Rootstock effects on scion phenotypes in a 'Chambourcin' experimental vineyard

Abstract

Understanding how root systems modulate shoot system phenotypes is a fundamental question in plant biology and will be useful in developing resilient agricultural crops. Grafting is a common horticultural practice that joins the roots (rootstock) of one plant to the shoot (scion) of another, providing an excellent method for investigating how these two organ systems affect each other. In this study, we used the French-American hybrid grapevine 'Chambourcin' (Vitis L.) as a model to explore the rootstock-scion relationship. We examined leaf shape, ion concentrations, and gene expression in 'Chambourcin' grown ungrafted as well as grafted to three different rootstocks ('SO4', '1103P' and '3309C') across 2 years and three different irrigation treatments. We found that a significant amount of the variation in leaf shape could be explained by the interaction between rootstock and irrigation. For ion concentrations, the primary source of variation identified was the position of a leaf in a shoot, although rootstock and rootstock by irrigation interaction also explained a significant amount of variation for most ions. Lastly, we found rootstock-specific patterns of gene expression in grafted plants when compared to ungrafted vines. Thus, our work reveals the subtle and complex effect of grafting on 'Chambourcin' leaf morphology, ionomics, and gene expression.

Keywords: Natural variation in plants; Plant sciences; Plant signalling.

Fig. 1. Variation in leaf morphology assessed using the shape descriptors aspect ratio, circularity, roundness and solidity.

a A linear model was estimated for shape descriptors including the factors block (indicating position in the orchard, as visualized in Fig. S1), year of sample (2014 or 2016), rootstock (ungrafted, ‘1103P’, ‘3309C’, or ‘SO4’), irrigation (none, partial, or full irrigation) and rootstock by irrigation interaction. The percent variance explained by each factor in the model is indicated using color for those factors which explain a significant portion of the variance (p < 0.05). b Boxplots indicating circularity based on historical irrigation treatment. c Boxplots indicating circularity based on rootstock

Fig. 2. Quantifying leaf shape using persistent homology, a Topological Data Analysis (TDA) method.

a A 2D point cloud represents each leaf contour. b A Gaussian density estimator estimates the density of neighboring pixels around each pixel. Pixels near serrations and lobes tend to have higher density values. c 16 concentric rings are used to partition the data as an d annulus kernel. e Multiplication of the annulus kernel by the Gaussian density estimator isolates sub-features of the leaf. f A side projection shows clearly the isolated density features of the leaf. g Proceeding from high density values to low (1–5) the number of connected components (a topological feature) is recorded as a function of density. The resulting curves from each ring are discretized and quantify leaf shape

Fig. 3. A linear model was estimated for morphometric PCs 1 to 20.

The model included block (based on position in the orchard, as indicated in Fig. S1), year (2014 or 2016), rootstock (ungrafted, ‘1103P’, ‘3309C’, or ‘SO4’), irrigation (none, partial, or full), and rootstock by irrigation interaction (Fig. 3). The amount of variance explained by each PC is listed in parenthesis with the first 20 PCs capturing a total of 68.13% of the variance in leaf shape. Only factors which explained a significant portion of the variance (p < 0.05) are plotted. The percent variance explained by each factor is indicated using color

Fig. 4. A linear model was estimated for each element measured using ionomics.

The model included block (position in the orchard, as indicated in Fig. S1), irrigation (none, partial or full), leaf (old, mid, or young based on position of the leaf in the shoot sampled), irrigation by leaf interaction, rootstock (ungrafted, ‘1103P’, ‘3309C’, or ‘SO4’), rootstock by irrigation interaction, rootstock by leaf interaction, and year of sampling (2014 or 2016). Only factors which explained a significant portion of the variance (p < 0.05) are plotted. The percent variance explained by each factor is indicated using color

Fig. 5. Boxplots showing the distribution of elements by the factor that explained the largest amount of variance.

The distribution visualized are: a Ca based on leaf position b K based on to leaf position c Ni based on to rootstock d Mo based on rootstock by irrigation interaction

Fig. 6. The Venn diagram includes genes with significant temporal expression changes in ungrafted ‘Chambourcin’.

Expression profiles of these genes were compared to leaves sampled from ‘Chambourcin’ grafted to each rootstock to determine significant differences between groups