Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Article
  • Published:

Clonal analysis of Salmonella-specific effector T cells reveals serovar-specific and cross-reactive T cell responses

Nature Immunology volume 19, pages 742–754 (2018)Cite this article

Subjects

A Publisher Correction to this article was published on 12 March 2019

This article has been updated

Abstract

To tackle the complexity of cross-reactive and pathogen-specific T cell responses against related Salmonella serovars, we used mass cytometry, unbiased single-cell cloning, live fluorescence barcoding, and T cell–receptor sequencing to reconstruct the Salmonella-specific repertoire of circulating effector CD4+ T cells, isolated from volunteers challenged with Salmonella enterica serovar Typhi (S. Typhi) or Salmonella Paratyphi A (S. Paratyphi). We describe the expansion of cross-reactive responses against distantly related Salmonella serovars and of clonotypes recognizing immunodominant antigens uniquely expressed by S. Typhi or S. Paratyphi A. In addition, single–amino acid variations in two immunodominant proteins, CdtB and PhoN, lead to the accumulation of T cells that do not cross-react against the different serovars, thus demonstrating how minor sequence variations in a complex microorganism shape the pathogen-specific T cell repertoire. Our results identify immune-dominant, serovar-specific, and cross-reactive T cell antigens, which should aid in the design of T cell–vaccination strategies against Salmonella.

This is a preview of subscription content, access via your institution

Access options

Buy this article

  • Purchase on Springer Link
  • Instant access to full article PDF
Buy now

Prices may be subject to local taxes which are calculated during checkout

Fig. 1: Mass cytometry identification of effector CD4+ T cells responding to Salmonella infection.
Fig. 2: CD38+CCR7–CD4+ T cells contain a homogeneous population of IFNγ- and MIP-1β-producing cells.
Fig. 3: Analysis of libraries of CD38+CCR7– T cell clones and fluorescence barcoding to dissect the antigen-specific repertoire of effector CD4+ T cells.
Fig. 4: Cross-reactivity of Salmonella-specific CD4+ T cell clones against different Salmonella serovars.
Fig. 5: HlyE and CdtB are S. Typhi– and S. Paratyphi A–specific immunodominant T cell antigens.
Fig. 6: Sequence-dependent recognition of different Salmonella serovars by CdtB-specific T cell clones.
Fig. 7: Characterization of the specificity of T cell clones cross-reactive against nontyphoidal Salmonella.
Fig. 8: Clonal expansion of Salmonella-specific effector T cells.

Similar content being viewed by others

Change history

  • 12 March 2019

    In the version of this article initially published, the first affiliation lacked ‘MRC’; the correct name of the institution is ‘MRC Weatherall Institute of Molecular Medicine’. Two designations (SP110Y and ST110H) were incorrect in the legend to Fig. 6f,h,i. The correct text is as follows: for panel f, “...loaded with either the CdtB(105–125)SP110Y (DRB4*SP110Y) or the CdtB(105–125)ST110H (DRB4*ST110H) peptide variants...”; for panel h, “...decorated by the DRB4*SP110Y tetramer (lower-right quadrant), the DRB4*ST110H (upper-left quadrant)...”; and for panel i, “...stained ex vivo with DRB4*SP110Y, DRB4*ST110H...”. In Fig. 8e, the final six residues (LTEAFF) of the sequence in the far right column of the third row of the table were missing; the correct sequence is ‘CASSYRRTPPLTEAFF’. In the legend to Fig. 8d, a designation (HLyE) was incorrect; the correct text is as follows: “(HlyE?).” Portions of the Acknowledgements section were incorrect; the correct text is as follows: “This work was supported by the UK Medical Research Council (MRC) (MR/K021222/1) (G.N., M.A.G., A.S., V.C., A.J.P.),...the Oxford Biomedical Research Centre (A.J.P., V.C.),...and core funding from the Singapore Immunology Network (SIgN) (E.W.N.) and the SIgN immunomonitoring platform (E.W.N.).” Finally, a parenthetical element was phrased incorrectly in the final paragraph of the Methods subsection “T cell cloning and live fluorescence barcoding”; the correct phrasing is as follows: “...(which in all cases included HlyE, CdtB, Ty21a, Quailes, NVGH308, and LT2 strains and in volunteers T5 and T6 included PhoN)...”. Also, in Figs. 3c and 4a, the right outlines of the plots were not visible; in the legend to Fig. 3, panel letter ‘f’ was not bold; and in Fig. 8f, ‘ND’ should be aligned directly beneath DRB4 in the key and ‘ND’ should be removed from the diagram at right, and the legend should be revised accordingly as follows: “...colors indicate the HLA class II restriction (gray indicates clones for which restriction was not determined (ND)). Clonotypes are grouped on the basis of pathogen selectivity (continuous line), protein specificity (dashed line) and epitope specificity; for ten HlyE-specific clones (pixilated squares), the epitope specificity was not determined...”. The errors have been corrected in the HTML and PDF versions of the article.

References

  1. Crump, J. A., Luby, S. P. & Mintz, E. D. The global burden of typhoid fever. Bull. World Health Organ. 82, 346–353 (2004).

    PubMed  PubMed Central  Google Scholar 

  2. Crump, J. A. & Mintz, E. D. Global trends in typhoid and paratyphoid fever. Clin. Infect. Dis. 50, 241–246 (2010).

    Article  Google Scholar 

  3. Mogasale, V. et al. Burden of typhoid fever in low-income and middle-income countries: a systematic, literature-based update with risk-factor adjustment. Lancet Glob. Health 2, e570–e580 (2014).

    Article  Google Scholar 

  4. Dougan, G. & Baker, S. Salmonella enterica serovar Typhi and the pathogenesis of typhoid fever. Annu. Rev. Microbiol. 68, 317–336 (2014).

    Article  CAS  Google Scholar 

  5. Gal-Mor, O., Boyle, E. C. & Grassl, G. A. Same species, different diseases: how and why typhoidal and non-typhoidal Salmonella enterica serovars differ. Front. Microbiol. 5, 391 (2014).

    Article  Google Scholar 

  6. McClelland, M. et al. Comparison of genome degradation in Paratyphi A and Typhi, human-restricted serovars of Salmonella enterica that cause typhoid. Nat. Genet. 36, 1268–1274 (2004).

    Article  CAS  Google Scholar 

  7. Holt, K. E. et al. Pseudogene accumulation in the evolutionary histories of Salmonella enterica serovars Paratyphi A and Typhi. BMC Genomics 10, 36 (2009).

    Article  Google Scholar 

  8. Coward, C. et al. The effects of vaccination and immunity on bacterial infection dynamics in vivo. PLoS Pathog. 10, e1004359 (2014).

    Article  Google Scholar 

  9. Griffin, A. J. & McSorley, S. J. Development of protective immunity to Salmonella, a mucosal pathogen with a systemic agenda. Mucosal Immunol. 4, 371–382 (2011).

    Article  CAS  Google Scholar 

  10. Lee, S. J., Dunmire, S. & McSorley, S. J. MHC class-I-restricted CD8 T cells play a protective role during primary Salmonella infection. Immunol. Lett. 148, 138–143 (2012).

    Article  CAS  Google Scholar 

  11. Dunstan, S. J. et al. Variation at HLA-DRB1 is associated with resistance to enteric fever. Nat. Genet. 46, 1333–1336 (2014).

    Article  CAS  Google Scholar 

  12. Waddington, C. S. et al. An outpatient, ambulant-design, controlled human infection model using escalating doses of Salmonella Typhi challenge delivered in sodium bicarbonate solution. Clin. Infect. Dis. 58, 1230–1240 (2014).

    Article  CAS  Google Scholar 

  13. McCullagh, D. et al. Understanding paratyphoid infection: study protocol for the development of a human model of Salmonella enterica serovar Paratyphi A challenge in healthy adult volunteers. BMJ Open 5, e007481 (2015).

    Article  Google Scholar 

  14. Blohmke, C. J. et al. Interferon-driven alterations of the host’s amino acid metabolism in the pathogenesis of typhoid fever. J. Exp. Med. 213, 1061–1077 (2016).

    Article  CAS  Google Scholar 

  15. Levine, J. H. et al. Data-driven phenotypic dissection of aml reveals progenitor-like cells that correlate with prognosis. Cell 162, 184–197 (2015).

    Article  CAS  Google Scholar 

  16. Krutzik, P. O. & Nolan, G. P. Fluorescent cell barcoding in flow cytometry allows high-throughput drug screening and signaling profiling. Nat. Methods 3, 361–368 (2006).

    Article  CAS  Google Scholar 

  17. Haghjoo, E. & Galán, J. E. Salmonella typhi encodes a functional cytolethal distending toxin that is delivered into host cells by a bacterial-internalization pathway. Proc. Natl. Acad. Sci. USA 101, 4614–4619 (2004).

    Article  CAS  Google Scholar 

  18. Song, J., Gao, X. & Galán, J. E. Structure and function of the Salmonella Typhi chimaeric A(2)B(5) typhoid toxin. Nature 499, 350–354 (2013).

    Article  CAS  Google Scholar 

  19. Liang, L. et al. Immune profiling with a Salmonella Typhi antigen microarray identifies new diagnostic biomarkers of human typhoid. Sci. Rep. 3, 1043 (2013).

    Article  Google Scholar 

  20. Rodriguez-Rivera, L. D., Bowen, B. M., den Bakker, H. C., Duhamel, G. E. & Wiedmann, M. Characterization of the cytolethal distending toxin (typhoid toxin) in non-typhoidal Salmonella serovars. Gut Pathog. 7, 19 (2015).

    Article  Google Scholar 

  21. von Rhein, C. et al. ClyA cytolysin from Salmonella: distribution within the genus, regulation of expression by SlyA, and pore-forming characteristics. Int. J. Med. Microbiol. 299, 21–35 (2009).

    Article  Google Scholar 

  22. Oscarsson, J. et al. Characterization of a pore-forming cytotoxin expressed by Salmonella enterica serovars typhi and paratyphi A. Infect. Immun. 70, 5759–5769 (2002).

    Article  CAS  Google Scholar 

  23. Barat, S. et al. Immunity to intracellular Salmonella depends on surface-associated antigens. PLoS Pathog. 8, e1002966 (2012).

    Article  CAS  Google Scholar 

  24. Stubbington, M. J. T. et al. T cell fate and clonality inference from single-cell transcriptomes. Nat. Methods 13, 329–332 (2016).

    Article  Google Scholar 

  25. Lindestam Arlehamn, C. S. et al. Memory T cells in latent Mycobacterium tuberculosis infection are directed against three antigenic islands and largely contained in a CXCR3+CCR6+ Th1 subset. PLoS Pathog. 9, e1003130 (2013).

    Article  Google Scholar 

  26. Bhuiyan, S. et al. Cellular and cytokine responses to Salmonella enterica serotype Typhi proteins in patients with typhoid fever in Bangladesh. Am. J. Trop. Med. Hyg. 90, 1024–1030 (2014).

    Article  CAS  Google Scholar 

  27. Reynolds, C. J. et al. The serodominant secreted effector protein of Salmonella, SseB, is a strong CD4 antigen containing an immunodominant epitope presented by diverse HLA class II alleles. Immunology 143, 438–446 (2014).

    Article  CAS  Google Scholar 

  28. Sheikh, A. et al. Interferon-γ and proliferation responses to Salmonella enterica serotype Typhi proteins in patients with S. Typhi bacteremia in Dhaka, Bangladesh. PLoS Negl. Trop. Dis. 5, e1193 (2011).

    Article  CAS  Google Scholar 

  29. Wahid, R., Fresnay, S., Levine, M. M. & Sztein, M. B. Cross-reactive multifunctional CD4+ T cell responses against Salmonella enterica serovars Typhi, Paratyphi A and Paratyphi B in humans following immunization with live oral typhoid vaccine Ty21a. Clin. Immunol. 173, 87–95 (2016).

    Article  CAS  Google Scholar 

  30. Fuentes, J. A., Villagra, N., Castillo-Ruiz, M. & Mora, G. C. The Salmonella Typhi hlyE gene plays a role in invasion of cultured epithelial cells and its functional transfer to S. Typhimurium promotes deep organ infection in mice. Res. Microbiol. 159, 279–287 (2008).

    Article  CAS  Google Scholar 

  31. Galán, J. E. Typhoid toxin provides a window into typhoid fever and the biology of Salmonella Typhi. Proc. Natl. Acad. Sci. USA 113, 6338–6344 (2016).

    Article  Google Scholar 

  32. Groisman, E. A. The pleiotropic two-component regulatory system PhoP-PhoQ. J. Bacteriol. 183, 1835–1842 (2001).

    Article  CAS  Google Scholar 

  33. Dobinson, H. C. et al. Evaluation of the clinical and microbiological response to Salmonella paratyphi a infection in the first paratyphoid human challenge model. Clin. Infect. Dis. 64, 1066–1073 (2017).

    Article  CAS  Google Scholar 

  34. Wong, M. T. et al. Mapping the diversity of follicular helper T cells in human blood and tonsils using high-dimensional mass cytometry analysis. Cell Reports 11, 1822–1833 (2015).

    Article  CAS  Google Scholar 

  35. Finck, R. et al. Normalization of mass cytometry data with bead standards. Cytometry A 83, 483–494 (2013).

    Article  Google Scholar 

  36. Chen, H. et al. Cytofkit: a Bioconductor package for an integrated mass cytometry data analysis pipeline. PLOS Comput. Biol. 12, e1005112 (2016).

    Article  Google Scholar 

  37. Kotecha, N., Krutzik, P. O. & Irish, J. M. Web-based analysis and publication of flow cytometry experiments. Curr. Protoc. Cytom. 53, 10.17.1–10.17.24 (2010).

  38. Traunecker, A., Oliveri, F. & Karjalainen, K. Myeloma based expression system for production of large mammalian proteins. Trends Biotechnol. 9, 109–113 (1991).

    Article  CAS  Google Scholar 

  39. Olsen, L. R. et al. BlockLogo: visualization of peptide and sequence motif conservation. J. Immunol. Methods 400-401, 37–44 (2013).

    Article  CAS  Google Scholar 

  40. Bolotin, D. A. et al. MiXCR: software for comprehensive adaptive immunity profiling. Nat. Methods 12, 380–381 (2015).

    Article  CAS  Google Scholar 

  41. Shugay, M. et al. Towards error-free profiling of immune repertoires. Nat. Methods 11, 653–655 (2014).

    Article  CAS  Google Scholar 

  42. Shugay, M. et al. VDJtools: unifying post-analysis of T cell receptor repertoires. PLOS Comput. Biol. 11, e1004503 (2015).

    Article  Google Scholar 

Download references

Acknowledgements

This work was supported by the UK Medical Research Council (MRC) (MR/K021222/1) (G.N., M.A.G., A.S., V.C., A.J.P.), Cancer Research UK (CRUK) (C399/A2291) (V.C.), the NIHR Clinical Research Network Thames Valley (A.J.P.), the Oxford Biomedical Research Centre (A.J.P., V.C.), the Bill & Melinda Gates Foundation (OPP1084259) (A.J.P.), the European Vaccine Initiative (PIM) (A.J.P.), and core funding from the Singapore Immunology Network (SIgN) (E.W.N.) and the SIgN immunomonitoring platform (E.W.N.). G.D. is supported by The Wellcome Trust. We thank C. Waugh and the WIMM FACS facility for assistance with cell sorting, and T. Rostron for assistance with next-generation sequencing. The DRB4 tetramers were provided by B. Kwok and I.-T. Chow, from the Tetramer Core Laboratory at the Benaroya Research Institute at Virginia Mason.

Author information

Authors and Affiliations

  1. MRC Human Immunology Unit, MRC Weatherall Institute of Molecular Medicine, University of Oxford, Oxford, UK

    Giorgio Napolitani, Prathiba Kurupati, Margarida Rei, Anna Aulicino, Lorena Preciado-Llanes, Lauren Howson, Paola de Haas, Mariolina Salio, Laura Scifo, Alison Simmons & Vincenzo Cerundolo

  2. Singapore Immunology Network, Agency of Science, Technology and Research, Singapore, Singapore

    Karen Wei Weng Teng, Michael Thomas Wong, Etienne Becht & Evan William Newell

  3. Department of Paediatrics, Oxford Vaccine Group, University of Oxford and NIHR Oxford Biomedical Research Centre, Oxford, UK

    Malick M. Gibani, Christoph J. Blohmke, Claire Jones, Hazel Dobinson, Danielle Campbell, Helene B. Juel, Helena Thomaides-Brears & Andrew J. Pollard

  4. Institute of Infection and Global Health, University of Liverpool, Liverpool, UK

    Lorena Preciado-Llanes & Melita A. Gordon

  5. Department of Bio and Health Informatics, Technical University of Denmark, Copenhagen, Denmark

    Lars Rønn Olsen

  6. Department of Biochemistry, Micron Oxford, University of Oxford, Oxford, UK

    David Miguel Susano Pinto

  7. Wellcome Trust Sanger Institute, Hinxton, UK

    Derek Pickard & Gordon Dougan

  8. Biozentrum, University of Basel, Basel, Switzerland

    Dirk Bumann

  9. Hospital for Tropical Diseases, Wellcome Trust Major Overseas Programme, Oxford University Clinical Research Unit, Ho Chi Minh City, Vietnam

    Stephen Baker

  10. Malawi-Liverpool-Wellcome Trust Clinical Research Programme, Blantyre, Malawi

    Melita A. Gordon

Authors
  1. Giorgio Napolitani
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Prathiba Kurupati
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Karen Wei Weng Teng
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Malick M. Gibani
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Margarida Rei
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Anna Aulicino
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Lorena Preciado-Llanes
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Michael Thomas Wong
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Etienne Becht
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Lauren Howson
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. Paola de Haas
    View author publications

    You can also search for this author in PubMed Google Scholar

  12. Mariolina Salio
    View author publications

    You can also search for this author in PubMed Google Scholar

  13. Christoph J. Blohmke
    View author publications

    You can also search for this author in PubMed Google Scholar

  14. Lars Rønn Olsen
    View author publications

    You can also search for this author in PubMed Google Scholar

  15. David Miguel Susano Pinto
    View author publications

    You can also search for this author in PubMed Google Scholar

  16. Laura Scifo
    View author publications

    You can also search for this author in PubMed Google Scholar

  17. Claire Jones
    View author publications

    You can also search for this author in PubMed Google Scholar

  18. Hazel Dobinson
    View author publications

    You can also search for this author in PubMed Google Scholar

  19. Danielle Campbell
    View author publications

    You can also search for this author in PubMed Google Scholar

  20. Helene B. Juel
    View author publications

    You can also search for this author in PubMed Google Scholar

  21. Helena Thomaides-Brears
    View author publications

    You can also search for this author in PubMed Google Scholar

  22. Derek Pickard
    View author publications

    You can also search for this author in PubMed Google Scholar

  23. Dirk Bumann
    View author publications

    You can also search for this author in PubMed Google Scholar

  24. Stephen Baker
    View author publications

    You can also search for this author in PubMed Google Scholar

  25. Gordon Dougan
    View author publications

    You can also search for this author in PubMed Google Scholar

  26. Alison Simmons
    View author publications

    You can also search for this author in PubMed Google Scholar

  27. Melita A. Gordon
    View author publications

    You can also search for this author in PubMed Google Scholar

  28. Evan William Newell
    View author publications

    You can also search for this author in PubMed Google Scholar

  29. Andrew J. Pollard
    View author publications

    You can also search for this author in PubMed Google Scholar

  30. Vincenzo Cerundolo
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

G.N. conceived and designed the study, processed the samples, performed and supervised the experiments, analyzed the data, and wrote the manuscript. P.K. processed the samples, performed experiments, and analyzed the data. L.H., P.d.H., L.P.-L., L.S., and M.S. assisted with experiments. A.A. designed and performed bacteria–macrophage coculture experiments. M.R. and D.M.S.P. optimized and performed TCR-repertoire experiments and analysis. K.W.W.T., E.B., M.T.W., and E.W.N. developed the metal-conjugated antibody panels, coordinated and performed the mass cytometry experiments, and provided assistance with the analysis of mass cytometry experiments. A.J.P. conceived the development of the human-challenge model and directed the clinical trial. H.D. and M.M.G. designed and directed the clinical trial. D.C., C.J., and C.J.B. coordinated the clinical trial and sampling. C.J., H.T.-B., and H.B.J. coordinated sampling and processed samples. L.R.O. designed peptide pools containing overlapping peptides. S.B., G.D., D.B., and D.P. provided reagents. G.N., M.A.G., A.S., V.C., and A.J.P. coordinated the study. V.C. conceived and designed the study, edited the manuscript, and supervised performing of experiments. All authors discussed the results and commented on the manuscript.

Corresponding author

Correspondence to Giorgio Napolitani.

Ethics declarations

Competing interests

The authors declare no competing interests.

Additional information

Publisher's note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Integrated supplementary information

Supplementary Figure 1 Characterization of Ki67+ cells accumulating during enteric fever.

(a)Gating strategy to identify CD4+ and CD4+Ki67+ cells in Mass Cytometry experiments. Frequency of Cluster 19 cells within Ki67+ (b) and total CD4+ T cells (c) at different time points after infection (n = 6). (b) Friedman Test p = 0.0147, Dunn’s Multiple Comparison Test D0 vs D4, D28, D90 p = ns, D0 vs EF p = 0.027. (c)Friedman Test p = 0.0159, Dunn’s Multiple Comparison Test D0 vs D4, D28, D90 p = ns, D0 vs EF p = 0.0247. (d) Total Ki67+ events (grey) and Cluster 19 events (red) across all samples were concatenated and expression of 20 markers against CD38 in the distinct populations is shown.

Supplementary Figure 2 CCR7–CD38+CD4+ cells are enriched in Ki67+ accumulating during enteric fever.

(a) Expression of CCR7 and CD38 in the 20 distinct clusters identified by PhenoGraph analysis. (b) Proportion of PhenoGraph clusters within Ki67+ CD38+CCR7- cells at the peak of Ki67+ cell accumulation. (c) Frequency of Ki67+ CD38+CCR7- cells (Friedman Test p = 0.0130, Dunn’s Multiple Comparison Test D0 vs D4, D28, D90p = ns, D0 vs EF p = 0.0139) and of KI67 + cells non comprised within CD38+CCR7- cells over time in challenged volunteers (n = 6) (Friedman Test p = 0.23, Dunn’s Multiple Comparison Test D0 vs D4, EF, D28, D90 p = ns). Box extends from the 25th to the 75th percentile and whiskers from the minimum to the maximum value; line indicates median.

Supplementary Figure 3 Accumulation of CD4+CD38+CCR7– in whole blood of volunteers with enteric fever

(a) Identification of CD4+CD38+CCR7- cells during enteric fever by Multicolour Flow Cytometry in whole blood from a representative volunteer challenged with S. Typhi, and one challenged with S. Paratyphi A. (b) Accumulation of CD38+CCR7- cells after diagnosis in volunteers challenged with S. Typhi and Paratyphi A who developed (Diagnosed) but not in those who did not develop enteric fever (Not Diagnosed). In challenged volunteers not diagnosed blood was collected 14 days after challenge (D14). Friedman Multi comparison Test p = 0.006 (S. Typhi), p < 0.0001 (S. Paratyphi). Dunn’s Multiple Comparison Test ED vs D0, p = 0.0065 (Typhi, n = 9); p = 0.0002 (S. Paratyphi A, n = 9). Box extends from the 25th to the 75th percentile and whiskers from the minimum to the maximum value; line indicates median.

Supplementary Figure 4 Gating strategy for live fluorescence barcoding of T cell clones

Shown is an example with four live fluorescent dyes. Cell were labelled with 9 combinations of three distinct dilution of Violet and Far red dyes. Cells with each of the nine cluster were labelled with 6 combinations of 4 distinct dilutions of CFSE and and 3 of CMTMR orange dyes. Dilutions used for adjacent clusters across the 640-670 axis were labelled with alternate dilutions of CFSE and CMTMR Orange (as shown in the overlaid plots) in order to reduce spill-over in case of suboptimal separation between clusters across the channel-dye combination with the lowest stain index.

Supplementary Figure 5 Increased frequency of HlyE- and CdtB- specific T cells in volunteers diagnosed with enteric fever

(a) Frequency of HlyE, CdtB and Flu specific T cell responses 28 days after challenge measured by IFN-g ELISPOT in volunteers challenged with S. Typhi or S. Paratyphi A who developed (Diagnosed, D, n = 12) or did not develop (Not Diagnosed, ND, n = 9)) enteric fever compared to uninfected controls (HC. n = 10). Freshly isolated PBMC were rested overnight and incubated with 5μg/ml of HlyE or CdtB peptide pools, or with inactivated seasonal influenza vaccine (Flu). Mann Witney two tailed t-test. * = p < 0.05; ** = p < 0.005. Box extends from the 25th to the 75th percentile and whiskers from the minimum to the maximum value; line indicates median.

Supplementary Figure 6 CdtB-specific clones help macrophages limit bacterial spread

(a) Monocyte derived macrophages (MF) were differentiated for 5 days in the lower chamber of 24 well transwell plates. At day 5 a pool of 3 CdtB specific clones (T) was added to the upper well in the presence or absence of CdtB peptide pool (CdtB). After O.N. incubation, upper well was removed, macrophages were washed, infected with S. Typhimurium (LT2 strain) for 30 m, washed again and incubated with Gentamicin (30 mg/ml) to limit bacterial overgrowth. 2 h after infection cell culture supernatant was plated in agar plates and colonies counted after over night culture. Indicated is the percentage of bacterial growth compared to macrophages alone (n = 6, Friedman test (p = 0.0008) with Dunn’s multiple comparisons test (control vs T + CdtB p = 0.0169). Box extends from the 25th to the 75th percentile and whiskers from the minimum to the maximum value; line indicates median. (b) CFU after O.N. culture of macrophage supernatant in agar plate in one representative experiment.

Supplementary Figure 7 Increased frequency of CdtB, HlyE, and PhoN T cell responses after diagnosis with enteric fever

(a) Frequency of HlyE, CdtB, and Flu specific T cell responses at baseline and 28 days after challenge measured by IFN-g ELISPOT in frozen PBMC from volunteers challenged with S. Typhi or S. Paratyphi A who developed (n = 19) enteric fever (Wilcoxon matched-pairs signed rank test). (b) Frequency of HlyE, CdtB, PhoN and Flu specific T cell responses in HLADRB4 + and HLADRB4- volunteers 28 days after challenge (Mann Whitney test two-tailed p value). (c) Frequency of PhoN specific T cell responses at baseline and 28 days after challenge measured by IFN-g ELISPOT in frozen PBMC from volunteers challenged with S. Typhi or S. Paratyphi A who developed (n = 19) enteric fever and in HLADRB4+ and HLADRB4- individuals. ELISPOT analysis done on the same frozen samples as in panel a and b.

Supplementary Figure 8 Clonal expansion of Salmonella-specific effector T cells

(a) Rarefaction curve describing the estimated diversity of the CDR3β repertoire of CD38+CCR7- and CD38-CCR7- subsets in volunteers P1 and T4. Dots between continuous and dashed curves indicate the number of TCRs identified within each sample. Number of cells probed: P1 CD38+CCR7- = 5087 cells, CD38-CCR7- = 1.15e105 cells; T4 CD38+CCR7- = 9130 cells, CD38-CCR7- = 1.3e105 cells. (b) Pathogen selectivity, antigen specificity, restriction, CDR3α and CDR3β sequence of clones isolated from volunteer P1. Starred(*) sequences were identified also in the polyclonal analysis. (c) Pie Chart depicting the frequency of CDR3β sequences within the polyclonal repertoire of CD38+CCR7- cells (5087 cells probed) from T4, highlighted are the CDR3β sequences identified in the isolated clones. (d) Frequency of CDR3β within the polyclonal repertoire of CD38+CCR7- cells and CD38-CCR7- cells in volunteer P1 (1.15e105 cells probed). Red circles represent CDR3β sequences identified in the isolated clones.

Supplementary information

Supplementary Text and Figures

Supplementary Figures 1–8 and Supplementary Tables 1–8

Reporting Summary

Supplementary Note

Peptide pools

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Napolitani, G., Kurupati, P., Teng, K.W.W. et al. Clonal analysis of Salmonella-specific effector T cells reveals serovar-specific and cross-reactive T cell responses. Nat Immunol 19, 742–754 (2018). https://doi.org/10.1038/s41590-018-0133-z

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41590-018-0133-z

This article is cited by

Search

Advanced search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing