# Molecular Networks Involved in the Immune Control of BK Polyomavirus

Authors: Eva Girmanova; Irena Brabcova; Jiri Klema; Petra Hribova; Mariana Wohlfartova; Jelena Skibova; Ondrej Viklicky
Journal: Clinical and Developmental Immunology
 (2012)

---

## Abstract

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 (n=11), infected asymptomatic patients (n=9), and patients with BK virus nephropathy (n=10). Analysis of biopsies from asymptomatic viruria patients resulted in the identification of 5 differentially expressed genes (CD3E, CD68, CCR2, ICAM-1, and SKI) (P<0.05), and functional analysis showed a significantly heightened presence of costimulatory signals (e.g., CD40/CD40L; P<0.05). 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.


---

## Body

## 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 [2–5].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) (n=9) 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) (n=11) 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 (n=10) 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.Table 1
Demographics and transplant related variables.
CG (n=11)
BKVN (n=10)
AV (n=9)
P
* value
At baseline
Recipient age (years)
50 ± 10
44 ± 21
47 ± 10
ns
Donor age (years)
49.2 ± 13
47 ± 18
46 ± 15
ns
Living donor (n, %)
1/9%
3/30%
1/22%
ns
Gender (male, %)
6/54%
3/30%
6/66%
ns
HLA mismatches
3 ± 1
4 ± 1
2 ± 1
ns
Peak level of PRA
5 ± 7
44 ± 40
1 ± 1
ns
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 ± 10
27 ± 15
32 ± 26
ns
At 3M biopsy
Time after TX
94.8 ± 5.8
343 ± 268
85.6 ± 13.5
ns
BMI (kg/m2)
23.5 ± 4.8
27.3 ± 4.3
27.2 ± 4.7
ns
Immunosuppression
Induction
36%
50%
44%
ns
Tacrolimus
73%
80%
89%
ns
Cyclosporin A
27%
0%
0
ns
mTOR
18%
40%
11%
ns
Mycophenolate mofetil
55%
70%
89%
ns
Prednisone
73%
90%
89%
ns
CIT (deceased donor)
20 ± 3
13 ± 9
19 ± 3
ns
Serum creatinine (μmol/L)
112 ± 60.3
210 ± 67.2
126 ± 35.3
**
Serum creatinine at 36-month followup
122.6 ± 37.3
NA
144.1 ± 69.5
ns
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; **(P<0.01) BKVN versus negative control group; ns: not significant; *P values for categorical data χ2 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 (2-ΔΔCt) 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 doi:10.1155/2012/972102. 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 theχ2 test, of continuous parametric and nonparametric variables using student’s t-test and Mann-Whitney U tests. The Bonferroni correction was used when appropriate. Supervised hierarchical clustering was performed using the MeV (V.3) software in order to visualize results. Gene expression data were compared using the Mann-Whitney U 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].


## 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) (n=9) 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) (n=11) 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 (n=10) 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.Table 1
Demographics and transplant related variables.
CG (n=11)
BKVN (n=10)
AV (n=9)
P
* value
At baseline
Recipient age (years)
50 ± 10
44 ± 21
47 ± 10
ns
Donor age (years)
49.2 ± 13
47 ± 18
46 ± 15
ns
Living donor (n, %)
1/9%
3/30%
1/22%
ns
Gender (male, %)
6/54%
3/30%
6/66%
ns
HLA mismatches
3 ± 1
4 ± 1
2 ± 1
ns
Peak level of PRA
5 ± 7
44 ± 40
1 ± 1
ns
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 ± 10
27 ± 15
32 ± 26
ns
At 3M biopsy
Time after TX
94.8 ± 5.8
343 ± 268
85.6 ± 13.5
ns
BMI (kg/m2)
23.5 ± 4.8
27.3 ± 4.3
27.2 ± 4.7
ns
Immunosuppression
Induction
36%
50%
44%
ns
Tacrolimus
73%
80%
89%
ns
Cyclosporin A
27%
0%
0
ns
mTOR
18%
40%
11%
ns
Mycophenolate mofetil
55%
70%
89%
ns
Prednisone
73%
90%
89%
ns
CIT (deceased donor)
20 ± 3
13 ± 9
19 ± 3
ns
Serum creatinine (μmol/L)
112 ± 60.3
210 ± 67.2
126 ± 35.3
**
Serum creatinine at 36-month followup
122.6 ± 37.3
NA
144.1 ± 69.5
ns
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; **(P<0.01) BKVN versus negative control group; ns: not significant; *P values for categorical data χ2 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 (2-ΔΔCt) 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 doi:10.1155/2012/972102. 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 theχ2 test, of continuous parametric and nonparametric variables using student’s t-test and Mann-Whitney U tests. The Bonferroni correction was used when appropriate. Supervised hierarchical clustering was performed using the MeV (V.3) software in order to visualize results. Gene expression data were compared using the Mann-Whitney U 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 (Figure1).Figure 1
Hierarchical clustering of 3 studied groups. This heat map represents all 90 immune targets assayed (plus endogenous control). Medians of logarithmed relative quantification (RQ) values for each group were used to create the heat map. Red denotes genes with relative increased expression while green denotes genes with relative decreased expression.


### 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 (P<0.05) (Table 2).Table 2
Genes with different regulation in asymptomatic viruria group.
Gene
Control group
Asymptomatic viruria group
P value*
CCR2
0 (0–6.38)
0.3 (0–31.7)
0.02
CD3E
0.53 (0.03–37.3)
117 (0.15–1120)
0.04
CD68
0.61 (0.05–1.38)
1.15 (0.72–4.96)
0.04
SKI
2.32 (0.29–13.2)
0.47 (0.07–13)
0.04
ICAM1
0.03 (0–0.08)
0.34 (0.02–2.12)
0.05
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 (P<0.05) 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).Table 3
Biological networks within graft tissue associated with asymptomatic BK viruria.
GO term
Genes annotated
P value*
Soluble fraction
GO:0005625GO:0005625
ACTB, CCL3, CCR2, CD40LG, FAS, IL13, SELP
0.0296
B lymphocyte proliferation, B cell proliferation
GO:0042100
BCL2, CD40, CD40LG, IL10
0.0364
Transmembrane receptor tyrosine kinase pathway
GO:0007169
CD4, CD8A, FN1
0.0394
Proteolysis
GO:0006508
ACE, ECE1, GZMB, LTA, REN
0.0416
Protein kinase binding
GO:0019901
CD3E, CD4, PTPRC
0.0426
Platelet activation
GO:0030168
CD40, CD40LG
0.0428
Positive regulation of T cell proliferation
GO:0042102
CD28, CD3E, IL12A, IL4, PTPRC
0.0444
Antiapoptosis
GO:0006916
BCL2, BCL2L1, CCL2, CD40LG, FAS, IL10, IL1A  “IL1B,” “IL2," “TNF,” “TNFRSF18”
0.0466
Leukocyte cell-cell adhesion
GO:0007159
CD40LG, ICAM1
0.0466
T cell receptor complex
GO: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 wereCCL2, CCL5, CCR2, CD4, CD68, FASL, GNLY, IL1B, IL2RA, IL8, PRF1, PTPRC, and TNF (all P<0.01 compared to the negative control group). These genes are primarily involved in T cell signaling, chemotaxis, activation, and cytotoxicity (Table 4).Table 4
Genes with different regulation in polyoma BK nephropathy.
Gene
Control group
BKVN group
P 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 theFASL/FAS (P<0.05) and CD28, CD80, CD86 (P<0.05), 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.Table 5
Biological networks in kidney graft tissues that are associated with BKVN.
GO term
Genes annotated
P value*
Negative regulation of transcription
GO:0048661
EDN1, TNF
0.003
Positive regulation of NF kappa B transcription
GO:0016481
TGFB1, TNF
0.004
Activation of MAPK activity
GO:0051092
IKBKB, TGFB1, TNF
0.005
Positive regulation of phosphorylation
GO:0000187
IL1B, TNF
0.007
Intracellular signal transduction
GO:0001934
IL1B, TNF
0.007
Negative regulation of viral genome replication
GO:0007242
CD80, IL8
0.008
Signal transducer activity
GO:0045071
CCL5, TNF
0.010
Organ morphogenesis
GO:0004871
CCL2, CCL3, CCL5, HMOX1, IL12B, IL13, IL15, IL18, IL1A, IL1B, STAT3
0.011
Positive regulation of transription, DNA dependent
GO:0009887
CCL2, IL7, TGFB1, TNF
0.011
Transcription activator activity
GO:0045941
CD80, CD86, TNF
0.011
Exocytosis
GO:0016563
CD80, CD86, IKBKB, TGFB1
0.011
Chemoattractant activity
GO:0006887
CCL3, CCL5
0.013
Angiogenesis
GO:0042056
CCL3, CCL5
0.013
Induction of positive  chemotaxis
GO:0001525
IL18, IL1B, IL8, VEGF
0.013
Regulation of cell adhesion
GO:0050930
IL8, VEGF
0.015
Regulation of isotype switching
GO:0030183
IL10, IL4
0.015
Regulation of cell adhesion
GO:0045191
IL10, IL4
0.015
Response to oxidative stress
GO:0030155
ICAM1, IL18, IL8
0.016
Protein phosphorylation
GO:0006979
CCL5, PTGS2
0.016
Positive regulation of T helper 2 cell differentiation
GO:0006468
CCL2, IKBKB, TGFB1
0.017
Cellular component movement
GO:0045630
CD86, IL6
0.018
Positive regulation of B cell proliferation
GO:0006928
ACTB, CCL3, CCL5, CXCR3, IFNG, IL13, IL8, PTGS2, STAT3
0.019
Cellular calcium ion homeostasis
GO:0030890
IL4, IL7, PTPRC
0.019
Chemokine activity
GO:0006874
CCL19, CCL2, CCL3, CCL5
0.022
Response to glucocorticoid stimulus
GO:0008009
CCL19, CCL2, CCL3, CCL5, CXCL10, CXCL11, IL8
0.024
Positive regulationof T cell proliferation
GO:0051384
IL10, IL6, TNF
0.024
T cell differentiation
GO:0042102
CD28, CD3E, IL12A, IL4, PTPRC
0.024
Defense response to virus
GO:0030217
IL2, PTPRC
0.025
Induction of apoptosis by extracellular signals
GO:0051607
BCL2, PTPRC
0.026
Negative regulation of transcription from RNA polymerase II promoter
GO:0008624
CD38, FAS, FASLG
0.027
Positive regulation of protein kinase activity
GO:0000122
SMAD3, STAT3, TNF
0.030
Response to virus
GO:0045860
CD4, PTPRC
0.033
Negative regulation of cytokine secretion involved in immune  response
GO:0009615
CCL19, CCL5, IFNG, TNF
0.033
Negative regulation of interleukin-6 production
GO:0002740
IL10, TNF
0.033
Receptor biosynthetic process
GO:0032715
IL10, TNF
0.033
Positive regulation of cytokine production
GO:0032800
IL10, TNF
0.033
Positive regulation of transcription from RNA polymerase II promoter
GO:0050715
IL10, TNF
0.033
Positive regulation of isotype switching to IgG isotypes
GO:0045944
IL4, IL6, SMAD3, TNF
0.034
Regulation of immune response
GO:0048304
IFNG, IL4, TBX21
0.035
Activation of caspase activity
GO:0050776
IFNG, IL4, TBX21
0.035
Receptor binding
GO:0006919
BAX, SMAD3, TNF
0.035
Antigen processing and presentation
GO:0005102
C3, REN
0.038
Activation a proapoptotic gene products
GO:0019882
CD8A, IFNG
0.038
Cell cycle arrest
GO:0008633
BCL2, FAS, FASLG
0.039
Protein kinase binding
GO:0007050
IFNG, IL12A, IL12B, IL8, SMAD3, TGFB1
0.042
Cell adhesion
GO:0019901
CD3E, CD4, PTPRC
0.043
JAK-STAT cascade
GO:0007155
CCL2, CCL5, CD34, CD4, CXCR3, FN1, SELE, SELP
0.044
Plasma membrane
GO:0007259
CCL2, CCR2, STAT3
0.046
Neutrophil chemotaxis
GO: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 process
GO:0045086
IFNG, IL1B, IL8
0.046
*P values by ANOVA followed by Bonferroni adjustment.


## 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 (Figure1).Figure 1
Hierarchical clustering of 3 studied groups. This heat map represents all 90 immune targets assayed (plus endogenous control). Medians of logarithmed relative quantification (RQ) values for each group were used to create the heat map. Red denotes genes with relative increased expression while green denotes genes with relative decreased expression.


## 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 (P<0.05) (Table 2).Table 2
Genes with different regulation in asymptomatic viruria group.
Gene
Control group
Asymptomatic viruria group
P value*
CCR2
0 (0–6.38)
0.3 (0–31.7)
0.02
CD3E
0.53 (0.03–37.3)
117 (0.15–1120)
0.04
CD68
0.61 (0.05–1.38)
1.15 (0.72–4.96)
0.04
SKI
2.32 (0.29–13.2)
0.47 (0.07–13)
0.04
ICAM1
0.03 (0–0.08)
0.34 (0.02–2.12)
0.05
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 (P<0.05) 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).Table 3
Biological networks within graft tissue associated with asymptomatic BK viruria.
GO term
Genes annotated
P value*
Soluble fraction
GO:0005625GO:0005625
ACTB, CCL3, CCR2, CD40LG, FAS, IL13, SELP
0.0296
B lymphocyte proliferation, B cell proliferation
GO:0042100
BCL2, CD40, CD40LG, IL10
0.0364
Transmembrane receptor tyrosine kinase pathway
GO:0007169
CD4, CD8A, FN1
0.0394
Proteolysis
GO:0006508
ACE, ECE1, GZMB, LTA, REN
0.0416
Protein kinase binding
GO:0019901
CD3E, CD4, PTPRC
0.0426
Platelet activation
GO:0030168
CD40, CD40LG
0.0428
Positive regulation of T cell proliferation
GO:0042102
CD28, CD3E, IL12A, IL4, PTPRC
0.0444
Antiapoptosis
GO:0006916
BCL2, BCL2L1, CCL2, CD40LG, FAS, IL10, IL1A  “IL1B,” “IL2," “TNF,” “TNFRSF18”
0.0466
Leukocyte cell-cell adhesion
GO:0007159
CD40LG, ICAM1
0.0466
T cell receptor complex
GO: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 wereCCL2, CCL5, CCR2, CD4, CD68, FASL, GNLY, IL1B, IL2RA, IL8, PRF1, PTPRC, and TNF (all P<0.01 compared to the negative control group). These genes are primarily involved in T cell signaling, chemotaxis, activation, and cytotoxicity (Table 4).Table 4
Genes with different regulation in polyoma BK nephropathy.
Gene
Control group
BKVN group
P 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 theFASL/FAS (P<0.05) and CD28, CD80, CD86 (P<0.05), 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.Table 5
Biological networks in kidney graft tissues that are associated with BKVN.
GO term
Genes annotated
P value*
Negative regulation of transcription
GO:0048661
EDN1, TNF
0.003
Positive regulation of NF kappa B transcription
GO:0016481
TGFB1, TNF
0.004
Activation of MAPK activity
GO:0051092
IKBKB, TGFB1, TNF
0.005
Positive regulation of phosphorylation
GO:0000187
IL1B, TNF
0.007
Intracellular signal transduction
GO:0001934
IL1B, TNF
0.007
Negative regulation of viral genome replication
GO:0007242
CD80, IL8
0.008
Signal transducer activity
GO:0045071
CCL5, TNF
0.010
Organ morphogenesis
GO:0004871
CCL2, CCL3, CCL5, HMOX1, IL12B, IL13, IL15, IL18, IL1A, IL1B, STAT3
0.011
Positive regulation of transription, DNA dependent
GO:0009887
CCL2, IL7, TGFB1, TNF
0.011
Transcription activator activity
GO:0045941
CD80, CD86, TNF
0.011
Exocytosis
GO:0016563
CD80, CD86, IKBKB, TGFB1
0.011
Chemoattractant activity
GO:0006887
CCL3, CCL5
0.013
Angiogenesis
GO:0042056
CCL3, CCL5
0.013
Induction of positive  chemotaxis
GO:0001525
IL18, IL1B, IL8, VEGF
0.013
Regulation of cell adhesion
GO:0050930
IL8, VEGF
0.015
Regulation of isotype switching
GO:0030183
IL10, IL4
0.015
Regulation of cell adhesion
GO:0045191
IL10, IL4
0.015
Response to oxidative stress
GO:0030155
ICAM1, IL18, IL8
0.016
Protein phosphorylation
GO:0006979
CCL5, PTGS2
0.016
Positive regulation of T helper 2 cell differentiation
GO:0006468
CCL2, IKBKB, TGFB1
0.017
Cellular component movement
GO:0045630
CD86, IL6
0.018
Positive regulation of B cell proliferation
GO:0006928
ACTB, CCL3, CCL5, CXCR3, IFNG, IL13, IL8, PTGS2, STAT3
0.019
Cellular calcium ion homeostasis
GO:0030890
IL4, IL7, PTPRC
0.019
Chemokine activity
GO:0006874
CCL19, CCL2, CCL3, CCL5
0.022
Response to glucocorticoid stimulus
GO:0008009
CCL19, CCL2, CCL3, CCL5, CXCL10, CXCL11, IL8
0.024
Positive regulationof T cell proliferation
GO:0051384
IL10, IL6, TNF
0.024
T cell differentiation
GO:0042102
CD28, CD3E, IL12A, IL4, PTPRC
0.024
Defense response to virus
GO:0030217
IL2, PTPRC
0.025
Induction of apoptosis by extracellular signals
GO:0051607
BCL2, PTPRC
0.026
Negative regulation of transcription from RNA polymerase II promoter
GO:0008624
CD38, FAS, FASLG
0.027
Positive regulation of protein kinase activity
GO:0000122
SMAD3, STAT3, TNF
0.030
Response to virus
GO:0045860
CD4, PTPRC
0.033
Negative regulation of cytokine secretion involved in immune  response
GO:0009615
CCL19, CCL5, IFNG, TNF
0.033
Negative regulation of interleukin-6 production
GO:0002740
IL10, TNF
0.033
Receptor biosynthetic process
GO:0032715
IL10, TNF
0.033
Positive regulation of cytokine production
GO:0032800
IL10, TNF
0.033
Positive regulation of transcription from RNA polymerase II promoter
GO:0050715
IL10, TNF
0.033
Positive regulation of isotype switching to IgG isotypes
GO:0045944
IL4, IL6, SMAD3, TNF
0.034
Regulation of immune response
GO:0048304
IFNG, IL4, TBX21
0.035
Activation of caspase activity
GO:0050776
IFNG, IL4, TBX21
0.035
Receptor binding
GO:0006919
BAX, SMAD3, TNF
0.035
Antigen processing and presentation
GO:0005102
C3, REN
0.038
Activation a proapoptotic gene products
GO:0019882
CD8A, IFNG
0.038
Cell cycle arrest
GO:0008633
BCL2, FAS, FASLG
0.039
Protein kinase binding
GO:0007050
IFNG, IL12A, IL12B, IL8, SMAD3, TGFB1
0.042
Cell adhesion
GO:0019901
CD3E, CD4, PTPRC
0.043
JAK-STAT cascade
GO:0007155
CCL2, CCL5, CD34, CD4, CXCR3, FN1, SELE, SELP
0.044
Plasma membrane
GO:0007259
CCL2, CCR2, STAT3
0.046
Neutrophil chemotaxis
GO: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 process
GO:0045086
IFNG, 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 (P<0.1) 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 shownin 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 [25–27]. 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.



---

*Source: 972102-2012-12-05.xml*