Difference between revisions of "Part:BBa J23104:Experience"
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With the previous results of the characterization of the promoters there is concluded that the promoter J23107, is the strongest because it produces more RPUs” | With the previous results of the characterization of the promoters there is concluded that the promoter J23107, is the strongest because it produces more RPUs” | ||
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| + | <partinfo>BBa_J23100 AddReview 5</partinfo> | ||
| + | <I>University of Texas at Austin iGEM 2019</I> | ||
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| + | ====Characterization of the Anderson series through burden monitoring by the University of Texas at Austin's 2019 iGEM team==== | ||
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| + | ===Description=== | ||
| + | Our team transformed the Anderson series of RFP reporters (J23101, J23113, J23104, J23107, J23117 in pSBC13 backbone) into our constitutive GFP burden monitor <I>E. coli</I> strain and measured the relative burden of each part. They contained a constitutive GFP sequence in the genome which serves as a way to measure the constructs’ ribosome allocation. Our results show a certain reduction ingrowth rate for each part as a result of ribosome misallocation away from the genome and towards the plasmid containing the construct. The promoter strengths associated with each RFP reporter construct shows that parts with stronger promoters express less GFP and have a reduced growth rate when compared to the constructs containing weaker promoters. The promoters associated with these RFP reporters were used to create a series of BFP reporters, also transformed into our GFP burden monitor strain, and create the regression on the figure below. For more information on characterization of these parts through burden monitoring and evolutionary stability experiments, visit our team’s wiki page: [https://https://2019.igem.org/Team:Austin_UTexas] | ||
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| + | [[Image: AndersonCharacterization.jpg|450px]] | ||
Revision as of 04:15, 10 October 2019
This experience page is provided so that any user may enter their experience using this part.
Please enter
how you used this part and how it worked out.
Applications of BBa_J23104
Unexpected LuxR-AHL repressible behaviour
This promoter shows activity repression as a function of AHL in presence of LuxR [ADD REFERENCE LINK]. Results are shown below for a set of widely used promoters tested in the same conditions (TOP10, M9 medium supplemented with casamino acids and glycerol, assayed in a microplate reader). Promoters drive the BBa_I13507 RFP expression device. The TACTAGTG scar is present between promoter and RBS, except for PLlacO1 (BBa_R0011), PlacIQ (BBa_I14032) and PR (BBa_R0051) where the scar is TACTAGAG. RFP was measured and used to compute Scell (arbitrary units) or RPU values. Promoters were assembled in pSB4C5 and were co-transformed with BBa_S03119 in pSB3K3 in TOP10. Blue bars represent the activity when no AHL was added to the medium, while green bars represent the repressed activity (AHL 20 µM was added to the medium). Only BBa_J23101, BBa_J23102 and BBa_J23104 showed repression and, for them, percent repression is reported.
User Reviews
UNIQbe94ad3a1ac20f70-partinfo-00000000-QINU UNIQbe94ad3a1ac20f70-partinfo-00000001-QINU
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for iGEM-Team Goettingen 2012 |
Characterization experiment by qrtPCR on BBa_J23100, BBa_J23104, BBa_J23105, BBa_J23106, BBa_J23109, BBa_J23112, BBa_J23113, BBa_J23114 by iGEM Team Göttingen (by C. Krüger and J. Kampf)DescriptionWe used quantitative real-time PCR as a powerful tool for quantification of gene expression. We used this method to examine the expression rate of the Tar receptor gene under control of promoters from the Anderson family of the parts registry. The BioBricks (K777001-K777008) we used for this experiment can be found here. The reported activities of these promoters are given as the relative fluorescence of these plasmids in strain TG1 [1]. Promoter constructs were cloned into the vector pSB1C3 and expressed in E.coli BL21DE3 grown in LB-media (lysogeny broth). The measurements were performed for each construct and reference as a triplet. Additionally, we included H2O as negative control to predict possible contamination. For the evaluation of our results, the 2–ΔΔCT (Livak) method was applied. We used the weakest promoter with the lowest expression rate as calibrator for the calculations and as reference the housekeeping gene rrsD of E.coli.
You can find detailed information of the qrtPCR approach [http://2012.igem.org/Team:Goettingen/Project/Methods#-.3E_Experimental_design here].
Results & Discussion Comparison of relative expression rates of constitutive promoters by qrtPCR and relative fluorescence (see parts registry,Anderson family). The blue bar indicates the measured expression rates for our constructs (J23100, J23104, J23105, J23106, J23109, J23112, J23113, J23114) and the red ones those for the literature values represented in the “parts registry”. The measurements are illustrated in a logarithmic application. The standard variation was calculated for our measured values (black error bar). As mentioned before, both datasets were collected by methods which produce data at different points after the gene expression. Quantitative real time PCR measures the amount of expressed mRNA while relative fluorescence measurements quantify on protein level. In perspective of stability and half-life periods of mRNA and proteins or due to protein modification, it is comprehensible to obtain varying data-sets and expression rates. Another problem that occurred during our quantitative real-time measurements was the deviation in some of biological replicates. This problem was also observed in another group’s experiments ([http://www.jbioleng.org/content/3/1/4 Kelly et al., 2009]). They mentioned variations across experimental conditions in the absolute activity of the BioBricks. To reduce variation in promoter activity, they measured the activity of promoters relative to BBa_J23101. Furthermore, the iGEM team of Groningen which participated in 2009 also measured the relative fluorescence of TG1 strain with the promoters J23100, J23109 and J23106 via Relative Promoter Units (RPUs). Their values indicated the comparable tendency to our documented values |
iGEM CINVESTAV_IPN_UNAM CHARACTERIZATION OF IGEM DISTRIBUTION BIOPARTS
For contribute to the parts registry our team decided to make the characterization of constitutive promoters, in E. coli, belonging to the family isolated from a small combinatorial library (J23101 , J23102, J23104, J23107, J23108, J2311, and J23115) which were attached to GFP, in psB1C3, to determine promoter activity, using the equipment Victor X3 Multilabel Plate Reader.
Fig. 1 Construction of the promoter J23104 expressing GFP.
Methods
With the selected colonies, an overnight culture was made in M9 media(minimal media supplemented with 0.2% CAA). After 12 hours the culture was transferred to a 96 well plate at a 1:10 dilution (20 μl of culture and 180 μL of fresh M9 medium). OD and fluorescence measurements of the selected colonies were performed at intervals of 30 minutes for 16 h. From the results the PopS were calculated (polymerases per second).
Modeling
The ecuations used for calulated de promoter activity were based on (R. K. Jason et. al 2009).
Results
In the following graphs there is shown the GFP expression in function of th time and the realtive promotor intensity.
With the previous results of the characterization of the promoters there is concluded that the promoter J23107, is the strongest because it produces more RPUs”
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University of Texas at Austin iGEM 2019 |
Characterization of the Anderson series through burden monitoring by the University of Texas at Austin's 2019 iGEM teamDescriptionOur team transformed the Anderson series of RFP reporters (J23101, J23113, J23104, J23107, J23117 in pSBC13 backbone) into our constitutive GFP burden monitor E. coli strain and measured the relative burden of each part. They contained a constitutive GFP sequence in the genome which serves as a way to measure the constructs’ ribosome allocation. Our results show a certain reduction ingrowth rate for each part as a result of ribosome misallocation away from the genome and towards the plasmid containing the construct. The promoter strengths associated with each RFP reporter construct shows that parts with stronger promoters express less GFP and have a reduced growth rate when compared to the constructs containing weaker promoters. The promoters associated with these RFP reporters were used to create a series of BFP reporters, also transformed into our GFP burden monitor strain, and create the regression on the figure below. For more information on characterization of these parts through burden monitoring and evolutionary stability experiments, visit our team’s wiki page: [2]
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