Disentangling the developmental drivers of behavioral individuality using a clonal fish

本提案的目标是了解个性是如何发展的，并研究个体差异背后的分子和神经机制。你是独一无二的，就像其他人一样。这句古老的格言抓住了一个非常真实的生物学现象：在整个动物王国，个体表现出独特的行为模式，类似于人类的个性。然而，尽管它很普遍，我们对行为个性是如何以及为什么出现的理解仍然有限。信息整合理论预测了动物在一生中将如何利用信息和经验来塑造它们的行为，但这通常很难进行实证检验，部分原因是绝大多数个体在基因上也是不同的。这个项目利用一种自然克隆的鱼——亚马逊茉莉，来分离经验对行为的影响，并查明产生这些变化的分子机制。一种创新的跟踪系统将跟踪单个molly从出生到一生的行为，提供前所未有的洞察力，了解行为如何响应不同的线索而变化。理解产生行为个性的分子机制将阐明这些变化是多么容易被触发，以及一旦发生，它们可能会持续多久。通过测试信息整合理论的关键预测，这个项目的结果将提高我们预测一个人对不同线索的反应的能力，甚至在他们经历这个线索之前。这可能会对我们预测物种对气候变化的反应的能力或对动物和人类病理行为的治疗干预的功效产生重大影响。此外，该项目还包括为加州大学戴维斯分校的本科生以及加州戴维斯地区少数民族高中的高中生开发基于课堂的真实研究体验。如果我们能够理解个人使用、重视和整合他们一生中接收到的信息的机制，我们就能更好地预测个人的行为方式和原因。本提案的目的是测试贝叶斯更新是否提供了一个框架，可以预测个体如何整合母体和个人的线索来产生他们独特的行为表型。该项目将系统地操纵个体是否从母亲和/或自己的经历中获得线索，以1)测试贝叶斯更新是否预测行为变化，2)通过跟踪脑神经激活、基因表达和甲基化状态的变化来研究这种变化的潜在近因机制，3)测试这些行为变化的潜在适应价值。使用基因相同的亚马逊茉莉提供了一个严格的实验系统，通过控制个体之间的遗传变异来精确确定经验对行为的影响。如果个体使用贝叶斯或类似贝叶斯的过程来建立他们的行为表型，那么他们的行为发展应该遵循可预测的变化模式，这是基于他们对先前预期的信心，我们可以使用我们的创新跟踪系统来测量，以及他们收到的关于最可能的环境状态的新信息，我们可以在实验环境中控制。通过将统一的机制框架与强大的动物模型和创新的跟踪系统相结合，这项工作将为行为个性的发展驱动因素提供深入的见解。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

The goal of this proposal is to understand how individuality develops and investigate the molecular and neurological mechanisms underlying individual variation. You are unique, as is everyone else. This age-old adage captures a very real biological phenomenon: across the animal kingdom, individuals exhibit distinctive patterns of behavior, similar to personality in humans. However, despite its prevalence we still have a limited understanding of how and why behavioral individuality emerges. Information integration theory predicts how animals will use information and experiences to shape their behavior over their lifetimes, but this is often difficult to test empirically, in part because the vast majority of individuals are also genetically distinct. This project exploits a naturally clonal fish, the Amazon molly, to isolate the effects of experience on behavior and pinpoint the molecular mechanisms generating these changes. An innovative tracking system will follow the behavior of individual mollies from birth throughout their entire lives providing unprecedented insight into how behavior changes in response to different cues. Understanding the molecular mechanisms generating behavioral individuality will clarify how easily such changes are triggered, and once they are, how durable they may be. By testing key predictions from information integration theory, the results of this project will improve our ability to predict an individual’s response to different cues before they have even experienced that cue. This could have major implications for our ability to predict species’ responses to climate change or the efficacy of therapeutic interventions on pathological behavior in animals and humans alike. In addition, the project includes the development of classroom-based authentic research experiences for undergraduates at UC Davis, as well as for high school students at minority-serving high schools in the Davis, CA area.If we can understand the mechanisms through which individuals use, value, and integrate the information they receive over their lives, we can better predict how and why individuals behave the way they do. The goal of this proposal is to test whether Bayesian updating provides a framework that can predict how individuals integrate maternal and personal cues to generate their unique behavioral phenotypes. This project will systematically manipulate whether individuals receive cues from their mothers and/or their own experiences to 1) test whether Bayesian updating predicts behavioral change, 2) investigate potential proximate mechanisms underlying such change by following changes in brain neural activation, gene expression, and methylation status and 3) test the potential adaptive value of these behavioral changes. The use of the genetically identical Amazon molly provides a rigorous experimental system to pinpoint experiential effects on behavior by controlling for genetic variation among individuals. If individuals are using Bayesian or Bayesian-like processes to build their behavioral phenotypes, then their behavioral development should follow predictable patterns of change based on the confidence in their prior expectations, which we can measure using our innovative tracking system, and the new information they receive about the most likely state of the environment, which we can control in an experimental setting. By combining a unifying mechanistic framework with a powerful animal model and an innovative tracking system, this work will provide deep insight into the developmental drivers of behavioral individuality.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.