Team:UChile Biotec/Overview








Overview

Last year during BiMaToX project we developed a novel biosensor based in aptazymes in order to detect a paralytic toxin called saxitoxin, produced during Harmful Algal Blooms (HAB). Tenzyme Vilu project will expand this goal to design a platform to obtain functional aptazymes for the biosensing of other marine toxins.

To achieve this goal, we look for the best way to select aptamers and Aptazymes. The well-studied SELEX method is an experimental way to obtain aptamers, but it has implications such as time, work and associated costs. That is why we consider bioinformatics as a more viable option to achieve our goal.

One of the known methods which we are already working with consists of the JAWS software, which allows the generation of Aptazymes sequences from the DNAzyme Horseradish Peroxidase Mimicking (HRP mimicking) and an aptamer for any other molecule, taking into consideration for the selection of the best sequence the free energy of a certain state and other thermodynamic parameters.

In order to validate this method, we worked with an already studied Aptazyme that binds the AMP molecule [1]. The objective is to compare the activity of the known aptazyme with others obtained by JAWS.

The validation of JAWS as a method to obtain aptamers, would allow us to proceed to the next step: generating the best aptazymes for new ligands, toxins associated with the red tide.

Finally, to complete the prototype BiMaTox Kit (see design section) in order to get closer to the biosensor that we are developing, we carried out experiments that validated the possibility of lyophilizing the entire molecular machinery, that had been previously studied [2]. For biosafety, we lyophilized an AMP aptazyme, as way to validate our method.

References

[1] Teller, C., Shimron, S., & Willner, I. (2009). Aptamer−DNAzyme hairpins for amplified biosensing. Analytical chemistry, 81(21), 9114-9119.

[2] Pardee, K., Green, A. A., Ferrante, T., Cameron, D. E., DaleyKeyser, A., Yin, P., & Collins, J. J. (2014). Paper-based synthetic gene networks. Cell, 159(4), 940-954.