Experimental Laboratory

Bacterial communities are inherently characterized by recurring cycles of feast and famine. Since bacterial biomass on Earth stays approximately constant, every new bacterial cell must be balanced by the death of another cell; next to lysis due to phage infection, one of the main causes for bacterial death is nutrient starvation. Especially in carbon starvation, bacterial cells are deprived of their central energy source, leading to dissipating chemical gradients, accumulation of oxidative damage and reduced protein synthesis. A way to counteract these processes and promote survival is to scavenge biomass released by dead cells. In bacterial communities, this creates a distinct form of competition in which cells compete for recycled necromass; prolonged survival then depends on population-specific physiological traits such as nutrient uptake and maintenance demand. Since bacterial cells deplete available nutrients rapidly, they spend extended times in starvation, optimizing physiological traits that enhance survival under nutrient-depleted conditions is presumably under equally strong selection as optimizing exponential growth.
In this work, we develop a quantitative framework for the competition of isogenic Escherichia coli populations whose starvation physiology is tuned by prior growth history. If starved individually, fast-grown populations have higher maintenance demands and die slightly faster, while slow-grown populations are better adapted to starvation, leading to a slower decline in cell density. However, when mixing both populations, these differences are exaggerated in a frequency-dependent manner: Populations with high maintenance demand die several-fold faster than in monoculture, whereas better adapted populations reduce their death rate below that of stationary-phase adapted monocultures. Our framework shows that survival in competition is governed by a self-amplifying necromass recycling feedback, in which the majority population sets the shared energy pool, constituted by released biomass from dead cells. If the majority population dies slowly, little energy is released, and the shared energy pool remains low, while for a fast-dying majority population, the shared energy pool rises. The minority strain therefore experiences an external energy pool that is different from its own monoculture, which effectively changes its death rate, thus exaggerating prior fitness differences arising from history-dependent physiological traits between both populations. Our results show that phenotypic differences alone are sufficient to predict selection under starvation, and our framework provides a mechanistic and predictive basis as a starting point for modelling multi-species environments such as biofilms or trade-off scenarios between rapid growth and long-term survival.

Adaptive stress resistance in microbes is mostly attributed to the expression of stress response genes, including heat-shock proteins. Here, we report a response of E. coli to heat stress caused by degradation of an enzyme in the methionine biosynthesis pathway (MetA). While MetA degradation can inhibit growth, which by itself is detrimental for fitness, we show that it directly benefits survival at temperatures exceeding 50°C, increasing survival chances by more than 1,000-fold. Using both experiments and mathematical modeling, we show quantitatively how protein expression, degradation rates, and environmental stressors cause long-term growth inhibition in otherwise habitable conditions. Because growth inhibition can be abolished with simple mutations, namely point mutations of MetA and protease knockouts, we interpret the breakdown of methionine synthesis as a system that has evolved to halt growth at high temperatures, analogous to “thermal fuses” in engineering that shut off electricity to prevent overheating.

Fitness of bacteria is determined both by how fast cells grow when nutrients are abundant and by how well they survive when conditions worsen. Here, we study how prior growth conditions affect the death rate of Escherichia coli during carbon starvation. We control the growth rate prior to starvation either via the carbon source or via a carbon‐limited chemostat. We find a consistent dependence where death rate depends on the prior growth conditions only via the growth rate, with slower growth leading to exponentially slower death. Breaking down the observed death rate into two factors, maintenance rate and recycling yield, reveals that slower growing cells display a decreased maintenance rate per cell volume during starvation, thereby decreasing their death rate. In contrast, the ability to scavenge nutrients from carcasses of dead cells (recycling yield) remains constant. Our results suggest a physiological trade‐off between rapid proliferation and long survival. We explore the implications of this trade‐off within a mathematical model, which can rationalize the observation that bacteria outside of lab environments are not optimized for fast growth.

To break down organismal fitness into molecular contributions, costs and benefits of cellular components must be analyzed in all phases of the organism’s life cycle. Here, we establish the required quantitative approach for the death phase of the model bacterium Escherichia coli. We show that in carbon starvation, an exponential decay of viability emerges as a collective phenomenon, with viable cells recycling nutrients from cell carcasses to maintain viability. The observed collective death rate is determined by the maintenance rate of viable cells and the amount of nutrients recovered from dead cells. Using this relation, we study the cost of a wasteful enzyme during starvation and the benefit of the stress response sigma factor RpoS. While the enzyme increases maintenance and thereby the death rate, RpoS improves biomass recycling, decreasing the death rate. Our approach thus enables quantitative analyses of how cellular components affect the survival of non-growing cells.

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TUM Press Release (deutsch)

TUM Press Release (english)

A grand challenge of systems biology is to predict the kinetic responses of living systems to perturbations starting from the underlying molecular interactions. Changes in the nutrient environment have long been used to study regulation and adaptation phenomena in microorganisms and they remain a topic of active investigation. Although much is known about the molecular interactions that govern the regulation of key metabolic processes in response to applied perturbations, they are insufficiently quantified for predictive bottom-up modelling. Here we develop a top-down approach, expanding the recently established coarse-grained proteome allocation models from steady-state growth into the kinetic regime. Using only qualitative knowledge of the underlying regulatory processes and imposing the condition of flux balance, we derive a quantitative model of bacterial growth transitions that is independent of inaccessible kinetic parameters. The resulting flux-controlled regulation model accurately predicts the time course of gene expression and biomass accumulation in response to carbon upshifts and downshifts (for example, diauxic shifts) without adjustable parameters. As predicted by the model and validated by quantitative proteomics, cells exhibit suboptimal recovery kinetics in response to nutrient shifts owing to a rigid strategy of protein synthesis allocation, which is not directed towards alleviating specific metabolic bottlenecks. Our approach does not rely on kinetic parameters, and therefore points to a theoretical framework for describing a broad range of such kinetic processes without detailed knowledge of the underlying biochemical reactions.