Journal of Neuroimmunology 270 (2014) 61–66

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Gene expression changes in chronic inflammatory demyelinating polyneuropathy skin biopsies Stefania Puttini a,b,1, Petrica-Adrian Panaite a,1, Nicolas Mermod b, Susanne Renaud a,c, Andreas J. Steck a,d, Thierry Kuntzer a,⁎ a

Department of Clinical Neurosciences, Nerve-Muscle Unit, Lausanne University Hospital (CHUV), rue du Bugnon 46, CH-1011 Lausanne, Switzerland Institute of Biotechnology, University of Lausanne (UNIL), chemin des Alambiques, CH-1015 Lausanne, Switzerland Neurology Division, Hôpital Neuchâtelois, Maladière 45, CH-2000 Neuchâtel, Switzerland d Department of Neurology, Basel University Hospital, Spitalstrasse 21, 4031 Basel, Switzerland b c

a r t i c l e

i n f o

Article history: Received 10 January 2014 Received in revised form 15 February 2014 Accepted 2 March 2014 Keywords: Chronic inflammatory demyelinating polyneuropathy (CIDP) Gene expression analysis Skin biopsies Biomarkers

a b s t r a c t Chronic-inflammatory demyelinating polyneuropathy (CIDP) is an immune-mediated disease with no known biomarkers for diagnosing the disease or assessing its prognosis. We performed transcriptional profiling microarray analysis on skin punch biopsies from 20 CIDP patients and 17 healthy controls to identify diseaseassociated gene expression changes. We demonstrate changes in expression of genes involved in immune and chemokine regulation, growth and repair. We also found a combination of two upregulated genes that can be proposed as a novel biomarker of the disorder. © 2014 Elsevier B.V. All rights reserved.

1. Introduction Chronic inflammatory demyelinating polyradiculoneuropathy (CIDP) is a rare autoimmune peripheral neuropathy that targets the myelin sheaths of peripheral nerve (Vallat et al., 2010). CIDP presents highly clinically heterogeneous sensory and motor manifestations, leading to various degrees of disability. Diagnosis is based on combined clinical, laboratory and nerve conduction investigations (The French CIDP Study Group, 2008; Joint Task Force of the EFNS and the PNS, 2010). Gene expression analysis in a small number of CIDP patients identified a panel of differentially regulated genes in sural nerve and in forearm skin biopsies of affected patients but none emerged as a biological marker or prognostic factor (Renaud et al., 2005; Lee et al., 2010; Dalakas, 2011; Sommer and Toyka, 2011). With the goal of identifying new biomarkers for the disease and potential targets for treatment, we performed transcriptional profiling microarray analysis on skin punch biopsies and found 26 differentially expressed genes in CIDP patients, most of which are involved in signaling immunity or

⁎ Corresponding author at: Department of Clinical Neurosciences, Nerve-Muscle Unit, CHUV, Rue du Bugnon 46, 1011 Lausanne, Switzerland. Tel.: +41 21 314 12 91; fax: +41 21 314 12 56. E-mail address: [email protected] (T. Kuntzer). 1 These authors contributed equally to the manuscript.

http://dx.doi.org/10.1016/j.jneuroim.2014.03.002 0165-5728/© 2014 Elsevier B.V. All rights reserved.

inflammation, and in proliferation or repair. We also found a combination of two upregulated genes that can be proposed as a novel biomarker of the disorder. 2. Patients and methods 2.1. Patients Twenty patients (14 men, 6 women, average age 59 years, range 31 to 77) and 17 normal volunteers (9 men, 8 women, average age 45 years, range 27 to 69) were recruited prospectively for this hospital's ethics committee approved study and all gave their informed consent. CIDP was diagnosed according to the EFNS and PNS Joint Task Force guidelines (Joint Task Force of the EFNS and the PNS, 2010). For each patient an extensive neurological examination and work-up were performed, and all volunteers were examined to ascertain that they were healthy control subjects. Clinical data for this cohort has been recently published in a study comparing their respective impairment and disability (Panaite et al., 2013), according to their CIDP disease activity status or CDAS (Gorson et al., 2010); 6 patients were in remission, 11 were stable, 1 was improving and 2 had unstable active disease. Two patients did not receive any treatment before the biopsy, 6 received treatment at least 3 months before the biopsy and 12 were under treatment at the time of biopsy. The subsequent RNA extraction and microarray analysis were performed using batch processing: a

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first group with 5 patients with predominant sensory involvement, all receiving treatment at the time of biopsy, and 5 age/sex matched controls; and a second heterogeneous group of 15 patients and 12 controls. 2.2. Skin biopsies and RNA extraction Immediately after the clinical examination, each subject underwent a 3 mm skin punch biopsy, 10 cm above the external malleolus (Lauria et al., 2010). The biopsy was snap-frozen in liquid nitrogen and stored at − 80 °C. Biopsies were then cut into 10 μm sections with a cryostat (Leica CM3050 S, Leica Microsystems GmbH, Wetzlar, Germany), and total RNA isolated and purified using the miRvana extraction kit (Ambion, Life Technologies, Carlsbad, CA, USA). RNA quantification was assessed using a NanoDrop®ND-1000 spectrophotometer and the quality determined using RNA 6000 NanoChips with the Agilent 2100 Bioanalyzer (Agilent, Palo Alto, USA).

PCR Master Mix Rox (Roche, Basel, Switzerland) mixed with primers at a final concentration of 0.3 μM. For each qPCR, cycling conditions were 50 °C for 2 min, 95 °C for 10 min, 40 cycles of 95 °C for 15 s and 60 °C for 1 min, and a final dissociation stage for SYBR Green. Three technical replicates were performed per cDNA sample. Quantification cycle (or threshold cycle, CT) values were recorded with SDS software 2.3. CT scores were imported into qBasePLUS software 1.3 (Biogazelle, Zwijnaarde, Belgium). To correct for any variation in cDNA content, CT scores of the candidate genes were normalized using the 3 following reference genes: ACTB (actin, beta), RPL27 (ribosomal protein L27) and TBP (TATA box binding protein). Mean relative quantities (RQs) for each sample were calculated and Pearson's correlation test was performed for each gene to determine whether qPCR confirmed the microarrays results. Graphics and additional statistical analysis were performed using GraphPad Prism (GraphPad Software, Inc., La Jolla, CA, USA).

2.3. Microarray analysis

3. Results

Microarray experiments and analysis were performed at the Genomic Technological Facilities of the University of Lausanne. For each sample, 100 ng of total RNA was amplified using the Whole Transcript (WT) sense strand Target Labelling kit (Affymetrix Cat.no. 900223, Affymetrix, Santa Clara, CA, USA); 5.5 μg of the resulting sense cDNA was fragmented by uracil DNA glycosylase (UDG) and APE 1 (apurinic/apyrimidic endonuclease 1) and biotin-labelled with TdT (terminal deoxynucleotidyl transferase) using the GeneChip® WT Terminal labeling kit (Affymetrix, Cat.no. 900671). Affymetrix Human Gene 1.0 ST arrays (Affymetrix) were hybridized with 2.7 μg of biotinylated target, at 45 °C for 17 h washed and stained according to the protocol described in the Affymetrix GeneChip® Expression Analysis Manual (Fluidics protocol FS450_0007). The arrays were scanned using the GeneChip® Scanner 3000 7G (Affymetrix) and raw data were extracted from the scanned images and analyzed with the Affymetrix Power Tools software package. Hybridization quality was assessed using the Expression Console software (Affymetrix). Normalized expression signals were calculated from Affymetrix CEL files using Robust Multi Averaging normalization method. Differential hybridized features were identified using the Bioconductor package “Limma” that implements linear models for microarray data (Smyth, 2004). The P values were adjusted for multiple testing with Benjamini and Hochberg's method to control for false discovery rate (FDR) (Hochberg and Benjamini, 1990), and hierarchical clustering was performed according to the Ward's method (Ward, 1963).

3.1. Microarray analysis The RNA was processed in two batches, 3 months apart, according to two groups of subjects. Hierarchical clustering by Ward’s method showed a “batch effect” between the two data sets; therefore, the results obtained for the first data set were filtered using the result obtained from the second one. From the 28,536 genes analyzed on the gene chip, 1664 were significantly differentially regulated in patients compared to controls (p b 0.05) with a fold change N 1.2 or b− 1.2. 900 were upregulated and 764 downregulated. The filtering of these results with the second set of differentially regulated genes resulted in 190 differentially expressed genes, 152 upregulated and 38 downregulated (Table-S2, Supporting information). These genes were uploaded to MetaCore™ GeneGo portal (Thompson Reuters) for pathway ontological analysis (Fig. 1). From the genes found to be involved in immune and inflammatory responses, nervous system development, cell adhesion, wound response, angiogenesis and apoptotic processes, we selected 48 for qPCR validation based on their putative role in the CIDP pathogenesis.

2.4. qPCR analysis Specific qPCR primers were designed with the assistance of qPrimerDepot or the NCBI web site. Primers were obtained from Microsynth AG (Balgach, Switzerland). Final primer pairs (TableS1, Supporting information) were selected based on amplification efficiency and dissociation curves. As an internal control we evaluated a series of housekeeping genes such as ACTB, RPL27, TBP, ACTG1, RPS9, RPL5, PNP22, GAPDH, EEF1, TUBB2C, HPRT and RPL13. We chose ACTB, RPL27 and TBP that had the lowest standard deviation between all samples in microarray studies. 200 ng of RNA was reverse transcribed to yield first-strand cDNA using the SuperScript II FirstStrand Synthesis System Invitrogen (Life Technologies). The reverse transcription reactions were then diluted 1:20 in 10 mM Tris pH 8.5. qPCRs were performed in 384-well optical reaction plates, assembled with a Tecan Freedom Evo liquid handler (Tecan group Ltd., Männedorf, Switzerland) and processed on an ABI Prism 7900HT Sequence Detection System (Life Technologies). The final reaction volume of 12 μl contained 2 μl diluted cDNA and 10 μl 0SYBR Green

Fig. 1. Gene Ontology processes significantly affected in CIDP. The histograms represent the most statistically significant GO processes involved in CIDP patients, according to GeneGo MetaCore™ pathways ontological analysis (Thompson Reuters). For every process the red bars represent the number of the upregulated and the blue bars the downregulated network objects, and the logarithmic P value is shown on top of each bar.

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Table 1 List of 26 confirmed differentially expressed genes. Gene symbol

Gene description

Fold change

p value

Chemokines CXCL12 CCR2 A2M

Chemokine (C-X-C motif) ligand 12 Chemokine (C-C motif) receptor 2 Alpha-2-macroglobulin

1.30 −1.36 1.22

0.023 0.009 0.025

Immune response C2 HLA-DPA1 HLA-DPB1 HLA-DQB2 TRL4

Complement component 2 Major histocompatibility complex, class II, DP alpha 1 Major histocompatibility complex, class II, DP beta 1 Major histocompatibility complex, class II, DQ beta 2 Toll-like receptor 4

1.32 −1.35 −1.52 −1.58 1.20

0.012 0.017 0.007 0.025 0.010

Cell adhesion CEACAM VCAM

Carcinoembryonic antigen-related cell adhesion molecule 1 Vascular cell adhesion molecule 1

1.50 1.45

0.047 0.034

Transport LYVE1 GOLM1 SLC39A8 NOSTRIN CAV2 STK39 PRKD1

Extracellular link domain containing 1 Golgi membrane protein 1 Solute carrier family 39 (zinc transporter), member 8 Nitric oxide synthase trafficker Caveolin 2 Serine threonine kinase 39 Protein kinase D1

1.40

0.020

1.34 1.50 1.20 1.46 1.27

0.005 0.020 0.022 0.008 0.010

Protein synthesis, regulation CPE

Carboxypeptidase E

1.32

0.017

Cytoskeletal protein WIPF1

WAS/WASL interacting protein family, member 1

1.20

0.018

Proliferation DDR2 KDR/VEGFR PDGFD

Discoidin domain receptor tyrosine kinase 2 Kinase insert domain receptor (a type III receptor tyrosine kinase) Platelet derived growth factor D

1.33 1.35 1.33

0.002 0.003 0.015

Extracellular matrix components BGN COL6A3 TNC SPARC

Biglycan Collagen, type VI, alpha 3 Tenascin C Secreted protein, acidic, cysteine-rich (osteonectin)

1.40 1.26 1.60 1.26

0.040 0.010 0.037 0.037

List of 26 confirmed differentially expressed genes. The list contains genes that were differentially expressed in CIDP patient skin biopsies compared to healthy controls by microarrays analysis, according to their mean fold change. These changes were confirmed by qPCR on a group of 5 healthy controls and 5 patients. Fold change and p values in the table refer to microarrays results.

3.2. qPCR assessment of gene expression and microarray validation

4. Discussion

Suitable qPCR primers were identified for 41 genes out of 48 (85%) and RT-qPCR was performed on the first batch of RNA extraction in 5 CIDP patients and 5 controls. This confirmed 26 microarrays results (63%, Table 1) with 15 not confirmed. To better represent the correlation between microarray and qPCR results, we compared the fold induction in microarrays versus qPCR in each individual for the 26 genes, as shown for 4 genes in Fig. 2. Out of the 26, 22 were upregulated up to 1.6 fold compared to controls, and 4 genes were downregulated. Of particular interest are the genes that are involved in immune regulation (C2, HLA-DPA1, HLA-DPB1, HLA-DQB2, LYVE-1, TLR4), genes encoding chemokines (CXCL12, CCR2), or those that are involved in proliferation and repair (PDGF1, VEGFR or KDR, A2M, CAV2 and NOSTRIN). qPCR confirmed the microarray results of downregulation of HLA-DPA1, HLA-DPB and, HLA-DQB2 genes in 75% of CIDP patients. We evaluated if the expression of particular combinations of deregulated genes provides a signature for the disease. The best combination of genes consisted of KDR (a VEGF receptor) and DDR2 (a tyrosine kinase receptor). The expression values were significantly higher in 95% of the CIDP patients compared to controls, with an area under the ROC curve of 1 for the first batch and 0.78 for the second batch (Fig. 3). Seven genes, all upregulated, for which we could not find primers (CD46, CNTN4, CNTNAP, CRISP3, MSR1, PCDHB5, PPEF1, PRSS3, TAC1) are not discussed further.

Our microarray analysis with qPCR validation of human skin biopsies revealed several genes differentially expressed in CIDP versus control subjects, and these genes are mostly involved in immune and chemokine regulation and growth and repair. We highlight genes that are of special interest due to their possible role in the pathophysiology of CIDP and, based on our data, conclude with plausible disease mechanisms and potential biomarkers. 4.1. HLA-DPA1, HLA-DPB1, HLA-DQB2 These members of the MHC class II family are downregulated in CIDP patients. This seems surprising since upregulation of HLA class II molecules is strongly associated with some autoimmune disorders. However, viruses such as HIV, CMV or Multiple Sclerosis retrovirus (MSRV) have all been shown to downregulate expression of HLA-II antigens (Seidl et al., 1999; Pascual et al., 2001) and MSRV has recently been incriminated in the pathogenesis of MS and CIDP (Perron and Lang, 2010; Garcia-Montojo et al., 2013), with antibodies against MSRV reported in 40% of CIDP patients (Faucard et al., 2013). The ENV protein of MSRV is known to link HLA-II and V beta chain of T-cell receptors, resulting in antigen super activation (Perron et al., 2001). As a consequence, this may lead to a downregulation of HLA-II. Whether our results of a downregulation of HLA-II point to a role of MSRV in the pathogenesis of CIDP remains to be demonstrated.

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Fig. 2. Confirmation of gene expression microarray results by qPCR analysis for selected genes. Histograms represent 4 examples of gene expression fold changes from skin biopsies in 5 CIDP patients and 5 healthy controls, using either microarray (dark bars) or qPCR (light bars) analysis. Fold change values are relative to the mean gene expression in controls, in microarray and qPCR analysis respectively. The r and p values were obtained using Pearson's correlation analysis between microarray and qPCR results.

4.2. LYVE-1 and TLR4 These 2 genes are upregulated in diseased patients. LYVE-1 (lymphatic vessel endothelial hyaluronan receptor), is expressed by

lymphatic vessels and macrophages, and TLR4 (toll-like receptor 4) by monocytes, the typical inflammatory cells involved in CIDP (Sanvito et al., 2009). LYVE-1 and TLR4 bind degraded hyaluronan oligosaccharides, present in inflammatory diseases, in order to reduce

Fig. 3. Fold change of mRNA amounts in skin biopsies of healthy versus CIDP patients in batch 1 and batch 2 studies. A, B: Scatter plots of mRNA mean fold change of DDR2 and KDR gene expression in CIDP patients and healthy controls compared to the average value in the control subjects. Graphs represent mean values ± SEM; P values lower than 0.05 are considered significant. C, D: ROC curves for mean fold change of DDR2 and KDR gene expression in batch 1 and 2, respectively. For the homogenous batch 1 the use of a combination of DDR2 and KDR appears as a perfect marker, while for the heterogeneous batch 2, the use of this gene combination could be used a fair marker, with an area under the ROC curve of 0.78.

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inflammation. Moreover, TLR4 plays a fundamental role in pathogen recognition and activation of innate immunity, and is upregulated in response to HIV1 infection (Hernandez et al., 2012). The envelope protein from MSRV that induces oxidative stress also activates TLR4. 4.3. TNC Tenascin C is a glycoprotein related to the extracellular matrix and is expressed by neurons, glia and also immune cells. TNC gene expression is upregulated in our study as well as in the peripheral nerve of inherited demyelinating neuropathy animal models (Fruttiger et al., 1995; Martini et al., 1995), and the protein has been detected by immunostaining in sural nerve biopsies in patients with inherited demyelinating neuropathies (Palumbo et al., 2002). The relationship between the TNC gene and immune system components is bidirectional: TNC enables macrophage translation of pro-inflammatory cytokines upon bacterial lipopolysaccharide activation of TLR4 but may also suppress the synthesis of anti-inflammatory cytokines (Piccinini and Midwood, 2012). 4.4. A2M Alpha-2-Macroglobulin (A2M) is upregulated. It is primarily known for its ability to inhibit a broad spectrum of proteases and to regulate cytokine and growth factor activity. For instance, A2M can bind to PDGF a chemo-attractant protein for inflammatory cells, decreasing its availability locally at site of inflammation (Solchaga and Zale, 2012). A2M can also bind to VEGF, a factor that increases blood nerve barrier (BNB) permeability (Watanabe et al., 1998) thus preventing VEGF from binding to its KDR receptor. A2M also binds to myelin basic protein (MBP) released after demyelination. This may protect myelin basic protein from degradation (protease inhibitor) (Gunnarsson and Jensen, 1998). In conclusion, upregulation of A2M expression and its binding to PDGF, VEGF and MBP may act to reduce the inflammation in the peripheral nerves. 4.5. PDGFD The platelet-derived growth factor-D, whose gene is upregulated in our study, is a potent proliferation factor for oligodendrocytes and Schwann cells in vitro. It is also a chemo-attractant protein for inflammatory cells and cells involved in wound repair, thus important in normal inflammation and repair. Two molecules of PDGF bind covalently to one molecule of A2M (see above). It is suggested that A2M decreases locally released PDGF at sites of inflammation (Solchaga and Zale, 2012). 4.6. KDR (VEGFR) KDR, encoding the VEGF receptor, is upregulated. VEGF has been described as a pathogenic factor in POEMS syndrome, characterized by a CIDP-like polyneuropathy: the mechanism by which VEGF exerts its function is by increasing micro-vascular permeability of the blood– nerve barrier (Watanabe et al., 1998).

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The best-characterized endothelium-derived relaxing factor is nitric oxide (NO), which is synthesized by endothelial nitric oxide synthase (eNOS) to increase vasospasm, thrombosis, penetration of macrophages and cellular growth (Michel and Vanhoutte, 2010). NOSTRIN (for eNOStrafficking inducer) decreases eNOS activity. Moreover NOSTRIN binds to CAV1, which is also a well-established inhibitor of eNOS, and independent binding of each of the two proteins to eNOS is possible. The upregulation of NOSTRIN in our study may therefore decrease oxidative stress and inflammation in CIDP nerves. 5. Conclusions We have demonstrated changes in expression of genes involved in immune and chemokine regulation and growth and repair in skin biopsies of CIDP patients. The functional analysis of the reported changes is compatible with the following pathophysiological events in CIDP. The downregulation of HLA II genes observed may be indirect evidence of activation of dormant MSRV viral particles from a previous viral infection. MSRV viral particles are known to display proinflammatory activities through the engagement of TLR4. The immunological breakdown in CIDP may therefore result from activation of autoimmune mechanisms by TLR4. Activation of innate immunity through TLR4 can lead to neuro-inflammatory phenomena via activation of macrophages and nitrosative stress. Indirect evidence for the role of MRSV in CIDP pathogenesis has recently emerged from data showing antibodies against MSRV in a proportion of CIDP patients (Faucard et al., 2013). Finally TLR4 has also been found to inhibit remyelination. Our study also reveals the activation of repair mechanisms. The upregulation of A2M is thought to be important because it inhibits known inflammatory factors such as PDGFD and VEGF, which are upregulated in many inflammatory neuropathies. The upregulation of NOSTRIN may also contribute to decrease inflammation in CIDP nerves. Furthermore the activation of CAVEOLIN, a gene involved in cholesterol transport may be important for increasing re-myelination capacity. Gene studies may also provide useful information about potential biomarkers. Our data show that a combination of two genes, KDR and DDR2 has the best diagnostic predictive value, being upregulated in 19 out of 20 CIDP patients. LYVE-1, a gene already reported to be upregulated in a previous study (Lee et al., 2010) is also a candidate biomarker. In summary, our study shows the interplay of key pro-inflammatory factors involved in innate autoimmunity together with the activation of oxidative stress mechanisms. On the other hand, several repair and protective factors are also activated. It will be the subject of future research to determine whether these changes are related to the process of denervation/reinnervation or to a primary inflammatory process, and to analyze their sequential profile. For this, a careful analysis of CIDP patients according to the phase of disease will be necessary. In addition, the expression of the products of the potential gene markers needs to be evaluated in the target tissues, keeping in mind that a combination of genes might represent a more sensitive marker. We do not yet know how treatment may affect some of these gene changes and whether targeting some of these pathways may be feasible for therapy. Acknowledgments

4.7. CAV2 and NOSTRIN Caveolin2 (CAV2) protein is a major component of caveolae in the plasma membrane and its gene expression is responsive to changes in cellular cholesterol levels (which constitute up to 25% of myelin lipid), possibly facilitating cholesterol transport in Schwann cells (Mikol et al., 1999). Increased CAV1 expression is found during periods of active myelination in vivo (Mikol et al., 2002). Furthermore, CAV1 colocalizes with myelin basic protein in myelinating Schwann cells. The CAV2 gene is upregulated in our study and we hypothesize that it plays a role in remyelination.

Thank to all CIDP patients, relatives and other volunteers for accepting to give the skin biopsy. Parts of this study were presented in abstract/poster form at the 2013 PNS Biennial Meeting of the Peripheral Nerve Society, held in St. Malo, France, June 29–July 3, 2013 We thank Keith Harshman, Alexandra Paillusson, Mélanie Dupasquier for Microarrays Experiments, Sylvain Pradervand and Leonore Wigger for statistical analysis and Hannes Richter for help in using TECAN and BioGazelle software, members of the DNA Genomic Facility of University of Lausanne; Marie-Claire Haymoz

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Gene expression changes in chronic inflammatory demyelinating polyneuropathy skin biopsies.

Chronic-inflammatory demyelinating polyneuropathy (CIDP) is an immune-mediated disease with no known biomarkers for diagnosing the disease or assessin...
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