Bioinformatics For Translational Spinal Cord Injury Research

项目总结/摘要 脊髓损伤（SCI）产生多方面的综合征，其特征在于丧失活动性，丧失膀胱， 肠和性功能，病理性疼痛，以及自主性的丧失。在过去的20年里， 我们在动物模型中模拟人类SCI的许多特征的能力取得了进展，但很少有实验 治疗方法已经从实验室转移到人类患者身上。翻译的一个主要障碍是缺乏 关于SCI的结果指标在不同实验室、菌株、类型之间具有可比性的信息 伤害和物种。确定这些重要的共同成果指标是 拟议项目。我们假设实验性SCI产生了一种生物行为综合征， 不是一个结果，而是许多不同结果的一致模式。这一观点隐含着 许多实验性SCI研究人员在评估实验性疗法时， 相同实验对象的结果。然而，这些数据尚未使用 复杂的多元信息处理程序，旨在检测，测量， 量化复杂数据集中的疾病模式。通过汇集来自多个实验室的数据并进行交叉- 物种的比较，我们将利用现有的实验数据，以确定共同的指标，SCI， 用于评估SCI跨物种翻译的机制。我们提出以下目标：1） 建立现有实验啮齿动物和灵长类SCI研究数据的汇集数据库，提供平台 用于知识发现和跨不同结果和实验模型的多变量量化。我们 我将从5个主要SCI研究中心的数据开始，为其他研究中心的后续贡献提供一个框架。 研究团体。2)使用相同的多变量技术识别啮齿动物SCI模型中的综合征指标 临床研究人员经常使用它来定义和测量复杂的疾病状态。3)确定哪个多变量 啮齿动物模型的结果模式对分级损伤的影响最敏感， 随着时间的推移对变化敏感，目的是提高敏感性和简化治疗性药物的测试。 干预措施。4)确定非人灵长类动物中哪些多变量结果模式对 随着时间的推移，SCI和恢复的影响，提供有关最敏感的结果的重要信息， 在这个有价值的临床前模型中进行治疗测试。5)对啮齿动物进行平移多变量比较 和灵长类SCI数据，以确定哪些结果模式在实验模型中最好地转换， 是物种和模型特异性的，为未来与人类数据的多变量比较奠定了基础。这 代表了实验SCI领域的一个新方向，我们希望这种方法有助于定义 跨物种可比的结果指标，促进转化SCI研究。

PROJECT SUMMARY/ABSTRACT Spinal cord injury (SCI) produces a multifaceted syndrome characterized by loss of mobility, loss of bladder, bowel and sexual function, pathological pain, and a loss of autonomy. The past 20 years have seen significant progress in our ability to emulate many features of human SCI in animal models, yet few experimental therapies have translated from the laboratory to human patients. One major obstacle to translation is the lack of information about which outcome metrics for SCI are comparable across different laboratories, strains, types of injuries, and species. Identification of these important common outcome metrics is a major goal of the proposed project. We hypothesize that experimental SCI produces a bio-behavioral syndrome that is reflected not by only one outcome, but rather consistent patterns across many different outcomes. This view is implicitly assumed by many experimental SCI researchers when they evaluate experimental therapeutics using several outcomes from the same experimental subjects. However these data have not been analyzed using sophisticated multivariate information processing procedures which are designed to detect, measure, and quantify disease patterns in complex datasets. By pooling data from several laboratories and making cross- species comparisons, we will leverage existing experimental data to identify common metrics of SCI that can be used for evaluating mechanism of SCI that translate across species. We propose the following Aims: 1) Build a pooled database of existing experimental rodent and primate SCI research data to provide a platform for knowledge discovery and multivariate quantification across diverse outcomes and experimental models. We will start with data from 5 major SCI research centers to provide a framework for later contributions from other research groups. 2) Identify syndrome measures in rodent SCI models, using the same multivariate techniques often used by clinical researchers to define and measure complex disease states. 3) Identify which multivariate outcome patterns in rodent models are most sensitive to the effects of graded injury and which are most sensitive to change over time, with the goal of improving sensitivity and streamlining testing of therapeutic interventions. 4) Identify which multivariate outcome patterns in non-human primates are most sensitive to the effects of SCI and recovery over time, providing important information about the most sensitive outcomes for therapeutic testing in this valuable preclinical model. 5) Make translational multivariate comparisons of rodent and primate SCI data to identify which outcome patterns best translate across experimental models and which are species- and model-specific, setting the stage for future multivariate comparisons to human data. This represents a new direction for the field of experimental SCI and we expect this approach to help define outcome metrics that are comparable across species, facilitating translational SCI research.