experiment Results

promoter library construction

Through our wet lab work, we have got 23 promoter strength data. The measured values are as follows:

Table.1 promoter strength characterized by a reporter gene

The fluorescence from the luciferase gene without a promoter and from P.fluorescence pf-5, that contains no luciferase gene, was also determined. Since predecessors have less research on the promoter collection and characterization of P. fluorescence, we have done an innovative work and set a standard for the promoter strength in P.fluorescence.We set P16s promoter as the standard, so the activity of the promoters was compared to the activity of the 13P16s promoter in our project. Some of the promoters which resulted from this approach, turned out to be very strong (more than 7-fold the P16s promoter), others quite weak (almost 0.5-fold lower than the P16s promoter)

Nowadays, the focus on metabolic engineering research is shifting from massive overexpression and inactivation of genes towards the model-based fine tuning of gene expression. In other words, being able to rationally designing a promoter would be extremely profitable in the context of a model-based metabolic engineering. Our project therefore attempts to link the promoter sequence to its strength. To this end, modelling strategies have been applied. (To learn more, please read our Model )

By solving the matrix sparse solution algorithm, we can conclude that there is indeed a linear relationship between the total 64 data of AAA-GGG and the promoter strength. Notice that we used merely 22 sets of data to approximately solve the sparse solutions of 64 equations with a fitting precision higher than 95%, therefore, when we provide as much data as possible, the fitting precision of the model will greatly increase.

We notice that there is a significant difference in the magnitude of the measurements between our group and the other groups. In order to better contribute our results to other teams, we will use the magnitude of our measurements and classify the promoters by numerical size.

The promoter intensity level corresponding to the data is as follows:

Using this standard, we quickly categorized 23 known promoters:

 Fig.1 The strength level of different promoters


Based on our promoter library, we selected three promoters (promoter 4, promoter 5 and promoter11) of different intensities to regulate the expression of the key nicotine-degrading gene nicA2.We’ve constructed lasmid, pBBR1-km-amp-cm-promoter-nic, and electroporated it into our new chassis bacteria, Pseudomonas fluorescences pf-5.

We found that there are only a few colonies of P. fluorescences pf-5 (containing nicA2 with promoters in front of it)on the LB plate,but lots of colonies on the CK plate(control group electroporated with pBBR1-km-amp-cm-nic without promoter). Combining the fact that theE.coliusually lost a part of (sometimes the whole plasmid)when we want to use high-copy vector to carry this gene cluster,we wondered if it NicA2 is a poisonous enzyme so that E.coli lost this plasmid during DNA replication under stress.

Plasmid in P. fluorescences pf-5

Strength of promoter

CFU on LB plate













In order to test if our plasmids really work in P. fluorescences pf-5, we demonstrate our project in three levels, transcription, protein expression, and substrate degradation efficiency. We used SDS-PAGE, to demonstrate the expression of NicA2 (52.5 kDa). In the picture,we can see the clear expression of NicA2 in P. fluorescences pf-5 containing plasmid with promoter. It can be qualitatively known that our promoter can initiate transcription normally, and the nicotine-degrading gene cluster has also been successfully heterologously expressed.

Fig.2 Whole protein SDS-PAGE electrophoresis: <b>Expression of NicA2 (52.5kDa, showed in red) in pBBR1-km-amp-cm-nic (ck), pBBR1-km-amp-cm-promoter4-nic (promoter4), pBBR1-km-amp-cm-promoter5-nic (promoter5) and pBBR1-km-amp-cm-promoter11-nic (promoter11) in P.fluorescens pf-5.</b>

As for transcription, we are going to use real-time PCR to compare the ability ofinitiating transcription of three promoters .

In addition, we are comparing the degradation efficiency by HPLC. NicA2 can convert nicotine to pseudo-oxidation in a whole-cell reaction, and the rest of the gene cluster will convert pseudo-oxidation to 2,5-DHP.

However, due to time limitation, we were unable to complete the last two experiments, but we will do a supplementary explanation in our presentation. Please stay tuned.