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Combination of gene expression patterns in whole blood discriminate between tuberculosis infection states

Mihret, Adane ; Loxton, Andre G. ; Bekele, Yonas ; Kaufmann, Stefan H. E. ; Kidd, Martin ; Haks, Marielle C. ; Ottenhoff, Tom H. M. ; Aseffa, Abraham ; Howe, Rawleigh ; Walzl, Gerhard (2014-05)

CITATION: Mihret, A. et al. 2014. Combination of gene expression patterns in whole blood discriminate between tuberculosis infection states. BMC Infectious Diseases, 14(1):257, doi:10.1186/1471-2334-14-257.

The original publication is available at http://www.biomedcentral.com/1471-2334/14/257


Background Genetic factors are involved in susceptibility or protection to tuberculosis (TB). Apart from gene polymorphisms and mutations, changes in levels of gene expression, induced by non-genetic factors, may also determine whether individuals progress to active TB. Methods We analysed the expression level of 45 genes in a total of 47 individuals (23 healthy household contacts and 24 new smear-positive pulmonary TB patients) in Addis Ababa using a dual colour multiplex ligation-dependent probe amplification (dcRT-MLPA) technique to assess gene expression profiles that may be used to distinguish TB cases and their contacts and also latently infected (LTBI) and uninfected household contacts. Results The gene expression level of BLR1, Bcl2, IL4d2, IL7R, FCGR1A, MARCO, MMP9, CCL19, and LTF had significant discriminatory power between sputum smear-positive TB cases and household contacts, with AUCs of 0.84, 0.81, 0.79, 0.79, 0.78, 0.76, 0.75, 0.75 and 0.68 respectively. The combination of Bcl2, BLR1, FCGR1A, IL4d2 and MARCO identified 91.66% of active TB cases and 95.65% of household contacts without active TB. The expression of CCL19, TGFB1, and Foxp3 showed significant difference between LTBI and uninfected contacts, with AUCs of 0.85, 0.82, and 0.75, respectively, whereas the combination of BPI, CCL19, FoxP3, FPR1 and TGFB1 identified 90.9% of QFT- and 91.6% of QFT+ household contacts. Conclusions Expression of single and especially combinations of host genes can accurately differentiate between active TB cases and healthy individuals as well as between LTBI and uninfected contacts.

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