Detection of reciprocal quantitative trait loci for acute ethanol withdrawal and ethanol consumption in heterogeneous stock mice.
- Authors
- Hitzemann, R; Edmunds, S; Wu, W; Malmanger, B; Walter, N; Belknap, J; Darakjian, P; McWeeney, S
- Year
- 2009
- Journal
- Psychopharmacology
- PMID
- 19052728
- DOI
- 10.1007/s00213-008-1418-y
- PMCID
- PMC5851459
RATIONALE: Previous studies have suggested that there is an inverse genetic relationship between ethanol consumption (two-bottle choice, continuous access) and ethanol withdrawal (e.g., Metten et al., Behav Brain Res 95:113-122, 1998a). OBJECTIVES: The current study used short-term selective breeding from heterogeneous stock (HS) animals to examine this relationship. The primary goal of the current study was to determine if reciprocal quantitative trait loci (QTLs) could be found in the selectively bred lines. The advantage of detecting QTLs in HS animals is that it is possible to extract a haplotype signature for the QTL, which in turn can be used to narrow the number of candidate genes generated from gene expression and sequence databases (see, e.g., Hitzemann et al., Mamm Genome 14:733-747, 2003). RESULTS: Seven reciprocal QTLs were detected on chromosomes (Chr) 1 (two), 3, 6, 11, 16, and 17 that exceeded the nominal LOD threshold of 10; genetic drift, which occurs during selection, dramatically increases the LOD threshold. The proximal Chr 1 QTL was examined in some detail. The haplotype structure of the QTL was such that the LP/J allele was associated with low withdrawal and high consumption. The QTL appears to be located in a gene-poor region between 170 and 173 Mbp. Based on available sequence data, two plausible candidate genes emerge-Nos1ap and Atf6alpha. CONCLUSIONS: The data presented here confirm some aspects of the negative genetic relationship between acute ethanol withdrawal and ethanol consumption. The QTL data point to the potential involvement of NO signaling and/or the unfolded protein response.
Ethanol consumption and acute ethanol withdrawal in HS4, generation 19, mice. For acute withdrawal, animals were first tested at T−30 min and T−10 min for baseline withdrawal scores. At T−0, animals were injected with 4 g/kg of ethanol. Testing for HICs began at T+2 h and continued for every hour up to 12 h. Animals were always phenotyped for consumption first. a The relationship between consumption and withdrawal for individual mice (N=348). b The relationship when the data are collapsed across families (N=48). Consumption is expressed as grams of ethanol consumed per 24 h. Withdrawal is expressed as the CAUC for HICs. To calculate the CAUC, the average baseline score was subtracted from the postethanol scores
LLM interpretation
This figure consists of two scatter plots showing the relationship between ethanol consumption (y-axis, g/kg) and acute ethanol withdrawal, measured as the CAUC for handling-induced convulsions (x-axis). Panel **a** displays individual mouse data (N=348) with a near-zero correlation ($r = -0.02, p > 0.68$), while panel **b** displays data collapsed across families (N=48) showing a weak negative trend ($r = -0.24, p > 0.09$). Neither plot shows a statistically significant correlation between consumption and withdrawal.
Short-term selection for acute ethanol withdrawal. a The response to selection in the High and Low responding lines. The average CAUC for the parents at each generation of selection is also illustrated. b The effects of selection on the corrected maximum HIC score (CMAX). c The effects of selection on the average baseline score. Asterisks indicate High line significantly different from respective Low line value. **p<10−2; ****p<10−4
LLM interpretation
This figure consists of three line graphs (a, b, and c) showing the effects of short-term selection on ethanol withdrawal across six generations ($S_0$ to $S_5$). Panels (a) and (b) show a divergence between the "High Line" and "Low Line" for Corrected Area (CAUC) and Corrected Maximum HIC (CMAX), with the High Line increasing significantly by $S_5$. Panel (c) shows a general decrease in Average Baseline HIC for both lines over time, with the High Line remaining significantly higher than the Low Line at $S_4$. Statistical significance is indicated by asterisks, with $p < 10^{-2}$ (**) and $p < 10^{-4}$ (****).
Time course for acute ethanol and acute pentobarbital withdrawal in animals selected for acute ethanol withdrawal at the S5 generation. a The time course for acute ethanol withdrawal. b The time course for acute pentobarbital withdrawal. All animals were drug naïve until testing. N=16 to 20 animals per line. Data are reported as the uncorrected HIC score
LLM interpretation
These two line graphs show the time course of HIC scores for the $S_5$ generation of "High Line" and "Low Line" animals following acute administration of ethanol (a) and pentobarbital (b). In both figures, the High Line exhibits a significantly higher peak in HIC scores compared to the Low Line, which remains relatively flat. The x-axes represent time points (B1 through T12 for ethanol; B1 through T8 for pentobarbital), and the y-axes measure the uncorrected HIC score.
Short-term selection for ethanol consumption (two-bottle choice, water vs 10% ethanol, continuous access). a The response to selection in the High and Low responding lines. Consumption in the parents at each generation of selection is also illustrated. b The effects of selection on ethanol preference. Preference is measured as the ratio of ethanol fluid consumed/total fluid consumed. c The effects of selection on total fluid consumed. Asterisks indicate High line significantly different from respective Low line value. ****p<10−4
LLM interpretation
This figure consists of three line graphs (a, b, and c) tracking ethanol consumption, preference, and total fluid intake across five generations ($S_0$ to $S_4$) for "High" and "Low" selection lines. Panel (a) shows a steady increase in ethanol consumption (g/kg) for the High line compared to the Low line and their respective parents, with significant differences (****p < $10^{-4}$) appearing from $S_2$ onwards. Panel (b) illustrates a corresponding increase in the ethanol preference ratio for the High line, while panel (c) shows that total fluid consumption remains relatively stable between both lines until a slight increase in the High line at $S_4$.
Time course of acute ethanol withdrawal in S4 animals selected for High and Low ethanol consumption. Data are reported as the uncorrected HIC score. N=20/line
LLM interpretation
This line graph shows the time course of HIC scores for "High Line" (filled circles) and "Low Line" (open circles) animals in the $S_4$ generation. The y-axis represents the HIC Score, and the x-axis tracks time points from baseline (B1, B2) through withdrawal (T0 to T12). The Low Line animals exhibit consistently higher HIC scores than the High Line animals, with both groups peaking around T8 and T10.
No entities extracted from this document yet.
No uploaded files.
In this knowledge base
External
| Title | Authors | Journal | Year | Link |
|---|---|---|---|---|
| Modeling Brain Gene Expression in Alcohol Use Disorder with Genetic Animal Models. | Hitzemann R et al. | — | 2025 | → |
| Cyfip2 allelic variation in C57BL/6J and C57BL/6NJ mice alters free-choice ethanol drinking but not binge-like drinking or wheel-running activity. | Hartmann MC et al. | — | 2023 | → |
| On the Use of Heterogeneous Stock Mice to Map Transcriptomes Associated With Excessive Ethanol Consumption. | Hitzemann R et al. | — | 2021 | → |
| Affective Disruption During Forced Ethanol Abstinence in C57BL/6J and C57BL/6NJ Mice. | Hartmann MC et al. | — | 2020 | → |
| Phenotypic and gene expression features associated with variation in chronic ethanol consumption in heterogeneous stock collaborative cross mice. | Hitzemann R et al. | — | 2020 | → |
| RNA-Seq Analysis of Genetic and Transcriptome Network Effects of Dual-Trait Selection for Ethanol Preference and Withdrawal Using SOT and NOT Genetic Models. | Kozell LB et al. | — | 2020 | → |
| Decoding the transcriptional programs activated by psychotropic drugs in the brain. | Zygmunt M et al. | — | 2019 | → |
| Using Heterogeneous Stocks for Fine-Mapping Genetically Complex Traits. | Solberg Woods LC et al. | — | 2019 | → |
| Apremilast Alters Behavioral Responses to Ethanol in Mice: II. Increased Sedation, Intoxication, and Reduced Acute Functional Tolerance. | Blednov YA et al. | — | 2018 | → |
| On the relationships in rhesus macaques between chronic ethanol consumption and the brain transcriptome. | Iancu OD et al. | — | 2018 | → |
| Effects of selection for ethanol preference on gene expression in the nucleus accumbens of HS-CC mice. | Colville AM et al. | — | 2017 | → |
| Identifying genes for neurobehavioural traits in rodents: progress and pitfalls. | Baud A et al. | — | 2017 | → |
| A Systems Approach Implicates a Brain Mitochondrial Oxidative Homeostasis Co-expression Network in Genetic Vulnerability to Alcohol Withdrawal. | Walter NA et al. | — | 2016 | → |
| PPAR Agonists: II. Fenofibrate and Tesaglitazar Alter Behaviors Related to Voluntary Alcohol Consumption. | Blednov YA et al. | — | 2016 | → |
| Dual-trait selection for ethanol consumption and withdrawal: genetic and transcriptional network effects. | Metten P et al. | — | 2014 | → |
| High-precision genetic mapping of behavioral traits in the diversity outbred mouse population. | Logan RW et al. | — | 2013 | → |
| Modeling the diagnostic criteria for alcohol dependence with genetic animal models. | Crabbe JC et al. | — | 2013 | → |
| Selection for drinking in the dark alters brain gene coexpression networks. | Iancu OD et al. | — | 2013 | → |
| Selective breeding for ethanol-related traits alters circadian phenotype. | McCulley WD et al. | — | 2013 | → |
| Behavioral actions of alcohol: phenotypic relations from multivariate analysis of mutant mouse data. | Blednov YA et al. | — | 2012 | → |
| Discovering genes involved in alcohol dependence and other alcohol responses: role of animal models. | Buck KJ et al. | — | 2012 | → |
| Selective breeding for magnitude of methamphetamine-induced sensitization alters methamphetamine consumption. | Scibelli AC et al. | — | 2011 | → |
| The influence of selection for ethanol withdrawal severity on traits associated with ethanol self-administration and reinforcement. | Ford MM et al. | — | 2011 | → |
| The influence of sex and estrous cycle on QTL for emotionality and ethanol consumption. | Izídio GS et al. | — | 2011 | → |
| A comparison of selected quantitative trait loci associated with alcohol use phenotypes in humans and mouse models. | Ehlers CL et al. | — | 2010 | → |
| Consilience of rodent and human phenotypes relevant for alcohol dependence. | Crabbe JC | — | 2010 | → |
| Genetic research: who is at risk for alcoholism. | Foroud T et al. | — | 2010 | → |
| The complexity of alcohol drinking: studies in rodent genetic models. | Crabbe JC et al. | — | 2010 | → |
| A verification of previously identified QTLs for cocaine-induced activation using a panel of B6.A chromosome substitution strains (CSS) and A/J x C57Bl/6J F2 mice. | Boyle AE et al. | — | 2009 | → |
| Differential activation of limbic circuitry associated with chronic ethanol withdrawal in DBA/2J and C57BL/6J mice. | Chen G et al. | — | 2009 | → |