Difference between revisions of "Part:BBa K2541001"
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Figure 5. Temperature response of the heat inducible RNA-based thermosensor. The green solid line represents the activity of the RNA-based thermosensor as a function of temperature. Key quantitative features of the response such as threshold and sensitivity are emphasized. | Figure 5. Temperature response of the heat inducible RNA-based thermosensor. The green solid line represents the activity of the RNA-based thermosensor as a function of temperature. Key quantitative features of the response such as threshold and sensitivity are emphasized. | ||
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Revision as of 04:03, 13 October 2018
Heat-inducible RNA-based thermosensor-1
A RNA-based thermosensor that can be used for temperature sensitive post-transcriptional regulation which is based on the change of RNA sencondary structure. The heat-inducible RNA-based thermosensors can initiate translation of downstream genes at high temperatures.
1. Usage and Biology
Heat-inducible RNA-based thermosensors are RNA genetic control systems that sense temperature changes. At low temperatures, the mRNA adopts a stem-loop conformation that masks the Shine–Dalgarno (SD) sequence within the 5’-untranslated region (5’-UTR) and, in this way, prevents ribosome binding and translation. At elevated temperatures, the RNA secondary structure melts locally, thereby giving the ribosomes access to the SD sequence to initiate translation (Figure 1). Whereas natural RNA-based thermosensors have a relatively complicated secondary structure with multiple stems, hairpin loops and bulges. The highly complex RNA secondary structures into which most naturally occurring RNA-based thermosensors can be folded has led to the hypothesis that RNA-based thermosensors may not function as simple on/off switches.
Our team designed synthetic heat-inducible RNA-based thermosensors that are considerably simpler than naturally occurring thermosensors and can be exploited as convenient on/off switches of gene expression.
Figure 1. Mechanism of heat inducible RNA-based thermosensors.
2. Design
The temperature response of these thermosensors was designed on the basis of the melting temperature of the minimum free energy structure. The 5’-UTR contained an ASD (anti-SD sequence) and the downstream consensus SD sequence (5’-AAGGAG-3’) was followed by 8-nt spacer derived from----the bacteriophage T7 gene 10 leader sequence. To optimize the thermosensors for the desired melting temperature, intensity and sensitivity, a number of structural parameters come into consideration: stem length, loop size and mismatches or bulges in the stem.
Stem length is determined by ASD sequence because the SD sequence is conserved. Adding stem length can optimize heat inducible RNA-based thermosensors to more high temperature, while decreasing stem length has the opposite effect. The stem length is 4 base parings in K2541001. Loop size can moderate thermosensors melting temperature to a suitable temperature. In K2541001, the loop sequence is AAUAA. Finally, we get the sequence as the figure 2.
Figure 2. Design of synthetic heat inducible RNA-based thermosensor. (A) The RNA secondary structure is predictred by mFOLD. (B) Thermosensor sequence, ASD sequence, loop sequence, the site of mismatch or bulge in the stem and △G are in the table.
3.Characterization
The thermosensor sequence is constructed on the pSB1C3 vector by GoldenGate assembly. The measurement device is composed of Anderson promotor (BBa_J23104), thermosensor (BBa_K2541001), sfGFP_optimism (BBa_K2541400) and terminator (BBa_B0015). We select a
strong constitutive Anderson promoter J23014 as a suitable promoter by doing pre-experiment. The sfGFP_optimism has faster folding speed and higher fluorescence intensity. The B0015 is a double terminator that can reduce leakge (Figure 3). We characterized RNA-based thermosensors in E.coli DH5a.
On the left side of the figure 4 is K2541001. On the right of figure 4 is positive control. K2415029, K2514013, K2514037 are three heat inducible RNA-based thermosensors with different intensity. The final normalized fluorescence was calculated as follows: normalized fluorescence = [(GFP/Abs)TS - (GFP/Abs)neg] / [(GFP/Abs)pos - (GFP/Abs)neg] ( TS = thermosensor, pos = positive control, and neg = E.coli DH5a ). As shown in the figure 4, the thermosensor K2541001 melting temperature range is 37°C to 42°C. Its intensity is between K2541029 and K2541013 .
Figure 4. Characteristics of synthetic heat inducible RNA-based thermosensors. Each set of six bars represents the activity level of a different thermosensor. The bar colors purple, cyan,green,orange,red and brown represent the temperatures 29, 31, 35, 37, 39 and 42°C, respectively. The height of the bars corresponds to the normalized fluorescence.
This year, our objective is to design a collection of RNA-based thermosensors with different melting temperatures, intensity and sensitivity. We used a combination of experimental measurements and computations of RNA secondary structures to achieve this objective. We studied a set of measured synthetic RNA-based thermosensors, finding consistency with measured results and among our experimental and computational analyses.
Figure 5. Temperature response of the heat inducible RNA-based thermosensor. The green solid line represents the activity of the RNA-based thermosensor as a function of temperature. Key quantitative features of the response such as threshold and sensitivity are emphasized.
Figure 6. Experimental measurements of the collection of heat inducible RNA-based thermosensors show a variety of responses. (A) Rows represent activity levels of different thermosensors. (B) Each set of six bars represents the activity level of a different thermosensor. The bar colors purple, cyan,green,orange,red and brown represent the temperatures 29, 31, 35, 37, 39 and 42°C, respectively. The height of the bars corresponds to the normalized fluorescence.
4. Conclusion
Our data show that efficient RNA-based thermosensors with different melting temperatures, intensity and sensitivity can be built from a single small RNA stem-loop structure masking the Shine–Dalgarno (SD) sequence, thus providing useful RNA-based toolkit for the regulation of gene expression.
Sequence and Features
- 10COMPATIBLE WITH RFC[10]
- 12COMPATIBLE WITH RFC[12]
- 21COMPATIBLE WITH RFC[21]
- 23COMPATIBLE WITH RFC[23]
- 25COMPATIBLE WITH RFC[25]
- 1000COMPATIBLE WITH RFC[1000]