Gene expression profiles of bone marrow cells from micephenotype-selected for maximal or minimal acute inflammations:

searching for genes in acute inflammation modifier loci

Introduction

Mouse lines phenotype-selected for the maximum (AIR-

max) or minimum (AIRmin) acute inflammatory reac-

tivity (AIR) were used to study the impact of the

genetic control of non-specific immunity on susceptibil-

ity to autoimmune,1 neoplasic2 and infectious diseases.3

AIRmax and AIRmin mice were developed through

bidirectional selection, starting from a highly poly-

morphic population (F0) derived from the intercrossing

of eight inbred mouse strains (A, DBA2, P, SWR, CBA,

SJL, BALB/c and C57BL/6). The selection phenotypes

chosen were localized leucocyte influx and exudated

plasma proteins 24 hr after the subcutaneous injection

of polyacrylamide beads (Biogel; Bio-Rad, Hercules,

CA), a non-antigenic, insoluble, and chemically inert

substance.4 The progressive divergence of the AIRmax

and AIRmin lines during successive generations of selec-

tive breeding reached 20- and 2�5-fold differences in

leucocyte infiltration and exudated protein concentra-

tions, respectively. These differences resulted from the

accumulation of alleles endowed with opposite and

additive effects on the inflammatory response. We can

consider AIRmax and AIRmin as outbred stock mice

developed from eight inbred lines for strong and weak

acute inflammation phenotypes after extensive selective

processes, while avoiding inbreeding. As such, they

maintain a heterogeneous genetic background yet have

homozygosis in acute inflammation modifier loci in

each line. Analysis of the selective processes indicated

Patrıcia dos S. Carneiro, Luciana

C. Peters, Francisca Vorraro,

Andrea Borrego, Orlando

G. Ribeiro, Nancy Starobinas, Jose

R. Jensen, Wafa H. K. Cabrera,

Olga M. Ibanez and Marcelo De

Franco

Laboratorio de Imunogenetica, Instituto

Butantan, Sao Paulo, Brazil

doi:10.1111/j.1365-2567.2008.03032.x

Received 9 October 2008; revised 21

November 2008; accepted 1 December 2008.

Correspondence: M. De Franco, Laboratorio

de Imunogenetica, Instituto Butantan. Av.

Vital Brasil, 1500 – Sao Paulo, Brazil

05503900.

Email: mdfranco@butantan.gov.br

Senior author: Marcelo De Franco

Summary

Two mouse lines were phenotype-selected for maximum (AIRmax) or

minimum (AIRmin) acute inflammation responses to polyacrylamide bead

(Biogel) injection. These lines differ in terms of bone marrow granulo-

poiesis, neutrophil resistance to apoptosis, and inflammatory cytokine

production during acute inflammation responses. We compared gene

expression profiles in bone marrow cells (BMC) of AIRmax and AIRmin

mice during acute inflammatory reactions. The BMC from femurs were

recovered 24 hr after subcutaneous injections of Biogel. Global gene

expression analysis was performed on CodeLink Bioarrays (36K genes)

using RNA pools of BMC from both control and treated AIRmax and

AIRmin mice. Differentially expressed genes were statistically established

and the over-represented gene ontology biological process categories were

identified. Upregulations of about 136 and 198 genes were observed in

the BMC of Biogel-treated AIRmax and AIRmin mice, respectively, but

740 genes were found to be downregulated in AIRmin mice compared

with 94 genes in AIRmax mice. The over-represented biological themes of

the differently expressed genes among AIRmax and AIRmin mice repre-

sent inflammatory response, signal transduction, cell proliferation and

immune cell chemotaxis. We were able to demonstrate a broad down-

modulation of gene transcripts in BMC from AIRmin mice during acute

inflammation, and significant differentially expressed genes colocalized

with previously mapped regions for inflammation-related phenotypes in

chromosomes 1, 3, 6 and 11.

Keywords: immunogenetics; inflammation; microarray; neutrophils;

selected mouse lines

Please cite this article in press as: Carneiro P. S. et al. Gene expression profiles of bone marrow cells from mice phenotype-selected for maximal or

minimal acute inflammations: searching for genes in acute inflammation modifier loci, Immunology (2009) doi: 10.1111/j.1365-2567.2008.03032.x

e562 � 2009 Blackwell Publishing Ltd, Immunology, 128, e562–e571

I M M U N O L O G Y O R I G I N A L A R T I C L E

that the AIR phenotype involves at least 11 quantitative

trait loci (QTL).2

The acute inflammation response to Biogel, as well as

susceptibilities to pristine-induced arthritis,5 to Salmo-

nella enterica serotype typhimurium infection, and to

the lipopolysaccharide of the bacteria were all modified

in these mice; and the genotyping of microsatellite

markers suggests the presence of QTL in chromosomes

1, 6 and 11, which are relevant to these phenotypes.6

Susceptibility to lung, colon and skin carcinogenesis was

also distinct in these two mouse lines. In previous stud-

ies we demonstrated that the pulmonary adenoma sus-

ceptibility 1 (Pas1) locus (which plays a major role in

predisposition to this tumour in mice) is involved in

AIR control.7 This relationship was indicated by the

lung–tumour response and by the segregation of the

resistant and susceptible haplotypes in AIRmax and

AIRmin mice, respectively, at genetic markers inside a

452-kilobase region in the Pas1 locus on chromosome

6. Interestingly, an inverse genetic predisposition to

colon carcinogenesis was observed in these mice, with

the AIRmax line being more susceptible to chemically-

induced colon cancer.8

Tissue repair was also investigated in these two lines,

revealing that AIRmax mice present a high capacity for

wound healing in comparison to AIRmin mice. Inflam-

matory QTL on chromosomes 1 (Slc11a1 gene region)

and 14 were found to be involved in the wound healing

phenotype in this model.9 Additionally, the same chro-

mosome 1 QTL seems to regulate leucocyte and protein

influx during acute inflammation, as well as arthritis inci-

dence and severity.5

Alterations in bone marrow granulopoiesis in response

to haematopoietic factors and the production of chemo-

tactic factors by infiltrated or local resident cells both

contribute to phenotypic differences between the two

lines. Convergent phenotypes in AIRmax mice were

observed that were characterized by high neutrophil

production in bone marrow, a high number of neu-

trophils in the blood, high concentrations of chemotac-

tic agents, and increased resistance to spontaneous

apoptosis.10

In the present study, we compared the gene expression

profiles of bone marrow cells (BMC) from control and

Biogel-treated AIRmax and AIRmin mice to identify dif-

ferentially expressed genes correlating with previously

mapped QTL involved in inflammation-related pheno-

types.

Materials and methods

Mouse lines

AIRmax and AIRmin mice from generation 47 were used.

Two experiments were carried out with equivalent

numbers of 2- to 3-month-old male and female mice

maintained under standard conditions in our animal

facilities. All procedures involving animals were approved

by the Committee for Ethics in Animal Experimentation

of the Instituto Butantan.

Biogel treatment and RNA preparation

The animals were shaved and 750 ll of a sterile 67% sus-

pension (53 mg dry weight/ml) of Biogel P100 (Biorad)

in phosphate-buffered saline was injected subcutaneously

into their backs. After 24 hr, BMC were obtained from

the femurs of six treated and six untreated animals of

each line, and total RNA was individually isolated using

the RNeasy mini kit (Qiagen, Valencia, CA). RNA pools

were prepared (from Biogel-treated and untreated AIR-

max, and from Biogel-treated and untreated AIRmin) by

mixing equal amounts of their RNAs. Identical aliquots

of each pool were used for microarray analysis after treat-

ment with DNaseI Amplification Grade (Invitrogen,

Carlsbad, CA), purified with RNeasy kit (Qiagen). Other

aliquots of these same pools were reverse-transcribed

using the Superscript III kit (Invitrogen) and used to vali-

date the microarray data by quantitative polymerase chain

reaction (qPCR).

Microarray expression analysis

Whole genome expression analysis was performed on

CodeLink mouse Bioarrays 36K genes obtained from GE

Healthcare (formerly Amersham Bioscience, Piscataway,

NJ) according to the manufacturer’s protocols. Briefly,

1 lg high-quality total RNA was reverse transcribed using

T7-oligo-dT primer and double-stranded complementary

DNA (cDNA), in vitro transcription and biotin labelling

of cRNA were carried out using the CodeLink mouse Bio-

array reagents. The samples of fragmented biotinylated

cRNA (10 lg cRNA each) were prepared for hybridiza-

tion to the bioarrays using the expression assay reagent

kit (GE Healthcare). Slides were incubated for 18 hr at

37� while shaking at 250 g (Innova 4080, New Brunswick

Scientific, Edison, NJ). After hybridization, each slide was

incubated in TNT buffer (Tris–HCl, NaCl, Tween-20) at

42� for 60 min and then washed. The signal was devel-

oped using streptavidin-Cy5 (GE Healthcare) for 30 min

at room temperature. Excess dye was removed by washing

four times with TNT buffer and slides were then dried

under centrifugation. The processed slides were scanned

on an Axon GenePix Scanner (Axon, Molecular Devices,

Union City, CA) at 635 nm with the photomultiplier tube

at 600 V, and using a scan resolution of 5 nm. CODELINK

expression analysis software (version 4.1, Amersham Bio-

science) was used to analyse the images of each slide. Sig-

nal intensities of the spots were normalized and their

quality was evaluated using data flagging from CODELINK

� 2009 Blackwell Publishing Ltd, Immunology, 128, e562–e571 e563

Gene expression profiles of bone marrow cells from AIRmax and AIRmin mice

expression analysis software. Spots with intensities below

that of the negative control (absence of an

oligonucleotide probe) were excluded, as were those spots

with irregular shapes or near-background intensities.

Biological replicates of the four groups were performed

for statistical analysis.

Real-time quantitative reverse transcription–PCR

Microarray data were validated by quantitative real-time

PCR using gene-specific primers. Real-time PCR amplifica-

tion mixtures contained 0�5 ll template cDNA, 12�5 ll

SYBRGreen PCR master mix (Invitrogen), and 0�3 lM

specific PCR primers were run through a Chromo 4

(Biorad, Hercules, CA) sequence detection system. The

mouse hydroxymethylbilane synthase (Hmbs) gene was

amplified with primers 50-aaagtgccgtgggaaccag-30 and 50-

gaggcgggtgttgaggtttc-30 and used as a housekeeping control

for possible differences in cDNA amounts. Target genes

were amplified with primers Il1b 50-ttgacggaccccaaaagatg-30

and 50-agaaggtgctcatgtcctca-30, Il6 50-gttctctgggaaatcgtgga-

30 and 50-tgtactccaggtagctatgg-30, Il10 50-atgcaggactttaaggg-

ttacttg-30 and 50-tagacaccttggtcttggagctta-30, Il8rb 50-gac-

tgttcacctaaacggtg-30 and 50-cataccaagatggaagggagc-30.

Relative differences were calculated according to the

delta–delta Ct method.11 Microarray data were correlated

with qPCR results using the Pearson analysis.

Statistical analysis

The gene expression observed in each array was log-trans-

formed to approximate Gaussian distribution and then

standardized over the array to adjust for systematic differ-

ences in their expressions. Differentially expressed genes

were detected using significance of analysis of microarray

software (two class unpaired, false discovery rate � 5%)

that analysed both biological replicate data.12 To increase

the statistical power of microarray analysis, we have con-

sidered fourfold (or higher) differentially expressed genes.

We analysed all of the significant differentially expressed

genes for the over-represented biological themes using

EXPRESSION ANALYSIS SYSTEMATIC EXPLORER (EASE) software.13

This program automates the process of biological theme

determination using gene ontology (GO) classification.

EASE calculates over-representation with respect to the

total number of genes assayed and annotated within each

system, allowing for side-by-side comparisons of catego-

ries from categorization systems with varying levels of

annotation. In this way, EASE rapidly converts a list of

genes in an ordered table of robust biological themes.

Calculating statistics on thousands of gene categories,

however, can lead to a few seemingly significant probabil-

ities simply by chance. To address this multiple compari-

son issue, we used the Bonferroni-type probability

correction.

Results

Overview of differentially expressed genes in AIRmaxand AIRmin BMC

Significance of analysis of microarray (SAM) analysis

using fourfold change minimal differences revealed dis-

tinct gene expression profiles of BMC from AIRmax and

AIRmin mice. Twenty-four hours after Biogel treatment,

upregulations of 136 and 198 genes were observed in

AIRmax and AIRmin BMC, respectively, while 740 genes

were downmodulated in AIRmin and 94 in AIRmax as

compared with control mice (Fig. 1a). Comparisons of

AIRmax/AIRmin gene expression ratios indicated similar

numbers of differentially expressed genes in control

groups, whereas greater numbers of genes with higher

expression in AIRmax than in AIRmin were observed in

Control

AIRmax AIRmin

Num

ber

of g

enes

4-f

old

diffe

rent

ially

expr

esse

d / 3

6 k

gene

s

0

100

200

300

400

500

600

700

Num

ber

of g

enes

4-f

old

diffe

rent

ially

expr

esse

d / 3

6 k

gene

s

0

100

200

300

400

500

600

700

AIRmax/AIRmin ratio > 4

AIRmax/AIRmin ratio < 0·25

Biogel/control ratio > 4(a)

(b)

Biogel/control ratio < 0·25

Biogel

Figure 1. Numbers of differentially-expressed genes in mice with

maximum and minimum acute inflammation responses (AIRmax

and AIRmin mice, respectively). Significance of analysis of micro-

array method using fourfold change minimal difference and false dis-

covery rate < 5% revealed distinct gene expression profile of bone

marrow cells from AIRmax and AIRmin mice, comparing Biogel/

control (a) or AIRmax/AIRmin (b) groups.

e564 � 2009 Blackwell Publishing Ltd, Immunology, 128, e562–e571

P. S. Carneiro et al.

Biogel-treated mice (Fig. 1b). Downmodulated genes in

AIRmin (Fig. 1a) are mostly responsible (75%) for the

higher expressed genes observed in Biogel treated AIRmax

mice (Fig. 1b).

Gene expression and chromosome gene clusters

We correlated the signal intensities of microarray with

qPCR experiments for several genes involved in acute

inflammatory reactions to confirm the microarray results.

Figure 2 indicates a significant correlation between the

two methods (Pearson r = 0�77, P < 0�001), validating

the microarray data and allowing us to analyse gene

expression in the over-represented biological process cate-

gories or in chromosome regions indicated by the EASE

analysis.

The signal intensities of genes located in previously

mapped chromosome loci in both lines are shown in

Fig. 3. These chromosomes were also indicated by EASE

analysis for over-represented genes (not shown). In most

cases, a downmodulation was observed in AIRmin BMC.

Two gene clusters were verified in chromosome 1: at the

proximal (70 Mb) Slc11a1 gene region colocated with

Il1r2, Cd28 and Il8rb genes; and at the distal region

(130 Mb) encompassing Il10 and Chi3l1 genes. An inter-

esting cluster of differentially expressed genes related to

the inflammatory response was observed on chromosome

3, around 90 Mb, including Tlr2, Adam15, Ilf2, Mcl1,

Cd53 and Vcam1. Chromosome 6 also demonstrates a

significant EASE score, showing differentially expressed

genes related to cell proliferation and to signal transduc-

tion, while most of the differentially expressed genes on

chromosome 11 are involved in cell migration and

inflammatory reactions. It is notable that several genes,

namely Cd28, IL-8rb, IL10, Tlr2, Adam15 and Stat3

located in these regions are also differentially expressed in

AIRmax and AIRmin control mice.

Inflammatory response is the most significant biological

process category that is over-represented among the most

highly expressed genes in Biogel-treated AIRmax BMC

(Fig. 4 and Table 2). The acute-phase related Saa3 gene

represents an interesting inflammatory biomarker because

it is highly activated in both lines. Among the other

inflammatory-response-related genes, however, significant

downmodulation after Biogel treatment was observed

only in the AIRmin line.

Over-represented gene ontology categories indifferentially expressed genes between AIRmax andAIRmin lines

Over-represented biological process categories associated

with differentially expressed genes were identified using

EASE analysis (Tables 1 and 2). Signal transduction and

cell communication were the most significant functional

categories among upregulated AIRmax genes, while

immune response genes were downmodulated in this line

after Biogel treatment. Interestingly, there are many genes

related to inflammatory reactions among them, such as

Il1b, Cxcl2, Ccl6 and Tnfsf13b. Protein modification and

5·0

–5·0AIRmax ct AIRmin ct AIRmin biogelAIRmax biogel AIRmax ct AIRmin ct AIRmin biogelAIRmax biogel

AIRmax ct AIRmin ct AIRmin biogelAIRmax biogelAIRmax ct AIRmin ct AIRmin biogelAIRmax biogel

2·5

–2·5

0·0

5·0 qPCRMicroarray

–5·0

2·5

–2·5

0·0

5·0

–5·0

2·5

–2·5

0·0

5·0

–5·0

2·5

–2·5

II10

mR

NA

gen

e ex

pres

sion

II8r

b m

RN

A g

ene

expr

essi

onII6

mR

NA

gen

e ex

pres

sion

II1 m

RN

A g

ene

expr

essi

on

0·0

Figure 2. Correlation between microarray and quantitative polymerase chain reaction (qPCR) experiments. Signal intensities of gene expression

(Log2) were correlated between microarray and qPCR data using the Pearson method (r = 0�77, P < 0�01).

� 2009 Blackwell Publishing Ltd, Immunology, 128, e562–e571 e565

Gene expression profiles of bone marrow cells from AIRmax and AIRmin mice

cell communication biological process categories were

predominant in upregulated genes; while cell cycle, cell

proliferation and DNA and RNA metabolism processes

were the most over-represented categories among down-

modulated genes in the AIRmin line (Table 1).

In comparing BMC gene expression in untreated AIR-

max and AIRmin animals, the differentially expressed

genes grouped in the categories of digestion, inflamma-

tory response and positive regulation of interleukin-6 bio-

synthesis in the � 4 AIRmax : AIRmin ratio cluster; while

DNA metabolism, translation and RNA processing were

identified in the � 0�25 AIRmax/AIRmin ratio cluster

(Table 2). After Biogel injection, however, the inflamma-

tory response is the most significant over-represented bio-

logical process category among the genes most highly

expressed in AIRmax BMC (Fig. 4 and Table 2), followed

by macromolecules biosynthesis, and immune cell chemo-

taxis; while meiosis, morphogenesis, cell proliferation and

G-protein-coupled receptor protein signalling pathways

were the over-represented categories of highly expressed

AIRmin genes.

Discussion

Our results show distinct gene expression profiles in

BMC from the two mouse lines produced through

bidirectional phenotype-based selection for AIR, demon-

strating broad downmodulation gene expression of the

low-responder AIRmin mice compared with the high-

responder AIRmax BMC gene transcripts. The selective

process was asymmetric, with AIRmax mice demonstrat-

ing a higher phenotypic increase than AIRmin mice,

which showed an inflammatory reaction similar to a

number of inbred laboratory mice.4,14 High inflammation

in AIRmax mice seems to result from the accumulation

of three convergent elements during their selection:

higher number of neutrophils in their bone marrow as a

consequence of an elevated response to granulopoietic

6

5

4

3

2

1

0

–1

–2

–3

70 MbII1r2 II8rb II10S/c11a1 Chi3I1 TIr2 McI1IIf2Adam15 Vcam1Cd53

130 Mb 80 Mb 100 Mb

80 Mb120 Mb 140 Mb

Cd9 Ccnd2 IappKras2Tnfrsf1Atp 6v1

100 Mb

Chromosome 6

Chromosome 1

Gen

e ex

pres

sion

inte

nsity

(lo

g2)

6

5

4

3

2

1

0

–1

–2

–3Icam2Itga2bEpxStat3Igfbp4CcI8

Gen

e ex

pres

sion

inte

nsity

(lo

g2)

6

5

4

3

2

1

0

–1

–2

–3

Gen

e ex

pres

sion

inte

nsity

(lo

g2)

6

5

4

3

2

1

0

–1

–2

–3

Gen

e ex

pres

sion

inte

nsity

(lo

g2)

Chromosome 3

AIRmax controlAIRmin control

AIRmin biogelAIRmax biogel

Chromosome 11

Cd28

Figure 3. Signal intensities of differentially-expressed genes from mice with maximum and minimum acute inflammation responses (AIRmax

and AIRmin mice, respectively) in chromosomes 1, 3, 6 and 11. Gene signal intensities were normalized and evaluated by data flagging using

CodeLink expression analysis software. Statistical analysis using the significance of analysis of microarray (SAM) algorithm was performed on

both pooled RNA biological replicates. Signal intensity was expressed as the mean ± SE.

e566 � 2009 Blackwell Publishing Ltd, Immunology, 128, e562–e571

P. S. Carneiro et al.

cytokines; large amounts of chemotatic factors in their ex-

udates; and the strong resistance of their neutrophils to

spontaneous apoptosis.10 In light of our results, these

three convergent elements modified during the selective

process could be controlled by the unrestrained expres-

sion of inflammatory response, signal transduction,

macromolecule biosynthesis and immune cell chemotaxis-

related genes (Tables 1 and 2). In agreement with these

alterations, AIRmax mice are more susceptible to arthri-

tis,1,5 lipopolysaccharide shock6 and colon carcinogenesis.8

However, this high-responder line demonstrated extreme

resistance to bacterial infections3 as well as to lung7 and

skin carcinogenesis.2

Neutrophils are the first cells to be recruited to a dam-

aged site15 and CXC chemokines, including CXCL2 and

CXCL1, are the most critical inflammatory mediators for

such recruitment.16 We observed basal differences

between AIRmax and AIRmin lines in terms of the

expression of these two genes in BMC, as well as of the

Il8rb gene that expresses the receptor for CXCL2 granulo-

cyte chemokine (Fig. 4 and Table 2). The same differences

were verified in the Biogel treatment, with AIRmin mice

demonstrating significant downmodulation, resulting in

higher relative transcript amounts of these chemokines in

AIRmax mice. Similar profiles could be observed for

other chemokines, adhesion molecules and receptors such

as Ccl8, Ccr1, Ccr2, Itgam, Itga2b, Icam2, Il8r and Vcam1

(Figs 3 and 4). However, differences among these genes

were only detected after Biogel injection. Granulocyte col-

ony-stimulating factor is a glycoprotein that influences

the survival, proliferation, differentiation and functioning

of mature neutrophil granulocytes and their precursors;17

it also influences its receptor, which modulates the matu-

ration of bone marrow cell precursors as well as the num-

ber of peripheral neutrophils.18 Csfr3 expression was also

significantly downmodulated in AIRmin mice, resulting

in significant transcript differences between both lines

(Fig. 4). Several other genes involved in granulopoiesis

and neutrophil maturation were more highly expressed in

AIRmax than in AIRmin mice, such as Stat3, Cebpb, Tlr2,

Tnfrsf1a, Il6, Il10, Il1b and Il8rb (Table 2). Some of these

were confirmed by qPCR, so validating the microarray

results (Fig. 2). The partial lack of correlation between

microarray and qPCR for some samples was in part the

result of accuracy differences between the two methods.

Quantitative PCR is much more precise than microarray

experiments, as variations within replicates tend to be

higher in the latter. However, our results demonstrated

very reproducible microarray data, as shown in Figs 3

and 4.

We also observed differences in gene expression related

to apoptosis, which could be associated with the resis-

tance of AIRmax neutrophils to spontaneous cell death;10

with most differences being clustered in the over-repre-

sented gene ontology categories of cell cycle and cell pro-

liferation (Table 1). Jun, Cybb, Casp3, Tlr2, Nfkb1 and

Il1b genes involved in the regulation of apoptosis19–22

were among the most highly expressed genes in BMC

from Biogel-treated AIRmax mice and in downregulated

AIRmin transcripts (Tables 1 and 2). A highly significant

over-represented gene category linked to immune

response was found to be downmodulated in AIRmax

BMC after Biogel treatment. This downmodulation could

be related to NAD+ production at inflammatory exudates

induced by polyacrylamide beads, which provoke the

death of naive T lymphocytes, so inhibiting primary

immune responses.23 Conversely, activated T cells repress

the NAD+ receptors ART2 and P2RX7 and become more

resistant to apoptosis. The elimination of some of the

naive T cells allows the expansion of primed T lympho-

cytes – increasing the secondary response that is impor-

tant during infection processes. NAD+ also acts as a

damage signal to recruit neutrophils to the inflammation

site, which then interact with monocytes, dendritic cells

and T and B cells in a bidirectional, multicompartmental

manner, shaping the immune response through cell–cell

contact or through secreted products.24 In the same line,

as a compensatory event, mice selected for low antibody

production25 present resistant or susceptible phenotypes

for bacterial infection,26,27 pristane-induced arthritis28 and

skin tumorigenesis29 resembling AIRmax mice.

Expression profiling data were used to identify differen-

tially expressed genes within the critical intervals of previ-

ously mapped inflammation-related QTLs in chromosomes

1, 3, 6 and 11 as potential candidate genes.6,9,28 Slc11a1 is

the strongest candidate gene in chromosome 1; however,

AIRmax controlAIRmin control

AIRmin biogelAIRmax biogel

6

5

4

3

2

1

0

–1

8

7

Saa3 CxcI2 Ncf1 Csf3r ItgamFcer1gCcr1 Ccr2CybbMyd88

Inflammatory response genes

Gen

e ex

pres

sion

inte

nsity

(lo

g2)

Figure 4. Signal intensities of the inflammatory response genes in

mice with maximum and minimum acute inflammation responses

(AIRmax and AIRmin mice, respectively). Gene signal intensities

were normalized and evaluated by data flagging using codelink

expression analysis software. Statistical analysis using the significance

of analysis of microarray (SAM) algorithm was performed on both

pooled RNA biological replicates. Signal intensity was expressed as

the mean ± SE. These genes were identified using SAM and expres-

sion analysis systematic explorer (EASE).

� 2009 Blackwell Publishing Ltd, Immunology, 128, e562–e571 e567

Gene expression profiles of bone marrow cells from AIRmax and AIRmin mice

some other linked genes were differentially expressed in the

two lines (Fig. 3). The Slc11a1 gene mutation correspond-

ing to a non-functional protein is determined by a

gly169asp substitution.30 This lack of functionality results

in the accumulation of ions inside the phagosome, favour-

ing replication of the pathogens.31 Homozygous AIRmax

Table 1. Over-represented gene ontology categories of up- and down-regulated genes in AIRmax and AIRmin mice after Biogel treatment

Groups

Biogel/

control

ratio

Gene category GO

biological process

Gene

number

EASE

score Gene symbol

AIRmax

upregulated

> 4 Signal transduction 32 1�43e-002 Fyn, Ctnnbip1, Grit, Fgf13, Agtr1b, Olfr983, Trim23, Per3,

Gpr124, Pde9a, Il1r2, Il17ra, Il10ra, Il4ra, Adam28, Sipa1,

V1rg12, V1rh1, V1rh9,Ccr7, Cd3e, Gtpbp1, Smad3, Glra1,

Gnas, Ghrh, Gnal, Axin1, Map3k7, Lck, Gabrb1, Rbm9

Cell communication 37 1�82e-002 Fyn, Ctnnbip1, Grit, Fgf13, Agtr1b, Olfr983, Chrne,

Trim23, Per3, Gpr124, Pde9a, Il1r2, Il11, Il17ra, Il10ra,

Il4ra, Adam28, Fgf21, Sipa1, Vcan, V1rg12, V1rh1, V1rh9,

Ccr7, Cd3e, Gtpbp1, Gpld1, Smad3, Glra1, Gnas, Ghrh,

Gnal, Axin1, Map3k7, Lck, Gabrb1, Rbm9

AIRmax

downregulated

< 0�25 Immune response 20 3�87e-006 Ppbp, Irf2, Igll1, Igk-V33, Igk-V38, Il1b, Ighg1, Igj, Igk-V,

Serpina1e, Pf4, Cxcl2, Ccl6, Tcrb-V13, Vpreb1, H2-Q10,

Tyrobp, Cd3d, Ly96, Tnfsf13b

AIRmin

upregulated

> 4 Protein modification 10 9�00e-003 Stk40, Prkaca, Lrrk2, Pdgfra, Ppt2, Pias4, Havcr2, Rps6kl1,

Cdc25a, Gria1

Cell communication 21 1�38e-002 Lama4, Glra3, Olfr170, Clec14a, Prkaca, Olfr507, Hapln2,

Trim23, Pdgfra, Olfr1269, Olfr493, Pias4, Drd3, Scg5,

V1rc5, Foxa3, Mapk8ip2, Gria1, Pcdhb21, Chrnb4, Tac4

Protein metabolism 16 2�68e-002 Stk40, Prkaca, Lrrk2, 4930432H15Rik, Pdgfra, Erap1, Ppt2,

Pias4, Barhl2, BarH, Havcr2, Rps6kl1, Rps3, Cdc25a,

Tpp2, Gria1, Nr5a2

AIRmin

downregulated

< 0�25 Cell cycle 50 3�96e-007 Cct2, Cetn2, Smchd1, Gadd45g, Prim1, Ppp1ca, Kras2,

Ccnb1, Ppm1g, Mobkl3, Pttg1, Nuf2, Kit, Jun, Itgb1,

Ebna1bp2, Aurka, Stag2, Aurkb, Mapre1, Psmg2, Cdkl1,

Sept6, Nf2, Siah1a, Nek2, Nek1, Cul4a, Csf1r, Sfn, Uba1,

Rnf2, Cul1, Cdc25b, Cdkn2c, Ccnd3, Cct7, Mcm2, Mcm6,

Gspt1, Smc4, Ccne2, Rbl1, Casp3, Calm2, Bub3, Rad9,

Rad21, Wdr20a, Orc4l

Cell proliferation 62 5�79e-007 Cct2, Cetn2, Smchd1, Gadd45g, Prim1, Ppp1ca, Kras2, Ccnb1,

Ppm1g, Mobkl3, Fancl, Pttg1, Nuf2, Kit, Jun, Itgb1, Ebna1bp2,

Aurka, Stag2, Aurkb, Mapre1, Psmg2, Cdkl1, Pbef1,

Sept6, Nf2, Slfn3, Gtpbp8, Siah1a, Nek2, Nek1, Nfkb1, Mxi1,

Cul4a, Csf1r, Hdgf, Sfn, Uba1, Rnf2, Cul1, Cdc25b, Dock2,

Cdc42, Cdkn2c, Ccnd3, Cct7, Mcm2, Mcm6, Gspt1, Smc4,

Ccne2, Rbl1, Casp3, Ctnnb1, Calm2, Cd40lg, Tgfbi, Btg2,

Bub3, Rad9, Rad21, Wdr20a, Orc4l

DNA metabolism 38 4�30e-006 Gadd45g, Prim1, Fancl, Pttg1, Smarcd2, Asf1b, Parp1, Hat1,

Supt4h1, Aurkb, Dnmt3a, Ruvbl1, Actl6a, Csnk1d, Hp1bp3,

Zc3h11a, Hist1h2bc, Mylc2b, Hdac2, Mll3, Uba1, H2afy,

Polr2g, Recql, Mcm2, Mcm6, Cbx1, Mela, Ccne2, Cbx3,

Mbd3, Btg2, Xab2, Rad21, Smarca5, Rad51ap1, Morf4l1,

Orc4l

RNA metabolism 32 5�01e-006 Cct2, Cpsf3l, Pcbp1, Auh, Farsb, Sfrs4, Papola, Cstf2t,

Ebna1bp2, Sip1, Pcbp2, Vars2, Spop, Hnrnpu, Pabpn1, Dhx15,

Rpgrip1, Prmt1, Cugbp1, Hnrnpa1, Ddx24, Dnajb11, Papolg,

Hnrnpa2b1, Exosc9, Rbm6, Trnt1, Sfrs1, Rbm39, Xab2, Csdc2,

Skiv2l2

Genes are listed from the most to the least differently expressed. Biological process categories over-represented from differentially expressed genes

were identified by EASE analysis at P < 0�05 significance level (after Bonferroni correction).

GO, gene ontology; EASE, expression analysis systematic Explorer.

e568 � 2009 Blackwell Publishing Ltd, Immunology, 128, e562–e571

P. S. Carneiro et al.

and AIRmin sub-lines for wild-type and non-functional

Slc11a1 alleles were produced to study the interaction of

this gene with the acute inflammatory loci and significant

cell infiltration differences were observed between AIRmax

sub-lines bearing distinct alleles.5,6 The Slc11a1 gene is

pleiotropic and interferes with macrophage activation, oxi-

dative and nitrosamine bursts,32 tumour necrosis factor-aand interleukin-1b production,33 as well as with the expres-

sion of major histocompatibility complex class II mole-

cules.34 We observed interline differences in the transcripts

of these molecules in the present work.

The rat collagen-induced arthritis (Cia7) locus is syn-

tenic to the mouse chromosome 3 region (80–100 Mb). It

has been linked to experimental arthritis and neutrophil

influx28,35 and colocalizes with QTL described for mouse

models of rheumatoid arthritis, namely Cia5,36 Pgia2637

and Prtia.28 Several interesting genes are strong candidates

in this region, but the combined effects of multiple genes

must be taken into account, for it seems to occur at the

Cia5 region where a cluster of close-linked genes has been

reported to be differentially expressed during arthritis

progression.38

Table 2. Over-represented gene ontology categories of fourfold differentially expressed genes comparing AIRmax versus AIRmin mice

Groups

AIRmax/

AIRmin

ratio

Gene category GO

biological process

Gene

number

EASE

score Gene symbol

Control > 4 Digestion 3 7�48e-003 Npy, Tac4, Csn1s1

Inflammatory response 4 3�16e-002 Cxcl2, Cxcl1, Il1b, Tlr6

Positive regulation of

interleukin-6

biosynthesis

2 3�37e-002 Il1b, Tlr6

Control < 0�25 DNA metabolism 19 5�45e-008 Prim1, Prim2, Asf1b, Parp1, Hells, Hat1,

4933411G06Rik, Ruvbl1, Actl6a, Ctcf, Mre11a,

Ahcy, Mela, Gtf2h1, Ccne2, Rad23a,

Gcn5l2, Rad21, Usp8

Translation 9 6�52e-004 Farsb, Prim2, Gfm1, Eif4g2, Eif4a2, Spnb2,

Cct2, Lgtn, Rpgrip1

RNA processing 10 2�00e-003 Farsb, Prim2, Ppp1r8, Spnb2, Cct2,

Hnrnpu, Rpgrip1, Nol5, Rbm6, Sfrs1

Biogel > 4 Inflammatory response 17 2�97e-008 Saa3, Cxcl2, Fos, Itgb2, Fcer1g, Ccl8, Itgam, Il10,

Ncf1, Myd88, Cybb, Csf3r, Ccr2, Ccr1,

Dock2, Cd14, Tnfrsf1a, Tlr1

Macromolecule

biosynthesis

41 1�12e-005 Atp6v1b2, Atp6v1a, Impdh1, Atp5b, Rpl14, Eef1g,

Atp6v0d1, Gadd45g, Farsb, Ireb2, Paics, Prkag1,

Isyna1, Eif4e3, Eif3e, Adsl, Il6, Vars2, Il10, Eef1a1,

Ampd2, Mterfd1, Eif4g2, Nmt1, St8sia1, Gadd45b,

Acly, Gyg, Eif4h, Cebpb, Edem2, Gys1, Cct2, Cd37,

Agpat2, Tlr1, Atic, B3gnt5, Rad21, Chst14,

Mapk13

Purine nucleotide

biosynthesis

9 1�91e-005 Atp6v1b2, Atp6v1a, Impdh1, Atp5b, Atp6v0d1,

Paics, Adsl, Ampd2, Atic

Coenzyme and prosthetic

group metabolism

13 6�22e-005 Atp6v1b2, Atp6v1a, Atp5b, Atp6v0d1, Ubiad1,

Idh1, Ncf1, Acly, Cpox, Gcat, Rsad2, Gsr, Cyb5r4

Immune cell chemotaxis 5 1�95e-004 Itgb2, Itgam, Csf3r, Ccr1, Dock2

Biogel < 0�25 Meiosis 4 5�18e-003 Sycp1, Rec8, Clgn, Wdr20a

Morphogenesis 16 6�30e-003 Plce1, Pitx1, Pdgfra, Elf3, Prelp, Dmd, Myom2,

Hoxd3, Myl4, Hoxd13, Tlx3, Dpysl3, Cdkn2a,

Lhx8, Ptprz1, Bglap1

Cell proliferation 14 6�57e-003 Ets1, Pdgfra, Sycp1, Elk1, Rec8, Hoxd13, Clgn, Ccnj,

Cdkn2a, Rab8a, Cd28, Nr6a1, Wdr20a, Cdk8

G-protein coupled receptor

protein signaling pathway

16 3�16e-002 Mrgprb2, Olfr570, Olfr1125, Olfr1377, Olfr170,

Olfr1378, Plce1, Gpr85, Olfr507, Olfr771, Olfr1356,

Olfr498, Olfr642, Olfr114, V1rh5, Tac4

Genes are listed from the most to the least differently-expressed. Biological process categories over-represented from differentially expressed genes

were identified by EASE analysis at P < 0�05 significance level (after Bonferroni correction).

GO, gene ontology; EASE, expression analysis systematic Explorer.

� 2009 Blackwell Publishing Ltd, Immunology, 128, e562–e571 e569

Gene expression profiles of bone marrow cells from AIRmax and AIRmin mice

A highly significant linkage disequilibrium pattern was

observed in AIRmax and AIRmin mice among marker

alleles located within a 452-kilobase pulmonary adenoma

susceptibility 1 (Pas1) locus on chromosome 6, so defin-

ing the location of gene candidacy for inflammatory

response and for lung cancer susceptibility.7 Kras2 is the

candidate gene in this region and it was downregulated

in AIRmin BMC after Biogel injection (Fig. 3), together

with the other differentially expressed linked genes. This

same profile was observed with the genes on chromo-

some 11 that were previously determined to be involved

in acute inflammation and bacterial infection resis-

tance.6,39 This region is syntenic to human chromosome

5q31–q33, which is also related to resistance to several

diseases.40 Stat3 is the major candidate gene for this

QTL, as it has been implicated in neutrophil migration,41

carcinogenesis,42 resistance to apoptosis43 and endotoxic

shock.44 However, we cannot exclude its interaction with

other differentially expressed genes close to this region in

modulating inflammation, as observed in another

study.45

Cytokine signalling via the STAT–SOCS (signal trans-

ducer and activator of transcription/suppressor of cyto-

kine signalling) pathways regulate several cell types

involved in the initiation and resolution of inflamma-

tion.46 Resolution of inflammation is a tightly regulated

process that is controlled by distinct mediators, including

cytokines, chemokines, their receptors, as well as resolvins

and lipoxins.47 The higher number of downmodulated

genes in AIRmin mice could represent an impaired mech-

anism of inflammatory resolution in this strain. In fact,

several genes known to be involved in the dampening of

inflammation are repressed in this line (Table 2). The

high susceptibility of AIRmin mice to lung and skin

tumorigenesis was in agreement with these results, in

which a persistent chronic inflammation was detected

only in the AIRmin line.2,7

Taken together, our results demonstrated distinct gene

expression profiles of the BMC from high and low

inflammatory responder mice, pointing to gene clusters

in QTL related to several inflammatory phenotypes.

These differences increase during Biogel-induced acute

inflammation, as a result of the downmodulation of

gene transcripts in AIRmin mice. The main pathways

closely associated with these gene expression profiles are

linked to inflammatory response, signal transduction

and cell proliferation, with several of the differently

expressed genes mapped in QTL implicated in the regu-

lation of inflammatory phenotypes. A finer resolution of

each of these loci will still be necessary to identify single

gene effects. Our approach represents an alternative to

models of knockout or transgenic mice and allows the

evaluation of allelic gene combinations instead of gene

mutations, and might be satisfactorily applicable to

humans.

Acknowledgements

This work was supported by grants from the Fundacao de

Amparo a Pesquisa do Estado de Sao Paulo (FAPESP)

and the Conselho Nacional de Desenvolvimento Cientıfi-

co e Tecnologico (CNPq). We would like to thank Mr

Roy Funch for the English revision and Dr Carlos Alberto

Moreira Filho and Dr Osvaldo Keith Okamoto for their

permission to use the Microarray facility at the Albert

Einstein Hospital.

Disclosures

None.

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Gene expression profiles of bone marrow cells from AIRmax and AIRmin mice