Metabolic modeling identifies determinants of thermal growth responses in Arabidopsis thaliana

Abstract

Introduction

Global food security depends on crop yields that are severely threatened by more fluctuating and increasing temperatures – a hallmark of future climate scenarios (Wheeler & von Braun, 2013). Ambient temperature affects all aspects of the plant life cycle, from development and growth to reproduction (Casal & Balasubramanian, 2019; Zhu et al., 2022). Plant responses to temperature changes are most immediately observed at the level of metabolism, followed by changes in gene expression to reestablish homeostasis (Casal & Balasubramanian, 2019). Considering that metabolism is tightly linked to plant growth (Meyer et al., 2007; Pyl et al., 2012), metabolic changes can facilitate rapid plant adaptation to temperature changes at a minimal growth penalty. While we understand that metabolic flexibility is achieved by rerouting nutrient flows within the plant metabolic network, we know little about (1) which enzymes limit plant metabolic changes in temperature? And (2) how these limits emerge from temperature-dependent biochemical constraints under which the metabolic network operates? The availability of a mathematical model that can accurately predict genetic and molecular determinants that affect plant temperature responses will address both questions.

A few metabolic models have already considered the effect of temperature on processes that directly affect plant growth (Clark et al., 2020; Wendering & Nikoloski, 2023). For instance, the classical mathematical model of C₃ photosynthesis (Farquhar et al., 1980) – an indispensable metabolic pathway for photoautotrophic growth – has been extended to predict effects of temperature changes in net CO₂ assimilation (Scafaro et al., 2023). However, this and other modeling efforts addressing responses of metabolic pathways to temperature change (Kannan et al., 2019; Herrmann et al., 2020; Inoue & Noguchi, 2021) consider only a few, lumped metabolic reactions. As a result, these models cannot be used to identify all gene targets modulating plant thermal responses, thus restricting their capacity to predict mitigation strategies. In addition, they cannot be used to make predictions about plant growth responses, due to the limited focus on one selected metabolic pathway. By contrast, genome-scale metabolic models, representing the entirety of known metabolic reactions in a system, have been successfully used to predict growth-related phenotypes and genetic engineering strategies for their modulation using approaches from the constraint-based modeling framework (Herrmann et al., 2019; Tong et al., 2023; Wendering & Nikoloski, 2023). These models allow the design of rational engineering strategies to modulate metabolic phenotypes, including growth (Küken & Nikoloski, 2019). Temperature effects have already been considered in genome-scale metabolic models of Escherichia coli (Chang et al., 2013) and Saccharomyces cerevisiae (Li et al., 2021); however, these studies either focused on a relatively narrow temperature range (Chang et al., 2013) or required additional parameter tuning to reproduce growth rates at superoptimal growth temperatures (Li et al., 2021).

Here, we present the first plant metabolic model that, by capturing temperature effects on enzyme properties and photosynthesis-related parameters, can accurately predict growth of Arabidopsis thaliana at different temperatures. Due to the fine-grained representation of metabolism, our model can correctly identify genes affecting temperature-dependent growth in A. thaliana. Due to the enzyme-constrained formulation of the model, the prediction of growth is also accompanied by predictions of reaction fluxes and enzyme abundances. Our contribution also facilitates the identification of temperature-specific growth-limiting metabolites and proteins, pointing to additional ways to improve plant temperature resilience. Therefore, our study provides a novel direction for engineering temperature-resilient plants for future climate scenarios.