1. Immunology and Inflammation
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Expression levels of MHC class I molecules are inversely correlated with promiscuity of peptide binding

  1. Paul E Chappell
  2. El Kahina Meziane
  3. Michael Harrison
  4. Łukasz Magiera
  5. Clemens Hermann
  6. Laura Mears
  7. Antoni G Wrobel
  8. Charlotte Durant
  9. Lise Lotte Nielsen
  10. Søren Buus
  11. Nicola Ternette
  12. William Mwangi
  13. Colin Butter
  14. Venugopal Nair
  15. Trudy Ahyee
  16. Richard Duggleby
  17. Alejandro Madrigal
  18. Pietro Roversi
  19. Susan M Lea  Is a corresponding author
  20. Jim Kaufman  Is a corresponding author
  1. University of Oxford, United Kingdom
  2. University of Cambridge, United Kingdom
  3. University of Copenhagen, Denmark
  4. Target Discovery Institute, University of Oxford, United Kingdom
  5. Pirbright Institute, United Kingdom
  6. Anthony Nolan Research Institute, The Royal Free Hospital, United Kingdom
  7. University College London, United Kingdom
  8. Basel Institute for Immunology, Switzerland
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Cite this article as: eLife 2015;4:e05345 doi: 10.7554/eLife.05345

Abstract

Highly polymorphic major histocompatibility complex (MHC) molecules are at the heart of adaptive immune responses, playing crucial roles in many kinds of disease and in vaccination. We report that breadth of peptide presentation and level of cell surface expression of class I molecules are inversely correlated in both chickens and humans. This relationship correlates with protective responses against infectious pathogens including Marek's disease virus leading to lethal tumours in chickens and human immunodeficiency virus infection progressing to AIDS in humans. We propose that differences in peptide binding repertoire define two groups of MHC class I molecules strategically evolved as generalists and specialists for different modes of pathogen resistance. We suggest that differences in cell surface expression level ensure the development of optimal peripheral T cell responses. The inverse relationship of peptide repertoire and expression is evidently a fundamental property of MHC molecules, with ramifications extending beyond immunology and medicine to evolutionary biology and conservation.

https://doi.org/10.7554/eLife.05345.001

eLife digest

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.

https://doi.org/10.7554/eLife.05345.002

Introduction

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.

Results

Chicken class I molecules show an inverse correlation between cell surface expression and peptide-binding repertoire

We reported that the relative level of class I molecules on the surface of erythrocytes is ten-fold lower for adult chickens of the B21 MHC haplotype compared to the B4, B12, B15, and B19 haplotypes, as assessed by flow cytometry with two different monoclonal antibodies (mAb) (Kaufman et al., 1995). Extending this work, we examined another cell type, spleen lymphocytes, with a quantitative flow cytometric assay using a mAb to chicken heavy chain. We found a similar rank order for these and other haplotypes (Figure 1), with the difference between the highest and lowest ranging up to fourfold over many experiments. The B2, B14, and B21 haplotypes can be considered as ‘low-expressing haplotypes’, while B4, B12, B15, and B19 haplotypes are ‘high-expressing haplotypes’, with low-expression correlating historically with resistance to Marek's disease.

Cell surface expression levels of class I molecules vary markedly between chicken haplotypes, as determined by a quantitative flow cytometric assay.

Spleen cells from various inbred experimental chicken lines (with MHC haplotypes indicated) were stained with the monoclonal antibody F21-2 against chicken major histocompatibility complex (MHC) class I heavy chain and the specific antigen binding capacity (SABC, which reflects number of epitopes per cell calculated in reference to specific antibody-binding calibration beads). Results are means of triplicate stains, with error bars indicating standard deviation.

https://doi.org/10.7554/eLife.05345.003

For the high-expressing haplotypes B4, B12, B15, and B19, the dominantly expressed class I molecules all have definite peptide motifs, with two or three peptide positions each bearing one or two chemically similar residues (Kaufman et al., 1995; Wallny et al., 2006). In contrast, much less peptide material was isolated from low expressing B21 cells, with pool sequences showing many amino acids in every peptide position (Koch et al., 2007). Extending this work, we sequenced pools of peptides from erythrocytes and leukocytes of chickens with B2 and B14 haplotypes and found that they also showed many amino acids in each position, mostly with very different chemical characteristics (Figure 2A). Sequencing of single peptides from B2, B14, and B21 haplotypes confirmed this sequence diversity, with no obvious anchor positions bearing one or two residues with similar chemical characteristics (Figure 2B).

Peptides isolated from class I molecules of B2, B14, and B21 chickens show promiscuity of peptide binding.

For all panels, amino acids are in single letter code, with basic residues shown in blue, acidic in red, polar in green, hydrophobic in black. (A) Sequences of peptides bound to class I molecules isolated from three chicken strains determined from peptide pools showing apparent anchor, strong and weak signals. (B) Sequences of individual peptides, with confirmed anchor residues in bold. (C) Peptide anchor residues in large letters (or question marks for unknown) superimposed on a model of class I α1 and α2 domains with those residues of the major (above) and minor (below) class I sequences that are both polymorphic and potentially peptide contacts indicated as smaller letters; numbering based on human class I (HLA-A2) sequence.

https://doi.org/10.7554/eLife.05345.004

Thus, the chicken class I molecules from high-expressing haplotypes have clear peptide motifs, at least as fastidious as such motifs described in humans and mice. In contrast, the class I molecules from low-expressing haplotypes have no obvious peptide motifs, with promiscuous binding unlike what has been described in mammals. To confirm that these properties result in different numbers of distinct peptides on the cell surface, we isolated class I molecules from equal numbers of cells from the B19 cell line 265L and the B21 cell line AVOL-1, and analysed the bound peptides by mass spectrometry. Despite the B19 line having twice as many class I molecules on the cell surface as the B21 line as assessed by quantitative flow cytometry, there were only one third as many distinct peptides identified by mass spectrometry (Figure 3). Thus, a promiscuous class I molecule can and does bind a greater variety of peptides that appear on the surface of the cells, in comparison to a fastidious molecule.

There is an inverse correlation between the cell surface expression levels of class I molecules and the variety of peptides isolated from class I molecules.

(A) The B19 cell line 265L and the B21 cell line AVOL-1 were analysed by flow cytometry by staining with the mAb F21-2 to chicken class I molecules. AVOL-1 had slightly more autofluorescence, so the settings on the FACScan were adjusted so that the mean fluorescence intensity of the isotype control sample was the same as for 265L. The histogram shows the fluorescence intensity in the FL1 channel on the x-axis and the number of events on the y-axis. (B) In the same flow cytometry experiment, the calibration beads from the QIFIKIT were stained separately with the secondary antibody for calibration curves to calculate the SABC, which reflects the absolute numbers of epitopes on the cell surface. As a separate experiment, the class I molecules were isolated from each cell line by affinity chromatography with F21-2 and analysed by LC-MS/MS. Table shows the SABC and the number of different peptides found for each cell line.

https://doi.org/10.7554/eLife.05345.005

The low-expressing BF2*2101 molecule remodels the binding site to accommodate a variety of peptides with very different anchor residues

Unlike high-expressing haplotypes (Wallny et al., 2006), the wire models of the dominantly expressed class I of the B21 haplotype, BF2*2101, gave no clue as to which peptides might bind (Figure 2C). Crystal structures, based on heavy chain and β2-microglobulin (β2m) expressed in bacteria and refolded with synthetic peptides, showed that BF2*2101 remodels the binding site to accommodate two peptides with completely different sequences, including the anchor residues (Koch et al., 2007). Small residues bordering the groove lead to a large cavity in the middle of the groove, within which Asp24 and Arg9 can move, creating different configurations, as illustrated for the 11mer peptide GHAEEYGAETL and the 10mer peptide REVDEQLLSV (Figure 4A,B). The peptide position P2 His of the 11mer interacts with the Asp24, while the Pc-2 Glu interacts with the Arg9 (and Pc Leu fits in a hydrophobic pocket at the end of the groove). The 10mer remodels the binding site, so that the P2 Glu interacts with Asp24 that also interacts with Arg9 in a so-called charge transfer mechanism, creating a hydrophobic pocket that accommodates Pc-2 Leu (and Pc Val fits in the hydrophobic pocket).

Structures of BF2*2101 with different peptides show several modes of promiscuous binding through remodelling of the binding site.

Left panels, top down view with peptide as sticks (N-terminus to the left; carbon atoms, yellow; nitrogen atoms, blue; oxygen atoms, red) and class I molecule as solid surface (grey except for positions of Asp24 side chain oxygen atoms in pink and Arg9 side chain nitrogen atoms as cyan). Right panels, side view from α2 domain side with peptide, Asp24 and Arg9 as sticks (hydrogen bonds, dotted lines; carbon atoms of Asp24 and Arg9, white; all else as in left panels). (A) GHAEEYGAETL (peptide P316; PDB 3BEV); (B) REVDEQLLSV (P330; 3BEW); (C) TNPESKVFYL (P458; 2YEZ); (D) TAGQEDYDRL (P394; 4D0B); (E) TAGQSNYDRL (P399; 4D0C); (F) YELDEKFDRL (P400; 4CVZ).

https://doi.org/10.7554/eLife.05345.006

Additional peptides eluted from MHC molecules of B21 cells (Figure 2B) show that other amino acids at P2 and Pc-2 can be accommodated by the critical Asp24 and Arg9 within the large central cavity in the binding site. In fact, the 10mer TNPESKVFYL binds BF2*2101 in similar way to the 10mer REVDEQLLSV (Table 1, Figure 4C), with P2 Asn binding the Asp24 aided by charge transfer with Arg9, and with the rearrangement of Arg9 permitting the accommodation of Pc-2 Phe, just as for Pc-2 Leu in the previous structure.

Table 1

Data collection and refinement statistics

https://doi.org/10.7554/eLife.05345.007
PDBID4d0b4d0c4cvz2yez4cvx4d0d4cw1
Data collection
 Space groupP212121P212121P212121P212121P65P212121P212121
 Cell dimensions
  a, b, c (Å)60.960.560.560.6173.988.162.2
69.268.969.069.0173.992.590.58
95.494.893.794.887.5223.6144.8
  α, β, γ (°)90909090909090
90909090909090
909090901209090
 Resolution (Å)56.03–2.80 (2.95–2.80)51.00–2.82 (2.89–2.82)50.84–2.39 (2.56–2.39)41.03–2.45 (2.58–2.45)75.66–3.30 (3.56–3.30)88.08–3.13 (3.21–3.13)72.38–2.58 (2.65–2.58)
Rsym or Rmerge0.118 (0.30)0.12 (0.30)0.15 (0.91)0.07 (0.22)0.17 (0.56)0.18 (0.69)0.14 (0.59)
II8.1 (2.8)8.2 (1.9)11.9 (2.1)9.5 (1.5)11.1 (3.8)8.8 (2.1)8.8 (2.3)
 Completeness (%)91.2 (89.8)90.8 (76.0)99.9 (100)85.9 (53.0)100 (99.9)99.6 (98.8)99.3 (98.4)
 Redundancy4.0 (3.6)3.8 (1.9)6.4 (6.5)2.8 (1.6)7.3 (7.5)4.6 (4.4)5.5 (4.2)
Refinement
 Resolution (Å)56.03–2.8050.92–2.8150.84–2.3941.03–2.4575.66–3.3088.08–3.1372.38–2.58
 No. reflections9361896716,086891321,70732,91626,228
Rwork/Rfree27.5/29.6%27.5/28.2%25.9/26.9%25.2/27.3%23.8/26.2%29.2/29.9%28.6/29.1%
 Number of atoms
  Protein3061305730793091605212,1026044
  Ligand/ion04240000
  Water53142308977
 B-factors
  Protein30.3034.2430.2332.2277.8647.0240.02
  Ligand/ion26.3246.25
  Water14.3312.8824.388.6134.8734.3129.50
 Root mean square (r.m.s.) deviations
  Bond lengths (Å)0.0080.0070.0070.0070.0040.0070.007
  Bond angles (°)0.890.860.850.830.7590.820.84
  1. Highest resolution shell is shown in parenthesis.

However, the two structures with the 10mers TAGQEDYDRL and TAGQSNYDRL display a completely different mode of binding (Table 1, Figure 4D,E), for which the P2 Ala does not interact with the MHC molecule at all (and therefore is not an anchor residue), with Arg9 interacting as a bridge between Asp24 and Pc-2 Asp. A further mode of binding is shown by the 10mer YELDEKFDRL (Table 1, Figure 4F), for which both anchor residues at positions P2 and Pc-2 are acidic. In this structure, the P2 Glu interacts with both Asp24 and Arg9, and Pc-2 Asp interacts with Arg9. These various modes of binding are presumably just a few out of many and illustrate the promiscuous binding of BF2*2101, unlike anything seen for mammalian class I molecules.

The low expressing BF2*0201 and BF2*1401 molecules bind a variety of peptides through broad hydrophobic pockets

Similar to B21, the peptides isolated from cells of the low expressing haplotypes B2 and B14 have no obvious motifs. Pool sequences show no position at which only one or two chemically similar amino acids are present as anchor residues (Figure 2A). Individual peptides have no classic pattern of anchor residues, but some features are discernable (Figure 2B).

The B2 peptides fall into two groups. One small group of peptides has P2 Arg and mostly P1 Arg and Pc Tyr (Figure 2B), much like the dominantly expressed class I molecule of the high-expressing B15 haplotype, BF2*1501 (Wallny et al., 2006). These three peptides are likely to have been isolated from the poorly expressed molecule encoded by the minor gene of the B2 haplotype, BF1*0201, the wire model of which (Figure 2C) looks very similar to BF2*1501. Size exclusion chromatography (SEC) of heavy chain and β2m expressed in bacteria and refolded with peptide showed that BF1*0201 but not BF2*0201 binds these peptides (Figure 5).

The dominantly expressed class I molecule BF2*0201 binds VIFPAKSL but not RRALREGY, while the minor class I molecule BF1*0201 binds RRALREGY but not VIFPAKSL.

(A and B) Size exclusion chromatography (SEC) traces for BF2*0201 or BF1*0201 heavy chains expressed in bacteria refolded with or without β2-microglobulin (β2m) and peptide. The heavy chain BF2*0201 refolded with β2m and the appropriate peptide migrates as a native monomer, whereas refolded with the inappropriate or no peptide migrates in the same position as heavy chain alone. In contrast, all these conditions for the heavy chain BF1*0201 result in molecules that migrate roughly the same mobility. (C through F) Mass spectrometry (MALDI-TOF) analysis of the monomer peaks of heavy chain refolded with β2m and peptide shows that VIFPAKSL but not RRALREGY can be recovered from BF2*0201, while RRALREGY but not VIFPAKSL can be recovered from BF1*0201. Note the many peaks for BF1*0201 with VIFPAKSL and for BF2*0201 with RRALREGY, representing background contaminants detected as sensitivity was increased in the search for the synthetic peptide. Comparable results were obtained with YPYLGPNTL, RRALREGY, RRGGVKRI, and the B15 (BF2*1501) peptide KRLIGKRY.

https://doi.org/10.7554/eLife.05345.008

All of the other B2 peptides have generally smaller hydrophobic amino acids at P2 and somewhat larger hydrophobic amino acids at Pc (Figure 2B). Crystal structures with two of these peptides (Table 1, Figure 6A,B), YPYLGPNTL and VIFPAKSL, show that Pro and Ile at peptide position P2 and Leu at Pc bind shallow hydrophobic pockets (Figure 6A,B), similar to the way in which anchor residues bind pockets B and F in many mammalian class I molecules. This mode of binding is completely different from BF2*2101 that remodels the binding site, despite the fact that BF2*0201 shares Asp24 and Arg9 with BF2*2101. In these BF2*0201 structures, Arg9 interacts mostly with peptide main chain atoms and Asp24 interacts with Tyr43 (residue 45 being the equivalent position in human class I molecules), a residue that differs between BF2*0201 and BF2*2101, ultimately leading to a more hydrophobic pocket B to accommodate P2 residues (Figure 6A,B).

Structures of BF2*0201 and BF2*1401 show promiscuous binding via hydrophobic binding pockets for the anchor residues at peptide position P2 and Pc, with the class I residues at positions 24 and 9 playing supporting roles, and with residues lining the pockets explaining the relative size of anchor residues.

Upper panels, side view from α2 domain side with peptide as sticks (N-terminus of peptide to the left; carbon atoms of peptide, yellow; carbon atoms of class I molecule, white; nitrogen atoms, blue; oxygen atoms, red; hydrogen bonds, dotted lines; carbon atoms of Asp24 and Arg9, white). Middle (pocket B) and bottom (pocket F) panels, side view cut-away from α2 domain side with peptide and selected class I residues as sticks (numbering based on chicken class I sequence) and with rest of MHC molecule as solid surface. (A) YPYLGPNTL bound to BF2*0201 (peptide P377; PDB 4CVX); (B) VIFPAKSL bound to BF2*0201 (P473; 4D0D); (C) SWFRKPMTR bound to BF2*1401 (P479; 4CW1).

https://doi.org/10.7554/eLife.05345.009

The B14 peptides all share two features, generally a medium to large hydrophobic amino acid at P2 and one or more basic amino acids at the end of the peptide (Figure 2B). A wire model of BF2*1401 shows small hydrophobic and polar residues in and around the beginning of the groove and reveals many acidic residues at the end of the groove, well placed to interact with the basic residues at the peptide C-terminus (Figure 2C). The crystal structure of peptide SWFRKPMTR bound to BF2*1401 shows that Trp at position P2 interacts with hydrophobic surfaces in pocket B, while Arg at Pc interacts with Asp73(human position 74), Asp76(77) and Asp113(116) in pocket F (Table 1, Figure 6C). Both these pockets are much deeper in BF2*1401 compared to BF2*0201, which explains the larger anchor residues found in the B14 peptides compared to most B2 peptides. The differences in pocket sizes are primarily due to difference in size between Val43(45) and Tyr43(45) for pocket B and between Asp113(116) and Met113(116) for pocket F, which are supported by a network of hydrogen bonds from Thr24 and Gln9 in BF2*1401 and from Asp24 and Arg9 in BF2*0201 (Figure 6).

These data show that the three class I molecules with lower expression on the cell surface all bind a wide range of peptides with no obvious peptide motif, although the mechanism by which the promiscuous binding is achieved is different for each molecule. One feature that may be important is the width of the peptide-binding groove (Figure 7), which is the narrowest in the high expressing BF2*0401 molecule (Zhang et al., 2012) and the widest in the low expressing BF2*2101.

Structures of chicken class I molecules show differences in the width of the peptide-binding groove, with the fastidious BF2*0401 having the narrowest groove and the promiscuous BF2*2101 being the widest in the centre of the groove.

Top down view with peptide as sticks (N-terminus to the left; carbon atoms, yellow; nitrogen atoms, blue; oxygen atoms, red) and class I molecule as grey solid surface. (A) VIFPAKSL bound to BF2*0201 (P473; 4D0D); (B) IDWFEGKE bound to BF2*0401 (IE8; 4G43); (C) SWFRKPMTR bound to BF2*1401 (P479; 4CW1); (D) YELDEKFDRL bound to BF2*2101 (P400; 4CVZ).

https://doi.org/10.7554/eLife.05345.010

Human class I molecules also show an inverse correlation between cell surface expression and peptide-binding repertoire

The structural analysis of BF2*2101 showed that this low expressing class I molecule achieves promiscuous binding by remodelling the peptide binding site in a way never described for a mammalian class I molecule. However, analysis of BF2*0201 and BF2*1401 shows promiscuous binding by chicken molecules using anchor positions into pockets B and F, much like many mammalian classical class I molecules. In order to determine whether the inverse correlation between cell surface expression level and peptide binding promiscuity is a feature just of chickens or is indicative of a more fundamental property, we examined some human class I alleles.

Only a few studies have explored the extent of the peptide-binding repertoire of different human class I molecules. One of these studies reported the predicted peptide-binding repertoires for four human class I alleles, finding a rank hierarchy from the extremely fastidious HLA-B*57:01 to HLA-B*27:05 to HLA-B*07:02 to the highly promiscuous HLA-B*35:01, that correlated directly with progression from HIV infection to AIDS (Kosmrlj et al., 2010). The rank hierarchy of these alleles is the same as that determined by binding of peptide libraries (Paul et al., 2013).

We identified two mAbs reported (Apps et al., 2009) to react with all four HLA-B alleles (along with HLA-C alleles, which are poorly expressed on blood cells), but not with certain HLA-A alleles. Volunteers with the proper combinations of homozygous HLA-A and HLA-B alleles were recruited from a large group of bone marrow donors (Table 2), and the cell surface expression levels on their blood lymphocytes and monocytes were examined by quantitative flow cytometry. The two mAb (Tu149 and B1.23.2) reacted with all four HLA-B alleles and gave similar (but not identical) results for both lymphocytes and monocytes, with a rank hierarchy in cell surface expression ranging from HLA-B*57:01 as the highest to HLA-B*27:05 to HLA-B*07:02 to HLA-B*35:01 as the lowest (Figure 8A–D).

Table 2

Anonymized donors with class I alleles and inferred HLA-C expression

https://doi.org/10.7554/eLife.05345.011
DonorHLA-A allelesHLA-B allelesHLA-C allelesHLA-C expression
5701/0202:01, 02:0157:01, 57:0106:02, 15:02high, unknown
5701/0301:01, 01:0157:01, 57:0106:02, 06:02high, high
5701/0401:01, 03:0157:01, 57:0106:02, 07:01high, low
2705/103:01, 68:0127:05, 27:0501:02, 02:02high, high
2705/202:06, 11:0127:05, 27:0501:02, 03:03high, low
2705/302:01, 02:0127:05, 27:0501:02, 12:03high, high
2705/402:01, 02:1227:05, 27:05unknown
0702/102:01, 03:0107:02, 07:0207:02, 07:02low, low
0702/203:01, 03:0107:02, 07:0207:02, 07:02low, low
0702/302:01, 03:0107:02, 07:0207:02, 07:02low, low
0702/403:01, 11:0107:02, 07:0207:02, 07:02low, low
3501/111:01, 11:0135:01, 35:0104:01, 04:01high, high
3501/211:01, 11:0135:01, 35:0104:01, 04:01high, high
3501/303:01, 03:0135:01, 35:0104:01, 04:01high, high
3501/411:01, 11:0135:01, 35:0104:01, 04:01high, high
Human class I molecules show an inverse correlation between cell surface expression level and peptide binding promiscuity.

Levels of specific antibody binding capacity (SABC) for different mAb: (A and B) Tu149, (C and D) B1.2.23, (E and F) 22E-1 for (A, C and E) ex vivo lymphoctyes and (B, D and F) ex vivo monocytes. Each point represents the sample from a particular donor (identified with anonymous labels that correlate with haplotypes in Table 2; donor B57:01/01 failed to donate); bars indicate the mean for each HLA-B allele.

https://doi.org/10.7554/eLife.05345.012

One potential concern with these data was that the difference in expression level of the HLA-B alleles might actually be due to differences in HLA-C levels, which are known to vary between alleles (Thomas et al., 2009; Apps et al., 2013). However, inspection shows that there is no correlation of expression levels reported for the HLA-C alleles found for the individual donors and the expression level of the mAb binding (Table 2), if anything the reverse with the low expressing HLA-B*35:01 donors all having HLA-C alleles reported to have high expression.

Another concern was that the difference in expression level of the HLA-B alleles actually reflects differences in affinity of binding by the two mAb. The fact that both antibodies gave similar but non-identical results suggests that they do not recognize exactly the same epitopes and therefore are unlikely by chance to vary similarly in affinity. We tested whether the two antibodies bound independently and found that B1.23.2 inhibited both itself and Tu149 very well, while Tu149 inhibited both itself and B1.23.2 much less well (Figure 9). These results suggest that B1.23.2 has a much higher affinity than Tu149 and that their epitopes overlap. We also utilized a third antibody (22E-1) that reacts with the HLA-B*57:01 and HLA-B*27:05 but not HLA-B*07:02 and HLA-B*35:01 (nor with any HLA-C allele), and therefore must recognize a different epitope. We found the same relationship of expression level for HLA-B*57:01 and HLA-B*27:05 as with the other two antibodies (Figure 8E,F).

The epitopes on HLA-B57:01 for mAb B1.23.2 and Tu149 are overlapping, with B1.23.2 having a much higher affinity.

Win cells were treated with saturating amounts of unlabelled Tu149 (blue), saturating amounts of unlabelled B1.23.2 (green) or PBS (red), and then stained with either A Tu149 conjugated to APC or B B1.23.2 conjugated to Alexa Fluor 647. Results were compared to staining with only isotype control (grey).

https://doi.org/10.7554/eLife.05345.013

Thus, in humans the high expressing class I molecules have fastidious peptide binding, and the low expressing molecules have promiscuous binding. We conclude that the correlation between peptide-binding repertoire and expression level is the same in chickens and humans and is indicative of a fundamental property of class I molecules.

Discussion

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 Pc-2 (and hydrophobic at Pc), 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 Pc.

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 Pc-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 Pc 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.

Materials and methods

Cells

Ex vivo chicken cells were from inbred chicken lines with known MHC haplotypes (Shaw et al., 2007), kept at the University of Cambridge. All procedures were performed under appropriate Home Office Licenses and after review by the Ethics Committee at the University of Cambridge. Spleens were mashed through 100-μm nylon cell strainers (Falcon) in RPMI-1640 medium, supernatants taken after 5 min settling, and spleen cells recovered after centrifugation at 400×g for 5 min. All procedures were carried out under Home Office licenses and with ethical approval.

Chicken cell lines were from the Pirbright Institute. AVOL-1 was derived from in vitro transformation of spleen cells from a line 0 (B21) chicken by the reticuloendotheliosis virus REV-T (Smith, 2004; Yao et al., 2008; W Mwangi and V Nair, unpublished data). The MDCC-265L cell line was established from a liver lymphoma of a line P2a (B19) chicken infected with the RB-1B virus derived from a BAC clone (as in Yao et al., 2009; W Mwangi and V Nair, unpublished data). Both lines were maintained in RPMI 1640 medium containing 10% foetal bovine serum, 10% tryptose phosphate broth and 1% sodium pyruvate, and at 38.5°C in 5% CO2.

Ex vivo human cells were from Anthony Nolan registrants typed as homozygous for particular HLA-A and HLA-B locus alleles, who signed written consent forms, and with all procedures carried out under Human Tissue Act licenses and with ethical approval. Blood samples were collected by general practice or Walk-in Clinic phlebotomists and were couriered to the Anthony Nolan Research Institute within 24 hr. Whole blood was diluted 1:1 upon arrival with transport media (RPMI 1640 [Lonza, Belgium] supplemented with 0.6% tri-sodium citrate and 50 nM 2-mercaptoethanol), and most samples were rocked at room temperature overnight. In a similar manner as originally described for cord blood (Figueroa-Tentori et al., 2008), peripheral blood mononuclear cells were isolated (all steps at 20°C) using a density gradient centrifugation (Ficoll–Paque Plus 1077, GE Healthcare) at 840×g for 30 min with no brake, with the buffy coat washed twice with two volumes RPMI-1640 media, spun once at 680×g for 10 min and once at 540×g for 10 min. All samples were stained, fixed, and anonymized before transfer to Cambridge for analysis by flow cytometry.

The human homozygous HLA-B*57 typing cell line WIN (alias IHW9095 from 10th International Workshop, gift of W Bultitude and S Marsh, Anthony Nolan Research Institute) was maintained in RPMI1640 with 10% foetal bovine serum and 1 mM glutamine in 5% CO2 at 37°C.

Flow cytometry

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As described by instructions for quantitative flow cytometry from manufacturer (QIFIKIT, Dako) and following previous work (Smith and Ellis, 1999), 5 × 105 cells were incubated on ice in 96-well (U-bottom for chicken, V-bottom for human) microtiter plates (Nunc) with saturating primary antibody followed by washing and then by incubation with goat anti-mouse secondary antibody conjugated to fluorescein followed by washing, and data acquired using a FACscan (Becton-Dickenson). Set-up beads and calibration beads were stained separately with the secondary antibody for calibration curves to calculate the specific antigen binding capacity, which reflects the absolute numbers of epitopes on the cell surface. Primary mAb include 200 µl tissue culture supernatant of mouse mAb F21-2 for chicken class I molecules (Crone et al., 1985) and three that react with certain HLA-B antigens but not certain HLA-A alleles (Apps et al., 2009): 200 µl tissue culture supernatant of Tu149 (Uchańska-Ziegler et al., 1993; gift of J Trowsdale, University of Cambridge), 20 µl 1 mg/ml B1.2.23 (Rebaï and Malissen, 1983; bought from eBioscience) diluted in PBS and 20 µl 1 mg/ml 22E1 (Tahara et al., 1990; bought from Caltag Medsystems) diluted in PBS. Saturation was confirmed by staining with dilutions of antibodies on each set of cells, except for human ex vivo cells, for which saturation was confirmed by staining WIN cells.

For the inhibition assay, Tu149 conjugated to APC (Invitrogen; kind gift of S Ashraf and J Trowsdale, University of Cambridge) and purified B1.23.2 conjugated to Alexa Fluor 647 (purified mAb from eBioscience conjugated using the Antibody Labeling kit, Molecular Probes/Life Technologies, according to manufacturer's instructions) were used. As above, 5 × 105 WIN cells were incubated with saturating amounts of unlabelled B1.23.2 or Tu149 (or with PBS) for 1 hr, washed and then incubated with a directly labelled antibody for 1 hr, before washing the cells again. Data were acquired using the red laser on a Cytek FACSanalyser (Becton Dickinson).

Peptides from ex vivo chicken cells

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Inbred chicken lines, with known MHC haplotypes (Shaw et al., 2007), were kept at the Institute for Animal Health at Compton. All procedures were performed under appropriate Home Office Licenses and after review by the Ethics Committee at the Institute for Animal Health. As previously described (Wallny et al., 2006; Koch et al., 2007), erythrocytes (or whole mashed up spleens) were solubilized in detergent, and class I molecules were isolated by affinity chromatography using mAb F21-2 against chicken class I molecules or F21-21 against chicken β2m. Peptides were eluted using trifluoroacetic acid and separated by reverse phase high-pressure liquid chromatography with single peaks of abundant peptides as well as pools of non-abundant peptides sequenced by Edman degradation.

Peptides from chicken cell lines

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Immunoaffinity beads were produced, with all steps at room temperature. Protein G-Sepharose beads (Expedeon) were washed with 50 mM borate, 50 mM KCl, pH 8.0, the equivalent of 1-ml packed beads was incubated with 3 mg F21-2 (produced by the Microbiological Media Services of the Pirbright Institute) for 1 hr, treated with 40 mM dimethyl pimelimidate dihydrochloride (Sigma) in 0.1 M triethanolamine, pH 8.3 for 1 hr to cross-link the antibody to the protein G, washed with 100 mM citric acid pH 3.0, and equilibrated in 50 mM Tris, pH 8.0. The two cell lines AVOL1 and 265L were washed with PBS. Pellets of 109 cells were incubated with 10 ml 1% Igepal 630, 300 mM NaCl, 100 mM Tris pH 8.0 for 30 min at 4°C, subcellular debris was pelleted by centrifugation at 300×g for 10 min and 15,000×g for 30 min at 4°C, and the cleared lysates were incubated with 1 ml immunoaffinity beads for 1 hr at 4°C. The beads were washed with 50 mM TrisCl, pH 8.0 buffer, first with 150 mM NaCl, then with 400 mM NaCl and finally with no salt. Bound material was eluted with 10% acetic acid. The eluted material was dried, resuspended in 3% acetonitrile, 0.1% formic acid in water, loaded directly onto on a 4.6 × 50 mm ProSwiftTM RP-1S column (ThermoFisher) and eluted at 500 μl/min flow rate for 10 min with a linear gradient from 2 to 35% buffer B (0.1% formic acid in acetonitrile) in buffer A (0.1% formic acid in water) using an Ultimate 3000 HPLC system (ThermoFisher), with fractions collected from 2 to 15 min. Protein detection was performed at 280 nm absorbance, with fractions eluting before β2m pooled and dried.

For liquid chromatography tandem mass spectrometry (LC-MS/MS), peptides were analysed using either a Q-Exactive (Thermo Scientific) or a TripleTOF 5600 (AB Sciex) system. For the Q-Exactive system, peptides were separated on a Ultimate 3000 RSLCnano System utilizing a PepMap C18 column, 2 μm particle size, 75 μm × 50 cm (Thermo Scientific) with a linear gradient from 3% to 35% buffer B in buffer A (as above) at a flow rate of 250 nl/min (∼65 MPa) for 60 min, and the 15 most intense precursors per full MS scan were selected for MS/MS analysis using HCD fragmentation. For the TripleTOF system, peptides were separated with a 15 cm × 75 µm ChromXP C18-CL (3 µm particle size) using an ekspert nanoLC 400 cHiPLC system (Eksigent) with a linear gradient from 8% to 35% buffer B in buffer A (as above) at a flow rate of 300 nl/min (∼1600 psi) for 60 min, and CID fragmentation was induced on the 30 most abundant ions per full MS scan. All fragmented precursor ions were actively excluded from repeated selection for 15 s. Data were analysed using Peaks 7 (Bioinformatics Solutions) using a database containing all 24,092 Uniprot entries for the organism Gallus gallus combined with protein translations (>8 amino acids) of either all six reading frames of gallid herpesvirus 2 (NCBI entry NC_002229.3; 10,026 entries) or reticuloendotheliosis virus (NC_006934.1; 412 entries). Results were filtered using a false discovery rate of 1% that was determined by parallel searching of a randomized decoy database.

Expression and assembly of class I molecules

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As previously described (Koch et al., 2007), the extracellular sequence of mature BF2*2101 heavy chain and chicken β2m cloned in pET22b(+) were expressed separately as inclusion bodies in BL21 (λDE3) pLysS Rosetta bacterial cells, solubilized in urea and assembled together with synthetic peptide (synthesized commercially by fluorenyl-methoxy-carbonyl [fMOC] chemistry) by dilution in a renaturation buffer. Assembled class I molecules were isolated after SEC using a HiLoad 26/60 Superdex 200 column (GE Healthcare, UK). Similar procedures were used for BF1*0201, BF2*0201 and BF2*1401, except that a codon-optimized gene was synthesized (GenScript) for BF2*0201, which was then cloned into a pET28a(+) vector followed by a factor X site for cleavage, a BirA site for biotinylation and a His tag for purification (Leisner et al., 2008).

Assembly assays

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As previously described (Koch et al., 2007), heavy chains and β2m were expressed in bacteria as inclusion bodies and denatured in urea; β2m was refolded and purified by SEC. For each renaturation sample, solutions containing 30 µg heavy chain plus or minus 42 µg β2m and/or 10 µg synthetic peptide were added to refold buffer and incubated at 4°C with stirring for roughly 40 hr. After centrifugation and passage through a 0.45-µm sterile filter, each sample was loaded on a Superdex 200 10/300 GL SEC column (GE Healthcare, UK) as part of an AKTA 920 with 100 mM NaCl, 25 mM TrisCl, pH 8.0 running at 1 ml/min at room temperature. Peak fractions were collected and concentrated first using a pre-rinsed Amicon Ultra-4 10 kDa column and then a Vivaspin 500 10 kDa column to roughly 100 µl, buffer-exchanged into 50 mM NaCl, 10 mM Tris, pH 8.0, concentrated to roughly 20 µl, and then transferred into a polypropylene microfuge tube. Acetic acid was added to 5% and the sample was concentrated to 2–4 µl using a SpeedVac at 40°C before analysis by MALDI-TOF (Protein and Nucleic Acid Chemistry services, Department of Biochemistry, University of Cambridge).

Crystallography

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Recombinant MHC class I complexes were crystallized using the sitting-drop method (see Table 3 for conditions). Crystals were flash frozen in liquid nitrogen and native data sets for each crystal were collected at 100K [Diamond Light Source, Harwell (beamlines I02, I03, I04 or I04-1), or the ESRF, Grenoble (beamline ID29)]. Data were processed using either AUTOPROC (Vonrhein et al., 2011) or XIA2 (Winter, 2010) with XDS (Kabsch, 2010) for integration and AIMLESS (Evans, 2006) or SCALA (Evans and McCoy, 2008) for scaling. All structures were solved by molecular replacement, as implemented in Phaser (McCoy et al., 2007), part of the CCP4 software package (Winn et al., 2011). Starting molecular replacement models were generated using CHAINSAW (Stein, 2008) and the atomic co-ordinates of the chicken B21 MHC class I molecule (PDBID: 3BEV) with the peptide removed. Model building and refinement were carried out using COOT (Emsley et al., 2010) and AUTOBUSTER (Bricogne et al., 2011) or REFMAC (Murshudov et al., 1997), with the heavy and light chains of the MHC molecule rebuilt first before the peptide was modelled into residual electron density (see Table 1 for refinement statistics, Table 3 for data collection and refinement information, Table 4 for Ramachandran statistics).

Table 3

Crystallization conditions, data collection, and refinement information

https://doi.org/10.7554/eLife.05345.014
Structure (PDB ID)Crystallization conditionsCryo-protectantBeamlineData processingRefinement program
4 d0b0.1M MMT buffer, pH 5.0, 25% PEG 150015% ethylene glycolI02 (Diamond Light Source)AUTOPROC SUITE with XDS & SCALAAUTOBUSTER
4d0c0.05M KH2PO4, 20% PEG 800020% ethylene glycolI02 (Diamond Light Source)XIA2 with XDS & AIMLESSAUTOBUSTER
2yez0.1M MMT buffer, pH 4.0, 25% PEG 150015% ethylene glycolI03 (Diamond Light Source)AUTOPROC SUITE with XDS & SCALAAUTOBUSTER
4cvz0.1M sodium acetate, pH 5.0, 1.5 M ammonium sulphate8M sodium formateI04-1 (Diamond Light Source)XIA2 with XDS & AIMLESSAUTOBUSTER
4cvx0.1M MMT buffer, pH 7.0, 25% PEG 150015% ethylene glycolI04 (Diamond Light Source)XIA2 with XDS & AIMLESSREFMAC
4 d0d0.1M MMT buffer, pH 7.0, 25% PEG 150015% ethylene glycolI04 (Diamond Light Source)XIA2 with XDS & AIMLESSAUTOBUSTER
4cw10.1M MIB buffer, pH 5.0, 25% PEG 150015% ethylene glycolID29 (ESRF)XIA2 with XDS & AIMLESSAUTOBUSTER
Table 4

Ramachandran statistics

https://doi.org/10.7554/eLife.05345.015
Structure (PDB ID)Ramachandran outliers number (%)Ramachandran favoured number (%)
4cvz0 (0%)371 (98.15%)
4 d0b0 (0%)356 (94.43%)
4d0c0 (0%)361 (95.76%)
2yez1 (0.26%)364 (96.04%)
4cvx0 (0%)710 (95.69%)
4 d0d0 (0%)1430 (96.30%)
4cw10 (0%)730 (98.25%)

Data availability

The following data sets were generated
The following previously published data sets were used

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Decision letter

  1. Arup K Chakraborty
    Reviewing Editor; Massachusetts Institute of Technology, United States

eLife posts the editorial decision letter and author response on a selection of the published articles (subject to the approval of the authors). An edited version of the letter sent to the authors after peer review is shown, indicating the substantive concerns or comments; minor concerns are not usually shown. Reviewers have the opportunity to discuss the decision before the letter is sent (see review process). Similarly, the author response typically shows only responses to the major concerns raised by the reviewers.

Thank you for sending your work entitled “Expression Levels of MHC Class I molecules are Inversely Correlated with Promiscuity of Peptide Binding” for consideration at eLife. Your article has been favorably evaluated by Tadatsugu Taniguchi (Senior editor), a Reviewing editor, and three reviewers, one of whom, Andrej Kosmrlj, has agreed to reveal his identity.

The Reviewing editor and the reviewers discussed their comments before we reached this decision, and the Reviewing editor has assembled the following comments to help you prepare a revised submission.

All reviewers agree that this paper may represent an important study. You aim to demonstrate both in chickens and humans that the expression levels of MHC molecules on the surface of cells are inversely correlated with promiscuity of peptide binding. Interestingly MHC types, which are promiscuous for peptide binding and are thus expressed at lower levels, are associated with resistance to Marek's disease in chickens, while in humans they are associated with faster progression to AIDS. The opposite is true for MHC types, which are more restrictive for peptide binding. However, all reviewers also agree that several important points need to be addressed, and some speculations refined, before this paper is acceptable for publication. The points that need to be addressed are summarized below:

1) The authors state in the Results section, that both antibodies used to measure HLA*B expression levels gave similar but non-identical results, which suggested to them that they recognize different epitopes on the HLA-B molecules. This is very likely to be an incorrect conclusion, as Apps et al. (2009) present data strongly indicating that these antibodies actually do recognize the same epitope. The possibility that expression levels of HLA-B correlate with peptide binding promiscuity in humans is very intriguing, but the authors are encouraged to expand their monoclonals to a set that will more definitively measure HLA-B allotype expression. There are antibodies that recognize certain HLA-B allotypes specifically and with equal affinity, including pairs of the allotypes included in Figure 7. The bridging of this work from chickens (where the correlation data between peptide binding promiscuity and expression levels are clear cut) to humans is a major point of the paper, so the expression data need to be definitive. Finally, if possible, inclusion of data on HLA-A would strengthen conclusions drawn from Figure 7?

2) Repertoire size in the study has been defined on the basis of the breadth of the primary anchor motif. But, it is not clear that this is the same as the number of peptides actually bound and presented. An allele with a diverse motif, defined here as having a large repertoire, might in the end present fewer peptides because nothing really binds well. Conversely, an allele with a narrow motif might bind everything that has the motif, and in the end have a large repertoire. In this respect, defining repertoire size on the basis of motif diversity, and not on the basis of actually bound peptides, is a limitation. Some way to quantify repertoire size beyond just the number of amino acids preferred at the various anchor residues needs to be established.

3) The demarcation of alleles into generalist and specialists is interesting. However, this is likely to be a continuum, and not a strict dichotomy. At the same time, surface expression is also likely to be a continuum. The present study suggests that these two continuums run in parallel (but opposite directions). There is too little data to convincingly establish this. An example of a manifestation of the looseness of this argument is the following. According to the authors, the HLA-B57 allele would be classified as a specialist for a particular pathogen. However, it is known that they are associated with protection against HIV and hepatitis C virus (HCV). On the other hand the more promiscuous allele HLA-B8 would be classified as a generalist, but this allele is known to be associated with faster disease progression in HIV and HCV. In this regard, it may be good to also list T cell cross-reactivity as a potentially significant variable for highly mutable pathogens (see Kosmrlj et al), and the importance of presenting peptides from mutationally vulnerable regions of HIV (see Dahirel et al, 2011), Ferguson et al, 2013, and other references). What can be said about other infections where there is significant HLA association?

4) The authors suggest that the difference in cell surface expression level in chickens might have to do with peptide loading and editing? Is there any data that suggest peptide loading affects surface expression levels? This would be important to cite.

5) Is there any data to suggest that lower expression levels of class I leads to escape from negative selection (which is implied in the Discussion section, paragraph eight)?

6) The Discussion is thought-provoking, but a bit long and could do with some tightening and perhaps less speculation in cases where background data for the models do not yet exist.

https://doi.org/10.7554/eLife.05345.016

Author response

All reviewers agree that this paper may represent an important study. You aim to demonstrate both in chickens and humans that the expression levels of MHC molecules on the surface of cells are inversely correlated with promiscuity of peptide binding. Interestingly MHC types, which are promiscuous for peptide binding and are thus expressed at lower levels, are associated with resistance to Marek's disease in chickens, while in humans they are associated with faster progression to AIDS. The opposite is true for MHC types, which are more restrictive for peptide binding. However, all reviewers also agree that several important points need to be addressed, and some speculations refined, before this paper is acceptable for publication. The points that need to be addressed are summarized below:

1) The authors state in the Results section that both antibodies used to measure HLA*B expression levels gave similar but non-identical results, which suggested to them that they recognize different epitopes on the HLA-B molecules. This is very likely to be an incorrect conclusion, as Apps et al. (2009) present data strongly indicating that these antibodies actually do recognize the same epitope. The possibility that expression levels of HLA-B correlate with peptide binding promiscuity in humans is very intriguing, but the authors are encouraged to expand their monoclonals to a set that will more definitively measure HLA-B allotype expression. There are antibodies that recognize certain HLA-B allotypes specifically and with equal affinity, including pairs of the allotypes included in Figure 7. The bridging of this work from chickens (where the correlation data between peptide binding promiscuity and expression levels are clear cut) to humans is a major point of the paper, so the expression data need to be definitive. Finally, if possible, inclusion of data on HLA-A would strengthen conclusions drawn from Figure 7?

We are glad that, in this part of the review, the reviewers consider the work with chicken class I molecules to be definitive, and we share their desire to see even more data for human class I molecules. To put this request into perspective, the two papers on the expression level of human class I molecules (Nature Genetics and Science, for which the level of proof should have been high) describe data with one single monoclonal antibody directed to HLA-C and large panels of humans bearing all kinds of HLA types, mostly heterozygote for HLA-C, giving a vast range of expression levels from which the authors derive a statistical result. Inspection of the data shows that the concordance of alleles with expression level is not perfect, even for identified homozygotes.

We chose a different approach, which was to pick individuals examined in great detail for their MHC alleles that had the right array of class I alleles to allow us to examine the four HLA-B alleles without confounding effects of heterozygosity or HLA-A cross-reaction. It took us over two years to get all these data, in large part because these carefully chosen individuals are only willing to donate at times of their own convenience. As appealing as it is (for us as well as for the reviewers) to gather substantially more data, recruitment of new donors and subsequent experiments are likely to take us at least another two years, and therefore are outside the scope of this current paper.

We also chose to use two different monoclonal antibodies, twice as many as were used in the two previous publications. On the basis of the similar but non-identical results of the two antibodies with the four kinds of donors, we suggested that the epitopes for these antibodies are likely not to be identical. The reviewer countered that data in the Apps et al 2013 paper suggests that the epitopes are identical, presumably (since they did not state why) because of concordance of reaction with a range of class I alleles, with the implication that the two antibodies were not independent and thus were no better than previous data with HLA-C. We have responded in two ways. First, we test whether either antibody will inhibit the other, finding that the two antibodies do inhibit each other and therefore have at the least overlapping epitopes, but that one inhibited the other much better than the reverse suggesting that they have very different affinities. Second, we used a third monoclonal antibody that recognizes only HLA-B57 and HLA-B27 (and thus is extremely likely to recognize a separate epitope) and found the same pattern with two donors as with the other two antibodies.

Finally, the reviewers ask us to examine many other HLA allele pairs with monoclonal antibodies. We chose the four HLA-B alleles because they have been examined in the Kosmrlj et al, 2010, Nature article (for which, again, the level of proof should have been high) that had gone some way towards understanding the story we have been working on for 20 years. It took us two years to recruit the donors for the experiments involving two monoclonal antibodies with these four alleles, and a further two months to recruit two further donors to examine with an additional antibody in response to the review. A study with the many antibodies envisaged by the reviewers would be extremely interesting, but it likely to take years to complete in a worthwhile fashion, and therefore is outside the scope of the current paper.

2) Repertoire size in the study has been defined on the basis of the breadth of the primary anchor motif. But, it is not clear that this is the same as the number of peptides actually bound and presented. An allele with a diverse motif, defined here as having a large repertoire, might in the end present fewer peptides because nothing really binds well. Conversely, an allele with a narrow motif might bind everything that has the motif, and in the end have a large repertoire. In this respect, defining repertoire size on the basis of motif diversity, and not on the basis of actually bound peptides, is a limitation. Some way to quantify repertoire size beyond just the number of amino acids preferred at the various anchor residues needs to be established.

This is an important point that we should have considered. As a result, we now include an experiment comparing B19 and B21 cell lines. The B19 cells have twice as many class I molecules on the cell surface as assessed by flow cytometry, but have only one third as many sequences of peptides as assessed by immuno-isolation followed by mass spectrometry analysis. These results show that promiscuous class I alleles can and do bind a greater variety of peptides that are presented on the cell surface.

3) The demarcation of alleles into generalist and specialists is interesting. However, this is likely to be a continuum, and not a strict dichotomy. At the same time, surface expression is also likely to be a continuum. The present study suggests that these two continuums run in parallel (but opposite directions). There is too little data to convincingly establish this.

This portion of the comment appears to repeat our arguments back to us. In the Discussion we say “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”, although due to space limitations we only mention the two groups in the Abstract. Of course more data is always welcome, but we judge that we have shown enough data to support our argument.

An example of a manifestation of the looseness of this argument is the following. According to the authors, the HLA-B57 allele would be classified as a specialist for a particular pathogen. However, it is known that they are associated with protection against HIV and hepatitis C virus (HCV). On the other hand the more promiscuous allele HLA-B8 would be classified as a generalist, but this allele is known to be associated with faster disease progression in HIV and HCV. In this regard, it may be good to also list T cell cross-reactivity as a potentially significant variable for highly mutable pathogens (see Kosmrlj et al), and the importance of presenting peptides from mutationally vulnerable regions of HIV (see Dahirel et al, 2011, Ferguson et al, 2013, and other references). What can be said about other infections where there is significant HLA association?

We are glad that reviewers find the generalist and specialist idea interesting, but it is only one of several ways we propose as ways to look at this phenomenon, and we are careful to say that the different proposals are not necessarily mutually exclusive. In the Discussion we state: “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.” The fact that B57 protects from both HIV and HCV, while B8 does not, may lend support to the proposal that fastidious class I molecules are better for viruses with mutable regions while promiscuous molecules are better for more stable pathogens with larger genomes. However, such an analysis as suggested is well beyond the scope of the present paper.

4) The authors suggest that the difference in cell surface expression level in chickens might have to do with peptide loading and editing? Is there any data that suggest peptide loading affects surface expression levels? This would be important to cite.

The first sentence is phrased as a question, and there is no doubt that we have made this point as a proposal in the Discussion. To our knowledge, there is little pertinent data published even for mammals; we already have cited Rizvi et al, 2014, J Immunol.

5) Is there any data to suggest that lower expression levels of class I leads to escape from negative selection (which is implied in the Discussion section, paragraph eight)?

We are not aware of any published data on this point, but it is stated explicitly (not implied as asserted by the reviewer) as part of our proposal in the Discussion that flows directly from our interpretation of the data presented in this manuscript and is an ongoing area of research in our laboratory. Extension of data into novel hypotheses is standard scientific practice, as a way of stimulating interest in further experimentation. For instance, in their 2010 Nature article Kosmrlj et al proposed that promiscuous class I molecules led to pauperized T cell repertoires, a proposal for which they presented no experimental evidence but which has stimulated further thought (not the least for us).

6) The Discussion is thought-provoking, but a bit long and could do with some tightening and perhaps less speculation in cases where background data for the models do not yet exist.

We have made changes to the Discussion to clarify and add support to the proposals made. In any case, our main text is within 150 words of the 5000 words recommended by the current Author Guides and Policies published on-line by eLife. Therefore, we see no reason to shorten or limit the Discussion, which we hope is full of interesting new ideas.

https://doi.org/10.7554/eLife.05345.017

Article and author information

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.

Funding

Wellcome Trust (Programme grant 089305)

  • El Kahina Meziane
  • Michael Harrison
  • Łukasz Magiera
  • Clemens Hermann
  • Laura Mears
  • Antoni G Wrobel
  • Charlotte Durant
  • Jim Kaufman

Biotechnology and Biological Sciences Research Council (BBSRC) (Core Funding to the Pirbright Institute)

  • William Mwangi
  • Colin Butter
  • Venugopal Nair

Biotechnology and Biological Sciences Research Council (BBSRC) (PhD studentship)

  • Paul E Chappell

Wellcome Trust (Senior Investigator Award)

  • Susan M Lea

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

Acknowledgements

Structure factors and atomic coordinates have been deposited with the Protein Data Bank under accession codes 2YEZ (BF2*2101 with TNPESKVFYL), 4D0B (BF2*2101 with TAGQEDYDRL), 4D0C (BF2*2101 with TAGQSNYDRL), 4CVZ (BF2*2101 with YELDEKFDRL), 4CVX (BF2*0201 with YPYLGPNTL), 4D0D (BF2*0201 with VIFPAKSL), and 4CW1 (BF2*1401 with SWFRKPMTR). We thank many colleagues at the IAH (now rebranded as the Pirbright Institute) for much historical help in processing materials for analysis of MHC-bound peptides, especially Larry Hunt from the Protein Chemistry facility for analysis of eluted peptides by Edman degradation and for provision of synthetic peptides.

We thank Diamond Light Source for access to beamlines I02, I03, I04, and I04-1 (mx9306) that contributed to the results presented here. The B14 structural experiments were performed on the ID29 beamline at the European Synchrotron Radiation Facility (ESRF), Grenoble, France. We are grateful to Local Contact at ESRF for providing assistance in using beamline ID29. We also thank Ed Lowe from the Biochemistry Department, Oxford for data collection and members of the Lea lab for crystallography discussions. We thank Steve Marsh from the Anthony Nolan, Tom Pizzari from Oxford Zoology, and Lars Råberg from Lund University for helpful discussions. We thank Gillian Griffiths, Alison Schuldt, Michaela Fakiola, and Mike Ratcliffe for critical reading of the manuscript. The authors declare no conflicts of interest.

Ethics

Human subjects: Anthony Nolan registrants signed written consent forms, with all procedures carried out under Human Tissue Act licensing number 22513 and with approval of the local Research Ethics committee (REC).

Animal experimentation: All procedures involving chickens were carried out at the University of Cambridge under Home Office project license PPL 80/2420 and with ethical approval of the Local Ethical Review Committee.

Reviewing Editor

  1. Arup K Chakraborty, Massachusetts Institute of Technology, United States

Publication history

  1. Received: October 26, 2014
  2. Accepted: April 10, 2015
  3. Accepted Manuscript published: April 10, 2015 (version 1)
  4. Version of Record published: May 6, 2015 (version 2)

Copyright

© 2015, Chappell et al.

This article is distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use and redistribution provided that the original author and source are credited.

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Further reading

    1. Biochemistry and Chemical Biology
    2. Immunology and Inflammation
    Gerald Willimsky et al.
    Research Article

    Proteasome catalyzed peptide splicing (PCPS) of cancer-driving antigens could generate attractive neoepitopes to be targeted by TCR-based adoptive T cell therapy. Based on a spliced peptide prediction algorithm TCRs were generated against putative KRASG12V and RAC2P29L derived neo-splicetopes with high HLA-A*02:01 binding affinity. TCRs generated in mice with a diverse human TCR repertoire specifically recognized the respective target peptides with high efficacy. However, we failed to detect any neo-splicetope specific T cell response when testing the in vivo neo-splicetope generation and obtained no experimental evidence that the putative KRASG12V- and RAC2P29L-derived neo-splicetopes were naturally processed and presented. Furthermore, only the putative RAC2P29L-derived neo-splicetopes was generated by in vitro PCPS. The experiments pose severe questions on the notion that available algorithms or the in vitro PCPS reaction reliably simulate in vivo splicing and argue against the general applicability of an algorithm-driven 'reverse immunology' pipeline for the identification of cancer-specific neo-splicetopes.

    1. Immunology and Inflammation
    Jinwei Chai et al.
    Research Article

    Antimicrobial peptides form part of the innate immune response and play a vital role in host defense against pathogens. Here we report a new antimicrobial peptide belonging to the cathelicidin family, cathelicidin-MH (cath-MH), from the skin of Microhyla heymonsivogt frog. Cath-MH has a single α-helical structure in membrane-mimetic environments and is antimicrobial against fungi and bacteria, especially Gram-negative bacteria. In contrast to other cathelicidins, cath-MH suppresses coagulation by affecting the enzymatic activities of tissue plasminogen activator, plasmin, β-tryptase, elastase, thrombin, and chymase. Cath-MH protects against lipopolysaccharide (LPS)- and cecal ligation and puncture-induced sepsis, effectively ameliorating multiorgan pathology and inflammatory cytokine through its antimicrobial, LPS-neutralizing, coagulation suppressing effects as well as suppression of MAPK signaling. Taken together, these data suggest that cath-MH is an attractive candidate therapeutic agent for the treatment of septic shock.