Protein-protein and protein-nucleic acid interactions are fundamental to all biological processes, and comprehensive determination of such interactions in an organism provides a framework for understanding it as an integrated system. Posttranscriptional regulatory networks are the current frontier in systems biology: the proteins mediating RNA transactions comprise only 3-11% of proteomes, yet macromolecular complexes acting on RNA constitute highly interconnected nodes in the scale-free cellular functional networks, as evidenced by high-throughput functional mapping of cellular pathways and direct analyses of interactomes. These networks are presently best understood in Saccharomyces cerevisiae. Yet despite its well-known advantages and densely populated interactome datasets, the power of yeast as a model only extends only so far, partly because this system differs from more complex eukaryotes in a number of key attributes of gene expression, e.g. it lacks the small RNA pathways.
In Arabidopsis, the situation is essentially opposite, i.e. its gene expression pathways are rich in biological complexity but its molecular interactions space is grossly undersampled. Particularly striking is near-absence of biochemically defined macromolecular complexes, which stands in contrast with high density and quality of genetic data as well as exponentially growing transcriptome data. The systems-level comprehension of Arabidopsis biology is greatly limited by such lopsidedness. Therefore, this project seeks to develop and deploy the genome-enabled tools for identifying the composition and the targets of the key macromolecular complexes, particularly those acting on RNA. The awesome power of information about the system’s elements, followed by modeling its behavior and generation and testing the hypotheses explaining it. Integrating the results of experimental probing of the system’s components with the outcomes of hypotheses testing then allows for synthesis of new concepts about the system’s behavior and emergent properties, leading to another round of testable hypotheses. Characterization of the system’s elements empowers this cycle. Targeting macromolecular complexes is a highly effective way to do so, because such complexes capture and define network topology as well as its functionality. The overwhelming degree of chemical and structural diversity of native macromolecular complexes and their constituents represents a formidable problem. However, it can be bypassed via genetically engineered affinity tags. Recent advances in affinity tag-assisted purifications, mass spectrometry, microarray and deep sequencing technologies enable the development of pipelines for large-scale, high-definition studies of macromolecular complexes. However, affinity-tagging tools remain suboptimal in plants, representing a major obstacle to further progress of plant systems biology. Building on our prior success in affinity tagging in E.coli, Saccharomyces and Arabidopsis we intend to optimize and deliver such tools for the Arabidopsis community (see Tags link).