Short- and Long-Term Biomarkers for Bacterial Robustness: A Framework for Quantifying Correlations between Cellular Indicators and Adaptive Behavior

The ability of microorganisms to adapt to changing environments challenges the prediction of their history-dependent behavior. Cellular biomarkers that are quantitatively correlated to stress adaptive behavior will facilitate our ability to predict the impact of these adaptive traits. Here, we present a framework for identifying cellular biomarkers for mild stress induced enhanced microbial robustness towards lethal stresses. Several candidate-biomarkers were selected by comparing the genome-wide transcriptome profiles of our model-organism Bacillus cereus upon exposure to four mild stress conditions (mild heat, acid, salt and oxidative stress). These candidate-biomarkers—a transcriptional regulator (activating general stress responses), enzymes (removing reactive oxygen species), and chaperones and proteases (maintaining protein quality)—were quantitatively determined at transcript, protein and/or activity level upon exposure to mild heat, acid, salt and oxidative stress for various time intervals. Both unstressed and mild stress treated cells were also exposed to lethal stress conditions (severe heat, acid and oxidative stress) to quantify the robustness advantage provided by mild stress pretreatment. To evaluate whether the candidate-biomarkers could predict the robustness enhancement towards lethal stress elicited by mild stress pretreatment, the biomarker responses upon mild stress treatment were correlated to mild stress induced robustness towards lethal stress. Both short- and long-term biomarkers could be identified of which their induction levels were correlated to mild stress induced enhanced robustness towards lethal heat, acid and/or oxidative stress, respectively, and are therefore predictive cellular indicators for mild stress induced enhanced robustness. The identified biomarkers are among the most consistently induced cellular components in stress responses and ubiquitous in biology, supporting extrapolation to other microorganisms than B. cereus. Our quantitative, systematic approach provides a framework to search for these biomarkers and to evaluate their predictive quality in order to select promising biomarkers that can serve to early detect and predict adaptive traits

Scale-up of an intensified bioprocess for the expansion of bovine adipose-derived stem cells (bASCs) in stirred tank bioreactors

Cultivated meat is an emerging field, aiming to establish the production of animal tissue for human consumption in an in vitro environment, eliminating the need to raise and slaughter animals for their meat. To realise this, the expansion of primary cells in a bioreactor is needed to achieve the high cell numbers required. The aim of this study was to develop a scalable, microcarrier based, intensified bioprocess for the expansion of bovine adipose-derived stem cells as precursors of fat and muscle tissue. The intensified bioprocess development was carried out initially in spinner flasks of different sizes and then translated to fully controlled litre scale benchtop bioreactors. Bioprocess intensification was achieved by utilising the previously demonstrated bead-to-bead transfer phenomenon and through the combined addition of microcarrier and medium to double the existing surface area and working volume in the bioreactor. Choosing the optimal time point for the additions was critical in enhancing the cell expansion. A significant fold increase of 114.19 ± 1.07 was obtained at the litre scale in the intensified bioprocess compared to the baseline (**p < .005). The quality of the cells was evaluated pre- and post-expansion and the cells were found to maintain their phenotype and differentiation capacity

Visualization and Curve-Parameter Estimation Strategies for Efficient Exploration of Phenotype Microarray Kinetics

The Phenotype MicroArray (OmniLog® PM) system is able to simultaneously capture a large number of phenotypes by recording an organism's respiration over time on distinct substrates. This technique targets the object of natural selection itself, the phenotype, whereas previously addressed '-omics' techniques merely study components that finally contribute to it. The recording of respiration over time, however, adds a longitudinal dimension to the data. To optimally exploit this information, it must be extracted from the shapes of the recorded curves and displayed in analogy to conventional growth curves.The free software environment R was explored for both visualizing and fitting of PM respiration curves. Approaches using either a model fit (and commonly applied growth models) or a smoothing spline were evaluated. Their reliability in inferring curve parameters and confidence intervals was compared to the native OmniLog® PM analysis software. We consider the post-processing of the estimated parameters, the optimal classification of curve shapes and the detection of significant differences between them, as well as practically relevant questions such as detecting the impact of cultivation times and the minimum required number of experimental repeats.We provide a comprehensive framework for data visualization and parameter estimation according to user choices. A flexible graphical representation strategy for displaying the results is proposed, including 95% confidence intervals for the estimated parameters. The spline approach is less prone to irregular curve shapes than fitting any of the considered models or using the native PM software for calculating both point estimates and confidence intervals. These can serve as a starting point for the automated post-processing of PM data, providing much more information than the strict dichotomization into positive and negative reactions. Our results form the basis for a freely available R package for the analysis of PM data

Bacterial growth: a statistical physicist's guide

Bacterial growth presents many beautiful phenomena that pose new theoretical challenges to statistical physicists, and are also amenable to laboratory experimentation. This review provides some of the essential biological background, discusses recent applications of statistical physics in this field, and highlights the potential for future research

Characterization of uptake and hydrolysis of fluorescein diacetate and carboxyfluorescein diacetate by intracellular esterases in Saccharomyces cerevisiae, which result in accumulation of fluorescent product.

Flow cytometry is a rapid and sensitive method which may be used for the detection of microorganisms in foods and drinks. A key requirement for this method is a sufficient fluorescence staining of the target cells. The mechanism of staining of the yeast Saccharomyces cerevisiae by fluorescein diacetate (FDA) and 5- (and 6-)carboxyfluorescein diacetate (cFDA) was studied in detail. The uptake rate of the prefluorochromes increased in direct proportion to the concentration and was not saturable, which suggests that transport occurs via a passive diffusion process. The permeability coefficient for cFDA was 1.3 x 10(-8) m s-1. Once inside the cell, the esters were hydrolyzed by intracellular esterases and their fluorescent products accumulated. FDA hydrolysis (at 40 degrees C) in cell extracts could be described by first-order reaction kinetics, and a rate constant (K) of 0.33 s-1 was calculated. Hydrolysis of cFDA (at 40 degrees C) in cell extracts was described by Michaelis-Menten kinetics with an apparent Vmax and Km of 12.3 nmol.min-1.mg of protein-1 and 0.29 mM, respectively. Accumulation of fluorescein was most likely limited by the esterase activity, since transport of FDA was faster than the hydrolysis rate. In contrast, accumulation of carboxyfluorescein was limited by the much slower transport of cFDA through the cell envelope. A simple mathematical model was developed to describe the fluorescence staining. The implications for optimal staining of yeast cells with FDA and cFDA are discussed