Difference between revisions of "Team:Jilin China/Design"

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This year, our project aims to design a thermosensor toolkit, which could sense different temperature and hold different intensity, sensitivity. After three versions of the upgrade, our toolkit consist of four types of thermosensors, which areheat-inducible, heat-repressible, cold-inducible and cold-repressible RNA-based thermosensors. We name this as SynRT toolkit, Synthetic RNA-based thermosensors, which principle is similar to natural RNA thermosensors. Naturally occurring RNA thermomsensors exhibit complex secondary structures which are believed to undergo a series of gradual structural changes in response to temperature shifts. However, they lack standardization and only respond to temperatures in a narrow range，which impends application process[1]. Therefore, we focus on more rational and engineering design after understanding the principles of natural RNA thermosensors response.

RNA-based thermosensors mediate the temperature-controlled access of the ribosome to the SD sequence, because of the SD sequence is buried in a stem-loop structure, which is melting as temperatures rising[2].Stem-loop intramolecular base pairing is a pattern that can occur in single-stranded DNA or, more commonly, in RNA. A base pair (bp) is a unit consisting of two nucleobases bound to each other by hydrogen bonds, such as A-U or G-C.Based on natural RNA thermosensors’ stem-loop sequence, we would like to modify the stem-loop region in order to amplify our tool kits with different melting temperature. With the increasing of base pairing or hydrogen bonds in the stem-loop structure, the free energy required for conformation transitions would be higher. According to this, we could know qualitatively that increasing base pairing or GC content could increase the melting temperature, which means the temperature for unfolded mRNA moleculars occupying the half of mRNA moleculars. But we need further quantitative parameters of thermosensors, such like the detailed free energy of conformation transition, before our wet experiment, so we apply mfold web server (Mfold) to help us engineer our designs. Mfold describes a number of closely related software applications available on the World Wide Web for the prediction of the secondary structure of single stranded nucleic acids[3]. RNA secondary structure is predicted by energy minimization using nearest neighbor energy parameters. After prediction and selection, desired RNA-based thermosensors have been obtained and then they have been measured in E.coil.

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Heat-inducible RNA-based thermosensors

1.What is heat-inducible RNA-based thermosensors?

Natural heat-inducible RNA-based thermosensors form a complex structure in the 5’untranslated region of mRNAs[4], which modulate the expression of upstream genes. There is evidence suggesting that heat-inducible RNA-based thermosensors act in an RNA-only manner, without the aid of additional factors[5]. Increasing the temperature gradually shifts the equilibrium between the closed and open conformations towards the open structure in a zipper-like manner, thereby increasing the efficiency of translation initiation. At high temperatures, RBS could be accessed to ribosome, and translation proceeds efficiently. At low temperatures, mRNA folds in such a manner so as to prevent ribosome access and inhibit translation[6].

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Nevertheless, natural heat-inducible RNA-based thermosensors have a relatively complicated secondary structure with multiple stems, loops, and bulges, which bring difficulty to engineering toolkit. So our project focuses on designing simpler heat-inducible RNA-based thermosensors with only a single stem-loop structure where SD sequence is buried in stem.

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2. How to design it?

The temperature response of these thermosensors was designed on the basis of the melting temperature of the minimum free energy structure. The 5’-UTR was designed on the basis of minimal sequence. The 5’-UTR consists of anti-SD sequence(ASD sequence), loop sequence, consensus SD sequence (5’-AAGGAG-3’) and 8-nt spacer. The different synthetic 5’-UTR constructs differ in loop size and/or in the extent of complementarity between ASD and SD. For the estimate of thermosensors activity in E.coil, We select a constitutive Anderson promoter J23104 as an appropriate promoter. We apply superfolder Green Fluorescent Protein (sfGFP) as a reporter. Thus, the efficiency of thermosensors can be quickly assessed. To optimize the thermosensors for the melting temperature, three structural parameters come into consideration: stem length, loop size and the presence of mismatches or bulges in the stem. Prediction of the secondary structure formation and calculation of their free energies was performed using the algorithms of the Mfold web server.

Loop size can moderate thermosensors melting temperature to suitable temperature. In our design, three kinds of loop are chosen: AAUAA, AAAUAUAAA, AAAAUAUAUAUAAAA .

Furthermore, we change base composition in ASD sequence in order to make mismatch or bulges in stem which can decrease the melting temperature.

Finally, we get a series of heat-inducible RNA-based theromsensors sequence.

Additionally, these theromsensors are predicted by computational methods (mFOLD) to identify optimum thermosensors structures and adjust the switch properties to the desired temperature range.

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Heat-repressible RNA thermosensor

Most naturally-occurring RNA thermosensors are heat-inducible and they perform function by sequestering the ribosome binding site (RBS) in a stem-loop structure at low temperatures and exposing the RBS upon stem-loop destabilization at high temperatures. In order to acquire more kinds of engineered thermosensors and broaden our toolkit application, we designed the heat-repressible thermosensor based on RNase E which also follow the zipper-like manner[7].

1. What is RNase E?

RNase E is an endoribonuclease, with a preference for regions of single-stranded RNA[8]. This allows for targeted degradation of RNA in its unfolded form, which occurs at higher temperatures. What is more, RNase E is native to E.coli, alleviating the need for the expression of a heterologous protein. Finally, both RNase E and its homologue RNase G are common in E.coli and other organism[9]. So our heat-repressible thermosensor can be applied to a variety of chassis.

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2. How heat-repressible RNA thermosensor work?

Each heat-repressible RNA thermosensor sequence was inserted between downstream of the transcription start site and upstream of the SD sequence. At high temperatures, the RNase E cleavage site (RC) is exposed, thus mRNA was cleaved by RNase E, leading to low expression. At low temperatures, the RC binds to the anti-RNase E cleavage site (ARC) and forms a stable stem-loop structure. This structure sequesters the RC, leading to high expression.

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3. How to design heat-repressible RNA thermosensors?

In order to design heat-repressible RNA-based thermosensores with different melting temperatures, intensity and sensitivity, we change the ARC sequence because the RC sequence is conserved. Three structural parameters come into consideration: stem length, loop size and mismatches or bulges in the stem.

Stem length is determined by ARC sequence. Increasing stem length can optimize heat-repressible RNA-based thermosensors to higher melting temperature, while decreasing stem length has the opposite effect. Each heat-repressible RNA-based thermosensors have common RBS. Upstream of the RBS sequence is RC sequence (UCUUCC). We also change ARC sequence to have different stem length. Several types of loop size (AAUAA, AAAUAA, AAAAUAUAAA, AAAAAAUAUAAA, AAAAAUAUAUAUAAAA) are chosen to moderate thermosensors to desired melting temperature. Mismatch or bulges in stem loop can decrease the melting temperature so we also change the base composition in ARC to make mismatch or bulges in ARC.

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Heat-repressible RNA-based thermosensors structures and melting temperature were estimated by the Mfold Web Server. Based on results, we identify optimum thermosensors structures and select desired RNA thermosensors.

Cold-repressible RNA-based thermosensor

In order to extend temperature sensing range of thermosensors, we further design cold-repressible RNA thermosensor which can make response in the lower temperature range. Its design principle also follows the zipper-manner based on RNase III, an enzyme native to E.coli . RNase III cleaves double-strained RNA (dsRNA) specifically[10].

1. What is RNase III?

In bacterial cells, RNase III cleavage of dsRNA is a key step in the maturation and degradation of coding and noncoding RNAs, and can regulate gene expression by controlling RNA stability and translational efficiency. The processing reactivities of RNase III substrates in E.coli are determined in part by the sequence content of two discrete double-helical elements, termed the distal box (db) and proximal box (pb). The db and pb function as the positive recognition determinant. The pb is also a site of catalytic anti-determinant action. A db+pb set is sufficient to specify a cleavage site[10].

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2. How do Cold-repressible RNA-based thermosensors work?

Based on it, we utilized a minimal RNase III cleavage site in E. coli as our cold-repressible RNA-based thermosensors. Each thermosensor part was inserted between downstream of the transcription start site and upstream of the RBS. At low temperatures, the stem-loop structure formed, allowing RNase III to cleave the double stranded mRNA, resulting the mRNA degradation, and the gene expression turns ‘off’. At high temperatures, the stem-loop structure is destabilized. RNase III can’t recognize the single stranded mRNA so the ribosome can access the RBS and gene expression turns ‘on’ .

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3. How do we design Cold-repressible RNA thermosensor?

We keep the db and pb sequence conserved because it is necessary for RNase III to recognize and cleave mRNA. And we change their adjacent base pairs to increase or decrease the stem length to design cold-repressible RNA-based thermosensors with different melting temperatures, intensity and sensitivity. Moreover, changing adjacent base pairs may also influence RNase III catalytic efficiency.

Considering the RNase III catalytic efficiency, stem length is from to 11-base to 13-base. And two different loop sequences are chosen: GAGA, GCAA.

Thermosensors structures and parameters are also estimated by the Mfold Web Server to identify optimum thermosensors structures and adjust the switch properties to the desired temperature range.

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Cold-inducible RNA Thermosensor

Besides cold-repressible RNA-based thermosensors，We design a series of cold-inducible RNA thermosensors to completely meet users’ need, which design is based on cspA (cold shock protein A) mRNA 5’UTR.

1. What is the CspA ?

In nature, as temperature shifting, E.coli enters the cold-acclimation phase and a set of cold-shock protein (Csp) is transiently expressed. CspA represent csp family, which has been quite extensively studied for the mechanism of its cold response[11]. The expression of cspA is regulated by temperature. CspA is hardly detectable at 37 ℃ , but its expression is significantly enhanced after cold shock, when its level reaches 2% of the total proteins[12].

2. How does it response to temperature？

The cspA mRNA is key for cspA temperature sensing, adopting functionally distinct structures at different temperatures, even without the aid of trans-acting factors. Unlike other RNA-based thermosensors, these structural rearrangements do not result from melting of stem-loop structures. CspA mRNA has different conformation at 10℃ and 37℃. The 10℃ conformation of cspA mRNA can make translation proceeds efficiently. Conversely, 37℃ conformation limit translational efficiency[13] .

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The structural rearrangements is due to pseudoknot which is formed by long-distance base pairing [14]. At low temperatures, mRNA contains pseudoknot structure resulting in translating initiation region (TIR) to a conformation that facilitates ribosome recruitment. But at high temperatures, because of its thermodynamic instability, pseudoknot is unfolded so mRNA rearranges conformation which TIR is buried in double-stranded RNA, limiting translation efficiency. Furthermore, the 37℃ conformation of cspA mRNA contain various of high sensitivity RNase recognize site, such as RNase V1, RNase V2, which makes mRNA easier to be degraded, leading to further translation inhibition[15]. In consequence, the formation of RNA pseudoknot is vitally crucial to cspA mRNA conformation transformation, and its sensitivity to temperature gives cspA mRNA different translating capacity at different temperature.

3. How to design?

Based on pseudoknot structure, we design a series of cold-inducible RNA thermosensors with different temperature sensing range and different sensitivity. Several structural parameters come into consideration to optimize cold-inducible RNA thermosensors: base pairing, base pair position, GC content in pseudoknot region. Increasing base pairing can raise the melting temperature of pseudoknot, thus enlarging the temperature range of open conformation. We also add the same base pair at the different position of pseudoknot region. Increasing GC content can also raise the melting temperature of pseudoknot. What is more, we delete the cold box which is at the upstream of cspA 5’UTR. Cold box is a conservative region where CspA itself interferes with pseudoknot formation to repress CspA expression at the end of the cold acclimation phase.

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