Study of strategies for genetic variant discrimination and detection by optosensing

Resumen

Current medicine is moving towards a more personalized approach based on the patients' molecular diagnosis through the study of specific biomarkers. Diagnosis, prognosis and therapy selection, applying this molecular principle, rely on identifying specific variations in the human genome, such as single nucleotide variations (SNV). A wide range of technologies is available to detect these biomarkers. However, many of the employed methods have limitations such as high cost, complexity, long analysis times, or requiring specialized personnel and equipment, making their massive incorporation in most healthcare systems impossible. Therefore, there is a need to research and develop analytical solutions that provide information on genetic variants that can be implemented in different health scenarios with competitive and economically feasible performances. The main objective of this thesis has been to develop innovative strategies to solve the challenge of multiple detection of genetic variants that are found in a minority amount in patient samples, covering the demands associated with the clinical setting. Research tasks focused on the combination of allelic discrimination reactions with selective DNA amplification and the development of versatile optical detection systems. In order to meet the wide range of needs, in the first chapter, the analytical performances of the polymerase chain reaction (PCR) were improved by incorporating a thermocycling step and a blocking agent to amplify selectively minority variants that were monitored by real-time fluorescence. In the second chapter, allelic discrimination was achieved by combining oligonucleotide ligation with recombinase polymerase amplification (RPA), which operates at a constant temperature, allowing point-of-care (POC) detection. SNV identification was carried out by hybridization in microarray format, using Blu-Ray technology as the assay platform and detector. RPA was integrated with allele-specific hybridization chain reaction (AS-HCR), in an array format to genotype SNV from genomic DNA on a chip in the third chapter. The reading of the results was performed using a smartphone. In the last chapter, a new bioluminescent reagent was synthesized. It was applied to real-time and endpoint DNA biomarker monitoring based on bioluminescence resonance energy transfer (BRET), eliminating the need for an excitation source. All the strategies allowed specific recognition of the target variant, even in samples containing as few as 20 copies of target genomic DNA. Sensitive (limit of detection 0.5% variant/total), reproducible (relative standard deviation < 19%), simple (3 steps or less), fast (short times of 30-200 min) results were achieved, allowing simultaneous analysis of several genes. As proof of concept, these strategies were applied to detect and identify biomarkers associated with colorectal cancer and cardiological diseases in clinical samples. The results were validated by comparison with reference methods such as NGS and PCR, proving that the technical requirements and cost-effectiveness were improved. In conclusion, the developed research made it possible to develop genotyping tools with competitive analytical properties and versatile, applicable to different healthcare scenarios, from hospitals to limited-resource environments. These results are promising since they respond to the demand for alternative technologies for personalized molecular diagnostics.