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Team:Peking/Project/BioSensors
From 2013.igem.org
Biosensors
One advantage of biosensors is that the detection profile of a particular biosensor is usually limited to a few specific signals, thus to make the output of biosensors highly informative. Another advantage is the responding procedure of a biosensor: it usually involves no more than transcription and translation, both of which are automatically operated by the living cells themselves; so it is more convenient to use, compared with conventional chemical methods that rely heavily on complicated measuring devices. Additionally, originating from functional elements of natural biological systems, biosensors are subject to various tuning methods such as directed evolution, point mutagenesis or other genetic manipulations. A biosensor typically consists of a detector and a reporter; the input signal activates the detector, through which the reporter is stimulated to emit the output signal. In our project, the input signal for biosensor is aromatic compounds and the output is the expression of a reporter gene; the aromatics are supposed to be sensed by transcriptional regulators from the bacteria living in aromatics-rich environment. Through the bioinformatic mining, we obtained 22 aromatics-sensing transcriptional regulators (see Sensor Mining). Noting the fact that their expected aromatics-sensing profiles overlap a lot, we finally determined 8 transcriptional regulators whose profiles could combine to cover the overall spectrum of aromatic compound species (Table 1). Next we focused on constructing biosensors with low basal level, high induction ratio, and robust detection profiles.
| Sensor | Expected Aromatics-sensing Profiles | Sources |
|---|---|---|
| XylS | BzO; 2-MeBzO; 3-MeBzO; 2,3-MeBzO; 3,4-MeBzO | Pseudomonas putida |
| XylR | Toluene; m-Xylene; 3-ClTOL; 3-MePhl | Pseudomonas putida |
| HbpR | o-Phenylphenol; 2,6'-DiHydroxybiphenol | Pseudomonas azelaica |
| HcaR | 2-HPASCoA; 3-HPASCoA; 4-HPASCoA | Escherichia coli |
| HpaR | 3-HPAA; 4-HPAA; 3,4-DHPAA | Escherichia coli |
| PaaX | PAASCoA | Escherichia coli |
| DmpR | Phl; 2-MePhl; 3-MePhl; 4-MePhl; 2-ClPhl | Pseudomonas putida |
| NahR | 4-MeSaA; 4-C1SaA; 5-C1SaA; SaA 3-IBzO | Pseudomonas putida |
The coding sequences of these 8 transcriptional regulators were obtained by either chemical synthesis or PCR amplification. They were then incorporated into our biosensor circuit design (Fig. 1): (1) A constitutive promoter (Pc) constitutively expresses the transcriptional regulator protein; (2) The cognate promoter of the transcriptional regulator controls the expression of the reporter gene, super-fold green fluorescent protein (sfGFP, a novel and robust GFP variant designed for in vivo measurement of protein expression levels). We tested fluorescence intensity to show induction ratio of each expression system when exposed to their inducers through ELIZA and flow cytometry. Naturally, the performances of these transcriptional factors are not well characterized and needs further tuning to be biosensors.
Based on the data, we selectively changed Pc and RBS strength, tuning expression intensity of these transcriptional factors and sfGFP respectively, to optimize induction behavior.(Fig 2)
Up to now, we have constructed several well-performed types of aromatic biosensors including NahR, XylS, HbpR and DmpR. (Fig 3)
The well-characterized aromatic biosensors consist a comprehensive aromatic detection toolkit. Various aromatic compounds are involved in our toolkit’s detection range. The performance of these biosensors propose a possibility for pathway coupling, complex sample analysis and further band pass circuit application.
To apply the well-characterization biosensors we built in multicomponent analysis, the nonexistence of synergistic or antagonistic effects, in another word, orthogonality, among inducers should be confirmed. We tested the orthogonality for all our fine-tuned biosensors. (please click here for further information) The result shows that within a general inducer concentration, the orthogonality of our biosensors fits the requirements of multi-components sample detection. (Fig. 4)
Fig 1. Schematic diagram of expression system The transcriptional factor (TF) is ligated with Pc promoter on low-copy backbone pSB4K5. Reporter gene sfGFP is located downstream of promoter which is regulated by corresponding TF. The backbone is high-copy pUC57 simple. The dark purple arrowhead refers to Pc promoter, while the promoter regulated by corresponding TF is shown in cyan, dark green oval stands for Ribosome Binding Site (RBS), terminator is in dark red hexagon, dark blue square represents gene coding sequence.
Fig 2. Tuning on Pc promoter and RBS intensity In order to obtain optimal induction performance, we constructed Pc promoter library, selecting J23106, J23105, J23114, J23117, J23109, J23113 and linked it with transcriptional factors. Besides, we constructed RBS library, adopting B0031, B0032, B0034 and put it upstream of reporter gene sfGFP. The arrowheads with blue gradient refer to different intensity of Pc promoter. The ovals with green gradient stand for distinct intensity of RBS.
Fig 3 well-performed aromatic biosensors and their detective range Each color in the middle ring represents the detection range of a biosensor. Structural formula with color background stands for the aromatic compounds detected by our biosensors .┝ means plug in, connecting an enzyme with existing biosensor .
Fig. 4 Summary of the orthogonality between four sensors’ inducers. The inducers between XylS and NahR, XylS and HbpR, NahR and HbpR, XylS and DmpR, NahR and DmpR, and HbpR and DmpR are all highly orthogonal.
