Metabolic robustness to growth temperature of cold adapted bacterium
Riccardi, C.; Calvanese, M.; Ghini, V.; Alonso-Vasquez, T.; Perrin, E.; Turano, P.; Giurato, G.; Weisz, A.; Parrilli, E.; Tutino, M. L.; Fondi, M.
Show abstract
Microbial communities experience continuous environmental changes, among which temperature fluctuations are arguably 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 possible adaptations of microbial communities to a changing environment. In this work, we investigated which are the mechanisms through which metabolic homeostasis is maintained in a cold-adapted bacterium during growth at temperatures that differ widely (15 and 0{degrees}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 two 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.
Matching journals
The top 5 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Mixing and matching methylotrophic enzymes to design a novel methanol utilization pathway in E. coli 96%
- Thermodynamic limitations of metabolic strategies for PHB production from formate and fructose in Cupriavidus necator 95%
- Evolution guided tolerance engineering of Pseudomonas putida KT2440 for production of the sustainable aviation fuel precursor isoprenol 95%
Similar papers in this journal
- The impact of PrsA over-expression on the Bacillus subtilis transcriptome during fed-batch fermentation of alpha-amylase production 95%
- The isolate Caproiciproducens sp. 7D4C2 produces n-caproate at mildly acidic conditions from hexoses: genome and rBOX comparison with related strains and chain-elongating bacteria 94%
- Machine learning uncovers a data-driven transcriptional regulatory network for the Crenarchaeal thermoacidophile Sulfolobus acidocaldarius 94%
Similar papers in this journal
- Metabolic Response of a Chemolithoautotrophic Archaeon to Carbon Limitation 95%
- Reconstruction and analysis of thermodynamically-constrained models reveal metabolic responses of a deep-sea bacterium to temperature perturbations 95%
- Integrative genome-scale metabolic modeling reveals versatile metabolic strategies for methane utilization in Methylomicrobium album BG8 94%
Similar papers in this journal
- Quantitative Dynamic Analysis of de novo Sphingolipid Biosynthesis in Arabidopsis thaliana 93%
- Enzyme-constrained models and omics analysis of Streptomyces coelicolor reveal metabolic changes that enhance heterologous production 93%
- Quantitative modeling of pentose phosphate pathway response to oxidative stress reveals a cooperative regulatory strategy 93%
Similar papers in this journal
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.