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. 2022 May 19;11:e75670. doi: 10.7554/eLife.75670

T cells discriminate between groups C1 and C2 HLA-C

Malcolm J W Sim 1,†,✉, Zachary Stotz 1, Jinghua Lu 1, Paul Brennan 1, Eric O Long 1, Peter D Sun 1,✉
Editors: Chyung-Ru Wang2, Tadatsugu Taniguchi3
PMCID: PMC9177145  PMID: 35587797

Abstract

Dimorphic amino acids at positions 77 and 80 delineate HLA-C allotypes into two groups, C1 and C2, which associate with disease through interactions with C1 and C2-specific natural killer cell receptors. How the C1/C2 dimorphism affects T cell recognition is unknown. Using HLA-C allotypes that differ only by the C1/C2-defining residues, we found that KRAS-G12D neoantigen-specific T cell receptors (TCRs) discriminated between C1 and C2 presenting the same KRAS-G12D peptides. Structural and functional experiments, and immunopeptidomics analysis revealed that Ser77 in C1 and Asn77 in C2 influence amino acid preference near the peptide C-terminus (pΩ), including the pΩ-1 position, in which C1 favors small and C2 prefers large residues. This resulted in weaker TCR affinity for KRAS-G12D-bound C2-HLA-C despite conserved TCR contacts. Thus, the C1/C2 dimorphism on its own impacts peptide presentation and HLA-C-restricted T cell responses, with implications in disease, including adoptive T cell therapy targeting KRAS-G12D-induced cancers.

Research organism: Human

Introduction

The classical class I human leukocyte antigens (HLA-I; HLA-A, HLA-B, and HLA-C) are the most polymorphic genes across human populations and are associated with a myriad of human diseases (Dendrou et al., 2018; Robinson et al., 2017). HLA-I molecules present short peptides on the cell surface of nucleated cells where they form ligands for receptors of the immune system including T cell receptors (TCRs) and natural killer (NK) cell receptors. HLA-I-bound peptides are collectively known as the immunopeptidome, which defines the subset of the proteome bound as peptides to a particular HLA-I allotype (Istrail et al., 2004). Immunopeptidomes can be highly diverse, consisting of hundreds to thousands of different sequences as they are shaped by polymorphic amino acid residues that form pockets (A to F) in the HLA-I peptide-binding groove (PBG) (Di Marco et al., 2017; Madden, 1995; Sarkizova et al., 2020). These pockets restrict the peptide repertoire to those peptides with certain amino acid side chains at particular positions, which form anchor residues that define allotype-specific motifs (Di Marco et al., 2017; Falk et al., 1991; Sarkizova et al., 2020). The ‘anchor’ residues are often position 2 (p2) or p3 (B and C pockets) and the C-terminus (pΩ; F pocket), while residues outside the anchors can vary extensively allowing immunopeptidome diversification (Di Marco et al., 2017; Falk et al., 1991; Madden, 1995; Sarkizova et al., 2020). HLA-I allotypes with similar PBGs bind similar peptides, but subtle amino acid differences can modify the side-chain orientation impacting interactions with TCRs and NK cell receptors (Illing et al., 2018; Illing et al., 2012; Saunders et al., 2020; Stewart-Jones et al., 2012; Tynan et al., 2005). Understanding how HLA-I polymorphism impacts the immunopeptidome and interactions with immunoreceptors is critical for constructing molecular mechanisms that underpin disease risk associated with specific HLA-I alleles (Dendrou et al., 2018).

HLA-C is thought to play a lesser role in T cell responses compared to that of HLA-A and HLA-B due to its lower cell surface expression level (Apps et al., 2015; McCutcheon et al., 1995). However, HLA-C-restricted T cells are implicated in several disease settings. In HIV infection, HLA-C allotypes with a high expression were associated with increased T cell responses and slower progression to AIDS, while higher HLA-C expression was also linked with susceptibility to Crohn’s disease (Apps et al., 2013). In psoriasis, the strongest genetic association is with HLA-C*06:02, thought to be mediated by HLA-C*06:02-restricted T cells (Chen and Tsai, 2018; Lande et al., 2014). Furthermore, recent efforts to define the specificities of tumor-infiltrating lymphocytes (TIL) readily identified HLA-C-restricted T cells (Levin et al., 2021; Lu et al., 2014; Murata et al., 2020; Tran et al., 2015; Tran et al., 2016). Most notably, multiple HLA-C*08:02-restricted TCRs specific for the oncogenic hotspot mutation KRAS-G12D were identified in several cancer patients (Tran et al., 2015; Tran et al., 2016). Adoptive transfer of expanded KRAS-G12D-specific TILs successfully treated a patient with metastatic colorectal cancer, leading to complete regression of all but one metastatic lesion (Tran et al., 2016). The remaining lesion lost HLA-C*08:02 from its genome demonstrating conclusively that HLA-C-restricted T cells can mediate effective antitumor responses (Tran et al., 2016). Despite the clinical relevance of HLA-C-restricted T cells, few studies have examined TCR-HLA-C interactions with a molecular and structural focus. Indeed, our recent study on HLA-C*08:02-restricted KRAS-G12D-specific TCRs was the first to present crystal structures of any TCR in complex with HLA-C (Sim et al., 2020).

HLA-C is the most recently evolved classical HLA-I gene, present in only humans and other apes, and has several unique features (Adams and Parham, 2001). HLA-C, not HLA-A or HLA-B, is expressed on fetal extravillous trophoblast cells, making it the most polymorphic molecule expressed at the maternal–fetal interface (King et al., 2000). In addition, all HLA-C allotypes serve as ligands for the killer-cell immunoglobulin-like receptors (KIR), whereas only a subset of HLA-A and HLA-B allotypes bind KIRs (Hilton and Parham, 2017; Moesta et al., 2008). The KIR are a family of activating and inhibitory receptors expressed primarily on NK cells (Hilton and Parham, 2017; Saunders et al., 2015). HLA-C allotypes form two groups, C1 and C2, based on two dimorphic amino acid residues at positions 77 and 80 (Biassoni et al., 1995; Parham et al., 2012). C1 HLA-C allotypes carry Ser77 and Asn80 and are ligands for the inhibitory receptors KIR2DL2/3. The inhibitory receptor KIR2DL1 binds C2 HLA-C allotypes, which carry Asn77 and Lys80. The KIR discriminate C1 and C2 HLA-C largely via position 44 in the KIR (Winter and Long, 1997) and direct interactions with HLA-C position 80 (Boyington et al., 2000; Boyington and Sun, 2002; Fan et al., 2001). Genetic association studies link C1/C2 status and the presence or absence of specific KIR-HLA combinations with risk of multiple human diseases, including cancer, infectious diseases, autoimmunity, and disorders of pregnancy (Hiby et al., 2004; Khakoo et al., 2004; Kulkarni et al., 2008; Littera et al., 2016; Parham and Moffett, 2013; Rajagopalan and Long, 2005; Venstrom et al., 2012). Whether the C1/C2 dimorphism impacts HLA-C interactions with other immunoreceptors is unknown.

Given the clinical relevance of HLA-C-restricted T cells and that all HLA-C allotypes are either C1 or C2, it is important to ask whether this dimorphism impacts T cell recognition. It was hitherto assumed that the C1/C2 dimorphism would not directly affect T cell recognition as it lies outside the general TCR footprint. To answer this question, we studied two HLA-C*08:02 (C1)-restricted TCRs specific for different KRAS-G12D neoantigens. TCR9a is specific for G12D-9mer (GADGVGKSA), while TCR10 is specific for G12D-10mer (GADGVGKSAL) (Sim et al., 2020; Tran et al., 2016). TCR9a and TCR10 do not share TCR V genes and recognize their peptide antigens in different ways (Sim et al., 2020; Tran et al., 2016). We compared TCR9a and TCR10 recognition of HLA-C*08:02 (C1) with that of HLA-C*05:01, a C2 allotype that is identical in sequence to HLA-C*08:02 apart from the C1/C2 defining residues at positions 77 and 80 (Sim et al., 2017). In a previous study, we showed that 28 different ‘self’ peptides eluted from HLA-C*05:01 bound to both HLA-C*05:01 (C2) and HLA-C*08:02 (C1), suggesting a minimal impact of the C1/C2 dimorphism on peptide binding to HLA-C (Sim et al., 2017). Here, we found that T cells discriminate between C1 and C2 HLA-C due to differences in peptide binding to HLA-C and in peptide presentation to TCR, which revealed unsuspected features of the C1/C2 dimorphism governing interactions of HLA-C with immunoreceptors. Further, our data suggest that HLA-C*05:01-positive individuals may be eligible for immunotherapies targeting KRAS-G12D-10mer.

Results

Impact of the C1/C2 dimorphism to T cell recognition of KRAS neoantigens

HLA-C*05:01 (C*05) and HLA-C*08:02 (C*08) differ only by the C1/C2 dimorphism defined by positions 77 and 80, which are located on the α1 helix, near the peptide C-terminal amino acid (pΩ) (Figure 1A and B). We expressed C*08 and C*05 to similar levels in 721.221 cells, which are otherwise deficient in classical HLA-I, HLA-A, -B and -C (Figure 1—figure supplement 1A). These cells displayed canonical dimorphic recognition by KIR, with KIR2DL2/3 demonstrating strong binding to C*08, and KIR2DL1 demonstrating exclusive binding to C*05 (Figure 1—figure supplement 1B). We next used these cells as targets in functional experiments with Jurkat-T cells expressing C*08-restricted KRAS-G12D-specific TCRs, TCR9a, or TCR10. As expected, both the 9mer and 10mer G12D but not their WT KRAS peptides activated their respective TCR9a and TCR10 expressing T cells in the presence of C*08 (Figure 1C and D; Sim et al., 2020). Notably, we observed no activation of TCR9a and only weak activation of TCR10 when the 9mer and 10mer G12D KRAS peptides were presented by C*05, respectively (Figure 1C and D). To confirm this was not due to inefficient peptide loading, we utilized C*08 and C*05-expressing cell lines deficient in transporter associated with antigen presentation (TAP) (Figure 1—figure supplement 1C). These experiments yielded similar results using a different functional readout (expression of CD69), as G12D-9mer loaded on C*05 did not activate Jurkat-TCR9a cells, while G12D-10mer loaded on C*05 did stimulate Jurkat-TCR10 cells but required higher peptide concentrations than when loaded on C*08 (Figure 1—figure supplement 1C). Thus, the HLA-C1/C2 dimorphism impacts T cell recognition of KRAS-G12D neoantigen even though neither residue 77 nor 80 is in contact with the TCR. We next explored if the differences in T cell recognition of C*08 and C*05 were due to differences in the binding of peptide (Figure 2) or TCR (Figure 3) to HLA-C.

Figure 1. HLA-C C1/C2 dimorphism impacts T cell recognition of KRAS-G12D neoantigen.

(A) Sequence of C1/C2 dimorphism in HLA-C*08:02 and HLA-C*05:01 at positions 77 and 80. All other residues are identical. (B) Location of C1/C2 dimorphism on the structure of HLA-C*08:02, close to peptide C-terminus (PDB:6ULI). (C, D) Stimulation of TCR9a+ Jurkat cells (C) and TCR10+ Jurkat cells (D) by 221C*08:02 (left) or 221C*05:01 (right) preloaded with KRAS WT or G12D peptides at indicated concentrations. Means and standard errors of IL-2 concentration in culture supernatant measured by ELISA are shown from two independent biological replicates. Source data available in Figure 1—source data 1.

Figure 1—source data 1. ELISA readings for Figure 1C, D.

Figure 1.

Figure 1—figure supplement 1. Impact of C1/C2 dimorphism in T cell recognition of HLA-C.

Figure 1—figure supplement 1.

(A) Expression of HLA-C*08:02 and HLA-C*05:01 on 721.221 (221) cells. (B) Recombinant KIR-Ig fusion protein (KIR-Fc) binding to 221, 221C*05:01, and 221C*08:02. KIR-Fc was conjugated to protein-A APC and used at 3.6 μg/ml. (C) Stimulation of Jurkat-TCR9a (left) and Jurkat-TCR10 (right) with indicated peptides loaded on TAP-deficient cells expressing HLA-C*08:02 and HLA-C*05:01. Jurkat T cell activation was measured by CD69 expression by flow cytometry. Means and standard errors from at least three independent experiments are shown.
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Figure 2. C1 but not C2 HLA-C allotypes present peptides with C-terminal (pΩ) Ala.

(A) Stabilization of HLA-C on TAP-deficient 221 cells expressing HLA-C*08:02 or HLA-C*05:01. (B) Data from (A) shown as fold median fluorescent intensity (MdFI) relative to no peptide (NP) from a minimum of four independent experiments. (C) Stimulation of TCR9a+ Jurkat cells by 221C*05:01 cells preloaded with KRAS peptides at indicated concentrations. Means and standard errors of IL-2 concentration in culture supernatant measured by ELISA from two biological replicates are shown. (D) Frequency of indicated residues at the C-terminus (pΩ) in peptides eluted from HLA-C*08:02 or HLA-C*05:01. (E) Frequency of pΩ Ala in peptides eluted from HLA-C*08:02 or HLA-C*05:01. (F) Volcano plot displaying pΩ amino acid frequency from 21 HLA-C allotypes. The C2/C1 ratio is shown for the average frequency of each amino acid. (G) The frequency of pΩ Ala from 21 HLA-C allotypes by C1/C2 status. Statistical significance was assessed by unpaired t-test with Welsh’s correction, *p<0.05, **p<0.001, ****p<0.0001. Source data available in Figure 2—source data 1. Peptide sequences and p9 frequency analysis are available in Figure 2—source data 2.

Figure 2—source data 1. Normalized MdFI values for Figure 2B, ELISA readings for Figure 2C and amino acids frequencies for Figure 2D-G.
Figure 2—source data 2. HLA-C Peptide sequences used for analysis.

Figure 2.

Figure 2—figure supplement 1. Impact of peptide length on frequency of C-terminal (pΩ) Ala in peptides eluted from C*08:02 and C*05:01.

Figure 2—figure supplement 1.

Frequency of peptides with C-terminal Ala in 8mers, 9mers, and 10mers eluted from HLA-C*08:02 and HLA-C*05:01.
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Figure 3. T cell receptor (TCR) binding is weaker to C2 HLA-C.

(A) Binding of TCR9a to captured HLA-C*05:01-KRAS-G12D-A18L determined by surface plasmon resonance (SPR). Dissociation constant was determined by kinetic curve fitting of 12 curves from two independent experiments. (B, C) Binding of HLA-C*08:02 or HLA-C*05:01 tetramers to Jurkat T-cells expressing TCR9a or TCR10 at indicated tetramer concentrations. HLA-C was refolded with indicated peptides. (C) Summary of (B) from two independent experiments displayed as average (mean) and standard error of median fluorescence intensity (MdFI). (D, E) Binding of TCR10 to captured HLA-C*08:02-G12D-10mer and HLA-C*05:01-G12D-10mer by SPR. One experiment of four is shown. (F) Mean, standard deviation, and nonlinear curve fitting of TCR10 binding to G12D-10mer bound to C*08:02 and C*05:01. Dissociation constants were derived from four independent binding experiments with twofold serial dilutions of TCR10 starting at 10 μM (two experiments), 40 μM (one experiment), and 80 μM (one experiment). (G) Association binding of TCR10 with G12D-10mer presented by C*08:02 or C*05:01. SA, streptavidin; Tet, tetramer. Source data available in Figure 3—source data 1.

Figure 3—source data 1. Raw data for TCR response curves in Figure 3A, D, E and G.
MdFI of tetramer binding experiments in 3C. Responses of all TCR10 binding assays in 3F.

Figure 3.

Figure 3—figure supplement 1. C*08 is a better ligand for TCR9a than C*05.

Figure 3—figure supplement 1.

(A) Binding of TCR9a to C*05-A18L-9mer by surface plasmon resonance (SPR). Binding was measured with six serial twofold dilutions with a highest concentration of 1200 nM. The plots display raw and fitted data from two independent experiments. (B) Activation of Jurkat-TCR9a cells by 221C*08:02 and 221C*05:01 loaded with A18L-9mer. Means and standard errors of IL-2 concentration in culture supernatant from three independent experiments are shown.
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KRAS peptide C-terminal residue distinguishes C1 and C2 HLA-C allotypes

To characterize KRAS-G12D peptide binding to C*08 and C*05, we carried out peptide loading assays with TAP-deficient cells expressing C*08 or C*05. Similar to C*08, C*05 was not stabilized by WT-KRAS peptides likely due to the absence of Asp/Glu at p3, a critical anchor for C*08 and C*05 (Figure 2A and B). However, G12D-9mer only stabilized C*08 and not C*05, while G12D-10mer stabilized both allotypes (Figure 2A and B). An unusual feature of the G12D-9mer was pΩ Ala as its short side chain does not fill the pΩ pocket (F pocket), in contrast to G12D-10mer with pΩ Leu (Sim et al., 2020). We previously demonstrated that substitution of pΩ Ala to Leu in G12D-9mer (A18L-9mer: 10GADGVGKSL) improved binding to HLA-C*08:02, and T cell recognition by Jurkat-TCR9a (Sim et al., 2020). Similarly, A18L-9mer bound C*05 well (Figure 2A and B) and stimulated Jurkat-TCR9a cells (Figure 2C, Figure 1—figure supplement 1C). Thus, the inability of G12D-9mer to bind C*05 accounts for its lack of activation of Jurkat-TCR9a cells. Further, these data suggest that the C1/C2 dimorphism selectively impacts peptide binding to HLA-C in a sequence-dependent manner.

To explore if pΩ Ala is a general feature of peptides that bind C1 allotypes (like C*08) and not C2 allotypes (like C*05), we examined C-terminal amino acid usage in the immunopeptidomes of HLA-C allotypes using two publicly available datasets (see Materials and methods) (Di Marco et al., 2017; Sarkizova et al., 2020). We initially analyzed both datasets independently and observed similar amino acid frequencies, thus we present a combined analysis of a merged dataset with duplicate sequences removed. However, we cannot rule out that differences in cell line origin, data acquisition, false discovery rates, or analytical pipelines may have influenced our analysis. In addition, our analysis was limited to that of amino acid frequency and did not incorporate peptide abundance. We first compared the immunopeptidomes of C*08 and C*05 as the residues that form the pΩ binding pocket of C*08 and C*05 are identical and accordingly the five most abundant pΩ residues (Leu, Val, Ile, Phe, and Met) were the same in both allotypes, accounting for 97% of C*08% and 98% of C*05 sequences (Figure 2D). Leucine was the most common pΩ in both allotypes but had a higher frequency in C*08 peptides than C*05 (C*08 = 73%, C*05 = 37%) (Figure 2D). The significance of this difference is unknown; however, peptides with pΩ Leu, Val, Ile, Phe, and Met bind well to both C*08 and C*05 (Sim et al., 2017). Among 9mer peptides, the frequency of Ala at pΩ position was 1.5% (30 out of 1986) in C*08, significantly higher (p=0.0016 Fisher’s exact test) than that of 0.13% (1 out of 732) in C*05-bound peptides (Figure 2E). Similar results were observed for 10mers, where pΩ Ala was more common among C*08 peptides (5/349, 1.4%) than C*05 peptides (0/152, 0%) (Figure 2—figure supplement 1). We next analyzed the pΩ amino acid frequency amongst 9mer sequences eluted from 14 C1 allotypes and 7 C2 allotypes and looked for amino acids with the greatest difference in frequency between C1 and C2 eluted peptides. In concordance with the comparison of C*05 and C*08 immunopeptidomes, pΩ Ala was the one most enriched in C1 allotypes, with a frequency of 1.45% ± 1.4% in C1 allotypes compared to 0.27% ± 0.19% in C2 allotypes (Figure 2F and G). In addition, Gln and Ser were significantly enriched at pΩ of C1 allotypes, but at lower frequencies than Ala. Thus, C1 allotypes bind peptides with pΩ Ala to a greater extent than C2 allotypes, consistent with the ability of G12D-9mer to bind C*08 and not C*05.

Impact of C1/C2 dimorphism on TCR binding to HLA-C

We next explored the impact of the C1/C2 dimorphism on TCR binding to HLA-C using recombinant HLA-C refolded with individual peptides. The solution-binding affinities of the two neoantigen-specific TCRs, TCR9a and TCR10, to their cognate peptides presented by C*08 or C*05 were determined by surface plasmon resonance (SPR) using recombinant TCRs as analytes. As G12D-9mer with pΩ Ala does not bind C*05 (Figure 2A), we chose the Leu anchor variant A18L-9mer as the peptide for C*05 to ensure HLA-C stability. While TCR9a bound to the C1 allotype HLA-C*08 in the presence of G12D-9mer peptide with 16 nM affinity (Sim et al., 2020), it displayed a binding affinity of 108 nM for the C2 allotype HLA-C*05 in the presence of Leu anchor variant of G12D-9mer, an approximately sevenfold affinity reduction compared to the C1 allotype (Figure 3A, Figure 3—figure supplement 1F). Consistent with their solution-binding affinities, the C*08-G12D-9mer tetramers bound TCR9a-expressing Jurkat cells with lower EC50 than C*05-A18L-9mer tetramers (Figure 3B and C). This reduced binding affinity and avidity may explain the reduced sensitivity of Jurkat-TCR9a cells for A18L-9mer loaded on C*05 compared to C*08 (Figure 3—figure supplement 1B). In solution, TCR10 bound both the C1 and C2 allotypes with similar μM affinities (Figure 3D–F), though slightly weaker affinity was observed with C*05. By steady-state kinetics, TCR10 binding was less than twofold weaker to C*05:01-G12D-10mer (KD = 13 ± 6 μM) than C*08:02-G12D-10mer (KD = 8 ± 5 μM) (Figure 3D and E). However, by kinetic analysis of the binding curves (Figure 3G), TCR10 had almost 20-fold slower on rate with C*05:01-G12D-10mer (Kon = 6.2 ± 1.2 × 103 M–1 s–1) than with C*08:02-G12D-10mer (Kon = 1.2 ± 0.9 × 105 M–1 s–1). Consistently, C*08-G12D-10mer exhibited better binding to TCR10-expressing Jurkat T cells than C*05-G12D-10mer, which failed to reach binding saturation at the highest concentration of tetramer tested (40 μg/ml) (Figure 3B and C). Thus, both TCR9a and TCR10 exhibited better binding to the C1 allotype than C2.

Neither TCR9a nor TCR10 make direct contacts to HLA-C positions Ser 77 and Asn 80 (Sim et al., 2020). To understand the impact of C2 residues Asn 77 and Lys 80 on T cell recognition, we determined the crystal structure of TCR9a in complex with C*05-A18L-9mer to a high resolution of 1.9 Å (Supplementary file 1). TCR9a adopted the same binding mode when in complex with C*05, as in the C*08 complex structure (Figure 4A). TCR9a recognition of C*05-A18L-9mer was via CDR3α Q98 that forms hydrogen (h) bonds with Arg 156 and Gln 155 of HLA-C and the peptide backbone of peptide p5 Val (Figure 4B). TCR9a CDR2β Tyr48 and Glu49 coordinated peptide p7 Lys (Figure 4C), while CDR3β of TCR9a E95 formed a salt bridge with C*05 Arg 69 (Figure 4D). All these contacts observed between TCR9a and the C2 HLA-C*05 are conserved in the structure of TCR9a complexed with C*08 (Supplementary file 2). Like the C1 HLA-C*08, the dimorphic residues 77 and 80 on the C2 HLA-C*05 also do not interact with TCR9a. The closest TCR residue to HLA-C position 77 or 80 is Glu 49 of the TCRβ chain that interacts with Lys p7 of the peptide but is greater than 7 Å away from HLA-C position 80. Docking TCR10 onto a structure of C*05:01-G12D-10mer using our TCR10-C*08:02-G12D-10mer complex revealed no obvious impact on TCR contacts (Figure 4—figure supplement 1; Bai et al., 2021; Sim et al., 2020), suggesting the dimorphic positions on C*05 do not directly contact TCR.

Figure 4. Minimal impact of C1/C2 dimorphism on TCR:HLA-C complex structure.

(A) Side view of TCR9a in complex with HLA-C*05:01-G12D-A18L (left) and HLA-C*08:02-G12D-9mer (right; PDB:6ULR). (B–D) Interactions of TCR9a with HLA-C*05:01-A18L (blue) and HLA-C*08:02-G12D-9mer (red). TCR9a α chain, green; β chain, pink; β2-microglobulin (β2M), gold.

Figure 4.

Figure 4—figure supplement 1. Modeling TCR10 binding to C*05:01-G12D-10mer.

Figure 4—figure supplement 1.

Crystal structure of TCR10 in complex with C*08:02-G12D-10mer (PDB:6UON) was used to dock C*05:01-G12D-10mer (PDB:6JTO). Docking was carried out in PyMOL.
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The C1/C2 dimorphism affects pΩ-1 backbone position and side-chain preference

The lack of direct contacts between TCR and the HLA-C1/C2 dimorphic residues raised the question if the C1/C2 dimorphism indirectly affected TCR binding through peptide conformation. The position 77 side chain is orientated directly into the PBG, while position 80 sits atop the α1-helix (Figure 1B). Position 77 is Ser in C*08 (C1) and Asn in C*05 (C2) and both form an h-bond of the same distance with the amide of the terminal peptide bond between pΩ and p8 (pΩ-1) (Figure 5A). Comparing the structures of C*05 and C*08, the HLA-C α1-helices were displaced relative to each other and peptide p8 was higher out of the PBG in C*05 compared to C*08 (Figure 5B–D). This displacement was measured by comparing the distance between the Cα of peptide p8 (Ser) with that of the Cβ of HLA-C position 77 (the first carbon of the position 77 side chain). This distance was 4.7 Å and 5.7 Å in the C*08 and C*05 structures, respectively (Figure 5B and C). We next examined the same distance in 10 additional published HLA-C structures, 4 C1 (HLA-C*03:04, PDB:1EFX, HLA-C*07:02, PDB:5VGE, HLA-C*08:01, PDB:4NT6, HLA-C*08:02-G12D-10mer, PDB: 6ULK) and 6 C2 (HLA-C*04:01, PDB:1QQD, HLA-C*05:01-G12D-10mer, PDB: 6JTO, HLA-C*05:01, PDB: 5VGD, HLA-C*06:02, PDB:5W6A, 5W69, 5W67). We observed that the difference between the Cα of peptide p8 and HLA-C 77 Cβ was maintained between C1 and C2 HLA-C structures (C1 = 4.6 ± 0.11 Å, C2 = 5.7 ± 0.22 Å) (Figure 5E). In contrast, a conserved contact with the peptide N terminus and Tyr171 Oγ was the same between C1 and C2 HLA-C structures (2.70 Å vs. 2.76 Å) (Figure 5F and G). A consequence of this displacement is the proximity of the p8 side chain to the HLA-C α1 helix (Figure 5H). We compared the distance between the p8 side chain and HLA-C Val 76 in the 10 HLA-C structures. In all five C1 allotype structures, the p8 side chain was within van der Waals range of Val 76 (3.3–4.0 Å). In contrast, the p8 side chain was out of contact range with Val 76 in all but one C2 HLA-C structures (Figure 5H and I). This appears to be due in part to the torsion angle of the terminal peptide bond that orientates p8 towards the α1 helix in C1 allotypes and out of the groove in C2 allotypes (Figure 5J). Thus, the C1/C2 dimorphism impacts the position and orientation of the peptide at pΩ-1, suggesting that position 77 is the major determinant of the difference between C*05 and C*08. To interrogate the role of positions 77 and 80 individually, each one was substituted in C*05 with the corresponding residue in C*08 (Figure 5—figure supplement 1). Cells expressing C*05, C*05 Asn77Ser (N77S), C*05 Lys80Asn (K80N), and C*08 loaded with G12D-9mer or G12D-10mer were used as targets for Jurkat-TCR9a and Jurkat-TCR10 cells, respectively. The lack of stimulation of Jurkat-TCR9a by C*05 was minimally impacted by C*05-K80N, while C*05-N77S recovered Jurkat-TCR9a responses, though not completely (Figure 5K). In contrast, stimulation of Jurkat-TCR10 cells by HLA-C*05:01 was not improved by single substitutions at positions 77 and 80, suggesting a synergistic effect of positions 77 and 80 on recognition of HLA-C*08:02 by TCR10.

Figure 5. C1/C2 dimorphism determines the distance between peptide p8 and HLA-C.

(A) Hydrogen bond between HLA-C position 77 and amide in terminal peptide bond in C*08:02-G12D-9mer and C*05:01-A18L-9mer structures. (B–D) Distance between HLA-C position 77 Cβ and Cα of peptide p8 Ser of HLA-C*08:02-G12D-9mer (B), HLA-C*05:01-G12D-A18L (C), and both overlaid (D). (E) Distances between HLA-C position 77 Cβ and Cα of peptide pΩ-1 in 12 HLA-C crystal structures. (F) Distance between Tyr 171 Oγ and peptide N-terminus of C*08:02 and C*05:01. (G) Distance between Tyr 171 Oγ and peptide N-terminus in 12 HLA-C crystal structures. (H) Distance between peptide p8 side chain and HLA-C position 77 Val side chain in structures of C*08:02 and C*05:01. (I) Distance between peptide pΩ-1 side chain and HLA-C position 76 Val side chain in 12 HLA-C structures. (J) Torsion angle of terminal peptide bond in 12 HLA-C structures. (K) Stimulation of Jurkat-TCR9a+ and Jurkat-TCR10+ by 221 cells expressing HLA-C*08:02, HLA-C*05:01, HLA-C*05:01-N77S, or HLA-C*05:01-K80N preloaded with G12D-9mer (left) or G12D-10mer (right). Means and standard errors of IL-2 concentration in culture supernatant measured by ELISA from three biological replicates are shown. Significance in (E), (G), (I), and (J) was measured using an unpaired t-test with Welch’s correction, **p<0.01, ****p<0.0001. Source data available in Figure 5—source data 1.

Figure 5—source data 1. Raw distances for Figure 5E, G, I.
Raw angles for 5J. ELISA readings for 5K.

Figure 5.

Figure 5—figure supplement 1. Expression HLA-C in 721.221 cells with position 77 and 80 substitutions.

Figure 5—figure supplement 1.

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We next explored if the shift in pΩ-1 position is associated with changes in the HLA-C immunopeptidome other than those at pΩ (Figure 2). Analyzing 9mer sequences unique to C*08 and C*05, we compared the amino acid frequency at each position (1–9) by Pearson correlation. Strong correlations (r > 0.9) were observed at positions 1–3, while the pΩ was slightly weaker (r = 0.8), reflecting the small differences in pΩ usage (Figure 6A, Figure 6—figure supplement 1A). In contrast, there was a weak correlation (r = 0.46) at p7 and no correlation in amino acid usage (r = –0.01) at p8 (pΩ-1) (Figure 6A). At pΩ-1, Ala and Ser made up over 30% of sequences unique to C*08, but only 5% of sequences unique to C*05. In contrast, Leu, Lys, and Arg were found in approximately 40% of sequences unique to C*05, but only 7% of sequences unique to C*08 (Figure 6A). We observed similar findings for p9 (pΩ-1) in an analysis of 10mers (Figure 6—figure supplement 1B). Next, we analyzed the relative amino acid frequencies at p8 of 9mers eluted from 14 C1 allotypes and 7 C2 allotypes (Figure 6B). We found that at p8, C1 allotypes are enriched for Ser, Ala, and Pro, while C2 allotypes are enriched for Phe, Leu, Lys, Met, Arg, Trp, and Tyr (Figure 6B, Figure 6—figure supplement 1C). There was a strong correlation between amino acid enrichment at p8 in C2 allotypes and amino acid side-chain volume, demonstrating that the C1/C2 dimorphism imposes a size constraint on the peptides bound to HLA-C (Figure 1C). In peptides eluted from C1 allotypes, the preference for smaller residues at p8 was accentuated in peptides with pΩ-Ala compared to those with pΩ-Leu (Figure 6—figure supplement 1D), suggesting that size at pΩ-1 is especially important for peptides with pΩ-Ala.

Figure 6. The C1/C2 dimorphism selects for side-chain size at pΩ-1.

(A) Correlation of amino acid frequency at p2, p7, and p8 (pΩ-1) of 9mer peptides unique to HLA-C*08:02 (uC*08:02) and unique to HLA-C*05:01 (uC*05:01). (B) Volcano plot displaying relative p8 amino acid frequency from 9mer peptides eluted from 14 HLA-C allotypes based on C1/C2 status. Amino acids with twofold enrichment and statistically significant differences (p<0.05) determined by Student’s t-test are shown. A total of 26,543 peptide sequences were included. (C) Correlation (Pearson) of amino acid volume with amino acid enrichment at p8 of peptides eluted from HLA-C defined by C2/C1 status. Source data available in Figure 6—source data 1. Peptide sequences and p8 frequency analysis are available in Figure 2—source data 2.

Figure 6—source data 1. Amino acid frequencies for Figure 6A, fold change and p vlaues for 6B, fold change and amino acid volume for 6C.

Figure 6.

Figure 6—figure supplement 1. Impact of C1/C2 dimorphism on HLA-C immunopeptidomes.

Figure 6—figure supplement 1.

(A) Workflow for comparing amino acid frequency of 9mer peptides eluted from HLA-C*05:01 and HLA-C*08:02. Amino acid frequencies were compared by Pearson correlation. (B, left) Correlation of amino acid frequencies at p9 (pΩ-1) of 10mers eluted from HLA-C*05:01 and HLA-C*08:02. Frequencies of Ser and Ala and Lys, Leu and Arg at pΩ-1 from peptides eluted from HLA-C*08:02 and HLA-C*05:01 (right). (C) Average frequencies of indicated amino acids at p8 (pΩ-1) of 9mers eluted from 14 C1 and 7 C2 HLA-C allotypes. (D) Amino acid frequencies at p8 (pΩ-1) of 9mers eluted from C1 allotypes with Ala or Leu at the C-terminus (pΩ).
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Large amino acid side chains at peptide pΩ-1 position impair TCR9a recognition

Finally, we tested the importance of side-chain size at pΩ-1 by substituting p8 Ser in G12D-9mer and A18L-9mer to Ala, Gly, Glu, Val, Leu, Lys, and Arg. Stimulation of Jurkat-TCR9a by C*08 cells loaded with G12D-9mer p8 substitutions to Leu, Lys, and Arg substantially increased the EC50 by approximately 1000-fold, while substitution to Ala, Gly, Glu, Val had a minimal impact on the EC50 (Figure 7A–C). The same p8 substitutions in A18L-9mer had much less impact on Jurkat-TCR9a stimulation; however, p8 Lys, Arg, and Leu increased the EC50 by roughly 10-fold (Figure 7D–F). There was not a strict correlation between Jurkat-TCR9a stimulation and stabilization of HLA-C on TAP-deficient C*08+ cells (Figure 7—figure supplement 1). For example, in G12D-9mer, S8L was a poor ligand for Jurkat-TCR9a, but stabilized HLA-C better than S8G, which was a better ligand for Jurkat-TCR9a. Further, substitution at p8 in A18L-9mer had a minimal effect on HLA-C stabilization, but still impacted Jurkat-TCR9a activation (Figure 7F, Figure 7—figure supplement 1). This may be due in part to differences in sensitivity between these assays but also suggests a complex relationship between p8 sequence, HLA-C stabilization, and T cell recognition. In line with our other data, the impact of p8 size demonstrates how molecular changes outside TCR contacts can directly impact T cell recognition.

Figure 7. Large residues at p8 diminish T cell recognition of C1 HLA-C.

(A–F) Stimulation of Jurkat-TCR9a+ cells by 221C*08:02 cells preloaded with pΩ-1 substitutions of G12D-9mer (A, B) and A18L-9mer (D, E) at indicated concentrations. Each peptide was tested individually in two independent experiments and displayed by p8 sequence as indicated. (C, F) Data from (A, B) and (D, E) are summarized in (C) and (F), respectively, by pooling data by indicated p8 substitutions. Means and standard errors of IL-2 concentration in culture supernatant measured by ELISA are shown. Source data available in Figure 7—source data 1.

Figure 7—source data 1. ELISA readings for Figure 7.

Figure 7.

Figure 7—figure supplement 1. Impact of p8 size on stabilization of HLA-C*08:02.

Figure 7—figure supplement 1.

Stabilization of HLA-C on HLA-C*08:02-expressing TAP-deficient cells by G12D-9mer (left) and A18L-9mer (right) with indicated p8 (pΩ-1) substitutions. Data are from five independent experiments and normalized to cells with no peptide (NP).
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Discussion

HLA-I molecules form ligands for many immunoreceptors, and the interpretation of disease associations with HLA-I requires modeling interactions with different immune populations (Debebe et al., 2020). The C1/C2 dimorphism in HLA-C plays a critical role in innate immunity by forming ligands for the NK cell receptors KIR2DL1 and KIR2DL2/3. By comparing T cell recognition of two HLA-C allotypes identical in sequence except for the C1/C2 dimorphism, we assessed how this dimorphism affects adaptive immunity. We examined two clinically relevant, HLA-C*08:02 restricted neoantigens, G12D-9mer and G12D-10mer derived from the oncogenic hotspot mutation KRAS-G12D. We reject the null hypothesis that the C1/C2 dimorphism exhibits no effect on T cell recognition as C*05 was a poor ligand for TCR9a+ and TCR10+ T cells. Our study identified a clear and underestimated role for the C1/C2 dimorphism on peptide binding to HLA-C; specifically, as G12D-9mer bound only C*08 and not C*05. Further, we found that the C1/C2 dimorphism shapes the HLA-C immunopeptidome, exerting a size preference at pΩ-1 with C1 allotypes favoring smaller residues. Additionally, we identified an impact of the C1/C2 dimorphism on TCR binding as TCR9a and TCR10 binding to C*05 was weaker than binding to C*08 in the presence of the same peptides.

By investigating the exclusive restriction of TCR9a to C*08, we uncovered a novel role for the C1/C2 dimorphism in peptide presentation. Specifically, the C1/C2 dimorphism defines two distinct PBG structures that impose size constraints at pΩ-1 and allow C1 but not C2 allotypes to bind peptides with shorter pΩ anchors, like Ala in G12D-9mer. The primary cause of these distinct PBGs is likely the size of position 77, which forms a conserved h-bond with the amide of pΩ in both C1 and C2 HLA-C. As Asn (C2) is larger than Ser (C1), it results in peptide pΩ-1 protruding out of the groove, thus permitting larger amino acids at pΩ-1 in C2 allotypes compared to C1. In C1, pΩ-1 sits lower in the groove, restricting pΩ-1 side chains to smaller residues to avoid clashing with HLA-C position Val 76. Consistently, substitutions at p8 in G12D-9mer to the larger Leu, Lys, or Arg substantially reduced C1 recognition by TCR9a, while substitution to Gly had only a minor decrease. Further, the penalty imposed for large residues at p8 was diminished with A18L-9mer, suggesting a canonical pΩ anchor can accommodate unfavorable p8 residues, likely via orientating p8 side chain out of the groove like peptides in C2 allotypes. In C1 allotypes, the additional contacts between Val 76 and p8 may explain the ability of C*08:02 to bind peptides with short, suboptimal pΩ anchors. In contrast, pΩ-1 in C2, which is higher in the PBG and orientated away from the α1 helix, is unable to contact Val 76 and therefore cannot support peptides with pΩ Ala. Consistently, substitution of Asn 77 to Ser in C*05 substantially recovered TCR9a recognition of G12D-9mer.

In addition to peptide binding to HLA-C, TCR-binding affinity and T cell sensitivity were reduced in the presence of C2 residues. Neither N77S or K80N substitutions improved the sensitivity of TCR10 for C*05 and G12D-10mer, suggesting a synergistic effect of the C2 residues on reducing TCR recognition. TCR10 displayed a much slower on rate in binding C*05-G12D-10mer and even after controlling for a poor pΩ anchor, TCR9a was more sensitive to A18L-9mer with C*08 than C*05. Comparing our TCR9a-C*05:01-A18L-9mer structure with that of TCR9a:C*08:02-G12D-9mer revealed no impact on TCR contacts. Further, modeling TCR10 in complex with a recently solved crystal structure of C*05-G12D-10mer revealed no impact on TCR contacts (6JTO) (Bai et al., 2021). The most likely explanation for diminished TCR binding in the presence of C2 residues is that of conformational flexibility. Clearly, TCR9a and TCR10 can bind C*05; however, the ability to make the same contacts with C*08 in C*05 likely requires more energetically unfavorable conformational changes, in either peptide, HLA-C, or both, exemplified by the slow on rate of TCR10 with C*05-G12D-10mer. Indeed, recent evidence suggests that TCR recognition can be optimized by peptide substitutions that allow TCR contacts to form more readily (Devlin et al., 2020; Duru et al., 2020). It is likely that the converse is also true; slight alterations in amino acid side-chain orientations or flexibility may impose a higher energy barrier for efficient TCR recognition.

The G12D-9mer and G12D-10mer are considered public or shared neoantigens due to the high frequency of cancers with KRAS-G12D mutations (Cox et al., 2014; Pearlman et al., 2021; Stephen et al., 2014). Therefore, the small differences between C*08 and C*05 investigated here have clinical implications for mounting an effective antitumor immune response (Tran et al., 2016). The ability of C*05 to bind G12D-10mer means HLA-C*05:01-positive individuals may be eligible for such therapies; however, identifying a more sensitive receptor than TCR10 would be needed to translate these findings for clinical application.

The consequences of our findings may have broad implications for T cell recognition of HLA-C beyond the specific example of KRAS-G12D recognition in cancer. For example, in infectious diseases, viral escape mutants may eliminate T cell recognition of C1 allotypes by increasing side chain size at pΩ-1. Similarly, escape mutants that substitute pΩ to smaller residues like Ala may escape T cell recognition of C2 HLA-C but not C1. For peptide vaccines, pΩ-1 size could be optimized depending on whether the peptide targets a C1 or C2 HLA-C allotype. A reasonable prediction from our work is that if a T cell epitope contains a canonical pΩ, it will be immunogenic in individuals carrying either C*08 and C*05. Indeed, there is evidence that HIV-infected individuals can respond to the same epitope (SAEPVPLQL) if they carry HLA-Cw5 or HLA-Cw8 (Addo et al., 2001). However, it is possible that preference for pΩ-1 size could engender differential T cell immunity based on differences in HLA-C and TCR binding. These effects need to be investigated in further experiments of HLA-C-restricted T cells. It is worth noting that the impact of the C1/C2 dimorphism on TCR recognition is intrinsic to the structure of HLA-C and thus may also affect interactions with other immunoreceptors. In particular, the differences in amino acid preferences at pΩ-1 may have enormous consequences for KIR recognition of HLA-C as this residue is central to the KIR binding site (Boyington et al., 2000; Boyington and Sun, 2002; Fan et al., 2001). Our study highlights how small HLA polymorphisms can impact interactions with both peptide and immunoreceptors with implications for immune responses and cancer immunotherapy. Finally, our study establishes that T cells, like NK cells, can discriminate between the dimorphic HLA-C allotypes.

Materials and methods

Key resources table.

Reagent type (species) or resource Designation Source or reference Identifiers Additional information
Cell line (human) 721.221 (221)
221C*05:01
221C*08:02
221C*05:01-N77S
221C*05:01-K80N		 Original 221, PMID:3257565
221C*05:01 and 221C*08:02, Sim et al., 2017, PMID:28352266
221C*05:01-N77S and 221C*05:01-K80N – this paper		 Plasmids encoding HLA-C*05:01-N77S and HLA-C*05:01-K80N were generated by QuikChange mutagenesis and expressed in 221 cells via retroviral transduction
Strain, strain background (Escherichia coli) BL21 (DE3) Novagen 70235-4 For recombinant protein production
Recombinant DNA reagent pet30a-HLA-C*05:01 This paper Synthesized by GenScript, USA

Residues 1–278

Sequence from https://www.ebi.ac.uk/ipd/imgt/hla/

Soluble HLA-C*05:01 heavy chain for production in E. coli and in vitro refolding
Recombinant DNA reagent pet30a-HLA-C*08:02 Sim et al., 2020; PMID:32461371 Soluble HLA-C*08:02 heavy chain for production in E. coli and in vitro refolding
Recombinant DNA reagent pet30a-B2M Sim et al., 2020; PMID:32461371 Soluble B2M for production in E. coli and in vitro refolding
Antibody Anti-HLA-A, -B, -C
(mouse monoclonal, clone W6/32)		 BioLegend 311402 For SPR
10 μg/ml
Antibody Anti-β2m
(mouse monoclonal, clone 2M2) APC		 BioLegend 316312 For peptide-loading experiments
1:50
Antibody Anti-human CD3
APC-Cy7 (mouse monoclonal)		 BioLegend 300426 For Jurkat-T cell functional assays
1:100
Antibody Anti-human CD69
APC (mouse monoclonal)		 BioLegend 555533 For Jurkat-T cell functional assays
1:100
Cell line (human) Jurkat-TCR9a, Jurkat TCR10 Sim et al., 2020; PMID:32461371 For Jurkat-T cell functional assays
Cell line (human) 221C*05:01-ICP47 Sim et al., 2017; PMID:28352266
Cell line (human) 221C*08:02-TAP-KO This paper Cas9 was expressed in 221C*08:02 cells and gRNA to TAP1 were introduced by lentiviral transduction
Recombinant DNA reagent TCR9a alpha chain Sim et al., 2020; PMID:32461371 Soluble TCR9a alpha chain for production in E. coli and in vitro refolding
Recombinant DNA reagent TCR9a beta chain Sim et al., 2020; PMID:32461371 Soluble TCR9a beta chain for production in E. coli and in vitro refolding
Recombinant DNA reagent TCR10 alpha chain Sim et al., 2020; PMID:32461371 Soluble TCR10 alpha chain for production in E. coli and in vitro refolding
Recombinant DNA reagent TCR10 beta chain Sim et al., 2020; PMID:32461371 Soluble TCR10 beta chain for production in E. coli and in vitro refolding
Peptide, recombinant protein Streptavidin PE Agilent PJRS25-1 For HLA-C tetramers
Peptide, recombinant protein WT and G12D KRAS peptides with substitutions This paper and Sim et al., 2020; PMID:32461371 Synthesized by GenScript, USA
Commercial assay or kit Human IL-2 ELISA BioLegend 431804 For Jurkat T-cell functional assays
Recombinant DNA reagent TAP1 gRNA GenScript 1 gRNA sequence:
CCCAGATGTCTTAGTGCTAC
2 gRNA sequence:
ACCTGTAGCACTAAGACATC		 pLentiCRISPR v2 vector
Used to knockout TAP in 221C*08:02-Cas9 cells by lentiviral transduction
Commercial assay or kit Amine coupling kit GE Healthcare Life Sciences BR100050 For immobilization of protein on SPR chip
Commercial assay or kit CM5 chips Cytiva 29149604 For SPR
Peptide, recombinant protein Biotinylated HLA-C monomers NIH Tetramer Core Facility For HLA-C tetramers
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Cell lines and culture

Cell lines were cultured in IMDM and 10% fetal calf serum (FCS) at 37°C and 5% CO2. 721.221 (221) cells expressing HLA-C*05:01 and the TAP inhibitor ICP47 (221C*05:01-ICP47) were described previously (Sim et al., 2017; Sim et al., 2019). Cas9-expressing 221 cells were a generated by lentiviral transduction. cDNA encoding full-length HLA-C*08:02 was expressed in 221-Cas9 cells by retroviral transduction with vectors on the pbabe backbone. Two TAP1 gRNAs (GenScript, USA) were introduced by lentiviral transduction. Jurkat T cells expressing TCR9a and TCR10 were previously described (Sim et al., 2020).

Peptide loading assay

Peptide loading assays were performed as described (Sim et al., 2017; Sim et al., 2019). 221C*05:01-ICP47 and 221-Cas9-C*08:02-TAP cells were cultured overnight at 26°C with 100 μM of peptide. Cells were then stained with anti-mAb (APC, clone 2M2, BioLegend, USA, #316312). Expression of HLA-C was determined by flow cytometry. Each peptide was tested at least twice in independent experiments. Peptides were synthesized at >95% purity (GenScript).

T cell activation assay

Peptides were incubated with 105 221-HLA-C+target cells for 4 hr at 37°C before incubation with 105 Jurkat-TCR+ cells overnight. The following day, cell culture supernatants were recovered, and IL-2 measured by ELISA using the ELISA MAX Deluxe Set Human IL-2, BioLegend (#431804). For assays with TAP-deficient target cells, 105 target cells were incubated overnight at 26°C with peptide ranging from 1 μM to 0.01 nM. The following day, target cells were mixed with TCR+ Jurkat T cells for 6 hr at 37°C. Cells were then washed twice in PBS and stained with mAbs to CD69 (APC, FN50, BD Biosciences, USA, #555533) and CD3 (APC-Cy7, UCHT1, BioLegend, #300426). Expression of CD69 was measured on CD3+ cells by flow cytometry. All peptides were tested at indicated concentrations at least twice in independent experiments.

HLA-C immunopeptidomics

For the analysis of HLA-C immunopeptidomes, we used published datasets from two studies (Di Marco et al., 2017; Sarkizova et al., 2020). These data are found in public depositories; Di Marco et al., PRIDE Project PXD009531, Sarkizova et al., MASSIVE MSV000084172. Both studies used cells transfected with single HLA-I allotypes. Sarkizova et al. used the HLA-A, -B, -C-deficient 721.221 cells, while Di Marco et al. used C1R cells. C1R cells express low levels of HLA-B*35:03 and HLA-C*04:01, and therefore, peptides aligning with motifs for these allotypes were removed from HLA-C allotype-specific sequences. The false discovery rates were 1 and 5% for Sarkizova et al. and Di Marco et al., respectively. For comparing the immunopeptidomes of C1 and C2 HLA-C allotypes, 9mer peptides eluted from 21 different HLA-C allotypes were obtained. The list of allotype-specific sequences from each study was merged and duplicate sequences removed. For some HLA-C allotypes, data was only available from one study and therefore no merging occurred. The C1 HLA-C allotypes were HLA-C*01:02, C*03:02, C*03:03, C*03:04, C*07:01, C*07:02, C*07:04, C*08:01, C*08:02, C*12:02, C*12:03, C*14:02, C*14:03, and C*16:01. The C2 HLA-C allotypes were HLA-C*02:02, C*04:01, C*04:03, C*05:01, C*06:02, C*15:02, and C*17:01. For C*03:02, C*04:03, C*07:04, C*08:01, C*12:02, annd C*14:03, data were only available from one study (Sarkizova et al., 2020). A total of 18,275 peptides from C1 allotypes and 8268 peptides from C2 allotypes were studied. The amino acid frequency at positions 8 (pΩ-1) and 9 (pΩ) was determined for each allotype independently and collected to derive an average amino acid frequency for C1 and C2 allotypes. The relative amino acid frequency between C2 and C1 allotypes (C2/C1 fold difference) was determined for each amino acid and significant differences determined by two-sided Student’s t-test. For comparing C*08 and C*05 immunopeptidomes, a total of 1928 C*05 and 3182 C*08 nonredundant 9mer sequences were compared of which 1196 sequences were shared between C*05 and C*08 (Figure 5—figure supplement 1).

Protein expression, purification, and crystallization

HLA-C*05:01 and TCR proteins were produced largely as described (Clements et al., 2002; Garboczi et al., 1992; Sim et al., 2020; Tikhonova et al., 2012). DNA encoding residues 1–278 of HLA-C*05:01, 1–99 of β2M and TCR alpha (1–208) and beta (1–244) chains were synthesized and cloned into the bacterial expression vector pET30a via NdeI and XhoI (GenScript). Proteins were expressed as inclusion bodies in BL21 (DE3) cells (Novagen, USA) and dissolved in 8 M urea, 0.1 M Tris pH 8. Proteins were refolded by rapid dilution in 0.5 M L-arginine, 0.1 M Tris pH 8, 2 mM EDTA, 0.5 mM oxidized glutathione, 5 mM reduced glutathione. Proteins were purified by ion-exchange chromatography (Q column, GE Healthcare, USA) followed by size-exclusion chromatography with a Superdex 200 column (GE Healthcare). HLA-C*05:01-G12D-A18L-9mer and TCR9a were concentrated to 10 mg/ml and crystals grown under the same conditions as TCR9a/d with HLA-C*08:02-G12D-9mer (Sim et al., 2020) and were 22% PEG 3350, 0.1 M MOPS pH 7.1, and 0.25 M MgSO4.

Data collection, structure determination, and refinement

Crystals were immersed in cryoprotectant (crystallization condition plus 20% glycerol) before flash-cooling in liquid nitrogen. A single dataset was collected on the SER-CAT 22 ID beamline (Argonne National Laboratory, IL, USA), processed and merged using HKL2000 (Otwinowski and Minor, 1997). TCR9a:HLA-C*05:01-G12D-A18L-9mer structure was solved by molecular replacement method with Phaser in the CCP4 package using the TCR9a:HLA-C*08:02-G12D-9mer complex as a search model (PDB: 6ULR) (Sim et al., 2020). The model was built and refined with Coot and Phenix (Adams et al., 2002; Collaborative Computational Project, 1994; Emsley and Cowtan, 2004; McCoy et al., 2007; Otwinowski and Minor, 1997). The complex contained one complex per asymmetric unit and belonged to the P21 space group. Peptide and CDR loops were added manually using 2Fo-Fc electron density maps. Graphical figures were generated in PyMOL.

Surface plasmon resonance

SPR was performed largely as described (Sim et al., 2020) with a BIAcore 3000 instrument and analyzed with BIAevalution software v4.1 (GE Healthcare). The HLA-A, -B, -C-specific mAb W6/32 (#311402, BioLegend) was immobilized to CM5 chips (Cytiva, USA) in 10 mM sodium acetate pH 5.5 at 5000–7000 response units (RU) by primary amine-coupling with a 2 μl/min flow rate. HLA-C was captured by W6/32 at 400–700 RU in PBS. Soluble TCR heterodimers were used as analytes in 10 mM HEPES pH 7.5 and 0.15 M NaCl with a flow rate of 50 μl/min. TCRs were injected for 2 min followed by a dissociation of 10 min. Binding was measured with serial dilutions of TCR from 80 μM to 1.25 μM for TCR10 and 1200 nM to 37.5 nM for TCR9a. Four independent TCR10-binding experiments were carried out with initial concentrations of 10 μM (two experiments), 40 μM (one experiment), and 80 μM (one experiment). Dissociation constants were obtained by modeling steady-state kinetics for TCR10 and kinetic curve fitting for TCR9a with BIAevaluation software.

Flow cytometry

Flow cytometry was performed on an LSR II or Fortessa X20 (BD Biosciences) and a Cytoflex S (Beckman Coulter, USA). Data were analyzed using FlowJo software (Treestar V10, USA). Cytometer setup and tracking beads were run daily, and single mAb-stained beads were used to determine compensation settings for multicolor experiments.

Statistical analysis

Statistical analyses were carried out in GraphPad Prism (version 8) and Microsoft Excel.

Protein database deposition and files

The C*05:01-A18L-9mer-TCR9a complex was assigned the PDB code: 7SU9. The following PDBs were used in this article: 6ULI, 6ULR, 1EFX, 5VGE, 4NT6, 1QQD, 6JTO, 5VGD, 5W6A, 5W69, and 5W67.

Acknowledgements

We thank Dr. Sumati Rajagopalan for critical reading of the manuscript. We thank Dr. Ludmila Krymskaya of the LIG Flow Core for assistance with cell sorting and staff at Argonne National Laboratory for assistance with collecting X-ray diffraction data. We thank the NIH Tetramer Core Facility for providing biotinylated HLA-C monomers. This work was supported by the Intramural Research Program of the NIH, National Institute of Allergy and Infectious Diseases.

Funding Statement

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

Contributor Information

Malcolm J W Sim, Email: mjwsim@gmail.com.

Peter D Sun, Email: PSUN@niaid.nih.gov.

Chyung-Ru Wang, Northwestern University, United States.

Tadatsugu Taniguchi, Institute of Industrial Science, The University of Tokyo, Japan.

Funding Information

This paper was supported by the following grants:

  • NIAID Division of Intramural Research Funding AI000697 to Peter D Sun.

  • NIAID Division of Intramural Research Funding AI000525 to Eric O Long.

  • National Institute of Allergy and Infectious Diseases to Peter D Sun.

  • Argonne National Laboratory to Eric O Long.

Additional information

Competing interests

No competing interests declared.

No competing interests declared.

Author contributions

Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Project administration, Supervision, Writing – original draft, Writing – review and editing.

Investigation, Writing – review and editing.

Data curation, Formal analysis, Investigation, Methodology, Writing – review and editing.

Investigation, Writing – review and editing.

Conceptualization, Formal analysis, Project administration, Supervision, Writing – original draft, Writing – review and editing.

Conceptualization, Data curation, Formal analysis, Funding acquisition, Methodology, Project administration, Resources, Supervision, Writing – original draft, Writing – review and editing.

Additional files

Supplementary file 1. Structural data and refinement statistics for TCR9a-C*05:01-A18L complex.

Data for outer shell shown in parentheses.

elife-75670-supp1.docx (14.3KB, docx)
Supplementary file 2. Major contacts between TCR9a and HLA-C*08:02 (6ULR) and HLA-C*05:01 (7SU9).
elife-75670-supp2.docx (15.4KB, docx)
Transparent reporting form

Data availability

Diffraction data have been deposited in PDB under the accession code 7SU9.

The following dataset was generated:

Stotz Z, Lu J, Brennan P, Long EO, Sun PD. 2022. KRAS-G12D specific TCR9a in complex with C*05-GADGVGKSL. RCSB Protein Data Bank. 7SU9

The following previously published datasets were used:

Sarkizova S, Klaeger S, Oliveira G, Keshishian H, Hartigan CH, Zhang W, Braun DA, Ligon KL, Bachireddy P, Zervantonakis IK, Rosenbluth JM, Ouspenskaia T, Law T, Justeson S, Stevens J, Lane WJ, Eisenhaure T, Zhang GL, Clauser KR, Hacohen N, Carr SA, Wu CJ, Keskin DB. 2020. Mono-allelic datasets for: A large peptidome dataset improves HLA class I epitope prediction across most of the human population. MassIVE: Mass Spectrometry Interactive Virtual environment.

Marco M Di, Schuster H, Backert L, Ghosh M, Rammensee HG, Stevanović S. 2017. Unveiling the Peptide Motifs of HLA-C and HLA-G from Naturally Presented Peptides and Generation of Binding Prediction Matrices. PRIDE. PXD009531

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Editor's evaluation

Chyung-Ru Wang 1

In this study, the authors use structural, functional, and immunopeptidomics analysis to provide insights into how HLA-C C1/C2 dimorphism impacts T cell recognition. This knowledge is important in immunotherapies targeting HLA-C-specific T cells.

Decision letter

Editor: Chyung-Ru Wang1
Reviewed by: Anthony W Purcell2, Dirk Zajonc

Our editorial process produces two outputs: i) public reviews designed to be posted alongside the preprint for the benefit of readers; ii) feedback on the manuscript for the authors, including requests for revisions, shown below. We also include an acceptance summary that explains what the editors found interesting or important about the work.

Decision letter after peer review:

Thank you for submitting your article "T cells discriminate between groups C1 and C2 HLA-C" for consideration by eLife. Your article has been reviewed by 3 peer reviewers, and the evaluation has been overseen by a Reviewing Editor and Tadatsugu Taniguchi as the Senior Editor. The following individuals involved in review of your submission have agreed to reveal their identity: Anthony W Purcell (Reviewer #1); Dirk Zajonc (Reviewer #3).

Reviewer #1 (Recommendations for the authors):

Points for response buy the authors

1) Was the pHLA complex stability for the WT and heteroclitic peptide assessed for HLA C*05 and C*08??

2) Can the authors comment on the expected conformation of the WT 9mer in C*05 to that observed for the heteroclitic variant? A direct comparison of the bound peptides bound to both C-allotypes would be useful.

3) In Figure 2 the peptidome analysis is shown. In Figure 2G which allotype is the outlier for HLA C1 allotypes? How does this data point impact on the significance? It is important to provide more qualitative information about the data sets used in this analysis = - how do the different data sets compare – some are clearly more comprehensive than others – how many of the datasets are based on monoallelic data vs inferred allotype specificity from other cell lines? Are the two studies performed using similar technology, were the same FDR rates used to report peptides and has this been taken into account in the authors reanalysis? Which databases and search engines were used – it would be most accurate to re-search the raw data together to provide a common analytic workflow to remove biases in the data. At the very least the authors should comment on these influences on the composition of the peptides used for this analysis.

4) It would be interesting to see the results of peptide stabilization assays (akin to T2 stabilization) of the peptides in the TAP deficient 221 cell lines to address the anticipated density of pHLA used for the various TCR readouts/ Likewise was W6/32 flowed over the SPR chis to show similar levels of conformation pHLA were immobilized prior to the SPR analysis? This would seem a critical control?

Reviewer #2 (Recommendations for the authors):

1. Supplementary Figure S1B shows that KIR2DL2 binds quite well to 221-C*05:01, would this be a consideration for any therapies targeting the T cell response?

2. Why were 2 different functional readouts utilized for assessing responses from the Jurkat cell lines (e.g. Figure 1C vs Figure S1C)?

3. It was slightly unclear to me why the G12D-A18L peptide was not used for both C*05:01 and C*08:02 for the SPR/tetramer experiments (or as a target for crystallography), given this peptide binds to both alleles and shows improved recognition by TCR9a.

4. The authors state that TCR10 bound C*05 with slightly weaker affinity but this is difficult to judge with such minor differences and large error values.

5. The legend indicates Figure 3C is the summary of 2 experiments and shows mean +/- standard error but the error bars appear to be missing. Moreover, the text indicates the highest concentration of peptide tested was 40 ug/ml, yet only up to 20 ug/ml is shown.

6. The saturation plot in Figure 3F shows a TCR concentration up to 80 μm was used for SPR, yet the highest concentration in 3D and 3E is 40 µM. Was there a reason the highest concentration was not shown on the sensorgrams in Figure 3D and 3E?

7. For Figure 3, it is unclear if the KDs provided are the average KDs from the independent experiments or if it is the KD derived from the curves shown. Was there any variation between the independent experiments or is the large error due to poor curve fitting? Please clarify.

8. It would be good to provide the kinetic fit curves overlaid with the raw data for TCR9a binding to C*05:01-KRAS-G12D-A18L (Figure 3A).

9. What the level of surface expression of the mutant HLA-C*0501-N77S and C*05:01K-80N equivalent to the wild-type?

10. Given the importance of position 77 and the observation that the C*05:01 N77S at least partially recovered recognition by Jurkat TCR9a, is the mutant C*08:01 S77N no longer recognised by TCR9a (or TCR10)? These are important experiments to confirm the importance of position 77.

11. Typo line 53: should be Crohn's disease.

12. Typo line 193: should read Lys (Figure 4C).

13. Typo in figure legend to Sup Figure 1 221-C805:01.

Reviewer #3 (Recommendations for the authors):

There is some discussion/speculation about the impact of the different peptides in KIR2DL1-3 recognition. Especially in the scenario where the large amino acid at P(Omega)-1 would diminish T cell recognition. Would the authors think that this would also impact KIR2DL binding and thereby increase NK cell cytotoxicity? Since Dr. Long is an expert in KIR's, these assays should be easy to perform.

eLife. 2022 May 19;11:e75670. doi: 10.7554/eLife.75670.sa2

Author response

Reviewer #1 (Recommendations for the authors):

Points for response buy the authors

1) Was the pHLA complex stability for the WT and heteroclitic peptide assessed for HLA C*05 and C*08??

We assessed the ability of KRAS peptides to stabilize C*05 and C*08 using peptide loading assays with TAP-deficient cells expressing C*08 or C*05 (please see Figure 2AandB). WT KRAS peptides were unable to stabilize C*08 or C*05 as they lack a key anchor residue at p3. G12D10mer and A18L-9mer stabilized both C*08 and C*05, while G12D-9mer only stabilized C*08. In addition, G12D-10mer and A18L-9mer successfully refolded recombinant C*05 protein, while in our previous study we found that WT peptides could not refold recombinant C*08 protein (Sim et al., PNAS 2020). Another study also found that WT KRAS peptides and G12D9mer were unable to refold C*05, while G12D-10mer could (Bai et al., Sci China Life, 2021).

2) Can the authors comment on the expected conformation of the WT 9mer in C*05 to that observed for the heteroclitic variant? A direct comparison of the bound peptides bound to both C-allotypes would be useful.

The conformations of WT-9mer and G12D-9mer are hypothetical as neither one can stabilize C*05. We would expect the G12D-9mer conformation in C*05 to be very similar to that of A18L9mer, even though it lacks a suitable C-terminal anchor. Our analysis suggests that while Cterminal sequence is essential for conferring HLA-C stability, it is the interaction between HLA-C position 77 and pΩ-1 side chain size that determines the peptide confirmation near the peptide C-terminus (Figure 5A-J). Indeed, the C-terminal residue conformation was the same when comparing G12D-9mer and G12D-10mer bound to C*08 (Sim et al., PNAS 2020). It is difficult to predict the conformation of WT9mer in C*05 as it lacks both a suitable C-terminal anchor and p3 anchor and would therefore be held on the groove at each end only by the conserved interactions with N and C termini. This would lead to considerable conformational flexibility, and binding would likely be unstable. However, if binding occurred, it is likely that the pΩ-1 conformation would follow the pattern observed in other peptides bound to C2 HLA-C allotypes.

3) In Figure 2 the peptidome analysis is shown. In Figure 2G which allotype is the outlier for HLA C1 allotypes? How does this data point impact on the significance?

The outlier is HLA-C*08:01, and removing the data does not alter the conclusion that C1 allotypes have a higher frequency of p9 Ala. After removing C*08:01, the p-value changes from 0.0098 to 0.0003, by unpaired t test with Welch’s correction.

It is important to provide more qualitative information about the data sets used in this analysis =- how do the different data sets compare – some are clearly more comprehensive than others – how many of the datasets are based on monoallelic data vs inferred allotype specificity from other cell lines? Are the two studies performed using similar technology, were the same FDR rates used to report peptides and has this been taken into account in the authors reanalysis? Which databases and search engines were used – it would be most accurate to re-search the raw data together to provide a common analytic workflow to remove biases in the data. At the very least the authors should comment on these influences on the composition of the peptides used for this analysis.

We appreciate the reviewer's concern that there was not enough detail covering these datasets. We have updated both the corresponding results and methods sections.

We chose these datasets as both studies covered a large number of HLA-C allotypes in the same study, as opposed to pooling studies where only one or two HLA-C allotypes were reported. Our initial analysis used the data by Di Marco et al., as it was published first. After publication of the larger dataset by Sarkizova et al., we analysed this data separately and observed similar amino acid frequencies based on C1/C2 status, reaching the same conclusions. Therefore, when preparing our manuscript, we merged the datasets for a combined analysis, rather than displaying the two analyses separately that confirmed each other. In our original manuscript, these details were omitted from our methods. Furthermore, the reviewer is correct that there are differences between the studies that should also be mentioned including cell line origin, data acquisition and data analysis. Another caveat is that our analysis does not account for peptide abundance.

We have added the following to the corresponding results and methods sections describing our analysis:

Results: ‘To explore if pΩ Ala is a general feature of peptides that bind C1 allotypes (like C*08) and not C2 allotypes (like C*05), we examined C-terminal amino acid usage in the immunopeptidomes of HLA-C allotypes using two publicly available datasets (see methods) (Di Marco et al., 2017; Sarkizova et al., 2020). We initially analysed both datasets independently and observed similar amino acid frequencies, thus we present a combined analysis of a merged dataset with duplicate sequences removed. However, we cannot rule out that differences in cell line origin, data acquisition, false-discovery rate or analytical pipelines may have influenced our analysis. In addition, our analysis was limited to that of amino acid frequency and did not incorporate peptide abundance. We first compared the immunopeptidomes of C*08 and C*05 as…’

Methods: HLA-C immunopeptidomics

For the analysis of HLA-C immunopeptidomes, we used published datasets from two studies (Di Marco et al., 2017; Sarkizova et al., 2020). These data are found in public depositories; Di Marco et al., PRIDE Project PXD009531, Sarkizova et al., MASSIVE MSV000084172. Both studies used cells transfected with single HLA-I allotypes. Sarkizova et al., used the HLA-A, -B, -C deficient 721.221 cells, while Di Marco et al., used C1R cells. C1R cells express low levels of HLA-B*35:03 and HLA-C*04:01 and therefore peptides aligning with motifs for these allotypes were removed from HLA-C allotype specific sequences. The false discovery rate was 1% and 5% for Sarkizova et al., and Di Marco et al., respectively. For comparing the immunopeptidomes of C1 and C2 HLAC allotypes, 9mer peptides eluted from 21 different HLA-C allotypes were obtained. The list of allotype specific sequences from each study were merged and duplicate sequences removed. For some HLA-C allotypes, data was only available from one study and therefore no merging occurred. The C1 HLA-C allotypes were; HLA-C*01:02, C*03:02, C*03:03, C*03:04, C*07:01, C*07:02, C*07:04, C*08:01, C*08:02, C*12:02, C*12:03, C*14:02, C*14:03 and C*16:01. The C2 HLA-C allotypes were; HLA-C*02:02, C*04:01, C*04:03, C*05:01, C*06:02, C*15:02 and C*17:01. For C*03:02, C*04:03, C*07:04 C*08:01, C*12:02, C*14:03, data were only available from one study. A total of 18275 peptides from C1 allotypes and 8268 peptides from C2 allotypes were studied. The amino acid frequency at positions 8 (pΩ-1) and 9 (pΩ) were determined for each allotype independently and collected to derive an average amino acid frequency for C1 and C2 allotypes. The relative amino acid frequency between C2 and C1 allotypes (C2/C1 fold difference) was determined for each amino acid and significant differences determined by two-sided student t-test. For comparing C*08 and C*05 immunopeptidomes, a total of 1928 C*05 and 3182 C*08 non-redundant 9mer sequences were compared of which 1196 sequences were shared between C*05 and C*08 (S Figure 6).’

4) It would be interesting to see the results of peptide stabilization assays (akin to T2 stabilization) of the peptides in the TAP deficient 221 cell lines to address the anticipated density of pHLA used for the various TCR readouts/ Likewise was W6/32 flowed over the SPR chis to show similar levels of conformation pHLA were immobilized prior to the SPR analysis? This would seem a critical control?

We performed peptide loading experiments, the results can be found in Figure 2AandB and S Figure 7. For the SPR experiments, HLA-C molecules were conjugated to CM5 chips via immobilised W6/32. As such, only HLA-C stably bound to W6/32 was analysed for TCR binding and chips were loaded with similar levels of HLA-C.

Reviewer #2 (Recommendations for the authors):

1. Supplementary Figure S1B shows that KIR2DL2 binds quite well to 221-C*05:01, would this be a consideration for any therapies targeting the T cell response?

KIR2DL2 binding to C2 HLA-C has been reported before and is dependent on HLA-C bound peptides (Sim et al., 2017, Front. Imm). Blocking KIR antibodies showed some efficacy in preclinical studies, but this is likely through an impact on NK cells (Romagne et al., 2009, Blood). As KIR can be expressed on T cells, it would be wise to deplete inhibitory KIR+ expressing cells prior to adoptive T cell therapy. There is also evidence that KIR expression correlates with PD-1 expression and that blocking both PD-1 and KIR may be beneficial in immunotherapies (He et al., 2018, Drug Des Devel Ther).

2. Why were 2 different functional readouts utilized for assessing responses from the Jurkat cell lines (e.g. Figure 1C vs Figure S1C)?

We used two functional readouts to validate our findings using complementary approaches (flow cytometry and ELISA)

3. It was slightly unclear to me why the G12D-A18L peptide was not used for both C*05:01 and C*08:02 for the SPR/tetramer experiments (or as a target for crystallography), given this peptide binds to both alleles and shows improved recognition by TCR9a.

Our rationale was to prioritise the natural variant (G12D-9mer) as much as possible. This led to the inconsistency identified by the reviewer as we compared A18L-9mer in C*05 with G12D9mer in C*08. However, we believe this inconsistency does not change our conclusion that TCR9a binds HLA-C*08:02 with higher avidity and affinity. For peptide loading experiments, A18L-9mer appeared to stabilise C*05:01 and C*08:02 to similar levels, and was better than G12D-9mer for stabilising C*08:02 (Figure 2AandB). However, G12D-9mer is a good, stable ligand for C*08:02 and in no experiment did C*05:A18L-9mer outperform C*08:02 with G12D-9mer. Hypothetically, if C*05:A18L-9mer was a better ligand for TCR9a than C*08:02-G12D-9mer, then we would agree that the additional experiments with C*08:02-A18L-9mer would be warranted. Our data suggest that peptide stability and C-terminal sequence is not the main source of C*08:02 superiority for TCR9a especially when it also does not appear to explain the superiority of C*08:02 over C*05:01, as a ligand for TCR10.

4. The authors state that TCR10 bound C*05 with slightly weaker affinity but this is difficult to judge with such minor differences and large error values.

The displayed dissociation constants were determined by modelling steady state kinetics and by this analysis, there is only a minor two-fold difference between TCR10 binding to C*08 and C*05. However, by kinetic analysis of association rates, TCR10 had a slower on-rate with C*05:01-G12D-10mer than C*08:02-G12D-10mer. Furthermore, C*05:01-G12D-10mer tetramers had considerably lower avidity for Jurkat-TCR10+ cells than C*08:02-G12D-10mer tetramers. Combining these two results allow us to conclude that the TCR10 interaction with C*05 is weaker than that with C*08 (please see point 7, for more details of TCR10 experiments).

5. The legend indicates Figure 3C is the summary of 2 experiments and shows mean +/- standard error but the error bars appear to be missing. Moreover, the text indicates the highest concentration of peptide tested was 40 ug/ml, yet only up to 20 ug/ml is shown.

The error bars are too small to be observed. The reviewer is correct that the highest concentration tested was 40 ug/ml, however we truncated the x-axis for clarity as it allows the lower tetramer concentrations to be more visible. We include the non-truncated graph for the reviewer.

6. The saturation plot in Figure 3F shows a TCR concentration up to 80 μm was used for SPR, yet the highest concentration in 3D and 3E is 40 uM. Was there a reason the highest concentration was not shown on the sensorgrams in Figure 3D and 3E?

The sensorgrams shown in 3D and 3E were from an experiment where 40µM was the highest TCR concentration. This experiment had less ‘buffer jumps’ than the experiment with an 80µM max TCR concentration, making the sensorgrams more suitable for display (see point 7 for more details of TCR10 experiments). We include the sensorgrams from all four independent experiments for the reviewer.

7. For Figure 3, it is unclear if the KDs provided are the average KDs from the independent experiments or if it is the KD derived from the curves shown. Was there any variation between the independent experiments or is the large error due to poor curve fitting? Please clarify.

For 3A, the dissociation constant for TCR9a with C*05-A18L-9mer (mean and standard deviation) was derived from 12 curves from two independent experiments where association and dissociation rates were fitted separately. To demonstrate good curve fitting, we reanalysed these binding data by fitting association and dissociation simultaneously. This analysis revealed a good fit for all curves, deriving a similar Kd of 214 nM. These curves are now shown in S Figure 3B.

For TCR10 experiments (Figure 3D, E, F) 4 independent experiments were carried out with serial dilutions of TCR10 with different starting TCR concentrations. Our initial two experiments used a highest concentration of 10 µM, however this was below the KD of TCR10 for C*05-G12D10mer and therefore to ensure binding saturation, we repeated TCR10 binding with an additional two experiments. The first used a maximum concentration of 40 µM and the second experiment a highest of 80 µM. We reanalysed all binding curves for TCR10 binding experiments to recalculate the dissociation constants. Combining binding data from all four experiments to derive a single dissociation constant gave Kd=7 µM and Kd=14 µM for C*08 and C*05 respectively, but with large confidence intervals. Deriving constants from each binding experiment separately and taking a mean and standard deviation, gave dissociation constants of Kd=8±5 µM and 13±6 µM for C*08 and C*05 respectively. Thus, by both analysis methods TCR10 had weaker binding to C*05 than C*08 but with large overlap in confidence/standard deviation. The variation does not appear to due to poor fitting but due to one experiment (3) where the Kd was higher for both C*08 and C*05. In three of the four experiments, TCR10 binding to C*08 was stronger than with C*05. In the remaining experiment, binding was similar. We include all binding curves for the reviewer.

We have modified the legend to read

“(F) Mean, standard deviation and non-linear curve fitting of TCR10 binding to G12D-10mer bound to C*08:02 and C*05:01. Dissociation constants were derived from four independent binding experiments with two-fold serial dilutions of TCR10 starting at 10 µM (two experiments), 40 µM (one experiment) and 80 µM (one experiment).”

8. It would be good to provide the kinetic fit curves overlaid with the raw data for TCR9a binding to C*05:01-KRAS-G12D-A18L (Figure 3A).

We include fitted curves for both experiments in S Figure 3A.

9. What the level of surface expression of the mutant HLA-C*0501-N77S and C*05:01K-80N equivalent to the wild-type?

We apologize for not including this data, the expression levels of the mutant cell lines were similar to that of WT C*08 and C*05. This data is now shown in new S Figure 5.

10. Given the importance of position 77 and the observation that the C*05:01 N77S at least partially recovered recognition by Jurkat TCR9a, is the mutant C*08:01 S77N no longer recognised by TCR9a (or TCR10)? These are important experiments to confirm the importance of position 77.

These data are shown in the same figure (Figure 5K). As C*05:01 and C*08:02 differ only by positions 77 and 80, C*08:02 – S77N (77N 80N), is the same as C*05:01 K80N (77N 80N), While the N77S substitution in C*05:01 (S77 K80) was able to substantially recover TCR9a activation, it had no impact on TCR10.

11. Typo line 53: should be Crohn's disease.

We have corrected the typo.

12. Typo line 193: should read Lys (Figure 4C).

We have corrected the typo.

13. Typo in figure legend to Sup Figure 1 221-C805:01.

We have corrected the typo.

Reviewer #3 (Recommendations for the authors):

There is some discussion/speculation about the impact of the different peptides in KIR2DL1-3 recognition. Especially in the scenario where the large amino acid at P(Omega)-1 would diminish T cell recognition. Would the authors think that this would also impact KIR2DL binding and thereby increase NK cell cytotoxicity? Since Dr. Long is an expert in KIR's, these assays should be easy to perform.

We thank the reviewer for their evaluation of our paper and for this question. We have studied the impact of peptide sequence on KIR recognition in published works (Sim, MJW et al., 2017, Frontiers in Immunology and Sim, MJW et al., 2019, PNAS) and in as yet unpublished data. We can confirm that large residues at p8 are not tolerated by KIR2DL2/3 binding to C*08, while they are by KIR2DL1 binding to C*05. These data are part of an extensive study of peptide sequence recognition by different KIRs and thus are not presented in this publication. We include some unpublished data demonstrating the impact of side chain size on inhibitory KIR binding for the reviewer.

References

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5;340(6128):87-91. doi: 10.1126/science.1232685. PMID: 23559252; PMCID: PMC3784322.

Bai, P., Zhou, Q., Wei, P. et al. Rational discovery of a cancer neoepitope harboring the KRAS G12D driver mutation. Sci. China Life Sci. 64, 2144–2152 (2021).

https://doi.org/10.1007/s11427-020-1888-1

He Y, Liu S, Mattei J, Bunn PA Jr, Zhou C, Chan D. The combination of anti-KIR monoclonal antibodies with anti-PD-1/PD-L1 monoclonal antibodies could be a critical breakthrough in overcoming tumor immune escape in NSCLC. Drug Des Devel Ther. 2018 Apr 24;12:981-986.

doi: 10.2147/DDDT.S163304. PMID: 29731605; PMCID: PMC5923225.

Romagne F, Andre P, Spee P, Zahn S, Anfossi N, Gauthier L, et al. Preclinical characterization of 1-7F9, a novel human anti-KIR receptor therapeutic antibody that augments natural killermediated killing of tumor cells. Blood. 2009;114(13):2667-77.

Sim, M.J.W., Lu, J., Spencer, M., Hopkins, F., Tran, E., Rosenberg, S.A., Long, E.O., and Sun, P.D. (2020). High-affinity oligoclonal TCRs define effective adoptive T cell therapy targeting mutant KRAS-G12D. Proc Natl Acad Sci U S A.

Sim MJW, Rajagopalan S, Altmann DM, Boyton RJ, Sun PD, Long EO. Human NK cell receptor KIR2DS4 detects a conserved bacterial epitope presented by HLA-C. Proc Natl Acad Sci U S A. 2019;116(26):12964-73.

Sim MJ, Malaker SA, Khan A, Stowell JM, Shabanowitz J, Peterson ME, et al. Canonical and

Cross-reactive Binding of NK Cell Inhibitory Receptors to HLA-C Allotypes Is Dictated by Peptides Bound to HLA-C. Front Immunol. 2017;8:193

Associated Data

    This section collects any data citations, data availability statements, or supplementary materials included in this article.

    Data Citations

    1. Stotz Z, Lu J, Brennan P, Long EO, Sun PD. 2022. KRAS-G12D specific TCR9a in complex with C*05-GADGVGKSL. RCSB Protein Data Bank. 7SU9
    2. Sarkizova S, Klaeger S, Oliveira G, Keshishian H, Hartigan CH, Zhang W, Braun DA, Ligon KL, Bachireddy P, Zervantonakis IK, Rosenbluth JM, Ouspenskaia T, Law T, Justeson S, Stevens J, Lane WJ, Eisenhaure T, Zhang GL, Clauser KR, Hacohen N, Carr SA, Wu CJ, Keskin DB. 2020. Mono-allelic datasets for: A large peptidome dataset improves HLA class I epitope prediction across most of the human population. MassIVE: Mass Spectrometry Interactive Virtual environment. [DOI] [PMC free article] [PubMed]
    3. Marco M Di, Schuster H, Backert L, Ghosh M, Rammensee HG, Stevanović S. 2017. Unveiling the Peptide Motifs of HLA-C and HLA-G from Naturally Presented Peptides and Generation of Binding Prediction Matrices. PRIDE. PXD009531 [DOI] [PubMed]

    Supplementary Materials

    Figure 1—source data 1. ELISA readings for Figure 1C, D.
    elife-75670-fig1-data1.xlsx (11.4KB, xlsx)
    Figure 2—source data 1. Normalized MdFI values for Figure 2B, ELISA readings for Figure 2C and amino acids frequencies for Figure 2D-G.
    elife-75670-fig2-data1.xlsx (18.8KB, xlsx)
    Figure 2—source data 2. HLA-C Peptide sequences used for analysis.
    elife-75670-fig2-data2.xlsx (8.2MB, xlsx)
    Figure 3—source data 1. Raw data for TCR response curves in Figure 3A, D, E and G.

    MdFI of tetramer binding experiments in 3C. Responses of all TCR10 binding assays in 3F.

    elife-75670-fig3-data1.xlsx (208KB, xlsx)
    Figure 5—source data 1. Raw distances for Figure 5E, G, I.

    Raw angles for 5J. ELISA readings for 5K.

    elife-75670-fig5-data1.xlsx (16.2KB, xlsx)
    Figure 6—source data 1. Amino acid frequencies for Figure 6A, fold change and p vlaues for 6B, fold change and amino acid volume for 6C.
    elife-75670-fig6-data1.xlsx (14.6KB, xlsx)
    Figure 7—source data 1. ELISA readings for Figure 7.
    elife-75670-fig7-data1.xlsx (15.7KB, xlsx)
    Supplementary file 1. Structural data and refinement statistics for TCR9a-C*05:01-A18L complex.

    Data for outer shell shown in parentheses.

    elife-75670-supp1.docx (14.3KB, docx)
    Supplementary file 2. Major contacts between TCR9a and HLA-C*08:02 (6ULR) and HLA-C*05:01 (7SU9).
    elife-75670-supp2.docx (15.4KB, docx)
    Transparent reporting form
    elife-75670-transrepform1.docx (111.5KB, docx)

    Data Availability Statement

    Diffraction data have been deposited in PDB under the accession code 7SU9.

    The following dataset was generated:

    Stotz Z, Lu J, Brennan P, Long EO, Sun PD. 2022. KRAS-G12D specific TCR9a in complex with C*05-GADGVGKSL. RCSB Protein Data Bank. 7SU9

    The following previously published datasets were used:

    Sarkizova S, Klaeger S, Oliveira G, Keshishian H, Hartigan CH, Zhang W, Braun DA, Ligon KL, Bachireddy P, Zervantonakis IK, Rosenbluth JM, Ouspenskaia T, Law T, Justeson S, Stevens J, Lane WJ, Eisenhaure T, Zhang GL, Clauser KR, Hacohen N, Carr SA, Wu CJ, Keskin DB. 2020. Mono-allelic datasets for: A large peptidome dataset improves HLA class I epitope prediction across most of the human population. MassIVE: Mass Spectrometry Interactive Virtual environment.

    Marco M Di, Schuster H, Backert L, Ghosh M, Rammensee HG, Stevanović S. 2017. Unveiling the Peptide Motifs of HLA-C and HLA-G from Naturally Presented Peptides and Generation of Binding Prediction Matrices. PRIDE. PXD009531

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