State Key Laboratory of Plant Diversity and Specialty Crops and Beijing Key Laboratory of Grape Science and Enology, Institute of Botany, the Chinese Academy of Sciences, Beijing, 100093, China.
China National Botanical Garden, Beijing 100093, China.
Ann Bot. 2023 Nov 30;132(5):1033-1050. doi: 10.1093/aob/mcad165.
Anthocyanin composition is responsible for the red colour of grape berries and wines, and contributes to their organoleptic quality. However, anthocyanin biosynthesis is under genetic, developmental and environmental regulation, making its targeted fine-tuning challenging. We constructed a mechanistic model to simulate the dynamics of anthocyanin composition throughout grape ripening in Vitis vinifera, employing a consensus anthocyanin biosynthesis pathway. The model was calibrated and validated using six datasets from eight cultivars and 37 growth conditions. Tuning the transformation and degradation parameters allowed us to accurately simulate the accumulation process of each individual anthocyanin under different environmental conditions. The model parameters were robust across environments for each genotype. The coefficients of determination (R2) for the simulated versus observed values for the six datasets ranged from 0.92 to 0.99, while the relative root mean square errors (RRMSEs) were between 16.8 and 42.1 %. The leave-one-out cross-validation for three datasets showed R2 values of 0.99, 0.96 and 0.91, and RRMSE values of 28.8, 32.9 and 26.4 %, respectively, suggesting a high prediction quality of the model. Model analysis showed that the anthocyanin profiles of diverse genotypes are relatively stable in response to parameter perturbations. Virtual experiments further suggested that targeted anthocyanin profiles may be reached by manipulating a minimum of three parameters, in a genotype-dependent manner. This model presents a promising methodology for characterizing the temporal progression of anthocyanin composition, while also offering a logical foundation for bioengineering endeavours focused on precisely adjusting the anthocyanin composition of grapes.
花色苷组成决定了葡萄浆果和葡萄酒的红色，并有助于其感官质量。然而，花色苷生物合成受遗传、发育和环境调控，使其成为具有挑战性的靶向微调目标。我们构建了一个机械模型，采用共识花色苷生物合成途径，模拟了酿酒葡萄成熟过程中花色苷组成的动态变化。该模型使用来自 8 个品种和 37 个生长条件的 6 个数据集进行了校准和验证。调整转化和降解参数可以使我们能够在不同的环境条件下准确模拟每个花色苷单体的积累过程。模型参数在每个基因型的不同环境中都是稳健的。六个数据集的模拟值与观测值之间的决定系数（R2）范围为 0.92 至 0.99，而相对均方根误差（RRMSE）介于 16.8 至 42.1%之间。三个数据集的留一法交叉验证的 R2 值分别为 0.99、0.96 和 0.91，RRMSE 值分别为 28.8、32.9 和 26.4%，表明模型具有较高的预测质量。模型分析表明，不同基因型的花色苷图谱在响应参数扰动时相对稳定。虚拟实验进一步表明，通过以基因型依赖的方式操纵最小三个参数，可以实现目标花色苷图谱。该模型为描述花色苷组成的时间变化提供了一种有前途的方法，同时也为专注于精确调整葡萄花色苷组成的生物工程努力提供了逻辑基础。
