Identification of Gene Networks and Pathways Associated with Guillain-Barré Syndrome

PLOS ONE, Jan 2012

Background The underlying change of gene network expression of Guillain-Barré syndrome (GBS) remains elusive. We sought to identify GBS-associated gene networks and signaling pathways by analyzing the transcriptional profile of leukocytes in the patients with GBS. Methods and Findings Quantitative global gene expression microarray analysis of peripheral blood leukocytes was performed on 7 patients with GBS and 7 healthy controls. Gene expression profiles were compared between patients and controls after standardization. The set of genes that significantly correlated with GBS was further analyzed by Ingenuity Pathways Analyses. 256 genes and 18 gene networks were significantly associated with GBS (fold change ≥2, P<0.05). FOS, PTGS2, HMGB2 and MMP9 are the top four of 246 significantly up-regulated genes. The most significant disease and altered biological function genes associated with GBS were those involved in inflammatory response, infectious disease, and respiratory disease. Cell death, cellular development and cellular movement were the top significant molecular and cellular functions involved in GBS. Hematological system development and function, immune cell trafficking and organismal survival were the most significant GBS-associated function in physiological development and system category. Several hub genes, such as MMP9, PTGS2 and CREB1 were identified in the associated gene networks. Canonical pathway analysis showed that GnRH, corticotrophin-releasing hormone and ERK/MAPK signaling were the most significant pathways in the up-regulated gene set in GBS. Conclusions This study reveals the gene networks and canonical pathways associated with GBS. These data provide not only networks between the genes for understanding the pathogenic properties of GBS but also map significant pathways for the future development of novel therapeutic strategies.

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Identification of Gene Networks and Pathways Associated with Guillain-Barré Syndrome

et al. (2012) Identification of Gene Networks and Pathways Associated with Guillain-Barre Syndrome. PLoS ONE 7(1): e29506. doi:10.1371/journal.pone.0029506 Identification of Gene Networks and Pathways Associated with Guillain-Barre Syndrome Kuo-Hsuan Chang 0 Tzi-Jung Chuang 0 Rong-Kuo Lyu 0 Long-Sun Ro 0 Yih-Ru Wu 0 Hong-Shiu Chang 0 Chin- Chang Huang 0 Hung-Chou Kuo 0 Wen-Chuin Hsu 0 Chun-Che Chu 0 Chiung-Mei Chen 0 Joseph Najbauer, City of Hope National Medical Center and Beckman Research Institute, United States of America 0 Department of Neurology, Chang Gung Memorial Hospital Linkou Medical Center and College of Medicine, Chang Gung University , Taiwan , Republic of China Background: The underlying change of gene network expression of Guillain-Barre syndrome (GBS) remains elusive. We sought to identify GBS-associated gene networks and signaling pathways by analyzing the transcriptional profile of leukocytes in the patients with GBS. Methods and Findings: Quantitative global gene expression microarray analysis of peripheral blood leukocytes was performed on 7 patients with GBS and 7 healthy controls. Gene expression profiles were compared between patients and controls after standardization. The set of genes that significantly correlated with GBS was further analyzed by Ingenuity Pathways Analyses. 256 genes and 18 gene networks were significantly associated with GBS (fold change $2, P,0.05). FOS, PTGS2, HMGB2 and MMP9 are the top four of 246 significantly up-regulated genes. The most significant disease and altered biological function genes associated with GBS were those involved in inflammatory response, infectious disease, and respiratory disease. Cell death, cellular development and cellular movement were the top significant molecular and cellular functions involved in GBS. Hematological system development and function, immune cell trafficking and organismal survival were the most significant GBS-associated function in physiological development and system category. Several hub genes, such as MMP9, PTGS2 and CREB1 were identified in the associated gene networks. Canonical pathway analysis showed that GnRH, corticotrophin-releasing hormone and ERK/MAPK signaling were the most significant pathways in the up-regulated gene set in GBS. Conclusions: This study reveals the gene networks and canonical pathways associated with GBS. These data provide not only networks between the genes for understanding the pathogenic properties of GBS but also map significant pathways for the future development of novel therapeutic strategies. - Funding: This study was sponsored by Chang Gung Memorial Hospital, Taipei, Taiwan (CMRPG 37056 and CMRPG 38138), and National Science Council, Executive Yuan, Taiwan (NSC 99-2628-B-182A-063-MY3). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Competing Interests: The authors have declared that no competing interests exist. Guillain-Barre syndrome (GBS) is an inflammatory demyelinating disease of the peripheral nervous system that is characterized by acute areflexic paralysis [1]. As the major cause of acute neuromuscular paralysis around the world, the annual incidence of GBS is 0.62 to 2.66 cases per population of 100,000 [2]. GBS is thought to be an autoimmune disease triggered by antecedent infection [1,3,4,5,6,7]. Currently the underlying mechanisms of this immune-mediated invasion of nerves remain elusive. A number of infectious agents, such as Campylobacter jejuni and Mycoplasma, are proposed to induce T cell-mediated immune process against myelin sheath proteins or gangliosides [7,8,9,10,11,12,13]. The activated T cells could induce the production of autoantibodies or recruit macrophages on the surface of myelin sheath or the node of Ranvier [14,15,16,17]. The mediators released by activated macrophages may cause destruction of myelin sheath or axons [18,19]. Although a number of studies have shown the crucial role of inflammatory infiltration in such demyelination or axonal degeneration [15,16,20,21,22], the alteration of cellular entity in these inflammatory cells has not been completely revealed. So far a long list of GBS-associated biomarkers, including myelin basic protein [23], neurofilaments [24], anti-ganglioside antibodies [25], neuron-specific enolase [26], S100B [26], hypocretin-1 [27], cystatin C [28], transthyretin [29], haptoglobin [30,31], carbonylation of albumin [32], and different cytokines and complement factors [33,34,35], has been disclosed. These studies, carried out on body fluid analysis, did not provide critical information on the molecular modifications in the inflammatory cells. Moreover, these studies did not reveal information about the changes of systemic signaling networks associated with GBS. In this study, we address both these questions by analyzing the global quantitative gene expression profile in peripheral blood leukocytes. This examination provides the opportunity for understanding the evolution of cell responses and sheds light on screening novel therapeutic targets for GBS. Leucocyte transcription profile in GBS patients A total of 2794 transcripts were significantly associated with GBS (P,0.05). Of these, 256 genes reached the minimum fold changes ($2). 246 genes were up-regulated and 10 genes are down-regulated in GBS group, respectively (Table 1 and Table S2). Of 15 genes quantified by RT-PCR, 8 up-regulated genes (FOS, PTGS2, HMGB2, MMP9, LY96, TTRAP, ANXA3, CREB1) were in good agreement with the results of microarray (Table 2). Furthermore, the ANXA3 expression level is proportionally correlated with the score of GBS disability scale [36] (Fig. 1A, P = 0.006). The GBS group also displayed a significantly higher serum level of MMP9 (Fig. 1B, 153.74635.68 ng/mL) than the control group (52.7065.67 ng/mL, P = 0.013). The serum level of MMP9 is also positively correlated with GBS disability scale score (Fig. 1C, P = 0.001). Gene network analysis To determine significant biological functions and to reveal transcriptional correlations among genes associated with GBS, the 256 significant genes were subjected to gene network analysis. The most significant disease and disorder biological functions associated with GBS-correlated genes were inflammatory response, infectious disease, and respiratory disease (Table 3 and Table S3). Cell death, cellular development and cellular movement were the top significant molecular and cellular functional categories. Hematological system development and function, immune cell trafficking and organismal survival were the most significant categories in physiological development and system function. Eighteen significant gene networks were noted in GBS (Table 4 and Table S4). MMP9, PTGS2, and CREB1 were the hub genes in the two top significant gene networks (Fig. 2AB). Canonical pathway analysis To gain further insights into the pathogenesis of GBS, we analyzed the GBS-correlated genes to elucidate dominant canonical pathways. 246 up-regulated and 10-down-regulated GBS correlated transcripts were subjected to canonical pathway analysis, which showed that 101 significant pathways in the upregulated GBS gene set (Table S5). GnRH, Corticotropin releasing hormone and ERK/MAPK signaling pathways were the most significant pathways in the up-regulated GBS gene set (Table 5). Only two pathways, including Eicosanoid signaling and Pyruvate metabolism, were significant in the down-regulated GBS gene set (Table 6). To demonstrate the biological interactions of these genes within these pathways and highlight hub genes controlling the signaling transduction, the top three up-regulated pathways are shown in Fig. 3. In this study, we analyzed global gene expression of peripheral blood leukocytes in a clinically well-characterized and ethnically homogeneous cohort of GBS, and found several novel or reported candidate gene markers associated with the disease. Using gene networks and pathways analyses, we confirmed a likely role of several previously described biological processes and uncovered new important pathways that may be involved in the pathogenesis of GBS. There were several interesting genes in our study that showed strong evidence of up-regulation, such as FOS, PTGS2, HMGB2, MMP9, LY96, TTRAP, ANXA3 and CREB1. Among them, FOS, PTGS2, HMGB2, LY96, TTRAP, ANXA3 and CREB1 have never been reported to be associated with GBS. FOS gene encodes a transcription factor that has critical functions in regulating cell proliferation, differentiation, and transformation. The binding of FOS and JUN forms a dimeric transcription factor complex, activator protein-1 (AP-1). AP-1 affects the severity of inflammation by activation of cytokine production in cooperation with NFAT transcription factors and regulates the expression of IL-2, IL-3, GM-CSF, IL-4, IL-5, IL-13, IFN-gamma, TNF-alpha, CD40L, CD5, CD25, and IL-8 [37]. Therefore, FOS represents a GBS candidate gene for exploring the pathogenesis and also for a potential therapeutic target. The protein encoded by PTGS2 is a member of cyclo-oxygenase [38] family, a rate limiting enzyme catalyzing the synthesis of prostaglandins from arachidonic acid. It has been shown that a significant up-regulation of PTGS2 was detected in sural nerves from patients with GBS and other demyelinating polyneuropathies [39]. In experimental autoimmune neuritis (EAN), an animal model for GBS, the administration of COX inhibitors significantly Fold change by arrays (GBS vs Control) Prostaglandin-endoperoxide synthase 2 High mobility group box 2 Matrix metallopeptidase 9 Defensin, alpha 3, neutrophil-specific Lymphocyte antigen 96 CREB binding protein TRAF and TNF receptor-associated protein cAMP responsive element binding protein 1 Selenium binding protein 1 Hemoglobin, theta 1 Prostaglandin D2 synthase decreased clinical, neurophysiologic, and histomorphologic signs of the disease, indicating that COX and prostaglandins represent important factors in the regulation of the inflammatory demyelination of the peripheral nerves [40,41,42]. HMGB2 encodes a member of the non-histone chromosomal high mobility group protein family and is associated with chromosomes during mitosis. Although the association of HMGB2 and inflammation remains unclear, a closely related gene, HMGB1, has been demonstrated to exhibit an important extracellular function in mediation of inflammation processes [43]. MMP9 is involved in the breakdown of extracellular matrix in normal physiological processes [44]. MMP9 may degrade myelin basic protein, one of the principal myelin components of the peripheral nervous system [45]. Similar to this report, it has been shown that elevated serum level of MMP9 was associated with disease severity and electrophysiological changes in GBS patients [18,46,47]. MMP9 expression can be detected in the damaged nerve of patients with GBS [48]. MMP9 has also been implicated in the pathogenesis of EAN [49,50]. In particular, MMP9 is increased early in the course of EAN, peaking with maximum disease severity, and detected in nerve tissue in Schwann cells, endoneurial vessels, and infiltrating immune cells [49,50]. The administration of an MMP inhibitor decreased severity of EAN [50,51]. Thus, the inhibition of MMP9 could be a potential therapeutic strategy for GBS. LY96 is a small secreted glycoprotein that binds with cytokinelike affinities to both the hydrophobic portion of lipopolysaccharide and to the extracellular domain of TLR4 [52], which plays a critical role in Campylobacter jejuni-induced dendritic cell activation and B cell proliferation [11]. TLR4/LY96 complex is specific for recognition of lipopolysaccharide and promotes phagocytosis [52,53]. In addition to inducing innate immune responses to microbial membrane components, TLR4/LY96 may sense tissue damage by responding endogenous ligands released from damaged tissues and induce inflammation [54]. Thus the elevation of LY96 is probably an indicator of inflammatory process. TTRAP is reported to interact with members of the tumor necrosis factor receptor superfamily and may inhibit inflammation by inhibition of NFkB [55,56]. The role of the up-regulation of TTRAP in GBS or other neuroimmunological diseases remains to be clarified. ANXA3 encoded a calcium-dependent phospholipid-binding protein that belongs to the annexin family [57]. The function of ANXA3 is yet to be fully elucidated. It has been suggested that ANXA3 expression is increased in post-ischemic brain [58]. In addition, ANXA3 also plays an important role in angiogenesis and neural tissue regeneration [58,59]. In this study, ANXA3 expression level is significantly correlated with the clinical severity in GBS, suggesting that ANXA3 may be used as a potential marker for prognostic monitoring in GBS patients. The protein encoded by CREB1 appears to regulate gene expression by constitutively binding to conserved cAMP-responsive elements [60]. Its pivotal role in gene networks has been revealed by bioinformatic analysis, which has estimated that there are approximately 4000 human genes containing conserved cAMP-responsive elements adjacent to the transcription start site [61]. Activation of CREB1 by phosphorylation has been shown to up-regulate the expression of IL-2 and IL-6 [62,63], and to induce the transcriptional activation of PTGS2 [64], whereby playing a critical role in inflammatory diseases. Beyond the identification of individual genes, our analysis also focused on the identification and characterization of biological functions associated with these genes. The most significant biological functions involving genes with significantly altered expression included inflammatory response, infectious disease, cell death, cellular development, hematological system development and function, and immune cell trafficking. These data are consistent with findings of other studies revealing the altered cellular and immunological function in GBS [5,65,66,67,68,69]. Although statistical significance of expression level changes may be one way to select a candidate gene for a given disease, gene network analysis offers the advantage of understanding the interaction of significant genes associated with a disease and the ability to find hub genes within a network that interact with several other genes up- and downstream of them. The high interconnectivity of hub genes with other correlated genes within a biological network may imply functional and biological importance of these genes. In this study, a number of hub genes of gene networks significantly associated with GBS, such as CREB1, MMP9 and PTGS, have been identified. Regulating the expression of these hub genes could be important in the treatment of GBS. The most significant canonical pathways involving genes with significantly altered expression included GnRH, corticotrophin releasing hormone and ERK/MAPK signaling. Extensive investigations suggest that the immune system may also modulate the hypothalamic-pituitary-gonadal and hypothalamic-pituitary-adrenal axis [70]. Generally, an increased immune response is coupled with an enhanced hypothalamic-pituitary-adrenal axis [71]. The up-regulation of GnRH and corticotrophin-releasing hormone signaling in GBS leukocytes may be a response in the immune system of patients affected by autoimmune diseases. In addition to its crucial role in the production of proinflammatory cytokines [72], ERK/MAPK signaling is also involved in the demyelination process [73,74,75,76]. Selective activation of ERK/MAPK signaling or alternatively overexpression of RAF, a molecule effector upstream of ERK1/2, prevents Schwann cell differentiation [74,75]. RAF also induces demyelination of Schwann cell [75]. Furthermore, the blockage of ERK/ MAPK signaling can rescue the demyelination caused by sustained activation of ERK/MAPK signaling [75]. Thus blockade of ERK/MAPK signaling could potentially inhibit both the inflammatory and demyelination processes, serving as a novel therapeutic target for GBS. In the down-regulated gene set, Eicosanoid signaling and Pyruvate metabolism pathways were significantly involved. However, due to the paucity of gene hits, the alterations of these pathways need to be validated further. In summary, this is the first report applying gene transcription analysis in the search for potential gene markers, studying gene biological functions and canonical pathways involved in GBS. As MMP9 has been shown in the damaged nerves of patients with GBS, and MMP9 expression in leucocytes is correlated to the clinical disability score, the level of peripheral nerve damages can be reflected by the changes in peripheral leukocytes. While the identification of reported GBS-associated genes MMP9 authenticates this study, the discovery of novel candidate genes and the application of gene networks analysis in these markers highlight the transcriptional relationships among GBS-associated genes. It should be kept in mind that there are certain limits to in silico analysis. The small size of samples constrains the detection power in microarray. Since there are many undetermined gene-gene interactions, the actual relationship between genes may not be accurately revealed by the literature-based computational network. Despite these limitations, this is the first study describing a large number of GBS-associated genes in inflammatory cells. Further investigations are needed to confirm the clinical relevance of these biomarkers, and clarify the potential of ERK/MAPK signaling pathways as therapeutic targets in GBS disease models. Materials and Methods Ethics statement This study was performed under a protocol approved by the Institutional Review Boards of Chang Gung Memorial Hospital (ethical license No: 96-0285B) and all examinations were performed after obtaining written informed consents. Study population All the patients and controls were residents of Taiwan. Patient group consisted of GBS patients fulfilling the required diagnostic criteria [77]. None of the patients or the controls had systemic infection, autoimmune diseases, malignancies, or chronic renal, cardiac, or liver dysfunction. Disease and disorder Molecular and Cellular functions Physiological system development and function Organismal injury and abnormalities Cellular death and proliferation Amino acid metabolism Hematological system development and function Immune cell trafficking 1.01E-10 - 9.55E-03 1.39E-08 - 1.12E-02 7.71E-07 - 7.50E-03 1.71E-06 - 1.11E-02 1.71E-06 - 9.48E-03 2.32E-12 - 1.13E-02 8.26E-09 - 1.11E-02 5.59E-08 - 1.11E-02 5.45E-07 - 6.32E-03 1.32E-06 - 5.23E-03 5.45E-07 - 1.11E-02 1.79E-06 - 8.60E-03 4.12E-06 - 7.06E-03 9.30E-06 - 1.11E-02 9.82E-06 - 6.34E-03 Sample collection Venous puncture was performed between 1 and 2 weeks after onset of disease. The blood was collected into PaxgeneTM blood RNA tube (Pre-AnalytiX, Qiagen). Total RNA of leukocytes was extracted using the PaxgeneTM blood RNA Extraction Kit (PreAnalytiX, Qiagen), and transferred into the RNeasy MinElute spin column (RNeasyH MinEluteHCleanup Kit, Qiagen) for RNA purification and concentration. RNA quality was determined was determined using the A260/A280 absorption ratio and capillary electrophoresis on an Agilent 2100 Bioanalyzer automated analysis system (Agilent). Microarray mRNA expression profiling analysis Genome-wide mRNA expression data of peripheral blood leukocytes in 7 treatment-nave GBS patients (3 females and 4 males, age of onset: 52.43615.06 years, mean score of GBS disability scale: 2.5760.90, preceding infectious event: 1) and 7 healthy volunteers (3 females and 4 males, mean age: 50.00614.06 years) were determined by Affymetrix Human Genome U133 plus 2.0 Arrays. All the samples from the patients with GBS were obtained within one month after disease onset. Biotin-labelled cRNA was generated and linearly amplified from 5 mg total RNA using the GeneChipH IVT Labeling Kit (Affymetrix) as described by the protocol. Array hybridization, chemiluminescence detection, image acquisition and analysis were performed using Partek >Genomics Suite following the manufacturers instructions. Briefly, each microarray was first pre-hybridized at 55uC for 1 h in hybridization buffer with blocking reagent. Twenty mg biotinlabeled cRNA targets were first fragmented, mixed with internal control target and hybridized to the prehybridized microarrays in a volume of 1.5 ml at 55uC for 18 h. After hybridization, the arrays were washed with hybridization wash buffer and chemiluminescence rinse buffer. Enhanced chemiluminescent signals were generated by incubating arrays with alkaline phosphatase Network Top functions Score Focus genes Up-regulated genes in network Cardiovascular disease, Hematological disease, Neurological disease Amino acid metabolism, Post-translational modification, Small molecule biochemistry Cell-to-cell signalling and interaction, Hematological system development and function, Hematopoiesis Cellular development, Hematological system development and function, Hematopoiesis BAZ1A, CD36, CIR1, CREBBP, GTF2B, HIST1H2AD, IGF2R, KLF4, KLHL2, KYNU, LAP3, LTF, MME, MMP9, MXD1, NFE2L2, PADI4, PTGS2, SENP6, SNW1, TANK, YME1L1 ACTN1, ADCY7, AKAP13, ATP2B1, CD55, CD97, CREB1, CREB5, DUSP1, DUSP6, FYB, NAMPT, NFIL3, PRKAR1A, PTPRE, RAPGEF2, RGS2, RHOB, SGK1, TRIB1, ZFP36L1 AIM2, C5AR1, CAMP, CASP1, CD163, CD1D, CSTA, DEFA3, FPR1, FPR2, G0S2, GBP2, GNAI3, IRAK3, IRF2, LY96, MCL1, NOD2, TLR1, TLR8, TNFAIP6 ANXA1, CD58, CRISPLD2, DPYSL2, HMGB2, HNRNPA2B1, HSPA6, KCTD12, MAP3K7, MARCKS, PICALM, PRKCB, PTGDS, RBM5, SP100, SRPK1, STXBP3, SUB1, TAOK3, ZMYND8 ACSL1, ARHGAP26, ATG3, ATG12, FOS, HHEX, IL10RB, ITGAM, LIMK2, MAFB, PLSCR1, PRKCD, RB1CC1, SELENBP1, SNX2, TSC22D1 2log (P value) Corticotropin releasing hormone signaling cAMP-mediated signaling Molecular mechanisms of cancer P2Y purigenic receptor signalling Toll-like receptor signaling LPS-stimulated MAPK signalling Renin-angiotensin signaling conjugated anti-digoxigenin antibody followed by incubation with Chemiluminescence Enhancing Solution and a final addition of Chemiluminescence Substrate. Images were collected for each microarray using the Affymetrix GeneChipH Scanner and the chemiluminescent signals were quantified, corrected for background and spot size, and spatially normalized. Obtained data were imported into GeneSpring GX 11.01 software for analysis (Agilent). The fold changes were analyzed by filtering the dataset using P value,0.05, two tailed Students t-test. Additional filtering (minimum 2-fold change) was applied to identify the diseaserelated genes, which were analyzed using Ingenuity Pathway Analysis (IPA) software (Ingenuity Systems). Those genes with known gene symbols and their expression values were uploaded into the software. Each gene symbol was mapped to its own gene object in the Ingenuity Pathways Knowledge Database. Networks of these genes were assigned a score based on their connectivity. The score reflected the number of focus genes in the network and how relevant this network is to the original list of focus genes. A network graph was shown to present the molecular relationship between individual genes. The significance of the association between the data set and the canonical pathway was determined by a P value calculated using Fishers exact test. P,0.05 was considered statistically significant. Microarray data are MIAME compliant and the raw data have been deposited with the NCBI 2log (P value) RAF1, PAK2, CDC42, CREBBP, CREB5, GNAI3, FOS, MAP3K7, PRKCD, CREB1, ADCY7, PRKAR1A, PRKCB GNAI3, RAF1, FOS, PRKCD, CREB1, PTGS2, CREB5, ADCY7, PRKAR1A, PRKCB RAF1, AKAP13, RGS2, MAPKSP1, DUSP1, DUSP6, CREB1, TDP2, CREB5, ADCY7, PRKAR1A RAF1, PAK2, CDC42, CREBBP, JAK2, NBN, GNAI3, FOS, MAPKSP1, RHOB, MAP3K7, PRKCD, CFLAR, ADCY7, PRKCB, PRKAR1A GNAI3, RAF1, ITGAM, PAK2, RHOB, PRKCD, LIMK2, PTGS2, IRAK3, MMP9, PRKCB GNAI3, RAF1, FOS, PRKCD, CREB1, CREB5, ADCY7, PRKAR1A, PRKCB FOS, TLR1, LY96, MAP3K7, TLR8 (includes EG:51311), IRAK3 RAF1, FOS, CDC42, MAP3K7, PRKCD, CREB1, PRKCB RAF1, FOS, PAK2, PRKCD, JAK2, ADCY7, PRKAR1A, PRKCB Gene Expression Omnibus (http://www.ncbi.nlm.nih.gov/geo) under accession number GSE31014. Real-time polymerase chain reaction (RT-PCR) Total RNAs were collected from the peripheral blood leukocytes of 16 treatment-nave GBS patients (6 females and 10 males, age of onset: 47.06615.28 years, mean score of GBS disability scale: 2.7561.39, preceding infectious event: 3) within one month after disease onset and 20 healthy volunteers (10 females and 10 males, mean age: 51.85611.25 years). RNA was converted to cDNA using the SuperScriptH III First-Strand (Invitrogen). PCR results were generated using the 59-nuclease assay (TaqMan) and the ABI 7900HT Sequence Detection System (Applied Biosystems). Each reaction included cDNA from 100 ng of RNA, 900 nM of each primer and 100 nM of probe and Universal PCR Master Mix (Applied Biosystems). Assay sequence information is indicated in Table S1. PCR parameters were 50uC for 2 min, 95uC for 10 min, 40 cycles of 95uC for 15 sec, 60uC for 1 min. Each sample was assessed in duplicate. Relative expression values were normalized to b-actin. Relative gene expressions were calculated using the 2DCT method, DCT = CT (b-actin)2CT (target gene), in which CT indicates cycle threshold (the fractional cycle number where the fluorescent signal reaches detection threshold). Students t- test was used to compare the differences between GPS and control groups. The correlation between gene expression level and GBS disability scale score was assessed by linear regression analysis. Enzyme-linked immunosorbant assay (ELISA) Serum from the above population groups was collected for RTPCR analysis. The serum level of MMP9 was assessed with a Quantikine ELISA kit (R&D System) according to the manufacturers instruction. Students t- test was used to compare the differences between GBS and control groups. The correlation between serum level of MMP9 and GBS disability scale score was assessed by linear regression analysis. Lists of assay ID and probe sequence for RT Supporting Information Lists of significant gene networks in GBS The authors gratefully acknowledge the technical support of this study by Genomic Medicine Research Core Laboratory, Chang Gung Memorial Hospital, Taiwan. Conceived and designed the experiments: K-HC C-MC. Performed the experiments: T-JC. Analyzed the data: K-HC C-MC T-JC. Contributed reagents/materials/analysis tools: K-HC R-KL L-SR Y-RW H-SC C-CH H-CK W-CH C-CC C-MC. Wrote the paper: K-HC C-MC. 1. Hughes RAC , Cornblath DR ( 2005 ) Guillain-Barre syndrome . Lancet 366 : 1653 - 1666 . 2. Sejvar JJ , Baughman AL , Wise M , Morgan OW ( 2011 ) Population incidence of Guillain-Barre syndrome: a systematic review and meta-analysis . Neuroepidemiology 36 : 123 - 133 . 3. Cosi V , Versino M ( 2006 ) Guillain-Barre syndrome . Neurol Sci 27 Suppl 1 : S47 - 51 . 4. Hughes RA , Rees JH ( 1997 ) Clinical and epidemiologic features of GuillainBarre syndrome . J Infect Dis 176 Suppl 2 : S92 - 98 . 5. Kang JH , Sheu JJ , Lin HC ( 2010 ) Increased risk of Guillain-Barre syndrome following recent herpes zoster: A population-based study across Taiwan . Clin Infect Dis 51 : 525 - 530 . 6. Rees JH , Soudain SE , Gregson NA , Hughes RA ( 1995 ) Campylobacter jejuni infection and Guillain-Barre syndrome . N Engl J Med 333 : 1374 - 1379 . 7. Wim Ang C , Jacobs BC , Laman JD ( 2004 ) The Guillain-Barre syndrome: a true case of molecular mimicry . Trends Immunol 25 : 61 - 66 . 8. Susuki K , Odaka M , Mori M , Hirata K , Yuki N ( 2004 ) Acute motor axonal neuropathy after Mycoplasma infection: Evidence of molecular mimicry . Neurology 62 : 949 - 956 . 9. Kwa MSG , van Schaik IN , Brand A , Baas F , Vermeulen M ( 2001 ) Investigation of serum response to PMP22, connexin 32 and P0 in inflammatory neuropathies . J Neuroimmunol 116 : 220 - 225 . 10. Kusunoki S , Kaida K ( 2011 ) Antibodies against ganglioside complexes in Guillain-Barre syndrome and related disorders . J Neurochem 116 : 828 - 832 . 11. Kuijf ML , Samsom JN , van Rijs W , Bax M , Huizinga R , et al. ( 2010 ) TLR4- mediated sensing of Campylobacter jejuni by dendritic cells is determined by sialylation . J Immunol 185 : 748 - 755 . 12. Hao Q , Saida T , Kuroki S , Nishimura M , Nukina M , et al. ( 1998 ) Antibodies to gangliosides and galactocerebroside in patients with Guillain-Barre syndrome with preceding Campylobacter jejuni and other identified infections . J Neuroimmunol 81 : 116 - 126 . 13. Gabriel CM , Gregson NA , Hughes RAC ( 2000 ) Anti-PMP22 antibodies in patients with inflammatory neuropathy . J Neuroimmunol 104 : 139 - 146 . 14. Willison HJ , Yuki N ( 2002 ) Peripheral neuropathies and anti-glycolipid antibodies . Brain 125 : 2591 - 2625 . 15. Hafer-Macko CE , Sheikh KA , Li CY , Ho TW , Cornblath DR , et al. ( 1996 ) Immune attack on the schwann cell surface in acute inflammatory demyelinating polyneuropathy . Ann Neurol 39 : 625 - 635 . 16. Hafer-Macko C , Hsieh ST , Ho TW , Sheikh K , Cornblath DR , et al. ( 1996 ) Acute motor axonal neuropathy: An antibody-mediated attack on axolemma . Ann Neurol 40 : 635 - 644 . 17. Griffin J , Li C , Macko C , Ho T , Hsieh S , et al. ( 1996 ) Early nodal changes in the acute motor axonal neuropathy pattern of the Guillain-Barre syndrome . J Neurocytol 25 : 33 - 51 . 18. Creange A , Sharshar T , Planchenault T , Christov C , Poron F , et al. ( 1999 ) Matrix metalloproteinase-9 is increased and correlates with severity in GuillainBarre syndrome . Neurology 53 : 1683 - 1691 . 19. Kieseier BC , Kiefer R , Gold R , Hemmer B , Willison HJ , et al. ( 2004 ) Advances in understanding and treatment of immune-mediated disorders of the peripheral nervous system . Muscle Nerve 30 : 131 - 156 . 20. Asbury AK , Arnason BG , Adams RD ( 1969 ) The inflammatory lesion in idiopathic polyneuritis. Its role in pathogenesis . Medicine 48 : 173 - 215 . 21. Prineas JW ( 1981 ) Pathology of the Guillain-Barre syndrome . Ann Neurol 9 : 6 - 19 . 22. Griffin JW , Li CY , Ho TW , Tian M , Gao CY , et al. ( 1996 ) Pathology of the motor-sensory axonal Guillain-Barre syndrome . Ann Neurol 39 : 17 - 28 . 23. Marchiori PE , Dos Reis M , Quevedo ME , Callegaro D , Hirata MT , et al. ( 1990 ) Cerebrospinal fluid and serum antiphospholipid antibodies in multiple sclerosis, Guillain-Barre syndrome and systemic lupus erythematosus . Arq Neuropsiquiatr 48 : 465 - 468 . 24. Petzold A , Hinds N , Murray NF , Hirsch NP , Grant D , et al. ( 2006 ) CSF neurofilament levels: A potential prognostic marker in Guillain-Barre syndrome . Neurology 67 : 1071 - 1073 . 25. Mata ` S, Galli E , Amantini A , Pinto F , Sorbi S , et al. ( 2006 ) Anti-ganglioside antibodies and elevated CSF IgG levels in Guillain-Barre syndrome . Eur J Neurol 13 : 153 - 160 . 26. Mokuno K , Kiyosawa K , Sugimura K , Yasuda T , Riku S , et al. ( 1994 ) Prognostic value of cerebrospinal fluid neuron-specific enolase and S-100b protein in Guillain-Barre syndrome . Acta Neurol Scand 89 : 27 - 30 . 27. Nishino S , Kanbayashi T , Fujiki N , Uchino M , Ripley B , et al. ( 2003 ) CSF hypocretin levels in Guillain-Barre syndrome and other inflammatory neuropathies . Neurology 61 : 823 - 825 . 28. Nagai A , Murakawa Y , Terashima M , Shimode K , Umegae N , et al. ( 2000 ) Cystatin C and cathepsin B in CSF from patients with inflammatory neurologic diseases . Neurology 55 : 1828 - 1832 . 29. Chiang HL , Lyu RK , Tseng MY , Chang KH , Chang HS , et al. ( 2009 ) Analyses of transthyretin concentration in the cerebrospinal fluid of patients with GuillainBarre syndrome and other neurological disorders . Clin Chim Acta 405 : 143 - 147 . 30. Jin T , Hu LS , Chang M , Wu J , Winblad B , et al. ( 2007 ) Proteomic identification of potential protein markers in cerebrospinal fluid of GBS patients . Eur J Neurol 14 : 563 - 568 . 31. Chang KH , Lyu RK , Tseng MY , Ro LS , Wu YR , et al. ( 2007 ) Elevated haptoglobin level of cerebrospinal fluid in Guillain-Barre syndrome revealed by proteomics analysis . Proteomics Clin Appl 1 : 467 - 475 . 32. D'Aguanno S , Franciotta D , Lupisella S , Barassi A , Pieragostino D , et al. ( 2010 ) Protein profiling of Guillain-Barre syndrome cerebrospinal fluid by twodimensional electrophoresis and mass spectrometry . Neurosci Lett 485 : 49 - 54 . 33. Weller M , Stevens A , Sommer N , Melms A , Dichgans J , et al. ( 1991 ) Comparative analysis of cytokine patterns in immunological, infectious, and oncological neurological disorders . J Neurol Sci 104 : 215 - 221 . 34. Sainaghi PP , Collimedaglia L , Alciato F , Leone MA , Naldi P , et al. ( 2010 ) The expression pattern of inflammatory mediators in cerebrospinal fluid differentiates Guillain-Barre syndrome from chronic inflammatory demyelinating polyneuropathy . Cytokine 51 : 138 - 143 . 35. Hartung HP , Schwenke C , Bitter-Suermann D , Toyka KV ( 1987 ) GuillainBarre syndrome: activated complement components C3a and C5a in CSF . Neurology 37 : 1006 - 1009 . 36. Hughes RA , Newsom-Davis JM , Perkin GD , Pierce JM ( 1978 ) Controlled trial prednisolone in acute polyneuropathy . Lancet 2 : 750 - 753 . 37. Wagner EF , Eferl R ( 2005 ) Fos/AP-1 proteins in bone and the immune system . Immunol Rev 208 : 126 - 140 . 38. Wang W , Lin C , Lu D , Ning Z , Cox T , et al. ( 2008 ) Chromosomal transposition of PiggyBac in mouse embryonic stem cells . Proc Natl Acad Sci U S A 105 : 9290 - 9295 . 39. Hu W , Mathey E , Hartung HP , Kieseier BC ( 2003 ) Cyclo-oxygenases and prostaglandins in acute inflammatory demyelination of the peripheral nerve . Neurology 61 : 1774 - 1779 . 40. Miyamoto K , Oka N , Kawasaki T , Miyake S , Yamamura T , et al. ( 2002 ) New cyclooxygenase-2 inhibitors for treatment of experimental autoimmune neuritis . Muscle Nerve 25 : 280 - 282 . 41. Miyamoto K , Oka N , Kawasaki T , Satoi H , Akiguchi I , et al. ( 1998 ) The effect of cyclooxygenase-2 inhibitor on experimental allergic neuritis . Neuro Report 9 : 2331 - 2334 . 42. Miyamoto K , Oka N , Kawasaki T , Satoi H , Matsuo A , et al. ( 1999 ) The action mechanism of cyclooxygenase-2 inhibitor for treatment of experimental allergic neuritis . Muscle Nerve 22 : 1704 - 1709 . 43. Kumar K , Singal A , Rizvi MM , Chauhan VS ( 2008 ) High mobility group box (HMGB) proteins of Plasmodium falciparum: DNA binding proteins with proinflammatory activity . Parasitol Int 57 : 150 - 157 . 44. Randell SH , Shimizu T , Bakewell W , Ramaekers FC , Nettesheim P ( 1993 ) Phenotypic marker expression during fetal and neonatal differentiation of rat tracheal epithelial cells . Am J Respir Cell Mol Biol 8 : 546 - 555 . 45. Proost P , Van Damme J , Opdenakker G ( 1993 ) Leukocyte gelatinase B cleavage releases encephalitogens from human myelin basic protein . Biochem Biophys Res Commun 192 : 1175 - 1181 . 46. Sharshar T , Durand MC , Lefaucheur JP , Lofaso F , Raphael JC , et al. ( 2002 ) MMP-9 correlates with electrophysiologic abnormalities in Guillain-Barre syndrome . Neurology 59 : 1649 - 1651 . 47. Nyati KK , Prasad KN , Verma A , Paliwal VK ( 2010 ) Correlation of matrix metalloproteinases-2 and -9 with proinflammatory cytokines in Guillain-Barre syndrome . J Neurosci Res 88 : 3540 - 3546 . 48. Kieseier BC , Clements JM , Pischel HB , Wells GM , Miller K , et al. ( 1998 ) Matrix metalloproteinases MMP-9 and MMP-7 are expressed in experimental autoimmune neuritis and the Guillain-Barre syndrome . Ann Neurol 43 : 427 - 434 . 49. Hughes P , Wells G , Clements J , Gearing A , Redford E , et al. ( 1998 ) Matrix metalloproteinase expression during experimental autoimmune neuritis . Brain 121 : 481 - 494 . 50. Redford E , Smith K , Gregson N , Davies M , Hughes P , et al. ( 1997 ) A combined inhibitor of matrix metalloproteinase activity and tumour necrosis factor-alpha processing attenuates experimental autoimmune neuritis . Brain 120 : 1895 - 1905 . 51. Zhao XL , Li GZ , Sun B , Zhang ZL , Yin YH , et al. ( 2010 ) MMP-mediated cleavage of beta-dystroglycan in myelin sheath is involved in autoimmune neuritis . Biochem Biophys Res Commun 392 : 551 - 556 . 52. Viriyakosol S , Tobias PS , Kitchens RL , Kirkland TN ( 2001 ) MD-2 Binds to Bacterial Lipopolysaccharide . J Biol Chem 276 : 38044 - 38051 . 53. Shimazu R , Akashi S , Ogata H , Nagai Y , Fukudome K , et al. ( 1999 ) MD-2, a molecule that confers lipopolysaccharide responsiveness on Toll-like receptor 4 . J Exp Med 189 : 1777 - 1782 . 54. Miyake K ( 2007 ) Innate immune sensing of pathogens and danger signals by cell surface Toll-like receptors . Sem Immunol 19 : 3 - 10 . 55. Wang BY , Xu GL , Zhou CH , Tian L , Xue JL , et al. ( 2010 ) WC31 integrase interacts with TTRAP and inhibits NFkB activation . Mol Biol Rep 37 : 2809 - 2816 . 56. Pype S , Declercq W , Ibrahimi A , Michiels C , Van Rietschoten JGI , et al. ( 2000 ) TTRAP, a novel protein that associates with CD40, tumor necrosis factor (TNF) receptor-75 and TNF receptor-associated factors (TRAFs), and that inhibits nuclear factor-kB activation . J Biol Chem 275 : 18586 - 18593 . 57. Gerke V , Moss SE ( 2002 ) Annexins: From structure to function . Physiol Rev 82 : 331 - 371 . 58. Kessler C , Junker H , Balseanu TA , Oprea B , Pirici D , et al. ( 2008 ) Annexin A3 expression after stroke in the aged rat brain . Rom J Morphol Embryol 49 : 27 - 35 . 59. Park JE , Lee DH , Lee JA , Park SG , Kim NS , et al. ( 2005 ) Annexin A3 is a potential angiogenic mediator . Biochem Biophys Res Commun 337 : 1283 - 1287 . 60. Mayr B , Montminy M ( 2001 ) Transcriptional regulation by the phosphorylation-dependent factor CREB . Nat Rev Mol Cell Biol 2 : 599 - 609 . 61. Zhang X , Odom DT , Koo SH , Conkright MD , Canettieri G , et al. ( 2005 ) Genome-wide analysis of cAMP-response element binding protein occupancy, phosphorylation, and target gene activation in human tissues . Proc Natl Acad Sci U S A 102 : 4459 - 4464 . 62. Gomez-Mart n D, D az-Zamudio M , Crispn JC , Alcocer-Varela J ( 2009 ) Interleukin 2 and systemic lupus erythematosus: Beyond the transcriptional regulatory net abnormalities . Autoimmun Rev 9 : 34 - 39 . 63. Sitaraman SV , Merlin D , Wang L , Wong M , Gewirtz AT , et al. ( 2001 ) Neutrophil-epithelial crosstalk at the intestinal lumenal surface mediated by reciprocal secretion of adenosine and IL-6 . J Clin Invest 107 : 861 - 869 . 64. Schroer K , Zhu Y , Saunders MA , Deng WG , Xu XM , et al. ( 2002 ) Obligatory role of cyclic adenosine monophosphate response element in cyclooxygenase-2 promoter induction and feedback regulation by inflammatory mediators . Circulation 105 : 2760 - 2765 . 65. Jander S , Heidenreich F , Stoll G ( 1993 ) Serum and CSF levels of soluble intercellular adhesion molecule-1 (ICAM-1) in inflammatory neurologic diseases . Neurology 43 : 1809 - 1813 . 66. Del Giudice E , Savoldi G , Notarangelo L , Di Benedetto L , Manganelli F , et al. ( 2003 ) Acute inflammatory demyelinating polyradiculoneuropathy associated with perforin-deficient familial haemophagocytic lymphohistiocytosis . Acta Paediatrica 92 : 398 - 401 . 67. Harness J , McCombe PA ( 2008 ) Increased levels of activated T-cells and reduced levels of CD4/CD25+ cells in peripheral blood of Guillain-Barre syndrome patients compared to controls . J Clin Neurosci 15 : 1031 - 1035 . 68. Weishaupt A , Bru ck W , Hartung T , Toyka KV , Gold R ( 2001 ) Schwann cell apoptosis in experimental autoimmune neuritis of the Lewis rat and the functional role of tumor necrosis factor-a . Neurosci Lett 306 : 77 - 80 . 69. ren A , White LR , Aasly J ( 2001 ) Apoptosis in neurones exposed to cerebrospinal fluid from patients with multiple sclerosis or acute polyradiculoneuropathy . J Neurol Sci 186 : 31 - 36 . 70. Tomaszewska-Zaremba D , Herman A ( 2009 ) The role of immunological system in the regulation of gonadoliberin and gonadotropin secretion . Reprod Biol 9 : 11 - 23 . 71. Battaglia DF , Brown ME , Krasa HB , Thrun LA , Viguie C , et al. ( 1998 ) Systemic challenge with endotoxin stimulates corticotropin-releasing hormone and arginine vasopressin secretion into hypophyseal portal blood: Coincidence with gonadotropin-releasing hormone suppression . Endocrinology 139 : 4175 - 4181 . 72. Dong C , Davis RJ , Flavell RA ( 2002 ) MAP kinases in the immune response . Ann Rev Immunol 20 : 55 - 72 . 73. Sheu JY , Kulhanek DJ , Eckenstein FP ( 2000 ) Differential patterns of ERK and STAT3 phosphorylation after sciatic nerve transection in the rat . Exp Neurol 166 : 392 - 402 . 74. Ogata T , Iijima S , Hoshikawa S , Miura T , Yamamoto S , et al. ( 2004 ) Opposing extracellular signal-regulated kinase and Akt pathways control Schwann cell myelination . J Neurosci 24 : 6724 - 6732 . 75. Harrisingh MC , Perez-Nadales E , Parkinson DB , Malcolm DS , Mudge AW , et al. ( 2004 ) The Ras/Raf/ERK signalling pathway drives Schwann cell dedifferentiation . EMBO J 23 : 3061 - 3071 . 76. Agthong S , Kaewsema A , Tanomsridejchai N , Chentanez V ( 2006 ) Activation of MAPK ERK in peripheral nerve after injury . BMC Neurosci 7 : 45 . 77. Asbury AK , Cornblath DR ( 1990 ) Assessment of current diagnostic criteria for Guillain-Barre syndrome . Ann Neurol 27 Suppl: S21 - 24 .


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Kuo-Hsuan Chang, Tzi-Jung Chuang, Rong-Kuo Lyu, Long-Sun Ro, Yih-Ru Wu, Hong-Shiu Chang, Chin-Chang Huang, Hung-Chou Kuo, Wen-Chuin Hsu, Chun-Che Chu, Chiung-Mei Chen. Identification of Gene Networks and Pathways Associated with Guillain-Barré Syndrome, PLOS ONE, 2012, DOI: 10.1371/journal.pone.0029506