLoS One, 13, e0201838, 10.1371/journal.pone.0201838 · DOI
  131. Hughes, 1988, Pattern of nucleotide substitution at major histocompatibility complex class I loci reveals overdominant selection, Nature, 335, 167, 10.1038/335167a0 · DOI
  132. Hughes, 1989, Nucleotide substitution at major histocompatibility complex class II loci: Evidence for overdominant selection, Proceedings of the National Academy of Sciences of the United States of America, 86, 958, 10.1073/pnas.86.3.958 · DOI
  133. Hyland, 2021, Adaptive evolution in TRIF leads to discordance between human and mouse innate immune signaling, Genome Biology and Evolution, 13, evab268, 10.1093/gbe/evab268 · DOI
  134. International HIV Controllers Study, 2010, The major genetic determinants of HIV-1 control affect HLA class I peptide presentation, Science, 330, 1551, 10.1126/science.1195271 · DOI
  135. Irestedt, 2022, A guide to avian museomics: Insights gained from resequencing hundreds of avian study skins, Molecular Ecology Resources, 22, 2672, 10.1111/1755-0998.13660 · DOI
  136. Irving, 2021, Lessons from the host defences of bats, a unique viral reservoir, Nature, 589, 363, 10.1038/s41586-020-03128-0 · DOI
  137. Jagoda, 2018, Disentangling immediate adaptive introgression from selection on standing Introgressed variation in humans, Molecular Biology and Evolution, 35, 623, 10.1093/molbev/msx314 · DOI
  138. Johnson, 2008, Ranavirus infection of free-ranging and captive box turtles and tortoises in the United States, Journal of Wildlife Diseases, 44, 851, 10.7589/0090-3558-44.4.851 · DOI
  139. Jolles, 2015, Beyond mice and men: Environmental change, immunity and infections in wild ungulates, Parasite Immunology, 37, 255, 10.1111/pim.12153 · DOI
  140. Jorgensen, 2017, Programmed cell death as a defence against infection, Nature Reviews. Immunology, 17, 151, 10.1038/nri.2016.147 · DOI
  141. Kanehisa, 2000, KEGG: Kyoto encyclopedia of genes and genomes, Nucleic Acids Research, 28, 27, 10.1093/nar/28.1.27 · DOI · Paper page
  142. Kaufman, 2018, Generalists and specialists: A new view of how MHC class I molecules fight infectious pathogens, Trends in Immunology, 39, 367, 10.1016/j.it.2018.01.001 · DOI
  143. Kaufman, 1999, The chicken B locus is a minimal essential major histocompatibility complex, Nature, 401, 923, 10.1038/44856 · DOI
  144. Keestra, 2008, Unique properties of the chicken TLR4/MD-2 complex: Selective lipopolysaccharide activation of the MyD88-dependent pathway, Journal of Immunology, 181, 4354, 10.4049/jimmunol.181.6.4354 · DOI
  145. Khan, 2019, Comparative physiological and metabolic analysis reveals a complex mechanism involved in drought tolerance in chickpea (Cicer arietinum L.) induced by PGPR and PGRs, Scientific Reports, 9, 2097, 10.1038/s41598-019-38702-8 · DOI
  146. Kirkham, 2014, Complexity and diversity of the NKR-P1: Clr (Klrb1:Clec2) recognition systems, Frontiers in Immunology, 5, 214, 10.3389/fimmu.2014.00214 · DOI
  147. Klein, 1994, Mhc polymorphism and parasites, Philosophical Transactions of the Royal Society of London Series B-Biological Sciences, 346, 351, 10.1098/rstb.1994.0152 · DOI
  148. Koch, 2007, Structures of an MHC class I molecule from B21 chickens illustrate promiscuous peptide binding, Immunity, 27, 885, 10.1016/j.immuni.2007.11.007 · DOI
  149. Kolora, 2021, Origins and evolution of extreme lifespan in Pacific Ocean rockfishes, Science, 374, 842, 10.1126/science.abg5332 · DOI
  150. Kosakovsky Pond, 2005, Not so different after all: A comparison of methods for detecting amino acid sites under selection, Molecular Biology and Evolution, 22, 1208, 10.1093/molbev/msi105 · DOI
  151. Kosakovsky Pond, 2005, HyPhy: Hypothesis testing using phylogenies, Bioinformatics, 21, 676, 10.1093/bioinformatics/bti079 · DOI · Paper page
  152. Kosakovsky Pond, 2006, Automated phylogenetic detection of recombination using a genetic algorithm, Molecular Biology and Evolution, 23, 1891, 10.1093/molbev/msl051 · DOI
  153. Kosch, 2016, Major histocompatibility complex selection dynamics in pathogen-infected túngara frog (Physalaemus pustulosus) populations, Biology Letters, 12, 20160345, 10.1098/rsbl.2016.0345 · DOI
  154. Kosch, 2017, Characterization of MHC class IA in the endangered southern corroboree frog, Immunogenetics, 69, 165, 10.1007/s00251-016-0965-3 · DOI
  155. Košmrlj, 2010, Effects of thymic selection of the T-cell repertoire on HLA class I-associated control of HIV infection, Nature, 465, 350, 10.1038/nature08997 · DOI
  156. Králová, 2018, Signatures of diversifying selection and convergence acting on passerine toll-like receptor 4 in an evolutionary context, Molecular Ecology, 27, 2871, 10.1111/mec.14724 · DOI
  157. Krause-Kyora, 2018, Ancient DNA study reveals HLA susceptibility locus for leprosy in medieval Europeans, Nature Communications, 9, 1569, 10.1038/s41467-018-03857-x · DOI
  158. Krchlíková, 2021, Repeated MDA5 gene loss in birds: An evolutionary perspective, Viruses, 13, 2131, 10.3390/v13112131 · DOI
  159. Krchlíková, 2023, Dynamic evolution of avian RNA virus sensors: Repeated loss of RIG-I and RIPLET, Viruses, 15, 3, 10.3390/v15010003 · DOI
  160. Kurtz, 2004, Major histocompatibility complex diversity influences parasite resistance and innate immunity in sticklebacks, Proceedings of the Biological Sciences, 271, 197, 10.1098/rspb.2003.2567 · DOI
  161. Lachish, 2009, Demography, disease and the devil: Life-history changes in a disease-affected population of Tasmanian devils (Sarcophilus harrisii), The Journal of Animal Ecology, 78, 427, 10.1111/j.1365-2656.2008.01494.x · DOI
  162. Laky, 2008, Notch signaling in CD4 and CD8 T cell development, Current Opinion in Immunology, 20, 197, 10.1016/j.coi.2008.03.004 · DOI
  163. Lazzaro, 2020, Antimicrobial peptides: Application informed by evolution, Science, 368, eaau5480, 10.1126/science.aau5480 · DOI
  164. Lenz, 2013, Evaluating patterns of convergent evolution and trans-species polymorphism at MHC immunogenes in two sympatric stickleback species, Evolution, 67, 2400, 10.1111/evo.12124 · DOI
  165. Levy, 2020, Evidence of pathogen-induced Immunogenetic selection across the large geographic range of a wild seabird, Molecular Biology and Evolution, 37, 1708, 10.1093/molbev/msaa040 · DOI
  166. Ley, 1996, Mycoplasma gallisepticum isolated from house finches (Carpodacus mexicanus) with conjunctivitis, Avian Diseases, 40, 480, 10.2307/1592250 · DOI
  167. Liu, 2020, Evolutionary history of the toll-like receptor gene family across vertebrates, Genome Biology and Evolution, 12, 3615, 10.1093/gbe/evz266 · DOI
  168. Luttrell, 1998, Natural Mycoplasma gallisepticum infection in a captive flock of house finches, Journal of Wildlife Diseases, 34, 289, 10.7589/0090-3558-34.2.289 · DOI
  169. Luzzatto, 2012, Sickle cell anaemia and malaria, Mediterranean Journal of Hematology and Infectious Diseases, 4, e2012065, 10.4084/mjhid.2012.065 · DOI
  170. Machado, 2015, An evolutionary history of defensins: A role for copy number variation in maximizing host innate and adaptive immune responses, Frontiers in Immunology, 6, 115, 10.3389/fimmu.2015.00115 · DOI
  171. Mallikarjunappa, 2021, Johne's disease in dairy cattle: An Immunogenetic perspective, Frontiers in Veterinary Science, 8, 718987, 10.3389/fvets.2021.718987 · DOI
  172. Mallikarjunappa, 2018, Short communication: Uncovering quantitative trait loci associated with resistance to Mycobacterium avium ssp. paratuberculosis infection in Holstein cattle using a high-density single nucleotide polymorphism panel, Journal of Dairy Science, 101, 7280, 10.3168/jds.2018-14388 · DOI
  173. Malmstrøm, 2016, Evolution of the immune system influences speciation rates in teleost fishes, Nature Genetics, 48, 1204, 10.1038/ng.3645 · DOI
  174. Marchalonis, 2002, Natural recognition repertoire and the evolutionary emergence of the combinatorial immune system, The FASEB Journal, 16, 842, 10.1096/fj.01-0953hyp · DOI
  175. Margres, 2020, Spontaneous tumor regression in Tasmanian devils associated with RASL11A activation, Genetics, 215, 1143, 10.1534/genetics.120.303428 · DOI
  176. Martin, 2018, Killer cell immunoglobulin-like receptor 3DL1 variation modifies HLA-B*57 protection against HIV-1, The Journal of Clinical Investigation, 128, 1903, 10.1172/JCI98463 · DOI
  177. McLaren, 2015, Polymorphisms of large effect explain the majority of the host genetic contribution to variation of HIV-1 virus load, Proceedings of the National Academy of Sciences of the United States of America, 112, 14658, 10.1073/pnas.1514867112 · DOI
  178. Medzhitov, 2012, Disease tolerance as a defense strategy, Science, 335, 936, 10.1126/science.1214935 · DOI · Paper page
  179. Mestas, 2004, Of mice and not men: Differences between mouse and human immunology, The Journal of Immunology, 172, 2731, 10.4049/jimmunol.172.5.2731 · DOI · Paper page
  180. Meyer-Lucht, 2016, Adaptive and neutral genetic differentiation among Scottish and endangered Irish red grouse (Lagopus lagopus scotica), Conservation Genetics, 17, 615, 10.1007/s10592-016-0810-0 · DOI
  181. Migalska, 2018, Profiling of the TCRβ repertoire in non-model species using high-throughput sequencing, Scientific Reports, 8, 11613, 10.1038/s41598-018-30037-0 · DOI
  182. Minias, 2018, A global analysis of selection at the avian MHC, Evolution, 72, 1278, 10.1111/evo.13490 · DOI
  183. Minias, 2019, Evolution of copy number at the MHC varies across the avian tree of life, Genome Biology and Evolution, 11, 17, 10.1093/gbe/evy253 · DOI
  184. Minias, 2022, Selection balancing at innate immune genes: Adaptive polymorphism maintenance in toll-like receptors, Molecular Biology and Evolution, 39, msac102, 10.1093/molbev/msac102 · DOI
  185. Morger, 2015, Distinct haplotype structure at the innate immune receptor toll-like receptor 2 across bank vole populations and lineages in Europe, Biological Journal of the Linnean Society, 116, 124, 10.1111/bij.12593 · DOI
  186. Morrissey, 2021, The molecular assembly of the marsupial γμ T cell receptor defines a third T cell lineage, Science, 371, 1383, 10.1126/science.abe7070 · DOI
  187. Mueller, 2020, Avian Immunome DB: An example of a user-friendly interface for extracting genetic information, BMC Bioinformatics, 21, 502, 10.1186/s12859-020-03764-3 · DOI
  188. Muirhead, 2001, Consequences of population structure on genes under balancing selection, Evolution, 55, 1532
  189. Murrell, 2013, FUBAR: A fast, unconstrained Bayesian AppRoximation for inferring selection, Molecular Biology and Evolution, 30, 1196, 10.1093/molbev/mst030 · DOI
  190. Murrell, 2015, Gene-wide identification of episodic selection, Molecular Biology and Evolution, 32, 1365, 10.1093/molbev/msv035 · DOI
  191. Nadachowska-Brzyska, 2012, Interspecific hybridization increases MHC class II diversity in two sister species of newts, Molecular Ecology, 21, 887, 10.1111/j.1365-294X.2011.05347.x · DOI
  192. Natsopoulou, 2012, Parasites and parallel divergence of the number of individual MHC alleles between sympatric three-spined stickleback Gasterosteus aculeatus morphs in Iceland, Journal of Fish Biology, 81, 1696, 10.1111/j.1095-8649.2012.03430.x · DOI
  193. Nei, 1997, Evolution by the birth-and-death process in multigene families of the vertebrate immune system, Proceedings of the National Academy of Sciences, 94, 7799, 10.1073/pnas.94.15.7799 · DOI
  194. Nei, 2005, Concerted and birth-and-death evolution of multigene families, Annual Review of Genetics, 39, 121, 10.1146/annurev.genet.39.073003.112240 · DOI · Paper page
  195. Nielsen, 2005, A scan for positively selected genes in the genomes of humans and chimpanzees, PLoS Biology, 3, 976, 10.1371/journal.pbio.0030170 · DOI
  196. Nomiyama, 2013, Systematic classification of vertebrate chemokines based on conserved synteny and evolutionary history, Genes to Cells, 18, 1, 10.1111/gtc.12013 · DOI
  197. Norris, 2000, Ecological immunology: Life history trade-offs and immune defense in birds, Behavioral Ecology, 11, 19, 10.1093/beheco/11.1.19 · DOI
  198. Nozawa, 2009, Reliabilities of identifying positive selection by the branch-site and the site-prediction methods, Proceedings of the National Academy of Sciences, 106, 6700, 10.1073/pnas.0901855106 · DOI
  199. Nunes, 2021, How natural selection shapes genetic differentiation in the MHC region: A case study with native Americans, Human Immunology, 82, 523, 10.1016/j.humimm.2021.03.005 · DOI
  200. O'Connor, 2018, The evolution of immunity in relation to colonization and migration, Nature Ecology and Evolution, 2, 841, 10.1038/s41559-018-0509-3 · DOI

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Metadata

Authors
Vinkler, M., Fiddaman, S. R., Těšický, M., O'Connor, E. A., Savage, A. E., Lenz, T. L., Smith, A. L., Kaufman, J., Bolnick, D. I., Davies, C. S., Dedić, N., Flies, A. S., Samblás, M. M. G., Henschen, A. E., Novák, K., Palomar, G., Raven, N., Samaké, K., Slade, J., Veetil, N. K., Voukali, E., Höglund, J., Richardson, D. S., & Westerdahl, H.
Affiliations
  • Department of Zoology Faculty of Science Charles University Prague Czech Republic
  • Department of Biology University of Oxford Oxford UK
  • Department of Biology Lund University Lund Sweden
  • Department of Biology University of Central Florida Florida Orlando USA
  • Research Unit for Evolutionary Immunogenomics Department of Biology University of Hamburg Hamburg Germany
  • Department of Veterinary Medicine University of Cambridge Cambridge UK
  • Department of Ecology and Evolutionary Biology University of Connecticut Storrs Connecticut USA
  • School of Biological Sciences University of East Anglia Norwich UK
  • Department of Botany and Zoology Masaryk University Brno Czech Republic
  • Menzies Institute for Medical Research University of Tasmania Hobart Tasmania Australia
  • Department of Parasitology University of Granada Granada Spain
  • Department of Biological Sciences University of Memphis Memphis Tennessee USA
  • Department of Genetics and Breeding Institute of Animal Science Prague Uhříněves Czech Republic
  • Faculty of Biology Institute of Environmental Sciences Jagiellonian University Kraków Poland
  • Department of Science Engineering and Build Environment Deakin University Victoria Waurn Ponds Australia
  • Department of Genetics and Microbiology Faculty of Science Charles University Prague Czech Republic
  • Department of Biology California State University Fresno California USA
  • Department of Ecology and Genetics Uppsala Universitet Uppsala Sweden
ORCID
Venue
Journal of Evolutionary Biology
Publisher
Oxford University Press (OUP)
Published
2023-06-01
Volume
36
Issue
6
Pages
847-873
ISSN
  • 1420-9101
  • 1010-061X
DOI
10.1111/jeb.14181
PMC
PMC10247546
Cited by
55
References
11
Language
en
Licence
Funders
  • Biotechnology and Biological Sciences Research Council (BB/K004468/1, BB/M011224/1, BB/N023803/1, BB/V000756/1)
  • Department for Environment, Food and Rural Affairs, UK Government (OD0221)
  • Deutsche Forschungsgemeinschaft (437857095)
  • Grantová Agentura České Republiky (19-20152Y, 19‐20152Y)
  • Grantová Agentura, Univerzita Karlova (646119)
  • H2020 European Research Council (ERC-2019-StG-853272-PALAEOFARM, ERC‐2019‐StG‐853272‐PALAEOFARM)
  • John Fell Fund, University of Oxford (0005172)
  • Ministerstvo Školství, Mládeže a Tělovýchovy (SVV 260684/2023)
  • Ministerstvo Zemědělství (MZE-RO0723, MZE‐RO0723)
  • National Institutes of Health (1R01AI123659-01A1, 1R01AI123659‐01A1)
  • Univerzita Karlova v Praze (START/SCI/113 with reg. no. CZ.02.2.69/0.0/0.0/19_)
  • Vetenskapsrådet (2020-04285, 2020‐04285)

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CoChat. (2026). Citation check: Understanding the evolution of immune genes in jawed vertebrates. https://app.cochat.ai/papers/10.1111/jeb.14181
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Vinkler, M., Fiddaman, S. R., Těšický, M., O'Connor, E. A., Savage, A. E., Lenz, T. L., Smith, A. L., Kaufman, J., Bolnick, D. I., Davies, C. S., Dedić, N., Flies, A. S., Samblás, M. M. G., Henschen, A. E., Novák, K., Palomar, G., Raven, N., Samaké, K., Slade, J., . . . Westerdahl, H. (2023). Understanding the evolution of immune genes in jawed vertebrates. Journal of Evolutionary Biology. https://doi.org/10.1111/jeb.14181