A transcriptomic approach to the discovery of novel biomarkers of blood doping and training response

Abstract

Investigating the whole transcriptome or exome is a prevailing approach that is extensively employed for the discovery phase in biomedical research. Microarray technology is an emerging tool for observing the expression levels of thousands of genes simultaneously under a particular condition and it could be exploited for the discovery of novel biomarkers by comparing gene expression patterns between different conditions (e.g., healthy to diseased cells, modifying dosage of therapy). The aim of this research is to apply the latest microarray technology to two independent cohorts in response to an acute and more chronic stimulus in an attempt to elucidate the molecular mechanisms of the phenotypic response and outcome. Specifically, to identify novel biomarkers of the response to acute exercise, exercise training and blood transfusion. In the ABT study, analysis of the whole blood transcriptome of 15 subjects revealed a modest transcriptomic signature hours after autologous blood transfusion, with fold changes not exceeding 1.5. Highly enriched pathways estrogen-dependent nuclear events downstream of ESR-membrane signalling and PIP3 activates AKT signalling were associated with changes in gene expression and cellular function. In the GeneSMART study, 47 participants underwent a 4-week supervised HIIT. Changes in exercise parameters were observed, with significant improvements in peak power, lactate threshold, and citrate synthase enzyme activity. 400 and more than 1,200 genes were differentially expressed immediately and 3hours post HIIE session (adj. p 1.1), respectively. Highly enriched pathways associated with leukocytes circulation, activation, and migration as well as with natural killer cell mediated cytotoxicity and signalling by interleukins were linked to forty-two overlapping genes between the two time points. However, no gene transcripts were differentially expressed after 4 weeks of training. Correlation analysis identified associations between gene expression patterns and changes in exercise parameters. While the studies had limitations in terms of sample size and potential biases, they contribute to the growing field of personalized medicine by providing valuable insights into the potential of microarray technology in identifying molecular markers. Future investigations should aim for larger sample sizes, diverse populations, and integration of other omics technologies to validate and expand upon these findings. Overall, this study underscores the potential of gene expression profiling and microarray technology in advancing our understanding of individual responses to interventions, paving the way for personalized approaches in healthcare and optimizing outcomes for individuals.

Date of Award	Jun 2023
Original language	English
Awarding Institution	University of Brighton
Supervisor	Yannis Pitsiladis (Supervisor), Guan Wang (Supervisor) & Nir Eynon (Supervisor)