Introduction

Synthetic biology is currently one of the most rapidly developing fields in science. Recent advancements, particularly in genetic engineering, allow us to tackle major societal challenges. Gene engineering is even being applied to humans, with the use of gene therapy. Gene therapy is an experimental technique that uses genes to treat or prevent among others severe genetic disorders. However, major concerns are raised about the misuse of gene editing techniques, particularly for human enhancement. Gene doping, the misuse of gene therapy to enhance athletes’ performances, is one example. Thus to promote responsible use of synthetic biology and to help eliminate gene doping from sport, we developed a complete gene doping detection method: ADOPE, the Advanced Detection of Performance Enhancement.

ADOPE

We designed ADOPE to detect gene doping in blood samples by targeting the most striking difference between the natural and gene doping DNA, namely the exon-exon junctions that only exist in doping DNA (Beiter et al., 2011). ADOPE consists of 4 main steps: sample preparation, prescreening, library preparation, and sequencing.

Figure 2. A flow diagram of our detection method ADOPE. With the four steps sample preparation, prescreening, library preparation and sequencing.

1. Sample preparation

Blood samples are commonly taken from athletes during regular doping tests. We used these blood samples to extract DNA from serum or the buffycoat (serum and white blood cells) required for testing (Ni et al., 2011). We built an extensive gene doping kinetics model to predict the amount of gene doping fragments in blood over time based an input concentration of gene doping vectors and an injection frequency. We used this model to determine the appropriate time window for gene doping testing based on the measured sensitivity of DNA extraction.

2. Prescreening

3.Targeted library preparation

Positive prescreened samples proceed to our novel rapid targeted next generation sequencing. Targeted library preparation relies on our innovative fusion protein consisting of a Tn5 transposase and a dxCas9. The fusion of these two proteins resulted in a target specific transposition. As a proof of concept, we showed that our fusion protein is target specific with an in vitro targeted integration assay, that was verified by visualising the amplified integration products with gel electrophoresis. Once we established the functionality and optimal conditions, we implemented the fusion protein in the established rapid next generation sequencing library preparation protocol from Oxford Nanopore Technologies (ONT). We replaced the original transposase, responsible for random integration of the sequencing adaptors, by our novel fusion protein. In our case, a specifically designed sgRNA will guide the fusion protein to the exon-exon junction target site and prepare only gene doping DNA for sequencing. Additionally, we developed a sgRNA model which identified the optimal exon-exon target site by searching for the least sgRNA’s required to cover all possible variation of the target sites due to synonymous mutations of EPO coding sequence. A sgRNA array of the resulting 12 sgRNAs can be used in a single library preparation, utilizing the multiplexing capability of dxCas9 (Cong et al., 2013). As a result, we could simultaneously test for the gene doping variants, improving the efficiency of our method.

4. Targeted sequencing

After library preparation, we sequence the samples using a MinION, portable real-time sequencing device, from ONT. Only targeted sequences with adapters are translocated through the pores of ONT’s via the nanopore motor protein. The remaining untagged DNA will not be sequenced. By making a simple enzyme substitution, we transformed ONT’s established next generation sequencing platform into a targeted next generation sequencing platform.

Figure 6. Process flow diagram of our Targeted Next Generation Sequencing platform.

We processed the data obtained from ONT MinION sequencing runs with our tailor-made data analysis software tool. The software consists of an algorithm that aligns all the files generated by our sequencing run sequences with our pre-existing database, containing expected gene doping sequences. Based on the alignment score, the sequences are classified into gene doping DNA or non-gene doping DNA, eliminating any false positives that might have been sequenced. The software strengthens the robustness and reliability of our method, allowing us to determine whether gene doping DNA was present in the athlete’s blood sample. To go a step further, our algorithm has the capacity of expanding the database as it detects new gene doping sequences, thereby simultaneously evolving with gene doping.

Impact

We believe that by establishing and integrating a novel detection method into valid testing systems, we will discourage athletes from using high-risk gene doping technologies. Not only has the Dutch Doping Authority, the Delft Sports Engineering Institute and Dutch Trotting and Flat Racing Association (NDR) shown interest in our technology, but so has various other non gene doping related stakeholders. Sanquin, the Dutch blood bank, and the Dutch Research Department for Food Safety (RIKILT) at Wageningen University also emphasized the potential and value of our targeted sequencing technology. These stakeholders’ interests highlight the possible applications of our targeted sequencing method in other fields, such as cancer detection, non-invasive prenatal screening, food safety regulations, and even strain identification.

Figure 8. Future applications of our targeted next generation sequencing.

References

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