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Measuring system dynamics: mRNA, protein and metabolite profiling.

机译：测量系统动力学：mRNA，蛋白质和代谢物谱。

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摘要

Compared to the traditional reductionist approach, systems biology seeks to explain biological phenomenon, not on a gene-by-gene basis, but through the net interactions of all cellular and biochemical components within a cell or organism. As systems biology is driving technological developments, we sought to improve high-throughput measurements of the major cellular molecules and apply multiple molecular profiling approaches to measure cellular system dynamics.; We focused on three classes of molecules: mRNA, proteins and metabolites. For mRNA, expression deconvolution, a new algorithm for expression pattern analysis, was proposed to reveal dynamic changes in cell populations by reinterpretation of DNA microarray data. For proteins, a novel statistical method was established to calculate protein expression levels from shotgun proteomics; protein levels measured by this approach correlate well with protein abundance measured by Western blot and 2D gels. For metabolites, we took advantage of the extended 13C NMR spectral range and developed 1H-13C 2D-NMR for in vitro and in vivo metabolic profiling of cells.; With these technologies, we combined mRNA, protein and metabolite profiling to study one carbon metabolism and the yeast cell cycle. Integrating various "omic" data, we showed that local changes in one carbon metabolism (AdoMet hyperaccumulation) causes a gross change in the global metabolome, accompanied by both transcriptional and post-transcriptional responses, ultimately leading to a G1-delay defect in the cell cycle. We began mapping the yeast cell cycle in terms of dynamic abundance changes of the major cellular molecules. All these studies indicate that for many cases the measurement of mRNA is not predictive of the corresponding protein or metabolite abundances. Consequently, these different types of data provide complementary information to elucidate control mechanisms otherwise evident. This validates an essential idea of systems biology: it is only by integrating different levels of biological information that the cell's state can be fully described.
机译：与传统的还原论方法相比，系统生物学试图不是通过逐个基因而是通过细胞或生物体内所有细胞和生化成分的净相互作用来解释生物学现象。随着系统生物学推动技术发展，我们寻求改进主要细胞分子的高通量测量，并应用多种分子谱分析方法来测量细胞系统动力学。我们专注于三类分子：mRNA，蛋白质和代谢产物。对于mRNA，提出了表达解卷积（一种表达模式分析的新算法），以通过重新解释DNA微阵列数据揭示细胞群体的动态变化。对于蛋白质，建立了一种新颖的统计方法来从shot弹枪蛋白质组学计算蛋白质表达水平。通过这种方法测得的蛋白质水平与通过蛋白质印迹和2D凝胶测得的蛋白质丰度有很好的相关性。对于代谢物，我们利用扩展的13C NMR光谱范围开发了1H-13C 2D-NMR，用于细胞的体外和体内代谢谱分析。通过这些技术，我们结合了mRNA，蛋白质和代谢物谱，研究了一种碳代谢和酵母细胞周期。整合各种“组学”数据，我们发现一个碳代谢的局部变化（AdoMet过度积累）会导致整体代谢组发生重大变化，并伴随转录和转录后反应，最终导致细胞中的G1延迟缺陷周期。我们开始根据主要细胞分子的动态丰度变化绘制酵母细胞周期图。所有这些研究表明，在许多情况下，mRNA的测量不能预测相应的蛋白质或代谢产物的丰度。因此，这些不同类型的数据提供了补充信息，以阐明原本显而易见的控制机制。这证实了系统生物学的基本思想：只有整合不同水平的生物学信息，才能充分描述细胞状态。

著录项

  • 作者

    Lu, Peng.;

  • 作者单位

    The University of Texas at Austin.;

  • 授予单位 The University of Texas at Austin.;
  • 学科 Biology Molecular.
  • 学位 Ph.D.
  • 年度 2006
  • 页码 153 p.
  • 总页数 153
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类 分子遗传学;
  • 关键词

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