Immunochemistry-Based Diagnosis of Extrapulmonary Tuberculosis: A Strategy for Large-Scale Production of MPT64-Antibodies for Use in the MPT64 Antigen Detection Test

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  • معلومة اضافية
    • Publication Information:
      MDPI AG, 2021.
    • Publication Date:
      2021
    • Collection:
      LCC:Immunologic diseases. Allergy
    • Abstract:
      Tuberculosis (TB) is a global health problem. The immunohistochemistry (IHC)-based MPT64 antigen detection test has shown promising results for diagnosing extrapulmonary TB in previous studies. However, the anti-MPT64 antibody currently used in the test is in limited supply, and reproduction of a functional antibody is a prerequisite for further large-scale use. Various antigen-adjuvant combinations and immunisation protocols were tested in mice and rabbits to generate monoclonal and polyclonal antibodies. Antibodies were screened in IHC, and the final new antibody was validated on clinical human specimens. We were not able to generate monoclonal antibodies that were functional in IHC, but we obtained multiple functional polyclonal antibodies through careful selection of antigen-adjuvant and comprehensive screening in IHC of both pre-immune sera and antisera. To overcome the limitation of batch-to-batch variability with polyclonal antibodies, the best performing individual polyclonal antibodies were pooled to one final large-volume new anti-MPT64 antibody. The sensitivity of the new antibody was in the same range as the reference antibody, while the specificity was somewhat reduced. Our results suggest that it possible to reproduce a large-volume functional polyclonal antibody with stable performance, thereby securing stable supplies and reproducibility of the MPT64 test, albeit further validation remains to be done.
    • File Description:
      electronic resource
    • ISSN:
      10030034
      2073-4468
    • Relation:
      https://www.mdpi.com/2073-4468/10/3/34; https://doaj.org/toc/2073-4468
    • Accession Number:
      10.3390/antib10030034
    • Accession Number:
      edsdoj.78b3f7e8aef34328b054c854d2c67339