Difference between revisions of "Part:BBa K4387005"
Line 5: Line 5:
 
This composite part consists of the inducible <html><a href="https://parts.igem.org/Part:BBa_K4387000">pNorVβ promoter</a></html>, a <html><a href="https://parts.igem.org/Part:BBa_K2553008">superfolder GFP</a></html> preceded by one strong ribosomal binding site (<html><a href="https://parts.igem.org/Part:BBa_B0034">BBa_B0034</a></html>), the <html><a href="https://parts.igem.org/Part:BBa_K4387001">NorR regulator</a></html>, and a <html><a href="https://parts.igem.org/Part:BBa_B0015">double forward terminator</a></html>. We chose a high-copy backbone from Twist Bioscience for this part. Due to the competitive binding of the activated and inactivated NorR on the promoter, we decided on this construct with a positive feedback loop that adjusted the levels of NorR based on the amount of nitric oxide present [1]. The presence of nitric oxide would activate pNorVβ to induce GFP and NorR expression. Thereby, we ensure that high amounts of NorR will be produced only when NO is present.
 
This composite part consists of the inducible <html><a href="https://parts.igem.org/Part:BBa_K4387000">pNorVβ promoter</a></html>, a <html><a href="https://parts.igem.org/Part:BBa_K2553008">superfolder GFP</a></html> preceded by one strong ribosomal binding site (<html><a href="https://parts.igem.org/Part:BBa_B0034">BBa_B0034</a></html>), the <html><a href="https://parts.igem.org/Part:BBa_K4387001">NorR regulator</a></html>, and a <html><a href="https://parts.igem.org/Part:BBa_B0015">double forward terminator</a></html>. We chose a high-copy backbone from Twist Bioscience for this part. Due to the competitive binding of the activated and inactivated NorR on the promoter, we decided on this construct with a positive feedback loop that adjusted the levels of NorR based on the amount of nitric oxide present [1]. The presence of nitric oxide would activate pNorVβ to induce GFP and NorR expression. Thereby, we ensure that high amounts of NorR will be produced only when NO is present.
  
−
This construct was tested in the bacterial strain E.coli Nissle 1917.
+
This construct was tested in the bacterial strain <i>E.coli Nissle 1917<i>.
  
  
 
==Characterization==
 
==Characterization==
  
−
We measured the GFP expression upon NO induction with a plate reader over 16 hours. Below is the dose-response curve of pNorVβ, measured in a plate reader.  
+
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:
−
For all measurements, we used the following conditions:
+
−
<ul>
+
−
<li>Overnight growth and experiment were done in minimal M9 medium supplemented with Ampicillin at 37°C.</li>
+
−
<li>Start of experiment in 96 well plate at an OD600 of 0.05.</li>
+
−
<li>Settings for GFP measurements: excitation at 485nm, emission at 520nm.</li>
+
−
<li>Every condition was measured over three technical and three biological replicates.</li>
+
−
<li>GFP emission was normalized to OD600.</li>
+
−
</ul>
+
  
−
If high GFP expression is required, but some leakiness does not matter much, we recommend choosing <html><a href="https://parts.igem.org/Part:BBa_K4387006">BBa_K4387006</a></html>. If lower leakiness is essential, but GFP expression does not need to be very high, we recommend using parts <html><a href="https://parts.igem.org/Part:BBa_K4387005">BBa_K4387005</a></html> or <html><a href="https://parts.igem.org/Part:BBa_K4387007">BBa_K4387007</a></html> instead.
+
(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.
  
−
===Measurements===
+
We performed all analyses using in-house R scripts.
  
−
[[File:DoseResponse1rbs.jpeg|450px|thumb|left|'''Figure 1:''' Induction response of the pNorVβ promoter to different DETA/NO concentrations with respect to time.]]
+
===Time-Lapse Plate Reader Assay===
  
−
[[File:Response1rbs.jpeg|450px|thumb|left|'''Figure 2:''' Dose response of the pNorVβ promoter at different DETA/NO concentrations.]]
+
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 show the averages and standard deviations for the biological replicates for each sample for each time point.
 +
 
 +
===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 glycerol, 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 in 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).
  
  

Revision as of 16:29, 9 October 2022

Nitric Oxide Sensing Genetic Circuit With One Ribosomal Binding Site

Usage and Biology

This composite part consists of the inducible pNorVβ promoter, a superfolder GFP preceded by one strong ribosomal binding site (BBa_B0034), the NorR regulator, and a double forward terminator. We chose a high-copy backbone from Twist Bioscience for this part. Due to the competitive binding of the activated and inactivated NorR on the promoter, we decided on this construct with a positive feedback loop that adjusted the levels of NorR based on the amount of nitric oxide present [1]. The presence of nitric oxide would activate pNorVβ to induce GFP and NorR expression. Thereby, we ensure that high amounts of NorR will be produced only when NO is present.

This construct was tested in the bacterial strain 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:

  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 show the averages and standard deviations for the biological replicates for each sample for each time point.

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 glycerol, 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 in 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).


Sequence and Features


Assembly Compatibility:
  • 10
    COMPATIBLE WITH RFC[10]
  • 12
    COMPATIBLE WITH RFC[12]
  • 21
    INCOMPATIBLE WITH RFC[21]
    Illegal XhoI site found at 703
  • 23
    COMPATIBLE WITH RFC[23]
  • 25
    COMPATIBLE WITH RFC[25]
  • 1000
    COMPATIBLE WITH RFC[1000]


References

  • [1] 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