Components of Water Use Efficiency Have Unique Genetic Signatures in the Model C4 Grass Setaria

Plasma membrane aquaporins regulate root hydraulic conductivity in the model plant Setaria viridis

The high rate of productivity observed in panicoid crops is in part due to their extensive root system. Recently, green foxtail (Setaria viridis) has emerged as a genetic model system for panicoid grasses. Natural accessions of S. viridis originating from different parts of the world, with differential leaf physiological behavior, have been identified. This work focused on understanding the physiological and molecular mechanisms controlling root hydraulic conductivity and root-to-shoot gas exchange signaling in S. viridis. We identified 2 accessions, SHA and ZHA, with contrasting behavior at the leaf, root, and whole-plant levels. Our results indicated a role for root aquaporin (AQP) plasma membrane (PM) intrinsic proteins in the differential behavior of SHA and ZHA. Moreover, a different root hydraulic response to low levels of abscisic acid between SHA and ZHA was observed, which was associated with root AQPs. Using cell imaging, biochemical, and reverse genetic approaches, we identified PM intrinsic protein 1;6 (PIP1;6) as a possible PIP1 candidate that regulates radial root hydraulics and root-to-shoot signaling of gas exchange in S. viridis. In heterologous systems, PIP1;6 localized in the endoplasmic reticulum, and upon interaction with PIP2s, relocalization to the PM was observed. PIP1;6 was predominantly expressed at the root endodermis. Generation of knockout PIP1;6 plants (KO-PIP1;6) in S. viridis showed altered root hydraulic conductivity, altered gas exchange, and alteration of root transcriptional patterns. Our results indicate that PIPs are essential in regulating whole-plant water homeostasis in S. viridis. We conclude that root hydraulic conductivity and gas exchange are positively associated and are regulated by AQPs.

A comparative analysis of agronomic water-use efficiency and its proxy measures as derived from key morpho-physiological and supportive quantitative genetics attributes of perennial ryegrass under imposed drought

Water-use efficiency (WUE) is an under-researched but very important drought tolerance trait in forage breeding. This research estimated quantitative genetic parameters of morpho-physiological traits linked to agronomic water-use efficiency (WUEA) and its proxy measures based on δ13C (WUEi) or gas exchange (evapotranspiration, WUEAET, or stomatal conductance WUEASC) of genotypes from half-sib families of Lolium perenne L. (PRG) in a simulated summer drought cycle. Principal component analysis (PCA) of trait data distinguished a group of PRG genotypes where high WUEA and dry matter yield was associated with deep rooting, leaf hydration at more negative leaf osmotic and water potential, and reduced soil moisture depletion. Plants with this trait association sustained net assimilation and postdefoliation regrowth in drought. However, WUEi, WUEASC, and WUEAET were poorly correlated with most traits of interest at p < .05. Another PCA revealed a weak association between WUEA and its proxy measures under conditions tested. Quantitative genetic parameters including high estimates of narrow-sense heritability (h n 2 > 0.7 ; p < .05 ) of WUEA and related traits emphasized the genetic potential of the key trait combination for selecting PRG for improved drought tolerance. Research findings highlight the relative importance of WUEA and its proxy measures in the broad definition of PRG drought tolerance for breeding purposes.

Explainable deep learning in plant phenotyping

The increasing human population and variable weather conditions, due to climate change, pose a threat to the world's food security. To improve global food security, we need to provide breeders with tools to develop crop cultivars that are more resilient to extreme weather conditions and provide growers with tools to more effectively manage biotic and abiotic stresses in their crops. Plant phenotyping, the measurement of a plant's structural and functional characteristics, has the potential to inform, improve and accelerate both breeders' selections and growers' management decisions. To improve the speed, reliability and scale of plant phenotyping procedures, many researchers have adopted deep learning methods to estimate phenotypic information from images of plants and crops. Despite the successful results of these image-based phenotyping studies, the representations learned by deep learning models remain difficult to interpret, understand, and explain. For this reason, deep learning models are still considered to be black boxes. Explainable AI (XAI) is a promising approach for opening the deep learning model's black box and providing plant scientists with image-based phenotypic information that is interpretable and trustworthy. Although various fields of study have adopted XAI to advance their understanding of deep learning models, it has yet to be well-studied in the context of plant phenotyping research. In this review article, we reviewed existing XAI studies in plant shoot phenotyping, as well as related domains, to help plant researchers understand the benefits of XAI and make it easier for them to integrate XAI into their future studies. An elucidation of the representations within a deep learning model can help researchers explain the model's decisions, relate the features detected by the model to the underlying plant physiology, and enhance the trustworthiness of image-based phenotypic information used in food production systems.

Indicators of water use efficiency across diverse agroecosystems and spatiotemporal scales

Understanding the relationship between water and production within and across agroecosystems is essential for addressing several agricultural challenges of the 21st century: providing food, fuel, and fiber to a growing human population, reducing the environmental impacts of agricultural production, and adapting food systems to climate change. Of all human activities, agriculture has the highest demand for water globally. Therefore, increasing water use efficiency (WUE), or producing 'more crop per drop', has been a long-term goal of agricultural management, engineering, and crop breeding. WUE is a widely used term applied across a diverse array of spatial scales, spanning from the leaf to the globe, and over temporal scales ranging from seconds to months to years. The measurement, interpretation, and complexity of WUE varies enormously across these spatial and temporal scales, challenging comparisons within and across diverse agroecosystems. The goals of this review are to evaluate common indicators of WUE in agricultural production and assess tradeoffs when applying these indicators within and across agroecosystems amidst a changing climate. We examine three questions: (1) what are the uses and limitations of common WUE indicators, (2) how can WUE indicators be applied within and across agroecosystems, and (3) how can WUE indicators help adapt agriculture to climate change? Addressing these agricultural challenges will require land managers, producers, policy makers, researchers, and consumers to evaluate costs and benefits of practices and innovations of water use in agricultural production. Clearly defining and interpreting WUE in the most scale-appropriate way is crucial for advancing agroecosystem sustainability.

The influences of stomatal size and density on rice abiotic stress resilience

A warming climate coupled with reductions in water availability and rising salinity are increasingly affecting rice (Oryza sativa) yields. Elevated temperatures combined with vapour pressure deficit (VPD) rises are causing stomatal closure, further reducing plant productivity and cooling. It is unclear what stomatal size (SS) and stomatal density (SD) will best suit all these environmental extremes. To understand how stomatal differences contribute to rice abiotic stress resilience, we screened the stomatal characteristics of 72 traditionally bred varieties. We found significant variation in SS, SD and calculated anatomical maximal stomatal conductance (gsmax ) but did not identify any varieties with SD and gsmax as low as transgenic OsEPF1oe plants. Traditionally bred varieties with high SD and small SS (resulting in higher gsmax ) typically had lower biomasses, and these plants were more resilient to drought than low SD and large SS plants, which were physically larger. None of the varieties assessed were as resilient to drought or salinity as low SD OsEPF1oe transgenic plants. High SD and small SS rice displayed faster stomatal closure during increasing temperature and VPD, but photosynthesis and plant cooling were reduced. Compromises will be required when choosing rice SS and SD to tackle multiple future environmental stresses.

Physiological and genetic control of transpiration efficiency in African rice, Oryza glaberrima Steud

Improving crop water use efficiency, the amount of carbon assimilated as biomass per unit of water used by a plant, is of major importance as water for agriculture becomes scarcer. In rice, the genetic bases of transpiration efficiency, the derivation of water use efficiency at the whole-plant scale, and its putative component trait transpiration restriction under high evaporative demand remain unknown. These traits were measured in 2019 in a panel of 147 African rice (Oryza glaberrima) genotypes known to be potential sources of tolerance genes to biotic and abiotic stresses. Our results reveal that higher transpiration efficiency is associated with transpiration restriction in African rice. Detailed measurements in a subset of highly contrasted genotypes in terms of biomass accumulation and transpiration confirmed these associations and suggested that root to shoot ratio played an important role in transpiration restriction. Genome wide association studies identified marker-trait associations for transpiration response to evaporative demand, transpiration efficiency, and its residuals, with links to genes involved in water transport and cell wall patterning. Our data suggest that root-shoot partitioning is an important component of transpiration restriction that has a positive effect on transpiration efficiency in African rice. Both traits are heritable and define targets for breeding rice with improved water use strategies.

Multi-omics intervention in Setaria to dissect climate-resilient traits: Progress and prospects

Millets constitute a significant proportion of underutilized grasses and are well known for their climate resilience as well as excellent nutritional profiles. Among millets, foxtail millet (Setaria italica) and its wild relative green foxtail (S. viridis) are collectively regarded as models for studying broad-spectrum traits, including abiotic stress tolerance, C₄ photosynthesis, biofuel, and nutritional traits. Since the genome sequence release, the crop has seen an exponential increase in omics studies to dissect agronomic, nutritional, biofuel, and climate-resilience traits. These studies have provided first-hand information on the structure, organization, evolution, and expression of several genes; however, knowledge of the precise roles of such genes and their products remains elusive. Several open-access databases have also been instituted to enable advanced scientific research on these important crops. In this context, the current review enumerates the contemporary trend of research on understanding the climate resilience and other essential traits in Setaria, the knowledge gap, and how the information could be translated for the crop improvement of related millets, biofuel crops, and cereals. Also, the review provides a roadmap for studying other underutilized crop species using Setaria as a model.

Coordination of stomata and vein patterns with leaf width underpins water-use efficiency in a C4 crop

Despite its importance for crop water use and productivity, especially in drought-affected environments, the underlying mechanisms of variation in intrinsic water-use efficiency (iWUE = net photosynthesis/stomatal conductance for water vapour, g_sw ) are not well understood, especially in C₄ plants. Recently, we discovered that leaf width (LW) correlated negatively with iWUE and positively with g_sw across several C₄ grasses. Here, we confirmed these relationships within 48 field-grown genotypes differing in LW in Sorghum bicolor, a C₄ crop adapted to dry and hot conditions. We measured leaf gas exchange and modelled leaf energy balance three times a day, alongside anatomical traits as potential predictors of iWUE. LW correlated negatively with iWUE and stomatal density, but positively with g_sw , interveinal distance of longitudinal veins, and the percentage of stomatal aperture relative to maximum. Energy balance modelling showed that wider leaves needed to open their stomata more to generate a more negative leaf-to-air temperature difference, especially at midday when air temperatures exceeded 40°C. These results highlight the important role that LW plays in shaping iWUE through coordination of vein and stomatal traits and by affecting stomatal aperture. Therefore, LW could be used as a predictor of higher iWUE among sorghum genotypes.

Genetics as a key to improving crop photosynthesis

Since the basic biochemical mechanisms of photosynthesis are remarkably conserved among plant species, genetic modification approaches have so far been the main route to improve the photosynthetic performance of crops. Yet, phenotypic variation observed in wild species and between varieties of crop species implies there is standing natural genetic variation for photosynthesis, offering a largely unexplored resource to use for breeding crops with improved photosynthesis and higher yields. The reason this has not yet been explored is that the variation probably involves thousands of genes, each contributing only a little to photosynthesis, making them hard to identify without proper phenotyping and genetic tools. This is changing, though, and increasingly studies report on quantitative trait loci for photosynthetic phenotypes. So far, hardly any of these quantitative trait loci have been used in marker assisted breeding or genomic selection approaches to improve crop photosynthesis and yield, and hardly ever have the underlying causal genes been identified. We propose to take the genetics of photosynthesis to a higher level, and identify the genes and alleles nature has used for millions of years to tune photosynthesis to be in line with local environmental conditions. We will need to determine the physiological function of the genes and alleles, and design novel strategies to use this knowledge to improve crop photosynthesis through conventional plant breeding, based on readily available crop plant germplasm. In this work, we present and discuss the genetic methods needed to reveal natural genetic variation, and elaborate on how to apply this to improve crop photosynthesis.

Time-series transcriptomics reveals a drought-responsive temporal network and crosstalk between drought stress and the circadian clock in foxtail millet

Drought stress is a serious factor affecting crop growth and production worldwide. The circadian clock has been identified as key to improving regional adaptability of plants. However, our understanding of the contribution of the circadian clock to drought response and the impacts of drought stress on the circadian clock in plants is still limited. To explore the interactions between the circadian clock and drought stress, foxtail millet seedlings were treated with simulated drought (20% polyethylene glycol-6000) treatment starting at the day (DD) onset zeitgeber time 0 (ZT0, lights on) and at the night (DN) onset zeitgeber time 16 (ZT16, lights off). A high temporal-resolution transcriptomic investigation was performed using DD and DN samples collected at intervals of 2 or 4 h within a 24-h drought-treatment period. Overall, we identified 13 294 drought-responsive genes (DRGs). Among these DRGs, 7931 were common between DD and DN samples, 2638 were specific to DD, and 2725 were specific to DN. Additionally, we identified 1257 circadian genes, of which 67% were DRGs. Interestingly, with drought treatment starting at the day for 8, 12 or 16 h, the circadian phase shifted to 12 h. We also found that the circadian clock led to different day and night drought-responsive pathways. The identification of DRG_Clock (DRG and circadian clock) and DRG_NonClock (DRG and not circadian clock) genes provides a reference for selecting candidate drought resistance genes. Our work reveals the temporal drought-response process and crosstalk between drought stress and the circadian clock in foxtail millet.

High-throughput field crop phenotyping: current status and challenges

In contrast to the rapid advances made in plant genotyping, plant phenotyping is considered a bottleneck in plant science. This has promoted high-throughput plant phenotyping (HTP) studies, resulting in an exponential increase in phenotyping-related publications. The development of HTP was originally intended for use as indoor HTP technologies for model plant species under controlled environments. However, this subsequently shifted to HTP for use in crops in fields. Although HTP in fields is much more difficult to conduct due to unstable environmental conditions compared to HTP in controlled environments, recent advances in HTP technology have allowed these difficulties to be overcome, allowing for rapid, efficient, non-destructive, non-invasive, quantitative, repeatable, and objective phenotyping. Recent HTP developments have been accelerated by the advances in data analysis, sensors, and robot technologies, including machine learning, image analysis, three dimensional (3D) reconstruction, image sensors, laser sensors, environmental sensors, and drones, along with high-speed computational resources. This article provides an overview of recent HTP technologies, focusing mainly on canopy-based phenotypes of major crops, such as canopy height, canopy coverage, canopy biomass, and canopy stressed appearance, in addition to crop organ detection and counting in the fields. Current topics in field HTP are also presented, followed by a discussion on the low rates of adoption of HTP in practical breeding programs.

Insights Into the Genetic Architecture of Complex Traits in Napier Grass (Cenchrus purpureus) and QTL Regions Governing Forage Biomass Yield, Water Use Efficiency and Feed Quality Traits

Napier grass is the most important perennial tropical grass native to Sub-Saharan Africa and widely grown in tropical and subtropical regions around the world, primarily as a forage crop for animal feed, but with potential as an energy crop and in a wide range of other areas. Genomic resources have recently been developed for Napier grass that need to be deployed for genetic improvement and molecular dissection of important agro-morphological and feed quality traits. From a diverse set of Napier grass genotypes assembled from two independent collections, a subset of 84 genotypes (although a small population size, the genotypes were selected to best represent the genetic diversity of the collections) were selected and evaluated for 2 years in dry (DS) and wet (WS) seasons under three soil moisture conditions: moderate water stress in DS (DS-MWS); severe water stress in DS (DS-SWS) and, under rainfed (RF) conditions in WS (WS-RF). Data for agro-morphological and feed quality traits, adjusted for the spatial heterogeneity in the experimental blocks, were collected over a 2-year period from 2018 to 2020. A total of 135,706 molecular markers were filtered, after removing markers with missing values >10% and a minor allele frequency (MAF) <5%, from the high-density genome-wide markers generated previously using the genotyping by sequencing (GBS) method of the DArTseq platform. A genome-wide association study (GWAS), using two different mixed linear model algorithms implemented in the GAPIT R package, identified more than 35 QTL regions and markers associated with agronomic, morphological, and water-use efficiency traits. QTL regions governing purple pigmentation and feed quality traits were also identified. The identified markers will be useful in the genetic improvement of Napier grass through the application of marker-assisted selection and for further characterization and map-based cloning of the QTLs.

Machine learning-enabled phenotyping for GWAS and TWAS of WUE traits in 869 field-grown sorghum accessions

Sorghum (Sorghum bicolor) is a model C4 crop made experimentally tractable by extensive genomic and genetic resources. Biomass sorghum is studied as a feedstock for biofuel and forage. Mechanistic modeling suggests that reducing stomatal conductance (gs) could improve sorghum intrinsic water use efficiency (iWUE) and biomass production. Phenotyping to discover genotype-to-phenotype associations remains a bottleneck in understanding the mechanistic basis for natural variation in gs and iWUE. This study addressed multiple methodological limitations. Optical tomography and a machine learning tool were combined to measure stomatal density (SD). This was combined with rapid measurements of leaf photosynthetic gas exchange and specific leaf area (SLA). These traits were the subject of genome-wide association study and transcriptome-wide association study across 869 field-grown biomass sorghum accessions. The ratio of intracellular to ambient CO2 was genetically correlated with SD, SLA, gs, and biomass production. Plasticity in SD and SLA was interrelated with each other and with productivity across wet and dry growing seasons. Moderate-to-high heritability of traits studied across the large mapping population validated associations between DNA sequence variation or RNA transcript abundance and trait variation. A total of 394 unique genes underpinning variation in WUE-related traits are described with higher confidence because they were identified in multiple independent tests. This list was enriched in genes whose Arabidopsis (Arabidopsis thaliana) putative orthologs have functions related to stomatal or leaf development and leaf gas exchange, as well as genes with nonsynonymous/missense variants. These advances in methodology and knowledge will facilitate improving C4 crop WUE.

Machine learning-enabled phenotyping for GWAS and TWAS of WUE traits in 869 field-grown sorghum accessions

Sorghum (Sorghum bicolor) is a model C4 crop made experimentally tractable by extensive genomic and genetic resources. Biomass sorghum is studied as a feedstock for biofuel and forage. Mechanistic modeling suggests that reducing stomatal conductance (gs) could improve sorghum intrinsic water use efficiency (iWUE) and biomass production. Phenotyping to discover genotype-to-phenotype associations remains a bottleneck in understanding the mechanistic basis for natural variation in gs and iWUE. This study addressed multiple methodological limitations. Optical tomography and a machine learning tool were combined to measure stomatal density (SD). This was combined with rapid measurements of leaf photosynthetic gas exchange and specific leaf area (SLA). These traits were the subject of genome-wide association study and transcriptome-wide association study across 869 field-grown biomass sorghum accessions. The ratio of intracellular to ambient CO2 was genetically correlated with SD, SLA, gs, and biomass production. Plasticity in SD and SLA was interrelated with each other and with productivity across wet and dry growing seasons. Moderate-to-high heritability of traits studied across the large mapping population validated associations between DNA sequence variation or RNA transcript abundance and trait variation. A total of 394 unique genes underpinning variation in WUE-related traits are described with higher confidence because they were identified in multiple independent tests. This list was enriched in genes whose Arabidopsis (Arabidopsis thaliana) putative orthologs have functions related to stomatal or leaf development and leaf gas exchange, as well as genes with nonsynonymous/missense variants. These advances in methodology and knowledge will facilitate improving C4 crop WUE.

High light and temperature reduce photosynthetic efficiency through different mechanisms in the C4 model Setaria viridis

C₄ plants frequently experience high light and high temperature conditions in the field, which reduce growth and yield. However, the mechanisms underlying these stress responses in C₄ plants have been under-explored, especially the coordination between mesophyll (M) and bundle sheath (BS) cells. We investigated how the C₄ model plant Setaria viridis responded to a four-hour high light or high temperature treatment at photosynthetic, transcriptomic, and ultrastructural levels. Although we observed a comparable reduction of photosynthetic efficiency in high light or high temperature treated leaves, detailed analysis of multi-level responses revealed important differences in key pathways and M/BS specificity responding to high light and high temperature. We provide a systematic analysis of high light and high temperature responses in S. viridis, reveal different acclimation strategies to these two stresses in C₄ plants, discover unique light/temperature responses in C₄ plants in comparison to C₃ plants, and identify potential targets to improve abiotic stress tolerance in C₄ crops.

The genetic architecture of leaf stable carbon isotope composition in Zea mays and the effect of transpiration efficiency on leaf elemental accumulation

With increased demand on freshwater resources for agriculture, it is imperative that more water-use efficient crops are developed. Leaf stable carbon isotope composition, δ13C, is a proxy for transpiration efficiency and a possible tool for breeders, but the underlying mechanisms effecting δ13C in C4 plants are not known. It has been suggested that differences in specific leaf area (SLA), which potentially reflects variation in internal CO2 diffusion, can impact leaf δ13C. Furthermore, although it is known that water movement is important for elemental uptake, it is not clear how manipulation of transpiration for increased water-use efficiency may impact nutrient accumulation. Here, we characterize the genetic architecture of leaf δ13C and test its relationship to SLA and the ionome in five populations of maize. Five significant QTL for leaf δ13C were identified, including novel QTL as well as some that were identified previously in maize kernels. One of the QTL regions contains an Erecta-like gene, the ortholog of which has been shown to regulate transpiration efficiency and leaf δ13C in Arabidopsis. QTL for δ13C were located in the same general chromosome region, but slightly shifted, when comparing data from two different years. Our data does not support a relationship between δ13C and SLA, and of the 19 elements analyzed, only a weak correlation between molybdenum and δ13C was detected. Together these data add to the genetic understanding of leaf δ13C in maize and suggest that improvements to plant water use may be possible without significantly influencing elemental homeostasis.

Improving C4 photosynthesis to increase productivity under optimal and suboptimal conditions

Although improving photosynthetic efficiency is widely recognized as an underutilized strategy to increase crop yields, research in this area is strongly biased towards species with C3 photosynthesis relative to C4 species. Here, we outline potential strategies for improving C4 photosynthesis to increase yields in crops by reviewing the major bottlenecks limiting the C4 NADP-malic enzyme pathway under optimal and suboptimal conditions. Recent experimental results demonstrate that steady-state C4 photosynthesis under non-stressed conditions can be enhanced by increasing Rubisco content or electron transport capacity, both of which may also stimulate CO2 assimilation at supraoptimal temperatures. Several additional putative bottlenecks for photosynthetic performance under drought, heat, or chilling stress or during photosynthetic induction await further experimental verification. Based on source-sink interactions in maize, sugarcane, and sorghum, alleviating these photosynthetic bottlenecks during establishment and growth of the harvestable parts are likely to improve yield. The expected benefits are also shown to be augmented by the increasing trend in planting density, which increases the impact of photosynthetic source limitation on crop yields.

Variation in climatic tolerance, but not stomatal traits, partially explains Pooideae grass species distributions

BACKGROUND AND AIMS

Grasses in subfamily Pooideae live in some of the world's harshest terrestrial environments, from frigid boreal zones to the arid windswept steppe. It is hypothesized that the climate distribution of species within this group is driven by differences in climatic tolerance, and that tolerance can be partially explained by variation in stomatal traits.

METHODS

We determined the aridity index (AI) and minimum temperature of the coldest month (MTCM) for 22 diverse Pooideae accessions and one outgroup, and used comparative methods to assess predicted relationships for climate traits versus fitness traits, stomatal diffusive conductance to water (gw) and speed of stomatal closure following drought and/or cold.

KEY RESULTS

Results demonstrate that AI and MTCM predict variation in survival/regreening following drought/cold, and gw under drought/cold is positively correlated with δ 13C-measured water use efficiency (WUE). However, the relationship between climate traits and fitness under drought/cold was not explained by gw or speed of stomatal closure.

CONCLUSIONS

These findings suggest that Pooideae distributions are at least partly determined by tolerance to aridity and above-freezing cold, but that variation in tolerance is not uniformly explained by variation in stomatal traits.

Hyperspectral reflectance-based phenotyping for quantitative genetics in crops: Progress and challenges

Many biochemical and physiological properties of plants that are of interest to breeders and geneticists have extremely low throughput and/or can only be measured destructively. This has limited the use of information on natural variation in nutrient and metabolite abundance, as well as photosynthetic capacity in quantitative genetic contexts where it is necessary to collect data from hundreds or thousands of plants. A number of recent studies have demonstrated the potential to estimate many of these traits from hyperspectral reflectance data, primarily in ecophysiological contexts. Here, we summarize recent advances in the use of hyperspectral reflectance data for plant phenotyping, and discuss both the potential benefits and remaining challenges to its application in plant genetics contexts. The performances of previously published models in estimating six traits from hyperspectral reflectance data in maize were evaluated on new sample datasets, and the resulting predicted trait values shown to be heritable (e.g., explained by genetic factors) were estimated. The adoption of hyperspectral reflectance-based phenotyping beyond its current uses may accelerate the study of genes controlling natural variation in biochemical and physiological traits.

Functional physiological phenotyping with functional mapping: A general framework to bridge the phenotype-genotype gap in plant physiology

The recent years have witnessed the emergence of high-throughput phenotyping techniques. In particular, these techniques can characterize a comprehensive landscape of physiological traits of plants responding to dynamic changes in the environment. These innovations, along with the next-generation genomic technologies, have brought plant science into the big-data era. However, a general framework that links multifaceted physiological traits to DNA variants is still lacking. Here, we developed a general framework that integrates functional physiological phenotyping (FPP) with functional mapping (FM). This integration, implemented with high-dimensional statistical reasoning, can aid in our understanding of how genotype is translated toward phenotype. As a demonstration of method, we implemented the transpiration and soil-plant-atmosphere measurements of a tomato introgression line population into the FPP-FM framework, facilitating the identification of quantitative trait loci (QTLs) that mediate the spatiotemporal change of transpiration rate and the test of how these QTLs control, through their interaction networks, phenotypic plasticity under drought stress.

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.

Small Millets for Enduring Food Security Amidst Pandemics

Food security is an ongoing problem, and current staple foods are not sufficient to overcome challenges such as the present COVID-19 pandemic. We propose here that small millets have the potential to become new staple crops, especially in hunger hotspots. Currently, the absence of intensification of millet farming, lack of deployment of genetic tools for trait improvement, and the need for optimization of storage and supply chains limit crop production. We highlight a roadmap to strengthen small millet cultivation, such as identifying varieties suitable for particular environments and targeting trait improvement using genetic and genomic approaches. These approaches will help to combat hunger and malnutrition and also economically benefit the farmers and stakeholders involved in small millet cultivation amidst the ongoing pandemic.

A genome resource for green millet Setaria viridis enables discovery of agronomically valuable loci

Wild and weedy relatives of domesticated crops harbor genetic variants that can advance agricultural biotechnology. Here we provide a genome resource for the wild plant green millet (Setaria viridis), a model species for studies of C4 grasses, and use the resource to probe domestication genes in the close crop relative foxtail millet (Setaria italica). We produced a platinum-quality genome assembly of S. viridis and de novo assemblies for 598 wild accessions and exploited these assemblies to identify loci underlying three traits: response to climate, a 'loss of shattering' trait that permits mechanical harvest and leaf angle, a predictor of yield in many grass crops. With CRISPR-Cas9 genome editing, we validated Less Shattering1 (SvLes1) as a gene whose product controls seed shattering. In S. italica, this gene was rendered nonfunctional by a retrotransposon insertion in the domesticated loss-of-shattering allele SiLes1-TE (transposable element). This resource will enhance the utility of S. viridis for dissection of complex traits and biotechnological improvement of panicoid crops.

Physiological and molecular responses of Setaria viridis to osmotic stress

Drought-tolerant species, such as Setaria viridis, a C₄ model plant, make physiological and biochemical adjustments water limitation and recover from the stress upon its release. We investigated S. viridis (A10.1 accession) responses to continuing osmotic stress. The osmotic stress was imposed using polyethylene glycol (PEG) 8000 (7.5%) for 10 days. Morphological traits and stomatal conductance were measured daily for the 10 days. On days 6 and 10, the following traits were measured separately for root and shoot: relative water content (RWC), osmotic potential (OP), electrolytic leakage (EL), and proline content. qPCR analysis was used to evaluate the expression of five selected genes in roots (SvLEA, SvDREB1C, SvPIP2-1, SvHSP20, and SvP5CS2), and chlorophyll a fluorescence was measured on three key days. The morphological data demonstrated a drastic reduction in shoot biomass as an effect of water deficit caused by the osmotic stress. Shoot biomass reduction could be associated with putative ABA-dependent signaling involved in SvDREB1C expression. Stomatal conductance and photosynthesis were severely affected up until day 6, however, stomatal conductance and some photosynthetic parameters such as F_V/F_M, ABS/RC, and DI₀/RC showed total or slight recovery on day 10. Root EL decreased in treated plants suggesting an investment in membrane protection by osmoregulator expression such as dehydrin (SvLEA) and proline (SvP5CS2) genes. Our data suggest that S. viridis exhibited a partial recovery from an imposed and constant osmotic stress within 10 days.

Increased Power and Accuracy of Causal Locus Identification in Time Series Genome-wide Association in Sorghum

The phenotypes of plants develop over time and change in response to the environment. New engineering and computer vision technologies track these phenotypic changes. Identifying the genetic loci regulating differences in the pattern of phenotypic change remains challenging. This study used functional principal component analysis (FPCA) to achieve this aim. Time series phenotype data were collected from a sorghum (Sorghum bicolor) diversity panel using a number of technologies including conventional color photography and hyperspectral imaging. This imaging lasted for 37 d and centered on reproductive transition. A new higher density marker set was generated for the same population. Several genes known to control trait variation in sorghum have been previously cloned and characterized. These genes were not confidently identified in genome-wide association analyses at single time points. However, FPCA successfully identified the same known and characterized genes. FPCA analyses partitioned the role these genes play in controlling phenotypes. Partitioning was consistent with the known molecular function of the individual cloned genes. These data demonstrate that FPCA-based genome-wide association studies can enable robust time series mapping analyses in a wide range of contexts. Moreover, time series analysis can increase the accuracy and power of quantitative genetic analyses.

A genetic link between leaf carbon isotope composition and whole-plant water use efficiency in the C4 grass Setaria

Genetic selection for whole-plant water use efficiency (yield per transpiration; WUE_plant ) in any crop-breeding programme requires high-throughput phenotyping of component traits of WUE_plant such as intrinsic water use efficiency (WUE_i ; CO₂ assimilation rate per stomatal conductance). Measuring WUE_i by gas exchange measurements is laborious and time consuming and may not reflect an integrated WUE_i over the life of the leaf. Alternatively, leaf carbon stable isotope composition (δ¹³ C_leaf ) has been suggested as a potential time-integrated proxy for WUE_i that may provide a tool to screen for WUE_plant . However, a genetic link between δ¹³ C_leaf and WUE_plant in a C₄ species has not been well established. Therefore, to determine if there is a genetic relationship in a C₄ plant between δ¹³ C_leaf and WUE_plant under well watered and water-limited growth conditions, a high-throughput phenotyping facility was used to measure WUE_plant in a recombinant inbred line (RIL) population created between the C₄ grasses Setaria viridis and S. italica. Three quantitative trait loci (QTL) for δ¹³ C_leaf were found and co-localized with transpiration, biomass accumulation, and WUE_plant . Additionally, WUE_plant for each of the δ¹³ C_leaf QTL allele classes was negatively correlated with δ¹³ C_leaf , as would be predicted when WUE_i influences WUE_plant . These results demonstrate that δ¹³ C_leaf is genetically linked to WUE_plant , likely to be through their relationship with WUE_i , and can be used as a high-throughput proxy to screen for WUE_plant in these C₄ species.

Multi-trait random regression models increase genomic prediction accuracy for a temporal physiological trait derived from high-throughput phenotyping

Random regression models (RRM) are used extensively for genomic inference and prediction of time-valued traits in animal breeding, but only recently have been used in plant systems. High-throughput phenotyping (HTP) platforms provide a powerful means to collect high-dimensional phenotypes throughout the growing season for large populations. However, to date, selection of an appropriate statistical genomic framework to integrate multiple temporal traits for genomic prediction in plants remains unexplored. Here, we demonstrate the utility of a multi-trait RRM (MT-RRM) for genomic prediction of daily water usage (WU) in rice (Oryza sativa) through joint modeling with shoot biomass (projected shoot area, PSA). Three hundred and fifty-seven accessions were phenotyped daily for WU and PSA over 20 days using a greenhouse-based HTP platform. MT-RRMs that modeled additive genetic and permanent environmental effects for both traits using quadratic Legendre polynomials were used to assess genomic correlations between traits and genomic prediction for WU. Predictive abilities of the MT-RRMs were assessed using two cross-validation (CV) scenarios. The first scenario was designed to predict genetic values for WU at all time points for a set of accessions with unobserved WU. The second scenario was designed to forecast future genetic values for WU for a panel of known accessions with records for WU at earlier time periods. In each scenario we evaluated two MT-RRMs in which PSA records were absent or available for time points in the testing population. Weak to strong genomic correlations between WU and PSA were observed across the days of imaging (0.29-0.870.38-0.80). In both CV scenarios, MT-RRMs showed better predictive abilities compared to single-trait RRM, and prediction accuracies were greatly improved when PSA records were available for the testing population. In summary, these frameworks provide an effective approach to predict temporal physiological traits that are difficult or expensive to quantify in large populations.

Latent Space Phenotyping: Automatic Image-Based Phenotyping for Treatment Studies

Association mapping studies have enabled researchers to identify candidate loci for many important environmental tolerance factors, including agronomically relevant tolerance traits in plants. However, traditional genome-by-environment studies such as these require a phenotyping pipeline which is capable of accurately measuring stress responses, typically in an automated high-throughput context using image processing. In this work, we present Latent Space Phenotyping (LSP), a novel phenotyping method which is able to automatically detect and quantify response-to-treatment directly from images. We demonstrate example applications using data from an interspecific cross of the model C₄ grass Setaria, a diversity panel of sorghum (S. bicolor), and the founder panel for a nested association mapping population of canola (Brassica napus L.). Using two synthetically generated image datasets, we then show that LSP is able to successfully recover the simulated QTL in both simple and complex synthetic imagery. We propose LSP as an alternative to traditional image analysis methods for phenotyping, enabling the phenotyping of arbitrary and potentially complex response traits without the need for engineering-complicated image-processing pipelines.

Climate change and abiotic stress mechanisms in plants

Predicted global climatic change will perturb the productivity of our most valuable crops as well as detrimentally impact ecological fitness. The most important aspects of climate change with respect to these effects relate to water availability and heat stress. Over multiple decades, the plant research community has amassed a highly comprehensive understanding of the physiological mechanisms that facilitate the maintenance of productivity in response to drought, flooding, and heat stress. Consequently, the foundations necessary to begin the development of elite crop varieties that are primed for climate change are in place. To meet the food and fuel security concerns of a growing population, it is vital that biotechnological and breeding efforts to harness these mechanisms are accelerated in the coming decade. Despite this, those concerned with crop improvement must approach such efforts with caution and ensure that potentially harnessed mechanisms are viable under the context of a dynamically changing environment.

Water Use Efficiency as a Constraint and Target for Improving the Resilience and Productivity of C3 and C4 Crops

The ratio of plant carbon gain to water use, known as water use efficiency (WUE), has long been recognized as a key constraint on crop production and an important target for crop improvement. WUE is a physiologically and genetically complex trait that can be defined at a range of scales. Many component traits directly influence WUE, including photosynthesis, stomatal and mesophyll conductances, and canopy structure. Interactions of carbon and water relations with diverse aspects of the environment and crop development also modulate WUE. As a consequence, enhancing WUE by breeding or biotechnology has proven challenging but not impossible. This review aims to synthesize new knowledge of WUE arising from advances in phenotyping, modeling, physiology, genetics, and molecular biology in the context of classical theoretical principles. In addition, we discuss how rising atmospheric CO₂ concentration has created and will continue to create opportunities for enhancing WUE by modifying the trade-off between photosynthesis and transpiration.