Team:BostonU/Data

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Data Collected

As a measurement team, we completed the Interlab Study. For more information about our Interlab Study results, please refer to our Interlab Study page.

Flow Cytometry Data



pTet-pBad RFP Characterization with atc and arabinose

For the first tandem promoter flow cytometry experiment, we tested pBad-pTet-BCD2-E1010m-B0015 and pTet-pBad-BCD2-E1010m-B0015. We planned on inducing each promoter separately with it's corresponding small molecule (either arabinose or atc) and also planned on inducing both together. We followed the flow cytometry workflow. By growing the constructs in different concentrations of media, we hoped to see RFP fluorescence increase as the small molecule concentration increased. For each concentration, we also had a negative. We also ran controls including: J04B2RM (RFP Positive), J04B2GM (GFP Positive), COXGR, COXRG, and DH5alpha. For an explanation of how we chose our controls, please refer to our Software Tools page.The pTet-pBad graph turned out the way we expected and showed the anticipated function. For the 5,000 and 10,000 ng/ul atc concentrations (for both graphs), the cells died because of the high concentrations. This is why the graph dropped rapidly.
Figure 1: Flow Cytometry graph for pTet-pBad level 1 construct with RFP for three conditions: atc (red), arabinose (blue), atc and arabinose (purple)


pBad-pTet RFP Characterization with atc and arabinose

For the pBad-pTet construct, we used the same controls as mentioned above. According to literature, the pTet-pBad construct has not previously functioned as suspected. This was possibly due to position-dependence interference [1]. Conversely, our flow cytometer data showed that pTet-pBad had a greater range of fluorescence than pBad-pTet. We will need to do further investigating to find out why this was the case. Unlike results in the literature, our pTet-pBad construct worked well, but the pBad-pTet didn't show anticipated function. We are predicting that the arabinose concentrations were too low for this experiment and the atc concentrations were too high. We are planning to run another flow experiment before the jamboree with new small molecule concentrations. We hope that this will improve function (fluorescence expression) and reduce the error bars.
Figure 2: Flow Cytometry graph for pBad-pTet level 1 construct with RFP for three conditions: atc (red), arabinose (blue), atc and arabinose (purple)


Testing of p15A origin of replication

Flow cytometry testing of the p15A origin was performed with the flow cytometry workflow using constitutively-expressed GFP and RFP controls. The similarities in fluorescence between the transcriptional units with the pMB1 and p15A origins were not expected, as only approximately 10-25 plasmids should be present per cell with the p15A origin in contrast with the pMB1 origin, which allows for over 100 plasmids per cell. This result may be due to the use of strong promoters and RBSs in the experiment, which may have caused overexpression of the fluorescent reporter and overshadowed any change brought about by changing the origin of replication.
Figure 3: Flow Cytometry graph for the p15A and pMB1 origins of replication with constitutively expressed GFP and RFP.
Alternatively, these results may be due to a deviation in the protocol necessitated by poor cell growth - on day 2 of the flow cytometry workflow, the 8-hour liquid cultures were grown in contaminated deep-well plates that resulted in no cell growth. The transformed colonies had to be re-picked and grown after this delay, which may have affected the folding and expression of the fluorescent proteins. This experiment will be repeated with freshly-transformed cells and a weaker promoter and RBS, and its results will be presented at the Jamboree.


Collaboration with Team WPI-Worcester

This data is a result of our collaboration with Team WPI-Worcester. They gave us two copies of the same construct, in different antibiotic resistant backbones. This construct expresses BclA-YFP , a cell surface targeted protein that expresses YFP on the cell surface. Our intention was to compare BclA expression with expression of our internal YFP control (J23104+BCD2+YFP+B0015). As evident from Figure 4, the Internal YFP has an expression of over 2 * 10^4 MEFLs, while none of the BclA constructs have expression more than 10^4 MEFLs. This data is, however, inconclusive as the internal YFP is in a Kanamycin resistant backbone and as we have shown, different backbones can lead to greatly varied data.
Figure 4: Expression of BclA-YFP constructs as compared to Internal YFP expression
Below are the micrographs team WPI-Worcester took for us. They used confocal microscopy to view cells containing tandem promoter + RFP testing constructs. These constructs were also tested using flow cytometry as described above. For details on experimental setup, click here. Both constructs expressed RFP for 100mM arabinose (on state) and did not express RFP for 1mM. While we did not test 100mM in our previous flow experiment, the confocal microscopy shows the same trend that we would expect for arabinose. For atc, the confocal experiment differed from our flow cytometry results because we expected an on state for 1000 ng/mL. This will require further investigation and a repeat experiment. It is possible that we mixed up the media that we gave to WPI. Likewise, the presence of GFP in some of the cells suggests we may have gave them a mixed culture since we didn't see GFP for the same constructs when we ran flow cytometry analysis.
Figure 5: Micrographs showing the expression of RFP and GFP at different small molecule concentrations for pTet+pBAD+ RFP Figure 6: Micrographs showing the expression of RFP and GFP at different small molecule concentrations for pBAD+pTet +RFP



Fusion Protein GFP Expression

Though we were unable to successfully clone the testing constructs listed here, we tested the single transcriptional units with GFP and YFP fusion proteins to check whether there was any protein expression at all. It was hypothesized that because the two heavy gene sequences in a fusion protein are very close to each other, GFP expression wouldn't be as high as it would in a single GFP control. The hypothesis was accepted based on the results on Figure 7. However, it should be noted that the fluorescence for both tetR+GFP and araC+GFP is not greatly different from that for just GFP. This cannot be used to conclude whether the fusion proteins affect regulator function. For that, we will need to test the testing constructs designed. We should be able to present that data at the Jamboree.
Figure 7: Flow Cytometry results comparing GFP expression of fused proteins and that for the single GFP control.


Eugene


Here are the Eugene files for the tandem promoters, tandem promoter test devices, fusion proteins, and the Priority Encoder.
Tandem promoter basic parts
Tandem promoter test devices
Fusion protein basic parts
Priority encoder

Raven


Here is the Raven input files for tandem promoters, tandem promoter test devices, and the Priority Encoder.
Tandem Promoters
Tandem Promoter Test Devices
Priority Encoder

SBOL


Here is the SBOL file for the Priority Encoder.
Priority Encoder

Primer Designs

Primer Design for Tandem Promoters and Repressor Genes

 Device Name   Forward Primer   Sequence 5' to 3'    Reverse Primer   Sequence 5' to 3'  
 BetI_CD   BetI_For_C   ATGAAGACGTAATGGTGCCGAAACTGGGTATGCAGAGC   BetI_Rev_D   ACGAAGACCTACCTTTAATCGGTCGGCAGATGCTGGGT 
 PhlF_CD   PhlF_For_C   ATGAAGACGTAATGATGGCACGTACCCCGAGCCGTAGC   PhlF_Rev_D   ACGAAGACCTACCTTTAACGCTGTGTACCCGGACAAAC 
 BM3R1_CD   BM3R1_For_C   ATGAAGACGTAATGATGGAAAGCACCCCGACCAAACAG   BM3R1_Rev_D   ACGAAGACCTACCTTTAGCTCTGACGGCTCAGTGCTGC 
 LmrA_CD   LmrA_For_C   ATGAAGACGTAATGATGAGCTATGGTGATAGCCGTGAA   LmrA_Rev_D   ACGAAGACCTACCTTTAACGTTTCAGCAGATCCGGAAT 
 SrpR_CD   SrpR_For_C   ATGAAGACGTAATGATGGCACGTAAAACCGCAGCAGAA   SrpR_Rev_D   ACGAAGACCTACCTTTATTCGAAGGATTTCACCTGTTT 
 pTet_AK   pTet_For_A    ATGAAGACGTGGAGTCCCTATCAGTGATAGAGATTGAC   pTet_Rev_K   ACGAAGACCTGCATTTCGGTCAGTGCGTCCTGCTGATG 
 pTet_KB   pTet_For_K   ATGAAGACGTATGCTCCCTATCAGTGATAGAGATTGAC   pTet_Rev_B   ACGAAGACCTAGTATTCGGTCAGTGCGTCCTGCTGATG 
 pBad_AK   pBad_For_A   ATGAAGACGTGGAGAAGAAACCAATTGTCCATATTGCA   pBad_Rev_K   ACGAAGACCTGCATTATGGAGAAACAGTAGAGAGTTGC 
 pBad_KB   pBad_For_K    ATGAAGACGTATGCAAGAAACCAATTGTCCATATTGCA   pBad_Rev_B   ACGAAGACCTAGTATATGGAGAAACAGTAGAGAGTTGC 
 pSrpR_KB   pSrpR_For_K    ATGAAGACGTATGCTTCGTTACCAATTGACAGCTAGCT   pSrpR_Rev_B   ACGAAGACCTAGTAGTTTACAAACAAACAAGCATGTAT 
 pLmrA_FK   pLmrA_For_F    ATGAAGACGTCGCTTTCGTTACCAATTGACAACTGGTG   pLmrA_Rev_K   ACGAAGACCTGCATAAATATAGTGACTGGTCTATTATC 
 pBetI_EB   pBet_For_E    ATGAAGACGTGCTTTTCATGGATTCGTTACCAATTGAC   pBetI_Rev_B   ACGAAGACCTAGTAGCTAGCATTATATTGAACGTCCAA 
 pPhlF_GB   pPhlF_For_G    ATGAAGACGTTGCCTTCGTTACCAATTGACATGATACG   pPhlF_Rev_B   ACGAAGACCTAGTAACCTTAACGATACGGTACGTTTCG 
 pBM3R1_FB   pBM3R1_For_F    ATGAAGACGTCGCTTTCGTTACCAATTGACGGAATGAA   pBM3R1_Rev_B   ACGAAGACCTAGTAGCTAGCATTATCGGAATGAACGTT 

Primer Design for Fusion Proteins

 Device Name   Primer   Sequence 5' to 3'  
 C0080_CI   C0080_Rev_I   ACGAAGACCTTAGACAACTTGACGGCTACATCATTCAC 
 C0040_CI   C0040_Rev_I   ACGAAGACCTTAGACAACTTGACGGCTACATCATTCAC  
 E0040m_ID   E0040m_For_I   ATGAAGACGTTCTAGAATGCGTAAAGGAGAAGAACTTTTC  
 E0030_ID   E0030_For_I   ATGAAGACGTTCTAGAATGGTGAGCAAGGGCGAGGAGCTG  

Primer design for Origins of Replication

 Device Name   Forward Primer   Sequence 5' to 3'    Reverse Primer   Sequence 5' to 3'  
 DVL1_AE   DVL1_AE_FWD   ATCGATCAATTGGATTATCAAAAAGGATCTTCACCT   DVL1_AE_REV   ATCGATGGCGCGCCTTTTTACGGTTCCTGGCCTTTTGC 
 DVL2_AF   DVL2_AF_FWD   ATCGATCAATTGGATTATCAAAAAGGATCTTCACCT   DVL2_AF_REV   ATCGATGGCGCGCCTTTTTACGGTTCCTGGCCTTTTGC 
 ColE1   ColE1_FWD   ATCGATCAATTGGGCCGCGTTGCTGGCGTTTTTCCA   ColE1_REV   ATCGATGGCGCGCCTCATGACCAAAATCCCTTAACGTG 
 p15A   p15A_FWD   ATCGATCAATTGAAATATTTTATCTGATTAATAAGA   p15A_REV   ATCGATGGCGCGCCTAGCGGAGTGTATACTGGCTTACT 
 pSC101   pSC101_FWD   ATCGATCAATTGTCAGATCCTTCCGTATTTAGCCAG   pSC101_REV   ATCGATGGCGCGCCATGTCTGAATTAGTTGTTTTCAAA 


References

[1] A. Tamsir, J. Tabor, C. Voigt (2011). “Robust multicellular computing using genetically encoded NOR gates and chemical ‘wires’.” Nature 469: 212-215







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