Our immune system has the remarkable ability to recognize and destroy damaged cells or invading microbes while leaving the healthy cells in the body alone. Groups of proteins called ‘MHC class I molecules’ play important roles in defence against invading microbes. If a cell becomes infected with a virus or a bacterium, these molecules can recognize and bind to fragments of foreign or unusual proteins inside the cell, and display them on the surface of the cell. This allows immune cells to identify and kill the infected cells.

Cells produce many different MHC class I molecules that are able to bind to different protein fragments. Some MHC molecules can bind to a wider variety of protein fragments than others, and the number of these molecules present on the cell surface can also vary between individuals. Researchers have noticed that individuals with particular MHC molecules tend to be more or less resistant to particular diseases. For instance, individuals with certain MHC molecules tend to take longer to develop AIDS if they become infected with HIV. However, it is not clear how either the variety of protein fragments bound or the numbers of MHC class I molecules on the surface of cells could alter the immune response.

Here, Chappell, Meziane et al. studied MHC class I molecules in chickens and humans. The experiments reveal that the MHC class I molecules that can bind to a larger variety of protein fragments (so-called ‘generalists’) are present in lower numbers on the surface of cells than molecules that can bind to a smaller variety of fragments (so-called ‘specialists’).

Furthermore, generalist MHC molecules were found to provide resistance to Marek's disease in chickens—which causes paralysis—but some specialists slowed the progression of HIV infections into AIDS in humans. Chappell, Meziane et al. propose that these two types of MHC class I molecules evolved to perform different roles in immune responses. This is a new way of looking at the role of MHC molecules in fighting disease, and the next challenge is to explore the implications for medicine and evolutionary biology.

Highly polymorphic class I molecules encoded by the major histocompatibility complex (MHC) are crucial in the adaptive immune response to viruses and some intracellular bacteria, binding peptides inside the cell and presenting them on the cell surface to CD8 T lymphocytes (Blum et al., 2013; International HIV Controllers Study et al., 2013; Trowsdale and Knight, 2013). The impact of the MHC in response to human immunodeficiency virus (HIV) is well recognized, with class I alleles like HLA-B*35:01 leading to rapid onset of AIDS while HLA-B*57:01 and HLA-B*27:05 confer long-term non-progression (Carrington et al., 1999; Goulder and Walker, 2012; International HIV Controllers Study et al., 2013). Various explanations for these associations have been suggested, including antigen presentation of a particularly effective peptide or of a number of peptides to cytotoxic CD8 T cells or recognition (primarily) independent of peptide by natural killer (NK) cells (Carrington et al., 1999; Martin et al., 2002; Kosmrlj et al., 2010; International HIV Controllers Study et al., 2013). Recently, the level of cell surface expression of HLA-C alleles correlated with CD8 T cell cytotoxicity has been proposed as one important basis for control (Thomas et al., 2009; Apps et al., 2013). Understanding how cell expression level might impact on disease resistance is complicated in humans due to the presence of three class I loci, so we began by studying a simpler animal system before examining human class I alleles.

Long ago, we reported that the relative expression level of MHC class I molecules on the surface of chicken red blood cells as assessed by flow cytometry varies significantly, with cells of the MHC haplotype B21 approximately ten-fold lower than B4, B12, B15, and B19 (Kaufman et al., 1995). This finding was of interest because the level of cell surface expression is inversely correlated with the reported levels of MHC-determined resistance to Marek's disease, an economically important disease caused by the oncogenic herpesvirus, Marek's disease virus (MDV). Decades of investigation identified B21 (and other haplotypes like B2, B6, and B14) as generally conferring resistance and B19 (and other haplotypes such as B4, B12, and B15) as generally conferring susceptibility (reviewed in Plachy et al., 1992). On this basis, we proposed that MHC-determined resistance to Marek's disease could be due to the cell surface expression polymorphism of class I molecules (Kaufman et al., 1995; Kaufman and Salomonsen, 1997).

Compared to humans, the chicken MHC is relatively simple (Kaufman et al., 1999), with two classical class I genes BF1 and BF2 that flank the genes for the transporter associated with antigen presentation (TAP). The TAP transporter pumps peptides from the cytoplasm to the lumen of the endoplasmic reticulum for loading nascent class I molecules, and in typical mammals is functionally monomorphic, pumping a wide variety of peptides for all members of the polymorphic class I multigene family. In contrast, chicken TAP genes are highly polymorphic, with each haplotype encoding a TAP molecule with a peptide-translocation specificity matching the peptide-binding specificity of the dominantly expressed class I molecule encoded by the BF2 gene, with the BF1 gene expressed poorly or not at all (Wallny et al., 2006; Shaw et al., 2007; Walker et al., 2011).

In chickens, the peptide-binding specificity of the dominantly expressed class I molecule can determine resistance to infectious pathogens as well as responses to vaccines. The peptide motifs from B4, B12, B15, and B19 class I molecules are if anything more fastidious than human and mouse motifs, with only one or two amino acids found at the positions of anchor residues, with the motifs explaining the immune response to infection and vaccination (Kaufman et al., 1995). The fastidious binding of these molecules could be easily understood from wire models of the binding site, in which charged and hydrophobic residues were found in appropriate places to interact in a simple manner with the anchor residues of the bound peptides (Wallny et al., 2006). This view was confirmed from the crystal structure of the dominantly expressed class I molecule BF2*0401 from the B4 haplotype, with positive-charged residues in a narrow groove allowing only certain anchor residues from the peptide to be accommodated (Zhang et al., 2012). In contrast, much less peptide material was isolated from B21 cells, with many amino acids found in every peptide position. Crystal structures demonstrated that the dominantly expressed class I molecule BF2*2101 remodels the binding site to accommodate two peptides with completely different sequences, including the anchor residues (Koch et al., 2007). Thus, the lack of a clear peptide motif could be explained as promiscuous peptide binding due to the remodelling of the peptide-binding site.

In this paper, we find that two other haplotypes known to confer resistance to Marek's disease also have low cell surface expression and promiscuous peptide motifs, and examine the structural basis for promiscuous binding in three low expressing molecules. We show that the same relationship between cell surface expression and peptide-binding repertoire is found for four human class I molecules, associated with progression to AIDS. Finally, we propose how promiscuous peptide binding might confer resistance to some pathogens, what the basis for the cell surface expression polymorphism might be, and how these properties may relate to different strategies for resistance to pathogens.

In this article, we make three major points. First, we extend previous work showing that the chicken BF2*2101 achieves promiscuity by remodelling the peptide binding site in a way unknown for mammals, but we find that two other low expressing chicken class I molecules achieve promiscuity with the same pockets typically used in mammals. By extension, these latter results provide a potential molecular explanation for recent reports that human class I molecules vary in peptide repertoire and other reports that they vary in cell surface expression level. Second, we show that peptide-binding repertoire and level of cell surface expression are inversely correlated in both chickens and humans, indicating that this relationship is a fundamental property of class I molecules. Finally, we find that there is a clear association with resistance to infectious diseases, with the low expressing promiscuous class I molecules associated with resistance to Marek's disease in chickens but associated with faster progression to AIDS in humans. We discuss each of these points in turn.

The first point is the structural and mechanistic basis for differences in peptide repertoire of chicken class I molecules. In this article, we confirm and extend the finding (Koch et al., 2007) that BF2*2101 binds 10mer and 11mer peptides with three anchor positions, with many different amino acids with different chemical characteristics, mostly charged and polar at positions P₂ and P_c-2 (and hydrophobic at P_c), which is unlike any mammalian molecule described thus far. In contrast, we find that BF2*0201 and BF2*1401 bind 8mer and 9mer peptides with two anchor residues (much like typical mammalian class I molecules), each with a variety of amino acids at P₂ and P_c.

One question is why BF2*2101 and BF2*0201 bind in such different ways, given that both have Asp24 and Arg9 in their binding sites. In part, this may be due to the difference in residues lining the peptide-binding groove. In BF2*2101, small residues like Gly68(human position 69), Ser69(70), Ser97(99), and Gly152(155) create a big cavity in the centre of the binding groove, allowing Asp24 and Arg9 to move. In BF2*0201 and BF2*1401, the two Ser residues are replaced by the large residues Asn69(70) and Tyr97(99), creating a constricted groove much more like narrow grooves found in high expressing fastidious molecules such as BF2*0401. It is also possible that the length of peptides is determined at least in part by TAP specificity. Together, the narrower groove and the shorter peptides ensure that Asp24 and Arg9 in BF2*0201 (as well as Thr24 and Gln9 in BF2*1401) cannot reach the P₂ and P_c-2 positions, and thus they interact (if at all) with the main chain atoms of the peptide.

Another question is how chicken class I molecules BF2*0201 and BF2*1401 compare to human molecules like HLA-A2, all of which bind peptides with hydrophobic anchor residues. HLA-A2 has one of the widest peptide repertoires among human class I molecules, as assessed by peptide-binding studies (Paul et al., 2013). The anchor residues for HLA-A2 fit into narrow and specific-binding pockets, which allow almost only P₂ Met and Leu for pocket B and P_c Val and Leu for pocket F (Guo et al., 1993). Although Leu and Val in particular are relatively common in proteins (so that the number of peptides bearing these amino acids should be relatively high), there is a much larger number of hydrophobic amino acids that are accommodated by the shallower pockets in both BF2*0201 and BF2*1401. Hence, it seems likely that the peptide repertoire of the most promiscuous chicken class I molecules will be greater than the most promiscuous human class I molecules.

Finally, are there obvious mechanisms by which differences in peptide repertoire arise? In chickens, the peptides presented depend on the peptide-binding specificities of the class I molecule, but also on the peptide-translocation specificities of the TAPs and perhaps also on peptide-editing by tapasin. The genes for chicken TAP and tapasin have at least as many alleles as class I genes (Walker et al., 2005, 2011; van Hateren et al., 2013). On this basis, we propose that the difference in peptide repertoire (and in cell surface expression level) between chicken class I alleles is determined at the level of peptide loading and editing, likely to take place mostly in the peptide-loading complex. It might not be immediately apparent whether such an explanation could account for the differences in peptide repertoire and cell surface expression of human class I molecules, since human TAPs and tapasin are functionally monomorphic. However, a recent paper suggests that the assembly and cell surface expression of different HLA-B alleles is determined by interaction with the near invariant tapasin molecule, based on variation in the certain HLA-B residues (Rizvi et al., 2014). Indeed, it is striking that the rank order of expression level that we find for the four HLA-B alleles tested is exactly the same as the rank order of tapasin dependence found in this recent paper.

The second important point for discussion is why the cell surface expression would be inversely correlated with peptide-binding repertoire. A proximal explanation might be that the biochemical mechanism of peptide loading, as mentioned in the paragraph above, leads to this relationship. Another explanation, similar to the suggestion that multiple MHC molecules lead to a greater chance of autoimmunity (Kaufman et al., 1995), is that promiscuous class I molecules confer more resistance to pathogens but also lead to greater autoimmunity compared to fastidious class I molecules.

We favour a third possibility, prompted by the experimental and supporting theoretical evidence for the notion that a greater number of MHC loci would lead to greater level of negative selection in the thymus (Vidović and Matzinger, 1988; Nowak et al., 1992). In a similar way, one might expect that class I molecules presenting a lesser variety of peptides would negatively select fewer T cell clones, but class I molecules presenting a greater variety of peptides would negatively select many more T cells and create a pauperized T cell repertoire. Therefore, we propose that the peptide-binding repertoire and the cell surface expression levels are inversely correlated precisely to allow similar numbers of T cell clones to survive negative selection and be available in the periphery. In this view, low expressing and promiscuous class I molecules might present many more peptides but each individual peptide would be present on the surface at a much lower level, so that T cells would escape the negative selection that might have occurred at a higher concentration of a particular MHC-peptide complex. In essence, we propose that peptide-binding repertoires and cell surface expression levels have evolved to optimize peripheral T cell responses. Many papers have examined naïve CD8 T cell repertoire in humans and mice (for example, Alanio et al., 2010; Jenkins and Moon, 2012; Lo et al., 2014); such analyses with high and low expressing haplotypes would be informative.

The third important point for discussion is the fact that the inverse correlation between peptide binding repertoire and cell surface expression level also correlates with resistance and susceptibility to certain infectious pathogens. One important question is how it might work.

In chickens, these phenomena are correlated with MHC-determined resistance to historic Marek's disease. Many genetic loci contribute to resistance to Marek's disease, but historically the B locus (containing the MHC) was by far the most important, and under strong selection by MDV (Plachy et al., 1992; Vallejo et al., 1998). Here, we show that the low expressing promiscuous class I molecules are associated with resistance to Marek's disease, while the high expressing fastidious class I molecules are associated with susceptibility. A consequence of promiscuous binding is that these low expressing class I molecules present a larger repertoire of peptides which should activate a wider range of T cell clones, which may be beneficial in the immune response to certain pathogens. We propose that this breadth of antigen presentation leads to a breadth of T cell response that is the basis of MHC-determined resistance to Marek's disease. Conversely, a narrow T cell response to Marek's disease would be the basis for MHC-determined susceptibility, and there is already evidence for a limited repertoire of CD8 T cell clones infiltrating tumours in susceptible B19 chickens (Mwangi et al., 2011). Parenthetically, if true this model suggests that the narrowing of CD8 T cell clonality characteristic of responses in humans and mice (Yewdell and Bennink, 1999; Yewdell and Del Val, 2004; Akram and Inman, 2012) may not be a feature of responses involving promiscuous class I molecules in chickens.

For our example in humans, however, we find the opposite disease association. We find that the low expressing promiscuous HLA-B alleles are associated with rapid progression from HIV infection to AIDS, while the high expressing fastidious HLA-B alleles are found in non-progressors (Carrington et al., 1999; Goulder and Walker, 2012; International HIV Controllers Study et al., 2013). The correlation with peptide binding repertoire (as assessed by prediction) of HLA-B alleles had already been noted (Kosmrlj et al., 2010), but the relationship with cell surface expression level has not. The controller alleles HLA-B*57:01 and HLA-B*27:05 are known to bind and present particular viral peptides that are both protective and difficult for the virus to change without a loss in fitness (Gillespie et al., 2006; Schneidewind et al., 2007). Thus, particular class I molecules may be selected because their fastidious motif presents a particularly protective peptide.

Is there an obvious evolutionary basis for differences in peptide-binding repertoire? The fact that the fastidious HLA-B*57:01 confers resistance to AIDS while the promiscuous BF2*2101 confers resistance to Marek's disease may be no paradox, in that breadth of peptide presentation may be an appropriate response to some pathogens, while presentation of a particular peptide may be more suitable for other pathogens. Viewed from this perspective, the work presented in this paper has begun to define two groups (or a range between two extremes) of class I molecules that are strategically evolved for different modes of resistance.

One possible strategy is that fastidious class I molecules with pauci-clonal T cell responses might be better suited for rapidly evolving viruses with a limited scope for immune evasion, for which a particular peptide might be the most efficient way to achieve resistance. In contrast, promiscuous class I molecules might be better suited for pathogens with coding potential for many immune evasion genes and more stability over evolutionary time, such as large DNA viruses, bacteria, and even parasites. There are few reports about pathogen peptides that are presented in chickens, but the large literature on the MHC restriction of viruses, bacteria, and parasites in humans and mice (for example, Fitzmaurice et al., 2015; Goulder and Walker, 2012; Macnamara et al., 2010; Moss and Khan, 2004; Steven et al., 1997; Yim and Selvaraj, 2010) may be reinterpreted, as the peptide repertoire and cell surface expression levels of the class I molecules become known.

Another potentially fruitful way of considering the strategies of class I molecules (not necessarily exclusive of the first view) might be as generalists and specialists. There is a large literature in biology that examines the generalist/specialist paradigm at the species level, typically testing the model that ‘the jack of all trades is the master of none’ (McArthur, 1972; Futuyma and Moreno, 1988). Recent examples, among many others, include breadth of diet by insect herbivore species (Ali and Agrawal, 2012), niche breadth by bird species (Julliard et al., 2006), competition on shared hosts by aphid parasitoids (Straub et al., 2011), and weasel predation of voles (Sundell and Ylönen, 2008), as well as mathematical models to disentangle the contributions by various factors (Remold, 2012; Büchi and Vuilleumier, 2014). Indeed, the terms generalist and specialist MHC alleles have already been used for describing correlations of class II alleles of the striped mouse in Africa with number of nematode species carried (Froeschke and Sommer, 2012).

In this view, the promiscuous class I molecules would be generalists, which one might expect to suffice for protection against a wide variety of the most common pathogens. However, such generalists might not suffice for protection from a new and/or an especially virulent pathogen that suddenly appears, at which point there would be a strong selection for a specialist class I molecule that was particularly suited to deal with the new threat. The properties of fastidious class I molecules are consistent with selection as specialists for particular pathogens, perhaps including those no longer a danger in the current population.

The importance of these concepts for immunology and medicine may be clear from the discussion above, but there are also ramifications for evolutionary biology, ecology, and conservation. As one example, the number of MHC alleles is considered a key measure for population diversity in estimating the risk of extinction, both as a measure of overall genome diversity and in terms of fitness, including resilience to infection (Sommer, 2005). However, a population with a few generalist MHC alleles might remain healthy compared to a population with many inappropriate specialist MHC alleles.

1. 1. Chappell PE
   2. Roversi P
   3. Harrison MC
   4. Mears LE
   5. Kaufman JF
   6. Lea SM

   (2011) COMPLEX OF A B21 CHICKEN MHC CLASS I MOLECULE AND A 10MER CHICKEN PEPTIDE

   Publicly available at the RCSB Protein Data Bank 2YEZ.

   http://www.rcsb.org/pdb/explore/explore.do?structureId=2YEZ
2. 1. Chappell PE
   2. Roversi P
   3. Harrison MC
   4. Mears LE
   5. Kaufman JF
   6. Lea SM

   (2014) COMPLEX OF A B21 CHICKEN MHC CLASS I MOLECULE AND A 10MER CHICKEN PEPTIDE

   Publicly available at the RCSB Protein Data Bank (4D0B).

   http://www.rcsb.org/pdb/search/structidSearch.do?structureId=4D0B
3. 1. Chappell PE
   2. Roversi P
   3. Harrison MC
   4. Mears LE
   5. Kaufman JF
   6. Lea SM

   (2014) COMPLEX OF A B21 CHICKEN MHC CLASS I MOLECULE AND A 10MER CHICKEN PEPTIDE

   Publicly available at the RCSB Protein Data Bank (4D0C).

   http://www.rcsb.org/pdb/search/structidSearch.do?structureId=4D0C
4. 1. Chappell PE
   2. Roversi P
   3. Harrison MC
   4. Mears LE
   5. Kaufman JF
   6. Lea SM

   (2014) COMPLEX OF A B21 CHICKEN MHC CLASS I MOLECULE AND A 10MER CHICKEN PEPTIDE

   Publicly available at the RCSB Protein Data Bank (4CVZ).

   http://www.rcsb.org/pdb/search/structidSearch.do?structureId=4CVZ
5. 1. Chappell PE
   2. Roversi P
   3. Harrison MC
   4. Mears LE
   5. Kaufman JF
   6. Lea SM

   (2014) COMPLEX OF A B2 CHICKEN MHC CLASS I MOLECULE AND A 9MER CHICKEN PEPTIDE

   Publicly available at the RCSB Protein Data Bank (4CVX).

   http://www.rcsb.org/pdb/search/structidSearch.do?structureId=4CVX
6. 1. Chappell PE
   2. Roversi P
   3. Harrison MC
   4. Mears LE
   5. Kaufman JF
   6. Lea SM

   (2014) COMPLEX OF A B2 CHICKEN MHC CLASS I MOLECULE AND A 8MER CHICKEN PEPTIDE

   Publicly available at the RCSB Protein Data Bank (4D0D).

   http://www.rcsb.org/pdb/search/structidSearch.do?structureId=4D0D
7. 1. Chappell PE
   2. Roversi P
   3. Harrison MC
   4. Mears LE
   5. Kaufman JF
   6. Lea SM

   (2014) COMPLEX OF A B14 CHICKEN MHC CLASS I MOLECULE AND A 9MER CHICKEN PEPTIDE

   Publicly available at the RCSB Protein Data Bank (4CW1).

   http://www.rcsb.org/pdb/search/structidSearch.do?structureId=4CW1

1. 1. Koch M
   2. Kaufman J
   3. Jones Y

   (2007) 11mer Structure of an MHC class I molecule from B21 chickens illustrate promiscuous peptide binding

   Publicly available at the RCSB Protein Data Bank (3BEV).

   http://www.rcsb.org/pdb/explore/explore.do?structureId=3BEV
2. 1. Koch M
   2. Kaufman J
   3. Jones Y

   (2007) 10mer Crystal Structure of chicken MHC class I haplotype B21

   Publicly available at the RCSB Protein Data Bank (3BEW).

   http://www.rcsb.org/pdb/explore/explore.do?structureId=3BEW

1. 1. Akram A
   2. Inman RD

   (2012) Immunodominance: a pivotal principle in host response to viral infections

   Clinical Immunology 143:99–115.

   https://doi.org/10.1016/j.clim.2012.01.015

   • Google Scholar
2. 1. Alanio C
   2. Lemaitre F
   3. Law HK
   4. Hasan M
   5. Albert ML

   (2010) Enumeration of human antigen-specific naive CD8+ T cells reveals conserved precursor frequencies

   Blood 15:3718–3725.

   https://doi.org/10.1182/blood-2009-10-251124

   • Google Scholar
3. 1. Ali JG
   2. Agrawal AA

   (2012) Specialist versus generalist insect herbivores and plant defense

   Trends in Plant Science 17:293–302.

   https://doi.org/10.1016/j.tplants.2012.02.006

   • Google Scholar
4. 1. Apps R
   2. Murphy SP
   3. Fernando R
   4. Gardner L
   5. Ahad T
   6. Moffett A

   (2009) Human leucocyte antigen (HLA) expression of primary trophoblast cells and placental cell lines, determined using single antigen beads to characterize allotype specificities of anti-HLA antibodies

   Immunology 127:26–39.

   https://doi.org/10.1111/j.1365-2567.2008.03019.x

   • Google Scholar
5. 1. Apps R
   2. Qi Y
   3. Carlson JM
   4. Chen H
   5. Gao X
   6. Thomas R
   7. Yuki Y
   8. Del Prete GQ
   9. Goulder P
   10. Brumme ZL
   11. Brumme CJ
   12. John M
   13. Mallal S
   14. Nelson G
   15. Bosch R
   16. Heckerman D
   17. Stein JL
   18. Soderberg KA
   19. Moody MA
   20. Denny TN
   21. Zeng X
   22. Fang J
   23. Moffett A
   24. Lifson JD
   25. Goedert JJ
   26. Buchbinder S
   27. Kirk GD
   28. Fellay J
   29. McLaren P
   30. Deeks SG
   31. Pereyra F
   32. Walker B
   33. Michael NL
   34. Weintrob A
   35. Wolinsky S
   36. Liao W
   37. Carrington M

   (2013) Influence of HLA-C expression level on HIV control

   Science 340:87–91.

   https://doi.org/10.1126/science.1232685

   • Google Scholar
6. 1. Blum JS
   2. Wearsch PA
   3. Cresswell P

   (2013) Pathways of antigen processing

   Annual Review of Immunology 31:443–473.

   https://doi.org/10.1146/annurev-immunol-032712-095910

   • Google Scholar
7. Book

   1. Bricogne G
   2. Blanc E
   3. Brandl M
   4. Flensburg C
   5. Keller P
   6. Paciorek W
   7. Roversi P
   8. Sharff A
   9. Smart OS
   10. Vonrhein C
   11. Womack TO

   (2011)

   BUSTER version 2.11.4

   Cambridge, United Kingdom: Global Phasing Ltd.

   • Google Scholar
8. 1. Büchi L
   2. Vuilleumier S

   (2014) Coexistence of specialist and generalist species is shaped by dispersal and environmental factors

   The American Naturalist 183:612–624.

   https://doi.org/10.1086/675756

   • Google Scholar
9. 1. Carrington M
   2. Nelson GW
   3. Martin MP
   4. Kissner T
   5. Vlahov D
   6. Goedert JJ
   7. Kaslow R
   8. Buchbinder S
   9. Hoots K
   10. O'Brien SJ

   (1999) HLA and HIV-1: heterozygote advantage and B*35-Cw*04 disadvantage

   Science 283:1748–1752.

   https://doi.org/10.1126/science.283.5408.1748

   • Google Scholar
10. 1. Crone M
    2. Simonsen M
    3. Skjødt K
    4. Linnet K
    5. Olsson L

    (1985) Mouse monoclonal antibodies to class I and class II antigens of the chicken MHC. Evidence for at least two class I products of the B complex

    Immunogenetics 21:181–187.

    https://doi.org/10.1007/BF00364870

    • Google Scholar
11. 1. Emsley P
    2. Lohkamp B
    3. Scott WG
    4. Cowtan K

    (2010) Features and development of Coot

    Acta Crystallographica Section D, Biological Crystallography 66:486–501.

    https://doi.org/10.1107/S0907444910007493

    • Google Scholar
12. 1. Evans P

    (2006) Scaling and assessment of data quality

    Acta Crystallographica Section D, Biological Crystallography 62:72–82.

    https://doi.org/10.1107/S0907444905036693

    • Google Scholar
13. 1. Evans P
    2. McCoy A

    (2008) An introduction to molecular replacement

    Acta Crystallographica Section D, Biological Crystallography 64:1–10.

    https://doi.org/10.1107/S0907444907051554

    • Google Scholar
14. 1. Figueroa-Tentori D
    2. Querol S
    3. Dodi IA
    4. Madrigal A
    5. Duggleby R

    (2008) High purity and yield of natural Tregs from cord blood using a single step selection method

    Journal of Immunological Methods 339:228–235.

    https://doi.org/10.1016/j.jim.2008.09.019

    • Google Scholar
15. 1. Fitzmaurice K
    2. Hurst J
    3. Dring M
    4. Rauch A
    5. McLaren PJ
    6. Günthard HF
    7. Gardiner C
    8. Klenerman P
    9. Irish HCV Research Consortium and the Swiss HIV Cohort Study

    (2015) Additive effects of HLA alleles and innate immune genes determine viral outcome in HCV infection

    Gut 64:813–819.

    https://doi.org/10.1136/gutjnl-2013-306287

    • Google Scholar
16. 1. Froeschke G
    2. Sommer S

    (2012) Insights into the complex associations between MHC class II DRB polymorphism and multiple gastrointestinal parasite infestations in the striped mouse

    PLOS ONE 7:e31820.

    https://doi.org/10.1371/journal.pone.0031820

    • Google Scholar
17. 1. Futuyma DJ
    2. Moreno G

    (1988) The evolution of ecological specialization

    Annual Review of Ecology and Systematics 19:207–233.

    https://doi.org/10.1146/annurev.es.19.110188.001231

    • Google Scholar
18. 1. Gillespie GM
    2. Stewart-Jones G
    3. Rengasamy J
    4. Beattie T
    5. Bwayo JJ
    6. Plummer FA
    7. Kaul R
    8. McMichael AJ
    9. Easterbrook P
    10. Dong T
    11. Jones EY
    12. Rowland-Jones SL

    (2006) Strong TCR conservation and altered T cell cross-reactivity characterize a B*57-restricted immune response in HIV-1 infection

    The Journal of Immunology 177:3893–3902.

    https://doi.org/10.4049/jimmunol.177.6.3893

    • Google Scholar
19. 1. Goulder PJ
    2. Walker BD

    (2012) HIV and HLA class I: an evolving relationship

    Immunity 37:426–440.

    https://doi.org/10.1016/j.immuni.2012.09.005

    • Google Scholar
20. 1. Guo HC
    2. Madden DR
    3. Silver ML
    4. Jardetzky TS
    5. Gorga JC
    6. Strominger JL
    7. Wiley DC

    (1993) Comparison of the P2 specificity pocket in three human histocompatibility antigens: HLA-A*6801, HLA-A*0201, and HLA-B*2705

    Proceedings of the National Academy of Sciences of USA 90:8053–8057.

    https://doi.org/10.1073/pnas.90.17.8053

    • Google Scholar
21. 1. International HIV Controllers Study
    2. Pereyra F
    3. Jia X
    4. McLaren PJ
    5. Telenti A
    6. de Bakker PI
    7. Walker BD
    8. Ripke S
    9. Brumme CJ
    10. Pulit SL
    11. Carrington M
    12. Kadie CM
    13. Carlson JM
    14. Heckerman D
    15. Graham RR
    16. Plenge RM
    17. Deeks SG
    18. Gianniny L
    19. Crawford G
    20. Sullivan J
    21. Gonzalez E
    22. Davies L
    23. Camargo A
    24. Moore JM
    25. Beattie N
    26. Gupta S
    27. Crenshaw A
    28. Burtt NP
    29. Guiducci C
    30. Gupta N
    31. Gao X
    32. Qi Y
    33. Yuki Y
    34. Piechocka-Trocha A
    35. Cutrell E
    36. Rosenberg R
    37. Moss KL
    38. Lemay P
    39. O'Leary J
    40. Schaefer T
    41. Verma P
    42. Toth I
    43. Block B
    44. Baker B
    45. Rothchild A
    46. Lian J
    47. Proudfoot J
    48. Alvino DM
    49. Vine S
    50. Addo MM
    51. Allen TM
    52. Altfeld M
    53. Henn MR
    54. Le Gall S
    55. Streeck H
    56. Haas DW
    57. Kuritzkes DR
    58. Robbins GK
    59. Shafer RW
    60. Gulick RM
    61. Shikuma CM
    62. Haubrich R
    63. Riddler S
    64. Sax PE
    65. Daar ES
    66. Ribaudo HJ
    67. Agan B
    68. Agarwal S
    69. Ahern RL
    70. Allen BL
    71. Altidor S
    72. Altschuler EL
    73. Ambardar S
    74. Anastos K
    75. Anderson B
    76. Anderson V
    77. Andrady U
    78. Antoniskis D
    79. Bangsberg D
    80. Barbaro D
    81. Barrie W
    82. Bartczak J
    83. Barton S
    84. Basden P
    85. Basgoz N
    86. Bazner S
    87. Bellos NC
    88. Benson AM
    89. Berger J
    90. Bernard NF
    91. Bernard AM
    92. Birch C
    93. Bodner SJ
    94. Bolan RK
    95. Boudreaux ET
    96. Bradley M
    97. Braun JF
    98. Brndjar JE
    99. Brown SJ
    100. Brown K
    101. Brown ST
    102. Burack J
    103. Bush LM
    104. Cafaro V
    105. Campbell O
    106. Campbell J
    107. Carlson RH
    108. Carmichael JK
    109. Casey KK
    110. Cavacuiti C
    111. Celestin G
    112. Chambers ST
    113. Chez N
    114. Chirch LM
    115. Cimoch PJ
    116. Cohen D
    117. Cohn LE
    118. Conway B
    119. Cooper DA
    120. Cornelson B
    121. Cox DT
    122. Cristofano MV
    123. Cuchural G Jr
    124. Czartoski JL
    125. Dahman JM
    126. Daly JS
    127. Davis BT
    128. Davis K
    129. Davod SM
    130. DeJesus E
    131. Dietz CA
    132. Dunham E
    133. Dunn ME
    134. Ellerin TB
    135. Eron JJ
    136. Fangman JJ
    137. Farel CE
    138. Ferlazzo H
    139. Fidler S
    140. Fleenor-Ford A
    141. Frankel R
    142. Freedberg KA
    143. French NK
    144. Fuchs JD
    145. Fuller JD
    146. Gaberman J
    147. Gallant JE
    148. Gandhi RT
    149. Garcia E
    150. Garmon D
    151. Gathe JC Jr
    152. Gaultier CR
    153. Gebre W
    154. Gilman FD
    155. Gilson I
    156. Goepfert PA
    157. Gottlieb MS
    158. Goulston C
    159. Groger RK
    160. Gurley TD
    161. Haber S
    162. Hardwicke R
    163. Hardy WD
    164. Harrigan PR
    165. Hawkins TN
    166. Heath S
    167. Hecht FM
    168. Henry WK
    169. Hladek M
    170. Hoffman RP
    171. Horton JM
    172. Hsu RK
    173. Huhn GD
    174. Hunt P
    175. Hupert MJ
    176. Illeman ML
    177. Jaeger H
    178. Jellinger RM
    179. John M
    180. Johnson JA
    181. Johnson KL
    182. Johnson H
    183. Johnson K
    184. Joly J
    185. Jordan WC
    186. Kauffman CA
    187. Khanlou H
    188. Killian RK
    189. Kim AY
    190. Kim DD
    191. Kinder CA
    192. Kirchner JT
    193. Kogelman L
    194. Kojic EM
    195. Korthuis PT
    196. Kurisu W
    197. Kwon DS
    198. LaMar M
    199. Lampiris H
    200. Lanzafame M
    201. Lederman MM
    202. Lee DM
    203. Lee JM
    204. Lee MJ
    205. Lee ET
    206. Lemoine J
    207. Levy JA
    208. Llibre JM
    209. Liguori MA
    210. Little SJ
    211. Liu AY
    212. Lopez AJ
    213. Loutfy MR
    214. Loy D
    215. Mohammed DY
    216. Man A
    217. Mansour MK
    218. Marconi VC
    219. Markowitz M
    220. Marques R
    221. Martin JN
    222. Martin HL Jr
    223. Mayer KH
    224. McElrath MJ
    225. McGhee TA
    226. McGovern BH
    227. McGowan K
    228. McIntyre D
    229. Mcleod GX
    230. Menezes P
    231. Mesa G
    232. Metroka CE
    233. Meyer-Olson D
    234. Miller AO
    235. Montgomery K
    236. Mounzer KC
    237. Nagami EH
    238. Nagin I
    239. Nahass RG
    240. Nelson MO
    241. Nielsen C
    242. Norene DL
    243. O'Connor DH
    244. Ojikutu BO
    245. Okulicz J
    246. Oladehin OO
    247. Oldfield EC III
    248. Olender SA
    249. Ostrowski M
    250. Owen WF Jr
    251. Pae E
    252. Parsonnet J
    253. Pavlatos AM
    254. Perlmutter AM
    255. Pierce MN
    256. Pincus JM
    257. Pisani L
    258. Price LJ
    259. Proia L
    260. Prokesch RC
    261. Pujet HC
    262. Ramgopal M
    263. Rathod A
    264. Rausch M
    265. Ravishankar J
    266. Rhame FS
    267. Richards CS
    268. Richman DD
    269. Rodes B
    270. Rodriguez M
    271. Rose RC III
    272. Rosenberg ES
    273. Rosenthal D
    274. Ross PE
    275. Rubin DS
    276. Rumbaugh E
    277. Saenz L
    278. Salvaggio MR
    279. Sanchez WC
    280. Sanjana VM
    281. Santiago S
    282. Schmidt W
    283. Schuitemaker H
    284. Sestak PM
    285. Shalit P
    286. Shay W
    287. Shirvani VN
    288. Silebi VI
    289. Sizemore JM Jr
    290. Skolnik PR
    291. Sokol-Anderson M
    292. Sosman JM
    293. Stabile P
    294. Stapleton JT
    295. Starrett S
    296. Stein F
    297. Stellbrink HJ
    298. Sterman FL
    299. Stone VE
    300. Stone DR
    301. Tambussi G
    302. Taplitz RA
    303. Tedaldi EM
    304. Telenti A
    305. Theisen W
    306. Torres R
    307. Tosiello L
    308. Tremblay C
    309. Tribble MA
    310. Trinh PD
    311. Tsao A
    312. Ueda P
    313. Vaccaro A
    314. Valadas E
    315. Vanig TJ
    316. Vecino I
    317. Vega VM
    318. Veikley W
    319. Wade BH
    320. Walworth C
    321. Wanidworanun C
    322. Ward DJ
    323. Warner DA
    324. Weber RD
    325. Webster D
    326. Weis S
    327. Wheeler DA
    328. White DJ
    329. Wilkins E
    330. Winston A
    331. Wlodaver CG
    332. van't Wout A
    333. Wright DP
    334. Yang OO
    335. Yurdin DL
    336. Zabukovic BW
    337. Zachary KC
    338. Zeeman B
    339. Zhao M

    (2013) The major genetic determinants of HIV-1 control affect HLA class I peptide presentation

    Science 330:1551–1557.

    https://doi.org/10.1126/science.1195271

    • Google Scholar
22. 1. Jenkins MK
    2. Moon JJ

    (2012) The role of naive T cell precursor frequency and recruitment in dictating immune response magnitude

    The Journal of Immunology 188:4135–4140.

    https://doi.org/10.4049/jimmunol.1102661

    • Google Scholar
23. 1. Julliard R
    2. Clavel J
    3. Devictor V
    4. Jiguet F
    5. Couvet D

    (2006) Spatial segregation of specialists and generalists in bird communities

    Ecology Letters 9:1237–1244.

    https://doi.org/10.1111/j.1461-0248.2006.00977.x

    • Google Scholar
24. 1. Kabsch W

    (2010) XDS

    Acta crystallographica. Section D, Biological crystallography 66:125–132.

    https://doi.org/10.1107/S0907444909047337

    • Google Scholar
25. 1. Kaufman J
    2. Milne S
    3. Göbel TW
    4. Walker BA
    5. Jacob JP
    6. Auffray C
    7. Zoorob R
    8. Beck S

    (1999) The chicken B locus is a minimal essential major histocompatibility complex

    Nature 401:923–925.

    https://doi.org/10.1038/44856

    • Google Scholar
26. 1. Kaufman J
    2. Salomonsen J

    (1997) The ‘Minimal Essential MHC’ revisited: both peptide-binding and cell surface expression level of MHC molecules are polymorphisms selected by pathogens in chickens

    Hereditas 127:67–74.

    https://doi.org/10.1111/j.1601-5223.1997.t01-1-00067.x

    • Google Scholar
27. 1. Kaufman J
    2. Völk H
    3. Wallny H-J

    (1995) A ‘minimal essential Mhc’ and an ‘unrecognized Mhc’: two extremes in selection for polymorphism

    Immunological Reviews 143:63–88.

    https://doi.org/10.1111/j.1600-065X.1995.tb00670.x

    • Google Scholar
28. 1. Koch M
    2. Camp S
    3. Collen T
    4. Avila D
    5. Salomonsen J
    6. Wallny HJ
    7. van Hateren A
    8. Hunt LG
    9. Jacob JP
    10. Johnston F
    11. Marston DA
    12. Shaw I
    13. Dunbar PR
    14. Cerundolo V
    15. Jones EY
    16. Kaufman J

    (2007) Structures of an MHC class I molecule from B21 chickens illustrate promiscuous peptide binding

    Immunity 27:885–899.

    https://doi.org/10.1016/j.immuni.2007.11.007

    • Google Scholar
29. 1. Kosmrlj A
    2. Read EL
    3. Qi Y
    4. Allen TM
    5. Altfeld M
    6. Deeks SG
    7. Pereyra F
    8. Carrington M
    9. Walker BD
    10. Chakraborty AK

    (2010) Effects of thymic selection of the T-cell repertoire on HLA class I-associated control of HIV infection

    Nature 465:350–354.

    https://doi.org/10.1038/nature08997

    • Google Scholar
30. 1. Leisner C
    2. Loeth N
    3. Lamberth K
    4. Justesen S
    5. Sylvester-Hvid C
    6. Schmidt EG
    7. Claesson M
    8. Buus S
    9. Stryhn A

    (2008) One-pot, mix-and-read peptide-MHC tetramers

    PLOS ONE 3:e1678.

    https://doi.org/10.1371/journal.pone.0001678

    • Google Scholar
31. 1. Lo WL
    2. Solomon BD
    3. Donermeyer DL
    4. Hsieh CS
    5. Allen PM

    (2014) T cell immunodominance is dictated by the positively selecting self-peptide

    eLife 3:e01457.

    https://doi.org/10.7554/eLife.01457

    • Google Scholar
32. 1. Martin MP
    2. Gao X
    3. Lee JH
    4. Nelson GW
    5. Detels R
    6. Goedert JJ
    7. Buchbinder S
    8. Hoots K
    9. Vlahov D
    10. Trowsdale J
    11. Wilson M
    12. O'Brien SJ
    13. Carrington M

    (2002) Epistatic interaction between KIR3DS1 and HLA-B delays the progression to AIDS

    Nature Genetics 31:429–434.

    https://doi.org/10.1038/ng934

    • Google Scholar
33. 1. Macnamara A
    2. Rowan A
    3. Hilburn S
    4. Kadolsky U
    5. Fujiwara H
    6. Suemori K
    7. Yasukawa M
    8. Taylor G
    9. Bangham CR
    10. Asquith B

    (2010) HLA class I binding of HBZ determines outcome in HTLV-1 infection

    PLOS Pathogens 6:e1001117.

    https://doi.org/10.1371/journal.ppat.1001117

    • Google Scholar
34. Book

    1. McArthur RH

    (1972)

    Geographical ecology

    New York, USA: Harper & Row.

    • Google Scholar
35. 1. McCoy AJ
    2. Grosse-Kunstleve RW
    3. Adams PD
    4. Winn MD
    5. Storoni LC
    6. Read RJ

    (2007) Phaser crystallographic software

    Journal of Applied Crystallography 40:658–674.

    https://doi.org/10.1107/S0021889807021206

    • Google Scholar
36. 1. Moss P
    2. Khan N

    (2004) CD8(+) T-cell immunity to cytomegalovirus

    Human Immunology 65:456–464.

    https://doi.org/10.1016/j.humimm.2004.02.014

    • Google Scholar
37. 1. Murshudov GN
    2. Vagin AA
    3. Dodson EJ

    (1997) Refinement of macromolecular structures by the maximum-likelihood method

    Acta Crystallographica Section D, Biological Crystallography 53:240–255.

    https://doi.org/10.1107/S0907444996012255

    • Google Scholar
38. 1. Mwangi WN
    2. Smith LP
    3. Baigent SJ
    4. Beal RK
    5. Nair V
    6. Smith AL

    (2011) Clonal structure of rapid-onset MDV-driven CD4+ lymphomas and responding CD8+ T cells

    PLOS Pathogens 7:e1001337.

    https://doi.org/10.1371/journal.ppat.1001337

    • Google Scholar
39. 1. Nowak MA
    2. Tarczy-Hornoch K
    3. Austyn JM

    (1992) The optimal number of major histocompatibility complex molecules in an individual

    Proceedings of the National Academy of Sciences of USA 89:10896–10899.

    https://doi.org/10.1073/pnas.89.22.10896

    • Google Scholar
40. 1. Paul S
    2. Weiskopf D
    3. Angelo MA
    4. Sidney J
    5. Peters B
    6. Sette A

    (2013) HLA class I alleles are associated with peptide-binding repertoires of different size, affinity, and immunogenicity

    The Journal of Immunology 191:5831–5839.

    https://doi.org/10.4049/jimmunol.1302101

    • Google Scholar
41. 1. Plachy J
    2. Pink JRL
    3. Hala K

    (1992)

    Biology of the chicken MHC (B complex)

    Critical Reviews in Immunology 12:47–79.

    • Google Scholar
42. 1. Rebaï N
    2. Malissen B

    (1983) Structural and genetic analyses of HLA class I molecules using monoclonal xenoantibodies

    Tissue Antigens 22:107–117.

    https://doi.org/10.1111/j.1399-0039.1983.tb01176.x

    • Google Scholar
43. 1. Remold S

    (2012) Understanding specialism when the jack of all trades can be the master of all

    Proceedings Biological Sciences/The Royal Society 279:4861–4869.

    https://doi.org/10.1098/rspb.2012.1990

    • Google Scholar
44. 1. Rizvi SM
    2. Salam N
    3. Geng J
    4. Qi Y
    5. Bream JH
    6. Duggal P
    7. Hussain SK
    8. Martinson J
    9. Wolinsky SM
    10. Carrington M
    11. Raghavan M

    (2014) Distinct assembly profiles of HLA-B molecules

    The Journal of Immunology 192:4967–4976.

    https://doi.org/10.4049/jimmunol.1301670

    • Google Scholar
45. 1. Schneidewind A
    2. Brockman MA
    3. Yang R
    4. Adam RI
    5. Li B
    6. Le Gall S
    7. Rinaldo CR
    8. Craggs SL
    9. Allgaier RL
    10. Power KA
    11. Kuntzen T
    12. Tung CS
    13. LaBute MX
    14. Mueller SM
    15. Harrer T
    16. McMichael AJ
    17. Goulder PJ
    18. Aiken C
    19. Brander C
    20. Kelleher AD
    21. Allen TM

    (2007) Escape from the dominant HLA-B27-restricted cytotoxic T-lymphocyte response in Gag is associated with a dramatic reduction in human immunodeficiency virus type 1 replication

    Journal of Virology 81:12382–12393.

    https://doi.org/10.1128/JVI.01543-07

    • Google Scholar
46. 1. Shaw I
    2. Powell TJ
    3. Marston DA
    4. Baker K
    5. van Hateren A
    6. Riegert P
    7. Wiles MV
    8. Milne S
    9. Beck S
    10. Kaufman J

    (2007) Different evolutionary histories of the two classical class I genes BF1 and BF2 illustrate drift and selection within the stable MHC haplotypes of chickens

    The Journal of Immunology 178:5744–5752.

    https://doi.org/10.4049/jimmunol.178.9.5744

    • Google Scholar
47. 1. Smith KB
    2. Ellis SA

    (1999) Standardisation of a procedure for quantifying surface antigens by indirect immunofluorescence

    Journal of Immunological Methods 228:29–36.

    https://doi.org/10.1016/S0022-1759(99)00087-3

    • Google Scholar
48. Book

    1. Smith LP

    (2004)

    Development of a tetracycline-inducible expression system of the Marek's disease virus oncoprotein, meq. BSc (Hons) Thesis

    Oxford Brookes University.

    • Google Scholar
49. 1. Sommer S

    (2005) The importance of immune gene variability (MHC) in evolutionary ecology and conservation

    Frontiers in Zoology 2:16.

    https://doi.org/10.1186/1742-9994-2-16

    • Google Scholar
50. 1. Stein N

    (2008) CHAINSAW: a program for mutating pdb files used as templates in molecular replacement

    Journal of Applied Crystallography 41:641–643.

    https://doi.org/10.1107/S0021889808006985

    • Google Scholar
51. 1. Steven NM
    2. Annels NE
    3. Kumar A
    4. Leese AM
    5. Kurilla MG
    6. Rickinson AB

    (1997) Immediate early and early lytic cycle proteins are frequent targets of the Epstein-Barr virus-induced cytotoxic T cell response

    The Journal of Experimental Medicine 185:1605–1617.

    https://doi.org/10.1084/jem.185.9.1605

    • Google Scholar
52. 1. Straub CS
    2. Ives AR
    3. Gratton C

    (2011) Evidence for a trade-off between host-range breadth and host-use efficiency in aphid parasitoids

    The American Naturalist 177:389–395.

    https://doi.org/10.1086/658177

    • Google Scholar
53. 1. Sundell J
    2. Ylönen H

    (2008) Specialist predator in a multi-species prey community: boreal voles and weasels

    Integrative Zoology 3:51–63.

    https://doi.org/10.1111/j.1749-4877.2008.00077.x

    • Google Scholar
54. 1. Tahara T
    2. Yang SY
    3. Khan R
    4. Abish S
    5. Hämmerling GJ
    6. Hämmerling U

    (1990) HLA antibody responses in HLA class I transgenic mice

    Immunogenetics 32:351–360.

    https://doi.org/10.1007/BF00211650

    • Google Scholar
55. 1. Thomas R
    2. Apps R
    3. Qi Y
    4. Gao X
    5. Male V
    6. O'hUigin C
    7. O'Connor G
    8. Ge D
    9. Fellay J
    10. Martin JN
    11. Margolick J
    12. Goedert JJ
    13. Buchbinder S
    14. Kirk GD
    15. Martin MP
    16. Telenti A
    17. Deeks SG
    18. Walker BD
    19. Goldstein D
    20. McVicar DW
    21. Moffett A
    22. Carrington M

    (2009) HLA-C cell surface expression and control of HIV/AIDS correlate with a variant upstream of HLA-C

    Nature Genetics 41:1290–1294.

    https://doi.org/10.1038/ng.486

    • Google Scholar
56. 1. Trowsdale J
    2. Knight JC

    (2013) Major histocompatibility complex genomics and human disease

    Annual Review of Genomics and Human Genetics 14:301–323.

    https://doi.org/10.1146/annurev-genom-091212-153455

    • Google Scholar
57. 1. Uchańska-Ziegler B
    2. Nossner E
    3. Schenk A
    4. Ziegler A
    5. Schendel DJ

    (1993) Soluble T cell receptor-like properties of an HLA-B35-specific monoclonal antibody

    European Journal of Immunology 23:734–738.

    https://doi.org/10.1002/eji.1830230325

    • Google Scholar
58. 1. Vallejo RL
    2. Bacon LD
    3. Liu HC
    4. Witter RL
    5. Groenen MA
    6. Hillel J
    7. Cheng HH

    (1998)

    Genetic mapping of quantitative trait loci affecting susceptibility to Marek's disease virus induced tumors in F2 intercross chickens

    Genetics 148:349–360.

    • Google Scholar
59. 1. van Hateren A
    2. Carter R
    3. Bailey A
    4. Kontouli N
    5. Williams T
    6. Kaufman J
    7. Elliott T

    (2013) A mechanistic basis for the co-evolution of chicken tapasin and major histocompatibility complex class I (MHC I) proteins

    The Journal of Biological Chemistry 288:32797–32808.

    https://doi.org/10.1074/jbc.M113.474031

    • Google Scholar
60. 1. Vidović D
    2. Matzinger P

    (1988) Unresponsiveness to a foreign antigen can be caused by self-tolerance

    Nature 336:222–225.

    https://doi.org/10.1038/336222a0

    • Google Scholar
61. 1. Vonrhein C
    2. Flensburg C
    3. Keller P
    4. Sharff A
    5. Smart O
    6. Paciorek W
    7. Womack T
    8. Bricogne G

    (2011) Data processing and analysis with the autoPROC toolbox

    Acta Crystallographica Section D, Biological Crystallography 67:293–302.

    https://doi.org/10.1107/S0907444911007773

    • Google Scholar
62. 1. Walker BA
    2. Hunt LG
    3. Sowa AK
    4. Skjødt K
    5. Goebel TW
    6. Lehner PJ
    7. Kaufman J

    (2011) The dominantly expressed class I molecule of the chicken MHC is explained by co-evolution with the polymorphic peptide transporter (TAP) genes

    Proceedings of the National Academy of Sciences of USA 108:8396–8401.

    https://doi.org/10.1073/pnas.1019496108

    • Google Scholar
63. 1. Walker BA
    2. van Hateren A
    3. Milne S
    4. Beck S
    5. Kaufman J

    (2005) Chicken TAP genes differ from their human orthologues in locus organisation, size, sequence features and polymorphism

    Immunogenetics 57:232–247.

    https://doi.org/10.1007/s00251-005-0786-2

    • Google Scholar
64. 1. Wallny HJ
    2. Avila D
    3. Hunt LG
    4. Powell TJ
    5. Riegert P
    6. Salomonsen J
    7. Skjødt K
    8. Vainio O
    9. Vilbois F
    10. Wiles MV
    11. Kaufman J

    (2006) Peptide motifs of the single dominantly expressed class I molecule explain the striking MHC-determined response to Rous sarcoma virus in chickens

    Proceedings of the National Academy of Sciences of USA 103:1434–1439.

    https://doi.org/10.1073/pnas.0507386103

    • Google Scholar
65. 1. Winn MD
    2. Ballard CC
    3. Cowtan KD
    4. Dodson EJ
    5. Emsley P
    6. Evans PR
    7. Keegan RM
    8. Krissinel EB
    9. Leslie AG
    10. McCoy A
    11. McNicholas SJ
    12. Murshudov GN
    13. Pannu NS
    14. Potterton EA
    15. Powell HR
    16. Read RJ
    17. Vagin A
    18. Wilson KS

    (2011) Overview of the CCP4 suite and current developments

    Acta Crystallographica Section D, Biological Crystallography 67:235–242.

    https://doi.org/10.1107/S0907444910045749

    • Google Scholar
66. 1. Winter G

    (2010) xia2: an expert system for macromolecular crystallography data reduction

    Journal of Applied Crystallography 43:186–190.

    https://doi.org/10.1107/S0021889809045701

    • Google Scholar
67. 1. Yao Y
    2. Zhao Y
    3. Smith LP
    4. Lawrie CH
    5. Saunders NJ
    6. Watson M
    7. Nair V

    (2009) Differential expression of microRNAs in Marek's disease virus-transformed T-lymphoma cell lines

    The Journal of General Virology 90:1551–1559.

    https://doi.org/10.1099/vir.0.009902-0

    • Google Scholar
68. 1. Yao Y
    2. Zhao Y
    3. Xu H
    4. Smith LP
    5. Lawrie CH
    6. Watson M
    7. Nair V

    (2008) MicroRNA profile of Marek's disease virus-transformed T-cell line MSB-1: predominance of virus-encoded microRNAs

    Journal of Virology 82:4007–4015.

    https://doi.org/10.1128/JVI.02659-07

    • Google Scholar
69. 1. Yewdell JW
    2. Bennink JR

    (1999) Immunodominance in major histocompatibility complex class I-restricted T lymphocyte responses

    Annual Review of Immunology 17:51–88.

    https://doi.org/10.1146/annurev.immunol.17.1.51

    • Google Scholar
70. 1. Yewdell JW
    2. Del Val M

    (2004) Immunodominance in TCD8+ responses to viruses: cell biology, cellular immunology, and mathematical models

    Immunity 21:149–153.

    https://doi.org/10.1016/j.immuni.2004.06.015

    • Google Scholar
71. 1. Yim JJ
    2. Selvaraj P

    (2010) Genetic susceptibility in tuberculosis

    Respirology 15:241–256.

    https://doi.org/10.1111/j.1440-1843.2009.01690.x

    • Google Scholar
72. 1. Zhang J
    2. Chen Y
    3. Qi J
    4. Gao F
    5. Liu Y
    6. Liu J
    7. Zhou X
    8. Kaufman J
    9. Xia C
    10. Gao GF

    (2012) Narrow groove and restricted anchors of MHC class I molecule BF2*0401 plus peptide transporter restriction can explain disease susceptibility of B4 chickens

    The Journal of Immunology 189:4478–4487.

    https://doi.org/10.4049/jimmunol.1200885

    • Google Scholar

Author details

1. Paul E Chappell

   Sir William Dunn School of Pathology, University of Oxford, Oxford, United Kingdom

   Contribution

   PC, Acquisition of data, Analysis and interpretation of data, Drafting or revising the article

   Contributed equally with

   El Kahina Meziane

   Competing interests

   The authors declare that no competing interests exist.

2. El Kahina Meziane

   Department of Pathology, University of Cambridge, Cambridge, United Kingdom

   Contribution

   EKM, Acquisition of data, Analysis and interpretation of data, Drafting or revising the article

   Contributed equally with

   Paul E Chappell

   Competing interests

   The authors declare that no competing interests exist.

3. Michael Harrison

   Department of Pathology, University of Cambridge, Cambridge, United Kingdom

   Contribution

   MH, Acquisition of data, Analysis and interpretation of data, Drafting or revising the article

   Competing interests

   The authors declare that no competing interests exist.

4. Łukasz Magiera

   Department of Pathology, University of Cambridge, Cambridge, United Kingdom

   Contribution

   ŁM, Acquisition of data, Analysis and interpretation of data

   Competing interests

   The authors declare that no competing interests exist.

5. Clemens Hermann

   Department of Pathology, University of Cambridge, Cambridge, United Kingdom

   Contribution

   CH, Acquisition of data, Analysis and interpretation of data

   Competing interests

   The authors declare that no competing interests exist.

6. Laura Mears

   Department of Pathology, University of Cambridge, Cambridge, United Kingdom

   Contribution

   LM, Acquisition of data, Analysis and interpretation of data

   Competing interests

   The authors declare that no competing interests exist.

7. Antoni G Wrobel

   Department of Pathology, University of Cambridge, Cambridge, United Kingdom

   Contribution

   AGW, Acquisition of data, Analysis and interpretation of data

   Competing interests

   The authors declare that no competing interests exist.

8. Charlotte Durant

   Department of Pathology, University of Cambridge, Cambridge, United Kingdom

   Contribution

   CD, Acquisition of data, Analysis and interpretation of data

   Competing interests

   The authors declare that no competing interests exist.

9. Lise Lotte Nielsen

   Faculty of Health Sciences, University of Copenhagen, Copenhagen, Denmark

   Contribution

   LLN, Provided unique constructs and bacterial cells

   Competing interests

   The authors declare that no competing interests exist.

10. Søren Buus

    Faculty of Health Sciences, University of Copenhagen, Copenhagen, Denmark

    Contribution

    SB, Provided unique constructs and bacterial cells

    Competing interests

    The authors declare that no competing interests exist.

11. Nicola Ternette

    Target Discovery Institute, University of Oxford, Oxford, United Kingdom

    Contribution

    NT, Acquisition of data, Analysis and interpretation of data, Drafting or revising the article

    Competing interests

    The authors declare that no competing interests exist.

12. William Mwangi

    Pirbright Institute, Compton, United Kingdom

    Contribution

    WM, Acquisition of data, Analysis and interpretation of data, Drafting or revising the article

    Competing interests

    The authors declare that no competing interests exist.

13. Colin Butter

    Pirbright Institute, Compton, United Kingdom

    Contribution

    CB, Acquisition of data, Drafting or revising the article, Contributed unpublished essential data or reagents

    Competing interests

    The authors declare that no competing interests exist.

14. Venugopal Nair

    Pirbright Institute, Compton, United Kingdom

    Contribution

    VN, Acquisition of data, Drafting or revising the article, Contributed unpublished essential data or reagents

    Competing interests

    The authors declare that no competing interests exist.

15. Trudy Ahyee

    Anthony Nolan Research Institute, The Royal Free Hospital, London, United Kingdom

    Contribution

    TA, Acquisition of data, Drafting or revising the article

    Competing interests

    The authors declare that no competing interests exist.

16. Richard Duggleby

    Anthony Nolan Research Institute, The Royal Free Hospital, London, United Kingdom

    Contribution

    RD, Acquisition of data, Drafting or revising the article

    Competing interests

    The authors declare that no competing interests exist.

17. Alejandro Madrigal

    1. Anthony Nolan Research Institute, The Royal Free Hospital, London, United Kingdom
    2. University College London, London, United Kingdom

    Contribution

    AM, Acquisition of data, Drafting or revising the article

    Competing interests

    The authors declare that no competing interests exist.

18. Pietro Roversi

    Sir William Dunn School of Pathology, University of Oxford, Oxford, United Kingdom

    Present address

    Department of Biochemistry, University of Oxford, Oxford, United Kingdom

    Contribution

    PR, Acquisition of data, Analysis and interpretation of data

    Competing interests

    The authors declare that no competing interests exist.

19. Susan M Lea

    Sir William Dunn School of Pathology, University of Oxford, Oxford, United Kingdom

    Contribution

    SML, Conception and design, Analysis and interpretation of data, Drafting or revising the article

    For correspondence

    susan.lea@path.ox.ac.uk

    Competing interests

    The authors declare that no competing interests exist.

20. Jim Kaufman

    1. Department of Pathology, University of Cambridge, Cambridge, United Kingdom
    2. Pirbright Institute, Compton, United Kingdom
    3. Basel Institute for Immunology, Basel, Switzerland
    4. Department of Veterinary Medicine, University of Cambridge, Cambridge, United Kingdom

    Contribution

    JK, Conception and design, Acquisition of data, Analysis and interpretation of data, Drafting or revising the article, Contributed unpublished essential data or reagents

    For correspondence

    jfk31@cam.ac.uk

    Competing interests

    The authors declare that no competing interests exist.

• 4,417

  Page views

• 928

  Downloads

• 96

  Citations

Article citation count generated by polling the highest count across the following sources: Scopus, Crossref, PubMed Central.