Abstract
Responsible implementation of engineered nanomaterials (ENMs) into
commercial applications is an important societal issue, driving demand
for new approaches for rapid and comprehensive evaluation of their
bioactivity and safety. An essential part of any research focused on
identifying potential hazards of ENMs is the appropriate selection of
biological endpoints to evaluate. Herein, we use a tiered strategy
employing both targeted biological assays and untargeted quantitative
proteomics to elucidate the biological responses of human THP-1 derived
macrophages across a library of metal/metal oxide ENMs, raised as
priority ENMs for investigation by NIEHS’s Nanomaterial Health
Implications Research (NHIR) program. Our results show that
quantitative cellular proteome profiles readily distinguish ENM types
based on their cytotoxic potential according to induction of biological
processes and pathways involved in the cellular antioxidant response,
TCA cycle, oxidative stress, endoplasmic reticulum stress, and immune
responses as major processes impacted. Interestingly, bioinformatics
analysis of differentially expressed proteins also revealed new
biological processes that were influenced by all ENMs independent of
their cytotoxic potential. These included biological processes that
were previously implicated as mechanisms cells employ as adaptive
responses to low levels of oxidative stress, including cell adhesion,
protein translation and protein targeting. Unsupervised clustering
revealed the most striking proteome changes that differentiated ENM
classes highlight a small subset of proteins involved in the oxidative
stress response (HMOX1), protein chaperone functions (HS71B, DNJB1),
and autophagy (SQSTM), providing a potential new panel of markers of
ENM-induced cellular stress. To our knowledge, the results represent
the most comprehensive profiling of the biological responses to a
library of ENMs conducted using quantitative mass spectrometry-based
proteomics. The results provide a basis to identify the patterns of a
diverse set of cellular pathways and biological processes impacted by
ENM exposure in an important immune cell type, laying the foundation
for multivariate, pathway-level structure activity assessments of ENMs
in the future.
Keywords: engineered nanomaterials, proteomics, cytotoxicity, oxidative
stress, macrophage
Graphical Abstract
graphic file with name nihms-1546573-f0001.jpg
1. Introduction
As advances in nanoscience continue to drive transformations in
consumer products, energy efficiency, medical imaging, drug delivery
and other important applications, the responsible implementation of
these technologies remains a critical societal issue. In particular,
the enhanced potential for human exposure to engineered nanomaterials
(ENMs) through ingestion, inhalation, or dermal penetration due to
their increased prevalence in commercial applications is an important
concern. The vast diversity of ENMs under development is also driving
demand for new approaches to comprehensively assess bioactivity and
potential hazard of ENMs, increasing the importance of in vitro
(cellular) test systems that can be conducted with rapid throughput.
Accordingly, the U.S. National Institutes of Environmental Health
Sciences implemented the Nanomaterials Health Implications Research
(NHIR) Consortium, to advance research linking the physical and
chemical properties of ENMs to biological responses, with the goal of
predicting potential health impacts associated with high priority and
emerging classes of ENMs. ENMs composed of metal and metal oxides
remain high priority materials for investigation due to their broad
applications including food packaging ([44]Pulizzi 2016; [45]Sharma et
al. 2017), as anti-microbial agents ([46]Arias et al. 2018), as medical
diagnostics and imaging agents ([47]Parveen et al. 2012), and in a
variety of semiconductor applications ([48]Waiskopf et al. 2016).
An essential aspect of any research focused on identifying bioactivity
and potential hazard of ENMs is the appropriate selection of biological
endpoints to evaluate. Unfortunately, the primary focus of many
nanotoxicity studies is limited to measures of the direct cytotoxic or
pro-inflammatory effects of ENMs, with little attention to more subtle
alterations in biological function. For instance, previous studies from
us and others have shown that ENMs which are often categorized as
biologically inert based on cytotoxicity analysis alone can modulate
the expression of hundreds of gene products ([49]Kodali et al. 2013;
[50]Perkins et al. 2012; [51]Waters et al. 2009). In the case of
macrophages, an important immune cell type critical for clearance of
foreign particles, exposure to some classes of ENMs even at
subcytotoxic levels can disrupt important innate immune functions and
hinder cellular phagocytic capacity against foreign pathogens ([52]Kim
et al. 2011; [53]Kodali et al. 2013; [54]Thrall et al. 2019).
Identifying the underlying pathways involved in these responses to
engineered particulates is not only important to advance predictive
nanotoxicology, but could provide new insight into the mechanisms by
which ambient air particulate exposures increase risk of lung
infections such as pneumonia, particularly in children and elderly
([55]Loeb et al. 2009; [56]Neupane et al. 2010). Similar mechanisms of
bio-nano interactions may also underlie the increased risks of
morbidity and lung infections observed in welders exposed to fumes that
are rich in nanoscale metal oxide particles ([57]Andujar et al. 2014;
[58]Coggon et al. 1994; [59]Palmer et al. 2003).
Omics-based technologies offer attractive approaches for both an
unbiased and multivariate system-level characterization of nano-bio
interactions. Although transcriptomics studies have provided insights
into the cellular responses to a variety of ENMs ([60]Costa et al.
2018; [61]Frohlich 2017; [62]Kodali et al. 2013; [63]Zhao et al.
2015a), it is also widely understood that changes in mRNA abundance are
not necessarily predictive of changes at the protein level. However,
assessing global proteome responses to broad types of ENMs at both a
quantitative and comprehensive level remains challenging, despite
providing a more direct read-out of signaling and cell phenotypes than
provided by transcriptomics. While proteomics approaches have been
commonly used to qualitatively characterize the corona of adsorbed
proteins on ENMs ([64]Shannahan et al. 2013; [65]Zhang et al. 2011),
quantitative mass spectrometry-based proteomics is only beginning to be
fully explored as a strategy for hazard assessment and mechanistic
studies of ENM toxicity ([66]Zhang et al. 2018). Inclusion of
multivariate omics endpoints as part of a hierarchical
structure-activity assessment for ENMs has the potential to link
intrinsic properties of ENMs with specific biological processes and/or
pathway-level responses ([67]Cai et al. 2018). Since regulation of key
biological processes such as phagocytic function and endoplasmic
reticulum (ER) stress involve multiple proteins and mediators, it is
anticipated that compared to targeted biological endpoints, omics
measurements of the impact of ENM exposure will reduce uncertainty in
assessing the potential bioactivity of ENMs.
The ability of ENMs to induce cellular oxidative stress has emerged as
one of the leading paradigms for predicting the toxicity of ENMs,
particularly for metals and metal oxides ([68]Kodali and Thrall 2015;
[69]Meng et al. 2009; [70]Thrall et al. 2019). Previous work from our
laboratory comparing response of macrophages to three types of metal
oxide ENMs revealed a clear relationship between the level of cellular
protein oxidative modification (S-glutathionylation) induced and the
degree of altered macrophage function ([71]Duan et al. 2016). These
studies also demonstrated that oxidative modifications across the
proteome following low levels of ENM-induced oxidative stress were not
stochastic, but selective to proteins involved in cellular adaption
pathways (e.g., protein translation and ER stress). In contrast, ENMs
which displayed cytotoxic potential showed reduced specificity for the
proteins targeted by oxidative stress across a broader set of pathways
associated with classical stress responses, mitochondrial energetics
(e.g., glycolysis), and apoptosis ([72]Duan et al. 2016).
In this study, we extend our previous studies and use a tiered strategy
employing both targeted biological endpoints and untargeted
quantitative proteomics to elucidate the biological responses of human
THP-1 derived macrophages across a broader library of metal and metal
oxide ENMs. Twenty metal and metal oxide ENMs were selected as
priorities for investigation by the NHIR consortium due to their
increasing prevalence in commercial applications and human exposure
potential. These ENMs varied widely in their ability to induce
cytotoxicity, inflammasome activation, or ER stress, as determined by
targeted endpoint assays. Quantitative proteomic profiling of a subset
of 11 representative ENMs not only distinguished ENMs with different
cytotoxic potential, confirming the role of important stress-response
pathways, but also revealed a subset of common biological processes
that were generically influenced by particle exposure independent of
the cytotoxic potential of the ENM. Our results suggest new sets of
adaptive response pathways that are broadly triggered by particle
interactions that could contribute to altered macrophage functions and
reveal interesting new protein markers which could serve as sensitive
markers of cellular redox stress in response to ENMs.
2. Material and Methods
2.1. Preparation and characterization of ENMs
All ENMs used in this study ([73]Table 1) were provided by the
Engineered Nanomaterials Resource and Coordination Core (ERCC), which
is the primary resource for ENM synthesis and characterization for the
NIEHS Nanotechnology Health Implications Research (NHIR) Consortium.
ENM suspensions were prepared as described previously ([74]DeLoid et
al. 2017). Briefly, ENMs were subjected to sonication in ultra-pure
water to form a stable suspension and subsequently measured by dynamic
light scattering (DLS). Dispersed ENMs were then diluted in RPMI-1640
culture media supplemented with 10% fetal bovine serum to the desired
concentration for cell dosing. Characterization of ENMs provided by the
ERCC and our in-house DLS measurements are summarized in [75]Table 1.
Details on physicochemical characterization of the ENMs has also been
described previously ([76]Ahn et al. 2018; [77]Beltran-Huarac et al.
2018; [78]Zimmerman et al. 2019).
Table 1.
Characterization of metal and metal oxide ENMs used in this study
ENM type Serial Number Particle description Primary particle diameter
(nm)^[79]a[80]b Effective Diameter (nm)^[81]c Zeta Potential (mV)^[82]a
Polydispersity Index Agglomeration State Density (g/mL)^[83]a
Cell-deposited dose fraction^[84]e
Ag AG-20NM- JB20170316–1:T10 Silver (20 nm) 25.6±6.5 211.1±16.5
−10.5±0.5 0.371 agglomerate 2.16 0.138
Ag_Cit AG-20NM-CITRATECONV:PC20170324–1:T10 Silver Citrate Capped (20
nm) 21.6±2.8 115.5±0.9 −17.2±0.4 0.259 protein-coated primary particles
1.583 0.166
1%Ag_SiO[2] 1%AG-SIO2-JB20161109–1:T10 Silver (∼8 nm) Supported on
Silica (∼7 nm) 10.6±7.1 219.2±3.1 −10.4±0.6 0.322 agglomerate 1.074
0.089
10%Ag_SiO[2] 10%AG-SIO2-JB20161109–1:T10 Silver (∼7 nm) Supported on
Silica (∼10 nm) 7.8±4.3 213.1±19.5 −12.9±0.2 0.309 agglomerate 1.087
0.09
Al[2]O[3] AL2O3–30NM:JB20161003–1:T10 Aluminum Oxide (30 nm) 28.2±13.1
150.9±3.3 −14.0±1.3 0.358 agglomerate 1.943 0.151
Au AU-15NM-CITRATECONV:PC20160930–1:T10 Gold Citrate Capped (15 nm)
18.4±1.8 38.6±0.6 −17.7±2.4 0.341 non-porous agglomerate 19.3 0.384
CdS CdS-60NM-NS-B-1 Cadmium Sulfide (60 nm) 10.7±3.1 162.2±17.4
−9.6±1.1 0.343 agglomerate 2.982 0.413
CeO[2]_10nm CEO2–10NM-JB201611205–1:T10 Cerium (IV) Oxide (10 nm)
10.5±5.0 219.3±13.3 −14.7±0.9 0.295 agglomerate 1.375 0.092
CeO[2]_30 nm CEO2–30NM-JB20161126–1:T10 Cerium (IV) Oxide (30 nm)
34.8±22.9 336.3±27.5 −11.2±1.9 0.266 agglomerate 1.774 0.138
CuO CUO-50NM-SA-B-1 Copper Oxide (50 nm) 50.2±11.0 341.9±58.1 −11.9±1.5
0.368 agglomerate 1.227 0.075
Fe[2]O[3]_10nm FE2O3–10NM-JB20161128–1:T10 Iron (III) Oxide (10 nm)
9.9±3.8 181.5±0.5 −13.3±0.7 0.338 agglomerate 1.226 0.099
Fe[2]O[3]_100nm FE2O3–100NM-JB20161128–1:T10 Iron (III) Oxide (100 nm)
108.9±47.6 2408.9±488.2 −10.7±0.2 0.328 agglomerate 1.504 1
MgO MGO-20NM-SC-B-1 Magnesium Oxide (20 nm) 23.8±7.6 40.9±2.2 −12.0±1.3
0.27 protein-coated primary particles 1.125 0.205
SiO[2] SIO2–15NM:GP20160930–1:T10 Silicon Oxide (15 nm) NA^[85]d
120.0±19.5 −11.3±2.0 0.37 agglomerate 1.135 0.117
TiO[2]_E171 TIO2-E171–100NM-PF-B-1 Titanium Oxide Food Grade E171 (100
nm) 113.4±37.2 389.7±33.0 −11.0±1.2 0.268 agglomerate 1.512 0.169
TiO[2]_P25 TIO2-P25–30NM-AO-B-1 Titanium Oxide Degussa P25 (30 nm)
28.8±11.1 342.3±6.5 −12.8±0.5 0.271 agglomerate 1.512 0.13
V[2]O[5] V2O5–100NM-B-NS Vanadium Pentoxide (100 nm) 310.8±214.7
656.9±118.5 −11.4±1.6 0.366 agglomerate 1.453 1
WO[3] WO3–15NM- JB20170929–1 Tungsten Oxide (15 nm) 20.9±9.4 65.6±1.1
−12.4±1.3 0.364 protein-coated primary particles 1.588 0.182
ZnO ZNO-50NM-MT-B-1 Zinc Oxide (50 nm) 45.7±17.4 57.8±5.7 −12.4±1.6
0.365 protein-coated primary particles 1.552 0.181
ZnS ZnS-100NM-B-NS Zinc Sulfide (100 nm) 211.4±77.6 243.5±5.0 −11.9±0.5
0.373 protein-coated primary particles 1.557 0.35
[86]Open in a new tab
Note: All measurements were done in triplicate. Data represented as
mean ± standard deviation. Both effective diameter and density were
measured in the presence of the RPMI-1640 culture media with 10% FBS.
^a
Data provided by ERCC.
^b
Primary size was determined by TEM.
^c
Effective diameter was determined by DLS.
^d
SiO[2] lacks discrete particle borders and therefore is not possible to
define TEM based diameter.
^e
Determined by the ISD3 model (see [87]Methods for details)
2.2. Cell dosimetry analysis
The agglomeration state and the cell-deposited dose fraction of each
ENM ([88]Table 1) after dilution in cell culture medium were determined
by our in vitro dosimetry model, ISD3 ([89]Thomas et al. 2018), which
is an experimentally-validated particokinetic model for predicting the
combined effects of particle sedimentation, diffusion and dissolution
on cellular dosimetry for in vitro systems. The agglomeration state of
the particles was assessed by determining if the measured density and
size values are consistent with the formula used in the ISD3 model for
the agglomeration density,
[MATH: ρp=(1−ε)ρp1+ερf,
:MATH]
where, ρ[p] is the agglomerate density, ε is the agglomerate porosity,
ρ[p1] is the density of the primary particle, and ρ[f] is the density
of the liquid media (1g/mL). The porosity is calculated using the
equation,
[MATH:
ε=1−(DpDp1)DF−3, :MATH]
where, DF is the fractal dimension of the agglomerate (1 < DF ≤ 3),
D[p]is the agglomerate diameter, and D[p1] is the primary particle
diameter. The DF value for each nanoparticle is estimated using the
measured values (listed in [90]Table 1) for D[p], D[p1], ρ[p1], and
ρ[p], and by setting ρ[f] to 1 g/mL. If the DF value fell outside its
valid range (between 1 and 3), then the nanoparticle was not considered
an agglomerate, but a protein-coated primary particle, because lower
density values and larger sizes of the non-agglomerates compared to
that of the primary particles can be attributed to the presence of
protein corona on the surface of the primary particles ([91]Thomas et
al. 2018). Based on our analysis, the nanoparticles that behave as
protein-coated primary particles were MgO, WO[3], ZnO, ZnS, and Ag_Cit.
The calculated thickness of the protein layers ranged from 6.05 for the
ZnO nanoparticles to 47 nm for the citrate-stabilized silver (Ag_Cit)
nanoparticles.
The cell-deposited dose fraction (proportion of administered particles
that reach the cell monolayer) was also calculated using the ISD3 model
for ENMs suspended in RPMI media over a 24-hour time period, based on a
media height, volume and temperature equal to 6.3 mm, 0.2 ml, and 310
K, respectively ([92]Table 1).
2.3. Cell culture and cell viability
THP-1 human monocytes were obtained from the American Type Culture
Collection (ATCC TIB-202). Cells were maintained in RPMI-1640 culture
media supplemented with 10% fetal bovine serum (Gibco, Gaithersburg,
MD), 1% antibiotic/antimycotic mixture (Gibco, Gaithersburg, MD), and 2
mM of L-glutamine (Gibco, Gaithersburg, MD) at 37°C with 5% CO[2]. For
all experiments, cells were differentiated into macrophages with 80 nM
of phorbol 12-myristate 13-acetate (PMA) (Sigma) for 48 h followed by
another 24 h incubation without PMA ([93]Gatto et al. 2017; [94]Zhao et
al. 2015b). Cell viability was defined using the MTT assays as
described previously ([95]Lanone et al. 2009; [96]Waters et al. 2009).
Briefly, THP-1 cells were seeded in 96-well plates at 8.0 × 10^4 cells
per well in 200 μl culture media and differentiated as described above.
Cells were treated with ENMs at concentrations of 6.25, 12.5, 25, and
50 μg/mL in 200 μl total volume for 24 hr. After incubation with ENMs
culture medium was removed and each well was rinsed with 200 μl of
Hank’s Balanced Salt Solution (HBSS). Cells were then incubated with
200 μl of MTT solution (0.5 mg/ml in HBSS) for 1.5 h at 37 °C. MTT
solution was then removed and 100 μl of dimethyl sulfoxide was added to
each well. Plates were shaken prior to reading absorbance
spectrophotometrically with a SpectraMax Plus 384 microplate reader
(Molecular Devices) at a wavelength of 570 nm. Four biological
replicates were performed for the MTT assay and the following gene
expression and proteomics experiments.
2.4. Gene expression analysis
Quantitative reverse transcription polymerase chain reaction (qRT-PCR)
assays were designed to measure mRNA expression of reporter gene
products for oxidative stress response (HMOX1), ER stress (CHOP),
inflammasome activation (IL1B) based on results from our previous
studies ([97]Duan et al. 2016; [98]Kodali et al. 2013). For gene
expression analysis THP-1 cells are seeded in 6-well plates at 1.0 ×
10^6 cells per well in 3 mL culture media and differentiated as
described above. Cells were treated with ENMs at concentrations of 12.5
or 25 μg/mL in 3 mL total volume for 6 or 24 hr. After incubation with
ENMs the culture medium was removed and each well was washed with 3 mL
HBSS, and RNA extraction was performed using the Qiagen RNeasy Mini Kit
(Qiagen) according to the manufacturer’s protocol for adherent
mammalian cells. For reverse transcription, 1 μg total RNA was used as
template with the QuantiTect Reverse Transcription Kit (Qiagen)
according to the manufacturer’s instructions. qRT-PCR was performed
using SYBR Green PCR Master Mix per the manufacturer’s instructions
(Applied Biosystems). Relative mRNA levels were calculated using the
(2^−ΔΔCt) method using CYPA as the internal reference control using the
following forward and reverse primers respectively: CYPA
5’-atgctggacccaacacaaat-3’; CYPA 5’-tctttcactttgccaaacacc-3’; CHOP
5’-gacctgcaagaggtcctgtc-3’; CHOP 5’-gcagggtcaagagtggtgaa-3’; IL1B
5’-tacctgtcctgcgtgttgaa-3’; IL1B 5’-tctttgggtaatttttgggatct-3’; HMOX1
5’-ggcagagggtgatagaagagg-3’; HMOX1 5’-agctcctgcaactcctcaaa-3’.
2.5. Proteomics sample processing
For mass spectrometry analysis THP-1 cells were seeded in 60 mm plates
at 1.5 × 10^6 cells per plate in 5 mL culture media and differentiated
as described above. Cells were treated with ENMs at concentrations of
12.5 and 25 μg/mL in 5 mL total volume for 12 hr. After incubation with
ENMs the culture medium was removed, and each well was washed with 3 mL
HBSS. Cell lysis was performed in 100 μl of lysis buffer (8 M urea in
100 mM NH[4]CO[3], pH 7.8) by sonication and vortexing. The lysate was
centrifuged at 14,000 rpm for 10 min at 4°C, and the supernatant was
collected. The bicinchoninic acid assay (BCA) (Thermo Fisher
Scientific) was used to determine the protein concentration, and an
aliquot of 75 μg protein was transferred to a 96-well plate. Reduction
of protein thiols was performed with 10 mM dithiothreitol (DTT) for 30
min at 37°C, followed by alkylation with 40 mM iodoacetamide (IAA) for
1 h in the dark. The samples were diluted to a urea concentration of 1
M using 50 mM NH[4]CO[3], pH 7.8, and subjected to trypsin digestion
(1:50 mass ratio of enzyme to proteins) at 37°C for overnight.
Desalting of the peptide samples was performed with 96-Well SPE Plates
(Phenomenex).
2.6. LC-MS/MS analysis
Peptide samples were re-dissolved in 0.1% formic acid and adjusted to a
concentration of 0.1 μg/ μl, and 7 μl was analyzed by a nanoAcquity
ultra performance liquid chromatography (UPLC) system (Waters) coupled
to a Q Exactive Plus (Thermo Scientific). The peptides were trapped on
an in-house prepared C18 trapping column (4 cm x 150 μm i.d., 5 um
particle size of Jupiter C18, Phenomenex), and LC separations were
performed with a custom packed analytical C18 column (70 cm x 75 μm
i.d., 3 um particle size of Jupiter C18, Phenomenex). Binary mobile
phases comprising of buffer A (0.1% formic acid in water) and buffer B
(0.1% formic acid in acetonitrile) were used at a flow rate of 300
nl/min. For peptide elution, the percentage of buffer B was increased
linearly as follows: from 0.1–8% over 0–4 min; from 8–12% over 4–36
min; from 12–30% over 36–135 min; from 30–45% over 135–175min; from
45–95% over 175–180 min. A 10 min wash with 95% buffer B and a final 1
min wash with 100% buffer B was also included.
Full MS scans were conducted at 400–2000 m/z range with a resolution of
35, 000 in the Orbitrap followed by top 12 data-dependent MS/MS
acquisitions. The automatic gain control (AGC) was set to 3e6, and the
maximum injection time (IT) was 20 ms. The MS/MS isolation window was
set as 2 m/z, and higher-energy collisional dissociation (HCD) of NCE
(normalized collision energy) was 35. MS/MS settings were as follows:
resolution of 17, 500; AGC of 1e5; and maximum IT of 100 ms, and a 30 s
dynamic exclusion window.
2.7. Proteomics data processing and statistical analysis
The MS data were processed by MaxQuant (version 1.6.2.0). Raw data
files were searched against the non-redundant human Uniprot database
containing 20,198 entries. Carbamidomethylation on cysteine was set as
fixed modification, while oxidation on methionine and acetylation on
protein N-terminus were set as variable modifications. A tryptic rule
with a maximum of two missed cleavages was applied. Mass tolerance of
peptide for the first and second search was set as 20 and 4.5 ppm,
respectively. The MS/MS search tolerance was 0.5 Da. The minimum
peptide length was 7 amino acid residues, and the minimum score for a
modified peptide was 40. The search also set the false discovery rate
(FDR) at 0.01 for both peptide and protein identifications. Label-free
quantification (LFQ) was used for all experiments with the “match
between run” option enabled. A minimum LFQ ratio count was set to 2
with default normalization method enabled.
ANOVA (analysis of variance) tests with permutation-based FDR
calculation were performed on all experimental groups using Perseus.
Student t test for pair-wised comparison was performed to determine the
statistical significance between each experimental group and controls.
Statistically significant proteins were defined using strict criteria
of FDR < 0.01 and p-value < 0.01. Gene ontology analysis was performed
with the Database for Annotation, Visualization and Integrated
Discovery (DAVID, [99]https://david.ncifcrf.gov/).
2.9. Other methods
The role of ENM dissolution potential on oxidative stress was
investigated by plotting HMOX1 expression levels as a function of
[MATH: ΔHMe<
/mi>+ :MATH]
, a quantum-mechanical description defined as the enthalpy of formation
of a gaseous cation having the same oxidation state as that of the
metal in the metal oxide structure. The values of
[MATH: ΔHMe<
/mi>+ :MATH]
, obtained by quantum-mechanical calculations, were described
previously ([100]Puzyn et al. 2011). For ENMs where the values of
[MATH: ΔHMe<
/mi>+ :MATH]
are not available, a regression model was used to derive a value for
[MATH: ΔHMe<
/mi>+ :MATH]
from the electronegativity scale (χi) and oxidation number (χox) of the
metal cation as described previously ([101]Li and Xue 2006; [102]Thrall
et al. 2019).
3. Results
3.1. Characterization of ENM properties and cell dosimetry
The physiochemical properties for all ENMs used in this study are
summarized in [103]Table 1. Particle size distributions as measured by
transmission electron microscopy (TEM) showed that most ENMs were in
the range of 7 to 100 nm. Effective particle diameters in the culture
media, as determined by DLS, displayed much larger values compared to
TEM. In most cases, the larger effective size values are indicative of
particle agglomeration in medium. However, comparative analysis using
the ISD3 model suggested that the increased effective diameter of a
subset of ENMs (Ag_Cit, MgO, WO3, ZnO, ZnS) measured by DLS is likely
due to decreased effective density resulting from formation of a
protein corona rather than particle agglomeration. In addition, all
ENMs in this study exhibited a net negative zeta potential in culture
media, in agreement with metal oxide chemistry and the fact that ENMs
adsorb serum proteins, which on average bear net negative charge on
average.
Due to the number of diverse metal/metal oxide ENMs used in this study,
it was not practical to quantify cell dose and uptake through direct
experimental measures. Therefore, we applied the ISD3 model, which was
previously experimentally validated using metal oxides, to estimate the
relative cell-deposited dose fractions for each ENM ([104]Thomas et al.
2018). As shown in [105]Table 1, for most ENMs, less than 20% of the
applied concentration of ENMs deposit on the cell monolayer over 24 hrs
(based on culture conditions used in cytotoxicity assays, described
below). Furthermore, the deposited dose fraction varied by over 13-fold
across the library of ENMs. In particular, Fe[2]O[3] and V[2]O[5] ENMs
displayed very high dose deposition rates, resulting in 100% deposition
at 24 hrs. In fact, analysis of the fraction of deposited ENM as a
function of time revealed that, all of the applied V[2]O[5] particles
are predicted to settle on the cell layer within only 2 h incubation
time (not shown), in large contrast to the dosimetry behavior of the
majority of the other ENMs.
3.2. Impact on cell viability and oxidative stress
The impact of each ENM on cell viability was assessed by the MTT assay.
PMA-differentiated THP-1 macrophages were exposed with a range of ENM
concentrations (6.25–50 μg/mL) for 24 h. Initial studies showed that 12
hr exposure was insufficient to reveal ENM-mediated cytotoxicity, thus
subsequent experiments focused on the 24 hr time point. Significant
effects on the percentage of metabolically viable cells was found for
only a subset of ENMs, including Ag_Cit, ZnO, CuO, and V[2]O[5]
([106]Fig 1A). In contrast, many of the ENMs tested including ZnS, CdS,
Au, SiO[2], Fe[2]O[3] (10 nm), Fe[2]O[3] (100 nm), CeO[2] (10 nm),
CeO[2] (30 nm), MgO, TiO[2]_P25, and 1% Ag_SiO[2,] did not
significantly affect cell viability even at the highest concentration
administered (50 μg/mL, p > 0.01). Other ENMs such as 10% Ag_SiO[2],
Al[2]O[3], Ag, and WO[3] showed a minor decrease in cell viability
(around 20%) with high dosage exposure.
Figure 1.
[107]Figure 1.
[108]Open in a new tab
Cell viability and HMOX1 expression of THP-1 cells exposed to ENMs. (A)
MTT assay for cell viability. The cytotoxicity of 20 ENMs was evaluated
in THP-1 cells under different dosages for 24 h. ENMs were ranked in
the figure legend based on the percentage of cell viability at the
highest dosage tested (50 μg/mL). The median value of coefficient of
variations for MTT assays was 5.1%. Data points with a black circle
denote p < 0.01. (B) Relative mRNA expression of HMOX1. The mRNA level
in control samples was normalized to 1 (red dotted line). ENMs were
ranked based on the mRNA level at the high dose (25 μg/ml). Data was
represented as mean ± standard deviation. In both experiments, four
biological replicates were conducted. *: p < 0.01.
One proposed mechanism of ENM-induced cytotoxicity is through reactive
oxygen species (ROS)-dependent cellular stress. For instance, we and
others have previously found that mRNA induction of heme-oxygenase 1
(HMOX1), a Nrf2-regulated gene product, is a good surrogate for
assessing redox stress induced by ENMs in cells ([109]Meng et al. 2009;
[110]Thrall et al. 2019). While the impact of ENMs on cellular redox
state can also be assessed by GSH and GSSG/GSH ratio, our previous
studies demonstrated mRNA induction of HMOX1 occurs at much lower
levels of ENM-induced redox stress than is necessary for altering
cellular GSH level ([111]Duan et al. 2016). Thus, we used HMOX1 mRNA
induction as a sensitive indicator to quantify cellular oxidative
stress levels following ENM exposure. As expected, some of the ENMs
which displayed the greatest cytotoxic potential, such as CuO, ZnO and
Ag_Cit, induced marked increases in HMOX1 gene expression ([112]Fig.
1B, [113]Table S1). Significant increases in HMOX1 were also observed
by several non-cytotoxic ENMs such as Fe[2]O[3], AgSiO[2] and Ag,
albeit at lower levels. Interestingly, a decrease in HMOX1 was observed
following V[2]O[5] exposure, possibly due to a high degree of
cytotoxicity observed with this ENM. As discussed in more detail, the
high level of cytotoxicity and decrease in HMOX1 mRNA associated with
V[2]O[5] treatment must be interpreted with caution, given the rapid
settling of these ENMs in culture systems. Moreover, statistical
association between cell viability and HMOX1 change was also evaluated,
which showed a moderate negative correlation (r = −0.565 at the high
dose, [114]Fig. S1).
The potential role of dissolution (metal cation release) in mediating
the cytotoxicity of metal oxide ENMS has also been reported for several
of the ENM types studied here, including Ag, ZnO and CuO ENMs
([115]Mihai et al. 2015; [116]Smith et al. 2018; [117]Zhang et al.
2012). Since it was not practical to conduct ICP-MS analyses across the
diverse types of ENMs within this library, we also investigated the
potential role of ion dissolution as a factor for inducing oxidative
stress using first principle quantum descriptors of physicochemical
properties previously used in a quantitative structure-activity
relationship model of metal oxide toxicity in bacteria ([118]Puzyn et
al. 2011). Specifically, we compared the level of HMOX1 induction as a
measure of cellular oxidative stress with the physical property
[MATH: ΔHMe<
/mi>+ :MATH]
, which is a quantum mechanical estimate of the enthalpy of formation
of a gaseous cation from a metal oxide ([119]Puzyn et al. 2011). A
general decrease in HMOX1 induction as
[MATH: ΔHMe<
/mi>+ :MATH]
increases was found for most ENMs except for MgO ([120]Fig. S2). In
other words, the lower the energy barrier necessary for the metal oxide
nanoparticle to release its metal cation, the higher the observed HMOX1
response. For strongly cytotoxic ENMs in particular, this result is
consistent with a role for cation release accompanied by release of
electrons as a mechanism for the induction of oxidative stress
underlying their cytotoxic effects and is consistent with previous
experimental results ([121]Mihai et al. 2015; [122]Zhang et al. 2012).
3.3. Cellular proteome responses following ENM exposure
To systematically evaluate cellular responses to ENMs, we selected a
subset of ENMs based on the targeted screening results and performed
quantitative proteomics profiling analyses ([123]Fig. 2A). Eleven ENMs
were selected, including Ag, Ag_Cit, AgSiO[2] (comprised of 1% and 10%
Ag, respectively), CuO, Fe[2]O[3], MgO, SiO[2], V[2]O[5], ZnO and ZnS,
providing a representative subset of ENMs that display a broad range of
physicochemical properties ([124]Table 1), cytotoxic potential, and
ability to induce oxidative stress ([125]Fig. 1). For each ENM, both a
low (12.5 μg/ml) and a moderate (25 μg/ml) concentration were used to
investigate dose-dependent cellular responses. Cell exposures were
conducted for 12 hr, based on pilot time course analyses (data not
shown) which found this time period was sufficient to allow for gene
and protein expression changes that precede any significant
cytotoxicity (measured at 24 hr). Four biological replicates were
included for each ENM exposure. The panel of 11 ENMs were assigned into
6 analytical blocks, with each block containing a nested set of
untreated samples as the experimental control to both enable
large-scale quantification and ensure quality control between blocks
([126]Fig. 2B). The analysis yielded the identification and
quantification of an average of 4800 proteins in each analytical block
([127]Supplemental Data File 1) with quantitative data for 1505
proteins obtained in all samples ([128]Supplemental Data File 2).
Figure 2.
[129]Figure 2.
[130]Open in a new tab
ENMs exposures cause different levels of proteomic changes. (A)
Schematic overview of the proteomics workflow. A) PMA-differentiated
THP1 macrophages were treated with 11 ENMs at two different doses: 12.5
(low) or 25 (high) μg/mL for 12 h. Samples were subjected to protein
extraction, digestion and LC-MS analysis. (B) Two ENMs, along with a
set of controls, were assigned to each block for LC-MS analysis. (C) QC
samples were run before and after each block, samples in which were
completely randomized. (D) Volcano plots displaying the overall change
of the proteome induced by ENMs. The extent of protein abundance
change, expressed as log2 fold change (ENM treatment over the control),
is shown in the x axis. Significance of the pair-wised comparisons,
expressed as negative logarithm of the p values, is shown on the y
axis. Blue and red dots represent proteins that are down- and
up-regulated, respectively, with p value of 0.01 as a cutoff
(horizontal black dashed lines). Note that data with the high dose (25
μg/ml) treatment were shown. For volcano plots with low dose (12.5
μg/ml) treatment, see [131]Fig. S2.
To visualize the overall levels of proteome changes induced by ENMs,
volcano plots were created that display the statistical significance (y
axis) and the extent of change in protein abundances (x axis) under
each treatment condition ([132]Fig. 2C, [133]Fig. S3). As anticipated,
treatment with non-cytotoxic ENMs induced relatively small
perturbations in the proteome with a narrower distribution of the log2
fold change (typically −0.5 to 0.5). In contrast, a larger portion of
the overall proteome was found to be significantly altered following
exposure to ENMs which had greater cytotoxic potential.
To investigate whether concentration-dependence can be observed from
the overall proteome changes, we plotted the extent of protein
abundance changes between high concentration and low concentration for
each ENM ([134]Fig. S4). The directionality of changes in the cellular
proteome between the two exposure concentrations were consistent for
all ENMs. While the cytotoxic ENMs displayed a general
concentration-dependent alteration of the cellular proteome, there was
no obvious trend observed for the non-cytotoxic ENMs, possibly due to
an overall small change in the proteome under these treatments. In
summary, the overall levels of proteome abundance changes correlate
with both ENM cytotoxic potential and the administered ENM
concentrations, which supports the robustness of the label-free global
quantitative proteomics approach.
3.4. Unsupervised clustering of proteomics data differentiates cytotoxic
potential of ENMs
To assess global cellular proteome responses to various ENMs, we first
attempted a data-driven approach to group and visualize the complex
dataset collected ([135]Fig. 3). Unsupervised hierarchical clustering
analysis using 1505 proteins that were quantified in all samples
revealed six distinct sample (or dataset) clusters (A-F) corresponding
to different ENM types and treatment concentrations. Of note, the high
treatment concentration of V[2]O[5] and ZnO formed two separate
clusters (A and B, respectively), while the low concentration of these
two ENMs grouped into one cluster (cluster D). Samples treated with CuO
and Ag_cit at both the high and the low concentrations clustered as a
single group (cluster C). Collectively, these four ENM types were
grouped together and comprised the ENMs that display the greatest
cytotoxic potential. On the other hand, the remaining seven ENMs, which
were predominantly weakly or noncytotoxic, clustered separately within
two related groups (cluster E and F, respectively). While all four
biological replicates for each treatment condition (ENM by
concentration) were within the same cluster in the cytotoxic group,
biological replicates were not always within the same cluster E or F
for the non-cytotoxic group, presumably due to the more subtle
differences in the proteome changes observed between these treatment
conditions. Thus, at the level of global response profiles, the data
readily distinguished ENM classes based on bioactivity and support the
hypothesis that some common mechanisms (protein pathways) are likely
involved in the response across ENM types.
Figure 3.
[136]Figure 3.
[137]Open in a new tab
Hierarchical clustering of THP-1 proteomes following exposure to ENMs.
The relative protein abundance, expressed as log2 fold change between
each experiment group and control, was used for clustering. Rows
represent proteins quantified in all samples (1505 in total), which are
hierarchically clustered in into five groups (Cluster I-V). Columns
represent THP-1 samples treated with 11 ENMs at two dosages (12.5 and
25 μg/ml as the low and the high concentration, respectively), which
are segregated to two major groups based on the cytotoxicity of ENMs:
cytotoxic (Cluster A-D) and non-cytotoxic groups (Cluster E and
F).Labeling of columns (experiment groups): name of ENM, followed by
dose (L: low; H: high), followed by replicates number (1–4).
3.5. Biological processes impacted by ENM exposure
Unsupervised clustering of the protein abundance patterns also revealed
five protein clusters that displayed distinct patterns of abundance
change across the library of ENM treatment groups ([138]Fig. 3, see
y-axis ). We hypothesize that this pattern may indicate distinct
functional processes that are differentially affected by ENM
physicochemical properties. To further evaluate this concept, proteins
from each cluster, except cluster Ⅱ, were subjected to gene ontology
(GO) based enrichment analysis in order to identify biological
processes represented in these clusters. The top 10 biological
processes represented by each group were shown in [139]Fig. 4. Proteins
in cluster Ⅱ were analyzed separately since this cluster did not
contain a sufficient number of proteins for a statistical-based
enrichment analysis. For cluster I, the protein abundances were
impacted across a broad set of ENMs and the top two enriched biological
processes were translational initiation and cell-cell adhesion, both of
which showed down-regulation following ENM exposure ([140]Fig. 4A). For
cluster III, proteins were statistically enriched in metabolic
processes such as tricarboxylic acid (TCA) cycle and fatty acid
beta-oxidation and were generally up-regulated following exposure to
ENMs ([141]Fig. 4B). For cluster IV, the top enriched biological
processes were mRNA splicing and tRNA export from the nucleus, where
proteins were both up- or down-regulated depending on treatment
conditions ([142]Fig. 4C). Cluster V featured mRNA processing and
protein folding processes, which were predominantly up-regulated
([143]Fig. 4D). It is noteworthy that the protein abundances changes
for biological processes represented in clusters III-V were most
strongly impacted by cytotoxic classes of ENMs. Nonetheless,
significant downregulation of proteins involved in translational
initiation and cell adhesion (Cluster I) was observed broadly across
ENM types.
Figure 4.
[144]Figure 4.
[145]Open in a new tab
Distinctive biological processes in ENM-perturbed THP-1 proteomes from
four protein clusters. (A-D) The enriched biological processes of
protein cluster I, III, IV and V, respectively. For the top two
enriched biological process in each protein cluster, a heat map was
created using the top five proteins ranked by average protein fold
change.
Interestingly, cluster II was driven by robust changes in four proteins
(HMOX1, HS71B, DNJB1, and SQSTM1). These proteins displayed an overall
significant increase in abundance mainly in response to the cytotoxic
ENMs, except V[2]O[5]. [146]Fig. 5A shows the fold change values of
these proteins as a function of ENM type. Similar to the mRNA
expression screening data, we observed a 5–16 fold increase of HMOX1
protein abundance in THP-1 cells following exposure to ENMs which
display stronger cytotoxic potential, with the exception of V[2]O[5],
but much smaller, yet significant changes with some of the
non-cytotoxic ENMs. Similar trends were observed for the other three
proteins ([147]Fig. 5A), all of which showed strong positive
correlations with HMOX abundance in terms of protein fold change in
response to ENM treatments ([148]Fig. S5), suggesting that all four of
these proteins are responsive to oxidative stress. Both HS71B and DNJB1
are heat shock-related molecular chaperons that function in a wide
range of processes in response to stress (37, 38). DNJB1 interact with
HSP70, and the up-regulation of DNAJB1 mRNA expression in response to
ENMs has been reported ([149]Moos et al. 2011). Heat shock proteins
have long been recognized as cytoprotective molecules involved in the
facilitation of protein folding, assembly of transportation of protein
complex, and proteolysis of misfolded proteins under oxidative stress
([150]Ikwegbue et al. 2017; [151]Tang et al. 2007). SQSTM1, also known
as p62 or A170, is a multifunctional adaptor protein that plays an
essential role in the delivery of the polyubiquitinated protein cargo
to the autophagosome in the process of selective macroautophagy
([152]Jeong et al. 2019). While general roles of these proteins in
stress response pathways have been described, our discovery that they
are highly responsive to ENM exposure is novel.
Figure 5.
[153]Figure 5.
[154]Open in a new tab
Cluster II proteins and their associations with the overall proteome
change and cell viability. (A) Fold change of cluster II proteins
following exposure to ENMs. (B) The degree of overall proteome
perturbation indicated by the number of significantly changed proteins.
(C) Correlation between the protein fold change of HMOX1 and the number
of significantly changed proteins. (D) Correlation between the protein
fold change of HMOX1 and cell viability. In C and D, red lines indicate
linear model fit, and r denote the Pearson’s correlation coefficient.
Blue lines denote the normalized HMOX1 expression levels of control
samples as 1. V[2]O[5] are represented as triangles since it a
cytotoxic particle that causes significant proteome changes and cell
death, but a decrease in HMOX1 expression. All the other ENMs were
represented as circles and used for the Pearson’s correlation analysis.
We also compared the number of statistically significant proteins that
show altered abundance levels as a function of the ENM type. In line
with the clustering analysis, more significantly altered proteins were
identified in samples treated with cytotoxic ENMs ([155]Fig. 5B,
[156]Supplemental Data File 3). Given that cellular oxidative stress
represents a major initiator of the adverse biological outcomes of ENMs
([157]Kodali and Thrall 2015; [158]Meng et al. 2009; [159]Zhang et al.
2012) and that HMOX1 expression has been commonly used as a reporter of
cellular oxidative stress levels ([160]Di Cristo et al. 2016; [161]Lenz
et al. 2013; [162]Tabei et al. 2016; [163]Thrall et al. 2019), we aimed
to correlate HMOX1 protein abundance with proteome changes. As
expected, a strong positive correlation (r = 0.90) was observed between
the fold change of HMOX1 and the number of significant proteins
observed across different ENM treatment conditions ([164]Fig. 5C).
Moreover, a good correlation between the mRNA expression (by qRT-PCR)
and protein abundance (by LC-MS) of HMOX1 was observed for all ENM
treatments ([165]Fig. S6), which provides orthogonal validation of our
proteomics analyses. We also observed a negative correlation (r = −
0.51) between the fold change of HMOX1 protein abundance (at 12 h) and
cell viability following exposure to ENMs measured later at 24 h
([166]Fig. 5D), which supports the role of oxidative stress in
initiating cellular responses to ENMs. V[2]O[5] appears to be an
exception to these observed correlations, as discussed later. However,
our overall data support the conclusion that the observed cellular
proteome responses are ROS-dependent or ROS-driven, which in turn leads
to different levels of cytotoxicity.
3.6. Common and specific cellular pathways impacted by ENM exposure
To further assess whether there are unique pathways initiated by
cytotoxic ENMs versus non-cytotoxic ENMs, we performed pathway
enrichment analysis for the significant proteins identified from each
ENM condition. The top significantly enriched biological processes from
each ENM treatment were identified, and their significance (p-value)
across all ENM groups were plotted in [167]Fig. 6. This analysis
revealed two general categories of biological processes, one broadly
altered by most ENMs, and the other more uniquely impacted by ENMs
which display cytotoxicity after 24 hr exposure. The commonly altered
biological processes include cell-cell adhesion, translation, and mRNA
catabolic processes, which involved proteins that were generally
downregulated in abundance ([168]Fig. S7). The impact on these
biological processes is further amplified in the case of cytotoxic
ENMs, suggesting that these processes may reflect a general adaptive
cellular response to oxidative stress induced by ENM exposure, even at
low levels of ENM-induced stress. The biological processes that were
uniquely altered in response to cytotoxic ENMs included the TCA cycle,
fatty acid beta-oxidation, response to unfolded protein, and NIK/NF-κB
signaling ([169]Fig. 6 and [170]Fig. S8). These uniquely induced
biological processes likely reflect pathological responses triggered by
the higher level of oxidative stress induced by this subset of ENMs
that ultimately lead to the loss of cell regulation and the observed
cytotoxicity. The biological processes identified here are also
consistent with our previous study ([171]Duan et al. 2016), in which we
found that mitochondrial proteins involved in key cellular metabolism
and energetics pathways (including TCA cycle and beta-oxidation), are
substrates for oxidative modification at high levels of ENM-induced
oxidative stress.
Figure 6.
[172]Figure 6.
[173]Open in a new tab
Biological processes responsive to ENMs exposure. The significance of
the gene ontology enrichment is represented by the negative logarithm
of the p value (0.01 as the significance cutoff). White blocks denote
the absent of significant biological processes in enrichment.
Some of the specific protein signatures that distinguish the cytotoxic
class of ENMs discovered in this study are shown in [174]Fig. 7.
Proteins showing induced expression by the cytotoxic ENMs include
enzymes involved in cellular redox processes of ROS such as SODM,
TRXR2, PRDX3, and PRDX5 ([175]Fig. 7A), as well as proteins involved in
the unfolded protein response such as ERO1A, CH60, HS106, and SERPH,
([176]Fig. 7B). Interestingly, a down regulation of protein expression
was found for proteins involved in NIK/NF-κB signaling (HMGB1, PSB6,
PSME1, and ACTN4, [177]Fig. 7C).
Figure 7.
[178]Figure 7.
[179]Open in a new tab
Protein signatures in different cellular responses to ENMs. The
relative abundance of proteins involved in responses to ROS (A),
unfolded protein responses in the ER (B), and NF-kB signaling (C), were
plotted. The boxplots show the range of protein fold change under the
treatment of non-cytotoxic and cytotoxic ENMs, respectively. Each dot
represents the average from four biological replicates. Signature
proteins were selected based on the statistical significance between
the two treatment groups (Student t test, *: p < 0.1; **: p < 0.05;
***: p < 0.01). For each boxplot, Q1 and Q3 define the boundaries of
the box, while the minimum and maximum are the whiskers. The median is
shown as the central black line. All data points including outliers are
plotted.
To further confirm the observed ER stress response and inflammatory
response, we used qRT-PCR to measure induction of CHOP and
interleukin-1beta (IL1B) mRNA, as markers for ER stress and
inflammation, respectively ([180]Duan et al. 2016; [181]Khan et al.
2013; [182]Kodali et al. 2013; [183]Li et al. 2014). Increased cellular
ROS has been shown to induce inflammasome activation and IL-1β
secretion ([184]Duan et al. 2016; [185]Yazdi et al. 2010). Indeed, CHOP
and IL1B mRNA expression were induced following exposure to cytotoxic
ENMs as compared to non-cytotoxic ENMs ([186]Fig. 8, [187]Table S1).
The mRNA levels for these reporter genes were tightly distributed
following exposure to non-cytotoxic ENMs, but showed a much wider
distribution of expression following exposure to cytotoxic ENMs. It
should be noted that the THP-1 cells used here were differentiated
using PMA treatment, but were not further ‘primed’ with LPS prior to
ENM exposure, and hence are referred to as a “M0” phenotype. The
induction of IL-1β mRNA expression following ENM exposure is thereby
consistent with a shift toward a proinflammatory M1-like polarization
state. However, in pilot proteomic studies (data not shown) comparing
the profile of THP-1 cells that were polarized with either classical M1
stimuli (LPS/IFN-γ) or M2 stimuli (IL-4/IL-13), no clear pattern of
polarization was observed following ENM exposure, suggesting
ENM-mediated effects on the proteome are more complex than a simple
shift in classical polarization states. Further studies are warranted
to investigate the potential role that macrophage polarization has in
modulating the response to ENM exposure.
Figure 8.
[188]Figure 8.
[189]Open in a new tab
Gene expression of CHOP (A) and IL1B (B) in response to non-cytotoxic
and cytotoxic ENMs. The relative mRNA expression was shown fold change
normalized to the untreated control, and the boxplots show the range of
mRNA fold change under the treatment of non-cytotoxic and cytotoxic
ENMs, respectively. Each dot represents the average from four
biological replicates. Statistical signature was found between the two
treatment groups for IL1B (Student t test, **: p < 0.05).
4. Discussion
While transcriptomics and proteomics have been previously applied to
analyze the cellular responses to ENMs ([190]Kodali et al. 2013;
[191]Tarasova et al. 2017; [192]Zhang et al. 2018), the current work,
to our best knowledge, represents the most comprehensive profiling of
the broad biological responses to a library of ENMs using global
quantitative proteomics. The comprehensive data ([193]Fig. 3) provide a
basis to identify the patterns of a diverse set of cellular pathways
and biological processes impacted by ENM exposure in an important
immune cell type, laying the foundation for more comprehensive
pathway-level structure activity-based assessments of ENMs in the
future.
Interestingly, a clear correlation was observed between the extent of
proteome changes, the levels of induction of ROS-responsive proteins
(e.g., HMOX1, HS71B, DNJB1, SQSTM), and the eventual cytotoxic outcome
from ENM exposure ([194]Fig. 5). These data collectively suggest that
the cellular responses of THP-1 macrophages to the metal and
metal-oxide ENMs are likely modulated through an ROS-dependent
mechanism where common adaptive pathways are initially activated by
particle interactions, and may progress to pathological responses such
as cell death depending on the levels of ROS or oxidative stress
induced by the ENMs. This observation is consistent with the notion
that oxidative stress represents an initiating mechanism that is
predictive of ENM cytotoxicity, as we and others have proposed
([195]Duan et al. 2016; [196]Meng et al. 2009; [197]Nel et al. 2006;
[198]Thrall et al. 2019). Indeed, pathway analyses revealed clearly two
distinctive categories of biological processes ([199]Fig. 6) in
response to the gradient of cellular oxidative stress elicited by
various ENMs, as estimated by HMOX1 expression levels: 1) biological
processes commonly impacted by many ENMs independent of cytotoxic
potential, and 2) biological processes that ultimately invoke
cytotoxicity, which are uniquely observed in response to cytotoxic
ENMs.
The set of biological processes (e.g., translation, cell adhesion)
commonly impacted by the majority of ENMs most likely represent
cellular processes that facilitate adaption to low levels of oxidative
stress triggered by particle interactions. These adaptive stress
responses are critical for cellular homeostasis regulation and cell
survival. The observation of adaptive stress responses with low levels
of oxidative stress from non-cytotoxic ENMs is also consistent with our
previous findings that ENMs that lack cytotoxic activity caused
transcriptional reprogramming and dysregulation of various biological
pathways, such as those involved in the oxidative stress response
([200]Kodali et al. 2013). Significantly, alterations in these
biological processes could resulted in an impaired innate immune
response such as reduced phagocytic function. This has already been
demonstrated in our previous work, in which bone marrow-derived
macrophages pretreated with non-cytotoxic amorphous silica or
superparamagnetic iron oxide showed a diminished phagocytic activity
toward the lung pathogen Streptococcus pneumoniae ([201]Kodali et al.
2013). One of the most commonly reported adaptive responses to cellular
stress is translational inhibition. The inhibition of global protein
synthesis was previously observed as a common adaptive response under
stress conditions, including exposure to particles ([202]Lin et al.
2019; [203]Shenton et al. 2006; [204]Simpson and Ashe 2012; [205]Topf
et al. 2018). The observation that translation-related proteins are
primarily downregulated suggests that ENM-induced stress similarly
contributes to the attenuation of translation, possibly to prevent
accumulation of misfolded proteins. Indeed, oxidative stress has been
shown to elicit multiple levels of translational regulation including
inhibition of both translational initiation and elongation, as well as
selective increase of translation of stress protective mRNAs
([206]Grant 2011; [207]Shenton et al. 2006). For example, our data
revealed that ENM exposure caused downregulation of GCN1, which has
been previously shown to regulate protein translation in response to
H[2]O[2] by acting as a positive activator of the eukaryotic initiation
factor-2a protein kinase ([208]Shenton et al. 2006). In addition, many
ribosomal proteins are down-regulated after ENM exposure, but to a
greater extent following exposure to cytotoxic ENMs, suggesting that
the inhibition of translation is dependent on the level of oxidative
stress. Related to translation, our analyses also identify
SRP-dependent co-translational protein targeting to membrane as a
commonly regulated process. In this process, nascent proteins with
membrane-targeting sequences are recognized by SRP on the ribosome
during translation and are then translocated into the ER ([209]Nyathi
et al. 2013). The down-regulation of key regulators of protein
synthesis and protein loading into the ER under ENM-induced stress is
consistent with our previous findings that showed proteins within the
ER are highly sensitive to oxidative modification following exposure to
metal oxide ENMs ([210]Duan et al. 2016). Whether redox modification of
these proteins is directly linked to a change in their abundance, and
the underlying mechanisms involved, warrants additional investigation.
In addition to protein translation, cell-cell adhesion was identified
as another biological process commonly impacted by ENMs, where most
proteins in this category were also down-regulated following exposure
to ENMs ([211]Fig. S7). Proteins involved in cell adhesion complexes
are known to be required for many additional cellular functions such as
phagocytosis, production of cytokines, and antigen presentation in
macrophages ([212]Prieto et al. 1994). The degree of down-regulation on
these proteins also correlates with the level of alteration of HMOX1,
again suggesting ROS plays a regulatory role in this process. ROS have
been reported as essential mediators of cell adhesion through integrin
signaling primarily via oxidative inhibition of protein tyrosine
phosphatase ([213]Chiarugi et al. 2003). We identified several
significantly changed proteins including KTN1, RACK1, and SWP70 that
interact or regulate integrins ([214]Liliental and Chang 1998;
[215]Sivalenka and Jessberger 2004; [216]Tran et al. 2002), suggesting
that ENMs exposure could disrupt cell-cell adhesion via ROS-mediated
down-regulation of integrin-associated processes. Additionally, a
ROS-dependent loss of cell-cell adhesin was also found in endothelial
cells from primary human umbilical veins, where phosphorylation on
alpha cadherin was increased ([217]van Wetering et al. 2002). Our data
showed that IQGAP1, a Ras GTPase-activating-like protein and a key
regulator of cadherin, was generally down-regulated ([218]Fig. S7). The
concept that ENMs exposure may induce an ROS-dependent disruption of
cell adhesion is also consistent with our prior observation of reduced
phagocytic activity of macrophages following exposure to a variety of
metal oxide ENMs ([219]Kodali et al. 2013; [220]Duan et al. 2016;
[221]Thrall et al. 2019).
The second broad category of biological processes found to be uniquely
impacted by exposure to cytotoxic ENMs included responses to ROS,
unfolded protein responses, immune-related responses (e.g., antigen
processing and NIK/NF-κB signaling), as well as energy metabolism
processes such as the TCA cycle and fatty acid beta-oxidation.
Collectively these data clearly illustrate activation of the classical
antioxidant defense system, but with greater detail from a global
proteome perspective. For instance, many antioxidant enzymes such as
SODM, PRDX3, and PRDX5 and other redox-regulating proteins such as
TRXR2 are up-regulated ([222]Fig. S8). The activation of ROS response
following exposure to cytotoxic ENMs is also closely connected to ER
stress response or the unfolded protein response (UPR), which has been
previously reported for ENM exposure by us and others ([223]Chen et al.
2014; [224]Chen et al. 2015; [225]Duan et al. 2016; [226]Tsai et al.
2011). In line with this, our study provided solid evidence for an
up-regulation of UPR, suggesting an overload of the ER system under
oxidative stress and an attempt of macrophages to improve or maintain
ER homeostasis following exposure to ENMs.
Closely linked with the UPR is the observation that impaired
immune-related responses such as NF-κB signaling were specifically
impacted by cytotoxic ENMs. For instance, several proteins involved in
NF-κB are down-regulated by cytotoxic ENMs. High mobility group box 1
(HMGB1), a DNA-binding protein, has been demonstrated to activate NF-κB
signaling in bone marrow macrophages via the induction of gene
expression and increase in protein abundance of RelB ([227]Toia et al.
2015). Actin-binding protein alpha-actinin 4 (ACTN4) is an actin
binding protein that can be localized to the nuclear to interact with
and thus activate RelA/p65 subunit of NF-kB ([228]Aksenova et al.
2013). Proteasome subunit beta type-6 (PSB6) is a component of the 20S
proteasome complex which degrades intracellular misfolded or damaged
proteins, and is predicted to be involved in the activation of NF-kB
(predicted by Reactome, [229]https://reactome.org/). Proteasome
activator complex subunit 1 (PSME1) is implicated in the
immunoproteasome assembly and its involvement in NF-kB signaling has
also been predicted by Reactome. Significant down-regulation of these
proteins ([230]Fig. 7C) suggest that cytotoxic ENMs led to an impaired
NF-κB signaling. Consistent with this prediction, previous studies had
demonstrated that ENMs exposure can block the activation of NF-κB. For
example, Ma et al showed that polyethylene glycol coated gold
nanoparticles inhibit lipopolysaccharide (LPS)-induced activation of
NF-κB in RAW264.7 cells ([231]Ma et al. 2010). Similarly, the blocking
effect on LPS-induced activation of NF-κB in RAW 264.7 macrophages
following exposure to ZnO[2] nanoparticles was also reported ([232]Kim
and Jeong 2015). Consistent with our results, the latter study also
demonstrated that zinc oxide nanoparticles induce the expression of
A20, a negative regulator of NF-κB. Collectively, the data suggest that
ENMs could inhibit NF-κB signaling by down-regulating positive
regulators in NF-κB signaling, leading to altered immune regulation.
Regardless of the specific mechanism, the inhibition of NF-κB may also
lead to the observed increase in IL-1β production, as it has been
demonstrated that NF-κB is a negative regulator of IL-1β secretion
([233]Greten et al. 2007).
Finally, our data revealed several highly sensitive protein signatures
to ENM-induced oxidative stress. In addition to HMOX1 mRNA induction
which has been used as a sensitive general readout for the relatively
magnitude cellular oxidative stress, we identified molecular chaperons
(HS71B and DNJB1) and autophagy receptor (SQSTM1) that were also highly
sensitive. The induction of molecular chaperons by exposure to ENMs has
also been reported ([234]Safar et al. 2019; [235]Sundarraj et al.
2017a; [236]Sundarraj et al. 2017b). The degree of up-regulation of
these proteins is consistent with the degree of cellular oxidative
stress as indicated by the strong correlations with HMOX1 expression,
suggesting potentially common regulatory mechanisms are involved. In
addition to a general cytoprotective role, these proteins have also
been shown to regulate the states of macrophage activation
([237]Henderson and Henderson 2009) and phagocytosis ([238]Wang et al.
2006). Under oxidative stress, misfolded proteins can aggregate,
forming irreversibly oxidized molecules that can be recycled by
autophagy ([239]Filomeni et al. 2015; [240]Vasconcellos et al. 2016).
It has been demonstrated that ENMs could induce the formation of
autophagosome ([241]Li and Ju 2018; [242]Ma et al. 2011). However, the
role of SQSTM1 in the response to ENMs has not been reported
previously. Our novel finding on the up-regulation of SQSTM1 following
exposure to cytotoxic ENMs may suggest that SQSTM1-mediated autophagy
is coordinated with the antioxidant defense system to control oxidative
stress-induced cellular damage. Future studies are needed to
investigate these potential mechanisms.
Besides the observations of distinctive cellular pathways in response
to ENMs, potential limitations of this analysis should be acknowledged.
First, we used HMOX1 expression as a reporter of cellular oxidative
stress based on previous evidence ([243]Meng et al. 2009; [244]Thrall
et al. 2019). This strategy is warranted over alternative measures of
redox stress since fluorescent assays (e.g., DCF) have limited dynamic
range and sensitivity, and our previous studies have directly
demonstrated that protein redox modifications following metal oxide ENM
exposure are detectable well below oxidative stress levels needed to
alter glutathione status in cells ([245]Duan et al. 2016). Second,
although the same concentrations for 11 ENMs were used for cytotoxicity
and proteomics profiling, the final dose of ENMs reaching the cells
through diffusion and gravitation varied significantly across ENM
types. In particular, the high cytotoxic effect of V[2]O[5], which was
an outlier for most correlation analysis, was likely due to the fact
that it readily precipitates in culture systems and rapidly deposits on
the cells ([246]Table 1). Thus, while V[2]O[5] and CuO appear to have
similar cytotoxic potency (at higher concentrations), it is important
to realize that the cells received up to 13-fold greater delivered dose
of V[2]O[5] as compared to CuO. In fact, dosimetry calculations using
the experimentally validated ISD3 model show that essentially all of
the applied V[2]O[5] concentration is deposited on the cells within 2
hrs. In addition, the general pattern of higher increases of the
cluster Ⅱ proteins (including HMOX1) upon exposure to cytotoxic ENMs
does not apply to V[2]O[5]. This is unlikely due to a lack of cellular
ROS production, as a previous study has shown that V[2]O[5] generates
long-lived O2^•– radicals ([247]Wang et al. 2017). Rather, it is likely
that the rapid and high cell dose of V[2]O[5] causes cell death through
different mechanisms, which are unlikely to be relevant in vivo. Such
observations point toward the critical importance of evaluating
delivered cell dose as a part of any hazard analysis or mechanistic
study of nanotoxicity.
5. Conclusions
The combined use of targeted endpoint assays and untargeted
quantitative proteomics profiling used here reveals a breadth of
biological pathways and processes that can be impacted by metal and
metal oxide ENMs even when the ENMs have no apparent cytotoxicity.
While our results support the oxidative stress paradigm as a predictive
strategy, the use of global proteomics also sheds insights into new
biological processes that have been previously implicated in adaptive
cellular mechanisms at lower levels of redox stress, including
translational inhibition and disruption of cell adhesion. To our
knowledge this is the first study to apply quantitative LC-MS based
proteomics across a library of ENMs this size. The results highlight
the potential for extending this strategy to develop multivariate-based
structure-function relationships at the cellular process or pathway
level, rather than focus on single endpoints. From the perspective of
hazard ranking approaches for emerging ENMs, the use of a simple
endpoints such as cytotoxicity are clearly useful for rapidly assessing
the potential acute potential of ENMs to induce toxicity. However, this
approach must be balanced with the reality that typical human exposures
to ENMs will be at low doses and occur in the context of mixed
exposures to additional environmental challenges, such as bacteria. The
important question of whether adverse effects of ENMs may be manifested
by altering susceptibility to concurrent environmental exposures is
often overlooked, despite mounting evidence that important health
impact from environmental and occupational nanoparticle exposures in
humans include indirect effects such as suppressed innate immune
function ([248]Andujar et al. 2014; [249]Coggon et al. 1994;
[250]Neupane et al. 2010; [251]Palmer et al. 2003; [252]Thrall et al.
2019). Identification of the biological pathways and proteins that are
broadly affected by ENMs, such as cell adhesion proteins that are also
critical for normal macrophage function, can provide insight into the
molecular mechanisms by ENM exposures may alter susceptibility to other
environmental challenges. Consistent with this idea, our previous work
found that oxidative modification of actin-binding proteins necessary
for both cell adhesion and macrophage phagosomal function is associated
with suppression of normal macrophage phagocytic clearance to bacterial
pathogens ([253]Duan et al. 2016). Additional research is needed to
develop strategies for the integration of these ‘omics’-level pathway
data into quantitative structure-activity relationship modeling
frameworks. However, the pathway markers identified in this study lay
an important foundation towards this future direction.
Supplementary Material
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[254]NIHMS1546573-supplement-1.xlsx^ (12.7MB, xlsx)
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[255]NIHMS1546573-supplement-2.xlsx^ (1.6MB, xlsx)
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[256]NIHMS1546573-supplement-3.xlsx^ (5.2MB, xlsx)
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[257]NIHMS1546573-supplement-4.docx^ (1.2MB, docx)
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[258]NIHMS1546573-supplement-5.xlsx^ (68.3KB, xlsx)
Highlights.
* The biological responses of THP-1 derived macrophage to a library
of metal/metal oxide engineered nanomaterials (ENMs) were evaluated
by both conventional biological endpoint assays and proteomics.
* The overall levels of ENMs-induced proteome changes and responses
of specific pathways correlated positively with the level of
oxidative stress they caused.
* Adaptive responses for biological processes such as translation and
cell adhesion were observed across all ENMs even for those ENMs
which induced a low level of oxidative stress.
* Specific biological processes such as unfolded protein response
were only triggered by cytotoxic ENMs which induced a much higher
level of oxidative stress.
* In addition to HMOX1, several sensitive protein markers to
ENM-induced oxidative stress were identified, including HS71B,
DNJB1, and SQSTM.
Acknowledgements