Plant growth and grain filling are the key agronomical traits for grain weight and yield of rice. The continuous improvement in rice yield is required for a future sustainable global economy and food security. The heterotrimeric G protein complex containing a canonical α subunit (RGA1) couples extracellular signals perceived by receptors to modulate cell function including plant development and grain weight. We hypothesized that, besides RGA1, three atypical, extra-large GTP-binding protein (XLG) subunits also regulate panicle architecture, plant growth, development, grain weight, and disease resistance. Here, we identified a role of XLGs in agronomic traits and stress tolerance by genetically ablating all three rice XLGs individually and in combination using the CRISPR/Cas9 genome editing in rice. For this study, eight (three single, two double, and three triple) null mutants were selected. Three XLG proteins combinatorically regulate seed filling, because loss confers a decrease in grain weight from 14% with loss of one XLG and loss of three to 32% decrease in grain weight. Null mutations in XLG2 and XLG4 increase grain size. The mutants showed significantly reduced panicle length and number per plant including lesser number of grains per panicle compared to the controls. Loss-of-function of all individual XLGs contributed to 9% more aerial biomass compared to wild type (WT). The double mutant showed improved salinity tolerance. Moreover, loss of the XLG gene family confers hypersensitivity to pathogens. Our findings suggest that the non-canonical XLGs play important roles in regulating rice plant growth, grain filling, panicle phenotype, stress tolerance, and disease resistance. Genetic manipulation of XLGs has the potential to improve agronomic properties in rice.

Since rice (Oryza sativa) is an important crop and the most advanced model for monocotyledonous species, acceding to its physiological status is important for many fundamental and applied purposes. Although this physiological status can be obtained by measuring the transcriptional regulation of marker genes, the tools to perform such analysis are often too expensive, non flexible or time consuming. Here we manually selected 96 genes considered as biomarkers of important processes taking place in rice leaves based on literature analysis. We monitored their transcriptional regulation under several treatments (disease, phytohormone inoculation, abiotic stress...) using Fluidigm method that allows to perform ~10 000 RT-QPCR reactions in one single run. This technique allowed us to verify a large part of known regulations but also to identify new, unsuspected regulations. Together, our set of genes, coupled to our data analysis protocol with Fluidigm brings a new opportunity to have a fast and reasonably cheap access to the physiological status of rice leaves in a high number of samples.