Team:Bielefeld-CeBiTec/Part Collection

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Part Collection

Short Summary

Choosing the optimal promoter and RBS combination for a gene of interest can be crucial, since small changes in the protein expression level can lead to large changes in the resulting effect inside synthetic gene circuits. To address the challenge of choosing the right promoter, we designed a promoter-RBS library as this year’s parts collection. With our measurement vector, the library could be easily expanded by future iGEM teams and the results are comparable due to normalization of the reporter signal with the help of a second reporter. Our collection contains a variety of iGEM standard promoters like the Anderson promoter library, as well as inducible promoters. Furthermore we added a vector (BBa_K2638560) to assess the promoter-RBS combination expression strength accurately, based on two reporter genes. This collection is closely involved in our whole project. We tested all of our promoter-RBS combinations which are important for all of our parts. It is also possible to determine only the strength of the promoter or the RBS. With our part collection we improved our siRNA toolbox, which offers the probability to choose the strength of a knock-down, when a specific promoter is used. Furthermore, we used the Promoter-RBS combination to determine the optimal expression of our membrane proteins and our anti-toxicity project. To sum up, we analyzed 26 promoter-RBS combinations, modeled 37 more and therefore provided the iGEM community with detailed information regarding their future projects. In addition, we designed a database that allows us to easily find a promoter or promoter-RBS combination. If you want to express a slightly toxic protein, you can find a weak combination and if you want to express a reporter geneyou can choose the optimal strength.

Design

Analyzing the expression strength of individual Promoter-RBS combinations is quite challenging. The main reasons hindering accurate promoter-RBS characterization, are volatile copy-number changes of the expression plasmid (QUELLE) or growth phase specific expression changes (QUELLE). To avoid these errors, we designed a measurement vector carrying two reporter genes, which enables us to normalize the expression strength of the measured promoter-RBS combination to the relative abundance of the vector. Our measurement vector is based on the expression strength of the different promoter-RBS combinations from our library, cloned in front of mRFP and a double terminator (BBa_K2638426) inside the pSB1C3 restriction site. Furthermore, our measurement vector carries a eCFP (BBa_E0022) under control of a strong/weak Anderson promoter (BBa_J23100) and RBS (BBa_J61100) combination and a double terminator in the plasmid’s backbone (Fig. 1). The constitutive eCFP expression is proportional to the plasmid’s copy-number. This enables normalization of the mRFP expression to the plasmid’s copy-number and direct assessment of our library’s promoter-RBS combinations expression strength. As this measurement is independent of plasmid effects it enables comparison with our modeling as well as with other expression constructs.

Figure 1: Map of Biobrick BBa_K2638560. The vektor of our measurement system with changeable promoterand RBS between prefix and suffix.

Figure 3: Protein structures of the wild-type human ferritin (A, RCSB ID 4oYN) and the mutated human ferritin (B, RCSB ID 3ES3). Despite the mutations of ten amino acids the ferritin retains its shape. The protein structeres were generated with Chimera (Pettersen et al., 2004).

Modeling

For modeling of our promoter-RBS combinations we used the given strength of the Anderson promoters (BBa_J23119,BBa_J23100 to BBa_J23110) and the strength of different RBS (BBa_J61100, BBa_B0030, BBa_B0031) to determine an estimate for their absolute strength. Prior to the experiments, we modeled the expression strength of different promoter and RBS combinations to create a database for our experiments. Therefore we used the given strength of the Anderson promoters and the strength of the different known RBS to determine and visualize their absolute strength shown in Fig.: ?. When generating these results, we do not only wanted to consider the use of different Anderson promoters, but also analyze the expression strength of different promoters in combinations with different RBS. Especially for our siRNA system, it was interesting to see the difference between inducible and constitutive promoters. In addition, we modeled other promoters of the parts registry.

Characterization

After cloning all parts were checked by Sanger sequencing. The correct plasmids were transformed in E. coli DH5α and grown in LB media. Over-night cultures were diluted to an OD600 of 0.1 and incubated at 37°C and 300 rpm for one hour. Afterwards the fluorescence strength of mRFP and eCFP were measured using the Tecan Reader with excitation wavelengths of 558 nm and 435 nm and emission detected at 608 nm and 485 nm.

Results

In addition to the analyzed expression strengths we also tested the influence of gene expression on the bacteria growth by detection of the OD600. The experiment was performed over 14 hours, by 37 °C and 300 rpm. To analyze the production of the substrate we also used real-time PCR for some constructs to determine the total number of transkripts. We visualized the results in Fig. ?.

Outlook

With this database we can predict the right Promoter and RBS for every system. We have weak combinations for proteins which are slightly toxic for the cells or cause stress responses and we have powerful combinations for anti-toxicity and membrane proteins.

Figure 4: Possible applications of nanoparticles produced with ferritin.

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References ▾

Molecular graphics and analyses performed with UCSF Chimera, developed by the Resource for Biocomputing, Visualization, and Informatics at the University of California, San Francisco, with support from NIH P41-GM103311.
References ▾

De Mey, M., Maertens, J., Lequeux, G. J., Soetaert, W. K., & Vandamme, E. J. (2007). Construction and model-based analysis of a promoter library for E. coli: an indispensable tool for metabolic engineering. BMC biotechnology, 7(1), 34.
Ipsaro, J. J., & Joshua-Tor, L. (2015). From guide to target: molecular insights into eukaryotic RNA-interference machinery. Nature structural & molecular biology, 22(1), 20.
Jahn, M., Vorpahl, C., Hübschmann, T., Harms, H., & Müller, S. (2016). Copy number variability of expression plasmids determined by cell sorting and Droplet Digital PCR. Microbial cell factories, 15(1), 211.
Kannan, S., Sams, T., Maury, J., & Workman, C. T. (2018). Reconstructing dynamic promoter activity profiles from reporter gene data. ACS synthetic biology, 7(3), 832-841.
Köker, T., Fernandez, A., & Pinaud, F. (2018). Characterization of Split Fluorescent Protein Variants and Quantitative Analyses of Their Self-Assembly Process. Scientific reports, 8(1), 5344.
Rizzo, M. A., Springer, G. H., Granada, B., & Piston, D. W. (2004). An improved cyan fluorescent protein variant useful for FRET. Nature biotechnology, 22(4), 445.
Rudge, T. J., Brown, J. R., Federici, F., Dalchau, N., Phillips, A., Ajioka, J. W., & Haseloff, J. (2016). Characterization of intrinsic properties of promoters. ACS synthetic biology, 5(1), 89-98.