Deep sequencing approaches to investigate the dynamics and evolution of interaction networks of Candida pathogens and the human host
This thesis describes the application of Next Generation Sequencing, especially RNA sequencing, on the investigation of the pathogenic yeast Candida parapsilosis. Pathogenic yeasts of the Candida clade are one of the most common hospital derived infections, often with a fatal outcome. We applied mod...
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| Tipo de documento: | tese |
| Estado: | Versão publicada |
| Data de publicação: | 2017 |
| País: | España |
| Recursos: | CBUC, CESCA |
| Repositório: | TDR. Tesis Doctorales en Red |
| OAI Identifier: | oai:www.tdx.cat:10803/663874 |
| Acesso em linha: | http://hdl.handle.net/10803/663874 |
| Access Level: | Acceso aberto |
| Palavra-chave: | Transcriptomics Pathogenic yeast Noncoding Transcriptòmica Llevat patogen No codificant 577 |
| Resumo: | This thesis describes the application of Next Generation Sequencing, especially RNA sequencing, on the investigation of the pathogenic yeast Candida parapsilosis. Pathogenic yeasts of the Candida clade are one of the most common hospital derived infections, often with a fatal outcome. We applied modern tools in RNA sequencing based transcriptomics to investigate the unknown, noncoding part of the yeasts transcriptome. The investigation led to a potential noncoding RNA with an important impact on the ability of the yeast to tolerate physiological temperatures and therefore colonize humans. Additionally, using modern transcriptomics, we developed a pipeline that classifies and quantifies allelic expression regulation with limited parental information. The pipeline is specifically designed for the analysis of nonmodel species. Lastly, in the scope of the thesis, conclusions on the pathogen responses of a human cell line were analyzed and described to evaluate its potential as model system. |
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