Key circRNAs, lncRNAs, and mRNAs of ShenQi Compound in protecting vascular endothelial cells from high glucose identified by whole transcriptome sequencing

doi:10.21203/rs.3.rs-1967023/v1

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Key circRNAs, lncRNAs, and mRNAs of ShenQi Compound in protecting vascular endothelial cells from high glucose identified by whole transcriptome sequencing

https://doi.org/10.21203/rs.3.rs-1967023/v1

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Background: Vascular endothelial cell (VEC), which make up the inner wall of blood arteries, are susceptible to damage from oxidative stress and apoptosis caused by hyperglycemia. According to certain reports, noncoding RNAs (ncRNAs) are involved in controlling oxidative stress and apoptosis. Shen Qi Compound (SQC), a traditional herbal remedy, has been successfully treating diabetic vascular disease in China for more than 20 years. Although it is well established that SQC protects the vascular endothelium, the molecular mechanism remain unknown.

Methods:GK rats that consistently consume a high-fat diet were chosen as model animals. Six groups (control group, model group, metformin group, and 7.2 g/kg/d SQC group, 14.4 g/kg/d SQC group, and 28.8 g/kg/d SQC group) were included in this work, 15 rats each group. The approach of administration was gavage and the same volume (5.0 ml/kg/d) was given in each group, once a day, 12 weeks. The thoracic aortas were removed after the rats were sacrificed. Oxidative reduction profile in thoracic aorta, histopathological observation of thoracic aorta, endothelial cell apoptosis in thoracic aorta, whole transcriptome sequencing, bioinformatic analyses, and qRT-PCR were carried out.

Results：As a result, SQC prevented the oxidative stress and apoptosis induced by a high glucose concentration. Under hyperglycemia condition, ncRNAs, including one downregulated novel circRNA (circRNA.3121), three downregulated lncRNAs (Skil.cSep08, Shawso.aSep08-unspliced, and MSTRG.164.2), and one upregulated mRNA (Pcdh17), were clearly reverse-regulate by SQC.

Conclusion：SQC plays a role in protecting VEC from high glucose mainly by mediating ncRNA to inhibit cell apoptosis and oxidative stress.

ShenQi compound

endothelial cell

high glucose

lncRNA

circRNA.

SQC prevented the oxidative stress and apoptosis induced by a high glucose concentration.
ncRNAs including three lncRNAs (Skil.cSep08, shawso.aSep08-unspliced, and MSTRG.164.2), one new circRNA (circRNA.3121), and one mRNA (Pcdh17) are the main mediators that SQC uses to protect VEC against hyperglycemia.

High glucose levels will cause damage to the endothelium, a protective layer lining the inside of blood vessels, which is known to be the primary contributing factor to the onset and progression of vascular disease[1, 2]. Diabetes is the root cause of many vascular disorders and is one of the most urgent global health concerns of the twenty-first century [3]. Nitric oxide (NO) production is reduced and reactive oxygen species are increased when endothelial nitric oxide (NO) synthase is inactive under conditions of high hyperglycemia (ROS). As a result, oxidative stress brought on by hyperglycemia causes cell death, which in turn triggers vascular disorders[4-7]. On the one hand, increased ROS brought on by high glucose encourages vascular endothelial cells' (VECs') caspase-dependent death by causing the release of cytochrome c somatic (CYCS) [8]. ICAM1 (intercellular adhesion molecule 1) and VCAM1 (vascular cell adhesion molecule 1), two inflammatory cytokines, are abnormally expressed in endothelial cells as a result of chronic hyperglycemia (ECs) [9, 10]. Additionally, the oxidative stress brought on by the buildup of ROS reduces NO release, reduces the protective effects of NO on endothelial cells, and causes VEC death [11]. It is therefore crucial to identify key regulatory elements that might shield the vascular endothelium from hyperglycemia.

Non-coding RNA (ncRNA), in addition to mRNA, has recently been shown to be closely linked to human disorders[12]. The human genome can only be translated into proteins to a degree of roughly 2%. According to the length of the RNA chain, non-coding transcripts (ncRNAs), also known as long non-coding RNA (lncRNA, >200 nt) and short non-coding RNAs (snRNA, 200 nt), are separated into two categories. Parallel to this, microRNAs (miRNA, 18–22 nucleotides) and circular RNAs (circRNAs, covalently closed loop structure) can be used to further categorize snRNAs. It's interesting to note that these ncRNAs are not just transcriptional dross. These ncRNAs are crucial in the emergence of diabetes and related cardiovascular illnesses, according to mounting data[13, 14].LncRNAs are emerging as key regulators of cell apoptosis in a variety of vascular diseases, including cardiovascular disease [15, 16] and diabetic vascular complications [17, 18]. Examples of lncRNAs that were considerably up-regulated in vascular endothelium cells in high-glucose circumstances were ANRIL, MIAT, LOC100129973, MALAT1, MEG3, and TUG1. This suggests that lncRNAs are crucial for preventing endothelial cell damage in vascular disease situations[19-23]. Additionally, circRNAs with high sensitivity and specificity play important roles in a variety of human disorders in terms of early detection and efficient treatments [24]. Furthermore, it is well known that competing endogenous RNAs, such as circRNAs containing miRNA response elements (MREs), can interact with miRNAs to influence the production of downstream mRNAs (ceRNAs) [25]. The regulatory function of ncRNAs has opened up new possibilities for the creation of innovative therapeutics for vascular disease [12, 26]. New treatments haven't made much headway toward clinical usage, though.

The treatment of different vascular disorders can benefit greatly from the practical experience of traditional Chinese medicine (TCM), a part of China's intangible cultural legacy. The possible protective benefits of herbal medicine on endothelial cells have recently been the subject of a growing number of studies, which are helpful for the treatment or prevention of vascular disorders. Shen Qi Compound (SQC), a TCM recipe developed from the Zi-Cui-Yin was reported in Yi Xue Zong Zhong Can Xi Lu compiled by Xi-chun Zhang, has been used to therapeutically treat diabetic vascular disease in China for more than 20 years [27]. SQC consists of eight herbs: Panax Ginseng (Panax quinquefolius L., Ren Shen, RS), Astragali Radix (Astragalus aaronii (Eig) Zohary, Huang Qi, HQ), Rhizoma Dioscoreae (Dioscorea oppositifolia L., Shan Yao, SY), Corni Fructus (Cornus officinalis Siebold & Zucc., Shan Zhu Yu, SZY), Rehmanniae Radix (Rehmannia glutinosa (Gaertn.) DC., Di Huang, DH), Salviae Miltiorrhizae Radix et Rhizoma (Salvia miltiorrhiza Bunge, Dan Shen, DS), Radix Trichosanthis (Trichosanthes baviensis Gagnep.,Tian Hua Fen, THF) and Rhei Radix et Rhizoma (Rheum officinale Baill., Da Huang, DH), with a dose proportion of 15:15:10:10:10:10:10:6. Several previous studies have shown the prevention and treatment of SQC for diabetic vascular complications [28, 29], and the underlying mechanisms may be associated with oxidative stress and cell apoptosis. However, there are still significant gaps in our knowledge of how ncRNAs affect SQC's ability to protect VEC from excessive glucose. Therefore, identifying critical SQC regulatory ncRNAs for the preservation of the vascular endothelium has therapeutic implications for preventing diabetic vascular problems and offers a viable path for the development of treatments for vascular disease.

Drugs

Herbal medicine: The Pharmacy Department of Sichuan Hospital of Traditional Chinese Medicine validated each of the eight SQC herbs before they were obtained from Xin He-hua Chinese Medicine Co., Ltd. (Chengdu, China). the voucher number of each herb are as follow:Ren Shen (1801074),Huang Qi (1805070),Shan Yao (1804032),ShanZhuYu (1806028),Sheng Di Huang (1806029),Dan Shen (1805047),Tian Hua Fen (1710067),Da Huang (1803026). SQC extracts were created using the Chinese Pharmacopoeia's technique (version 2020). Before being cooked for 40 minutes, the herbal material was accurately weighed and steeped in cold water 10 times for 30 minutes. Thus, the first decoction was produced. To create the second decoction, the residue was cooked a second time for 40 minutes with the addition of eight times as much water. The first and second decoctions were then combined, filtered through a 0.45 mm filter, and concentrated to produce a 1.44 g/ml final extract.

Quality control: the chemical fingerprint of SQC water extract was identified through UPLC-MS. First, a 25 ml centrifuge tube containing 10 ml water and 10 ml carbinol was filled with SQC water extract (1 g), sonicated for 20 minutes, and then centrifuged (Eppendorf, Melbourne, Australia) at 4 000 r/min for 20 minutes. The test solution was prepared by resolving the dried supernatant with 50% acetonitrile in 2 ml and filtering it through 0.45 m. Opalshell C18 column (100 mm 4.6 mm, 2.6 m) at 0.1% aqueous formic acid (A) and acetonitrile containing 0.1% formic acid (B) were employed as the mobile phases for analysis in LC (2010A, Shimadzu, Japan). A flow rate of 0.3 ml/min was used. The 254 nm detective wavelength was chosen. For analysis, a 20ml sample was injected. The following gradient program was used to apply the elution condition: 0% B for 0  minutes; 5% B for 5  minutes; 5% B for 10 minutes; 10% B for 20  minutes; 10% B for 25 minutesutes; 20% B for 35 minutes; 25% B for 40 minutes; 40% B for 50 minutes, 40% B for 55   minutes; 60% B for 70 minutes; 80% B for 75 minutes; 0% B for 80  minutes; 0% B for 95 minutes. The following conditions were employed with the UHPLC+Q-Exactive MS (U3000, Thermo Fisher Scientific, American): The scanning quality range is 70-1055 m/z, the resolution ratio is 70000, the sheath gas flow is 35 ml/min, and the auxiliary gas flow is 15 ml/min. 350 °C for the capillary temperature, 35 KV for the auxiliary gas heating temperature, and 35 KV for the capillary tube voltage.

Metformin: a positive control drug for blood glucose control. Produced by Chengdu Kelong Chemical Reagent Factory, each pill contains 50mg.

Animals and ethics statement

90 rats (male, 7-8 weeks old, body weight 22020), including 75 Goto-Kakizaki (GK) rats and 15 Wistar rats, were bought from SLRC Laboratory Animals (Shanghai, China) (certification number: SCXK (hu) 2017-0005) and housed in the SPF experimental barrier system of the Laboratory Animal Center of Sichuan Academy of Chinese Medicine (license number: SYXK (Sichuan) 2018-100).

The US guidelines (NIH Publication #85-23, amended in 1985) for the use and care of laboratory animals in research were followed in the conduct of this study. Additionally, the Institutional Animal Care and Use Committee of the Medical Ethics Committee of the Hospital of Chengdu University of Traditional Chinese Medicine authorized this animal experiment (2021DL008).

Modeling, grouping and intervention

Modeling: GK rats (type 2 diabetes rats, fasting glucose level > 7.0 mmol/L) were selected as model animals and continuously fed a high-fat diet (HFD) for four weeks after seven days of acclimatization, while the Wistar rats were fed a standard diet. The HFD diet comprised 88.2% standard diet, 10% lard oil, 1.5% cholesterol, and 0.3% pig bile salts; the standard diet comprised 71% carbohydrate, 4.5% fat, 20% protein, and 4.5% minerals.

Grouping: a total of six groups (control group, model group, metformin group, and 7.2 g/kg/d SQC group, 14.4 g/kg/d SQC group, and 28.8 g/kg/d SQC group) were included in this work, 15 rats each group. Among these, Wistar rats were set as the control group, and all GK rats were randomly divided into the other five groups.

Intervention: the approach of administration was gavage and the same volume (5.0 ml/kg/d) was given in each group, once a day, 12 weeks. Gavage SQC extracts of 7.2 g/kg/d, 14.4 g/kg/d, and 28.8 g/kg/d were used in the 7.2 g/kg/d, 14.4 g/kg/d, and 28.8 g/kg/d groups, respectively. The Metformin group received 0.1 g/kg/d metformin, whereas the Model and Control groups received an equal volume of sterile water. All rats were fed standard rodent water during the entire experimental period.SQC concentrations, doses, routes, and frequencies were determined based on our previous[29-31]. All groups were assured that the same volume (5.0 ml/kg/d) of medication was given daily.

Sacrifice and sample collection of rats

All rats were sacrificed via intraperitoneal administration of sodium pentobarbital (40 mg/kg). The thoracic aortas were removed after the rats were sacrificed, and the adjoining connective tissues were carefully cleaned in saline. The aortas were cut into several ring segments of approximately 2 mm in length each and frozen in liquid nitrogen for subsequent procedures. The serum from the abdominal aorta was separated by centrifugation for biochemical analysis.

Detecting of glucose in blood and oxidative reduction profile in thoracic aorta

At the end of each week, the 12-h fasted rats were weighed and fasting blood glucose was determined. The levels of key oxidative stress factors such as 8-iso-prostaglandinF2 (8-iso-PGF2), superoxide dismutase (SOD), malonaldehyde (MDA), and glutathione peroxidase (GSH-Px) in thoracic aorta were measured in aorta tissue using rat commercial kits (8-iso-PGF2a:No F16555; SOD: No. F16742; MDA: No. F16194; GSH-Px: No. F15626).

Histopathological observation of thoracic aorta

HE staining was used to observe the aorta's histological alterations. Fresh aorta samples were embedded in paraffin after being fixed in 10% formalin solution for 48 hours. For the cross section, five successive slices of equal thickness were cut, and HE staining was used to perform standard histology (No. C0105; Beyotime Biotechnology; Shanghai; China). The pathology analysis program pro-plus 6.0 examined the media thickness. A microscope (Olympus, Tokyo, Japan, BX53) was used to randomly choose aorta fields at 200 magnification, and pictures (16001200 pixels) were recorded using a CCD (DP73; Olympus, Tokyo, Japan).

Analysis of endothelial cell apoptosis in thoracic aorta

TUNEL labeling was used to assess the aorta's level of apoptosis. The manufacturer's instructions were followed in the preparation of the aorta samples (A113, Vazyme; Nanjing, China). DAPI (62248, Thermo Fisher Scientific) was used to counterstain slices in most cases. Images were taken using an epi-fluorescent confocal microscope (ZEISS AX10 imager A2/AX10 cam, HRC, Heidelberg, Germany).

Whole transcriptome sequencing and bioinformatic analysis

Following the manufacturer's instructions, total RNA was extracted from each group of rats (n = 4) using the RNeasy Mini Kit (Cat# 74106, Qiagen), and RNA integrity was verified using an Agilent Bioanalyzer 2100 by looking for a RIN number (Agilent Technologies, Santa Clara, CA, US). The RNAClean XP Kit (Cat A63987, Beckman Coulter, Inc., Kraemer Boulevard, Brea, CA, USA) and the RNase-Free DNase Set (Cat#79254, QIAGEN, GmBH, Germany) were used to further purify the qualified total RNA. Shanghai Biotechnology Corporation prepared whole-transcriptome libraries and carried out next-generation sequencing (NGS) (Shanghai, China). On an Illumina HiSeq 4000 platform, RNA-seq was carried out, and 150-bp paired-end reads were produced in accordance with Illumina's instructions. Sequencing information was sent to the NCBI.All bioinformatic analyses were performed within the R environment (version 3.6.2, https://www.r-project.org/).

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

Following the manufacturer's instructions, total RNA from aortas was extracted using Trizol reagent (Thermo Fisher Scientific, Waltham, MA, USA), and reverse transcription into cDNA was carried out as explained in our earlier work. The Ct technique was used to calculate the relative gene expression levels using ACTIN as a reference gene. Chengdu Youkang Biotechnology Co., LTD. created the primer sequences. The qRT-PCR primers used were as follows: Irf4 (forward 5’-TCTCCCTATCTCACCAT AAGGTCCT-3’, reverse: 5’-ATGTCATCCTACACTAA CACCACGG-3’). Pcdh17 (forward: 5’-CTTGCCGATGTTGCCTAT-3’, reverse: 5’-CCATCTGTTGCTGCTTTC-3’) Skil.cSep08(forward: 5’-TGTCAGGAAGGAGGCACT-3’, reverse: 5’-ATGGGACTCGTGATAGGTG-3), shawso.aSep08-unspliced(forward:5’-GCTACTGCGAGCCTAACTT-3’, reverse: 5’-CTGATGCCTGAATCCTCC-3), MSTRG.164.2(forward: reverse:5’-GCTTGATGCCTCAGTGTA-3’, reverse: 5’-CTCGCACCCACAAATAAC-3’), circRNA.3121(forward: reverse:5’-CCTTTCTCAACGGCAACA-3’, reverse: 5’-CAGGCTTCTCAAACTAAACTCA-3’), GAPDH (forward: 5’-GCGAGATCCCGCTAACATCA-3’, reverse: 5’-CTCGTGGTTCACACCCATC A-3’).

Statistical analysis

Data was presented as the mean ± SEM. Comparisons of means were conducted using one-way ANOVA (comparisons among multiple groups) and Student's t-test (two independent samples). GraphPad Prism 8 (GraphPad Software, USA) was used for graphics and SPSS 16.0 (SPSS, Systat Software, San Jose, CA, USA) was performed for analyses. A value of P<0.05 was considered statistically significant.

The chemical fingerprint of SQC water extract

The results of the UPLC-MS analysis yielded a total of 20 components and the details are shown in table 1.

SQC inhibits hyperglycemia and oxidative stress

First, this work found that SQC inhibited hyperglycemia. As shown in figure 1, the fasting glucose level in the model group was higher than in the control group (P＜0.01) during the whole experimental phase. However, the fasting glucose level was significantly reduced at 7.2 g/kg, 14.4 g/kg, and 28.8 g/kg SQC groups after intervention (P＜0.05) (Figure.1A), without a dose-dependent manner, and metformin, as a positive drug, showed good efficacy in controlling glycemia (P＜0.01) (Figure.1A) indicating the the feasibility of the experiment. There was no significant difference in body weight among each group during the whole experimental phase, indicating the absence of side-effects of SQC (Figure.1B) (P＞0.05). Oxidative stress induced by hyperglycemia is the major causer of subsequent endothelial cell death. MDA, 8-isoPGF2 and SOD, GSH-Px are considered as the representative oxidative stress biomarkers of oxidative stress. In this work, the levels of MDA and 8-isoPGF2 substantially rise in the model group compared to the control group,while the levels of SOD and GSH-Px obviously decline in the model group compared to the control group, which indicates hyperglycemia induced oxidative damage in model rats (Figure.1C-F). Notably, SQC reduces MDA (Figure.1C) (P 0.01) and 8-isoPGF2- levels (Figure.1D) (P ＜0.01) while increasing SOD (Figure.1E) (P 0.01) and GSH-Px (Figure.1F) (P ＜0.01) without a dose-dependent effect. The results showed that SQC protects vascular endothelium from hyperglycemia and high-glucose-induced oxidative stress.

SQC improves histopathological injury of aorta

This work showed that SQC improves histopathological injury of the aorta. First, the model rats are consistent with characterization as vascular injury, such as intimal rupture, and endothelial cells swell and fall off (Figure 2). As expected, SQC improved the histopathological injury of the aorta through repairing vascular abnormalities and preventing endothelial cell abscission (Figure 2).

SQC inhibits the cell apoptosis of aorta

It is shown that SQC inhibits the endothelium apoptosis of the aorta. TUNEL staining revealed that the number of apoptotic cells is found in the model group (Figure 3A), and apoptosis rat of endothelial cells is significantly increased in the model group compared to the control group (Figure 3B). On the one hand, SQC decreases the number of apoptotic cells (Figure 3A) and the proportion of endothelial cell apoptosis (Figure 3B) (p<0.01) without a dose-dependent manner.

Global Responses of mRNA to SQC treatment

Twenty transcriptome libraries were created from the thoracic aorta of five different groups, each of which received 7.2 g/kg, 14.4 g/kg, and 28.8 g/kg of SQC, control, or model treatment. RNA-seq produced 15.3 billion raw reads with an average of 0.77 billion reads per sample. 96.1% (14.7 billion) of the readings representing clean reads were processed for further analysis after quality control. Each sample had an average rRNA ratio of roughly 0.16%. With an average mapping ratio of almost 92.1%, these reads were mapped to the beet genome. 30,958 genes were found to be expressed after gene expression levels were quantified as fragments per kilobase of transcript per million mapped reads (FPKM). Gene expression's principal component analysis (PCA) (Figure 1A) demonstrated the validity of the test and the soundness of sample selection. 1039 differentially expressed mRNAs (DEmRNAs) (562 up-regulated and 477 down-regulated) were found between the model group and the control group. Figure 1B and Table S1-4 show that 101 (7 up-regulated, 94 down-regulated, and 101 (54 up-regulated, 57 down-regulated) genes were found when SQC groups at 7.2 g/kg, 14.4 g/kg, and 28.8 g/kg were compared to the model group. This suggests that SQC regulates the overall response of mRNA. Although some DEmRNA expression was dosage-dependent, two (Irf4, Pcdh17) were present in all three dose groups for the SQC model (Figure 1C), suggesting that these mRNAs are crucial for SQC regulation.

To further investigate the roles of DEmRNAs, Gene Ontology (GO) annotation and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment were carried out (Figure 1D–G). Figure 1D displays the GO categorization for each group. Both the model group and the SQC three dosage group had an enrichment of 164 GO words (Table S5). The three most prevalent biological processes among them were the cellular process, single-organism process, and metabolic process (BP). In both the model group and the SQC three treatment groups, the highest number of DEmRNAs were annotated to cells and cell parts for cellular component (CC). In addition, a large number of DEmRNAs belonging to the binding, catalytic activity, and transporter activity were found both in the model group and the SQC three treatment groups under molecular function (MF). KEGG enrichment analysis showed that Cyanoamino acid metabolism (rno00460), Taurine and hypotaurine metabolism (rno00430) and Pentose phosphate pathway (rno00030) in the model group (Figure E1), whereas African trypanosomiasis (rno05143), Vitamin B6 metabolism (rno00750) and Malaria (rno05144) were significantly enriched in 7.2 SQC group (Figure E2), Legionellosis (rno05134), One carbon pool by folate (rno00670), and NOD-like receptor signaling pathway (rno04621) were significantly enriched in the 14.4 SQC group (Figure E3), and NOD-like receptor signaling pathway (rno04621), Legionellosis (rno05134), and Amyotrophic lateral sclerosis (ALS) (rno05014) were significantly enriched in 28.8 SQC group (Figure E4). A total of 37 common pathways were enriched in both the model group and the SQC three dose group (Table S6), such as the cGMP-PKG signaling pathway (rno04022), Amoebiasis (rno05146), and the AGE-RAGE signaling pathway in diabetic complications (rno04933).

Global Responses of lncRNA to SQC treatment

Apart from mRNAs, a total of 25554 potential lncRNAs were identified from the RNA-seq data (Table S7). Among these, 3410 lncRNAs identified here are novel lncRNAs. When mRNA and lncRNA were compared, most of the mRNA was more than 1000 bp long, whereas the main lncRNA was less than 1000 bp long (Figure 6A). In addition, 1 to 3 exons were observed for a higher proportion of lncRNAs (Figure 6B), which also had a lower FPKM value (Figure 6C). Finally, the number of DElncRNAs, in the model group compared to the control group, was 763 (470 up-regulated, 293 down-regulated)(Figure 6E1), while in 7.2 g/kg, 14.4 g/kg, and 28.8 g/kg SQC groups are found to be 92 (54 up-regulated, 38 down-regulated) (Figure 6E2), 107 (60 up-regulated, 47 down-regulated) (Figure 6E3), and 221 (123 up-regulated, 98 down-regulated), indicating a more robust response of SQC at the lncRNA level. Three (Skil.cSep08, shawso.aSep08-unspliced, and MSTRG.164.2) were common to the model and SQC three dose group (Figure 6D), which likely are the key mRNAs to SQC regulation. What’s more, to explore the potential functions of these DElncRNAs, their targeted mRNAs were predicted by cis-regulated and trans-regulated analysis. In the model group compared to the control group, 17442 suitable targeted mRNAs were found. Meanwhile, 2557, 3001, and 6204 were identified for 61 DElncRNAs in 7.2 g/kg, 14.4 g/kg, and 28.8 g/kg SQC groups compared to the model group, respectively (Table S8-11). The enriched top 30 GO terms of these targets in each group are shown in Figure 6F, while a total of 1385 common GO terms were enriched both in the model group and the SQC three dose groups (Table S12). Whereas 236 same enriched pathways were indicated by KEGG enrichment analysis (Figure 6G, top 30 pathway enrichment in each group), such as Metabolic pathways (rno01100), Olfactory transduction (rno04740), and Pathways in Cancer (rno05200), both in the model group and SQC three dose groups (Table S13).

Global Responses of circRNA to SQC treatment

A total of 10387 potential circRNAs were identified (Table S14). Among these, 0, 8.14, and 91.84% belong to the intergenic region type, intron type, and exon type, respectively (Figure 7A). In the mode group compared to the control group, 103 DEcircRNAs were identified (32 up-regulated, 71 down-regulated), whereas in the model group (Figure 7B1 and Table S15), 26 were found in the 7.2 SQC group (13 up-regulated, 13 down-regulated) (Figure 7B2 and Table S16), 32 in the 14.4 SQC group (19 up-regulated, 13 down-regulated) (Figure 7B3 and Table S17), and 41 in the 28.8 SQC group (14 up-regulated, 27 down-regulated) (Figure 7B4 and Table S18), and one (circRNA.3121) was was found in both model and SQC three groups (Figure 7B), suggesting that circRNAs are also the regulatory RNA of SQC for protecting endothelial cell from hyperglycemia.

Expression verification of key circRNAs, lncRNAs, and miRNAs to SQC treatment by qRT-PCR

qRT-PCR was performed to confirm the results of RNA-seq and to validate the expression of those key circRNAs, lncRNAs, and miRNAs. The qRT-PCR results agree well with RNA-seq data regarding expression trends (Figure 8). However, qPCR fold-change of some genes was different from the sequencing data, especially for circRNAs, lncRNAs, likely due to the different principles underlying these two test methods. Although the level of Irf4 increases in the model group compared to control group, there are not a significant difference between SQC three dose group and model group, suggesting Irf4 dose not mediate the effect of SQC in protecting VEC from high glucose. This result not only demonstrates that the RNA-seq data in this study is reliable, but also validates the role of key RNAs, including lncRNAs (Skil.cSep08, shawso.aSep08-unspliced, MSTRG.164.2), one novel circRNAs (circRNA.3121), and one mRNAs (Irf4, Pcdh17), in SQC protecting VEC from high glucose.

Diabetes-induced hyperglycemia has been shown to harm endothelial cells and cause vascular disease[10]. Therefore, the goal of treatment for diabetic vascular disease is to prevent endothelial cell damage. lncRNAs and circRNAs, for example, are ncRNAs that have a role in the pathophysiology of several human disorders. The development of new vascular disease treatments is given new chances and directions by ncRNA-mediated therapies. The findings show for the first time that SQC, a potential herbal treatment for diabetic vascular disease, protects vascular endothelial cells from hyperglycemia by mediating important lncRNAs (Skil.cSep08, shawso.aSep08-unspliced, MSTRG.164.2), important circRNAs (circRNA.3121), and important mRNAs (Irf4, Pcdh17).

Several lncRNAs have been discovered to act as regulators in vascular cells when glucose levels are high, establishing them as a novel class of drug development targets[26]. As reported, MALAT1, lncRNA AK087124, lncRNA H19, lncRNA OIP5-AS1, and XIST aggravate apoptosis of endothelium, while MEG3, RNCR3, and MIAT inhibit apoptosis of endothelium[14-17, 20, 22]. To our knowledge, no research has been done on the expression of lncRNAs Skil.cSep08, shawso.aSep08-unspliced, and MSTRG.164.2 in endothelial cells under high glucose. The important lncRNA for SQC's ability to protect the endothelium cell from hyperglycemia may include the lncRNAs Skil.cSep08 (NONRATT016293.2), shawso.aSep08-unspliced (NONRATT026659.2), and MSTRG.164.2, which we originally found.

CircRNAs have been linked to a variety of diseases, including diabetes and cardiovascular disease [24, 25].Certain circRNAs have specific roles in vascular endothelial cells that are stimulated by hyperglycemia, hypoxia, or other inducers, according to some study[32]. As reported, circRNA.3121 was verified to be less expressed in high glucose-induced human umbilical vein endothelial cells (HUVECs), and it was predicted to be a sponge of miR-29b-2-5p, miR-7161-3p, miR-7848-3p,miR-1255a, and miR-608 [32]. In this work, the circRNA.3121 similarly showed a clear down-regulate in the model group while SQC up-regulated the normal expression.

Protocadherin 17 (PCDH17), a member of the protocadherin family, is considered a methylated gene [33, 34]. In recent decades, a growing body of studies has demonstrated the methylation process is the most important regulatory factor for diabetes and associated vascular disease[35, 36]. For example, methylation in pericytes after acute injury promotes chronic kidney disease[37]. Endothelial dysfunction is mostly caused by histone methylation of Nox4 and eNOS, which makes them viable targets for metabolic memory interventions[38]. Another methylated protein, such as H3K4me1, H3K9me2, and H3K9me3, promotes endothelial dysfunction by inducing ROS[35]. Additionally, hyperglycemia causes a reduction in DNA methylation of the p66 Shc promoter in human aortic endothelial cells, which accelerates endothelial dysfunction by causing oxidative stress from the production of p66 Shc[39]. Nevertheless, PCDH17, known as a tumour suppressor, mainly induces tumour cell apoptosis and autophagy[33], but a lack of understanding of PCDH17 in endothelial cell injury led by hyperglycemia.In this study, SQC upregulated the normal expression whereas PCDH17 was clearly upregulated in the model group. This work provides evidence for PCDH17's involvement in endothelial cell damage caused by high glucose levels as well as SQC's regulatory function in endothelial cell defense against high glucose.

Of course, there are some restrictions on this work. On the one hand, further research is needed to completely understand how circRNA and lncRNA function in endothelial cells when glucose levels are high. On the other side, genetically flawed animals must be employed to support the future function of SQC.

This work generally establishes a platform for future research by methodically demonstrating that certain circRNA, lncRNA, and mRNA mediated the action of SQC in protecting VEC against high glucos.

According to the current study's data, SQC's ability to protect VEC from high glucose was mediated by important circRNAs, lncRNAs, and mRNAs. There were two outcomes: (1) In a rat model, SQC prevented the oxidative stress and apoptosis brought on by a high glucose concentration. (2) Three lncRNAs (Skil.cSep08, shawso.aSep08-unspliced, and MSTRG.164.2), one mRNA (Pcdh17), and one new circRNA (circRNA.3121) are the main RNAs that SQC uses to protect VEC against hyperglycemia.

VEC, vascular endothelial cell；ncRNA, noncoding RNA; SQC, Shen Qi Compound; lncRNA, long noncoding RNA; circRNA, circular RNA; NO, nitric oxide; ROS, reactive oxygen species; CYCS, cytochrome c, somatic; ICAM1, intercellular adhesion molecule 1; and VCAM1, vascular cell adhesion molecule 1; ECs, endothelial cells; snRNA, small non-coding RNA; TCM, Traditional Chinese medicine; PCDH17,Protocadherin 17.

Ethics approval and consent to participate

Not applicable.

Consent for publication

Not applicable.

Availability of data and materials

Supplementary material related to this article can be found in the online https://data.mendeley.com/datasets/publish-confirmation/6s4955hx2t/1?folder=1ffd0875-ef8e-41d7-97b3-e0d6fd23c794.

Competing interests

The authors declare no competing interests regarding the publication of this manuscript.

Funding

This work was financially supported by National Natural Science Foundation of China (Grant Number.81774302 and 82004341), Sichuan Science and Technology Program (Grant Number. 2020JDTD0022) and China Postdoctoral Science Foundation (Grant Number.2022M712286).

Authors' contributions

Chan Yang, Chunguang Xie participated in the research design and conceptualization; Chan Yang, Hanyu Liu, Guocheng Rao, Xinqiong Li, and Xixi Peng conducted the experiments. Chan Yang, Hanyu Liu，Guocheng Rao, and Qiangfei Yang performed the data curation and analysis. Lian Du, Xinqiong Li, and Ziyan Xie contributed to project administration and resources. Chunguang Xie contributed to project supervision; Chan Yang and Qiangfei Yang wrote the original draft. Chunguang Xie confirmed the final article.

Acknowledgements

Not applicable.

Authors' information

¹Division of Endocrinology and Metabolism, State Key Laboratory of Biotherapy, West China Hospital, Sichuan University and Collaborative Innovation Center of Biotherapy, Chengdu 610041, Sichuan, China.²Hospital of Chengdu University of Traditional Chinese Medicine, TCM regulating metabolic diseases key Laboratory of Sichuan province, 610075, Sichuan, Chengdu, China.³Jianyang City People's Hospital，610040,Sichuan, China.⁴Chengdu University of Traditional Chinese Medicine.

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Key circRNAs, lncRNAs, and mRNAs of ShenQi Compound in protecting vascular endothelial cells from high glucose identified by whole transcriptome sequencing

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