IRD, DIADE, Université de Montpellier, 34394 Montpellier Cedex 5, France.
CIRAD, AGAP, Université de Montpellier, 34398 Montpellier Cedex 5, France.
Plant Physiol. 2018 Jul;177(3):896-910. doi: 10.1104/pp.17.01648. Epub 2018 May 11.
Recent progress in root phenotyping has focused mainly on increasing throughput for genetic studies, while identifying root developmental patterns has been comparatively underexplored. We introduce a new phenotyping pipeline for producing high-quality spatiotemporal root system development data and identifying developmental patterns within these data. The SmartRoot image-analysis system and temporal and spatial statistical models were applied to two cereals, pearl millet () and maize (). Semi-Markov switching linear models were used to cluster lateral roots based on their growth rate profiles. These models revealed three types of lateral roots with similar characteristics in both species. The first type corresponds to fast and accelerating roots, the second to rapidly arrested roots, and the third to an intermediate type where roots cease elongation after a few days. These types of lateral roots were retrieved in different proportions in a maize mutant affected in auxin signaling, while the first most vigorous type was absent in maize plants exposed to severe shading. Moreover, the classification of growth rate profiles was mirrored by a ranking of anatomical traits in pearl millet. Potential dependencies in the succession of lateral root types along the primary root were then analyzed using variable-order Markov chains. The lateral root type was not influenced by the shootward neighbor root type or by the distance from this root. This random branching pattern of primary roots was remarkably conserved, despite the high variability of root systems in both species. Our phenotyping pipeline opens the door to exploring the genetic variability of lateral root developmental patterns.
近年来,根系表型分析的研究重点主要集中在提高遗传研究的通量上,而对根系发育模式的研究则相对较少。我们引入了一种新的表型分析方法,用于生成高质量的时空根系发育数据,并在这些数据中识别发育模式。SmartRoot 图像分析系统和时空统计模型应用于两种谷物,珍珠粟()和玉米()。半马尔可夫切换线性模型用于基于侧根生长速率曲线对其进行聚类。这些模型在两个物种中揭示了三种具有相似特征的侧根类型。第一种类型对应于快速和加速生长的根,第二种类型对应于快速停止生长的根,第三种类型对应于中间类型,即根在几天后停止伸长。在一个受生长素信号转导影响的玉米突变体中,这些类型的侧根以不同的比例被回收,而在严重遮荫的玉米植株中,第一种最活跃的类型不存在。此外,在珍珠粟中,生长速率曲线的分类反映了解剖学特征的排序。然后使用变量阶马尔可夫链分析侧根类型沿主根连续出现的潜在依赖性。侧根类型不受梢向相邻根类型或与该根的距离的影响。尽管两种物种的根系具有高度的可变性,但这种主根的随机分枝模式仍然非常保守。我们的表型分析方法为探索侧根发育模式的遗传可变性开辟了道路。