Integration of relative metabolomics and transcriptomics time-course data in a metabolic model pinpoints effects of ribosome biogenesis defects on Arabidopsis thaliana metabolism

Abstract

Ribosome biogenesis is tightly associated to plant metabolism due to the usage of ribosomes in the synthesis of proteins necessary to drive metabolic pathways. Given the central role of ribosome biogenesis in cell physiology, it is important to characterize the impact of different components involved in this process on plant metabolism. Double mutants of the Arabidopsis thaliana cytosolic 60S maturation factors REIL1 and REIL2 do not resume growth after shift to moderate 10 [Formula: see text] chilling conditions. To gain mechanistic insights into the metabolic effects of this ribosome biogenesis defect on metabolism, we developed TC-iReMet2, a constraint-based modelling approach that integrates relative metabolomics and transcriptomics time-course data to predict differential fluxes on a genome-scale level. We employed TC-iReMet2 with metabolomics and transcriptomics data from the Arabidopsis Columbia 0 wild type and the reil1-1 reil2-1 double mutant before and after cold shift. We identified reactions and pathways that are highly altered in a mutant relative to the wild type. These pathways include the Calvin-Benson cycle, photorespiration, gluconeogenesis, and glycolysis. Our findings also indicated differential NAD(P)/NAD(P)H ratios after cold shift. TC-iReMet2 allows for mechanistic hypothesis generation and interpretation of system biology experiments related to metabolic fluxes on a genome-scale level.

Figure 1

Morphometric analyses of reil1-1 reil2-1 and wild type after shift from optimized (20 ${}^{\circ }$C) to low temperatures (10 ${}^{\circ }$C). Reil1-1 reil2-1 double mutants and A. thaliana Col-0 wild type plants were shifted at developmental stage 1.10. Week-0 plants were grown at 20 ${}^{\circ }$C and assayed before the temperature shift. Rosette diameter, (A); leaf area, (B), (mean +/− standard deviation; $n=$3–10 plants), for original data and definitions of morphometric parameters refer to Schmidt et al. 2013. The R coefficients represent the Pearson correlation between mutant and wild type with respect to the Diameter (A) (P-value = $2.{91}^{-11}$) and Leaf area (B) (P-value = $1.{53}^{-5}$).

Figure 2

Changes in predicted sum of Fluxes. Shown are the optimal values of the Euclidean distance (displayed on y-axis) at each corresponding time point or time step (displayed on x-axis). Distances were visualized by plotting the Euclidean distance value above each bar. (A) Displayed are the sums of flux difference between wild type and mutant at each corresponding time point. (B) Displayed are the sums of flux differences between wild type fluxes and mutant fluxes between each two time consecutive points.

Figure 3

Overview of K-means clustering based on relative changes in reaction fluxes. K-means with Euclidean distance was used to identify seven clusters (C) of reactions (excluding transporters and artificial reactions). (A) Shows flux difference values normalized to the absolute maximum difference of each reaction for each time point. Corresponding nominal flux differences are shown in (B).

Figure 4

Pathways enriched in reactions with highly altered fluxes. Displayed are pathways significantly ($\mathit{P}<=0.05$) enriched in regulated reactions based on (A) relative and (B) nominal differences. They are descending ordered according to their respective P-value. Size of the dots corresponds to the count of reactions present in the pathway. Bar size represents the negative logarithm of the P-value (x-axis).