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− | BioBrick Improvement: | + | BioBrick Improvement: Documentation and characterization of <a href="http://parts.igem.org/Part:BBa_K1645998">BBa_K1645998</a> and <a href="http://parts.igem.org/Part:BBa_K1645999">BBa_K1645999</a> |
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Part of the <a href="https://2015.igem.org/Team:Waterloo/Parts">Waterloo iGEM 2015</a> project's work involved modifying an sgRNA such that its spacer was flanked by restriction sites to enable exchange of new spacers using basic molecular biology techniques. Adding these restriction sites required modification of the sgRNA scaffold, thus raising concern that the additions would disrupt the scaffold secondary structure and affect dCas9's performance. We created a BioBrick of an sgRNA, with no modifications, designed to target the lacI promoter of an RFP gene (<a href="http://parts.igem.org/Part:BBa_R0010">BBa_R0020</a>). This year, we brought this experiment back to complete its documentation and characterization with better flow cytometry experiments that produced more robust data. We successfully demonstrated that this BioBrick is able to repress RFP expression when compared to controls. We also demonstrated that the modified sgRNA allows dCas9 to repress RFP expression with very similar performance. | Part of the <a href="https://2015.igem.org/Team:Waterloo/Parts">Waterloo iGEM 2015</a> project's work involved modifying an sgRNA such that its spacer was flanked by restriction sites to enable exchange of new spacers using basic molecular biology techniques. Adding these restriction sites required modification of the sgRNA scaffold, thus raising concern that the additions would disrupt the scaffold secondary structure and affect dCas9's performance. We created a BioBrick of an sgRNA, with no modifications, designed to target the lacI promoter of an RFP gene (<a href="http://parts.igem.org/Part:BBa_R0010">BBa_R0020</a>). This year, we brought this experiment back to complete its documentation and characterization with better flow cytometry experiments that produced more robust data. We successfully demonstrated that this BioBrick is able to repress RFP expression when compared to controls. We also demonstrated that the modified sgRNA allows dCas9 to repress RFP expression with very similar performance. | ||
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+ | Additionally, we used a reporter plasmid that contained a GFP gene cassette (<a href="http://parts.igem.org/Part:BBa_I20260">BBa_I20260</a>) and RFP gene casette (<a href="http://parts.igem.org/Part:BBa_J04450">BBa_204450</a>) for last year's flow cytometry experiments. These two gene cassettes were cloned together into pSB1C3, but the characterization of this BioBrick was never published last year. This year, we've successfully demonstrated that this BioBrick works by showing flow cytometry data indicative of GFP and RFP expression. | ||
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Revision as of 18:10, 15 October 2016
Human neurodegenerative diseases (NDDs) like Creutzfeldt-Jakob, Alzheimer’s, and Parkinson’s disease are associated with multiple metabolic complications in neurons. To name a known few, the metabolism of cholesterol, copper, iron, heme, NAD+, and some neurotransmitters have all been shown to be affected by prion proteins affiliated with these NDDs1,2,3,4,5. It is important to continue studying the metabolic implications of NDDs to further elucidate their pathological mechanisms. For diseases like Alzheimer’s, years of work have been put in, yet treatment and therapy are only beginning to progress more rapidly. We hope to contribute to this field by providing a synthetic biology approach that enables an alternative method for metabolic studies in neurons affected by prion proteins.
This year, we’ve designed a negative feedback loop using a novel regulatory element capable of overexpressing or repressing target proteins upon transitioning from a neutral state to a prion state. As a proof-of-concept, we use the Sup35 prion protein associated with the [psi-] (healthy) / [PSI+] (disease-like) state of Saccharomyces cerevisiae cells. We use read-through of a premature stop codon in fusions with Hsp104 or dCas9 to respectively overexpress or repress Hsp104. Both overexpression and repression of Hsp104, through different mechanisms outlined here, cures the [PSI+] state – from priON to OFF.
This system’s design consists of a three-step process: trigger, signal, and control.
Sup35’s translational terminator activity, which allows it to remove ribosomes from mRNAs when they’ve reached a stop codon, is lost during the [PSI+] state which we induce via introduction of an extra Sup35 gene fused to a green fluorescent protein (GFP) on a plasmid. Our specially designed fusion of cyan fluorescent protein (CFP) and a Sup35 disaggregase (Hsp104) has a premature stop codon positioned upstream of CFP’s fluorophore. The loss in translation terminator activity upon transitioning to [PSI+] increases the likelihood of ribosomes translating the entire CFP::Hsp104 fusion protein due to read-through of the premature stop codon. This essentially triggers the increased production of CFP::Hsp104 in response to developing [PSI+]state.
By inputting dCas9 in lieu of the Hsp104 ORF in the plasmid, we can use mathematical modeling to predict the ability of optimized sgRNAs to repress the expression of the native Hsp104 gene in the S. cerevisiae genome.. This is based on the concept that overexpression of Hsp104 beyond an upper threshold cures the [PSI+] state, and repression below a lower threshold prevents propagation of the Sup35 aggregates to daughter cells, thus curing future generations.
While the [PSI+] state develops, the cell’s GFP signal increases due to Sup35::GFP overexpression. In response, the CFP signal also increases as it is triggered by the loss of translational terminator activity due to aggregated Sup35. With fluorimetry, we monitor the changes in the GFP and CFP signal shown in our Results Section. We also observe the changes between [psi-] and [PSI+] through other phenotypic outputs: the colour of the colonies, and the cellular granularity determined by flow cytometry.
Within a cell in the [PSI+] state the CFP::Hsp104 concentration increases beyond an upper threshold where it effectively disaggregates the Sup35 prions and aids in refolding them into their healthy functional form. This enables the premature stop codon in the CFP::Hsp104 to prevent full translation of the transcript into its functional form. Ultimately, triggering of the premature stop codon read-through can control the expression of a target protein and, in this case, allows for the [PSI+] state to return to psi- by Hsp104-mediated curing.
It is with great enthusiasm that we present a study of the sociological network of iGEM team collaborations. By educating our community about the attributes that inherently contribute to the likelihood of Jamboree success, we will empower teams to increase competitiveness and improve the overall quality of their science.
Part of the Waterloo iGEM 2015 project's work involved modifying an sgRNA such that its spacer was flanked by restriction sites to enable exchange of new spacers using basic molecular biology techniques. Adding these restriction sites required modification of the sgRNA scaffold, thus raising concern that the additions would disrupt the scaffold secondary structure and affect dCas9's performance. We created a BioBrick of an sgRNA, with no modifications, designed to target the lacI promoter of an RFP gene (BBa_R0020). This year, we brought this experiment back to complete its documentation and characterization with better flow cytometry experiments that produced more robust data. We successfully demonstrated that this BioBrick is able to repress RFP expression when compared to controls. We also demonstrated that the modified sgRNA allows dCas9 to repress RFP expression with very similar performance.
Additionally, we used a reporter plasmid that contained a GFP gene cassette (BBa_I20260) and RFP gene casette (BBa_204450) for last year's flow cytometry experiments. These two gene cassettes were cloned together into pSB1C3, but the characterization of this BioBrick was never published last year. This year, we've successfully demonstrated that this BioBrick works by showing flow cytometry data indicative of GFP and RFP expression.