Analyses of the Competing Endogenous RNA Network in Rats with Middle Cerebral Artery Occlusion Based on lncRNA, miRNA, and mRNA Expression

Summary

This study used bioinformatics analyses and experimental validation to systematically investigate the regulatory roles and underlying mechanisms of long noncoding RNAs (lncRNAs) functioning as competitive endogenous RNAs (ceRNAs) in ischemic stroke.

Abstract

This study aims to investigate the regulatory roles and underlying mechanisms of lncRNAs acting as ceRNAs in ischemic stroke. Based on the ceRNA hypothesis, lncRNAs, miRNAs, and mRNAs were identified as components of a regulatory network involved in stroke. Key lncRNAs from the resulting subnetwork were selected for detailed analysis. Functional enrichment analysis using Gene Ontology and pathway mapping through the Kyoto Encyclopedia of Genes and Genomes revealed critical interactions within the lncRNA-associated ceRNA network. Key pathways, including calcium signaling, gap junction signaling, and neuroactive ligand receptor interaction, were further validated using Western blot analysis. The constructed ceRNA network comprised 334 lncRNAs, miRNAs, and mRNAs, with functional enrichment analysis predicting their biological roles. Three lncRNAs with high degree centrality were selected to construct a representative ceRNA subnetwork. Western blot analysis revealed that, compared to the sham group, the expression levels of key proteins -- CaMKII, calmodulin, CX36, PKC, CX43, GRIA3, GABRA6, and NPY1R were significantly downregulated in the model group, while the expression of CaN was significantly upregulated (P < 0.05). These findings suggest that lncRNAs are significantly involved in stroke pathogenesis. In conclusion, lncRNAs acting as ceRNAs serve critical regulatory roles in the pathogenesis of ischemic stroke.

Introduction

Ischemic stroke, a prevalent neurological disorder, leads to permanent brain damage, long-term disability, or death^1,2,3,4. MicroRNAs (miRNAs or miR), long noncoding RNAs (lncRNAs), and messenger RNAs (mRNA) form RNA-mediated regulatory networks^5,6,7,8. These molecules regulate cellular functions through various complex mechanisms^9,10are increasingly recognized as contributors to the pathophysiology of ischemic stroke. However, despite their growing association with the condition, the precise roles of these noncoding RNAs remain unclear. Further investigation of novel noncoding RNAs is essential for clarifying the molecular mechanisms underlying ischemic stroke.

LncRNAs act as key regulators of gene expression during the initiation and progression of various pathological conditions. Serving as competing endogenous RNAs (ceRNAs), lncRNAs bind to miRNAs to exert specific regulatory effects¹¹. Wei et al. report that lncRNA AK038897 acts as a ceRNA by targeting miR-26a-5p, thereby modulating death-associated protein kinase 1 (DAPK1) to exacerbate cerebral ischemia-reperfusion injury¹². Studies show that long noncoding RNA SNHG1 (lncRNA SNHG1) functions as a ceRNA to regulate cerebrovascular diseases by modulating the HIF-1α/VEGF signaling pathway through interaction with miR-18a¹³. Additional studies show that the long noncoding RNA maternally expressed gene 3 (lncRNA MEG3) modulates neuronal apoptosis through the miR-21/PDCD4 signaling cascade¹⁴. While numerous lncRNAs, miRNAs, and mRNAs have been identified through high-throughput sequencing or microarray analysis, the functions of these molecules in stroke remain unclear, and the systematic establishment of RNA-mediated regulatory networks is urgently needed.

Therefore, this study aims to construct a comprehensive lncRNA-miRNA-mRNA network based on the ceRNA hypothesis using previously collected data, and to analyze selected ceRNA subnetworks through Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment to better understand lncRNA functions. The results could reveal that several lncRNAs function as ceRNAs, potentially regulating specific miRNAs and their target mRNAs. GO enrichment could highlight key biological processes, such as cellular development, signal transduction, and cell cycle regulation, while pathway enrichment could reveal their involvement in stroke-related pathways, immune responses, and metabolism. These findings could highlight the potential roles of lncRNAs in diverse cellular processes and disease mechanisms.

Several key improvements are offered over existing ceRNA network analysis methods in ischemic stroke research, as supported by comparisons with related studies: first, integrate multi-layered validation by combining network construction with functional pathway verification; unlike Li et al.¹⁵, which focuses primarily on bioinformatic profiling of immune-related ceRNA networks using public transcriptome data, construct a ceRNA network comprising 334 lncRNAs, miRNAs, and mRNAs and validate critical signaling pathways (calcium signaling, gap junction signaling, and neuroactive ligand-receptor interaction) through Western blot analysis, addressing the limitation of over-reliance on in silico predictions as seen in Fan et al.¹⁶, which constructs a circRNA-associated ceRNA network but lacks experimental validation of downstream pathways, and confirm the functional relevance of the predicted network by quantifying protein expression (e.g., CaMKII, CX36, GRIA3) in both model and sham group; focus on core lncRNAs with high topological importance, enhancing the specificity of findings; unlike Cheng et al.¹⁷, which constructs a lncRNA-miRNA-mRNA ceRNA network with 3 lncRNAs, 2 miRNAs, and 24 mRNAs but does not prioritize key regulators, identify 3 lncRNAs with high degree centrality to build a representative subnetwork, ensuring mechanistic insights are anchored to the most influential nodes and improving interpretability compared to broader, less focused networks like those described in Li et al.¹⁸, which includes 62 lncRNAs but lacks targeted subnetwork analysis; provide detailed experimental parameters to improve reproducibility: use 500 ng-1 µg of total RNA extracted from brain tissues (6 rats per group: 20 MCAO models and 20 sham-operated controls) for library preparation, with RNA integrity number (RIN) > 8.0 to ensure quality, a detail not specified in Wang et al.¹⁹, which validates circRNA biomarkers but omits RNA input specifications; employ male SPF SD rats (220 ± 30 g) with MCAO, a widely used model, but explicitly note its limitations, it does not fully replicate human ischemic stroke's vascular complexity or immune responses as highlighted in Li et al. -- and mitigate this by stratifying rats by neurological scores (1-3) to standardize infarct severity; acknowledge study limitations: the ceRNA network may not capture post-translational modifications, and the sample size for Western blot validation (n = 6 per group) could be expanded, with future work to include larger cohorts and proteomic analyses to address these gaps.

Protocol

The animal experiments were approved by the Experimental Ethics Committee of Anhui University of Chinese Medicine (license number: AHUCM-rats-2024006) and conducted in accordance with institutional guidelines and JoVE's animal use standards. Forty male SPF-grade SD rats (220 g ± 30 g) were used in this study. The reagents and equipment used are listed in the Table of Materials.

1. Experimental animals

1. House the animals in the Animal Experimental Center under standard experimental conditions. Maintain a 12 h light/dark cycle, change bedding daily, and provide free access to food and water.
2. Euthanize the animals at the end of the experiment by administering 150 mg/kg sodium pentobarbital via intraperitoneal injection to induce a deep anesthetic state (following institutionally approved protocols). Confirm deep anesthesia by the absence of reflexive response to paw pinch, monitor respiration to ensure complete loss of vital signs, and then proceed with decapitation.

2. Induction of the middle cerebral artery occlusion (MCAO) model in rats

1. Induce anesthesia by administering an intraperitoneal injection of 1% pentobarbital sodium at a dose of 40 mg/kg²⁰(following institutionally approved protocols). Evaluate anesthetic depth using the toe pinch and corneal reflexes.
   1. Continuously monitor respiratory and heart rate throughout the procedure. Apply sterile mineral oil-based eye ointment immediately after anesthesia induction for eye protection²¹.
2. Once fully anesthetized, make a midline neck incision and carefully expose the right common carotid artery (CCA), internal carotid artery (ICA), and external carotid artery (ECA). Under direct visualization, use appropriate microsurgical instruments (e.g., forceps and scissors) to gently separate the surrounding connective tissue and fully expose each artery.
   1. After exposing the arteries, ligate the ECA and CCA using surgical sutures. Tie secure surgical knots to occlude blood flow and minimize bleeding during subsequent procedures. Ensure proper tension to create a stable field for suture insertion.
   2. Make a small incision at the bifurcation of the ECA and ICA using ophthalmic scissors to prepare for suture insertion. Select a nylon suture with a diameter of 0.25 mm and a length of 18 mm from the tip of the rounded (ball-shaped) end. Insert the suture through the incision site at the ECA-ICA junction and gently advance it along the ICA to a depth of approximately 18.5 mm ± 0.5 mm to occlude the middle cerebral artery and induce focal cerebral ischemia.
   3. Secure the inserted nylon suture to the surrounding tissue or blood vessels using surgical sutures to prevent displacement. After maintaining MCAO for 2 h, induce ischemia/reperfusion injury. Use fine surgical forceps to gently grasp the nylon suture and slowly withdraw approximately 5 mm to partially restore cerebral blood flow and induce reperfusion injury. In the sham group, ligate only the carotid artery without inserting a thread embolus²².
3. One week after the successful establishment of the MCAO model, deeply anesthetize the rats. Once there is no response to paw pinch, quickly decapitate and harvest the brains. Rinse the brain tissues with normal saline, then store them in a −80 °C refrigerator for cryopreservation²³.
4. Neurological function score
   1. Evaluate neurological function using the Longa scoring method to determine the success of the MCAO model²⁴. At 24 h post-reperfusion, evaluate neurological function in each rat and assign a score based on the following criteria: Score 0: no observable neurological deficit; Score 1: flexion of the left forelimb during tail suspension, forelimb not fully extended; Score 2: flexion of the left forelimb during tail suspension with markedly reduced resistance on the affected (left) side when placed on a flat surface; Score 3: same as score 2, with added circling or involuntary turning movements while crawling; Score 4: unconsciousness or death within 24 h.
      NOTE: A higher score indicates a more severe behavioral disorder and greater neurological impairment.
5. Calculate cerebral infarction volume using 2,3,5-triphenyltetrazolium chloride (TTC) staining
   1. Section each ex vivo rat brain along the coronal plane into five slices of equal thickness. Ensure each slice is precisely 2.0 mm ± 0.1 mm thick. Fully immerse the sections in freshly prepared 2% (w/v) TTC staining solution.
      1. Wrap the staining container in aluminium foil to protect the solution from light and incubate the samples in a constant-temperature incubator at 37 °C ± 0.5 °C for 30 min. After staining, observe rose red staining in normal brain tissue, while ischemic infarcted areas remain unstained (white) due to loss of dehydrogenase activity.
   2. Transfer TTC-stained brain slices into a 4% (w/v) paraformaldehyde solution and fix at 4 °C for 24 h to preserve tissue morphology. After fixation, capture digital images of the slices using a high-resolution imaging system. Record and archive the data 24 h post-fixation.
   3. Use ImageJ software to quantify infarct volume and total brain tissue volume for each rat group following TTC staining. Calculate the corrected infarct volume percentage using the following formula²⁵:
      ​Corrected infarct volume (%) = {[total lesion volume − (left hemisphere volume − right hemisphere volume)] / right hemisphere volume} × 100%.
   4. Use GraphPad Prism software to visualize and statistically analyze corrected infarct volumes via an unpaired t-test. Define statistical significance as P < 0.05.

3. RNA high-throughput sequencing

1. Select brain tissues from three rats in each group. Remove ribosomal RNA (rRNA) from the total RNA samples using the Ribo-Zero rRNA Removal Kit according to the manufacturer's instructions.
   1. Construct complementary DNA (cDNA) libraries using the TruSeq RNA Library Prep Kit following the standard protocol. Use purified cDNA libraries for cluster generation and sequencing.
2. Use the core data previously generated²⁶, which provided all sequencing libraries and datasets for this study, without reliance on newly constructed libraries. Focus the current work on the integrated fusion analysis of these existing datasets without generating new RNA-seq libraries or sequencing data.
   1. Confirm approval from relevant institutional ethics committees for the previous study. Conduct high-throughput sequencing to systematically assess the miRNA expression profile in rats with MCAO, adhering to standardized protocols as previously described²⁷.
      NOTE: Quality control data for RNA-sequencing library is provided in Supplementary File 1.

4. Differential expression analysis of long noncoding RNAs, microRNAs, and messenger RNAs

1. Obtain annotations for protein-coding genes, miRNAs, and lncRNAs from Ensembl (https://www.ensembl.org/index.html), miRBase (https://www.mirbase.org/), and NONCODE (http://www.noncode.org/), as appropriate^28,29,30. Quantify gene expression using StringTie³¹ (https://ccb.jhu.edu/software/stringtie/). 
   1. Assemble the aligned reads from each sample into transcriptomes using the StringTie assembler. Use the StringTie merge module to integrate the transcriptome assemblies from all samples, generating a unified set of transcripts consistent across the cohort. Use this merged transcriptome to estimate transcript abundance³².
   2. Perform quantification of protein-coding genes and lncRNAs by applying Trimmed Mean of M-values (TMM) normalization and calculating Fragments Per Kilobase of transcript per Million mapped reads (FPKM) for each transcript^33,34.
2. Perform differential expression analysis using the edgeR package (https://bioconductor.org/) to identify differentially expressed lncRNAs (DELs), miRNAs (DEMis), and mRNAs (DEMs) between the MCAO and control groups. Convert the filtered raw count matrix into an edgeR DGEList object. Estimate the common dispersion and assign the same dispersion value to each gene or miRNA. 
   1. Perform an exact test to compare gene expression between groups. Retrieve the log2 fold change (log2FC), log counts per million (logCPM), P-values, and false discovery rate (FDR)-adjusted P-values for each differentially expressed gene³⁵. Define statistical significance as P < 0.05 with a fold-change threshold of |log2(FC)| > 1.

5. Prediction of long noncoding RNA and messenger RNA targets of differentially expressed microRNAs

1. Retrieve the sequences of DEMis and DELs from the appropriate databases. Use the miRNA-lncRNA interaction prediction tools miRanda³⁶ and RNAhybrid³⁷ to identify potential interactions between DEMis and DELs. Obtain miRNA-mRNA interaction pairs from the miRWalk 3.0 database (http://mirwalk.umm.uni-heidelberg.de/)³⁸

6. Construction of the long noncoding RNA - microRNA - messenger RNA regulatory network

1. Construct the lncRNA-miRNA-mRNA regulatory network based on the ceRNA hypothesis^39,40.
   1. Calculate the Pearson correlation coefficient (PCC) between DELs and DEMs. Select lncRNA-mRNA pairs for further analysis if PCC > 0.99 and P < 0.01.
   2. For each co-expressed lncRNA-mRNA pair, identify shared miRNAs targeting both transcripts. Use these shared miRNAs to construct the miRNA-mRNA-lncRNA co-expression network.
   3. Use Cytoscape (https://cytoscape.org/) to construct and visualize the lncRNA-miRNA-mRNA interaction network^41,42. Follow these steps:
      1. Use the CytoNCA plugin to calculate betweenness centrality (BC), closeness centrality (CC), and degree centrality (DC) for each node.
      2. Select genes with centrality scores exceeding the median values of all three metrics to construct the final key subnetwork.
      3. Use the CytoHubba plugin to identify the top 10 hub genes in the protein-protein interaction network and extract key nodes identified in Step 2.
      4. Calculate the node degree for each component of the ceRNA network.

7. Functional enrichment analyses

1. Perform GO and KEGG pathway enrichment analyses to further investigate RNA function⁴³. Use the clusterProfiler package (https://bioconductor.org/) to visualize the enrichment results⁴⁴.

8. Western blot analysis to detect the expression levels of key proteins involved in lncRNA - ceRNA-related pathways

1. Under deep anesthesia, quickly remove the entire brain. After carefully removing the meninges and blood vessels, isolate the cortical tissue in the infarcted area of the rat⁴⁵. Use fine scissors to cut the tissue into small pieces, ensuring each piece weighs between 50 mg and 100 mg. Transfer the weighed tissue samples into pre-cooled homogenization tubes.
   1. Prepare the protein lysis buffer at a volume ratio of PMSF to RIPA lysis buffer of 1:100 (final concentration 1 mM PMSF). Add the lysis buffer at a ratio of 200 µL per 20 mg of tissue. Homogenize the samples thoroughly using a heat-sterilized glass homogenizer until the tissue is finely broken down.
   2. Incubate the homogenates on ice for 30 min, gently agitating every 10 min to facilitate complete lysis. Centrifuge the samples at 28,656.6 × g for 15 min at 4 °C. Carefully aspirate the supernatant containing total protein without disturbing the pellet.
2. Add 5× sodium dodecyl sulfate-polyacrylamide gel electrophoresis (SDS-PAGE) loading buffer to the protein sample at a ratio of 1:4 (1 volume of 5× buffer to 4 volumes of protein sample). After dilution, the final working concentration of the SDS-PAGE loading buffer is 1×.
   1. Mix thoroughly and heat in a boiling water bath for 10 min to completely denature the proteins. Allow the samples to cool to room temperature. Load 30 µg of each sample into the SDS-PAGE gel wells.
   2. Perform electrophoresis in two stages: apply 80 V for 30 min to resolve proteins through the stacking gel, then increase to 120 V for 1 h to separate proteins in the resolving gel.
3. Prepare pre-cut filter paper and polyvinylidene difluoride (PVDF) membranes to match the size of the gel. Soak the PVDF membrane in methanol for 3 min to activate it. Immerse both the PVDF membrane and filter paper in the transfer buffer for 5 min.
   1. Assemble the transfer sandwich in the following order (top to bottom): anode plate, three layers of filter paper, PVDF membrane, gel, three layers of filter paper, cathode plate. Ensure proper alignment of the gel, membrane, and filter paper layers. Remove all air bubbles at each step to ensure even transfer.
4. Immediately after protein transfer, rinse the PVDF membrane in Western washing buffer for 5 min to remove residual transfer buffer. Block the membrane at room temperature for 2 h with gentle shaking in Western blocking buffer containing 5% skimmed milk powder, or use 5% BSA if detecting phosphorylated proteins.
5. After blocking, incubate the membrane with the primary antibody (1:1000) at 4 °C overnight with gentle shaking. The following day, incubate the membrane with the secondary antibody (1:10,000) for 2 h at room temperature.
6. Perform chemiluminescent detection in a dark room to prevent light interference. Place the protein side of the PVDF membrane flat at the center of the exposure plate in the automatic imaging system.
   1. Mix equal volumes of ECL reagent A and ECL reagent B in a clean centrifuge tube immediately before use. Apply the mixed ECL solution evenly to the membrane surface to initiate the luminescent reaction.
   2. After 1-2 min of incubation at room temperature, gently remove excess liquid without disturbing the membrane surface. Adjust the exposure parameters on the imaging system according to luminescence intensity to optimize signal capture.

9. Statistical analysis

1. Analyze Western blot results using ImageJ software to quantify band gray values. Use GraphPad Prism for statistical analysis of experimental data. Express all data as mean ± standard deviation (x ± SD). Evaluate statistical differences between groups using an unpaired t-test.
   1. Enter data from the two independent sample groups into the software's data table, with each group corresponding to a separate column and the samples independent of each other. Verify normality by selecting "Normality and symmetry tests" under the "Analyze" function and applying the Shapiro-Wilk test to both groups.
   2. If P > 0.05 for both groups, consider the data normally distributed. On this basis, select Unpaired t-test under t-tests in the Analyze function to perform the analysis. Define statistical significance as P < 0.05.

Results

The success rate verification experimental results of each group of rat models are shown in Figure 1. After modeling, neurological function scoring was performed on the rats (Figure 1A). The model group exhibited severe neurological deficits, and those with scores ranging from 1 to 3 were included in subsequent experiments. Figure 1B and C show the TTC staining results, with red representing normal brain tissue and white indicating ...

Discussion

To investigate the molecular mechanisms underlying stroke pathogenesis and identify potential diagnostic and therapeutic targets, RNA-Seq and small RNA-Seq (sRNA-Seq) analyses were conducted on brain tissues from rats following MCAO. This study primarily aims to investigate the complex regulatory networks involving lncRNAs, miRNAs, and mRNAs. A comprehensive analysis of the RNA-Seq and sRNA-Seq datasets from MCAO-induced rat brain tissues was conducted to elucidate the lncRNA-miRNA-mRNA regulatory network. An extensive n...

Materials

List of materials used in this article

Name	Company	Catalog Number	Comments
2% (w/v) TTC staining solution	Beijing Kangle Clone Biotechnology Co., Ltd.	20230805	TTC staining
4% (w/v) paraformaldehyde solution	Beyotime	P0099	Fixed brain tissue
40 male SPF-grade SD Rats	Hagzhou Ziyuan Laboratory Animal Breeding Co.,Ltd	SCXK [zhe] 2024-0004	laboratory animal
5% skimmed milk powder	Scientific Phygene	PH1519	WB
agarose electrophoresis system	Beijing Liuyi Biotechnology Co., Ltd.	DYCP-44P	WB
automatic exposure instrument	Shanghai Peiqing Technology Co., Ltd.	JS-M6P	WB
Automatic exposure meter	Shanghai Peiqing Technology Co., LTD	JS-M6P	WB
Automatic ice maker	Changshu City Xueke Electric Appliance Co., LTD	IMS-20	WB
Calmodulin	Affinity	49B2443	WB
CamKII	Affinity	14G0796	WB
centrifuge	Haimen Qilinbeier Instrument Manufacturing Co., Ltd.	LX 300	WB
clusterProfiler package	Bioconductor	clusterProfiler_4.17	enrichment analyses
CX36	ZENBIO	N24AP24	WB
CX43	ZENBIO	M08NO01	WB
ECL ultra-sensitive chemiluminescence kit	biosharp	BL520B	WB
Electric thermostatic air drying oven	Shanghai Sanfa Scientific Instrument Co., LTD	DHG-9070	WB
Electrophoresis apparatus	Shanghai Tianeng Technology Co., LTD. (Tanon)	EPS300	WB
electrophoresis apparatus	Shanghai Tanon Science & Technology Co., Ltd.	EPS300	WB
electrophoresis tank	Shanghai Tanon Science & Technology Co., Ltd.	VE-180	WB
GABRA6	Affinity	46V5371	WB
Goat Anti-mouse IgG	Zs-BIO	142637	WB
Goat Anti-Rabbit IgG	Zs-BIO	139931	WB
GRIA3	Affinity	0C33051	WB
High speed refrigerated centrifuge	Anhui Jiawen instrument equipment Co., LTD	JW-3021HR	WB
HiSeq 2500 system	illumina	/	RNA high-throughput sequencing
ImageJ software	National Institutes of Health	ImageJ 1.54k	TTC staining
Magnetic heating agitator	Changzhou city and instrument factory	JJ-79-1	WB
Micropipette	Germany Eppendorf	/	WB
Normal temperature micro centrifuge	Haimen Qi Limber Instrument Manufacturing Co., LTD. LX300	LX300	WB
NPY1R	Affinity	2D38248	WB
pipette	eppendorf	0.5-10ul	WB
PKC	Affinity	17E3745	WB
pre-stained protein Marker	biosharp	BL712C	WB
PVDF membrane	Millipore	IPVH00010	WB
Ribo-Zero rRNA Removal kit	Illumina, San Diego, California, USA	/	RNA extraction
RIPA lysate	Biosharp	BL504A	WB
Sodium pentobarbital	Beijing Think-Far Technology Co., Ltd.	MERCK	anesthetic
transfer membrane instrument	Shanghai Tanon Science & Technology Co., Ltd.	VE-186	WB
Transmembrane apparatus	Shanghai Tianeng Technology Co., LTD. (Tanon)	VE-186	WB
β-actin	Zs-BIO	19AW0505	WB