Photosynthesis and translation are targets of metabolic control and development in plants, yet how stress signals coordinately regulate these opposing energy-producing and consuming processes remains enigmatic. Here, we unravel a growth control circuit that ties photosynthesis to translational control in response to biotic and abiotic signals. Our findings reveal that the L10-INTERACTING MYB DOMAIN-CONTAINING PROTEIN (LIMYB), a key player of the NUCLEAR SHUTTLE PROTEIN-INTERACTING KINASE 1 (NIK1)/ RIBOSOMAL PROTEIN L10 (RPL10) antiviral signaling pathway, not only downregulates translation genes, but also represses photosynthesis-related genes and photosynthesis itself. LIMYB repressor activity, regulated by phosphorylation, is crucial for the decline in photosynthesis under stress. NIK1 activation by PAMPs or the phosphomimetic NIK1-T474D represses photosynthesis-related genes and photosynthesis in control but not in limyb lines. Furthermore, heat and osmotic stress also activate the NIK1/RPL10/LIMYB signaling circuit in wild type. These stresses induce NIK1 phosphorylation, but not marker gene repression, in limyb, indicating that LIMYB connects NIK1 activation to stress-mediated downregulation of translation- and photosynthesis-related genes. This coordinated repression via the NIK1/RPL10/LIMYB module may help plants adapt to changing environments.

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.