Metabolomic biomarkers and instrumentation for assessment of radiation injury

描述（由申请人提供）：该提案将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结合起来，用于快速和经济地评估选定的代谢物。总体目标是从易于获取的生物流体中开发一个可靠的人体辐射代谢组学生物标志物数据库，然后改进子集，以便使用仪器对选定的生物标志物进行评估，从而为临床和潜在的现场应用提供基础。该提议的研究建立在辐射代谢组学的广泛成就记录上，以及方法的使用，特别是差分迁移率光谱法（DMS），允许对感兴趣的代谢物生物标志物进行高通量评估。在辐射代谢组学方面，我们的实验室及其合作者在建立这一领域方面做出了重大贡献，在各种动物模型和人类细胞中使用了现代液相色谱（LC）质谱（MS）方法。我们已经在过去几年的出版物中表明，电离辐射（IR）剂量后尿液代谢组学反应具有剂量依赖性和时间依赖性，这是NIAID的优先事项。最近，我们已经证明，与另一种相关应激源相比，体内IR反应具有特异性，后者模仿败血症的炎症反应。我们的团队还发表了大量关于DMS的开发和改进的文章，这应该允许对代谢物生物标志物进行选择性“调整”，而无需繁琐的LC。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的微型MS系统进行检测。在人类生物标志物开发方面，我们收集了大量接受全身照射（TBI）的患者的生物体液，并已经证明TBI后尿液中有显著的代谢组学反应。目标1将是使用我们的高端实验室LCMS方法开发一个强大的人体暴露代谢组学生物标志物数据库。由于TBI患者的剂量和时间采样是有限的，我们将使用我们广泛的正在进行的小鼠模型数据集，这些数据集由不同的机制资助，以及合作者提供的非人类灵长类动物样本来模拟更大范围的暴露。在辐射毒性的情况下，我们有令人兴奋的初步数据表明，毒性和致死率，大约发生在照射后2周，可以早在照射后1天在小鼠中区分出来。目标2将开发生物标志物面板，以区分IR生物标志物特征与其他损伤和疾病过程。现代生物信息学管道，其中包括新颖的内部算法，已经开发，以促进生物标志物的发现。在开发了一个强大的红外数据集之后，我们将在目标3中专注于开发方便且具有成本效益的仪器，为临床和最终的现场场景提供基础，并完善红外生物标志物的子集，这些子集可以通过我们的方法（如DMS-MS）有效测量。

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.