Plant cell wall remodeling is an important process during plant responses to heat stress. Pectins, a group of cell wall polysaccharides with a great diversity of complex chemical structures, are also involved in heat stress responses. Enzymatic activity of the pectin methyl esterases, which remove methyl groups from pectins in the cell wall, is regulated by DUF642 proteins, as described in different plants, including Arabidopsis thaliana and Oryza sativa. Our results demonstrated that heat stress altered the expression of the DUF642 gene, BIIDXI. There was an important decrease in BIIDXI expression during the first hour of HS, followed by an increase at 24 h. bdx-1 seedlings had less tolerance to heat stress but presented a normal heat stress response; HSFA2 and HSP22 expressions were highly increased, as they were in WT seedlings. Thermopriming triggered changes in pectin methyl esterase activity in WT seedlings, while no increases in PME activity were detected in bdx-1 seedlings at the same conditions. Taken together, our results suggest that BIIDXI is involved in thermotolerance via PME activation.

Summary: (1) Rationale: Quantifying and predicting plant morphology is central to understanding development and evolution, yet many plant forms lack homologous features required for traditional morphometrics. We apply the Euler Characteristic Transform (ECT), an injective descriptor from topological data analysis, to encode 2D plant shapes. The ECT converts contours into image-like representations that preserve shape information while enabling deep learning. (2) Methods: We computed ECTs for large datasets of leaf and pavement cell shapes and used convolutional neural networks (CNNs) for classification. We also trained CNNs to approximate the inverse mapping, predicting leaf shape masks from radial ECTs. (3) Key results: ECT-based models achieved high classification accuracy, surpassing previous approaches on millions of herbarium-derived leaves. Notably, grapevine leaf venation was predicted from blade geometry alone, demonstrating that vascular structure is encoded in the outline. (4) Main conclusion: The ECT provides a compact, information-preserving representation of biological shape that integrates naturally with deep learning. It enables both accurate classification and predictive reconstruction, revealing latent morphological information and offering new opportunities to study plant form across scales.