Limits...
Fluorescent Reporter Libraries as Useful Tools for Optimizing Microbial Cell Factories: A Review of the Current Methods and Applications.

Delvigne F, Pêcheux H, Tarayre C - Front Bioeng Biotechnol (2015)

Bottom Line: The level of protein/metabolite is thus proportional to a fluorescence signal.Actually, different kinds of reporter systems are available, as well as specific cultivation devices allowing the on-line recording of the fluorescent signal.Cell-to-cell variability is another important phenomenon that can be integrated into the screening procedures for the selection of more efficient microbial cell factories.

View Article: PubMed Central - PubMed

Affiliation: Microbial Processes and Interactions (MiPI), Gembloux Agro-Bio Tech, University of Liège , Gembloux , Belgium.

ABSTRACT
The use of genetically encoded fluorescent reporters allows speeding up the initial optimization steps of microbial bioprocesses. These reporters can be used for determining the expression level of a particular promoter, not only the synthesis of a specific protein but also the content of intracellular metabolites. The level of protein/metabolite is thus proportional to a fluorescence signal. By this way, mean expression profiles of protein/metabolites can be determined non-invasively at a high-throughput rate, allowing the rapid identification of the best producers. Actually, different kinds of reporter systems are available, as well as specific cultivation devices allowing the on-line recording of the fluorescent signal. Cell-to-cell variability is another important phenomenon that can be integrated into the screening procedures for the selection of more efficient microbial cell factories.

No MeSH data available.


(A) Scheme showing the different molecular mechanisms affecting the intracellular content in GFP. (B) Scaling law adapted from Silander et al. (2012) showing the relationship between the level of promoter activity (in log scale) and its cell-to-cell variability or noise (the noise is calculated as the ratio between the standard deviation and the mean GFP content among a population of microbial cell, in log scale). The red arrow shows a typical evolution in GFP expression in real bioprocessing conditions (Baert et al., 2015).
© Copyright Policy
Related In: Results  -  Collection

License
getmorefigures.php?uid=PMC4585110&req=5

Figure 2: (A) Scheme showing the different molecular mechanisms affecting the intracellular content in GFP. (B) Scaling law adapted from Silander et al. (2012) showing the relationship between the level of promoter activity (in log scale) and its cell-to-cell variability or noise (the noise is calculated as the ratio between the standard deviation and the mean GFP content among a population of microbial cell, in log scale). The red arrow shows a typical evolution in GFP expression in real bioprocessing conditions (Baert et al., 2015).

Mentions: Reporter-based fluorescent biosensors have been widely used in the context of bioprocess optimization (Polizzi and Kontoravdi, 2014). However, some precautions must be taken since several factors can have a significant impact on the synthesis of the reporter molecules. These factors are summarized at Figure 2A and comprises the plasmid copy number, the strength of ribosome binding site (RBS), the folding rate of the GFP and its stability, and the potential release of GFP to the extracellular medium. Plasmid copy number is known to affect the degree of expression of GFP since this number can vary during cultures according to many environmental and intrinsic conditions. However, it has been shown that this effect can be limited by using low-copy number plasmids or by considering chromosomal integration (Freed et al., 2008; Silander et al., 2012). The strength of the RBS can also affect the rate by which mRNA is translated into GFP and this factor is now integrated when designing synthetic gene circuits (Xu et al., 2012; Jones et al., 2015). Typical folding rate of engineered GFP (e.g., GFPmut2 and GFPmut3) are below 4 min (Cormack, 1996), but the stability is very high (>24 h) leading to cumulative signal. Destabilized version of GFP have been designed and used to monitor gene expression in bioprocessing conditions (Han et al., 2013; Hentschel et al., 2013). Destabilization of GFP relies on the use of specific ssrA tags that can be recognized by the internal protease machinery (i.e., ClpXP mainly). By varying the sequence of the last three amino acids, the affinity of the proteases can be modulated, leading to medium (e.g., GFPAAV for E. coli with a half-life of 40 min) to highly destabilized variants (e.g., GFPLAA for E. coli with a half-life of 10 min). However, in this case, the GFP response is affected not only by the real promoter activity but also by the content of intracellular protease and the adenosine triphosphate (ATP) availability since ClpXP is ATP dependent (Purcell et al., 2012; Han et al., 2013). These side-reactions can notably artificially enhance the cell-to-cell variability at the level of the GFP content (Baert et al., 2015). A last effect potentially affecting the overall level of GFP is its potential release to the extracellular medium. Indeed, increase in membrane permeability and protein leakage is known to occur when microbial cells are exposed to nutrient limitation, a condition encountered in fed-batch bioprocesses (Shokri, 2002, 2004). At this level, it has been shown that GFP can be subjected to such release (Delvigne et al., 2011; Brognaux et al., 2014). In the case of promoter-dependent reporter systems, all these side-reactions affect the reliability of the fluorescent signal according to the real activity of the targeted promoter. Also, the impact of cultivation conditions can also have a significant impact both on the mean expression level of GFP and on its cell-to-cell variability (called noise). Noise in GFP expression is indeed a very important factor that can have detrimental effect on productivity (Delvigne and Goffin, 2014; Delvigne et al., 2014). However, noise in gene expression is also recognized as a beneficial factor in the context of microbial robustness. Indeed, cell-to-cell heterogeneity increases the potential resistance of the microbial population upon stress exposure by a mechanism named bet-hedging (Holland et al., 2013; Nikel et al., 2014b; Martins and Locke, 2015). Since productivity and robustness are two traits that are generally expected from an efficient cell factory, the effect of noise have to be balanced between these two phenotypic traits and advanced analytical devices are awaited at this level (see Figure 1 for a description of such devices). At this level, the use of an E. coli GFP clone library (Zaslaver et al., 2006) in connection with high-throughput flow cytometry has allowed for the design of a scaling law (Figure 2B). The collection comprised more than 1500 promoter-based biosensors and has been used for a genome-wide screen of the relationship between the promoter activity and the noise (Silander et al., 2012). The scaling-law shows a clear relationship between the mean promoter activity and the noise in GFP expression, i.e., promoters with high expression level exhibiting a low amount of noise, whereas weak promoters tend to exhibit larger level of noise (Figure 2B). More recently, this scaling-law has been validated by on-line flow cytometry in real bioprocessing conditions (Baert et al., 2015). This scaling-law constitutes an invaluable tool for a better understanding of the expression efficiency of microbial systems. The study of microbial physiology based on the use of fluorescent reporter system is thus critical in order to increase our knowledge for the development of more efficient microbial cell factories. However, this development is slowed down by the development of cultivation devices compatible with single-cell analysis and by the need for reliable fluorescent reporter systems (Polizzi and Kontoravdi, 2014). Indeed, we have shown that promoter-based fluorescent reporters are subject to many side-reactions affecting the interpretation of the fluorescent signal. Efforts must thus be given at the level of the development of more reliable expression systems. Promoter-independent fluorescent reporter systems described in the Section “Detection of Metabolite Productivity at the Intracellular Level” and the developments of synthetic devices described in the Section “Synthetic Biology and the Optimization of Artificial Metabolic Pathways” are very promising at this level.


Fluorescent Reporter Libraries as Useful Tools for Optimizing Microbial Cell Factories: A Review of the Current Methods and Applications.

Delvigne F, Pêcheux H, Tarayre C - Front Bioeng Biotechnol (2015)

(A) Scheme showing the different molecular mechanisms affecting the intracellular content in GFP. (B) Scaling law adapted from Silander et al. (2012) showing the relationship between the level of promoter activity (in log scale) and its cell-to-cell variability or noise (the noise is calculated as the ratio between the standard deviation and the mean GFP content among a population of microbial cell, in log scale). The red arrow shows a typical evolution in GFP expression in real bioprocessing conditions (Baert et al., 2015).
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4585110&req=5

Figure 2: (A) Scheme showing the different molecular mechanisms affecting the intracellular content in GFP. (B) Scaling law adapted from Silander et al. (2012) showing the relationship between the level of promoter activity (in log scale) and its cell-to-cell variability or noise (the noise is calculated as the ratio between the standard deviation and the mean GFP content among a population of microbial cell, in log scale). The red arrow shows a typical evolution in GFP expression in real bioprocessing conditions (Baert et al., 2015).
Mentions: Reporter-based fluorescent biosensors have been widely used in the context of bioprocess optimization (Polizzi and Kontoravdi, 2014). However, some precautions must be taken since several factors can have a significant impact on the synthesis of the reporter molecules. These factors are summarized at Figure 2A and comprises the plasmid copy number, the strength of ribosome binding site (RBS), the folding rate of the GFP and its stability, and the potential release of GFP to the extracellular medium. Plasmid copy number is known to affect the degree of expression of GFP since this number can vary during cultures according to many environmental and intrinsic conditions. However, it has been shown that this effect can be limited by using low-copy number plasmids or by considering chromosomal integration (Freed et al., 2008; Silander et al., 2012). The strength of the RBS can also affect the rate by which mRNA is translated into GFP and this factor is now integrated when designing synthetic gene circuits (Xu et al., 2012; Jones et al., 2015). Typical folding rate of engineered GFP (e.g., GFPmut2 and GFPmut3) are below 4 min (Cormack, 1996), but the stability is very high (>24 h) leading to cumulative signal. Destabilized version of GFP have been designed and used to monitor gene expression in bioprocessing conditions (Han et al., 2013; Hentschel et al., 2013). Destabilization of GFP relies on the use of specific ssrA tags that can be recognized by the internal protease machinery (i.e., ClpXP mainly). By varying the sequence of the last three amino acids, the affinity of the proteases can be modulated, leading to medium (e.g., GFPAAV for E. coli with a half-life of 40 min) to highly destabilized variants (e.g., GFPLAA for E. coli with a half-life of 10 min). However, in this case, the GFP response is affected not only by the real promoter activity but also by the content of intracellular protease and the adenosine triphosphate (ATP) availability since ClpXP is ATP dependent (Purcell et al., 2012; Han et al., 2013). These side-reactions can notably artificially enhance the cell-to-cell variability at the level of the GFP content (Baert et al., 2015). A last effect potentially affecting the overall level of GFP is its potential release to the extracellular medium. Indeed, increase in membrane permeability and protein leakage is known to occur when microbial cells are exposed to nutrient limitation, a condition encountered in fed-batch bioprocesses (Shokri, 2002, 2004). At this level, it has been shown that GFP can be subjected to such release (Delvigne et al., 2011; Brognaux et al., 2014). In the case of promoter-dependent reporter systems, all these side-reactions affect the reliability of the fluorescent signal according to the real activity of the targeted promoter. Also, the impact of cultivation conditions can also have a significant impact both on the mean expression level of GFP and on its cell-to-cell variability (called noise). Noise in GFP expression is indeed a very important factor that can have detrimental effect on productivity (Delvigne and Goffin, 2014; Delvigne et al., 2014). However, noise in gene expression is also recognized as a beneficial factor in the context of microbial robustness. Indeed, cell-to-cell heterogeneity increases the potential resistance of the microbial population upon stress exposure by a mechanism named bet-hedging (Holland et al., 2013; Nikel et al., 2014b; Martins and Locke, 2015). Since productivity and robustness are two traits that are generally expected from an efficient cell factory, the effect of noise have to be balanced between these two phenotypic traits and advanced analytical devices are awaited at this level (see Figure 1 for a description of such devices). At this level, the use of an E. coli GFP clone library (Zaslaver et al., 2006) in connection with high-throughput flow cytometry has allowed for the design of a scaling law (Figure 2B). The collection comprised more than 1500 promoter-based biosensors and has been used for a genome-wide screen of the relationship between the promoter activity and the noise (Silander et al., 2012). The scaling-law shows a clear relationship between the mean promoter activity and the noise in GFP expression, i.e., promoters with high expression level exhibiting a low amount of noise, whereas weak promoters tend to exhibit larger level of noise (Figure 2B). More recently, this scaling-law has been validated by on-line flow cytometry in real bioprocessing conditions (Baert et al., 2015). This scaling-law constitutes an invaluable tool for a better understanding of the expression efficiency of microbial systems. The study of microbial physiology based on the use of fluorescent reporter system is thus critical in order to increase our knowledge for the development of more efficient microbial cell factories. However, this development is slowed down by the development of cultivation devices compatible with single-cell analysis and by the need for reliable fluorescent reporter systems (Polizzi and Kontoravdi, 2014). Indeed, we have shown that promoter-based fluorescent reporters are subject to many side-reactions affecting the interpretation of the fluorescent signal. Efforts must thus be given at the level of the development of more reliable expression systems. Promoter-independent fluorescent reporter systems described in the Section “Detection of Metabolite Productivity at the Intracellular Level” and the developments of synthetic devices described in the Section “Synthetic Biology and the Optimization of Artificial Metabolic Pathways” are very promising at this level.

Bottom Line: The level of protein/metabolite is thus proportional to a fluorescence signal.Actually, different kinds of reporter systems are available, as well as specific cultivation devices allowing the on-line recording of the fluorescent signal.Cell-to-cell variability is another important phenomenon that can be integrated into the screening procedures for the selection of more efficient microbial cell factories.

View Article: PubMed Central - PubMed

Affiliation: Microbial Processes and Interactions (MiPI), Gembloux Agro-Bio Tech, University of Liège , Gembloux , Belgium.

ABSTRACT
The use of genetically encoded fluorescent reporters allows speeding up the initial optimization steps of microbial bioprocesses. These reporters can be used for determining the expression level of a particular promoter, not only the synthesis of a specific protein but also the content of intracellular metabolites. The level of protein/metabolite is thus proportional to a fluorescence signal. By this way, mean expression profiles of protein/metabolites can be determined non-invasively at a high-throughput rate, allowing the rapid identification of the best producers. Actually, different kinds of reporter systems are available, as well as specific cultivation devices allowing the on-line recording of the fluorescent signal. Cell-to-cell variability is another important phenomenon that can be integrated into the screening procedures for the selection of more efficient microbial cell factories.

No MeSH data available.