Team:Jilin China/Background2

BACKGROUND
Thermosensor


Promoters Principles Engineering Toolkit

Toolkits Design

  • RNA-based thermosensors mediate more effective feed-forward controls

    What’s the feed-forward control? In engineering, it describes the disturbances are measured and accounted for before they have time to affect the system[1] (figure 1). Concerning intracellular temperature sensing, RNA-based thermosensors can not only respond to physiological temperature in direct and rapid way before temperature-induced damages occurring, but also reduce the time lag of transcriptional thermoregulation. Therefore, RNA-based thermoregulation performs in a more immediate and effective manner[2].

  • How do RNA-based thermosensors work?

    The translation rate of mRNAs depends on many biochemical factors. The most important examples of them are mRNA stability and ribosome-mRNA interaction.

    mRNAs are undergoing degradation induced by RNA ribonucleases while being synthesized at the same time. Therefore, the translation rate is determined by the mRNA concentration when it gets equilibrium. Additionally, the translation of a mRNA starts form its translation initiation region (TIR), which contains RBS within Sine-Dalgarno sequence (SD sequence) and AUG start codon. The 30s ribosomal subunit recognize and bind to SD sequence, consequently promoting the translation initiation. Thus the ribosome accessibility also plays an important role in translation process[3].

    But how do cis-acting RNA elements influence mRNA stability and ribosome accessibility? RNA-based thermosensors utilize a common but efficient way -- conformational change[4]. The secondary or tertiary structure of mRNA such as stem-loops or pseudoknots could transform into different conformations at different temperatures[5,6]. The mRNA molecules with different conformations have different free energy. Since temperature can be described as the energy availability of mRNA molecules, there is very different probability to form the same conformation that could facilitate translation efficiently at the same temperatures (figure 2). There are two mechanisms of conformational change responding to temperature and control translation efficiency[7] (figure 3):

    1.Zipper-like mechanism

    This RNA element is in equilibrium between closed an open conformation. At low temperatures, the closed conformation hides the SD sequence by base-paring with vicinal anti-SD sequence. By contrast, stem-loop melts gradually at elevated temperature, finally resulting the full liberation of the SD sequence and start codon. This conformational change promotes the entire ribosome accessibility of RBS.

    Furthermore, the hidden region at low temperatures can also be substituted for RNase recognition site, which could mediate the variations of mRNA stability in different RNase accessibility.

    2.Switch-like mechanism

    Switch mode is also thought as two-state system. It consists of two mutually exclusive structures that depend on temperature shifts. These two structures have different mRNA stability and ribosome accessibility, thereby regulating the translation rate at different temperatures.

  • From natural to synthetic

    What I cannot create, I do not understand.
    -----Prof. Richard Feynman

    In many emerging fields of synthetic biology, it has been persistent pursuit of scientists and engineers to rationally engineer biomolecular systems for biosensing[8]. With respect to biological thermosensing, engineered thermosensors would be a huge boost in foundation and application, such as periodical drug delivery, diagnostics in vivo, temperature-induced gene expression and purification, fermentation and other large-scale processes[7]. These potential uses require the simplificity, accuracy and controlability of RNA-based thermosensors. Since natural RNA-based thermosensors hold complex structure such as ROSE thermometer in rpoH and ibpAB mRNA[9], eliciting the minimal temperature-sensing element is critical to design synthetic and standard RNA-based thermosensors.

    In 2008, an approach attempted de novo design of simple one-stem for manipulation of bacterial gene expression in response to temperature[5]. This modular work illustrated that the stability and loop size of heat-inducible RNA-based thermosensors were systematically altered and the regulatory consequences were monitored. In 2015, another group attempted to de novo design of simple heat-repressible RNA-based thermosensors, which was based on the interaction between RNase E and mRNA[9]. It modulates mRNA degradation and stability, and these RTSs allow a response in the physiological temperature range. This suggests that there are numerous ways in which temperature-responsive RNA sensors can be set up. Inspired by this, we designed and demonstrated standard heat-inducible and heat-repressible RNA-based thermosensors with different sensing temperatures, and also designed new types of cold-inducible and cold-repressible RNA-based thermosensors, which are based on existing hypothesized principles. After that we created an integrated and standard RNA-based thermosensors toolkit successfully.

  • SynRT: The collection and toolkit

    The four types of synthetic and standard RNA-based thermosensors we designed are shown as following:

    1)Heat-inducible RNA-based thermosensors
    2)Heat-repressible RNA-based thermosensors
    3)Cold-inducible RNA-based thermosensors
    4)Cold-repressible RNA-based thermosensors

    Firstly, we designed and standardized them based on free-energy method, which could broaden the temperature sensing range of these engineered thermosensors. It would Adapt to a variety of possible situations of application. Secondly, we predicted their theoretical sensing temperature thorough software and detected the practical threshold by experimenting setting temperature gradient. Thirdly we constructed evaluation model based on random forest, which we use to judge if the thermosensor is desirable or undesirable. Then we used biophysical mathematical model to analyse their abilities to sense temperature and modulate the expression intensity, such as critical temperature, maximal expression intensity and sensitivity.

    After these systematic analytical works, we created a tool box and matched search engine for potential users in numerous foundational and applied areas. We name this as SynRT toolkits, and the updated version is SynRT toolkit 3.0.

  • References

    • [1]Haugen, F. (2009). Basic Dynamics and Control. ISBN 978-82-91748-13-9
    • [2]Kemmer C, Neubauer P. Antisense RNA based down-regulation of RNaseE in E. coli[J]. Microbial Cell Factories, 2006, 5(1):38-38.
    • [3]Narberhaus F, Waldminighaus T, Chowdhury S. RNA thermometers[J]. Fems Microbiology Reviews, 2006, 30(1):3–16.
    • [4]Riesner D, Steger G, Zimmat R, et al. Temperature-gradient gel electrophoresis of nucleic acids: analysis of conformational transitions, sequence variations, and protein-nucleic acid interactions[J]. Electrophoresis, 2010, 10(5-6):377-389.
    • [5]Giuliodori A M, Di P F, Marzi S, et al. The cspA mRNA is a thermosensor that modulates translation of the cold-shock protein CspA.[J]. Molecular Cell, 2010, 37(1):21-33.
    • [6]Kortmann J, Narberhaus F. Bacterial RNA thermometers: molecular zippers and switches.[J]. Nature Reviews Microbiology, 2012, 10(4):255-65.
    • [7]Dy A J, Collins J J. Engineering Models to Scale[J]. Cell, 2016, 165(3):516-517.
    • [8]Gaubig L C, Waldminghaus T, Narberhaus F. Multiple layers of control govern expression of the Escherichia coli ibpAB heat-shock operon[J]. Microbiology, 2011, 157(1):66-76.
    • [9]Hoyneso'Connor A, Hinman K, Kirchner L, et al. De novo design of heat-repressible RNA thermosensors in E. coli[J]. Nucleic Acids Research, 2015, 43(12):6166-6179.
    • [10]Elowitz M, Lim W A. Build life to understand it[J]. Nature, 2010, 468(7326):889.