Team:TU Darmstadt/Lab/MetabolicBurden
METABOLIC BURDEN
ABSTRACT
Artificial plasmids are a significant burden to the host. The design of pathways, e.g. the combination of different promoter and RBS systems, results in different amounts of product. Measurement of the metabolic burden is a key for quantitative optimization of metabolic engineering approaches. We want to establish a new approach to iGEM by providing a measurement strain to the community. As described by F. Ceroni et al., we integrated one copy of the GFP gene into the genome of E. coli, which offers us a highly accurate and instantaneous measurement of the impact of our plasmids on the host. Metabolic burden measurement is of economical interest, because it enables academic and industrial research testing several different pathways at once in a short period of time by using microplate reader. For the integration we used the λ‑Integrase site‑specific recombination pathway, described by A. Landy in 2015 [9]. Therefore, we designed two plasmids (BBa_K1976000 and BBa_K1976001) and measured them using single cell measurement via microplate reader.
Metabolic Burden
In synthetic biology, the term "metabolic burden" describes the influence of heterologously expressed genes on the distribution availability of resources in the host cell. Thereby the extent of metabolic burden is not only dependent on the consumption of energy and reaction equivalents but is also influenced by a number of different factors. In addition to size and copy number of the used plasmid [1], the specific properties of the heterologous proteins play an important role. Proteins that interfere with the host's metabolism, influence the proton gradient or are toxic in a different way can pose a strong burden for the cell already at low expression levels [1, 2]. I. Shachrai et al. could show that ribosome availability represents a highly limiting factor for metabolic performance [3].
If the metabolic burden on the host cell is too high, the physiology and biochemistry of the cell will be drastically altered, e.g. the viability or proliferation of the cell could be disturbed. In addition, the error rate in translation increases which heightens the immunogenicity of the incorporated proteins and can lead to a reduced protein activity and stability [1]. Metabolic burden is a big issue especially in the industrial sector [4], because lowered expression efficiency decreases product yield.
Often used methods to reduce the metabolic burden include for example the separation of the biosynthetic pathway into multiple organisms through co‑cultivating them or the identification and deletion of expendable genes as a part of strain optimization [6]. Another approach is the regulation of protein expression with Dynamic Sensor and Regulatory Systems (DSRSs). Such systems are based on the usage of transcription factors which detect certain key‑metabolites and regulate the transcription simultaneously [7].
One possibility to quantify the metabolic burden in vivo was described by F. Ceroni et al. In this method a GFP reporter gene is integrated in the genome of E. coli using the λ‑integrase [8]. Through measuring the fluorescence it could be shown that the constitutive expression of GFP after transformation with expression‑plasmids drastically decreases in comparison to not transformed cells.
Measurement of the Metabolic Burden via Microplate Reader
In addition to the stress forced onto the cell, the total amount of formed GFP is significantly influenced by the cell amount. For this reason the measurement of GFP with a microplate reader was performed under continuous observation of the cell density. With this kind of measurement many samples, like in 96‑well‑plates, can be analyzed. Since the measurement of different combinations of plasmid and cell‑type, would enable to determine the artificially caused stress on the cells proportional to the decrease in GFP expression, this kind of measurement is assumed to be a more economical approach than the single cell measurement described in the next paragraph.
Single-cell Measurement
While measuring every single cell individually, the cell density can be neglected which leads to a smaller error in the fluorescence measurement. This method enables the detection of the influence of the metabolic burden based on the GFP production. Therefore, it is assumed to be a more exact method than the previously described method using a microplate reader.
Genomic Integration
The λ‑integrase, originally derived from the λ‑phage, catalyzes the recombination of the phage genome with the chromosomal genome of its host in combination with several assisting proteins. Therefore, two attachment sites are necessary: one located on the bacterial genome (attB) and the other located on the λ‑genome (attP), which also contains several binding sites for regulatory proteins. The attachment sites contain homologous recognition sequences, called BOB' region (attB) and COC' region (attP). These regions can be connected by the λ‑integrase and the bacterial integration host factor (IHF) via Holliday junction forming an intasome, a DNA‑protein‑complex, producing hybrid attachment sites attL and attR.
Integration Plasmid and Helper Plasmid
For the integration of the gene of interest (GOI) into the chromosomal genome of E. coli two plasmids are needed. The integration plasmid contains the constitutive generator of GFP, which is also the necessary reporter for the measurement of the metabolic burden and should be integrated into the genome of E. coli. To ensure that only temporary fluorescence are measured, a LVA degradation tag was added to the GFP. The plasmid also contains the attP site that enables the integration. Additionally, two bidirectional terminators are located on each side of the attP to protect the GFP operon from the transcription during biosynthesis of neighbouring genes. To create the integration plasmid E0240 (RBS(BB0032+GFP)) was cloned in a J61002 backbone to locate the GFP gene under the control of the promoter J23101. The construct J23101+E0240 was mutated via mutagenesis PCR for optimizing BBa_I11023 and afterwards cloned in the high‑copy vector pSB1C3 to increase the yield of the plasmid preparation. The mutagenesis PCR aimed to mutate attp2 to λ‑attP. To finalize the construct, we changed the backbone to pSB4A5 which possesses a low copy ori to facilitate the later performed plasmid curing.
The second plasmid is a helper plasmid which is necessary for transposing the GFP gene into the chromosomal genome as it contains the λ‑integrase generator. For inscription in the registry the construct was cloned in a pSB1C3 backbone and , additionally, to facilitate additional performed plasmid curing via sustained lack of selection pressure, we again chose the low‑copy vector pSB4K5 .
To verify the success of the recombination, PCR was performed, using specific primers to the attB site of the E. coli and the VR primer binding on every BioBrick standard vectors. Since the first primer binds to the genome and the other is complimentary to the plasmid, a PCR amplicon could be optained only if the integration has succeeded.
Plasmid Curing
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Integration Strains
A suitable genomic integration strain needs to carry the attB sequence which is necessary for the λ‑integrase mediated homologous recombination. Many commonly used E. coli strains carry the λ‑phage integrated in their genome. But since the attB site was often used to integrate genes via λ recombination, e.g. T7 polymerase in BL21 (DE3), in many strains the attachment site is not accessible anymore. For our integration we chose the E. coli JM109 strain because it matches all our demands and is also freely and easily available to our lab.
References
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- [9] A. Landy, Dynamic, structural, and regulatory aspects of lambda site-specific recombination, Annual Review of Biochemisty, vol. 58, pp. 913-949, 1989.
