Literature DB >> 24408611

Microbial heterogeneity affects bioprocess robustness: dynamic single-cell analysis contributes to understanding of microbial populations.

Frank Delvigne1, Philippe Goffin.   

Abstract

Heterogeneity or segregation of microbial populations has been the subject of much research, but the real impact of this phenomenon on bioprocesses remains poorly understood. The main reason for this lack of knowledge is the difficulty in monitoring microbial population heterogeneity under dynamic process conditions. The main concepts resulting in microbial population heterogeneity in the context of bioprocesses have been summarized by two distinct hypotheses. The first involves the individual history of microbial cells or the "path" followed during their residence time inside the process equipment. The second hypothesis involves a coordinated response by the microbial population as a bet-hedging strategy, in order to cope with process-related stresses. The respective contribution of each hypothesis to microbial heterogeneity in bioprocesses is still unclear. This illustrates the fact that, although microbial phenotypic heterogeneity has been thoroughly investigated at a fundamental level, the implications of this phenomenon in the context of microbial bioprocesses are still subject to debate. At this time, automated flow cytometry is the best technique for investigating microbial heterogeneity under process conditions. However, dedicated software and relevant biomarkers are needed for the proper integration of flow cytometry as a bioprocess control tool.
Copyright © 2013 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Keywords:  Bioreactor heterogeneity; Microbial stress; Scale-up; Single cell; Stress biosensor

Mesh:

Substances:

Year:  2013        PMID: 24408611     DOI: 10.1002/biot.201300119

Source DB:  PubMed          Journal:  Biotechnol J        ISSN: 1860-6768            Impact factor:   4.677


  34 in total

1.  Making variability less variable: matching expression system and host for oxygenase-based biotransformations.

Authors:  Martin Lindmeyer; Daniel Meyer; Daniel Kuhn; Bruno Bühler; Andreas Schmid
Journal:  J Ind Microbiol Biotechnol       Date:  2015-04-16       Impact factor: 3.346

Review 2.  Stochastic developmental variation, an epigenetic source of phenotypic diversity with far-reaching biological consequences.

Authors:  Günter Vogt
Journal:  J Biosci       Date:  2015-03       Impact factor: 1.826

3.  Coculturing Bacteria Leads to Reduced Phenotypic Heterogeneities.

Authors:  Jasmine Heyse; Benjamin Buysschaert; Ruben Props; Peter Rubbens; Andre G Skirtach; Willem Waegeman; Nico Boon
Journal:  Appl Environ Microbiol       Date:  2019-04-04       Impact factor: 4.792

4.  Potential of Integrating Model-Based Design of Experiments Approaches and Process Analytical Technologies for Bioprocess Scale-Down.

Authors:  Peter Neubauer; Emmanuel Anane; Stefan Junne; Mariano Nicolas Cruz Bournazou
Journal:  Adv Biochem Eng Biotechnol       Date:  2021       Impact factor: 2.635

5.  Plasmid expression level heterogeneity monitoring via heterologous eGFP production at the single-cell level in Cupriavidus necator.

Authors:  Catherine Boy; Julie Lesage; Sandrine Alfenore; Nathalie Gorret; Stéphane E Guillouet
Journal:  Appl Microbiol Biotechnol       Date:  2020-05-02       Impact factor: 4.813

6.  Bacillus sp.: A Remarkable Source of Bioactive Lipopeptides.

Authors:  A Théatre; A C R Hoste; A Rigolet; I Benneceur; M Bechet; M Ongena; M Deleu; P Jacques
Journal:  Adv Biochem Eng Biotechnol       Date:  2022       Impact factor: 2.635

Review 7.  Metabolic Engineering Strategies for Improved Lipid Production and Cellular Physiological Responses in Yeast Saccharomyces cerevisiae.

Authors:  Wei Jiang; Chao Li; Yanjun Li; Huadong Peng
Journal:  J Fungi (Basel)       Date:  2022-04-21

Review 8.  Beyond the bulk: disclosing the life of single microbial cells.

Authors:  Katrin Rosenthal; Verena Oehling; Christian Dusny; Andreas Schmid
Journal:  FEMS Microbiol Rev       Date:  2017-11-01       Impact factor: 16.408

9.  The dynamic balance of import and export of zinc in Escherichia coli suggests a heterogeneous population response to stress.

Authors:  Hiroki Takahashi; Taku Oshima; Jon L Hobman; Neil Doherty; Selina R Clayton; Mudassar Iqbal; Philip J Hill; Toru Tobe; Naotake Ogasawara; Shigehiko Kanaya; Dov J Stekel
Journal:  J R Soc Interface       Date:  2015-05-06       Impact factor: 4.118

10.  Variability in subpopulation formation propagates into biocatalytic variability of engineered Pseudomonas putida strains.

Authors:  Martin Lindmeyer; Michael Jahn; Carsten Vorpahl; Susann Müller; Andreas Schmid; Bruno Bühler
Journal:  Front Microbiol       Date:  2015-10-01       Impact factor: 5.640

View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.