Targeted Next Generation Sequencing strategy to detect gene doping

Code: 23GD04KL

The abuse of gene transfer methods in sports is a real and growing concern, the more successes one realizes in gene therapy treatment of hereditary diseases, the more likely the abuse in healthy individuals becomes to improve sports performance. Although the administration manner remains unclear, one can speculate that the most likely method would be a local injection of the transgene(s). Published PCR based methods target cDNA sequences of these transgenes, more specifically the exon-exon junctions. In collaboration with the group of Haisma et al. (University of Groningen, Netherlands), we successfully transferred their published targeted NGS approach to detect exon-exon junctions of 5 potential doping genes to the UZ-Ghent NGS core. However, these proof-of-concept (POC) studies were performed for only a limited number of potential doping genes and with high quality DNA extracted from cell lines. The quality of the input material is similar as the quality from DNA extracted from a fresh blood sample. We would like to optimize this protocol on cfDNA extracted from urine samples so noninvasive samples can be obtained from an athlete. In the POC study, probes were designed to capture the exon-exon boundaries of all transcripts of EPO, GH1, GH2 and IGF. We will now expand the target region with additional potential doping genes, including the coding regions of these genes to evaluate other relevant gene doping marks. A genomic fingerprint will be added so samples can be assigned to correct individual. This method ensures the accurate identification and tracking of biological samples in various fields, including genetics, forensics, and clinical research. Single nucleotide Polymorphisms (SNPs) are genetic variations that involve a single nucleotide change at a specific position in the DNA sequence. They are commonly used as genetic markers due to their abundance and stability. These SNPs should exhibit high variability among individuals or populations to differentiate samples accurately. The unique genetic profiles generated by SNP genotyping allow for accurate sample identification, preventing errors and ensuring the integrity of data and results. In a second work package we will reinvent the data analysis part. Manual counting of the reads (as published by De Boer et al. 2019) is not feasible, especially not if the number of samples to be tested increase. We will further optimize the data analysis to an (semi) automated pipeline for read counting and program an additional module to take non-human traces and gDNA variants into account. Overall, these adjustments to the initial proof of concept study will prepare this analysis to be implemented as a gene doping test.