The diagnosis and prognosis of tuberculosis remains challenging and necessitates the development of a new test that can accurately diagnose and monitor treatment responses. In this regard, miRNA is becoming a potential diagnostic and prognostic biomarker which differentiates treatment respondents from non-respondents for various non-infectious and infectious diseases, including tuberculosis. The concentration of miRNAs varies based on cell type, disease, and site of infection, implicating that selection of an optimal reference gene is crucial, and determines the quantification of transcript level and biological interpretation of the data. Thus, the study evaluated the stability and expression level of five candidate miRNAs (let-7i-5p, let-7a-5p, miRNA-16-5p, miRNA-22-3p and miRNA-93-5p), including U6 Small Nuclear RNA (RNU6B) to normalize circulating miRNAs in the plasma of 68 participants (26 healthy controls, 23 latent, and 19 pulmonary tuberculosis infected) recruited from four health centers and three hospitals in Addis Ababa, Ethiopia. The expression levels of miRNAs isolated from plasma of culture confirmed newly diagnosed pulmonary tuberculosis patients were compared with latently infected and non-infected healthy controls. The qPCR data were analyzed using four independent statistical tools: Best Keeper, Genorm, Normfinder and comparative delta-Ct methods, and the data showed that miRNA-22-3p and miRNA-93-5p were suitable plasma reference miRNAs in a tuberculosis study.

In conjunction with the spread of HIV infection, tuberculosis (TB) remains a major cause of illness and death worldwide. The Ethiopian national report reveals that extra pulmonary tuberculosis is on the rise and that case detection rate is exceeding that of smear positive or negative cases in many parts of the country. Different studies indicated that host and/or pathogen related factors are associated with the rise of extra pulmonary cases. However, the reason for this is not clearly known in our setting. Specimens were taken from clinically suspected extra pulmonary patients and confirmed by cytology, histopathology and culture. Deletion typing and Spoligotyping was utilized to identify the strains. The isolates were then assigned to lineage using conformal Bayesian network (rules model) algorithm and dendrograms were drawn using UPGMA methods. In addition, drug sensitivity test was done using the indirect proportion and 24 well plate methods. Out of the 200 clinically suspected extra pulmonary tuberculosis patients, 106 (53 %) were between 15 and 35 years of age and 167 (83.5 %) were new while 33 (16.5 %) were retreatment cases. The culture yield was 29.5 % (59). Of these only one was M. bovis and 58 were M. tuberculosis strains with 31 different spoligotype patterns grouped into seven clusters. The largest cluster (ST53) comprised 12 (20.3 %) isolates. There was higher clustering of CAS isolates in TBLN than in any other form of extra pulmonary tuberculosis cases. Resistance to rifampicin was higher (22 %) than that for INH, STM and EMB (8.1 %, 5 % and 3 % respectively). Out of the 37 isolates tested for resistance, only 2 isolates were resistant for both STM and INH and no MDR strain was found. There is an ongoing active recent transmission among extra pulmonary tuberculosis in the study areas as shown by the presence of clusters. Although no MDR case was observed, there is a risk of emergence of MDR as noted from the high proportion of resistance to rifampicin. Detailed study at population level is recommended to monitor its trend.

Tuberculosis infection exhibits different forms, namely, pulmonary, extrapulmonary, and latent. Here, diagnostic markers based on the gene expression of cytokines and chemokines for differentiating between tuberculosis infection state(s) were identified. Gene expression of seven cytokines (Interferon gamma (IFN-γ), Interferon gamma-induced protein 10 (IP-10), Interleukin-2 receptor (IL-2R), C-X-C Motif Chemokine Ligand 9 (CXCL-9), Interleukin 10 (IL-10), Interleukin 4 (IL-4), and Tumor Necrosis Factor alpha (TNF-α)) in response to tuberculosis antigen was analyzed using real-time polymerase reaction. The sensitivity and specificity of relative quantification (2^^-ΔΔCt) of mRNA expression were analyzed by constructing receiver operating characteristic curves and measuring the area under the curve (AUC) values. Combinations of cytokines were analyzed using the R statistical software package. IFN-γ, IP-10, IL2R, and CXCL-9 showed high expression in latent and active tuberculosis patients (p = 0.001), with a decrease in IL10 expression, and no statistical difference in IL-4 levels among all the groups (p = 0.999). IL-10 differentiated pulmonary tuberculosis patients from latent cases with an AUC of 0.731. IL10 combined with CXCL-9 distinguished pulmonary tuberculosis patients from extrapulmonary cases with a sensitivity, specificity, and accuracy of 85.7%, 73.9%, and 81.0%, respectively. IL-10 together with IP-10 and IL-4 differentiated pulmonary tuberculosis from latent cases with a sensitivity and specificity of 77.1% and 88.1%, respectively. Decision tree analysis demonstrated that IFN-γ IL-2R, and IL-4 can diagnose tuberculosis infection with a sensitivity, specificity, and accuracy of 89.7%, 96.1%, and 92.7%, respectively. A combination of gene expression of cytokines and chemokines might serve as an effective marker to differentiate tuberculosis infection state(s).

Rapid detection of drug-resistant tuberculosis (DR-TB) is crucial for timely treatment and management. The GenoType MTBDRplus and MTBDRsl (MTBDR) assays have been endorsed by the World Health Organization (WHO) for the detection of DR-TB. However, MTBDR assays cannot simultaneously detect multidrug-resistant TB (MDR-TB) and extensively drug-resistant TB (XDR-TB). Furthermore, interpretation of the MTBDR assay requires trained people, and the assay has low sample throughput, processing only up to 12 samples in parallel. We have developed the Quantamatrix Multiplexed Assay Platform (QMAP) to detect MDR-/XDR-TB simultaneously. The interpretation of QMAP results is automated, and the platform can process up to 96 samples in parallel. To compare the performance of QMAP with MTBDR assays, we performed QMAP and the MTBDR assay on 76 smear-positive, Mycobacterium tuberculosis culture-positive sputum specimens. Compared with phenotypic drug susceptibility testing (DST) results, the sensitivity and specificity of QMAP were 100 and 98% for rifampin resistance, 80 and 100% for isoniazid resistance, 44.4 and 100% for ethambutol resistance, 100 and 100% for fluoroquinolone resistance, and 100 and 100% for second-line injectable drug resistance, respectively. The sensitivity and specificity of MTBDR assays were 100 and 98% for rifampin resistance, 80 and 100% for isoniazid resistance, 44.4 and 98.1% for ethambutol resistance, 100 and 100% for fluoroquinolone resistance, and 100 and 100% for second-line injectable drug resistance, respectively. The sensitivity and specificity of QMAP were 85.0 and 100%, respectively, for the detection of MDR-TB and 100 and 100%, respectively, for XDR-TB. The sensitivity and specificity of MTBDR assays was consistent with those of QMAP. Our study showed that the QMAP assay has sensitivity and specificity equivalent to that of MTBDR assays in smear-positive sputum specimens. In combination with phenotypic DST, QMAP might be useful as a supplementary DST assay for rapid detection of MDR-/XDR-TB.