Méline Valérian, Brin Chrystelle, Lebreton Guillaume, Ledroit Lydie, Sochard Daniel, Hunault Gilles, Boureau Tristan, Belin Etienne
Emersys, SFR 4207 QUASAV, IRHS, UMR1345, Université d'Angers, Angers, France.
ImHorPhen, SFR 4207 QUASAV, IRHS, UMR1345, Université d'Angers, Angers, France.
Front Plant Sci. 2020 Feb 26;11:213. doi: 10.3389/fpls.2020.00213. eCollection 2020.
Phenotyping biotic stresses in plant-pathogen interactions studies is often hindered by phenotypes that can hardly be discriminated by visual assessment. Particularly, single gene mutants in virulence factors could lack visible phenotypes. Chlorophyll fluorescence (CF) imaging is a valuable tool to monitor plant-pathogen interactions. However, while numerous CF parameters can be measured, studies on plant-pathogen interactions often focus on a restricted number of parameters. It could result in limited abilities to discriminate visually similar phenotypes. In this study, we assess the ability of the combination of multiple CF parameters to improve the discrimination of such phenotypes. Such an approach could be of interest for screening and discriminating the impact of bacterial virulence factors without prior knowledge. A computation method was developed, based on the combination of multiple CF parameters, without any parameter selection. It involves histogram Bhattacharyya distance calculations and hierarchical clustering, with a normalization approach to take into account the inter-leaves and intra-phenotypes heterogeneities. To assess the efficiency of the method, two datasets were analyzed the same way. The first dataset featured single gene mutants of a strain which differed only by their abilities to secrete bacterial virulence proteins. This dataset displayed expected phenotypes at 6 days post-inoculation and was used as ground truth dataset to setup the method. The efficiency of the computation method was demonstrated by the relevant discrimination of phenotypes at 3 days post-inoculation. A second dataset was composed of transient expression (agrotransformation) of Type 3 Effectors. This second dataset displayed phenotypes that cannot be discriminated by visual assessment and no prior knowledge can be made on the respective impact of each Type 3 Effectors on leaf tissues. Using the computation method resulted in clustering the leaf samples according to the Type 3 Effectors, thereby demonstrating an improvement of the discrimination of the visually similar phenotypes. The relevant discrimination of visually similar phenotypes induced by bacterial strains differing only by one virulence factor illustrated the importance of using a combination of CF parameters to monitor plant-pathogen interactions. It opens a perspective for the identification of specific signatures of biotic stresses.
在植物 - 病原体相互作用研究中，对生物胁迫进行表型分析往往受到难以通过视觉评估区分的表型的阻碍。特别是，毒力因子中的单基因突变体可能缺乏可见表型。叶绿素荧光（CF）成像 是监测植物 - 病原体相互作用的宝贵工具。然而，虽然可以测量众多CF参数，但植物 - 病原体相互作用的研究通常集中在数量有限的参数上。这可能导致区分视觉上相似表型的能力有限。在本研究中，我们评估了多个CF参数组合提高此类表型区分能力的效果。这种方法对于在没有先验知识的情况下筛选和区分细菌毒力因子的影响可能具有重要意义。我们开发了一种基于多个CF参数组合的计算方法，无需任何参数选择。它涉及直方图巴氏距离计算和层次聚类，并采用归一化方法来考虑叶间和表型内的异质性。为了评估该方法的效率，我们以相同的方式分析了两个数据集。第一个数据集包含一个菌株的单基因突变体，这些突变体仅在分泌细菌毒力蛋白的能力上有所不同。该数据集在接种后6天显示出预期的表型，并用作建立该方法的基准数据集。接种后3天对表型的相关区分证明了计算方法的有效性。第二个数据集由III型效应子的瞬时表达（农杆菌转化）组成。第二个数据集显示出无法通过视觉评估区分的表型，并且对于每个III型效应子对叶片组织的各自影响无法有先验认识。使用计算方法导致根据III型效应子对叶片样本进行聚类，从而证明了对视觉上相似表型的区分能力有所提高。仅在一个毒力因子上不同的细菌菌株诱导的视觉上相似表型的相关区分说明了使用CF参数组合来监测植物 - 病原体相互作用的重要性。它为识别生物胁迫的特定特征开辟了前景。
