Ruthie Angelovici

Targeted Enhancement of Glutamate-to-γ-Aminobutyrate Conversion in Arabidopsis Seeds Affects Carbon-Nitrogen Balance and Storage Reserves in a Development-Dependent Manner

In seeds, glutamate decarboxylase (GAD) operates at the metabolic nexus between carbon and nitrogen metabolism by catalyzing the unidirectional decarboxylation of glutamate to form γ-aminobutyric acid (GABA). To elucidate the regulatory role of GAD in seed development, we generated Arabidopsis (Arabidopsis thaliana) transgenic plants expressing a truncated GAD from Petunia hybrida missing the carboxyl-terminal regulatory Ca2+-calmodulin-binding domain under the transcriptional regulation of the seed maturation-specific phaseolin promoter. Dry seeds of the transgenic plants accumulated considerable amounts of GABA, and during desiccation the content of several amino acids increased, although not glutamate or proline. Dry transgenic seeds had higher protein content than wild-type seeds but lower amounts of the intermediates of glycolysis, glycerol and malate. The total fatty acid content of the transgenic seeds was 50% lower than in the wild type, while acyl-coenzyme A accumulated in the transgenic seeds. Labeling experiments revealed altered levels of respiration in the transgenic seeds, and fractionation studies indicated reduced incorporation of label in the sugar and lipid fractions extracted from transgenic seeds. Comparative transcript profiling of the dry seeds supported the metabolic data. Cellular processes up-regulated at the transcript level included the tricarboxylic acid cycle, fatty acid elongation, the shikimate pathway, tryptophan metabolism, nitrogen-carbon remobilization, and programmed cell death. Genes involved in the regulation of germination were similarly up-regulated. Taken together, these results indicate that the GAD-mediated conversion of glutamate to GABA during seed development plays an important role in balancing carbon and nitrogen metabolism and in storage reserve accumulation.

Alteration of the Interconversion of Pyruvate and Malate in the Plastid or Cytosol of Ripening Tomato Fruit Invokes Diverse Consequences on Sugar But Similar Effects on Cellular Organic Acid, Metabolism, and Transitory Starch Accumulation

Summary: Normal tomato ripening is influenced by alterations on both cytosolic phosphoenolpyruvate carboxykinase and plastidic NADP-malic enzyme. This study provides compelling evidence of their roles in starch biosynthesis, respiration rates, and tricarboxylic acid cycle flux. The aim of this work was to investigate the effect of decreased cytosolic phosphoenolpyruvate carboxykinase (PEPCK) and plastidic NADP-dependent malic enzyme (ME) on tomato (Solanum lycopersicum) ripening. Transgenic tomato plants with strongly reduced levels of PEPCK and plastidic NADP-ME were generated by RNA interference gene silencing under the control of a ripening-specific E8 promoter. While these genetic modifications had relatively little effect on the total fruit yield and size, they had strong effects on fruit metabolism. Both transformants were characterized by lower levels of starch at breaker stage. Analysis of the activation state of ADP-glucose pyrophosphorylase correlated with the decrease of starch in both transformants, which suggests that it is due to an altered cellular redox status. Moreover, metabolic profiling and feeding experiments involving positionally labeled glucoses of fruits lacking in plastidic NADP-ME and cytosolic PEPCK activities revealed differential changes in overall respiration rates and tricarboxylic acid (TCA) cycle flux. Inactivation of cytosolic PEPCK affected the respiration rate, which suggests that an excess of oxaloacetate is converted to aspartate and reintroduced in the TCA cycle via 2-oxoglutarate/glutamate. On the other hand, the plastidic NADP-ME antisense lines were characterized by no changes in respiration rates and TCA cycle flux, which together with increases of pyruvate kinase and phosphoenolpyruvate carboxylase activities indicate that pyruvate is supplied through these enzymes to the TCA cycle. These results are discussed in the context of current models of the importance of malate during tomato fruit ripening.

ZEAXANTHIN EPOXIDASE Activity Potentiates Carotenoid Degradation in Maturing Seed

Zeaxanthin epoxidase-dependent epoxidation of carotenoids accelerates degradation by carotenoid cleavage enzymes during late seed Arabidopsis maturation and seed desiccation. Elucidation of the carotenoid biosynthetic pathway has enabled altering the composition and content of carotenoids in various plants, but to achieve desired nutritional impacts, the genetic components regulating carotenoid homeostasis in seed, the plant organ consumed in greatest abundance, must be elucidated. We used a combination of linkage mapping, genome-wide association studies (GWAS), and pathway-level analysis to identify nine loci that impact the natural variation of seed carotenoids in Arabidopsis (Arabidopsis thaliana). ZEAXANTHIN EPOXIDASE (ZEP) was the major contributor to carotenoid composition, with mutants lacking ZEP activity showing a remarkable 6-fold increase in total seed carotenoids relative to the wild type. Natural variation in ZEP gene expression during seed development was identified as the underlying mechanism for fine-tuning carotenoid composition, stability, and ultimately content in Arabidopsis seed. We previously showed that two CAROTENOID CLEAVAGE DIOXYGENASE enzymes, CCD1 and CCD4, are the primary mediators of seed carotenoid degradation, and here we demonstrate that ZEP acts as an upstream control point of carotenoid homeostasis, with ZEP-mediated epoxidation targeting carotenoids for degradation by CCD enzymes. Finally, four of the nine loci/enzymatic activities identified as underlying natural variation in Arabidopsis seed carotenoids also were identified in a recent GWAS of maize (Zea mays) kernel carotenoid variation. This first comparison of the natural variation in seed carotenoids in monocots and dicots suggests a surprising overlap in the genetic architecture of these traits between the two lineages and provides a list of likely candidates to target for selecting seed carotenoid variation in other species.

Network-guided GWAS improves identification of genes affecting free amino acids

A metabolic network-guided genome-wide association study of seed free amino acids facilitates the identification of a histidine-specific transporter in Arabidopsis. Amino acids are essential for proper growth and development in plants. Amino acids serve as building blocks for proteins but also are important for responses to stress and the biosynthesis of numerous essential compounds. In seed, the pool of free amino acids (FAAs) also contributes to alternative energy, desiccation, and seed vigor; thus, manipulating FAA levels can significantly impact a seed’s nutritional qualities. While genome-wide association studies (GWAS) on branched-chain amino acids have identified some regulatory genes controlling seed FAAs, the genetic regulation of FAA levels, composition, and homeostasis in seeds remains mostly unresolved. Hence, we performed GWAS on 18 FAAs from a 313-ecotype Arabidopsis (Arabidopsis thaliana) association panel. Specifically, GWAS was performed on 98 traits derived from known amino acid metabolic pathways (approach 1) and then on 92 traits generated from an unbiased correlation-based metabolic network analysis (approach 2), and the results were compared. The latter approach facilitated the discovery of additional novel metabolic interactions and single-nucleotide polymorphism-trait associations not identified by the former approach. The most prominent network-guided GWAS signal was for a histidine (His)-related trait in a region containing two genes: a cationic amino acid transporter (CAT4) and a polynucleotide phosphorylase resistant to inhibition with fosmidomycin. A reverse genetics approach confirmed CAT4 to be responsible for the natural variation of His-related traits across the association panel. Given that His is a semiessential amino acid and a potent metal chelator, CAT4 orthologs could be considered as candidate genes for seed quality biofortification in crop plants.

Editing of an alpha-kafirin gene family increases digestibility and protein quality in sorghum

Single-consensus guide RNA partially reduces kafirin levels in Sorghum bicolor grain, leading to an increased proportion of non-kafirins and improved digestibility and protein quality. Kafirins are the major storage proteins in sorghum (Sorghum bicolor) grains and form protein bodies with poor digestibility. Since kafirins are devoid of the essential amino acid lysine, they also impart poor protein quality to the kernel. The α-kafirins, which make up most of the total kafirins, are largely encoded by the k1C family of highly similar genes. We used a clustered regularly interspaced short palindromic repeats (CRISPR)/CRISPR-associated protein 9 (Cas9) gene editing approach to target the k1C genes to create variants with reduced kafirin levels and improved protein quality and digestibility. A single guide RNA was designed to introduce mutations in a conserved region encoding the endoplasmic reticulum signal peptide of α-kafirins. Sequencing of kafirin PCR products revealed extensive edits in 25 of 26 events in one or multiple k1C family members. T1 and T2 seeds showed reduced α-kafirin levels, and selected T2 events showed significantly increased grain protein digestibility and lysine content. Thus, a single consensus single guide RNA carrying target sequence mismatches is sufficient for extensive editing of all k1C genes. The resulting quality improvements can be deployed rapidly for breeding and the generation of transgene-free, improved cultivars of sorghum, a major crop worldwide.

Topological Data Analysis as a Morphometric Method: Using Persistent Homology to Demarcate a Leaf Morphospace

Current morphometric methods that comprehensively measure shape cannot compare the disparate leaf shapes found in seed plants and are sensitive to processing artifacts. We explore the use of persistent homology, a topological method applied as a filtration across simplicial complexes (or more simply, a method to measure topological features of spaces across different spatial resolutions), to overcome these limitations. The described method isolates subsets of shape features and measures the spatial relationship of neighboring pixel densities in a shape. We apply the method to the analysis of 182,707 leaves, both published and unpublished, representing 141 plant families collected from 75 sites throughout the world. By measuring leaves from throughout the seed plants using persistent homology, a defined morphospace comparing all leaves is demarcated. Clear differences in shape between major phylogenetic groups are detected and estimates of leaf shape diversity within plant families are made. The approach predicts plant family above chance. The application of a persistent homology method, using topological features, to measure leaf shape allows for a unified morphometric framework to measure plant form, including shapes, textures, patterns, and branching architectures.

Persistent homology demarcates a leaf morphospace

Current morphometric methods that comprehensively measure shape cannot compare the disparate leaf shapes found in seed plants and are sensitive to processing artifacts. We explore the use of persistent homology, a topological method applied across the scales of a function, to overcome these limitations. The described method isolates subsets of shape features and measures the spatial relationship of neighboring pixel densities in a shape. We apply the method to the analysis of 182,707 leaves, both published and unpublished, representing 141 plant families collected from 75 sites throughout the world. By measuring leaves from throughout the seed plants using persistent homology, a defined morphospace comparing all leaves is demarcated. Clear differences in shape between major phylogenetic groups are detected and estimates of leaf shape diversity within plant families are made. This approach does not only predict plant family, but also the collection site, confirming phylogenetically invariant morphological features that characterize leaves from specific locations. The application of a persistent homology method to measure leaf shape allows for a unified morphometric framework to measure plant form, including shape and branching architectures.

Subset-based genomic prediction provides insights into the genetic architecture of free amino acid levels in dry Arabidopsis thaliana seeds

Amino acids play a central role in plant growth, development, and human nutrition. A better understanding of the genetic architecture of amino acid traits will enable researchers to integrate this information for plant breeding and biological discovery. Despite a collection of successfully mapped genes, a fundamental understanding of the types of genes driving the genetic architecture of amino acid related traits in crop seeds and model systems such Arabidopsis has remained unresolved. To address this issue, we applied genomic prediction using distinct subsets of genes, including those belonging to the known amino acid biochemical pathways, to quantify their contributions to the genetic variation of free amino acid levels in dry seeds. First, we demonstrate that genomic prediction of free amino acid levels is moderately accurate in Arabidopsis seeds. Then, we explore whether specific subsets of SNPs corresponding to amino acid pathways exhibit enhanced predictability for amino acid traits. Surprisingly, for several of the traits we studied, SNPs within the amino acid pathways were no more predictive than randomly generated sets of control SNPs. This may imply a complex genetic architecture that includes other genes related to cellular processes or development. Conversely, a subset of amino acid traits did exhibit enhanced predictability based on pathway SNPs compared to control SNPs. We propose that this latter set of traits may correspond to a simpler genetic architecture. Ultimately, this study provides a potential strategy to assess the involvements of candidate genes in the genetic architecture of a traits using subset-based genomic prediction.Plant growth, development, and nutritional quality depends upon amino acid homeostasis, especially in seeds. However, our understanding of the underlying genetics influencing amino acid content and composition remains limited, with only a few candidate genes and quantitative trait loci identified to date. Improved knowledge of the genetics and biological processes that determine amino acid levels will enable researchers to use this information for plant breeding and biological discovery. Towards this goal, we used genomic prediction to identify biological processes that are associated with, and therefore potentially influence, free amino acid (FAA) composition in seeds of the model plant Arabidopsis thaliana. Markers were split into categories based on metabolic pathway annotations and fit using a genomic partitioning model to evaluate the influence of each pathway on heritability explained, model fit, and predictive ability. Selected pathways included processes known to influence FAA composition, albeit to an unknown degree, and spanned four categories: amino acid, core, specialized, and protein metabolism. Using this approach, we identified associations for pathways containing known variants for FAA traits, in addition to finding new trait-pathway associations. Markers related to amino acid metabolism, which are directly involved in the FAA regulation, improved predictive ability for branched chain amino acids and histidine. The use of genomic partitioning also revealed patterns across biochemical families, in which serine-derived FAAs were associated with protein related annotations and aromatic FAAs were associated with specialized metabolic pathways. Taken together, these findings provide evidence that genomic partitioning is a viable strategy to uncover the relative contributions of biological processes to FAA traits in seeds, offering a promising framework to guide hypothesis testing and narrow the search space for candidate genes.

Mobile Image Analysis for Microscopic Images of Seeds

Seeds sustain the beginning stage of a plant [1]. Seed kernel size and shape are crucial because they represent one of the major components of yield [2]. Therefore, it is important to develop an accurate, lowcost, and high-throughput morphometry method that allows for a detailed analysis of seed samples for the purpose of testing plant yield and vigor. Traditional methods of measuring individual seed morphometry or counting seeds in a cluster involve heavy manual work [3]. They have been proven to be labor-intensive, slow and inconsistent. Several tools that are based on image analysis have been developed to accelerate the measuring process, but it still requires a considerable length of time for sample preparation since the seeds need to be carefully placed either on a tray or a scanner in most cases. For accuracy, seeds should not be touching each other during the measurement. Ensuring so is particularly hard for small seeds such as Arabidopsis seeds.