Rebecca
Klaper
*,
Devrah
Arndt
,
Jared
Bozich
and
Gustavo
Dominguez
School of Freshwater Sciences, University of Wisconsin-Milwaukee, 600 East Greenfield Ave., Milwaukee, Wisconsin, USA. E-mail: rklaper@uwm.edu; Tel: +1 414-382-1713
First published on 5th December 2013
The toxicity of nanomaterials depends on the basic interaction of the chemistry of the material with the molecular pathways in an organism. To design safe and sustainable nanomaterials, more detailed information on the molecular interaction and biochemical machinery that is altered in an organism upon contact with a nanomaterial is needed. There are a multitude of papers now on the toxicity of nanomaterials to various model organisms from human to ecological models, but many focus on acute high dose exposures and research on the toxicity of other chemicals has shown that the dose of a chemical can have a tremendous impact on the pathways that are affected within the organism. The most common pathways investigated in nanotoxicity experiments are related to oxidative stress, yet oxidative stress can be a temporary and natural response to an insult without a negative outcome. There are a multitude of other potential mechanisms that may be triggered in response to a toxin at sublethal exposures. Here we present a review documenting the evidence to date on the indicators of the molecular response to nanomaterials from in vitro and in vivo studies. Alternative pathways as indicated by single biomarker, global gene expression studies and next generation sequencing approaches are discussed as well as the impacts of nanomaterial type, dose, and the types of system studied. Specific mechanisms that are impacted by a nanomaterial can be used as the basis of better high-throughput methods for evaluating how nanomaterial chemistry impacts toxicity and support models to predict the toxicity of future nanomaterials.
There are currently many papers on the toxicology of nanomaterials that describe acute mortality to cells and a select number of organisms. These include a variety of human, mouse, and rat cell lines as well as whole mice and rat studies and limited studies on invertebrates and other non-human model organisms. Overall, it appears from the literature that many of the current suites of available nanomaterials are not acutely toxic. Estimated exposure to nanomaterials, based on data from similar sized particulates, estimated wastewater treatment effluents, and life-cycle modeling efforts are predicted to be orders of magnitude below what is considered lethal for many nanomaterials.1 Select nanomaterials, such as metallic nanomaterials are toxic at lower doses, but this can be due to dissolution in certain aquatic environments rather than by the nanomaterial itself. Interactions with the environment can also increase toxicity, as is the case with metal oxides that are photoreactive.2,3 Realistically, the greatest impacts from nanomaterials will most likely be the result of long-term low-dose exposures. There is a significant gap regarding these types of potential impacts.
Low-dose chronic exposures often have more subtle impacts and alter different biochemical pathways in an organism than corresponding to high-dose exposures. High concentration exposures of many chemicals initiate a “global” stress response that often includes oxidative stress and pathways associated with necrosis. At lower concentrations the same chemicals trigger reactions that can show a very different pattern of molecular response, including unique gene or protein expression patterns that reflect the specific interaction of a chemical with cellular components.4,5 Slight variations in the chemical composition of a drug or pollutant have also been shown change the molecular responses and these gene expression signatures are predictive of impacts on important endpoints such as reproduction or development.6–9 These low-dose chronic exposures have become particularly important in the study of other emerging contaminants as these chemicals, like many nanomaterials, are not acutely toxic. However, research over the last two decades has shown that there is a potential for such chemicals to exert impacts on pathways associated with the reproductive system, immune system, nervous system, cancer pathways, metabolic pathways and others, even at these low doses.10,11
Long-term chronic studies do not readily lend themselves to rapid high-throughput analyses, which are desirable to quickly screen for potential impacts of exposures. Toxicity testing using molecular biomarkers that are known to be linked to negative outcomes may provide a mechanism to develop high-throughput tools that are predictive of long-term outcomes. Global gene expression patterns can be used as a sensitive tool to interrogate the interaction of the cell, tissue or whole organism to nanomaterials of differing chemistries and provide an indication of potential future impacts. Currently, there are a few individual biomarkers being explored in this capacity and they are often limited to pathways involved in oxidative stress, which is known to be a complicated biomarker. Here we review the current status of the field on the molecular impacts of nanomaterials, the limitations on the number of pathways that have been investigated to date, and the potential for next-generation sequencing platforms to provide novel information on the toxicity of nanomaterials. We discuss the potential impact of these methods on the development of high-throughput assays to evaluate the environmental health and safety of nanomaterials.
Oxidative stress can also be dose dependent where high levels of a toxin can cause an initial oxidative stress response that ultimately indicates a decline in cell viability, but lower doses do not instigate the same response. For example, in mice exposed to silver nanoparticles at concentrations of 100, 500 and 1000 mg kg−1, only the highest concentrations significantly induced genes involved in oxidative stress in the frontal cortex, while the lowest dose administrated minimally affected the same genes.23 Similarly, E. coli exposed to a high, medium and low concentration of TiO2 and silver nanoparticles (1, 10, 50 ppm) exhibited dose-dependent differential gene expression, with higher concentrations inducing many oxidative stress related genes (50 and 42, respectively) and the low concentration affecting only a few genes involved in stress (four and three, respectively).24
Other pathways associated with oxidative stress include inflammation, apoptosis, and general stress pathways (Fig. 2). Each of these mechanisms has been commonly studied in response to nanomaterial exposure across in vitro and in vivo study systems and also indicate that the effect of nanomaterials on these pathways is concentration and nanomaterial dependent (Table 1). Inflammation is a protective immune associated response that serves to instigate phagocytosis of a foreign object or pathogen, destroy the invading organism using ROS mechanisms. It also stimulates the secondary immune system to recognize and destroy future similar invasions. In an acute time frame inflammation is a beneficial response, however, if stimulated chronically it can destroy surrounding tissues and create disease. Cytokines such as IL-1, IL-6 and TNF are generic primary immune responses and have been measured by several studies in vertebrates in response to mainly titanium dioxide nanomaterials, carbon black and silica nanomaterials but at relatively high concentrations. Some secondary immune pathways have also been studied including IL-5 and IL-10 indicating that the reaction to nanomaterials can move beyond a simple inflammatory reaction. Exposures of mice to TiO2 indicate differential regulation of genes and proteins important in the COX-2 and MAPK/P13-k/Akt signaling pathways, apoptosis and inflammation at higher concentrations.25,26
Pathways | Gene/protein | Model/cell line | Nanoparticle dimensions (nm); shape | Nanoparticle typea | Modeb | Dose | Durationc | Ref. |
---|---|---|---|---|---|---|---|---|
a Nanoparticle/NP functional group: QDs – quantum dots, DP – 4,6-diamino-2-pyrimidinethiol, SWCNT – single walled carbon nanotubes, CIT – citrate, PEI – polycationic polyethylenimine, PMAO – polyanionic polymaleic anhydride-alt-1-octadecene, PVP – polyvinyl pyrrolidone, OH – hydroxylated, THF – tetrahydrofuran. b Mode of administration: IV – intravenous, IN – intranasal, IG – intragastric, IT – intratracheal, IP – intraperitoneal, O – oral, IH – inhalation, AM – added to media. c Exposure duration: h – hour(s), d – day(s), w – week(s). | ||||||||
Apoptosis | TLR-2 and B-actin | C28/I2, PDL, SCC-9 | 56 | Ag | AM | 10 μg mL−1 | 20 h | 31 |
p53, Bax, parp-1 and smac/DIABLO, caspase-3,-7,-8 and -9 | A549 | Ag | AM | 43 μg mL−1 | 6 h | 32 | ||
p53, Bcl-xl, Bcl-2, NF-κB, CDkNIA, TNFSF10, TNFSR1A and GAPDH | CET | 160 | Al2O3 | AM | 280 μg mL−1 | 12 h | 34 | |
Bax, Bak, caspase-3, caspase-8 and GAPDH | A549, A431 | 17; spheres | Au | AM | 48.9 (A549) and 65.2 (A431) μg mL−1 | 24 h | 75 | |
p53, Bax, caspase-3 & caspase-9, Bcl-2, GAPDH and B-actin | MCF-7 | — | Au | AM | 200 μg mL−1 | 24 h | 40 | |
Bax, caspase-3 and -8 | HEPG2 | 5 | CdTe | AM | 10 μg mL−1 | 24 h | 39 | |
Bcl2-like 4, HyCasp3, foxO and Hsp-70 | H. vulgaris | 3.2 | CdTe QDs | AM | 10 nM | 1–48 h | 21 | |
Bax and p53 | CD14+ | 30 | CeO2 | AM | 5 μg mL−1 | 20 and 40 h | 37 | |
Akt, procaspase-3, Bcl-2, Bax and caspase-3 | BGC-823 | 77; spheres | PEG-PCL-paclitaxel/tetrandrine | AM | 0.05 and 10 μM | 48 h | 38 | |
MRP-1, LRP, Bcl2 and NFκB | Y79 | 236 | Polymer–folate–curcumin | AM | 2 μg mL−1 | 48 h | 36 | |
Bax, caspase-3, Bcl-2 and p-p53 | HUVEC | 20; spheres | Si | AM | 200 μg mL−1 | 24 h | 44 | |
p53, Bax, Bcl-2 and caspase-3 | HEPG2 | 14 | Si | AM | 200 μg mL−1 | 72 h | 30 | |
Bcl-2, BclXL, Mcl-1, Bax, Bak and Bim | MM, PMB, U266 | 300; spheres | Si–snake venom | AM | 10 ng mL−1 | 12 h | 35 | |
p53, cytochrome C, Bax, Bcl-2 and caspase-9 | HFL-I | 20 and 80 | SiO2 | AM | 100 mg mL−1 | 24 h | 46 | |
Pdai2 and ada | Mice (CD-1 ICR) | 6 | TiO2 | IN | 10 mg kg−1 | 90 d | 26 | |
Birc5, Crap2 and Tfrc | Mice | 6 | TiO2 | IG | 10 mg kg−1 | 90 d | 29 | |
BNIP3 | BEAS-2B | 14–25 | ZnO | AM | 5 μg mL−1 | 24 h | 28 | |
Cell functions | Adh5 | Zebrafish (wild type WIK eggs) | 10 | Ag | AM | 5 μg L−1 | 24 and 48 h | 81 |
FliG, fliN, fliM, CheW, cheB, cheY, cheZ, cheR, cheA, and motB | E. coli | — | Au-DP | AM | 10 mg L−1 | 4 h | 61 | |
CXC, CXCL9 and 1/CCL2 | Mice (BALB/c) | 14 and 95; spheres | Carbon Black (CB) | IN | 125 μg | 4 w | 71 | |
c-Jun, ERK 1/2 and p38 | HEPG2 | 5 | CdTe | AM | 10 μg mL−1 | 24 h | 39 | |
c-Jun | SMMC-7721 | 20–30; hexahedrals | CeO2 | AM | 50 μg mL−1 | 24 h | 55 | |
p-c-Jun and c-Jun | HUVEC | 20; spheres | Si | AM | 25–200 μg mL−1 | 24 h | 44 | |
Sri-74, srv-7, srx-22 and srx-69 | C. elegans | 73, nanotubes | SWCNT | AM | 500 mg L−1 | 48 h | 59 | |
CCL11, GM-CSF, IFN-g, CXCL1, CCL2, CCL3, CCL4, FGF-basic, MCSF and VEGF | Mice (C57BL/6) | — | TiO2–rutile | IT | 18 54 and 162 μg | 1, 3 and 28 d | 20 | |
Inflammation immune | IL-6, IL-8 and NFκB | IMR-90, U251 | 6–20 | Ag | AM | 400 μg mL−1 | 48 h | 78 |
IL-1b and TNF-alpha | Rats (liver) | 10 and 50; spheres and hexagons | Au | IP | 22 μg kg−1 | 1 d | 22 | |
IL-6 and TNF-alpha | Rats (liver) | 50 nm hexagons | Au | IP | 22 μg kg−1 | 1 d | 22 | |
IL-6 and TNF-alpha | Rats (kidney) | 50 nm hexagons | Au | IP | 22 μg kg−1 | 1 d | 22 | |
1 alpha/CCL3, Il-1-beta and TNF-alfa | Mice (BALB/c) | 14 and 95; spheres | CB | IN | 125 μg | 4 w | 71 | |
Il1rn, Il1b, Il18pb, Il18, Ifi47, Igtp, Irf1, Irf8, RT1-A2, RT1CE12, RT1-M6-2, RT1-CE1 and RT1-M3-1 | Rats | 20; spheres | Cd doped Si | IT | 1 mg | 7 d | 62 | |
IL-1b, TNF-a and CCL5 | A549, THP-1 | 20 | CdSe QDs-PEG-COOH | AM | 2 nM | 24 h | 73 | |
p-ERK, ERK, p-JNK, JNK, p-p53 and NF-κB | HUVEC | 20; spheres | Si | AM | 200 μg mL−1 | 24 h | 44 | |
COX-2 MAPKs and P-13/Akt, JNK and ERK | Mice (CD-1 ICR) | 5.5 | TiO2 | IG | 10 mg kg−1 | 15, 30, 45, 60, 75, 90 d | 25 | |
Def-b4, H2-Oa, Chi313, Alox5ap, and IL1b | Mice (CD-1 ICR) | 6 | TiO2 | IN | 10 mg kg−1 | 90 d | 26 | |
Bcl6, Cfi and Cfd | Mice | 6 | TiO2 | IG | 10 mg kg−1 | 90 d | 29 | |
Akr1c18 | Mice (CD-1 ICR) | 6 | TiO2 | IG | 10 mg kg−1 | 90 d | 63 | |
Ccl3, Ccl6, clec5a, cxcl1, Il1r2 | Mice (C57BL/6) | — | TiO2–rutile | IA | 54 μg | 1 d | 20 | |
C3, Ccl2, Ccl3, Ccl4, Ccl7, Ccl8, Ccl9, Ccl9, Ccl12, Ccl17, Ccl20, Ccl22, Ccr1, Ccr2, Ccr3, Ccr4, Ccr5, clec5a, Cxcl1, Cxcl9, Cxcl10, Cxcr2, Cxcr3, Ifng, Il10ra, 1l1a, Il1b, Ilr2, Il4, Itgam, spp1, TgFb1, Timp1, Tnf, Tnfrsf1b | Mice (C57BL/6) | — | TiO2–rutile | IA | 162 μg | 1 d | 20 | |
C3, Ccl2, Ccl3, Ccl4, Ccl9, Ccr5, Ccr8, clec5a, Cxcl5, Oas1f, spp1, Timp1, Vnn1 | Mice (C57BL/6) | — | TiO2–rutile | IA | 162 μg | 28 d | 20 | |
Cell cycle/proliferation | Cyclin B, cyclin E, CDC2 and CDK5R21 | IMR-90, U251 | 6–20 | Ag | AM | 400 μg mL−1 | 48 h | 78 |
Pim3, Cdkn1b, Ptprf, Fkbp1a, GClm, Adamts1, Ddx24, Usp7, Uba5, Rchy1 | Rats | 20; spheres | Cd doped Si | IT | 1 mg | 30 d | 62 | |
Hymyc1 | H. vulgaris | 3.2 | CdTe Qds | AM | 10 nM | 1–48 h | 21 | |
Akt-1, cbp-1, daf-2, daf-12, daf-21, mek-2 and mpk-1 | C. elegans | 73; nanotubes | SWCNT | AM | 500 mg L−1 | 48 h | 59 | |
Metabolism | atpD and atpA | E. coli | — | Au-DP | AM | 10 mg L−1 | 4 h | 61 |
fruA, fruK, nuoK, nuoG, nuoF, frdD, frdC and frdB | S. typhimurium (TA100) | 71 | C60 | AM | 2 mg L−1 | 24 h | 57 | |
CYP1A and POR | MSC | <50 | CB | AM | 50 and 100 μg mL−1 | 24 h | 27 | |
rnt, thiS, cysW, yciW, cysI, ilvG, cysN, and pyrB | E. coli | 6–40 | CeO2 | AM | 100 mg L−1 | 1 h | 56 | |
GULP1, SLC30A8, NEGR1, SEC16B, MTCH2, MAF, MC4R, TMEM195, INSIG2, NAMPT, MTMR9, PFKP, KCTD15, LPL and GNPDA2 | Adipocytes | — | FeO | AM | — | 30 h | 66 | |
Cytochrome C | HFL-I | 20 and 80 | SiO2 | AM | 100 mg mL−1 | 24 h | 46 | |
Cdkn1a and Cdkn1c | Mice (CD-1 ICR) | 6 | TiO2 | IN | 10 mg kg−1 | 90 d | 26 | |
Cyp171a | Mice (CD-1 ICR) | 6 | TiO2 | IG | 10 mg kg−1 | 90 d | 63 | |
Ch25h, Fabp3, Aldob | Mice (C57BL/6) | — | TiO2–rutile | IA | 54 μg | 28 d | 20 | |
Membrane transport | ar, tsr and tap | E. coli | — | Au-DP | AM | 10 mg L−1 | 4 h | 61 |
Slc25a30, Rgn, Tmlhe, Sec62, Cldn16, Ktn1 | Rats | 20; spheres | Cd doped Si | IT | 1 mg | 30 d | 62 | |
cmr, yajr, and emrE, and dnaK | E. coli (k12) | 10 | TiO2 | AM | 10 mg L−1 | 2 h | 24 | |
DNA repair | recN, uvrA, ybfE, yebG, ssb, sbmc and nfo | E. coli (k12) | 60 | Ag | AM | 10 mg L−1 | 2 h | 24 |
XRCC1, XRCC3, FEN1, RED51C and RPA1 | IMR-90, U251 | 6–20 | Ag | AM | 400 μg mL−1 | 48 h | 78 | |
RecA and lexA | E. coli (k12) | 10 | TiO2 | AM | 10 mg L−1 | 2 h | 24 | |
Oxidative stress/general stress response | sodA, sodC, and katE | E. coli (k12) | 60 | Ag | AM | 10 mg L−1 | 2 h | 24 |
Sod-3 | C. elegans | <100; spheres | Ag | AM | 0.1 mg L−1 | 24 h | 41 | |
pkm2a, pkmb2 and etv5a | Zebrafish (WT/WIK eggs) | 10 | Ag | AM | 5 μg L−1 | 24 and 48 h | 81 | |
AhpC | E. coli | — | Au-DP | AM | 10 mg L−1 | 4 h | 61 | |
CAT, GST | D. magna | 100 | C60-Hx, C60-OH and TiO2 | AM | 7.5 (C60-Hx) 100 (C60-OH) and 7.5 mg L−1 (TiO2) | 24 h | 17 | |
GSTM3 and GSR | MSC | <50 | CB | AM | 50 and 100 μg mL−1 | 24 h | 27 | |
GSH, SOD, CAT, GST, and Nrf2 | HEPG2 | 5 | CdTe | AM | 10 μg mL−1 | 24 h | 39 | |
ERK 1/2, p38, GAPDH, MDA, SOD, GSH and CAT | SMMC-7721 | 20–30; hexahedrals | CeO2 | AM | 50 μg mL−1 | 24 h | 55 | |
Sod-3, Hsp-70, cyp-35A2, dnaK and katG | C. elegans | 73, nanotubes | SWCNT | AM | 500 mg L−1 | 48 h | 59 | |
Axud1, Cyp4a12a, Cyp4a12b, Cyp4a14 and Cyp2d9 | Mice | 6 | TiO2 | IG | 10 mg kg−1 | 90 d | 29 | |
Cryab and Alkbh7 | Mice (CD-1 ICR) | 6 | TiO2 | IN | 10 mg kg−1 | 90 d | 26 | |
PRDX3, PRNP and TXNRD1 | BEAS-2B | 14–25 | ZnO | AM | 5 μg mL−1 | 24 h | 28 | |
Reproduction | CrEcR | C. riparius (larvae) | — | Ag | AM | 0.2 mg L−1 | 1–72 h | 45 |
chc-1, col-51, col-183, flna1, lit and par-3 | C. elegans | 73, nanotubes | SWCNT | AM | 500 mg L−1 | 48 h | 59 |
At lower exposure doses, which are most likely the more realistic environmental exposure scenario for many organisms, nanomaterials illicit changes in a wider range of pathways (Table 1). For example mice exposed to three concentrations of TiO2 NPs (18, 54 and 162 μg per mouse), responded differently depending upon concentration. At 28 days, exposure of mice to 18 μg (the lowest dose) TiO2 induced changes in muscle contraction and striated tissue development, whereas after the same 28 day duration, exposure to 162 μg (the highest dose) was dominated by the inflammatory response.20
Generalized responses to xenobiotics are also implicated in the nanotoxicity literature. Metabolizing enzymes such as CYP1A, involved in xenobiotic metabolism has been shown to be triggered by carbon black nanoparticles in cell cultures.27 Some of these genes are also related to antioxidant enzymes that protect the cells from oxidative stress from contaminants, including PRDX3 and BNIP3 which have been found to be expressed in cell lines in response to ZnO nanomaterials28 or oxidize compounds such as CYP2d9 in response to TiO2 nanomaterial exposure.29
Apoptosis is a normal process important in balancing cellular structure associated with growth and development within an organism. However apoptosis can also be associated with cellular damage and death due to injury. This can be due to oxidative or free radical damage, or it can be an independent signal of an inflammatory response that induces ROS generation by the primary immune cells of the organism. The apoptosis process also instigates cellular alterations such as shrinkage, DNA degradation, and cell surface alterations that trigger phagocytosis by other cells to remove foreign substances. In cell cultures a variety of nanomaterials have been shown to directly increase genes associated with apoptosis such as caspases and cytochrome C.30–40 The suppression of certain gene families that in turn suppress cell death can also trigger apoptosis and immune response and in studies of titanium dioxide25,26,29 these genes including Birc5 and Crap2 were suppressed after 90 day exposures which increased apoptosis in whole organism models. However, in contrast to in vitro cell line studies, whole organism studies to date do not suggest significant expression of pathways associated with apoptosis associated with nanomaterial exposures.
In addition to these studies, other studies show the same response in a variety of organisms and cell lines, with higher concentrations of nanoparticles inducing genes associated with general stress, oxidative stress, and apoptosis and lower concentrations affecting other molecular pathways such as groups of genes in the major facilitator superfamily, such as drug resistance or detoxification genes and other cell signaling genes and transcription factors.24,41,42 Variations in dose will differentially alter gene expression with low and high concentrations displaying a unique molecular fingerprint.21,43,44 However, there are some exceptions of genomic overlapping, where higher concentrations trigger different genes but the same pathways of genes or affect the same genes.27,41,45,46
These studies demonstrate the need for testing nanomaterials at lower exposure concentrations so the more specific interactions of nanomaterials and cells that occur at environmentally relevant concentrations can be elucidated. This shows that there are other non-oxidative stress mechanisms present regarding nanoparticle toxicity. Testing these materials at low, environmentally relevant concentrations will better enable the development of biomarkers for assessing nanomaterial toxicity.
Other factors that limit current models include the measurement of a limited set of endpoints and time points as well as large dose exposures. Effects are also often time and chemical dependent therefore using an exaggerated response over a short time period introduces bias into modeling.54 In addition, low-dose effects are often hormetic in nature, where low-dose exposures and high-dose exposures have opposite effects, low can be stimulatory to many pathways and high doses cause a toxic response. As a result models using higher concentrations will inaccurately predict effects at doses that are most environmentally realistic. Developing assays that provide unbiased parameters and give information regarding different mechanisms of action will ultimately provide better models. As has been seen with other emerging contaminants such as endocrine disruptors, alternative pathways of effect that occur over chronic low-dose exposures and potentially over generations may in the end be the ultimate concern. There is a need to test nanomaterials at multiple concentrations and time points to help develop biomarkers to assess nanomaterial toxicity. More relevant and useful information for modeling is garnered from low-dose exposure studies that examine multiple endpoints and pathways of impact. Sublethal concentrations elicit more specific biological pathway responses.
Processes | # Results |
---|---|
Cell function | 2470 |
Oxidative stress | 694 |
Apoptosis | 574 |
Cell proliferation | 490 |
Inflammation | 474 |
Biological development | 449 |
Lipid metabolism | 327 |
Immune | 240 |
Reproductive | 191 |
Cell cycle | 184 |
Neurotransmitter | 181 |
Cell signaling | 173 |
Membrane transport | 139 |
Translation | 121 |
Transductional signaling | 91 |
Transcription | 89 |
Glucose metabolism | 45 |
DNA repair | 40 |
P450 | 11 |
Receptor mediated response | 11 |
Ion homeostasis | 9 |
Muscle regulation | 5 |
The types of nanomaterials that have been tested across different toxicity studies differ tremendously and the diversity of nanomaterials studied in any one study is very narrow. This makes it difficult to draw conclusions regarding which nanomaterial may cause the greatest impact, what size and shape may be most toxic, and the pathways that may provide the most information. For example, individual studies have shown separately that pathways involved in cell cycle and cell proliferation have been shown to be impacted by TiO2,26,29 SiO2,64 CdSi,62 CuNP,47 and Ag nanomaterials65 at various concentrations and exposure conditions. Cell signaling pathways, which are heavily involved in disease and impact development and immunity as well as other functions have been shown to be impacted by only TiO2 (ref. 20 and 29) and CdSi nanomaterials.62 Pathways associated with metabolism transcription, translation and some metabolism pathways have been significantly less studied. The lack of information on these other pathways speaks not to the lack of impact on these pathways but to the general lack of data and consistency of evaluating multiple pathways of effect.
There are singular studies, most often cell-based assays, which have investigated impacts on other less commonly studied pathways. For example, in one study the response of adipocytes to superparamagnetic iron oxide nanoparticles included differential regulation of genes associated with lipid and glucose metabolism.66 Expression of genes associated with the transduction signaling of TGF-beta pathway was altered after exposure of Fe3O4 nanoparticles in HeLa cells indicating interference with that pathway.67 Other studies have indicated endoplasmic reticulum stress response,68 transduction signaling of Epidermal Growth Factor (EGF) receptor,69 and hypoxia associated responses to exposure to nanomaterials.70
Surface chemistry may play a large role in the interaction of a nanomaterial with an organism or cell. Some studies suggest that the charge of the surface of the nanomaterial is a big determinant of impact and that positively charged surfaces have caused a greater biological response. For example Yang and co-workers72 found that Azotobacter vinelandii (a nitrogen fixing bacteria commonly found in wastewater treatment facilities) exposed to quantum dots (QD) coated with cationic polyethylenimine (PEI) exhibited the up-regulation of gene cad R, a gene associated with metal contamination, more so than QD coated with anionic polymaleic anhydride-alt-1-octadecene (PMAO). Additionally, the up-regulation of several types of nitrogenases (nif D, nif H, anf D, anf K, vnf D and vnf H) were observed in the QD-PEI (but not QD-PMAO) exposed bacteria, indicating that nitrogen fixation is stimulated upon exposure to the positively charged QD-PEI particles.72 In another study QD COOH-pQDs were more toxic and instigated the expression of scavenger receptor (SRA) endocytic pathway and the downstream NF-kB signaling cascades which are involved in innate and adaptive immunity, inflammation, and stress responses, in comparison to NH2-PEG-pQDs and HO-PEG-pQD which had an overexpression of p38 AP-1 cell signaling cascades instead.73 In a whole organism study, Daphnia magna exposed to polyvinyl pyrrolidone (PVP) and citrate (CIT) capped silver nanoparticles reported differences in gene expression related to stress response upon exposure to the differently coated silver nanoparticles. PVP coated particles, although less toxic, induced stress genes metallothionein (MT) and DNA repair gene (REV1) significantly, while CIT coated particles did not.74 Collectively, these results show that the various surface coatings and functional groups of a variety of nanoparticles can affect the expression of genes involved in numerous pathways.
Molecular data has also indicated that other factors such as the interactions of nanomaterials with cellular media and endogenous biomolecules, differences in the tissue or cell line, and location of accumulation of nanomaterial can impact the molecular interaction with a nanomaterial. The interactions of nanoparticles with chemical compounds present in cell culture media and their subsequent impacts on toxicity tests and molecular response have been minimally explored. However several studies have shown that cellular media and molecules can coat nanomaterial and change their properties and their interactions with cells. For example, cytotoxicity decreases for citrate coated Au-NPs due to, in the presence of fetal bovine serum (FBS) medium.75 The media components were shown to alter the citrate-coated gold crystals as Au crystals, naked and citrate-coated, deposited in FBS exhibited a frequency decrease that was higher than the Au crystals, naked or functionalized, incubated in RPMI without FBS present. Due to the importance of the interactions of nanoparticles with biological systems,76,77 there is a significant concern that there are only a handful of human cell studies that mention this interaction with free molecules present in the supplemented FBS media.
Differences among cell lines used for toxicity studies lead to differences regarding the pathways instigated by exposure to nanomaterials. Cancer cells lines such as U251 cells78 and MCF7 (ref. 79) are more susceptible to nanoparticle exposure than normal cells. The animal where the cell line originated is also important. For example murine macrophages were shown to be more sensitive than human cell lines when exposed to BSA-stabilized silica nanoparticles80 and silver-doped silica nanoparticles induce more toxicity in human hepatoma cell line (Huh7) than fibroblast-like fathead minnow (FHM, Pimephales promelas) cells.68 Cell lines from different tissues of the same organism also respond differently to nanoparticle treatment. Toll-like receptor 2 (TLR-2) gene expression, a gene responsible for cell membrane receptors that recognize foreign substances, was expressed to a greater extent in human chondrocytes (C28/I2) and periodontal ligament (PDL) cells in response to Ag-NPs exposure than in squamous cell carcinoma from the tongue (SCC-9).31 Because of these differences among cell types one might expect that the deposition location in an organism can also impact the molecular response. If nanomaterials induce toxicity to vital organs, including the lung, brain, liver, kidney, spleen, and ovary, each of these tissues may be specific in their response due to their differing functionality. All of these factors point towards the need to explore a diversity of cells, tissues and organisms to fully understand these molecular interactions.
There have been several studies to date that have investigated the biological response of cells or organisms to nanomaterials using global gene expression patterns from microarrays that have provided some key insights to the molecular response to exposure.20,26,29,47,62,63 Similar to other xenobiotics these studies have shown that when comparing a nanomaterial exposed organism or cell to a control there can be nanomaterial specific gene regulation42,56 and the gene expression patterns can be used to separate the effects of the nanomaterial from the other components in a suspension such as any metal ions that may be emitted from a metal nanomaterial.47 Toxicity of metal nanomaterials has been investigated more than other nanomaterial types and such experiments have identified that common pathways of impact include oxidative stress as well as inflammation and apoptosis. Genomics tools have been used as a tool to separate the impacts of metal ions from the dissolution of metal nanomaterials in solution from the nanomaterial itself to determine the underlying mechanism of toxicity of nanomaterials. For example similar genomic profiles are expressed in silver nanomaterial or silver ion exposures suggesting that both types of exposures induce toxicity by similar mechanisms.81,82 However, other studies show that nanomaterial toxicity acts by mechanisms that are unique to the nanomaterial and not a consequence of metal ion dissolution. Some novel genetic pathways associated with metal nanomaterials versus their metal ion counterparts in these studies include apoptosis, cell proliferation and differentiation, and cancer progression for copper nanoparticles;47 energy metabolism for copper nanoparticles;83 ribosome activity and elongation factors for TiO2 nanoparticles47 and metal detoxification, metabolic processes, and radical scavenging for silver nanoparticles.84 Variations in the responses found in these studies might be a result of the type of exposure (terrestrial, aqueous, or inhalation/instillation) and the type of particle, as different nanomaterials behave differently in various types of media.
Next generation sequencing technologies also hold promise for distinguishing the impacts of nanomaterials of differing properties. For example, our lab has investigated the differences in toxicity and associated molecular pathways instigated in response to fullerene and carbon nanotubes exposures with varying surface chemistries in several organisms. To examine global gene expression, RNA was extracted, cDNA created and sequenced using Roche 454 sequencer from the aquatic toxicology and genomic model Daphnia magna (Fig. 3) that had been exposed to 50 ppm fullerene nanomaterials for 48 hours. This concentration has been shown to be at the LC25 for this species in our previous work but was also shown not to instigate oxidative stress17 (for toxicity and nanomaterial characterization information see Klaper et al. 2009, and Arndt et al. 2013).17,85 As a comparison, daphnids exposed to control water and 50 ppm of fullerols were also sequenced (C60 (OH)24). Sequences were screened for quality control and approximately 500000 fragments were sequenced for a pool of three replicates of 20 adult daphnids for each treatment. Sequences were compared to each other and overlapping sequences (with greater than 95% overlap) from all treatments were assembled into one set of contigs, or longer gene sequence fragments, and then annotation by comparing these longer sequences to public databases. Fragments that only appeared once or had no overlap with other sequences were excluded from analysis. Associated pathway information for each gene fragment was also identified. Approximately 30000 contigs per treatment remained after this cleanup and were used in an RNA-Seq analysis, which compares the number of times a given sequence is represented between treatments. Sequences that were overrepresented in one treatment versus another were also analyzed with respect to pathways represented. Some of the key findings of this analysis include a significant overlap in the sequences represented across treatments with a large number of unique sequences represented in each treatment (Fig. 4). In addition, when the sequences are analyzed for their associated molecular pathways it is clear that although overlaps exist the difference in the surface chemistry of these two particles causes not only a difference in the toxicity but at sublethal concentrations it causes a difference in the molecular pathways that are expressed (Fig. 5). Genomic analyses can separate not only the various impacts of xenobiotic exposures but reaction to natural stressors compared to xenobiotic exposures as well. For this same experiment we compared the expression of genes in the nanomaterial exposures above to Daphnia pulex exposed to several different xenobiotic exposures and other stressor exposures in experiments related to our lab and the lab of others conducted through the Daphnia Genome Consortium and sequenced by the Joint Genome Institute. These included titanium dioxide and fullerene exposures as well as exposures to heavy metal exposures, hypoxia, temperature stress, exposure to pheromones from predators, and different life stages. When the Daphnia magna exposed to fullerenes and fullerols are compared to the Daphnia pulex exposures it is clear that the D. magna fullerene and fullerol nanomaterials related expression clusters with the D. pulex titanium dioxide and fullerene nanomaterial exposures as well as the other chemical exposures. They are less similar to hypoxia related stress or the more biological stressors of starvation and predator presence (Fig. 6). This indicates that the genomic response to nanomaterials is more similar to other chemical exposures than more natural stressors. The types of genes expressed upon nanomaterial exposure could provide an indicator of the differences in the molecular pathways instigated by nanomaterials versus other stressors.
Fig. 3 Daphnia magna is a model organism for toxicity and the interaction of the genome with the environment. |
Genomic data may not provide all answers to the impacts of nanomaterials on organisms. There is some possibility that some of the pathways expressed may be similar across nanomaterials as there are a set of key responses that cells and tissues use to respond to xenobiotics. However, as shown previously the data available through in vitro cellular assays indicates the complete expression pattern can be used to distinguish among stressors. The current issue is in deciphering the endpoints related with the global gene expression response. In addition, it should be noted that the data generated in genomic experiments is complicated by the volume of sequences generated and in organisms where the genome has not been sequenced and annotated full interpretation of the data will be incomplete. However, overall the molecular response described by examining the co-expression of these multiple biochemical pathways in a systems approach illustrates that the response is more complicated than oxidative stress and inflammation. It also provides a more complete idea of the response of an organism to nanomaterial exposure.
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