Difference between revisions of "Part:BBa K2116002"

 
(Usage and Biology)
 
(22 intermediate revisions by 4 users not shown)
Line 1: Line 1:
 
 
__NOTOC__
 
__NOTOC__
<partinfo>BBa_K2116002 short</partinfo>
+
<partinfo>BBa_K1153000 short</partinfo>
 +
 
 +
Our Biobrick has been designed to enable the detection of NO using the norV gene promoter, cloned from E.coli K12 TOP10 cells.
 +
In the presence of NOx it will promote the downstream genes in the plasmid, producing a protein in response to the environmental
 +
stimulus.
 +
 
 +
 
 +
<h1>Usage and Biology</h1>
 +
Promoter norV '''(PnorV)''' is the native promoter controlling the nitric oxide reduction operon (norRVW) in E. Coli. [1]
 +
It's transcriptional regulator, NorR, can bind to nitric oxide and activate gene expression.
 +
 
 +
 
 +
<h1>Characterisation of the Promoter</h1>
 +
<p>We cloned PnorV upstream of superfolder GFP for characterisation [[Part:BBa_K2116088]] This construct was expressed on a
 +
medium copy plasmid (ori:p15A). Since ''E.coli'' natively produces NorR, we relied on this to activate the promoter. Below
 +
is the dose response curve we obtained under the following experimental conditions, using a plate reader:</p>
 +
<ul>
 +
<li> Overnight growth and experiment in minimal M9 medium, with 25μg/μL chloramphenicol.
 +
<li> Plating at an OD<sub>600</sub> of 0.05, in M9, with 25μg/μL chloramphenicol.
 +
<li> Induction at OD<sub>600</sub>0.5.
 +
<li> Settings for GFP measurement: excitation-488nm, emission- 530nm.
 +
<li> Samples were always in three technical replicates, and the fluorescence measurements were normalized to OD<sub>600</sub>.
 +
</ul>
 +
 
 +
[[File:PnorVdoseresponse.png|500px|thumb|center|PnorV dose response curve for a range of DETA/NO concentrations that corresponds
 +
to 7-70μM of NO. Above 15000uM of DETA/NO affects cell growth and is not included in the dose response. Measured 6 hours
 +
after induction. Error bars represent S.D. from three technical replicates.]]
 +
 
 +
<p>In order to show that the native NorR is essential for activating PnorV, we used a [http://cgsc.biology.yale.edu/KeioList.php
 +
Keio] norR knock out strain (&Delta;norR) and compared it to it's parent wild type strain (WT). It was shown that PnorV
 +
can be activated by DETA/NO in the parent strain, but not in the norR KO strain.</p>
 +
 
 +
[[File:PnorV native norR functionality.png|500px|thumb|center|A norR KO strain was used as a negative control to demonstrate
 +
that the native norR can activate PnorV.Induced with 5000μM DETA/NO and measured 6 hours after induction. Error bars represent
 +
S.D. from three technical replicates.]]
 +
 
 +
<h1> Materials </h1>
 +
We used DETA/NO to as a nitric oxide source. It has a half life of roughly 20h, and releases NO at a relatively constant
 +
rate [2].
 +
 
 +
 
 +
<h1>'''Contribution by UZurich 2022'''</h1>
 +
*'''Group:''' UZurich 2022
 +
*'''Author:''' Jana Mehdy, Lea Brüllmann, Marine Mausy
 +
*'''Summary:''' We compared the GFP expression rates of the ETH promoter to the <html><a href="https://parts.igem.org/Part:BBa_K4387000">pNorVβ promoter</a></html> to sense nitric oxide.
 +
 
 +
 
 +
==Usage and Biology==
 +
 
 +
We wanted to compare this ETH pNorV promoter to the <html><a href="https://parts.igem.org/Part:BBa_K4387000">pNorVβ promoter</a></html> [3] and see which one was better suited for sensing nitric oxide at lower concentration ranges and which one expresses more GFP. Therefore we created a genetic circuit where we implemented this ETH promoter followed by one strong ribosomal binding site (<html><a href="https://parts.igem.org/Part:BBa_B0034">BBa_B0034</a></html>), which was attached to a <html><a href="https://parts.igem.org/Part:BBa_K2553008">superfolder GFP</a></html>, the regulator <html><a href="https://parts.igem.org/Part:BBa_K4387001">NorR</a></html>, and a <html><a href="https://parts.igem.org/Part:BBa_B0015">double forward terminator</a></html>. This composite part can be found <html><a href="https://parts.igem.org/Part:BBa_K4387004">here</a></html>, and it is similar to the genetic circuit <html><a href="https://parts.igem.org/Part:BBa_K4387005">BBa_K4387005</a></html>, which had the pNorVβ promoter instead of this ETH promoter. We chose a high-copy backbone from Twist Bioscience for these experiments.
 +
 
 +
This promoter was tested in the bacterial strain <i>E.coli Nissle 1917</i>.
 +
 
 +
==Characterization==
 +
 
 +
Measuring parts with different approaches and comparing them to provide a more insightful and multilayered characterization is essential in Synthetic Biology. Here, we focused on two methods:
 +
 
 +
(i) time-lapse plate reader assays to measure the sensitivity of our circuit to NorR in a dynamic manner and under different concentrations of inducer; and
 +
 
 +
(ii) endpoint flow cytometry assays to measure the behavior of our circuits at the single-cell scale.
 +
 
 +
With the first assay, we uncovered essential kinetic information about the circuits on the populational level (every measurement is an average of the individual expression patterns in the sample). With the second assay, we delved deeper into the cell populations to characterize other essential properties of our system, such as expression noise and dose-dependent responses to different inducer concentrations.
 +
 
 +
We performed all analyses using in-house R scripts.
 +
 
 +
===Time-Lapse Plate Reader Assay===
 +
 
 +
To make our experiments reproducible, during plate reader assays (PHERAstar FSX - λEx: 485 nm, λEm: 530 nm), we measured each sample for 16 hours at 37°C and constant orbital shaking, using three biological replicates (three individual colonies per circuit) and three technical replicates (three wells per biological replicate).
 +
 
 +
We performed the data analysis as follows:
 +
<ol>
 +
<li>Subtracted blanks from raw data</li>
 +
<li>Normalize GFP by OD600</li>
 +
<li>Calculate technical means of GFP/OD600 normalized data</li>
 +
<li>Calculate the biological means of GFP/OD600 normalized data</li>
 +
<li>Calculate the biological standard deviation of GFP/OD600 normalized data</li>
 +
<li>When necessary, perform imputation. Usually, the first normalized measurements are noisy and unreliable as OD600 values can be very low and significantly impact normalization. Thus, when individual normalized values are extremely high or low (sometimes negative due to blank correction), imputation was used following a na_kalman() function from the ImputeTS R package.</li>
 +
<li>Additional transformations, such as log transformations.</li>
 +
</ol>
 +
 
 +
Hence, our plots (Figure 1a and Figure 1b) show the averages and standard deviations for the biological replicates for each sample for each time point.
 +
 
 +
[[File:DoseresponseAll.jpeg|400px|thumb|left|<b>Figure 1a</b>: Dose response curves of GFP/OD for the different plasmids to various DETA/NO concentrations, with pNorVβ being the most expressive promoter, while pNorV of the 2016 ETH iGEM team being the least expressive promoter.]] [[File:ETHvs1P2mM.jpeg|400px|thumb|right|<b>Figure 1b: Response of pNorV vs. pNorVβ to 2mM DETA/NO</b>: This graph shows GFP expression normalized to the OD of pNorV and pNorVβ at 2mM DETA/NO.]]
 +
 
 +
 
 +
 
 +
 
 +
 
 +
 
 +
 
 +
 
 +
 
 +
 
 +
 
 +
 
 +
 
 +
 
 +
 
 +
 
 +
 
 +
 
 +
 
 +
 
 +
 
 +
 
 +
 
 +
 
 +
 
 +
 
 +
 
 +
 
 +
 
 +
 
 +
 
 +
 
 +
Because the standard deviations overlap, we thought that we might be able to reduce the standard deviations and get a clearer result if we had more samples. To get more samples, we also performed a flow cytromety.
 +
 
 +
===Endpoint Flow Cytometry Assay===
 +
 
 +
For the flow cytometry experiment, cell cultures were grown overnight in LB medium supplemented with antibiotic, diluted in 2mL of M9 (supplemented with glucose, cas amino acids and an antibiotic) in a 1:10 ratio (v/v), induced with different NO concentrations and grown for 7 hours in a shaker (37C, 220 RPM). Samples were then chilled on ice to halt cell growth and diluted in 1mL of cold PBS (1:500 v/v ratio). A total of 100,000 cells per sample was measured in a BD FACSCanto II flow cytometer (FSC: 625V, SSC: 420V, FITC: 650V, Event threshold: FSC & SSC > 200, Channel: FITC (λEx 488 nm / λEm. 530/30 nm, High flow rate: ~ 10,000 events/s).
 +
 
 +
[[File:ViolinPlotsNegETH1RBS.jpeg|400px|thumb|left|<b>Figure 2a: Violin Plots and Boxplots for the Genetic Circuit Containing 2022 UZurich's pNorVβ Promoter Including 1 RBS and the Genetic Circuit Containing 2016 ETH's pNorV Promoter Including 1 RBS. The Negative Control Consists of the Same Set-up But Without Any Promoter</b> These plots represent the distribution of FITC-H (green fluorescence) values for the two different promoters at 4 different concentrations of DETA/NO. Each measurement consisted of 100'000 cells.]] [[File:NoiseLevelsETH.jpeg|400px|thumb|right|<b>Figure 2b: Noise Levels of 2022 UZurich's pNorVβ Promoter and 2016 ETH's pNorV Promoter.</b>: Noise levels are measured as the coefficient of variation for construct sample and NO concentration. The constructs show low noise levels at high concentrations.]]
 +
 
 +
 
 +
 
 +
 
 +
 
 +
 
 +
 
 +
 
 +
 
 +
 
 +
 
 +
 
 +
 
 +
 
 +
 
 +
 
 +
 
 +
 
 +
 
 +
 
 +
 
 +
 
 +
 
 +
 
 +
 
 +
 
 +
 
 +
The results showed that our construct with pNorVβ has a higher response to induction with DETA/NO than the construct with pNorV (figure 2a). It further revealed that the constructs with pNorVβ and pNorV have relatively low noise compared to the negative control (figure 2b). Due to these reasons and the large sample size (100'000 sample points per plasmid), we can infer that pNorVβ is more responsive to DETA/NO induction than pNorV.
 +
 
 +
 
 +
 
 +
 
 +
 
 +
 
 +
 
 +
 
 +
 
 +
 
 +
<h1>References:</h1>
 +
<ul>
 +
<li> [1] Gardner, A. M. "Regulation Of The Nitric Oxide Reduction Operon (Norrvw) In Escherichia Coli. ROLE OF Norr AND Sigma
 +
54 IN THE NITRIC OXIDE STRESS RESPONSE". Journal of Biological Chemistry 278.12 (2003): 10081-10086.
 +
<li>[2] Roselle, Dominick C and Daniel J Smith. "Characterization And Nitric Oxide Release Studies Of Lipophilic 1-Substituted
 +
Diazen-1-Ium-1,2-Diolates". Journal of Controlled Release 51.2-3 (1998): 131-142. Web.
 +
<li>[3] Xiaoyu J. Chen, Baojun Wang, Ian P. Thompson, and Wei E. Huang et al. Rational Design and Characterization of Nitric Oxide Biosensors in E. coli Nissle 1917 and Mini SimCells ACS Synthetic Biology 2021 10 (10), 2566-2578 <html><a href="https://pubs.acs.org/doi/abs/10.1021/acssynbio.1c00223">DOI: 10.1021/acssynbio.1c00223</a></html></li>
  
PnorV is a promoter regulated by norR.
 
  
<!-- Add more about the biology of this part here
+
<!-- Add more about the biology of this part here
 
===Usage and Biology===
 
===Usage and Biology===
  
 
<!-- -->
 
<!-- -->
<span class='h3bb'>Sequence and Features</span>
+
<span class='h3bb'>Sequence and Features</span>
<partinfo>BBa_K2116002 SequenceAndFeatures</partinfo>
+
<partinfo>BBa_K1153000 SequenceAndFeatures</partinfo>
  
  
<!-- Uncomment this to enable Functional Parameter display  
+
<!-- Uncomment this to enable Functional Parameter display  
 
===Functional Parameters===
 
===Functional Parameters===
<partinfo>BBa_K2116002 parameters</partinfo>
+
<partinfo>BBa_K1153000 parameters</partinfo>
 
<!-- -->
 
<!-- -->

Latest revision as of 18:20, 13 October 2022

NorV promoter. NOx detector.

Our Biobrick has been designed to enable the detection of NO using the norV gene promoter, cloned from E.coli K12 TOP10 cells. In the presence of NOx it will promote the downstream genes in the plasmid, producing a protein in response to the environmental stimulus.


Usage and Biology

Promoter norV (PnorV) is the native promoter controlling the nitric oxide reduction operon (norRVW) in E. Coli. [1] It's transcriptional regulator, NorR, can bind to nitric oxide and activate gene expression.


Characterisation of the Promoter

We cloned PnorV upstream of superfolder GFP for characterisation Part:BBa_K2116088 This construct was expressed on a medium copy plasmid (ori:p15A). Since E.coli natively produces NorR, we relied on this to activate the promoter. Below is the dose response curve we obtained under the following experimental conditions, using a plate reader:

  • Overnight growth and experiment in minimal M9 medium, with 25μg/μL chloramphenicol.
  • Plating at an OD600 of 0.05, in M9, with 25μg/μL chloramphenicol.
  • Induction at OD6000.5.
  • Settings for GFP measurement: excitation-488nm, emission- 530nm.
  • Samples were always in three technical replicates, and the fluorescence measurements were normalized to OD600.
PnorV dose response curve for a range of DETA/NO concentrations that corresponds to 7-70μM of NO. Above 15000uM of DETA/NO affects cell growth and is not included in the dose response. Measured 6 hours after induction. Error bars represent S.D. from three technical replicates.

In order to show that the native NorR is essential for activating PnorV, we used a [http://cgsc.biology.yale.edu/KeioList.php Keio] norR knock out strain (ΔnorR) and compared it to it's parent wild type strain (WT). It was shown that PnorV can be activated by DETA/NO in the parent strain, but not in the norR KO strain.

A norR KO strain was used as a negative control to demonstrate that the native norR can activate PnorV.Induced with 5000μM DETA/NO and measured 6 hours after induction. Error bars represent S.D. from three technical replicates.

Materials

We used DETA/NO to as a nitric oxide source. It has a half life of roughly 20h, and releases NO at a relatively constant rate [2].


Contribution by UZurich 2022

  • Group: UZurich 2022
  • Author: Jana Mehdy, Lea Brüllmann, Marine Mausy
  • Summary: We compared the GFP expression rates of the ETH promoter to the pNorVβ promoter to sense nitric oxide.


Usage and Biology

We wanted to compare this ETH pNorV promoter to the pNorVβ promoter [3] and see which one was better suited for sensing nitric oxide at lower concentration ranges and which one expresses more GFP. Therefore we created a genetic circuit where we implemented this ETH promoter followed by one strong ribosomal binding site (BBa_B0034), which was attached to a superfolder GFP, the regulator NorR, and a double forward terminator. This composite part can be found here, and it is similar to the genetic circuit BBa_K4387005, which had the pNorVβ promoter instead of this ETH promoter. We chose a high-copy backbone from Twist Bioscience for these experiments.

This promoter was tested in the bacterial strain E.coli Nissle 1917.

Characterization

Measuring parts with different approaches and comparing them to provide a more insightful and multilayered characterization is essential in Synthetic Biology. Here, we focused on two methods:

(i) time-lapse plate reader assays to measure the sensitivity of our circuit to NorR in a dynamic manner and under different concentrations of inducer; and

(ii) endpoint flow cytometry assays to measure the behavior of our circuits at the single-cell scale.

With the first assay, we uncovered essential kinetic information about the circuits on the populational level (every measurement is an average of the individual expression patterns in the sample). With the second assay, we delved deeper into the cell populations to characterize other essential properties of our system, such as expression noise and dose-dependent responses to different inducer concentrations.

We performed all analyses using in-house R scripts.

Time-Lapse Plate Reader Assay

To make our experiments reproducible, during plate reader assays (PHERAstar FSX - λEx: 485 nm, λEm: 530 nm), we measured each sample for 16 hours at 37°C and constant orbital shaking, using three biological replicates (three individual colonies per circuit) and three technical replicates (three wells per biological replicate).

We performed the data analysis as follows:

  1. Subtracted blanks from raw data
  2. Normalize GFP by OD600
  3. Calculate technical means of GFP/OD600 normalized data
  4. Calculate the biological means of GFP/OD600 normalized data
  5. Calculate the biological standard deviation of GFP/OD600 normalized data
  6. When necessary, perform imputation. Usually, the first normalized measurements are noisy and unreliable as OD600 values can be very low and significantly impact normalization. Thus, when individual normalized values are extremely high or low (sometimes negative due to blank correction), imputation was used following a na_kalman() function from the ImputeTS R package.
  7. Additional transformations, such as log transformations.

Hence, our plots (Figure 1a and Figure 1b) show the averages and standard deviations for the biological replicates for each sample for each time point.

Figure 1a: Dose response curves of GFP/OD for the different plasmids to various DETA/NO concentrations, with pNorVβ being the most expressive promoter, while pNorV of the 2016 ETH iGEM team being the least expressive promoter.
Figure 1b: Response of pNorV vs. pNorVβ to 2mM DETA/NO: This graph shows GFP expression normalized to the OD of pNorV and pNorVβ at 2mM DETA/NO.

















Because the standard deviations overlap, we thought that we might be able to reduce the standard deviations and get a clearer result if we had more samples. To get more samples, we also performed a flow cytromety.

Endpoint Flow Cytometry Assay

For the flow cytometry experiment, cell cultures were grown overnight in LB medium supplemented with antibiotic, diluted in 2mL of M9 (supplemented with glucose, cas amino acids and an antibiotic) in a 1:10 ratio (v/v), induced with different NO concentrations and grown for 7 hours in a shaker (37C, 220 RPM). Samples were then chilled on ice to halt cell growth and diluted in 1mL of cold PBS (1:500 v/v ratio). A total of 100,000 cells per sample was measured in a BD FACSCanto II flow cytometer (FSC: 625V, SSC: 420V, FITC: 650V, Event threshold: FSC & SSC > 200, Channel: FITC (λEx 488 nm / λEm. 530/30 nm, High flow rate: ~ 10,000 events/s).

Figure 2a: Violin Plots and Boxplots for the Genetic Circuit Containing 2022 UZurich's pNorVβ Promoter Including 1 RBS and the Genetic Circuit Containing 2016 ETH's pNorV Promoter Including 1 RBS. The Negative Control Consists of the Same Set-up But Without Any Promoter These plots represent the distribution of FITC-H (green fluorescence) values for the two different promoters at 4 different concentrations of DETA/NO. Each measurement consisted of 100'000 cells.
Figure 2b: Noise Levels of 2022 UZurich's pNorVβ Promoter and 2016 ETH's pNorV Promoter.: Noise levels are measured as the coefficient of variation for construct sample and NO concentration. The constructs show low noise levels at high concentrations.














The results showed that our construct with pNorVβ has a higher response to induction with DETA/NO than the construct with pNorV (figure 2a). It further revealed that the constructs with pNorVβ and pNorV have relatively low noise compared to the negative control (figure 2b). Due to these reasons and the large sample size (100'000 sample points per plasmid), we can infer that pNorVβ is more responsive to DETA/NO induction than pNorV.






References:

  • [1] Gardner, A. M. "Regulation Of The Nitric Oxide Reduction Operon (Norrvw) In Escherichia Coli. ROLE OF Norr AND Sigma 54 IN THE NITRIC OXIDE STRESS RESPONSE". Journal of Biological Chemistry 278.12 (2003): 10081-10086.
  • [2] Roselle, Dominick C and Daniel J Smith. "Characterization And Nitric Oxide Release Studies Of Lipophilic 1-Substituted Diazen-1-Ium-1,2-Diolates". Journal of Controlled Release 51.2-3 (1998): 131-142. Web.
  • [3] Xiaoyu J. Chen, Baojun Wang, Ian P. Thompson, and Wei E. Huang et al. Rational Design and Characterization of Nitric Oxide Biosensors in E. coli Nissle 1917 and Mini SimCells ACS Synthetic Biology 2021 10 (10), 2566-2578 DOI: 10.1021/acssynbio.1c00223

  • Sequence and Features


    Assembly Compatibility:
    • 10
      COMPATIBLE WITH RFC[10]
    • 12
      COMPATIBLE WITH RFC[12]
    • 21
      COMPATIBLE WITH RFC[21]
    • 23
      COMPATIBLE WITH RFC[23]
    • 25
      COMPATIBLE WITH RFC[25]
    • 1000
      COMPATIBLE WITH RFC[1000]