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. As^III 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.

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.

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 to this effect. In the final application, this task is performed by our app.

Chromium

Chromium is an essential part of the earth´s crust (Mitchell D. Cohen et al.), but most of it is produced trough industrial uses (Paustenbach et al. 2003). We built a biosensor for the detection of hexavalent (Cr^VI), because it is toxic and has cancerogenic effects on the human body. An intoxication of chromium can lead to damages of the nervous system. The World Health Organization recommends a limit of 50 µg/L in drinking water (Guidelines for drinking-water quality 2011, WHO 2003).

in vivo

The chromium sensor (BBa_K1758313) was constructed by using the basic construction we showed in Our biosensors. We work with the chromate inducible operon of Ochrobactrum tritici 5bvl1 which enables a resistance for chromium VI and superoxide. For our sensor we used the Cr^VI dependent repressor chrB which was introduced by team BIT 2013 (BBa_K1058007), and optimized this sequence for the use in E. coli . The repressor protein becomes deactivated by the binding of Cr⁶⁺-ions The associated chromium responsive promoter is chrP (introduced by BIT 2013) (BBa_K1058007). For output we used sfGFP and a 5’UTR untranslated region in front of sfGFP to optimize the expression of the reporter protein and increase its fluorescence (see figure 2).

Our sensor for chromium detection consists of ChrB the repressor and the chromate specific promoter ChrP. The promoter is regulated by ChrB, which binds Cr⁶⁺-ions. Behind the promoter is a sfGFP for detection of a fluorescence signal.

We tested our in vivo chromium sensor with sfGFP as reporter gene, to test the functionality of the system. Moreover we tested different chromium concentrations. The kinetic of our sensors response to different chromium concentrations is shown in figure 3. The first 30 hours show a strong increase in fluorescence. After that the increase in fluorescence is slower. For better visualization the kinetics of figure 3 are represented as bars in figure 4. A fluorescence level difference for 60 min, 150 min and 650 min is represented.

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. Additionally the bar chart showed that the chromium sensor needs a long time to get different fluorescence levels at different chromium concentrations in in vivo experiments. The bar chart showed significant differences between the chromium concentrations after 650 minutes.

in vitro

For the characterization of the chromium sensor with CFPS we used parts differing from that we used in vivo characterization. For the in vitro characterization we used a cell extract produced from cells which contain the plasmid (BBa_K1758310). The plasmid contains the gene chrB under the control of a constitutive promoter, so that the cell extract is enriched with repressor molecules. In addition to that we added plasmid-DNA of the chromium specific promoter chrP with 5’UTR-sfGFP under the control of T7-promoter (BBa_K1758314 (figure 6))to the cell extract. The T7-promoter is needed to get a better fluorescence expression.

Chromium’s influence on the cell extract as shown in figure 7 is minimal for low concentrations. Higher chromium concentrations have a measurable impact on the viability of the cell extract, which is visible at concentrations of 120 µg/L and obvious at concentrations of 240 µg/L chromium.

The decrease of fluorescence for higher chromium concentrations in chromium specific cell extract is shown in figure 8. An increase of fluorescence at higher chromium concentrations would have been expected resulting out of the induction of the chromium sensor. A factor which should be considered is the influence of high chromium concentrations to the cell extract. The test for influence of chromium on the specific cell extract, illustrated in figure 7 showed that the influence of chromium at low concentrations is not significant. But the graphic shows that high concentrations of chromium induce fatal damages to the cell extract.

Taking the influence of different chromium concentrations under consideration measured fluorescence can be normalized on chromium’s influence on the cell extract (figure 9). Normalized data suggest, that higher concentrations of chromium induce fluorescence in relevance to chromium’s influence on the cell extract.

In addition to the measurements of our chromium sensor in CFPS we measured our chromium inducible promoter with the repressor of team Dundee (figure 10, 11), which works similar to ours. In contrast to our repressor only first 15 codons of their repressor are codon-optimized. Measurements with their repressor showed tendencies similar to our measured repressor. After normalization induction with higher chromium concentrations showed a detectable fluorescence response for both measured datasets.

References

summarize

Our chromium sensor detects the presence of chromium in vivo, but the outcome differed from our expectations. We would have expected an increase in fluorescence by increasing chromium concentrations. Our in vitro data suggest that these decrease in fluorescence could be explained by chromium’s influence on E. coli which is not reflected in growth but shown by chromium´s influence on the cell extract. Before normalizing the in vitro data the same pattern as in vivo could be observed. After normalization an increase in signal is noticeable. Therefore with optimization our chromium sensor would be compatible to our cell free sensor system.

Copper

There are some possibilities for the contamination of drinking water with copper, for the production of pipes, valves and fittings copper is used (Guidelines for Drinking-water Quality, Fourth Edition ). Copper is an essential trace element for humans, animals and plants, but an overdose can lead to anemia, liver and brain damages (US EPA ORD NCEA Integrated Risk Information System (IRIS) 2014). Additionally high input of copper is associated with aging diseases as Atherosclerosis and Alzheimer’s disease (Brewer 2012). These damages can finally cause death. The World Health Organization recommends a limit of 2 mg/L in drinking water (Guidelines for Drinking-water Quality, Fourth Edition). .

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 Cu ²⁺-ions. We also used a sfGFP downstream the promoter for detection through a fluorescence signal.

For our copper sensor we used the native operator of cooper homeostasis from E.coli K12. We constructed a part(BBa_K1758324) using the basic genetic structur showed in Our biosensors.The operator sequence, which includes the promoter (copAP), is regulated by the activator CueR. In BBa_K1758324 we combined a codon optimized version of cueR (BBa_K1758320) under the control of a constitutive promoter with sfGFP under the control of the corresponding promoter copAP (BBa_K1758321)(figure 2). Through the addition of a 5’UTR before the sfGFP we optimized the expression of sfGFP and increased fluorescence.

We tested our in vivo ccopper sensor with sfGFP as reporter gene, to test the functionality of the system. Moreover we tested different copper concentrations. The kinetic of our sensors response to different copper concentrations is shown in figure 3. The first 10 hours show a strong increase in fluorescence. After that the increase in fluorescence is slower. For better visualization the kinetics of figure 3 are represented as bars in figure 4. A fluorescence level difference for 60 min, 150 min and 650 min is represented.

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

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

For the characterization of the copper sensor with CFPS we used parts differing from that we used in vivo characterization. For the in vitro characterization we used a cell extract out of cells which contain the plasmid (BBa_K1758320)(figure 5), so that the resulting extract is enriched with the avtivator CueR To this extract we added plasmid-DNA of the copper specific promoter copAP with 5’UTR-sfGFP under the control of T7-promoter (BBa_K1758325)to the cell extract. The T7-promoter is needed to get a better fluorescence expression.

The results presented in figure 7 illustrates the influences of different copper concentrations on the cell extract.

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

As shown in figure 7 copper has no negative influence on the functionality of our cell extract. Therefore a relatively stable system for copper sensing is provided. First tests with specific cell extract and different copper concentrations lead to further tests and normalizations, illustrated in figure 8.

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

Fluorescence normalized on coppers influence to the cell extract are shown in figure 10 and figure 11.

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

After normalizing on coppers influence to the cell extract these differences were even more obvious.

References

To summarize

Our copper sensors in vivo data show that detection of different copper concentrations is possible. The fluorescence levels defer clearly between different induction concentrations. As shown above higher copper concentration, higher the fluorescence signal. Therefore the concept of our sensor is functional even if the concentration needed for induction are to high to reach sensitively concerning the WHO guidelines for copper. Our sensor has been tested in vitro as well. For copper we tested our original CopAP construct without a T7 promoter in front of the inducible at first. After realizing that the sensor shows the right tendencies but the general fluorescence is quite low we created an inducible promoter under the control of a T7 promoter to use in CFPS. Fluorescence levels of this device showed the same tendencies as the one without but were higher fluorescence’s, which helps detecting it.

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 PbrAP. The promoter is regulated by the RcnR, which binds Pb²⁺-ions. As the former sensors this one encloses a sfGFP for detection via fluorescence.

Our lead sensor consists of parts of the chromosomal lead operon of Cupriavidus metallidurans (figure 2). This operon includes the promoter PbrAP (BBa_K1758332 ), which is regulated by the repressor PbrR. The PbrR belongs to the MerR family, of metal-sensing regulatory proteins, and is Pb²⁺-inducible. Our sensor system comprises pbrR ( BBa_K1758330 ), which is under the control of a constitutive Promoter and PbrAP and a 5’ untranslated region, which controls the transcription of a sfGFP and increases the fluorescence. Fluorescence implemented by sfGFP protein is the measured output signal (figure 3 and figure 4).

BBa_K1758332), which consists of the repressor under the control of a constitutive promoter ( BBa_K17583230) and the operator and promoter sequence of the lead inducible promoter. An untranslated region in front of the sfGFP, which is used for detection, enhances its expression ( BBa_K1758332).">

We tested our lead sensor with sfGFP as reporter gene, to test the functionality of the system. Moreover we tested different concentrations. The kinetic of our sensors response to different lead concentrations is shown in figure 3. The first 40 hours show a strong increase in fluorescence. After that the increase in fluorescence is slower. For better visualization the kinetics of figure 3 are represented as bars in figure 4. A fluorescence level difference for 60 min, 150 min and 650 min is represented.

The results of the lead sensor show in vivo no significant differences between the different concentrations (figure 3). But you can see that the decreasing concentrations show a decrease in fluorescence. This biosensor showed the right trend. For using this sensor it has to be optimized. We did not use this sensor in Cell-free-Protein-synthesis, because of the low expression of sfGFP and a lack of time for in vivo tests. In future, it should be characterized with CFPS to show, that this sensor have potential in this system despite in vivo the results. The differences between inductions with various lead concentrations are really slight. Therefore, this sensor needs further optimization which was not possible in the limited time. Nevertheless, there is a fluorescence response to lead. Therefore, this sensor should work as expected. In the future a characterization in CFPS systems would be interesting

References

To summarize

Our lead sensor was characterized in vivo only. 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

The main sources of mercury exposure are generated through humans. For example mercury contamination can be caused by medical waste (damaged measurement instruments), Fluorescent-lamps, Chloralkali plants and thermal power plants. In the environment, mercury is one of the most toxic elements . Acute effects of a mercury intoxication can range from diseases of the liver, kidney, gastrointestinal tract, to neuromuscular and neurological problems. A chronic intoxication of mercury results in kidney changes, changes in the central nervous system and other effects like cancer. The World Health Organization recommends a limit of 6 µg/L in drinking water.

in vivo

One of the already existing sensors we used for our system is the mercury sensor consisting of MerR the activator and the mercury specific promoter pmerT. The promoter is regulated by the MerR, which binds Hg²⁺-ions. Similar to the former sensors we added a sfGFP for detection via fluorescence.

For our mercury sensor we used parts of the mercury sensor constructed by iGEM team Peking 2010. These parts consist of the mercury dependent mer operon from Shigella flexneri R100 plasmid Tn21. The expression of the genes in the mer operon depends on the regulation by MerR its activator and promoter PmerT. For our sensor we used the codon optimized activator (BBa_K1758340), under control of a constitutive promoter,(BBa_K346001). Additionally to this activator we designed and constructed the specific promoter PmerT(BBa_K346002)(figure 2). For our sensor we added a 5’-UTR downstreamd of this promoter, which increased the fluorscence of the used reporter protein sfGFP.

BBa_K1758343), which consists of the activator under the control of a constitutive promoter BBa_K1758340)and the operator and promoter sequence of the mercury inducible promoter. An untranslated region in front of the sfGFP, which is used for detection, enhances its expression ( BBa_K1758342).">

We tested our mercury sensor with sfGFP as reporter gene, to test the functionality of the system. Moreover we tested different concentrations. The kinetic of our sensors response to different mercury concentrations is shown in figure 3. A strong increase in fluorescence levels is notecible after induction with mercury after 120 min. For better visualization the kinetics of figure 3 are represented as bars in figure 4. A fluorescence level difference for 120 min and 190 min is represented.

In vivo data show a highly significant, well working sensor, which even reacts to concentrations below the threshold of the water guidelines by the WHO (Figure 3 and 4).

The mercury detection was measured during the cultivation of E. coli KRX at 37 °C (Figure 3 and 4). The strain contained the plasmid with the activator merR under the control of a constitutive promoter and the specific promoter with an operator binding site, which reacts to the activator with bound Hg ²⁺-ions. The specific promoter is located upstream of the sfGFP CDS. Therefore, the mercury in the medium is detected via formation of sfGFP. In vivothis 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

For the characterization of the mercury sensor with CFPS we used parts differing from that we used in the in vivo characterization. For the in vitro characterization we used a cell extract out of cells, which contained the plasmid ( BBa_K1758340)(figure 5). In addition, we added plasmid DNA to the cell extract. This plasmid consisted of the mercury specific promoter pmerT with 5’-UTR-sfGFP. The entire sequence was placed under the control of of T7-promoter ( BBa_K1758344)(figure 6). The T7-promoter is needed to get a better fluorescence expression.

In vitro this sensor showed good results. The fluorescence level was high at low concentrations. Additionally, it showed that the expression level at 6 µg/L (Guideline of WHO for Mercury) reached the maximal signal. This result indicated the potential for measurement of concentrations under 6 µg/L.To confirm this hypothesis, it takes more experiments and tests with lower concentrations. Due to the high expression of sfGFP at low concentrations and the same expression level at different concentrations, it is not possible to quantify mercury with CFPS analyses . , Our model predicted this observation. During the measurement we noticed that the heavy metals have negative influences on the cell extract. Because of this fact, we used a correction factor, which resulted from the heavy metals influence on the CFPS system. This already optimized sensor showed the high potential of optimized sensors in CFPS.

To summarize

We demonstrated, that our mercury sensor works well in vivo (figure 4). There is a clearly noticeable increase in fluorescence after induction with mercury. Even the threshold concentration, which is given in the WHO guideline, can be measured. This well working sensor was tested in in vitro as well. The presented data suggested, that maximal output is reached at concentrations of 6 µg/ L,which represent the value of the former mentioned WHO guideline. Further optimization could lead to a decreasing in vitro detection threshold.

Nickel

Naturally nickel occurrences are quite low and rare. In Germany most drinking water pollutions by nickel take place in the last meters of the plumbing system. The reason for these pollutions are wrong tap ware. There may be higher concentrations in drinking-water in special cases of release from natural or industrial nickel deposits. Higher nickel concentrations in water can lead to dermatitis as itching of fingers and other parts of the body through long term skin contact. The World Health Organization (WHO) recommends a limit of 70 µg/L in drinking water.

in vivo

We aimed to construct a sensor for nickel detection. It consists of rcnR the repressor and the nickel specific promoter prcnA. The promoter is regulated by the RcnR, which binds Ni²⁺-ions. As the former sensors this one encloses a sfGFP for detection via fluorescence.

Our nickel biosensor consists of parts of the rcn-operon from E. coli, which encodes a nickel- and cobalt-efflux system. This system is highly sensitive to nickel. In absence of nickel or cobalt RcnR binds to the operator and inhibits the nickel responsive promoter. With Ni²⁺-ions present the repression of the promoter prcnA will be reversed, because the repressor RcnR binds Ni²⁺-ions and cannot attach to the DNA. For our biosensor we construct the part BBa_K1758353 by using the basic construction shown in figure 2. For this part we used the repressor RcnR under control of a constitutive promoter ( BBa_K1758350 ) and the nickel specific promoter PrcnA with a 5’UTR in front of sfGFP ( BBa_K1758352 ) as reporter protein.

BBa_K1758354), which consists of the activator under the control of a constitutive promoter ( BBa_K1758350)and the operator and promoter sequence of the nickel inducible promoter. An untranslated region in front of the sfGFP, which is used for detection, enhances its expression ( BBa_K1758352) ">

We tested our nickel sensor with sfGFP as reporter gene, to test the functionality of the system. Moreover we tested different concentrations. The kinetic of our sensors response to different nickel concentrations is shown in figure 3. The first five hours show a strong decrease in fluorescence. After that there is a slight increase in fluorescence. Starting levels of fluorescence are not reached. For better visualization the kinetics of figure 3 are represented as bars in figure 4. A fluorescence level difference for 60 min, 150 min and 650 min is represented.

The data for our nickel sensor show a trend that differs for that of the other sensors. There is no indication for a working sensor in vivo (Figure 3 and 4). There is a fluorescence signal, but it decreases in the first five hours. After reaching a minimum the fluorescence increases slowly. Additionally, there is no difference in fluorescence as response to various nickel concentration. Nickel could influence the cells and thereby caused a precipitation, which could result in decrease of fluorescence. With this sensor no production of sfGFP via fluorescence level change could be detected. Therefore, this sensor is not suitable for our approach. Due to this observation, no in vitro data using CFPS were taken.

To summarize

With this sensor no production of sfGFP via fluorescence level change could be detected. Therefore, this sensor is not suitable for our approach. Consequently, no in vitro tests were performed. To create a working sensor based on this concept further optimization is needed.