Genetics and quantum chemistry as tools for unknown metabolite identification

项目摘要/摘要 SARS-CoV-2病毒和随之而来的新冠肺炎大流行已经在我们的 一辈子。我们已经组建了一支在疫苗开发、环境保护方面具有专业知识的调查团队 暴露、免疫学、代谢组学、脂类组学和建模以发现代谢预测生物标记物 (Mpbs)在雪貂中感染。我们将使用雪貂，因为它们已经被证明是有效的 人类新冠肺炎病的动物模型，目前正被用于疫苗开发。 我们的研究建立在美国国立卫生研究院资助的一项联合感染研究的基础上，在该研究中，雪貂将感染4种不同的 感染SARS-CoV-2之前的呼吸道病毒。这项研究将确定感染的严重程度和 免疫反应，但不包括代谢组学测量。关于混合感染的假说是 SARS-CoV-2感染的严重程度将因另一种病毒的混合感染而减轻。我们将添加 一组雪貂在感染SARS之前将暴露于全氟烷基物质和多氟烷基物质(PFA)- CoV-2。已有研究表明，全氟辛烷磺酸能抑制小鼠的免疫系统，但已有数量有限的研究 证实了病毒感染的严重程度与全氟辛烷磺酸水平之间的关联。全氟辛烷磺酸在体内生物积累 纸巾是许多日常用品中常用的化学品，如塑料瓶和不粘烹饪 因此，这种常见的环境暴露可能是新冠肺炎症状的一个重要变量。这个 雪貂模型为研究PFAS对SARS-CoV-2感染进程的影响提供了一种理想的方法 结果。对于研究中的每一组(合并感染、PFAS或对照)，将从以下位置收集15个血清样本 每只动物(每组6只)持续约1个月，SARS-CoV-2感染发生在 抽样调查。因此，我们将能够得出代谢物和血脂的详细时间进程测量结果，并 将这些信号与表型结果联系起来。 我们有3个具体目标：1)在BSL-3控制和控制中进行混合感染和PFAS暴露研究 收集免疫学和传染性数据。血清样本将被收集并由生物安全机构灭活- 批准的协议。2)利用非靶向LC-MS和核磁共振技术测定代谢物和血脂。核磁共振速度更快， 成本较低，将用于LC-MS样品的优先排序。动物舍发出的背景PFAS信号 设备将被确定。3)用表型结果模拟代谢物和脂类。我们还将为 PFAS暴露对脂质体的影响以更好地了解PFAS的分子机制 免疫毒性。 我们还启动了Slack工作区，用于在世界各地的不同团体之间进行交流 致力于新冠肺炎代谢组学研究。此工作区提供协议和数据的共享，发布 在这一领域的最新研究，以及一个问答论坛。 我们研究产生的所有数据一旦通过我们的系统适合性测试就会被公开分享。

Project Summary/Abstract The SARS-CoV-2 virus and resulting COVID-19 pandemic has created the biggest global health crisis in our lifetime. We have assembled a team of investigators with expertise in vaccine development, environmental exposures, immunology, metabolomics, lipidomics, and modeling to discover metabolic predictive biomarkers (MPBs) of infection in ferrets. We will use ferrets, because they have already been shown to be an effective animal model for human COVID-19 disease, and they are currently being used for vaccine development. Our study builds upon an NIH funded co-infection study in which ferrets will be infected with 4 different common respiratory viruses before infection by SARS-CoV-2. That study will determine the severity of infections and immune responses, but it did not include metabolomics measurements. The hypothesis of the co-infections is that the severity of SARS-CoV-2 infection will be attenuated with co-infection by another virus. We will be adding a group of ferrets that will be exposed to per- and polyfluoroalkyl substances (PFAS) prior to infection by SARS- CoV-2. PFAS have been shown to suppress the immune system in mice, and a limited number of studies have demonstrated associations between severity of virus infection and levels of PFAS. PFAS bioaccumulate in tissues and are common chemicals used in many everyday items such as plastic bottles and non-stick cooking pans, so this common environmental exposure could be an important variable in COVID-19 symptoms. The ferret model provides an ideal way to study the effect of PFAS on SARS-CoV-2 infection progression and outcomes. For each group in the study (co-infection, PFAS, or control), 15 serum samples will be collected from each animal (n=6 for each group) over about 1 month, with SARS-CoV-2 infection occurring at the midpoint of the sampling. Thus, we will be able to derive detailed time-course measurements of metabolites and lipids and associate these signals with phenotypic outcomes. We have 3 specific aims: 1) Conduct the co-infection and PFAS exposure studies in BSL-3 containment and collect immunological and infectivity data. Serum samples will be collected and inactivated by a biosafety- approved protocol. 2) Measure metabolites and lipids using non-targeted LC-MS and NMR. NMR is faster and less expensive and will be used to prioritize samples for LC-MS. Background PFAS signals from animal housing equipment will be determined. 3) Model the metabolites and lipids with phenotypic outcomes. We will also model the influence of PFAS exposure on the lipidome to better understand the molecular mechanisms of PFAS immunotoxicity. We have also started a Slack workspace for communication between different groups around the world working on COVID-19 metabolomics. This workspace provides for sharing of protocols and data, posting the latest research in this area, as well as a forum for questions and answers. All data generated from our study will be shared publicly as soon as it passes our system suitability tests.