Team:BostonU/Data

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         <td scope="col" colspan="2"><h3>pTet-pBad RFP Characterization with atc and arabinose</h3></td></tr><tr><td scope="col">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 <a href= "https://2014.igem.org/Team:BostonU/Protocols">flow cytometry</a> 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 <a href = "https://2014.igem.org/Team:BostonU/Software">Software Tools</a> 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.  </td>
         <td scope="col" colspan="2"><h3>pTet-pBad RFP Characterization with atc and arabinose</h3></td></tr><tr><td scope="col">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 <a href= "https://2014.igem.org/Team:BostonU/Protocols">flow cytometry</a> 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 <a href = "https://2014.igem.org/Team:BostonU/Software">Software Tools</a> 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.  </td>
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<td scope="col"><img src="https://static.igem.org/mediawiki/2014/b/b0/PTet_pBad_RFP_all_three.png" width="600" style="float:right" style= "margin-left:10px"><capt>Figure 1: Flow Cytometry graph for pTet-pBad level 1 construct with RFP for three conditions: atc (red), arabinose (blue), atc and arabinose (purple)</capt></td>
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<td scope="col"><img src="https://static.igem.org/mediawiki/2014/b/b0/PTet_pBad_RFP_all_three.png" width="500" style="float:right" style= "margin-left:10px"><capt>Figure 1: Flow Cytometry graph for pTet-pBad level 1 construct with RFP for three conditions: atc (red), arabinose (blue), atc and arabinose (purple)</capt></td>
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Revision as of 17:28, 16 October 2014



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.

Primer Design for Tandem Promoters and Repressor Genes

Device Name Forward Primer Sequence Reverse Primer Sequence
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
C0080_CI C0080_Rev_I ACGAAGACCTTAGACAACTTGACGGCTACATCATTCAC
C0040_CI C0040_Rev_I ACGAAGACCTTAGACAACTTGACGGCTACATCATTCAC
E0040m_ID E0040m_For_I ATGAAGACGTTCTAGAATGCGTAAAGGAGAAGAACTTTTC
E0030_ID E0030_For_I ATGAAGACGTTCTAGAATGGTGAGCAAGGGCGAGGAGCTG

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)


Place holder text:)




This data is a result of our collaboration with Team WPI-Worcestor. 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 (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

Eugene


Raven


SBOL





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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