Purchase this article with an account.
Raj P. Kandpal, Harsha Rajasimha, Matthew Brooks, Jacob Nellissery, Inhan Lee, Jun Wan, Jiang Qian, Timothy S. Kern, Anand Swaroop; Identification of Target mRNAs for Significantly Altered miRNAs in Diabetic Retinopathy. Invest. Ophthalmol. Vis. Sci. 2012;53(14):2409.
Download citation file:
© ARVO (1962-2015); The Authors (2016-present)
It is predicted that miRNAs may regulate the expression of approximately 2/3rd of the genes encoded in the human genome. Thus, miRNAs are likely to regulate important pathways involved in etiologies of various diseases. Our goal is to generate global miRNA profiles from retinas of nondiabetic and diabetic mice, and to identify target mRNAs for miRNAs that are significantly altered in diabetic mice.
Mice were made diabetic for eight months by injecting streptozotocin for 5 days, and retinas were harvested from nondiabetic and diabetic mice. Total RNA was isolated, and cDNA libraries corresponding to miRNAs were constructed. The libraries were sequenced using the Illumina platform. The alignment of miRNA sequence reads was carried out by GERALD analysis using ELAND algorithm. The data were normalized and differential expression was calculated as ratios of two samples. Alternatively, we applied SAM (significance analysis of microarrays) to RNA sequence reads to determine changes in the abundance of miRNAs. The significantly altered miRNAs were defined by false discovery rate of less than 50% and absolute fold change of greater than 1.5. Target mRNAs for significantly altered miRNAs were identified by using miBridge and mirFilter algorithms.
The analytical strategies resulted in two sets of miRNAs that were significantly altered in retinas from diabetic animals, and contained several common miRNAs. Notable among the common miRNAs were miR-31 and miR-184. While the target prediction programs such as PICTAR, MICROCOSM and MIRANDA yield a large number of mRNA targets for a specific miRNA, miBridge, a proprietary database that takes advantage of novel complementarities between mRNAs and miRNAs, predicts a set of mRNAs with a low likelihood of false positives. mirFilter, on the other hand, allows selection of miRNAs based on changes in the abundance of target mRNAs. Using miBridge and mirFilter, we have identified Dock5, Egr1, Islr, Slc24a6, Timp3 and Wnt7b as mRNA targets for significantly altered miRNAs such as miR-181b, miR-1948, miR-543, miR690 and miR-877.
Our results indicate the advantages of miBridge and mirFilter algorithms for identifying an accurate set of mRNAs targeted by specific miRNAs that are significantly altered in diabetic retinopathy. These analytical tools appear to decrease the numbers of false positive target mRNAs for specific miRNAs.
This PDF is available to Subscribers Only