ISOTOPE ASSISTED METABOLOMICS OF ARABIDOPSIS

这个子项目是许多研究子项目中的一个 由NIH/NCRR资助的中心赠款提供的资源。子项目及 研究者（PI）可能从另一个NIH来源获得了主要资金， 因此可以在其他CRISP条目中表示。所列机构为 研究中心，而研究中心不一定是研究者所在的机构。 核磁共振（NMR）已成为代谢组学领域的重要分析工具。 实际上，用于复杂NMR谱的统计分析的所有当前方法都依赖于平滑过程（分箱）来压缩谱复杂性并校正峰位置的可变性。 在本文中，我们表明，传统的分箱方法是实验变异性的主要来源。 针对这一点，我们介绍了自动过滤的NMR光谱（AFNS）。 AFNS是一种处理程序，它使用滚动分箱算法、多个分箱宽度和基于t统计的过滤作为识别复杂光谱中重要特征的手段。 作为该程序的初步应用，我们分析了两种小鼠品系的肝脏提取物的质子光谱：C57 BL 6/J（B6）和BTBR，在遗传性肥胖（ob/ob）和瘦（+/+）条件下。 虽然这两种品系的瘦小鼠都很健康，但BTBR-ob/ob小鼠会患上严重的糖尿病。 相比之下，尽管肥胖，B6-ob/ob小鼠仍然对糖尿病具有抵抗力。 当使用传统的分组方法时，来自B6（+/+和ob/ob）和BTBR（+/+和ob/ob）的质子谱不能被主成分分析（PCA）线性分离，并且个体的分布受到分组宽度的选择的强烈影响。 相反，用AFNS处理的光谱显示B6（+/+和ob/ob）和BTBR（+/+和ob/ob）小鼠具有不同的分子谱，其可以沿着单个主成分沿着彼此线性分离。 我们归因于成功的AFNS减少binwidth相关的变异性和更强大的选择的光谱特征。

This subproject is one of many research subprojects utilizing the resources provided by a Center grant funded by NIH/NCRR. The subproject and investigator (PI) may have received primary funding from another NIH source, and thus could be represented in other CRISP entries. The institution listed is for the Center, which is not necessarily the institution for the investigator. Nuclear Magnetic Resonance (NMR) has emerged as an important analytical tool in the field of metabolomics. Virtually all of the current methods for statistical analysis of complex NMR spectra rely on a smoothing procedure (binning) to condense spectral complexity and correct for variability in peak location. In this paper, we show that the traditional binning methods are a major source of experimental variability. In response to this, we introduce Automated Filtering of NMR Spectra (AFNS). AFNS is a processing program that uses a rolling binning algorithm, multiple binwidths, and t-statistic based filtering as a means of identifying significant features in complex spectra. As an initial application of this program, we analyzed proton spectra of liver extracts from two strains of mice: the C57BL6/J (B6) and BTBR, in both genetically obese (ob/ob) and lean (+/+) conditions. Although lean mice from both strains are healthy, the BTBR-ob/ob mice develop severe diabetes. In contrast, B6-ob/ob mice remain resistant to diabetes despite obesity. When traditional binning methods were used, proton spectra from B6(+/+ and ob/ob) and BTBR(+/+ and ob/ob) could not be linearly separated by Principal Components Analysis (PCA) and the distribution of individuals was strongly influenced by the selection of binwidth. In contrast, spectra processed with AFNS revealed that B6(+/+ and ob/ob) and BTBR(+/+ and ob/ob) mice have distinct molecular profiles that can be linearly separated from one another along a single principal component. We attribute the success of AFNS to reduced binwidth related variability and a more robust selection of spectral features.