Access to this full-text is provided by Frontiers.
Content available from Frontiers in Neuroscience
This content is subject to copyright.
fnins-17-1147274 March 7, 2023 Time: 13:35 # 1
TYPE Original Research
PUBLISHED 13 March 2023
DOI 10.3389/fnins.2023.1147274
OPEN ACCESS
EDITED BY
Aaron Sathyanesan,
University of Dayton, United States
REVIEWED BY
Balapal Basavarajappa,
Langone Medical Center, New York University,
United States
Sebastiano Bariselli,
National Institute on Alcohol Abuse
and Alcoholism (NIH), United States
*CORRESPONDENCE
Kelly J. Huffman
kelly.huffman@ucr.edu
SPECIALTY SECTION
This article was submitted to
Neurodevelopment,
a section of the journal
Frontiers in Neuroscience
RECEIVED 18 January 2023
ACCEPTED 21 February 2023
PUBLISHED 13 March 2023
CITATION
Perez RF Jr, Conner KE, Erickson MA,
Nabatanzi M and Huffman KJ (2023) Alcohol
and lactation: Developmental deficits in a
mouse model.
Front. Neurosci. 17:1147274.
doi: 10.3389/fnins.2023.1147274
COPYRIGHT
© 2023 Perez, Conner, Erickson, Nabatanzi and
Huffman. This is an open-access article
distributed under the terms of the Creative
Commons Attribution License (CC BY). The
use, distribution or reproduction in other
forums is permitted, provided the original
author(s) and the copyright owner(s) are
credited and that the original publication in this
journal is cited, in accordance with accepted
academic practice. No use, distribution or
reproduction is permitted which does not
comply with these terms.
Alcohol and lactation:
Developmental deficits in a
mouse model
Roberto F. Perez Jr.1, Kathleen E. Conner2, Michael A. Erickson1,
Mirembe Nabatanzi1and Kelly J. Huffman1,2*
1Department of Psychology, University of California, Riverside, Riverside, CA, United States,
2Interdepartmental Neuroscience Program, University of California, Riverside, Riverside, CA,
United States
It is well documented that prenatal ethanol exposure via maternal consumption
of alcohol during pregnancy alters brain and behavioral development in offspring.
Thus, the Centers for Disease Control (CDC) advises against maternal alcohol
consumption during pregnancy. However, little emphasis has been placed on
educating new parents about alcohol consumption while breastfeeding. This is
partly due to a paucity of research on lactational ethanol exposure (LEE) effects
in children; although, it has been shown that infants exposed to ethanol via
breast milk frequently present with reduced body mass, low verbal IQ scores,
and altered sleeping patterns. As approximately 36% of breastfeeding mothers in
the US consume alcohol, continued research in this area is critical. Our study
employed a novel murine LEE model, where offspring were exposed to ethanol
via nursing from postnatal day (P) 6 through P20, a period correlated with infancy
in humans. Compared to controls, LEE mice had reduced body weights and
neocortical lengths at P20 and P30. Brain weights were also reduced in both ages
in males, and at P20 for females, however, female brain weights recovered to
control levels by P30. We investigated neocortical features and found that frontal
cortex thickness was reduced in LEE males compared to controls. Analyses of
dendritic spines in the prelimbic subdivision of medial prefrontal cortex revealed
a trend of reduced densities in LEE mice. Results of behavioral tests suggest that
LEE mice engage in higher risk-taking behavior, show abnormal stress regulation,
and exhibit increased hyperactivity. In summary, our data describe potential
adverse brain and behavioral developmental outcomes due to LEE. Thus, women
should be advised to refrain from consuming alcohol during breastfeeding until
additional research can better guide recommendations of safe maternal practices
in early infancy.
KEYWORDS
alcohol, behavior, neocortex, lactation, anatomy, brain development, postnatal
neocortical development, lactational ethanol exposure
Introduction
Alcohol is known as a developmental teratogen in mammalian systems. However,
research in this area has primarily focused on exposures during the prenatal period.
Maternal consumption of alcohol during pregnancy can result in Fetal Alcohol Spectrum
Disorders (FASD) in offspring and children with FASD may exhibit physical, cognitive,
Frontiers in Neuroscience 01 frontiersin.org
fnins-17-1147274 March 7, 2023 Time: 13:35 # 2
Perez et al. 10.3389/fnins.2023.1147274
emotional, and behavioral phenotypes related to the exposure
(May et al.,2009,2014;Hoyme et al.,2016). Thus, Centers
for Disease Control (CDC) have released a statement that no
amount of alcohol is safe to consume during pregnancy (Centers
for Disease Control and Prevention,2022a). Generally, these
recommendations are followed, as demonstrated by a reduction
in alcohol consumption during pregnancy. However, consumption
levels approach preconception levels shortly after birth in some
populations (Little et al.,1990;Giglia and Binns,2006). The
prevalence of breastfeeding mothers consuming alcohol is high,
ranging from 20% in Canada (Popova et al.,2013), 36% in the
United States (May et al.,2016), and 60% in Australia (Tay et al.,
2017). For a specific example, in Seattle, Washington, 80% of
women consumed alcohol during the month before conception,
40% consumed alcohol during the last trimester of pregnancy, and
70% were drinking 3 months postpartum. Notably, this study also
reported that 10% of breastfeeding mothers reported drinking more
than once a day (>15 g alcohol) (Little et al.,1990).
Given the prevalence of maternal alcohol consumption during
breastfeeding, it is important to understand how this can represent
a teratogenic exposure for infants. Studies have shown that the
levels of alcohol in the breast milk mirror the amount of alcohol
in the blood (Lawton,1985;Chien et al.,2005). These levels
peak at 30–60 min after ethanol consumption and continue to be
detected 2–3 h after consumption (Chien et al.,2005;Centers for
Disease Control and Prevention,2019). Although these levels are
lower than the percentage in alcoholic beverages, they are non-
zero values. In infants, exposure to breast milk containing alcohol
may result in reduced body mass and verbal IQ scores (May et al.,
2016). Congruently, exposure to alcohol via breast milk may result
in a dose-dependent reduction of cognitive functions as seen when
testing exposed children aged 6–7 years (Gibson and Porter,2018)
and dose-dependent reductions in children’s academic abilities
up to grade 5 (Gibson and Porter,2020). Additionally, deficits
in abstract reasoning skills are observed at age 7 in lactational-
exposed children (Oei,2019). Changes in sociability can also occur
as exposed infants scored below, or within the monitoring zone,
on the scale of the personal-social interactions at 12 months of
age (Tay et al.,2017). Despite these potential negative effects of
alcohol compromised breast milk on offspring development, there
is a disconnect between conclusions drawn from scientific literature
and behaviors in many new mothers.
In humans, there is variability in maternal behavior in terms of
infant feeding preferences. In the US from 2012 to 2019, around
80% of mothers breastfed their infants, with just over half of them
breastfeeding exclusively [from the National Immunization Survey
(Centers for Disease Control and Prevention,2019)]. Additionally,
there is variability among women in their ability to metabolize
alcohol and to respond to stressors, which can moderate infant
exposure. Indeed, higher tolerance and stress may result in the
increase of the consumption of alcohol, for certain populations
(Guinle and Sinha,2020). Women who consume alcohol during
pregnancy are also more likely to drink while breastfeeding (May
et al.,2016), suggesting certain populations may be considered
high-risk for breast milk contamination. Additionally, unplanned,
and drastic lifestyle changes may influence alcohol consumption
levels. For example, the COVID-19 pandemic and subsequent
“stay-at-home” orders, rapidly emerged as a public and/or personal
health concern for many. In response to this novel stressor, women
in the United States showed an increase in their Alcohol Use
Disorders Identification Test scores during the COVID-19 “stay-
at-home” order (Boschuetz et al.,2020). These results translate
to an increase in frequency and quantity of alcohol ingested
in those who already used alcohol; congruently, factors such as
having children at home and a history of substance abuse were
positively associated with an increase in alcohol use during the
pandemic (Boschuetz et al.,2020). Similar results were observed
in Australia (Bramness et al.,2021), Norway (Rossow et al.,2021),
and Belgium (Vanderbruggen et al.,2020), and thus, the pandemic
and “stay-at home” orders may have unintentionally increased
infant alcohol exposure via increased maternal consumption.
These studies show an increase in alcohol consumption in certain
child rearing populations, elucidating the deleterious effects of
postnatal ethanol exposure via breast milk, and bolster the
importance of alcohol abstinence during breastfeeding. However,
published postnatal alcohol exposure paradigms (via breast milk)
tend to be uncontrolled, unstandardized, and often limited to
humans. Much of the existing data leave questions of dosing,
timing, and how the developing nervous system is affected by
lactational ethanol exposure (LEE). Data from animal models
are not always consistent, most likely due to the variability in
postnatal ethanol exposure methods, ranging from direct ethanol
exposure to combined prenatal and postnatal exposure. In one
study, researchers exposed rat pups to ethanol via intragastric
intubation from postnatal (P) day 4 to 8 and reported increased
male body weights but no increases in cerebral cortex weight
(Light et al.,1989). Another direct exposure study reported a
reduction of stem cell progenitor cells in the hippocampus and
reduced adult neurogenesis after a singular subcutaneous injection
of alcohol at P7 (Ieraci and Herrera,2007). A study from Vilaró
et al. (1987) exposed rat pups to alcohol via an alcohol-treated
mother and reported a reduction in weight of rat pups at age
P15 compared to controls; however, this study exposed rats to
ethanol during gestation as well as postnatally. These studies
provide much-needed evidence toward the damaging effects of
postnatal ethanol exposure; however, they do not target a particular
time window in mammalian brain development. Hence, many
of their results are contradictory. To combat this, an analogous
age range for exposure must be established between mice and
humans. To begin, the brain growth spurt (BGS) is a time window
where the mammalian brain undergoes rapid growth (Dobbing
and Sands,1979). In humans this period ranges from the third
trimester of pregnancy to about the first 2 years of life, peaking
at the birth (Dobbing and Sands,1979). In murine models, this
period ranges from the first week postnatal to the third week,
peaking around P7 (Dobbing and Sands,1979). A study has shown
that exposure to alcohol during the BGS induces deficits such as
a reduction in long-term cerebellar growth and altered rotarod
performance in a rat model (Goodlett et al.,1991). However, this
study used artificial-rearing procedures to directly expose pups to
ethanol during the P4–P9 time window and was a binge model
(Goodlett et al.,1991). Furthermore, ethanol exposure has been
shown to cause alterations in synaptic pruning (Kyzar et al.,2016).
In mice, synaptic pruning reaches its peak 2–3 weeks postnatal
(Lewis,2011), this is within the BGS, providing further evidence
of sensitivity toward perturbations early in postnatal development.
Clearly, additional research is needed to illuminate the specific
details of risk including dose-dependencies and the interaction
Frontiers in Neuroscience 02 frontiersin.org
fnins-17-1147274 March 7, 2023 Time: 13:35 # 3
Perez et al. 10.3389/fnins.2023.1147274
of developmental time and exposure. Here, we are specifically
interested in how maternal drinking while breastfeeding impacts
brain and behavioral development of offspring. The exposure
period we targeted is within the BGS but begins on a postnatal day
roughly equivalent to the day of human birth, to better mimic the
time when breastfeeding would begin in humans.
In the current study, we targeted early LEE in our mouse
model by estimating typical human birth in murine time. When
making cross-species comparisons for developmental stage, the first
postnatal week in mice relates to the third trimester in humans
(Clancy et al.,2007). As our study did not aim to model human
prenatal alcohol exposure, or FASD, we began our maternal dosing
of ethanol at the end of the first week of murine life (evening of
postnatal day 6). This way, offspring will have consumed alcohol
via breast milk by P7. Estimates of human day of birth (full term)
is between 245 and 265 days post conception with the mouse
equivalent between 7 and 9 days postnatal (Clancy et al.,2007;
Jukic et al.,2013). Specifically, we exposed CD-1 pups to breast
milk contaminated with ethanol, via maternal consumption, at the
end of the postnatal week until weaning. By mimicking human
postpartum drinking behavior, our results revealed potential effects
of LEE on offspring outcomes. We measured maternal blood
ethanol content to assure exposure validity and blood osmolality
to assess hydration. We analyzed several outcome measures in
offspring to determine to what degree ethanol exposure via
lactation altered key features of neuroanatomical development and
whether these phenotypes were read out in behavior. As predicted,
LEE resulted in abnormal brain and behavioral development.
Materials and methods
Animal care
All breeding and experimental studies were conducted in
accordance with protocol guidelines approved by the Institutional
Animal Care and Use Committee (IACUC) at the University of
California, Riverside (UCR). CD-1 mice, initially purchased from
Charles River Laboratories (Wilmington, MA, USA), were used for
breeding. We chose to use the outbred CD-1 mouse strain in this
lactational model because these mice show superior maternal care
compared to inbred strains and because we had validated them as
a model for prenatal ethanol exposure (PrEE) in our prior work (El
Shawa et al.,2013). Mice were housed in animal facilities located
at UCR that were kept at approximately 22◦C on a 12-h light/dark
cycle. Mouse chow and water (for controls), or mouse chow and
a 25% ethanol solution in water, were provided ad libitum to the
dams according to the dosing schedule.
Breeding and lactational ethanol
exposure paradigm
Adult female and male mice, aged P90-150, were paired just
before the start of the dark cycle. Once a vaginal plug was detected,
the male was removed from the cage. Throughout pregnancy,
mouse chow and water were provided ad libitum to all dams. Dams
were undisturbed through pregnancy and birth until the pups were
6 days old, when litter sizes were recorded (Figure 1). During
this time, we pseudo-randomly assigned each dam to the control
or experimental group (Lactational Ethanol Exposed, LEE group).
LEE dams had their water replaced with a 25% v/v ethanol in water
solution throughout the exposure period from the evening of P6 to
P20, while control dams remained on water. The liquid bottle tip
was placed high in the cage so that developing pups could not reach
it, thus, their only liquid intake was via dam breast. There were no
alterations to the dam’s food supply through the exposure period
for any experimental condition. Measurements were taken daily
for maternal liquid and food consumption during the exposure
period for both conditions. At wean (P20), litter size was assessed,
control and LEE pups were weighed and divided into two subsets.
Subsets A and B had different sacrificial end dates of P20 and P30,
respectively. Subset B control and LEE pups were weighed and
subjected to no more than two behavioral assays. The division of the
litters into subsets allowed us to evaluate the short and long-term
effects of LEE with an array of techniques. To avoid litter effects, we
distributed pups from multiple litters for each assay tested.
Dam and pup blood ethanol
concentration and plasma osmolality
measurements
To measure dam and pup blood ethanol concentration (BEC)
and blood plasma osmolality (pOsm), a measure of hydration,
animals from control and LEE groups were subjected to a whole
blood collection protocol. Whole blood was collected at the time of
weaning for dams and pups via cardiac puncture. After collection,
blood was placed in an untreated 1.5 ml centrifuge tube and allowed
to clot for 30 min at room temperature. The entire sample was
then centrifuged at 4,000 ×gfor 15 min at 4◦C to separate serum
from whole blood. To determine BEC in control and LEE groups,
an alcohol dehydrogenase (ADH) based enzymatic assay (Pointe
Scientific, Canton, MI, USA) was employed. In brief, ethanol,
and nicotinamide adenine dinucleotide (NAD+) become catalyzed
by ADH and this interaction causes the oxidation of ethanol
to acetaldehyde and reduces NAD+ to NADH. The modified
sample was read on a Nanodrop 2000 Spectrophotometer (Thermo
Fisher Scientific) at 340 nm. To determine pOsm, freshly extracted
serum from control and LEE groups were subjected to testing
using an osmometer.
Brain tissue preparation and collection
Pups from all conditions were randomly assigned for gross
anatomical studies. Mice were weighed then sacrificed using a
lethal dose of sodium pentobarbital (100 mg/kg) administered
via intraperitoneal injection. Mice were transcardially perfused
with 0.9% saline followed by 4% paraformaldehyde in PBS (PFA,
pH: 7.4) for fixation. The skulls were post-fixed in a 4% PFA
solution overnight, then the brains were extracted, weighed, and
imaged. Dorsal views of whole brains were imaged using a
Zeiss (Oberkochen, Germany) Axio high-resolution (HRm) camera
attached to a dissecting microscope. Extracted brains were stored in
4% PFA for later use.
Frontiers in Neuroscience 03 frontiersin.org
fnins-17-1147274 March 7, 2023 Time: 13:35 # 4
Perez et al. 10.3389/fnins.2023.1147274
FIGURE 1
Experimental paradigm. Mice were designated as control or LEE at P6. LEE dams received 25% EtOH when pups were P6-P20. At P20 pups were
weaned, divided into two subsets, and no longer exposed to EtOH. Subset A was subjected to a variety of measurements at P20. Subset B was
subjected to measurements as well as behavioral tests at P30.
Anatomical measurements
Brain and body weights were assessed at P20 and P30 for both
sexes and conditions. They were compared using statistical analyses
and a brain/body weight ratio was computed to determine if any
changes in brain or body weight were independent of one another.
Typically, in normal development, brain and body size/weight are
related. Larger animals within the same species tend to have larger
brains. We calculated the ratio able to differentiate whether the
exposure was causing a decrease in brain size alone, or whether
decreases in brain size from our perturbation could be related to
overall decrease in body size. Next, to measure cortical length of
all brains, we used a digital micrometer in ImageJ (NIH, Bethesda,
MD, USA), using the dorsal whole-brain images. To examine
anatomical cortical areas, perfused brain tissues were hemisected
and cryoprotected using a 30% sucrose (w:v) in PBS solution. Tissue
was then sectioned using a Leica cryostat at 40 µm thick in the
coronal plane, mounted on subbed slides, and stained for Nissl
bodies using a 0.1% Cresyl Violet solution staining protocol then
imaged using a Zeiss Axio Upright Imager microscope equipped
with a Zeiss Axio HRm camera. To control for comparisons
between groups, the Allen Mouse Brain Atlas (Allen Institute for
Brain Science,2004)1and the Paxinos Developing Mouse Brain
Atlas (Paxinos et al.,2007) were used to determine matching
planes of section between groups (anatomical landmarks used:
corpus callosum, hippocampus, and subcortical structures). Once
images were selected, regions of interest (ROIs) were measured
using the ImageJ (NIH) electronic micrometer function by trained
researchers blind to treatment conditions, as previously reported
in Abbott et al. (2016). In brief, cortical thickness was measured
with respect to the cortical sheet, by drawing perpendicular
lines from the most superficial region of layer I to the deepest
region of layer VI. Cortical regions measured include the frontal
1brain-map.org
cortex (the boundary of layer 2/3of the secondary motor area
to boundary of layer 2/3of the orbital area), prelimbic cortex,
primary somatosensory cortex (S1), primary auditory cortex (A1),
and primary visual cortex (V1).
Dendritic spine density measurements
P20 and P30 brains were hemisected and placed into a modified
Golgi-Cox solution (Bayram-Weston et al.,2016;Zaqout and
Kaindl,2016) for 14 days in the dark at room temperature. Brains
were then removed from the solution and placed in 30% sucrose
in PBS for 2 days. Brains were then embedded in 5% agarose and
sliced on a vibratome at 100 µm and mounted on subbed slides.
Slides were allowed to dry for 2–3 days before developing. Slides
were dipped in distilled water for 10 min, then 20% ammonia for
10 min, then distilled water for 10 min, then 70, 95, and 100%
ethanol (EtOH) for 5 min each, and xylenes for 40 min. Slides were
then immediately coverslipped with permount solution. Images of
dendritic spines, of pyramidal cells in layer IV/V of the Prelimbic
and Frontal cortices, were then imaged using a 630X oil immersion
objective on a Leica Dmi8 bright field stereoscope using an attached
Leica DFC 450C camera. Dendritic spine density was calculated for
the entire length of the dendrites using ImageJ by an experimenter
blind to condition. Counted spines were then divided by the length
of the dendrite measured, then an average of dendritic spines was
taken for each mouse as multiple neurons were sampled from each
individual subject. In depth dendritic spine staining methodology
has been previously described elsewhere (Bottom et al.,2022).
Behavioral assays
Due to higher than zero BEC levels in LEE pups at wean,
behavioral assays were only performed at P30. Therefore the
Frontiers in Neuroscience 04 frontiersin.org
fnins-17-1147274 March 7, 2023 Time: 13:35 # 5
Perez et al. 10.3389/fnins.2023.1147274
10-day post wean period was considered a “wash out” period
in the paradigm. Mice were subjected to a maximum of two
behavioral tests during the testing period with the forced swim
test (FST) always being last due to the high-stress nature of the
test. All behavioral analyses and scoring were performed and
analyzed by trained researchers blind to experimental conditions.
All apparatuses were cleaned using Virkon before and after each
testing session.
Elevated plus maze
The elevated plus maze (EPM) has been historically employed
to measure anxiety-like behaviors in rodents (Handley and Mithani,
1984;Rodgers and Dalvi,1997). Notably, young CD-1 mice are
known to contradict this measure of anxiety-like behaviors and they
typically interact with the lower anxiety-associated metrics of this
assay at higher portions; therefore, this behavior is thought to be
considered risk-taking behavior (Macrì et al.,2002). This test has
been used in our laboratory’s PrEE mouse model (Bottom et al.,
2022). In a dimly lit room, we employed the use of a plus “+”
shaped apparatus that is designed to provide test mice with two
different arm environments (arm specifications; 54 cm wide and
30 cm long). The first arm type (closed arms) shields the mouse
from the testing room using 15 cm high non-transparent panels
that laterally enclose the mouse, with an opening on top of the
apparatus. This provides the mouse with a shaded semi-enclosed
space. The second arm type (open arms) exposes the mouse to
the testing room through omission of the non-transparent panels.
These arm types are arranged adjacently to one another on the
apparatus, such that each environment is flanked by the opposing
environment. Additionally, the apparatus is lifted 50 cm above the
ground using stilts. In sum, mice were subjected to a single 5-min
trial on the EPM where the mouse was placed in the center of the
apparatus and could move freely for the entire testing period. The
amount of time spent in each arm, as well as entries and total time
was recorded. Video recordings were made of each testing session.
A longer time spent in the open arms may indicate increased
risk-taking behavior or active exploratory behavior.
Forced swim test
Designed to assess the effects of antidepressant drugs in the late
1970s (Porsolt et al.,1978), the FST was originally used to measure
depressive-like behaviors (Lucki et al.,2001). More recently, studies
have re-evaluated the interpretation of the test. Mouse performance
in the water (either actively swimming/attempting to climb or
floating immobile) has been viewed as a response to the stressful
environment; the mice could respond with a passive coping style
(immobility) or an active stress-coping style (swimming/climbing).
The active stress coping has also been hypothesized to be related to
hyperactivity (Commons et al.,2017;Conner et al.,2020;Armario,
2021). This technique has been used in our laboratory previously
in our PrEE mice (Abbott et al.,2018;Conner et al.,2020;Bottom
et al.,2022). Mice were placed in an acrylic glass cylinder (30 cm
in height and 12 cm in diameter) filled to two-thirds total volume
with room temperature (27◦C) water for 6 min. The initial 2 min
were an acclimation period and the remaining 4 min (240 s) were
video-recorded and the time in which the animal was immobile in
the water was recorded. Mice had light placed directly above them
throughout the testing period and no more than two experimenters
were allowed to be present during the testing period. Percentage of
time spent immobile was calculated for each mouse.
Accelerated rotarod
The accelerated rotarod (AR) test was used to examine motor
ability, learning, grip strength, and coordination (Rustay et al.,
2003;Buitrago et al.,2004). This test has been used in our
laboratory’s PrEE mouse model (Abbott et al.,2018;Bottom et al.,
2022). Briefly, the mice were subjected to four, 5-min trials on the
rotarod apparatus with each trial separated by a 10-min interval.
The AR (Ugo Basile; Germonio, Italy) consists of a rod (diameter
28.5 mm) that rotates and gradually increases speed from 4 to
40 rpm. Mice are scored for the amount of time they are able to
stay balanced on the AR. If they are able to maintain balance for the
entire trial length, they are given a perfect score of 300 s.
Statistical analyses
All statistical analyses were completed using R (v4.1.2; R
Core Team,2021). Between-subjects tests were carried out using
ANOVA with Type III sums of squares (via the car package, v3.0.12;
Fox and Weisberg,2019). Repeated measures tests were performed
using multilevel models via the lme4 R package (v1.1.27.1; Bates
et al.,2015). Planned comparisons and simple effect tests were
carried out using the emmeans R package (v1.7.2; Lenth,2022).
Results
Model verification: Blood ethanol
concentration and blood plasma
osmolality in dams and pups
To ensure adequate maternal intake of ethanol, we measured
BEC at wean. As expected, at wean, LEE dams had significantly
greater BEC when compared to control dams, t(4) = 33.30,
p<0.001 (Figure 2). Additionally, to assess maternal hydration
during the ethanol self-administration period, we measured dam
blood plasma osmolality (pOsm). No significant differences in
pOsm were found between LEE and control dams at wean,
t(7.95) = 1.66, p= 0.1366, suggesting similar levels of hydration in
dams across conditions. Ethanol treated dams showed lower caloric
consumption and body weights when compared to control dams.
From P6 through P20, LEE dams consumed fewer calories from
food and ethanol combined (M= 75.1, SD = 9.6, 95% CI [68.3,
82.0]) than control dams (from food alone; M= 98.5, SD = 11.6,
95% CI [87.8, 109.2]), t(11.37) = 4.39, p= 0.001. At wean, LEE
dams (M= 37.9 g, SD = 4.4 g, 95% CI [35.5, 40.7]) weighed less
than control dams (M= 46.0 g, SD = 4.7 g, 95% CI [42.7, 49.2]),
t(12.53) = 3.59, p= 0.003. In the case of the ethanol treated dams
in the current study, they engaged in higher rates of infanticide and
cannibalism [from P6 through P20, more of the LEE dams’ pups
died (M= 5.2, SD = 3.3, 95% CI [3.0, 7.4]) than control dams’
(M= 1.3, SD = 1.4, 95% CI [0.2, 2.4]), t(14.08) = 3.49, p= 0.004],
which would reduce their requirements to produce milk, and, to
some degree, compensate for lower food intake.
Frontiers in Neuroscience 05 frontiersin.org
fnins-17-1147274 March 7, 2023 Time: 13:35 # 6
Perez et al. 10.3389/fnins.2023.1147274
FIGURE 2
Blood ethanol concentration and pOsm measurements. (A) BEC measurements in Control and LEE dams at wean after a 14 day exposure to water
(control) or 25% EtOH. LEE mice exposed to 25% EtOH had an average BEC of 119.8 mg/dL compared to controls which had a BEC of 0 mg/dL
(N= 10). (B) BEC measurements in Control and LEE pups at wean after dams were exposed to water or 25% EtOH for 15 days. LEE pups had greater
BECs (68.9 mg/dL on average) compared to controls at 0.0 mg/dL (N= 14). (C) No significant differences observed between control
(M= 324.8 mOsm/kg, SD = 8.8 mOsm/kg) and LEE (M= 337.7 mOsm/kg, SD = 16.3 mOsm/kg) dam plasma osmolality (pOsm) at wean (N= 11).
(D) No significant differences in pup pOsm at wean between control (M= 287.1 mOsm/kg, SD = 20.0 mOsm/kg) and LEE (M= 280.6 mOsm/kg)
offspring (N= 16; *p<0.05, ***p<0.001). (A,B) Triangles represent individual data points taken for each experimental condition. Data expressed as
mean ±SEM.
Lactational ethanol exposure pups at wean, as anticipated, had
greater BEC than control pups, t(6) = 3.41, p<0.014, although
considerable variation was observed between individual measures.
We endeavored to account for this variation by examining the
relationship between both litter size and pups’ sex on LEE pups’
BEC at wean. Neither litter size [t(6) = 0.34, p= 0.742] nor sex
[t(6) = 0.49, p= 0.642], however, was a significant predictor of BEC.
Nevertheless, additional possible explanations for the increased
variability are discussed in the section on study limitations and
future directions.
There were no significant differences in blood plasma
osmolality (pOsm) found between LEE and control pups at wean,
t(8.38) = 0.40, p= 0.700, also suggesting similar levels of hydration
in pups across conditions. These results confirm that non-zero
levels of EtOH intoxication occur in LEE dams and pups at wean.
Furthermore, these results indicate no disparity in dam or pup
pOsm due to the exposure paradigm.
P20 and P30 pup gross measurements
To examine the ability of our exposure paradigm to produce
gross alterations in pup central nervous system (CNS), and overall
development, we evaluated body and brain weights, body-brain
weight ratio (Figure 3), and cortical length measurements
(Figure 5) at P20/P30 and by sex.
A three-way, condition (Control vs. LEE) ×age (P20 vs.
P30) ×sex (male vs. female) ANOVA (Type III SS) identified
a three-way condition ×age ×sex interaction on pups’ weight,
F(1,302) = 4.22, p= 0.0409 (Figure 3). In light of this three-
way interaction, lower order interactions and main effects should
be considered with caution. Nevertheless a two-way age ×sex
interaction was also present, F(1,302) = 31.02, p<0.001, as
was a main effect of condition, F(1,302) = 58.45, p<0.0001,
and age F(1,302) = 434.23, p<0.0001. To examine the three-
way interaction, the two-way condition ×age interactions were
examined separately for male and females. For males, the two-
way interaction was significant, F(1,302) = 12.36, p= 0.005,
indicating that the effect of condition was greater at P30 than
at P20. For females, the two-way interaction failed to reach
significance, F(1,302) = 0.22, p= 0.637. Sidak corrected planned
comparisons were carried out to examine the difference between
the weight of the control and LEE pups at each combination
of age and sex. These indicated that control pups weighed
more in all four combinations: P20 male, t(302) = 7.343,
p<0.0001, P30 male, t(302) = 10.602, p<0.0001, P20
female, t(302) = 7.646, p<0.0001, P30 female, t(302) = 6.296,
p<0.0001.
Frontiers in Neuroscience 06 frontiersin.org
fnins-17-1147274 March 7, 2023 Time: 13:35 # 7
Perez et al. 10.3389/fnins.2023.1147274
FIGURE 3
Offspring measures: Gross development. (A) A significant (p<0.001) reduction in body weight for LEE pups was observed in every age and sex
group, when compared to controls (N= 310). (B) Significant reductions in brain weights were observed for LEE males at P20 (p= 0.003) and P30
(p= 0.0332) developmental time points. However, significant reductions were only observed in P20 LEE females (p= 0.0085) and no significance is
observed in P30 LEE females (p= 0.1184) compared to controls (N= 70). (C) Significant increases to the brain/body ratio are observed in LEE males
at both developmental time points. Significant increases to the brain/body ratio were only observed in P20 LEE females and not P30 females as
compared to controls (N= 70). Data expressed as mean ±SEM.
Next, a three-way, condition (Control vs. LEE) ×age (P20
vs. P30) ×sex (male vs. female) ANOVA (Type III SS) identified
main effects of condition, F(1,62) = 10.26, p= 0.002, and age
F(1,62) = 9.12, p= 0.004 on the weight of pups’ brains. As described
previously, Sidak corrected planned comparisons were carried out
to examine differences between control and LEE pups weights at
each combination of age and sex. Results indicated that control
pups’ brains weighed more in three of the four combinations: P20
male, t(62) = 4.24, p= 0.0003, P30 male, t(62) = 2.72, p= 0.0332,
and P20 female, t(62) = 3.20, p= 0.0085, but not P30 female,
t(62) = 2.207, p= 0.1184.
Lastly, to consider the relationship between body and brain
weight, we examined the ratio of pups’ brain to body weight via
a three-way, condition (Control vs. LEE) ×age (P20 vs. P30) ×sex
(male vs. female) ANOVA (Type III SS). This analysis identified
main effects of condition, F(1,62) = 128.75, p<0.001, and age
F(1,62) = 243.95, p<0.0011 on the on the ratio of pups’ brain to
body weight. These main effects, however, should be considered in
Frontiers in Neuroscience 07 frontiersin.org
fnins-17-1147274 March 7, 2023 Time: 13:35 # 8
Perez et al. 10.3389/fnins.2023.1147274
FIGURE 4
Dorsal views. Representative images of perfused and extracted
brains of male (A1–A4) and female (B1–B4) pups at P20
(A1,A2,B1,B2) and P30 (A3,A4,B3,B4) after dams were exposed to
water (A1,B1,A3,B3) or EtOH (A2,B2,A4,B4) for 14 days. Images
oriented rostral (R) to the left and caudal (C) to the right. Scale bar,
1 cm.
FIGURE 5
Cortical lengths. No significant differences in cortical length were
observed between control and LEE pups at P20 or P30 (N= 70).
Data expressed as mean ±SEM.
light of interactions between age and condition, F(1,62) = 61.82,
p<0.001, and sex and condition, F(1,62) = 8.47, p= 0.005. The
age ×condition interaction provided evidence that the effect of
exposure to EtOH diminished between P20 (M= 0.022, 95% CI
[0.018, 0.023]) and P30 (M= 0.004, 95% CI [0.002, 0.007]), and
the sex ×condition interaction provided evidence that the effect
of exposure to EtOH was greater for male (M= 0.016, 95% CI
[0.014, 0.019]) than female (M= 0.009, 95% CI [0.007, 0.012])
pups. As described previously, we carried out Sidak corrected
planned comparisons to examine the difference between the ratio
of pups’ brain to body weight in the control and LEE pups at each
combination of age and sex. These indicated that LEE pups’ brain-
body weight ratio was greater in three of the four combinations: P20
male, t(62) = 15.88, p<0.001, P30 male, t(62) = 5.03, p<0.001,
and P20 female, t(62) = 11.35, p<0.001, but not P30 female,
t(62) = 0.49, p= 0.981.
To examine cortical length (Figures 4,5), we performed a
three-way, condition (Control vs. LEE) ×age (P20 vs. P30) ×sex
(male vs. female) ANOVA (Type III SS) that identified main effects
of condition, F(1,68) = 5.52, p= 0.022, and age F(1,68) = 12.69,
p= 0.001 on the length of pups’ brains. As described previously,
Sidak corrected planned comparisons were carried out to examine
the difference between the weight of the control and LEE pups
at each combination of age and sex. None of these comparisons
indicated a significant difference between the lengths of control and
LEE pups’ brains: P20 male, t(68) = 1.00, p= 0.7860, P30 male,
t(68) = 2.27, p= 0.1015, P20 female, t(68) = 2.35, p= 0.0843, and
P30 female, t(68) = 1.37, p= 0.5370.
Altogether, these results suggest that our exposure paradigm
produces long-lasting gross alterations in CNS and general
development in the LEE pups.
P20 and P30 pup cortical
neuroanatomical measurements
To assess the effects of the exposure paradigm on cortical
thickness development, we measured from five distinct regions
(frontal, prelimbic, somatosensory, auditory, and visual cortices)
in Nissl-stained coronal sections in both LEE and control pups
at both milestone dates (Figures 6,7). We carried out three-way
condition (Control vs. LEE) ×age (P20 vs. P30) ×sex (male vs.
female) ANOVAs (Type III SS) on the cortical thicknesses of pups’
brains in each region. In these analyses, none of the main effects
or interactions were significant although the main effect of age
in the visual cortex trended toward greater thickness at age P30
(M= 0.664 ±0.0173) than at age P20 (M= 0.590 ±0.0171),
F(1,33) = 3.22, p= 0.0820. The corresponding Sidak-corrected
planned comparisons we carried out to examine the difference
between the cortical thickness in the control and LEE pups at
each combination of age and sex also failed to show significant
differences with the exception of the frontal cortex in the P20 male
pups, t(34) = 2.94, p= 0.0235 (Figure 6A5).
These results suggest that there were only modest alterations to
frontal cortical thickness in the development of the LEE mice.
Dendritic spine measurements
An analysis on dendritic spine density (spines/um) was
employed to explore the impact of our exposure paradigm on
spine density at both milestone dates via Golgi-Cox-stained
coronal sections (Figures 8,9). Because we measured spinal
density on multiple dendrites from individual mice, the data
were analyzed using a multilevel model in which condition
(Control vs. LEE) ×age (P20 vs. P30) ×sex (male vs. female)
Frontiers in Neuroscience 08 frontiersin.org
fnins-17-1147274 March 7, 2023 Time: 13:35 # 9
Perez et al. 10.3389/fnins.2023.1147274
FIGURE 6
Cortical thickness measurements – males. High magnification coronal sections of Nissl-stained hemisections. Measurements include frontal cortex
(A1–A5;N= 21), prelimbic cortex (B1–B5;N= 16), somatosensory cortex (C1–C5;N= 22), auditory cortex (D1–D5;N= 19), and visual cortex
(E1–E5;N= 28). No significant differences between control and LEE males, except in the frontal cortex at P20 (A5, p= 0.0235). Data expressed as
mean ±SEM. Images oriented dorsal (D) up and lateral (L) to the right. *Indicates p<0.05. Scale bar, 1 mm.
were fixed factors and mouse was included as a random factor.
In prelimbic cortex, this analysis indicated a main effect of
sex on spinal density (male, M= 0.662 spines/µm±0.0305;
female, M= 0.719 spines/µm±0.0304), t(26.24) = 2.26,
p= 0.0326. There was also a trend toward an effect of
condition (control, M= 0.708 spines/µm±0.0307; LEE,
M= 0.673 spines/µm±0.0302), t(23.73) = 2.05, p= 0.0517,
and an interaction between sex and condition (male LEE –
control, M= 0.0308 spines/µm±0.0610; female LEE – control,
M=−0.1011 spines/µm±0.0607), t(24.97) = 1.73, p= 0.0954.
Sidak-corrected planned comparisons failed to show significant
differences between the spinal densities of neurons in the prelimbic
cortex of control and LEE pups for either male or female pups
at either age. In frontal cortex, this analysis did not indicate any
significant effects or interactions, nor did any of the planned
comparisons show significant differences at any combination of sex
and age. Overall, these results suggest a possible modest difference
between the experimental group and controls moderated by sex
in prelimbic cortex, but provided no evidence of differences in
dendritic spine density in frontal cortex.
P30 behavioral analyses
To assess the impact of the exposure paradigm on behavioral
development, we employed a number of behavioral tests to
investigate potential differences. The included tests were:
EPM, FST, and AR.
The EPM provides a measure of anxiety-like and risk-taking
behaviors. We investigated the risk-taking behaviors by recording
the percent of time mice spent in the open arms of the maze
(Figure 10). A two-way, condition (Control vs. LEE) ×sex (male
vs. female) ANOVA (Type III SS) failed to identify a significant
effect of condition or sex on the time pups spent in the open
arms of the maze. There was, however, a trend toward LEE pups
(23.0 ±1.63%) spending more time in open arms than control pups
(17.2 ±1.59%), F(1,37) = 3.40, p= 0.0733 (Figure 10A). Sidak
Frontiers in Neuroscience 09 frontiersin.org
fnins-17-1147274 March 7, 2023 Time: 13:35 # 10
Perez et al. 10.3389/fnins.2023.1147274
FIGURE 7
Cortical thickness measurements – females. High magnification coronal sections of Nissl-stained hemisections. Measurements include frontal
cortex (A1–A5;N= 19), prelimbic cortex (B1–B5;N= 11), somatosensory cortex (C1–C5;N= 26), auditory cortex (D1–D5;N= 20), and visual cortex
(E1–E5;N= 20). No significant differences between control and LEE pups. Data expressed as mean ±SEM. Images oriented dorsal (D) up and lateral
(L) to the right. Scale bar, 1 mm.
corrected planned comparisons were carried out to examine the
difference between the percent of time the control and LEE pups
spent in open arms for male and female pups separately. These
comparisons similarly failed to indicate significant differences
(Figure 10B). The results suggest that LEE mice may spend
more time on the uncovered arms of the apparatus compared to
controls, regardless of sex (Figure 10A) suggesting the possibility
of increased risk-taking behavior.
In the FST, immobility may be understood as a measure
of passive coping behavior. A two-way, condition (Control vs.
LEE) ×sex (male vs. female) ANOVA (Type III SS) failed to identify
a significant effect of condition or sex on the percent of time each
mouse was immobile. Sidak corrected planned comparisons were
carried out to examine the difference between the percent of time
the control and LEE pups spent immobile for male and female
pups separately. Here, it was found that male LEE pups spent less
time immobile than male control pups, t(30) = 3.31, p= 0.0049
(Figure 11A). For female pups, however, the difference between
the time spent immobile in the two groups was not significant,
t(30) = 1.31, p= 0.3588.
The AR test measures motor ability, balance, coordination, and
learning through repeated measures. Because the AR task extends
across four trials for each mouse, the data were analyzed using
a multilevel model in which condition (Control vs. LEE) ×sex
(male vs. female) ×trial (1–4) were fixed factors, and mouse
was included as a random factor. The analysis indicated a main
effect of trial, χ2(3) = 104.67, p<0.001. Planned polynomial
contrasts showed significant linear [t(114) = 3.54, p= 0.0006]
and quadratic [t(114) = 2.15, p= 0.0338] effects of trial as
well as a three-way interaction between condition, sex, and the
quadratic trial contrast [t(114) = 2.07, p= 0.0405]. This interaction
can be understood by considering the pattern of the effect of
condition across trials for males (trial 1 M= 75.76, 95% CI [−4.29,
155.81]; trial 2 M=−74.39, 95% CI [−154.44, 5.66]; trial 3
Frontiers in Neuroscience 10 frontiersin.org
fnins-17-1147274 March 7, 2023 Time: 13:35 # 11
Perez et al. 10.3389/fnins.2023.1147274
FIGURE 8
Dendritic spine density – males. Representative images of
secondary dendrites of pyramidal cells in layers 4/5 of the frontal
and prelimbic cortices of male control (A1,B1,A3,B3) and LEE
(A2,B2,A4,B4) pups at P20 and P30. Comparison of dendritic spine
density of males indicated no significant differences in frontal
(A5;N= 14) and prelimbic (B5;N= 16) cortices. Data expressed as
mean ±SEM. Scale bar, 250 µm.
M=−31.77, 95% CI [−111.82, 48.28]; trial 4 M=−29.40, 95%
CI [−109.45, 50.65]), and for females (trial 1 M= 12.576, 95% CI
[−71.04, 96.17]; trial 2 M= 3.48, 95% CI [−80.13, 87.09]; trial 3
M= 35.05, 95% CI [−48.56, 118.66]; trial 4 M=−3.58, 95% CI
[−87.19, 80.03]). Other main effects and interactions did not reach
significance (Figures 11B, C). Additional Sidak corrected planned
comparisons between adjacent trials within each combination of
sex and condition yielded significant differences between trials 1
FIGURE 9
Dendritic spine density – females. Representative images of
secondary dendrites of pyramidal cells in layers 4/5 of the frontal
and prelimbic cortices of female control (A1,B1,A3,B3) and LEE
(A2,B2,A4,B4) pups at P20 and P30. Comparison of dendritic spine
density of males indicated no significant differences in frontal
(A5;N= 16) and prelimbic (B5;N= 16) cortices. Data expressed as
mean ±SEM. Scale bar, 250 µm.
and 2 for Control, t(114) = 2.68, p= 0.0417, and LEE, t(114) = 2.97,
p= 0.0191, females (Figure 11C) and for LEE males, t(114) = 6.61,
p<0.0001 (Figure 11B).
Overall, these data suggest that our exposure paradigm
generates behavioral aberrations at P30 including increased risk-
taking behaviors in LEE mice regardless of sex as well as abnormal
stress regulation, active stress-coping styles and/or hyperactivity
in male LEE mice.
Frontiers in Neuroscience 11 frontiersin.org
fnins-17-1147274 March 7, 2023 Time: 13:35 # 12
Perez et al. 10.3389/fnins.2023.1147274
FIGURE 10
Behavioral assays at P30: EPM. (A) No significant differences in time
spent in the open arms of the EPM when evaluated by sex and a
marginal effect of condition (p= 0.07). (B) No effects observed in
planned comparisons for male and female pups (N= 41). Data
expressed as mean ±SEM.
Discussion
Fifty years ago, several physicians at the University of
Washington Medical School studied a small group of children
who exhibited a particular set of developmental delays. The
commonality among the children was that they were all born to
alcoholic mothers. This was the first of many studies that aimed
to identify and understand the condition that would be later
known as Fetal Alcohol Syndrome (FAS) (Jones et al.,1973). Our
laboratory has studied the effects of PrEE for over 10 years now and
although we have gained insight on FAS, or its spectrum disorder,
FASD, our work was limited to prenatal exposures. Unfortunately,
maternal alcohol consumption may continue during pregnancy,
or if the mother abstained from drinking while pregnant, it may
begin in the early postnatal period. Many new mothers report
that after 9 months of abstinence, they begin to drink again after
the baby is born (Jagodzinski and Fleming,2007). The advice
by physicians for drinking alcohol while breastfeeding is quite
variable, and this presents a possible health issue for infants of
drinking mothers. In fact, the CDC warn against heavy drinking
FIGURE 11
Behavioral assays at P30: FST and AR. (A) Male LEE pups
(p= 0.0049) spent less time immobile than male controls in the FST.
No significant differences in time spent immobile for females
(p= 0.3588) on the FST (N= 34). No significant differences in
performance on the AR for males (B) or females (C) (N= 42). Data
expressed as mean ±SEM.
during breastfeeding but suggest that “moderate consumption of
alcohol” is not harmful to offspring (Centers for Disease Control
and Prevention,2022b). Compared to research on prenatal alcohol
exposure, studies examining the effects of maternal drinking
during lactation are mostly limited to epidemiological reports
Frontiers in Neuroscience 12 frontiersin.org
fnins-17-1147274 March 7, 2023 Time: 13:35 # 13
Perez et al. 10.3389/fnins.2023.1147274
with a paucity of papers in animal models where changes in the
developing nervous system are investigated. Thus, we developed
a novel postnatal alcohol exposure model in breastfeeding mice,
using the murine strain utilized in our PrEE studies. In this LEE
model, we demonstrate that maternal consumption of alcohol
while breastfeeding can induce gross developmental deficits in
LEE pups including decreased body weights, brain weights, and
cortical lengths. Additionally, we discovered some sex-specific,
LEE-related phenotypes in the neuroanatomy of the frontal lobe
and prelimbic cortex, as well as behavioral deficits in stress-coping
styles and risk-taking behaviors in LEE offspring. Our findings that
postnatal, indirect ethanol exposure (as modeled by our lactational
experimental paradigm) can negatively impact various aspects of
development represents an important advancement in solidifying
the significance of conscientious, informed parental care.
A novel murine lactational ethanol
exposure model: Impact of LEE on gross
anatomical changes in offspring
Our results suggest that ethanol exposure via lactation is
correlated with reduced body weights in both males and females
at P20 and 30. These findings are consistent with human studies
where children exposed to ethanol through contaminated breast
milk can have consistently lower body weights and growth
trajectories (May et al.,2016). Although there is a paucity of rodent
data on offspring outcomes after ethanol exposure via lactation,
a study from Vilaró et al. (1987) reported a reduction in body
weight of ethanol-exposed rats after a period of maternal ethanol
consumption while nursing her pups. In terms of brain size and
morphology, we find some sex-specific effects of LEE in our model.
Specifically, while LEE males show sustained low brain weights
compared to controls at P20 and P30, LEE females only show
deficits in brain weights at P20, with recovery to control weights
by P30. Thus, LEE females show a faster rate of recovery when
compared to males.
Few rodent models have examined brain weight changes in
LEE mice; however, one study reported a decrease in weights of
the forebrain, cerebellum, and brainstem in alcohol treated pups
(Chen et al.,1998). When examining PrEE paradigms, sustained
reductions in body weight and brain weights are observed from P0
to P50 in mice, consistent with findings in LEE offspring (Abbott
et al.,2016;2018). This suggests that LEE and PrEE may impact
brain and body growth through similar mechanisms.
Considering the sustained growth retardation in PrEE and
LEE mice, the reduction of body and brain weights might be
due to the gut’s inability to efficiently extract nutrients when
alcohol is ingested. Acute and chronic ethanol administration
results in a reduction of protein synthesis in the small intestine
(Rajendram and Preedy,2005) and can block absorption of
micro- and macronutrients (Seitz and Homann,2001;Seitz and
Suter,2002). Additionally, nutrient deficiency has the potential
to manifest in epigenetic changes, as seen in the populations
affected by the Dutch Hunger Winter (Dutch Famine) (Heijmans
et al.,2008). We found that, in our PrEE model, epigenetic
modifications occurred via changes in DNA methylation, which led
to epigenetic and heritable phenotypes spanning three generations
of mice (Abbott et al.,2018). It is possible that examination of
epigenetic markers in LEE mice could provide further insight into
mechanisms underlying LEE-induced phenotypes.
Impact of LEE on cortical length
In mammals, much of our sophisticated behavior, including
language, sociability, decision making, and even fine motor skills
and coordination, originates with complex functions of cells within
the neocortex. In FASD or other alcohol-induced conditions, the
abnormal phenotypes in humans are often related to presumed
dysfunction within the neocortex (El Shawa et al.,2013). Thus, we
chose to focus our study of the novel LEE model on development of
the neocortex and the behaviors that are mediated, to some extent,
by its function. To begin, we measured cortical length at both
P20 and P30 ages in male and female LEE and control mice. We
found that while the cortex expanded in length significantly from
P20 to P30 in all mice, LEE cortices remained consistently smaller,
regardless of sex. Few rodent models have examined the impact of
LEE on cortical development, and, to our knowledge, there are no
studies that specifically measure cortical length after LEE. Similarly,
studies from our laboratory demonstrated a reduction in cortical
length in PrEE mice (El Shawa et al.,2013;Abbott et al.,2018). As
the cortex continues to grow and develop from birth to puberty in
mice, we posit here that alcohol exposure via lactation may lead
to apoptosis, increased oxidative stress, and interference with the
activity of growth factors as is suggested for prenatal exposures
(Goodlett and Horn,2001).
Neocortical thickness
In mice, neocortical lamination is present by around P5, when
barrels become apparent in later IV of somatosensory cortex.
According to a comprehensive set of papers from our laboratory,
the areal patterning period ends around this time, P5–6, when
cortical areas have adult-like connections and lamination. Beyond
P6, cortical thickness continues to increase, although the changes
are minimal (Dye et al.,2011a,b). Here, we measured cortical
thickness across several neocortical sensory and motor regions at
P20 and P30 in LEE and control mice. Given that the frontal
cortex develops later than other cortical regions, and that the time
of exposure is after the areal patterning period closes, it is not
surprising that the only LEE-related phenotype we found was a
reduction in cortical thickness in the frontal cortex of P20 LEE
males. This phenotype was recovered by P30 in the LEE male mice.
Subsequent measurements in prelimbic, somatosensory, auditory,
and visual cortices, at both milestone dates, produced no observable
differences. Few rodent models have examined the effects of LEE on
neocortex, and to our knowledge there are no studies that examine
cortical thickness changes after LEE. There are, however, reports of
alcohol-induced changes in cortical thickness measures after PrEE.
Our laboratory demonstrated changes spanning from birth to P50
in cortical thickness measures in the brains of PrEE mice (Abbott
et al.,2016). PrEE models impact cortical thickness at a higher
extent due to exposure during gestation, as this is the primary time
when the cortex develops layer-specific organization of cell types
Frontiers in Neuroscience 13 frontiersin.org
fnins-17-1147274 March 7, 2023 Time: 13:35 # 14
Perez et al. 10.3389/fnins.2023.1147274
and matures from a simply organized, single layer to a complex 6-
layered structure. As the lactational exposure occurs after cortical
areas subdivision and lamination, the exposure timing may be too
late in development to induce significant changes in neocortical
thickness.
LEE and dendritic spine densities in
frontal cortex
Through Golgi-Cox staining we aimed to evaluate the impact
of LEE on dendritic spine densities, as ethanol exposure has
the potential to alter synaptogenesis (Adams et al.,2022) and
synaptic pruning (Kyzar and Pandey,2015;Kyzar et al.,2016). In
typically developing mice, cortex wide synaptic pruning has been
reported to reach its peak 14–21 days postnatal (Lewis,2011). In
early alcohol exposure models, acute exposures led to increased
dendritic pruning in the prefrontal cortex, resulting in significant
synapse loss (Socodato et al.,2020). Also, acute ethanol exposure
during synaptogenesis (from P5 to P7) led to drastically decreased
spine densities in the caudate/putamen, however, these densities
recovered to normal levels by around P30 (Clabough et al.,2022).
Here, we exposed mice to ethanol via lactation within this
postnatal sensitive period and conducted intensive spine counts in
frontal lobe ROIs in male and female mice, aged P20 and P30. While
we did not find any significant changes in our measured frontal
cortex spine densities, we did find a trend toward significance
for prelimbic cortex (a subregion of the medial prefrontal cortex)
between LEE and control mice. There were no age- or sex-
dependent effects observed, but the overall reduction in spine
densities observed in the prelimbic cortex of LEE mice could
impact later development, and this could be possibly caused by
ethanol-induced impairment to synaptogenesis or to increased
synaptic pruning as the insult take places during a sensitive
period for both. Of note, whether spine densities in the prelimbic
cortex decrease or increase is age dependent (Galaj et al.,2020);
however, alterations due to alcohol exposure have been associated
with altered behavior regardless of the direction of change (Fox
et al.,2020). This is not surprising given that the prelimbic
cortex is a region shown to play a role in alcohol-drinking
reinforcement (Engleman et al.,2020). These data are consistent
with other brain areas (basal ganglia) where reductions in spine
densities observed immediately after exposure seemed to reverse
by 1 month of age (Clabough et al.,2022). It is possible that
alterations occurred in synaptogenesis and/or pruning earlier in the
exposure period and recovered by weaning when the first measures
were taken.
Impact of LEE on behavioral
development
While it is important to uncover changes in the developing
nervous system that are associated with ethanol exposure through
lactation, understanding the potential behavioral effects of the
postnatal exposure is critical. In our current study we implemented
a battery of behavioral assays to examine LEE’s effect on behavioral
development. The EPM is a classic way to measure anxiety
in rodents (Walf and Frye,2007). However, researchers have
also looked beyond the initial interpretation of the EPM and
created alternative hypotheses about how time spent in open
arms versus closed arms can be interpreted. Most importantly,
if an animal spends more time in the open arm, it may
indicate increased risk taking or increased exploratory behavior
(Macrì et al.,2002,Kozanian et al.,2018). Also, as alcohol
exposure impacts fear memory learning, affecting an animal’s
ability to learn a natural fear response, increased time in open
arms could be from inhibited fear learning, as was observed
in our PrEE model (Kozanian et al.,2018). Here, we found
that, overall, LEE mice spent a significantly longer time in
open arms when compared to control mice, without sex-specific
effects. This suggests that exposure to ethanol via lactation may
increase risk taking or exploratory behavior. This is consistent
with exposure to ethanol via lactation in humans, as May
et al. (2016) found that LEE children exhibited phenotypic
variability consistent with FASD, with increased risk taking
and cognitive deficits often present in children with FASD
(Fast and Conry,2009).
A hallmark of FASD and alcoholism is depression (Pei et al.,
2011;Kuria et al.,2012) and the FST is a classic test used to detect
depressive-like behaviors in animal models (Lucki et al.,2001).
Like the EPM, behavioral results associated with the FST have been
interpreted differently over time in the literature. Specifically, the
FST test has been a successful method used to test for the effects
of antidepressant drugs in that they increase the animal’s activity
in the swim well (Porsolt et al.,1978). Researchers who use the
test for other model systems have identified that time immobile
may represent a more complex measure than simple depressive
behaviors. How the animal responds to being in the swim well, with
floating (immobility) or active swimming/climbing can be viewed
as different adaptive reactions to the stressful environment. For
example, Armario (2021) determined that mice react according
to their coping style, either passively or actively, and that the
FST may be a more accurate measure of coping style rather than
behavioral despair. This may also be correlated with hyperactivity
or possibly response to fearful stimuli. Here, we found that LEE
males demonstrated reduced time immobile when compared to
control males in this task, with the effect not observed in female
LEE mice. This indicates that LEE may cause abnormal stress
regulation and hyperactivity in males, consistent with findings in
humans with FASD (Hellemans et al.,2008). For example, alcohol
compromised breast milk has been found to have an activating
effect in humans, as behavioral states of infants showed increased
variability, such as spending less time in quiet sleep and increased
crying (Schuetze et al.,2002). It is also possible that increased time
spent immobile during the FST for male LEE mice could indicate
alteration in fear responsivity, as we showed abnormal fear learning
in our FASD model mice (Kozanian et al.,2018). This behavioral
phenotype may be related to reduced frontal lobe thickness in
males (Figure 6), as the frontal cortex is likely to be involved in
depression (Zhang et al.,2018) and fear responsivity (Gilmartin
et al.,2014).
The AR test measures motor ability, balance, coordination and
learning through repeated measures. Previously, we found that
rotarod performance was altered in PrEE mice; specifically, first
generation PrEE mice showed deficits in performance in the first
two trials compared to controls at both P20 and P30 (Abbott et al.,
Frontiers in Neuroscience 14 frontiersin.org
fnins-17-1147274 March 7, 2023 Time: 13:35 # 15
Perez et al. 10.3389/fnins.2023.1147274
2018;Bottom et al.,2022). Additionally, postnatal alcohol exposure
in rats can impact AR performance (Goodlett et al.,1991;Cebolla
et al.,2009). In our LEE model, male LEE mice showed increased
variability in performance in trials 1–2. Specifically, the change in
performance was appreciably different from controls: the male LEE
mice performed worse on trial 1 but showed a significantly greater
degree of improvement between trials 1 and 2. After training, LEE
mice performed similar to controls on the AR. In summary, male
LEE mice show a greater deficit in trial 1 and showed an abrupt
learning profile that differs significantly from both controls and
female LEE mice.
Collectively, our results from our behavioral studies suggest
LEE may impact offspring in ways similar to prenatal exposures,
with increased risk-taking, hyperactivity, active stress-coping
responses to environmental stressors, and transient deficits in
motor coordination. Additionally, some of these LEE-induced
deficits may be sex-specific.
Critical periods, pubescence, and
plasticity
Developmental critical periods are described as times when
systems are “plastic” or open to change from environmental
experience, such as with learning, or insult, such as with early
alcohol exposure. For brain development, these are precise time
points where neuronal plasticity is heightened and cortical circuits
are particularly susceptible to regulation by specific sensory
modalities (Jeanmonod et al.,1981). Initial explorational work in
somatosensory cortical reorganization found that the removal of
mouse vibrissae at birth resulted in an absence of the associated
barrels (Van der Loos and Woolsey,1973). Since then, studies
have refined these events and have assigned a critical period range
(first week of life in mice) for proper barrel formation (Lo et al.,
2017). Additionally, the critical period for the visual system has
been extensively studied. A literature review from Hooks and Chen
(2007), places the critical period prior to eye opening in mice, at
P0–P10. Perturbations in this period may alter cortical retinotopic
maps (Hooks and Chen,2007) along with gene expression and
intra neocortical connections (Dye et al.,2012). How perturbations,
insults, or changes in input impact a developing animal depends on
the critical period for development in the relevant system. If events
occur after closure of a critical period, the animal may be protected
from detrimental harm. Unfortunately, if these events occur outside
the critical period, the ability of the brain to repair itself with
plasticity mechanisms may also be reduced. Understanding critical
periods when comparing the impact of prenatal versus postnatal
alcohol exposure, on the developing nervous system, is critical.
Compared to the effects of prenatal alcohol exposure in our
mouse model of FASD, LEE has more mild phenotypes associated
with the exposure, although the changes we observed in our LEE
mice could have debilitating consequences if mimicked in human
systems. The difference in severity of outcomes between PrEE and
LEE is possibly related to critical periods for development. As
described previously, much of cortical development (lamination,
arealization) in the mouse reaches an adult-like state by the first
postnatal week, whereas during the prenatal period and the first few
days of life, the developing brain is very susceptible to change. Thus,
LEE animals may be somewhat protected, when compared to PrEE,
from the more severe effects of the alcohol exposure because the key
elements of cortical development, particularly those regulated by
gene expression, such as the development of the intricate neuronal
circuitry, are near complete.
Interestingly, there are sex differences revealed in our data.
Specifically, we found that LEE females recovered brain and
body weights more quickly when compared to LEE males, and
that frontal cortex phenotypes and atypical behavior on the
FST were observed only in LEE males. Also, LEE male rotarod
performance demonstrated an abrupt learning pattern that was
markedly different from controls and LEE females. One hypothesis
as to why LEE females fare better, when compared to LEE males,
related to differences in puberty onset compared to the timing of
exposure and dependent measures. Typical onset of puberty for
wild-type mice begins around P28 in males, and P25 for females
(Ismail et al.,2011;Molenhuis et al.,2014). Alcohol exposure prior
to this period may impact the milieu of hormones that regulate
onset of puberty. For example, a gradual increase of Gonadotropin
Releasing Hormone (GnRH) is responsible for the typical onset of
puberty; its expression is diminished in the presence of alcohol,
resulting in a pubertal onset delay (Srivastava et al.,2014;Dees et al.,
2017). Therefore, our model can potentially delay puberty onset in
LEE mice. Considering that female mice go through puberty earlier
than males, it is not surprising that LEE has a greater impact on
male behavior at P30.
Study limitation and future directions
With this study, we attempted to model offspring exposure to
ethanol, naturally, via maternal consumption during lactation and
active breastfeeding in an outbred mouse strain. With this comes
limitations. For example, outbred mice have inherent variability,
unlike inbred strains where genetics are controlled. However,
inbred mice, such as C57BL/6 are less hardy than CD-1 mice
and tend to provide inferior maternal care to their offspring.
Additionally, the self-administration design of this experiment
leads to variation in maternal ethanol consumption as well
as milk production and composition. These factors could play
influential roles in offspring outcome in addition to the impact that
ethanol provides.
Another limitation is the variability in pup BEC we observed
in our data. Although the variability in dam BEC was small, we
believe there were several factors besides maternal ethanol levels
that influenced pup BEC. The LEE pups were small at P20 and
obtaining blood samples in a great enough volume for the assays
was difficult. This resulted in a lower sample size. Also, by P20,
some pups had begun eating chow in addition to nursing, possibly
reducing ethanol intake and time from the last nursing event was
variable from pups selected for analysis. Mice metabolize ethanol
quickly, so increased variability in measured BEC is expected when
time since the last dose is unknown. Additionally, competition for
breast milk access can result in variability among pups. Also, timing
of maternal alcohol consumption relative to the period of nursing
that preceded the pup sampling could also introduce variability.
Despite the observed variability in pup BEC, the BECs were
Frontiers in Neuroscience 15 frontiersin.org
fnins-17-1147274 March 7, 2023 Time: 13:35 # 16
Perez et al. 10.3389/fnins.2023.1147274
non-zero in all LEE pups and the level was significantly higher than
controls in all LEE cases.
Future studies could include shorter time periods of exposure,
as human mothers sometimes breastfeed for abbreviated periods
of time post-partum. Also, additional studies of gene expression
analyses in the frontal cortex as well as intraneocortical connectivity
would be warranted and behavior tests of fear conditioning and
learning as we observed phenotypes in these domains in our
PrEE models. Finally, additional behavioral assays including tests
to better assess hyperactivity, such as open field and assays
that can detect cognitive deficits such as Morris water maze or
radial arm maze.
Conclusion
A preponderance of evidence from researchers studying
prenatal alcohol exposure and FASD led the CDC to correct its
stance on drinking in pregnancy. They now clearly state “There
is no known safe amount of alcohol use during pregnancy or
while trying to get pregnant” (Centers for Disease Control and
Prevention,2022a). To date, the CDC has not made a similar
statement regarding drinking while breastfeeding, despite research
demonstrating high frequency of maternal alcohol consumption
while nursing (Backstrand et al.,2004;Parackal et al.,2007;Giglia
et al.,2008;Giglia,2010;Lange et al.,2016). In their review, May
et al. (2016) make a compelling argument that alcohol consumption
during pregnancy can result in poor childhood outcomes.
Our data from our novel LEE model supports this notion, as our
LEE model demonstrates similar phenotypes as our PrEE model;
therefore, abstaining from alcohol consumption during BOTH
the prenatal period and while breastfeeding is the safest option.
Although the effects of LEE are mild compared to PrEE, most likely
due to exposure outside critical periods for typical development,
offspring exposure to ethanol via breast milk can have deleterious
effects on developing brain and behavior and should be avoided.
Data availability statement
The raw data supporting the conclusions of this article will be
made available by the authors, without undue reservation.
Ethics statement
This animal study was reviewed and approved by the
Institutional Animal Care and Use Committee (IACUC) at the
University of California, Riverside (UCR).
Author contributions
RP assisted with research design, conducted the experiments,
collected, analyzed, and interpreted the data, and wrote the
manuscript. KC conducted the experiments, collected the data, and
wrote the manuscript. ME contributed to the statistical analysis and
interpretation of data, and wrote the manuscript. MN conducted
the experiments and collected the data. KH established the research
design, interpreted the data, and wrote the manuscript. All authors
contributed to the article and approved the submitted version.
Acknowledgments
The authors thank Riley Bottom, Olga Kozanian, Caitlyn
Gueverra, Diego Reyes, Grace Garcia, Andrew Dysico, Isabella
Olimpiada, Jennifer Hyunh, and Megan Ung for their assistance in
collecting behavioral data.
Conflict of interest
The authors declare that the research was conducted in the
absence of any commercial or financial relationships that could be
construed as a potential conflict of interest.
Publisher’s note
All claims expressed in this article are solely those of the
authors and do not necessarily represent those of their affiliated
organizations, or those of the publisher, the editors and the
reviewers. Any product that may be evaluated in this article, or
claim that may be made by its manufacturer, is not guaranteed or
endorsed by the publisher.
References
Abbott, C. W., Kozanian, O. O., Kanaan, J., Wendel, K. M., and Huffman, K. J.
(2016). The impact of prenatal ethanol exposure on neuroanatomical and behavioral
development in mice. Alcoholism 40, 122–133. doi: 10.1111/acer.12936
Abbott, C. W., Rohac, D. J., Bottom, R. T., Patadia, S., and Huffman, K. J. (2018).
Prenatal ethanol exposure and neocortical development: A transgenerational model of
FASD. Cereb. Cortex 28, 2908–2921. doi: 10.1093/cercor/bhx168
Adams, J. W., Negraes, P. D., Truong, J., Tran, T., Szeto, R. A., Guerra, B. S., et al.
(2022). Impact of alcohol exposure on neural development and network formation
in human cortical organoids. Mol. Psychiatry 27, 1–14. doi: 10.1038/s41380-022-0
1862-7
Allen Institute for Brain Science, (2004). Allen mouse brain atlas [dataset]. Seattle,
WA: Allen Institute for Brain Science.
Armario, A. (2021). The forced swim test: Historical, conceptual and
methodological considerations and its relationship with individual behavioral
traits. Neurosci. Biobehav. Rev.s 128, 74–86. doi: 10.1016/j.neubiorev.2021.06.014
Backstrand, J. R., Goodman, A. H., Allen, L. H., and Pelto, G. H. (2004). Pulque
intake during pregnancy and lactation in rural Mexico: Alcohol and child growth from
1 to 57 months. Eur. J. Clin. Nutr. 58, 1626–1634. doi: 10.1038/sj.ejcn.1602019
Bates, D., Maechler,M., Bolker, B., and Walker, S. (2015). Fitting linear mixed-effects
models using lme4. J. Stat. Softw. 67, 1–48. doi: 10.18637/jss.v067.i01
Bayram-Weston, Z., Olsen, E., Harrison, D. J., Dunnett, S. B., and Brooks, S. P.
(2016). Optimising Golgi–Cox staining for use with perfusion-fixed brain tissue
validated in the zQ175 mouse model of Huntington’s disease. J. Neurosci. Methods
265, 81–88. doi: 10.1016/j.jneumeth.2015.09.033
Frontiers in Neuroscience 16 frontiersin.org
fnins-17-1147274 March 7, 2023 Time: 13:35 # 17
Perez et al. 10.3389/fnins.2023.1147274
Boschuetz, N., Cheng, S., Mei, L., and Loy, V. M. (2020). Changes in alcohol use
patterns in the United States during COVID-19 pandemic. WMJ 119, 171–176.
Bottom, R. T., Kozanian, O. O., Rohac, D. J., Erickson, M. A., and Huffman, K. J.
(2022). Transgenerational effects of prenatal ethanol exposure in prepubescent mice.
Front. Cell Dev. Biol. 10:812429. doi: 10.3389/fcell.2022.812429
Bramness, J. G., Bye, E. K., Moan, I. S., and Rossow, I. (2021). Alcohol use during
the COVID-19 pandemic: Self-reported changes and motives for change. Eur. Addict.
Res. 27, 257–262. doi: 10.1159/000515102
Buitrago, M. M., Schulz, J. B., Dichgans, J., and Luft, A. R. (2004). Short and long-
term motor skill learning in an accelerated rotarod training paradigm. Neuro Biol.
Learn. Mem. 81, 211–216. doi: 10.1016/j.nlm.2004.01.001
Cebolla, A. M., Cheron, G., Hourez, R., Bearzatto, B., Dan, B., and Servais, L. (2009).
Effects of maternal alcohol consumption during breastfeeding on motor and cerebellar
Purkinje cells behavior in mice. Neurosci. Lett. 455, 4–7. doi: 10.1016/j.neulet.2009.0
3.034
Centers for Disease Control and Prevention (2019). Alcohol, breastfeeding and
special circumstances. Washington, DC: U.S. Department of Health and Human
Services.
Centers for Disease Control and Prevention, (2022a). Alcohol use during pregnancy.
U.S. Department of health and human services. Available online at: https://www.cdc.
gov/ncbddd/fasd/alcohol-use.html (accessed January 5, 2023).
Centers for Disease Control and Prevention (2022b). Alcohol.U.S. Department of
health and human services. Available online at: https://www.cdc.gov/breastfeeding/
breastfeeding-special- circumstances/vaccinations-medications- drugs/alcohol.html
(accessed January 5, 2023).
Chen, W. J. A., Parnell, S. E., and West, J. R. (1998). Neonatal alcohol and nicotine
exposure limits brain growth and depletes cerebellar Purkinje cells. Alcohol 15, 33–41.
doi: 10.1016/S0741-8329(97)00084- 0
Chien, Y. C., Liu, J. F., Huang, Y. J., Hsu, C., Sen, and Chao, J. C. J. (2005).
Alcohol levels in Chinese lactating mothers after consumption of alcoholic diet during
postpartum “doing-the-month” ritual. Alcohol 37, 143–150. doi: 10.1016/j.alcohol.
2006.02.001
Clabough, E., Ingersoll, J., Reekes, T., Gleichsner, A., and Ryan, A. (2022).
Acute ethanol exposure during synaptogenesis rapidly alters medium spiny neuron
morphology and synaptic protein expression in the dorsal striatum. Int. J. Mol. Sci.
23:290. doi: 10.3390/ijms23010290
Clancy, B., Finlay, B. L., Darlington, R. B., and Anand, K. J. S. (2007). Extrapolating
brain development from experimental species to humans. Neurotoxicology 28, 931–
937. doi: 10.1016/j.neuro.2007.01.014
Commons, K. G., Cholanians, A. B., Babb, J. A., and Ehlinger, D. G. (2017). The
rodent forced swim test measures stress-coping strategy, not depression-like behavior.
ACS Chem. Neurosci. 8, 955–960. doi: 10.1021/acschemneuro.7b00042
Conner, K. E., Bottom, R. T., and Huffman, K. J. (2020). The impact of paternal
alcohol consumption on offspring brain and behavioral development. Alcoholism 44,
125–140. doi: 10.1111/acer.14245
Dees, W. L., Hiney, J. K., and Srivastava, V. K. (2017). Alcohol and puberty:
Mechanisms of delayed development. Alcohol Res. 38:277.
Dobbing, J., and Sands, J. (1979). Comparative aspects of the brain growth spurt.
Early Hum. Dev. 3, 79–83. doi: 10.1016/0378-3782(79)90022- 7
Dye, C. A., Abbott, C. W., and Huffman, K. J. (2012). Bilateral enucleation alters
gene expression and intraneocortical connections in the mouse. Neural Dev. 7:5.
doi: 10.1186/1749-8104- 7-5
Dye, C. A., El Shawa, H., and Huffman, K. J. (2011a). A lifespan analysis of
intraneocortical connections and gene expression in the mouse I. Cereb. Cortex
(New York, N.Y. : 1991) 21, 1311–1330. doi: 10.1093/cercor/bhq212
Dye, C. A., El Shawa, H., and Huffman, K. J. (2011b). A lifespan analysis of
intraneocortical connections and gene expression in the mouse II. Cereb. Cortex
(New York, N.Y. : 1991) 21, 1331–1350. doi: 10.1093/cercor/bhq213
El Shawa, H., Abbott, C. W. III, and Huffman, K. J. (2013). Prenatal ethanol exposure
disrupts intraneocortical circuitry, cortical gene expression, and behavior in a mouse
model of FASD.J. Neurosci. 33, 18893–18905. doi: 10.1523/JNEUROSCI.3721-13.2013
Engleman, E. A., Ingraham, C. M., Rodd, Z. A., Murphy, J. M., McBride, W. J.,
and Ding, Z.-M. (2020). The reinforcing effects of ethanol within the prelimbic
cortex and ethanol drinking: Involvement of local dopamine D2 receptor-mediated
neurotransmission. Drug Alcohol Depend. 214, 108165. doi: 10.1016/j.drugalcdep.
2020.108165
Fast, D. K., and Conry, J. (2009). Fetal alcohol spectrum disorders and the criminal
justice system. Dev. Disabil. Res. Rev. 15, 250–257. doi: 10.1002/ddrr.66
Fox, J., and Weisberg, S. (2019). An R companion to applied regression, 3rd Edn.
Thousand Oaks, CA: Sage.
Fox, M. E., Chandra, R., Menken, M. S., Larkin, E. J., Nam, H., Engeln, M.,
et al. (2020). Dendritic remodeling of D1 neurons by RhoA/Rho-kinase mediates
depression-like behavior. Mol. Psychiatry 25, 1022–1034. doi: 10.1038/s41380-018-
0211-5
Galaj, E., Guo, C., Huang, D., Ranaldi, R., and Ma, Y.-Y. (2020). Contrasting
effects of adolescent and early-adult ethanol exposure on prelimbic cortical pyramidal
neurons. Drug Alcohol Depend. 216, 108309. doi: 10.1016/j.drugalcdep.2020.108309
Gibson, L., and Porter, M. (2018). Drinking or smoking while breastfeeding and later
cognition in children. Pediatrics 142:e20174266. doi: 10.1542/peds.2017-4266
Gibson, L., and Porter, M. (2020). Drinking or smoking while breastfeeding and
later developmental health outcomes in children. BMC Res. Notes 13:232. doi: 10.1186/
s13104-020- 05072-8
Giglia, R. C. (2010). Alcohol and lactation: An updated systematic review. Nutr.
Dietetics 67, 237–243. doi: 10.1111/j.1747-0080.2010.01469.x
Giglia, R. C., Binns, C. W., Alfonso, H. S., Scott, J. A., and Oddy, W. H. (2008). The
effect of alcohol intake on breastfeeding duration in Australian women. Acta Paediatr.
97, 624–629. doi: 10.1111/j.1651-2227.2008.00760.x
Giglia, R., and Binns, C. (2006). Alcohol and lactation: A systematic review. Nutr.
Dietetics 63, 103–116. doi: 10.1111/j.1747-0080.2006.00056.x
Gilmartin, M. R., Balderston, N. L., and Helmstetter, F. J. (2014). Prefrontal cortical
regulation of fear learning. Trends Neurosci. 37, 455–464. doi: 10.1016/j.tins.2014.0
5.004
Goodlett, C. R., and Horn, K. H. (2001). Mechanisms of alcohol-induced damage to
the developing nervous system. Alcohol Res. Health 25, 175–184.
Goodlett, C. R., Thomas, J. D., and West, J. R. (1991). Long-term deficits in
cerebellar growth and rotarod performance of rats following “binge-like” alcohol
exposure during the neonatal brain growth spurt. Neurotoxicol. Teratol. 13, 69–74.
doi: 10.1016/0892-0362(91)90029- V
Guinle, M. I. B., and Sinha, R. (2020). The role of stress, trauma, and negative
affect in alcohol misuse and alcohol use disorder in women. Alcohol Res. 40:05. doi:
10.35946/arcr.v40.2.05
Handley, S. L., and Mithani, S. (1984). Effects of alpha-adrenoceptor agonists
and antagonists in a maze-exploration model of ’fear’-motivated behaviour. Naunyn
Schmiedebergs Arch. Pharmacol. 327, 1–5. doi: 10.1007/BF00504983
Heijmans, B. T., Tobi, E. W., Stein, A. D., Putter, H., Blauw, G. J., Susser, E. S., et al.
(2008). Persistent epigenetic differences associated with prenatal exposure to famine in
humans. Proc. Natl. Acad. Sci. U.S.A. 105, 17046–17049. doi: 10.1073/pnas.0806560105
Hellemans, K. G., Verma, P., Yoon, E., Yu, W., and Weinberg, J. (2008). Prenatal
alcohol exposure increases vulnerability to stress and anxiety-like disorders in
adulthood. Ann. N. Y. Acad. Sci. 1144, 154–175. doi: 10.1196/annals.1418.016
Hooks, B. M., and Chen, C. (2007). Critical periods in the visual system: Changing
views for a model of experience-dependent plasticity. Neuron 56, 312–326. doi: 10.
1016/j.neuron.2007.10.003
Hoyme, H. E., Kalberg, W. O., Elliott, A. J., Blankenship, J., Buckley, D., Marais,
A. S., et al. (2016). Updated clinical guidelines for diagnosing fetal alcohol spectrum
disorders. Pediatrics 138:e20154256. doi: 10.1542/peds.2015-4256
Ieraci, A., and Herrera, D. G. (2007). Single alcohol exposure in early life damages
hippocampal stem/progenitor cells and reduces adult neurogenesis. Neurobiol. Dis. 26,
597–605. doi: 10.1016/j.nbd.2007.02.011
Ismail, N., Garas, P., and Blaustein, J. D. (2011). Long-term effects of pubertal
stressors on female sexual receptivity and estrogen receptor-αexpression in CD-1
female mice. Hormones Behav. 59, 565–571. doi: 10.1016/j.yhbeh.2011.02.010
Jagodzinski, T., and Fleming, M. F. (2007). Postpartum and alcohol-related factors
associated with the relapse of risky drinking. J. Stud. Alcohol Drugs 68, 879–885.
doi: 10.15288/jsad.2007.68.879
Jeanmonod, D., Rice, F. L., and Van der Loos, H. (1981). Mouse somatosensory
cortex: Alterations in the barrelfield following receptor injury at different early
postnatal ages. Neuroscience 6, 1503–1535. doi: 10.1016/0306-4522(81)90222- 0
Jones, K. L., Smith, D. W., Ulleland, C. N., and Streissguth, A. P. (1973). Pattern of
malformations in offspring of alcoholic mothers. Lancet 1, 1267–1271. doi: 10.1016/
S0140-6736(73)91291- 9
Jukic, A. M., Baird, D. D., Weinberg, C. R., McConnaughey, D. R., and Wilcox, A. J.
(2013). Length of human pregnancy and contributors to its natural variation. Hum.
Reprod. 28, 2848–2855. doi: 10.1093/humrep/det297
Kozanian, O. O., Rohac, D. J., Bavadian, N., Corches, A., Korzus, E., and Huffman,
K. J. (2018). Long-lasting effects of prenatal ethanol exposure on fear learning and
development of the amygdala. Front. Behav. Neurosci. 12:200. doi: 10.3389/fnbeh.2018.
00200
Kuria, M. W., Ndetei, D. M., Obot, I. S., Khasakhala, L. I., Bagaka, B. M.,
Mbugua, M. N., et al. (2012). The association between alcohol dependence and
depression before and after treatment for alcohol dependence. Int. Scholarly Res.
Notices 2012:482802. doi: 10.5402/2012/482802
Kyzar, E. J., and Pandey,S. C. (2015). Mole cular mechanisms of synaptic remodeling
in alcoholism. Neurosci. Lett. 601, 11–19. doi: 10.1016/j.neulet.2015.01.051
Kyzar, E. J., Floreani, C., Teppen, T. L., and Pandey, S. C. (2016). Adolescent
alcohol exposure: Burden of epigenetic reprogramming, synaptic remodeling, and
adult psychopathology. Front. Neurosci. 10:222. doi: 10.3389/fnins.2016.00222
Frontiers in Neuroscience 17 frontiersin.org
fnins-17-1147274 March 7, 2023 Time: 13:35 # 18
Perez et al. 10.3389/fnins.2023.1147274
Lange, S., Quere, M., Shield, K., Rehm, J., and Popova, S. (2016). Alcohol use
and self-perceived mental health status among pregnant and breastfeeding women in
Canada: A secondary data analysis. BJOG 123, 900–909. doi: 10.1111/1471-0528.13525
Lawton, M. E. (1985). Alcohol in breast milk. Aust. N. Zeal. J. Obstetr. Gynaecol. 25,
71–73. doi: 10.1111/j.1479-828X.1985.tb00609.x
Lenth, R. V. (2022). emmeans: Estimated marginal means, aka least-squares means.
R package Version 1.7.2. Available online at: https://CRAN.R-project.org/package=
emmeans.
Lewis, S. (2011). Pruning the dendritic tree. Nat. Rev. Neurosci. 12:493. doi: 10.1038/
nrn3099
Light, K. E., Serbus, D. C., and Santiago, M. (1989). Exposure of rats to ethanol from
postnatal days 4 to 8: Alterations of cholinergic neurochemistry in the cerebral cortex
and corpus striatum at day 20. Alcoholism 13, 29–35. doi: 10.1111/j.1530-0277.1989.
tb00279.x
Little, R. E., Lambert, M. D., and Worthington-Roberts, B. (1990). Drinking and
smoking at 3 months postpartum by lactation history. Paediatr. Perinat. Epidemiol. 4,
290–302. doi: 10.1111/j.1365-3016.1990.tb00653.x
Lo, S. Q., Sng, J. C., and Augustine, G. J. (2017). Defining a critical period for
inhibitory circuits within the somatosensory cortex. Sci. Rep. 7, 1–13. doi: 10.1038/
s41598-017- 07400-8
Lucki, I., Dalvi, A., and Mayorga, A. J. (2001). Sensitivity to the
effects of pharmacologically selective antidepressants in different strains
of mice. Psychopharmacology (Berl). 155, 315–322. doi: 10.1007/s0021301
00694
Macrì, S., Adriani, W., Chiarotti, F., and Laviola, G. (2002). Risk taking during
exploration of a plus-maze is greater in adolescent than in juvenile or adult mice.
Anim. Behav. 64, 541–546. doi: 10.1006/anbe.2002.4004
May, P. A., Gossage, J. P., Kalberg, W. O., Robinson, L. K., Buckley, D., Manning,
M., et al. (2009). Prevalence and epidemiologic characteristics of FASD from various
research methods with an emphasis on recent in-school studies. Dev. Disabil. Res. Rev.
15, 176–192. doi: 10.1002/ddrr.68
May, P. A., Hamrick, K. J., Corbin, K. D., Hasken, J. M., Marais, A. S., Brooke,
L. E., et al. (2014). Dietary intake, nutrition, and fetal alcohol spectrum disorders in
the Western Cape Province of South Africa. Reprod. Toxicol. 46, 31–39. doi: 10.1016/j.
reprotox.2014.02.002
May, P. A., Hasken, J. M., Blankenship, J., Marais, A.-S., Joubert, B., Cloete, M.,
et al. (2016). Breastfeeding and maternal alcohol use: Prevalence and effects on child
outcomes and fetal alcohol spectrum disorders. Reprod. Toxicol. 63, 13–21. doi: 10.
1016/j.reprotox.2016.05.002
Molenhuis, R. T., de Visser, L., Bruining, H., and Kas, M. J. (2014). Enhancing the
value of psychiatric mouse models; differential expression of developmental behavioral
and cognitive profiles in four inbred strains of mice. Eur. Neuropsychopharmacol. 24,
945–954. doi: 10.1016/j.euroneuro.2014.01.013
Oei, J.-L. (2019). Risky maternal alcohol consumption during lactation decreases
childhood abstract reasoning at school age. Pediatrics 22:20174266. doi: 10.1136/
ebnurs-2018- 102999
Parackal, S., Ferguson, E., and Harraway, J. (2007). Alcohol and tobacco
consumption among 6–24-months post-partum New Zealand women. Maternal Child
Nutr. 3, 40–51. doi: 10.1111/j.1740-8709.2007.00064.x
Paxinos, G., Watson, C., Koutcherov, Y., Halliday, G. M., and Wang, H. (2007). Atlas
of the developing mouse brain at E17.5, P0 and P6. Amsterdam: Elsevier Science.
Pei, J., Denys, K., Hughes, J., and Rasmussen, C. (2011). Mental health issues in fetal
alcohol spectrum disorder. J. Ment. Health 20, 473–483. doi: 10.3109/09638237.2011.
577113
Popova, S., Lange, S., and Rehm, J. (2013). Twenty percent of breastfeeding women
in canada consume alcohol. J. Obstetr. Gynaecol. Canada 35, 695–696. doi: 10.1016/
S1701-2163(15)30858- 6
Porsolt, R. D., Anton, G., Blavet, N., and Jalfre, M. (1978). Behavioural despair
in rats: A new model sensitive to antidepressant treatments. Eur. J. Pharmacol. 47,
379–391. doi: 10.1016/0014-2999(78)90118- 8
R Core Team (2021). R: A language and environment for statistical computing.
Vienna: R Foundation for Statistical Computing.
Rajendram, R., and Preedy, V. R. (2005). Effect of alcohol consumption on the gut.
Digestive Dis. 23, 214–221. doi: 10.1159/000090168
Rodgers, R. J., and Dalvi, A. (1997). Anxiety, defense and the elevated
plus-maze. Neurosci. Biobehav. Rev. 21, 801–810. doi: 10.1016/S0149-7634(96)0
0058-9
Rossow, I., Bye, E. K., Moan, I. S., Kilian, C., and Bramness, J. G. (2021). Changes
in alcohol consumption during the COVID-19 pandemic—small change in total
consumption, but increase in proportion of heavy drinkers. Int. J. Environ. Res. Public
Health 18:4231. doi: 10.3390/ijerph18084231
Rustay, N. R., Wahlsten, D., and Crabbe, J. C. (2003). Influence of task para- meters
on rotarod performance and sensitivity to ethanol in mice. Behav. Brain Res. 141,
237–249. doi: 10.1016/S0166-4328(02)00376- 5
Schuetze, P., Das Eiden, R., and Chan, A. W. K. (2002). The effects of alcohol in
breast milk on infant behavioral state and mother-infant feeding interactions. Infancy
3, 349–363. doi: 10.1207/S15327078IN0303_4
Seitz, H. K., and Homann, N. (2001). “Effect of alcohol on the orogastrointestinal
tract, the pancreas and the liver,” in International handbook of alcohol de- pendence
and problems, eds N. Heather, T. Peters, and T. Stockwell (Chichester: Wiley),
149–167.
Seitz, H. K., and Suter, P. M. (2002). “Ethanol toxicity and nutritional status,” in
Nutritional toxicology, 2 Edn, eds F. Kotsouis and M. Mackey (London: Taylor &
Francis), 122–154.
Socodato, R., Henriques, J. F., Portugal, C. C., Almeida, T. O., Tedim-Moreira, J.,
Alves, R. L., et al. (2020). Daily alcohol intake triggers aberrant synaptic pruning
leading to synapse loss and anxiety-like behavior. Sci. Signal. 13:eaba5754. doi: 10.
1126/scisignal.aba5754
Srivastava, V. K., Hiney, J. K., and Dees, W. L. (2014). Actions and interactions
of alcohol and transforming growth factor β1 on prepubertal hypothalamic
gonadotropin-releasing hormone. Alcoholism 38, 1321–1329. doi: 10.1111/acer.1
2374
Tay, R. Y., Wilson, J., McCormack, C., Allsop, S., Najman, J., Burns, L., et al. (2017).
Alcohol consumption by breastfeeding mothers: Frequency, correlates and infant
outcomes. Drug Alcohol Rev. 36, 667–676. doi: 10.1111/dar.12473
Van der Loos, H., and Woolsey, T. A. (1973). Somatosensory cortex: Structural
alterations following early injury to sense organs. Science 179, 395–398. doi: 10.1126/
science.179.4071.395
Vanderbruggen, N., Matthys, F., Van Laere, S., Zeeuws, D., Santermans, L., Van
den Ameele, S., et al. (2020). Self-reported alcohol, tobacco, and cannabis use during
COVID-19 lockdown measures: Results from a web-based survey. Eur. Addict. Res. 26,
309–315. doi: 10.1159/000510822
Vilaró, S., Viñas, O., Remesar, X., and Herrera, E. (1987). Effects of chronic
ethanol consumption on lactational performance in rat: Mammary gland and milk
composition and pups’ growth and metabolism. Pharmacol. Biochem. Behav. 27,
333–339. doi: 10.1016/0091-3057(87)90577- 6
Walf, A. A., and Frye, C. A. (2007). The use of the elevated plus maze as an assay
of anxiety-related behavior in rodents. Nat. Protocols 2, 322–328. doi: 10.1038/nprot.
2007.44
Zaqout, S., and Kaindl, A. M. (2016). Golgi-Cox staining step by step. Front.
Neuroanat. 10:38. doi: 10.3389/fnana.2016.00038
Zhang, F. F., Peng, W., Sweeney, J. A., Jia, Z. Y., and Gong, Q. Y. (2018).
Brain structure alterations in depression: Psychoradiological evidence. CNS Neurosci.
Therapuetics 24, 994–1003. doi: 10.1111/cns.12835
Frontiers in Neuroscience 18 frontiersin.org
Available via license: CC BY
Content may be subject to copyright.