Microbial population heterogeneity versus bioreactor heterogeneity: Evaluation of Redox Sensor Green as an exogenous metabolic biosensor

Microbial heterogeneity in metabolic performances has attracted a lot of attention, considering its potential impact on industrial bioprocesses. However, little is known about the impact of extracellular perturbations (i.e. bioreactor heterogeneity) on cell‐to‐cell variability in metabolic performances (i.e. microbial population heterogeneity). In this work, we have evaluated the relevance of Redox Sensor Green (RSG) as an exogenous biosensor of metabolic activity at the single‐cell level. RSG signal is proportional to the activity of the electron transport chain and its signal is strongly affected by metabolic burden, availability of electron final acceptor, and side metabolisms (i.e. overflow and mixed acid fermentation). RSG can also be used for the estimation of the impact of scale‐down conditions on microbial metabolic robustness. The relationship linking averaged RSG activity and its cell‐to‐cell variability (noise) has been highlighted but seems unaffected by environmental perturbations.

[1]  E. O’Shea,et al.  Noise in protein expression scales with natural protein abundance , 2006, Nature Genetics.

[2]  T. Strovas,et al.  Respiration Response Imaging for Real-Time Detection of Microbial Function at the Single-Cell Level , 2010, Applied and Environmental Microbiology.

[3]  F. Bolivar,et al.  A novel plasmid vector designed for chromosomal gene integration and expression: use for developing a genetically stable Escherichia coli melanin production strain. , 2013, Plasmid.

[4]  C. Hewitt,et al.  Physiological responses to mixing in large scale bioreactors. , 2001, Journal of biotechnology.

[5]  Wolfgang Wiechert,et al.  Single-cell microfluidics: opportunity for bioprocess development. , 2014, Current opinion in biotechnology.

[6]  J. François,et al.  Use of noise in gene expression as an experimental parameter to test phenotypic effects , 2016, Yeast.

[7]  M. Boxus,et al.  Bioreactor mixing efficiency modulates the activity of a prpoS : : GFP reporter gene in E . coli , 2009 .

[8]  Jan Kok,et al.  Bet-hedging during bacterial diauxic shift , 2014, Proceedings of the National Academy of Sciences.

[9]  Alvaro R. Lara,et al.  Vitreoscilla hemoglobin expression in engineered Escherichia coli: improved performance in high cell-density batch cultivations. , 2011, Biotechnology journal.

[10]  Philippe Nghe,et al.  Single-Cell Dynamics Reveals Sustained Growth during Diauxic Shifts , 2013, PloS one.

[11]  T. Terwilliger,et al.  Protein tagging and detection with engineered self-assembling fragments of green fluorescent protein , 2005, Nature Biotechnology.

[12]  Martin Ackermann,et al.  Analysis of fluorescent reporters indicates heterogeneity in glucose uptake and utilization in clonal bacterial populations , 2013, BMC Microbiology.

[13]  P. Kallio,et al.  Bacterial hemoglobins and flavohemoglobins: versatile proteins and their impact on microbiology and biotechnology. , 2003, FEMS microbiology reviews.

[14]  V. de Lorenzo,et al.  Noise and robustness in prokaryotic regulatory networks. , 2010, Annual review of microbiology.

[15]  Meike T. Wortel,et al.  Lost in Transition: Start-Up of Glycolysis Yields Subpopulations of Nongrowing Cells , 2014, Science.

[16]  Frank Delvigne,et al.  A methodology for the design of scale-down bioreactors by the use of mixing and circulation stochastic models , 2006 .

[17]  Jay D Keasling,et al.  Development of biosensors and their application in metabolic engineering. , 2015, Current opinion in chemical biology.

[18]  Thomas Bley,et al.  Origin and analysis of microbial population heterogeneity in bioprocesses. , 2010, Current opinion in biotechnology.

[19]  Frank Delvigne,et al.  Metabolic variability in bioprocessing: implications of microbial phenotypic heterogeneity. , 2014, Trends in biotechnology.

[20]  A. Burkovski,et al.  Destabilized eYFP variants for dynamic gene expression studies in Corynebacterium glutamicum , 2012, Microbial biotechnology.

[21]  H. Westerhoff,et al.  The Glycolytic Flux in Escherichia coli Is Controlled by the Demand for ATP , 2002, Journal of bacteriology.

[22]  Peter Neubauer,et al.  Scale-down simulators for metabolic analysis of large-scale bioprocesses. , 2010, Current opinion in biotechnology.

[23]  S. Sørensen,et al.  Design of growth‐dependent biosensors based on destabilized GFP for the detection of physiological behavior of Escherichia coli in heterogeneous bioreactors , 2013, Biotechnology progress.

[24]  Frank Delvigne,et al.  Structured mixing model for stirred bioreactors: An extension to the stochastic approach , 2005 .

[25]  Frank Delvigne,et al.  Modelling of the substrate heterogeneities experienced by a limited microbial population in scale-down and in large-scale bioreactors , 2006 .

[26]  P. Swain,et al.  Intrinsic and extrinsic contributions to stochasticity in gene expression , 2002, Proceedings of the National Academy of Sciences of the United States of America.

[27]  J. Derisi,et al.  Single-cell proteomic analysis of S. cerevisiae reveals the architecture of biological noise , 2006, Nature.

[28]  S. Sørensen,et al.  A low-cost, multiplexable, automated flow cytometry procedure for the characterization of microbial stress dynamics in bioreactors , 2013, Microbial Cell Factories.

[29]  U. Alon,et al.  A Genome-Wide Analysis of Promoter-Mediated Phenotypic Noise in Escherichia coli , 2012, PLoS genetics.

[30]  M. Reuss,et al.  Multi-scale spatio-temporal modeling: lifelines of microorganisms in bioreactors and tracking molecules in cells. , 2010, Advances in biochemical engineering/biotechnology.

[31]  D. Webster,et al.  Site-directed mutagenesis of bacterial hemoglobin: the role of glutamine (E7) in oxygen-binding in the distal heme pocket. , 1998, Archives of Biochemistry and Biophysics.

[32]  K. Bettenbrock,et al.  Basic Regulatory Principles of Escherichia coli's Electron Transport Chain for Varying Oxygen Conditions , 2014, PloS one.

[33]  F. Delvigne,et al.  Dynamic single-cell analysis of Saccharomyces cerevisiae under process perturbation: comparison of different methods for monitoring the intensity of population heterogeneity , 2015 .

[34]  Robert Altenloh.  From a Novel , 1953 .

[35]  F. Delvigne,et al.  Microbial heterogeneity affects bioprocess robustness: Dynamic single‐cell analysis contributes to understanding of microbial populations , 2014, Biotechnology journal.

[36]  L. Blank,et al.  Metabolic and Transcriptional Response to Cofactor Perturbations in Escherichia coli , 2010, The Journal of Biological Chemistry.

[37]  Alvaro R. Lara,et al.  Aerobic expression of Vitreoscilla hemoglobin efficiently reduces overflow metabolism in Escherichia coli. , 2014, Biotechnology journal.

[38]  Patrick Fickers,et al.  Phenotypic variability in bioprocessing conditions can be tracked on the basis of on-line flow cytometry and fits to a scaling law. , 2015, Biotechnology journal.

[39]  Paul J. Choi,et al.  Quantifying E. coli Proteome and Transcriptome with Single-Molecule Sensitivity in Single Cells , 2010, Science.

[40]  V. Shahrezaei,et al.  The stochastic nature of biochemical networks. , 2008, Current opinion in biotechnology.

[41]  Cleo Kontoravdi,et al.  Genetically-encoded biosensors for monitoring cellular stress in bioprocessing. , 2015, Current opinion in biotechnology.