Microbial community profiling using high-throughput sequencing relies in part on the preservation of the DNA and the effectiveness of the DNA extraction method. This study aimed at understanding to what extent these parameters affect the profiling. We obtained samples treated with and without a preservation solution. Also, we compared DNA extraction kits from Qiagen and Zymo-Research. The types of samples were defined strains, both as single species and mixtures, as well as undefined indigenous microbial communities from soil. We show that the use of a preservation solution resulted in substantial changes in the 16S rRNA gene profiles either due to an overrepresentation of Gram-positive bacteria or to an underrepresentation of Gram-negative bacteria. In addition, 16S rRNA gene profiles were substantially different depending on the type of kit that was used for extraction. The kit from Zymo extracted DNA from different types of bacteria in roughly equal amounts. In contrast, the kit from Qiagen preferentially extracted DNA from Gram-negative bacteria while DNA from Gram-positive bacteria was extracted less effectively. These differences in kit performance strongly influenced the interpretation of our microbial ecology studies.
Fine-tuning cellular physiology in response to intracellular and environmental cues requires precise temporal and spatial control of gene expression. High-resolution imaging technologies to detect mRNAs and their translation state have revealed that all living organisms localize mRNAs in subcellular compartments and create translation hotspots, enabling cells to tune gene expression locally. Therefore, mRNA localization is a conserved and integral part of gene expression regulation from prokaryotic to eukaryotic cells. In this Review, we discuss the mechanisms of mRNA transport and local mRNA translation across the kingdoms of life and at organellar, subcellular and multicellular resolution. We also discuss the properties of messenger ribonucleoprotein and higher order RNA granules and how they may influence mRNA transport and local protein synthesis. Finally, we summarize the technological developments that allow us to study mRNA localization and local translation through the simultaneous detection of mRNAs and proteins in single cells, mRNA and nascent protein single-molecule imaging, and bulk RNA and protein detection methods.
Julius Battjes will work on the ZeroYeast project together with Chr Hansen, and make a proteome-constrained model of Pichia kluyveri with the aim to transfer knowledge from S cerevisiae to another industrial yeast.
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.
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.
Many different organisms secrete ‘overflow products’ in
conditions where they grow and reproduce fast. For example, many yeasts produce
ethanol, Escherichia coli produces
acetate, and cancer cells produce lactate. This behaviour seems
counter-intuitive because the production of overflow products is inefficient in
terms of energy. The cells do not fully use the energy stored in their growth
substrates at high growth rates, while they do fully use this energy at lower
To study this phenomenon, many mathematical/computational
models have been proposed, based on different biological fundaments and using different
assumptions. However, since the models can all reproduce overflow metabolism, no
final conclusion could be drawn about which model is closest to reality.
We therefore decided to analyse all constraint-based
optimization approaches that describe overflow metabolism. Although these
models appear very different: the approaches range from relatively simple flux
balance analyses to self-fabricating metabolism and expression models, we could
rewrite all of them in one standard form. We then looked for a common feature,
for this could unveil the cause of overflow metabolism, and for differences,
for these can be used to design experiments that test the different models and
We found that all models predict overflow metabolism when
two growth-limiting constraints are hit, while the specific nature of the
constraints differed between the models. This is in line with recent theory: (https://doi.org/10.1371/journal.pcbi.1006858).
We concluded that finding the true cause of overflow metabolism in a specific
organism thus amounts to proving which two constraints limit the growth of that
organism. However, we did not find such decisive proofs for any constraint.
Therefore, we decided to list all the proposed constraints, such that these can
hopefully all be tested by specific perturbation experiments in the future.