Copy number

Copy number of plasmids vary when used in different bacterial hosts and this significantly alters behavior of parts (Segall-Shapiro et al., (2018)). We used a modeling approach to study the behavior of a genetic implementation of an iFFL. Our model shows complete independence to copy number of the steady-state gene expression.

• Results -- Copy number independence﹀

  The expression levels in a genetic circuit are strongly correlated to the plasmid copy number of the DNA template (Segall-Shapiro et al., (2018)). The amount of gene plasmid copy number can change when the plasmid is transferred between organisms. Therefore there is a need for expression levels independent of plasmid copy number if the same genetic circuit is used in different organisms. The steady-state solution of our model tells us that when our repressor binding is fully non-cooperative, we have complete independence of plasmid copy number.

  
  
  

  The model has the same expression level independent of plasmid copy number (figure 1). We therefore can transfer our circuit between organisms and expect the expression of the gene of interest to be independent of the changes in plasmid copy number of our orthogonal plasmid.

Portable T7 expression system

• Experimental design﹀

  To demonstrate the compatibility of our system with the portable T7 RNAP expression system we first cloned our T7 promoter based optimized iFFL into low and medium copy number backbones (pICH82113 , and pICH82094 respectively) from the MoClo toolkit. These plasmid backbones contain origins of replication compatible with ColE1. The clones were then co-transformed with the plasmid containing the portable T7 expression system into E. coli TOP10. E. coli TOP10 does express T7 RNAP.</p>

  To reduce dependency on host transcriptional machinery, we co-transformed these constructs with the UBER portable T7 expression system. The UBER system expresses T7 RNAP at a stable level as described on our <a href="https://2019.igem.org/Team:TUDelft/Design" > design page </a>.

  Contents

  • 1 Results
  • 2 Conclusion
  • 3 Transcriptional variation
    • 3.1 Results
    • 3.2 Results
    • 3.3 Results
    • 3.4 Conclusion
  • 4 Translational variation
    • 4.1 Conclusion
  • 5 Expression across different organisms
    • 5.1 Results
    • 5.2 Conclusion
  • 6 Cross species codon harmonization
  • 7 Future Plan
    • 7.1 References

  Results

                                 <img src="" style="width:80%;border:1px solid #00a6d6;" class="centermodel"
                                      alt="TALE system">
                                 <figcaption class="centermodel"> Figure 1: Fluorescence histogram of cotransformations of obtained by flow cytometry. Black is regular E. coli TOP10 cells, green is cotransformation of our <a href="http://parts.igem.org/Part:BBa_K2918010" > T7 promoter based optimized iFFL </a>, pink is cotransformation of an iFFL without repression. </figcaption> 
   
  

  Figure 1 shows the fluorescence hisogram obtained by flow cytometry. Clearly a shift in fluorescence is observed when our plasmid is cotransformed with the UBER plasmids.

                             </ul>
                         </li>
                     </ul>
  

  Conclusion

  Our unique iFFL genetic circuits are compatible with the portable T7 expression system and we have set the basis for portable expression systems for controllable gene expression across bacterial species.

                 </div>
  

  Transcriptional variation

                     Behavior of promoters (transcriptional rates) significantly changes across different bacterial hosts <a href=”https://www.ncbi.nlm.nih.gov/pubmed/29061047”> (Yang S et al., 2017) </a>. Hence, promoters either need to be re-characterized for each bacterial hosts or promoters specific to the host need to be identified. Using iFFL, we demonstrated gene expression independent of transcriptional rates  when the transcription rate of both genes (TALE and GOI) maintain the same ratio, as predicted by <a href="https://2019.igem.org/Team:TUDelft/Model#TranscriptionalVariations" > modeling </a>. 
                     
   
  
  

  • <a class="toggle " href="javascript:void(0);" >Results -- Independence to promoter strengths﹀</a>

    To validate our model prediction, we designed T7 promoters based iFFL systems with varying promoter strengths. We compared our <a href="http://parts.igem.org/Part:BBa_K2918040">wild-type T7 promoter based iFFL system</a> (figure 1) to a iFFL system based on a T7 promoter variant with 50% strength compared to the wild-type (figure 2) (Ryo Komura et al., 2018) (<a href="http://parts.igem.org/Part:BBa_K2918048">medium T7 based iFFL system</a>). As a control we express GFP without any TALE (figure 3).

                                   <img src="" style="width:60%;border:1px solid #00a6d6;" class="centermodel"
                                        alt="TALE system">
                                   <figcaption class="centermodel">Figure 1: T7 based iFFL. Both genes are controlled by a T7 promoter. </figcaption>
                                   
    
                                   <img src="" style="width:60%;border:1px solid #00a6d6;" class="centermodel"
                                        alt="TALE system">
                                   <figcaption class="centermodel">Figure 2: Medium T7 based iFFL. Both genes are controlled by a medium strength version of a T7 promoter. </figcaption>
                                   
    
                                   <img src="https://static.igem.org/mediawiki/2019/4/4e/T--TUDelft--notale.png#00a6d6;" class="centermodel"
                                        alt="TALE system">
                                   <figcaption class="centermodel">Figure 3: Negative control, T7 promoter controlling GFP. </figcaption>
    

    
    

    The output GFP fluorescence was measured using flow cytometry during logarithmic growth phase after induction with 1mM IPTG. As a reference for background fluorescence we use E. coli BL21DE(3) cells without any plasmid. The most dense region (determined by eye) in the scatter plot (forward and side-scatter) is selected for gating in order to only compare cells of similar morphology. Furthermore, the fluorescence histogram is gated to discern between cells that are "off" or "on" (expressing GFP or not), by gating the E. coli BL21DE(3) cells without any plasmid.

    
    

    The median of the background is subtracted from the median of the samples and the resulting values are plotted (figure 4).

    
    

    Results

    <img src="https://static.igem.org/mediawiki/2019/5/5c/T--TUDelft--promoter_variation_wetlab_model.svg" style="width:60%;border:1px solid #00a6d6;" class="centermodel" alt="TALE system"> <figcaption class="centermodel">Figure 4: Steady-state GFP fluorescence measurement of promoter variation using flow cytometry. The graph depicts T7 and medium T7 iFFL systems, expected to give the same fluorescence according to the model. As a control, GFP under control of an unrepressed T7 promoter was used. </figcaption>
    <p>In figure 4, similar GFP fluorescence can be observed for the iFFL systems while the unrepressed control system shows higher fluorescence. This suggests successful insulation of gene expression from change in promoter strengths.

  • <a class="toggle " href="javascript:void(0);" >Results -- Independence to IPTG concentration﹀</a>

    Aside from testing gene expression independent of transcriptional variation by using promoters of different strengths, the effect of different concentrations of IPTG on the iFFL loop was tested. Change in IPTG concentrations, changes in-vivo concentrations of T7 RNAP and this contributes to variations in transcriptional rates. In unrepressed systems, the expression of the GOI is a function of IPTG concentrations. However, in iFFL systems, since the transcriptional rates of TALE and GFP are under control of T7 promoters, similar GOI expression is expected (figure 1). As a control we expressed GFP under the control of T7sp1 promoter was used (figure 2). <img src="" style="width:60%;border:1px solid #00a6d6;" class="centermodel" alt="TALE system"> <figcaption class="centermodel">Figure 1: Optimized T7 based iFFL. TALE is under cotntrol of weak T7 promtoer. GFP is controlled by a T7 promoter. </figcaption>
    <img src="https://static.igem.org/mediawiki/2019/4/4e/T--TUDelft--notale.png#00a6d6;" class="centermodel" alt="TALE system"> <figcaption class="centermodel">Figure 2: Negative control, T7 promoter controlling GFP. </figcaption>

    The output GFP fluorescence was measured using flow cytometry during logarithmic growth phase after induction with 1mM IPTG. As a reference for background fluorescence we use E. coli BL21DE(3) cells without any plasmid. The most dense region (determined by eye) in the scatter plot (forward and side-scatter) is selected for gating in order to only compare cells of similar morphology. Furthermore, the fluorescence histogram is gated to discern between cells that are "off" or "on" (expressing GFP or not), by gating the E. coli BL21DE(3) cells without any plasmid.

    
    

    The median of the background is subtracted from the samples and are compared.

    
    

    Results

    <p> In figure 3 the GFP fluorescence of the unrepressed control changes with changing concentrations of IPTG while the iFFL system shows the same GFP expression across different IPTG concentrations. Thus, the iFFL system has been shown to insulate gene expression against changes in transcription rates (achieved by varying IPTG concentrations). There is a high difference in levels of expression between the controlled and uncontrolled samples. The difference could be due a combination of expression of two proteins instead of one and thus partitioning of resources, and the repression by the TALE protein.

                               <img src="https://static.igem.org/mediawiki/2019/a/a9/T--TUDelft--IPTGtitration.svg" style="width:60%;border:1px solid #00a6d6;" class="centermodel"
                                    alt="TALE system">
                               <figcaption class="centermodel">Figure 3: Steady-state GFP fluorescence measurement of IPTG titration using FACS. The graph depicts a T7 iFFL system induced using different levels of IPTG, which according to the model should give the same result. As a control GFP under control of an unrepressed T7 promoter was used. 
                               The unrepressed promoter is depicted on the left yaxis and the stabilized system uses the right axis.</figcaption>
                               
    
    

  • <a class="toggle " href="javascript:void(0);" >Results -- Tunability﹀</a>

    The predictions made by modeling not only tell us that we can maintain the same level of gene expression but also that we can easily tune the expression levels by changing one of the promoters. By changing one of the promoters to another variant of T7 we can expect a different level of expression, while at the same time expect it to behave similarly when transferred between organisms. Through the use of T7 variants we could achieve wide ranges of expression levels, which can be used to establish complex genetic circuits, while also expecting it to work similarly in different biological contexts.
    
    We tested the prediction by changing the promoter controlling the TALE protein to a variant of T7 which has been shown to be about 10% in strength compared to the wild-type (Ryo Komura et al., 2018). According to the model the expression should increase.

    We measure GFP fluorescence using flow cytometry. As a reference for background fluorescence we use E. coli BL21DE(3) cells without any plasmid. The most dense region (determined by eye) in the scatter plot (forward and side-scatter) is selected for gating in order to only compare cells of similar morphology. Furthermore, the fluorescence histogram is gated to discern between cells that are "off" or "on" (expressing GFP or not), by gating the E. coli BL21DE(3) cells without any plasmid.

    
    

    The median of the background is subtracted from the samples and are compared.

    
    

    Results

    <p> Figure 1 shows higher fluorescence when a lower T7 promoter version is used to express the TALE protein in comparison to our systems with the same ratio in promoter strengths for both genes.

                               <img src="https://static.igem.org/mediawiki/2019/5/5d/T--TUDelft--tunability.svg" style="width:60%;border:1px solid #00a6d6;" class="centermodel"
                                    alt="TALE system">
                               <figcaption class="centermodel">Figure 1: Steady-state GFP fluorescence measurement of E. coli BL21DE(3) cells expressing our iFFL systems. The graph depicts a different T7 iFFL systems, one with both promoters T7, one with both medium strength and one where the promoter controlling of the TALE is weak. As a control GFP under control of an unrepressed T7 promoter was used. </figcaption>
                               
    
    

  Conclusion

  Results above indicate successful implementation of the iFFL system to insulate from transcriptional variations. Transcriptional variations were achieved by using T7 promoters of different strengths and by induction at different IPTG concentrations. Furthermore, we demonstrated the tunability of the iFFL system to achieve different levels of gene expression. As predicted by our model, we achieved gene expression independent of changes in transcriptional rates by maintaining constant ratios of transcriptional rates of TALE and GFP genes.

                     Strengths of ribosome binding sites (RBSs) across organisms change due to differing anti-Shine Dalgarno sequences (Salis, Mirsky, & Voigt, 2009) therefore, RBSs need to be recharacterized across different bacterial species. The iFFL system can be used to achieve controllable gene expression despite differences in RBS strengths.  In order to see the effect of translational variation on the expression levels of the gene of interest (GOI) we modeled our system for a range of translational rates for both genes (TALE and GOI).
  

  Translational variation

                     In order to see the effect of translational variation on the expression levels of the gene of interest (GOI) we modeled our system for a range of translational rates for both genes (TALE and GOI). 
  

  • <a class="toggle " href="javascript:void(0);" >Results﹀</a>

    As can be seen in figure … the steady-state expression levels of GFP remain the same when the translation rates are kept constant.

  Conclusion

  According to our model solution we can maintain the same level of GOI expression when both translation rates remain constant. We therefore designed our system to contain the same RBS in front of both TALE and the GOI.

  
  

  Expression across different organisms

  To achieve similar gene expression across different organisms the iFFL system needs to be robust to changes in copy number, transcriptional and translational rates. We experimentally demonstrated gene expression independent of transcriptional variations and through modelling showed adaptation variations in copy number and translational variation of our iFFL system. Through the implementation of the iFFL loop using engineered broad host range promoters, we successfully demonstrated similar GFP expression across E. coli and P. putida. Thereby demonstrating gene expression insulated from variations associated to microbial hosts.

  
  

  • <a class="toggle " href="javascript:void(0);" >Results -- Expression across organisms﹀</a>

    Broad host range promoter (P_BHR) was designed by combining the conserved -10 and -35 regions from E.coli and B.subtilis <a href=""> (Yang S et al., 2018) </a> and the promoter was engineered to contain a binding site for TALE repressor (P_BHRsp1). Using the P_BHR and P_BHRsp1, we constructed an iFFL genetic circuit driving GFP expression. The circuit was transformed in E. coli and P. putida and, output fluorescence was measured by flow cytometry during logarithmic growth phase. To correct for background fluorescence, E. coli and P. putida without plasmids were used as blanks. GFP under the control of P_BHRsp1 was used as a positive (unrepressed) control. As the cell morphologies of E. coli and P. putida are different they cannot be compared directly, gating was based on the most dense regions in the scatter plot for each organism. In order to compare the GFP expression levels between each organism, the background fluorescence for each organism was subtracted by its respective blank .

    Results

    <p> Figure 1 clearly shows higher fluorescence when a lower T7 promoter version is used to express the TALE protein in comparison to our systems with the same ratio in promoter strengths for both genes.

                               <img src="https://static.igem.org/mediawiki/2019/4/4a/T--TUDelft--difforgs.svg" style="width:60%;border:1px solid #00a6d6;" class="centermodel"
                                    alt="TALE system">
                               <figcaption class="centermodel">Figure 1: : Steady-state GOI production while translation rates of both TALE and GOI are changed. The lines indicate the constant rate of the translation rates. </figcaption>
                               
    
    

    In figure 1, the median fluorescence of the gated populations is plotted. Significantly large difference in expression levels is observed between the unrepressed controls and the broad host range promoter based iFFL systems in E.coli and P.putida. However, similar levels of expression were observed from iFFL systems in E.coli and P.putida . The difference in expression levels between the unrepressed circuit is significantly higher than the difference in expression levels between the iFFL system (578530 and 2351.2 respectively).

  Conclusion

  The implementation of the iFFL significantly decreased the differences in expression levels between organisms. Our system sets the basis for controllability across organisms.

  Cross species codon harmonization

  • <a class="toggle " href="javascript:void(0);" >Design﹀</a>

             </div>
  

  Future Plan

  text

  
  

  
  

  References

  • <a target="_blank" href="http://doi.org/10.3389/fmicb.2018.02154">Sun, D. (2018). Pull in and Push Out: Mechanisms of Horizontal Gene Transfer in Bacteria. Frontiers in Microbiology, 9, 2154. https://doi.org/10.3389/fmicb.2018.02154</a>
  • <a target="_blank" id="Weber2011" href="http://doi.org/10.1371/journal.pone.0016765">Weber, E., Engler, C., Gruetzner, R., Werner, S., & Marillonnet, S. (2011). A modular cloning system for standardized assembly of multigene constructs. PloS One, 6(2), e16765. http://doi.org/10.1371/journal.pone.0016765</a>
  • <a href="https://www.ncbi.nlm.nih.gov/pubmed/27665861"> Adams, B. (2016). The Next Generation of Synthetic Biology Chassis: Moving Synthetic Biology from the Laboratory to the Field. ACS Synthetic Biology, 5(12), 1328-1330.</a>
  • <a href="https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0088717"> Meysman, P., Collado-Vides, J., Morett, E., Viola, R., Engelen, K., & Laukens, K. (2014, 2 7). Structural properties of prokaryotic promoter regions correlate with functional features. PLoS ONE, 9(2). </a>
  • <a href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4535381/"> Omotajo, D., Tate, T., Cho, H., & Choudhary, M. (2015, 8 14). Distribution and diversity of ribosome binding sites in prokaryotic genomes. BMC Genomics, 16(1). </a>
  • <a href="https://www.ncbi.nlm.nih.gov/pubmed/15728743"> Sharp, P., Bailes, E., Grocock, R., Peden, J., & Sockett, R. (2005). Variation in the strength of selected codon usage bias among bacteria. Nucleic Acids Research, 33(4), 1141-1153. </a>

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