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Original article 543

Genetics of behaviour: phenotypic and molecular study of rats derived from high- and low-alcohol consuming lines Elena Terenina-Rigaldiea , Marie-Pierre Moisana , Anthony Colasa , Françoise Beaugeć , Kavita V. Shahb , Byron C. Jonesb and Pierre Morme`dea The aim of this study was to analyse the genetic basis of excessive ethanol consumption and its relationship with emotional reactivity. The high-ethanol preferring line of rats used is characterized by a: (i) high voluntary consumption of alcohol; (ii) high sensitivity to taste reinforcement (saccharine, quinine); (iii) high locomotor activity in a novel environment; and (iv) low emotional reactivity, these features being opposite in the WistarKyoto (WKY) rat strain. The F2 population demonstrated a very large variability in these behavioural traits, and factor analysis revealed that these characteristics appear to be largely unrelated to each other. The molecular bases for these differences were investigated by quantitative trait loci (QTL) analysis. For this purpose, the 196 F2 rats were genotyped with regularly distributed markers on the whole genome, and genetic linkage maps were generated for all subsequent QTL analyses. A locus with a maximum LOD score of 7.6 and accounting for approximately 61% of the genetic variance of the trait in the F2 population was detected on chromosome 4 for alcohol drinking. In the same region, we found a QTL related to the reinforcement properties of saccharin, with a significant LOD score of 4.9 and explaining 46% of the variance of the trait. Other

Introduction Alcoholism is a complex behavioural trait that is influenced by multiple genes. A powerful approach to identifying the quantitative trait loci (QTL) influencing alcohol consumption and alcoholism is accomplished through the study of well-characterized animal lines, with behavioural phenotypes similar to those seen in humans. It has been well established that alcohol effects are influenced by genetic factors in animals [1– 11]. A QTL is a region of a chromosome that has been shown, through genetic mapping, to contain one or more genes contributing to phenotypic differences. Each QTL may contribute to only a small degree of these differences in sensitivity but, together, the several QTL that affect a trait (e.g. a response to a drug) essentially account for all the genetic contribution to differences in a particular phenotype with non-genetic factors also playing a major role [4].

significant QTL were found for plus maze behaviour and open field activity on chromosome 1. Current research aims to identify the gene(s) involved. Pharmacogenetics 13:543–554 & 2003 Lippincott Williams & Wilkins

Pharmacogenetics 2003, 13:543–554 Keywords: rat lines, alcoholism, behaviour genetics, multivariate analysis, quantitative trait locus a Laboratoire de Neurogeńe´tique et Stress, UMR 1243 INRA – Universite´ Victor Segalen, Inserm U471, Institut François Magendie, Bordeaux, France, b Department of Biobehavioral Health, The Pennsylvania State University, University Park, Pennsylvania, USA and c Centre de Recherches Pernod-Ricard, Cre´teil, France.

Sponsorship: The authors gratefully acknowledge support from Institut National de la Sante´ et la Recherche Me´dicale for a fellowship (Poste Orange) to B.C.J., the Mission Interministe´rielle de Lutte contre la Drogue et la Toxicomanie (MILDT, contract no. 96D08) and the Institut de Recherches Scientifiques sur les Boissons (IREB). Correspondence and requests for reprints to Pierre Morme`de, Laboratoire de Neurogeńe´tique et Stress, Institut François Magendie, rue Camille Saint-Sae¨ns, 33077 Bordeaux cedex, France. Tel: +33 5575 73751; fax: +33 5575 73752; e-mail: [email protected] Received 30 January 2003 Accepted 16 June 2003

has been extremely difficult in humans [12–14]. Recent efforts have therefore focused on the genetic dissection of complex traits in animal models to identify putative genes for study in humans [4,15].

Because of a multitude of confounding factors, identification and confirmation of QTL that confer a predisposition to alcoholism, a complex, composite disorder,

In recent years, we have come to recognize that alcohol use and misuse are not single entities but clusters of different conditions each with its own aetiology and outcome. Multiple typologies of alcoholism is an accepted concept among the scientific and clinical communities, with each type having its own characteristic set of related traits and genetic influence. For example, Cloninger’s [16] type I alcoholic is characterized as harm-avoiding and passive-dependent, whereas the type II alcoholic exhibits a more cavalier, noveltyseeking personality. Whether alcohol use and misuse is primary or a consequence of pre-existing personality traits remains to be seen. However, what is important is the heuristic value of recognizing different alcoholrelated syndromes and, subsequently, to develop animal models in an effort to understand behavioural and neurobiological mechanisms shared between types and

0960-314X & 2003 Lippincott Williams & Wilkins

DOI: 10.1097/01.fpc.0000054120.14659.8c

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544 Pharmacogenetics 2003, Vol 13 No 9

unique to each type. Moving among levels of investigation, the problem becomes one of how to use genetically defined animals to demonstrate relationships between alcohol and drug seeking (and other phenotypes) and other behaviours such as activity, fear and aggression, and then to show how these behaviours relate to neurobiology and genetics. In a recent study, Overstreet et al. [17] conducted a factor analysis of activity/reactivity in nine inbred strains and selected lines of rats. Using a battery of tests, these authors reported factors containing both reactivity measures (open field defecation, stimulus-induced ultrasonic vocalization, time in the open arm of the plus maze) and alcohol consumption in male rats.

generation (mWKY 3 fHEP), 45 males and 48 females; and Group 3, F2 generation (mHEP 3 fWKY), 52 males and 51 females.

The aim of the present study was to analyse the genetic basis of high ethanol consumption and its relationship with emotionality traits. We bred a segregating population (F2 intercross) between two contrasting rat strains differing for voluntary alcohol intake and emotional reactivity, as measured by their behaviour in the open field and elevated plus maze, and their sensitivity to gustatory stimuli. The high-ethanol preferring (HEP) line of rats [18] is characterized by a: (i) high voluntary consumption of alcohol; (ii) high sensitivity to taste reinforcement (saccharine, quinine); (iii) high locomotor activity in a novel environment; and (iv) low emotional reactivity, with these features being opposite in the Wistar-Kyoto (WKY) rat strain [19–21]. The F2 population demonstrated a very large variability in these behavioural traits and we report factor analysis using multiple activity/reactivity measures in genetically segregating animals and in both sexes. The inclusion of females is important because female rodents generally show consistently higher consumption of ethanol [22], and we believed it important to determine whether both sexes would demonstrate similar patterns of factors. Molecular mechanisms were investigated by the QTL approach based on the correlation between phenotypes and 131 genetic markers covering the genome at 10–20 cM. Genetic linkage maps were then generated for all subsequent QTL analyses.

Phenotyping

Methods Animals

Parental stocks of the inbred WKY rat strain were kindly provided by Edith Hendley (U. Vermont at Burlington, Vermont, USA) and parental stocks of the HEP rat line (from the S6 and S7 generations of selection) were kindly provided by Robert D. Myers (East Carolina University at Greenville, North Carolina, USA). The animals used for this study consisted of three groups of male and female rats from the parental strains and their reciprocal F2 intercross generations: Group 1, parental WKY (five males and 11 females) and HEP (eight males and eight females) rats; Group 2, F2

All animals were reared in our vivarium and were 60– 70 days of age at the beginning of the study. The ambient conditions in the vivarium were temperature, 218C and relative humidity at 40–50%. The light cycle was on a 12-h rotation with lights on at 06.00 h. Throughout the duration of testing, all animals were weighed weekly. All procedures used in this study were in compliance with the European Convention for the Protection of Vertebrate Animals Used for Experimental and Other Scientific Purposes, 18 March 1986.

All animals were subjected to a battery of behavioural tests and subsequently tested for consumption of solutions of saccharine, quinine and ethanol. The test battery is described (in order) below. Activity

Animals were placed into an automated activity monitor (length 38.5 3 width 23.5 3 height 23 cm, Imetronic Inc., Pessac, France) for 60 min with front-to-back traverses, rearings and total activity detected by infrared photo beams. Open field

The rats were placed for 5 min into an 1 m2 enclosure marked in 25 squares, 16 next to the 40-cm wall (outer squares) and nine located more centrally (inner squares). Behaviours were scored by hand and included the number of outer squares entered, number of inner squares entered, supported rearings (the animal leaning on or touching the wall), unsupported rearings and time spent in the inner squares. Plus maze

This apparatus and its application are described in detail elsewhere [20]. Briefly, it comprised a platform in the shape of an equilateral cross with two opposing arms enclosed by a wall and two opposing arms open but with a 1-cm lip. The animals were placed initially in the centre with a view of open and closed arms. The duration of the test was 5 min and behaviours measured included open arm entries, closed arm entries and time spent in each type of arm and in the centre. Two-choice saccharine

All rats were given a choice of fluids to drink between 7.5 mmol saccharine (as the sodium salt) in tap water and tap water on two consecutive days. The bottles containing the fluid were weighed, refilled and rotated for position each day within the same 1-h interval. The data were converted to a saccharine index, the volume of saccharine plus the volume of water consumed in


Molecular genetics of alcohol drinking in rat Terenina-Rigaldie et al. 545

the same 24-h period divided by the normal volume of water consumed in 24 h (measured over the 2 days before presentation of saccharin), expressed in percentage.

lite markers were purchased from Eurogentec (Seraing, Belgium), or from Research Genetics (Huntsville, Alabama, USA). Genomic DNA was prepared from lung tissue following standard DNA extraction protocols by classical phenol/chloroform method.

Two-choice quinine

This followed the same protocol as for saccharine, with concentration of quinine of 2 mol. Forced ethanol

All animals were given 10% (v/v) ethanol as their sole source of drinking fluid for 2 days. Volumes consumed were taken each day and the position of the bottle changed. Data are expressed in g/kg for each of the 2 days. Two-choice ethanol

Immediately following forced ethanol, the animals were given a choice between water and 10% (v/v) ethanol. The bottles containing water and ethanol were weighed (and refilled if necessary) every 2 days over 14 days (i.e. for seven 2-day periods). Data are expressed as alcohol (g) per kg body weight and in selection ratios (volume of alcohol in ratio to volume of alcohol plus water expressed as percentage). Ethanol consumption versus concentration

To test the hypothesis that some animals may adjust their alcohol intake, based on its concentration and thereby achieve consistent alcohol intake, we varied alcohol concentrations at 2.5, 5.0, 15.0 and 20.0% (v/v). The procedure was two-choice as with 10% ethanol. The animals were divided into four groups and the order of presentation of the different concentrations was based on a replicated Latin square design. Alcohol disappearance rates in the parental lines

Separate groups of parental HEP and WKY male and female rats were tested for alcohol disappearance rates. All animals were injected i.p. with ethanol (20% v/v in 0.9% saline) at a dose of 1.3 g/kg. At 30, 60, 120, 165 and 210 min after administration, 100 l of blood was taken by microcapillary tube from the retro-orbital sinus of each animal. Blood ethanol concentrations (BEC) were determined by the micro-enzymatic method [23]. Zero-order disappearance rates for ethanol were determined by linear regression analysis of BEC versus time with data expressed in mg per 100 ml/h. Genotyping

Rat microsatellite markers were used for polymerase chain reaction (PCR). We initially selected markers polymorphic for H 3 W F2 rats. From the 650 markers tested, 150 were polymorphic (23%). A total of 131 informative markers were chosen to cover the genome at 10–20-cM intervals and genetic linkage maps were generated for all subsequent QTL analyses. Microsatel-

Genotype determinations were performed by PCR. In a 20-l reaction volume, 50 ng of genomic DNA was mixed with 5 pmol of each primer and 0.4 U of Taq DNA polymerase (Promega, Charbonnie`res, France) in Promega type A buffer. Amplification was performed in microtitre plates on a Hybaid OmniGene thermocycler (Hybaid Limited, Teddington, UK). The PCR conditions were: initial denaturation at 968C for 4 min, followed by 35 cycles of 928C for 40 s, 55–638C (depending on the microsatellite) for 1 min, and 728C for 30 s, then a final extension period at 728C for 2 min. PCR products were separated on 3% agarose gels and visualized with ethidium bromide staining under ultraviolet light. The homologous loci used for the construction of the comparative rat, mouse and human genetic maps were compiled from the following databases: the ARB Rat Genetic database (http://www.nih.gov/niams/scientific/ ratgbase); the RATMAP (http://ratmap.gen.gu.se); the Genome Database (GDB; http://gdbwww.gdb.org); the Mouse Genome Database (MGD; http://www.informatics. jax.org); the Seldin/DeBry Human/Mouse Homology Map (http://www3.ncbi.nlm.nih.gov/Homology) and the Otsuka Pharmaceutical Co. comparative maps (http:// ratmap.ims.u-tokyo.ac.jp/cgi-bin/comparative_home.pl). Statistical analysis

All univariate analyses for parental stocks were performed using two between-subjects variables (rat line and sex) analysis of variance (ANOVA). Effect size was estimated by estimated ø2 [24]. In the F2 population following ANOVA (for sex and reciprocal type, Group 2 versus Group 3), it was apparent that there were differences between the means for some phenotypes based on reciprocal breeding. These differences are difficult to interpret because the animals were tested in two separate cohort groups. Alternatively, there are also some sex differences (Table 1); however, there were no significant interactions between sex and cohort group. Thus, the data were corrected for cohort effect before principal components analysis. Principal components analysis (PCA) is a standard multivariate statistical technique that identifies commonalties among a set of dependent variables. The major advantage of PCA, compared to ordinary correlational analysis, is that it can uncover so-called latent associations [i.e. patterns of relationships (factors) that appear when controlling for the influence of other variables]. Thus, PCA is useful for examining relationships among variables that might otherwise seem to be unrelated. For the purposes of



Table 1.

Means and SD for behavioural measures

Behaviour Automated back and forth Males Females Open field outer squares entered Males Females Open field inner squares entered Males Females Open field total squares entered Males Females Plus maze closed arm entries Males Females Plus maze open arm time Males Females Plus maze centre time Males Females Quinine consumption Males Females Saccharine consumption Males Females Forced ethanol Males Females Two-choice (2.5% g/kg) Males Females Two-choice (5% g/kg) Males Females Two-choice (10% g/kg) Males Females Two-choice (15% g/kg) Males Females Two-choice (20% g/kg) Males Females

Number of rats 195 96 99 196 97 99 196 97 99 196 97 99 195 97 99 195 97 99 195 97 98 196 97 99 196 97 99 196 97 99 196 97 99 196 97 99 196 97 99 196 97 99 196 97 99

Mean

SD

116 113 120 (NS) 87 77 96 13.4 11.4 15.3

41 46 36 26 24 24 8.6 7.8 9.0 100 30 89 28 112 28 5.8 3.6 5.2 3.0 6.4 4.0 26.4 26.4 15.7 21.8 27.5 29.3 16.3 17.5 18.5 20.1 14.3 (NS) 14.3 29.4 19.7 27.5 18.5 31.3 (NS) 20.8 222 126 194 108 250 136 4.9 1.38 4.2 1.13 5.6 1.26 1.3 1.32 1.0 0.72 1.7 1.21 2.1 1.53 1.7 1.16 2.5 1.74 2.4 1.83 1.8 1.37 2.9 2.08 2.5 1.78 2.0 1.29 3.0 2.05 2.3 1.46 1.9 1.16 2.7 1.61

Differences between the sexes: P , 0.01; P , 0.001.

this study, we employed PCA to search for associations among alcohol-related and indices of activity/reactivity. Multivariate analyses were performed using PCA with varimax rotation on standardized data obtained from F2 animals, male and female cohorts being analysed separately. Factors with eigenvalues greater than one were considered to be significant and variables with loadings greater than 0.5 were judged to be important for that factor. The behavioural data were corrected as above for both sex and cohort effects, and standardized; phenotype– genotype relationships were studied by analyses of variance and specific mapping programs. Genetic maps were generated using Mapmaker/EXP (version 3.0b) [25]. QTL were detected in scans using MapMaker/ QTL (version 1.1) [25,26]. Linkage analyses were confirmed by computer program Map Manager

QTXb13 [27]. In virtually all cases, the genetic maps produced with the H 3 W F2 rats correlated very well with those published online (http://www.informatics.org/locus.html and http://ratmap.gen.gu.se). A QTL was considered as suggestive when the LOD score reached 2.8 and significant when the LOD score reached 4.3, as suggested by Lander and Kruglyak [28].

Results Parental line analysis Activity measures

Figure 1 illustrates performance, both in the automated activity monitor (Fig. 1a) and in the open field (Fig. 1b,c). For the automated activity data, ANOVA revealed a main effect of rat line (F1,28 ¼ 142.48, P , 0.001) with no effect for sex nor line by sex interaction observed. The proportion of variance accounted for by rat line was 0.79 (estimated ø2 ). A similar result was observed for total squares entered in the open field. The effect for rat line was significant (F1,28 ¼ 132.14, P , 0.001) with approximately 80% of the total variance attributable to rat line. In both measures, the HEP rats generated two- to four-fold higher activity scores than the WKY rats.

Elevated plus maze

Closed arm entries and time spent in the open arms are presented in Fig. 1(d,e). We observed a main effect of line (F1,28 ¼ 24.39, P , 0.001), but no sex or sex by line interaction. The HEP rats showed more closed arm entries than did the WKY rats and the proportion of variance accounted was 0.44 (estimated ø2 ). For the measure, time spent in the open arms, we again observed a significant main effect of line (F1,28 ¼ 4.21, P , 0.05). The HEP rats spent nearly three times the amount of time on the open arms, compared to WKY rats.

Saccharine and quinine free choice tests

Figure 1(f) presents the index for saccharine consumption in a free-choice test (versus water). There was a main effect for rat line on both days (F1,28 ¼ 4.69, P , 0.05; F1,28 ¼ 4.63, P , 0.05, respectively) with HEP rats showing greater indices than WKY rats. The proportion of variance attributable to rat line was 0.11 on day 1 and 0.10 on day 2. There was no significant effect for sex or interaction between line and sex. The results for quinine (not shown) also showed a strain effect for day 1 (F1,28 ¼ 5.17, P , 0.03); however, the results for day 2 failed to reach significance (F1,28 ¼ 3.76, P , 0.07). Again, HEP rats evinced a greater consumption of quinine than the WKY rats with the proportion of variance accounted for by strain estimated at 0.11.



Fig. 1

WKY - males WKY - females HEP - males HEP - females 40

140 b

c

180

140 120 100 80 60 40 20 0 8

30

100

25 80 20 60 15 40

10

20

5 0 350

0 30 e

f 25

6 5 4 3

20 15 10

2 1 0

5 0

EMP – time in open arms (sec)

d 7 EPM – closed arms entries

35

120

OF – number of squares crossed

160

300 250 200 150 100

saccharine reinforcement index

a OF – number of squares crossed

activity cages – locomotion (units/1 hr)

200

50 0

(a) Number of front-to-back traverses of the cage made for 1 h in automated activity monitors. Number of squares visited in the periphery (b) and in the centre (c) of the open field for 5 min. (d) Number of closed arm entries and (e) total time spent in the open arms for 5 min in the elevated plus maze. (f) Increase of fluid consumption induced by the availability of a saccharine solution (7.5 mmol) in free choice with water for two consecutive days. Data reported consists of the volume of saccharine plus the volume of water consumed in the same 24-h period divided by the normal volume of water (i.e. without choice) consumed in 24 h, expressed as percentage.

Ethanol consumption

Figure 2 presents the results of the forced ethanol and 14-day two-choice of ethanol (10% v/v) versus water. For the forced ethanol test, we observed significant line and sex main effects on day 1 (F1,28 ¼ 41.70, F1,28 ¼ 63.32, P , 0.001, estimated ø2 ¼ 0.33, 0.52, respectively) and on day 2 (F1,28 ¼ 5.74, F1,28 ¼ 29.98, P , 0.05, 0.001, estimated ø2 ¼ 0.08, 0.33, respectively). Overall, HEP rats consumed more ethanol than WKY rats and females consumed more than males. For the seventh 2-day block of two-choice ethanol (10%) test, ANOVA revealed a significant effect for line (F1,28 ¼ 44.29, P , 0.0001) and sex (F1,28 ¼ 33.95, P , 0.001), without significant interaction. Line accounted

for 38% of the variance and sex accounted for 29% of the overall variance. Overall, HEP rats consumed more ethanol than did the WKY rats and females consumed more than males (seventh block of free choice: WKY males 0.56 0.19 g/kg/day, preference 10.2 3.7%, n ¼ 5; WKY females 2.54 0.50 g/kg/day, preference 45.7 10.9%, n ¼ 11; HEP males 3.05 0.56 g/kg/day, preference 51.1 9.9%, n ¼ 8; HEP females 7.06 0.63 g/kg/day, preference 84.9 5.8%, n ¼ 8). We also observed a significant line-by-day interaction (F6,168 ¼ 2.96, P , 0.05). This interaction reflects an overall trend for the HEP rats of both sexes to increase their ethanol consumption across the seven 2-day blocks. Further analysis of difference values, block 7 minus block 1, revealed a main effect for line (F1,28 ¼ 8.93,



Fig. 2

10

ethanol drinking (g/kg.day)

10 WKY – males WKY – females HEP – males HEP – females

8

8

6

6

4

4

2

2

and sex (F1,28 ¼ 108.50, 51.81, respectively, P , 0.001, estimated ø2 ¼ 0.50, 0.24, respectively). The interaction between line and sex was also significant (F1,28 ¼ 25.10, P , 0.001, estimated ø2 ¼ 0.11). As before, we observed that the HEP rats consumed more ethanol overall than the WKY rats and females more than males. Additionally, as the concentration of ethanol increased, there was a tendency for consumption to increase but, between 10% and 20%, we observed an increase in the HEP females but a decrease in the HEP males and WKY females. Ethanol disappearance and metabolism

0

⫺2 ⫺1 1 days forced intake

2

3 4 5 6 blocks of 2 days free choice

7

0

Ethanol consumption. Left: forced consumption. All animals were given 10% (v/v) ethanol as their sole source of liquid for two consecutive days. Data reported are ethanol (g) per kg body weight. Right: twochoice ethanol (versus water) consumption across 14 days. Animals were given a choice between ethanol (10% v/v) and tap water. Data reported are ethanol (g) per kg body weight per day averaged for seven 2-day blocks.

P , 0.01, estimated ø2 ¼ 0.20) with the HEP rats showing a greater increase in ethanol consumption compared to WKY rats. Figure 3 presents the results of ethanol consumption at concentrations of ethanol varying between 2.5 and 20% (v/v). Analysis of variance revealed main effects for line

Fig. 3

10

ethanol intake (g/kg.day)

9 8

10 WKY – males WKY – females HEP – males HEP – females

9 8

For the WKY rats, the disappearance rate for males was 33.6 1.1 mg/dl/h and 41.3 3.1 mg/dl/h for females (315 6 and 379 8 mg/kg/h for males and females, respectively). For the HEP rats, ethanol disappearance rates were 29.6 5.5 versus 38.6 10.1 mg/dl/h (288 12 and 327 21 mg/kg/h for males and females, respectively). Principal component analysis of reactivity and ethanol behaviours in WXH and HXW F2 rats

Table 1 presents means and standard deviations for all of the phenotypes measured, by sex for the F2 generation of animals. Table 2 presents principal component results separately for males and females. Principal component analysis, using varimax rotation identified four factors for males and females. For the males, the first factor loaded forced ethanol and consumption of all ethanol concentrations, except for 2.5%. The second factor contained loadings from activity and elevated plus maze measures and the third factor concerned saccharine and quinine intake. The fourth factor showed a negative loading of time spent in the centre of the open field and a positive loading of consumption of 2.5% ethanol. For females, factor loadings were quite similar with small differences. The measures of ethanol consumption were split into two factors (1 and 3). Factor 2 included all of the automated activity measures, open field measures and elevated plus maze indices. Factor 4 showed negative loadings for saccharine and quinine consumption.

7

7

6

6

5

5

4

4

Molecular genetics

3

3

2

2

1

1

Several QTLs were identified for alcohol intake, taste reinforcement and emotionality traits. Significant association results are given in Table 3 and Fig. 4.

0

2.5

5.0 10.0 15.0 alcohol concentration (% v/v)

20.0

0

Ethanol consumption versus ethanol concentration. All animals were given a choice between ethanol at concentrations of 2.5, 5, 15 and 20% (v/v) and water for two days each in a balanced Latin-square order of presentation. The values for 10% ethanol are the values from the seventh two-day block of two-choice testing (Fig. 2). Data reported are in grams ethanol per kilogram body weight per day.

A locus with a maximum LOD score of 7.6 was detected on chromosome 4 for alcohol consumption. This QTL (named Coet5, for the ethanol 5% consumption in two-bottle choice versus water) accounted for approximately 61% of the total variance of the trait in the F2 population. Figure 5 shows the effect of the genotype at Coet5 on the phenotype (5% alcohol consumption) of F2 animals compared to the values



Table 2 Varimax rotated factor loadings for reactivity and alcohol-related behaviours in male and female F2 rats derived from high-ethanol preferring (HEP) and Wistar-Kyoto (WKY) matings Males Measure

Factor 1

Activity Open field total squares Open field time in centre Plus maze closed arm entries Plus maze open arm time Saccharine Quinine Ethanol measures Forced ethanol 2.5% 5% 10% 15% 20% Explained variance (% of total)

Table 3

Factor 2

Females Factor 3

Factor 4

Factor 1

Factor 2

0.718 0.718

Factor 3

Factor 4

0.709 0.812 0.581 0.533 0.605

0.732 0.787 0.548

0.829 0.875

0.852 0.789 0.681

0.710 0.603

0.760 0.799 0.845 0.775 24.1

0.636 16.1

11.6

Relevant association results on the total F2 population (n

Tests Two-choice water/5% ethanol Two-choice water/saccharine (7.5 mmol) Plus maze (time in the centre) Open field (total activity) Forced ethanol (10%) Plus maze (time in open arms) Two-choice water/saccharine Two-choice water/10% ethanol (for seven 2-day periods) Plus maze (time in the centre) Two-choice water/5% ethanol

0.807 0.788

Chr

Marker + cM

4 4 1 1 2 1 6 4 8 6

D4Rat124 + 2.0 D4Rat52 + 2.4 D1Mgh8 + 2.8 D1Mgh7 + 4.2 D2Rat118 + 2.9 D1Mgh8 + 3.3 D6Rat148 + 5.9 D4Rat57 + 4.8 D8Rat2 + 5.2 D6Rat46 + 7.3

10.3

17.3

17.0

0.777 0.533 0.578 16.1

12.3

196) LOD % Var 7.6 4.9 4.9 4.6 4.1 4.0 3.9 3.6 3.6 3.1

61 46 29 24 18 32 31 35 32 38

H/H

H/W

W/W

F

P

2.4 0.3 310.0 20.7 19.0 2.1 120.8 14.4 6.0 2.0 56.0 2.6 221.2 6.7 2.6 0.4 58.2 1.5 3.6 0.8

1.8 0.2 271.4 15.4 15.0 1.1 107.0 12.0 5.4 1.6 51.3 1.2 211.0 3.6 3.1 0.5 52.4 2.2 3.0 0.4

0.8 0.2 219.5 12.1 9.0 2.4 86.0 9.6 5.1 1.6 49.0 3.1 169.5 9.0 3.3 0.4 49.8 2.9 2.1 0.9

8.6 6.9 7.6 6.5 6.1 4.9 6.4 7.7 3.9 3.6

3.0 3 105 1.6 3 105 1.0 3 105 3.9 3 104 2.2 3 104 3.5 3 104 3.9 3 104 1.5 3 104 5.0 3 103 3.0 3 103

Chr, Chromosome; Marker + cM, the closest marker and distance to peak of quantitative trait loci (QTL); LOD, LOD score of QTL; F and P, data of ANOVA between the three genotype; % Var, percentage of the variation of a phenotype, explained by QTL in the analysed population; W/W, H/W, H/H, means values of phenotype of rats, respectively, homozygotes for the alleles Wistar-Kyoto (WKY), heterozygotes and homozygotes for the alleles high-ethanol preferring (HEP) of closest markers to peak of QTL.

measured in the grandparent lines. The genotype at the QTL influenced drinking of 5% ethanol solution (F2,189 ¼ 8.57, P , 0.001), with WKY alleles decreasing the magnitude of the phenotype, without any influence of sex (F1,189 ¼ 2.44, P . 0.10) or sex–genotype interaction (F2,189 ¼ 0.30). In the same region, a significant QTL was mapped related to the increase of fluid consumption when a saccharin solution (7.5 mmol) was freely available in a two-bottle choice with water (significant LOD score of 4.9) and a suggestive QTL related to alcohol drinking in the two-bottle choice between water and ethanol 10% (LOD score of 3.6, accounting for 35% of the total variability). The phenotypic effect of this QTL was inverted compared to the parental lines (i.e. the F2 rats with HEP alleles drank more than F2 rats with WKY alleles) (Table 3). However, the phenotypic differences between the groups (homozygous for the WKY alleles, heterozygous and homozygous for the HEP alleles of the closest markers to peak of QTL) did not reach significance and no interaction was found between the genotype at the QTL and sex.

Significant associations were found for the time spent in the centre of the elevated plus maze and open field activity on chromosome 1 with LOD scores 4.9 and 4.6, respectively. The whole genome search revealed six provisional QTL, with suggestive LOD scores of between 3.0 and 4.1. These provisional QTL are located on chromosomes 1, 2, 4, 6 and 8. It is interesting to note that no interaction was found between the effect of QTL and sex or direction of the initial cross.

Discussion Behaviour genetic analysis involving two groups of genetically defined animals will invariably reveal differences in several behavioural phenotypes. In the present case, we observed large differences in activity, anxietylike and consummatory behaviours. The obvious question becomes one of how (if at all) these behaviours are related and more importantly if they are under common genetic influence. Of course, with only two types of animals, this question is virtually unapproachable. Solutions to the problem include adding more genetically defined groups of animals for genetic correlation analy-



D4Mit20 D4Wox16

D4Mgh7

D4Rat57

D4Mgh15

D4Rat24

D4Rat14

D4Mgh22

(a)

D4Rat52 D4Mit17

Npy D4Mit7 D4Rat34 Coet5 D4Rat124

Fig. 4

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(a) LOD scores for several quantitative trait loci (QTL) on chromosome 4: ethanol intake in a two-bottle choice between water and ethanol 5% (v/v) (significant LOD score of 7.6) and ethanol 10% (v/v) (suggestive LOD score of 3.6), increase of fluid drinking when saccharine (7.5 mmol) is available (significant LOD score of 4.9). Npy, position of plausible candidate gene Npy; Coet5, QTL for alcohol drinking identified in present study; (b) LOD scores for two QTLs on chromosome 1, for the time spent in the centre of the elevated plus maze (significant LOD score of 4.9) and for the total activity score in the open field (significant LOD score of 4.6).



animals, we found little evidence of subject characteristics, other than their ethanol distinguishing characteristics as contributory to their ethanol consumption. These results are coherent with the data reviewed by Overstreet et al. [29] showing that there are only limited relationships between measures of emotional reactivity and alcohol intake among lines of rats with diverging ethanol preferences.

Fig. 5

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CoEt5 Mean ethanol intake in the two-bottle choice between water and ethanol 5% (v/v) in the parental lines on the left part [high-ethanol preferring (HEP) and Wistar-Kyoto (WKY)] and in the F2 rats according to their genotype at the Coet5 locus on the right part. W/W, H/W, H/ H, respectively, homozygous for the WKY alleles, heterozygous and homozygous for the HEP alleles of closest markers to peak of quantitative trait loci Coet5. M, males; F, females.

sis, or to intercross the two original types twice (F1 to F2 or backcross) to produce genetically segregating populations. Either a phenotypic correlational analysis can be performed or, if there are multiple measures and the aim is to discover overt or latent factors that combine several factors, a factor analysis may be performed. In the present study, we chose the latter approach, by producing a genetically segregating population of animals derived from two parental lines chosen primarily for their differences in ethanol consumption. In addition, the parental lines differed nearly on every task performed, from automated activity to consumption of saccharine and quinine. We thus believed that these lines would provide a good starting place to investigate putative subject characteristics other than alcohol consumption that would be related to this latter phenotype. At least for females, factor analysis revealed alcohol or tastant or emotional reactivity factors that collected measures of the same kind only, even at different concentrations of ethanol. However, for males, although the first three factors were related to alcohol, or activity or tastants, the fourth factor contained two seemingly disparate measures, time spent in the centre of the open field with a negative loading and consumption of 2.5% ethanol. Thus, those animals showing avoidance of the centre of an open field (i.e. showing more timidity-like behaviours) might be expected to consume more of the low concentration of ethanol and vice versa. However, in the main, with these groups of

Although this might be seen as a lack of support by way of animal models for the typologies proposed by Cloninger [16] and Babor et al. [30], it must be recognized that the kinds of phenotypes we measured are in fact polygenic, together with the fact that using narrowly genetically defined animals likely results in failure to select for many alleles relevant to both alcohol consumption and activity/reactivity. Thus, a similar study with different groups of animals chosen for their differences in activity/reactivity could produce a different outcome. Moreover, the models proposed by Cloninger [16] and Babor et al. [30] apply to people with long-standing alcohol-related difficulties. Rather little is known about individual characteristics and the initial approach to and consumption of ethanol and, more importantly, how these factors might be related to the development of alcohol-related difficulties. Finally, we observed sex differences not only in the amount of ethanol consumed, but also in that the factors underlying ethanol consumption appear to be different between the sexes. We observed two fairly robust factors for ethanol consumption in the females, one that collected mostly higher concentrations of ethanol and one that contained mostly lower concentrations of ethanol. Moreover, both factors containing ethanol had ethanol measures only. In males, we observed a similar pattern for the first three factors as was observed for the females; however, we observed only one factor containing ethanol consumption measures. This finding provides evidence showing, at least in these rats, that female ethanol consumption may be both quantitatively and qualitatively different from male ethanol consumption. This has been confirmed at the molecular level by the finding of sex-specific factors [10]. Recently, progress was made in identifying specific chromosome regions (i.e. QTL) that are relevant to alcohol abuse and development of physical dependence to alcohol and other drugs. More QTL mapping work has been carried out for alcohol-related responses than for other drugs of abuse, mostly in mice [1,4,6,9–11, 31–38] but also in rats [3,39]. We found here a locus on chromosome 4 (Coet5) with a highly significant influence on the amount of alcohol consumed when offered as a free choice between water and a 5% (v/v) ethanol solution; this locus influences ethanol drinking



equally in both sexes. This locus is approximately 5 cM distal from a previously described QTL influencing alcohol preference in an F2 population from alcoholpreferring (P) by alcohol-nonpreferring (NP) rats [3]. Indeed, the HEP line was obtained by initially crossing male P and female Sprague-Dawley rats, and the present study is therefore a confirmation of a role for this locus in ethanol preference. The fact that this locus has the same influence in males and females, and that the F2 rats homozygous for the HEP allele at the locus do not reach the same level of alcohol intake as the grandparental line, suggests that other loci are also involved in the difference between these lines. Indeed, another QTL (suggestive only) was found in chromosome 4 with a LOD score of 3.6. The phenotypic effect of this QTL was inverted compared to the parental lines (i.e. the F2 rats with HEP alleles drank less than F2 rats with WKY alleles) (Table 3). It is interesting to note that this QTL is localized approximately in the same region as another previously described QTL [40,41] in a Lewis 3 SHR F2 intercross. The direction of the phenotypic effect of this QTL (named Ofil1, for open field inner locomotion) is at the opposite of the parental lines. It is worth noting that the SHR strain used by Ramos et al. [40] derives from the WKY strain used in the present study [42], and therefore carries part of the WKY genome. This QTL might link emotional reactivity and alcohol drinking, and this hypothesis is currently under investigation. The gene Npy was mapped in the same region of rat chromosome 4 as Coet5 (Fig. 4a). It encodes neuropeptide Y (NPY), a 36 amino acid neuropeptide widely expressed throughout the nervous system and particularly in the hypothalamus, where it is involved in the regulation of food intake and energy balance. Indeed, NPY has been shown to influence alcohol consumption. Lower concentrations of NPY have been measured in the amygdala of alcohol-preferring P and HAD rats compared to NP and LAD rats, respectively, but disparate findings were found in other regions, including the hypothalamus [43–44]. Tissue levels of NPY were also shown to be influenced by chronic alcohol intake or withdrawal, although these changes could result from modifications in food intake [43,45]. Thiele et al. [46] studied genetically engineered mice in which the Npy gene was either knocked-out (KO) or overexpressed. In KO mice that lacked the gene for NPY, alcohol preference and consumption were increased, and the sensitivity to alcohol, as measured by its sedative/hypnotic effects, was reduced. Preference for other tastants (sucrose and quinine) was not modified, as well as food and water intake, and body weight. In mice overexpressing NPY, alcohol preference and consumption were decreased, and the sensitivity to ethanol sedative effects increased. Anxiety-like behaviours, as measured in the elevated plus maze, were not modi-

fied. These data show the selectivity of changes in NPY expression for alcohol-related behaviours. Finally, the repeated administration of NPY in the cerebral ventricles inhibits the development of tolerance to the hypothermic effect of ethanol [47]. In humans, no correlation was found between NPY levels in the cerebrospinal fluid of male alcoholics compared to controls [48]. On the other hand, two out of three studies found an association between a genetic polymorphism in the signal peptide part of NPY and alcohol drinking [49–51]. Together, these data reinforce the hypothesis that Npy is a candidate gene in the aetiology of alcohol-related disorders. Our molecular genetic results also suggest that a possible relationship exists between alcohol and saccharin consumption because a QTL was found in the same region for both traits. In rodents, the volume of ethanol consumed depends in part on its flavour [52], but the relationship between intake and flavour is not well understood. A sweet component to alcohol taste has been demonstrated in rats, which is consistent with several studies showing that the proclivity to drink alcohol is associated with elevated sweet preferences [29,53]. Alcohol-preferring rats have a tendency to consume sucrose and saccharin solutions far beyond the limits of their normal fluid intake, or otherwise prefer quinine-adulterated solutions, and this has been proposed to be a model of clinical phenomenons known as loss of control or sensation seeking [29,54–56]. Furthermore, some rat and mice strains genetically bred to prefer alcohol tend to choose more concentrated sweet solutions compared to animals which do not prefer alcohol [57–59]. Similar tendencies to prefer ultra-sweet solutions have been noted in studies of alcoholic subjects, with most alcoholics preferring sweeter sucrose solutions than controls [60]. However, in other experiments, no such correlation was found between alcohol and sweet solution intake [19,61,62]. Furthermore, among strains selected for high alcohol drinking, some eventually prefer sweet solutions to alcohol whereas, in other strains, alcohol drinking is unaffected by the concurrent presentation of sweet solutions [61,63–65]. In mice, a high genetic correlation between saccharin and alcohol intake was reported in 15 inbred mouse strains [58]. A similar correlation between sucrose consumption and alcohol intake can be seen in the F2 generation of crosses between alcohol-preferring C57BL/6ByJ and alcohol-avoiding 129/J strains of mice [57]. The data of Bachmanov et al. [57] suggest that differences in ethanol and sucrose intake between the C57BL/6ByJ and 129/J strains of mice depend on a few to many genes. These gene sets partly overlap between sucrose and ethanol intakes. The presence of a genetically determined link between the consumption of ethanol and that of sweet solutions may result from pleiotropy, and this can be explained



by several physiological mechanisms, including the sensitivity to the sweet-taste component of ethanol flavour [66], or common brain mechanisms [67]. Much of the difference in sweetener preferences among mouse strains is attributed to allelic variation of the saccharin preference (Sac) locus on the distal part of chromosome 4 [35,68]. In addition to sweetener preferences, the Sac genotype influences the afferent responses of gustatory nerves to sweeteners [69–70], suggesting that the Sac gene is involved in peripheral taste transduction and may encode a sweet-taste receptor. In the P/NP lines, Foroud et al. [71] showed that QTL related to saccharin preference were not colocalized with the loci influencing alcohol drinking. A search of the Rat Genome Database yielded potential candidate genes for a number of the QTL mapped in the present study; however, no potential candidate gene could be identified for the other putative QTL listed in Table 1 (on chromosomes 1, 2, 6 and 8). Clearly, with the current state of knowledge, it is not possible to state whether any of these potential candidate genes are involved in determining alcohol preference. In conclusion, the present results clearly demonstrate that, at least in the genetic model studied here, the level of voluntary alcohol drinking is a specific trait largely independent of general emotional reactivity factors or sensitivity to reinforcement by saccharin. Molecular investigations confirm the implication of a locus on rat chromosome 4 in alcohol drinking, where the gene coding neuropeptide Y is located. Further studies should investigate in more detail the implication of this neuropeptide in alcohol-related phenotypes. Positive findings would open new avenues to the treatment of alcohol-related disorders.

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Acknowledgements The authors thank Dr Robert D. Myers (East Carolina U., Greenville, North Carolina, USA) for supplying HEP breeders, Dr Edith D. Hendley (University of Burlington, Vermont, USA) for supplying WKY breeders, and Yannick Mellerin for his assistance in the care and breeding of the animals.

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