- Open Access
Influence of global gene regulatory networks on single cell heterogeneity of green fluorescent protein production in Bacillus subtilis
© The Author(s) 2018
- Received: 12 August 2018
- Accepted: 24 August 2018
- Published: 30 August 2018
Gram-positive bacterium Bacillus subtilis has been extensively studied as a microbial cell factory for high-level producing a wide range of interesting products. Green fluorescent protein (GFP) is commonly used as a marker for determining the strength of a given promoter or for the subcellular localization of a fusion protein. However, the inherent heterogeneity of GFP expression among individual cells that can arise from global regulation differences in the expression host, has not yet been systematically assessed. B. subtilis strains with single mutation(s) in the two major transcriptional regulators CcpA and/or CodY were earlier found to improve overall heterologous protein production levels. Here, we investigate the dynamic production performance of GFP in the reporter strains with chromosomally integrated Physpank-sfGFP(Sp).
The mutation R214C in the DNA-binding domain of CodY effectively enhances GFP production at the population level relative to two other strains, i.e. wildtype (WT) and CcpAT19S. During the late stationary phase, the high- and low-level GFP-producing cells coexist in the WT population, while the CodYR214C population at the single-cell level shows higher phenotypic homogeneity of fluorescence signals.
Expression of GFP is prominently heterogeneous in the WT B. subtilis cells, and this phenotypic heterogeneity can be significantly reduced by CodYR214C mutation. The rates of production heterogeneity show a high correlation to the overall GFP yields. Moreover, the toolkit of flow cytometry and fluorescence microscopy that can achieve real-time profiles of GFP production performance in various strains may facilitate the further use of B. subtilis as a cell factory.
- Bacillus subtilis
- Superfolder green fluorescent protein (sfGFP)
- Heterogeneous expression
- Global transcriptional regulation
- Production level
- Single cell analysis
- Phenotypic noise
The gradual but very rapid accumulation of genetic information and the fast development of experimental approaches have opened up many new frontiers in the cellular investigation . The traditional bulk-scale measurements that only investigate the average values for a population of cells give an incomplete picture of what happens in bacterial cultures. The information on individual cells is needed for correctly monitoring biological processes. It has become evident that various subpopulations of bacteria can exist under certain conditions, with cells in distinct physiological or developmental states [2, 3]. Multiple studies have been focused on the development and utilization of single-cell techniques, which aid the research on the cellular behavior of individual cells in bacterial populations [4, 5].
It is widely recognized that bacterial cells with the same genetic information (clonal populations) can display a multitude of distinct phenotypes, even when exposed to the same environment; this phenomenon is known as phenotypic heterogeneity . Bacillus subtilis, the best-characterized member of low-GC Gram-positive bacterial species, has been studied extensively with respect to phenotypic diversity. When nutrients are limited, B. subtilis in the stationary phase generates a mixed population, in which some cells form spores that are highly resistant to external stresses . Additionally, a subset of cells that have entered into the sporulation state can secrete an extracellular ‘killing factor’ and toxin to block sister cells from sporulating and to stimulate the lysis of them . In certain conditions, a subpopulation of the B. subtilis cells can enter into the competent state, enabling them to take up DNA from the environment [9, 10]. Heterogeneity also plays an important role in biofilm formation, which results in a subpopulation generating extracellular matrix material that tightly holds the surrounding cells together to form a robust biofilm . Moreover, during exponential growth, a fraction of cells manage to express sigD, which is necessary for flagellar production, resulting in the cells to be motile .
Phenotypic heterogeneity, which mostly results from heterogeneous gene expression, increases the survival chance of a subpopulation that is better adapted to changing conditions [12–15]. There are three main factors that control the dynamic cellular behavior: (i) the circuit architecture or regulatory interaction patterns; (ii) quantitative parameters, such as promoter strengths; and (iii) stochastic fluctuations or “noise”, which depends on the availability of certain cellular components . In general, the noise of gene expression arises from two sources. The “intrinsic” noise is generated by the inherent stochasticity of biochemical processes such as transcription and translation, causing a particular gene to be expressed at different levels in the cells at precisely the same state. On the other hand, the fluctuations in the states or accumulations of crucial cellular components such as regulatory proteins and polymerases represent “extrinsic” noise, leading indirectly to particular gene expression variation and which has a global effect [4, 17].
A wide variety of proteins have been chosen as reporters for benchmarking gene expression in order to study the mechanisms of phenotypic heterogeneity. In B. subtilis, the mostly used reporters include lacZ, encoding the β-galactosidase from E. coli , luxAB, encoding the luciferase from Vibrio harveyi , mCherry, encoding an enhanced red fluorescent protein from Discosoma sp.  and gfp, encoding the green fluorescent protein (GFP) from Aequorea victoria . GFP and its derivatives have been extensively utilized in the study of protein localization or promoter activity in living cells , which has tremendously increased our knowledge of bacterial cell biology [23–25]. These analyses can be carried out using flow cytometry, fluorescence microscopy or both [26, 27]. Flow cytometry facilitates the rapid analysis of cells in the population, while time-lapse microscopy follows the behavior of individual cells over time and dynamic movements of proteins within a single cell [28–31]. A previous study from our laboratory benchmarked the expression of a library of GFP variants in three model microorganisms, i.e. B. subtilis, Streptococcus pneumoniae, and Lactococcus lactis . Surprisingly, the superfolder GFP with codon optimization specifically for S. pneumoniae–sfGFP(Sp) displayed the highest fluorescence intensity and relatively low phenotypic noise in B. subtilis.
In B. subtilis, the pleiotropic transcriptional regulators CcpA and CodY behave either as a repressor or activator of gene expression by specifically binding to a sequence located in or near the promoter region of target genes. Therefore, these two regulatory proteins provide a top layer of metabolic networks by regulating genes that are involved in the carbon overflow, and citric acid cycle pathways, BCAA biosynthetic pathway, and the interplay between carbon and nitrogen metabolism . In an earlier study, we explored the heterologous protein production potential of B. subtilis by genetically altering the two global regulators, which demonstrated that amino acid substitutions among the DNA-binding regions . The mutations CodYR214C and CcpAT19S in one cell resulted in the reorganization of metabolic networks, which eventually improved the intracellular synthesis of β-galactosidase (Physpank-lacZ) and other soluble proteins. In the present research, the robustly folded version of GFP–sfGFP(Sp) was utilized as a reporter protein to quantify the productivity of the wildtype and the obtained mutant CodYR214CCcpAT19S over time, both at the population and single-cell level. Notably, this investigation points to altered production levels of GFP and great variation between single cells, depending on the central regulatory metabolic pathways operating in the WT and mutant cells.
The alteration of global regulatory networks significantly impacts the GFP production in B. subtilis
As presented previously, we selected out desired phenotypes with higher product yields of the reporter protein (β-galactosidase) by consecutively screening the randomly mutagenized libraries of CodY and CcpA . The best mutant strain CodYR214CCcpAT19S that contains crucial mutations within the DNA-binding HTH motifs, shows significantly reprogrammed central carbon and nitrogen metabolic pathways, and this overall metabolic shift leads to a twofold increase of β-galactosidase production (Physpank-lacZ) in comparison to the WT . To investigate the expression of another classic reporter, GFP, in the genetically modified expression hosts, the sfGFP(Sp) was utilized in this research. Moreover, since the plasmid-based expression systems can cause additional heterogeneity due to copy number variation and polar fixation effects [35, 36], we integrated the expression cassette Physpank-sfGFP(Sp) into the amyE locus in B. subtilis 168 WT, CodYR214C, CcpAT19S, CodYR214CCcpAT19S to obtain the four reporter strains.
The rewired central nitrogen metabolism plays a crucial role in the GFP production enhancement
Phenotypic noise, related to global regulation, negatively correlates to the overall GFP production level
Characterization of GFP production at the single-cell level
Characterization of GFP production at the subpopulation level
Metabolic burden might affect the heterologous expression of GFP
Metabolic burden, a known phenomenon for heterologous expression systems, is caused by the fact that the overexpression pathways of foreign proteins can take up a large proportion of the nutrient source fluxes, which then influences the original metabolic distribution in the cell, and cause serious physiological problems and finally results in lower yields of target products [41–43]. In a previous study, we reprogrammed the metabolic regulatory networks, and found that a more strongly repressed carbon metabolism and de-repressed nitrogen metabolism coordinately contribute to an increase of the reporter protein β-galactosidase production in B. subtilis . The production improvements were found to be consistent with upregulation of several nitrogen metabolic operons, and this was regarded to reduce the metabolic burden of β-gal overexpression in the genetically modified strains. The balanced and modified metabolic networks with increased uptake and utilization ability of arginine, ornithine, citrulline, and histidine could also weaken the extrinsic noise of GFP expression in the CodYR214CCcpAT19S. Different from the previous observation, strain CcpAT19S does not have an advantage in the expression of sfGFP(Sp), which is slightly lower than the WT control. This is in accordance with the fact that protein production improvement is performed in a protein-specific way . Nevertheless, based on population-scale analysis, the mutation CcpAT19S can still further improve the GFP expression on the basis of the improvement in CodYR214C. This shows that the effects of mutation CodYR214C and CcpAT19S on the final production of sfGFP(Sp) are more complex than simple addition. To sum up, the CodYR214CCcpAT19S strain displays balanced metabolic flux distributions between essential cellular processes and heterologous over-expression pathway probably has a lower metabolic burden. This not only increased the overall product yield but also decreased the phenotypic heterogeneity of sfGFP(Sp) expression in B. subtilis, a property generally useful for overproduction of any soluble intracellular protein.
In this study, we investigated the production of sfGFP(Sp) in strains with mutation(s) in CodY and/or CcpA and the WT strain as the control. We demonstrated that the mutation CodYR214C improves the overall expression of reporter protein sfGFP(Sp) significantly, with a slight decrease of the growth rate, while the CcpAT19S mutant slightly reduces the GFP synthesis. Nevertheless, when the two amino acid substitutions among the DNA-binding HTH motif of CodY and CcpA were combined, this yielded the best GFP producer—CodYR214CCcpAT19S. Furthermore, the phenotypic noise clearly differs between different mutants of the global regulator(s). This extrinsic noise comes from global regulation and is shown to be negatively correlated with GFP production in our cell factories. In addition, the single-cell and subpopulation analyses demonstrated that the cells of WT and CcpAT19S show stronger heterogeneity during the expression process over time. Although the full understanding of the mechanisms underlying expression heterogeneity is still incomplete, this study provides novel insights into decreasing cellular diversity and directs the way to further increase heterologous protein production in cell factories.
Plasmids, bacterial strains, and medium
The plasmids and bacterial strains used in this study
Strains and plasmids
Phenotype or property
Source or references
trpC2, amyE::Physpank-sfgfp(Sp) spcr
trpC2, codYR214C cmr, amyE::Physpank-sfgfp(Sp) spcr
trpC2, ccpAT19S kmr, amyE::Physpank-sfgfp(Sp) spcr
trpC2, codYR214C cmr, ccpAT19S kmr, amyE::Physpank- sfgfp(Sp) spcr
F−, araD139, Δ(ara-leu)7696, Δ(lac)X74, galU, galK, hsdR2, mcrA, mcrB1, rspL
Recombinant DNA techniques and oligonucleotides
Procedures for DNA purification, restriction, ligation, gel electrophoresis and transformation of E. coli were carried out as previously described . B. subtilis was naturally transformed as described before . T4 DNA ligase, Fastdigest Restriction enzymes and DNA polymerases (Phusion and DreamTaq) were purchased from Thermo Fisher Scientific (Landsmeer, Netherlands). Chromosomal DNA of the B. subtilis 168 and the constructed plasmids in this research were used as templates for PCR. The NucleoSpin Plasmid EasyPure and Gel & PCR Clean-up kits were purchased from BIOKE (Leiden, Netherlands). All the reagents used were bought from Sigma unless otherwise indicated. Oligonucleotides were synthesized by Biolegio (Nijmegen, Netherlands). Sequencing of all our constructs was performed at MacroGen (Amsterdam, Netherlands).
Construction of bacterial strains
Bacillus subtilis strain 168_sfGFP(Sp)_CodYR214C was obtained by homologous double crossover recombination of plasmid pJV153 into the flanking region of codY in B. subtilis 168. Strain 168_sfGFP(Sp)_CcpAT19S was obtained by the integration of plasmid pCH3_CcpAT19S into the specific chromosomal region of B. subtilis 168. Transformants were selected on LB agar plates containing appropriate antibiotic(s), after overnight incubation at 37 °C. Correct integration was verified by PCR and sequence analysis. The strain 168_sfGFP(Sp)_CodYR214CCcpAT19S was constructed in the same way as described above.
Single colonies of required strains were picked from LB agar plates with antibiotics and were incubated at 37 °C, 220 rpm overnight. The day after, the O/N cultures were diluted in a 96-well microtiter plate to OD600-0.1 with 200 µl fresh LB medium containing 1.0% glucose and 0.1 mM ITPG. Plates were incubated at 37 °C and 220 rpm shaking in the plate reader-VarioskanLUX (Thermo Fisher) with a GFP filter set (excitation at 485/20 nm, emission 535/25), and the absorbance was measured at 600 nm. The values of GFP intensity and OD600 were automatically recorded every 15 min for 22 h, data of all samples were collected in triplicates. All the optical density and fluorescence values were corrected for the background of the medium by the following formula: (GFPreporter − GFPmedium)/(ODreporter − ODmedium) .
All the strains were streaked on LB agar plates supplemented with a specific antibiotic, and the single colonies were picked up and grown overnight in LB medium at 37 °C, 220 rpm. Next morning, the pre-cultures were diluted to OD600-0.1 in fresh LB supplemented with 1.0% glucose and 0.1 mM IPTG and further incubated in a 37 °C shaker. Subsequently, the cultures of each time point were prepared for flow cytometry as described before [3, 12]. Cells were diluted 10–20 times in phosphate buffered saline (PBS) and directly measured on the Becton–Dickinson FACSCanto (BD BioSciences, USA) with an Argon laser (488 nm). For each sample, the green fluorescent signals of 50,000 cells were collected by a FITC filter. The fluorescent intensity was calculated in Arbitrary Units (AU). All the captured data was further analyzed using Flowing Software (http://www.flowingsoftware.com/).
In parallel, the above-described cultures of each time point were also prepared for fluorescence microscopy and applied to agarose slides as described before . The expression of the fluorescent protein was analyzed by fluorescence microscopy (Nikon Eclipse Ti, Japan) equipped with a CoolsnapHQ2 CCD camera. Fluorescent signals from cells were visualized using 450–490 nm excitation and 500–550 nm emission for GFP fluorescence channel and an Intensilight light as phase contrast channel. Software NIS-Elements AR  was used for image capturing by 0.2 s exposure, and the final images for publication were generated by ImageJ software .
Conceived and designed experiments: HC, OPK; performed the experiments and data analysis: HC; wrote the manuscript: HC, OPK. Both authors read and approved the final manuscript.
We thank Ard Jan Grimbergen (Molecular Genetics, University of Groningen) for critically reading the manuscript. We are grateful to Yanglei Yi (Molecular Genetics, University of Groningen) for helpful technical assistance in fluorescence microscopy.
The authors declare that they have no competing interests.
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