Longitudinal genome-wide association study reveals early QTL that predict biomass accumulation under cold stress in sorghum.

Introduction: Sorghum bicolor is a promising cellulosic feedstock crop for bioenergy due to its high biomass yields. However, early growth phases of sorghum are sensitive to cold stress, limiting its planting in temperate environments. Cold adaptability is crucial for cultivating bioenergy and grain sorghum at higher latitudes and elevations, or for extending the growing season. Identifying genes and alleles that enhance biomass accumulation under early cold stress can lead to improved sorghum varieties through breeding or genetic engineering. Methods: We conducted image-based phenotyping on 369 accessions from the sorghum Bioenergy Association Panel (BAP) in a controlled environment with early cold treatment. The BAP includes diverse accessions with dense genotyping and varied racial, geographical, and phenotypic backgrounds. Daily, non-destructive imaging allowed temporal analysis of growth-related traits and water use efficiency (WUE). A genome-wide association study (GWAS) was performed to identify genomic intervals and genes associated with cold stress response. Results: The GWAS identified transient quantitative trait loci (QTL) strongly associated with growth-related traits, enabling an exploration of the genetic basis of cold stress response at different developmental stages. This analysis of daily growth traits, rather than endpoint traits, revealed early transient QTL predictive of final phenotypes. The study identified both known and novel candidate genes associated with growth-related traits and temporal responses to cold stress. Discussion: The identified QTL and candidate genes contribute to understanding the genetic mechanisms underlying sorghum's response to cold stress. These findings can inform breeding and genetic engineering strategies to develop sorghum varieties with improved biomass yields and resilience to cold, facilitating earlier planting, extended growing seasons, and cultivation at higher latitudes and elevations.

Natural variation in Brachypodium distachyon responses to combined abiotic stresses.

The demand for agricultural production is becoming more challenging as climate change increases global temperature and the frequency of extreme weather events. This study examines the phenotypic variation of 149 accessions of Brachypodium distachyon under drought, heat, and the combination of stresses. Heat alone causes the largest amounts of tissue damage while the combination of stresses causes the largest decrease in biomass compared to other treatments. Notably, Bd21-0, the reference line for B. distachyon, did not have robust growth under stress conditions, especially the heat and combined drought and heat treatments. The climate of origin was significantly associated with B. distachyon responses to the assessed stress conditions. Additionally, a GWAS found loci associated with changes in plant height and the amount of damaged tissue under stress. Some of these SNPs were closely located to genes known to be involved in responses to abiotic stresses and point to potential causative loci in plant stress response. However, SNPs found to be significantly associated with a response to heat or drought individually are not also significantly associated with the combination of stresses. This, with the phenotypic data, suggests that the effects of these abiotic stresses are not simply additive, and the responses to the combined stresses differ from drought and heat alone.

The plant response to high CO2 levels is heritable and orchestrated by DNA methylation.

Plant responses to abiotic environmental challenges are known to have lasting effects on the plant beyond the initial stress exposure. Some of these lasting effects are transgenerational, affecting the next generation. The plant response to elevated carbon dioxide (CO2 ) levels has been well studied. However, these investigations are typically limited to plants grown for a single generation in a high CO2 environment while transgenerational studies are rare. We aimed to determine transgenerational growth responses in plants after exposure to high CO2 by investigating the direct progeny when returned to baseline CO2 levels. We found that both the flowering plant Arabidopsis thaliana and seedless nonvascular plant Physcomitrium patens continue to display accelerated growth rates in the progeny of plants exposed to high CO2 . We used the model species Arabidopsis to dissect the molecular mechanism and found that DNA methylation pathways are necessary for heritability of this growth response. More specifically, the pathway of RNA-directed DNA methylation is required to initiate methylation and the proteins CMT2 and CMT3 are needed for the transgenerational propagation of this DNA methylation to the progeny plants. Together, these two DNA methylation pathways establish and then maintain a cellular memory to high CO2 exposure.

Toward a data infrastructure for the Plant Cell Atlas.

We review how a data infrastructure for the Plant Cell Atlas might be built using existing infrastructure and platforms. The Human Cell Atlas has developed an extensive infrastructure for human and mouse single cell data, while the European Bioinformatics Institute has developed a Single Cell Expression Atlas, that currently houses several plant data sets. We discuss issues related to appropriate ontologies for describing a plant single cell experiment. We imagine how such an infrastructure will enable biologists and data scientists to glean new insights into plant biology in the coming decades, as long as such data are made accessible to the community in an open manner.

First Plant Cell Atlas symposium report.

The Plant Cell Atlas (PCA) community hosted a virtual symposium on December 9 and 10, 2021 on single cell and spatial omics technologies. The conference gathered almost 500 academic, industry, and government leaders to identify the needs and directions of the PCA community and to explore how establishing a data synthesis center would address these needs and accelerate progress. This report details the presentations and discussions focused on the possibility of a data synthesis center for a PCA and the expected impacts of such a center on advancing science and technology globally. Community discussions focused on topics such as data analysis tools and annotation standards; computational expertise and cyber-infrastructure; modes of community organization and engagement; methods for ensuring a broad reach in the PCA community; recruitment, training, and nurturing of new talent; and the overall impact of the PCA initiative. These targeted discussions facilitated dialogue among the participants to gauge whether PCA might be a vehicle for formulating a data synthesis center. The conversations also explored how online tools can be leveraged to help broaden the reach of the PCA (i.e., online contests, virtual networking, and social media stakeholder engagement) and decrease costs of conducting research (e.g., virtual REU opportunities). Major recommendations for the future of the PCA included establishing standards, creating dashboards for easy and intuitive access to data, and engaging with a broad community of stakeholders. The discussions also identified the following as being essential to the PCA's success: identifying homologous cell-type markers and their biocuration, publishing datasets and computational pipelines, utilizing online tools for communication (such as Slack), and user-friendly data visualization and data sharing. In conclusion, the development of a data synthesis center will help the PCA community achieve these goals by providing a centralized repository for existing and new data, a platform for sharing tools, and new analytical approaches through collaborative, multidisciplinary efforts. A data synthesis center will help the PCA reach milestones, such as community-supported data evaluation metrics, accelerating plant research necessary for human and environmental health.

Heat stress changes mineral nutrient concentrations in Chenopodium quinoa seed.

Quinoa is a popular seed crop, often consumed for its high nutritional quality. We studied how heat stress in the roots or the shoots of quinoa plants affected the concentrations of 20 elements (aluminum, arsenic, boron, calcium, cadmium, cobalt, copper, iron, potassium, magnesium, manganese, molybdenum, sodium, nickel, phosphorous, rubidium, sulfur, selenium, strontium, and zinc) in quinoa seed. Elemental concentrations in quinoa seed were significantly changed after an 11-day heat treatment during anthesis. The type of panicle (main, secondary, and tertiary) sampled and the type of heat treatment (root only, shoot only, or whole plants) significantly affected elemental profiles in quinoa seed. Plants were also divided into five sections from top to bottom to assess the effect of panicle position on seed elemental profiles. Plant section had an effect on the concentrations of arsenic, iron, and sodium under control conditions and on copper with heat treatment. Overall, the time of panicle development in relation to the time of heat exposure had the largest effect on seed elemental concentrations. Interestingly, the quinoa plants were exposed to heat only during anthesis of the main panicle, but the elemental concentrations of seeds produced after heat treatment ended were still significantly changed, indicating that heat stress has long-lasting effects on quinoa plants. These findings demonstrate how the nutritional quality of quinoa seeds can be changed significantly even by relatively short heat spells.

Large structural variations in the haplotype-resolved African cassava genome.

Cassava (Manihot esculenta Crantz, 2n = 36) is a global food security crop. It has a highly heterozygous genome, high genetic load, and genotype-dependent asynchronous flowering. It is typically propagated by stem cuttings and any genetic variation between haplotypes, including large structural variations, is preserved by such clonal propagation. Traditional genome assembly approaches generate a collapsed haplotype representation of the genome. In highly heterozygous plants, this results in artifacts and an oversimplification of heterozygous regions. We used a combination of Pacific Biosciences (PacBio), Illumina, and Hi-C to resolve each haplotype of the genome of a farmer-preferred cassava line, TME7 (Oko-iyawo). PacBio reads were assembled using the FALCON suite. Phase switch errors were corrected using FALCON-Phase and Hi-C read data. The ultralong-range information from Hi-C sequencing was also used for scaffolding. Comparison of the two phases revealed >5000 large haplotype-specific structural variants affecting over 8 Mb, including insertions and deletions spanning thousands of base pairs. The potential of these variants to affect allele-specific expression was further explored. RNA-sequencing data from 11 different tissue types were mapped against the scaffolded haploid assembly and gene expression data are incorporated into our existing easy-to-use web-based interface to facilitate use by the broader plant science community. These two assemblies provide an excellent means to study the effects of heterozygosity, haplotype-specific structural variation, gene hemizygosity, and allele-specific gene expression contributing to important agricultural traits and further our understanding of the genetics and domestication of cassava.

Vision, challenges and opportunities for a Plant Cell Atlas.

With growing populations and pressing environmental problems, future economies will be increasingly plant-based. Now is the time to reimagine plant science as a critical component of fundamental science, agriculture, environmental stewardship, energy, technology and healthcare. This effort requires a conceptual and technological framework to identify and map all cell types, and to comprehensively annotate the localization and organization of molecules at cellular and tissue levels. This framework, called the Plant Cell Atlas (PCA), will be critical for understanding and engineering plant development, physiology and environmental responses. A workshop was convened to discuss the purpose and utility of such an initiative, resulting in a roadmap that acknowledges the current knowledge gaps and technical challenges, and underscores how the PCA initiative can help to overcome them.

Heating quinoa shoots results in yield loss by inhibiting fruit production and delaying maturity.

Increasing global temperatures and a growing world population create the need to develop crop varieties that provide higher yields in warmer climates. There is growing interest in expanding quinoa cultivation, because of the ability of quinoa to produce nutritious grain in poor soils, with little water and at high salinity. The main limitation to expanding quinoa cultivation, however, is the susceptibility of quinoa to temperatures above approximately 32°C. This study investigates the phenotypes, genes and mechanisms that may affect quinoa seed yield at high temperatures. Using a differential heating system where only roots or only shoots were heated, quinoa yield losses were attributed to shoot heating. Plants with heated shoots lost 60-85% yield as compared with control plants. Yield losses were the result of lower fruit production, which lowered the number of seeds produced per plant. Furthermore, plants with heated shoots had delayed maturity and greater non-reproductive shoot biomass, whereas plants with both heated roots and heated shoots produced higher yields from the panicles that had escaped the heat, compared with the control. This suggests that quinoa uses a type of avoidance strategy to survive heat. Gene expression analysis identified transcription factors differentially expressed in plants with heated shoots and low yield that had been previously associated with flower development and flower opening. Interestingly, in plants with heated shoots, flowers stayed closed during the day while the control flowers were open. Although a closed flower may protect the floral structures, this could also cause yield losses by limiting pollen dispersal, which is necessary to produce fruit in the mostly female flowers of quinoa.

Genome-wide association study (GWAS) of leaf cuticular wax components in Camelina sativa identifies genetic loci related to intracellular wax transport.

BACKGROUND: It is important to explore renewable alternatives (e.g. biofuels) that can produce energy sources to help reduce reliance on fossil oils, and reduce greenhouse gases and waste solids resulted from fossil oils consumption. Camelina sativa is an oilseed crop which has received increasing attention due to its short life cycle, broader adaptation regions, high oil content, high level of omega-3 unsaturated fatty acids, and low-input requirements in agriculture practices. To expand its Camelina production areas into arid regions, there is a need to breed for new drought-tolerant cultivars. Leaf cuticular wax is known to facilitate plant development and growth under water-limited conditions. Dissecting the genetic loci underlying leaf cuticular waxes is important to breed for cultivars with improved drought tolerance. RESULTS: Here we combined phenotypic data and single nucleotide polymorphism (SNP) data from a spring C. sativa diversity panel using genotyping-by-sequencing (GBS) technology, to perform a large-scale genome-wide association study (GWAS) on leaf wax compositions. A total of 42 SNP markers were significantly associated with 15 leaf wax traits including major wax components such as total primary alcohols, total alkanes, and total wax esters as well as their constituents. The vast majority of significant SNPs were associated with long-chain carbon monomers (carbon chain length longer than C28), indicating the important effects of long-chain carbon monomers on leaf total wax biosynthesis. These SNP markers are located on genes directly or indirectly related to wax biosynthesis such as maintaining endoplasmic reticulum (ER) morphology and enabling normal wax secretion from ER to plasma membrane or Golgi network-mediated transport. CONCLUSIONS: These loci could potentially serve as candidates for the genetic control involved in intracellular wax transport that might directly or indirectly facilitate leaf wax accumulation in C. sativa and can be used in future marker-assisted selection (MAS) to breed for the cultivars with high wax content to improve drought tolerance.

Antiviral ARGONAUTEs Against Turnip Crinkle Virus Revealed by Image-Based Trait Analysis.

RNA-based silencing functions as an important antiviral immunity mechanism in plants. Plant viruses evolved to encode viral suppressors of RNA silencing (VSRs) that interfere with the function of key components in the silencing pathway. As effectors in the RNA silencing pathway, ARGONAUTE (AGO) proteins are targeted by some VSRs, such as that encoded by Turnip crinkle virus (TCV). A VSR-deficient TCV mutant was used to identify AGO proteins with antiviral activities during infection. A quantitative phenotyping protocol using an image-based color trait analysis pipeline on the PlantCV platform, with temporal red, green, and blue imaging and a computational segmentation algorithm, was used to measure plant disease after TCV inoculation. This process captured and analyzed growth and leaf color of Arabidopsis (Arabidopsis thaliana) plants in response to virus infection over time. By combining this quantitative phenotypic data with molecular assays to detect local and systemic virus accumulation, AGO2, AGO3, and AGO7 were shown to play antiviral roles during TCV infection. In leaves, AGO2 and AGO7 functioned as prominent nonadditive, anti-TCV effectors, whereas AGO3 played a minor role. Other AGOs were required to protect inflorescence tissues against TCV. Overall, these results indicate that distinct AGO proteins have specialized, modular roles in antiviral defense across different tissues, and demonstrate the effectiveness of image-based phenotyping to quantify disease progression.

Genetic Diversity and Population Structure of a Camelina sativa Spring Panel.

There is a need to explore renewable alternatives (e.g., biofuels) that can produce energy sources to help reduce the reliance on fossil oils. In addition, the consumption of fossil oils adversely affects the environment and human health via the generation of waste water, greenhouse gases, and waste solids. Camelina sativa, originated from southeastern Europe and southwestern Asia, is being re-embraced as an industrial oilseed crop due to its high seed oil content (36-47%) and high unsaturated fatty acid composition (>90%), which are suitable for jet fuel, biodiesel, high-value lubricants and animal feed. C. sativa's agronomic advantages include short time to maturation, low water and nutrient requirements, adaptability to adverse environmental conditions and resistance to common pests and pathogens. These characteristics make it an ideal crop for sustainable agricultural systems and regions of marginal land. However, the lack of genetic and genomic resources has slowed the enhancement of this emerging oilseed crop and exploration of its full agronomic and breeding potential. Here, a core of 213 spring C. sativa accessions was collected and genotyped. The genotypic data was used to characterize genetic diversity and population structure to infer how natural selection and plant breeding may have affected the formation and differentiation within the C. sativa natural populations, and how the genetic diversity of this species can be used in future breeding efforts. A total of 6,192 high-quality single nucleotide polymorphisms (SNPs) were identified using genotyping-by-sequencing (GBS) technology. The average polymorphism information content (PIC) value of 0.29 indicate moderate genetic diversity for the C. sativa spring panel evaluated in this report. Population structure and principal coordinates analyses (PCoA) based on SNPs revealed two distinct subpopulations. Sub-population 1 (POP1) contains accessions that mainly originated from Germany while the majority of POP2 accessions (>75%) were collected from Eastern Europe. Analysis of molecular variance (AMOVA) identified 4% variance among and 96% variance within subpopulations, indicating a high gene exchange (or low genetic differentiation) between the two subpopulations. These findings provide important information for future allele/gene identification using genome-wide association studies (GWAS) and marker-assisted selection (MAS) to enhance genetic gain in C. sativa breeding programs.

An automated, high-throughput method for standardizing image color profiles to improve image-based plant phenotyping.

High-throughput phenotyping has emerged as a powerful method for studying plant biology. Large image-based datasets are generated and analyzed with automated image analysis pipelines. A major challenge associated with these analyses is variation in image quality that can inadvertently bias results. Images are made up of tuples of data called pixels, which consist of R, G, and B values, arranged in a grid. Many factors, for example image brightness, can influence the quality of the image that is captured. These factors alter the values of the pixels within images and consequently can bias the data and downstream analyses. Here, we provide an automated method to adjust an image-based dataset so that brightness, contrast, and color profile is standardized. The correction method is a collection of linear models that adjusts pixel tuples based on a reference panel of colors. We apply this technique to a set of images taken in a high-throughput imaging facility and successfully detect variance within the image dataset. In this case, variation resulted from temperature-dependent light intensity throughout the experiment. Using this correction method, we were able to standardize images throughout the dataset, and we show that this correction enhanced our ability to accurately quantify morphological measurements within each image. We implement this technique in a high-throughput pipeline available with this paper, and it is also implemented in PlantCV.

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

Plant growth and water use are interrelated processes influenced by genetically controlled 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 improvement of WUE using traditional methods has proven difficult due to the low throughput and environmental heterogeneity of field settings. To overcome these limitations, this study utilizes a high-throughput phenotyping platform to quantify plant size and water use of an interspecific Setaria italica × 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 are correlated strongly in this system; 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 all were 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 can be utilized to predictably modulate trait values given a specified precipitation regime in the model C4 genus Setaria.

Raspberry Pi-powered imaging for plant phenotyping.

PREMISE OF THE STUDY: Image-based phenomics is a powerful approach to capture and quantify plant diversity. However, commercial platforms that make consistent image acquisition easy are often cost-prohibitive. To make high-throughput phenotyping methods more accessible, low-cost microcomputers and cameras can be used to acquire plant image data. METHODS AND RESULTS: We used low-cost Raspberry Pi computers and cameras to manage and capture plant image data. Detailed here are three different applications of Raspberry Pi-controlled imaging platforms for seed and shoot imaging. Images obtained from each platform were suitable for extracting quantifiable plant traits (e.g., shape, area, height, color) en masse using open-source image processing software such as PlantCV. CONCLUSIONS: This protocol describes three low-cost platforms for image acquisition that are useful for quantifying plant diversity. When coupled with open-source image processing tools, these imaging platforms provide viable low-cost solutions for incorporating high-throughput phenomics into a wide range of research programs.

PlantCV v2: Image analysis software for high-throughput plant phenotyping.

Systems for collecting image data in conjunction with computer vision techniques are a powerful tool for increasing the temporal resolution at which plant phenotypes can be measured non-destructively. Computational tools that are flexible and extendable are needed to address the diversity of plant phenotyping problems. We previously described the Plant Computer Vision (PlantCV) software package, which is an image processing toolkit for plant phenotyping analysis. The goal of the PlantCV project is to develop a set of modular, reusable, and repurposable tools for plant image analysis that are open-source and community-developed. Here we present the details and rationale for major developments in the second major release of PlantCV. In addition to overall improvements in the organization of the PlantCV project, new functionality includes a set of new image processing and normalization tools, support for analyzing images that include multiple plants, leaf segmentation, landmark identification tools for morphometrics, and modules for machine learning.

P-SAMS: a web site for plant artificial microRNA and synthetic trans-acting small interfering RNA design.

SUMMARY: The Plant Small RNA Maker Site (P-SAMS) is a web tool for the simple and automated design of artificial miRNAs (amiRNAs) and synthetic trans-acting small interfering RNAs (syn-tasiRNAs) for efficient and specific targeted gene silencing in plants. P-SAMS includes two applications, P-SAMS amiRNA Designer and P-SAMS syn-tasiRNA Designer. The navigation through both applications is wizard-assisted, and the job runtime is relatively short. Both applications output the sequence of designed small RNA(s), and the sequence of the two oligonucleotides required for cloning into 'B/c' compatible vectors. AVAILABILITY AND IMPLEMENTATION: The P-SAMS website is available at http://p-sams.carringtonlab.org. CONTACT: acarbonell@ibmcp.upv.es or nfahlgren@danforthcenter.org.

CG gene body DNA methylation changes and evolution of duplicated genes in cassava.

DNA methylation is important for the regulation of gene expression and the silencing of transposons in plants. Here we present genome-wide methylation patterns at single-base pair resolution for cassava (Manihot esculenta, cultivar TME 7), a crop with a substantial impact in the agriculture of subtropical and tropical regions. On average, DNA methylation levels were higher in all three DNA sequence contexts (CG, CHG, and CHH, where H equals A, T, or C) than those of the most well-studied model plant Arabidopsis thaliana. As in other plants, DNA methylation was found both on transposons and in the transcribed regions (bodies) of many genes. Consistent with these patterns, at least one cassava gene copy of all of the known components of Arabidopsis DNA methylation pathways was identified. Methylation of LTR transposons (GYPSY and COPIA) was found to be unusually high compared with other types of transposons, suggesting that the control of the activity of these two types of transposons may be especially important. Analysis of duplicated gene pairs resulting from whole-genome duplication showed that gene body DNA methylation and gene expression levels have coevolved over short evolutionary time scales, reinforcing the positive relationship between gene body methylation and high levels of gene expression. Duplicated genes with the most divergent gene body methylation and expression patterns were found to have distinct biological functions and may have been under natural or human selection for cassava traits.

A Versatile Phenotyping System and Analytics Platform Reveals Diverse Temporal Responses to Water Availability in Setaria.

Phenotyping has become the rate-limiting step in using large-scale genomic data to understand and improve agricultural crops. Here, the Bellwether Phenotyping Platform for controlled-environment plant growth and automated multimodal phenotyping is described. The system has capacity for 1140 plants, which pass daily through stations to record fluorescence, near-infrared, and visible images. Plant Computer Vision (PlantCV) was developed as open-source, hardware platform-independent software for quantitative image analysis. In a 4-week experiment, wild Setaria viridis and domesticated Setaria italica had fundamentally different temporal responses to water availability. While both lines produced similar levels of biomass under limited water conditions, Setaria viridis maintained the same water-use efficiency under water replete conditions, while Setaria italica shifted to less efficient growth. Overall, the Bellwether Phenotyping Platform and PlantCV software detected significant effects of genotype and environment on height, biomass, water-use efficiency, color, plant architecture, and tissue water status traits. All ∼ 79,000 images acquired during the course of the experiment are publicly available.