Team:BostonU/Data

From 2014.igem.org

Revision as of 22:29, 16 October 2014

• TEAM
• PROJECT
  • Overview
  • Priority Encoder
  • MoClo Assembly Method
  • Low Copy Backbones
  • Tandem Promoters
  • Fusion Proteins
  • Repressor Proteins
  • Multiplexing
  • BioDesign Automation Tools
  • Future Work
• ACHIEVEMENTS
  • Data Collected
  • Parts Submitted
  • Chimera Workflow
  • Chimera Example
  • Medal Fulfillment
• NOTEBOOK
  • Training
  • Protocols
  • Backbones
  • Tandem Promoters and Repressors
  • Fusion Proteins
• CONSIDERATIONS
  • Collaborations
  • Measurement Track
  • Safety
  • Interlab Study
  • NEGEM
  • Policy and Practices
• ACKNOWLEDGEMENTS

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
The similarities in fluorescence between high- and medium-copy plasmids 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. The Phase 1 cloning strategy must be modified to ensure proper incorporation of the p15A origins by the cells to see the predicted change in fluorescence.
	Figure 3: Flow Cytometry graph for the p15A and pMB1 origins of replication with constitutively expressed GFP and RFP.

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.
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

Raven

SBOL

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

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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