Singapore Institute of Food & Biotechnology Innovation, Agency for Science, Technology and Research A*STAR, Proteos, 61 Biopolis Drive, 138673, Singapore, Singapore.
Synthetic Biology for Clinical and Technological Innovation (SynCTI), National University of Singapore, 28 Medical Drive, 117456, Singapore, Singapore.
Sci Rep. 2020 Apr 3;10(1):5878. doi: 10.1038/s41598-020-62804-3.
For any dynamical system, like living organisms, an attractor state is a set of variables or mechanisms that converge towards a stable system behavior despite a wide variety of initial conditions. Here, using multi-dimensional statistics, we investigate the global gene expression attractor mechanisms shaping anaerobic to aerobic state transition (AAT) of Escherichia coli in a bioreactor at early times. Out of 3,389 RNA-Seq expression changes over time, we identified 100 sharply changing genes that are key for guiding 1700 genes into the AAT attractor basin. Collectively, these genes were named as attractor genes constituting of 6 dynamic clusters. Apart from the expected anaerobic (glycolysis), aerobic (TCA cycle) and fermentation (succinate pathways) processes, sulphur metabolism, ribosome assembly and amino acid transport mechanisms together with 332 uncharacterised genes are also key for AAT. Overall, our work highlights the importance of multi-dimensional statistical analyses for revealing novel processes shaping AAT.
对于任何动力系统，如生物体，吸引子状态是一组变量或机制，尽管存在各种初始条件，但它们仍能趋向于稳定的系统行为。在这里，我们使用多维统计方法，研究了塑造大肠杆菌在生物反应器中从厌氧到需氧状态转变（AAT）的全局基因表达吸引子机制。在 3389 个随时间变化的 RNA-Seq 表达变化中，我们鉴定了 100 个急剧变化的基因，这些基因是引导 1700 个基因进入 AAT 吸引子盆地的关键。这些基因共同构成了 6 个动态簇的吸引子基因。除了预期的厌氧（糖酵解）、需氧（三羧酸循环）和发酵（琥珀酸途径）过程外，硫代谢、核糖体组装和氨基酸转运机制以及 332 个未表征的基因对于 AAT 也很关键。总的来说，我们的工作强调了多维统计分析对于揭示塑造 AAT 的新过程的重要性。
