
==== Front
Medicine (Baltimore)
Medicine (Baltimore)
MD
Medicine
0025-7974
1536-5964
Lippincott Williams & Wilkins Hagerstown, MD

39029007
MD-D-24-00673
00009
10.1097/MD.0000000000038886
3
3400
Research Article
Observational Study
The effect of overexpression of CyPA on gene expression in human umbilical vein endothelial cells
Yang Wenwen MM yww81873073@163.com
a
Zhou XinRong MM 1051675381@qq.com
b
Li Qiuju MBs 2858724904@qq.com
a
Yin Mingyue MBs wjk961102921@163.com
a
https://orcid.org/0000-0001-7270-948X
Wang Ning PhD ac*
a The First Department of General Internal Medicine, the First Affiliated Hospital of Xinjiang Medical University, Urumqi, China
b The Coronary Heart Disease Care Unit, CCU, The First Affiliated Hospital of Xinjiang Medical University, Urumqi, China
c State Key Laboratory of Pathogenesis, Prevention and Treatment of High Incidence Diseases in Central Asia.
* Correspondence: Ning Wang, The First Department of General Internal Medicine, the First Affiliated Hospital of Xinjiang Medical University, No. 137, Liyushan South Road, Xinshi District, Urumqi 830000, China (e-mail: 13999994126@163.com).
19 7 2024
19 7 2024
103 29 e3888618 1 2024
21 3 2024
20 6 2024
Copyright © 2024 the Author(s). Published by Wolters Kluwer Health, Inc.
2024
https://creativecommons.org/licenses/by-nc/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution-Non Commercial License 4.0 (CCBY-NC), where it is permissible to download, share, remix, transform, and buildup the work provided it is properly cited. The work cannot be used commercially without permission from the journal.

The aim of this study is to screen the differentially expressed genes and genes with alternative splicing in PPIA overexpressing cells by transcriptome sequencing. Transcriptome sequencing was performed to identify differentially expressed genes and genes with altered alternative splicing in PPIA overexpressing cells and results were validated by real-time quantitative polymerase chain reaction. The biological function and pathways of those genes were further explored through Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes analyses. A total of 157 significantly upregulated genes and 171 significantly downregulated genes were identified in PPIA overexpressing cells, and the splicing pattern of LHPP, APH1A, BRD1, and ORAI3 was found to be altered. GO analyses showed that the most enriched GO terms of the 157 upregulated genes included extracellular region, protein binding, and metal ion, and the most enriched GO terms of the 171 downregulated genes included binding neuron projection, protein binding, and endoplasmic reticulum unfolded protein response. Kyoto Encyclopedia of Genes and Genomes analyses showed that the 157 upregulated genes were mainly enriched in gastric acid secretion, Mitogen-activated protein kinase signaling pathway, etc, and the 171 downregulated genes were mainly enriched in transcriptional misregulation in cancer, Tumor necrosis factor signaling pathway, etc. The overexpression of PPIA in human umbilical vein endothelial cells causes changes in the expression of downstream genes and induces alternative splicing in multiple genes. PPIA alters the expression or the alternative splicing pattern of downstream genes, leading to pathogenesis of vascular endothelial injury by high glucose mediated through CyPA.

CyPA
diabetes
endothelial dysfunction
HUVEC
PPIA
vascular disease
OPEN-ACCESSTRUE
SDCT
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pmc1. Introduction

Diabetes and cardiovascular diseases, which are closely related, have become important noninfectious diseases threatening the global human health. The prevalence rate of diabetes in China has reached 11.2%, and the number of patients with diabetes ranks first in the world.[1] According to the Framingham Heart Study, diabetes increases the risk of cardiovascular diseases by 2 to 3 times, and thus diabetes is considered an equivalent of coronary artery disease.[2] Coronary atherosclerosis in patients with diabetes presents serious and diffuse characteristics, with larger area of atherosclerotic tissues, and higher proportion of macrophage infiltration. Clinically, coronary artery stenosis is serious, and the incidence of thrombosis is higher,[3] which has become the primary cause of death in diabetic patients.[4] The relationship between diabetes and cardiovascular diseases has attracted much attention. The hyperglycemia, insulin resistance, inflammation, and oxidative stress of diabetes are closely related to vascular diseases, especially endothelial dysfunction. Endothelial dysfunction, especially endothelium-dependent relaxation reduction, is the earliest change of cardiovascular diseases such as atherosclerosis and also the early pathophysiological change of atherosclerosis caused by type 2 diabetes. The in-depth study of the molecular mechanism of endothelial dysfunction is conducive to the understanding of the pathogenesis of cardiovascular diseases.

Cyclophilin A (CyPA), encoded by peptidylprolyl isomerase A (PPIA), belongs to the immune affinity protein family, has peptidylprolyl cis-trans isomerase activity, and can regulate protein folding and transport.[5] CyPA is expressed in coronary atherosclerosis,[6] and oxidative stress can also induce the secretion of CyPA by vascular smooth muscle cells.[7] Studies[8,9] have shown that CyPA/CD147 signaling pathway is closely related to cardiovascular diseases and diabetes. Inhibition of CyPA can promote insulin secretion, reduce the pancreatic β cell apoptosis caused by high glucose, and alleviate inflammation and oxidative stress.[10] CyPA is involved in inflammatory reactions, which is closely related to diabetes and atherosclerosis.[11] CyPA is also a newly identified RNA binding protein. RNA binding proteins are essential binding partners of RNA in cells. Their dynamic binding to RNA plays an important role in the regulation of transcription and posttranscriptional gene expression, participates in RNA splicing, polyadenylation, sequence editing, and RNA transport, and maintains the stability and degradation, intracellular localization, and other aspects of RNA metabolism.[12,13]

Preliminarily, we searched the DisGeNET database of human diseases and found that there were 1506 genes related to diabetes and 980 genes related to coronary artery diseases (data not shown). There were 433 genes that were both related to diabetes and coronary artery diseases. Then, 16 genes were obtained after taking the intersection of these 433 genes with 1455 known RNA binding proteins, and PPIA was one of them. Herein, we overexpressed PPIA in human umbilical vein endothelial cells. Then, the transcriptome sequencing and bioinformatics analysis were performed to screen the differentially expressed genes (DEGs) and genes with alternative splicing. The functions of these genes were annotated. The possible mechanisms of PPIA in regulating type 2 diabetes-related cardiovascular diseases and its possible functions in alternative splicing were analyzed and discussed. Our findings may provide evidence for the development of new treatment strategies and drug targets for diabetes and cardiovascular diseases.

2. Materials and methods

2.1. Cell culture and infection

Human umbilical vein endothelial cells (Sciencell, San Diego) were cultured in Endothelial Cell Medium complete medium at 37°C, 5% CO2. Following overnight culture, cells were infected with the empty lentivirus (titer 1.35 × 109 TU/mL; Multiple of Infection = 18; Genepharma, Suzhou, China) and the lentivirus overexpressing PPIA (titer 2.23 × 109 TU/mL; Multiple of Infection = 18; Genepharma). After infection for 24 hours, the culture medium was replaced with a complete medium, and cells were cultured for another 60 hours before collection. The overexpression of CyPA was validated with western blot.

2.2. Western blot

Total proteins were extracted from cells and subjected to Sodium dodecyl sulfure-polyacrylamide gel electrophoresis electrophoresis. Then, the proteins were transferred to the Polyvinylidene fluoride membranes (Millipore, Boston, ISEQ00010). After blocking with 5% skimmed milk at room temperature for 1 hour, the membrane was successively probed with primary and secondary antibodies. The primary antibodies included anti-Lamin A/C (Proteintech, 10298-1-AP), anti-CyPA (ABclonal, A0993), and anti-β-Tubulin (ABclonal, Boston, A12289). Finally, the membrane was visualized with Clarity Western ECL Substrate (Bio-Rad, Hercules, 170506). The density of each protein band was analyzed with Image J software (National Institutes of Health (Bethesda).

2.3. Transcriptome sequencing

Transcriptome sequencing was performed on cells with PPIA overexpression and control cells. The edgeR software (edgeR-Zueich, Switzerland) was used to analyze the DEGs in the data set.

2.4. Real-time quantitative PCR

Eight DEGs and 7 genes with alternative splicing were validated by real-time quantitative polymerase chain reaction (PCR). Briefly, RNA was extracted from cells and reverse transcription was performed. The primer sequences are shown in Table S1, Supplemental Digital Content, http://links.lww.com/MD/N196.

The real-time quantitative PCR was performed with the SYBR Green PCR Reagents Kit (Yeasen, Shanghai, CHINA) on the StepOne RealTime PCR System. The PCR procedures were 95°C for 10 minutes, 40 cycles of 95°C for 15 seconds, and 60°C for 1 minute.

2.5. Analysis of alternative splicing

The RNA seq data were aligned to the unique mapped reads on the reference genome for alternative splicing analysis. The splicing sites detected by TopHat2 were analyzed and classified as a whole through the program ABLas. The change of the same splicing type of each gene in the 2 samples was compared by T test. The screening criteria for significantly different alternative splicing was P ≤ .05. T value > 0 indicated an upregulated splicing type in the overexpression group than the control group, and T value < 0 suggested a downregulated splicing type in the overexpression group than the control group.

2.6. GO enrichment and KEGG pathway analysis

The DEGs and genes with alternative splicing were subjected to Gene Ontology (GO) enrichment and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis.

2.7. Statistical analysis

The measurement data were expressed as the mean ± standard deviation. The gene expression levels assessed by sequencing were compared using R language. The gene expression levels assessed by real-time quantitative PCR were compared using 2-way analysis of variance. P < .05 was considered significant.

3. Results

3.1. PPIA is overexpressed in human umbilical vein endothelial cells

To verify whether PPIA is overexpressed in human umbilical vein endothelial cells, western blot was conducted to detect CyPA expression. As shown in Figure 1, the levels of CyPA and CyPA-FLAG were significantly higher in the cells with PPIA overexpression than those in control cells (P < .05).

Figure 1. Western blot analysis of CyPA expression after PPIA overexpression. Representative and quantitative western blot results are shown. ***P < .001. CyPA = cyclophilin A.

3.2. Identification of DEGs

Transcriptome sequencing showed that a total of 29,329 genes were expressed in human umbilical vein endothelial cells after PPIA overexpression. Among them, 328 DEGs were identified, including 157 upregulated genes and 171 downregulated genes. The volcano plot (Fig. 2A) and heat map (Fig. 2B) of DEGs were shown, respectively.

Figure 2. Analysis of differentially expressed genes between PPIA overexpression and control groups. (A) Volcano plot. (B) Heat map.

3.3. GO enrichment and KEGG pathway analysis of upregulated DEGs

The upregulated DEGs were first subjected to GO enrichment analysis. The top 10 enriched GO terms in cellular component and molecular function were listed. As shown in Figure 3A, the enriched GO terms in cellular components included extracellular region, extracellular space, integral to membrane, plasma membrane, integral to plasma membrane, cytosol, nucleus, and cytoplasm. The enriched GO terms in molecular function included protein binding and metal ion binding. However, there was no enriched GO term in the biological process.

Figure 3. Functional analysis of differentially expressed genes with upregulation. (A) GO enrichment analysis of differentially expressed genes with upregulation. The top 10 enriched items in cellular component and molecular function are shown. (B) KEGG pathway analysis of differentially expressed genes with upregulation. The top 10 enriched pathways are listed. GO = Gene Ontology, KEGG = Kyoto Encyclopedia of Genes and Genomes.

Then, KEGG pathway analysis was performed. The results showed that the upregulated DEGs were mainly enriched in signaling pathways such as gastric acid secretion, protein digestion and absorption, pantothenate and CoA biosynthesis, Mitogen-activated protein kinase signaling pathway, neuroactive ligand-receptor interaction, terpenoid backbone biosynthesis, histidine metabolism, phototransduction, pentose phosphate pathway, and beta-Alanine metabolism (Fig. 3B).

3.4. GO enrichment and KEGG pathway analysis of downregulated DEGs

GO enrichment analysis of downregulated DEGs (Fig. 4A) showed that the enriched GO terms in cellular components included neuron projection, mitochondrion, extracellular space, cytosol, nucleus, nucleoplasm, endoplasmic reticulum, Golgi apparatus, nucleolus, and extracellular region. The enriched GO terms in molecular function included protein binding, double-stranded DNA binding, sequence-specific DNA binding, growth factor activity, cytokine activity, transcription factor binding, sequence-specific DNA binding transcription factor binding, protein homodimerization activity, protein heterodimerization activity, and DNA binding. Additionally, the enriched GO terms in biological process included endoplasmic reticulum unfolded protein response, amino acid transport, activation of signaling protein activity involved in unfolded protein response, defense response to virus, cellular response to hypoxia, immune response, ion transport, transmembrane transport, negative regulation of transcription, DNA-dependent, and apoptotic process.

Figure 4. Functional analysis of differentially expressed genes with downregulation. (A) GO enrichment analysis of differentially expressed genes with downregulation. The top 10 enriched items in cellular components, molecular function, and biological process are shown. (B) KEGG pathway analysis of differentially expressed genes with downregulation. The top 10 enriched pathways are listed. GO = Gene Ontology, KEGG = Kyoto Encyclopedia of Genes and Genomes.

KEGG pathway analysis of downregulated DEGs revealed that they were enriched in signaling pathways such as transcriptional misregulation in cancer, alcoholism, systemic lupus erythematosus, Tumor necrosis factor signaling pathway, glycine serine and threonine metabolism, apoptosis, nonalcoholic fatty liver disease, biosynthesis of amino acids, neuclear factor kappa B signaling pathway, and mTOR signaling pathway (Fig. 4B).

3.5. The expression of some DEGs

Based on previous studies,[14,15] apoptosis has a close relationship with diabetes and coronary artery disease. Thus, genes involved in apoptosis signal pathway were further studied in this study. In the GO analysis of the significantly downregulated DEGs, genes involved in the apoptosis pathway included PMAIP1 (phorbol-12-myristate-13-acetate-induced protein 1), ATCAY (cayman ataxia), SGK1 (serum/glucocorticoid regulated kinase 1), GADD45B (growth arrest and DNA damage-inducible protein 45B), BIRC3 (baculoviral IAP repeat-containing 3), GDF6 (growth differentiation factor 6), SULF1 (sulfatase 1), PPP1R15A (protein phosphatase 1, regulatory [inhibitor] subunit 15A), and FAM215A (family with sequence similarity 215 member A), and the gene involved in the immune-inflammatory pathway was XBP 1 (X-box binding protein 1) (Table S2, Supplemental Digital Content, http://links.lww.com/MD/N196). In the KEGG analysis of the significantly downregulated DEGs, the genes involved in the apoptosis pathway were DDIT3 (DNA damage-inducible transcript 3), PMAIP1, FOS (Fos proto-oncogene), GADD45B, BIRC3, and activating transcription factor 4 (ATF4).

3.6. Real-time quantitative PCR validation of 9 DEGs

The expression levels of 9 DEGs were subjected to validation with real-time quantitative PCR. As shown in Figure 5, the levels of ATF4, PPP1R15A, GADD45B, SGK1, DDIT3, PMAIP1, SULF1, GDF6, and XBP1 were significantly reduced in the PPIA overexpression group compared to the control group, which were consistent with the sequencing results.

Figure 5. Real-time quantitative PCR validation of 9 differentially expressed genes. Compared with control, ***P < .001. PCR = polymerase chain reaction.

3.7. Analysis of gene alternative splicing

Multiple genes underwent alternative splicing after overexpression of PPIA. Of note, the number of genes with intron retention and alternative splicing at the 5’ end of an exon was the largest (Table S3, Supplemental Digital Content, http://links.lww.com/MD/N196) (Fig. 6A, B).

Figure 6. The genes involved in alternative slicing. (A) The bar plot shows the number of all significant regulated alternative splicing events (RASEs). x-axis: RASE number. y-axis: the different types of AS events. (B) Hierarchical clustering heat map of all significant RASEs based on PSI. AS = Alternative Splicing, PSI = Ployfunctional Index Strength.

3.8. GO enrichment and KEGG pathway analysis of DEGs with alternative splicing

The DEGs with alternative splicing were first subjected to GO enrichment analysis. The top 10 enriched GO terms in cellular component, molecular function, and biological process were listed. As shown in Figure 7A, the enriched GO terms in cellular components included nucleus, cytoplasm, nucleolus, mitochondrion, nucleoplasm, lamellipodium, stress fiber, cytosol, ruffle, and, Promyelocytic Leukemia body. The enriched GO terms in molecular function included protein binding, chromatin binding, DNA binding, transcription factor binding, repressing transcription factor binding, nucleic acid binding, transcription corepressor activity, Adenosine Triphosphate binding, protein kinase binding, and, metal ion binding. Additionally, the enriched GO terms in the biological process included ncRNA metabolic process, G2/M transition of mitotic cell cycle, negative regulation of transcription from RNA polymerase II promoter, spliceosomal snRNP assembly, cell migration, autophagy, transcription, DNA-dependent, nerve growth factor receptor signaling pathway, mitotic cell cycle, and small molecule metabolic process. The apoptosis pathway was also found in the enriched GO terms with a P value < .05, although it was not in the top 10 GO terms. This indicates that the apoptosis pathway may be associated with genes undergoing alternative splicing.

Figure 7. Functional analysis of differentially expressed genes with significant alternative slicing. (A) GO enrichment analysis of differentially expressed genes with significant alternative slicing. The top 10 enriched items in cellular components, molecular function, and biological process are shown. (B) KEGG pathway analysis of differentially expressed genes with significant alternative slicing. The top 10 enriched pathways are listed. GO = Gene Ontology, KEGG = Kyoto Encyclopedia of Genes and Genomes.

Then, KEGG pathway analysis was performed. The top 10 enriched signaling pathways were vitamin digestion and absorption, MAPK signaling pathway, chronic myeloid leukemia, Human T-lymphotropic virus 1 infection, p53 signaling pathway, glycerophospholipid metabolism, etc (Fig. 7B).

3.9. Genes with significant alternative splicing

As shown in Table S4, Supplemental Digital Content, http://links.lww.com/MD/N196, the levels of alternative splicing in the genes of LHPP (phospholysine phosphohistidine inorganic pyrophosphate phosphatase), APH1A (anterior pharynx defective 1 homolog A), SERGEF (secretion regulating guanine nucleotide exchange factor), NFX1 (nuclear transcription factor X-box binding 1), PCBP4 (poly (rC) binding protein 4), BRD1 (bromodomain-containing protein 1), and ORAI3 (ORAI calcium release-activated calcium modulator 3) changed significantly. They may have an effect on blood vessels by participating in apoptosis.

3.10. Real-time quantitative PCR validation of 7 genes with significant alternative slicing

The expression levels of 7 genes with significant alternative slicing were validated with real-time quantitative PCR. As expected, LHPP, APH1A, BRD1, and ORAI3 genes had an elevated probability of alternative splicing events in the PPIA overexpression group compared to the control group (Fig. 8). Unexpectedly, the probability of an alternative splicing event in the PPIA overexpression group for the NFX1 gene was elevated compared with the control group. The probability of an alternative splicing event in the SERGEF and PCBP4 was not statistically different between the 2 groups.

Figure 8. Real-time quantitative PCR validation of 7 differentially expressed genes with significant alternative slicing. *P < .05, **P < .01, ****P < .001; ns = not significant, PCR = polymerase chain reaction.

4. Discussion

CyPA is mainly found in endothelial cells, vascular smooth muscle cells, monocytes, macrophages, and activated platelets.[8,9,14] Under inflammatory stimuli such as hypoxia and oxidative stress, CyPA can be secreted into the extracellular space. Both extracellular and intracellular CyPA may significantly contribute to the processes of inflammation, myocardial ischemia, reperfusion injury, and myocardial remodeling.[14,15] It is suggested that circulating CyPA is closely related to coronary artery disease and acute myocardial infarction.[16] CyPA levels were positively correlated with the number of coronary artery lesion branches, metalloproteinase-9, C-reactive protein, and Gensini scores.[17] Plasma CyPA can assess the risk of all-cause death, readmission, and coronary revascularization in patients with coronary artery disease.[18] Mechanically, when there is ischemia-reperfusion injury, CyPA can upregulate the expression of adhesion molecules in endothelial cells through proinflammatory pathways such as Neclear factor kappa B and ERK1/2[19] and promote the production of reactive oxygen radicals and the proliferation of vascular smooth muscle cells via activating ERK1/2 pathway.[20]

It is shown that CyPA deficiency can lead to thrombosis defects and hinder arterial thrombosis in vivo.[21] The most common thrombotic ischemic disease caused by CyPA deficiency is myocardial infarction in patients with coronary artery disease. For patients at high risk of thrombotic ischemic events, CyPA regulation of thrombosis is a target for diagnosis and treatment. Seizer et al reported that compared with patients with stable angina pectoris, the expression level of CyPA on platelet surface in patients with acute myocardial infarction was lower,[9] which is inconsistent with the previous results.[22] They further found that in patients with symptomatic coronary artery disease, the expression of CyPA on the platelet surface was independently related to all-cause mortality. Moreover, CyPA single nucleotide polymorphism (CyPA PPIA rs6850) was reported to be significantly and independently related to recurrent myocardial infarction.[8] The single nucleotide polymorphism rs6850 A > G locus was associated with the increased plasma CyPA level in patients with coronary artery disease and those with coronary artery disease and diabetes.[23] Thus, it is suggested that the expression level of CyPA on the platelet surface and CyPA single nucleotide polymorphism may have a certain clinical value in identifying coronary artery disease patients with high risk of adverse events.

In this study, KEGG pathway analysis was carried out on the upregulated DEGs after overexpression of PPIA. However, we found that the enriched pathways had little association with type 2 diabetes and coronary artery disease, and the number of genes was small. Thus, we focused on the downregulated DEGs. GO and KEGG analysis of downregulated DEGs found that most genes were enriched in the apoptosis pathway. Studies[24,25] have shown that apoptosis is closely related to diabetes and coronary artery disease. Therefore, we focused on genes involved in apoptosis in this study. Among the apoptosis-related genes regulated by PPIA, the genes of ATF4, PPP1R15A, GADD45B, SGK1, DDIT3, PMAIP1, SULF1, and GDF6 have been reported to be related to type 2 diabetes and coronary artery disease.[26–28] PPIA may play a role in diabetes with cardiovascular disease by regulating the expression of these genes.

ATF4 belongs to the activating transcription factor/cyclic AMP-response element-binding protein family. It plays an important role in the responses induced by hypoxia, amino acid deficiency, oxidative stress, endoplasmic reticulum stress, and other stress signals.[29] In addition, ATF4 can induce the expression of VEGF and play an important role in the vascular injury of the cardiovascular system.[30] Wu et al[31] showed that Icariside II could prevent hypertensive heart disease by blocking the Protein kinase RNA-like endoplasmic reticulum kinase (PERK)/ATF4/CHOP-C/EBP-homologous protein signal pathway, thus alleviating the apoptosis of myocardial cells induced by endoplasmic reticulum stress. Ma et al[32] showed that Guanxinkang could inhibit PERK-eIF2 α-ATF4 signal pathway and increase the expression of antiapoptotic factor Bcl-2, thereby alleviating atherosclerosis. It has been reported[33] that palmitic acid induced the apoptosis of H9c2 cardiomyocytes by upregulating the expression level of proteins related to endoplasmic reticulum apoptosis signal pathways, such as PERK, ATF4, and CHOP. In this study, the expression of ATF4 in the PPIA overexpression group was lower than that in the control group. It is suggested that the overexpression of PPIA could alleviate the vascular endothelial injury in diabetes patients possibly by inhibiting the expression of ATF4 and reducing the endoplasmic reticulum stress of vascular endothelial cells.

PPP1R15A was the second most downregulated differential gene between the PPIA overexpression group and the control group in this study. PPP1R15A regulates eIF2 α (α subunit of eukaryotic translation initiation factor 2) induced by stress and dephosphorylates eIF2 α.[34] Phosphorylated eIF2 α can reduce protein synthesis and prevent the accumulation of misfolded proteins in the endoplasmic reticulum.[34] The diseases related to PPP1R15A gene include multiple, early-onset diabetes.[35] It has been shown[36] that mice lacking active PPP1R15A were healthier and more resistant to endoplasmic reticulum stress than wild-type control group.

GADD45 family is related to stress signals and is widely involved in regulating cellular events, such as cell survival, aging, and apoptosis.[37] Brain ischemia and reperfusion injury in rats induce the protein and mRNA expression of GADD45B, which can mediate neuron apoptosis and may stimulate axon plasticity and recovery after stroke.[38] Xue et al showed that GADD45B promoted glucose-induced apoptosis of renal tubular epithelial-mesenchymal cells through p38 MAPK and c-Jun N-terminal kinase signaling pathways.[39] In the present study, overexpression of PPIA in human vein endothelial cells led to a decrease in the expression of GADD45B, thus affecting vascular endothelial cell damage by regulating cell apoptosis. However, the specific mechanisms need further study.

SGK1 is a member of the serine/threonine kinase gene family. It is regulated by serum glucocorticoids, can regulate the expression of different enzymes, transcription factors, ion channels, and transporters, and participates in a variety of metabolic processes and pathophysiological processes.[40] Voelkl et al showed that SGK1 was a key regulator of vascular calcification and promoted vascular calcification, at least partly, via Neclear factor kappa B activation.[41] Li et al revealed that the favorable effect of an SGK1 inhibitor on hyperglycemia was partly due to decreased glucose absorption through SGLT1 in the small intestine.[42] SGK1 plays an equally significant role in the complications of diabetes. SGK1 is engaged in glucose absorption and excretion in the intestine and kidney and participates in the progression of hyperglycemia-induced secondary organ damage.[43] It is shown that high glucose concentration can increase SGK1 transcription, and SGK1 is highly expressed in the renal tissue of diabetes patients.[42] This study preliminarily concluded that PPIA may participate in apoptosis by inhibiting the expression of SGK1, thus affecting the occurrence and development of diabetes combined with cardiovascular disease.

Alternative splicing can produce different mRNAs through different splicing types and then induce the formation of different protein isomers, which participate in cell differentiation and tissue development.[44] Herein, after overexpression of PPIA, the number of genes with intron retention and alternative splicing at the 5’ end of an exon was the largest, suggesting that these alternative splicing types may play an important role in the regulation of gene expression by PPIA in human umbilical vein endothelial cells. Alternative splicing is involved in vascular diseases by affecting lipid metabolism, changing the permeability of ion channels, and other mechanisms.[43] The specific role of intron retention and exon 5’ end selective splicing in vascular endothelial cells needs further research.

GO analysis of DEGs with significant alternative splicing found that one of the enriched terms was the apoptotic signal pathway, indicating that the apoptotic pathway may be related to the alternative splicing of genes. Studies[8,9] have shown that PPIA may affect the occurrence and development of diabetes complicated with coronary artery disease by regulating alternative splicing of LHPP,[45] APH1A,[46] SERGEF,[47] NFX1,[48] PCBP4, BRD1,[49] and ORAI3[50]genes. By KEGG analysis, we found that DEGs with significant alternative splicing were enriched in MAPK signaling pathway, which is related to many important cellular physiological/pathological processes, such as cell growth, differentiation, stress response, and inflammatory response.[51,52] Previous study has shown that p38MAPK could significantly increase myocardial cell apoptosis caused by ischemia-reperfusion and reduce cardiac function.[53] From the perspective of alternative splicing, this study provides a new idea for exploring the pathogenesis of diabetes complicated with cardiovascular disease in the future. However, the results of sequencing and bioinformatics analysis need to be further verified by experiments.

5. Conclusion

Overexpression of PPIA in human umbilical vein endothelial cells induced 328 genes to be differentially expressed, and multiple differential genes were with alternative splicing. Based on the results of the literature review and GO and KEGG pathway analysis, genes involved in cell apoptosis and genes with alternative splicing were verified by real-time quantitative PCR. Our findings may provide insights into the molecular mechanisms of diabetes-related atherosclerosis and provide targets for the treatment of diabetes-related coronary artery disease.

Author contributions

Conceptualization: Wenwen Yang, XinRong Zhou, Ning Wang

Formal analysis: Wenwen Yang

Investigation: Wenwen Yang, XinRong Zhou, Qiuju Li, Mingyue Yin

Methodology: Wenwen Yang

Project administration: Wenwen Yang

Writing – original draft: Wenwen Yang, XinRong Zhou, Mingyue Yin

Writing – review & editing: Wenwen Yang, Ning Wang

Software: Qiuju Li

Funding acquisition: Ning Wang

Resources: Ning Wang

Supervision: Ning Wang

Supplementary Material

Abbreviations:

APH1A anterior pharynx defective 1 homolog A

ATCAY cayman ataxia

ATF4 activating transcription factor 4

BIRC3 baculoviral IAP repeat-containing 3

BRD1 bromodomain-containing protein 1

CyPA cyclophilin A

DDIT3 DNA damage-inducible transcript 3

DEGs differentially expressed genes

eIF2 α α subunit of eukaryotic translation initiation factor 2

FAM215A family with sequence similarity 215 member A

FOS Fos proto-oncogene

GADD45B growth arrest and DNA damage-inducible protein 45

GDF6 growth differentiation factor 6

GO Gene Ontology

KEGG Kyoto Encyclopedia of Genes and Genomes

LHPP phospholysine phosphohistidine inorganic pyrophosphate phosphatase

NFX1 nuclear transcription factor X-box binding 1

ORAI3 ORAI calcium release-activated calcium modulator 3

PCBP4 poly (rC) binding protein 4

PMAIP1 phorbol-12-myristate-13-acetate-induced protein 1

PPIA peptidylprolyl isomerase A

PPP1R15A protein phosphatase 1, regulatory (inhibitor) subunit 15A

SERGEF secretion regulating guanine nucleotide exchange factor

SGK1 serum/glucocorticoid regulated kinase 1

SULF1 sulfatase 1

XBP 1 X-box binding protein 1.

This study was supported by the State Key Laboratory of Pathogenesis, Prevention and Treatment of High Incidence Diseases in Central Asia (SKL-HIDCH-2020-19).

The authors have no conflicts of interest to disclose.

The data that support the findings of this study are openly available at: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE227860.

Supplemental Digital Content is available for this article.

How to cite this article: Yang W, Zhou X, Li Q, Yin M, Wang N. The effect of overexpression of CyPA on gene expression in human umbilical vein endothelial cells. Medicine 2024;103:29(e38886).
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