Metabolomic biomarkers and instrumentation for assessment of radiation injury

描述(由申请人提供)：这项建议结合了我们的代谢组学和辐射信号专业知识与团队成员在仪器方面的专业知识，以快速和经济有效地评估选定的代谢物。总体目标是从易于获取的生物体液中开发一个可靠的人体辐射代谢生物标记物数据库，然后提炼子集(S)，使其能够通过仪器对选定的生物标记物进行评估，为临床和潜在的现场应用奠定基础。这项拟议的研究建立在辐射代谢组学成就的广泛记录基础上，并使用了各种方法，特别是允许对感兴趣的代谢物生物标志物进行高通量评估的差示迁移率光谱分析(DMS)。在辐射代谢组学方面，我们的实验室及其合作伙伴在各种动物模型和人类细胞中使用现代液体色谱(LC)质谱学(MS)方法建立这一领域做出了重大贡献。我们在过去几年的出版物中已经表明，在NIAID优先考虑的电离辐射(IR)剂量之后，它们在尿液代谢组学谱中是剂量依赖和时间依赖的反应。最近，我们已经证明，与另一种相关的应激源相比，体内IR反应具有特异性，后者模仿脓毒症的炎症反应。我们的团队还发表了大量关于DMS的开发和改进的文章，这应该允许在没有繁琐的LC的情况下对代谢物生物标记物进行选择性的“调整”。我们已经证明，DMS允许分离选定的代谢物，这样它们就可以用简化和微型的MS系统进行检测。在人类生物标记物的开发方面，我们收集了大量接受全身照射(TBI)的患者的生物液，并已在TBI后的尿液中显示出显著的代谢反应。目标1将是利用我们的高端实验室LCMS方法开发一个强大的代谢生物标记物数据库，用于人类暴露。由于脑损伤患者的剂量和时间过程采样有限，我们将使用我们正在进行的由不同机制资助的广泛的小鼠模型数据集，以及合作者提供的非人类灵长类样本来模拟更广泛的暴露范围。在辐射毒性方面，我们有令人振奋的初步数据表明，在辐射后大约2周发生的毒性和致死性，最早可以在辐射后1天在小鼠身上区分开来。AIM 2将开发生物标记物面板，将IR生物标记物信号与其他损伤和疾病过程区分开来。一个现代生物信息学管道，其中包括新的内部算法，已经开发出来，以促进生物标记物的发现。在开发了强大的IR数据集之后，我们将在目标3中专注于开发方便和成本效益高的仪器，为临床和最终现场方案的使用提供基础，并改进可以用我们的方法(如DMS-MS)有效测量的IR生物标志物的子集。

DESCRIPTION (provided by applicant): This proposal combines our metabolomics and radiation-signaling expertise with the expertise of team members in instrumentation for rapid and cost-effective assessment of select metabolites. The overall goals are to develop a reliable database of radiation metabolomic biomarkers in humans from easily-accessible biofluids, and then to refine subset(s) that will allow assessment of select biomarkers with instrumentation that could provide the basis for application in clinical and potentially in-field scenarios. The propose study builds on an extensive track record of accomplishments in radiation metabolomics, as well as the use of approaches, particularly differential mobility spectrometry (DMS), that allows for high-throughput assessment of metabolite biomarkers of interest. In the case of radiation metabolomics, our laboratory and its collaborators have made major contributions in establishing this field using a modern liquid chromatography (LC) mass spectrometry (MS) approach in a variety of animal models as well as in human cells. We have shown in publications over the last several years that they are dose-dependent and timecourse-dependent responses in urine metabolomics profiles after doses of ionizing radiation (IR) that are a NIAID priority. Recently, we have shown that there is evidence for specificity in the IR response in vivo compared to another relevant stressor, which mimics the inflammatory response to sepsis. Our team has also published extensively on the development and refinement of DMS, which should allow for selective "tuning" for metabolite biomarkers without cumbersome LC. We have demonstrated that DMS allows isolation of select metabolites so that they can then be detected with a simplified and miniature MS system. In the case of human biomarker development, we have a collection of biofluids from a large number of patients undergoing total body irradiation (TBI) and have already demonstrated significant metabolomic responses in urine after TBI. Aim 1 will be to develop a robust metabolomic biomarker database for human exposure using our high-end laboratory LCMS approach. Since dose and timecourse sampling is limited in TBI patients, we will use our extensive on-going mouse model datasets, which are funded by a different mechanism, as well as non-human primate samples provided by collaborators to model a much wider range of exposures. In the case of radiation toxicity, we have exciting preliminary data demonstrating that toxicity and lethality, which occurs approximately 2 wk after irradiation, can be distinguished as early as 1 day after irradiation in mice. Aim 2 will develop biomarker panels to distinguish IR biomarker signatures from other injury and disease processes. A modern bioinformatics pipeline, which includes novel in-house algorithms, has been developed to facilitate biomarker discovery. Having developed a robust IR dataset, we will then focus in aim 3 on development of convenient and cost-effective instrumentation that can provide the basis for use in clinical and ultimately in-field scenarios, and refine subsets of IR biomarkers that can be effectively measured with our approaches such as DMS-MS.