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Gosselin-Monplaisir, T.

Publications and source records attributed to Gosselin-Monplaisir, T..

3 recordsLinked to original sources

Acetate promotes nutritional adaptation in Escherichia coli

The long-held view that acetate, one of the main fermentation by-products of Escherichia coli, is toxic to microbial growth is currently challenged. Here, we demonstrate that acetate promotes E. coli adaptation to nutrient changes by accelerating growth resumption, with as little as 250 {micro}M acetate being sufficient to shorten the lag phase by several hours. Acetate was found to be consumed via acetyl-CoA synthetase very early after the nutrient change. Transcriptomics, metabolomics and 13C-isotope labeling experiments show that acetate replenishes metabolic pools in the tricarboxylic acid cycle and upper glycolysis. Single-cell analyses reveal that acetate increases the adaptation speed of individual cells switching to the new nutrient. We conclude that the reuse of excreted acetate by E. coli facilitates metabolic adaptation by transiently replenishing central metabolite pools. This work identifies an unexpected role of acetate in the nutritional adaptation of E. coli, providing new insights into the physiological relevance of overflow metabolism. HighlightsO_LIAcetate facilitates E. coli adaptation from one nutrient to another. C_LIO_LILess than 250 {micro}M acetate is sufficient to halve lag times. C_LIO_LIAcetate helps replenish metabolite pools in central carbon metabolism. C_LIO_LIAcetate excretion is an adaptative strategy to overcome resource fluctuations. C_LI

systems biology↗

Acetate as a metabolic booster for glucose-based bioproduction in Escherichia coli

Escherichia coli, a key workhorse in industrial biotechnology, is commonly grown on glucose, which supports rapid growth but leads to acetate overflow, which instead inhibits growth, diverts carbon from the production pathway, and reduces productivity. Recent studies suggest that acetate may also have beneficial effects in glucose-grown E. coli, but its potential in bioprocesses remains unexplored. In this study, we systematically investigated acetates impact on bioproduction using a kinetic model of glucose and acetate metabolism in E. coli. The model predicts that acetate can enhance bioproduction in glucose-grown E. coli through three mechanisms: (i) by minimizing acetate overflow, thereby reducing carbon loss, (ii) by increasing acetyl-CoA levels, thereby boosting the biosynthetic flux of acetyl-CoA-derived compounds, and (iii) by promoting biomass accumulation, thus improving overall productivity. We experimentally validated the predictions of the model for mevalonate and 3-hydroxypropionate production, where acetate supplementation increased productivity by 117% and 34%, respectively. Our findings provide a valuable framework for optimizing E. coli-based bioprocesses and highlight acetates underutilized potential in biotechnology. By leveraging acetate from waste streams as a metabolic booster, this approach could contribute to more sustainable and environmentally friendly bioprocesses. Highlights- A kinetic model was used for rational optimization of E. coli-based bioprocesses - Acetate can enhance growth and production of acetyl-CoA-derived bioproducts - Model predictions were validated for mevalonate and 3-hydroxypropionate production - Acetate can be used as a metabolic booster for glucose-based bioproduction in E. coli

systems biology↗

MultiNMRFit: A software to fit 1D and pseudo-2D NMR spectra

Nuclear Magnetic Resonance (NMR) is widely used for quantitative analysis of metabolic systems. Accurate extraction of NMR signal parameters - such as chemical shift, intensity, coupling constants, and linewidth - is essential for obtaining information on the structure, concentration, and isotopic composition of metabolites. We present MultiNMRFit, an open-source software designed for high-throughput analysis of one-dimensional NMR spectra, whether acquired individually or as pseudo-2D experiments. MultiNMRFit extracts signal parameters (e.g. intensity, area, chemical shift, and coupling constants) by fitting the experimental spectra using built-in or user-defined signal models that account for multiplicity, providing high flexibility along with robust and reproducible results. The software is accessible both as a Python library and via a graphical user interface, enabling intuitive use by end-users with or without computational expertise. We demonstrate the robustness and flexibility of MultiNMRFit on 1H, 13C, and 31P NMR datasets collected in metabolomics and isotope labeling studies. Availability and ImplementationMultiNMRFit is implemented in Python 3 and was tested on Unix, Windows, and MacOS platforms. The source code and the documentation are freely distributed under GPL3 license at https://github.com/NMRTeamTBI/MultiNMRFit/. Supplementary dataSupplementary data are available online. ContactCyril Charlier (charlier@insa-toulouse.fr) and Pierre Millard (millard@insa-toulouse.fr)

bioinformatics↗