Curion, Fabiola (2016) Network Inference on RNA-Seq Data from Mammalian Retina. [Tesi di dottorato]

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Tipologia del documento: Tesi di dottorato
Lingua: English
Titolo: Network Inference on RNA-Seq Data from Mammalian Retina
Autori:
AutoreEmail
Curion, Fabiolafabiola.curion@gmail.com
Data: 31 Marzo 2016
Numero di pagine: 156
Istituzione: Università degli Studi di Napoli Federico II
Dipartimento: Medicina Molecolare e Biotecnologie Mediche
Scuola di dottorato: Biotecnologie
Dottorato: Biologia computazionale e bioinformatica
Ciclo di dottorato: 28
Coordinatore del Corso di dottorato:
nomeemail
Cocozza, Sergiosergio.cocozza@unina.it
Tutor:
nomeemail
Surace, Enrico Maria[non definito]
di Bernardo, Diego[non definito]
Data: 31 Marzo 2016
Numero di pagine: 156
Parole chiave: Retina, RNA-Seq, Artificial Transcription Factors, Network Inference
Settori scientifico-disciplinari del MIUR: Area 05 - Scienze biologiche > BIO/11 - Biologia molecolare
Area 06 - Scienze mediche > MED/03 - Genetica medica
Depositato il: 13 Apr 2016 20:50
Ultima modifica: 31 Ott 2016 09:26
URI: http://www.fedoa.unina.it/id/eprint/10966

Abstract

The mammalian retina is an intricate network of cells communicating and cooperating to convey light stimuli to the visual cortex of the brain. Moreover, it is the most accessible part of the Central Nervous System and hence a valuable model to study the CNS. A hierarchical scheme of transcription factors (TF) that determine each cells’ identity is regularly expressed following a precise timeline, since the early stages of development of the embryo. The interplay of those TF controls univocal flows of transcription and genetic programs which direct cells’ identities, maintain their specific expression patterns and guarantee the survival of each cell type. Despite the large interest of the scientific community on retina, and the large variety of databases collecting gene expression profiles from multiple species, very few Next Generation Sequencing experiments on this tissue were collected in public available data. We generated a co-expression net work using porcine whole retina RNA-seq data produced in our laboratory to characterise the retina specific Gene Regulatory Networks, which are disrupted in retinal diseases. Our inferred network shows good performance and reliability of the predicted connections. We characterised retina-specific processes by comparing our dataset with a RNA-seq study on 10 porcine tissues. Furthermore, we characterised the genome-wide functional effects of a synthetic transcription factor composed of a DNA-binding domain targeted to a 20 bp of Rhodopsin (RHO) cis-regulatory sequence, which induced RHO specific transcriptional silencing upon adeno-associated viral (AAV) vector delivery. Finally, we assessed the rod-specific repression of RHO after FACS-sorting photoreceptors interfered with our construct, and confirmed this results on single cells by qPCR.

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