# Effects of low doses of bisphenol A on the metabolome of perinatally exposed CD-1 mice

Nicolas J. Cabaton, Cécile Canlet, Perinaaz R. Wadia, Marie

Tremblay-Franco, Roselyne Gautier, Jerome Molina, Carlos Sonnenschein,

Jean Pierre J. P. Cravedi, Beverly S. Rubin, Ana Soto, et al.

► To cite this version:

Nicolas J. Cabaton, Cécile Canlet, Perinaaz R. Wadia, Marie Tremblay-Franco, Roselyne Gautier, et al.. Effects of low doses of bisphenol A on the metabolome of perinatally exposed CD-1 mice. Environmental Health Perspectives, 2013, 121 (5), pp.586-593. 10.1289/ehp.1205588. http://hal-02646392

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# Effects of Low Doses of Bisphenol A on the Metabolome of Perinatally Exposed CD-1 Mice

Nicolas J. Cabaton,<fnref n="1" />,<fnref n="2" /> Cécile Canlet,<fnref n="1" />,<fnref n="2" /> Perinaaz R. Wadia,³ Marie Tremblay-Franco,<fnref n="1" />,<fnref n="2" /> Roselyne Gautier,<fnref n="1" />,<fnref n="2" />

Jérôme Molina,<fnref n="1" />,<fnref n="2" /> Carlos Sonnenschein,³ Jean-Pierre Cravedi,<fnref n="1" />,<fnref n="2" /> Beverly S. Rubin,³ Ana M. Soto,³ and Daniel Zalko<fnref n="1" />,<fnref n="2" />

**BACKGROUND:** Bisphenol A (BPA) is a well-known endocrine disruptor used to manufacture polycarbonate plastics and epoxy resins. Exposure of pregnant rodents to low doses of BPA results in pleiotropic effects in their offspring.

**OBJECTIVE:** We used metabolomics—a method for determining metabolic changes in response to nutritional, pharmacological, or toxic stimuli—to examine metabolic shifts induced *in vivo* by perinatal exposure to low doses of BPA in CD-1 mice.

**METHODS:** Male offspring born to pregnant CD-1 mice that were exposed to vehicle or to 0.025, 0.25, or 25 µg BPA/kg body weight/day, from gestation day 8 through day 16 of lactation, were examined on postnatal day (PND) 2 or PND21. Aqueous extracts of newborns (PND2, whole animal) and of livers, brains, and serum samples from PND21 pups were submitted to ¹H nuclear magnetic resonance spectroscopy. Data were analyzed using partial least squares discriminant analysis.

**RESULTS:** Examination of endogenous metabolic fingerprints revealed remarkable discrimination in whole extracts of the four PND2 newborn treatment groups, strongly suggesting changes in the global metabolism. Furthermore, statistical analyses of liver, serum, and brain samples collected on PND21 successfully discriminated among treatment groups. Variations in glucose, pyruvate, some amino acids, and neurotransmitters (γ-aminobutyric acid and glutamate) were identified.

**CONCLUSIONS:** Low doses of BPA disrupt global metabolism, including energy metabolism and brain function, in perinatally exposed CD-1 mouse pups. Metabolomics can be used to highlight the effects of low doses of endocrine disruptors by linking perinatal exposure to changes in global metabolism.

**KEY WORDS:** bisphenol A, endocrine disruptor, fetal origins of adult disease, low dose, metabolomics, metabonomics, NMR fingerprints, partial least-squares discriminant analysis (PLS-DA), perinatal exposure, toxicology. *Environ Health Perspect* 121:586–593 (2013). http://dx.doi.org/10.1289/ehp.1205588 [Online 21 February 2013]

Bisphenol A (BPA) is manufactured at a rate of > 3.1 million tons/year; thus, exposure to this compound is ubiquitous. Its possible impact on human health is reflected in recent worldwide regulatory legislation. For example, the Canadian authorities, followed by the European Union, have recently banned the use of BPA in infant feeding bottles, a landmark move to safeguard the health of infants and the general population (Asimakopoulos et al. 2012; European Commission 2011). Nevertheless, BPA is still widely used in polycarbonates, epoxy resins, paints, lacquers, and medical devices. BPA is also used as a base compound for the manufacture of flame retardants, brake fluids, and thermal papers (Fernandez et al. 2007). BPA monomers migrate out of these products and contaminate, for example, food, beverages, and intravenous infusions, (Goodson et al. 2004). Although the main route of contamination is through ingestion, the transdermal route could also contribute to BPA exposure in humans when direct contact with BPA (free monomer) occurs (Zalko et al. 2011). Detectable levels of BPA were present in urine samples of > 92% of Americans tested in the 2003–2004 National Health and Nutrition Examination Survey (Calafat et al.

2008). Higher exposure levels were found in children and adolescents compared with adults. Of particular concern is the finding of high levels of BPA in premature infants being treated in neonatal intensive care units (Calafat et al. 2009). BPA has also been detected in maternal and fetal plasma, in human placenta, and in the milk of nursing mothers (Calafat et al. 2006; Sun et al. 2004). Animal studies have highlighted the estrogenic effects of BPA (vom Saal et al. 2007), although BPA is considered by some to be a weak estrogen due to its low potency compared with estradiol in reporter gene assays involving nuclear receptors (Blair et al. 2000). However, recent work has shown that BPA can be as potent as or more potent than estradiol in promoting some estrogenic activities (Alonso-Magdalena et al. 2006, 2012). BPA can also bind to membrane receptors (G protein-coupled receptor 30 and the membrane form of estrogen receptor-α) to produce effects similar to those of estradiol (Thomas and Dong 2006; Welshons et al. 2006; Wozniak et al. 2005).

Perinatal BPA exposure has been reported to decrease fertility and fecundity in female CD-1 mice (Cabaton et al. 2011) and to decrease fertility in male offspring of exposed

rats (Salian et al. 2011). Additional effects of perinatal BPA exposure include masculinization of behaviors and brain structures in female CD-1 mice (Richter et al. 2007; Rubin et al. 2006). Exposure to BPA through placenta and milk has been shown to increase early adipose storage and adipogenesis in a sex-specific and dose-dependent manner in rats and mice, with consequences on body weight later in life (Rubin and Soto 2009; Somm et al. 2009). BPA exposure has been linked to altered glucose homeostasis in pregnant female rodents and their male offspring (Alonso-Magdalena et al. 2010) and has been postulated to be a contributing factor in predisposing populations to the development of obesity and diabetes later in life (Heindel and vom Saal 2009; vom Saal et al. 2012).

The aim of metabonomics is to measure the “global, dynamic metabolic response of living systems to biological stimuli” (Nicholson and Lindon 2008). Metabolomics have furthered our understanding of drug toxicology while complementing more traditional approaches (Coen 2010). The integration of metabolomics and conventional toxicological studies is expected to provide valuable information for risk assessment of endocrine disruptors (EDs) such as BPA. Metabolic fingerprints based on nuclear magnetic resonance (NMR) spectroscopy, combined with appropriate statistical methods, could detect slight changes in the metabolome of cells, tissues, or organisms exposed to EDs, opening the way to examine whether exposure to an ED results in global

Address correspondence to D. Zalko, INRA, UMR 1331, TOXALIM (Research Centre in Food Toxicology), 180 Chemin de Tournefeuille, BP 93173, 31027 Toulouse Cedex 3, France. Telephone: 33 561 285 004. E-mail: dzalko@toulouse.inra.fr

Supplemental Material is available online (http://dx.doi.org/10.1289/ehp.1205588).

We thank W.V. Welshons for testing the rodent diet for estrogenicity; J. Iordachescu, M. Askenase, A. Tharp, and G. Whitt for their excellent technical assistance; and C. Schaeberle for editing the manuscript.

This work was supported by the National Institute of Environmental Health Sciences (grant ES08314 and its supplement), the European Commission-funded CASCADE network of Excellence (FP6, FOOD-CT-2004-506319), and the French National Research Agency (ANR).

The authors declare they have no actual or potential competing financial interests.

Received 18 September 2012; accepted 4 February 2013.

<footnote n="1">¹Institut National de la Recherche Agronomique (INRA), UMR1331, TOXALIM (Research Centre in Food Toxicology), Toulouse, France;</footnote>

<footnote n="2">²Université de Toulouse, UMR1331, Toulouse, France; ³Department of Anatomy and Cellular Biology, Tufts University School of Medicine, Boston, Massachusetts, USA</footnote>
alterations of metabolism and whether these
changes persist after cessation of exposure.

Metabolomic data are characterized by a
large number of variables and by high correla-
tions among these variables. Multivariate
methods, such as principal components analy-
sis (PCA) and partial least squares discriminant
analysis (PLS-DA), which aim to solve both
problems mentioned above, are thus the meth-
ods of choice for analyzing metabolomic data.
PCA is used to determine the variation in the
data set, regardless of the origin of this varia-
tion. PLS-DA is an alternative to PCA that
allows discriminating observations according
to classes defined a priori. Besides, PLS-DA
is preferred to PCA for sample discrimination
because the dimension reduction provided by
PLS is explicitly guided by variability among
groups, whereas PCA can identify only gross
variability directions and is incapable of distin-
guishing variability that occurs among groups
and within groups (Quintás et al. 2012).
When more than two groups are included in
the analysis, PLS-DA is more appropriate than
PCA (Lindon et al. 2004).

We hypothesized that the global metabo-
lism of mice perinatally exposed to BPA may
be disrupted. The present study was designed
to test this hypothesis in the outbred CD-1
mouse, a common animal model in BPA
studies. Rodent strains vary in their suscep-
tibility to low-dose BPA exposure (Richter
et al. 2007). Rodent placentation at late stages
of gestation is histologically close to human
placentation (Zalko et al. 2003), and the
mouse and rat have been shown to be excel-
lent models for understanding the human
syndrome observed in the offspring of moth-
ers exposed to diethylstilbestrol during preg-
nancy (Vandenberg mice. 2009). Previous
work using CD-1 mice has shown that, fol-
lowing transplacental transfer, fetuses become
exposed to their mother's BPA burden and
BPA metabolites (Markey et al. 2001; Wadia
et al. 2007; Zalko et al. 2003). The body of
literature clearly demonstrates multiple tar-
gets affected by low doses of BPA, includ-
ing metabolism, especially when the exposure
occurs at critical periods of development
(Vandenberg et al. 2012).

In the present study, CD-1 fetuses and neo-
nates were exposed to low doses of BPA [0.025,
0.25, or 25 µg BPA/kg body weight (bw)/day]
administered to their mothers from the end
of gestation day (GD) 8 through day 16 of
lactation. Female offspring were examined
for reproductive outcomes and found to have
decreased fertility and fecundity (Cabaton
et al. 2011), whereas the male offspring were
examined for changes in global metabolism
by assessing ¹H NMR profiling and analyz-
ing the data using multivariate statistics. Male
offspring were examined on postnatal day
(PND) 2 (whole body) and on PND21. For

the latter group, samples included the liver (the
main metabolizing organ), serum (circulating
metabolites), and the brain, given the fact that
recent studies have suggested that perinatal
exposure to low doses of BPA could have per-
sistent effects on brain structure, function, and
behavior in rodents (Richter et al. 2007).

Materials and Methods

**Chemicals.** Dimethylsulfoxide [DMSO; Chemical Abstracts Service Registry Number (CASRN) 67-68-5)], bisphenol A (4,4'-dihydroxydiphenyldimethylmethane; CAS 80-05-7; product #239658, lot #03105ES; purity ≥ 99%), and butylated hydroxytoluene (BHT) were purchased from Sigma Chemical Company (St. Louis, MO, USA). Stock purity of BPA was confirmed as described previously (Cabaton et al. 2011). We purchased acetonitrile from Scharlab SL (Sentmenat, Spain), and deuterium oxide (D₂O) and sodium 3-trimethylsilyl-2,2,3,3-tetradeuteriopropionate (TMSP) from Eurisotop (Saint-Aubin, France).

**Animals.** Twelve-week-old female CD-1 mice and proven breeder male mice (Charles River Laboratories, Wilmington, MA, USA) were maintained in temperature- and light-controlled (14 hr/10 hr light/dark cycle) conditions at the Human Nutrition and Research Center animal facility (Tufts University), a facility approved by the Association for Assessment and Accreditation of Laboratory Animal Care. All experimental procedures were approved by the Tufts University-Tufts Medical Center Institutional Animal Care and Use Committee. The animals were treated humanely and with regard for alleviation of suffering. Water (glass bottles) and food (Harlan Teklad 2018; Harlan Laboratories Inc., Indianapolis, IN, USA) were supplied *ad libitum*. The estrogenicity of food lots was measured by the E-SCREEN assay (Soto et al. 1992) and was also tested independently by W.V. Welshons (University of Missouri-Columbia, Columbia, MO, USA) (Welshons et al. 1990), confirming a negligible estrogenic activity (< 20 pmol of estrogen equivalents per gram). Cages, water, and bedding all tested negligible for estrogenicity by the E-SCREEN assay (Soto et al. 1992).

Mice were allowed to acclimatize for
5 days before being mated. The morning on
which a vaginal plug was detected was con-
sidered GD1. On the evening of GD8, dams
were implanted subcutaneously with Alzet
osmotic pumps (model 2004; Alza Corp., Palo
Alto, CA, USA) following the manufacturer's
recommendations. Pumps were designed to
deliver vehicle alone (50% DMSO in water)
or one of three doses of BPA—0.025, 0.25, or
25 µg/kg bw/day—based on the mother's
body weight at GD6. These pumps continued
to release at a constant rate (0.25 µL/hr)

until day 16 of lactation (PND16); however,
because the BPA dose was calculated based
on the weight of the mother at GD6 and
because maternal body weight increased from
this point throughout pregnancy, the actual
delivered dose of BPA decreased as pregnancy
progressed. More than 90% of the dams deliv-
ered naturally; the F1 litters were culled to
eight pups each, with no differences in sex
ratio between treatment groups on the day
after birth. Litters were weaned on PND21.

*Experimental design.* On PND2, one F1 male mouse was randomly chosen from each litter and euthanized by decapitation (control, *n* = 20; 0.025 µg BPA, *n* = 18; 0.25 µg BPA, *n* = 14; 25 µg BPA, *n* = 11). On PND21, one F1 male mouse was randomly chosen from each litter and euthanized by CO₂ asphyxia tion. Blood samples (control, *n* = 11; BPA 0.025 µg/kg, *n* = 12; 0.25 µg BPA, *n* = 14; BPA 25 µg/kg, *n* = 12) and brain and liver samples (control, *n* = 11; BPA 0.025 µg/kg, *n* = 11; 0.25 µg BPA, *n* = 13; BPA 25 µg/kg, *n* = 14) were collected from each animal.

**Sample preparation for ¹H NMR spectroscopy.** Trunk blood was collected into serum tubes (Sarstedt, Newton, NC, USA). Blood samples were centrifuged for 5 min at 10,000 × g and 20°C. Serum was collected into microtubes and stored at –20°C. For analysis, serum samples (100 µL) were diluted with 600 µL D₂O and centrifuged at 5,000 × g for 10 min before they were placed in 5-mm NMR tubes.

For liver, brain, and whole-pup samples,
extraction procedures were derived from
Folch et al. (1951) and from the method
described by Waters et al. (2002). Samples of
tissue (100 mg liver, whole brain, and whole
pup) were homogenized using a Polytron
PT2100 homogenizer (Kinematica, Lucerne,
Switzerland) in acetonitrile/H₂O (50/50,
vol/vol) containing 0.1% BHT in an ice-
water bath. Homogenates were centrifuged at
5,000 × g for 10 min at 4°C, and the super-
natants were removed and lyophilized. The
lyophilisates were reconstituted in 600 µL
D₂O containing 0.25 mM TMSP (as
a chemical shift reference at 0 ppm). The
reconstituted solutions were transferred to
NMR tubes.

*¹H NMR analyses.* All ¹H NMR spectra were obtained on a Bruker DRX-600-Avance NMR spectrometer (Bruker, Wissembourg, France) operating at 600.13 MHz for ¹H resonance frequency using an inverse detection 5 mm ¹H-¹³C-¹⁵N cryoprobe attached to a cryoplatform (the preamplifier cooling unit).

The ¹H NMR spectra were acquired at
300K using the Carr-Purcell-Meiboom-Gill
(CPMG) spin-echo pulse sequence with pre-
saturation, with a total spin-echo delay (2nτ) of
100 msec to attenuate broad signals from pro-
teins and lipoproteins. A total of 128 transients
were collected into 32,000 data points using a spectral width of 12 ppm, a relaxation delay of 2.5 sec, and an acquisition time of 2.28 sec. Prior to Fourier transformation, we applied an exponential line-broadening function of 0.3 Hz to the free induction decay.

To confirm the chemical structure of metabolites of interest, we performed two dimensional (2D) ¹H-¹H COSY (correlation spectroscopy) and 2D ¹H-¹³C-HSQC (heteronuclear single quantum coherence spectroscopy) NMR experiments on selected samples.

**Data reduction and multivariate statistical analyses.** All NMR spectra were phased and baseline corrected, then data was reduced using AMIX (version 3.8; Bruker Analytik, Rheinstetten, Germany) to integrate 0.01 ppm-wide regions corresponding to the δ 10.0–0.5 ppm region. The δ 5.1–4.5 ppm region, which includes water resonance, was excluded. A total of 791–861 NMR buckets were included in the data matrices. To account for differences in sample amount, each integrated region was normalized to the total spectral area. Multivariate analyses were used to study the effect of the treatment (DMSO or 0.025 µg, 0.25 µg, or 25 µg BPA/kg bw/day) on the metabolome. We first performed PCA to reveal intrinsic treatment-related clusters and detect eventual outliers. PLS-DA was then used to model the relationship between group and spectral data. PLS-DA is similar to PCA but uses discriminant variables that correlate to class membership. Before analysis, we used orthogonal signal correction filtering (Wold

et al. 1998) to remove variation not linked to the treatment (physiological, experimental, or instrumental variation). Filtered data were mean centered and scaled (unit variance or Pareto scaling). For the figures, we used Hotelling's T² statistics to construct 95% confidence ellipses. The R²Y parameter represents the explained variance. Seven-fold cross-validation was used to determine the number of latent variables to include in the PLS-DA model and to estimate the predictive ability (Q² parameter) of the adjusted model. In addition, the robustness and validity of the PLS-DA results were calculated using a permutation test (200 permutations). Discriminant variables were determined using VIP (variable importance in the projection), an appropriate quantitative statistical parameter ranking the descriptors according to their ability to discriminate different doses. We used this global measure of the influence of each variable on the PLS components to derive a subset of the most important metabolites for the separation of experimental groups. We then used the Kruskal-Wallis test to determine which metabolites were significantly different between the groups. SIMCA-P software (V12; Umetrics AB, Umea, Sweden) was used to perform the multivariate analyses.

## Results

We observed no statistical differences in the weight of PND2 pups (whole body) or PND21 livers or brains. PCA results for the NMR spectral data sets [aqueous extracts of

PND2 pups (whole body) and serum, liver, and brain from PND21 male mice] are available in Supplemental Material, Figures S1–S4 (http://dx.doi.org/10.1289/ehp.1205588). The PCA score plots allowed a primary separation between groups. We then used a supervised PLS-DA model to further investigate the differences between groups.

**PND2 pups (whole body).** For the four experimental groups, the analysis generated a PLS-DA model with three latent components, characterized by a faithful representation of the data (R²Y = 71.5%) and, more important, by a good cumulative predictive capacity (Q² = 0.557) (Figure 1A). The score plot of the PLS-DA showed a clear separation between the control group and the BPA groups; 83 variables had a VIP value > 1.0 (arbitrary threshold), and the median of 71 buckets was statistically different by the Kruskal-Wallis test. These differences corresponded to 20 metabolites, according to 2D NMR spectra identification. Endogenous metabolite variations induced by BPA exposure (25 µg) in PND2 pups (whole body samples) showed increases in lactate, glucose, cholines, creatine, and glycine compared with control samples. Conversely, we observed decreases in valine, leucine, isoleucine, and lysine (Table 1). In addition, taken separately and using a pairwise comparison, all groups could be successfully discriminated, including the 0.025 µg BPA and 0.25 µg BPA groups (Table 2; Figure 1B). The latter analysis generated a PLS-DA model with three latent components, characterized by a very faithful representation of the data (R²Y = 98.0%), with > 90% variability explained along axis 1, and by a very good cumulative predictive capacity (Q² = 0.731). This analysis identified 13 metabolites that contributed to the difference in metabolic profiles between 0.025 µg BPA and 0.25 µg BPA groups by VIP (> 1) and Kruskal-Wallis test (Table 1).

**PND21 serum.** The score plot of the PLS-DA using the entire data set (all groups taken together) showed a clear separation between the 25 µg BPA group and all other groups along the first latent component, and between the 0.25 µg BPA and 0.025 µg BPA groups (Figure 2A). This analysis generated a PLS-DA model with two latent components, with R²Y = 55.3% and Q² = 0.450. Thirteen metabolites were identified as discriminant parameters in the metabolic profiles. Further pairwise comparisons between control and 0.025 µg BPA samples and between control and 0.25 µg BPA samples demonstrated a clear discrimination between the corresponding groups, and 14 and 11 metabolites, respectively, were identified as discriminant biomarkers (Figure 2B,C). BPA exposure (25 µg) induced a decrease in lipids [low-density lipoprotein (LDL) and very-low-density lipoprotein

<figure><img src="image_2.png" /><figcaption>**Figure 1.** Two-dimensional PLS-DA score plot of integrated ¹H NMR spectra of PND2 whole body extracts. (A) PLS-DA results for all treatment treatment groups (control, *n* = 20; 0.025 µg BPA, *n* = 18; 0.25 µg BPA, *n* = 14; and 25 µg BPA, *n* = 11) (first and second latent variable of three components: R²Y = 71.5% and Q² = 0.557). (B) PLS-DA results for 0.025 µg BPA and 0.25 µg BPA taken separately (first and second latent variable of three components: R²Y = 73.1% and Q² = 0.731).</figcaption></figure>
(VLDL)] and lactate concentrations and an increase in glucose, taurine, and cholines compared with controls (Table 1).

**PND21 liver.** The four-group comparison generated a PLS-DA model with two latent components, characterized by a correct representation of the data (R²Y = 48.3%) and Q² equal to 0.421. The score plot of the PLS-DA showed a clear separation between control, 0.25 µg BPA, and 25 µg BPA, as well as between 0.25 µg BPA and 25 µg BPA (Figure 3A). The median of 46 buckets was significantly different according to the Kruskal–Wallis test, corresponding to three metabolites discriminating the three BPA-exposed groups, eight metabolites discriminating between 0.25 µg BPA and 25 µg BPA, and five metabolites separating control and 0.025 µg BPA from 0.25 µg BPA and 25 µg BPA (Table 2). For more specificity, pairwise comparisons were carried out between the 0.025 µg BPA group and all other groups, which led to the separations displayed in Figure 3B–D and further detailed in Table 1. When comparing 25 µg BPA samples with control samples, endogenous metabolite variations induced by BPA exposure showed increases in taurine, glutamate, and glutathione, as well as decreases in lactate, glucose, cholines, and glycogen (Table 1).

**PND21 brain.** The four-group comparison generated a PLS-DA model with three latent components, characterized by a faithful representation of the data (R²Y = 78.9%) and a good cumulative confidence criterion of prediction (Q² = 0.564). The score plot of the PLS-DA showed a correct separation between controls and all BPA groups (Figure 4); 101 variables had a VIP value > 1.5, and the median of 76 buckets was statistically different according to the Kruskal–Wallis test, corresponding to 21 metabolites. Taken separately using pairwise comparisons, all groups could be discriminated from each other (Table 2). Endogenous metabolite variations between 25 µg BPA and control showed an increase in glutamine, glycine, and aspartic acid, and a decrease in cholines, glutamate, and γ-aminobutyric acid (GABA) (Table 1).

## Discussion

Developmental exposure to BPA affects the reproductive system and fertility, alters brain development and behavior, disrupts glucose homeostasis, and may contribute to the development of obesity and metabolic syndrome (Alonso-Magdalena et al. 2010; Cabaton et al. 2011; Richter et al. 2007; Ryan et al., 2010). In the present study, we examined whether a novel approach based on metabolomics profiling could detect subtle changes in the metabolome following BPA exposure at low to very low doses, and whether metabolic profiling could reveal differences that persist beyond the end of the exposure

period. The BPA doses used in this study correspond to 1/2,000 to 1/2,000,000 of the no observed adverse effect level (NOAEL) for BPA (European Food Safety Authority 2006; U.S. Food and Drug Administration 2008), and the time of BPA exposure coincided with critical periods of development (GD8 to PND16).

Factors that affect fetal growth are also associated with the postnatal growth rate and with adult body weight in humans as well as

in laboratory animals (Barker 2004; Coe et al. 2008). The classical tools of toxicology are not designed to detect the effects of an early exposure to low doses of endocrine disruptors. Recent developments in metabolomics allow us to further explore global changes in biological systems. NMR spectroscopy is broadly used in research to characterize metabolite structure. NMR spectroscopy fingerprinting, combined with multivariate statistical analysis,

<table><caption>**Table 1.** Endogenous metabolite variations induced by BPA exposure (25 µg) in PND2 pups and in serum, liver, and brain of PND21 mice compared with controls.</caption><thead><tr><th rowspan="2">Metabolites</th><th rowspan="2">¹H NMR chemical shifts δ (ppm)</th><th colspan="1">PND2</th><th colspan="3">PND21</th></tr><tr><th>Whole body</th><th>Serum</th><th>Liver</th><th>Brain</th></tr></thead><tbody><tr><td>Lipids<sup>a</sup></td><td>0.86 (m); 0.90 (m); 1.26 (m); 1.30 (m)</td><td>—</td><td>—</td><td>—</td><td>—</td></tr><tr><td>Lactate</td><td>1.33 (d); 4.11 (q)</td><td>+</td><td>—</td><td>—</td><td>—</td></tr><tr><td>Glucose</td><td>3.54 (m); 3.66 (m); 3.70 (m); 3.74 (m); 3.78 (m); 3.82 (m); 3.86 (m); 3.90 (m)</td><td>+</td><td>+</td><td>—</td><td>—</td></tr><tr><td>Taurine</td><td>3.26 (t); 3.42 (t)</td><td>—</td><td>+</td><td>+</td><td>—</td></tr><tr><td>Cholines<sup>b</sup></td><td>3.20 (s); 3.22 (s); 3.23 (s)</td><td>+</td><td>+</td><td>—</td><td>—</td></tr><tr><td>Creatine</td><td>3.03 (s); 3.93 (s)</td><td>+</td><td>—</td><td>—</td><td>—</td></tr><tr><td>Glutamate</td><td>2.08 (m); 2.34 (m)</td><td>—</td><td>+</td><td>—</td><td>—</td></tr><tr><td>Glutamine</td><td>2.14 (m); 2.46 (m)</td><td>—</td><td>—</td><td>+</td><td>—</td></tr><tr><td>Glycine</td><td>3.55 (s)</td><td>+</td><td>—</td><td>+</td><td>+</td></tr><tr><td>Valine, leucine, isoleucine</td><td>0.94 (d); 0.96 (d); 1.0 (d); 1.01 (d); 1.05 (d)</td><td>—</td><td>—</td><td>—</td><td>—</td></tr><tr><td>Lysine</td><td>1.72 (m); 2.98 (m)</td><td>—</td><td>—</td><td>—</td><td>—</td></tr><tr><td>Glutathione</td><td>2.17 (m); 2.56 (m); 2.94 (m)</td><td>—</td><td>—</td><td>+</td><td>—</td></tr><tr><td>Glycogen</td><td>5.42 (m)</td><td>—</td><td>—</td><td>—</td><td>—</td></tr><tr><td>Aspartic acid</td><td>2.65 (dd); 2.80 (dd)</td><td>—</td><td>—</td><td>—</td><td>+</td></tr><tr><td>GABA</td><td>1.90 (q); 2.27 (t); 3.01 (t)</td><td>—</td><td>—</td><td>—</td><td>—</td></tr></tbody></table>

Abbreviations: +, significantly increased concentration; —, significantly decreased concentration; GABA, γ-aminobutyric acid. Chemical shifts (ppm) are relative to TMSP (¹H, δ). Multiplicity of signals is indicated in parentheses: d, doublet; dd, doublet of doublet; m, multiplet; q, quadruplet; s, singlet; t, triplet.

<sup>a</sup>Low-density lipoprotein and very-low-density lipoprotein. <sup>b</sup>Phosphatidylcholine and glycerophosphocholine.

<table><caption>**Table 2.** Pairwise model parameter comparison of PND2 whole body and PND21 liver, brain, and serum between treatment groups.</caption><thead><tr><th>Groups compared</th><th>n</th><th>PLS components</th><th>R²Y (%)</th><th>Q² (cumulative)</th></tr></thead><tbody><tr><td><strong>PND2</strong></td><td></td><td></td><td></td><td></td></tr><tr><td>Whole body</td><td></td><td></td><td></td><td></td></tr><tr><td>Control/0.025 µg BPA</td><td>38</td><td>2</td><td>99.3</td><td>0.979</td></tr><tr><td>Control/0.25 µg BPA</td><td>34</td><td>2</td><td>99.4</td><td>0.970</td></tr><tr><td>Control/25 µg BPA</td><td>31</td><td>1</td><td>99.5</td><td>0.989</td></tr><tr><td>0.025 µg BPA/0.25 µg BPA</td><td>31</td><td>3</td><td>98.0</td><td>0.731</td></tr><tr><td>0.025 µg BPA/25 µg BPA</td><td>29</td><td>1</td><td>98.3</td><td>0.943</td></tr><tr><td>0.25 µg BPA/25 µg BPA</td><td>25</td><td>2</td><td>98.9</td><td>0.969</td></tr><tr><td><strong>PND21</strong></td><td></td><td></td><td></td><td></td></tr><tr><td>Liver</td><td></td><td></td><td></td><td></td></tr><tr><td>Control/0.025 µg BPA</td><td>21</td><td>1</td><td>89.7</td><td>0.822</td></tr><tr><td>Control/0.25 µg BPA</td><td>24</td><td>1</td><td>99.5</td><td>0.980</td></tr><tr><td>Control/25 µg BPA</td><td>24</td><td>2</td><td>99.7</td><td>0.990</td></tr><tr><td>0.025 µg BPA/0.25 µg BPA</td><td>23</td><td>3</td><td>99.5</td><td>0.896</td></tr><tr><td>0.025 µg BPA/25 µg BPA</td><td>24</td><td>3</td><td>99.4</td><td>0.950</td></tr><tr><td>0.25 µg BPA/25 µg BPA</td><td>26</td><td>2</td><td>98.8</td><td>0.928</td></tr><tr><td>Brain</td><td></td><td></td><td></td><td></td></tr><tr><td>Control/0.025 µg BPA</td><td>22</td><td>2</td><td>99.0</td><td>0.955</td></tr><tr><td>Control/0.25 µg BPA</td><td>22</td><td>2</td><td>99.0</td><td>0.963</td></tr><tr><td>Control/25 µg BPA</td><td>21</td><td>1</td><td>90.0</td><td>0.664</td></tr><tr><td>0.025 µg BPA/0.25 µg BPA</td><td>23</td><td>3</td><td>98.5</td><td>0.932</td></tr><tr><td>0.025 µg BPA/25 µg BPA</td><td>25</td><td>3</td><td>99.5</td><td>0.941</td></tr><tr><td>0.25 µg BPA/25 µg BPA</td><td>26</td><td>4</td><td>99.4</td><td>0.895</td></tr><tr><td>Serum</td><td></td><td></td><td></td><td></td></tr><tr><td>Control/0.025 µg BPA</td><td>23</td><td>3</td><td>99.6</td><td>0.826</td></tr><tr><td>Control/0.25 µg BPA</td><td>25</td><td>4</td><td>98.9</td><td>0.932</td></tr><tr><td>Control/25 µg BPA</td><td>23</td><td>3</td><td>99.8</td><td>0.991</td></tr><tr><td>0.025 µg BPA/0.25 µg BPA</td><td>26</td><td>3</td><td>96.3</td><td>0.891</td></tr><tr><td>0.025 µg BPA/25 µg BPA</td><td>23</td><td>2</td><td>99.0</td><td>0.937</td></tr><tr><td>0.25 µg BPA/25 µg BPA</td><td>26</td><td>3</td><td>99.3</td><td>0.973</td></tr></tbody></table>
provides a powerful tool to detect metabolic
changes induced by very low doses of EDs,
allowing discrimination between several
experimental groups on the basis of over- or
underexpression of endogenous molecules. In
the present study, we used ¹H-NMR followed
by PCA and PLS-DA analyses. PCA was
used to detect potential outliers. PCA usually
allows only a first-step discrimination between
groups. When there are more than two groups,
PLS-DA is more appropriate (Lindon et al.
2004). We performed PLS-DA—now rou-
tinely used in the field of metabolomics—to
explore NMR fingerprints linked with peri-
natal exposure to BPA. Linear combinations
of NMR buckets were constructed and then
used to visualize differences (or similarities)
between groups. One primary objective of our
study was to identify exposure biomarkers that
would correlate with the metabolic changes
triggered by perinatal exposure to BPA, thus
providing a proof of concept that ¹H-NMR,
completed by robust statistical analysis, is a
powerful tool that would discriminate between
exposed versus nonexposed animals.

PLS-DA is a classification method widely
used in metabolomics to discriminate between
treatment groups and to identify biomarkers
responsible for this discrimination (Gavaghan
et al. 2002; Martin et al. 2009). Our mod-
els were cross-validated, and their robustness
was assessed with permutation tests. A robust
model is associated with a Q² value > 0.40
(McCombie et al. 2009). This was the case for
all our models. The parameters of the PLS-DA
models were estimated based on data sets com-
prising 49 (PDN21) to 63 (PND2) individu-
als. Because a large number of variables had to
be processed, cross-validation was necessary to
avoid overfitting. Results of permutation tests
demonstrated that our analyses were robust
and not obtained by chance. Discriminating
variables were identified using the VIP and
Kruskal–Wallis test, providing variables that
explain the discrimination between groups. The
next step was to connect some discriminant
metabolites with possible mechanisms of action
and to suggest some exposure biomarkers.

We were able to identify groups of pups
that were perinatally exposed to low doses of

BPA (0.025 µg/kg bw/day) with robust statisti-
cal models (Q₂ > 0.4), even at a very early age
(PND2). Among the discriminating variables,
glucose was affected by perinatal exposure to
BPA. Alonso-Magdalena et al. (2010) have
also shown that BPA exposure to mice dur-
ing pregnancy disrupts glucose homeostasis in
6-month-old male offspring. It makes sense
that the shift observed for glucose would in
turn be involved in the disruption of pyruvate
biosynthesis through glycolysis. Pyruvate sup-
plies energy to living cells through the Krebs
cycle (aerobic respiration) and, alternatively,
ferments to produce lactate (anaerobic respira-
tion); pyruvate was also increased in PND2
pups. In this case, lactate could still be utilized
by neurons, as already demonstrated in mice,
rats, and humans (Wyss et al. 2011; Zilberter
et al. 2010). At PND2, two other variables
(increased levels of creatine and glycine) con-
tributed to the discrimination between the
control and 25 µg BPA groups, respectively.
Creatine, a product of amino acid degradation
(including glycine), is a major metabolite found
mainly in muscle and brain, and it appears

<figure><img src="image_3.png" /></figure>

<figure><img src="image_4.png" /><figcaption>**Figure 2.** Two-dimensional PLS-DA score plot of integrated ¹H NMR spectra of PND21 serum. (A) PLS-DA results for all four treatment groups [control, *n* = 11; 0.025 µg BPA, *n* = 12; 0.25 µg BPA, *n* = 14; and 25 µg BPA, *n* = 12 (two components): R²Y = 55.3% and Q² = 0.450]. (B) PLS-DA results for control and 0.025 µg BPA (three components: R²Y = 99.6% and Q² = 0.826) (C) PLS-DA results for control and 0.25 µg BPA (four components: R²Y = 98.9% and Q² = 0.932).</figcaption></figure>
to be affected by prenatal exposure to BPA. An increase in creatine levels might lead to an increase in ATP production and, consequently, to increased energy metabolism. Glycine, a key precursor of porphyrins involved in heme production, is an inhibitory neurotransmitter in the central nervous system, especially in the spinal cord and brainstem, as well as in the retina. An increase in glycine may disrupt the global energy metabolism as well as brain and neurologic functions. Conversely, a decrease in essential amino acids, namely valine, leucine, and isoleucine, could reflect a disruption in their degradation pathways, which would be consistent with previous observations in GD18 rat fetuses exposed to butylbenzylphthalate (Sumner et al. 2009). Finally, we found that BPA exposure increased concentrations of cholinines in PND2 mice, which may affect membrane integrity and could favor a decrease in acetylcholine production in the brain.

In PND21 tissues, we were able to distinguish all BPA exposure groups from controls. As observed in PND2 mice, glucose in serum and liver was affected by perinatal

BPA exposure. Similarly, cholinines were also decreased, reflecting a potential disruption in membrane integrity. In PND21 serum samples, a decrease in lipids (VLDL and

HDL) and an increase in taurine (involved in the conjugation of bile acids) suggest that lipid metabolism may also be affected by BPA exposure. Taurine is a key amino

<figure><img src="image_5.png" /><figcaption>**Figure 4.** Two-dimensional PLS-DA score plot of integrated ¹H NMR spectra of PND21 brain extracts from control (n = 11), 0.025 µg BPA (n = 11), 0.25 µg BPA (n = 13), and 25 µg BPA (n = 14) groups (first and second latent variable of three components: R²Y = 78.9% and Q² = 0.564).</figcaption></figure>

<figure><img src="image_6.png" /></figure>

<figure><img src="image_7.png" /><figcaption>**Figure 3.** Two-dimensional PLS-DA score plot of integrated ¹HNMR spectra of PND21 liver extracts. (A) PLS-DA results for all four treatment groups [control, n = 11; 0.025 µg BPA, n = 11; 0.25 µg BPA, n = 13; 25 µg BPA, n = 14 (first and second latent variable of three components: R²Y 48.3% and Q² = 0.421). (B) PLS-DA results for control and 0.025 µg BPA (one component: R²Y = 89.7% and Q² = 0.822); red and green lines indicate ± 3 SD and ± 2 SD, respectively. (C) PLS-DA results for 0.025 µg BPA and 0.25 µg BPA (three components: R²Y = 99.5% and Q² = 0.896). (D) PLS-DA results for 0.025 µg BPA and 25 µg BPA (three components: R²Y = 99.4% and Q² = 0.950).</figcaption></figure>
acid for cardiovascular function and for the
development and function of skeletal mus-
cle. Taurine, also a major constituent of bile,
was decreased in liver (and serum) in PND21
mice, reflecting a possible disruption in the
digestive process. In liver extracts, an increase
in glutathione in animals exposed to 25 µg
BPA suggested a possible modulation of this
detoxification pathway and also a hyper-
production of pyruvate, a key metabolite
involved in the Krebs cycle, glycolysis, and
glycogenesis. These changes may affect energy
metabolism pathways.

Regarding PND21 brain extracts, recent
studies have highlighted alterations in brain
development following perinatal exposure to
BPA (Itoh et al. 2012; Kunz et al. 2011). In the
present study, two neurotransmitters (GABA
and glutamate) were significantly decreased
in all BPA-exposed animals compared with
controls. Glutamate, the major excitatory
transmitter in the brain, is the precursor for
the synthesis of GABA, the major inhibitory
transmitter in the adult brain. It is important
to note that prior to their neurotransmission
roles during adulthood, both glutamate and
GABA are thought to influence processes of
neural development, including proliferation,
migration, differentiation, and survival (Lujan
et al. 2005). Therefore, decreased levels of these
neurotransmitters during postnatal develop-
ment could be expected to exert lasting effects
on the brain. In the present study, glutamine
(the precursor of glutamate) was increased in
the brain, whereas glutamate levels decreased,
suggesting a conversion problem and possibly
disruption and/or damage in brain function.
Aspartate, produced from oxaloacetate (by
transamination), is another neurotransmitter
that stimulates N-methyl-D-aspartate receptors,
the predominant molecule that controls synap-
tic plasticity and memory function. An increase
in aspartate, known as an excitatory neuro-
transmitter, which increases the likelihood of
depolarization in the postsynaptic membrane,
might induce behavioral hyperactivity (Ishido
et al. 2011).

Conclusion

We observed significant and unequivocal
shifts in the global metabolism of young male
mice perinatally exposed to low doses of BPA.
Our metabolomics studies on extracts from
PND2 and PND21 males suggest that energy
metabolism and brain function are the main
targets. Work is ongoing on samples from
female mice perinatally exposed to the same
doses of BPA, euthanized at later time points
with the aim of better understanding the
mechanisms of action of BPA and the effects
of BPA long after the time of exposure.

This study provides a proof of concept that
metabolomics, here as NMR-based metabolic
fingerprints, can be used as a novel approach

and a powerful tool to detect metabolic shifts
following perinatal exposure to BPA, even
at very low doses. A similar approach could
be used for other EDs, and we expect this
approach to contribute to a better under-
standing of the modulations/disruption of the
metabolome triggered during critical windows
of development. Metabolomics could also be
suitable and reliable for the analysis of biolog-
ical fluids, such as urine or blood, in animal
models and in human populations.

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