Anna-Lena Heins

Physiological heterogeneities in microbial populations and implications for physical stress tolerance.

BackgroundTraditionally average values of the whole population are considered when analysing microbial cell cultivations. However, a typical microbial population in a bioreactor is heterogeneous in most phenotypes measurable at a single-cell level. There are indications that such heterogeneity may be unfavourable on the one hand (reduces yields and productivities), but also beneficial on the other hand (facilitates quick adaptation to new conditions - i.e. increases the robustness of the fermentation process). Understanding and control of microbial population heterogeneity is thus of major importance for improving microbial cell factory processes.ResultsIn this work, a dual reporter system was developed and applied to map growth and cell fitness heterogeneities within budding yeast populations during aerobic cultivation in well-mixed bioreactors. The reporter strain, which was based on the expression of green fluorescent protein (GFP) under the control of the ribosomal protein RPL22a promoter, made it possible to distinguish cell growth phases by the level of fluorescence intensity. Furthermore, by exploiting the strong correlation of intracellular GFP level and cell membrane integrity it was possible to distinguish subpopulations with high and low cell membrane robustness and hence ability to withstand freeze-thaw stress. A strong inverse correlation between growth and cell membrane robustness was observed, which further supports the hypothesis that cellular resources are limited and need to be distributed as a trade-off between two functions: growth and robustness. In addition, the trade-off was shown to vary within the population, and the occurrence of two distinct subpopulations shifting between these two antagonistic modes of cell operation could be distinguished.ConclusionsThe reporter strain enabled mapping of population heterogeneities in growth and cell membrane robustness towards freeze-thaw stress at different phases of cell cultivation. The described reporter system is a valuable tool for understanding the effect of environmental conditions on population heterogeneity of microbial cells and thereby to understand cell responses during industrial process-like conditions. It may be applied to identify more robust subpopulations, and for developing novel strategies for strain improvement and process design for more effective bioprocessing.

Challenges in industrial fermentation technology research

Industrial fermentation processes are increasingly popular, and are considered an important technological asset for reducing our dependence on chemicals and products produced from fossil fuels. However, despite their increasing popularity, fermentation processes have not yet reached the same maturity as traditional chemical processes, particularly when it comes to using engineering tools such as mathematical models and optimization techniques. This perspective starts with a brief overview of these engineering tools. However, the main focus is on a description of some of the most important engineering challenges: scaling up and scaling down fermentation processes, the influence of morphology on broth rheology and mass transfer, and establishing novel sensors to measure and control insightful process parameters. The greatest emphasis is on the challenges posed by filamentous fungi, because of their wide applications as cell factories and therefore their relevance in a White Biotechnology context. Computational fluid dynamics (CFD) is introduced as a promising tool that can be used to support the scaling up and scaling down of bioreactors, and for studying mixing and the potential occurrence of gradients in a tank.

Cell mass and cell cycle dynamics of an asynchronous budding yeast population: experimental observations, flow cytometry data analysis, and multi-scale modeling.

Despite traditionally regarded as identical, cells in a microbial cultivation present a distribution of phenotypic traits, forming a heterogeneous cell population. Moreover, the degree of heterogeneity is notably enhanced by changes in micro‐environmental conditions. A major development in experimental single‐cell studies has taken place in the last decades. It has however not been fully accompanied by similar contributions within data analysis and mathematical modeling. Indeed, literature reporting, for example, quantitative analyses of experimental single‐cell observations and validation of model predictions for cell property distributions against experimental data is scarce. This study focuses on the experimental and mathematical description of the dynamics of cell size and cell cycle position distributions, of a population of Saccharomyces cerevisiae, in response to the substrate consumption observed during batch cultivation. The good agreement between the proposed multi‐scale model (a population balance model [PBM] coupled to an unstructured model) and experimental data (both the overall physiology and cell size and cell cycle distributions) indicates that a mechanistic model is a suitable tool for describing the microbial population dynamics in a bioreactor. This study therefore contributes towards the understanding of the development of heterogeneous populations during microbial cultivations. More generally, it consists of a step towards a paradigm change in the study and description of cell cultivations, where average cell behaviors observed experimentally now are interpreted as a potential joint result of various co‐existing single‐cell behaviors, rather than a unique response common to all cells in the cultivation. Biotechnol. Bioeng. 2013; 110: 812–826.

Applying Mechanistic Models in Bioprocess Development

The available knowledge on the mechanisms of a bioprocess system is central to process analytical technology. In this respect, mechanistic modeling has gained renewed attention, since a mechanistic model can provide an excellent summary of available process knowledge. Such a model therefore incorporates process-relevant input (critical process variables)-output (product concentration and product quality attributes) relations. The model therefore has great value in planning experiments, or in determining which critical process variables need to be monitored and controlled tightly. Mechanistic models should be combined with proper model analysis tools, such as uncertainty and sensitivity analysis. When assuming distributed inputs, the resulting uncertainty in the model outputs can be decomposed using sensitivity analysis to determine which input parameters are responsible for the major part of the output uncertainty. Such information can be used as guidance for experimental work; i.e., only parameters with a significant influence on model outputs need to be determined experimentally. The use of mechanistic models and model analysis tools is demonstrated in this chapter. As a practical case study, experimental data from Saccharomyces cerevisiae fermentations are used. The data are described with the well-known model of Sonnleitner and Käppeli (Biotechnol Bioeng 28:927-937, 1986) and the model is analyzed further. The methods used are generic, and can be transferred easily to other, more complex case studies as well.

Untargeted GC-MS Metabolomics Reveals Changes in the Metabolite Dynamics of Industrial Scale Batch Fermentations of Streptoccoccus thermophilus Broth

An industrial scale biomass production using batch or fed‐batch fermentations usually optimized by selection of bacterial strains, tuning fermentation media, feeding strategy, and temperature. However, in‐depth investigation of the biomass metabolome during the production may reveal new knowledge for better optimization. In this study, for the first time, the authors investigated seven fermentation batches performed on five Streptoccoccus thermophilus strains during the biomass production at Chr. Hansen (Denmark) in a real life large scale fermentation process. The study is designed to investigate effects of batch fermentation, fermentation time, production line, and yeast extract brands on the biomass metabolome using untargeted GC‐MS metabolomics. Processing of the raw GC‐MS data using PARAFAC2 revealed a total of 90 metabolites out of which 64 are identified. Partitioning of the data variance according to the experimental design was performed using ASCA and revealed that batch and fermentation time effects and their interaction term were the most significant effects. The yeast extract brand had a smaller impact on the biomass metabolome, while the production line showed no effect. This study shows that in‐depth metabolic analysis of fermentation broth provides a new tool for advanced optimization of high‐volume‐low‐cost biomass production by lowering the cost, increase the yield, and augment the product quality.