Provitamin A biofortification of cassava enhances shelf life but reduces dry matter content of storage roots due to altered carbon partitioning into starch

Storage roots of cassava (Manihot esculenta Crantz), a major subsistence crop of sub-Saharan Africa, are calorie rich but deficient in essential micronutrients, including provitamin A β-carotene. In this study, β-carotene concentrations in cassava storage roots were enhanced by coexpression of transgenes for deoxy-d-xylulose-5-phosphate synthase (DXS) and bacterial phytoene synthase (crtB), mediated by the patatin-type 1 promoter. Storage roots harvested from field-grown plants accumulated carotenoids to ≤50 lg/g DW, 15- to 20-fold increases relative to roots from nontransgenic plants. Approximately 85%–90% of these carotenoids accumulated as all-trans-β-carotene, the most nutritionally efficacious carotenoid. β-Carotene-accumulating storage roots displayed delayed onset of postharvest physiological deterioration, a major constraint limiting utilization of cassava products. Large metabolite changes were detected in β-carotene-enhanced storage roots. Most significantly, an inverse correlation was observed between β-carotene and dry matter content, with reductions of 50%–60% of dry matter content in the highest carotenoid-accumulating storage roots of different cultivars. Further analysis confirmed a concomitant reduction in starch content and increased levels of total fatty acids, triacylglycerols, soluble sugars and abscisic acid. Potato engineered to co-express DXS and crtB displayed a similar correlation between β-carotene accumulation, reduced dry matter and starch content and elevated oil and soluble sugars in tubers. Transcriptome analyses revealed a reduced expression of genes involved in starch biosynthesis including ADP-glucose pyrophosphorylase genes in transgenic, carotene-accumulating cassava roots relative to nontransgenic roots. These findings highlight unintended metabolic consequences of provitamin A biofortification of starch-rich organs and point to strategies for redirecting metabolic flux to restore starch production

Cross-species complementation reveals conserved functions for EARLY FLOWERING 3 between monocots and dicots

Plant responses to the environment are shaped by external stimuli and internal signaling pathways. In both the model plant Arabidopsis thaliana (Arabidopsis) and crop species, circadian clock factors are critical for growth, flowering, and circadian rhythms. Outside of Arabidopsis, however, little is known about the molecular function of clock gene products. Therefore, we sought to compare the function of Brachypodium distachyon (Brachypodium) and Setaria viridis (Setaria) orthologs of EARLY FLOWERING 3, a key clock gene in Arabidopsis. To identify both cycling genes and putative ELF3 functional orthologs in Setaria, a circadian RNA-seq dataset and online query tool (Diel Explorer) were generated to explore expression profiles of Setaria genes under circadian conditions. The function of ELF3 orthologs from Arabidopsis, Brachypodium, and Setaria was tested for complementation of an elf3 mutation in Arabidopsis. We find that both monocot orthologs were capable of rescuing hypocotyl elongation, flowering time, and arrhythmic clock phenotypes. Using affinity purification and mass spectrometry, our data indicate that BdELF3 and SvELF3 could be integrated into similar complexes in vivo as AtELF3. Thus, we find that, despite 180 million years of separation, BdELF3 and SvELF3 can functionally complement loss of ELF3 at the molecular and physiological level

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

The next generation of training for arabidopsis researchers: Bioinformatics and Quantitative Biology

It has been more than 50 years since Arabidopsis (Arabidopsis thaliana) was first introduced as a model organism to understand basic processes in plant biology. A well-organized scientific community has used this small reference plant species to make numerous fundamental plant biology discoveries (Provart et al., 2016). Due to an extremely well-annotated genome and advances in high-throughput sequencing, our understanding of this organism and other plant species has become even more intricate and complex. Computational resources, including CyVerse,3 Araport,4 The Arabidopsis Information Resource (TAIR),5 and BAR,6 have further facilitated novel findings with just the click of a mouse. As we move toward understanding biological systems, Arabidopsis researchers will need to use more quantitative and computational approaches to extract novel biological findings from these data. Here, we discuss guidelines, skill sets, and core competencies that should be considered when developing curricula or training undergraduate or graduate students, postdoctoral researchers, and faculty. A selected case study provides more specificity as to the concrete issues plant biologists face and how best to address such challenges

Arabidopsis bioinformatics resources: the current state, challenges, and priorities for the future

Effective research, education, and outreach efforts by the Arabidopsis thaliana community, as well as other scientific communities that depend on Arabidopsis resources, depend vitally on easily available and publicly-shared resources. These resources include reference genome sequence data and an ever-increasing number of diverse data sets and data types. TAIR (The Arabidopsis Information Resource) and Araport (originally named the Arabidopsis Information Portal) are community informatics resources that provide tools, data, and applications to the more than 30,000 researchers worldwide that use in their work either Arabidopsis as a primary system of study or data derived from Arabidopsis. Four years after Araport’s establishment, the IAIC held another workshop to evaluate the current status of Arabidopsis Informatics and chart a course for future research and development. The workshop focused on several challenges, including the need for reliable and current annotation, community-defined common standards for data and metadata, and accessible and user-friendly repositories / tools / methods for data integration and visualization. Solutions envisioned included (1) a centralized annotation authority to coalesce annotation from new groups, establish a consistent naming scheme, distribute this format regularly and frequently, and encourage and enforce its adoption. (2) Standards for data and metadata formats, which are essential, but challenging when comparing across diverse genotypes and in areas with less-established standards (e.g. phenomics, metabolomics). Community-established guidelines need to be developed. (3) A searchable, central repository for analysis and visualization tools. Improved versioning and user access would make tools more accessible. Workshop participants proposed a “one-stop shop” website, an Arabidopsis “Super-Portal” to link tools, data resources, programmatic standards, and best practice descriptions for each data type. This must have community buy-in and participation in its establishment and development to encourage adoption

Morphological Plant Modeling: Unleashing Geometric and Topological Potential within the Plant Sciences

The geometries and topologies of leaves, flowers, roots, shoots, and their arrangements have fascinated plant biologists and mathematicians alike. As such, plant morphology is inherently mathematical in that it describes plant form and architecture with geometrical and topological techniques. Gaining an understanding of how to modify plant morphology, through molecular biology and breeding, aided by a mathematical perspective, is critical to improving agriculture, and the monitoring of ecosystems is vital to modeling a future with fewer natural resources. In this white paper, we begin with an overview in quantifying the form of plants and mathematical models of patterning in plants. We then explore the fundamental challenges that remain unanswered concerning plant morphology, from the barriers preventing the prediction of phenotype from genotype to modeling the movement of leaves in air streams. We end with a discussion concerning the education of plant morphology synthesizing biological and mathematical approaches and ways to facilitate research advances through outreach, cross-disciplinary training, and open science. Unleashing the potential of geometric and topological approaches in the plant sciences promises to transform our understanding of both plants and mathematics

tiller-test_set2-A versatile phenotyping system and analytics platform reveals diverse temporal responses to water limitation in Setaria

<p>Set of images with manual tiller measurementments</p

biomass_set1_A versatile phenotyping system and analytics platform reveals diverse temporal responses to water limitation in Setaria

<p>This is the set of images that we have manual fresh weight biomass measurements for.</p

PlantCV release v1.0: Plant image analysis using Open Computer Vision (OpenCV)

<p>PlantCV is an imaging processing package specific for plants that is built upon open-source software platforms OpenCV, NumPy, and MatPlotLib. PlantCV was created at the Donald Danforth Plant Science Center in 2014 to analyze data from high-throughput plant phenotyping systems. PlantCV release v1.0 marks the final commit used in our manuscript [1]. If you want to repeat an analysis from the paper, checkout tag v1.0 after cloning the repository. This Figshare repository is an archival record of release v1.0.</p> <p>1. Fahlgren N, Feldman M, Gehan MA, Wilson MS, Shyu C, Bryant DW, Hill ST, McEntee CJ, Warnasooriya SN, Kumar I, Ficor T, Turnipseed S, Gilbert KB, Brutnell TP, Carrington JC, Mockler TC, Baxter I (2015) A versatile phenotyping system and analytics platform reveals diverse temporal responses to water availability in Setaria. Molecular Plant, in press.</p

Plant Biology 2015 Poster: Open-source tools for high-throughput plant phenotyping

<p>Demand for food, fuel, and other plant products is expected to increase dramatically over the next century. At the same time, environmental considerations require that increases in agricultural output must occur using less water, land, fertilizer, and other inputs per unit of yield. One strategy to sustainably increase productivity is to develop new crops and cultivars that use resources more efficiently. While decreasing DNA sequencing costs has enabled rapid genetic screening of crop germplasm, only recently has the development of robotic imaging platforms and low-cost sensors led to major improvements in phenotyping throughput. Here we present PlantCV, an open-source framework for analyzing high-throughput plant phenotyping data. We demonstrate the utility of PlantCV and high-throughput phenotyping by analyzing the phenotypic diversity of a population of <em>Camelina sativa</em> natural accessions using the Bellwether Phenotyping Platform at the Donald Danforth Plant Science Center. <em>C. sativa</em> is an oilseed crop from the family <em>Brassicaceae</em> that is an emerging source of oil for fuel and is also being developed as a production platform for high-value compounds. Analysis of images taken daily for five weeks was used to measure natural diversity in above ground biomass, growth rates, days to flowering, and other traits. Additional analysis of seed phenotypes including yield per plant, seed size, and oil content was used to identify <em>C. sativa</em> accessions that could enhance breeding efforts. Although PlantCV was developed for the LemnaTec-based Bellwether Phenotyping Platform, we demonstrate that PlantCV can also be applied to low-cost phenotyping solutions and encourage community input in future development.</p