Hyperspectral reflectance-based phenotyping for quantitative genetics in crops: Progress and challenges
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59 BASIC BIOLOGICAL SCIENCES↗
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Induced and natural variation affect traits independently in hybrid Populus
Abstract The genetic control of many plant traits can be highly complex. Both allelic variation (sequence change) and dosage variation (copy number change) contribute to a plant's phenotype. While numerous studies have investigated the effect of allelic or dosage variation, very few have documented both within the same system, leaving their relative contribution to phenotypic effects unclear. The Populus genome is highly polymorphic, and poplars are fairly tolerant of gene dosage variation. Here, using a previously established Populus hybrid F1 population, we assessed and compared the effect of natural allelic variation and induced dosage variation on biomass, phenology, and leaf morphology traits. We identified QTLs for many of these traits, but our results indicate limited overlap between the QTLs associated with natural allelic variation and induced dosage variation. Additionally, the integration of data from both allelic and dosage variation identifies a larger set of QTLs that together explain a larger percentage of the phenotypic variance. Finally, our results suggest that the effect of the large indels might mask that of allelic QTLs. Our study helps clarify the relationship between allelic and dosage variation and their effects on quantitative traits.
Guo, Weier (ORCID:0000000251789334)↗
Genome-sequenced bacterial collection from sorghum aerial root mucilage
ABSTRACT A collection of 47 bacteria isolated from the mucilage of aerial roots of energy sorghum is available at the Great Lakes Bioenergy Research Center, Michigan State University, Michigan, USA. We enriched bacteria with putative plant-beneficial phenotypes and included information on phenotypic diversity, taxonomy, and whole genome sequences.
Mechan-Llontop, Marco E.↗
Genome-sequenced bacterial collection from sorghum epicuticular wax
ABSTRACT A collection of 44 isolates isolated from the epicuticular wax of stems of energy sorghum is available at the Great Lakes Bioenergy Researcher Center, Michigan State University, MI, USA. We enriched bacteria with putative plant-beneficial phenotypes and include information on their phenotypic diversity, taxonomy, and whole-genome sequences.
Mechan-Llontop, Marco E.↗
Automated Sorghum Phenotyping and Trait Development Platform
There is an urgent need to accelerate energy crop development for the production of renewable transportation fuels from biomass. Our interdisciplinary team developed mobile, ground-based and aerial phenotyping platforms and advanced remote sensing data analysis tools to acquire and process imagery from different types of cameras (e.g. RGB, multispectral, hyperspectral, and thermal sensors) and LiDAR point cloud data in large-scale sorghum field trials during phase 1 of the project. Phase 2 focused on Technology to Market activities to deliver these systems to the market place coupled with targeted research programs to address specific limitations or challenges for these systems.
09 BIOMASS FUELS↗
Validation of a metabolite–GWAS network for Populus trichocarpa family 1 UDP-glycosyltransferases
Metabolite genome-wide association studies (mGWASs) are increasingly used to discover the genetic basis of target phenotypes in plants such as Populus trichocarpa , a biofuel feedstock and model woody plant species. Despite their growing importance in plant genetics and metabolomics, few mGWASs are experimentally validated. Here, we present a functional genomics workflow for validating mGWAS-predicted enzyme–substrate relationships. We focus on uridine diphosphate–glycosyltransferases (UGTs), a large family of enzymes that catalyze sugar transfer to a variety of plant secondary metabolites involved in defense, signaling, and lignification. Glycosylation influences physiological roles, localization within cells and tissues, and metabolic fates of these metabolites. UGTs have substantially expanded in P. trichocarpa , presenting a challenge for large-scale characterization. Using a high-throughput assay, we produced substrate acceptance profiles for 40 previously uncharacterized candidate enzymes. Assays confirmed 10 of 13 leaf mGWAS associations, and a focused metabolite screen demonstrated varying levels of substrate specificity among UGTs. A substrate binding model case study of UGT-23 rationalized observed enzyme activities and mGWAS associations, including glycosylation of trichocarpinene to produce trichocarpin, a major higher-order salicylate in P. trichocarpa. We identified UGTs putatively involved in lignan, flavonoid, salicylate, and phytohormone metabolism, with potential implications for cell wall biosynthesis, nitrogen uptake, and biotic and abiotic stress response that determine sustainable biomass crop production. Our results provide new support for in silico analyses and evidence-based guidance for in vivo functional characterization.
59 BASIC BIOLOGICAL SCIENCES↗
Image-Based Methods to Score Fungal Pathogen Symptom Progression and Severity in Excised Arabidopsis Leaves
Image-based symptom scoring of plant diseases is a powerful tool for associating disease resistance with plant genotypes. Advancements in technology have enabled new imaging and image processing strategies for statistical analysis of time-course experiments. There are several tools available for analyzing symptoms on leaves and fruits of crop plants, but only a few are available for the model plant Arabidopsis thaliana (Arabidopsis). Arabidopsis and the model fungus Botrytis cinerea (Botrytis) comprise a potent model pathosystem for the identification of signaling pathways conferring immunity against this broad host-range necrotrophic fungus. Here, we present two strategies to assess severity and symptom progression of Botrytis infection over time in Arabidopsis leaves. Thus, a pixel classification strategy using color hue values from red-green-blue (RGB) images and a random forest algorithm was used to establish necrotic, chlorotic, and healthy leaf areas. Secondly, using chlorophyll fluorescence (ChlFl) imaging, the maximum quantum yield of photosystem II (Fv/Fm) was determined to define diseased areas and their proportion per total leaf area. Both RGB and ChlFl imaging strategies were employed to track disease progression over time. This has provided a robust and sensitive method for detecting sensitive or resistant genetic backgrounds. A full methodological workflow, from plant culture to data analysis, is described.
59 BASIC BIOLOGICAL SCIENCES↗
Genetic separation of phototropism and blue light inhibition of stem elongation
Blue light-induced regulation of cell elongation is a component of the signal response pathway for both phototropic curvature and inhibition of stem elongation in higher plants. To determine if blue light regulates cell elongation in these responses through shared or discrete pathways, phototropism and hypocotyl elongation were investigated in several blue light response mutants in Arabidopsis thaliana. Specifically, the blu mutants that lack blue light-dependent inhibition of hypocotyl elongation were found to exhibit a normal phototropic response. In contrast, a phototropic null mutant (JK218) and a mutant that has a 20- to 30-fold shift in the fluence dependence for first positive phototropism (JK224) showed normal inhibition of hypocotyl elongation in blue light. F1 progeny of crosses between the blu mutants and JK218 showed normal phototropism and inhibition of hypocotyl elongation, and approximately 1 in 16 F2 progeny were double mutants lacking both responses. Thus, blue light-dependent inhibition of hypocotyl elongation and phototropism operate through at least some genetically distinct components.
Non-NASA Center↗
Geology and climate influence rhizobiome composition of the phenotypically diverse tropical tree Tabebuia heterophylla
Plant-associated microbial communities have diverse phenotypic effects on their hosts that are only beginning to be revealed. We hypothesized that morpho-physiological variations in the tropical tree Tabebuia heterophylla, observed on different geological substrates, arise in part due to microbial processes in the rhizosphere. We characterized the microbiota of the rhizosphere and soil communities associated with T. heterophylla trees in high and low altitude sites (with varying temperature and precipitation) of volcanic, karst and serpentine geologies across Puerto Rico. We sampled 6 areas across the island in three geological materials including volcanic, serpentine and karst soils. Collection was done in 2 elevations (>450m and 0-300m high), that included 3 trees for each site and 4 replicate soil samples per tree of both bulk and rhizosphere. Genomic DNA was extracted from 144 samples, and 16S rRNA V4 sequencing was performed on the Illumina MiSeq platform. Proteobacteria, Actinobacteria, and Verrucomicrobia were the most dominant phyla, and microbiomes clustered by geological substrate and elevation. Volcanic samples were enriched in Verrucomicrobia; karst was dominated by nitrogen-fixing Proteobacteria, and serpentine sites harbored the most diverse communities, with dominant Cyanobacteria. Sites with similar climates but differing geologies showed significant differences on rhizobiota diversity and composition demonstrating the importance of geology in shaping the rhizosphere microbiota, with implications for the plant's phenotype. Our study sheds light on the combined role of geology and climate in the rhizosphere microbial consortia, likely contributing to the phenotypic plasticity of the trees.
59 BASIC BIOLOGICAL SCIENCES↗
Root hairs vs. trichomes: Not everyone is straight!
Trichomes show 47 morphological phenotypes, while literature reports only two root hair phenotypes in all plants. However, could hair-like structures exist below-ground in a similar wide range of morphologies like trichomes? Genetic mutants and root hair stress phenotypes point to the possibility of uncharacterized morphological variation existing belowground. For example, such root hairs in Arabidopsis (Arabidopsis thaliana) can be wavy, curled, or branched. We found hints in the literature about hair-like structures that emerge before root hairs belowground. As such, these early emerging hair structures can be potential exceptions to the contrasting morphological variation between trichomes and root hairs. Here, in this work, we show a previously unreported ‘hooked’ hair structure growing below-ground in common bean. The unique ‘hooking’ shape distinguishes the ‘hooked hair’ morphologically from root hairs. Currently, we cannot fully characterize the phenotype of our observation due to the lack of automated methods for phenotyping root hairs. This phenotyping bottleneck also handicaps the discovery of more morphology types that might exist below-ground as manual screening across species is slower than computer-assisted high-throughput screening.
59 BASIC BIOLOGICAL SCIENCES↗
Root phenotypes for improved nitrogen capture
Abstract Background Suboptimal nitrogen availability is a primary constraint for crop production in low-input agroecosystems, while nitrogen fertilization is a primary contributor to the energy, economic, and environmental costs of crop production in high-input agroecosystems. In this article we consider avenues to develop crops with improved nitrogen capture and reduced requirement for nitrogen fertilizer. Scope Intraspecific variation for an array of root phenotypes has been associated with improved nitrogen capture in cereal crops, including architectural phenotypes that colocalize root foraging with nitrogen availability in the soil; anatomical phenotypes that reduce the metabolic costs of soil exploration, improve penetration of hard soil, and exploit the rhizosphere; subcellular phenotypes that reduce the nitrogen requirement of plant tissue; molecular phenotypes exhibiting optimized nitrate uptake kinetics; and rhizosphere phenotypes that optimize associations with the rhizosphere microbiome. For each of these topics we provide examples of root phenotypes which merit attention as potential selection targets for crop improvement. Several cross-cutting issues are addressed including the importance of soil hydrology and impedance, phenotypic plasticity, integrated phenotypes, in silico modeling, and breeding strategies using high throughput phenotyping for co-optimization of multiple phenes. Conclusions Substantial phenotypic variation exists in crop germplasm for an array of root phenotypes that improve nitrogen capture. Although this topic merits greater research attention than it currently receives, we have adequate understanding and tools to develop crops with improved nitrogen capture. Root phenotypes are underutilized yet attractive breeding targets for the development of the nitrogen efficient crops urgently needed in global agriculture.
60 APPLIED LIFE SCIENCES↗
OPEN-Augmented Reality GUI for Bioenergy Crop Phenotyping and Precision Agriculture (Donald Danforth Plant Science Center Final Scientific Technical Report)
The project led by the Donald Danforth Plant Science Center, in collaboration with Arizona State University, George Washington University, and Saint Louis University, has made significant strides in advancing the phenotypic analysis of bioenergy crops through the development of an innovative AI processing pipeline. This initiative was primarily funded by ARPA-E, with additional cost-sharing provided by the participating institutions. The project successfully utilized a variety of sensors—3D scanners, thermal, RGB, and hyperspectral—to refine algorithms for data-driven trait signature identification and improve the classification and visualization of plant traits. The developed AI processing pipeline is capable of handling the complex, multidimensional data characteristic of dynamic agricultural environments. 1) Contributions to understanding: The research has advanced the field of plant phenomics by showcasing the synergistic use of various sensor data to enhance the precision of trait analysis in bioenergy crops. Through the integration of 3D scanners, thermal, RGB, and hyperspectral sensors, the project has developed robust data-driven trait signature algorithms and visualization techniques. These innovations have facilitated detailed monitoring and management of plant traits, providing vital insights into plant growth dynamics and stress responses. Further, the project has broadened our understanding of how machine learning can be effectively applied in multi-sensor environments to refine trait analysis. By leveraging diverse datasets, the research has not only improved the accuracy of phenotypic assessments but also established a versatile methodological framework that can be extended beyond agriculture to other fields requiring detailed phenotypic analysis. 2) Technical effectiveness and economic feasibility: The AI processing pipeline developed in this project demonstrated significant technical effectiveness, achieving high throughput analysis of extensive phenotypic data and meeting targeted accuracies. This system exemplified the capability of advanced machine learning technologies to efficiently manage and analyze large, complex datasets. Economically, the implementation of the project-developed pipelines may offer substantial cost savings across multiple sectors. It enhances data analysis processes and significantly reduces the need for manual data interpretation, thereby decreasing both the time and resources required. 3) Public benefit: The project has significantly broadened the scope of agricultural methodologies to enhance phenotypic analysis, with potential applications in various sectors beyond agriculture. Additionally, the initiative fostered an enriching educational and collaborative environment, significantly enhancing the technical skills of participants. It also made substantial contributions to the scientific community by providing open-access data sets and tools, encouraging ongoing research and development across various disciplines. Overall, the project not only met its scientific goals but also showcased the extensive utility of integrating advanced machine learning and sensor data analysis technologies. These advancements have proven instrumental in driving forward both theoretical research and practical applications, setting a strong foundation for future explorations and innovations in data-driven science.
60 APPLIED LIFE SCIENCES↗
Phytochrome-Dependent Regulation of ZFP6 and ZFPH Impacts Photomorphogenesis in Arabidopsis thaliana
Phytochromes (phy) are key regulators of photomorphogenesis in plants. Among the different phys characterized in higher plants (i.e., phyA to phyE), phyA and phyB primarily regulate phenotypic responses in plants under far-red (FR) and red (R) conditions, respectively. Recent findings suggest that some zinc finger proteins (ZFPs) are involved in plant light-modulated morphogenesis. However, the interaction(s) between phyA, phyB and ZFP homologs potentially involved in photomorphogenesis, as well as their phenotypic and molecular effects in Arabidopsis seedlings exposed to R and FR light remain to be elucidated fully. Prior analyses with phytochrome chromophore deficient lines indicated that ZFP6 expression is misregulated compared to levels in Col-0 wild type (WT). Here, we used plants with phytochrome chromophore or apoprotein (specifically phyA and phyB) deficiencies, lines with mutations in ZFP6 and ZFP6 HOMOLOG ( ZFPH ) genes, and plants overexpressing ZFP6 to examine regulatory interactions between phytochromes, ZFP6, and ZFPH. Our results indicate that phytochromes are required for downregulation of ZFP6 and ZFPH and suggest a role for light-regulated control of ZFP levels in phytochrome-dependent photomorphogenesis. Conversely, PHYB is downregulated in zfp6 mutants under R light. Analyses of a zfp6zfph double mutant confirmed disruption in photomorphogenic phenotypes, including the regulation of hypocotyl elongation in seedlings grown under FR light. In addition, PIF3 and PIF4 levels are transcriptionally regulated by ZFP6 and ZFPH in a gibberellic acid-dependent manner. ZFP6 overexpression resulted in opposite phenotypic responses to those observed in the zfp6 and zfph mutants grown in FR and R light, as well as a reduction in the rosette size of mature ZFP6 OX plants relative to WT under white light. Based on these observations, we provide insight into how phy and ZFPs interact to regulate specific aspects of light-dependent processes in Arabidopsis.
59 BASIC BIOLOGICAL SCIENCES↗
A rapid assay for assessing bacterial effects on Arabidopsis thermotolerance
Abstract Background The role of beneficial microbes in mitigating plant abiotic stress has received considerable attention. However, the lack of a reproducible and relatively high-throughput screen for microbial contributions to plant thermotolerance has greatly limited progress in this area, this slows the discovery of novel beneficial isolates and the processes by which they operate. Results We designed a rapid phenotyping method to assess the effects of bacteria on plant host thermotolerance. After testing multiple growth conditions, a hydroponic system was selected and used to optimize an Arabidopsis heat shock regime and phenotypic evaluation. Arabidopsis seedlings germinated on a PTFE mesh disc were floated onto a 6-well plate containing liquid MS media, then subjected to heat shock at 45 °C for various duration. To characterize phenotype, plants were harvested after four days of recovery to measure chlorophyll content. The method was extended to include bacterial isolates and to quantify bacterial contributions to host plant thermotolerance. As an exemplar, the method was used to screen 25 strains of the plant growth promoting Variovorax spp. for enhanced plant thermotolerance. A follow-up study demonstrated the reproducibility of this assay and led to the discovery of a novel beneficial interaction. Conclusions This method enables rapid screening of individual bacterial strains for beneficial effects on host plant thermotolerance. The throughput and reproducibility of the system is ideal for testing many genetic variants of Arabidopsis and bacterial strains.
59 BASIC BIOLOGICAL SCIENCES↗
Dissecting the Genetic Architecture of Carbon Partitioning in Sorghum Using Multiscale Phenotypes
Carbon partitioning in plants may be viewed as a dynamic process composed of the many interactions between sources and sinks. The accumulation and distribution of fixed carbon is not dictated simply by the sink strength and number but is dependent upon the source, pathways, and interactions of the system. As such, the study of carbon partitioning through perturbations to the system or through focus on individual traits may fail to produce actionable developments or a comprehensive understanding of the mechanisms underlying this complex process. Using the recently published sorghum carbon-partitioning panel, we collected both macroscale phenotypic characteristics such as plant height, above-ground biomass, and dry weight along with microscale compositional traits to deconvolute the carbon-partitioning pathways in this multipurpose crop. Multivariate analyses of traits resulted in the identification of numerous loci associated with several distinct carbon-partitioning traits, which putatively regulate sugar content, manganese homeostasis, and nitrate transportation. Using a multivariate adaptive shrinkage approach, we identified several loci associated with multiple traits suggesting that pleiotropic and/or interactive effects may positively influence multiple carbon-partitioning traits, or these overlaps may represent molecular switches mediating basal carbon allocating or partitioning networks. Conversely, we also identify a carbon tradeoff where reduced lignin content is associated with increased sugar content. The results presented here support previous studies demonstrating the convoluted nature of carbon partitioning in sorghum and emphasize the importance of taking a holistic approach to the study of carbon partitioning by utilizing multiscale phenotypes.
59 BASIC BIOLOGICAL SCIENCES↗
Brachypodium Phenylalanine Ammonia Lyase (PAL) Promotes Antiviral Defenses against Panicum mosaic virus and Its Satellites
Brachypodium distachyon has recently emerged as a premier model plant for monocot biology, akin to Arabidopsis thaliana. We previously reported genome-wide transcriptomic and alternative splicing changes occurring in Brachypodium during compatible infections with Panicum mosaic virus (PMV) and its satellite virus (SPMV). Here, we dissected the role of Brachypodium phenylalanine ammonia lyase 1 (PAL1), a key enzyme for phenylpropanoid and salicylic acid (SA) biosynthesis and the induction of plant defenses. Targeted metabolomics profiling of PMV-infected and PMV- plus SPMV-infected (PMV/SPMV) Brachypodium plants revealed enhanced levels of multiple defense-related hormones and metabolites such as cinnamic acid, SA, and fatty acids and lignin precursors during disease progression. The virus-induced accumulation of SA and lignin was significantly suppressed upon knockdown of B. distachyon PAL1 (BdPAL1) using RNA interference (RNAi). The compromised SA accumulation in PMV/SPMV-infected BdPAL1 RNAi plants correlated with weaker induction of multiple SA-related defense gene markers (pathogenesis related 1 [PR-1], PR-3, PR-5, and WRKY75) and enhanced susceptibility to PMV/SPMV compared to that of wild-type (WT) plants. Furthermore, exogenous application of SA alleviated the PMV/SPMV necrotic disease phenotypes and delayed plant death caused by single and mixed infections. Together, our results support an antiviral role for BdPAL1 during compatible host-virus interaction, perhaps as a last resort attempt to rescue the infected plant.
59 BASIC BIOLOGICAL SCIENCES↗
Microbiome-enabled genomic selection improves prediction accuracy for nitrogen-related traits in maize
Root-associated microbiomes in the rhizosphere (rhizobiomes) are increasingly known to play an important role in nutrient acquisition, stress tolerance, and disease resistance of plants. However, it remains largely unclear to what extent these rhizobiomes contribute to trait variation for different genotypes and if their inclusion in the genomic selection protocol can enhance prediction accuracy. To address these questions, we developed a microbiome-enabled genomic selection method that incorporated host SNPs and amplicon sequence variants from plant rhizobiomes in a maize diversity panel under high and low nitrogen (N) field conditions. Our cross-validation results showed that the microbiome-enabled genomic selection model significantly outperformed the conventional genomic selection model for nearly all time-series traits related to plant growth and N responses, with an average relative improvement of 3.7%. The improvement was more pronounced under low N conditions (8.4–40.2% of relative improvement), consistent with the view that some beneficial microbes can enhance N nutrient uptake, particularly in low N fields. However, our study could not definitively rule out the possibility that the observed improvement is partially due to the amplicon sequence variants being influenced by microenvironments. Using a high-dimensional mediation analysis method, our study has also identified microbial mediators that establish a link between plant genotype and phenotype. Some of the detected mediator microbes were previously reported to promote plant growth. The enhanced prediction accuracy of the microbiome-enabled genomic selection models, demonstrated in a single environment, serves as a proof-of-concept for the potential application of microbiome-enabled plant breeding for sustainable agriculture.
60 APPLIED LIFE SCIENCES↗
Installation and imaging of thousands of minirhizotrons to phenotype root systems of field-grown plants
Roots are vital to plant performance because they acquire resources from the soil and provide anchorage. However, it remains difficult to assess root system size and distribution because roots are inaccessible in the soil. Existing methods to phenotype entire root systems range from slow, often destructive, methods applied to relatively small numbers of plants in the field to rapid methods that can be applied to large numbers of plants in controlled environment conditions. Much has been learned recently by extensive sampling of the root crown portion of field-grown plants. But, information on large-scale genetic and environmental variation in the size and distribution of root systems in the field remains a key knowledge gap. Minirhizotrons are the only established, non-destructive technology that can address this need in a standard field trial. Prior experiments have used only modest numbers of minirhizotrons, which has limited testing to small numbers of genotypes or environmental conditions. This study addressed the need for methods to install and collect images from thousands of minirhizotrons and thereby help break the phenotyping bottleneck in the field. Over three growing seasons, methods were developed and refined to install and collect images from up to 3038 minirhizotrons per experiment. Modifications were made to four tractors and hydraulic soil corers mounted to them. High quality installation was achieved at an average rate of up to 84.4 minirhizotron tubes per tractor per day. A set of four commercially available minirhizotron camera systems were each transported by wheelbarrow to allow collection of images of mature maize root systems at an average rate of up to 65.3 tubes per day per camera. This resulted in over 300,000 images being collected in as little as 11 days for a single experiment. The scale of minirhizotron installation was increased by two orders of magnitude by simultaneously using four tractor-mounted, hydraulic soil corers with modifications to ensure high quality, rapid operation. Image collection can be achieved at the corresponding scale using commercially available minirhizotron camera systems. Along with recent advances in image analysis, these advances will allow use of minirhizotrons at unprecedented scale to address key knowledge gaps regarding genetic and environmental effects on root system size and distribution in the field.
54 ENVIRONMENTAL SCIENCES↗