SYSTEMS BIOLOGY AND TRANSCRIPTOMIC ANALYSIS TO STUDY THE IMMUNE RESPONSE TO INFECTIONS AND VACCINES

Given the complexity of living systems, and the difficulty of measuring and interpreting data from these systems, biomedical science has been adopting a reductionist approach over the years. However, the rapid technological advances and the progress in molecular biology and computation are changing this establishment. Transcriptomics is one of the technologies that has revolutionized the way we study the response of organisms to various situations, such as infections, vaccines and cancer. By measuring the changes in the gene expression, we can capture important information about the pathophysiology of diseases, mechanism of action of vaccines, among other biological processes. By integrating transcriptomic data with cytokines, for instance, we uncovered a systemic recall immune response to lung pneumococcal infection, describing the main factors driving this process. By combining systems biology and Machine Learning algorithms, the biological signatures of three different cohorts studying the recombinant vesicular stomatitis virus vaccine against Ebola were compared. We showed that different methods can capture distinct signatures, especially when the molecular perturbation is less evident. The use of Feature Selection and Machine learning algorithms can help us to focus on a gene level characterization, which is an important feature in the precision medicine era. Finally, in this work transcriptomics has also contributed to characterize the response to a mucosal immunization with a recombinant bacteria expressing the CTH522, a Chlamydia trachomatis antigen. We have shown that the intravaginal priming with the recombinant vector modulated the systemic response to the antigen, using a model of splenocytes in vitro stimulated after different immunization schedules. Rather than focus on a specific vaccine or infection, the aim of this thesis was to explore the range of tools available for the analysis of transcriptomics data in a systems biology perspective. Using data from different studies, involving both experimental models and clinical studies, the thesis offered a great opportunity to approach different themes and leverage different tools to deal with the challenges of extracting meaningful biological information from large data sets