Difference between revisions of "Team:Bielefeld-CeBiTec/Results/HeavyMetals"

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     <p>Compared to the <i>in vivo</i> results, the response to arsenic was relatively small and we measured a high background signal. We assume that this is due to the different construct we used <i>in vitro</i>. This construct had been optimized for our CFPS by exchanging the natural promoter for the T7 promoter and exchanging mRFP1 for our optimized sfGFP. However, we assume that the repression in the presence of ArsR was not effective enough to observe a clear induction. The reason is most likely that the distance between the T7 promoter and the arsenic operator was too large. The distance was a result of our cloning strategy and would likely be suitable for <i>E. coli</i> promoters. However, the T7 promoter requires the operator to be very close for an efficient repression (<a href="https://2015.igem.org/Team:Bielefeld-CeBiTec/Results/HeavyMetals#Karig2012">Karig et al. 2012</a>). </p>
 
     <p>Compared to the <i>in vivo</i> results, the response to arsenic was relatively small and we measured a high background signal. We assume that this is due to the different construct we used <i>in vitro</i>. This construct had been optimized for our CFPS by exchanging the natural promoter for the T7 promoter and exchanging mRFP1 for our optimized sfGFP. However, we assume that the repression in the presence of ArsR was not effective enough to observe a clear induction. The reason is most likely that the distance between the T7 promoter and the arsenic operator was too large. The distance was a result of our cloning strategy and would likely be suitable for <i>E. coli</i> promoters. However, the T7 promoter requires the operator to be very close for an efficient repression (<a href="https://2015.igem.org/Team:Bielefeld-CeBiTec/Results/HeavyMetals#Karig2012">Karig et al. 2012</a>). </p>
 +
    <p>In addition, we performed an experiment in which the arsenic repressor was not present in the reaction from the beginning, but was encoded on a second plasmid. The plasmid concentrations we used had been predicted by our model. In accordance with the aforementioned results, we observed no clear repression and addition of arsenic showed no effect. This experiment is discussed on the <a href="https://2015.igem.org/Team:Bielefeld-CeBiTec/Modeling/Application">Modeling pages</a></p>
  
 
<h3>References</h3>
 
<h3>References</h3>

Revision as of 16:53, 15 September 2015

iGEM Bielefeld 2015


Heavy Metals

Zusammenfassung in ganz wenigen Worten.

The different sensors we worked with were characterized in vivo as well as in vitro.



We tested the influence of each heavy metal on our sensors in vivo Therefore we used heavy metal concentrations based on heavy metal occurrences measured all over the world.


Adjusting the detection limit
Influence of heavy metals on the growth of E.coli KRX shown is the standard deviation of three biological replicates. For induction concentrations of 20 µg/L lead, 60 µg/L mercury, 60 µg/L chromium, 80 µg/L nickel, 40 mg/L copper which represent ten times of the WHO guideline were used.


E. colis growth. Moreover there is no significant difference between the curves with heavy metals and the controls. This first experiment showed us, in vivo characterization with these sensors under the tested heavy metal concentrations is possible. Most of our sensors were cultivated in the BioLector. Due to the accuracy of this device we could measure our sample in duplicates.



Arsenic

We choose to work with the chromosomal arsenic operon of E. coli, which was used by the team from Edinburgh in 2006. This operon encodes an efflux pump which confers resistance against arsenic. The expression is controlled by the repressor ArsR, which negatively autoregulates its own expression. AsIII can bind to three cysteine residues in ArsR. The resulting conformational change deactivates the repressor (Chen, Rosen 2014).

By placing a reporter gene downstream of arsR, an arsenic biosensor can be constructed. In this case, both the repressor and the reporter are under the control of the same promoter. In this respect, the arsenic sensor is different from the other heavy metal biosensors we worked with, as their repressors or activators are expressed constitutively. However, the genetic build-up of the arsenic sensor is well established. Consequently, we decided to keep this deviating design.

in vivo

We tested an arsenic sensor with mRFP1 as reporter gene in vivo to confirm that the sensor is functional and test whether it is possible to detect the safety limit as defined by the WHO. We observed a reaction approximately five hours after addition of arsenic. The safety limit of 10 µg/L could clearly be distinguished from the negative control and the fluorescence signal increased up to a concentration of 500 µg/L. The signal in the presence of 1000 µg/L was slightly lower than in the presence of 500 µg/L.

Adjusting the detection limit
Time course of the induction of an arsenic biosensor with RFP for different arsenic concentrations in vivo. Error bars represent the standard deviation of three biological replicates.

in vitro

E. coli is resistant to arsenic because it posseses an efflux pump. The cell extract is not protected by such mechanisms, therefore we tested the effect of arsenic on the synthesis of sfGFP. We observed no significant effect for the relevant safety limits of 10 µg/L and 50 µg/L.

Influence of arsenic on CFPS
Influence of arsenic on cell-free protein synthesis.

In order to test the arsenic sensor in our cell-free protein synthesis, we cloned a device that contains the arsenic operator between the T7 promoter and sfGFP with our optimized untranslated region (UTR). We tested this device in a cell extract that had been generated from cells expressing the arsenic repressor. We observed an induction when adding arsenic up to a concentration of 1.87 mg/L. As high arsenic concentrations inhibit the performance of the CFPS, we normalized the results for this effect. In the final application, this task is performed by our app.

Adjusting the detection limit
Induction of arsenic sensor in vitro. For this experiment, a cell extract which already containes the arsenic repressor was used. Error bars represent the standard deviation of three biological replicates.

Compared to the in vivo results, the response to arsenic was relatively small and we measured a high background signal. We assume that this is due to the different construct we used in vitro. This construct had been optimized for our CFPS by exchanging the natural promoter for the T7 promoter and exchanging mRFP1 for our optimized sfGFP. However, we assume that the repression in the presence of ArsR was not effective enough to observe a clear induction. The reason is most likely that the distance between the T7 promoter and the arsenic operator was too large. The distance was a result of our cloning strategy and would likely be suitable for E. coli promoters. However, the T7 promoter requires the operator to be very close for an efficient repression (Karig et al. 2012).

In addition, we performed an experiment in which the arsenic repressor was not present in the reaction from the beginning, but was encoded on a second plasmid. The plasmid concentrations we used had been predicted by our model. In accordance with the aforementioned results, we observed no clear repression and addition of arsenic showed no effect. This experiment is discussed on the Modeling pages

References

Chen, Jian; Rosen, Barry P. (2014): Biosensors for inorganic and organic arsenicals. In Biosensors 4 (4), pp. 494–512. DOI: 10.3390/bios4040494.

Karig, David K.; Iyer, Sukanya; Simpson, Michael L.; Doktycz, Mitchel J. (2012): Expression optimization and synthetic gene networks in cell-free systems. In Nucleic acids research 40 (8), pp. 3763–3774. DOI: 10.1093/nar/gkr1191.

Chromium



in vivo



In vivo we could show that the addition of different concentrations of chromium have different effects to transcription of sfGFP.



Adjusting the detection limit
Time course of the induction of a chromium biosensor with sfGFP for different chromium concentrations in vivo. The data are measured with BioLector and normalized on OD600. Error bars represent the standard deviation of two biological replicates.
Adjusting the detection limit
Fluorescence levels at three different stages of cultivation. Shown are levels after 60 minutes, 150 minutes and 650 minutes. Error bars represent the standard deviation of three biological replicates.

Our data lead to the conclusion that in a cell based system it is possible to detect chromium. In contrast to our expectations with higher chromium concentrations we got lower fluorescence levels. These observations needed further investigation.


in vitro

Adjusting the detection limit
Influence of different chromium concentrations on our crude cell extract. Error bars represent the standard deviation of three biological replicates.
Adjusting the detection limit
Chromium specific cell extract made from E. coli cells which already expressed the repressor before cell extract production. Induction with different chromium concentrations. Error bars represent the standard deviation of three biological replicates.
Adjusting the detection limit
Chromium specific cell extract made from E. coli cells which already expressed the repressor before cell extract production. Induction with different chromium concentrations. Error bars represent the standard deviation of three biological replicates.Data are normalised on chromiums influence to the cell extrat.
Adjusting the detection limit
Chromium sensor with alternative repressor build by team Dundee 2015, which has only the first 15 codons optimized in chromium specific cell extract under the induction withdifferent chromium concentrations. Error bars represent the standard deviation of three biological replicates.
Adjusting the detection limit
Chromium sensor with alternative repressor build by team Dundee 2015, which has only the first 15 codons optimized in chromium specific cell extract under the induction withdifferent chromium concentrations. Error bars represent the standard deviation of three biological replicates.Data are normalised on chromiums influence to the cell extrat.

Copper

in vivo



Our sensor for copper detection consists of CueR a MerR like activator and the copper specific promoter CopAP. The promoter is regulated by CueR, which binds Cu2+-ions. We also used a sfGFP behind the promoter for detection trough a fluorescence signal.



Adjusting the detection limit
Time course of the induction of a copper biosensor with sfGFP for different copper concentrations in vivo. The data are measured with BioLector and normalized on OD600. Error bars represent the standard deviation of two biological replicates.


In vivo we could show that the adding different concentrations of copper has effects on the transcription levels of sfGFP.



Adjusting the detection limit
Fluorescence levels at three different stages of cultivation. Shown are levels after 60 minutes, 150 minutes and 650 minutes.

The shown data suggest that sensing copper with our device is possible even if the detectable concentrations are higher than the desireble sensitivity limits. Therfore we tested the copper sensor in our in vitro transcription translation approach.



in vitro



In the following graphic the influences of different copper concentrations on the cell extact are shown
Adjusting the detection limit
Influence of different copper concentrations on our crude cell extract. Error bars represent the standard deviation of three biological replicates.

As shown above copper has no negatice influence on the functuality of our cell extact. Therefore a ralatively stable system for copper sensing is provided.




First tests with specific cell extract and different copper concentrations lead to further tests and normilisations.


Adjusting the detection limit
Copper specific cell extract made from E. coli cells which have already expressed the activator before cell extract production. Induction of copper inducible promoter without T7 in front of the operator site with different copper concentrations. Error bars represent the standard deviation of three biological replicates.

Fluorescences normalised on coppers influence to the cell extract are shown above.


Adjusting the detection limit
Copper specific cell extract made from E. coli cells which have already expressed the activator before cell extract production. Induction of copper inducible promoter without T7 in front of the operator site with different copper concentrations. Error bars represent the standard deviation of three biological replicates. Data are normalized on coppers influence to the cell extract.

In addition to the native promoter, operator device as measured above reporter constructs under the control of T7 promoter were tested.


Adjusting the detection limit
Copper specific cell extract made from E. coli cells which have already expressed the activator before cell extract production. Induction with different copper concentrations. Error bars represent the standard deviation of three biological replicates.

Compared to the former fluorecence leves the T7 reporter device showed higher levels therefore a reporter device under the control of T7 promoter is more suitable for our CFPS.


Adjusting the detection limit
Copper specific cell extract made from E. coli cells which have already expressed the activator before cell extract production. Induction of copper inducible promoter with different copper concentrations. Error bars represent the standard deviation of three biological replicates. Data are normalized on coppers influence to the cell extract.

After normalising on coppers influcence to the cell extract these differecnces were even more obvious.


Lead

in vivo


In addition to these we constructed a sensor for lead detection. It consists of PbrR, the repressor, and the lead specific promoter PbrA. The promoter is regulated by the RcnR, which binds Pb-ions. As the former sensors this one encloses a sfGFP for detection via fluorescence.


Adjusting the detection limit
Time course of the induction of a lead biosensor with sfGFP for different lead concentrations in vivo. The data are measured with BioLector and normalized on OD600. Error bars represent the standard deviation of two biological replicates.
Adjusting the detection limit
Fluorescence levels at three different stages of cultivation. Shown are levels after 60 minutes, 150 minutes and 650 minutes. Error bars represent the standard deviation of three biological replicates.

The differences between inductions with various lead concentrations are really slight therefore this sensor needs further optimization which was not possible in this limited time. But as there is a fluorescence response to lead this sensor has the potential work as expected. In the future a characterization in CFPS systems would be interesting.


Mercury


in vivo


One of the already existing sensors we use for our system is the mercury sensor consisting of MerR the activator and the mercury specific promoter MerT. The promoter is regulated by the MerR, which binds Hg-ions. Similar to the former sensors we added a sfGFP for detection via fluorescence.


Adjusting the detection limit
During cultivation the sfGFP signal in reaction to different mercury concentrations was measured. The induction with mercury happened after 165 minutes. Error bars represent the standard deviation of three biological replicates.

In vivo data show a highly significant, well working sensor which even reacts to concentrations which are mentioned as drinking water guidelines by the WHO.


Adjusting the detection limit
Fluorescence levels at two different stages of cultivation. Shown are levels after 120 minutes and 190 minutes. Error bars represent the standard deviation of three biological replicates.

The mercury detection was measured during the cultivation of E. coli KRX at 37 °C. The strain contains the plasmid with the activator MerRunder the control of a constitutive promoter and the specific promoter with operator site which reacts to the activator with bound Hg-ions. The specific promoter is in front of sfGFP for measurment , so the mercury in the medium is detected directly.In vivo this sensor devise shows a fast answer to occurrence of his heavy metal contrary to the other sensor systems in vivo.


Therefore we tested our sensor in vitro to check if an already functioning highly optimized sensor provides required data for guideline detections


in vitro

Adjusting the detection limit
Influence of different mercury concentrations on our crude cell extract. Error bars represent the standard deviation of three biological replicates.
Adjusting the detection limit
Influence of different mercury concentrations on our crude cell extract. Error bars represent the standard deviation of three biological replicates.
Adjusting the detection limit
Mercury specific cell extract made from E. coli cells which have already expressed the activator before cell extract production. Induction of mercury inducible promoter without T7 in front of the operator site with different mercury concentrations. Error bars represent the standard deviation of three biological replicates.
Adjusting the detection limit
Mercury specific cell extract made from E. coli cells which have already expressed the activator before cell extract production. Induction of mercury inducible promoter without T7 in front of the operator site with different mercury concentrations. Error bars represent the standard deviation of three biological replicates.

Nickel

in vivo


In addition to these we aimed to construct a sensor for nickel detection. It consists of RcnR the repressor and the nickel specific promoter RcnA. The promoter is regulated by the RcnR, which binds Ni-ions. As the former sensors this one encloses a sfGFP for detection via Fluorescence.


Adjusting the detection limit
Time course of the induction of a lead biosensor with sfGFP for different nickel concentrations in vivo. The data are measured with BioLector and normalized on OD600. Error bars represent the standard deviation of two biological replicates.
Adjusting the detection limit
Fluorescence levels at three different stages of cultivation. Shown are levels after 60 minutes, 150 minutes and 650 minutes. Error bars represent the standard deviation of three biological replicates.

With this sensor no production of sfGFp via fluorescence level change could be detected. Therefore this sensor is not suitable for approach. Therefore no in vitro data using CFPS were taken.