Jamboree final presentation!
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Presentation from the 2014 Waterloo iGEM team at the Giant Jamboree in Boston. Read more about Staphylocide, our microbe engineered to silence antiobiotic resistance, on our 2014 wiki: http://2014.igem.org/Team:Waterloo. This presentation is also available on the iGEM website: http://2014.igem.org/files/presentation/Waterloo_Championship.pdf
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Jamboree final presentation!
- 1. STAPHYLOCIDE: Delivering Antibiotic Resistance Gene Silencing Mechanisms to a MRSA Population using Bacterial Conjugation
- 2. "The problem is so serious that it threatens the achievements of modern medicine. ” - World Health Organization, Antimicrobial Resistance: Global Report on Surveillance 2014
- 3. 80 461
- 4. 11 285
- 5. Infectious Diseases Society of America Clin Infect Dis. 2011; 52:S397-S428Adapted from: Data collected from hospital intensive care units that participate in the National Nosocomial Infections Surveillance System of the Centers for Disease Control. MRSA Cases by Year 16 14 12 7 4 2 0 2 4 6 8 10 12 14 16 18 Number of new antimicrobial agents approved by the FDA for humans CasesinThousands
- 6. MRSA resistance in a nutshell Penicillin PBP Chromosome Cell Wall Staphylococcus aureus
- 7. MRSA resistance in a nutshell Methicillin Resistant Staphylococcus aureus mecA gene Penicillin PBP2A Chromosome Cell Wall
- 8. mecA mRNA Transcription Translation PBP2 A mecA mRNA Transcription Translation PBP2 A mecA mRNA Transcription Translation PBP2 A MRSA STAPHYLOCIDE
- 10. 9 Promoters 1. sarA P1 – Strong constitutive 2. Xylose inducible promoter construct Ribosome Binding Sites 1. sodA RBS 2. Optimized TIR RBS Terminators 1. sarA rho-independent Staphylococcal Parts Selection Markers 1. ermM – Erythromycin resistance 2. aadD – Kanamycin resistance 3. spC – Spectinomycin resistance Origin of Replication 1. pSK41 - S. aureus - Theta Replictation - Low copy Reporters - DsRed - YFP
- 11. 10 S. epidermidis (ATCC 12228) • Level 1 organism • Native to human microbiota • Able to conjugate with S. aureus • No endogenous CRISPR system unlike other S. epidermidis strains Staphylococcal Strain
- 12. 11 Reporter Gene: DsRed E. coli S. epidermidis -ve control
- 13. 12 E. coli-Staphylococcus Shuttle Vector BBa_K1323017 ErmRoriVE. coliCmR oriVS.aureus P SRFP Expression Cassette (BBa_J04450) VF2 VR Improved pSB1C3 by making it more versatile: pSB1C3 parts Parts we introduced
- 14. 13 Shuttle Vector: Antibiotic Resistance • Stably maintained in S. epidermidis • Confers erythromycin resistance
- 18. 17 Silence: CRISPRi dCas9-sgRNA complex blocks RNA polymerase
- 21. 20 Silence: CRISPRi Sensitivity • Sensitive to mRNA degradation rate Therefore… • Targeting of translation will improve silencing!
- 22. 21 Silence: RNAi sRNA-Hfq complex blocks ribosome
- 26. 25 pSB1A3 AmpRoriVE.coli ErmR oriVS. aureus TTsgRNA Pconst Silence: CRISPRi dCas9 xylose XylR TT
- 28. 27 pSB1A3 AmpRoriVE.coli ErmR oriVS. aureus TTsRNA Pconst Silence: RNAi Hfq xylose TTxylR
- 29. 28 Silence: RNAi Design YFP CDSScarRBS YFP mRNA 5’ 3’ … sRNA 1 sRNA 2 sRNA 3
- 30. 29 Co- Transform Silence: RNAi Preliminary Tests pSB3K3 E. coli DH5α Measure fluorescence pSB1A3
- 31. 30 Silence: RNAi Preliminary Test YFP Alone Control sRNA1 sRNA2 sRNA3 RFU/OD600
- 32. 31 • Characterize silencing systems in S. epidermidis • Integrate yfp into S. epidermidis genome • Incorporate the mecA gene regulation Silence: Future Directions
- 34. 33 Conjugation in Staphylococcus Solid Surface RecipientDonor
- 35. 34 Deliver: Conjugation Advantages: • Large carrying capacity • Independently propagates • Opportunity to contribute to an underdeveloped area of research Disadvantage: • Not efficient
- 36. 35 Conjugation Parts: pGO1 pGO1: S. aureus conjugational plasmid oriT-nes: BBa_K1323003 oriT nesRBS TT 2.2 kb trs Region: Still in progress trs: 13.5 kb
- 37. 36 Conjugation Test Construct pSBS1A3 ErmR AmpRoriVE.coli P DsRed TTRBS oriVS. aureusnes TTRBSoriTtrs genes S RecipientsDonor Transconjugants Filter Mating Assays
- 38. 37 Deliver: Modeling Challenge: Modeling conjugation between cells spread across a lab plate or a patient’s skin
- 39. 38 Deliver: Modeling Two novel models: Partial Differential Equation (PDE) is deterministic and computationally efficient Agent-Based Approach is stochastic and considers the spatial relationships between individual cells Output: time needed for silencing to spread
- 40. 39 Deliver: Agent Based Model Staphylococcus conjugation rate Susceptible Staphylococcus MRSA Sufficient conjugation rate t = 0 ht = 0 h
- 41. 40 Deliver: Agent Based Model Staphylococcus conjugation rate Susceptible Staphylococcus MRSA Sufficient conjugation rate t = 6 h t = 6 h
- 42. 41 Deliver: Agent Based Model Staphylococcus conjugation rate Susceptible Staphylococcus MRSA Sufficient conjugation rate t = 12 h t = 12 h
- 43. 42 Deliver: Agent Based Model Staphylococcus conjugation rate Susceptible Staphylococcus MRSA Sufficient conjugation rate t = 24 h t = 24 h
- 44. 43 Deliver: Agent Based Results
- 45. 44 Deliver: PDE Model Results
- 46. 45 Deliver: Future Uses of Model + Find igem-waterloo on GitHub!
- 47. 46 • Improve conjugation efficiency with error prone PCR mutagenesis and selective mating assays Deliver: Future Directions • Test conjugational efficiency in S. epidermidis
- 56. 55 Submitted 19 BioBricks, 8 characterized Improved BioBrick backbone to develop shuttle vector Produced and validated several models of the silencing and delivery systems Explored scalability of project Collaborated on uOttawa iGEM & Virginia Tech project and assisted with oGEM Accomplishments
- 57. 56 Accomplishments: Outreach High School Enrichment ProgramScience Club Lab Skills Video Series Sir John A. Macdonald Secondary School
- 58. 57 Acknowledgements Dr. Marc Aucoin Dr. Brian Ingalls Dr. Matthew Scott Dr. Trevor Charles Dr. Barbara Moffat Dr. Andrew Doxey
- 59. Questions?
- 60. 59 Bayer, M. G., Heinrichs, J. H., & Cheung, A. L. (1996). The molecular architecture of the sar locus in Staphylococcus aureus. Journal of Bacteriology, 178(15): 4563-70 Bikard, D., Jiang, W., Samai, P., Hochschild, A., Zhang, F., & Marraffini, L. a. (2013). Programmable repression and activation of bacterial gene expression using an engineered CRISPR-Cas system. Nucleic Acids Research, 41(15): 7429–3 Bose, J. L., Fey, P. D., & Bayles, K. W. (2013). Genetic Tools to Enhance the Study of Gene Function and Regulation in Staphylococcus aureus. Applied Environmental Microbiology, 79(7): 2218- 2224. Caryl, J. A. and O’Neill, A. J. (2009). Complete nucleotide sequence of pGO1, the prototype conjugative plasmid from the staphylococci. Plasmid, 62: 35-38 Cirino, P. C., Mayer, K. M., and Umeno, D. (2002). Chapter 1: Generating Mutant Libraries Using Error-Prone PCR, Methods in Molecular Biology, vol. 231. New Jersey: Humana Press Inc. Climo, M. W., Sharma, V. K., and Archer, G. L. (1996). Identification and Characterization of the Origin of Conjugative Transfer (oriT) and a Gene (nes) Encoding a Single-Stranded Endonuclease on the Staphylococcal Plasmid pGO1. Journal of Bacteriology, 178 (16): 4975-83 Fey, P. D. (2014). Staphylococcus epidermidis: methods and protocols. New York: Springer Science + Business Media, LLC. Horstmann, N., Orans, J., Valentin-Hansen, P., Shelburne III, S. A., & Brennan, R. G. (2012). Structural mechanism of Staphylococcus aureus Hfq binding to an RNA A-tract. Nucleic Acids Research, 1-13. Jinek, M., Chylinski, K., Fonfara, I., Hauer, M., Doudna, J. A., and Charpentier, E. (2012). A Programmable Dual-RNA–Guided DNA Endonuclease in Adaptive Bacterial Immunity. Science, 337: 816-821. Katze, M. J., He, Y., and Gale, M. (2002). Viruses and Interferon: A Fight for Supremacy. Nature Reviews, 2: 675-687. Larson, M. H., Gilbert, L. A., Wang, X., Lim, W. A., Weissman, J. S., and Qi, L. S. (2013). Nature Protocols, 8 (11): 2180-2196. References
- 61. 60 Malone, C. L., Boles, B. R., Lauderdale, K. J., Thoendel, M., Kavanaugh, J. S., & Horswill, A. R. (2009). Fluorescent Reporters for Staphylococcus aureus. Journal of Microbiological Methods, 77(3): 251-260. Qi, L. S., Larson, M. H., Gilbert, L. a, Doudna, J. a, Weissman, J. S., Arkin, A. P., & Lim, W. a. (2013). Repurposing CRISPR as an RNA-guided platform for sequence-specific control of gene expression. Cell, 152(5): 1173–83. Yoo, S. M., Na, D., & Lee, S. Y. (2013). Design and use of synthetic regulatory small RNAs to control gene expression in Escherichia coli. Nature Protocol, 8 (9): 1694-1707. Zhang, Y-Q. (2003). Genome-based analysis of virulence genes in a non-biofilm-forming Staphylococcus epidermidis strain (ATCC 12228). Molecular Microbiology, 49(6): 1577-1593. Zhao, H. (2004). Staggered Extension Process In Vitro DNA Recombination, Methods in Enzymology, vol. 388, 42-49. References
Notes de l'éditeur
- Expanding the registry Improve the collection of Staphylococcal parts
- Starting off with the silencing of translation, this slide shows the various entities involved in CRISPR interference. CRISPRi is based off the well-known CRISPR bacterial immune system, but uses a catalytically dead version of the Cas9 protein, dCas9. So the sgRNA binds dCas9 and guides it to the target sequence, but rather than cleaving the DNA the complex simply binds and prevents transcription elongation by RNA polymerase. This is an advantage for our system because it poses less risk to the cell, which we hope will pass on the antibiotic susceptibility.
- From this system, we developed a model of the network from which mass action kinetics could be derived. The relevant mRNA for dCas9, sgRNA and YFP are produced according to rates corresponding to their respective promoters. The complex forms and then binds to the DNA.
- So after some parameter searching and some parameter optimization, we found the followin time series silencing from CRISPR. The silencing actually occurs on quite a satisfactory time scale- a few hours- but depending on the target location we may not acheive the needed repression level. So, we decided to do a sensitivity analysis to find
- A local sensitivity analysis showed us which parameter would have the greatest influence on YFP repression if it were changed. The table shows the percent change in final YFP concentration when the parameters are changed by 1% - it was exciting to see that the system was quite senstive to YFP mRNA degradation, because increasing the level of YFP mRNA degradation is just what we hope to do with translational silencing.
- That takes us right into our sytem for blocking translation, which we call the RNA interference system. A silencing mRNA associates with Host Factor 1 (Hfq) and then binds to the target mRNA using its target binding sequence. The bound complex prevents the ribosome from advancing along the mRNA and blocks translation.
- Here's the expanded network- you can see that we again have terms for mRNA and protein production and degradation as well as complex formation. Using the same assumptions of tcreated a second ordinary differential equation using this network and found the following result
- So the RNA interference system acts more slowly than the CRISPR interference system, but the final repression of YFP is nearly complete, unlike the case of the CRISPRi system. We again wanted to see what we could do to improve this system, so we performed a local sensitivity analysis
- Early in the design of our silencing system, we realized there were two possible targets of silencing: transcription of target mRNA or translation into the target protein. To explore these silencing possibilities, we developed models for a CRISPR interference system that targets transcription and an RNA interference system that targets translation.
- Here is the diagram of our construct with sgRNA under a weak promoter and dCas9 under a xylose inducible promoter Note that in order for our system to work in S.epidermidis, we designed a shuttle vector containing a origin of replication and an antibiotic resistance marker in S.epidermids.
- Early in the design of our silencing system, we realized there were two possible targets of silencing: transcription of target mRNA or translation into the target protein. To explore these silencing possibilities, we developed models for a CRISPR interference system that targets transcription and an RNA interference system that targets translation.
- Here is the diagram of our construct with sgRNA under a weak promoter and dCas9 under a xylose inducible promoter Note that in order for our system to work in S.epidermidis, we designed a shuttle vector containing a origin of replication and an antibiotic resistance marker in S.epidermids.
- we have designed three sRNA constructs for our project a
- Our system is broken down into three parts. First, we will discuss the details of DELIVERY of genetic material through conjugation. Then, we will explore the SILENCING of antibiotic resistance genes through CRISPRi and RNAi systems, and finally, we will discuss what it takes to TRANSLATE our project from the lab into a viable treatment.
- Conjugation is our mode of delivering our silencing constructs. Conjugation is the horizontal gene transfer of DNA between bacteria. In E.coli, this molecular process has been well characterized. However, in gram positive bacteria such as Staph, it is not. In Staphylococcus, a solid surface is required for a donor cell to transmit DNA to a recipient. And this has relevance in our models, as you will soon see.
- Large carrying capacity compared to viruses available for Staphylococcus Opportunity to contribute to an underdeveloped area of research
- pGO1, well studied S. aureus conjugational plasmid. This plasmid was graciously gifted to us by Dr. Alex O’Neill’s Lab at the University of Leeds A paper published by Climo et al. (1996) found that the minimal parts needed for conjugation were: ~2 kb oriT and nes region which contains the origin of transfer and nickase protein ~14 kb trs region where other proteins associated with conjugation are located. This region is not as well characterized as the equivalent tra region in gram negative bacteria We successfully PCR amplified and sub-cloned the Nes region and submitted it to the parts registry The trs region is much larger so we are still currently making attempts to clone this region
- Here shows what our pSBS1A3 conjugative plasmid would look like.
- We wanted to know how long it would take our delivery system to silence resistance in a S. aureus population. There are models of conjugation out there, but they make the assumption that the donors and recipients are in a well-mixed environment, rather than spread across our lab plates or a future patient's skin. Our non-motile and spread out Staphlococcus cannot be described by these models.
- So we created two novel models of conjugation: the partial differential equation model and the agent-based model. Both models give us the time series data, but they have specific advantages- Our partial differential equation model is able to model arbitrarily large cell populations, since it is computationally efficient and has deterministic behaviour. However, the agent-based model allows us to consider the spatial relationships between individual cells, so that a donor cell that is unable to come into contact with any recipients isn't able to affect the overall silencing. Both models show the time needed for the population of cells to receive the Staphylocide plasmid. I'll show you the results of the agent-based model first:
- So in this video, you can see that the flat surface on which our cells conjugate is divided into a series of hexagons. The pink hexagons are empty, the red hexagons show MRSA and the blue hexagons show Staphylocide. We're comparing two populations of cells, one of which has an idealized high conjugation rate (can I say bacillus here?) and one which has a conjugation rate closer to staphylococcus. As you can see, by the end of our simulation, antibiotic susceptibility has spread all across the population on the left, while our staphylococcal population still contains a large MRSA population. We made this model to check our PDE model validity. ----- Meeting Notes (2014-10-26 17:42) ----- change this to bacillus- at least for presentation, its relevantadd legend for parameters
- So in this video, you can see that the flat surface on which our cells conjugate is divided into a series of hexagons. The pink hexagons are empty, the red hexagons show MRSA and the blue hexagons show Staphylocide. We're comparing two populations of cells, one of which has an idealized high conjugation rate (can I say bacillus here?) and one which has a conjugation rate closer to staphylococcus. As you can see, by the end of our simulation, antibiotic susceptibility has spread all across the population on the left, while our staphylococcal population still contains a large MRSA population. We made this model to check our PDE model validity. ----- Meeting Notes (2014-10-26 17:42) ----- change this to bacillus- at least for presentation, its relevantadd legend for parameters
- So in this video, you can see that the flat surface on which our cells conjugate is divided into a series of hexagons. The pink hexagons are empty, the red hexagons show MRSA and the blue hexagons show Staphylocide. We're comparing two populations of cells, one of which has an idealized high conjugation rate (can I say bacillus here?) and one which has a conjugation rate closer to staphylococcus. As you can see, by the end of our simulation, antibiotic susceptibility has spread all across the population on the left, while our staphylococcal population still contains a large MRSA population. We made this model to check our PDE model validity. ----- Meeting Notes (2014-10-26 17:42) ----- change this to bacillus- at least for presentation, its relevantadd legend for parameters
- So in this video, you can see that the flat surface on which our cells conjugate is divided into a series of hexagons. The pink hexagons are empty, the red hexagons show MRSA and the blue hexagons show Staphylocide. We're comparing two populations of cells, one of which has an idealized high conjugation rate (can I say bacillus here?) and one which has a conjugation rate closer to staphylococcus. As you can see, by the end of our simulation, antibiotic susceptibility has spread all across the population on the left, while our staphylococcal population still contains a large MRSA population. We made this model to check our PDE model validity. ----- Meeting Notes (2014-10-26 17:42) ----- change this to bacillus- at least for presentation, its relevantadd legend for parameters
- To show the simulation results in another way, you can see in this graph that the bacteria quickly grow to fill all the spaces on the grid and then the number of MRSA cells very slowly decays- it takes roughly 15 hours for only 10% of the cells to be resistant.
- This is the same result we get with the PDE model, which is actually quite exciting since PDE and Agent-Based models were developed independently of one another. Eventually the population becomes susceptible, but the time scale is unacceptable
- This is the same result we get with the PDE model, which is actually quite exciting since PDE and Agent-Based models were developed independently of one another. Eventually the population becomes susceptible, but the time scale is unacceptable
- This leads us into our next steps for delivery: one of our top priorities is to attempt to increase the efficiency of staphylococcal conjugation, perhaps using error prone PCR mutagenesis of the WHICH THING? Throughout the model, we worked with parameters from the literature, but we’d like to perform our own characterization of conjugational efficiency in S. epidemidis Finally, we need to complete our cloning of the conjugation parts.
- Our system is broken down into three parts. First, we will discuss the details of DELIVERY of genetic material through conjugation. Then, we will explore the SILENCING of antibiotic resistance genes through CRISPRi and RNAi systems, and finally, we will discuss what it takes to TRANSLATE our project from the lab into a viable treatment.
- As a future direction, our project can be advanced by optimizing the construct and implementing it into Staphyloccocus epidermidis. Next, a complimentary cream can be made. Feedback ----Construct: either don’t have the selectable markers OR don’t say final construct. Say “topical ointment” throughout (be consistent). -final construct will not contain the antibiotic resistance genes (change image) -one way to treat skin infections of MRSA: stress that ointment is one directly scalable application -other avenues are possible Healthcare professionals section: -only available through trained medical professionals -not over the counter
- After successfully overcoming design hurdles, the treatment itself would be a cyclic process (apply cream, then antibiotic, then repeat until no evidence of MRSA). Using the agent-based model used to simulate conjugation, we will derive the optimal application time for this cyclic process. To ensure the Staphylocide is safe, the treatment would be administered by trained professionals specifically regarding Biosafety.
- After successfully overcoming design hurdles, the treatment itself would be a cyclic process (apply cream, then antibiotic, then repeat until no evidence of MRSA). Using the agent-based model used to simulate conjugation, we will derive the optimal application time for this cyclic process. To ensure the Staphylocide is safe, the treatment would be administered by trained professionals specifically regarding Biosafety.
- After successfully overcoming design hurdles, the treatment itself would be a cyclic process (apply cream, then antibiotic, then repeat until no evidence of MRSA). Using the agent-based model used to simulate conjugation, we will derive the optimal application time for this cyclic process. To ensure the Staphylocide is safe, the treatment would be administered by trained professionals specifically regarding Biosafety.
- After successfully overcoming design hurdles, the treatment itself would be a cyclic process (apply cream, then antibiotic, then repeat until no evidence of MRSA). Using the agent-based model used to simulate conjugation, we will derive the optimal application time for this cyclic process. To ensure the Staphylocide is safe, the treatment would be administered by trained professionals specifically regarding Biosafety.
- Our project is of potential interest to: -pharma in relation to healthcare and agriculture (humans and animals suffer from MRSA infections -furthermore, with additional characterization and improvement of conjugation, other research fields could use conjugation as an alternative delivery mechanism for DNA (especially when constructs limit the use of phage) Use logos (similar to existing theme) that have the various fields…
- Our system is broken down into three parts. First, we will discuss the details of DELIVERY of genetic material through conjugation. Then, we will explore the SILENCING of antibiotic resistance genes through CRISPRi and RNAi systems, and finally, we will discuss what it takes to TRANSLATE our project from the lab into a viable treatment.
- We submitted 19 BioBricks, 8 of which were characterized (specifically for staph). Of these, the team improved an existing BioBrick backbone by developing a E. coli- Staphylococcal shuttle vector –making it more versatile. Our experimentation showed repressing of YFP in E. coli using RNA interference system and suggesting functionality in S. epidermidis. The team also developed several models to simulate the silencing and delivery systems –which helped with out design. The conjugation model in particular is novel and can help future iGEM teams ___. Next, the team explored the safety concerns associated with taking our project into the real world through consulting pharmacy professionals. Finally, we collaborated with our friends at UOttawa iGEM by helping with modeling as well as Virginia Tech with helping collect surveys for their human practices project.
- Can we get a quote from somebody who participated in and enjoyed these? Finally, on top of working on these pursuits, we also sought to connect other communities with our project. - 2 very successful high school outreach workshops [- SJAM: draw with bacteria + Shad Valley: transforming bacteria to produce a red fluorescence] - promote synbio as an opportunity to apply their talents - lab techniques video series [- students first entering a lab, co-op work, or a certain synthetic biology club] - solid foundation for future lab environments show pictures
- Fix the aesthetics of this slide.