Patients with active pulmonary TB were consecutively enrolled from the Affiliated Hospital of Weifang Medical University and Weifang Chest Hospital, China, from September 2011 to January 2012. The demographic and clinical characteristics of patients with active TB, LTBI and healthy controls were summarized in Table 1. Diagnosis of active pulmonary TB was based on typical clinical symptoms, such as cough, fever, pulmonary fibro-cavitation and infiltrates on chest radiograph, and at least one positive sputum smear. Biochemical tests and PCR method were used to identify Mtb. Patients were excluded if they had a history of diabetes, cancer or coinfection with other pathogens, such as HIV, HBV or HCV. Peripheral venous blood was drawn from the patients prior to initiation of anti-TB treatment. Individuals with LTBI and healthy control participants were recruited from the staff of two hospitals mentioned above, and participants were excluded if they had a prior history of TB, diabetes, cancer or other coinfected disease. Tuberculin skin test and interferon-γ (IFN-γ) release assay (IGRA; T-SPOT. TB, Oxford Immunuotec, Oxfordshire, UK) were used to distinguish LTBI participants from healthy controls. There were no significant differences among the groups in terms of age or gender.

The study was performed with the approval of Weifang Medical University local ethics committee and carried out in compliance with the Helsinki Declaration. All the participants provided informed consent before beginning the study.

Peripheral venous blood was drawn from each participant and PBMCs were separated by density gradient centrifugation using Ficoll (TBD, China). Untouched CD4⁺ T cells were purified from PBMCs by negative selection using CD4⁺ T Cell Isolation Kit (R&D Systems, Minneapolis, MN, USA) according to the manufacturer's instructions. Flow cytometric analysis of a subset of samples showed that the purity of isolated CD4⁺ T cells was greater than 90%. Samples were stored in liquid nitrogen until RNA was extracted.

Total RNA was isolated using Trizol reagent (Invitrogen, Carlsbad, CA,USA) and further purified with a miRNeasy mini kit (Qiagen, Dusseldorf, Germany) according to the manufacturer's protocol. RNA quality control procedure was followed to ensure that only high-quality RNA sample was used for microarray analysis. Spectrophotometry was performed to examine RNA purity and only sample with OD260/OD280 ∼2 and OD260/OD230 >1.8 was acceptable. Electrophoresis was used to analyse RNA integrity and only sample with 28S/18S >2 was adequate in the study.

For each sample (n = 4 for each group, randomly selected from the participants in Table 1), 1 μg total RNA was 3′-end-labelled with a miRCURYTM Hy3TM power labelling kit (Exiqon, Vedbaek, Denmark) and then subsequently hybridized to Exiqon Human miRCURY™ LNA Array (v.16.0) that contained ∼1223 capture probes (each probe being replicated four times on each array) according to the manufacturer's instructions. Arrays were scanned and each spot signal intensity was calculated by subtracting local background (based on the median signal intensity of the area surrounding each spot) from total intensity using Lowess Normalization (MIDAS, TIGR Microarray Data Analysis System). Intensity value of each miRNA was normalized to that of reference gene U6 RNA. After normalization, obtained average value for each miRNA was used for statistics, and a false discovery rate (FDR) value of less than 0.05 was considered statistically significant. The results were presented as fold change in miRNA expression. The expression profiles of the differentially expressed miRNAs between the groups were then subjected to hierarchical clustering using the Pearson correlation with average linkage to create a condition tree.

Five miRNAs were randomly selected from the deregulated miRNAs to be further confirmed using reverse transcription quantitative real-time PCR (RT-qPCR). RT reactions contained 700 ng RNA, 15 nM RT primers, 1× RT buffer, 0.25 mM each of dNTPs, 2 U/μl reverse transcriptase and 0.6 U/μl RNase inhibitor. The 20 μl reactions were incubated for 30 min. at 16°C, 42 min. at 42°C, and then 5 min. at 85°C. The cDNA product was used for the following PCR analysis. The 10 μl reactions included 1× Master Mix, 0.5 μM each primer and 2 μl cDNA. Reactions were incubated at 95°C for 10 min., followed by 40 cycles at 95°C for 10 sec. and 60°C for 1 min. on an ABI PRISM 7900 system (Applied Biosystems, Foster City, CA, USA). The primers (if needed, their sequences were available) were designed by optimization of parameters including melting temperature, GC content, complementarity and secondary structure, as well as primer length. At the same time, no template control (including all RT-PCR reagents except RNA template) was run to rule out cross-contamination of reagents and surfaces, and no amplification control (containing all RT-PCR reagents except reverse transcriptase) was used to eliminate genomic DNA contamination. All reactions were run in triplicate.

The Δ cycle threshold (Ct) values were obtained for each sample in each group for each miRNA tested. Human U6 small RNA level was used as the endogenous control. Normalized average value was used for statistics by the 2^−ΔΔCt method, and a P < 0.05 for each miRNA was considered statistically significant.

Three target prediction databases (mirbase, miranda and targetscan) were used to identify potential target genes of the differentially expressed miRNAs. The overlapping targets predicted by the three databases were subjected to gene ontology (GO) analysis and Kyoto encyclopedia of genes and genomes (KEGG) pathway analysis. P < 0.05 was considered statistically significant.

As T cells are reported to be the main producers of IFN-γ, which is critical for innate and adaptive immune response to Mtb infection, IFN-γ mRNA expression in CD4⁺ T cells was further assessed in the study. First-strand cDNA was generated using random primers. PCR was then performed with human IFN-γ specific primers (Forward 5′-3′ AGTTATATCTTGGCTTTTCA; Reverse 5′-3′ TTCGACTGATTAATAAGC). The data were analysed using the 2^−ΔΔCt method, and a P < 0.05 was considered statistically significant.

CD4⁺ T cells freshly isolated from LTBI patient were seeded in triplicate in each well of 24-well plates at a concentration of 1 × 10⁵ cells/well in 500 μl culture medium containing serum (complete medium) at 37°C in a 5% CO₂ incubator. Cells were then stimulated with purified protein derivative (PPD) from Mtb (25 μg/ml) for 24 hrs. RT-qPCR assay was used to measure miR-29 and IFN-γ mRNA expression. The data were analysed using the 2^−ΔΔCt method, and a P < 0.05 was considered statistically significant.

MiR-29 mimics (GenePharma, Shanghai, China) were used to further assess the effect of altered miR-29 level on IFN-γ production in stimulated CD4⁺ T cells with PPD in vitro. For the transfection procedure, CD4⁺ T cells were seeded in triplicate in each well of 24-well plates at a concentration of 1 × 10⁵ cells/well in 100 μl complete medium. Immediately, cells were either left untreated (medium only, untreated control) or transiently transfected with miR-29 mimics (70 nM) or negative mimics control (70 nM) using HiPerFect Transfection Reagent (Qiagen, Valencia, CA, USA) according to the manufacturer's protocols. Cells treated with transfection reagent but no mimics were used as mock control. Cells were incubated with transfection complexes under their normal growth conditions for 6 hrs. Then, 400 μl of complete medium containing 25 μg/ml PPD was added to each well and incubated for 24 hrs. RT-qPCR assay was used to determine IFN-γ mRNA expression. The data were analysed using the 2^−ΔΔCt method, and a P < 0.05 was considered statistically significant.

Data were presented as mean ± SD. The differences in levels of miRNAs among groups were analysed using two-tailed t-tests or one-way anova for experiments with more than two groups. A P < 0.05 was considered statistically significant.

To identify the most significant candidates, miRNAs with at least twofold expression change were selected. Under the criteria, 82 miRNAs were up-regulated and 53 miRNAs were down-regulated in the active TB group compared with the control group (Fig. 1); 33 miRNAs were increased and 46 miRNAs were decreased in the LTBI group compared with the control group (Fig. 1); 62 miRNAs were overexpressed and 62 miRNAs were downexpressed in the LTBI group compared with the active TB group (Fig. 1, Table 2; FDR < 0.05).

A total of 27 miRNAs (Fig. 2) were differentially expressed among the three groups (FDR < 0.05). Among the 27 miRNAs, 26 miRNAs had similar expression trend in both the active TB and LTBI group compared with the control group; 14 miRNAs were increased; and 12 miRNAs were decreased in both the active TB and LTBI group. Among the 27 miRNAs, 11 miRNAs (miR-136-5p, miR-340-5p, miR-451a, miR-32-5p, miR-27a-3p, miR-29a, miR-29b, miR-4292, miR-H8*, miR-1915-3p, miR-4258) were differentially expressed between the active TB group and the LTBI group; five miRNAs (miR-136-5p, miR-4292, miR-H8*, miR-1915-3p, miR-4258) were up-regulated in the LTBI group; and six miRNAs were down-regulated. Among the 11 miRNAs differentially expressed between the active TB group and the LTBI group mentioned above, six miRNAs (miR-136-5p, miR-340-5p, miR-451a, miR-32-5p, miR-27a-3p, miR-29b) were increased and four miRNAs (miR-4292, miR-H8*, miR-1915-3p, miR-4258) were decreased in both the LTBI and active TB group compared with the control group.

To confirm the microarray results, five miRNAs (miR-451a, miR-340-5p, miR-136-5p, miR-4292, miR-29b) were randomly selected for further confirmation using RT-qPCR. Among the five selected miRNAs, all of them except miR-136-5p were differentially expressed between the active TB group and the LTBI group (P < 0.05); three miRNAs (miR-451a, miR-340-5p, miR-29b) were increased in the active TB group; and only miR-4292 was decreased (Fig. 3). Among the five selected miRNAs, four miRNAs (miR-451a, miR-340-5p, miR-136-5p, miR-29b) were down-regulated in comparison of the control group with the LTBI group, as well as the control group with the active TB group (P < 0.05). The results were generally consistent with the microarray data.

As the number of experimentally validated miRNA targets is very limited, to explore the functions of 27 differentially expressed miRNAs among the three groups, we predicted their potential target genes. The biology of the predicted target genes was further explored regarding their molecular functions and involvement in known regulatory pathways. The predicated miRNA targets were categorized into several biological functions, of which protein binding and focal adhesion were shown to be the most enriched ones (Fig. 4A and B; BH-corrected P < 0.05). Among the enriched signalling pathways, only a few ones, such as mitogen-activated protein kinase (MAPK) signalling pathway, have been found to be involved in response to TB infection [13,14]; however, other signalling pathways have never been reported, such as extracellular matrix (ECM)–receptor interaction and GnRH signalling pathway.

Both microarray and RT-qPCR results showed that miR-29 levels in CD4⁺ T cells were decreased in comparisons of the control group with the LTBI group, as well as the control group with the active TB group. On the basis of IFN-γ as one target of miR-29 [15], it has been suggested that up-regulated miR-29 in CD4⁺ T cells might in turn inhibit IFN-γ mRNA expression in both the LTBI and active TB group. Consistent with our hypothesis, IFN-γ mRNA levels were decreased in both the LTBI group and the active TB group compared with the controls, and reduced more in the active TB group compared with the LTBI group (P < 0.05). The results indicated an inverse correlation between miR-29 level and IFN-γ mRNA expression in CD4⁺ T cells response to Mtb infection.

To further determine the correlation between the expression of miR-29 and that of IFN-γ in CD4⁺ T cells response to TB infection, several experiments were performed. We found that activated CD4⁺ T cells with PPD had higher expression of IFN-γ mRNA, but lower miR-29 mRNA expression than did non-activated CD4⁺ T cells (Fig. 5A and B), which demonstrated an inverse correlation between the expression of miR-29 and that of IFN-γ in activated CD4⁺ T cells with PPD.

MiR-29 mimics (synthetic miRNAs that mimic the function of endogenous miR-29) were used to further assess the effect of increased miR-29 level on IFN-γ production in activated CD4⁺ T cells with PPD. We found that miR-29 mimics significantly inhibited IFN-γ production in activated CD4⁺ T cells with PPD (Fig. 5C), which suggested that miR-29 might be involved in negative regulation of IFN-γ in response to TB infection.