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MicroRNA profiling in bovine serum according to the stage of Mycobacterium avium subsp. paratuberculosis infection.

Sung-Woon ChoiSuji KimHong-Tae ParkHyun-Eui ParkJeong-Soo ChoiHan-Sang Yoo
Published in: PloS one (2021)
Mycobacterium avium subsp. paratuberculosis (MAP) is the causative agent of Johne's disease (JD), and it causes diarrhea and weakness in cattle. During a long subclinical stage, infected animals without clinical signs shed pathogens through feces. For this reason, the diagnosis of JD during the subclinical stage is very important. Circulating miRNAs are attracting attention as useful biomarkers in various veterinary diseases because of their expression changes depending on the state of the disease. Based on current knowledge, circulating miRNAs extracted from bovine serum were used to develop a diagnostic tool for JD. In this study, the animals were divided into 4 groups according to fecal shedding, the presence of antibodies, and clinical signs. Gene expression was analyzed by performing miRNA sequencing for each group, and it was identified that the miRNA expression changed more as the MAP infection progressed. The eight miRNAs that were differentially expressed in all infected groups were selected as biomarker candidates based on their significant differences compared to the control group. These biomarker candidates were validated by qRT-PCR. Considering the sequencing data, two upregulated miRNAs and two downregulated miRNAs showed the same trend in the validation results. Network analysis was also conducted and the results showed that mRNAs (IL-10, TGF-β1) associated with regulatory T cells were predicted to be activated in the subclinical stage. Taken together, our data suggest that two miRNAs (bta-miR-374b, bta-miR-2887) may play major roles in the immune response to MAP infection during the subclinical stage.
Keyphrases
  • regulatory t cells
  • gene expression
  • poor prognosis
  • network analysis
  • single cell
  • long non coding rna
  • mycobacterium tuberculosis
  • dna methylation
  • healthcare
  • big data
  • machine learning