When Chocolate Seeking Becomes Compulsion: Gene-Environment Interplay (2015)

  • Enrico Patrono ,
  • Matteo Di Segni ,
  • Loris Patella,
  • Diego Andolina,
  • Alessandro Valzania,
  • Emanuele Claudio Latagliata,
  • Armando Felsani,
  • Assunta Pompili,
  • Antonella Gasbarri,
  • Stefano Puglisi-Allegra,
  • Rossella Ventur

Published: March 17, 2015

http://dx.doi.org/10.1371/journal.pone.0120191

Abstract

Background

Eating disorders appear to be caused by a complex interaction between environmental and genetic factors, and compulsive eating in response to adverse circumstances characterizes many eating disorders.

Materials and Methods

We compared compulsion-like eating in the form of conditioned suppression of palatable food-seeking in adverse situations in stressed C57BL/6J and DBA/2J mice, two well-characterized inbred strains, to determine the influence of gene-environment interplay on this behavioral phenotype. Moreover, we tested the hypothesis that low accumbal D2 receptor (R) availability is a genetic risk factor of food compulsion-like behavior and that environmental conditions that induce compulsive eating alter D2R expression in the striatum. To this end, we measured D1R and D2R expression in the striatum and D1R, D2R and α1R levels in the medial prefrontal cortex, respectively, by western blot.

Results

Exposure to environmental conditions induces compulsion-like eating behavior, depending on genetic background. This behavioral pattern is linked to decreased availability of accumbal D2R. Moreover, exposure to certain environmental conditions upregulates D2R and downregulates α1R in the striatum and medial prefrontal cortex, respectively, of compulsive animals. These findings confirm the function of gene-environment interplay in the manifestation of compulsive eating and support the hypothesis that low accumbal D2R availability is a “constitutive” genetic risk factor for compulsion-like eating behavior. Finally, D2R upregulation and α1R downregulation in the striatum and medial prefrontal cortex, respectively, are potential neuroadaptive responses that parallel the shift from motivated to compulsive eating.

Citation: Patrono E, Di Segni M, Patella L, Andolina D, Valzania A, Latagliata EC, et al. (2015) When Chocolate Seeking Becomes Compulsion: Gene-Environment Interplay. PLoS ONE 10(3): e0120191. doi:10.1371/journal.pone.0120191

Academic Editor: Henrik Oster, University of Lübeck, GERMANY

Received: August 7, 2014; Accepted: February 4, 2015; Published: March 17, 2015

Copyright: © 2015 Patrono et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited

Data Availability: All relevant data are within the paper and its Supporting Information files.

Funding: The work was supported by Ministero dell’Istruzione dell’Università e della Ricerca: Ateneo 2013 (C26A13L3PZ); FIRB 2010 (RBFR10RZ0N_001), Italy.

Competing interests: The authors have declared that no competing interests exist.

Introduction

Eating disorders are caused by environmental and genetic factors and their complex interactions [1, 2]. However, there are few gene- environment studies on human eating disorders [2] and animal studies that have examined environmental and genetic factors in compulsive food seeking and intake [36].

Stressful experiences interact with genetic factors and increase the risk for addictive behaviors inducing changes in the corticostriatal dopamine (DA) and norepinephrine (NE) signals that mediate motivational salience attribution [79]. Mounting evidence has implicated dopamine receptors in motivated behavior [1014] and D2Rs in the proclivity toward compulsion-driven behaviors, such as addiction [1517].

Inbred strains of mice provide valuable models for studying the interaction between genetic and environmental factors [18]. C57Bl6 ⁄ J (C57) and DBA2⁄ J (DBA) mice are among the most frequently studied inbred strains with regard to psychobiology because they are characterized by clear differences in a number of behavioral responses. The functional and anatomical characteristics of their brain neurotransmitter systems, as well as the behavioral outputs to reinforcing and aversive stimuli, have been examined extensively in these strains, thus providing important information on how the response of different neural systems to the same environmental stimuli is related to genetic background, leading to different (or also opposite) behavioral outputs [1923]. In particular, C57 and DBA mice are commonly used in drug abuse research because of their different sensitivity to the incentive properties of, and differential responses to, addictive drugs, such as alcohol, psychomotor stimulants, and opiates [7, 20, 21, 2431]. Moreover, with regard to psychopathological endophenotypes [3234], disparities between C57 and DBA mice in D2R-associated phenotypes appear to depend on gene-environment interactions [3537].

DBA mice are poorly responsive to rewarding stimuli compared with C57 mice, a state that is highlighted by chronic stressful experiences, increasing drug responsiveness in DBA/2 mice [24]. Thus, we hypothesize that chronic stress exposure (caloric restriction) induces a similar motivational drive toward palatable food in the DBA strain. We examined compulsive eating with regard to conditioned suppression of palatable food-seeking under adverse conditions [38], in C57 and DBA mice. Food restriction in rodents is commonly considered a stressful conditions leading to, among other effects, altered sensitization of brain reward systems and affecting the attribution motivational salience processes [8, 24, 3942]. Moreover, it has been reported that greater sensitization of the reward system can lead to excessive intake of highly palatable food [38, 43, 44], and repeated stimulation of reward pathways through highly palatable food may lead to neurobiological adaptations that make the intake behavior more compulsive [45]. Of the environmental factors that influence some eating disorders, the availability of seductive foods is the most obvious [45] and it has been demonstrated that different foods establish different levels of compulsive behaviors [45, 46]. Of all palatable foods, chocolate has been showed to have rewarding properties in animals [9, 4749], and it is the food most typically associated with reports of food craving in humans. Thus, chocolate craving and addiction have been proposed in humans [50].

Because caloric restriction is a stressful experience [24], animals were placed on a moderate food-restriction schedule [38], and because pre-exposure to palatable food is a significant factor in eating disorders [51], they were also pre-exposed to chocolate. Overeating shares several neural substrates with compulsive drug-seeking [52, 53]. Based on the function of DA receptors in drug- and food-related behaviors [17, 51, 54, 55], we measured D1R and D2R subtype levels in the caudate putamen (CP), nucleus accumbens (NAc), and medial prefrontal cortex (mpFC) and alpha-1 adrenergic receptors (α1Rs) in the mpFC because prefrontal NE is required for compulsive food-seeking [38] and α1Rs mediate motivation and drug-reinforcing effects [5658].

We found that exposure to environmental conditions induces compulsion-like eating behavior, depending on the genetic background. This behavioral pattern was linked to decreased availability of accumbal D2Rs. Moreover, such exposure upregulated D2Rs and downregulated α1Rs in the striatum and medial prefrontal cortex, respectively, of compulsive animals.

These findings confirm the function of gene-environment interplay in the expression of compulsive eating and support the hypothesis that low accumbal D2R availability is a “constitutive” genetic risk factor of compulsion-like behavior. Thus we propose that D2R upregulation and α1R downregulation in the striatum and medial prefrontal cortex, respectively, are potential neuroadaptive responses that parallel the shift from motivated to compulsive eating.

Materials and Methods

Animals

Male C57BL/6JIco and DBA/2J mice (Charles River, Como, Italy), 8–9 weeks old at the time of the experiments, were group-housed and maintained on a12-h/12-h light/dark cycle (light on between 7 AM and 7 PM), as described [9, 38]. All experiments were conducted per the Italian Law (Decreto Legislativo no. 116, 1992) and the European Communities Council Directive of November 24, 1986 (86/609/EEC) regulating the use of animals for research. All experiments of this study were approved by the ethics committee of the Italian Ministry of Health and therefore conducted under license/approval ID #: 10/2011-B, according with Italian regulations on the use of animals for research (legislation DL 116/92) and NIH guidelines on animal care. Adequate measures were taken to minimize the pain and discomfort of animals. Control groups were subjected only to the “brief pre-exposure” to chocolate (2 days); Stressed groups were subjected to the “pre-exposure” to chocolate, “caloric restriction” and “brief pre-exposure” to chocolate before the conditioned suppression procedure started (see above for methodological details).

All experiments were conducted during the light phase.

Conditioned Suppression Procedure

The apparatus for the conditioned suppression test has been previously described [38]. A Plexiglas cup (3.8 cm in diameter) was placed in each chamber and fixed to prevent movement: 1 cup contained 1 g of milk chocolate (Kraft) (Chocolate-Chamber, C-C), and the other cup was empty (Empty Safe- Chamber, ES-C).

Briefly, the procedure was as follows: from Day 1 to Day 4 (training phase), mice (Control, Stressed groups for each strain) were placed individually in the alley, and the sliding doors were opened to allow them to enter both chambers freely and explore the entire apparatus for 30 minutes. On Day 5, the animals were exposed to light-foot shock pairings. Acquisition of the conditioned stimulus (CS) (light)-shock association was established in a different apparatus, comprising a 15×15×20 cm Plexiglas chamber with a black-and-white-striped pattern on 2 walls (to differentiate it from the conditioned suppression apparatus) and a stainless steel grid floor through which the shocks were delivered. The light was produced by a halogen lamp (10W, Lexman) under the grid floor that was turned on for 5, 20-sec periods every 100 sec.; in each period, after the light had been on for 19 sec, a 1-sec 0.15-mA scrambled foot shock was delivered. This session of light-shock association lasted for 10 min and was followed by a 10-min rest period, after which another identical 10-min light-shock association session was administered; overall, the mice received 10 light-foot shock pairings in a 30-min session. On Days 6–8, the mice were left undisturbed in their home cage. On Day 9, conditioned suppression of chocolate-seeking was measured in a test session (conditioned suppression test day), in which the mice had access to chocolate in 1 of the 2 chambers in which chocolate had been placed during the training phase. In the chamber that contained chocolate (C-C), the CS (light) was presented according to the paradigm for the light-foot shock association (except for the 10-min rest period, which was eliminated). The light was produced by a halogen lamp under the grid floor that was turned on for 20-sec periods every 100 sec. This session lasted 20 min; overall, the mice received 10 20-sec periods in a 20-min session.

The test session began with the first 20-sec burst of light. The time that was spent in each of the 2 chambers was recorded throughout the session. All experiments were performed in experimental sound-attenuated rooms that were indirectly lit by a standard lamp (60 W). For all behavioral tests, data were collected and analyzed using “EthoVision” (Noldus, The Netherlands), a fully automated video-tracking system. The acquired digital signal was then processed by the software to extract “time spent” (in seconds) in the chambers, which was used as raw data for preference/aversion scores in each sector of the apparatus for each subject.

Two groups of mice for each strain were used in the conditioned suppression experiment: control (Control n = 6) and stressed (Stressed n = 8).

Experimental Procedure

The experimental procedure is depicted in Fig. 1.

thumbnail

Download:

PowerPoint slide

larger image (45KB)

original image (196KB)

Fig 1. Timeline of Experimental Procedure. (See Methods for details.)

http://dx.doi.org/10.1371/journal.pone.0120191.g001

Pre-exposure to chocolate

Animals in the stressed groups (Stressed C57 and Stressed DBA) were exposed to chocolate for 7 days until 18 (from day -24 to day -18, Fig. 1) days before the conditioned suppression procedure began. Mice were “randomly” isolated daily for 4 hours; milk chocolate and standard food were delivered ad libitum. Two days after the end of this schedule (day -15, Fig. 1), mice in the Stressed group were subjected to caloric restriction (food restriction, FR).

Caloric Restriction

Mice were assigned to a feeding regimen: they either received food ad libitum (Control groups) or were subjected to a food restricted regimen (FR, Stressed groups). In the caloric restriction condition, food was delivered once daily (07.00 p.m.) in a quantity adjusted to induce a loss of 15% of the original body weight. In the ad libitum condition, food was given once daily (07.00 p.m.) in a quantity adjusted to exceed daily consumption [38].

Animals were placed on a moderate FR schedule [29] for 10 days (from day -15 to day -6, Fig. 1), until 6 days before the conditioned suppression procedure began (day 1, Fig. 1). Six days before the training phase started, the animals were returned to ad libitum feeding in order to rule out any effects of dietary deficiency on the conditioned suppression test day.

Brief pre-exposure to chocolate

To prevent any unspecific novelty responses to chocolate in the groups that were not subjected to the “pre-exposure” condition described above (Control groups), both control and Stressed groups, were exposed to chocolate on the same schedule for 2 days, 2 days before the conditioned suppression procedure started (“brief pre-exposure”).

Chocolate intake and animal weight

Chocolate intake during the various phases of the conditioned suppression procedure (pre-exposure, training, test) was measured, and the animals weight was recorded. Mice were weighed on: the first day of the experiment (before the experimental procedure began), the training phase days, and the day of the conditioned suppression test.

Dopaminergic and noradrenergic receptors expression in Control and Stressed DBA mice

α1R, D1R and D2R receptors expression in 3 brain regions [mpFC (α1R, D1R, D2R); NAc (D1R, D2R); and CP (D1R, D2R)] was measured by western blot in control (Control DBA n = 6) and stressed animals (Stressed DBA n = 8), the same groups used in the conditioned suppression experiment.

Dopaminergic and noradrenergic receptor expression in naïve C57 and DBA mice

Baseline D1R, and D2R receptors expression in the mpFC, NAc, and CP as well as baseline α1R in the mpFC was measured in naïve animals of both strains [naïve C57 (n = 6) and naïve DBA (n = 6)] by western blot. This experiment was performed in animals subjected neither to environmental conditions (pre-exposure to chocolate, FR) nor to the conditioned suppression procedure (naïve groups) in order to test the hypothesis that low striatal D2 receptors availability is a genetic risk factor of food compulsion-like behavior.

Western blotting

The mice were sacrificed by decapitation, and the brains were removed 1 h after the conditioned suppression test, except for the naïve groups. The prefrontal, accumbal, and striatal tissue was dissected and kept in liquid nitrogen. Punches of the mpFC, NAc, and CP were obtained from frozen brain slices as reported [59] (S1 Fig.) and stored in liquid nitrogen until the day of the assay. Each tissue sample was homogenized at 4°C in lysis buffer (20 mM Tris (pH 7.4), 1 mM EDTA, 1 mM EGTA, 1% Triton X-100) with protease inhibitor cocktail (Sigma-Aldrich, St. Louis, MO, USA).

The tissue extract was centrifuged at 12,000 g at 4°C for 30 min. The supernatant was treated in the same way as the tissue extract. Finally, The supernatant was removed and stored at 80°C.

Protein content was measured by Bradford assay (BioRad Laboratories, Hercules, CA, USA).

The mpFC, NAc, and CP were analyzed using, 60 ug, 30 ug, and 30 ug, respectively, of each protein sample after addition of sample buffer (0.5 M Tris, 30% glycerol, 10% SDS, 0.6 M dithiothreitol, 0.012% bromophenol blue) and boiling for 5 min at 95°C. Proteins were separated by electrophoresis on 10% acrylamide/bisacrylamide gels and transferred electrophoretically to nitrocellulose membranes, which were then blocked for 1 h at 22°C–25°C in Tris-buffered saline (in mM: 137 NaCl and 20 Tris-HCl, pH 7.5), containing 0.1% Tween 20 (TBS-T) and 5% low-fat milk.

The membranes were incubated with primary antibodies [rabbit anti-dopamine D1 (Immunological Sciences) and rabbit anti-dopamine D2 receptor (Immunological Sciences), diluted 1:800 in TBS-T with 5% low-fat, or rabbit anti-alpha1-adrenergic receptor (Abcam), diluted 1:400 with 1% low-fat milk overnight at 4°C. After being washed extensively in TBS-T, the membranes were incubated for 1 h at room temperature (22°C–25°C) with HRP-linked secondary antibodies [anti-rabbit IgG diluted 1:8000 (immunological sciences) in TBS-T with 5% low-fat milk] and developed with ECL-R (Amersham). The signals were digitally scanned and quantified using densitometric image software (imagej 64), normalized to tubulin.

Statistics

Conditioned Suppression experiment.

For the conditioned suppression test, statistical analyses were performed for the time (sec) spent in the center (CT), in the chamber that contained chocolate (C-C) and in the empty safe chamber (ES-C) during the training phase (overall mean of 4 days of training) and on the day of the conditioned suppression test. The data were analyzed using repeated-measures ANOVA, with 2 between-group factors (strain, 2 levels: C57, DBA; treatment, 2 levels: Control, Stressed) and 1 within-group factor (chamber, 3 levels: CT, C-C, ES-C). Mean time spent in the C-C and ES-C chambers was compared using repeated-measures ANOVA within each group. Between-group comparisons were analyzed when appropriate by one-way ANOVA.

Chocolate intake and weight.

Chocolate intake during training (overall mean of 4 days) and on the conditioned suppression test day was analyzed by two-way ANOVA (strain, 2 levels: C57, DBA; treatment, 2 levels: Control, Stressed). Chocolate intake during the pre-exposure phase was analyzed by one-way ANOVA (strain: Stressed C57, Stressed DBA). The animals weight was also recorded on the first day of the experiment (before the experimental procedure), during the training phase, and on the day of the conditioned suppression test. The data were analyzed by two-way ANOVA (strain, 2 levels: C57, DBA; treatment, 2 levels: Control, Stressed).

Dopaminergic and noradrenergic receptors expression in Control and Stressed DBA mice.

D1R and D2R expression in the mpFC, NAc, and CP and D1R, D2R, and α1R levels in Stressed DBA versus Control DBA were analyzed by one-way ANOVA (treatment, 2 levels: Control DBA, Stressed DBA).

Dopaminergic and noradrenergic receptors expression in naïve C57 and DBA mice.

D1R and D2R expression in the mpFC, NAc, and CP and D1R, D2R, and α1R levels in naïve C57 and DBA animals (naïve C57, naïve DBA) were analyzed by one-way ANOVA (strain, 2 levels: C57, DBA).

Results

Conditioned suppression experiment: Food-seeking behavior in Stressed DBA mice

In order to assess the interplay between genetic background and environmental conditions exposure on the expression of compulsive eating behavior, the time spent in C-C and ES-C on the different phases (training and test) of the conditioned suppression procedure shown by Stressed and Control groups of both strains was assessed (Control C57, Control DBA, Stressed C57, Stressed DBA).

In the analysis of the training phase, we observed a significant strain x treatment x chamber interaction (F(1,72) = 6.52; p< 0.001). Comparison of the time spent in the C-C and ES-C in each group indicated that only the Control C57 and Stressed DBA groups preferred the C-C versus the ES-C during the training phase (Control C57: F(1,10) = 6.32; p< 0.05; Stressed DBA: F(1,14) = 15.60; p< 0.05) (Fig. 2), spending more time in the C-C than ES-C.

Fig 2. Conditioned Suppression Training in C57 and DBA mice.

Time spent (sec ± SE) in the chamber containing chocolate (C-C) and in the empty safe chamber (ES-C) during training phase by Control C57/DBA groups (n = 6 for each group) (A) and Stressed C57/DBA mice (n = 8 for each group) (B). * p< 0.05 in comparison with ES-C.

http://dx.doi.org/10.1371/journal.pone.0120191.g002

Concerning the test results, we observed a significant interaction between strain, treatment and chamber (F(1,72) = 6.0; p< 0.001). The two strains showed different patterns of time spent in the C-C and ES-C. Both control groups (C57, DBA) spent more time in ES-C in comparison with the chamber that contained chocolate (C-C), in which the conditioned stimulus (CS) was present (C57: F (1,10) = 6.04; p < 0.05; DBA: F (1,10) = 12.32; p < 0.01), indicating conditioned suppression of chocolate-seeking during presentation of the CS. In contrast, whereas Stressed C57 mice showed no significant tendency or aversion for either chamber (F (1,14) = .381; n.s.), Stressed DBA animals spent more time in the C-C in comparison with the ES-C, (F (1,14) = 7.38; p< 0.05) (Fig. 3), thus indicating food-seeking behavior despite its possible harmful consequences.

 

Fig 3. Conditioned Suppression Test in C57 and DBA mice.

Time spent (sec ± SE) in the chamber containing chocolate (C-C) and in the empty safe chamber (ES-C) during conditioned suppression test by Control C57/DBA groups (n = 6 for each group) (A) and Stressed C57/DBA mice (n = 8 for each group) (B). * p< 0.05; ** p< 0.01 in comparison with C-C.

http://dx.doi.org/10.1371/journal.pone.0120191.g003

These results indicate that the exposure to our environmental conditions rendered chocolate-seeking impervious to punishment signals, transforming adaptive food-seeking behavior into compulsive seeking only in DBA mice (Fig. 3).

Chocolate intake and weight

To evaluate the chocolate intake shown by Control and Stressed groups of both strains (Control C57, Control DBA, Stressed C57, Stressed DBA), the consumption of chocolate was assessed during the different phases (pre-exposure, training, test) of the conditioned suppression procedure.

With regard to chocolate intake on pre-exposure phase, there was no significant difference between stressed C57 and Stressed DBA mice (F(1,14) = 0.83; n.s.) (Fig. 4).

 

Fig 4. Chocolate intake in C57/DBA Control and Stressed groups.

Chocolate intake in C57/DBA Control (n = 6 for each group) and Stressed (n = 8 for each group) animals recorded during pre-exposure (A), training (B), and test (C). Data are expressed as mean grams (overall mean of days ± SE for A and B). * p < 0.05; *** p< 0.001 in comparison with the control group of the same strain. ### p < 0.001 in comparison with the same group of the other strain.

http://dx.doi.org/10.1371/journal.pone.0120191.g004

With regard to chocolate intake during the training phase, there was a significant interaction between strain and treatment F(1,24) = 20.10; p< 0.001). In the individual between-group comparisons, we noted a significant difference between Control DBA versus Stressed DBA ((F(1,12) = 46.17; p< 0.001), Control C57 versus Stressed C57 ((F(1,12) = 24.25; p< 0.001), and Stressed C57 versus Stressed DBA mice ((F(1,14) = 27.52; p< 0.001) (Fig. 4). Stressed DBA animals showed significantly higher chocolate intake compared to all other groups.

Analysis of chocolate intake on the test day revealed a significant strain x treatment interaction (F(1,24) = 21.48; p< 0.005). Individual between-group comparisons showed a significant difference between control and Stressed DBA ((F(1,12) = 38.49; p< 0.001), Control and Stressed C57 ((F(1,12) = 7.90; p< 0.05) and Stressed C57 and Stressed DBA mice ((F(1,14) = 33.32; p< 0.001) (Fig. 4). Stressed DBA animals experienced significantly greater chocolate intake compared with all other groups, suggesting compulsive chocolate consumption, in agreement with the seeking behavior in the conditioned suppression test.

Finally, concerning weight results, statistical analysis showed that the animals weight did not differ significantly between groups on the first day of the experiment (before the experimental procedure started (F(1,24) = 2.22; ns), on the training phase (F(1,24) = 2.97; n.s.) and on the day of the conditioned suppression test (F(1,24) = 0.58; n.s.) (Fig. 5).

Fig 5. Animal weight.

Weight in Control (n = 6 for each group) and Stressed (n = 8 for each group) C57/DBA groups measured before manipulation started (A), on the first training day (B) and on the Test day (C). Data are expressed as gram ± SE.

http://dx.doi.org/10.1371/journal.pone.0120191.g005

Overall, our data demonstrate a strong interaction between genetic factors and environmental conditions in the expression of compulsive eating, consistent with previous studies that reported a critical function of these factors in certain eating disorders [35, 38].

Dopaminergic and noradrenergic receptor expression in mpFC, NAc, and CP of Stressed DBA vs Control DBA mice

To assess the expression of dopaminergic and noradrenergic receptors in animal showing compulsion-like eating behavior (Stressed DBA), the expression of α1R, D1R and D2R in the mpFC as well as D1R and D2R in the NAc and CP was evaluated in Stressed vs. Control DBA mice (Fig. 6).

 

Fig 6. Expression of DA and NE Receptors in DBA strain.

Expression of D1R and D2R in CP and NAc (A) and D1R, D2R and α1 in mpFC (B) of Stressed DBA (n = 8) and Control group (n = 6). * p< 0.05; ** p< 0.01 in comparison with control group. Data are shown as relative ratio ± SE.

http://dx.doi.org/10.1371/journal.pone.0120191.g006

D2Rs were upregulated in the NAc (F(1,12) = 5.58; p< 0.05) and in the CP (F(1,12) = 10.74; p< 0.01) of Stressed DBA compared with Control DBA mice (Fig. 6), indicating a selective effect on striatal D2 receptors in animals showing compulsion-like eating behavior. No significant effect was evident for D1 receptors. α1Rs expression was lower in the mpFC of Stressed DBA group compared to Control DBA mice (F(1,12) = 7.27; p< 0.05) (Fig. 6). No significant effect was observed for prefrontal D1R or D2R receptors expression.

Dopaminergic and noradrenergic receptor expression in mpFC, NAc, and CP of naïve DBA versus naïve C57 mice

In order to evaluate the baseline receptors availability of α1R, D1R and D2R, the expression of α1R, D1R and D2R in the mpFC as well as D1R and D2R in the NAc and CP was evaluated in two different groups of naïve animals of both strains (naïve C57 and naïve DBA) (Fig. 7).

 

Fig 7. Expression of DA and NE Receptors in naïve C57 and DBA animals.

Expression of D1R and D2R in CP and NAc (A) and D1R, D2R, and α1 in mpFC (B) of naïve C57/DBA groups (n = 6 for each group). ** p<0.01 in comparison with naïve group of the other strain. Data are shown as relative ratio ± SE.

http://dx.doi.org/10.1371/journal.pone.0120191.g007

We observed significantly selective lower D2R availability in the NAc of naïve DBA versus naïve C57 mice (F(1,10) = 11.80; p< 0.01). No other significant difference was seen in D1R, D2R, or α1R in the other areas of the brain (Fig. 7). These results, consistent with previous data [4, 54, 60, 61], support the hypothesis that low D2R availability is a “constitutive” risk genetic factor underlying the vulnerability to maladaptive eating.

Discussion

We assessed compulsive eating in terms of conditioned suppression of palatable food-seeking/intake under adverse conditions [38] in C57 and DBA mice. Exposure to environmental conditions induced compulsion-like eating behavior, depending on genetic background. Moreover, this behavioral pattern appeared to be linked to low availability of accumbal D2 receptors. We also observed D2R upregulation and α1R downregulation in the striatum and mpFC, respectively – a potentially neuroadaptive response that parallel the shift from motivated to compulsion-like eating behavior.

Our experiments suggest that the interaction between access to chocolate pre-exposure and caloric restriction renders chocolate-seeking impervious to signals of punishment, transforming adaptive food-seeking behavior into compulsion-like eating behavior. Notably, this behavior depends strongly on genotype. The conditioned suppression test results indicate that only Stressed DBA animals showed food-seeking behavior, despite possible harmful consequences.

This effect can’t be ascribed to a difference in shock sensitivity between C57 and DBA mice, as shown by the supporting experiment (see S1 Methods and S2 Fig.) and as reported by other groups [62]. Moreover, food-seeking behavior developed, in Stressed DBA animals, in parallel to intake behavior as demonstrated by the high chocolate intake shown by this group. Although consuming large quantities of palatable foods can indicate increased motivation for food, doing so despite harmful consequences, such as tolerating punishment to obtain it, reflects pathological motivation for food (compulsion) [5].

Thus, whereas DBA mice constitute an “ideal model” of resistance to drugs of abuse [24] and food-related disorders under normal conditions (present results), they become most sensitive to drug- [24] and food-related effects when subjected to specific environmental pressures. Moreover, preliminary experiments indicate that exposure to only one of these variables (pre-exposure to chocolate or caloric restriction, separately) fails to induce this phenotype (S1 Methods and S3 Fig.). Thus, only the addictive effect of the environmental conditions (pre-exposure to chocolate and caloric restriction) makes eating behavior refractory to signals of punishment (compulsion-like eating behavior). This result is consistent with evidence that shows that availability of palatable [46, 51], stress exposure [1, 6365], and a synergistic relationship between stress and calorie restriction are the most important factors that promote eating disorders in humans and animal models [6567].

The shift from motivated to compulsion-like eating behavior shown by Stressed DBA mice seems to be related to altered dopaminergic and noradrenergic receptors expression in the pFC-NAc-CP circuit. In fact, Stressed DBA mice, which exhibited compulsive eating behavior (as shown by the absence of conditioned suppression), showed an upregulation of D2R in the NAc and CP and a downregulation of α-1AR in the mpFC, compared to control DBA. To rule out that the effects observed could be induced by different amount of chocolate consumption on the test session shown by Control and Stressed DBA, an additional experiment was performed. The experimental conditions and the procedure were as described for Control and Stressed DBA, but receptors expression was performed on the brains removed from mice without chocolate consumption (on the test day). Results from this experiment (S1 Methods and S4 Fig.), clearly exclude that the upregulation of D2R in the NAc and CP as well as the downregulation of α-1AR in the mpFC shown by Stressed DBA can be induced to chocolate consumption.

The results observed in the NAc and CP of Stressed DBA mice do not allow us to determine the effects on DA transmission – i.e., whether the changes increase dopaminergic tone, necessitating more detailed information on the D2 receptor form – e.g., the proportion of the 2 alternative mRNA splice variants, D2R-long (D2L) and D2R-short (D2S) – in the 2 areas, because the relative proportion of the isoforms in the striatum influences neural and behavioral outcomes of D1R and D2/3R co-activation [6870]. We hypothesize that the increase in postsynaptic receptors and consequent rise in dopamine transmission sustain motivation and invigorate food-seeking behavior [11]. However, more details studies are needed to investigate which type of D2Rs is affected in our experimental procedure.

Increased striatal D2R expression in Stressed DBA mice seems to be in contrast with the hypothesis suggesting that the downregulation of striatal D2R is a neuroadaptive response to the overconsumption of palatable food. However, downregulation of striatal D2R has been reported to be a neuroadaptive response to overconsumption of palatable food and drug intake in humans and animals [4, 44, 60, 7175] but also a genetic risk factor underlying vulnerability to maladaptive eating [4, 54, 60, 61, 75]. The greater striatal D2R expression that we observed in this study could be the result of a neuroadaptive response to our environmental conditions (pre-exposure, calorie restriction) underlying a specific symptom (compulsive eating) that is shared by other, more complex eating disorders. The debate over this issue has often considered obesity and binge eating disorders, in which complex behavioral patterns (such as increased weight, intermittent feeding episodes, extended access to a high-fat diet) develop—not compulsion-like eating behavior per se, as evaluated in this study.

Increasing evidence implicates striatal D1R and D2R in the cost-benefit computation that determines the willingness to expend effort in obtaining a preferred reward, thus affecting motivated behavior [1014]. Moreover, optimal goal-directed behaviors and motivation appear to correlate with higher D2R levels in the striatum [12, 7679]. Our study indicates that excessive striatal D2R expression is also linked to a pathological behavioral phenotype, prompting the hypothesis that optimal D2R expression is a neural correlate of ideal goal-directed behaviors and motivation.

Another significant result was the lower availability of D2R in the NAc of naïve DBA versus naïve C57 mice. As discussed, reduced D2R expression has been suggested to be a genetic risk factor of the vulnerability to maladaptive eating [4, 54, 60, 61, 75]. Moreover, decreased D2/D3 dopaminergic receptor availability in the ventral striatum has been proposed to confer an increased propensity to escalate drug intake and correlate with high impulsivity [16, 79, 80]. Further, DBA/2 mice have been reported to have high impulsivity levels [81, 82]. Thus, we speculate that low accumbal D2R availability observed in naïve DBA mice accounts for the disparate inclination toward the development of compulsive eating under specific environmental conditions, such caloric restriction and availability of palatable food—factors that affect the development and expression of eating disorders [4, 46, 64, 83, 84].

We observed decreased prefrontal α1R expression in Stressed versus Control DBA mice. Although prefrontal NE transmission has been suggested to be required for food-related motivated behavior [9] and although NE neurons (in particular through α1Rs) mediate the reinforcing effects of drugs of abuse [57, 58, 85], no study has examined the involvement of prefrontal noradrenergic receptors in compulsion-like eating behavior. Our results extend previous findings on the function of prefrontal NE transmission in food-related motivated behavior, suggesting that specific receptors govern aberrant motivation related to compulsive eating. Downregulation of α1R in the mpFC could be indicative of an adaptive process that underlies the shift from motivated toward compulsive behavior, driven by a faded role of the cortex and a dominant function of the striatum. However, further studies are needed to investigate this hypothesis.

The hypothalamus is one of the most important brain area regulating food-intake [8688]. However, different brain circuits, other than those regulating hunger and satiety, have been suggested to be involved in food consumption [60, 89]. Moreover, several neurotransmitters and hormones, including DA, NE, acetylcholine, glutamate, cannabinoids, opiods and serotonine, as well as neuroptides involved in homeostatic regulation of food intake, such as orexin, leptin and ghrelin, are implicated in the rewarding effects of food [60, 9092]. Thus, the regulation of food intake by the hypothalamus seems to be related to different neural circuits processing the rewarding and motivational aspects of food intake [60], such as prefrontal-accumbal system. It’s to note that C57 and DBA mice show numerous behavioral differences and the functional and anatomical characteristics of their brain neurotransmitter systems have been extensively examined in these inbred strains [19, 23], thus suggesting a different, strain-dependent, regulation of motivation, reward, learning, and control circuits.

The best-established mechanism involved in processing the rewarding and motivational aspects of food (and drug) is the brain’s dopaminergic reward circuitry [45, 51, 60]. Repeated stimulation of DA reward pathways is believed to trigger neurobiological adaptations in various neural circuits, thus making seeking behavior “compulsive” and leading to a loss of control over one’s intake of food (or drugs) [51, 60].

It has been suggested that under different access conditions, the potent reward-inducing capacity of palatable foods can drive behavioral modification through neurochemical alterations in brain areas linked to motivation, learning, cognition, and decision making that mirror the changes induced by drug abuse [83, 9399]. In particular, the changes in the reward, motivation, memory, and control circuits following repeated exposure to palatable food is similar to the changes observed following repeated drug exposure [60, 95]. In individuals who are vulnerable to these changes, consuming high quantities of palatable food (or drugs) can disrupt the balance between motivation, reward, learning, and control circuits, thereby increasing the reinforcing value of the palatable food (or drug) and weakening the control circuits [51, 60].

Based on this observation and on results from present study, it can proposed that the shift from motivated behavior to compulsive eating behavior observed in DBA mice could be related to an interplay between genetic vulnerability (low accumbal D2 receptors availability observed in this study as well as differences in other neurotransmitters and hormones involved in food-related brain circuits) and exposure to environmental conditions that, inducing a D2R upregulation and α1R downregulation in the striatum and mpFC, respectively, can lead at an “unbalanced” interaction between circuits that motivate behavior and circuits that control and inhibit pre-potent responses [60, 95].

Conclusions

There are few studies on gene-environment interaction in human eating disorders [2]. The animal model that we propose here could be used to understand how environmental factors interact with genetic liability and neurobiological factors to promote the expression of compulsion-like eating behavior, also providing new insights into drug addiction.

Supporting Information

S1_Fig.tif

https://s3-eu-west-1.amazonaws.com/ppreviews-plos-725668748/1951833/preview.jpg

 

figshare

 

1 / 5

Representative position of punching in the medial preFrontal Cortex (mpFC) (A), Nucleus Acumbens (NAc) and Caudate-Putamen (CP) (B).

S1 Fig. Punching position.

Representative position of punching in the medial preFrontal Cortex (mpFC) (A), Nucleus Acumbens (NAc) and Caudate-Putamen (CP) (B).

doi:10.1371/journal.pone.0120191.s001

(TIFF)

S2 Fig. Shock sensitivity threshold in C57 and DBA mice.

Shock sensitivity in C57 and DBA animals (Methods S1). Mean (μA ± SE) shock threshold observed in C57 and DBA animals.

doi:10.1371/journal.pone.0120191.s002

(TIFF)

S3 Fig. Conditioned Suppression Test in DBA mice.

Time spent (sec ± SE) in chamber containing chocolate (C-C) empty-safe chamber (ES-C) during Conditioned Suppression Test by DBA pre-exposed and DBA Food Restricted Groups.

doi:10.1371/journal.pone.0120191.s003

(TIFF)

S4 Fig. Expression of DA and NE Receptors in DBA mice.

Expression of D2 receptors in the CP and NAc as well as of α1 in the mpFC of Stressed and Control DBA mice (n = 6 for each group). * p< 0.05 in comparison with Control group. Data are shown as relative ratio ± SE.

doi:10.1371/journal.pone.0120191.s004

(TIFF)

S1 Methods. Supporting Materials and Methods.

doi:10.1371/journal.pone.0120191.s005

(DOC)

Acknowledgments

We thank Dr. Sergio Papalia for his skillful assistance.

Author Contributions

Conceived and designed the experiments: RV EP MDS. Performed the experiments: EP MDS DA ECL AF LP AV. Analyzed the data: RV AP AG SPA. Contributed reagents/materials/analysis tools: AF EP MDS. Wrote the paper: RV SPA EP MDS.

References

  1. 1. Campbell IC, Mill J, Uher R, Schmidt U (2010) Eating disorders, gene-environment interactions and epigenetics. Neuroscience Biobehav Rev 35: 784–793. doi: 10.1016/j.neubiorev.2010.09.012
  2. 2. Bulik CM (2005) Exploring the gene-environment nexus in eating disorders. J Psychiatry Neurosci 30: 335–339. pmid:16151538
  3. View Article
  4. PubMed/NCBI
  5. Google Scholar
  6. View Article
  7. PubMed/NCBI
  8. Google Scholar
  9. View Article
  10. PubMed/NCBI
  11. Google Scholar
  12. View Article
  13. PubMed/NCBI
  14. Google Scholar
  15. View Article
  16. PubMed/NCBI
  17. Google Scholar
  18. View Article
  19. PubMed/NCBI
  20. Google Scholar
  21. View Article
  22. PubMed/NCBI
  23. Google Scholar
  24. View Article
  25. PubMed/NCBI
  26. Google Scholar
  27. View Article
  28. PubMed/NCBI
  29. Google Scholar
  30. View Article
  31. PubMed/NCBI
  32. Google Scholar
  33. View Article
  34. PubMed/NCBI
  35. Google Scholar
  36. View Article
  37. PubMed/NCBI
  38. Google Scholar
  39. View Article
  40. PubMed/NCBI
  41. Google Scholar
  42. View Article
  43. PubMed/NCBI
  44. Google Scholar
  45. View Article
  46. PubMed/NCBI
  47. Google Scholar
  48. View Article
  49. PubMed/NCBI
  50. Google Scholar
  51. View Article
  52. PubMed/NCBI
  53. Google Scholar
  54. View Article
  55. PubMed/NCBI
  56. Google Scholar
  57. View Article
  58. PubMed/NCBI
  59. Google Scholar
  60. View Article
  61. PubMed/NCBI
  62. Google Scholar
  63. View Article
  64. PubMed/NCBI
  65. Google Scholar
  66. 3. Heyne A, Kiesselbach C, Sahùn I (2009) An animal model of compulsive food-taking behaviour. Add Biol 14: 373–383. doi: 10.1111/j.1369-1600.2009.00175.x
  67. View Article
  68. PubMed/NCBI
  69. Google Scholar
  70. View Article
  71. PubMed/NCBI
  72. Google Scholar
  73. View Article
  74. PubMed/NCBI
  75. Google Scholar
  76. View Article
  77. PubMed/NCBI
  78. Google Scholar
  79. View Article
  80. PubMed/NCBI
  81. Google Scholar
  82. View Article
  83. PubMed/NCBI
  84. Google Scholar
  85. View Article
  86. PubMed/NCBI
  87. Google Scholar
  88. View Article
  89. PubMed/NCBI
  90. Google Scholar
  91. View Article
  92. PubMed/NCBI
  93. Google Scholar
  94. View Article
  95. PubMed/NCBI
  96. Google Scholar
  97. View Article
  98. PubMed/NCBI
  99. Google Scholar
  100. View Article
  101. PubMed/NCBI
  102. Google Scholar
  103. View Article
  104. PubMed/NCBI
  105. Google Scholar
  106. View Article
  107. PubMed/NCBI
  108. Google Scholar
  109. View Article
  110. PubMed/NCBI
  111. Google Scholar
  112. View Article
  113. PubMed/NCBI
  114. Google Scholar
  115. View Article
  116. PubMed/NCBI
  117. Google Scholar
  118. View Article
  119. PubMed/NCBI
  120. Google Scholar
  121. View Article
  122. PubMed/NCBI
  123. Google Scholar
  124. View Article
  125. PubMed/NCBI
  126. Google Scholar
  127. View Article
  128. PubMed/NCBI
  129. Google Scholar
  130. View Article
  131. PubMed/NCBI
  132. Google Scholar
  133. View Article
  134. PubMed/NCBI
  135. Google Scholar
  136. View Article
  137. PubMed/NCBI
  138. Google Scholar
  139. View Article
  140. PubMed/NCBI
  141. Google Scholar
  142. View Article
  143. PubMed/NCBI
  144. Google Scholar
  145. View Article
  146. PubMed/NCBI
  147. Google Scholar
  148. View Article
  149. PubMed/NCBI
  150. Google Scholar
  151. View Article
  152. PubMed/NCBI
  153. Google Scholar
  154. View Article
  155. PubMed/NCBI
  156. Google Scholar
  157. View Article
  158. PubMed/NCBI
  159. Google Scholar
  160. View Article
  161. PubMed/NCBI
  162. Google Scholar
  163. View Article
  164. PubMed/NCBI
  165. Google Scholar
  166. View Article
  167. PubMed/NCBI
  168. Google Scholar
  169. View Article
  170. PubMed/NCBI
  171. Google Scholar
  172. View Article
  173. PubMed/NCBI
  174. Google Scholar
  175. View Article
  176. PubMed/NCBI
  177. Google Scholar
  178. View Article
  179. PubMed/NCBI
  180. Google Scholar
  181. View Article
  182. PubMed/NCBI
  183. Google Scholar
  184. View Article
  185. PubMed/NCBI
  186. Google Scholar
  187. View Article
  188. PubMed/NCBI
  189. Google Scholar
  190. View Article
  191. PubMed/NCBI
  192. Google Scholar
  193. View Article
  194. PubMed/NCBI
  195. Google Scholar
  196. View Article
  197. PubMed/NCBI
  198. Google Scholar
  199. View Article
  200. PubMed/NCBI
  201. Google Scholar
  202. View Article
  203. PubMed/NCBI
  204. Google Scholar
  205. View Article
  206. PubMed/NCBI
  207. Google Scholar
  208. View Article
  209. PubMed/NCBI
  210. Google Scholar
  211. View Article
  212. PubMed/NCBI
  213. Google Scholar
  214. View Article
  215. PubMed/NCBI
  216. Google Scholar
  217. View Article
  218. PubMed/NCBI
  219. Google Scholar
  220. View Article
  221. PubMed/NCBI
  222. Google Scholar
  223. View Article
  224. PubMed/NCBI
  225. Google Scholar
  226. View Article
  227. PubMed/NCBI
  228. Google Scholar
  229. View Article
  230. PubMed/NCBI
  231. Google Scholar
  232. View Article
  233. PubMed/NCBI
  234. Google Scholar
  235. View Article
  236. PubMed/NCBI
  237. Google Scholar
  238. View Article
  239. PubMed/NCBI
  240. Google Scholar
  241. View Article
  242. PubMed/NCBI
  243. Google Scholar
  244. View Article
  245. PubMed/NCBI
  246. Google Scholar
  247. View Article
  248. PubMed/NCBI
  249. Google Scholar
  250. View Article
  251. PubMed/NCBI
  252. Google Scholar
  253. View Article
  254. PubMed/NCBI
  255. Google Scholar
  256. View Article
  257. PubMed/NCBI
  258. Google Scholar
  259. View Article
  260. PubMed/NCBI
  261. Google Scholar
  262. View Article
  263. PubMed/NCBI
  264. Google Scholar
  265. View Article
  266. PubMed/NCBI
  267. Google Scholar
  268. View Article
  269. PubMed/NCBI
  270. Google Scholar
  271. View Article
  272. PubMed/NCBI
  273. Google Scholar
  274. View Article
  275. PubMed/NCBI
  276. Google Scholar
  277. View Article
  278. PubMed/NCBI
  279. Google Scholar
  280. View Article
  281. PubMed/NCBI
  282. Google Scholar
  283. View Article
  284. PubMed/NCBI
  285. Google Scholar
  286. View Article
  287. PubMed/NCBI
  288. Google Scholar
  289. View Article
  290. PubMed/NCBI
  291. Google Scholar
  292. View Article
  293. PubMed/NCBI
  294. Google Scholar
  295. 4. Johnson PM, Kenny PJ (2010) Addiction-like reward dysfunction and compulsive eating in obese rats: role for dopamine D2 receptors. Nat Neuroscience 13: 635–641. doi: 10.1038/nn.2519. pmid:20348917
  296. 5. Oswald KD, Murdaugh DL, King VL, Boggiano MM (2011) Motivation for palatable food despite consequences in an animal model of binge eating. Int J Eatg Disord 44: 203–211. doi: 10.1002/eat.20808. pmid:20186718
  297. 6. Teegarden SL, Bale TL (2008) Effects of stress on dietary preference and intake are dependent on access and stress sensitivity. Physiol & Behav 93:713–723. doi: 10.1016/j.physbeh.2007.11.030
  298. 7. Cabib S, Puglisi-Allegra S (2012) The mesoaccumbens dopamine in coping with stress. Neurosci Biobehav Rev 36:79–89. doi: 10.1016/j.neubiorev.2011.04.012. pmid:21565217
  299. 8. Ventura R, Latagliata EC, Morrone C, La Mela I, Puglisi-Allegra S (2008) Prefrontal norepinephrine determines attribution of “high” motivational salience. PLoS ONE, 3:e3044. Biol Psychiatry 71:358–365. doi: 10.1371/journal.pone.0003044. pmid:18725944
  300. 9. Ventura R, Morrone C, Puglisi-Allegra S (2007) Prefrontal/accumbal catecholamine system determines motivational salience attribution to both reward- and aversion-related stimuli. Proc Natl Acad Sci USA 104: 5181–5186. pmid:17360372 doi: 10.1073/pnas.0610178104
  301. 10. Salamone JD, Correa M (2012) The mysterious motivational functions of mesolimbic dopamine. Neuron 76: 470–485. doi: 10.1016/j.neuron.2012.10.021. pmid:23141060
  302. 11. Salamone JD, Correa M, Farrar A, Mingote SM (2007) Effort-related functions of nucleus accumbens dopamine and associated forebrain circuits. Psychopharmacology 191: 461–482. pmid:17225164 doi: 10.1007/s00213-006-0668-9
  303. 12. Trifilieff P, Feng B, Urizar E, Winiger V, Ward RD, Taylor KM, et al.(2013) Increasing dopamine D2 receptor expression in adult nucleus accumbens anhances motivation. Mol Psychiatry 18: 1025–1033. doi: 10.1038/mp.2013.57. pmid:23711983
  304. 13. Van den Bos R, van der Harst J, Jonkman S, Schilders M, Sprijt B (2006) Rats assess costs and benefits according to an internal standard. Behav Brain Res 171: 350–354. pmid:16697474 doi: 10.1016/j.bbr.2006.03.035
  305. 14. Ward RD, Simpson EH, Richards VL, Deo G, Taylor K, Glendinning JI, et al.(2012) Dissociation of hedonic reaction to reward and incentive motivation in an animal model of the negative symptoms of schizophrenia. Neuropsychopharmacology 37: 1699–1707. doi: 10.1038/npp.2012.15. pmid:22414818
  306. 15. Bertolino A, Fazio L, Caforio G, Blasi G, Rampino A, Romano R, et al. (2009) Functional variants of the dopamine receptor D2 gene modulate prefronto-striatal phenotypes in schizophrenia. Brain 132:417–425. doi: 10.1093/brain/awn248. pmid:18829695
  307. 16. Everitt BJ, Belin D, Economidou D, Pelloux Y, Dalley J, Robbins TW (2008) Neural mechanisms underlying the vulnerability to develop compulsive drug-seeking habits and addiction. Phylos Transact R S London Series B: Biological Sciences 363: 3125–3135. doi: 10.1098/rstb.2008.0089. pmid:18640910
  308. 17. Volkow ND, Fowler JS, Wang GJ, Baler R, Telang F (2009) Imaging dopamine’s role in drug abuse and addiction. Neuropharmacology 1: 3–8. doi: 10.1016/j.neuropharm.2008.05.022
  309. 18. Crawley JN, Belknap JK, Collins A, Crabbe JC, Frankel W, Henderson N, et al. (1997) Behavioral phenotypes of inbred mouse strains: implications and recommendations for molecular studies. Psychopharmacology (Berl) 132:107–124. pmid:9266608 doi: 10.1007/s002130050327
  310. 19. Cabib S, Puglisi-Allegra S, Ventura R (2002) The contribution of comparative studies in inbred strains of mice to the understanding of the hyperactive phenotype. Behav Brain Res 130: 103–109. pmid:11864725 doi: 10.1016/s0166-4328(01)00422-3
  311. 20. Puglisi-Allegra S, Ventura R (2012) Prefrontal/accumbal catecholamine system processes emotionally driven attribution of motivational salience. Rev Neurosci 23: 509–526. doi: 10.1515/revneuro-2012-0076. pmid:23159865
  312. 21. Puglisi-Allegra S, Ventura R (2012) Prefrontal/accumbal catecholamine system processes high motivational salience. Front Behav Neurosci 6:31. doi: 10.3389/fnbeh.2012.00031. pmid:22754514
  313. 22. Alcaro A, Huber R, Panksepp J (2007) Behavioral functions of the mesolimbic dopaminergic system: an affective neuroethological perspective. Brain Res Rev 56: 283–321. pmid:17905440 doi: 10.1016/j.brainresrev.2007.07.014
  314. 23. Andolina D, Maran D, Viscomi MT, Puglisi-Allegra S (2014) Strain-dependent variations in stress coping behavior are mediated by a 5-HT/GABA interaction within the prefrontal corticolimbic system. International Journal of Neuropsychopharmacology doi: 10.1093/ijnp/pyu074.
  315. 24. Cabib S, Orsini C, Le Moal M, Piazza PV (2000) Abolition and Reversal of Strain Differences in Behavioral Responses to Drugs of Abuse After a Brief Experience. Science 289: 463–465. pmid:10903209 doi: 10.1126/science.289.5478.463
  316. 25. Orsini C, Bonito-Oliva A, Conversi D, Cabib S (2005) Susceptibility to conditioned place preference induced by addictive drugs in mice of the C57BL/6 and DBA/2 inbred strains. Psychopharmacology (Berl) 181: 327–336. pmid:15864555 doi: 10.1007/s00213-005-2259-6
  317. 26. Orsini C, Bonito-Oliva A, Conversi D, Cabib S (2008) Genetic liability increases propensity to prime-induced reinstatement of conditioned place preference in mice exposed to low cocaine. Psychopharmacology (Berl) 198: 287–296. doi: 10.1007/s00213-008-1137-4. pmid:18421441
  318. 27. van der Veen R, Piazza PV, Deroche-Gamonet V (2007) Gene environment interactions in vulnerability to cocaine intravenous self-administration: a brief social experience affects intake in DBA/2J but not in C57BL/6J mice. Psychopharmacology (Berl) 193: 179–186. pmid:17396246 doi: 10.1007/s00213-007-0777-0
  319. 28. Young JW, Light GA, Marston HM, Sharp R, Geyer MA (2009) The 5-choice continuous performance test: evidence for a translational test of vigilance for mice. PLoS ONE 4, e4227. doi: 10.1371/journal.pone.0004227. pmid:19156216
  320. 29. Elmer GI, Pieper JO, Hamilton LR, Wise RA (2010) Qualitative differences between C57BL/6J and DBA/2J mice in morphine potentiation of brain stimulation reward and intravenous self-administration. Psychopharmacology 208: 309–321. doi: 10.1007/s00213-009-1732-z. pmid:20013116
  321. 30. Fish EW, Riday TT, McGuigan MM, Faccidomo S, Hodge CW, Malanga CJ (2010) Alcohol, cocaine, and brain stimulation-reward in C57Bl6/J and DBA2/J mice. Alcohol Clin Exp Res 34:81–89. doi: 10.1111/j.1530-0277.2009.01069.x. pmid:19860803
  322. 31. Solecki W, Turek A, Kubik J, Przewlocki R (2009) Motivational effects of opiates in conditioned place preference and aversion paradigm—a study in three inbred strains of mice. Psychopharmacology 207:245–255. doi: 10.1007/s00213-009-1672-7. pmid:19787337
  323. 32. Caspi A, Moffitt TE (2006) Gene-environment interactions in psychiatry: joining forces with neuroscience. Nat Rev Neurosci 7: 583–590. pmid:16791147 doi: 10.1038/nrn1925
  324. 33. Rutter M (2008) Biological implications of gene-environment interaction. J Abnorm Child Psychol 36: 969–975. doi: 10.1007/s10802-008-9256-2. pmid:18642072
  325. 34. Volkow N, Li TK (2005) The neuroscience of addiction. Nat Neurosci 8: 1429–1430. pmid:16251981 doi: 10.1038/nn1105-1429
  326. 35. Cabib S, Puglisi-Allegra S, Oliverio A (1985) A genetic analysis of stereotypy in the mouse: dopaminergic plasticity following chronic stress. Behav Neural Biol 44: 239–248. pmid:4062778 doi: 10.1016/s0163-1047(85)90254-7
  327. 36. Cabib S, Giardino L, Calza L, Zanni M, Mele A, Puglisi-Allegra S (1998) Stress promotes major changes in dopamine receptor densities within the mesoaccumbens and nigrostriatal systems. Neuroscience 84, 193–200. pmid:9522373 doi: 10.1016/s0306-4522(97)00468-5
  328. 37. Puglisi-Allegra S, Cabib S (1997) Psychopharmacology of dopamine: the contribution of comparative studies in inbred strains of mice. Prog Neurobiol 51: 637–61. pmid:9175160 doi: 10.1016/s0301-0082(97)00008-7
  329. 38. Latagliata EC, Patrono E, Puglisi-Allegra S, Ventura R (2010) Food seeking in spite of harmful consequences is under prefrontal cortical noradrenergic control. BMC Neurosci 8: 11–15. pmid:21478683 doi: 10.1186/1471-2202-11-15
  330. 39. Carr KD (2002) Augmentation of drug reward by chronic food restriction:behavioral evidence and underlying mechanisms. Physiol Behav 76: 353–364. pmid:12117572 doi: 10.1016/s0031-9384(02)00759-x
  331. 40. Rougé-Pont F, Marinelli M, Le Moal M, Simon H, Piazza PV (1995) Stress-induced sensitization and glucocorticoids. II. Sensitization of the increase in extracellular dopamine induced by cocaine depends on stress-induced corticosterone secretion. J Neurosi 15:7189–7195. pmid:7472473
  332. 41. Deroche V, Marinelli M, Maccari S, Le Moal M, Simon H, Piazza PV (1995) Stress-induced sensitization and glucocorticoids. I. Sensitization of dopamine dependent locomotor effects of amphetamine and morphine depends on stress-induced corticosterone secretion. J Neurosi 15: 7181–7188. pmid:7472472 doi: 10.1016/0006-8993(92)90205-n
  333. 42. Guarnieri DJ, Brayton CE, Richards SM, Maldonado-Aviles J, Trinko JR, Nelson J, et al. (2012) Gene profiling reveals a role for stress hormones in the molecular and behavioral response to food restriction. Biol Psychiatry 71:358–365. doi: 10.1016/j.biopsych.2011.06.028. pmid:21855858
  334. 43. Adam TC, Epel ES (2007) Stress, eating and the reward system. Physiol Behav 91: 449–458. pmid:17543357 doi: 10.1016/j.physbeh.2007.04.011
  335. 44. Corwin RL, Avena NM, Boggiano MM (2011) Feeding and reward: perspectives from three rat models of binge eating. Physiol and Behav 104:87–97. doi: 10.1016/j.physbeh.2011.04.041. pmid:21549136
  336. 45. Volkow ND, Wise RA (2005) How can drug addiction help us understand obesity? Nat Neurosci 8, 555–556. pmid:15856062 doi: 10.1038/nn1452
  337. 46. Ifland JR, Preuss HG, Marcus MT, Rourke KM, Taylor WC, Burau K, et al. (2009) Refined food addiction: a classic substance use disorders. Mel Hypoth 72: 518–526. doi: 10.1016/j.mehy.2008.11.035
  338. 47. Bray GA, Nielsen SJ, Popkin BM (2004) Consumption of high-fructose corn syrup in beverages may play a role in the epidemic of obesity. Am J Clin Nutrition 79: 537–543. pmid:15051594
  339. 48. Rogers PJ, Smit HJ (2000) Food craving and food ‘‘addiction”: a critical review of the evidence from a biopsychosocial perspective. Pharmacol Biochem Behav 66: 3–14. pmid:10837838
  340. 49. Kalra SP, Kalra PS (2004) Overlapping and interactive pathways regulating appetite and craving. J Addict Dis 23: 5–21. pmid:15256341 doi: 10.1300/j069v23n03_02
  341. 50. Parker G, Parker I, Brotchie H (2006) Mood state effects of chocolate. J Affect Dis 92: 149–159. pmid:16546266 doi: 10.1016/j.jad.2006.02.007
  342. 51. Volkow ND, Wang GJ, Telang F, Fowler JS, Thanos PK, Logan J, et al. (2008) Low dopamine striatal D2 receptors are associated with prefrontal metabolism in obese subjects: possible contributing factors. Neuroimage 42: 1537–1543. doi: 10.1016/j.neuroimage.2008.06.002. pmid:18598772
  343. 52. Berridge KC, Ho CY, Richard JM, Difeliceantonio AG (2010) The tempted brain eats: Pleasure and desire circuits in obesity and eating disorders. Brain Res 1350: 43–64. doi: 10.1016/j.brainres.2010.04.003. pmid:20388498
  344. 53. Volkow ND, Wang GJ, Tomasi D, Baler RD (2013) Obesity and addiction: neurobiological overlaps. Obese Rev 14: 2–18. doi: 10.1111/j.1467-789x.2012.01031.x
  345. 54. Bello NT, Hajnal A (2010) Dopamine and Binge Eating Behaviors. Pharmacol Biochem Behav 97: 25–33. doi: 10.1016/j.pbb.2010.04.016. pmid:20417658
  346. 55. Wang GJ, Volkow ND, Thanos PK, Fowler JS (2009) Imaging of brain dopamine pathways: implications for understanding obesity. J Addict Med 3: 8–18. doi: 10.1097/ADM.0b013e31819a86f7. pmid:21603099
  347. 56. Sara SJ, Bouret S (2012) Orienting and reorienting: the Locus Coeruleus mediates cognition through arousal. Neuron rev 76: 130–141. doi: 10.1016/j.neuron.2012.09.011. pmid:23040811
  348. 57. Drouin C, Darracq L, Trovero F, Blanc G, Glowinski J, Cotecchia S, et al. (2002) Alpha1b-adrenergic receptors control locomotor and rewarding effects of psychostimulants and opiates. J Neurosci 22: 2873–2884. pmid:11923452
  349. 58. Weinshenker D, Schroeder JPS (2007) There and back again: a tale of norepinephrine and drug addiction. Neuropsychopharmacology 32: 1433–1451. pmid:17164822 doi: 10.1038/sj.npp.1301263
  350. 59. Puglisi-Allegra S, Cabib S, Pascucci T, Ventura R, Cali F, Romano V (2000) Dramatic brain aminergic deficit in a genetic mouse model of phenylketonuria. Neuroreport 11: 1361–1364. pmid:10817622 doi: 10.1097/00001756-200004270-00042
  351. 60. Volkow ND, Wang GJ, Baler RD (2011) Reward, dopamine and the control of food intake: implications for obesity. Trends in Cogn Sci 15: 37–46. doi: 10.1016/j.tics.2010.11.001. pmid:21109477
  352. 61. Stice E, Spoor S, Bohon C, Small DM (2008) Relation between obesity and blunted striatal response to food is moderated by TaqIA A1 allele. Science 322: 449–452. doi: 10.1126/science.1161550. pmid:18927395
  353. 62. Szklarczyk K, Korostynski M, Golda S, Solecki W, Przewlocki R (2012) Genotype-dependent consequences of traumatic stress in four inbred mouse strains. Genes, Brain and Behav 11: 977–985. doi: 10.1111/j.1601-183x.2012.00850.x
  354. 63. Cifani C, Polidori C, Melotto S, Ciccocioppo R, Massi M (2009) A preclinical model of binge eating elicited by yo-yo dieting and stressful exposure to food: effect of sibutramine, fluoxetine, topiramate, and midazolam. Psychopharmacology 204: 113–125. doi: 10.1007/s00213-008-1442-y. pmid:19125237
  355. 64. Dallman MF, Pecoraro N, Akana SF, La Fleur SE, Gomez F, Houshyar H, et al. (2003) Chronic stress and obesity: A new view of “comfort food”. Proc Natl Acad Sci U S A 100: 11696–11701. pmid:12975524 doi: 10.1073/pnas.1934666100
  356. 65. Hagan MM, Chandler PC, Wauford PK, Rybak RJ, Oswald KD (2003) The role of palatable food and hunger as trigger factors in an animal model of stress induced binge eating. Int Journal Eating Disorders 34:183–197. pmid:12898554 doi: 10.1002/eat.10168
  357. 66. Casper RC, Sullivan EL, Tecott L (2008) Relevance of animal models to human eating disorders and obesity. Psychopharmacology 199: 313–329. doi: 10.1007/s00213-008-1102-2. pmid:18317734
  358. 67. Parylak SL, Koob GF, Zorrilla EP (2011) The dark side of food addiction. Physiol and Behav 104: 149–156. doi: 10.1016/j.physbeh.2011.04.063. pmid:21557958
  359. 68. Colelli V, Fiorenza MT, Conversi D, Orsini C, Cabib S (2010) Strain-specific proportion of the two isoforms of the dopamine D2 receptor in the mouse striatum: associated neural and behavioral phenotypes. Genes Brain Behav 9: 703–711. doi: 10.1111/j.1601-183X.2010.00604.x. pmid:20546314
  360. 69. Fetsko LA, Xu R, Wang Y (2003) Alterations in D1/D2 synergism may account for enhanced stereotypy and reduced climbing in mice lacking dopamine D2L receptor. Brain Res 967:191–200. pmid:12650980 doi: 10.1016/s0006-8993(02)04277-4
  361. 70. Usiello A, Baik JH, Rougé-Pont F, Picetti R, Dierich A, LeMeur M, et al. (2000) Distinct functions of the two isoforms of dopamine D2 receptors. Nature 408: 199–203. pmid:11089973 doi: 10.1038/35041572
  362. 71. Colantuoni C, Schwenker J, McCarthy J, Rada P, Ladenheim B, Cadet JL (2001) Excessive sugar intake alters binding to dopamine and mu-opioid receptors in the brain. Neuroreport 12: 3549–3552. pmid:11733709 doi: 10.1097/00001756-200111160-00035
  363. 72. Halpern CH, Tekriwal A, Santollo J, Keating JG, Wolf JA, Daniels D, et al. (2013) Amelioration of binge eating by nucleus accumbens shell deep brain stimulation in mice involves D2 receptor modulation. J Neurosci 33:7122–7129. doi: 10.1523/JNEUROSCI.3237-12.2013. pmid:23616522
  364. 73. Olsen CM (2011) Natural rewards, neuroplasticity, and non-drug addictions. Neuropharmacology 61:1109–1122. doi: 10.1016/j.neuropharm.2011.03.010. pmid:21459101
  365. 74. Stice E, Yokum S, Blum K, Bohon C (2010) Weight gain is associated with reduced striatal response to palatable food. J Neurosci 30: 13105–13109. doi: 10.1523/JNEUROSCI.2105-10.2010. pmid:20881128
  366. 75. Stice E, Yokum S, Zald D, Dagher A (2011) Dopamine-based reward circuitry responsitivity, genetics, and overeating. Curr Top Behav Neurosci 6: 81–93. doi: 10.1007/7854_2010_89. pmid:21243471
  367. 76. Gjedde A, Kumakura Y, Cumming P, Linnet J, Moller A (2010) Inverted-U-shaped correlation between dopamine receptor availability in striatum and sensation seeking. Proc Natl Acad Sci USA 107: 3870–3875. doi: 10.1073/pnas.0912319107. pmid:20133675
  368. 77. Stelzel C, Basten U, Montag C, Reuter M, Fiebach CJ (2010) Frontostriatal involvement in task switching depends on genetic differences in d2 receptor density. J Neurosci 30:14205–12. doi: 10.1523/JNEUROSCI.1062-10.2010. pmid:20962241
  369. 78. Tomer R, Goldstein RZ, Wang GJ, Wong C, Volkow ND (2008) Incentive motivation is associated with striatal dopamine asymmetry. Biol Psychol 77: 98–101. pmid:17868972 doi: 10.1016/j.biopsycho.2007.08.001
  370. 79. Trifilieff P, Martinez D (2014) Imaging addiction: D2 receptors and dopamine signaling in the striatum as biomarkers for impulsivity. Neuropharmacology 76: 498–509. doi: 10.1016/j.neuropharm.2013.06.031. pmid:23851257
  371. 80. Dalley JW, Fryer TD, Brichard L, Robinson ES, Theobald DE, Lääne K, et al. (2007) Nucleus accumbens D2/3 receptors predict trait impulsivity and cocaine reinforcement. Science 315: 1267–1270. pmid:17332411 doi: 10.1126/science.1137073
  372. 81. Gubner NR, Wilhelm CJ, Phillips TJ, Mitchell SH (2010) Strain differences in behavioral inhibition in a Go/No-go task demonstrated using 15 inbred mouse strains. Alcohol Clin Exp Res 34: 1353–1362. doi: 10.1111/j.1530-0277.2010.01219.x. pmid:20491731
  373. 82. Patel S, Stolerman IP, Asherson P, Sluyter F (2006) Attentional performance of C57BL/6 and DBA/2 mice in the 5-choice serial reaction time task. Behav Brain Res 170: 197–203. pmid:16616787 doi: 10.1016/j.bbr.2006.02.019
  374. 83. Avena NM, Rada P, Hoebel B (2008) Evidence for sugar addiction: Behavioral and neurochemical effects of intermittent, excessive sugar intake. Neurosci Biobehav Rev 32: 20–39. pmid:17617461 doi: 10.1016/j.neubiorev.2007.04.019
  375. 84. Hoebel BG, Avena NM, Bocarsly ME, Rada P (2009) Natural addiction: a behavioral and circuit model based on sugar addiction in rats. J Add Med.3, 33–41. doi: 10.1097/adm.0b013e31819aa621
  376. 85. Zhang XY, Kosten TA (2005) Prazosin, an alpha-1 adrenergic antagonist, reduces cocaine-induced reinstatement of drug-seeking. Biol Psychiatry 57: 1202–1204. pmid:15866561 doi: 10.1016/j.biopsych.2005.02.003
  377. 86. Blouet C, Schwartz GJ (2010) Hypothalamic nutrient sensing in the control of energy homeostasis. Behav. Brain Res 209: 1–12. doi: 10.1016/j.bbr.2009.12.024. pmid:20035790
  378. 87. Coll AP, Farooqi IS, O’Rahilly S (2007) The hormonal control of food intake. Cell 129: 251–262. pmid:17448988 doi: 10.1016/j.cell.2007.04.001
  379. 88. Dietrich M, Horvath T (2009) Feeding signals and brain circuitry. Eur. J. Neurosci 30: 1688–1696. doi: 10.1111/j.1460-9568.2009.06963.x. pmid:19878280
  380. 89. Rolls ET (2008) Functions of the orbitofrontal and pregenual cingulate cortex in taste, olfaction, appetite and emotion. Acta Physiol. Hung 95: 131–164. doi: 10.1556/APhysiol.95.2008.2.1. pmid:18642756
  381. 90. Avena NM, Bocarsly ME (2012) Dysregulation of brain reward systems in eating disorders: neurochemical information from animal models of binge eating, bulimia nervosa, and anorexia nervosa. Neuropharmacology 63:87–96. doi: 10.1016/j.neuropharm.2011.11.010. pmid:22138162
  382. 91. Alsiö J, Olszewski PK, Levine AS, Schiöth HB (2012) Feed-forward mechanisms: addiction-like behavioral and molecular adaptations in overeating. Front Neuroendocrinol 33(2), 127–139. doi: 10.1016/j.yfrne.2012.01.002. pmid:22305720
  383. 92. Hadad NA, Knackstedt LA (2014) Addicted to palatable foods: comparing the neurobiology of Bulimia Nervosa to that of drug addiction. Psychopharmacology 231:1897–912. doi: 10.1007/s00213-014-3461-1. pmid:24500676
  384. 93. Lenoir M, Serre F, Cantin L, Ahmed SH (2007) Intense sweetness surpasses cocaine reward. PLoS ONE 2:e698. pmid:17668074 doi: 10.1371/journal.pone.0000698
  385. 94. Petrovich GD, Ross CA, Holland PC, Gallagher M (2007) Medial prefrontal cortex is necessary for an appetitive contextual conditioned stimulus to promote eating in sated rats. J Neurosci 27:6436–6441. pmid:17567804 doi: 10.1523/jneurosci.5001-06.2007
  386. 95. Volkow ND, Wang GJ, Fowler JS, Telang F (2008) Overlapping neuronal circuits in addiction and obesity: evidence of systems pathology. Philos Trans R Soc Lond B Biol Sci. 363: 3191–3200. doi: 10.1098/rstb.2008.0107. pmid:18640912
  387. 96. Fallon S, Shearman E, Sershen H, Lajtha A (2007) Food reward-induced neurotransmitter changes in cognitive brain regions. Neurochem Res 32: 1772–1782. pmid:17721820 doi: 10.1007/s11064-007-9343-8
  388. 97. Wang GJ, Volkow ND, Thanos PK, Fowler JS (2004) Similarity between obesity and drug addiction as assessed by neurofunctional imaging: a concept review. J Addict Dis 23: 39–53. pmid:15256343 doi: 10.1300/j069v23n03_04
  389. 98. Schroeder BE, Binzak JM, Kelley AE (2001) A common profile of prefrontal cortical activation following exposure to nicotine- or chocolateassociated contextual cues. Neuroscience 105:535–545. pmid:11516821 doi: 10.1016/s0306-4522(01)00221-4
  390. 99. Volkow ND, Fowler JS, Wang GJ (2003) The addicted human brain: insights from imaging studies. J Clin Invest 111: 1444–1451. pmid:12750391 doi: 10.1172/jci18533