Trends in Biotechnology
OpinionMetabolic variability in bioprocessing: implications of microbial phenotypic heterogeneity
Section snippets
Impact of microbial phenotypic heterogeneity on microbial bioprocesses
Until now, microbial phenotypic heterogeneity has generally only been explored in basic scientific research, and few studies have taken this important phenomenon into account in the context of industrial biotechnological applications 1, 2. The production of bio-based compounds is dependent on stochastic cellular mechanisms, leading to difficulties in controlling bioprocessing. It is thus of primary importance to increase our knowledge of the mechanisms involved in phenotypic diversification.
What are the sources of microbial phenotypic heterogeneity?
Although stochasticity occurs at the level of gene expression [3], stochastic effects can be observed at the level of the metabolic pathways themselves (Figure 1A and Box 1). Metabolic reactions can be directly influenced by bioprocessing conditions, and thus deeper understanding of their influence of microbial phenotypic heterogeneity would lead to technical solutions that can be implemented in the design of more robust microbial cell factories. The fact that a clonal population of microbial
How to measure phenotypic heterogeneity?
Distributions of cell parameters are generally measured by optical methods such as flow cytometry or microscopy. Flow cytometry is a very promising technique because it can be coupled to the bioreactor, delivering on-line measurement of cell distributions during a bioprocess, but the technique also has some drawbacks that can be potentially avoided by complementary techniques. We now address the actual methodologies available for the characterization of microbial phenotypic heterogeneity in
How to control microbial phenotypic heterogeneity?
Technologically relevant methodologies aimed at controlling microbial phenotypic heterogeneity will now be considered. Metabolic engineering can be designed to reduce phenotypic plasticity. To control metabolic variability, cofactor engineering, in particular, must be considered. In addition, synthetic biology may provide access to specific DNA components and devices aimed at controlling noise in gene expression 41, 42. Bioreactor design can also be modified to exploit phenotypic heterogeneity.
Concluding remarks and future perspectives
Microbial phenotypic heterogeneity clearly plays an important role in bioprocess robustness, but additional data will be necessary to address its implications more fully. Indeed, recent results have revealed stochasticity at the level of metabolic reactions without any intervention of gene expression mechanisms, multiplying the possible sources of phenotypic heterogeneity. Dedicated single cell analytical tools are thus needed for this purpose, and solutions are already available for this
Acknowledgments
F.D. gratefully acknowledges the Belgian fund for scientific research [Fonds de la Recherche Scientifique (FRS), Fonds National de la Recherche Scientifique (FNRS), Brussels, Belgium] for support through several research grants. Q.Z. is the recipient of a FRIA (Fonds pour la Formation à la Recherche dans l’Industrie et dans l’Agriculture) PhD grant provided by the FRS–FNRS. A.R.L acknowledges support by CONACyT and PROMEP grants 183911 and 10828, respectively. F.D. and A.R.L. acknowledge the
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