Ramin Yadegari, Ph.D. - US grants

Wheat is the single most important source of plant protein in the human diet and together with other cereal grains (rice, corn, sorghum, barley, millet, and oats) accounts for most of the food directly consumed by humans.

The challenge for the post-sequencing era is to identify the biological functions of sequenced genes. Reverse genetics - the discovery of gene function by searching for lesions in specific genes - plays an essential role in that process. The large, complex genomes of important crop species like wheat necessitate the development of a large number of mutants, and genetic screening methods developed to date do not support the high throughput necessary for making this search efficient. We intend to optimize and demonstrate a model screening system applicable to any large genome species.

Research resources
We will produce a collection of lines carrying small deletions in the genome in the cultivated diploid wheat, Triticum monococcum. Screening of this mutant collection for lesions in genes of interest can be accomplished efficiently through a pooling scheme combined with a method termed DEALING (DEtecting Adduct Lesions IN Genomes). We will demonstrate the utility of this approach by identifying mutations that affect reproductive development in wheat. We will also develop a database, WIRE (the Wheat Information Resource), to disseminate the information generated by this and future DEALING experiments.

The proposed research will determine functions for genes expressed in the Arabidopsis female gametophyte (FG, embryo sac) with the long-term goal of identifying the gene regulatory networks controlling FG cell specification and differentiation. The FG is an integral part of the plant life cycle and plays critical roles in essentially every step of the angiosperm reproductive process including pollen tube guidance, fertilization, activation of seed development, and maternal control of seed development. Using mRNA-based assays, the proposed research will identify a battery of genes including members of transcription factor gene families expressed in each of the FG cells of Arabidopsis. FG cell-specific patterns of gene expression will be further characterized using promoter:reporter lines. To determine whether any of these genes have functions in FG cell specification or differentiation, corresponding T-DNA mutant lines will be analyzed for defects in FG development and function. To establish the positions of FG transcription-factor genes relative to one another in the gene regulatory circuitry of the FG, altered patterns of promoter:reporter expression will be assayed in the presence of various FG mutations. Finally, to elucidate the gene-regulatory networks operating in these cells, a combination of biochemical, genetic, and molecular biology approaches will be used to establish direct connections between selected transcription-factor genes expressed in specific cells of the FG and their targets. Data from these experiments including the identity of confirmed genes expressed in the FG and all corresponding patterns of expression will be shared with the community at fgproject.biosci.arizona.edu or www.femalegametophyte.org . Research activities will be coordinated through interaction with research groups in the US and abroad with interests in FG development and other aspects of the project. The significance of the proposed work in relation to the overall 2010 Project objectives in determining gene functions is twofold. First, the proposed studies will identify gene expression patterns and any corresponding functions required for plant reproduction. Second, identification of gene-regulatory networks of the FG will contribute to the goal of understanding how gene circuitries control plant processes. Ultimately, results from these studies will be critical for designing strategies to modify reproductive processes in plants for improved seed yield and quality.

The broader educational impacts of this project will enhance the infrastructure of research and training at the Universities of Utah and Arizona through the continued instruction of undergraduate and graduate students, and training of postdoctoral researchers. An important goal of the project is to increase the representation of underrepresented minorities in science and to communicate science through outreach to K-12 teachers and to nonscientists. Graduate and undergraduate researchers will be recruited from the local community colleges and/or underrepresented student populations at the Universities of Utah and Arizona.

Project Websites: http://fgproject.biosci.arizona.edu/ or http//www.femalegametophyte.org

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PI: Ramin Yadegari (University of Arizona)

CoPIs: Amy Clore (New College of Florida), Joanne Dannehoffer (Central Michigan University), Gary Drews (University of Utah) and Brian Larkins (University of Nebraska)


The goal of this research project is to understand the gene networks that control endosperm development and function in maize. The endosperm of the seed is biologically and economically important. More than 50% of the calories in the human diet originate from cereal endosperm, which also serves as a raw material for manufacturing numerous industrial products. Early endosperm development in maize, the period between 0 and 8 days after pollination, encompasses the time when the endosperm grows from a single cell into a multicellular structure composed of different cell types. Although the molecular mechanisms that control this period of development in maize have not been elucidated, they are likely to affect many important processes, including embryo nutrition, starch and storage protein accumulation during grain filling, and seed maturation. In this project a network of maize transcription factors expressed during early endosperm development will be identified, and their temporal and spatial expression patterns will be determined. The function of a subset of these genes will be characterized in maize and Arabidopsis plants. In addition, the cytological patterns of early endosperm development will be described in a diverse set of maize inbred lines. These inbreds are currently being genetically and phenotypically characterized by a large number of laboratories, and these data will make it possible to evaluate the importance of the identified genes to a variety of grain quality traits.

The broader educational impacts of this project will enhance the infrastructure of research and training at all the participating institutions through the continued instruction of undergraduate and graduate students and training of postdoctoral researchers. A significant component of the proposed research will involve faculty members at institutions with predominately (Central Michigan University CMU) or exclusively (New College of Florida NCF) undergraduate populations. Student researchers from CMU and NCF will have an opportunity to spend a summer each year at the University of Arizona and the University of Utah for further training and education. Undergraduate researchers will be recruited from the classroom, local community colleges, and underrepresented student populations at all institutions. In addition, an important goal of the project is to increase the representation of underrepresented minorities in science and to communicate science through outreach to K-12 teachers and to nonscientists. The scientific and educational outcomes of this project will be disseminated through the project website http://cals.arizona.edu/earlyendosperm and Gramene (http://www.gramene.org/).

In biology, network techniques have been applied to interpret the interactions between genes, including the physical interactions of proteins and regulatory relationships between transcription factors and targets. Although numerous methods have been developed to infer a network from expression data, several computational challenges remain unsolved, such as, how to derive non-linear relationships between transcription factors and targets, how to properly decompose a network into individual sub-network modules, how to predict biologically significant genes via network-scale comparisons, how to integrate and use the heterogeneous forms of biological interaction data to facilitate network analysis, and how to seamlessly visualize a large-scale network for interactive data mining. To solve these problems, the primary goal of this project is to develop a software package - the Gini Network Analysis Toolkit (GNAT) that utilizes the Gini-based methodologies: a family of mathematical solutions that have been widely used in economics, physics, informatic networks, and social networks in analyzing non-normally distributed data. The core functional modules and algorithms in the GNAT include the use of supervised machine learning methods to infer transcriptional networks, the Gini correlation coefficient to derive non-linear regulatory relationships, the Gini regression analysis to decompose a time-series network, the Gini index to measure and compare the distributions of the network properties of modules and genes under different biological conditions, and eventually the discovery of biologically important genes with system perturbation and decision tree analysis. The PI will also develop a network explorer, BioNetscape, to efficiently organize and visualize the tremendous amount of network data generated from the GNAT using the k-core decomposition algorithm, Ajax technology and GPU (graphical processing unit) computing techniques. The GNAT will be implemented in R and organized as a streamlined workflow to compensate the shortcomings of the traditional gene-scale transcriptome analysis methods.

The GNAT software will greatly facilitate the ongoing network development projects in plant research. The GNAT will be made available to be integrated into the iPlant Discovery Environment, The Arabidopsis Information Resource (TAIR), Plant Expression Database (PLEXdb) and other consortium databases to enhance the function of network analysis and gene discovery in plants. The GNAT will also be integrated into the Galaxy and GenePattern platforms to provide a user-friendly graphical interface. The source-code and R packages will be released into the public domain for broader use in plant, animal and microbial biology. To integrate research into education, the PI?s laboratory will develop a web-based Virtual Next Generation Sequencing Workshop for training biologists who are not specialists in bioinformatics to analyze genomic, epigenomic, transcriptomic and small RNA data. The workshop courseware is composed of teaching materials prepared in the PI's class, self-practice datasets and a virtual UNIX web-console for training biologists to analyze different types of next generation sequencing data with minimal requirements for programming skills. This project explicitly addresses cross-disciplinary research training at multiple levels that will encourage the participation of underrepresented groups in computer sciences and mathematics at the University of Arizona, who will work to answering biological questions. The students from the ASEMS (Arizona Science, Engineering, and Math Scholars) and IGERT programs at the University of Arizona will participate in the PI's team to develop the GNAT, BioNetscape and VNW, and use these tools in their research.

PI: Ramin Yadegari (University of Arizona)

CoPIs: Philip W. Becraft (Iowa State University), Joanne M. Dannenhoffer (Central Michigan University), and Gary N. Drews (University of Utah)

Endosperm is a major component of the seed, and is biologically and economically important. In all angiosperms, endosperm provides nutrients and signals to the embryo during seed development. In cereal grains, endosperm composes a large proportion of the mature seed, and contains large amounts of carbohydrates and proteins, which provide more than 50% of the calories in the human diet, either directly or indirectly through animal feed. Cereal endosperm also serves as a raw material for manufacturing important industrial products, including ethanol. Maize is an excellent model system for molecular genetic analysis of cereal endosperm and is the primary subject of this research project which focuses on maize early endosperm development, encompassing the time when the endosperm grows from a single cell into a multicellular structure composed of different specialized cell types. Although the molecular mechanisms that control this developmental period have not been elucidated, they are likely to affect many economically important processes including, but not limited to, the regulation of seed size, and accumulation of starch and protein during grain filling. In addition to the training of students and postdoctoral associates, the project will provide summer research training internships for undergraduate students from Central Michigan University.

To identify genetic pathways that control endosperm cell differentiation, gene expression profiling will be performed on each endosperm cell type at multiple time points; this will be accomplished through a combination of laser-capture microdissection to collect specific endosperm cell types and the RNA-seq method (high throughput sequencing of cDNA) to identify all the genes expressed in the collected tissues. This information will be used as a framework for gene-network analysis in two of the endosperm cell types: the starchy endosperm and basal transfer layer. Starchy endosperm is the major repository for starch and protein accumulation, while the basal transfer layer is important for transporting sugar and amino acid building blocks into the growing grain; both tissues are important for grain yield and quality. The gene networks will be revealed by performing gene co-expression analysis and key transcription factors will be studied with protein-DNA assays and genetic analyses. Early endosperm development is highly sensitive to drought stress which can decrease grain yield, even if favorable moisture conditions are restored. To identify the gene-expression perturbations caused by drought stress in maize kernels, gene expression profiling will be performed with kernels from drought-stressed plants. In addition, this project will profile the genes expressed during early endosperm development in a closely related cereal, sorghum, and use comparative genomics with maize to reveal fundamental and species-specific aspects of endosperm development. Data generated can be accessed through the project website (www.grainendosperm.org) as well as through long-term repositories such as MaizeGDB and NCBI's GEO and SRA.