Sun Yi, Chen Jingqiu, Pruckmayr Gregory, Baumgardner James E, Eckmann David M, Eckenhoff Roderic G, Kelz Max B
Department of Anesthesiology and Critical Care, University of Pennsylvania School of Medicine, Philadelphia, USA.
BMC Anesthesiol. 2006 Nov 10;6:13. doi: 10.1186/1471-2253-6-13.
Anesthetic sensitivity is determined by the interaction of multiple genes. Hence, a dissection of genetic contributors would be aided by precise and high throughput behavioral screens. Traditionally, anesthetic phenotyping has addressed only induction of anesthesia, evaluated with dose-response curves, while ignoring potentially important data on emergence from anesthesia.
We designed and built a controlled environment apparatus to permit rapid phenotyping of twenty-four mice simultaneously. We used the loss of righting reflex to indicate anesthetic-induced unconsciousness. After fitting the data to a sigmoidal dose-response curve with variable slope, we calculated the MAC(LORR) (EC50), the Hill coefficient, and the 95% confidence intervals bracketing these values. Upon termination of the anesthetic, Emergence timeRR was determined and expressed as the mean +/- standard error for each inhaled anesthetic.
In agreement with several previously published reports we find that the MAC(LORR) of halothane, isoflurane, and sevoflurane in 8-12 week old C57BL/6J mice is 0.79% (95% confidence interval = 0.78-0.79%), 0.91% (95% confidence interval = 0.90-0.93%), and 1.96% (95% confidence interval = 1.94-1.97%), respectively. Hill coefficients for halothane, isoflurane, and sevoflurane are 24.7 (95% confidence interval = 19.8-29.7%), 19.2 (95% confidence interval = 14.0-24.3%), and 33.1 (95% confidence interval = 27.3-38.8%), respectively. After roughly 2.5 MAC(LORR) x hr exposures, mice take 16.00 +/- 1.07, 6.19 +/- 0.32, and 2.15 +/- 0.12 minutes to emerge from halothane, isoflurane, and sevoflurane, respectively.
This system enabled assessment of inhaled anesthetic responsiveness with a higher precision than that previously reported. It is broadly adaptable for delivering an inhaled therapeutic (or toxin) to a population while monitoring its vital signs, motor reflexes, and providing precise control over environmental conditions. This system is also amenable to full automation. Data presented in this manuscript prove the utility of the controlled environment chambers and should allow for subsequent phenotyping of mice with targeted mutations that are expected to alter sensitivity to induction or emergence from anesthesia.
麻醉敏感性由多个基因的相互作用决定。因此,精确且高通量的行为学筛选将有助于剖析遗传因素。传统上,麻醉表型分析仅涉及麻醉诱导,通过剂量 - 反应曲线进行评估,而忽略了麻醉苏醒过程中潜在的重要数据。
我们设计并构建了一个可控环境装置,以允许同时对24只小鼠进行快速表型分析。我们使用翻正反射消失来指示麻醉诱导的无意识状态。将数据拟合到具有可变斜率的S形剂量 - 反应曲线后,我们计算了MAC(LORR)(EC50)、希尔系数以及这些值的95%置信区间。麻醉终止后,确定苏醒时间RR,并将其表示为每种吸入麻醉剂的平均值±标准误差。
与先前发表的几份报告一致,我们发现8 - 12周龄C57BL / 6J小鼠中,氟烷、异氟烷和七氟烷的MAC(LORR)分别为0.79%(95%置信区间 = 0.78 - 0.79%)、0.91%(95%置信区间 = 0.90 - 0.93%)和1.96%(95%置信区间 = 1.94 - 1.97%)。氟烷、异氟烷和七氟烷的希尔系数分别为24.7(95%置信区间 = 19.8 - 29.7%)、19.2(95%置信区间 = 14.0 - 24.3%)和33.1(95%置信区间 = 27.3 - 38.8%)。在大约2.5 MAC(LORR)×小时的暴露后,小鼠从氟烷、异氟烷和七氟烷中苏醒分别需要16.00±1.07、6.19±0.32和2.15±0.12分钟。
该系统能够以比先前报告更高的精度评估吸入麻醉反应性。它广泛适用于在监测其生命体征、运动反射并精确控制环境条件的同时,向群体输送吸入性治疗药物(或毒素)。该系统也适用于完全自动化。本手稿中呈现的数据证明了可控环境舱的实用性,并应允许对预期会改变麻醉诱导或苏醒敏感性的靶向突变小鼠进行后续表型分析。