A new collaborative paper with Greg Bokinsky has just came out in Nature Communications! You find it here: paper link.
When temperature changes, the kinetics of enzymes change (think of the Arrhenius law) as well as their diffusive properties (the diffusion coefficient depends linearly on temperature, at constant viscosity). The change in the diffusion coefficient of cytosolic and membrane proteins is different since membrane proteins are dependent on the change in membrane fluidity (via, the membrane viscosity change). Some of those changes are large, whereas others are small. Their magnitudes depend on the precise biochemistry of the associated molecules, which is outside of the realm of control by the microbe, and all molecular changes to temperature propagate in a nonlinear way to the phenotype. The only measure a microbe has — to somehow control this — is by adjusting protein expression and hereby change its molecular composition, including the lipid composition of its membrane. To achieve this, the microbe needs mechanisms akin to thermometers and homeostatic controls — similarly to the thermostats in your house!
Dr Greg Bokinsky, from the nanoscience department of the TU Delft, developed a mass spectrometry based method for the measurement of the most important intermediates and enzymes of fatty acid and lipid metabolism in Escherichia coli. He used this method to monitor the temperature response of this metabolic system as function of time. Since this bacterium does not measure membrane fluidity, like other organisms do, he was interested in figuring out how fluxes in lipid metabolism are repartitioned in response to temperature to adjust the membrane lipid composition such that membrane fluidity remains (almost) independent of temperature.
We got involved in this because Greg found that several adaptive mechanisms act concertedly and on different time scales. Metabolic regulation on a time scale of second to minutes, and gene expression adaptation on a time scale of tens of minutes. Also, it remained unclear which enzymes were likely temperature sensitive — this is hard to determine in vitro because of the complexity of the substrates and the cell-free extract assays. We helped Greg to figure these things out, together with his students. The model we eventually ended up with was remarkably simple and powerful in describing the data, giving us confidence in our understanding of this complex adaptive mechanism.
What I enjoyed most of this study, in addition to solving this puzzle with Greg, is the experiment that addresses the consequences of temperature maladaptation. It turns that the diffusion of one of the components of the respiratory chain can become rate-limiting when the temperature drops, the membrane fluidity is increased which effectively removes this rate limitation and restores fast growth. This is shown in Figure 5 of the paper, when E. coli grows on succinate.
We hope you enjoy this work!
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