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DOI 10.1186/s12879-017-2910-y

Медицинская библиотека · BMC infectious diseases · 2017

ORFeome-based identification of biomarkers for serodiagnosis of Mycobacterium tuberculosis latent infection.

Zhou Fangbin, Xu Xindong, Wu Sijia, Cui Xiaobing, Pan Weiqing

СтатьяПоддержка исследования: вне правительства СШАПеревод пока не подготовлен

Аннотация

BACKGROUND

The challenges posed by Mycobacterium tuberculosis infection require the gradual removal of the pool of latent tuberculosis infection (LTBI). The current cell-immune-based diagnostic tests used to identify LTBI individuals have several irreversible drawbacks. In the present study, we attempted to identify novel diagnostic antigens for LTBI.

METHODS

A high-throughput glutathione S-transferase (GST)-fusion technology was used to express over 409 TB proteins and sera from LTBI and healthy individuals was used to interrogate these GST-TB fusion proteins.

RESULTS

Of 409 TB proteins, sixty-three reacted seropositive and defined the immuno-ORFeome of latent M. tuberculosis. Within the immuno-ORFeome, the rare targets were predominantly latency-associated proteins and secreted proteins, while the preferentially recognized antigens tended to be transmembrane proteins. Six of novel highly-reactive antigens had the potential to distinguish LTBI from active TB and healthy individuals. A multiple-antigen combination set was selected through analysis of various combinations. A panel of 94 archived serum samples was used to validate the diagnostic performance of the multiple-antigen combination set, which had sensitivity of 66.1% (95% CI 52.9, 77.4) and specificity of 87.5% (95% CI 70.1, 95.1).

CONCLUSION

These results provide experimental evidence of the immunogenicity of novel TB proteins that are suitable for the development of serodiagnostic tools for LTBI.

Для цитирования: Zhou Fangbin, Xu Xindong, Wu Sijia, Cui Xiaobing, Pan Weiqing. ORFeome-based identification of biomarkers for serodiagnosis of Mycobacterium tuberculosis latent infection.. BMC infectious diseases. 2017. DOI: 10.1186/s12879-017-2910-y.

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