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 . 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 . 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 , neurofilaments , anti-ganglioside
antibodies , neuron-specific enolase , S100B ,
hypocretin-1 , cystatin C , transthyretin , haptoglobin
[30,31], carbonylation of albumin , 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  (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
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 . 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
 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
. 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 .
MMP9 is involved in the breakdown of extracellular matrix in
normal physiological processes . MMP9 may degrade myelin
basic protein, one of the principal myelin components of the
peripheral nervous system . 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 . 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 , which plays a
critical role in Campylobacter jejuni-induced dendritic cell
activation and B cell proliferation . 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 . Thus the
elevation of LY96 is probably an indicator of inflammatory
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
ANXA3 encoded a calcium-dependent phospholipid-binding
protein that belongs to the annexin family . The function of
ANXA3 is yet to be fully elucidated. It has been suggested that
ANXA3 expression is increased in post-ischemic brain . 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 . 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
. 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 , 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-adrenal axis . Generally, an increased immune response is coupled
with an enhanced hypothalamic-pituitary-adrenal axis . 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 , 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 . Furthermore, the blockage of ERK/
MAPK signaling can rescue the demyelination caused by sustained
activation of ERK/MAPK signaling . 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
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.
All the patients and controls were residents of Taiwan. Patient
group consisted of GBS patients fulfilling the required diagnostic
criteria . 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
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
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,
ACSL1, ARHGAP26, ATG3, ATG12, FOS, HHEX, IL10RB, ITGAM, LIMK2, MAFB, PLSCR1,
PRKCD, RB1CC1, SELENBP1, SNX2, TSC22D1
2log (P value)
Corticotropin releasing hormone signaling
Molecular mechanisms of cancer
P2Y purigenic receptor signalling
Toll-like receptor signaling
LPS-stimulated MAPK signalling
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,
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
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
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
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.
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