Correlation and co-localization of QTL for stomatal density, canopy temperature, and productivity with and without drought stress in Setaria

AI-assisted image analysis and physiological validation for progressive drought detection in a diverse panel of Gossypium hirsutum L

Introduction

Drought detection, spanning from early stress to severe conditions, plays a crucial role in maintaining productivity, facilitating recovery, and preventing plant mortality. While handheld thermal cameras have been widely employed to track changes in leaf water content and stomatal conductance, research on thermal image classification remains limited due mainly to low resolution and blurry images produced by handheld cameras.

Methods

In this study, we introduce a computer vision pipeline to enhance the significance of leaf-level thermal images across 27 distinct cotton genotypes cultivated in a greenhouse under progressive drought conditions. Our approach involved employing a customized software pipeline to process raw thermal images, generating leaf masks, and extracting a range of statistically relevant thermal features (e.g., min and max temperature, median value, quartiles, etc.). These features were then utilized to develop machine learning algorithms capable of assessing leaf hydration status and distinguishing between well-watered (WW) and dry-down (DD) conditions.

Results

Two different classifiers were trained to predict the plant treatment-random forest and multilayer perceptron neural networks-finding 75% and 78% accuracy in the treatment prediction, respectively. Furthermore, we evaluated the predicted versus true labels based on classic physiological indicators of drought in plants, including volumetric soil water content, leaf water potential, and chlorophyll a fluorescence, to provide more insights and possible explanations about the classification outputs.

Discussion

Interestingly, mislabeled leaves mostly exhibited notable responses in fluorescence, water uptake from the soil, and/or leaf hydration status. Our findings emphasize the potential of AI-assisted thermal image analysis in enhancing the informative value of common heterogeneous datasets for drought detection. This application suggests widening the experimental settings to be used with deep learning models, designing future investigations into the genotypic variation in plant drought response and potential optimization of water management in agricultural settings.

Variations of stomata development in tea plant (Camellia sinensis) leaves in different light and temperature environments and genetic backgrounds

Stomata perform important functions in plant photosynthesis, respiration, gas exchange, and interactions with environments. However, tea plant stomata development and functions are not known. Here, we show morphological changes during stomata development and genetic dissection of stomata lineage genes regulating stomata formation in tea developing leaves. Different tea plant cultivars displayed clear variations in the stomata development rate, density and size, which are closely related to their tolerance against dehydration capabilities. Whole sets of stomata lineage genes were identified to display predicted functions in regulating stomatal development and formation. The stomata development and lineage genes were tightly regulated by light intensities and high or low temperature stresses, which affected stomata density and function. Furthermore, lower stomatal density and larger size were observed in triploid tea varieties as compared to those in diploid plant. Key stomata lineage genes such as CsSPCHs, CsSCRM, and CsFAMA showed much lower expression levels, whereas negative regulators CsEPF1 and CsYODAs had higher expression levels in triploid than in diploid tea varieties. Our study provides new insight into tea plant stomatal morphological development and the genetic regulatory mechanisms on stomata development under abiotic stresses and genetic backgrounds. The study lays a foundation for future exploring of the genetic improvement of water use efficiency in tea plants for living up to the challenge of global climate change.

The Global Assessment of Oilseed Brassica Crop Species Yield, Yield Stability and the Underlying Genetics

The global demand for oilseeds is increasing along with the human population. The family of Brassicaceae crops are no exception, typically harvested as a valuable source of oil, rich in beneficial molecules important for human health. The global capacity for improving Brassica yield has steadily risen over the last 50 years, with the major crop Brassica napus (rapeseed, canola) production increasing to ~72 Gt in 2020. In contrast, the production of Brassica mustard crops has fluctuated, rarely improving in farming efficiency. The drastic increase in global yield of B. napus is largely due to the demand for a stable source of cooking oil. Furthermore, with the adoption of highly efficient farming techniques, yield enhancement programs, breeding programs, the integration of high-throughput phenotyping technology and establishing the underlying genetics, B. napus yields have increased by >450 fold since 1978. Yield stability has been improved with new management strategies targeting diseases and pests, as well as by understanding the complex interaction of environment, phenotype and genotype. This review assesses the global yield and yield stability of agriculturally important oilseed Brassica species and discusses how contemporary farming and genetic techniques have driven improvements.

Quantitative trait loci mapping reveals important genomic regions controlling root architecture and shoot biomass under nitrogen, phosphorus, and potassium stress in rapeseed (Brassica napus L.)

Plants rely on root systems for nutrient uptake from soils. Marker-assisted selection helps breeders to select desirable root traits for effective nutrient uptake. Here, 12 root and biomass traits were investigated at the seedling stage under low nitrogen (LN), low phosphorus (LP), and low potassium (LK) conditions, respectively, in a recombinant inbred line (RIL) population, which was generated from Brassica napus L. Zhongshuang11 and 4D122 with significant differences in root traits and nutrient efficiency. Significant differences for all the investigated traits were observed among RILs, with high heritabilities (0.43-0.74) and high correlations between the different treatments. Quantitative trait loci (QTL) mapping identified 57, 27, and 36 loci, explaining 4.1-10.9, 4.6-10.8, and 4.9-17.4% phenotypic variances under LN, LP, and LK, respectively. Through QTL-meta analysis, these loci were integrated into 18 significant QTL clusters. Four major QTL clusters involved 25 QTLs that could be repeatedly detected and explained more than 10% phenotypic variances, including two NPK-common and two specific QTL clusters (K and NK-specific), indicating their critical role in cooperative nutrients uptake of N, P, and K. Moreover, 264 genes within the four major QTL clusters having high expressions in roots and SNP/InDel variations between two parents were identified as potential candidate genes. Thirty-eight of them have been reported to be associated with root growth and development and/or nutrient stress tolerance. These key loci and candidate genes lay the foundation for deeper dissection of the NPK starvation response mechanisms in B. napus.

Genome-wide association study revealed TaHXK3-2A as a candidate gene controlling stomatal index in wheat seedlings

Stomata are important channels for the control of gas exchange between plants and the atmosphere. To examine the genetic architecture of wheat stomatal index, we performed a genome-wide association study (GWAS) using a panel of 539 wheat accessions and 450 678 polymorphic single nucleotide polymorphisms (SNPs) that were detected using wheat-specific 660K SNP array. A total of 130 SNPs were detected to be significantly associated with stomatal index in both leaf surfaces of wheat seedlings. These significant SNPs were distributed across 16 chromosomes and involved 2625 candidate genes which participate in stress response, metabolism and cell/organ development. Subsequent bulk segregant analysis (BSA), combined with GWAS identified one major haplotype on chromosome 2A, that is responsible for stomatal index on the abaxial leaf surface. Candidate gene association analysis revealed that genetic variation in the promoter region of the hexokinase gene TaHXK3-2A was significantly associated with the stomatal index. Moreover, transgenic analysis confirmed that TaHXK3-2A overexpression in wheat decreased the size of leaf pavement cells but increased stomatal density through the glucose metabolic pathway, resulting in drought sensitivity among TaHXK3-2A transgenic lines due to an increased transpiration rate. Taken together, these results provide valuable insights into the genetic control of the stomatal index in wheat seedlings.

Plasticity in stomatal behaviour across a gradient of water supply is consistent among field-grown maize inbred lines with varying stomatal patterning

Stomata regulate leaf CO₂ assimilation (A) and water loss. The Ball-Berry and Medlyn models predict stomatal conductance (g_s ) with a slope parameter (m or g₁ ) that reflects the sensitivity of g_s to A, atmospheric CO₂  and humidity, and is inversely related to water use efficiency (WUE). This study addressed knowledge gaps about what the values of m and g₁ are in C₄ crops under field conditions, as well as how they vary among genotypes and with drought stress. Four inbred maize genotypes were unexpectedly consistent in how m and g₁ decreased as water supply decreased. This was despite genotypic variation in stomatal patterning, A and g_s . m and g₁ were strongly correlated with soil water content, moderately correlated with predawn leaf water potential (Ψ_pd ), but not correlated with midday leaf water potential (Ψ_md ). This implied that m and g₁ respond to long-term water supply more than short-term drought stress. The conserved nature of m and g₁ across anatomically diverse genotypes and water supplies suggests there is flexibility in structure-function relationships underpinning WUE. This evidence can guide the simulation of maize g_s across a range of water supply in the primary maize growing region and inform efforts to improve WUE.

Phenotyping stomatal closure by thermal imaging for GWAS and TWAS of water use efficiency-related genes

Stomata allow CO2 uptake by leaves for photosynthetic assimilation at the cost of water vapor loss to the atmosphere. The opening and closing of stomata in response to fluctuations in light intensity regulate CO2 and water fluxes and are essential for maintaining water-use efficiency (WUE). However, a little is known about the genetic basis for natural variation in stomatal movement, especially in C4 crops. This is partly because the stomatal response to a change in light intensity is difficult to measure at the scale required for association studies. Here, we used high-throughput thermal imaging to bypass the phenotyping bottleneck and assess 10 traits describing stomatal conductance (gs) before, during and after a stepwise decrease in light intensity for a diversity panel of 659 sorghum (Sorghum bicolor) accessions. Results from thermal imaging significantly correlated with photosynthetic gas exchange measurements. gs traits varied substantially across the population and were moderately heritable (h2 up to 0.72). An integrated genome-wide and transcriptome-wide association study identified candidate genes putatively driving variation in stomatal conductance traits. Of the 239 unique candidate genes identified with the greatest confidence, 77 were putative orthologs of Arabidopsis (Arabidopsis thaliana) genes related to functions implicated in WUE, including stomatal opening/closing (24 genes), stomatal/epidermal cell development (35 genes), leaf/vasculature development (12 genes), or chlorophyll metabolism/photosynthesis (8 genes). These findings demonstrate an approach to finding genotype-to-phenotype relationships for a challenging trait as well as candidate genes for further investigation of the genetic basis of WUE in a model C4 grass for bioenergy, food, and forage production.

Optical topometry and machine learning to rapidly phenotype stomatal patterning traits for maize QTL mapping

Stomata are adjustable pores on leaf surfaces that regulate the tradeoff of CO2 uptake with water vapor loss, thus having critical roles in controlling photosynthetic carbon gain and plant water use. The lack of easy, rapid methods for phenotyping epidermal cell traits have limited discoveries about the genetic basis of stomatal patterning. A high-throughput epidermal cell phenotyping pipeline is presented here and used for quantitative trait loci (QTL) mapping in field-grown maize (Zea mays). The locations and sizes of stomatal complexes and pavement cells on images acquired by an optical topometer from mature leaves were automatically determined. Computer estimated stomatal complex density (SCD; R2 = 0.97) and stomatal complex area (SCA; R2 = 0.71) were strongly correlated with human measurements. Leaf gas exchange traits were genetically correlated with the dimensions and proportions of stomatal complexes (rg = 0.39-0.71) but did not correlate with SCD. Heritability of epidermal traits was moderate to high (h2 = 0.42-0.82) across two field seasons. Thirty-six QTL were consistently identified for a given trait in both years. Twenty-four clusters of overlapping QTL for multiple traits were identified, with univariate versus multivariate single marker analysis providing evidence consistent with pleiotropy in multiple cases. Putative orthologs of genes known to regulate stomatal patterning in Arabidopsis (Arabidopsis thaliana) were located within some, but not all, of these regions. This study demonstrates how discovery of the genetic basis for stomatal patterning can be accelerated in maize, a C4 model species where these processes are poorly understood.

Correlation and co-localization of QTL for stomatal density, canopy temperature, and productivity with and without drought stress in Setaria

Mechanistic modeling indicates that stomatal conductance could be reduced to improve water use efficiency (WUE) in C4 crops. Genetic variation in stomatal density and canopy temperature was evaluated in the model C4 genus, Setaria. Recombinant inbred lines (RILs) derived from a Setaria italica×Setaria viridis cross were grown with ample or limiting water supply under field conditions in Illinois. An optical profilometer was used to rapidly assess stomatal patterning, and canopy temperature was measured using infrared imaging. Stomatal density and canopy temperature were positively correlated but both were negatively correlated with total above-ground biomass. These trait relationships suggest a likely interaction between stomatal density and the other drivers of water use such as stomatal size and aperture. Multiple quantitative trait loci (QTL) were identified for stomatal density and canopy temperature, including co-located QTL on chromosomes 5 and 9. The direction of the additive effect of these QTL on chromosome 5 and 9 was in accordance with the positive phenotypic relationship between these two traits. This, along with prior experiments, suggests a common genetic architecture between stomatal patterning and WUE in controlled environments with canopy transpiration and productivity in the field, while highlighting the potential of Setaria as a model to understand the physiology and genetics of WUE in C4 species.