Proteomics of Restenosis Model in LDLR-deficient Hamster Coupled With the Proliferative Vascular Smooth Muscle Cells Reveal New Mechanism of Vascular Remodeling Diseases

doi:10.21203/rs.3.rs-671001/v1

Download PDF

Research

Proteomics of Restenosis Model in LDLR-deficient Hamster Coupled With the Proliferative Vascular Smooth Muscle Cells Reveal New Mechanism of Vascular Remodeling Diseases

https://doi.org/10.21203/rs.3.rs-671001/v1

This work is licensed under a CC BY 4.0 License

Version 1

posted

You are reading this latest preprint version

Background

Vascular remodeling diseases is a global health problem, and the proliferation and migration of vascular smooth muscle cells (VSMCs) is an important pathological mechanism. Golden hamsters have potential value in the study of the pathogenesis and molecular characteristics of vascular remodeling diseases, due to their lipid distribution similar to that of humans.

Methods

We detected a large number of differential proteins in vascular remodeling disease using proteomic methods, and verified the proteins (PFKL and LDHA) of concern by immunofluorescence and Western Blot.

Results

In this study, the molecular characteristics of vascular remodeling diseases were studied by golden hamster carotid artery proteomics and in vitro cultured rat VSMCs proteomics, and 1,246 and 1,070 differentially abundant proteins were detected, respectively. According to bioinformatics analysis, differentially abundant proteins are mainly related to carbohydrate metabolism, amino acid metabolism, acute inflammatory response, complement activation and cytoskeleton.

Conclusions

Among the numerous differentially abundant proteins, we found two proteins (PFKL and LDHA) of interest and verified them. Maybe they will become new biomarkers or drug targets for vascular remodeling diseases. Therefore, we not only provide a better animal model for the study of vascular remodeling diseases, but also provide new perspective for the pathological mechanism and treatment of vascular remodeling diseases.

Molecular Biology

Proteomics

Vascular remodeling diseases

VSMC

Hamster

Glycolysis/Gluconeogenesis

Vascular remodeling diseases, including atherosclerosis[1], hypertension[2] and restenosis after angioplasty[3], are vascular wall thickening and luminal stenosis diseases caused by endothelial injury, vascular smooth muscle cell (VSMC) proliferation and extracellular matrix deposition, which are the leading cause of mortality among humans throughout the world[4]. VSMC phenotypic transformation, which were transformed from contractile type to synthetic type, losing contractile function and gaining the ability to proliferate, migrate and secrete extracellular matrix, is of great importance to the formation and development of vascular remodeling diseases[5-8].

Percutaneous angioplasty and endovascular stenting have substantially reduced the mortality rate of coronary atherosclerotic heart disease. However, current rates of restenosis after drug eluting stent implantation in clinical practice remain higher than 10%[9]. The mechanism of vascular restenosis has been studied extensively for many years, but results from animal models have not yet yielded effective drugs or devices to prevent restenosis after interventional therapy. Utilizing suitable human-like experimental animal models, the pathogenesis and development of new potential interventions of vascular remodeling diseases may be further clarified. Recently, studies suggested that golden hamsters are small rodents that exhibit many features that are similar to those of humans, including lipoprotein profile, high plasma CETP levels, and apoB editing only in the intestine[10, 11]. Golden hamsters exhibit metabolic features similar to humans, suggesting that they could be a highly selective animal model for translational studies in the field of vascular remodeling diseases. Therefore, the restenosis model of LDLR-deficient hamster after arterial injury may provide a new breakthrough in the study of human vascular remodeling diseases.

Percutaneous coronary angioplasty (PTCA) is an effective treatment for coronary artery diseases. Although drug-eluting stents have significantly reduced the risk of restenosis, it remains a major cause of stent failure[12]. In people with diabetes and insulin resistance, there is a significant increase in the rate of restenosis after PTCA, which is related to the increased migration and proliferation of VSMCs[13].

Proteomics is widely used in the study of human diseases in order to explore the mechanisms and pathways underlying disease occurrence at the molecular level, determine biomarkers, and find effective therapeutic targets[14]. In recent years proteomics has been successfully applied to study proteins involved in many pathological processes of vascular remodeling diseases. Thus, proteomic studies have been carried out focusing on different elements such as vascular tissues (arteries)[15], artery layers[16], cells looking at proteomes and secretomes[17], plasma/serum[18], exosomes[19], lipoproteins and metabolites[20]. Here, we performed proteomic analysis in proliferated VSMCs and the common carotid arteries in hamsters after ligation for the first time. Through bioinformatics annotation, we found that many kinds of metabolic abnormalities had occurred both in the LDLR^-/+ hamster restenosis model and the proliferative VSMCs, especially in amino acid metabolism and carbon metabolism.

1 Cell culture

VSMCs were isolated from the thoracoabdominal aorta of 60-80 g male Sprague-Dawley rats by tissue mass adherent culture. VSMCs were grown in low glucose Dulbecco’s-modified Eagle’s medium (DMEM) (Invitrogen, US) with 10% fetal bovine serum (FBS), 100 U/mL penicillin and 100 µg/mL streptomycin. The VSMCs were cultured at 37°C in a humidified atmosphere containing 5% CO2, and only 3-5 generations of cells were selected for the experiments. When the density of cells grew to about 70-80%, Quiescent VSMCs were induced by 3% FBS starvation for 24 hours, and proliferative VSMCs were obtained from quiescent VSMCs after treatment with 10 ng/mL PDGF-BB (R&D Systems Inc) for 24 hours.

2 Establishment of hamster artery ligation model

Syrian golden hamsters were purchased from Invivo Biotech Co. (Shijiazhuang, China) and kept individually under a 12-hour light/12-hour dark cycle with free access to water and chow in the SPF animal laboratory of Hebei Medical University. All experiments were approved by the Animal Care and Use Committee of Hebei Medical University. In our studies, male animals of 8–12 weeks of age were used. Inhaled isoflurane anesthesia was used throughout the operation. An incision about 0.5 cm was made in the middle of the neck with surgical scissors, and ophthalmological scissors went deep into the incision to separate the subcutaneous tissue, subcutaneous fat and glands gently, so as to broaden the surgical field. Find the left common carotid artery, carefully separate the left common carotid artery from the vagus nerve, and dissect it to the bifurcation site. Non-absorbable sutures were taken through the left common carotid artery and ligated at the bifurcation site. The right common carotid artery was the control side, only the carotid artery was separated but not ligated. After the operation, the incision was sutured with 6-0 absorbable suture, and the hamsters were put back into the cage after they woke up.

3 B-ultrasound of hamster carotid artery

The peak blood flow, vessel wall thickness and lumen diameter of the left and right common carotid arteries were measured by B ultrasonography of small animals (Vevo 2100 Imaging System, FUJIFILM Visual Sonics Inc Canada) at different time points (0, 7, 14, 21, 28 days) after ligation. These data were analyzed by GraphPad Prism 7 software.

4 HE staining and Morphometry

For molecular histology studies, left and right common carotid arteries were collected at different time points (14, 21, 28 days). The carotids were fixed with 4% paraformaldehyde and were embedded in OCT compound and stored at -80°C until use. HE staining was conducted according to routine protocols. Briefly, after 5 μm serial sections were prepared, soak in distilled water for 1min. Sections were stained with hematoxylin solution for 3 min followed by 3 dips in distilled water and 3-5 seconds in 1% acid ethanol (3mL 38% HCl in 97mL 75% ethanol), and then rinsed in distilled water for 3 times. Then the sections were stained with eosin solution for 15 seconds and followed by dehydration with graded alcohol (70%, 80%, 90%, anhydrous ethanol) and clearing in xylene. The mounted slides were then examined and photographed using an Leica DM2000 LED Microsystems. The thickness of the endometrial and medial membrane of the vessel wall was measured by Image-Pro Plus 5.1 software and expressed as I/M Ratio.

5 Proteomic analysis

For cell samples: Sample was sonicated three times on ice using a high intensity ultrasonic processor (Scientz) in lysis buffer (8 M urea, 1% Protease Inhibitor Cocktail). The remaining debris was removed by centrifugation at 12,000 g at 4 °C for 10 min. Finally, the supernatant was collected and the protein concentration was determined with BCA kit according to the manufacturer’s instructions.

For tissue samples: The sample was grinded by liquid nitrogen into cell powder and then transferred to a 5-mL centrifuge tube. After that, four volumes of lysis buffer (8 M urea, 1% Protease Inhibitor Cocktail) was added to the cell powder, followed by sonication three times on ice using a high intensity ultrasonic processor (Scientz). (Note: For PTM experiments, inhibitors were also added to the lysis buffer, e.g. 3 μM TSA and 50 mM NAM for acetylation.) The remaining debris was removed by centrifugation at 12,000 g at 4 °C for 10 min. Finally, the supernatant was collected and the protein concentration was determined with BCA kit according to the manufacturer’s instructions.

Trypsin Digestion: For digestion, the protein solution was reduced with 5 mM dithiothreitol for 30 min at 56°C and alkylated with 11 mM iodoacetamide for 15 min at room temperature in darkness. The protein sample was then diluted by adding 100 mM TEAB to urea concentration less than 2M. Finally, trypsin was added at 1:50 trypsin-to-protein mass ratio for the first digestion overnight and 1:100 trypsin-to-protein mass ratio for a second 4 h-digestion.

LC-MS/MS Analysis: The tryptic peptides were dissolved in 0.1% formic acid (solvent A), directly loaded onto a home-made reversed-phase analytical column (15-cm length, 75 μm i.d.). The gradient was comprised of an increase from 6% to 23% solvent B (0.1% formic acid in 98% acetonitrile) over 26 min, 23% to 35% in 8 min and climbing to 80% in 3 min then holding at 80% for the last 3 min, all at a constant flow rate of 400 nL/min on an EASY-nLC 1000 UPLC system. The peptides were subjected to NSI source followed by tandem mass spectrometry (MS/MS) in Q ExactiveTM Plus (Thermo) coupled online to the UPLC. The electrospray voltage applied was 2.0 kV. The m/z scan range was 350 to 1800 for full scan, and intact peptides were detected in the Orbitrap at a resolution of 70,000. Peptides were then selected for MS/MS using NCE setting as 28 and the fragments were detected in the Orbitrap at a resolution of 17,500. A data-dependent procedure that alternated between one MS scan followed by 20 MS/MS scans with 15.0s dynamic exclusion. Automatic gain control (AGC) was set at 5E4. Fixed first mass was set as 100 m/z.

GO Annotation: Gene Ontology (GO) annotation proteome was derived from the UniProt-GOA database (http://www.ebi.ac.uk/GOA/). Firstly, converting identified protein ID to UniProt ID and then mapping to GO IDs by protein ID. If some identified proteins were not annotated by UniProt-GOA database, the InterProScan soft would be used to annotated protein’s GO functional based on protein sequence alignment method. Then proteins were classified by Gene Ontology annotation based on three categories: biological process, cellular component and molecular function.

Domain Annotation: Identified proteins domain functional description was annotated by InterProScan (a sequence analysis application) based on protein sequence alignment method, and the InterPro domain database was used.

KEGG Pathway Annotation: Firstly, using KEGG online service tools KAAS to annotated protein’s KEGG database description. Then mapping the annotation result on the KEGG pathway database using KEGG online service tools KEGG mapper.

Subcellular Localization: we used wolfpsort a subcellular localization predication soft to predict subcellular localization.

Enrichment of Gene Ontology analysis: Proteins were classified by GO annotation into three categories: biological process, cellular compartment and molecular function. For each category, a two-tailed Fisher’s exact test was employed to test the enrichment of the differentially expressed protein against all identified proteins. The GO with a corrected p-value < 0.05 is considered significant.

Enrichment of pathway analysis: Encyclopedia of Genes and Genomes (KEGG) database was used to identify enriched pathways by a two-tailed Fisher’s exact test to test the enrichment of the differentially expressed protein against all identified proteins. The pathway with a corrected p-value<0.05 was considered significant. These pathways were classified into hierarchical categories according to the KEGG website.

Enrichment of protein domain analysis: For each category proteins, InterPro (a resource that provides functional analysis of protein sequences by classifying them into families and predicting the presence of domains and important sites) database was researched and a two-tailed Fisher’s exact test was employed to test the enrichment of the differentially expressed protein against all identified proteins. Protein domains with a corrected p-value < 0.05 were considered significant.

Enrichment-based Clustering: For further hierarchical clustering based on differentially expressed protein functional classification (such as: GO, Domain, Pathway, Complex). We first collated all the categories obtained after enrichment along with their P values, and then filtered for those categories which were at least enriched in one of the clusters with P value <0.05. This filtered P value matrix was transformed by the function x = −log10 (P value). Finally, these x values were z-transformed for each functional category. These z scores were then clustered by one-way hierarchical clustering (Euclidean distance, average linkage clustering) in Genesis. Cluster memberships were visualized by a heat map using the “heatmap.2” function from the “gplots” R-package.

6 Western blot analysis

Whole-cell lysate was extracted from rat VSMCs using RIPA Lysis Buffer (1% Triton X-100, 150 mM NaCl, 10mM Tris-HCl, pH 7.4, 1 mM EDTA, 1 mM EGTA, pH 8.0, 0.2 mM Na3VO4, and 0.5% NP-40, add 1mM PMSF before use). Protein concentration of the samples was measured by a microplate protein assay (Spectra Max i3), and equal amounts of protein (PFKL: 80μg, LDHA: 20μg) were separated by 10% SDS-PAGE, and electrotransfered to a PVDF membrane. Membranes were blocked with 5% BSA (bovine serum albumin) for 2 hours at room temperature, and incubated with primary antibodies against PFKL (1:200, Immunoway) and LDHA (1:1000, abClone) overnight at 4℃, then with the HRP-conjugated secondary antibody (1:10000) for 2 hours at room temperature. The blots were evaluated with the enhanced chemiluminescence (Thermo fisher, Image Quant LAS4000) detection system. These experiments were replicated six times.

7 Immunofluorescence confocal microscopy analysis

The frozen tissue slides were fixed in 4% paraformaldehyde and permeabilized with 0.1% Triton X-100 at room temperature for 30 minutes, and then blocked with 1% NGS (Normal Goat Serum) for 1 hour. Rabbit anti-PFKL (Abcam) and LDHA (abClone) primary antibody was diluted 1:20 in 1% NGS and slides were incubated overnight at 4℃, then, TRITC conjugated goat anti-rabbit secondary antibody (sera care) was diluted 1:100 in 1% NGS and added to the slides for 1 hour at room temperature. Finally, the slides were mounted with DAPI Fluoromount-G (Cell Signaling Technology, Inc.). Confocal microscopy was performed with the Confocal Laser Scanning Microscope Systems (Leica).

8 Statistical analysis

All data were analyzed using SPSS 21.0 and GraphPad Prism 7 software. Differences between two groups for normally distributed variables were tested using independent-samples t-test. Repeated measurement data were tested using Multivariate analysis of variance (ANOVA). Multivariate ANOVA or degree of freedom adjustment is needed when the data does not satisfy the hypothesis of spherical distribution. All data were expressed as mean ± SEM unless otherwise stated, and differences were considered significant when P-value<0.05.

1 Identification of differentially abundant proteins in the VSMCs before and after PDGF-BB stimulation

To maximize proteome depth and coverage from the proliferative and quiescent VSMCs, the tryptic peptides were fractionated into fractions by high pH reverse-phase HPLC using Agilent 300Extend C18 column (5 μm particles, 4.6 mm ID, 250 mm length) and the peptides were subjected to NSI source followed by tandem mass spectrometry (MS/MS) in Q Exactive^TM Plus (Thermo) coupled online to the UPLC.

The resulting MS/MS data were processed using Maxquant search engine (v.1.5.2.8) and 385,728 secondary spectra were identified. A total of 180,242 available effective spectra were obtained, and the utilization rate was 46.7%. A total of 33111 peptides were identified by spectrogram analysis, among which 31,085 were specific peptides. A total of 5,428 proteins were identified, of which 4,146 were quantifiable (FDR was set at 1% for both peptide and protein identification) (Fig. S1a). Finally, differential protein expression analysis showed that when p<0.05, the change of differential expression level was more than 1.3 as the significantly up-regulated change threshold, and less than 1/1.3 as the significantly down-regulated change threshold, and a total of 1,070 proteins exhibited a significant difference between the two groups, of which 551 proteins were up-regulated and 519 were down-regulated (Fig. 1a, b). The quantitative reproducibility of the protein was evaluated by principal component analysis (PCA), relative standard deviation (RSD) and Pearson Correlation Coefficient. The results showed that the biological replicates were statistically consistent (Fig. S1b-d).

To determine the characteristics of the differentially abundant proteins, we first annotated the 1,070 proteins in detail from the subcellular localization, and COG functional classification. Annotation of the subcellular localization showed that 33.36% of all identified significantly different proteins were localized to the cytoplasm, 22.9% to the nucleus, 14.11% to the mitochondria, 11.4% to the extracellular, 8.69% to the plasma membrane, 3.55% to the cytoplasm and nucleus, and 5.98% to other locations (Fig. 1c). COG/KOG functional classification revealed that most of these differentially abundant proteins played a role in signal transduction mechanisms, posttranslational modification, protein turnover, chaperones, intracellular trafficking, secretion, vesicular transport, cytoskeleton, lipid transport and metabolism, energy production and conversion, carbohydrate transport and metabolism, amino acid transport and metabolism (Fig. 1d).

Furthermore, Gene Ontology (GO) classification was performed among the differentially abundant proteins. After PDGF-BB stimulation, the biological process (BP) of rat VSMCs underwent many abnormal changes, such as nucleotide phosphorylation, cofactor and coenzyme metabolic process, carbohydrate metabolic process, ribose phosphate and monocarboxylic acid metabolic process, carboxylic acid, organic acid and oxoacid metabolic process, etc. (Fig. 1e). Protein classification by CC revealed that most proteins were implicated in MCM complex, stress fiber, DNA packaging complex, contractile actin filament bundle, actomyosin and extracellular matrix (Fig. 1f). MF classification showed that most differentially abundant proteins were related to lyase activity (carboxy lyase, carbon-carbon lyase), ligase activity, oxidoreductase activity and isomerase activity (Fig. 1g).

Subsequent Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis revealed that the most significantly altered pathways were involved in glycometabolic pathway (glycolysis, gluconeogenesis and pentose phosphate pathway) and biosynthesis of amino acids. At the same time, the metabolism of carbon, galactose, fructose, mannose, pyruvate and various amino acids (tryptophan, valine, leucine, isoleucine, cysteine, methionine, glycine, serine, threonine, alanine, aspartate, glutamate) is also significantly affected (Fig. 1h).

2 Restenosis model of LDLR-deficient hamster after artery injury

Recently, LDLR-deficient hamsters exhibit metabolic features similar to humans[10, 11], suggesting that they could be a highly selective animal model for translational studies in the field of vascular remodeling diseases. Therefore, the left common carotid artery ligation was performed in wild type (WT), LDLR heterozygous (LDLR^-/+) and LDLR knockout (LDLR^-/-) hamsters. Before and after ligation at 7, 14, 21 and 28 days, the thickness of vessel wall and peak blood flow were measured by small animal B-ultrasound. The vessel wall was significantly thickened with the extension of ligation time (P<0.05) (Fig. 2a). Compared with WT group, the thickness of blood vessel wall in LDLR^-/+ and LDLR^-/- hamsters was significantly different at 14 and 28 days after ligation (P<0.05). However, vascular wall thickness between LDLR^-/+ and LDLR^-/-group has no change at 21 and 28 days after ligation (Fig. 2a). Therefore, the blood peak velocity decreased rapidly after ligation (P<0.05) (Fig. 2b). HE staining showed that the intima and media of the vessel wall was significantly thickened. Compared with WT hamster, LDLR^-/+ and LDLR^-/- hamster showed more obvious thickness of intima and media (Fig. 2c). Compared with WT hamster, the intimal/medial ratio (I/M) of the LDLR^-/+ and LDLR^-/- hamster showed a statistical difference at 14 days and 28 days after ligation (P<0.05) (Fig. 2d). However, LDLR^-/- group showed no statistically significant difference compared with LDLR^-/+ group (P>0.05) (Fig. 2d). Since LDLR^-/+ hamsters can quickly show the symptoms of restenosis after vascular injury, LDLR^-/+ hamsters were selected for further study.

3 Proteomic analysis of restenosis model after artery injury in LDLR^-/+ hamsters

First, ligation was performed at the bifurcation of the left common carotid artery of 6 LDLR^-/+ hamsters. 14 days after ligation, left common carotid arteries were collected used as the experimental group for proteomic analysis. The right common carotid artery was used as the control group LC-MS/MS identified 652,728 secondary spectra. A total of 179,591 available effective spectra were obtained, and the utilization rate was 27.5%. A total of 38,369 peptides were identified by spectrogram analysis, among which 35,395 were specific peptides. A total of 4,259 proteins were identified, of which 3,305 were quantifiable (FDR was set at 1% for both peptide and protein identification) (Fig. S2a). Finally, differential protein expression analysis showed that when p<0.05, the change of differential expression level was more than 1.5 as the significantly up-regulated change threshold, and less than 1/1.5 as the significantly down-regulated change threshold, and a total of 1,246 proteins exhibited a significant difference between the two groups, of which 740 proteins were up-regulated and 506 were down-regulated (Fig. 3a, b). The quantitative reproducibility of the protein was evaluated by principal component analysis (PCA), relative standard deviation (RSD) and Pearson Correlation Coefficient. The results showed that the biological replicates were statistically consistent (Fig. S2b-d).

To determine the characteristics of the differentially abundant proteins, we first annotated the 1,246 proteins in detail from the subcellular localization, COG functional classification and GO secondary annotation classification. Annotation of the Subcellular Localization showed that 32.05% of all identified significantly different proteins were localized to the cytoplasm, 21.69% to the nucleus, 18.55% to the extracellular, 9.88% to the mitochondria, 9.72% to the plasma membrane, 3.37% to the cytoplasm and nucleus, 2.09% to the endoplasmic reticulum, and 2.65% to other locations (Fig. 3c). COG/KOG functional classification revealed that most of these differentially abundant proteins played a role in signal transduction mechanisms, posttranslational modification, protein turnover, chaperones, cytoskeleton, intracellular trafficking, secretion, vesicular transport, RNA processing and modification, energy production and conversion, carbohydrate transport and metabolism, transcription, lipid transport and metabolism, as general function predictions (Fig. 3d).

In addition, GO classification KEGG pathway and protein domain enrichment analysis was performed among the differentially abundant proteins. Classification of proteins by BP indicated that, after arterial injury, a variety of abnormal biological processes occur in the common carotid artery of LDLR-deficient hamsters, such as glycosphingolipid metabolic process, ATP hydrolysis coupled transmembrane transport, regulation of complement activation and regulation of acute inflammatory response, etc. (Fig. 3e). Protein classification by CC revealed that most proteins were implicated in glycoprotein complexes (especially in dystrophin-associated glycoprotein complex) and ribosome (cytosolic ribosome and ribosomal subunit) (Fig. 3f). MF classification showed that most differentially abundant proteins were related to ATPase activity (calcium- and cation-transporting ATPase activity, hydrogen-exporting ATPase activity) (Fig. 3g). Subsequent KEGG analysis revealed that the most significantly altered pathways were involved in glycosphingolipid biosynthesis and glycosaminoglycan degradation, as well as significant effects on vascular smooth muscle contraction, dilated cardiomyopathy (DCM) and hypertrophic cardiomyopathy (HCM) (Fig. 3h).

4 Metabolic abnormalities occurred both in the LDLR^-/+ hamster restenosis model and the proliferative VSMCs

We compared the differential proteins in LDLR^-/+ hamster artery restenosis and the proliferative VSMCs. Venn diagram showed that 130 differentially expressed proteins appeared in the two models simultaneously, including 67 up-regulated proteins (Fig. 4a) and 63 down-regulated proteins (Fig. 4b). We performed GO and KEGG analysis of these common differential proteins using DAVID Bioinformatics Resources 6.8 (https://david.ncifcrf.gov/). Visual analysis of enrichment GO term histogram and KEGG enrichment dot bubble were performed using web-version analysis tools (http://www.bioinformatics.com.cn/) (Fig. 4c, d). Classification of proteins by BP indicated that various proteins were expressed abnormal during carbohydrate metabolic process (Fig. 4c). KEGG enrichment dot bubble showed that the differential proteins were mainly concentrated in metabolic pathways, accounting for 25% of the analyzed proteins (Fig. 4d). Therefore, we counted the differentially abundant proteins in the glycolytic pathways of the two models respectively (Fig. 6e, f), and labeled the common proteins (Fig. 4g). The glycolytic pathway showed up-regulation of phosphofructokinase 1 of liver type (PFKL), phosphoglycerate mutase (PGM), enolase, and Lactate dehydrogenase A (LDHA), while down-regulation of pyruvate dehydrogenase (PHD).

5 Validation of candidate protein abundant in the proliferative VSMCs and artery restenosis of LDLR^-/+ hamster

PFKL and LDHA are the key enzymes in the glycolysis, so Western Blot and immunofluorescence analysis were used respectively to examine their abundant in rat VSMCs and hamster carotid artery slices. Consistent with the LC-MS/MS data, Western Blot assay showed that the levels of PFKL and LDHA were significantly up-regulated in PDGF-BB stimulated VSMCs, compared to those in the control group (P<0.05) (Fig. 5a, b). Similarly, in LDLR^-/+ hamsters, immunofluorescence analysis results showed that PFKL and LDHA expression were significantly increased in left common carotid artery after 21 days ligation, compared with the control group (P<0.05) (Fig. 5c, d).

To our knowledge, the current study is the first comprehensive proteomics analysis of the proliferative VSMCs and the ligation-reduced restenosis in LDLR^-/+ hamster, whereas previous studies have focused on plasma[18, 21], extracellular matrix[22-24], atherosclerotic plaques[16] or urine[25]. In this study, we mainly explored the molecular features of vascular remodeling disease and revealed the VSMC metabolic disorder in vascular remodeling disease. The proteins associated with vascular remodeling diseases were selected by combining carotid artery proteomics in LDLR-deficient hamster artery restenosis model with rat VSMC proteomics.

Studies have shown that the activation of complement system is a key mechanism of vascular remodeling. Anaphylatoxins C3a and C5a and their receptors (C3aR and C5aR) are expressed in human atherosclerotic coronary plaques[26]. Moreover, C5a colocalizes with matrix metalloproteinases (MMP-1 and MMP-9) in vivo[27], suggesting that this association may have some influence on the pathogenesis of atherosclerosis, especially plaque instability and rupture. C5a is also involved in the recruitment of inflammatory cells (such as neutrophils, eosinophils, monocytes, and T lymphocytes) and the activation of phagocytes[28]. In addition, complement was activated in Ang II-induced hypertension and cardiac injury mouse models, and C5aR expression played an important role in mediating Ang II-induced myocardial injury and remodeling[29]. Researches have also shown that the increased expression of complement 3 (C3) in VSMCs of spontaneously hypertensive rats (SHRs) converts VSMC from a contractile phenotype to a synthetic phenotype by inducing KLF5[30]. This was verified precisely in our LDLR^-/+hamster common carotid artery proteomic results, and BP showed that differential proteins were enriched in the regulation of complement activation, including C3, C5, C8, C9, C1q, etc.

KEGG pathway analysis showed that the differential proteins were enriched in Valine, leucine and isoleucine degradation, and almost all the enzymes involved in this pathway were significantly reduced. We know that Valine, leucine and isoleucine are branched chain amino acids (BCAAs), and they are the most abundant essential amino acids in the human body[31]. They are not only substrates for the synthesis of nitrogen-containing compounds, but also signal molecules that regulate carbohydrate, lipid and protein metabolism intestinal health and immunity through specialized signaling networks[32]. BCAAs are catabolized through the branched-chain aminotransferase (BCAT) and branched-chain ketoacid dehydrogenase (BCKD) complex, and eventually generate acetyl-CoA and succinyl-CoA through a series of enzymatic reactions, which participate in the metabolism of tricarboxylic acid (TCA) cycle[33]. Studies have shown that defects in BCAAs catabolism are associated with cardiovascular disease, and BCAAs catabolism is impaired in a variety of cardiometabolic diseases[34, 35]. Interestingly, increased expression of branched-chain amino acid transaminase 1 (BCAT1) was detected in a number of reduced BCAAs catabolic enzymes. Studies have shown that PI3K/Akt/mTOR pathway is abnormal in breast cancer patients[36], and by activating mTOR signal, BCAT1 can partially promote the growth and colony formation of breast cancer cells, and enhance the biogenesis and function of mitochondria[37, 38]. Therefore, the PI3K/Akt/mTOR pathway may be a target for cancer therapy. It is worth considering whether the same mechanism also exists in VSMCs to promote its metabolic abnormalities and cell proliferation.

Glycolysis is important in cellular energy metabolism. Multiple proteins are upregulated in the Glycolysis/Gluconeogenesis pathway (HK, G-6-Pase, PFKL, TPI, GAPDH, PGK, PGM, Enolase, PK, LDHA), indicating abnormal VSMC metabolism, active glycolysis and increased ATP supply, similar to the Warburg effect in tumor cell proliferation. Studies have shown that increasing the rate of glycolysis is a necessary condition for vascular smooth muscle migration, during which the expression of HK2 increases[39]. PKM2 is highly expressed in VSMCs of rabbit atherosclerotic plaques of the femoral artery and increases with the development of atherosclerosis[40]. Carotid plaque tissue from 159 endarterectomy patients, were reported increased glycolysis, amino acid utilization, and decreased fatty acid oxidation[41]. Finally, we identified two differentially abundant proteins PFKL and LDHA, as the two key enzymes in glycolysis，and verified them. These proteins play a very important role in the occurrence and development of vascular remodeling diseases, and together with the risk factors of vascular remodeling diseases, provide strong evidence for clinical disease risk prediction.

Phosphofructokinase 1 (PFK1, ATP: D-fructose-6-phosphate-1-phosphotransferase, EC 2.7.1.11) plays a central role in the regulation of glycolysis. This enzyme catalyzes the conversion of fructose-6-phosphate (F6P), to fructose-1,6-bisphosphate (F1,6BP)[42, 43]. Mammalian PFK1 is composed of three different isoforms, muscle (PFKM), liver (PFKL), and platelet (PFKP), which are randomly combined into homologous or heterologous tetramers to function[42]. PFK1 coordinates the activities of the pentose phosphate, hexosamine biosynthetic, and glycerolipid synthesis pathways by directly modulating glycolytic intermediate entry into subsidiary pathways and indirectly regulating mitochondrial cataplerosis[44]. This is also an important pathway for normal and malignant cells to proliferate[45]. PFK1, especially PFKL, has a strong influence on glycolysis, which may also be a key regulatory point in the process of oncogenic transformation[46, 47]. PFK1 association with f-actin is partially responsible for the so-called ‘‘Warburg effect’’, a characteristic of tumors that accounts for ability to proliferate. Besides, PFKL tetramers can assemble into filaments with retained catalytic activity and enriched at the plasma membrane[48]. Several other enzymes in glycolysis (pyruvate kinase, aldolase and lactate dehydrogenase) are also localized to pseudopodia, and reversibly associated with components of the actin cytoskeleton[49]. This is similar to the formation of the multienzyme metabolic complex for glucose metabolism, and the acetylation of PFKL is necessary for the formation of the multienzyme metabolic complex[50].

Lactate dehydrogenase (LDH) is an essential metabolic enzyme，and the main function of LDHA is to convert pyruvate to lactate, and transform NADH to NAD+[51, 52]. Although most studies on LDHA have focused on the cancer model and considered it as a target to control the proliferation and metastasis of cancer cells[53-55], some studies have also shown that LDHA is a key enzyme necessary for the proliferation and migration of VSMC[56]. In proliferating vascular smooth muscle cells, the rate of glycolysis increases Warburg-like, and this characteristic metabolic reprogramming has become a key factor in determining the phenotypes and signal transduction processes. In addition, LDHA-specific siRNA inhibition of LDHA can regulate the expression of inflammatory genes and inhibit the inflammatory response[57].

So far, we have only shown that these proteins are significantly altered in vascular remodeling diseases. However, it is still not clear how they affect the occurrence and development of disease, or through which signaling pathways or molecular mechanisms they act. Therefore, further studies on these proteins are needed to clarify the new role in vascular remodeling diseases. We know that proteomics results can be false positive, so using a combination of the two proteomics analyses not only narrows the screening range, but also makes the screened proteins more meaningful and convincing. Inevitably, some information is lost. For example, the proteomic analysis of rat VSMCs is limited to the protein in VSMCs, while the proteomic analysis of hamster carotid artery tissue also includes arterial plaques, endothelial cells, extracellular matrix, and inflammatory cells after lesion, etc. Still and all, there's no denying that in the midst of this huge amount of information, we've found a group of proteins that have potential clinical significance. For example, as a biomarker, it can predict the occurrence of vascular remodeling diseases, help to judge the severity of vascular remodeling diseases, and is likely to become a molecular target for future disease treatment.

In summary, our study is the first to use rat VSMCs proteomics and hamster common carotid artery proteomics to identify a series of proteins of significance in vascular remodeling disease and also validated PFKL and LDHA.

Acknowledgements

The authors thank Jingjie PTM Biolab Co. Ltd. (Hangzhou, China) for the performance of LC-MS/MS.

Authors’ contributions

Li-Hua Dong and Zhao-Kun Hu designed the research; Li-Hua Dong, Zhao-Kun Hu, Jiang-Ling Niu and Jia-Jie Lin performed the study; Zhao-Kun Hu analyzed the data; Li-Hua Dong, Zhao-Kun Hu and Jia-Jie Lin wrote and revised the manuscript. All authors read and approved the final manuscript.

Funding

This study was supported by grants from the National Natural Science Foundation of China (81670394), the Natural Science Foundation of Hebei Province (C2019206022) and the Hebei Scientific and Technological Research Projects (ZD2020302).

Availability of data and materials

Not applicable.

Ethics approval and consent to participate

The use of animals was in accordance with the regulations of the welfare ethics committee of Hebei Medical University.

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.

1. Basatemur, G.L., et al., Vascular smooth muscle cells in atherosclerosis. Nat Rev Cardiol, 2019. 16(12): 727-44.

2. Brown, I.A.M., et al., Vascular Smooth Muscle Remodeling in Conductive and Resistance Arteries in Hypertension. Arterioscler Thromb Vasc Biol, 2018. 38(9): 1969-85.

3. Méndez-Barbero, N., et al., A major role of TWEAK/Fn14 axis as a therapeutic target for post-angioplasty restenosis. EBioMedicine, 2019. 46: 274-89.

4. Virani, S.S., et al., Heart Disease and Stroke Statistics-2020 Update: A Report From the American Heart Association. Circulation, 2020. 141(9): e139-e596.

5. Jaminon, A., et al., The Role of Vascular Smooth Muscle Cells in Arterial Remodeling: Focus on Calcification-Related Processes. Int J Mol Sci, 2019. 20(22).

6. Gomez, D. and G.K. Owens, Smooth muscle cell phenotypic switching in atherosclerosis. Cardiovasc Res, 2012. 95(2): 156-64.

7. Campbell, J.H. and G.R. Campbell, Smooth muscle phenotypic modulation--a personal experience. Arterioscler Thromb Vasc Biol, 2012. 32(8): 1784-9.

8. Heusch, G., et al., Cardiovascular remodelling in coronary artery disease and heart failure. Lancet, 2014. 383(9932): 1933-43.

9. Giacoppo, D., et al., Treatment strategies for coronary in-stent restenosis: systematic review and hierarchical Bayesian network meta-analysis of 24 randomised trials and 4880 patients. Bmj, 2015. 351: h5392.

10. Guo, X., et al., LDL Receptor Gene-ablated Hamsters: A Rodent Model of Familial Hypercholesterolemia With Dominant Inheritance and Diet-induced Coronary Atherosclerosis. EBioMedicine, 2018. 27: 214-24.

11. Reaves, S.K., et al., Regulation of intestinal apolipoprotein B mRNA editing levels by a zinc-deficient diet and cDNA cloning of editing protein in hamsters. J Nutr, 2000. 130(9): 2166-73.

12. Byrne, R.A., et al., Coronary balloon angioplasty, stents, and scaffolds. Lancet, 2017. 390(10096): 781-92.

13. Li, Q., et al., Homozygous receptors for insulin and not IGF-1 accelerate intimal hyperplasia in insulin resistance and diabetes. Nat Commun, 2019. 10(1): 4427.

14. Wang, C., et al., Small intestine proteomics coupled with serum metabolomics reveal disruption of amino acid metabolism in Chinese hamsters with type 2 diabetes mellitus. J Proteomics, 2020. 223: 103823.

15. Peterson, S.M., et al., Notch2 and Proteomic Signatures in Mouse Neointimal Lesion Formation. Arterioscler Thromb Vasc Biol, 2018. 38(7): 1576-93.

16. Hansmeier, N., et al., Identification of Mature Atherosclerotic Plaque Proteome Signatures Using Data-Independent Acquisition Mass Spectrometry. J Proteome Res, 2018. 17(1): 164-76.

17. Alrefai, M.T., et al., Functional Assessment of Pluripotent and Mesenchymal Stem Cell Derived Secretome in Heart Disease. Ann Stem Cell Res, 2019. 2(1): 29-36.

18. Lee, R., et al., A novel workflow combining plaque imaging, plaque and plasma proteomics identifies biomarkers of human coronary atherosclerotic plaque disruption. Clin Proteomics, 2017. 14: 22.

19. Zhang, H., et al., Serum exosomes mediate delivery of arginase 1 as a novel mechanism for endothelial dysfunction in diabetes. Proc Natl Acad Sci U S A, 2018. 115(29): E6927-e36.

20. Tuñón, J., et al., Proteomics and metabolomics in biomarker discovery for cardiovascular diseases: progress and potential. Expert Rev Proteomics, 2016. 13(9): 857-71.

21. Ozaki, T., et al., Proteomic analysis of protein changes in plasma by balloon test occlusion. J Clin Neurosci, 2020. 72: 397-401.

22. Suna, G., et al., Extracellular Matrix Proteomics Reveals Interplay of Aggrecan and Aggrecanases in Vascular Remodeling of Stented Coronary Arteries. Circulation, 2018. 137(2): 166-83.

23. Ucciferri, N., et al., Extracellular matrix characterization in plaques from carotid endarterectomy by a proteomics approach. Talanta, 2017. 174: 341-46.

24. Langley, S.R., et al., Extracellular matrix proteomics identifies molecular signature of symptomatic carotid plaques. J Clin Invest, 2017. 127(4): 1546-60.

25. Yu, Y., et al., Protein signatures from blood plasma and urine suggest changes in vascular function and IL-12 signaling in elderly with a history of chronic diseases compared with an age-matched healthy cohort. Geroscience, 2020.

26. Oksjoki, R., et al., Receptors for the anaphylatoxins C3a and C5a are expressed in human atherosclerotic coronary plaques. Atherosclerosis, 2007. 195(1): 90-9.

27. Speidl, W.S., et al., The complement component C5a is present in human coronary lesions in vivo and induces the expression of MMP-1 and MMP-9 in human macrophages in vitro. Faseb j, 2011. 25(1): 35-44.

28. Guo, R.F. and P.A. Ward, Role of C5a in inflammatory responses. Annu Rev Immunol, 2005. 23: 821-52.

29. Zhang, C., et al., Complement 5a receptor mediates angiotensin II-induced cardiac inflammation and remodeling. Arterioscler Thromb Vasc Biol, 2014. 34(6): 1240-8.

30. Chen, L., et al., Increased Complement 3 With Suppression of miR-145 Induces the Synthetic Phenotype in Vascular Smooth Muscle Cells From Spontaneously Hypertensive Rats. J Am Heart Assoc, 2019. 8(10): e012327.

31. Neinast, M., D. Murashige, and Z. Arany, Branched Chain Amino Acids. Annu Rev Physiol, 2019. 81: 139-64.

32. Nie, C., et al., Branched Chain Amino Acids: Beyond Nutrition Metabolism. Int J Mol Sci, 2018. 19(4).

33. Adeva-Andany, M.M., et al., Enzymes involved in branched-chain amino acid metabolism in humans. Amino Acids, 2017. 49(6): 1005-28.

34. Li, Y., et al., Branched chain amino acids exacerbate myocardial ischemia/reperfusion vulnerability via enhancing GCN2/ATF6/PPAR-α pathway-dependent fatty acid oxidation. Theranostics, 2020. 10(12): 5623-40.

35. Tobias, D.K., et al., Circulating Branched-Chain Amino Acids and Incident Cardiovascular Disease in a Prospective Cohort of US Women. Circ Genom Precis Med, 2018. 11(4): e002157.

36. Costa, R.L.B., H.S. Han, and W.J. Gradishar, Targeting the PI3K/AKT/mTOR pathway in triple-negative breast cancer: a review. Breast Cancer Res Treat, 2018. 169(3): 397-406.

37. Mangge, H., et al., Branched-chain amino acids are associated with cardiometabolic risk profiles found already in lean, overweight and obese young. J Nutr Biochem, 2016. 32: 123-7.

38. Zhang, L. and J. Han, Branched-chain amino acid transaminase 1 (BCAT1) promotes the growth of breast cancer cells through improving mTOR-mediated mitochondrial biogenesis and function. Biochem Biophys Res Commun, 2017. 486(2): 224-31.

39. Heiss, E.H., et al., Increased aerobic glycolysis is important for the motility of activated VSMC and inhibited by indirubin-3'-monoxime. Vascul Pharmacol, 2016. 83: 47-56.

40. Zhao, X., et al., PKM2-dependent glycolysis promotes the proliferation and migration of vascular smooth muscle cells during atherosclerosis. Acta Biochim Biophys Sin (Shanghai), 2020. 52(1): 9-17.

41. Tomas, L., et al., Altered metabolism distinguishes high-risk from stable carotid atherosclerotic plaques. Eur Heart J, 2018. 39(24): 2301-10.

42. Vora, S. and U. Francke, Assignment of the human gene for liver-type 6-phosphofructokinase isozyme (PFKL) to chromosome 21 by using somatic cell hybrids and monoclonal anti-L antibody. Proc Natl Acad Sci U S A, 1981. 78(6): 3738-42.

43. Sola-Penna, M., et al., Regulation of mammalian muscle type 6-phosphofructo-1-kinase and its implication for the control of the metabolism. IUBMB Life, 2010. 62(11): 791-6.

44. Gibb, A.A., et al., Integration of flux measurements to resolve changes in anabolic and catabolic metabolism in cardiac myocytes. Biochem J, 2017. 474(16): 2785-801.

45. Li, L., et al., TAp73-induced phosphofructokinase-1 transcription promotes the Warburg effect and enhances cell proliferation. Nat Commun, 2018. 9(1): 4683.

46. Feng, Y., et al., A20 targets PFKL and glycolysis to inhibit the progression of hepatocellular carcinoma. Cell Death Dis, 2020. 11(2): 89.

47. Wang, J., et al., Development and validation of a hypoxia-related prognostic signature for breast cancer. Oncol Lett, 2020. 20(2): 1906-14.

48. Webb, B.A., et al., The glycolytic enzyme phosphofructokinase-1 assembles into filaments. J Cell Biol, 2017. 216(8): 2305-13.

49. Attanasio, F., et al., Novel invadopodia components revealed by differential proteomic analysis. Eur J Cell Biol, 2011. 90(2-3): 115-27.

50. Kohnhorst, C.L., et al., Identification of a multienzyme complex for glucose metabolism in living cells. J Biol Chem, 2017. 292(22): 9191-203.

51. Feng, Y., et al., Lactate dehydrogenase A: A key player in carcinogenesis and potential target in cancer therapy. Cancer Med, 2018. 7(12): 6124-36.

52. Kolappan, S., et al., Structures of lactate dehydrogenase A (LDHA) in apo, ternary and inhibitor-bound forms. Acta Crystallogr D Biol Crystallogr, 2015. 71(Pt 2): 185-95.

53. Cai, H., et al., LDHA Promotes Oral Squamous Cell Carcinoma Progression Through Facilitating Glycolysis and Epithelial-Mesenchymal Transition. Front Oncol, 2019. 9: 1446.

54. Pathria, G., et al., Targeting the Warburg effect via LDHA inhibition engages ATF4 signaling for cancer cell survival. Embo j, 2018. 37(20).

55. Jin, L., et al., Phosphorylation-mediated activation of LDHA promotes cancer cell invasion and tumour metastasis. Oncogene, 2017. 36(27): 3797-806.

56. Kim, J.H., et al., Lactate dehydrogenase-A is indispensable for vascular smooth muscle cell proliferation and migration. Biochem Biophys Res Commun, 2017. 492(1): 41-47.

57. Song, Y.J., et al., Inhibition of lactate dehydrogenase A suppresses inflammatory response in RAW 264.7 macrophages. Mol Med Rep, 2019. 19(1): 629-37.

supplementalfile.pdf

Download PDF

Version 1

posted

You are reading this latest preprint version

Proteomics of Restenosis Model in LDLR-deficient Hamster Coupled With the Proliferative Vascular Smooth Muscle Cells Reveal New Mechanism of Vascular Remodeling Diseases

Status:

Version 1

Abstract

Figures

Introduction

Materials And Methods

Results

Discussion

Conclusion

Declarations

References

Supplementary Files

Status:

Version 1