Difference between revisions of "Part:BBa J23104:Experience"

 
 
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This experience page is provided so that any user may enter their experience using this part.<BR>Please enter
 
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===Applications of BBa_J23104===
 
===Applications of BBa_J23104===
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====Unexpected LuxR-AHL repressible behaviour====
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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.
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[[Image:pv_luxrep_unexp.jpg|700px|center]]
  
 
===User Reviews===
 
===User Reviews===
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{|width='80%' style='border:1px solid gray'
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|-
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|width='10%'|
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<partinfo>BBa_J23100 AddReview 5</partinfo>
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<I>for iGEM-Team Goettingen 2012</I>
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|width='60%' valign='top'|
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====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)====
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=====Description=====
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We 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 [https://parts.igem.org/Promoters/Catalog/Anderson Anderson family] of the parts registry. The BioBricks (K777001-K777008) we used for this experiment can be found [https://parts.igem.org/Part:BBa_K777001 here].
 +
 +
The reported activities of these promoters are given as the relative fluorescence of these plasmids in strain TG1 [https://parts.igem.org/Promoters/Catalog/Anderson]. Promoter constructs were cloned into the vector pSB1C3 and expressed in <i>E.coli</i> BL21DE3 grown in LB-media (lysogeny broth). The measurements were performed for each construct and reference as a triplet. Additionally, we included H<sub>2</sub>O as negative control to predict possible contamination. For the evaluation of our results, the 2<sup>–ΔΔCT</sup> (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 <i>E.coli</i>.
 +
You can find detailed information of the qrtPCR approach [http://2012.igem.org/Team:Goettingen/Project/Methods#-.3E_Experimental_design here].<br><br>
 +
 +
 +
=====Results & Discussion=====
 +
[[Image:QrtPCR_ready.png|thumb|center|700px|Comparison of relative expression rates of constitutive promoters by qrtPCR and relative fluorescence (see parts registry,[https://parts.igem.org/Promoters/Catalog/Anderson 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 [https://parts.igem.org/wiki/index.php/Part:BBa_J23109 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 <br>
 +
For a more detailed description of our results [http://2012.igem.org/Team:Goettingen/Notebook/Results click here].
 +
 +
|}
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 +
 +
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<I>'''iGEM CINVESTAV_IPN_UNAM''' CHARACTERIZATION OF IGEM DISTRIBUTION BIOPARTS</I>
 +
 +
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.
 +
 +
[[Image:Gfp1.jpg‎]]
 +
 +
'''Fig. 1 Construction of the promoter J23104 expressing GFP.'''
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 +
'''Methods'''
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 +
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).
 +
 +
[[Image:ecu4.jpg]]
 +
 +
[[Image:ecu5.jpg]]
 +
 +
'''Results'''
 +
 +
In the following graphs there is shown the GFP expression in function of th time and the realtive promotor intensity.
 +
 +
 +
[[Image:graf1.jpg]]
 +
 +
[[Image:graf2.jpg]]
 +
 +
 +
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”
 +
 +
 +
{|width='100%' style='border:1px solid gray'
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|-
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|width='10%'|
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<partinfo>BBa_J23100 AddReview 5</partinfo>
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<I>University of Texas at Austin iGEM 2019</I>
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|width='60%' valign='top'|
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<h3>UT Austin iGEM 2019: Characterization of metabolic burden of the Anderson Series</h3>
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<h4>Description</h4>
 +
The 2019 UT Austin iGEM team transformed the Anderson Series promoters into our 'burden monitor' DH10B strain of E. coli, which contains a constitutive GFP cassette in the genome of the cell. GFP expression fluctuates depending on the number of ribosomes available. Using this strain, we characterized the relative burden (percent reduction in growth rate) of each Anderson Series part. Our results showed a range of growth rate reductions for each of these parts due to ribosomal reallocation from the genome of the host cell, towards the expression of RFP. Anderson Series parts with strong promoters are depicted with darker red colors and Anderson Series parts with weak promoters are depicted with lighter pink colors to show relative RFP expression.
 +
We saw a positive correlation between relative promoter strength and metabolic burden; parts with stronger promoters expressed less GFP and had a lower growth rate than parts with weaker promoters. The regression line for the graph below was constructed by measuring the burden of 5 parts that were created by the 2019 UT Austin iGEM team that each contained an Anderson Series promoter (<partinfo>J23104</partinfo> or <partinfo>J23110</partinfo>), an RBS of varying strength, and a BFP reporter. For more information on characterization of these parts through the burden monitor, visit our team’s wiki page: [https://https://2019.igem.org/Team:Austin_UTexas]
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<html>
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<figure>
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<div class = "left">
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<img src = "https://static.igem.org/mediawiki/parts/a/a0/AndersonCharacterization.jpg" style = "width:550px;height:500px">
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</div>
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<figcaption><b>Fig.1:</b>Growth vs GFP Expression graph showing the relative burden positions of the Anderson Series promoters. The parts with strong promoters are depicted in dark red and are clustered near the bottom of the graph because they have lower growth rates and express lower levels of GFP as a result of high cellular burden. The parts with weaker promoter are depicted in light pink ad are clustered near the top of the graph because they have higher growth rates and express higher levels of GFP as a result of low cellular burden.</figcaption>
 +
</figure>
 +
</html>
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<br><br>
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<html>
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<figure>
 +
<div class = "left">
 +
<img src = "https://static.igem.org/mediawiki/parts/8/80/T--Austin_Utexas--andersontable.png" style = "width:545px;height:375px">
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</div>
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<figcaption><b>Table.1:</b> Burden measurements for the Anderson Series promoters measured as percent reduction in growth rate ± 95% confidence interval. </figcaption>
 +
</figure>
 +
</html>
 +
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<h4>Importance of Characterizing Burden</h4>
 +
<p> Although often we cannot avoid using a specific burdensome part, knowing in advance that it is burdensome, and that it has a high chance of mutating into a non-functional genetic device, can help with troubleshooting and coming up with alternatives. In the specific case of fluorescent protein-expressing devices, Fluorescence-activated cell sorting (FACS) can be used to filter out individual cells that meet a certain fluorescence threshold. This way, the cells expressing lower levels of the fluorescent protein are weeded out of the population.</p>

Latest revision as of 20:06, 21 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.

Pv luxrep unexp.jpg

User Reviews

UNIQ9589dd85cd25d0b9-partinfo-00000000-QINU UNIQ9589dd85cd25d0b9-partinfo-00000001-QINU



•••••

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)

Description

We 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
For a more detailed description of our results [http://2012.igem.org/Team:Goettingen/Notebook/Results click here].



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.

Gfp1.jpg

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).

Ecu4.jpg

Ecu5.jpg

Results

In the following graphs there is shown the GFP expression in function of th time and the realtive promotor intensity.


Graf1.jpg

Graf2.jpg


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”


•••••

University of Texas at Austin iGEM 2019

UT Austin iGEM 2019: Characterization of metabolic burden of the Anderson Series

Description

The 2019 UT Austin iGEM team transformed the Anderson Series promoters into our 'burden monitor' DH10B strain of E. coli, which contains a constitutive GFP cassette in the genome of the cell. GFP expression fluctuates depending on the number of ribosomes available. Using this strain, we characterized the relative burden (percent reduction in growth rate) of each Anderson Series part. Our results showed a range of growth rate reductions for each of these parts due to ribosomal reallocation from the genome of the host cell, towards the expression of RFP. Anderson Series parts with strong promoters are depicted with darker red colors and Anderson Series parts with weak promoters are depicted with lighter pink colors to show relative RFP expression. We saw a positive correlation between relative promoter strength and metabolic burden; parts with stronger promoters expressed less GFP and had a lower growth rate than parts with weaker promoters. The regression line for the graph below was constructed by measuring the burden of 5 parts that were created by the 2019 UT Austin iGEM team that each contained an Anderson Series promoter (BBa_J23104 or BBa_J23110), an RBS of varying strength, and a BFP reporter. For more information on characterization of these parts through the burden monitor, visit our team’s wiki page: [2]

Fig.1:Growth vs GFP Expression graph showing the relative burden positions of the Anderson Series promoters. The parts with strong promoters are depicted in dark red and are clustered near the bottom of the graph because they have lower growth rates and express lower levels of GFP as a result of high cellular burden. The parts with weaker promoter are depicted in light pink ad are clustered near the top of the graph because they have higher growth rates and express higher levels of GFP as a result of low cellular burden.


Table.1: Burden measurements for the Anderson Series promoters measured as percent reduction in growth rate ± 95% confidence interval.

Importance of Characterizing Burden

Although often we cannot avoid using a specific burdensome part, knowing in advance that it is burdensome, and that it has a high chance of mutating into a non-functional genetic device, can help with troubleshooting and coming up with alternatives. In the specific case of fluorescent protein-expressing devices, Fluorescence-activated cell sorting (FACS) can be used to filter out individual cells that meet a certain fluorescence threshold. This way, the cells expressing lower levels of the fluorescent protein are weeded out of the population.