Neurotoxicology 100 (2024) 55–71
Contents lists available at ScienceDirect
Neurotoxicology
journal homepage: www.elsevier.com/locate/neuro
The contributions of neonatal inhalation of copper to air pollution-induced
neurodevelopmental outcomes in mice
Janine Cubello a, *, Elena Marvin a, Katherine Conrad a, Alyssa K. Merrill a, Jithin V. George a,
Kevin Welle b, Brian P. Jackson c, David Chalupa a, Günter Oberdörster a, Marissa Sobolewski a,
Deborah A. Cory-Slechta a, *
a
b
c
Department of Environmental Medicine, University of Rochester Medical Center, Rochester, NY 14642, USA
Proteomics Core, University of Rochester Medical Center, Rochester, NY 14642, USA
Department of Earth Sciences, Dartmouth College, Hanover, NH 03755, USA
A R T I C L E I N F O
A B S T R A C T
Edited by Dr. P Lein and Dr. R Westerink
Exposures to ambient ultrafine particle (UFP) air pollution (AP) during the early postnatal period in mice
(equivalent to human third trimester brain development) produce male-biased changes in brain structure,
including ventriculomegaly, reduced brain myelination, alterations in neurotransmitters and glial activation, as
well as impulsive-like behavioral characteristics, all of which are also features characteristic of male-biased
neurodevelopmental disorders (NDDs). The purpose of this study was to ascertain the extent to which inhaled
Cu, a common contaminant of AP that is also dysregulated across multiple NDDs, might contribute to these
phenotypes. For this purpose, C57BL/6J mice were exposed from postnatal days 4–7 and 10–13 for 4 hr/day to
inhaled copper oxide (CuxOy) nanoparticles at an environmentally relevant concentration averaging 171.9 ng/
m3. Changes in brain metal homeostasis and neurotransmitter levels were determined following termination of
exposure (postnatal day 14), while behavioral changes were assessed in adulthood. CuxOy inhalation modified
cortical metal homeostasis and produced male-biased disruption of striatal neurotransmitters, with marked in­
creases in dopaminergic function, as well as excitatory/inhibitory imbalance and reductions in serotonergic
function. Impulsive-like behaviors in a fixed ratio (FR) waiting-for-reward schedule and a fixed interval (FI)
schedule of food reward occurred in both sexes, but more prominently in males, effects which could not be
attributed to altered locomotor activity or short-term memory. Inhaled Cu as from AP exposures, at environ­
mentally relevant levels experienced during development, may contribute to impaired brain function, as shown
by its ability to disrupt brain metal homeostasis and striatal neurotransmission. In addition, its ability to evoke
impulsive-like behavior, particularly in male offspring, may be related to striatal dopaminergic dysfunction that
is known to mediate such behaviors. As such, regulation of air Cu levels may be protective of public health.
Keywords:
Air pollution
Ultrafine particulate matter
Copper
Neurodevelopmental disorders
Metal dyshomeostasis
Dopamine
1. Introduction
Male-biased neurodevelopmental disorders (NDDs), such as atten­
tion deficit/hyperactivity disorder, and autism spectrum disorder, as
well as psychiatric disorders such as schizophrenia, are considered to be
a consequence of both genetic vulnerability and environmental triggers
(Palladino et al., 2019; Schaafsma et al., 2017; Zhang et al., 2022a;
Zhang et al., 2022b). Notably, prevalence of these disorders has
continued to increase over the years (Charlson et al., 2018; Yang et al.,
2022; Zablotsky and Black, 2020; Zablotsky et al., 2019), with preva­
lence rates differing by geographic, demographic, and socioeconomic
conditions (Choi et al., 2012; Frances et al., 2022; Liu et al., 2015; Yang
et al., 2022; Zablotsky and Black, 2020; Zablotsky et al., 2019; Zeidan
et al., 2022). While the variability in prevalence has been attributed at
least in part to variations in diagnostic criteria and analysis across
studies, other reports indicate that variations in prevalence cannot be
explained by study methodology alone (Catala-Lopez et al., 2012; Gesi
et al., 2021; Zeidan et al., 2022). Collectively, these findings highlight
the importance of environmental contributions in the etiology of these
disorders.
In correspondence with the potential for environmental triggers,
accumulating evidence points to an association of air pollution (AP)
* Corresponding authors.
E-mail addresses: janine_cubello@urmc.rochester.edu (J. Cubello), deborah_cory-slechta@urmc.rochester.edu (D.A. Cory-Slechta).
https://doi.org/10.1016/j.neuro.2023.12.007
Received 5 October 2023; Received in revised form 5 December 2023; Accepted 6 December 2023
Available online 9 December 2023
0161-813X/© 2023 The Author(s). Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/bync-nd/4.0/).
J. Cubello et al.
Neurotoxicology 100 (2024) 55–71
exposure with various NDDs (McGuinn et al., 2020; Morgan et al., 2023;
Oudin et al., 2019; Volk et al., 2013) and psychiatric disorders including
schizophrenia (Newbury et al., 2021; Saxena and Dodell-Feder, 2022).
For example, a study of 2750 children between 10 and 12 years of age in
the Netherlands reported that higher AP exposure levels were associated
with more severe symptoms of both autism spectrum disorder and
attention deficit/hyperactivity disorder (Santos et al., 2023). A study of
tailpipe and non-tailpipe particulate matter pollution that included
4559 children diagnosed with autism spectrum disorder found an
increased risk related to gestational exposures (Rahman et al., 2023). A
recent systematic review of 13 studies indicated that short-term expo­
sures to various markers of air pollution increased the risk for schizo­
phrenia (Song et al., 2022). While diagnoses of these NDDs is typically
based on their unique features, it is also the case that these NDDs and
psychiatric disorders share numerous features, including changes in
brain neurotransmitter function as well as alterations in cognitive
functions and impulsive behaviors (Cory-Slechta et al., 2023). These
shared features may be targeted by AP and thus explain the breadth of
AP effects in relation to NDDs and psychiatric disorders.
In our prior studies in mice, whole-body inhalation exposure to
concentrated ambient ultrafine particles (UFPs; ≤ 0.1 µM or 100 nm
aerobic diameter) during the murine equivalent of human third
trimester brain development (postnatal days 4–7, 10–13; (Clancy et al.,
2007a; Clancy et al., 2007b)) recapitulated several of the male-biased
vulnerabilities and phenotypic characteristics that are found in these
disorders. This included male-biased impairments in waiting behavior
for free reward deliveries, consistent with increases in impulsive-like
behavior, and deficits in short-term recognition memory as measured
in adulthood (Allen et al., 2013; Allen et al., 2017; Cory-Slechta et al.,
2018).
While the mechanism(s) of action by which ultrafine particle (UFP)
air pollution exposure recapitulates male-biased features characteristic
of these disorders is not entirely understood, one potential basis could be
a result of exposures to the associated metal and trace element con­
taminants of AP and direct nose-to-brain translocation of UFPs. UFPs are
considered the most reactive of AP particles with a greater surface area
to mass ratio for the adsorption of contaminants such as metals and trace
elements. Furthermore, these nanoparticles and their adsorbed con­
taminants can travel from nasal mucosa along the olfactory nerve
directly into brain, bypassing the blood brain barrier (Garcia and Kim­
bell, 2009; Oberdorster et al., 2004). Correspondingly, analyses of
brains from mice exposed to UFPs during the early postnatal period in
our studies showed brain metal dyshomeostasis that included increases
in brain iron (Fe), selenium (Se), calcium (Ca) and copper (Cu) at the
apparent expense of zinc (Zn) and manganese (Mn) (Cory-Slechta et al.,
2019; Cory-Slechta et al., 2020).
Notably, Cu imbalance has been reported in NDDs and psychiatric
disorders (Pandey et al., 2022), including alterations in serum Cu and
urinary Cu levels in relation to autism spectrum disorder and attention
deficit/hyperactivity disorder risk (Feng et al., 2023; Rezaei et al., 2022;
Skogheim et al., 2021; Zhang et al., 2022). A recent report cites an
increased risk of autism spectrum disorder in relation to Cu levels in air
pollution exposure (Rahman et al., 2023). Increased serum Cu levels
have also been reported in individuals diagnosed with schizophrenia
(Mazhari et al., 2020; Saghazadeh et al., 2020), as have alterations in the
Cu transporter CTR1 in the hippocampus (Schoonover et al., 2021).
Studies have also reported altered basal ganglia structure in children in
response to high airborne Cu exposures (Pujol et al., 2016).
Although Cu is essential for various brain functions, it is also redox
active, as it cycles between oxidative states Cu2+/Cu+, leading to
oxidative stress and the production of reactive oxygen species such as
hydrogen peroxide, superoxide radical, and hydroxyl radicals. In gen­
eral, metal dyshomeostasis is a neuropathological feature shared across
NDDs and neurodegenerative diseases (Cilliers, 2021; Saghazadeh et al.,
2020). While normally under tight regulation in the brain, as a
contaminant of UFPs, Cu can directly translocate to the brain via the
olfactory nerves, thereby bypassing the blood brain barrier (Garcia and
Kimbell, 2009; Garcia et al., 2015; Kozlovskaya et al., 2014; Li et al.,
2019; Lochhead et al., 2015). Moreover, such exposures essentially
begin in utero with exposure of the mother to UFPs.
Based on this collective evidence, the current study sought to
determine the extent to which inhaled Cu nanoparticulate matter, as a
contaminant of UFPs, might play an etiological role in producing
phenotypic features of NDDs and psychiatric disorders such as schizo­
phrenia. For that purpose, mice were exposed via inhalation during the
early postnatal period, as in our prior studies of UFPs, to Cu nanoparticle
aerosols, after which changes in behavioral and brain functions previ­
ously seen to be sensitive to UFP exposures were assessed.
2. Materials and methods
2.1. Animals
Eight-week-old male and female C57BL/6J mice (n = 35/sex) pur­
chased from Jackson Laboratories (Bar Harbor, ME), were housed with
1/8” high performance bedding (BioFresh, VA, USA), and mated
monogamously, as previously described (Allen et al., 2013; Allen et al.,
2014a; Allen et al., 2014c; Allen et al., 2017; Cory-Slechta et al., 2018;
Cory-Slechta et al., 2019). Briefly, all mice were acclimated to vivarium
housing for two weeks post-delivery under a 12 h light/dark cycle at 22
± 2 ◦ C, and fed standard rodent chow1 (LabDiet, 0001326). Synchro­
nization of estrous cycles in the three days prior to mating was per­
formed by adding dirty bedding from male cages into female cages.
Pre-pregnancy weights were noted and mice were paired for three
days or until a sperm plug was confirmed. Pregnancy was confirmed 10
days later with a 4 g or greater weight gain, after which dams were
housed alone with their respective litters until weaning at postnatal day
(PND) 25. Amongst the 13 litters born within two days of each other and
assigned for exposures, the average litter size was 5.8 ± 0.5 pups, of
which 50.5% were female and 49.5% male. Based on sex distributions
across litters, after whole litters were assigned to a given exposure
group, each pup was designated to a specific end point purpose (i.e.,
postnatal day (PND) 14 micro-dissections or adulthood behavior). Body
weights were recorded for entire litters on two days in the first week of
life, reflecting PND4/5 or PND6/7, and again on PND14. As in our prior
studies, brains from a subset of all PND14 offspring were
micro-dissected for specific brain regions (striatum, frontal cortex,
midbrain, and cerebellum) and flash-frozen at an acute timepoint of
approximately 0900 h at PND14, i.e., 24 h after exposures ceased for
subsequent neurotransmitter analyses (Fig. 1A). To minimize potential
maternal stress, PND14 pup removals never encompassed more than
43% of the original litter size, which equated to no more than 1–2
pups/sex/litter removal. Remaining littermates were aged to adulthood
before behavioral testing occurred from PND60-PND398. Since it is well
established that behavioral history contributes to behavior, the
sequence of behavioral tests proceeded in order of increasing complexity
and potential stress, with assessments of baseline activity levels (loco­
motor), short-term recognition memory (novel object recognition), and
olfactory deficits (discrimination) preceding assessments of more com­
plex cognitive assessments (i.e., fixed ratio waiting-for-reward or fixed
interval schedules of reinforcement) which are influenced by behavioral
presentations observed in earlier assessments (Fig. 1B). All animal
protocols within this study were approved by the University of
Rochester Institutional Animal Care and Use Committee (approval
#102208/2010–046E) and performed in accordance with NIH
guidelines.
1
LabDiet standard rodent chow (0001326) contains 19 ppm copper in the
form of copper sulfate.
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Neurotoxicology 100 (2024) 55–71
Fig. 1. Experimental design for exposures, micro-dissections, and adulthood behavior. A. Schematic depicting the experimental design of postnatal CuxOy
exposures and micro-dissected brain tissue from a subset of offspring. B. Schedule of behavioral testing for remaining offspring in litters used during PND14 microdissections. Created with BioRender.com.
2.2. CuxOy inhalation exposures
NY, USA). Particle number concentration was adjusted by altering
electric spark discharge frequency. Particle size and counts were
measured in real-time via a particle Scanning Mobility Analyzer (SMPS,
model 3934 TSI Inc, St Paul, MN, USA) and a Condensation Particle
Counter (CPC, model 3022 A; TSI Inc, St Paul, MN, USA), respectively.
CuxOy particles were generated by adding a low flow of oxygen
(~50 mL/min) into the argon flow (~5 L/min) entering the spark
discharge chamber. The oxygen concentration within the exposure
chamber was brought up to 21% through the addition of oxygen
(~1 L/min) to the exiting aerosol flow and verified using an O2 sensor
(MAXO2 − 250E, Maxtec, Salt Lake City, UT, USA). This procedure
produced particle sizes exclusively in the ultrafine size range with a
count median diameter (CMD) of approximately 15–21 nm. Generated
particles were then fed into the whole-body exposure chamber at
25–30 L/min. Using Inductively Coupled Plasma – Optical Emission
Spectrometry (ICP-OES), mass concentrations of Cu were measured on
nitrocellulose membrane filters (0.8 µm, AAWP02500, Millipore Ltd.,
Tullagreen, Cork, IRL) collected daily (5 L/min for 60 min, 300 L total
volume) from the filtered air and ultrafine CuxOy particle exposure
chambers.
From PND4–7 and 10–13, consistent with third trimester human
brain development (Clancy et al., 2007a; Clancy et al., 2007b; Rice and
Barone, 2000) and with our prior traffic-related UFP exposure studies
(Allen et al., 2013; Allen et al., 2014a; Allen et al., 2014c; Allen et al.,
2017; Cory-Slechta et al., 2018; Cory-Slechta et al., 2019), whole litters
were randomly assigned for exposure to either HEPA-filtered Air (Air;
n = 7 litters) or copper oxide (CuxOy) nanoparticles (≤0.1 µM in aero­
dynamic diameter) (Cu; n = 6 litters) for 4 hr/day between 0900 and
1300 h. As in our previously published UFP studies, the gap in exposure
from PND8–9 was incorporated to replicate the decrease in AP seen
during weekends. For this exposure, the intended mass concentration of
Cu was approximately 175 ng/m3. This concentration was selected
based on reported Cu levels (0.76–430 ng/m3) measured in ambient fine
particulate matter (PM2.5; ≤ 2500 nm in diameter) across various
geographic locations in recent studies (Das et al., 2015; Lavigne et al.,
2019; Liu et al., 2018; McNeill et al., 2020; Pujol et al., 2016; Sarnat
et al., 2015; Sharma and Mandal, 2023; Soleimani et al., 2018; Ventura
et al., 2017; Wu et al., 2022). Filter analyses from our previous studies of
gestational exposures to ambient UFPs generated Cu levels averaging
40.8 ± 27.8 ng/m3 (Klocke et al., 2017) and averaged 69.01 (ranged
from 11.6 to 138.2) ng/m3 in a prior postnatal exposure study (un­
published data). Since litters used in this exposure were born across the
span of two days, to ensure that all exposures occurred at ages PND4–7
and 10–13, litters that were born on the second day (Air = two litters, Cu
= three litters) began exposure one day later than those born on the first
(Air = five litters, Cu = three litters), with Cu and Air pups being
exposed simultaneously each day. Following exposure each day, pups
were returned to their corresponding dams.
Similar to our previous studies generating iron (Fe) oxide UFP
nanoparticles (Eckard et al., 2023a; Sobolewski et al., 2022), in
collaboration with the University of Rochester Medical Center Inhala­
tion Exposure Facility, a GFG-1000 Palas® Aerosol generator (Palas,
GmbH, Karlshrue, Germany) was used to generate Cu oxide (CuxOy)
nanoparticles via electric spark discharge between two 99.99999% pure
Cu rods (3N5 Purity, ESPI Metals, Ashland, OR, USA). The CuxOy par­
ticles were fed into a compartmentalized whole-body mouse exposure
chamber, while HEPA-filtered air was delivered to the control chamber,
as in prior studies in our inhalation facility (Oberdorster et al., 2000).
Boltzmann equilibrium was achieved by passing the airborne particles
through a deionizer (Isotope Po-210, model P-2031, NRD, Grand Island,
2.3. Metal analysis
Given that elemental constituents within ultrafine particulate matter
can be taken up by olfactory and trigeminal nerves (Garcia and Kimbell,
2009; Garcia et al., 2015; Kozlovskaya et al., 2014; Li et al., 2019;
Lochhead et al., 2015) and concomitantly aggregate in lungs upon
inhalation (Oberdorster et al., 2000), metal levels were measured via
Inductively Coupled Plasma Mass Spectrometry (ICP-MS) in collabora­
tion with the Dartmouth Trace Element Analysis Core. Tissue samples of
PND14 offspring were weighed, digested with acid in a 1.5 mL Eppen­
dorf tube, and heated at 80 ◦ C for 1 h using a Fisher Scientific heat block.
Briefly, sample-dependent volumes of 9:1 HNO3:HCl (Optima grade,
Fisher Scientific) were used to digest specific tissues. Olfactory bulbs
(6.1–12.1 mg) were digested in 200 µL of acid mix and diluted to a final
volume of 2 mL with deionized water, whereas brain cortices (75.4 –
102 mg) were digested in 500 µL of acid mix and diluted to a final
volume of 5 mL with deionized water. Weights of all final sample di­
lutions were recorded. In parallel with tissue samples, six blanks and six
reference material samples (NIST 2976 Mussel Tissue, 6–18 mg) were
also digested and diluted similar to cortices.
Tissue metal levels were measured via ICP-MS (Agilent 8900,
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Neurotoxicology 100 (2024) 55–71
Wilmington, DE) in helium and oxygen modes. A multi-element cali­
bration curve was generated using NIST-traceable primary standards as
reference materials and a calibration check was performed after each
calibration and after every 10 samples using second source standards.
Calibration checks were additionally performed using USGS water pro­
ficiency samples (P76, T-245) three times throughout analysis of sam­
ples. Five analytical duplicates and five analytical spikes were run and
metal levels are reported as µg/g. Quality control data are presented as
Supplementary Table 1.
control, which was calculated by dividing each Cu sample value by the
group mean of the air control group for that given neurotransmitter.
2.5. Spontaneous locomotor activity
At PND60, vertical and horizontal ambulation along with stereotypic
non-ambulatory movements were assessed using an automated loco­
motor activity chamber with 48 infrared beam arrays covering the x, y,
and z axes (model ENV-510S, Med Associates Inc, St. Albans, VT, USA)
as previously described (Cory-Slechta et al., 2018; Eckard et al., 2023a).
Locomotion was categorized by number and directional plane of pho­
tobeam breaks. Ambulatory distance traveled (in centimeters) was
defined by movement that generated three or more consecutive hori­
zontal beam breaks. Stereotypic counts refer to the stereotypic distance
traveled when fewer than three consecutive breaks are broken in a 2 × 2
beam area. Vertical time, or time (in seconds) spent rearing, was defined
by vertical movement that generates simultaneous beam breaks in the xor y- and z-axes. Jump time, or time (in seconds) spent jumping, was
defined by time without beam breaks in the x- or y-axes. All of the above
measures were recorded and presented in 5-minute bins plotted for
60-minute sessions across 12 sessions.
2.4. Neurotransmitter analysis
In collaboration with the University of Rochester Mass Spectrometry
Core, concentrations of dopaminergic (Tyrosine (Tyr), Homovanillic
Acid (HVA), 3,4-Dihydroxyphenylacetic acid (DOPAC), Dopamine (DA),
Norepinephrine (NE)), serotonergic (Tryptophan (Trp), Kynurenine
(Kyn), 5-hydroxytryptophan (5-HTP), 5-hydroxytryptamine (5-HT), 5hydroxyindoleacetic acid (5-HIAA)), and glutamatergic (Glutamine
(Gln), Glutamate (Glu), γ-Aminobutyric acid (GABA)) neurotransmitters
were quantified in micro-dissected striatum, frontal cortex, midbrain,
and cerebellum from PND14 offspring. Brain regions were thawed from
− 80 ◦ C storage, weighed, diluted in 75 µL of ice-cold acetonitrile (50%
v/v), and homogenized for 10 pulses via ultra-sonication (SLPe digital
sonifier, Branson Ultrasonics Corp., Danbury, CT, USA). Suspensions
were incubated on ice for 10 min before centrifugation at 10,000 g at
4 ◦ C for 20 min. The supernatants were then collected and centrifuged at
10,000 g at 4 ◦ C for 20 min. The final supernatant was aliquoted into a
new tube and stored at − 80 ◦ C until analysis. All neurotransmitter stock
solutions (Sigma-Aldrich, St. Louis, MO, USA) were made to a concen­
tration of 5 mg/mL, using 0.2 M HCl for Tyr and ddH2O for the
remaining neurotransmitters (Gln, Glu, GABA, Trp, Kyn, 5-HTP, 5-HT,
5HIAA, HVA, DOPAC, DA, and NE). Previous range-finding studies and
regional heterogeneity in endogenous neurotransmitter levels guided
the various analyte concentrations used in the subsequent ddH2O stan­
dard mixture. Internal standards for each neurotransmitter within this
stock solution were created via an adapted method from Wong et al.
(2016) which uses 13C6 Benzoyl chloride (BzCl, Sigma-Aldrich, St.
Louis, MO, USA) as a derivatizing agent. Once derivatized, the internal
standard mixture was aliquoted and frozen at − 80 ◦ C for long-term
storage. When ready to prepare samples, internal standard aliquots
were thawed and diluted in 50% acetonitrile with 1% sulfuric acid. Prior
to analysis, samples were derivatized following the same procedure.
Briefly, debris was first removed via centrifugation at 16,000 g for
5 min, then 20 µL of the resulting supernatant was dispensed into a clean
LoBind Eppendorf tube. Subsequently, 10 µL of 100 mM sodium car­
bonate, 10 µL of 2% BzCl in acetonitrile, and 10 µL of the respective
internal standard was added in that order. To diminish the organic
concentration prior to injection, 50 µL of ddH2O was then added. Sam­
ples were again centrifuged to pellet remaining protein before the su­
pernatant was transferred to a clean autosampler vial.
LC-MS/MS analysis was performed with a Dionex Ultimate 3000
UHPLC coupled to a Q Exactive Plus mass spectrometer (Thermo Fisher
Scientific, Waltham, MA, USA). Analytes were separated on a Waters
Acquity HSS T3 column. The mobile phases were: A) 10 mM ammonium
formate in 0.1% formic acid and B) acetonitrile. The flow rate was set to
400 µL/min and the column oven temperature to 27 ◦ C. After injection
of 5 µL of each sample, the analytes were separated using a 12 min
multi-step gradient. The Q Exactive Plus was operated in positive mode
and derivatized molecules were detected using a parallel reaction
monitoring method (PRM). Fragment ions were extracted with a 10 ppm
mass error using the LC Quan node of the XCalibur software (Thermo
Fisher Scientific, Waltham, MA, USA). Relative abundance was deter­
mined by comparing endogenous analyte peak areas to corresponding
internal standards. Within each tissue sample, relative abundance values
for detected neurotransmitters were then normalized to the wet weight
of that tissue. Neurotransmitter data is presented as the percent of
2.6. Novel object recognition (NOR) task
Three days after the measurement of spontaneous locomotor activity
(approximately PND63), learning/short-term memory was assessed in a
NOR paradigm carried out in an open Plexiglas arena (dimensions:
30.5 cm × 30.5 cm × 30.5 cm) as previously described (Allen et al.,
2014c; Cory-Slechta et al., 2018). In session one, mice were given
10 min to explore the arena which contained two fixed-in-place iden­
tical white doorknobs in opposing corners of the arena. This session
permitted assessments of individual variabilities in response to novelty,
levels and patterns in exploratory behavior, side preferences, and neo­
phobia. Mice were then returned to their home cage for one hour prior to
beginning the probe trial in session two. In session two, subjects were
given 5 min to explore the same area, however, one of the white door­
knobs from session one, which now represented a familiar object, was
replaced with an orange Pyrex bottle cap, a novel object. Session two
tests the animal’s short-term memory and ability to distinguish familiar
vs unfamiliar stimuli. To avoid biased placement preferences in object
choice, placement of each object was counterbalanced across treatment
groups and subjects; the arena and objects were thoroughly cleaned
between tests for each animal using disinfectant. Video recordings
across sessions were reviewed by a treatment-blinded individual using
Noldus Observer software. Exploration of a given object was defined by
head entry into a 2 cm circular boundary line surrounding each object
with orientation towards the object. Time spent with each of the objects
was evaluated in the first two minutes of the probe trial and calculated
by the average time spent per bout of investigation. A recognition index
for each mouse was then calculated by comparing the amount of time
spent interacting with the novel object to time spent collectively with
both the novel and familiar objects (Recognition index duration = Novel
object duration/ (novel object duration + familiar object duration)).
2.7. Olfactory discrimination
To determine whether inhalational exposure to CuxOy nanoparticles
resulted in olfactory impairments in male and female offspring, five days
after completion of NOR (approximately PND68), an olfactory
discrimination assay was carried out in an open Plexiglas arena (di­
mensions: 30.5 cm × 30.5 cm×30.5 cm) with covered sides as previ­
ously described (Anderson et al., 2021). Briefly, this assay tested
discrimination between two scents, almond (McCormick and Company,
Inc.) and vanilla extract (Hunt Valley, Maryland), with one being
designated as the positive (reinforced) scent (extract + water) and the
negative scent being a water mixture containing the other extract with
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1% of the bittering agent denatonium benzoate (D5765, Sigma-Aldrich,
St. Louis, MO). Mice were trained to drink from water containing the
assigned positive scent and avoid water with the negative scent, with the
assignment of extract mixes to positive and negative being
counter-balanced across animals and treatment groups to prevent scent
preference interference. Approximately 48 h prior to every training and
testing session, mice were water-restricted to enhance thirst. After each
session was completed, mice were given ad libitum access to water for at
least 24 h before being restricted again for the next session and weights
were collected daily to monitor indicators of dehydration.
Mice were first placed into the Plexiglas arena for 10 min to habit­
uate before being progressively trained across four sessions to drink
from decreasing volumes of water in a round petri dish within two mi­
nutes (filled, half-full, 40 µL, 20 µL). In positive association training,
mice were prompted to drink from a dish containing their assigned
positive scent in water within two minutes. With each successful
training session, the volume of extract was gradually increased to 8 µL in
an overall drop volume of 20 µL and mice were considered trained after
drinking in three consecutive two-minute sessions. For negative asso­
ciation training, the water droplet instead contained 8 µL of their
assigned negative scent mixed with 1% denatonium benzoate and mice
were considered trained when they successfully avoided drinking for
three consecutive sessions.
The ability of mice to discriminate between the positive and negative
scent (always included 1% denatonium benzoate) was then tested across
varying ratios of the two scents together in their respective dishes, with
discrimination becoming more difficult as these ratios became closer
(100:0, 90:10, 80:20, 70:30, 60:40). Dish placement in the arena was
counter-balanced across animals and treatment groups, both containing
20 µL droplets, and training criteria used for association training was
maintained for each ratio prior to discrimination testing. Mice began
with the easiest to discriminate ratio (100:0 = 8 µL positive scent in
12 µL water vs 8 µL negative scent in 12 µL water) and ended with the
most difficult to discriminate (60:40 positive mix = 4.8 µL positive +
3.2 µL negative in 12 µL water, 60:40 negative mix = 4.8 µL negative +
3.2 µL positive in 12 µL water). Drinking from only the positive dish was
scored as a correct choice, whereas drinking from the negative dish,
from both, or not drinking from either was scored as an incorrect choice.
Mice that did not pass training for specific ratios were not included in
discrimination tests at that ratio.
reward. Subjects were considered trained once 25 rewards were earned
in a 30-minute session. Impulsivity, or response inhibition/ability to
wait, in mice was then evaluated in 20-minute sessions in which 25
correct lever press responses (FR = 25) produced a food delivery fol­
lowed immediately by the onset of a ‘wait’ interval during which free
reward deliveries were available in the absence of lever press responses.
However, the length of the interval between free rewards increased after
each free reward delivery. Any lever press response during a wait in­
terval reset the fixed ratio requirement to re-start the wait intervals for
free reward deliveries. Initial interval wait times and wait increment
values tested were 2.5 or 5.0 s. Sessions ended after 50 rewards were
received or 20 min had elapsed, whichever occurred first. Measures of
FR wait included average FR lever response rate (total number of
responses/total time to complete the FR ratio), number of FR resets,
mean long wait time, and the mean responses per reinforcer.
2.8. Fixed ratio (FR) waiting for reward behavioral paradigm
Our prior studies have repeatedly revealed sex-specific responses to
UFP exposures (Allen et al., 2014a, 2017) and in the current study an
ANOVA with sex and exposure assigned as factors, likewise found
frequent sex differences that were brain region- and outcome measurespecific, providing rationale for overall statistical analyses to be sepa­
rated by sex. All sample analyses and assessment of animal behaviors
were counter-balanced in order. Amongst the 6–7 litters/treatment used
within this study, PND14 neurotransmitter analyses were conducted
using 6–7 pups/sex/treatment group and behavioral testing was per­
formed on 7–8 adult offspring/sex/treatment group ranging from the
ages of PND60-PND414 depending on the test. Additionally, aside from
two Air males, all adult offspring assigned to behavioral testing were
corresponding littermates of offspring used for analyses at PND14.
Two-tailed t-tests were utilized for the majority of analyses; treat­
ment effects in behavioral paradigms carried out across sessions were
analyzed using repeated-measures analysis of variance. To assess metal
dyshomeostasis in the olfactory bulb and cortex, multivariate correla­
tion analysis was performed and represented in a color map of the
Pearson Product-Moment Correlation values with marginal or signifi­
cant correlations noted. For discrete data (correct/incorrect choice) in
olfactory discrimination tests, two-sided Fisher’s Exact tests were used.
Outliers within treatment groups were determined using Grubb’s test
in GraphPad Prism 10, with a maximum of one exclusion per treatment
group per sex. For multivariate correlation analyses between metals and
in datasets where repeated-measures analysis of variance was
2.9. Fixed interval (FI) schedule-controlled behavior
At PND368, one day following completion of the assessment of FR
Wait behavior, a Fixed Interval (FI) 60 s schedule of reinforcement was
imposed (Allen et al., 2014c) in the operant chamber. On the FI
schedule, reinforcement was available for the first correct lever press
response that occurred after a 60 s interval had elapsed; responses
during the interval itself had no programmed consequences and could
not accelerate reward delivery. The delivery of reinforcement also
initiated the next 60 s interval. FI 60 s behavior was measured across
sixteen 20-minute sessions, carried out five days a week (M-F). This was
followed by 5 consecutive sessions of the FI 60 s schedule in which
conditions were identical but no reinforcer was delivered (FI extinction).
FI performance measures included the overall response rates (defined as
the total number of correct (left) lever responses/overall session dura­
tion), the average post-reinforcement pause time (mean PRP; defined as
the average time elapsed between the beginning of the 60 s interval and
the first response), run rates (defined as the total number of response­
s/overall session duration excluding the PRP time), and inter-response
times (IRT; defined as the average time between successive responses)
(Cory-Slechta et al., 1998; Rossi-George et al., 2011; Sobolewski et al.,
2018).
2.10. Statistics
At PND244, approximately two weeks after olfactory recognition
tests were completed, mice had free access to water while being grad­
ually food-restricted to achieve and maintain 85% of their ad libitum
body weight. As previously described (Cory-Slechta et al., 1985), mice
were then autoshaped to press levers for food pellet rewards (F0163,
20 mg Dustless Precision Pellets, Rodent, Grain-Based, Bio-Serv, Fle­
mington, NJ, USA) in an operant-conditioning chamber (ENV-307A,
Med Associates Inc, St. Albans, VT, USA) housed in a sound attenuating
cubicle equipped with fans for ventilation and white noise. In this
chamber, three fixed levers, only one of which was designated the cor­
rect lever for reward delivery (left lever), were horizontally aligned
directly across from a hopper connected to a pellet dispenser. In an
initial six-hour session, free rewards were given at varying intervals of
time (on average 60 s) for up to 20 min. Any single correct lever press
during that time additionally yielded a reward; completion of 10 such
lever presses during the 20-minute interval terminated the free reward
deliveries. In subsequent sessions, a single response on the correct lever
was required for food delivery. Mice were deemed trained when 45 or
greater earned rewards was achieved within a session.
As in our previous studies (Allen et al., 2013; Cory-Slechta, 1986),
mice were then trained on a fixed-ratio (FR) schedule of food rein­
forcement which entailed gradually increasing the required number of
responses (ratio) values from 1 to 25 lever presses to earn a pellet
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performed, after outlier exclusion, omitted data were replaced with the
mean value of the remaining values. Otherwise, outliers were not
replaced. Furthermore, prior to completion of the last three sessions of
FI60, one Air female subject died of an unknown cause. Since Grubb’s
test had not deemed her values as an outlier in any of the previous 13
sessions of FI60, the mean of the group without her was assigned as her
value for the last three sessions. No other Air females were permitted to
be excluded or replaced in those sessions and FI Extinction analysis
excluded her altogether. All statistical analyses were conducted using
JMP Pro 16 (SAS Institute Inc., Cary, N.C.). Aside from Cu exposure
characteristics (reported as mean ± standard deviation), all descriptive
statistics were reported as mean ± standard error of the mean (SEM).
Data were plotted using GraphPad Prism 10, and graphical schematics
were created using BioRender.com. In all graphs, statistically significant
values were defined by a p-value < 0.05 (graphically annotated with *),
whereas marginally significant findings were defined by a p-value
< 0.10 (graphically annotated with #).
female weights observed within Air and Cu litters were not significantly
different during the first week of life at either PND4/5 (Female: F(1,10)
= 2.16, p = 0.17; Male: F(1,11) = 1.19, p = 0.30) or PND6/7 (Female: F
(1,10) = 1.55, p = 0.24; Male: F(1,11) = 0.66, p = 0.43). Similarly,
same-sex PND14 offspring average body weights did not differ between
treatment groups of either sex (Air versus Cu females: 6.48 ± 0.14
versus 6.19 ± 0.13 g; Air versus Cu males: 6.00 ± 0.16 versus 5.91
± 0.15 g) and were not significantly affected by exposure to inhaled Cu
particles (Females: F(1,37) = 2.53, p = 0.12; Males: F(1,32) = 0.18,
p = 0.68).
3.3. Tissue metal levels and correlations
Tissue metal levels from PND14 CuxOy male and female olfactory
bulb and cortex are shown as percent of Air control mean values in
Supplementary Figure 1A and 1B, respectively, and the mean ± SEM are
reported in Supplementary Table 2. In the olfactory bulb, Mn, Cu, and Se
levels were unaffected by CuxOy exposure, while marginally significant
increases in Fe (F(1,10) = 4.06, p = 0.072) and marginally significant
decreases in Zn (F(1,11) = 3.99, p = 0.071) were observed in males and
females, respectively. In the cortex, CuxOy exposure did not significantly
affect Mn, Fe, Cu, Zn, or Se levels in either sex.
To assess potential metal dyshomeostasis within the olfactory bulb
and cortex, correlation profiles among metals in PND14 brain were
examined (Figs. 3A and 3B, respectively). CuxOy exposure perturbed the
patterns, strength, and directionality of brain metal correlations. In male
olfactory bulb, significant positive correlations were found in controls
between Cu/Zn (r = 0.95, p = 0.004) and Fe/Se (r = 0.89, p = 0.017)
that were absent after CuxOy exposure (Cu/Zn, Fe/Se: r = 0.19,
p = 0.68). In contrast, a significant positive Cu/Zn correlation was also
observed in the olfactory bulb of Air females (r = 0.92, p = 0.003) but
this correlation was not influenced by CuxOy exposure (r = 0.90,
p = 0.013). Air females additionally exhibited a significant positive Cu/
Se correlation in olfactory bulb, which was reduced to a marginally
significant correlation after CuxOy exposure (Air: r = 0.82, p = 0.023;
Cu: r = 0.80, p = 0.054).
In male cortex, non-significant positive correlations between Cu/Zn
(r = 0.19, p = 0.71), Cu/Se (r = 0.39, p = 0.44), and Zn/Se (r = 0.27,
p = 0.61) observed in Air males were strengthened and reached signif­
icance after exposure to CuxOy nanoparticles (Cu/Zn: r = 0.94,
p = 0.002; Cu/Se: r = 0.77, p = 0.044; Zn/Se: r = 0.89, p = 0.008),
while a significant positive Zn/Fe correlation in Air males (r = 0.92,
p = 0.010) disappeared after CuxOy exposure (r = 0.60, p = 0.16). In
female cortex, a marginally significant negative correlation between Fe/
Mn (r = − 0.71, p = 0.075) and a significant positive correlation be­
tween Cu/Fe (r = 0.86, p = 0.014) in Air females were lost after CuxOy
exposure (Fe/Mn: r = 0.47, p = 0.35; Cu/Fe: r = 0.53, p = 0.28), while
the statistically significant positive Se/Zn correlation (r = 0.78,
3. Results
3.1. Cu exposure characteristics
Daily averages for particle diameter (nm), mass concentrations (ng/
m3), and particle number concentration (particles/cm3) are presented
across the 8 days of Cu exposure representing PND4–7 and 10–13 in
Fig. 2. Across the overall span of exposures, pure Cu nanoparticles were
successfully generated with Count Median Diameter (CMD) particle
sizes that met UFP size criterion (≤ 100 nm) ranging from 15.5 to 21 nm
with an average Geometrical Standard Deviation (GSD) of 1.1. The daily
mass concentrations of Cu ranged from 106.9 to 219.2 ng/m3 with an
overall eight-day exposure average of 171.9 ng/m3, similar to the
intended target of 175 ng/m3 and well-within Cu mass concentrations
observed globally in PM2.5, which drove this selected concentration (Das
et al., 2015; Lavigne et al., 2019; Liu et al., 2018; McNeill et al., 2020;
Pujol et al., 2016; Sarnat et al., 2015; Sharma and Mandal, 2023; Sol­
eimani et al., 2018; Ventura et al., 2017; Wu et al., 2022). Furthermore,
this eight-day average mass concentration of Cu for the Cu exposure
group was 2.2-fold greater than levels measured on the last day of
exposure for the filtered air group (77.2 ng/m3). The daily particle
number concentration ranged from 2.38E+ 04–2.61E+ 04 parti­
cles/cm3, with an overall eight-day exposure (average ± standard de­
viation) of 2.47E+ 04 ± 7.95E+ 02 particles/cm3.
3.2. Litter size and body weights
There were no significant differences in average litter size between
litters selected for either exposure (Air: 5.43 ± 0.78 pups; Cu: 6.17
± 0.75; F(1,11) = 0.46, p = 0.51). Additionally, average male and
Fig. 2. Exposure characteristics at ages PND4–7 and 10–13. A. Mean ± standard deviation values for mass concentration of CuxOy exposures (ng/m3; open
triangles) and of particle diameter (nm; gray circles) across the 8 days of postnatal exposure. B. Mean ± standard deviation values for particle number concentrations
(particles /cm3) across the 8 days of postnatal exposure.
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Fig. 3. The effects of inhalational Cu exposure on correlations between metals within the olfactory bulb and cortex of PND14 male and female offspring.
Color maps of the Pearson Product-Moment correlation coefficient (r) values for olfactory bulb (left two columns) and cortex (right two columns) metals of male
offspring (Panel A) and female offspring (Panel B). The strength and directionality of correlations are indicated by the intensity and color hue (red = positive, blue =
negative) of the square, respectively. N = 5–7/sex/region/treatment/metal (see Supplementary Table 2). * = p-value < 0.05, # = p-value < 0.10.
p = 0.041) in Air female cortex was diminished to a marginally signifi­
cant positive correlation (r = 0.81, p = 0.052) after CuxOy exposure.
New significant and marginally significant positive correlations addi­
tionally emerged in CuxOy -exposed female cortex that were not seen in
Air female cortex, including Fe/Zn (Air: r = 0.16, p = 0.73; Cu: r = 0.93,
p = 0.007), Cu/Se (Air: r = 0.59, p = 0.17; Cu: r = 0.95, p = 0.003) and
Fe/Se (Air: r = 0.52, p = 0.23; Cu: r = 0.75, p = 0.084).
Additionally, while not statistically significant across all metals, in
both Air males and females, the strength and directionality of multiple
correlations between metals, as indicated by the intensity and color hue
used within the color maps, respectively, were altered after CuxOy
exposure in a region-dependent manner. For example, CuxOy exposure
reversed the direction of correlation between Cu and Mn in the olfactory
bulb from a non-significant positive to negative correlation in both
males (Air: r = 0.36, p = 0.49; Cu: r = − 0.11, p = 0.81) and females
(Air: r = 0.097, p = 0.84; Cu: r = − 0.31, p = 0.56). Similar findings
were observed in the cortex, however, the relationship between Cu and
Mn switched from a non-significant negative to positive correlation in
both males (Air: r = − 0.46, p = 0.36; Cu: r = 0.58, p = 0.17) and fe­
males (Air: r = − 0.61, p = 0.15; Cu: r = 0.78, p = 0.067).
a precursor for DA synthesis, the ratio of DA/Tyr was significantly
increased in CuxOy -exposed males (575%, F(1,9) = 204.90, p < 0.001).
Aside from dopaminergic signaling, CuxOy exposure prompted
striatal reductions in GABAergic inhibitory tone, with marginal re­
ductions in the neurotransmitter GABA (− 16%, F(1,11) = 4.66,
p = 0.055) and significant increases in the ratios of excitatory/inhibi­
tory neurotransmitters (Gln/GABA: 130%, F(1,11) = 21.74, p < 0.001;
Glu/GABA: 115%, F(1,10) = 17.05, p = 0.002). Furthermore, signifi­
cant reductions in serotonergic neurotransmitters (5HTP: − 51%, F(1,9)
= 14.11, p = 0.005; 5HIAA: − 28%, F(1,11) = 9.02, p = 0.012) and
serotonin (5HT) turnover (− 25%, 5HIAA/5HT: F(1,10) = 14.84,
p = 0.003) were observed as a consequence of CuxOy exposure in males.
Unlike the striatum, glutamatergic, serotonergic, and dopaminergic
signaling in the frontal cortex, midbrain, and cerebellum of males were
largely unaffected by CuxOy exposure, with the only exceptions being
marginal and significant increases in cerebellar Gln/GABA (112%, F
(1,11) = 3.89, p = 0.074) and Glu/GABA (112%, F(1,10) = 7.66,
p = 0.020) levels, respectively.
As shown for males, neurotransmitter levels in the striatum, frontal
cortex, midbrain, and cerebellum of PND14 Cu females are shown as the
percent of Air female values in Fig. 5A-D, respectively, and the mean
± SEM are reported in Supplementary Tables 3–5. Interestingly, sensi­
tivities to CuxOy exposure observed in male striatal neurotransmitter
systems were not observed in females. In fact, in females, neurotrans­
mitters in the striatum, frontal cortex, midbrain, and cerebellum were
largely unaffected by CuxOy exposure, with the only exception being
significant increases in the ratio of Gln/Glu (108%, F(1,10) = 5.60,
p = 0.040) in the cerebellum.
3.4. Neurotransmitter analysis
Neurotransmitter levels in the striatum, frontal cortex, midbrain, and
cerebellum of PND14 CuxOy males are shown as the percent of Air
control male values in Fig. 4A-D, respectively, and the mean ± SEM
absolute values are reported in Supplementary Tables 3–5. In males,
perturbations in glutamatergic, serotonergic, and dopaminergic neuro­
transmitter systems attributed to CuxOy exposure were predominantly
striatal-specific. In the striatum, the most notable effects were signifi­
cant increases in the dopamine metabolites HVA (163%, F(1,10)
= 11.28, p = 0.007) and DOPAC (246%, F(1,10) = 5.98, p = 0.035), and
of dopamine (DA) itself (583%, F(1,10) = 33.84, p < 0.001), which
occurred concurrently with significant reductions in dopamine turnover
(HVA/DA (− 72%, F(1,10) = 109.16, p < 0.001) and DOPAC/DA
(− 60%, F(1,10) = 73.81, p < 0.001)). Additionally, in the absence of a
significant effect of CuxOy exposure on Tyr (F(1,11) = 0.057, p = 0.82),
3.5. Locomotor activity levels
To determine whether CuxOy exposure affected locomotor activity,
spontaneous locomotor activity was measured over the course of 60 min
in both male and female offspring as shown across 12 five-minute time
bins in Figs. 6A and 6B, respectively. In males, exposure to CuxOy
nanoparticles did not significantly affect ambulatory distance traveled,
stereotypic counts, or time spent jumping. However, there was
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Fig. 4. Changes in striatal, frontal cortical, midbrain, and cerebellar neurotransmitter levels in CuxOy- exposed PND14 male offspring. Group mean
± standard error percent of filtered air control mean values for glutamatergic (Gln=glutamine; Glu=glutamate; GABA=γ aminobutyric acid; Gln/Glu=glutamine/
glutamate; Glu/GABA=glutamate/γ aminobutyric acid), serotonergic (Trp=tryptophan; Kyn=kynurenine; 5HTP=5 hydroxytryptophan; 5HT=serotonin, 5HIAA=5
hydroxyindoleacetic acid; 5HIAA/5HT=5 hydroxyindoleacetic acid/ serotonin) and dopaminergic (tyr=tyrosine; HVA=homovanillic acid; DOPAC=3,4-dihydrox­
yphenylacetic acid; DA=dopamine; NE=norepinephrine; HVA/DA= homovanillic acid/dopamine; DOPAC/DA=3,4-dihydroxyphenylacetic acid/dopamine; DA/
Tyr=dopamine/tyrosine) neurotransmitters from male PND14 striatum (Panel A), frontal cortex (Panel B), midbrain (Panel C) and cerebellum (Panel D) following
exposure to CuxOy. N = 4–7/sex/region/treatment/neurotransmitter (see Supplementary Tables 3–5). * = p-value < 0.05, # = p-value < 0.10.
significant variation in the effect of CuxOy on vertical time throughout
the course of the session (time x treatment: F(11,154) = 2.75,
p = 0.003), such that Cu males spent greater amounts of time than Air
males in the vertical plane (rearing behavior) during time bins 5–11, but
not in the initial 20 min or the final 5 min of the 60-minute session. In
females, exposure to CuxOy nanoparticles did not appear to affect
ambulatory distance traveled, stereotypic counts, or vertical time.
However, a main effect of CuxOy exposure was observed for jump time,
wherein Cu females were spending significantly less time jumping than
Air females over the course of the session (treatment: F(1,14) = 4.82,
p = 0.045).
p = 0.73; females: F(1,14) = 1.96, p = 0.18). Validity of the paradigm
was demonstrated by the fact that in session 2, 28 of the total 32 mice
had recognition index duration values above 50%, indicating a prefer­
ence for time spent with the novel object as opposed to the familiar
object (see Supplementary Figure 2).
3.7. Olfactory discrimination
To discern whether inhalational exposure to CuxOy nanoparticles
affected olfactory capabilities, mice were presented with a two-choice
olfactory discrimination test with varying ratios of the positive:nega­
tive scents (see Supplementary Figure 3). All subjects (N = 8/sex/
treatment) successfully passed training criteria for positive and negative
scent association training and the training sessions preceding olfactory
discrimination testing at 100:0, 90:10, and 80:20 positive:negative scent
ratios. However, not all mice were able to successfully pass training
3.6. Novel object recognition memory
Novel object recognition memory was not significantly influenced by
exposure to CuxOy nanoparticles in either sex (males: F(1,14) = 0.13,
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Fig. 5. Changes in striatal, frontal cortical, midbrain, and cerebellar neurotransmitter levels in CuxOy- exposed PND14 female offspring. Group mean
± standard error percent of filtered air control mean values for glutamatergic (Gln=glutamine; Glu=glutamate; GABA=γ aminobutyric acid; Gln/Glu=glutamine/
glutamate; Glu/GABA=glutamate/γ aminobutyric acid), serotonergic (Trp=tryptophan; Kyn=kynurenine; 5HTP=5 hydroxytryptophan; 5HT=serotonin, 5HIAA=5
hydroxyindoleacetic acid; 5HIAA/5HT=5 hydroxyindoleacetic acid/ serotonin) and dopaminergic (tyr=tyrosine; HVA=homovanillic acid; DOPAC=3,4-dihydrox­
yphenylacetic acid; DA=dopamine; NE=norepinephrine; HVA/DA= homovanillic acid/dopamine; DOPAC/DA=3,4-dihydroxyphenylacetic acid/dopamine; DA/
Tyr=dopamine/tyrosine) neurotransmitters from female PND14 striatum (Panel A), frontal cortex (Panel B), midbrain (Panel C) and cerebellum (Panel D)
following exposure to CuxOy. N = 5–7/sex/region/treatment/neurotransmitter (see Supplementary Tables 3–5). * = p-value < 0.05.
criteria for participation in the 70:30 (1 Cu male) and 60:40 ratios (1 Air
female, 3 Cu females, 3 Air males, 4 Cu males). Of those that did, ol­
factory discrimination was not significantly influenced by exposure to
CuxOy nanoparticles in either sex at 100:0 or 90:10 positive:negative
scent ratios (p = 1.00), in which all animals unanimously made the
correct choice by drinking from the positive scent dish only. Further­
more, CuxOy nanoparticle exposure did not significantly affect olfactory
discrimination at the 80:20 (Females, Males: p = 1.00), 70:30 (Females:
p = 0.57, Males: p = 1.00), or 60:40 (Females: p = 0.56, Males:
p = 1.00) ratios. As expected, and irrespective of treatment group, the
total number of incorrect choices appeared to increase as the discrimi­
nation task became more challenging at the 70:30 and 60:40 ratios.
3.8. FR waiting behavior
Figs. 7 and 8 depict the effects of CuxOy nanoparticle exposure on FR
waiting behavior across four 20-minute sessions. With the initial delay
interval and subsequent increment set to 2.5 s, CuxOy -exposed males
exhibited marginally greater FR response rates and a marginally greater
number of total FR resets than Air males (FR response rate: main effect of
treatment, F(1,14) = 4.41, p = 0.054; FR resets: main effect of treatment
F(1,14) = 3.88, p = 0.069) (Fig. 7A), even while neither mean long wait
time nor number of responses per reinforcer in male mice were signifi­
cantly affected by CuxOy nanoparticles. However, in females, early ef­
fects of CuxOy nanoparticles on FR response rates were observed (time x
treatment: F(3,42) = 3.08, p = 0.038), with CuxOy females exhibiting
increased FR response rates relative to Air females in the first session, as
well as an increase in the mean number of responses per reinforcer (time
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Fig. 6. The effects of inhalational Cu exposure on spontaneous locomotor activity assessments in PND60 male and female offspring. Group mean
± standard error levels of ambulatory distance traveled (centimeters), stereotypic counts (number), jump time (seconds) and vertical time (seconds) in locomotor
activity assessments plotted in 5-minute bins over the course of a 60 min session in males (Panel A) and females (Panel B) of air-exposed (filled-in symbols) and
CuxOy -exposed (open symbols) offspring. N = 8/sex/treatment. * = p-value < 0.05, # = p-value < 0.10.
Fig. 7. The effects of inhalational Cu exposure on the performance of male and female offspring in a Fixed Ratio wait-for-reward schedule of rein­
forcement (initial wait ¼ 2.5 s, incremental increase ¼ 2.5 s). Group mean ± standard error levels of FR response rate (responses/minute; first column), total FR
resets (number; second column), mean long wait time (seconds; third column), and mean responses per reinforcer (number; fourth column) of male (Panel A) and
female (Panel B) air-exposed (filled-in symbols) and CuxOy -exposed (open symbols) offspring across 4 sessions of the FR wait schedule with an initial wait of 2.5 s
and subsequent increments of 2.5 s. Time x treatment=interaction effect in repeated measures analyses of variance; N = 8/sex/treatment. * = p-value < 0.05, # = pvalue < 0.10.
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Fig. 8. The effects of inhalational Cu exposure on the performance of male and female mice in a Fixed Ratio wait-for-reward schedule of reinforcement
(initial wait ¼ 5.0 s, incremental increase ¼ 5.0 s). Group mean ± standard error levels of FR response rate (responses/minute; first column), total FR resets
(number; second column), mean long wait time (seconds; third column), and mean responses per reinforcer (number; fourth column) of male (Panel A) and female
(Panel B) air-exposed (filled-in symbols) and CuxOy -exposed (open symbols) offspring across 4 sessions of the FR wait schedule with an initial wait of 5.0 s and
subsequent increments of 5.0 s. Treatment = main effect of treatment in repeated measures analyses of variance; time x treatment = interaction effect in repeated
measures analyses of variance; N = 8/sex/treatment. * = p-value < 0.05, # = p-value < 0.10.
by treatment: F(3,42)= 3.02, p = 0.0205), but this treatment effect did
not persist across sessions 2–4. Total FR resets and mean long wait time
were not significantly affected by CuxOy exposure in females (Fig. 7B).
When the FR wait schedule was changed to an initial 5.0 second wait
with each interval subsequently increased by 5.0 ss, FR response rates in
Cu males were marginally greater than those in Air males (main effect of
treatment, F(1,14) = 4.33, p = 0.056) as observed with a 2.5 s wait and
increment (Fig. 8A). Interestingly, the marginally significant increases
in the number of total FR resets observed in Cu males at the 2.5 s wait
and increment were further increased at the 5.0 s wait and increment
and reached statistical significance (main effect of treatment F(1,14)
= 6.12, p = 0.027). Additionally, at the longer 5.0 sec wait and incre­
ment, CuxOy -exposed males exhibited reductions in mean long wait
time during the second session (time x treatment: F(3,42) = 7.78,
p < 0.001) as well as a marginally significant increase in the number of
responses per reinforcer (main effect of treatment F((1,14)= 4.55,
p = 0.051). In contrast, CuxOy exposure did not significantly impact
female FR waiting behavior with a 5.0 sond wait and increment
(Fig. 8B).
correct lever), run rates, post-reinforcement pause times, and mean
inter-response time values for male and female offspring are shown
across sixteen 20-minute sessions of an FI60 schedule of reinforcement
in Figs. 9A and 9B, respectively. Increases in overall rates of Cu males
ranged from 40% to 68% above Air control, while corresponding values
for Cu females ranged from 10% to 86% of Air control values (males:
main effect of treatment, F(1,14) = 12.36, p = 0.003; females: main
effect of treatment, F(1,14) = 4.75, p = 0.047). In both sexes, there were
marginally significant increases in run rate (males, main effect of
treatment, F(1,14) = 4.27, p = 0.058; females: main effect of treatment,
F(1,14) = 4.37, p = 0.055). This effect was accompanied by significant
reductions in mean IRT values in Cu males that differed in magnitude
across sessions (time x treatment: F(15,210) = 1.93, p = 0.022) and
marginally significant reductions in females across sessions (main effect
of treatment, F(1,14) = 3.71, p = 0.075). Post-reinforcement pause
times, however, were not significantly affected in either sex after CuxOy
nanoparticle exposure (males: F(1,14) = 1.74, p = 0.21; females: F
(1,14) = 2.10, p = 0.17).
3.10. Fixed interval extinction
3.9. Fixed interval behavior
As expected, when lever pressing no longer produced reinforcement
delivery during the FI60 schedule of reinforcement, overall response
rates of all mice declined across sessions. CuxOy nanoparticle exposure
significantly slowed these reductions in FI overall response rate and in
run rate in males (Fig. 10A), particularly in the first session (overall
response rate: time x treatment, F(4,56) = 3.54, p = 0.012; run rate:
As expected, the average number of lever presses in a given session
was highest for the reward-yielding left lever as the lowest average
number of left lever presses observed in a session was 534.4, while the
highest average number of presses on the right and center levers in a
session were 15.9 and 22.4, respectively. Overall FI response rates (left/
65
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Neurotoxicology 100 (2024) 55–71
Fig. 9. The effects of inhalational Cu exposure on the performance of male and female mice in a Fixed Interval 60-second schedule. Group mean ± standard
error levels of overall response rate (responses per minute; first column), run rate (responses per minute; second column), mean post-reinforcement pause time
(seconds; third column) and mean inter-response time (seconds; fourth column) across the course of 16 sessions on a Fixed Interval (FI) 60-second schedule of food
reward in male (Panel A) and female (Panel B) air-exposed (filled-in symbols) and CuxOy -exposed (open symbols) offspring. Treatment = main effect of treatment in
repeated measures analyses of variance; time x treatment = interaction effect in repeated measures analyses of variance; N = 8/sex/treatment. * = p-value < 0.05, #
= p-value < 0.10.
Fig. 10. The effects of inhalational Cu exposure on the performance of male and female mice in a Fixed Interval 60-second schedule of extinction. Group
mean ± standard error levels of overall response rate (responses per minute; first column), run rate (responses per minute; second column), mean post-reinforcement
pause time (seconds; third column) and mean inter-response time (seconds; fourth column) across the course of 5 sessions on a Fixed Interval (FI) 60 s schedule of
extinction in male (Panel A) and female (Panel B) air-exposed (filled-in symbols) and CuxOy -exposed (open symbols) offspring. Treatment = main effect of
treatment in repeated measures analyses of variance; time x treatment = interaction effect in repeated measures analyses of variance; N = 7–8/sex/treatment. * = pvalue < 0.05, # = p-value < 0.10.
time x treatment, F(4,56) = 3.46, p = 0.014). Correspondingly, re­
ductions in mean IRTs in males also occurred in response to CuxOy
nanoparticle exposure (F(1,14) = 5.16, p = 0.039). CuxOy-exposed fe­
males (Fig. 10B) likewise showed a slower rate of decline in FI overall
response rates (time x treatment, F(4,52) = 4.86, p = 0.002), again
especially in session 1, while run rates of CuxOy-exposed females
remained consistently elevated across the 5 sessions of FI extinction
(main effect of treatment, F(1,13) = 7.95, p = 0.015) and mean IRTs
remained unaffected (F(1,13) = 0.62, p = 0.45). Post-reinforcement
pause times were not significantly affected in either sex as a conse­
quence of CuxOy nanoparticle exposure (males: F(1,14) = 1.44,
p = 0.25; females: F(1,13) = 0.28, p = 0.61).
4. Discussion
AP exposures have increasingly been associated with risk for various
NDDs and psychiatric disorders (Cory-Slechta et al., 2023), raising the
question of what chemical contaminants of AP underlie such associa­
tions. The current study sought to determine whether developmental
exposure to inhaled Cu, a prevalent metal constituent within AP that has
also been implicated as a risk factor in such disorders (Devanarayanan
et al., 2016; Pandey et al., 2022; Schoonover et al., 2021), could underlie
66
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Neurotoxicology 100 (2024) 55–71
phenotypes associated with these disorders. Inhalation of CuxOy nano­
particles in mice during the first two weeks of postnatal development, a
time period equivalent to human third trimester brain development
(Clancy et al., 2007a; Clancy et al., 2007b), at environmentally-relevant
mass concentrations, altered brain metal homeostasis in both sexes,
despite not increasing brain Cu levels. This CuxOy exposure in males, but
not females, also resulted in marked increases in dopaminergic function,
as well as excitatory/inhibitory imbalances and reductions in seroto­
nergic function within the striatum at PND14. Upon maturing into
adulthood, alterations in behavior were observed most prominently in
males, with impulsive-like features that included impairments in wait­
ing for delayed rewards in a FR wait-for-reward program and increases
in FI response rates.
The changes in striatal profiles across the three classes of neuro­
transmitters in CuxOy-exposed PND14 males not only recapitulate per­
turbations in striatal neurotransmission implicated in individuals
diagnosed with various NDDs (Abi-Dargham et al., 1998; Nakamura
et al., 2010; Puts et al., 2020; Stahl, 2018), but also aligns with current
understanding of the interactive functions of striatal dopamine, gluta­
mate, and serotonergic circuits. For example, glutamate has been shown
to be able to regulate both spontaneous and evoked release of dopamine
in the striatum in rats (Kulagina et al., 2001). Furthermore, early studies
in the striatum demonstrated a role of endogenous serotonin in regula­
tion of dopamine release (Jacocks and Cox, 1992), and that activation of
group II metabotropic receptors can increase turnover of dopamine and
5-HT (Cartmell et al., 2000).
The elevations in striatal dopamine levels in CuxOy -exposed PND14
males were particularly striking, with increases of nearly 600%
compared to Air males. The specific mechanism(s) by which this
occurred is not yet known, nor is it yet known how dorsal and ventral
sub-regions of the striatum (caudate putamen and nucleus accumbens,
respectively) might be individually altered. For example, Cu could be
acting upon astrocytic calcium signaling and GABA uptake transporters,
both of which have recently been identified as crucial local modulators
of striatal dopaminergic activity and underlying GABAergic inhibitory
tone (Corkrum et al., 2020; Roberts et al., 2020; Roberts et al., 2021).
Alternatively, Cu exposure could be affecting the Cu-dependent enzyme
dopamine-beta-hydroxylase, which converts dopamine to norepineph­
rine (Kaler and Holmes, 2013). Finally, the absence of an increase in the
dopamine precursor, tyrosine, coupled with the increases in both
dopamine and its metabolites HVA and DOPAC, could also suggest a
deficit in re-uptake mechanisms (Nissbrandt et al., 1991) and/or in­
creases in dopamine release and dopamine receptor blockade (Rayevsky
et al., 1995).
Outside of the striatum, the CuxOy-associated increases in the ratios
of cerebellar excitatory/inhibitory neurotransmitters in males (Gln/
GABA) and of the glutamine precursor to its product glutamate (Gln/
Glu) in females indicate that CuxOy exposures could be affecting cere­
bellar outputs known to modulate striatal dopaminergic release. Such
systems are commonly dysregulated in NDDs (Fatemi and Folsom, 2015;
Fatemi et al., 2018; Yeganeh-Doost et al., 2011) and alter neuro­
modulatory actions between dopaminergic, glutamatergic, and
GABAergic neurons originating in the ventral tegmental area; the bal­
ance of which is crucial for reward-mediated behaviors, as many of these
neurons have efferent projections to the nucleus accumbens of the
striatum (Al-Hasani et al., 2021; Dobi et al., 2010; Omelchenko and
Sesack, 2009; Van Bockstaele and Pickel, 1995; van Zessen et al., 2012).
It would also be informative to evaluate the way Cu acts upon hippo­
campal glutamatergic afferents extending from the ventral subiculum to
ventral tegmental area dopaminergic neurons that enervate the nucleus
accumbens (Floresco et al., 2001).
The observed striatal neurochemical imbalances that occurred in
early postnatal ages persisted into male-biased alterations in adulthood
such as deficits in waiting-for-reward behavior, increased FI response
rates, and an impaired ability to adapt to new environmental contin­
gencies (FI extinction), findings which align with their crucial roles in
maintaining excitation/inhibition balance within mesocorticolimbic
brain regions (i.e. striatum) and in governing behavioral flexibility,
impulsivity, and reward-mediated behaviors (Chen et al., 2013; Des­
rochers et al., 2021; Hariri et al., 2006). Alterations in FR wait behavior
and behavior on the FI schedule of food reward in mice inhalationally
exposed to CuxOy nanoparticles tended to be more consistent and of
greater magnitude in males. In the case of the FR wait-for-reward
paradigm, an impaired ability to wait for rewards also resulted in a
greater number of responses emitted for each reinforcer delivery, while
in the FI schedule, response rate increases cannot accelerate reinforcer
availability. This is noteworthy because increases in FI response rates
were observed in both males and females exposed to CuxOy nano­
particles, however, CuxOy- associated increases in the number of FR
resets were exclusive to males.
Collectively, these findings corroborate our previous observations
following developmental UFP exposures that yielded a mass concen­
tration averaging 96 µg/m3, wherein sex- and brain region- biased
perturbations in mesocorticolimbic neurotransmission within the first
two weeks of life resulted in male-biased increases in FR resets on the FR
wait schedule (Allen et al., 2013; Allen et al., 2014a), a behavior which
has specifically been associated with hyperactivity within the ventral
striatum (i.e., nucleus accumbens) in humans (Hariri et al., 2006) and,
like increases in response rates in FI schedules of reinforcement
(Darcheville et al., 1992, 1993), serves as an indicator of response
inhibition/impulsivity. Impulsive-like behaviors are commonly
observed amongst individuals with NDDs, including autism spectrum
disorder (Geurts et al., 2014; Lopez et al., 2005; Mosconi et al., 2009),
attention deficit/hyperactivity disorder (Demurie et al., 2012; Yin et al.,
2022), and schizophrenia (Hoptman et al., 2018; Nolan et al., 2011).
Moreover, the striatum, and particularly dopaminergic pathways within
it, are known to play crucial roles in mediating impulsive-like behaviors,
particularly choice impulsivity (Dalley et al., 2008; Kim and Im, 2019).
For example, micro-infusion of the dopamine/norepinephrine reuptake
inhibitor methylphenidate into the nucleus accumbens in rats was
shown to increase premature responding on the 5-choice serial reaction
time test (Economidou et al., 2012). In humans, a more rapid decline in
the discounting rate for delayed rewards occurred with reduced dopa­
mine synthesis capacity in putamen (Smith et al., 2016). In the context
of airborne Cu exposure in humans, alterations in caudate nucleus
cytoarchitecture and reductions in connectivity to the frontal cortex
were associated with shorter reaction times (Pujol et al., 2016). Further,
we have also previously reported the mediation of FI
schedule-controlled behavior via nucleus accumbens dopamine systems
(Cory-Slechta et al., 1997). Collectively, additional behavioral assess­
ments (e.g., progressive ratio schedule) could be used to further discern
the specific behavioral mechanisms of action by which Cu exposure
increases impulsive-like behaviors in males and whether these effects
are perhaps linked to alterations in motivation for reward.
Neurotransmitter changes reported here were determined at PND14,
while behavioral functions were determined in adulthood. Clearly,
future studies will need to determine the persistence of the early
neurotransmitter dysfunctions observed, their direct relationship to
subsequent behavioral changes, as well as the specific roles of various
regions of striatum (i.e., nucleus accumbens, dorsal striatum) in such
effects. Importantly, however, our neurochemical and behavioral data
do suggest that mesolimbic (ventral tegmental area→nucleus accum­
bens) circuitry, a circuitry often neurochemically dysregulated in rodent
models and human investigations of male-biased NDDs such as autism
spectrum disorder (Chao et al., 2020), attention deficit/hyperactivity
disorder (Jucaite et al., 2005; Volkow et al., 2007), and schizophrenia
(Katzel et al., 2020), is especially vulnerable to inhaled CuxOy nano­
particle exposure in the first two weeks of life.
Additionally, while male- and striatal- specific sensitivities in
neurotransmitter systems and oxidative stress have been reported with
developmental UFP (Allen et al., 2014b; Allen et al., 2014c) and adult­
hood UFP exposures (Guerra et al., 2013), the underlying reason for sex67
J. Cubello et al.
Neurotoxicology 100 (2024) 55–71
and regional- specificity within these contexts remains unclear and
should be further investigated. Such specificity could be in part a
consequence of inherent sex- and region- specific variability in dopa­
minergic (Bhatt and Dluzen, 2005; Brundage et al., 2022; Connell et al.,
2004; Dluzen et al., 2008; Mozley et al., 2001; Walker et al., 2006),
GABAergic (Kalamarides et al., 2023; McCarthy et al., 2002), gluta­
matergic (Burton and Fletcher, 2012; Wickens et al., 2018), and sero­
tonergic (Campanelli et al., 2021; Connell et al., 2004)
neurotransmission systems; all of which factor into known sex-specific
variabilities in striatal DA release and behavioral presentations of
impulsivity (Cross et al., 2011; Eckard et al., 2023b; Jentsch and Taylor,
2003; Jupp et al., 2013; Weafer and de Wit, 2014).
As expected, given the heterogenous nature of NDDs and in the
composition of AP, which contains various neurotoxic contaminants in
addition to Cu that likely contribute to our previously observed ambient
UFP exposure-associated phenotypes, findings within this study suggest
that postnatal exposure to CuxOy nanoparticles alone does contribute to
some of the neuropathological and behavioral phenotypes characteristic
to UFP exposures and NDDs. Further investigations are therefore war­
ranted for the expansion of our current understanding of the neuro­
pathological and behavioral susceptibilities to inhalational CuxOy
nanoparticle exposure, which could also prompt and direct protective
public health regulations of air Cu levels.
Alyssa K.: Data curation, Investigation. Conrad Katherine: Data
curation, Investigation.
Declaration of Competing Interest
The authors declare that they have no known competing financial
interests or personal relationships that could have appeared to influence
the work reported in this paper.
Data Availability
Data will be made available on request.
Appendix A. Supporting information
Supplementary data associated with this article can be found in the
online version at doi:10.1016/j.neuro.2023.12.007.
References
Abi-Dargham, A., Gil, R., Krystal, J., Baldwin, R.M., Seibyl, J.P., Bowers, M., van Dyck, C.
H., Charney, D.S., Innis, R.B., Laruelle, M., 1998. Increased striatal dopamine
transmission in schizophrenia: confirmation in a second cohort. Am. J. Psychiatry
155 (6), 761–767.
Al-Hasani, R., Gowrishankar, R., Schmitz, G.P., Pedersen, C.E., Marcus, D.J., Shirley, S.
E., Hobbs, T.E., Elerding, A.J., Renaud, S.J., Jing, M., Li, Y., Alvarez, V.A., Lemos, J.
C., Bruchas, M.R., 2021. Ventral tegmental area GABAergic inhibition of cholinergic
interneurons in the ventral nucleus accumbens shell promotes reward reinforcement.
Nat. Neurosci. 24 (10), 1414–1428.
Allen, J.L., Conrad, K., Oberdorster, G., Johnston, C.J., Sleezer, B., Cory-Slechta, D.A.,
2013. Developmental exposure to concentrated ambient particles and preference for
immediate reward in mice. Environ. Health Perspect. 121 (1), 32–38.
Allen, J.L., Liu, X., Pelkowski, S., Palmer, B., Conrad, K., Oberdorster, G., Weston, D.,
Mayer-Proschel, M., Cory-Slechta, D.A., 2014a. Early postnatal exposure to ultrafine
particulate matter air pollution: persistent ventriculomegaly, neurochemical
disruption, and glial activation preferentially in male mice. Environ. Health
Perspect. 122 (9), 939–945.
Allen, J.L., Liu, X., Weston, D., Conrad, K., Oberdorster, G., Cory-Slechta, D.A., 2014b.
Consequences of developmental exposure to concentrated ambient ultrafine particle
air pollution combined with the adult paraquat and maneb model of the Parkinson’s
disease phenotype in male mice. Neurotoxicology 41, 80–88.
Allen, J.L., Liu, X., Weston, D., Prince, L., Oberdorster, G., Finkelstein, J.N., Johnston, C.
J., Cory-Slechta, D.A., 2014c. Developmental exposure to concentrated ambient
ultrafine particulate matter air pollution in mice results in persistent and sexdependent behavioral neurotoxicity and glial activation. Toxicol. Sci. 140 (1),
160–178.
Allen, J.L., Oberdorster, G., Morris-Schaffer, K., Wong, C., Klocke, C., Sobolewski, M.,
Conrad, K., Mayer-Proschel, M., Cory-Slechta, D.A., 2017. Developmental
neurotoxicity of inhaled ambient ultrafine particle air pollution: parallels with
neuropathological and behavioral features of autism and other neurodevelopmental
disorders. Neurotoxicology 59, 140–154.
Anderson, T., Merrill, A.K., Eckard, M.L., Marvin, E., Conrad, K., Welle, K.,
Oberdorster, G., Sobolewski, M., Cory-Slechta, D.A., 2021. Paraquat inhalation, a
translationally relevant route of exposure: disposition to the brain and male-specific
olfactory impairment in mice. Toxicol. Sci. 180 (1), 175–185.
Bhatt, S.D., Dluzen, D.E., 2005. Dopamine transporter function differences between male
and female CD-1 mice. Brain Res 1035 (2), 188–195.
Brundage, J.N., Mason, C.P., Wadsworth, H.A., Finuf, C.S., Nelson, J.J., Ronstrom, P.J.
W., Jones, S.R., Siciliano, C.A., Steffensen, S.C., Yorgason, J.T., 2022. Regional and
sex differences in spontaneous striatal dopamine transmission. J. Neurochem 160
(6), 598–612.
Burton, C.L., Fletcher, P.J., 2012. Age and sex differences in impulsive action in rats: the
role of dopamine and glutamate. Behav. Brain Res 230 (1), 21–33.
Campanelli, F., Marino, G., Barsotti, N., Natale, G., Calabrese, V., Cardinale, A.,
Ghiglieri, V., Maddaloni, G., Usiello, A., Calabresi, P., Pasqualetti, M., Picconi, B.,
2021. Serotonin drives striatal synaptic plasticity in a sex-related manner. Neurobiol.
Dis. 158, 105448.
Cartmell, J., Salhoff, C.R., Perry, K.W., Monn, J.A., Schoepp, D.D., 2000. Dopamine and
5-HT turnover are increased by the mGlu2/3 receptor agonist LY379268 in rat
medial prefrontal cortex, nucleus accumbens and striatum. Brain Res 887 (2),
378–384.
Catala-Lopez, F., Peiro, S., Ridao, M., Sanfelix-Gimeno, G., Genova-Maleras, R.,
Catala, M.A., 2012. Prevalence of attention deficit hyperactivity disorder among
children and adolescents in Spain: a systematic review and meta-analysis of
epidemiological studies. BMC Psychiatry 12, 168.
Chao, O.Y., Pathak, S.S., Zhang, H., Dunaway, N., Li, J.S., Mattern, C., Nikolaus, S.,
Huston, J.P., Yang, Y.M., 2020. Altered dopaminergic pathways and therapeutic
effects of intranasal dopamine in two distinct mouse models of autism. Mol. Brain 13
(1), 111.
5. Conclusions
This study sought to determine the potential contributions of Cu in
air pollution to the neurotoxic consequences of developmental expo­
sures to inhaled ambient UFPs seen in our prior studies, many features of
which are characteristic of male-biased NDDs. In the absence of alter­
ations in total metal concentrations as a consequence of inhalational
exposure to CuxOy nanoparticles, correlations between brain metals
were altered, especially in the cortex. This exposure also resulted in
male-biased imbalances across dopaminergic, glutamatergic, and sero­
tonergic neurotransmitter systems within the striatum, and additionally
affected excitatory/inhibitory balance in the cerebellum irrespective of
sex. Whereas ambulatory locomotor activity and short-term memory
were not impaired, as with UFPs, CuxOy exposure produced male-biased
changes in adulthood in a FR wait-for-reward paradigm and a FI
schedule of food reward, behaviors known to be associated with striatal
dopaminergic dysfunction. Collectively, these findings highlight the
potential for Cu contamination in AP matter to affect the developing
nervous system and to result in persisting behavioral effects.
Funding
This work was supported by NIH grants R01 ES032260; R35
ES031689; P30 ES001247; and the NCI Cancer Center Support Grant
P30 CA023108 (Dartmouth College). JVG is a trainee in the Medical
Scientist Training Program funded by NIH T32 GM007356. The content
is solely the responsibility of the authors and does not necessarily
represent the official views of the National Institute of General Medical
Science or NIH.
CRediT authorship contribution statement
Sobolewski Marissa: Conceptualization, Formal analysis, Method­
ology, Resources, Writing – review & editing. Oberdörster Günter:
Conceptualization, Methodology, Resources. Chalupa David: Data
curation, Investigation. Jackson Brian P.: Data curation, Investigation.
Cubello Janine: Data curation, Formal analysis, Investigation, Writing
– original draft, Writing – review & editing. Cory-Slechta Deborah A.:
Conceptualization, Formal analysis, Funding acquisition, Methodology,
Resources, Writing – original draft, Writing – review & editing. Marvin
Elena: Data curation, Investigation. Welle Kevin: Data curation,
Investigation. George Jithin V.: Data curation, Investigation. Merrill
68
J. Cubello et al.
Neurotoxicology 100 (2024) 55–71
Charlson, F.J., Ferrari, A.J., Santomauro, D.F., Diminic, S., Stockings, E., Scott, J.G.,
McGrath, J.J., Whiteford, H.A., 2018. Global epidemiology and burden of
schizophrenia: findings from the global burden of disease study 2016. Schizophr.
Bull. 44 (6), 1195–1203.
Chen, J.Y., Wang, E.A., Cepeda, C., Levine, M.S., 2013. Dopamine imbalance in
Huntington’s disease: a mechanism for the lack of behavioral flexibility. Front
Neurosci. 7, 114.
Choi, M.R., Eun, H.J., Yoo, T.P., Yun, Y., Wood, C., Kase, M., Park, J.I., Yang, J.C., 2012.
The effects of sociodemographic factors on psychiatric diagnosis. Psychiatry Invest. 9
(3), 199–208.
Cilliers, K., 2021. Trace element alterations in Alzheimer’s disease: a review. Clin. Anat.
34 (5), 766–773.
Clancy, B., Finlay, B.L., Darlington, R.B., Anand, K.J., 2007a. Extrapolating brain
development from experimental species to humans. Neurotoxicology 28 (5),
931–937.
Clancy, B., Kersh, B., Hyde, J., Darlington, R.B., Anand, K.J., Finlay, B.L., 2007b. Webbased method for translating neurodevelopment from laboratory species to humans.
Neuroinformatics 5 (1), 79–94.
Connell, S., Karikari, C., Hohmann, C.F., 2004. Sex-specific development of cortical
monoamine levels in mouse. Brain Res. Dev. Brain Res 151 (1–2), 187–191.
Corkrum, M., Covelo, A., Lines, J., Bellocchio, L., Pisansky, M., Loke, K., Quintana, R.,
Rothwell, P.E., Lujan, R., Marsicano, G., Martin, E.D., Thomas, M.J., Kofuji, P.,
Araque, A., 2020. Dopamine-evoked synaptic regulation in the nucleus accumbens
requires astrocyte activity. Neuron 105 (6), 1036–1047 e1035.
Cory-Slechta, D.A., 1986. Prolonged lead exposure and fixed ratio performance.
Neurobehav. Toxicol. Teratol. 8 (3), 237–244.
Cory-Slechta, D.A., Weiss, B., Cox, C., 1985. Performance and exposure indices of rats
exposed to low concentrations of lead. Toxicol. Appl. Pharm. 78 (2), 291–299.
Cory-Slechta, D.A., Pazmino, R., Bare, C., 1997. The critical role of the nucleus
accumbens dopamine systems in the mediation of fixed interval schedule-controlled
operant behavior. Brain Res. 764, 253–256.
Cory-Slechta, D.A., O’Mara, D.J., Brockel, B.J., 1998. Nucleus accumbens dopaminergic
medication of fixed interval schedule-controlled behavior and its modulation by lowlevel lead exposure. J. Pharm. Exp. Ther. 286 (2), 794–805.
Cory-Slechta, D.A., Allen, J.L., Conrad, K., Marvin, E., Sobolewski, M., 2018.
Developmental exposure to low level ambient ultrafine particle air pollution and
cognitive dysfunction. Neurotoxicology 69, 217–231.
Cory-Slechta, D.A., Sobolewski, M., Marvin, E., Conrad, K., Merrill, A., Anderson, T.,
Jackson, B.P., Oberdorster, G., 2019. The impact of inhaled ambient ultrafine
particulate matter on developing brain: potential importance of elemental
contaminants. Toxicol. Pathol. 47 (8), 976–992.
Cory-Slechta, D.A., Sobolewski, M., Oberdorster, G., 2020. Air pollution-related brain
metal dyshomeostasis as a potential risk factor for neurodevelopmental disorders
and neurodegenerative diseases. Atmosphere 11 (10).
Cory-Slechta, D.A., Merrill, A., Sobolewski, M., 2023. Air pollution-related neurotoxicity
across the life span. Annu Rev. Pharm. Toxicol. 63, 143–163.
Cross, C.P., Copping, L.T., Campbell, A., 2011. Sex differences in impulsivity: a metaanalysis. Psychol. Bull. 137 (1), 97–130.
Dalley, J.W., Mar, A.C., Economidou, D., Robbins, T.W., 2008. Neurobehavioral
mechanisms of impulsivity: fronto-striatal systems and functional neurochemistry.
Pharm. Biochem Behav. 90 (2), 250–260.
Darcheville, J.C., Riviere, V., Wearden, J.H., 1992. Fixed-interval performance and selfcontrol in children. J. Exp. Anal. Behav. 57, 187–199.
Darcheville, J.C., Riviere, V., Wearden, J.H., 1993. Fixed-interval performance and selfcontrol in infants. J. Exp. Anal. Behav. 60, 239–254.
Das, R., Khezri, B., Srivastava, B., Datta, S., Sikdar, P.K., Webster, R.D., Wang, X.F., 2015.
Trace element composition of PM2.5 and PM10 from Kolkata - a heavily polluted
Indian metropolis. Atmos. Pollut. Res 6 (5), 742–750.
Demurie, E., Roeyers, H., Baeyens, D., Sonuga-Barke, E., 2012. Temporal discounting of
monetary rewards in children and adolescents with ADHD and autism spectrum
disorders. Dev. Sci. 15 (6), 791–800.
Desrochers, S.S., Lesko, E.K., Magalong, V.M., Balsam, P.D., Nautiyal, K.M., 2021. A role
for reward valuation in the serotonergic modulation of impulsivity.
Psychopharmacol. (Berl. ) 238 (11), 3293–3309.
Devanarayanan, S., Nandeesha, H., Kattimani, S., Sarkar, S., Jose, J., 2016. Elevated
copper, hs C-reactive protein and dyslipidemia in drug free schizophrenia: relation
with psychopathology score. Asian J. Psychiatr. 24, 99–102.
Dluzen, D.E., Bhatt, S., McDermott, J.L., 2008. Differences in reserpine-induced striatal
dopamine output and content between female and male mice: implications for sex
differences in vesicular monoamine transporter 2 function. Neuroscience 154 (4),
1488–1496.
Dobi, A., Margolis, E.B., Wang, H.L., Harvey, B.K., Morales, M., 2010. Glutamatergic and
nonglutamatergic neurons of the ventral tegmental area establish local synaptic
contacts with dopaminergic and nondopaminergic neurons. J. Neurosci. 30 (1),
218–229.
Eckard, M.L., Marvin, E., Conrad, K., Oberdorster, G., Sobolewski, M., Cory-Slechta, D.A.,
2023a. Neonatal exposure to ultrafine iron but not combined iron and sulfur aerosols
recapitulates air pollution-induced impulsivity in mice. Neurotoxicology 94,
191–205.
Eckard, M.L., Welle, K., Sobolewski, M., Cory-Slechta, D.A., 2023b. A behavioral timing
intervention upregulates striatal serotonergic markers and reduces impulsive action
in adult male mice. Behav. Brain Res. 440, 114267.
Economidou, D., Theobald, D.E., Robbins, T.W., Everitt, B.J., Dalley, J.W., 2012.
Norepinephrine and dopamine modulate impulsivity on the five-choice serial
reaction time task through opponent actions in the shell and core sub-regions of the
nucleus accumbens. Neuropsychopharmacology 37 (9), 2057–2066.
Fatemi, S.H., Folsom, T.D., 2015. GABA receptor subunit distribution and FMRP-mGluR5
signaling abnormalities in the cerebellum of subjects with schizophrenia, mood
disorders, and autism. Schizophr. Res 167 (1–3), 42–56.
Fatemi, S.H., Wong, D.F., Brasic, J.R., Kuwabara, H., Mathur, A., Folsom, T.D., Jacob, S.,
Realmuto, G.M., Pardo, J.V., Lee, S., 2018. Metabotropic glutamate receptor 5 tracer
[(18)F]-FPEB displays increased binding potential in postcentral gyrus and
cerebellum of male individuals with autism: a pilot PET study. Cerebellum Ataxias 5,
3.
Feng, J., Shan, L., Miao, C., Xue, Y., Yue, X., Jia, F., 2023. The association of vitamin A,
zinc and copper levels with clinical symptoms in children with autism spectrum
disorders in Jilin Province, China. BMC Pedia 23 (1), 173.
Floresco, S.B., Todd, C.L., Grace, A.A., 2001. Glutamatergic afferents from the
hippocampus to the nucleus accumbens regulate activity of ventral tegmental area
dopamine neurons. J. Neurosci. 21 (13), 4915–4922.
Frances, L., Quintero, J., Fernandez, A., Ruiz, A., Caules, J., Fillon, G., Hervas, A.,
Soler, C.V., 2022. Current state of knowledge on the prevalence of
neurodevelopmental disorders in childhood according to the DSM-5: a systematic
review in accordance with the PRISMA criteria. Child Adolesc. Psychiatry Ment.
Health 16 (1), 27.
Garcia, G.J., Kimbell, J.S., 2009. Deposition of inhaled nanoparticles in the rat nasal
passages: dose to the olfactory region. Inhal. Toxicol. 21 (14), 1165–1175.
Garcia, G.J., Schroeter, J.D., Kimbell, J.S., 2015. Olfactory deposition of inhaled
nanoparticles in humans. Inhal. Toxicol. 27 (8), 394–403.
Gesi, C., Migliarese, G., Torriero, S., Capellazzi, M., Omboni, A.C., Cerveri, G.,
Mencacci, C., 2021. Gender differences in misdiagnosis and delayed diagnosis
among adults with autism spectrum disorder with no language or intellectual
disability. Brain Sci. 11 (7).
Geurts, H.M., van den Bergh, S.F., Ruzzano, L., 2014. Prepotent response inhibition and
interference control in autism spectrum disorders: two meta-analyses. Autism Res 7
(4), 407–420.
Guerra, R., Vera-Aguilar, E., Uribe-Ramirez, M., Gookin, G., Camacho, J., OsornioVargas, A.R., Mugica-Alvarez, V., Angulo-Olais, R., Campbell, A., Froines, J.,
Kleinman, T.M., De Vizcaya-Ruiz, A., 2013. Exposure to inhaled particulate matter
activates early markers of oxidative stress, inflammation and unfolded protein
response in rat striatum. Toxicol. Lett. 222 (2), 146–154.
Hariri, A.R., Brown, S.M., Williamson, D.E., Flory, J.D., de Wit, H., Manuck, S.B., 2006.
Preference for immediate over delayed rewards is associated with magnitude of
ventral striatal activity. J. Neurosci. 26 (51), 13213–13217.
Hoptman, M.J., Parker, E.M., Nair-Collins, S., Dias, E.C., Ross, M.E., DiCostanzo, J.N.,
Sehatpour, P., Javitt, D.C., 2018. Sensory and cross-network contributions to
response inhibition in patients with schizophrenia. Neuroimage Clin. 18, 31–39.
Jacocks 3rd, H.M., Cox, B.M., 1992. Serotonin-stimulated release of [3H]dopamine via
reversal of the dopamine transporter in rat striatum and nucleus accumbens: a
comparison with release elicited by potassium, N-methyl-D-aspartic acid, glutamic
acid and D-amphetamine. J. Pharm. Exp. Ther. 262 (1), 356–364.
Jentsch, J.D., Taylor, J.R., 2003. Sex-related differences in spatial divided attention and
motor impulsivity in rats. Behav. Neurosci. 117 (1), 76–83.
Jucaite, A., Fernell, E., Halldin, C., Forssberg, H., Farde, L., 2005. Reduced midbrain
dopamine transporter binding in male adolescents with attention-deficit/
hyperactivity disorder: association between striatal dopamine markers and motor
hyperactivity. Biol. Psychiatry 57 (3), 229–238.
Jupp, B., Caprioli, D., Saigal, N., Reverte, I., Shrestha, S., Cumming, P., Everitt, B.J.,
Robbins, T.W., Dalley, J.W., 2013. Dopaminergic and GABA-ergic markers of
impulsivity in rats: evidence for anatomical localisation in ventral striatum and
prefrontal cortex. Eur. J. Neurosci. 37 (9), 1519–1528.
Kalamarides, D.J., Singh, A., Wolfman, S.L., Dani, J.A., 2023. Sex differences in VTA
GABA transmission and plasticity during opioid withdrawal. Sci. Rep. 13 (1), 8460.
Kaler, S.G., Holmes, C.S., 2013. Catecholamine metabolites affected by the copperdependent enzyme dopamine-beta-hydroxylase provide sensitive biomarkers for
early diagnosis of menkes disease and viral-mediated ATP7A gene therapy. Adv.
Pharm. 68, 223–233.
Katzel, D., Wolff, A.R., Bygrave, A.M., Bannerman, D.M., 2020. Hippocampal
hyperactivity as a druggable circuit-level origin of aberrant salience in
schizophrenia. Front Pharm. 11, 486811.
Kim, B., Im, H.I., 2019. The role of the dorsal striatum in choice impulsivity. Ann. N. Y
Acad. Sci. 1451 (1), 92–111.
Klocke, C., Allen, J.L., Sobolewski, M., Mayer-Proschel, M., Blum, J.L., Lauterstein, D.,
Zelikoff, J.T., Cory-Slechta, D.A., 2017. Neuropathological consequences of
gestational exposure to concentrated ambient fine and ultrafine particles in the
mouse. Toxicol. Sci. 156 (2), 492–508.
Kozlovskaya, L., Abou-Kaoud, M., Stepensky, D., 2014. Quantitative analysis of drug
delivery to the brain via nasal route. J. Control Release 189, 133–140.
Kulagina, N.V., Zigmond, M.J., Michael, A.C., 2001. Glutamate regulates the
spontaneous and evoked release of dopamine in the rat striatum. Neuroscience 102
(1), 121–128.
Lavigne, A., Sterrantino, A.F., Liverani, S., Blangiardo, M., de Hoogh, K., Molitor, J.,
Hansell, A., 2019. Associations between metal constituents of ambient particulate
matter and mortality in England: an ecological study. BMJ Open 9 (12).
Li, Y., Wang, C., Zong, S., Qi, J., Dong, X., Zhao, W., Wu, W., Fu, Q., Lu, Y., Chen, Z.,
2019. The trigeminal pathway dominates the nose-to-brain transportation of intact
polymeric nanoparticles: evidence from aggregation-caused quenching probes.
J. Biomed. Nanotechnol. 15 (4), 686–702.
Liu, K., Shang, Q., Wan, C., Song, P., Ma, C., Cao, L., 2018. Characteristics and sources of
heavy metals in PM2.5 during a Typical Haze Episode in Rural and Urban Areas in
Taiyuan, China. Atmosphere 9 (1), 2.
69
J. Cubello et al.
Neurotoxicology 100 (2024) 55–71
Liu, T., Zhang, L., Pang, L., Li, N., Chen, G., Zheng, X., 2015. Schizophrenia-related
disability in China: prevalence, gender, and geographic location. Psychiatr. Serv. 66
(3), 249–257.
Lochhead, J.J., Wolak, D.J., Pizzo, M.E., Thorne, R.G., 2015. Rapid transport within
cerebral perivascular spaces underlies widespread tracer distribution in the brain
after intranasal administration. J. Cereb. Blood Flow. Metab. 35 (3), 371–381.
Lopez, B.R., Lincoln, A.J., Ozonoff, S., Lai, Z., 2005. Examining the relationship between
executive functions and restricted, repetitive symptoms of Autistic Disorder.
J. Autism Dev. Disord. 35 (4), 445–460.
Mazhari, S., Arjmand, S., Eslami Shahrbabaki, M., Karimi Ghoughari, E., 2020.
Comparing copper serum level and cognitive functioning in patients with
schizophrenia and healthy controls. Basic Clin. Neurosci. 11 (5), 649–657.
McCarthy, M.M., Auger, A.P., Perrot-Sinal, T.S., 2002. Getting excited about GABA and
sex differences in the brain. Trends Neurosci. 25 (6), 307–312.
McGuinn, L.A., Windham, G.C., Kalkbrenner, A.E., Bradley, C., Di, Q., Croen, L.A.,
Fallin, M.D., Hoffman, K., Ladd-Acosta, C., Schwartz, J., Rappold, A.G.,
Richardson, D.B., Neas, L.M., Gammon, M.D., Schieve, L.A., Daniels, J.L., 2020.
Early life exposure to air pollution and autism spectrum disorder: findings from a
multisite case-control study. Epidemiology 31 (1), 103–114.
McNeill, J., Snider, G., Weagle, C.L., Walsh, B., Bissonnette, P., Stone, E., Abboud, I.,
Akoshile, C., Anh, N.X., Balasubramanian, R., Brook, J.R., Coburn, C., Cohen, A.,
Dong, J.L., Gagnon, G., Garland, R.M., He, K.B., Holben, B.N., Kahn, R., Kim, J.S.,
Lagrosas, N., Lestari, P., Liu, Y., Jeba, F., Joy, K.S., Martins, J.V., Misra, A.,
Norford, L.K., Quel, E.J., Salam, A., Schichtel, B., Tripathi, S.N., Wang, C., Zhang, Q.,
Brauer, M., Gibson, M.D., Rudich, Y., Martin, R.V., 2020. Large global variations in
measured airborne metal concentrations driven by anthropogenic sources. Sci. Rep.
-Uk 10 (1).
Morgan, Z.E.M., Bailey, M.J., Trifonova, D.I., Naik, N.C., Patterson, W.B., Lurmann, F.W.,
Chang, H.H., Peterson, B.S., Goran, M.I., Alderete, T.L., 2023. Prenatal exposure to
ambient air pollution is associated with neurodevelopmental outcomes at 2 years of
age. Environ. Health 22 (1), 11.
Mosconi, M.W., Kay, M., D’Cruz, A.M., Seidenfeld, A., Guter, S., Stanford, L.D.,
Sweeney, J.A., 2009. Impaired inhibitory control is associated with higher-order
repetitive behaviors in autism spectrum disorders. Psychol. Med 39 (9), 1559–1566.
Mozley, L.H., Gur, R.C., Mozley, P.D., Gur, R.E., 2001. Striatal dopamine transporters
and cognitive functioning in healthy men and women. Am. J. Psychiatry 158 (9),
1492–1499.
Nakamura, K., Sekine, Y., Ouchi, Y., Tsujii, M., Yoshikawa, E., Futatsubashi, M.,
Tsuchiya, K.J., Sugihara, G., Iwata, Y., Suzuki, K., Matsuzaki, H., Suda, S.,
Sugiyama, T., Takei, N., Mori, N., 2010. Brain serotonin and dopamine transporter
bindings in adults with high-functioning autism. Arch. Gen. Psychiatry 67 (1),
59–68.
Newbury, J.B., Stewart, R., Fisher, H.L., Beevers, S., Dajnak, D., Broadbent, M.,
Pritchard, M., Shiode, N., Heslin, M., Hammoud, R., Hotopf, M., Hatch, S.L.,
Mudway, I.S., Bakolis, I., 2021. Association between air pollution exposure and
mental health service use among individuals with first presentations of psychotic and
mood disorders: retrospective cohort study. Br. J. Psychiatry 219 (6), 678–685.
Nissbrandt, H., Engberg, G., Pileblad, E., 1991. The effects of GBR 12909, a dopamine reuptake inhibitor, on monoaminergic neurotransmission in rat striatum, limbic
forebrain, cortical hemispheres and substantia nigra. Naunyn Schmiede Arch.
Pharm. 344 (1), 16–28.
Nolan, K.A., D’Angelo, D., Hoptman, M.J., 2011. Self-report and laboratory measures of
impulsivity in patients with schizophrenia or schizoaffective disorder and healthy
controls. Psychiatry Res 187 (1–2), 301–303.
Oberdorster, G., Finkelstein, J.N., Johnston, C., Gelein, R., Cox, C., Baggs, R., Elder, A.C.,
2000. Acute pulmonary effects of ultrafine particles in rats and mice. Res Rep. Health
Eff. Inst. (96) 5–74, 75–86 (disc).
Oberdorster, G., Sharp, Z., Atudorei, V., Elder, A., Gelein, R., Kreyling, W., Cox, C., 2004.
Translocation of inhaled ultrafine particles to the brain. Inhal. Toxicol. 16 (6–7),
437–445.
Omelchenko, N., Sesack, S.R., 2009. Ultrastructural analysis of local collaterals of rat
ventral tegmental area neurons: GABA phenotype and synapses onto dopamine and
GABA cells. Synapse 63 (10), 895–906.
Oudin, A., Frondelius, K., Haglund, N., Kallen, K., Forsberg, B., Gustafsson, P.,
Malmqvist, E., 2019. Prenatal exposure to air pollution as a potential risk factor for
autism and ADHD. Environ. Int 133 (Pt A), 105149.
Palladino, V.S., McNeill, R., Reif, A., Kittel-Schneider, S., 2019. Genetic risk factors and
gene-environment interactions in adult and childhood attention-deficit/
hyperactivity disorder. Psychiatr. Genet 29 (3), 63–78.
Pandey, M.K., Grabrucker, A.M., Mehta, S.Q., Harvey, R.J., 2022. Editorial: autism
spectrum disorders and metal dyshomeostasis. Front Mol. Neurosci. 15, 861483.
Pujol, J., Fenoll, R., Macia, D., Martinez-Vilavella, G., Alvarez-Pedrerol, M., Rivas, I.,
Forns, J., Deus, J., Blanco-Hinojo, L., Querol, X., Sunyer, J., 2016. Airborne copper
exposure in school environments associated with poorer motor performance and
altered basal ganglia. Brain Behav. 6 (6).
Puts, N.A., Ryan, M., Oeltzschner, G., Horska, A., Edden, R.A.E., Mahone, E.M., 2020.
Reduced striatal GABA in unmedicated children with ADHD at 7T. Psychiatry Res
Neuroimaging 301, 111082.
Rahman, M.M., Carter, S.A., Lin, J.C., Chow, T., Yu, X., Martinez, M.P., Levitt, P.,
Chen, Z., Chen, J.C., Rud, D., Lewinger, J.P., Eckel, S.P., Schwartz, J., Lurmann, F.
W., Kleeman, M.J., McConnell, R., Xiang, A.H., 2023. Prenatal exposure to tailpipe
and non-tailpipe tracers of particulate matter pollution and autism spectrum
disorders. Environ. Int 171, 107736.
Rayevsky, K.S., Gainetdinov, R.R., Grekhova, T.V., Sotnikova, T.D., 1995. Regulation of
dopamine release and metabolism in rat striatum in vivo: effects of dopamine
receptor antagonists. Prog. Neuropsychopharmacol. Biol. Psychiatry 19 (8),
1285–1303.
Rezaei, M., Rezaei, A., Esmaeili, A., Nakhaee, S., Azadi, N.A., Mansouri, B., 2022. A casecontrol study on the relationship between urine trace element levels and autism
spectrum disorder among Iranian children. Environ. Sci. Pollut. Res Int 29 (38),
57287–57295.
Rice, D., Barone Jr., S., 2000. Critical periods of vulnerability for the developing nervous
system: evidence from humans and animal models. Environ. Health Perspect. 108,
511–533. Suppl 3(Suppl 3).
Roberts, B.M., Doig, N.M., Brimblecombe, K.R., Lopes, E.F., Siddorn, R.E., Threlfell, S.,
Connor-Robson, N., Bengoa-Vergniory, N., Pasternack, N., Wade-Martins, R.,
Magill, P.J., Cragg, S.J., 2020. GABA uptake transporters support dopamine release
in dorsal striatum with maladaptive downregulation in a parkinsonism model. Nat.
Commun. 11 (1), 4958.
Roberts, B.M., Lopes, E.F., Cragg, S.J., 2021. Axonal modulation of striatal dopamine
release by local gamma-aminobutyric acid (GABA) signalling. Cells 10 (3).
Rossi-George, A., Virgolini, M.B., Weston, D., Thiruchelvam, M., Cory-Slechta, D.A.,
2011. Interactions of lifetime lead exposure and stress: behavioral, neurochemical
and HPA axis effects. Neurotoxicology 32 (1), 83–99.
Saghazadeh, A., Mahmoudi, M., Shahrokhi, S., Mojarrad, M., Dastmardi, M.,
Mirbeyk, M., Rezaei, N., 2020. Trace elements in schizophrenia: a systematic review
and meta-analysis of 39 studies (N = 5151 participants). Nutr. Rev. 78 (4), 278–303.
Santos, J.X., Sampaio, P., Rasga, C., Martiniano, H., Faria, C., Cafe, C., Oliveira, A.,
Duque, F., Oliveira, G., Sousa, L., Nunes, A., Vicente, A.M., 2023. Evidence for an
association of prenatal exposure to particulate matter with clinical severity of
Autism Spectrum Disorder. Environ. Res 228, 115795.
Sarnat, S.E., Winquist, A., Schauer, J.J., Turner, J.R., Sarnat, J.A., 2015. Fine particulate
matter components and emergency department visits for cardiovascular and
respiratory diseases in the st. louis, missouri-illinois, metropolitan area. Environ.
Health Persp 123 (5), 437–444.
Saxena, A., Dodell-Feder, D., 2022. Explaining the association between urbanicity and
psychotic-like experiences in pre-adolescence: the indirect effect of urban exposures.
Front Psychiatry 13, 831089.
Schaafsma, S.M., Gagnidze, K., Reyes, A., Norstedt, N., Mansson, K., Francis, K., Pfaff, D.
W., 2017. Sex-specific gene-environment interactions underlying ASD-like
behaviors. Proc. Natl. Acad. Sci. USA 114 (6), 1383–1388.
Schoonover, K.E., Farmer, C.B., Morgan, C.J., Sinha, V., Odom, L., Roberts, R.C., 2021.
Abnormalities in the copper transporter CTR1 in postmortem hippocampus in
schizophrenia: a subregion and laminar analysis. Schizophr. Res 228, 60–73.
Sharma, S.K., Mandal, T.K., 2023. Elemental composition and sources of fine particulate
matter (PM(2.5)) in Delhi, India. Bull. Environ. Contam. Toxicol. 110 (3), 60.
Skogheim, T.S., Weyde, K.V.F., Engel, S.M., Aase, H., Surén, P., Øie, M.G., Biele, G.,
Reichborn-Kjennerud, T., Caspersen, I.H., Hornig, M., Haug, L.S., Villanger, G.D.,
2021. Metal and essential element concentrations during pregnancy and associations
with autism spectrum disorder and attention-deficit/hyperactivity disorder in
children. Environ. Int 152, 106468.
Smith, C.T., Wallace, D.L., Dang, L.C., Aarts, E., Jagust, W.J., D’Esposito, M.,
Boettiger, C.A., 2016. Modulation of impulsivity and reward sensitivity in
intertemporal choice by striatal and midbrain dopamine synthesis in healthy adults.
J. Neurophysiol. 115 (3), 1146–1156.
Sobolewski, M., Singh, G., Schneider, J.S., Cory-Slechta, D.A., 2018. Different behavioral
experiences produce distinctive parallel changes in, and correlate with, frontal
cortex and hippocampal global post-translational histone levels. Front Integr.
Neurosci. 12, 29.
Sobolewski, M., Conrad, K., Marvin, E., Eckard, M., Goeke, C.M., Merrill, A.K., Welle, K.,
Jackson, B.P., Gelein, R., Chalupa, D., Oberdorster, G., Cory-Slechta, D.A., 2022. The
potential involvement of inhaled iron (Fe) in the neurotoxic effects of ultrafine
particulate matter air pollution exposure on brain development in mice. Part Fibre
Toxicol. 19 (1), 56.
Soleimani, M., Amini, N., Sadeghian, B., Wang, D., Fang, L., 2018. Heavy metals and
their source identification in particulate matter (PM(2.5)) in Isfahan City, Iran.
J. Environ. Sci. (China) 72, 166–175.
Song, R., Liu, L., Wei, N., Li, X., Liu, J., Yuan, J., Yan, S., Sun, X., Mei, L., Liang, Y., Li, Y.,
Jin, X., Wu, Y., Pan, R., Yi, W., Song, J., He, Y., Tang, C., Liu, X., Cheng, J., Su, H.,
2022. Short-term exposure to air pollution is an emerging but neglected risk factor
for schizophrenia: a systematic review and meta-analysis. Sci. Total Environ.,
158823
Stahl, S.M., 2018. Beyond the dopamine hypothesis of schizophrenia to three neural
networks of psychosis: dopamine, serotonin, and glutamate. CNS Spectr. 23 (3),
187–191.
Van Bockstaele, E.J., Pickel, V.M., 1995. GABA-containing neurons in the ventral
tegmental area project to the nucleus accumbens in rat brain. Brain Res 682 (1–2),
215–221.
van Zessen, R., Phillips, J.L., Budygin, E.A., Stuber, G.D., 2012. Activation of VTA GABA
neurons disrupts reward consumption. Neuron 73 (6), 1184–1194.
Ventura, L.M.B., Mateus, V.L., de Almeida, A.C.S.L., Wanderley, K.B., Taira, F.T.,
Saint’Pierre, T.D., Gioda, A., 2017. Chemical composition of fine particles (PM2.5):
water-soluble organic fraction and trace metals. Air Qual. Atmos. Health 10 (7),
845–852.
Volk, H.E., Lurmann, F., Penfold, B., Hertz-Picciotto, I., McConnell, R., 2013. Trafficrelated air pollution, particulate matter, and autism. JAMA Psychiatry 70 (1), 71–77.
Volkow, N.D., Wang, G.J., Newcorn, J., Telang, F., Solanto, M.V., Fowler, J.S., Logan, J.,
Ma, Y., Schulz, K., Pradhan, K., Wong, C., Swanson, J.M., 2007. Depressed dopamine
activity in caudate and preliminary evidence of limbic involvement in adults with
attention-deficit/hyperactivity disorder. Arch. Gen. Psychiatry 64 (8), 932–940.
70
J. Cubello et al.
Neurotoxicology 100 (2024) 55–71
Walker, Q.D., Ray, R., Kuhn, C.M., 2006. Sex differences in neurochemical effects of
dopaminergic drugs in rat striatum. Neuropsychopharmacology 31 (6), 1193–1202.
Weafer, J., de Wit, H., 2014. Sex differences in impulsive action and impulsive choice.
Addict. Behav. 39 (11), 1573–1579.
Wickens, M.M., Bangasser, D.A., Briand, L.A., 2018. Sex differences in psychiatric
disease: a focus on the glutamate system. Front Mol. Neurosci. 11, 197.
Wong, J.M., Malec, P.A., Mabrouk, O.S., Ro, J., Dus, M., Kennedy, R.T., 2016. Benzoyl
chloride derivatization with liquid chromatography-mass spectrometry for targeted
metabolomics of neurochemicals in biological samples. J. Chromatogr. A 1446,
78–90.
Wu, Y.J., Li, G.Y., An, T.C., 2022. Toxic metals in particulate matter and health risks in
an e-waste dismantling park and its surrounding areas: analysis of 3 PM size groups.
Int J. Environ. Res. Publ. Health 19 (22).
Yang, Y., Zhao, S., Zhang, M., Xiang, M., Zhao, J., Chen, S., Wang, H., Han, L., Ran, J.,
2022. Prevalence of neurodevelopmental disorders among US children and
adolescents in 2019 and 2020. Front Psychol. 13, 997648.
Yeganeh-Doost, P., Gruber, O., Falkai, P., Schmitt, A., 2011. The role of the cerebellum in
schizophrenia: from cognition to molecular pathways. Clinics 66, 71–77. Suppl 1
(Suppl 1).
Yin, W., Li, T., Mucha, P.J., Cohen, J.R., Zhu, H., Zhu, Z., Lin, W., 2022. Altered neural
flexibility in children with attention-deficit/hyperactivity disorder. Mol. Psychiatry
27 (11), 4673–4679.
Zablotsky, B., Black, L.I., 2020. Prevalence of children Aged 3-17 years with
developmental disabilities, by urbanicity: United States, 2015-2018. Natl. Health
Stat. Rep. (139), 1–7.
Zablotsky, B., Black, L.I., Maenner, M.J., Schieve, L.A., Danielson, M.L., Bitsko, R.H.,
Blumberg, S.J., Kogan, M.D., Boyle, C.A., 2019. Prevalence and trends of
developmental disabilities among children in the United States: 2009-2017.
Pediatrics 144 (4).
Zeidan, J., Fombonne, E., Scorah, J., Ibrahim, A., Durkin, M.S., Saxena, S., Yusuf, A.,
Shih, A., Elsabbagh, M., 2022. Global prevalence of autism: a systematic review
update. Autism Res 15 (5), 778–790.
Zhang, H., Khan, A., Kushner, S.A., Rzhetsky, A., 2022a. Dissecting schizophrenia
phenotypic variation: the contribution of genetic variation, environmental
exposures, and gene-environment interactions. Schizophr. (Heide ) 8 (1), 51.
Zhang, H., Khan, A., Rzhetsky, A., 2022b. Gene-environment interactions explain a
substantial portion of variability of common neuropsychiatric disorders. Cell Rep.
Med 3 (9), 100736.
Zhang, Y., Maimaiti, R., Lou, S., Abula, R., Abulaiti, A., Kelimu, A., 2022. Risk prediction
of autism spectrum disorder behaviors among children based on blood elements by
nomogram: a cross-sectional study in Xinjiang from 2018 to 2019. J. Affect Disord.
318, 1–6.
71