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Phenotypic Implications of Genetic Interaction Networks in Saccharomyces Cerevisiae Bede Phillip Busby

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Phenotypic Implications of Genetic Interaction Networks in Saccharomyces Cerevisiae Bede Phillip Busby Phenotypic implications of genetic interaction networks in Saccharomyces cerevisiae By Bede Phillip Busby A thesis submitted to the Victoria University of Wellington in fulfilment of the requirements for the degree of Doctor of Philosophy In Cell and Molecular Bioscience Victoria University of Wellington 2015 Abstract Gene functions were studied as extensive networks comprising synergistic functional interactions between overlapping pairs of genes. Elucidation of such networks related to drug phenotypes (statins in this thesis) provides additional information to classical genetics as to what genes and metabolic pathways might be involved in phenotypes and, importantly, where side-effects might arise in drug effects. A key question is whether there are genetic interaction networks that vary with individuals and with phenotypes. To answer this question a panel of twenty-six fully sequenced yeast strains from the Saccharomyces Genome Resequencing Project (SGRP; Sanger Institute) was screened for statin resistance to approximate a model for individuals in a human population. Three strains (Y55, SK1 and YPS606) were shown to be 500% more resistant to atorvastatin than the S288C laboratory control strain and were selected for further analysis. Synthetic genetic array analysis (SGA) and chemical genetic profiling were utilised to elucidate genetic interaction networks in the four different strains. SGA analysis depends on the availability of a genome-wide deletion mutant array (DMA) which already exists for S288C and the current studies constructed equivalent Y55-, SK1-, and YS606-strain specific deletion mutant arrays called here “ssDMA’s”. Creating the new ssDMAs involved six back-crossings (1-1/26) of Y55, SK1 and YPS606 with S288C to place the genome-wide deletion mutations of S288C on the genetic background of the strains using appropriate selection markers between each backcrossing. The four DMAs were then subjected to chemical genetic profiling with two statin drugs and also subjected to SGA analysis utilising five query genes chosen for their involvement with ii the cellular response to statins. The query genes HMG1, HMG2, ARV1, BTS1 and OPI3 were constructed to be strain specific and generated a total 25 genetic interaction networks. The chemical genetic profiles in the ssDMAs identified off-target interactions genes associated with the resistance phenotype in Y55, SK1, and YPS606 that were not observed to show genetic interactions in the more sensitive S288c strain. There was little conservation of the genetic interaction networks elicited by the specific query genes between the strains with the exception of OPI3. There was, however, conservation of fundamental cellular processes, as might be expected, but the genes encoding these processes in the SGAs of the different strains were for the most part different. Therefore, we conclude that the genetic interaction networks concerning statins are different between individuals. iii Dedicated to Phil Busby (29th December 1947 – 8th March 2013) iv Acknowledgements I would like to thank my supervisor Professor Paul Atkinson for giving me the opportunity to further my studies to this level; without your encouragement and guidance I would have never made it this far. Your continual insight, forward thinking and overall passion for every aspect of science has been and will always be an inspiration to me. I am grateful for your willingness to let me do my own thing and allowing me to think for myself. Over the years you have gone well beyond the requirements of supervision, the many hours reading over work, preparing for talks and generally being an all-round great guy. To my secondary supervisor Dr Paul Teesdale-Spittle, your scientific knowledge, diplomatic way of thinking and your words of wisdom were always appreciated. To Dr David Maass, your obscure way of thinking reminds me of an unorganised genetic interaction network, there is an answer in there somewhere but you or I have no idea exactly what it is. Your immense knowledge of molecular biology and pretty much any little aspect of science has been appreciated in many ways over the years. I have always admired your ability to turn up at any seminar, pick apart any speakers talk and ask a multitude of random and in depth questions and in the process confusing both the audience and the speaker. All that aside, your overall knowledge and enthusiasm is an inspiration to everyone. I would like to thank other faculty members who helped me along the way and give me insight and advice on my project especially, Professor John Miller and Dr Andrew Munkacsi, your advice and informal chats were always welcomed. v Thank you to all the members of the chemical genetics lab. To Peter, we have come a long way since our first experience melting agar together; we then worked out the best way to utilise our knowledge of yeast biology was to make beer, and for all your continual input into my project. To Katie for keeping Peter in line and for your generosity and friendship. To James, although it was not always smooth sailing with us, over the years you have been an inspiration to me, your pure enthusiasm, passion for science and your willingness to pass it on to anyone who needed it (whether they wanted it or not); you have also been a great friend over the years. To Christina, oh the fun times we had and your love for absinthe and vodka Nostrovia! To Yee and Ploi, you show people how to work like slaves in the most entertaining way possible. To Darryl, Kai pai, your relaxed attitude towards life is something we should all strive for. To the remaining humans of the chemical genetics lab: Namal, such a small boy with a big heart; Aunty Seeseei, thanks for helping me along and proof reading and making the figures for my thesis; without you, all my figures and graphs would look terrible; Natalie, thanks for all the help you have given me and making me laugh; Richard, the world is your playground. To Dini, thanks with all your help with the data analysis and for being the calmest person on the planet. Thanks to everyone else who has been in the chemical genetics lab over the years and had to endure working with me. I would like to acknowledge Professor Charlie Boone (University of Toronto) for kindly giving us the DMA. To my family, we have been down a rocky road which bought us closer than ever. Dad, you were an inspiration to me and always taught me to strive for greatness, your passion for life and hard work ethic has made a lasting impression on me and will vi never be forgotten. Mum, thank you for all your help and support over my many years of study (which has finally come to an end), without your support, life would not be the same. To my Grandma, your help and prayers are irreplaceable, you have always been encouraging and supportive of me. To my Aunty Colleen, your constant support and many suggestions to hurry up and get a job has finally paid off. To all of Dad’s family your help and support during Dad’s illness was indispensable, without it I may not have made it this far; for that I thank you. Finally to Ingrid, my Wife, my soulmate and my rock, without your love, patience and support I would never have made it this far, for that I thank you. The wait for my perpetual studentship to end is over; now we can travel the world and buy all those houses you want. vii List of Tables Table 1 Yeast strains used in this study ....................................................................... 22 Table 2 Antibiotic media supplements ........................................................................ 26 Table 3 Plasmids used in this study ............................................................................. 27 Table 4 General PCR reaction mixture ......................................................................... 29 Table 5 Primers used .................................................................................................... 29 Table 6 Statin screening concentrations ...................................................................... 50 Table 7 Atorvastatin – summary of chemical genetic interactions ............................. 51 Table 8 GO-slim categories of atorvastatin chemical genetic interactions ................. 53 Table 9 Cerivastatin - summary of chemical genetic interactions ............................... 57 Table 10 GO-slim categories of cerivastatin chemical genetic interactions ................ 59 Table 11 ARV1 – numerical summary of genetic interactions ..................................... 74 Table 12 GO slim categories of ARV1 query gene hits ................................................ 76 Table 13 BTS1 - summary of genetic interactions ....................................................... 80 Table 14 - GO slim categories of BTS1 query gene hits ............................................... 82 Table 15 HMG1 numerical summary of genetic interactions ...................................... 85 Table 16 GO slim categories of HMG1 query gene hits ............................................... 88 Table 17 HMG2 – numerical summary of genetic interactions ................................... 90 Table 18 GO slim categories of HMG2 query gene hits ............................................... 92 Table 19 OPI3 – numerical summary of genetic interactions ...................................... 93 Table 20 GO slim categories of OPI3 query gene hits ................................................. 96 viii List of Figures Figure 1 Proportion of deaths under the age 70 years. ................................................
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