
==== Front
Biochem Biophys Rep
Biochem Biophys Rep
Biochemistry and Biophysics Reports
2405-5808
Elsevier

S2405-5808(24)00178-X
10.1016/j.bbrep.2024.101814
101814
Research Article
In vitro analysis of VEGF-mediated endothelial permeability and the potential therapeutic role of Anti-VEGF in severe dengue
Lim Sheng Jye a
Gan Seng Chiew a
Ong Hooi Tin ac
Ngeow Yun Fong ngeowyf@utar.edu.my
ab⁎
a Faculty of Medicine and Health Sciences, Universiti Tunku Abdul Rahman, Sungai Long Campus, Jalan Sungai Long, Bandar Sungai Long, Cheras 43000, Kajang, Selangor, Malaysia
b Centre for Research on Communicable Diseases, Faculty of Medicine and Health Sciences, Universiti Tunku Abdul Rahman, Sungai Long Campus, Jalan Sungai Long, Bandar Sungai Long, Cheras 43000, Kajang, Selangor, Malaysia
c Center for Cancer Research, Faculty of Medicine and Health Sciences, Universiti Tunku Abdul Rahman, Sungai Long Campus, Jalan Sungai Long, Bandar Sungai Long, Cheras 43000, Kajang, Selangor, Malaysia
⁎ Corresponding author. Universiti Tunku Abdul Rahman, Jalan Sungai Long, Bandar Sungai Long, Cheras, 43000, Kajang, Selangor, Malaysia. ngeowyf@utar.edu.my
22 8 2024
9 2024
22 8 2024
39 10181412 6 2024
8 8 2024
19 8 2024
© 2024 The Authors. Published by Elsevier B.V.
2024

https://creativecommons.org/licenses/by-nc/4.0/ This is an open access article under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).
Background

Vascular endothelial growth factor (VEGF) is one of the proteins involved in dengue immunopathogenesis. It is overexpressed in severe dengue and contributes to vascular permeability and plasma leakage. In this study, we investigated the effects of VEGF and anti-VEGF treatments on endothelial cells in vitro, to assess the potential use of anti-VEGF antibodies in managing severe dengue.

Methods

Human pulmonary microvascular endothelial cells were treated with VEGF and a VEGF/anti-VEGF combination. The effects of the treatments were studied using an endothelial permeability assay and microarray gene expression profiling. In the permeability assay, the fluorescein isothiocyanate (FITC)-dextran fluorescence signal across the endothelial monolayer was recorded, and the cells were stained with PECAM-1 to detect gap formation. RNA was extracted from treated cells for microarray gene profiling and analysis. The results were analyzed for differentially expressed genes (DEGs) and gene enrichment analysis. The DEGs were subjected to STRING to construct the protein-protein interaction network and then Cytoscape to identify the hub genes.

Results

VEGF-treated endothelial cells showed greater movement of FITC-dextran across the monolayer than VEGF/anti-VEGF-treated cells. There were 111 DEGs for VEGF-treated cells and 118 DEGs for VEGF/anti-VEGF-treated cells. The genes upregulated in VEGF-treated cells were enriched in inflammatory responses and regulation of the endothelial barrier, nitric oxide synthesis, angiogenesis, and the nucleotide-binding oligomerization domain-like receptor signaling pathway. Top 10 hub genes were identified from the DEGs.

Conclusions

VEGF treatment increased permeability across endothelial cells, while anti-VEGF reduced this leakage. Analysis of VEGF-treated endothelial cells identified hub genes implicated in severe dengue. The top 10 hub genes were TNF, IL1B, IL6, CCL2, PTGS2, ICAM1, CXCL2, CXCL1, CSF2, and TLR2. The results of this study show that using anti-VEGF antibodies to neutralize VEGF may be a promising therapy to prevent the progression of dengue to severe dengue.

Graphical abstract

Image 1

Highlights

• The in vitro effects of VEGF and anti-VEGF on endothelial cells were investigated.

• VEGF treatment increased permeability across endothelial cells.

• Anti-VEGF treatment reduced the degree of leakage caused by VEGF.

• Genes upregulated by VEGF encode proteins implicated in severe dengue.

• Inhibiting VEGF using anti-VEGF may stop the progression of dengue to severe dengue.

Keywords

Vascular endothelial growth factor
Anti-Vascular endothelial growth factor
Endothelial cells
Severe dengue
Vascular permeability
Gene expression profiling
==== Body
pmcAbbreviations

VEGF Vascular endothelial growth factor

HPMEC Human pulmonary microvascular endothelial cells

FITC Fluorescein isothiocyanate

PBS Phosphate buffered saline

BSA Bovine serum albumin

GO Gene ontology

FC Fold change

DEG Differentially expressed gene

PPI Protein-protein interaction

CT Cycle threshold

DHF Dengue hemorrhagic fever

HUVEC Human umbilical vein endothelial cells

NO Nitric oxide

1 Introduction

Dengue fever is a self-limiting illness that presents with various constitutional symptoms or asymptomatic infections [1]. A small percentage of cases progress to severe dengue, which is characterized by increased vascular permeability, plasma leakage, hemoconcentration, and thrombocytopenia, presenting with circulatory failure (hypovolemia, hypotension, and shock), edema in body cavities (pleural, abdominal, and cardiac), and internal bleeding [2]. This infection is caused by four serotypes of the dengue virus (DENV 1, 2, 3, and 4) transmitted by mosquitoes, mainly Aedes aegypti and Aedes albopictus [1,3]. Although all four serotypes can cause dengue, severe dengue mostly appears as a secondary infection caused by a serotype that is different from that causing the first infection [4]. However, the pathogenesis of severe dengue is not completely understood. It is hypothesized that the antibodies and T cells generated during a primary infection provide incomplete immunity to subsequent heterotypic infection and elicit altered immune responses that lead to severe disease [5].

Vascular endothelial growth factor (VEGF) has been implicated in the immunopathogenesis of severe dengue. The VEGF protein family is involved in normal physiological functions in the skeletal, neural, and hematopoietic systems [6]. VEGF-A is a protein that belongs to the VEGF family. It plays a role in vasculogenesis and promotes angiogenesis for homeostasis. However, it has also been implicated in several diseases [7]. In severe dengue, VEGF is overexpressed [[8], [9], [10], [11], [12]] and responsible for the severe plasma leakage that follows a VEGF-induced increase in vascular permeability [12,13].

VEGF inhibitors target either VEGF or its receptor [7,14]. Anti-VEGF monoclonal antibodies, such as those found in bevacizumab and ranibizumab have been used to treat eye diseases and various cancers [15,16], but not severe dengue. We hypothesized that VEGF-induced plasma leakage is a significant contributor to the pathophysiology of severe dengue and anti-VEGF treatment may reverse the effects of VEGF-induced plasma leakage in severe dengue. To test this, the study investigated the effects of VEGF and anti-VEGF treatments on endothelial cells through in vitro permeability assays and gene expression studies.

2 Methods

2.1 Cell culture

Human pulmonary microvascular endothelial cells (HPMECs) were acquired from PromoCell (Heidelberg, Germany). The revived HPMECs were maintained in the endothelial cell growth medium MV2 (PromoCell), and subsequent maintenance, subculture, and cryopreservation of the cell line were performed according to the manufacturer's protocol.

2.2 In-vitro permeability assay

An in-vitro permeability assay was performed to test the permeability-inducing effect of VEGF and determine whether treatment with anti-VEGF could reduce the induced permeability. These procedures were performed as described previously [17]. Before seeding endothelial cells, the cell culture inserts were coated with diluted Matrigel (Corning, NY, USA) at a 3:1 ratio in cold Dulbecco's Modified Eagles Medium. Next, the insert with the Matrigel film was incubated at 37 °C for 30 min to solidify the Matrigel. Approximately 200,000 endothelial cells were seeded on each insert. After 30 min of incubation at 37 °C, another 200 μL of culture medium was added to the insert, and 1 mL of culture medium was added to the well of the 24-well plate holding the insert. The entire 24-well plate was incubated at 37 °C for 24 h. After the incubation period, another 200,000 endothelial cells were seeded into each insert. The cells were further incubated at 37 °C for another 24 h before they were used for the vascular permeability assay.

During the permeability assay, fluorescein isothiocyanate (FITC)-dextran was added to the lower chamber of the cell culture insert at a final concentration of 10 μg/mL. The VEGF used in this study was a recombinant VEGF protein (Catalogue # GF615, Merck, Darmstadt, Germany), whereas the anti-VEGF used was a human VEGF monoclonal antibody (Catalogue # MAB293, R&D Systems, MN, USA). In VEGF-treated wells, only VEGF was added to the upper and lower chambers at a final concentration of 200 pg/mL. For the anti-VEGF-treated wells, VEGF and anti-VEGF were added separately into the upper and lower chambers at final concentrations of 200 and 400 pg/mL, respectively. Triplicates were prepared for treated and untreated wells. At different time intervals (0, 5, 15, 30, 45, 60, 120, and 180 min) after treatment, 10 μL of culture medium from the upper chamber was removed and diluted with 90 μL of deionized water for transfer to a 96-well plate. After removing the medium at the final time point, the fluorescence intensity of each sample was measured using a fluorescence microplate reader (Infinite 200 Pro, Tecan, Zurich, Switzerland) at excitation and emission wavelengths of 485 and 535 nm, respectively. The permeability of the endothelial cells was measured based on the FITC-dextran signal that passed through the cells. The higher the fluorescence signal, the higher the permeability of endothelial cells, as it allows FITC-dextran to pass through the endothelial cells from the lower chamber to the upper chamber. The experiments were repeated in triplicate.

2.3 Immunofluorescence staining of HPMECs

Immunofluorescence staining was performed as described previously [17]. HPMECs were plated onto Matrigel-coated cell culture inserts and treated with VEGF, VEGF/anti-VEGF for 180 min, or left untreated. For immunofluorescence staining, the cell culture medium was removed, and the upper and lower chambers of each insert were rinsed twice with 1 × phosphate-buffered saline (PBS). The cells were fixed with 4 % paraformaldehyde in PBS for 30 min, washed twice with PBS for 5 min, incubated in 0.1 M glycine in PBS for 10 min to quench free paraformaldehyde radicals, and further washed twice with PBS. Next, the cells were permeabilized with diluted Perm/Wash Buffer (BD, Franklin Lakes, NJ, USA) for 15 min at 27 °C. This procedure was followed by incubating the cells for 30 min in 10 % goat serum (100 μL/well) to block nonspecific binding of the secondary antibody and further incubation for 1 h with 1:200 anti-PECAM-1 mouse antibody in PBS containing 0.1 % bovine serum albumin (BSA). The cells were washed thrice with PBS containing 0.1 % BSA and stained for 1 h with 1:100 FITC goat anti-mouse antibody. Subsequently, the cells were washed three times with PBS containing 0.1 % BSA to remove any remaining antibodies. The cell nuclei were stained with 4′,6-diamidino-2-phenylindole for 30 min at room temperature. The abovementioned steps involving washing with PBS were performed, and the permeable membrane was removed from the cell culture insert using a clean scalpel and placed onto a clean glass slide with the cells facing up. The glass slides were mounted using a fluorescence mounting medium for visualization under an inverted fluorescence microscope (Carl Zeiss, Baden-Württemberg, Germany).

2.4 Gene expression study on treated and untreated HPMECs using microarray analysis

Total RNA from HPMECs that were not treated or treated with either VEGF or VEGF/anti-VEGF (untreated, n = 2; VEGF-treated, n = 3; VEGF/anti-VEGF, n = 3) was extracted using the Ribospin II RNA Purification Kit (GeneAll, Seoul, South Korea), following the manufacturer's instructions. The extracted RNA was subjected to microarray analysis using the SurePrint G3 Human Gene Expression v2 8 × 60K Microarray (Agilent, CA, USA). Briefly, 10–200 ng of total RNA was reverse transcribed to cDNA, amplified, and labeled with Cy3 fluorescent dye to produce the final product of Cy3-labeled cRNA. The cRNA was further purified using the RNeasy Mini Kit (Qiagen, Hilden, Germany), fragmented at 60 °C for 30 min, and loaded onto the microarray slide for hybridization at 65 °C for 17 h. After hybridization, the slide was washed with Gene Expression Wash Buffer I and II to remove any non-hybridized cRNA and scanned immediately using the Agilent SureScan Microarray Scanner and Agilent Scan Control software (version 9.1.3). The microarray scan images were extracted using the Agilent Feature Extraction Software. The extracted.txt files were analyzed using the GeneSpring software (version 14.9).

The.txt files uploaded to the GeneSpring software were normalized using quantile normalization to reduce variation in the distribution of the samples. The next step involved filtering by expression to remove saturated or background signals, with a 20%–100 % cut-off. After preprocessing the data, statistical analysis was performed. One-way analysis of variance (ANOVA) was used to compare the three samples (untreated, VGEF-treated, and VEGF/anti-VEGF-treated cells). The fold-change (FC) pairing options for comparison were as follows: anti-VEGF-treated vs. untreated, and VEGF-treated vs. untreated. The results that passed the criteria of a p-value <0.05 and FC ≥ 2 were selected and presented as FC. The processed data were subjected to Gene Ontology (GO) analysis using the GeneSpring software. In addition, the Database for Annotation, Visualization, and Integrated Discovery (DAVID; version 6.8) [18] was used to study the enriched functions in the differentially expressed genes (DEGs). The DEGs were further subjected to STRING to construct the protein-protein interaction (PPI) network before being exported to Cytoscape to identify the top 10 hub genes using the MCC algorithm in the CytoHubba plugin. Random genes among the upregulated and downregulated genes were selected for validation using qRT-PCR. The random genes included CXCL2, CSF3, IL23A, CSF2, SELE, F3, CXCL3, TRIL, CH25H, and PDK4. The cycle threshold (CT) value was obtained for each sample, and the 2-ΔΔCt method was used to determine the relative changes in gene expression for the samples [19].

2.5 Statistical analysis

Statistical differences between untreated and treated cells in the permeability assay were calculated using student t-test. Statistical significance was set at p < 0.05.

3 Results

3.1 Effects of VEGF and anti-VEGF treatments on endothelial cells

In the permeability study (Fig. 1), the movement of FITC-dextran across the monolayer was significantly higher in VEGF-treated wells than in untreated wells at 120 and 180 min. In contrast, in the wells treated with VEGF/anti-VEGF, the movement of FITC-dextran across the monolayer was less than that in VEGF-treated wells but slightly more than that in untreated wells. The VEGF/anti-VEGF treatment led to a statistically significant reduction in signal compared to the VEGF treatment at 120 and 180 min only (Fig. 1B).Fig. 1 In vitro permeability assay. (A) Brightfield image of human pulmonary microvascular endothelial cells (HPMECs) during culture before seeding for permeability assay. Scale bar = 200 μm. (B) Graph of fluorescein isothiocyanate (FITC)-dextran fluorescence signal over time (n = 3). Asterisks indicate a significant increase in signal from VEGF-treated cells compared to untreated cells at *p < 0.01. Hashtags indicate a significant reduction in signal from VEGF/anti-VEGF-treated cells compared to VEGF-treated cells at #p < 0.01. A higher FITC-dextran signal represents increased movement over the endothelial layer and greater endothelial permeability. VEGF-treated cells showed higher endothelial permeability compared to untreated cells and VEGF/anti-VEGF-treated cells. (C) Immunostaining images of untreated and treated cells (incubated for 180 min) stained with PECAM-1 (n = 3). The formation of gaps between cells allowed FITC-dextran to cross the endothelial cell monolayer. Anti-VEGF treatment reduced the number of gaps formed between cells. Scale bar = 25 μm.

Fig. 1

After recording fluorescence intensity, the treated cells were stained with the PECAM-1 antibody. In Fig. 1C, the endothelial cells treated with VEGF showed more gap formation between cells than the untreated cells or cells treated with VEGF/anti-VEGF. As endothelial cells treated with VEGF showed increased paracellular gap formation, the rate of FITC-dextran movement across the cell monolayer also increased. The untreated cells showed the lowest leakage rate because the tight linkage between endothelial cells prevented movement across the cell-cell junctions. In VEGF/anti-VEGF-treated cells, the number of gaps formed between cells was less than that in the VEGF-treated cells. This suggests that treatment with anti-VEGF agents can prevent the gaps or leakages caused by VEGF. Fluorescence images of gap formation (Fig. 1C) correlated with the trend in endothelial cell permeability shown in Fig. 1B.

3.2 Gene expression profiling of treated endothelial cells

The genome-wide microarray analysis featured 50,599 probes covering 24,588 genes. After VEGF treatment, 111 genes showed more than 2-fold differential expression, of which 103 were upregulated and 8 were downregulated. Following VEGF/anti-VEGF treatment, 118 genes showed more than 2-fold differential expression compared with untreated endothelial cells. Of these, 106 genes were upregulated, and 12 genes were downregulated. Table 1 shows the top 10 upregulated and downregulated genes for both endothelial cell treatments with fold change value. Fig. 2 shows the volcano plot of DEGs in VEGF-treated cells and VEGF/anti-VEGF-treated cells, highlighting the top 10 upregulated and downregulated genes. Unsupervised hierarchical clustering analysis grouped treated and untreated HPMEC samples into three distinct clusters. The two treated samples formed separate clusters but were grouped closer to each other than to the untreated cells (Fig. 3). qRT-PCR validation of randomly selected DEGs confirmed the same patterns of upregulation and downregulation as observed in the microarray profiling (data not shown).Table 1 Top 10 upregulated and downregulated genes in VEGF and VEGF/Anti-VEGF treated HPMECs, P < 0.05, FC ≥ 2.

Table 1VEGF vs. untreated	Fold-change	Regulation	VEGF/anti-VEGF vs. untreated	Fold-change	Regulation	
F3	330.17	Up	CSF2	425.06	Up	
CSF2	299.61	Up	F3	256.10	Up	
LIF	191.54	Up	TNF	252.29	Up	
TNF	180.82	Up	LIF	242.85	Up	
CXCL3	175.95	Up	CXCL3	201.89	Up	
CSF3	92.73	Up	CSF3	140.07	Up	
C2CD4A	85.62	Up	IL23A	95.34	Up	
IL23A	68.99	Up	C2CD4A	89.70	Up	
IL1B	66.47	Up	CXCL2	86.34	Up	
CXCL2	57.75	Up	SELE	66.96	Up	
OR9A2	−2.90	Down	CH25H	−4.45	Down	
TRIL	−2.83	Down	CUL4A	−3.49	Down	
TBX1	−2.61	Down	PDK4	−3.43	Down	
RAB37	−2.24	Down	TRIL	−3.35	Down	
SLC2A12	−2.17	Down	OR9A2	−3.10	Down	
TACC3	−2.16	Down	TBX1	−2.80	Down	
BRD3	−2.08	Down	ADAMST12	−2.41	Down	
NRDE2	−2.00	Down	DDIT4L	−2.39	Down	
			RAB37	−2.28	Down	
			BRD3	−2.22	Down	

Fig. 2 Volcano plot of differentially expressed genes (DEGs) in VEGF-treated cells (left) and VEGF/anti-VEGF-treated cells (right). The plot showed top 10 upregulated and downregulated genes. Red represents upregulated genes, blue represents downregulated genes, and black represents genes with no significant change in fold change. (For interpretation of the references to color in this figure legend, the reader is referred to the Web version of this article.)

Fig. 2

Fig. 3 Unsupervised hierarchical clustering of untreated and treated HPMECs. The treated and untreated HPMECs were grouped into three distinct clusters. Treated cells formed different subgroups, indicating that each treatment elicited a different cellular response. The color bar shows the fold-change range, with red indicating upregulated genes and green indicating downregulated genes. (For interpretation of the references to color in this figure legend, the reader is referred to the Web version of this article.)

Fig. 3

In this study, GO enrichment analysis of DEGs revealed 387 GO terms satisfying the corrected p-value cut-off of 0.05 (Fig. 4), grouped under biological processes (46.5 %), molecular functions (46.1 %), and cellular components (7.5 %). For biological processes, 12 subcategories were topped by biological regulation (17.3 %), response to stimuli (14.3 %), and cellular processes (13.2 %). The molecular function category included only binding (78.8 %) and molecular function regulators (21.2 %), while four subcategories were listed in the cellular component category, namely, cell (29.2 %), cell parts (29.2 %), membrane (20.8 %), and membrane parts (29.2 %).Fig. 4 Gene Ontology (GO) enrichment analysis of the differentially expressed genes (DEGs) in untreated and treated endothelial cells (p-value <0.05). The DEGs were divided into three GO categories: molecular function, cellular component, and biological processes. The percentage of genes enriched in these categories were as follows: biological processes (46.5 %), molecular function (46.1 %), and cellular component (7.5 %).

Fig. 4

DAVID enrichment analysis showed that the upregulated genes in VEGF-treated cells were enriched for the inflammatory response, regulation of the endothelial barrier, angiogenesis, nitric oxide (NO) biosynthesis, and the nucleotide-binding oligomerization domain (NOD)-like receptor signaling pathway (Table 2). The PPI network was constructed from the DEGs in VEGF-treated cells (Fig. 5A). Based on the PPI network, the hub genes were identified using Cytoscape. The top 10 hub genes were TNF, IL1B, IL6, CCL2, PTGS2, ICAM1, CXCL2, CXCL1, CSF2, and TLR2 (Fig. 5B and Table 3). All the genes in the top 10 hub genes were upregulated genes. Analysis of DEGs in VEGF/anti-VEGF treated cells with STRING and Cytoscape revealed the same hub genes as those found in VEGF-treated cells.Table 2 Enriched functions in upregulated genes in VEGF-treated cells.

Table 2Regulation of endothelial barrier (Fold enrichment: 51.6), p = 0.038	
Gene	Fold change	
TNF	180.82	
IL1B	66.47	
Regulation of nitric oxide biosynthesis (Fold enrichment: 29.39), p < 0.0001	
TNF	180.82	
IL1B	66.47	
IL6	42.33	
PTGS2	12.99	
SOD2	4.82	
ICAM1	4.49	
SMAD3	2.36	
NOD-like receptor signaling pathway (Fold enrichment: 21.55), p < 0.0001	
TNF	180.82	
IL1B	66.47	
CXCL2	57.75	
IL6	42.33	
NOD2	33.84	
BIRC3	20.46	
TNFAIP3	15.88	
CXCL1	12.21	
NFKBIA	4.21	
CCL2	2.82	
Inflammatory response (Fold enrichment: 9.53), p < 0.0001	
Gene	Fold change	
TNF	180.82	
CXCL3	175.95	
IL23A	68.99	
IL1B	66.47	
CXCL2	57.75	
SELE	44.25	
IL6	42.33	
CXCL6	20.11	
TNFAIP3	15.88	
BDKRB1	15.62	
PTGS2	12.99	
CXCL1	12.21	
BMP2	10.53	
ZC3H12A	9.01	
NFKBIZ	8.32	
TLR2	3.58	
CXCL5	3.44	
CSF1	3.19	
CCL2	2.82	
TNFRSF11B	2.38	
Regulation of angiogenesis (Fold enrichment: 7.85), p = 0.0037	
F3	330.17	
IL1B	66.47	
CX3CL1	15.31	
ZC3H12A	9.01	
ETS1	3.53	

Fig. 5 Protein-protein interaction (PPI) network of differentially expressed genes. A PPI network identified from DEGs of VEGF-treated endothelial cells using STRING. This PPI network was used to identify hub genes using Cytoscape, interaction score >0.4. Top 10 hub genes for VEGF-treated endothelial cells identified by CytoHubba, plugin in Cytoscape, using MCC algorithm. All the genes identified were upregulated genes in VEGF-treated cells. Red represents genes with high MCC score and yellow represents genes with low MCC score. (For interpretation of the references to color in this figure legend, the reader is referred to the Web version of this article.)

Fig. 5

Table 3 Top 10 hub genes identified using the MCC algorithm in the CytoHubba plugin of Cytoscape.

Table 3Rank	Gene	MCC Score	
1	TNF	2.21E+11	
2	IL1B	2.21E+11	
3	IL6	2.21E+11	
4	CCL2	2.21E+11	
5	PTGS2	2.21E+11	
6	ICAM1	2.21E+11	
7	CXCL2	2.20E+11	
8	CXCL1	2.20E+11	
9	CSF2	2.20E+11	
10	TLR2	2.08E+11	

Next, we compared the FC between the upregulated genes in VEGF- and VEGF/anti-VEGF-treated cells. Genes that showed a reduction in FC after VEGF/anti-VEGF treatment compared to VEGF treatment were further subjected to DAVID enrichment analysis. The upregulated genes in VEGF/anti-VEGF-treated cells that showed a lower FC than in VEGF-treated cells were enriched in the regulation of angiogenesis, cytokine secretion, and cytokine-mediated signaling pathways (Table 4). In both VEGF- and VEGF/anti-VEGF-treated cells, no enriched functions among the downregulated genes were observed.Table 4 Enriched functions in upregulated genes in VEGF/anti-VEGF-treated cells that showed reduced FC compared to the VEGF-treated cells.

Table 4Regulation of cytokine secretion (Fold enrichment: 138.78), p = 0.0137	
Gene	Fold change	
	VEGF-treated cells	Anti-VEGF-treated cells	
SOCS1	20.92	20.43	
TLR2	3.58	3.39	
Regulation of angiogenesis (Fold enrichment: 19.91), p = 0.009	
	VEGF-treated cells	Anti-VEGF-treated cells	
F3	330.18	256.10	
IL1B	66.47	56.56	
ETS1	3.53	3.26	
Cytokine-mediated signaling pathway (Fold enrichment: 17.48), p = 0.0115	
	VEGF-treated cells	Anti-VEGF-treated cells	
F3	330.18	256.10	
IL1B	66.47	56.56	
SOCS1	20.92	20.43	

4 Discussion

In this study, we investigated the effects of VEGF and anti-VEGF treatments on the permeability of endothelial cells and performed gene profiling of the untreated and treated cells using a genome-wide microarray. Based on the permeability assay, VEGF treatment increased the permeability across endothelial cells, whereas adding anti-VEGF reduced the degree of leakage caused by VEGF. These findings were consistent with the observations of Monaghan−Benson and Burridge [20], who showed that visible gaps between adjacent endothelial cells provide a route for the passage of macromolecules. Hence, permeability is the highest at the vascular endothelial junctions, and VEGF regulates microvascular permeability by altering the integrity of adhesion molecules [20].

The binding of anti-VEGF to VEGF prevents it from increasing endothelial permeability. This anti-permeability effect was demonstrated by Peters et al. [21], who used bevacizumab, a drug containing anti-VEGF monoclonal antibodies, on choroidal endothelial cells. Our results showed a decrease in endothelial cell permeability after anti-VEGF treatment, based on the reduced movement of FITC-dextran across endothelial cells. In addition, the increased endothelial cell permeability after VEGF treatment was supported by the immunostaining images, which revealed that more gaps had formed between the endothelial cells after VEGF treatment, compared with VEGF/anti-VEGF treatment.

Among the top 10 hub genes for the DEGs in VEGF-treated cells, TNF is identified as the most significant hub gene. TNF is linked to the regulation of endothelial barrier function and is a potent cytokine that contributes to uncontrolled proinflammatory cytokines production during severe dengue [[22], [23], [24]]. During early dengue virus infection, CD8+ T lymphocytes not only kill virus-infected cells through release of perforin and granzymes but also produce proinflammatory cytokines such as TNF-α and IFN-γ to help control the infection [25]. Due to inefficient killing of infected cells, the presence of a large number of infected cells promotes CD8+ T lymphocytes to increase cytokines production, including TNF-α [25]. Consequently, high levels of TNF-α had been detected in the sera from patients with dengue hemorrhagic fever [26]. Overexpression of TNF-α increases endothelial permeability by reducing the expression of the tight junction protein ZO-1 via Src/PI3K/Akt signaling pathway [27]. Moreover, TNF-α has been reported to promote the effects of VEGF on endothelial cells through upregulation of the VEGF receptors expression and increase VEGF production via the NF-kB pathway [8,28,29].

Pan et al. [30] demonstrated that IL-1B induces vascular leakage in human umbilical vein endothelial cells (HUVECs) after IL-1B overexpression and in interferon-alpha/beta receptor 1-deficient mice after dengue virus infection. In an earlier study by Du et al. [31], IL-1B treatment caused an increase in endothelial permeability of the HUVEC monolayer and decreased VE-cadherin level. Thus, the IL-1B treatment disrupts the endothelial integrity and further increases the permeability of HUVECs [31]. There is evidence showing that IL-1B and VEGF share similar pathways and biological functions, particularly the mitogen-activated protein kinase (MAPK) cascade, which also can be activated through NOD-like receptor signaling pathway, contributing to inflammatory and angiogenesis [32].

Several studies have reported that IL-6 levels are upregulated in severe dengue patients compared to dengue fever patients [33,34]. The production of IL-6 enhances the expression of adhesion molecules such as PECAM-1 and VCAM-1, leading to inflammation and damage to endothelial cells. Consequently, this process results in increased vascular permeability [24]. Additionally, TNF-α, IL-1B, and IL-6 are also involved in the regulation of nitric oxide (NO) biosynthesis. These cytokines induce NO secretion [35,36], which enhances vascular endothelial permeability in vivo and in vitro [37,38]. Furthermore, endothelial nitric oxide synthetase-induced NO mediates VEGF-induced angiogenesis and vascular permeability [35].

CCL2 (also known as MCP-1) is one of the chemokines that is significantly higher in dengue hemorrhagic fever (DHF) patients compare to dengue patients. In several studies, endothelial cells were exposed to recombinant CCL2, and human monocytes were exposed to dengue virus 2 [39,40]. Both studies reported increased CCL2 expression after dengue virus 2 infection. In the study by Lee et al., the authors found that both conditions (exposure to CCL2 and dengue virus) increased the vascular permeability of endothelial cells caused by the disturbance to the distribution of ZO-1 on the cell membrane [39]. Additionally, another study showed the involvement of CCL2 in VEGF-induced angiogenesis and vascular leakage. CCL2 enhances the vascular leakage through the binding of VEGF to the binding site, AP1 on its promoter region [41].

PTGS2 is not widely discussed in dengue studies, except in a study by Li et al. This study obtained the GEO dataset of gene expression from whole blood samples of dengue patients, severe dengue patients, and healthy individuals. The study found that PTGS2 was upregulated in DF and downregulated in DHF [42]. In our study, PTGS2 was found to be upregulated in both VEGF and VEGF/anti-VEGF treated cells. In the context of cancer, PTGS2 expression was found to be affecting the VEGF signaling pathway in promoting angiogenesis [43].

ICAM1 is a type of adhesion molecule, and its level was higher in DHF patients compared to dengue patients [44]. The high level of ICAM1 suggests the activation of endothelial cells by promoting adhesion of immune cells to endothelial cells, and possibly causing damage to the endothelial cells [45,46]. Miyamoto et al. showed that VEGF-induced vascular permeability was partly due to the regulation of ICAM-1 [47,48]. CXCL1 is a leukocyte chemoattractant that attracts and promotes the adhesion of leukocytes to endothelial cells. This action causes rearrangement of junctional proteins such as ICAM1 and brings further disruption to the blood vessel [49,50]. CXCL2 is known as chemotactic factor that attracts immune cells to infection site, and it was reported to be released by dengue virus infected endothelial cells [51,52].

Solorzano et al. reported that 6 out of 15 studies showed higher levels of CSF2 in severe dengue patients compared to non-severe dengue patients or healthy individuals [53]. CSF2 may involve in severe dengue by stimulate the progenitor immune cells into monocytes, which in turn promotes production of more pro-inflammatory cytokines and leads to endothelial activation [49]. Elevated TLR2 expression is linked to the progression of severe dengue. TLR2 plays an important role in the activation of the inflammatory response through the sensing of dengue virus infection. The production of proinflammatory cytokines is facilitated by TLR2 through activation of NF-kB pathway [54]. Furthermore, TLR2 increases VEGF production which further downregulates ZO-1 in human mesothelial cells [55].

In this study, we used a VEGF/anti-VEGF treatment combination on endothelial cells to mimic the in vivo conditions in patients with dengue, in whom VEGF levels are likely to increase when anti-VEGF treatment is prescribed. For the in vitro permeability study, treatment with anti-VEGF reversed the VEGF-induced permeability significantly. However, the gene expression pattern after VEGF/anti-VEGF treatment was almost identical to that after VEGF treatment. The incomplete neutralization of VEGF by anti-VEGF could be explained by the small dose of anti-VEGF used in this study, which may have been capable of only partially blocking the VEGF-induced effects. Peters et al. [21] used 1 mg/mL bevacizumab to block 100 ng/mL VEGF-induced permeability completely, whereas, in this study, only 400 pg/mL anti-VEGF antibody was used to neutralize the effects induced by 200 pg/mL VEGF, with the same incubation time. Therefore, the gene expression from the microarray study must be treated with caution because anti-VEGF effects were observed in the permeability assay.

VEGF is key factor in promoting vascular permeability. Its levels increase during the febrile and defervescence stages, peaking coinciding with plasma leakage. Intervening with anti-VEGF treatments before VEGF peaking may inhibit VEGF activity, helping maintain endothelial integrity and prevent the progression to plasma leakage [29,56]. VEGF also plays a role in the inflammatory response, possibly through interactions with cytokines such as TNF-α, IL-1B, IL6, and others, which are highly elevated after VEGF treatment seen in this study. Anti-VEGF therapy could help mitigate the inflammatory response by reduce the production of pro-inflammatory cytokines, leading to less endothelial activation and damage [29,57].

This study had certain limitations, which included the timing of VEGF and anti-VEGF addition. In this study, co-treatment with VEGF and anti-VEGF was used. The effects of VEGF may have been neutralized soon after the addition of anti-VEGF. Since we aimed to examine the effects of anti-VEGF at high levels of VEGF, co-treatment was selected in this case. Thus, further research using animal models must be conducted on anti-VEGF concentration, time of treatment and duration, and the time of sample retrieval after treatment.

5 Conclusion

Using endothelial cell monolayers, we showed that in vitro VEGF treatment increased permeability across endothelial cells, while the addition of anti-VEGF reduced the leakage induced by VEGF. Genes upregulated by VEGF treatment were found to be enriched in functions related to inflammatory responses, the regulation of the endothelial barrier, NO synthesis, angiogenesis, and the NOD-like receptor signaling pathway. The top 10 hub genes among the upregulated genes in VEGF-treated cells were identified, which include TNF, IL1B, IL6, CCL2, PTGS2, ICAM1, CXCL2, CXCL1, CSF2, and TLR2. Many of these genes encode proteins associated with severe dengue. Investigating the roles of these genes in the pathophysiology of severe dengue could provide valuable insights for clinical diagnosis and patient management. Additionally, future study might explore the identification of reliable diagnostic biomarkers for early detection of severe dengue, based on the discovery of the hub genes in this study. Further studies are needed to examine the genes dysregulation of genes following anti-VEGF treatment. The ability of anti-VEGF agents to inhibit VEGF-mediated endothelial permeability suggests that anti-VEGF therapy may help prevent the progression from dengue to severe dengue.

Data availability

The data that support the findings of this study are available upon request from the corresponding author.

Funding

This work was supported by 10.13039/501100002671 Universiti Tunku Abdul Rahman (UTAR) [grant numbers IPSR/RMC/UTARSRF/PROJECT 2014-C1/007 , IPSR/RMC/UTARRF/2018-C2/G01 ].

CRediT authorship contribution statement

Sheng Jye Lim: Writing – original draft, Validation, Methodology, Formal analysis, Data curation, Conceptualization. Seng Chiew Gan: Writing – review & editing, Supervision, Resources, Methodology, Funding acquisition, Conceptualization. Hooi Tin Ong: Writing – review & editing, Supervision, Resources. Yun Fong Ngeow: Writing – review & editing, Supervision, Resources, Methodology.

Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
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