Human CD1c-autoreactive T-cells recognise Mycobacterium tuberculosis-infected antigen-presenting cells and display cytotoxic effector programmes

Tuberculosis (TB), caused by Mycobacterium tuberculosis (Mtb), is a major global epidemic (Wallis et al., 2016; World Health Organization, 2021; Hui et al., 2026). The only licensed vaccine, Bacille Calmette-Guérin (BCG), has variable efficacy against adult pulmonary disease, which drives transmission, and therefore new vaccines are urgently needed (Nguipdop-Djomo et al., 2016). Typically, novel TB vaccines have focussed on eliciting conventional T-cell responses to peptide antigens presented by highly polymorphic major histocompatibility complex (MHC) molecules (O’Garra et al., 2013; Jasenosky et al., 2015). Although progress has been made in developing new immunisation tools, one of the most promising candidates, M72, based on immunogenic peptides, achieved only 50% efficacy against TB (Ndiaye et al., 2015; Tameris et al., 2013; Schrager et al., 2020; Tait et al., 2019). This partial efficacy highlights the need to explore alternative approaches, including non-peptide antigens, to achieve the level of protection required for global TB control. Consequently, there is growing interest in harnessing unconventional T-cell responses as a complementary or alternative strategy (Joosten et al., 2019; Nunes-Alves et al., 2014). Generally, unconventional T-cells recognise antigens bound to non-polymorphic antigen-presenting molecules and therefore are not restricted by genetic variability. This group includes MR1-restricted mucosal-associated invariant T-cells, CD1d-restricted invariant natural killer T-cells (iNKT), HLA-E-, and CD1 group 1-restricted T-cells (Joosten et al., 2019; Ruibal et al., 2021).

The CD1 family (CD1a, CD1b, CD1c, CD1d) are non-polymorphic MHC class I-like proteins that bind and present self or foreign lipid antigens to αβ and γδ T-cells (Chancellor et al., 2018). CD1c is the most ubiquitously expressed group 1 CD1 molecule, present on antigen-presenting cells (APCs) such as dendritic cells (DCs), foamy macrophages, and B-cells (Mori et al., 2016; Melián et al., 1999). Several studies suggest a role for CD1c in host immunity to Mtb. CD1c presents Mtb-derived lipids to T-cells, including mannosyl-β1-phosphomycoketide and phosphomycoketide (PM) (de Jong et al., 2007; Moody et al., 2000; Matsunaga et al., 2004; Scharf et al., 2010; Roy et al., 2014). CD1c-restricted and Mtb lipid-specific T-cells expand in the circulation of TB patients, and CD1c-PM-loaded tetramers detect PM-specific T-cells in individuals with latent TB (Moody et al., 2000; Ly et al., 2013).

However, it is notable that the majority of CD1c-restricted T-cells exhibit autoreactivity against CD1c expressing APCs in the absence of exogenous microbial antigens, suggesting recognition of CD1c bound to endogenous self-lipids (de Lalla et al., 2011; de Jong et al., 2010). These CD1c-autoreactive T-cells recognise diverse self-lipids, including phospholipids, cholesteryl-esters, methyl-lysophosphatidic acids (mLPAs), and monoacylglycerols (Wun et al., 2018; Mansour et al., 2016; Lepore et al., 2014). CD1c-autoreactive T-cells are relatively abundant in the circulation of healthy adults and are proposed to become activated in response to host lipids in autoimmune disease and cancer (de Lalla et al., 2011). CD1c-autoreactive T-cells isolated from patients with systemic lupus erythematosus provide help to CD1c⁺ B-cells, enhancing the production of IgG and contributing to immunopathology (Sieling et al., 2000). CD1c-autoreactive T-cells infiltrate lesions in Hashimoto’s thyroiditis and Graves’ disease, contributing to tissue destruction (Roura-Mir et al., 2005). CD1c-autoreactive T-cells recognise tumour-associated self-lipid antigens (mLPA) derived from leukaemic cells (Lepore et al., 2014). mLPA-specific T-cells efficiently kill CD1c⁺ acute leukaemia cells and protect immunodeficient mice against CD1c⁺ human leukaemia cells (Lepore et al., 2014). Therefore, CD1c-autoreactive T-cells contribute to the immune response in human autoimmune disease and tumour immunosurveillance. However, historically these diseases will not have exerted the dominant evolutionary pressure relative to mortality from infection.

Humans have co-evolved with bacteria, including gut commensals (Suzuki et al., 2022) and environmental and pathogenic mycobacteria (Brites and Gagneux, 2015). This long-standing relationship with microorganisms may have exerted evolutionary pressure on CD1c-reactive T-cells to contribute to immune defence against pathogenic organisms (Brites and Gagneux, 2015). Nevertheless, although CD1c-autoreactive T-cells are well described in human blood and have been implicated in autoimmunity and cancer, their role during infection remains poorly defined. This is an important knowledge gap because CD1c is positioned to present infection-altered lipid antigens, and autoreactive CD1-restricted T-cells may respond not only to steady-state self-lipids but also to infection-induced changes in lipid antigen presentation. Indeed, CD1c-autoreactive T-cells expand after Mtb infection of CD1 transgenic mice (hCD1Tg) in vivo (Felio et al., 2009). Furthermore, autoreactive T-cells restricted by other CD1 family members also exhibit enhanced responses to TLR agonists, mycobacteria, and mycobacterial lipids, thereby indicating that these T-cells may also be activated during infection (Li et al., 2011; Van Rhijn et al., 2016). However, whether human CD1c-autoreactive T-cells directly respond to Mtb-infected APCs, whether this response is CD1c-TCR-dependent, and whether these cells mediate cytotoxic or antimicrobial effector functions has not been established.

Here, we define the response of human CD1c-autoreactive T-cells to Mtb-infected APCs using complementary human in vitro systems. We first examine CD1c expression in human TB lung tissue and determine how Mtb infection affects CD1c expression on primary DCs and engineered THP1-CD1c APCs. We then use CD1c-expressing APCs to expand and validate CD1c-autoreactive T-cell lines, and test their activation, cytokine secretion, cytotoxicity, and capacity to reduce relative Mtb burden in infected phagocytes. To establish whether enhanced recognition of infected APCs can be mediated through the TCR, we clone dominant CD1c-reactive TCRs and test their function in a Jurkat expression system. Finally, we use single-cell transcriptomic profiling of ex vivo CD1c-autoreactive T-cells to define their phenotypic landscape and effector programmes. Together, these data identify a previously unrecognised human CD1c-autoreactive T-cell response to Mtb-infected APCs and support these cells as candidate contributors to lipid-directed immunity during TB infection.

The majority of human CD1c-restricted T-cells are activated by exposure to CD1-expressing APCs in the absence of foreign lipid antigens (de Lalla et al., 2011; de Jong et al., 2010). Indeed, such CD1c-autoreactive T-cells comprise 2% of all circulating human αβ T-cells (de Lalla et al., 2011), and studies have suggested a functional role for these T-cells in human autoimmune disease (Roura-Mir et al., 2005) and cancer (Lepore et al., 2014). However, for almost the entirety of human evolution, infection is likely to have exerted a greater selective pressure affecting survival and reproduction than autoimmune disease or cancer (Karlsson et al., 2014; Fumagalli et al., 2011). Here, we found that polyclonal CD1c-autoreactive T-cells produced higher levels of Th1 and Th2 cytokines when stimulated with Mtb-infected APCs relative to uninfected cells, mediated increased cytotoxicity against Mtb-infected APCs in a CD1c-mediated manner, and reduced the relative viable Mtb burden under in vitro co-culture conditions (Bielecka et al., 2017). To gain insight into the cellular programmes associated with this antimicrobial effector potential, we performed scRNA-seq of ex vivo isolated CD1c-autoreactive T-cells from healthy donors. These cells were transcriptionally diverse but predominantly exhibited cytotoxic effector-memory phenotypes. Notably, they expressed high levels of cytolytic molecules, including GZMA, GZMK, and GNLY, as well as effector mediators such as CCL5 and NKG7, consistent with a role in direct target-cell killing (Stenger et al., 1998a). Granulysin is one of the few T-cell-derived molecules with demonstrated antimicrobial activity against Mtb (Stenger et al., 1998a), yet few human T-cell subsets express it in this context. Combined with our finding that CD1c-autoreactive T-cells can reduce Mtb luminescence in vitro, this places them among a rare population with antimicrobial activity in this experimental setting. These findings provide a mechanistic explanation for how CD1c-autoreactive T-cells could mediate cytotoxicity and contribute to control of intracellular Mtb, supporting their classification as functionally distinct effector T-cells rather than merely autoreactive bystanders. To our knowledge, this is the first single-cell transcriptional analysis of human CD1c-autoreactive T-cells, offering preliminary insight into their phenotype and potential functional diversity. Our data suggest that CD1c-responsive T-cells, previously described as ‘autoreactive’, respond more robustly to Mtb-infected APCs, raising the possibility that, in addition to roles in autoimmunity and cancer, these cells may also contribute to antimicrobial responses. Together, these findings address a previously unresolved question: whether human CD1c-autoreactive T-cells, known mainly in the context of autoimmunity and cancer, can functionally respond to Mtb-infected APCs. By integrating engineered APC systems, TCR transfer, cytotoxicity assays, and ex vivo single-cell transcriptomics, we show that these cells exhibit CD1c-dependent activation, cytotoxicity, cytokine production, and antimicrobial activity in response to Mtb-infected APCs. This links CD1c-autoreactivity to infection-associated effector function and supports these cells as candidate contributors to lipid-directed immunity in TB.

Two previous studies utilising either in vitro activation-based assays or CD1c-endo tetramers have confirmed the presence of CD1c-autoreactive T-cells in human peripheral blood (de Lalla et al., 2011; Gherardin et al., 2021). Although there was a discrepancy in their estimation of ex vivo frequencies for CD1c-autoreactive T-cells, both studies showed that these T-cells are a natural component of the human T-cell pool (de Lalla et al., 2011; Gherardin et al., 2021). Moreover, both showed that CD1c-autoreactive T-cells were enriched for CD4⁺ or DN T-cells, and exhibited memory and adaptive T-cell features closely aligned with conventional MHC-restricted T-cells (de Lalla et al., 2011; Gherardin et al., 2021). CD1c-autoreactive T-cells expressed diverse TCRs, including αβ, γδ, and δ/αβ (Gherardin et al., 2021). Although the TCR repertoire was generally diverse, they were enriched for the TRAV17, TRAV38-1, and TRBV4-1 gene segments (Gherardin et al., 2021; Guo et al., 2018). Functionally, CD1c-autoreactive T-cell clones exhibit a Th1- or a Th0-like phenotype, releasing IFN-γ, TNF-α, GM-CSF, IL-4, and IL-5 (de Lalla et al., 2011). Corroborating these earlier studies, we detected CD1c-autoreactive T-cells in the blood of healthy donors, and demonstrated that CD1c-autoreactive T-cells were polyfunctional. The variable expansion observed across donors likely reflects heterogeneity in CD1c-autoreactive precursor frequency, TCR repertoire composition, and activation state, as expected for primary human autoreactive T-cell populations (de Lalla et al., 2011). The functional and single-cell datasets presented capture different levels of this repertoire. The functional assays used two donor-derived CD1c-autoreactive T-cell lines that were CD4+αβTCR+, whereas ex vivo scRNA-seq revealed broader CD1c-endo-binding populations, including both CD4+ and CD8+ cytotoxic effector-memory T-cells. These approaches are therefore complementary: the T-cell lines provide mechanistic evidence of CD1c-dependent function, while single-cell profiling captures the wider phenotypic diversity of the CD1c-autoreactive compartment.

We generated CD1c-restricted αβ T-cell lines with a baseline autoreactive response that was significantly greater when responding to Mtb-infected cells, with increased activation markers, cytotoxicity, and cytokine secretion, and an associated reduction in Mtb luminescence in infected APCs. The Jurkat T-cell experiments with EM1 and EM2 TCRs support a key role for the TCR in mediating response through direct recognition of CD1c. Because Jurkat cells are not cytotoxic effector cells (Grailer et al., 2023), these experiments were designed to test TCR-dependent recognition and activation rather than target-cell killing. These findings suggest that changes in CD1c expression or cytokine secretion alone are unlikely to explain the enhanced T-cell activity observed towards Mtb-infected cells and instead point to a role for lipid antigen presentation on CD1c. Our findings align with a study in 2005 where autoreactive CD1 group 1 restricted T-cell clones were generated by limited dilution, after stimulation of CD4-depleted T-cells with MoDCs pulsed with hydrophobic microbial extracts derived from Mtb, Escherichia coli, and Yersinia enterocolitica (Vincent et al., 2005). A panel of 15 autoreactive clones were generated with the majority being CD1a- and CD1b-restricted and only two clones being CD1c-autoreactive (Vincent et al., 2005). While the clones exhibited weak autoreactivity, the response to microbial antigens far exceeded the self-reactive response seen in assays of T-cell proliferation (Vincent et al., 2005), similar to our findings and suggesting an antimicrobial role. TCR transfer experiments supported a role for the TCRs in promoting this dual reactivity to self and foreign antigen. However, further data from clone Mt2.33, which was a CD1c-autoreactive clone generated through Mtb antigen stimulation, was not provided. Further evidence for CD1c-restricted dual reactivity comes from a study identifying Vδ1⁺ γδ T-cells using CD1c-PM tetramers, where Jurkat cells transduced with these γδ TCRs exhibited spontaneous activation and stronger responses to microbial lipid antigens, again implicating TCR-driven recognition of CD1c-presented ligands (Roy et al., 2016). Another study investigated the CD1b-autoreactive clone HJ1, which was isolated from hCD1Tg mice, in Listeria monocytogenes infection (Li et al., 2011). While HJ1 T-cells exhibited autoreactivity towards CD1b, responses were enhanced by treatment of APCs with Listeria or the TLR agonists Pam3Cys (TLR2) and LPS (TLR4) (Li et al., 2011). Together, these observations suggest that some autoreactive CD1-responsive T-cells may have evolved as anti-microbial effectors, but when studied experimentally without such stimuli, a residual autoreactive phenotype is observed.

TLR activation during infection of APCs promotes the accumulation of stimulatory self-lipid antigens, contributing to the enhanced activation of iNKT cells (Brennan et al., 2011). Indeed, treatment of APCs with LPS or bacteria induced the accumulation of stimulatory iNKT self-lipid agonists b-d-glucopyranosylceramides (b-GlcCer) (Brennan et al., 2011). In our system, Mtb infection of THP1-CD1c APCs enhanced cytotoxicity by CD1c-autoreactive T-cells, but this effect was not reproduced by TLR2 or TLR4 agonists alone. This suggests that generic TLR2/4-mediated APC activation is insufficient to explain the enhanced response to Mtb-infected cells. Together with the TCR transfer experiments, these data point towards a CD1c-dependent signal that is increased or altered during Mtb infection. The molecular identity of this signal remains unresolved. One possibility is that Mtb infection alters the CD1c-presented lipid repertoire, either through presentation of Mtb-derived lipids (Vincent et al., 2005; Roy et al., 2016), infection-induced host ‘stress lipids’ (Mansour et al., 2016), or bacterial and mammalian shared lipids similar to those described for CD1b-autoreactive T-cells (Van Rhijn et al., 2016; Reinink et al., 2019). Alternatively, infection may alter lipid processing, trafficking or loading onto CD1c, increasing presentation of permissive self-lipids that can sit deeply within the CD1c groove and expose the CD1c surface for TCR recognition (Wun et al., 2018). Stimulatory lipids could include cholesteryl esters (Mansour et al., 2016) or other infection-induced lipid species. Thus, the additional signal provided by Mtb-infected THP1-CD1c cells is likely to be a qualitative or quantitative change in CD1c-associated lipid presentation, rather than CD1c expression alone or generic APC activation, a hypothesis supported by our TCR transfer experiments.

A limitation of this study is that we did not directly identify the lipids presented by CD1c on Mtb-infected APCs, making it unclear whether the relevant ligands are host-derived, bacterial, or shared lipid species. Future studies should isolate CD1c molecules from Mtb-infected APCs and use mass spectrometry-based lipidomics to define the lipid species associated with enhanced T-cell activation (Huang et al., 2023). Comparing these autoreactive populations with non-autoreactive CD1c-restricted T-cells that recognise defined microbial lipids will also be key, as the two may represent complementary modes of lipid immune surveillance. Although Mtb luminescence correlates closely with CFU counts in this system (Bielecka et al., 2017), it remains an indirect measure of viable bacterial burden, and future studies using CFU plating or complementary bacterial quantification methods will be important to confirm the extent of CD1c-dependent growth restriction. A further limitation is that the scRNA-seq analysis was performed on CD1c-endo-binding T-cells from two donors. Although subsampling analysis supported the robustness of the transcriptional patterns within this dataset, larger donor cohorts will be required to determine the consistency of cytotoxic effector-memory programmes across the broader human CD1c-autoreactive T-cell repertoire. In addition, our experiments rely on in vitro human cell systems, and in vivo validation of CD1c-restricted responses will benefit from specialised CD1-transgenic models that accommodate human CD1c biology, beyond what is possible in standard mouse systems (Felio et al., 2009; Zhao et al., 2015).

Our observation of CD1c expression within human lung TB granulomas provides further evidence for a potential role for lipid-specific T-cell immunity in the host immune response to Mtb. Our findings are consistent with reports suggesting that CD1c expression is targeted by Mtb and BCG, leading to reduced expression on APCs such as DCs (Stenger et al., 1998b; Wen et al., 2016; Aquino et al., 2011; Gagliardi et al., 2004). A recent study identified a potential mechanism, with Mtb driving expression of the microRNA miR-381-3p and thereby suppressing CD1c (Wen et al., 2016). This has important implications for interpreting functional assays using primary DCs. Although primary CD1c-expressing DCs are biologically relevant APCs, Mtb-induced CD1c downregulation would make it difficult to determine whether altered T-cell activation reflects changes in the CD1c-presented lipid repertoire or simply reduced CD1c at the cell surface. For this reason, the engineered THP1-CD1c system provides a controlled reductionist model in which CD1c expression is maintained during infection, allowing CD1c-TCR-dependent activation to be tested directly. This difference between primary MoDCs and THP1-CD1c APCs likely reflects the engineered nature of the THP1-CD1c system. Rather than regulation from the endogenous CD1C locus, CD1c is expressed from a heterologous CD1c-β2m fusion construct lacking the 3'-untranslated region where miR-381-3p can bind to disrupt translation (Wen et al., 2016). Therefore, our data are consistent with the idea that CD1c-autoreactive responses may contribute to protective host immunity, and that Mtb-mediated suppression of CD1c expression on primary APCs may represent an immune evasion strategy (Wen et al., 2016). Future studies comparing TB lesions with healthy lung and non-TB inflammatory lung tissue will be important to determine whether the spatial pattern of CD1c expression observed here is specific to TB or reflects broader inflammatory remodelling of lung APC populations.

Taken together, our findings suggest that T-cells previously described as autoreactive may also play a role in anti-mycobacterial immunity, and that this phenotype could have been shaped by evolutionary adaptation to mycobacterial exposure. Mtb has killed many more humans than any other single infection, and humans and Mtb may have co-evolved for 70,000 years (Comas et al., 2013; Paulson, 2013), demonstrating the significant impact mycobacteria will have had on human immune development (Reichmann et al., 2026). We identify functional responses to Mtb-infected cells by CD1c-autoreactive T-cells, including activation, cytotoxicity, cytokine secretion, and reduction in Mtb luminescence in vitro, that clearly exceed the autoreactive phenotype. Together, our findings suggest that CD1c-autoreactive T-cells may contribute to immune responses against infection and warrant further investigation in the context of vaccine development.

Source data files are provided with this article. scRNA-seq data are available from the European Nucleotide Archive under accession PRJEB94332. The previously published bulk RNA-seq dataset analysed in this study is available under accession PRJNA478394. Newly generated cell lines and TCR constructs are available from the corresponding author upon reasonable request.

1. 1. Pacis A
   2. Mailhot-Léonard F
   3. Randolph HE
   4. Yotova V
   5. Dumaine A
   6. Grenier J-C
   7. Barreiro LB

   (2026) Seqout

   ID PRJNA478394. Gene activation precedes DNA demethylation in response to infection in human dendritic cells.

   https://seqout.org/p/PRJNA478394
2. 1. Milton M
   2. Farag S
   3. Garay-Baquero DJ
   4. Gullick J
   5. Niedobecka K
   6. Burns D
   7. Szoke-Kovacs R
   8. Trimby-Smith P
   9. Look A
   10. Stopforth RJ
   11. Lepore M
   12. Cole DK
   13. Denney L
   14. White A
   15. Sharpe S
   16. Leslie A
   17. Vallejo A
   18. Tezera LB
   19. Elkington PT
   20. Mansour S

   (2025) European Nucleotide Archive

   ID PRJEB94332. sc-RNAseq on CD1c restricted T-cells.

   https://www.ebi.ac.uk/ena/browser/view/PRJEB94332

1. 1. Aquino A
   2. Graziani G
   3. Franzese O
   4. Prete SP
   5. Bonmassar E
   6. Bonmassar L
   7. D’Atri S

   (2011) Exogenous control of the expression of Group I CD1 molecules competent for presentation of microbial nonpeptide antigens to human T lymphocytes

   Clinical & Developmental Immunology 2011:790460.

   https://doi.org/10.1155/2011/790460

   • PubMed
   • Google Scholar
2. 1. Bielecka MK
   2. Tezera LB
   3. Zmijan R
   4. Drobniewski F
   5. Zhang X
   6. Jayasinghe S
   7. Elkington P

   (2017) A bioengineered three-dimensional cell culture platform integrated with microfluidics to address antimicrobial resistance in tuberculosis

   mBio 8:e02073-16.

   https://doi.org/10.1128/mBio.02073-16

   • PubMed
   • Google Scholar
3. 1. Brennan PJ
   2. Tatituri RVV
   3. Brigl M
   4. Kim EY
   5. Tuli A
   6. Sanderson JP
   7. Gadola SD
   8. Hsu F-F
   9. Besra GS
   10. Brenner MB

   (2011) Invariant natural killer T cells recognize lipid self antigen induced by microbial danger signals

   Nature Immunology 12:1202–1211.

   https://doi.org/10.1038/ni.2143

   • PubMed
   • Google Scholar
4. 1. Brites D
   2. Gagneux S

   (2015) Co-evolution of Mycobacterium tuberculosis and Homo sapiens

   Immunological Reviews 264:6–24.

   https://doi.org/10.1111/imr.12264

   • PubMed
   • Google Scholar
5. 1. Chancellor A
   2. Gadola SD
   3. Mansour S

   (2018) The versatility of the CD1 lipid antigen presentation pathway

   Immunology 154:196–203.

   https://doi.org/10.1111/imm.12912

   • PubMed
   • Google Scholar
6. 1. Comas I
   2. Coscolla M
   3. Luo T
   4. Borrell S
   5. Holt KE
   6. Kato-Maeda M
   7. Parkhill J
   8. Malla B
   9. Berg S
   10. Thwaites G
   11. Yeboah-Manu D
   12. Bothamley G
   13. Mei J
   14. Wei L
   15. Bentley S
   16. Harris SR
   17. Niemann S
   18. Diel R
   19. Aseffa A
   20. Gao Q
   21. Young D
   22. Gagneux S

   (2013) Out-of-Africa migration and Neolithic coexpansion of Mycobacterium tuberculosis with modern humans

   Nature Genetics 45:1176–1182.

   https://doi.org/10.1038/ng.2744

   • PubMed
   • Google Scholar
7. 1. Cotton RN
   2. Shahine A
   3. Rossjohn J
   4. Moody DB

   (2018) Lipids hide or step aside for CD1-autoreactive T cell receptors

   Current Opinion in Immunology 52:93–99.

   https://doi.org/10.1016/j.coi.2018.04.013

   • PubMed
   • Google Scholar
8. 1. de Jong A
   2. Arce EC
   3. Cheng TY
   4. van Summeren RP
   5. Feringa BL
   6. Dudkin V
   7. Crich D
   8. Matsunaga I
   9. Minnaard AJ
   10. Moody DB

   (2007) CD1c presentation of synthetic glycolipid antigens with foreign Alkyl Branching Motifs

   Chemistry & Biology 14:1232–1242.

   https://doi.org/10.1016/j.chembiol.2007.09.010

   • PubMed
   • Google Scholar
9. 1. de Jong A
   2. Peña-Cruz V
   3. Cheng T-Y
   4. Clark RA
   5. Van Rhijn I
   6. Moody DB

   (2010) CD1a-autoreactive T cells are a normal component of the human αβ T cell repertoire

   Nature Immunology 11:1102–1109.

   https://doi.org/10.1038/ni.1956

   • PubMed
   • Google Scholar
10. 1. de Lalla C
    2. Lepore M
    3. Piccolo FM
    4. Rinaldi A
    5. Scelfo A
    6. Garavaglia C
    7. Mori L
    8. De Libero G
    9. Dellabona P
    10. Casorati G

    (2011) High-frequency and adaptive-like dynamics of human CD1 self-reactive T cells

    European Journal of Immunology 41:602–610.

    https://doi.org/10.1002/eji.201041211

    • PubMed
    • Google Scholar
11. 1. Felio K
    2. Nguyen H
    3. Dascher CC
    4. Choi H-J
    5. Li S
    6. Zimmer MI
    7. Colmone A
    8. Moody DB
    9. Brenner MB
    10. Wang C-R

    (2009) CD1-restricted adaptive immune responses to Mycobacteria in human group 1 CD1 transgenic mice

    The Journal of Experimental Medicine 206:2497–2509.

    https://doi.org/10.1084/jem.20090898

    • PubMed
    • Google Scholar
12. 1. Fumagalli M
    2. Sironi M
    3. Pozzoli U
    4. Ferrer-Admetlla A
    5. Pattini L
    6. Nielsen R

    (2011) Signatures of environmental genetic adaptation pinpoint pathogens as the main selective pressure through human evolution

    PLOS Genetics 7:e1002355.

    https://doi.org/10.1371/journal.pgen.1002355

    • PubMed
    • Google Scholar
13. 1. Gagliardi MC
    2. Teloni R
    3. Mariotti S
    4. Iona E
    5. Pardini M
    6. Fattorini L
    7. Orefici G
    8. Nisini R

    (2004) Bacillus Calmette-Guérin shares with virulent Mycobacterium tuberculosis the capacity to subvert monocyte differentiation into dendritic cell: implication for its efficacy as a vaccine preventing tuberculosis

    Vaccine 22:3848–3857.

    https://doi.org/10.1016/j.vaccine.2004.07.009

    • PubMed
    • Google Scholar
14. 1. Gherardin NA
    2. Redmond SJ
    3. McWilliam HEG
    4. Almeida CF
    5. Gourley KHA
    6. Seneviratna R
    7. Li S
    8. De Rose R
    9. Ross FJ
    10. Nguyen-Robertson CV
    11. Su S
    12. Ritchie ME
    13. Villadangos JA
    14. Moody DB
    15. Pellicci DG
    16. Uldrich AP
    17. Godfrey DI

    (2021) CD36 family members are TCR-independent ligands for CD1 antigen-presenting molecules

    Science Immunology 6:eabg4176.

    https://doi.org/10.1126/sciimmunol.abg4176

    • PubMed
    • Google Scholar
15. 1. Grailer J
    2. Cheng ZJ
    3. Hartnett J
    4. Slater M
    5. Fan F
    6. Cong M

    (2023) A novel cell-based luciferase reporter platform for the development and characterization of T-cell redirecting therapies and vaccine development

    Journal of Immunotherapy 46:96–106.

    https://doi.org/10.1097/CJI.0000000000000453

    • PubMed
    • Google Scholar
16. 1. Guo T
    2. Koo MY
    3. Kagoya Y
    4. Anczurowski M
    5. Wang CH
    6. Saso K
    7. Butler MO
    8. Hirano N

    (2018) A subset of human autoreactive CD1c-Restricted T cells preferentially expresses TRBV4-1⁺ TCRs

    The Journal of Immunology 200:500–511.

    https://doi.org/10.4049/jimmunol.1700677

    • PubMed
    • Google Scholar
17. 1. Huang S
    2. Shahine A
    3. Cheng TY
    4. Chen YL
    5. Ng SW
    6. Balaji GR
    7. Farquhar R
    8. Gras S
    9. Hardman CS
    10. Altman JD
    11. Tahiri N
    12. Minnaard AJ
    13. Ogg GS
    14. Mayfield JA
    15. Rossjohn J
    16. Moody DB

    (2023) CD1 lipidomes reveal lipid-binding motifs and size-based antigen-display mechanisms

    Cell 186:4583–4596.

    https://doi.org/10.1016/j.cell.2023.08.022

    • PubMed
    • Google Scholar
18. 1. Hui D
    2. Sahu S
    3. Ditiu L
    4. Marais BJ
    5. Yeboah-Manu D
    6. Goletti D
    7. Walzl G
    8. Zumla A

    (2026) Global tuberculosis response off track: urgent priorities to end the world’s top infectious killer

    The Lancet 407:1126–1129.

    https://doi.org/10.1016/S0140-6736(25)02433-X

    • Google Scholar
19. 1. Jasenosky LD
    2. Scriba TJ
    3. Hanekom WA
    4. Goldfeld AE

    (2015) T cells and adaptive immunity to Mycobacterium tuberculosis in humans

    Immunological Reviews 264:74–87.

    https://doi.org/10.1111/imr.12274

    • PubMed
    • Google Scholar
20. 1. Joosten SA
    2. Ottenhoff THM
    3. Lewinsohn DM
    4. Hoft DF
    5. Moody DB
    6. Seshadri C
    7. Collaboration for Tuberculosis Vaccine Discovery - Donor-Unrestricted T-cells Working Group, Bill and Melinda Gates Foundation

    (2019) Harnessing donor unrestricted T-cells for new vaccines against tuberculosis

    Vaccine 37:3022–3030.

    https://doi.org/10.1016/j.vaccine.2019.04.050

    • PubMed
    • Google Scholar
21. 1. Karlsson EK
    2. Kwiatkowski DP
    3. Sabeti PC

    (2014) Natural selection and infectious disease in human populations

    Nature Reviews. Genetics 15:379–393.

    https://doi.org/10.1038/nrg3734

    • PubMed
    • Google Scholar
22. 1. Lepore M
    2. de Lalla C
    3. Gundimeda SR
    4. Gsellinger H
    5. Consonni M
    6. Garavaglia C
    7. Sansano S
    8. Piccolo F
    9. Scelfo A
    10. Häussinger D
    11. Montagna D
    12. Locatelli F
    13. Bonini C
    14. Bondanza A
    15. Forcina A
    16. Li Z
    17. Ni G
    18. Ciceri F
    19. Jenö P
    20. Xia C
    21. Mori L
    22. Dellabona P
    23. Casorati G
    24. De Libero G

    (2014) A novel self-lipid antigen targets human T cells against CD1c(+) leukemias

    The Journal of Experimental Medicine 211:1363–1377.

    https://doi.org/10.1084/jem.20140410

    • PubMed
    • Google Scholar
23. 1. Li S
    2. Choi HJ
    3. Felio K
    4. Wang CR

    (2011) Autoreactive CD1b-restricted T cells: a new innate-like T-cell population that contributes to immunity against infection

    Blood 118:3870–3878.

    https://doi.org/10.1182/blood-2011-03-341941

    • PubMed
    • Google Scholar
24. 1. Ly D
    2. Kasmar AG
    3. Cheng T-Y
    4. de Jong A
    5. Huang S
    6. Roy S
    7. Bhatt A
    8. van Summeren RP
    9. Altman JD
    10. Jacobs WR Jr
    11. Adams EJ
    12. Minnaard AJ
    13. Porcelli SA
    14. Moody DB

    (2013) CD1c tetramers detect ex vivo T cell responses to processed phosphomycoketide antigens

    The Journal of Experimental Medicine 210:729–741.

    https://doi.org/10.1084/jem.20120624

    • PubMed
    • Google Scholar
25. 1. Mansour S
    2. Tocheva AS
    3. Cave-Ayland C
    4. Machelett MM
    5. Sander B
    6. Lissin NM
    7. Molloy PE
    8. Baird MS
    9. Stübs G
    10. Schröder NWJ
    11. Schumann RR
    12. Rademann J
    13. Postle AD
    14. Jakobsen BK
    15. Marshall BG
    16. Gosain R
    17. Elkington PT
    18. Elliott T
    19. Skylaris C-K
    20. Essex JW
    21. Tews I
    22. Gadola SD

    (2016) Cholesteryl esters stabilize human CD1c conformations for recognition by self-reactive T cells

    PNAS 113:E1266–E1275.

    https://doi.org/10.1073/pnas.1519246113

    • PubMed
    • Google Scholar
26. 1. Matsunaga I
    2. Bhatt A
    3. Young DC
    4. Cheng TY
    5. Eyles SJ
    6. Besra GS
    7. Briken V
    8. Porcelli SA
    9. Costello CE
    10. Jacobs WR Jr
    11. Moody DB

    (2004) Mycobacterium tuberculosis pks12 produces a novel polyketide presented by CD1c to T cells

    The Journal of Experimental Medicine 200:1559–1569.

    https://doi.org/10.1084/jem.20041429

    • PubMed
    • Google Scholar
27. 1. Melián A
    2. Geng YJ
    3. Sukhova GK
    4. Libby P
    5. Porcelli SA

    (1999) CD1 expression in human atherosclerosis. A potential mechanism for T cell activation by foam cells

    The American Journal of Pathology 155:775–786.

    https://doi.org/10.1016/S0002-9440(10)65176-0

    • PubMed
    • Google Scholar
28. 1. Moody DB
    2. Ulrichs T
    3. Mühlecker W
    4. Young DC
    5. Gurcha SS
    6. Grant E
    7. Rosat JP
    8. Brenner MB
    9. Costello CE
    10. Besra GS
    11. Porcelli SA

    (2000) CD1c-mediated T-cell recognition of isoprenoid glycolipids in Mycobacterium tuberculosis infection

    Nature 404:884–888.

    https://doi.org/10.1038/35009119

    • PubMed
    • Google Scholar
29. 1. Mori L
    2. Lepore M
    3. De Libero G

    (2016) The immunology of CD1- and MR1-restricted T cells

    Annual Review of Immunology 34:479–510.

    https://doi.org/10.1146/annurev-immunol-032414-112008

    • PubMed
    • Google Scholar
30. 1. Ndiaye BP
    2. Thienemann F
    3. Ota M
    4. Landry BS
    5. Camara M
    6. Dièye S
    7. Dieye TN
    8. Esmail H
    9. Goliath R
    10. Huygen K
    11. January V
    12. Ndiaye I
    13. Oni T
    14. Raine M
    15. Romano M
    16. Satti I
    17. Sutton S
    18. Thiam A
    19. Wilkinson KA
    20. Mboup S
    21. Wilkinson RJ
    22. McShane H
    23. MVA85A 030 trial investigators

    (2015) Safety, immunogenicity, and efficacy of the candidate tuberculosis vaccine MVA85A in healthy adults infected with HIV-1: a randomised, placebo-controlled, phase 2 trial

    The Lancet. Respiratory Medicine 3:190–200.

    https://doi.org/10.1016/S2213-2600(15)00037-5

    • PubMed
    • Google Scholar
31. 1. Nguipdop-Djomo P
    2. Heldal E
    3. Rodrigues LC
    4. Abubakar I
    5. Mangtani P

    (2016) Duration of BCG protection against tuberculosis and change in effectiveness with time since vaccination in Norway: a retrospective population-based cohort study

    The Lancet. Infectious Diseases 16:219–226.

    https://doi.org/10.1016/S1473-3099(15)00400-4

    • PubMed
    • Google Scholar
32. 1. Nunes-Alves C
    2. Booty MG
    3. Carpenter SM
    4. Jayaraman P
    5. Rothchild AC
    6. Behar SM

    (2014) In search of a new paradigm for protective immunity to TB

    Nature Reviews. Microbiology 12:289–299.

    https://doi.org/10.1038/nrmicro3230

    • PubMed
    • Google Scholar
33. 1. O’Garra A
    2. Redford PS
    3. McNab FW
    4. Bloom CI
    5. Wilkinson RJ
    6. Berry MPR

    (2013) The immune response in tuberculosis

    Annual Review of Immunology 31:475–527.

    https://doi.org/10.1146/annurev-immunol-032712-095939

    • PubMed
    • Google Scholar
34. 1. Pacis A
    2. Mailhot-Léonard F
    3. Tailleux L
    4. Randolph HE
    5. Yotova V
    6. Dumaine A
    7. Grenier JC
    8. Barreiro LB

    (2019) Gene activation precedes DNA demethylation in response to infection in human dendritic cells

    PNAS 116:6938–6943.

    https://doi.org/10.1073/pnas.1814700116

    • PubMed
    • Google Scholar
35. 1. Paulson T

    (2013) Epidemiology: A mortal foe

    Nature 502:S2–S3.

    https://doi.org/10.1038/502S2a

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

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

    Nature Protocols 9:171–181.

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

    • PubMed
    • Google Scholar
37. 1. Reichmann MT
    2. Tezera LB
    3. Denney L
    4. Schiff H
    5. Vallejo A
    6. Mansour S
    7. Leslie A
    8. Garay-Baquero DJ
    9. Elkington PT

    (2026) A co-evolutionary perspective on humans and Mycobacterium tuberculosis in the era of systems biology

    eLife 14:RP108175.

    https://doi.org/10.7554/eLife.108175

    • PubMed
    • Google Scholar
38. 1. Reinink P
    2. Souter MNT
    3. Cheng T-Y
    4. van Gorkom T
    5. Lenz S
    6. Kubler-Kielb J
    7. Strle K
    8. Kremer K
    9. Thijsen SFT
    10. Steere AC
    11. Godfrey DI
    12. Pellicci DG
    13. Moody DB
    14. Van Rhijn I

    (2019) CD1b presents self and Borrelia burgdorferi diacylglycerols to human T cells

    European Journal of Immunology 49:737–746.

    https://doi.org/10.1002/eji.201847949

    • PubMed
    • Google Scholar
39. 1. Reljic R
    2. Stylianou E
    3. Balu S
    4. Ma JK

    (2010) Cytokine interactions that determine the outcome of Mycobacterial infection of macrophages

    Cytokine 51:42–46.

    https://doi.org/10.1016/j.cyto.2010.04.005

    • PubMed
    • Google Scholar
40. 1. Roura-Mir C
    2. Catálfamo M
    3. Cheng T-Y
    4. Marqusee E
    5. Besra GS
    6. Jaraquemada D
    7. Moody DB

    (2005) CD1a and CD1c activate intrathyroidal T cells during Graves’ disease and Hashimoto’s thyroiditis

    Journal of Immunology 174:3773–3780.

    https://doi.org/10.4049/jimmunol.174.6.3773

    • PubMed
    • Google Scholar
41. 1. Roy S
    2. Ly D
    3. Li N-S
    4. Altman JD
    5. Piccirilli JA
    6. Moody DB
    7. Adams EJ

    (2014) Molecular basis of mycobacterial lipid antigen presentation by CD1c and its recognition by αβ T cells

    PNAS 111:E4648–E4657.

    https://doi.org/10.1073/pnas.1408549111

    • PubMed
    • Google Scholar
42. 1. Roy S
    2. Ly D
    3. Castro CD
    4. Li NS
    5. Hawk AJ
    6. Altman JD
    7. Meredith SC
    8. Piccirilli JA
    9. Moody DB
    10. Adams EJ

    (2016) Molecular analysis of lipid-reactive Vδ1 γδ T cells identified by CD1c tetramers

    Journal of Immunology 196:1933–1942.

    https://doi.org/10.4049/jimmunol.1502202

    • PubMed
    • Google Scholar
43. 1. Ruibal P
    2. Voogd L
    3. Joosten SA
    4. Ottenhoff THM

    (2021) The role of donor-unrestricted T-cells, innate lymphoid cells, and NK cells in anti-mycobacterial immunity

    Immunological Reviews 301:30–47.

    https://doi.org/10.1111/imr.12948

    • PubMed
    • Google Scholar
44. 1. Scharf L
    2. Li N-S
    3. Hawk AJ
    4. Garzón D
    5. Zhang T
    6. Fox LM
    7. Kazen AR
    8. Shah S
    9. Haddadian EJ
    10. Gumperz JE
    11. Saghatelian A
    12. Faraldo-Gómez JD
    13. Meredith SC
    14. Piccirilli JA
    15. Adams EJ

    (2010) The 2.5 Å structure of CD1c in complex with a mycobacterial lipid reveals an open groove ideally suited for diverse antigen presentation

    Immunity 33:853–862.

    https://doi.org/10.1016/j.immuni.2010.11.026

    • PubMed
    • Google Scholar
45. 1. Schrager LK
    2. Vekemens J
    3. Drager N
    4. Lewinsohn DM
    5. Olesen OF

    (2020) The status of tuberculosis vaccine development

    The Lancet. Infectious Diseases 20:e28–e37.

    https://doi.org/10.1016/S1473-3099(19)30625-5

    • PubMed
    • Google Scholar
46. 1. Sieling PA
    2. Porcelli SA
    3. Duong BT
    4. Spada F
    5. Bloom BR
    6. Diamond B
    7. Hahn BH

    (2000) Human double-negative T cells in systemic lupus erythematosus provide help for IgG and are restricted by CD1c

    Journal of Immunology 165:5338–5344.

    https://doi.org/10.4049/jimmunol.165.9.5338

    • PubMed
    • Google Scholar
47. 1. Spada FM
    2. Grant EP
    3. Peters PJ
    4. Sugita M
    5. Melián A
    6. Leslie DS
    7. Lee HK
    8. van Donselaar E
    9. Hanson DA
    10. Krensky AM
    11. Majdic O
    12. Porcelli SA
    13. Morita CT
    14. Brenner MB

    (2000) Self-recognition of CD1 by gamma/delta T cells: implications for innate immunity

    The Journal of Experimental Medicine 191:937–948.

    https://doi.org/10.1084/jem.191.6.937

    • PubMed
    • Google Scholar
48. 1. Stenger S
    2. Hanson DA
    3. Teitelbaum R
    4. Dewan P
    5. Niazi KR
    6. Froelich CJ
    7. Ganz T
    8. Thoma-Uszynski S
    9. Melián A
    10. Bogdan C
    11. Porcelli SA
    12. Bloom BR
    13. Krensky AM
    14. Modlin RL

    (1998a) An antimicrobial activity of cytolytic T cells mediated by granulysin

    Science 282:121–125.

    https://doi.org/10.1126/science.282.5386.121

    • PubMed
    • Google Scholar
49. 1. Stenger S
    2. Niazi KR
    3. Modlin RL

    (1998b) Down-regulation of CD1 on antigen-presenting cells by infection with Mycobacterium tuberculosis

    The Journal of Immunology 161:3582–3588.

    https://doi.org/10.4049/jimmunol.161.7.3582

    • PubMed
    • Google Scholar
50. 1. Suzuki TA
    2. Fitzstevens JL
    3. Schmidt VT
    4. Enav H
    5. Huus KE
    6. Mbong Ngwese M
    7. Grießhammer A
    8. Pfleiderer A
    9. Adegbite BR
    10. Zinsou JF
    11. Esen M
    12. Velavan TP
    13. Adegnika AA
    14. Song LH
    15. Spector TD
    16. Muehlbauer AL
    17. Marchi N
    18. Kang H
    19. Maier L
    20. Blekhman R
    21. Ségurel L
    22. Ko G
    23. Youngblut ND
    24. Kremsner P
    25. Ley RE

    (2022) Codiversification of gut microbiota with humans

    Science 377:1328–1332.

    https://doi.org/10.1126/science.abm7759

    • PubMed
    • Google Scholar
51. 1. Tait DR
    2. Hatherill M
    3. Van Der Meeren O
    4. Ginsberg AM
    5. Van Brakel E
    6. Salaun B
    7. Scriba TJ
    8. Akite EJ
    9. Ayles HM
    10. Bollaerts A
    11. Demoitié M-A
    12. Diacon A
    13. Evans TG
    14. Gillard P
    15. Hellström E
    16. Innes JC
    17. Lempicki M
    18. Malahleha M
    19. Martinson N
    20. Mesia Vela D
    21. Muyoyeta M
    22. Nduba V
    23. Pascal TG
    24. Tameris M
    25. Thienemann F
    26. Wilkinson RJ
    27. Roman F

    (2019) Final analysis of a trial of M72/AS01_E vaccine to prevent tuberculosis

    The New England Journal of Medicine 381:2429–2439.

    https://doi.org/10.1056/NEJMoa1909953

    • PubMed
    • Google Scholar
52. 1. Tameris MD
    2. Hatherill M
    3. Landry BS
    4. Scriba TJ
    5. Snowden MA
    6. Lockhart S
    7. Shea JE
    8. McClain JB
    9. Hussey GD
    10. Hanekom WA
    11. Mahomed H
    12. McShane H
    13. MVA85A 020 Trial Study Team

    (2013) Safety and efficacy of MVA85A, a new tuberculosis vaccine, in infants previously vaccinated with BCG: a randomised, placebo-controlled phase 2b trial

    Lancet 381:1021–1028.

    https://doi.org/10.1016/S0140-6736(13)60177-4

    • PubMed
    • Google Scholar
53. 1. Van Rhijn I
    2. van Berlo T
    3. Hilmenyuk T
    4. Cheng T-Y
    5. Wolf BJ
    6. Tatituri RVV
    7. Uldrich AP
    8. Napolitani G
    9. Cerundolo V
    10. Altman JD
    11. Willemsen P
    12. Huang S
    13. Rossjohn J
    14. Besra GS
    15. Brenner MB
    16. Godfrey DI
    17. Moody DB

    (2016) Human autoreactive T cells recognize CD1b and phospholipids

    PNAS 113:380–385.

    https://doi.org/10.1073/pnas.1520947112

    • PubMed
    • Google Scholar
54. 1. Vincent MS
    2. Xiong X
    3. Grant EP
    4. Peng W
    5. Brenner MB

    (2005) CD1a-, b-, and c-restricted TCRs recognize both self and foreign antigens

    Journal of Immunology 175:6344–6351.

    https://doi.org/10.4049/jimmunol.175.10.6344

    • PubMed
    • Google Scholar
55. 1. Wallis RS
    2. Maeurer M
    3. Mwaba P
    4. Chakaya J
    5. Rustomjee R
    6. Migliori GB
    7. Marais B
    8. Schito M
    9. Churchyard G
    10. Swaminathan S
    11. Hoelscher M
    12. Zumla A

    (2016) Tuberculosis--advances in development of new drugs, treatment regimens, host-directed therapies, and biomarkers

    The Lancet. Infectious Diseases 16:e34–e46.

    https://doi.org/10.1016/S1473-3099(16)00070-0

    • PubMed
    • Google Scholar
56. 1. Wen Q
    2. Zhou C
    3. Xiong W
    4. Su J
    5. He J
    6. Zhang S
    7. Du X
    8. Liu S
    9. Wang J
    10. Ma L

    (2016) MiR-381-3p regulates the antigen-presenting capability of dendritic cells and represses antituberculosis cellular immune responses by targeting CD1c

    Journal of Immunology 197:580–589.

    https://doi.org/10.4049/jimmunol.1500481

    • PubMed
    • Google Scholar
57. Report

    1. World Health Organization

    (2021)

    Global tuberculosis report 2021

    Geneva: World Health Organization.

    • Google Scholar
58. 1. Wun KS
    2. Reijneveld JF
    3. Cheng T-Y
    4. Ladell K
    5. Uldrich AP
    6. Le Nours J
    7. Miners KL
    8. McLaren JE
    9. Grant EJ
    10. Haigh OL
    11. Watkins TS
    12. Suliman S
    13. Iwany S
    14. Jimenez J
    15. Calderon R
    16. Tamara KL
    17. Leon SR
    18. Murray MB
    19. Mayfield JA
    20. Altman JD
    21. Purcell AW
    22. Miles JJ
    23. Godfrey DI
    24. Gras S
    25. Price DA
    26. Van Rhijn I
    27. Moody DB
    28. Rossjohn J

    (2018) T cell autoreactivity directed toward CD1c itself rather than toward carried self lipids

    Nature Immunology 19:397–406.

    https://doi.org/10.1038/s41590-018-0065-7

    • PubMed
    • Google Scholar
59. 1. Zhao J
    2. Siddiqui S
    3. Shang S
    4. Bian Y
    5. Bagchi S
    6. He Y
    7. Wang C-R

    (2015) Mycolic acid-specific T cells protect against Mycobacterium tuberculosis infection in a humanized transgenic mouse model

    eLife 4:e08525.

    https://doi.org/10.7554/eLife.08525

    • PubMed
    • Google Scholar

Author details

1. Matthew Milton

   School of Clinical and Experimental Sciences, NIHR Southampton Biomedical Research Centre, Faculty of Medicine, University of Southampton, Southampton, United Kingdom

   Contribution

   Data curation, Formal analysis, Investigation, Methodology, Writing – original draft

   Contributed equally with

   Sahar H Farag

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0003-2509-7171

2. Sahar H Farag

   School of Clinical and Experimental Sciences, NIHR Southampton Biomedical Research Centre, Faculty of Medicine, University of Southampton, Southampton, United Kingdom

   Contribution

   Investigation

   Contributed equally with

   Matthew Milton

   Competing interests

   No competing interests declared

3. Diana Garay-Baquero

   School of Clinical and Experimental Sciences, NIHR Southampton Biomedical Research Centre, Faculty of Medicine, University of Southampton, Southampton, United Kingdom

   Contribution

   Data curation, Formal analysis, Investigation, Methodology

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-9450-8504

4. Jennie Gullick

   1. School of Clinical and Experimental Sciences, NIHR Southampton Biomedical Research Centre, Faculty of Medicine, University of Southampton, Southampton, United Kingdom
   2. Research and Evaluation, UK Health Security Agency, Porton Down, United Kingdom

   Contribution

   Data curation, Formal analysis, Investigation, Methodology

   Competing interests

   No competing interests declared

5. Kinga Niedobecka

   School of Clinical and Experimental Sciences, NIHR Southampton Biomedical Research Centre, Faculty of Medicine, University of Southampton, Southampton, United Kingdom

   Contribution

   Investigation

   Competing interests

   No competing interests declared

6. Daniel Burns

   School of Clinical and Experimental Sciences, NIHR Southampton Biomedical Research Centre, Faculty of Medicine, University of Southampton, Southampton, United Kingdom

   Contribution

   Investigation, Methodology

   Competing interests

   No competing interests declared

7. Rita Szoke-Kovacs

   Immunocore Limited, Abingdon, United Kingdom

   Contribution

   Data curation, Investigation, Methodology

   Competing interests

   Employee of Immunocore Limited

8. Patrick Trimby-Smith

   School of Clinical and Experimental Sciences, NIHR Southampton Biomedical Research Centre, Faculty of Medicine, University of Southampton, Southampton, United Kingdom

   Contribution

   Data curation, Formal analysis, Investigation

   Competing interests

   No competing interests declared

9. Alex Look

   School of Clinical and Experimental Sciences, NIHR Southampton Biomedical Research Centre, Faculty of Medicine, University of Southampton, Southampton, United Kingdom

   Contribution

   Formal analysis, Investigation

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-8492-0888

10. Richard Stopforth

    School of Clinical and Experimental Sciences, NIHR Southampton Biomedical Research Centre, Faculty of Medicine, University of Southampton, Southampton, United Kingdom

    Contribution

    Investigation

    Competing interests

    No competing interests declared

11. Marco Lepore

    Immunocore Limited, Abingdon, United Kingdom

    Contribution

    Supervision

    Competing interests

    Employee of Immunocore Limited

12. David K Cole

    Immunocore Limited, Abingdon, United Kingdom

    Contribution

    Supervision

    Competing interests

    Employee of Immunocore Limited

13. Laura Denney

    School of Clinical and Experimental Sciences, NIHR Southampton Biomedical Research Centre, Faculty of Medicine, University of Southampton, Southampton, United Kingdom

    Contribution

    Investigation, Methodology

    Competing interests

    No competing interests declared

14. Andrew White

    Research and Evaluation, UK Health Security Agency, Porton Down, United Kingdom

    Contribution

    Supervision, Funding acquisition

    Competing interests

    No competing interests declared

    "This ORCID iD identifies the author of this article:" 0000-0001-9481-0079

15. Sally Sharpe

    Research and Evaluation, UK Health Security Agency, Porton Down, United Kingdom

    Contribution

    Funding acquisition, Investigation

    Competing interests

    No competing interests declared

16. Alasdair Leslie

    1. Africa Health Research Institute, Durban, South Africa
    2. School of Laboratory Medicine and Medical Sciences, University of KwaZulu-Natal, Durban, South Africa
    3. Division of Infection and Immunity, University College London, London, United Kingdom

    Contribution

    Supervision, Investigation, Writing – review and editing

    Competing interests

    No competing interests declared

17. Andres Vallejo

    1. School of Clinical and Experimental Sciences, NIHR Southampton Biomedical Research Centre, Faculty of Medicine, University of Southampton, Southampton, United Kingdom
    2. Institute for Life Sciences, University of Southampton, Southampton, United Kingdom

    Contribution

    Supervision, Investigation, Methodology

    Competing interests

    No competing interests declared

18. Liku Tezera

    1. School of Clinical and Experimental Sciences, NIHR Southampton Biomedical Research Centre, Faculty of Medicine, University of Southampton, Southampton, United Kingdom
    2. Institute for Life Sciences, University of Southampton, Southampton, United Kingdom

    Contribution

    Supervision, Investigation, Methodology, Writing – review and editing

    Competing interests

    No competing interests declared

    "This ORCID iD identifies the author of this article:" 0000-0002-7898-6709

19. Paul Elkington

    1. School of Clinical and Experimental Sciences, NIHR Southampton Biomedical Research Centre, Faculty of Medicine, University of Southampton, Southampton, United Kingdom
    2. Institute for Life Sciences, University of Southampton, Southampton, United Kingdom

    Contribution

    Supervision, Funding acquisition, Investigation, Methodology, Writing – original draft, Writing – review and editing

    Competing interests

    No competing interests declared

    "This ORCID iD identifies the author of this article:" 0000-0003-0390-0613

20. Salah Mansour

    1. School of Clinical and Experimental Sciences, NIHR Southampton Biomedical Research Centre, Faculty of Medicine, University of Southampton, Southampton, United Kingdom
    2. Institute for Life Sciences, University of Southampton, Southampton, United Kingdom

    Contribution

    Conceptualization, Formal analysis, Supervision, Funding acquisition, Investigation, Writing – original draft, Project administration, Writing – review and editing

    For correspondence

    s.mansour@soton.ac.uk

    Competing interests

    No competing interests declared

    "This ORCID iD identifies the author of this article:" 0000-0002-5982-734X

• 816

  views

• 32

  downloads

• 0

  citations

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