(643f) Developing Genome-Scale Whole-Plant Models for Poplar (Populus deltoides) and Switchgrass (Panicum virgatum)
- Conference: AIChE Annual Meeting
- Year: 2018
- Proceeding: 2018 AIChE Annual Meeting
- Group: Food, Pharmaceutical & Bioengineering Division
- Time: Thursday, November 1, 2018 - 2:00pm-2:18pm
The GSM for P. deltoides covers primary and secondary metabolism for a compartmentalized plant cell based on the P. deltoides genome, accounting for 3555 metabolites and 3015 metabolic reactions. The initial draft model was constructed using the PoplarCyc database  and BLAST-matched Arabidopsis thaliana proteins. Due to the paucity of cellular component ontologies (CO) for poplar proteins, a constraint-based method was developed for predicting subcellular localization of reactions based on the underlying metabolic network. The developed method predicts localization scores for unannotated reactions by maximizing the flux through reactions with known (subcellular) localizations while parsimoniously filling in network gaps to ensure the production of biomass precursors. Reactions with unknown localizations were thus distributed among eight subcellular compartments â cytosol, plastid, peroxisome, vacuole, mitochondria, plasma membrane, thylakoid membrane, and inner mitochondria matrix. Among the reactions annotated thusly, localizations for ~53% of the predicted reactions were found to be same as listed in the BRENDA database for organisms other than P. deltoides and A. thaliana. For instance, 3-hydroxyacyl-CoA dehydrogenase was predicted to be located exclusively in the peroxisome, as seen in plants where the -oxidation cycle is only present in the peroxisome . Subsequently, this scaffold model will be used to construct a whole-plant metabolic model encompassing the leaf, stem, and root tissues, all connected by the vascular system. The envisioned model will capture carbon and nitrogen flows between tissues as a function of its growth cycle and sequester biomass in organ-specific ratios. The model will be used to explore pathways carrying the biggest lever on target processes such as growth, nitrogen utilization, feedstock degradability, and lignin valorization.
Similarly, the metabolic model for Panicum virgatum accounts for over 2500 metabolites and 3400 reactions and provides a framework for accounting for the substantial genetic diversity present in its two cultivars (upland and lowland). Although there are differences in pathways in switchgrass (a perennial) and maize (an annual), both are members of the Panicoideae subfamily of grasses and have a form of C4carbon fixation. Therefore, we used MaizeGrow and an earlier maize leaf model  as a reference during model construction by performing protein-protein BLAST to systematically search for homology for the gene-product of each gene contained in the source model . Each match having an Expect value of 10-10 was reverse searched into the Maize protein database; if the best result was the initial Maize protein, the switchgrass gene and the corresponding reactions were added to the model. SwitchgrassCyc and the literature were used to bring in additional reactions and gene associations such as the NAD-malic enzyme . Transcript data  were used to localize reactions into the mesophyll and bundle sheath cells of the leaf. We describe how the modelis expanded and adapted into organs, specifically the root, stalk, and leaf connected by vascular tissues using representative data for biomass growth [7,8]. The resulting model is used to determine the metabolic changes that occur between various growth conditions, to capture carbon and nitrogen flows between tissues as a function of growth stage, pinpointing tissue-specific bottlenecks in nitrogen metabolism, and to identify key switchgrass genes involved with yield and composition and metabolic tradeoffs between target phenotypes.
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