PERKing up our understanding of the proline-rich extensin-like receptor kinases, a forgotten plant receptor kinase family

How plants sense and respond to osmotic stress

Drought is one of the most serious abiotic stresses to land plants. Plants sense and respond to drought stress to survive under water deficiency. Scientists have studied how plants sense drought stress, or osmotic stress caused by drought, ever since Charles Darwin, and gradually obtained clues about osmotic stress sensing and signaling in plants. Osmotic stress is a physical stimulus that triggers many physiological changes at the cellular level, including changes in turgor, cell wall stiffness and integrity, membrane tension, and cell fluid volume, and plants may sense some of these stimuli and trigger downstream responses. In this review, we emphasized water potential and movements in organisms, compared putative signal inputs in cell wall-containing and cell wall-free organisms, prospected how plants sense changes in turgor, membrane tension, and cell fluid volume under osmotic stress according to advances in plants, animals, yeasts, and bacteria, summarized multilevel biochemical and physiological signal outputs, such as plasma membrane nanodomain formation, membrane water permeability, root hydrotropism, root halotropism, Casparian strip and suberin lamellae, and finally proposed a hypothesis that osmotic stress responses are likely to be a cocktail of signaling mediated by multiple osmosensors. We also discussed the core scientific questions, provided perspective about the future directions in this field, and highlighted the importance of robust and smart root systems and efficient source-sink allocations for generating future high-yield stress-resistant crops and plants.

Silveira S, Brommonschenkel SH, Fontes EPB. RLPredictiOme, a Machine Learning-Derived Method for High-Throughput Prediction of Plant Receptor-like Proteins, Reveals Novel Classes of Transmembrane Receptors

Cell surface receptors play essential roles in perceiving and processing external and internal signals at the cell surface of plants and animals. The receptor-like protein kinases (RLK) and receptor-like proteins (RLPs), two major classes of proteins with membrane receptor configuration, play a crucial role in plant development and disease defense. Although RLPs and RLKs share a similar single-pass transmembrane configuration, RLPs harbor short divergent C-terminal regions instead of the conserved kinase domain of RLKs. This RLP receptor structural design precludes sequence comparison algorithms from being used for high-throughput predictions of the RLP family in plant genomes, as has been extensively performed for RLK superfamily predictions. Here, we developed the RLPredictiOme, implemented with machine learning models in combination with Bayesian inference, capable of predicting RLP subfamilies in plant genomes. The ML models were simultaneously trained using six types of features, along with three stages to distinguish RLPs from non-RLPs (NRLPs), RLPs from RLKs, and classify new subfamilies of RLPs in plants. The ML models achieved high accuracy, precision, sensitivity, and specificity for predicting RLPs with relatively high probability ranging from 0.79 to 0.99. The prediction of the method was assessed with three datasets, two of which contained leucine-rich repeats (LRR)-RLPs from Arabidopsis and rice, and the last one consisted of the complete set of previously described Arabidopsis RLPs. In these validation tests, more than 90% of known RLPs were correctly predicted via RLPredictiOme. In addition to predicting previously characterized RLPs, RLPredictiOme uncovered new RLP subfamilies in the Arabidopsis genome. These include probable lipid transfer (PLT)-RLP, plastocyanin-like-RLP, ring finger-RLP, glycosyl-hydrolase-RLP, and glycerophosphoryldiester phosphodiesterase (GDPD, GDPDL)-RLP subfamilies, yet to be characterized. Compared to the only Arabidopsis GDPDL-RLK, molecular evolution studies confirmed that the ectodomain of GDPDL-RLPs might have undergone a purifying selection with a predominance of synonymous substitutions. Expression analyses revealed that predicted GDPGL-RLPs display a basal expression level and respond to developmental and biotic signals. The results of these biological assays indicate that these subfamily members have maintained functional domains during evolution and may play relevant roles in development and plant defense. Therefore, RLPredictiOme provides a framework for genome-wide surveys of the RLP superfamily as a foundation to rationalize functional studies of surface receptors and their relationships with different biological processes.