Tyler A. Roy, Jason A. Bubier, Price E. Dickson, Troy D. Wilcox, Juliet Ndukum, James W. Clark, Stacey J. Sukoff Rizzo, John C. Crabbe, James M. Denegre, Karen L. Svenson, Robert E. Braun, Vivek Kumar, Stephen A. Murray, Jacqueline K. White, Vivek M. Philip, Elissa J. Chesler
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引用次数: 0
摘要
物质使用障碍是一种以强迫性使用毒品为特征的遗传性疾病,其生物学机制在很大程度上仍不为人所知。遗传相关性显示,易受药物影响的表型(包括焦虑、抑郁、新奇偏好和感觉寻求)可预测药物使用表型,从而暗示了共同的遗传机制。利用基因敲除(KO)小鼠进行高通量行为筛选可以有效地发现基因的功能。我们在两轮候选基因优先排序中使用了这一策略,根据易吸毒表型确定了 33 个吸毒候选基因,并最终验证了 22 个基因的扰动是药物摄入的因果驱动因素。我们选择了 19/221 个 KO 株系(8.5%),这些株系至少在一种药物新药预测行为表型上与对照有差异,并确定 15/19 株系(约 80%)影响了酒精、甲基苯丙胺或两者的消费或偏好。没有突变体在尼古丁消费或偏好方面表现出差异,这可能与糖精有关。在第二轮优先排序中,我们采用了多元方法来识别异常值,并使用甲基苯丙胺双瓶选择和乙醇暗饮方案进行了验证。我们确定了 15/401 株 KO 株系(占 3.7%,其中包括第一组中的一个基因)在易感表型方面与对照组差异最大。15 个基因缺失中有 8 个(53%)会影响酒精、甲基苯丙胺或两者的摄入量或偏好。通过多变量和生物信息学分析,我们观察到易感行为和药物摄入之间存在多种关系,揭示了这些关系背后许多不同的生物行为过程。本研究确定的一系列小鼠模型可用于进一步描述这些与成瘾相关的过程。
Discovery and validation of genes driving drug-intake and related behavioral traits in mice
Substance use disorders are heritable disorders characterized by compulsive drug use, the biological mechanisms for which remain largely unknown. Genetic correlations reveal that predisposing drug-naïve phenotypes, including anxiety, depression, novelty preference and sensation seeking, are predictive of drug-use phenotypes, thereby implicating shared genetic mechanisms. High-throughput behavioral screening in knockout (KO) mice allows efficient discovery of the function of genes. We used this strategy in two rounds of candidate prioritization in which we identified 33 drug-use candidate genes based upon predisposing drug-naïve phenotypes and ultimately validated the perturbation of 22 genes as causal drivers of substance intake. We selected 19/221 KO strains (8.5%) that had a difference from control on at least one drug-naïve predictive behavioral phenotype and determined that 15/19 (~80%) affected the consumption or preference for alcohol, methamphetamine or both. No mutant exhibited a difference in nicotine consumption or preference which was possibly confounded with saccharin. In the second round of prioritization, we employed a multivariate approach to identify outliers and performed validation using methamphetamine two-bottle choice and ethanol drinking-in-the-dark protocols. We identified 15/401 KO strains (3.7%, which included one gene from the first cohort) that differed most from controls for the predisposing phenotypes. 8 of 15 gene deletions (53%) affected intake or preference for alcohol, methamphetamine or both. Using multivariate and bioinformatic analyses, we observed multiple relations between predisposing behaviors and drug intake, revealing many distinct biobehavioral processes underlying these relationships. The set of mouse models identified in this study can be used to characterize these addiction-related processes further.