The transcriptomics of running: Statistical analysis of gene expression and blood cell counts in long-distance races
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Tutor / directorPerera Lluna, Alexandre
Document typeBachelor thesis
Rights accessRestricted access - author's decision
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The study of the health effects of sport has been studied over the years from many perspectives. Microarray technology allows studies to be carried out from the perspective of gene expression. However, existing studies show a large number of genes and consequently altered biological pathways after intense exercise. This large number of genes makes it difficult to interpret the results. At the same time, it has been observed that after an intense exercise session significant variations in the levels of most blood cells occur. Taking these two factors into account, the following question arise. Is there a relationship between variation in gene expression and variation in blood cell counts? The main objective of this study is to explain the variation in total blood differential expression in response to running intervention by while controlling the variation of the values of the blood cell count. For this purpose, genetic expression data (HuGene2.0st microarrays), complete blood count data and other biological variables (sex, age, performance) are collected from runners before and immediately after the competition “Volta a la Cerdanya 2013”. Runners are stratified by three distance categories; A) 14 km (N=30, Age= 35.07 ± 9.76), B) 35 km (N=40, Age=35.91 ± 7.65) and C) 55 km (N=24, Age=36.58 ± 4.64). A linear regression model was fitted to each TC variation in expression data. The level count of erythrocytes, neutrophils, lymphocytes, monocytes, eosinophils and platelets have been used as explanatory variables. Finally, gene enrichment analysis has been computed over the Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) Pathway. 91 genes are reported as statistically significant (independent of the cell counts), 774 related to neutrophils, 11 related to lymphocytes, 69 related to monocytes, and 28 related to eosinophils. 138 GO therms and 16 KEGG pathways are reported as statistically significant (independent of the cell counts), 257 GO therms and 16 KEGG pathways related to neutrophils, 22 GO therms related to lymphocytes, 154 GO therms and 12 KEGG pathways related to monocytes, and 1 GO therm related to eosinophils.
DegreeGRAU EN ENGINYERIA EN TECNOLOGIES INDUSTRIALS (Pla 2010)