Genome- scale metabolic model based engineering of Escherichia coli enhances recombinant single chain antibody fragment production

doi:10.21203/rs.3.rs-412220/v2

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Genome- scale metabolic model based engineering of Escherichia coli enhances recombinant single chain antibody fragment production

https://doi.org/10.21203/rs.3.rs-412220/v2

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Escherichia coli is an attractive and cost-effective cell factory for producing recombinant proteins such as single-chain variable fragments (scFvs). AntiEpEX-scFv is a small antibody fragment that has received considerable attention for its ability to target the epithelial cell adhesion molecule (EpCAM), a cancer-associated biomarker of solid tumors. Due to its metabolic burden, scFv recombinant expression causes a remarkable decrease in the maximum specific growth rate of the scFv-producing strain. In the present study, a genome-scale metabolic model (GEM)-guided engineering strategy is proposed to identify gene targets for improved antiEpEX-scFv production in E. coli. For improving scFv production, flux balance analysis (FBA) and FVSEOF methods identified a number of potential genetic engineering targets localized in the glucose uptake system and pentose phosphate pathway. From the predicted targets, glk gene encoding glucokinase was chosen to be overexpressed in the parent strain Escherichia coli BW25113 (DE3). By overexpressing glk, the growth capacity of the recombinant E. coli strain was recovered. Moreover, the engineered strain with glk overexpression successfully lead to increased scFv production. This approach can be considered for the improvement of other recombinant proteins production.

AntiEpEX-scFv

Escherichia coli

FVSEOF

Genome-scale metabolic model

glk

Glucokinase

Nowadays, most clinically valuable proteins are recombinantly produced. For such proteins, expression is directly influenced by the metabolism of the producing cell factory. E. coli is one of the well-established microbial cell factories characterized by rapid growth and high-yield production of recombinant proteins (Ferrer-Miralles and Villaverde 2013). Despite continual efforts, there are still limiting barriers to overexpression of recombinant proteins. Metabolic burden observed in recombinant protein-producing cells leads to biomass yield reduction, which is part of the stress response triggered by protein overexpression. This stress response, in turn, can hamper the production of recombinant protein.

In the past few decades, metabolic engineering strategies have been successfully employed in developing industrial cell factories (Fernández-Cabezón and Nikel 2020). In metabolic engineering, specific biochemical reactions are modified by deletion and/or overexpression of specific genes, leading toward optimal pathway usage (Ko et al. 2020). These modifications lead to better productivity of the strains by changing the flux of certain metabolic reactions. Based on this strategy, improved production of numerous heterologous products has been reported. However, a systematic understanding of the involved metabolic pathways, as well as the regulatory mechanisms, is highly needed for rational metabolic engineering.

Systems-level metabolic modeling strategies have been developed in recent years to guide metabolic engineering. These novel approaches such as genome-scale metabolic model (GEM)-guided engineering have shown promising results in practice. Genome-scale modeling can predict the cellular phenotype at the systems level and recommend genetic manipulations to modulate the relationship between target protein overexpression and biomass production so that strains with high growth and maximal protein productivity can be attained. High accuracy in the prediction of cell phenotype and minimization of consumed laboratory resources and time for developing productive strains are the most momentous advantages of model-guided metabolic engineering (Orth et al. 2011).

In GEM-based metabolic engineering, flux balance analysis (FBA) and flux variability analysis (FVA) can be utilized to compute the maximum production rate of biomass, as the objective function. Various algorithms such as FSEOF (flux scanning based on enforced objective flux) (Choi et al. 2010) as an FBA-based method and FVSEOF (flux variability scanning based on enforced objective flux) (Park et al. 2012) and OptForce (Ranganathan et al. 2010) as FVA-based methods have been employed in metabolic engineering to determine appropriate genes to be amplified in order to achieve improved industrial cell factories. However, in practice, most of the GEM-based studies have focused on the production of small-molecule chemicals, rather than recombinant proteins (Ko et al. 2020) .

The scFv (single-chain variable fragment) antibody against EpEX (EpCAM extracellular domain) has drawn great attention in biomedicine for dual therapeutic and diagnostic applications due to its small size and low immunogenicity (Eyvazi et al. 2018). In this study, scFv is considered as a model protein to be overproduced in E. coli with the help of metabolic engineering techniques. FVSEOF method was used to determine appropriate modifications of the genes in order to increase flux towards antiEpEX-scFv overproduction. By analyzing the FVSEOF results, glk gene was chosen for overexpression. Experimental tests were then performed to evaluate the effect of glk overexpression on antiEpEX-scFv overproduction, which proved the appropriateness GEM-based model for achieving a better cell factory.

Metabolic modeling and target gene prediction

The iJO1366 metabolic model of E. coli (Orth et al. 2011) was used in the COBRA Toolbox v2.0 (Schellenberger et al. 2011) with 'glpk' as the solver. The metabolic reaction of antiEpEX-scFv production was added to iJO1366 according to the previously described method (Behravan et al. 2022).

In order to determine strategies for increasing antiEpEX-scFv production, FVSEOF algorithm was used. Metabolic network and FVSEOF algorithm can be used to predict the effect of an enforced increase in theoretical scFv production on the flux changes of the other metabolic reactions in E. coli cells. FVSEOF finds a list of metabolic reactions whose fluxes are changed as a result of a theoretical increase of scFv production. Based on FVSEOF results, the in vitro changes in metabolic reactions that had altered rates during the theoretical increase of scFv can potentially lead to the increased scFv production in vitro.

Bacterial strains, plasmids, and cultivation conditions

Escherichia coli DH5α was used for recombinant plasmid cloning. Escherichia coli BW25113 (DE3) (kindly provided by Prof. Leimkühler, University of Potsdam) was utilized for protein expression (Bühning et al. 2017). The plasmid pETDuet-1 (a gift from Dr. Bandehpour, Shahid Beheshti University of Medical Sciences, Tehran, Iran) was utilized as a co-expression vector. The plasmid pETDuet-antiEpEX-scFv was previously constructed in our laboratory (Behravan and Hashemi 2021). Pfu DNA polymerase, T4 DNA ligase, protein molecular weight markers and restriction enzymes were obtained from Thermo Fisher Scientific (USA). DNA fragments were purified from agarose gel using the gel extraction kit (Roche Diagnostics GmbH, Germany). M9 minimal media containing (per liter) 0.5 g of NaCl, 6 g of Na₂HPO₄, 3 g of KH₂PO₄, 1 g of NH₄Cl supplemented with 5 g⋅L^− 1 glucose, 2 mmol⋅L^− 1 MgSO₄, 0.01 mmol⋅L^− 1 FeCl₃, 0.1 mmol⋅L^− 1 CaCl₂, and 0.1 mL 1000x trace metals element (Teknova-USA), and LB medium composed of (per liter) 10 g of tryptone, 5 g of yeast extract, and 10 g of NaCl were used as the culture media. All other chemicals were purchased from Merck in analytical grade.

Construction of recombinant plasmid

The glk gene was amplified from the genome of E. coli BW25113 employing the primers glk-F, 5'- CCGGAATTCTGAAGAATGACAAAGTATGC − 3' (the EcoRI site is marked) and glk-R, 5'- AAACTGCAGCCCGATATAAAAGGAAGGAT − 3' (the PstI site is marked). Cycling condition was 94°C for 3 min followed by 30 cycles of 94°C, 30 s; 56°C, 35 s, 72°C 1 min and 30 s and 1 cycle of 72°C, 10 min. To generate plasmid pETDuet-glk, the PCR product was digested with restriction enzymes PstI, EcoRI, and then ligated to pETDuet-1 treated with the same two enzymes. pETDuet-glk-antiEpEX-scFv expression plasmid was constructed by digesting the pGH vector carrying the antiEpEX-scFv gene with XhoI and NdeI to get the gene with a polyhistidine tag in its C-terminal, which was then ligated with pETDuet-glk treated by NdeI/XhoI. The restriction enzyme digestion assay and sequencing were used to confirm the constructs. Then, the recombinant plasmids were transformed into the chemically competent E. coli BW25113 (DE3) cells.

Expression of antiEpEX-scFv

For antiEpEX-scFv expression, a single colony of BW25113 harboring pETDuet-glk-antiEpEX-scFv was inoculated into 5 mL of LB medium supplemented with appropriate amounts of ampicillin and incubated for 18 h at 37°C with shaking (200 rpm). After centrifugation (6000×g for 5 min at 4°C), the pellet was resuspended into 100 mL of M9 minimal medium containing ampicillin (50 mg⋅mL^-1). When cell density reached an optical density at 600 nm (OD₆₀₀) value of 0.8, the expression of antiEpEX-scFv was induced with 0.8 mmol⋅L^-1 IPTG at 37°C. Using centrifugation (10000×g, 10 min, 4°C), cells were harvested after 24 h. For initial determination of protein expression level, the cell pellets were suspended in 30 mL of lysis buffer containing 50 mmol⋅L^-1 Tris pH 7.5, 1 mmol⋅L^-1 EDTA, 1 mg⋅mL^-1 lysozyme, 150 mmol⋅L^-1 NaCl, 1% triton X1005, and then, sonicated for 30 min (20 s ON, 10 s OFF at 400 W). After centrifugation of the cell lysate (10000×g for 30 min at 4°C), protein samples were electrophoresed on a 15% SDS-PAGE gel and visualized using Coomassie brilliant blue G-250 dye. By using a wet Trans-Bolt (Bio-Rad, USA), the proteins were electro-transferred from the gel into the polyvinylidene difluoride (PVDF) membrane to perform a Western blot analysis. The transferred membrane was blocked in 5% nonfat milk for 1 h, and then, was washed three times with TBST and then incubated in His-tag antibody (Sigma, UK) overnight. After washing again, the membrane was incubated in anti-mouse HRP conjugated immunoglobulin (Sigma, UK) as the secondary antibody for two hours, and then, detected by means of a solution of 3,3′-diaminobenzidine (DAB) (Sigma, UK). The recombinant antiEpEX-scFv was purified using the Ni–NTA affinity chromatography column under denaturing conditions based on the manufacturer's protocol (Qiagen, Netherlands). Utilizing the bicinchoninic acid assay (BCA assay), the concentration of the purified protein was measured (Takara BCA Protein Assay Kit, Takara, Japan).

Growth profile and glucose analysis

To investigate the cell growth profile, OD₆₀₀ was determined every hour, using spectrophotometer (E-Chrome Tech, Taipei, Taiwan). Logarithmic derivation of the optical density curve used for calculation of growth rate. In order to determine glucose concentration, one milliliter of sample from culture broth was harvested in one-hour intervals. The supernatant was collected following 10 min of centrifugation at 10000×g. The concentration of glucose was measured using a commercial enzymatic kit (Megazyme, Wicklow, Ireland).

RT-qPCR analysis

To compare the relative expression of glk as a target gene between E. coli BW25113/Duet-glk-scFv, wild type strain E. coli BW25113 and E. coli BW25113/Duet-scFv RT-qPCR were employed. E. coli strains were cultured in 50 mL M9 medium and induced with 0.8 mmol⋅L^− 1 IPTG in OD₆₀₀ = 0.8. After 3 hours, samples were collected and diluted to OD₆₀₀ = 0.4. Based on the manufacturer’s protocol, total RNA was extracted from bacterial cells utilizing TRIzol reagent (Ambion). The purity and quantity of the isolated RNA were measured by Synergy HTX multimode reader (BioTek), and subsequently, it was stored at -80°C for further use. Then, a cDNA synthesis kit (YT450; Yekta Tajhiz Azma) was employed to synthesize cDNA according to the instruction provided by the manufacturer. Reviewing the literature, primers were selected and assessed for GC content, specificity, secondary structures, and amplicon size. Primer sequences synthesized by Metabion (Germany) are presented in Table 1. StepOne Real-Time PCR System (Applied Biosystems) was employed for SYBR-Green qPCR reactions in 48 well optical reaction plates. cDNA (0.5 ng/reaction) was used as the template for qPCR reactions with 5 µL SYBR Green PCR Master Mix (2×) (YT2551; Yekta Tajhiz Azma) and primers at 10 µM final concentration. Samples were exposed to thermal plan as follows: 95°C, 30 s followed by 40 cycles of 95°C, 5 s and 60°C, 30 s. The PCR reactions were done in three technical replicates for more accuracy. The 2^−ΔΔCt method was used to evaluate relative gene expression against the reference gene (Livak and Schmittgen 2001).

Table 1. Sequences of RT-qPCR primers

Name	Oligonucleotide Sequences (5^׳ -> 3^׳)
16S	F: TACCGCATAACGTCGCAAGA
16S	R: AGTCTGGACCGTGTCTCAGT
glk	F: CTGTATTGCCATCGCTTGCC
glk	R: TTACCTTCGACCGGTTCTGC

Prediction of overexpression targets

The genome-scale metabolic model of E. coli, iJO1366 (Orth et al. 2011), was employed for the prediction of metabolic engineering targets that can improve antiEpEX-scFv production. To this end, the FVSEOF algorithm was exploited, as explained in the Materials and Methods section. In iJO1366, the FVSEOF algorithm predicted ten metabolic reactions whose flux increase can enhance antiEpEX-scFv production. Therefore, the enzymes that catalyze these reactions were considered potential targets of overexpression. Among their associated genes, two genes were related to the import of glucose (galP and glk), four genes were related to the pentose phosphate (PP) pathway (zwf, rpe, pgl, gnd), two genes were related to the folate biosynthesis pathway (focA and purU), and two genes were related to alternative carbon metabolism subsystem (xylA, mak). These reactions and their associated metabolic genes are illustrated in Fig. 1.

The transcriptome analysis showed that glk is upregulated during the recombinant protein production in E. coli (Oh and Liao 2000). Therefore, in the present study, the glk gene was chosen as the target gene for overexpression.

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Effects of glk overexpression on antiEpEX-scFv production

The glk gene was amplified from the E. coli genome by PCR, resulting in a 1004 bp glk gene which can encode 321 amino acids with a molecular weight of about 35 kDa. Then, glk and antiEpEX-scFv coding sequences were inserted into the first and second multiple cloning sites (MCS) of pETDuet-1 to generate the plasmid pETDuet-glk-antiEpEX-scFv (Fig. 2). The constructed pETDuet-glk-antiEpEX-scFv plasmid was confirmed by restriction enzyme digestion (Fig. 3) and sequencing. The plasmid was then transformed into E. coli BW25113 (DE3). Recombinant E. coli BW25113/Duet-glk-scFv and E. coli BW25113/Duet-scFv were cultured in the M9 minimal medium containing suitable antibiotics to an OD₆₀₀ of 0.8, induced with 0.8 mmol⋅L^− 1 IPTG for 24 h. The SDS-PAGE analysis confirmed the presence of two separate protein bands with molecular weights of 35 kDa (glk) and 29 kDa (antiEpEX-scFv) (Fig. 4a). The expressed scFv protein was confirmed using Western blot analysis (Fig. 4b). According to the concentration of purified antiEpEX-scFv, the glk-overexpressing strain, after 24 h post-induction cultivation, showed an increase in antiEpEX-scFv titer (235.4 ± 9.5 µg⋅mL^− 1; 0.428 g⋅g_DCW⁻¹), which was approximately 110% higher than that in strain without glk overexpression (110.2 ± 7.7 µg⋅mL^− 1; 0.202 g⋅g_DCW⁻¹) (Fig. 4c). The result confirmed that altered glucose metabolism by glk overexpression could improve the antiEpEX-scFv production.

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Growth and glucose consumption profiles

To examine how co-expression of glk with antiEpEX-scFv can affect bacterial growth rate and glucose consumption rate, wild-type strain (BW25113), recombinant E. coli BW25113/Duet-glk-scFv and E. coli BW25113/Duet-scFv, were cultured in the M9 minimal medium containing 5 or 10 g⋅L^− 1 glucose at 37°C. IPTG in final concentration of 0.8 mmol⋅L^− 1 was added in OD₆₀₀ = 0.8 for protein induction.

All strains in the M9 minimal medium containing 5 g⋅L^− 1 glucose (Figs. 5a and 5b) grow logarithmically as long as glucose is available in the medium. When glucose is depleted, strains enter the stationary phase. As shown in Fig. 4a, maximum specific growth rate in recombinant E. coli BW25113/Duet-scFv and BW25113/Duet-glk-scFv (µ_max = 0.46 ± 0.03 and µ_max = 0.55 ± 0.003 respectively) was lower than that in the parent strain (µ_max = 0.63 ± 0.01). Since recombinant protein and cell biomass share common precursors, increased protein expression at the expense of decreased cell density is presumably due to the alteration of intracellular fluxes through the biomass precursors towards protein synthesis in recombinant strains. However, recombinant E. coli BW25113/Duet-glk-scFv has a greater specific growth rate than the E. coli BW25113/Duet-scFv (Table 2), especially when more glucose is available in the medium. As shown in Fig. 4c and Table 2, µ_max is much higher for E. coli BW25113/Duet-glk-scFv (0.81 ± 0.04) in comparison to E. coli BW25113/Duet-scFv (0.59 ± 0.003) and wild type strain (0.73 ± 0.02). As expected, in the M9 minimal medium containing 10 g⋅L^− 1 glucose, maximum specific growth rates of all strains are higher than those in the medium supplemented with 5 g⋅L^− 1 glucose.

Table 2

Specific growth rate of different strains in M9 minimal medium in different glucose concentrations
Strains	Growth rate (h^− 1) in M9 + 5 g/L glucose	Growth rate (h^− 1) in M9 + 10 g/L glucose
BW25113	0.637 ± 0.013	0.729 ± 0.022
BW25113-Duet-scFv	0.462 ± 0.034	0.592 ± 0.003
BW25113-Duet-glk-scFv	0.552 ± 0.003	0.81 ± 0.043

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As shown in Table 3, the glucose consumption rate by E. coli BW25113/Duet-glk-scFv (1.145 ± 0.01) is greater than that of the E. coli BW25113/Duet-scFv (0.968 ± 0.02) and the parental strain (0.851 ± 0.05). Also, as illustrated in Fig. 4b, the required time for the complete consumption of glucose for the parental strain and the E. coli BW25113/Duet-glk-scFv was 10 h, while E. coli BW25113/Duet-scFv needs 12 h to consume all the glucose in the medium. Interestingly, the required time for complete consumption of glucose for the parental strain and for E. coli BW25113/Duet-glk-scFv is similar (about 24 h), while E. coli BW25113/Duet-scFv does not consume all of the available glucose in 24 h (Fig. 5d).

Table 3

Glucose consumption rate in different strains in M9 minimal medium in different glucose concentrations
Strains	uptake rate (g/g DCW h) in M9 + 5 g/L glucose	uptake rate (g/g DCW h) in M9 + 10 g/L glucose
BW25113	0.851 ± 0.05	0.730 ± 0.034
BW25113-Duet-scFv	0.968 ± 0.02	0.706 ± 0.039
BW25113-Duet-glk-scFv	1.145 ± 0.01	1.175 ± 0.03

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Evaluation of glk transcription level

In order to validate the overexpression of glk, the real-time PCR experiment was employed. The E. coli BW25113 harboring pETDuet-glk-scFv showed significantly higher levels of glk transcription compared to the E. coli BW25113 harboring pETDuet-scFv. After 3 h of induction, the relative quantification of glk transcript revealed that glk level was upregulated in E. coli BW25113/Duet-glk-scFv by 14.78-fold in comparison to the E. coli BW25113/Duet-scFv.

Development of hosts that have desirable metabolic phenotypes and the ability to produce heterologous products is an important issue in microbial metabolic engineering. Various GEM-based approaches have enabled scientists to recognize gene deletion or overexpression targets for developing cell factories. Using MOMA simulations, l-valine biosynthesis was successfully improved in an engineered E. coli strain (Park et al. 2007). Also, amplification of the idi gene selected by FSEOF together with the dxs gene led to lycopene overproduction (Choi et al. 2010). However, predicting the metabolic consequences of gene deletion is often much simpler than those of gene overexpression. In the case of a deleted gene, its corresponding metabolic flux can simply be assumed as zero, while, due to complex regulation and control of metabolism, fluxes of overexpressed genes do not necessarily increase. Moreover, even if gene overexpression results in a metabolic flux increment, the amount of this increase is hardly predictable.

In this study, in order to increase flux towards antiEpEX-scFv overproduction, among several other targets predicted by FVSEOF, the glk gene was selected for overexpression. According to our results, recombinant expression of antiEpEX-scFv and glk resulted in a decrease in the maximum specific growth rate of recombinant strains compared with the parent strain. A decrease in growth rate is normally detectable in bacteria transformed with multicopy plasmids to produce a recombinant protein. One should keep in mind that plasmid DNA replication, plasmid-encoded mRNA synthesis and translation in bacteria often impose a metabolic burden on the engineered strains, which in turn, results in growth retardation (Flores et al. 2004). This metabolic burden may be due to the cell's inability to supply the extra demand of energy and building blocks required for plasmid replication and foreign multicopy genes expression (Li and Rinas 2020). However, a significant increase was observed in the µ_max of the recombinant strains from 0.592 ± 0.003 in BW25113-Duet-scFv to 0.81 ± 0.043 in BW25113-Duet-glk-scFv when the expression of the glk gene was increased, which is comparable to the wild-type strain (0.729 ± 0.022). E. coli cellular performance is improved by engineering glucose uptake systems. De Anda et al., for example, showed that the overexpression of galP-glk in E. coli reduces acetate accumulation as well as improvement of the cellular growth rate and recombinant protein production (De Anda et al. 2006).

The glk gene encodes the enzyme glucokinase catalyzing the ATP-dependent phosphorylation of the glucose that was imported by GalP. Overexpression of glk presumably leads to carbon flux redirection into the glycolysis and PP pathway compensating for the special metabolic demands of the engineered strain (Hernández-Montalvo et al. 2003). The PP pathway, which is closely interconnected with glycolysis, normally provides some of the required blocks for biosynthesis of histidine, nucleotide and aromatic amino acids, e.g., erythrose-4-phosphate and ribose-5-phosphate (Stincone et al. 2015). Also, NADPH, a power source of biosynthetic reactions, is reduced in the oxidative branch of this pathway (Christodoulou et al. 2018). In a similar study, the PP pathway of E. coli has been engineered leading to a reduction of the metabolic load caused by recombinant protein production (Flores et al. 2004). In the present work, using this approach, a significant positive effect was observed on the productivity of the scFv producing strain. The glk-overexpressed strain produced approximately 2.1 times higher titer of scFv than the strain with no glk overexpression. One can conclude that the metabolic engineering target predicted in our study was validated via the improvement observed in the scFv production.

Using DNA microarray, Oh et al. revealed that overproduction of recombinant non-toxic LuxA could lead to the downregulation of ppc, fba, gnd, and atpA genes, as well as upregulation of heat shock and glk genes in E. coli strains including JM109, MC4100, and VJS676A. Based on the transcriptome profile obtained in that study, instead of the phosphotransferase system (PTS), glucose kinase was suggested to have the major role to provide glucose-6-phosphate in protein overproducing conditions in the E. coli cells (Oh and Liao 2000). On the other hand, overexpression of recombinant proteins was shown to induce heat shock genes and rapid stress response. Interestingly, glk has been reported to play an essential role in bacterial stress responses (Zhang et al. 2020). Although this gene plays a minor role in glucose metabolism, under stress conditions like heterologous protein expression or growth in acidic conditions, this glycolytic enzyme is required for a sufficient supply of glucose-6-phosphate (Arora and Pedersen 1995; Zhang et al. 2020). Therefore, glk seems to be a suitable target gene to be overexpressed to achieve increased recombinant protein productivity, which is consistent with our results.

In conclusion, the goal of our work was to improve the maximum specific growth rate of a scFv-producing E. coli strain, and consequently, to increase the scFv production yield. GEM-guided metabolic engineering strategies have been previously used for increasing recombinant protein production (Nocon et al. 2014; Fouladiha et al. 2020; Behravan et al. 2021). Here, the GEM-guided metabolic engineering strategy was used to improve the scFv production in E. coli BW25113 (DE3). We applied FBA and FVSEOF methods to identify potential genetic engineering targets. From the predicted targets, glk gene encoding glucokinase was chosen to be overexpressed in the parent strain. The engineered strain with glk overexpression successfully increased scFv production to 2.1-fold, which proves the suitability of the exploited rational design strategy for strain development. This approach can be employed to determine the bottleneck in the intracellular metabolic pathways by modification of cellular behavior on the basis of metabolic engineering, which can be considered for improving the production of other recombinant proteins. Finally, we believe that our method for the production of scFv is a successful example of GEM-guided metabolic engineering, and can be applied to other recombinant protein production systems to achieve higher productivity and product yields.

Acknowledgements

We thanked Prof. Dr. Silke Leimkühler and Dr. Bandehpour for providing Escherichia coli BW25113 (DE3) and plasmid pETDuet-1 respectively.

Funding

This work was supported by the research deputy of Shahid Beheshti University of Medical Sciences in Tehran, Iran (IR.SBMU.PHARMACY.REC.1399.372). Atieh Hashemi has received research support. The authors declare that no funds, grants, or other support were received during the preparation of this manuscript.

Competing interests

The authors have no relevant financial or non-financial interests to disclose.

Author Contributions

AH and SAM have made substantial contributions to conception and design. AB and HF carried out the experiments and analyzed the data. The first draft of the manuscript was written by AB. AH and SAM reviewed and edited the article for spelling, grammar, and intellectual content. AH and SAM organized and supervised the whole project. AH provided the facilities and materials required for the project. All authors agreed to be responsible for the content of the work.

Ethics approval

This is an observational study. The school of pharmacy and nursing and midwifery Shahid Beheshti University of Medical Sciences’ Ethics Committee has confirmed that no ethical approval is required.

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Genome- scale metabolic model based engineering of Escherichia coli enhances recombinant single chain antibody fragment production

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Version 2

Abstract

Figures

Introduction

Materials And Methods

Metabolic modeling and target gene prediction

Bacterial strains, plasmids, and cultivation conditions

Construction of recombinant plasmid

Expression of antiEpEX-scFv

Growth profile and glucose analysis

RT-qPCR analysis

Results

Prediction of overexpression targets

Growth and glucose consumption profiles

Discussion

Declarations

References

Status:

Version 2