Microbes and infection · 2022
Novel serological biomarker panel using protein microarray can distinguish active TB from latent TB infection.
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Li Jie, Wang Yaguo, Yan Liang, Zhang Chunlan, He Yanbin, Zou Jun, Zhou Yanhong, Zhong Cheng, Zhang Xueyu
Аннотация
BACKGROUND: Rapid laboratory technologies which can effectively distinguish active tuberculosis (ATB) from controls and latent tuberculosis infection (LTBI) are lacked.The objective of this study is to explore MTB biomarkers in serum that can distinguish ATB from LTBI.
METHODS: We constructed a tuberculosis protein microarray containing 64 MTB associated antigens. We then used this microarray to screen 180 serum samples, from patients with ATB and LTBI, and healthy volunteer controls. Both SAM (Significance analysis of microarrays) and ROC curve analysis were used to identify the differentially recognized biomarkers between groups. Extra 300 serum samples from patients with ATB and LTBI, and healthy volunteer controls were employed to validate the identified biomarkers using ELISA-based method.
RESULTS: According to the results, the best biomarker combinations of 4 proteins (Rv1860, RV3881c, Rv2031c and Rv3803c) were selected. The biomarker panel containing these 4 proteins has reached a sensitivity of 93.3% and specificity of 97.7% for distinguishing ATB from LTBI, and a sensitivity of 86% and specificity of 97.6% for distinguishing ATB from HC.
CONCLUSION: The biomarker combination in this study has high sensitivity and specificity in distinguishing ATB from LTBI, suggesting it is worthy for further validation in more clinical samples.
METHODS: We constructed a tuberculosis protein microarray containing 64 MTB associated antigens. We then used this microarray to screen 180 serum samples, from patients with ATB and LTBI, and healthy volunteer controls. Both SAM (Significance analysis of microarrays) and ROC curve analysis were used to identify the differentially recognized biomarkers between groups. Extra 300 serum samples from patients with ATB and LTBI, and healthy volunteer controls were employed to validate the identified biomarkers using ELISA-based method.
RESULTS: According to the results, the best biomarker combinations of 4 proteins (Rv1860, RV3881c, Rv2031c and Rv3803c) were selected. The biomarker panel containing these 4 proteins has reached a sensitivity of 93.3% and specificity of 97.7% for distinguishing ATB from LTBI, and a sensitivity of 86% and specificity of 97.6% for distinguishing ATB from HC.
CONCLUSION: The biomarker combination in this study has high sensitivity and specificity in distinguishing ATB from LTBI, suggesting it is worthy for further validation in more clinical samples.