Evaluation of molecular apoptosis signaling pathways and its correlation with EBV viral load in SLE patients using systems biology approach

Abstract

BACKGROUND:

Considerable evidence supports that SLE could be related to apoptotic cells and EBV infection.

OBJECTIVE:

The aim of this study was to identify the transcriptional signature of EBV infection in SLE patients for survey of the molecular apoptosis signaling pathways.

METHODS:

The PBMCs gene expression profiles of healthy control and SLE patients were obtained from GEO. Functional annotation and signaling pathway enrichment were carried out using DAVID, KEGG. To validate bioinformatics analysis the changes in genes expression of some of obtained genes, Real time PCR was performed on PBMCs from 28 SLE patients and 18 controls.

RESULTS:

We found that mean viral load was 6013 $\pm$ 390.1 copy/ $\mu$ g DNA from PBMCs in all patients. QRT-PCR results showed that the expression of the DUSP1 and LAMP3 genes which had most changes in the logFC among 4 candidate genes, increased significantly in comparison with control. The consistent expression of LMP2 as viral latency gene involve in apoptosis signaling pathways was detected in SLE patients with EBV viral load and some controls.

CONCLUSIONS:

The study indicated that some cellular genes may have an important role in pathogenesis of SLE through apoptosis signaling pathways. Beside, EBV infection as an environmental risk factor for SLE may affect the dysfunction of apoptosis.

Keywords

Epstein-Barr virus viral load systemic lupus erythematous apoptosis signaling pathways system biology

1. Introduction

Apoptosis is an important physiological process in development and disease. Apoptotic cells are a major source of self-antigens, but usually evade immune responses [1]. Clearing apoptotic cells inefficiently and accumulating apoptotic cell debris provoke a chronic inflammatory response, which may lead to the breakdown of self-tolerance [2].

Systemic lupus erythematosus is considered as a chronic autoimmune connective tissue disease and one of the most common forms of heterogeneous autoimmunity. Autoimmunity in systemic lupus erythematosus (SLE) is believed to be driven by autoantigens [3]. Considerable evidence supports the notion that SLE autoimmunity could be related to impaired or delayed clearance of apoptotic cells. Although the precise causes of SLE are unknown, increased apoptosis rate, defect in cleaning the products of apoptosis, and high activity of B lymphocytes have been often related to a higher incidence of SLE [4, 5]. Recognizing autoantigens by Toll like receptor (TLR), as well as nucleic acid sensors in the antigen presenting cells (APC) results in creating the constitutive activation of type I interferons and changes in immune cell signaling [6, 7]. Some believe that genetic and environmental factors determine the risk of initiation and progression to SLE. The Epstein-Barr virus (EBV) is regarded among the environmental factors which can contribute to SLE development and progression since seroconversion and viremia are reported in these patients [8, 9].

An increase of 5–40 times in EBV blood load was demonstrated in SLE patients compared with control group which seems to be independent of treatment with immunosuppressive agents [10, 11]. Apoptosis and clearance of apoptotic cells are tightly regulated processes, the dysfunction of which is considered in the etiology of SLE. Lack of control in apoptosis pathways results in clearing exogenous agent like EBV infection defectively [12, 13]. EBV latency gene products are involved in viral persistence and oncogenesis through interfering with multiple cellular processes such as apoptosis pathways [14]. Latent membrane proteins 1 (LMP1) and 2A (LMP2A) are expressed in many EBV-associated malignancies and play essential roles in maintaining latent infection, EBV-induced B-cell transformation and apoptosis signaling pathway [15, 16]. In addition, some genetic factors induce abnormality in apoptosis pathways [17, 18].

The dual-specificity phosphatase 1 (DUSP1) known as MAPK Phosphatase-1 can inhibit MAPK pathway, which plays an important role in the apoptotic pathway, which is mediated through inhibiting the MAPK pathway in the process of growth and cell death. Some studies have been conducted on the role of dual-specificity phosphatase (DUSP) family in cell signaling pathways and SLE pathogenesis [19, 20, 21]. The lysosome-associated membrane glycoprotein 3 (LAMP3), which was reported to be highly expressed in SLE patients, is thought to play role in regulating cell viability and apoptosis [22, 23]. The leukocyte tyrosine kinase (LTK) and neuropilin-1 (NRP1) genes are known for their role in immunological disorders through different immune cell subsets and regulation of various molecular pathways [24, 25].

To the best of our knowledge, few studies have focused on regulating the apoptosis signaling pathways in SLE patients with EBV infection. The present study aimed to use available high-throughput data in SLE patients in order to identify the genes involved in apoptosis signaling pathwaysand the relationship between SLE and EBV infection through their role in regulating the apoptosis signaling pathways.

2. Materials and methods

2.1 In silico study

First, the Peripheral blood mononuclear cells (PBMCs) gene expression profile of control group and SLE patients was obtained from GEO database. The dataset code GSE50772 with the GPL570 platform code performed by Affymetrix Gene Chip Human Genome U133 plus 2.0 [HG-U133_Plus_2] which contains the gene expression data of the PBMCs samples including 61 SLE patients and 20 control samples was conducted at the University of Michiganto confirm the presence of the genes involved in interferon and other genes. Then, the Deferential Expressed Genes (DEGs) were extracted using GEO2R tool. In addition, the STRING and GeneMania biological network databases were used to evaluate the interactions and communications of these genes together with other genes and pathways involved in apoptosis. Further, the interaction network of the gene list was analyzed using Cytoscape software. In the next step, the network was analyzed and clustered to select the genes which are separate clusters. Finally, primer designing tools, Primer-Blast program, and Oligoanalyzer software were implemented for selecting primers in both selected and controlled genes.

2.2 Subjects

The samples included the patients diagnosed with SLE based on American Rheumatology Association criteria and referred to the rheumatology clinic and Autoimmune Diseases Research Center of Kashan Shahid Beheshti Hospital for regular follow-up treatment. In addition, the control group was recruited from the same hospital without having any lupus. However, the SLE patients with other simultaneous rheumatologic, active infectious diseases, and other viral infections such as HBV, HCV, and HIV were excluded Considering the results of our previous study indicating that the amount of infection with EBV in SLE patients was high, 18 control and 28 systemic lupus patients who had EBV viral load (VL) were examined in the present study [10]. Since the proportion of the SLE is nine times more common in women than that of men, just the females aged 13–63 years were selected.

2.3 PBMCs preparation

PBMCs were immediately isolated using Ficoll-Paque ${}^{\text{TM}}$ – plus according to the established protocol after collecting 10 mL whole blood from each subject in the presence of heparin. PBMCs were washed twice with phosphate buffered saline solution (PBS), resuspended in RPMI 1640 (Gibco Life Technologies, USA) supplemented with 10% FCS (Gibco), and stored in liquid nitrogen for future use. Finally, the cells were counted using a hemacytometer with trypan blue (Lonza) to determine cell viability [26].

2.4 DNA extraction and EBV viral load quantitation

First, high Pure Viral Nucleic acid kit (Roche, Germany) was used for extracting DNA from PBMCs. Then, the concentration of the extracted DNA was determined by assessing absorbance at 260 nm. In addition, the purified DNA was washed with 50 $\mu$ l of elution buffer and the samples were stored at $-$ 20 ${}^{\circ}$ C. Further, EBV viral load was quantified using RealStar ${}^{\@setsize{\scriptsize}{8pt}{\viipt}{\@viipt}\textregistered}$ EBV PCR Kit 1.0 (Altona, Germany). In summary, 10 $\mu$ l of extracted DNA and 20 $\mu$ l master mix were used in a total reaction of 30 $\mu$ l. The thermocycling profile for real time PCR was 10 min at 95 ${{}^{\circ}}$ C and 15 sec at 95 ${{}^{\circ}}$ C, followed by 45 cycles with dye acquisition during 1 min at 58 ${{}^{\circ}}$ C. Four standards of known concentrations were run to generate standard curve for quantitative analysis. Additionally, an internal control was used for each sample to identify possible PCR inhibitor and confirm the reliability of the reagents in the kit. EBV DNA detection limit was defined as the values of 10 copy number. Finally, the virus copy number was calculated based on copy/ $\mu$ g.

2.5 RNA extraction and quantitative real-time PCR

The RNA from 200 $\mu$ l of PBMCs was extracted using the High Pure Viral Nucleic Acid Kit (Roche, Mannheim, Germany). Then, the extracted RNA was eluted in 50 $\mu$ l of RNase-free water, and the RNA samples were treated with RNasefree DNase I (Invitrogen, Carlsbad, CA) to eliminate contaminating genomic DNA according to the manufacturer’s instructions. In the next step, the purity and concentration of total RNA were determined using a NanoDrop ND-1000 spectrophotometer (Thermo Fisher Scientific, Wilmington, DE, USA). Further, the extracted RNAs from samples were adjusted to a final concentration of 1 $\mu$ g/ml before being stored at $-$ 80 ${}^{\circ}$ C. cDNA was synthesized from total RNA (1 $\mu$ g) using the superscript RT kit (Roche), and a single cycle in the final volume of 20 $\mu$ l as 60 min at 37 ${}^{\circ}$ C, 30 min at 48 ${}^{\circ}$ C for RT, and 5 min at 95 ${}^{\circ}$ C. Relative quantitative (RQ) RT-PCR was applied to quantify the mRNA levels of DUSP1, LAMP3, LTK, NRP1 and EBV latent membrane protein 2A (LMP2A) using SYBR ${}^{\@setsize{\scriptsize}{8pt}{\viipt}{\@viipt}\textregistered}$ GreenER ${}^{\text{TM}}$ qPCR SuperMix Universal (Invitrogen) on the ABI step one plus (Applied Biosystems, Carlsbad, CA, USA). Briefly, PCR amplification reactions were performed in 20 $\mu$ l reaction mixtures containing cDNA (tenfold diluted), 2X SYBR Green Supermix, and 10 pmol of each primers (Table 1). The reactions were incubated at 95 ${}^{\circ}$ C for 10 min, followed by 50 cycles of 95 ${}^{\circ}$ C for 15 s, 57 ${}^{\circ}$ C for 30 s, and 68 ${}^{\circ}$ C for 30 s. In addition, a melting curve analysis was performed to confirm single gene-specific peaks. The linearity and accuracy of real-time RT-PCR were evaluated using beta-actin ( $\square$ -actin), as a reference gene, and standard curve derived from amplifying serially diluted pooled cDNA. Gene expression patterns were analyzed according to the 2 ${}^{-\Delta\Delta\text{CT}}$ method [18]. Finally, the relative values of each gene were expressed as a change fold to compare mRNA levels among the subjects.

Table 1
List of primer sequences used for RT-PCR analysis

Gene	Amplicon size	Sequence (5’ $\to$ 3’)	Length
Beta-actin	137	Forward Primer TGGACTTCGAGCAAGAGATG	20
		Reverse Primer GAAGGAAGGCTGGAAGAGTG	20
DUSP1	77	Forward Primer AGTACCCCACTCTACGATCAGG	22
		Reverse Primer GAAGCGTGATACGCACTGC	19
LAMP3	104	Forward Primer AGCAAGCACCTCACCAAACTT	21
		Reverse Primer TGTAGTCGCTGGGGTAGTTGT	21
LTK	178	Forward Primer GCGCCATTCTCTGCTCTAGC	20
		Reverse Primer CCGCAGGTAGAAAACAGCCAA	21
NRP1	90	Forward Primer ACCCAAGTGAAAAATGCGAATG	22
		Reverse Primer CCTCCAAATCGAAGTGAGGGTT	22
LMP2A	69	Forward Primer AGCTGTAACTGTGGTTTCCATGAC	24
		Reverse Primer CGATGAGGAACGTGAATCTAATG	16

F: Forward primer. R: reverse primer.

2.6 Statistical analysis

The 2 ${}^{-\Delta\text{Ct}}$ method was used to compare the gene expression between SLE and control group. To analyze the data, the Ct values of the target genes were normalized to the reference gene. Then, by comparing all of the group with the control group, 2 ${}^{-\Delta\text{Ct}}$ was calculated for all group. The sensitivity and specificity of the test were examined using ROC curve analysis. In all tests, $P$ value $<$ 0.05 was considered as the significant difference. Accuracy and cut-off point selection in all the genes were determined using a generalization of the Youden index. $T$ -test was used to calculate the significant level of the observed difference between the groups. The gene expression difference was calculated using Genex 6 software and drawn the diagrams with Graph pad prism 7 software. SPSS version 22 was used for all statistical analyses (SPSS Inc., Chicago, IL, USA).

3. Results

3.1 Demographic and clinical characteristics of SLE patients with EBV viral load

Table 2 shows demographic and clinical characteristics of 28 SLE patients with EBV viral load who followed up in the rheumatology clinic of Shahid Beheshti Hospital, Kashan. All patients were women with a mean $\pm$ SD age of 36.00 $\pm$ 2.485, $n=$ 28.

Table 2
Clinical characteristics of SLE patients with EBV viral load

Characteristics of SLE patients with high EBV viral load	Percentage (%)
Age (mean $\pm$ SD, years)	36.00 $\pm$ 2.485, $n=$ 28
Age at diagnosis (mean $\pm$ SD, years)	27.64 $\pm$ 1.961, $n=$ 28
History of systemic involvement (%)
Musculoskeletal involvement	67.85
Photosensitivity	25
Malar Rash	42.85
Discoid lesions	21.42
Aphthous/ulcers	21.42
Pericarditis	17.85
Hematological involvement (%)	42.85
Anemia	35.71
Leucopoenia	14.28
Thrombocytopenia	10.71
Renal involvement (%)	28.57
Neuropsychiatric involvement (%)	14.28
Laboratory tests (%)
ANA	96.42
Anti-dsDNA	89.28
ENA (%)	57.14
Anti-Sm	7.14
C3 (below normal values)	42.85
C4 (below normal values)	35.71

Note: SD Standard Deviation, ANA Anti-Nuclear Antibody.

3.2 PBMCs count

The PBMCs count was not significantly different between SLE patients and control group, a higher PBMCs count was reported among SLE patients (914500 $\pm$ 19224, $n=$ 28 vs 897778 $\pm$ 15268, $n=$ 18 $P=$ 0.53) (Fig. 1). In addition, the amount of EBV viral load in SLE patients was significantly higher than that of control group (6013 $\pm$ 390.1, $n=$ 28 vs 12.28 $\pm$ 7.424, $n=$ 18) (Fig. 2).

Figure 1.

Comparison of the PBMCs count between SLE patients and control group.

Figure 2.

Comparison of the of EBV viral load in SLE patients and control group.

3.3 Gene selection

Based on the analysis of interaction network in the selected effective genes in SLE, DUSP1, LAMP3, LTK, and NRP1 were clustered separately. The DUSP1 and LAMP3 were overexpressed while LTK and NRP1 were under-expressed in microarray data analysis. Figure 3 displays the interaction network of effective genes in the SLE.

Figure 3.

The parts of interaction network of effective genes in the SLE. Candidate genes are shown in square boxes.

3.4 The quantity of gene expression

Based on the results, elevated expression of the DUSP1 and LAMP3 genes in PBMCs samples of SLE patients with EBV viral load was higher compared to the control group. While the mRNA levels of LTK was significantly lower in the patients than control group, no significant difference was found in the mRNA transcriptional levels between SLE patients with EBV viral load in expressing the NRP1 gene compared to the control group (Table 3).

Table 3
In-silico selected biomarker validation in SLE patients with EBV viral load

Biomarker	SLE mean $\pm$ SEM	Healthy mean $\pm$ SEM	$p$ value
DUSP1	1.119 $\pm$ 0.3440	0.07653 $\pm$ 0.04568	$P<$ 0.0001
LAMP3	0.8184 $\pm$ 0.2284	0.01004 $\pm$ 0.005829	$P<$ 0.0001
LTK	0.005968 $\pm$ 0.002557	0.01363 $\pm$ 0.008293	$P<$ 0.0001
NRP1	0.02605 $\pm$ 0.02341	0.04651 $\pm$ 0.03560	$P<$ 0.034

Note: Quantification expressed as mean change fold $\pm$ SEM, and $p$ values were calculated with Mann-Whitney test.

The sensitivity and specificity of DUSP1 (75% vs. 83.33), LAMP3 (92.86% vs. 83.33%), LTK (53.57% vs. 94.44%) and LMP2A (96.43% vs. 55.56%) were statistically significant. In contrast, the sensitivity and specificity of NRP1 (82.14% vs. 44.44) was not significant (Fig. 4A–J). Based on the results, DUSP1, LAMP3, LTK genes, and viral LMP2A gene can be considered as candidate genes as biomarkers in SLE patients with EBV viral load

Figure 4.

Determining biomarker role using dct and Roc curve analysis in candidate genes. qRT-PCR quantification of candidate genes (dct) between control and SLE patients with EBV viral load for (A) DUSP1, (C) LAMP3, (E) LTK, (G) NRP1 and (I) LMP2A are shown. Student’s $t$ -test was performed to obtain statistical significance in the data. Asterisk on the error bar corresponds to ${}^{**}P\leqslant$ 0.01, ${}^{****}P\leqslant$ 0.0001 and ns $P>$ 0.05. The sensitivity and specificity of candidate genes (B) DUSP1, (D) LAMP3, (F) LTK, (H) NRP1 and (J) LMP2A was determined by drawing the Roc curve. The highest total area under the curve (AUC) were found for LAMP3 (AUC $=$ 93 %, $P<$ 0.0001) and DUSP1 (AUC $=$ 84%, $P<$ 0001) which indicate that LAMP3 and DUSP1 have suitable ability to discriminate correctly between SLE and control samples. Graphs were plotted using GraphPad prism.

Figure 4.

Continued.

4. Discussion

Increased apoptosis, defects in apoptotic clearance, and high activity of B lymphocytes may be considered as the underlying reasons although the exact cause of SLE is unknown [27]. In addition, EBV as an environmental factor is important in the etiology of SLE. In fact, the virus produces some proteins which interfere in various phases of the apoptosis process. Therefore, the number of viral and host factors plays an important role in developing the SLE and associated malignancies [28]. Systems biology approaches depend on high-throughput techniques with data analysis platforms which can assess proteins, metabolites, genes, and network analysis of complex biologic or pathways influenced during specific autoimmune diseases. Better-coordinated multi-omics approaches and standardized translational research, along with the skills related to clinicians, engineers, biologists, and bioinformaticians are required to contribute to the discovery of validated and qualified biomarkers [29, 30]. In the present study, regarding the network and pathway analysis, four differentially-expressed genes including DUSP1 and LAMP3 as two highly-expressed genes and LTK and NRP1 as two low-expressed genes were selected for evaluating the results among Iranian SLE patients with EBV infection. The LMP2A gene was selected to study the EBV latent genes in SLE patients since the persistent expression of LMP2A may be considered as an important risk factor in EBV-associated diseases and apoptosis signaling pathways.

DUSP1is known as MAPK Phosphatase-1 and MAPK pathway inhibitor. Involvement in the apoptotic pathway is considered as one of the important roles of this gene, which is mediated through the inhibitory role of the MAPK pathway in the process of growth and cell death [20]. The present study showed an increase of 14.72-fold DUSP1 gene expression in SLE patients with EBV virus compared to the control group, which is consistent with the previous studies indicating that DUSP family can be a diagnostic biomarker in the SLE [31, 32]. Chuang et al. reported that the reduced expression of DUSP22 in human T cells is an important biomarker in diagnosing lupus erythematosus nephritis [33]. The result of another study showed that DUSP23 as another protein of this family had an increased expression in TCD4 $+$ cells of patients with systemic lupus erythematosus [34]. Therefore, DUSP family proteins and their role in the apoptosis signaling pathway should be considered in future studies to elaborate the complexities of this disease.

The LAMP3 gene is located in the lysosome, which is involved in the process of antigen presentation in dendritic cells through transferring between lysosomes, endosomes, and plasma membrane via exocytosis. Increased expression of LAMP3 in TCD4 $+$ cells of SLE patients and its effect on anti-TCR interaction were demonstrated by altering the surface expression of TCR and co-stimulatory molecules in the previous studies [35, 36]. The results of the present study indicated a 81.51-fold increase in this gene as a diagnostic biomarker in SLE patients with EBV VL. Liu et al. showed that an increased expression of LAMP3 gene can lead to an increase in cell survival and a decrease in apoptosis. Some reported that LAMP3 can be an important diagnostic biomarker in apoptosis-related diseases and their complications in the patients with autoimmune disorders and SLE [22].

Based on the result, neuropilin-1 (NRP1) is low- expressed gene in selected microarray data of SLE patients. Similarly, the laboratory results of this study confirm low-expressed NRP1 with 2.28 fold in SLE patients with EBV VL ( $P<$ 0.0001). The results of the previous studies indicated that this surface molecule plays a role in developing the nervous system, as well as in several immune processes such as maintaining immune synapses and developing regulatory T-cell. On the other hand, some studies demonstrated that NRP1 plays an important role in maintaining proper peripheral tolerance, and its reduced expression leads to uncontrolled autoimmune responses in the patients with autoimmune disease [37, 38, 39].

LTK as another gene has been emphasized in different studies. However, the tyrosine kinase ligand and its physiological role are not well-understood. Some have shown the role of polymorphisms in this protein in signaling pathways and highlighted the important role of this gene product in the process of cell proliferation and death [40, 41]. Some have shown that these polymorphisms result in increasing PI3K pathway and promoting autologous B cell proliferation in the SLE patients. The PI3K as one of the cascading signaling pathways regulates various cellular processes such as cell proliferation and survival, and plays a significant role in autoimmune allergies. On the other hand, some reported that these tyrosine kinases are involved in the phosphorylation of JAK/STAT pathway proteins, which are included in cell survival homeostasis. Therefore, increased expression of LTK with increased cell survival has been considered in many studies [24, 41, 42]. The present study showed a 1.78-fold decrease in the expression of this gene although no significant difference was observed between SLE and control group. The generalization of these genes to SLE and their role in apoptosis, as well as the recent advances in high throughput technology, is regarded as a promising point for identifying important diagnostic biomarkers and identifying the pathway for personalized medicine.

Furthermore, apoptosis is the most extensively investigated programed cell death during viral infection. Apoptosis elicited by virus infection has both negative and positive influence on viral replication. Host cells eliminate virally infected cells via apoptosis, which aborts virus infection [43, 44]. On the other hand, some viruses take advantage of inducing apoptosis as a way to release and disseminate progeny viruses. EBV-infected cells release Fas ligand in exosomal fractions and induce Fas-mediated extrinsic pathway in a number of different cell types including B, T, and epithelial cells. Both EBV latent membrane protein 1 (LMP1) and protein 2A (LMP2A) sensitize the infected B cells to Fas-mediated apoptosis through increasing Fas expression, which are susceptible to elimination by the immune system [45]. EBV is an interesting factor contributing to SLE since seroconversion and viremia are reported in the patients. In addition, the presence of LMP2A gene in this study showed that this latency protein has high expression levels in these patients. Further, apoptosis, as an important phenomenon in EBV infection and SLE, should be considered in the complexity of SLE pathogenesis.

5. Conclusion

Based on the results, the evaluation of the relationship between SLE and EBV infection through considering their role in regulating the apoptosis signaling pathway will provide new insight into SLE complex pathogenesis.

Footnotes

Acknowledgments

The authors of the study are grateful to Dr. Kaveh Sadeghi and Mrs. Mona Marzban for assisting with sample collection. This research was supported by Kashan University of Medical Sciences (Grant No: 9656).

Conflict of interest

The authors declare no conflict of interest.

References

Yakoub

A.M.

Schulz

Seiffert

and Sadek

, Autoantigen-harboring apoptotic cells hijack the coinhibitory pathway of T cell activation, Sci. Rep 8 (2018), 1–15.

Mahajan

Herrmann

and Muñoz

L.E.

, Clearance deficiency and cell death pathways: A model for the pathogenesis of SLE, Front. Immunol 7 (2016), 35.

Kado

, Systemic Lupus erythematosus for primary care, Prim Care 45 (2018), 257–270.

Liao

Reihl

A.M.

and Luo

X.M.

, Breakdown of immune tolerance in systemic lupus erythematosus by dendritic cells, J. Immunol. Res 2016 (2016), 6269157.

Nicholas

M.W.

Dooley

M.A.

Hogan

S.L.

Anolik

Looney

Sanz I

et al., A novel subset of memory B cells is enriched in autoreactivity and correlates with adverse outcomes in SLE, Clin Immunol 126 (2008), 189–201.

Deng

and Tsao

B.P.

, Updates in lupus genetics, Curr Rheumatol Rep 19 (2017), 68.

Rönnblom

Eloranta

M.L.

and Alm

G.V.

, The type I interferon system in systemic lupus erythematosus, Arthritis Rheum 54 (2006), 408–420.

Sullivan

K.E.

, Genetics of systemic lupus erythematosus: Clinical implications, Rheum Dis Clin North Am 26 (2000), 229–256.

Niller

H.H.

Wolf

and Minarovits

, Regulation and dysregulation of Epstein-Barr virus latency: Implications for the development of autoimmune diseases, Autoimmunity 41 (2008), 298–328.

10.

Piroozmand

Haddad Kashani

and Zamani

, Correlation between Epstein-Barr virus infection and disease activity of systemic lupus erythematosus: A cross-sectional study, Asian Pac J Cancer Prev 18 (2017), 523–527.

11.

Zandman-Goddard

and Shoenfeld

, Infections and SLE, Autoimmunity 38 (2005), 473–485.

12.

Ren

Tang

Mok

M.Y.

Chan

A.W.

and Lau

C.S.

, Increased apoptotic neutrophils and macrophages and impaired macrophage phagocytic clearance of apoptotic neutrophils in systemic lupus erythematosus, Arthritis Rheum 48 (2003), 2888–2897.

13.

Francis

and Perl

, Infection in systemic lupus erythematosus: Friend or foe? Int J Clin Rheumtol 5 (2010), 59–74.

14.

Teodoro

J.G.

and Branton

P.E.

, Regulation of apoptosis by viral gene products, J Virol 71 (1997), 1739–1749.

15.

Dawson

C.W.

Port

R.J.

and Young

L.S.

, The role of the EBV-encoded latent membrane proteins LMP1 and LMP2 in the pathogenesis of nasopharyngeal carcinoma (NPC), Semin Cancer Biol 22 (2012), 144–153.

16.

El-Sharkawy

Al Zaidan

and Malki

, Epstein-Barr virus-associated malignancies: Roles of viral oncoproteins in carcinogenesis, Front Oncol 8 (2018), 265.

17.

Ceccarelli

Perricone

Borgiani

Ciccacci

Rufini

Cipriano

et al., Genetic factors in systemic lupus erythematosus: Contribution to disease phenotype, J Immunol Res 2015 (2015), 745647.

18.

Kiraz

Adan

Kartal Yandim

and Baran

, Major apoptotic mechanisms and genes involved in apoptosis, Tumour Biol 37 (2016), 8471–8486.

19.

Calvisi

D.F.

Pinna

Meloni

Ladu

Pellegrino

Sini

et al., Dual-specificity phosphatase 1 ubiquitination in extracellular signal-regulated kinase-mediated control of growth in human hepatocellular carcinoma, Cancer Res 68 (2008), 4192–4200.

20.

Keyse

S.M.

, Dual-specificity MAP kinase phosphatases (MKPs) and cancer, Cancer Metastasis Rev 27 (2008), 253–261.

21.

Chen

H.F.

Chuang

H.C.

and Tan

T.H.

, Regulation of dual-specificity phosphatase (DUSP) ubiquitination and protein stability, Int J Mol Sci 20 (2019), 2668.

22.

Liu

Yue

Han

and Zhang

, LAMP3 plays an oncogenic role in osteosarcoma cells partially by inhibiting TP53, Cell Mol Biol Lett 23 (2018), 33.

23.

Jiang

D.S.

Huo

Liu

X.X.

Zhu

X.H.

et al., The potential role of lysosome-associated membrane protein 3 (LAMP3) on cardiac remodeling, Am J Transl Res 8 (2016), 37–48.

24.

Bernards

and de la Monte

S.M.

, The ltk receptor tyrosine kinase is expressed in preâ€B lymphocytes and cerebral neurons and uses a nonâ€AUG translational initiator, EMBO J 9 (1990), 2279–2287.

25.

van der Leun

A.M.

Yofe

Lubling

Gelbard-Solodkin

van Akkooi

A.C.J.

et al., Dysfunctional CD8 T Cells Form a Proliferative, Dynamically Regulated Compartment within Human Melanoma, Cell 176 (2019), 775–789.

26.

Lan

Verma

S.C.

Murakami

Bajaj

and Robertson

E.S.

, Isolation of Human Peripheral Blood Mononuclear Cells (PBMCs), Curr Protoc Microbiol 6 (2007), Appendix 4: Appendix 4C.

27.

Liphaus

B.L.

and Kiss

M.H.

, The role of apoptosis proteins and complement components in the etiopathogenesis of systemic lupus erythematosus, Clinics 65 (2010), 327–333.

28.

Draborg

A.H.

Sandhu

Larsen

Lisander Larsen

Jacobsen

and Houen

, Impaired cytokine responses to Epstein-Barr virus antigens in systemic lupus Erythematosus patients, J Immunol Res 2016 (2016), ID 6473204.

29.

Kim

H.Y.

Kim

H.R.

and Lee

S.H.

, Advances in systems biology approaches for autoimmune diseases, Immune Netw 14 (2014), 73–80.

30.

Ghosh

and Poisson

L.M.

, “Omics” data and levels of evidence for biomarker discovery, Genomics 93 (2009), 13–16.

31.

Chuang

H.C.

and Tan

T.H.

, MAP4K family kinases and DUSP family phosphatases in T-cell signaling and systemic lupus erythematosus, Cells 8 (2019), 1433.

32.

Lang

and Raffi

F.A.M.

, Dual-specificity phosphatases in immunity and infection: An update, Int J Mol Sci 20 (2019), 2710.

33.

Chuang

H.C.

Chen

Y.M.

Hung

W.T.

J.P.

Chen

D.Y.

Lan

J.L.

et al., Downregulation of the phosphatase JKAP/DUSP22 in T cells as a potential new biomarker of systemic lupus erythematosus nephritis, Oncotarget 7 (2016), 57593–57605.

34.

Balada

Felip

Ordi-Ros

and Vilardell-Tarrés

, DUSP23 is overâ€expressed and linked to the expression of DNMTs in CD4+ T cells from systemic lupus erythematosus patients, Clin Exp Immunol 187 (2017), 242–250.

35.

Kyogoku

Smiljanovic

Grün

J.R.

Biesen

Schulte-Wrede

Häupl

et al., Cell-specific type I IFN signatures in autoimmunity and viral infection: What makes the difference? PLoS One 8 (2013), e83776.

36.

Haynes

W.A.

Haddon

D.J.

Diep

V.K.

Khatri

Bongen

Yiu

et al., A Unified Molecular Signature of Systemic Lupus Erythematosus Revealed by Integrated, Multi-Cohort Transcriptomic Analysis, bioRxiv (2019), 834093.

37.

Campos-Mora

Morales

Gajardo

Catalan

and Pino-Lagos

, Neuropilin-1 in transplantation tolerance, Front. Immunol 4 (2013), 405.

38.

Torres-Salido

M.T.

Sanchis

Solé

Moliné

Vidal

et al., Urinary Neuropilin-1: A Predictive Biomarker for Renal Outcome in Lupus Nephritis, Int J Mol Sci 20 (2019), 4601.

39.

Nishide

and Kumanogoh

, The role of semaphorins in immune responses and autoimmune rheumatic diseases, Nat Rev Rheumatol 14 (2018), 19–31.

40.

Wheeler

D.L.

and Yarden

, Receptor tyrosine kinases: Structure, functions and role in human disease, 1st ed., Springer Press, New York, 2015, 439–440.

41.

Nakamura

Jiang

Tsurui

Matsuoka

Abe

et al., Gain-of-function polymorphism in mouse and human Ltk: Implications for the pathogenesis of systemic lupus erythematosus, Hum Mol Genet 13 (2004), 171–179.

42.

Shao

W.H.

and Cohen

P.L.

, The role of tyrosine kinases in systemic lupus erythematosus and their potential as therapeutic targets, Expert Rev Clin Immunol 10 (2014), 573–582.

43.

Zhou

Jiang

Liu

and Liang

, Virus infection and death receptor-mediated apoptosis, Viruses 9 (2017), 316.

44.

Roulston

Marcellus

R.C.

and Branton

P.E.

, Viruses and apoptosis, Annu Rev Microbiol 53 (1999), 577–628.

45.

Ahmed

Philip

P.S.

Attoub

and Khan

, Epstein-Barr virus-infected cells release Fas ligand in exosomal fractions and induce apoptosis in recipient cells via the extrinsic pathway, J Gen Virol 96 (2015), 3646–3659.

Evaluation of molecular apoptosis signaling pathways and its correlation with EBV viral load in SLE patients using systems biology approach

Abstract

BACKGROUND:

OBJECTIVE:

METHODS:

RESULTS:

CONCLUSIONS:

Keywords

1. Introduction

2. Materials and methods

2.1 In silico study

2.2 Subjects

2.3 PBMCs preparation

2.4 DNA extraction and EBV viral load quantitation

2.5 RNA extraction and quantitative real-time PCR

Table 1 List of primer sequences used for RT-PCR analysis

3. Results

3.1 Demographic and clinical characteristics of SLE patients with EBV viral load

Table 2 Clinical characteristics of SLE patients with EBV viral load

Table 3 In-silico selected biomarker validation in SLE patients with EBV viral load

5. Conclusion

Footnotes

Acknowledgments

Conflict of interest

References

Table 1
List of primer sequences used for RT-PCR analysis

Table 2
Clinical characteristics of SLE patients with EBV viral load

Table 3
In-silico selected biomarker validation in SLE patients with EBV viral load