In perennial energy crop breeding programmes, it can take several years before a mature yield is reached when potential new varieties can be scored. Modern plant breeding technologies have focussed on molecular markers, but for many crop species, this technology is unavailable. Therefore, prematurity predictors of harvestable yield would accelerate the release of new varieties. Metabolic biomarkers are routinely used in medicine, but they have been largely overlooked as predictive tools in plant science. We aimed to identify biomarkers of productivity in the bioenergy crop, Miscanthus, that could be used prognostically to predict future yields. This study identified a metabolic profile reflecting productivity in Miscanthus by correlating the summer carbohydrate composition of multiple genotypes with final yield 6 months later. Consistent and strong, significant correlations were observed between carbohydrate metrics and biomass traits at two separate field sites over 2 years. Machine‐learning feature selection was used to optimize carbohydrate metrics for support vector regression models, which were able to predict interyear biomass traits with a correlation (R) of >0.67 between predicted and actual values. To identify a causal basis for the relationships between the glycome profile and biomass, a ¹³C‐labelling experiment compared carbohydrate partitioning between high‐ and low‐yielding genotypes. A lower yielding and slower growing genotype partitioned a greater percentage of the ¹³C pulse into starch compared to a faster growing genotype where a greater percentage was located in the structural biomass. These results supported a link between plant performance and carbon flow through two rival pathways (starch vs. sucrose), with higher yielding plants exhibiting greater partitioning into structural biomass, via sucrose metabolism, rather than starch. Our results demonstrate that the plant metabolome can be used prognostically to anticipate future yields and this is a method that could be used to accelerate selection in perennial energy crop breeding programmes.

中文翻译：

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使用碳水化合物代谢谱作为生物标志物预测芒草的未来生物量产量。

在多年生能源作物育种计划中，可能需要几年时间才能达到成熟产量，然后才能对潜在的新品种进行评分。现代植物育种技术侧重于分子标记，但对于许多作物品种来说，这种技术是不可用的。因此，可收获产量的早产预测因子将加速新品种的发布。代谢生物标志物通常用于医学，但它们在植物科学中作为预测工具在很大程度上被忽视了。我们旨在确定生物能源作物芒草中生产力的生物标志物，可用于预测未来产量。本研究确定了反映芒草生产力的代谢特征通过将多个基因型的夏季碳水化合物组成与 6 个月后的最终产量相关联。在 2 年内，在两个不同的田间地点，碳水化合物指标和生物量性状之间观察到一致且强烈的显着相关性。机器学习特征选择用于优化支持向量回归模型的碳水化合物指标，该模型能够预测年际生物量性状，预测值和实际值之间的相关性 ( R ) > 0.67。为了确定糖组谱和生物量之间关系的因果基础，一项¹³ C 标记实验比较了高产基因型和低产基因型之间的碳水化合物分配。产量较低且生长较慢的基因型在¹³与生长较快的基因型相比，C 脉冲进入淀粉，其中较大百分比位于结构生物质中。这些结果支持植物性能和通过两种竞争途径（淀粉与蔗糖）的碳流量之间的联系，高产植物通过蔗糖代谢而不是淀粉表现出更大的结构生物量分配。我们的研究结果表明，植物代谢组可用于预测未来产量，这是一种可用于加速多年生能源作物育种计划选择的方法。