RESUMEN

Abiotic factors, including heat stress, significantly impact the growth and development of lentil across the globe. Although these stresses impact the plant's phenotypic, genotypic, metabolic, and yield development, predicting those traits in lentil is challenging. This study aimed to construct a machine learning-based yield prediction model for lentil using various yield attributes under two different sowing conditions. Twelve genotypes were planted in open-field conditions, and images were captured 45 days after sowing (DAS) and 60 DAS to make predictions for agro-morphological traits with the assessment for the influence of high-temperature stress on lentil growth. Greening techniques like Excess Green, Modified Excess Green (ME × G), and Color Index of Plant Extraction (CIVE) were used to extract 35 vegetative indices from the crop image. Random forest (RF) regression and artificial neural network (ANN) models were developed for both the normal-sown and late-sown lentils. The ME × G-CIVE method with Otsu's thresholding provided superior performance in image segmentation, while the RF model showed the highest level of model generalization. This study demonstrated that yield per plant and number of pods per plant were the most significant attributes for early prediction of lentil production in both conditions using the RF models. After harvesting, various yield parameters of the selected genotypes were measured, showing significant reductions in most traits for the late-sown plants. Heat-tolerant genotypes like RLG-05, Kota Masoor-1, and Kota Masoor-2 depicted decreased yield and harvest index (HI) reduction than the heat-sensitive HUL-57. These findings warrant further study to correlate the data with more stress-modulating attributes. Supplementary Information: The online version contains supplementary material available at 10.1007/s13205-024-04031-5.

RESUMEN

The calmodulin-binding transcription activator (CAMTA) is a family of transcriptional factors containing a cluster of calmodulin-binding proteins that can activate gene regulation in response to stresses. The presence of this family of genes has been reported earlier, though, the comprehensive analyses of rice CAMTA (OsCAMTA) genes, their promoter regions, and the proteins were not deliberated till date. The present report revealed the existence of seven CAMTA genes along with their alternate transcripts in five chromosomes of rice (Oryza sativa) genome. Phylogenetic trees classified seven CAMTA genes into three clades indicating the evolutionary conservation in gene structure and their association with other plant species. The in silico study was carried out considering 2 kilobases (kb) promoter regions of seven OsCAMTA genes regarding the distribution of transcription factor binding sites (TFbs) of major and plant-specific transcription factors whereas OsCAMTA7a was identified with highest number of TFbs, while OsCAMTA4 had the lowest. Comparative modelling, i.e., homology modelling, and molecular docking of the CAMTA proteins contributed the thoughtful comprehension of protein 3D structures and protein-protein interaction with probable partners. Gene ontology annotation identified the involvement of the proteins in biological processes, molecular functions, and localization in cellular components. Differential gene expression study gave an insight on functional multiplicity to showcase OsCAMTA3b as most upregulated stress-responsive gene. Summarization of the present findings can be interpreted that OsCAMTA gene duplication, variation in TFbs available in the promoters, and interactions of OsCAMTA proteins with their binding partners might be linked to tolerance against multiple biotic and abiotic cues.

Asunto(s)

Oryza , Expresión Génica , Perfilación de la Expresión Génica , Regulación de la Expresión Génica de las Plantas , Simulación del Acoplamiento Molecular , Familia de Multigenes , Oryza/genética , Oryza/metabolismo , Filogenia , Proteínas de Plantas/genética , Proteínas de Plantas/metabolismo , Regiones Promotoras Genéticas , Estrés Fisiológico/genética

RESUMEN

Plant growth, development, and ultimately crop productivity are largely impacted by the interaction of plants with different abiotic and biotic factors throughout their life cycle. Perception of different abiotic stresses, such as salt, cold, drought, heat, and heavy metals, and interaction with beneficial and harmful biotic agents by plants lead to transient, sustained, or oscillatory changes of [calcium ion, Ca2+]cyt within the cell. Significant progress has been made in the decoding of Ca2+ signatures into downstream responses to modulate differential developmental and physiological responses in the whole plant. Ca2+ sensor proteins, mainly calmodulins (CaMs), calmodulin-like proteins (CMLs), and others, such as Ca2+-dependent protein kinases (CDPKs), calcineurin B-like proteins (CBLs), and calmodulin-binding transcription activators (CAMTAs) have played critical roles in coupling the specific stress stimulus with an appropriate response. This review summarizes the current understanding of the Ca2+ influx and efflux system in plant cells and various Ca2+ binding protein-mediated signal transduction pathways that are delicately orchestrated to mitigate abiotic and biotic stresses. The probable interactions of different components of Ca2+ sensor relays and Ca2+ sensor responders in response to various external stimuli have been described diagrammatically focusing on established pathways and latest developments. Present comprehensive insight into key components of the Ca2+ signaling toolkit in plants can provide an innovative framework for biotechnological manipulations toward crop improvability in near future.