Review Article | Published:

Evolutionary insights into host–pathogen interactions from mammalian sequence data

Nature Reviews Genetics volume 16, pages 224236 (2015) | Download Citation

Abstract

Infections are one of the major selective pressures acting on humans, and host-pathogen interactions contribute to shaping the genetic diversity of both organisms. Evolutionary genomic studies take advantage of experiments that natural selection has been performing over millennia. In particular, inter-species comparative genomic analyses can highlight the genetic determinants of infection susceptibility or severity. Recent examples show how evolution-guided approaches can provide new insights into host–pathogen interactions, ultimately clarifying the basis of host range and explaining the emergence of different diseases. We describe the latest developments in comparative immunology and evolutionary genetics, showing their relevance for understanding the molecular determinants of infection susceptibility in mammals.

Key points

  • Infections are possibly the major selective pressure acting on humans, and host–pathogen interactions contribute to shaping the genetic diversity of both organisms.

  • Comparisons among species provide a snapshot of selective events that have been unfolding over long timescales. These approaches use extant genetic diversity and phylogenetic relationships among species to identify positively selected sites.

  • Positive selection often acts on a limited number of sites in a protein that is otherwise selectively constrained; one example is the localized signal of selection at Niemann–Pick C1 protein (NPC1), the receptor for the Ebola virus.

  • As epitomized by the evolutionary history of tripartite motif-containing 5 (TRIM5), past infection events may leave a signature that affects the ability of extant species to fight emerging pathogens.

  • Protein regions at the host–pathogen interface are expected to be targeted by the strongest selective pressure (this is the case for dipeptidyl peptidase 4 (DPP4) and angiotensin-converting enzyme 2 (ACE2), which act as receptors for coronaviruses).

  • Other mammals host a wide range of viruses that are highly pathogenic for humans. Sequencing the genomes of these pathogens will be instrumental in refining our understanding of the process of host–pathogen interaction.

  • Pathogen-driven natural selection is not limited to the immune system: genes that encode incidental pathogen receptors and components of the contact system and coagulation cascade can also be targeted.

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References

  1. 1.

    et al. Signatures of environmental genetic adaptation pinpoint pathogens as the main selective pressure through human evolution. PLoS Genet. 7, e1002355 (2011).

  2. 2.

    A new evolutionary law. Evol. Theory 1, 1–30 (1973).

  3. 3.

    et al. Running with the Red Queen: the role of biotic conflicts in evolution. Proc. Biol. Sci. (2014).

  4. 4.

    The Causes of Evolution (Longmans, Green, & Co, 1932).

  5. 5.

    , , & Revenge of the phages: defeating bacterial defences. Nature Rev. Microbiol. 11, 675–687 (2013).

  6. 6.

    , & Genomic variability as a driver of plant-pathogen coevolution? Curr. Opin. Plant Biol. 18, 24–30 (2014).

  7. 7.

    & Mainstreaming Caenorhabditis elegans in experimental evolution. Proc. Biol. Sci. 281, 20133055 (2014).

  8. 8.

    & Insights from natural host-parasite interactions: the Drosophila model. Dev. Comp. Immunol. 42, 111–123 (2014).

  9. 9.

    & From evolutionary genetics to human immunology: how selection shapes host defence genes. Nature Rev. Genet. 11, 17–30 (2010).

  10. 10.

    & Population genetic tools for dissecting innate immunity in humans. Nature Rev. Immunol. 13, 280–293 (2013).

  11. 11.

    & The Red Queen's long race: human adaptation to pathogen pressure. Curr. Opin. Genet. Dev. 29, 31–38 (2014).

  12. 12.

    & Human genome variability, natural selection and infectious diseases. Curr. Opin. Immunol. 30, 9–16 (2014).

  13. 13.

    , & Natural selection and infectious disease in human populations. Nature Rev. Genet. 15, 379–393 (2014).

  14. 14.

    The Red Queen and the Court Jester: species diversity and the role of biotic and abiotic factors through time. Science 323, 728–732 (2009).

  15. 15.

    , & Phylogenies reveal new interpretation of speciation and the Red Queen. Nature 463, 349–352 (2010).

  16. 16.

    et al. Host–parasite 'Red Queen' dynamics archived in pond sediment. Nature 450, 870–873 (2007).

  17. 17.

    , , & A matching-allele model explains host resistance to parasites. Curr. Biol. 23, 1085–1088 (2013).

  18. 18.

    et al. Antagonistic coevolution accelerates molecular evolution. Nature 464, 275–278 (2010).

  19. 19.

    , , , & Biological and biomedical implications of the co-evolution of pathogens and their hosts. Nature Genet. 32, 569–577 (2002).

  20. 20.

    et al. Global trends in emerging infectious diseases. Nature 451, 990–993 (2008).

  21. 21.

    & Origins of HIV and the AIDS pandemic. Cold Spring Harb. Perspect. Med. 1, a006841 (2011).

  22. 22.

    et al. The cytoplasmic body component TRIM5α restricts HIV-1 infection in Old World monkeys. Nature 427, 848–853 (2004).

  23. 23.

    , , & Positive selection of primate TRIM5α identifies a critical species-specific retroviral restriction domain. Proc. Natl Acad. Sci. USA 102, 2832–2837 (2005).

  24. 24.

    , & Restriction of an extinct retrovirus by the human TRIM5α antiviral protein. Science 316, 1756–1758 (2007). This landmark paper was the first to demonstrate that past infections contribute to shaping susceptibility to novel pathogens in extant species.

  25. 25.

    et al. Positive selection of primate genes that promote HIV-1 replication. Virology 454–455, 291–298 (2014).

  26. 26.

    et al. HIV-1 capsid-cyclophilin interactions determine nuclear import pathway, integration targeting and replication efficiency. PLoS Pathog. 7, e1002439 (2011).

  27. 27.

    et al. Studying immunity to zoonotic diseases in the natural host — keeping it real. Nature Rev. Immunol. 13, 851–861 (2013). This is an excellent review highlighting the importance of non-model organisms in understanding zoonotic infections, with a closing remark on the 'One-Health' concept.

  28. 28.

    et al. Diversification of an emerging pathogen in a biodiversity hotspot: Leptospira in endemic small mammals of Madagascar. Mol. Ecol. 23, 2783–2796 (2014).

  29. 29.

    et al. Patterns of positive selection in six mammalian genomes. PLoS Genet. 4, e1000144 (2008).

  30. 30.

    et al. Genome analysis reveals insights into physiology and longevity of the Brandt's bat Myotis brandtii. Nature Commun. 4, 2212 (2013).

  31. 31.

    et al. Molecular characterization of the interaction between sialylated Neisseria gonorrhoeae and factor H. J. Biol. Chem. 286, 22235–22242 (2011). This work helps to clarify the species specificity of N. gonorrhoeae infection by analysing the binding of sialylated gonococci to human and chimpanzee CFH.

  32. 32.

    et al. Humanized TLR4/MD-2 mice reveal LPS recognition differentially impacts susceptibility to Yersinia pestis and Salmonella enterica. PLoS Pathog. 8, e1002963 (2012).

  33. 33.

    , , & Lipid A modification systems in gram-negative bacteria. Annu. Rev. Biochem. 76, 295–329 (2007).

  34. 34.

    , , & Structural basis of species-specific endotoxin sensing by innate immune receptor TLR4/MD-2. Proc. Natl Acad. Sci. USA 109, 7421–7426 (2012). This paper presents the crystal structure of the mouse TLR4–LY96–lipid IVa complex and compares it to the human counterpart, elucidating elements that may account for different responsiveness in the two species.

  35. 35.

    , & Signatures of positive selection in Toll-like receptor (TLR) genes in mammals. BMC Evol. Biol. 11, 368 (2011).

  36. 36.

    , & Mass extinctions, biodiversity and mitochondrial function: are bats 'special' as reservoirs for emerging viruses? Curr. Opin. Virol. 1, 649–657 (2011).

  37. 37.

    et al. Comparative analysis of bat genomes provides insight into the evolution of flight and immunity. Science 339, 456–460 (2013). An extremely interesting study providing an overview of the evolutionary history of three bat genomes, with possible implications for immunity-related (and other) traits.

  38. 38.

    , & Evidence for ACE2-utilizing coronaviruses (CoVs) related to severe acute respiratory syndrome CoV in bats. J. Virol. 86, 6350–6353 (2012). A good example of how evolutionary studies can provide insight into host range and disease emergence.

  39. 39.

    , , , & Retargeting of coronavirus by substitution of the spike glycoprotein ectodomain: crossing the host cell species barrier. J. Virol. 74, 1393–1406 (2000).

  40. 40.

    et al. Isolation and characterization of viruses related to the SARS coronavirus from animals in southern China. Science 302, 276–278 (2003).

  41. 41.

    et al. Severe acute respiratory syndrome coronavirus-like virus in Chinese horseshoe bats. Proc. Natl Acad. Sci. USA 102, 14040–14045 (2005).

  42. 42.

    et al. Isolation and characterization of a bat SARS-like coronavirus that uses the ACE2 receptor. Nature 503, 535–538 (2013).

  43. 43.

    et al. Full-genome deep sequencing and phylogenetic analysis of novel human betacoronavirus. Emerg. Infect. Dis. 19, 736–742B (2013).

  44. 44.

    et al. Dipeptidyl peptidase 4 is a functional receptor for the emerging human coronavirus-EMC. Nature 495, 251–254 (2013). A central work showing that DPP4 of human and bat origin acts as a functional receptor for MERS-CoV.

  45. 45.

    et al. Host species restriction of Middle East respiratory syndrome coronavirus through its receptor, dipeptidyl peptidase 4. J. Virol. 88, 9220–9232 (2014).

  46. 46.

    , , & Adaptive evolution of bat dipeptidyl peptidase 4 (dpp4): implications for the origin and emergence of Middle East respiratory syndrome coronavirus. Virol. J. 10, 304 (2013).

  47. 47.

    et al. Molecular basis of binding between novel human coronavirus MERS-CoV and its receptor CD26. Nature 500, 227–231 (2013).

  48. 48.

    & Adaptation and constraint at Toll-like receptors in primates. Mol. Biol. Evol. 27, 2172–2186 (2010).

  49. 49.

    et al. Contrasted evolutionary histories of two Toll-like receptors (Tlr4 and Tlr7) in wild rodents (MURINAE). BMC Evol. Biol. 13, 194 (2013).

  50. 50.

    , , , & Variation matters: TLR structure and species-specific pathogen recognition. Trends Immunol. 30, 124–130 (2009).

  51. 51.

    et al. Extensive evolutionary and functional diversity among mammalian AIM2-like receptors. J. Exp. Med. 209, 1969–1983 (2012).

  52. 52.

    et al. Ancient and recent selective pressures shaped genetic diversity at AIM2-like nucleic acid sensors. Genome Biol. Evol. 6, 830–845 (2014).

  53. 53.

    et al. RIG-I-like receptors evolved adaptively in mammals, with parallel evolution at LGP2 and RIG-I. J. Mol. Biol. 426, 1351–1365 (2014).

  54. 54.

    , , , & Molecular basis for specific recognition of bacterial ligands by NAIP/NLRC4 inflammasomes. Mol. Cell 54, 17–29 (2014).

  55. 55.

    & Rules of engagement: molecular insights from host-virus arms races. Annu. Rev. Genet. 46, 677–700 (2012).

  56. 56.

    & Evolutionary conflicts between viruses and restriction factors shape immunity. Nature Rev. Immunol. 12, 687–695 (2012).

  57. 57.

    & A cross-species view on viruses. Curr. Opin. Virol. 2, 561–568 (2012).

  58. 58.

    , , & Positional cloning of the mouse retrovirus restriction gene Fv1. Nature 382, 826–829 (1996).

  59. 59.

    & Paleovirology and virally derived immunity. Trends Ecol. Evol. 27, 627–636 (2012).

  60. 60.

    , , & Evolution of the retroviral restriction gene Fv1: inhibition of non-MLV retroviruses. PLoS Pathog. 10, e1003968 (2014). A study in wild mice showing that FV1 antiviral activity is broader than previously thought. It identifies positively selected residues in the C terminus that contribute to antiviral specificity.

  61. 61.

    et al. Evolution-guided identification of antiviral specificity determinants in the broadly acting interferon-induced innate immunity factor MxA. Cell Host Microbe 12, 598–604 (2012). A seminal paper that applies an evolution-guided approach to detect MX1 residues that confer antiviral specificity.

  62. 62.

    et al. Human MX2 is an interferon-induced post-entry inhibitor of HIV-1 infection. Nature 502, 559–562 (2013).

  63. 63.

    et al. Evolutionary analysis identifies an MX2 haplotype associated with natural resistance to HIV-1 infection. Mol. Biol. Evol. 31, 2402–2414 (2014).

  64. 64.

    , , & Manipulation of costimulatory molecules by intracellular pathogens: veni, vidi, vici!! PLoS Pathog. 8, e1002676 (2012).

  65. 65.

    & MHC class I antigen presentation: learning from viral evasion strategies. Nature Rev. Immunol. 9, 503–513 (2009).

  66. 66.

    et al. An evolutionary analysis of antigen processing and presentation across different timescales reveals pervasive selection. PLoS Genet. 10, e1004189 (2014).

  67. 67.

    et al. A 175 million year history of T cell regulatory molecules reveals widespread selection, with adaptive evolution of disease alleles. Immunity 38, 1129–1141 (2013).

  68. 68.

    et al. The intertransmembrane region of Kaposi's sarcoma-associated herpesvirus modulator of immune recognition 2 contributes to B7-2 downregulation. J. Virol. 86, 5288–5296 (2012).

  69. 69.

    et al. The Nef protein of HIV-1 induces loss of cell surface costimulatory molecules CD80 and CD86 in APCs. J. Immunol. 175, 4566–4574 (2005).

  70. 70.

    , , , & Members of adenovirus species B utilize CD80 and CD86 as cellular attachment receptors. Virus Res. 122, 144–153 (2006).

  71. 71.

    et al. Structural basis for langerin recognition of diverse pathogen and mammalian glycans through a single binding site. J. Mol. Biol. 405, 1027–1039 (2011).

  72. 72.

    , , , & Hiding lipid presentation: viral interference with CD1d-restricted invariant natural killer T (iNKT) cell activation. Viruses 4, 2379–2399 (2012).

  73. 73.

    et al. A threonine-based targeting signal in the human CD1d cytoplasmic tail controls its functional expression. J. Immunol. 184, 4973–4981 (2010).

  74. 74.

    et al. Evolutionary history of copy-number-variable locus for the low-affinity Fcγ receptor: mutation rate, autoimmune disease, and the legacy of helminth infection. Am. J. Hum. Genet. 90, 973–985 (2012). One of the few studies of helminth-driven selective pressure in mammals that also integrates evolutionary analysis with epidemiological information.

  75. 75.

    & Thrombosis as an intravascular effector of innate immunity. Nature Rev. Immunol. 13, 34–45 (2013).

  76. 76.

    , , , & Dual host-virus arms races shape an essential housekeeping protein. PLoS Biol. 11, e1001571 (2013). An extremely interesting study extending the arms race scenario to a housekeeping protein, the transferrin receptor, which acts as a viral receptor.

  77. 77.

    et al. Evolutionary reconstructions of the transferrin receptor of caniforms supports canine parvovirus being a re-emerged and not a novel pathogen in dogs. PLoS Pathog. 8, e1002666 (2012).

  78. 78.

    & Nutritional immunity. Escape from bacterial iron piracy through rapid evolution of transferrin. Science 346, 1362–1366 (2014).

  79. 79.

    et al. Mammalian NPC1 genes may undergo positive selection and human polymorphisms associate with type 2 diabetes. BMC Med. 10, 140 (2012).

  80. 80.

    et al. Niemann–Pick C1 (NPC1)/NPC1-like1 chimeras define sequences critical for NPC1's function as a flovirus entry receptor. Viruses 4, 2471–2484 (2012).

  81. 81.

    et al. Cell entry by a novel European filovirus requires host endosomal cysteine proteases and Niemann–Pick C1. Virology 468–470, 637–646 (2014).

  82. 82.

    et al. Multiple cationic amphiphiles induce a Niemann–Pick C phenotype and inhibit Ebola virus entry and infection. PLoS ONE 8, e56265 (2013).

  83. 83.

    et al. Inhibition of ebola virus infection: identification of Niemann–Pick C1 as the target by optimization of a chemical probe. ACS Med. Chem. Lett. 4, 239–243 (2013).

  84. 84.

    et al. Small molecule inhibitors reveal Niemann–Pick C1 is essential for Ebola virus infection. Nature 477, 344–348 (2011).

  85. 85.

    et al. Evolutionary analysis of the contact system indicates that kininogen evolved adaptively in mammals and in human populations. Mol. Biol. Evol. 30, 1397–1408 (2013).

  86. 86.

    , , & Positive selection during the evolution of the blood coagulation factors in the context of their disease-causing mutations. Mol. Biol. Evol. 31, 3040–3056 (2014).

  87. 87.

    et al. Induction of vascular leakage through release of bradykinin and a novel kinin by cysteine proteinases from Staphylococcus aureus. J. Exp. Med. 201, 1669–1676 (2005).

  88. 88.

    et al. Viral immune modulators perturb the human molecular network by common and unique strategies. Nature 487, 486–490 (2012).

  89. 89.

    et al. Genome-wide RNAi screen identifies human host factors crucial for influenza virus replication. Nature 463, 818–822 (2010).

  90. 90.

    , & A novel test for selection on cis-regulatory elements reveals positive and negative selection acting on mammalian transcriptional enhancers. Mol. Biol. Evol. 30, 2509–2518 (2013).

  91. 91.

    PAML 4: phylogenetic analysis by maximum likelihood. Mol. Biol. Evol. 24, 1586–1591 (2007).

  92. 92.

    , & Accuracy and power of Bayes prediction of amino acid sites under positive selection. Mol. Biol. Evol. 19, 950–958 (2002).

  93. 93.

    , & Bayes empirical Bayes inference of amino acid sites under positive selection. Mol. Biol. Evol. 22, 1107–1118 (2005).

  94. 94.

    et al. Detecting individual sites subject to episodic diversifying selection. PLoS Genet. 8, e1002764 (2012).

  95. 95.

    & High sensitivity to aligner and high rate of false positives in the estimates of positive selection in the 12 Drosophila genomes. Genome Res. 21, 863–874 (2011).

  96. 96.

    & Class of multiple sequence alignment algorithm affects genomic analysis. Mol. Biol. Evol. 30, 642–653 (2013).

  97. 97.

    et al. Estimates of positive Darwinian selection are inflated by errors in sequencing, annotation, and alignment. Genome Biol. Evol. 1, 114–118 (2009).

  98. 98.

    & The effects of alignment error and alignment filtering on the sitewise detection of positive selection. Mol. Biol. Evol. 29, 1125–1139 (2012).

  99. 99.

    , & Effect of recombination on the accuracy of the likelihood method for detecting positive selection at amino acid sites. Genetics 164, 1229–1236 (2003).

  100. 100.

    , & Evaluation of an improved branch-site likelihood method for detecting positive selection at the molecular level. Mol. Biol. Evol. 22, 2472–2479 (2005).

  101. 101.

    & The effect of insertions, deletions, and alignment errors on the branch-site test of positive selection. Mol. Biol. Evol. 27, 2257–2267 (2010).

  102. 102.

    & Multiple hypothesis testing to detect lineages under positive selection that affects only a few sites. Mol. Biol. Evol. 24, 1219–1228 (2007).

  103. 103.

    et al. A random effects branch-site model for detecting episodic diversifying selection. Mol. Biol. Evol. 28, 3033–3043 (2011).

  104. 104.

    , , & Modeling the site-specific variation of selection patterns along lineages. Proc. Natl Acad. Sci. USA 101, 12957–12962 (2004).

  105. 105.

    & Performance of standard and stochastic branch-site models for detecting positive selection among coding sequences. Mol. Biol. Evol. 31, 484–495 (2014).

  106. 106.

    & Statistical properties of the branch-site test of positive selection. Mol. Biol. Evol. 28, 1217–1228 (2011).

  107. 107.

    , , & Towards a systems understanding of MHC class I and MHC class II antigen presentation. Nature Rev. Immunol. 11, 823–836 (2011).

  108. 108.

    & CD1 antigen presentation: how it works. Nature Rev. Immunol. 7, 929–941 (2007).

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Acknowledgements

D.F. is supported by fellowships of the Doctorate School of Molecular Medicine, University of Milan, Italy.

Author information

Affiliations

  1. Bioinformatics, Scientific Institute IRCCS E. Medea, 23842 Bosisio Parini, Italy.

    • Manuela Sironi
    • , Rachele Cagliani
    •  & Diego Forni
  2. Department of Physiopathology and Transplantation, University of Milan, 20090 Milan, Italy.

    • Mario Clerici
  3. Don C. Gnocchi Foundation ONLUS, IRCCS, 20148 Milan, Italy.

    • Mario Clerici

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Competing interests

The authors declare no competing financial interests.

Corresponding author

Correspondence to Manuela Sironi.

Supplementary information

PDF files

  1. 1.

    Supplementary information S1 (box)

    Evolutionary analysis of mammalian DDP4.

  2. 2.

    Supplementary information S2 (table)

    LRT statistics for DPP4.

Glossary

Positive selection

The accumulation of favourable amino acid-replacing substitutions, which results in more non-synonymous changes than expected under neutrality (dN/dS > 1).

Episodic selection

Positive selection localized to a subset of sites or confined to a few species in a phylogeny.

Orthologues

Genes that evolved from a common ancestral gene through speciation.

Paralogues

Homologous genes created by a duplication event within the same genome.

dN

The observed number of non-synonymous substitutions per non-synonymous site.

dS

The observed number of synonymous substitutions per synonymous site.

Purifying selection

The elimination of deleterious amino acid-replacing substitutions, which results in fewer non-synonymous changes than expected under neutrality (dN/dS < 1).

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https://doi.org/10.1038/nrg3905