Schramm Gunnar, Zapatka Marc, Eils Roland, König Rainer
Theoretical Bioinformatics, German Cancer Research Center (DKFZ), Heidelberg, Germany.
BMC Bioinformatics. 2007 May 8;8:149. doi: 10.1186/1471-2105-8-149.
Biochemical investigations over the last decades have elucidated an increasingly complete image of the cellular metabolism. To derive a systems view for the regulation of the metabolism when cells adapt to environmental changes, whole genome gene expression profiles can be analysed. Moreover, utilising a network topology based on gene relationships may facilitate interpreting this vast amount of information, and extracting significant patterns within the networks.
Interpreting expression levels as pixels with grey value intensities and network topology as relationships between pixels, allows for an image-like representation of cellular metabolism. While the topology of a regular image is a lattice grid, biological networks demonstrate scale-free architecture and thus advanced image processing methods such as wavelet transforms cannot directly be applied. In the study reported here, one-dimensional enzyme-enzyme pairs were tracked to reveal sub-graphs of a biological interaction network which showed significant adaptations to a changing environment. As a case study, the response of the hetero-fermentative bacterium E. coli to oxygen deprivation was investigated. With our novel method, we detected, as expected, an up-regulation in the pathways of hexose nutrients up-take and metabolism and formate fermentation. Furthermore, our approach revealed a down-regulation in iron processing as well as the up-regulation of the histidine biosynthesis pathway. The latter may reflect an adaptive response of E. coli against an increasingly acidic environment due to the excretion of acidic products during anaerobic growth in a batch culture.
Based on microarray expression profiling data of prokaryotic cells exposed to fundamental treatment changes, our novel technique proved to extract system changes for a rather broad spectrum of the biochemical network.
过去几十年的生化研究已阐明了细胞代谢日益完整的图景。为了在细胞适应环境变化时获得代谢调节的系统观点，可以分析全基因组基因表达谱。此外，利用基于基因关系的网络拓扑结构可能有助于解释这大量信息，并提取网络中的显著模式。
将表达水平解释为具有灰度强度的像素，将网络拓扑结构解释为像素之间的关系，可实现细胞代谢的类似图像表示。虽然常规图像的拓扑结构是晶格网格，但生物网络呈现无标度架构，因此诸如小波变换等先进的图像处理方法不能直接应用。在本文报道的研究中，追踪一维酶 - 酶对以揭示生物相互作用网络的子图，这些子图显示出对变化环境的显著适应性。作为案例研究，研究了异养发酵细菌大肠杆菌对缺氧的反应。使用我们的新方法，正如预期的那样，我们检测到己糖营养物质摄取和代谢以及甲酸发酵途径的上调。此外，我们的方法揭示了铁加工的下调以及组氨酸生物合成途径的上调。后者可能反映了大肠杆菌在分批培养中厌氧生长期间由于酸性产物的排泄而对日益酸性环境的适应性反应。
基于原核细胞暴露于基本处理变化的微阵列表达谱数据，我们的新技术证明能够提取生化网络相当广泛范围内的系统变化。
