BioMed Research International
Volume 2018 (2018), Article ID 3109251, 14 pages
https://doi.org/10.1155/2018/3109251

Research Article

A Possible Mechanism: Vildagliptin Prevents Aortic Dysfunction through Paraoxonase and Angiopoietin-Like 3

Qian Zhang, Xinhua Xiao, Jia Zheng, Ming Li, Miao Yu, Fan Ping, Tong Wang, and Xiaojing Wang

Key Laboratory of Endocrinology, Ministry of Health, Department of Endocrinology, Peking Union Medical College Hospital, Peking Union Medical College, Chinese Academy of Medical Sciences, Beijing, 100730, China

Correspondence should be addressed to Xinhua Xiao; xiaoxh2014@vip.163.com

Received 18 January 2018; Revised 3 April 2018; Accepted 19 April 2018; Published 23 May 2018

Academic Editor: Koichiro Wada

Copyright © 2018 Qian Zhang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract.

The collected data have revealed the beneficial effects of dipeptidyl peptidase-4 (DPP-4) inhibitors on the vascular endothelium, including vildagliptin. However, the involved mechanisms are not yet clear. In this study, Sprague-Dawley rats were randomly divided into the following four groups: control, diabetic, diabetic + low-dose vildagliptin (10 mg/kg/d), and diabetic + high-dose vildagliptin (20 mg/kg/d). The diabetic model was created by feeding a high-fat diet for four weeks and injection of streptozotocin. Then, vildagliptin groups were given oral vildagliptin for twelve weeks, and the control and diabetic groups were given the same volume of saline. The metabolic parameters, endothelial function, and whole genome expression in the aorta were examined. After 12 weeks of treatment, vildagliptin groups showed significantly reduced blood glucose, blood total cholesterol, and attenuated endothelial dysfunction. Notably, vildagliptin may inhibit angiopoietin-like 3 (Angptl3) and betaine-homocysteine S-methyltransferase (Bhmt) expression and activated paraoxonase-1 (Pon1) in the aorta of diabetic rats. These findings may demonstrate the vasoprotective pathway of vildagliptin in vivo.

1. Introduction

The incidence and prevalence of diabetes mellitus are dramatically increasing worldwide [1]. Epidemiological research shows that diabetic patients have a higher risk of cardiovascular diseases [2]. The main cause of morbidity and mortality in diabetic patients is cardiovascular diseases.

Glucagon-like peptide-1 (GLP-1) is produced in gut L-cells. It contains 30 amino acids. The main physiological function of GLP-1 is stimulation of insulin secretion from pancreatic β cells when glucose is orally taken up in the human body. GLP-1 can also inhibit glucose production and appetite, activate adipose and muscle glucose uptake and storage, and thus moderate insulin sensitivity. However, GLP-1 is quickly hydrolyzed by dipeptidyl peptidase-4 (DPP-4).

DPP-4 inhibitors are a new class of GLP-1 based antidiabetic drugs. As one type of DPP-4 inhibitors, vildagliptin controls blood glucose by inhibiting the enzymatic activity of DPP-4. DPP-4 is also found on endothelial cells in the cardiovascular system, and increasing research has focused on the benefit of DPP-4 inhibitors on cardiovascular function. Sitagliptin (one type of DPP-4 inhibitor) has been proven to significantly attenuate heart failure-related left ventricular (LV) end-diastolic pressure, systolic performance, and chamber stiffness in an ablation-induced cardiac dysfunction rat model [3]. In a clinical trial, sitagliptin enhanced global and regional LV function in type 2 diabetes mellitus (T2DM) patients with coronary artery [4]. Vildagliptin exerts cardioprotective effects in obesity-based insulin resistance [5, 6], myocardial infarction (MI) [7], and ischemia-reperfusion (I/R) injury rat models [8]. However, the exact mechanism of the beneficial effect of vildagliptin on the aorta in diabetic rats remains to be elucidated.

In this study, we hypothesized that vildagliptin improved aorta function through multiple pathways. We employed a whole genomic expression array and bioinformatics method to explore the pathway involved in aorta vascular function moderation in diabetic rats.

2. Materials and Methods

2.1. Animal Treatments and Diets

Five-week-old male Sprague-Dawley rats were obtained from the Institute of Laboratory Animal Science, Chinese Academy of Medical Sciences, and Peking Union Medical College (Beijing, China, SCXK-2014-0013). The animal protocol was approved by the Animal Care Committee of the Peking Union Medical Hospital Animal Ethics Committee (Project XHDW-2015-0051, 15 Feb 2015), and all efforts were made to minimize suffering. All the rats were fed in a light/dark cycle (12 hours : 12 hours) environment and were free to drink water. Three days after arrival, rats were randomly divided into four groups ( per group): normal control group, diabetic group, low-dose vildagliptin (vil-low), and high-dose vildagliptin (vil-high). The normal control group was fed a standard rodent diet (kcal%: 10% fat, 20% protein, and 70% carbohydrate; 3.85 kcal/gm). Other groups were fed a high-fat diet (kcal%: 45% fat, 20% protein, and 35% carbohydrate; 4.73 kcal/gm, Research Diet, New Brunswick, NJ, USA). After 4 weeks, diabetic, low-dose vildagliptin, and high-dose vildagliptin groups were given a single injection of streptozotocin (STZ, 30 mg/kg body weight, i.p., Sigma-Aldrich, St. Louis, MO, USA). Fasting blood glucose > 11.1 mmol/L was the standard for the diabetic model. Then, vil-low and vil-high groups were treated with 10 mg or 20 mg vildagliptin (Novartis Pharma AG, Basel, Switzerland)/kg of body weight by daily gavage for 12 weeks. Normal control and diabetic groups were given normal saline. After 12 weeks of treatment, the rats were anesthetized using ketamine (100 mg/kg i.p., Pharmacia and Upjohn Ltd., Crawley, UK), followed by withdrawal of food overnight. Blood samples were obtained from the abdominal aorta. Then, the rats were sacrificed by decapitation. The thoracic aorta was quickly removed. Some aortas were placed in Krebs solution (120 mmol/L of NaCl, 4.7 mmol/L of KCl, 1.18 mmol/L of KH₂PO₄, 2.25 mmol/L of CaCl₂, 24.5 mmol/L of NaHCO₃, 1.2 mmol/L of MgSO₄·7H₂O, 11.1 mmol/L of glucose, and 0.03 mmol/L of EDTA) and aerated with 95% O₂ and 5% CO₂. Other aortas were frozen in liquid nitrogen and stored at −80°C for a gene microarray experiment.

2.2. Body Weight and Fasting Blood Glucose Measurements

The rats were weighed every 4 weeks. Fasting blood glucose levels were measured by Bayer Contour TS glucometer (Hamburg, Germany).

2.3. Oral Glucose Tolerance Test (OGTT)

An OGTT was performed after 12 weeks of treatment. Blood glucose levels were measured at 30, 60, and 120 min after an oral administration of 20% glucose at a dose of 2 g/kg. The area under the curve (AUC) was calculated by the linear trapezoid method [9].

2.4. Serum Insulin and Lipid Panel Measurements

Serum fasting insulin was analyzed using an ELISA kit (Millipore, Billerica, MA, USA). The homeostasis model assessment of insulin resistance (HOMA-IR) was calculated by the following formula: FBG (mmol/L) × fasting insulin (μIU/mL)/22.5. Serum total cholesterol (TC), triglyceride (TG), high-density lipoprotein (HDL), and low-density lipoprotein (LDL) were measured using an enzyme end-point kit (Roche Diagnostics GmbH, Mannheim, Germany).

2.5. Isometric Contractile Tension Assay

Isometric contractile tension was determined as follows and described previously [10]. The aortic ring segments (3 mm width) were incubated with Krebs solution (120 mmol/L of NaCl, 4.7 mmol/L of KCl, 1.18 mmol/L of KH₂PO₄, 2.25 mmol/L of CaCl₂, 24.5 mmol/L of NaHCO₃, 1.2 mmol/L of MgSO₄·7H₂O, 11.1 mmol/L of glucose, and 0.03 mmol/L of EDTA), aerated with 95% O₂ and 5% CO₂, and then preconstricted with phenylephrine (Phe, 10⁻⁷ mmol/L). Isometric contractile tension was measured by a BL-410 Biological function system (Chengdu Tai Meng Science and Technology Co., Ltd., Chengdu, China). Once a basal contraction was obtained, 10⁻¹⁰ to 10⁻⁴ mol/L of acetylcholine (Ach) was cumulatively added to the solution.

2.6. RNA Extraction, Amplification, Labeling, and Hybridization

To look for the differentially expressed genes under vildagliptin treatment in diabetic rats, we performed gene whole transcript-based array in vil-high group and diabetic group ( in each group). Total RNA was extracted from aortas using a mirVana™ RNA Isolation Kit (Ambion, San Paulo, SP, Brazil). The RNA was purified using an RNeasy Kit (Qiagen, Hilden, Germany), quantified by NanoDrop ND-2000 spectrophotometry (Nanodrop Tech, Rockland, Del, Wilmington, DE, USA), and qualified by agarose gel electrophoresis. Total RNA (100 ng) was used for cDNA synthesis. cRNA was synthesized followed by two-strand cDNA. Biotin-labeled cRNA was hybridized to an Affymetrix GeneChip Rat Gene 2.0 ST whole transcript-based array (Affymetrix Technologies, Santa Clara, CA, USA). After hybridization, the microarrays were washed, stained, and scanned with an Affymetrix Scanner 3000 7G (Santa Clara, CA, USA).

The microarray signals were analyzed using Expression Console software (version 1.4.1, Affymetrix, Santa Clara, CA, USA). The significance of the difference in genes was determined by one-way ANOVA. Differentially expressed genes were defined as genes with a fold change > 2.0 and < 0.05. The microarray raw data were submitted to the Gene Expression Omnibus (GEO) repository (GSE102196).

2.7. Bioinformatics Analysis for Microarray

DAVID (Database for Annotation, Visualization and Integrated Discovery) software (https://david.abcc.ncifcrf.gov/) [11] was used to perform gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses. The Search Tool for the Retrieval of Interacting Genes (STRING) database (https://string-db.org/) [12] was used to analyze the interaction network for differentially expressed genes.

2.8. Validation of Differentially Expressed Genes by Quantitative PCR

The expression levels of three differentially expressed genes were measured by qPCR to validate the results of the microarray. All primers are listed in Table 1. Actin was used as an internal control.

Table 1: Oligonucleotide sequences for qPCR analysis.

Gene symbol
GenBank ID
Forward primer
Reverse Primer
Product size

Angptl3
NM_001025065
AAAGGGTTTTGGGAGGCTTGA
CCCAAAAGCGCTATGGTCTC
117

Bhmt
NM_030850
GATGCTTGGGGAGTGACGAA
TGTGGCTACTGTGCGGATTT
119

Pon1
NM_032077
AAGCTGGCTACACCCACATC
CAACATTCGTTGGTGAGCGG
103

Angptl3: angiopoietin-like 3; Bhmt: betaine-homocysteine S-methyltransferase; Pon1: paraoxonase 1.

2.9. Statistical Analyses

Data are presented as the means ± standard deviation (SD). One-way ANOVA analysis followed by Student’s -test was used to compare differences among groups. was considered significant.

3. Results

3.1. Vildagliptin Moderated Blood Glucose and Insulin Resistance

After 12 weeks of treatment, vildagliptin significantly reduced fasting blood glucose AUC in OGTT (, Figures 1(b) and 1(f)). Additionally, fasting serum insulin and HOMA-IR index in the vildagliptin-treated group were lower than in the diabetic group (, Figures 1(c) and 1(d)).

(a)

(b)

(c)

(d)

(e)

(f)

(g)

(h)

(i)

(j)

(k)

Figure 1: Effect of vildagliptin on metabolic parameters in rats. (a) Body weight, (b) fasting blood glucose, (c) insulin, (d) homeostasis model assessment (HOMA-IR), (e) blood glucose in oral glucose tolerance test (OGTT), (f) area under the curve (AUC) in OGTT, (g) total cholesterol, (h) triglyceride, (i) high-density lipoprotein cholesterol (HDL-C), (j) low-density lipoprotein cholesterol (LDL-C), and (k) relaxation responses to Ach in aortic rings. Values are mean ± SD (), , , versus control group; , versus diabetic group. vil-low: low dose of vildagliptin; vil-high: high dose of vildagliptin.

3.2. Vildagliptin Decreased Blood Lipid Levels

Vildagliptin decreased TC and LDL-C levels in diabetic rats (, Figures 1(g) and 1(j)).

3.3. Vildagliptin Depressed Vasorelaxation Responses of Aortic Rings to Ach

As shown in Figure 1(k), endothelium-dependent vasodilation was impaired in diabetic rats compared with normal rats. Treatment with vildagliptin ameliorated this impairment .

3.4. Identification of Differentially Expressed Genes

After filtering genes that had a cutoff of a 1.5-fold change or greater, we found that 150 genes were upregulated and 120 genes were downregulated in the vil-high group compared with the diabetic group ().

3.5. GO Term Enrichment Analysis of Differentially Expressed Genes

In general, 91 GO terms were significantly enriched (, Table 2). The top 20 most significant biological process (BP) terms are shown in Figure 2 (). They were negative regulation of endopeptidase activity, epoxygenase P450 pathway, acute-phase response, blood coagulation, oxidation-reduction processes, cytoskeleton organization, negative regulation of fibrinolysis, microtubule-based processes, phosphatidylcholine metabolic processes, fibrinolysis, organ regeneration, complement activation classical pathway, inflammatory response, positive regulation of lipid catabolic process, triglyceride homeostasis, aging, cellular response to interferon gamma, cholesterol metabolic process, cholesterol homeostasis, and tissue regeneration.

Table 2: The enriched GO terms with differentially expressed genes .

Term ID
Term name
Count
-value
Fold Enrichment
catalogue
Genes

GO:0010951
negative regulation of endopeptidase activity
16
7.22 × 10⁻¹⁰
8.425
Biology Process
KNG1, MUG2, MUG1, SERPINA10, PZP, C5, AHSG, AMBP, SERPINA3N, SERPINF2, SERPINA4, SERPINC1, ITIH4, HRG, ITIH2, SERPIND1

GO:0019373
epoxygenase P450 pathway
7
5.88 × 10⁻⁷
21.821
Biology Process
CYP2C6V1, CYP2B3, CYP2J4, CYP2C23, CYP2C13, CYP2A1, CYP2A2

GO:0006953
acute-phase response
7
7.97 × 10⁻⁶
14.356
Biology Process
KNG1, MUG1, CRP, ITIH4, SAA4, SERPINA1, AHSG

GO:0007596
blood coagulation
8
2.08 × 10⁻⁵
9.446
Biology Process
F13B, F12, SERPINA10, C9, F9, SERPIND1, CPB2, PLG

GO:0055114
oxidation-reduction process
23
3.29 × 10⁻⁵
2.753
Biology Process
ALDH8A1, CYP2B3, CYP2J4, HSD3B5, DECR2, RGD1304810, CYP2D3, EGLN1, KMO, P4HTM, VAT1, IYD, CYP2C6V1, TDO2, CYP3A23/3A1, HIF1AN, CYP4F4, CYP2C23, CYP2C13, SH3BGRL3, RDH16, CYP2A1, CYP2A2

GO:0007010
cytoskeleton organization
10
3.95 × 10⁻⁵
6.089
Biology Process
CCL24, TNIK, CAP2, TUBB2A, SVIL, CAMSAP1, STRIP2, TUBB6, TUBA1A, TUBB4B

GO:0051918
negative regulation of fibrinolysis
4
6.93 × 10⁻⁵
44.533
Biology Process
SERPINF2, HRG, CPB2, PLG

GO:0007017
microtubule-based process
6
1.68 × 10⁻⁴
11.405
Biology Process
TUBB2A, TUBB5, TUBB6, TUBA1A, DCTN1, TUBB4B

GO:0046470
phosphatidylcholine metabolic process
4
6.75 × 10⁻⁴
22.267
Biology Process
APOA5, PON1, GPLD1, LIPC

GO:0042730
fibrinolysis
4
8.35 × 10⁻⁴
20.782
Biology Process
F12, HRG, CPB2, PLG

GO:0031100
organ regeneration
7
0.00110
5.995
Biology Process
APOA2, BAAT, ADH1, ALDOC, APOA5, PRPS2, AHSG

GO:0006958
complement activation, classical pathway
5
0.00123
10.532
Biology Process
C9, C5, CRP, C4BPB, C4BPA

GO:0006954
inflammatory response
12
0.00158
3.149
Biology Process
CCL24, KNG1, TLR10, SERPINA3N, TNFRSF10B, MUG1, CCL21, C5, HRH4, CCL9, SERPINA1, CXCR3

GO:0050996
positive regulation of lipid catabolic process
3
0.00236
38.967
Biology Process
APOA2, APOA5, ANGPTL3

GO:0070328
triglyceride homeostasis
4
0.00431
11.990
Biology Process
APOC4, APOA5, LIPC, ANGPTL3

GO:0007568
aging
11
0.00745
2.721
Biology Process
ADRB3, CYP3A23/3A1, CRYAB, ENDOG, ALDOC, CRP, SPINK1, IGFBP1, ATP5G3, SREBF2, HTR2A

GO:0071346
cellular response to interferon-gamma
5
0.00766
6.388
Biology Process
CCL24, SERPINA3N, CCL21, CCL9, CFH

GO:0008203
cholesterol metabolic process
5
0.00811
6.285
Biology Process
APOA2, PON1, LIPC, ANGPTL3, SREBF2

GO:0042632
cholesterol homeostasis
5
0.00906
6.089
Biology Process
CAV3, APOA2, APOA5, LIPC, ANGPTL3

GO:0042246
tissue regeneration
4
0.00996
8.907
Biology Process
SERPINA10, APOA5, IGFBP1, PLG

GO:0046330
positive regulation of JNK cascade
5
0.0112
5.730
Biology Process
DIXDC1, CCL21, SERPINF2, MAP3K10, TPD52L1

GO:0050921
positive regulation of chemotaxis
3
0.0134
16.700
Biology Process
CCL21, C5, CXCR3

GO:0006956
complement activation
3
0.0153
15.587
Biology Process
C8A, C5, CFH

GO:2000649
regulation of sodium ion transmembrane transporter activity
3
0.0153
15.587
Biology Process
CAV3, SCN1B, SCN2B

GO:0042573
retinoic acid metabolic process
3
0.0173
14.613
Biology Process
ALDH8A1, ADH1, RDH16

GO:0009395
phospholipid catabolic process
3
0.0217
12.989
Biology Process
APOA2, ENPP2, ANGPTL3

GO:0001666
response to hypoxia
9
0.0230
2.598
Biology Process
CRYAB, ANG, CAMK2G, ALDOC, CRP, SERPINA1, EGLN1, PAK1, LIPC

GO:0055117
regulation of cardiac muscle contraction
3
0.0241
12.305
Biology Process
CAV3, P2RX4, CALM3

GO:0055090
acylglycerol homeostasis
2
0.0254
77.933
Biology Process
APOA5, ANGPTL3

GO:0034370
triglyceride-rich lipoprotein particle remodeling
2
0.0254
77.933
Biology Process
APOA2, APOA5

GO:0031116
positive regulation of microtubule polymerization
3
0.0265
11.690
Biology Process
CAV3, MAP1B, DCTN1

GO:0032956
regulation of actin cytoskeleton organization
4
0.0287
5.995
Biology Process
DIXDC1, HRG, PAK1, SH3BGRL3

GO:0048247
lymphocyte chemotaxis
3
0.0317
10.627
Biology Process
CCL24, CCL21, CCL9

GO:0070098
chemokine-mediated signaling pathway
4
0.0363
5.469
Biology Process
CCL24, CCL21, CCL9, CXCR3

GO:0045959
negative regulation of complement activation, classical pathway
2
0.0378
51.956
Biology Process
C4BPB, C4BPA

GO:0002542
Factor XII activation
2
0.0378
51.956
Biology Process
KNG1, F12

GO:0009725
response to hormone
5
0.0381
3.936
Biology Process
ANG, APOA5, LIPC, ANGPTL3, SREBF2

GO:0042311
vasodilation
3
0.0402
9.352
Biology Process
KNG1, P2RX4, ALB

GO:0006631
fatty acid metabolic process
4
0.0413
5.196
Biology Process
BAAT, DECR2, LIPC, ANGPTL3

GO:0007399
nervous system development
7
0.0428
2.741
Biology Process
PLXNA4, SCN2B, CAMK2G, KREMEN1, MAP1B, DPYSL3, NUMBL

GO:0048675
axon extension
3
0.0431
8.992
Biology Process
SEMA7A, MAP1B, POU4F3

GO:0072659
protein localization to plasma membrane
4
0.0485
4.871
Biology Process
CAV3, TNIK, FGF13, CDH1

GO:0006935
chemotaxis
4
0.0485
4.871
Biology Process
CCL24, ENPP2, C5, CXCR3

GO:0006810
transport
6
0.0489
3.017
Biology Process
P2RX4, DYNC1LI1, AFM, LOC360919, ALB, LOC500473

GO:0072562
blood microparticle
22
1.84 × 10⁻¹⁸
14.631
Cellular Components
KNG1, GC, C9, MUG1, APCS, C4BPA, PLG, AHSG, C8A, AMBP, APOA2, AFM, SERPINF2, ALB, HPX, APOA5, PON1, CFH, SERPINC1, ITIH4, HRG, ITIH2

GO:0005615
extracellular space
49
2.19 × 10⁻¹¹
2.922
Cellular Components
MUG2, MUG1, PZP, CRP, GPLD1, SPINK1, ANPEP, AZGP1, APOA2, ANG, SEMA7A, SERPINA4, APOA5, CFH, SERPINA1, SEMA3B, KNG1, F12, APCS, LOC360919, F9, C8A, AMBP, SERPINA3N, SERPINF2, SCGB2A2, GC, SERPINA10, ENPP2, C5, CCL9, AHSG, CCL24, ALB, CCL21, SERPINC1, ANGPTL3, C4BPB, DPYSL3, C4BPA, PLG, AFM, HPX, PON1, METRNL, SERPIND1, IGFBP1, LIPC, CPB2

GO:0034364
high-density lipoprotein particle
7
4.31 × 10⁻⁸
32.313
Cellular Components
APOA2, APOC4, APOA5, PON1, GPLD1, SAA4, LIPC

GO:0070062
extracellular exosome
66
6.06 × 10⁻⁸
1.957
Cellular Components
ALDH8A1, CYP2J4, MUG1, TUBB2A, CRP, GPLD1, ANPEP, CKB, AZGP1, APOA2, DES, PACSIN3, ANG, SERPINA4, ITIH4, TUBB5, CFH, TUBB6, ITIH2, SEMA3B, SERPINA1, TUBA1A, KNG1, F12, TNIK, APCS, CRYAB, F9, METTL7A, VAT1, AMBP, C8A, RPS16, SERPINF2, BHMT, PRNP, SLC27A2, PRPS2, GC, C9, SERPINA10, ALDOC, C5, CDH1, KMO, AHSG, ALCAM, ALB, SERPINC1, DOPEY2, HRG, TUBB4B, COTL1, PLG, P2RX4, AFM, HPX, ARF3, PON1, CALM3, PAPPA2, METRNL, SERPIND1, SH3BGRL3, MYH14, CPB2

GO:0031090
organelle membrane
11
2.10 × 10⁻⁷
9.591
Cellular Components
CYP2C6V1, CYP2B3, UGT2B37, UGT2B17, CYP3A23/3A1, CYP4F4, CYP2C23, CYP2D3, CYP2C13, CYP2A1, CYP2A2

GO:0030018
Z disc
9
1.24 × 10⁻⁴
6.037
Cellular Components
CAV3, JPH2, DES, CRYAB, BAG3, PDLIM3, PAK1, HOMER1, FLNC

GO:0014704
intercalated disc
6
4.60 × 10⁻⁴
9.232
Cellular Components
CAV3, SCN1B, DES, ATP2A2, FGF13, PAK1

GO:0030426
growth cone
9
6.13 × 10⁻⁴
4.772
Cellular Components
ANG, CRP, MAP1B, CALM3, DPYSL3, FGF13, MYH14, PAK1, HAP1

GO:0005874
microtubule
11
8.97 × 10⁻⁴
3.658
Cellular Components
DYNC1LI1, TUBB2A, CAMSAP1, MAP1B, TUBB5, TUBB6, FGF13, TUBA1A, DCTN1, TUBB4B, GLYATL2

GO:0043034
costamere
4
0.00196
15.695
Cellular Components
SVIL, SYNM, HOMER1, FLNC

GO:0005576
extracellular region
19
0.00456
2.088
Cellular Components
APCS, C9, CRP, NTN4, GPLD1, SAA4, C4BPB, FGF13, PTH2, C4BPA, PLG, LOC500473, CCL24, C8A, SERPINA3N, ALB, APOA5, HRG, LIPC

GO:0005789
endoplasmic reticulum membrane
16
0.00504
2.258
Cellular Components
CYP2B3, CDIPT, HSD3B5, CYP2D3, SREBF2, CYP2C6V1, UGT2B37, UGT2B17, CYP3A23/3A1, ATP2A2, CYP4F4, CYP2C23, CYP2C13, CYP2A1, CYP2A2, SLC27A2

GO:0030424
axon
12
0.00657
2.609
Cellular Components
GC, ALCAM, P2RX4, CRYAB, ALDOC, MAP1B, FGF13, CDH1, MYH14, PAK1, HOMER1, HTR2A

GO:0034361
very-low-density lipoprotein particle
3
0.0192
13.848
Cellular Components
APOA2, APOC4, APOA5

GO:0043231
intracellular membrane-bounded organelle
16
0.0302
1.825
Cellular Components
KNG1, CYP2B3, CAV3, CYP2J4, HSD3B5, GPLD1, PLG, SREBF2, AMBP, UGT2B17, CYP3A23/3A1, PON1, CYP2C23, UGT2A3, CYP2A1, SLC27A2

GO:0005856
cytoskeleton
8
0.0345
2.605
Cellular Components
TNIK, DES, FILIP1, MAP1B, FLNC, COTL1, TPM2, HAP1

GO:0044216
other organism cell
2
0.0376
52.316
Cellular Components
C4BPB, C4BPA

GO:0033017
sarcoplasmic reticulum membrane
3
0.0457
8.719
Cellular Components
JPH2, ATP2A2, CAMK2G

GO:0004867
serine-type endopeptidase inhibitor activity
15
6.34 × 10⁻¹¹
11.167
Molecular Function
MUG2, PZP, MUG1, SERPINA10, SPINK1, AMBP, SERPINA3N, SERPINF2, SERPINA4, SERPINC1, ITIH4, HRG, ITIH2, SERPINA1, SERPIND1

GO:0070330
aromatase activity
9
2.65 × 10⁻⁸
18.039
Molecular Function
CYP2C6V1, CYP2B3, CYP3A23/3A1, CYP4F4, CYP2C23, CYP2D3, CYP2C13, CYP2A1, CYP2A2

GO:0008392
arachidonic acid epoxygenase activity
8
1.97 × 10⁻⁷
18.393
Molecular Function
CYP2C6V1, CYP2B3, CYP2J4, CYP2C23, CYP2D3, CYP2C13, CYP2A1, CYP2A2

GO:0008395
steroid hydroxylase activity
8
5.35 × 10⁻⁷
16.035
Molecular Function
CYP2C6V1, CYP2B3, CYP2J4, CYP2C23, CYP2D3, CYP2C13, CYP2A1, CYP2A2

GO:0020037
heme binding
13
1.46 × 10⁻⁶
6.122
Molecular Function
CYP2B3, CYP2J4, CYP2D3, AMBP, CYP2C6V1, TDO2, CYP3A23/3A1, CYP4F4, CYP2C23, HRG, CYP2C13, CYP2A1, CYP2A2

GO:0008092
cytoskeletal protein binding
8
1.07 × 10⁻⁵
10.423
Molecular Function
DES, PACSIN3, CRYAB, ALDOC, PDLIM3, CDH1, FLNC, ABCG2

GO:0005506
iron ion binding
13
1.11 × 10⁻⁵
5.031
Molecular Function
CYP2B3, CYP2J4, CYP2D3, EGLN1, P4HTM, CYP2C6V1, CYP3A23/3A1, HIF1AN, CYP4F4, CYP2C23, CYP2C13, CYP2A1, CYP2A2

GO:0004866
endopeptidase inhibitor activity
5
5.40 × 10⁻⁴
13.028
Molecular Function
MUG2, MUG1, C5, SERPINA1, AHSG

GO:0017080
sodium channel regulator activity
5
6.95 × 10⁻⁴
12.214
Molecular Function
CAV3, SCN1B, SCN2B, GPLD1, FGF13

GO:0016712
oxidoreductase activity, acting on paired donors, with incorporation or reduction of molecular oxygen, reduced flavin or flavoprotein as one donor, and incorporation of one atom of oxygen
5
8.78 × 10⁻⁴
11.495
Molecular Function
CYP2B3, CYP2J4, CYP3A23/3A1, CYP2D3, CYP2A1

GO:0005200
structural constituent of cytoskeleton
6
0.00223
6.514
Molecular Function
TUBB2A, TUBB5, TUBB6, SYNM, TUBA1A, TUBB4B

GO:0016706
oxidoreductase activity, acting on paired donors, with incorporation or reduction of molecular oxygen, 2-oxoglutarate as one donor, and incorporation of one atom each of oxygen into both donors
4
0.00262
14.213
Molecular Function
HIF1AN, RGD1304810, EGLN1, P4HTM

GO:0008201
heparin binding
8
0.00268
4.283
Molecular Function
SERPINA10, ANG, APOA5, SERPINC1, CFH, HRG, SERPIND1, LIPC

GO:0016705
oxidoreductase activity, acting on paired donors, with incorporation or reduction of molecular oxygen
5
0.00297
8.316
Molecular Function
CYP2C6V1, CYP2B3, CYP2C23, EGLN1, CYP2C13

GO:0005102
receptor binding
12
0.00666
2.598
Molecular Function
KNG1, HAO1, P2RX4, BAAT, ANG, LRRC4B, DECR2, HRG, HOMER1, SLC27A2, HAP1, PLG

GO:0004497
monooxygenase activity
5
0.00944
6.013
Molecular Function
CYP2C6V1, CYP2B3, CYP2C23, CYP2D3, CYP2C13

GO:0004622
lysophospholipase activity
3
0.01328
16.751
Molecular Function
ENPP2, LIPC, GDPD1

GO:0042803
protein homodimerization activity
19
0.01474
1.845
Molecular Function
RBPMS2, CRYAB, GIMAP7, CAMK2G, CRP, NR4A1, TPD52L1, ABCG2, AMBP, ADRB3, APOA2, HIF1AN, ANG, SERPINF2, ADH1, TENM3, PON1, RDH16, PRPS2

GO:0005543
phospholipid binding
5
0.0198
4.825
Molecular Function
APOA2, APOA5, MAP1B, PON1, SYTL3

GO:0044325
ion channel binding
6
0.0233
3.693
Molecular Function
CAV3, CALM3, FGF13, PRNP, HOMER1, HAP1

GO:0048020
CCR chemokine receptor binding
3
0.0239
12.342
Molecular Function
CCL24, CCL21, CCL9

GO:0043395
heparan sulfate proteoglycan binding
3
0.0289
11.167
Molecular Function
CFH, HRG, LIPC

GO:0030215
semaphorin receptor binding
3
0.0289
11.167
Molecular Function
SEMA7A, SEMA3B, SH3BGRL3

GO:0005509
calcium ion binding
16
0.0330
1.797
Molecular Function
MATN2, F12, APCS, CALR4, TBC1D9, ENPP2, CRP, DECR2, F9, CDH1, P4HTM, ATP2A2, PON1, ITIH4, CALM3, SYTL3

GO:0008307
structural constituent of muscle
3
0.0371
9.771
Molecular Function
PDLIM3, SYNM, TPM2

GO:0003779
actin binding
8
0.0406
2.511
Molecular Function
GC, DIXDC1, CAP2, ANG, DIAPH3, MAP1B, COTL1, TPM2

GO:0015020
glucuronosyltransferase activity
3
0.0460
8.685
Molecular Function
UGT2B37, UGT2B17, UGT2A3

GO:0005507
copper ion binding
4
0.0463
4.963
Molecular Function
P2RX4, ANG, PRNP, ATP7B

GO:0005515
protein binding
29
0.0484
1.427
Molecular Function
CAV3, GC, SCN1B, ALDOC, CRP, CDH1, RHOV, CKB, A1CF, TUBB5, SERPINC1, SERPINA1, PAK1, TUBA1A, HAP1, TUBB4B, SCN2B, CRYAB, MAP1B, HRK, NR4A1, HOMER1, NUMBL, P2RX4, ATP2A2, CALM3, SLC27A2, PRPS2, HTR2A

Figure 2: Enriched biological process for the differentially expressed genes between vil-high group and diabetic group.

3.6. Pathway Enrichment Analysis of Differentially Expressed Genes

In the pathway enrichment analysis, 12 pathway terms were significantly enriched (, Table 3). The top 10 most significant pathway terms were complement and coagulation cascades, retinol metabolism, steroid hormone biosynthesis, chemical carcinogenesis, linoleic acid metabolism, inflammatory mediator regulation of TRP channels, Gap junction, prion diseases, metabolic pathways, and arachidonic acid metabolism.

Table 3: The enriched Kegg pathway with differentially expressed genes .

Pathway ID
Pathway name
Count
-value
Fold Enrichment
Genes

rno04610
Complement and coagulation cascades
16
7.92 × 10⁻¹⁴
14.904
KNG1, F12, C9, C5, F9, C4BPB, C4BPA, PLG, F13B, C8A, SERPINF2, CFH, SERPINC1, SERPINA1, SERPIND1, CPB2

rno00830
Retinol metabolism
12
3.00 × 10⁻⁸
9.697
CYP2C6V1, CYP2B3, UGT2B37, UGT2B17, CYP3A23/3A1, ADH1, CYP2C23, CYP2C13, UGT2A3, CYP2A1, RDH16, CYP2A2

rno00140
Steroid hormone biosynthesis
10
2.63 × 10⁻⁶
8.280
CYP2C6V1, CYP2B3, UGT2B37, UGT2B17, CYP3A23/3A1, HSD3B5, CYP2C23, CYP2D3, CYP2C13, UGT2A3

rno05204
Chemical carcinogenesis
9
5.47 × 10⁻⁵
6.633
CYP2C6V1, CYP2B3, UGT2B37, UGT2B17, CYP3A23/3A1, ADH1, CYP2C23, CYP2C13, UGT2A3

rno00591
Linoleic acid metabolism
5
3.01 × 10⁻³
8.179
CYP2C6V1, CYP2J4, CYP3A23/3A1, CYP2C23, CYP2C13

rno04750
Inflammatory mediator regulation of TRP channels
7
6.98 × 10⁻³
4.082
CYP2C6V1, CYP2J4, CAMK2G, CYP2C23, CALM3, CYP2C13, HTR2A

rno04540
Gap junction
6
9.57 × 10⁻³
4.573
TUBB2A, TUBB5, TUBB6, TUBA1A, TUBB4B, HTR2A

rno05020
Prion diseases
4
1.14 × 10⁻²
8.384
C8A, C9, C5, PRNP

rno01100
Metabolic pathways
30
1.17 × 10⁻²
1.556
CYP2B3, CYP2J4, PGS1, CDIPT, TUSC3, ALDOC, HSD3B5, KMO, ANPEP, ATP5G3, CKB, TDO2, ADH1, CYP2C13, UGT2A3, CYP2C6V1, MAN2A2, HAO1, UGT2B37, UGT2B17, BAAT, CYP3A23/3A1, BHMT, PON1, CYP2C23, LIPC, RDH16, CYP2A1, CYP2A2, PRPS2

rno00590
Arachidonic acid metabolism
5
3.16 × 10⁻²
4.140
CYP2C6V1, CYP2B3, CYP2J4, CYP2C23, CYP2C13

rno04726
Serotonergic synapse
6
3.63 × 10⁻²
3.245
CYP2C6V1, CYP2J4, CYP2C23, CYP2D3, CYP2C13, HTR2A

rno04360
Axon guidance
6
4.08 × 10⁻²
3.144
PLXNA4, SEMA7A, NTN4, SEMA3B, PAK1, UNC5C

3.7. Gene Interaction Network

Based on 270 differentially expressed genes, a gene interaction network was analyzed by the String software. We found that 257 nodes and 338 edges were constructed (Figure 3). Twenty-five genes had more than 5 edges. The top 10 genes by degree are listed in Table 4.

Table 4: The top 10 genes from gene interaction analysis.

Gene Accession
Gene Symbol
gene name
degree

NM_019369
Itih4
inter-alpha-trypsin inhibitor heavy chain family, member 4
17

NM_053491
Plg
plasminogen
16

NM_001014006
F12
coagulation factor XII (Hageman factor)
12

NM_001012027
Serpinc1
serpin peptidase inhibitor, clade C (antithrombin), member 1
11

NM_138514
Cyp2c13
cytochrome P450, family 2, subfamily c, polypeptide 13
9

NM_019287
Apob
apolipoprotein B
8

ENSRNOT00000074103
Cyp2c6
cytochrome P450, family 2, subfamily C, polypeptide 6, variant 1
8

NM_053318
Hpx
hemopexin
8

NM_022519
Serpina1
clade A (alpha-1 antiproteinase, antitrypsin), member 1
8

NM_001108802
Speg
SPEG complex locus
7

Figure 3: Interaction of differentially expressed genes between vil-high group and diabetic group from String software. Angptl3, Bhmt, and Pon1 are circled in red.

3.8. Confirmation with qPCR

From the gene expression array results, we found that Pon1 and Bhmt were in “metabolic pathway” (KEGG ID: rno01100), and Angptl3 and Pon1 were in several GO terms such as “positive regulation of lipid catabolic process” (GO: 0050996), “triglyceride homeostasis” (GO: 0070328), “cholesterol metabolic process” (GO: 0008203), “cholesterol homeostasis” (GO: 0042632), “response to hypoxia” (GO: 0001666), and “acylglycerol homeostasis” (GO: 0055090). So, we focused on these three genes in further study. As shown in Figure 4, the relative mRNA levels of angiopoietin-like 3 (Angptl3) and betaine-homocysteine S-methyltransferase (Bhmt) in diabetic rats were significantly higher than those of normal rats (), whereas the expression of these two genes was significantly reduced in vildagliptin-treated groups compared with those in diabetic rats (). Conversely, paraoxonase 1 (Pon1) decreased in the diabetic group. However, Pon1 increased in vildagliptin-treated rats (). These results were consistent with the microarray results.

Figure 4: Confirmation of three representative differentially expressed genes by qPCR. Values are mean ± SD . Angptl3: angiopoietin-like 3; Bhmt: betaine-homocysteine S-methyltransferase; Pon1: paraoxonase 1. versus control group; versus diabetic group. vil-low: low dose of vildagliptin; vil-high: high dose of vildagliptin.

4. Discussion

In our study, we found that vildagliptin reduced blood glucose, TC, and LDL-C. In a clinical trial, sitagliptin-combined metformin add-on therapy led to greater improvement in HbA1c than the metformin monotherapy group after 18 weeks of treatment. Moreover, sitagliptin combined with metformin significantly reduced TG, TC, and LDL-C and increased HDL-C compared with the metformin group [13].

We used aortic rings to test the Ach-induced endothelium-dependent vasodilation. In diabetic rats, Ach-induced endothelium-dependent vasodilation was impaired. Vildagliptin augmented endothelial function. Other DDP-4 inhibitors also had similar vasoprotective effects. Saxagliptin treatment for 8 weeks increased aortic nitric oxide (NO) release by 22% and reduced mean arterial pressure in spontaneously hypertensive rats [14]. Moreover, sitagliptin treatment for 2 weeks protected endothelial function and reduced systolic blood pressure in spontaneously hypertensive rats through GLP-1 signaling [15].

In our research, the expression of Angptl3 was downregulated in vildagliptin-treated rats. Angptl3 is a key regulator, which can inhibit lipoprotein lipase (LPL) activity [16, 17]. The enzyme LPL hydrolyzes TG to free fatty acids (FFA). ANGPTL3 overexpression mice had high plasma TG levels [18]. Patients with a loss-of-function mutation of Angptl3 are characterized by low plasma TC, TG, HDL-C, and LDL-C [19, 20]. In 2008, Kathiresan et al. first identified an SNP site near ANGPTL3 that was associated with plasma TG levels in a genome-wide association study [21]. In obesity and T2D subjects, the ANGPTL3 level was higher than that in healthy subjects [22]. Moreover, the level of ANGPTL3 was increased in the livers of insulin-deficient and insulin-resistant diabetic mice [23]. In mice and monkeys, using ANGPTL3-specific antibodies led to reduced plasma TG [24–26]. The therapeutic targets of the inhibition activity of ANGPTL require further research to treat dyslipidemia [16, 17].

We also found that the expression of Bhmt was reduced in the vildagliptin-treated group. Methionine (Met) was produced from methylate homocysteine (Hcy) by BHMT enzymes with betaine. Thus, BHMT can reduce Hcy levels and increase Met levels. Met can then be converted to S-adenosylmethionine (SAM). In vivo, SAM is a main methyl donor to regulate methionine metabolism in many reactions. An increase in BHMT activity and SAM levels was observed in the livers of both type 1 diabetic rats [27] and the type 2 diabetic model [28, 29]. Insulin treatment in the rat hepatoma cell line and STZ-induced diabetic rats can inhibit the excess expression of BHMT and SAM [28, 30].

Moreover, our results showed that Pon1 was upregulated in the vildagliptin-treated group. Pon1 is an enzyme that has antioxidant functions. In serum, Pon1 is located in HDL. Pon1 protects LDL from oxidation and hydrolyzed oxidized LDL [31–34]. HDL levels are negatively associated with the risk of developing coronary artery disease (CAD), but high levels of oxidized LDL (oxLDL) in the aorta lead to cholesterol accumulation, foam cell formation, and atherosclerotic lesions [31, 35]. Low Pon1 expression accelerates aortic lesion development in mice [36]. In humans, serum PON level is reduced in patients with a history of myocardial infarction [32] and diabetic patients [37]. PON1 transgenic mice had decreased oxidative stress and atherosclerotic lesions [38, 39], whereas PON1 knockout mice had increased serum oxidative stress and were more susceptible to high-fat-diet-induced atherosclerosis [34, 40].

5. Conclusion

In conclusion, this study has confirmed that vildagliptin significantly attenuates endothelial dysfunction in diabetic rats. Importantly, our study indicates that vildagliptin may activate Pon1 expression and inhibit the expression of Bhmt and Angptl3 in the aorta. This study may increase the understanding of the pathways that contribute to which DPP-4 inhibitor attenuates endothelial dysfunction. More studies are needed to investigate whether or not vildagliptin treatment directly alters Agptl3, Bhmt, and Pon1 expression in the aorta of diabetic rats, independent of blood glucose (Figure 5).

Figure 5: The possible mechanism of attenuation of vildagliptin on endothelial dysfunction. Vildagliptin attenuates endothelial dysfunction in diabetic rats through activating Pon1 expression in aorta to inhibit oxLDL production; inhibiting Bhmt expression to reduce Met and SAM production; inhibiting Angptl3 expression to activate LPL activity and thereby reduce plasma TG level. LDL: low-density lipoprotein; Pon1: paraoxonase 1; oxLDL: oxidized low-density lipoprotein; Hcy: homocysteine; Bhmt: betaine-homocysteine S-methyltransferase; Met: methionine, SAM: S-adenosylmethionine; TG: triglyceride.

Data Availability

The data used to support the findings of this study are included within the article.

Disclosure

The funding institutions had no role in study design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.

Conflicts of Interest

The authors declare no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Authors’ Contributions

Xinhua Xiao designed the experiments and contributed reagents and materials. Qian Zhang, Jia Zheng, Tong Wang, and Xiaojing Wang conducted the experiments. Miao Yu, Ming Li, and Fan Ping analyzed the data. Qian Zhang wrote the manuscript.

Acknowledgments

This work was supported by grants from the National Key R&D Program of China (2017YFC1309603), National Key Research and Development Program of China (2016YFA0101002), National Natural Science Foundation of China (nos. 81170736 and 81570715), National Natural Science Foundation for Young Scholars of China (no. 81300649), China Scholarship Council foundation (201308110443), PUMC Youth Fund (33320140022) and Fundamental Research Funds for the Central Universities, Scientific Activities Foundation for Selected Returned Overseas Professionals of Human Resources and Social Security Ministry, and Novartis AG. The authors are very grateful to Beijing Compass Biotechnology Company for the excellent technical assistance with the microarray experiments.

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