Chemometric explorations of plant metabolomics

植物仍然是生物活性化合物的重要来源，尽管在合成和组合化学方面取得了进展，但在1981年至2010年期间开发的新化学实体中有超过70%来自天然产物。这些包括合成的模拟物，来自天然产物和结构类似物的半合成化合物。单个植物物种内的化学复杂性是巨大的;然而，植物王国内化学化合物的多样性可以通过植物的种间杂交进一步增强。由杂种植物产生的化学化合物可以是：（1）与它们的亲本相似，（2）介于它们的亲本之间，（3）以与亲本不同的浓度表达，（4）不存在，因为杂种不表达在任何亲本中发现的化学物质，或（5）新的，因为杂种表达在任何亲本中不发现的化学物质。化学特征的这种变化可以作为生产、开发和/或发现生物活性化合物的极好来源。在植物次生代谢物化学中发现新的生物活性化合物需要一种分析方法来检测和解释每个植物样品中的大量化学信号。色谱、光谱技术和方法的进步使研究人员能够获得重要的、高维的和复杂的数据集，使这种研究成为可能。然而，关于这类实验的设计存在重大争议，缺乏合适的标准，数据分析也没有得到充分发展。我建议开发统计工具，以便对数据进行解释、优先排序和评估，从而有效地消除错误发现并识别生物活性化合物。拟议的研究将进行代谢组学研究，以调查亲本和杂交植物标本和相关物种的化学使用青蒿作为选定的模型系统。A. annua是生物活性化合物的丰富来源，包括高效抗疟疾化合物青蒿素。代谢组学与单变量和多变量分析，以检查生产和植物内的生物活性化合物的发展，育种对生物合成途径的影响，并最终通过它可以发现新的化合物的手段将进行调查。此外，这项研究还将有助于更好地了解A. annua和影响植物选择和育种中生物合成过程的因素，以提高作物产量。

Plants remain a significant source of bioactive compounds, despite advances in synthetic and combinational chemistry, with >70% of new chemical entities developed between 1981 and 2010 derived from natural products. These include synthetic mimics, semi-synthetic compounds derived from natural products and structural analogs. The chemical complexity within individual plant species is immense; yet the diversity of chemical compounds within the plant kingdom can be further enhanced through inter-specific hybridization of plants. Chemical compounds produced by hybrid plants may be: (1) similar to that of their parents, (2) intermediate between those of the their parents, (3) expressed in different concentrations than in the parents, (4) absent, in that the hybrids do not express the chemicals found in either parents, or (5) novel, in that the hybrids express chemicals not found in either parents. Such changes in chemical profiles could serve as an excellent source for the production, development and/or discovery of bioactive compounds. Discovering new bioactive compounds in the secondary metabolite chemistry of plants requires an analytical approach to detect and interpret a large number of chemical signals within each plant sample. Advances in chromatography, spectrometric technologies and approaches have enabled researchers to acquire significant, high-dimensional and complicated data sets making such investigations possible. However, there is a significant debate about the design of such experiments, there is a lack of suitable standards and the data analysis is not fully developed. I propose to develop statistical tools that allow for interpretation, prioritization and evaluation of data to effectively eliminate false discoveries and identify bioactive compounds.. The proposed research will undertake metabolomic studies to investigate the chemistry of parent and hybrid plant specimens and related species using Artemesia annua as the selected model system. A. annua is a rich source of bioactive compounds including the highly effective anti-malarial compound artemisinin. Metabolomics with univariate and multivariate analyses to examine the production and development of bioactive compounds within plants, the impact of breeding on biosynthetic pathways and ultimately a means through which novel compounds can be discovered will be investigated. Further this study should also allow for the development of a better understanding of the biosynthesis of the major active constituents with A. annua and the factors that can influence biosynthetic processes in plant selection and breeding for improved crop production.