Latest revision as of 09:33, 14 October 2019

Hg_Purple

This composite part is a mercury biosensor. It is composed of the MerR activator under the control of a constitutive promoter, the mercury specific promoter PmerT, strong rbs, the tsPurple chromoprotein and a transcription terminator. In presence of mercury, bacteria turn into different purple color intensities according to the concentration of mercury.

UPNAvarra_Spain 2019

Our biosensor for mercury detection is based on the mercury dependent mer operon from Shigella flexneri R100 plasmid Tn21. It is composed of a regulatory sequence made up by the MerR activator and the mercury specific promoter PmerT. The promoter is regulated by the MerR, which binds Hg²⁺ ions (Brown et al., 2003), that is under the control of a constitutive promoter (BBa_K608002). Then we used a purple chromoprotein (tsPurple BBa_K1033906) downstream the promoter for a first sight detection, with a strong RBS (BBa_B0030). The correct construction of this plasmid was confirmed by sequencing (Figure 1).

Figure 1. Construction of expression vector Hg_Purple (BBa_3287003) from parts of 2010_Peking and 2015_Bielefeld-CeBiTec teams: BBa_K1758340 and BBa_K346002; and tsPurple coming from the chromoprotein collection of 2011_Uppsala-Sweden team. The MerR-PmerT-tsPurple composite part is cloned pSB1C3 vector through the BioBrick suffix-prefix site.

We transformed the plasmid containing BBa_K3287003 into E. coli competent cells and cultured at 37ºC until OD = 0.4. Then we add CuCl<ub>2</sub> at different concentrations to induce the expression at 37 ℃ for 24 hours. After that, 2 mL of the bacterial culture were centrifuged at 3,000 r.p.m for 3 minutes, so we could observe at first sight the result of PmerT promoter being activated by mercury (Figure 2).

Figure 2. tsPurple expression levels under increasing concentrations of HgCl₂.

For these experimental results we generated a mathematical model, in order to prove that the (imaging) data we have gathered in the lab is in fact learnable by a simple regression model. We have opted out by a standard Least-Square error (linear) regression model, which has been run on the dataset obtained in the imaging part. This dataset consists of the average RGB color in the colored part of the pellets used at different concentrations of HgCl₂ (Figure 3A). For each color, we have subselected the channels that we are interest for the problem. That are the Red and Green channels in this case. It can be seen how the data is easily learnable by a linear regression model (Figure 3B) and, moreover, the error in the model training is quite small (0.96).

Figure 3. Modeling a Cooper biosensor. A) Input data; B) Regression model.

Reference

• Brown, N. L., J. V. Stoyanov, et al. (2003). "The MerR family of transcriptional regulators." FEMS Microbiol Rev 27(2-3): 145-163.

Sequence and Features