Abstract
Vascular smooth muscle cells (VSMCs) are the major contributor to vascular repair and remodeling, which showed high level of phenotypic plasticity. Abnormalities in VSMC plasticity can lead to multiple cardiovascular diseases, wherein alternative splicing plays important roles. However, alternative splicing variants in VSMC plasticity are not fully understood. Here we systematically characterized the long-read transcriptome and their dysregulation in human aortic smooth muscle cells (HASMCs) by employing the Oxford Nanopore Technologies long-read RNA sequencing in HASMCs that are separately treated with platelet-derived growth factor, transforming growth factor, and hsa-miR-221-3P transfection. Our analysis reveals frequent alternative splicing events and thousands of unannotated transcripts generated from alternative splicing. HASMCs treated with different factors exhibit distinct transcriptional reprogramming modulated by alternative splicing. We also found that unannotated transcripts produce different open reading frames compared to the annotated transcripts. Finally, we experimentally validated the unannotated transcript derived from gene CISD1, namely CISD1-u, which plays a role in the phenotypic switch of HASMCs. Our study characterizes the phenotypic modulation of HASMCs from an insight of long-read transcriptome, which would promote the understanding and the manipulation of HASMC plasticity in cardiovascular diseases.
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Introduction
Vascular smooth muscle cells (VSMCs) are located within the vasculature and constitute the major cells of the medial layer of arteries1,2. VSMCs participate in arterial contraction and extracellular matrix (ECM) production, which are highly differentiated in healthy vessels with fully functional phenotypes. The phenotypic modulation of VSMCs from a state to another, such as from differentiated to dedifferentiated state, has been shown to play important roles in various cardiovascular diseases, such as atherosclerosis and restenosis3, hypertension4, and other aging-related diseases5. Two major phenotypes of VSMCs are contractile and non-contractile (synthetic), wherein contractile phenotype, including quiescent and differentiated VSMCs, mainly modulate the size reduction or shortening of muscles, while the synthetic phenotype, which is more likely to migrate or proliferate, contributes to the vascular remodeling under various pathophysiological conditions6. A variety of factors could induce phenotypic changes in VSMCs. The platelet-derived growth factor (PDGF), which is a potent mitogen, makes a major contribution to the phenotypic switch from contractile to proliferative state of VSMCs7,8, is characterized by high expression of constriction genes, such as osteopontin (OPN), kruppel like factor 4 (KLF4), KLF5, and Cyclin D1 (CCND1)9. The transforming growth factor beta (TGFβ) has been demonstrated to enhance the proliferation10 and maintain the differentiation state11 of VSMCs, wherein contractile markers include smooth muscle α-actin (SMαA), smooth muscle 22α (SM22α), smooth muscle calponin (CNN1), smooth muscle myosin heavy chain (SM-myh11), and smoothelin-B (SMTN-B), and transgelin (TAGLN)12. Growing evidence has shown that microRNAs (such as miR-14513, miR-22114, miR-12415, and miR-2216) play an important role in regulating VSMC phenotypic modulation. These are all suitable factors for the treatment of VSMCs to establish good models of phenotypic changes.
Aberrant alternative splicing has been demonstrated to play crucial roles in human complex diseases, including cancer17,18 and cardiovascular diseases19,20. Different transcripts that might perform distinct functions could be produced from the same genes through alternative splicing21,22,23. The extensive applications of high-throughput RNA sequencing (RNA-seq) technologies have accelerated the discovery of alternative splicing events that are frequently dysregulated in VSMCs59 (version 2.2.1). This map** strategy adopted possible splicing junctions in all samples. Final read alignments were subject to the StringTie60 (version 2.1.8) software for reference-based transcript assembly. Gene annotation of GENCODE v38 was utilized as the transcript model reference to guide the assembly in each sample. Finally, the merge mode implemented in StringTie was run to merge transcripts identified in all samples to produce a nonredundant master set of transcripts.
Quantification of transcripts in nanopore and Illumina RNA-seq data
The quantify function implemented in FLAIR software was adopted to quantify transcripts identified in nanopore RNA-seq data, which only considered reads with alignment scores no less than 1. Transcripts detected from Illumina RNA-seq data were quantified by using StringTie (version 2.1.8) with assembled transcript annotation. Then quantification was normalized in the unit of transcripts per million mapped reads (TPM). Transcripts that expressed no less than 0.1 TPM in at least one sample remained for downstream analysis. In addition, the DESeq261 software (version 1.30.1) was employed to identify DETs between different groups. Transcripts with fold change > 1.5 and FDR < 0.05 were considered as DETs.
Enrichment analysis of transcripts
Annotated transcripts were directly mapped to known genes according to the GENCODE (v38) annotation, while genes of unannotated transcripts were searched by genomic coordinates using the BEDTools software (version 2.29.2)62. Only protein-coding genes remained for enrichment analysis. Then the clusterProfiler R package (version 4.1.4)63 was employed to conduct enrichment analysis in pathways curated in the Kyoto Encyclopedia of Genes and Genomes (KEGG) database64. The Benjamini-Hochberg corrected p values were adopted as the enrichment significance.
Identification of alternative splicing events
Normalized transcript expression matrix and gene annotation were subject to SUPPA2 (version 2.3)65 for the identification of alternative splicing events (ASEs). Specifically, the “generateEvent” function with “-f ioe” option was used to generate local ASEs from the gene annotation GTF file. The percent spliced-in (PSI) value was calculated for each ASE in every sample by using the “psiPerEvent” function implemented in SUPPA2. ASEs with PSI values less than 0.1 in all samples were not included in downstream analysis. Then the Wilcoxon signed-rank test was employed to identify differentially spliced ASEs between different sample groups.
Detection of isoform switching events
Genome-wide isoform switching events were identified by using the IsoformSwitchAnalyzeR package (version 1.17.04)66 with default parameters. Changes in isoform usage were computed by comparing the miR-221, PDGF, and TGFβ with the control group, respectively. The genome-wide enrichment was assessed by counting isoform switches of specific alternative splicing types and comparing the number of gains and losses by using the Proportion test (prop.test function in R) and Fisher’s exact test (fisher.test function in R). For each pairwise comparison, the mean gene and isoform expression values were used to calculate the isoform fraction. A switching event with absolute difference in isoform fraction > 0.1 and adjusted p-value < 0.05 was considered significant. Functional consequences of isoform switching events were also predicted. Particularly, we used external analyses with CAPT, IUPred2, SignalP, and Pfam tools to predict the coding capabilities, protein structure stability, peptide signaling, and shifts in protein domain usage of significantly switching isoforms. These analyses were imported back into IsoformSwitchAnalyzeR to infer downstream biological consequences.
SiRNA transfection and validation
The silencing of CISD1-u was performed with the specific siRNA 5′-AAAUGAGUCUAAACAUGUCCA-3′ (Genepharma). CISD1-u siRNA transfection was conducted according to the protocol of DNA & siRNA transfection Reagent (jetPRIME). Cells were incubated in PDGF (10 ng/ml; MedChemExpress) for 24 h after siRNA transfection. Cellular protein and RNA were harvested after 24 h incubation in PDGF and the level of primary CISD1 was quantified by western blot and CISD1-u silencing was analyzed by qPCR.
Quantitative real-time PCR
Total RNA was isolated from HASMC cells with TRIzol reagent (Takara). The RNA was reverse-transcribed to cDNA using HiScript® III All-in-one RT SuperMix Perfect for qPCR (Vazyme). Quantitative RT-PCR (qRT-PCR) was performed using ChamQ SYBR Color qPCR Master Mix (Vazyme) according to the manufacturer’s instructions. The gene expression was then detected by using the QuantStudio™ 7 Flex Real-Time PCR system (Applied Biosystems). Primer sequences were synthesized by GENEWIZ as follows. All data were quantified using the Comparative Cт method. qRT-PCR primers used in this study is provided in Supplementary Table 3.
Western blotting
HASMC cells were lysed in RIPA buffer (Beyotime Biotechnology, Shanghai, China) in the presence of phenylmethylsulfonyl fluoride (PMSF, Beyotime). Protein content was determined by the BCA Protein Assay (Beyotime Biotechnology, Shanghai, China). The lysate was loaded in SDS-polyacrylamide gel and then subject to electrophoresis and electrical transfer. For CISD1, Cyclin D1, SMα-actin and GAPDH protein detection, rabbit anti-human CISD1 (ABclonal, A10317, 1:1000), rabbit anti-human Cyclin D1 (ABclonal, A11022, 1:1000), rabbit anti-human SMα-actin (Sigma-Aldrich, A2547, 1:500) and mouse anti-human GAPDH (ABclonal, AC002, 1:10000) antibodies were used, respectively. HRP Goat Anti-Rabbit IgG (Abclonal, AS014, 1:10000) and HRP Goat Anti-Mouse IgG (Abclonal, AS003, 1:5000) were used as secondary antibodies. In each lane, 15 μg protein was loaded. Detection of protein expression was performed by using Immobilon Western HRP (Millipore, MA, USA).
5′ RACE assays
Total RNAs were isolated from the HASMCs using RNAiso plus kit (Takara). RNA samples were subject to 5′-RACE reaction using the HiScript-TS 5′/3′RACE Kit (Vazyme, Nan**g, China). Firstly, the first-strand cDNA was synthesized according to the manufacturer’s instructions. Secondly, the synthesized cDNA was subject to a 5’-RACE reaction using forward and reverse primers: forward primers were the Universal Primer Mix, while reverse primer was a specific primer (5′-GCCCCATCACAGAATGGGAA-3′) for the novel transcript CISD1-u. Finally, the 5′-RACE product was subject to agarose gel electrophoresis, special bands extraction, and then confirmed by Sanger sequencing (Genewiz) using the following primers: upstream (5′-AGCATCGCGGAGTCGGT-3′) and downstream primer (5′-GCCCCATCACAGAATGGGAA-3′).
Semiquantitative RT-PCR
To determine the percent splicing inclusion of novel transcript CISD1-u, total RNAs were isolated from HASMCs using RNAiso plus kit (Takara) and performed reverse transcription to generate cDNA according to the instruction of HiScript® III All-in-one RT SuperMix Perfect for qPCR (Vazyme, Nan**g, China). The cDNA was then subject to a PCR reaction by following primers: GAPDH (F- GGACCTGACCTGCCGTCTAGAA; R-GGTGTCGCTGTTGAAGTCAGAG); CISD1-u (F-TGAGTTGTATGACGGCCACC; R-GCCCCATCACAGAATGGGAA); CISD1 (F-ACCCGTTTGAGCTCGGTATC; R-TGTGAGCCCCATCACAGAAT). The PCR products were analyzed in 2% agarose gels containing Gel Red (Vazyme, Nan**g, China) and photographed under ultraviolet light. Finally, the grayscale values of PCR bands were analyzed using ImageJ software.
Human aortic tissue collection
Aortic dissection tissues were collected during surgery from patients undergoing aortic root and ascending aorta replacement in the Department of Cardiac Surgery, Wuhan Asia Heart Hospital Affiliated to Wuhan University of Science and Technology. Control aortic tissues were collected from age-matched patients undergoing heart transplant surgery without aortic aneurysm, dissection or previous aortic repair. The study was reviewed and approved by the Ethics Committee of the Wuhan Asia Heart Hospital Affiliated to Wuhan University of Science and Technology and written informed consents were obtained from all patients.
Proliferation activity (CCK8 assay)
Approximately 4 × 104 primary HASMCs of passages 3 to 6 were plated in each well of a 96-well plate for 24 h. Cells were transfected with scramble or si-CISD1-u for another 36 h. Subsequently, vehicle control or PDGF (15 ng/mL) was added and incubated for 24 h. 10× diluted CCK8 solution (Solarbio, Bei**g, China) was added to the culture medium and incubated for 2 h. The absorbance of the solution was measured at 450 nm using a MRX II absorbance reader (Dynex Technologies, Chantilly, VA).
Migration activity (scrape assay)
Primary HASMCs of passages 3 to 6 were seeded in 24-well plates at a concentration of 1 × 105 cells/well and transfected with scramble or si-CISD1-u for 36 h. A linear wound was gently introduced in the center of the cell monolayer using a 200-µl tip followed by washing away the cellular debris with PBS. Thereafter, cells were stimulated with or without PDGF (15 ng/mL) and incubated for another 24 h. The borders of the scrape were outlined on the bottom of the plate, images were acquired, and the area of migration was measured by automated planimetry using ImageJ software (National Institutes of Health).
Statistics and reproducibility
Statistical analysis and data visualization in this study were performed by using the R software (R Foundation for Statistical Computing, Vienna, Austria; http://www.r-project.org) and the GraphPad Prism 8.0 software. Normality was examined using the Shapiro-Wilk test. Two-tailed unpaired Student’s t-test was performed to compare two datasets. Multiple comparisons were tested using one or two-way analysis of variance (ANOVA) followed by Bonferroni’s post-test. Unless specific statements, all tests were performed in two-sided, and p or FDR values < 0.05 were considered statistically significant.
Reporting summary
Further information on research design is available in the Nature Portfolio Reporting Summary linked to this article.
Data availability
The raw nanopore long-read RNA-seq and Illumina short-read RNA-seq data generated in this study was deposited in the GEO database with the accession number of GSE209739. The alignment bam files of nanopore long-read and Illumina short-read RNA-seq data were deposited in the Sequence Read Archive (SRA) database with the accession of PRJNA1001518. Software and resources used for analysis and visualization are described in each method section. All results generated in this study can be found in supplemental tables. Source data for all figures except for Fig. 4a are provided in Supplementary Data 7. Source data underlying Fig. 4a are provided in Supplementary Data 8.
Code availability
Scripts and codes that were used for data analysis and visualization were deposited in GitHub67: https://github.com/lishenglilab/ONT-LR-seq-in-HVSMC.
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Acknowledgements
This study was supported by the Science and Technology Commission of Shanghai Municipality (20410761000, 21QA1407400, 21ZR1481200, and 21YF1436900), the National Natural Science Foundation of China (82170253, 82241018, 32100517, and 82101257), National Key R&D Program of China (2021YFA1102300), Integrated Innovation Fund of Shanghai Jiao Tong University (2021JCPT04), and Shanghai General Hospital Startup Funding (02.06.01.20.06 and 02.06.01.20.04).
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S.L., D.K., and Z.L. designed and supervised the project; H.W. performed the data analysis and visualization; Y.L. conducted the experimental validation; Z.D., J.W., and M.L. assisted in cell culture and data curation; H.X. and J.F. collected human aortic tissues; X.H. assisted in data analysis; Y.W., S.H., T.L., and Y.F. interpreted results; S.L. and D.K. wrote the manuscript with comments from all the other authors; All listed authors read and approved the final manuscript.
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Wu, H., Lu, Y., Duan, Z. et al. Nanopore long-read RNA sequencing reveals functional alternative splicing variants in human vascular smooth muscle cells. Commun Biol 6, 1104 (2023). https://doi.org/10.1038/s42003-023-05481-y
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DOI: https://doi.org/10.1038/s42003-023-05481-y
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