All free-living microorganisms homeostatically maintain the fluidity of their membranes by adapting lipid composition to environmental temperatures. Here, we quantify enzymes and metabolic intermediates of the Escherichia coli fatty acid and phospholipid synthesis pathways, to describe how this organism measures temperature and restores optimal membrane fluidity within a single generation after a temperature shock. A first element of this regulatory system is a temperature-sensitive metabolic valve that allocates flux between the saturated and unsaturated fatty acid synthesis pathways via the branchpoint enzymes FabI and FabB. A second element is a transcription-based negative feedback loop that counteracts the temperature-sensitive valve. The combination of these elements accelerates membrane adaptation by causing a transient overshoot in the synthesis of saturated or unsaturated fatty acids following temperature shocks. This strategy is comparable to increasing the temperature of a water bath by adding water that is excessively hot rather than adding water at the desired temperature. These properties are captured in a mathematical model, which we use to show how hard-wired parameters calibrate the system to generate membrane compositions that maintain constant fluidity across temperatures. We hypothesize that core features of the E. coli system will prove to be ubiquitous features of homeoviscous adaptation systems.

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One-carbon (C1) feedstocks, such as carbon monoxide (CO), formate (HCO2H), methanol (CH3OH), and methane (CH4), can be obtained either through stepwise electrochemical reduction of CO2 with renewable electricity or via processing of organic side streams. These C1 substrates are increasingly investigated in biotechnology as they can contribute to a circular carbon economy. In recent years, noncanonical redox cofactors (NCRCs) emerged as a tool to generate synthetic electron circuits in cell factories to maximize electron transfer within a pathway of interest. Here, we argue that expanding the use of NCRCs in the context of C1-driven bioprocesses will boost product yields and facilitate challenging redox transactions that are typically out of the scope of natural cofactors due to inherent thermodynamic constraints.@en

Non-dividing Saccharomyces cerevisiae cultures are highly relevant for fundamental and applied studies. However, cultivation conditions in which non-dividing cells retain substantial metabolic activity are lacking. Unlike stationary-phase (SP) batch cultures, the current experimental paradigm for non-dividing yeast cultures, cultivation under extreme calorie restriction (ECR) in retentostat enables non-dividing yeast cells to retain substantial metabolic activity and to prevent rapid cellular deterioration. Distribution of F-actin structures and single-cell copy numbers of specific transcripts revealed that cultivation under ECR yields highly homogeneous cultures, in contrast to SP cultures that differentiate into quiescent and non-quiescent subpopulations. Combined with previous physiological studies, these results indicate that yeast cells subjected to ECR survive in an extended G₁ phase. This study demonstrates that yeast cells exposed to ECR differ from carbon-starved cells and offer a promising experimental model for studying non-dividing, metabolically active, and robust eukaryotic cells.

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The inherent stochasticity of molecular reactions prevents us from predicting the exact state of single-cells in a population. However, when a population grows at steady-state, the probability to observe a cell with particular combinations of properties is fixed. Here we validate and exploit existing theory on the statistics of single-cell growth in order to predict the probability of phenotypic characteristics such as cell-cycle times, volumes, accuracy of division and cell-age distributions, using real-time imaging data for Bacillus subtilis and Escherichia coli. Our results show that single-cell growth-statistics can accurately be predicted from a few basic measurements. These equations relate different phenotypic characteristics, and can therefore be used in consistency tests of experimental single-cell growth data and prediction of single-cell statistics. We also exploit these statistical relations in the development of a fast stochastic-simulation algorithm of single-cell growth and protein expression. This algorithm greatly reduces computational burden, by recovering the statistics of growing cell-populations from the simulation of only one of its lineages. Our approach is validated by comparison of simulations and experimental data. This work illustrates a methodology for the prediction, analysis and tests of consistency of single-cell growth and protein expression data from a few basic statistical principles.

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