Microarray analysis in B cells among siblings with/without MS

1755-8794-1-2 1755-8794 Research article Microarray analysis in B cells among siblings with/without MS - role for transcription factor TCF2 Avasarala R Jagannadha javasarala@yahoo.com Chittur V Sridar schittur@albany.edu George D Ajish ajishg@gmail.com Tine A John jtine@csc.albany.edu

Multiple Sclerosis Specialty Care, Kansas Neurological Consultants, PA, Wichita, KS 67218, USA

Center for Functional Genomics and Department of Biomedical Sciences, University at Albany, SUNY, Rensselaer, NY 12144, USA

BMC Medical Genomics 1755-8794 2008 1 1 2 http://www.biomedcentral.com/1755-8794/1/2 18237449 10.1186/1755-8794-1-2 17 7 2007 31 1 2008 31 1 2008 2008 Chittur et al; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Background

We investigated if global gene expression and transcription networks in B-lymphocytes of siblings with multiple sclerosis (MS) were different from healthy siblings.

Results

Using virus-transformed immortalized B cells and human whole genome bioarrays with validation using RT-qPCR, we found that in siblings with MS, genes for CXCL10, serpin B1 and FUT4 were up regulated whereas CDC5L, TNFRSF19 and HLA-DR were down regulated, among others; transcription analysis showed two intersecting clusters of transcriptional factors - the larger, governed by the upregulated transcription factor 2 (TCF2) and the smaller network regulated by the downregulated CDC5L.

Conclusion

No study has linked TCF2 to MS and to better understand the role of TCF2 in MS, studies in larger cohorts are required.

Background

MS is a complex genetic disease associated with inflammation predominantly in the white matter of brain and spinal cord. It is thought to be mediated by autoreactive T cells 12. Susceptibility to MS is determined by both inherited and non-inherited factors 3. Approximately 15-20% of MS patients have a family history of MS, but large extended pedigrees are uncommon. Studies in twins 45 and conjugal pairs 6 show that much of the familial clustering is the result of shared genetic risk factors.

MS susceptibility is linked to HLA-DR2 7. Increased risk of MS in women has been detected with interleukin-1 receptor antagonist (IL-1RA) allele 2 8, 5G5G genotype of plasminogen activator inhibitor 1 (PAI-1) gene 9 and interaction between estrogen receptor 1 (ESR1) and HLA-DR2 10. B cells are implicated in MS and have been found in the cerebrospinal fluid (CSF) of MS patients 11. Additionally, oligoclonal bands identified in CSF point to the role of B cells in MS pathogenesis 12. Furthermore, antibody-secreting B cells contribute to tissue injury 13. We hypothesized that B cells derived from MS patients could harbor genes that confer a higher MS risk as compared to B cell gene expression in healthy siblings.

Human B-cells have a receptor for Epstein-Barr virus (EBV) and can become immortalized after in vitro infection with EBV. Additionally, the link between EBV and MS is highly impressive 14 though inconclusive; we hypothesized that analysis of gene expression and transcription networks in EBV-transformed B cells between siblings with and without MS could yield important clues to understanding the pathology of MS.

Large-scale analyses of transcripts from peripheral blood cells or brain lesions from MS patients have created possibilities for therapeutics 15 and global gene expression analysis using microarrays is a sensitive method to investigate molecular heterogeneity 16. In this study, we tested a new software tool that is in development, to map transcription networks in the microarray data.

Our objectives were to i) to determine if gene expression and transcription networks in B-lymphocytes of siblings with MS were different from healthy siblings in EBV-transformed B cells and ii) to validate data using qPCR techniques.

Methods

EBV-transformed B cell lines from Coriell Institute for medical research (Camden, NJ, USA) and the National Institute of General Medical Sciences (NIGMS, Bethesda, MD) were obtained for our study. As shown in Table 1, B cells were harvested from one family (# 2108, proband, affected sister) and an unaffected brother (control); cells from another family (# 2112, proband, affected brother) and three unaffected brothers (controls). Cells from the third family (# 2102) comprised of proband and an affected sister but no unaffected controls. None of the patients were on immunomodulatory agents (IMAs) to treat MS.

Table 1

Cell lines obtained from Corielle for our study.

Sample ID

Relation

Family

Affected

Sex

Age

GM8923

Proband

2108

Yes

Male

GM8922A

Sister

2108

Yes

Female

GM8830

Proband

2102

Yes

Female

GM8839

Sister

2102

Yes

Female

GM9013

Proband

2112

Yes

Male

GM9016

Brother

2112

Yes

Male

GM9018

Brother

2112

Male

GM9017

Brother

2112

Male

GM9023

Brother

2112

Male

GM8921A

Brother

2108

Male

Cells were harvested in 4 ml of Tri-Reagent (Molecular research center, Inc) and the total RNA was isolated using standard protocols. Briefly, B-cells were incubated at 37°C and 5% CO₂overnight, the day after cells were counted. Cells were expanded in RPMI 1640 with 2 mM L-glutamine and 15% fetal bovine serum following Coriell's lymphoblast line maintenance protocols. 2 vials were frozen and 1 × 10⁷cells were spun down and resuspended in 2 ml of Tri-reagent. The cell mixture was frozen at -80°C until further analysis. The RNA was isolated using the standard Tri-reagent protocol and further cleaned up using RNeasy kits (Qiagen) using a protocol that included an on column Dnase step. RNA samples were checked for quality on a Bioanalyzer and a nanodrop spectrophotometer. Samples with 28s/18s ratio > 1.8 were deemed acceptable for the microarray experiment. The total RNA (1 ug) was first converted to double stranded cDNA using the standard Codelink protocol. This was then purified on Qiaquick PCR columns and in vitro transcribed to labeled cRNA using biotin-11-UTP (Perkin Elmer). The labeled cRNA was purified on Qiagen RNeasy Mini columns and 10 ug was fragmented as per the Codelink protocol following which it was hybridized to the Codelink Human Whole Genome bioarrays for 18 h at 37°C. The arrays were subsequently washed, stained with Cy5-Streptavidin (GE Amersham) and scanned on an Axon Genepix 4000B scanner. The data were collected using Codelink Expression Analysis software and analyzed using Genespring software version 7.3.1 (Agilent). All microarray data are deposited at Gene Expression Omnibus as GSE 10064 and linked via 17.

Microarray data analysis

Values below 0.01 were set to 0.01. Each measurement was divided by the 50th percentile of all measurements in that sample. All samples were normalized to the median of the control samples. Each measurement for each gene in those specific samples was divided by the median of that gene's measurements in the corresponding control samples. The gene list (54,902 transcripts) was filtered using the cross gene error model for replicates to select genes with good signal-to-noise ratios (31, 168 genes). This gene list was subjected to a student's t-test (p-value < 0.05) and yielded 1, 417 genes with significant differences between the two conditions. This list was further filtered for confidence using a Benjamini-Hochberg false discovery rate correction to obtain 1405 genes. A 1.5-fold cut-off filter was then applied to identify genes that were preferentially upregulated (260) or downregulated (452) genes in samples with disease as compared to controls. These 712 genes were further filtered to only select those which were consistently 'absent" or "present" in 3 of 4 controls and 5 of 6 diseased samples. The final gene list had 705 genes; 259 upregulated and 446 downregulated genes while using 2-fold cut off resulted in 84 upregulated and 111 downregulated genes (Tables 2 &3).

Table 2

Representative upregulated genes (out of 259 total).

Probe ID

Fold Change

Common

Genbank

Description

Upregulated genes

GE53598

5.88

TINAG

NM_014464

tubulointerstitial nephritis antigen, complete cds

GE59642

5.77

CXCL10

NM_001565

gamma-interferon inducible early response gene (with homology to platelet proteins)

GE57550

4.74

CRYM

NM_001888

mu-crystallin mRNA, complete cds

GE518123

4.27

IGLL1

NM_020070

immunoglobulin lambda-like polypeptide 1 (IGLL1), transcript variant 1, mRNA

GE58736

4.20

DACT1

NM_016651

dapper homolog 1, antagonist of beta-catenin (xenopus) (DACT1), mRNA

GE79033

4.15

CD9

NM_001769

CD9 antigen (p24) (CD9), mRNA

GE55346

3.57

LRRC16

NM_017640

leucine rich repeat containing 16 (LRRC16), mRNA

GE61239

3.50

SERPINB1

NM_030666

serine (or cysteine) proteinase inhibitor, clade B (ovalbumin), member 1 (SERPINB1), mRNA

GE62781

3.50

MT1F

NM_005949

metallothionein 1F (functional) (MT1F), mRNA

GE87501

3.49

FXYD2

NM_001680

FXYD domain containing ion transport regulator 2 (FXYD2), transcript variant a, mRNA

GE61946

3.26

ARHGAP6

NM_001174

Rho GTPase activating protein 6 (ARHGAP6), transcript variant 2, mRNA

GE56932

3.22

EGLN3

NM_022073

egl nine homolog 3 (C. elegans) (EGLN3), mRNA

GE61093

3.18

ACTN1

NM_001102

actinin, alpha 1 (ACTN1), mRNA

GE87626

2.97

OSR2

NM_053001

odd-skipped-related 2A protein (OSR2), mRNA

GE59655

2.93

ATP1B1

NM_001677

ATPase, Na+/K+ transporting, beta 1 polypeptide (ATP1B1), mRNA

GE58575

2.91

PTPLA

NM_014241

protein tyrosine phosphatase-like (proline instead of catalytic arginine), member a (PTPLA), mRNA

GE57767

2.84

COL9A3

NM_001853

collagen, type IX, alpha 3 (COL9A3), mRNA

GE57621

2.83

LGALS3BP

NM_005567

lectin, galactoside-binding, soluble, 3 binding protein (LGALS3BP), mRNA

GE81389

2.82

CCR6

NM_004367

chemokine (C-C motif) receptor 6 (CCR6), transcript variant 1, mRNA

GE79898

2.81

LYPLA3

NM_012320

lysophospholipase 3 (lysosomal phospholipase A2) (LYPLA3), mRNA

GE62292

2.79

CPXM

NM_019609

carboxypeptidase X (M14 family) (CPXM), mRNA

GE57547

2.66

TCN2

NM_000355

transcobalamin II; macrocytic anemia (TCN2), mRNA

GE81525

2.52

IFI27

NM_005532

ISG12 protein

GE79641

2.48

FUT4

NM_002033

fucosyltransferase 4 (alpha (1,3) fucosyltransferase, myeloid-specific) (FUT4), mRNA

GE81272

2.47

VIM

NM_003380

vimentin (VIM), mRNA

GE57737

2.45

ENPP2

NM_006209

autotaxin mRNA, complete cds

GE80150

2.38

ELF3

NM_004433

E74-like factor 3 (ets domain transcription factor, epithelial-specific) (ELF3), mRNA

GE58207

2.34

ENG

NM_000118

endoglin (Osler-Rendu-Weber syndrome 1) (ENG), mRNA

GE57048

2.33

CCR1

NM_001295

chemokine (C-C motif) receptor 1 (CCR1), mRNA

GE57883

2.28

SELL

NM_000655

selectin L (lymphocyte adhesion molecule 1) (SELL), mRNA

GE60214

2.27

NRGN

NM_006176

neurogranin (protein kinase C substrate, RC3) (NRGN), mRNA

GE498904

2.22

DACT1

NM_016651

dapper homolog 1, antagonist of beta-catenin (xenopus) (DACT1), mRNA

GE81340

2.17

PSTPIP1

NM_003978

proline-serine-threonine phosphatase interacting protein 1 (PSTPIP1), mRNA

GE58931

2.16

TCF8

U12170

zinc finger homeodomain protein mRNA, complete cds

GE61464

2.15

SERPINB9

NM_004155

serine (or cysteine) proteinase inhibitor, clade B (ovalbumin), member 9 (SERPINB9), mRNA

GE82947

2.12

SNX22

NM_024798

sorting nexin 22 (SNX22), mRNA

GE60134

2.09

UPP1

NM_003364

uridine phosphorylase (UP), transcript variant 1, mRNA

GE54152

2.07

APOL1

NM_003661

apolipoprotein L, 1 (APOL1), transcript variant 1, mRNA

GE56786

2.02

DUSP4

NM_001394

dual specificity phosphatase 4 (DUSP4), transcript variant 1, mRNA

GE54528

2.02

TNFSF12;

NM_003809

tumor necrosis factor (ligand) superfamily, member 12 (TNFSF12), transcript variant 1, mRNA

GE61789

2.02

TDE2L

NM_178865

tumor differentially expressed protein 2 (TDE2), mRNA

GE61399

2.01

LLT1

NM_013269

lectin-like NK cell receptor (LLT1), mRNA

Fold change = diseased/normal

Table 3

Representative downregulated genes (out of 446 total)

Probe ID

Fold change

Common

Genbank

Description

Down regulated genes

GE54030

0.09

EIF1AY

NM_004681

eukaryotic translation initiation factor 1A, Y-linked (EIF1AY), mRNA

GE79500

0.1

RPS4Y

NM_001008

ribosomal protein S4, Y-linked (RPS4Y), mRNA

GE54029

0.12

DDX3Y

NM_004660

DEAD/H (Asp-Glu-Ala-Asp/His) box polypeptide, Y chromosome (DBY), mRNA

GE59227

0.12

JARID1D

NM_004653

Smcy homolog, Y-linked (mouse) (SMCY), mRNA

GE55836

0.15

MLAT4

NM_018192

myxoid liposarcoma associated protein 4 (MLAT4), mRNA

GE54974

0.19

TNFRSF19

NM_148957

tumor necrosis factor receptor superfamily, member 19 (TNFRSF19), transcript variant 2, mRNA

GE788416

0.28

KRTAP4-9

AJ406941

partial mRNA for keratin associated protein 49 (KRTAP4.9 gene)

GE489986

0.32

ZFY

NM_003411

zinc finger protein, Y-linked (ZFY), mRNA

GE58359

0.35

GCHFR

NM_005258

GTP cyclohydrolase I feedback regulatory protein (GCHFR), mRNA

GE87376

0.36

BAIAP2L1

NM_018842

insulin receptor tyrosine kinase substrate (LOC55971), mRNA

GE561268

0.39

FOXD4L2

NM_199135

FOXD4-like 2 (FOXD4L2), mRNA

GE54486

0.4

DAPK2

NM_014326

death-associated protein kinase 2 (DAPK2), mRNA

GE79999

0.4

CAMK1

NM_003656

calcium/calmodulin-dependent protein kinase I (CAMK1), mRNA

GE58178

0.4

KAL1

NM_000216

Kallmann syndrome (KAL) mRNA, complete cds

GE82446

0.4

BAIAP2L1

NM_018842

insulin receptor tyrosine kinase substrate (LOC55971), mRNA

GE58327

0.41

CD83

NM_004233

CD83 antigen (activated B lymphocytes, immunoglobulin superfamily) (CD83), mRNA

GE567535

0.42

NGFRAP1

AF187064

p75NTR-associated cell death executor (NADE) mRNA

GE62339

0.42

CSE-C

NM_018978

sialic acid-specific 9-O-acetylesterase I mRNA, complete cds

GE82815

0.42

FA2H

NM_024306

fatty acid 2-hydroxylase (FA2H), mRNA

GE82367

0.44

LAPTM4B

NM_018407

lysosomal associated protein transmembrane 4 beta (LAPTM4B), mRNA phosphodiesterase 4A, cAMP-specific (phosphodiesterase E2 dunce homolog, Drosophila)

GE81609

0.45

PDE4A

NM_006202

(PDE4A), mRNA

GE61123

0.45

ZNF239

NM_005674

zinc finger protein 239 (ZNF239), mRNA

GE53189

0.46

AUTS2

NM_015570

autism susceptibility candidate 2 (AUTS2), mRNA

GE81426

0.46

USP9Y

NM_004654

ubiquitin specific protease 9, Y chromosome (fat facets-like Drosophila) (USP9Y), mRNA

GE63274

0.48

CENTG3

AL537305

FETAL BRAIN cDNA clone CS0DF025YC08 5-PRIME

GE83170

0.48

BCL2L14

NM_030766

apoptosis regulator BCL-G (BCLG), transcript variant 2, mRNA

GE58554

0.49

PRO1914

NM_014106

PRO1914 protein (PRO1914), mRNA

GE86889

0.49

SLC26A7

NM_052832

solute carrier family 26, member 7 (SLC26A7), transcript variant 1, mRNA

GE81520

0.49

BCAT1

NM_005504

branched chain aminotransferase 1, cytosolic (BCAT1), mRNA

Fold change = diseased/normal

Hierarchical clustering

This gene list was subjected to a hierarchical clustering routine in GeneSpring using a Pearson correlation similarity measure across conditions and a standard correlation similarity measure across genes. The final tree is represented in Figure 1.

Figure 1

Hierarchical clustering of all statistically significant genes that show differential expression between diseased and normal samples

Hierarchical clustering of all statistically significant genes that show differential expression between diseased and normal samples. Upregulated are colored red and downregulated genes are colored green. Clustering of genes is shown on the left while condition based tree is shown on the top.

Pathway Analysis

The 1.5 fold list was further analyzed using Onto-Express and Pathway-Express, part of the Onto-Tools package available from the Draghici laboratory. This software allows for identification of pathways that show enrichment in the microarray data and is a novel tool that translates lists of differentially regulated genes into functional profiles characterizing the impact of the condition studied.

Quantitative PCR validation

To verify expression data obtained by the Codelink analysis, Taqman gene expression assays were obtained (Applied Biosystems) to evaluate the expression of selected genes by RT-qPCR. The assays utilized were as follows: TCF2 (Applied Biosystems assay Hs01001609_m1), CXCL10 (Hs00171042_m1), FUT4 (Hs00275643_s1), SERPINB1 (Hs00229084_m1), CD9 (Hs01124026_m1), LYPLA3 (Hs00202399_m1), SERPINB9 (Hs00394497_m1), CD83 (Hs00188486_m1), CDC5L (Hs00904622_m1), TNFRSF19 (Hs00218634_m1), HLA-DRA (Hs00219578_m1). All assays are designed to detect the appropriate transcripts at an exon-exon junction to ensure the detection of mRNA and not genomic DNA.

The expression levels of these 11 genes were evaluated by two-step Taqman-based RT-qPCR. First strand cDNA was synthesized with M-MLV reverse transcriptase (Ambion, Inc., Austin, TX) and primed with oligo-dT per the manufacturer's specifications. The cDNA equivalent of 20 ng starting RNA was then included in reactions containing 1× Taqman Master Mix (Applied Biosystems, Foster City, CA), and 1× gene-specific assay reagents as recommended by the manufacturer (Applied Biosystems). All reactions were run in triplicate. Reactions that did not contain template cDNA were included as negative controls.

Reaction plates were processed on an Applied Biosystems 7900 HT Sequence Detection System. The AmpliTaq Gold polymerase was activated at 95°C for 10 min followed by 40 cycles consisting of denaturation for 15 seconds at 95°C and annealing and extension for 60 seconds at 60°C.

Amplification data was analyzed with the ABI Prism SDS 2.1 software (Applied Biosystems). Although run individually, samples GM8921A, 9017, 9018, and 9023 were pooled for analysis as the control sample, and samples GM8830, 8922A, 8923, 8839, 9013, and 9016 were pooled for analysis as the MS sample, unless otherwise noted. Relative quantification of gene expression was performed by the ΔΔCt method 18. GAPDH expression was used as an endogenous control to normalize expression within each sample.

Transcription network analysis

We employed a software program that one of our authors continues to develop (G.A.) to recast the matrix of regulatory interactions found in a prior step as a directed graph overlaying color information for gene responses. Our regulatory network was constructed by integrating transcription factor binding site information (from TRANSFAC) with gene expression data using graph TFs (Bioconductor functions for transcription network analysis). The standard microarray experiments cannot measure the transcription factor activities (TFAs) directly, since TFAs are subject to post-translational modifications or ligand binding in order to exert their function.

We hypothesized that decoding some of the important TFAs involved in B cells of MS patients might provide important clues to disease pathogenesis or uncover potential molecular targets for therapy. We started with an initial list of all genes changing at the 1.5-fold level. In this dual sample study, we assumed all pairs of genes moving in the same direction to be concordant and all pairs moving in opposite directions to be discordant. Combining this with the potential for regulatory relationships inferred from a TRANSFAC binding site dataset, we were able to extrapolate the potential transcription regulatory networks driving expression changes. We found that TCF2 and CDC5L bind very similar promoter motifs and seem to control overlapping sets of up and down regulated genes in this study. The transcripts we uncovered in this dataset are most notably, CCR1 and CXCR4, molecules which are associated with MS. The multiplication of corresponding cells in the TFBS and correlation matrices yields a regulation matrix where the sign of the interaction score reflects positive or negative regulation and the magnitude of the score reflects the amount of supporting evidence, as shown in the grid below (Figure 2). We examined these networks for various thresholds of fold-change and TRANSFAC evidence scores to obtain the picture of regulation of downstream molecules by TCF2 and CDCL5 shown in Figure 3.

Figure 2

Analysis of transcription networks from microarray data

Analysis of transcription networks from microarray data.

Figure 3

Transcription factor network deciphered from genes differentially expressed at a 1.5 fold cutoff in microarray data

Transcription factor network deciphered from genes differentially expressed at a 1.5 fold cutoff in microarray data. The color 'red" shows upregulation while "green" shows downregulated expression. Genes are depicted by circles while transcription factors are represented as rectangles. Green arrows indicate that the nodes at each end of the arrow are regulated in the same direction (up or down) while the red arrows connect the nodes that are anticorrelated.

Results

As shown in Figure 1, the rows represent hierarchical clustering of all differentially expressed genes between the diseased (MS) and control (normal siblings) samples. The MS samples were 8923, 8922 A, 8830, 8839, 9013 and 9016, respectively. The unaffected siblings were 9017, 9018, 9023 and 8921A, respectively. Onto-Express analyses depicted in Figure 4 shows that transcription factor binding is a highly significant component in our microarray data. Results from Pathway-Express analysis are listed in Table 4, and these show that immune function and neurospecific pathways are also highly implicated in this data set.

Figure 4

Results from functional profiling of data using Onto-Express

Results from functional profiling of data using Onto-Express. (A) The figure shows the significant (red) biological processes represented in a set of genes differentially expressed between two clinical groups. (B) The figure shows the significant (red) molecular function represented in a set of genes differentially expressed between two clinical groups.

Table 4

Results of pathway profiling using Pathway Express.

Rank

Database Name

Pathway Name

Impact Factor

#Genes in Pathway

#Input Genes in Pathway

#Pathway Genes on Chip

%Input Genes in Pathway

%Pathway Genes in Input

p-value

KEGG

Cell adhesion molecules (CAMs)

6.94

132

126

2.099

8.333

9.69E-04

KEGG

Axon guidance

6.94

130

126

2.099

8.462

9.69E-04

KEGG

Leukocyte transendothelial migration

5.288

117

113

1.718

7.692

0.005054

KEGG

Type I diabetes mellitus

5.232

0.954

11.364

0.005344

KEGG

Regulation of actin cytoskeleton

4.461

206

198

2.29

5.825

0.011556

KEGG

Natural killer cell mediated cytotoxicity

4.11

128

114

1.527

6.25

0.016401

KEGG

Focal adhesion

3.949

194

188

2.099

5.67

0.019276

KEGG

Tight junction

3.187

119

112

1.336

5.882

0.041314

KEGG

Antigen processing and presentation

3.112

0.954

5.814

0.044499

KEGG

Adherens junction

2.721

0.954

6.494

0.065823

KEGG

Colorectal cancer

2.676

0.954

6.494

0.068828

KEGG

Circadian rhythm

2.386

0.382

11.111

0.091979

KEGG

Maturity onset diabetes of the young

1.975

0.382

0.138816

KEGG

VEGF signaling pathway

1.972

0.763

5.556

0.139208

KEGG

Fc epsilon RI signaling pathway

1.83

0.763

5.333

0.160493

KEGG

mTOR signaling pathway

1.811

0.573

6.122

0.16353

KEGG

Huntington's disease

1.558

0.382

6.667

0.210455

KEGG

Wnt signaling pathway

1.474

147

144

1.145

4.082

0.229071

KEGG

MAPK signaling pathway

1.441

273

268

1.908

3.663

0.236637

KEGG

Toll-like receptor signaling pathway

1.416

0.763

4.396

0.242667

KEGG

B cell receptor signaling pathway

1.318

0.573

4.762

0.267565

KEGG

T cell receptor signaling pathway

1.294

0.763

4.301

0.274064

KEGG

Long-term potentiation

1.235

0.573

4.478

0.290928

KEGG

Insulin signaling pathway

1.143

135

131

0.954

3.704

0.318761

KEGG

Type II diabetes mellitus

1.117

0.382

4.545

0.327167

KEGG

Dentatorubropallidoluysian atrophy (DRPLA)

1.044

0.191

6.667

0.352194

KEGG

Epithelial cell signaling in Helicobacter pylori infection

0.997

0.382

4.348

0.368822

KEGG

Parkinson's disease

0.992

0.191

6.25

0.370684

KEGG

Cytokine-cytokine receptor interaction

0.963

256

241

1.527

3.125

0.381922

KEGG

Jak-STAT signaling pathway

0.936

153

144

0.954

3.268

0.392344

KEGG

Apoptosis

0.86

0.573

3.571

0.423249

KEGG

Complement and coagulation cascades

0.6

0.382

2.899

0.548544

KEGG

Olfactory transduction

0.523

0.191

3.226

0.592482

KEGG

SNARE interactions in vesicular transport

0.468

0.191

2.778

0.62642

KEGG

Phosphatidylinositol signaling system

0.421

0.382

2.532

0.656227

KEGG

Taste transduction

0.317

0.191

1.887

0.728441

KEGG

GnRH signaling pathway

0.277

0.382

2.062

0.758254

KEGG

Gap junction

0.27

0.382

2.02

0.763352

KEGG

Adipocytokine signaling pathway

0.145

0.191

1.449

0.864684

KEGG

Long-term depression

0.124

0.191

1.333

0.882976

KEGG

Neuroactive ligand-receptor interaction

0.102

302

279

0.954

1.656

0.902917

KEGG

ECM-receptor interaction

0.086

0.191

1.149

0.91743

KEGG

Cell cycle

0.042

112

110

0.191

0.893

0.958921

KEGG

Calcium signaling pathway

0.041

176

173

0.382

1.136

0.959904

We selected a subset of genes that were up and down regulated in the MS samples for further validation by RT-qPCR. Our results generally confirmed the results obtained with the Codelink arrays. TCF2, CXCL10, and FUT4 were all up regulated in the MS samples whereas CDC5L, TNFRSF19 and HLA-DR were down regulated (Table 5).

Table 5

Relative Expression of Selected Genes by RT-qPCR.

Control

+/- SD

TCF2

1.00

0.68

7.95

4.25

CXCL10

1.00

0.47

5.93

3.85

FUT4

1.00

0.43

3.93

2.23

SERPIN B1

1.00

0.57

1.71

1.07