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Title: High-throughput chronological lifespan screening of the fission yeast deletion library using barcode sequencing
Author: Romila, Catalina-Andreea
ISNI:       0000 0004 8508 066X
Awarding Body: UCL (University College London)
Current Institution: University College London (University of London)
Date of Award: 2019
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Ageing is associated with the development of several chronic illnesses, including cardiovascular diseases, diabetes and cancer. To understand the genetic components driving cellular ageing in higher organisms, like ourselves, we study simple eukaryotic model systems which are more accessible and easier to manipulate than higher eukaryotes. This is possible due to the remarkably conserved ageing mechanisms that occurs between species. Here, we employ fission yeast one of the simplest eukaryotic model organisms to study cellular ageing. In this work, we de- coded the fission yeast deletion collection using our in-house developed pipeline, developed an improved version of Bar-seq along with a custom-developed analysis pipeline, determined a method for high-quality RNA extraction and RNA-seq from long-term quiescent yeast cells, and finally, performed a high-throughput Bar-seq screen to profile the chronological lifespan of our decoded strains. We describe bar- code decoding of 94% of the gene deletions; validation of our Bar-seq developed method; identification of ncRNAs as elements important for the cellular quiescence maintenance; Bar-seq screening of the competitively grown decoded strains which identified several long-lived and short-lived mutants following glucose-starvation and cellular culture re-growth; and also, validation of the top hits using isogenic cell cultures revealing eight novel gene deletions important for the early life maintenance, as well as ten novel gene deletion mutants with pro-ageing effects. Overall, in addition to providing rich datasets, we describe several high-throughput methods that can be used for future genome-wide studies, whereby the complementarity of genomics and transcriptomics can be coupled together to further advance our understanding of the genetic factors underpinning cellular ageing in humans.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available