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Comparative Analysis of Cytokine mRNA Expressions in Human Tissues with Mycobacterium tuberculosis Infection

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Submitted:

13 May 2021

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14 May 2021

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Abstract
In the present study, we aimed to investigate whether an automated molecular diagnostic method based on PCR-reverse blot hybridization assay can discriminate between human Mycobacterium tuberculosis (MTB)-positive and -negative FFPE tissues and to compare the relative mRNA expression levels of various host immune markers between MTB-infected and uninfected human tissues using quantitative reverse transcription (qRT) PCR. A total of 52 human FFPE tissue samples from various regions of the body, including the lungs, lymph nodes, tendons, colon, and appendix, were collected and used for the molecular identification of Mycobacterium species and analysis of cytokine mRNA expression. As a result, IFN-γ, TNF-α, IP-10, CXCL9, CXCL11, and GM-CSF mRNA expression levels in MTB-infected tissues were significantly higher than those in uninfected samples. Additionally, the differences in the mRNA expression levels of IFN-γ, CXCL9, and GM-CSF between MTB-infected and uninfected tissues were statistically significant were statistically significant (p < 0.05). Correlation curve analysis indicated that the mRNA expression of IFN-γ was inversely proportional to that of IP-10 and that the mRNA expression levels of IFN-γ, TNF-α, CXCL9, CXCL11, GM-CSF, and TNFR were proportional and well-correlated. Furthermore, to establish marker profiles for detecting MTB infection, the statistically significant expression levels of three markers were combined. We confirmed that the combined profile of IFN-γ, CXCL9, and GM-CSF expression levels was statistically significant (P < 0.001). Although the mRNA expression patterns of host immune markers may vary according to MTB infection status, these patterns may be highly correlated and can be simultaneously used as an additional indicator for diagnosing TB.
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Subject: Biology and Life Sciences  -   Anatomy and Physiology
Copyright: This open access article is published under a Creative Commons CC BY 4.0 license, which permit the free download, distribution, and reuse, provided that the author and preprint are cited in any reuse.
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