Construction of competing endogenous RNA network of long non-coding RNA with microRNA and mRNA in middle cerebral artery occlusion

doi:10.21203/rs.3.rs-1592189/v1

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Construction of competing endogenous RNA network of long non-coding RNA with microRNA and mRNA in middle cerebral artery occlusion

https://doi.org/10.21203/rs.3.rs-1592189/v1

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In our previous studies, we have identified that a series of message RNAs (mRNAs), microRNAs (miRNAs) and long noncoding RNAs (lncRNAs) may contribute to stroke. However, the network of RNA regulation with stroke remains well-known enough. This study explored the regulatory mechanism and function of lncRNAs as ceRNAs in stroke. According to the competitive endogenous RNA(ceRNA) hypothesis, a group of lncRNA, miRNA and mRNAs were found, which revealed the interaction between lncRNA, miRNA and mRNA and stroke. Several important lncRNAs were selected for in-depth analysis including sub-ceRNA-network, GO enrichment analyis, and pathway enrichment analysis. Importantly, the candidate lncRNAs, miRNAs and mRNAs are contributed to the ceRNA network, which can be used as potential therapeutic targets for stroke for further research.

Competing endogenous RNA

Middle Cerebral Artery Occlusion

MCAO

long non-coding RNA

lncRNA-miRNA-mRNA-net

Stroke is recognized as a cerebrovascular accident, which may lead to permanent brain injury, long-term disability and even death [1; 2]. Many biological processes are related to stroke, hypoxia and strong inflammatory response included [3; 4]. There are many types of RNAs, such as microRNAs (miRNAs), long noncoding RNAs (lncRNAs) and even meassage RNAs (mRNAs), involved in RNA-mediated regulatory network [5; 6; 7; 8], and regulate important cellular events through various complex mechanisms [9; 10], which are increasingly argued to involve in ischemic stroke in previous work. However, there is still not well-known of ischemic stroke. To better understand the precise molecular mechanism of ischemic stroke. Novel noncoding RNA (ncRNA) needs further analysis.

LncRNAs has been proved to be a key gene regulator for development and disease ^1–3. LncRNAs could bingding to miRNA as ceRNA element to perform fuctions ⁴, which is the focus of this study. Wei et, al found that lncRNA AK038897 can target miR-26a-5p as ceRNA to DAPK1, further to aggravates cerebral injury by ischemia/reperfusion ⁵. It has been proved that LncRNA SNHG1 can play as ceRNA element to regulates cerebrovascular pathologies by interacting with miR-18a through HIF-1α/VEGF signaling in ischemic stroke ⁶. Notch1 signaling pathway disorder is performed by LncRNA GAS5 regulatesce miR-137 as ceRNA ⁷. LncRNA CHRF, as a kind of ceRNA, regulates ischemic injury by targeting miR-126/SOX6 signaling pathway [18]. LncRNA MEG3 modulates the neuronal death via miR-21/PDCD4 signaling pathway ⁸. Although a large number of lncRNAs, miRNAs and mRNAs are being identified by High throughput sequencing or microarray, to date, the functional RNA molecules in stroke there is not known enough, and RNA-mediated regulation network is urgent to establish.

Based on the hypothesis of ceRNA, a global lncRNA-miRNA-mRNA was constructed by using the data from our previous papers. To better comprehend the effect of the lncRNAs, some subnet of ceRNA-network was selected for further analysis including GO and pathway enrichment.

2.1 Data availability

The MCAO-induced focal cerebral ischemia model prepared as described in above was prepared by using a 7-week-old male Sprague-Dawley rat (weight 200 ± 20g) with a specific pathogen-free grade, and total RNA was isolated from the left hemisphere. Ribosomal RNA was removed from total RNA using Ribo-Zero rRNA removal beads (Illumina, USA). Libraries were bulit according to the standard TruSeq protocol. Under the manufacturer's agreement, the purified cDNA libraries were used for cluster generation and sequencing on Illumina hiseq 2500 (Illumina, Inc., San Diego, California, USA). The first-hand data were provided by Duan et al, which has been described in our previous study ⁹, and the ethics statement was previously provided. In order to generate the high-throughput sequencing data of MCAO rat microRNA, we refer to our previous research [21].

2.2 Differential expression analysis lncRNAs, miRNAs and mRNAs

All known protein-coding genes, miRNAs, and lncRNAs were annotated to ensemble, mirBase and noncode / ensemble, respectively ^10–12. Genes count were quantified by Stringtie ¹³. Protein-coding and lncRNA counts by pruning the M value of mean normalization (TMM)¹⁴, and Perl script number per one million per thousand base transcription in map reading (FPKM) on the number of pieces ¹⁵. Meanwhile, TPM is computational for miRNA. Edger software was used to analyze DELs, DEMis and DEMs of rats with MCAO and control rats. Screening threshold is P < 0.05, |log2 (multiple change) |>1.

2.3 Prediction the DEMis of target lncRNAs and mRNAs

After the DEMis and DELs sequence were obtained from the corresponding database, The DEMis of the DELs were predicted using Miranda ¹⁶ and RNAhybrid ¹⁷, which identifies miRNA and lncRNA interactions. The interactions pairs of miRNA and mRNA were obtained from miRwalk 3.0 (http://mirwalk.umm.uni-heidelberg.de/) ¹⁸.

2.4 Construction of the lncRNA-miRNA-mRNA network

Based on the theory of ceRNA rebuilt the lncRNA - microRNAs- mRNA network as follows ^{19, 20} (1) to calculate the Pearson correlation coefficient (PCC) between DELs and DEMs, and select with PCC > 0.99 and P < 0.01 lncRNA - mRNA expression of as a total of lncRNA - mRNA. (2) For a given co-expressed lncRNA-mRNA pair, both mRNA and lncRNA in the pair are targeted and negatively expressed together with a common miRNA, which is identified as a triplet competing for co-expression. Then, by Cytoscape build and visualize lncRNA - miRNAs - mRNA network [33], finally calculated ceRNA network degree of all nodes.

2.5 Functional analysis

In order to better understand the function of RNA, GO [34; 35] and the KEGG pathway [36] analyses of were run by clusterProfiler package [37]. The package has realized the function of genes and gene cluster analysis, and visualization characteristics (GO and KEGG) method. And statistically significant should set threshold of P < 0.05.

3.1 Screening of differentially expressed lncRNAs, miRNAs and mRNAs

A total of 920 DEMs and 710 DELs with abnormal expression in the MCAO were identified, with thresholds of log2FC > 1.0 and P < 0.05, respectively. Meanwhile, 16 miRNAs with abnormal expression in MCAO were identified using edgeR.

3.2 Construction of miRNA-mRNA and miRNA-lncRNA network

LncRNA could play a ceRNA element by competitively interact with miRNAs. MiRanda and RNAhybrid were performed to prognose the interactions between the lncRNAs and the miRNAs, the lncRNA should perfect nucleotide pair between the seed sequences of miRNAs. At last, 717 pairs of lncRNA-miRNA were obtained, including 16 miRNAs and 121 lncRNAs (Fig. 1). Mirwalk was used for predicting the interactions between miRNA and mRNA, 7813 pairs were identified, Joint analysis with DEM showed that 587 pairs were negatively correlated including 334 target mRNAs and 16 miRNAs (Fig. 2).

3.3 Construction of the lncRNA-miRNA-mRNA network

As shown in Fig. 5A, Combining the miRNA-mRNA and miRNA-lncRNA pairs, by Cytoscape build and visualize the triple lncRNA - miRNAs - mRNA network (Fig. 3), The network presents the interaction among DELs, DEMis and DEMs, and consists of 240 DELs, 16 DEMis, 334 DEMs and 1304 edges. To illustrate the lncRNA competition mechanism and the potential biological function in the MCAO, Cytoscape software was used to study the ceRNA network node degree distribution (Table 1). Figure 4 shows the degree of RNA in ceRNA network.

3.4 LncRNA function was predicted based on the relationship among the three RNA networks of lncRNA-miRNA-mRNA

A total of 334 genes GO and pathway enrichment analysis can be used by clusterProfiler for analysis. For GO analysis, when considering BPs, the top ten enriched terms leukocyte cell-cell adhesion (GO:0007159), regulation of cell adhesion mediated by integrin (GO:0033628), collagen fibril organization (GO:0030199), cell adhesion mediated by integrin (GO:0033627), positive regulation of calcium-mediated signaling (GO:0050850), heterophilic cell-cell adhesion (GO:0007157), monocyte chemotaxis (GO:0002548), response to fluid shear stress (GO:0034405), cellular extravasation (GO:0045123), endoderm formation (GO:0001706). In terms of CC, the top ten enriched terms were integrin complex (GO:0008305), condensed chromosome kinetochore (GO:0000777), condensed chromosome, centromeric region (GO:0000779), kinetochore (GO:0000776), transport vesicle (GO:0030133), external side of plasma membrane (GO:0009897), perikaryon (GO:0043204), caveola (GO:0005901), chromosome, centromeric region (GO:0000775), extrinsic component of cytoplasmic side of plasma membrane (GO:0031234). In terms of MF, oligosaccharide binding (GO:0070492), hyaluronic acid binding (GO:0005540), protein kinase activator activity (GO:0030295), kinase activator activity (GO:0019209), integrin binding (GO:0005178), cytokine binding (GO:0019955), growth factor receptor binding (GO:0070851), monosaccharide binding (GO:0048029), cytokine receptor activity (GO:0004896), growth factor binding (GO:0019838) were enriched for the top10 (Fig. 5A). Pathway enrichment analysis indicated that dozens of pathways were significantly enriched including: Cell adhesion molecules (CAMs) (rno04514), Long-term depression (rno04730), p53 signaling pathway (rno04115), Hematopoietic cell lineage (rno04640), Complement and coagulation cascades (rno04610), Tryptophan metabolism (rno00380), Galactose metabolism (rno00052), Phototransduction (rno04744), Glioma (rno05214), Viral myocarditis (rno05416), Chagas disease (American trypanosomiasis) (rno05142), Cytokine-cytokine receptor interaction (rno04060), ECM-receptor interaction (rno04512), Cell cycle (rno04110), ABC transporters (rno02010), Neuroactive ligand-receptor interaction (rno04080), Gap junction (rno04540), Calcium signaling pathway (rno04020), Focal adhesion (rno04510), etc (Fig. 5B).

3.5 Key lncRNA-miRNA-mRNA sub-network

It is we all known that the central nodes play an extremely critical role in a biological networks. According to the previous study of Han et al., who described the centre as node degree is more than 5, we learned that can choose 39 node as the center, the numbers of lncRNA, miRNA and mRNA were 21,16 and 2, respectively, and the results are shown in Table 1 and Fig. 3. In addition, we also through calculation,obtained the lncRNA-miRNA primary relationship pair and mirNA-mrna secondary relationship between the number of the two. The results are shown in Table 2. The interesting thing is, we found that six lncRNAs (NONRATT023334.2, NONRATT018501.2, NONRATT006800.2, NONRATT017723.2, NONRATT025255.2, NONRATT028448.2) in addition to had a higher on the basis of the node degree, and on this basis, also had a larger number of lncRNA - miRNA and miRNA – mRNA pairs. This suggests that the nine kinds of lncRNA in MCAO has played a key role in the origin and development and could be chosen to the pivotal of lncRNAs. As shown in Fig. 3 and Table 1, rno-miR-653-3pp had the highest node degree, with a node value of 74.

Therefore, we refined three important lncRNAs (total degree > 60) and their related mRNAs and miRNAs in the global triple network, and reconstructed a few new subnets on this basis. As shown in Fig. The lncRNA NONRATT023334.2-miRNA-mRNA sub-network by 1 DEL, 8 DEMis, 43 DEMs and 75 edges (Fig. 6A). The lncRNA NONRATT018501.2-miRNA-mRNA sub-network consists of 1 DEL, 5 DEMis, 43 DEMs and 62 edges (Fig. 7A). The lncRNA NONRATT006800.2-miRNA-mRNA sub-network by1 DEL, 5 DEMis, 40 DEMs and 61 edges (Fig. 8A). GO and pathway analysis indicated that the NONRA TT023334.2-miRNA-mRNA subnetwork was enriched in 86 GO terms and 5 pathways. A total of 81 GO terms and 5 pathways were enriched in the lncRNA NONRA TT018501.2-miRNA-mRNA subnet and 104 GO terms and 4 pathways were enriched in the lncRNA NONRA TT006800.2-miRNA-mRNA subnet. The results demonstrated in the GO interactive network are shown in Figs. 6B, 7B, and 8B. Figures 6B, 7B, and 8B show the top 10 important GO items, with abundant pathways shown in Table 3. These three lncRNAs are associated with calcium signaling pathway (Rno04020) and neuroactive ligand-receptor interaction (Rno04080), respectively.

For the purpose of better understanding of molecular mechanisms with MCAO that will be confide for revealing how to diagnose stroke early and remedy it. So, the RNA-seq and sRNA-seq data of rat brain tissues with MCAO were systematically used for analyzing to identify the regulatory networks that including the lncRNA, miRNA and mRNA. Our work constructs a comprehensive regulatory network consist of lncRNA, miRNA and mRNA to clarify of RNA interaction events acting on the pathogenesis of stroke. In this ceRNA network, we identifed 344 protein-coding genes which participated in the leukocyte cell-cell adhesion, integrin-mediated regulation of cell adhesion, collagen fibril organization, cell adhesion mediated by integrin, calcium-mediated positive regulation of signal transduction, heterophilic cell-cell adhesion, monocyte chemotaxis after ischemic stroke. Consistent with our results, many protein-coding genes such as Mmp9 ^21–23, C5ar1 ^24–26 which be dyregulated with stroke are shown in other previously research.

LncRNAs could bingding to miRNA as ceRNA element to perform fuctions ⁴. Wei et, al found that lncRNA AK038897 can target miR-26a-5p as ceRNA to DAPK1, further to aggravates cerebral injury by ischemia/reperfusion [15]. It has been proved that LncRNA SNHG1 can play as ceRNA element to regulates cerebrovascular pathologies by interacting with miR-18a through HIF-1α/VEGF signaling in ischemic stroke [16]. Notch1 signaling pathway disorder is performed by LncRNA GAS5 regulatesce miR-137 as ceRNA [17]. LncRNA CHRF functions as a ceRNA, regulates ischemic injury by targeting miR-126/SOX6 signaling pathway [18]. LncRNA MEG3 modulates the neuronal death through miR-21/PDCD4 signaling pathway [19]. In our works, we identify some lncRNA can function as ceRNA to regulate stroke by calcium signaling pathway ^27–29, Neuroactive ligand-receptor interaction ³⁰.

In in briefly, our research results show that lncRNAs is doubly regulated in stroke, which indicates that lncRNAs may participate in stroke pathophysiology. In addition, systematic bioinformatics analysis showed that lncRNA several subgroups may through the lncRNA micrornas mRNA axis to participate in the process of stroke pathophysiology. In our works, the systematic lncRNA-miRNA-mRNA network in stroke have been constructed, which discovers the complex functional regulatory network of ncRNAs and protein-coding mRNAs. Sure, Whether regulatory specific lncRNAs can be used as a diagnosis or therapeutic target is the focus of our future research.

By analyzing the data of high throughput RNA-seq and small RNA-seq for MCAO, we construct the network among lncRNA-miRNA-mRNA in brain tissue via MCAO. Some key-lncRNA-miRNA-mRNA were identified. Perhaps, these findings have greatly improved our understanding of the latent mechanisms of lncRNAs, miRNAs and protein-coding mRNAs in MCAO.

MCAO, middle cerebral artery occlusion; lncRNAs, Long non-coding RNAs; DELs, differentially expression lncRNAs; miRNA, microRNA; mRNA, message RNA; DEMs, differentially expression mRNAs; ceRNA, competing endogenous RNAs; PCC, Pearson correlation coefficient; GO, Gene Ontology; KEGG, Kyoto Encyclopedia of Genes and Genomes

Acknowledgments

The authors gratefully acknowledge Mr. Qiang Fan (Ao-Ji Bio-tech Co., Ltd., Shanghai, China) for help in data analysis.

Author Contributions

Furui Chu and Changyi Fei conceived and designed the study. Furui Chu, Ni Wang, Changyi Fei and Lingyu Pan performed the experiments. Yongzhong Wang, Ling Fang, Daiyin Pen, Xianchun Duan and Weidong Chen analyzed the data and drafted the manuscript. All authors read and approved the final manuscript.

Conflict of Interest Statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Funding

This research was supported by the National Natural Science Fund Regional Innovation and Development Joint Fund Project (No.U19A2009), National Natural Science Foundation of China (Grant No. 82074059), Anhui University Collaborative Innovation Project (GXXT-2019-043), Anhui Provincial College Natural Science Research Key Project(No.KJ2019A0466,No.KJ2020A0409), Excellent and Top Talents Program in Colleges and Universities (No. gxyq2019034) and Anhui Provincial Key Laboratory of Traditional Chinese Medicine Compounds (2019AKLCMF03).

Ethics Statement

Bioinformatics analysis based on previous data does not involve ethical proof.

Data Availability

The data used to support the findings of this study are available from the corresponding author upon request.

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Tables 1-3 are not available with this version.

No competing interests reported.

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Construction of competing endogenous RNA network of long non-coding RNA with microRNA and mRNA in middle cerebral artery occlusion

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Version 1

Abstract

Figures

1. Introduction

2. Materials And Methods

2.1 Data availability

2.2 Differential expression analysis lncRNAs, miRNAs and mRNAs

2.3 Prediction the DEMis of target lncRNAs and mRNAs

2.4 Construction of the lncRNA-miRNA-mRNA network

2.5 Functional analysis

3. Results

3.1 Screening of differentially expressed lncRNAs, miRNAs and mRNAs

3.2 Construction of miRNA-mRNA and miRNA-lncRNA network

3.3 Construction of the lncRNA-miRNA-mRNA network

3.5 Key lncRNA-miRNA-mRNA sub-network

4. Discussion

5. Conclusion

Abbreviations

Declarations

References

Tables

Additional Declarations

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