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. Author manuscript; available in PMC: 2012 Aug 15.
Published in final edited form as: Toxicol Appl Pharmacol. 2011 May 23;255(1):18–31. doi: 10.1016/j.taap.2011.05.012

Multi-Walled Carbon Nanotube-Induced Gene Expression in the Mouse Lung: Association with Lung Pathology

M Pacurari 1, Y Qian 2,*, DW Porter 2, M Wolfarth 2, Y Wan 1, D Luo 1, M Ding 2, Castranova V 1,2, NL Guo 1,3,*
PMCID: PMC3148292  NIHMSID: NIHMS296874  PMID: 21624382

Abstract

Due to the fibrous shape and durability of multi-walled carbon nanotubes (MWCNT), concerns regarding their potential for producing environmental and human health risks, including carcinogenesis, have been raised. This study sought to investigate how previously identified lung cancer prognostic biomarkers and the related cancer signaling pathways are affected in the mouse lung following pharyngeal aspiration of well-dispersed MWCNT. A total of 63 identified lung cancer prognostic biomarker genes and major signaling biomarker genes were analyzed in mouse lungs (n = 80) exposed to 0, 10, 20, 40, or 80 μg of MWCNT by pharyngeal aspiration at 7 and 56 days post-exposure using quantitative PCR assays. At 7 and 56 days post-exposure, a set of 7 genes and a set of 11 genes, respectively, showed differential expression in the lungs of mice exposed to MWCNT vs. the control group. Additionally, these significant genes could separate the control group from the treated group over the time series in a hierarchical gene clustering analysis. Furthermore, 4 genes from these two sets of significant genes, coiled-coil domain containing-99 (Ccdc99), muscle segment homeobox gene-2 (Msx2), nitric oxide synthase-2 (Nos2), and wingless-type inhibitory factor-1 (Wif1), showed significant mRNA expression perturbations at both time points. It was also found that the expression changes of these 4 overlapping genes at 7 days post-exposure were attenuated at 56 days post-exposure. Ingenuity Pathway Analysis (IPA) found that several carcinogenic-related signaling pathways and carcinogenesis itself were associated with both the 7 and 11 gene signatures. Taken together, this study identifies that MWCNT exposure affects a subset of lung cancer biomarkers in mouse lungs.

Keywords: Nanoparticles, Lung cancer, Biomarker, Gene expression, Molecular network analysis

Introduction

Carbon nanotubes (CNT) represent an important class of engineered nanomaterials. They can be synthesized either as single-walled carbon nanotubes (SWCNT) or as multi-walled carbon nanotubes (MWCNT). SWCNT consist of a single sheet of graphite rolled-up in the form of a cylinder with a diameter of 1–2 nm and lengths ranging up to several micrometers. MWCNT consist of several stacked single wall carbon nanotubes with diameters up to 100 nm and lengths up to several micrometers (Pacurari et al., 2010). Due to their unique physicochemical properties, MWCNT have been widely used for various industrial applications, including, but not limited to, supercapacitors, batteries, automotive industry, aerospace industry, electronics, pharmaceutics, bio-engineering, medical devices, and biomedicine (Pacurari et al., 2010).

Concern over potential MWCNT-induced toxicity has emerged, particularly due to the structural similarity between asbestos and MWCNT. Asbestos is a group of naturally occurring hydrated silicate minerals with fibrous morphology (Pacurari et al., 2010). They consist of fibers of various lengths and diameters, with lengths ranging from 0.1 to greater than 200 μm. It has been well known that asbestos exposure leads to the development of pulmonary fibrosis (asbestosis), bronchogenic lung cancer, pleural plaques, and malignant mesothelioma (Pacurari et al., 2010). Numerous studies have demonstrated that fiber length, diameter, and durability are critical factors involved in the pathogenicity of asbestos (Pacurari et al., 2010). Although MWCNT have a tendency to bundle together, MWCNT aerosols may contain many long, thin, fiber-like nanotube structures similar to asbestos (Pacurari et al., 2010). In addition, the resistance of MWCNT to high temperature or acid treatment indicates that these engineered fibers are durable. It has been proposed that these unique asbestos-like properties of MWCNT may potentially induce pathogenic and carcinogenic effects (Service, 1999; Donaldson et al., 2006; Tian et al., 2006). Indeed, several studies reported that MWCNT induce inflammation, granuloma formation, and biopersistence in the rodent lung after 60 days post-exposure (Muller et al., 2005; Elgrabli et al., 2008; Porter et al., 2010). Other studies have shown that MWCNT exposure increases the genotoxic potential both in vivo and in vitro (Muller et al., 2008).

Recently, our group conducted an in vivo dose-response and time course study of MWCNT exposure in mice in order to investigate the ability of MWCNT to induce pulmonary inflammation, damage, and fibrosis (Porter et al., 2010). Mice were exposed to 0, 10, 20, 40, or 80 μg of MWCNT by pharyngeal aspiration. At 1, 7, 28, and 56 days post-exposure, MWCNT-induced pulmonary toxicity was evaluated. The results demonstrate that pulmonary inflammation and damage were dose-dependent, appeared 1 day post-exposure, and peaked 7 days post-exposure. In contrast, morphometric analysis of lung tissue from this study by Mercer et al. (Mercer et al., 2011) indicates that MWCNT-induced interstitial fibrosis increased at day 28 and progressed through 56 days post-exposure. These results indicate that MWCNT exposure rapidly produces significant pulmonary inflammation, damage, and fibrosis.

Several studies have indicated that fibrosis, a pulmonary fibrotic scarring, could be a precursor to lung cancer. A clinical study found that lung scarring was associated with elevated lung cancer risk, and furthermore, they found that pulmonary scarring and lung cancer occurred to the same lung and extended over time (Yu et al., 2008). In a review over a 21 year period of lung cancer patients, it was found that 45% of all peripheral lung cancers originated in a lung scar (Auerbach et al., 1979). A study with computed tomographic (CT) scans and pathologic specimen analysis found that 47 patients of 57 histologically proved lung cancers had pulmonary fibrosis, indicating a possible association between lung cancer and fibrosis (Sakai et al., 2003).

A casual relationship between lung fibrosis and lung cancer has been observed in crystalline silica exposed patients. Literature review indicated that an increased risk of lung cancer among patients with silicosis, a progressive lung fibrosis, might be an effect of the lung fibrosis rather than a direct effect of silica exposure (Peretz et al., 2006). Analysis of the relationship between tuberculosis-induced lung fibrosis and lung cancer indicated that there is an association between tuberculosis and increased lung cancer risk (Shiels et al., 2011), which implicates chronic pulmonary scarring in the etiology of lung cancer.

One study indicated that pulmonary fibrosis may not be a precursor for lung cancer, rather it may increase the susceptibility of fibrosis patients to develop a lung cancer over time. A comparative study of metaplastic epithelia in lungs of usual interstitial pneumonia (UIP), idiopathic pulmonary fibrosis, with or without lung cancer showed that 32 of 70 UIP autopsy cases had lung cancer, and quantitative assessment of the metaplastic epithelia in lungs demonstrated that squamous metaplasia occurred more frequently in UIP with lung cancer than in UIP without lung cancer (Hironaka and Fukayama, 1999).

Molecular biological analysis also supports that lung fibrosis and lung cancer could be associated. Immunohistochemical analysis found that TGF-beta expression is increased in asbestos-induced fibrosis (Jagirdar et al., 1997). TGF-beta is a ubiquitous and essential regulator of cellular proliferation, differentiation, migration, cell survival, and angiogenesis (Elliott and Blobe, 2005). Alterations in the TGF-beta have been associated with human cancers, including lung cancer. TGF-beta signaling promotes epithelial to mesenchymal transition, a process by which cells lose their epithelial characteristics and acquire more migratory and metastatic mesenchymal properties (Toonkel et al., 2010). An increase in TGF-beta expression in fibrosis may imply that fibrotic scarring may increase the risk of carcinogenesis. Several biological and pathological characteristics of lung fibrosis are similar to that of lung cancer, including genetic alterations, uncontrolled proliferation, and tissue invasion (MacKinnon et al., 2010; Vancheri et al., 2010).

Although more research is needed for the assessment of the clinical outcome of lung fibrosis, Vancheri et al. (Vancheri et al., 2010) suggested that the abnormal fibroblast proliferation observed in idiopathic pulmonary fibrosis may be associated with the development of lung cancer. The current study was designed to follow up our previous investigation of MWCNT-induced pulmonary inflammation, damage, and fibrosis in the mouse. The aim of this study was to successfully profile MWCNT-induced gene expression changes within a set of previously identified lung cancer biomarkers (Guo et al., 2006; Guo et al., 2008) using MWCNT-exposed mouse lungs from a previous study (Porter et al., 2010). Ultimately, the association between MWCNT-induced pulmonary inflammation, damage and fibrosis and genetic markers for lung carcinogenesis was investigated.

In our previous lung cancer-related genome-wide DNA microarray analysis, a 35-gene signature was identified from 86 lung adenocarcinoma patients in order to predict tumor recurrence (Guo et al., 2006). This 35-gene prognostic signature was further validated in 348 non-small cell lung cancer (NSCLC) patients using their transcriptional profiles generated from microarray studies (Guo et al., 2008). The gene expression of the 35 biomarkers was confirmed with real-time RT-PCR analysis of independent snap-frozen human lung cancer tumors (Guo et al., 2008). In the Director’s Challenge Study, a separate set of patient cohorts from multiple hospitals was analyzed, and a new 12-gene signature was identified from genome-wide DNA microarray analysis. This 12-gene signature provides accurate prognostic stratification of 442 lung adenocarcinoma patients including those with early stage tumors (Wan et al., 2010). We hypothesize that expression profiling of lung cancer biomarkers in MWCNT-exposed mouse lungs will identify a subset of biomarkers affected by the exposure, suggesting a possible association between MWCNT exposure and lung cancer risk. These biomarkers could be relevant for early detection or medical surveillance of lung cancer risk in occupationally-exposed workers. In the current study, in order to identify biomarkers relevant for risk prediction of lung carcinogenesis after MWCNT exposure, quantitative RT-PCR low density arrays (LDA) were designed to measure the gene expression profiles of the identified 47 lung cancer prognostic markers (Guo et al., 2006; Guo et al., 2008; Wan et al., 2010) as well as several major signaling transduction pathway genes in lung tissues from mice exposed to different doses of MWCNT at 7 and 56 days post-exposure (Table 1).

Table 1.

Information for the 63 genes analyzed in this study.

Gene Symbol Gene name Assay ID Function Pathways Remarks
Ahnak Desmoyokin Mm00613028_m1 Protein:protein binding Signal transduction Prognostic maker for lung cancer (Guo et al., 2006; Guo et al., 2008)
Akt1 Protein kinase B subunit alpha Mm01331624_m1 Kinase activity Signal transduction Oncogen2 ((Sun et al., 2001)
Arhgap19 Rho GTPase activating protein-19 Mm01198653_m1 GTPase activity Signal transduction Prognostic maker for lung cancer (Guo et al., 2006; Guo et al., 2008)
Arhgdig Rho-related GDP- dissociation inhibitor Mm00433576_m1 Cell signaling protein Signal transduction Prognostic maker for lung cancer (Guo et al., 2006; Guo et al., 2008)
Atp5a1 ATP synthase alpha- subunit-1 Mm00431960_m1 Atpase, hydrogen- transporting, ATP synthase activity Prognostic maker for lung cancer (Guo et al., 2006; Guo et al., 2008)
Atp6v0d1 vacuolar (V−) ATPase proton pump subunit D1 Mm00442694_m1 Atpase H+-exporting Organelles acidification Prognostic maker for lung cancer (Wan et al., 2010)
Atp8a2 P4-type ATPases or Phosphatidylserine Flippase Mm00443740_m1 Atpase, negative regulation of cell proliferation, aminophospholipid transport Transport across membranes Prognostic maker for lung cancer (Guo et al., 2006; Guo et al., 2008)
Atrx Alpha thalassemia/mental retardation syndrome X- linked Mm01225442_m1 Transcriptional regulator, chromatin remodeling Transcription Prognostic maker for lung cancer (Guo et al., 2006; Guo et al., 2008)
Bcl2 B-cell leukemia/lymphoma-2 Mm00477631_m1 Protein binding, anti-apoptosis Signal transduction
Cav1 Caveolin-1 Mm00483057_m1 Vasculogenesis, endocytosis Tumor suppressor gene candidate
Ccdc99 Coiled-coil domain containing −99 Mm00510371_m1 Establishment of mitotic spindle orientation Cell cycle Prognostic marker for lung cancer (Wan et al., 2010)
Chd4 Chromodomain helicase DNA binding protein-4 Mm01190896_m1 Transcription regulator Transcription Prognostic maker for lung cancer (Guo et al., 2006; Guo et al., 2008)
Cox2 Prostaglandin G/H synthase-2 Mm01307329_m1 Inflammation, mitogenesis Signal transduction
Creb3 cAMP responsive element binding protein 3 Mm00457268_m1 Transcription factor Transcription Prognostic maker for lung cancer (Guo et al., 2006; Guo et al., 2008)
Dhh Deserted hedgehog Mm01310203_m1 Protein binding, peptidase activity, cell-cell signaling Signal transduction
Dlc1 Deleted in liver cancer-1 Mm00507957_m1 GTPase activity Signal transduction A candidate tumor suppressor gene; Prognostic marker for lung cancer (Wan et al., 2010)
E2f4 E2F transcription factor-4 Mm00514160_m1 Transcriptional factor, cell cycle apoptosis Transcription Prognostic maker for lung cancer (Guo et al., 2006; Guo et al., 2008)
Egf Epidermal growth factor Mm01316968_m1 Growth factor Signal transduction Oncogene; Prognostic maker for lung cancer (Guo et al., 2006; Guo et al., 2008)
Erk1/Mapk 3 Extracellular signal- regulated kinase-1 Mm00662375_g1 Serine/threonine kinase activity Signal transduction
Erk2/Mapk1 Extracellular signal- regulated kinase-2 Mm00442479_m1 Serine/threonine kinase activity Signal transduction
Fam164A Family with sequence similarity 164, member A Mm00557410_m1 Unknown Unknown Prognostic marker for lung cancer (Wan et al., 2010)
Fut7 Fucosyltransferase-7 Mm01330673_g1 Protein glycosylation Metabolism Prognostic maker for lung cancer (Guo et al., 2006; Guo et al., 2008)
Gli1 Glioma-associated oncogene homolog -1 Mm00494645_m1 DNA, protein binding Transcription
Gnb1 Guanine nucleotide binding protein (G protein), beta polypeptide -1. Mm00515002_m1 GTPase activity Signal transduction
Gpx3 Plasma gluthatione peroxidase-3 Mm00492427_m1 Response to oxidative stress Antioxidant activity
HnrnpA3 Heterogeneous nuclear ribonucleoprotein-A3 Mm00817148_g1 mRNA binding, protein binding Signal transduction Prognostic maker for lung cancer (Guo et al., 2006; Guo et al., 2008)
Igl-v1 Immunoglobulin lambda variable 1–44 Mm01611614_m1 Signaling in immune system Immunity Prognostic maker for lung cancer (Guo et al., 2006; Guo et al., 2008)
Ihh Indian hedgehog Mm01259021_m1 Development processes Signal transduction
Ilf3 Interleukin enhancer- binding factor-3 Mm00494960_m1 DNA, protein binding Transcription Prognostic maker for lung cancer (Guo et al., 2006; Guo et al., 2008)
Insr Insulin receptor Mm01211875_m1 Growth factor receptor, insulin receptor Signal transduction Oncogene; Prognostic maker for lung cancer (Guo et al., 2006; Guo et al., 2008)
Lmf1 Lipase maturation factor-1 Mm00512942_m1 Maturation of specific proteins in ER, transport of lipoprotein lipase Protein transport Prognostic marker for lung cancer (Wan et al., 2010)
Mark2 MAP/microtubule affinity-regulating kinase-2 Mm00433039_m1 Serine/threonin kinase activity Signal transduction Prognostic maker for lung cancer (Guo et al., 2006; Guo et al., 2008)
Msx2 Muscle segment homeobox gene-2 Mm00442992_m1 Transcription suppressor genes Transcription Prognostic maker for lung cancer (Guo et al., 2006; Guo et al., 2008)
Mt3 Metallothionin-3 Mm00496661_g1 Cellular metal ion homeostasis, removal of superoxide radicals, binds to heavy metals Antioxidant activity Oncogene; Prognostic maker for lung cancer (Guo et al., 2006; Guo et al., 2008)
Nos1 Nitric oxide synthase-1 Mm00435175_m1 Nitric oxide synthesis, neurotransmiter Signal transduction
Nos2 Nitric oxide synthase-2 Mm00440488_m1 Response to hypoxia; positive regulation of leukocyte mediated cytotoxicity, endothelial cell proliferation, blood vessel remodeling, nitric oxide synthesis Signal transduction Biomarker for bronchopulmonary dysplasia
Nos3 Nitric oxide syntahse-3 Mm01164908_m1 Nitric oxide synthesis, diverse biological functions Signal transduction Biomarker for prostatic carcinoma
Nsfl N-ethylmaleimide- sensitive factor Mm01243864_g1 Vesicle transport/target membrane fusion Cellular transport
Ogt O-linked N- acetylglucosamine (GlcNAc) transferase Mm00507317_m1 Glycosilation Metabolism Prognostic maker for lung cancer (Guo et al., 2006; Guo et al., 2008)
Pdpk1 Pyruvate dehydrogenase kinase, isozyme-1 Mm00440707_m1 Protein kinase activity Signal transduction Oncogene, Prognostic marker for lung cancer (Wan et al., 2010)
Pik3r1 Phosphoinositide-3- kinase, regulatory subunit-1 (alpha) Mm00803160_m1 Protein localization in plasma membrane Signal transduction
Prmt1 Protein arginine methyltransferase-1 Mm00480133_m1 Tranferease histone methyltransferase Signal transduction Prognostic maker for lung cancer (Guo et al., 2006; Guo et al., 2008)
Ptch1 Patched-1 Mm00436014_m1 Receptor activity Signal transduction Tumor suppressor gene
Rer1 Retention in endoplasmic reticulum-1 Mm00471276_m1 Endoplasmic reticulum membrane proteins Protein retention in ER Prognostic maker for lung cancer (Guo et al., 2006; Guo et al., 2008)
Scly Selenocysteine lyase Mm00489563_m1 Catalyzes the decomposition of L-selenocysteine Metabolism Prognostic marker (Wan et al., 2010)
Shh Sonic hedgehog Mm00436527_m1 Proliferation, differentiation, development, specification, signaling, survival, migration, growth Signal transduction
Smo Smoothened Mm01162710_m1 Signal transducer Signal transduction
Smpd1 Sphingomyelin phosphodiesterase-1, acid lysosomal Mm00488318_m1 Converts sphingomyelin to ceramide Metabolism Prognostic marker for lung cancer (Wan et al., 2010)
Sod1 Superoxide dismutase-1 Mm01344231_g1 Oxidative stress response Antioxidant signaling
Sod2 Superoxide dismutase-2 Mm00449726_m1 Oxidative stress response Antioxidant signaling
Sod3 Superoxide dismutase-3 Mm00448831_s1 Oxidative stress response, xenobiotic metabolism signaling Antioxidant signaling
Stk24 Serine/threonine kinase- 24 Mm00520930_m1 Protein kinase activity Signal transduction Prognostic marker for lung cancer (Wan et al., 2010)
Tal2 T-cell acute lymphocytic leukemia-2 Mm00447439_s1 DNA, and protein binding factor Transcription regulation T cell acute lymphoblastic leukemia.Oncogene; Prognostic maker for lung cancer (Guo et al., 2006; Guo et al., 2008)
Tnfsf9 Tumor necrosis factor (ligand) superfamily, member-9 Mm00437154_m1 Bidirectional signal transducer Signal transduction Oncogene; Prognostic maker for lung cancer (Guo et al., 2006; Guo et al., 2008)
Tuba1a Tubulin, alpha-1a Mm00846967_g1 Microtubule- based movement, protein polymerization Structural activity Prognostic maker for lung cancer (Guo et al., 2006; Guo et al., 2008)
Uba1 Ubiquitin-like modifier activating enzyme-1 Mm00493988_m1 Ubiquitin- conjugation for protein degradation Protein degradation Prognostic maker for lung cancer (Guo et al., 2006; Guo et al., 2008)
Ube2i Ubiquitin-conjugating enzyme E2I Mm00495850_m1 Ubiquitin- activating protein Protein degradation Prognostic maker for lung cancer (Guo et al., 2006; Guo et al., 2008)
Vac14 Protein Vac14 homolog Mm00461494_m1 Regulates the level of PtdIns(3,5) P2 Signal transduction Prognostic maker for lung cancer (Guo et al., 2006; Guo et al., 2008)
Vegfa Vascular endothelial growth factor A Mm01281449_m1 Angiogenesis Signal transduction
Wif1 WNT inhibitory factor-1 Mm00442355_m1 Development, Wnt receptor signaling pathway Signal transduction Tumor suppressor gene
Wnt3a Wingless-type MMTV integration site family, member-3A Mm00437337_m1 Protein binding, extracellular matrix structural constituent Signal transduction Oncogene
Xpo1 Exportin-1 Mm00506129_m1 Protein transporter activity Protein transport Prognostic marker for lung cancer (Wan et al., 2010)
Zak Leucine zipper- and sterile alpha motif- containing kinase Mm00458198_m1 Protein kinase activity, cell cycle control Signal transduction Biomarker for diagnosis of cervical cancer; Prognostic marker for lung cancer (Wan et al., 2010);. Oncogene
18S 18S ribosomal RNA Protein synthesis
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Materials and Methods

MWCNT

MWCNT used in this study were a gift from Mitsui-&-Company (MWCNT-7, lot # 05072001K28). The characterization of MWCNT has been published (Porter et al., 2010). Briefly, the bulk MWCNT exhibit a distinctive crystalline structure with the number of walls ranging from 20 to 50 walls. Overall, MWCNT trace metal contamination was 0.78%, including sodium (0.41%) and iron (0.32%) with no other metals present above 0.02%. Transmission electron microscopy (TEM) micrographs of MWCNT dispersed in dispersion medium (DM) demonstrated that DM promotes significant dispersion of MWCNT. The quantitative analysis of TEM micrographs revealed that the median length of this MWCNT sample was 3.86 μm (GSD 1.94) and the count mean width was 49 ±13.4 (S.D.) nm. The zeta potential of the MWCNT in the DM was determined to be −11mV.

Animals

Male C57BL/6J mice (7 weeks old) were obtained from Jackson Laboratories (Bar Harbor, ME). Since male mice fight and injury each other if caged together, individual mice were housed one per cage in polycarbonate isolator ventilated cages, which were provided HEPA-filtered air, with fluorescent lighting from 0700 to 1900 hours. Autoclaved Alpha-Dri virgin cellulose chips and hardwood Beta-chips were used as bedding. Mice were monitored to be free of endogenous viral pathogens, parasites, mycoplasms, Helicobacter and CAR Bacillus. Mice were maintained on Harlan Teklad Rodent Diet 7913 (Indianapolis, IN), and tap water was provided ad libitum. Animals were allowed to acclimate for at least 5 days before use. All animals used in this study were housed in an AAALAC-accredited, specific pathogen-free, environmentally controlled facility.

MWCNT pharyngeal aspiration exposure

Suspensions of MWCNT were prepared in DM as previously described by our laboratory (Porter et al., 2008). Each treatment group consisted of 8 mice. Mice were anesthetized with isoflurane (Abbott Laboratories, North Chicago, IL). When fully anesthetized, the mouse was positioned with its back against a slant board and suspended by the incisor teeth using a rubber band. The mouth was opened, and the tongue gently pulled aside from the oral cavity. A 50μl aliquot of sample was pipetted at the base of the tongue, and the tongue was restrained until at least 2 deep breaths were completed (but for not longer than 15 seconds). Following release of the tongue, the mouse was gently lifted off the board, placed on its left side, and monitored for recovery from anaesthesia. Mice received either DM (vehicle control), 10, 20, 40 or 80 μg MWCNT.

RNA extraction

Total RNA was extracted from the frozen mouse lung tissue sample (−80 °C) in RNAlater using the RNeasy Fibrous Tissue Mini Kit according to the manufacturer’s protocol (Qiagen, USA). Total RNA was eluted in RNase-free water and stored at −80°C until further analysis. The quality and the concentration of each RNA sample was determined using a Nanodrop-1000 Spectrophotometer (NanoDrop Tech, Germany).

Quantitative real-time PCR (qPCR)

cDNA was generated using a TaqMan high capacity RT kit according to manufacturer’s protocol (Applied Biosystems, CA). Real-time qPCR was performed using TaqMan microfluidic low density arrays (LDA) with custom designed genes on an ABI 7900HT Fast RT-PCR instrument (Applied Biosystems). The reports were generated using SDS2.3 software (Applied Biosystems), and the expression level of each gene was given by the number of cycles required to reach threshold fluorescence (Ct). 18S was used as an endogenous control gene.

Statistical analysis

At every time point measured, each treatment and control group consisted of eight mice. To assess the gene expression fold change in each treated group versus the control group, the gene expression (Ct value) of three replicates was first averaged for each sample. Second, each gene expression value was normalized using the average expression of 18S for the entire plate, i.e. plate-wise normalization. Third, the fold change between treatment groups versus the control group was calculated using 2−ΔΔCt method (Livak and Schmittgen, 2001). Finally, the statistical significance was assessed with two-sample t-tests using software SAS 9.2. In order to assess post-exposure effects over the time series, a two-sample t-test was also performed on the fold change values between two time points for each gene.

Functional Pathway Analysis

Ingenuity Pathway Analysis (IPA) software (Ingenuity Systems, Redwood City, CA) was used to derive curated molecular interactions reported in the literature. These interactions included both physical and functional interactions, as well as interactions representing pathway relevance. In this study, in order to delineate molecular networks of genes interacting with the 7-gene or 11-gene biomarker sets, a core analysis was employed to identify the most significant biological functions and processes from the derived interactions reported in mouse studies with IPA. The statistical significance of canonical signaling pathways and associated disease and disorder functions were assessed using Fisher’s exact tests adjusted with BH tests.

Hierarchical clustering analysis and heatmap

Hierarchical two-dimensional clustering analysis was performed using genes that exhibited significant differential expression after treatment at 7 and 56 days post-exposure, using the Heatplus function in the R package. Similarity metrics were manhattan distance, and the cluster method was complete linkage. Heatmaps were then generated in the R package.

Results

A 7-gene biomarker set identified at 7 days post-exposure

A total of 63 lung cancer prognostic and major signaling pathway biomarker genes were analyzed using qPCR assays in mouse lungs exposed by aspiration to 0, 10, 20, 40, or 80 μg of MWCNT at 7 days post-exposure. Overall, it was found that only 7 genes had different expression levels compared to the control group. These 7 genes were Rho GTPase activating protein-19 (Arhgap19), coiled-coil domain containing-99 (Ccdc99), muscle segment homeobox gene-2 (Msx2), methalothione-3 (Mt3), nitric oxide synthase-2 (Nos2), sonic hedgehog homolog (Shh), and wingless-type inhibitory factor-1 (Wif1). The expression levels of Arhgap19, Msx2, and Shh were significantly increased only at the exposure dose of 40 μg MWCNT. The expression level of Wif1 was found to be significantly decreased only at the dose of 80 μg MWCNT exposure. The expression levels of Ccdc99 and Nos2 were significantly increased at doses of 20, 40, and 80 μg of MWCNT, whereas the expression of Mt3 was significantly increased at doses of 40 and 80 μg of MWCNT (Figure 1). These results indicate that several lung cancer biomarker signature genes are affected during the earlier stage of MWCNT exposure.

Figure 1.

Figure 1

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7-gene biomarker set in the mouse lung at 7 days post-exposure to MWCNT. Total RNA was extracted from the frozen lung tissue of mice that were exposed to different doses of MWCNT at 7 days post-exposure as indicated, followed by cDNA generation. Real-time PCR was performed to profile 63 lung cancer biomarker genes using LDA. Genes shown in the figure underwent significant changes in expression when compared to the negative control. A, Arhgap19. B, Ccdc99. C, Msx2. D, Mt3. E, Nos2. F, Shh. G, Wif1. H, 18S. Fold change was calculated after normalization to 18S and relative to control samples. Values given are mean ± SEM (p ≤ 0.05, n=8). * significantly different compared to control (unpaired t-tests).

An 11-gene biomarker set identified at 56 days post-exposure

qRT-PCR analysis was applied to assess the expression changes of 63 lung cancer prognostic and major signaling biomarker genes in mouse lungs exposed by aspiration to 0, 10, 20, 40, or 80 μg of MWCNT at 56 days after a single exposure. At 56 days post-exposure, it was found overall that 11 genes exhibited differential expression levels compared to the control group (Figure 2). These 11 genes were B-cell leukemia/lymphoma-2 (Bcl2), caveolin-1 (Cav1), Ccdc9, desert hedgehog homolog (Dhh), gluthatione peroxidase-3 (Gpx3), Msx2, Nos2, phosphatidylinositol 3-kinase regulatory subunit-1 (Pi3kr1), patched-1 (Ptch1), Wif1, and leucine-zipper and sterile-alpha motif kinase (Zak) (Figure 2). Of these 11 genes, eight genes, Bcl2, Cav1, Dhh, Gpx3, Pi3kr1, Ptch1, Zak, and Wif1, had a significant decreased expression level at the highest tested dose (80 μg). The expression of Msx2 exhibited significant decreases at the doses of 20 and 80 μg. In contrast, Nos2 expression level was significantly increased at the doses of 10 and 80 μg. Ccdc99 expression was significantly increased at 40 μg of MWCNT (Figure 2). These results indicate that after 56 days post-exposure to MWCNT, the expression levels of several lung cancer biomarkers have differential expression levels compared to control samples.

Figure 2.

Figure 2

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11-gene biomarker set in the mouse lung at 56 days post-exposure to MWCNT. Total RNA was extracted from the frozen stored lung tissue of mice that were exposed to different doses of MWCNT at 56 days post-exposure as indicated, followed by cDNA generation. Real-time PCR was performed to profile 63 lung cancer biomarker genes using LDA. Genes shown in the figure underwent significant changes in expression when compared to the negative control. A, Bcl2. B, Cav1. C, Ccdc99. D, Dhh. E, Gpx3. F, Msx2. G, Nos2. H, Pi3kr1. I, Ptch1. J, Wif1. K, Zak. L, 18S. Fold change was calculated after normalization to 18S and relative to control samples. Values given are mean ± SEM (p ≤ 0.05, n=8). * significantly different compared to control (unpaired t-tests).

A common 4-gene biomarker set between 7 and 56 days post-exposure

Further analysis found that 4 genes appeared in the gene biomarker sets for both the 7-and 56 days post-exposure samples. These four overlapping genes were Ccdc99, Msx2, Nos2, and Wif1 (Figure 3). A comparison of the expression of these overlapping genes between 7 days (7-gene set) and 56 days (11-gene set) post-exposure samples revealed that the expression of three genes, Ccdc99, Nos2 and Msx2, decreased at 56 days post-exposure compared to their expression at 7 days post-exposure (Figure 3A-C). Statistical analysis demonstrated that the expression levels of Ccdc99 and Nos2 at 7 days were significantly higher compared with those at 56 days post-exposure (Figure 3A, 3C). These results further indicate that the expression of selected lung cancer biomarkers show differential expression levels during the more extended post-exposure interval.

Figure 3.

Figure 3

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An overlapping 4-gene biomarker set in the mouse lung between 7- and 11-gene set at 7 and 56 days post-exposure to MWCNT, respectively. A, Ccdc99. B, Msx2. C, Nos2. D, Wif1. Fold change was calculated after normalization to 18S and relative to control samples. Values given are mean ± SEM (p ≤ 0.05, n=8). # significantly different compared to 56 days post-exposure.

Gene clustering analysis

Hierarchical clustering analysis showed that the 7-gene biomarker set was able to separate MWCNT-treated animals from the control animals at 7 days post-exposure (Figure 4A). The 7-gene biomarker set showed elevated gene expression in the treated animals, except for Wif1 (Figure 4A and Figure 1). At 56 days post-exposure, the expression pattern of the 11-gene biomarker set was not as homogenous among the MWCNT-treated animals compared with that at 7 days post-exposure (Figure 4B). Most genes exhibited decreased expression in the majority of MWCNT-exposed animals compared with a middle cluster containing four vehicle control-exposed animals (marked in Figure 4B).

Figure 4.

Figure 4

Figure 4

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Hierarchical clustering analysis. DS: control animal. The number after “MW” denotes the dosage used in the treatment. (A) Clustering analysis using the 7-gene biomarker set at 7 days post-exposure. (B) Clustering analysis using the 11-gene biomarker set at 56 days post-exposure. The marked cluster contains 4 control samples.

Molecular network analysis

To investigate significant molecular network interactions and canonical signaling pathways associated with these differentially expressed genes, the Ingenuity Pathway Analysis (IPA) software was applied to analyze 7- and 11-gene biomarker sets, respectively. The results revealed that a total of 25 and 60 genes interacted with 7- and 11-gene biomarker sets, respectively (Figure 5). The analysis also demonstrated that 25 canonical pathways were associated with the 7-gene biomarker set. Among these 25 pathways, 13 were statistically significant (p<0.05). Ranking the pathways by their significance, the top five most significant were: corticotrophin releasing hormone signaling, the role of macrophages, fibroblasts and endothelial cells in rheumatoid arthritis, sonic hedgehog signaling, MIF regulation of innate immunity and MSP-RON signaling pathway (Figure 6A). For the 11-gene biomarker set, 154 associated canonical pathways were identified. Among these 154 pathways, 81 were statistically significant (p<0.05). Based on the ranking of the significance, the top five most significant pathways were: molecular mechanisms of cancer, docosahexaenoic acid (DHA) signaling, the role of osteoblasts, osteoclasts and chondrocytes in rheumatoid arthritis, MSP-RON signaling pathway and glucocorticoid receptor signaling (Figure 6B). Notably, the molecular mechanisms of cancer pathway was ranked as the most significant among the 11-gene associated canonical pathways at 56 days post-exposure.

Figure 5.

Figure 5

Figure 5

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Molecular network analysis using IPA. (A). Network associated with the 7-gene biomarker set identified from animals treated with MWCNT at 7 days post-exposure. (B). Network associated with the 11-gene biomarker set identified from animals treated with MWCNT at 56 days post-exposure.

Figure 6.

Figure 6

Figure 6

Figure 6

Figure 6

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Canonical pathways, diseases and functional disorders related to the networks generated from the 7- and 11-gene biomarker sets using IPA. (A). Canonical pathway analysis in the network related to the 7-gene biomarker set. (B). Canonical pathway analysis in the network related to the 11-gene biomarker set. (C). Comparison of canonical pathways in the 7- and 11-gene biomarker set network analysis. (D). Top 5 disease and disorder functions related to the 7-gene biomarker set network (at 7 day post-exposure) and the 11-gene biomarker set network (at 56 day post-exposure).

The IPA also identified 12 common canonical pathways between the 7- and 11-gene biomarker sets. Notably, basal cell carcinoma signaling pathway was ranked as the most significant among these 12 overlapping pathways (Figure 6C). An analysis of the 7- and 11-gene biomarker sets-associated potential diseases and biological function disorders showed that carcinogenesis was ranked fifth among the 7-gene set-associated diseases and function disorders (at 7 days post-exposure). For the 11-gene set network (at 56 days post exposure), carcinogenesis was ranked thesecond most significant disease function (Figure 6D). Taken together, these results indicate that exposure to MWCNT may be potentially carcinogenic in humans, which could be enhanced over a prolonged post-exposure period.

Discussion

Several studies have demonstrated that exposure to CNT substantially induces harmful effects on the lungs, including inflammatory granulomas and lung fibrosis in animal models (Lam et al., 2004; Muller et al., 2005; Shvedova et al., 2005; Porter et al., 2010). Furthermore, it has been proposed that CNT exposure can induce an asbestos-like pathogenicity and may pose a similar carcinogenic risk as exposure to asbestos fiber does (Takita et al., 1986; Donaldson et al., 2006; Poland et al., 2008). In fact, MWCNT are man-made fiber-shaped materials. The concern of their potential health hazards has been raised due to their unique physical properties, such as high aspect ratio (length/diameter), nanoscale diameters, micrometer length fiber like-shape and durability, which are analogous to asbestos fibers (Muller et al., 2005; Donaldson et al., 2006; Kobayashi et al., 2010). Particularly, the high aspect ratio and micrometer length are two important features that may contribute to the fiber toxicity (Stanton et al., 1981). Whether MWCNT fit the classical fiber ‘paradigm’ has not yet been fully established. Poland et al. (Poland et al., 2008) reported an asbestos-like acute inflammatory effect of long MWCNT when they were administered into abdominal cavity of mice. They found that the asbestos-like effects were fiber length-dependent since results showed that short fibers failed to induce abdominal inflammation or granulomas. Conversely, long fibers induced inflammation and granulomas at the peritoneal side of the diaphragm at 24 h and 7 days post-exposure. Another study by Takagi et al. (Takagi et al., 2008) reported that intra-abdominal exposure to MWCNT induced mesothelioma, a form of cancer of the lining of the body cavities, in p53+/− mice. In this study, the p53+/− mouse was chosen because it is sensitive to induction of tumors. Histopathological analysis of the peritoneal cavity of MWCNT-treated p53+/− mice found the presence of several asbestos-like pathological changes, such as fibrous scars, granulomas, and typical epithelial mesotheliomas. Particularly, the characteristics of MWCNT-induced mesotheliomas were hobnail appearance to large tumors along with high mitotic rate cells and central necrosis due to high grade of malignant mesothelioma. Furthermore, recent two studies show that MWCNT can reach the pleural tissue in mice, the site of mesothelioma, after pulmonary exposure to occupationally relevant burdens of MWCNT (Ryman-Rasmussen et al., 2009; Mercer et al., 2010) These studies conclude that the fibrous structure of MWCNT may pose a carcinogenic risk similar to that for asbestos fibers.

It has been well-established that the biopersistence and pulmonary penetration potential of asbestos are the critical factors associated with asbestos pathogenicity and carcinogenicity (Vallyathan et al., 1998; Shukla et al., 2003). Studies have shown that MWCNT exposure has a similar pattern of biopersistence and pulmonary penetration as asbestos (Elgrabli et al., 2008; Kim et al., 2010). Recent studies found that MWCNT were persistent in lungs up to 60 days post-exposure (Muller et al., 2005; Porter et al., 2010). In addition, histopathological analyses found that upon pulmonary exposure to MWCNT the particles penetrated through alveolar walls and were transported to the alveolar interstitium, subpleural tissue and subpleural lymphatics, finally reaching the intrapleural space (Porter et al., 2010).

Several in vitro studies have shown that MWCNT exposure induces genotoxicity. Zhu et al. (Zhu et al., 2007) found that MWCNT exposure induces DNA damage and increases mutation frequency in mouse embryonic stem cells. Karlsson et al.(Karlsson et al., 2008) demonstrated that MWCNT exposure significantly damages DNA in A549 type II epithelial cells. Sargent et al. (Sargent et al., 2011) found that MWCNT induce mitotic abnormality with one mitotic spindle pole, showing the evidence of the mechanism responsible for the disruption of cell division by MWCNT. Their results indicate that MWCNT are genotoxic, which may result in carcinogenic potential for MWCNT. These results are in agreement with our present results of MWCNT-induced lung cancer biomarker gene expression changes. Although whether MWCNT would induce tumors can’t be addressed in the present study, our data indicate an association between MWCNT exposure and lung cancer biomarker changes. Such an association has been used to justify a current project within NIOSH to evaluate lung tumor formation over the course of 1 year after a 2 week inhalation of MWCNT in a cancer susceptible mouse strain.

Taken together, the fibrous characteristics of MWCNT, their biopersistence, their ability to penetrate the lungs, and their genotoxicity have prompted the hypothesis that exposure to MWCNT may contribute to the initiation and progression of asbestos-like pathological responses, such as lung carcinogenesis (Donaldson et al., 2006; Pacurari et al., 2010).

In the present study, we sought to determine whether MWCNT exposure would affect lung cancer biomarker gene expression through profiling a set of lung cancer biomarker genes in mouse lungs following exposure to MWCNT. The design of this study was established based on our previous in vivo mouse model for pharyngeal aspiration of MWCNT (Porter et al., 2010). The mouse lung tissue specimens at both 7 and 56 days post-MWCNT exposure were taken from this previous investigation and were analyzed for the expression changes of a set of 63 lung cancer biomarker genes using RT-PCR analysis (Table 1). Of these 63 genes, 47 were previously shown to be prognostic markers of human lung cancer recurrence and metastasis (Guo et al., 2006; Guo et al., 2008; Wan et al., 2010), and the remaining 16 genes are well established hallmarks of cancer signaling pathways. The 47 lung cancer prognostic biomarkers were from a 35-gene (Guo et al., 2006) and a 12-gene signature (Guo et al., 2008), respectively. Both lung cancer gene signatures could identify more aggressive tumors from stage I non-small cell lung cancer tumors, which were at the early stage of cancer formation by the time of diagnosis. We have data showing that 45 out of 47 prognostic markers were also significantly differentially expressed in NSCLC tumors vs. normal lung tissues (currently unpublished results). Specifically, Msx2 and Ccdc99 were both over-expressed in lung cancer tumor tissues with a fold change of 2.3 and 1.8, respectively, which are concordant with the over-expression patterns observed in the MWCNT-treated mouse lungs in this study. The functions of these 63 genes and the associated signaling transduction pathways were extracted from the literatures and are listed in Table 1.

The results obtained from our study demonstrated that 7 genes out of these 63 lung cancer biomarker genes showed differential expression changes at 7 days post-exposure to MWCNT. These 7 genes are Arpgap19, Nos2, Shh, Wif1, Mt3, Ccdc99, and Msx2. The functional analysis of 7 genes showed that: 1) Arpgap19, Nos2, Shh, and Wif1 are involved in signaling transduction, 2) Mt3 is involved in metal detoxification, cell proliferation, and cell protection against damage, 3) Ccdc99 is involved in cell cycle regulation, and 4) Msx2 is a transcription factor and an oncogene (Table 1). Particularly, Shh, a tissue developmental and angiogenic factor, is involved in tumor angiogenesis in a variety of cancers, and Mt3, modulated by oxidative stress, plays a role in carcinogenesis (Pola et al., 2001; Garrett et al., 2002). The increase in expression of both genes upon MWCNT exposure in mouse lungs may indicate the potential molecular mechanisms related to MWCNT-induced potential carcinogenic effects. Such a relationship warrants further study.

At 56 days post-exposure to MWCNT, the gene profiling showed that 11 genes underwent different expression changes. These 11 genes are: Dhh, Nos2, Ptch1, Pik3r1, Zak, Wif1, Ccdc99, Cav1, Bcl2, Msx2, and Gpx3. The functional analysis of these 11 genes showed that: 1) Dhh, Nos2, Ptch1, Pik3r1, Zak, and Wif1 are involved in signaling transduction, 2) Ccdc99 is a cell cycle protein, 3) Cav1 is associated with plasma membrane, 4) Bcl2 is an anti-apoptotic gene, 5) Msx2 is a transcription factor and an oncogene, and 6) Gpx3 is involved in antioxidant activity (Table 1). Notably, Bcl2 is an anti-apoptotic gene and plays a major role in tumorigenesis (Coultas and Strasser, 2003). The decrease in Bcl-2 gene expression in this study indicates that MWCNT exposure may damage anti-oncogenic suppressor genes in mouse lungs.

Furthermore, these two sets of significant genes at 7 and 56 days post-exposure share 4 common genes between them. These genes are: Ccdc99, Msx2, Nos2, and Wif1 (Figure 3). The data analysis showed that the expression changes of these 4 overlapping genes at 7 days post-exposure were attenuated at 56 days post-exposure. Interestingly, our previous in vivo studies have shown that MWCNT-induced pulmonary inflammation and damage peaked at 7 days post-exposure of MWCNT (Porter et al., 2010); while interstitial fibrosis was significantly elevated at 28 days post-exposure and peaked at 56 days (Mercer et al., 2011). Therefore, the time-dependent changes in gene expression profiling correlate with the previously published results of histopathological measurements in mouse lungs, indicating a transient inflammatory/damage response and persistent fibrosis. These results demonstrate that gene profiling may well reveal alterations in the molecular processes at the level of the gene transcription and signaling transduction pathways upon MWCNT exposure in mouse lungs.

The notion of predicting chemical toxicity by gene expression profiling has been proposed and applied in genotoxicity for several decades (Aubrecht and Caba, 2005). Particularly, the recent development of high-throughput technologies has made it possible to identify key predictive biomarkers through global gene expression profiling. It has become increasingly important to use gene expression signatures identified with bioinformatics methods for toxicity predication (Shi et al., 2010), risk assessment, and screening (Afshari et al., 2011). A similar gene expression and copy number profiling study was conducted to identify important allelic imbalance in asbestos-related lung cancer (Wikman et al., 2007). Blood gene expression signatures have been used to predict exposure levels of APAP (Bushel et al., 2007). The unique advantage of these studies, as well our present study, is the ability to detect toxic injury at the molecular level and to identify the signaling pathways that lead to organ injury long before the clinical symptoms occur. In these biomarker and gene signature studies, the emphasis is on the prediction of potential risk or toxicity. In this study, the lung tissues were collected from the mice at 7 and 56 days post-exposure, which are not long enough for mice to develop lung cancer. In the case of asbestos exposure, the latency period between the first exposure and diagnosis of mesothelioma ranges from 20 to 40 years (Pacurari et al., 2010). An in vivo animal study demonstrated that asbestos exposure induces malignant mesothelioma in mouse lungs at 60 weeks post-exposure (Vaslet et al., 2002). Thus, neither asbestos nor MWCNT would be able to induce lung carcinogenesis in mice at 56 days post-exposure. In vivo animal model-generated gene profiling reveals information that approximates the complexity of the human body and its cellular, biochemical, and molecular systems that are involved in responses to chemical agents. Several studies have shown that animal model-based gene expression profiling can predict human target organ toxicities reliably for numerous human diseases, including cancer (Nuwaysir et al., 1999; Newton et al., 2004). The combined results from the gene expression profiling in this study and the histopathological changes observed in the previous study (Porter et al., 2010) indicate that exposure to MWCNT may potentially enhance the risk of pulmonary diseases, including lung carcinogenesis, in humans. Thus, results from the present study have been used to justify a further investigation by NIOSH to address whether the changes observed in gene expression in lung target cells are involved in the pathogenesis of lung cancer. Specifically, NIOSH is currently conducting a two week inhalation exposure study with histological evaluation through 1 year post-exposure in a cancer susceptible mouse strain.

Ingenuity Pathway Analysis (IPA) software analyzes the gene information from more than 200,000 published full text articles in order to create global gene networks, an interactome. At the same time, IPA relates specific biological functions and potential diseases and functional disorders to individual genes (Calvano et al., 2005; Long et al., 2008). The software is used to identify significant networks, biological functions, potential diseases and canonical signaling pathways associated with the differentially expressed gene profiles. The results obtained from IPA show a correlation between our gene expression profiling and an empirical classification of biological functions and potential diseases. Among the top five most significant canonical pathways associated with the 7-gene biomarker set, 4 pathways (corticotrophin releasing hormone signaling, Sonic hedgehog signaling, MIF regulation of innate immunity, and MSP-RON signaling pathway) are involved in carcinogenesis. Furthermore, the results of IPA showed that cancer is one of top 5 diseases and functional disorders associated with the 7-gene biomarker set. Similarly, 4 out of the 5 most significant canonical pathways associated with 11-gene biomarker set are involved in carcinogenesis and cancer is ranked as the second most significant of the potential diseases and functional disorders listed. Moreover, the comparison of results from analyses of both gene sets showed that there are 12 overlapping significant canonical signaling pathways and that basal cell carcinoma signaling is ranked as the most significant pathway.

Our IPA results also showed that MWCNT induced potentially carcinogenic alterations in the genes of mice and that these changes were time dependent. The Signaling Pathway of Molecular Mechanisms of Cancer is ranked as the most significant among the 11-gene biomarker set-associated canonical pathways at 56 days post-exposure. Notably, it is ranked much lower among the 7-gene biomarker set-associated canonical pathways at 7 days post-exposure. Furthermore, cancer is ranked as thesecond most significant among the 11-gene set-associated diseases and functional disorders whereas in the 7-gene set analysis, its significance falls to the fifth. These results are consistent with those of our histopathological measurements (Porter et al., 2010; Mercer et al., 2011). They demonstrate a shift from rapid/transient inflammation/damage to a persistent fibrotic response involving remodeling signals and growth factor damages, and as well as epithelial hyperplasia and hypertrophy (Porter et al., 2010). Such changes have been associated with initiation of carcinogenesis.

The IPA results were focused on the 7- and 11-gene sets identified in this study. As only cancer-related genes were initially analyzed in the experiments, the results would be biased toward cancer related pathways and functions, as opposed to those obtained from genome-wide expression studies. However, the identified biomarkers are involved in multiple diseases. Pathological disorders, such as organismal disorders and injuries (including fibrosis), were also retrieved from the IPA database as significant functions, indicating that the genes identified in this study were associated with the observed MWCNT-induced lung pathology.

Overall, the results obtained from this study have demonstrated that MWCNT exposure induces changes in lung cancer biomarker gene expression in mouse lungs, which may indicate a potential association between MWCNT exposure-induced lung inflammatory, damage and fibrotic responses and lung carcinogenesis. The results of the IPA also indicate that MWCNT exposure may induce alterations in several disease-related signaling transduction pathways. In light of this finding, long term inhalation studies of MWCNT to monitor possible induction of lung tumors and/or mesothelioma are justified. NIOSH is currently conducting a two week inhalation exposure to MWCNT in a cancer susceptible mouse strain with histological evaluation through 1 year post-exposure to address this issue. Additionally, the results obtained from this study indicate these gene signature alterations of MWCNT exposure and could conceivably be used for the medical surveillance of occupationally-exposed MWCNT workers.

Acknowledgments

We thank Rebecca Raese for her help in editing the manuscript. This study is supported by NIH/NLM R01LM009500 (PI: Guo) and NCRR P20RR16440 and Supplement (PD: Guo).

Footnotes

Disclaimer: The findings and conclusions in this report are those of the author(s) and do not necessarily represent the views of the National Institute for Occupational Safety and Health.

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