Unveiling Iso- and Aniso-Hydric Disparities in Grapevine-A Reanalysis by Transcriptome Portrayal Machine Learning

Abstract

Mechanisms underlying grapevine responses to water(-deficient) stress (WS) are crucial for viticulture amid escalating climate change challenges. Reanalysis of previous transcriptome data uncovered disparities among isohydric and anisohydric grapevine cultivars in managing water scarcity. By using a self-organizing map (SOM) transcriptome portrayal, we elucidate specific gene expression trajectories, shedding light on the dynamic interplay of transcriptional programs as stress duration progresses. Functional annotation reveals key pathways involved in drought response, pinpointing potential targets for enhancing drought resilience in grapevine cultivation. Our results indicate distinct gene expression responses, with the isohydric cultivar favoring plant growth and possibly stilbenoid synthesis, while the anisohydric cultivar engages more in stress response and water management mechanisms. Notably, prolonged WS leads to converging stress responses in both cultivars, particularly through the activation of chaperones for stress mitigation. These findings underscore the importance of understanding cultivar-specific WS responses to develop sustainable viticultural strategies in the face of changing climate.

Keywords: SOM machine learning; climate changes; grapevine; stilbenoid biosynthesis; thiamine biosynthesis; transcriptome portrayal; water(-deficient) stress.

Figure A1

Covariance and population structure of the SOM expression landscape. (A) The 5947 genes significantly modulated in leaves under our experimental conditions were taken from the SAM analysis in [15] and mapped into our SOM. Occupied metagenes (pixels) are marked in greyish. They refer mostly to metagenes of high and moderate variance of gene expression (see part C). (B) The covariance structure of the SOM was estimated by calculating a weighted topological overlap (WTO) network between the spot modules [65]. It reveals strong anticorrelations (w < 0) between the two cultivars (SG vs. MP) and between the two conditions (STRS vs. CTRL; see the scheme on the right). It thus assigns the “modulated genes” (part A) to up- and downregulation under the different conditions. Note also that the SOM decomposes the “modulated genes” into clusters of co-regulated genes called spot modules. (C) The variance map of the metagenes reveals a variance gradient of gene expression from the center of the SOM (blue) towards its edges (brown). The population map color codes the number of genes per metagene from high (red) to low (blue). The gene density changes across the SOM. Also, the Euclidean distances between neighboring metagenes are variant and “amplify” regions of increased gene density. The K-means map provides a space-filling segmentation of the SOM, enabling it to consider any region for downstream analysis, such as function mining.

Figure A2

Genes specifically modulated in the accessions were taken from supporting data [15] and compared to the respective waterline portraits. Genes modulated in SG and MP accumulate in the overexpressed areas in SG and MP, respectively.

Figure A3

Genes modulated specifically under stress conditions. The cutoffs defining upregulated (UP in STRS/CTRL) and downregulated (DOWN in STRS/CTRL) genes were taken from [15] (fold change > 2 and top 20% variant genes) and mapped into the SOM. Clusters of genes accumulate in different areas of the SOM in and near the spots. The respective profiles are shown on the right, together with the respective biological functions. As expected, the profiles confirm the UP/DOWN under STRS conditions, showing the respective changes, especially under lasting stress at T3. The profiles also show that the expression changes are about one order of magnitude smaller compared with that observed in the spots. The bars are color-coded as in the other figures.

Figure A4

Genes specifically modulated under recovery from WS. Recovery conditions were achieved by re-watering the plants 46 days after the onset of WS, and the post-recovery plant material was collected 70 days after the onset of WS (see Materials and Methods in [15]). Upregulated (UP) and downregulated (DOWN) genes and their respective profiles are shown separately. The profiles refer to the stress experiment. Recovery virtually reverses expression changes observed for lasting stress (STRS at T3).

Figure 1

Transcriptome portrayal of the WS experiment reveals distinct trajectories for isohydric (MP) and anisohydric (SG) cultivars. (A) SOM transcriptome portraits of all samples studied. (B) The pairwise correlation map of the SOM portraits indicates two distinct clusters for the two cultivars, correlation squares along the diagonal due to the replicates, and off-diagonal correlations between different time points and stress-induced effects in both cultivars (see arrows). (C) The independent component plot of the portraits shows linear trajectories along the IC2 axis of both cultivars. IC1, IC2, and IC3 denote the first three independent components. (D) Sample SOM represents a two-dimensional presentation of the trajectories: Transcriptome trajectories separate due to isohydric and anisohydric cultivars in the horizontal direction and develop with time vertically with an additional increment due to WS. Dots inside each “Sample SOM” represent samples. The color code of samples is indicated in the bottom right corner.

Figure 2

Transcriptome dynamics under WS in isohydric (MP) and anisohydric (SG) water management. (A) The regions of characteristic overexpression as red areas labeled A–F (see “Overexpression map”) agree with regions of highest expression variance (see “Variance map”). (B) Spots usually contain a few hundred genes of different functional contexts (left). Expression profiles of the spots across all conditions reveal characteristic courses of transcriptomic co-regulation (right). (C) Transcriptome dynamics under WS is characterized by waterline portraits revealing different stress trajectories for SG and MP (gray arrows), time, as well as by spot activation patterns (spots jointly activated in the portraits are connected by lines at the time points indicated). The number of jointly expressed spots increases under WS, thus indicating a more complex transcriptomic pattern compared to the controls (see text).

Figure 3

Topology of expression trajectories under WS. (A) The stress trajectories in the three-dimensional expression landscape. (B) Schematic spot activation along the T1-T2-T3 trajectory. (C) Gene ontology enrichment in spots A–F (biological processes in green, molecular functions in pink text).

Figure 4

Stilbenoid, diarylheptanoid, and gingerol biosynthetic pathway. (A) Genes of the pathway (downstream flow visualized by black arrows) are along the two STRS trajectories (gray arrows), indicating their condition-specific activation. (B) The heatmap shows the expression changes in response to WS. (C) KEGG pathway with gene color code derived from their positions in the SOM portrait indicated by the colored waterline portraits of SG CTRL and STRS and MP CTRL and STRS (see the lower right part).

Figure 5

Thiamine biosynthetic pathway. (A) Map of the pathway (downstream flow visualized by black arrows). Genes accumulate in areas related to stress response, and the two STRS trajectories are shown by gray arrows. (B) Heatmap depicting the thiamine biosynthetic pathway gene expression in response to WS. (C) KEGG pathway with gene color code derived from their positions in the SOM portrait indicated by the colored waterline portraits of SG CTRL and STRS and MP CTRL and STRS (see the upper right part).