Micrornas (mirnas) are upstream regulators of gene expression and are involved in several biological processes. The purpose of the present study was to obtain a detailed spatiotemporal miRNA expression profile in mouse retina, to identify one or more mirnas that are key to mouse retinal development and to investigate the roles and mechanisms of these mirnas. The mirna expression pattern of the developing mouse retina was acquired from locked nucleic acid microarrays. data were processed to identify differentially expressed mirnas (de-mirnas) using the linear model in Python 3.6. Following bioinformatics analysis and reverse transcription-quantitative polymerase chain reaction validation, 8 mirnas (mir-9-5p, mir-130a-3p, mir-92a-3p, mir-20a-5p, mir-93-5p, mir-9-3p, mir-709 and miR-124) were identified as key DE-miRNAs with low variability during mouse retinal development. Gene ontology analysis revealed that the target genes of the de-mirnas were enriched in cellular metabolic processes. Kyoto encyclopedia of Genes and Genomes analysis demonstrated that the target genes of the de-mirnas were significantly enriched in Pi3K/aKT/mTor, class o of forkhead box transcription factors, mitogen-activated protein kinase (MaPK), neurotrophin and transforming growth factor (TGF)-β signaling, as well as focal adhesion and the axon guidance pathway. Pi3K, aKT, PTen, MaPK1, Son of Sevenless, sphingosine-1-phosphate receptor 1, Bcl-2l11, TGF-β receptor type 1/2 and integrin α (iTGa)/iTGaB, which are key components of the aforementioned pathways and were revealed to be target genes of several of the de-mirnas. The present study used a linear model to identify several de-mirnas, as well as their target genes and associated pathways, which may serve crucial roles in mouse retinal development. Therefore, the results obtained in the present study may provide the groundwork for further experiments.

Purpose: To demonstrate an interaction-based method for the refinement of Gene Set Enrichment Analysis (GSEA) results.Method: Intravitreal injection of miR-124-3p antagomir was used to knockdown the expression of miR-124-3p in mouse retina at postnatal day 3 (P3). Whole retinal RNA was extracted for mRNA transcriptome sequencing at P9. After preprocessing the dataset, GSEA was performed, and the leading-edge subsets were obtained. The Apriori algorithm was used to identify the frequent genes or gene sets from the union of the leading-edge subsets. A new statistic d was introduced to evaluate the frequent genes or gene sets. Reverse transcription quantitative PCR (RT-qPCR) was performed to validate the expression trend of candidate genes after the knockdown of miR-124-3p.Results: A total of 115,140 assembled transcript sequences were obtained from the clean data. With GSEA, the NOD-like receptor signaling pathway, C-type-like lectin receptor signaling pathway, phagosome, necroptosis, JAK-STAT signaling pathway, Toll-like receptor signaling pathway, leukocyte transendothelial migration, chemokine signaling pathway, NF-kappa B signaling pathway and RIG-I-like signaling pathway were identified as the top 10 enriched pathways, and their leading-edge subsets were obtained. After being refined by the Apriori algorithm and sorted by the value of the modulus of d, Prkcd, Irf9, Stat3, Cxcl12, Stat1, Stat2, Isg15, Eif2ak2, Il6st, Pdgfra, Socs4 and Csf2ra had the significant number of interactions and the greatest value of d to downstream genes among all frequent transactions. Results of RT-qPCR validation for the expression of candidate genes after the knockdown of miR-124-3p showed a similar trend to the RNA-Seq results.Conclusion: This study indicated that using the Apriori algorithm and defining the statistic d was a novel way to refine the GSEA results. We hope to convey the intricacies from the computational results to the low-throughput experiments, and to plan experimental investigations specifically.