Team:Bielefeld-CeBiTec/Results/HeavyMetals

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.

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

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.

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 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.

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.

Chromium

in vivo

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

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.

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

Influence of different chromium concentrations on our crude cell extract. Error bars represent the standard deviation of three biological replicates.

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.

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.

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.

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.

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.

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

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.

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.

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.

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.

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.

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.

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

TEXT. Error bars represent the standard deviation of three biological replicates.

TEXT. Error bars represent the standard deviation of three biological replicates.

in vitro

TEXT. Error bars represent the standard deviation of three biological replicates.

TEXT. Error bars represent the standard deviation of three biological replicates.

TEXT. Error bars represent the standard deviation of three biological replicates.

TEXT. Error bars represent the standard deviation of three biological replicates.

Nickel

in vivo

TEXT. Error bars represent the standard deviation of three biological replicates.

TEXT. Error bars represent the standard deviation of three biological replicates.

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