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  • Research Paper
  • Open Access

Modulation of gene expression in endothelial cells by hyperlipaemic postprandial serum from healthy volunteers

  • 1,
  • 2,
  • 3,
  • 1Email author and
  • 1
Genes & NutritionStudying the relationship between genetics and nutrition in the improvement of human health20105:166

https://doi.org/10.1007/s12263-010-0166-x

  • Received: 17 June 2009
  • Accepted: 3 January 2010
  • Published:

Abstract

A single high-fat challenge induces plasmatic pro-inflammatory and pro-oxidative responses in the postprandial state, even in healthy men. This period is also associated with vascular endothelial dysfunction, which is an early event in the development of cardiovascular diseases. However, knowledge about the mechanisms involved in postprandial hyperlipaemia-induced endothelial dysfunction is sparse. An objective of our study was to characterize the behaviour and gene expression of vascular endothelial cells exposed to postprandial hyperlipaemic sera. Human umbilical vein endothelial cells (HUVECs) were cultured in media containing 10% serum from healthy men withdrawn either before or 4 h after a high-fat challenge. Endothelial cell proliferation, adhesion and migration were then assessed. The transcriptomic profiles of endothelial cells exposed to pre and postprandial sera were also compared. Exposure to postprandial hyperlipaemic sera significantly decreased HUVEC proliferation when compared to preprandial serum (P < 0.0001), while no changes in migration or endothelial/monocyte interactions were observed. The transcriptomic analysis revealed changes in the expression of 675 genes, of which 431 have a known function. Among them, a set of differentially expressed genes was linked to cell cycle regulation and apoptosis and are regulated in favour of cell cycle arrest or death. This result was confirmed by measuring the induction of apoptosis after postprandial sera exposure (P = 0.011). Taken together, the transcriptomic results and pathway analysis showed that postprandial serum promotes apoptosis in HUVECs, potentially through the activation of the p53 network. We conclude that upon postprandial serum exposure, vascular endothelial cells transcriptionally regulate genes involved in the control of cell cycle and death to favour growth arrest and apoptosis. These findings support the hypothesis that postprandial hyperlipaemia is associated with vascular dysfunction and offer new insights into the mechanisms involved.

Keywords

  • Postprandial hyperlipaemia
  • Vascular endothelial cells
  • Nutrigenomics
  • Apoptosis atherogenesis

Introduction

Since the early report of Zilversmit [45], the postprandial increase in blood lipids, and triacylglycerol-rich lipoproteins in particular, has been proposed to play a causal role in the aetiology of cardiovascular disease. Indeed, human studies have shown that a transient increase in blood triacylglycerol and fatty acid concentrations impairs endothelium-dependent vasodilation, which is an early event in atherogenesis [16], in healthy subjects [8]. Postprandial lipaemia has also been associated with elevated concentrations of circulating pro-inflammatory and pro-oxidative molecules such as tumour necrosis factor-alpha, interleukin-6, interleukin-8 and nitrotyrosine [5]. Based on these data, postprandial vascular dysfunction has been proposed to be mediated by inflammation and oxidative stress [5, 28], but the exact mechanisms remain unclear.

Recent studies using transcriptomic approaches have identified new signalling pathways and genomic signatures of atherosclerotic plaques [27, 34]. For this purpose, samples of aortic tissues and atherosclerotic plaques collected from patients and transcriptomic analysis of different stages of atheroma progression have been performed [14, 20, 31, 34]. Among the wide genomic studies found in theses studies, atherosclerotic progression was shown to be associated with the differential expression of genes involved in cell proliferation, adhesion, chemotaxis and in the organization of the cytoskeleton. These results are a significant resource for the identification of new biosignatures and new signalling pathways important in the development of cardiovascular diseases. Recently, Volger et al. [40] reported a transcriptomic study of endothelium from human large arteries isolated by laser microdissection in early and advanced stages of atherosclerosis. They showed a particular enrichment of distinct sets of chemokines and nuclear factor-kB-, p53- and transforming growth factor-β-related genes in advanced plaques. This study also revealed a broad distinction between the genomic profiles of the early and advanced stages, as shown by others in whole atherosclerotic plaques [20]. The results suggest that different stages of atheroma progression have specific transcriptomic profiles.

As postprandial hyperlipaemia-associated endothelial dysfunction could be considered a very early stage in the pathogenesis of vascular disease, we set out to establish the genomic profiles of vascular endothelial cells exposed to postprandial sera. Given the challenges of obtaining human arterial tissues, we performed an ex vivo study. To this purpose, we exposed human umbilical vein endothelial cells (HUVECs) to media containing the sera from healthy men withdrawn either before or 4 h after a high-fat challenge. Cellular proliferation, adhesion and migration were then assessed. The transcriptomes of endothelial cells exposed to pre and postprandial sera from each individual were compared in a pair-wise manner, thus limiting genetic and environmental confounders.

Materials and methods

Study design

The study group consisted of seven healthy normolipaemic, non-smoking males. All volunteers had normal physical examinations without any medical history of digestive, renal, cardiovascular, endocrine or chronic diseases. The physical characteristics of the subjects were (mean ± SD) age (year) 49.3 ± 7.6; body weight (kg) 73.9 ± 9.4; BMI (kg/m2) 25.0 ± 2.8; cholesterolaemia (mmol/l) 5.15 ± 0.52 and triacylglycerolaemia (mmol/l) 0.84 ± 0.22. The purpose and potential risks of the study were explained to all subjects, and their written consent was obtained before participation. The study was carried out in accordance with the guidelines of the Declaration of Helsinki after approval by the Ethics Committee of the Auvergne area. After an overnight fast of at least 12 h, blood was withdrawn in the morning (baseline, 0 h). Blood samples were collected into 6-ml EDTA-containing Vacutainer tubes. After centrifugation (4,000 rpm, 5 min, 20°C), plasma was collected, divided into aliquots (250 μl) and immediately frozen at −80°C. For the sera, blood was collected in 7-ml Vacutainer glass serum tubes, placed for 1 h at room temperature, centrifuged (4,000 rpm, 15 min, 20°C) and divided into aliquots. The volunteers then received an oral fat challenge consisting of fresh cream (total lipid content 42 g/100 wet wt., with the percentage composition SFA/MUFA/PUFA of about 68/29/3) 50 g fat per 1 m² of body surface. Body surface area was used because it is less affected by abnormal adipose mass and thus represents a better indicator of metabolic mass than body weight. The body surface area was calculated with the Dubois and Dubois formula [11]. Consumption of the cream was completed within 15 min from the first (baseline, 0 h) blood withdrawal. Plasma and serum samples were withdrawn 2, 4, 6 and 8 h after the cream was consumed. The volunteers abstained from consuming any food and drinks except water during this 8-h period.

Cell culture, proliferation, adhesion, migration and cell death

HUVECs were cultured in M199 containing 10% foetal calf serum, endothelial cell growth supplement (ECGS) (150 μg/ml) and heparin (5 U/ml) on 2% gelatin-coated dishes. In all of the experiments, the cells were seeded in growth medium. After 24 h, the medium was changed in order to expose the cells to a medium containing 10% of human pre or postprandial serum instead of 10% foetal calf serum. Proliferation assays were performed on HUVECs at low density (7,500/cm2), cultured in a medium containing 10% human serum. After 4 days, the cells were trypsinized, stained with trypan blue solution (0.4%) and the viable cells were counted using a Burker chamber. All experiments were repeated at least four times for both pre and postprandial conditions for all volunteers. Migration of HUVECs cultured in the presence of pre or postprandial sera was determined using an in vitro model of wound repair as previously described [15]. Briefly, confluent endothelial cells were wounded and treated with hepatocyte growth factor (HGF) (20 ng/ml) for 18 h. The number of cells migrating from the wound origin was counted with a light microscope at 100× magnification using a grid. The adhesion assay was performed according to Maier et al. [24] at least three times for both conditions and for each volunteer. Finally, apoptosis was assessed with the Cell Death Detection ELISAPLUS Kit (Roche, Mannheim, Germany), which qualitatively and quantitatively detects the amount of cleaved DNA/histone complexes (nucleosomes) using a sandwich enzyme immunoassay-based method. To this purpose, the cells were cultured in the presence of postprandial sera for 4 days. TNF-α (50 ng/ml) was used as a positive control. Statistical analysis was carried out using SAS software (Version 8.1, SAS Institute Inc., Cary, NC, USA). For cellular proliferation, migration and adhesion assays, the statistical significance of the differences between means was assessed using the Student’s t-test (threshold 0.05). The results of the statistical analyses are expressed as means ± SEM.

Biochemical measurements

Cholesterol and triacylglycerol concentrations were determined enzymatically using commercial kits from BioMerieux (Charbonnières-les-Bains, France). Free fatty acids in the plasma of volunteers were measured with a kit from Wako (NEFA-C kit Unipath, Dardilly, France). The data were analysed using one-way repeated measures ANOVA. A P value of <0.05 was considered statistically significant.

Microarray procedure

For the microarray study, total RNA was extracted from cells exposed to pre or postprandial serum for 4 h from five of the seven volunteers (chosen randomly). Cultured cells were preserved in RNAlater (Sigma, Steiheim, Germany) and frozen at −20°C to maintain RNA integrity until the RNA was extracted. Total RNA was extracted using the RNeasy Kit (Qiagen, Courtaboeuf, France) as recommended by the manufacturer. Ten total RNA extractions were performed with cells exposed to the pre and postprandial sera for each of the five volunteers. Subsequently, RNA concentrations were assessed spectrophotometrically (260/280 absorbance ratios), and RNA quality was determined by gel electrophoresis.

The dye-swap design approach was used in the microarray study, meaning that pre and postprandial sera-exposed cells were labelled in the complement colours and hybridized on the same slide, and then inversely coloured on the second slide. This dye-swap approach eliminates artefacts that result from using different dyes in the experimental and control samples. Thus, ten microarray hybridizations were completed in order to compare the transcriptomic profiles of cells exposed to postprandial sera and preprandial sera from the five volunteers. Five micrograms of total RNA were used to synthesize fluorescently labelled cDNA using the Pronto ChipShot™ Direct Labelling Kit (Corning, Avon, France) according to the manufacturer’s protocol. After purification, the quantity and labelling efficiency of the procedure were determined by quantitating the absorbance at 260, 550 and 650 nm using a Lambda 25 UV/VIS spectrometer (Perkin Elmer Instruments, Courtaboeuf, France). Hybridization was performed with a Pronto!™ Universal Microarray Hybridization Kit as recommended by the manufacturer. Briefly, arrays were pretreated (in pre-soak and pre-hybridization buffers), washed and dried by centrifugation at 500 rpm. Labelled cDNAs were dissolved in Pronto!™ Long Oligo/cDNA Hybridization Solution. Hybridizations were conducted on the RNG/MRC 25 k human oligonucleotides microarray [22]. After heating to 95°C for 5 min and then cooling at room temperature, the mixture was applied to the slides, covered by a coverslip and incubated for 14 h at 42°C. The slides were subsequently washed and dried by centrifugation at 500 rpm. The 10 slides (for a total of five independent comparisons) were scanned for both dye channels with an Affymetrix 428 Array Scanner (MWG Biotech, Roissy, France).

Microarray image and statistical analyses

The signal and background intensity values for each spot in both channels were obtained using ImaGene 6.0 software (Biodiscovery, Inc., Proteigene, Saint Marcel, France). Data were filtered using the ImaGene “empty spot” option, which automatically flags low-expressed and missing spots in order to remove them from the analyses. After log base 2 transformation, data were corrected for systemic dye bias by Lowess normalization using GeneSight 4.1 software (BioDiscovery, Inc, Proteigene, Saint Marcel, France). Ratios of the signal of postprandial cells compared to preprandial cells from the same volunteers were obtained and filtered according to their variability among the five comparisons. Statistical analyses were performed using the free R 2.1 software (http://www.r-project.org). Log ratios were analysed with an ANOVA model and a standard Student’s t-test in order to detect differentially expressed genes between the two nutritional conditions. Probability values were adjusted using the Bonferroni correction for multiple testing at 1% to eliminate false positives. Genes identified by these criteria and with a ratio greater than 1.15 or less than 0.87 (1/1.15) are referred to as the “differentially expressed genes”. Differentially expressed genes were first annotated using the online software, DAVID [9]. They were further classified according their Gene Ontology annotation using the online Babelomics tools [1]. Pathway analysis was performed using MetaCore version 3.2 (GeneGo Inc., St Joseph, MI, USA). MetaCoreTM is based on a proprietary manually curated database of human protein–protein, protein–DNA and protein compound interactions, metabolic and signalling pathways and the effects of bioactive molecules in gene expression.

Results

Baseline fasting plasma cholesterol, triacylglycerol and free fatty acid concentrations were 6.03 mmol/l ± 0.43, 0.75 mmol/l ± 0.18 and 0.41 mmol/l ± 0.15, respectively. After the oral fat challenge, the plasma triacylglycerol and free fatty acid concentrations peaked at 4 h (P < 0.01) to gradually return to the baseline within 8 h (Fig. 1). We did not observe a statistically significant influence of fat ingestion on plasma cholesterol levels (Fig. 1) nor glucose level (data not shown). Since plasma free fatty acids and triacylglycerols peaked at 4 h, we selected times 0 h (t0) and 4 h (t4) to study the behavioural and genomic response of HUVECs to postprandial hyperlipaemic sera. HUVECs were exposed to media containing 10% sera withdrawn at t0 or t4, and cell proliferation, adhesion and migration were then compared. Of the parameters measured, only cell proliferation was significantly affected. Indeed, exposure of the cells to postprandial hyperlipaemic sera caused a decrease in cell proliferation when compared to preprandial sera (Fig. 2) (P < 0.0001). Differential gene expression profiles were generated from the RNA extracted from HUVECs exposed to pre and postprandial sera from five healthy men. The comparison of the transcriptomes of HUVECs exposed to pre and postprandial sera led to the identification of 675 differentially expressed genes. Specifically, 338 genes were shown to be upregulated, and 337 genes were downregulated in the postprandial versus the preprandial condition. Among these differentially expressed genes, 431 have a known function, as revealed by ontological analyses (not shown). Ontological analyses of the results revealed that many differentially expressed genes are linked to apoptosis, cell cycle, lipid metabolism and immune and inflammatory responses. These genes are presented in Tables 1 and 2. Moreover, a great number of the differentially expressed genes have calcium ion binding capabilities (Table 3). As it may be possible that these genes are regulated in response to changes in intracellular calcium levels, we also present the differentially expressed genes linked to calcium-associated biological process and with ion channel activity in Table 3. Finally, using bioinformatics analysis (MetaCore) we identified the 10 master regulators potentially involved in the genomic regulation of gene expression in HUVECs exposed to pre and postprandial sera (Table 4). Among these, hepatocyte nuclear factor 4-alpha (HNF-4α) is the most likely to be involved in the postprandial response, as 78 genes in the set of differentially expressed genes are a target of this transcription factor. Finally, in order to confirm the apoptotic phenotype suggested by the transcriptomic results, we repeated the ex vivo experiment and measured apoptosis in the cells. We found an increase in cell death in the HUVECs exposed to postprandial sera compared to those exposed to preprandial sera (P = 0.011) (Fig. 3).
Fig. 1
Fig. 1

Changes in postprandial plasma lipid concentrations after high-fat ingestion. The mean and standard deviation of plasma (A) cholesterol, (B) triacylglycerols (TG) and (C) free fatty acids (FFA) during an 8-h postprandial period are shown. Values are means ± SD, n = 7. **P < 0.01

Fig. 2
Fig. 2

Changes in HUVEC proliferation after preprandial (0 h) or postprandial (4 h) sera exposure. The means and standard deviations of cell numbers are shown. Values are means ± SD, n = 36. ***P < 0.0001

Table 1

Genes regulated by postprandial serum in HUVECs that are involved in cell cycle and apoptosis

Gene ID

Gene symbol

Gene name

FC

Cell cycle genes

 11200

CHEK2

CHK2 CHECKPOINT HOMOLOG (S. POMBE)

1.46

 1031

CDKN2C

CYCLIN-DEPENDENT KINASE INHIBITOR 2C (P18, INHIBITS CDK4)

1.31

 10769

PLK2

POLO-LIKE KINASE 2 (DROSOPHILA)

1.31

 51696

HECA

HEADCASE HOMOLOG (DROSOPHILA)

1.30

 10459

MAD2L2

MAD2 MITOTIC ARREST DEFICIENT-LIKE 2 (YEAST)

1.24

 5719

PSMD13

PROTEASOME (PROSOME, MACROPAIN) 26S SUBUNIT, NON-ATPASE, 13

1.21

 602

BCL3

B-CELL CLL/LYMPHOMA 3

1.19

 83593

RASSF5

RAS ASSOCIATION (RALGDS/AF-6) DOMAIN FAMILY 5

1.16

 8451

CUL4A

CULLIN 4A

0.85

 990

CDC6

CDC6 CELL DIVISION CYCLE 6 HOMOLOG (S. CEREVISIAE)

0.85

 2672

GFI1

GROWTH FACTOR INDEPENDENT 1

0.84

 5228

PGF

PLACENTAL GROWTH FACTOR, VASCULAR ENDOTHELIAL GROWTH FACTOR-RELATED PROTEIN

0.84

 997

CDC34

CELL DIVISION CYCLE 34

0.83

 140690

CTCFL

CCCTC-BINDING FACTOR (ZINC FINGER PROTEIN)-LIKE

0.81

 11040

PIM2

PIM-2 ONCOGENE

0.78

 2935

GSPT1

G1 TO S PHASE TRANSITION 1

0.71

 10634

GAS2L1

GROWTH ARREST-SPECIFIC 2 LIKE 1

0.69

Apoptosis

 27113

BBC3

BCL2-BINDING COMPONENT 3

1.27

 597

BCL2A1

BCL2-RELATED PROTEIN A1

1.27

 83593

RASSF5

RAS ASSOCIATION (RALGDS/AF-6) DOMAIN FAMILY 5

1.16

 117584

RFFL

RING FINGER AND FYVE-LIKE DOMAIN CONTAINING 1

1.16

 1917

EEF1A2

EUKARYOTIC TRANSLATION ELONGATION FACTOR 1 ALPHA 2

0.86

 1616

DAXX

DEATH-ASSOCIATED PROTEIN 6

0.85

 8451

CUL4A

CULLIN 4A

0.85

 9093

DNAJA3

DNAJ (HSP40) HOMOLOG, SUBFAMILY A, MEMBER 3

0.85

 598

BCL2L1

BCL2-LIKE 1

0.84

 6885

MAP3K7

MITOGEN-ACTIVATED PROTEIN KINASE KINASE KINASE 7

0.83

 56616

DIABLO

DIABLO HOMOLOG (DROSOPHILA)

0.81

 8477

GPR65

G PROTEIN-COUPLED RECEPTOR 65

0.80

 11040

PIM2

PIM-2 ONCOGENE

0.78

 246184

CDC26

CELL DIVISION CYCLE 26

0.76

 2935

GSPT1

G1 TO S PHASE TRANSITION 1

0.71

Table 2

Genes regulated by postprandial serum in HUVECs that are involved in lipid metabolism and immune and inflammatory responses

Gene ID

Gene symbol

Gene name

FC

Lipid metabolism genes

 1577

CYP3A5

CYTOCHROME P450, FAMILY 3, SUBFAMILY A, POLYPEPTIDE 5

1.28

 79966

SCD5

STEAROYL-COA DESATURASE 5

1.27

 35

ACADS

ACYL-COENZYME A DEHYDROGENASE, C-2 TO C-3 SHORT CHAIN

1.23

 80011

NIP30

NEFA-INTERACTING NUCLEAR PROTEIN NIP30

1.22

 8443

GNPAT

GLYCERONEPHOSPHATE O-ACYLTRANSFERASE

1.21

 3158

HMGCS2

3-HYDROXY-3-METHYLGLUTARYL-COENZYME A SYNTHASE 2 (MITOCHONDRIAL)

1.21

 54965

PIGX

PHOSPHATIDYLINOSITOL GLYCAN, CLASS X

1.21

 1387

CREBBP

CREB-BINDING PROTEIN (RUBINSTEIN-TAYBI SYNDROME)

1.20

 8856

NR1I2

NUCLEAR RECEPTOR SUBFAMILY 1, GROUP I, MEMBER 2 (PXR)

1.20

 6817

SULT1A1

SULFOTRANSFERASE FAMILY, CYTOSOLIC, 1A, PHENOL-PREFERRING, MEMBER 1

1.18

 34

ACADM

ACYL-COENZYME A DEHYDROGENASE, C-4 TO C-12 STRAIGHT CHAIN

1.16

 8087

FXR1

FRAGILE X MENTAL RETARDATION, AUTOSOMAL HOMOLOG 1

1.16

 85320

ABCC11

ATP-BINDING CASSETTE TRANSPORTER MRP8

0.86

 3712

IVD

ISOVALERYL COENZYME A DEHYDROGENASE

0.85

 9619

ABCG1

ATP-BINDING CASSETTE, SUB-FAMILY G (WHITE), MEMBER 1

0.85

 1545

CYP1B1

CYTOCHROME P450, FAMILY 1, SUBFAMILY B, POLYPEPTIDE 1

0.85

 7804

LRP8

LOW-DENSITY LIPOPROTEIN RECEPTOR-RELATED PROTEIN 8, APOLIPOPROTEIN E RECEPTOR

0.84

 9420

CYP7B1

CYTOCHROME P450, FAMILY 7, SUBFAMILY B, POLYPEPTIDE 1

 

 10026

PIGK

PHOSPHATIDYLINOSITOL GLYCAN, CLASS K

0.83

 345

APOC3

APOLIPOPROTEIN C-III

0.82

 50487

PLA2G3

PHOSPHOLIPASE A2, GROUP III

0.79

 10351

ABCA8

ATP-BINDING CASSETTE, SUB-FAMILY A (ABC1), MEMBER 8

0.75

 347735

SERINC2

SERINE INCORPORATOR 2

0.73

Immune and inflammatory response genes

 8581

LY6D

LYMPHOCYTE ANTIGEN 6 COMPLEX, LOCUS D

1.41

 8029

CUBN

CUBILIN (INTRINSIC FACTOR-COBALAMIN RECEPTOR)

1.34

 4033

LRMP

LYMPHOID-RESTRICTED MEMBRANE PROTEIN

1.31

 221938

MMD2

MONOCYTE TO MACROPHAGE DIFFERENTIATION-ASSOCIATED 2

1.29

 462

SERPINC1

SERPIN PEPTIDASE INHIBITOR, CLADE C (ANTITHROMBIN), MEMBER 1

1.24

 2833

CXCR3

CHEMOKINE (C-X-C MOTIF) RECEPTOR 3

1.23

 5732

PTGER2

PROSTAGLANDIN E RECEPTOR 2 (SUBTYPE EP2), 53KDA

1.21

 8993

PGLYRP1

PEPTIDOGLYCAN RECOGNITION PROTEIN 1

1.20

 23446

SLC44A1

SOLUTE CARRIER FAMILY 44, MEMBER 1

1.18

 57549

IGSF9

IMMUNOGLOBULIN SUPERFAMILY, MEMBER 9

1.18

 10410

IFITM3

INTERFERON-INDUCED TRANSMEMBRANE PROTEIN 3 (1-8U)

1.18

 7441

VPREB1

PRE-B LYMPHOCYTE GENE 1

1.17

 51156

SERPINA10

SERPIN PEPTIDASE INHIBITOR, CLADE A (ALPHA-1 ANTIPROTEINASE, ANTITRYPSIN), MEMBER 10

1.16

 2151

F2RL2

COAGULATION FACTOR II (THROMBIN) RECEPTOR-LIKE 2

1.16

 56123

PCDHB13

PROTOCADHERIN BETA 13

0.86

 5737

PTGFR

PROSTAGLANDIN F RECEPTOR (FP)

0.86

 3565

IL4

INTERLEUKIN 4

0.85

 9332

CD163

CD163 ANTIGEN

0.85

 990

CDC6

CDC6 CELL DIVISION CYCLE 6 HOMOLOG (S. CEREVISIAE)

0.85

 8277

TKTL1

TRANSKETOLASE-LIKE 1

0.85

 5228

PGF

PLACENTAL GROWTH FACTOR, VASCULAR ENDOTHELIAL GROWTH FACTOR-RELATED PROTEIN

0.84

 3433

IFIT2

INTERFERON-INDUCED PROTEIN WITH TETRATRICOPEPTIDE REPEATS 2

0.83

 997

CDC34

CELL DIVISION CYCLE 34

0.83

 6349

CCL3L3

CHEMOKINE (C–C MOTIF) LIGAND 3-LIKE 1

0.82

 64072

CDH23

CADHERIN-LIKE 23

0.81

 11027

LILRA2

LEUCOCYTE IMMUNOGLOBULIN-LIKE RECEPTOR, SUBFAMILY A (WITH TM DOMAIN), MEMBER 2

0.81

 3562

IL3

INTERLEUKIN 3 (COLONY-STIMULATING FACTOR, MULTIPLE)

0.81

 79626

TNFAIP8L2

TUMOUR NECROSIS FACTOR, ALPHA-INDUCED PROTEIN 8-LIKE 2

0.80

 148170

CDC42EP5

CDC42 EFFECTOR PROTEIN (RHO GTPASE BINDING) 5

0.77

 23418

CRB1

CRUMBS HOMOLOG 1 (DROSOPHILA)

0.77

 246184

CDC26

CELL DIVISION CYCLE 26

0.76

 3824

KLRD1

KILLER CELL LECTIN-LIKE RECEPTOR SUBFAMILY D, MEMBER 1

0.75

 4804

NGFR

NERVE GROWTH FACTOR RECEPTOR (TNFR SUPERFAMILY, MEMBER 16)

0.73

 26548

ITGB1BP2

INTEGRIN BETA 1 BINDING PROTEIN (MELUSIN) 2

0.67

 2243

FGA

FIBRINOGEN ALPHA CHAIN

0.66

 91179

SCARF2

SCAVENGER RECEPTOR CLASS F, MEMBER 2

0.63

Table 3

Genes regulated by postprandial serum in HUVECs that are involved in calcium-associated biological process and code for proteins with calcium ion binding and ion channel activity

Gene ID

Gene symbol

Gene name

FC

Calcium-associated biological process

 6283

S100A12

S100 CALCIUM-BINDING PROTEIN A12 (CALGRANULIN C)

1.33

 51475

CABP2

CALCIUM-BINDING PROTEIN 2

1.26

 23284

LPHN3

LATROPHILIN 3

1.21

 5336

PLCG2

PHOSPHOLIPASE C, GAMMA 2 (PHOSPHATIDYLINOSITOL-SPECIFIC)

1.20

 124583

CANT1

CALCIUM-ACTIVATED NUCLEOTIDASE 1

1.16

 1909

EDNRA

ENDOTHELIN RECEPTOR TYPE A

0.84

Calcium ion binding

 8029

CUBN

CUBILIN (INTRINSIC FACTOR-COBALAMIN RECEPTOR)

1.34

 6283

S100A12

S100 CALCIUM-BINDING PROTEIN A12 (CALGRANULIN C)

1.33

 7125

TNNC2

TROPONIN C TYPE 2 (FAST)

1.28

 5534

PPP3R1

PROTEIN PHOSPHATASE 3 (FORMERLY 2B), REGULATORY SUBUNIT B, 19KDA, ALPH …

1.27

 51475

CABP2

CALCIUM-BINDING PROTEIN 2

1.26

 114327

EFHC1

HYPOTHETICAL PROTEIN FLJ10466

1.23

 5336

PLCG2

PHOSPHOLIPASE C, GAMMA 2 (PHOSPHATIDYLINOSITOL-SPECIFIC)

1.20

 51661

FKBP7

FK506 BINDING PROTEIN 7

1.18

 54947

AYTL1

ACYLTRANSFERASE LIKE 1

1.17

 124583

CANT1

CALCIUM-ACTIVATED NUCLEOTIDASE 1

1.16

 5551

PRF1

PERFORIN 1 (PORE FORMING PROTEIN)

 

 56123

PCDHB13

PROTOCADHERIN BETA 13

0.86

 4324

MMP15

MATRIX METALLOPEPTIDASE 15 (MEMBRANE-INSERTED)

0.85

 8277

TKTL1

TRANSKETOLASE-LIKE 1

0.85

 7804

LRP8

LOW-DENSITY LIPOPROTEIN RECEPTOR-RELATED PROTEIN 8

0.84

 147968

CAPN12

CALPAIN 12

0.84

 64072

CDH23

CADHERIN-LIKE 23

 

 80144

FRAS1

KIAA1500 PROTEIN

0.81

 50487

PLA2G3

PHOSPHOLIPASE A2, GROUP III

0.79

 23418

CRB1

CRUMBS HOMOLOG 1 (DROSOPHILA)

0.77

 92737

DNER

DELTA-NOTCH-LIKE EGF REPEAT-CONTAINING TRANSMEMBRANE

0.76

 26548

ITGB1BP2

INTEGRIN BETA 1 BINDING PROTEIN (MELUSIN) 2

0.67

Ion channel activity

 1143

CHRNB4

CHOLINERGIC RECEPTOR, NICOTINIC, BETA 4

2.51

 8001

GLRA3

GLYCINE RECEPTOR, ALPHA 3

1.78

 2890

GRIA1

GLUTAMATE RECEPTOR, IONOTROPIC, AMPA 1

1.54

 3761

KCNJ4

POTASSIUM INWARDLY-RECTIFYING CHANNEL, SUBFAMILY J, MEMBER 4

1.30

 50801

KCNK4

DKFZP566E164 PROTEIN

1.27

 7881

KCNAB1

POTASSIUM VOLTAGE-GATED CHANNEL, SHAKER-RELATED SUBFAMILY, BETA MEMBER 1

1.19

 6330

SCN4B

SODIUM CHANNEL, VOLTAGE-GATED, TYPE IV, BETA

1.16

 283518

KCNRG

POTASSIUM CHANNEL REGULATOR

0.86

 146212

KCTD19

POTASSIUM CHANNEL TETRAMERIZATION DOMAIN CONTAINING 19

0.78

 27133

KCNH5

POTASSIUM VOLTAGE-GATED CHANNEL, SUBFAMILY H (EAG-RELATED), MEMBER 5

0.76

 3783

KCNN4

POTASSIUM INTERMEDIATE/SMALL CONDUCTANCE CALCIUM-ACTIVATED CHANNEL

0.70

Table 4

Master regulators identified by transcriptomic analysis

Transcription factor

Processes

P value

Regulated genes

Nb

Examples

HNF-4α

Induction of apoptosis via death domain receptors

2.52E-143

78

LRP5, APOC3, NR1I2, PXR, ABG1, CIP1B1, CYP3A5

Energy metabolism

Response to chemical stimulus

SP1

Neurotransmitter uptake

6.10E-76

42

EDNRB, KRT16, TNNT2, PGE2R2, PGF2aR, nAChR alpha-5 and alpha-4

Intermediate filament cytoskeleton organization and biogenesis

Ketone body biosynthetic process

P53

DNA damage response, signal transduction resulting in induction of apoptosis

1.51E-37

21

BCL2L1, CREBBP, CDC6, CHEK2, DAXX, BCL2A1, BBC3

Regulation of mitochondrial membrane permeability

DNA damage response, signal transduction by p53 class mediator resulting in induction of apoptosis

c-Myc

DNA damage response, signal transduction by p53 class mediator resulting in induction of apoptosis

9.74E-36

20

BBC3, NOTCH1, BCL-3

Induction of apoptosis by intracellular signals

Release of cytochrome c from mitochondria

AP-1

Cell death

1.58E-28

16

CBP, DAXX, IL-3

Regulation of apoptosis

Regulation of apoptosis

c-Jun

Regulation of survival gene product activity

9.91E-27

15

CBP, DAXX, IL-3

Regulation of biological quality

Catecholamine uptake during transmission of nerve impulse

CREB1

Regulation of nucleobase, nucleoside, nucleotide and nucleic acid metabolic process

6.16E-25

14

IL-4, NPR1, SOX15

Regulation of cellular metabolic process

Regulation of transcription

ESR1

Steroid hormone receptor signalling pathway

3.80E-23

13

ABCC11, CYP1B1, CYP7B1

Oestrogen receptor signalling pathway

Regulation of transcription, DNA-dependent

HNF1-alpha

Catecholamine transport

3.80E-23

13

APOC3, Fibrinogen alpha, Anithrombin III

Positive regulation of cellular process

Catecholamine uptake during transmission of nerve impulse

NF-kB

Regulation of apoptosis

2.33E-21

12

SOD2, DAXX, Pim-2

Response to stress

Regulation of programmed cell death

Transcription factors identified by MetaCore are presented. Transcription factors are organized by a major biological process in which they are involved, P value assigned by MetaCore, number of genes present in the array that they potentially regulate and examples of these genes

Fig. 3
Fig. 3

Changes in apoptosis induction in HUVECs after preprandial (T0) or postprandial (T4) sera exposure. Cell death was assessed using a cell death detection ELISA kit (Roche), which detects the amount of cleaved DNA/histone complexes. Values are means ± SD, n = 5. **P = 0.011

Discussion

Consistent with previous studies [3739], we found that plasmatic triacylglycerols and free fatty acids increased in plasma after a high-fat challenge in healthy men, and peaked 4 h after fat consumption (Fig. 1). In these studies, postprandial hyperlipaemia was shown to be associated with a general metabolic stress, characterized by the rise in oxidative stress and endothelial dysfunction. Pro-inflammatory cytokine concentrations have also been shown to be elevated during the postprandial period (4). Thus, postprandial blood after fat ingestion could be considered a mixture of molecules with the capacity to either activate or impair the functions of the endothelium, thus leading to vascular dysfunction. To study the influence of fat ingestion on vascular endothelial cells, we exposed HUVECs to sera withdrawn either before or 4 h after the fat challenge. HUVEC responses were then explored by evaluating: (1) proliferation, migration and endothelial/monocyte interactions and (2) modulation of gene expression as measured by transcriptomic analysis.

The first major finding of this study is the observation that HUVEC proliferation decreases (approximately 30%) when exposed to postprandial hyperlipaemic sera (Fig. 2). To our knowledge, this is the first time that the HUVEC response to lipaemic sera has been studied. Previously, the response of HUVECs to different isolated pro-atherogenic molecules, such as oxidized LDL, has been explored. Seibold et al. [32] showed that oxidized LDL leads to either cell proliferation or apoptotic cell death, depending on the lipoprotein concentration. Other known pathogenic factors of atherosclerosis, such as oxygen-free radicals or pro-inflammatory cytokines, which are elevated in plasma during the postprandial hyperlipaemic period [5], induce programmed cellular death in endothelial cells [10]. In our study, the decreased cell number after exposure to hyperlipaemic sera was partly due to the induction of apoptosis.

Another major finding of our work is the demonstration of the modulation of genes linked to cell cycle control and the promotion of apoptosis (Table 1). Indeed, genes coding for pro-apoptotic proteins (chk2, bbc3, rassf5, crebbp, cdkn2c and bcl3) were upregulated and genes coding for anti-apoptotic and checkpoint proteins were downregulated (cdc6, cdc34, cdc26, bcl2l1, gstp1) in HUVECs exposed to postprandial versus preprandial sera. Among these genes, chk2 encodes a protein that has the capacity to phosphorylate and, consequently, activate p53 [6]. p53 is a nuclear receptor that affects cellular functions that include transcription, DNA synthesis and repair, cell cycle arrest, senescence and apoptosis [44]. The CREB-binding protein gene, crebbp, a transcriptional co-activator that has the capacity to stimulate p53 transcriptional activity, is also overexpressed in HUVECs after hyperlipaemic serum exposure. The gene, bbc3 (bcl2-binding component 3, also known as puma) was also found to be upregulated by the transcriptomic analysis and is an essential mediator of p53-induced apoptosis [42]. Thus, the concomitant upregulation of crebbp, chk2 and bbc3 suggests that p53 activation occurs in HUVECs exposed to postprandial hyperlipaemic sera. This hypothesis is also supported by the identification of p53 as one of the three major transcription factors by the bioinformatics analysis performed with MetaCore (Table 4). Furthermore, the pathway analysis also reveals the potential implications of other regulators, such as c-Myc, which mediates p53 signal transduction [17], or c-jun/AP-1, which is implicated in the control of p53 activation [2]. Other genes involved in the induction of apoptosis are upregulated after high-fat serum exposure. Among them, rassf5 (also known as nore1) encodes a specific effector that regulates the pro-apoptotic action of the oncogenic Ras protein [7]. Our transcriptome analysis suggests that various cell death pathways are upregulated. Finally, microarray analysis reveals that checkpoint proteins, such as cdc6, cdc34, cdc26 and gstp1, are transcriptionally downregulated in the postprandial condition. Among them, the Cdc6 protein is an essential component of pre-replication complexes, which assemble at the origins of DNA replication during the G1 phase of the cell cycle [3]. Cdc6 stability is also controlled by the p53 pathway [12]. Overall, our results show that postprandial serum induces gene expression modifications in genes that are involved in the control of cell cycle and death to favour growth arrest and apoptosis in vascular endothelial cells.

Taken together, our results show that postprandial hyperlipaemic sera can induce stress and/or impair HUVEC function leading to apoptosis. This outcome could be ascribed to pro-oxidant molecules or to the cytokine, TNF-α, which can both induce apoptosis in HUVECs [10] and have been showed to be elevated in plasma after a high-fat challenge in healthy men [28]. Alternatively, the apoptosis of HUVECs could be due to low levels of growth and survival factors in postprandial sera. Indeed, cellular survival is dependent upon the availability of growth factors that can inhibit intrinsic programmed cell death. Among these growth factors, interleukin-3 (IL-3) and placental growth (PlGF) factor are transcriptionally downregulated in our study (Table 2). IL-3 has been showed to inhibit apoptosis through the activation of different signalling pathways [19, 35] and can induce vascular endothelial cell proliferation in vitro [4]. PlGF, a member of the VEGF family, is angiogenic and promotes endothelial cell proliferation in vitro [30]. Therefore, the inhibition of HUVEC proliferation observed in this study could be mediated, at least in part, by the downregulation of IL-3 or PlGF.

Intracellular concentrations of potassium and calcium ions also play an important role in apoptosis [21, 26]. The microarray analysis of vascular endothelial cells exposed to pre or postprandial sera indicates the importance of a set of genes associated with calcium-dependent biological processes, calcium ion binding capacity or ion channel activity (Table 3). Suicidal cell death involves and requires the activation of potassium and calcium channels [21]. Indeed, increased levels of calcium may lead to the activation of Ca2+-dependent kinases or phosphatases or could lead to the increased release of cytochrome c, thus resulting in the activation of the apoptotic pathway [21, 26]. Moreover, cellular loss of potassium favours apoptosis in a wide variety of cells. Thus, the transcriptomic regulation of genes linked to the control of intracellular ion concentrations could also explain the induction of apoptosis in HUVECs exposed to postprandial serum.

Among the 431 regulated genes with a known function, 23 are linked to lipid-related biological processes (Table 2). Some genes implicated in fatty acid degradation, including acads and acadm, which are involved in the breakdown of fatty acids and hmgcs2, which is involved in ketone body production from fatty acids, were found to be upregulated in the postprandial condition. These results are consistent with a metabolic response activated by HUVECs in response to the elevation of lipid uptake during fatty sera exposure. Other genes involved in bile acid and drug detoxification are regulated in HUVECs after hyperlipaemic serum exposure. These genes include cyp3a5, cyp1b1 and cyp7b1 and abcc11, abcg1 and abca8. Interestingly, cytochrome P450 (CYP) [23] and ATP-binding cassette (ABC) transporters have already been implicated in cardiovascular disease [29]; however, their pathological involvements are not clear. The genes coding for drug and bile acid transporters and CYP enzymes are generally regulated by the nuclear receptors FXR, PXR, CAR and HNF-4α through complex and overlapping regulatory networks [13, 25]. Among theses regulators, FXR (fxr1) and PXR (nr1i2) were found to be upregulated in the present study (Table 2). Furthermore, special attention should be paid to HNF-4α regulation in our genomic study. This transcription factor was identified by MetaCore pathway analysis as the key regulator of gene expression (Table 4). HNF-4α potentially regulates 78 of the genes differentially expressed in our study. HNF-4α was first identified in the liver, where it regulates the expression of a broad number of genes involved in several different functions, including energy metabolism, xenobiotic detoxification, bile acid synthesis, serum protein production and intracellular lipid control [41]. The transcriptional activity of HNF-4α transcriptional activity may be modulated by the binding of fatty acyl-CoA thioesters, fatty acyl CoA or fatty acids [18]. Gene knockout studies of hnf-4α have demonstrated its role in controlling PXR expression [41]. Moreover, co-activation of HNF-4α and PPAR-γ has been linked to the increase in fxr mRNA levels during fasting that results in the reduction of triglyceride production and secretion and fatty acid β-oxidation [43]. In our study, HNF-4α may represent a major regulator of the HUVEC metabolic response to postprandial hyperlipaemia, partially through its capacity to upregulate fxr and pxr expression.

Taken together, our results show that postprandial hyperlipaemia induces an apoptotic phenotype in vascular endothelial cells. Indeed, postprandial hyperlipaemic serum induces (1) a decrease in HUVEC proliferation, (2) the transcriptional regulation of major cell death-associated genes and (3) elevation of the amount of cleaved nucleosomes. This phenotype could be partially mediated by pro-oxidant or pro-inflammatory molecules previously shown to be elevated in plasma during the postprandial period following a high-fat challenge in healthy men. A link between endothelial apoptosis and the pathology of atherosclerosis has been previously suggested [36]. Apoptotic endothelial cells have been detected on the luminal surface of atherosclerotic coronary vessels, but not in normal vessels [36]. Endothelial apoptosis in an atherosclerotic lesion may lead to the exposure of collagen fibres. Importantly, apoptosis is suggested to play an important role in the rupture of the plaque and in thrombus formation. In conclusion, our findings support the hypothesis that postprandial hyperlipaemia is associated with an endothelial dysfunction that promotes atherogenesis.

Declarations

Acknowledgments

This work was supported by grants from the Fondation pour la Recherche Médicale. The authors thank Dominique Bayle and Severine Thien for their technical assistance with the biochemical measurements and Agnès Thomas and Christiane Legay for lipid analyses.

Authors’ Affiliations

(1)
Unité de Nutrition Humaine, UMR1019, INRA, Clermont-Fd/Theix, 63122 St Genès Champanelle, France
(2)
Department of Preclinical Sciences LITA Vialba, University of Milan, Via GB Grassi 74, 20157 Milan, Italy
(3)
Centre Hospitalier Universitaire de Clermont-Ferrand, Service d’Endocrinologie et Maladies Métaboliques, Hôpital G. Montpied, Clermont-Ferrand et Université d’Auvergne, Faculté de Médecine, Clermont-Ferrand, France

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