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).

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Despite the availability of genomic methods for determining the origin and divergence of domestic cattle in East Africa, particularly Ethiopia, knowledge regarding their genetic adaptability and divergence remain limited. To investigate signatures of selection and linkage disequilibrium Ethiopian cattle populations were genotyped with an 80K SNP array. European beef cattle breeds were also used for comparison purposes. Across Ethiopian cattle populations, the mean observed and expected heterozygosity were 0.403 and 0.400, respectively. Similarly, for European cattle, observed and expected heterozygosity were 0.25 and 0.26 respectively. PCA and NJ-tree revealed a separation of Ethiopian cattle breeds from European beef breeds. Similarly, NJ-tree grouped the study cattle according to their breed group with close clustering of Ethiopian cattle populations. The average r2 values were 0.22 ± 0.25, 0.23 ± 0.25, and 0.22 ± 0.25 in Boran, Begait, and Fogera, respectively. For Angus, Herford and Charolais it was 0.17 ± 0.28, 0.17 ± 0.28 and 0.18 ± 0.29, respectively. The top 1% FST values were considered to delimit genomic regions under positive selection. Some of the candidate genes are involved in biological processes and pathways linked to meat quality attributes. Furthermore, some of the candidate genes are associated with tropical adaptation to heat tolerance and resistance to disease. Significant SNP variation found in this study suggests that these markers could be useful for genetic research in Ethiopian cattle breeds.

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