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http://hdl.handle.net/10609/138310
Title: | Análisis transcriptómico en pacientes con infección del virus SARS-CoV-2. Identificación de asociación de perfiles de riesgo/protectores frente a la enfermedad |
Author: | Duarte Herrera, Israel David |
Tutor: | Ylla Bou, Guillem |
Others: | Perez-Navarro, Antoni ![]() Gómez De Oña, Juan |
Abstract: | SARS-CoV2 is a virus of the Coronaviridae family, which causes the disease COVID-19, capable of producing severe acute respiratory syndrome. This agent is responsible for the global pandemic situation declared in the first quarter of 2020 and which is still in force, in which more than 277 million people have been infected worldwide, with more than 5 million dying as a direct cause of the disease. A series of clinical variables related to a worse prognosis of this pathology (hypertension, dyslipidemia, sedentary lifestyle, etc) and genetic variables, such as genes coding for HLA III, HLA-A, HLA-B, HLA-C, IFN, TNF¿, IL-6 and IL-8 among others, have been established so far. In this work we study the changes in the expression generated in the transcriptome of patients infected by SARS-CoV2 and who have developed different degrees of severity of the syndrome secondary to the infection. An RNA-seq analysis has been performed using several bioinformatics tools. As a result, we obtained 2042 genes differentially expressed between patients classified as severe (CV) versus those classified as critical (ICU). Thus, we found some genes that can explain different levels of severity of COVID-19, such as PGLYRP1, HDAC9 and FUT4 . There are also others with real potential for future analysis: ABCF1, ABHD16A and IER3 among others. |
Keywords: | bioinformatics COVID-19 genetics |
Document type: | info:eu-repo/semantics/masterThesis |
Issue Date: | Dec-2021 |
Publication license: | http://creativecommons.org/licenses/by-nc-nd/3.0/es/ ![]() |
Appears in Collections: | Trabajos finales de carrera, trabajos de investigación, etc. |
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File | Description | Size | Format | |
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iduartehTFM1221memoria.pdf | Memoria del TFM | 4,39 MB | Adobe PDF | ![]() View/Open |
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