Biology subjects

Baxter, I.

Publications and source records attributed to Baxter, I..

11 recordsLinked to original sources

bioRxiv2018

Increased temperatures may safeguard the nutritional quality of crops under future elevated CO2 concentrations

Iron (Fe) and zinc (Zn) deficiencies are a global human health problem that may worsen by growth of crops at elevated atmospheric CO2 concentration (eCO2). However, climate change will also involve higher temperature, but it is unclear how the combined effect of eCO2 and higher temperature will affect the nutritional quality of food crops. To begin to address this question, we grew soybean (Glycine max) in a Temperature by Free-Air CO2 Enrichment (T-FACE) experiment in 2014 and 2015 under ambient (400 mol mol-1) and elevated (600 mol mol-1) CO2 concentration and under ambient and elevated temperatures (+2.7 {degrees}C day and +3.4 {degrees}C at night). In our study, eCO2 significantly decreased Fe concentration in soybean seeds in both seasons (-8.7% and -7.7%) and Zn concentration in one season (-8.9%) while higher temperature (at ambient CO2 concentration) had the opposite effect. The combination of eCO2 with elevated temperature generally restored seed Fe and Zn concentrations to levels obtained under ambient CO2 and temperature conditions, suggesting that the potential threat to human nutrition by increasing CO2 concentration may not be realized. In general, seed Fe concentration was negatively correlated with yield suggesting inherent limitations to increasing seed Fe. In addition, we confirm our previous report that the concentration of seed storage products and several minerals varies with node position at which the seeds developed. Overall, these results demonstrate the complexity of predicting climate change effects on food security when various environmental parameters change in an interactive manner.

plant biology↗

bioRxiv2018

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

Increasing whole plant water use efficiency (yield per transpiration; WUEplant) through plant breeding can benefit the sustainability of agriculture and improve crop yield under drought. To select for WUEplant, an efficient phenotyping method that reports on the genetic contribution of component traits such as transpiration efficiency (TEi; rate of CO2 assimilation per stomatal conductance) must be developed. Leaf carbon stable isotope composition ({delta}13Cleaf) has been proposed as a high-throughput proxy for TEi, and a negative correlation between {delta}13Cleaf and both WUEplant and TEi has previously been demonstrated in several C4 grass species. Therefore, the aim of the research presented here was to determine if the same loci control {delta}13Cleaf, WUEplant, and TEi under well-watered and water-limited conditions in a recombinant inbred line (RIL) population of closely related C4 grasses Setaria viridis and S. italica. Three quantitative trait loci (QTL) for {delta}13Cleaf were co-localized with transpiration, biomass, and a linear model of WUE. When WUEplant was calculated for allele classes based on the three QTL for {delta}13Cleaf, {delta}13Cleaf was negatively correlated with WUEplant as theory predicts when WUEplant is in part driven by differences in TEi. In any population, multiple traits can influence WUEplant; however, the analysis of {delta}13Cleaf in this RIL population demonstrates that there is genetic control of TEi that significantly contributes to WUEplant. Furthermore, this research suggests that {delta}13Cleaf can be used in marker-assisted breeding to select for TEi and as a tool to better understand the physiology and genetic architecture of TEi and WUEplant in C4 species.\n\nSignificance StatementOverextended water resources and drought are major agricultural problems worldwide. Therefore, selection for increased plant water use efficiency (WUEplant) in food and biofuel crop species is an important trait in plant breeding programs. Leaf carbon isotopic composition ({delta}13Cleaf) has potential as a rapid and effective high throughput phenotyping method for intrinsic transpiration efficiency (TEi), an important leaf-level component trait of WUEplant. Our research shows that {delta}13Cleaf and WUEplant share a common genetic architecture through their shared relationship with TEi. This suggests that {delta}13Cleaf can be used as a screen for TEi in marker-assisted plant breeding programs to improve crop drought resistance and decrease agricultural water consumption.

plant biology↗

bioRxiv2017

Multivariate analysis reveals environmental and genetic determinants of element covariation in the maize grain ionome

Plants obtain elements from the soil through genetic and biochemical pathways responsive to physiological state and environment. Most perturbations affect multiple elements which leads the ionome, the full complement of mineral nutrients in an organism, to vary as an integrated network rather than a set of distinct single elements. To examine the genetic basis of covariation in the accumulation of multiple elements, we analyzed maize kernel ionomes from Intermated B73 x Mo17 (IBM) recombinant inbred populations grown in 10 environments. We compared quantitative trait loci (QTL) determining single-element variation to QTL that predict variation in principal components (PCs) of multiple-element covariance. Single-element and multivariate approaches detected partially overlapping sets of loci. In addition to loci co-localizing with single-element QTL, multivariate traits within environments were controlled by loci with significant multi-element effects not detectable using single-element traits. Gene-by-environment interactions underlying multiple-element covariance were identified through QTL analyses of principal component models of ionome variation. In addition to interactive effects, growth environment had a profound effect on the elemental profiles and multi-element phenotypes were significantly correlated with specific environmental variables.\n\nAuthor SummaryA multivariate approach to the analysis of element accumulation in the maize kernel shows that elements are not regulated independently. By describing relationships between element accumulation we identified new genetic loci invisible to single-element approaches. The mathematical combinations of elements distinguish groups of plants based on environment, demonstrating that observed variation derives from interactions between genetically controlled factors and environmental variables. These results suggest that successful application of ionomics to improve human nutrition and plant productivity requires simultaneous consideration of multiple-element effects and variation of such effects in response to environment.

plant biology↗

bioRxiv2017

Trait components of whole plant water use efficiency are defined by unique, environmentally responsive genetic signatures in the model C4 grass Setaria

Plant growth and water use are interrelated processes influenced by the genetic control of both plant morphological and biochemical characteristics. Improving plant water use efficiency (WUE) to sustain growth in different environments is an important breeding objective that can improve crop yields and enhance agricultural sustainability. However, genetic improvements of WUE using traditional methods have proven difficult due to low throughput and environmental heterogeneity encountered in field settings. To overcome these limitations the study presented here utilizes a high-throughput phenotyping platform to quantify plant size and water use of an interspecific Setaria italica x Setaria viridis recombinant inbred line population at daily intervals in both well-watered and water-limited conditions. Our findings indicate that measurements of plant size and water use in this system are strongly correlated; therefore, a linear modeling approach was used to partition this relationship into predicted values of plant size given water use and deviations from this relationship at the genotype level. The resulting traits describing plant size, water use and WUE were all heritable and responsive to soil water availability, allowing for a genetic dissection of the components of plant WUE under different watering treatments. Linkage mapping identified major loci underlying two different pleiotropic components of WUE. This study indicates that alleles controlling WUE derived from both wild and domesticated accessions of the model C4 species Setaria can be utilized to predictably modulate trait values given a specified precipitation regime.

plant biology↗

bioRxiv2017

Integrating co-expression networks with GWAS detects genes driving elemental accumulation in maize seeds

BackgroundGenome wide association studies (GWAS) have identified thousands of loci linked to hundreds of traits in many different species. However, because linkage equilibrium implicates a broad region surrounding each identified locus, the causal genes often remain unknown. This problem is especially pronounced in non-human, non-model species where functional annotations are sparse and there is frequently little information available for prioritizing candidate genes.\n\nResultsTo address this issue, we developed a computational approach called Camoco (Co-Analysis of Molecular Components) that systematically integrates loci identified by GWAS with gene co-expression networks to prioritize putative causal genes. We applied Camoco to prioritize candidate genes from a large-scale GWAS examining the accumulation of 17 different elements in maize seeds. Camoco identified statistically significant subnetworks for the majority of traits examined, producing a prioritized list of high-confidence causal genes for several agronomically important maize traits. Two candidate genes identified by our approach were validated through analysis of mutant phenotypes. Strikingly, we observed a strong dependence in the performance of our approach on the type of co-expression network used: expression variation across genetically diverse individuals in a relevant tissue context (in our case, maize roots) outperformed other alternatives.\n\nConclusionsOur study demonstrates that co-expression networks can provide a powerful basis for prioritizing candidate causal genes from GWAS loci, but suggests that the success of such strategies can highly depend on the gene expression data context. Both the Camoco software and the lessons on integrating GWAS data with co-expression networks generalize to species beyond maize.

systems biology↗

bioRxiv2017

Multivariate analysis of the cotton seed ionome reveals integrated genetic signatures of abiotic stress response

To mitigate the effects of heat and drought stress, a better understanding of the genetic control of physiological responses to these environmental conditions is needed. To this end, we evaluated an upland cotton (Gossypium hirsutum L.) mapping population under water-limited and well-watered conditions in a hot, arid environment. The elemental concentrations (ionome) of seed samples from the population were profiled in addition to those of soil samples taken from throughout the field site to better model environmental variation. The elements profiled in seeds exhibited moderate to high heritabilities, as well as strong phenotypic and genotypic correlations between elements that were not altered by the imposed irrigation regimes. Quantitative trait loci (QTL) mapping results from a Bayesian classification method identified multiple genomic regions where QTL for individual elements colocalized, suggesting that genetic control of the ionome is highly interrelated. To more fully explore this genetic architecture, multivariate QTL mapping was implemented among groups of biochemically related elements. This analysis revealed both additional and pleiotropic QTL responsible for coordinated control of phenotypic variation for elemental accumulation. Machine learning algorithms that utilized only ionomic data predicted the irrigation regime under which genotypes were evaluated with very high accuracy. Taken together, these results demonstrate the extent to which the seed ionome is genetically interrelated and predictive of plant physiological responses to adverse environmental conditions.\n\nOne sentence summaryThe cotton seed ionome has a shared genetic basis that provides insight into the physiological status of the plant.

genetics↗

bioRxiv2017

Elemental Accumulation in Kernels of the Maize Nested Association Mapping Panel Reveals Signals of Gene by Environment Interactions

Elemental accumulation in seeds is the product of a combination of environment and a wide variety of genetically controlled physiological processes. We measured the kernel elemental composition of the Nested Association Mapping (NAM) of maize (Zea mays L.) grown in 4 different environments. Analysis of variance revealed strong effects of genotype, environment and genotype by environment interactions. Using Joint-linkage mapping on a set of 7000 markers we identified 354 quantitative trait loci (QTL) across 20 elements, four environments and a combination of the environments. Leveraging 20 M SNPs derived from genome resequencing on the parents of the population, genome-wide association mapping studies (GWAS) detected 8573 loci. While most of the GWAS SNPs were located near genes not previously implicated in elemental regulation, several SNPs were located next to orthologs of well-characterized elemental regulation genes.

plant biology↗

bioRxiv2017

High fidelity detection of crop biomass QTL from low-cost imaging in the field

Above-ground biomass production is a key target for studies of crop abiotic stress tolerance, disease resistance and yield improvement. However, biomass is slow and laborious to evaluate in the field using traditional destructive methods. High-throughput phenotyping (HTP) is widely promoted as a potential solution that can rapidly and non-destructively assess plant traits by exploiting advances in sensor and computing technology. A key potential application of HTP is for quantitative genetics studies that identify loci where allelic variation is associated with variation in crop production. And, the value of performing such studies in the field, where environmental conditions match that of production farming, is recognized. To date, HTP of biomass productivity in field trials has largely focused on expensive and complex methods, which - even if successful - will limit their use to a subset of wealthy research institutions and companies with extensive research infrastructure and highly-trained personnel. Even with investment in ground vehicles, aerial vehicles and gantry systems ranging from thousands to millions of dollars, there are very few examples where Quantitative trait loci (QTLs) detected by HTP of biomass production in a field-grown crop are shown to match QTLs detected by direct measures of biomass traits by destructive harvest techniques. Until such proof of concept for HTP proxies is generated it is unlikely to replace existing technology and be widely adopted. Therefore, there is a need for methods that can be used to assess crop performance by small teams with limited training and at field sites that are remote or have limited infrastructure. Here we use an inexpensive and simple, miniaturized system of hemispherical imaging and light attenuation modeling to identify the same set of key QTLs for biomass production as traditional destructive harvest methods applied to a field-grown Setaria mapping population. This provides a case study of a HTP technology that can deliver results for QTL mapping without high costs or complexity.

plant biology↗

bioRxiv2017

Coordinated Changes In The Accumulation Of Metal Ions In Maize (Zea mays ssp. mays L.) In Response To Inoculation With The Arbuscular Mycorrhizal Fungus Funneliformis mosseae

Arbuscular mycorrhizal symbiosis is an ancient interaction between plants and fungi of the phylum Glomeromycota. In exchange for photosynthetically fixed carbon, the fungus provides the plant host with greater access to soil nutrients via an extensive network of root-external hyphae. Here, to determine the impact of the symbiosis on the host ionome, the concentration of nineteen elements was determined in the roots and leaves of a panel of thirty maize varieties, grown under phosphorus limiting conditions, with, or without, inoculation with the fungus Funneliformis mosseae. Although the most recognized benefit of the symbiosis to the host plant is greater access to soil phosphorus, the concentration of a number of other elements responded significantly to inoculation across the panel as a whole. In addition, variety-specific effects indicated the importance of plant genotype to the response. Clusters of elements were identified that varied in a coordinated manner across genotypes, and that were maintained between non-inoculated and inoculated plants.\n\nAbbreviations

plant biology↗

bioRxiv2017

High-Throughput Profiling Identifies Resource Use Efficient And Abiotic Stress Tolerant Sorghum Varieties

Sorghum (Sorghum bicolor (L.) Moench) is a rapidly growing, high-biomass crop prized for abiotic stress tolerance. However, measuring genotype-by-environment (G x E) interactions remains a progress bottleneck. Here we describe strategies for identifying shape, color and ionomic indicators of plant nitrogen use efficiency. We subjected a panel of 30 genetically diverse sorghum genotypes to a spectrum of nitrogen deprivation and measured responses using high-throughput phenotyping technology followed by ionomic profiling. Responses were quantified using shape (16 measurable outputs), color (hue and intensity) and ionome (18 elements). We measured the speed at which specific genotypes respond to environmental conditions, both in terms of biomass and color changes, and identified individual genotypes that perform most favorably. With this analysis we present a novel approach to quantifying color-based stress indicators over time. Additionally, ionomic profiling was conducted as an independent, low cost and high throughput option for characterizing G x E, identifying the elements most affected by either genotype or treatment and suggesting signaling that occurs in response to the environment. This entire dataset and associated scripts are made available through an open access, user-friendly, web-based interface. In summary, this work provides analysis tools for visualizing and quantifying plant abiotic stress responses over time. These methods can be deployed as a time-efficient method of dissecting the genetic mechanisms used by sorghum to respond to the environment to accelerate crop improvement.

plant biology↗

bioRxiv2016

Time dependent genetic analysis links field and controlled environment phenotypes in the model C4 grass Setaria

Vertical growth of plants is a dynamic process that is influenced by genetic and environmental factors and has a pronounced effect on overall plant architecture and biomass composition. We have performed twelve controlled growth trials of an interspecific Setaria italica x Setaria viridis recombinant inbred line population to assess how the genetic architecture of plant height is influenced by developmental queues, water availability and planting density. The nondestructive nature of plant height measurements has enabled us to monitor vertical growth throughout the plant life cycle in both field and controlled environments. We find that plant height is reduced under water limitation and high density planting and affected by growth environment (field vs. growth chamber). The results support a model where plant height is a heritable, polygenic trait and that the major genetic loci that influence plant height function independent of growth environment. The identity and contribution of loci that influence height changes dynamically throughout development and the reduction of growth observed in water limited environments is a consequence of delayed progression through the genetic program which establishes plant height in Setaria. In this population, alleles inherited from the weedy S. viridis parent act to increase plant height early, whereas a larger number of small effect alleles inherited from the domesticated S. italica parent collectively act to increase plant height later in development.

plant biology↗