Biology subjects

Terradot, G.

Publications and source records attributed to Terradot, G..

3 recordsLinked to original sources

bioRxiv2021

The proton motive force determines Escherichia coli's robustness to extracellular pH

Maintaining intracellular homeostases is a hallmark of life, and key physiological variables, such as cytoplasmic pH, osmotic pressure, and proton motive force (PMF), are typically interdependent. Using a mathematical model, we argue that near neutral pH homeostasis implies that cells must export ions other than protons to generate physiological electrical potential across their plasma membrane. For Escherichia coli, proton:ion antiporters are the only known cation efflux pumps, and we therefore predict that principal function of antiporters is to generate an out-of-equilibrium plasma membrane potential and so maintain the PMF at the constant levels observed. Consequently, the strength of the PMF determines the range of extracellular pH over which the cell is able to preserve its near neutral cytoplasmic pH, and the non-zero PMF is needed to maintain membrane potential. In support, we concurrently measure the PMF and cytoplasmic pH in single cells and demonstrate both that decreasing the PMFs strength impairs E. colis ability to maintain its pH and that artificially collapsing the PMF destroys the out-of-equilibrium plasma membrane potential. We further predict the observed ranges of extracellular pH for which three of E. colis antiporters are expressed, through defining their cost by the rate at which they divert protons from being imported to generate ATP. Taken together, our results suggest a new perspective on bacterial electrophysiology, where cells regulate the plasma membrane potential to maintain

microbiology↗

bioRxiv2019

Escherichia coli’s physiology can turn membrane voltage dyes into actuators

The electrical membrane potential (Vm) is one of the components of the electrochemical potential of protons across the biological membrane (proton motive force), which powers many vital cellular processes, and Vm also plays a role in signal transduction. Therefore, measuring it is of great interest, and over the years a variety of techniques has been developed for the purpose. In bacteria, given their small size, Nernstian membrane voltage probes are arguably the favourite strategy, and their cytoplasmic accumulation depends on Vm according to the Nernst equation. However, a careful calibration of Nernstian probes that takes into account the trade-offs between the ease with which the signal from the dye is observed, and the dyes interactions with cellular physiology, is rarely performed. Here we use a mathematical model to understand such trade-offs and, based on the knowledge gained, propose a general work-flow for the characterization of Nernstian dye candidates. We demonstrate the work-flow on the Thioflavin T dye in Escherichia coli, and identify conditions in which the dye turns from a Vm probe into an actuator.\n\nSIGNIFICANCE STATEMENT\n\nThe phospholipid bilayer of a biological membrane is virtually impermeable to charged molecules. Much like in a rechargeable battery, cells harness this property to store an electrical potential that fuels life reactions but also transduces signals. Measuring this electrical potential, also referred to as membrane voltage, is therefore of great interest and a variety of techniques have been employed for the purpose, starting as early as the 1930s. For the case of bacteria, which are smaller in size and possess a stiffer cell wall, arguably the most popular approach to measuring membrane voltage are Nernstian probes that accumulate across the bacterial membrane according to the Nernst potential. The present study characterizes the undesired effects Nernstian probes can have on cell physiology, which can be crucial for the accurate interpretation of experimental results. Using mathematical modelling and experiments, the study provides a general, simple workflow to characterise and minimise these effects.

biophysics↗

bioRxiv2018

Stochasticity of cellular growth: sources, propagation and consequences

Cellular growth impacts a range of phenotypic responses. Identifying the sources of fluctuations in growth and how they propagate across the cellular machinery can unravel mechanisms that underpin cell decisions. We present a stochastic cell model linking gene expression, metabolism and replication to predict growth dynamics in single bacterial cells. In addition to several population-averaged data, the model quantitatively recovers how growth fluctuations in single cells change across nutrient conditions. We develop a framework to analyse stochastic chemical reactions coupled with cell divisions and use it to identify sources of growth heterogeneity. By visualising cross-correlations we then determine how such initial fluctuations propagate to growth rate and affect other cell processes. We further study antibiotic responses and find that complex drug-nutrient interactions can both enhance and suppress heterogeneity. Our results provide a predictive framework to integrate single-cell and bulk data and draw testable predictions with implications for antibiotic tolerance, evolutionary biology and synthetic biology.

systems biology↗