Difference between revisions of "Part:BBa K4387003"
 
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<li> <html><a href="https://parts.igem.org/Part:BBa_K4387006">Nitric Oxide Sensing Genetic Circuit With Two Ribosomal Binding Sites</a></html></li>  
 
<li> <html><a href="https://parts.igem.org/Part:BBa_K4387006">Nitric Oxide Sensing Genetic Circuit With Two Ribosomal Binding Sites</a></html></li>  
 
<li><html><a href="https://parts.igem.org/Part:BBa_K4387007">Nitric Oxide Sensing Genetic Circuit With Three Ribosomal Binding Sites</a></html></li>
 
<li><html><a href="https://parts.igem.org/Part:BBa_K4387007">Nitric Oxide Sensing Genetic Circuit With Three Ribosomal Binding Sites</a></html></li>
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<li><html><a href="https://parts.igem.org/Part:BBa_K4387009">Nitric Oxide Sensing Genetic Circuit Without the NorR regulator BBa_K4387001</a></html></li>
 
</ul>
 
</ul>
  
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This construct was tested in the bacterial strain E.coli Nissle 1917.  
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This construct was tested in the bacterial strain <i>E.coli Nissle 1917</i>.  
  
  
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<span class='h3bb'/span>
 
<span class='h3bb'/span>
 
<partinfo>BBa_K4387003 SequenceAndFeatures</partinfo>
 
<partinfo>BBa_K4387003 SequenceAndFeatures</partinfo>
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==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.
 +
 +
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>
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<li>Subtracted blanks from raw data</li>
 +
<li>Normalize GFP by OD600</li>
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<li>Calculate technical means of GFP/OD600 normalized data</li>
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<li>Calculate the biological means of GFP/OD600 normalized data</li>
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<li>Calculate the biological standard deviation of GFP/OD600 normalized data</li>
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<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>
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<li>Additional transformations, such as log transformations.</li>
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</ol>
 +
 +
Hence, our plots show the averages and standard deviations for the biological replicates for each sample for each time point.
 +
 +
 +
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 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).
 +
 +
We performed all analyses using in-house R scripts.
 +
 +
All plots can be found in the respective parts listed in "Usage and Biology."
 +
 +
  
  

Latest revision as of 15:08, 11 October 2022

Negative Control of Nitric Oxide Sensing Genetic Circuit

Usage and Biology

This composite part consists of a superfolder GFP, the transcriptional regulator NorR, and a double forward terminator. We chose a high-copy backbone from Twist Bioscience for this part. To assess the proper induction of our construct by nitric oxide, we removed the pNorV promoter to create a negative control. With the removal of the promoter, the presence of nitric oxide should not affect the expression of GFP. Therefore, this part was used for the autofluorescence of the EcN cultures in the following constructs:

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


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 439
  • 23
    COMPATIBLE WITH RFC[23]
  • 25
    COMPATIBLE WITH RFC[25]
  • 1000
    COMPATIBLE WITH RFC[1000]


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.

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.


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 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).

We performed all analyses using in-house R scripts.

All plots can be found in the respective parts listed in "Usage and Biology."