A dolabralexin-deficient mutant provides insight into specialized diterpenoid metabolism in maize.

Two major groups of specialized metabolites in maize (Zea mays), termed kauralexins and dolabralexins, serve as known or predicted diterpenoid defenses against pathogens, herbivores, and other environmental stressors. To consider the physiological roles of the recently discovered dolabralexin pathway, we examined dolabralexin structural diversity, tissue-specificity, and stress-elicited production in a defined biosynthetic pathway mutant. Metabolomics analyses support a larger number of dolabralexin pathway products than previously known. We identified dolabradienol as a previously undetected pathway metabolite and characterized its enzymatic production. Transcript and metabolite profiling showed that dolabralexin biosynthesis and accumulation predominantly occur in primary roots and show quantitative variation across genetically diverse inbred lines. Generation and analysis of CRISPR-Cas9-derived loss-of-function Kaurene Synthase-Like 4 (Zmksl4) mutants demonstrated dolabralexin production deficiency, thus supporting ZmKSL4 as the diterpene synthase responsible for the conversion of geranylgeranyl pyrophosphate precursors into dolabradiene and downstream pathway products. Zmksl4 mutants further display altered root-to-shoot ratios and root architecture in response to water deficit. Collectively, these results demonstrate dolabralexin biosynthesis via ZmKSL4 as a committed pathway node biochemically separating kauralexin and dolabralexin metabolism, and suggest an interactive role of maize dolabralexins in plant vigor during abiotic stress.

X-ray microscopy enables multiscale high-resolution 3D imaging of plant cells, tissues, and organs.

Capturing complete internal anatomies of plant organs and tissues within their relevant morphological context remains a key challenge in plant science. While plant growth and development are inherently multiscale, conventional light, fluorescence, and electron microscopy platforms are typically limited to imaging of plant microstructure from small flat samples that lack a direct spatial context to, and represent only a small portion of, the relevant plant macrostructures. We demonstrate technical advances with a lab-based X-ray microscope (XRM) that bridge the imaging gap by providing multiscale high-resolution three-dimensional (3D) volumes of intact plant samples from the cell to the whole plant level. Serial imaging of a single sample is shown to provide sub-micron 3D volumes co-registered with lower magnification scans for explicit contextual reference. High-quality 3D volume data from our enhanced methods facilitate sophisticated and effective computational segmentation. Advances in sample preparation make multimodal correlative imaging workflows possible, where a single resin-embedded plant sample is scanned via XRM to generate a 3D cell-level map, and then used to identify and zoom in on sub-cellular regions of interest for high-resolution scanning electron microscopy. In total, we present the methodologies for use of XRM in the multiscale and multimodal analysis of 3D plant features using numerous economically and scientifically important plant systems.

Organizing your space: The potential for integrating spatial transcriptomics and 3D imaging data in plants.

Plant cells communicate information for the regulation of development and responses to external stresses. A key form of this communication is transcriptional regulation, accomplished via complex gene networks operating both locally and systemically. To fully understand how genes are regulated across plant tissues and organs, high resolution, multi-dimensional spatial transcriptional data must be acquired and placed within a cellular and organismal context. Spatial transcriptomics (ST) typically provides a two-dimensional spatial analysis of gene expression of tissue sections that can be stacked to render three-dimensional data. For example, X-ray and light-sheet microscopy provide sub-micron scale volumetric imaging of cellular morphology of tissues, organs, or potentially entire organisms. Linking these technologies could substantially advance transcriptomics in plant biology and other fields. Here, we review advances in ST and 3D microscopy approaches and describe how these technologies could be combined to provide high resolution, spatially organized plant tissue transcript mapping.

Optimisation of root traits to provide enhanced ecosystem services in agricultural systems: A focus on cover crops.

Roots are the interface between the plant and the soil and play a central role in multiple ecosystem processes. With intensification of agricultural practices, rhizosphere processes are being disrupted and are causing degradation of the physical, chemical and biotic properties of soil. However, cover crops, a group of plants that provide ecosystem services, can be utilised during fallow periods or used as an intercrop to restore soil health. The effectiveness of ecosystem services provided by cover crops varies widely as very little breeding has occurred in these species. Improvement of ecosystem service performance is rarely considered as a breeding trait due to the complexities and challenges of belowground evaluation. Advancements in root phenotyping and genetic tools are critical in accelerating ecosystem service improvement in cover crops. In this study, we provide an overview of the range of belowground ecosystem services provided by cover crop roots: (1) soil structural remediation, (2) capture of soil resources and (3) maintenance of the rhizosphere and building of organic matter content. Based on the ecosystem services described, we outline current and promising phenotyping technologies and breeding strategies in cover crops that can enhance agricultural sustainability through improvement of root traits.

Three-Dimensional Time-Lapse Analysis Reveals Multiscale Relationships in Maize Root Systems with Contrasting Architectures.

Understanding how an organism's phenotypic traits are conditioned by genetic and environmental variation is a central goal of biology. Root systems are one of the most important but poorly understood aspects of plants, largely due to the three-dimensional (3D), dynamic, and multiscale phenotyping challenge they pose. A critical gap in our knowledge is how root systems build in complexity from a single primary root to a network of thousands of roots that collectively compete for ephemeral, heterogeneous soil resources. We used time-lapse 3D imaging and mathematical modeling to assess root system architectures (RSAs) of two maize (Zea mays) inbred genotypes and their hybrid as they grew in complexity from a few to many roots. Genetically driven differences in root branching zone size and lateral branching densities along a single root, combined with differences in peak growth rate and the relative allocation of carbon resources to new versus existing roots, manifest as sharply distinct global RSAs over time. The 3D imaging of mature field-grown root crowns showed that several genetic differences in seedling architectures could persist throughout development and across environments. This approach connects individual and system-wide scales of root growth dynamics, which could eventually be used to predict genetic variation for complex RSAs and their functions.

Site-Selective Acylation of Pyranosides with Oligopeptide Catalysts.

Herein, we report the oligopeptide-catalyzed site-selective acylation of partially protected monosaccharides. We identified catalysts that invert site-selectivity compared to N-methylimidazole, which was used to determine the intrinsic reactivity, for 4,6-O-protected glucopyranosides (trans-diols) as well as 4,6-O-protected mannopyranosides (cis-diols). The reaction yields up to 81% of the inherently unfavored 2-O-acetylated products with selectivities up to 15:1 using mild reaction conditions. We also determined the influence of protecting groups on the reaction and demonstrate that our protocol is suitable for one-pot reactions with multiple consecutive protection steps.

Comprehensive 3D phenotyping reveals continuous morphological variation across genetically diverse sorghum inflorescences.

●Inflorescence architecture in plants is often complex and challenging to quantify, particularly for inflorescences of cereal grasses. Methods for capturing inflorescence architecture and for analyzing the resulting data are limited to a few easily captured parameters that may miss the rich underlying diversity. ●Here, we apply X-ray computed tomography combined with detailed morphometrics, offering new imaging and computational tools to analyze three-dimensional inflorescence architecture. To show the power of this approach, we focus on the panicles of Sorghum bicolor, which vary extensively in numbers, lengths, and angles of primary branches, as well as the three-dimensional shape, size, and distribution of the seed. ●We imaged and comprehensively evaluated the panicle morphology of 55 sorghum accessions that represent the five botanical races in the most common classification system of the species, defined by genetic data. We used our data to determine the reliability of the morphological characters for assigning specimens to race and found that seed features were particularly informative. ●However, the extensive overlap between botanical races in multivariate trait space indicates that the phenotypic range of each group extends well beyond its overall genetic background, indicating unexpectedly weak correlation between morphology, genetic identity, and domestication history.

In Situ Switching of Site-Selectivity with Light in the Acetylation of Sugars with Azopeptide Catalysts.

We present a novel concept for the in situ control of site-selectivity of catalytic acetylations of partially protected sugars using light as external stimulus and oligopeptide catalysts equipped with an azobenzene moiety. The isomerizable azobenzene-peptide backbone defines the size and shape of the catalytic pocket, while the π-methyl-l-histidine (Pmh) moiety transfers the electrophile. Photoisomerization of the E- to the Z-azobenzene catalyst (monitored via NMR) with an LED (λ = 365 nm) drastically changes the chemical environment around the catalytically active Pmh moiety, so that the light-induced change in the catalyst shape alters site-selectivity. As a proof of principle, we employed (4,6-O-benzylidene)methyl-α-d-pyranosides, which provide a change in regioselectivity from 2:1 (E) to 1:5 (Z) for the monoacetylated products at room temperature. The validity of this new catalyst-design concept is further demonstrated with the regioselective acetylation of the natural product quercetin. In situ irradiation NMR spectroscopy was used to quantify photostationary states under continuous irradiation with UV light.

Convergent evolution of root system architecture in two independently evolved lineages of weedy rice.

Root system architecture (RSA) is a critical aspect of plant growth and competitive ability. Here we used two independently evolved strains of weedy rice, a de-domesticated form of rice, to study the evolution of weed-associated RSA traits and the extent to which they evolve through shared or different genetic mechanisms. We characterised 98 two-dimensional and three-dimensional RSA traits in 671 plants representing parents and descendants of two recombinant inbred line populations derived from two weed × crop crosses. A random forest machine learning model was used to assess the degree to which root traits can predict genotype and the most diagnostic traits for doing so. We used quantitative trait locus (QTL) mapping to compare genetic architecture between the weed strains. The two weeds were distinguishable from the crop in similar and predictable ways, suggesting independent evolution of a 'weedy' RSA phenotype. Notably, comparative QTL mapping revealed little evidence for shared underlying genetic mechanisms. Our findings suggest that despite the double bottlenecks of domestication and de-domestication, weedy rice nonetheless shows genetic flexibility in the repeated evolution of weedy RSA traits. Whereas the root growth of cultivated rice may facilitate interactions among neighbouring plants, the weedy rice phenotype may minimise below-ground contact as a competitive strategy.

The Persistent Homology Mathematical Framework Provides Enhanced Genotype-to-Phenotype Associations for Plant Morphology.

Efforts to understand the genetic and environmental conditioning of plant morphology are hindered by the lack of flexible and effective tools for quantifying morphology. Here, we demonstrate that persistent-homology-based topological methods can improve measurement of variation in leaf shape, serrations, and root architecture. We apply these methods to 2D images of leaves and root systems in field-grown plants of a domesticated introgression line population of tomato (Solanum pennellii). We find that compared with some commonly used conventional traits, (1) persistent-homology-based methods can more comprehensively capture morphological variation; (2) these techniques discriminate between genotypes with a larger normalized effect size and detect a greater number of unique quantitative trait loci (QTLs); (3) multivariate traits, whether statistically derived from univariate or persistent-homology-based traits, improve our ability to understand the genetic basis of phenotype; and (4) persistent-homology-based techniques detect unique QTLs compared to conventional traits or their multivariate derivatives, indicating that previously unmeasured aspects of morphology are now detectable. The QTL results further imply that genetic contributions to morphology can affect both the shoot and root, revealing a pleiotropic basis to natural variation in tomato. Persistent homology is a versatile framework to quantify plant morphology and developmental processes that complements and extends existing methods.

Characterizing 3D inflorescence architecture in grapevine using X-ray imaging and advanced morphometrics: implications for understanding cluster density.

Inflorescence architecture provides the scaffold on which flowers and fruits develop, and consequently is a primary trait under investigation in many crop systems. Yet the challenge remains to analyse these complex 3D branching structures with appropriate tools. High information content datasets are required to represent the actual structure and facilitate full analysis of both the geometric and the topological features relevant to phenotypic variation in order to clarify evolutionary and developmental inflorescence patterns. We combined advanced imaging (X-ray tomography) and computational approaches (topological and geometric data analysis and structural simulations) to comprehensively characterize grapevine inflorescence architecture (the rachis and all branches without berries) among 10 wild Vitis species. Clustering and correlation analyses revealed unexpected relationships, for example pedicel branch angles were largely independent of other traits. We identified multivariate traits that typified species, which allowed us to classify species with 78.3% accuracy, versus 10% by chance. Twelve traits had strong signals across phylogenetic clades, providing insight into the evolution of inflorescence architecture. We provide an advanced framework to quantify 3D inflorescence and other branched plant structures that can be used to tease apart subtle, heritable features for a better understanding of genetic and environmental effects on plant phenotypes.

The Quantitative Genetic Control of Root Architecture in Maize.

Roots remain an underexplored frontier in plant genetics despite their well-known influence on plant development, agricultural performance and competition in the wild. Visualizing and measuring root structures and their growth is vastly more difficult than characterizing aboveground parts of the plant and is often simply avoided. The majority of research on maize root systems has focused on their anatomy, physiology, development and soil interaction, but much less is known about the genetics that control quantitative traits. In maize, seven root development genes have been cloned using mutagenesis, but no genes underlying the many root-related quantitative trait loci (QTLs) have been identified. In this review, we discuss whether the maize mutants known to control root development may also influence quantitative aspects of root architecture, including the extent to which they overlap with the most recent maize root trait QTLs. We highlight specific challenges and anticipate the impacts that emerging technologies, especially computational approaches, may have toward the identification of genes controlling root quantitative traits.

Quantitative trait locus mapping reveals regions of the maize genome controlling root system architecture.

The quest to determine the genetic basis of root system architecture (RSA) has been greatly facilitated by recent developments in root phenotyping techniques. Methods that are accurate, high throughput, and control for environmental factors are especially attractive for quantitative trait locus mapping. Here, we describe the adaptation of a nondestructive in vivo gel-based root imaging platform for use in maize (Zea mays). We identify a large number of contrasting RSA traits among 25 founder lines of the maize nested association mapping population and locate 102 quantitative trait loci using the B73 (compact RSA)×Ki3 (exploratory RSA) mapping population. Our results suggest that a phenotypic tradeoff exists between small, compact RSA and large, exploratory RSA.

3D phenotyping and quantitative trait locus mapping identify core regions of the rice genome controlling root architecture.

Identification of genes that control root system architecture in crop plants requires innovations that enable high-throughput and accurate measurements of root system architecture through time. We demonstrate the ability of a semiautomated 3D in vivo imaging and digital phenotyping pipeline to interrogate the quantitative genetic basis of root system growth in a rice biparental mapping population, Bala × Azucena. We phenotyped >1,400 3D root models and >57,000 2D images for a suite of 25 traits that quantified the distribution, shape, extent of exploration, and the intrinsic size of root networks at days 12, 14, and 16 of growth in a gellan gum medium. From these data we identified 89 quantitative trait loci, some of which correspond to those found previously in soil-grown plants, and provide evidence for genetic tradeoffs in root growth allocations, such as between the extent and thoroughness of exploration. We also developed a multivariate method for generating and mapping central root architecture phenotypes and used it to identify five major quantitative trait loci (r(2) = 24-37%), two of which were not identified by our univariate analysis. Our imaging and analytical platform provides a means to identify genes with high potential for improving root traits and agronomic qualities of crops.

How can we harness quantitative genetic variation in crop root systems for agricultural improvement?

Root systems are a black box obscuring a comprehensive understanding of plant function, from the ecosystem scale down to the individual. In particular, a lack of knowledge about the genetic mechanisms and environmental effects that condition root system growth hinders our ability to develop the next generation of crop plants for improved agricultural productivity and sustainability. We discuss how the methods and metrics we use to quantify root systems can affect our ability to understand them, how we can bridge knowledge gaps and accelerate the derivation of structure-function relationships for roots, and why a detailed mechanistic understanding of root growth and function will be important for future agricultural gains.

Preparation of Spirocyclic Vinylic Carbonates from Allylic Alcohols.

We present the synthesis of exo- and endo-spirovinylethylene carbonates, starting from various cyclic allylic alcohols. This one-pot cascade reaction to the spirocyclic scaffold was optimized using a design of experiments approach. The introduction of spirovinylethylene carbonates broadens the scope of using these in catalytic applications and provides an easy synthetic entry into spirocyclic scaffolds of various ring sizes.

DNA binding of centromere protein C (CENPC) is stabilized by single-stranded RNA.

Centromeres are the attachment points between the genome and the cytoskeleton: centromeres bind to kinetochores, which in turn bind to spindles and move chromosomes. Paradoxically, the DNA sequence of centromeres has little or no role in perpetuating kinetochores. As such they are striking examples of genetic information being transmitted in a manner that is independent of DNA sequence (epigenetically). It has been found that RNA transcribed from centromeres remains bound within the kinetochore region, and this local population of RNA is thought to be part of the epigenetic marking system. Here we carried out a genetic and biochemical study of maize CENPC, a key inner kinetochore protein. We show that DNA binding is conferred by a localized region 122 amino acids long, and that the DNA-binding reaction is exquisitely sensitive to single-stranded RNA. Long, single-stranded nucleic acids strongly promote the binding of CENPC to DNA, and the types of RNAs that stabilize DNA binding match in size and character the RNAs present on kinetochores in vivo. Removal or replacement of the binding module with HIV integrase binding domain causes a partial delocalization of CENPC in vivo. The data suggest that centromeric RNA helps to recruit CENPC to the inner kinetochore by altering its DNA binding characteristics.

Topological data analysis expands the genotype to phenotype map for 3D maize root system architecture.

A central goal of biology is to understand how genetic variation produces phenotypic variation, which has been described as a genotype to phenotype (G to P) map. The plant form is continuously shaped by intrinsic developmental and extrinsic environmental inputs, and therefore plant phenomes are highly multivariate and require comprehensive approaches to fully quantify. Yet a common assumption in plant phenotyping efforts is that a few pre-selected measurements can adequately describe the relevant phenome space. Our poor understanding of the genetic basis of root system architecture is at least partially a result of this incongruence. Root systems are complex 3D structures that are most often studied as 2D representations measured with relatively simple univariate traits. In prior work, we showed that persistent homology, a topological data analysis method that does not pre-suppose the salient features of the data, could expand the phenotypic trait space and identify new G to P relations from a commonly used 2D root phenotyping platform. Here we extend the work to entire 3D root system architectures of maize seedlings from a mapping population that was designed to understand the genetic basis of maize-nitrogen relations. Using a panel of 84 univariate traits, persistent homology methods developed for 3D branching, and multivariate vectors of the collective trait space, we found that each method captures distinct information about root system variation as evidenced by the majority of non-overlapping QTL, and hence that root phenotypic trait space is not easily exhausted. The work offers a data-driven method for assessing 3D root structure and highlights the importance of non-canonical phenotypes for more accurate representations of the G to P map.