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AQ curious about

Promoters?

Joshua Baker, Haley Everroad, and Madison Powell

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Introduction

Promoters are an important part of mRNA transcription and indicate the beginning of an operon
Potential promoters are often found in gaps between a reverse gene and a forward gene
Located before the gene’s DNA sequence, the promoter is a region of DNA that allows RNA polymerase to bind and begin the process of transcription

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The Main Question

Many phages in the AQ cluster have very similar nucleotide sequences and have a gap between genes 10 & 11 and genes 83 & 84 where potential promoters could be located
What promoters exist in the AQ cluster? Specifically identify a consensus sequence between genes 10 & 11 and genes 83 & 84
Our group focused on AQ phages Amigo, Anansi, Gorgeous, Rings, and SorJuana

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DY Wu et al. Nature 462, 1056-1060 (2009) doi:10.1038/nature08656

Actinobacteria

Gammaproteobacteria

Take away:

E. coli and Arthrobacter are not very related. Are the promoter sequences conserved?

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MEthods

Examine nucleotide sequences between genes 10 & 11 and genes 83 & 84 of annotated AQ phages
Run a sigma 70 promoter predictor in DNA Master

Sigma 70 employs the E. coli consensus sequence TTGACA_17_TATAAT

Compile data
Develop a consensus sequence using averages
Verify the presence of promoters with a termination prediction program
Compare to AN consensus sequence and E. coli consensus sequence

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Results

Average Gap Size Between Genes

10 & 11: 17.64285714

Average Gap Size Between Genes

83 & 84:

17.48484848

	1	2	3	4	5	6
Gene 10/11 -10 Promoter	C/A	A	T	A	T/A	T
Gene 83/84 -10 Promoter	T	A	T	A	A/T	T
-10 Promoter Consensus	T	A	T	A	A/T	T
Gene 10/11 -35 Promoter	T/G	T	G	A	A/G	A
Gene 83/84 -35 Promoter	T	T	G	A/C	C/T/A	A
-35 Promoter Consensus	T	T	G	A	A/C	A

So to get our results, we took the 6bp promoter sequences that DNAMaster gave us. Then we located the ones that were in the gaps between genes 10&11 and 83&84. We then took all of these sequences and put them into an excel sheet. Using equations, the 6bp promoter were separated into a single base pair for each of the 6bp spots. This was done for each sequence and then each spot was averaged the the amount that each nucleotide showed up in that spot. If a single nucleotide held a large majority, than it was concluded that that nucleotide was the consensus for that specific spot. If the were two nucleotide that had percentages closer to each other, then they were both possible nucleotides for the consensus sequence. The one in large print had the greater percentage. Lastly, the gap was measured in between the -10 and -35 location for both observed promoter locations. Both of these averages fell around the average for an arthrobacter phage, 17.

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Results

	1	2	3	4	5	6
-10 CONSENSUS	T	A	T	A	A/T	T
-35 CONSENSUS	T	T	G	A	A/C	A

	1	2	3	4	5	6
-10 CONSENSUS	T	T	G	A	T	T
-35 CONSENSUS	T	T	G	A	C	G

	1	2	3	4	5	6
-10 CONSENSUS	T	A	T	A	A	T
-35 CONSENSUS	T	T	G	A	C	A

AQ PHAGES

AVERAGE GAP:

17.56385281

AN PHAGES

AVERAGE GAP:

16.82692308

E. coli

AVERAGE GAP:

17

Here we took the consensus sequences for the AQ phages from the previous slide and compared them to the consensus sequences of AN phages and E. coli. These sequences were taken from a previous project. The yellow boxes show where the AQ consensus differs from the E. coli consensus. As you can see, they only differ in one of the twelve nucleotide spaces. This is something that was surprising since Arthrobacter and E. coli were so far apart on the phylogenetic tree shown earlier. The green shows where the AN and AQ consensus sequences differed. Obviously this showed much more diversity, despite both being arthrobacter phages. The blue shows difference between AN phages and E. coli. This once again, must diversity can be seen. Also, you can once again see the gaps, and they still lie right around the 17 range.

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Rho - Independent Transcription Terminase Prediction

Verified the presence of promoters by predicting the terminating region of entire operons
Used ARNold predicting software

Functions by using two complementary programs, Erpin and RNAmotif.

Erpin is given a structure-annotated alignment of 1200 terminator sequences from B. subtilis and E. coli
RNAmotif recognizes E. coli terminators, however it can be applied to find terminators from any species

To provide a uniform scoring scheme for Erpin and RNAmotif hits, ARNold computes the free energy of the predicted terminator stem-loop structure using RNAfold. This free energy value may be used as a confidence value for predicted terminators.

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Gene 10 & 11 Promoter Verification

Predicted Promoter Region

Reverse Strand Predicted Terminase (1364 bp)

Score: -16.10

Predicted Protein Coding Sequence

Forward Strand Predicted Terminase (32151 bp) Score: -16.40

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Gene 83 & 84 Promoter Verification

Predicted Promoter Region

Predicted Protein Coding Sequence

Reverse Strand Predicted Terminase (47445 bp)

Score: -17.60

Forward Strand Predicted Terminase (32151 bp) Score: -16.40

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10/11 Reverse Strand Termination Sequence

10/11 Forward Strand Termination Sequence

83/84 Reverse Strand Termination Sequence

83/84 Forward Strand Termination Sequence

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Discussion

When comparing our consensus sequence to that of E. coli, the sequences are nearly identical
Comparison of our consensus sequence to that of the AN phages reveals less similarities
Similar consensus sequences could indicate similar promoters in bacteria and phages
Small sample size could have affected results
Presence of a terminase verifies that a promoter exists in the areas studied

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Conclusion

Due to the supporting evidence that was gathered, it can be argued that a promoter exists between genes 10 & 11 and genes 83 & 84 of phages in the AQ cluster
Identification of promoters in phages leads to a better understanding of prokaryotic transcription and subsequent gene expression
More insight into phages could lead to important advances in the medical field, such as the development of phage therapy

Based on all the evidence that we’ve gathered, we can conclude that there are promoters located between genes 10 and 11 and genes 83 and 84 of phages in the AQ cluster

As we continue to learn more about promoters, we can use that knowledge to better understand how promoters control and regulate transcription

If we understand how transcription is controlled by promoters, I think that there is a possibility for us to figure out how to manipulate promoters, leading to the manipulation of transcription and subsequent gene expression

As we continue to learn more about phages and their genomes, further advancements can be made in the area of phage therapy

If we are able to learn how to control the promoters of phages, then we could learn how to control gene expression of phages, which could help further phage therapy research

More research needs to be done to develop a program that could predict promoters in Arthrobacter phages

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REFERENCES

Harley, C B, and R P Reynolds. “Analysis of E. Coli Promoter Sequences.” Nucleic Acids Research 15.5 (1987): 2343–2361. Print.
Adhya, S et al. “Location, Function, and Nucleotide Sequence of a Promoter for Bacteriophage T3 RNA Polymerase.” Proceedings of the National Academy of Sciences of the United States of America 78.1 (1981): 147–151. Print.
Shlomit Lisser and Hanah Margalit. “Compilation of E. coli mRNA promoter sequences.” Nucleic Acids Research 21.7 (1993): 1507-1516. Print.

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ACKNOWLEDGEMENTS

We would like to thank Tamarah Adair Ph.D. for granting us this opportunity, as well as Lathan Lucas, Jennifer Wilson, and Ashley Young for their assistance in lab. Furthermore we would like to thank the SEA-PHAGE program for their funding.

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Questions?