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Doing, G.

Publications and source records attributed to Doing, G..

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A Multimodal Strategy Used By A Large c-di-GMP Network

The Pseudomonas fluorescens genome encodes for 50+ proteins involved in -di-GMP signaling. Here, we demonstrate that when tested across 188 nutrients, these enzymes and effectors appear capable of impacting biofilm formation. Transcriptional analysis of network members across [~]50 nutrient conditions indicates that altered gene expression can explain a subset, but not all, of biofilm-formation responses to the nutrients. Additional organization of the network is likely achieved through physical interaction, as determined via probing [~]2000 interactions by bacterial two-hybrid assays. Our analysis revealed a multimodal regulatory strategy, using combinations of ligand-mediated signals, protein-protein interaction and/or transcriptional regulation to further fine-tune c-di-GMP-mediated responses. These results create a profile of a large c-di-GMP network that is used to make important cellular decisions, opening the door to future model building and the ability to engineer this complex circuitry in other bacteria.\n\nAbstract ImportanceCyclic diguanylate (c-di-GMP) is a key signalling molecule regulating bacterial biofilm formation, and many microbes have up to dozens of proteins that make, break or bind this dinucleotide. Thus, a major open question in the field is how signalling specificity is conferred in this context with a soluble signalling molecule. Here, we take a systems approach, using mutational analysis, transcriptional studies and bacterial two-hybrid analysis to interrogate this network. We find that the network typically combines two or more modes of regulation (i.e., transcriptional control with protein-protein interaction) to generate an observed output.

microbiology

PathCORE: Visualizing globally co-occurring pathways in large transcriptomic compendia

BackgroundInvestigators often interpret genome-wide data by analyzing the expression levels of genes within pathways. While this within-pathway analysis is routine, the products of any one pathway can affect the activity of other pathways. Past efforts to identify relationships between biological processes have evaluated overlap in knowledge bases or evaluated changes that occur after specific treatments. Individual experiments can highlight condition-specific pathway-pathway relationships; however, constructing a complete network of such relationships across many conditions requires analyzing results from many studies. ResultsWe developed PathCORE-T framework by implementing existing methods to identify pathway-pathway transcriptional relationships evident across a broad data compendium. PathCORE-T is applied to the output of feature construction algorithms; it identifies pairs of pathways observed in features more than expected by chance as functionally co-occurring. We demonstrate PathCORE-T by analyzing an existing eADAGE model of a microbial compendium and building and analyzing NMF features from the TCGA dataset of 33 cancer types. The PathCORE-T framework includes a demonstration web interface, with source code, that users can launch to (1) visualize the network and (2) review the expression levels of associated genes in the original data. PathCORE-T creates and displays the network of globally co-occurring pathways based on features observed in a machine learning analysis of gene expression data. ConclusionsThe PathCORE-T framework identifies transcriptionally co-occurring pathways from the results of unsupervised analysis of gene expression data and visualizes the relationships between pathways as a network. PathCORE-T recapitulated previously described pathway-pathway relationships and suggested experimentally testable additional hypotheses that remain to be explored.

bioinformatics