Fingerprinting the Metabolom of Wine

葡萄酒代谢指纹图谱

• 批准号: 0716049
• 负责人: Eric Anslyn
• 金额: $ 38.1万
• 依托单位: University of Texas at Austin
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2007
• 资助国家: 美国
• 起止时间: 2007-08-15 至 2011-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0716049&HistoricalAwards=false
• 关键词: Fingerprinting; Metabolom; Wine

AbstractThe mammalian sense of taste involves the analysis of complex mixtures of analytes present in food and beverages. The mechanism of taste involves a metabolomic fingerprint of the solution, and in many cases the sense of taste can distinguish subtly different mixtures, as well as differentiate minor differences in chemical structures. Most tastants, and odorants as well, are identified through a composite of responses from non-specific interactions. The pattern created by the simultaneous response of a series of differential receptors is specific for a particular set of stimuli. For example, wine tasters are able to distinguish very subtle taste characteristics as a key to the success of their careers.Our plan is to use wine as a test-bed solution to optimize the art of differential sensing techniques. The reason for our choice of wine is its complexity and the very unique chemical structures. Further, wine is an excellent choice because human test panels are available for comparison to our artificial approach. We propose to target several classes of analytes present in wine: carboxylic acids, sugars, and tannins (and analogs). These classes present interesting challenges for molecular recognition and fingerprinting. We will use "in-hand" receptors for carboxylates and sugars. Alternatively, the receptors for the tannins will be derived from combinatorial chemistry. Further, we plan to create specific receptors for those components of wine known to have health advantages: resveratrol and quercetin.To accomplish our goals, a collaboration has been established between Dr. Eric Anslyn at the University of Texas at Austin and Dr. Hildegarde Heymann at the University of California Davis. Dr. Anslyn will create the complex organic receptors, the signaling protocols, and apply the appropriate pattern recognition protocols. Dr. Heymann will use more standard approaches to metabolomic profiling, and will oversee trained human test panels. The data collected at U.T. and U.C.D. will be used to answer several questions as described herein, a few of which are: 1) How many receptors, and what structures, are needed to accomplish the fingerprinting of our analyte classes? 2) What correlations will we find between fingerprints of the analyte classes created at U.T. and at U.C.D., 3) Will specific chemicals dominate the fingerprints and test panel responses at U.C.D., 4) Will specific chemicals found important to the U.C.D. fingerprints be evident in the U.T. fingerprints, even if we did not train on those specific chemicals, 5) Which chemical fingerprints found at either U.C.D. or U.T. will best reflect the sensory panel input, and 6) Can fingerprints found at U.T. be used in a predictive manner for sensory panel response.The broad impact of this proposal resides in exploring a general approach that can have impact on the fields of medical, environmental, defense, and food diagnostics. We feel that the power of an array of synthetic receptors, when coupled with pattern recognition protocols, cannot be surpassed for array sensor applications. In practice, synthetic receptors suffer interference from similar analytes due to their simplicity. Therefore, synthetic receptors are naturally cross-reactive, the exact attribute that is desired in an array setting. We predict that this attribute of synthetic receptors will allow chemists to use them to analyze solutions for which the components are not exactly known. Furthermore, the use of synthetic combinatorial chemistry in the creation of unnatural receptors naturally compliments this requirement of cross-reactivity. The "big-picture" goal of this proposal is to teach this lesson to supramolecular and analytical chemists, while the choice of wine to prove the techniques leads to a general societal interest and dissemination of the results in non-traditional academic media.

摘要哺乳动物的味觉包括对食物和饮料中复杂混合物的分析。 味觉的机制涉及溶液的代谢指纹，在许多情况下，味觉可以区分微妙的不同混合物，以及区分化学结构的微小差异。 大多数味觉和气味都是通过非特异性相互作用的复合反应来识别的。 由一系列不同受体的同时反应所产生的模式是特定于一组特定刺激的。例如，品酒师能够区分非常微妙的味道特征，这是他们职业生涯成功的关键。我们的计划是使用葡萄酒作为测试平台解决方案，以优化差分传感技术的艺术。 我们选择葡萄酒的原因是它的复杂性和非常独特的化学结构。 此外，葡萄酒是一个很好的选择，因为人类测试面板可用于与我们的人工方法进行比较。 我们建议针对葡萄酒中存在的几类分析物：羧酸，糖和单宁（和类似物）。这些类别对分子识别和指纹识别提出了有趣的挑战。 我们将使用“在手”的羧酸盐和糖受体。 或者，单宁的受体将衍生自组合化学。 此外，我们计划为那些已知对健康有益的葡萄酒成分：白藜芦醇和槲皮素创造特定的受体。为了实现我们的目标，德克萨斯大学奥斯汀分校的埃里克·安斯林博士和加州戴维斯大学的希尔德加德·海曼博士之间建立了合作。 Anslyn博士将创建复杂的有机受体，信号协议，并应用适当的模式识别协议。 Heymann博士将使用更标准的方法进行代谢组学分析，并将监督经过培训的人体测试小组。 在U.T.收集的数据和加州大学将用于回答如本文所述的几个问题，其中一些是：1）需要多少受体和什么结构来完成我们的分析物类别的指纹识别？2)我们将在德克萨斯大学创建的分析物类别的指纹之间发现哪些相关性在加州大学3)特定的化学物质是否会在加州大学圣地亚哥分校的指纹和测试小组的反应中占据主导地位，4)会不会发现对U.C.D.很重要的化学物质。指纹在美国很明显。指纹，即使我们没有对这些特定的化学品进行培训，5）在U.C.D.或者德州大学将最好地反映感官面板输入，以及6）可以在U. T.该建议的广泛影响在于探索一种通用方法，可以对医学，环境，国防和食品诊断领域产生影响。 我们认为，当与模式识别协议相结合时，合成受体阵列的能力对于阵列传感器应用来说是无法超越的。 在实践中，合成受体由于其简单性而受到类似分析物的干扰。因此，合成受体是天然交叉反应性的，这正是阵列设置中所需的属性。我们预测，这种属性的合成受体将允许化学家使用它们来分析解决方案的组成部分是不完全知道。此外，在非天然受体的产生中使用合成组合化学自然地满足了交叉反应性的这一要求。 该提案的“大局”目标是向超分子和分析化学家传授这一课程，而选择葡萄酒来证明这些技术会引起社会的普遍兴趣，并在非传统学术媒体上传播结果。