Fingerprinting the Metabolom of Wine

摘要哺乳动物的味觉包括对食物和饮料中存在的分析物的复杂混合物的分析。味觉的机制涉及溶液的代谢指纹，在许多情况下，味觉可以细微地区分不同的混合物，以及区分化学结构上的微小差异。大多数味料和气味都是通过非特定相互作用的反应组合来识别的。由一系列不同受体的同时反应产生的模式对一组特定的刺激是特定的。例如，品酒师能够分辨出非常微妙的味道特征，这是他们事业成功的关键。我们的计划是将葡萄酒作为一种试验台解决方案，以优化差动传感技术的艺术。我们之所以选择葡萄酒，是因为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，因为可以使用人体测试板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(及其类似物)。这些课程对分子识别和指纹识别提出了有趣的挑战。我们将使用“手中”受体来识别羧酸盐和糖类。或者，单宁的受体将来自组合化学。此外，我们计划为葡萄酒中那些已知对健康有好处的成分创造特定的受体：白藜芦醇和栎素。为了实现我们的目标，德克萨斯大学奥斯汀分校的埃里克·安斯林博士和加州大学戴维斯分校的希尔德加德·海曼博士建立了合作关系。安斯林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。海曼博士将使用更标准的方法进行代谢组分析，并将监督训练有素的人体测试小组。在德州大学和加州大学学院收集的数据将被用来回答这里描述的几个问题，其中几个是：1)需要多少受体和什么结构，才能完成我们的分析物类别的指纹识别？2)我们将发现在德州大学和加州大学学院创建的分析物类别的指纹之间有什么相关性，3)特定的化学物质是否会主导UC.D.的指纹和测试小组的反应，4)被发现对UC.D.指纹重要的特定化学物质在U.T.指纹中是否明显，即使我们没有对这些特定的化学物质进行培训，5)在加州大学和德州大学发现的哪些化学指纹最能反映感官小组的输入，以及6)在德州大学发现的指纹是否可以以预测的方式用于感觉小组的反应。这项提议的广泛影响在于探索一种可以对医疗、环境、国防和食品诊断领域产生影响的一般方法。我们认为，在阵列传感器应用中，当结合模式识别协议时，合成接收器阵列的能力是无法超越的。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，合成的受体自然具有交叉反应，这正是阵列设置中所需的属性。我们预测，合成受体的这一属性将允许化学家使用它们来分析成分未知的溶液。此外，在创造非天然受体时使用合成组合化学自然地补充了这种交叉反应的要求。这项提议的“宏大”目标是向超分子和分析化学家传授这一课，而选择葡萄酒来证明技术会引起社会的普遍兴趣，并在非传统的学术媒体上传播结果。

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.