A multi-organ maize metabolic model connects temperature stress with energy production and reducing power generation

Abstract

Climate change has adversely affected maize productivity. Thereby, a holistic understanding of metabolic crosstalk among its organs is important to address this issue. Thus, we reconstructed the first multi-organ maize metabolic model, iZMA6517, and contextualized it with heat and cold stress transcriptomics data using expression distributed reaction flux measurement (EXTREAM) algorithm. Furthermore, implementing metabolic bottleneck analysis on contextualized models revealed differences between these stresses. While both stresses had reducing power bottlenecks, heat stress had additional energy generation bottlenecks. We also performed thermodynamic driving force analysis, revealing thermodynamics-reducing power-energy generation axis dictating the nature of temperature stress responses. Thus, a temperature-tolerant maize ideotype can be engineered by leveraging the proposed thermodynamics-reducing power-energy generation axis. We experimentally inoculated maize root with a beneficial mycorrhizal fungus, Rhizophagus irregularis, and as a proof-of-concept demonstrated its efficacy in alleviating temperature stress. Overall, this study will guide the engineering effort of temperature stress-tolerant maize ideotypes.

Keywords: Environmental science; Microbial metabolism.

Graphical abstract

Figure 1

Transcriptomics data analysis and introduction to EXTREAM algorithm (A) Scatterplot of control and cold stress transcriptomics data. (B) Scatterplot of control and heat stress transcriptomics data. (C) Scatterplot of heat and cold stresses transcriptomics data. (D) Correlation matrix among control, heat stress, and cold stress (95% confidence interval, two-tail test). (E) K-means clustering analysis of heat and cold stress data. (F) E-flux and EXTREAM both predicted correct biomass production and carbon fixation for three selected cross sections of leaf, whereas only EXTREAM predicted the correct leaf starch content.

Figure 2

Overview of iZMA6517 reconstruction and contextualization (A) Individual metabolic models for root, stalk, kernel, and leaf. (B) Connection between individual tissues via the vascular tissues. (C) Comparison of reactions among the individual GSMs. (D) Comparison of metabolites among the individual GSMs. (E) Comparison of unique genes among the individual GSMs. (F) Incorporating heat and cold stress related transcriptomics data with the iZMA6517 using the EXTREAM algorithm. (G) EXTREAM algorithm predicted lower biomass production in each organ and in the whole plant (WP) during the cold stress compared to the heat stress.

Figure 3

Identification of temperature stress bottlenecks (A) Organ specific bottleneck reactions for heat stress (number of reactions is indicated inside the column). (B) Organ specific bottleneck reactions for cold stress, (number of reactions is indicated inside the column). (C) Cytochrome b5 reductase bottleneck in the fatty acid metabolism for heat and cold stress. (D) Thermodynamic driving force analysis in fatty acid metabolism. (E) Pyruvate-phosphate dikinase and malate dehydrogenase bottlenecks in the photosynthetic pathway for heat stress. (F) Thermodynamic driving force analysis of the photosynthetic pathway.

Figure 4

Maize plant responses to the AMF R irregularis (A) Volcano plot of root transcriptomic data for control and inoculated plants. (B) Volcano plot of leaf transcriptomic data for control and inoculated plants. (C) Comparison between root transcriptomic data for control and inoculated plants. (D) Comparison between leaf transcriptomic data for control and symbiotic conditions. (E) Root and leaf transcriptomic data integration using the iZMA6517. (F) R. irregularis inoculation of maize root. (G) Root inoculated contextualized iZMA6517 predicted the pattern of biomass growth rate, matched with experimental growth pattern. WP: Whole Plant. (H) Picture of plants used for the study displaying higher biomass under inoculation conditions compared to control. (I) In the symbiotic interaction, 65% of bottlenecks, identified in cold stress, were alleviated.

Figure 5

Overall workflow of this project We reconstructed the first ever multi-organ GSM of maize, iZMA6517. We integrated heat and cold stress transcriptomics data with iZMA6517 with the EXTREAM. Later, we devised MBA to find metabolic bottlenecks of heat and cold stress conditions. We showed that, metabolic bottlenecks on both conditions are guided by thermodynamic principles. We then proposed protein engineering strategies to improve metabolic bottlenecks. Finally, we showed that R. irregularis symbiosis with maize root can also alleviate major metabolic bottlenecks.