The function of ribonucleic acid (RNA) was long thought to be only an intermediate product in the protein synthesis machinery, but this view changed since the biological significance of the non-coding protein fraction of RNA was discovered. More recently a still evolving class of RNAs with a length above 200 nucleotides, the long non-coding RNAs (lncRNA) come into the spotlight. Up- or downregulation have been associated with human diseases, such as neurodegenerative and psychiatric diseases, cardiac diseases, diabetes as well as cancer, offering the possibility to use lncRNA as biomarker for diagnosis and prognosis.
The currently used method for detection of lncRNA in human samples (qPCR) is complicated and only a reliable method for experienced researcher with the necessary lab facilities. The expensive qPCR-arrays needs sophisticated equipment and is a time-consuming process. Additionally, the expanded landscape of RNA in the human transcriptome is outgrown of the traditional trial-and-error experimental methods. To use the potential efficiently, computational analysis and model-based experimental design before any biomolecular implementation has to be the new standard.
To exploit the full potential of lncRNA as new biomarker for several diseases, the detection method needs to be simplified. This would allow to use lncRNA as biomarker on-spot in private praxis and integrate lncRNA in the routine diagnostic of clinics.
The goal of the Proof of concept proposal LONGSENSE is to use new classes of engineered regulatory RNAs, small transcriptional activators (STARs) and toehold switches for the development of a biosensor for lncRNA. The system can be used as high throughput format for clinical research or can be adapted to be used on the spot, without the need of sophisticated equipment.
Within LONGSENSE the technology was utilized for the first time to detect lncRNAs. For this proof-of-concept study, we worked with the lncRNA PCA3, an FDA approved biomarker for prostate cancer. Using secondary structure prediction methods, we designed a scoring function that ranks all potential nucleotide hairpin sensor sequences with respect to all candidate subsequences of PCA3. With this method, sequences can rationally be selected for targeted test series in the laboratory and large time-consuming, trial-and-error screening series can be avoided. With promising candidates, we run functional tests for detection of PCA3 by lncRNA transcribed from a vector incorporated in the TX-TL system. By using different plasmid concentrations, we were able to show a promising dose-response relationship for our RNA sensor.
A useful property of RNA sensors in TX-TL systems is the option of freeze-drying and thus uncomplicated storage and transport (i.e. without cold chain). Also for our PCA3-sensor, the reaction mixture was freeze-dried and we were able to initiate a fluorescence sensor output after rehydration. For application as a point-of care test device a visually detectable output will ensure simple usage. As a first step we were able to demonstrate that the detection reaction for the lncRNA PCA3 is visible by eye.
During the course of the project, we were able to lay the foundation for further development of the technology of STARs as biosensors for lncRNA for clinical application and prepare for possible commercialization.
