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Logo of nihpaAbout Author manuscriptsSubmit a manuscriptHHS Public Access; Author Manuscript; Accepted for publication in peer reviewed journal;
 
J Toxicol Environ Health B Crit Rev. Author manuscript; available in PMC 2012 December 4.
Published in final edited form as:
PMCID: PMC3513758
NIHMSID: NIHMS402992

New Perspectives for In Vitro Risk Assessment of Multi-Walled Carbon Nanotubes: Application of Coculture and Bioinformatics

Abstract

Nanotechnology is a rapidly expanding field with wide application for industrial and medical use; therefore, understanding the toxicity of engineered nanomaterials is critical for their commercialization. While short term in vivo studies have been performed to understand the toxicity profile of various nanomaterials, there is a current effort to shift toxicological testing from in vivo observational models to predictive and high-throughput in vitro models. However, conventional monoculture results of nanoparticle exposure are often disparate and not predictive of in vivo toxic effects. A coculture system of multiple cell types allows for cross-talk between cells and better mimics the in vivo environment. This review proposes that advanced coculture models, combined with integrated analysis of genome-wide in vivo and in vitro toxicogenomic data, will lead to the development of predictive multi-gene expression-based models to better determine toxicity profiles of and the potential human health risk of exposure to nanomaterials.

Keywords: Multi wall carbon nanotubes, nanotoxicology, lung injury, biomarker, cell cultures

Introduction

The interaction with and manipulation of nanomaterials has been evident since the 4th century A.D. and has become one of the most heavily invested technologies in the past decade (http://nano.gov). Defined by the United States National Nanotechnology Initiative as “the understanding and control of matter at the nanoscale, at dimensions between 1 and 100 nanometers, where unique phenomena enable novel applications”, nanotechnology is the study of materials that have at least one dimension in the range of 1–100 nm (http://nano.gov). Nanomaterial may refer to materials occurring naturally through gas-to-particle conversions, combustion reactions, microbial activity, and mineral weathering or to engineered materials that are intentionally produced to exploit their novel properties for use in a variety of commercial fields (Nel et al., 2006; Oberdorster et al., 2005b; Wigginton et al., 2007). From appliances, automotive, and electronic goods to kitchenware, food additives, and personal health items, the list of goods incorporating nanotechnology is ever growing (Scholars, 2011).

The rapid expansion of nanotechnology requires the assessment of the interaction of nanoparticles with biological systems. A framework for the responsible development of nanotechnology has been suggested to fully evaluate nanomaterial, determine the potential risks, and implement appropriate safety measures (www.nanoriskframework.com). Occupational exposures during synthesis, incorporation into products, and product use and disposal, as well as public exposures through environmental contamination and the use of commercial goods, are concerns for the growing industry (Oberdorster, 2010). Although numerous short-term studies have been performed to determine a product’s safety before market, the effects of chronic exposure are largely unknown (Teow et al., 2011). Nanotoxicology, the study of the interaction of nanoparticles with biological systems, aims to determine the resulting responses to nanoparticle exposure and the time frame in which these responses occur (Castranova, 2011). Nanoparticle uptake occurs through multiple routes of administration, such as inhalation or dermal contact, during industrial processes, as well as dermal and gastrointestinal tract absorption through medical devices, food products, and environmental contamination (Hagens et al., 2007). The method of absorption of nanomaterials may affect their toxicity due to differences in penetration and clearance. Once exposure occurs, the diverse characteristics of nanoparticles, their various physical, chemical, and catalytic properties, induce a range of responses within a biological system. Translocation from the site of absorption and interaction with proteins may alter the metabolism, distribution, and elimination or possible accumulation of the nanomaterial in the body (Zhao and Castranova, 2011). In addition, the metabolism of nanomaterial may contribute to unexpected toxicity due to altered chemical composition and structural characteristics. One critical characteristic of nanoparticles is their high surface area per mass as compared to larger particles of the same composition. As particle size decreases, the relative surface area increases, enhancing the possibility for electronic disruption, structural defects, and the number of potential surface oxidant reactive sites, thus intensifying the possible biological reactivity of the material (Kim et al., 2010; Nel et al., 2006; Pacurari et al., 2010). Multiple in vitro studies showed that, on an equal mass basis, nano-sized particles produced an increased cytotoxic effect compared to their fine-sized counterparts, and this effect was attributable to differences in surface area (Ding et al., 2009; Sager et al., 2008; Warheit et al., 2009a). Additional attributes of engineered nanoparticles, such as surface coatings and treatments or the ability to agglomerate, may also alter their toxic potential (Nel et al., 2006).

The unique physical and reactive properties of nanomaterials can lead to unpredictable toxic effects; therefore, understanding the pharmacokinetics of such materials is paramount to accurately assess toxicity. To determine the ultimate effect of nanoparticles after exposure, it is important to know the distribution to target organs, accumulation and persistence in these organs, and the adverse effects exerted by the nanoparticle on individual cells so that safe exposure levels can be determined (Hagens et al., 2007). In vivo studies of nanoparticle exposure by various routes (intravenous, intraperitoneal, topical, subcutaneous, and pulmonary [inhalation, intratracheal instillation, or pharyngeal aspiration]) in animal studies showed a range of distribution patterns dependent on the route of administration with possible translocation of nanomaterial to the blood, liver, spleen, lung, brain, heart, and kidneys. Nanoparticle exposure most often results in the initiation of an inflammatory response and may exhibit the ability to induce fibrosis in both lung and liver after inhalation and intravenous administration, respectively (Hagens et al., 2007; Li et al., 2010).

While the necessity of animal studies to accurately determine toxicity profiles of nanomaterials is unquestionable, there is a current effort to shift toxicological testing from an in vivo observational standpoint to an in vitro predictive model using advanced in vitro techniques to mimic the in vivo situation. Building on the tenants sent forth by W.M.S. Russell and R.L. Burch in 1959, the need to refine experiments so as to minimize animal distress, reduce the number of animal experiments performed while maintaining quality information, and eventually replace a majority of animal testing with in vitro methods is a focus of current U.S. Environmental Protection Agency (EPA) and National Toxicology Program (NPA) strategic plans to shift in vivo animal studies to in vitro assays and computational modeling for toxicity assessment in humans (Collins et al., 2008; Maynard et al., 2006; Russell and Burch, 1959). In addition to obvious cost-saving strategies, in vitro biochemical and cell-based assays will allow for high-throughput screening which, when combined with computational toxicology, can infer predictive outcomes upon human exposure while refining and prioritizing animal studies (Collins et al., 2008).

Although monoculture systems of cells are the predominant model system for in vitro toxicological testing, many studies found little correlation between results obtained in vitro and those obtained in vivo (Sayes et al., 2007; Seagrave et al., 2003; Warheit et al., 2009b). Seagrave et al. (2003) determined, through their analysis of the toxicity of gasoline and diesel emissions, that particles which exerted the least cytotoxic effect in vitro had the most potent effect in vivo. Conversely, the most potent particle in vitro was among the least potent during in vivo studies. This same conflict was seen by both Sayes et al. (2007) and Warheit et al. (2009b). Sayes et al. (2007) found little to no correlation in cytotoxic and inflammatory markers between in vivo exposures to 5 different particle types when compared to in vitro results. This conclusion was echoed by Warheit et al. (2009b) as cytokine profiles of lung epithelial cells and macrophages in monoculture exposed to both fine and nanoscale zinc oxide (ZnO) did not correlate with potency profiles determined after instillation or inhalation in vivo. Therefore, there is a need for improved cell culture techniques and models for high-throughput screening to predict nanomaterial toxicity before in vivo testing. A more advanced cell culture system in which multiple cell types are grown together to allow cross-talk between cells needs to better mimic the in vivo situation and provide more reliable information of nanoparticle toxicity when compared to conventional monoculture systems.

Newly developed high-throughput-based global gene expression profiling techniques have made it possible to identify key predictive gene signatures from various specimens. Additionally, it has become increasingly important to use gene expression signatures identified with bioinformatics methods for toxicity prediction, risk assessment, and screening (Afshari et al., 2011; Shi et al., 2006). In 2005, the EPA established the National Center for Computational Toxicology (NCCT) to integrate high-throughput screening into its testing program. The combined computational modeling of in vivo and in vitro toxicogenomics data could prioritize critical toxicity pathways so as to design intervention strategies and identify novel biomarkers for risk assessment of human health ramifications (Collins et al., 2008). Recently, bioinformatic analyses of genomic data have been identified by both the US Food and Drug Administration (FDA) and EPA as key opportunities to advance personalized medicine and environmental risk assessment (Dix et al., 2006; Frueh, 2006; Lesko and Woodcock, 2004). The MicroArray Quality Control (MACQ) project shows inter- and intra-platform reproducibility of gene expression measurements, substantiating the feasibility of establishing a framework for the use of microarrays in clinical and regulatory settings for drug discovery, medical diagnosis, and risk assessment (Shi et al., 2006).

While there are numerous naturally occurring and engineered nanoparticles, each with their own distinct physico-chemical characteristics, this review will focus on multi-walled carbon nanotubes (MWCNT) as an example of one type of nanoparticle, examine their effects on both in vivo and in vitro systems, discuss the need for a combination of both advanced in vitro systems and in vivo evaluation to fully comprehend the toxicity profile of such nanomaterials, and provide new perspectives for the use of computational systems biology in human health assessment of MWCNT exposure. The use of in vitro coculture studies coupled with genome-wide computational toxicogenomic analysis is a novel approach to the assessment of nanotoxicology which provides a high-throughput and cost-effective strategy to the refinement of in vivo toxicological testing. While this review will focus on the use of advanced in vitro techniques for MWCNT risk assessment, several comprehensive and insightful reviews are available for an in depth analysis of MWCNT and other nanomaterials (Johnston et al., 2010; Pacurari et al., 2010; Zhao and Castranova, 2011).

MWCNT in vivo studies

First described by Iijima in 1991, MWCNT are structures that resemble sheets of graphite rolled into hollow tubes forming concentric cylinders inside one another. The number of concentric cylinders varies, ranging from 2 up to 50 or more, with the diameter of the tube staying within the nanometer range even though the length of the cylinder may reach into the micrometer range (Iijima, 1991). MWCNT have many unique physico-chemical properties. Their closed cage phenotype and hexagonal pattern of carbon rings permits an inert environment and electron sharing which establishes aromaticity and efficient electronic conductivity. The strong carbon bonds permit immense strength which allow MWCNT to undergo extreme strain without breakage or deformity (Yakobson and Smalley, 1997). Of particular interest to the biomedical industry, the tube-like channels within MWCNT exert strong capillary forces resulting in the ability of MWCNT to act as nanocarriers for a variety of drug delivery systems (Ajayan, 1999).

The unique and valuable physico-chemical properties of MWCNT put them at the forefront of the booming nanotechnology field; therefore, the need to understand their toxicity profile after both short-term and chronic exposure is necessary. Respiratory studies to determine the pulmonary effects of MWCNT lead the toxicology field in evaluation, since aerosolization during production, use, and disposal is a main route of occupational MWCNT exposure. Muller et al. (2005) determined that, after intratracheal instillation, MWCNT had the ability to produce both inflammatory and fibrotic responses in the rat lung. An increase in lactate dehydrogenase (LDH) as well as tumor necrosis factor-α (TNF-α) was indicative of inflammation up to 15 days after particle exposure. Sixty days after exposure, increased collagen levels and granuloma formation were observed, indicating that MWCNT had the ability to produce persistent injury and also induce a fibrotic response after a single exposure (Muller et al., 2005). Additional in vivo studies of MWCNT exposure have confirmed an increase in inflammatory and fibrotic activity in the mouse lung, uptake of the MWCNT into alveolar macrophages (AM) and other alveolar cells, and impaired lymphatic drainage (Mercer et al., 2010, 2011; Porter et al., 2010). Injection of MWCNT into the peritoneal cavity or scrotum so as to mimic exposure to the chest mesothelial lining was found to elicit a length-dependent inflammatory response and granuloma formation in both mice and rats followed by the slow development of mesothelioma (Muller et al., 2009; Poland et al., 2008; Sakamoto et al., 2009; Takagi et al., 2008).

The ability of the body to clear nanomaterial after either intentional or unintentional exposure will affect the toxicity profile of the material. Upon inhalation, nanoparticles travel through the upper respiratory tract and deposit into the lungs. The nanoscale diameter of MWCNT contributes to their pulmonary deposition as the majority of MWCNT may be able to pass through the conducting airways and reach the alveolar regions. Indeed, Mercer et al. (2010) reported that after aspiration of MWCNT in a mouse model, 18% of the particles deposit in the conducting airways while 81% deposit in the alveolar region of the lung. Analysis of the distribution pattern after inhalation gave similar results (Porter, personal communication). While MWCNT which deposit on the conducting airway are rapidly cleared, those in the alveolar region may pass through the alveolar epithelial lining by either a paracellular or transcellular route and reach the lung interstitium where time-dependent fibrosis occurs (Mercer et al., 2010, 2011). MWCNT further translocate to the extracellular space and lymphatics and may be transported from subpleural tissue to the intrapleural space (Mercer et al., 2010). The ability of macrophages to engulf and destroy foreign material is essential to nanoparticle clearance and biopersistence, and this clearance is dependent upon the size and shape of the particle. Nano-sized particles have been shown to induce numerous toxicological responses in comparison to their fine-sized counterparts, including a greater inflammatory response in the lung and longer retention time in the body, which may increase the time of exposure to nanomaterials even after the initial exposure has passed (Oberdorster, 1994, 2010). The fibrous nature of MWCNT results in a high aspect ratio as the length of MWCNT greatly surpasses its nanoscale diameter. Toxicity of high aspect ratio nanomaterials has been well studied in the case of asbestos. Rigid asbestos fibers have the increased ability to induce pulmonary inflammation, fibrosis, mesothelioma, and cancer after exposure as compared to more flexible forms of asbestos due to their long, inflexible form (Mossman and Churg, 1998; Mossman et al., 2011; Osinubi et al., 2000). This pulmonary toxicity is thought to progress through the inability of macrophages to efficiently engulf and remove long fibrous materials. As fiber length increases, macrophages cannot fully surround the invading particle and become “frustrated”, secreting pro-inflammatory cytokines and eliciting an immune response (Donaldson et al., 2010). The rigid fibrous shape of MWCNT is analogous to rigid fibrous asbestos; therefore, a similar mode of frustrated phagocytosis has been postulated to occur after MWCNT exposure (Donaldson et al., 2010; Pacurari et al., 2010). Comparative analysis of pulmonary exposure studies with MWCNT, flexibile asbestos, and rigid asbestos conclude that all have the ability to induce inflammatory and fibrotic responses to some degree due in part to a high aspect ratio and biopersistence (Donaldson et al., 2010; Mercer et al., 2010; Muller et al., 2005). The rate of clearance of MWCNT, or lack thereof, and the role of clearance rate in the etiology of MWCNT-induced inflammatory and fibrotic responses require further study (Donaldson et al., 2010).

MWCNT monoculture in vitro studies

In vitro testing has determined that MWCNT have a range of adverse effects. Reactive oxygen species (ROS) production is a common characteristic of MWCNT exposure in a variety of cell types (He et al., 2011; Pacurari et al., 2012; Ye et al., 2009). ROS are primarily created by mitochondria as a byproduct of aerobic respiration and as an antimicrobial agent by phagocytic cells. Direct mitochondrial damage from MWCNT exposure results in depolarization of the mitochondrial membrane potential, leading to increased ROS production in lung fibroblasts (He et al., 2011). The oxidative stress that arises from the generation of these reactive oxygen species exerts a number of deleterious effects on the cell including activation of pro-inflammatory signaling cascades, DNA damage, and cell death (Nel et al., 2006). Using an in vitro model of lung epithelial cells, Ye et al. (2009) determined that ROS production increased after alveolar epithelial (A549) cell exposure to MWCNT. This increase in ROS resulted in enhanced production of the pro-inflammatory mediator IL-8 due to ROS-induced activation of the redox-sensitive transcription factor, NF-κB. Additional studies in A549 cells also found that MWCNT exposure increased the expression levels of IL-1β, IL-6, IL-10, and MCP1, and this was also attributable to an increase in ROS-induced NF-κB signaling (He et al., 2011). Therefore, MWCNT have the ability to induce the production of multiple inflammatory markers, most likely through ROS production and induction of NF-κB signaling. In addition to upregulation of inflammatory mediators, MWCNT were able to induce a concentration-dependent activation of genes associated with oxidative stress and apoptosis, such as p53 and caspase-3, at both the mRNA and protein level in A549 cells (Srivastava et al., 2011). The aspect ratio of MWCNT was determined to be a key component in MWCNT-induced cytotoxicity of human embryonic lung cells and this effect was independent of trace metal contamination (Kim et al., 2010; Porter et al., 2010). Exposure of keratinocytes to MWCNT in vitro induced both IL-8 and IL-1β production, implicating MWCNT in potential inflammatory reactions after dermal exposure as well and potentially contributing to inflammation and progressive disease states of the skin (Witzmann and Monteiro-Riviere, 2006). With regard to MWCNT-induced toxicity to the intestine due to occupational and environmental ingestion, no observable cytotoxicity was induced by either pristine or oxidized MWCNT and uptake of MWCNT into Caco-2 human intestinal enterocytes was not observed (Clark et al., 2012). Although MWCNT did interact with the microvilli of the cells, their effect was minor, and the toxic effect of MWCNT in the intestine requires further study (Clark et al., 2012).

Witzmann and Monteiro-Riviere (2006) determined overall MWCNT-induced in vitro proteomic alterations in keratinocytes through 2D gel electrophoresis and peptide mass fingerprinting of significantly altered proteins at 24 h and 48 h post-exposure. These significantly altered proteins were involved in diverse cellular signaling pathways such as apoptosis, metabolism, and growth and differentiation (Witzmann and Monteiro-Riviere, 2006). A proteomics approach to determine overall significant protein alterations to a monoblastic leukemia cell line after MWCNT exposure has also been undertaken by Haniu et al. (2010, 2011). Alterations in proteins associated with the cell cycle, transcription, and metabolism, among others, have been noted after MWCNT exposure (Haniu et al., 2010). While these toxicoproteomic-based evaluations suggest a novel approach to the determination of MWCNT-induced toxicity, their use in in vitro cellular systems involving relevant exposure concentrations in the lung and in vivo models remains to be determined.

In vitro exposure to MWCNT also induced double-strand DNA breaks and chromosomal aberrations in rat lung epithelial cells (Muller et al., 2008). It was proposed that the mode of action was either a direct interaction of the MWCNT with DNA molecules or secondary actions such as ROS production. The ability of MWCNT to induce chromosomal aberrations may be due to the structure of MWCNT themselves and their ability to interact with and replace various components of the cellular machinery. MWCNT have the ability to interact with alpha and beta tubulin subunits in a cell-free system and self-assemble into functional tubulin-MWCNT hybrids with the MWCNT acting as an internal structure onto which the alpha and beta tubulin subunits adsorb (Dinu et al., 2009). While this unique trait of MWCNT may be beneficial from a bioengineering standpoint, it raises concern that a similar mechanism may occur in vivo after either intentional or unintentional MWCNT exposure. Microtubules are dynamic structures of alpha and beta subunits that continually polymerize and depolymerize in response to signals from the cell, while MWCNT are fibrous, strong, static structures which do not deform easily in size or shape. If MWCNT had the ability to enter into a cell and incorporate into microtubules during assembly, they may also have the ability to interact with the mitotic spindle and thus interfere with chromosome segregation. It is here that MWCNT could play a detrimental role in chromosomal segregation, resulting in aberrant chromosome numbers and creating a potential disease state that is characterized by loss of chromosomal stability (Sargent et al., 2010). Additionally, DNA strand breaks are a consequence of an altered balance of ROS production. As an increase in ROS production is a well-known characteristic of MWCNT exposure, ROS-induced DNA damage occurs, additional ROS are produced, and a cycle is created through which chronic diseases may progress (Ma, 2010). Ultimately, MWCNT exposure may lead to apoptosis of exposed cells as MWCNT have been shown to induce cell death (He et al., 2011; Kim et al., 2010; Srivastava et al., 2011).

In vivo, MWCNT produced a rapid and persistent interstitial fibrosis, which was associated with translocation of MWCNT into the alveolar walls (Mercer et al., 2011; Porter et al., 2010). In vitro studies showed that dispersed MWCNT induced the secretion of fibrogenic mediators from lung epithelial cells and collagen production by lung fibroblasts (Mishra et al., 2011). The production of TGFβ and PDGF from macrophages upon MWCNT exposure is also indicative of a pro-fibrogenic environment (He et al., 2011).

While the physical attributes of MWCNT alone are enough to produce a harmful effect after exposure, their chemical composition is also a vital piece of information to determine toxicity. Multiple techniques are available for the production of MWCNT, such as electric-arc discharge and laser vaporization, which generate MWCNT through the high temperature vaporization of a graphite target in an inert atmosphere followed by chemical vapor deposition. Metal catalyst particles are added into the reaction to lower the temperature needed for efficient deposition, and Awasthi et al. (2005) reported that through these production techniques, metal contaminants may become entrapped in the nanomaterial . Iron, nickel, and cobalt are commonly used metal catalysts that can become incorporated into MWCNT and may potentially elicit a toxic effect of their own. Pulskamp et al. (2007) suggested that purification techniques, which lower the amount of metal contamination in MWCNT samples, may alleviate some of the cellular toxicities seen upon MWCNT exposure, thus suggesting that the toxicity of MWCNT is due to metal impurities and not the MWCNT themselves. However, studies showed that even highly purified MWCNT may still induce cytotoxicity and that MWCNT in acellular systems do not have the ability to form ROS, indicating that their potential toxicity is independent of metal contaminants (Porter et al., 2010; Tsukahara and Haniu, 2011). A summary of the in vitro MWCNT monoculture studies presented is provided in Table 1.

Table 1
Summary of MWCNT monoculture in vitro studies.

MWCNT: in vitro vs. in vivo results

For in vitro assays to be effective and predictive screening tests of nanoparticle in vivo bioactivity, the following factors need to be considered. In vitro MWCNT concentrations must be relevant to in vivo lung burdens, and effort should be made to disperse MWCNT for in vitro testing so that structure sizes are similar to inhaled MWCNT structures upon aerosolization of dry material. In vitro assays need to be selected which are most relevant to in vivo mechanisms of action. In general, the physico-chemical properties of MWCNT and other nanoparticles should be well-characterized and compared to appropriate controls for efficient toxicity testing (Warheit and Donner, 2010).

A review of the current literature indicates that in vitro studies of MWCNT pulmonary toxicity use exposure concentrations as high as 200 µg/ml (Table 1). Such in vitro doses are much higher than achieved in pulmonary exposure studies in animal models. Relevant in vitro concentrations are achieved by using MWCNT mass concentrations/surface area of cultured cells which mimic MWCNT mass burdens/alveolar epithelial surface area from animal studies. Porter et al. (2010) determined, based upon studies of alveolar surface area and peak airborne concentrations of MWCNT in laboratory and occupational settings, relevant concentrations of MWCNT for use during in vivo exposure (Han et al., 2008; Maynard et al., 2004; Stone et al., 1992). Taking into consideration peak MWCNT aerosol levels found previously in an occupational setting, MWCNT mass median aerodynamic diameter, minute ventilation, and human alveolar epithelium surface area, Porter et al. concluded that a 10 µg MWCNT exposure to a mouse would approximate one month of exposure to a human in a work environment with aerosol concentrations of 400 µg/m3 MWCNT (Han et al., 2008; Porter et al., 2010). In occupational settings where airborne levels of MWCNT have been found to be lower, the 10 µg dose of MWCNT was suggested to approximate exposure levels between 9 months and 7.5 years (Maynard et al., 2004; Porter et al., 2010). Porter et al. (2010) concluded that aerosol levels of MWCNT at 400 µg/m3 suggest human exposure to be 226 µg MWCNT/m2 of human alveolar epithelium per month of exposure. In vivo experiments concluded that a 10 µg exposure of MWCNT induced significant inflammation that returned to control levels 7 days after exposure. Additional exposures at 20, 40, and 80 µg MWCNT induced persistent inflammation up to 56 days post-exposure with signs of fibrosis at the 80 µg dose (Mercer et al., 2011; Porter et al., 2010). These concentrations can be extrapolated for use during in vitro studies with 226 µg MWCNT/m2 corresponding to a MWCNT concentration of less than 1 µg/ml in standard tissue culture protocols. Higher in vivo exposure levels (20, 40, and 80 µg), correlate to in vitro concentrations approximately less than or equal to 1 µg/ml. Therefore, low concentrations of MWCNT in in vitro studies are suggested to be the most biologically relevant.

In addition to biologically relevant doses, it is also necessary to understand the behavior of different nanoparticles in cell culture media with regard to particle settling, diffusion, and aggregation as the size, density, and physico-chemical properties of a particular nanomaterial may affect it’s interaction with neighboring nanoparticles, cell culture media, and underlying cells when placed into a liquid suspension (Oberdorster et al., 2005a; Teeguarden et al., 2007). In vivo studies concerning the fibrotic response to aspiration of a CNT suspension indicate that dispersed CNT are more fibrogenic than agglomerated CNT (Mercer et al., 2008). Shvedova et al. (2008) showed that the generation of a dry CNT aerosol for inhalation exposure produces structure sizes significantly smaller than CNT suspensions in phosphate-buffered saline used for aspiration exposure. As a consequence, CNT inhalation resulted in a greater pulmonary response than aspiration of poorly dispersed CNT. Therefore, to mimic response to inhaled MWCNT, properly dispersed MWCNT need to be used for in vitro test systems. Indeed, dispersed CNT exhibit fibrogenic activity with fibroblast cell cultures, as expected from animal studies, while agglomerated preparations do not (Wang et al., 2010a, 2010b). To alleviate the various modifications that suspension of MWCNT in cell culture media may induce, in vitro models may be modified with the addition of an air-liquid interface to mimic exposure in vivo. Epithelial cells grown on permeable supports are hydrated basolaterally while their apical surface is exposed to a controlled air and exposure environment, inducing a well-differentiated epithelial culture that mimics the epithelial state in vivo (Brandenberger et al., 2010; de Bruijne et al., 2009; Fulcher et al., 2005; Hill and Button, 2012; Lenz et al., 2009). Evaluations of such systems with nanomaterials suggested direct deposition of particles with the alleviation of agglomeration, diffusion, and particle loss associated with submerged culture (Brandenberger et al., 2010). While the effects of MWCNT in an in vitro air-liquid interface environment remain to be resolved, this in vitro method is a promising alternative to fully submerged cell culture experiments with regard to nanoparticle-induced pulmonary toxicity.

Nel et al. (2006) proposed that oxidative stress may be a predictive paradigm for the bioactivity of nanoparticles. Indeed, Rushton et al. (2010) found that, for a set of spherical particles, in vitro generation of ROS by alveolar macrophages was predictive of their inflammatory potential in the lung. In contrast, the fibrogenic potential of CNT in vivo does not appear to require persistent oxidative injury and inflammation (Porter et al., 2010; Shvedova et al., 2005). At doses relevant to lung burdens in animal studies, CNT do not induce substantial oxidant injury. However, low concentrations of CNT in vitro do stimulate proliferation and collagen production in cultured lung fibroblasts and induce TGFβ production by lung epithelial cells (Mishra et al., 2011; Wang et al., 2010a, 2010b). Therefore, for in vitro assays to be predictive of in vivo responses, they must be directed toward mechanisms of action relevant to the in vivo response.

Currently, there are some disparate conclusions concerning the toxicity of MWCNT when comparing monoculture in vitro results to those seen in vivo that suggest the need for enhanced cell culture techniques. An in vitro study of the circulatory compatibility of MWCNT determined that MWCNT in vitro had the ability to induce the intrinsic pathway of coagulation as well as increase platelet numbers. Functionalization of the MWCNT by either amidation or carboxylation enhanced this pro-coagulant activity. As seen with previous studies of nanomaterial, in vivo results were not concordant and showed an opposite effect with decreased coagulant activity and decreased platelet numbers upon functionalization (Burke et al., 2011). Functionalization of MWCNT with carboxylic polyacid groups facilitated internalization into a monoculture of macrophages in vitro and showed similar oxidative and inflammatory responses to non-functionalized MWCNT; however, in vivo results suggested that the oxidative and inflammatory responses to functionalized MWCNT were increased when compared to non-functionalized MWCNT (Tabet et al., 2011). A study of the ability of MWCNT to irritate both the skin and eyes determined that results were concordant between in vitro and in vivo results with regard to skin in that both showed little to no irritation; however, in vitro results suggested that MWCNT were non-irritating to the eye while in vivo results suggested irritation (Kishore et al., 2009). Recent studies have shown that MWCNT are able to induce genotoxic effects in both in vivo and in vitro assays; however, the extent to which these effects occur in vivo compared to in vitro is still a matter of discussion (Ema et al., 2012; Kato et al., 2012).

Coculture models to predict nanoparticle toxicity

One method to improve the discrepancies between cellular and animal studies is the use of coculture cell models. These complex models mimic the cellular cross-talk of an in vivo signaling environment and are an expanding in vitro methodology to better predict the potential in vivo effects of nanomaterials. While animal models may be the most relevant to human occupational or environmental exposures, in vitro testing allows for better evaluation of cellular functions and molecular pathways. In addition, in vitro assays are low cost and afford the high rate of throughput required for screening the growing number of nanomaterials being developed for commercialization (Collins et al., 2008; Rothen-Rutishauser et al., 2008). As inhalation of nanomaterials remains the predominant potential route of human exposure, coculture models aiming to mimic the alveolar-capillary units of the lung are essential for the understanding of the complex interactions of cells in the lung. Although air-liquid interface using epithelial cells alone has been the focus of numerous studies to allow for biologically relevant exposure methods of the lung epithelium, the additional need for microvascular or immune cells in the culture system is essential to allow for cross-talk between pulmonary cell types and incorporation of air-liquid interface into coculture models (Brandenberger et al., 2010; Diabate et al., 2008).

To simulate the alveolar-capillary unit in vitro, various cell types of epithelial, endothelial, and immune origin are grown on opposite sides of a permeable filter so that physiologically relevant barriers to nanomaterial in vivo can be created. Exposure of epithelial cells in the apical well of the two-part chamber, whether by submerged culture or air-liquid interface, aims to mimic the exposure to nanomaterial after inhalation, while cells representing the interstitium or vasculature in the basolateral chamber can receive signals from the exposed epithelial barrier and respond accordingly. This is an advantage over conventional cell culture techniques as monoculture does not have the ability to demonstrate interplay between different cell types (Hermanns et al., 2004). In a case of silica exposure, a coculture model of apically seeded human lung H441 cells and basolaterally seeded human microvascular endothelial ISO-HAS-1 cells showed less toxicity, i.e. impaired barrier function, upon exposure to silica nanoparticles when compared to separate monocultures (Kasper et al., 2011). This coculture also determined that low levels of silica exposure produced increased ICAM-1, IL-6, and IL-8 expression, suggesting an inflammatory response, where monocultures had little or no reaction. As no nanoparticles were found in the basolateral chamber of the coculture after apical exposure and endothelial cells alone in the basolateral chamber were not able to induce the same effect, it was suggested that this difference in cellular signaling was due to cross-talk between the two cells types in coculture (Kasper et al., 2011). Through a similar evaluation of the interaction between H441 epithelial cells and human microvascular endothelial cells, the nanocarrier polyethyleneimine (PEI) was determined to have differential cellular uptake between monoculture and coculture systems during an in vitro test of gene delivery. While PEI was readily detectable in both epithelial and endothelial cells during monoculture, coculture of cells showed little to no detectable uptake of PEI after epithelial exposure under the same conditions (Hermanns et al., 2010). These coculture results echo similar findings obtained in vivo. While pre-clinical studies of adenoviral gene delivery determined that small doses of vector could amend defective chloride transport in a monoculture in vitro model of cystic fibrosis, aerosolized delivery of the vector to both mouse and human lung disease models resulted in low efficiency of uptake and little improvement in chloride transport (Grubb et al., 1994). Hermanns et al. (2010) proposed that cells behave differently in coculture, that an epithelial/endothelial coculture representation of the alveolar-capillary barrier behaves in a more in-vivo-like manner than either cell type alone, and that paracrine signaling between cell types may be responsible for the establishment of a tighter epithelial barrier in coculture than that found in monoculture. The effect of cell-cell communication in coculture may represent a more physiologically relevant model for the study of nanoparticle uptake, toxicity, and potential cellular mechanisms as well as provide a better model for lung drug delivery.

In addition to modeling lung epithelial and endothelial barrier function, a model of the interaction between lung epithelial cells and the surrounding immune cells is also necessary for an in vitro prediction of a possible immune response to nanoparticle exposure. Coculture models of lung epithelial and endothelial barriers that incorporate various cells of the immune system found that cellular reactions to nanomaterials were either mitigated or amplified when compared to monoculture (Muller et al., 2010). Coculture of lung epithelial cells with macrophages suggested greater sensitivity to nanomaterial as a significant increase in inflammatory markers was induced as compared to separate monoculture (Wottrich et al., 2004). A coculture of lung epithelial and endothelial cells which also incorporated macrophages and mast cells into the epithelial chamber resulted in greatly increased inflammatory signals above those predicted by monoculture after particulate matter exposure. This coculture response more closely matched inflammatory effects seen in vivo (Alfaro-Moreno et al., 2008). Interestingly, cocultures of lung epithelial cells with mast cells and macrophages gave a different cytokine profile than a similar coculture that also included an endothelial layer, suggesting that even amongst cocultures, the interplay between multiple cell types is evident, increasing the validity of coculture systems over conventional monoculture (Alfaro-Moreno et al., 2008). While the effects of nanoparticle exposure on coculture of pulmonary cells are the most prevalent, coculture models have also been adopted to determine the effect of nanomaterial exposure on other organ systems, such as the brain and intestinal tract, with similar congruent results to in vivo studies (Bouwmeester et al., 2011; Meng et al., 2007).

Although there are currently no published coculture studies of MWCNT, the lack of association between results obtained in vitro and results obtained in vivo suggests that more relevant concentrations, nanoparticle dispersion, mechanisms, and methods of exposure must be incorporated into in vitro techniques to study MWCNT toxicity. Additionally, improved in vitro techniques could be combined with in silico computational analysis of both in vivo and in vitro data so as to mimic and predict in vivo effects based upon genomic data modeling for risk assessment.

Computational toxicogenomics to infer toxicity pathways and molecular mechanisms

Genome-wide expression analysis could infer critical toxicity pathways and aid molecular mechanistic studies for intervention of environmental diseases by evaluating the relevance of signaling pathways representing significantly perturbed genes (Afshari et al., 2011; Hamadeh et al., 2010). A few studies used in vivo or in vitro genome-wide mRNA expression profiling to infer toxicity pathways induced by MWCNT in a rat model (Alazzam et al., 2010; Ellinger-Ziegelbauer and Pauluhn, 2009; Peng et al., 2010). A combination of benchmark dose (BMD) methods, microarray data, and Gene Ontology (GO) functional annotations was applied to estimate a dose range of adverse biological processes in toxicity tests (Thomas et al., 2007, 2011, 2012b). Using the BMD methods, the expression profile of each gene was fitted with parametric models, including a linear model, a second-degree polynomial model, a third-degree polynomial model, and a power model; and, the best-fit model was selected to derive the safe dose range of this gene (Thomas et al., 2007; 2011, 2012b). The median benchmark dose of all the genes belonging to a biological process was used to present the safe dose range of this process. Alternatively, the dose-response models could also be fit with parametric models including exponential, linear, Gaussian, quadratic, and sigmoid models (Burgoon and Zacharewski, 2008; Kopec et al., 2010). In these studies, histological phenomena were observed but were not used in the gene expression modeling to identify gene activities or biological processes that resemble the observed histological patterns (Burgoon and Zacharewski, 2008; Kopec et al., 2010; Thomas et al., 2007, 2011, 2012b). Furthermore, these studies did not delve into rigorous simulation of pathway activities associated with the observed in vivo time-course and dose-response histopathological phenomena. In an effort to mathematically model gene transcriptional activities resembling the observed in vivo histological patterns, clustering approaches and Bayesian Decomposition methods have been explored in previous studies. Traditional clustering approaches partitioned each gene into a single coexpression group, although genes were often coexpressed in different groups depending on time or dose conditions (Tamayo et al., 1999; Waring et al., 2001; Yeung and Ruzzo, 2001). More sophisticated methods based on Bayesian Decomposition were computationally inefficient and thus not practically scalable to a genome-wide model to infer pathway activities (Moloshok et al., 2002; Ochs et al., 2009).

In order to overcome the limitations of these methods, a recent study developed a novel and computationally efficient model based on non-negative matrix factorization, Monte Carlo Markov Chain simulation, and Gene Set Enrichment Analysis (Devarajan, 2008; Dymacek and Guo, 2011; Lee and Seung, 1999; Russell and Norvig, 2003; Subramanian et al., 2005). This method is non-parametric and is thus robust to model pathway activities resembling any time-course dose-response histopathological patterns observed in animal studies. This novel computational model identified relevant processes of MWCNT-induced inflammation and fibrosis from dose-response time-series DNA microarray data in mouse lungs following MWCNT aspiration (Dymacek and Guo, 2011). For the identified processes, their transcriptional activities closely resembled the observed time-course dose-response histopathological patterns of lung inflammation and fibrosis in the animal studies. Predicted mRNA gene expression changes from the analysis of in vivo microarray data were validated in vitro through mRNA and protein expression analysis of small airway epithelial cells exposed to MWCNT (Snyder-Talkington et al., personal communication). The in vitro mRNA and protein expression levels of selected genes matched those seen after in vivo exposure and thus indicated that the computational model was sufficient to predict cellular changes after in vivo exposure that can be validated through in vitro mechanistic studies (Snyder-Talkington et al., personal communication). The evaluation of the most significantly represented pathways throughout this comprehensive evaluation can inform further mechanistic studies focusing on these signaling pathways for investigation of MWCNT-induced inflammation and fibrosis, thus refining the mechanistic studies from an observational standpoint to one of predictive outcomes.

Comparative toxicogenomic analyses of gene expression data from in vivo animal studies and in vitro human cells identified critical biological processes and toxicity pathways (Deng et al., 2010; Doktorova et al., 2012; Heise et al., 2012; Kienhuis et al., 2009; Ord et al., 2005; Robinson et al., 2011). In these studies, genes showing concordant expression changes in both in vivo and in vitro systems were selected and their functional pathway involvements evaluated with bioinformatics tools such as Gene Ontology and Ingenuity Pathway Analysis. Using these comparative in vivo and in vitro analyses, multiple genes could be selected to construct a model to predict the in vivo and in vitro toxicity response (Cheng et al., 2011). Proteomic techniques, including 2-dimensional gel electrophoresis (2-DE) and liquid chromatography tandem mass spectrometry (LC-MS/MS), have also been used in the search for toxicity mechanisms and biomarkers in both in vivo and in vitro studies (Van Summeren et al., 2012).

In order to facilitate the screening and prioritization of chemicals for in vivo testing, the United States EPA initiated the ToxCast project and the Tox21 consortium to characterize in vitro biological activities of chemicals, including toxicogenomic data. A similar initiative, REACH, was implemented in Europe to test both new and existing chemicals. By using chemical structure descriptors, in vitro assays, and genomic data, a mathematic model could be constructed to classify in vivo responses of each chemical (Thomas et al., 2009, 2012a). Potentially, this classifier could infer the in vivo toxicity response of a new chemical. Results showed that the predictive power of the in vitro assays was not significantly different from the chemical structure metrics and aggregating in vitro assays with selected gene markers reduced predictive performance (Thomas et al., 2012a). These results indicate the challenges of using in vitro assays in combination with toxicogenomic analysis to predict the in vivo response during chemical screening. More sophisticated gene selection methods beyond simple t-tests may be required to accomplish such tasks.

Computational genomic data modeling to identify biomarkers for risk assessment in humans

Integrated in vivo and in vitro assays combined with genome-wide analyses could reveal toxicity pathways for mechanistic studies and identify biomarkers for risk assessment. Nevertheless, as discussed above, there is often a gap between in vivo and in vitro MWCNT-induced toxicity studies and risk assessment in humans. Computational analysis of microarray data could prioritize critical toxicity pathways and identify innovative biomarkers for risk assessment in exposed individuals (Collins et al., 2008). In vivo and in vitro gene expression signatures associated with specific histopathological phenotypes could be identified from toxicogenomics data to predict human health ramifications and risk assessment based on similarities of gene expression profiles between the exposed in vivo and in vitro samples and those of humans (Amin et al., 2004; Bushel et al., 2007; Hamadeh et al., 2002, 2004; Luhe et al., 2003; Paules, 2003; Powell et al., 2006).

Studies suggest that animal model-based gene expression profiling can successfully predict target organ toxicities for numerous human diseases (Aubrecht and Caba, 2005; Bushel et al., 2007; Newton et al., 2004; Nuwaysir et al., 1999). Specifically, in the study by Bushel et al. (2007), blood gene expression signatures identified from acetaminophen (APAP)-exposed rats could separate APAP-intoxicated patients from unexposed controls, indicating that gene expression data from peripheral blood cells can provide valuable information about environmental disease well before liver damage is detectable by classical parameters. The unique advantage of such studies is the ability to detect toxic injury at the molecular level and to identify the molecular events that lead to organ injury long before the clinical symptoms occur.

In a recent genome-wide expression analysis of mouse lungs exposed to MWCNT, gene signatures were identified to predict lung cancer risk and progression in human patient samples (Guo et al., 2012). This study estimated the health hazards of MWCNT exposures by comparing the similarity between MWCNT-induced gene alterations and gene expression profiles in patient samples. Lung adenocarcinoma patients having more similar gene expression patterns to that in the MWCNT-treated mice were found to have more aggressive tumors with a poor clinical outcome, whereas patient tumors showing a less similar gene expression pattern to that in the MWCNT-treated mice had less metastatic potential with a relatively better clinical outcome (Guo et al., 2012). These MWCNT-induced gene signatures were also shown to be associated with increased risk for developing lung cancer using animal and human patient data (Guo et al., 2012). The microarray results were confirmed in qRT-PCR analysis of a set of previously identified lung cancer biomarkers and related signaling pathway genes (Guo et al., 2008; Pacurari et al., 2011; Wan et al., 2010). This study used the nearest centroid classification method to predict human lung cancer progression by measuring the correlation between mouse gene expression data and patient gene expression profiles. This algorithm is robust to account for different microarray platforms and, in this case, different species and was successfully used to classify breast cancer subtypes in clinics based on gene expression profiles quantified with different microarray platforms (Perou et al., 1999, 2000; Sorlie et al., 2003; van 't Veer et al., 2002). This approach could estimate the risk in individual patients based on gene expression profiles, which is different from the risk assessment methods such as benchmark dose or dose-response models that estimate a safe dose range for a gene or a biological process (Burgoon and Zacharewski, 2008; Kopec et al., 2010; Thomas et al., 2007, 2011, 2012b).

In the study by Guo et al. (2012), lung tissue specimens were collected from mice exposed to MWCNT from 1 to 56 days post-exposure, which was not sufficient time for mice to develop lung cancer. However, in vivo animal model-generated gene profiling revealed information that approximated the complexity of the human body and its cellular, biochemical, and molecular systems that are involved in responses to chemical agents. The unique advantage of this study was the ability to detect responses at the molecular level that may lead to pathology long before clinical symptoms are detectable. The emphasis of this study was on the prediction of potential risk or toxicity at an early stage. The ability of MWCNT-induced gene sets to correlate with carcinogenesis of lung cancer patients provided justification for a further long term study to determine the temporal association between MWCNT-induced gene alterations and development of pre-cancerous lesions and/or tumors in the mouse lung, which is currently underway at the National Institute for Occupational Safety and Health (Qian, personal communication). Given the nature of biomarker research, clinically applied biomarkers need to be validated in the following three phases: retrospective studies, prospective evaluation, and clinical trials. The study by Guo et al. (2012) that utilized multiple retrospective patient cohorts to validate the identified biomarker genes is an initial step, i.e., the identification of potentially useful gene signatures for further study, for the development of a surveillance approach for early detection of lung cancer and prognosis with MWCNT in the workplace.

Gene expression profiling has yielded two commercially available, clinically used breast cancer prognostic tests, MammaPrint© and Oncotype DX© (Paik et al., 2004; van 't Veer et al., 2002; van de Vijver et al., 2002). In these routine clinical gene tests, mRNA expression, not protein expression, is used to predict clinical outcome in patients. The commonly accepted approach in these biomarker studies is to use mRNA expression for clinical diagnosis or prognosis, instead of protein expression, because mRNA quantification is considered reliable for clinical tests, whereas current protein expression assays, such as immunohistochemistry or western blots, are semi-quantitative and thus are not favored for developing multi-gene assays as clinical tests.

Conclusion

The growing nanotechnology field calls for the improved, rapid, and cost-effective testing of nanoparticle toxicology. Hazards of nanoparticle exposure exist during synthesis and disposal, commercial use, and various nanomedicine diagnostic and therapeutic interventions (Oberdorster, 2010). Although multiple studies have attempted to collate the undesirable health effects of nanoparticles for efficient risk assessment, current knowledge remains insufficient for the development of an informed conclusion (Becker et al., 2011; Card et al., 2011; Dhawan and Sharma, 2010; Fadeel and Garcia-Bennett, 2010; Kuhlbusch et al., 2011; Oberdorster, 2010). As regulatory agencies and advocacy groups call for a shift from primary observational science through animal testing to a more predictive science through the development of sophisticated in vitro approaches, nanotoxicology methods must adapt to the changing era (Collins et al., 2008; Stokstad, 2009). Though, at the present time, in vitro assays cannot fully replicate the intricate balance of interactions in vivo, there is a distinct difference in signaling between cells in monoculture and cells in coculture (Hermanns et al., 2010; Kasper et al., 2011; Muller et al., 2010; Wottrich et al., 2004). This change in cellular signaling is attributed to the cross-talk between the different cell types in culture. Although validation of coculture in vitro studies to mimic the in vivo environment will remain difficult, the ability to incorporate cellular cross-talk into in vitro models allows for more relevant cellular signaling. Preliminary data suggests that, in a coculture model of human small airway epithelial cells (SAEC) and human microvascular endothelial cells (HMVEC), HMVEC display increased ROS production, increased actin disruption and decreased VE-cadherin expression at the cell membrane after epithelial exposure to 1.2 µg/ml MWCNT. An overall increase of VEGFA protein expression in both chambers, in addition to an increase in HMVEC angiogenic ability, suggests cross-talk between the two cells types after epithelial exposure (Snyder-Talkington, personal communication). Additionally, genome-wide mRNA microarray analysis suggests multiple gene expression changes when comparing SAEC and HMVEC grown in monoculture to SAEC and HMVEC grown together in coculture, even in the absence of MWCNT exposure (Snyder-Talkington, personal communication). Therefore, cells of pulmonary origin may have distinct cellular signaling variations based upon their growth conditions and studies are underway to determine the correlation of mRNA expression among monoculture, coculture, and in vivo gene expression (Snyder-Talkington, personal communication). The use of low concentrations of adequately dispersed MWCNT and appropriate mechanistic endpoints demonstrate the biological relevance of this system and demonstrate the potential of the coculture model to study MWCNT-induced pulmonary toxicity.

While some studies on the effect of various nanomaterials in coculture systems have been performed, the effects of MWCNT in coculture systems remain to be evaluated. As the number of commercial applications involving MWCNT advances, so must in vitro testing technology to accurately predict the effects of purity, length, width, and functionalization, etc. on toxicity. The use of MWCNT in coculture models coupled with in silico gene expression profiling is a necessary development to adequately determine their toxicity in a manner most relevant to in vivo exposure studies. Coculture models will also advance the ability to elucidate the underlying cellular mechanisms governing bioactivity and the development of relationships between physico-chemical properties and bioactivity to assist assignment of an untested nanoparticle into a relative toxicity category, i.e. high, mid, or low toxicity. The toxicity category, along with process parameter information such as degree and duration of exposure, could be used for control banding, a qualitative strategy for determining the degree of exposure controls necessary in the absence of official exposure limits.

The institution of in vitro assay systems that parallel results from in vivo models will provide a means for the determination of cellular mechanisms associated with nanotoxicity. These enhanced in vitro data, coupled with in silico bioinformatics based gene expression profiling, can help to determine the underlying mechanisms associated with biological responses to nanomaterial exposure and would inform refinement of in vivo toxicological testing and risk assessment (Figure 1). Traditional toxicological studies focus on observations at the level of disease-specific models in vivo; whereas this review proposes a strategy to focus on broad mechanism-based biological observations in vitro with the support of in vivo studies. With this approach, the data derived from in vitro systems are applied together with that from in vivo animal studies, as well as that from human origin, in order to identify novel biomarkers, early characteristic genomic patterns, and mechanisms of MWCNT-induced pulmonary toxicity in humans. This strategy will lead to the development of methods for early detection and interventions of MWCNT-induced pulmonary diseases, particularly fibrosis, in humans.

Figure 1
A schematic of the proposed strategy to incorporate in vivo and in vitro toxicological data with computational toxicological modeling for prediction and early detection of disease and relevant mechanisms.

Acknowledgements

N.L. Guo is funded by NIH R01LM009500 (PI: Guo), NCRR P20RR16440 and supplement (PD: Guo).

Footnotes

Publisher's Disclaimer: 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.

References

  • Afshari CA, Hamadeh HK, Bushel PR. The evolution of bioinformatics in toxicology: advancing toxicogenomics. Toxicol Sci. 2011;120(Suppl 1):S225–S237. [PMC free article] [PubMed]
  • Ajayan PM. Nanotubes from carbon. Chem Rev. 1999;99:1787–1800. [PubMed]
  • Alazzam A, Mfoumou E, Stiharu I, Kassab A, Darnel A, Yasmeen A, Sivakumar N, Bhat R, Al Moustafa AE. Identification of deregulated genes by single wall carbon-nanotubes in human normal bronchial epithelial cells. Nanomedicine. 2010;6:563–569. [PubMed]
  • Alfaro-Moreno E, Nawrot TS, Vanaudenaerde BM, Hoylaerts MF, Vanoirbeek JA, Nemery B, Hoet PH. Co-cultures of multiple cell types mimic pulmonary cell communication in response to urban PM10. Eur Respir J. 2008;32:1184–1194. [PubMed]
  • Amin RP, Vickers AE, Sistare F, Thompson KL, Roman RJ, Lawton M, Kramer J, Hamadeh HK, Collins J, Grissom S, Bennett L, Tucker CJ, Wild S, Kind C, Oreffo V, Davis JW, 2nd, Curtiss S, Naciff JM, Cunningham M, Tennant R, Stevens J, Car B, Bertram TA, Afshari CA. Identification of putative gene based markers of renal toxicity. Environ Health Persp. 2004;112:465–479. [PMC free article] [PubMed]
  • Aubrecht J, Caba E. Gene expression profile analysis: an emerging approach to investigate mechanisms of genotoxicity. Pharmacogenomics. 2005;6:419–428. [PubMed]
  • Awasthi K, Srivastava A, Srivastava ON. Synthesis of carbon nanotubes. J Nanosci Nanotechnol. 2005;5:1616–1636. [PubMed]
  • Becker H, Herzberg F, Schulte A, Kolossa-Gehring M. The carcinogenic potential of nanomaterials, their release from products and options for regulating them. Int J Hyg Environ Health. 2011;214:231–238. [PubMed]
  • Bouwmeester H, Poortman J, Peters RJ, Wijma E, Kramer E, Makama S, Puspitaninganindita K, Marvin HJ, Peijnenburg AA, Hendriksen PJ. Characterization of translocation of silver nanoparticles and effects on whole-genome gene expression using an in vitro intestinal epithelium coculture model. ACS Nano. 2011;5:4091–4103. [PubMed]
  • Brandenberger C, Rothen-Rutishauser B, Muhlfeld C, Schmid O, Ferron GA, Maier KL, Gehr P, Lenz AG. Effects and uptake of gold nanoparticles deposited at the air-liquid interface of a human epithelial airway model. Toxicol Appl Pharmacol. 2010;242:56–65. [PubMed]
  • Burgoon LD, Zacharewski TR. Automated quantitative dose-response modeling and point of departure determination for large toxicogenomic and high-throughput screening data sets. Toxicol Sci. 2008;104:412–418. [PubMed]
  • Burke A, Singh R, Carroll D, Owen J, Kock N, D'Agostino R, Torti F, Torti S. Determinants of the thrombogenic potential of multiwalled carbon nanotubes. Biomaterials. 2011;32:5970–5978. [PMC free article] [PubMed]
  • Bushel PR, Heinloth AN, Li J, Huang L, Chou JW, Boorman GA, Malarkey DE, Houle CD, Ward SM, Wilson RE, Fannin RD, Russo MW, Watkins PB, Tennant RW, Paules RS. Blood gene expression signatures predict exposure levels. Proc Natl Acad Sci U S A. 2007;104:18211–18216. [PubMed]
  • Card JW, Jonaitis TS, Tafazoli S, Magnuson BA. An appraisal of the published literature on the safety and toxicity of food-related nanomaterials. Crit Rev Toxicol. 2011;41:22–49. [PubMed]
  • Castranova V. Overview of current toxicological knowledge of engineered nanoparticles. J Occup Environ Med. 2011;53:S14–S17. [PubMed]
  • Cheng F, Theodorescu D, Schulman IG, Lee JK. In vitro transcriptomic prediction of hepatotoxicity for early drug discovery. J Theor Biol. 2011;290:27–36. [PMC free article] [PubMed]
  • Clark KA, O'Driscoll C, Cooke CA, Smith BA, Wepasnick K, Fairbrother DH, Lees PS, Bressler JP. Evaluation of the interactions between multiwalled carbon nanotubes and Caco-2 cells. J Toxicol Environ Health A. 2012;75:25–35. [PubMed]
  • Collins FS, Gray GM, Bucher JR. Toxicology. Transforming environmental health protection. Science. 2008;319:906–907. [PMC free article] [PubMed]
  • de Bruijne K, Ebersviller S, Sexton KG, Lake S, Leith D, Goodman R, Jetters J, Walters GW, Doyle-Eisele M, Woodside R, Jeffries HE, Jaspers I. Design and testing of Electrostatic Aerosol in Vitro Exposure System (EAVES): an alternative exposure system for particles. Inhal Toxicol. 2009;21:91–101. [PubMed]
  • Deng Y, Johnson DR, Guan X, Ang CY, Ai J, Perkins EJ. In vitro gene regulatory networks predict in vivo function of liver. BMC Syst Biol. 2010;4:153. [PMC free article] [PubMed]
  • Devarajan K. Nonnegative matrix factorization: an analytical and interpretive tool in computational biology. PLoS Comput Biol. 2008;4:e1000029. [PMC free article] [PubMed]
  • Dhawan A, Sharma V. Toxicity assessment of nanomaterials: methods and challenges. Anal Bioanal Chem. 2010;398:589–605. [PubMed]
  • Diabate S, Mulhopt S, Paur HR, Krug HF. The response of a co-culture lung model to fine and ultrafine particles of incinerator fly ash at the air-liquid interface. Altern Lab Anim. 2008;36:285–298. [PubMed]
  • Ding M, Kisin ER, Zhao J, Bowman L, Lu Y, Jiang B, Leonard S, Vallyathan V, Castranova V, Murray AR, Fadeel B, Shvedova AA. Size-dependent effects of tungsten carbide-cobalt particles on oxygen radical production and activation of cell signaling pathways in murine epidermal cells. Toxicol Appl Pharmacol. 2009;241:260–268. [PubMed]
  • Dinu CZ, Bale SS, Zhu G, Dordick JS. Tubulin encapsulation of carbon nanotubes into functional hybrid assemblies. Small. 2009;5:310–315. [PubMed]
  • Dix DJ, Gallagher K, Benson WH, Groskinsky BL, McClintock JT, Dearfield KL, Farland WH. A framework for the use of genomics data at the EPA. Nat Biotechnol. 2006;24:1108–1111. [PubMed]
  • Doktorova TY, Ellinger-Ziegelbauer H, Vinken M, Vanhaecke T, van Delft J, Kleinjans J, Ahr HJ, Rogiers V. Comparison of hepatocarcinogen-induced gene expression profiles in conventional primary rat hepatocytes with in vivo rat liver. Arch Toxicol. 2012 In Press. [PubMed]
  • Donaldson K, Murphy FA, Duffin R, Poland CA. Asbestos, carbon nanotubes and the pleural mesothelium: a review of the hypothesis regarding the role of long fibre retention in the parietal pleura, inflammation and mesothelioma. Part Fibre Toxicol. 2010;7:5. [PMC free article] [PubMed]
  • Dymacek J, Guo N. Systems Approach to Identifying Relevant Pathways from Phenotype Information in Dose-Dependent Time Series Microarray Data. IEEE Bioinform Biomed. 2011:290–293.
  • Ellinger-Ziegelbauer H, Pauluhn J. Pulmonary toxicity of multi-walled carbon nanotubes (Baytubes) relative to alpha-quartz following a single 6h inhalation exposure of rats and a 3 months post-exposure period. Toxicology. 2009;266:16–29. [PubMed]
  • Ema M, Imamura T, Suzuki H, Kobayashi N, Naya M, Nakanishi J. Evaluation of genotoxicity of multi-walled carbon nanotubes in a battery of in vitro and in vivo assays. Regul Toxicol Pharmacol. 2012;63:188–195. [PubMed]
  • Fadeel B, Garcia-Bennett AE. Better safe than sorry: Understanding the toxicological properties of inorganic nanoparticles manufactured for biomedical applications. Adv Drug Deliv Rev. 2010;62:362–374. [PubMed]
  • Frueh FW. Impact of microarray data quality on genomic data submissions to the FDA. Nat Biotechnol. 2006;24:1105–1107. [PubMed]
  • Fulcher ML, Gabriel S, Burns KA, Yankaskas JR, Randell SH. Well-differentiated human airway epithelial cell cultures. Methods Mol Med. 2005;107:183–206. [PubMed]
  • Grubb BR, Pickles RJ, Ye H, Yankaskas JR, Vick RN, Engelhardt JF, Wilson JM, Johnson LG, Boucher RC. Inefficient gene transfer by adenovirus vector to cystic fibrosis airway epithelia of mice and humans. Nature. 1994;371:802–806. [PubMed]
  • Guo NL, Wan Y, Denvir J, Porter D, Pacurari M, Wolfarth M, Castranova V, Qian Y. Multi-walled carbon nanotube-induced gene signatures in the mouse lung: potential predictive value for human lung cancer risk and prognosis. J Toxicol Environ Health A. 2012;75:1–25. [PMC free article] [PubMed]
  • Guo NL, Wan YW, Tosun K, Lin H, Msiska Z, Flynn DC, Remick SC, Vallyathan V, Dowlati A, Shi X, Castranova V, Beer DG, Qian Y. Confirmation of gene expression-based prediction of survival in non-small cell lung cancer. Clin Cancer Res. 2008;14:8213–8220. [PMC free article] [PubMed]
  • Hagens WI, Oomen AG, de Jong WH, Cassee FR, Sips AJ. What do we (need to) know about the kinetic properties of nanoparticles in the body? Reg Toxicol Pharmacol. 2007;49:217–229. [PubMed]
  • Hamadeh HK, Jayadev S, Gaillard ET, Huang Q, Stoll R, Blanchard K, Chou J, Tucker CJ, Collins J, Maronpot R, Bushel P, Afshari CA. Integration of clinical and gene expression endpoints to explore furan-mediated hepatotoxicity. Mutat Res. 2004;549:169–183. [PubMed]
  • Hamadeh HK, Knight BL, Haugen AC, Sieber S, Amin RP, Bushel PR, Stoll R, Blanchard K, Jayadev S, Tennant RW, Cunningham ML, Afshari CA, Paules RS. Methapyrilene toxicity: anchorage of pathologic observations to gene expression alterations. Toxicol Pathol. 2002;30:470–482. [PubMed]
  • Hamadeh HK, Todd M, Healy L, Meyer JT, Kwok AM, Higgins M, Afshari CA. Application of genomics for identification of systemic toxicity triggers associated with VEGF-R inhibitors. Chem Res Toxicol. 2010;23:1025–1033. [PubMed]
  • Han JH, Lee EJ, Lee JH, So KP, Lee YH, Bae GN, Lee SB, Ji JH, Cho MH, Yu IJ. Monitoring multiwalled carbon nanotube exposure in carbon nanotube research facility. Inhal Toxicol. 2008;20:741–749. [PubMed]
  • Haniu H, Matsuda Y, Takeuchi K, Kim YA, Hayashi T, Endo M. Proteomics-based safety evaluation of multi-walled carbon nanotubes. Toxicol Appl Pharmacol. 2010;242:256–262. [PubMed]
  • Haniu H, Matsuda Y, Usui Y, Aoki K, Shimizu M, Ogihara N, Hara K, Okamoto M, Takanashi S, Ishigaki N, Nakamura K, Kato H, Saito N. Toxicoproteomic evaluation of carbon nanomaterials in vitro. Journal of Proteomics. 2011;74:2703–2712. [PubMed]
  • He X, Young SH, Schwegler-Berry D, Chisholm WP, Fernback JE, Ma Q. Multiwalled carbon nanotubes induce a fibrogenic response by stimulating reactive oxygen species production, activating NF-kappaB signaling, and promoting fibroblast-to-myofibroblast transformation. Chem Res Toxicol. 2011;24:2237–2248. [PubMed]
  • Heise T, Schug M, Storm D, Ellinger-Ziegelbauer H, Ahr HJ, Hellwig B, Rahnenfuhrer J, Ghallab A, Guenther G, Sisnaiske J, Reif R, Godoy P, Mielke H, Gundert-Remy U, Lampen A, Oberemm A, Hengstler JG. In vitro - in vivo correlation of gene expression alterations induced by liver carcinogens. Curr Med Chem. 2012;19:1721–1730. [PubMed]
  • Hermanns MI, Kasper J, Dubruel P, Pohl C, Uboldi C, Vermeersch V, Fuchs S, Unger RE, Kirkpatrick CJ. An impaired alveolar-capillary barrier in vitro: effect of proinflammatory cytokines and consequences on nanocarrier interaction. J R Soc Interface. 2010;7(Suppl 1):S41–S54. [PMC free article] [PubMed]
  • Hermanns MI, Unger RE, Kehe K, Peters K, Kirkpatrick CJ. Lung epithelial cell lines in coculture with human pulmonary microvascular endothelial cells: development of an alveolo-capillary barrier in vitro. Lab Invest. 2004;84:736–752. [PubMed]
  • Hill DB, Button B. Establishment of respiratory air-liquid interface cultures and their use in studying mucin production, secretion, and function. Methods Mol Biol. 2012;842:245–258. [PubMed]
  • Iijima S. Helical microtubules of graphitic carbon. Nature. 1991;354:56–58.
  • Johnston HJ, Hutchison GR, Christensen FM, Peters S, Hankin S, Aschberger K, Stone V. A critical review of the biological mechanisms underlying the in vivo and in vitro toxicity of carbon nanotubes: The contribution of physico-chemical characteristics. Nanotoxicology. 2010;4:207–246. [PubMed]
  • Kasper J, Hermanns MI, Bantz C, Maskos M, Stauber R, Pohl C, Unger RE, Kirkpatrick JC. Inflammatory and cytotoxic responses of an alveolar-capillary coculture model to silica nanoparticles: comparison with conventional monocultures. Part Fibre Toxicol. 2011;8:6. [PMC free article] [PubMed]
  • Kato T, Totsuka Y, Ishino K, Matsumoto Y, Tada Y, Nakae D, Goto S, Masuda S, Ogo S, Kawanishi M, Yagi T, Matsuda T, Watanabe M, Wakabayashi K. Genotoxicity of multi-walled carbon nanotubes in both in vitro and in vivo assay systems. Nanotoxicology. 2012 [PubMed]
  • Kienhuis AS, van de Poll MC, Dejong CH, Gottschalk R, van Herwijnen M, Boorsma A, Kleinjans JC, Stierum RH, van Delft JH. A toxicogenomics-based parallelogram approach to evaluate the relevance of coumarin-induced responses in primary human hepatocytes in vitro for humans in vivo. Toxicol In Vitro. 2009;23:1163–1169. [PubMed]
  • Kim JS, Song KS, Joo HJ, Lee JH, Yu IJ. Determination of cytotoxicity attributed to multiwall carbon nanotubes (MWCNT) in normal human embryonic lung cell (WI-38) line. J Toxicol Environ Health A. 2010;73:1521–1529. [PubMed]
  • Kishore AS, Surekha P, Murthy PB. Assessment of the dermal and ocular irritation potential of multi-walled carbon nanotubes by using in vitro and in vivo methods. Toxicology Lett. 2009;191:268–274. [PubMed]
  • Kopec AK, Burgoon LD, Ibrahim-Aibo D, Burg AR, Lee AW, Tashiro C, Potter D, Sharratt B, Harkema JR, Rowlands JC, Budinsky RA, Zacharewski TR. Automated dose-response analysis and comparative toxicogenomic evaluation of the hepatic effects elicited by TCDD, TCDF, and PCB126 in C57BL/6 mice. Toxicol Sci. 2010;118:286–297. [PMC free article] [PubMed]
  • Kuhlbusch TA, Asbach C, Fissan H, Gohler D, Stintz M. Nanoparticle exposure at nanotechnology workplaces: a review. Part Fibre Toxicol. 2011;8:22. [PMC free article] [PubMed]
  • Lee DD, Seung HS. Learning the parts of objects by non-negative matrix factorization. Nature. 1999;401:788–791. [PubMed]
  • Lenz AG, Karg E, Lentner B, Dittrich V, Brandenberger C, Rothen-Rutishauser B, Schulz H, Ferron GA, Schmid O. A dose-controlled system for air-liquid interface cell exposure and application to zinc oxide nanoparticles. Part Fibre Toxicol. 2009;6:32. [PMC free article] [PubMed]
  • Lesko LJ, Woodcock J. Translation of pharmacogenomics and pharmacogenetics: a regulatory perspective. Nat Rev Drug Discov. 2004;3:763–769. [PubMed]
  • Li M, Al-Jamal KT, Kostarelos K, Reineke J. Physiologically based pharmacokinetic modeling of nanoparticles. ACS Nano. 2010;4:6303–6317. [PubMed]
  • Luhe A, Hildebrand H, Bach U, Dingermann T, Ahr HJ. A new approach to studying ochratoxin A (OTA)-induced nephrotoxicity: expression profiling in vivo and in vitro employing cDNA microarrays. Toxicol Sci. 2003;73:315–328. [PubMed]
  • Ma Q. Transcriptional responses to oxidative stress: pathological and toxicological implications. Pharmacol Ther. 2010;125:376–393. [PubMed]
  • Maynard AD, Aitken RJ, Butz T, Colvin V, Donaldson K, Oberdorster G, Philbert MA, Ryan J, Seaton A, Stone V, Tinkle SS, Tran L, Walker NJ, Warheit DB. Safe handling of nanotechnology. Nature. 2006;444:267–269. [PubMed]
  • Maynard AD, Baron PA, Foley M, Shvedova AA, Kisin ER, Castranova V. Exposure to carbon nanotube material: aerosol release during the handling of unrefined single-walled carbon nanotube material. J Toxicol Environ Health A. 2004;67:87–107. [PubMed]
  • Meng W, Kallinteri P, Walker DA, Parker TL, Garnett MC. Evaluation of poly (glycerol-adipate) nanoparticle uptake in an in vitro 3-D brain tumor co-culture model. Exp Biol Med (Maywood) 2007;232:1100–1108. [PubMed]
  • Mercer RR, Hubbs AF, Scabilloni JF, Wang L, Battelli LA, Friend S, Castranova V, Porter DW. Pulmonary fibrotic response to aspiration of multi-walled carbon nanotubes. Part Fibre Toxicol. 2011;8:21. [PMC free article] [PubMed]
  • Mercer RR, Hubbs AF, Scabilloni JF, Wang L, Battelli LA, Schwegler-Berry D, Castranova V, Porter DW. Distribution and persistence of pleural penetrations by multi-walled carbon nanotubes. Part Fibre Toxicol. 2010;7:28. [PMC free article] [PubMed]
  • Mercer RR, Scabilloni J, Wang L, Kisin E, Murray AR, Schwegler-Berry D, Shvedova AA, Castranova V. Alteration of deposition pattern and pulmonary response as a result of improved dispersion of aspirated single-walled carbon nanotubes in a mouse model. Am J Physiol Lung Cell Mol Physiol. 2008;294:L87–L97. [PubMed]
  • Mishra A, Rojanasakul Y, Castranova V, Mercer R, Wang L. Assessment of fibrogenic biomarkers induced by multi wall carbon nanotubes. The Toxicologist. 2011;120:A1183.
  • Moloshok TD, Klevecz RR, Grant JD, Manion FJ, Speier WFt, Ochs MF. Application of Bayesian decomposition for analysing microarray data. Bioinformatics. 2002;18:566–575. [PubMed]
  • Mossman BT, Churg A. Mechanisms in the pathogenesis of asbestosis and silicosis. Am J Respir Crit Care Med. 1998;157:1666–1680. [PubMed]
  • Mossman BT, Lippmann M, Hesterberg TW, Kelsey KT, Barchowsky A, Bonner JC. Pulmonary endpoints (lung carcinomas and asbestosis) following inhalation exposure to asbestos. J Toxicol Environ Health B. 2011;14:76–121. [PMC free article] [PubMed]
  • Muller J, Decordier I, Hoet PH, Lombaert N, Thomassen L, Huaux F, Lison D, Kirsch-Volders M. Clastogenic and aneugenic effects of multi-wall carbon nanotubes in epithelial cells. Carcinogenesis. 2008;29:427–433. [PubMed]
  • Muller J, Delos M, Panin N, Rabolli V, Huaux F, Lison D. Absence of carcinogenic response to multiwall carbon nanotubes in a 2-year bioassay in the peritoneal cavity of the rat. Toxicol Sci. 2009;110:442–448. [PubMed]
  • Muller J, Huaux F, Moreau N, Misson P, Heilier JF, Delos M, Arras M, Fonseca A, Nagy JB, Lison D. Respiratory toxicity of multi-wall carbon nanotubes. Toxicol Appl Pharmacol. 2005;207:221–231. [PubMed]
  • Muller L, Riediker M, Wick P, Mohr M, Gehr P, Rothen-Rutishauser B. Oxidative stress and inflammation response after nanoparticle exposure: differences between human lung cell monocultures and an advanced three-dimensional model of the human epithelial airways. J R Soc Interface. 2010;7(Suppl 1):S27–S40. [PMC free article] [PubMed]
  • Nel A, Xia T, Madler L, Li N. Toxic potential of materials at the nanolevel. Science. 2006;311:622–627. [PubMed]
  • Newton RK, Aardema M, Aubrecht J. The utility of DNA microarrays for characterizing genotoxicity. Environ Health Persp. 2004;112:420–422. [PMC free article] [PubMed]
  • Nuwaysir EF, Bittner M, Trent J, Barrett JC, Afshari CA. Microarrays and toxicology: the advent of toxicogenomics. Mol Carcinog. 1999;24:153–159. [PubMed]
  • Oberdorster G. Safety assessment for nanotechnology and nanomedicine: concepts of nanotoxicology. J Intern Med. 2010;267:89–105. [PubMed]
  • Oberdorster G, Ferin J, Lehnert BE. Correlation between particle size, in vivo particle persistence, and lung injury. Environ Health Persp. 1994;102(Suppl 5):173–179. [PMC free article] [PubMed]
  • Oberdorster G, Maynard A, Donaldson K, Castranova V, Fitzpatrick J, Ausman K, Carter J, Karn B, Kreyling W, Lai D, Olin S, Monteiro-Riviere N, Warheit D, Yang H. Group, I.R.F.R.S.I.N.T.S.W. Principles for characterizing the potential human health effects from exposure to nanomaterials: elements of a screening strategy. Part Fibre Toxicol. 2005a;2:8. [PMC free article] [PubMed]
  • Oberdorster G, Oberdorster E, Oberdorster J. Nanotoxicology: an emerging discipline evolving from studies of ultrafine particles. Environ Health Persp. 2005b;113:823–839. [PMC free article] [PubMed]
  • Ochs MF, Rink L, Tarn C, Mburu S, Taguchi T, Eisenberg B, Godwin AK. Detection of treatment-induced changes in signaling pathways in gastrointestinal stromal tumors using transcriptomic data. Cancer Res. 2009;69:9125–9132. [PMC free article] [PubMed]
  • Ord JJ, Streeter EH, Roberts IS, Cranston D, Harris AL. Comparison of hypoxia transcriptome in vitro with in vivo gene expression in human bladder cancer. Br J Cancer. 2005;93:346–354. [PMC free article] [PubMed]
  • Osinubi OY, Gochfeld M, Kipen HM. Health effects of asbestos and nonasbestos fibers. Environ Health Perspect. 2000;108(Suppl 4):665–674. [PMC free article] [PubMed]
  • Pacurari M, Castranova V, Vallyathan V. Single- and multi-wall carbon nanotubes versus asbestos: are the carbon nanotubes a new health risk to humans? J Toxicol Environ Health A. 2010;73:378–395. [PubMed]
  • Pacurari M, Qian Y, Fu W, Schwegler Berry D, Ding M, Castranova V, Guo NL. Cell permeability, migration, and reactive oxygen species induced by multiwalled carbon nanotubes in human microvascular endothelial cells. J Toxicol Environ Health A. 2012;75:112–128. [PMC free article] [PubMed]
  • Pacurari M, Qian Y, Porter DW, Wolfarth M, Wan Y, Luo D, Ding M, Castranova V, Guo NL. Multi-walled carbon nanotube-induced gene expression in the mouse lung: association with lung pathology. Toxicol Appl Pharmacol. 2011;255:18–31. [PMC free article] [PubMed]
  • Paik S, Shak S, Tang G, Kim C, Baker J, Cronin M, Baehner FL, Walker MG, Watson D, Park T, Hiller W, Fisher ER, Wickerham DL, Bryant J, Wolmark N. A multigene assay to predict recurrence of tamoxifen-treated, node-negative breast cancer. N Engl J Med. 2004;351:2817–2826. [PubMed]
  • Paules R. Phenotypic anchoring: linking cause and effect. Environ Health Persp. 2003;111:A338–A339. [PMC free article] [PubMed]
  • Peng L, Barczak AJ, Barbeau RA, Xiao Y, LaTempa TJ, Grimes CA, Desai TA. Whole genome expression analysis reveals differential effects of TiO2 nanotubes on vascular cells. Nano Lett. 2010;10:143–148. [PMC free article] [PubMed]
  • Perou CM, Jeffrey SS, van de Rijn M, Rees CA, Eisen MB, Ross DT, Pergamenschikov A, Williams CF, Zhu SX, Lee JC, Lashkari D, Shalon D, Brown PO, Botstein D. Distinctive gene expression patterns in human mammary epithelial cells and breast cancers. Proc Natl Acad Sci U S A. 1999;96:9212–9217. [PubMed]
  • Perou CM, Sorlie T, Eisen MB, van de Rijn M, Jeffrey SS, Rees CA, Pollack JR, Ross DT, Johnsen H, Akslen LA, Fluge O, Pergamenschikov A, Williams C, Zhu SX, Lonning PE, Borresen-Dale AL, Brown PO, Botstein D. Molecular portraits of human breast tumours. Nature. 2000;406:747–752. [PubMed]
  • Poland CA, Duffin R, Kinloch I, Maynard A, Wallace WA, Seaton A, Stone V, Brown S, Macnee W, Donaldson K. Carbon nanotubes introduced into the abdominal cavity of mice show asbestos-like pathogenicity in a pilot study. Nat Nanotechnol. 2008;3:423–428. [PubMed]
  • Porter D. Unpublished data.
  • Porter DW, Hubbs AF, Mercer RR, Wu N, Wolfarth MG, Sriram K, Leonard S, Battelli L, Schwegler-Berry D, Friend S, Andrew M, Chen BT, Tsuruoka S, Endo M, Castranova V. Mouse pulmonary dose- and time course-responses induced by exposure to multi-walled carbon nanotubes. Toxicology. 2010;269:136–147. [PubMed]
  • Powell CL, Kosyk O, Ross PK, Schoonhoven R, Boysen G, Swenberg JA, Heinloth AN, Boorman GA, Cunningham ML, Paules RS, Rusyn I. Phenotypic anchoring of acetaminophen-induced oxidative stress with gene expression profiles in rat liver. Toxicol Sci. 2006;93:213–222. [PMC free article] [PubMed]
  • Pulskamp K, Diabate S, Krug HF. Carbon nanotubes show no sign of acute toxicity but induce intracellular reactive oxygen species in dependence on contaminants. Toxicol Lett. 2007;168:58–74. [PubMed]
  • Qian Y. Unpublished data.
  • Robinson JF, Theunissen PT, van Dartel DA, Pennings JL, Faustman EM, Piersma AH. Comparison of MeHg-induced toxicogenomic responses across in vivo and in vitro models used in developmental toxicology. Reprod Toxicol. 2011;32:180–188. [PubMed]
  • Rothen-Rutishauser B, Blank F, Muhlfeld C, Gehr P. In vitro models of the human epithelial airway barrier to study the toxic potential of particulate matter. Expert Opin Drug Metab Toxicol. 2008;4:1075–1089. [PubMed]
  • Rushton EK, Jiang J, Leonard SS, Eberly S, Castranova V, Biswas P, Elder A, Han X, Gelein R, Finkelstein J, Oberdorster G. Concept of assessing nanoparticle hazards considering nanoparticle dosemetric and chemical/biological response metrics. J Toxicol Environ Health A. 2010;73:445–461. [PubMed]
  • Russell S, Norvig P. Artifical Interlligence: A Modern Approach. Prentice Hall; 2003.
  • Russell WMS, Burch RL. The Principles of Humane Experimental Technique. London: Methuen; 1959.
  • Sager TM, Kommineni C, Castranova V. Pulmonary response to intratracheal instillation of ultrafine versus fine titanium dioxide: role of particle surface area. Part Fibre Toxicol. 2008;5:17. [PMC free article] [PubMed]
  • Sakamoto Y, Nakae D, Fukumori N, Tayama K, Maekawa A, Imai K, Hirose A, Nishimura T, Ohashi N, Ogata A. Induction of mesothelioma by a single intrascrotal administration of multi-wall carbon nanotube in intact male Fischer 344 rats. J Toxicol Sci. 2009;34:65–76. [PubMed]
  • Sargent LM, Reynolds SH, Castranova V. Potential pulmonary effects of engineered carbon nanotubes: in vitro genotoxic effects. Nanotoxicology. 2010;4:396–408. [PubMed]
  • Sayes CM, Reed KL, Warheit DB. Assessing toxicity of fine and nanoparticles: comparing in vitro measurements to in vivo pulmonary toxicity profiles. Toxicol Sci. 2007;97:163–180. [PubMed]
  • Scholars WWICf. Consumer Products: An Inventory of Nanotechnology-based Consumer Products Currenly on the Market. 2011 http://www.nanotechproject.org. Accessed 11/2/11.
  • Seagrave J, Mauderly JL, Seilkop SK. In vitro relative toxicity screening of combined particulate and semivolatile organic fractions of gasoline and diesel engine emissions. J Toxicol Environ Health A. 2003;66:1113–1132. [PubMed]
  • Shi L, Reid LH, Jones WD, Shippy R, Warrington JA, Baker SC, Collins PJ, de Longueville F, Kawasaki ES, Lee KY, Luo Y, Sun YA, Willey JC, Setterquist RA, Fischer GM, Tong W, Dragan YP, Dix DJ, Frueh FW, Goodsaid FM, Herman D, Jensen RV, Johnson CD, Lobenhofer EK, Puri RK, Schrf U, Thierry-Mieg J, Wang C, Wilson M, Wolber PK, Zhang L, Amur S, Bao W, Barbacioru CC, Lucas AB, Bertholet V, Boysen C, Bromley B, Brown D, Brunner A, Canales R, Cao XM, Cebula TA, Chen JJ, Cheng J, Chu TM, Chudin E, Corson J, Corton JC, Croner LJ, Davies C, Davison TS, Delenstarr G, Deng X, Dorris D, Eklund AC, Fan XH, Fang H, Fulmer-Smentek S, Fuscoe JC, Gallagher K, Ge W, Guo L, Guo X, Hager J, Haje PK, Han J, Han T, Harbottle HC, Harris SC, Hatchwell E, Hauser CA, Hester S, Hong H, Hurban P, Jackson SA, Ji H, Knight CR, Kuo WP, LeClerc JE, Levy S, Li QZ, Liu C, Liu Y, Lombardi MJ, Ma Y, Magnuson SR, Maqsodi B, McDaniel T, Mei N, Myklebost O, Ning B, Novoradovskaya N, Orr MS, Osborn TW, Papallo A, Patterson TA, Perkins RG, Peters EH, Peterson R, Philips KL, Pine PS, Pusztai L, Qian F, Ren H, Rosen M, Rosenzweig BA, Samaha RR, Schena M, Schroth GP, Shchegrova S, Smith DD, Staedtler F, Su Z, Sun H, Szallasi Z, Tezak Z, Thierry-Mieg D, Thompson KL, Tikhonova I, Turpaz Y, Vallanat B, Van C, Walker SJ, Wang SJ, Wang Y, Wolfinger R, Wong A, Wu J, Xiao C, Xie Q, Xu J, Yang W, Zhang L, Zhong S, Zong Y, Slikker W., Jr The MicroArray Quality Control (MAQC) project shows inter- and intraplatform reproducibility of gene expression measurements. Nat Biotechnol. 2006;24:1151–1161. [PMC free article] [PubMed]
  • Shvedova AA, Kisin E, Murray AR, Johnson VJ, Gorelik O, Arepalli S, Hubbs AF, Mercer RR, Keohavong P, Sussman N, Jin J, Yin J, Stone S, Chen BT, Deye G, Maynard A, Castranova V, Baron PA, Kagan VE. Inhalation vs. aspiration of single-walled carbon nanotubes in C57BL/6 mice: inflammation, fibrosis, oxidative stress, and mutagenesis. Am J Physiol Lung Cell Mol Physiol. 2008;295:L552–L565. [PubMed]
  • Shvedova AA, Kisin ER, Mercer R, Murray AR, Johnson VJ, Potapovich AI, Tyurina YY, Gorelik O, Arepalli S, Schwegler-Berry D, Hubbs AF, Antonini J, Evans DE, Ku BK, Ramsey D, Maynard A, Kagan VE, Castranova V, Baron P. Unusual inflammatory and fibrogenic pulmonary responses to single-walled carbon nanotubes in mice. Am J Physiol Lung Cell Mol Physiol. 2005;289:L698–L708. [PubMed]
  • Snyder-Talkington BN. Unpublished data.
  • Snyder-Talkington BN, Dymacek J, Qian Y, Porter D, Wolfarth M, Pacurari M, Denvir J, Castranova V, Guo NL. Unpublished data.
  • Sorlie T, Tibshirani R, Parker J, Hastie T, Marron JS, Nobel A, Deng S, Johnsen H, Pesich R, Geisler S, Demeter J, Perou CM, Lonning PE, Brown PO, Borresen-Dale AL, Botstein D. Repeated observation of breast tumor subtypes in independent gene expression data sets. Proc Natl Acad Sci U S A. 2003;100:8418–8423. [PubMed]
  • Srivastava RK, Pant AB, Kashyap MP, Kumar V, Lohani M, Jonas L, Rahman Q. Multi-walled carbon nanotubes induce oxidative stress and apoptosis in human lung cancer cell line-A549. Nanotoxicology. 2011;5:195–207. [PubMed]
  • Stokstad E. Putting chemicals on a path to better risk assessment. Science. 2009;325:694–695. [PubMed]
  • Stone KC, Mercer RR, Gehr P, Stockstill B, Crapo JD. Allometric relationships of cell numbers and size in the mammalian lung. Am J Respir Cell Mol Biol. 1992;6:235–243. [PubMed]
  • Subramanian A, Tamayo P, Mootha VK, Mukherjee S, Ebert BL, Gillette MA, Paulovich A, Pomeroy SL, Golub TR, Lander ES, Mesirov JP. Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc Natl Acad Sci U S A. 2005;102:15545–15550. [PubMed]
  • Tabet L, Bussy C, Setyan A, Simon-Deckers A, Rossi M, Boczkowski J, Lanone S. Coating carbon nanotubes with a polystyrene-based polymer protects against pulmonary toxicity. Particle Fibre Toxicol. 2011;8:3–3. [PMC free article] [PubMed]
  • Takagi A, Hirose A, Nishimura T, Fukumori N, Ogata A, Ohashi N, Kitajima S, Kanno J. Induction of mesothelioma in p53+/− mouse by intraperitoneal application of multi-wall carbon nanotube. J Toxicol Sci. 2008;33:105–116. [PubMed]
  • Tamayo P, Slonim D, Mesirov J, Zhu Q, Kitareewan S, Dmitrovsky E, Lander ES, Golub TR. Interpreting patterns of gene expression with self-organizing maps: methods and application to hematopoietic differentiation. Proc Natl Acad Sci U S A. 1999;96:2907–2912. [PubMed]
  • Teeguarden JG, Hinderliter PM, Orr G, Thrall BD, Pounds JG. Particokinetics in vitro: dosimetry considerations for in vitro nanoparticle toxicity assessments. Toxicol Sci. 2007;95:300–312. [PubMed]
  • Teow Y, Asharani PV, Hande MP, Valiyaveettil S. Health impact and safety of engineered nanomaterials. Chem Commun (Camb) 2011;47:7025–7038. [PubMed]
  • Thomas RS, Allen BC, Nong A, Yang L, Bermudez E, Clewell HJ, 3rd, Andersen ME. A method to integrate benchmark dose estimates with genomic data to assess the functional effects of chemical exposure. Toxicol Sci. 2007;98:240–248. [PubMed]
  • Thomas RS, Bao W, Chu TM, Bessarabova M, Nikolskaya T, Nikolsky Y, Andersen ME, Wolfinger RD. Use of short-term transcriptional profiles to assess the long-term cancer-related safety of environmental and industrial chemicals. Toxicol Sci. 2009;112:311–321. [PubMed]
  • Thomas RS, Black M, Li L, Healy E, Chu TM, Bao W, Andersen M, Wolfinger R. A Comprehensive Statistical Analysis of Predicting In Vivo Hazard Using High-Throughput In Vitro Screening. Toxicol Sci. 2012a In Press. [PubMed]
  • Thomas RS, Clewell HJ, 3rd, Allen BC, Wesselkamper SC, Wang NC, Lambert JC, Hess-Wilson JK, Zhao QJ, Andersen ME. Application of transcriptional benchmark dose values in quantitative cancer and noncancer risk assessment. Toxicol Sci. 2011;120:194–205. [PubMed]
  • Thomas RS, Clewell HJ, 3rd, Allen BC, Yang L, Healy E, Andersen ME. Integrating pathway-based transcriptomic data into quantitative chemical risk assessment: a five chemical case study. Mutat Res. 2012b;746:135–143. [PubMed]
  • Tsukahara T, Haniu H. Cellular cytotoxic response induced by highly purified multi-wall carbon nanotube in human lung cells. Mol Cell Biochem. 2011;352:57–63. [PubMed]
  • van 't Veer LJ, Dai H, van de Vijver MJ, He YD, Hart AA, Mao M, Peterse HL, van der Kooy K, Marton MJ, Witteveen AT, Schreiber GJ, Kerkhoven RM, Roberts C, Linsley PS, Bernards R, Friend SH. Gene expression profiling predicts clinical outcome of breast cancer. Nature. 2002;415:530–536. [PubMed]
  • van de Vijver MJ, He YD, van't Veer LJ, Dai H, Hart AA, Voskuil DW, Schreiber GJ, Peterse JL, Roberts C, Marton MJ, Parrish M, Atsma D, Witteveen A, Glas A, Delahaye L, van der Velde T, Bartelink H, Rodenhuis S, Rutgers ET, Friend SH, Bernards R. A gene-expression signature as a predictor of survival in breast cancer. N Engl J Med. 2002;347:1999–2009. [PubMed]
  • Van Summeren A, Renes J, van Delft JH, Kleinjans JC, Mariman EC. Proteomics in the search for mechanisms and biomarkers of drug-induced hepatotoxicity. Toxicol In Vitro. 2012;26:373–385. [PubMed]
  • Wan YW, Sabbagh E, Raese R, Qian Y, Luo D, Denvir J, Vallyathan V, Castranova V, Guo NL. Hybrid models identified a 12-gene signature for lung cancer prognosis and chemoresponse prediction. PLoS One. 2010;5:e12222. [PMC free article] [PubMed]
  • Wang L, Castranova V, Mishra A, Chen B, Mercer RR, Schwegler-Berry D, Rojanasakul Y. Dispersion of single-walled carbon nanotubes by a natural lung surfactant for pulmonary in vitro and in vivo toxicity studies. Part Fibre Toxicol. 2010a;7:31. [PMC free article] [PubMed]
  • Wang X, Xia T, Ntim SA, Ji Z, George S, Meng H, Zhang H, Castranova V, Mitra S, Nel AE. Quantitative techniques for assessing and controlling the dispersion and biological effects of multiwalled carbon nanotubes in mammalian tissue culture cells. ACS Nano. 2010b;4:7241–7252. [PubMed]
  • Warheit DB, Donner EM. Rationale of genotoxicity testing of nanomaterials: regulatory requirements and appropriateness of available OECD test guidelines. Nanotoxicology. 2010;4:409–413. [PubMed]
  • Warheit DB, Reed KL, Sayes CM. A role for nanoparticle surface reactivity in facilitating pulmonary toxicity and development of a base set of hazard assays as a component of nanoparticle risk management. Inhal Toxicol. 2009a;21(Suppl 1):61–67. [PubMed]
  • Warheit DB, Sayes CM, Reed KL. Nanoscale and fine zinc oxide particles: can in vitro assays accurately forecast lung hazards following inhalation exposures? Environ Sci Technol. 2009b;43:7939–7945. [PubMed]
  • Waring JF, Ciurlionis R, Jolly RA, Heindel M, Ulrich RG. Microarray analysis of hepatotoxins in vitro reveals a correlation between gene expression profiles and mechanisms of toxicity. Toxicol Lett. 2001;120:359–368. [PubMed]
  • Wigginton NS, Haus KL, Hochella MF., Jr Aquatic environmental nanoparticles. J Environ Monit. 2007;9:1306–1316. [PubMed]
  • Witzmann FA, Monteiro-Riviere NA. Multi-walled carbon nanotube exposure alters protein expression in human keratinocytes. Nanomedicine. 2006;2:158–168. [PubMed]
  • Wottrich R, Diabate S, Krug HF. Biological effects of ultrafine model particles in human macrophages and epithelial cells in mono- and co-culture. Int J Hyg Environ Health. 2004;207:353–361. [PubMed]
  • Yakobson BI, Smalley RE. Fullerene Nanotubes: C 1,000,000 and Beyond: Some unusual new molecules - long, hollow fibers with tantalizing electronic and mechanical properties - have joined diamonds and graphite in the carbon family. Am Scientist. 1997;85:324–337.
  • Ye SF, Wu YH, Hou ZQ, Zhang QQ. ROS and NF-kappaB are involved in upregulation of IL-8 in A549 cells exposed to multi-walled carbon nanotubes. Biochem Biophys Res Commun. 2009;379:643–648. [PubMed]
  • Yeung KY, Ruzzo WL. Principal component analysis for clustering gene expression data. Bioinformatics. 2001;17:763–774. [PubMed]
  • Zhao J, Castranova V. Toxicology of nanomaterials used in nanomedicine. J Toxicol Environ Health B. 2011;14:593–632. [PubMed]