Uncoupling differential water usage from drought resistance in a dwarf Arabidopsis mutant

Characterization of tomato autophagy-related SlCOST family genes

Autophagy is a eukaryote-specific cellular process that can engulf unwanted targets with double-membrane autophagosomes and subject them to the vacuole or lysosome for breaking down and recycling, playing dual roles in plant growth and environmental adaptions. However, perception of specific environmental signals for autophagy induction is largely unknown, limiting its application in agricultural usage. Identification of plant-unique DUF641 family COST1 (Constitutively Stressed 1) protein directly links drought perception and autophagy induction, shedding light on manipulating autophagy for breeding stress tolerant crops. In this study, we performed a genome-wide analysis of DUF641/COST family in tomato, and identified five SlCOST genes SlCOST1, -2, -3, -4, and -5. SlCOST genes show both overlapping and distinct expression patterns in plant growth and stress responding. In addition, SlCOST1, -3, -4, -5 proteins demonstrate co-localization with autophagy adaptor protein ATG8e, and all five SlCOST proteins show interactions ATG8e in planta. However, only SlCOST1, the closest ortholog of Arabidopsis AtCOST1, can restore cost1 mutant to WT level, suggesting conserved role of COST1 and functional diversification of SlCOST family in tomato. Our study provides clues for future investigation of autophagy-related COST family and its promising implementations in breeding crops with robust environmental plasticity.

5-Aminolevulinic acid improves cold resistance through regulation of SlMYB4/SlMYB88-SlGSTU43 module to scavenge reactive oxygen species in tomato

Cold stress severely affects the growth and quality of tomato. 5-Aminolevulinic acid (ALA) can effectively improve tomato's cold stress tolerance. In this study, a tomato glutathione S-transferase gene, SlGSTU43, was identified. Results showed that ALA strongly induced the expression of SlGSTU43 under cold stress. SlGSTU43-overexpressing lines showed increased resistance to cold stress through an enhanced ability to scavenge reactive oxygen species. On the contrary, slgstu43 mutant lines were sensitive to cold stress, and ALA did not improve their cold stress tolerance. Thus, SlGSTU43 is a key gene in the process of ALA improving tomato cold tolerance. Through yeast library screening, SlMYB4 and SlMYB88 were preliminarily identified as transcription factors that bind to the SlGSTU43 promoter. Electrophoretic mobility shift, yeast one-hybrid, dual luciferase, and chromatin immunoprecipitation assays experiments verified that SlMYB4 and SlMYB88 can bind to the SlGSTU43 promoter. Further experiments showed that SlMYB4 and SlMYB88 are involved in the process of ALA-improving tomato's cold stress tolerance and they positively regulate the expression of SlGSTU43. The findings provide new insights into the mechanism by which ALA improves cold stress tolerance. SlGSTU43, as a valuable gene, could be added to the cold-responsive gene repository. Subsequently, it could be used in genetic engineering to enhance the cold tolerance of tomato.

Variability in drought gene expression datasets highlight the need for community standardization

Physiologically relevant drought stress is difficult to apply consistently, and the heterogeneity in experimental design, growth conditions, and sampling schemes make it challenging to compare water deficit studies in plants. Here, we re-analyzed hundreds of drought gene expression experiments across diverse model and crop species and quantified the variability across studies. We found that drought studies are surprisingly uncomparable, even when accounting for differences in genotype, environment, drought severity, and method of drying. Many studies, including most Arabidopsis work, lack high-quality phenotypic and physiological datasets to accompany gene expression, making it impossible to assess the severity or in some cases the occurrence of water deficit stress events. From these datasets, we developed supervised learning classifiers that can accurately predict if RNA-seq samples have experienced a physiologically relevant drought stress, and suggest this can be used as a quality control for future studies. Together, our analyses highlight the need for more community standardization, and the importance of paired physiology data to quantify stress severity for reproducibility and future data analyses.

A Comparison of Different Stomatal Density Phenotypes of Hordeum vulgare under Varied Watering Regimes Reveals Superior Genotypes with Enhanced Drought Tolerance

Enhancing the water-use efficiency (WUE) of barley cultivars may safeguard yield deficits during periods of low rainfall. Reduced stomatal density is linked to enhanced WUE, leading to improved drought resistance across plant genera. In this study, 10 barley varieties exhibiting a range of stomatal density phenotypes were grown under differing soil water contents to determine whether stomatal density influences the capacity of genotypes to resist low water availability. The low-stomatal-density genotype Hindmarsh showed the least impact on biomass production during early development, with a 37.13% decrease in dry biomass during drought treatment. Low-stomatal-density genotypes additionally outcompeted high-stomatal-density genotypes under water-deprivation conditions during the reproductive phase of development, exhibiting 19.35% greater wilting resistance and generating 54.62% more heads relative to high-stomatal-density genotypes (p < 0.05). Finally, a correlation analysis revealed a strong negative linear relationship between stomatal density and the traits of head number (r = -0.71) and the number of days until wilting symptoms (r = -0.67) (p < 0.05). The combined results indicate that low-stomatal-density genotypes show promising attributes for high WUE, revealing novel barley varieties that may be useful to future breed improvement for drought tolerance.

Evaluating Plant Drought Resistance with a Raspberry Pi and Time-lapse Photography

Identifying genetic variations or treatments that confer greater resistance to drought is paramount to ensuring sustainable crop productivity. Accurate and reproducible measurement of drought stress symptoms can be achieved via automated, image-based phenotyping. Many phenotyping platforms are either cost-prohibitive, require specific technical expertise, or are simply more complex than necessary to effectively evaluate drought resistance. Certain mutations, allelic variations, or treatments result in plants that constitutively use less water. To accurately identify genetic differences or treatments that confer a drought phenotype, plants from all experimental groups must be subjected to equal levels of drought stress. This can be easily achieved by growing and imaging plants that are grown in the same pot. Here, we provide a detailed protocol to configure a Raspberry Pi computer and camera module to image seedlings of multiple genotypes growing in shared pots and to transfer images and metadata via the cloud for downstream analyses. Also detailed is a method to calculate percent soil water content of pots while being imaged to allow for comparison of stress symptoms with water availability. This protocol was recently used to uncouple differential water usage from drought resistance in a dwarf Arabidopsis thaliana mutant chiquita1-1/cost1 compared to the wild-type control. It is cost effective, suitable for any plant species, customizable to various biological questions, and requires no prior experience with electronics or basic software programming.

Time for a drought experiment: Do you know your plants' water status?

Drought stress is an increasing concern because of climate change and increasing demands on water for agriculture. There are still many unknowns about how plants sense and respond to water limitation, including which genes and cellular mechanisms are impactful for ecology and crop improvement in drought-prone environments. A better understanding of plant drought resistance will require integration of several research disciplines. A common set of parameters to describe plant water status and quantify drought severity can enhance data interpretation and research integration across the research disciplines involved in understanding drought resistance and would be especially useful in integrating the flood of genomic data being generated in drought studies. Water potential (ψw) is a physical measure of the free energy status of water that, along with related physiological measurements, allows unambiguous description of plant water status that can apply across various soil types and environmental conditions. ψw and related physiological parameters can be measured with relatively modest investment in equipment and effort. Thus, we propose that increased use of ψw as a fundamental descriptor of plant water status can enhance the insight gained from many drought-related experiments and facilitate data integration and sharing across laboratories and research disciplines.