Most cells in the body make proteins called human leukocyte antigen class I (or HLA-I). These proteins sit on the cell surface, where they help the immune system distinguish between healthy and diseased cells. A groove in each HLA-I protein holds a fragment of a protein chain, called a peptide, from inside the cell. In healthy cells, all the peptides come from normal proteins. Yet in diseased or infected cells, the peptides may come from abnormal or foreign proteins – those encoded by viruses, for example. When the immune system sees these abnormal peptides, it responds by killing the cell.

Across the human population, there are thousands of types of HLA-I, each able to carry a different set of peptides. Any individual person can only make a maximum of six types of the HLA-I, meaning we each show a different combination of peptides to our immune cells. This difference will change the way different people respond to the same disease.

Before a peptide can be assembled into HLA-I, it must be moved to the correct part of the cell by a transporter known as TAP. This transport favors peptides with certain characteristics, but these characteristics do not always match the preferences of the individual's HLA-I proteins. For example, TAP is less likely to transport peptides where the second building block in the chain is a proline, but these peptides will still fit into the binding grooves of some HLA-I variants.

Here, Yarzabek, Zaitouna, Olson et al. obtained blood from healthy human donors to answer questions about what happens when TAP and HLA-I have different preferences. Specifically, how many HLA-I molecules reach the surface, how long do they last, and which peptides do they carry?

This analysis revealed that, when there was a mismatch between HLA-I and TAP, the amount of some HLA-I types on the surface of white blood cells called lymphocytes dropped. These HLA-I types were also able to pick up new peptides from their environment, indicating that some HLA-I were at the surface of the cell without a peptide. The role of these empty HLA-I remains to be fully defined.

The reverse was true for other white blood cells called monocytes; HLA-I variants that were mismatched with TAP became more abundant on the cell surface. Monocytes also had more HLA-I molecules inside and did not pick up peptides from the environment. This suggests that monocytes may load peptides via new pathways, filling grooves left empty in lymphocytes, although other mechanisms might also explain the differences between the two types of white blood cells. Taken together, the findings reveal that HLA-I on the surface of cells depends on both the type of HLA-I and the type of immune cell.

HLA-I proteins play a key role in the immune system’s ability to recognize and kill diseased cells. A better knowledge of how HLA-I variants differ could help us to understand why people respond differently to the same disease. A better grasp of HLA-I could in the future lead to improved drug and vaccine design.

Major histocompatibility complex (MHC) class I proteins are cell surface proteins that control immune responses by CD8⁺ T cells and natural killer (NK) cells. MHC class I proteins are comprised of a heavy chain, a light chain, β2-microglobulin (β2m), and a short peptide that is bound to a peptide-binding groove in the heavy chain (Bjorkman and Parham, 1990). Heavy chains of human MHC molecules (human leukocyte antigens (HLA)) are encoded by three sets of genes, which are the HLA-A, HLA-B and HLA-C genes. These genes are highly polymorphic, with about 5000 known alleles in the case of HLA-B and fewer alleles in the case of HLA-A and HLA-C genes (Robinson et al., 2015). Polymorphic residues are localized to the peptide-binding groove of HLA class I proteins and determine their specificities for peptide binding (Bjorkman and Parham, 1990). T cell receptors (TCR) of cytotoxic T cells have specificities for combinations of MHC class I and peptide (Rossjohn et al., 2015). Binding of a CD8⁺ T cell TCR to peptide-MHC class I complexes triggers CD8⁺ T cell cytokine production and cytotoxic activity. Conversely, NK cells have inhibitory receptors that recognize HLA class I (Saunders et al., 2015). Engagement of MHC class I by NK cell inhibitory receptors suppresses NK cell activity (Parham and Moffett, 2013). NK cell activity is induced by MHC class I down-modulation, a strategy frequently used by viruses and cancers to evade CD8⁺ T cell responses.

MHC class I assembly involves a complex pathway that is initiated by the formation of chaperone-guided heterodimers of heavy chains and β2m. In the absence of a peptide ligand, heavy chain-β2m heterodimers are generally unstable and retained in the endoplasmic reticulum (ER) via the peptide loading complex (PLC). The PLC facilitates peptide loading of MHC class I, and comprises peptide-deficient forms of MHC class I molecules in complex with the transporter associated with antigen processing (TAP), the assembly factor tapasin, and the ER chaperones calreticulin and ERp57. The binding of a peptide releases MHC class I from the PLC, and allows for trafficking to the cell surface via the Golgi network (Blum et al., 2013; Raghavan and Geng, 2015). HLA class I alleles have strong influences upon disease progression outcomes in infectious diseases and cancers (Carrington and Walker, 2012; Tang et al., 2012). Specific alleles are also linked to autoimmune diseases (Brown et al., 2016; Price et al., 1999) and drug hypersensitivities (Illing et al., 2013). Since the presence of a ‘foreign’ peptide is the key activation signal for CD8⁺ T cell responses, the peptide-binding characteristics of individual HLA class I molecules are important determinants of their associations with many disease outcomes. This is well-studied in the case of HIV infections (Pereyra et al., 2010).

The cell surface stabilities of HLA class I-peptide complexes can be influenced by multiple factors, including the nature of peptide-MHC interactions, the abundance of factors that mediate their assembly, and the extent of peptide-deficient HLA class I expression. A given HLA class I molecule can bind to a large number of peptides that have specific sequence motifs (for example, Figure 1—figure supplement 1) and length constraints (the HLA class I peptidome, for example those characterized in [Abelin et al., 2017]). Not all HLA class I binding peptides are transported equivalently by the TAP transporter. The use of peptide libraries fixed at specific positions with single amino acids has revealed strong sequence preferences for peptide transport by TAP (Uebel et al., 1997). In general, peptide residue 2 (P₂) and the C-terminal residues of peptides (P_C) are strong determinants of peptide binding to HLA class I. TAP also has strong preferences within this region; hydrophobic C-terminal residues, generally preferred by HLA class I molecules, are also preferred by TAP. At the P₂ position, however, proline is strongly disfavored by TAP, but highly preferred by a subset of HLA-B molecules - those within the B7 supertype (Figure 1—figure supplement 1). The functional consequences of such mismatches in TAP and HLA class I binding preferences are unknown. It can be hypothesized that the mismatch causes suboptimal assembly in the ER, and for some allotypes, reduced cell surface stability and increased ability to sample peptides from unconventional sources.

A number of studies have indicated that tapasin, via the PLC, facilitates HLA class I-peptide assembly and also optimizes the HLA class I peptide repertoire towards high affinity sequences (Chen and Bouvier, 2007; Wearsch and Cresswell, 2007; Williams et al., 2002). HLA-B allotypes differ markedly in their dependencies on tapasin for their cell surface expression (Peh et al., 1998; Rizvi et al., 2014). Tapasin-independent HLA-B allotypes generally have higher intrinsic stabilities of their peptide-deficient forms (Rizvi et al., 2014), and thus may be more prone to exit the ER as suboptimally loaded versions, particularly when peptide is limiting.

Previous studies have shown that mRNA differences and regulatory polymorphisms affect HLA class I and class II expression (Raj et al., 2016; Thomas et al., 2009). The HLA-B locus is the most polymorphic of the HLA class I loci (and thus the most rapidly evolving), with dominant influences upon disease outcomes (Kiepiela et al., 2004). HLA-B alleles do not vary in mRNA expression (Ramsuran et al., 2017), but there are known variations in the assembly and peptide-binding characteristics of HLA-B allotypes, as described above. It is unknown whether such variations can result in global cell surface stability differences, ER retention differences and subsequent cell surface expression differences in primary human cells. In this study, we addressed the hypothesis that peptide pool limitations induced by mismatched peptide-binding preferences between TAP and HLA class I allotypes affects cell surface expression levels of HLA class I molecules, via suboptimal assembly. To address this hypothesis, we used freshly-isolated human lymphocytes and monocytes and quantitative flow cytometry to examine the expression levels of HLA-B alleles in an Ann Arbor, United States cohort of healthy donors. Where expression differences were significant, we also undertook cell surface stability measurements to assess whether these variations explain the expression differences. Finally, we compared exogenous peptide receptivity of HLA-B allotypes with high or low cell surface stability to assess variations.

HLA-B alleles do not vary in their mRNA expression in individual lymphocyte subsets, as previously described in bulk PBMC (Figure 1—figure supplements 4 and 5 and (Ramsuran et al., 2017). However, both allele-intrinsic and cell-intrinsic factors exert important influences on HLA-B cell surface protein expressions levels. Nonetheless, there is no global high or low cell surface expressing allotype among the 12 HLA-B alleles considered in this study; rather, measured variations are cell-specific (Figure 9). Furthermore, there are allele and cell type-dependent thresholds for passing ER quality control checkpoints rather than a global threshold for HLA class I stability.

Figure 9

Download asset Open asset

Figure 9—source data 1

Blood Donor demographics.

Top table: All genotyped donors. Bottom table: Blood donors whose samples were used for the data shown in Figures 1–7.

https://doi.org/10.7554/eLife.34961.037

Download elife-34961-fig9-data1-v1.docx

Mismatch of HLA peptide binding preference with TAP transport specificity (P₂P preferences of the B7 superfamily) is a potential determinant of low cell surface expression in lymphocytes. A tapasin-independent assembly pathway is also a potential factor for reduced cell surface expression and stability and enhanced exogenous peptide-receptivity in lymphocytes, particularly when the intracellular peptide pool is also limiting. High affinity peptides are better able to displace tapasin from HLA class I than low affinity peptides (Rizvi and Raghavan, 2006), consistent with recent structural studies (Jiang et al., 2017; Thomas and Tampé, 2017) that support models involving competition between peptide and tapasin. For MHC class I allotypes assembling in the context of the PLC, the threshold peptide affinity required to exit the ER is expected to be dictated by the affinity of the tapasin-MHC interaction. For allotypes such as HLA-B*35:01 and B*07:02 that can also assemble efficiently independently of the PLC, this threshold affinity would be lower than allotypes such as B*08:01, which are more dependent on the PLC for assembly. Since tapasin-independent assembly is also linked to higher stability of the peptide-deficient form (Rizvi et al., 2014), allotypes such as HLA-B*35:01 and B*07:02 may escape PLC-mediated and other ER quality control mechanisms when devoid of peptides or when bound to a sub-optimal peptide, resulting in enhanced peptide receptivity in lymphocytes. Alternatively, tapasin-dependent allotypes such as B*51:01 that have P₂P preferences may face an intracellular assembly bottleneck. Conversely, high expression and stability of B*08:01 in lymphocytes is linked to its unique peptide-binding characteristics (Figure 8), an ample peptide supply and tapasin-dependent assembly.

With regard to cell-intrinsic factors, in monocytes, an intracellular AP-1⁺ compartment contains a large pool of intracellular HLA-B, and assembly parameters within this compartment are likely to account for monocyte-specific HLA-B cell surface expression differences (Figure 4). While further studies are needed to understand the differences between HLA-B assembly in monocytes and lymphocytes, it is apparent that a more substantial pool of HLA-B is intracellularly localized in monocytes than lymphocytes (Figure 4A). Peptides that enter the intracellular compartments independently of TAP may provide a robust peptide source for members of the B7 supertype in monocytes. Indeed, in a related study (Geng et al., 2018), we find that HLA-B allotypes with P₂P preferences generally tend to express at higher levels under TAP-deficiency conditions. Furthermore, despite the presence of higher levels of tapasin in monocytes (Figure 4—figure supplement 1), the parallel increases in HLA class I levels (and reduced tapasin/HLA class I ratios; (Figure 4B)) are likely to induce greater competition for the formation of stoichiometric tapasin:HLA class I complexes within the PLC. Such competition, combined with the particular environment within the AP-1⁺ compartment, could selectively reduce the assembly efficiency and/or the assembly quality of allotypes such as HLA-B*44:02 and B*08:01 in monocytes. Overall, the measurements with lymphocytes and monocytes highlight the importance of the cellular assembly landscape as a key HLA-B cell surface expression determinant (Figure 9).

A recent study reported higher expression of HLA-B*57 and HLA-B*27 relative to other Bw4 allotypes including HLA-B*44 in bulk PBMCs (Boudreau et al., 2016). Those results can be explained by variations in recognition by the anti-Bw4 antibody that was used, which was the same antibody used as in our Bw4 measurements (Figure 6 and Figure 6—figure supplements 1 and 4). Another recent study found low cell surface expression among some chicken MHC allotypes, which was attributed to their promiscuous binding of peptides lacking recognizable anchor residues (Chappell et al., 2015). Using a different set of antibodies than anti-Bw4/Bw6, the study also reported high expression of cell surface HLA-B in lymphocytes and monocytes of homozygous donors expressing HLA-B*27:05 and HLA-B*57:01 compared with donors expressing B*07:02 and B*35:01. Higher promiscuity of the B*07:02 and B*35:01 peptidomes relative to those of B*27:05 and HLA-B*57:01 was suggested based on prior studies (Chappell et al., 2015). While it is likely that mass spectrometry does not capture the full peptidome diversity, Shannon Entropy plots of mass spectrometry-derived 9-mer peptides suggest higher P₂ diversity for B*57:01 compared to B*35:01, confirmed by motif assessments with a different peptidome dataset (Figure 8 and Figure 8—figure supplement 2). Assessments of length diversities indicate similar preferences of B*57:01 compared to B*35:01 for 9-mer and 10-mer peptides relative to other lengths (data not shown). Overall, our findings suggest that there is a not a simple relationship between HLA-B expression levels and peptidome diversity, and indeed that intracellular assembly variations can induce different peptidome diversities for the same allotype in different cells.

The HLA 8.1 ancestral haplotype, which includes the HLA-B*08:01 allele, has been associated with a number of autoimmune diseases (Candore et al., 2002; Price et al., 1999). Recent genetic studies show that the strongest individual allelic associations for polymyositis are with HLA-B*08:01 (Miller et al., 2015) and another study showed strong HLA-B*08:01 associations with idiopathic inflammatory myopathies (Rothwell et al., 2016). Our findings indicate high cell-surface expression and high cell-surface stability of HLA-B*08:01 in lymphocytes, relating to the specifics of peptide interactions with HLA-B*08:01 (Figure 8), which may lead to increased probability of CD8⁺ T cell activation in autoimmune myopathies. Conditional upregulation of HLA class I in muscle is sufficient to induce some characteristics of autoimmune myositis in mouse models of disease (Nagaraju et al., 2000). Important questions that stem from our current observations relate to whether high expression of HLA-B*08:01, measurable in lymphocytes, is also maintained in muscle, which could have key relevance to the autoimmunity linkages of HLA-B*08:01.

The findings described in this study (Figure 9) are relevant to further understanding of how variations in HLA-B expression, stability and peptide occupancy influence immunity to pathogens such as HIV that preferentially target CD4⁺ T cells and macrophages. The influences of HLA class I expression levels on the lysis of HIV-infected CD4⁺ T cells by cytotoxic CD8⁺ T cells is well-studied (Collins et al., 1998). HLA-B alleles have strong influences on AIDS progression outcomes and viral loads (Bashirova et al., 2014; Carrington and Walker, 2012). High HLA-B expression in APC lineage cells would favor effective priming of an HIV-specific CD8⁺ T cell response and their activity against infected macrophages, whereas high HLA-B expression in CD4⁺ T cells would favor efficient CD8⁺ T cell-mediated lysis of infected CD4⁺ T cells. The B*57:01 advantage in HIV infections relative to less protective HLA-Bw4 allotypes such as B*51:01 and B*44:02 may derive in part from the relatively high expression of B*57:01 in both cell lineages. Similarly, the B*35:01 disadvantage may relate in part to its lower expression, lower stability and higher proportion of peptide-deficient versions in CD4⁺ T cells. Further studies are needed to assess influences of HIV infection on HLA-B expression, stability and peptide occupancy in CD4⁺ T cells and macrophages.

All data generated or analysed during this study are included in the manuscript and supporting files. Source data files have been provided for several figures. Datasets will be made available using Dyrad.

1. 1. Yarzabek B
   2. Zaitouna A
   3. Olson E
   4. Geng J
   5. Raghavan M
   6. Silva G
   7. Aviva Geretz
   8. Thomas R
   9. Krishnakumar S
   10. Ramon D

   (2018) Data from: Variations in HLA-B cell surface expression, Half-Life and extrecellular antigen receptivity

   Available at Dryad Digital Repository under a CC0 Public Domain Dedication.

   http://dx.doi.org/10.5061/dryad.f0s23r8

1. 1. Pellicci DG
   2. Uldrich AP
   3. Le Nours J
   4. Ross F
   5. Chabrol E
   6. Eckle SB

   (2014) Crystal Structure of HLA B*0801 in complex with ELK_IYM, ELKRKMIYM

   Publicly available at the RCSB Protein Data Bank (accession no.4QRS).

   http://www.rcsb.org/pdb/explore/explore.do?structureId=4QRS
2. 1. Maenaka K
   2. Maenaka T
   3. Tomiyama H
   4. Takiguchi M
   5. Stuart DI
   6. Jones
   7. EY

   (2014) Crystal Structure of HLA B*3501-IPS in complex with a Delta-Beta TCR, clone 12 TCR

   Publicly available at the RCSB Protein Data Bank (accession no.4QRR).

   http://www.rcsb.org/pdb/explore/explore.do?structureId=4qrr
3. 1. Maenaka K
   2. Maenaka T
   3. Tomiyama H
   4. Takiguchi M
   5. Stuart DI
   6. Jones
   7. EY

   (2000) Nonstandard peptide binding of HLA-B*5101 complexed with HIV immunodominant epitope KM1(LPPVVAKEI)

   Publicly available at the RCSB Protein Data Bank (accession no. 1E27).

   http://www.rcsb.org/pdb/explore/explore.do?structureId=1e27
4. 1. Chessman D
   2. Kostenko L
   3. Lethborg T
   4. Purcell AW
   5. Williamson NA

   (2008) Crystal Structure of HLA-B*5701, presenting the self peptide, LSSPVTKSF

   Publicly available at the RCSB Protein Data Bank (accession no. 2RFX).

   http://www.rcsb.org/pdb/explore/explore.do?structureId=2RFX

1. 1. Abelin JG
   2. Keskin DB
   3. Sarkizova S
   4. Hartigan CR
   5. Zhang W
   6. Sidney J
   7. Stevens J
   8. Lane W
   9. Zhang GL
   10. Eisenhaure TM
   11. Clauser KR
   12. Hacohen N
   13. Rooney MS
   14. Carr SA
   15. Wu CJ

   (2017) Mass spectrometry profiling of HLA-Associated peptidomes in mono-allelic cells enables more accurate epitope prediction

   Immunity 46:315–326.

   https://doi.org/10.1016/j.immuni.2017.02.007

   • PubMed
   • Google Scholar
2. 1. Anders S
   2. Pyl PT
   3. Huber W

   (2015) HTSeq--a Python framework to work with high-throughput sequencing data

   Bioinformatics 31:166–169.

   https://doi.org/10.1093/bioinformatics/btu638

   • PubMed
   • Google Scholar
3. 1. Apps R
   2. Meng Z
   3. Del Prete GQ
   4. Lifson JD
   5. Zhou M
   6. Carrington M

   (2015) Relative expression levels of the HLA class-I proteins in normal and HIV-infected cells

   The Journal of Immunology 194:3594–3600.

   https://doi.org/10.4049/jimmunol.1403234

   • PubMed
   • Google Scholar
4. 1. Barnstable CJ
   2. Bodmer WF
   3. Brown G
   4. Galfre G
   5. Milstein C
   6. Williams AF
   7. Ziegler A

   (1978) Production of monoclonal antibodies to group A erythrocytes, HLA and other human cell surface antigens-new tools for genetic analysis

   Cell 14:9–20.

   https://doi.org/10.1016/0092-8674(78)90296-9

   • PubMed
   • Google Scholar
5. 1. Bashirova AA
   2. Martin-Gayo E
   3. Jones DC
   4. Qi Y
   5. Apps R
   6. Gao X
   7. Burke PS
   8. Taylor CJ
   9. Rogich J
   10. Wolinsky S
   11. Bream JH
   12. Duggal P
   13. Hussain S
   14. Martinson J
   15. Weintrob A
   16. Kirk GD
   17. Fellay J
   18. Buchbinder SP
   19. Goedert JJ
   20. Deeks SG
   21. Pereyra F
   22. Trowsdale J
   23. Lichterfeld M
   24. Telenti A
   25. Walker BD
   26. Allen RL
   27. Carrington M
   28. Yu XG

   (2014) LILRB2 interaction with HLA class I correlates with control of HIV-1 infection

   PLoS Genetics 10:e1004196.

   https://doi.org/10.1371/journal.pgen.1004196

   • PubMed
   • Google Scholar
6. 1. Bjorkman PJ
   2. Parham P

   (1990) Structure, function, and diversity of class I major histocompatibility complex molecules

   Annual Review of Biochemistry 59:253–288.

   https://doi.org/10.1146/annurev.bi.59.070190.001345

   • PubMed
   • Google Scholar
7. 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

   • PubMed
   • Google Scholar
8. 1. Bolger AM
   2. Lohse M
   3. Usadel B

   (2014) Trimmomatic: a flexible trimmer for Illumina sequence data

   Bioinformatics 30:2114–2120.

   https://doi.org/10.1093/bioinformatics/btu170

   • PubMed
   • Google Scholar
9. 1. Boudreau JE
   2. Mulrooney TJ
   3. Le Luduec JB
   4. Barker E
   5. Hsu KC

   (2016) KIR3DL1 and HLA-B density and binding calibrate NK education and response to HIV

   The Journal of Immunology 196:3398–3410.

   https://doi.org/10.4049/jimmunol.1502469

   • PubMed
   • Google Scholar
10. 1. Brown MA
    2. Kenna T
    3. Wordsworth BP

    (2016) Genetics of ankylosing spondylitis--insights into pathogenesis

    Nature Reviews Rheumatology 12:81–91.

    https://doi.org/10.1038/nrrheum.2015.133

    • PubMed
    • Google Scholar
11. 1. Candore G
    2. Lio D
    3. Colonna Romano G
    4. Caruso C

    (2002) Pathogenesis of autoimmune diseases associated with 8.1 ancestral haplotype: effect of multiple gene interactions

    Autoimmunity Reviews 1:29–35.

    https://doi.org/10.1016/S1568-9972(01)00004-0

    • PubMed
    • Google Scholar
12. 1. Carrington M
    2. Walker BD

    (2012) Immunogenetics of spontaneous control of HIV

    Annual Review of Medicine 63:131–145.

    https://doi.org/10.1146/annurev-med-062909-130018

    • PubMed
    • Google Scholar
13. 1. Chappell P
    2. Meziane elK
    3. Harrison M
    4. Magiera Ł
    5. Hermann C
    6. Mears L
    7. Wrobel AG
    8. Durant C
    9. Nielsen LL
    10. Buus S
    11. Ternette N
    12. Mwangi W
    13. Butter C
    14. Nair V
    15. Ahyee T
    16. Duggleby R
    17. Madrigal A
    18. Roversi P
    19. Lea SM
    20. Kaufman J

    (2015) Expression levels of MHC class I molecules are inversely correlated with promiscuity of peptide binding

    eLife 4:e05345.

    https://doi.org/10.7554/eLife.05345

    • PubMed
    • Google Scholar
14. 1. Chen M
    2. Bouvier M

    (2007) Analysis of interactions in a tapasin/class I complex provides a mechanism for peptide selection

    The EMBO Journal 26:1681–1690.

    https://doi.org/10.1038/sj.emboj.7601624

    • PubMed
    • Google Scholar
15. 1. Collins KL
    2. Chen BK
    3. Kalams SA
    4. Walker BD
    5. Baltimore D

    (1998) HIV-1 Nef protein protects infected primary cells against killing by cytotoxic T lymphocytes

    Nature 391:397–401.

    https://doi.org/10.1038/34929

    • PubMed
    • Google Scholar
16. 1. Ehrenberg PK
    2. Geretz A
    3. Baldwin KM
    4. Apps R
    5. Polonis VR
    6. Robb ML
    7. Kim JH
    8. Michael NL
    9. Thomas R

    (2014) High-throughput multiplex HLA genotyping by next-generation sequencing using multi-locus individual tagging

    BMC Genomics 15:864.

    https://doi.org/10.1186/1471-2164-15-864

    • PubMed
    • Google Scholar
17. 1. Ehrenberg PK
    2. Geretz A
    3. Sindhu RK
    4. Vayntrub T
    5. Fernández Viña MA
    6. Apps R
    7. Michael NL
    8. Thomas R

    (2017) High-throughput next-generation sequencing to genotype six classical HLA loci from 96 donors in a single MiSeq run

    HLA 90:284–291.

    https://doi.org/10.1111/tan.13133

    • PubMed
    • Google Scholar
18. 1. Fischbach H
    2. Döring M
    3. Nikles D
    4. Lehnert E
    5. Baldauf C
    6. Kalinke U
    7. Tampé R

    (2015) Ultrasensitive quantification of TAP-dependent antigen compartmentalization in scarce primary immune cell subsets

    Nature Communications 6:6199.

    https://doi.org/10.1038/ncomms7199

    • PubMed
    • Google Scholar
19. 1. García-Ruano AB
    2. Méndez R
    3. Romero JM
    4. Cabrera T
    5. Ruiz-Cabello F
    6. Garrido F

    (2010) Analysis of HLA-ABC locus-specific transcription in normal tissues

    Immunogenetics 62:711–719.

    https://doi.org/10.1007/s00251-010-0470-z

    • PubMed
    • Google Scholar
20. 1. Geng J
    2. Zaitouna AJ
    3. Raghavan M

    (2018)

    Selected HLA-B allotypes are resistant to inhibitors or deficiency of the transporter associated with antigen processing (TAP)

    PLoS Pathogens In press.

    • Google Scholar
21. 1. Illing PT
    2. Vivian JP
    3. Purcell AW
    4. Rossjohn J
    5. McCluskey J

    (2013) Human leukocyte antigen-associated drug hypersensitivity

    Current Opinion in Immunology 25:81–89.

    https://doi.org/10.1016/j.coi.2012.10.002

    • PubMed
    • Google Scholar
22. 1. Jiang J
    2. Natarajan K
    3. Boyd LF
    4. Morozov GI
    5. Mage MG
    6. Margulies DH

    (2017) Crystal structure of a TAPBPR-MHC I complex reveals the mechanism of peptide editing in antigen presentation

    Science 358:1064–1068.

    https://doi.org/10.1126/science.aao5154

    • PubMed
    • Google Scholar
23. 1. Kiepiela P
    2. Leslie AJ
    3. Honeyborne I
    4. Ramduth D
    5. Thobakgale C
    6. Chetty S
    7. Rathnavalu P
    8. Moore C
    9. Pfafferott KJ
    10. Hilton L
    11. Zimbwa P
    12. Moore S
    13. Allen T
    14. Brander C
    15. Addo MM
    16. Altfeld M
    17. James I
    18. Mallal S
    19. Bunce M
    20. Barber LD
    21. Szinger J
    22. Day C
    23. Klenerman P
    24. Mullins J
    25. Korber B
    26. Coovadia HM
    27. Walker BD
    28. Goulder PJ

    (2004) Dominant influence of HLA-B in mediating the potential co-evolution of HIV and HLA

    Nature 432:769–775.

    https://doi.org/10.1038/nature03113

    • PubMed
    • Google Scholar
24. 1. Kulpa DA
    2. Del Cid N
    3. Peterson KA
    4. Collins KL

    (2013) Adaptor protein 1 promotes cross-presentation through the same tyrosine signal in major histocompatibility complex class I as that targeted by HIV-1

    Journal of Virology 87:8085–8098.

    https://doi.org/10.1128/JVI.00701-13

    • PubMed
    • Google Scholar
25. 1. Lundegaard C
    2. Lamberth K
    3. Harndahl M
    4. Buus S
    5. Lund O
    6. Nielsen M

    (2008) NetMHC-3.0: accurate web accessible predictions of human, mouse and monkey MHC class I affinities for peptides of length 8-11

    Nucleic Acids Research 36:W509–W512.

    https://doi.org/10.1093/nar/gkn202

    • PubMed
    • Google Scholar
26. 1. Maenaka K
    2. Maenaka T
    3. Tomiyama H
    4. Takiguchi M
    5. Stuart DI
    6. Jones EY

    (2000) Nonstandard peptide binding revealed by crystal structures of HLA-B*5101 complexed with HIV immunodominant epitopes

    The Journal of Immunology 165:3260–3267.

    https://doi.org/10.4049/jimmunol.165.6.3260

    • PubMed
    • Google Scholar
27. 1. Miller FW
    2. Chen W
    3. O'Hanlon TP
    4. Cooper RG
    5. Vencovsky J
    6. Rider LG
    7. Danko K
    8. Wedderburn LR
    9. Lundberg IE
    10. Pachman LM
    11. Reed AM
    12. Ytterberg SR
    13. Padyukov L
    14. Selva-O'Callaghan A
    15. Radstake TR
    16. Isenberg DA
    17. Chinoy H
    18. Ollier WE
    19. Scheet P
    20. Peng B
    21. Lee A
    22. Byun J
    23. Lamb JA
    24. Gregersen PK
    25. Amos CI
    26. Myositis Genetics C
    27. Myositis Genetics Consortium

    (2015) Genome-wide association study identifies HLA 8.1 ancestral haplotype alleles as major genetic risk factors for myositis phenotypes

    Genes & Immunity 16:470–480.

    https://doi.org/10.1038/gene.2015.28

    • PubMed
    • Google Scholar
28. 1. Müller CA
    2. Engler-Blum G
    3. Gekeler V
    4. Steiert I
    5. Weiss E
    6. Schmidt H

    (1989) Genetic and serological heterogeneity of the supertypic HLA-B locus specificities Bw4 and Bw6

    Immunogenetics 30:200–207.

    https://doi.org/10.1007/BF02421207

    • PubMed
    • Google Scholar
29. 1. Nagaraju K
    2. Raben N
    3. Loeffler L
    4. Parker T
    5. Rochon PJ
    6. Lee E
    7. Danning C
    8. Wada R
    9. Thompson C
    10. Bahtiyar G
    11. Craft J
    12. Hooft Van Huijsduijnen R
    13. Plotz P

    (2000) Conditional up-regulation of MHC class I in skeletal muscle leads to self-sustaining autoimmune myositis and myositis-specific autoantibodies

    PNAS 97:9209–9214.

    https://doi.org/10.1073/pnas.97.16.9209

    • PubMed
    • Google Scholar
30. 1. Parham P
    2. Barnstable CJ
    3. Bodmer WF

    (1979)

    Use of a monoclonal antibody (W6/32) in structural studies of HLA-A,B,C, antigens

    Journal of immunology 123:342–349.

    • PubMed
    • Google Scholar
31. 1. Parham P
    2. Moffett A

    (2013) Variable NK cell receptors and their MHC class I ligands in immunity, reproduction and human evolution

    Nature Reviews Immunology 13:133–144.

    https://doi.org/10.1038/nri3370

    • PubMed
    • Google Scholar
32. 1. Park SY
    2. Guo X

    (2014) Adaptor protein complexes and intracellular transport

    Bioscience Reports 34:381–390.

    https://doi.org/10.1042/BSR20140069

    • PubMed
    • Google Scholar
33. 1. Pearson H
    2. Daouda T
    3. Granados DP
    4. Durette C
    5. Bonneil E
    6. Courcelles M
    7. Rodenbrock A
    8. Laverdure JP
    9. Côté C
    10. Mader S
    11. Lemieux S
    12. Thibault P
    13. Perreault C

    (2016) MHC class I-associated peptides derive from selective regions of the human genome

    Journal of Clinical Investigation 126:4690–4701.

    https://doi.org/10.1172/JCI88590

    • PubMed
    • Google Scholar
34. 1. Peh CA
    2. Burrows SR
    3. Barnden M
    4. Khanna R
    5. Cresswell P
    6. Moss DJ
    7. McCluskey J

    (1998) HLA-B27-restricted antigen presentation in the absence of tapasin reveals polymorphism in mechanisms of HLA class I peptide loading

    Immunity 8:531–542.

    https://doi.org/10.1016/S1074-7613(00)80558-0

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

    (2010) 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

    • PubMed
    • Google Scholar
36. 1. Picelli S
    2. Faridani OR
    3. Björklund AK
    4. Winberg G
    5. Sagasser S
    6. Sandberg R

    (2014) Full-length RNA-seq from single cells using Smart-seq2

    Nature Protocols 9:171–181.

    https://doi.org/10.1038/nprot.2014.006

    • PubMed
    • Google Scholar
37. 1. Price P
    2. Witt C
    3. Allcock R
    4. Sayer D
    5. Garlepp M
    6. Kok CC
    7. French M
    8. Mallal S
    9. Christiansen F

    (1999) The genetic basis for the association of the 8.1 ancestral haplotype (A1, B8, DR3) with multiple immunopathological diseases

    Immunological Reviews 167:257–274.

    https://doi.org/10.1111/j.1600-065X.1999.tb01398.x

    • PubMed
    • Google Scholar
38. 1. Raghavan M
    2. Geng J

    (2015) HLA-B polymorphisms and intracellular assembly modes

    Molecular Immunology 68:89–93.

    https://doi.org/10.1016/j.molimm.2015.07.007

    • PubMed
    • Google Scholar
39. 1. Raj P
    2. Rai E
    3. Song R
    4. Khan S
    5. Wakeland BE
    6. Viswanathan K
    7. Arana C
    8. Liang C
    9. Zhang B
    10. Dozmorov I
    11. Carr-Johnson F
    12. Mitrovic M
    13. Wiley GB
    14. Kelly JA
    15. Lauwerys BR
    16. Olsen NJ
    17. Cotsapas C
    18. Garcia CK
    19. Wise CA
    20. Harley JB
    21. Nath SK
    22. James JA
    23. Jacob CO
    24. Tsao BP
    25. Pasare C
    26. Karp DR
    27. Li QZ
    28. Gaffney PM
    29. Wakeland EK

    (2016) Regulatory polymorphisms modulate the expression of HLA class II molecules and promote autoimmunity

    eLife 5:e12089.

    https://doi.org/10.7554/eLife.12089

    • PubMed
    • Google Scholar
40. 1. Ramsköld D
    2. Luo S
    3. Wang YC
    4. Li R
    5. Deng Q
    6. Faridani OR
    7. Daniels GA
    8. Khrebtukova I
    9. Loring JF
    10. Laurent LC
    11. Schroth GP
    12. Sandberg R

    (2012) Full-length mRNA-Seq from single-cell levels of RNA and individual circulating tumor cells

    Nature Biotechnology 30:777–782.

    https://doi.org/10.1038/nbt.2282

    • PubMed
    • Google Scholar
41. 1. Ramsuran V
    2. Hernández-Sanchez PG
    3. O'hUigin C
    4. Sharma G
    5. Spence N
    6. Augusto DG
    7. Gao X
    8. García-Sepúlveda CA
    9. Kaur G
    10. Mehra NK
    11. Carrington M

    (2017) Sequence and Phylogenetic Analysis of the Untranslated Promoter Regions for HLA Class I Genes

    The Journal of Immunology 198:2320–2329.

    https://doi.org/10.4049/jimmunol.1601679

    • PubMed
    • Google Scholar
42. 1. Rao X
    2. Costa AI
    3. van Baarle D
    4. Kesmir C

    (2009) A comparative study of HLA binding affinity and ligand diversity: implications for generating immunodominant CD8+ T cell responses

    The Journal of Immunology 182:1526–1532.

    https://doi.org/10.4049/jimmunol.182.3.1526

    • PubMed
    • Google Scholar
43. 1. Rizvi SM
    2. Raghavan M

    (2006) Direct peptide-regulatable interactions between MHC class I molecules and tapasin

    PNAS 103:18220–18225.

    https://doi.org/10.1073/pnas.0605131103

    • PubMed
    • 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

    • PubMed
    • Google Scholar
45. 1. Robb ML
    2. Eller LA
    3. Kibuuka H
    4. Rono K
    5. Maganga L
    6. Nitayaphan S
    7. Kroon E
    8. Sawe FK
    9. Sinei S
    10. Sriplienchan S
    11. Jagodzinski LL
    12. Malia J
    13. Manak M
    14. de Souza MS
    15. Tovanabutra S
    16. Sanders-Buell E
    17. Rolland M
    18. Dorsey-Spitz J
    19. Eller MA
    20. Milazzo M
    21. Li Q
    22. Lewandowski A
    23. Wu H
    24. Swann E
    25. O'Connell RJ
    26. Peel S
    27. Dawson P
    28. Kim JH
    29. Michael NL
    30. Team RVS
    31. RV 217 Study Team

    (2016) Prospective Study of Acute HIV-1 Infection in Adults in East Africa and Thailand

    New England Journal of Medicine 374:2120–2130.

    https://doi.org/10.1056/NEJMoa1508952

    • PubMed
    • Google Scholar
46. 1. Robinson J
    2. Halliwell JA
    3. Hayhurst JD
    4. Flicek P
    5. Parham P
    6. Marsh SG

    (2015) The IPD and IMGT/HLA database: allele variant databases

    Nucleic Acids Research 43:D423–D431.

    https://doi.org/10.1093/nar/gku1161

    • PubMed
    • Google Scholar
47. 1. Rossjohn J
    2. Gras S
    3. Miles JJ
    4. Turner SJ
    5. Godfrey DI
    6. McCluskey J

    (2015) T cell antigen receptor recognition of antigen-presenting molecules

    Annual Review of Immunology 33:169–200.

    https://doi.org/10.1146/annurev-immunol-032414-112334

    • PubMed
    • Google Scholar
48. 1. Rothwell S
    2. Cooper RG
    3. Lundberg IE
    4. Miller FW
    5. Gregersen PK
    6. Bowes J
    7. Vencovsky J
    8. Danko K
    9. Limaye V
    10. Selva-O'Callaghan A
    11. Hanna MG
    12. Machado PM
    13. Pachman LM
    14. Reed AM
    15. Rider LG
    16. Cobb J
    17. Platt H
    18. Molberg Ø
    19. Benveniste O
    20. Mathiesen P
    21. Radstake T
    22. Doria A
    23. De Bleecker J
    24. De Paepe B
    25. Maurer B
    26. Ollier WE
    27. Padyukov L
    28. O'Hanlon TP
    29. Lee A
    30. Amos CI
    31. Gieger C
    32. Meitinger T
    33. Winkelmann J
    34. Wedderburn LR
    35. Chinoy H
    36. Lamb JA
    37. Myositis Genetics C
    38. Myositis Genetics Consortium

    (2016) Dense genotyping of immune-related loci in idiopathic inflammatory myopathies confirms HLA alleles as the strongest genetic risk factor and suggests different genetic background for major clinical subgroups

    Annals of the Rheumatic Diseases 75:1558–1566.

    https://doi.org/10.1136/annrheumdis-2015-208119

    • PubMed
    • Google Scholar
49. 1. Saunders PM
    2. Vivian JP
    3. O'Connor GM
    4. Sullivan LC
    5. Pymm P
    6. Rossjohn J
    7. Brooks AG

    (2015) A bird's eye view of NK cell receptor interactions with their MHC class I ligands

    Immunological Reviews 267:148–166.

    https://doi.org/10.1111/imr.12319

    • PubMed
    • Google Scholar
50. 1. Stam NJ
    2. Vroom TM
    3. Peters PJ
    4. Pastoors EB
    5. Ploegh HL

    (1990) HLA-A- and HLA-B-specific monoclonal antibodies reactive with free heavy chains in western blots, in formalin-fixed, paraffin-embedded tissue sections and in cryo-immuno-electron microscopy

    International Immunology 2:113–125.

    https://doi.org/10.1093/intimm/2.2.113

    • PubMed
    • Google Scholar
51. 1. Tang M
    2. Lautenberger JA
    3. Gao X
    4. Sezgin E
    5. Hendrickson SL
    6. Troyer JL
    7. David VA
    8. Guan L
    9. McIntosh CE
    10. Guo X
    11. Zheng Y
    12. Liao J
    13. Deng H
    14. Malasky M
    15. Kessing B
    16. Winkler CA
    17. Carrington M
    18. Dé The G
    19. Zeng Y
    20. O'Brien SJ

    (2012) The principal genetic determinants for nasopharyngeal carcinoma in China involve the HLA class I antigen recognition groove

    PLoS Genetics 8:e1003103.

    https://doi.org/10.1371/journal.pgen.1003103

    • PubMed
    • Google Scholar
52. 1. Thammavongsa V
    2. Schaefer M
    3. Filzen T
    4. Collins KL
    5. Carrington M
    6. Bangia N
    7. Raghavan M

    (2009) Assembly and intracellular trafficking of HLA-B*3501 and HLA-B*3503

    Immunogenetics 61:703–716.

    https://doi.org/10.1007/s00251-009-0399-2

    • PubMed
    • Google Scholar
53. 1. Thomas C
    2. Tampé R

    (2017) Structure of the TAPBPR-MHC I complex defines the mechanism of peptide loading and editing

    Science 358:1060–1064.

    https://doi.org/10.1126/science.aao6001

    • PubMed
    • Google Scholar
54. 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

    • PubMed
    • Google Scholar
55. 1. Thomsen MC
    2. Nielsen M

    (2012) Seq2Logo: a method for construction and visualization of amino acid binding motifs and sequence profiles including sequence weighting, pseudo counts and two-sided representation of amino acid enrichment and depletion

    Nucleic Acids Research 40:W281–W287.

    https://doi.org/10.1093/nar/gks469

    • PubMed
    • Google Scholar
56. 1. Uebel S
    2. Kraas W
    3. Kienle S
    4. Wiesmüller KH
    5. Jung G
    6. Tampé R

    (1997) Recognition principle of the TAP transporter disclosed by combinatorial peptide libraries

    PNAS 94:8976–8981.

    https://doi.org/10.1073/pnas.94.17.8976

    • PubMed
    • Google Scholar
57. 1. Wang C
    2. Krishnakumar S
    3. Wilhelmy J
    4. Babrzadeh F
    5. Stepanyan L
    6. Su LF
    7. Levinson D
    8. Fernandez-Viña MA
    9. Davis RW
    10. Davis MM
    11. Mindrinos M

    (2012) High-throughput, high-fidelity HLA genotyping with deep sequencing

    PNAS 109:8676–8681.

    https://doi.org/10.1073/pnas.1206614109

    • PubMed
    • Google Scholar
58. 1. Wearsch PA
    2. Cresswell P

    (2007) Selective loading of high-affinity peptides onto major histocompatibility complex class I molecules by the tapasin-ERp57 heterodimer

    Nature Immunology 8:873–881.

    https://doi.org/10.1038/ni1485

    • PubMed
    • Google Scholar
59. 1. Williams AP
    2. Peh CA
    3. Purcell AW
    4. McCluskey J
    5. Elliott T

    (2002) Optimization of the MHC class I peptide cargo is dependent on tapasin

    Immunity 16:509–520.

    https://doi.org/10.1016/S1074-7613(02)00304-7

    • PubMed
    • Google Scholar
60. 1. Wu TD
    2. Nacu S

    (2010) Fast and SNP-tolerant detection of complex variants and splicing in short reads

    Bioinformatics 26:873–881.

    https://doi.org/10.1093/bioinformatics/btq057

    • PubMed
    • Google Scholar
61. 1. Zarling AL
    2. Luckey CJ
    3. Marto JA
    4. White FM
    5. Brame CJ
    6. Evans AM
    7. Lehner PJ
    8. Cresswell P
    9. Shabanowitz J
    10. Hunt DF
    11. Engelhard VH

    (2003) Tapasin is a facilitator, not an editor, of class I MHC peptide binding

    The Journal of Immunology 171:5287–5295.

    https://doi.org/10.4049/jimmunol.171.10.5287

    • PubMed
    • Google Scholar

Author details

1. Brogan Yarzabek

   Department of Microbiology and Immunology, Michigan Medicine, University of Michigan, Michigan, United States

   Contribution

   Formal analysis, Investigation, Visualization, Methodology, Writing—review and editing

   Contributed equally with

   Anita J Zaitouna and Eli Olson

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-9632-096X

2. Anita J Zaitouna

   Department of Microbiology and Immunology, Michigan Medicine, University of Michigan, Michigan, United States

   Contribution

   Formal analysis, Investigation, Visualization, Methodology, Writing—review and editing

   Contributed equally with

   Brogan Yarzabek and Eli Olson

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0001-5513-6886

3. Eli Olson

   1. Department of Microbiology and Immunology, Michigan Medicine, University of Michigan, Michigan, United States
   2. Graduate Program in Immunology, Michigan Medicine, University of Michigan, Michigan, United States

   Contribution

   Formal analysis, Investigation, Visualization, Methodology, Writing—review and editing

   Contributed equally with

   Brogan Yarzabek and Anita J Zaitouna

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0003-2319-7144

4. Gayathri N Silva

   Department of Microbiology and Immunology, Michigan Medicine, University of Michigan, Michigan, United States

   Present address

   Department of Chemistry, University of Colombo, Colombo, Sri Lanka

   Contribution

   Formal analysis, Investigation, Methodology, Writing—review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-2687-2144

5. Jie Geng

   Department of Microbiology and Immunology, Michigan Medicine, University of Michigan, Michigan, United States

   Contribution

   Formal analysis, Investigation, Visualization, Methodology, Writing—review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0001-8722-2228

6. Aviva Geretz

   1. US Military HIV Research Program, Walter Reed Army Institute of Research, Silver Spring, United States
   2. Henry M. Jackson Foundation for the Advancement of Military Medicine, Bethesda, United States

   Contribution

   Data curation, Methodology, Software, Writing-review and editing

   Competing interests

   No competing interests declared

7. Rasmi Thomas

   1. US Military HIV Research Program, Walter Reed Army Institute of Research, Silver Spring, United States
   2. Henry M. Jackson Foundation for the Advancement of Military Medicine, Bethesda, United States

   Contribution

   Formal analysis, Investigation, Visualization, Methodology, Writing—review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-2116-2418

8. Sujatha Krishnakumar

   Sirona Genomics, Immucor, Inc, California, United States

   Contribution

   Formal analysis, Investigation, Methodology, Writing—review and editing

   Competing interests

   Sujatha Krishnakumar is affiliated with Sirona Genomics, as VP Research and Development. The author has no financial interests to declare.

   "This ORCID iD identifies the author of this article:" 0000-0003-1961-3432

9. Daniel S Ramon

   Department of Laboratory Medicine and Pathology, Mayo Clinic, Arizona, United States

   Contribution

   Formal analysis, Investigation, Methodology, Writing—review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-3067-5653

10. Malini Raghavan

    Department of Microbiology and Immunology, Michigan Medicine, University of Michigan, Michigan, United States

    Contribution

    Conceptualization, Formal analysis, Supervision, Funding acquisition, Validation, Visualization, Methodology, Writing—original draft, Project administration, Writing—review and editing

    For correspondence

    malinir@umich.edu

    Competing interests

    No competing interests declared

    "This ORCID iD identifies the author of this article:" 0000-0002-1345-9318

• 5,115

  views

• 449

  downloads

• 39

  citations

Views, downloads and citations are aggregated across all versions of this paper published by eLife.