Dynamic resource allocation drives growth under nitrogen starvation in eukaryotes

Juan D Tibocha-Bonilla,Manish Kumar,Anne Richelle,Karsten Zengler,Cristal Zuñiga,Rubén D Godoy-Silva

doi:10.1038/s41540-020-0135-y

Abstract

Cells can sense changes in their extracellular environment and subsequently adapt their biomass composition. Nutrient abundance defines the capability of the cell to produce biomass components. Under nutrient-limited conditions, resource allocation dramatically shifts to carbon-rich molecules. Here, we used dynamic biomass composition data to predict changes in growth and reaction flux distributions using the available genome-scale metabolic models of five eukaryotic organisms (three heterotrophs and two phototrophs). We identified temporal profiles of metabolic fluxes that indicate long-term trends in pathway and organelle function in response to nitrogen depletion. Surprisingly, our calculations of model sensitivity and biosynthetic cost showed that free energy of biomass metabolites is the main driver of biosynthetic cost and not molecular weight, thus explaining the high costs of arginine and histidine. We demonstrated how metabolic models can accurately predict the complexity of interwoven mechanisms in response to stress over the course of growth.

Highlights

Perturbations in environmental conditions require organisms to withstand transient phases from nutrient abundance to nutrient depletion
We identified a ubiquitous trend of mitochondrial carbon and nitrogen load reduction, caused by the activity reduction in reactions of energy (tricarboxylic acid cycle (TCA) and oxidative pentose phosphate pathway (PPP)) and amino acid metabolism (Fig. 2)
We study how nitrogen starvation decreases the productivity of essential nitrogen-rich compounds, causing cells to prioritize higher-energy nitrogen-free biomolecule synthesis, such as those for lipids, carbohydrates, and sterols

Summary

INTRODUCTION

Perturbations in environmental conditions require organisms to withstand transient phases from nutrient abundance to nutrient depletion. Using a reduced number of known uptake rates, or constraints[17], M-model simulations predict growth and flux distributions (phenotypes) under diverse genetic and environmental conditions, identifying the main drivers of metabolism[14]. In this approach, the formulation of the biomass objective function (BOF) is highly important to obtain biologically relevant flux distributions. Each biomass precursor in the BOF pulls resources from the network depending on its stoichiometric coefficient; ideally each coefficient is experimentally determined Considering this dependence, we devised a strategy to compute dynamic flux distributions by using time-course biomass composition data, expanding the scope of M-models from a steady state to several pseudo-steady states encompassing growth under stress conditions and in time-dependent processes[1].

RESULTS

Phaeodactylum tricornutum iCZ8433 P H iLB102721 P

DISCUSSION

(2) REFERENCES