All Title Author
Keywords Abstract

PLOS ONE  2008 

Pattern Recognition in Pulmonary Tuberculosis Defined by High Content Peptide Microarray Chip Analysis Representing 61 Proteins from M. tuberculosis

DOI: 10.1371/journal.pone.0003840

Full-Text   Cite this paper   Add to My Lib

Abstract:

Background Serum antibody-based target identification has been used to identify tumor-associated antigens (TAAs) for development of anti-cancer vaccines. A similar approach can be helpful to identify biologically relevant and clinically meaningful targets in M.tuberculosis (MTB) infection for diagnosis or TB vaccine development in clinically well defined populations. Method We constructed a high-content peptide microarray with 61 M.tuberculosis proteins as linear 15 aa peptide stretches with 12 aa overlaps resulting in 7446 individual peptide epitopes. Antibody profiling was carried with serum from 34 individuals with active pulmonary TB and 35 healthy individuals in order to obtain an unbiased view of the MTB epitope pattern recognition pattern. Quality data extraction was performed, data sets were analyzed for significant differences and patterns predictive of TB+/?. Findings Three distinct patterns of IgG reactivity were identified: 89/7446 peptides were differentially recognized (in 34/34 TB+ patients and in 35/35 healthy individuals) and are highly predictive of the division into TB+ and TB?, other targets were exclusively recognized in all patients with TB (e.g. sigmaF) but not in any of the healthy individuals, and a third peptide set was recognized exclusively in healthy individuals (35/35) but no in TB+ patients. The segregation between TB+ and TB? does not cluster into specific recognition of distinct MTB proteins, but into specific peptide epitope ‘hotspots’ at different locations within the same protein. Antigen recognition pattern profiles in serum from TB+ patients from Armenia vs. patients recruited in Sweden showed that IgG-defined MTB epitopes are very similar in individuals with different genetic background. Conclusions A uniform target MTB IgG-epitope recognition pattern exists in pulmonary tuberculosis. Unbiased, high-content peptide microarray chip-based testing of clinically well-defined populations allows to visualize biologically relevant targets useful for development of novel TB diagnostics and vaccines.

References

[1]  Lee SY, Jeoung D (2007) The reverse proteomics for identification of tumor antigens. J Microbiol Biotechnol 17: 879–890.
[2]  Jager D, Karbach J, Pauligk C, Seil I, Frei C, et al. (2005) Humoral and cellular immune responses against the breast cancer antigen NY-BR-1: definition of two HLA-A2 restricted peptide epitopes. Cancer Immun 5: 11.
[3]  Odunsi K, Qian F, Matsuzaki J, Mhawech-Fauceglia P, Andrews C, et al. (2007) Vaccination with an NY-ESO-1 peptide of HLA class I/II specificities induces integrated humoral and T cell responses in ovarian cancer. Proc Natl Acad Sci U S A 104: 12837–12842.
[4]  Odunsi K, Old LJ (2007) Tumor infiltrating lymphocytes: indicators of tumor-related immune responses. Cancer Immun 7: 3.
[5]  Kaufmann SH, Parida SK (2008) Tuberculosis in Africa: learning from pathogenesis for biomarker identification. Cell Host Microbe 4: 219–228.
[6]  Tully G, Kortsik C, Hohn H, Zehbe I, Hitzler WE, et al. (2005) Highly focused T cell responses in latent human pulmonary Mycobacterium tuberculosis infection. J Immunol 174: 2174–2184.
[7]  Mueller H, Detjen AK, Schuck SD, Gutschmidt A, Wahn U, et al. (2008) Mycobacterium tuberculosis-specific CD4(+), IFNgamma(+), and TNFalpha(+) multifunctional memory T cells coexpress GM-CSF. Cytokine.
[8]  Jacobsen M, Detjen AK, Mueller H, Gutschmidt A, Leitner S, et al. (2007) Clonal expansion of CD8+ effector T cells in childhood tuberculosis. J Immunol 179: 1331–1339.
[9]  Andersen P, Smedegaard B (2000) CD4(+) T-cell subsets that mediate immunological memory to Mycobacterium tuberculosis infection in mice. Infect Immun 68: 621–629.
[10]  Mueller H, Detjen AK, Schuck SD, Gutschmidt A, Wahn U, et al. (2008) Mycobacterium tuberculosis-specific CD4+, IFNgamma+, and TNFalpha+ multifunctional memory T cells coexpress GM-CSF. Cytokine 43: 143–148.
[11]  Andersen P, Munk ME, Pollock JM, Doherty TM (2000) Specific immune-based diagnosis of tuberculosis. Lancet 356: 1099–1104.
[12]  Lyashchenko K, Manca C, Colangeli R, Heijbel A, Williams A, et al. (1998) Use of Mycobacterium tuberculosis complex-specific antigen cocktails for a skin test specific for tuberculosis. Infect Immun 66: 3606–3610.
[13]  Pottumarthy S, Wells VC, Morris AJ (2000) A comparison of seven tests for serological diagnosis of tuberculosis. J Clin Microbiol 38: 2227–2231.
[14]  Robinson WH, DiGennaro C, Hueber W, Haab BB, Kamachi M, et al. (2002) Autoantigen microarrays for multiplex characterization of autoantibody responses. Nat Med 8: 295–301.
[15]  Banerjee S, Nandyala A, Podili R, Katoch VM, Murthy KJ, et al. (2004) Mycobacterium tuberculosis (Mtb) isocitrate dehydrogenases show strong B cell response and distinguish vaccinated controls from TB patients. Proc Natl Acad Sci U S A 101: 12652–12657.
[16]  Meena LS, Goel S, Sharma SK, Jain NK, Banavaliker JN, et al. (2002) Comparative study of three different mycobacterial antigens with a novel lipopolysaccharide antigen for the serodiagnosis of tuberculosis. J Clin Lab Anal 16: 151–155.
[17]  Blythe MJ, Zhang Q, Vaughan K, de Castro R Jr., Salimi N, et al. (2007) An analysis of the epitope knowledge related to Mycobacteria. Immunome Res 3: 10.
[18]  Lee JH, Geiman DE, Bishai WR (2008) Role of stress response sigma factor SigG in Mycobacterium tuberculosis. J Bacteriol 190: 1128–1133.
[19]  Lee JH, Karakousis PC, Bishai WR (2008) Roles of SigB and SigF in the Mycobacterium tuberculosis sigma factor network. J Bacteriol 190: 699–707.
[20]  Michele TM, Ko C, Bishai WR (1999) Exposure to antibiotics induces expression of the Mycobacterium tuberculosis sigF gene: implications for chemotherapy against mycobacterial persistors. Antimicrob Agents Chemother 43: 218–225.
[21]  Barry CE 3rd, Lee RE, Mdluli K, Sampson AE, Schroeder BG, et al. (1998) Mycolic acids: structure, biosynthesis and physiological functions. Prog Lipid Res 37: 143–179.
[22]  Rao V, Fujiwara N, Porcelli SA, Glickman MS (2005) Mycobacterium tuberculosis controls host innate immune activation through cyclopropane modification of a glycolipid effector molecule. J Exp Med 201: 535–543.
[23]  Andersen P, Doherty TM (2005) The success and failure of BCG - implications for a novel tuberculosis vaccine. Nat Rev Microbiol 3: 656–662.
[24]  Kaufmann SH, Baumann S, Nasser Eddine A (2006) Exploiting immunology and molecular genetics for rational vaccine design against tuberculosis. Int J Tuberc Lung Dis 10: 1068–1079.
[25]  van der Wel N, Hava D, Houben D, Fluitsma D, van Zon M, et al. (2007) M. tuberculosis and M. leprae translocate from the phagolysosome to the cytosol in myeloid cells. Cell 129: 1287–1298.
[26]  Predki PF, Mattoon D, Bangham R, Schweitzer B, Michaud G (2005) Protein microarrays: a new tool for profiling antibody cross-reactivity. Hum Antibodies 14: 7–15.
[27]  Michaud GA, Salcius M, Zhou F, Bangham R, Bonin J, et al. (2003) Analyzing antibody specificity with whole proteome microarrays. Nat Biotechnol 21: 1509–1512.
[28]  Skjot RL, Brock I, Arend SM, Munk ME, Theisen M, et al. (2002) Epitope mapping of the immunodominant antigen TB10.4 and the two homologous proteins TB10.3 and TB12.9, which constitute a subfamily of the esat-6 gene family. Infect Immun 70: 5446–5453.
[29]  Nahtman T, Jernberg A, Mahdavifar S, Zerweck J, Schutkowski M, et al. (2007) Validation of peptide epitope microarray experiments and extraction of quality data. J Immunol Methods 328: 1–13.
[30]  Tusher VG, Tibshirani R, Chu G (2001) Significance analysis of microarrays applied to the ionizing radiation response. Proc Natl Acad Sci U S A 98: 5116–5121.
[31]  Tibshirani R, Hastie T, Narasimhan B, Chu G (2002) Diagnosis of multiple cancer types by shrunken centroids of gene expression. Proc Natl Acad Sci U S A 99: 6567–6572.

Full-Text

comments powered by Disqus