Department of Electrical and Computer Engineering, North Carolina State University, Raleigh, North Carolina, United States of America.
State Key Laboratory of Tree Genetics and Breeding, Northeast Forestry University, Harbin, China.
PLoS Comput Biol. 2020 Apr 10;16(4):e1007197. doi: 10.1371/journal.pcbi.1007197. eCollection 2020 Apr.
Accurate manipulation of metabolites in monolignol biosynthesis is a key step for controlling lignin content, structure, and other wood properties important to the bioenergy and biomaterial industries. A crucial component of this strategy is predicting how single and combinatorial knockdowns of monolignol specific gene transcripts influence the abundance of monolignol proteins, which are the driving mechanisms of monolignol biosynthesis. Computational models have been developed to estimate protein abundances from transcript perturbations of monolignol specific genes. The accuracy of these models, however, is hindered by their inability to capture indirect regulatory influences on other pathway genes. Here, we examine the manifestation of these indirect influences on transgenic transcript and protein abundances, identifying putative indirect regulatory influences that occur when one or more specific monolignol pathway genes are perturbed. We created a computational model using sparse maximum likelihood to estimate the resulting monolignol transcript and protein abundances in transgenic Populus trichocarpa based on targeted knockdowns of specific monolignol genes. Using in-silico simulations of this model and root mean square error, we showed that our model more accurately estimated transcript and protein abundances, in comparison to previous models, when individual and families of monolignol genes were perturbed. We leveraged insight from the inferred network structure obtained from our model to identify potential genes, including PtrHCT, PtrCAD, and Ptr4CL, involved in post-transcriptional and/or post-translational regulation. Our model provides a useful computational tool for exploring the cascaded impact of single and combinatorial modifications of monolignol specific genes on lignin and other wood properties.
准确操纵木质素单体生物合成中的代谢物是控制木质素含量、结构和其他对生物能源和生物材料产业重要的木材特性的关键步骤。该策略的一个关键组成部分是预测单基因和组合基因敲低如何影响木质素单体特异性基因转录本的丰度，木质素单体蛋白是木质素单体生物合成的驱动机制。已经开发了计算模型来根据木质素单体特异性基因的转录本扰动估计蛋白质丰度。然而，这些模型的准确性受到其无法捕捉对其他途径基因的间接调控影响的限制。在这里，我们检查了这些间接影响在转基因转录本和蛋白丰度上的表现，确定了当一个或多个特定木质素途径基因受到干扰时发生的潜在间接调控影响。我们使用稀疏最大似然法创建了一个计算模型，用于根据特定木质素基因的靶向敲低来估计转基因杨Populus trichocarpa 中的木质素转录本和蛋白丰度。通过对该模型和均方根误差的仿真模拟，我们表明与以前的模型相比，当单个和木质素基因家族受到干扰时，我们的模型更准确地估计了转录本和蛋白丰度。我们利用从我们的模型中获得的推断网络结构的洞察力来识别潜在的基因，包括 PtrHCT、PtrCAD 和 Ptr4CL，它们涉及转录后和/或翻译后调控。我们的模型为探索木质素单体特异性基因的单基因和组合修饰对木质素和其他木材特性的级联影响提供了一个有用的计算工具。
