Centre for Advanced Computational Solutions (CfACS), Agriculture and Life Sciences Division Lincoln University, Canterbury, New Zealand.
BMC Bioinformatics. 2011 Aug 17;12:343. doi: 10.1186/1471-2105-12-343.
The numerous diverse metabolic pathways by which plant compounds can be produced make it difficult to predict how colour pigmentation is lost for different tissues and plants. This study employs mathematical and in silico methods to identify correlated gene targets for the loss of colour pigmentation in plants from a whole cell perspective based on the full metabolic network of Arabidopsis. This involves extracting a self-contained flavonoid subnetwork from the AraCyc database and calculating feasible metabolic routes or elementary modes (EMs) for it. Those EMs leading to anthocyanin compounds are taken to constitute the anthocyanin biosynthetic pathway (ABP) and their interplay with the rest of the EMs is used to study the minimal cut sets (MCSs), which are different combinations of reactions to block for eliminating colour pigmentation. By relating the reactions to their corresponding genes, the MCSs are used to explore the phenotypic roles of the ABP genes, their relevance to the ABP and the impact their eliminations would have on other processes in the cell.
Simulation and prediction results of the effect of different MCSs for eliminating colour pigmentation correspond with existing experimental observations. Two examples are: i) two MCSs which require the simultaneous suppression of genes DFR and ANS to eliminate colour pigmentation, correspond to observational results of the same genes being co-regulated for eliminating floral pigmentation in Aquilegia and; ii) the impact of another MCS requiring CHS suppression, corresponds to findings where the suppression of the early gene CHS eliminated nearly all flavonoids but did not affect the production of volatile benzenoids responsible for floral scent.
From the various MCSs identified for eliminating colour pigmentation, several correlate to existing experimental observations, indicating that different MCSs are suitable for different plants, different cells, and different conditions and could also be related to regulatory genes. Being able to correlate the predictions with experimental results gives credence to the use of these mathematical and in silico analyses methods in the design of experiments. The methods could be used to prioritize target enzymes for different objectives to achieve desired outcomes, especially for less understood pathways.
植物化合物可以通过众多不同的代谢途径产生，这使得难以预测不同组织和植物的颜色色素是如何丢失的。本研究从整个细胞的角度出发，采用数学和计算机方法，根据拟南芥的全代谢网络，从整个细胞的角度识别植物颜色色素丢失的相关基因靶标。这涉及从 AraCyc 数据库中提取一个自我包含的类黄酮子网，并计算其可行的代谢途径或基本模式 (EM)。那些导致花青素化合物的 EM 被认为构成花青素生物合成途径 (ABP)，并将它们与其余 EM 的相互作用用于研究最小切割集 (MCS)，这是不同的反应组合，用于阻止消除颜色色素。通过将反应与相应的基因相关联，MCS 用于探索 ABP 基因的表型作用、它们与 ABP 的相关性以及消除它们对细胞中其他过程的影响。
不同 MCS 消除颜色色素的模拟和预测结果与现有实验观察结果相符。有两个例子：i）需要同时抑制 DFR 和 ANS 基因以消除颜色色素的两个 MCS，对应于在 Aquilegia 中同时调控相同基因以消除花色素的观察结果；ii）需要抑制 CHS 的另一个 MCS 的影响，对应于发现早期基因 CHS 的抑制消除了几乎所有的类黄酮，但不影响负责花香的挥发性苯类的产生。
从鉴定的各种消除颜色色素的 MCS 中，有几个与现有的实验观察结果相关，表明不同的 MCS 适合不同的植物、不同的细胞、不同的条件，并且可能与调节基因有关。能够将预测结果与实验结果相关联，为在实验设计中使用这些数学和计算机分析方法提供了依据。这些方法可用于为不同目标确定优先靶酶，以实现预期的结果，尤其是对于不太了解的途径。
