Data-driven integrative modeling of photorespiratory metabolism
Bioinformatics and Theoretical Biology
Plant Physiology
Theoretical Chemistry: Molecules, Materials, Surfaces
Final Report Abstract
Photorespiratory metabolism is an inevitable by-pathway of carbon fixation and is present across all photosynthetically active species. It includes reactions involving multiple compartmentalized pools, and mounting evidence points at its important role in diverse cellular processes. Nevertheless, the implications of photorespiration and modulation of its function on the entirety of plant metabolism remain fragmented. Quantitative modeling of photorespiration offers the means for in silico investigation of the effect its modulation has on the plant system. Coupled with integration of large-scale data, we used genome-scale modeling to determine which parts of a metabolic network are mostly affected by tempering with enzymatic steps in the photorespiratory metabolism. The main scientific contribution of our project is a constraint-based approach for genome-wide differential flux profiling based on the integration of relative metabolite levels from different scenarios (e.g. between mutant and wild type or between treatment and control) in a plant genome-scale metabolic model. This approach, named iReMet-Flux, is based on the assumption that the rate of each enzymatic reaction can be cast in a mass-action-like kinetic form and that the flux distributions between different scenarios is minimized while ensuring compliance with the integrated date. The approach can also account for additional constraints due to the relative differences in enzyme (or transcript) abundance between two conditions, and has been extended to account for deviations from the mass action kinetic form of the reaction fluxes. Most importantly, iReMet-Flux is independent of reaction rate constants and can be used with largescale models provided sufficient coverage of measurable metabolite levels. To facilitate interpretation of the predictions about differential flux profiling, iReMet-Flux was complemented by an advanced flux variability analysis to determine the reactions and pathways with robustly predicted flux differences. We used iReMet-Flux with metabolomics data from Arabidopsis thaliana Col0 and photorespiratory mutants to determine the reactions which were most affected by the gene knock-outs. We validated that the predictions were in line with growth and gas exchange measurements and knowledge about enzymatic regulation. The approach can be applied not only in plant science, but also in biotechnological and medical fields which are becoming more dependent on differential flux profiling. Overcoming the drawbacks of photorespiration on plant growth and yield has been already addressed by introducing metabolic pathways aimed at bypassing parts of the photorespiratory pathway. Therefore, we investigate if the existing large-scale metabolic models of Arabidopsis can reproduce the experimental observations, as a first step in the feasibility of designing intervention strategies around the photorespiratory pathway. Our study indicated that constraintbased modeling has indeed the potential to identify target reactions and pathways whose insertion could lead to increase in performance of plant species; however, this can be achieved by considering more physiologically meaningful constraints, rather than only simple optimization criteria. Interestingly, this conclusion contrasts the current approaches in engineering microbial strains.
Publications
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(2014). Analysis of short-term metabolic alterations in Arabidopsis following changes in the prevailing environmental conditions. Mol Plant. 7:893-911
Florian A, Nikoloski Z, Sulpice R, Timm S, Araújo WL, Tohge T, Bauwe H, Fernie AR
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(2014).Variability of metabolite levels is linked to differential metabolic pathways in Arabidopsis's responses to abiotic stresses. PLoS Comput Biol.;10:e1003656
Töpfer N, Scossa F, Fernie A, Nikoloski Z
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(2016). iReMet-flux: constraint-based approach for integrating relative metabolite levels into a stoichiometric metabolic models. Bioinformatics 32:i755-i762
Sajitz-Hermstein M, Töpfer N, Kleessen S, Fernie AR, Nikoloski Z
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(2016). Photorespiratory bypasses lead to increased growth in Arabidopsis thaliana: Are predictions consistent with experimental evidence? Frontiers in Bioengineering and Biotechnology 4:31
Basler, G, Küken, A, Nikoloski Z
