Biology subjects

Baxter, I.

Publications and source records attributed to Baxter, I..

At least 19 recordsLinked to original sources

bioRxiv2026

Heritability of leaf stable carbon isotope signature in a diversity panel of the C4 plant Sorghum bicolor

A strategy to improve agricultural water productivity is to increase water use efficiency (WUE) at the level of plant transpiration through genetic selection. This requires detectable genetic variability in WUE and the ability to phenotype and select plants with higher WUE within a population. A proxy for phenotyping leaf level WUE by measuring carbon isotope signature ({delta}13Cleaf) has been supported by theory and data in C4 species. However, the functional relationship of {delta}13Cleaf and WUE in C4 species can be driven by genetics and environment. Therefore, a wide survey of existing natural variation is needed to quantify the heritability and identify various genetic factors that influence {delta}13Cleaf and WUE. In this study a genome-wide association panel was used to quantify the heritability of {delta}13Cleaf. We measured {delta}13Cleaf across a population of 360 genetically diverse lines of the C4 species Sorghum bicolor with single nucleotide polymorphic (SNP) markers determined from whole-genome resequencing. This analysis was conducted on two independent field environments where heritability of {delta}13Cleaf was evident and was driven by small genetic effects from loci that were consistently identified across environments. Candidate genes are presented that offer insights on future targets to manipulate and explore the functional relationship between {delta}13Cleaf and WUEi in C4 plants.

plant biology↗

bioRxiv2026

The carbon-nitrogen metabolism gene E1OGDH1 influences maize root system architecture and nitrogen plasticity

Synthetic nitrogen fertilizers have greatly increased crop yields, yet much of the applied nitrogen is lost from agroecosystems and contributes to environmental pollution and higher economic costs. Improving nitrogen uptake efficiency (NUpE) benefits from understanding how root system architecture (RSA) governs soil nitrogen capture. Although root traits have seldom been explicit breeding targets, selection for variation in above-ground nitrogen accumulation has also likely shaped differences in RSA. The Illinois Protein Strain Recombinant Inbred population, derived from more than a century of divergent selection for seed protein concentration, offers a powerful resource for dissecting RSA variation. Using multi-year field phenotyping of excavated root crowns and genome-wide association analysis, we identified a quantitative trait locus on chromosome 10 containing E1OGDH1, which encodes the E1 subunit of the 2-oxoglutarate dehydrogenase (OGDH) complex. OGDH performs a key step in the tricarboxylic acid cycle that also modulates 2-oxoglutarate, an important entry point into nitrogen metabolism and a co-factor for enzymes involved in hormone and secondary product synthesis. Long-read sequencing of inbreds derived from the divergent IHP and ILP parental populations revealed promoter polymorphisms defining E1OGDH1 alleles and differed in E1OGDH1 expression in root tissue. Field experiments in IPSRI lines carrying IHP- or ILP-associated E1OGDH1 alleles showed differences in root architectural traits over two years. CRISPR-Cas9 knockout mutants confirmed a functional role for E1OGDH1 in whole-plant performance and nitrogen-responsive root development. Mutants were shorter, had reduced biomass, and exhibited altered architectural responses to soil nitrogen levels. Transcriptome analysis further showed that loss of E1OGDH1 altered basal and nitrogen-responsive expression of genes associated with root development and nitrogen uptake and metabolism. Together, these findings identify E1OGDH1 as a strong candidate quantitative regulator of maize RSA and nitrogen plasticity, suggesting that central carbon-nitrogen metabolic genes can contribute to root developmental responses relevant to NUpE.

plant biology↗

bioRxiv2026

PanvaR: An R package for fine-mapping and visualizing results from genome-wide association studies

Genome-wide association studies (GWAS) use statistical models to correlate single nucleotide polymorphisms (SNPs) to a phenotype of interest. This scan of the entire genome identifies regions of association with a phenotype, but due to linkage disequilibrium (LD), GWAS on their own cannot identify single genes responsible for phenotypic variation. Rather, fine-mapping of GWAS regions is required, necessitating the use of additional tools and software. With the introduction of more pangenomic resources in a number of crops (Guo et al. 2025; Hufford et al. 2021), the fidelity of these fine-mapping efforts is growing, presenting the opportunity to leverage new information about allelic variation towards gene discovery (Shi et al. 2023; Della Coletta et al. 2021). Panvar is a tool developed to integrate existing software and resources to perform GWAS and fine-mapping in one seamless step. For each identified GWAS peak, panvaR outputs information about LD and SNP effect prediction for each SNP and by layering locations of nearby genes, creates a refined list of possible candidate genes. We have implemented Panvar as an R package, "panvaR", which runs the analysis functions, creates interactive and static visualizations, and outputs results tables. This tool seeks to bridge the gap between GWAS and gene speeding up an important step of quantitative genetic studies.

genomics↗

bioRxiv2026

Smartphone image capture system and image analysis pipelines enable accurate and efficient phenotyping of spaced plant mapping populations

Plain Language SummaryThe lack of low-cost, user-friendly and expedient methods for plant phenotyping challenges researchers ability to efficiently collect accurate phenotypic data in large field experiments. Here, we demonstrate the use of a novel, smartphone-based image capture system and two user-friendly image analysis pipelines (utilizing PlantCV or Biodock AI) to increase the throughput of plant phenotyping in two large, spaced plant populations. We showed that the image capture system collected images of adequate quality for downstream analysis using either the PlantCV or Biodock AI pipeline. Both image analysis pipelines produced phenotype values in line with those obtained using manual image annotation. Together, these results provide researchers with a low-cost, user-friendly image-based phenotyping method that can be widely applied to increase phenotyping throughout in field experiments.

plant biology↗

bioRxiv2025

Developing future resilience from signatures of adaptation across the sorghum pangenome

While the green revolution adapted a handful of crops to homogenous and high-input industrialized agriculture, much of the global population still relies on local food production from low-input smallholder farms that grow highly variable crop cultivars. The high diversity of the grain and bioenergy crop sorghum 1-4, and many other crops that were not homogenized during the green revolution 5, not only provides the raw materials for breeders to make substantial gains in cultivar improvement, but also constrains breeding efforts due to highly specialized locally adapted plant phenotypes 6. Here, we construct a 33-member pangenome and identify trait-associated variants in 1,988 cultivars and landraces. We then apply these resources to explore the complex interplay between historical contingency, ongoing adaptation, and the potential for future gains through climate-aware genome-enabled breeding. Specifically, our analyses conclusively demonstrate that multiple nested, deeply diverged, and previously uncharacterized structural variants in the domestication gene SHATTERING1 distinguish the previously established multicentric origin of sorghum. We then apply landscape genomics tests to reveal how gene flow, adaptation, and secondary contact created the complex genetic mosaic in current global breeding networks. Further analysis of climate-gene associations highlights candidate loci underlying adaptation, including the biosynthetic gene cluster for the cyanogenic glucoside dhurrin. Combined, the pangenome-informed variants developed here will enable both trait discovery and subsequent marker assays to accelerate breeding and provide a framework for similar applications in other diverse and non-model crops.

genomics↗

bioRxiv2025

Development of male-sterile lines of Setaria viridis to accelerate C4 model plant genetics

Setaria viridis is a diploid C4 grass in the Poaceae family, notable for its rapid life cycle of 6-8 weeks from sowing to seed--much shorter than the 4-5 months required by crops such as Zea mays and Sorghum bicolor. This fast growth makes S. viridis a valuable model for C4 crop research. Genetic crosses are essential for studying gene function, but manual crossing is labor-intensive and time-consuming. To address this, we developed a male-sterile line by targeting the S. viridis ortholog of Setaria italica NO POLLEN 1 (SiNP1), which encodes a glucose-methanol-choline oxidoreductase required for pollen exine formation. Using Cas9 and TREX2-mediated genome editing, we generated SiNP1 knockouts in both the S. viridis ME034V and A10.1 backgrounds that were fully male-sterile. Backcrossing T0 male-sterile plants to ME034V yielded a stable line homozygous for a 59 bp deletion, easily genotyped by PCR. Using this line, we developed a simple and efficient crossing protocol that eliminates the need for emasculation. This method enables a single person to perform up to 100 crosses per day--compared to 15 using traditional methods--and yields 20-32 F1 hybrid seeds per panicle with 100% genetic purity. We also quantified pollen flow and outcrossing frequencies under greenhouse conditions to develop optimal bagging strategies and prevent unintended pollination.This resource accelerates genetic research in S. viridis, enhancing its utility as a premier C4 model for mapping and functional genomics.

plant biology↗

bioRxiv2024

Nutrient Challenges in a Changing Atmosphere: Investigating Biomass Growth and Mineral Concentration Changes in Soybean Plants under Elevated CO2

Rising atmospheric CO2 levels, projected to reach [~]650 ppm by 2050, threaten the nutritional value of food crops. This rise is expected to increase biomass yield in C3 plants through enhanced photosynthesis and water-use efficiency. However, elevated CO2 (eCO2) reduces protein, nitrogen, and essential minerals like zinc (Zn) and iron (Fe) in plant leaves and seeds, posing a global nutrition risk. We conducted an experiment using Open Top Chambers to examine the response of three soybean cultivars (Clark, Flyer, and Loda) to ambient ([~]410 ppm) and eCO2 ([~]610 ppm) conditions. These cultivars were selected due to their contrasting responses to eCO2. Measurements of physiological parameters (i.e., biomass, and nutrient concentration) were taken at different growth stages. Our results showed that eCO2 increased carbon assimilation, leading to higher aboveground biomass and seed yield (through increased seed number) while root biomass remained unchanged. eCO2 also reduced stomatal conductance and transpiration. There was a significant decrease in seed nutrient concentration at maturity, particularly iron (Fe), phosphorous (P), potassium (K), and magnesium (Mg), in plants grown in eCO2. These findings suggest that increased yield, reduced transpiration, and unchanged root biomass are key drivers of nutrient dilution in seeds under eCO2.

plant biology↗

bioRxiv2024

Elemental profiling and genome-wide association mapping reveal genomic variants modulating ionomic composition in Populus trichocarpa leaves

The ionome represents elemental composition in plant tissues and can be an indicator of nutrient status as well as overall plant performance. Thus, identifying genetic determinants governing elemental uptake and storage is an important goal in plant breeding and engineering. In this study, we coupled high-throughput ionome characterization with high-resolution genome-wide association studies (GWAS) to uncover genetic loci that modulate ionomic composition in leaves of 584 black cottonwood poplar (Populus trichocarpa) genotypes. Congruence of alternate ionomic profiling platforms, i.e., inductively coupled plasma-mass spectrometry (ICP-MS), neutron activation analysis (NAA) and laser-induced breakdown spectroscopy (LIBS), was performed on leaf samples from a subset of the population. Significant agreement was observed across the three platforms with some notable exceptions for individual elements. Subsequently, we used the ICP-MS platform to profile the 584 genotypes focusing on 20 elements. GWAS performed using a set of high-density (>8.2 million) single nucleotide polymorphisms (SNP), identified multiple loci significantly associated with variations in these mineral elements. The potential causal genes for variations in the ionome were significantly enriched in genes whose homologs were previously associated to ion homeostasis in other species. Notably, a polymorphic copy of the high-affinity molybdenum transporter MOT1 was found directly associated to molybdenum content in leaf tissues. The results of the GWAS also provided evidence of physiological and genetic interactions between mineral elements in poplar. The new candidate genes predicted to play a key role in cross-homeostasis of multiple elements are new targets for engineering a variety of traits of interest in tree species.

plant biology↗

bioRxiv2023

Competition for resources during development drives allometric patterns in the grass Setaria

Plant growth and resilience is greater than the sum of its component traits, with important traits influencing one another. This interdependency makes it challenging to identify the genetic determinants of key agronomic traits, such as water use efficiency. Measuring traits such as plant height and size is possible via image analysis software, such as PlantCV, but these traits are often highly correlated. Furthermore, plant size is estimated using a 2D projection of a 3D object, which is more difficult for plants with complex body plans. To address these problems, we developed a generalizable, biologically-informed model describing the temporal coordination of semi-sequential phytomeric growth in the model grass Setaria. Our approach integrates time dependence with water usage, the growth of phytomers, and the emergence of side shoots or tillers, improving our ability to estimate both phytomer-level phenotypes and water usage traits in high-throughput. We developed new PlantCV methods to inform the Phytomeric Growth Model, and estimated parameter traits using a high-throughput phenotyping dataset of a recombinant inbred line (RIL) panel in well-watered and drought conditions. Model parameter estimates identified additional quantitative trait loci (QTL) for new traits compared to the directly measured PlantCV-derived estimates, and predicted the relationships underlying QTL control of composite traits through co-localization of model parameters to loci controlling size and water use metrics. The Phytomeric Growth Model estimates improved our ability to estimate the growth of tillers and obtain a model-based estimate of marginal water use efficiency (defined here as the ratio of biomass gained per gram of water transpired), both identified as highly influential on plant drought responses through random forest classification. Our approach demonstrates the value of integrating mechanistic modeling with high-throughput imaging to extract new information at scale.

plant biology↗

bioRxiv2023

A comparative approach for selecting orthologous candidate genes underlying signal in genome-wide association studies across multiple species

Advances in quantitative genetics have enabled researchers to identify genomic regions associated with changes in phenotype. However, genomic regions can contain hundreds to thousands of genes, and progressing from genomic regions to causative genes is still challenging. In genome-wide association studies (GWAS) measuring elemental accumulation (ionomic) traits, only 5% of loci overlap genes known to control the ionome - indicating that many causal genes are still unknown. To identify candidates for the remaining 95% loci, we developed a method that uses GWAS studies across multiple species to identify conserved causative genes. By Filtering the Results of Multi-species, Analogous, GWAS Experiments (FiREMAGE) we were able to take the GWAS of 19 ionomic traits in Arabidopsis, soybean, rice, maize, and sorghum, and identify alleles affecting trait variation at conserved genes. Permutation testing demonstrated that GWAS loci affecting the same trait contained homologs more often than expected by chance. Most of the top 10% most significant conserved candidate sets encoded alleles in all five species, highlighting the conservation of ionomic genetic regulators in flowering plants. The candidates include proteins with known biochemical functions that regulate the ionome, validating the approach. In addition to genes with known functions, this approach also identified many conserved genes underlying GWAS loci affecting the same trait in multiple species that have no previously identified function in regulating the ionome, providing a path to discover the as yet unknown mechanisms of element accumulation in plants. This method enables the identification of conserved genes of previously unknown function via GWAS. Author summaryQuantitative genetics identifies a genomic region of interest but not the causal gene. We developed an approach to narrow these gene lists using genetic loci affecting elemental (i.e., calcium, iron, zinc) accumulation. Comparative genomics and GWAS demonstrates that alleles at evolutionarily conserved genes alter the same phenotype in multiple species. This produced a list of conserved candidate genes including previously known elemental regulators and genes whose elemental accumulation mechanism has yet to be determined. Combining datasets across species boosted the signal at these loci. This approach accelerates the discovery of new functional roles of genes.

genetics↗

bioRxiv2022

Mycorrhizal status impacts the genetic architecture of mineral accumulation in field grown maize (Zea mays ssp. mays L.)

Arbuscular mycorrhizal fungi (AMF) establish symbioses with major crop species, providing their hosts with greater access to mineral nutrients and promoting tolerance to heavy metal toxicity. There is considerable interest in AMF as biofertilizers and for their potential in breeding for greater nutrient efficiency and stress tolerance. However, it remains a challenge to estimate the nutritional benefits of AMF in the field, in part due to a lack of suitable AMF-free controls. Here we evaluated the impact of AMF on the concentration of 20 elements in the leaves and grain of field grown maize using a custom genetic mapping population in which half of the families carry the AMF-incompatibility mutation castor. By comparing AMF-compatible and AMF-incompatible families, we confirmed the benefits of AMF in increasing the concentration of essential mineral nutrients (e.g., P, Zn, and Cu) and reducing the concentration of toxic elements (e.g., Cd and As) in a medium-input subtropical field. We characterised the genetic architecture of element concentration using quantitative trait mapping and identified loci that were specific to AMF-compatible or AMF-incompatible families, consistent with their respective involvement in mycorrhizal or direct nutrient uptake. Patterns of element covariance changed depending on AMF status and could be used to predict variation in mycorrhizal colonisation. We comment on the potential of AMF to drive genotype-specific differences in the host ionome across fields and to impact the alignment of biofortification breeding targets. Our results highlight the benefits of AMF in improving plant access to micronutrients while protecting from heavy metals, and indicate the potential benefits of considering AMF in biofortification programs.

plant biology↗

bioRxiv2021

A novel paradigm for optimal mass feature peak picking in large scale LC-MS datasets using the isopair: isoLock, autoCredential and anovAlign

Untargeted metabolomics enables direct quantification of metabolites without apriori knowledge of their identity. Liquid chromatography mass spectrometry (LC-MS), a popular method to implement untargeted metabolomics, identifies metabolites via combined mass/charge (m/z) and retention time as mass features. Improvements in the sensitivity of mass spectrometers has increased the complexity of data produced, leading to computational obstacles. One outstanding challenge is calling metabolite mass feature peaks rapidly and accurately in large LC-MS datasets (dozens to thousands of samples) in the presence of measurement and other noise. While existing algorithms are useful, they have limitations that become pronounced at scale and lead to false positive metabolite predictions as well as signal dropouts. To overcome some of these shortcomings, biochemists have developed hybrid computational and carbon labeling techniques, such as credentialing. Credentialing can validate metabolite signals, but is laborious and its applicability is limited. We have developed a suite of three computational tools to overcome the challenges of unreliable algorithms and inefficient validation protocols: isolock, autoCredential and anovAlign. Isolock uses isopairs, or metabolite-istopologue pairs, to calculate and correct for mass drift noise across LC-MS runs. autoCredential leverages statistical features of LC-MS data to amplify naturally present 13C isotopologues and validate metabolites through isopairs. This obviates the need to artificially introduce carbon labeling. anovAlign, an anova-derived algorithm, is used to align retention time windows across samples to accurately delineate retention time windows for mass features. Using a large published clinical dataset as well as a plant dataset with biological replicates across time, genotype and treatment, we demonstrate that this suite of tools is more sensitive and reproducible than both an open source metabolomics pipelines, XCMS, and the commercial software progenesis QI. This software suite opens a new era for enhanced accuracy and increased throughput for untargeted metabolomics.

bioinformatics↗

bioRxiv2021

Selection-Enriched Genomic Loci (SEGL) Reveals Genetic Loci for Environmental Adaptation and Photosynthetic Productivity in Chlamydomonas reinhardtii.

This work demonstrates an approach to produce and select hybrid algal strains exhibiting increased photosynthetic productivity under multiple environmental conditions. This simultaneously addresses two major impediments to improving algal bioenergy production: 1) generating new genetic variants with improved performance; and 2) disentangling complex interactions between genetic and physiological factors contributing to these improvements. We pooled progeny generated from mating two environmental isolates of the green alga Chlamydomonas reinhardtii and cultured the pools under multiple environmental conditions. Strains from the outcompeting populations showed substantial (in some cases over 3 fold) increases in productivity over the parental lines under certain environments related to biomass production, including laboratory conditions as well as hyperoxia, fluctuating light, high salinity and high temperature. The results indicate that C. reinhardtii has remarkable, untapped, directed evolution capacity that may be harnessed using breeding and competition approaches. The populations were deep sequenced at multiple time points to identify "Selection-Enriched Genomic Loci" (SEGL) that accumulated in the populations, and thus likely confer increased fitness under the respective environmental conditions. With improved resolution, SEGL mapping can identify allelic combinations used for targeted breeding approaches, generating elite algal lines with multiple desirable traits, as well as to further understand the genetic and mechanistic bases of photosynthetic productivity. Significance StatementIncreasing the photosynthetic efficiency of algae during biomass production is perhaps the most critical hurdle for economically sustainable algal based biofuels. This presents unique challenges because modifications designed to increase photosynthesis often result in decreased fitness, due to production of toxic reactive oxygen species when photosynthesis is not adequately regulated. These problems are exacerbated under natural and outdoor production environments because of the complex nature of photosynthesis and the multifaceted interactions between genetic, environmental and physiological factors. Here, we demonstrate a high throughput biotechnological screening approach that simultaneously produces algal strains with highly increased autotrophic productivity and identifies genomic loci contributing to these improvements. Our results demonstrate that Chlamydomonas reinhardtii exhibits high directed evolutionary capacity readily accessed through breeding and selection.

evolutionary biology↗

bioRxiv2021

Strengthened mutualistic adaptation between teosinte and its rhizosphere biota in cold climates

O_LIWhile abiotic environments consistently shape local adaptation, the strength of local adaptation to biotic interactions may vary more. One theory, COCO (CO-evolutionary Outcomes across Conditionality), predicts it may be strongest where species experience greater stress, because stress increases fitness impacts of species interactions. For example, in plant interactions with rhizosphere biota, positive outcomes increase with stress from low soil fertility, drought and cold. C_LIO_LITo investigate the influence of abiotic stress gradients on adaptation between plants and rhizosphere biota, we used a greenhouse common garden experiment recombining teosinte, Zea mays ssp. mexicana (wild relative of maize), and rhizosphere biota, collected across a stress gradient (elevational variation in temperature, precipitation, and nutrients). C_LIO_LIWe found stronger local adaptation between teosinte and rhizosphere biota from colder, more stressful sites, as expected by COCO. However, biota from less stressful, warmer sites provided greater average benefits across teosinte populations. Links between plant traits and 20-element profiles of plant leaves explained fitness variation, persisted in the field, were influenced by both plants and biota, and largely reflected patterns of local adaptation. C_LIO_LIIn sum, we uncovered greater local adaptation to biotic interactions in colder sites, and that both plants and rhizosphere biota affect the expression of plant phenotypes. C_LI

evolutionary biology↗

bioRxiv2021

Intraspecific variation in elemental accumulation and its association with salt tolerance in Paspalum vaginatum

Most plant species, including most crops, perform poorly in salt-affected soils because high sodium levels are cytotoxic and can disrupt uptake of water and important nutrients. Halophytes are species that have evolved adaptations to overcome these challenges and may be a useful source of knowledge for salt tolerance mechanisms and genes that may be transferable to crop species. The salt content of saline habitats can vary dramatically by location, providing ample opportunity for different populations of halophytic species to adapt to their local salt concentrations; however, the extent of this variation, and the physiology and polymorphisms that drive it, remain poorly understood. Differential accumulation of inorganic elements between genotypes or populations may play an important role in local salinity adaptation. To test this, we investigated the relationships between population structure, tissue ion concentrations (i.e., ionomic profiles) and salt tolerance in 17 "fine-textured" genotypes of the halophytic turfgrass seashore paspalum (Paspalum vaginatum Swartz). A high-throughput ionomics pipeline was used to quantify the shoot concentration of 18 inorganic elements across three salinity treatments. We found a significant relationship between population structure and ion accumulation, with strong correlations between principal components derived from genetic and ionomic data. Additionally, genotypes with higher salt tolerance accumulated more K and Fe and less Ca than less tolerant genotypes. Together these results indicate that differences in ion accumulation between P. vaginatum populations may reflect locally adapted salt stress responses.

plant biology↗

bioRxiv2021

Genome-wide association study reveals an independent genetic basis of zinc and cadmium concentrations in fresh sweet corn kernels

Despite being one of the most consumed vegetables in the United States, the elemental profile of sweet corn (Zea mays L.) is limited in its dietary contributions. To address this through genetic improvement, a genome-wide association study was conducted for the concentrations of 15 elements in fresh kernels of a sweet corn association panel. In concordance with mapping results from mature maize kernels, we detected a probable pleiotropic association of zinc and iron concentrations with nicotianamine synthase5 (nas5), which purportedly encodes an enzyme involved in synthesis of the metal chelator nicotianamine. Additionally, a pervasive association signal was identified for cadmium concentration within a recombination suppressed region on chromosome 2. The likely causal gene underlying this signal was heavy metal ATPase3 (hma3), whose counterpart in rice, OsHMA3, mediates vacuolar sequestration of cadmium and zinc in roots, whereby regulating zinc homeostasis and cadmium accumulation in grains. In our association panel, hma3 associated with cadmium but not zinc accumulation in fresh kernels. This finding implies that selection for low cadmium will not affect zinc levels in fresh kernels. Although less resolved association signals were detected for boron, nickel, and calcium, all 15 elements were shown to have moderate predictive abilities via whole-genome prediction. Collectively, these results help enhance our genomics-assisted breeding efforts centered on improving the elemental profile of fresh sweet corn kernels.

genetics↗

bioRxiv2020

Correlation and co-localization of QTL for stomatal density and canopy temperature under 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 (RIL) derived from a Setaria italica x 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 QTLs were identified for stomatal density and canopy temperature, including co-located QTLs on chromosomes 5 and 9. The direction of the additive effect of these QTLs on chromosome 5 and 9 were in accordance with the positive phenotypic relationship between these two traits. This suggests a common genetic architecture between stomatal patterning in the greenhouse and canopy transpiration in the field, while highlighting the potential of setaria as a model to understand the physiology and genetics of WUE in C4 species. HighlightThis article reports a phenotypic and genetic relationship between two water use related traits operating at leaf level and canopy level in a C4 model crop species.

plant biology↗

bioRxiv2020

The Genetic Architecture of Leaf Stable Carbon Isotope Composition in Zea mays and the Effect of Transpiration Efficiency on 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, {delta}13C, is a proxy for transpiration efficiency and a possible tool for breeders, but the underlying mechanisms effecting {delta}13C in C4 plants are not known. It has been suggested that differences in specific leaf area, which potentially reflects variation in internal CO2 diffusion, can impact leaf {delta}13C. However, at this point the relationship has not been tested in maize. 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 underlying genetic architecture of leaf {delta}13C and test its relationship to specific leaf area and the ionome in four biparental populations of maize. Five significant QTL for leaf {delta}13C were identified, including both 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 {delta}13C in Arabidopsis. Our data does not support a relationship between {delta}13C and specific leaf area, and of the 19 elements analyzed, only a weak correlation between molybdenum and {delta}13C was detected. Together these data begin to build a genetic understanding of leaf {delta}13C in maize and suggest the potential to improve plant water use without significantly influencing elemental homeostasis. Article SummaryQuantitative genetics approaches were used to investigate the genetic architecture of leaf stable carbon isotope discrimination ({delta}13C) in maize. Developing a better understanding of leaf {delta}13C could facilitate its use in breeding for reduced transpirational water loss. Several genomic regions were identified that contribute to the variation observed in leaf {delta}13C. Furthermore, contrary to what has been observed in other species, leaf {delta}13C was not correlated with specific leaf area. Finally, a leaf ionomic analysis indicates that a reduction in transpiration, and thus mass flow, would not result in a decrease in nutrient accumulation.

plant biology↗