A COMPREHENSIVE RESOURCE FOR HIGH-THROUGHPUT PROFILING OF WORM AND ZEBRAFISH METABOLOMES

蠕虫(秀丽线虫)和斑马鱼(Danio Rerio)是具有 对与疾病相关的代谢过程提供了深刻的洞察。因此，蠕虫的很大一部分 斑马鱼的研究重点是新陈代谢。自2010年以来，约70%的蠕虫和斑马鱼出版物 与新陈代谢有关。然而，引人注目的是，这些出版物中有1%利用了代谢组学。 代谢组学是一项相对较新的技术，它为研究代谢相关问题提供了重要的帮助。 任何其他方法。优势包括前所未有的灵敏度、卓越的定量准确性和 以高全程方式显著提高了化合物的覆盖率。于是问题就出现了，为什么 新陈代谢技术在蠕虫和斑马鱼的研究中一直没有得到充分利用。 随着在过去十年中建立的代谢组设施的数量不断增加， 障碍与可用性无关。相反，该领域一直受到数据解释挑战的制约。 当用液相色谱/质谱仪进行非靶向代谢实验时 (LC/MS)，从动物样品中检测到~40K信号。然而，即使有最先进的生物信息学 工具，大多数信号仍然无法识别。这严重限制了对数据的生物学解释。 我们已经开发了一套创新的方法来注释LC/MS中的每个非目标信号 新陈代谢数据集。我们的工作表明，LC/MS检测到的~40K信号只对应于 几千种非冗余、独特的代谢物。在这里，我们建议识别所有独特的代谢物， 经LC/MS检测，产生全面的线虫和斑马鱼参考代谢物。我们会 然后配置LC/MS参数，使蠕虫和斑马鱼参考中的每个可检测代谢物 通过有针对性的实验来分析代谢物。与非靶向实验不同，靶向实验 具有提供自动识别和定量的主要优势。因此，蠕虫和斑马鱼 工作人员将能够使用我们的资源来量化数千种生化命名的代谢物，而不需要 阻碍该领域的复杂数据分析障碍。这将显著扩展可访问性 对蠕虫、鱼类和其他模型动物研究人员的代谢组学。 为了增加对我们资源的全面覆盖，我们将整合非定向代谢组数据 在不同条件下从蠕虫和斑马鱼到每个参考代谢组的注释。这些 实验将提供一个机会来回答一些有趣的问题：代谢物如何变化 在开发过程中？动物体内不同细胞类型的代谢物是如何变化的？代谢物是如何 会因为压力而改变？参考代谢物的哪个部分在动物之间共享？我们期待着 共享的代谢组将是实质性的，我们在这里开发的自动化分析方法 因此，蠕虫和斑马鱼的参考代谢组将广泛适用于所有动物模型的研究。

Worms (Caenorhabditis elegans) and zebrafish (Danio rerio) are premier animal models that have provided profound insight into metabolic processes associated with disease. As such, a major fraction of worm and zebrafish research focuses on metabolism. Since 2010, ~70% of worm and zebrafish publications have been related to metabolism. Strikingly, however, <1% of these publications have utilized metabolomics. Metabolomics is a relatively new technology that offers significant benefits for studying metabolism relative to any other approach. Advantages include unprecedented sensitivity, superior quantitative accuracy, and dramatically improved compound coverage in a high-throughout fashion. The question then arises why metabolomic technologies have been so vastly underutilized in worm and zebrafish research. With the increasing number of metabolomic facilities that have been established over the last decade, the barrier is not related to availability. Rather, the field has been inhibited by the challenges of data interpretation. When an untargeted metabolomic experiment is performed with liquid chromatography/mass spectrometry (LC/MS), ~40k signals are detected from animal samples. Yet, even with the most advanced bioinformatic tools, the majority of signals remain unidentified. This severely limits biological interpretation of the data. We have developed a suite of innovative approaches to annotate each signal in LC/MS untargeted metabolomic data sets. Our work has revealed that the ~40k signals detected by LC/MS only correspond to a few thousand non-redundant, unique metabolites. Here we propose to identify all unique metabolites that can be detected by LC/MS to generate comprehensive C. elegans and zebrafish reference metabolomes. We will then configure LC/MS parameters that enable each detectable metabolite in the worm and zebrafish reference metabolome to be analyzed with a targeted experiment. Unlike untargeted experiments, targeted experiments have the major advantage of providing automated identification and quantitation. Thus, worm and zebrafish workers will be able to use our resource to quantify thousands of biochemically named metabolites without the barrier of complex data analysis that has hindered the field. This will significantly extend the accessibility of metabolomics to worms, fish, and other model animal researchers. To increase the comprehensive coverage of our resource, we will integrate untargeted metabolomic data annotations from worms and zebrafish under various conditions into each reference metabolome. These experiments will provide an opportunity to answer some interesting questions: How do metabolites change during development? How do metabolites change between cell types within an animal? How do metabolites change with stress? What fraction of the reference metabolomes is shared between animals? We expect that the shared metabolome will be substantial and that the methods we develop here to automate analysis of the worm and zebrafish reference metabolome will therefore be broadly applicable to all animal model research.