Advertisement

Genetica

, Volume 147, Issue 5–6, pp 337–350 | Cite as

Episodic positive diversifying selection on key immune system genes in major avian lineages

  • Jennifer AntonidesEmail author
  • Samarth Mathur
  • J. Andrew DeWoody
Original Paper
  • 78 Downloads

Abstract

The major histocompatibility complex (MHC) of the adaptive immune system and the toll-like receptor (TLR) family of the innate immune system are involved in the detection of foreign invaders, and thus are subject to parasite-driven molecular evolution. Herein, we tested for macroevolutionary signatures of selection in these gene families within and among all three major clades of birds (Paleognathae, Galloanserae, and Neoaves). We characterized evolutionary relationships of representative immune genes (Mhc1 and Tlr2b) and a control gene (ubiquitin, Ubb), using a relatively large and phylogenetically diverse set of species with complete coding sequences (34 orthologous loci for Mhc1, 29 for Tlr2b, and 37 for Ubb). Episodic positive diversifying selection was found in the gene-wide phylogenies of the two immune genes, as well as at specific sites within each gene (8.5% of codon sites in Mhc1 and 2.7% in Tlr2b), but not in the control gene (Ubb). We found 20% of lineages under episodic diversifying selection in Mhc1 versus 9.1% in Tlr2b. For Mhc1, selection was relaxed in the Galloanserae and intensified in the Neoaves relative to the other clades, but no differences were detected among clades in the Tlr2b gene. In summary, we provide evidence of episodic positive diversifying selection in key immune genes and demonstrate differential strengths of selection within Class Aves, with the adaptive gene showing an increased divergence and evolutionary rate over the innate gene, contributing to the growing understanding of vertebrate immune gene evolution.

Keywords

Major histocompatibility complex Toll-like receptor Paleognathae Galloanserae Neoaves 

Notes

Acknowledgements

We thank M. Christie, J. Dunning, R. Ricklefs, C. Searle and members of the DeWoody Lab for critical review of a previous version of this manuscript.

Funding

This work was funded by the U.S. National Institute of Food and Agriculture, Purdue’s Department of Forestry & Natural Resources, and the University Faculty Scholar program.

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.

Supplementary material

10709_2019_81_MOESM1_ESM.txt (37 kb)
Supplementary material 1 (TXT 36 kb)Fig. S1. The nucleotide CDS fasta file for the 34 avian species with Mhc1 sequences used in this study. Sequences were required to be complete for the entire alpha chain, including both the variable regions α1 and α2, which forms the peptide-binding groove, and the conserved region α3 which encodes the alpha chain immunoglobulin domain
10709_2019_81_MOESM2_ESM.txt (66 kb)
Supplementary material 2 (TXT 66 kb)Fig. S2. The nucleotide CDS fasta file for the 29 avian species with Tlr2b sequences used in this study. Sequences were required to be complete with the variable region, the extracellular N-terminal LRR (leucine-rich repeat) region which is involved in pathogen recognition, the conserved TIR (Toll/interleukin-1 receptor) region, and the intracellular domain that initiates a signal cascade for downstream immune response
10709_2019_81_MOESM3_ESM.txt (33 kb)
Supplementary material 3 (TXT 33 kb)Fig. S3. The nucleotide CDS fasta file for the 37 avian species with Ubb sequences used in this study. Sequences were required to be complete with repeat conserved ubiquitin domains
10709_2019_81_MOESM4_ESM.rtf (568 kb)
Supplementary material 4 (RTF 568 kb)Fig. S4. Curated peptide MSA alignment of Mhc1 for 34 species produced by T-coffee (Di Tommaso P 2011) and Gblocks (Castresana 2000) in TranslatorX (Abascal et al. 2010), and visualized with the Boxshade server (https://embnet.vital-it.ch/software/BOX_form.html). Black shading represents identical amino acids, gray shading designates similar amino acids (>=50%), and white shading indicates no amino acid similarity
10709_2019_81_MOESM5_ESM.rtf (646 kb)
Supplementary material 5 (RTF 646 kb)Fig. S5. Curated peptide MSA alignment of Tlr2b for 29 species produced by T-coffee (Di Tommaso P 2011) and Gblocks (Castresana 2000) in TranslatorX (Abascal et al. 2010), and visualized with the Boxshade server (https://embnet.vital-it.ch/software/BOX_form.html). Black shading represents identical amino acids, gray shading designates similar amino acids (>=50%), and white shading indicates no amino acid similarity
10709_2019_81_MOESM6_ESM.rtf (111 kb)
Supplementary material 6 (RTF 110 kb)Fig. S6. Curated peptide MSA alignment of Ubb for 37 species produced by T-coffee (Di Tommaso P 2011) and Gblocks (Castresana 2000) in TranslatorX (Abascal et al. 2010), and visualized with the Boxshade server (https://embnet.vital-it.ch/software/BOX_form.html). Black shading represents identical amino acids, gray shading designates similar amino acids (>=50%), and white shading indicates no amino acid similarity
10709_2019_81_MOESM7_ESM.rtf (1.2 mb)
Supplementary material 7 (RTF 1238 kb)Fig. S7. In-frame codon-based nucleotide MSA alignment of Mhc1 for 34 species produced by back translation of curated peptide MSA alignment in TranslatorX (Abascal et al. 2010), and visualized with the Boxshade server (https://embnet.vital-it.ch/software/BOX_form.html). Black shading represents identical nucleotides, gray shading designates similar nucleotides (>=50%), and white shading indicates no nucleotide similarity
10709_2019_81_MOESM8_ESM.rtf (1.5 mb)
Supplementary material 8 (RTF 1493 kb)Fig. S8. In-frame codon-based nucleotide MSA alignment of Tlr2b for 29 species produced by back translation of curated peptide MSA alignment in TranslatorX (Abascal et al. 2010), and visualized with the Boxshade server (https://embnet.vital-it.ch/software/BOX_form.html). Black shading represents identical nucleotides, gray shading designates similar nucleotides (>=50%), and white shading indicates no nucleotide similarity
10709_2019_81_MOESM9_ESM.rtf (614 kb)
Supplementary material 9 (RTF 613 kb)Fig. S9. In-frame codon-based nucleotide MSA alignment of Uaa for 37 species produced by back translation of curated peptide MSA alignment in TranslatorX (Abascal et al. 2010), and visualized with the Boxshade server (https://embnet.vital-it.ch/software/BOX_form.html). Black shading represents identical nucleotides, gray shading designates similar nucleotides (>=50%), and white shading indicates no nucleotide similarity
10709_2019_81_MOESM10_ESM.jpg (2 mb)
Supplementary material 10 (JPEG 2097 kb)Fig. S10. A) The species tree/cladogram from the NCBI taxonomy database (www.ncbi.nlm.nih.gov/taxonomy) for the 34 species used for Mhc1. B) The Mhc1 gene tree topology based on the majority-rule consensus maximum-likelihood tree of 37 species produced by IQ-Tree (Trifinopoulos et al. 2016). The Robinson-Foulds distance is 18 and Euclidean distance is 14.3. Lighter colors indicate less similarity in the topology as indicated by Phylo.io (Robinson et al. 2016). between the Mhc1 gene tree and its species tree were 18 and 14.3, respectively, while for Tlr2b the distances were 15 and 13.5
10709_2019_81_MOESM11_ESM.jpg (1.8 mb)
Supplementary material 11 (JPEG 1794 kb)Fig. S11. A) The species tree/cladogram from the NCBI taxonomy database (www.ncbi.nlm.nih.gov/taxonomy) for the 29 species used for Tlr2b. B) The Tlr2b gene tree topology based on the majority-rule consensus maximum-likelihood tree of 37 species produced by IQ-Tree (Trifinopoulos et al. 2016). The Robinson-Foulds distance is 15 and Euclidean distance is 13.5. Lighter colors indicate less similarity in the topology as indicated by Phylo.io (Robinson et al. 2016)
10709_2019_81_MOESM12_ESM.jpg (2.3 mb)
Supplementary material 12 (JPEG 2358 kb)Fig. S12. A) The species tree/cladogram from the NCBI taxonomy database (www.ncbi.nlm.nih.gov/taxonomy) for the 37 species used for Uaa. B) The Uaa gene tree topology based on the majority-rule consensus maximum-likelihood tree of 37 species produced by IQ-Tree (Trifinopoulos et al. 2016). The Robinson-Foulds distance is 6 and Euclidean distance is 4.0. Lighter colors indicate less similarity in the topology as indicated by Phylo.io (Robinson et al. 2016)
10709_2019_81_MOESM13_ESM.xlsx (13 kb)
Supplementary material 13 (XLSX 12 kb)Table S1. The 34 avian species with Mhc1 sequences used in this study. The CDS was required to be complete for the entire alpha chain, including both the variable regions α1 and α2, which forms the peptide-binding groove, and the conserved region α3 which encodes the alpha chain immunoglobulin domain
10709_2019_81_MOESM14_ESM.xlsx (13 kb)
Supplementary material 14 (XLSX 12 kb)Table S2. The 29 avian species with Tlr2b sequences used in this study. The CDS was required to be complete with the variable region, the extracellular N-terminal LRR (leucine-rich repeat) region which is involved in pathogen recognition, the conserved TIR (Toll/interleukin-1 receptor) region, and the intracellular domain that initiates a signal cascade for downstream immune response. Species in which the Tlr locus was not specified as Tlr2a or Tlr2b were not included
10709_2019_81_MOESM15_ESM.xlsx (13 kb)
Supplementary material 15 (XLSX 13 kb)Table S3. The 37 avian species with Ubb sequences used in this study. The CDS was required to be complete with repeat conserved ubiquitin domains
10709_2019_81_MOESM16_ESM.xlsx (12 kb)
Supplementary material 16 (XLSX 12 kb)Table S4. Statistical results of model fits for ration or intensification of selection pressure at each clade relative to the rest of the tree for Mhc1 and Tlr2b, as determined by the RELAX algorithm (Wertheim et al. 2015) in the Datamonkey server (Weaver et al. 2018). The best fit model for each of the three clades is bolded

References

  1. Abascal F, Zardoya R, Telford MJ (2010) TranslatorX: multiple alignment of nucleotide sequences guided by amino acid translations. Nucleic Acids Res 38:W7–13.  https://doi.org/10.1093/nar/gkq291 CrossRefPubMedPubMedCentralGoogle Scholar
  2. Acevedo-Whitehouse K, Cunningham AA (2006) Is MHC enough for understanding wildlife immunogenetics? Trends Ecol Evol 21:433–438.  https://doi.org/10.1016/j.tree.2006.05.010 CrossRefPubMedGoogle Scholar
  3. Alcaide M, Edwards SV (2011) Molecular evolution of the Toll-like receptor multigene family in birds. Mol Biol Evol 28:1703–1715CrossRefGoogle Scholar
  4. Alcaide M, Liu M, Edwards SV (2013) Major histocompatibility complex class I evolution in songbirds: universal primers, rapid evolution and base compositional shifts in exon 3. PeerJ 1:e86.  https://doi.org/10.7717/peerj.86 CrossRefPubMedPubMedCentralGoogle Scholar
  5. Alfaro ME et al (2009) Nine exceptional radiations plus high turnover explain species diversity in jawed vertebrates. Proc Natl Acad Sci USA 106:13410–13414.  https://doi.org/10.1073/pnas.0811087106 CrossRefPubMedGoogle Scholar
  6. Antonides J, Ricklefs R, DeWoody JA (2017) The genome sequence and insights into the immunogenetics of the bananaquit (Passeriformes: Coereba flaveola). Immunogenetics 69:175–186.  https://doi.org/10.1007/s00251-016-0960-8 CrossRefPubMedGoogle Scholar
  7. Antonides J, Mathur S, Sundaram M, Ricklefs R, DeWoody JA (2019) Immunogenetic response of the bananaquit in the face of malarial parasites. BMC Evol Biol 19:107.  https://doi.org/10.1186/s12862-019-1435-y CrossRefPubMedPubMedCentralGoogle Scholar
  8. Apanius V, Penn D, Slev PR, Ruff LR, Potts WK (1997) The nature of selection on the major histocompatibility complex. Crit Rev Immunol 17:179–224.  https://doi.org/10.1615/CritRevImmunol.v17.i2.40 CrossRefPubMedGoogle Scholar
  9. Areal H, Abrantes J, Esteves PJ (2011) Signatures of positive selection in Toll-like receptor (TLR) genes in mammals. BMC Evol Biol.  https://doi.org/10.1186/1471-2148-11-368 CrossRefPubMedPubMedCentralGoogle Scholar
  10. Bellemain E, Bermingham E, Ricklefs RE (2008) The dynamic evolutionary history of the bananaquit (Coereba flaveola) in the Caribbean revealed by a multigene analysis. BMC Evol Biol 8:240.  https://doi.org/10.1186/1471-2148-8-240 CrossRefPubMedPubMedCentralGoogle Scholar
  11. Best A, White A, Boots M (2009) The implications of coevolutionary dynamics to host–parasite interactions. Am Nat 173:779–791.  https://doi.org/10.1086/598494 CrossRefPubMedGoogle Scholar
  12. Bonneaud C, Perez-Tris J, Federici P, Chastel O, Sorci G (2006) Major histocompatibility alleles associated with local resistance to malaria in a passerine. Evolution 60:383–389.  https://doi.org/10.1554/05-409.1 CrossRefPubMedGoogle Scholar
  13. Borghans JA, Beltman JB, De Boer RJ (2004) MHC polymorphism under host-pathogen coevolution Immunogenetics 55:732–739PubMedGoogle Scholar
  14. Brownlie R, Allan B (2011) Avian toll-like receptors. Cell Tissue Res 343:121–130.  https://doi.org/10.1007/s00441-010-1026-0 CrossRefPubMedGoogle Scholar
  15. Campos MA et al (2001) Activation of Toll-like receptor-2 by glycosylphosphatidylinositol anchors from a protozoan parasite. J Immunol 167:416–423CrossRefGoogle Scholar
  16. Castresana J (2000) Selection of conserved blocks from multiple alignments for their use in phylogenetic analysis. Mol Biol Evol 17:540–552.  https://doi.org/10.1093/oxfordjournals.molbev.a026334 CrossRefPubMedGoogle Scholar
  17. Dalton DL, Vermaak E, Smit-Robinson HA, Kotze A (2016) Lack of diversity at innate immunity Toll-like receptor genes in the critically endangered white-winged Flufftail (Sarothrura ayresi). Sci Rep.  https://doi.org/10.1038/srep36757 CrossRefPubMedPubMedCentralGoogle Scholar
  18. Di Tommaso P et al (2011) T-Coffee: a web server for the multiple sequence alignment of protein and RNA sequences using structural information and homology extension. Nucleic Acids Res 39:W13–17.  https://doi.org/10.1093/nar/gkr245 CrossRefPubMedPubMedCentralGoogle Scholar
  19. Downing T, Lloyd AT, O’Farrelly C, Bradley DG (2010) The differential evolutionary dynamics of avian cytokine and TLR gene classes. J Immunol 184:6993–7000.  https://doi.org/10.4049/jimmunol.0903092 CrossRefPubMedGoogle Scholar
  20. Eo SH, Bininda-Emonds ORP, Carroll JP (2009) A phylogenetic supertree of the fowls (Galloanserae, Aves). Zool Scr 38:465–481.  https://doi.org/10.1111/j.1463-6409.2008.00382.x CrossRefGoogle Scholar
  21. Eriksson EM, Sampaio NG, Schofield L (2014) Toll-like receptors and malaria—sensing and susceptibility. J Trop Dis 2:2CrossRefGoogle Scholar
  22. Fahey Ricklefs RE, Latta SC, DeWoody JA (2012) Comparative historical demography of migratory and nonmigratory birds from the Caribbean island of Hispaniola. Evol Biol 39:400–414CrossRefGoogle Scholar
  23. Feduccia A (2003) ‘Big bang’ for tertiary birds? Trends Ecol Evol 18:172–176.  https://doi.org/10.1016/S0169-5347(03)00017-X CrossRefGoogle Scholar
  24. Foster JT, Woodworth BL, Eggert LE, Hart PJ, Palmer D, Duffy DC, Fleischer RC (2007) Genetic structure and evolved malaria resistance in Hawaiian honeycreepers. Mol Ecol 16:4738–4746.  https://doi.org/10.1111/j.1365-294X.2007.03550.x CrossRefPubMedGoogle Scholar
  25. Gill FB (2007) Ornithology. Macmillan, New YorkGoogle Scholar
  26. Gilroy DL, van Oosterhout C, Komdeur J, Richardson DS (2017) Toll-like receptor variation in the bottlenecked population of the endangered Seychelles warbler. Anim Conserv 20:235–250.  https://doi.org/10.1111/acv.12307 CrossRefGoogle Scholar
  27. Grueber CE, Wallis GP, Jamieson IG (2013) Genetic drift outweighs natural selection at toll-like receptor (TLR) immunity loci in a re-introduced population of a threatened species. Mol Ecol 22:4470–4482.  https://doi.org/10.1111/mec.12404 CrossRefPubMedGoogle Scholar
  28. Grueber CE, Wallis GP, Jamieson IG (2014) Episodic positive selection in the evolution of avian Toll-like receptor innate immunity genes. PLoS ONE.  https://doi.org/10.1371/journal.pone.0089632 CrossRefPubMedPubMedCentralGoogle Scholar
  29. Grueber CE et al (2015) Toll-like receptor diversity in 10 threatened bird species: relationship with microsatellite heterozygosity. Conserv Genet 16:595–611.  https://doi.org/10.1007/s10592-014-0685-x CrossRefGoogle Scholar
  30. Guindon S, Dufayard JF, Lefort V, Anisimova M, Hordijk W, Gascuel O (2010) New algorithms and methods to estimate maximum-likelihood phylogenies: assessing the performance of PhyML 3.0. Syst Biol 59:307–321.  https://doi.org/10.1093/sysbio/syq010 CrossRefPubMedGoogle Scholar
  31. Hall RJ, Altizer S, Bartel RA (2014) Greater migratory propensity in hosts lowers pathogen transmission and impacts. J Anim Ecol 83:1068–1077.  https://doi.org/10.1111/1365-2656.12204 CrossRefPubMedPubMedCentralGoogle Scholar
  32. Hamilton WD (1980) Sex versus non-sex versus parasite. Oikos 35:282–290.  https://doi.org/10.2307/3544435 CrossRefGoogle Scholar
  33. Hedrick PW (1994) Evolutionary genetics of the major histocompatibility complex. Am Nat 143:945–964.  https://doi.org/10.1086/285643 CrossRefGoogle Scholar
  34. Hess CM, Edwards SV (2002) The evolution of the major histocompatibility complex in birds. Bioscience 52:423–431CrossRefGoogle Scholar
  35. Higuchi M et al (2008) Combinational recognition of bacterial lipoproteins and peptidoglycan by chicken Toll-like receptor 2 subfamily. Dev Comp Immunol 32:147–155.  https://doi.org/10.1016/j.dci.2007.05.003 CrossRefPubMedGoogle Scholar
  36. Hoang DT, Chernomor O, von Haeseler A, Minh BQ, Vinh LS (2018) UFBoot2: improving the ultrafast bootstrap approximation. Mol Biol Evol 35:518–522.  https://doi.org/10.1093/molbev/msx281 CrossRefPubMedGoogle Scholar
  37. Holm S (1979) A simple sequentially rejective multiple test procedure. Scand J Stat 6:65–70Google Scholar
  38. Huang YH, Temperley ND, Ren LM, Smith J, Li N, Burt DW (2011) Molecular evolution of the vertebrate TLR1 gene family—a complex history of gene duplication, gene conversion, positive selection and co-evolution. BMC Evol Biol.  https://doi.org/10.1186/1471-2148-11-149 CrossRefPubMedPubMedCentralGoogle Scholar
  39. Janeway C (2005) Immunobiology: the immune system in health and disease, 6th edn. Garland Science, New YorkGoogle Scholar
  40. Jarvis ED et al (2014) Whole-genome analyses resolve early branches in the tree of life of modern birds. Science 346:1320–1331.  https://doi.org/10.1126/science.1253451 CrossRefPubMedPubMedCentralGoogle Scholar
  41. Jetz W, Thomas GH, Joy JB, Hartmann K, Mooers AO (2012) The global diversity of birds in space and time. Nature 491:444–448.  https://doi.org/10.1038/nature11631 CrossRefPubMedGoogle Scholar
  42. Kalyaanamoorthy S, Minh BQ, Wong TKF, von Haeseler A, Jermiin LS (2017) ModelFinder: fast model selection for accurate phylogenetic estimates. Nat Methods 14:587–589.  https://doi.org/10.1038/nmeth.4285 CrossRefPubMedPubMedCentralGoogle Scholar
  43. Khan I et al (2019) The vertebrate TLR supergene family evolved dynamically by gene gain/loss and positive selection revealing a host-pathogen arms race in birds. Diversity 11:131CrossRefGoogle Scholar
  44. Kiemnec-Tyburczy KM, Richmond JQ, Savage AE, Lips KR, Zamudio KR (2012) Genetic diversity of MHC class I loci in six non-model frogs is shaped by positive selection and gene duplication. Heredity 109:146–155.  https://doi.org/10.1038/hdy.2012.22 CrossRefPubMedPubMedCentralGoogle Scholar
  45. Kimura Y, Tanaka K (2010) Regulatory mechanisms involved in the control of ubiquitin homeostasis. J Biochem 147:793–798.  https://doi.org/10.1093/jb/mvq044 CrossRefPubMedGoogle Scholar
  46. Klein J (1986) Natural history of the major histocompatibility complex. Wiley, New YorkGoogle Scholar
  47. Kobe B, Kajava AV (2001) The leucine-rich repeat as a protein recognition motif. Curr Opin Struct Biol 11:725–732.  https://doi.org/10.1016/S0959-440x(01)00266-4 CrossRefPubMedGoogle Scholar
  48. Kosiol C, Holmes I, Goldman N (2007) An empirical codon model for protein sequence evolution. Mol Biol Evol 24:1464–1479CrossRefGoogle Scholar
  49. Krishnegowda G et al (2005) Induction of proinflammatory responses in macrophages by the glycosylphosphatidylinositols of Plasmodium falciparum—cell signaling receptors, glycosylphosphatidylinositol (GPI) structural requirement, and regulation of GPI activity. J Biol Chem 280:8606–8616.  https://doi.org/10.1074/jbc.m413541200 CrossRefPubMedGoogle Scholar
  50. Lee KA, Wikelski M, Robinson WD, Robinson TR, Klasing KC (2008) Constitutive immune defences correlate with life-history variables in tropical birds. J Anim Ecol 77:356–363.  https://doi.org/10.1111/j.1365-2656.2007.01347.x CrossRefPubMedGoogle Scholar
  51. Liao W, Atkinson CT, LaPointe DA, Samuel MD (2017) Mitigating future avian malaria threats to hawaiian forest birds from climate change. PLoS ONE.  https://doi.org/10.1371/journal.pone.0168880 CrossRefPubMedPubMedCentralGoogle Scholar
  52. Minias P, Pikus E, Whittingham LA, Dunn PO (2018) A global analysis of selection at the avian MHC. Evolution 72:1278–1293.  https://doi.org/10.1111/evo.13490 CrossRefPubMedGoogle Scholar
  53. Minias P, Pikus E, Anderwald D (2019) Allelic diversity and selection at the MHC class I and class II in a bottlenecked bird of prey, the White-tailed Eagle. BMC Evol Biol 19:2CrossRefGoogle Scholar
  54. Mukherjee S, Sarkar-Roy N, Wagener DK, Majumder PP (2009) Signatures of natural selection are not uniform across genes of innate immune system, but purifying selection is the dominant signature. Proc Natl Acad Sci USA 106:7073–7078.  https://doi.org/10.1073/pnas.0811357106 CrossRefPubMedGoogle Scholar
  55. Murrell B, Wertheim JO, Moola S, Weighill T, Scheffler K, Kosakovsky Pond SL (2012) Detecting individual sites subject to episodic diversifying selection. PLoS Genet 8:e1002764.  https://doi.org/10.1371/journal.pgen.1002764 CrossRefPubMedPubMedCentralGoogle Scholar
  56. Murrell B et al (2015) Gene-wide identification of episodic selection. Mol Biol Evol 32:1365–1371.  https://doi.org/10.1093/molbev/msv035 CrossRefPubMedPubMedCentralGoogle Scholar
  57. Muse SV, Gaut BS (1994) A likelihood approach for comparing synonymous and nonsynonymous nucleotide substitution rates, with application to the chloroplast genome. Mol Biol Evol 11:715–724PubMedGoogle Scholar
  58. Nei M, Gu X, Sitnikova T (1997) Evolution by the birth-and-death process in multigene families of the vertebrate immune system. Proc Natl Acad Sci USA 94:7799–7806CrossRefGoogle Scholar
  59. Nei M, Rogozin IB, Piontkivska H (2000) Purifying selection and birth-and-death evolution in the ubiquitin gene family. Proc Natl Acad Sci USA 97:10866–10871CrossRefGoogle Scholar
  60. O’Connor EA, Cornwallis CK, Hasselquist D, Nilsson JA, Westerdahl H (2018) The evolution of immunity in relation to colonization and migration. Nat Ecol Evol 2:841–849.  https://doi.org/10.1038/s41559-018-0509-3 CrossRefPubMedGoogle Scholar
  61. Piertney SB, Oliver MK (2006) The evolutionary ecology of the major histocompatibility complex. Heredity 96:7–21.  https://doi.org/10.1038/sj.hdy.6800724 CrossRefPubMedGoogle Scholar
  62. Piontkivska H, Rooney AP, Nei M (2002) Purifying selection and birth-and-death evolution in the histone H4 gene family. Mol Biol Evol 19:689–697.  https://doi.org/10.1093/oxfordjournals.molbev.a004127 CrossRefPubMedGoogle Scholar
  63. Pond SLK, Frost SDW, Muse SV (2004) HyPhy: hypothesis testing using phylogenies. Bioinformatics 21:676–679.  https://doi.org/10.1093/bioinformatics/bti079 CrossRefPubMedGoogle Scholar
  64. Ricklefs RE, Dodge Gray J, Latta SC, Svensson-Coelho M (2011) Distribution anomalies in avian haemosporidian parasites in the southern Lesser Antilles. J Avian Biol 42:570–584CrossRefGoogle Scholar
  65. Roach JC et al (2005) The evolution of vertebrate Toll-like receptors. Proc Natl Acad Sci USA 102:9577–9582.  https://doi.org/10.1073/pnas.0502272102 CrossRefPubMedGoogle Scholar
  66. Robinson DF, Foulds LR (1981) Comparison of phylogenetic trees. Math Biosci 53:131–147CrossRefGoogle Scholar
  67. Robinson O, Dylus D, Dessimoz C (2016) Phylo.io: interactive viewing and comparison of large phylogenetic trees on the web. Mol Biol Evol 33:2163–2166.  https://doi.org/10.1093/molbev/msw080 CrossRefPubMedPubMedCentralGoogle Scholar
  68. Sepil I, Lachish S, Hinks AE, Sheldon BC (2013) Mhc supertypes confer both qualitative and quantitative resistance to avian malaria infections in a wild bird population. Proc R Soc B.  https://doi.org/10.1098/rspb.2013.0134 CrossRefPubMedGoogle Scholar
  69. Shimodaira H, Hasegawa M (1999) Multiple comparisons of log-likelihoods with applications to phylogenetic inference. Mol Biol Evol 16:1114CrossRefGoogle Scholar
  70. Shultz AJ, Sackton TB (2019) Immune genes are hotspots of shared positive selection across birds and mammals. eLife 8:e41815CrossRefGoogle Scholar
  71. Sibley CG, Monroe BL (1990) Distribution and taxonomy of birds of the world. Yale University Press, New HavenGoogle Scholar
  72. Smith MD, Wertheim JO, Weaver S, Murrell B, Scheffler K, Kosakovsky Pond SL (2015) Less is more: an adaptive branch-site random effects model for efficient detection of episodic diversifying selection. Mol Biol Evol 32:1342–1353.  https://doi.org/10.1093/molbev/msv022 CrossRefPubMedPubMedCentralGoogle Scholar
  73. Sommer S (2005) The importance of immune gene variability (MHC) in evolutionary ecology and conservation. Front Zool 2:16.  https://doi.org/10.1186/1742-9994-2-16 CrossRefPubMedPubMedCentralGoogle Scholar
  74. Soubrier J, Steel M, Lee MSY, Der Sarkissian C, Guindon S, Ho SYW, Cooper A (2012) The influence of rate heterogeneity among sites on the time dependence of molecular rates. Mol Biol Evol 29:3345–3358.  https://doi.org/10.1093/molbev/mss140 CrossRefPubMedGoogle Scholar
  75. Suh A, Smeds L, Ellegren H (2015) The dynamics of incomplete lineage sorting across the ancient adaptive radiation of neoavian birds. PLoS Biol.  https://doi.org/10.1371/journal.pbio.1002224 CrossRefPubMedPubMedCentralGoogle Scholar
  76. Trifinopoulos J, Nguyen LT, von Haeseler A, Minh BQ (2016) W-IQ-TREE: a fast online phylogenetic tool for maximum likelihood analysis. Nucleic Acids Res 44:W232–235.  https://doi.org/10.1093/nar/gkw256 CrossRefPubMedPubMedCentralGoogle Scholar
  77. Velova H, Gutowska-Ding MW, Burt DW, Vinkler M (2018) Toll-like receptor evolution in birds: gene duplication, pseudogenisation and diversifying selection. Mol Biol Evol.  https://doi.org/10.1093/molbev/msy119 CrossRefPubMedPubMedCentralGoogle Scholar
  78. Vinkler M, Albrecht T (2009) The question waiting to be asked: innate immunity receptors in the perspective of zoological research. Folia Zool 58:15–28Google Scholar
  79. Weaver S, Shank SD, Spielman SJ, Li M, Muse SV, Pond SLK (2018) Datamonkey 2.0: a modern web application for characterizing selective and other evolutionary processes. Mol Biol Evol 35:773–777.  https://doi.org/10.1093/molbev/msx335 CrossRefPubMedPubMedCentralGoogle Scholar
  80. Wertheim JO, Murrell B, Smith MD, Kosakovsky Pond SL, Scheffler K (2015) RELAX: detecting relaxed selection in a phylogenetic framework. Mol Biol Evol 32:820–832.  https://doi.org/10.1093/molbev/msu400 CrossRefPubMedGoogle Scholar
  81. Willoughby JR et al (2015) The reduction of genetic diversity in threatened vertebrates and new recommendations regarding IUCN conservation rankings. Biol Conserv 191:495–503.  https://doi.org/10.1016/j.biocon.2015.07.025 CrossRefGoogle Scholar
  82. Wlasiuk G, Nachman MW (2010) Adaptation and constraint at Toll-like receptors in primates. Mol Biol Evol 27:2172–2186.  https://doi.org/10.1093/molbev/msq104 CrossRefPubMedPubMedCentralGoogle Scholar
  83. Yang Z (1994) Maximum likelihood phylogenetic estimation from DNA sequences with variable rates over sites: approximate methods. J Mol Evol 39:306–314CrossRefGoogle Scholar
  84. Yang Z (1995) A space-time process model for the evolution of DNA sequences. Genetics 139:993–1005PubMedPubMedCentralGoogle Scholar
  85. Yang ZH, Nielsen R, Goldman N, Pedersen AMK (2000) Codon-substitution models for heterogeneous selection pressure at amino acid sites. Genetics 155:431–449PubMedPubMedCentralGoogle Scholar
  86. Zdobnov EM et al (2017) OrthoDB v9.1: cataloging evolutionary and functional annotations for animal, fungal, plant, archaeal, bacterial and viral orthologs. Nucleic Acids Res 45:D744–D749.  https://doi.org/10.1093/nar/gkw1119 CrossRefPubMedGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Department of Forestry and Natural ResourcesPurdue UniversityWest LafayetteUSA
  2. 2.Department of Biological SciencesPurdue UniversityWest LafayetteUSA

Personalised recommendations