Characterizing long-range propagation of injury information during regeneration

项目摘要/摘要 一些动物在受损后可以再生复杂的器官或附属物。在以下方面取得了重大进展 干细胞分化的分子生物学，这有助于解释新的细胞构建块的起源 组织。然而，关于新的生长如何与身体的其他部分相协调的主要知识差距仍然存在。 大小、形状和位置，以及整个系统如何知道缺少什么以及何时完成(以便它可以停止 增殖和重塑)。该项目利用了高度可再生和易处理的模式系统- 日本涡虫--这证明了修复复杂的大脑、周围神经系统、 肌肉和肠道是可能的。最近在脊椎动物再生方面的大部分进展都集中在伤口、新的生长、 和干细胞库。然而，莱文实验室在青蛙腿再生和大脑修复方面的工作，以及其他研究小组在 小鼠的研究表明，存在远程信号，让健康组织知道发生了什么损害 曾经是。例如，当一只青蛙的腿被截肢时，另一侧(未接触的)腿上的细胞表现出戏剧性的生物电 在与另一腿截肢平面相同的位置进行去极化。类似地，当青蛙的肢体 截肢和治疗后，大脑会改变基因表达谱，不同的治疗方法会有所不同。 这些数据表明，有关解剖缺陷的重要信息可能会传播到其他器官，并有助于 协调系统层面的综合应对措施。他们还提出了将这种知识转化为生物医学的可能性 作为替代部位诊断和治疗患者中距离较难的部位的细胞的技术 (正如东道主实验室用于大脑修复和癌症正常化的青蛙模型所显示的那样)。但是，这么远的距离 再生过程中的信号特征很差。在这里，我建议使用行星动物模型来发现一个可靠的 远程损伤信息的分子标记，以及2)构建和测试定量的生物物理模型 信息在传播。具体地说，我将使用候选方法和公正的转录方法来询问 当尾巴被截断时，扁虫头部的基因上调/下调，反之亦然。使用减法 分析，我们将寻找明确显示蠕虫的一端检测到 其他的。我将把这些知识融入现有的神经和非神经生物电信号方面的工作中，并且 分子遗传学的级联，在一个构造主义的生物物理模型中，浮游生物的远程信号。这款车型将是 分析具体的预测，然后我将验证这些预测并改进模型。这项工作将使 我用新的计算和生物物理方法来增强我的分子生物学背景，并为我 在生物医学背景下从事远程诊断和功能修复信号的独立职业。

Project Summary/Abstract Some animals can regenerate complex organs or appendages after damage. Significant progress has been made on the molecular biology of stem cell differentiation, which helps explain the origin of the cellular building blocks of new tissue. However, major knowledge gaps remain about how new growth is coordinated with the rest of the body with respect to size, shape, and location, and how the whole system knows what's missing and when it is complete (so that it can stop proliferation and remodeling). This project takes advantage of the highly regenerative and tractable model system – the planarian Dugesia japonica – which serves as a proof of principle that repair of complex brains, peripheral nervous systems, muscle, and gut is possible. Most of the recent advances in planarian regeneration have focused on the wound, new growth, and stem cell pool. However, work in the Levin lab on frog leg regeneration and brain repair, and other groups working in mice, have suggested the presence of long-range signals that let healthy tissue know that damage has occurred and what it was. For example, when a frog leg is amputated, cells in the opposite side (untouched) leg exhibit a dramatic bioelectric depolarization at the same location as the amputation plane that occurred in the other leg. Similarly, when a frog limb is amputated and treated, the brain changes gene expression profiles that are different depending on the treatment applied. These kinds of data suggest that important information about anatomical defects may propagate to other organs and help coordinate system-level integrated responses. They also raise the possibility of translating this knowledge to biomedicine as techniques for surrogate site diagnostics and treatments of cells at some distance from a difficult location in the patient (as has been shown in the frog model for brain repair and cancer normalization by the host lab). However, such long-range signals during regeneration are very poorly characterized. Here, I propose to use the planarian model to 1) discover a reliable molecular marker of long-range damage information, and 2) construct and test a quantitative, biophysical model of how the information propagates. Specifically, I will use a candidate approach and an unbiased transcriptomic approach to ask which genes are up/down-regulated in the head of the flatworm when the tail is amputated, and vice-versa. Using subtractive analysis, we will look for transcripts that specifically reveal that one end of the worm has detected what is missing at the other. I will incorporate this knowledge into the existing body of work on neural and non-neural bioelectric signaling, and molecular-genetic cascades, in a constructivist biophysical model of planarian long-range signaling. This model will be analyzed for specific predictions, and I will then validate those predictions and improve the model. This work will allow me to augment my background of molecular biology with novel computational and biophysical approaches and set me up for an independent career in pursuing long-range diagnostics and functional repair signals in biomedical contexts.