Scalable extracellular expression of tag-free MPT64 protein in E. coli via pelB signal optimization: a step toward tuberculosis diagnostic antigen preparation
Tuberculosis (TB) remains a global health burden, requiring affordable and scalable diagnostic tools. The MPT64 protein is a secreted biomarker specific to Mycobacterium tuberculosis and holds promise for rapid antigen-based diagnostics. However, existing recombinant expression systems often yield l...
Saved in:
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Published: |
Taylor and Francis
2025
|
| Subjects: | |
| Online Access: | http://psasir.upm.edu.my/id/eprint/123394/ https://www.tandfonline.com/doi/full/10.1080/10826068.2025.2551369 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Summary: | Tuberculosis (TB) remains a global health burden, requiring affordable and scalable diagnostic tools. The MPT64 protein is a secreted biomarker specific to Mycobacterium tuberculosis and holds promise for rapid antigen-based diagnostics. However, existing recombinant expression systems often yield low extracellular amounts, complicating purification and limiting downstream application. This study aimed to optimize the extracellular, tag-free expression of MPT64 protein in Escherichia coli by employing the pelB signal peptide in combination with Response Surface Methodology (RSM). A Box-Behnken design was used to analyze the interactive effects of rhamnose concentration, induction timing, and medium composition. The optimal condition (4 mM rhamnose, 2-h induction, and 1.8-fold medium enrichment) yielded 0.0293 mg/mL of extracellular MPT64. The identity and antigenicity of the secreted protein were validated using Sodium Dodecyl Sulfate Polyacrylamide Gel Electrophoresis (SDS-PAGE) and lateral flow immunoassay (LFIA), respectively. This study demonstrates that fine-tuning expression parameters can significantly enhance extracellular protein yield, providing a cost-effective production strategy for MPT64-based TB diagnostics and laying the foundation for future scalable diagnostic development. |
|---|