title: Multi-scale modeling of Arabidopsis thaliana response to different CO2 conditions: From gene expression to metabolic flux.
abstract: Multi-scale investigation from gene transcript level to metabolic activity is important to uncover plant response to environment perturbation. Here we integrated a genome-scale constraint-based metabolic model with transcriptome data to explore Arabidopsis thaliana response to both elevated and low CO2 conditions. The four condition-specific models from low to high CO2 concentrations show differences in active reaction sets, enriched pathways for increased/decreased fluxes, and putative post-transcriptional regulation, which indicates that condition-specific models are necessary to reflect physiological metabolic states. The simulated CO2 fixation flux at different CO2 concentrations is consistent with the measured Assimilation-CO2intercellular curve. Interestingly, we found that reactions in primary metabolism are affected most significantly by CO2 perturbation, whereas secondary metabolic reactions are not influenced a lot. The changes predicted in key pathways are consistent with existing knowledge. Another interesting point is that Arabidopsis is required to make stronger adjustment on metabolism to adapt to the more severe low CO2 stress than elevated CO2 . The challenges of identifying post-transcriptional regulation could also be addressed by the integrative model. In conclusion, this innovative application of multi-scale modeling in plants demonstrates potential to uncover the mechanisms of metabolic response to different conditions.
authors.affiliation: School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, 200240, China.
authors.forename: Lin
authors.initials: L
authors.lastname: Liu
authors.affiliation: School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, 200240, China.
authors.forename: Fangzhou
authors.initials: F
authors.lastname: Shen
authors.affiliation: Key Laboratory of Computational Biology, CAS-MPG Partner Institute for Computational Biology, Shanghai Institutes of Biological Sciences, Chinese Academy of Sciences, Shanghai, 200031, China.|Shanghai Botanical Garden, Shanghai, 200231, China.
authors.forename: Changpeng
authors.initials: C
authors.lastname: Xin
authors.affiliation: School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, 200240, China.|Key Laboratory of Computational Biology, CAS-MPG Partner Institute for Computational Biology, Shanghai Institutes of Biological Sciences, Chinese Academy of Sciences, Shanghai, 200031, China.
authors.forename: Zhuo
authors.initials: Z
authors.lastname: Wang
mesh_terms: D017360:Arabidopsis / Q000187:drug effects / Q000235:genetics*; D002245:Carbon Dioxide / Q000494:pharmacology*; D020869:Gene Expression Profiling; D018506:Gene Expression Regulation, Plant / Q000187:drug effects*; D064688:Metabolic Flux Analysis; D053858:Metabolic Networks and Pathways / Q000187:drug effects / Q000235:genetics; D008954:Models, Biological; D010100:Oxygen / Q000378:metabolism; D014158:Transcription, Genetic / Q000187:drug effects; D059467:Transcriptome / Q000187:drug effects / Q000235:genetics
chemical_list: D002245:Carbon Dioxide; D010100:Oxygen
keywords: Metabolic model; gene expression; low/elevated CO2; multi-scale analysis; post-transcriptional regulation