Skip to main content

Advertisement

SpringerLink
Log in

Evaluation of immunodominant peptides of in vivo expressed mycobacterial antigens in an ELISA-based diagnostic assay for pulmonary tuberculosis

  • Clinical Microbiology - Research Paper
  • Published:
Brazilian Journal of Microbiology Aims and scope Submit manuscript

Abstract

Non-sputum-based biomarker assay is urgently required as per WHO’s target product pipeline for diagnosis of tuberculosis. Therefore, the current study was designed to evaluate the utility of previously identified proteins, encoded by in vivo expressed mycobacterial transcripts in pulmonary tuberculosis, as diagnostic targets for a serodiagnostic assay. A total of 300 subjects were recruited including smear+, smear− pulmonary tuberculosis (PTB) patients, sarcoidosis patients, lung cancer patients and healthy controls. Proteins encoded by eight in vivo expressed transcripts selected from previous study including those encoded by two topmost expressed and six RD transcripts (Rv0986, Rv0971, Rv1965, Rv1971, Rv2351c, Rv2657c, Rv2674, Rv3121) were analyzed for B-cell epitopes by peptide arrays/bioinformatics. Enzyme-linked immunosorbent assay was used to evaluate the antibody response against the selected peptides in sera from PTB and controls. Overall 12 peptides were selected for serodiagnosis. All the peptides were initially screened for their antibody response. The peptide with highest sensitivity and specificity was further assessed for its serodiagnostic ability in all the study subjects. The mean absorbance values for antibody response to selected peptide were significantly higher (p<0.001) in PTB patients as compared to healthy controls; however, the sensitivity for diagnosis of PTB was 31% for smear+ and 20% for smear− PTB patients. Thus, the peptides encoded by in vivo expressed transcripts elicited a significant antibody response, but are not suitable candidates for serodiagnosis of PTB.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2

Similar content being viewed by others

Data availability

All the data generated during this study is included in the current research article and its supplementary information files.

References

  1. WHO (2014) High-priority target product profiles for new tuberculosis diagnostics. WHO, Report of a consensus meeting, Geneva, Switzerland

    Google Scholar 

  2. Weyer K, Carai S, Nunn P (2011) Viewpoint TB diagnostics: what does the world really need? J Infect Dis 204(suppl_4):S1196–S1202. https://doi.org/10.1093/infdis/jir452

    Article  PubMed  Google Scholar 

  3. Bulterys MA, Wagner B, Redard-Jacot M, Suresh A, Pollock NR, Moreau E, Denkinger CM, Drain PK, Broger T (2019) Point-of-care urine LAM tests for tuberculosis diagnosis: a status update. J Clin Med 9(1):111

    PubMed  PubMed Central  Google Scholar 

  4. Lawn SD (2012) Point-of-care detection of lipoarabinomannan (LAM) in urine for diagnosis of HIV-associated tuberculosis: a state of the art review. BMC Infect Dis 12:103

    PubMed  PubMed Central  Google Scholar 

  5. Peter JG, Theron G, van Zyl-Smit R, Haripersad A, Mottay L, Kraus S, Binder A, Meldau R, Hardy A, Dheda K (2012) Diagnostic accuracy of a urine lipoarabinomannan strip-test for TB detection in HIV-infected hospitalised patients. Eur Respir J 40:1211–1220

    PubMed  PubMed Central  Google Scholar 

  6. Mosquera-Restrepo SF, Zuberogoïtia S, Gouxette L, Layre E, Gilleron M, Stella A, Rengel D, Burlet-Schiltz O, Caro AC, Garcia LF, Segura C, Peláez Jaramillo CA, Rojas M, Nigou J (2022) A Mycobacterium tuberculosis fingerprint in human breath allows tuberculosis detection. Nat Commun 13:7751

    CAS  PubMed  PubMed Central  Google Scholar 

  7. Hiza H, Hella J, Arbués A, Magani B, Sasamalo M, Gagneux S, Reither K, Portevin D (2021) Case-control diagnostic accuracy study of a non-sputum CD38-based TAM-TB test from a single milliliter of blood. Sci Rep 11:13190

    CAS  PubMed  PubMed Central  Google Scholar 

  8. Sivakumaran D, Ritz C, Gjøen JE, Vaz M, Selvam S, Ottenhoff THM, Doherty TM, Jenum S, Grewal HMS (2021) Host blood RNA transcript and protein signatures for sputum-independent diagnostics of tuberculosis in adults. Front Immunol 11:626049

    PubMed  PubMed Central  Google Scholar 

  9. WHO (2011) Commercial serodiagnostic tests for diagnosis of tuberculosis policy statement. WHO, Policy Statement, Geneva, Switzerland

    Google Scholar 

  10. Gonzalez JM, Francis B, Burda S, Hess K, Behera D, Gupta D, Agarwal AN, Verma I, Verma A, Myneedu VP, Niedbala S, Laal S (2014) Development of a POC test for TB based on multiple immunodominant epitopes of Mtb specific cell-wall proteins. PloS One 9:e106279

    PubMed  PubMed Central  Google Scholar 

  11. Kunnath-Velayudhan S, Salamon H, Wang HY, Davidow AL, Molina DM, Huynh VT, Cirillo DM, Michel G, Talbot EA, Perkins MD, Felgner PL, Liang X, Gennaro ML (2010) Dynamic antibody responses to the Mycobacterium tuberculosis proteome. Proc Natl Acad Sci USA 107:14703–14708

    CAS  PubMed  PubMed Central  Google Scholar 

  12. Broger T, Basu Roy R, Filomena A, Greef CH, Rimmele S, Havumaki J, Danks D, Schneiderhan-Marra N, Gray CM, Singh M, Rosenkrands I, Andersen P, Husar GM, Joos TO, Gennaro ML, Lochhead MJ, Denkinger CM, Perkins MD (2017) Diagnostic performance of tuberculosis-specific IgG antibody profiles in patients with presumptive tuberculosis from two continents. Clin Infect Dis 64:947–955

    CAS  PubMed  PubMed Central  Google Scholar 

  13. Hao X, Bai J, Ding Y, Wang J, Liu Y, Yao L, Pan W (2020) Characterization of antibody response against 16kD and 38kD of M. tuberculosis in the assisted diagnosis of active pulmonary tuberculosis. Ann Transl Med 8:945

    PubMed  PubMed Central  Google Scholar 

  14. Hu Y, Liu M, Hu H, Yang H, Qin L, Hu Z, Zhu C, Liu Z (2021) Accuracy of multitarget indirect enzyme-linked immunoassay assay for detection of tuberculosis antibody. Ann Transl Med 9:1731

    CAS  PubMed  PubMed Central  Google Scholar 

  15. Davidow A, Kanaujia GV, Shi L, Kaviar J, Guo X, Sung N, Kaplan G, Menzies D, Gennaro ML (2005) Antibody profiles characteristic of Mycobacterium tuberculosis infection state. Infect Immun 73:6846–6851

    CAS  PubMed  PubMed Central  Google Scholar 

  16. Parkash O, Singh BP, Pai M (2009) Regions of differences encoded antigens as targets for immunodiagnosis of tuberculosis in humans. Scand J Immunol 70:345–357

    CAS  PubMed  Google Scholar 

  17. Brodin P, Majlessi L, Brosch R, Smith D, Bancroft G, Clark S, Williams A, Leclerc C, Cole ST (2004) Enhanced protection against tuberculosis by vaccination with recombinant Mycobacterium microti vaccine that induces T cell immunity against region of difference 1 antigens. J Infect Dis 190:115–122

    CAS  PubMed  Google Scholar 

  18. Shaban K, Amoudy HA, Mustafa AS (2013) Cellular immune responses to recombinant Mycobacterium bovis BCG constructs expressing major antigens of region of difference 1 of Mycobacterium tuberculosis. Clin Vaccine Immunol 20:1230–1237

    CAS  PubMed  PubMed Central  Google Scholar 

  19. Mustafa AS (2013) In silico analysis and experimental validation of Mycobacterium tuberculosis-specific proteins and peptides of Mycobacterium tuberculosis for immunological diagnosis and vaccine development. Med Princ Pract 22:43–51

    PubMed  PubMed Central  Google Scholar 

  20. Goyal B, Kumar K, Gupta D, Agarwal R, Latawa R, Sheikh JA, Verma I (2014) Utility of B-cell epitopes based peptides of RD1 and RD2 antigens for immunodiagnosis of pulmonary tuberculosis. Diagn Microbiol Infect Dis 78:391–397

    CAS  PubMed  Google Scholar 

  21. Wang S, Chen J, Zhang Y, Diao N, Zhang S, Wu J, Lu C, Wang F, Gao Y, Shao L, Jin J, Weng X, Zhang W (2013) Mycobacterium tuberculosis region of difference (RD) 2 antigen Rv1985c and RD11 antigen Rv3425 have the promising potential to distinguish patients with active tuberculosis from M. bovis BCG-vaccinated individuals. Clin Vaccine Immunol 20:69–76

    CAS  PubMed  PubMed Central  Google Scholar 

  22. Goyal B, Sheikh JA, Agarwal R, Verma I (2017) Levels of circulating immune complexes containing Mycobacterium Tuberculosis-specific antigens in pulmonary tuberculosis and sarcoidosis patients. Indian J Med Microbiol 35:290–292

    CAS  PubMed  Google Scholar 

  23. Sharma S, Ryndak MB, Aggarwal AN, Yadav R, Sethi S, Masih S, Laal S, Verma I (2017) Transcriptome analysis of mycobacteria in sputum samples of pulmonary tuberculosis patients. PLos One 12:e0173508

    PubMed  PubMed Central  Google Scholar 

  24. Vordermeier HM, Whelan A, Cockle PJ, Farrant L, Palmer N, Hewinson RG (2001) Use of synthetic peptides derived from the antigens ESAT-6 and CFP-10 for differential diagnosis of bovine tuberculosis in cattle. Clin Diagn Lab Immunol 8(3):571–578

    CAS  PubMed  PubMed Central  Google Scholar 

  25. Yao B, Zhang L, Liang S, Zhang C (2012) SVMTriP: a method to predict antigenic epitopes using support vector machine to integrate tri-peptide similarity and propensity. PloS One 7:e45152

    CAS  PubMed  PubMed Central  Google Scholar 

  26. Sweredoski MJ, Baldi P (2009) COBEpro: a novel system for predicting continuous B-cell epitopes. Protein Eng Des Sel PEDS 22:113–120

    CAS  PubMed  Google Scholar 

  27. Singh H, Ansari HR, Raghava GPS (2013) Improved method for linear B-cell epitope prediction using antigen’s primary sequence. PLoS One 8:1–8

    CAS  Google Scholar 

  28. Wang HW, Lin YC, Pai TW, Chang HT (2011) Prediction of B-cell linear epitopes with a combination of support vector machine classification and amino acid propensity identification. J Biomed Biotechnol 2011:432830

    PubMed  PubMed Central  Google Scholar 

  29. Sakamuri RM, Ryndak MB, Singh KK, Laal S (2020) Evolution of antibodies to epitopes of PE_PGRS51 in the spectrum of active pulmonary tuberculosis. J Infect Dis 221:1538–1541

    CAS  PubMed  Google Scholar 

  30. Shen G, Behera D, Bhalla M, Nadas A, Laal S (2009) Peptide-based antibody detection for tuberculosis diagnosis. Clin Vaccine Immunol 16:49–54

    CAS  PubMed  Google Scholar 

  31. DGN M, Smith DA, EJ MM, Reese V, Coler RN, Parish T (2011) Mycobacterium tuberculosis Rv0198c, a putative matrix metalloprotease is involved in pathogenicity. Tuberc Edinb Scotl 91:111–116

    Google Scholar 

  32. Abhishek S, Saikia UN, Gupta A, Bansal R, Gupta V, Singh N, Laal S, Verma I (2018) Transcriptional profile of Mycobacterium tuberculosis in an in vitro model of intraocular tuberculosis. Front Cell Infect Microbiol 8:330

    CAS  PubMed  PubMed Central  Google Scholar 

  33. Mathiesen MJ, Christiansen M, Hansen K, Holm A, Asbrink E, Theisen M (1998) Peptide-based OspC enzyme-linked immunosorbent assay for serodiagnosis of Lyme borreliosis. J Clin Microbiol 36:3474–3479

    CAS  PubMed  PubMed Central  Google Scholar 

  34. Andresen H, Grotzinger C (2009) Deciphering the antibodyome-peptide arrays for serum antibody biomarker diagnostics. Curr Proteomics 6:1–12

    CAS  Google Scholar 

  35. Gaseitsiwe S, Valentini D, Mahdavifar S, Magalhaes I, Hoft DF, Zerweck J, Schutkowski M, Andersson J, Reilly M, Maeurer MJ (2008) Pattern recognition in pulmonary tuberculosis defined by high content peptide microarray chip analysis representing 61 proteins from Mtb. PLoS One 3:e3840

    PubMed  PubMed Central  Google Scholar 

  36. Mendes TA, Reis Cunha JL, de Almeida LR, Rodrigues Luiz GF, Lemos LD, dos Santos AR, da Câmara AC, Galvão LM, Bern C, Gilman RH, Fujiwara RT, Gazzinelli RT, Bartholomeu DC (2013) Identification of strain-specific B-cell epitopes in Trypanosoma cruzi using genome-scale epitope prediction and high-throughput immunoscreening with peptide arrays. PLoS Negl Trop Dis 7:e2524

    PubMed  PubMed Central  Google Scholar 

  37. Goyal B, Kumar K, Gupta D, Agarwal R, Latawa R, Sheikh JA, Verma I (2014) Utility of B-cell epitopes based peptides of RD1 and RD2 antigens for immunodiagnosis of pulmonary tuberculosis. Diagn Microbiol Infect Dis 78:391–397

    CAS  PubMed  Google Scholar 

  38. Padmaja RJ, Halami PM (2016) Taming C-terminal peptides of Staphylococcus aureus leukotoxin M for B-cell response: implication in improved subclinical bovine mastitis diagnosis and protective efficacy in vitro. Toxicon 119:99–105

    CAS  PubMed  Google Scholar 

  39. Wang HW, Pai TW (2014) Machine learning-based methods for prediction of linear B-cell epitopes. Methods Mol Biol Clifton NJ 1184:217–236

    Google Scholar 

  40. Wang S, Inci F, De Libero G, Singhal A, Demirci U (2013) Point-of-care assays for tuberculosis: role of nanotechnology/microfluidics. Biotechnol Adv 31:438–449

    PubMed  PubMed Central  Google Scholar 

  41. Zhou F, Xu X, Wu S, Cui X, Fan L, Pan W (2015) Protein array identification of protein markers for serodiagnosis of Mycobacterium tuberculosis infection. Sci Rep 5:15349

    CAS  PubMed  PubMed Central  Google Scholar 

  42. Rachman H, Strong M, Ulrichs T, Grode L, Schuchhardt J, Mollenkopf H, Kosmiadi GA, Eisenberg D, Kaufmann SH (2006) Unique transcriptome signature of Mycobacterium tuberculosis in pulmonary tuberculosis. Infect Immun 74:1233–1242

    CAS  PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgements

The help from Dr. Prabhdeep Kaur during the standardization of ELISA is highly acknowledged.

Funding

This work was supported by intramural research grant No.71/8-Edu-15/1610 provided to Dr. Indu Verma by PGIMER, NIH/FIC AITRP grant No. TW001409 provided to Dr. Suman Laal and Research grant 09/141(0180)2011-EMR-I provided to Dr. Sumedha Sharma as fellowship by Council of Scientific and Industrial Research, Human Resource Development Group.

Author information

Authors and Affiliations

  1. Department of Biochemistry, Post Graduate Institute of Medical Education and Research, Chandigarh, 160012, India

    Sumedha Sharma & Indu Verma

  2. Advanced Pediatric Centre, Post Graduate Institute of Medical Education and Research, Chandigarh, 160012, India

    Deepti Suri

  3. Department of Pulmonary Medicine, Post Graduate Institute of Medical Education and Research, Chandigarh, 160012, India

    Ashutosh N. Aggarwal

  4. Department of Medical Microbiology, Post Graduate Institute of Medical Education and Research, Chandigarh, 160012, India

    Rakesh Yadav & Sunil Sethi

  5. Department of Pathology, New York University School of Medicine, New York, NY, 10010, USA

    Suman Laal

Authors
  1. Sumedha Sharma
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Deepti Suri
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Ashutosh N. Aggarwal
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Rakesh Yadav
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Sunil Sethi
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Suman Laal
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Indu Verma
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

Conceptualization: S.S., I.V.; methodology: S.S., I.V; data curation: S.S., D.S.; formal analysis: S.S., I.V., A.N.A.; resources: IV, ANA, SL, S.Sethi, R.Y.; writing—original draft: S.S.; writing—review and editing: S.S., I.V., funding: I.V., S.L., S.S.

Corresponding author

Correspondence to Indu Verma.

Ethics declarations

Ethics approval

The Institutional Ethics Committee PGIMER provided approval for current study vide no. 8818/PG11-1TRG/168235.

Consent to participate

Informed consent was obtained from the study subjects.

Conflict of interest

The authors declare no competing interests.

Additional information

Responsible Editor: Fernando R. Pavan

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary Information.

ESM 1:

HPLC Report of commercially synthesized peptides used in current study.

ESM 2:

Table S1: Representative table showing the raw data obtained for peptide arrays.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Sharma, S., Suri, D., Aggarwal, A.N. et al. Evaluation of immunodominant peptides of in vivo expressed mycobacterial antigens in an ELISA-based diagnostic assay for pulmonary tuberculosis. Braz J Microbiol 54, 1751–1759 (2023). https://doi.org/10.1007/s42770-023-00998-0

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s42770-023-00998-0

Keywords

Search

Navigation