Plant Reactome Knowledgebase: A Resource for Plant Synthetic Biology | AIChE

Plant Reactome Knowledgebase: A Resource for Plant Synthetic Biology

Authors 

Naithani, S. - Presenter, Oregon State University
Preece, J., Oregon State University
Gupta, P., Oregon State University
D’Eustachio, P., NYU School of Medicine
Elser, J., Oregon State University
Jaiswal, P., Oregon State University
In-silico systems-level pathway knowledgebases are key reference resources that provide conceptual frameworks for plant biologists aiming to design, model, and test synthetic metabolic pathways in plants. Plant Reactome (https://plantreactome.gramene.org) is an open-source, free-of-cost pathway portal of the Gramene project (www.gramene.org). Plant Reactome features plant metabolic, signaling, transport, genetic, regulatory, developmental and stress-response pathways for 83 plant species, from unicellular photoautotrophs to higher plants. We utilize a combinatorial approach involving manual curation of reference pathways and automated gene orthology-based projection to rapidly scale up pathway knowledge from reference species rice (Oryza sativa) to all other species. Pathway clustering across the broad phylogenetic spectrum of photosynthetic organisms shows distinct gene-pathway association patterns reflecting evolutionary history and ploidy levels. Plant researchers can compare reference rice pathways with the projected pathways from any hosted plant species to discover species-specific pathway enrichment, regulatory events, and loss/gain of reactions, thereby providing necessary clues and hypotheses for building potential pathway engineering and synthetic biology strategies for the improvement of the physiology, utility, performance and synthesis of bioproducts. Users can also upload their data for pathway analysis, visualization and integration of gene-gene interactions, and access Plant Reactome pathway data in various standardized formats via an embeddable widget, web services, and other download mechanisms. The Plant Reactome is funded by the NSF (IOS-1127112), the NIH (U41 HG003751), ENFIN (LSHG-CT-2005-518254), the Ontario Research Fund, and the EBI Industry Programme.