The growth rate of single bacterial cells is continuously disturbed by random fluctuations in biosynthesis rates and by deterministic cell-cycle events, such as division, genome duplication, and septum formation. It is not understood whether, and how, bacteria reject these growth-rate disturbances. Here, we quantified growth and constitutive protein expression dynamics of single Bacillus subtilis cells as a function of cell-cycle progression. We found that, even though growth at the population level is exponential, close inspection of the cell cycle of thousands of single Bacillus subtilis cells reveals systematic deviations from exponential growth. Newborn cells display varying growth rates that depend on their size. When they divide, growth-rate variation has decreased, and growth rates have become birth size independent. Thus, cells indeed compensate for growth-rate disturbances and achieve growth-rate homeostasis. Protein synthesis and growth of single cells displayed correlated, biphasic dynamics from cell birth to division. During a first phase of variable duration, the absolute rates were approximately constant and cells behaved as sizers. In the second phase, rates increased, and growth behavior exhibited characteristics of a timer strategy. These findings demonstrate that, just like size homeostasis, growth-rate homeostasis is an inherent property of single cells that is achieved by cell-cycle-dependent rate adjustments of biosynthesis and growth.
Growing cells in constant conditions at a fixed exponential growth rate in a shake flask — balanced growth — is arguably the most basic experiment in microbiology. We are so used to it that we sometimes forget to realise that this is not at all so obvious.
Not only the average growth rate remains fixed, but also the average cell size at birth and division. Either this emerges for free, and no active homeostatic mechanisms are required, or balanced growth requires evolved regulatory mechanisms. Analysis of experimental data, using a deceptively, simple theory, by Susan et al. , indicates that active homeostatic mechanisms are at work during steady-state growth and that without those balanced growth would be possible.
That cell-size homeostasis mechanisms exist and are required for balanced growth was already known — mostly from work by Suckjoon Jun’s lab. But what about growth-rate homeostasis? Do active mechanisms exist that maintain a constant average (and variance) of single-cell growth rate? Here we show that that is indeed the case for Bacillus subtilis.
We also found that its cell cycle is composed out of two phases. A first one, during which cells with variations in birth size correct size differences — they behave as “sizers” — and a second one during which cells behave as “timers” — they grow for a nearly fixed duration.
Our most surprising, and novel, finding was that cells experience a great disturbance of growth rate at division, with smaller cells outgrowing larger ones, while at division that growth rate variation has largely disappeared and growth rate became independent of cell size at birth.
So, also a growth-rate homeostasis mechanism is at work in Bacillus subtilis— like it is for cell size. How it works, we are currently figuring out.
Jurgen Haanstra and Bas Teusink collaborated with several research groups on interdisciplinary research combining wetlab experiments with computational models.
Haanstra and Teusink collaborated with research groups in Gothenburg (Sweden), Groningen and Heidelberg (Germany). Their work on a quantitative analysis of amino acid metabolism in liver cancer, with Jurgen as co-first author, was just published in PNAS.
Metabolic changes are a well-known hallmark of cancer, but an integrative view on how metabolic fluxes sustain (high) growth rates is often lacking. In this study the authors used a combination of experimental measurements and computational modelling to understand metabolism of HepG2 liver cancer cells during in vitro growth at different glucose levels. The measured fluxes of glucose, pyruvate, lactate and amino acids during growth were integrated with a genome-scale reconstruction of liver cancer metabolism to estimate the intracellular metabolic fluxes that should be operational to sustain this growth.
The analysis published in PNAS shows that many amino acids are consumed at rates exceeding the need for biomass formation. The intracellular fluxes indicate that a large part of the glutamine that is consumed is metabolised in the cytosol to support biosynthetic processes. A large part of the glutamate that results from these processes is excreted. This led to the hypothesis that inhibition of glutamate export would decrease growth and this was validated in an experiment where glutamate export was inhibited.
The work shows that genome-scale metabolic models constrained with measured fluxes can be used to estimate the effects of inhibitors of metabolic reactions.
Moritz Bieber, Christian Lieven (Biosustain, DTU), Brett Olivier and Bas Teusink (AIMMS, VU Amsterdam) and a worldwide community of scientists developed quality control tools for systems biology models in Nature Biotechnology
Mathematical models are key tool to
understand complex biological systems. In particular, constraint-based, genome
scale, models (GSM’s) can relate the physiological property of a cells, such as
growth and metabolism, to the chemical flows through the underlying metabolic
reaction network encoded by the genes. However, each model can in itself
contain thousands of components. Moreover there is a continuous increase in
number of these models being produced, using various semi-automated methods.
The question arises, how do you evaluate a models quality? Being able to answer
this question is critical to a model’s reproducibility and reusability.
In a recent correspondence published in Nature Biotechnology Moritz Bieber, Christian Lieven (Biosustain, DTU) and a large, worldwide community of scientists including Brett Olivier and Bas Teusink (AIMMS, VU Amsterdam) set out to address this problem. The tool presented in this contribution, MEMOTE, leverages the advantages of the latest community developed model encoding standards (SBML FBC), and implements a pipeline containing a set of tests that evaluates a model’s annotation, basic functionality and conceptual integrity. Hopefully, this will lead to a more efficient (re)use of genome-scale models in research and biotechnology.