Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

Horizontal gene transfer events reshape the global landscape of arm race between viruses and homo sapiens

Abstract

Horizontal gene transfer (HGT) drives the evolution of recipient organism particularly if it provides a novel function which enhances the fitness or its adaption to the environment. Virus-host co-evolution is attractive for studying co-evolutionary processes, since viruses strictly replicate inside of the host cells and thus their evolution is inexorably tangled with host biology. HGT, as a mechanism of co-evolution between human and viruses, has been widely documented, however, the roles HGT play during the interaction between human and viruses are still in their infancy. In this study, we performed a comprehensive analysis on the genes horizontally transferred between viruses and their corresponding human hosts. Our study suggests that the HGT genes in human are predominantly enriched in immune related GO terms while viral HGT genes are tend to be encoded by viruses which promote the invasion of immune system of hosts. Based on our results, it gives us a hint about the evolution trajectory of HGT events. Overall, our study suggests that the HGT between human and viruses are highly relevant to immune interaction and probably reshaped the arm race between hosts and viruses.

Introduction

Horizontal gene transfer has been well recognized as an important driving force of evolution, as it provides gene flow between evolutionary remote lineages which may facilitate the evolution of recipient spices by providing novel functions1,2,3,4,5,6,7,8,9. Since the first report of HGT from bacteriophage to corynebacterium diphtheriae in 195110, many other species varying from prokaryote to eukaryote have also been found to be the recipients of exogenous genes2,5,6,10. Most transferred genes are deleterious and will be wiped out by nature selection. In contrast, a small proportion of HGT events provide advantages against selection to hosts and thus can be retained in the species11,12. For instance, plant-parasitic nematodes gained cellulase genes and thus acquired cellulase activity resulting from HGT from several microbial donors, which significantly enhanced their parasitism and pathogenicity13. More interestingly, HGT was found to contribute to the plant parasitic mechanism of oomycetes, as some HGT genes are predicted to metabolize sugars, rutin, hemicellulose for feeding on plant tissues and some HGT genes are predicted to degrade structures components specific to plants which favors its attacking on plant cells14. HGT is common in animals as well, for example, a developmentally expressed functional gene in zebrafish was found to be laterally transferred from marine bacteria15.

Over the past hundreds of millions of years, viruses and hosts have been undergoing a continuous co-evolutionary process, involving both host defense and viral evasion mechanisms16,17. Viruses have evolved various strategies to evade host immune system. For instance, herpesviruses, a group of large DNA viruses, have developed to encode proteins that mimic cellular cytokines and cytokine receptors to modulate cytokine mediated signals during infection18,19,20. In contrast, host immune system is due to protect itself from invading pathogens, including viruses. HGT has been frequently reported between hosts and viruses and thus considered as an important contributor for host-virus co-evolution21,22,23,24,25,26,27,28.

The systematic screen on 26 animal species suggest that there are hundreds of HGT genes in primates, flies and nematodes29. Alastair Crisp et al. identified hundreds of HGT genes in the genome of human beings which originally transferred from plants, fungus and bacterial29. It is of particular interest that they also predicted the HGT which happened between 51 human encoding genes and 27 viral encoding genes. In this study, we investigated the characteristics, evolution history and significance of genes transferred between human and viruses.

Characterization of the 51 HGT Genes Encoded by Human

In order to study the functions of the 51 HGT genes encoded by human (Supplementary Table S1), the data set was submitted to PANTHER30,31 for gene list classification analysis. Among the 51 HGT genes, some genes could not be mapped to any GO or pathways terms for lack of annotation while others might be related to multiple terms. Therefore the total number of genes in each classification analysis is not equal to 51 because of overlapping of gene list in different catalogues. Briefly, 27, 29, 5 and 17 genes were successfully mapped in ‘molecular function’, ‘biological process’, ‘celluar process’ and ‘pathway’ terms respectively (Supplementary Table S2). According to the ‘molecular function classification’ result, 27 out of 51 genes were found to have ‘binding activity’(9), ‘receptor activity’(17), ‘catalytic activity’(18) and ‘antioxidant activity’(1). ‘Biological process classification’ suggests that 29 out of 57 genes were mainly mapped to ‘cellular process’, ‘localization’, ‘response to stimulus’, ‘metabolic process’ and ‘immune system process’, with over 18 genes in each catalog (Fig. 1a). In ‘cell components classification analysis’, 5 out of 51 genes were mapped (3 genes encode ‘membrane’, 4 encode ‘cell part’ and 1 encodes ‘organelle’ and the other 1 encodes ‘extracellular region’). Regarding ‘pathway classification’ analysis, 17 out of 51 genes were found to be involved in 7 pathways (Fig. 1b). Specifically, YES1 from ‘CCKR signalling map pathway’, Q86XF0 and P00374 from ‘formyltetrahydroformate biosynthesis pathway’, 10 genes (CCR3, CCR1, CXCR1, CCR5, CXCR2, CXCR6, CCR6, CCR4, XCR1 and CCR7) from ‘inflammation mediated by chemokine and cytokine signalling pathway’, DHFRL1 and DHFR from ‘folate biosynthesis’, YES1 and FGR from ‘parkinson disease pathway’, CXCR2, CXCR1, IL17A, IL17F, IL10 from ‘Interleukin signalling pathway’, YES1 from ‘cadherin signalling pathway’. After classification of those HGT genes, we performed a GO enrichment analysis. In total 46 GO terms were found to be enriched with the p value smaller than 0.05 (Supplementary Table S3). Surprisingly, the GO terms predominantly enriched in immune related terms. The most significant GO term is ‘chemokine-mediated signaling pathway’ with a p value 5.70E-18, followed by ‘dendritic cell chemotaxis’ with p value 1.43E-09 and then ‘dendritic cell migration ‘ (p value 7.27E-09). Actually, the top 10 GO terms are all immune related (Table 1). The disproportionate of enriched immune related GO terms strongly suggest that the HGT events between viruses and hosts have a clear link with immune response. When we checked the functional annotation of the 51 gene list, we found 27 genes were found to be directly related to immune response. Specifically, 19 genes encode chemokine receptors, 5 genes encode interleukin(interleukin 1 receptor antagonist, interleukin 1 receptor, type II, interleukin 10, interleukin 17A, interleukin 17F), and 3 genes encode cluster of differentiation (CD) molecules (CD200 molecule,CD59 molecule and CD69 molecule).

Figure 1: The biological process and pathway classification of the 51 HGT genes encoded by human.
figure1

(a) Biological process classification (b) Pathway classification.

Table 1 The top 10 GO enrichment of the 51 human encoded HGT genes.

The 27 viral HGT genes are encoded by herpesviridae, poxviridae, retroviridae and bornaviridae

Next, we investigated the origin of the HGT related genes derived from the viral genomes (Supplementary Table S1). The result shows that the 27 viral HGT related genes are found to be encoded by 14 virus species. Specifically, 5 out of 14 species belongs to herpesviridae (human herpesvirus type 5, 6, 8, equine herpesvirus 2, Saimiriine herpesvirus 2), 5 from poxviridae (deerpox virus, molluscum contagiosum virus, vaccinia virus, fowlpox virus, yaba monkey tumor virus), 3 from retroviridae (rous sarcoma virus, baboon endogenous virus, simian retrovirus), with the remaining one from bornaviridae (bornavirus). The common characteristics those viruses share is that they have the capability to establish latent infection, indicating that they are able to modify and evade the immune system of hosts32,33,34,35,36.

Orthologs of immune related HGT genes were mainly found in viruses capable of establishing latent infection

To test whether the 27 immune related HGT genes encoded by human have any homologs in other viruses species, we performed a blastp analysis using 27 viral HGT proteins against virus protein database. As the function as of a particular protein is largely determined by its structure, we usually infer the function of an unknown protein based on its similarity to the well-studied protein through homology structure modeling. However, the accuracy of modeling is highly dependent on the sequence identity between two proteins. Models tend to be reliable when sequence identity is above 50%, become less confident in the 30–50% range and show low-accuracy under 30% identity37. Therefore, we used two sequence identity cut-off values (30% and 50%) in the ortholog analysis. Our results indicated that there were 1019 records in viruses sharing similarity with the 27 immune related HGT genes encoded by human (Supplementary Table S4). When using a cut-off value of identity greater than 30% and removing the redundant hits, 151 unique viral proteins were left (Supplementary Table S5). We then traced the organisms of those proteins, those 151 viral proteins were mapped to 37 virus families (arteriviridae, asfarviridae, baculoviridae, betaflexiviridae, bromoviridae, bunyaviridae, caliciviridae, caulimoviridae, chrysoviridae, closteroviridae, coronaviridae, flaviviridae, hepadnaviridae, herpesviridae, iridoviridae, malacoherpesviridae, mimiviridae, myoviridae , narnaviridae, ophioviridae, orthomyxoviridae, paramyxoviridae, parvoviridae, phycodnaviridae, picornaviridae, plasmaviridae, podoviridae, poxviridae, reoviridae, retroviridae, rhabdoviridae, siphoviridae, togaviridae, tombusviridae, virgaviridae). Among those, 35 out of the 151 proteins belong to herpesviridae, 18 proteins belong to poxviridae and 11 proteins belong to retroviridae (Supplementary Table S4). In total around 42% (64 out of 151) viral proteins sharing more than 30% identity belong to herpesviridae, poxviridae and retroviridae. Viruses in those three families are well known for their ability of immune evasion and establishment of latent infection. When we increase the cut-off value of identity from 30% to 50%, this trend become even more obvious, only 12 unique viral proteins meet this criteria, and predominantly belongs to herpesviridae (10 out of 12). The remaining two proteins were encoded by retroviridae and bromoviridae respectively (Table 2).

Table 2 List of viral genes sharing similarity (identity > = 50%) to 27 human immune related HGT genes.

Similarity between viral encoding G-protein coupled receptor E1 and human encoding chemokine receptors

Of particular interest, we found out that 13 out of the 27 human immune related HGT proteins (chemokine (C-C motif) receptor(CCR) 1–7, CCR9, CCR-like 1 and CCR-like 2, C-X-C motif chemokine receptor 3, 4 and 6) harbor the best hit with the same viral protein, G-protein coupled receptor E1 (Q89609) encoded by equine herpesvirus 2 (EHV-2). The identity between those 13 host proteins and E1 vary from 33.5% to 55.8%. As the identity between CCR3 and E1 is the highest (55.8%), we performed a comparison analysis of protein sequence, transmembrane domains and 3D structure between CCR3 and E1. Overall, CCR3 and E1 exhibit very high degree of similarity. Due to alternative splicing, CCR3 possesses 6 isoforms CCR3-001 (2000bp, 355aa), CCR3-201(1786bp, 376aa), CCR3-002 (1581bp, 355aa), CCR3-007 (1201bp, 355aa), CCR3-003 (400bp, 80aa) and CCR3-004 (212bp, 21aa). E1 is composed of 1149 nucleotides, which is subsequently translated into 383 aa, with 28 more aa than CCR3-001. TMHMM38 transmembrane analysis suggests that CCR3 contains 7 transmemembrane domains while E1 contains 9 domains. SWISS MODEL39 predicted 3D structures indicate that the two proteins share almost identical structures except for some differences in the terminal regions (Fig. 2).

Figure 2: The similarity of protein sequence, secondary structure and 3D structure between human encoding CCR3 and viral protein E1.
figure2

(a) Gene model of human CCR3 (b) Gene model of EHV E1 (c) Alignment of protein sequences between CCR3 and E1 (d) Transmembrane domains and predicted 3D structure of CCR3 and E1.

Functional divergence after gene duplication contributes to the evolution of CCRs gene family in human

Gene duplication serves as an important source of functional divergence, as duplicated genes may gain novel functions or lose original ones through modifying their existing regulatory network by accumulation of mutations in coding or non-coding regions40,41. There are 9 copies of C-C chomokine receptors in human genome which share distinct extent of similarity to each other, it is possible that those genes come from functional divergence after gene duplication. Using DIVERGE 3 developed by Gu’s group42,43, we predicted the type I and type II functional divergence residuals among CCR paralogs. In type I functional divergence, the site is conserved in one group of paralogs while variable in the others. In contrast, type II functional divergence is the case that residuals are conserved in both paralog groups, though the property of amino acids differed between two clusters. Firstly, we constructed a maximum likelihood tree using 66 proteins sequences of CCR 2, 3, 5, 6, 8, 9 downloaded from KEGG. There are two distinct clades on the tree, indicating that all the 66 proteins are divided into two clusters. Cluster A includes CCR6 and CCR9 while cluster B is composed of CCR 2, 3, 5 and 8 (Fig. 3b). We performed the type I and type II functional divergence analysis, which revealed that there was no type I residuals, whereas 6 type II sites were identified at alignment positions 114, 148, 179, 206, 325 and 326 (Fig. 3a). We next studied the property of those amino acids in two clusters and found those residuals change from moderate to hydrophobic, hydrophobic to hydriphibic, positive charge to no charge and hydrophobic to moderate. We also mapped those functional divergence residuals to the 3D structure of CCR3, which supports the deduction that those 5 type II functional divergence residuals contribute to the functional difference between cluster 1 CCRs and cluster 2 CCRs (Fig. 3c).

Figure 3
figure3

Functional divergence of CCR3s paralogs.

Discussion

The gene flow between viruses and hosts normally happens after the integration of viral genomic fragments or whole genomes into host chromosomes. This phenomenon occurs frequently in retroviruses such as human immunodeficiency virus (HIV) through the integration of double stranded DNA (dsDNA) which were reverse transcripted from viral RNA genome44,45. As for the dsDNA viruses, several members of herpesviruses such as human herpesvirus 6 (HHV-6) and marek’s disease virus (MDV) have been found to be capable to integrate their genomes into host chromosomes under certain circumstances46,47,48. It is possible that herpesviruses integrate into the germ line chromosomes of the ancestor of homo sapiens, probably through non-homologous recombination or interaction with cellular retroelements49,50. If the immune related genes were horizontally transferred from viruses to host, the foreign genes were likely to be retained in the population for providing protective immunity for recipient hosts. Since then, the laterally transferred genes experienced functional divergence after gene duplication, which subsequently evolved into a family of CCRs with different functions. In our study, we predicted 6 amino acid residuals which might contribute to the functional divergence of CCR paralogs in human, however further research need to be done to validate the functional importance of those residuals. On the other hand, if those foreign genes were transferred from host to viruses, it would be a quite dissimilar story, viruses might acquire genomic fragments containing host origin genes when switching from latency to lytic infection.

In a recent report by Crisp et al., the direction of gene transfer between viruses and humans remains an open question29. In our opinion, there are two possible models to explain the HGT direction, the ‘host origin model’ in which those HGT genes were laterally transferred from host to viruses, and the ‘virus origin model’ in which those HGT genes were transferred in an opposite direction. In theory, these two scenarios could happen with equal chance. In the former scenario, namely ‘host origin model’, the integration of viral structural gene into host genome could lead to the expression of viral antigen which would stimulate the immune system to protect itself against the infection of viruses. In other words, host might utilize the viral DNA as ‘DNA vaccine’ by acquiring and expressing viral protein encoding genes51,52. However, the HGT genes are unlikely to be fixed in host population unless they meet two criteria. Firstly, the HGT genes need to be integrated into the chromosome of sperms, otherwise the HGT will be difficult to be inherited by offsprings of the HGT recipient individuals. Secondly, the HGT genes should provide enhanced fitness to the environment compared to counterparts without the transfer of foreign genes. Once the viral origin HGT genes got spread and fixed in the population, they probably experienced domestication such as the optimizing of codons and the gain of introns. During millions of years, those HGT genes evolved into a family of proteins playing diverse roles in immune system. In contrast, in the second scenario (‘host origin model’), those immune related factors might be used by viruses to trick the immune response of hosts to achieve the goal of immune evasion. Viruses have been reported to hijack host immune response related signaling pathways by mimicking host immune modifiers, either cytokines or chemokines, in order to facilitate the replicate and survive of viruses insides host cells53,54,55,56,57,58,59,60. In our study, among the 51 human encoding HGT genes transferred between human and viruses, several immune related genes were found to share high similarity with viral proteins encoded by herpesviridae, poxviridae and retroviridae. As a variety of viruses have been shown to restrict the immune system of the host by protein imitating the host immune functional molecular61,62, it is highly possible that those viruses investigated in this study utilize a similar mechanism to tackle with the host immune system. Viruses carrying genes related to immune response might facilitate them to evade the immune system, which enhances their opportunity to survive the attack from host immune system and pass on their genetic elements to progeny viruses. Taken together, ‘host origin model’ and ‘virus origin model’ are not necessarily mutually exclusive. It could be possible that some genes were transferred from hosts to viruses while others from viruses to hosts. And it is the bi-directional gene flow of immune related genes through HGT that drives the co-evolution of viruses and hosts and altered the attack and defense strategies of players during the lasting war among the two sides (Fig. 4).

Figure 4
figure4

Horizontal gene transfer drives the co-evolution of hosts and viruses.

The comparison analysis suggests a high similarity of both secondary structure and 3D structure between viral protein E1 and host protein CCR3, implying they share similar biological functions as well. As CCR3 is well known for its importance in inducing the migration of immune cells into inflammatory sites63,64,65, it is reasonable to speculate that viral protein E1 is also related to immune response. Since the particular mechanism by which E1 manipulate host immune system is not yet fully understood, more work needs to be done to clarify the roles of E1 in the interaction between viruses and their hosts. Intriguingly, the 51 HGT genes in human are enriched in immune related GO terms, which suggests the HGT events are tend to happen among immune genes. In addition, the 27 viral HGT genes are encoded by viruses from herpesviridae, retroviridae and bornaviridae, which are capable to establish latent infection, suggesting HGT might contribute to the immune evasion of those viral species. In conclusion, our study suggests that the HGT between viruses and hosts might play a pivotal role in reshaping the arm race between viruses and hosts, which eventually drives their co-evolution.

Methods

Classification and GO enrichment analysis of gene lists

The genes lists were analysed using PANTHER31 classification tool with the default parameters. The GO enrichment analysis was performed using online tool of Gene Ontology Consortium66. Specifically, PANTHER overrepresentation test (release 20150430) with Bonferroni correction and GO ontology database (released 2015-08-06) were used to evaluate the enrichment of GO terms for the 51 human HGT genes, all the GO terms with P value greater than 0.05 were removed.

Blast of 27 human encoding immune related genes

The virus protein database were created using makeblast command from blast package, using the virus sequences downloaded from Uniprot67. Blastp programme was used to scan homologs for query protein sequences. Then 27 human immune related HGT protein sequences were blasted against the virus protein databases. The Blastp results were outputted as table format using the parameter outfmt 6. The list of Blastp hit records were extracted for further analysis. Unique viral protein Id were extracted by removing the redundant contents. To investigate the origin of those proteins, the protein IDs were submitted to UniProt using retrieve/ID mapping tool.

Transmemembrane domains prediction

The transmembrane protein topology of E1 and CCR3 were predicted with TMHMM38 while 3D structures were modelled using SWISS MODEL39 with default parameters.

Functional divergence analysis of CCRs genes

The orthlogs of human CCRs were downloaded from KEGG database and analysed using DIVERGE 342,43.

Additional Information

How to cite this article: Chen, D.S. et al. Horizontal gene transfer events reshape the global landscape of arm race between viruses and homo sapiens. Sci. Rep. 6, 26934; doi: 10.1038/srep26934 (2016).

References

  1. 1

    Yue, J., Hu, X., Sun, H., Yang, Y. & Huang, J. Widespread impact of horizontal gene transfer on plant colonization of land. Nat. Commun. 3, 1152 (2012).

    ADS  Article  Google Scholar 

  2. 2

    Pál, C., Papp, B. & Lercher, M. J. Adaptive evolution of bacterial metabolic networks by horizontal gene transfer. Nat. Genet. 37, 1372–5 (2005).

    Article  Google Scholar 

  3. 3

    Ochman, H., Lawrence, J. G. & Groisman, E. A. Lateral gene transfer and the nature of bacterial innovation. Nature 405, 299–304 (2000).

    CAS  ADS  Article  Google Scholar 

  4. 4

    Gogarten, J. P. & Townsend, J. P. Horizontal gene transfer, genome innovation and evolution. Nat. Rev. Microbiol. 3, 679–87 (2005).

    CAS  Article  Google Scholar 

  5. 5

    Keeling, P. J. & Palmer, J. D. Horizontal gene transfer in eukaryotic evolution. Nat. Rev. Genet. 9, 605–18 (2008).

    CAS  Article  Google Scholar 

  6. 6

    Koonin, E. V., Makarova, K. S. & Aravind, L. Horizontal gene transfer in prokaryotes: quantification and classification. Annu. Rev. Microbiol. 55, 709–42 (2001).

    CAS  Article  Google Scholar 

  7. 7

    Polz, M. F., Alm, E. J. & Hanage, W. P. Horizontal gene transfer and the evolution of bacterial and archaeal population structure. Trends Genet. 29, 170–5 (2013).

    CAS  Article  Google Scholar 

  8. 8

    Boto, L. Horizontal gene transfer in evolution: facts and challenges. Proc. Biol. Sci. 277, 819–27 (2010).

    Article  Google Scholar 

  9. 9

    Soucy, S. M., Huang, J. & Gogarten, J. P. Horizontal gene transfer: building the web of life. Nat. Rev. Genet. 16, 472–482 (2015).

    CAS  Article  Google Scholar 

  10. 10

    Freeman, V. J. Studies on the virulence of bacteriophage-infected strains of Corynebacterium diphtheriae. J. Bacteriol. 61, 675–88 (1951).

    CAS  PubMed  PubMed Central  Google Scholar 

  11. 11

    Klassen, J. L. Pathway evolution by horizontal transfer and positive selection is accommodated by relaxed negative selection upon upstream pathway genes in purple bacterial carotenoid biosynthesis. J. Bacteriol. 191, 7500–8 (2009).

    CAS  Article  Google Scholar 

  12. 12

    Novozhilov, A. S., Karev, G. P. & Koonin, E. V. Mathematical modeling of evolution of horizontally transferred genes. Mol. Biol. Evol. 22, 1721–32 (2005).

    CAS  Article  Google Scholar 

  13. 13

    Mayer, W. E., Schuster, L. N., Bartelmes, G., Dieterich, C. & Sommer, R. J. Horizontal gene transfer of microbial cellulases into nematode genomes is associated with functional assimilation and gene turnover. BMC Evol. Biol. 11, 13 (2011).

    CAS  Article  Google Scholar 

  14. 14

    Richards, T. A. et al. Horizontal gene transfer facilitated the evolution of plant parasitic mechanisms in the oomycetes. Proc. Natl. Acad. Sci. USA 108, 15258–63 (2011).

    CAS  ADS  Article  Google Scholar 

  15. 15

    Sun, B.-F. et al. Horizontal functional gene transfer from bacteria to fishes. Sci. Rep. 5, 18676 (2015).

    CAS  ADS  Article  Google Scholar 

  16. 16

    Kim, W. M. & Sigalov, A. B. Viral pathogenesis, modulation of immune receptor signaling and treatment. Adv. Exp. Med. Biol. 640, 325–49 (2008).

    CAS  Article  Google Scholar 

  17. 17

    Moelling, K., Matskevich, A. & Jung, J.-S. Relationship between retroviral replication and RNA interference machineries. Cold Spring Harb. Symp. Quant. Biol. 71, 365–8 (2006).

    CAS  Article  Google Scholar 

  18. 18

    McFadden, G. & Murphy, P. M. Host-related immunomodulators encoded by poxviruses and herpesviruses. Curr. Opin. Microbiol. 3, 371–8 (2000).

    CAS  Article  Google Scholar 

  19. 19

    Alcami, A. & Koszinowski, U. H. Viral mechanisms of immune evasion. Immunol. Today 21, 447–55 (2000).

    CAS  Article  Google Scholar 

  20. 20

    Tortorella, D., Gewurz, B. E., Furman, M. H., Schust, D. J. & Ploegh, H. L. Viral subversion of the immune system. Annu. Rev. Immunol. 18, 861–926 (2000).

    CAS  Article  Google Scholar 

  21. 21

    Monier, A. et al. Horizontal gene transfer of an entire metabolic pathway between a eukaryotic alga and its DNA virus. Genome Res. 19, 1441–9 (2009).

    CAS  Article  Google Scholar 

  22. 22

    Koonin, E. V. & Dolja, V. V. Evolution of complexity in the viral world: the dawn of a new vision. Virus Res. 117, 1–4 (2006).

    CAS  Article  Google Scholar 

  23. 23

    Katzourakis, A., Tristem, M., Pybus, O. G. & Gifford, R. J. Discovery and analysis of the first endogenous lentivirus. Proc. Natl. Acad. Sci. USA 104, 6261–5 (2007).

    CAS  ADS  Article  Google Scholar 

  24. 24

    Filée, J., Forterre, P. & Laurent, J. The role played by viruses in the evolution of their hosts: a view based on informational protein phylogenies. Res. Microbiol. 154, 237–43 (2003).

    Article  Google Scholar 

  25. 25

    Awadalla, P. The evolutionary genomics of pathogen recombination. Nat. Rev. Genet. 4, 50–60 (2003).

    CAS  Article  Google Scholar 

  26. 26

    Moreira, D. & López-García, P. Ten reasons to exclude viruses from the tree of life. Nat. Rev. Microbiol. 7, 306–11 (2009).

    CAS  Article  Google Scholar 

  27. 27

    Moreira, D. & Brochier-Armanet, C. Giant viruses, giant chimeras: the multiple evolutionary histories of Mimivirus genes. BMC Evol. Biol. 8, 12 (2008).

    Article  Google Scholar 

  28. 28

    Bratke, K. A. & McLysaght, A. Identification of multiple independent horizontal gene transfers into poxviruses using a comparative genomics approach. BMC Evol. Biol. 8, 67 (2008).

    Article  Google Scholar 

  29. 29

    Crisp, A., Boschetti, C., Perry, M., Tunnacliffe, A. & Micklem, G. Expression of multiple horizontally acquired genes is a hallmark of both vertebrate and invertebrate genomes. Genome Biol. 16, 50 (2015).

    Article  Google Scholar 

  30. 30

    Mi, H., Muruganujan, A., Casagrande, J. T. & Thomas, P. D. Large-scale gene function analysis with the PANTHER classification system. Nat. Protoc. 8, 1551–66 (2013).

    Article  Google Scholar 

  31. 31

    Mi, H., Poudel, S., Muruganujan, A., Casagrande, J. T. & Thomas, P. D. PANTHER version 10: expanded protein families and functions, and analysis tools. Nucleic Acids Res. 44, D336–42 (2015).

    Article  Google Scholar 

  32. 32

    Cooper, L. N. Theory of an immune system retrovirus. Proc. Natl. Acad. Sci. USA 83, 9159–63 (1986).

    CAS  ADS  MathSciNet  Article  Google Scholar 

  33. 33

    Doehle, B. P. & Michael Gale, J. Innate Immune Evasion Strategies of HCV and HIV: Common Themes for Chronic Viral Infection. (2013).

  34. 34

    Griffin, B. D., Verweij, M. C. & Wiertz, E. J. H. J. Herpesviruses and immunity: the art of evasion. Vet. Microbiol. 143, 89–100 (2010).

    CAS  Article  Google Scholar 

  35. 35

    Seet, B. T. et al. Poxviruses and immune evasion. Annu. Rev. Immunol. 21, 377–423 (2003).

    CAS  Article  Google Scholar 

  36. 36

    Buller, R. M. & Palumbo, G. J. Poxvirus pathogenesis. Microbiol. Rev. 55, 80–122 (1991).

    CAS  PubMed  PubMed Central  Google Scholar 

  37. 37

    Baker, D. & Sali, A. Protein structure prediction and structural genomics. Science 294, 93–6 (2001).

    CAS  ADS  Article  Google Scholar 

  38. 38

    Krogh, A., Larsson, B., von Heijne, G. & Sonnhammer, E. L. Predicting transmembrane protein topology with a hidden Markov model: application to complete genomes. J. Mol. Biol. 305, 567–80 (2001).

    CAS  Article  Google Scholar 

  39. 39

    Biasini, M. et al. SWISS-MODEL: modelling protein tertiary and quaternary structure using evolutionary information. Nucleic Acids Res. 42, W252–8 (2014).

    CAS  Article  Google Scholar 

  40. 40

    Conant, G. C. & Wolfe, K. H. Turning a hobby into a job: how duplicated genes find new functions. Nat. Rev. Genet. 9, 938–50 (2008).

    CAS  Article  Google Scholar 

  41. 41

    Hughes, A. L. The evolution of functionally novel proteins after gene duplication. Proc. Biol. Sci. 256, 119–24 (1994).

    CAS  Article  Google Scholar 

  42. 42

    Gu, X. & Vander Velden, K. DIVERGE: phylogeny-based analysis for functional-structural divergence of a protein family. Bioinformatics 18, 500–1 (2002).

    CAS  Article  Google Scholar 

  43. 43

    Gu, X. et al. An update of DIVERGE software for functional divergence analysis of protein family. Mol. Biol. Evol. 30, 1713–9 (2013).

    CAS  Article  Google Scholar 

  44. 44

    Cohn, L. B. et al. HIV-1 integration landscape during latent and active infection. Cell 160, 420–32 (2015).

    CAS  Article  Google Scholar 

  45. 45

    Craigie, R. & Bushman, F. D. HIV DNA integration. Cold Spring Harb. Perspect. Med. 2, a006890 (2012).

    Article  Google Scholar 

  46. 46

    Morissette, G. & Flamand, L. Herpesviruses and chromosomal integration. J. Virol. 84, 12100–9 (2010).

    CAS  Article  Google Scholar 

  47. 47

    Osterrieder, N., Wallaschek, N. & Kaufer, B. B. Herpesvirus Genome Integration into Telomeric Repeats of Host Cell Chromosomes. Annu. Rev. Virol. 1, 215–235 (2014).

    Article  Google Scholar 

  48. 48

    Kaufer, B. B., Jarosinski, K. W. & Osterrieder, N. Herpesvirus telomeric repeats facilitate genomic integration into host telomeres and mobilization of viral DNA during reactivation. J. Exp. Med. 208, 605–15 (2011).

    CAS  Article  Google Scholar 

  49. 49

    Katzourakis, A. & Gifford, R. J. Endogenous viral elements in animal genomes. PLoS Genet. 6, e1001191 (2010).

    Article  Google Scholar 

  50. 50

    Holmes, E. C. The evolution of endogenous viral elements. Cell Host Microbe 10, 368–77 (2011).

    CAS  Article  Google Scholar 

  51. 51

    Fu, T. M., Friedman, A., Ulmer, J. B., Liu, M. A. & Donnelly, J. J. Protective cellular immunity: cytotoxic T-lymphocyte responses against dominant and recessive epitopes of influenza virus nucleoprotein induced by DNA immunization. J. Virol. 71, 2715–21 (1997).

    CAS  PubMed  PubMed Central  Google Scholar 

  52. 52

    Mancini, M. et al. DNA-mediated immunization in a transgenic mouse model of the hepatitis B surface antigen chronic carrier state. Proc. Natl. Acad. Sci. USA 93, 12496–501 (1996).

    CAS  ADS  Article  Google Scholar 

  53. 53

    Alcami, A. Viral mimicry of cytokines, chemokines and their receptors. Nat. Rev. Immunol. 3, 36–50 (2003).

    CAS  Article  Google Scholar 

  54. 54

    Streblow, D. N. et al. The Human Cytomegalovirus Chemokine Receptor US28 Mediates Vascular Smooth Muscle Cell Migration. Cell 99, 511–520 (1999).

    CAS  Article  Google Scholar 

  55. 55

    Bais, C. et al. G-protein-coupled receptor of Kaposi’s sarcoma-associated herpesvirus is a viral oncogene and angiogenesis activator. Nature 391, 86–9 (1998).

    CAS  ADS  Article  Google Scholar 

  56. 56

    Arvanitakis, L., Geras-Raaka, E., Varma, A., Gershengorn, M. C. & Cesarman, E. Human herpesvirus KSHV encodes a constitutively active G-protein-coupled receptor linked to cell proliferation. Nature 385, 347–50 (1997).

    CAS  ADS  Article  Google Scholar 

  57. 57

    Samson, M. et al. Resistance to HIV-1 infection in caucasian individuals bearing mutant alleles of the CCR-5 chemokine receptor gene. Nature 382, 722–5 (1996).

    CAS  ADS  Article  Google Scholar 

  58. 58

    Berger, E. A., Murphy, P. M. & Farber, J. M. Chemokine receptors as HIV-1 coreceptors: roles in viral entry, tropism, and disease. Annu. Rev. Immunol. 17, 657–700 (1999).

    CAS  Article  Google Scholar 

  59. 59

    Murphy, P. M. Viral exploitation and subversion of the immune system through chemokine mimicry. Nat. Immunol. 2, 116–22 (2001).

    CAS  Article  Google Scholar 

  60. 60

    Sodhi, A., Montaner, S. & Gutkind, J. S. Viral hijacking of G-protein-coupled-receptor signalling networks. Nat. Rev. Mol. Cell Biol. 5, 998–1012 (2004).

    CAS  Article  Google Scholar 

  61. 61

    Spencer, J. V. et al. Potent Immunosuppressive Activities of Cytomegalovirus- Encoded Interleukin-10. J. Virol. 76, 1285–1292 (2002).

    CAS  Article  Google Scholar 

  62. 62

    Eberhardt, M. K. et al. Host immune responses to a viral immune modulating protein: immunogenicity of viral interleukin-10 in rhesus cytomegalovirus-infected rhesus macaques. PLoS One 7, e37931 (2012).

    CAS  ADS  Article  Google Scholar 

  63. 63

    Spinetti, G. The chemokine receptor CCR8 mediates rescue from dexamethasone-induced apoptosis via an ERK-dependent pathway. J. Leukoc. Biol. 73, 201–207 (2003).

    CAS  Article  Google Scholar 

  64. 64

    Goya, I. et al. Identification of CCR8 as the Specific Receptor for the Human β-Chemokine I-309: Cloning and Molecular Characterization of Murine CCR8 as the Receptor for TCA-3. J. Immunol. 160, 1975–1981 (1998).

    CAS  PubMed  Google Scholar 

  65. 65

    Yabe, R. et al. CCR8 regulates contact hypersensitivity by restricting cutaneous dendritic cell migration to the draining lymph nodes. Int. Immunol. 27, 169–181 (2014).

    Article  Google Scholar 

  66. 66

    Ashburner, M. et al. Gene ontology: tool for the unification of biology. The Gene Ontology Consortium. Nat. Genet. 25, 25–9 (2000).

    CAS  Article  Google Scholar 

  67. 67

    The universal protein resource (UniProt). Nucleic Acids Res. 36, D190–5 (2008).

Download references

Acknowledgements

D.S.C., Y.Q.W., W.Z. and S.J.J. are funded by the scholarship from the China Scholarship Council.

Author information

Affiliations

Authors

Contributions

S.Z.C. and D.S.C. designed the study. D.S.C., Y.Q.W., W.Z. and S.J.J. performed the data analysis. S.Z.C., D.S.C., Y.Q.W., W.Z. and S.J.J. wrote the manuscript. All authors approved the final version of this manuscript.

Corresponding author

Correspondence to Shan-Ze Chen.

Ethics declarations

Competing interests

The authors declare no competing financial interests.

Supplementary information

Rights and permissions

This work is licensed under a Creative Commons Attribution 4.0 International License. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in the credit line; if the material is not included under the Creative Commons license, users will need to obtain permission from the license holder to reproduce the material. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Chen, DS., Wu, YQ., Zhang, W. et al. Horizontal gene transfer events reshape the global landscape of arm race between viruses and homo sapiens. Sci Rep 6, 26934 (2016). https://doi.org/10.1038/srep26934

Download citation

Further reading

Comments

By submitting a comment you agree to abide by our Terms and Community Guidelines. If you find something abusive or that does not comply with our terms or guidelines please flag it as inappropriate.

Search

Quick links