Abstract
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.
Highlights
Average values of the whole population are considered when analysing microbial cell cultivations
Understanding and control of microbial population heterogeneity is of major importance for improving biological production processes, and this has led to an increased interest from industry for methods to monitor population heterogeneity [6,7]
To investigate the behaviour of the growth reporter strain during different growth phases, batch cultivations in well-controlled stirred tank reactors were performed and the physiology was monitored both on the whole population by standard methods and on a single-cell level by flow cytometry
Summary
Average values of the whole population are considered when analysing microbial cell cultivations. A typical microbial population in a bioreactor is heterogeneous in most phenotypes measurable at a single-cell level. Research has shown that a typical microbial population in a bioreactor is heterogeneous in most phenotypes measurable at a single-cell level [1,2,3]. In industrial scale fermentation processes, phenotypic heterogeneity is further amplified as a result of deficient mixing, which leads to zones with diverse environmental conditions [8]. The microbial cells, experience sudden changes in the environmental conditions as they circulate from one zone to the other These changes may pose different types of stress (e.g. oxidative, temperature, pH) on the cells and affect their metabolism and fitness [4,8,9]. The heterogeneous environment in large-scale fermenters may lead to repeated cycles of production/re-assimilation of overflow metabolites and repeated induction/relaxation of stress responses resulting in reduced biomass yield and productivity [4,10]
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