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. 2019 May 27;20(1):417–432. doi: 10.3892/mmr.2019.10300

Bioinformatics analysis of a long non-coding RNA and mRNA regulation network in rats with middle cerebral artery occlusion based on RNA sequencing

Xianchun Duan 1,2,3, Lan Han 2,3, Daiyin Peng 2,3,✉, Can Peng 2,3, Ling Xiao 4, Qiuyu Bao 4, Huasheng Peng 2,3
PMCID: PMC6580035  PMID: 31180537

Abstract

Long non-coding RNAs (lncRNAs) have been proven to be critical gene regulators of development and disease. The main aim of the present study was to elucidate the lncRNA-mRNA regulation network in ischemic stroke induced by middle cerebral artery occlusion (MCAO) using RNA sequencing (RNA-seq) in rats. lncRNA expression profiles were screened in brain tissues to identify a number of differentially expressed lncRNAs (DELs) and genes (DEGs) by RNA-seq. Reverse transcription-quantitative polymerase chain reaction was performed to further confirm the lncRNA expression data. Furthermore, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis were used to mine mRNA functions, and a lncRNA-mRNA network was constructed. Additionally, cis- and trans-regulatory gene analyses of DELs were predicted. A total of 134 DELs (fold change >2, false discovery rate <0.05) and 1,006 DEGs (fold change >2 and P<0.05) were identified. Eighteen lncRNAs were predicted to regulate heme oxygenase 1, mitotic checkpoint serine/threonine kinase B, chemokine ligand 2 and DNA Topoisomerase IIα, amongst other genes. These genes are all associated with a cellular response to inorganic substances, alkaloids, estradiol, reactive oxygen species, metal ions, oxidative stress, and are associated with metabolic pathways, chemokine signaling pathways, malaria, Parkinson's disease, the cell cycle and other GO and KEGG pathway enrichments. The present study identifies novel DELs and an lncRNA-mRNA regulatory network that may allow for an improved understanding of the molecular mechanism of ischemic stroke induced by MCAO.

Keywords: middle cerebral artery occlusion, long non-coding RNA

Introduction

Stroke, universally acknowledged as a cerebrovascular accident, may result in lasting brain damage, long-term disability or even mortality (1,2). A multitude of biological processes are implicated in ischemic stroke, including oxygen deprivation, neuronal necrosis and an intense inflammatory response (3,4). MicroRNAs (miRNAs), long non-coding RNAs (lncRNAs) and even circular RNAs (circRNAs) contribute to RNA-mediated networks (5–8) that regulate notable cellular events through a variety of complicated mechanisms (9,10). These networks have been implicated in ischemic stroke in previous studies (5–10); however, there remain gaps in current knowledge in this regard, and novel ncRNAs need be mined in order to provide a better understanding of the precise molecular mechanisms involved in ischemic stroke.

lncRNAs have been proven to be critical gene regulators of development and disease (11–13). lncRNAs may also perform functions through competitively binding to miRNAs known as competitive endogenous RNAs (14). Washietl et al (15) systematically analyzed the conservatism of human lncRNA and other six mammalian lncRNA and identified that ~54% human lncRNA loci may be mapped to that of a rat. A previous study has demonstrated that significantly differentially expressed lncRNAs (DELs) may contribute to the stabilization of mRNA expressions in stroke (7). Stroke-induced lncRNAs may also interact with chromatin-modifying proteins and modulate genes associated with ischemic brain damage (16,17). Furthermore, lncRNA BC088414 was revealed to be involved with apoptosis-associated genes following hypoxic-ischemic brain damage (8). Similarly, another study suggested that lncRNA C2dat1 may modulate calcium/calmodulin-dependent protein kinase II expression to promote neuronal survival following cerebral ischemia (10). Although a host of lncRNAs have been identified by massive parallel sequencing, to date, little is known on functional RNA molecules and RNA-mediated regulation networks in ischemic stroke.

The main aim of the present study is to elucidate the lncRNA-mRNA regulation networks in ischemic stroke induced by middle cerebral artery occlusion (MCAO) using RNA sequencing (RNA-seq) in rats.

Materials and methods

MCAO model and tissue preparation

A focal cerebral ischemia model induced by MCAO, prepared as previously described (18), was prepared using 20 7-week-old male Sprague-Dawley rats of a specific pathogen-free grade (weighing 200±20 g), purchased from the experimental animal center of Anhui Medical University (Anhui, China). The study protocol was ethically approved by the Committee on the Ethics of Animal Experiments of Anhui University of Chinese Medicine (approval no. 2012AH-036-03). In brief, the animals were fasted overnight but allowed ad libitum access to water. They were then anesthetized with chloral hydrate (350 mg/kg, intraperitoneal injection). A 4-0 silicon-coated monofilament nylon suture with a round tip was inserted through an arteriectomy in the common carotid artery just below the carotid bifurcation and then advanced into the internal carotid artery ~18 mm distal to the carotid bifurcation until a mild resistance was felt. Following 2 h of MCAO, the filament was removed to allow reperfusion. As a control, control-operated rats underwent identical surgery but did not have the suture inserted. Four days subsequent to MCAO, the left hemispheres were collected and immediately frozen in liquid nitrogen.

RNA-seq

RNA-seq was performed by Ao-Ji Bio-Tech (Shanghai, China). Briefly, total RNA was extracted using an RNeasy Mini kit (Qiagen GmbH, Hilden, Germany), according to the manufacturer's protocol. The RNA quality control was performed using Nanodrop 2000 and Agilent 2100, and mainly depended on the concentration, purity and integrity of the RNA. Ribosomal RNA was removed from total RNA using Ribo-Zero rRNA removal beads (Illumina, Inc., San Diego, CA, USA). Libraries were constructed according to the standard TruSeq protocol (19). Purified cDNA libraries were prepared for cluster generation and sequencing on an Illumina HiSeq 2500 (Illumina, Inc.) according to the manufacturer's protocol. Subsequently, data analyses were performed in silico.

lncRNA annotation

Quality control of the RNA-Seq reads was conducted using FastQC (v0.11.3) (The Babraham Institute, Cambridge, UK). Reads were trimmed using the software seqtk (github.com/lh3/seqtk) for known Illumina TruSeq adapter sequences, poor reads and ribosome RNA reads. Trimmed reads were aligned to the rat genome (Rn6) using Hisat2 (version 2.0.4) (20). Transcripts were assembled using Stringtie (v1.3.0) (20,21). Transcripts constructed from Stringtie were compiled together by gffcompare (v0.9.8) (20,21). Transcripts detected in at least five samples (half of the total number) were considered to be bona fide transcripts. Transcripts, with the exception of those with just one exon and shorter than 200 base pairs, were further analyzed for the identification of lncRNAs. Transcripts with class codes ‘i,’ ‘u,’ and ‘x,’ were considered to be potential novel long transcripts. Pfam (22), Coding Potential Calculator (CPC) (23) and Coding-Non-Coding Index (CNCI) (24) were used to estimate the coding potential of each novel transcript. Transcripts with a Pfam score <0, CNCI <0 and CPC non-significant were considered to lack coding potential. Transcripts were compared with annotation databases, including NONCODE (v4) (http://www.noncode.org) and Ensembl (25). The matched transcripts were considered to be known lncRNAs, and others were considered to be novel lncRNAs. All lncRNAs were quantified using Stringtie. According to the positional association between lncRNA and mRNA in the genome, lncRNA may be classified into six types: Bidirectional, exonic_antisense, exonic_sense, intergenic, intronic_antisense and intronic_sense (26).

The lncRNA-mRNA coexpression network

Initially, the DELs and differentially expressed genes (DEGs) were analyzed using EdgeR (27). For DEGs, log2| [fold change (FC)] |>1 and P<0.05 were used as the cutoff values. Meanwhile, log2| (FC) |>1 and false discovery rate (FDR) <0.05 were used as the threshold for DELs. Hierarchical clustering of DELs was performed based on mean signals using a Euclidean distance function. In addition, a volcano plot was generated. The Pearson's correlation coefficient (PCC) between lncRNAs and mRNAs was calculated (cutoff value, PCC>0.9, P<0.05) and the lncRNA-mRNA regulatory network was structured using Cytoscape 2.8.3 (28).

Prediction of target genes and enrichment analysis

cis- or trans-acting algorithms were used to predict the potential targets of lncRNAs. The first algorithm predicted potential target genes of cis-acting lncRNAs that were physically located within 10 kb upstream or 20 kb downstream of lncRNAs using liftOver genome browser (genome.ucsc.edu/cgi-bin/hgLiftOver). The second algorithm predicted potential target genes of trans-acting lncRNAs based on the lncRNA-mRNA complementary sequences, and predicted lncRNA-mRNA duplex energy. First, BLASTN (29) was performed to detect potential target mRNA sequences with >95% identity and E value <1×10−5 (https://blast.ncbi.nlm.nih.gov/Blast.cgi). Then, RNAplex (30) was used to calculate the complementary energy between lncRNAs and their potential trans-regulated target genes with RNAplex-10−30. Gene Ontology (GO) (31) and Kyoto Encyclopedia of Genes and Genomes (KEGG) (32) enrichment analyses of the identified potential target genes were performed using the Database for Annotation, Visualization and Integrated Discovery (33); and P<0.05 was considered to indicate a statistically significant difference.

Reverse transcription-quantitative polymerase chain reaction (RT-qPCR)

Total RNA was extracted from left hemisphere samples using TRIzol® reagent (Invitrogen; Thermo Fisher Scientific, Inc., Waltham, MA, USA) and reverse-transcribed using a Thermo Fisher Scientific RevertAid First Strand cDNA Synthesis kit (cat. no. K1622; Thermo Fisher Scientific, Inc.) according to the manufacturer's protocol at 42°C for 60 min. To further confirm the expression data from RNA-seq, a cutoff value (FC>2, P<0.05) was randomly selected for qPCR verification. The expression levels of six randomly DELs (NONRATT027551.2, MSTRG.1836.1, MSTRG.4344.10, MSTRG.7720.11, NONRATT005132.2 and MSTRG.20633.3) were assayed using a SYBRGreen flurophore (Applied Biosystems; Thermo Fisher Scientific, Inc.) using the PikoReal real-time PCR system (Thermo Fisher Scientific, Inc.) under the following conditions: Initial denaturation at 95°C for 30 sec, followed by 40 cycles at 95°C for 30 sec and 60°C for 30 sec, and a final extension step at 4°C for 20 min. FC was determined using the 2−ΔΔCq method (34). GAPDH mRNA was used as an internal control. The primers used are listed in Table I.

Table I.

Primer sequences.

Gene Sequence Polymerase chain reaction product length (base pairs)
GAPDH F: 5′-CCTGGTATGACAACGAATTTG-3′ 131
R: 5′-CAGTGAGGGTCTCTCTCTTCC-3′
NONRATT027551.2 F: 5′- GGACCTGGAAGGTGAACAGG-3′ 118
R: 5′-TGAATGGGTGACCAACAGGG-3′
MSTRG.1836.1 F: 5′-CCATTGTCCTTCCATCCCCC-3′ 85
R: 5′-CCACCCTACCAAACTTCCCC-3′
MSTRG.4344.10 F: 5′-GACTTAGGCACAGTGGGTGG-3′ 119
R: 5′-ATGGCAGAGAGCGAATGGAG-3′
MSTRG.7720.11 F: 5′-TCCCTAGAGCAGTCCTCACC-3′ 97
R: 5′- ATCTCGGGTTCGCCTTTTGT-3′
NONRATT005132.2 F: 5′-CCTGACTATGGCACGTCCTC-3′ 152
R: 5′-CTGAGTCCAGTGTGCCTGTT-3′
MSTRG.20633.3 F: 5′-CTTTCACTCCGAGAACCCCC-3′ 117
R: 5′-GCAAGCAGGTTGGTTCCTTG-3′
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F, forward; R, reverse.

Statistical analysis

The comparisons between the MCAO group and the control group were determined using a Student's t-test for the RT-qPCR results by SPSS 22.0 statistical software (IBM Corp., Armonk, NY, USA). P<0.05 was considered to indicate a statistically significant difference. The PCC between lncRNAs and mRNAs was calculated using the Hmisc package in R based on the expression determined using RNA-seq (PCC>0.9, P<0.05). The correlation analysis between the RT-qPCR results and RNA-seq results was calculated in Excel 2013 (Microsoft Corporation, Redmond, WA, USA) with the function of CORR.

Results

lncRNA-sequencing data analysis

The present study characterized the lncRNA landscape and expression by performing deep RNA-seq experiments on three control and three MCAO tissue samples. Subsequent to the seqtk quality assessment of sequencing, >33 million total original reads for each sample were obtained, and the proportion of bases with quality values >20 was >94%. These results indicated that the quality of the sequencing results was acceptable (Table II). Subsequent to filtering out the adaptor sequence and low quality reads, the percentage of clean reads within the raw reads accounted for 94% of the total sequences in two groups. Hisat2 software was used to map the obtained clean reads to the Rattus norvegicus reference genome. As presented in Table II, ~97% of the trimmed reads were mapped onto the reference genome. In total, 24,304 lncRNAs were screened from six samples, and there were 23,255 shared lncRNAs detected in the MCAO and control groups (Fig. 1A). The majority of the identified lncRNAs were transcribed from protein-coding exons; others were from introns and intergenic regions (Fig. 1B). In addition, the present study analyzed the distribution of the identified lncRNAs on the rat chromosomes; 24,304 lncRNA transcripts were identified in all chromosomes, and chromosome 1 included the most lncRNAs (Fig. 1C).

Table II.

Results of the RNA sequencing.

Sample ID Raw reads Clean reads Clean ratio (%) rRNA trimmed Mapped reads Mapped ratio
MCAO 1 155190870 147282901 94.90 147214274 131290423 0.824026529
MCAO 2 144450130 136930064 94.79 136768708 120311195 0.812346697
MCAO 3 151411986 142862439 94.35 142724911 125540743 0.804354762
Control 1 169303916 160544960 94.83 160466568 142289885 0.819832697
Control 2 136533930 129672892 94.97 129609605 116072061 0.828973282
Control 3 124878376 118432971 94.84 118349299 105506286 0.821661742
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MCAO, middle cerebral artery occlusion.

Figure 1.

Figure 1.

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Class type and chromosome distribution of lncRNAs identified in the control and MCAO group. (A) Venn diagram of lncRNA in the control and MCAO groups. (B) According to the positional association between lncRNA and mRNA in the genome, lncRNAs may be classified into six types: Bidirectional, exonic antisense, exonic sense, intergenic, intronic antisense and intronic sense. (C) Number of lncRNAs on each chromosome in the MCAO and control groups. MCAO, Middle cerebral artery occlusion; lncRNA, long noncoding RNA.

Identification of DEGs and DELs

EdgeR was used to filter DEGs and DELs and differentiate their expression between the control and MCAO groups. A total of 1,007 DEGs (|FC|>2, P<0.05) were identified, including 785 upregulated genes and 222 downregulated genes. Similarly, as presented in Fig. 2, 134 DELs (|FC|>2, FDR<0.05) were identified in the MCAO group (Fig. 2A and B), including 77 upregulated and 57 downregulated DELs (Fig. 2C and D). In the present study, it was revealed that the FC values of certain DELs were equal to positive infinity and negative infinity, meaning that these lncRNAs are switched-on or off with MCAO. Essentially, positive or negative infinity indicates zero expression of the lncRNA in normal or MCAO groups. It was speculated that this may be associated with the abundance of lncRNAs and the sensitivity to RNA-seq. The top five upregulated DELs were NONRATT027551.2, MSTRG.1836.1, MSTRG.4344.10, NONRATT028102.2 and MSTRG.31500.2; the top five downregulated DELs were MSTRG.7720.11, NONRATT005132.2, MSTRG.20633.3, NONRATT020232.2 and MSTRG.1836.3.

Figure 2.

Figure 2.

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RNA-seq data on the differentially expressed lncRNAs between the model and control groups. (A) Hierarchical cluster of DELs between the MCAO and control groups. The color code in each heat map is linear, with green indicating the least and red indicating the greatest differentiation. The mean signals of the altered lncRNAs in each of the two groups were clustered using a Euclidean distance function. The lncRNAs with the most similar expression patterns were placed next to each other (n=3 per group). (B) A volcano plot of the RNA-seq FC and P-value of MCAO group compared with the control group. Blue and red points stand for DELs. Gray points represent lncRNAs which are not differentially expressed. (C) Upregulated and (D) downregulated DELs as exhibited in the red and blue boxes, respectively, represent the results. MCAO, Middle cerebral artery occlusion; lncRNA, long noncoding RNA; RNA-seq, RNA sequencing; FC, fold change; DEL, differentially expressed lncRNAs.

lncRNA-mRNA network

The cutoff correlation r-values (|PCC|>0.9) and P-values (P<0.05) were selected to structure a lncRNA-mRNA co-expression network between DEGs and DELs. As Fig. 3 presents, 46 DEGs, 104 DELs and 664 edges were filtered out using Cytoscape to construct the co-expression network. The co-expression-associated top 30 GO terms and pathway terms enrichment analyses presented in Figs. 4 and 5 suggest that these DELs were associated with the cellular response to inorganic substances, alkaloids, estradiols, reactive oxygen species, metal ions and oxidative stress. In particular, the heme oxygenase 1 (HO-1) gene participates in many of these functions. A multitude of pathways were implicated, including metabolic pathways, chemokine signaling pathways, malaria, Parkinson's disease and the cell cycle. Notably, the BUB1 mitotic checkpoint serine/threonine kinase B (BUB1B) and C-C motif chemokine ligand 2 (CCL2) genes were associated with the cell cycle.

Figure 3.

Figure 3.

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An lncRNA-gene-network based on Pearson's correlation coefficient. Pink nodes indicate the upregulated mRNAs or lncRNAs, and green nodes indicate the downregulated mRNAs or lncRNAs. lncRNA, long noncoding RNA.

Figure 4.

Figure 4.

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Top 30 significant enrichment of GO terms in the long noncoding RNA-mRNA network. GO, gene ontology; ERK, extracellular regulated kinase.

Figure 5.

Figure 5.

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Top 30 significant enrichment of KEGG pathway terms in the long noncoding RNA-mRNA network. KEGG, Kyoto Encyclopedia of Genes and Genomes; TNF, tumor necrosis factor; HIF-1, hypoxia-inducible factor 1; ECM, extracellular matrix.

Regulatory analysis of DELs

A total of 91 cis-regulatory genes of 94 DELs, including 55 upregulated lncRNAs in the MCAO group were identified; 14 of the 91 cis-regulatory genes exhibited differential expression. A total of 13 of the DEL/cis-regulatory gene pairs had positive correlations as follows: NONRATT021925.2 (Rho GDP dissociation inhibitor β), NONRATT004791.2 (G protein subunit γ transducing 2), NONRATT015286.2 (periostin), MSTRG.30235.10 (LRR binding FLII interacting protein 1), NONRATT015403.2 (IQ motif containing GTPase activating protein 3), NONRATT008267.2 (kinesin family member 14), NONRATT009960.2 (non-SMC condensin I complex subunit G), NONRATT011312.2 (retinol binding protein 3), NONRATT005985.2 (DNA topoisomerase IIα), NONRATT016680.2 (ENSRNOG00000053081), NONRATT013960.2 (galanin receptor 1), NONRATT016022.2 (CART prepropeptide) and NONRATT024616.2 (δ like non-canonical Notch ligand 1) (Table III). Additionally, 90 trans-regulatory genes of lncRNAs were filtered by BLASTN and RNAplex, with a negative correlation identified between ENSRNOT00000092040 and Ccl9 (Table IV).

Table III.

Differentially expressed lncRNAs mechanisms involved in cis-regulatory elements.

lncRNA ID log2FC Q value Up/downregulated Type Gene ID Gene name log2FC P-value Up/downregulated
NONRATT017723.2 −3.68 0.0125 Down cis ENSRNOG00000000633 Rhobtb1 0.85 0.1148 –
NONRATT008064.2 −6.73 0.0145 Down cis ENSRNOG00000001183 Hnf1a 1.25 0.7907 –
NONRATT000377.2 +∞ 0.0492 Up cis ENSRNOG00000001492 Slc8a2 0.13 0.9091 –
NONRATT006957.2 6.69 0.0000 Up cis ENSRNOG00000001982 Cblb 0.60 0.1402 –
NONRATT009831.2 −5.72 0.0425 Down cis ENSRNOG00000002137 Aasdh 0.21 0.7048 –
NONRATT009718.2 −5.48 0.0020 Down cis ENSRNOG00000002146 Pkd2 0.29 0.5035 –
NONRATT009780.2 −3.94 0.0473 Down cis ENSRNOG00000002208 Shroom3 0.83 0.1301 –
NONRATT005026.2 6.14 0.0000 Up cis ENSRNOG00000002607 Sox9 0.25 0.5566 –
NONRATT014565.2 −2.91 0.0492 Down cis ENSRNOG00000002864 Nacc1 0.26 0.5744 –
NONRATT005048.2 -∞ 0.0432 Down cis ENSRNOG00000003144 Gprc5c 0.48 0.5195 –
NONRATT024547.2 -∞ 0.0082 Down cis ENSRNOG00000003955 Spata7 0.26 0.6325 –
NONRATT026753.2 3.08 0.0450 Up cis ENSRNOG00000003993 Thap2 0.26 0.7629 –
NONRATT005132.2 −8.71 0.0471 Down cis ENSRNOG00000004049 Baiap2 −0.04 0.8211 –
NONRATT026300.2 +∞ 0.0469 Up cis ENSRNOG00000004628 Dazap2 0.13 0.8554 –
NONRATT026156.2 7.44 0.0000 Up cis ENSRNOG00000005332 Csdc2 −0.58 0.1263 –
NONRATT005775.2 3.69 0.0453 Up cis ENSRNOG00000005538 Psmd11 −0.03 0.7616 –
NONRATT023334.2 −4.85 0.0448 Down cis ENSRNOG00000005711 Ptprd −0.21 0.4658 –
NONRATT021925.2 +∞ 0.0000 Up cis ENSRNOG00000005809 Arhgdib 1.81 0.0074 Up
NONRATT004791.2 4.02 0.0218 Up cis ENSRNOG00000006108 Gngt2 4.95 0.0000 Up
MSTRG.22811.4 -∞ 0.0388 Down cis ENSRNOG00000006966 Nfia 0.21 0.6590 –
NONRATT025479.2 −4.14 0.0000 Down cis ENSRNOG00000007610 Gdf11 0.08 0.9402 –
NONRATT020232.2 −7.72 0.0000 Down cis ENSRNOG00000007804 C1galt1 0.45 0.2729 –
NONRATT008198.2 4.83 0.0305 Up cis ENSRNOG00000007887 Elk4 0.76 0.2762 –
NONRATT022252.2 +∞ 0.0305 Up cis ENSRNOG00000008099 Galnt12 0.85 0.2496 –
NONRATT024954.2 −3.98 0.0471 Down cis ENSRNOG00000008155 Dus4l −0.17 0.7340 –
NONRATT028439.2 5.06 0.0430 Up cis ENSRNOG00000008187 Ubash3b −0.21 0.4764 –
NONRATT022210.2 −4.24 0.0492 Down cis ENSRNOG00000008237 Unc13b 0.30 0.6027 –
NONRATT027551.2 9.08 0.0119 Up cis ENSRNOG00000008709 Arhgap32 −0.24 0.5090 –
NONRATT027576.2 -∞ 0.0061 Down cis ENSRNOG00000008757 Tmem218 0.16 0.8511 –
NONRATT021402.2 -∞ 0.0291 Down cis ENSRNOG00000009156 Tra2a 0.47 0.2267 –
NONRATT021972.2 +∞ 0.0335 Up cis ENSRNOG00000009338 Kras −0.11 0.6201 –
NONRATT022345.2 −5.23 0.0000 Down cis ENSRNOG00000009795 Nfib 0.04 0.9239 –
MSTRG.16900.3 −4.70 0.0248 Down cis ENSRNOG00000009882 Ppp3ca 0.07 1.0000 –
MSTRG.19870.10 +∞ 0.0000 Up cis ENSRNOG00000010993 Dpm1 −0.14 0.5966 –
NONRATT008272.2 −3.96 0.0431 Down cis ENSRNOG00000011063 Dennd1b 0.20 0.7240 –
MSTRG.10245.2 6.31 0.0002 Up cis ENSRNOG00000011704 Fbxo34 −0.37 0.2745 –
NONRATT001841.2 3.41 0.0430 Up cis ENSRNOG00000012110 Col17a1 −1.52 0.0483 Down
NONRATT002035.2 7.89 0.0003 Up cis ENSRNOG00000012324 Soga3 −0.04 0.7913 –
MSTRG.22390.2 -∞ 0.0000 Down cis ENSRNOG00000012634 Fbxo10 −0.41 0.1989 –
MSTRG.22390.1 +∞ 0.0000 Up cis ENSRNOG00000012634 Fbxo10 −0.41 0.1989 –
NONRATT015286.2 5.30 0.0425 Up cis ENSRNOG00000012660 Postn 3.66 0.0004 Up
MSTRG.1836.3 −7.60 0.0041 Down cis ENSRNOG00000012716 Chd2 0.06 0.9929 –
MSTRG.1836.1 9.24 0.0001 Up cis ENSRNOG00000012716 Chd2 0.06 0.9929 –
NONRATT016334.2 +∞ 0.0000 Up cis ENSRNOG00000012734 Dcun1d1 −0.20 0.5140 –
NONRATT015057.2 −4.70 0.0049 Down cis ENSRNOG00000012799 Prkaa1 −0.28 0.3193 –
NONRATT000212.2 −6.12 0.0378 Down cis ENSRNOG00000013194 Rps6ka2 −0.14 0.5704 –
NONRATT030198.2 5.34 0.0000 Up cis ENSRNOG00000013213 Epha4 0.35 0.5640 –
NONRATT010352.2 −6.23 0.0004 Down cis ENSRNOG00000013353 Tmem260 0.07 0.9952 –
NONRATT029471.2 4.91 0.0082 Up cis ENSRNOG00000013557 Lancl1 −0.17 0.5373 –
NONRATT028588.2 6.05 0.0378 Up cis ENSRNOG00000013829 Chrna3 1.17 0.2523 –
NONRATT023203.2 +∞ 0.0007 Up cis ENSRNOG00000013956 Rnf38 0.17 0.7841 –
MSTRG.29693.5 +∞ 0.0484 Up cis ENSRNOG00000013991 Creg2 −0.69 0.2339 –
MSTRG.12408.2 -∞ 0.0041 Down cis ENSRNOG00000014007 Gfod1 0.04 0.9704 –
NONRATT004566.2 3.44 0.0440 Up cis ENSRNOG00000015002 Abhd15 0.76 0.3266 –
MSTRG.15067.2 +∞ 0.0090 Up cis ENSRNOG00000015334 Fcho2 0.49 0.2280 –
NONRATT003289.2 7.56 0.0052 Up cis ENSRNOG00000015717 Ptpre 0.21 0.6977 –
NONRATT013960.2 −3.81 0.0248 Down cis ENSRNOG00000016654 Galr1 −3.38 0.0001 Down
NONRATT028604.2 4.71 0.0000 Up cis ENSRNOG00000017193 Lingo1 −0.52 0.2454 –
NONRATT016022.2 −5.74 0.0001 Down cis ENSRNOG00000017712 Cartpt −3.83 0.0001 Down
NONRATT015604.2 −5.60 0.0000 Down cis ENSRNOG00000018166 Prkab2 0.00 0.8495 –
NONRATT024616.2 −3.79 0.0143 Down cis ENSRNOG00000019584 Dlk1 −4.21 0.0000 Down
MSTRG.30235.10 8.50 0.0275 Up cis ENSRNOG00000019892 Lrrfip1 1.14 0.0479 Up
NONRATT026461.2 +∞ 0.0118 Up cis ENSRNOG00000020230 Pias4 0.17 0.8050 –
NONRATT018820.2 -∞ 0.0002 Down cis ENSRNOG00000020337 Sla2 0.52 0.5953 –
NONRATT004912.2 6.00 0.0041 Up cis ENSRNOG00000020658 Aarsd1 0.05 0.9927 –
NONRATT019712.2 −6.20 0.0446 Down cis ENSRNOG00000021262 Slc23a2 0.09 0.9742 –
NONRATT027268.2 5.27 0.0071 Up cis ENSRNOG00000022570 Pus7l 0.51 0.5096 –
MSTRG.12863.69 +∞ 0.0304 Up cis ENSRNOG00000023661 Celf2 0.30 0.5495 –
NONRATT030368.2 5.55 0.0043 Up cis ENSRNOG00000025527 Mtcl1 0.16 0.8136 –
NONRATT027862.2 −3.75 0.0304 Down cis ENSRNOG00000027145 Rora −0.16 0.5963 –
NONRATT015403.2 4.83 0.0409 Up cis ENSRNOG00000027894 Iqgap3 2.03 0.0137 Up
NONRATT003576.2 +∞ 0.0028 Up cis ENSRNOG00000028017 Tmem109 0.36 0.4097 –
NONRATT004361.2 −5.86 0.0409 Down cis ENSRNOG00000028341 Alkbh5 0.00 0.8591 –
NONRATT012903.2 −5.26 0.0000 Down cis ENSRNOG00000031706 AABR07027388.1 −0.85 0.1761 –
MSTRG.15111.2 +∞ 0.0000 Up cis ENSRNOG00000032735 Srek1 0.18 0.7203 –
MSTRG.15111.1 −7.42 0.0000 Down cis ENSRNOG00000032735 Srek1 0.18 0.7203 –
NONRATT028102.2 10.21 0.0000 Up cis ENSRNOG00000033809 Mlh1 0.76 0.1272 –
NONRATT019889.2 −4.60 0.0113 Down cis ENSRNOG00000034031 Vstm2l −0.45 0.3771 –
NONRATT008267.2 +∞ 0.0039 Up cis ENSRNOG00000037211 Kif14 5.80 0.0005 Up
NONRATT009960.2 4.06 0.0430 Up cis ENSRNOG00000038572 Ncapg 3.28 0.0054 Up
MSTRG.21884.7 +∞ 0.0492 Up cis ENSRNOG00000042458 Stau2 −0.20 0.5104 –
NONRATT030464.2 5.30 0.0042 Up cis ENSRNOG00000046053 Nudt10 −0.59 0.1573 –
MSTRG.15418.3 +∞ 0.0448 Up cis ENSRNOG00000048993 Metazoa_SRP 0.03 0.9881 –
MSTRG.28323.2 +∞ 0.0492 Up cis ENSRNOG00000049584 AABR07070555.1 −0.13 0.8591 –
MSTRG.4080.13 +∞ 0.0002 Up cis ENSRNOG00000049768 Adcy9 −0.04 0.7770 –
NONRATT025333.2 6.61 0.0000 Up cis ENSRNOG00000051719 AABR07065498.1 0.11 0.9304 –
NONRATT011312.2 +∞ 0.0479 Up cis ENSRNOG00000051911 Rbp3 9.84 0.0000 Up
NONRATT024648.2 -∞ 0.0492 Down cis ENSRNOG00000052540 SNORD113 0.45 0.6631 –
NONRATT005985.2 4.53 0.0490 Up cis ENSRNOG00000053047 Top2a 5.28 0.0000 Up
NONRATT016680.2 7.62 0.0041 Up cis ENSRNOG00000053081 3.59 0.0011 Up
NONRATT009382.2 8.35 0.0257 Up cis ENSRNOG00000056826 Arap2 0.21 0.6941 –
MSTRG.15263.2 -∞ 0.0041 Down cis ENSRNOG00000060329 Emb −0.06 0.7939 –
NONRATT015639.2 3.25 0.0005 Up cis ENSRNOG00000061058 Csde1 0.02 0.8739 –
NONRATT027585.2 7.19 0.0000 Up cis ENSRNOG00000061656 SNORD14 0.17 0.9160 –
Open in a new tab

lncRNA, long noncoding RNA; FC, fold change.

Table IV.

Differentially expressed lncRNA mechanisms involved in trans-regulatory elements.

lncRNA ID log2FC Q value Up/downregulated Type Gene name log2FC P-value Up/downregulated
ENSRNOT00000092040 −3.35 0.0204 Down Trans Ccl9 5.19 0.0017 Up
ENSRNOT00000092040 −3.35 0.0204 Down Trans Homer3 1.00 0.0611 –
ENSRNOT00000088402 +∞ 0.0471 Up Trans Aurkb 2.35 0.0723 –
ENSRNOT00000092040 −3.35 0.0204 Down Trans Galns 1.03 0.0724 –
ENSRNOT00000092040 −3.35 0.0204 Down Trans Slc39a1 0.92 0.0739 –
ENSRNOT00000092040 −3.35 0.0204 Down Trans AC099384.2 +∞ 0.1052 –
ENSRNOT00000092040 −3.35 0.0204 Down Trans Hsd3b7 0.83 0.1369 –
ENSRNOT00000092040 −3.35 0.0204 Down Trans Klrb1b 1.98 0.1413 –
ENSRNOT00000092040 −3.35 0.0204 Down Trans Me2 0.63 0.1558 –
ENSRNOT00000092040 −3.35 0.0204 Down Trans Pnma2 −0.51 0.1564 –
ENSRNOT00000092040 −3.35 0.0204 Down Trans Cass4 1.24 0.1599 –
ENSRNOT00000092040 −3.35 0.0204 Down Trans Klhl23 −0.44 0.2192 –
ENSRNOT00000092040 −3.35 0.0204 Down Trans Elac1 −0.49 0.2260 –
ENSRNOT00000092040 −3.35 0.0204 Down Trans Clcf1 1.26 0.2519 –
ENSRNOT00000092040 −3.35 0.0204 Down Trans Fzd6 0.80 0.2590 –
NONRATT009960.2 4.06 0.0430 Up Trans Lcorl −0.44 0.3199 –
ENSRNOT00000092040 −3.35 0.0204 Down Trans Gpr37l1 0.40 0.3297 –
ENSRNOT00000092040 −3.35 0.0204 Down Trans Mmab −0.35 0.3391 –
ENSRNOT00000092040 −3.35 0.0204 Down Trans Spon1 −0.28 0.3414 –
ENSRNOT00000092040 −3.35 0.0204 Down Trans Ifngr2 0.44 0.3660 –
ENSRNOT00000092040 −3.35 0.0204 Down Trans Foxred2 −0.35 0.3732 –
ENSRNOT00000092040 −3.35 0.0204 Down Trans Prr22 −0.67 0.3889 –
ENSRNOT00000092040 −3.35 0.0204 Down Trans Sppl2a 0.40 0.3894 –
ENSRNOT00000092040 −3.35 0.0204 Down Trans Haus5 0.55 0.4019 –
ENSRNOT00000092040 −3.35 0.0204 Down Trans Cpvl 0.80 0.4041 –
ENSRNOT00000092040 −3.35 0.0204 Down Trans Fam120b −0.24 0.4061 –
ENSRNOT00000092040 −3.35 0.0204 Down Trans Fbxo3 −0.21 0.4206 –
ENSRNOT00000092040 −3.35 0.0204 Down Trans Ypel4 −0.24 0.4477 –
ENSRNOT00000092040 −3.35 0.0204 Down Trans Klhl26 −0.20 0.4480 –
ENSRNOT00000092040 −3.35 0.0204 Down Trans Sync 0.43 0.5076 –
ENSRNOT00000092040 −3.35 0.0204 Down Trans Lhfpl5 −0.31 0.5231 –
ENSRNOT00000092040 −3.35 0.0204 Down Trans Mrpl52 0.44 0.5306 –
ENSRNOT00000092040 −3.35 0.0204 Down Trans Cldn15 −0.39 0.5315 –
ENSRNOT00000092040 −3.35 0.0204 Down Trans Mrps35 −0.18 0.5443 –
ENSRNOT00000092040 −3.35 0.0204 Down Trans Zfp382 −0.17 0.5640 –
ENSRNOT00000092040 −3.35 0.0204 Down Trans Ube2k −0.14 0.5697 –
ENSRNOT00000092040 −3.35 0.0204 Down Trans Drg1 −0.12 0.5755 –
ENSRNOT00000092040 −3.35 0.0204 Down Trans P2rx5 −0.36 0.5770 –
ENSRNOT00000092040 −3.35 0.0204 Down Trans Xpnpep3 0.25 0.5933 –
ENSRNOT00000092040 −3.35 0.0204 Down Trans RGD1311345 0.28 0.5950 –
ENSRNOT00000092040 −3.35 0.0204 Down Trans Acer2 0.32 0.6034 –
ENSRNOT00000092040 −3.35 0.0204 Down Trans Urgcp 0.28 0.6182 –
ENSRNOT00000092040 −3.35 0.0204 Down Trans Megf8 −0.10 0.6223 –
ENSRNOT00000092040 −3.35 0.0204 Down Trans Lrtm2 −0.14 0.6301 –
ENSRNOT00000092040 −3.35 0.0204 Down Trans RGD1562299 0.26 0.6325 –
ENSRNOT00000092040 −3.35 0.0204 Down Trans Rpl9 0.38 0.6347 –
ENSRNOT00000092040 −3.35 0.0204 Down Trans Slc25a44 −0.10 0.6348 –
ENSRNOT00000092040 −3.35 0.0204 Down Trans Ssmem1 −0.28 0.6350 –
ENSRNOT00000092040 −3.35 0.0204 Down Trans Stt3a 0.24 0.6468 –
ENSRNOT00000092040 −3.35 0.0204 Down Trans Spire1 −0.09 0.6631 –
ENSRNOT00000092040 −3.35 0.0204 Down Trans RGD1561777 −0.13 0.6846 –
ENSRNOT00000092040 −3.35 0.0204 Down Trans Luzp1 −0.09 0.6941 –
ENSRNOT00000092040 −3.35 0.0204 Down Trans Acot2 0.26 0.7000 –
ENSRNOT00000092040 −3.35 0.0204 Down Trans Fitm2 −0.07 0.7111 –
ENSRNOT00000092040 −3.35 0.0204 Down Trans Aox4 0.32 0.7374 –
ENSRNOT00000092040 −3.35 0.0204 Down Trans Golga1 −0.04 0.7775 –
ENSRNOT00000092040 −3.35 0.0204 Down Trans Lefty2 1.23 0.7977 –
ENSRNOT00000092040 −3.35 0.0204 Down Trans Zdhhc24 −0.04 0.8075 –
ENSRNOT00000092040 −3.35 0.0204 Down Trans Zfp329 −0.03 0.8205 –
ENSRNOT00000092040 −3.35 0.0204 Down Trans Tmem101 −0.03 0.8236 –
ENSRNOT00000092040 −3.35 0.0204 Down Trans Dhh 0.28 0.8353 –
ENSRNOT00000092040 −3.35 0.0204 Down Trans Ncr3 0.57 0.8354 –
ENSRNOT00000092040 −3.35 0.0204 Down Trans Ppp1r15b 0.14 0.8423 –
ENSRNOT00000092040 −3.35 0.0204 Down Trans Zfyve27 −0.01 0.8439 –
ENSRNOT00000092040 −3.35 0.0204 Down Trans Ints7 0.15 0.8457 –
ENSRNOT00000092040 −3.35 0.0204 Down Trans Dus1l 0.14 0.8545 –
ENSRNOT00000092040 −3.35 0.0204 Down Trans Ahsa2 0.15 0.8576 –
ENSRNOT00000092040 −3.35 0.0204 Down Trans Aif1l 0.15 0.8579 –
ENSRNOT00000092040 −3.35 0.0204 Down Trans Rsg1 0.16 0.8645 –
ENSRNOT00000092040 −3.35 0.0204 Down Trans Fuom 0.15 0.8883 –
ENSRNOT00000092040 −3.35 0.0204 Down Trans Cwf19l1 0.12 0.8894 –
ENSRNOT00000092040 −3.35 0.0204 Down Trans Paqr7 0.01 0.8942 –
ENSRNOT00000092040 −3.35 0.0204 Down Trans Coa5 0.02 0.8985 –
ENSRNOT00000092040 −3.35 0.0204 Down Trans Cdc14b 0.13 0.8989 –
ENSRNOT00000092040 −3.35 0.0204 Down Trans Crebl2 −0.01 0.9081 –
ENSRNOT00000092040 −3.35 0.0204 Down Trans RGD1564541 0.10 0.9210 –
ENSRNOT00000092040 −3.35 0.0204 Down Trans Slc15a1 −0.03 0.9211 –
ENSRNOT00000092040 −3.35 0.0204 Down Trans Ppm1k 0.03 0.9299 –
NONRATT025479.2 −4.14 0.0000 Down Cis and trans Gdf11 0.08 0.9402 –
ENSRNOT00000092040 −3.35 0.0204 Down Trans Fam20b 0.04 0.9436 –
ENSRNOT00000092040 −3.35 0.0204 Down Trans Anapc11 0.09 0.9536 –
ENSRNOT00000092040 −3.35 0.0204 Down Trans Tmem79 −0.04 0.9595 –
ENSRNOT00000092040 −3.35 0.0204 Down Trans Cwc25 0.06 0.9692 –
ENSRNOT00000092040 −3.35 0.0204 Down Trans Blvrb 0.07 0.9859 –
ENSRNOT00000092040 −3.35 0.0204 Down Trans Stk4 0.08 0.9880 –
ENSRNOT00000092040 −3.35 0.0204 Down Trans Rbm20 0.08 0.9896 –
ENSRNOT00000092040 −3.35 0.0204 Down Trans Tbc1d10b 0.06 0.9927 –
ENSRNOT00000092040 −3.35 0.0204 Down Trans Mapkbp1 0.07 0.9986 –
ENSRNOT00000092040 −3.35 0.0204 Down Trans Psma8 1.00 1.0000 –
ENSRNOT00000092040 −3.35 0.0204 Down Trans Rbbp8nl 0.37 1.0000 –
Open in a new tab

lncRNA, long non-coding RNA; FC, fold change.

Validation of expression of DELs by RT-qPCR

From the data in Fig. 6, NONRATT027551.2, MSTRG.1836.1 and MSTRG.4344.10 were identified to be significantly upregulated in the MCAO group compared with the control (P<0.05), consistent with the RNA-seq data, while MSTRG.7720.11, NONRATT005132.2 and MSTRG.20633.3 were significantly downregulated in the MCAO group compared with the control (P<0.01), also consistent with the RNA-seq data. These results, revealing that the RNA-seq results were consistent with the RT-qPCR results, verified that the RNA-seq results were reliable (Fig. 6).

Figure 6.

Figure 6.

Open in a new tab

Validation of lncRNA RNA-seq data by RT-qPCR. Fold changes represent the comparison of the MCAO group with the control group. Blue bars indicate the fold change were detected with RNA-seq. **P<0.01 vs. the control group. The orange bars indicate the fold change detected using RT-qPCR. $P<0.05 and $$P<0.01 vs. the control group. Comparison of the results obtained from RT-qPCR and RNA-seq revealed satisfactory consistency (R2=0.9338). MCAO, Middle cerebral artery occlusion; lncRNA, long noncoding RNA; RNA-seq, RNA sequencing; RT-qPCR, reverse transcription-quantitative polymerase chain reaction.

Discussion

A host of lncRNAs have been indicated to be involved in ischemic stroke by microarray or RNA-seq studies (35,36). Metastasis associated lung adenocarcinoma transcript 1 was identified to have a function in ischemic stroke through inhibiting endothelial cell death and inflammation (36,37). Additionally, the upregulation of H19 imprinted maternally expressed transcript may induce apoptosis and necrosis in cerebral ischemic reperfusion injury (38–40). In the present study, a total of 77 upregulated and 57 downregulated DELs (|FC|>2, P<0.05) were identified through reliable RNA-seq and validated using RT-qPCR in an ischemic stroke group induced by MCAO compared with a control group.

HO-1-mediated neurogenesis has been demonstrated to be enhanced in ischemic stroke in mice (41). HO-1 has been revealed to promote angiogenesis following cerebral ischemic reperfusion injury in rats (42). GO enrichment analysis suggested that HO-1 was associated with responses to alkaloids, cellular responses to oxidative stress and responses to reactive oxygen species. BUB1B has been reported to promote tumor proliferation in glioblastoma (43,44). Similarly, BUB1B has been implicated in tumor growth and the progression of prostate cancer (45), and overexpressed BUB1B has been demonstrated to be involved in lung adenocarcinoma in humans (46). The KEGG enrichment analysis in the present study indicated that BUB1B was associated with the cell cycle. It has previously been reported that upregulated CCL2 is associated with protection from stroke induced by hypoxic preconditioning (47), and the knockdown of CCL2 was used to successfully reverse the drug resistance of tumor cells in gastric cancer (48). In the KEGG enrichment analysis performed in the present study, CCL2 was additionally associated with the cell cycle. Furthermore, based on the data presented in Fig. 3, HO-1, BUB1B and CCL2 may be regulated by a number of novel lncRNAs, including NONRATT008267.2, NONRATT015286.2, NONRATT004791.2, MSTRG.15067.2, NONRATT003289.2, NONRATT004566.2, NONRATT005985.2, NONRATT008198.2, NONRATT028439.2, NONRATT026753.2, NONRATT027268.2, MSTRG.15418.3, NONRATT016680.2, NONRATT015403.2, MSTRG.29693.5, NONRATT009960.2, MSTRG.27670.3 and NONRATT000377.2. A previous study has suggested that the knockdown of DNA topoisomerase IIα (Top2a) may suppress proliferation and invasion of colon cancer cells (49); based on the present regulatory analysis of DELs, Top2a, as a cis-regulatory gene of NONRATT005985.2, may have a vital function in ischemic stroke. Overall, the analyzed data provide novel DELs and an lncRNA-mRNA regulatory network that may provide a better understanding of ischemic stroke induced by MCAO.

Acknowledgements

The authors would like to thank Mr. Qiang Fan (Ao-Ji Bio-tech Co., Ltd., Shanghai, China) for help with data analysis.

Funding

The present study was financially supported by the National Key Research and Development Plan (grant nos. 2017YFC1701600 and 2017YFC1701601), the National Natural Science Foundation of China (grant nos. 81473387, 81503291 and 81703805), the Anhui Provincial Natural Science Foundation of China (grant no. 1508085QH191) and the Key Project of the National Science Fund of Anhui Province (grant no. KJ2013A169).

Availability of data and materials

The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.

Authors' contributions

XD and DP conceived and designed the study. XD and QB performed the experiments. LH, CP, LX and HP analyzed the data and drafted the manuscript. All authors read and approved the final manuscript.

Ethics approval and consent to participate

The present study was approved by The Animal Experiments Ethics Committee of The Anhui University of Chinese Medicine (Hefei, China).

Patient consent for publication

Not applicable.

Competing interests

The authors declare that they have no competing interests.

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Data Availability Statement

The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.

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