FIT-103

Mild cognitive impairment (MCI) is an intermediate state between normal aging and dementia including Alzheimer's disease. Early detection of dementia, and MCI, is a crucial issue in terms of secondary prevention. Blood biomarker detection is a possible way for early detection of MCI. Although disease biomarkers are detected by, in general, using single molecular analysis such as t-test, another possible approach is based on interaction between molecules.

Differential correlation analysis, which detects difference on correlation of two variables in case/control study, was carried out to plasma microRNA (miRNA) expression profiles of 30 age- and race-matched controls and 23 Japanese MCI patients. The 20 pairs of miRNAs, which consist of 20 miRNAs, were selected as MCI markers. Two pairs of miRNAs (hsa-miR-191 and hsa-miR-101, and hsa-miR-103 and hsa-miR-222) out of 20 attained the highest area under the curve (AUC) value of 0.962 for MCI detection. Other two miRNA pairs that include hsa-miR-191 and hsa-miR-125b also attained high AUC value of ≥ 0.95. Pathway analysis was performed to the MCI markers for further understanding of biological implications. As a result, collapsed correlation on hsa-miR-191 and emerged correlation on hsa-miR-125b might have key role in MCI and dementia progression.

Differential correlation analysis, a bioinformatics tool to elucidate complicated and interdependent biological systems behind diseases, detects effective MCI markers that cannot be found by single molecule analysis such as t-test.

Relative quantification is the strategy of choice for processing RT-qPCR data in microRNAs (miRNAs) expression studies. Normalisation of relative quantification data is performed by using reference genes. In livestock species, such as pigs, the determination of reference miRNAs and the optimal number of them has not been widely studied. In this study, the stability of ten miRNAs (Ssc-let-7a, Ssc-miR-103, Ssc-miR-17-3p, Hsa-miR-25, Hsa-miR-93, Ssc-miR-106a, Ssc-miR-191, Ssc-miR-16, Ssc-miR-26a and Ssc-miR-17-5p) was investigated by RT-qPCR in different tissues (skeletal muscle, kidney, liver, ovary and uterus) and in different pig breeds (Iberian, Landrace, Large White, Meishan and Vietnamese) as variation factors. Stability values were calculated with geNorm and NormFinder algorithms obtaining high correlation between them (r(2) = 0.99). The analyses showed that tissue is an important variability factor in miRNAs expression stability whereas breed is not a determinant factor. All ten miRNAs analysed had good stability values and, therefore, can be used as reference miRNAs. When all tissues were considered, miR-93 was the most stable miRNA. Dividing data set by tissues, let-7a was the most stable in skeletal muscle and ovary, miR-17-5p in kidney, miR-26a in liver and miR-103 in uterus. Moreover, the optimal number of reference miRNAs to be used for proper normalisation data was determined. It is suggested the use of five reference miRNAs (miR-93, miR-25, miR-106a, miR-17-5p and miR-26a) in multi-tissue experimental designs and the use of three reference miRNAs as the optimal number in single tissues studies (let-7a, miR-17-5p and miR-25 in skeletal muscle; miR-17-5p, miR-93 and miR-26a in kidney, miR-26a, miR-103 and let-7a in liver, let-7a, miR-25 and miR-106a in ovary and miR-103, let-7a and miR-93 in uterus). Overall, this study provides valuable information about the porcine reference miRNAs that can be used in order to perform a proper normalisation when relative quantification by RT-qPCR studies is undertaken.