microarray_CK+inflam

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Biomarkers of acute respiratory allergen exposure: Screening for

sensitization potential

Cherie M. Pucheu-Haston a,, Lisa B. Copeland b, Beena Vallanat b, Elizabeth Boykin b, Marsha D.W. Ward b

a Curriculum in Toxicology, University of North Carolina-Chapel Hill, CB# 7270, Chapel Hill, NC 27599-7270, USAb National Health and Environmental Effects Research Laboratory, U.S. Environmental Protection Agency, 109 T.W. Alexander Drive, Research Triangle Park, NC 27711, USA

a b s t r a c ta r t i c l e i n f o

Article history:

Received 9 November 2009Revised 10 December 2009

Accepted 17 December 2009

Available online 4 January 2010

Keywords:

Asthma

Biomarkers

Hazard screening

Intratracheal aspiration

Gene expression microarray

Quantitative real-time polymerase chain

reaction (qRT-PCR)

Effective hazard screening will require the development of high-throughput or in vitro assays for the

identication of potential sensitizers. The goal of this preliminary study was to identify potential biomarkers

that differentiate the response to allergensvsnon-allergens following an acute exposure in nave individuals.

Female BALB/c mice received a single intratracheal aspiration exposure to Metarhizium anisopliae crude

antigen (MACA) or bovine serum albumin (BSA) in Hank's Balanced Salt Solution (HBSS) or HBSS alone. Mice

were terminated after 1, 3, 6, 12, 18 and 24 h. Bronchoalveolar lavage uid (BALF) was evaluated to

determine total and differential cellularity, total protein concentration and LDH activity. RNA was isolated

from lung tissue for microarray analysis and qRT-PCR. MACA administration induced a rapid increase in BALF

neutrophils, lymphocytes, eosinophils and total protein compared to BSA or HBSS. Microarray analysis

demonstrated differential expression of genes involved in cytokine production, signaling, inammatory cell

recruitment, adhesion and activation in 3 and 12 h MACA-treated samples compared to BSA or HBSS. Further

analyses allowed identication of100 candidate biomarker genes. Eleven genes were selected for further

assessment by qRT-PCR. Of these, 6 demonstrated persistently increased expression (Ccl17, Ccl22, Ccl7,

Cxcl10, Cxcl2, Saa1), while C3ar1 increased from 624 h. In conclusion, a single respiratory exposure of mice

to an allergenic mold extract induces an inammatory response which is distinct in phenotype and gene

transcription from the response to a control protein. Further validation of these biomarkers with additional

allergens and irritants is needed. These biomarkers may facilitate improvements in screening methods. 2009 Elsevier Inc. All rights reserved.

Introduction

One particularly challenging area of immunotoxicology is the

identication of agents with a high likelihood to induce allergic

sensitization. Traditional approaches to screening for these agents

require time consuming multiple exposure protocols. Unfortunately,

these methods are becoming increasingly incapable of meeting the

screening needs of the rapidly expanding list of newly synthesized

and bioengineered compounds, not to mention re-screening those

agents that have been discovered to persist in the environment for

longer than originally expected. Efcient identication of problematic

agentswill require a shift to more high-throughput methods of hazard

screening. It is probable that a multidisciplinary approach will be

required, where agents will be evaluated using multiple models, with

the nal decisions being made based upon the preponderance of

evidence.

We hypothesize that there is a set of differentially expressed

gene biomarkers that distinguishes the immune response associ-

ated with the induction of allergic sensitization from non-allergic

immune responsiveness following a single exposure. The objectives

of this study were two-fold: (1) to determine whether we could

identify biomarkers of acute exposure to a known respiratory

sensitizer as compared to a poorly allergenic compound or vehicle

control; (2) to use this information to aid in the development of a

more extensive validation study. The ultimate goal of this work is to

translate these results into the development of an in vitro screening

assay.

To test our hypothesis, we selected the entomopathogenic fungus

Metarhizium anisopliae extract which we have previously shown to

induce robust responses characteristic of human allergic asthma in

adult BALB/c mice following multiple exposures (Ward et al., 1998,

2000). These responses can be obtained following intratracheal

aspiration (IA) exposure to M. anisopliae crude antigen (MACA).

Neither intraperitoneal priming nor adjuvant administration is

required for sensitization (Ward et al., 2000). As low or non-allergic

agent, we selected bovine serum albumin (BSA) which does not

induceallergicor asthma-like responsesin ourmousemodel(Viana et

al., 2002; Ward et al., 2009).

Toxicology and Applied Pharmacology 244 (2010) 144155

Corresponding author. c/o U.S. Environmental Protection Agency 109 T. W.

Alexander Drive, Research Triangle Park, NC 27711, USA. Fax: +1 919 541 4284.

E-mail addresses: [email protected],[email protected]

(C.M. Pucheu-Haston).

0041-008X/$ see front matter 2009 Elsevier Inc. All rights reserved.

doi:10.1016/j.taap.2009.12.027

Contents lists available at ScienceDirect

Toxicology and Applied Pharmacology

j o u r n a l h o m e p a g e : w w w. e l s e v i e r. c o m / l o c a t e / y t a a p
mailto:[email protected]:[email protected]://dx.doi.org/10.1016/j.taap.2009.12.027http://www.sciencedirect.com/science/journal/0041008Xhttp://www.sciencedirect.com/science/journal/0041008Xhttp://dx.doi.org/10.1016/j.taap.2009.12.027mailto:[email protected]:[email protected]


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Methods

Animals. Eight-week-old female BALB/c mice were obtained from

Charles River Laboratories (Raleigh, NC). All mice were housed in

polycarbonate cages with hardwood chip bedding in an environmen-

tally controlled, Association for Assessment and Accreditation of

Laboratory Animal Care International (AAALAC)-accredited animal

facility. Environmental enrichment was provided in the form of

mouse nestlets (Ancare Corporation, Bellmore, NY). Sentinel micewere monitored serologically and were found to be free of Sendai,

mouse pneumonia, mouse hepatitis, other murine viruses, and

mycoplasma. Mice also were monitored for, and found to be free of,

ectoparasites and endoparasites. The animal holding room tempera-

ture (22 F 1.1 F) and relative humidity (50% 10%) were

maintained at the recommended levels. Mice received standard

rodent chow (Lab Diet 5P00, Purina Mills, St. Louis, MO) and water

ad libitum. Mice were acclimated 1 week at this facility prior to the

start of the experiment.

Antigen preparation. M. anisopliae strain 1080 was obtained from

USDA-ARS Entomopathogenic Fungus Collection in Ithaca, NY.

Extracts of M. anisopliae mycelium and spores/conidia as well as

inducible enzymes ltrate were produced as previously described

(Ward et al., 1998). Briey, a fungal extract was prepared by a

modication of the methods described by (Stankus and O'Neil,

1988). Mycelium (hyphae growth) was grown at 27 C for 72 h in

Sabouraud's maltose broth with aeration (150 rpm). Subsequently,

the mycelium was collected on Whatman No. 1 lter paper, washed

twice with saline to remove media contaminants, and air-dried

overnight in a biosafety cabinet. The conidia, grown at 27 C on

Sabouraud's maltose agar, were harvested by gently scraping culture

plates of 2- to 3-week-old cultures. Each component was ground into

a paste in saline (1:2530 w/v) and stirred overnight at 4 C. The

supernatant was collected following centrifugation at 18,000g.

Production of inducible proteases and chitinases by M. anisopliae is

enhanced in the absence of readily available nitrogen and carbon.

Therefore, a deprivation medium of unpuried chitin (Sigma

Chemical Co., St. Louis, MO) at 3% in water was inoculated with M.anisopliaeand incubated at 27 C for 72 h with aeration (150 rpm).

Theltrate was retained following ltration through Whatman No. 1

lter paper. The proteins with a molecular weight greater than 3000

were concentrated using a YM-3 lter (MWCO 3000) (Amicon,

Beverly, MA) and assayed for total protein concentration (as

described below). For each component, the pH was adjusted to 6.0

followed by lter sterilization through a 0.2-um syringe lter

(Acrodisc; Gelman Sciences, Ann Arbor, MI). Equal protein con-

centrations of each component were combined to form the M.

anisopliae crude antigen (MACA). Dosing aliquots were stored at

20 C for a maximum of 10 wks until use. Extract endotoxin

concentrations were minimal (0.0140.007 ng per 20 g dose) as

measured by LAL assay (Lonza Group Ltd., Basel, Switzerland).

Bovine serum albumin (BSA), fraction V, was purchased fromSigma-Aldrich (St. Louis, MO) and diluted in Hanks Balanced Salt

Solution (HBSS Gibco/Invitrogen, Carlsbad, CA). Dosing aliquots were

stored at 20 C until use.

Experimental design. Female BALB/c mice were anesthetized, then

given a single dose of MACA (20 g in 50 l HBSS), BSA (20 g in 50 l

HBSS) or HBSS alone (50 l) by intratracheal aspiration (IA) as

previously described (Ward et al., 2000). Briey, mice were

anesthetized by inhalation of a mixture of isourane and oxygen,

and then vertically suspended by their incisors to facilitate treatment

administration. Swallowing was prevented by gently pulling the

tongue out of the mouth in an upward direction. Antigen extract was

deposited into the oropharynx while the nose was briey occluded,

inducing aspiration of theextract. Sixmice from each treatmentgroup

were terminated by sodium pentobarbital overdose (Euthasol, Virbac

Animal Health, Fort Worth, TX) at each of six time points (1 h, 3 h, 6 h,

12 h, 18 h and 24 h post-treatment).

Bronchoalveolar lavage (BAL) and lung collection. Lung tissue and

bronchoalveolar lavage uid (BALF) samples were collected from all

mice at termination. The left lung was harvested and snap-frozen in

liquid nitrogen immediately after euthanasia, then stored at 80 C

until RNA isolation. The remaining lung tissue was lavaged twicewith 0.7 ml aliquots of HBSS. BALF aliquots for each animal were

pooled and stored on ice. The BALF was centrifuged at 800 rpm

(100g) for 15 min at 4 C. Aliquots of BALF supernatant were

assayed for total protein and lactate dehydrogenase (LDH) activity

(as described below), and the remainder was stored at 20 C for

IgE quantication by ELISA. The cell pellet was resuspended in 1 ml

HBSS and cytospin preparations of 200 l BALF were made by

centrifugation onto glass slides (200 rpm, 10 min on a Shandon

Cytospin; Shandon Inc., Pittsburg, PA). Following Wright-Giemsa

staining (Fisher Scientic, Fairlawn, NJ), cells were differentially

counted at 200 cells per slide (one slide per animal). Total BALF cell

counts were obtained from the resuspended cells using a Coulter

counter (Coulter Corp., Miami, FL).

Total protein and lactate dehydrogenase (LDH) assays. BALF samples

and fungal extract components were assayed for total protein using

Pierce Coomassie Plus Protein Assay Reagent (Pierce/Thermo Fisher

Scientic, Rockford, IL). Concentrations were determined from a

standard curve using BSA standards obtained from Sigma Chemical

Co. (St. Louis, MO). BSA works well as a general-purpose standard for

protein quantitation. However, due to differences in amino acid

concentrations and protein extinction coefcients, there may be some

imprecision in the measurement of any complex protein mixture

(such as a fungal extract or lavage uid) with any single protein

standard.

BALF samples were assayed for LDH activity using a commercially

prepared kit and controls from Sigma Chemical Co. Both the total

protein and LDH assays were modied for use on the KONELAB 30

clinical chemistry Spectrophotometer analyzer (Thermo ClinicalLabsystems, Espoo, Finland).

RNA isolation. Total RNA was isolated from all frozen lung tissue

samples using a RNeasy Mini kit (Qiagen Inc., Valencia, CA) according

to manufacturers instructions. Briey, whole left lungs were kept on

dry ice before homogenization in 1 ml of RLT lysis buffer using an

Omni TH homogenizer (Omni International, Marietta, GA). Lung

homogenate (350 l) was added to an equal volume of 70% ethanol,

vortexed and added to an RNeasy mini spin column. After

centrifugation, the column was washed once with RW1 buffer and

twice with RPE buffer. RNA was then eluted by the addition of RNAse-

free water. RNasin ribonuclease inhibitor (Promega Corporation,

Madison, WI) was added immediately after isolation to increase RNA

stability. RNA quantication was performed using a NanoDrop ND-1000 spectrophotometer (NanoDrop Technologies, Inc., Wilmington,

DE). RNA quality was assessed using an Agilent 2100 bioanalyzer

with the Agilent RNA 6000 nano kit (Agilent Technologies, Santa

Clara, CA).

Microarray hybridization. Three to 4 unique RNA samples from all 3

treatment groups at both the 3 and 12 h post-treatment time points

were submitted for microarray gene expression analysis (MACA 3 h

n =3, BSA 3 h n =3, HBSS 3 h n =3; MACA 12 h n =3, BSA 12 h

n =4, HBSS 12 h n =4; total n =24). These sample times were

chosen based upon a tendency for the BALF inammatory response to

peak at 6 and 18 h, or both (see Figs. 13). The microarray chosen for

this study was the Affymetrix Mouse Genome 430A 2.0 (Affymetrix

Inc., Santa Clara, CA, www.affymetrix.com), which contains over

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22,500 probe sets representing 14,500 well-characterized mouse

genes. A total of 21 gene chips (one per mouse) were used in this

study.

Overall gene expression data analysis strategy. The analysis of the

microarray data involved the comparison of a total of six data sets

from two time points (MACA 3 h, BSA 3 h, HBSS 3 h; MACA 12 h, BSA

12 h, HBSS 12 h). Identication of putative biomarkers was performed

in a stepwise fashion: (1) identify sequences differentially expressed

in either MACA-or BSA-treated animals as compared to time-matched

HBSS controls, (2) determine sequences differentially expressed in

MACA-treated samples only, (3) perform biomarkerlter enrichment,

(4) perform functional expression analysis on biomarker-ltered

data, and (5) select putative biomarkers. Additionally, differentially

expressed sequences were subjected to pathway analysis to identify

fundamental differences in the immune response to compounds of

high versus low allergenicity (Fig. 1).

Statistical analysis of microarray data. Data (.cel les) were

analyzed and statistically ltered using Rosetta Resolver version

7.1 software (Rosetta Inpharmatics, Kirkland, WA). For analysis

purposes, the data from individual 25-mer oligonucleotide probe

pairs were combined into sequence data. Each sequence represents a

portion of a gene transcript, and is generally comprised of 11

oligonucleotide probe pairs (reporters). A given gene may be

represented by one or more sequences, which allows for detection of

alternate transcripts.

Sequence expression intensities were averaged for each treatment

group. Sequences that were differentially expressed within a time

point between HBSS control groups and MACA or BSA treatment

groups were identied in Rosetta Resolver by one-way ANOVA with

Benjamini and Hochberg False Discovery Rate (FDR) analysis(pb0.01), followed by Scheffes post hoc data analysis (pb0.05).

Sequences identied as statistically different in intensity in either

MACA or BSA samples as compared to HBSS were selected for ratio

analysis. These sequences were then further ltered using an

experimental/control ratio of2.0.

Filtered sequence lists from MACA- and BSA-treated animals were

then compared within a time point (i.e., MACA 3 h versus BSA 3 h;

MACA 12 h versus BSA 12 h) using the list operations function of

Metacore GeneGo (GeneGo, Inc., St. Joseph, MI; www.genego.com ) to

identify sequences differentially expressed only in MACA-treated

animals (MACA-specic sequences). A second analysis on MACA-

specic sequences was then run to identify those sequences

differentially expressed at both MACA post-treatment time points

(MACA 3 h/12 h-speci

c).

Fig. 1.Overview of microarray data analysis strategy. RNA was harvested from snap-

frozen lung tissue and hybridized to a gene expression microarray (Affymetrix Mouse

Genome 430A 2.0). Sequences that were differentially expressed between HBSS control

groups and MACA or BSA treatment groups were identied by one-way ANOVA and

Benjamini and HochbergFDR (pb0.01)followed by Scheffe' post hocanalysis (pb0.05).

Genes thatchanged2-fold or greater relativeto HBSSwere selected for further analysis.

Sequences differentially expressed in MACA-treated animals were identied using

GeneGo/Metacore software. Sequences were then ltered for biomarker potential

using the biomarker function of Ingenuity Pathway Analysis software. Selected

sequences weresubjected to functional analysis usingDAVID (Databasefor Annotation,

Visualization and Integrated Discovery). Expression of putative biomarkers was then

evaluated using quantitative real-time PCR.

Fig. 2.BALF LDH and total protein levels. (A) Intratracheal administration (IA) of MACA

is associated with little acute cellular toxicity (LDH). (B) IA MACA induces a rapid

increase in BALF total protein levels, suggesting the development of pulmonary cellular

leakage and/or edema. N=6 per treatment per time point. Pb0.01, Pb0.001

relative to HBSS;Pb

0.001 relative to BSA.

Fig. 3. BALFtotal celullarity. IA MACAinduces a rapid increase in totalairway cellularity

relative to BSA-or HBSS-treated mice.Pb0.01, Pb0.001 relative to HBSS; Pb0.05,

Pb0.01,Pb0.001 relative to BSA.

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Biomarkerlter analysis. Sequences identied in Metacore GeneGo

asMACA 3 h/12 h-specicwere then subjected to biomarker lter

analysis using the Biomarker function of Ingenuity Pathway Analysis

Software (Ingenuity Systems, Redwood City, CA; www.ingenuity.

com). This feature facilitates prioritization of putative biomarker

genes based upon user-dened parameters such as tissue expression,

species of interest or disease association. For this analysis, potential

biomarkers were selectedbasedupon thepresence in any oneor more

of the following: immune system cells (e.g., T lymphocytes,neutrophils, dendritic cells, etc.), immune system cell lines, lung cell

lines, epidermis, lung, spleen or thymus.

Functional analysis. Putative biomarker sequences identied by

Ingenuity were then queried against the gene expression databases in

Metacore GeneGo, Ingenuity and DAVID (Database for Annotation,

Visualization and Integrated Discovery, National Institute of Allergy

and Infectious Diseases, NIH, USA; http://david.abcc.ncifcrf.gov/

home.jsp) software programs. Candidate biomarkers were prioritized

and selected based upon relevance to allergic sensitization and

likelihood of expression in available in vitroculture systems.

Pathway analysis. Sequences which met all the criteria for differ-

ential expression relative to HBSS control (i.e., ANOVA/FDRp b0.01,

Scheffe' p b0.05 and experiment/control ratio 2) were subjected to

pathway analysis in Ingenuity software. Pathway analysis allows

superimposition of user-specied sequence lists and expression data

on known biological pathways and functions in the Ingenuity

Pathways Knowledge Base. The signicance of the association

between the data set and canonical pathways was determined in

two ways: (1) a ratio was generated of the number of genes from the

data set that mapped to the pathway divided by the total number of

genes in the pathway, (2) Fischers exact test was used to calculate a

p-value determining the probability that the association between the

genes in the data set and the canonical pathway was explained by

chance alone.

Conrmation of selected microarray results using quantitative real-time

polymerase chain reaction (qRT-PCR). Total RNA from all samples (6samples per treatment per time point; n =108) was converted to

cDNA using a High-capacity RNA-to-cDNA kit (Applied Biosystems

Inc., Foster City, CA) according to manufacturers instructions. Briey,

3 g of total lung RNA was reverse transcribed using random hexamer

primers and Multiscribe reverse transcriptase in a total reaction

volume of 30 l. Cycling conditions were 25 C for 10 min, 37 C for

120 min and 85 C for 5 min. All cDNA was stored at20 C until use.

Quantitative real-time polymerase chain reaction (qRT-PCR) was

performed on cDNA using TaqMan Universal PCR Master Mix no

AmpErase UNG (Applied Biosystems). A 30 l total reaction volume

was used per well, consisting of 100 g of cDNA (in 5 l RNAse-free

water), 15 l of 2 Master Mix, 8.5 l of RNAse free water and 1.5 l of

primers and FAM-labeled MGB probes. All primerand probe sets were

selected from available inventoried assays and purchased fromApplied Biosystems.

Amplication was performed on an ABI PRISM 7900 thermocyclerat

thedefault cycling conditions (50C for2 min, 95 C for10 min, followed

by 40 cycles of 95 C for 15 s (melt) and 60 C for 1 min (anneal)). The

endogenous control used for these assays was 18 s. Analysis was

performed using a comparative CT method adapted from (Livak and

Schmittgen, 2001). The CT value for 2 replicates per sample was

averaged foreach targetgene andthe endogenous control. Theaveraged

CT value for 18 s was subtracted from the corresponding CT value for the

target gene in the same sample (CT target CT endogenous control) =CT.

Individual CT values were subtracted from the averagedCT value of the

HBSS control group for that time point=CT. The fold change was

calculated as 2CT. Average fold changes (relative to HBSS) and

standard errors were then calculated for each treatmenttime point.

Statistical analysis of BALF and qRT-PCR data. BALF LDH activity,

total protein concentration and total and differential cell counts were

analyzed by one-way analysis of variance (ANOVA) with Bonferroni's

multiple comparison post-test using GraphPad Prism version 4.03 for

Windows (GraphPad Software, San Diego, CA,www.graphpad.com).

For these analyses, values from MACA-treated and BSA-treated

animals were compared to HBSS-treated controls at the same time

point. A p-value of b0.05 was considered statistically signicant.

Reported values represent meansstandard error (SE).Gene transcription data obtained by qRT-PCR are expressed as

mean fold changes relative to HBSS-treated controlsSE. Relative

transcription levels for MACA-treated mice were compared to BSA-

treated mice at each time point in Graph Pad Prism using a two-tailed

nonparametric MannWhitney test. A value ofpb0.05 was considered

to be signicant. Where necessary to facilitate comparison in Table 4,

2CT valuesb1 were converted to negative fold change valuesusing

the formula1/(2CT). However, these converted values were not

otherwise used for display or analysis.

Results

BALF LDH activity and total protein concentration

LDH activity is frequently used as an indicator of non-specic

cellular damage. LDH activity in HBSS-treated control mice stayed

relatively constant through the experiment (Fig. 2A). MACA-treated

mice displayed a very small increase in LDH activity relative to HBSS

from 6 to 24 h post-treatment, but this increase was not statistically

signicant. LDH activity in BSA-treated mice was more variable and

displayed no clear temporal pattern.

BALF total protein concentration may indicate increased vascular

permeability, increased local secretion of inammatory mediators,

or cellular leakage secondary to damage or death. Administration of

MACA was associated with a signicant increase (relative to both

HBSS and BSA) in BALF total protein concentration at 12 and 18 h

post-treatment, with a more variable increase seen at 6 h ( Fig. 2B).

This increase in total protein in the absence of signicantly

increased LDH activity suggests that MACA administration isassociated with the induction of pulmonary edema and inamma-

tion but very little overt cytotoxicity during the time points

evaluated in this study.

Total and differential BALF cell counts

MACA-treated mice displayed a rapid increase in total BALF cell

numbers post-treatment (Fig. 3). These increases were signicant

compared to both HBSS and BSA at 6, 12 and 18 h. HBSS- and BSA-

treated mice demonstrated similar but minimal increases in BALF

cellularity after IA, with the greatest cell counts noted at the 18 h and

24 h time points.

BALF cellularity was also evaluated by individual cell type.

Macrophages demonstrated a small increase with time (up to 18 hpost-treatment) in all groups, including HBSS controls (Fig. 4A).

Neither BSA-treated nor HBSS-treated mice demonstrated apprecia-

ble changes in BALF neutrophils, lymphocytes or eosinophils. In

contrast, MACA-treated mice had signicant increases in all of these

cell types relative to both HBSS and BSA. Neutrophils were

signicantly increased by 3 h post-treatment, while lymphocytes

were signicantly increased by 6 h (Figs. 4B, C). Both cell types

remained elevated for the duration of the study. Eosinophil numbers

were signicantly increased at 12 and 24 h post-treatment ( Fig. 4D).

When the results of the BALF analyses were evaluated as a whole,

it was noted that most parameters rst demonstrated signicant

perturbations at 6 h after treatment administration, with many of

these values decreasing back towards baseline by 24 h. Thetriggers

for these changes would be expressed before phenotypic changes

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could be seen. Therefore, we selected samples from the 3 and 12 h

post-treatment times for all three treatment groups for microarray

gene expression analysis.

Microarray gene expression analysis

Our overall data analysis strategy is outlined inFig. 1. Sequences

dened as differentially expressed had to t all of the following

conditions: (1) sequence intensity signicantly different from time-

matched control as determined by one-way ANOVA/Benjamini-

Hochberg FDR pb0.01, (2) Scheffe's post hoc analysis pb0.05, and

(3) upregulated or downregulated at least two-fold relative to time-

matched control.

A total of 451 sequences from MACA-treated mice were differen-

tially expressed relative to control at the 3 h time point, with a fold

difference range of +50.37 to 18.68 (Supplementary Table 1). This

number increased at the 12 h time point, with 754 sequences

differentially expressed (range +49.99 to 15.10;SupplementaryTable 2). Compared to HBSS-treated control mice, BSA-treated mice

had a total of 124 differentially expressed sequences 3 h post-

administration, with a fold difference range of +35.12 to 24.04

(Supplementary Table 3). This number had decreased to 17 by 12 h

(range +3.44 to 2.80;Supplementary Table 4).

One objective of this study was to identify potential biomarkers of

exposure to respiratory sensitizers. Therefore, we used the list

operations function of GeneGo to select genes which were differen-

tially expressed only in MACA-treated animals at either the 3 or 12 h

time points. Genes which were also (or only) differentially expressed

after BSA administration were excluded from further consideration as

biomarkers, but were subjected to pathway analysis (see below). A

total of 299 and 620 MACA-specic genes were identied at the 3 and

12 h time points, respectively.

Theultimategoal of this work is to translateour results into a more

high-throughput format for hazard screening. Many high-throughput

assays are time-sensitive and therefore will likely require the use of

biomarkers that demonstrate persistent expression. For this reason,we used the list operations function of GeneGo to identify MACA-

specic genes that were differentially expressed at both the 3 h and

12 h time points (MACA 3 h/12 h genes; n =153 genes) (Table 1).

Biomarkerlter analysis

Our list of differentially expressed genes was generated from

whole lung homogenate RNA. It is possible that some of these genes

had been expressed by non-residentcellsor by cell types which might

not be easily modeledin vitro. For this reason, we imported the list of

MACA 3 h/12 h genes into Ingenuity Pathway Analysis Software to

conduct biomarker lter analysis. This allowed us to select genes

which would be expected to be expressed in a more limited selection

of organs or cell types. We chose genes that would be expressed inlung or immune system cells (such as dendritic cells or lymphocytes),

as well as in existing cell lines derived from these cell types. We

included genes expressed in thymus and spleen to represent primary

and secondary immune organs. Finally, since respiratory hypersensi-

tivity may be induced by cutaneous sensitization (Vanoirbeek et al.,

2004; Akei et al., 2006), we also included genes expressed in

epidermis. Application of these limits allowed us to narrow our list

of potential biomarkers down to 130 genes, represented by 201

sequences (Table 2).

Functional analysis and candidate biomarker selection

At this point, our list of potential biomarkers was composed of

genes that were differentially expressed in MACA-treated mice, and

Fig. 4. BALF differential cell counts. (A)An increasein BALF macrophages wasnoted overtimein all3 treatment groups. (BD). IA MACAinducesthe rapidrecruitment of neutrophils,

lymphocytes and eosinophils relative to BSA- or HBSS-treated mice. Pb0.05,Pb0.01,Pb0.001 relative to HBSS;Pb0.05,Pb0.01,Pb0.001 relative to BSA.

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Table 1

Candidate biomarker gene sequence sets differentially expressed 3 and 12 h after MACA administration.

Sequence

code

Primary

sequence

name

Fold

change

(3 h)

Fold

change

(12 h)

Sequence description Sequence

code

Primary

sequence

name

Fold

change

(3 h)

Fold

change

(12 h)

Sequence description

1450788_at Saa1 25.69 46.99 Serum amyloid A 1 1420904_at Il17ra 2.11 2.73 Interleukin 17 receptor A

1418652_at Cxcl9 7.60 27.46 Ch emokine ( C- X- C mot if) l igan d 9 1426909_ at Uck2 2.40 2.70 Ur idine-cyt idine kinase 2

1418930_at Cxcl10 19.11 19.91 Ch emokine ( C- X- C mot if) l igan d 10 1416111_ at Cd83 6.38 2.70 CD83 an tigen

1419607_at Tnf 7.67 19.54 Tumor necrosis factor 1419272_at Myd88 2.17 2.68 Myeloid differentiation

primary response gene 881428034_a_at Tnfrsf9 5.00 15.21 Tumor necrosis factor receptor

superfamily, member 9

1421207_ at Lif 5 .16 2.66 Leukemia inhibitor y f act or

1420591_at Gpr84 4.04 12.09 G protein-coupled receptor 84 1418936_at Maff 2.35 2.63 v-maf musculoaponeurotic

brosarcoma oncogene family,

protein F (avian)

1460227_at Timp1 4.38 12.05 Tissue inhibitor o f met allopr ot einase 1 1425374_ at Oas3 4.93 2.61 2-5oligoadenylate synthetase 3

1419209_at Cxcl1 12.42 11.98 Chemokine (C-X-C motif) ligand 1 1449184_at Pglyrp1 2.52 2.60 Peptidoglycan recognition protein 1

1425151_a_at Noxo1 5.83 11.43 NADPH oxidase organizer 1 1450698_at Dusp2 2.42 2.60 Dual specicity phosphatase 2

1421228_at Ccl7 11.88 11.32 Ch emokine ( C- C motif) l igan d 7 1449233_ at Bhlhb8 2.47 2.60 Basic helix-loop-helix domain

containing, class B, 8

1419192_at Il4i1 13.40 11.23 Interleukin 4 induced 1 1460220_a_at Csf1 4.24 2.57 Colony stimulating factor 1

(macrophage)

1460282_at Trem1 9.59 11.06 Triggering receptor expressed

on myeloid cells 1

1425850_a_at Nek6 2.13 2.56 NIMA (never in mitosis gene a)

-related expressed kinase 6

1451798_at Il1rn 6.88 10.86 Interleukin 1 receptor antagonist 1418262_at Syk 1.70 2.56 Spleen tyros ine kinas e

1419721_at Gpr109a 5.78 10.58 G protein-coupled receptor 109A 1422924_at Tnfsf9 4.77 2.55 Tumor necrosis factor (ligand)

superfamily, member 91420380_at Ccl2 17.44 10.24 Ch emokine ( C- C motif) l igan d 2 1453924_ a_at Ptgf r 2.13 2.53 Pr ostaglandin F r eceptor

1419413_at Ccl17 3.77 9.83 Chemokine (C-C motif) ligand 17 1450377_at Thbs1 2.70 2.51 Thrombospondin 1

1460469_at Tnfrsf9 4.18 9.79 Tumor necrosis factor receptor


1454005_at Fmo2 2.36 2.51 Flavin containing monooxygenase 2

1423017_a_at Il1rn 6.24 9.35 Interleukin 1 receptor antagonist 1438855_x_at Tnfaip2 3.43 2.50 Tumor necrosis factor, alpha-induced

protein 2

1420330_at Clec4e 4.48 8.93 C-type lectin domain family 4, member e 1460231_at Irf5 2.02 2.49 Interferon regulatory factor 5

1449227_at Ch25h 12.50 8.11 Ch olestero l 25-h ydr oxylase 1455265_ a_at Rgs16 3.79 2.47 Regulat or of G- prot ein sign alin g 16

1425829_a_at Steap4 4.61 8.06 STEAP family member 4 1423596_at Nek6 2.50 2.47 NIMA (never in mitos is gene a)

-related expressed kinase 6

1420331_at Clec4e 6.36 7.58 C- type lect in domain f amily 4, member e 1427257_ at Vcan 1.55 2.46 Versican

1417314_at Cfb 2.01 7.25 Complement factor B 1420723_at Vnn3 2.19 2.45 Vanin 3

1419561_at Ccl3 13.72 6.96 Chemokine (C-C motif) ligand 3 1448749_at Plek 2.63 2.42 Pleckstrin

1449399_a_at Il1b 8.75 6.88 Interleukin 1 beta 1418547_at Tfpi2 2.77 2.41 Tissue factor pathway inhibitor 2

1426037_a_at Rgs16 10.57 6.49 Regulator of G-protein signaling 16 1421712_at Sele 16.99 2.41 Selectin, endothelial cell

1448061_at Msr1 2.61 6.47 Macro phage scav enger recepto r 1 1438183_ x_at Sord 2.55 2.40 Sor bitol dehydrogenase 1

1420558_at Selp 7.25 6.21 Selectin, platelet 1418261_at Syk 2.07 2.40 Spleen tyrosine kinase

1449277_at Ccl19 5.68 6.05 Ch emokine ( C- C motif) l igan d 19 1425649_ at Slc39a14 2.30 2.39 Solut e carr ier f amily 39(zinc transporter), member 14

1417925_at Ccl22 4.40 6.01 Ch emokine ( C- C motif) l igan d 22 1450672_ a_at Tr ex1 2.22 2.38 Thre e prime r epair e xonuclease 1

1449984_at Cxcl2 11.22 5.99 Ch emokine ( C- X- C mot if) l igan d 2 1423006_ at Pim1 3.26 2.37 Pr oviral int egr ation sit e 1

1421408_at Igsf6 11.88 5.91 Immunoglobulin superfamily, member 6 1451452_a_at Rgs16 8.62 2.36 Regulator of G-protein signaling 16

1418806_at Csf3r 3.94 5.61 Colony stimulating factor 3 receptor

(granulocyte)

1448604_at Uck2 2.57 2.36 Uridine-cytidine kinase 2

1416576_at Socs3 6.08 5.42 Suppressor of cytokine signaling 3 1419714_at Cd274 2.16 2.35 CD274 antigen

1421694_a_at Vcan 2.99 5.41 Ch ondroitin sulfate prote oglycan 2 1423520_ at Lmn b1 3.15 2.34 Lamin B1

1419532_at I l1r2 3.63 5.40 In ter leukin 1 r eceptor, t ype I I 1438097_ at Rab20 2.52 2.34 RAB20, member RAS on cog ene f amily

1448951_at Tnfrsf1b 4.98 5.26 Tumor necrosis factor receptor

superfamily, member 1b

1420824_at Sema4d 1.57 2.33 Sema domain, immunoglobulin

domain (Ig), transmembrane domain

(TM) and short cytoplasmic domain,

(semaphorin) 4D

1449906_at Selp 11.66 5.26 Selectin, platelet 1451314_a_at Vcam1 2.85 2.33 Vascular cell adhesion molecule 1

1453076_at Batf3 3.52 5.17 Basic leucine zipper transcription

factor, ATF-like 3

1422905_s_at Fmo2 1.49 2.33 Flavin containing monooxygenase 2

1450318_a_at P2ry2 3.20 5.06 Purinergic receptor P2Y, G-protein coupled2

1415899_at Junb 4.10 2.33 Jun-B oncogene

1422062_at Msr1 2.72 5.03 Macro phage scav enger recepto r 1 1435415_ x_at Marcksl1 2.17 2.32 MARCKS- like 1

1419697_at Cxcl11 5.01 5.01 Chemokine (C-X-C motif) ligand 11 1419394_s_at S100a8 4.58 2.32 S100 calcium binding protein

A8 (calgranulin A)

1450200_s_at Csf2rb 2.91 4.99 Colony stimulating factor 2 receptor, beta,

low-afnity (granulocyte-macrophage)

1431843_a_at Nfkbie 4.64 2.32 Nuclear factor of kappa light

polypeptide gene enhancer in

B-cells inhibitor, epsilon

1425663_at I l1rn 4.24 4.98 In ter leukin 1 r eceptor antagon ist 1437226_ x_at Marcksl1 3.41 2.30 MARCKS- like 1

1419609_at Ccr1 4.12 4.93 Ch emokine ( C- C motif) receptor 1 1438841_ s_at Ar g2 1.87 2.30 Vesicle t ranspor t t hr oug h int eraction

with t-SNAREs 1b homolog

1419082_at Serpinb2 2.92 4.92 Serine (or cysteine) peptidase inhibitor,

clade B, member 2

1417856_at Relb 3.90 2.29 Avian reticuloendotheliosis viral

(v-rel) oncogene related B

1416273_at Tnfaip2 8.35 4.83 Tumor necrosis factor, alpha-induced

protein 2

1453851_a_at Gadd45g 3.10 2.28 Growth arrest and

DNA-damage-inducible 45 gamma

1419482_at C3ar 1 2.09 4.78 Co mplement co mpo nent 3a r ecept or 1 1425797_ a_at Syk 1.60 2.27 Spleen t yrosine kinase

(continued on next page)(continued on next page)



7/12

Table 1 (continued)

Sequence

code

Primary

sequence

name

Fold

change

(3 h)

Fold

change

(12 h)


code

Primary

sequence

name

Fold

change

(3 h)

Fold

change

(12 h)


1417523_at Plek 4.01 4.77 Pleckstrin 1426505_at Evi2b 2.10 2.24 Ecotropic viral integration site 2b

1425380_at Rasgrp4 3.60 4.73 RAS guanyl releasing protein 4 1452483_a_at Cd44 2.24 2.21 CD44 antigen

1452732_at Aspr v1 2.39 4.65 Aspart ic peptidase, r etr oviral-l ike 1 1432273_a_at D ar c 2.53 2.21 Duff y blood gr oup

1425434_a_at Msr1 1.35 4.63 Macrophage scavenger receptor 1 1434596_at Sema4d 1.18 2.20 Sema domain, immunoglobulin

domain (Ig), transmembrane domain

(TM) and short cytoplasmic domain,

(semaphorin) 4D

1427747_a_at Lcn2 2.26 4.59 Lipocalin 2 1421173_at Irf4 2.45 2.20 Interferon regulatory factor 4

1421578_at Ccl4 12.43 4.58 Chemokine (C-C motif) ligand 4 1427256_at Vcan 1.35 2.18 Versican

1448914_a_at Csf1 4.77 4.55 Colony stimulating factor 1 (macrophage) 1422904_at Fmo2 1.26 2.16 Flavin containing monooxygenase 2

1419149_at Serpine1 3.76 4.55 Serine (or cysteine) peptidase inhibitor,

clade E, member 1

1418099_at Tnfrsf1b 1.83 2.15 Tumor necrosis factor receptor

superfamily, member 1b

1431705_a_at Mcoln2 4.74 4.53 Mucolipin 2 1418830_at Cd79a 2.44 2.15 CD79A antigen (immunoglobulin-

associated alpha)

1435872_at Pim1 3.13 4.41 Proviral i ntegration site 1 1425154_a_at Csf1 2.77 2.15 Colony stimulating factor 1

(macrophage)

1419728_at Cxcl5 21.26 4.34 Chemokine (C-X-C motif) ligand 5 1448748_at Plek 2.30 2.15 Plecks trin

1448291_at Mmp9 9.99 4.33 Matrix metallopeptidase 9 1460302_at Thbs1 2.74 2.14 Thrombospondin 1

1422046_at Itgam 2.51 4.28 Integrin alpha M 1427683_at Egr2 2.88 2.13 Early growth response 2

1451416_a_at Tgm1 3.53 4.15 Transglutaminase 1, K polypeptide 1416871_at Adam8 3.20 2.13 A disintegrin and metallopeptidase

domain 8

1425902_a_at Nfkb2 4.69 4.08 Nuclear factor of kappa light polypeptide

gene enhancer in B-cells 2, p49/p100

1449360_at Csf2rb2 1.70 2.13 Colony stimulating factor 2

receptor, beta 2, low-afnity

(granulocyte-macrophage)1420823_at Sema4d 3.95 4.08 Sema domain, immunoglobulin domain

(Ig), transmembrane domain (TM) and

short cytoplasmic domain, (semaphorin)

4D

1416304_at Lit af 2.25 2.11 LP S-induce d TN fact or

1460197_a_at Steap4 3.09 4.04 STEAP family member 4 1420905_at Il17ra 2.14 2.10 Interleukin 17 receptor A

1420804_s_at Clec4d 3.63 3.92 C-type lectin domain family 4, member d 1424880_at Trib1 2.76 2.10 Tribbles homolog 1 (Drosophila)

1427429_at Csf2 10.51 3.91 Colony stimulating factor 2

(granulocyte-macrophage)

1451043_at Nek6 1.34 2.06 NIMA (never in mitosis gene a)-

related expressed kinase 6

1420349_at Ptgfr 1.50 3.73 Prostaglandin F receptor 1425197_at Ptpn2 2.33 2.05 Protei n tyrosine phosphatase, non-

receptor type 2

1451054_at Orm1 3.34 3.68 Orosomucoid 1 1449125_at Tnfaip8l1 2.39 2.05 Tumor necrosis factor, alpha-induced

protein 8-like 1

1417813_at Ikbke 3.71 3.42 Inh ibitor of kappaB kinase epsilon 1429128_x_at Nfkb2 2.28 1.91 RIKEN cDNA 1110007H17 gene

1448181_at Klf15 2.79 3.37 Kruppel -like factor 15 1429752_x_at Clip4 1.37 1.89 CAP-GLY domain containing linker

protein family, member 4

1418842_at Hcls1 2.05 3.37 Hemat opoiet ic cell specic Lyn substrate 1 1427682_a_at Egr2 3.71 1.85 Early growth response 2

1450783_at It1 2.16 3.36 Interferon-induced protein withtetratricopeptide repeats 1

1449828_at Ptgfr 2.07 1.84 Prostaglandin F receptor

1419483_at C3ar1 1.71 3.35 Complement component 3a receptor 1 1417140_a_at Ptpn2 1.85 1.83 Protein tyrosine phosphatase, non-

receptor type 2

1419610_at Ccr1 3.00 3.32 Chemokine (C-C motif) receptor 1 1418847_at Arg2 2.06 1.79 Arginase type II

1420394_s_at Lilrb4 2.48 3.32 Leukocyte immunoglobulin-like

receptor, subfamily B, member 4

1450749_a_at Nr4a2 1.41 1.77 Nuclear receptor subfamily 4, group

A, member 2

1449450_at Ptges 3.16 3.30 Prostaglandin E synthase 1425198_at Ptpn2 2.37 1.74 Protei n tyrosine phosphatase, non-

receptor type 2

1418465_at Ncf4 2.08 3.25 Neutrophil cytosolic factor 4 1416303_at Litaf 2.19 1.72 LPS-induced TN factor

1435458_at Pim1 4.11 3.25 Proviral i ntegration site 1 1453278_a_at Clip4 1.35 1.72 CAP-GLY domain containing linker


1450750_a_at Nr4a2 2.20 3.22 Nuclear receptor subfamily 4,

group A, member 2

1416298_at Mmp9 2.23 1.63 Matrix metallopeptidase 9

1415922_s_at Marcksl1 6.98 3.21 MARCKS-like 1 1417483_at Nfkbiz 2.93 1.60 Nuclear factor of kappa light

polypeptide gene enhancer in B-cells

inhibitor, zeta

1427994_at Cd300lf 2.14 3.20 CD300 antige n l ike f amily member F 1418800_at Bhlhb8 1.09 1.58 Basic he lixloophelix domain

containing, class B, 8

1425155_x_at Csf1 4.57 3.19 Colony stimulating factor 1 (macrophage) 1438562_a_at Ptpn2 1.73 1.55 Protein tyrosine phosphatase, non-

receptor type 2

1425412_at Nlrp3 2.74 3.17 NLR family, pyrin domain containing 3 1418642_at Lcp2 1.49 1.51 Lymphocyte cytosolic protein 2

1427313_at Ptgir 2.62 3.13 Prostaglandin I receptor (IP) 1435504_at Clip4 1.01 1.50 CAP-GLY domain containing linker


1427911_at Tmem173 2.83 3.11 Tr ansmembran e pr ote in 173 1426584_a_at S ord 1.61 1.43 Sor bitol deh ydrogenase

1425435_at Msr1 1.14 3.08 macrophage scavenger receptor 1 1421811_at Thbs1 1. 43 1.41 thrombos pondin 1

1449486_at Ces1 2.79 3.03 Carboxylesterase 1 1415989_at Vcam1 3.38 1.40 Vascular cell adhesion molecule 1

1418641_at Lcp2 2.86 2.99 Lymphocyte cytosolic protein 2 1421206_at Lif 1. 29 1.39 Leukemia inhibitory factor

1451340_at Ar id5a 4.92 2.99 AT r ich inter act ive domain 5A (Mr f1 l ike) 1423760_at Cd44 1.56 1.39 CD44 ant igen

1419135_at Ltb 2.72 2.90 Lymphotoxin B 1426506_at Evi2b 1.24 1.36 Ecotropic viral integration site 2b

1456212_x_at Socs3 3.11 2.90 Cytokine inducible SH2-containing

protein 3

1448162_at Vcam1 2.24 1.36 Vascular cell adhesion molecule 1

1450446_a_ at Socs1 3.35 2.88 Suppressor of cyt okine signaling 1 1448294_at Lit af 1.19 1.29 LP S-induce d TN fact or

1455899_x_at Socs3 2.98 2.87 Cytokine inducible SH2-containing

protein 3

1424881_at Trib1 1.33 1.26 Tribbles homolog 1 (Drosophila)



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which had been ltered for likely expression in lung and immune

system-related tissues (or cell lines derived from those tissues).

However, these genes were not necessarily related to the develop-

ment or perpetuation of allergic sensitization or asthma. To address

this possibility, we queried the list against three gene expression

databases to facilitate prioritization of biomarker candidates by

known or suspected relevance to allergic sensitization, to airway

hyperreactivity or to other pulmonary conditions associated with

asthma (such as airway remodeling).

The databases we used were the Ingenuity Knowledge database,

the GeneGo database and DAVID (Database for Annotation, Visuali-

zation and Integrated Discovery). Annotations for each of the

potential biomarker genes were manually reviewed. Genes with low

specicity (such as those encoding the multi-functional transcription

factorsJunband Nfkb2) were given low priority, as were genes known

to be associated with non-specic inammation (e.g., Tnf). Genes

with known relevance to allergic sensitization or inammation,

inammatory cell recruitment, airway hyperreactivity or remodeling

were given a high priority. This process enabled the selection of a nal

list of 11 high priority candidate biomarker genes (Table 2).

Pathway analysis

To gain additional insight into the nature of theimmune responses

following exposure to agents of high versus low allergenicity,

differentially expressed sequences were subjected to pathway

analysis in Ingenuity. Genes differentially expressed after MACA

exposure (either 3 h or 12 h post-administration) demonstrated a

highly signicant association with a number of canonical pathways

characteristic of an active and developing immuneresponse(Table 3).

In many of these pathways, increased expression of most members of

the NFB signaling family was seen. However, there was little to no

change in expression of the JAK2/STAT3 or ERK, JNK or p38 map

kinase signalingfamilies. A similar pattern of pathway association was

observed even when only MACA-specic genes were evaluated

(Table 3).

Differentially expressed genes in 3 h BSA-treated mice were

associated with pathways characteristic of a more general inamma-

tory response and oxidative stress. No clear pathway association was

noted in 12 h BSA samples, probably due to the low number of

differentially expressed genes at this time point (Table 3). Neither

time point appeared to be associated with consistent alterations in

any of the signaling families.

qRT-PCR analysis of candidate biomarker expression

Expression of the 11 high-priority candidate biomarker genes was

evaluated by qRT-PCR, both as a validation of the microarray resultsand to evaluate expression levels over the entire time course of the

study. In general, fold difference values (relative to HBSS-treated

mice) calculated from qRT-PCR data approximated those calculated

from the microarray intensity data at both the 3 and 12 h time points

in MACA-treated mice (Table 4). The largest discrepancies were seen

for Saa1, in which microarray and PCR fold differenceswere 25.69 and

12.59, and 46.99 and 135.74 at 3 and 12 h, respectively. Other notable

differences between microarray and qRT-PCR values were for Cxcl2

Table 1 (continued)

Sequence

code

Primary

sequence

name

Fold

change

(3 h)

Fold

change

(12 h)


code

Primary

sequence

name

Fold

change

(3 h)

Fold

change

(12 h)


1448728_a_at Nfkbiz 7.20 2.85 Nuclear factor of kappa light polypeptide

gene enhancer in B-cells inhibitor, zeta

1423521_at Lmnb1 1.52 1.21 Lamin B1

1421326_at Csf2rb 1.97 2.85 Colony stimulating factor 2 receptor, beta,

low-afnity (granulocyte-macrophage)

1419698_at Cxcl11 1.23 1.19 Chemokine (C-X-C motif) ligand 11

1427035_at Slc39a14 2.27 2.85 Solute carrier family 39 (zinc transporter),

member 14

1421174_at Irf4 1.75 1.12 Interferon regulatory factor 4

1449449_at Ptges 3.11 2.82 Prostaglandin E synthase 1450160_at Lif 3.33 1.12 Leukemia inhibitory factor

1425958_at I l1f9 5.25 2.75 I nt erleukin 1 family , member 9 1421481_at Tnfr sf 9 1.53 1.12 Tumor necrosis f acto r re cepto r


1419132_at Tlr2 2.93 2.75 Toll-like receptor 2 1417137_at Uck2 2.47 1.10 Uridine-cytidine kinase 2

1448756_at S100a9 4.03 2.73 S100 calcium binding protein A9

(calgranulin B)

For selection as candidate biomarkers, sequence sets had to t all of the following criteria: (1) signicantly different in expression relative to time-matched HBSS-treated mice (see

text), (2) not differentially expressed in BSA-treated mice, (3) differentially expressed in MACA-treated mice at both the 3 and 12 h time points, and (4) selected for potential

biomarker utility using Ingenuity Pathway Analysis Software. Values are presented as group average fold changes relative to HBSS-treated mice for each sequence set.

Table 2

Overview of high-priority candidate biomarkers of acute respiratory exposure to sensitizing agents.

Primary

sequence name

Sequence description Chemotactic Associated with

allergic disease

Associated with

pulmonary disease

Persistently expressed

(qRT-PCR)

CCL17 Chemokine (C-C motif) ligand 17; thymus and activation regulated chemokine

(TARC)

X X X

CCL22 Chemokine (C-C motif) ligand 22; macrophage derived chemokine (MDC) X X X

CCL19 Chemokine (C-Cmotif) ligand19;macrophage inhibitoryprotein3- (MIP-3) X

CCL7 Chemokine (C-C motif) ligand 7; monocyte chemoattractant protein 3 (MCP-3) X X

CXCL2 Chemokine (C-X-C motif) ligand 2; macrophage inammatory protein 2

(MIP-2)

X X

CXCL10 Chemokine (C-X-C motif) ligand 10; interferon inducible protein 10 (IP-10) X X X

SAA1 Serum amyloid A1 X X

C3aR1 Complement component 3a receptor 1 X X

VCAM1 Vascular cell adhesion molecule 1 X

SOCS3 Suppressor of cytokine signaling 3 X

Arg2 Arginase 2 X X

Biomarker andfunctional analysis of genes differentiallyexpressed at both3 and12 h timepoints allowed identication of 11 candidatebiomarker genes. Selectedgenes hadto meet

at least one of the following criteria: (1) involved in chemotaxis, (2) associated with allergic disease, and (3) known or strongly suspected associated with allergic or non-allergic

pulmonary disease. Candidate genes were also evaluated for persistence of gene expression (as determined by qRT-PCR) over the course of the study.



9/12

(11.22 and 24.01, and 5.99 and 17.44 at 3 and 12 h), Ccl7 (11.32 and

23.10 at 12 h) and Ccl19 (6.05 and 12.06 at 12 h). In contrast, direct

comparison between microarray and qRT-PCR values was usually not

possible for BSA-treated mice, as almost none of these sequence sets

met the statistical cutoff parameters during initial microarray analysis

(data not shown).

Table 3

Summary of pathway analyses.

Name p-value No. of genes in data set

mapping to pathway/Total

no. of genes in pathway

Ratio Net effect

MACA 3 h

IL-10 signaling 9.43E-14 16/71 0.225 Increased IL-1 signaling via NFB; decreased IL-10 signaling

IL-6 signaling 5.10E-12 17/96 0.177 Increased IL-1 and TNF signaling via NFB; increased

production of inammatory mediators

LXR/RXR activation 8.84E-12 15/85 0.176 Increased IL-1 and TNF signaling via NFB; increased productionof inammatory mediators

Dendritic cell maturation 3.61E-11 20/165 0.121 Increased production of inammatory mediators, adhesion and

costimulatory molecules

Acute phase response signaling 5.27E-11 21/178 0.118 Increased IL-1 and TNF signaling via NFB; increased production

of inammatory mediators

MACA 12 h

Acute phase response signaling 6.30E-14 30/178 0.169 Increased IL-1 and TNF signaling via NFB; increased production


TREM1 signaling 3.65E-12 17/69 0.246 Increased IL-1 and TNF signaling via AKT and NFB; increased




Role of pattern recognition receptors

in recognition of bacteria and viruses

1.04E- 09 17/88 0.193 I ncreased signaling by TLR-2 and complemen t r eceptor s; incr ease d


Hepaticbro sis/hepatic stellate cell activat ion 1.67E- 09 21/135 0.156 I ncre ased product ion of inammatory mediators

MACA-specic 3 h/12 h

Acute phase response signaling 6.48E-13 16/178 0.09 Increased IL-1 and TNF signaling via NFB; increased production ofinammatory mediators



TREM1 signaling 4.05E-11 10/69 0.145 Increased IL-1 and TNF signaling via AKT and NFB; increased


LXR/RXR activation 4.34E-10 10/85 0.118 Increased IL-1 and TNF signaling via NFB; increased production


Role of pattern recognition receptors in

recognition of bacteria and viruses

9.66E- 10 10/88 0.114 I ncreased signaling by TLR-2 and complemen t r eceptor s; incr ease d


BSA 3 h

Xenobi otic metabolism signaling 3.22E-04 7/254 0.028 Enhanced redox regulation; decreased oxidation

Aryl hydrocarbon receptor signaling 8.60E-04 5/155 0.032 Mixed effects on redox status

NRF-2 mediated oxidative stress response 2.34E-03 5/185 0.027 Increased antioxidant response

LPS/I L- 1 mediat ed inhibition of RX R function 3.43E- 03 5/198 0.025 I ncre ased l ipid me tabo lism

IL-10 signaling 4.28E-03 3/71 0.042 Small increase in IL-6 production

BSA 12 h

Coagulation system 3.14E-02 1/37 0.027 Increased production of coagulation factor X

CD40 signaling 5.59E-02 1/70 0.014 Not signicantMacropinocytosis 5.67E-02 1/72 0.014 Not signicant

CCR5 signaling in macrophages 5.76E-02 1/87 0.011 N ot signicant

IL-4 signaling 5.76E-02 1/72 0.014 Not signicant

Genes that were signicantly expressed in MACA- or BSA-treated mice were evaluated to determine canonical pathway association using Ingenuity Pathway Analysis software.

Table 4

Microarray and PCR comparison.

Sequence description Primary

sequence name

MACA fold

change 3 h

microarray

MACA fold

change 3 h

qRT-PCR

MACA fold

change 12 h

microarray

MACA fold

change 12 h

qRT-PCR

BSA fold

change 3 h

microarray

BSA fold

change 3 h

qRT-PCR

BSA fold

change 12 h

microarray

BSA fold

change 12 h

qRT-PCR

Serum amyloid A 1 Saa1 25.69 12.59 46.99 135.74 N/aa 1.50 N/a 6.55

Chemokine (C-X-C motif) ligand 10 Cxcl10 19.11 19.23 19.91 20.76 N/a 1.51 N/a 1.94

Chemokine (C-C motif) ligand 7 Ccl7 11.88 11.44 11.32 23.10 N/a 2.04 N/a 1.83

Chemokine (C-X-C motif) ligand 2 Cxcl2 11.22 24.01 5.99 17.44 N/a 1.05 N/a 7.19



Suppressor of cytokine signaling 3 Socs3 4.06b 3.62 3.73 2.89 1.49 1.09 N/a 1.92


Vascular cell adhesion molecule 1 Vcam1 2.82 4.55 1.70 1.03 N/a 1.01 N/a 1.09

Arginase type II Arg2 1.97 2.37 2.04 2.02 1.47 1.01 N/a 1.21

Complement component 3a receptor 1 C3ar1 1.90 2.10 4.07 2.36 N/a 1.21 N/a 1.03

For validation purposes, average microarray gene fold-expression values for 11 candidate biomarker genes were compared to the corresponding values obtained by qRT-PCR

analysis. qRT-PCR values were calculated relative to HBSS-treated control samples for each time point using the 2Ct method. For comparison purposes, 2Ct values b 1 were

converted to negative fold change values using the formula: 1/2Ct.a N/aindicates that the expression intensity for this parameter failed to meet the initial statistical cutoff.b

Where necessary, the expression values of genes represented by multiple sequence sets have been averaged.



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Fig. 5.Transcription of candidate biomarker genes as determined by qRT-PCR. Transcript levels were calculated using the CTmethod. Values are presented as average fold chang

(dened as fold change of 1 and indicated by a dotted line).


11/12


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Disclaimer

This research paper has been reviewed by the National Health and

Environmental Effects Research Laboratory, US Environmental Pro-

tection Agency and approved for publication. Approval does not

signify that the contents necessarily reect the views and policies of

the agency, nor does mention of trade names or commercial products

constitute endorsement or recommendation for use.

This work was supported by UNC/EPA training agreementCR83323701.

Declaration of interest

The Authors report no conicts of interest. The Authors alone are

responsible for the content and writing of the paper.

Acknowledgments

The Authors would like to thank Debora Andrews, Judy Richards,

Richard Jaskot and Dr. Yong Joo Chung of US EPA for technical and

intellectual assistance. Additionally, we would like to thank Drs.

Robert Luebke, MaryJane Selgrade, and Susan Hester for their critical

review of the manuscript.

Appendix A. Supplementary data

Supplementary data associated with this article can be found, in

the online version, atdoi:10.1016/j.taap.2009.12.027.

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