gene profile in the spleen under massive partial hepatectomy using complementary dna microarray and...

Arakawa Y. et al.

This article is protected by copyright. All rights reserved.

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Article type: Original Article - Hepatology (Experimental)

Received date: 28-Sep-2013

Accepted date: 24-Feb-2014

Original article

The gene profile in the spleen under massive partial

hepatectomy using cDNA microarray and pathway analysis 1

Running title: expression and change of splenic genes after hepatectomy

Key words: EGR1, GRO1, microarray, massive hepatectomy, splenectomy

Yusuke Arakawa, Mitsuo Shimada, Tohru Utsunomiya, Satoru Imura,

Yuji Morine, Tetsuya Ikemoto, Hiroki Mori, Mami Kanamoto, Shuichi

Iwahashi, Yu Saito, Chie Takasu

From the Department of Surgery, Institute of Health Biosciences,

The University of Tokushima, 3-18-15 Kuramoto-cho, Tokushima 770-8503, JAPAN

Correspondence and reprint requests:

YUSUKE ARAKAWA, MD

Assistant Professor, Institute of Health Biosciences, Department of Surgery,

The University of Tokushima Graduate School

3-18-15 Kuramoto, Tokushima. 770-8503 JAPAN.

This article has been accepted for publication and undergone full peer review but has not been through the

copyediting, typesetting, pagination and proofreading process, which may lead to differences between this

version and the Version of Record. Please cite this article as doi: 10.1111/jgh.12573 Acc

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(Phone) +81-88-633-7137 (FAX) +81-88-631-9698

(E-mail) [email protected]

Abbreviations

hepatectomy (Hx)

complementary DNA (cDNA)

early growth response-1 (EGR1)

Gene ontology (GO)

FBJ murine osteosarcoma (FOS)

Activating transcription factor 3 (ATF3)

Tumor necrosis factor alpha (TNFɑ)

Interferon gamma (IFNγ)

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Abstract

Background: In general, the spleen is one of the abdominal organs connected by the portal

system, and a splenectomy improves hepatic functions in the settings of partial hepatectomy

(Hx) for portal hypertensive cases or living donor liver transplantation with excessive portal

vein flow. Those precise mechanisms remain still unclear; therefore, we investigated the

DNA expression profile in the spleen after 90% Hx in rats using cDNA microarray and

pathway analysis.

Methods: mRNAs were prepared from three rat spleens at each time point (0, 3 and 6h

after 90% Hx). Using the gene chip, mRNA was hybridized to Affymetrix GeneChip Rat

Genome 230 2.0 Array (Affymetrix®) and pathway analysis was done with Ingenuity

Pathway Analysis (IPA®).

Results: We determined the 3h or 6h / 0h ratio to assess the influence of Hx, and

cut-off-values were set at more than 2.0-fold or less than 1/2 (0.5)-fold. Chemokine activity

related genes including Cxcl1 (GRO1) and Cxcl2 (MIP-2) related pathway were

up-regulated in the spleen. Also, immediate early response genes including early growth

response-1 (EGR1), FBJ murine osteosarcoma (FOS) and activating transcription factor 3

(ATF3) related pathway were up-regulated in the spleen.

Conclusions: We concluded that in the spleen the expression of numerous inflammatory

related genes would occur after 90% Hx. The spleen could take a harmful role and provide

a negative impact during post Hx phase due to the induction of chemokine and transcription

factors including GRO1 and EGR1. Acc

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Introduction

The results of hepatobiliary surgeries for malignant tumors such as hepatocellular

carcinoma or bile duct cancer have steadily improved (1, 2); however, liver failure after a

partial hepatectomy (Hx) was still the major cause of morbidity and mortality (3).

It is well recognized that the spleen is one of the abdominal organs, which connected

with the portal system. Kim et al. reported the negative relation between liver and spleen

after Hx (4). In the settings of Hx for portal hypertensive cases or living donor liver

transplantation, it had been reported that a splenectomy induced significant reductions in

excessive portal system pressure, resulting in improving hepatic functions including serum

albumin level, prothrombin time and number of platelets (5-7). Furthermore, recent reports

have suggested that a splenectomy might cause some anti-inflammatory effects in the portal

systems on massive Hx or hepatic ischemia reperfusion injury due to a reduction of the

number of splenic monocytes or macrophages, although the exact mechanism was still

unclear (8). We have previously reported that a splenectomy had beneficial effects owing to

the protection of the hepatocytes by the induction of heat shock proteins in a massive Hx rat

model (9). However, the large “black-box” still remained. No research has focused on what

happened in the spleen after Hx, although many reports and studies mentioned about the

gene profile in the liver under massive Hx (10, 11). This was because that a new study was

needed to be performed in this field to reveal the molecular mechanism and role of the

spleen under that condition.

Nowadays, large-scale gene expression analysis with complementary DNA (cDNA) Acc

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microarray has been gaining importance in biology and chemistry. The quantity of

information obtained from analysis of the expression of thousands of genes at once not only

created unique opportunities for research but also posed substantial challenges (10). As we

described recently, numerous genes and pathways were expressed in the liver of the rat with

massive Hx using cDNA microarray and pathway analysis (12).

In the current study, the molecular changes in the spleen after massive Hx were

investigated using cDNA microarray and pathway analysis methods.

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Materials and Methods

Animals. 6x-week-old male Wister rats, weighing 180-220g were obtained from Charles

River Laboratories (Kanagawa, Japan). Animals were provided with water and standard

laboratory diet for at least 7 days before use. Throughout the experiment, the animals were

maintained behind barriers under controlled conditions and had free access to tap water and

food before and after operation. The experiments and procedures were approved by the

Animal Care and Use Committee of the University of Tokushima.

Experimental protocols. Animals were randomly divided into following three groups (n=3,

each); sham operation group, 3h after 90% partial Hx (3h group) and 6h after 90% Hx (6h

group). Surgeries were performed under ether anesthesia. In sham operation group, mouse

underwent only simple laparotomy without hepatic resection. Hepatic resection was

performed according to the technique of Higgins and Anderson (9). Then splenic artery and

vein were ligated with 4-0 silk and spleen was removed from the abdominal space after the

hepatic resection was performed. Consecutively, animals were sacrificed at 3 and 6h after

surgery. Immediately before the sacrifice, the spleens for further study were obtained.

RNA isolation and purification. Total RNA was isolated from frozen spleens manually by

Trizol reagent (Invitrogen Corporation, Carls bad, California, USA) (13) and then purified

using RNeasy Mini Kit (QIAGEN, Inc, Valencia, CA, USA, Cat.No.74106.) (14). Total

RNA samples concentration and purity were examined and estimated by optical density Acc

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measurements at 260/280 nm (NanoDrop, ND-1000 Spectrophotometer, NanoDrop

Technologies, Inc, Wilmington, DE, USA). After RNA extraction, equal amounts of

samples from three rats were mixed together in each group for further microarray analysis.

Preparation of sample and gene chip analysis. We committed Bio Matrix Research Inc

(105 Higashifukai, Nagareyama-shi, 270-0101 Chiba, Japan) to the cDNA microarray.

Isolated total RNA were amplified and labeled as described in the GeneChip Expression

Analysis Technical Manual (Affymetrix, Santa Clara, CA). First, total RNA (1 µg) was

converted into double-stranded cDNA using the One-Cycle cDNA Synthesis Kit

(Affymetrix). Double-stranded cDNA was purified by using a GeneChip Sample Cleanup

Module (Affymetrix). In vitro transcription reactions were performed using a GeneChip

IVT Labeling Kit, which includes T7 RNA polymerase and Biotin-labeled ribonucleotides.

Biotin-labeled cRNA was purified using a GeneChip Sample Cleanup Module. The

concentration of cRNA was calculated from light absorbance at 260 nm using a UV

spectrophotometer. Next, cRNA (15 µg) was fragmented at 94°C in the presence of a

fragmentation buffer (Affymetrix). Fifteen micrograms of the cRNA was hybridized to

Affymetrix GeneChip Rat Genome 230 2.0 Array (Affymetrix). The array was incubated

for 16 hr at 45°C, then automatically washed and stained with GeneChip Hybridization,

Wash and Stain Kit (Affymetrix). The Probe Array was scanned using a GeneChip Scanner

3000 7G. The data was analyzed with Microarray Suite version 5.0 (MAS 5.0).

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Normalization and analysis of microarray data. Further analysis was carried out with

GeneSpring®7.3.1 (Agilent Technologies, Palo Alto, CA). Additionally, gene function was

evaluated using web tools. In per-chip normalization, a raw intensity value was divided by

the median value of the chip measurements. In per-gene normalization, each gene was

normalized to the respective control, to enable of relative changes in gene expression levels

between samples.

Then the normalized signal values and the fold changes of 3h or 6h group / sham group

could be obtained. Cut-off-values were set at more than 2.0-fold or less than 1/2 (0.5)-fold.

IPA pathway analysis. The Ingenuity Pathway Analysis (IPA) application (Ingenuity

Systems, Mountain View, CA, USA) was used to organize differentially expressed genes

into networks of interacting genes and to identify modules of functionally related genes that

correspond to pathways. A detailed description is given in the online repository

(http://www.ingenuity.com).

Quantitative real time polymerase chain reaction (PCR) for Egr1. After that, the

mRNA expression level of early growth response-1 (EGR1) was evaluated by Quantitative

real time PCR. Total RNA was extracted from 30mg of rat liver and spleen tissue using

RNeasy Mini Kit (QIAGEN, Inc, Valencia, CA, USA, Cat.No.74106). Quantitative

RT-PCR was performed by using an ABI 7500 Real-time PCR system (PE Applied

Biosystems). TaqMan gene expression systems (PE Applied Biosystems) for EGR1 (assay Acc

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ID Rn00561138) was used for quantification of mRNA expression of the respective genes.

To normalize, amplification of GAPDH (Taq man ribosomal RNA control reagents, assay

ID Rn9999916) was performed as an endogenous control.

Statistical Analysis. Real time PCR result was presented as mean ± SD and Mann-Whitney

U test was used for statistical analysis. The probability of a relationship between each

biological function and the identified genes was calculated by Fisher’s exact test. A p-value

of less than 0.05 was considered statistically significant.

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Results

Microarray Data Analysis and Biostatistics

Microarray data analysis identified 31099 probe sets that were differentially

expressed. Among these, 760 (up-regulated genes: 572, down-regulated genes: 188) after 3h

and 1452 (up-regulated genes: 1018, down-regulated genes: 434) after 6h uniquely known

genes were identified as “post Hx specific genes in the spleen.” Analysis of the these genes

with IPA indicated that the spleen under the massive hepatectomy was likely to have altered

the activities of “disease and disorders pathways”, “Molecular and Cellular Functions”,

“Physiological System Development and Function” and “Tox lists” (Table 1 and 2).

Top Canonical pathway from IPA in spleen

The top ten high level canonical pathways were identified by IPA as being

significantly overrepresented by differentially expressed genes in this dataset. In 3h,

inflammatory response related pathways including “Acute Phase Response Signaling”,

“Hepatic Fibrosis/Hepatic Stellate cell Activation”, “IL-10 signaling” and “IL-6 signaling”

were affected under the massive Hx (Figure 1A). In 6h, inflammatory response related

pathways including “IL-10 signaling”, “IL-6 signaling” and “Hepatic Fibrosis/Hepatic

Stellate cell Activation” were also strongly affected (Figure 1B).

Gene Ontology (GO) analysis on chemokines

The detailed GO analysis revealed that genes of the chemokine activity which has a Acc

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crucial role in the acute phase response showed significant different patterns between the

two groups. In GO “chemokine activity”, 8 genes (up-regulated: Cxcl1 (Gro1), Cxcl2

(Mip-2), Ccl7, Ccl2 (MCP-1), Ccl24, Cxcl5 and Ccl20, down-regulated: Xcl1) after 3h and 13

genes (up-regulated: Cxcl1 (Gro1), Ccl2 (MCP-1), Ccl20 (ST38), Cxcl2 (Mip-2), Ccl9, Ccl7,

Cxcl5, Ccl3 (MIP-1a), Ccl4 (Mip1-b), Cxcl9, Ccl6 (Mrp-1), Cxcl10 (IP-10), Ccl22) after 6h

unique genes were isolated (Table 3 and 4). We constructed several networks using these

genes. IPA revealed that chemokine activity formed an acute phase response network of

“chemokine activity.” IPA represented up-regulated (red) and down-regulated (green) genes

in the gene network. This pathway included many kinds of chemokine genes. Interestingly,

all of these genes were up-regulated except XCL1, so this meant these pathways were

up-regulated (Figure 2A, B).

GO analysis on transcription factor

In addition, another GO analysis revealed that genes of the transcription factor

activity which has a crucial role in the acute phase response showed significantly different

patterns between the two groups. In GO “transcriptional factor activity”, 30 genes

(up-regulated: 24, down-regulated: 6) after 3h and 56 (up-regulated: 44, down-regulated:

12) after 6h unique genes were isolated (Table 5 and 6). Those genes included key

transcription factors, such as EGR1, FBJ murine osteosarcoma (FOS), Activating

transcription factor 3 (ATF3), JUN and JUN-B belonging to immediately early response

genes. We constructed several networks using these genes. These pathways included these Acc

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stress response genes (Figure 3A and B). The qRT-PCR showed that in the spleen under

the massive Hx constitutively expressed a higher amount of EGR1 mRNA which was a key

molecule of the pathway (Figure 3C).

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Discussion

In general, the major known functions of the spleen were removal of aging

erythrocytes and recycling of iron, elicitation of immunity, removal of blood-borne

microorganism and cellular debris, and a supply of erythrocytes after hemorrhagic shock

(15). In response to inflammation, splenic monocytes such as dendritic cells or

macrophages increased their motility, exited the spleen, accumulated in the injured tissue,

and participated in wound healing (16). The blood flow was significantly lower and

vascular resistances became higher in the spleen after Hx; therefore, the congestion and

ischemia injury might occur in the spleen (17). As a result, it could affect the gene

expression in the spleen. Despite these reports, molecular changes in the spleen after Hx

have not been investigated and reported yet. Remarkably, this was the first report to

investigate the genetic change in the spleen after Hx, so we could know “what happens in

the spleen” expectantly.

First of all, the results of top bio functional gene analysis (Tables 1 and 2) suggested

that the altered genes included inflammatory response, inflammatory disease, cell death,

liver necrosis/cell death and hepatic fibrosis. The results of top canonical pathways (figures

1A and 1B) also suggested that the altered pathways included acute phase response

signaling, hepatic fibrosis/hepatic stellate cell activation and IL-6 signaling. These features

of genes and pathways expression showed an inflammatory response could occur in the

spleen, and they might provide a negative and harmful impact to the liver via the portal vein.

Actually, Kuriyama et al. reported splenectomy could not only decrease the portal vein flow Acc

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but also induce cytoprotective effect by decreased ET-1 and increased HO-1 (18). Takuya et

al. has also reported the limitation of splenic inflammatory cell recruitment into the liver

was the cytoprotective effect of splenectomy in small-for-size liver transplantation (19).

Furthermore we have already reported gene changes in the liver after 90% Hx and

protective effect of splenectomy (12).

In the current study, we could achieve the investigation or the gene-expression

changes in the rat spleens under the situation of 90% Hx on the two time points. Firstly, we

could find numerous “chemokines” related genes. Along with the accelerated rate of the

discovery of chemokines had come the realization that these molecules not only controlled

hemopoietic cell migration, but also they were involved in a number of other physiological

and pathological processes including induction of either monocytes, macrophages or

fibroblastic cells in inflammation (20). So it was expected that they were one of the triggers

in inflammatory response in the spleen. The message for the chemoattractants Ccl2

(MCP-1), Ccl7, Cxcl2 (MIP-2), Cxcl1 (GRO1), Ccl4 (MIP1-B) and Ccl3 (MIP-1A) were

extremely increasing. Especially, GRO1 showed the highest up-regulation in chemokine

related genes in this study. GRO1 encoding a potent chemoattractant belonging to the CXC

chemokine subfamily was most rapidly and markedly up-regulated after 90% Hx; therefore,

GRO1 played a vital role in acute liver injury after Hx (21). Cxcl4 and Cxcl3 were MIP-1

(macrophage inflammatory protein 1) family, indicating an acute inflammatory host

response at sites of injury mainly by recruiting pro-inflammatory cells, such as T-cell, NK

cell, neutrophil and eosinophil (22). Tumor necrosis factor alpha (TNFɑ) and interferon Acc

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gamma (IFNγ) were well-known as one of the pro-inflammatory cytokines, also

demonstrating to play a critical role in the liver injury. They formed a pro-inflammatory

cytokine network in this study. Moreover, Cxcl2 (MIP-2) which was induced by TNFɑ was

involved in the recruitment of neutrophils in the development of massive hepatocellular

apoptosis and necrosis (23). These findings implied the gene expressions in the spleen had a

negative impact on the remnant liver after Hx in terms of inducing inflammatory response.

Secondly, “transcription factors,” containing one or more DNA-binding domains

which attached to specific sequences of DNA adjacent to the genes that they regulated

including EGR1, EGR2, FOS, JUNB and ATF3 were up-regulated. JUNB and FOS were

reported as transcription factors on oxidative stress pathways (24). They were primary

immediate-early genes expressed by many cell types in response to cellular stress, and DNA

binding of their heterodimer was modulated by reduction-oxidation of a single conserved

cysteine residue in the DNA-binding domains of the two proteins (25). EGR1 was one of

the zinc-finger transcription factors and up-regulated at 3h and 6h in the current study. It

was reported as a master switch coordinating up-regulation of divergent gene families

related to liver ischemia-reperfusion injury including IL-1β, Cxcl2 (MIP-2), tissue factor

and plasminogen activating inhibitor 1(26). It was also reported that EGR1 had crucial roles

expressed in small-for-size grafts which had a small remnant of the liver after liver

transplantation and was similar to massive Hx (27).

We concluded that in the spleen numerous gene expressions would occur under the

massive Hx which was similar to the small-for-size graft. Based on these results, the spleen Acc

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could take a harmful role and provide a negative impact in this situation due to inducing

chemokine and transcription factor including GRO1 and EGR1. However, the precise

mechanism is not clear. We did not measure the level of cytokine and transcription factor in

the portal flow. In this microarray analysis the samples in each group were mixed together,

therefore; we couldn’t put SD. So we should interpret these data very carefully. As the

number of samples in this study was also relatively small, a large sample size is needed in

the further study.

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Acknowledgements

This study has no financial support.

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Figure legends

Figure 1. Canonical pathway analysis with the IPA application showed the pathways of

specific genes in the spleen significantly modified by 90% partial hepatectomy (Hx) using

Fisher’s exact test. The threshold lines represent a p value with 0.05. (A) 3h and (B) 6h

after Hx.

Figure 2. Isolation 90% Hx-induced changes in chemokine networks. Using the chemokine

related genes network. (A) 3h and (B) 6h after Hx. Genes up-regulated and down-regulated

by 2-fold were expressed in red and in green, respectively.

Figure 3. Isolation 90% Hx-induced changes in transcriptional factor networks. Using the

transcription factor related genes including EGR1, ATF3, JUNB and JUN network. (A) 3h

and (B) 6h after Hx. Genes up-regulated and down-regulated by 2-fold were expressed in

red and in green, respectively. The gene-expressions of EGR1 (C) in the spleen were

determined by real-time PCR. GAPDH RNA serves as an endogenous quantity control.

Values were means ± SD. (* p<0.05)

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Table 1. The list of top bio functions of the splenic genes in 3h after hepatectomy

P-value # Molecules

Disease and Disorders

Cancer 1.54E-17 167

Inflammatory Response 9.58E-15 96

Organismal Injury and Abnormalities 3.06E-12 62

Dermatological Disease and Conditions 1.72E-10 71

Reproductive System Disease 2.78E-10 86

Molecular and Cellular Functions

Cell Death 3.99E-12 132

Cellular Growth and Proliferation 5.71E-12 145

Cellular Movement 1.89E-11 94

Cellular Development 6.07E-10 129

Cell Morphology 1.73E-08 53

Physiological System Development and Function

Hematological System Development and Function 9.72E-09 88

Immune Cell Trafficking 9.72E-09 59

Tissue Morphology 1.63E-08 75

Organismal Survival 4.00E-08 56

Nervous System Development and Function 2.73E-07 48

Tox Lists

LXR/RXR Activation 1.49E-07 79

Cardiac Hypertrophy 1.28E-06 259

Liver Proliferation 1.42E-06 133

Renal Necrosis/Cell Death 1.92E-06 314

Cardiac Fibrosis 7.64E-06 95

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Table 2. The list of top bio functions of the splenic genes in 6h after hepatectomy

P-value # Molecules

Disease and Disorders

Cancer 1.78E-34 343

Inflammatory Response 2.14E-34 205

Inflammatory Disease 1.58E-32 316

Dermatological Diseases and Conditions 2.16E-27 163

Respiratory Disease 2.37E-26 144

Molecular and Cellular Functions

Cellular Movement 1.54E-31 206

Cell-To-Cell Signaling and Interaction 2.53E-31 230

Cell Death 7.95E-26 275

Cellular Growth and Proliferation 1.81E-25 293

Cellular Development 5.08E-21 263

Physiological System Development and Function

Tissue Development 2.53E-31 209

Organismal Survival 4.14E-28 152

Hematological System Development and Function 6.11E-27 209

Immune Cell Trafficking 6.11E-27 153

Tissue Morphology 4.89E-22 142

Tox lists

Cardiac Hypertrophy 3.69E-12 259

Liver Necrosis/Cell Death 4.31E-12 166

Liver Proliferation 1.04E-11 133

LXR/RXR Activation 3.86E-11 79

Hepatic Fibrosis 1.73E-10 85

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Table 3. Gene ontology (GO) analysis on “chemokine activity” related genes in 3h

Gene name Fold change Percentile

(median 2.3685356)

GenBank number

Cxcl1 (Gro1) 48.04672 0.998 NM_030845

Cxcl2 (Mip-2) 6.53484 0.906 NM_053647

Ccl7 3.706583 0.806 BF419899

Ccl2 (MCP-1) 3.393003 0.776 NM_031530

Ccl24 2.973374 0.706 BI294084

Cxcl5 2.925218 0.657 NM_022214

Ccl20 2.18812 0.382 AF053312

Xcl1 0.338448 0.018 NM_134361

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Table 4. Gene ontology (GO) analysis on “chemokine activity” related genes in 6h


(median 2.2727032)

GenBank number

Cxcl1 (Gro1) 439.649 1.000 NM_030845

Ccl2 (MCP-1) 70.00393 0.996 NM_031530

Ccl20 (ST38) 62.80219 0.995 AF053312

Cxcl2 (Mip-2) 54.54451 0.995 NM_053647

Ccl9 47.29132 0.992 AI169984

Ccl7 37.39671 0.989 BF419899

Cxcl5 27.58101 0.984 NM_022214

Ccl3 (MIP-1a) 11.08792 0.951 U22414

Ccl4 (Mip1-b) 4.445999 0.845 U06434

Cxcl9 3.993681 0.821 AI044222

Ccl6 (Mrp-1) 2.310009 0.516 BE095824

Cxcl10 (IP-10) 2.304766 0.513 U22520

Ccl22 2.287212 0.506 AF432871

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Table 5. Gene ontology (GO) analysis on “transcription factor” related genes in 3h


(median 2.3685356)

GenBank number

Top 20 genes

RGD1559697_predicted 26.59984 0.992 BE105492

Fos 9.197226 0.943 BF415939

Hit39 8.171527 0.928 AF277902

Crem 6.371599 0.902 NM_017334

Cebpd 6.35065 0.898 BF419200

Phox2a 6.192782 0.895 NM_053869

Ccl7 3.706583 0.806 BF419899

Ccl2 3.393003 0.776 NM_031530

Sox9 3.199223 0.750 AI548994

Ccl24 2.973374 0.706 BI294084

Gadd45g 2.763646 0.657 AI599423

Bhlhb2 2.443606 0.541 AI548256

Nfkbiz_predicted 2.354374 0.492 AI176265

Fkhl18 2.224701 0.401 AI008883

Zfp36l2 2.2081 0.395 AA943730

Fev 2.183003 0.379 U91679

Atf3 2.099832 0.313 NM_012912

Jundp2 2.070742 0.288 NM_053894

Egr1 2.063132 0.279 NM_012551

Mtf1_predicted 2.001628 0.223 BG375691

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Table 6. Gene ontology (GO) analysis on “transcription factor” related genes in 6h


(median 2.2727032)

GenBank number

Top 20 genes

Cebpd 9.097913 0.933 NM_013154

Neurod1 7.304114 0.917 NM_019218

Nfkbiz_predicted 5.75559 0.886 AI176265

Phox2a 5.332282 0.878 NM_053869

Batf_predicted 4.482911 0.848 AA819819

Ccl4 4.445999 0.845 U06434

Cebpb 4.111673 0.829 NM_024125

Fkhl18 3.672931 0.799 AI008883

Egr1 3.628126 0.795 NM_012551

Tfec 3.415391 0.768 L08812

Trim28 3.299043 0.754 BE098563

Fos 3.20988 0.743 BF415939

Gadd45g 3.055698 0.726 AI599423

Junb 3.027802 0.721 NM_021836

Crem 3.015733 0.718 NM_017334

Lhx1 2.862468 0.693 BI282093

Atf3 2.854969 0.691 NM_012912

Zfhx1b 2.729652 0.661 BG377397

Stat3 2.567718 0.620 BE113920

Jundp2 2.49348 0.596 NM_053894

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gene profile in the spleen under massive partial hepatectomy using complementary dna microarray and...

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