A Dynamic Multi-Tissue Flux Balance Model Captures Carbon and Nitrogen Metabolism and Optimal Resource Partitioning During Arabidopsis Growth.

Rahul Shaw,C Y Maurice Cheung

doi:10.3389/fpls.2018.00884

Abstract

Plant metabolism is highly adapted in response to its surrounding for acquiring limiting resources. In this study, a dynamic flux balance modeling framework with a multi-tissue (leaf and root) diel genome-scale metabolic model of Arabidopsis thaliana was developed and applied to investigate the reprogramming of plant metabolism through multiple growth stages under different nutrient availability. The framework allowed the modeling of optimal partitioning of resources and biomass in leaf and root over diel phases. A qualitative flux map of carbon and nitrogen metabolism was identified which was consistent across growth phases under both nitrogen rich and limiting conditions. Results from the model simulations suggested distinct metabolic roles in nitrogen metabolism played by enzymes with different cofactor specificities. Moreover, the dynamic model was used to predict the effect of physiological or environmental perturbation on the growth of Arabidopsis leaves and roots.

Highlights

In recent years, genome-scale metabolic models, containing reactions catalyzed by enzymes encoded in an organism’s genome, have been an emerging tool to study plant metabolic systems (Sweetlove and Ratcliffe, 2011; Seaver et al, 2012; de Oliveira Dal’Molin and Nielsen, 2013)
We applied dynamic flux balance analysis (FBA) on a multi-tissue metabolic model of Arabidopsis thaliana to explore the metabolic interactions between leaf and root in a diel cycle and the metabolic reprogramming across various growth stages under different environment conditions
First multi-tissue model of plant representing leaf-root of barley and their interaction using Dynamic Flux Balance Analysis (dFBA) was introduced by GrafahrendBelau et al (2013), in which the authors used a functional plant model (FPM) to obtain growth kinetics and fed the outputs of FPM to a FBA model as constraints to obtain flux solutions across multiple tissues during seed development from 53 to 70 day after sowing

Summary

Introduction

Genome-scale metabolic models, containing reactions catalyzed by enzymes encoded in an organism’s genome, have been an emerging tool to study plant metabolic systems (Sweetlove and Ratcliffe, 2011; Seaver et al, 2012; de Oliveira Dal’Molin and Nielsen, 2013). Most of the current plant genome-scale metabolic models were built using a top-down approach, where annotations from biochemical databases (such as BioCyc Caspi et al, 2016, KEGG Kanehisa et al, 2017, PMN Schlapfer et al, 2017 etc.) have been curated to obtain a functional network of biochemical reactions for different species, and such building procedures in most cases result in multiple problematic features including stoichiometric inconsistencies (Gevorgyan et al, 2008), dead metabolites, and disconnected sub-networks (Poolman et al, 2006). To avoid the limitations pertaining to top-down models, Arnold and Nikoloski (2014) used a bottomup approach to reconstruct a large scale Arabidopsis model relying solely on species specific annotations without the need of gap-filling algorithms

Methods

Results

Discussion

Conclusion