Open Access

Identification of key miRNAs and genes for mouse retinal development using a linear model

  • Authors:
    • Yishen Wang
    • Xiao Wang
    • Yukang Jiang
    • Ruyuan Liu
    • Di Cao
    • Jianying Pan
    • Yan Luo
  • View Affiliations

  • Published online on: April 21, 2020     https://doi.org/10.3892/mmr.2020.11082
  • Pages: 494-506
  • Copyright: © Wang et al. This is an open access article distributed under the terms of Creative Commons Attribution License.

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Abstract

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.

Introduction

MicroRNAs (miRNAs) are small, non-coding RNAs that are ~22 nucleotides in length. They serve important roles in regulating gene expression and are involved in a number of biological processes, including early development, vascular development (1,2), cell proliferation, differentiation and apoptosis (3). miRNAs are encoded and transcribed as primary transcripts, which are subsequently cleaved by 2 enzymes, Drosha and Dicer, to produce mature miRNAs. As part of the RNA-induced silencing complex, miRNAs regulate gene expression post-transcriptionally by binding to target mRNAs. Several genes are involved in the regulation of eye development and miRNAs have been shown to act upstream of these genes (4,5). A previous study revealed that miRNA dysregulation during mouse retinal development resulted in morphological and functional defects (6).

While several studies have investigated the roles of miRNA in cancer (7,8), few have investigated their role in development. In addition, a limited number of studies have examined the expression pattern of miRNAs in the mammalian retina. Karali et al (9) analyzed the spatiotemporal localization of several miRNAs in mouse embryonic and postnatal retinas. Hackler et al (10) used microarrays to characterize the miRNA expression patterns in the mouse retina from embryonic day 15 to adulthood. However, the aforementioned studies focused on the expression patterns of specific miRNAs or obtained a global expression profile for a limited number of time-points.

In mice, the period between postnatal day 0 (P0) and postnatal day 21 (P21) is important for to retinal development, particularly neuron development and angiogenesis (11,12). The aim of the present study was to gain a more detailed understanding of the postnatal miRNA expression pattern over several time-points and to identify the miRNAs required for mouse retinal development, using a linear model. The combination of bioinformatics analysis and microarray data allowed for the comprehensive analysis of the miRNA expression pattern. Initially, differentially expressed miRNAs (DE-miRNAs) associated with neuron development and angiogenesis were identified. The target genes of these miRNAs were subsequently predicted and pathway enrichment analysis was performed using Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG). Reverse-transcription quantitative polymerase chain reaction (RT-qPCR) was performed to validate the identified miRNAs. The present study identified critical miRNAs associated with mouse retinal development and investigated their biological functions and molecular interactions.

Materials and methods

Animals

C57BL/6J pregnant mice aged 6–8 weeks (32–40 g) were purchased from the Ophthalmic Animal Laboratory, Zhongshan Ophthalmic Center, Sun Yat-Sen University and housed in a pathogen-free environment with an average temperature of 20°C, 55% humidity and 12-h light/dark cycle with free access to food and water. The procedures for the care and use of the animals were approved by The Ethics Committee of the Sun Yat-Sen University Zhongshan Ophthalmic Center (ethical approval no. 2017-069). Institutional and governmental regulations concerning the ethical use of animals were followed. Animal health and behavior were monitored daily. Retinas from mice at P0, P1, P3, P4, P7, P8, P10, P14 and P21 were harvested and pooled. A total of 46 mice were used for the experiment and sacrifice was performed by cervical dislocation.

RNA extraction

Each sample consisted of 8–10 mixed retina tissues. Total RNA was isolated using TRIzol® (Invitrogen; Thermo Fisher Scientific, Inc.) and purified with the RNeasy Mini kit (Exiqon; Qiagen GmbH), according to the manufacturer's protocol. RNA quality and quantity were measured using a spectrophotometer (ND-1000; NanoDrop Technologies; Thermo Fisher Scientific, Inc.). To determine the RNA integrity, the isolated RNA with loading buffer was loaded and electrophoresed on a 1% Tris-Acetate-EDTA agarose gel.

miRNA labeling and array hybridization

The miRCURY™ Hy3™/Hy5™ Power labeling kit (Exiqon; Qiagen GmbH) was used for miRNA labeling, according to the manufacturer's protocol. The Hy3™-labeled samples were subsequently hybridized on the miRCURYTM Locked Nucleic Acid Array (version 18.0; Exiqon; Qiagen GmbH), according to the manufacturer's protocol. The resulting slides were scanned using the Axon GenePix 4000B microarray scanner (Molecular Devices, LLC).

Identification of key DE-miRNAs

The expression of each miRNA on microarrays at the developmental time-points of interest was analyzed using linear regression models. For each miRNA, time-points and their corresponding expression values were regarded as independent variable × and dependent variable y, respectively, as follows:

yi=β0+β1xi+εi

Where β0, β1 and εi represented the intercept, slope and error term, respectively. The aim was to find the estimated value ^β0 and ^β1 for the intercept and slope that would provide the best fit for all the time-points for each miRNA. In other words, the goal was to find the ^β0 and ^β1 values that minimized the sum of the squared error term (Q):

Q(β0,β1)=∑(yi-β0-β1xi)2 Q(βˆ0,βˆ1)=minβ0,β1 Q(β0,β1)

Using the least-squares approach, the intercept and slope of the fitted regression line of each miRNA were calculated. The greater the absolute value of the slope, the greater the change in miRNA expression during mouse retinal development. Since data from microarrays often have high variability, the linear model may not be appropriate for miRNAs with a high absolute value of the slope. Therefore, in order to reduce the false positive rate, the coefficient of determination (R2) of each regression line was calculated to evaluate the proportion of the variance:

R2=(∑(xi-x¯)(yi-y¯))2∑(xi-x¯)2∑(yi-y¯)2

Where x¯=1n∑xi and y¯=1n∑yi

The closer R2 is to 1, the better the estimated regression line is at explaining the relationship between × and y. miRNAs with a R2>0.6 were considered reliable. This implied that ≤60% of the variability between the 2 variables was accounted for by a linear regression model (Fig. 1). miRNAs with absolute values of the slopes in the top 1% and R2>0.6 were considered to be the most essential miRNAs during mouse retinal development and were defined as DE-miRNAs.

Identifying the target genes of the DE-miRNAs

TargetScan (version 7.2) (13) and miRDB (version 6.0) (14), 2 miRNA target gene prediction databases that are continuously updated, were used to identify the target genes of the DE-miRNAs using the default parameters. In order to increase the accuracy of the results obtained, any gene that was predicted by both databases for a specific miRNA was considered as a target gene for that miRNA.

GO and KEGG pathway enrichment analyses

A GO term describes the functions of specific genes in terms of their associated biological processes, cellular components and molecular functions. GO enrichment analysis is used to identify which GO terms are enriched in a given gene set (15,16). Based on the knowledge of molecular interactions, reactions and gene relation networks, potential target genes of interest were annotated in a collection of manually drawn pathway maps. The goal of KEGG pathway enrichment analysis is to determine the pathways in which a certain set of genes are over-represented (17). After the target genes were identified, GO and KEGG pathway enrichment analyses were performed using the online tool DAVID (version 6.8) (18,19).

RT-qPCR

RT-qPCR analysis of the final filtered DE-miRNAs was performed. Total RNA from retinas was isolated and RNA quality and quantity were measured as described earlier. The RNA was reverse transcribed with a PrimeScript RT reagent kit (Takara Bio, Inc.) using specific primers for each miRNA and U6 was used as the reference (Bulge-Loop miRNA qPCR Primers; RiboBio, Co., Ltd.; the sequences are a commercial secret). Real-time PCR was subsequently performed conducted on the resulting cDNA template using TB Green™ Premix Ex Taq™ II (Takara Bio, Inc.) on a StepOnePlus™ Real-Time PCR System (Applied Biosystems; Thermo Fisher Scientific, Inc.) using the following protocol: 95°C for 30 sec; 40 cycles of 95°C for 10 sec, 60°C for 20 sec, and 70°C for 1 sec. miRNA expression levels were quantified using the 2−ΔΔCq method and normalized to U6 levels (20). Efficiencies of qPCR reactions were also calculated by standard curve method (21).

Statistical analyses: The method of identifying DE-miRNAs using linear regression models has already been described

miRNA expression levels and efficiencies were calculated from the average of three independent RT-qPCR experiments. Linear regression models were also used to fit the RT-qPCR data. In enrichment of GO and KEGG pathway analyses, P<0.05 was considered to indicate a statistically significant difference. All the data processing and figure generation were performed using Python. (Python Software Foundation; Python Language Reference; version 3.6; http://www.python.org).

Results

Identification of key DE-miRNAs in mouse retinal development

The mean values of the concentration and OD 260/280 for the RNA samples were 1,156 ng/µl and a ratio of 2.02, respectively, indicating that the RNA samples were acceptable for microarray hybridization. The microarray data discussed in the present study have been deposited in the Gene Expression Omnibus (GEO) and are accessible through GEO Series accession number GSE115581 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE115581). A total of 1,114 mouse miRNAs were identified and analyzed from the microarrays. The slope values for the majority of the miRNAs were equal or nearly equal to 0, indicating that the expression of these miRNAs did not significantly change during mouse retinal development (Fig. 2).

A change in slope value can be seen at the far right of the horizontal axis (Fig. 2)

Using a top 1% cut-off, miRNAs with no significant trend were excluded. The R2 of each identified miRNA was evaluated and miRNAs were considered to be DE-miRNAs if the absolute value of the slope was in the top 1% and R2>0.6. Using this threshold, a total of 14 miRNAs were identified and included miR-709, miR-1839-3p, miR-9-5p, miR-130a-3p, miR-181a-5p, miR-181b-5p, miR-92a-3p, miR-20a-5p, miR-93-5p, miR-9-3p, miR-124-3p, miR-378a-3p, miR-129-1-3p and miR-125a-5p (Figs. 3 and 4).

Enrichment analyses of GO and KEGG pathway

A target gene set was retrieved from TargetScan and miRDB for each DE-miRNA. After target genes had been retrieved, GO and KEGG enrichment analyses were performed through the DAVID online tool. With an adjusted P<0.05, each gene set generated both a GO and a KEGG enrichment result. For biological processes, GO analysis results revealed that target genes of DE-miRNAs were enriched in cellular metabolic process-related terms (Table I).

Table I.

Gene ontology analysis for the biological process of predicted target genes associated with retina development.

Table I.

Gene ontology analysis for the biological process of predicted target genes associated with retina development.

miRNATermGene countAdjusted P-valuea
miR-9-5p GO:0043161~proteasome-mediated ubiquitin-dependent protein catabolic process21 8.37×10−3
GO:0036211~protein modification process96 8.98×10−3
GO:0006464~cellular protein modification process96 8.98×10−3
miR-130a-3pGO:0036211~protein modification process107 2.31×10−7
GO:0006464~cellular protein modification process107 2.31×10−7
GO:0010556~regulation of macromolecule biosynthetic process112 6.45×10−6
miR-181a/b-5p ‘GO:0006351~transcription, DNA-templated’129 5.97×10−9
GO:0006357~regulation of transcription from RNA polymerase II promoter88 3.98×10−9
GO:0051252~regulation of RNA metabolic process140 6.23×10−9
miR-93-5p GO:0010556~regulation of macromolecule biosynthetic process169 1.87×10−8
GO:0006357~regulation of transcription from RNA polymerase II promoter96 2.90×10−8
‘GO:0006351~transcription, DNA-templated’141 6.37×10−8
miR-92a-3pGO:0006464~cellular protein modification process97 4.68×10−7
GO:0036211~protein modification process97 4.68×10−7
GO:0006796~phosphate-containing compound metabolic process78 6.62×10−8
miR-20a-5p GO:0010556~regulation of macromolecule biosynthetic process169 6.83×10-12
GO:0006357~regulation of transcription from RNA polymerase II promoter95 2.03×10−11
‘GO:0006351~transcription, DNA-templated’140 4.40×10−11
miR-9-3pGO:0031325~positive regulation of cellular metabolic process87 3.67×10−3
GO:0010604~positive regulation of macromolecule metabolic process86 5.11×10−3
GO:0009893~positive regulation of metabolic process90 5.48×10−3
miR-124 GO:0006357~regulation of transcription from RNA polymerase II promoter113 2.51×10−8
GO:0007155~cell adhesion103 4.41×10−8
GO:0022610~biological adhesion103 4.57×10−8
miR-709GO:0050839~cell adhesion molecule binding44 1.39×10−8
GO:0098632~protein binding involved in cell-cell adhesion33 3.48×10−8
GO:0098631~protein binding involved in cell adhesion33 5.24×10−8

a Benjamini-Hochberg adjusted P-value. miRNA/miR, microRNA; GO, Gene Ontology.

KEGG pathway enrichment analysis indicated that target genes of DE-miRNAs were significantly enriched in development-related pathways, including PI3K/AKT/mTOR signaling, class O of forkhead box transcription factors (FOXO) signaling, mitogen-activated protein kinase (MAPK) signaling, neurotrophin signaling, transforming growth factor (TGF)-β signaling, focal adhesion and axon guidance pathways (Table II). miR-1839-3p, miR-378a-3p, miR-129-1-3p and miR-125a-5p showed no significant enrichment associated with retinal development in KEGG analysis.

Table II.

KEGG pathway analysis of predicted target genes associated with retina development.

Table II.

KEGG pathway analysis of predicted target genes associated with retina development.

miRNATermGene countAdjusted P-valueaGenes
miR-9-5pNeurotrophin signaling pathway10 3.84×10−2MAP3K3, MAP3K1, GSK3B, SORT1, NFKB1, SHC1, MAPKAPK2, SHC2, MAP2K7, ARHGDIA
MAPK signaling pathway14 3.03×10−2MEF2C, RPS6KA4, MAP3K3, MAP3K1, TGFBR1, TGFBR2, PDGFRB, CACNB2, NFKB1, MAPKAPK2, MAP2K7, NFATC3, STK3, ATF2
miR-130a-3pTGF-β signaling pathway9 3.92×10−3INHBB, ACVR2A, MAPK1, TNF, TGFBR2, SMAD5, BMPR2, TGFB2, ACVR1
mTOR signaling pathway7 1.65×10−2MAPK1, TNF, TSC1, PIK3CB, ULK2, PRKAA1, PTEN
FOXO signaling pathway9 4.32×10−2MAPK1, S1PR1, PIK3CB, TGFBR2, PRKAA1, PTEN, GADD45A, BCL2L11, TGFB2
miR-181a/b-5pFOXO signaling pathway12 5.37×10−3CCNB1, MAPK1, S1PR1, MAP2K1, SOS1, TGFBR1, NLK, GRM1, PTEN, SIRT1, AKT3, BCL2L11
mTOR signaling pathway7 2.30×10−2RPS6KA6, MAPK1, TNF, RPS6KB1, PTEN, AKT3, DDIT4
TGF-β signaling pathway8 2.88×10−2MAPK1, TNF, E2F5, SMAD7, TGFBR1, RPS6KB1, TGIF2, ACVR1C
miR-93-5pAxon guidance13 1.49×10−2SEMA5A, EPHA5, PAK7, EPHA4, EPHA7, LIMK1, CFL2, SEMA7A, DPYSL5, PPP3R1, SEMA4B, DPYSL2, SRGAP1
miR-92a-3pPI3K-Akt signaling pathway19 1.67×10−4IBSP, PHLPP2, PIK3CB, PTEN, COL5A1, BCL2L11, CCNE2, TSC1, ITGA6, CHRM2, ITGA5, ITGAV, COL27A1, ITGA8, CREB3L2, COL1A2, PIK3CA, FASL, PRKAA2
Focal adhesion14 7.07×10−4IBSP, PIK3CB, PPP1R12C, PTEN, COL5A1, ITGA6, ITGA5, ITGAV, ITGA8, COL27A1, COL1A2, PIK3CA, RAP1B, MAPK8
miR-20a-5pAxon guidance13 1.57×10−2SEMA5A, EPHA5, PAK7, EPHA4, EPHA7, LIMK1, CFL2, SEMA7A, DPYSL5, PPP3R1, SEMA4B, DPYSL2, SRGAP1
miR-9-3pAxon guidance10 5.32×10−2LRRC4, DCC, GNAI2, PLXNA2, GNAI1, CFL2, SEMA3A, SEMA4D, ITGB1, RASA1
miR-124Axon guidance14 2.95×10−2PLXNA3, NRP1, GNAI3, ROCK1, GNAI1, PLXNB2, SLIT1, ITGB1, EPHA2, SEMA6A, SEMA6D, UNC5D, NFATC2, SRGAP1
Focal adhesion18 2.84×10−2CAV1, TLN1, COL4A1, ROCK1, ACTN4, PGF, GRB2, ITGA3, ITGB1, FLNB, CRKL, CCND2, SOS1, SOS2, ITGA7, SHC1, LAMC1, RAPGEF1
miR-709Axon guidance15 1.88×10−4PLXNA4, EFNB3, EFNA1, LIMK1, PLXNA2, EFNB1, PAK6, NCK2, EPHA4, MAPK1, SEMA4G, SEMA3F, ROBO2, PAK1, SRGAP2

a Benjamini-Hochberg adjusted P-value. miRNA/miR, microRNA.

PI3K, AKT, PTEN, mitogen-activated protein kinase (MAPK)1, Son of Sevenless (SOS), sphingosine-1-phosphate receptor 1 (S1PR1), BCL2L11, TGF-β receptor (TGFBR)1/2 and integrin α (ITGA)/ITGB are important components of the significantly enriched pathways. Each gene was predicted for several DE-miRNAs in the present study. PI3K/AKT were predicted as target genes of miR-130a-3p and miR-9-3p. SOS, S1PR1 and BCL2L11 were predicted as the target of miR-124. PTEN and MAPK1 were predicted as the target of miR-181a/b-5p, miR-92a-3p, miR-709 and miR-130a-3p. TGFBR1/2 were predicted as target genes of miR-181a/b-5p, miR-130a-5p, miR-93-5p and miR-9-5p and ITGA/ITGB predicted for miR-9-3p, miR-124 and miR-92a-3p.

RT-qPCR validation

RT-qPCR was performed to validate the microarray results. The efficiencies calculated from the standard curves were all between 0.9 and 1.1 (Fig. 5), indicating that it was appropriate to use 2 as the base number for the 2−ΔΔCq method. The melt curves of the 10 DE-miRNAs exhibited only one peak at a similar temperature, indicating that the amplification was specific. Apart from miR-181a-5p and miR-181b-5p, the 8 remaining miRNAs exhibited a similar trend to the microarray results (Fig. 6).

Discussion

miRNAs regulate the expression of target genes and are involved in a number of cellular processes, including the cell cycle, self-renewal and differentiation. Tissue-specific and spatiotemporal changes in miRNA expression suggest that miRNAs are involved in the regulation of differentiation, development and maturation during mouse retinal development. Therefore, the present study filtered several key miRNAs according to the temporal characteristics of mouse retinal development.

In the field of genomics, the aim is to find the ‘trend’ or ‘difference’ among data sets. Previous studies often used an independent t-test or one-way analysis of variance (ANOVA) to compare 2 or more data sets. It is appropriate to use a t-test and ANOVA to process cross-sectional data when comparisons among experimental and control groups are performed at the same time and statistical results suggest an association between the data sets. The expression data of each miRNA during retinal development, however, were taken at successive time-points. Therefore, a t-test or ANOVA would not reflect the overall expression trend involving all time-points. For example, in a study conducted by Hackler et al (10), the expression change for each miRNA was defined as the difference between the maximum and minimum values among five time-points using a t-test. Therefore, data on the three remaining time-points were lost. In order to overcome the difficulty of identifying the ‘trend’ in the present study, linear regression models were used to analyze the expression of each miRNA at the developmental times. Slopes with a greater absolute value indicated a greater change in miRNA expression.

As the microarray data had high variability, the R2 was used to evaluate the variance in miRNA expression. The closer the value of R2 was to 1, the better the linear regression fit the expression data. The miRNAs with high slope and R2 values tended to have biological significance and were identified as DE-miRNAs. Subsequently, DE-miRNAs were screened for functional analysis.

Retinal vascular and neuron development are of great importance in the functionalization of the retina. Functionalization is a complex process with rapid changes in infant mice. Previous studies have reported that the blood plexus was almost completely absent in mice at P1 but became dense at P3. By P5, arterioles and venules had formed (11,22). Research has demonstrated that the period between P0 and P21 is critical for retinal neuron development and angiogenesis in mice (11,12). To obtain a more detailed description of the miRNA expression profile in the mouse retina in the present study, a total of 9 developmental time-points between P0 and P21 were used, with increased sampling density at the earlier time-points.

The linear model used allowed all the developmental time-points of interest to be considered. The miRNAs with absolute values of the slope in the top 1% were selected for further analysis. Along with the evaluation of R2, GO and KEGG analyses and RT-qPCR validation, 8 key DE-miRNAs were identified (Fig. 7). These were: miR-9-5p, miR-130a-3p, miR-92a-3p, miR-20a-5p, miR-93-5p, miR-9-3p, miR-709 and miR-124.

miR-124 and miR-9-5p/3p have been reported to be enriched in the mammalian retina (9,23,24). The present study revealed that miR-124a-3p exhibited an increasing trend in both the microarray and RT-qPCR. Previous studies have revealed that miR-124 promoted the human brain tumor growth and angiogenesis (25,26). Additionally, miR-124 is required for normal retinal neuronal development in Xenopus and rats (27,28).

A number of studies have considered miR-9-5p and miR-9-3p as a single combined entity (29,30). miR-9/9*. Yoo et al (29) reported that miR-9/9* and miR-124 induce the conversion of human fibroblasts into neurons. Other studies revealed that the 5′ strand and 3′ strand of miR-9 showed different effects. Sim et al (31) found that miR-9-3p, but not miR-9-5p, served an important role in hippocampal long-term potentiation in the adult mouse. miR-9-3p and miR-9-5p were reported to serve key roles in neurogenesis (32). In the present study, miR-9-3p and miR-9-5p were analyzed separately and each showed significant expression trends with high R2 values.

miR-181a-5p and miR-181b-5p are highly homologous and share similar predicted target genes. Therefore, in the present study, these 2 miRNAs were considered as miR-181a/b-5p. KEGG analysis revealed that miR-181a/b-5p was enriched in TGF-β and MAPK signaling pathways. Carrella et al (33) demonstrated that miR-181a/b is a key factor in the specification and growth of retinal axons in medaka fish and is involved in MAPK/ERK and TGF-β signaling. In the microarray data in the present study, they had similar expression patterns with a correlation coefficient >0.9 and were undetectable after P10. However, RT-qPCR in the present study revealed that miR-181a/b-5p exhibited minimal changes during retinal development. A previous study used RNA in situ hybridization to demonstrate that miR-181a was strongly expressed in both postnatal and adult mouse retina (34).

miR-92a-3p and miR-20a-5p are members of the mir-17–92 cluster, while miR-93-5p is a paralog. In the present study, these three miRNAs shared similar expression pattern. Previous studies have revealed associations between the aforementioned three miRNAs and angiogenesis (3537). Blood vessel growth in a mouse model of limb ischemia and myocardial infarction is enhanced following inhibition of miR-92a-3p (35). miR-20a is shown to target MKK3 and to regulate the migration and angiogenesis of human endothelial cells in vitro (36). Fang et al (37) demonstrated that miR-93 promotes angiogenesis in a human breast carcinoma cell line by targeting LATS2. However, to the best of the authors' knowledge, in vivo studies investigating the effect of these three miRNAs on retinal development under normal conditions have not previously been performed.

Functional studies of miR-709, miR-125a-5p, miR-129-1-3p, miR-130a-3p and miR-378a-3p on retinal development are lacking. The present authors are not aware of any studies identifying that miR-709 is associated with angiogenesis or neurogenesis. It has been reported that miR-125 was linked to neurogenesis by promoting cell differentiation in mice and humans (3840). La Torre et al (41) demonstrated that miR-125 is one of the key regulators of retinal progenitors and that overexpression of miR-125 increases progenitor progression in Dicer-CKO retinas in mice. miR-129 has been revealed to be highly expressed at the early stages of development in the Xenopus retina (42). Furthermore, miR-129-1 suppresses angiogenesis in human umbilical vein endothelial cells (43). miR-130a regulates neurodevelopment by inhibiting neurite outgrowth in rat brains (44). Previous studies have suggested that miR-378a-3p may promote tumor angiogenesis (45,46). However, apart from miR-709, KEGG analysis in the present study did not identify enrichment of miR-378a-3p, miR-129-1-3p or miR-125a-5p.

The enriched pathways identified in the present study are all associated with neurodevelopment or angiogenesis. Previous studies have reported that the PI3K/AKT/mTOR pathway can promote the growth of neural stem cells in mammal (47,48). In vivo, the PI3K/AKT/mTOR pathway has an important role in mammalian brain development, under both physiological and pathological conditions (4951). The MAPK signaling pathway is highly conserved and is associated with various cellular functions (52). It has been reported that, together with the PI3K/AKT pathway, the MAPK signaling pathway exhibits neuroprotective effects in neurons in rat retina (53) and increases their proliferation and transdifferentiation (54). The FOXO family of transcription factions serves essential roles in regulating the expression of genes. FOXO shows a temporal expression pattern in the developing zebra fish retina (55) and may promote angiogenesis by increasing the metabolism and proliferation of endothelial cells (56). Neurotrophin signaling and the axon guidance pathway are involved in the formation of the neuronal network. In the mouse retina, these 2 pathways regulate survival, differentiation and cytoskeletal organization of neural cells (57) and are also associated with other pathways. The axon guidance factor netrin-4 increases angiogenesis and the neurotrophin brain-derived neurotrophic factor promotes the maturation of neurons in mammals, both by increasing activation of MAPK (58,59).

TGF-β signaling pathway regulates several cellular processes, including proliferation, apoptosis, differentiation and migration. Studies have demonstrated that this pathway regulates the survival and growth of neurons and is associated with angiogenesis in both physiological and pathological conditions in the mammalian retina (33,6063). Focal adhesions serve important roles in biological processes. It has been reported that focal adhesions are associated with angiogenesis and neuron growth in humans and mice (64,65). Researchers have found that focal adhesion-associated proteins and signaling were necessary for retinal development in Xenopus and Drosophila (66,67). The overexpression of focal adhesion kinase, a focal adhesion-associated protein kinase, contributed to retinal angiogenesis in oxygen-induced retinopathy mice (68).

PI3K, AKT, PTEN, MAPK1, SOS, S1PR1, BCL2L11, TGFBR1/2 and ITGA/ITGAB serve essential roles in the enriched pathways identified in the present study and interact with the identified DE-miRNAs. PI3K and AKT are key factors in the majority of the enriched pathways, including PI3K/AKT/mTOR, MAPK, FOXO and neurotrophin signaling, as well as the focal adhesion pathway. PTEN, MAPK1 and SOS are important secondary effectors that participate in a number of signaling pathways (47,69). A previous study reported that S1PR1 is associated with vascular growth and development in mouse (70). Furthermore, Chae et al (71) demonstrated that S1PR1 promoted mouse limb morphogenesis. BCL2L11 acts as an apoptotic activator (72). S1PR1 and BCL2L11 are both downstream effectors, while TGFBR1/2 and ITGA/ITGB are upstream regulators that coordinate a series of downstream effects.

In conclusion, the present study identified several key DE-miRNAs using a linear model. Furthermore, the target genes and pathways that were identified may serve crucial roles in mouse retinal development. Following RT-qPCR validation, eight miRNAs were identified associated with retinal development. These were: miR-9-5p, miR-130a-3p, miR-92a-3p, miR-20a-5p, miR-93-5p, miR-9-3p, miR-709 and miR-124. The results obtained in the present study may provide the groundwork for further experiments.

Acknowledgements

Not applicable.

Funding

The present study was supported by grants from the National Natural Science Foundation of China (grant no. 81770971) and the Natural Science Foundation of Guangdong Province (grant no. 2017A030313787) to YL.

Availability of data and materials

The datasets generated and analyzed during the current study are available in the GEO repository (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE115581).

Authors' contributions

YW performed the bioinformatics analysis and contributed to writing manuscript. XW, RL and DC performed the microarray and RT-qPCR experiments. YJ and JP assisted with the bioinformatics analysis. YL designed the study. All authors read and approved the final manuscript.

Ethics approval and consent to participate

The procedures for care and use of animals were approved by the Ethics Committee of the Sun Yat-Sen University, Zhongshan Ophthalmic Center (ethical approval no. 2017-069).

Patient consent for publication

Not applicable.

Competing interests

The authors declare that they have no competing interests.

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APA
Wang, Y., Wang, X., Jiang, Y., Liu, R., Cao, D., Pan, J., & Luo, Y. (2020). Identification of key miRNAs and genes for mouse retinal development using a linear model. Molecular Medicine Reports, 22, 494-506. https://doi.org/10.3892/mmr.2020.11082
MLA
Wang, Y., Wang, X., Jiang, Y., Liu, R., Cao, D., Pan, J., Luo, Y."Identification of key miRNAs and genes for mouse retinal development using a linear model". Molecular Medicine Reports 22.1 (2020): 494-506.
Chicago
Wang, Y., Wang, X., Jiang, Y., Liu, R., Cao, D., Pan, J., Luo, Y."Identification of key miRNAs and genes for mouse retinal development using a linear model". Molecular Medicine Reports 22, no. 1 (2020): 494-506. https://doi.org/10.3892/mmr.2020.11082