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 (Jahn, M. et al,2016) or growth phase specific expression changes. 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.
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.
Promotor strength * RBS
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.: 2.
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.
Figure 2: Modeling of the Anderson promoter (BBa_J23119,BBa_J23100 to BBa_J23110)in combination with the RBS (BBa_J61100, BBa_B0030, BBa_B0031).
In the visualisation of the modeling, the modeled expression strength, influenced from the different RBS, are shown in different colours.
The modeling showed a significant influence of differnt RBS on the expression strength, independant fro the use of different promoters.
When the J61100 RBS is used, the expression strength of the construct is statistically larger (approximately eight times higher) than in the other modeled RBS. The modeling showed a relative small influence on the expression strength when the RBS B0030 or B0031 are used.
Results
In addition to the modeled expression strengths we also tested the influence of gene expression on the bacteria growth by detection of the OD600.
After cloning all parts were checked by Sanger sequencing. The correct plasmids, condaining different promoter-RBS combinations were transformed in E. coli DH5α and grown in LB media.
5mL cultures were inoculated at OD600 of 0.1 and incubated at 37°C and 300 rpm. After one hour, the fluorescence signals of mRFP and eCFP were measured using the Tecan Reader with excitation wavelengths of 558 nm and 435 nm and emission detection at 608 nm and 485 nm.
In a second experiment the samples were cultivated under same conditions over 14 hours, showing similar results.
The fluorescence signals of the cells containing the Anderson promoter BBa_J23119 and different RBS (J6100, B0030, B0031), was set as basis for the analyse of expression strength, influenced by the differnet RBS and promoters.
Figure 3: Results of the Anderson promoter (BBa_J23119,BBa_J23100 to BBa_J23110)in combination with the RBS (BBa_J61100, BBa_B0030, BBa_B0031).
Name
Sequence
Measured Strength RBS J61100
Measured Strength RBS B0030
Measured Strength RBS B0031
BBa_J23119
ttgacagctagctcagtcctaggtataatgctagc
1
1
1
BBa_J23100
ttgacggctagctcagtcctaggtacagtgctagc
0.41631
0.47372
0.40999
BBa_J23101
tttacagctagctcagtcctaggtattatgctagc
0.41057
0.38392
0.35113
BBa_J23102
ttgacagctagctcagtcctaggtactgtgctagc
0.32899
0.55225
0.49386
BBa_J23103
ctgatagctagctcagtcctagggattatgctagc
0.05543
0.07914
nd
BBa_J23104
ttgacagctagctcagtcctaggtattgtgctagc
0.66601
0.84445
0.66691
BBa_J23105
tttacggctagctcagtcctaggtactatgctagc
0.11405
0.00532
0.07979
BBa_J23106
tttacggctagctcagtcctaggtatagtgctagc
0.18257
0.2062
0.15317
BBa_J23107
tttacggctagctcagccctaggtattatgctagc
0.05682
0.01321
0.02459
BBa_J23108
ctgacagctagctcagtcctaggtataatgctagc
0.16366
0.25364
0.17165
BBa_J23109
tttacagctagctcagtcctagggactgtgctagc
0.00928
0.01173
nd
BBa_J23110
tttacggctagctcagtcctaggtacaatgctagc
0.29643
0.29876
0.29423
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.
For further characterization a real-time PCR could be performed.
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