HuR binds to flanking exons and regulates intron retention alternative splicing of cell cycle-related genes in smooth muscle cells

HuR binds to flanking exons and regulates intron retention alternative splicing of cell cycle-related genes in smooth muscle cells | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article HuR binds to flanking exons and regulates intron retention alternative splicing of cell cycle-related genes in smooth muscle cells Yin Shen, Zhihong Liu, Dongdong Xiao, Jianglin Zheng, Bandlamudi Uma Maheswara Rao, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7733072/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 7 You are reading this latest preprint version Abstract The RNA-binding protein HuR influences gene stability and translation, promoting vascular smooth muscle cell proliferation and being linked to inflammation. However, there is a paucity of studies focusing on the role of in HuR in vascular smooth muscle cells. Knocking down HuR in cerebral vascular smooth muscle cells altered gene expression, with 3,300 genes upregulated in cytokine signaling pathways and 1,998 downregulated in cell mitosis pathways. It also affected alternative splicing, resulting in 3,531 events mainly related to RNA splicing and the cell cycle. Pearson analysis linked 33 splicing events with gene expression, including cell cycle genes MCM5, UHRF1, RPA2, and PRC1. eCLIP-seq of HuR identified 5,582 binding peaks in CDS and 3'UTR regions, with 33 related to cell cycle genes like Atf5, Ier3, and Zfp36l2. This study is the first to explore how the HuR gene influences cell cycle gene expression through pre-mRNA alternative splicing in vascular smooth muscle cells, enhancing our understanding of HuR's role in cardiovascular diseases. Health sciences/Cardiology Biological sciences/Cell biology Biological sciences/Genetics Biological sciences/Molecular biology cerebral artery vascular smooth muscle cells HuR alternative splicing eCLIP-seq cell cycle intron retention Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 1 Introduction Cerebral aneurysms (CA), characterized by abnormal dilations of cerebral arteries, pose significant health risks due to their potential to rupture, leading to subarachnoid hemorrhage with high mortality and morbidity rates [ 1 ]. Aneurysms most frequently develop in areas where blood flow causes high wall shear stress (WSS), such as vessel bifurcations [ 1 ]. High WSS incites cerebral aneurysm formation, while low WSS propagates cerebral aneurysm growth through induced proinflammatory process [ 2 ]. The inflammatory process further leads to matrix metalloproteinases (MMPs)-mediated degradation of the extracellular matrix and apoptosis of smooth muscle cells (SMCs), which are the predominant matrix-synthesizing cells and maintain significant plasticity of the vascular wall [ 3 ]. These processes act in concert to weaken the arterial wall progressively, resulting in dilatation, aneurysm formation, and ultimately rupture. In cerebral aneurysm pathology, the phenotypic and functional changes of SMCs from primarily concerned with contraction to a pro-inflammatory and matrix remodeling phenotype play significant roles in cerebral aneurysm formation and rupture [ 4 ]. The proliferation of SMCs also plays an important role in the formation and development of cerebral aneurysms [ 5 ]. Alternative splicing (AS) is functionally important in many contexts, but primarily it diversifies the proteome and functions as key regulatory mechanism during development [ 6 ]. In cardiovascular and cerebrovascular system, AS also exerts crucial roles in the regulation of vascular walls’ function [ 7 – 9 ]. Alternatively spliced transcripts could encode different proteins, which might function distinctly in focal lesion and inflammatory responses of the vascular wall. For example, deep transcriptome sequencing of subarachnoid hemorrhage (SAH) patients revealed that some of the ELF2 ( E74-like factor 2 ) transcripts had opposite directions of expression changes in acute phase (RAA) compared to the chronic phase (RAC). The isoform upregulated in RAA is built from the initial 7 exons, whereas the downregulated transcript in RAC, possesses the terminal 6–7 exons [ 10 ]. The SNP (single nucleotide polymorphism) associated with CA in Versican gene resulted in two transcripts, the difference of which involved the retention of two largest exons (6 and 7), and the larger one had glycosaminoglycan attachment sites which can bind chondroitin sulfate chains exerting antiadhesive function. In patients with CA, the splicing process of Versican may be altered resulting in a higher proportion of larger isoforms and thus in diminished extracellular matrix assembly [ 11 ]. Hu antigen R (HuR), also known as HuA or ELAVL1, is one of the best-studied RNA-binding post-transcriptional regulators, which belongs to mammalian embryonic abnormal lethal vision-like (ELAVL) protein family with three other members (i.e. HuB, HuC, and HuD) [ 12 ]. HuR is ubiquitously expressed in human tissues and predominantly localized in the nucleus of resting cells [ 13 ]. HuR contains three RNA recognition motifs (RRMs) and usually targets mRNAs containing AU-rich elements at their 3’untranslated region, regulating mRNA transport from nucleus-to-cytoplasm, stability and translation [ 14 ]. Dysregulation of HuR is linked to diseases such as cancer, neurodegenerative disorders, and immune-related disorders [ 14 ]. HuR has also been reported to involve in alternative splicing of itself and other proteins. For example, the excessive HuR proteins bind to GU-rich element overlapped with its major polyadenylation signal (PAS2) in 3’UTR region, reducing PAS2’s recruitment of the CstF-64 subunit of the pre-mRNA cleavage stimulation factor and producing long HuR mRNA species, which contains an AU-rich element (ARE) that destabilizes the mRNAs and thus reduces the protein production output [ 15 ]. In breast cancer, HuR binds to intron 14 of glutaminase, favoring the kidney-type (KGA) isoform during splicing, and binds to the 3’UTRs of glutaminase isoform C (GAC) and KGA, increasing KGA and GAC’s mRNA stability. Knocking down HuR reduces KGA but increases GAC levels, enhancing glutamine anaplerosis into the TCA cycle and driving cells towards glutamine dependence [ 16 ]. Besides, ELVAL1 was predicted as marker genes distinguishing individuals with CA from healthy individuals and might involve in the proliferation of vascular smooth muscle cell [ 17 , 18 ]. However, less reports concern about HuR’s function in CA, especially in smooth muscle cells. In the current study, we report a comprehensive study of HuR regulation model in smooth muscle cells. In order to investigate the function of HuR protein in cerebral artery vascular smooth muscle cells, we knocked down HuR in these cells and conducted RNA-seq. The RNA-seq data showed that knocking down HuR led to the downregulation of cell cycle-related genes and upregulation of cytokines-mediated signaling-associated genes. HuR also contributed to differential alternative splicing, especially intron retention AS events in smooth muscle cells. Some genes that undergo intron retention splicing events contain premature termination codons. eCLIP-seq analysis showed that HuR predominantly binds to an exon facilitates splicing of the adjacent intron. Several cell cycle-related genes, such as Atf5 , Ier3 and Zfp36l2 , were significantly downregulated and these genes also contained intron retention AS events. These findings indicate that HuR affects the proliferation of smooth muscle cells by regulating alternative splicing events related to intron retention in cell cycle-associated genes. 2 Results 2.1 RNA-seq profiling elucidated the comprehensive transcriptional regulation of HuR in cerebrovascular smooth muscle cells To explore the function of HuR in cerebrovascular smooth muscle cells, the lentiviruses achieving HuR knockdown were established to infect mouse cerebral artery vascular smooth muscle cells. The efficacy of the viruses was examined and found the protein and transcript levels of HuR were significantly reduced (Fig. 1 A-B). Among three well-established lentiviruses, shRNA-1 led to the most reduced effect (approximately 50%) and was selected for further research (Fig. 1 B-C). To do the RNA-seq profiling, we constructed cDNA libraries prepared from control (NC) and HuR knockdown cells(shRNA-1) (three biological replicates). Effective depletion of HuR was further confirmed in RNA-seq analysis (Fig. 1 D). Principal component analysis (PCA) revealed that the samples divided into two distinct groups: NC and shRNA-1 (Fig. 1 E). Overall, HuR downregulation led to a noticeable decrease in transcript distribution in coding sequence (CDS) and an increase in introns (Fig. 1 F), suggesting that HuR knockdown promotes more intron retention genome-wide. In total, 5,298 genes were differentially expressed between the two groups, with 3,300 up-regulated and 1,998 down-regulated (Fig. 1 G and Table S1 shRNA-1 vs NC DEG ). In the biological process terms of GO analysis, the up-regulated DEGs in the HuR -knockdown cells were mainly enriched in monoatomic ion transmembrane transport, cytokine-mediated signaling and metal ion transport (Fig. 1 H and Table S2 DEGs enrich go BP ). The down-regulated DEGs were primarily enriched in cell division-related processes, such as mitotic sister chromatid segregation, nuclear chromosome segregation and mitotic nuclear division (Fig. 1 I and Table S2 DEGs enrich go BP )), particularly involving genes Foxm1 , KIF20A , ATF5 and KIF23 ( Fig. S1 A-D ). This result suggests HuR might affect cell division in cerebrovascular smooth vascular cells. 2.2 Alternative splicing events in smooth muscle cells were significantly regulated by HuR Given HuR was a multifunctional RNA binding protein, we inferred it might function as a splicing factor in smooth muscle cells. Thus, we conducted splicing site usage variation analysis (SUVA) to analyze AS events in depth, where the alternative splicing events were classified into five types: alt3p (alternative 3' splice site), alt5p (alternative 5' splice site), IR (intron retention), contain (splice junction containing), and olp (splice junction overlapping) [ 19 ]. The results indicated that IR events were the most affected differentially alternative splicing (DAS) events, followed by and alt5p and alt3p (Fig. 2 A and Table S3 diff RAS events suva stat ). This observation was consistent with the increased distribution of intron reads in HuR knockdown cells, suggesting that introns were more frequently retained after HuR knockdown. Corresponding to the classical splicing events, cassette exon events were the most frequently detected splicing events after HuR knockdown, followed by A5SS (alternative 5' splice site) and A3SS (alternative 3' splice site) (Fig. 2 B). Overall, the complex DAS events were more than simple events. We detected more than 1000 novel DAS events. This result highlighted the complexity and exploitability of AS events in smooth muscle cells (Fig. 2 C-D). Due to the low proportion of two transcripts involved in a single AS event across the gene, pSAR (proportion of each SUVA AS event reads) was proposed to measure the percentage of each AS event reads within its region. A total of 3,531 DAS events with pSAR over 50% were identified for further analysis (Fig. 2 E). The detailed pSAR values of AS events were listed in Supplemental Table 4 . PCA showed those DAS events with pSAR > 50% were also clearly divided into two subgroups, NC and shRNA-1 group. These data suggested that HuR globally regulated alternative splicing in smooth muscle cells (Fig. 2 F). The heatmap showed that most DAS events with a pSAR > 50% had increased splicing ratios following HuR knockdown, suggesting that HuR knockdown raised the proportion of AS events in smooth muscle cells (Fig. 2 G). GO-BP enrichment analysis revealed the genes regulated by HuR-mediated alternative splicing were highly enriched for mRNA processing, RNA splicing, chromatin remodeling and mitotic cell cycle phase (GO biological process terms, Fig. 2 H). Network analysis representation illustrates the top five enriched Gene Ontology (GO) biological processes derived from dataset, along with their associated genes, thereby highlighting the core position of RNA splicing and cell cycle-related proteins (Fig. 2 I). These findings demonstrate that HuR significantly influences AS events, particularly intron retention events, in cerebrovascular smooth muscle cells. 2.3 HuR may modulate genes expressions by regulating IR alternative splicing events in cerebrovascular smooth muscle cells To analyze whether intron retention serves as the principal mechanism by which HuR regulates AS events across various cell types. We download RNA-seq data from human skin fibroblasts (GSE161811) and human MIA-PaCa2 pancreatic cancer cells (GSE167525), each containing normal control and HuR knockdown groups, which were utilized to perform SUVA analysis. The results demonstrated that, in contrast to smooth muscle cells, HuR knockdown predominantly influenced alt5p and alt3p AS events in human skin fibroblasts and pancreatic cancer cells, respectively, thereby proving the cell specificity of HuR’s impact on AS events (Fig. 3 A). We analyzed GO-BP enrichment for genes with IR events in smooth muscle cells and found enrichment in cell cycle pathways like chromatin remodeling and mitotic cell cycle phase. This suggested HuR may influence smooth muscle cell proliferation through affect these genes IR events (Fig. 3 B). To examine whether genes undergoing IR alternative splicing were also impacted at the expression level, we identified the intersection between genes exhibiting IR events and DEGs. Venn diagram was drawn between DEGs and IR events genes. We found 109 genes with IR events were up-regulated and 132 genes with IR events were down-regulated (Fig. 3 C). Person correlation analysis were subsequently performed to identify genes whose expressions were significantly associated with IR events. Of the 33 genes exhibiting significantly correlation, 23 genes, including those related to the cell cycle-cycle, such as Mcm5 , Uhrf1 , Rpa2, Prc1 and Eme1 were found to be down-regulated, while 10 genes were up-regulated (Fig. 3 D). In light of the possibility that down-regulated expressions associated with IR events may result from premature terminated codons (PTCs), we also analyzed PTCs in these genes. Our findings revealed that more than half (14/23) down-regulated genes had PTCs, including Mcm5 , Uhrf1 and Rpa2 ( Fig. S2 ). Collectively, these results implied that HuR might participate improve smooth muscle cell proliferation by inducing cell cycle genes IR events. 2.4 eCLIP-seq data demonstrated that HuR specifically bond to CDS region and regulated alternative splicing To further investigated the association between HuR binding molecules and HuR-regulated alternative splicing genes. We proceeded enhanced cross-linking and immunoprecipitation sequencing (eCLIP-seq) and obtained a transcriptome-wide binding profile of HuR in mouse cerebrovascular smooth muscle cells. Two replicate libraries of HuR CLIP-seq (IP_1 and IP_2) were subjected to sequencing. The sequencing results revealed binding peaks totaling 14,549 and 12,589 respectively for the IP_1 and IP_2 assays. A total of 5,582 peaks were found to be overlapped between the two replicates (Fig. 4 A). The peaks distribution showed a broad range of binding sites to CDS region (68.2%) and 3’UTR region (11.42%) (Fig. 4 B). Motif analysis revealed that HuR mainly bond UA-rich sequence (Fig. 4 C). This binding feature was inconsistent with previous reports in the literature. It was hypothesized that this may represent a specific binding characteristic of HuR in vascular smooth muscle cells. To examine the association between HuR binding enrichment and splicing regulation, we constructed a HuR splicing map. This map integrates eCLIP enrichment data for splicing events responsive to HuR knockdown, averaged across a meta-exon. HuR eCLIP enrichment at the flank exon correlated with intron inclusion (Fig. 4 D). HuR protein was associated with increased intron inclusion upon knockdown. To investigate the regulatory role of HuR in cell cycle-related genes, we conducted an integrated analysis of HuR-bound genes associated with the cell cycle, focusing specifically on genes exhibiting IR events. We performed integrated analysis and found that 33 cell cycle-related genes overlapped between HuR-RASGs and HuR binding peaks in two replicates, including Rpa2 , Atf5 , Zfp36l2 and Ier3 (Fig. 4 E and Supplemental Table 5 ). This result indicated that HuR might directly bind to these targets and regulate IR alternative splicing in smooth muscle cells. Globally, the expression of HuR-associated intron was increased when HuR expression was down-regulated, indicating a negative correlation between HuR association and the intron inclusion. Here, we further selected Atf5 as an example to study the effect of HuR binding on the pre-mRNA splicing. Transcription factor Atf5 was abnormal expression in intracranial aneurysm superficial temporal artery samples [ 20 ]. In this study, the HuR eCLIP-seq dot plot revealed the presence of two HuR-bound peaks located in exon 1 and exon 2 of Atf5. As illustrated in the upper pannel of Fig. 4 F, an intron between exon 1 and exon 2 was inclusion following HuR knockdown. Comparable regulatory modules could also be observed in the cell cycle-related genes Ier3 and Zfp36l2 ( Fig. S3 ). HuR exhibited binding peaks on the exon regions flanking the introns of these genes. Silencing HuR would likely to enhance the retention of the central introns. 2.5 HuR also bound to the 3’UTR region of genes and modulated mRNA stability Previously, HuR had been documented to bind to 3’UTR region to modulate RNA stability [ 21 , 22 ]. Therefore, we conducted a more detailed analysis the correlation between the binding peaks within 3’UTR region and DEGs. As a result, a total of 123 genes with binding peaks in 3’UTR region were differentially expressed, of which 103 genes were down-regulated and 20 were up-regulated (Fig. 5 A). Following the HuR knockdown, the upregulated genes were predominantly enriched for the positive regulation of cold-induced thermogenesis (Fig. 5 B). Down-regulated genes were enriched in cell-matrix adhesion, intracellular transport and aortic valve morphogenesis (Fig. 5 C). The distribution of reads indicated that HuR bond to the 3’UTR region of Rcc2 gene. Furthermore, the expression of the gene Rcc2 was significant reduced following HuR knockdown (Fig. 5 D). In conclusion, HuR played diverse roles in cerebrovascular smooth muscle cells. Specifically, HuR bond to the CDS region of cell cycle-realted genes, facilitating intron exclusion, which in turn promoted their expressions to influence the proliferation of smooth muscle cells. Conversely, HuR bond to the 3’UTR region of extracellular matrix-related genes, thereby increasing mRNA stability and ensuring the proper execution of transcription and translation processes (Fig. 6 ). 3 Discussions Cerebral aneurysm (CA) is a severe cerebrovascular disease characterized by abnormal bulging of cerebral vessels that may rupture and cause a stroke. The expansion of the aneurysm is accompanied by the remodeling of vascular matrix, which is highly dependent on the phenotype of vascular smooth muscle cells (VSMCs) [ 23 ]. The phenotypic switching of VSMC is considered to be bidirectional, including the physiological contractile phenotype and alternative synthetic phenotype in response to injury [ 24 ]. The phenotypic transition, proliferation and migration of SMCs play variable roles in the formation and progression of cerebral aneurysms [ 25 ]. As a multifunctional RNA binding protein, HuR is involved in the regulation of mRNA alternative and stability. Several high-throughput approaches have been employed to comprehensively identify HuR targets, revealing and enrichment of HuR-bound peaks at 3’UTRs [ 22 , 26 ]. Furthermore, certain evidence has demonstrated that HuR serves as a biomarker distinguishing CA patients from healthy control and may play a role in regulating the proliferation of smooth muscle cells [ 17 , 18 ]. However, there is still no functional researches of HuR in smooth muscle cells. First, we knockdown HuR in smooth muscle cells, performed RNA sequencing and comprehensive analysis, and proved HuR’s regulation of DEGs and IR AS events, especially cell cycle-related genes. Then we initially employed the eCLIP-seq approach to elucidate the interactions between HuR and RNA within smooth muscle cells. Our findings enhance the comprehension of HuR’s role in cerebrovascular smooth muscle cells and contribute to elucidating the molecular pathology of CA. In the present research, we revealed large amount of differentially expressed genes regulated by HuR. Interestingly, we found that HuR activates the expression of numerous genes associated with the cell cycle in SMCs ( Supplemental Table 2 ). To be specific, transcription factor (TF) ATF5 (cyclic AMP-dependent transcription factor) was found abnormally expressed in arterial tissues from CA patients and also reported to promote the proliferation of cerebral cortical neuroprogenitor cells [ 20 , 27 ]. Another TF FOXM1 (forkhead box protein M1) was found to promote pulmonary artery smooth muscle cell expansion in pulmonary arterial hypertension [ 28 ]. Kinesin-like protein KIF23 was also found to be significantly increased in pulmonary arterial smooth muscle cells (PASMCs) and inhibiting KIF23 alleviated IPAH (idiopathic pulmonary arterial hypertension) through targeting pyroptosis and proliferation of PASMCs [ 29 ]. Another same family member KIF20A was also validated to be significantly upregulated both in the lung tissue of hypoxia-induced pulmonary hypertension mice and proliferative PASMCs [ 30 ]. In this study, the expression of Atf5, Foxm1, Kif23 and Kif20a were observed to be downregulated following HuR knockdown. Additionally, we also found the protein level of HuR was elevated in rat vascular wall tissues undergoing aortic endothelial balloon injury, a model characterized by the proliferation of smooth muscle cells. We proposed that Atf5, Foxm1, Kif23 and Kif20a might be associated with SMCs proliferation. And the transcriptional activity of these genes may be modulated by HuR protein. Notably, we first found the proportion of introns in the whole transcripts increased following HuR knockdown. It is hypothesized that HuR might contribute to an increased prevalence of these sequences through its regulatory role in alternative splicing. Subsequent SUVA analysis revealed that the most prevalent AS events following HuR knockdown was IR splicing events (= 1727/4690 = 36.8%). An analysis of previously published HuR knockdown RNA-seq data from human skin fibroblasts and MIA-PaCa2 pancreatic cancer cells revealed that the alternative splicing model exhibited cell-specific characteristics in cerebrovascular smooth muscle cells. Intron retention (IR) is a form of alternative splicing that has long been neglected in mammalian systems, because it was generally assumed mis-splicing, leading to the retention of introns, and had no physiological consequence other than reducing gene expression by nonsense-mediated decay (NMD) [ 31 ]. However, currently IR has been revealed as an independent mechanism of controlling and enhancing the complexity of gene expression, and it can also facilitate rapid responses to biological stimuli and generate novel protein isoforms [ 32 ]. The most exciting discoveries have highlighted the pivotal role that IR serves in normal and disease-related human biology. For example, neoepitopes derived from retained intron are processed and presented on MHC-I on the surface of cancer cell lines, which is considered for prospective personalized cancer vaccine development [ 33 ]. The transcript comparison between patients in RAA and controls adopting venous whole blood also found a pronounced decrease in the exon/intron ratio score for the differentially downregulated genes [ 10 ]. Combining our findings, we may infer HuR might be a key regulator of CA relying on intron retention, but further investigations are still needed. To substantiate our hypothesis that HuR selectively interacts with mRNA in vascular smooth muscle cells and modulates intron retention via alternative splicing mechanisms, we performed eCLIP-seq analysis targeting HuR. eCLIP-seq revealed that HuR bound to CDS region.Previously, iCLIP (individual nucleotide resolution cross-linking and immunoprecipitation) analysis of HuR protein in B cells showed 75% of HuR-RNA crosslink site were mapped to introns [ 34 ]. PAR-CLIP (photoactivatable-ribonucleoside-enhanced cross-linking and immunoprecipitation) analysis in Hela cells showed HuR binding sites are mainly enriched in 3’UTR region and introns are followed [ 22 ]. iCLIP analysis in MCF-7 cells revealed the main binding sites of HuR are in 3’UTR and exon region [ 26 ]. In our research, eCLIP-seq analysis in smooth muscle cells revealed HuR mainly bond in CDS region (68.2%) and 3’UTR region was followed, which is different from the reported conclusion and highlighting the cell specific regulation of HuR in AS events. But what decides the cell-specific differences needs more experiment investigations. We also integrated the RNA-seq data and the eCLIP-seq data. The results showed that in vascular smooth muscle cells, HuR enrichment at the flank exon promoting the intron inclusion. We introduced Atf5 pre-mRNA as a target of HuR protein. We showed that the downregulation of HuR in SMCs led to an increase of intron 1 inclusion. Atf5 could promote the proliferation of neural progenitor cells. Therefore, this finding provided a new proof that HuR could be implicated in SMCs proliferation processes, which was helpful to discover novel biological functions of HuR in the process of CA. 4 Materials and methods 4.1 Lentiviral packaging HuR-targeting shRNA constructs were assembled into lentiviral vector (pLVshRNA-EGFP(2A)-Puro) to knock down HuR’s expression. The shRNA sequences were designed and synthesized by Wuhan Boyuan Biotechnology Co., Ltd. The shRNA sequences were as follows. shRNA-1: 5’- GACATTGGGAGAACGAATTTACTCGAGTAAATTCGTTCTCCCAATGTCTTTTTG-3’; shRNA-2: 5’-GACATTGGGAGAACGAATTTAACTCGAGTTAAATTCGTTCTCCCAATGTTTTTTG-3’; shRNA-3: 5’-GCATTGGGAGAACGAATTTAATCTCGAGATTAAATTCGTTCTCCCAATGTTTTTG-3’. Lentivirus was packaged in HEK 293T cells using a three-plasmid system, including the shRNA expression plasmid, psPAX2 (packaging plasmid), and pMD2.G (envelope plasmid). Transfection was performed with Lipofectamine 3000 (Thermo Fisher Scientific) and lentiviral particles were collected from the culture supernatant 48 hours post transfection. The harvested viral supernatants were filtered through a 0.45 µm filter and concentrated by ultracentrifugation. 4.2 Cell culture Mouse cerebral artery vascular smooth muscle cells were purchased from Shanghai Zhong Qiao Xin Zhou Biotechnology Corporation (PRI-MOU-00160), and infected with the lentivirus in the presence of 8 µg/mL polybrene (Sigma-Aldrich). After 48 hours, the infected cells were screened by puromycin (2 µg/mL) and the efficiency was validated by quantitative reverse transcription PCR (RT-qPCR) and Western blot analysis. HEK 293T and vascular smooth muscle cells were maintained in high-glucose DMEM (Hyclone) supplemented with 10% fetal bovine serum (Gibco) and 1% penicillin-streptomycin. Mouse cerebral artery vascular smooth muscle cells were incubated in the complete medium (PCM-M-160) from Shanghai Zhong Qiao Xin Zhou Biotechnology Corporation. The incubation temperature was set at 37℃ with 5% CO 2 atmosphere. 4.3 Western blotting IP Lysis buffer (87787, Thermo Fisher Scientific) with Protease inhibitors Cocktail (04693132001, Roche) was adopted to extract the protein from cells, and the extraction was facilitated by ultra-sonication. The protein quantification was completed by BCA Protein Assay Kit (Thermo Fisher Scientific) and the protein separation and transfer were completed using SDS-PAGE gels and PVDF membranes (IPVH00010, Millipore). The blocking and the washing of membranes were proceeded in TBST buffer. The incubation of membrane and the first and second antibodies were at 4℃ and 25℃ respectively. The first antibodies used in this research were as follows: HuR (66549-1-lg, Proteintech, USA); GAPDH (60004-1-lg, Proteintech, USA) and β-actin (66009-1-lg, Proteintech, USA). Signals were detected by ECL (Tanon5200, Tanon) and quantified using ImageJ (NIH, v1.53e), normalized to β-actin/GAPDH. 4.4 Quantitative RT-PCR The RNA extraction of cells was proceeded in TRIzol reagent (15596026, Invitrogen) and following the manufacturer’s guidelines. The first strand synthesis was completed using NovoScript® Plus All-in-one 1st Strand cDNA Synthesis SuperMix (gDNA Purge) (E047, NovoProtein). NovoStart® SYBR High-Sensitivity qPCR SuperMix (E099, NovoProtein), the primers and cDNA (complementary DNA) were mixed and subjected to ABI QuantStudio™ 6 Flex System (ABI). The quantification were performed as per the established protocol. The primers used in this study were as follows: HuR (mouse)-F: AGACTGCAGGGATGACATTG; HuR (mouse)-R: CAGACTTCGTAGTTCCTCTTGG; β-actin (mouse)-F: GAGGTATCCTGACCCTGAAGTA; β-actin (mouse)-R: CACACGCAGCTCATTGTAGA. 4.5 RNA sequencing (RNA-seq) RNA-seq library preparation and sequencing were performed by Novogene Co., Ltd. Total RNA was extracted from vascular smooth muscle cells using TRIzol reagent (15596026, Invitrogen) according to the manufacturer’s protocol. Messenger RNA (mRNA) was purified from total RNA using poly-T oligo-attached magnetic beads. Following purification, the mRNA was fragmented into small pieces under elevated temperature. The first-strand cDNA was synthesized using random hexamer primers and reverse transcriptase, followed by second-strand cDNA synthesis. The resulting cDNA fragments underwent end repair, A-tailing, and ligation to sequencing adapters. After adapter ligation, cDNA fragments of the desired size were purified and amplified by PCR to create the final library. Libraries were quantified and assessed for quality using the Agilent 2100 Bioanalyzer. The libraries were sequenced on the Illumina NovaSeq 6000 platform to generate paired-end 150 bp reads. Raw sequence reads were quality-checked using FastQC ( http://www.bioinformatics.babraham.ac.uk/projects/fastqc ) and trimmed with fastp to remove low-quality bases and adapters. Clean reads were aligned to the mouse reference genome (GRCm39) using STAR (v2.7.10b), and uniquely mapped reads were used to calculate the read counts and normalized expression levels, measured as reads per kilobase of exon per million mapped fragments (FPKM) for each gene. Principal component analysis (PCA) was performed using the factoextra package ( https://cloud.r-project.org/package=factoextra ) to visualize clustering among samples. Heatmaps of gene expression and splicing ratios were generated using the pheatmap package ( https://cran.r-project.org/web/packages/pheatmap/index.html ), with Euclidean distance used for hierarchical clustering. The statistical analysis were conducted in R (v4.3.2) or Python (v3.9). 4.6 Differentially expressed genes (DEGs) analysis Differential expression analysis was performed using the DESeq2 package (v1.38.0) in R [ 35 ]. Gene expression levels were normalized to fragments per kilobase per million mapped reads (FPKM) before analysis. Differentially expressed genes (DEGs) were identified based on a fold change (FC) threshold of ≥ 2 or ≤ 0.5 and a false discovery rate (FDR) ≤ 0.05. Volcano plots were generated to visualize DEGs. 4.7 Splicing site usage variation analysis (SUVA) The alternative splicing events and regulated alternative splicing events (RAS) were defined and quantified by using the SUVA pipeline as described previously [ 19 ]. Splicing ratio difference and proportion of SUVA AS event reads (pSAR) of each AS events were calculated. To identify premature termination codons (PTCs) in IR (intron retention) events, a custom Python pipeline was used. Gene annotations in GTF format were parsed to extract exon and CDS coordinates, and the mRNA sequence with the retained intron was reconstructed. The CDS sequence was translated in silico to detect stop codons, and their positions were mapped to the retained intron to classify PTC status as "PTC_in_Intron," "PTC_not_in_Intron," or "No_PTC." Genome sequences in FASTA format were processed using Biopython, and IR events were annotated with their genomic regions (e.g., CDS, UTR, or non-coding) and PTC positions. This analysis determined the potential for IR events to trigger nonsense-mediated decay (NMD). 4.8 Functional enrichment analysis To identify functional categories of genes, we employed the clusterProfiler package (v4.6.2) [ 36 ], which enabled us to determine Gene Ontology (GO) terms and KEGG pathways. 4.9 eCLIP sequencing and analysis eCLIP (enhanced crosslinking and immunoprecipitation) was performed as previously described with minor modifications to detect HuR-binding sites in vascular smooth muscle cells [ 37 ]. Briefly, cells were UV crosslinked at 254 nm with an energy of 400 mJ/cm² to covalently stabilize RNA-protein complexes. Crosslinked cells were lysed and subjected to limited RNase I digestion to fragment unbound regions. A small aliquot (2%) was retained as the size-matched input (SMInput), while the majority (98%) proceeded to immunoprecipitation (IP) with anti-HuR antibody (Abcam, ab200342). After dephosphorylation and 3’ RNA adapter ligation, complexes were separated by SDS-PAGE and transferred to a nitrocellulose membrane. A gel slice 75 kDa above the expected molecular weight of the RNA-binding protein was excised. Protein-RNA complexes were recovered by Proteinase K digestion, and co-purified RNA was extracted. Reverse transcription was performed using primers with unique inline barcodes and randommers to enable PCR duplicate removal and error correction. The resulting cDNA was ligated to a 3’ DNA adapter, PCR amplified, and size-selected to complete the library. Libraries were sequenced using an Illumina NovaSeq platform with paired-end 150 bp reads. Raw reads were quality filtered using Cutadapt [ 38 ] to remove adapters and low-quality bases. rRNA contamination was removed using SortMeRNA [ 39 ]. Unique molecular identifiers (UMIs) embedded in the adapters were extracted, and reads sharing the same UID sequence were grouped into clusters. Within each UID cluster, reads were further sub-clustered based on sequence similarity (≤ 5 nt mismatch), followed by consensus sequence generation to perform error correction and deduplication. UID sequences were also error-corrected with a 1 nt mismatch tolerance. Clean reads were aligned to the mouse genome (GRCm39) using STAR [ 40 ]. To identify high-confidence binding regions, we clusters reads with at least 1 bp overlap into candidate peaks. For each gene, simulated random reads were generated 500 times with the same count and length distribution as real reads to model a null distribution. Peaks with heights exceeding the maximum height of the simulated random peaks (p-value < 0.05) were retained. Peak regions were annotated using HOMER annotatePeaks.pl, and motif enrichment analysis was performed on peak regions using HOMER findMotifs.pl [ 41 ]. Genes with HuR peaks in 3′UTRs were intersected with differentially expressed genes (DEGs) identified from RNA-seq analysis. To investigate HuR’s role in splicing regulation, peaks were integrated with intron retained (IR) events. Metagene analysis was performed to visualize HuR binding density near splice sites (± 50 bp), stratified by IR change direction (inclusion, exclusion, or no change). All genomic visualizations were generated using in-house scripts. 4.10 Statistical analysis The data in this research was analyzed using GraphPad Prism 8.0 (San Diego,USA), and was presented as the mean ± standard deviation (SD). Data was analyzed using student’s t-test for two groups or one-way ANOVA for multiple groups. P value of less than 0.05 was considered statistically as significant. Declarations Author contributions Yin Shen and Haifeng Yang were in charge of most of the investigations, data analysis and the draft writing. Dongdong Xiao, Zhihong Liu and Jianglin Zheng performed parts of the cell experiments. Bandlamudi Uma Maheswara Rao helped drawing and data organization. Xiaobing Jiang and Haifeng Yang led the design of the entire research and revised the manuscript. Funding No funding was received. Ethics Statement This study was performed in line with the principles of the Declaration of Helsinki. Approval was granted by the Ethics Committee of Union Hospital, Tongji Medical College, Huazhong University of Science and Technology. Competing interests The authors have no relevant financial or non-financial interests to disclose. Data availability Statement iCLIP-seq and RNA-seq data have been deposited in National Genomics Data Center with accession number PRJCA042738 Competing interests The authors declare no competing interests. Acknowledgments We are very thankful to members in Dr. Wen Chen’s team for their helpful discussions and experimental advice. References C. Toader, M.P. Radoi, C.A. Covlea, R.A. Covache-Busuioc, M.M. Ilie, L.A. Glavan, A.D. Corlatescu, H.P. Costin, M.D. Gica, N. 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Additional Declarations No competing interests reported. Supplementary Files S5actin.tif Suptable1shRNA1vsNCDEG.xls S4WBofHuR.tif Suptable5peakgeneolprasg.xls S5actin.tif Suptable3diffRASeventssuvastat.xls Suptable4pSARall.RAS.filter.0.550.xls Suptable2DEGsenrich.go.BP.xls FigureS1S2S3.docx Cite Share Download PDF Status: Under Review Version 1 posted Reviews received at journal 12 May, 2026 Reviewers agreed at journal 30 Apr, 2026 Reviewers invited by journal 23 Feb, 2026 Editor assigned by journal 03 Feb, 2026 Editor invited by journal 14 Oct, 2025 Submission checks completed at journal 09 Oct, 2025 First submitted to journal 09 Oct, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7733072","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":598552690,"identity":"5ed155e6-c8ce-42b7-b078-ea96e74085ee","order_by":0,"name":"Yin Shen","email":"","orcid":"","institution":"Huazhong University of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Yin","middleName":"","lastName":"Shen","suffix":""},{"id":598552691,"identity":"88529ae9-8f21-4732-b1cf-f8392af42bda","order_by":1,"name":"Zhihong Liu","email":"","orcid":"","institution":"Wuxue First People's Hospital","correspondingAuthor":false,"prefix":"","firstName":"Zhihong","middleName":"","lastName":"Liu","suffix":""},{"id":598552692,"identity":"87d681ec-5c50-4271-a702-0a5ed57ab708","order_by":2,"name":"Dongdong Xiao","email":"","orcid":"","institution":"Huazhong University of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Dongdong","middleName":"","lastName":"Xiao","suffix":""},{"id":598552693,"identity":"a299ae8e-1926-4dcf-9fbe-d82787e712c7","order_by":3,"name":"Jianglin Zheng","email":"","orcid":"","institution":"Huazhong University of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Jianglin","middleName":"","lastName":"Zheng","suffix":""},{"id":598552694,"identity":"7966d8e6-da31-420f-a5d5-43fd33ba76f0","order_by":4,"name":"Bandlamudi Uma Maheswara Rao","email":"","orcid":"","institution":"Huazhong University of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Bandlamudi","middleName":"Uma Maheswara","lastName":"Rao","suffix":""},{"id":598552695,"identity":"e2eac31a-a141-4de8-b035-e4878bcc98fc","order_by":5,"name":"Xiaobing Jiang","email":"","orcid":"","institution":"Huazhong University of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Xiaobing","middleName":"","lastName":"Jiang","suffix":""},{"id":598552696,"identity":"778a0202-5029-43fd-b090-367a21bcff05","order_by":6,"name":"Haifeng Yang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA9UlEQVRIiWNgGAWjYJACZgYGCX4w64OBjR3RWiQbgAzGGQVpycRqYQBrYeb5cIixgZByg+NnD78uqLGQ4Jduv/zZxuAAMwP74aMb8Go5k5dmPeOYhITknDMFxjkGd/gYeNLSbuDVciDHzJiHTaLO4EZOQnKOwTNmBgkeM/xazr8BavknIWEP1HLYwuAwYwNBLTdyjB/ztklIGEikH2xmIEaL5I03Zsy8fRISEjdymBl7DNKS2Qj5he98jvFnnm91Evwz0h9/+PHHxo6f/fAxvFoUDjCwSUCYPAZgig2fchCQb2Bg/gBhsj8gpHgUjIJRMApGKAAAsfJKBJdHrKcAAAAASUVORK5CYII=","orcid":"","institution":"Huazhong University of Science and Technology","correspondingAuthor":true,"prefix":"","firstName":"Haifeng","middleName":"","lastName":"Yang","suffix":""}],"badges":[],"createdAt":"2025-09-28 08:53:08","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7733072/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7733072/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":103740034,"identity":"7311e748-5dd7-49f8-84c0-32634e17a5ee","added_by":"auto","created_at":"2026-03-02 10:45:45","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":345888,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eTranscriptomic analysis of DEGs from vascular smooth muscle cells harboring HuR knockdown viruses and control\u003c/strong\u003e (\u003cstrong\u003eA\u003c/strong\u003e) Quantitative RT-PCR analysis of \u003cem\u003eHuR\u003c/em\u003e mRNA expression in NC and HuR knockdown groups (shRNA-1, shRNA-2, shRNA-3). \u003cem\u003eACTB \u003c/em\u003eserved as a reference gene. (\u003cstrong\u003eB-C\u003c/strong\u003e) Western blot (B) and quantification (C) analysis of HuR protein levels in negative control (NC) and HuR knockdown groups. b-actin served as a loading control. \u003cstrong\u003eDensitometry analysis was performed with respect to β-actin. Original blots are presented in Supplementary figure 4 and 5\u003c/strong\u003e. (\u003cstrong\u003eD\u003c/strong\u003e) Fragments per kilobase of transcript per million mapped reads (FPKM) values of HuR in NC and shRNA-1 groups. (\u003cstrong\u003eE\u003c/strong\u003e) Principal component analysis (PCA) of gene expression profiles in NC and shRNA-1 groups. (\u003cstrong\u003eF\u003c/strong\u003e) Distribution of RNA-seq reads across different genomic regions, including 5'UTR, 3'UTR, CDS, Nc_exon (non-coding exon), introns, and intergenic regions, for NC and shRNA groups. (\u003cstrong\u003eG\u003c/strong\u003e) Volcano plot of differentially expressed genes (DEGs) between NC and shRNA-1 groups, with upregulated (orange, 3,300 genes) and downregulated (blue, 1,998 genes) genes indicated. (\u003cstrong\u003eH-I\u003c/strong\u003e) Gene Ontology (GO) enrichment analysis of biological processes for upregulated (H) and downregulated (I) DEGs. Asterisks in this figure indicated significant differences between indicated group and NC group in a Student \u003cem\u003et\u003c/em\u003e-test. *\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05, ***\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.001, ****\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.0001.\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-7733072/v1/19c8918bf8053521621099a9.png"},{"id":103740047,"identity":"f6eb6c9e-f4e0-4808-9804-edda14bd7cc7","added_by":"auto","created_at":"2026-03-02 10:45:46","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":363267,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eTranscriptomic analysis of DAS events in vascular smooth muscle cells harboring HuR knockdown viruses and control \u003c/strong\u003e(\u003cstrong\u003eA\u003c/strong\u003e) Boxplot showing differential AS events (DAS) detected by SUVA with the number of events indicated. alt3p: alternative 3' splice site, alt5p: alternative 5' splice site, ir: intron retention, contain: splice junction containing, and olp: splice junction overlapping. (\u003cstrong\u003eB\u003c/strong\u003e) Boxplot showing annotation of DAS events into classical splicing types with the number of events indicated. 5pMXE: mutually exclusive 5ʹUTRs, A3SS: alternative 3' splice site, intron R: intron retention, 3pMXE: mutually exclusive 3ʹUTRs, A5SS: alternative 5' splice site, ES: exon skipping, and MXE: mutually exclusive exons. (\u003cstrong\u003eC\u003c/strong\u003e) Boxplot showing categorization of DAS events as simple or complex. (\u003cstrong\u003eD\u003c/strong\u003e) Boxplot showing classification of DAS events as known or novel based on prior annotations. (\u003cstrong\u003eE\u003c/strong\u003e) Boxplot showing the distribution of DAS events based on pSAR (proportion of each SUVA AS event reads) value. Events with pSAR \u0026gt; 50% are highlighted in green and used for further analysis. (\u003cstrong\u003eF\u003c/strong\u003e) Principal component analysis (PCA) of splicing ratios for DAS events with pSAR \u0026gt; 50%. (\u003cstrong\u003eG\u003c/strong\u003e) Heatmap displaying the splicing ratios of DAS events with pSAR \u0026gt; 50% across NC and shRNA-1k samples. Events are hierarchically clustered. (\u003cstrong\u003eH\u003c/strong\u003e) Gene Ontology (GO) enrichment analysis of biological processes associated with DAS events. (\u003cstrong\u003eI\u003c/strong\u003e) Network presentation of the top 5 enriched GO biological processes (from H) and their associated genes. Nodes represent genes and GO terms, with edges indicating associations.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-7733072/v1/670ab9766e1c7308f60c7826.png"},{"id":103740035,"identity":"efadf899-5485-4b6e-8c5b-2e3581aefcca","added_by":"auto","created_at":"2026-03-02 10:45:45","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":212261,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAnalysis of intron retention (IR) events upon HuR knockdown in various datasets\u003c/strong\u003e (\u003cstrong\u003eA\u003c/strong\u003e) Boxplot showing DAS detected by SUVA basing on data from GSE161811 and GSE167525 with the number of events indicated. (\u003cstrong\u003eB\u003c/strong\u003e) GO enrichment analysis of biological processes associated with genes undergoing IR events basing on data from vascular smooth muscle cells harboring HuR knockdown viruses and control. (\u003cstrong\u003eC\u003c/strong\u003e) Venn diagram displaying the overlap of upregulated (up), downregulated (down), and IR-associated genes in vascular smooth muscle cells. The percentage of IR-associated genes overlapping with up- or down-regulated genes was indicated. (\u003cstrong\u003eD\u003c/strong\u003e) Bar plot displaying the results derived from Person correlation analysis between up- or down-regulated genes with IR events from C. Color coding reflects the correlation coefficient, with yellow indicating positive and purple indicating negative correlation. Events labeled in red represent IR events containing premature termination codons (PTCs), potentially leading to nonsense-mediated mRNA decay (NMD).\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-7733072/v1/542c58e95f4263175914968f.png"},{"id":104400138,"identity":"6d1fee86-e48a-4992-be17-420ef04310b5","added_by":"auto","created_at":"2026-03-11 12:09:00","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":196711,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eeCLIP-seq analysis of HuR protein binding in the flanking region of retained introns in cerebrovascular smooth muscle cells\u003c/strong\u003e (\u003cstrong\u003eA\u003c/strong\u003e) Venn diagram showing the overlap of significant peaks detected from two biological replicates of HuR eCLIP-seq in vascular smooth muscle cells. (\u003cstrong\u003eB\u003c/strong\u003e) Genomic annotation of HuR binding sites. (\u003cstrong\u003eC\u003c/strong\u003e) Top 3 enriched motif sequences in HuR binding sites, identified by motif analysis. (\u003cstrong\u003eD\u003c/strong\u003e) Metagene plot showing the distribution of HuR peaks across retained introns and their flanking exons (±50 nt from the splice sites). IR events were categorized into exclusion (significantly decreased retention upon HuR knockdown), inclusion (significantly increased retention), and other (no significant change). (\u003cstrong\u003eE\u003c/strong\u003e) Venn diagram illustrating the overlap between cell cycle–related genes derived from HuR-bound genes (peak-associated) and RASG (IR-associated genes). (\u003cstrong\u003eF\u003c/strong\u003e) Genome browser view of the \u003cem\u003eAtf5 \u003c/em\u003elocus, showing increased intron retention in HuR knockdown (shRNA-1) compared to control (NC). Two HuR eCLIP peaks (peak_n_7_1041 and peak_n_7_1159) are located near the retained intron.\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-7733072/v1/b3e4bc6149408442dfc07470.png"},{"id":103740044,"identity":"eea78f2e-5e82-4217-ac25-6a6ac00e7f59","added_by":"auto","created_at":"2026-03-02 10:45:46","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":197063,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eeCLIP-seq analysis of HuR protein binding in the 3’UTR region in cerebrovascular smooth muscle cells \u003c/strong\u003e(\u003cstrong\u003eA\u003c/strong\u003e)\u003cstrong\u003e \u003c/strong\u003eVenn diagram showing the overlap between upregulated (up-deg), downregulated (down-deg), and 3′UTR-bound genes based on eCLIP-seq. A total of 717 genes had HuR binding peaks on their 3′UTRs. (\u003cstrong\u003eB\u003c/strong\u003e) GO biological process enrichment analysis of the 20 upregulated genes with HuR peaks on their 3′UTRs. (\u003cstrong\u003eC\u003c/strong\u003e) GO biological process enrichment analysis of the 103 downregulated genes with 3′UTR binding. (\u003cstrong\u003eD\u003c/strong\u003e) Genome browser view of the \u003cem\u003eRoc2 \u003c/em\u003egene, showing reduced expression in HuR knockdown (shRNA-1) compared to control (NC). Two HuR peaks (peak_8_4_3665 and peak_8_4_3667) are observed within the 3′UTR of \u003cem\u003eRcc2\u003c/em\u003e.\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-7733072/v1/91fef27614ef53839e1683b0.png"},{"id":103740038,"identity":"68ae7b72-fa8b-4a3c-b5f4-2a35e15d0db7","added_by":"auto","created_at":"2026-03-02 10:45:45","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":167948,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eProposed model of HuR-mediated regulation of gene expression through intron retention and 3′UTR binding in cerebrovascular smooth muscle cells.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-7733072/v1/cdabbb900caac27583db8705.png"},{"id":105562318,"identity":"52837c4e-2048-451b-acba-eb25f73b8bca","added_by":"auto","created_at":"2026-03-27 12:28:56","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2784918,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7733072/v1/bedb3f4e-8abf-494f-b345-e35f711439f8.pdf"},{"id":104399823,"identity":"91ee3258-19f1-4b04-b6b0-38764586bed4","added_by":"auto","created_at":"2026-03-11 12:07:45","extension":"tif","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":534581,"visible":true,"origin":"","legend":"","description":"","filename":"S5actin.tif","url":"https://assets-eu.researchsquare.com/files/rs-7733072/v1/ebe422ac9c7be7f6075ede50.tif"},{"id":103740041,"identity":"b88fb67c-8be4-4b36-b1d9-4ae0d89731db","added_by":"auto","created_at":"2026-03-02 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10:45:46","extension":"xls","order_by":5,"title":"","display":"","copyAsset":false,"role":"supplement","size":3656192,"visible":true,"origin":"","legend":"","description":"","filename":"Suptable2DEGsenrich.go.BP.xls","url":"https://assets-eu.researchsquare.com/files/rs-7733072/v1/6d4f877839f473c34d06e043.xls"},{"id":103740039,"identity":"8a1a1ba7-4f49-473f-bf55-4305ba8a788a","added_by":"auto","created_at":"2026-03-02 10:45:46","extension":"docx","order_by":6,"title":"","display":"","copyAsset":false,"role":"supplement","size":362729,"visible":true,"origin":"","legend":"","description":"","filename":"FigureS1S2S3.docx","url":"https://assets-eu.researchsquare.com/files/rs-7733072/v1/85263cd7e3584ab6adfd5ac4.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"HuR binds to flanking exons and regulates intron retention alternative splicing of cell cycle-related genes in smooth muscle cells","fulltext":[{"header":"1 Introduction","content":"\u003cp\u003eCerebral aneurysms (CA), characterized by abnormal dilations of cerebral arteries, pose significant health risks due to their potential to rupture, leading to subarachnoid hemorrhage with high mortality and morbidity rates [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Aneurysms most frequently develop in areas where blood flow causes high wall shear stress (WSS), such as vessel bifurcations [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. High WSS incites cerebral aneurysm formation, while low WSS propagates cerebral aneurysm growth through induced proinflammatory process [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. The inflammatory process further leads to matrix metalloproteinases (MMPs)-mediated degradation of the extracellular matrix and apoptosis of smooth muscle cells (SMCs), which are the predominant matrix-synthesizing cells and maintain significant plasticity of the vascular wall [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. These processes act in concert to weaken the arterial wall progressively, resulting in dilatation, aneurysm formation, and ultimately rupture. In cerebral aneurysm pathology, the phenotypic and functional changes of SMCs from primarily concerned with contraction to a pro-inflammatory and matrix remodeling phenotype play significant roles in cerebral aneurysm formation and rupture [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. The proliferation of SMCs also plays an important role in the formation and development of cerebral aneurysms [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAlternative splicing (AS) is functionally important in many contexts, but primarily it diversifies the proteome and functions as key regulatory mechanism during development [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. In cardiovascular and cerebrovascular system, AS also exerts crucial roles in the regulation of vascular walls\u0026rsquo; function [\u003cspan additionalcitationids=\"CR8\" citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Alternatively spliced transcripts could encode different proteins, which might function distinctly in focal lesion and inflammatory responses of the vascular wall. For example, deep transcriptome sequencing of subarachnoid hemorrhage (SAH) patients revealed that some of the \u003cem\u003eELF2\u003c/em\u003e (\u003cem\u003eE74-like factor 2\u003c/em\u003e) transcripts had opposite directions of expression changes in acute phase (RAA) compared to the chronic phase (RAC). The isoform upregulated in RAA is built from the initial 7 exons, whereas the downregulated transcript in RAC, possesses the terminal 6\u0026ndash;7 exons [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. The SNP (single nucleotide polymorphism) associated with CA in Versican gene resulted in two transcripts, the difference of which involved the retention of two largest exons (6 and 7), and the larger one had glycosaminoglycan attachment sites which can bind chondroitin sulfate chains exerting antiadhesive function. In patients with CA, the splicing process of Versican may be altered resulting in a higher proportion of larger isoforms and thus in diminished extracellular matrix assembly [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eHu antigen R (HuR), also known as HuA or ELAVL1, is one of the best-studied RNA-binding post-transcriptional regulators, which belongs to mammalian embryonic abnormal lethal vision-like (ELAVL) protein family with three other members (i.e. HuB, HuC, and HuD) [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. HuR is ubiquitously expressed in human tissues and predominantly localized in the nucleus of resting cells [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. HuR contains three RNA recognition motifs (RRMs) and usually targets mRNAs containing AU-rich elements at their 3\u0026rsquo;untranslated region, regulating mRNA transport from nucleus-to-cytoplasm, stability and translation [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Dysregulation of HuR is linked to diseases such as cancer, neurodegenerative disorders, and immune-related disorders [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. HuR has also been reported to involve in alternative splicing of itself and other proteins. For example, the excessive HuR proteins bind to GU-rich element overlapped with its major polyadenylation signal (PAS2) in 3\u0026rsquo;UTR region, reducing PAS2\u0026rsquo;s recruitment of the CstF-64 subunit of the pre-mRNA cleavage stimulation factor and producing long HuR mRNA species, which contains an AU-rich element (ARE) that destabilizes the mRNAs and thus reduces the protein production output [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. In breast cancer, HuR binds to intron 14 of glutaminase, favoring the kidney-type (KGA) isoform during splicing, and binds to the 3\u0026rsquo;UTRs of glutaminase isoform C (GAC) and KGA, increasing KGA and GAC\u0026rsquo;s mRNA stability. Knocking down HuR reduces KGA but increases GAC levels, enhancing glutamine anaplerosis into the TCA cycle and driving cells towards glutamine dependence [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Besides, \u003cem\u003eELVAL1\u003c/em\u003e was predicted as marker genes distinguishing individuals with CA from healthy individuals and might involve in the proliferation of vascular smooth muscle cell [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. However, less reports concern about HuR\u0026rsquo;s function in CA, especially in smooth muscle cells.\u003c/p\u003e \u003cp\u003eIn the current study, we report a comprehensive study of HuR regulation model in smooth muscle cells. In order to investigate the function of HuR protein in cerebral artery vascular smooth muscle cells, we knocked down HuR in these cells and conducted RNA-seq.\u0026nbsp;The RNA-seq data showed that knocking down HuR led to the downregulation of cell cycle-related genes and upregulation of cytokines-mediated signaling-associated genes. HuR also contributed to differential alternative splicing, especially intron retention AS events in smooth muscle cells. Some genes that undergo intron retention splicing events contain premature termination codons. eCLIP-seq analysis showed that HuR predominantly binds to an exon facilitates splicing of the adjacent intron. Several cell cycle-related genes, such as \u003cem\u003eAtf5\u003c/em\u003e, \u003cem\u003eIer3\u003c/em\u003e and \u003cem\u003eZfp36l2\u003c/em\u003e, were significantly downregulated and these genes also contained intron retention AS events. These findings indicate that HuR affects the proliferation of smooth muscle cells by regulating alternative splicing events related to intron retention in cell cycle-associated genes.\u003c/p\u003e"},{"header":"2 Results","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 RNA-seq profiling elucidated the comprehensive transcriptional regulation of HuR in cerebrovascular smooth muscle cells\u003c/h2\u003e \u003cp\u003eTo explore the function of HuR in cerebrovascular smooth muscle cells, the lentiviruses achieving \u003cem\u003eHuR\u003c/em\u003e knockdown were established to infect mouse cerebral artery vascular smooth muscle cells. The efficacy of the viruses was examined and found the protein and transcript levels of HuR were significantly reduced (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA-B). Among three well-established lentiviruses, shRNA-1 led to the most reduced effect (approximately 50%) and was selected for further research (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB-C). To do the RNA-seq profiling, we constructed cDNA libraries prepared from control (NC) and \u003cem\u003eHuR\u003c/em\u003e knockdown cells(shRNA-1) (three biological replicates). Effective depletion of \u003cem\u003eHuR\u003c/em\u003e was further confirmed in RNA-seq analysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eD). Principal component analysis (PCA) revealed that the samples divided into two distinct groups: NC and shRNA-1 (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eE). Overall, \u003cem\u003eHuR\u003c/em\u003e downregulation led to a noticeable decrease in transcript distribution in coding sequence (CDS) and an increase in introns (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eF), suggesting that \u003cem\u003eHuR\u003c/em\u003e knockdown promotes more intron retention genome-wide. In total, 5,298 genes were differentially expressed between the two groups, with 3,300 up-regulated and 1,998 down-regulated (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eG \u003cb\u003eand Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e shRNA-1 vs NC DEG\u003c/b\u003e). In the biological process terms of GO analysis, the up-regulated DEGs in the \u003cem\u003eHuR\u003c/em\u003e-knockdown cells were mainly enriched in monoatomic ion transmembrane transport, cytokine-mediated signaling and metal ion transport (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eH \u003cb\u003eand Table \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e DEGs enrich go BP\u003c/b\u003e). The down-regulated DEGs were primarily enriched in cell division-related processes, such as mitotic sister chromatid segregation, nuclear chromosome segregation and mitotic nuclear division (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eI \u003cb\u003eand Table \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e DEGs enrich go BP\u003c/b\u003e)), particularly involving genes \u003cem\u003eFoxm1\u003c/em\u003e, \u003cem\u003eKIF20A\u003c/em\u003e, \u003cem\u003eATF5\u003c/em\u003e and KIF23 (\u003cb\u003eFig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003eA-D\u003c/b\u003e). This result suggests HuR might affect cell division in cerebrovascular smooth vascular cells.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Alternative splicing events in smooth muscle cells were significantly regulated by HuR\u003c/h2\u003e \u003cp\u003eGiven HuR was a multifunctional RNA binding protein, we inferred it might function as a splicing factor in smooth muscle cells. Thus, we conducted splicing site usage variation analysis (SUVA) to analyze AS events in depth, where the alternative splicing events were classified into five types: alt3p (alternative 3' splice site), alt5p (alternative 5' splice site), IR (intron retention), contain (splice junction containing), and olp (splice junction overlapping) [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. The results indicated that IR events were the most affected differentially alternative splicing (DAS) events, followed by and alt5p and alt3p (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e2\u003c/span\u003eA \u003cb\u003eand Table \u003cspan refid=\"MOESM3\" class=\"InternalRef\"\u003eS3\u003c/span\u003e diff RAS events suva stat\u003c/b\u003e). This observation was consistent with the increased distribution of intron reads in HuR knockdown cells, suggesting that introns were more frequently retained after HuR knockdown. Corresponding to the classical splicing events, cassette exon events were the most frequently detected splicing events after HuR knockdown, followed by A5SS (alternative 5' splice site) and A3SS (alternative 3' splice site) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e2\u003c/span\u003eB). Overall, the complex DAS events were more than simple events. We detected more than 1000 novel DAS events. This result highlighted the complexity and exploitability of AS events in smooth muscle cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e2\u003c/span\u003eC-D). Due to the low proportion of two transcripts involved in a single AS event across the gene, pSAR (proportion of each SUVA AS event reads) was proposed to measure the percentage of each AS event reads within its region. A total of 3,531 DAS events with pSAR over 50% were identified for further analysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e2\u003c/span\u003eE). The detailed pSAR values of AS events were listed in \u003cb\u003eSupplemental Table\u0026nbsp;4\u003c/b\u003e. PCA showed those DAS events with pSAR\u0026thinsp;\u0026gt;\u0026thinsp;50% were also clearly divided into two subgroups, NC and shRNA-1 group. These data suggested that HuR globally regulated alternative splicing in smooth muscle cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e2\u003c/span\u003eF). The heatmap showed that most DAS events with a pSAR\u0026thinsp;\u0026gt;\u0026thinsp;50% had increased splicing ratios following \u003cem\u003eHuR\u003c/em\u003e knockdown, suggesting that \u003cem\u003eHuR\u003c/em\u003e knockdown raised the proportion of AS events in smooth muscle cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e2\u003c/span\u003eG). GO-BP enrichment analysis revealed the genes regulated by HuR-mediated alternative splicing were highly enriched for mRNA processing, RNA splicing, chromatin remodeling and mitotic cell cycle phase (GO biological process terms, Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e2\u003c/span\u003eH). Network analysis representation illustrates the top five enriched Gene Ontology (GO) biological processes derived from dataset, along with their associated genes, thereby highlighting the core position of RNA splicing and cell cycle-related proteins (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e2\u003c/span\u003eI). These findings demonstrate that HuR significantly influences AS events, particularly intron retention events, in cerebrovascular smooth muscle cells.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003e2.3 HuR may modulate genes expressions by regulating IR alternative splicing events in cerebrovascular smooth muscle cells\u003c/b\u003e \u003c/p\u003e \u003cp\u003eTo analyze whether intron retention serves as the principal mechanism by which HuR regulates AS events across various cell types. We download RNA-seq data from human skin fibroblasts (GSE161811) and human MIA-PaCa2 pancreatic cancer cells (GSE167525), each containing normal control and HuR knockdown groups, which were utilized to perform SUVA analysis. The results demonstrated that, in contrast to smooth muscle cells, HuR knockdown predominantly influenced alt5p and alt3p AS events in human skin fibroblasts and pancreatic cancer cells, respectively, thereby proving the cell specificity of HuR\u0026rsquo;s impact on AS events (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e3\u003c/span\u003eA). We analyzed GO-BP enrichment for genes with IR events in smooth muscle cells and found enrichment in cell cycle pathways like chromatin remodeling and mitotic cell cycle phase. This suggested HuR may influence smooth muscle cell proliferation through affect these genes IR events (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e3\u003c/span\u003eB). To examine whether genes undergoing IR alternative splicing were also impacted at the expression level, we identified the intersection between genes exhibiting IR events and DEGs. Venn diagram was drawn between DEGs and IR events genes. We found 109 genes with IR events were up-regulated and 132 genes with IR events were down-regulated (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e3\u003c/span\u003eC). Person correlation analysis were subsequently performed to identify genes whose expressions were significantly associated with IR events. Of the 33 genes exhibiting significantly correlation, 23 genes, including those related to the cell cycle-cycle, such as \u003cem\u003eMcm5\u003c/em\u003e, \u003cem\u003eUhrf1\u003c/em\u003e, \u003cem\u003eRpa2, Prc1\u003c/em\u003e and \u003cem\u003eEme1\u003c/em\u003e were found to be down-regulated, while 10 genes were up-regulated (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e3\u003c/span\u003eD). In light of the possibility that down-regulated expressions associated with IR events may result from premature terminated codons (PTCs), we also analyzed PTCs in these genes. Our findings revealed that more than half (14/23) down-regulated genes had PTCs, including \u003cem\u003eMcm5\u003c/em\u003e, \u003cem\u003eUhrf1\u003c/em\u003e and \u003cem\u003eRpa2\u003c/em\u003e (\u003cb\u003eFig. \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e\u003c/b\u003e). Collectively, these results implied that HuR might participate improve smooth muscle cell proliferation by inducing cell cycle genes IR events.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.4 eCLIP-seq data demonstrated that HuR specifically bond to CDS region and regulated alternative splicing\u003c/h2\u003e \u003cp\u003eTo further investigated the association between HuR binding molecules and HuR-regulated alternative splicing genes. We proceeded enhanced cross-linking and immunoprecipitation sequencing (eCLIP-seq) and obtained a transcriptome-wide binding profile of HuR in mouse cerebrovascular smooth muscle cells. Two replicate libraries of HuR CLIP-seq (IP_1 and IP_2) were subjected to sequencing. The sequencing results revealed binding peaks totaling 14,549 and 12,589 respectively for the IP_1 and IP_2 assays. A total of 5,582 peaks were found to be overlapped between the two replicates (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e4\u003c/span\u003eA). The peaks distribution showed a broad range of binding sites to CDS region (68.2%) and 3\u0026rsquo;UTR region (11.42%) (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e4\u003c/span\u003eB). Motif analysis revealed that HuR mainly bond UA-rich sequence (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e4\u003c/span\u003eC). This binding feature was inconsistent with previous reports in the literature. It was hypothesized that this may represent a specific binding characteristic of HuR in vascular smooth muscle cells.\u003c/p\u003e \u003cp\u003eTo examine the association between HuR binding enrichment and splicing regulation, we constructed a HuR splicing map. This map integrates eCLIP enrichment data for splicing events responsive to HuR knockdown, averaged across a meta-exon. HuR eCLIP enrichment at the flank exon correlated with intron inclusion (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e4\u003c/span\u003eD). HuR protein was associated with increased intron inclusion upon knockdown. To investigate the regulatory role of HuR in cell cycle-related genes, we conducted an integrated analysis of HuR-bound genes associated with the cell cycle, focusing specifically on genes exhibiting IR events. We performed integrated analysis and found that 33 cell cycle-related genes overlapped between HuR-RASGs and HuR binding peaks in two replicates, including \u003cem\u003eRpa2\u003c/em\u003e, \u003cem\u003eAtf5\u003c/em\u003e, \u003cem\u003eZfp36l2\u003c/em\u003e and \u003cem\u003eIer3\u003c/em\u003e(Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e4\u003c/span\u003eE \u003cb\u003eand Supplemental Table\u0026nbsp;5\u003c/b\u003e). This result indicated that HuR might directly bind to these targets and regulate IR alternative splicing in smooth muscle cells. Globally, the expression of HuR-associated intron was increased when \u003cem\u003eHuR\u003c/em\u003e expression was down-regulated, indicating a negative correlation between HuR association and the intron inclusion. Here, we further selected \u003cem\u003eAtf5\u003c/em\u003e as an example to study the effect of HuR binding on the pre-mRNA splicing. Transcription factor Atf5 was abnormal expression in intracranial aneurysm superficial temporal artery samples [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. In this study, the HuR eCLIP-seq dot plot revealed the presence of two HuR-bound peaks located in exon 1 and exon 2 of \u003cem\u003eAtf5.\u003c/em\u003eAs illustrated in the upper pannel of Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e4\u003c/span\u003eF, an intron between exon 1 and exon 2 was inclusion following \u003cem\u003eHuR\u003c/em\u003e knockdown. Comparable regulatory modules could also be observed in the cell cycle-related genes \u003cem\u003eIer3\u003c/em\u003e and \u003cem\u003eZfp36l2\u003c/em\u003e (\u003cb\u003eFig. \u003cspan refid=\"MOESM3\" class=\"InternalRef\"\u003eS3\u003c/span\u003e\u003c/b\u003e). HuR exhibited binding peaks on the exon regions flanking the introns of these genes. Silencing HuR would likely to enhance the retention of the central introns.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.5 HuR also bound to the 3\u0026rsquo;UTR region of genes and modulated mRNA stability\u003c/h2\u003e \u003cp\u003ePreviously, HuR had been documented to bind to 3\u0026rsquo;UTR region to modulate RNA stability [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Therefore, we conducted a more detailed analysis the correlation between the binding peaks within 3\u0026rsquo;UTR region and DEGs. As a result, a total of 123 genes with binding peaks in 3\u0026rsquo;UTR region were differentially expressed, of which 103 genes were down-regulated and 20 were up-regulated (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e5\u003c/span\u003eA). Following the HuR knockdown, the upregulated genes were predominantly enriched for the positive regulation of cold-induced thermogenesis (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e5\u003c/span\u003eB). Down-regulated genes were enriched in cell-matrix adhesion, intracellular transport and aortic valve morphogenesis (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e5\u003c/span\u003eC). The distribution of reads indicated that HuR bond to the 3\u0026rsquo;UTR region of \u003cem\u003eRcc2\u003c/em\u003e gene. Furthermore, the expression of the gene Rcc2 was significant reduced following HuR knockdown (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e5\u003c/span\u003eD).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eIn conclusion, HuR played diverse roles in cerebrovascular smooth muscle cells. Specifically, HuR bond to the CDS region of cell cycle-realted genes, facilitating intron exclusion, which in turn promoted their expressions to influence the proliferation of smooth muscle cells. Conversely, HuR bond to the 3\u0026rsquo;UTR region of extracellular matrix-related genes, thereby increasing mRNA stability and ensuring the proper execution of transcription and translation processes (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e6\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"3 Discussions","content":"\u003cp\u003eCerebral aneurysm (CA) is a severe cerebrovascular disease characterized by abnormal bulging of cerebral vessels that may rupture and cause a stroke. The expansion of the aneurysm is accompanied by the remodeling of vascular matrix, which is highly dependent on the phenotype of vascular smooth muscle cells (VSMCs) [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. The phenotypic switching of VSMC is considered to be bidirectional, including the physiological contractile phenotype and alternative synthetic phenotype in response to injury [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. The phenotypic transition, proliferation and migration of SMCs play variable roles in the formation and progression of cerebral aneurysms [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAs a multifunctional RNA binding protein, HuR is involved in the regulation of mRNA alternative and stability. Several high-throughput approaches have been employed to comprehensively identify HuR targets, revealing and enrichment of HuR-bound peaks at 3\u0026rsquo;UTRs [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Furthermore, certain evidence has demonstrated that HuR serves as a biomarker distinguishing CA patients from healthy control and may play a role in regulating the proliferation of smooth muscle cells [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. However, there is still no functional researches of HuR in smooth muscle cells. First, we knockdown HuR in smooth muscle cells, performed RNA sequencing and comprehensive analysis, and proved HuR\u0026rsquo;s regulation of DEGs and IR AS events, especially cell cycle-related genes. Then we initially employed the eCLIP-seq approach to elucidate the interactions between HuR and RNA within smooth muscle cells. Our findings enhance the comprehension of HuR\u0026rsquo;s role in cerebrovascular smooth muscle cells and contribute to elucidating the molecular pathology of CA.\u003c/p\u003e \u003cp\u003eIn the present research, we revealed large amount of differentially expressed genes regulated by HuR. Interestingly, we found that HuR activates the expression of numerous genes associated with the cell cycle in SMCs (\u003cb\u003eSupplemental Table\u0026nbsp;2\u003c/b\u003e). To be specific, transcription factor (TF) ATF5 (cyclic AMP-dependent transcription factor) was found abnormally expressed in arterial tissues from CA patients and also reported to promote the proliferation of cerebral cortical neuroprogenitor cells [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. Another TF FOXM1 (forkhead box protein M1) was found to promote pulmonary artery smooth muscle cell expansion in pulmonary arterial hypertension [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. Kinesin-like protein KIF23 was also found to be significantly increased in pulmonary arterial smooth muscle cells (PASMCs) and inhibiting KIF23 alleviated IPAH (idiopathic pulmonary arterial hypertension) through targeting pyroptosis and proliferation of PASMCs [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. Another same family member KIF20A was also validated to be significantly upregulated both in the lung tissue of hypoxia-induced pulmonary hypertension mice and proliferative PASMCs [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. In this study, the expression of \u003cem\u003eAtf5, Foxm1, Kif23\u003c/em\u003e and \u003cem\u003eKif20a\u003c/em\u003e were observed to be downregulated following HuR knockdown. Additionally, we also found the protein level of HuR was elevated in rat vascular wall tissues undergoing aortic endothelial balloon injury, a model characterized by the proliferation of smooth muscle cells. We proposed that \u003cem\u003eAtf5, Foxm1, Kif23\u003c/em\u003e and \u003cem\u003eKif20a\u003c/em\u003e might be associated with SMCs proliferation. And the transcriptional activity of these genes may be modulated by HuR protein.\u003c/p\u003e \u003cp\u003eNotably, we first found the proportion of introns in the whole transcripts increased following HuR knockdown. It is hypothesized that HuR might contribute to an increased prevalence of these sequences through its regulatory role in alternative splicing. Subsequent SUVA analysis revealed that the most prevalent AS events following HuR knockdown was IR splicing events (=\u0026thinsp;1727/4690\u0026thinsp;=\u0026thinsp;36.8%). An analysis of previously published HuR knockdown RNA-seq data from human skin fibroblasts and MIA-PaCa2 pancreatic cancer cells revealed that the alternative splicing model exhibited cell-specific characteristics in cerebrovascular smooth muscle cells. Intron retention (IR) is a form of alternative splicing that has long been neglected in mammalian systems, because it was generally assumed mis-splicing, leading to the retention of introns, and had no physiological consequence other than reducing gene expression by nonsense-mediated decay (NMD) [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. However, currently IR has been revealed as an independent mechanism of controlling and enhancing the complexity of gene expression, and it can also facilitate rapid responses to biological stimuli and generate novel protein isoforms [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. The most exciting discoveries have highlighted the pivotal role that IR serves in normal and disease-related human biology. For example, neoepitopes derived from retained intron are processed and presented on MHC-I on the surface of cancer cell lines, which is considered for prospective personalized cancer vaccine development [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. The transcript comparison between patients in RAA and controls adopting venous whole blood also found a pronounced decrease in the exon/intron ratio score for the differentially downregulated genes [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Combining our findings, we may infer HuR might be a key regulator of CA relying on intron retention, but further investigations are still needed.\u003c/p\u003e \u003cp\u003eTo substantiate our hypothesis that HuR selectively interacts with mRNA in vascular smooth muscle cells and modulates intron retention via alternative splicing mechanisms, we performed eCLIP-seq analysis targeting HuR. eCLIP-seq revealed that HuR bound to CDS region.Previously, iCLIP (individual nucleotide resolution cross-linking and immunoprecipitation) analysis of HuR protein in B cells showed 75% of HuR-RNA crosslink site were mapped to introns [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. PAR-CLIP (photoactivatable-ribonucleoside-enhanced cross-linking and immunoprecipitation) analysis in Hela cells showed HuR binding sites are mainly enriched in 3\u0026rsquo;UTR region and introns are followed [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. iCLIP analysis in MCF-7 cells revealed the main binding sites of HuR are in 3\u0026rsquo;UTR and exon region [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. In our research, eCLIP-seq analysis in smooth muscle cells revealed HuR mainly bond in CDS region (68.2%) and 3\u0026rsquo;UTR region was followed, which is different from the reported conclusion and highlighting the cell specific regulation of HuR in AS events. But what decides the cell-specific differences needs more experiment investigations. We also integrated the RNA-seq data and the eCLIP-seq data. The results showed that in vascular smooth muscle cells, HuR enrichment at the flank exon promoting the intron inclusion. We introduced Atf5 pre-mRNA as a target of HuR protein. We showed that the downregulation of HuR in SMCs led to an increase of intron 1 inclusion. Atf5 could promote the proliferation of neural progenitor cells. Therefore, this finding provided a new proof that HuR could be implicated in SMCs proliferation processes, which was helpful to discover novel biological functions of HuR in the process of CA.\u003c/p\u003e"},{"header":"4 Materials and methods","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e4.1 Lentiviral packaging\u003c/h2\u003e \u003cp\u003eHuR-targeting shRNA constructs were assembled into lentiviral vector (pLVshRNA-EGFP(2A)-Puro) to knock down HuR\u0026rsquo;s expression. The shRNA sequences were designed and synthesized by Wuhan Boyuan Biotechnology Co., Ltd. The shRNA sequences were as follows.\u003c/p\u003e \u003cp\u003eshRNA-1: 5\u0026rsquo;- GACATTGGGAGAACGAATTTACTCGAGTAAATTCGTTCTCCCAATGTCTTTTTG-3\u0026rsquo;;\u003c/p\u003e \u003cp\u003eshRNA-2: 5\u0026rsquo;-GACATTGGGAGAACGAATTTAACTCGAGTTAAATTCGTTCTCCCAATGTTTTTTG-3\u0026rsquo;;\u003c/p\u003e \u003cp\u003eshRNA-3: 5\u0026rsquo;-GCATTGGGAGAACGAATTTAATCTCGAGATTAAATTCGTTCTCCCAATGTTTTTG-3\u0026rsquo;.\u003c/p\u003e \u003cp\u003eLentivirus was packaged in HEK 293T cells using a three-plasmid system, including the shRNA expression plasmid, psPAX2 (packaging plasmid), and pMD2.G (envelope plasmid). Transfection was performed with Lipofectamine 3000 (Thermo Fisher Scientific) and lentiviral particles were collected from the culture supernatant 48 hours post transfection. The harvested viral supernatants were filtered through a 0.45 \u0026micro;m filter and concentrated by ultracentrifugation.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e4.2 Cell culture\u003c/h2\u003e \u003cp\u003eMouse cerebral artery vascular smooth muscle cells were purchased from Shanghai Zhong Qiao Xin Zhou Biotechnology Corporation (PRI-MOU-00160), and infected with the lentivirus in the presence of 8 \u0026micro;g/mL polybrene (Sigma-Aldrich). After 48 hours, the infected cells were screened by puromycin (2 \u0026micro;g/mL) and the efficiency was validated by quantitative reverse transcription PCR (RT-qPCR) and Western blot analysis.\u003c/p\u003e \u003cp\u003eHEK 293T and vascular smooth muscle cells were maintained in high-glucose DMEM (Hyclone) supplemented with 10% fetal bovine serum (Gibco) and 1% penicillin-streptomycin. Mouse cerebral artery vascular smooth muscle cells were incubated in the complete medium (PCM-M-160) from Shanghai Zhong Qiao Xin Zhou Biotechnology Corporation. The incubation temperature was set at 37℃ with 5% CO\u003csub\u003e2\u003c/sub\u003e atmosphere.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e4.3 Western blotting\u003c/h2\u003e \u003cp\u003eIP Lysis buffer (87787, Thermo Fisher Scientific) with Protease inhibitors Cocktail (04693132001, Roche) was adopted to extract the protein from cells, and the extraction was facilitated by ultra-sonication. The protein quantification was completed by BCA Protein Assay Kit (Thermo Fisher Scientific) and the protein separation and transfer were completed using SDS-PAGE gels and PVDF membranes (IPVH00010, Millipore). The blocking and the washing of membranes were proceeded in TBST buffer. The incubation of membrane and the first and second antibodies were at 4℃ and 25℃ respectively. The first antibodies used in this research were as follows: HuR (66549-1-lg, Proteintech, USA); GAPDH (60004-1-lg, Proteintech, USA) and β-actin (66009-1-lg, Proteintech, USA). \u003cb\u003eSignals were detected by ECL (Tanon5200, Tanon) and quantified using ImageJ (NIH, v1.53e), normalized to β-actin/GAPDH.\u003c/b\u003e\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e4.4 Quantitative RT-PCR\u003c/h2\u003e \u003cp\u003eThe RNA extraction of cells was proceeded in TRIzol reagent (15596026, Invitrogen) and following the manufacturer\u0026rsquo;s guidelines. The first strand synthesis was completed using NovoScript\u0026reg; Plus All-in-one 1st Strand cDNA Synthesis SuperMix (gDNA Purge) (E047, NovoProtein). NovoStart\u0026reg; SYBR High-Sensitivity qPCR SuperMix (E099, NovoProtein), the primers and cDNA (complementary DNA) were mixed and subjected to ABI QuantStudio\u0026trade; 6 Flex System (ABI). The quantification were performed as per the established protocol. The primers used in this study were as follows:\u003c/p\u003e \u003cp\u003e \u003cem\u003eHuR\u003c/em\u003e (mouse)-F: AGACTGCAGGGATGACATTG;\u003c/p\u003e \u003cp\u003e \u003cem\u003eHuR\u003c/em\u003e (mouse)-R: CAGACTTCGTAGTTCCTCTTGG;\u003c/p\u003e \u003cp\u003e \u003cem\u003eβ-actin\u003c/em\u003e (mouse)-F: GAGGTATCCTGACCCTGAAGTA;\u003c/p\u003e \u003cp\u003e \u003cem\u003eβ-actin\u003c/em\u003e (mouse)-R: CACACGCAGCTCATTGTAGA.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e4.5 RNA sequencing (RNA-seq)\u003c/h2\u003e \u003cp\u003eRNA-seq library preparation and sequencing were performed by Novogene Co., Ltd. Total RNA was extracted from vascular smooth muscle cells using TRIzol reagent (15596026, Invitrogen) according to the manufacturer\u0026rsquo;s protocol. Messenger RNA (mRNA) was purified from total RNA using poly-T oligo-attached magnetic beads. Following purification, the mRNA was fragmented into small pieces under elevated temperature.\u003c/p\u003e \u003cp\u003eThe first-strand cDNA was synthesized using random hexamer primers and reverse transcriptase, followed by second-strand cDNA synthesis. The resulting cDNA fragments underwent end repair, A-tailing, and ligation to sequencing adapters. After adapter ligation, cDNA fragments of the desired size were purified and amplified by PCR to create the final library. Libraries were quantified and assessed for quality using the Agilent 2100 Bioanalyzer.\u003c/p\u003e \u003cp\u003eThe libraries were sequenced on the Illumina NovaSeq 6000 platform to generate paired-end 150 bp reads. Raw sequence reads were quality-checked using FastQC (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.bioinformatics.babraham.ac.uk/projects/fastqc\u003c/span\u003e\u003cspan address=\"http://www.bioinformatics.babraham.ac.uk/projects/fastqc\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) and trimmed with fastp to remove low-quality bases and adapters. Clean reads were aligned to the mouse reference genome (GRCm39) using STAR (v2.7.10b), and uniquely mapped reads were used to calculate the read counts and normalized expression levels, measured as reads per kilobase of exon per million mapped fragments (FPKM) for each gene.\u003c/p\u003e \u003cp\u003ePrincipal component analysis (PCA) was performed using the factoextra package (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://cloud.r-project.org/package=factoextra\u003c/span\u003e\u003cspan address=\"https://cloud.r-project.org/package=factoextra\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) to visualize clustering among samples. Heatmaps of gene expression and splicing ratios were generated using the pheatmap package (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://cran.r-project.org/web/packages/pheatmap/index.html\u003c/span\u003e\u003cspan address=\"https://cran.r-project.org/web/packages/pheatmap/index.html\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), with Euclidean distance used for hierarchical clustering. The statistical analysis were conducted in R (v4.3.2) or Python (v3.9).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e4.6 Differentially expressed genes (DEGs) analysis\u003c/h2\u003e \u003cp\u003eDifferential expression analysis was performed using the DESeq2 package (v1.38.0) in R [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. Gene expression levels were normalized to fragments per kilobase per million mapped reads (FPKM) before analysis. Differentially expressed genes (DEGs) were identified based on a fold change (FC) threshold of \u0026ge;\u0026thinsp;2 or \u0026le;\u0026thinsp;0.5 and a false discovery rate (FDR)\u0026thinsp;\u0026le;\u0026thinsp;0.05. Volcano plots were generated to visualize DEGs.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e4.7 Splicing site usage variation analysis (SUVA)\u003c/h2\u003e \u003cp\u003eThe alternative splicing events and regulated alternative splicing events (RAS) were defined and quantified by using the SUVA pipeline as described previously [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Splicing ratio difference and proportion of SUVA AS event reads (pSAR) of each AS events were calculated.\u003c/p\u003e \u003cp\u003eTo identify premature termination codons (PTCs) in IR (intron retention) events, a custom Python pipeline was used. Gene annotations in GTF format were parsed to extract exon and CDS coordinates, and the mRNA sequence with the retained intron was reconstructed. The CDS sequence was translated \u003cem\u003ein silico\u003c/em\u003e to detect stop codons, and their positions were mapped to the retained intron to classify PTC status as \"PTC_in_Intron,\" \"PTC_not_in_Intron,\" or \"No_PTC.\" Genome sequences in FASTA format were processed using Biopython, and IR events were annotated with their genomic regions (e.g., CDS, UTR, or non-coding) and PTC positions. This analysis determined the potential for IR events to trigger nonsense-mediated decay (NMD).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e4.8 Functional enrichment analysis\u003c/h2\u003e \u003cp\u003eTo identify functional categories of genes, we employed the clusterProfiler package (v4.6.2) [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e], which enabled us to determine Gene Ontology (GO) terms and KEGG pathways.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003e4.9 eCLIP sequencing and analysis\u003c/h2\u003e \u003cp\u003eeCLIP (enhanced crosslinking and immunoprecipitation) was performed as previously described with minor modifications to detect HuR-binding sites in vascular smooth muscle cells [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. Briefly, cells were UV crosslinked at 254 nm with an energy of 400 mJ/cm\u0026sup2; to covalently stabilize RNA-protein complexes. Crosslinked cells were lysed and subjected to limited RNase I digestion to fragment unbound regions. A small aliquot (2%) was retained as the size-matched input (SMInput), while the majority (98%) proceeded to immunoprecipitation (IP) with anti-HuR antibody (Abcam, ab200342). After dephosphorylation and 3\u0026rsquo; RNA adapter ligation, complexes were separated by SDS-PAGE and transferred to a nitrocellulose membrane. A gel slice 75 kDa above the expected molecular weight of the RNA-binding protein was excised. Protein-RNA complexes were recovered by Proteinase K digestion, and co-purified RNA was extracted.\u003c/p\u003e \u003cp\u003eReverse transcription was performed using primers with unique inline barcodes and randommers to enable PCR duplicate removal and error correction. The resulting cDNA was ligated to a 3\u0026rsquo; DNA adapter, PCR amplified, and size-selected to complete the library. Libraries were sequenced using an Illumina NovaSeq platform with paired-end 150 bp reads.\u003c/p\u003e \u003cp\u003eRaw reads were quality filtered using Cutadapt [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e] to remove adapters and low-quality bases. rRNA contamination was removed using SortMeRNA [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. Unique molecular identifiers (UMIs) embedded in the adapters were extracted, and reads sharing the same UID sequence were grouped into clusters. Within each UID cluster, reads were further sub-clustered based on sequence similarity (\u0026le;\u0026thinsp;5 nt mismatch), followed by consensus sequence generation to perform error correction and deduplication. UID sequences were also error-corrected with a 1 nt mismatch tolerance.\u003c/p\u003e \u003cp\u003eClean reads were aligned to the mouse genome (GRCm39) using STAR [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. To identify high-confidence binding regions, we clusters reads with at least 1 bp overlap into candidate peaks. For each gene, simulated random reads were generated 500 times with the same count and length distribution as real reads to model a null distribution. Peaks with heights exceeding the maximum height of the simulated random peaks (p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05) were retained. Peak regions were annotated using HOMER annotatePeaks.pl, and motif enrichment analysis was performed on peak regions using HOMER findMotifs.pl [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eGenes with HuR peaks in 3\u0026prime;UTRs were intersected with differentially expressed genes (DEGs) identified from RNA-seq analysis. To investigate HuR\u0026rsquo;s role in splicing regulation, peaks were integrated with intron retained (IR) events. Metagene analysis was performed to visualize HuR binding density near splice sites (\u0026plusmn;\u0026thinsp;50 bp), stratified by IR change direction (inclusion, exclusion, or no change). All genomic visualizations were generated using in-house scripts.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003e4.10 Statistical analysis\u003c/h2\u003e \u003cp\u003eThe data in this research was analyzed using GraphPad Prism 8.0 (San Diego,USA), and was presented as the mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation (SD). Data was analyzed using student\u0026rsquo;s t-test for two groups or one-way ANOVA for multiple groups. \u003cem\u003eP\u003c/em\u003e value of less than 0.05 was considered statistically as significant.\u003c/p\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eYin Shen and Haifeng Yang were in charge of most of the investigations, data analysis and the draft writing. Dongdong Xiao, Zhihong Liu and Jianglin Zheng performed parts of the cell experiments. Bandlamudi Uma Maheswara Rao helped drawing and data organization. Xiaobing Jiang and Haifeng Yang led the design of the entire research and revised the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNo funding was received.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics Statement\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was performed in line with the principles of the Declaration of Helsinki. Approval was granted by the Ethics Committee of Union Hospital, Tongji Medical College, Huazhong University of Science and Technology.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors have no relevant financial or non-financial interests to disclose.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eiCLIP-seq and RNA-seq data have been deposited in National Genomics Data Center\u003c/p\u003e\n\u003cp\u003ewith accession number PRJCA042738\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe are very thankful to members in Dr. Wen Chen\u0026rsquo;s team for their helpful discussions and experimental advice.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eC. 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However, there is a paucity of studies focusing on the role of in HuR in vascular smooth muscle cells. Knocking down HuR in cerebral vascular smooth muscle cells altered gene expression, with 3,300 genes upregulated in cytokine signaling pathways and 1,998 downregulated in cell mitosis pathways. It also affected alternative splicing, resulting in 3,531 events mainly related to RNA splicing and the cell cycle. Pearson analysis linked 33 splicing events with gene expression, including cell cycle genes MCM5, UHRF1, RPA2, and PRC1. eCLIP-seq of HuR identified 5,582 binding peaks in CDS and 3'UTR regions, with 33 related to cell cycle genes like Atf5, Ier3, and Zfp36l2. This study is the first to explore how the HuR gene influences cell cycle gene expression through pre-mRNA alternative splicing in vascular smooth muscle cells, enhancing our understanding of HuR's role in cardiovascular diseases.\u003c/p\u003e","manuscriptTitle":"HuR binds to flanking exons and regulates intron retention alternative splicing of cell cycle-related genes in smooth muscle cells","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-03-02 10:45:37","doi":"10.21203/rs.3.rs-7733072/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorInvitedReview","content":"","date":"2026-05-12T22:46:54+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"261915544666623583343186174522997902714","date":"2026-04-30T19:49:26+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-02-23T13:44:04+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-02-03T11:15:01+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-10-14T14:59:43+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-10-10T02:49:14+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2025-10-10T02:45:46+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"91bb9ade-f248-4789-bd4b-f817ade41519","owner":[],"postedDate":"March 2nd, 2026","published":true,"recentEditorialEvents":[{"type":"editorInvitedReview","content":"","date":"2026-05-12T22:46:54+00:00","index":101,"fulltext":""},{"type":"reviewerAgreed","content":"261915544666623583343186174522997902714","date":"2026-04-30T19:49:26+00:00","index":100,"fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":63699026,"name":"Health sciences/Cardiology"},{"id":63699027,"name":"Biological sciences/Cell biology"},{"id":63699028,"name":"Biological sciences/Genetics"},{"id":63699029,"name":"Biological sciences/Molecular biology"}],"tags":[],"updatedAt":"2026-03-02T10:45:38+00:00","versionOfRecord":[],"versionCreatedAt":"2026-03-02 10:45:37","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7733072","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7733072","identity":"rs-7733072","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}