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

Abstract

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.

Figure 1

Gene expressions in household contacts and TB cases. Box plots are shown where the horizontal lines indicate medians of household contacts (white bars) and TB cases (grey bars) and the lower and upper edge of each boxes indicate the 25th and 75th percentiles, respectively. Data were analysed using the non-parameteric Mann- Whitney test with p-values indicating significant differences after transformation of Log2 values. *P < 0.05; **P < 0.001.

Figure 2

Receiver operator characteristics curves showing the accuracies of individual genes in discriminating between active TB cases and household contacts. Receiver operator characteristic (ROC) curves for the accuracies of single analytes to ifferentiate between active TB and household contacts. AUC = Area under the curve.

Figure 3

Frequency of individual genes in top 10 models for discriminating between active TB cases and household contacts. The columns represent the number of inclusions of individual markers into the most accurate five-analyte models by general discriminant for discriminating between active pulmonary TB cases and contacts.

Figure 4

Gene expression in Quantiferon⁺ and Quantiferon^- household contacts. Box plots are shown with the horizontal lines indicating median levels of Quantiferon⁺ (white bars) and Quantiferon^- (grey bars) household contacts. The lower and upper edge of each box indicates the 25th and 75th percentiles, respectively. Data were analysed using nonparametric Mann–Whitney test with p-values indicating significant differences after transformation of data to Log2 values. *P < 0.05.

Figure 5

Receiver operator characteristics curves showing the accuracies of individual genes in discriminating between LTBI and uninfected household contacts. Receiver operator characteristic (ROC) curves for the accuracies of single analytes to differentiate between LTBI and uninfected household contacts. AUC = Area under the curve.

Figure 6

Frequency of individual genes in top 10 models for discriminating between LTBI and uninfected household contacts. The columns represent the number of inclusions individual markers into the most accurate five-analyte models by general discriminant for discriminating between QFT⁺ and QFT^- contacts.