IMAGiNE:Dissecting neuronal and systemic responses to interacting environmental stressors

暴露在环境化学品中可能对生态系统和生物体有害，包括对人类。一种类型的化学物质可以产生活性氧物种(ROS)，这是破坏细胞的氧的形式。动物对ROS的反应是通过逃逸并产生降解ROS的分子作为一种防御机制。实验室实验通常侧重于分析动物对一种化学物质的反应，但现实情况涉及多种化学物质的混合。当考虑到混合化学品时，因操纵单一化学品而观察到的影响可能会增强或减弱。这个项目的重点是了解动物对以线虫为模式生物产生ROS的化学混合物的反应。关键问题包括生物体中的不同反应如何随时间变化，以及神经系统是否在产生这种反应中起重要作用。了解不同的条件和化学混合物是否会导致不同的反应也是一个关键目标。这项工作将建立对动物在现实场景中如何对环境化学品做出反应的理解，这对于理解这种暴露对人类和其他物种健康的影响是重要的。该项目还用于开发几种对更广泛的科学界有用的工具，例如用于测量线虫逃逸反应和产生防御酶的工具。此外，该项目还包括在与数据科学和统计相结合时，对生物科学方面的各种工作人员进行培训。最后，与该项目相关的外展计划侧重于与STEM中代表性不足群体的高中生接触。不同类型的有害环境可以驱动压力反应，并通过密切相关的遗传途径发挥作用。大多数研究集中于单一应激源的暴露，并测量单一生物体读数的反应。然而，现实的环境暴露是由长期复杂的混合物组成的。多个应激源如何相互作用以时间依赖的方式驱动应激反应，以及来自不同细胞和组织的不同反应如何协调机体反应，目前还不完全清楚。解决这些问题的一个挑战是需要从有机体的多个水平获取反应，并进行广泛的组合暴露，以探索广泛的有机体应激反应空间。这项工作使用模式生物线虫来回答：1.氧化剂和其他应激源是否在应激反应中表现出相互作用的影响；2.氧化剂检测是否在生物体的氧化应激反应中发挥作用，如果是，神经元和转录程序是否在不同的背景和时间尺度上运行。这项工作确定不同的暴露是否会引发不同的反应模式，包括测量具有组织特异性的多个生物单位(神经元信号、转录因子活性和抗氧化酶基因表达)的反应，并包括开发数据驱动模型以确定生物体中不同应激反应信号之间的功能关系。这项工作的一大重点是评估对化学混合物的反应，并研究生物反应的动力学。该项目的目标是通过整合几种互补的方法来实现的，包括微流体、荧光成像、光遗传学、神经元沉默和统计建模。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

Exposure to environmental chemicals can be harmful to ecosystems and organisms, including humans. One type of chemical can produce Reactive Oxygen Species (ROS), which are forms of oxygen that damage cells. Animals respond to ROS by escaping and producing molecules that degrade ROS as a defense mechanism. Laboratory experiments typically focus on analyzing animal responses to a single chemical, but realistic scenarios involve mixtures of multiple chemicals. Observed effects due to manipulating a single chemical could be heightened or silenced when mixtures of chemicals are considered. This project focuses on understanding how animals respond to mixtures of chemicals that produce ROS using nematodes as a model organism. Key questions include how different responses in the organism change with time, and whether the nervous system is important in generating such responses. Understanding whether different conditions and chemical mixtures result in different responses also is a key objective. This work will build understanding about how animals respond to environmental chemicals in realistic scenarios, which is important to understand the effects of such exposures on the health of humans and other species. This project also serves to develop several tools useful for the broader scientific community, such as tools for measuring escape responses in nematodes and the generation of defense enzymes. In addition, this project includes training of a diverse workforce in the biological sciences as it interfaces with data science and statistics. Finally, the outreach program associated with the project focuses on engaging with high school students from under-represented groups in STEM.Different types of noxious environments can drive the stress response and act through closely associated genetic pathways. Most studies focus on exposure to single stressors and measure responses from a single organismal readout. Yet, realistic environmental exposures consist of complex mixtures through long periods of time. How multiple stressors interact to drive the stress response in a time-dependent manner, and how diverse responses from different cells and tissues orchestrate the organismal response is not fully understood. A challenge to address these questions is the need to acquire responses from multiple levels of the organism, and to perform a broad range of combinatorial exposures that can probe a wide organismal stress response space. This work uses the model organism C. elegans to answer: 1. Whether oxidants and other stressors exhibit interacting effects on the stress response; and 2. Whether oxidant detection plays a role in the organismal oxidative stress response, and if so, whether neuronal and transcriptional programs operate in different contexts and time scales. This work determines whether different response modalities are elicited by different exposures, involves measurement of responses from multiple biological units (neuron signaling, transcription factor activity, and antioxidant enzyme gene expression) with tissue specificity, and includes development of data-driven models to identify functional relationships between different stress-response signals in the organism. A large focus of this work is on assessing the responses to combinations of chemical mixtures, and on studying the dynamics of the organismal responses. The project goals are achieved through integration of several complementary approaches, including microfluidics, fluorescence imaging, optogenetics, neuronal silencing, and statistical modeling.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.