Circulating miRNAs have been extensively investigated as novel and non-invasive diagnostic and prognostic markers. Many studies have shown that circulating miRNAs serve as potential biomarkers for the early detection of different cancers, such as breast cancer , non-small cell lung carcinomas [29, 30] and colorectal cancer [31–33]. More recently, the role of miRNA in pathogen-host interactions has attracted attention. Human miRNAs may play important roles in viral replication, limiting antiviral responses, inhibiting apoptosis and stimulating cellular growth . MiRNAs are also associated with immune effects and inflammatory response in bacterial infections [35, 36]. However, serum/plasma miRNAs as novel biomarkers for diagnosis of infectious diseases, such as viral or common bacterial infections, remain to be identified. The diagnosis of M. tuberculosis infection is more difficult to establish than almost every other common bacterial infection . Recently, differential miRNA levels as potential biomarkers in the diagnosis of TB have been described in peripheral blood mononuclear cells (PBMC) [16–18, 38] and serum  from TB patients. For example, Cheng et al. demonstrated that miR-144* was significantly altered in PBMC from active TB patients . Maertzdorf et al. showed that miR-361-5p, miR-889, and miR-576-3p were under-expressed in TB patients . Our studies demonstrated the potential utility of circulating miRNAs as diagnostic or prognostic biomarkers of TB infection by TLDA analysis of miRNAs that were shown to be differentially expressed in TB patient sera. The majority of these showed up-regulated expression while only seven miRNAs were down-regulated. However, differential expression of miR-144* was not identified by qRT-PCR in our study. This discrepancy may be due to the analysis of different sample types (PBMC and serum). However, serum is more easily obtained and is regarded as more stable and therefore represents a preferred sample type for the analysis of miRNA expression as a circulating diagnostic biomarker. In comparison with a previously published data , we didn’t observe miR-29a showing significantly difference in our qRT-PCR results, which may be attributed to the differences in microarray system, sample size, etc.
Tuberculosis is classified as a granulomatous inflammatory condition. Macrophages, T lymphocytes, B lymphocytes and fibroblasts are among the cells that aggregate to form granulomas, with lymphocytes surrounding the infected macrophages. All of these cells secrete miRNAs into the serum. We hypothesized that serum miRNAs were derived from the release of infected epithelial cells as well as from other cell types, including immune cells. The target gene prediction results also showed the miRNAs might involve in regulation of anti-TB immunity, respiratory system development and lung development. Therefore, analysis of a cluster of M tuberculosis-associated miRNAs in sera will notably improve the diagnosis of TB infection although the underlying mechanism requires further investigation.
Real-time qRT-PCR was performed in individual serum samples to further verify the ten candidate miRNAs identified by TLDA. Differential expression of seven miRNAs (miR-361-5p, miR-889, miR-576-3p, miR-210, miR-26a, miR-432, and miR-134) was confirmed among these ten candidate miRNAs. To evaluate the efficiency of these dysregulated miRNAs for diagnosis of TB infections, ROC curves were constructed for each miRNA. MiR-361-5p, miR-889, and miR-576-3p showed good ability to efficiently distinguish TB infections from other microbial infections, with an AUC value greater than 0.7 in diagnosing TB infection. MiR-361-5p showed greater ability to distinguish TB infection with an AUC value of 0.848. In this study, we aimed to increase the diagnosis efficiency of these markers by using a combination of several host miRNAs (miR-361-5p, miR-889, and miR-576-3p). Unfortunately, the efficiency was not significantly improved although the AUC value increased slightly from 0.848 to 0.863. In order to verify the specificity of the host miRNAs for TB infection, three different microbial infection serum pools (BP, VZV and EV) were also analyzed by TLDA. The data of three miRNAs (miR-361-5p, miR-889, and miR-576-3p) showed significant difference between TB and three other microbial infection groups (Table 2). Although these three different microbial infection samples were from children, there was no significant difference of expression of three miRNAs between adults and children healthy controls (data not shown). Therefore, miR-361-5p, miR-889, and miR-576-3p could serve as potential molecular markers for TB infection.
Previous studies demonstrated that miR-361-5p was relatively abundant in bleomycin-induced fibrosis in mouse lungs and that its potential target genes may contribute to the understanding of the molecular mechanisms of lung injury and fibrosis . Our studies showed for the first time that higher levels of miR-361-5p are expressed in TB patient sera compared with healthy controls. It can be speculated that this reflects the lung injury caused by TB infection although the mechanism remains to be elucidated. Some evidence suggests that miR-210 can be used as a circulating biomarker for lung cancer among individuals with CT-detected solitary pulmonary nodules  and miR-26a can be used for diagnosis of HBV-related hepatocellular carcinoma . MiR-134 has also been reported as a regulator of cell proliferation, apoptosis and migration involving lung septation . Further studies are required to establish their functions in active TB infection.
Several limitations of our study should be noted. Firstly, serum miRNA demonstrated only moderate capacity to differentiate between TB infected patients and controls. Furthermore, only partially dysregulated miRNAs were evaluated and other miRNA combinations may provide more efficient biomarkers. Secondly, our study represents a preliminary investigation of host responses associated with different forms of TB infection. Biomarkers are required for different situations including protection by vaccination, discrimination of latent and active disease to facilitate rapid diagnosis and assessment of treatment outcome and relapse risk [42, 43]. Furthermore, additional investigations conducted in larger numbers of patients and healthy volunteers are required to validate our findings.