Regulation of MOR by Different Abiotic Stresses in Arabidopsis thaliana

The climate is changing and as a consequence the environment is becoming hotter and drier. How different plants will react to these changes is unknown. Identification of genes involved in stress tolerance can help predict plant-environment interactions and lead to stress tolerant plants. The MOR gene (Modulator Of Root ROS, ROS = Reactive Oxygen Species) in the model plant Arabidopsis thaliana encodes a transcription factor that may regulate stress responses, as mor mutants are drought tolerant. We hypothesized that MOR expression changes in response to different abiotic stress stimuli. We tested MOR expression in response to salt (NaCl), abscisic acid (ABA), osmotic stress (Mannitol), drought, and heat. For the first three stresses wild type seedlings were grown under our standard conditions and then transferred to a treatment for 24 hours. For drought stress plants were grown in soil and watering was withheld from mature plants for 10 days. With all samples a quantitative reverse transcriptase Polymerase Chain Reaction (qRT-PCR) was run to find changes in gene expression. For heat stress, mutants and wild types plants were transferred in a 30°C environment and root growth was monitored daily. MOR gene expression did not change in the presence of salt or ABA but was slightly repressed by osmotic stress. However, drought stress strongly induced MOR. mor mutants showed less sensitivity to heat stress. Our data show that MOR is involved in drought and heat pathways, suggesting this gene is specific to certain types of stresses

Detecting Genomic Regions Responsible for Resistance in Arabidopsis

Ralstonia solanacearum is a soil-borne plant root colonizing pathogen and the casual agent of bacterial wilt (BW) disease. BW leads to severe yield loss in a wide variety of agricultural commodity crops, such as tomato, banana, and pepper. In this study, we look at the plant-pathogen interaction between Ralstonia solanacearum and various ecotypes of Arabidopsis thaliana with the goal of finding resistant ecotypes. To identify resistant ecotypes, seeds are first sterilized and left to soak in the dark. Then the seeds are plated on agar media, transferred to a growth chamber, and allowed to grow for 5 days. On day 5, plates are removed from the growth chamber, and plants are transferred to new plates that have been inoculated with or without R. solanacearum, and the root tips are marked. After 5 days, the inoculated plates are scanned and root length after inoculation measured. The vegetative growth is also removed and placed in 95% ethanol for chlorophyll analyses. Based on root growth and chlorophyll analyses, 5 out of 16 ecotypes showed increased resistance to R. solanacearum in comparison to the susceptible control line, Col-0. Identifying resistant ecotypes will allow genetic analysis of resistance to Ralstonia solanacearum, and will enhance our ability to search for homologs of Arabidopsis resistance genes in important agricultural commodity crops

Integrated functional networks of process, tissue, and developmental stage specific interactions in Arabidopsis thaliana

<p>Abstract</p> <p>Background</p> <p>Recent years have seen an explosion in plant genomics, as the difficulties inherent in sequencing and functionally analyzing these biologically and economically significant organisms have been overcome. <it>Arabidopsis thaliana</it>, a versatile model organism, represents an opportunity to evaluate the predictive power of biological network inference for plant functional genomics.</p> <p>Results</p> <p>Here, we provide a compendium of functional relationship networks for <it>Arabidopsis thaliana </it>leveraging data integration based on over 60 microarray, physical and genetic interaction, and literature curation datasets. These include tissue, biological process, and development stage specific networks, each predicting relationships specific to an individual biological context. These biological networks enable the rapid investigation of uncharacterized genes in specific tissues and developmental stages of interest and summarize a very large collection of <it>A. thaliana </it>data for biological examination. We found validation in the literature for many of our predicted networks, including those involved in disease resistance, root hair patterning, and auxin homeostasis.</p> <p>Conclusions</p> <p>These context-specific networks demonstrate that highly specific biological hypotheses can be generated for a diversity of individual processes, developmental stages, and plant tissues in <it>A. thaliana</it>. All predicted functional networks are available online at <url>http://function.princeton.edu/arathGraphle</url>.</p

GiA Roots: Software for the high throughput analysis of plant root system architecture

Background: Characterizing root system architecture (RSA) is essential to understanding the development and function of vascular plants. Identifying RSA-associated genes also represents an underexplored opportunity for crop improvement. Software tools are needed to accelerate the pace at which quantitative traits of RSA are estimated from images of root networks.Results: We have developed GiA Roots (General Image Analysis of Roots), a semi-automated software tool designed specifically for the high-throughput analysis of root system images. GiA Roots includes user-assisted algorithms to distinguish root from background and a fully automated pipeline that extracts dozens of root system phenotypes. Quantitative information on each phenotype, along with intermediate steps for full reproducibility, is returned to the end-user for downstream analysis. GiA Roots has a GUI front end and a command-line interface for interweaving the software into large-scale workflows. GiA Roots can also be extended to estimate novel phenotypes specified by the end-user.Conclusions: We demonstrate the use of GiA Roots on a set of 2393 images of rice roots representing 12 genotypes from the species Oryza sativa. We validate trait measurements against prior analyses of this image set that demonstrated that RSA traits are likely heritable and associated with genotypic differences. Moreover, we demonstrate that GiA Roots is extensible and an end-user can add functionality so that GiA Roots can estimate novel RSA traits. In summary, we show that the software can function as an efficient tool as part of a workflow to move from large numbers of root images to downstream analysis

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

Image-based plant wilting estimation

Abstract Background Environmental stress due to climate or pathogens is a major threat to modern agriculture. Plant genetic resistance to these stresses is one way to develop more resilient crops, but accurately quantifying plant phenotypic responses can be challenging. Here we develop and test a set of metrics to quantify plant wilting, which can occur in response to abiotic stress such as heat or drought, or in response to biotic stress caused by pathogenic microbes. These metrics can be useful in genomic studies to identify genes and genomic regions underlying plant resistance to a given stress. Results We use two datasets: one of tomatoes inoculated with Ralstonia solanacearum, a soilborne pathogen that causes bacterial wilt disease, and another of soybeans exposed to water stress. For both tomato and soybean, the metrics predict the visual wilting score provided by human experts. Specific to the tomato dataset, we demonstrate that our metrics can capture the genetic difference of bacterium wilt resistance among resistant and susceptible tomato genotypes. In soybean, we show that our metrics can capture the effect of water stress. Conclusion Our proposed RGB image-based wilting metrics can be useful for identifying plant wilting caused by diverse stresses in different plant species