Journal of Immunology Research

Journal of Immunology Research / 2012 / Article
Special Issue

Viruses and Immunity in Transplant Patients

View this Special Issue

Research Article | Open Access

Volume 2012 |Article ID 972102 |

Eva Girmanova, Irena Brabcova, Jiri Klema, Petra Hribova, Mariana Wohlfartova, Jelena Skibova, Ondrej Viklicky, "Molecular Networks Involved in the Immune Control of BK Polyomavirus", Journal of Immunology Research, vol. 2012, Article ID 972102, 9 pages, 2012.

Molecular Networks Involved in the Immune Control of BK Polyomavirus

Academic Editor: Rossana Cavallo
Received28 Aug 2012
Accepted05 Nov 2012
Published05 Dec 2012


BK polyomavirus infection is the important cause of virus-related nephropathy following kidney transplantation. BK virus reactivates in 30%–80% of kidney transplant recipients resulting in BK virus-related nephropathy in 1%–10% of cases. Currently, the molecular processes associated with asymptomatic infections in transplant patients infected with BK virus remain unclear. In this study we evaluate intrarenal molecular processes during different stages of BKV infection. The gene expression profiles of 90 target genes known to be associated with immune response were evaluated in kidney graft biopsy material using TaqMan low density array. Three patient groups were examined: control patients with no evidence of BK virus reactivation ( ), infected asymptomatic patients ( ), and patients with BK virus nephropathy ( ). Analysis of biopsies from asymptomatic viruria patients resulted in the identification of 5 differentially expressed genes (CD3E, CD68, CCR2, ICAM-1, and SKI) ( ), and functional analysis showed a significantly heightened presence of costimulatory signals (e.g., CD40/CD40L; ). Gene ontology analysis revealed several biological networks associated with BKV immune control in comparison to the control group. This study demonstrated that asymptomatic BK viruria is associated with a different intrarenal regulation of several genes implicating in antiviral immune response.

1. Introduction

Innovations to immunosuppressive regimens have improved patient and kidney transplant survival rates; however, drug-induced immune suppression has also resulted in significant increases in complications associated with infections. BK polyomavirus (BKV) infections have emerged as an important cause of virus-related nephropathy following kidney transplantation in the era of modern immunosuppressive therapies [1]. BKV has been shown to reactivate in 30%–80% of kidney transplant recipients but only in 1%–10% of cases resulted in the development of BKV nephropathy (BKVN) associated with subsequent kidney graft deterioration and failure [25].

Recently, the polymerase chain reaction (PCR) has been used for routine monitoring of BKV replication in peripheral blood [5]. However, PCR screening has demonstrated that a majority of kidney transplant recipients are BKV positive in urine but not blood and never develop BKV nephropathy with graft function deterioration. Furthermore, it was shown that patients with asymptomatic viruria presented with significant BKV viral loads in kidney graft biopsy specimens [6], suggesting successful control of the BKV infection by the host immune system.

Generally, innate immunity and nonspecific effector mechanisms represent a first line of defense against infection, followed by the development of more specific, acquired immune responses. Active viral replication in tissues has been considered to be a trigger point for inflammation, and cellular immunity has been suggested to play a critical role in viral control. Recently, it was shown that patients with self-limited BKV reactivation without therapeutic intervention developed BKV-specific T cells and cleared BKV rapidly compared to patients suffering from BKV associated-nephropathy [7].

However, the process of successful BKV control on molecular level has not been fully described.

In this study, we investigated the intrarenal specific transcripts during a different outcome of BKV infection in kidney graft.

2. Materials and Methods

2.1. Patients

Based on the BKV monitoring study [8] and histopathological archive of the Institute for Clinical and Experimental Medicine, Prague, different patient groups were established as follows. (i) The asymptomatic viruria group (AV) ( ) presented with BK viral urine loads higher than 107/mL [9] on the day of protocol biopsy (3 months after transplant). These patients never developed BKV-associated nephropathy and were negative for BKV replication screening in both urine and blood 12 months after transplantation. Protocol kidney graft biopsies revealed normal findings free of rejection and patients had stable graft function. (ii) Control group (NCG) ( ) consisted of patients who were neither BKV positive in urine nor in serum at any time-point after kidney transplantation. None of the patients exhibited allograft rejection; protocol biopsy was normal and patients had stable f graft function. (iii) The BK virus nephropathy (BKVN) group consisted of patients ( ) with BKV-associated nephropathy. Eight patients presented with BKV-associated nephropathy prior to the start of the study and archived biopsy specimens for molecular biology were used for analysis. In 2 cases, BKV-associated nephropathy developed during the course of the study. Histological confirmation of BKV-associated nephropathy was defined as detection of viral cytopathic changes with intranuclear inclusion bodies, associated renal tubular epithelial cell injury, including tubular epithelial cell necrosis, and denudation of basement membranes, as well as positive immunohistochemical staining for the SV40 T large antigen. Patients’ clinical and demographic variables are to be found in Table 1.

CG ( ) BKVN ( ) AV ( ) value

At baseline
 Recipient age (years) 50 ± 1044 ± 2147 ± 10ns
 Donor age (years) 49.2 ± 1347 ± 1846 ± 15ns
 Living donor (n, %)1/9%3/30%1/22%ns
 Gender (male, %)6/54%3/30%6/66%ns
 HLA mismatches 3 ± 14 ± 12 ± 1ns
 Peak level of PRA 5 ± 744 ± 401 ± 1ns
 CMV serostatus
  D−/R− 1/9%1/10%1/11%ns
  D−/R+ 1/9%1/10%1/11%ns
  D+/R− 3/27%1/10%1/11%ns
  D+/R+ 6/54%7/70%6/66%ns
 Dialysis before TX (months)19 ± 1027 ± 1532 ± 26ns

At 3M biopsy
 Time after TX94.8 ± 5.8343 ± 26885.6 ± 13.5ns
 BMI (kg/m2)23.5 ± 4.827.3 ± 4.327.2 ± 4.7ns
  Cyclosporin A27%0%0ns
  Mycophenolate mofetil55%70%89%ns
  CIT (deceased donor) 20 ± 313 ± 919 ± 3ns
  Serum creatinine (μmol/L) 112 ± 60.3210 ± 67.2126 ± 35.3 **
  Serum creatinine at 36-month followup122.6 ± 37.3NA144.1 ± 69.5ns

Data shown as mean ± standard deviation if not indicated otherwise. ESRD: end-stage renal disease; CIT: cold ischemia time; PRA: panel reactive antibody; BMI: body mass index; TX: transplantation; **( ) BKVN versus negative control group; ns: not significant; *P values for categorical data or Fisherś test and for continuous variables Mann-Whitney U test (Student’s t-test where appropriate).

Three months after transplantation, protocol kidney graft biopsies were performed in all patients from group I and II and a part of the biopsy specimen fixed for molecular biology analysis at a later date. The study protocol was approved by the Ethics Committee of the Institute for Clinical and Experimental Medicine in Prague and a written informed consent was obtained from all patients.

2.2. RNA Isolation and TaqMan Low Density Array (TLDA)

Small portions (~2 mm) of the cortical or juxtamedullary zone from biopsy specimens were immediately stored in RNA later (Ambion Corporation, Austin, TX). Renal tissues were homogenized; total RNA were extracted using StrataPrep Total RNA Microprep Kit (Stratagene, La Jolla, CA, USA) and reverse transcribed into complementary DNA (cDNA) as described elsewhere [10].

The gene expression profile of 90 candidate gene targets known to play roles in the elicitation of immune responses (e.g., genes involved in cytokine expression, costimulatory molecules, growth factors, chemokines, immune regulation, apoptosis markers, and ischemia markers) was determined using real-time RT-PCR ( ) with GAPDH as internal control and cDNA from a control kidney serving as the calibrator in 30 renal biopsy specimen analyses. All evaluated genes are described in Table S1 in Supplementary Material available online at Each immune TLDA profile contains lyophilized gene expression reagents (primers and probes (FAM labeled)) in a preconfigured 384 well format. Two samples in duplicate were analyzed per card. Each loading port was filled with a 100 μL cDNA, nuclease free water, and 2X TaqMan universal PCR master mix. Following centrifugation, cards were sealed with a TLDA sealer (Applied Biosystems, Foster City, CA) to prevent cross-contamination. RT-PCR amplification was performed using an ABI Prism 7900 H.T. Sequence Detection system (Applied Biosystems). TLDA cards were analyzed as relative quantification (RQ) and RQ manager 1.2. software for automated data analysis was used (Applied Biosystems).

2.3. Statistical and Functional Analyses

Continuous variables were presented as the mean ± SD (standard deviation). Statistical analysis of categorical characteristics was performed using the test, of continuous parametric and nonparametric variables using student’s -test and Mann-Whitney tests. The Bonferroni correction was used when appropriate. Supervised hierarchical clustering was performed using the MeV ( ) software in order to visualize results. Gene expression data were compared using the Mann-Whitney test followed by the Bonferroni correction.

For functional analysis, large-scale data management was used to identify specific transcript patterns. In the set analysis, genes were grouped into sets determined by their annotation and then compared between defined groups. For the functional analysis, 2 types of set analyses were used. Since a limited number of genes were assessed, the fully coupled flux analysis that reflects the analysis of the pathway fragment was used as the first set analysis [11]. Fully coupled flux represents a gene network that corresponds to a pathway in which non-zero flux for one reaction implies a nonzero flux for a second reaction, and vice versa. This flux represents the strongest qualitative connectivity that can be identified in a network. The genes coupled by their enzymatic fluxes were shown to have similar expression patterns, share transcriptional regulators, and frequently reside in the same operon. The second gene set type analysis included genes sharing a common gene ontology [12]. For the use of functional analysis, gene expression data were log transformed and compared between groups with one-way analysis of variance (ANOVA) followed by the Bonferroni correction to appropriately account for multiple comparisons. Set-level analysis was carried out using XGENE.ORG [13, 14].

3. Results

3.1. Gene Expression Profiles

We first determined whether the 90 candidate target transcripts identified differed in their levels of expression between the groups. Using these criteria, cohorts of patients with asymptomatic viruria and the negative control group were established; we also examined archived biopsies from patients with histologically proven BKV-associated nephropathy.

Using hierarchical clustering, different gene transcript profiles were identified among the study groups. Hierarchical clustering demonstrated that gene transcript profiles expressed in kidney graft tissues in patients not testing BKV positive were similar to profiles observed in patients presenting with asymptomatic BKV viruria who did not present with BKV positive blood samples or BKV-associated nephropathy. However, gene transcript profiles identified in patients with BKV-associated nephropathy formed a significantly different cluster (Figure 1).

3.2. Characterization of Intrarenal Gene Transcript Profiles in Patients with Asymptomatic BK Viruria

In order to identify the nature of intrarenal immune mechanisms associated with the control of BKV infections, intrarenal graft transcripts from patients with asymptomatic viruria or patients in the negative control group were analyzed. Five differentially expressed genes were identified, including upregulation of the T cell (CD3E) and macrophage (CD68) markers, in addition to genes encoding the receptor for the monocyte chemoattractant protein-1 (CCR2; a chemokine which specifically mediates monocyte chemotaxis) and the adhesion molecule, ICAM-1. The SKI protooncogene, involved in downregulation of TGF-β1 gene transcripts, was significantly downregulated in the asymptomatic BKV infection group ( ) (Table 2).

Control group
Asymptomatic viruria group
P value*

CCR2 0 (0–6.38)
0.3 (0–31.7)
CD3E 0.53 (0.03–37.3)
117 (0.15–1120)
CD68 0.61 (0.05–1.38)
1.15 (0.72–4.96)
SKI 2.32 (0.29–13.2)
0.47 (0.07–13)
ICAM1 0.03 (0–0.08)
0.34 (0.02–2.12)

Data are shown as median (minimum–maximum) of relative quantity (RQ) of gene expression calculated with regard to the reference gene (GAPDH) and calibrator.
* P values calculated by Mann-Whitney U test followed by the Bonferroni adjustment.

Functional analysis revealed that patients with asymptomatic viruria exhibited significantly higher expression levels of the costimulatory signals CD40/CD40L ( ) compared to the negative control group. In addition, groups of genes sharing a common ontology were analyzed, revealing that several biological networks were involved in BKV immune control. These networks were involved primarily with B cell proliferation (BCL2, CD40, CD40L, IL10), T cell proliferation (CD28, CD3E, IL12A, IL4, PTPRC), transmembrane receptor tyrosine kinase pathways (CD4, CD8A, FN1), proteolysis (ACE, ECE1, GZMB, LTA, REN), protein kinase binding (CD3E, CD4, PTPRC), antiapoptotic processes (BCL2, BCL2L, CCL2, CD40L, FAS, IL10, IL1A), and leukocyte adhesion (CD40L, ICAM1) (Table 3).

GO termGenes annotatedP value*

Soluble fractionGO:0005625
ACTB, CCL3, CCR2, CD40LG, FAS, IL13, SELP 0.0296
B lymphocyte proliferation, B cell proliferationGO:0042100 BCL2, CD40, CD40LG, IL10 0.0364
Transmembrane receptor tyrosine kinase pathwayGO:0007169 CD4, CD8A, FN1 0.0394
ProteolysisGO:0006508 ACE, ECE1, GZMB, LTA, REN 0.0416
Protein kinase bindingGO:0019901 CD3E, CD4, PTPRC 0.0426
Platelet activationGO:0030168 CD40, CD40LG 0.0428
Positive regulation of T cell proliferationGO:0042102CD28, CD3E, IL12A, IL4, PTPRC 0.0444
AntiapoptosisGO:0006916 BCL2, BCL2L1, CCL2, CD40LG, FAS, IL10, IL1A  
IL1B,” “IL2," “TNF,” “TNFRSF18
Leukocyte cell-cell adhesionGO:0007159 CD40LG, ICAM1 0.0466
T cell receptor complexGO:0042101 CD3E, CD4, CD8A  0.0484

* P values by ANOVA followed by Bonferroni adjustment.
3.3. Identification of Intrarenal Gene Transcripts Associated with BKV-Associated Nephropathy

The BKVN group displayed differential regulation of 33/90 genes analyzed. The most differentially regulated genes were CCL2, CCL5, CCR2, CD4, CD68, FASL, GNLY, IL1B, IL2RA, IL8, PRF1, PTPRC, and TNF (all compared to the negative control group). These genes are primarily involved in T cell signaling, chemotaxis, activation, and cytotoxicity (Table 4).

GeneControl groupBKVN groupP value*

C3 0.06 (0.02–0.6)0.375 (0.03–5.27)0.022
CCL2 0.06 (0.01–0.12)0.22 (0.03–0.85)0.005
CCL3 0.12 (0–0.65)0.71 (0.16–5.89)0.02
CCL5 3.26 (0.41–45.4)66.9 (7.9–235)0.003
CCR2 0 (0–6.38)20.7 (0–158)0.001
CCR7 0 (0–659)35.3 (0–789)0.017
CD19 0 (0–104)109 (0–2440)0.013
CD28 1.36 (0–75.1)59.3 (0.74–448)0.012
CD4 3.32 (0.22–15.1)27 (6.63–209)0.004
CD68 0.61 (0.05–1.38)1.68 (0.97–4.79)0.001
CD86 0.67 (0.01–16.9)10 (0.88–32.3)0.012
CSF1 1.42 (0.02–4.89)5.17 (0.57–71.3)0.049
CXCL10 0.05 (0–2.9)1.8 (0.06–3.76)0.015
CXCR3 0 (0–34.3)61.2 (0–671)0.013
EDN1 0.45 (0.14–1.32)1.18 (0.39–8.74)0.025
FASL 0 (0–67.2)145.72 (25.96–409.25)0.003
GNLY 0.36 (0.12–2.1)4.9 (1.45–52.2)0.001
HLADRA 0.39 (0.15–3.27)1.55 (0.503–7.6)0.021
HLA-DRB1 0 (0–0.39)0.85 (0–85.6)0.036
ICAM1 0.03 (0–0.8)0.235 (0.06–13.1)0.03
IFNG 2.65 (0–139)82.1 (0–519)0.023
IL12B 0 (0–3.83)3.49 (0–37.6)0.03
IL1B 0.17 (0–0.95)1.38 (0.3–3.67)0.002
IL2RA 0.14 (0–3.44)3.32 (0.47–19)0.009
IL6 0.05 (0.01–3.49)0.72 (0.07–9.49)0.048
IL8 0.55 (0–6.75)14.7 (0.11–53.4)0.004
LTA 0 (0–93.9)68.8 (0–903)0.036
PRF1 8.7 (0–43.3)117 (1.4–4100)0.007
PTPRC 1.52 (0.55–24.6)47.6 (4.7–303)0.001
TBX21 0 (0–15.2)7.14 (0–383)0.036
TGFB 0.73 (0.23–6.75)3.42 (0.73–13)0.025
TNF 0.61 (0.04–3.18)8.2 (0.95–30.7)0.002
TNFRSF18 0 (0–1.77)1.7 (0–19.5)0.01

Data are shown as median (minimum–maximum) of relative quantity (RQ) of gene expression calculated with regard to the reference gene (GAPDH) and calibrator.
* P values calculated by Mann-Whitney U test followed by the Bonferroni adjustment.

Functional analysis revealed that patients with BKVN had significantly higher expression levels of the flux for the FASL/FAS ( ) and CD28, CD80, CD86 ( ), signaling molecules associated with apoptosis, and costimulation. Moreover, a broad spectrum of molecular networks were shown to have an identical ontology, that is, genes associated with the positive regulation of NF-κB transcription and intracellular signal transduction (TNF, TGFB1), chemotaxis (CCL3, CCL5, IL18, IL1B, IL8, VEGF), activation of MAPK activity (IKBKB, TGFB1, TNF), and negative regulation of viral genome replication (CD80, IL8). A detailed description of ontology-related genes associated with BKV-associated nephropathy is listed in Table 5.

GO termGenes annotatedP value*

Negative regulation of transcriptionGO:0048661 EDN1, TNF 0.003
Positive regulation of NF kappa B transcriptionGO:0016481TGFB1, TNF 0.004
Activation of MAPK activityGO:0051092 IKBKB, TGFB1, TNF 0.005
Positive regulation of phosphorylationGO:0000187 IL1B, TNF 0.007
Intracellular signal transductionGO:0001934 IL1B, TNF 0.007
Negative regulation of viral genome replicationGO:0007242 CD80, IL8 0.008
Signal transducer activityGO:0045071 CCL5, TNF 0.010
Organ morphogenesisGO:0004871 CCL2, CCL3, CCL5, HMOX1, IL12B, IL13, IL15, IL18, IL1A, IL1B, STAT3 0.011
Positive regulation of transription, DNA dependentGO:0009887 CCL2, IL7, TGFB1, TNF 0.011
Transcription activator activityGO:0045941 CD80, CD86, TNF 0.011
ExocytosisGO:0016563 CD80, CD86, IKBKB, TGFB1 0.011
Chemoattractant activityGO:0006887 CCL3, CCL5 0.013
AngiogenesisGO:0042056 CCL3, CCL5 0.013
Induction of positive chemotaxisGO:0001525 IL18, IL1B, IL8, VEGF 0.013
Regulation of cell adhesionGO:0050930 IL8, VEGF 0.015
Regulation of isotype switchingGO:0030183 IL10, IL4 0.015
Regulation of cell adhesionGO:0045191 IL10, IL4 0.015
Response to oxidative stressGO:0030155 ICAM1, IL18, IL8 0.016
Protein phosphorylationGO:0006979 CCL5, PTGS2 0.016
Positive regulation of T helper 2 cell differentiationGO:0006468 CCL2, IKBKB, TGFB1 0.017
Cellular component movementGO:0045630 CD86, IL6 0.018
Positive regulation of B cell proliferationGO:0006928 ACTB, CCL3, CCL5, CXCR3, IFNG, IL13, IL8, PTGS2, STAT3 0.019
Cellular calcium ion homeostasisGO:0030890 IL4, IL7, PTPRC 0.019
Chemokine activityGO:0006874 CCL19, CCL2, CCL3, CCL5 0.022
Response to glucocorticoid stimulusGO:0008009 CCL19, CCL2, CCL3, CCL5, CXCL10, CXCL11, IL8 0.024
Positive regulationof T cell proliferationGO:0051384 IL10, IL6, TNF 0.024
T cell differentiationGO:0042102 CD28, CD3E, IL12A, IL4, PTPRC 0.024
Defense response to virusGO:0030217 IL2, PTPRC 0.025
Induction of apoptosis by extracellular signalsGO:0051607 BCL2, PTPRC 0.026
Negative regulation of transcription from RNA polymerase II promoterGO:0008624 CD38, FAS, FASLG 0.027
Positive regulation of protein kinase activityGO:0000122 SMAD3, STAT3, TNF 0.030
Response to virusGO:0045860 CD4, PTPRC 0.033
Negative regulation of cytokine secretion involved in immune responseGO:0009615 CCL19, CCL5, IFNG, TNF 0.033
Negative regulation of interleukin-6 productionGO:0002740 IL10, TNF 0.033
Receptor biosynthetic processGO:0032715 IL10, TNF 0.033
Positive regulation of cytokine productionGO:0032800 IL10, TNF 0.033
Positive regulation of transcription from RNA polymerase II promoterGO:0050715 IL10, TNF 0.033
Positive regulation of isotype switching to IgG isotypesGO:0045944 IL4, IL6, SMAD3, TNF 0.034
Regulation of immune responseGO:0048304 IFNG, IL4, TBX21 0.035
Activation of caspase activityGO:0050776 IFNG, IL4, TBX21 0.035
Receptor bindingGO:0006919 BAX, SMAD3, TNF 0.035
Antigen processing and presentationGO:0005102C3, REN 0.038
Activation a proapoptotic gene productsGO:0019882 CD8A, IFNG 0.038
Cell cycle arrestGO:0008633 BCL2, FAS, FASLG 0.039
Protein kinase bindingGO:0007050 IFNG, IL12A, IL12B, IL8, SMAD3, TGFB1 0.042
Cell adhesionGO:0019901 CD3E, CD4, PTPRC 0.043
JAK-STAT cascadeGO:0007155 CCL2, CCL5, CD34, CD4, CXCR3, FN1, SELE, SELP 0.044
Plasma membraneGO:0007259 CCL2, CCR2, STAT3 0.046
Neutrophil chemotaxisGO:0005886 ACE, AGTR1, AGTR2, CCR2, CCR4, CCR5, CCR7, CD19, CD28, CD34, CD38, CD3E, CD4, CD40, CD40LG, CD80, CD86, CD8A, CSF1, CXCR3, FAS, FASLG, ICAM1, ICOS, IL2RA, PTPRC, SELE, SELP, TFRC, TNF, TNFRSF18 0.046
Positive regulation of interleukin-2 biosynthetic processGO:0045086IFNG, IL1B, IL8 0.046

* P values by ANOVA followed by Bonferroni adjustment.

4. Discussion

Polyoma BK virus-associated nephropathy represents the one of the most challenging infectious complications associated with kidney transplantation [15].

A better understanding of the molecular processes associated with the immune control of BKV infections may facilitate improvement of clinical management strategies [16]. Evaluation of BKV in urine samples in this study was performed in a blinded fashion; therefore no changes in clinical management were carried out based on results obtained [8] since it would have influenced the transcript expression profiles present in biopsies collected 3 months after transplantation. To the best of our knowledge, this is the first molecular study in such patient cohort. This analysis demonstrated that effective BKV control 3 months after transplantation was associated with gene expression profiles with the potential of affecting cellular immune responses, including B and T cell signaling and anti-apoptotic gene networks whereas, in late BKV-associated nephropathy, the profound gene upregulation in networks covering T cell signaling, chemoattraction, activation, and cytotoxicity along with many other inflammatory networks were detected.

Specifically, 5 genes likely to be associated with the successful control of viral replication were identified. The heightened presence of T cells (CD3E), monocyte macrophages (CD68), and their chemoattractant chemokine CCR2 (as well as presence of adhesion molecule ICAM-1) were described. Previous studies have identified these molecules to be associated with viral infections [17, 18].

It is broadly known that T cell expansion and cytokine production are needed for the generation of effective antiviral immune responses [19]. CD8+ cytotoxic T cells secreting interferon-gamma (IFN-γ) or/and tumor necrosis factor alpha (TNF-α) are important components in mediating host immunity against viral infections and have been shown to play critical roles in BKV clearance [17]. In our study, however, the expression pattern of IFN-γ and TNF-α during asymptomatic viruria was just marginal ( ) that may reflect just a limited burden of immune injury. CD8+ T lymphocyte activation is tightly regulated, especially during primary responses elicited following positive and negative costimulation following BKV infections [20, 21]. In our study, molecules associated with costimulation were consistently upregulated in kidney tissues of asymptomatic viruria patients and also in biopsies from patients with confirmed BKV-associated nephropathy. Therefore, in order to further identify additional genes associated with protection from BKV infection, data from our study was further analyzed by carrying out functional analyses. Since a low number of genes were assessed compared to the number of genes that could be analyzed following a microarray analysis, we focused our research on describing smaller interactional and functional units using a gene ontology approach and network flux that corresponds to a part of the pathway.

Flux for CD40/CD40L costimulatory signal was significantly upregulated in biopsies from patients who successfully controlled BKV infection and presented with asymptomatic viruria. It is well known that activation of CD40 on antigen presenting cells following ligation of CD40L (expressed mainly on CD4+ T lymphocytes) contributes to proinflammatory responses necessary for eradication of infections caused by certain types of pathogens [22]. Studies focused on defining cellular immune responses with the potential of controlling BKV replication determined that the majority of the BKV-specific T cells expressed CD40L (CD154) [23].

Gene targets uncovered by gene ontology analysis identified genes typically associated with the elicitation of immune responses specific to viral infections where the interplay between B and T cell function and effective cellular proliferation represents a basic protective strategy.

The activation of cellular mechanisms in response to BKV infections represents an injury-repair immune response with fibrosis development as a consequence [9]. In the current study, patients with asymptomatic viruria (who never developed BKV-associated nephropathy) presented with normal graft function at the 36-month followup. This meant that successful immune control of viral infection was likely associated with limited or transient cytokine upregulation compared to BKV-associated nephropathy where the burden of injury initiated fibrosis development.

Currently, there is limited information regarding the molecular processes associated with BKV-associated nephropathy. The upregulation of large number of genes involved in cell cycle and proliferation was shown in vitro in BKV infected primary kidney epithelial cells that suggests stimulatory nature of BKV proteins [24]. In vivo, the analysis of genes upregulated in renal allografts affected by BKV-associated nephropathy identified proinflammatory genes (CD8 and related molecules associated with graft fibrosis) similar to the profile observed during cases of acute rejection; however, expression levels were larger in magnitude [25]. In our study, intraparenchymal upregulation of 33 genes was observed in BKV-associated nephropathy, confirming previous results [25], as well as demonstrating a similar expression profile to that observed during acute rejection [2527]. Moreover, using the functional analysis approach, another 50 biological processes were described in kidneys affected by BKV-associated nephropathy. Using hierarchical clustering, gene expression in BKV-associated nephropathy formed clearly different group.

In this study, the quantitative PCR analysis was performed. Compared to microarray-based analyses, this technique was fast and quantitative, and the results were more reliable. More specific tools for the study of specific immune responses associated with BKV infections would include ELISPOT and multiparameter flow cytometry analyses [7, 17, 28, 29].

5. Conclusion

In conclusion, this study demonstrated that asymptomatic BKV viruria reflecting successful immune system control of viral infections was associated with specific gene transcripts and immune processes, specifically transcripts associated with B lymphocyte signaling and costimulation. Furthermore, the degree of associated immune responses was much higher in patients presenting with BKV-associated nephropathy.

Conflict of Interests

The author declare that there is no conflict of interests.

Ethical Approval

Multicenter Ethics Committee of the Thomayer Hospital and Institute for Clinical and Experimental Medicine, Prague, approved the study protocol.


The authors would like to thank F. Zelezny and M. Holec for their help with data analysis using the X-gene platform. In addition, the authors are indebted to the patients and nurses for their cooperation and help. This work received a Grant from the Internal Grant Agency from the Ministry of Health (NS/9714/2008 and institutional support 00023001) and MZO 00023001.

Supplementary Materials

The gene expression profile of 90 candidate gene targets known to play a role in the elicitation of immune responses (e.g., genes involved in cytokine expression, costimulatory molecules, growth factors, chemokines, immune regulation, apoptosis and ischemia markers) was determined using real-time RT-PCR.

  1. Supplementary Table


  1. V. Nickeleit, H. H. Hirsch, I. F. Binet et al., “Polyomavirus infection of renal allograft recipients: from latent infection to manifest disease,” Journal of the American Society of Nephrology, vol. 10, no. 5, pp. 1080–1089, 1999. View at: Google Scholar
  2. C. Costa, M. Bergallo, S. Astegiano et al., “Monitoring of BK virus replication in the first year following renal transplantation,” Nephrology Dialysis Transplantation, vol. 23, no. 10, pp. 3333–3336, 2008. View at: Publisher Site | Google Scholar
  3. S. Hariharan, “BK virus nephritis after renal transplantation,” Kidney International, vol. 69, no. 4, pp. 655–662, 2006. View at: Publisher Site | Google Scholar
  4. C. Alméras, V. Foulongne, V. Garrigue et al., “Does reduction in immunosuppression in viremic patients prevent BK virus nephropathy in de novo renal transplant recipients? A prospective study,” Transplantation, vol. 85, no. 8, pp. 1099–1104, 2008. View at: Publisher Site | Google Scholar
  5. H. H. Hirsch, “BK virus: opportunity makes a pathogen,” Clinical Infectious Diseases, vol. 41, no. 3, pp. 354–360, 2005. View at: Publisher Site | Google Scholar
  6. C. Costa, M. Bergallo, F. Sidoti et al., “Polyomaviruses BK- and JC-DNA quantitation in kidney allograft biopsies,” Journal of Clinical Virology, vol. 44, no. 1, pp. 20–23, 2009. View at: Publisher Site | Google Scholar
  7. T. Schachtner, K. Muller, M. Stein et al., “BK virus-specific immunity kinetics: a predictor of recovery from polyomavirus BK-associated nephropathy,” American Journal of Transplantation, vol. 11, no. 11, pp. 2443–2452, 2011. View at: Google Scholar
  8. E. Girmanova, I. Brabcova, S. Bandur, P. Hribova, J. Skibova, and O. Viklicky, “A prospective longitudinal study of BK virus infection in 120 Czech renal transplant recipients,” Journal of Medical Virology, vol. 83, no. 8, pp. 1395–1400, 2011. View at: Publisher Site | Google Scholar
  9. H. H. Hirsch, D. C. Brennan, C. B. Drachenberg et al., “Polyomavirus-associated nephropathy in renal transplantation: interdisciplinary analyses and recommendations,” Transplantation, vol. 79, no. 10, pp. 1277–1286, 2005. View at: Publisher Site | Google Scholar
  10. C. Platzer, S. Ode-Hakim, P. Reinke, W. D. Docke, R. Ewert, and H. D. Volk, “Quantitative PCR analysis of cytokine transcription patterns in peripheral mononuclear cells after anti-CD3 rejection therapy using two novel multispecific competitor fragments,” Transplantation, vol. 58, no. 2, pp. 264–268, 1994. View at: Google Scholar
  11. R. A. Notebaart, B. Teusink, R. J. Siezen, and B. Papp, “Co-regulation of metabolic genes is better explained by flux coupling than by network distance,” PLoS Computational Biology, vol. 4, no. 1, article e26, 2008. View at: Publisher Site | Google Scholar
  12. M. Ashburner, C. A. Ball, J. A. Blake et al., “Gene ontology: tool for the unification of biology,” Nature Genetics, vol. 25, no. 1, pp. 25–29, 2000. View at: Publisher Site | Google Scholar
  13. M. Holec, F. Zelezny, J. Klema, and J. Tolar, “Integrating multiple-platform expression data through gene set features,” in Proceedings of the 5th International Conference on Bioinformatics Research and Applications (ISBRA '09), 2009. View at: Google Scholar
  14. M. Holec, F. Zelezny, J. Klema, and J. Tolar, “Cross-genome knowledge-based expression data fusion,” in Proceedings of the International Conference on Bioinformatics, Computational Biology, Genomics and Chemoinformatics (BCBGC '09), 2009. View at: Google Scholar
  15. J. Rhee, N. Al-Mana, and R. Freeman, “Immunosuppression in high-risk transplantation,” Current Opinion in Organ Transplantation, vol. 14, no. 6, pp. 636–642, 2009. View at: Publisher Site | Google Scholar
  16. N. Babel, H. D. Volk, and P. Reinke, “BK polyomavirus infection and nephropathy: the virus-immune system interplay,” Nature Reviews Nephrology, vol. 7, no. 7, pp. 399–406, 2011. View at: Publisher Site | Google Scholar
  17. P. Comoli, H. H. Hirsch, and F. Ginevri, “Cellular immune responses to BK virus,” Current Opinion in Organ Transplantation, vol. 13, no. 6, pp. 569–574, 2008. View at: Publisher Site | Google Scholar
  18. J. E. Christensen and A. R. Thomsen, “Co-ordinating innate and adaptive immunity to viral infection: mobility is the key,” Acta Pathologica, Microbiologica et Immunologica Scandinavica, vol. 117, no. 5-6, pp. 338–355, 2009. View at: Publisher Site | Google Scholar
  19. K. Mueller, T. Schachtner, A. Sattler et al., “BK-VP3 as a new target of cellular immunity in BK virus infection,” Transplantation, vol. 91, no. 1, pp. 100–107, 2011. View at: Publisher Site | Google Scholar
  20. G. van Kooten and J. Banchereau, “CD40-CD40 ligand,” Journal of Leukocyte Biology, vol. 67, no. 1, pp. 2–17, 2000. View at: Google Scholar
  21. P. Pandiyan, J. K. E. Hegel, M. Krueger, D. Quandt, and M. C. Brunner-Weinzierl, “High IFN-γ production of individual CD8 T lymphocytes is controlled by CD152 (CTLA-4),” Journal of Immunology, vol. 178, no. 4, pp. 2132–2140, 2007. View at: Google Scholar
  22. M. E. Munroe, “Functional roles for T cell CD40 in infection and autoimmune disease: the role of CD40 in lymphocyte homeostasis,” Seminars in Immunology, vol. 21, no. 5, pp. 283–288, 2009. View at: Publisher Site | Google Scholar
  23. W. Zhou, M. Sharma, J. Martinez et al., “Functional characterization of BK virus-specific CD4+ T cells with cytotoxic potential in seropositive adults,” Viral Immunology, vol. 20, no. 3, pp. 379–388, 2007. View at: Publisher Site | Google Scholar
  24. J. R. Abend, J. A. Low, and M. J. Imperiale, “Global effects of BKV infection on gene expression in human primary kidney epithelial cells,” Virology, vol. 397, no. 1, pp. 73–79, 2010. View at: Publisher Site | Google Scholar
  25. R. B. Mannon, S. C. Hoffmann, R. L. Kampen et al., “Molecular evaluation of BK polyomavirus nephropathy,” American Journal of Transplantation, vol. 5, no. 12, pp. 2883–2893, 2005. View at: Publisher Site | Google Scholar
  26. S. C. Hoffmann, D. A. Hale, D. E. Kleiner et al., “Functionally significant renal allograft rejection is defined by transcriptional criteria,” American Journal of Transplantation, vol. 5, no. 3, pp. 573–581, 2005. View at: Publisher Site | Google Scholar
  27. R. B. Mannon and A. D. Kirk, “Beyond histology: novel tools to diagnose allograft dysfunction,” Clinical journal of the American Society of Nephrology, vol. 1, no. 3, pp. 358–366, 2006. View at: Publisher Site | Google Scholar
  28. T. Schachtner, K. Müller, M. Stein et al., “BK virus-specific immunity kinetics: a predictor of recovery from polyomavirus BK-associated nephropathy,” American Journal of Transplantation, vol. 11, no. 11, pp. 2443–2452, 2011. View at: Publisher Site | Google Scholar
  29. C. A. Seemayer, N. H. Seemayer, U. Dürmüller et al., “BK virus large T and VP-1 expression in infected human renal allografts,” Nephrology Dialysis Transplantation, vol. 23, no. 12, pp. 3752–3761, 2008. View at: Publisher Site | Google Scholar

Copyright © 2012 Eva Girmanova et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

More related articles

 PDF Download Citation Citation
 Download other formatsMore
 Order printed copiesOrder

Related articles

Article of the Year Award: Outstanding research contributions of 2020, as selected by our Chief Editors. Read the winning articles.