Uwe Ohler - US grants

Scientific Goals: The long-term goal of this project is to define the cellular states that occur during root development in Arabidopsis when subjected to different stimuli. As a tractable approach to defining cellular state, transcriptional networks active in different cell types when grown under a variety of environmental conditions will be identified. To describe a first-order transcriptional network three types of data are needed: 1) transcriptional profiles at cell-type specific resolution under normal lab conditions and when subjected to external stimuli; 2) knowledge of transcription factor localization at cellular resolution and 3) information about the targets of transcription factors. For the first, a technique that involves sorting of fluorescently marked cell populations followed by microarray analysis of the RNA from the marked cells will be used. For the second, comparisons will be made of the expression patterns of constructs containing the promoter regions of transcription factors driving fluorescent reporter genes with constructs containing the same promoter regions driving the coding sequences of transcription factors fused to fluorescent reporter genes. For the third, yeast one-hybrid and two-hybrid methods will be used with libraries of tissue-specific transcription factors. Analyses will initially be performed on plants growing under standard laboratory conditions. To determine how expression is perturbed by external stimuli, plants will be systematically subjected to a variety of external stimuli and the effects on transcriptional profiles determined at cell-type specific resolution. To determine the effect of external stimuli on expression at high spatial and temporal resolution a novel technology called the "RootArray" will be developed. The combination of results, from these different approaches, will provide information concerning the function of the identified genes. Results from these studies will be posted on the Arabidopsis Gene Expression Database, at www.arexdb.org.


Broader Impact: To achieve continued improvement in plant traits while minimizing unwanted side effects will require a sophisticated understanding of the networks that control plant development and physiology. This research will provide a high-resolution dataset for the identification of transcriptional networks active during root development. It will also determine the effect on root transcriptional networks of stimuli relevant to plant growth in the field. Another outcome of the research will be detailed knowledge of the spatial and temporal expression patterns of large numbers of plant genes and the regulatory sequences able to confer specific expression patterns. These should be of immediate value in many plant research programs and are likely to be useful for agronomic purposes as well. Another important part of the project will be to train the next generation of plant scientists in Systems Biology, which integrates computational, engineering and experimental approaches. Postdoctoral fellows, graduate and undergraduate students will be trained in this research. The PIs also will participate actively in outreach efforts such as development of a Systems Biology curriculum at Duke. The research program will also leverage the currently funded NSF IGERT program in biologically inspired materials and material systems directed by Co-PI Clark. A significant focus of the IGERT program is placed upon recruitment and training of women and underrepresented minorities in engineering and the sciences.

Relevance to Arabidopsis 2010 goals: The research activities will contribute to all three of the primary goals of the Arabidopsis 2010 program. Enhancing the resolution of the root expression map under normal laboratory conditions and producing expression profiles at cell-type specific resolution in response to different stimuli will substantially aid in "Benchmarking Gene Function." Developing computational tools for cis-element identification and automated image analysis as well as developing the RootArray should provide "genome wide experimental approaches and tools for analyzing gene function and regulation." Perhaps most directly, the research has as its aim to identify the transcriptional networks acting in root development and physiology, thus "exploring exemplary networks and systems."

[unreadable] DESCRIPTION (provided by applicant): Transcription is at the heart of the regulation of gene expression, yet the computational analysis of transcription regulation currently faces a number of challenges and opportunities: The large number of sequenced genomes allows to study and exploit the conservation of regulatory sequences, but algorithms that do so in a rigorous framework are still scarce. Detailed data of spatiotemporal gene expression has become available, enabling us to use this information to elucidate regulatory interactions in the development of complex organisms. The long-term goal is to build computational models to infer regulatory networks and their evolution in the development of model organisms and ultimately humans. The objective of this particular proposal is to develop algorithms to analyze the conservation of gene regulation on the sequence level, as well as an integrated approach to model conserved regulatory regions important for development. Its specific aims are: (1) To decipher the precise requirements to define a functional transcription start site, based on a comparative study of the conservation of core promoter elements in two fly genomes, and build a model for genome-wide comparative annotation. (2) To develop and implement an efficient progressive multiple alignment algorithm for non-coding regulatory sequences based on phylogenetic hidden Markov models, and to study the evolution of core promoters in a wider set of species. (3) To extend the framework set by this algorithm to more complex regulatory modules (such as developmental enhancers and E2F target genes), and to incorporate prior information on putative upstream factors to predict regulatory interactions. Computational predictions will be validated by a small number of experiments. The proposed research is expected to advance the understanding on the evolution of regulatory regions, and how to build computational models that accurately utilize sequence information from several species. Relevance to public health: Understanding how gene regulation is encoded in the genome is undoubtedly one of the most interesting challenges in molecular biology today, and it is intuitive that errors occurring in this machinery lead to mis-expression of genes, and may often be important in genetically based diseases. Our research will help to find the exact regulatory regions in DNA, both computationally and experimentally, and to learn the mechanisms that control the expression of genes in model organisms and humans. [unreadable] [unreadable] [unreadable]

MicroRNAs (miRNAs) are extremely small (21-23 nucleotide) RNAs that do not encode proteins but serve as important post-transcriptional regulators. Specifically, miRNAs target messenger RNAs to which they are perfectly complementary for degradation, and those to which they can base pair with internal mismatches for translational repression. It is widely believed that miRNAs are embedded in an extensive array of gene regulatory circuits, thereby playing essential roles in establishing and maintaining cellular functions during development and differentiation. However, direct evidence in support of this proposal is lacking. Given the importance of miRNAs in gene regulation, one key question is how miRNA biogenesis itself is regulated at the levels of transcription and processing. To date, little has been learned about transcriptional regulation of miRNA genes, mainly due to the lack of information about the precise location of miRNA promoters. Mapping the transcriptional regulatory elements that comprise miRNA promoters is critical for determining the sequence motifs present and the factors with which they interact to turn on and off miRNA synthesis. The goal of this research project is to (1) use high throughput methods to globally determine the transcription start sites of miRNA genes in human cells; and (2) to use computational strategies to identify the sequence elements and regulatory factors involved in miRNA transcriptional control. Therefore, this study will serve as an initial yet critical step towards dissecting the molecular mechanism underlying regulated miRNA expression, bringing us one step closer to fully understanding miRNA-mediated gene regulation at the network/system level.
Broader Impacts: The proposed project provides unique training opportunities for undergraduate, graduate and postdoctoral fellows in genome science, including but not limited to genome technology development and computational biology. In addition, the research program will promote curriculum development for graduate and undergraduate courses with an emphasis on genome biology. By taking advantage of the existing infrastructure and outreach programs at Duke University, in particular at the Institute for Genome Sciences & Policy and its associated NIGMS National Center for Systems Biology, the project will reach out to scientists of diverse backgrounds as well as educate the general public.

PI: Philip N. Benfey
Co-PI: Uwe Ohler
IOS-1021619
Arabidopsis 2010: Regulatory Networks Controlling Root Growth and Differentiation

The long-term scientific goal of this project is to determine the dynamics of the gene regulatory networks that control root growth and differentiation in Arabidopsis. The simplifying aspects of root growth and anatomy will be exploited to identify networks involved in development, which will then be perturbed with environmental stimuli. Current work on determining the mRNA expression profile in every cell type of the root and along the developmental axis will be greatly enhanced through the use of a new set of markers that are specific for both cell type and developmental stage and through use of next-generation sequencing to profile expression. In a second aim, the expression data will be combined and extended by chromatin immunoprecipitation followed by sequencing and by yeast one-hybrid analysis to infer regulatory networks controlling biological processes central to agriculture and bioenergy production. In the third aim the response at cellular resolution will be determined for abiotic stresses such as temperature and drought as well as biotic stresses such as bacteria. To obtain parameters for the modeling of the dynamics of network responses to environmental stimuli, the RootArray platform will be used. This technology allows the simultaneous in vivo observation of expression responses of more than 60 seedlings. The fourth aim will be to identify how the tissue-specific response to an abiotic stress changes among natural isolates of Arabidopsis. All of these efforts will be informed by and analyzed in concert with a theory and modeling team, which will lead the effort on network identification, modeling dynamical responses and image analysis. This project also has several broader impacts. To achieve continued improvement in plant traits for food security and bioenergy production will require a sophisticated understanding of the networks that control plant growth and differentiation. This research will generate high-resolution datasets from which regulatory networks controlling biological processes central to real-world agricultural and bioenergy productivity can be identified and characterized. Another important part of the project will be to train the next generation of plant scientists in systems biology, which integrates quantitative and experimental approaches. The experience of all trainees will be enhanced by cross-training opportunities between computational and experimental biology within the Duke Center for Systems Biology as well as participation in outreach efforts such as helping to teach a course on Complex Genetic Traits at North Carolina Central University, a historically black university in Durham, NC and participating in summer undergraduate research programs for students from groups underrepresented in science.

Duke University is awarded a grant from the Faculty Early Career Development program (CAREER) to develop an integrated framework for the analysis and interpretation of biological image data. Images have been long used in molecular and developmental biology as a means to document the outcome of experiments, but are increasingly seen as quantitative data and not just qualitative descriptions. With recent advances in microscopy technology, as well as means to visualize biological molecules, the growing amount of available data has turned images into a new data type for computational biology with new and exciting challenges and possibilities. In particular, microscopy allows for measuring gene expression patterns at high resolution and in living organisms. Algorithms to extract, represent, and compare spatial and temporal expression patterns from images are still in early stages, and are often tailored to a particular scenario. The key contribution of this project lies in a principled probabilistic framework which utilizes top-down generative strategies to extract samples from images, model gene expression patterns from microscopy data, and integrate image expression data with other genomic data to understand gene regulation. Close collaborations with biologists working on animal and plant model systems will ensure that the developed methods are widely applicable, and will allow for the targeted validation of specific model predictions.

The interdiscplinary nature of this proposal reaches across both research and education. In concert with the research program, a graduate course in computational biology will be expanded to include case study modules combining methodological background with hands-on examples to analyze primary research data. Topics will include genome annotation, gene regulation, and image analysis. To increase the impact of this effort, the PI will closely collaborate with the ongoing NSF iPlant initiative and teach at workshops for educators at the high school, undergraduate, and graduate level. The PI will also continue to participate in development and teaching of systems biology curricula for undergraduates and graduates spearheaded by the Duke Center for Systems Biology. Ongoing international efforts by the Center include the development of a platform to allow for an open sharing of teaching resources.

DESCRIPTION (provided by applicant): All mRNA molecules are subject to posttranscriptional gene regulation (PTGR) involving sequence-dependent modulation of splicing, cleavage and polyadenylation, editing, transport, stability, and translation. In recent years, a paradigm shift has occurred in our understanding of PTGR, triggered by discoveries of regulatory RNAs and extensive RNA processing including alternative polyadenylation and splicing. These developments have led to the realization that virtually every human gene is subject to some degree of PTGR. Understanding the mechanisms of target RNA recognition, and the function of the hundreds of mRNA-binding proteins encod- ed in the human genome, is therefore an open challenge that requires the joint effort of multidisciplinary teams. The recent introduction of deep sequencing technologies has enabled the development of new methods for precisely mapping interaction sites between RNA-binding proteins (RBPs) and their RNA target sites in human cells. It is only now possible to resolve interdependencies and redundancies of binding of RBPs and ribonucleoprotein particles (RNPs) to mRNA molecules and evaluate their contribution to gene regulation in the context of organismal development or normal and disease states. The broad aim of this application is to identify and characterize the interaction networ of mRNA-binding proteins at the sequence, structural and functional level, with a particular focus on transport and shuttling - crucial PTGR mechanisms that have received little attention. A driving concept is whether larger chromatin- like packaging of RNPs facilitates transport and translational regulation. The specific aims include: (1) Global identification of target RNA site for a comprehensive panel of nucleocytoplasmic transport and shuttling proteins by Photoactivatable-Ribonucleoside-Enhanced Crosslinking and Immunoprecipitation (PAR- CLIP) involving deep sequencing of libraries for all proteins and computational analysis. (2) Integrated annotation of binding sites on transcripts across libraries, using a probabilistic graphical modeling approach to identify prevalent site configurations from heterogenenous data. PAR-CLIP data, complemented by expression as well as sequence and RNA structural features, will allow for the identification of targets for combinatorial and redundant regulation. (3) Development of experimental systems for assessment of the phenotypic outcomes of the perturbation of the transporter interaction network and elucidation of its role in normal and disease states. (4) Perform biophysical and structural studies substantiating the biochemical and computational findings. Natural, as well as designed RNA-recognition element (RRE)-representing RNA ligands, will by co-crystallized with their respective recombinant proteins placing emphasis on obtaining larger RNP structures. The proposed work will thus establish the first theoretical and experimental models that relate global maps of protein-RNA interactions to RNA transport, setting the stage for future systems-wide studies of PTGR.