Open Access

A dynamic model of gene expression in monocytes reveals differences in immediate/early response genes between adult and neonatal cells

  • Shelley Lawrence1,
  • Yuhong Tang2,
  • M Barton Frank2,
  • Igor Dozmorov2,
  • Kaiyu Jiang1,
  • Yanmin Chen1,
  • Craig Cadwell2,
  • Sean Turner2,
  • Michael Centola2 and
  • James N Jarvis1Email author
Contributed equally
Journal of Inflammation20074:4

DOI: 10.1186/1476-9255-4-4

Received: 26 September 2006

Accepted: 16 February 2007

Published: 16 February 2007

Abstract

Neonatal monocytes display immaturity of numerous functions compared with adult cells. Gene expression arrays provide a promising tool for elucidating mechanisms underlying neonatal immune function. We used a well-established microarray to analyze differences between LPS-stimulated human cord blood and adult monocytes to create dynamic models for interactions to elucidate observed deficiencies in neonatal immune responses.

We identified 168 genes that were differentially expressed between adult and cord monocytes after 45 min incubation with LPS. Of these genes, 95% (159 of 167) were over-expressed in adult relative to cord monocytes. Differentially expressed genes could be sorted into nine groups according to their kinetics of activation. Functional modelling suggested differences between adult and cord blood in the regulation of apoptosis, a finding confirmed using annexin binding assays. We conclude that kinetic studies of gene expression reveal potentially important differences in gene expression dynamics that may provide insight into neonatal innate immunity.

Background

The defects in neonatal adaptive immunity are relatively easy to understand a priori. Although there are complexities to be considered [1, 2], experimental evidence demonstrates that newborns, lacking prior antigen exposure, must develop immunologic memory based on postnatal experience with phogens and environmental immunogens [35].

It is less clear why there should be defects in newborns' innate immunity, although these defects are well documented. For example, newborns have long been known to exhibit defects in phagocytosis [6], chemotaxis [7, 8], and adherence [9], the latter possibly due to aberrant regulation of critical cell-surface proteins that mediate leukocyte-endothelial interactions [10]. Newborn monocytes also exhibit diminished secretion of numerous cytokines under both stimulated and basal conditions [1113].

Elucidating the causes of these defects is a crucial question in neonatal medicine, since infection remains a major cause of morbidity and mortality in the newborn period. However, unravelling the complex events in monocyte and/or neutrophil activation, from ligand binding to activation of effector responses, is clearly a daunting challenge. Any one of numerous pathways from the earliest cell signalling events to protein synthesis or secretion could be relevant, and focusing on any one may overlook critical aspects of cellular regulation. In this context, genomic and/or proteomic approaches may offer some important advantages, at least in the initial phases of investigation, by allowing investigators to survey the panoply of biological processes that may be relevant to identifying critical biological distinctions.

Recently published work has documented differences in gene expression between adult and cord blood monocytes [14], although these studies did not elucidate the fundamental, functional differences between cord blood and adult cells. The studies we report here demonstrate how computational analyses, applied to microarray data, can elucidate critical biological functions when analysis extends beyond the identification of differentially-expressed genes.

Methods

Cells and cellular stimulation

Monocytes were purified from cord blood of healthy, term infants and from the peripheral blood of healthy adults by positive selection using anti-CD-14 mAb-coated magnetic beads (Miltenyi Biotec, Auburn, CA, USA) according to the manufacturer's instructions. Informed consent was obtained from adult volunteers; collection of cord blood was ruled exempt from consent after review by the Oklahoma Health Sciences Center IRB. In brief, blood was collected into sterile tubes containing sodium citrate as an anticoagulant (Becton Dickinson, Franklin Lakes, NJ). Peripheral blood mononuclear cells (PBMC) were prepared from the anti-coagulated blood using gradient separation on Histopaque-1077 performed directly in the blood collection tubes. Cells were washed three times in Ca2+ and Mg2+-free Hanks's balanced salt solution. PBMC were incubated for 20 min at 4°C with CD14 microbeads at 20 μ l/1 × 107 cells. The cells were washed once, re-suspended in 500 μ l Ca2+ and Mg2+-free PBS containing 5% FBS/1 × 108 cells. The suspension was then applied to a MACs column. After unlabeled cells passed through, the column was washed with 3 × 500 μl Ca2+ and Mg2+-free PBS. The column was removed from the separator and was put on a new collection tube. One ml of Ca2+ and Mg2+-free PBS was then added onto the column, which was immediately flushed by firmly applying the plunger supplied with the column.

Purified monocytes were incubated with LPS from Escherichia coli 0111:4B (Sigma, St. Louis, MO) at 10 ng/ml for 45 min and 2-hours in RPMI 1640 with 10% fetal bovine serum or studied in the absence of stimulation ("zero time"). It should be noted that this product is not "pure," and stimulates both TLR-4 and TRL-2 signaling pathways [15]. A smaller number of replicates (n = 5) was analyzed after 24 hr incubation. After the relevant time points, monocytes were lysed with TriZol (Invitrogen, Carlsbad, CA, USA) and RNA was isolated as recommended by the manufacturer. Cells from eight different term neonates and eight different healthy adults were used for these studies.

Gene microarrays

The microarrays used in these experiments were developed at the Oklahoma Medical Research Foundation Microarray Research Facility and contained probes for 21,329 human genes. Slides were produced using commercially available libraries of 70 nucleotide long DNA molecules whose length and sequence specificity were optimized to reduce the cross-hybridization problems encountered with cDNA-based microarrays (Qiagen-Operon). The oligonucleotides were derived from the UniGene and RefSeq databases. The RefSeq database is an effort by the NCBI to create a true reference database of genomic information for all genes of known function. All 11,000 human genes of known or suspected function were represented on these arrays. In addition, most undefined open reading frames were represented (approximately 10,000 additional genes).

Oligonucleotides were spotted onto Corning® UltraGAPS™ amino-silane coated slides, rehydrated with water vapor, snap dried at 90°C, and then covalently fixed to the surface of the glass using 300 mJ, 254 nm wavelength ultraviolet radiation. Unbound free amines on the glass surface were blocked for 15 min with moderate agitation in a 143 mM solution of succinic anhydride dissolved in 1-methyl-2-pyrolidinone, 20 mM sodium borate, pH 8.0. Slides were rinsed for 2 min in distilled water, immersed for 1 min in 95% ethanol, and dried with a stream of nitrogen gas.

Labeling, hybridization, and scanning

Fluorescently labeled cDNA was separately synthesized from 2.0 μg of total RNA using an oligo dT12–18 primer, PowerScript reverse transcriptase (Clontech, Palo Alto, CA), and Cy3-dUTP (Amersham Biosciences, Piscataway, NJ) for 1 hour at 42°C in a volume of 40 μl. Reactions were quenched with 0.5 M EDTA and the RNA was hydrolyzed by addition of 1 M NaOH for 1 hr at 65°C. The reaction was neutralized with 1 M Tris, pH 8.0, and cDNA was then purified with the Montage PCR96 Cleanup Kit (Millipore, Billerica, MA). cDNA was added to ChipHybe™ hybridization buffer (Ventana Medical Systems, Tucson, AZ) containing Cot-1 DNA (0.5 mg/ml final concentration), yeast tRNA (0.2 mg/ml), and poly(dA)40–60 (0.4 mg/ml). Hybridization was performed on a Ventana Discovery system for 6 hr at 42°C. Microarrays were washed to a final stringency of 0.1× SSC, and then scanned using a dual-color laser (Agilent Biotechnologies, Palo Alto, CA). Fluorescent intensity was measured by Imagene™ software (BioDiscovery, El Segundo, CA).

PCR validation of array data

Reverse transcription

Three cord blood samples (C1, C2, and C5) and three adult samples (A1, A5, and A6) from the 45 minute time point were used for PCR validation. First strand cDNA was generated from 3.6 μg of total RNA per sample using the OmniScript Reverse Transcriptase and buffer (Qiagen, Valencia, CA), 1 μl of 100 μM oligo dT primer (dT15) in a 40 μl volume. Reactions were incubated 60 min at 37° and inactivated at 93° for 5 min. cDNA was diluted 1:100 in water and stored at -20°C.

Quantitative PCR

Gene-specific primers for 10 genes (Erbb3, Tmod, Dscr1l1, Sp1, Scya4, Gro2, Cri1, Scya3, Scya3l1, and Il-1a) were designed with a 60°C melting temperature and a length of 19–25 bp for PCR products with a length of 90–140 bp, using Applied Biosystems Inc (ABI, Foster City, CA) Primer Express 1.5 software. PCR was run with 2 μl cDNA template in 15 μl reactions in triplicate on an ABI SDS 7700 using the ABI SYBR Green I Master Mix and gene specific primers at a concentration of 1 μM each. The temperature profile consisted of an initial 95°C step for 10 minutes (for Taq activation), followed by 40 cycles of 95°C for 15 sec, 60°C for 1 min, and then a final melting curve analysis with a ramp from 60°C to 95°C over 20 min. Gene-specific amplification was confirmed by a single peak in the ABI Dissociation Curve software. No template controls were run for each primer pair. Since equal amounts of total RNA were used for cDNA synthesis, Ct values should reflect relative abundance [16]. These values were used to calculate the average group Ct (Cord vs. Adult) and the relative ΔCt was used to calculate fold change between the two groups [17].

Apoptosis assays

Exposed membrane phospholipids (a marker for early apoptosis) were detected in adult and neonatal monocytes after LPS stimulation using a commercially available annexin V binding assay. Monocytes from cord blood and adult peripheral blood were obtained as outlined above. Isolated monocytes were either labeled immediately with annexin V-FITC or were stimulated for 14 hours with LPS 10 ng/ml prior to labeling (this time point was derived empirically to maximize apoptosis). Annexin V-FITC staining was completed via the Annexin V-FITC Apoptosis Detection Kit I (BD Biosciences, San Jose, CA) using 5 μl of propidium iodine and 5 μl annexin V-FITC as recommended by the manufacturer. Analysis by flow cytometry was accomplished on a FACS Calibur automated benchtop flow cytometer. Data obtained by flow cytometry was analyzed by non-parametric t-test (Mann-Whitney test). An alpha level of 0.05 was considered statistically significant.

Statistical analysis

Microarrays were normalized and tested for differential expression using methods described previously [18]. Differential expression was concluded if the genes met the following criteria: a minimum expression level at least 10 times above background at one or more time points, a minimum 1.5-fold difference in the mean expression values between groups at one or more time points, and a minimum of 80% reproducibility using the jack-knife method. A jack-knife is the most common type of Leave-one-out-cross-validation (LOOCV); it is used here to cross-validate genes selected by differential analysis [19]. Time series analysis was performed using the hypervariable (HV) gene method previously described by our group [20].

After selection, HV genes are clustered and interrogated for gene-gene interactions. K-means clustering, an unsupervised technique, was performed on the HV genes to create unbiased clusters. Discriminate function analysis (DFA), a supervised technique, was used to determine and spatially map gene-to-gene interactions [21].

All statistical analysis was performed in Matlab R14 (Natick, MA) and Statistica v7 (Tulsa, OK, USA). An alpha level of 0.05 was considered statistically significant for all analyses.

Analysis of the apoptosis assays was undertaken using both parametric and non-parametric analysis methods. Parametric analysis was undertaken using the student's t-test; non-parametic analysis used the Mann-Whitney U-test. A p-value of > 0.05 was the threshold for rejecting the null hypothesis.

Discriminant function analysis

DFA is a method that identifies a subset of genes whose expression values can be linearly combined in an equation, denoted a root, whose overall value is distinct for a given characterized group. DFA therefore, allows the genes that maximally discriminate among the distinct groups analyzed to be identified. In the present work, a variant of the classical DFA, named the Forward Stepwise Analysis, was used to select the set of genes whose expression maximally discriminated among experimentally distinct groups. The Forward Stepwise Analysis was built systematically in an iterative manner. Specifically, at each step all variables were reviewed to identify the one that most contributes to the discrimination between groups. This variable was included in the model, and the process proceeded to the next iteration. The statistical significance of discriminative power of each gene was also characterized by partial Wilk's Lambda coefficients, which are equivalent to the partial correlation coefficient generated by multiple regression analyses. The Wilk's Lambda coefficient used a ratio of within-group differences and the sum of within-plus between-group differences. Its value ranged from 1.0 (no discriminatory power) to 0.0 (perfect discriminatory power).

Computer analysis of functional associations between differentially expressed genes

In addition to the above analyses, genes showing the most significant differences between neonatal and adult cells were characterized functionally using pre-existing databases such as PubMed, BIND, KEGG, and Ontoexpress. Biological associations of the differentially expressed genes were modelled using Ingenuity Pathways Analysis (Redwood City, CA). Data analyzed through this technique can then be resolved into cogent models of the specific biological pathways activated under the experimental conditions used in the microarray analyses.

Results

Differential gene expression analysis

Table 1 lists genes determined to be differentially expressed between cord and adult peripheral blood monocytes, as described above. No genes were found to be statistically significantly differentially expressed between adult and cord monocytes in the absence of LPS exposure. 168 genes were differentially expressed between adult and cord monocytes after 45 min incubation with LPS. 95% of these genes (159 of 168) were over-expressed in adult relative to cord monocytes. After 120 minutes of LPS exposure, 24 genes were differentially expressed between adult and cord monocytes. Of the latter genes, 23 were more highly expressed in cord than adult monocytes. This pattern of differentially expressed genes suggested an initial delayed response to LPS followed by an enhanced transcription of genes in cord relative to adult monocytes. To test this hypothesis, k-means clustering was used to categorize differentially expressed genes based on their temporal profiles. Relative decreases in gene transcription by cord monocytes at 45 min were seen in 6 of the 9 clusters (Figure 1). Each of these clusters contained between 15 and 46 genes. Examination of the clusters showed that differences between groups after 45 minutes of LPS exposure were attributable to a) genes in certain clusters that were up-regulated in adult monocytes only, b) genes in other clusters that were down-regulated in cord monocytes only, or c) genes in yet other clusters that were up-regulated in adult and down-regulated in cord monocytes. These results, summarized in a heat map in Figure 2, indicated a high complexity of gene expression differences between adult monocytes and cord blood monocytes in response to LPS.
Table 1

Differentially expressed genes between adult and cord monocytes at specific time points. T = time (min) at which the sample was taken. Numbers indicate corrected expression values.

    

Adult

Adult

Adult

Cord

Cord

 
 

Genbank #

Symbol

Gene Description

T = 0

t = 45

t = 120

t = 0

t = 45

t = 120

Apoptosis

         
 

NM_033423

CTLA1

Similar to granzyme B (granzyme 2, cytotoxic T-lymphocyte-associated serine esterase 1)

317

419

299

199

193

264

 

AB037796

PDCD6IP

Programmed cell death 6 interacting protein

75

155

68

79

70

81

 

NM_024969

TAIP-2

TGFb-induced apoptosis protein 2

63

113

107

53

68

116

 

NM_003127

SPTAN1

Spectrin, alpha, non-erythrocytic 1 (alpha-fodrin)

713

842

1171

724

824

2093

Protein synthesis, processing, degradation

         
 

AK001313

RPLP0

Ribosomal protein, large, P0

704

1465

947

703

756

669

 

NM_006799

PRSS21

Protease, serine, 21 (testisin)

204

789

457

169

360

400

 

NM_003774

GALNT4

UDP-N-acetyl-alpha-D-galactosamine:polypeptide N-acetylgalactosaminyltransferase 4 (GalNAc-T4)

576

651

648

528

378

578

 

AK057790

 

cDNA FLJ25061 fis, clone CBL04730

245

373

302

244

215

200

 

NM_004223

UBE2L6

Ubiquitin-conjugating enzyme E2L 6

128

191

146

108

99

109

 

NM_014710

GPRASP1

KIAA0443 gene product

122

182

106

113

119

95

 

NM_021090

MTMR3

Myotubularin related protein 3

109

171

137

108

87

138

 

AF339824

HS6ST3

Heparan sulfate 6-O-sulfotransferase 3

89

112

91

94

46

76

 

NM_012180

FBXO8

F-box only protein 8

40

67

42

45

33

43

 

U66589

RPL5

Ribosomal protein L5

34

48

37

30

26

36

 

NM_001870

CPA3

Carboxypeptidase A3 (mast cell)

183

495

610

146

949

756

 

NM_006145

DNAJB1

DnaJ (Hsp40) homolog, subfmaily B, member 1

179

277

408

168

299

745

 

AK025547

MRPL30

Mitochondrial ribosomal protein L30

83

118

126

81

101

211

 

NM_000439

PCSK1

Proprotein convertase subtilisin/kexin type 1

39

55

53

40

78

88

Cell/Organism Movement

         
 

NM_002067

GNA11

Guanine nucleotide binding protein (G protein), alpha 11 (Gq class)

555

870

607

540

468

664

 

NM_002465

MYBPC1

Myosin binding protein C, slow type

81

140

154

88

80

161

 

NM_003275

TMOD

Tropomodulin

276

151

481

257

344

503

 

AK026164

MYL6

Myosin, light polypeptide 6, alkali, smooth muscle and non-muscle

7

6

48

5

16

11

Small Molecule Interactions

         
 

NM_006030

CACNA2D2

Calcium channel, voltage-dependent, alpha 2/delta subunit 2

670

1390

1021

641

639

946

 

AK025170

SFXN5

FLJ21517 fis, clone COL05829

431

537

437

405

295

374

 

NM_021097

SLC8A1

Solute carrier family 8 (sodium/calcium exchanger), member 1

396

456

458

412

276

369

Signal Transduction

         
 

NM_032144

RAB6C

RAB6C

827

1658

1307

626

773

1251

 

NM_001982

ERBB3

V-erb-b2 erythroblastic leukemia viral oncogene homolog 3

603

1375

671

555

584

643

 

AK026479

SNX14

Sorting nexin 14

682

1207

879

624

567

883

 

NM_018979

PRKWNK1

Protein kinase, lysine deficient 1

451

813

782

516

480

792

 

NM_004811

LPXN

Leupaxin

329

539

445

323

298

503

 

BC005365

 

clone IMAGE:3829438, mRNA, partial cds

257

418

275

275

275

206

 

NM_004723

ARHGEF2

Rho/rac guanine nucleotide exchange factor (GEF) 2

215

300

228

197

176

186

 

AF130093

MAP3K4

Mitogen-activated protein kinase kinase kinase 4

237

285

275

221

171

223

 

AK000383

MKPX

Mitogen-activated protein kinase phosphatase x

218

221

244

233

126

197

 

NM_022304

HRH2

Histamine receptor H2

45

121

86

42

74

79

 

NM_030753

WNT3

Wingless-type MMTV integration site family member 3

105

117

92

109

63

81

 

AB024574

GTPBP2

GTP binding protein 2

89

90

99

74

57

92

 

NM_002836

PTPRA

Protein tyrosine phosphatase, receptor type, A

8

6

80

6

16

28

 

NM_003656

CAMK1

Calcium/calmodulin-dependent protein kinase I

4940

10131

4446

4785

4907

7190

Cellular Metabolism & Cell Division

         
 

NM_006170

NOL1

Nucleolar protein 1 (120 kD)

575

1815

1021

499

896

1093

 

AL133115

COVA1

Cytosolic ovarian carcinoma antigen 1

1381

1294

848

1309

658

808

 

D86962

GRB10

Growth factor receptor-bound protein 10

619

906

200

609

512

179

 

NM_005628

SLC1A5

Solute carrier family 1 (neutral amino acid transporter), member 5

338

801

600

311

397

524

 

D17525

MASP1

Mannan-binding lectin serine protease 1 (C4/C2 activating component of Ra-reactive factor)

372

654

43

361

325

55

 

NM_016518

PIPOX

Pipecolic acid oxidase

240

545

330

221

293

286

 

NM_012157

FBXL2

F-box and leucine-rich repeat protein 2

274

501

374

249

277

298

 

NM_018446

AD-017

Glycosyltransferase AD-017

301

369

337

288

223

327

 

NM_001609

ACADSB

Acyl-Coenzyme A dehydrogenase, short/branched chain

354

368

325

273

211

276

 

NM_001647

APOD

Apolipoprotein D

259

358

289

261

202

205

 

NM_012113

CA14

Carbonic anhydrase XIV

218

356

279

251

194

270

 

AB067472

DKFZP434L1435

KIAA1885 protein

150

213

186

166

119

163

 

NM_002916

RFC4

Replication factor C (activator 1) 4 (37 kD)

102

177

119

105

86

132

 

NM_004889

ATP5J2

ATP synthase, H+ transporting, mitochondrial F0 complex, subunit f, isoform 2

106

147

76

102

76

62

 

AK057066

 

cDNA FLJ32504 fis, clone SMINT1000016, weakly similar to 2-hydroxyacylsphingosine 1b

69

121

126

64

75

84

 

AK021722

AGPAT5

Lysophosphatidic acid acyltransferase, epsilon

37

71

48

42

39

46

 

NM_003664

AP3B1

Adaptor-related protein complex 3, beta 1 subunit

34

52

29

37

24

30

 

AF146760

Sept10

Septin 10

22

36

23

26

16

28

 

NM_004910

PITPNM

Phosphatidylinositol transfer protein, membrane-associated

2611

2809

2410

2974

4590

2675

 

NM_018216

FLJ10782

Pantothenic acid kinase

10

9

10

9

18

15

 

NM_001714

BICD1

Bicaudal D homolog 1 (Drosophila)

230

562

407

197

447

691

 

AK054944

LENG5

Leukocyte receptor cluster (LRC) member 5

67

100

91

78

74

158

Gene Expression

         
 

NM_005088

DXYS155E

DNA segment on chromosome X and Y (unique) 155 expressed sequence

4857

3489

3214

5177

2241

2725

 

NM_006298

ZNF192

Zinc finger protein 192

552

988

761

537

578

820

 

NM_004991

MDS1

Myelodysplasia syndrome 1

401

691

480

390

361

420

 

NM_021784

HNF3B

Hepatocyte nuclear factor 3, beta

320

632

367

347

361

391

 

AF153201

LOC58502

C2H2 (Kruppel-type) zinc finger protein

288

532

335

244

297

324

 

NM_025212

IDAX

Dvl-binding protein IDAX (inhibition of the Dvl and Axin complex)

297

490

311

303

254

241

 

AK022962

PBX1

Pre-B-cell leukemia transcription factor 1

237

456

326

245

261

345

 

NM_017617

NOTCH1

Notch-1 homolog

309

358

353

324

208

370

 

NM_001451

FOXF1

Forkhead box F1

165

347

306

177

208

328

 

NM_007136

ZNF80

Zinc finger protein 80 (pT17)

199

269

203

205

143

177

 

NM_021975

RELA

V-rel reticuloendotheliosis viral oncogene homolog A, nuclear factor of kappa light polypeptide gene

184

221

139

150

124

122

 

NM_031214

TARDBP

TAR DNA binding protein

76

154

109

74

91

90

 

NM_014007

ZNF297B

Zinc finger protein 297B

109

137

122

109

77

111

 

NM_014938

MONDOA

Mlx interactor

74

90

92

69

53

86

 

NM_005822

DSCR1L1

Down syndrome critical region gene 1-like 1

45

80

30

40

27

26

 

NM_004289

NFE2L3

Nuclear factor (erythroid-derived 2)-like 3

73

63

41

64

39

38

 

NM_054023

SCGB3A2

Secretoglobin family 3a, member 2

37

59

45

43

34

49

 

NM_012107

BP75

Bromodomain containing protein 75 kDa human homolog

44

51

34

37

22

30

 

NM_007212

RNF2

Ring finger protein 2

48

40

30

45

18

26

 

D89859

ZFP161

Zinc finger protein 161 homolog (mouse)

500

596

4280

458

481

6699

 

NM_014335

CRI1

CREBBP/EP300 inhibitory protein 1

52

84

86

57

72

196

Immune Function

         
 

NM_014889

MP1

Metalloprotease 1 (pitrilysin family)

352

401

398

379

260

351

 

NM_014312

CTXL

Cortical thymocyte receptor (X. laevis CTX) like

386

370

375

392

224

299

 

NM_002053

GBP1

Guanylate binding protein 1, interferon-inducible, 67 kD

259

369

334

245

214

251

 

NM_005356

LCK

Lymphocyte-specific protein tyrosine kinase

186

206

187

235

124

181

 

NM_000564

IL5RA

Interleukin 5 receptor, alpha

112

106

124

121

63

150

 

NM_001311

CRIP1

Cysteine-rich protein 1 (intestinal)

45

31

39

49

60

43

 

NM_002984

SCYA4

Small inducible cytokine A4 MIP1B

492

2001

2483

517

1523

3897

 

NM_002983

SCYA3

Small inducible cytokine A3 MIP1A

248

1798

2207

185

1364

3673

 

NM_014443

IL17B

Interleukin 17B

663

696

681

706

703

1155

 

NM_006018

HM74

Putative chemokine receptor-GTP-binding protein

13

25

19

15

26

34

Miscellaneous Functions

         
 

AB033041

VANGL2

Vang, van gogh-like 2 (Drosophila)

983

1246

1351

981

796

1304

 

AK021444

POSTN

Periostin, osteoblast specific factor

569

917

789

522

479

629

 

NM_003691

STK16

Serine/threonine kinase 16

403

777

458

395

348

393

 

NM_006438

COLEC10

Collectin sub-family member 10 (C-type lectin)

284

762

500

260

351

528

 

AK057699

 

FLJ33137 fis, clone UTERU1000077

375

637

613

369

392

616

 

NM_017671

C20orf42

Chromosome 20 open reading frame 42

362

557

551

280

323

478

 

AK054683

DCLRE1C

DNA cross-link repair 1C

486

555

574

476

293

515

 

NM_033060

KAP4.10

Keratin associated protein 4.10

210

245

197

154

123

172

 

AF319045

CNTNAP2

Contactin associated protein-like 2

112

215

173

120

113

176

 

NM_001046

SLC12A2

Solute carrier family 12 (sodium/potassium/chloride transporters), member 2

158

148

184

146

86

161

 

NM_016279

CDH9

Cadherin 9, type 2 (T1-cadherin)

77

112

69

65

51

64

 

NM_014208

DSPP

Dentin sialophosphoprotein

60

90

64

57

53

59

 

NM_015669

PCDHB5

Protocadherin beta 5

92

83

62

98

42

47

 

AK023198

OPRK1

Opioid receptor, kappa 1

58

76

41

48

46

38

 

NM_018240

KIRREL

Kin of IRRE like (Drosophila)

60

75

47

66

43

46

 

AK056781

ROCK1

Rho-associated, coiled-coil containing protein kinase 1

54

62

42

47

41

42

 

NM_022123

NPAS3

Basic-helix-loop-helix-PAS protein

17

22

9

16

12

13

 

NM_001246

ENTPD2

Ectonucleoside triphosphate diphosphohydrolase 2

3438

3272

3731

3767

3590

6309

Unknown Function

         
 

AK056884

 

FLJ32322 fis, clone PROST2003577

2007

2878

2008

1825

1548

1958

 

NM_017812

FLJ20420

Coiled-coil-helix-coiled-coil-helix domain containing 3

1105

1915

1370

1125

940

1358

 

AJ420459

LOC51184

Protein x 0004

661

1579

881

603

771

768

 

BC011575

 

Similar to RIKEN cDNA 0610031J06 gene, clone IMAGE:4639306

974

1556

1412

1020

844

1261

 

AK057357

FLJ32926

DKFZp434D2472

1188

1378

1159

1043

515

1136

 

NM_025019

TUBA4

tubulin, alpha 4

1446

1173

1330

1477

782

1366

 

AK023150

 

FLJ13088 fis, clone NT2RP3002102

798

1087

905

845

564

785

 

NM_017833

C21orf55

Chromosome 21 open reading frame 55

741

1079

799

687

508

665

 

BC001407

 

Similar to cytochrome c-like antigen

524

1004

629

506

502

577

 

AK023104

 

FLJ22648 fis, clone HSI07329

441

984

621

488

471

495

 

AK024617

 

FLJ20964 fis, clone ADSH00902

824

955

745

788

535

824

 

BC009536

 

IMAGE:3892368

553

924

775

597

498

671

 

AK056287

 

FLJ31725 fis, clone NT2RI2006716

435

862

907

405

459

893

 

AK021611

 

FLJ11549 fis, clone HEMBA1002968

535

812

675

545

392

630

 

BC015119

 

IMAGE:3951139

445

784

487

455

435

439

 

AK056492

 

FLJ31930 fis, clone NT2RP7006162

252

651

525

266

367

457

 

AB058711

KIAA1808

KIAA1808 protein

208

637

357

199

339

366

 

BC011266

 

IMAGE:4156795

354

632

432

356

328

460

 

AK023316

 

FLJ13254 fis, clone OVARC1000787

416

596

357

400

290

352

 

NM_024696

FLJ23058

Hypothetical protein FLJ23058

456

541

346

436

313

359

 

AF253316

 

Pheromone receptor (PHRET) pseudogene

136

520

425

128

301

347

 

AK056007

BICD1

Bicaudal D homolog 1 (Drosophila)

704

505

439

624

243

305

 

AB020632

KIAA0825

KIAA0825 protein

249

498

353

246

272

339

 

NM_017609

DKFZp434A1721

Hypothetical protein DKFZp434A1721

182

485

319

190

298

304

 

NM_018190

FLJ10715

Hypothetical protein FLJ10715

202

483

310

174

206

266

 

AK057046

 

FLJ32484 fis, clone SKNMC2001555

229

473

294

261

302

228

 

NM_013395

AD013

Proteinx0008

448

461

496

403

304

378

 

BC008501

MGC14839

Similar to RIKEN cDNA 2310030G06

379

414

329

443

264

290

 

AK021988

 

FLJ11926 fis, clone HEMBB1000374

321

411

399

280

218

288

 

AF119872

 

PRO2272

257

405

327

257

205

250

 

NM_022744

FLJ13868

Hypothetical protein FLJ13868

267

376

239

270

212

172

 

AK022364

 

FLJ12302 fis, clone MAMMA1001864

172

355

316

164

184

332

 

BC002644

MGC4859

Hypothetical protein MGC4859 similar to HSPA8

282

335

382

257

223

331

 

AK022201

 

FLJ12139 fis, clone MAMMA1000339

267

302

152

235

123

131

 

NM_017953

FLJ20729

Hypothetical protein FLJ20729

170

290

258

138

170

218

 

AK057473

 

FLJ32911 fis, clone TESTI2006210

160

268

265

163

123

247

 

U50383

RAI15

Retinoic acid induced 15

206

265

236

198

159

186

 

AK027027

 

FLJ23374 fis, clone HEP16126

134

261

170

134

152

141

 

AK057288

 

FLJ32726 fis, clone TESTI2000981

206

249

312

216

152

244

 

U79280

PIPPIN

Ortholog of rat pippin

274

229

189

238

117

134

 

AK023628

 

FLJ13566 fis, clone PLACE1008330

140

195

230

133

128

193

 

NM_025263

CAT56

CAT56 protein

126

194

147

127

101

130

 

AF311324

 

Ubiquitin-like fusion protein

191

189

179

190

106

138

 

NM_005708

GPC6

Glypican 6

107

185

144

109

88

146

 

AB037778

KIAA1357

KIAA1357 protein

153

180

156

149

118

146

 

AK055939

 

FLJ31377 fis, clone NESOP1000087

152

167

179

136

105

173

 

NM_018316

FLJ11078

Hypothetical protein FLJ11078

89

145

118

73

94

103

 

AF402776

BIC

BIC noncoding mRNA

82

136

171

96

88

153

 

BC003416

 

IMAGE:3450973

64

133

93

83

73

111

 

AL137491

 

DKFZp434P1530

62

130

88

57

72

74

 

AK057770

 

FLJ25041 fis, clone CBL03194

110

130

114

108

83

84

 

AB058769

KIAA1866

KIAA1866 protein

89

126

122

102

83

91

 

AB058747

WAC

WW domain-containing adapter with a coiled-coil region

60

124

103

57

76

77

 

AK054885

C6orf31

Chromosome 6 open reading frame 31

51

119

108

41

68

119

 

AK022235

 

FLJ12173 fis, clone MAMMA1000696

109

103

94

90

62

77

 

AK026853

AOAH

Acyloxyacyl hydrolase (neutrophil)

59

98

64

59

61

56

 

AK024877

 

FLJ21224 fis, clone COL00694

53

96

110

55

54

103

 

NM_003171

SUPV3L1

Suppressor of var1, 3-like 1 (S. cerevisiae)

65

93

60

60

55

58

 

NM_052933

TSGA13

Testis specific, 13

66

80

70

68

44

71

 

AK057907

 

FLJ25178 fis, clone CBR09176

42

77

31

47

43

41

 

AK055748

 

FLJ31186 fis, clone KIDNE2000335

88

67

68

79

44

71

 

BC013757

 

IMAGE:4525041

40

54

39

43

33

32

 

AL365511

 

Novel human gene mapping to chomosome 22

19

48

29

20

27

37

 

AK026889

APRIN

Androgen-induced proliferation inhibitor

31

35

42

34

21

34

 

AK057423

 

FLJ32861 fis, clone TESTI2003589

36

32

34

30

18

31

 

AK055543

MLSTD1

Male sterility domain containing 1

31

31

32

27

18

30

 

AK056513

 

FLJ31951 fis, clone NT2RP7007177

33

29

20

22

13

20

 

NM_013319

TERE1

Transitional epithelia response protein

22

28

19

24

17

22

 

AK026456

 

FLJ22803 fis, clone KAIA2685

15

26

14

16

13

17

 

AK021610

 

cDNA FLJ11548 fis, clone HEMBA1002944

34

26

29

31

15

28

 

AK026823

 

FLJ23170 fis, clone LNG09984

15

22

14

19

8

18

 

AK056805

 

FLJ32243 fis, clone PROST1000039

400

177

186

343

314

160

 

NM_012238

SIRT1

Sirtuin silent mating type information regulation 2 homolog 1 (S. cerevisiae)

149

156

170

178

134

109

 

NM_016099

GOLGA7

golgi autoantigen, golgin subfamily a, 7

10493

15165

9882

11947

11564

15698

 

AK022482

 

FLJ12420 fis, clone MAMMA1003049

6052

9099

5803

6362

7620

9309

 

AK026490

RAB32

RAB32, member RAS oncogene family

3677

7044

4641

3671

5553

7561

 

NM_020684

NPD007

NPD007 protein

674

794

764

630

720

1215

 

AL390158

ATXN7L3

Ataxin 7-like 3

319

460

378

339

403

598

 

NM_017752

FLJ20298

Hypothetical protein FLJ20298

146

237

282

133

233

493

 

AB037743

KIAA1322

KIAA1322 protein

236

202

199

239

246

319

 

AF339819

 

clone IMAGE:38177

77

111

110

96

125

174

 

AK055215

 

FLJ30653 fis, clone DFNES2000143

47

48

58

43

80

92

Figure 1

LPS-stimulated genes in cord blood and adult monocytes can be differentiated on the basis of kinetics of expression. Expression level (in relative intensity units) is shown of the y-axis and time on the x-axis. At the 45 min time point, significant differences in expression level were seen between adult and neonatal monocytes for each of the gene groups A-H.

Figure 2

Heat map representation of differences in gene expression of adult and cord blood monocytes in response to LPS. Z-transformed scores of the mean expression values for adult monocytes prior to (A0), after 45 min (A45), and after 120 min (A120) of LPS exposure are graphically shown to the left. Similar scores from cord blood monocytes prior to (C0), after 45 min (C45), and after 120 min C120) of LPS exposure, respectively. The heat map was produced using software from Spotfire Decision Site (Somerville, MA).

In addition to the above genes which differed in expression between groups following LPS exposure, 516 genes were also identified that were differentially expressed over time within a group. A supplementary table containing these data is available upon request. For these genes, a similar pattern of dynamic expression was seen as was observed in the other group. Therefore, these genes reflect common responses to LPS in monocytes from both sources.

A subset of genes that were differentially expressed either between adult and cord blood monocytes were selected for validation using the quantitative real-time polymerase chain reaction method (QRT-PCR). These included four genes that differed between groups after 45 min of LPS exposure (Erbb3, Tmod, Dscr1l1, and Sp1), and six genes that differed in expression after 2 hours of LPS exposure (Scya4, Gro2, Cri1, Scya3, Scya3l1, and Il-1a). Nine of the ten genes tested for QRT-PCR validation demonstrated similar levels of relative expression in QRT-PCR experiments as in the microarrays. Only CRI1 failed to corroborate the microarray data.

Hypervariable gene analysis

One hundred eighty-eight hypervariable (HV) genes were selected from expressed genes in adult and cord blood monocytes based on their changes across three time points. These genes exhibited significantly higher expression variation over time than the majority of genes. Differences in variation between two experimental sample sets, in this case adult and neonatal samples, can represent differences in homeostatic control mechanisms between these two sets [20]. The selected genes were hypervariable in both sample groups. HV genes with highly correlated expression levels in a given population are likely to share function [20]. A correlation based clustering procedure was carried out for these HV genes as described in the methods section. Genes belonging to the 5 largest clusters were used for creation of a graphical output, denoted a correlation mosaic. A correlation mosaic allows identification of the genes within clusters by visual inspection and subsequent functional analysis of genes within clusters (Figures 3A &3B). Figure 3A represents 110 genes of the same cluster allocation between adult and cord blood monocyte samples, demonstrating a very high similarity between cells from these two groups, as measured by the correlation coefficients between genes from adult and cord monocytes with value > 0.90 (figure 3A, black and white graph to the right). Three genes on this list (#101–103) were the exception: transcriptional regulator interacting with the PHS-bromodomain 2 (Trip-Br2), interleukin 1 beta (Il1b), and the GRO2 oncogene(Gro2). These genes may play a critical role in differentiation between adult and cord monocyte behaviour [22, 23]. The high similarity of these mosaics presents evidence for the presence of fundamental processes in monocyte development that appear to be quite similar in both groups of samples. The details of the genes used in Figure 3A are presented as Table 2. Another group of 78 genes were found that have different cluster designations between adult and cord blood monocytes (Figure 3B). Details of these genes are listed in Table 3.
Figure 3

Correlative mosaic for genes selected as HV-genes in cord blood and adult monocytes, belonging to five clusters of highest content. A. Genes of the same cluster in cord and adult; B. Genes of different cluster in cord and adult. Correlation coefficients are color-coded according to the key in the upper right. The correlation between the adult and cord blood monocyte profiles for each gene are shown in black and white, lower right.

Table 2

Genes from which correlation mosaics in Figure 3A were derived. Genes in this table show the highest level of correlation by DFA analysis comparing adult and cord blood monocytes.

Order in mosaic

Accession No.

Gene symbol

Description

1

NM_017614

BHMT2

Betaine-homocysteine methyltransferase 2

2

NM_001651

AQP5

Aquaporin 5

3

NM_020163

LOC56920

Semaphorin sem2

4

NM_012343

NNT

Nicotinamide nucleotide transhydrogenase

5

NM_000096

CP

Ceruloplasmin (ferroxidase)

6

NM_005819

STX6

Syntaxin 6

7

NM_052951

C20orf167

Chromosome 20 open reading frame 167

8

NM_001348

DAPK3

Death-associated protein kinase 3

9

X73502

KRT20

Cytokeratin 20

10

NM_052887

TIRAP

Toll-interleukin 1 receptor (TIR) domain-containing adapter protein

11

NM_019555

ARHGEF3

Rho guanine nucleotide exchange factor (GEF) 3

12

NM_014380

NGFRAP1

Nerve growth factor receptor (TNFRSF16) associated protein 1

13

NM_001272

CHD3

Chromodomain helicase DNA binding protein 3

14

NM_005842

SPRY2

Sprouty homolog 2 (Drosophila)

15

NM_012332

MT-ACT48

Mitochondrial Acyl-CoA Thioesterase

16

BC015041

VATI

Vesicle amine transport protein 1

17

NM_003872

NRP2

Neuropilin 2

18

NM_005849

IGSF6

Immunoglobulin superfamily, member 6

19

NM_014323

ZNF278

Zinc finger protein 278

20

NM_030674

SLC38A1

Solute carrier family 38, member 1

21

NM_004153

ORC1L

Origin recognition complex, subunit 1-like (yeast)

22

NM_005249

FOXG1B

Forkhead box G1B

23

NM_021048

MAGEA10

Melanoma antigen, family A, 10

24

M60502

FLG

Filaggrin

25

NM_004997

MYBPH

Myosin binding protein H

26

J05046

INSRR

Insulin receptor-related receptor

27

M33987

CA1

Carbonic anhydrase I

28

D31886

RAB3GAP

RAB3 GTPase-ACTIVATING PROTEIN

29

L24498

GADD45A

Growth arrest and DNA-damage-inducible, alpha

30

L07590

PPP2R3

Protein phosphatase 2 (formerly 2A), regulatory subunit B" (PR 72), alpha isoform and (PR 130), bet

31

D87024

IGLV4-3

Immunoglobulin lambda variable 4-3

32

L35848

MS4A3

Membrane-spanning 4-domains, subfamily A, member 3 (hematopoietic cell-specific)

33

M18216

CEACAM6

Carcinoembryonic antigen-related cell adhesion molecule 6 (non-specific cross reacting antigen)

34

M11952

TRBV7–8

T cell receptor beta variable 7–8

35

D89094

PDE5A

Phosphodiesterase 5A, cGMP-specific

36

M77140

GAL

Galanin

37

D13628

ANGPT1

Angiopoietin 1

38

M81635

EPB72

Erythrocyte membrane protein band 7.2 (stomatin)

39

D89859

ZFP161

Zinc finger protein 161 homolog (mouse)

40

D26069

CENTB2

Centaurin, beta 2

41

L10717

ITK

IL2-inducible T-cell kinase

42

L04282

ZNF148

Zinc finger protein 148 (pHZ-52)

43

L41944

IFNAR2

Interferon (alpha, beta and omega) receptor 2

44

M82882

ELF1

E74-like factor 1 (ets domain transcription factor)

45

L26339

RCD-8

Autoantigen

46

D87328

HLCS

Holocarboxylase synthetase (biotin-[proprionyl-Coenzyme A-carboxylase (ATP-hydrolysing)] ligase)

47

D00943

MYH6

Myosin, heavy polypeptide 6, cardiac muscle, alpha (cardiomyopathy, hypertrophic 1)

48

D00099

ATP1A1

ATPase, Na+/K+ transporting, alpha 1 polypeptide

49

L36531

ITGA8

Integrin, alpha 8

50

D42084

METAP1

Methionyl aminopeptidase 1

51

M76766

GTF2B

General transcription factor IIB

52

J04621

SDC2

Syndecan 2 (heparan sulfate proteoglycan 1, cell surface-associated, fibroglycan)

53

D31888

RCOR

REST corepressor

54

L32832

ATBF1

AT-binding transcription factor 1

55

D86981

APPBP2

Amyloid beta precursor protein (cytoplasmic tail) binding protein 2

56

M94362

LMNB2

Lamin B2

57

M54968

KRAS2

V-Ki-ras2 Kirsten rat sarcoma 2 viral oncogene homolog

58

D42046

DNA2L

DNA2 DNA replication helicase 2-like (yeast)

59

D86964

DOCK2

Dedicator of cyto-kinesis 2

60

D50683

TGFBR2

Transforming growth factor, beta receptor II (70–80 kD)

61

M96843

ID2B

Striated muscle contraction regulatory protein

62

M61906

PIK3R1

Phosphoinositide-3-kinase, regulatory subunit, polypeptide 1 (p85 alpha)

63

M12679

HUMMHCW1A

Cw1 antigen

64

M63623

OMG

Oligodendrocyte myelin glycoprotein

65

J04162

FCGR3B

Fc fragment of IgG, low affinity IIIb, receptor for (CD16)

66

L48516

PON3

Paraoxonase 3

67

M54927

PLP1

Proteolipid protein1 (Pelizaeus-Merzbacher disease, spastic paraplegia 2, uncomplicated)

68

D86973

GCN1L1

GCN1 general control of amino-acid synthesis 1-like 1 (yeast)

69

D43968

RUNX1

Runt-related transcription factor 1 (acute myeloid leukemia 1-aml1 oncogene)

70

L05500

ADCY1

Adenylate cyclase 1 (brain)

71

D80010

LPIN1

Lipin 1

72

D50918

SEPT6

Septin 6

73

D86988

RENT1

Regulator of nonsense transcripts 1

74

M90391

IL16

Interleukin 16 (lymphocyte chemoattractant factor)

75

M62324

MRF-1

Modulator recognition factor I

76

L77565

DGS-H

DiGeorge syndrome gene H

77

D86970

TIAF1

TGFB1-induced anti-apoptotic factor 1

78

D38169

ITPKC

Inositol 1,4,5-trisphosphate 3-kinase C

79

D87684

UBXD2

UBX domain-containing 2

80

D84454

SLC35A2

Solute carrier family 35 (UDP-galactose transporter), member 2

81

M97496

GUCA2A

Guanylate cyclase activator 2A (guanylin)

82

M95585

HLF

Hepatic leukemia factor

83

L38517

IHH

Indian hedgehog homolog (Drosophila)

84

L20860

GP1BB

Glycoprotein Ib (platelet), beta polypeptide

85

M26880

UBC

Ubiquitin C

86

D86962

GRB10

Growth factor receptor-bound protein 10

87

D63481

SCRIB

Scribble

88

D17525

MASP1

Mannan-binding lectin serine protease 1 (C4/C2 activating component of Ra-reactive factor)

89

L26584

RASGRF1

Ras protein-specific guanine nucleotide-releasing factor 1

90

M65066

PRKAR1B

Protein kinase, cAMP-dependent, regulatory, type I, beta

91

J05158

CPN2

Carboxypeptidase N, polypeptide 2, 83 kD

92

L36861

GUCA1A

Guanylate cyclase activator 1A (retina)

93

L11239

GBX1

Gastrulation brain homeo box 1

94

D90145

SCYA3L1

Small inducible cytokine A3-like 1

95

M96739

NHLH1

Nescient helix loop helix 1

96

M12959

TRA@

T cell receptor alpha locus

97

D80005

C9orf10

C9orf10 protein

98

M13231

TRGC2

T cell receptor gamma constant 2

99

D28588

SP2

Sp2 transcription factor

100

M57732

TCF1

Transcription factor 1, hepatic-LF-B1, hepatic nuclear factor (HNF1), albumin proximal factor

101

NM_014755

TRIP-Br2

Transcriptional regulator interacting with the PHS-bromodomain 2

102

NM_000576

IL1B

Interleukin 1, beta

103

NM_002089

GRO2

GRO2 oncogene

104

NM_002089x

GPRC5D

G protein-coupled receptor, family C, group 5, member D

105

NM_002713

PPP1R8

Protein phosphatase 1, regulatory (inhibitor) subunit 8

106

NM_014383

TZFP

Testis zinc finger protein

107

NM_012248

SPS2

Selenophosphate synthetase 2

108

AL137438

SEC15L

SEC15 (S. cerevisiae)-like

109

NM_005387

NUP98

Nucleoporin 98 kD

110

NM_003476

CSRP3

Cysteine and glycine-rich protein 3 (cardiac LIM protein)

Table 3

Genes from which the mosaic in Figure 3B were derived. Genes from which correlation mosaics in Figure 3B were derived. Genes in this table show the greatest differences by DFA analysis comparing adult and cord blood monocytes.

Order in Mosaic

Accession No.

Gene Symbol

Description

1

AK055855

CLDN10

Claudin 10

2

NM_000565

IL6R

Interleukin 6 receptor

3

NM_006150

LMO6

LIM domain only 6

4

NM_022787

NMNAT

NMN adenylyltransferase-nicotinamide mononucleotide adenylyl transferase

5

NM_002743

PRKCSH

Protein kinase C substrate 80K-H

6

NM_004847

AIF1

Allograft inflammatory factor 1

7

NM_021073

BMP5

Bone morphogenetic protein 5

* 8

AK025306

CLK1

CDC-like kinase 1

9

NM_004280

EEF1E1

Eukaryotic translation elongation factor 1 epsilon 1

* 10

NM_004432

ELAVL2

ELAV (embryonic lethal, abnormal vision, Drosophila)-like 2 (Hu antigen B)

11

NM_012181

FKBP8

FK506 binding protein 8 (38 kD)

12

NM_002091

GRP

Gastrin-releasing peptide

13

NM_016355

LOC51202

Hqp0256 protein

14

NM_021204

MASA

E-1 enzyme

15

NM_004204

PIGQ

Phosphatidylinositol glycan, class Q

16

NM_002928

RGS16

Regulator of G-protein signalling 16

17

NM_005839

SRRM1

Serine/arginine repetitive matrix 1

18

NM_003166

SULT1A3

Sulfotransferase family, cytosolic, 1A, phenol-preferring, member 3

19

NM_000356

TCOF1

Treacher Collins-Franceschetti syndrome 1

20

NM_016437

TUBG2

Tubulin, gamma 2

* 21

NM_022568

ALDH8A1

Aldehyde dehyrdogenase 8 family, member A1

22

AF209930

CHRD

Chordin

23

NM_005274

GNG5

Guanine nucleotide binding protein (G protein), gamma 5

24

NM_018384

IAN4L1

Immune associated nucleotide 4 like 1 (mouse)

25

NM_000640

IL13RA2

Interleukin 13 receptor, alpha 2

26

AK021692

LOC51141

Insulin induced protein 2

27

NM_012443

SPAG6

Sperm associated antigen 6

28

NM_003155

STC1

Stanniocalcin 1

29

NM_022003

FXYD6

FXYD domain-containing ion transport regulator 6

30

NM_002763

PROX1

Prospero-related homeobox 1

31

NM_002836

PTPRA

Protein tyrosine phosphatase, receptor type, A

32

AL136835

TOLLIP

Toll-interacting protein

33

AB058691

ALX4

Aristaless-like homeobox 4

34

AF112345

ITGA10

Integrin, alpha 10

35

NM_022788

P2RY12

Purinergic receptor P2Y, G protein-coupled, 12

36

NM_001213

C1orf1

Chromosome 1 open reading frame 1

37

NM_005860

FSTL3

Follistatin-like 3 (secreted glycoprotein)

38

NM_013320

HCF-2

Host cell factor 2

39

NM_058246

LOC136442

Similar to MRJ gene for a member of the DNAJ protein family

40

NM_020169

LXN

Latexin protein

41

BC008993

MGC17337

Similar to RIKEN cDNA 5730528L13 gene

42

BC002712

MYCN

V-myc myelocytomatosis viral related oncogene, neuroblastoma derived (avian)

43

AK026164

MYL6

Myosin, light polypeptide 6, alkali, smooth muscle and non-muscle

44

NM_006215

SERPINA4

Serine (or cysteine) proteinase inhibitor, clade A (alpha-1 antiproteinase, antitrypsin), member 4

45

NM_004790

SLC22A6

Solute carrier family 22 (organic anion transporter), member 6

46

NM_022911

SLC26A6

Solute carrier family 26, member 6

47

NM_003374

VDAC1

Voltage-dependent anion channel 1

48

NM_017818

WDR8

WD repeat domain 8

49

NM_003416

ZNF7

Zinc finger protein 7 (KOX 4, clone HF.16)

50

NM_002313

ABLIM

Actin binding LIM protein

51

NM_012074

CERD4

Cer-d4 (mouse) homolog

52

NM_000787

DBH

Dopamine beta-hydroxylase (dopamine beta-monooxygenase)

* 53

NM_000561

GSTM1

Glutathione S-transferase M1

54

BC014075

GTPBP1

GTP binding protein 1

55

NM_033260

HFH1

Winged helix/forkhead transcription factor

56

NM_033033

KRTHB2

Keratin, hair, basic, 2

57

NM_004789

LHX2

LIM homeobox protein 2

58

NM_014106

PRO1914

PRO1914 protein

* 59

NM_006799

PRSS21

Protease, serine, 21 (testisin)

* 60

NM_002900

RBP3

Retinol binding protein 3, interstitial

61

NM_033022

RPS24

Ribosomal protein S24

* 62

AB029021

TRIM35

Tripartite motif-containing 35

* 63

NM_020989

CRYGC

Crystallin, gamma C

* 64

BI198124

HMG1L10

High-mobility group (nonhistone chromosomal) protein 1-like 10

65

NM_014163

HSPC073

HSPC073 protein

66

AF181985

JIK

STE20-like kinase

67

NM_017607

PPP1R12C

Protein phosphatase 1, regulatory (inhibitor) subunit 12C

* 68

NM_002873

RAD17

RAD17 homolog (S. pombe)

69

NM_022095

ZNF335

Zinc finger protein 335

* 70

M90355

BTF3L2

Basic transcription factor 3, like 2

71

NM_002079

GOT1

Glutamic-oxaloacetic transaminase 1, soluble (aspartate aminotransferase 1)

72

NM_004146

NDUFB7

NADH dehydrogenase (ubiquinone) 1 beta subcomplex, 7 (18 kD, B18)

73

L38486

MFAP4

Microfibrillar-associated protein 4

* 74

AF111848

ACTB

Actin, beta

75

NM_001916

CYC1

Cytochrome c-1

We analyzed these genes using DFA in order to find those genes most likely to highlight the differences between cord and adult monocytes. DFA identified genes having high discriminatory capabilities. The DFA software selected genes from Table 3 with highest discriminatory capabilities for this case. A total of 12 genes (marked with asterisk in Table 3) were used by the DFA program to differentiate dynamical changes in both cord and adult monocytes after LPS stimulation. Values of the roots obtained by DFA analysis were used to graphically depict the differences of the gene expression values obtained in cord and adult samples in different stages after stimulation (Fig. 4). The spatial organization of the elements in this representation provides a measure of the overall similarity of the dynamic behaviour of these samples. The greatest temporal changes in gene expression for cord and adult monocytes noted above after 45 min of LPS exposure were also observed in the analysis using these 12 genes. However, almost no differences occurred at the 2 hr time point between cord and adult cells suggesting that the global behavior of the cells is similar, but the kinetics of change differ i.e. many of the changes are the same in both groups, but they occur at different rates.
Figure 4

DFA analysis of phases of monocyte activation comparing cord and adult cells. DFA identified a subset of genes (see Table 3) whose expression values can be linearly combined in an equation, denoted a root, whose overall value is distinct for a given characterized group. These roots used as coordinate for presentation of these groups of samples in scatterplot. Results from individual samples for adult monocyte (circles) and cord monocytes (triangles) are discussed in the text. Results from individual samples for adult monocyte (circles) and cord monocytes (triangles) are shown.

Apoptosis assays

The products of a subset of genes that were differentially expressed between groups after 45 min exposure to LPS are involved in apoptosis. We therefore performed a series of functional experiments comparing apoptosis in adult (n = 10) and neonatal (n = 10) cord bloods. Results of these assays are shown in Table 4. Annexin assays demonstrated that adult monocytes display different kinetics for both apoptosis and necrosis as compared with neonatal monocytes. Flow cytometry revealed that 43 ± 5% (mean + SD) of adult and 53 + 8% of neonatal monocytes are undergoing apoptosis after stimulation with LPS for 14 hours (p < 0.002), while 38 + 8% of adult and 25 + 9% of neonatal monocytes are necrotic after 14 hours of LPS stimulation (p < 0.003). The number of live monocytes after 14 hours of LPS stimulation was not statistically different between the two groups. There was also no statistically significant difference in the number of live, apoptotic, or necrotic monocytes between adult and neonatal samples prior to LPS stimulation (data not shown).
Table 4

Results of Annexin Binding Assays

Cell Type

Apoptotic Cells

Necrotic Cells

Significance

Adult monocytes

43 ± 5%

38 % ± 8%

P < 0.002

Cord blood monocytes

53 ± 8%

25% ± 9%

P < 0.003

Discussion

Following a given physiologic stimulus, signalling kinase activation, transcription factor translocation, and gene transcription all occur in rapid order. However, like all biological processes, mRNA accumulation (or decreases) does not occur uniformly, and we hypothesized that examining the kinetics of mRNA accumulation or disappearance might provide clues into relevant cellular dynamics. We used a well-developed and validated gene expression microarray to examine the dynamics of mRNA accumulation and differences between adult and neonatal monocytes in that process.

Genes were found to be differentially expressed between adult and cord monocytes after either 45 or 120 minutes of LPS exposure, with little difference at 24 hr (see Figure 4). Interestingly, no statistically significant differences in gene expression were observed between these groups in untreated cells. Previous reports by others indicated altered functions of cord blood monocytes in cytokine secretion and cellular adhesion. Results from this study cast new light on these findings and add complexity to understanding such differences. In some cases, our data support previous speculations about neonatal immune function. For example, the increased expression of IL-17B in neonatal monocytes is consistent with the observations of Vanden Eijnden and colleagues that newborns compensate for their relative immune deficiency by over-expression of the IL23-IL-17 signalling pathway in dendritic cells [24]. Similarly, we found significant elevations in cord monocyte transcripts of the chemokines MIP1B and MIP1A after 2 hrs of LPS exposure, consistent with Sullivan and colleagues' report of higher amounts of MIPα in cord blood samples compared with adults [25]. On the other hand, transcripts for cadherin 9, Rock1, periostin, heparin sulfate 6-O-sulfotransferase 3, and C20orf42, whose products participate in various mechanisms that are associated with adhesion [2628] were statistically significantly increased in adult monocytes after 45 min of LPS exposure, although no differences in expression for these genes between groups were detected at the later time point. These data suggest complex, dynamic relations for genes whose products are associated with cellular adhesion, and collectively highlight the importance of examining gene expression profiles (or related protein expression levels) over time.

The limits of gene expression profiling as a technique, albeit a very useful technique, must be acknowledged. The technique examines only RNA transcripts, not protein synthesis. Thus, alterations in other critical inflammatory mediators, such as eicosanoids, remain unobserved with this method. Furthermore, it is well known that there are many proteins, including critical inflammatory mediators, whose synthesis and secretion is not directly related in mRNA accumulation [29]. Thus, gene expression profiling should be complemented with other methods in order to maximize there potential.

In the final analysis, the utility of gene expression profiling will be demonstrated only if they provide insights into relevant physiologic or pathophysiologic function. For that reason, we elected to test the validity of the array data by examining a physiologic mechanism implicated by computer modelling of the array data. As noted in Table 1, adult monocytes over-expressed a small number of genes associated with the regulation of apoptosis. Since monocyte activation is a "balancing act" between signals inducing apoptosis and those inducing activation and differentiation [30, 31], differences in the kinetics of expression or activation of enzymes or transcription factors that regulate apoptosis could have a crucial outcome on whether monocyte responses are pro- or anti-inflammatory. Annexin assays confirmed that there are significant differences in the appearance of apoptotic cells between adults and newborn monocytes (Table 4). Since apoptotic cells dampen the inflammatory response, it is interesting to speculate that the related blunted neonatal response to inflammatory stimuli (including infection) may result, at least in part, from the excessive production of apoptotic cells during monocyte activation.

There has been, to our knowledge, one previously published paper using gene expression arrays to study neonatal monocyte function [14]. Our findings differ somewhat from those described by these authors. The most obvious difference was our finding of no statistically significant differences between adult and cord blood samples in the resting state. We should note, however, that it is otherwise difficult to compare the two studies. Jiang and colleagues used a 1000-fold greater dose of LPS to stimulate the monocytes, and RNA was prepared after 18 hr of stimulation. Thus, it is difficult to determine which of the effects observed by these authors were the direct result of LPS activation or were mediated through autocrine activation by proteins secreted in response to LPS. Furthermore, the non-physiologic dose of LPS used by those authors makes the biological/pathological relevance of that study difficult to interpret. Finally, we should note that the study by Jiang and colleagues used different methodologies for purifying monocytes. While our method, positive selection using CD14-coated microbeads, carries the theoretical risk of activating the cells through TLR-4/CD14 signaling pathways, adherence procedures carry the greater risk of activating the cells, as β2 integrins are activated during the adherence process.

From the bioinformatics standpoint, our data demonstrate how gene microarray experiments can quickly move from the generation of gene lists to the development of plausible and testable models of relevant biology and physiology. Specifically, they demonstrate that computer-assisted, physiologic modelling is another means of corroborating array findings and provides the advantage of providing an approach for immediately testing the biological relevance of microarray data before embarking on the sometimes laborious task of confirming differential expression of dozens or even hundreds of genes identified in a microarray experiment. As described in the results section, the differences between groups in gene expression at 45 min were attributable to a unique up-regulation of specific genes in adult monocytes, a unique down-regulation of other genes in cord monocytes, or a combination of both processes for other genes. We have searched for mechanisms that account for these patterns. Specifically, we have analyzed the genes within derived k-means clusters to determine if a large number of genes within a cluster are related to overlapping functions using Ingenuity Pathway Assist software, or alternatively to shared transcriptional response elements upstream of these genes. However, these strategies have failed to elucidate reasons to explain these findings.

Our studies also suggest that, while expensive and time-consuming to undertake, studying the kinetics of gene expression using microarrays can be highly informative. The previously reported study [14] examining gene expression differences between adult and cord blood monocytes was performed at only a single time point (18 hr after activation with a non-physiologic dose of LPS). Our studies suggest that the relevant biology may lie not in the specific genes that are differentially expressed at one particular time point, but, as one would predict with a dynamic system, which genes are expressed when. Timing of mRNA accumulation could determine, among other things, whether pro-apoptotic signals are processed in monocytes before cellular necrosis ensues.

The validity of the dynamic/kinetic approach is further supported by the correlation analyses (Figures 3 and 4). These analyses demonstrate clearly that the accumulation of a specific mRNA is not an independent event. Gene transcription and mRNA degradation are dynamic processes closely tied to the accumulation or degradation of other mRNAs and the transcription of their cognate proteins. We contend that, without this dynamic view of cellular activity, investigators attempting to use microarray data to elucidate relevant biological or pathological processes will encounter unnecessary obstacles in attempts to move from the generation of gene lists to testing specific hypotheses.

Notes

Abbreviations

LPS: 

Lipopolysaccharide

DFA: 

Discriminant function analysis

HV: 

Hypervariable

Declarations

Acknowledgements

Supported in part by the National Institutes of Health (NIH), National Center for Research Resources, a component of the NIH, General Clinical Research Center Grant MO1 RR-14467, NIH grants P20 RR020143-01, P20 RR15577, P20 RR17703, and P20 R016478-04 and by the Oklahoma Center for Science and Technology (OCAST).

The authors also wish to extend their thanks to Julie McGhee, M.D., for her review and thoughtful comments on this manuscript.

Authors’ Affiliations

(1)
Dept. of Pediatrics, Neonatal Section, University of Oklahoma College of Medicine
(2)
Arthritis & Immunology Program Oklahoma Medical Research Foundation

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© Lawrence et al; licensee BioMed Central Ltd. 2007

This article is published under license to 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.

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