Metabolic Robustness to Growth Temperature of a Cold- Adapted Marine Bacterium

Abstract

Microbial communities experience continuous environmental changes, with temperature fluctuations being the most impacting. This is particularly important considering the ongoing global warming but also in the "simpler" context of seasonal variability of sea-surface temperature. Understanding how microorganisms react at the cellular level can improve our understanding of their possible adaptations to a changing environment. In this work, we investigated the mechanisms through which metabolic homeostasis is maintained in a cold-adapted marine bacterium during growth at temperatures that differ widely (15 and 0°C). We have quantified its intracellular and extracellular central metabolomes together with changes occurring at the transcriptomic level in the same growth conditions. This information was then used to contextualize a genome-scale metabolic reconstruction, and to provide a systemic understanding of cellular adaptation to growth at 2 different temperatures. Our findings indicate a strong metabolic robustness at the level of the main central metabolites, counteracted by a relatively deep transcriptomic reprogramming that includes changes in gene expression of hundreds of metabolic genes. We interpret this as a transcriptomic buffering of cellular metabolism, able to produce overlapping metabolic phenotypes, despite the wide temperature gap. Moreover, we show that metabolic adaptation seems to be mostly played at the level of few key intermediates (e.g., phosphoenolpyruvate) and in the cross talk between the main central metabolic pathways. Overall, our findings reveal a complex interplay at gene expression level that contributes to the robustness/resilience of core metabolism, also promoting the leveraging of state-of-the-art multi-disciplinary approaches to fully comprehend molecular adaptations to environmental fluctuations. IMPORTANCE This manuscript addresses a central and broad interest topic in environmental microbiology, i.e. the effect of growth temperature on microbial cell physiology. We investigated if and how metabolic homeostasis is maintained in a cold-adapted bacterium during growth at temperatures that differ widely and that match measured changes on the field. Our integrative approach revealed an extraordinary robustness of the central metabolome to growth temperature. However, this was counteracted by deep changes at the transcriptional level, and especially in the metabolic part of the transcriptome. This conflictual scenario was interpreted as a transcriptomic buffering of cellular metabolism, and was investigated using genome-scale metabolic modeling. Overall, our findings reveal a complex interplay at gene expression level that contributes to the robustness/resilience of core metabolism, also promoting the use of state-of-the-art multi-disciplinary approaches to fully comprehend molecular adaptations to environmental fluctuations.

Keywords: cold-adaptation; genome-scale modeling; metabolomics; transcriptomics.

FIG 1

(A) Growth curve of PhTAC125 at 0°C. Numbers indicate the sampling points for metabolomic and transcriptomic experiments. (B) Growth curve of PhTAC125 at 15°C. Numbers indicate the sampling points for the metabolomic experiments. The transcriptome of PhTAC125 growing cells was sampled at time point 1. (C) Glutamate and gluconate uptake at 0°C. (D) Glutamate and gluconate uptake at 15°C.

FIG 2

(A) Normalized intracellular metabolites concentration across the 5 time points. (B) Correlation of each intracellular metabolite at 0 and 15°C (asterisks indicate Spearman correlation P value < 0.05). (C) Comparison of the concentration of each intracellular metabolite at 0 and 15°C (asterisks indicate statistically significant correlations, i.e., P value < 0.05). (D) All-against-all correlations between intracellular metabolites at 0 and 15°C.

FIG 3

(A) Normalized extracellular metabolites concentration across the 5 time points. (B) Correlation of each intracellular metabolite at 0 and 15°C (asterisks indicate Spearman correlation P value < 0.05) (C) Comparison of the concentration (in a.u.) of each extracellular metabolite at 0 and 15°C (asterisks indicate statistically significant correlations, i.e., P value < 0.05). (D) All-against-all correlations between extracellular metabolites at 0 and 15°C.

FIG 4

(A) Volcano plot of up- and downregulated genes. (B) COG categories of up- and downregulated genes (asterisks indicate significantly enriched functional categories). (C) Percentage of differentially expressed genes over the total gene of a subset of PhTAC125 central metabolic pathways. Differentially expressed genes are represented in green (downregulated) or red (upregulated).

FIG 5

(A) Comparison between experimental and simulated growth rates. (B) Distribution of TPM values of gene expression at T1. (C) log₂FC of metabolites intracellular concentration at T1 during growth at 15°C versus growth at 0°C. (D) REMI output in terms of Maximal and Theoretical Consistency Scores following transcriptomic and metabolomic data integration. (E) Genome-scale modeling prediction of metabolites log₂FC concentration showing a |log₂FC| > 1 during the metabolomic experiment. (F) Correlation between log₂FC of simulated (y axis) versus measured (x axis) internal metabolites concentration. (G) Working model of key metabolic adjustments at the 2 different temperatures. Blue and red arrows indicate fluxes predicted to increase during growth at 0° and 15°C, respectively. Boxplots represent the average flux values for each pathway. Asterisks indicate a significative (i.e., P value < 0.05) Kolmogorov-Smirnov statistical test.