Gosseau Florie, Blanchet Nicolas, Varès Didier, Burger Philippe, Campergue Didier, Colombet Céline, Gody Louise, Liévin Jean-François, Mangin Brigitte, Tison Gilles, Vincourt Patrick, Casadebaig Pierre, Langlade Nicolas
LIPM, INRA, CNRS, Université de Toulouse, Castanet-Tolosan, France.
AGIR, INRA, Université de Toulouse, Castanet-Tolosan, France.
Front Plant Sci. 2019 Jan 16;9:1908. doi: 10.3389/fpls.2018.01908. eCollection 2018.
Heliaphen is an outdoor platform designed for high-throughput phenotyping. It allows the automated management of drought scenarios and monitoring of plants throughout their lifecycles. A robot moving between plants growing in 15-L pots monitors the plant water status and phenotypes the leaf or whole-plant morphology. From these measurements, we can compute more complex traits, such as leaf expansion (LE) or transpiration rate (TR) in response to water deficit. Here, we illustrate the capabilities of the platform with two practical cases in sunflower (): a genetic and genomic study of the response of yield-related traits to drought, and a modeling study using measured parameters as inputs for a crop simulation. For the genetic study, classical measurements of thousand-kernel weight (TKW) were performed on a biparental population under automatically managed drought stress and control conditions. These data were used for an association study, which identified five genetic markers of the TKW drought response. A complementary transcriptomic analysis identified candidate genes associated with these markers that were differentially expressed in the parental backgrounds in drought conditions. For the simulation study, we used a crop simulation model to predict the impact on crop yield of two traits measured on the platform (LE and TR) for a large number of environments. We conducted simulations in 42 contrasting locations across Europe using 21 years of climate data. We defined the pattern of abiotic stresses occurring at the continental scale and identified ideotypes (i.e., genotypes with specific trait values) that are more adapted to specific environment types. This study exemplifies how phenotyping platforms can assist the identification of the genetic architecture controlling complex response traits and facilitate the estimation of ecophysiological model parameters to define ideotypes adapted to different environmental conditions.
Heliaphen是一个用于高通量表型分析的户外平台。它允许对干旱情景进行自动化管理,并在植物的整个生命周期内对其进行监测。一个在15升花盆中生长的植株间移动的机器人监测植物的水分状况,并对叶片或整株植物形态进行表型分析。通过这些测量,我们可以计算出更复杂的性状,如响应水分亏缺的叶片扩展(LE)或蒸腾速率(TR)。在这里,我们用向日葵的两个实际案例来说明该平台的功能:一项关于产量相关性状对干旱响应的遗传和基因组研究,以及一项使用测量参数作为作物模拟输入的建模研究。对于遗传研究,在自动管理的干旱胁迫和对照条件下,对一个双亲群体进行了千粒重(TKW)的经典测量。这些数据用于关联研究,该研究确定了TKW干旱响应的五个遗传标记。一项补充转录组分析确定了与这些标记相关的候选基因,这些基因在干旱条件下的亲本背景中差异表达。对于模拟研究,我们使用作物模拟模型来预测平台上测量的两个性状(LE和TR)对大量环境下作物产量的影响。我们利用21年的气候数据,在欧洲42个对比地点进行了模拟。我们定义了大陆尺度上发生的非生物胁迫模式,并确定了更适应特定环境类型的理想型(即具有特定性状值的基因型)。这项研究例证了表型分析平台如何能够协助识别控制复杂响应性状的遗传结构,并有助于估计生态生理模型参数以定义适应不同环境条件的理想型。