Gomes de Oliveira Dal'Molin Cristiana, Quek Lake-Ee, Saa Pedro A, Nielsen Lars K
Centre for Systems and Synthetic Biology, Australian Institute for Bioengineering and Nanotechnology, The University of Queensland Brisbane, Qld, Australia.
Front Plant Sci. 2015 Jan 22;6:4. doi: 10.3389/fpls.2015.00004. eCollection 2015.
Genome scale metabolic modeling has traditionally been used to explore metabolism of individual cells or tissues. In higher organisms, the metabolism of individual tissues and organs is coordinated for the overall growth and well-being of the organism. Understanding the dependencies and rationale for multicellular metabolism is far from trivial. Here, we have advanced the use of AraGEM (a genome-scale reconstruction of Arabidopsis metabolism) in a multi-tissue context to understand how plants grow utilizing their leaf, stem and root systems across the day-night (diurnal) cycle. Six tissue compartments were created, each with their own distinct set of metabolic capabilities, and hence a reliance on other compartments for support. We used the multi-tissue framework to explore differences in the "division-of-labor" between the sources and sink tissues in response to: (a) the energy demand for the translocation of C and N species in between tissues; and (b) the use of two distinct nitrogen sources (NO(-) 3 or NH(+) 4). The "division-of-labor" between compartments was investigated using a minimum energy (photon) objective function. Random sampling of the solution space was used to explore the flux distributions under different scenarios as well as to identify highly coupled reaction sets in different tissues and organelles. Efficient identification of these sets was achieved by casting this problem as a maximum clique enumeration problem. The framework also enabled assessing the impact of energetic constraints in resource (redox and ATP) allocation between leaf, stem, and root tissues required for efficient carbon and nitrogen assimilation, including the diurnal cycle constraint forcing the plant to set aside resources during the day and defer metabolic processes that are more efficiently performed at night. This study is a first step toward autonomous modeling of whole plant metabolism.
基因组尺度代谢建模传统上用于探索单个细胞或组织的代谢。在高等生物中，各个组织和器官的代谢是相互协调的，以确保生物体的整体生长和健康。理解多细胞代谢的依赖性和原理并非易事。在此，我们在多组织背景下推进了对AraGEM（拟南芥代谢的基因组尺度重建）的应用，以了解植物如何利用其叶、茎和根系统在昼夜循环中生长。我们创建了六个组织区室，每个区室都有其独特的一组代谢能力，因此依赖于其他区室的支持。我们使用多组织框架来探索源组织和库组织之间“分工”的差异，以应对：（a）组织间碳和氮物种转运的能量需求；以及（b）两种不同氮源（NO₃⁻或NH₄⁺）的使用。使用最小能量（光子）目标函数研究了区室之间的“分工”。对解空间进行随机抽样，以探索不同情景下的通量分布，并识别不同组织和细胞器中高度耦合的反应集。通过将此问题转化为最大团枚举问题，实现了对这些反应集的高效识别。该框架还能够评估能量限制对叶片、茎和根组织之间资源（氧化还原和ATP）分配的影响，这些组织对于有效的碳和氮同化是必需的，包括昼夜循环限制迫使植物在白天储备资源，并推迟在夜间更有效地进行的代谢过程。这项研究是迈向全植物代谢自主建模的第一步。
