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HLA class I signal peptide polymorphism determines the level of CD94/NKG2–HLA-E-mediated regulation of effector cell responses

Nature Immunology volume 24, pages 1087–1097 (2023)Cite this article

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Abstract

Human leukocyte antigen (HLA)-E binds epitopes derived from HLA-A, HLA-B, HLA-C and HLA-G signal peptides (SPs) and serves as a ligand for CD94/NKG2A and CD94/NKG2C receptors expressed on natural killer and T cell subsets. We show that among 16 common classical HLA class I SP variants, only 6 can be efficiently processed to generate epitopes that enable CD94/NKG2 engagement, which we term ‘functional SPs’. The single functional HLA-B SP, known as HLA-B/−21M, induced high HLA-E expression, but conferred the lowest receptor recognition. Consequently, HLA-B/−21M SP competes with other SPs for providing epitope to HLA-E and reduces overall recognition of target cells by CD94/NKG2A, calling for reassessment of previous disease models involving HLA-B/−21M. Genetic population data indicate a positive correlation between frequencies of functional SPs in humans and corresponding cytomegalovirus mimics, suggesting a means for viral escape from host responses. The systematic, quantitative approach described herein will facilitate development of prediction algorithms for accurately measuring the impact of CD94/NKG2–HLA-E interactions in disease resistance/susceptibility.

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Fig. 1: HLA class I signal peptide polymorphism influences HLA-E expression.
Fig. 2: JurkatNKG2 reporter cells respond differentially to VL9-pulsed .221 and .221-SPE cells.
Fig. 3: 221-SPE*01:03 cells differentially stimulate primary NK and NKL cells.
Fig. 4: Frequencies of combined functional signal peptides at each HLA locus across NMDP populations.
Fig. 5: HLA-A, HLA-B and HLA-C haplotypes and genotypes stratified by the presence of alleles encoding functional signal peptides.
Fig. 6: HLA-E expression levels on BLCLs and recognition of BLCLs by reporter cells suggest competition between SP variants in providing VL9 epitopes to HLA-E.
Fig. 7: Increasing copy number of alleles encoding SP-1C versus SP-6B associate with opposing effects on JurkatNKG2A reporter cell activity.

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Data availability

Publicly available HLA genetic datasets used in this study include Allele Frequency Net database (http://www.allelefrequencies.net/), IPD-IMGT/HLA database (https://www.ebi.ac.uk/ipd/imgt/hla/), NMDP Registry Haplotype Frequencies (https://frequency.nmdp.org/) and the 2014 1000 Genomes Project HLA data (https://www.internationalgenome.org/category/hla/). The structure of the human CD94/NKG2A complex with HLA-E (3CDG) was downloaded from the Protein Data Bank (https://www.rcsb.org/). Source data are provided with this paper. All other data are available within the article and Supplementary Information.

Code availability

PyMOL script is provided in the Supplementary Information.

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Acknowledgements

We thank Q. Hammer, G. Nelson and M. P. Martin for discussions and M. Thompson for technical assistance. We also thank the NIH Blood Bank, the University Medical Center Hamburg-Eppendorf, and donors for providing blood samples. This project has been funded in whole or in part with federal funds from the Frederick National Laboratory for Cancer Research, under contract no. 75N91019D00024. The content of this publication does not necessarily reflect the views or policies of the Department of Health and Human Services, nor does mention of trade names, commercial products or organizations imply endorsement by the US Government. This research was supported in part by the Intramural Research Program of the NIH, Frederick National Laboratory, Center for Cancer Research. A. Hoelzemer and M.B. are supported by the Federal Ministry of Education and Research (01KI2110) and DZIF (German Center for Infection Research).

Author information

Author notes

  1. These authors contributed equally: Zhansong Lin, Arman A. Bashirova.

Authors and Affiliations

  1. Ragon Institute of MGH, MIT and Harvard, Cambridge, MA, USA

    Zhansong Lin, Pedro Ojeda, Wilfredo F. Garcia-Beltran & Mary Carrington

  2. Basic Science Program, Frederick National Laboratory for Cancer Research, Frederick, MD, USA

    Arman A. Bashirova, Mathias Viard, Wojciech K. Kasprzak, Marjan Akdag, Yuko Yuki & Mary Carrington

  3. Laboratory of Integrative Cancer Immunology, Center for Cancer Research, National Cancer Institute, Bethesda, MD, USA

    Arman A. Bashirova, Mathias Viard, Wojciech K. Kasprzak, Marjan Akdag, Yuko Yuki & Mary Carrington

  4. Centre for Immuno-Oncology, University of Oxford, Oxford, UK

    Lee Garner, Max Quastel, Andrew J. McMichael & Geraldine M. Gillespie

  5. Leibniz Institute of Virology, Hamburg, Germany

    Maya Beiersdorfer & Angelique Hoelzemer

  6. 1st Department of Medicine, University Medical Center Hamburg-Eppendorf, Hamburg, Germany

    Maya Beiersdorfer & Angelique Hoelzemer

  7. German Center for Infection Research (DZIF), Site Hamburg-Lübeck-Borstel-Riems, Hamburg, Germany

    Maya Beiersdorfer & Angelique Hoelzemer

  8. Advanced Biomedical Computational Science, Frederick National Laboratory for Cancer Research, Frederick, MD, USA

    Wojciech K. Kasprzak

  9. Cancer Research Technology Program, Frederick National Laboratory for Cancer Research, Frederick, MD, USA

    Sudipto Das & Thorkell Andresson

  10. Massachusetts General Hospital Cancer Center, Division of Hematology/Oncology, Department of Medicine, Massachusetts General Hospital, Boston, MA, USA

    Vivek Naranbhai

  11. Dana-Farber Cancer Institute, Boston, MA, USA

    Vivek Naranbhai

  12. Center for the AIDS Programme of Research in South Africa, Durban, South Africa

    Vivek Naranbhai

  13. Department of Oncological Sciences, Precision Immunology Institute, Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA

    Amir Horowitz

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Contributions

Z.L. and A.A.B. designed and performed experiments, analyzed data and wrote the manuscript; M.V. performed bioinformatic analysis; L.G., M.Q. and G.M.G. performed SPR and peptide binding analyses; M.B. and A. Hoelzemer performed macrophage experiments; W.K.K. performed molecular dynamics simulation analysis; M.A. assisted Z.L. and A.A.B. in cloning and flow cytometry experiments; Y.Y. performed HLA typing; P.O. and W.F.G.-B. designed and made the JurkatNKG2 reporter cells; S.D. and T.A. performed mass spectrometry analysis; V.N., A. Horowitz, A.J.M., A. Hoelzemer, G.M.G. and W.F.G.-B. contributed to the design of the work; M.C. designed the study, supervised all work and wrote the manuscript. All authors reviewed and approved the manuscript.

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Correspondence to Mary Carrington.

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Nature Immunology thanks Peter Parham and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Peer reviewer reports are available. Primary handling editor: S. Houston, in collaboration with the Nature Immunology team.

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Extended data

Extended Data Fig. 1 HLA class I SP frequencies in human populations.

Allelic frequencies of SP variants were determined based on HLA class I allele frequencies obtained from www.allelefrequencies.net for four USA NMDP populations, including African (n = 28,557), European (n = 1,242,890), Chinese (n = 99,672), and Southeast Asian (n = 27,978). SP sequences and corresponding HLA class I alleles are shown in Table 1.

Source data

Extended Data Fig. 2 Differential binding of VL9 peptides to HLA-E.

a, HLA-E surface expression level on VL9-pulsed .221 cells measured by flow cytometry using 3D12 antibody. Cells were pulsed with peptides at 100, 30, and 10 μM concentrations. Peptide sequence alignments labeled with the corresponding SP variants are shown on the left. The expression index was calculated using 3D12 MFI as follows: ((sample – neg_ctrl) ÷ (pos_ctrl – neg_ctrl)) × 100, where neg_ctrl represents unpulsed .221 cells mixed with DMSO and pos_ctrl represents unpulsed .221 cells incubated constantly at 26 °C. Data represent triplicate experiments and reflect endogenous HLA-E*01:01 expression. b, VL9 binding to HLA-E*01:03 estimated using ELISA-based peptide binding and thermal stability assays. Bar charts represent absorbance signals at 450 nm reflecting the degree of VL9HLA-E complex recovery in the sandwich ELISA assay and thermal melting temperatures of VL9HLA-E determined using differential scanning fluorimetry. Data represent six experiments. a, b, Light gray bars depict the three peptides that showed the lowest binding levels consistently across experiments. Error bars represent the mean ± SD.

Source data

Extended Data Fig. 3 Differential HLA-E expression on the surface of .221-SPE cells and correlations of HLA-E expression levels between pairs of distinct measurements.

a, Cell surface expression levels of HLA-E on .221-SPE*01:01 and .221-SPE*01:03 cells measured by flow cytometry using anti-FLAG antibody. Data represent measurements on different days (n = 3). Error bars represent the mean ± SD. Amino acid sequence alignments of corresponding SP variants are shown on the left. VL9 peptide is shown in red. b, Correlations between HLA-E expression levels measured using anti-FLAG antibody and those measured using 3D12 antibody on the surface of .221-SPE*01:01 or .221-SPE*01:03 cells. c, Correlations between HLA-E expression levels on .221-SPE*01:01 cells and those on .221-SPE*01:03 cells measured using 3D12 or anti-FLAG antibodies. d, Correlations of HLA-E expression levels on .221-SPB*57:01 and those on .221-SPE*01:01 or .221-SPE*01:03. b-d, R2 was determined by Spearman correlation analysis and is shown with a two-tailed P value.

Source data

Extended Data Fig. 4 HLA-E expression levels on various cell types.

a, HLA-E expression levels on .221-SPE*01:03 cells, .221 cells pulsed with 100 μM VL9 peptides, PBMCs, and BLCLs. Error bars for PBMCs reflect variation across four donors (mean ± SD). The Y axis represents MFI obtained by 3D12 antibody staining minus MFI obtained by isotype control staining for each cell type. b, HLA-E expression levels on PBMCs and cell type subsets after 48 hours in culture with or without IFN-γ treatment. Data for two healthy donors (HD) are shown (HD75 and HD77) and represent triplicate experiments. Error bars represent the mean ± SD. P values for comparison between IFN-γ-treated and untreated cells were determined by a two-sided unpaired t-test: * - P < 0.05, ** - P < 0.01, *** - P < 0.001, **** - P < 0.0001.

Source data

Extended Data Fig. 5 JurkatNKG2A and JurkatNKG2C reporter activity against VL9-pulsed .221 cells.

a, Correlations between JurkatNKG2A and JurkatNKG2C reporter activity against VL9-pulsed (100uM) .221 expressing endogenous HLA-E*01:01 (shown in Fig. 2b). b, Absence of a correlation between HLA-E expression level on VL9-pulsed (100uM) .221 cells (shown in Fig. 1a) and Jurkat reporter cell activity against these cells (shown in Fig. 2b). Peptides are labeled with representative SPs. c, Correlations between Jurkat reporter cell activity against VL9-pulsed (100uM) .221 cells and KD values for HLA-E*01:03/VL9 binding to CD94/NKG2 determined by SPR analysis (Supplementary Table 1). a-c, R2 was determined by Spearman correlation analysis and is shown with a two-tailed P value.

Source data

Extended Data Fig. 6 Increase of HLA-E surface expression on monocyte-derived macrophages and enhanced JurkatNKG2A activity against these cells after IFN-γ treatment.

Monocytes were isolated from nine healthy donors and differentiated to monocyte-derived macrophages that were exposed to IFN-γ overnight (both unpulsed and VL9G-pulsed). Peptide pulsing was performed at 37°C for one hour prior to co-culture with reporter cells. P values for comparison between IFN-γ-treated and untreated cells were determined by a two-sided paired t-test.

Source data

Extended Data Fig. 7 Influence of valine at P7 of VL96B on CD94/NKG2A recognition.

a, Mutation analysis of P7 using JurkatNKG2A reporter cell response to .221-SPE*01:03 cells. Schematic representation of hybrid swap constructs transduced into .221 cells is shown on top. HLA-E expression level on .221-SPE*01:03 cells expressing wild-type (1A, 6B) and mutant (1AV, 6BL) SPs, and JurkatNKG2A activity against these cells are shown below. Data represent triplicate experiments. Error bars represent the mean ± SD. P values for comparisons between wild type and mutant SPs were determined by a two-sided unpaired t-test. b, Molecular dynamics simulation analysis of CD94/NKG2A–HLA-E*01:03 complexes in the presence of VL91A and VL96B peptides. Box plots show the root-mean-square deviations (RMSDs) of the full receptor, CD94, or NKG2A depending on the presence of the two distinct VL9 peptides in the binding groove during a 5 μs simulation (n = 500). Box boundaries span the 25–75 percentiles with the median marked in the middle; the whiskers extend to 10 and 90 percentiles, and the remaining data points are shown as gray circles. The RMSD box plots demonstrate higher receptor motions in VL96B-loaded complexes P values for comparisons between VL91A and VL96B were determined by a two-sided Mann-Whitney test.

Source data

Extended Data Fig. 8 NKG2A surface expression levels on different effector cells.

Jurkat reporter cells, primary NK cells from four healthy donors (HD), and NKL cells were stained with anti-NKG2A antibody (clone REA110). Flow cytometry histograms are shown on top, and graphical representation of the corresponding MFI data is shown below. The vertical line on the REA110 histogram separates NKG2A− from NKG2A+ populations. NKG2A expression levels are determined as MFI values of NKG2A+ cells obtained by REA110 staining minus MFI values obtained by isotype control staining for each cell type. (-) represents untransduced parental Jurkat cells.

Source data

Extended Data Fig. 9 Distribution of specific functional SP haplotype groups present in NMDP samples.

Each SP haplotype group presented in Fig. 5a (main text) was further stratified by specific SP variants encoded by HLA-A, -B, -C haplotypes. Haplotype groupings are labeled according to the encoded functional SP variants.

Source data

Extended Data Fig. 10 HLA-E surface expression on BLCLs and reporter cell recognition of BLCLs stratified by copy number of alleles encoding SP-1A, SP-2A, or SP-2C.

HLA-E surface expression levels on BLCLs measured by flow cytometry using 3D12 antibody is shown on top and the corresponding JurkatNKG2A reporter cell activity (% of CD69+ cells) is shown below (n = 360). Lines in each group represent the median. P values for multi-group comparisons were determined by the Kruskal-Wallis test.

Source data

Supplementary information

Supplementary Information

Supplementary Fig. 1.

Reporting Summary

Peer Review File

Supplementary Table 1

Equilibrium Kd values for HLA-E*01:03/VL9 binding to CD94/NKG2.

Supplementary Code 1

PyMOL script for root-mean-square deviation calculations in the molecular dynamics simulation analysis.

Supplementary Data 1

Antibody and reagents.

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Lin, Z., Bashirova, A.A., Viard, M. et al. HLA class I signal peptide polymorphism determines the level of CD94/NKG2–HLA-E-mediated regulation of effector cell responses. Nat Immunol 24, 1087–1097 (2023). https://doi.org/10.1038/s41590-023-01523-z

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