Location: Plant, Soil and Nutrition Research2012 Annual Report
1a. Objectives (from AD-416):
Biological research benefits extraordinarily from the integration of many different types of data both within and between species. The first specific objective of the proposal builds on existing and emerging data sets, providing resources to characterize, track and ultimately identify sequence associated with agronomically important traits. The second objective addresses infrastructure to manage, visualize and distribute complex datasets. The research makes use of four methodologies, data integration, software development, genome annotation, and evolutionary analysis. Throughout the proposal each objective builds upon each other. Combined they hold greater potential for providing a knowledge base for improving agricultural varieties. 1. Enhance our knowledge of plant genome structure, organization and evolution through computational and experimental approaches. 2. Develop and implement standards for plant genome databases. This includes development of vocabulary, methods, database structures and visualization software to facilitate data integration and interoperability.
1b. Approach (from AD-416):
We propose to leverage computational and experimental approaches, building on existing and new developed resources to create standardized baseline comparative maps and genome annotations across plant genomes, with an emphasis on crop grasses and other agriculturally important species as well as model genomes. As part of this work we will leverage existing infrastructure and build upon these to deliver data management and visualization tools for sequence, maps, diversity, and phenotype data sets.
3. Progress Report:
Aim (1 & 3) We expanded in collaboration with the NSF Gramene project from 10 to 19 genomes, covering plant species ranging from algae to early land plants to flowering plants and serving both research model plants as well as agriculturally and energy important crops. We performed genome-wide comparative analysis including gene-tree building and whole-genome alignments against rice. In continued support of reference genome assemblies, an updated B73 maize assembly and annotations, AGPv3, was generated. Our previous work on maize diversity was published. The project contributed to draft assemblies and comparative analyses of the wheat D genome wild tomato genome. A computational pipeline for integrating expression and methylation meta-data analyses was developed and prototyped to analyze maize and sorghum. We generated the draft maize metabolic pathway resource, MaizeCyc. Aim 2: We expanded existing efforts to characterize gene networks, targeting networks in root and floral architecture and response to abiotic stress. The miRNA- arabidopsis gene root network efforts were extended through the addition of promoter screens and the incorporation of Y2H data. In maize ~15 high-confidence candidate genes were identified as determinacy factors of developmental networks controlling inflorescence architecture, through differential mutant profiles. We refined previous workflows for core promoter motifs and applied this to 8 eukaryote genomes and have begun work to characterize cis regulatory motifs (CRMs) combinations in co-expression networks. Using three maize inbred Zea mays lines that showed different tolerance to waterlogging, 5 miRNAs families were identified and are likely key regulators under short-term waterlogging and participate in signal transduction at the early stage of hypoxia conditions. Our previous work in sorghum reported in last year’s report was published this year. Aim 3: This aim actively contributes to the scientific leadership, development, and community outreach for three collaborative infrastructure projects: Gramene/Ensembl (NSF, EBI), iPlant (NSF), and Systems Biology Knowledge Base (DOE) and contributes US-EC taskforce on Plant Biotechnology. The iPlant collaborative provides an infrastructure for hosting data; access to high performance computes from the command line or a graphical user interface called the Discovery Environment (DE); virtual hosting (Atmosphere) similar to the Amazon cloud; and community standards. More recent work has focused on evaluating de novo assembly tools to support plant genome assembly, expression profiling and genotyping services, and the development of the Taxon Name Resolution Service (TRNS). The KBase project award was made in summer 2011. The project is developing infrastructure to support modeling development for plants, microbes and meta-communities with a focus on DOE objectives. This work was done in collaboration with USDA ARS scientists at several locations, as well as public- and private-sector scientists at Cold Spring Harbor Laboratory, Texas A&M, UC Davis, European Bioinformatics Institute and and Pioneer/Dupont.
1. Characterization of miRNAs in response to short-term waterlogging in three inbred lines of Zea mays. It is necessary to increase agriculture output in the next 20 years to address the food needs of humans and animals and the increasing demand for renewable fuel. This will need to be done in the context of climate change and increased reductions of inputs. Global climate change will result in changes in temperature and rainfall which will directly impact agricultural outputs. It is not only drought but examples of irregular rainfalls, early and late in the lifecycle of a plant that can substantially impact yield. In order to understand a plants ability to adapt to these rapid and intense rainfall shortly after planting, ARS researchers at Ithaca, New York are looking at the molecular responses of corn plants that are adaptive to waterlogging of which leads to low oxygen levels (hypoxia) in the roots. We would like to understand which genes and proteins support a metabolic switch from aerobic respiration to anaerobic fermentation. Our recent study of corn varieties with different levels of tolerance to waterlogging allowed us to identify a set of non-coding genes that may contribute to one of many adaptive strategies a corn plant can make use of to survive intense rainfall that results in flooding of fields early after planting. This work was done in collaboration with scientist at National Key Lab. of Crop Genetic Improvement Huazhong Agricultural University, Wuhan.
Ware, D., Thannhauser, T.W., White, R.A., Giovannoni, J.J. 2012. The tomato genome sequence provides insight into fleshy fruit evolution. Nature. 485:635-641. DOI: 10.1038/nature11119.