global gene expression profiling in whole-blood samples

Global Gene Expression Profiling in Whole-Blood Samples From Individuals Exposed to Metal Fumes

CitationWang, Zhaoxi, Donna Neuburg, Cheng Li, Li Su, Jee Young Kim, Jiu Chiuan Chen, and David C. Christiani. 2005. Global gene expression profiling in whole-blood samples from individuals exposed to metal fumes. Environmental Health Perspectives 113, no. 2: 233-241.

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Exposure to ambient particulate air pollutionis associated with increases in morbidity andmortality from respiratory and cardiovasculardiseases (Godleski et al. 2000). The weldingprocess generates high levels of metal fumecontaining respirable particles. Epidemiologicstudies have shown that acute exposure towelding fume is associated with metal-fumefever (Mueller and Seger 1985) and increasedreversible respiratory symptoms (El-Zein et al.2003a; Wolf et al. 1997). There was anincreased prevalence of inflammatory lungdiseases, such as asthma and chronic bronchi-tis, among welders (El-Zein et al. 2003b).Additionally, accumulating epidemiologic evi-dence in the last decade has pointed to theassociations of particulate exposure withadverse cardiovascular effects (Dockery et al.1993; Mann et al. 2002; Peters et al. 2000,2001a; Pope et al. 2002). Limited evidenceindicates that welding-fume exposure also maybe associated with increased cardiovascularevents (Sjogren et al. 2002).

It has been proposed that inhaled particu-lates from air pollution may cause systemicalterations by the release of inflammatorycytokines subsequent to pulmonary inflamma-tion, which plays an important role in thepathogenesis of atherosclerosis and coronarydiseases. Indeed, elevated ambient particulatelevels have been shown to be associated with

increased levels of inflammatory markers, suchas white blood cell (WBC) counts (Schwartz2001), C-reactive protein (CRP; Peters et al.2001b; Seaton et al. 1999), and fibrinogen(Pekkanen et al. 2000; Schwartz 2001) inboth cross-sectional and longitudinal epidemi-ologic observations. In the experimental set-ting, animal studies have revealed thatconcentrated ambient particulate exposuresincrease the total WBC counts and the differ-ential count of circulating neutrophils (Clarkeet al. 2000; Gordon et al. 1998) in bothhealthy animals and those with pulmonaryhypertension. Intratracheal instillation ofresidual oil fly ash (ROFA) can induce asignificant elevation of plasma fibrinogen incardiopulmonary-compromised rats (Gardneret al. 2000). Suwa et al. (2002) in their impor-tant work showing progressive atherosclerosisrelated to particulate exposure in hyper-lipidemic rabbits also noted an increase in cir-culating polymorphonuclear leukocyte countscaused by exposures to particulate matter(PM) with a mass median aerodynamicdiameter ≤ 10 µm (PM10).

However, most previous studies evaluatedonly downstream markers for systemic inflam-matory responses. Direct human evidence isstill lacking that shows particulates can inducesystemic inflammation, although previoushuman studies and animal experiments did

generate data, suggesting the involvement ofinflammatory responses in particulate-mediatedacute cardiac events. If particulate-mediatedsystemic inflammation were responsible for theobserved adverse effects on the cardiovascularsystem, we would expect to see correspondingchanges in mRNA expression for particulate-mediated systemic inflammation. The studydescribed in this article addresses this mecha-nistic gap by investigating the systemicinflammatory response to welding-fume expo-sure using cDNA microarray technology onwhole-blood total RNA. Blood samples werecollected from welders and nonwelding con-trols before and after the work shift. Wehypothesized that welding-fume exposurewould be associated with systemic inflamma-tion, as indicated by the findings that genesinvolved in systemic inflammation have signif-icantly altered expressions. Furthermore, pre-vious epidemiologic studies have shown thatcigarette smoking significantly affects CRP,fibrinogen, and WBC levels (Frohlich et al.2003; Smith et al. 2003). Therefore, we alsohypothesized that smoking status wouldsignificantly affect the association betweenwelding fume and the various systemic inflam-matory gene expressions.

Microarray technology provides a formatfor the simultaneous measurement of theexpression of thousands of genes in a singleexperimental assay and quickly becomes oneof most the powerful and versatile tools forgenomics and biomedical research (Murphy2002). Peripheral blood is an essential tissuetype for biomedical and clinical researchbecause of its critical roles in immuneresponse and metabolism. Furthermore,considering the simplicity and ease of collec-tion, peripheral blood is also essential fordiscovery of biomarkers of hematologicdiseases and surrogate markers of a wide

Environmental Health Perspectives • VOLUME 113 | NUMBER 2 | February 2005 233

Address correspondence to D. Christiani, 665Huntington Ave., I-1402, Boston, MA 02115 USA.Telephone: (617) 432-3323. Fax: (617) 432-3441.E-mail: [email protected]

This work was supported by grant T32 ES07069from the National Institute of EnvironmentalHealth Sciences and by research grants ES009860,CA074386, CA090578, and CA092824 from theNational Institutes of Health.

The authors declare they have no competing finan-cial interests.

Received 21 May 2004; accepted 22 November2004.

Global Gene Expression Profiling in Whole-Blood Samples from IndividualsExposed to Metal Fumes

Zhaoxi Wang,1 Donna Neuburg,2 Cheng Li,2 Li Su,1 Jee Young Kim,1 Jiu Chiuan Chen,1 and David C. Christiani1,3

1Department of Environmental Health, Occupational Health Program, Harvard School of Public Health, Boston, Massachusetts, USA;2Department of Statistical Sciences, Dana Farber Cancer Institute, Harvard Medical School, Boston, Massachusetts, USA; 3Pulmonaryand Critical Care Unit, Department of Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, USA

Accumulating evidence demonstrates that particulate air pollutants can cause both pulmonary andairway inflammation. However, few data show that particulates can induce systemic inflammatoryresponses. We conducted an exploratory study using microarray techniques to analyze whole-blood total RNA in boilermakers before and after occupational exposure to metal fumes. A self-controlled study design was used to overcome the problems of larger between-individual variationinterferences with observations of relatively smaller changes caused by environmental exposure.Moreover, we incorporated the dichotomous data of absolute gene expression status in themicroarray analyses. Compared with nonexposed controls, we observed that genes with alteredexpression in response to particulate exposure were clustered in biologic processes related toinflammatory response, oxidative stress, intracellular signal transduction, cell cycle, and pro-grammed cell death. In particular, the preinflammatory cytokine interleukin 8 and one of itsreceptors, chemokine receptor 4, seemed to play important roles in early-stage response to heavymetal exposure and were down-regulated. Furthermore, most observed expression variations werefrom nonsmoking exposed individuals, suggesting that smoking profoundly affects whole-bloodexpression profiles. Our study is the first to demonstrate that with a paired sampling study designof pre- and postexposed individuals, small changes in gene expression profiling can be measured inwhole-blood total RNA from a population-based study. This technique can be applied to evaluatethe host response to other forms of environmental exposures. Key words: functional pathway, geneexpression profiling, inflammation, occupational particulate exposure, whole-blood total RNA.

[Online 22 November 2004]

Toxicogenomics Article

Environ Health Perspect 113:233–241 (2005). doi:10.1289/txg.7273 available via http://dx.doi.org/

range of nonhematologic disorders. Thus,applying microarray technology on periph-eral blood may provide new insights of varia-tions in global gene expression specificallyassociated with states of normal and diseaseand has the potential of applying the tech-nology in disease detection and diagnosis.However, with the challenges unique to theblood sample, including complex composi-tion of heterogeneous cell types and ex vivochanges of expression profiles induced bydifferent handling and processing methods,it is difficult to apply microarray technologyon whole-blood total RNA, and there arefew previous publications of such research.To this end, this study is also an exploratoryresearch with the purpose of developingproper methods for applying microarraytechnology on whole-blood total RNA.

Materials and Methods

Study population. The study was approved bythe institutional review board of the HarvardSchool of Public Health, and written informedconsent was obtained from each subject. Thestudy population consisted of 28 weldingapprentices, instructors, and union officers,recruited and monitored at an apprentice weld-ing school (Union Local 29, Quincy, MA). All18 exposed subjects actively welded in theworkshop, whereas 10 nonexposed controlsstayed in the office or classroom of the samebuilding during the work period. Blood sam-ples were collected from each subject beforeand after the welding workshop. A self-administered questionnaire was used to obtainrelevant information, including respiratorysymptoms and diseases, smoking history, andoccupational history. Exposure to fine particu-late matter (particulate matter with a massmedian aerodynamic diameter ≤ 2.5 µm,PM2.5) was assessed using KTL cyclones(GK2.05SH; BGI Inc., Waltham, MA). Theair sample was collected on a 37-mm polytetra-fluoroethylene membrane filter (GelmanLaboratories, Ann Arbor, MI), and the massconcentration was determined as previouslydescribed (Kim et al. 2003).

Blood measurements. Complete bloodcounts of all blood samples were carried outat Path Lab Inc. (Portsmouth, NH). Theblood parameters included total WBC countwith differential, red blood cell count,platelet count, hemoglobin, hematocrit, anderythrocyte indices (mean corpuscular vol-ume, mean corpuscular hemoglobin, meancorpuscular hemoglobin concentration, andred cell distribution width).

RNA preparation. Immediately after theblood was drawn, we added TRI Reagent BD(Molecular Research Center, Inc., Cincinnati,OH) and mixed to stabilize the whole-bloodtotal RNA. The stabilized samples were trans-ported to our laboratory on dry ice and stored

at –80°C until RNA extraction. Total RNAwas isolated later from 10 mL of whole bloodaccording to manufacturer protocols andpurified using the RNeasy mini kit (Qiagen,Chatsworth, CA). The yield and quality ofRNA were assessed by spectrophotometry andthe Agilent 2100 Bioanalyzer (AgilentTechnologies, Palo Alto, CA).

Microarray hybridization. For genomewideexpression profiling, we used AffymetrixHuman Genome U133A GeneChips(Affymetrix, Santa Clara, CA), which allowdetection of approximately 22,215 gene expres-sion probe sets. All RNA samples were sent toand analyzed at the Microarray Core Facility ofthe Dana-Farber Cancer Institute (Boston,MA), according to the manufacturer’s manual.The baseline and postexposure RNA samplesfrom each subject were processed together inone batch of microarray analysis to minimizeinherent variations. The quality of microarraysanalysis was initially assessed by examination ofthe 3´ to 5´ ratios of five housekeeping controlson U133A GeneChips.

Normalization and data extraction. Weused DNA-Chip Analyzer 1.3 (dChip;http://www.dchip.org/) software to normalizethe raw microarray signals and then calculatethe model-based expression values using adefault perfect-match–only model with outlierdetection. dChip software applied an invariantset normalization method, which chose a sub-set of perfect-matched probes with smallwithin-subset rank difference in the twomicroarrays to serve as the basis for fitting anormalization curve (Li and Wong 2001a,2001b). The outlier detection algorithm of thedChip software allowed further quality assess-ment of microarray data by cross-referencingone array with other arrays through a model-ing approach to identify problematic arrays(Li and Wong 2001b). To have a better fit inthe model for more precise estimations ofexpression values, we included 10 additionalmicroarrays in data normalization and extrac-tion. The detection of whether a genewas expressed (present) or not expressed(absent) in a RNA sample was carried out byAffymetrix Microarray Suite (MAS) 5.0 soft-ware (Affymetrix) using one-sided Wilcoxon’ssigned-ranked algorithm (Detection Calls; Liuet al. 2002).

Microarray data analysis. Initially weevaluated gene expression changes by compar-ing the large fold-changes of expression valuesbetween baseline and postexposed microarraysin both exposed (welders) and nonexposed(controls) subjects. Then, we focused onusing the paired t-test in the dChip softwarepackage to flush out genes with small expres-sion changes in response to metal particulateexposure. The results of the paired t-test wereadjusted by standard errors associated witheach gene expression value. Because dChip

software only gave an expression value to eachgene on an array without discriminatingwhether the gene was expressed, we attemptedto incorporate the Detection Calls informa-tion generated from the Affymetrix MAS 5.0software package in the data analyses.

Hierarchical clustering. The analyses werecarried out by dChip software using a hierar-chical clustering algorithm (Eisen et al. 1998)with average-linkage method. After lineartransformation to standardize the expressionvalues across all selected samples, the distancebetween two genes was calculated as 1-absolutestandard correlation coefficient and was usedin the subsequent repeated process to buildphylogenetic tree of genes and samples.

Clustering analyses using gene ontology.Testing for significant change in a single geneis difficult to accomplish given the stringentcriteria for significance in the multiple-testbackground of > 20,000 probe sets. Therefore,we focused instead on assessing the biologicfunctions enriched with genes identified fromthe paired t-test, using the annotations definedby the Gene Ontology Consortium (GO;http://www.geneontology.org/; Hakak et al.2001). The GO annotations are structured,controlled vocabulary for describing the rolesof genes in any organism. The probability ofobserving a particular number of genes in oneGO biologic process (bioprocess) was testedusing hypergeometric distribution as previ-ously described (Tavazoie et al. 1999). Briefly,we addressed the problem as what is the prob-ability of observation of at least (x) genes of acertain GO bioprocess annotation in a list of(k ) genes from paired t-test results, given thebackground that there are (n ) genes with thesame GO annotation from total (m ) genes(total annotated genes or a subclass of GOannotated genes on the entire Affymetrixarray). The p-values were calculated using thefollowing formula:

In this study, we focused only on the geneannotations of GO biologic process and usedAffymetrix nonredundant build of humanGO annotations downloaded 18 May 2004.The lists of genes were uploaded toAffymetrix NetAffx Analysis Center (http://www.affymetrix.com/analysis/index.affx) toobtain the numbers of genes within each GObioprocess.

A potential problem of significance testingusing GO annotations is that the hypergeo-metric distribution p-values are biased andsensitive to the total genes (m) used in thetests, which are not truly representing theentire genome because of the selection biases

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234 VOLUME 113 | NUMBER 2 | February 2005 • Environmental Health Perspectives

in array design and incomplete process of GOannotation. Furthermore, the problem ofmultiple testing is difficult to adjust becausethe GO bioprocesses are highly interrelatedand genes are often assigned into multipleGO bioprocesses. Because GO has a multiple-level structure of directed acyclic graphs witheach level of bioprocess linked through multi-ple parent–child relationships, there are oneor more pathways that could be identified bytracing back from any GO bioprocess to thetop using true-path–rule logic relationships.Thus, we adopted a conservative approach oftesting the hypergeometric distributions.First, we used three numbers of the totalgenes (m) corresponding to the top three lev-els of GO bioprocess. A GO bioprocess wasregarded as significant when it had ap < 0.005 at the lowest testing level and ap < 0.05 at the immediate upper level. Afunctional pathway was regarded as signifi-cant when it had three consecutive GO bio-processes tested significantly or had twoconsecutive significant bioprocesses but alsotested significantly in other pathway(s). Theresults were visualized using GoSurfer SoftMining Tool (https://www.affymetrix.com/analysis/query/go_analysis.affx).

Statistical analysis. Statistical analyseswere performed using SAS version 6.12 (SASInstitute Inc., Cary, NC). Exposure statuswas dichotomized as nonexposed controlsand welders. Study population characteris-tics between controls and welders were com-pared using two-sample t-tests, Wilcoxonrank-sum tests with exact p-values, andFisher’s exact test. The mean (SD) values ofthe PM2.5 concentrations were determinedfor controls and welders. Wilcoxonrank-sum tests with exact p-values were per-formed to compare the PM2.5 concentra-tions in controls and welders and also insmokers and nonsmokers. To account forthe repeated measurements, linear mixedmodels with an interaction term for expo-sure status and smoking status were used foranalysis. A generalized autoregressive covari-ance structure was used to account for theexponential decay of the correlation functionas the interval between the measurementsincreases (Verbeke and Molenberghs 1997).Restricted maximum likelihood was used toestimate the covariance parameters. Baselinemean (SEM) levels of systematic inflamma-tory markers in peripheral blood were calcu-lated in controls and welders according tosmoking status. Linear mixed models wereused to compare the baseline levels of thesystemic inflammatory markers in controlsand welders and in smokers and nonsmok-ers. The effect of age on the baseline levels ofthe systemic inflammatory markers also wasinvestigated. The level of significance for allanalyses was set at 0.05.

ResultsStudy population characteristics. A combinedapproach of using intraindividual (self-pairingsamples) and interindividual controls wasimplemented in the study to a) minimize thebiologic variability among individuals andb) compare more precisely the gene expres-sion profiles of different exposure states.Eighteen welders and 10 nonexposed controlsat an apprentice welding school wererecruited, and blood samples were collected attwo time points: baseline and after 5.8 ±0.6 hr of exposure to welding fume. Aftermicroarray hybridization, we had completedata sets on 44 arrays from 15 welders and7 controls available for subsequent analysis.Among the 6 excluded study subjects, 1 with-drew during the study, 4 had low yield orpoor quality of RNA extraction, and 1 had apoor quality of microarray hybridization.Population demographic data are summarizedin Table 1. All study subjects were male,including 18 Caucasians, 3 Hispanics, and1 African American. The age and smokingstatus were comparable between exposed andcontrol groups.

During the welding workshop, the welderswere exposed to metal fume and airborne PMfrom shielded metal arc welding, gas tungstenarc welding, plasma arc cutting, and grinding,with carbon steel being the most commonlyused base metal. The controls were exposedprimarily to ambient levels of PM while per-forming bookwork and office tasks at the weld-ing school. In this study, particulate sampleswere collected from all controls and welders.With comparable mean sampling timesbetween controls and welders, the medianPM2.5 concentrations of welders were signifi-cantly higher than those of nonexposed con-trols (p < 0.01). However, there were nosignificant differences in PM2.5 concentrationsaccording to smoking status in welders(p = 0.9). Previous occupational exposures, asmeasured by years of boilermaking, were notsignificantly different between controls andwelders (Table 1). Moreover, before the day ofsample collection, all controls and 12 of15 welders had at least a 5-day period of non-welding or nonboilermaking to wash out theeffects of previous metal-fume exposure.Among three welders with shorter than 5-day

washout periods, two performed welding 1 daybefore the welding workshop and one per-formed welding 2 days before the workshop.

Systemic inflammatory marker levels inperipheral blood. All study subjects, includingboth controls and welders, had their bloodcell counts within the normal ranges and hadsimilar baseline profiles of major systematicinflammatory markers. Controls and welderswere not found to have significantly differentmean baseline CRP (p = 0.4), fibrinogen (p =0.8), absolute neutrophil count (p = 0.1), andabsolute WBC counts (p = 0.1). However,smokers were found to have significantlyhigher mean baseline WBC (p < 0.01) andneutrophil (p < 0.001) levels than nonsmok-ers, among welders as well as in the entirestudied population.

The changes of the systemic inflammatorymarkers across two time points were not signif-icant in controls except for a significantincrease of fibrinogen [25 mg/dL; 95% confi-dence interval (95% CI), 4–45] in the post-exposure measurements. In contrast, there wasa significant increase in total WBC counts(mean change, +1.2 × 103/µL; 95% CI,0.6–1.8) in nonsmoking welders but not insmoking welders (mean change, +0.3 ×103/µL; 95% CI, –1.3 to 1.8). Relative andabsolute neutrophil counts were also increasedsignificantly in nonsmoking welders (p < 0.02)but not in smoking welders (p > 0.7). Thechange profiles of CRP levels were oppositethose of WBC and neutrophil counts, with asignificant increase in smokers (p = 0.02) and anonsignificant change in nonsmoking welders(p = 0.4). Fibrinogen levels did not change sig-nificantly between postexposure and baselinein both smoking and nonsmoking welders(p ≥ 0.6). Overall, our observations of acutemetal-fume exposure were consistent with pre-vious epidemiologic findings that increased lev-els of inflammatory markers were associatedwith elevated ambient particulate levels(Pekkanen et al. 2000; Peters et al. 2001b;Schwartz 2001; Seaton et al. 1999)

Finding genes with large expression varia-tions by fold-change analysis. Initially, wetried to find genes with large alterations ofexpressions between baseline and postexposedmicroarrays by comparing the large n-fold-changes of expression values in both exposed

Toxicogenomics | Blood gene expression profiles and metal-fume exposure


Table 1. Demographics of study population.

NonexposureWelders controls p-Value

No. of subjects 15 7No. of smokers (%) 6 (40) 1 (14) 0.35*

Age, years 32 (22–46) 40 (19–57) 0.69**

Years of boilermaking 3 (2–20) 3 (1.5–33) 0.61**

Number with hypertension (%) 1 (7) 2 (29) 0.23*

Welding fume exposure (PM2.5 concentration, mg/m3) 2.44 (1.30–3.42) 0.04 (0.02–0.17) < 0.001**

Unless specified, values are expressed as median (range) and were tested by the median test.*Fisher’s exact test. **Wilcoxon rank-sum test with exact p-value.

(welders) and nonexposed (controls) subjects.In both the welder and control groups, therewas no gene with a 2-fold greater difference ofthe mean expression levels between baselineand postexposure arrays and an absolute dif-ference > 50. Moreover, for each pair of base-line and postexposed arrays from the samesubject, we found that few genes had largefold-changes (median number of genes, 20;range, 1–123) regardless of exposure status. Inaddition, the correlation coefficients of theraw expression values across entire probe setswere high between baseline and postexposedarrays from the same subject (median, 0.971;range, 0.949–0.988). These observations sug-gested that the real signals of changes in geneexpression profiling in response to occupa-tional metal exposure were very small, whichcould be the compound results of mixed celltypes and large amounts of hemoglobin RNAin the whole-blood samples.

Identifying genes with altered expressionsby paired t-test. When all 22,215 probes onthe U133A GeneChip were included in thepaired t-test, we found more genes (p < 0.05)in welders (533 genes from 546 probes) thanin controls (86 genes from 88 probes)(Table 2). Considering the absolute geneexpression status, we further found that probesidentified by the paired t-test in controls had alarger proportion of noninformative probes(60.5%) that had absent calls assigned by theDetection Calls algorithm in every tested arraycompared with those in welders (47.3%).Regarding the entire set of probes onGeneChip, our data set had an overall 49.0%of noninformative probes among all baselinearrays. The initial observations suggested therewere only random variations and no statisti-cally significant changes in whole-blood expres-sion between postexposed and baseline samplesin individuals without metal particulate expo-sure. We then conducted a series of pairedt-tests in several subsets of genes, which hadPresent calls in at least one, 10%, 25%, and50% arrays. With the increase of Present calls,the number of genes identified by paired t-testsdropped, but the difference in the numbers ofidentified genes between welders and controlsincreased (Table 2). Taken together, consistentfindings of more genes identified by pairedt-tests in welders than in controls suggestedthere were alterations of global gene expressionprofiling in the whole-blood total RNA inresponse to acute metal-fume exposure. Inaddition, only one gene, RIO kinase 3(RIOK3), was identified and down-regulated inboth welders and controls.

Sample clustering using genes identified inpaired t-tests. Genes identified by pairedt-tests were used to classify RNA samples inhierarchical clustering analyses to further eval-uate the expression patterns in samples cate-gorized by different collection time points,

smoking status, and metal-fume exposurestatus. We tested various lists of genesobtained from paired t-tests in controls,welders, nonsmoking welders, and smokingwelders on the original expression data ofbaseline and postexposure arrays, as well asthe data of log2-transformed expression ratiosof postexposure over baseline. The clusteringresults neither revealed any distinct pattern ofgene expressions with any kind of combina-tion of selected genes and RNA samples norshowed any subgroup of samples or genes

with similar expression patterns. However, ingeneral, we found that > 70% of samples hadthe baseline and postexposure arrays of thesame individual always clustered next to eachother, regardless their exposure status(Figure 1). When RNA samples of non-smoking controls and welders were clusteredwith genes identified from paired t-tests ofnonsmoking welders, all study participantshad their baseline and postexposure arraysgrouped together in the phylogenetic tree ofsample clustering. Furthermore, samples of



Table 2. Genes identified by paired t-test: postexposure versus baseline microarrays.

Welders Nonexposed controls Gene ratioGene with Present calls in (28 arrays/14 pairs) (16 arrays/8 pairs) (welders:controls)

All arrays 533 86 6.20At least one array 281 34 8.26At least 10% arrays 236 28 8.43At least 25% arrays 186 23 8.09At least 50% arrays 139 17 8.18

Figure 1. Cluster analysis 44 RNA samples using 139 genes identified by paired t-test in welders. The clus-tering display was generated by dChip software with two-way data clustering. Each row represents anindividual gene, and each column corresponds to an individual array. Gene expression values were stan-dardized and color coded relative to the mean: blue, values less than the mean; red, values greater thanthe mean. RNA samples from the same individual were labeled with the same sample ID with different suf-fixes, representing different collection time points. Smoking status: N, nonsmoking; S, smoking. Exposurestatus: N, controls; Y, welders. Time point: B, baseline; P, postexposure. Experiment: the number indicatesthe hybridization batch in which a sample was analyzed.

Smoking statusExposure statusTime pointExperiment

Sample ID

–3.0 –2.6 –2.1 –1.7 –1.3 –0.9 –0.4 0 0.4 0.9 1.3 1.7 2.1 2.6 3.0

controls and welders seemed to be randomlymixed in any sample clustering analyses,including those using the data of log2-trans-formed expression ratios (data not shown).These observations further demonstrate thatthe real signals of gene expression changescaused by occupational metal exposure weresmaller than the interindividual variations.

Functional clustering using gene ontology.Next, the genes identified by paired t-testswere evaluated by hypergeometric distribu-tion testing based on GO annotations todefine any bioprocesses enriched with theidentified genes. To minimize the noise of thefalse-positive genes on the paired t-test, weapplied a set of highly stringent criteria to

define the significant GO bioprocesses andfunctional pathways and further observed thetrends and distribution of the significant bio-processes in four subsets of genes, withincreasing percentage of Present calls amongall arrays (at least one, 10%, 25%, and 50%arrays). The results are shown in Figure 2.With a decrease in the available numbers ofgenes and an increase in the percentages ofPresent calls, the main structures of GObioprocesses were preserved in both welders andcontrols except for a few low-level bioprocessesthat disappeared. In the nonexposed group, wedid not find that genes were significantlyenriched in any functional pathways except fortwo statistically significant bioprocesses:

response to DNA damage stimulus (GO ID6974) and nucleotide-excision repair (GO ID6289). These two bioprocesses also existed inthe welders but were not statistically signifi-cant. However, in contrast to the controls, inthe welder group many GO bioprocesses werefound to be significantly enriched with geneshaving significant alterations of expressionafter exposure to metal fume. Some of theGO bioprocesses tested significantly across allsubsets with different Present calls. In subsetsincluding genes with lower Present calls, thesignificant bioprocesses were distributed morediscretely, with fewer functional pathwaysidentified. With the increase of Present calls,more significant functional pathways showed



Figure 2. GoSurfer graphic view of hypergeometric distribution testing of gene clustering. Each node represents a GO biologic process, and a line connectingnodes represents parent–child relationship in the top-down direction. Because GO allows multiple parent–child relationships toward one biologic process butGoSurfer only plots one upstream and one downstream relationship for each node, one biologic process may appear several times in the GoSurfer plot. Rednodes represent significant GO bioprocesses tested by hypergeometric distribution as described in “Materials and Methods.” Numbered GO bioprocesses wereused in calculation in hypergeometric distribution testing: 1, biologic process; 2.1, cellular process; 2.2, development; 2.3, physiologic processes; 3.1, cell commu-nication; 3.2, cell growth and/or maintenance; 3.3, metabolism; 3.4, response to external stimulus; 3.5; response to stress; and 3.6, death.

Welders Nonexposed controls

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up in welders by connecting discrete bio-processes with newly appeared ones. In thesubset of at least 50% Present calls, mostsignificant bioprocesses were in the intercon-nected functional pathways. In the metal-exposed welders, functional pathways relatedto nucleic acid metabolism (including RNAmetabolism and DNA metabolism), and cel-lular morphogenesis disappeared with theincrease of Present calls.

We identified eight functional pathwayswith significant enrichment of genes havingaltered expressions in response to metal-fumeexposure in the subset of genes having Presentcalls in > 50% arrays (Table 3). These func-tional pathways contained many GO bio-processes related to proinflammatory andimmune responses, oxidative stress, phosphatemetabolism, cell proliferation, and pro-grammed cell death. Moreover, we identified35 genes from these significant pathways thathad altered expression levels in weldingfume–exposed individuals in comparison withtheir own baseline samples (Table 4). Amongthe identified genes, we found several genesinvolved in every aspect of the inflammatoryresponse, including proinflammatory media-tors, cytokine receptors, downstream signaltransduction genes, and cytotoxic granulysin.

Smoking effects on gene expressionprofiling. We assessed further the effects ofsmoking on acute particulate exposure expres-sion profiles. Of 15 welders and 7 nonex-posed controls, there were 6 smoking weldersand 1 smoking control. It appeared that mostobserved expression alterations were fromnonsmokers exposed to welding fume becausethe number of genes identified from thepaired t-test and the cluster of genes in GObioprocesses were comparable between thissubgroup of welders and the entire weldinggroup (Table 5). In contrast to nonsmokingwelders, fewer genes were identified from thepaired t-test in welding smokers, and they haddifferent patterns of gene clustering. A similarfinding was observed in the analysis of theperipheral WBC count as described in thepreceding section, and our results suggest thatsmoking may alter expression profiles inwhole-blood total RNA and is a confoundingfactor in the study of particulate exposure-induced gene expression profiling changes.

Discussion

In the present study, small expression alera-tions in several genes, caused by short-termoccupational exposure to metal particulates,could be detected in whole-blood total RNAby paired t-tests. Based on GO annotations,the significant genes were clustered in func-tional pathways related to proinflammatoryand immune responses, oxidative stress,phosphate metabolism, cell proliferation, andprogrammed cell death, suggesting systemic

reactions in peripheral blood in response toenvironmental particulate exposure. Moreover,the observations were confounded by smokingbecause most variations were observed in non-smoking welders exposed to welding fume.

Accumulating evidence proved thatmicroarray technology for the investigation ofglobal gene expression profiling is a powerfultool for basic biologic research and laboratoryinvestigations of patient materials, especially inthe field of cancer research and toxicology.Although this technology had been success-fully applied on fractionated blood samples(Klein et al. 2001; Locati et al. 2002) such asperipheral blood mononuclear cells (PBMCs),successful studies of gene expression profiles inwhole-blood total RNA have been limitedbecause of the difficult challenges of hetero-geneous cell types and potential ex vivochanges from blood handling and processing.

Compared with fractionated blood samples,whole-blood total RNA had lower detectionsensitivities mainly caused by a large amountof hemoglobin RNA from reticulocytes, whichcontributes up to 70% of the total RNA iso-lated from whole blood (Affymetrix 2003a,2003b). PBMCs have a more uniform cellpopulation, containing lymphocytes andmonocytes but excluding red blood cells andgranulocytes (eosinophils, basophils, neu-trophils), and are the most transcriptionallyactive cells in blood (DePrimo et al. 2003).However, the extra fractionation procedure forPBMCs requires a prolonged period beforeRNA stabilization, which results in significantex vivo changes in gene expression profiling(Affymetrix 2003a; Pahl and Brune 2002). Inthis study, because all blood samples werecollected within 1 day, it was beyond thecapacity of our laboratory to fractionate all



Table 3. Results of hypergeometric testing using annotations from the Gene Ontology Consortium (GO).a

Functional Genes annotated Genes from paired t-testb

pathway GO ID Biologic processes on array Welders Controls

1 9605 Response to external stimulus 959 18 042330 Taxis 88 5 06935 Chemotaxis 88 5 0

30595 Immune cell chemotaxis 2 1 030593 Neutrophil chemotaxis 1 1 0

2 9605 Response to external stimulus 959 18 09607 Response to biotic stimulus 653 15 06952 Defense response 595 12 06955 Immune response 548 12 0

45087 Innate immune response 147 5 06954 Inflammatory response 145 5 0

42119 Neutrophil activation 1 1 030593 Neutrophil chemotaxis 1 1 0

3 6950 Response to stress 616 16 29605 Response to external stimulus 959 18 09611 Response to wounding 213 7 06954 Inflammatory response 145 5 0

42119 Neutrophil activation 1 1 030593 Neutrophil chemotaxis 1 1 0

4 9607 Response to biotic stimulus 653 15 06950 Response to stress 616 16 29613 Response to pest/pathogen/parasite 364 11 06954 Inflammatory response 145 5 0

42119 Neutrophil activation 1 1 030593 Neutrophil chemotaxis 1 1 09615 Response to viruses 28 2 0

5 9605 Response to external stimulus 959 18 09607 Response to biotic stimulus 653 15 06979 Response to oxidative stress 34 3 06950 Response to stress 616 16 2

6 8219 Cell death 315 7 112501 Programmed cell death 292 7 16915 Apoptosis 291 7 16916 Anti-apoptosis 60 3 1

7 8283 Cell proliferation 762 14 17049 Cell cycle 491 14 1

67 DNA replication and chromosome cycle 127 6 184 S phase of mitotic cell cycle 100 4 0

6260 DNA replication 99 4 06270 DNA replication initiation 14 2 0

8 6793 Phosphorus metabolism 468 9 16796 Phosphate metabolism 468 9 1

16311 Dephosphorylation 78 4 0aGenes were identified from paired t-test with Present calls in at least 50% arrays. Annotations are from Gene OntologyConsortium (http://www.geneontology.org/). bAll listed biologic processes tested significantly in hypergeometric distribu-tion testing in welders but nonsignificantly in nonexposed controls.

blood samples in a timely fashion. Thus, thewhole-blood total RNA was extracted andapplied in all subsequent microarray assays.

Compared with person-to-person varia-tions of gene expressions, the exposure-induced gene expression changes were smaller.Regardless of exposure status, a pair of baselineand postexposed microarrays of the same sub-ject often had a higher correlation coefficientof raw signals across entire probe sets than apair of baseline microarrays randomly selected,and most pairs were clustered next to eachother in sample clustering analyses. In addi-tion, excess hemoglobin RNA and mixed celltypes in the whole blood made it more diffi-cult to observe the real changes in gene expres-sion profiles. Under such circumstances, wewere able to control better the biologic vari-ability among individuals and obtain moresensitive and precise measurements on geneexpression profiles by using self-paired con-trols. In our experiments, this test identified

more genes in the exposed group (139 genes)than in nonexposed controls (17 genes), withPresent calls in at least 50% arrays.

Affymetrix U133A GeneChip contains> 20,000 probes for measuring gene expres-sions in a single hybridization experiment.One major issue in data analysis is to deter-mine whether changes in gene expression areexperimentally significant, with the back-ground of thousands of individual genes testedsimultaneously. On a GeneChip, many genesare functionally interrelated or have unknownfunctions, and there are multiple probe setsdetecting the same gene. In addition, the weaksignals of exposure-induced changes made itvery difficult, or even impossible, to conduct avalid multiple testing adjustment. With theseconsiderations, we did not perform anyadjustments on the results of the paired t-testin the present study.

An alternative approach in the statistics ofmultiple testing is to estimate the false discovery

rate (FDR) by random permutations withinthe same data set (Tusher et al. 2001). Weestimated the FDR of paired t-test results bypermutating each pair of baseline and postex-posed arrays 500 times using dChip 1.3 soft-ware. There was a lower FDR (median,30.2%) in the exposed welders than in non-exposed controls (median, 112%), suggestingthat the paired t-test results of the exposedgroup contained genes with real changes inexpressions in response to occupational expo-sure. However, the permutation tests throughdChip software did not adjust for the prob-lem that multiple probe sets detect the samegene on a GeneChip, so the estimated FDRscould be inflated. Nevertheless, knowing thatan approximately 30% FDR was associatedwith a set of genes from the paired t-test lim-its our ability to identify individual geneswith statistically significant changes in expres-sion in response to particulate exposure.Instead of further testing the significantchange in a single gene, we focused on identi-fying significant pattern changes of biologicprocess in the genes identified from the pairedt-test, using the annotations defined by GO.The underlying hypothesis is that severalgenes of one functional bioprocess changetheir expressions in response to environmentalchallenge because genes are highly networkedand coordinated and do not act alone.Although one gene change may be small anddifficult to be detected accurately in a signifi-cance test, the significant enrichment of geneswith small changes in a biologic process and afunctional pathway may be assessable.

In this study we identified 35 genes fromeight significant functional pathways that hadaltered expression levels after metal-fume expo-sure. The most interesting finding was theidentification of several genes involved in everyaspect of the inflammatory response, includingproinflammatory mediators, cytokine recep-tors, downstream signal transduction genes,and cytotoxic granulysin. Five genes (IL8,IL1A, CXCR4, RALBP1, and SCYE1) havebeen implicated in chemotaxis of the earlyinflammatory response, especially IL8, which isa critical mediator for neutrophil-dependentacute inflammation (Mukaida 2000, 2003).IL8 has a wide range of actions on different celltypes, including neutrophils, lymphocytes,monocytes, endothelial cells, and fibroblasts.IL8 is produced from various cell types inresponse to a wide variety of stimuli, includingproinflammatory cytokines, microbes and theirproducts, and environmental changes such ashypoxia, reperfusion, and hyperoxia. Previousstudies on ROFA-exposed workers found anincrease in proinflammatory cytokines andpolymorphonuclear cells in the nasal lavagefluid, indicating that the particulate exposureresulted in acute upper airway inflammation(Hauser et al. 1995; Woodin et al. 1998). In



Table 4. Genes with altered expressions in response welding-fume exposure.

Welders ControlsAccession Gene Fold Paired Fold Pairednumbera Gene namea symbola change p-value change p-value

NM_00584 Interleukin 8 IL8 –1.22 0.004 1.02 0.923NM_000575 Interleukin 1, alpha IL1A –1.12 0.035 –1.04 0.673NM_003467 Chemokine (C-X-C motif) receptor 4 CXCR4 –1.26 0.038 1.02 0.900NM_004757 Small inducible cytokine subfamily E, member 1 SCYE1 –1.12 0.017 1.03 0.769

(endothelial monocyte-activating)NM_006788 ralA binding protein 1 RALBP1 –1.16 0.036 –1.09 0.629NM_004111 Flap structure-specific endonuclease 1 FEN1 –1.13 0.038 1.02 0.864NM_000416 Interferon gamma receptor 1 IFNGR1 –1.39 0.014 –1.10 0.483NM_078481 CD97 antigen CD97 1.21 0.049 –1.00 0.930NM_002339 Lymphocyte-specific protein 1 LSP1 1.21 0.040 1.14 0.225NM_012483 Granulysin GNLY –1.18 0.034 1.05 0.577NM_001766 CD1D antigen, d polypeptide CD1D –1.22 0.002 –1.09 0.394NM_001828 Charot-Leyden crystal protein CLC –1.21 0.033 –1.18 0.540NM_000633 B-cell CLL/lymphoma 2 BCL2 –1.12 0.010 1.08 0.198NM_006144 Granzyme A (granzyme 1, cytotoxic GZMA –1.27 0.045 1.00 0.985

T-lymphocyte-associated serine esterase 3)NM_080549 Protein tyrosine phosphatase, non-receptor PTPN6 1.10 0.032 –1.05 0.612

type 6NM_000345 Synuclein, alpha (non A4 component of SNCA –1.28 0.030 1.02 0.907

amyloid precursor)NM_002656 Pleiomorphic adenoma gene-like 1 PLAGL1 –1.20 0.039 –1.11 0.318NM_201397 Glutathione peroxidase 1 GPX1 1.14 0.035 –1.01 0.897NM_004417 Dual specificity phosphatase 1 DUSP1 –1.21 0.035 –1.09 0.331NM_001752 Catalase CAT –1.22 0.044 –1.07 0.521NM_004383 c-src tyrosine kinase CSK 1.18 0.013 –1.02 0.736NM_006999 Polymerase (DNA directed) sigma POLS –1.08 0.046 1.03 0.689NM_002835 Protein tyrosine phosphatase, non-receptor PTPN12 –1.23 0.042 –1.02 0.810

type 12NM_145906 RIO kinase 3 (yeast) RIOK3 –1.25 0.008 –1.05 0.737NM_181742 Origin recognition complex, subunit 4-like (yeast) ORC4L –1.13 0.032 –1.06 0.596NM_052811 ret finger protein 2 RFP2 –1.13 0.037 1.03 0.750NM_002577 p21 (CDKN1A)-activated kinase 2 PAK2 –1.20 0.020 1.03 0.751NM_002848 Protein tyrosine phosphatase, receptor type, O PTPRO –1.14 0.041 –1.12 0.209NM_015374 unc-84 homolog B (C. elegans) UNC84B 1.12 0.044 –1.03 0.462NM_004359 Cell division cycle 34 CDC34 1.17 0.013 –1.06 0.426NM-002958 RYK receptor-like tyrosine kinase RYK –1.19 0.033 1.01 0.976NM_014826 CDC42 binding protein kinase alpha (DMPK-like) CDC42BPA –1.21 0.020 1.04 0.843NM_016839 RNA binding motif, single stranded RBMS1 –1.18 0.023 –1.09 0.535

interacting protein 1NM_016113 Transient receptor potential cation channel, TRPV2 1.08 0.041 –1.07 0.208

subfamily V, member 2NM_032454 Serine/threonine kinase 19 STK19 –1.11 0.044 1.07 0.456aFrom Affymetrix NetAffx Analysis Center (http://www.affymetrix.com/analysis/index.affx).

our study, IL8 and other cytokines and recep-tor genes were transcriptionally down-regulated in whole-blood total RNA inresponse to metal particulate exposure.

Our findings that genes with alteredexpressions in whole-blood total RNA inresponse to metal particulate exposure wereclustered in the functional pathways related toinflammatory and immune responses supportthe hypothesis that particulates induce systemicinflammation. It has been well documentedthat particulate air pollutants can cause bothpulmonary parenchymal (Nel et al. 2001; Pope2000) and airway inflammation (Peden 2001).These particulate-mediated local inflammatoryresponses conform to those epidemiologic

observations that exposure to particulate airpollutants can lead to asthma exacerbation,increased pulmonary infections, decreased pul-monary functions, increased hospitalizationsdue to pulmonary and/or airway diseases, andincreased mortality. Recent studies using high-throughput technology for gene expressionprofiling have added to our understanding ofparticulate-mediated local inflammationunderlying those adverse effects on lungs andairway in response to air pollution. IncreasedRNA expression for stress response, inflamma-tory, and repair-related genes were observed inSprague-Dawley rats after intratracheal instilla-tion of ROFA (Nadadur and Kodavanti 2002).In co-cultures of alveolar macrophages and

primary human bronchial epithelial cells,mRNA levels of tumor necrosis factor(TNF)-α, granulocyte macrophage colonystimulating factor (GM-CSF), interleukinIL1β, IL6, and IL8 were increased within 2 hr(p < 0.05) after exposure to 100 µg/mL ofPM10 (Fujii et al. 2002), and mRNA levels ofleukemia inhibitory factor (LIF), GM-CSF,IL1α, and IL8 in primary human bronchialepithelial cells were increased after exposure toPM10 (Fujii et al. 2001).

In this study, we also demonstrated that itwas critical to apply a dichotomous definitionof absolute gene expression status, that is,expressed versus nonexpressed, in the data min-ing of the microarray data. Many algorithms



Table 5. Effects of smoking on acute metal exposure expression profiles.

Welders Nonexposed controlsAll welders Nonsmokers Smokers All controls Nonsmokers

Number of arrays 30 18 12 14 12Paired t-test

No. of genes with Present calls in all arrays 533 419 251 86 104No. of genes with Present calls in at least 50% arrays 139 154 85 17 16

Hypergeometric distribution test in gene with Present call in at least 50% arraysa

1b 9605c Response to external stimulusd (959)e 18f 18f 8f 0f 0f

42330 Taxis (88) 5 3 1 0 06935 Chemotaxis (88) 5 3 1 0 0

30595 Immune cell chemotaxis (2) 1 1 0 0 030593 Neutrophil chemotaxis (1) 1 1 0 0 0

2 9605 Response to external stimulus (959) 18 18 8 0 09607 Response to biotic stimulus (653) 15 16 6 0 0

6952 Defense response (595) 12 13 6 0 06955 Immune response (548) 12 13 5 0 0

45087 Innate immune response (147) 5 3 0 0 06954 Inflammatory response (145) 5 3 0 0 0

42119 Neutrophil activation (1) 1 1 0 0 030593 Neutrophil chemotaxis (1) 1 1 0 0 0

3 6950 Response to stress (616) 16 17 4 2 29605 Response to external stimulus (959) 18 18 8 0 0

9611 Response to wounding (213) 7 4 1 0 06954 Inflammatory response (145) 5 3 0 0 0


4 9607 Response to biotic stimulus (653) 15 16 6 0 06950 Response to stress (616) 16 17 4 2 2

9613 Response to pest/pathogen/parasite (364) 11 10 3 0 06954op Inflammatory response (145) 5 3 0 0 0


9615 Response to viruses (28) 2 1 0 0 05 9605 Response to external stimulus (959) 18 18 8 0 0

9607 Response to biotic stimulus (653) 15 16 6 0 06979 Response to oxidative stress (34) 3 2 0 0 0

6950 Response to stress (616) 16 17 4 2 26 8219 Cell death (315) 7 8 5 1 1

12501 Programmed cell death (292) 7 8 5 1 16915 Aptosis (291) 7 8 5 1 1

6916 Anti-apoptosis (60) 3 5 2 0 07 8283 Cell proliferation (762) 14 17 5 1 1

7049 Cell cycle (491) 14 14 4 1 167 DNA replication and chromosome cycle (127) 6 5 1 1 184 S phase of mitotic cell cycle (100) 4 5 0 0 0

6260 DNA replication (99) 4 5 0 0 06270 DNA replication initiation (14) 2 1 0 0 0

8 6793 Phosphorus metabolism (468) 9 5 3 1 06796 Phosphate metabolism (468) 9 5 3 1 0

16311 Dephosphorylation (78) 4 2 0 0 0aItalic numbers indicate that the functional pathway was tested significantly in hypergeometric distribution testing. bFunctional pathway. cGO identification number (from GOConsortium: http://www.geneontology.org/) dBiologic process. eThe number of genes on U133A array belonging to the functional pathway. f The number of genes identified by pairedt-test belonging to the functional pathway.

currently used for microarray analysis retrievethe expression data from raw signals as continu-ous data and do not distinguish the distinctdichotomous biologic status of a gene. If onegene was not expressed in a RNA sample, therewas always a meaningless expression value beinggenerated that could not be distinguished accu-rately from other samples that expressed thesame gene. In reality there should be no mRNAin a sample when a gene is not expressed. If theexpression values generated by an algorithmtruly represented reality, the data for expressedand nonexpressed genes should have differentdistributions. Therefore, without distinguishingexpression status, a large number of meaning-less data from nonexpressed genes would havedeteriorating effects on a statistical analysis thatassumed a normal distribution of data. Ourobservations that more functional pathwayswere associated with high content of Presentcalls in welders support this hypothesis.Furthermore, based on the absolute expressionstatus, microarray data may be divided intothree categories: consistently not expressed,turned on or off, and continuously expressed indifferent experimental conditions. The first cat-egory of genes was noninformative, and theanalyses of the second category of genes werevery complicated and difficult. Only the lastcategory of genes, those with a high percentageof Present calls across all arrays, was suitablefor parametric statistical analysis. At present,the Detection call algorithm of AffymetrixMAS 5.0 is the only one available for determin-ing the absolute gene expression status, withlimitations on both sensitivity and specificityto distinguish low-level expressed genes fromnonexpressed genes (Liu et al. 2002).

In conclusion, using a repeated measuredesign, peripheral blood gene expression pro-files revealed that environmental exposures tometal fume in healthy individuals producedobservable changes in gene expression clusteredin biologic processes related to inflammatory,oxidative stress, phosphate metabolism, cellproliferation, and programmed cell death.Smoking modified the observed responses.Finally, our study demonstrates the utility ofpaired sampling pre- and postexposure in anat-risk population.

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