Stomata regulates conductance, transpiration and photosynthetic traits in plants. Increased stomatal density may contribute to enhanced water loss and thereby help improve the transpirational cooling process and mitigate the high temperature-induced yield losses. However, genetic manipulation of stomatal traits through conventional breeding still remains a challenge due to problems involved in phenotyping and the lack of suitable genetic materials. Recent advances in functional genomics in rice identified major effect genes determining stomatal traits, including its number and size. Widespread applications of CRISPR/Cas9 in creating targeted mutations paved the way for fine tuning the stomatal traits for enhancing climate resilience in crops. In the current study, attempts were made to create novel alleles of OsEPF1 (Epidermal Patterning Factor), a negative regulator of stomatal frequency/density in a popular rice variety, ASD 16, using the CRISPR/Cas9 approach. Evaluation of 17 T₀ progenies identified varying mutations (seven multiallelic, seven biallelic and three monoallelic mutations). T₀ mutant lines showed a 3.7–44.3% increase in the stomatal density, and all the mutations were successfully inherited into the T₁ generation. Evaluation of T₁ progenies through sequencing identified three homozygous mutants for one bp insertion. Overall, T₁ plants showed 54–95% increased stomatal density. The homozygous T₁ lines (# E1-1-4, # E1-1-9 and # E1-1-11) showed significant increase in the stomatal conductance (60–65%), photosynthetic rate (14–31%) and the transpiration rate (58–62%) compared to the nontransgenic ASD 16. Results demonstrated that the genetic alterations in OsEPF1 altered the stomatal density, stomatal conductance and photosynthetic efficiency in rice. Further experiments are needed to associate this technology with canopy cooling and high temperature tolerance.

Phenomics technologies have advanced rapidly in the recent past for precision phenotyping of diverse crop plants. High-throughput phenotyping using imaging sensors has been proven to fetch more informative data from a large population of genotypes than the traditional destructive phenotyping methodologies. It provides accurate, high-dimensional phenome-wide big data at an ultra-super spatial and temporal resolution. Biomass is an important plant phenotypic trait that can reflect the agronomic performance of crop plants in terms of growth and yield. Several image-derived features such as area, projected shoot area, projected shoot area with height constant, estimated bio-volume, etc., and machine learning models (single or multivariate analysis) are reported in the literature for use in the non-invasive prediction of biomass in diverse crop plants. However, no studies have reported the best suitable image-derived features for accurate biomass prediction, particularly for fully grown rice plants (70DAS). In this present study, we analyzed a subset of rice recombinant inbred lines (RILs) which were developed from a cross between rice varieties BVD109 × IR20 and grown in sufficient (control) and deficient soil nitrogen (N stress) conditions. Images of plants were acquired using three different sensors (RGB, IR, and NIR) just before destructive plant sampling for the quantitative estimation of fresh (FW) and dry weight (DW). A total of 67 image-derived traits were extracted and classified into four groups, viz., geometric-, color-, IR- and NIR-related traits. We identified a multimodal trait feature, the ratio of PSA and NIR grey intensity as estimated from RGB and NIR sensors, as a novel trait for predicting biomass in rice. Among the 16 machine learning models tested for predicting biomass, the Bayesian regularized neural network (BRNN) model showed the maximum predictive power (R² = 0.96 and 0.95 for FW and DW of biomass, respectively) with the lowest prediction error (RMSE and bias value) in both control and N stress environments. Thus, biomass can be accurately predicted by measuring novel image-based parameters and neural network-based machine learning models in rice.

Tyrosine phosphorylation constitutes up to 5% of the total phophoproteome. However, only limited studies are available on protein tyrosine kinases (PTKs) that catalyze protein tyrosine phosphorylation in plants. In this study, domain analysis of the 27 annotated PTK genes in rice genome led to the identification of 18 PTKs with tyrosine kinase domain. The kinase domain of rice PTKs shared high homology with that of dual specificity kinase BRASSINOSTEROID-INSENSITIVE 1 (BRI1) of Arabidopsis. In phylogenetic analysis, rice PTKs clustered with receptor-like cytoplasmic kinases-VII (RLCKs-VII) of Arabidopsis. mRNAseq analysis using Genevestigator revealed that rice PTKs except PTK9 and PTK16 express at moderate to high level in most tissues. PTK16 expression was highly abundant in panicle at flowering stage. mRNAseq data analysis led to the identification of drought, heat, salt, and submergence stress regulated PTK genes in rice. PTK14 was upregulated under all stresses. qRT-PCR analysis also showed that all PTKs except PTK10 were significantly upregulated in root under osmotic stress. Tissue specificity and abiotic stress mediated differential regulation of PTKs suggest their potential role in development and stress response of rice. The candidate dual specificity PTKs identified in this study paves way for molecular analysis of tyrosine phosphorylation in rice.