Lipid Biomarker Efflux from the Brain following TBI

项目总结：TBI后脂质生物标志物从脑中流出，LaPlaca，M.C.，Fernández，F.M. 创伤性脑损伤（TBI）是美国和全球的主要健康问题，影响至少250万人 美国人每年TBI是非常可变的，从人到人，使伤害的标准化 分类和诊断具有挑战性。流体生物标志物可以潜在地提供个性化信息， 目前还没有批准用于TBI诊断或预后。大多数TBI生物标志物候选物是蛋白质 可能是从垂死的神经元和神经胶质细胞中释放出来的，但与正常人相比， 对其他器官，并容易受到氧化应激，使大脑脂质非常容易受到自由基 攻击此外，脂质可能比蛋白质更容易从大脑中泄漏出来，使其成为理想的 外围设备诊断的候选对象。生物标志物从大脑到血液的清除途径是 假设主要通过血脑屏障（BBB），然而，分子清除的胶质淋巴途径 现在已经认识到，将焦点从仅BBB运输转变为潜在的多种外排途径之一。 鉴于TBI特异性脂质生物标志物的前景和脂质生物标志物释放的未知动力学， 需要更好地了解TBI后的脂质流出途径。拟议工作的目标是确定 用于TBI诊断的新型脂质生物标志物以及它们作为时间的函数离开大脑的途径， 伤害严重程度。总体假设是TBI特异性脂质通过BBB和胶质淋巴途径， 这些流出动力学将根据损伤的严重程度和脂质性质而变化。三是相互- 目的1）确定反映TBI严重程度的血清脂质生物标志物组 和病理据推测，大脑和血液中改变的脂质的数量和群体将 表明损伤严重程度;目的2）确定TBI后脂质的脑流出途径和时间流出动力学。它 假设存在不止一种脑清除TBI产生的脂质生物标志物的途径， 流出动力学取决于脂质大小和损伤后时间;以及目的3）开发预测TBI严重程度的模型 基于血清脂质生物标志物水平。假设使用以下参数的房室模型 目的1和2将允许从血清中的脂质生物标志物谱预测TBI严重程度。发现 代谢组学技术识别血清中的脂质，成功区分损伤和 未受伤的大鼠将扩大到包括监测脑脊液和淋巴液中的急性和 在雄性和雌性大鼠中，TBI后亚急性时间对于一系列损伤严重程度。通过这项研究，我们 期望鉴定新的脂质生物标志物组并确定其从大脑释放的主要途径。 这是重要的和新颖的，因为，虽然生物标志物提供了一个独特的窗口，进入继发性损伤事件， 外排模式的变化直接影响临床解释和实施。

Project Summary: Lipid Biomarker Efflux from the Brain following TBI, LaPlaca, M.C., Fernández, F.M. Traumatic brain injury (TBI) is a major health problem in the US and worldwide, affecting at least 2.5 million Americans every year. TBI is extremely variable from person to person, making the standardization of injury classification and diagnosis challenging. Fluid biomarkers can potentially provide personalized information, yet none are currently approved for TBI diagnosis or prognosis. Most of the TBI biomarker candidates are proteins that are likely released from dying neurons and glial cells, yet the brain has a very high lipid content compared to other organs, and is vulnerable to oxidative stress, leaving brain lipids extremely vulnerable to free radical attack. Furthermore, lipids may leak out of the brain more readily than proteins, rendering them ideal candidates for peripheral diagnostics. The route for biomarker clearance from the brain to the blood has been assumed to be primarily via the blood brain barrier (BBB), however a glymphatic route for molecule clearance is now recognized, changing the focus from BBB-only transport to one of potentially multiple efflux routes. Given the promise of TBI-specific lipid biomarkers and the unknown dynamics of lipid biomarker release, there is a need to better understand lipid efflux routes following TBI. The objective of the proposed work is to identify novel lipid biomarkers for TBI diagnosis and the routes by which they exit the brain as a function of time and injury severity. The overall hypothesis is that TBI-specific lipids pass through BBB and glymphatic routes to the blood, and that these efflux kinetics will vary based on injury severity and lipid properties. Three mutually- informing aims will be pursued: Aim1) Identify a serum lipid biomarker panel that is reflective of TBI severity and pathology. It is hypothesized that the number and population of altered lipids in the brain and blood will indicate injury severity; Aim 2) Determine brain efflux routes and temporal efflux dynamics of lipids after TBI. It is hypothesized that more than one route of brain clearance of TBI-generated lipid biomarkers exists and the efflux dynamics depend on lipid size and time post-injury; and Aim 3) Develop a model to predict TBI severity based on serum lipid biomarker levels. It is hypothesized that a compartmental model using parameters from Aims 1 and 2 will allow prediction of TBI severity from lipid biomarker profiles in serum. Discovery metabolomics techniques identifying lipids in serum that successfully discriminates between injured and uninjured rats will be expanded to include surveillance in cerebrospinal fluid and lymph in the acute and subacute time post-TBI for a range of injury severities in both male and female rats. Through this research, we expect to identify novel lipid biomarker panels and determine the major route(s) for their release from the brain. This is significant and novel because, while biomarkers provide a unique window into secondary injury events, changes in efflux patterns directly impact clinical interpretation and implementation.