Mark Stitt

50 years of Arabidopsis research: highlights and future directions

922 I. 922 II. 922 III. 925 IV. 925 V. 926 VI. 927 VII. 928 VIII. 929 IX. 930 X. 931 XI. 932 XII. 933 XIII. Natural variation and genome-wide association studies 934 XIV. 934 XV. 935 XVI. 936 XVII. 937 937 References 937 SUMMARY: The year 2014 marked the 25(th) International Conference on Arabidopsis Research. In the 50 yr since the first International Conference on Arabidopsis Research, held in 1965 in Göttingen, Germany, > 54 000 papers that mention Arabidopsis thaliana in the title, abstract or keywords have been published. We present herein a citational network analysis of these papers, and touch on some of the important discoveries in plant biology that have been made in this powerful model system, and highlight how these discoveries have then had an impact in crop species. We also look to the future, highlighting some outstanding questions that can be readily addressed in Arabidopsis. Topics that are discussed include Arabidopsis reverse genetic resources, stock centers, databases and online tools, cell biology, development, hormones, plant immunity, signaling in response to abiotic stress, transporters, biosynthesis of cells walls and macromolecules such as starch and lipids, epigenetics and epigenomics, genome-wide association studies and natural variation, gene regulatory networks, modeling and systems biology, and synthetic biology.

Association mapping across numerous traits reveals patterns of functional variation in maize.

Phenotypic variation in natural populations results from a combination of genetic effects, environmental effects, and gene-by-environment interactions. Despite the vast amount of genomic data becoming available, many pressing questions remain about the nature of genetic mutations that underlie functional variation. We present the results of combining genome-wide association analysis of 41 different phenotypes in ∼5,000 inbred maize lines to analyze patterns of high-resolution genetic association among of 28.9 million single-nucleotide polymorphisms (SNPs) and ∼800,000 copy-number variants (CNVs). We show that genic and intergenic regions have opposite patterns of enrichment, minor allele frequencies, and effect sizes, implying tradeoffs among the probability that a given polymorphism will have an effect, the detectable size of that effect, and its frequency in the population. We also find that genes tagged by GWAS are enriched for regulatory functions and are ∼50% more likely to have a paralog than expected by chance, indicating that gene regulation and gene duplication are strong drivers of phenotypic variation. These results will likely apply to many other organisms, especially ones with large and complex genomes like maize.

Multiscale digital Arabidopsis predicts individual organ and whole-organism growth

Significance Plants respond to environmental change by triggering biochemical and developmental networks across multiple scales. Multiscale models that link genetic input to the whole-plant scale and beyond might therefore improve biological understanding and yield prediction. We report a modular approach to build such models, validated by a framework model of Arabidopsis thaliana comprising four existing mathematical models. Our model brings together gene dynamics, carbon partitioning, organ growth, shoot architecture, and development in response to environmental signals. It predicted the biomass of each leaf in independent data, demonstrated flexible control of photosynthesis across photoperiods, and predicted the pleiotropic phenotype of a developmentally misregulated transgenic line. Systems biology, crop science, and ecology might thus be linked productively in a community-based approach to modeling. Understanding how dynamic molecular networks affect whole-organism physiology, analogous to mapping genotype to phenotype, remains a key challenge in biology. Quantitative models that represent processes at multiple scales and link understanding from several research domains can help to tackle this problem. Such integrated models are more common in crop science and ecophysiology than in the research communities that elucidate molecular networks. Several laboratories have modeled particular aspects of growth in Arabidopsis thaliana, but it was unclear whether these existing models could productively be combined. We test this approach by constructing a multiscale model of Arabidopsis rosette growth. Four existing models were integrated with minimal parameter modification (leaf water content and one flowering parameter used measured data). The resulting framework model links genetic regulation and biochemical dynamics to events at the organ and whole-plant levels, helping to understand the combined effects of endogenous and environmental regulators on Arabidopsis growth. The framework model was validated and tested with metabolic, physiological, and biomass data from two laboratories, for five photoperiods, three accessions, and a transgenic line, highlighting the plasticity of plant growth strategies. The model was extended to include stochastic development. Model simulations gave insight into the developmental control of leaf production and provided a quantitative explanation for the pleiotropic developmental phenotype caused by overexpression of miR156, which was an open question. Modular, multiscale models, assembling knowledge from systems biology to ecophysiology, will help to understand and to engineer plant behavior from the genome to the field.

Quantifying protein synthesis and degradation in Arabidopsis by dynamic 13CO2 labeling and analysis of enrichment in individual amino acids in their free pools and in protein

A novel 13CO2 labeling technique measures synthesis and degradation rates for total and specific proteins and could be extended to cell wall synthesis and other determinants of growth. Protein synthesis and degradation represent substantial costs during plant growth. To obtain a quantitative measure of the rate of protein synthesis and degradation, we supplied 13CO2 to intact Arabidopsis (Arabidopsis thaliana) Columbia-0 plants and analyzed enrichment in free amino acids and in amino acid residues in protein during a 24-h pulse and 4-d chase. While many free amino acids labeled slowly and incompletely, alanine showed a rapid rise in enrichment in the pulse and a decrease in the chase. Enrichment in free alanine was used to correct enrichment in alanine residues in protein and calculate the rate of protein synthesis. The latter was compared with the relative growth rate to estimate the rate of protein degradation. The relative growth rate was estimated from sequential determination of fresh weight, sequential images of rosette area, and labeling of glucose in the cell wall. In an 8-h photoperiod, protein synthesis and cell wall synthesis were 3-fold faster in the day than at night, protein degradation was slow (3%–4% d−1), and flux to growth and degradation resulted in a protein half-life of 3.5 d. In the starchless phosphoglucomutase mutant at night, protein synthesis was further decreased and protein degradation increased, while cell wall synthesis was totally inhibited, quantitatively accounting for the inhibition of growth in this mutant. We also investigated the rates of protein synthesis and degradation during leaf development, during growth at high temperature, and compared synthesis rates of Rubisco large and small subunits of in the light and dark.

Trehalose 6-phosphate coordinates organic and amino acid metabolism with carbon availability

Trehalose 6-phosphate (Tre6P) is an essential signal metabolite in plants, linking growth and development to carbon metabolism. The sucrose-Tre6P nexus model postulates that Tre6P acts as both a signal and negative feedback regulator of sucrose levels. To test this model, short-term metabolic responses to induced increases in Tre6P levels were investigated in Arabidopsis thaliana plants expressing the Escherichia coli Tre6P synthase gene (otsA) under the control of an ethanol-inducible promoter. Increased Tre6P levels led to a transient decrease in sucrose content, post-translational activation of nitrate reductase and phosphoenolpyruvate carboxylase, and increased levels of organic and amino acids. Radio-isotope ((14)CO2) and stable isotope ((13)CO2) labelling experiments showed no change in the rates of photoassimilate export in plants with elevated Tre6P, but increased labelling of organic acids. We conclude that high Tre6P levels decrease sucrose levels by stimulating nitrate assimilation and anaplerotic synthesis of organic acids, thereby diverting photoassimilates away from sucrose to generate carbon skeletons and fixed nitrogen for amino acid synthesis. These results are consistent with the sucrose-Tre6P nexus model, and implicate Tre6P in coordinating carbon and nitrogen metabolism in plants.

Why measure enzyme activities in the era of systems biology

Information about the abundance and biological activities of proteins is essential to reveal how genes affect phenotypes. Over the past decade, mass spectrometry (MS)-based proteomics has revolutionized the identification and quantification of proteins, and the detection of post-translational modifications. Interpretation of proteomics data depends on information about the biological activities of proteins, which has created a bottleneck in research. This review focuses on enzymes in central metabolism. We examine the methods used for measuring enzyme activities, and discuss how these methods provide information about the kinetic and regulatory properties of enzymes, their turnover, and how this information can be integrated into metabolic models. We also discuss how robotized assays could enable the genetic networks that control enzyme abundance to be analyzed.

Phytotyping 4D : a light-field imaging system for non-invasive and accurate monitoring of spatio-temporal plant growth

Integrative studies of plant growth require spatially and temporally resolved information from high-throughput imaging systems. However, analysis and interpretation of conventional two-dimensional images is complicated by the three-dimensional nature of shoot architecture and by changes in leaf position over time, termed hyponasty. To solve this problem, Phytotyping(4D) uses a light-field camera that simultaneously provides a focus image and a depth image, which contains distance information about the object surface. Our automated pipeline segments the focus images, integrates depth information to reconstruct the three-dimensional architecture, and analyses time series to provide information about the relative expansion rate, the timing of leaf appearance, hyponastic movement, and shape for individual leaves and the whole rosette. Phytotyping(4D) was calibrated and validated using discs of known sizes, and plants tilted at various orientations. Information from this analysis was integrated into the pipeline to allow error assessment during routine operation. To illustrate the utility of Phytotyping(4D) , we compare diurnal changes in Arabidopsis thaliana wild-type Col-0 and the starchless pgm mutant. Compared to Col-0, pgm showed very low relative expansion rate in the second half of the night, a transiently increased relative expansion rate at the onset of light period, and smaller hyponastic movement including delayed movement after dusk, both at the level of the rosette and individual leaves. Our study introduces light-field camera systems as a tool to accurately measure morphological and growth-related features in plants.

Relationship between starch degradation and carbon demand for maintenance and growth in Arabidopsis thaliana in different irradiance and temperature regimes

Experiments were designed to compare the relationship between starch degradation and the use of carbon for maintenance and growth in Arabidopsis in source-limited and sink-limited conditions. It is known that starch degradation is regulated by the clock in source-limited plants, which degrade their starch in a linear manner such that it is almost but not completely exhausted at dawn. We asked whether this response is maintained under an extreme carbon deficit. Arabidopsis was subjected to a sudden combination of a day of low irradiance, to decrease starch at dusk, and a warm night. Starch was degraded in a linear manner through the night, even though the plants became acutely carbon starved. We conclude that starch degradation is not increased to meet demand in carbon-limited plants. This network property will allow stringent control of starch turnover in a fluctuating environment. In contrast, in sink-limited plants, which do not completely mobilize their starch during the night, starch degradation was accelerated in warm nights to meet the increased demand for maintenance and growth. Across all conditions, the rate of growth at night depends on the rate of starch degradation, whereas the rate of maintenance respiration decreases only when starch degradation is very slow.

A repeat protein links Rubisco to form the eukaryotic carbon-concentrating organelle

Significance Eukaryotic algae, which play a fundamental role in global CO2 fixation, enhance the performance of the carbon-fixing enzyme Rubisco by placing it into an organelle called the pyrenoid. Despite the ubiquitous presence and biogeochemical importance of this organelle, how Rubisco assembles to form the pyrenoid remains a long-standing mystery. Our discovery of an abundant repeat protein that binds Rubisco in the pyrenoid represents a critical advance in our understanding of pyrenoid biogenesis. The repeat sequence of this protein suggests elegant models to explain the structural arrangement of Rubisco enzymes in the pyrenoid. Beyond advances in basic understanding, our findings open doors to the engineering of algal pyrenoids into crops to enhance yields. Biological carbon fixation is a key step in the global carbon cycle that regulates the atmospheres composition while producing the food we eat and the fuels we burn. Approximately one-third of global carbon fixation occurs in an overlooked algal organelle called the pyrenoid. The pyrenoid contains the CO2-fixing enzyme Rubisco and enhances carbon fixation by supplying Rubisco with a high concentration of CO2. Since the discovery of the pyrenoid more that 130 y ago, the molecular structure and biogenesis of this ecologically fundamental organelle have remained enigmatic. Here we use the model green alga Chlamydomonas reinhardtii to discover that a low-complexity repeat protein, Essential Pyrenoid Component 1 (EPYC1), links Rubisco to form the pyrenoid. We find that EPYC1 is of comparable abundance to Rubisco and colocalizes with Rubisco throughout the pyrenoid. We show that EPYC1 is essential for normal pyrenoid size, number, morphology, Rubisco content, and efficient carbon fixation at low CO2. We explain the central role of EPYC1 in pyrenoid biogenesis by the finding that EPYC1 binds Rubisco to form the pyrenoid matrix. We propose two models in which EPYC1’s four repeats could produce the observed lattice arrangement of Rubisco in the Chlamydomonas pyrenoid. Our results suggest a surprisingly simple molecular mechanism for how Rubisco can be packaged to form the pyrenoid matrix, potentially explaining how Rubisco packaging into a pyrenoid could have evolved across a broad range of photosynthetic eukaryotes through convergent evolution. In addition, our findings represent a key step toward engineering a pyrenoid into crops to enhance their carbon fixation efficiency.

Genome-Wide Association of Carbon and Nitrogen Metabolism in the Maize Nested Association Mapping Population

Genetic variants of maize identify genes and regions that control core carbon and nitrogen metabolism. Carbon (C) and nitrogen (N) metabolism are critical to plant growth and development and are at the basis of crop yield and adaptation. We performed high-throughput metabolite analyses on over 12,000 samples from the nested association mapping population to identify genetic variation in C and N metabolism in maize (Zea mays ssp. mays). All samples were grown in the same field and used to identify natural variation controlling the levels of 12 key C and N metabolites, namely chlorophyll a, chlorophyll b, fructose, fumarate, glucose, glutamate, malate, nitrate, starch, sucrose, total amino acids, and total protein, along with the first two principal components derived from them. Our genome-wide association results frequently identified hits with single-gene resolution. In addition to expected genes such as invertases, natural variation was identified in key C4 metabolism genes, including carbonic anhydrases and a malate transporter. Unlike several prior maize studies, extensive pleiotropy was found for C and N metabolites. This integration of field-derived metabolite data with powerful mapping and genomics resources allows for the dissection of key metabolic pathways, providing avenues for future genetic improvement.