Samuel Telek

Phenotypic variability in bioprocessing conditions can be tracked on the basis of on-line flow cytometry and fits to a scaling law

Noise in gene and protein expression is a major cause for bioprocess deviation. However, this phenomenon has been only scarcely considered in real bioprocessing conditions. In this work, a scaling-law derived from genome-scale studies based on GFP reporter systems has been calibrated to an on-line flow cytometry device, allowing thus to get an insight at the level of promoter activity and associated noise during a whole microbial culture carried out in bioreactor. We show that most of the GFP reporter systems investigated and thus corresponding genes could be included inside the area covered by the scaling-law. The experimental results suggest that this scaling-law could be used to predict the dynamics of promoter activity, as well as the associated noise, in bioprocessing conditions. The knowledge acquired throughout this work could be used for the design of more robust expression systems.

Identification and characterization of EYD1, encoding an erythritol dehydrogenase in Yarrowia lipolytica and its application to bioconvert erythritol into erythrulose

In this study, gene YALI0F01650g has been isolated and characterized. Several experimental evidences suggest that the identified gene, renamed EYD1, encodes an erythritol dehydrogenase. An efficient bioreactor process for the bioconversion of erythritol into erythrulose was also developed. Using constitutive expression of EYD1 in a Y. lipolytica mutant containing a disrupted EYK1 gene, which encodes erythrulose kinase, erythrulose could be synthesized from erythritol at a rate of 0.116g/gDCW.h and with a bioconversion yield of 0.64g/g.

Enhancing erythritol productivity in Yarrowia lipolytica using metabolic engineering

Erythritol (1,2,3,4-butanetetrol) is a four-carbon sugar alcohol with sweetening properties that is used by the agrofood industry as a food additive. In this study, we demonstrated that metabolic engineering can be used to improve the production of erythritol from glycerol in the yeast Yarrowia lipolytica. The best results were obtained using a mutant that overexpressed GUT1 and TKL1, which encode a glycerol kinase and a transketolase, respectively, and in which EYK1, which encodes erythrulose kinase, was disrupted; the latter enzyme is involved in an early step of erythritol catabolism. In this strain, erythritol productivity was 75% higher than in the wild type; furthermore, the culturing time needed to achieve maximum concentration was reduced by 40%. An additional advantage is that the strain was unable to consume the erythritol it had created, further increasing the processs efficiency. The erythritol productivity values we obtained here are among the highest reported thus far.

Real-time monitoring of cell viability and cell density on the basis of a three dimensional optical reflectance method (3D-ORM): investigation of the effect of sub-lethal and lethal injuries

Cell density and cell viability have been followed on-line by using a three-dimensional optical reflectance method (3D-ORM) probe. This method has allowed to highlight the differences between a well-mixed and a scale-down bioreactor configured in order to reproduce mixing deficiencies during a fed-batch culture of Escherichia coli. These differences have been observed both for the obscuration factor (OBF) and the coincidence probability delivered by the probe. These parameters are correlated to flow cytometry measurement based on the PI-uptake test and cell density based on optical density measurement. This first set of results has pointed out the fact that the 3D-ORM probe is sensitive to sub-lethal injuries encountered by microbial cells in process-related conditions. The effect of lethal injuries has been further investigated on the basis of additional experiments involving heat stress and a sharp increase of the OBF has been observed indicating that cells are effectively injured by the increase of temperature. However, further improvement of the probe are needed in order to give access to single-cell measurements.

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.