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.
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Dedicated to Phil Busby
(29th December 1947 – 8th March 2013)
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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.
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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.
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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
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List of Figures
Figure 1 Proportion of deaths under the age 70 years...... 2
Figure 2 Mevalonate pathway...... 4
Figure 3 Comparative sequence analysis of sporulation genes ...... 8
Figure 4 Cystic Fibrosis interaction network...... 9
Figure 5 Synthetic lethal interactions ...... 11
Figure 6 Yeast DMA construction...... 12
Figure 7 Functional redundancy ...... 14
Figure 8 Phylogenetic tree of SGRP strains...... 20
Figure 9 Serial spot dilution assays with atorvastatin and cerivastatin ...... 38
Figure 10 Marker switching ...... 39
Figure 11 Mata mating-type specific promoter incorporation into the DMA...... 42
Figure 12 ssDMA backcross method ...... 43
Figure 13 Backcrossing strategy ...... 44
Figure 14 Statin resistant ssDMA's ...... 46
Figure 15 Atorvastatin chemical genetic interaction network ...... 52
Figure 16 Cerivastatin chemical genetic interaction network...... 58
Figure 17 ARV1 genetic interaction network ...... 75
Figure 18 BTS1- genetic interaction network ...... 81
Figure 19 HMG1 - genetic interaction network ...... 87
Figure 20 HMG2 - genetic interaction network ...... 91
Figure 21 OPI3 genetic interaction network ...... 95
Figure 22 N-linked glycosylation of dolichol pyrophosphate ...... 113
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Commonly used abbreviations bp base pair CoA Co-enzyme A CVD Cardiovascular disease DMA Deletion mutant array DMSO Dimethyl sulfoxide ER Endoplasmic reticulum FPP Farnesyl pyrophosphate gDNA Genomic DNA GET Golgi - ER trafficking GGPP Geranylgeranyl pyrophosphate GO Gene ontology HMG-CoA 3-hydroxy-3-methylglutaryl-coenzyme A NAT Nourseothricin NatR Nourseothricin resistance cassette OD Optical density ORF Open reading frame PCR Polymerase chain reaction QTL Quantitative trait loci SGA Synthetic genetic array ssDMA Strain specific DMA SSL Synthetic sick or synthetic lethal
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Table of contents
Abstract ...... ii
Acknowledgements ...... v
List of Tables ...... viii
List of Figures ...... ix
Commonly used abbreviations ...... x
Introduction ...... 1
1.1 Genetic interaction networks and phenotypes ...... 1
1.2 Cardiovascular Disease and statins ...... 2
1.2.1 Cardiovascular disease ...... 2
1.2.2 Statins...... 3
1.2.3 Mevalonate pathway ...... 4
1.2.4 HMG-CoA reductase ...... 5
1.3 Genes act in genetic networks ...... 5
1.3.1 The importance of genetic complexity in disease ...... 5
1.3.2 Defining complex traits ...... 7
1.3.3 Genetic interaction networks defining functional interactions ...... 8
1.3.4 Genome-wide Genetic Interaction Screening in S. cerevisiae ...... 9
1.3.5 Yeast Deletion Mutant Array (DMA) ...... 11
1.3.6 Generating genetic interaction networks in S. cerevisiae ...... 13
1.4 Conservation of genetic interaction networks ...... 15
1.4.1 Comparing genetic interaction networks ...... 15
1.4.2 Phenotypic variation S. cerevisiae strains ...... 16
1.4.3 Network conservation between S. cerevisiae and S. pombe ...... 16
1.4.4 Network conservation between S. cerevisiae and C. elegans ...... 17
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1.5 Saccharomyces Genome Resequencing Project (SGRP) strains...... 19
1.6 Research aims...... 21
Methods ...... 22
2.1 Yeast strains used in this study ...... 22
2.2 Media used ...... 25
Yeast Peptone Dextrose ...... 25
Synthetic complete ...... 25
Amino acid mix ...... 25
Drop out ...... 25
Enriched sporulation ...... 25
2.3 Plasmids used ...... 26
2.4 Yeast DNA extraction ...... 27
2.5 DNA Electrophoresis ...... 28
2.6 General PCR conditions ...... 28
2.7 Yeast transformation ...... 30
2.8 Synthetic genetic array ...... 31
2.8.1 Mating the wild strains with the DMA ...... 31
2.8.2 MATa/α diploid selection ...... 32
2.8.3 Sporulation ...... 32
2.8.4 MATa progeny selection ...... 32
2.8.5 Double mutant selection...... 33
2.8.6 SGA mini array analysis ...... 34
2.9 Chemicals and media for chemical genetic profiling ...... 34
2.10 Serial spot dilution assays ...... 34
2.11 Chemical genetic Screens ...... 35
2.12 Scoring synthetic lethality ...... 35
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2.13 Gene ontology ...... 36
Strain specific deletion mutant arrays (ssDMA) ...... 37
3.1 Determining statin resistant strains ...... 37
3.2 Strain Construction...... 38
3.2.1 Marker switching ...... 39
3.2.2 PCR amplification of URA3 genomic region from BY4742 ...... 40
3.2.3 his3∆0 construction ...... 40
3.3 Incorporation of the SGA reporter into the DMA ...... 41
3.4 ssDMA backcrossing ...... 42
3.5 Verification of ssDMA’s ...... 45
3.6 Discussion ...... 47
Results chemical genetic profiling ...... 49
4.1 Introduction...... 49
4.2 Chemical genetic screen optimisation ...... 50
4.3 Atorvastatin chemical genetic screen results ...... 50
4.3.1 Chemical genetic interactions atorvastatin which overlap in all DMA’s ...... 53
4.3.2 Chemical genetic interactions with atorvastatin which are unique to ssDMA’s ...... 54
4.3.3 Chemical genetic interactions atorvastatin which overlap in two ssDMA’s ...... 54
4.3.4 Gene ontology ...... 56
4.4 Cerivastatin chemical genetic screen results ...... 56
4.4.1 Chemical genetic interactions with cerivastatin which overlap in all DMA’s ...... 60
4.4.2 Chemical genetic interactions with cerivastatin which are unique to ssDMA’s ...... 60
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4.4.3 Chemical genetic interactions with cerivastatin which overlap in two ssDMA’s ...... 60
4.4.4 Gene ontology ...... 63
4.5 Discussion ...... 63
4.5.1 Genetic interactions conserved across all DMA’s ...... 63
4.5.2 Conservation of unfolded protein response genes ...... 65
4.5.3 Conservation of the chemical genetic interaction networks surrounding atorvastatin and cerivastatin ...... 65
4.5.4 Small GTPases in statin response...... 67
4.5.5 Implications of this study ...... 68
Results synthetic genetic array analysis using ssDMA’s ...... 70
5.1 Introduction...... 70
5.2 Results - Query strain construction ...... 72
5.3 Results ARV1 SGA’s ...... 73
5.3.1 Genetic interactions that are common to all the DMA’s ...... 77
5.3.2 Genetic interactions that are unique to the ssDMAs ...... 77
5.3.3 Gene ontology ...... 78
5.4 Results BTS1 SGA’s ...... 80
5.4.1 Genetic interactions common to all DMA’s ...... 83
5.4.2 Genes unique to ssDMAs ...... 83
5.4.3 Gene ontology ...... 83
5.5 Results HMG1 SGA’s ...... 85
5.5.1 Genes which interact with S288C, SK1 and Y55 ...... 88
5.5.2 Gene ontology ...... 89
5.6 Results HMG2 SGA’s ...... 89
5.6.1 Genes which interact with S288C, SK1 and Y55 ...... 92
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5.6.2 Gene ontology ...... 92
5.7 Results OPI3 SGA’s ...... 93
5.7.1 Genes which interact in the S288C, SK1 and Y55 DMAs...... 96
5.7.2 Gene ontology ...... 98
5.8 Discussion ...... 101
5.8.1 ARV1 SGA ...... 102
5.8.2 BTS1 SGA ...... 104
5.8.3 HMG1 and HMG2 SGA’s...... 107
5.8.4 OPI3 SGA ...... 114
5.8.5 Implications of this study ...... 115
Discussion ...... 117
6.1 Conceptual Limitations of the current study ...... 117
6.2 Reliability of ssDMA’s ...... 119
6.3 Chemical genetic interactions ...... 120
6.4 SGA analyses of ARV1, BTS1, HMG1, HMG2 and OPI3 ...... 121
6.5 Genetic interaction networks ...... 122
6.6 Future directions ...... 123
6.6.1 Chemical genetic and genetic interaction data ...... 123
6.6.2 Reproducibility of genetic interactions ...... 123
6.6.3 SK1 strain specific glycosylation interactions ...... 124
6.6.4 Lipid profiling of strains ...... 125
6.6.5 Inclusion of essential genes ...... 125
6.7 Conclusion ...... 126
References ...... 127
Appendix 1 Atorvastatin and Cerivastatin chemical genetic interactions ...... (CD)
Appendix 2 Genetic interactions of ARV1, BTS1, HMG1, HMG2 and OPI3 ...... (CD)
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Introduction
1.1 Genetic interaction networks and phenotypes
Phenotypes are rarely the result of single genes and are usually made up of multiple gene contributions. Human height for example is a polygenic trait comprising the additive contribution of at least 50 loci (Visscher, 2008; Visscher et al., 2010; Yang et al., 2010). Classical genetic studies have long investigated the additive role of genes in polygenic or quantitative traits (Falconer and MacKay, 1996) and the epistatic contribution of genes to phenotype (Mackay, 2014). In a departure from classical genetics, gene functions have also been implied in genetic networks comprising overlapping synthetic lethal epistatic interactions between pairs of non-essential genes (Boone et al., 2007). In the latter literature review, there is a broad but as yet unproven implication that genetic interaction networks also specify phenotypes
(Bloom et al., 2013; Bloom et al., 2015; Zuk et al., 2012). This implication gives rise to the question: are there genetic interaction networks that vary with individual phenotypes? The foregoing provides a contextual background on the choice of phenotype (statin effects) to study the fundamental question of whether differing genetic interaction networks can account for phenotypic changes between individuals.
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1.2 Cardiovascular Disease and statins
1.2.1 Cardiovascular disease Throughout the world in 2012 there were 56 million recorded deaths, of these 38 million were from non-communicable diseases. Of the 38 million non – communicable disease related deaths in 2012, cardiovascular disease represented 46%, of deaths, thus making it the leading cause of death by disease (Figure 1). This number has increased from 28 million in 2000, it is predicted that this number will increase to over
50 million by the year 2030 (WHO, 2015).
Figure 1 Proportion of deaths under the age 70 years. Reproduced from. Top, main causes of death; Bottom, types of non-communicable diseases causing death (WHO, 2015).
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One of the main risk factors of cardiovascular disease is hypercholesterolemia, characterised by increased levels of low density lipoprotein cholesterol (LDL-C )(Yusuf et al., 2001). Increased levels of LDL-C result in atherosclerotic plaque deposition in the arteries, caused by a combination of genetic and environmental factors. Familial hypercholesterolemia, an autosomal dominant disorder is among the most common inherited metabolic disorders, causing death at a young age. Mutations in the LDL receptor, apolipoprotein B, LDL receptor adaptor protein 1 and proprotein convertase subtilin/kexin 9 genes are linked to the inheritance of familial hypercholesterolemia
(Broome, 1991; Soutar and Naoumova, 2007). Premature morbidity can be lowered by early detection and treatment, namely statin therapy (Elis et al., 2011; Singh and
Bittner, 2015).
1.2.2 Statins Statins are competitive inhibitors of 3-hydroxy-3methylglutaryl-CoA (HMG-CoA) reductase, an enzyme that converts 3-hydroxy-3-methylglutaryl CoA to mevalonate in the sterol biosynthesis pathway depicted in Figure 2. Statins also inhibit the production of other important intermediates including coenzyme Q10, heme-A and isoprenylated proteins (Liao and Laufs, 2005). Therefore, it is unsurprising that in addition to their cholesterol lowering effects, statins exhibit a range of off-target effects (Laufs et al., 1998; Liao and Laufs, 2005).
Statins, the most prescribed of all human therapeutic drugs are highly successful in treatment of life-threatening atherosclerosis. However, they have side effects ranging from mild muscle pains to severe muscle myopathy, liver damage, gastro-intestinal upset and even neurological effects including memory loss and confusion. The severity
3 of the various symptoms often varies between individuals receiving treatment (Baker,
2005; Ghatak et al., 2009; Oh et al., 2007).
1.2.3 Mevalonate pathway Mevalonate biosynthesis and signalling pathways are highly conserved between yeast and humans, although structural differences exist in the end product. These features occur late in the synthesis pathway in response to specialised requirements related to yeasts utilising ergosterol as their major sterol (Bloch, 1983; Smith et al., 1996; Veen and Lang, 2005). In contrast to cholesterol in mammalian cells, the major end product from sterol synthesis in yeast is ergosterol, which differs from cholesterol in that it contains two extra double bonds and a methyl group on the side chain. However, ergosterol is synthesised, regulated, esterified and utilised in very similar processes to mammalian cells (Arora et al., 2004).
Figure 2 Mevalonate pathway. The major product of the mevalonate synthesis pathway in mammalian cells is cholesterol, however in yeast it is ergosterol. Adapted from (Brown and Goldstein, 1980; Goldstein and Brown, 1990; Paddon and Keasling, 2014).
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1.2.4 HMG-CoA reductase HMG-CoA reductase is an integral membrane protein of the endoplasmic reticulum
(ER) and is highly conserved between yeast and humans (Basson et al., 1988). In mammalian cells the protein is encoded by one gene, HMGCR, whereas in yeast it is encoded by two, HMG1 and HMG2 (Basson et al., 1986). In Saccharomyces cerevisiae a null mutation in either HMG1 or HMG2 causes a subtle growth defect, whereas cells that contain null mutations in both genes are inviable. However, the yeast double- mutant may be rescued with the insertion of the mammalian HMGCR gene (Basson et al., 1988) demonstrating the overall similarity of the yeast mevalonate pathway to the human one helping to validate the use of yeast, as a model for human disease.
1.3 Genes act in genetic networks
1.3.1 The importance of genetic complexity in disease Like most phenotypes, the majority of heritable diseases are genetically complex
(Weeks and Lathrop, 1995) and have recently been studied on the basis of genetic interaction networks (Feltus, 2014; Hartman et al., 2015; Louie et al., 2012).
Interestingly, examples of diseases classically considered to be caused by single gene mutations have recently been shown to be more genetically complex than originally thought. For example, cystic fibrosis which is commonly known to occur as a result of a mutation in the CFTR gene has been shown to have a number of smaller contributing mutations which mediate the severity of the disease phenotype (Hartman et al., 2015;
Wang et al., 2006). Furthermore, even in individuals with Mendelian disorders, the severity of disease phenotypes may depend on a complex set of genes modifying the single mutation causative gene (Vu et al., 2015). The concept of so called “modifier genes” (Cutting, 2010; Dowell et al., 2010; Nadeau, 2001) has been further developed
5 to describe genetic interaction network hubs that modify mutant phenotypes in C. elegans by acting in different functional pathways such as EGF/ras, Notch, and Wnt, genes often mutated in human diseases (Lehner et al., 2006).
Furthermore, cardiovascular disease and arthrosclerosis are known to involve partial contribution of numerous genes that are often unrelated by biochemical pathway as seen in increasing numbers of QTL studies (Boardman-Pretty et al., 2015; Ma et al.,
2014; Schadt et al., 2009; Schadt and Lum, 2006; Smallwood et al., 2014; Yang et al.,
2015) though such studies often lack resolution to single genes. It is an argument in this dissertation that genetic interaction network analysis may ultimately allow higher resolution.
Understanding the networks in which genes are interacting with each other is a fundamental step in our ability to better understand the detailed relationship of genotype to phenotype. At this time we know very little about the genetic interaction networks underlying specific phenotypes, owing to a lack of experimental data focussed on that specific question. Using SGA technology (Tong et al 2001), similarly in C. elegans and D. melanogaster using RNAi knockdown (Baryshnikova et al., 2013), it has become possible to study this question.
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1.3.2 Defining complex traits Classical genetics defines phenotypic heritability in two distinct ways. The first measure, called narrow sense heritability [h2] is defined by additive genetic effects plus environmental effects. The other is broad sense heritability [H2] which is additive and epistatic (synergistic) genetic effects plus the environment (Visscher et al., 2008).
Heritability is typically used as a population measure and therefore there may be many alleles (within a population) at a single locus contributing to a phenotype, thus, giving rise to quantitative trait loci (Falconer and MacKay, 1996; Mackay, 2014).
Furthermore, as discussed above, phenotypes are rarely the result of single genes and are usually made up of the contribution of multiple genes with multiple alleles in a population. The genetic term “repeatability”, which describes how variation between individuals can be attributed the variation seen within a given population, is useful in this general description (Boake, 1989). Thus, the genetic structure of a heritable trait in a specific population is not necessarily the same in another population of the same species (Deutschbauer and Davis, 2005; Visscher et al., 2008). An example of this was seen in a classical study where the quantitative trait sporulation efficiency in yeast was studied. Two yeast strains, S288C (low efficiency) and SK1 (high efficiency) were crossed and a single F1 segregant was selected, backcrossed against the S288C parent, from which a single high efficiency segregant was chosen and backcrossed with the low efficiency S288C parent. The resulting progeny resulted in 19 segregants displaying high efficiency sporulation, these were then genotyped, identifying four intervals with statistically biased SK1 inheritance and they identified the causative quantitative trait nucleotides (QTN) in three of genes (MKT1, TAO3 and RME1) contributing to the sporulation QTL. These three regions were then sequenced in 11
7 strains that had a large range in sporulation efficiency and as can be seen in Figure 3.
The three QTN identified in SK1 are not observed across all strains, based on this figure the authors make the statement “ different genes control sporulation efficiency in these 11 strains” (Deutschbauer and Davis, 2005). This suggests the effects of these
QTN alleles also need analysis in the light of modifier genes or specific genetic interaction networks.
Figure 3 Comparative sequence analysis of sporulation genes. The coding regions sequences of the genes RME1, TAO3 and MKT1 involved in sporulation, then asterisks in red are the QTN seen in SK1 (Reprinted by permission from Macmillan Publishers Ltd: Nature genetics (Deutschbauer and Davis, 2005) copyright, 2005).
1.3.3 Genetic interaction networks defining functional interactions An example of the power of phenotypic analysis by genetic interaction networks is seen in a study of cystic fibrosis, an inherited disease, which is mainly caused by a mutation in the cystic fibrosis transmembrane conductance receptor (CFTR) namely
∆F508 (Phe508 deletion). The deletion causes CFTR to misfold in the ER blocking transport to plasma membrane where it normally functions as a chloride channel.
Wang et al., 2006 elucidated a CFTR gene-product interactome network in BHK1 cells by mass spectrometry (multi-dimensional protein identification technology) defining
210 protein –protein (PPI) interactions in an overlapping network with CFTR (Figure
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4A). This network was then narrowed down to 31 interactors by filtering out all proteins except protein-folding chaperones. Wang et al then knocked down the 31 chaperones with siRNA (Figure 4B) and found that even moderate knock down of
AHA1 (activator of HSP90) restored the normal phenotype. (Wang et al., 2006). This example shows how genetic interaction networks can be used to narrowly hypothesise specific gene – gene or protein – protein epistatic interactions which would be overlooked in a classical genetics screen of narrow sense heritability owing to lack of resolution.
Figure 4 Cystic Fibrosis interaction network. 4A, Cystic fibrosis interactome network showing 210 interactors. 4B, the network seen in 4A narrowed down to 31 chaperone interactors, knocked down with siRNA. Reprinted from Cell, (Wang et al., 2006) copyright 2006, with permission from Elsevier.
1.3.4 Genome-wide Genetic Interaction Screening in S. cerevisiae Analysing gene function by genetic interaction networks is still a rapidly emerging field in the post genomic era and has been carried out in a number of eukaryotic model organisms with genetically tractable genetic tools. S. cerevisiae is one such model, whose genes are 30% conserved with humans and approximately 1000 genes have a
9 homologue implicated in human disease (Botstein et al., 1997; Botstein and Fink,
2011; Heinicke et al., 2007). It was the first eukaryote to be fully genome sequenced
(Goffeau et al., 1996) and there are a wide range of functional genomic tools available including: the haploid DMA, heterozygous and homozygous diploid deletion collections (Winzeler et al., 1999), the decreased abundance by mRNA perturbation
(DAmP) collection of conditional essential genes (Breslow et al., 2008), the GFP-fusion protein library (Huh et al., 2003), and the TAP-tagged library (Ghaemmaghami et al.,
2003). Additionally, a database exists of protein locations resulting from a number of genome-wide localisation studies (Bircham et al., 2011; Huh et al., 2003; Vizeacoumar et al., 2009).
This field of research becomes more relevant owing to studies attempting to uncover the means for functional redundancy and genetic back up pathways (Costanzo et al.,
2010b; Tong et al., 2004). Genetic interaction network analysis can explain why the majority of single gene knockouts exhibit little or no effect on phenotype (discussed below). It is suggested that naturally occurring biological networks are “small world” meaning any gene is functionally connected to others in pathways of 4-5 genes
(Barabasi and Oltvai, 2004; Hartman et al., 2001) and therefore rich in redundancy.
Such networks are therefore able to buffer/compensate for the effect of genetic perturbation at the single gene level.
High-throughput screening tools have allowed the assembly of genetic interaction networks on a genome-wide basis. A genetic interaction observed between two genes
(Figure 5) indicates there may be a functional relationship. Genetic interactions observed in this study are solely of the epistatic enhancement type. These are defined
10 by an interaction between non-allelic genes where a combination of their effects exceeds the sum (multiplication) of the expected effects of the individual genes
(Fisher, 1918, cited in (Phillips, 1998) such as that seen in Figure 5, that in its extreme form is called “synthetic lethality” (Boone et al., 2007; Tong et al., 2001; Tong et al.,
2004). These ideas will be explained in more detail in the following sections.
Figure 5 Synthetic lethal interactions. 5A. The combination of two otherwise viable single mutants resulting in an inviable synthetic lethal phenotype; 5B. A chemical stimulus (viable) + viable mutation in combination leading to an inviable phenotype – chemical induced synthetic lethality. Reprinted by permission from Macmillan Publishers Ltd, Nature Biotechnology (Parsons et al., 2004) copyright, 2003.
1.3.5 Yeast Deletion Mutant Array (DMA) The DMA is a genome-wide collection of strains each with a different gene systematically deleted. Genes are replaced by a dominant selectable marker
(KanRMX4) conferring resistance to kanamycin flanked by unique 20 bp oligonucleotide bar-codes (Figure 6). The DMA enables us to uncover redundant and unknown cellular functions using high throughput genetics, from which genetic
11 interaction networks can be deduced, such as those described in (Tong et al., 2001;
Tong et al., 2004).
Figure 6 Yeast DMA construction. Each of the 4800 strains in the yeast DMA was replaced with the kanamycin (KanR) dominant selectable marked flanked by unique 20 bp barcodes Reprinted by permission from Macmillan Publishers Ltd Nature Reviews, Genetics (Boone et al., 2007) copyright (2007).
The haploid DMA comprises approximately 4300 viable (non-essential) deletion strains (Giaever et al., 2002; Winzeler et al., 1999). Essential gene “deletions” must be used in heterozygous diploids or as conditional mutants in homozygotes or haploids.
Construction of the DMA led to the development of a number of high-throughput assays which can be used to screen for particular phenotypes under a variety of growth conditions, including (but not limited to) SGA analysis of query genes
(Costanzo et al., 2010a; Tong et al., 2001), fitness profiling using small molecule inhibitors (Giaever et al., 2004; Hillenmeyer et al., 2008; Parsons et al., 2006) and localisation studies (Bircham et al., 2011; Schuldiner et al., 2005), including resistance or susceptibility to xenobiotics such as drugs (Yibmantasiri et al., 2014).
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1.3.6 Generating genetic interaction networks in S. cerevisiae Synthetic genetic array (SGA) analysis allows systematic assessment of synthetic genetic interactions between a chosen query gene deletion strain and the entire non- essential genome (Figure 5). Query genes are genes of a known function or act in a pathway of interest, and therefore are used to investigate the gene’s genetic interaction networks. SGA relies on the ability of yeast to grow as haploids, mate to form diploids and undergo meiosis allowing for the selection of double-mutant haploids. Some of these non-essential gene double-mutants will result in inviable synthetic lethal (SL) or less viable synthetic-sick (SS) meiotic progeny defining a phenotype for genetic interactions (Tong and Boone, 2005; Tong et al., 2001; Tong et al., 2004). A small molecule inhibitor may take the place of one of the deletion mutants also defining a chemical genetic interaction (Hillenmeyer et al., 2008; Parsons et al.,
2004; Parsons et al., 2006) described in Figure 5B.
Synthetic lethal interactions generated by SGA can result from three different genetic interactions. The first being interactions between genes that act in parallel pathways regulating the same essential process termed a ‘between pathway’ interaction (Figure
7).
13
Figure 7 Functional redundancy. Synthetic lethality occurring in compensatory pathways encoding/regulating the same essential function, a “between pathway interaction” can be identified by SGA analysis where one gene in each pathway is inhibited. Modified from (Boone et al., 2007).
Secondly, genes acting at the same point in a specific redundant or compensatory pathway (i.e. paralogues or duplicated genes, for example HMG1 and HMG2 in this study) are termed a ‘within pathway interaction’. Thirdly, genes showing a previously uncharacterized synthetic interaction are termed an ‘indirect interaction’. This indirect interaction is likely to be the most useful in the annotation of uncharacterised genes and/or reviewing the significance of unknown pathways. Moreover, even the simple yeast system has a complex genetic architecture, thus systematic mapping of its genetic interaction networks may only be the tip of the iceberg (Baryshnikova et al., 2013).
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1.4 Conservation of genetic interaction networks
1.4.1 Comparing genetic interaction networks Genetic interaction networks are derived from screens consisting of a query – either a gene of interest (Tong et al., 2001) or a chemical/small molecule inhibitor (Parsons et al., 2006) against a biological system, for example the S. cerevisiae DMA. The resulting synthetic lethal/synthetic sick interactions are able to be assembled into genetic interaction networks. A fundamental question is how conserved are such networks?
As genomes evolve, new genes may emerge as older ones adapt to new functions through mutation or fusion and some genes may be completely deleted. As a species evolves it is can reveal the plasticity of its genetic networks (the ability for a given network to evolve along with environmental requirements for maintaining a normal phenotype), in which orthologous genes can evolve different roles, in closely related species or organisms due to changes/differences in environmental/functional requirements and/or selective pressures or mutations (Hamilton and Yu, 2012;
Harrison et al., 2007; Tischler et al., 2008). Although organisms display similar phenotypes that are highly conserved, the genetic interaction networks underpinning them may have significantly diverged. For example, two nematodes C. elegans and C. briggsae have very similar biology and early development in both species is identical.
However, knocking down the Wnt-pathway causes the opposite cell transformations in the two species. This example describes the phenomenon of developmental system shift, where changes at the molecular level are not necessarily reflected at the physical level (Verster et al., 2014). Moreover, there are also genes that are universally
15 conserved, some might be restricted to a particular common ancestor or species. It is reasonable to suggest that orthologous genes exist due to their conserved functions, as a result of an unfavourable phenotype if there is inappropriate activation/deactivation of the gene (Haag, 2014).
1.4.2 Phenotypic variation S. cerevisiae strains There has been no comparison in the literature of genetic interaction networks between different isolates of the same species of yeast. However, one study where a
DMA was created in the S. cerevisiae strain ∑1278b (Dowell et al., 2010) sharing
6848/6923 open reading frames and 99.7% sequence similarity (Chin et al., 2012) in comparison to the original s288c strain. Even with this high level of sequence similarity there is a major phenotypic difference between these strains, namely the ability of
∑1278b to undergo a dimorphic shift to a filamentous form (Ryan et al., 2012). In addition dispite the high level of sequence similarity, only 80% of S288C essential genes are required for viability in ∑1278b , thus suggesting that subtle mutations associated with natural variation in multiple genes can impact a phenotype as fundamental as cell growth. This high level of sequence similarity makes these strains similar to human individuals with an average frequency of 3.2 single nucleotide polymorphism’s (SNP) per 1 Kb (Chin et al., 2012; Consortium, 2010; Dowell et al.,
2010), and also implies that each strain will have strain specific phenotypes due to genetic interactions involving their individual sequence variance.
1.4.3 Network conservation between S. cerevisiae and S. pombe A number of studies have investigated the similarity of the genetic interaction networks between these two species of yeast which show approximately 75% sequence similarity. Moreover, 80% of the genes which are essential in S. pombe are
16 essential in S. cerevisiae (Koch et al., 2012). However, there is great evolutionary diversity between the two species. A major phenotypic difference between these two species is the replication of S. cerevisiae and S. pombe via budding and medial fission respectively. Thus, despite the high genetic similarity, it is apparent there has been significant rewiring of the genetic networks controlling these two organisms (Dixon et al., 2009).
When the genetic interaction networks generated via SGA analysis were compared, it was observed that there is only a 30% overlap between interacting genes and therefore 70% of interactions were deemed to be species specific (Baryshnikova et al.,
2013). Though the overlap was small, the authors conclude that the core set of interacting genes surrounding a particular process are highly conserved between the two species and are likely to be conserved in higher eukaryotes. Their findings indicate many complexes and biological processes have diverged through evolution in terms of their importance, i.e. acting as hub genes in one organism but not the other.
Moreover, close to 20% of protein complexes and 10% of Gene Ontology (Ashburner et al., 2000) biological processes may have undergone significant rewiring (Koch et al.,
2012). The highest level of conservation is understandably between the functional modules or complexes existing within the genetic networks in these two species, although cross talk between the modules can vary between species (Roguev et al.,
2008).
1.4.4 Network conservation between S. cerevisiae and C. elegans Initially it was thought there was little or no conservation of the genetic interaction networks between S. cerevisiae and C. elegans due to a meagre 4% overlap in genetic interactions (Byrne et al., 2007). However, upon further investigation 61% of essential
17 genes in S. cerevisiae have an orthologue in C. elegans, which also results in a lethal phenotype when knocked down by RNAi (Tischler et al., 2008). Tishler et al., therefore concluded that there is conservation between the functions of individual genes, the physical interactions and gene products and non-additive interactions are not conserved (Tischler et al., 2008).
However, when comparing genetic interaction networks caution must be exercised owing to the disparity between the methods used to construct networks in these two organisms. While S. cerevisiae and S. pombe deletion sets are generated by complete gene deletion (Kim et al., 2010; Winzeler et al., 1999), Drosophila melanogaster and
C. elegans collections use RNAi knockdown of gene expression of one or both genes
(Lehner et al., 2006; Ryder et al., 2007). These RNAi based methods do not completely eliminate ORF’s, which can result in a partially functional protein, even when mRNA expression is knocked down. Furthermore, the identification of subtle growth defects in the unicellular yeast organism is almost impossible to detect in the assays used for complex multicellular organisms (Dixon et al., 2008).
When investigating two different strains of C. elegans, Vu et al., (2015) focused on whether variation in gene expression contributes to the severity of the RNAi induced mutant phenotypes observed between the two strains. Their data suggest that altered gene expression in a given individual can lead to a more severe phenotype (relating back to an earlier point in chapter 1.4, that RNAi knockdown is variable between genes and even strains). Thus, natural variation in gene expression is able to significantly affect the severity of a mutant phenotype and would be a possible explanation for variability in the amount of RNAi knockdown seen in C. elegans expression changes
18 that could either be in the mutant gene itself or in the expression in genes that interact in specific pathways surrounding the mutant gene (Vu et al., 2015).
It has become apparent from the forgoing review that little is understood about the mechanisms which underlie complex traits. Understanding the relationship of genotype and phenotype is still very much a key question in biology. In this dissertation there is an attempt to answer the question “are genetic interaction networks conserved between individuals?” by comparing genetic interaction networks in S. cerevisiae when treated with statin drugs.
1.5 Saccharomyces Genome Resequencing Project (SGRP)
strains
S. cerevisiae strain S288C was the first eukaryote to undergo comprehensive full genome sequencing (Goffeau et al., 1996). Therefore it has been well studied and used as a reference strain for other sequencing projects (Balakrishnan et al., 2012; Liti et al., 2009). Since the initial sequencing project the Sanger institute has re-sequenced
S288C along with 35 other S. cerevisiae isolates from diverse backgrounds (Figure 8) including other lab strains, pathogenic strains, baking, wine, food spoilage, natural fermentation, sake, probiotic and plant isolate strains (Cubillos et al., 2009a; Liti et al.,
2009). The sequencing of these isolates has revealed the genetic diversity between these 26 phenotypically different strains and a valuable resource for classical and molecular genetics studies.
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Figure 8 Phylogenetic tree of Saccharomyces genome resequencing project (SGRP) strains. Showing diversity amongst the set, their location and their use. The scale bar indicates the frequency of base pair differences Reprinted by permission from Macmillan Publishers Ltd, Nature (Liti et al., 2009) copyright 2009.
As can be seen in Figure 8 natural populations evolve in different environmental conditions leading to genetic and phenotypic variation of traits within a species. A major challenge in biology is understanding complex polygenic nature of traits. It is one of the most difficult questions in biology. Strains which have been isolated from
20 different environments reveal adaptive changes of specific traits with the selection of specific alleles utilised in certain environments (Cubillos et al., 2009a). In this study members of this set of genetically diverse strains were used as a model to study divergence of the genetic interaction networks surrounding statin resistance in a human population. Based on the forgoing review it might be expected to see conservation of the functional processes underlying the cellular response to statin drugs along with species specific interactions unique to each strain. However, are the contributing genes conserved?
1.6 Research aims
The aim of this study is to investigate the genetic basis of phenotypic variation with respect to statin sensitivity by investigating whether genetic interaction networks are conserved between different strains. The investigation proceeds by generating genetic interaction networks in three strains that show the highest level of statin resistance for comparison of the networks characteristics.
1. Firstly, assess phenotypic variation with respect to statin sensitivity across a
panel of diverse S. cerevisiae strains (Chapter 3)
2. Secondly, to construct strain specific DMA’s (Chapter3)
3. Thirdly, to generate chemical genetic interaction networks on the newly
developed ssDMA’s using atorvastatin and cerivastatin (Chapter 4).
4. Finally, to generate genetic interaction networks using statin target genes
HMG1 and HMG2 and known statin interactors ARV1, BTS1 and OPI3 (Chapter
5).
5. Comparative analysis of the genetic interaction networks (Chapters 4, 5 & 6).
21
Methods
2.1 Yeast strains used in this study
Saccharomyces cerevisiae yeast strains were maintained in YPD glycerol stocks at -
800C and are displayed in Table 1 Yeast strains used in this study Table 1. All
Saccharomyces genome resequencing project (SGRP) strains were purchased from the
National collection of yeast cultures (NCYC, United Kingdom).
Table 1 Yeast strains used in this study
YCG # Strain Description Genotype Origin Mata wild- type lab Mata his3Δ1 leu2Δ0 met15Δ0 Open 103 BY4741 ( S288C) strain ura3Δ0 Biosystems Matα wild- type lab Matα his3Δ1 leu2Δ0 met15Δ0 Open 104 BY4742 ( S288C) strain ura3Δ0 Biosystems Matα can1Δ::STE2pr-Sp_his5 SGA lyp1Δ his3Δ1 leu2Δ0 ura3Δ0 111 Y7092 ( S288C) reporter met15Δ0 Boone lab Matα can1Δ::STE2pr-Sp_his5 Y7092 ( S288C) ARV1 query lyp1Δ his3Δ1 leu2Δ0 ura3Δ0 309 arv1∆ strain met15Δ0 arv1∆:: NatR This study Matα can1Δ::STE2pr-Sp_his5 Y7092 ( S288C) BTS1 query lyp1Δ his3Δ1 leu2Δ0 ura3Δ0 310 bts1∆ strain met15Δ0 bts1∆:: NatR This study Matα can1Δ::STE2pr-Sp_his5 Y7092 ( S288C) OPI3 query lyp1Δ his3Δ1 leu2Δ0 ura3Δ0 311 opi3∆ strain met15Δ0 opi3∆:: NatR This study HMG1 Matα can1Δ::STE2pr-Sp_his5 Y7092 ( S288C) query lyp1Δ his3Δ1 leu2Δ0 ura3Δ0 396 hmg1∆ strain met15Δ0 hmg1∆:: NatR This study HMG2 Matα can1Δ::STE2pr-Sp_his5 Y7092 ( S288C) query lyp1Δ his3Δ1 leu2Δ0 ura3Δ0 397 hmg2∆ strain met15Δ0 hmg2∆:: NatR This study 511 S288C SGRP strain Matα hoΔ::HPH ura3::KanR NCYC 512 UWOPS87-2421 SGRP strain Matα hoΔ::HPH ura3::KanR NCYC 513 378604X SGRP strain Matα hoΔ::HPH ura3::KanR NCYC 514 273614N SGRP strain Matα hoΔ::HPH ura3::KanR NCYC 515 YIIc17_E5 SGRP strain Matα hoΔ::HPH ura3::KanR NCYC
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516 Y55 SGRP strain Matα hoΔ::HPH ura3::KanR NCYC 517 UWOPS83-787.3 SGRP strain Matα hoΔ::HPH ura3::KanR NCYC 518 SK1 SGRP strain Matα hoΔ::HPH ura3::KanR NCYC 519 BC187 SGRP strain Matα hoΔ::HPH ura3::KanR NCYC 520 YJM978 SGRP strain Matα hoΔ::HPH ura3::KanR NCYC 521 YJM981 SGRP strain Matα hoΔ::HPH ura3::KanR NCYC 522 YJM975 SGRP strain Matα hoΔ::HPH ura3::KanR NCYC 523 DBVPG 1373 SGRP strain Matα hoΔ::HPH ura3::KanR NCYC 524 DBVPG 1106 SGRP strain Matα hoΔ::HPH ura3::KanR NCYC 525 DBVPG 6765 SGRP strain Matα hoΔ::HPH ura3::KanR NCYC 526 L-1374 SGRP strain Matα hoΔ::HPH ura3::KanR NCYC 527 L-1528 SGRP strain Matα hoΔ::HPH ura3::KanR NCYC 528 DBVPG 6044 SGRP strain Matα hoΔ::HPH ura3::KanR NCYC 529 NCYC 110 SGRP strain Matα hoΔ::HPH ura3::KanR NCYC 530 UWOPS03-461.4 SGRP strain Matα hoΔ::HPH ura3::KanR NCYC 531 UWOPS05-217.3 SGRP strain Matα hoΔ::HPH ura3::KanR NCYC 532 UWOPS05-227.2 SGRP strain Matα hoΔ::HPH ura3::KanR NCYC 533 Y12 SGRP strain Matα hoΔ::HPH ura3::KanR NCYC 534 YPS606 SGRP strain Matα hoΔ::HPH ura3::KanR NCYC 535 YPS128 SGRP strain Matα hoΔ::HPH ura3::KanR NCYC Matα can1Δ::STE2pr-Sp_his5 lyp1Δ his3Δ1 leu2Δ0 ura3Δ0 538 Y7092 URA3+ CEN-URA3 met15Δ0 this study DMA Matα his3Δ1 leu2Δ0 met15Δ0 587 BY4742 ( S288C) control ura3Δ0 [URA3_CEN] This study ssDMA starting Matα hoΔ::HPH ura3Δ0 588 Y55 strain [URA3_CEN] This study ssDMA starting Matα hoΔ::HPH ura3Δ0 589 sk1 strain [URA3_CEN] This study ssDMA starting Matα hoΔ::HPH ura3Δ0 590 YPS606 strain [URA3_CEN] This study HMG1 S288C (BY4742), query MATα ∆his3Δ1 leu2Δ0 lys2Δ0 595 ∆hmg1 strain ura3Δ0 hmg1Δ:: NatR This study HMG1 query MATα ∆his3Δ0 ura3Δ0 596 Y55, ∆hmg1 strain hoΔ::HPH hmg1Δ:: NatR This study HMG1 query MATα ∆his3Δ0 ura3Δ0 597 SK1, ∆hmg1 strain hoΔ::HPH hmg1Δ:: NatR This study
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HMG1 query MATα ∆his3Δ0 ura3Δ0 598 YPS606, ∆hmg1 strain hoΔ::HPH hmg1Δ:: NatR This study HMG2 S288C (BY4742), query MATα ∆his3Δ1 leu2Δ0 lys2Δ0 599 ∆hmg2 strain ura3Δ0 hmg2Δ:: NatR This study HMG2 query MATα ∆his3Δ0 ura3Δ0 600 Y55, ∆hmg2 strain hoΔ::HPH hmg2Δ:: NatR This study HMG2 query MATα ∆his3Δ0 ura3Δ0 601 SK1, ∆hmg2 strain hoΔ::HPH hmg2Δ:: NatR This study HMG2 query MATα ∆his3Δ0 ura3Δ0 602 YPS606, ∆hmg2 strain hoΔ::HPH hmg2Δ:: NatR This study S288C (BY4742), ARV1 query MATα ∆his3Δ1 leu2Δ0 lys2Δ0 603 ∆arv1 strain ura3Δ0 arv1Δ:: NatR This study ARV1 query MATα ∆his3Δ0 ura3Δ0 604 Y55, ∆arv1 strain hoΔ::HPH arv1Δ:: NatR This study ARV1 query MATα ∆his3Δ0 ura3Δ0 605 SK1, ∆arv1 strain hoΔ::HPH arv1Δ:: NatR This study ARV1 query MATα ∆his3Δ0 ura3Δ0 606 YPS606, ∆arv1 strain hoΔ::HPH arv1Δ:: NatR This study S288C (BY4742), BTS1 query MATα ∆his3Δ1 leu2Δ0 lys2Δ0 607 ∆bts1 strain ura3Δ0 bts1Δ:: NatR This study BTS1 query MATα ∆his3Δ0 ura3Δ0 608 Y55, ∆bts1 strain hoΔ::HPH bts1Δ:: NatR This study BTS1 query MATα ∆his3Δ0 ura3Δ0 609 SK1, ∆bts1 strain hoΔ::HPH bts1Δ:: NatR This study BTS1 query MATα ∆his3Δ0 ura3Δ0 610 YPS606, ∆bts1 strain hoΔ::HPH bts1Δ:: NatR This study S288C (BY4742), OPI3 query MATα ∆his3Δ1 leu2Δ0 lys2Δ0 612 ∆opi3 strain ura3Δ0 opi3Δ:: NatR This study OPI3 query MATα ∆his3Δ0 ura3Δ0 613 Y55, ∆opi3 strain hoΔ::HPH opi3Δ:: NatR This study OPI3 query MATα ∆his3Δ0 ura3Δ0 614 SK1, ∆opi3 strain hoΔ::HPH opi3Δ:: NatR This study OPI3 query MATα ∆his3Δ0 ura3Δ0 615 YPS606, ∆opi3 strain hoΔ::HPH opi3Δ:: NatR This study
24
2.2 Media used
All media were autoclaved at 121˚C for 21 min. Media were cooled to 65˚C prior to the addition of glucose and antibiotics. If liquid broth media was required the agar was omitted from the following recipes. All media components and chemicals were purchased from Sigma Aldrich NZ, unless otherwise stated.
Yeast Peptone Dextrose (YPD) Media: Yeast extract 10 g/L, peptone 20 g/L, agar 20 g/L and Glucose 2%
Synthetic complete (SC) Media: 1.7 g/L yeast nitrogen base without amino acids and ammonium sulphate, amino acid mix 2 g/L, agar 20 g/L and glucose 2%.
Amino acid mix for synthetic complete media: adenine 3 g, alanine 2 g, para – aminobenzoic acid 0.2 g, arginine 2 g, asparagine 2 g, aspartic acid 2 g, cysteine 2 g, glutamine 2 g, glutamic acid 2g, glycine 2 g, histidine 2 g, inositol 2 g, isoleucine 2 g, leucine 10 g, lysine 2 g, methionine 2 g, phenylalanine 2 g, proline 2 g, serine 2 g, threonine 2 g, tryptophan 2 g, tyrosine 2 g, uracil 2 g and valine 2 g.
Drop out (SD) media: is synthetic complete media minus the appropriate amino acid(s).
Enriched sporulation media: Potassium acetate 10 g/L, yeast extract 1 g/L, amino acid mix (SD + His, Leu, Lys, Ura).
Luria Bertani (LB) media: Bacto tryptone 10 g/L, yeast extract 5 g/L, sodium chloride
10 g/L and agar 20 g/L.
25
Table 2 Antibiotic media supplements
Antibiotic/selection Stock concentration Working concentration Nourseothricin (ClonNat, Werner BioAgents) 100 mg/mL (ddH20) 100 µg/mL Geneticin (G418, Invitrogen Life Technologies) 200 mg/mL (ddH20) 200 µg/mL Canavanine (Supplier) 50 mg/mL (ddH20) 50 µg/mL Thialysine (Supplier) 50 mg/mL (ddH20) 50 µg/mL Hygromycin B (HPH, Invitrogen Life Technologies) 100 mg/mL (ddH20) 200 µg/mL Ampicillin (Supplier) 100 mg/mL (ddH20) 100 µg/mL 5-Fluoroorotic Acid (5-FOA, Kaixuan Chemical Co) 100 mg/mL (DMSO) 1mg/ mL
2.3 Plasmids used
All plasmids are displayed in Table 3 and were maintained in Escherichia coli (DH5α) glycerol stocks at -800C. Plasmid DNA was transformed into MAX Efficiency® DH5α™ competent cells (Invitrogen Life Technologies, #18258-012) as per the manufacture’s protocol with minor modifications. A 25 µL aliquot of competent cells was thawed on ice before the addition of 5 ng of plasmid DNA. The bacteria was incubated on ice for
30 mins at which time the cells were heat shocked at 420C for 2 min followed by the addition of 500 µL of LB media. The cells were incubated at 370C for 1 hour with shaking at 250 rpm after which 100 µL of the recovered cells (undiluted, 1:10 and
1:100) were plated onto LB + ampicillin (amp) agar plates and incubated overnight at
370C. Single colonies were picked, and grown in 3 mL of LB+amp liquid media overnight at 370C with shaking for conformation by plasmid mini – prep (see below).
Confirmed transforments were then grown 3 mL of LB+amp liquid media overnight at
370C with shaking, then harvested and resuspended in 1.5 mL LB+15% glycerol and frozen at -800C for future use.
26
Plasmid DNA for molecular biology applications was extracted and purified from bacterial cultures using the Geneaid high speed plasmid mini kit (dnature, NZ) as per the manufacturer’s instructions.
Table 3 Plasmids used in this study
Plasmid Discription Source pIS374 his3 disintegrator Euroscarf, (Sadowski et al., 2007b) p4339 NatRMX4 Boone lab, (Tong et al., 2001) pAG60 URA3 from C. albicans Addgene, (Goldstein and McCusker, 1999) pRS316 CEN-URA3 ATCC, (Sikorski and Hieter, 1989)
2.4 Yeast DNA extraction
Yeast DNA was prepared as described (Harju et al., 2004; Lõoke et al., 2011). A single colony of yeast from an agar plate were used for each DNA extraction. The yeast colony was added to a micro centrifuge tube containing 0.3 g glass beads (0.5 mm, dnature NZ), 200 µL yeast breaking buffer (2% Triton X-100, 10% sodium dodecyl sulphate, 100 mM NaCl, 10 mM Tris-HCL (pH8) and 1 mM Ethylenediaminetetraacetic acid (EDTA, pH8.0)) and 200 µL phenol/chloroform/isoamyl alcohol (25:24:1). The tube was then vortexed for 5 min and centrifuged at 16,000 g for 5 min. The aqueous phase was collected and added to a new microcentrifuge tube containing 200 µL of chlorofom, vortexed for 30 seconds and centrifuged at 16,000 g for 5 min. The aqueous phase was removed and added to a new microcentrifuge tube containing 1 mL 100% ethanol and 40 µL 3 M potassium acetate (pH 5.3), inverted 10 times, incubated at 20oC for 20 minutes followed by centrifugation at 16,000 g for 5 min to pellet the DNA. The supernatant was removed, the DNA was air dried, dissolved in 40
µL TE buffer (10mM Tris-HCL and 1 mM and EDTA) and incubated at 65oC for 10 minutes then stored at 4oC for future use. 27
2.5 DNA Electrophoresis
DNA was visualised using agarose gel electrophoresis, 0.8%-2% agarose gels were prepared using 1X TBE buffer pH 8 (89 mM Tris base, 89 mM boric acid and 2mM
EDTA) Ethidium bromide was added to a concentration of 50 µg/mL. Samples were mixed with 5x loading dye (0.25 % bromophenol blue and 30 % w/v glycerol) before loaded onto the gel along with 1 Kb+ DNA ladder (Invitrogen Life Technologies) for size analysis. Electrophoresis was carried out using 1X TBE running buffer containing 50
µg/mL ethidium bromide at 100 V for 1 hour. DNA was visualised with a UV transilluminator at 365 nm.
2.6 General PCR conditions
Polymerase chain reactions in this thesis were carried out using TaKaRa Ex Taq™
(Takara Bio Inc, Japan) using the general reaction mixture outlined in Table 4. The PCR reactions were carried out in a Technie T-500 thermocycler using the following cycle conditions unless stated otherwise: 2 min at 94 oC, 35 cycles of: 30 seconds at 94 oC,
30 seconds at 54-58 oC (depending on Tm of primers) and 1 min/Kb at 72 oC; followed by a 10 min final extension at 72 oC. Upon completion, the samples were run on an agarose gel (0.8% or 1%) and stored at 4 oC for further use. All primers (Table 5) were
0 resuspended in ddH20 at a concentration of 100 µM and stored at -20 C.
28
Table 4 General PCR reaction mixture
10x Ex Taq Buffer 2.5 µL dNTP Mix (2.5 mM each) 2 µL Primer 1 0.5 µL Primer 2 0.5 µL Template (100 ng) 2 µL TaKaRa Ex Taq (5 units/ µL) 0.125 µL ddh20 17.375µL final reaction volume 25 µL
PCR products were run on a 1-2% agarose gel alongside a 1 Kb + ladder (Invitrogen life technologies, NZ) to ensure correct size of the product. Prior to use in transformation, the PCR products were purified using a Geneaid PCR clean up kit (dnature, NZ) following the manufactures instructions.
Table 5 Primers used
Lab No. Name Sequence 3 NAT_con_rev TACGAGACGACCACGAAGC 4 NAT_con_fwd TGGAACCGCCGGCTGACC 42 hmg1_con_rev CGCATGACTCAAGAGAAGC 43 hmg1_con_fwd AGTCTCTACGCCCGCTCG ATAGTGTATCATTGTCTAATTGTTGATACAAAGTA GATAAATACATAAAACAAGCACATGGAGGCCCAG 44 hmg1_Del_Fwd AATACCCT ACATGGTGCTGTTGTGCTTCTTTTTCAAGAGAATA CCAATGACGTATGACTAAGTCAGTATAGAGCGAC 45 hmg21_Del_Rev CAGCATTCAC 136 hmg2_con_fwd TCCCTTTCAACAGCGCGACA 137 hmg2_con_Rev AGCGCAGTGCTAGGCGATAA ACTTAATTGTGTTCTTTCCAAATTAGTTCAACAAG hmg2_del_fwd GTTCCCACATACAACCTCAAACATGGAGGCCCAG 138 AATACCCT TTAGAATAGCTAGACAATACAAAGATATAAAGTA hmg2_del_rev TCACCATGTAAACTACAAGAGCAGTATAGCGACC 139 AGCATTCAC 306 arv1_con_fwd GAATAGCCCTATGTGAAC 307 arv1_con_rev CGGTGAATTCTGCCAATGAGG 308 bts1_con_fwd CCAGCATAGCAGAAATTACG
29
309 bts1_con_rev GGAGTTTCAGAAATCGTGG 310 opi3_con_fwd CCACACATGCATCGTTGGTTTC 311 opi3_con_rev TGCGCTAGATGCTCTCATTG TGCGTCAGTGAACGGATGAAGCGCATCAAAAAGT arv1_Del_Fwd AGATATTAGCTGTGATAAACGACATGGAGGCCCA 321 GAATACCCT AAAATGCCAAAATAAGATTTTTGATACAAGTAATA arv_Del_Rev 322 CTGGATATTTTTTTATTTGCCAGTATAGCGACCA TTCAAAGAAGCTACTAATAGAAAGAGAACAAAGC bts1_Del_Fwd GTTTACGAGTCTGGAAAATCAACATGGAGGCCCA 323 GAATACCCT GAGAAGGCTTTATTTCTGACTATCTTCCTCCACTAA bts1_Del_Rev TTTGATTGATCAATTTATTCAGTATAGCGACCAGC 324 ATTCAC TTGGACAGAGCCATAAACAGCAATTGAAGACAAC opi3_Del_Fwd AAGAATAGCGCAAGTCAAGCGACATGGAGGCCC 325 AGAATACCCT AGAAACGGTAATAGCATAGGCTTCTAACATTATA opi3_Del_Rev GAATATATAGAAATAGAGCACCAGTATAGCGACC 326 AGCATTCAC 414 ura3_del_con_fwd AGAGCAGAGCGAGAGCATT 415 ura 3_del_con_rev ACACAGTGGAGCCTTGTCCTC 578 ura 3_del_fwd_1000 AAGTTACAGCAATGAAAGAGCA 579 ura 3_del_rev_1000 ACTCTGGGAGCTGCGATTGG 580 ura3_del_fwd_2000 GGCCCAATGCCACTGGTGCAA 581 ura 3_del_rev_2000 GCTCGGATATGCTCTTGCATGC 582 ura 3_del_fwd_2500 ACTCTGGTAACTTAAAGGGATG 583 ura3_del_rev_2500 GTTCGATTCCAATCCCGAAACC 584 ura3_del_fwd_3000 ATTGACAGAGAAGAATTTGGCA 585 ura 3_del_rev_3000 CCGGTTTAATCCACGCACTG 623 ura 3_con_fwd CTAGCATGTACGTGAGCGTATT 624 ura 3_con_rev ATGGAGGAGGAACATAACCATTC
2.7 Yeast transformation
Yeast were made competent by the lithium acetate method. Briefly, saturated yeast cultures were used to inoculate 50 ml of YPD media to a cell density of 5 x 106 cells/mL and grown for ~4 hours at 30˚C with shaking at 250 rpm. When the cells reached a
7 concentration of 2-5 x 10 cells/mL (OD600nm 1-1.5) they were harvested by
30 centrifugation and washed twice with 5 mL LiAc-TE (0.1 M lithium acetate, 10 mM Tris and 1 mM EDTA) and resuspended in 1 mL of LiAc-TE. Salmon sperm DNA (10 mg/mL) was denaturated by boiling for ten minutes and placed on ice. 250 µg denatured salmon sperm DNA and 50 µL PCR product or 10 µL plasmid DNA were added to 100
µL competent yeast cells along with 700 µL PEG-LiAc-TE (40% w/v PEG3350/LiAc-TE).The cells were incubated at 30˚C for 30 minutes, at which time 80 µL of di-methyl-sulfoxide
(DMSO) was added and the yeast subjected to heat shock at 42˚C for 40-60 minutes.
The transformed cells were harvested and washed with ddH2O, resuspended in either
1 mL ddH20 for prototrophic gene selection and plated onto selection media or resuspended in 1 mL YPD and incubated at 30˚C for 4 hours to allow expression of the antibiotic resistance gene product after which they were plated onto selection media for 2-4 days at 30˚C.
2.8 Synthetic genetic array
The MATa deletion mutant array (DMA) is the yeast gene deletion set maintained in
384 format with a control border strain MATa his3Δ::kanR to ensure that colony sizes on the outside borders were not biased (Tong et al., 2001; Winzeler et al., 1999) and was a kind gift from Charlie Boone. Replica plating (pinning) was performed using an automated robotic system, the Singer RoToR HDA (Singer Instrument Co. Ltd,
Somerset, UK). Yeast was grown at 300C unless stated otherwise.
2.8.1 Mating the wild strains with the DMA The yeast MATα query strains (∆xxx::NatR) were grown in 1536 colony format on
YPD+Nat. The MATa DMA was grown on YPD with G418. All replica plating (pinnings) were carried out using the Singer Rotor HDA robot (Singer Instruments Co, Somerset,
31
UK). The query strains were mated with the DMA by pinning the DMA on top of the query strain and incubated on rich media to produce diploid cells at 300C for 2 days.
2.8.2 MATa/α diploid selection The desired diploids (query + DMA) were selected by pinning onto YPD+NAT/G418 media. This media allows only the diploids to grow, as haploids will only be either G418 or NAT resistant, not both, and thus will be unviable.
2.8.3 Sporulation The selected diploids were then pinned onto media deficient in nutrients required for growth and incubated at a lower temperature (250C) to induce sporulation of the diploids. The resultant haploid spores were a combination of wild-type, single or double mutants due to independent assortment of the chromosomes and recombination within the chromosomes.
2.8.4 MATa progeny selection The spores were transferred onto SC –His/Arg/Lys + canavanine/thialysine, to allow for the selective germination of MATa meiotic progeny. Selectivity of only MATa haploids is ensure by, the use of the STE2pr promoter linked to the
Schizosaccharomyces pombe HIS5 gene (can1Δ::STE2pr-Sp_his5), which is able to complement S. cerevisiae HIS3, a gene required for histidine biosynthesis which is deleted from both the MATα query strain and MATa deletion mutant array strains, thus they require histidine supplementation for survival. STE2 encodes the α-factor pheromone receptor which is only expressed in MATa, thus (S. pombe) his5 is only expressed in MATa cells and able to survive without the addition of histidine.
Moreover, this genetic selection also prevents mating between MATa and MATα haploid cells since the resultant diploids cannot grow in the absence of histidine.
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However, mitotic recombination can occur between homologous chromosomes in
MATa/α diploids, a crossover event that can result in MATa/a or MATα/α diploids. To prevent this event from occurring, two recessive markers, can1Δ and lyp1Δ, are introduced into the genome. The wild type CAN1 gene product is an arginine permease which allows canavanine (a toxic analogue of arginine) to enter the cells causing cell death. Likewise the wild type LYP1 gene product is a lysine permease that allows thialysine (a toxic analogue of lysine) to enter the cells causing cell death. The presence of wild type LYP1 and CAN1 genes allows the respective antibiotics to enter the cells resulting in cell death; conversely, lyp1Δ and can1Δ cells do not have these permeases present, therefore, the cells are viable in the presence of these toxic analogues. Including these genetic mutations into the query strains ensures the specific selection of MATa haploid progeny and substantially reduces the potential for false positives. This step was repeated twice for greater assurance of MATa haploid selection.
2.8.5 Double mutant selection. The MATa meiotic progeny underwent two rounds of selection, first round selecting for the DMA deletion mutants (G418 alone) and the second round selecting for the query and DMA double mutants (G418 and NAT together).
The selection of the DMA deletion mutants is achieved by pinning the MATa meiotic progeny onto SC – His/Arg/Lys + canavanine/thialysine/G418 media, in which the
G418 selects for the MATa DMA deletions (ΔGene1…5000::KanR). The selection for the
DMA + query deletions is achieved by pinning the resulting MATa progeny onto SD–
His/Arg/Lys + canavanine/thialysine/G418/NAT media, thus selecting for double deletions containing the query strain NatR and DMA deletions. The progeny resulting
33 from this step were the MATa (haploid) double mutants of every non-essential gene and query gene of interest.
2.8.6 SGA mini array analysis Genes which show synthetic sick/lethal interactions in the SGA procedure were picked and arrayed in 384 colony format using the Singer Stinger attachment for the Singer
RoToR. These were then mated with the appropriate query strain to create haploid double mutants as described in the SGA procedure above.
2.9 Chemicals and media for chemical genetic profiling
Atorvastatin (Inter Chemical Hong Kong Ltd; WanChai, Hong Kong), was dissolved in dimethyl sulfoxide (DMSO, Sigma) at a stock concentration of 50mM (Blank et al.,
2007) and cerivastatin (Chengdu Caikun Biological Products Co., Ltd.; Chengdu,
Sichuan, China) was dissolved in DMSO at a stock concentration of 10 mM (Yoshida et al., 2001).
SC media was chosen over standard rich (YPD) yeast medium for use in all chemical experiments as it is chemically defined and does not contain yeast extract. Yeast extract is a major component of YPD and is made from concentrations of autolysed yeast. Yeast extract contains a variety of soluble peptides, amino acids and vitamins which could interfere with drug activity. Use of SC over YPD allows for alleviation of drug interactions with media components instead of cellular targets.
2.10 Serial spot dilution assays
Yeast strains were grown on agar plates and individual colonies of each strain were picked and arrayed in 96 well plates containing 100 µL of the appropriate liquid media and incubated for 24-48 hours (until saturation). The strains were then serially diluted
34 four times at a one in four dilution (50µl into 150µl) in fresh 96 well plates filled with
150 µl sterile ddH20, the dilutions were performed using a Bio Tek Precision XS liquid handler fitted with a Bio–Stack plate stacker (BioTek Instruments Inc. Winooski, USA).
The diluted cells were then immediately spotted onto SC drug agar plates using the
Singer RoToR HDA (96 format long pins) or the V&P scientific (VP 405) 96 format manual pinning tool. The agar plates were incubated for 24 and 48 hours at 30oC and photographed. The resulting spot assays were scored 0-4 based on colony sizes compared to a DMSO control and wild type controls on each plate.
2.11 Chemical genetic Screens
Chemical genetic screening of the created strain specific deletion mutant arrays
(ssDMA’s) were performed as follows. Atorvastatin was added to SC media at a concentration of 110 µM and cerivastatin at 60 µM, the plates were incubated at 30˚C for 48 hours, imaged and scored for synthetic genetic interactions.
2.12 Scoring synthetic lethality
Synthetic lethal (SL) and synthetic sick (SS) genetic interactions were inferred from reduced colony growth in which double mutants grew less than the expected combination of parental phenotypes (Boone et al., 2007). 1536-colony plates were photographed using a Cannon EOS 600D camera and colony size and circularity were measured using ‘Gitter in r’ (Wagih and Parts, 2014). These data were uploaded to
ScreenMill (Dittmar et al., 2010) alongside a control set comprising 5 independent replicates of the DMA and a file containing the known colony position of each gene in the yeast deletion mutant array (DMA). The resulting SL/SS data from ScreenMill were presented in positive z-score values (accompanied with a p-value showing
35 significance) i.e. growth on the control set was better than the experimental, in this study all z-scores with a value greater than 1.0 were considered SL/SS.
2.13 Gene ontology
Gene Ontology (GO) is a controlled vocabulary used to describe the biology of a gene product in any organism. GO annotations are able to compare functional, process or cellular localisation associations made between gene products and the GO terms that describe them (Ashburner et al., 2000; Gene Ontology Consortium, 2004). Statistically significant changes in GO term distribution compared to that of the whole genome suggest enrichment in the number of genes evolved in that particular process. Yeast mine (Balakrishnan et al., 2012) and GO-Slim (Cherry et al., 2012) were used for functional analysis of SGA and chemical genetic ‘hit’ genes. The fold change differences in distribution of GO terms for the ‘hit’ genes were compared to that of the genes in the DMA (p = 0.05)
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Strain specific deletion mutant arrays (ssDMA)
3.1 Determining statin resistant strains
The SGRP collection of 36 yeast strains was compared to growth of S288C (BY4742) on increasing concentrations of atorvastatin and subsequently cerivastatin (using the method described in Chapter 2.10). Briefly, strains were grown to saturation and diluted to 1x108 cells/mL, they were then serially diluted and spotted onto agar plates containing increasing concentrations of statin (Figure 9). Of the original 36 strains, eight showed varying levels of resistance from which three were chosen that showed the highest level of resistance (Figure 9).
The three resistant SGRP strains (Y55, SK1 and YPS606) showed little growth reduction at the highest concentration of atorvastatin (400 µM) when compared to the lab strain
BY4742 (Table 1) which showed growth inhibition 25 µM. When the same assay was repeated using cerivastatin observed growth inhibition of the lab strain at occurred at
10 µM compared to 80 µM in the three atorvastatin resistant SGRP strains. Therefore, the ssDMA’s described in this chapter were constructed using the strains SK1, Y55 and
YPS606.
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Figure 9 serial spot dilution assays with atorvastatin and cerivastatin. Serial cellular dilutions of the three most statin resistant SGRP strains (Y55, SK1 and YPS606) were and spotted onto increasing concentrations of: 1A, atorvastatin 10 µM – 400 µM; or 1B, cerivastatin 10 µM – 80 µM.
3.2 Strain Construction
Prior to mating the statin resistant wild yeast strains (BY4742, Y55, SK1 and YPS606,
Table 1) it was necessary to incorporate the appropriate selection markers and at the same time remove the conflicting KanR marker, the dominant selectable marker used in the DMA (Winzeler et al., 1999). Removal of this marker enables the selection of the DMA throughout the backcrossing. These markers include his3Δ0 null mutation to
38 enable the use of the HIS3 SGA reporter, the removal of the KanR resistance cassette from the URA3 locus replaced with a ura3Δ0 null mutation. Furthermore, it was necessary to incorporate the SGA Mata mating-type specific promoter
(can1Δ::STE2pr-Sp_his5), carried in the strain Y7092 into the commercially available
DMA, to enable the selection of MATa specific meiotic progeny (Tong and Boone,
2005).
3.2.1 Marker switching Genetic markers controlled by identical promoter/terminator pair’s i.e the “MX cassettes”, may be “switched” owing to the large areas of homology of the shared promoter/terminator regions flanking the marker described in Figure 10. This switch readily occurs via homologous recombination. The technique was used for changing the markers in the URA3 region of the strains purchased from NCYC (Cubillos et al.,
2009b), including switching the genotype from ura3Δ::KanR to ura3::URA3Ca
(Goldstein and McCusker, 1999).
Figure 10 Marker switching The CaURA3 marker from pAG60 was added in place of KanR.
To enable marker switching of the KanR cassette to the C. albicans URA3 gene, pAG60 was digested with BamHI (New England Biolabs) cutting out the linearized CaURA3 region. The digested product was then transformed into the appropriate strains using
39 the method described and selected on SD-URA media. Single transformant colonies were then picked and confirmed via PCR using primers 414 and 415 (Table 5) as described in Chapter 2.7.
3.2.2 PCR amplification of URA3 genomic region from BY4742 Primers were designed (Table 5) to amplify the region surrounding the URA3 locus using genomic DNA from the URA3 auxotrophic strain BY4742 (ura3Δ0), in which the
URA3 open reading frame is deleted (Brachmann et al., 1998). The regions 1000 bp,
2000 bp, 2500 bp and 3000 bp away from either the 3’ or 5’ end of the URA3 locus were amplified. To confirm correct integration of the PCR product, the genomic region surrounding the URA3 region was amplified (Chapter 2.6) to ensure that the URA3 ORF has been removed using primers 414 and 415, Table 5).
3.2.3 his3∆0 construction The HIS3 yeast “disintegrator plasmid” carrying a URA3 selectable marker was purchased from Euroscarf (Sadowski et al., 2007a). The disintegrator plasmid disrupts gene, using the two step gene disruption technique, resulting in disruption of the HIS3 gene, without the requirement of a selectable marker, as described (Sadowski et al.,
2007a) . Yeast were transformed as described in Chapter 2.7, selected on SD minus uracil media. Individual colonies were streaked onto selection media (SD-URA), grown for 48 hours, then streaked onto YPD media, grown for at least 72 hours to allow for non-selective growth. Cells were then streaked for single colonies on 5-FOA plates, incubated for 48 hours, individual colonies were patched onto YPD plates and grown for 48 hours then replica plated onto SD minus histidine and YPD to identify the clones with the gene disruption. The resulting strains were then verified for gene disruption
40 by PCR analysis (using primers 623 and 624, Table 5) of the genomic region surrounding gene (Sadowski et al., 2007a).
3.3 Incorporation of the SGA reporter into the DMA
Prior to mating the statin resistant strains with the DMA it was necessary to incorporate the SGA reporter (can1Δ::STE2pr-Sp_his5) into the DMA to allow for the selection of MATa specific haploids (
Figure 11). This was carried out by mating the Y7092 carrying the reporter along with the CEN-URA3 re-usable selectable marker (yCG538, Table 1) with the DMA using SGA methodology described in Chapter 2.8, (Tong and Boone, 2005). Once the reporter was incorporated cells containing the CEN-URA3 marker were selectively killed by pinning the DMA onto media containing 5-FOA (Boeke et al., 1984).
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Figure 11 Mata mating-type specific promoter incorporation into the DMA. Incorporating the SGA reporter (red) into the commercially available DMA via mating, selecting for diploids using the CEN-URA3 re-usable marker, sporulation, selection of MATa haploids and removal of the CEN-URA3 marker with a final pinning onto 5-FOA (Boeke et al., 1984; Tong and Boone, 2005). 3.4 ssDMA backcrossing
The three statin resistant strains SK1, YPS606 and Y55 (yCG588, yCG589 and yCG590 respectively, Table 1), were mated with the DMA and back crossed with these statin resistant parental strains 6 times to create a ssDMA with minimal genetic background from the original S288C DMA. As each strain was mated with the DMA, using SGA methodology, the resulting back-cross of DMA, selected for the presence of the KanR marker, was then used as the starting DMA for the next round of back-cross mating
(Figure 12).
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Figure 12 ssDMA backcross method The three statin resistant strains were backcrossed with the DMA using SGA methodology. The resulting DMA at the end of each SGA was then used as the starting DMA for the next cross.
Backcrossing is a well-established method, allowing the sequential and incremental introduction of the genome of the statin resistant strain. Each additional cross removed more of the original DMA background (Figure 13). The end result of back- crossing created an ssDMA with minimal original DMA genetic background. At the limit this will comprise the gene deletion flanking regions. Based on repeating the backcross six times (Figure 13) it can be calculated that the resulting ssDMA’s should inherit
98.5% of the genetic background from each of the SGRP strains, with only 1.5% remaining from the original DMA (Deutschbauer and Davis, 2005; Hill, 1998).
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Figure 13 Backcrossing strategy Blue shows the percentage of genetic material of statin resistant strain increasing from each round of backcrossing whereas the orange indicates the decreasing amount of genetic material from the original DMA.
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3.5 Verification of ssDMA’s
The 6-fold backcrossed ssDMAs were tested against atorvastatin and cerivastatin to ensure that they remained statin resistant. An entire ssDMA (and DMA) consists of 14
384-colony plates. Plate 10 was selected for representative testing in the ssDMAs so this and the original DMA plate 10 were treated to increasing concentrations of atorvastatin and cerivastatin in the 1536 colony format. The plates were then imaged after 48 hours and colony sizes were measured using the software package “Gitter in r” (Wagih and Parts, 2014). The average colony size was measured for each plate and residual growth was calculated using a carrier (DMSO) control. Figure 14 shows the residual growths for each of the four ssDMA’s. It is clear that the three new ssDMA’s in the genetic backgrounds of SK1, Y55 and YPS606 show the same drug resistant phenotypes as the original strains
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Figure 14 Statin resistant ssDMA's. Residual growth of plate 10 of the original DMA compared to the three newly created ssDMA’s in the genetic backgrounds of Y55, SK1 and YPS606. 6A is the DMA and ssDMA’s against increasing concentrations of atorvastatin and 6B is against increasing concentrations of cerivastatin.
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3.6 Discussion
The common lab strain widely used in DMAs was derived from S288C which was originally isolated from a rotting fig in California, USA circa. 1938 and has been well characterised and studied over the years (Mortimer and Johnston, 1986). S288C was also the first eukaryote species to be fully sequenced (Goffeau et al., 1996) which led to the construction of the original DMA (Winzeler et al., 1999). It is of interest to note that S288C diploid derivatives are of low sporulation efficiency (2%) whereas the three strains comprising the new ssDMAs have very high sporulation efficiencies (Tomar et al., 2013) of over 70% making them potentially useful for SGA research in their own right. This is because SGA amenity is limited by sporulation efficiency. This property likewise made the ssDMAs more amenable to the next step in this dissertation, namely the comparison of genetic interaction networks by SGA methodology described in the following chapters.
The three SGRP strains showing resistance to atorvastatin and cerivastatin are diverse and have been isolated from various places: YPS606 has been used as a lab strain and was isolated from an oak tree in Pennsylvania, USA; along with SK1 which has also been used as a common lab strain (Kane and Roth, 1974), and was isolated from soil in the USA; Y55 was originally used as a wine strain and was isolated in France and has also been used as a lab strain. These strains show similar divergence to human individuals (Chin et al., 2012; Consortium, 2010; Dowell et al., 2010), with the greatest divergence between the strains S288C and SK1 (Figure 8). These strains have a polymorphism on average every 175 bp (Deutschbauer and Davis, 2005), with a range
47 of one every 150 to 250 bp in the non-sub-telomeric regions of all sequenced S. cerevisiae strains which equates to 99.29 – 99.6% identity to S288C (Hospital, 2005;
Strope et al., 2015). Furthermore, a study conducted to improve heat tolerance to wine yeast underwent three back crosses and the strains were expected to carry
93.75% of the in-crossed parental genome Upon analysis of microsatellites it was shown that the backcross resultant strain harboured 91.6% of the genome inherited from the in-crossed parent (Marullo et al., 2009). This result, along with others like it is generally consistent with the efficacy of the backcross method.
Based on the newly created ssDMA’s statin resistance phenotype (compared to the original S288C DMA, Figure 14) the three new ssDMA deletion sets of genetically diverse backgrounds should have minimal genetic contribution from the original DMA, from which they were derived. This statement is based the revealed phenotype and also on the expected outcome of multiple backcrosses but is an assertion that can be verified by genome sequencing, a project that was precluded by time and money constraints in the dissertation work described here. Nonetheless, the newly created ssDMA’s described in this Chapter provide a reasonable basis for the next steps namely to study the genetic interaction networks of between individuals yeast strains and investigate the degree of variation between them. Furthermore, the behaviour of the ssDMAs in subsequent chapters tend to support this assertion.
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Results chemical genetic profiling
4.1 Introduction
The central assumption of chemical genetics (Hillenmeyer et al., 2008; Parsons et al.,
2004; Parsons et al., 2006) is that a drug or small molecule inhibitor (SMI) binds specifically to a gene product and alters its function, mimicking a mutation in the corresponding gene (Figure 5). Upon submitting the DMA to a growth inhibitory concentration of a drug, in the current studies atorvastatin or cerivastatin, it was possible to create a network comprising gene deletions which confer hypersensitivity to the drug, similar to those created using a query gene deletion (Parsons et al., 2006).
This Chapter describes an approach in which atorvastatin and cerivastatin were used as SMIs. Epistatic genetic interaction networks were so defined and obtained from the four ssDMA’s described in Chapter 3 for comparison. Therefore, the results described in this and the following chapter enabling comparison of the individual chemical genetic interaction networks surrounding the statin drugs in four genetically different
S. cerevisiae strains. Furthermore, the chemical genetic screening of atorvastatin and cerivastatin generated 1424 genetic interactions across the four DMA’s. The interacting strains were picked from their corresponding DMA using the Singer Stinger attachment for the Singer RoToR robot and confirmed via serial spot dilution analysis
(Chapter 2.10).
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4.2 Chemical genetic screen optimisation
Prior to chemical genetic profiling, one plate each from the commercial DMA and newly created ssDMA’s were pinned onto increasing concentrations of atorvastatin and cerivastatin to determine the concentration which inhibits growth by 40-50%. This concentration was used to enable detection of chemical genetic interactions in non- essential genes for DMA screening. These results are described in Chapter 3.5 and
Figure 14, and was the assay used to confirm the creation of statin resistant DMA’s.
Based on these results the concentrations used for chemical genetic screening are described in Table 6.
Table 6 Statin screening concentrations
Atorvastatin Cerivastatin Strain (DMA) concentration concentration DMA (Boone) 25 µM 10 µM ssDMA (control, BY4742) 25 µM 10 µM ssDMA (SK1) 100 µM 50 µM ssDMA(YPS606) 100 µM 50 µM ssDMA(Y55) 100 µM 50 µM
4.3 Atorvastatin chemical genetic screen results
The chemical screens on atorvastatin were performed as described in Chapter 2.11, and analysed with Screen Mill using a Z-score cut-off of 1.0 (Chapter 2.12), the genes that were considered hits are displayed in Appendix 1 and the number of hits are summarised in Table 7. All hits were independently verified as hits by spot dilution assays and each gene was scored on a scale of 0-4, 0 being no growth at all and 4 slight growth inhibition compared to the control. Genes with a growth score of less than 2 are considered to be strong chemical genetic interactions (Appendix 1).
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Table 7 Atorvastatin – summary of chemical genetic interactions
Strain Boone ssDMA Y55 ssDMA SK1 ssDMA YPS606 Boone 86 13 13 26 ssDMA Y55 13 268 33 36 ssDMA SK1 13 33 166 26 ssDMA YPS606 26 36 26 168
The data displayed in Table 7 are a summary of the chemical genetic screens performed with the S288C DMA and ssDMA’s showing the atorvastatin X ssDMA genetic interaction overlaps between the strains. As can be seen from this table there is little overlap between the strains. Furthermore, there are only five genes (HMG1,
MID1, MID2, PDR1 and VRP1) which interact in all strains, and ten genes (CKA2, MCP2,
RIM15, SPF1, SLT2, SYT1, YOR1, YFL032W, YLR257W and YLR255C) which interact with only the ssDMA’s (Figure 15). Finally, there are 44 genes (described below) which share interactions with atorvastatin in two out of three of the ssDMA’s (Figure 15). All descriptions of gene functions in this Chapter are summarised from the
Saccharomyces Genome Database (Cherry et al., 2012).
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Figure 15 Atorvastatin chemical genetic interaction network. Blue indicates a genetic interaction with one strain, green indicates genetic interactions with two strains, red indicates genetic interactions with three strains and pink indicates genetic interactions with four strains.
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Table 8 GO-slim categories of atorvastatin chemical genetic interactions
GO Term S288C SK1 Y55 YPS606 CKA1, CLB2, CLB5, IME2, CKB1, CLB1, CMK1, Protein AKL1, DBF2, KIN1, KSP1, KSS1, PTP3, RCK2, FUS3, PTC2 Phosphorylation PPS1, SNT1 MRK1, NNK1, SKY1, TPK3, YIH1 PSK1 CMK1, GPA1, FAR1, FUS3, MDS3, BEM2, EDE1, KSP1, MFA2, HAC1, KSS1, LRG1, PEX15, PTC2, RGD1, Signalling PPS1, SLM4, OPY2, RTG3, MFA2, PTP3, RCK2, RLM1, ROM2, RTC1, SNT1 SYG1 ROG3, RSR1, SLG1, TUS1 TPK3 regulation of CLB2, CLB5, CLB1, MCX1, PTP3, protein PPS1, SNT1 FUS3, PTC2 GIP1 YIH1 modification APL6, DJP1, APM3, AST1, ATG7, PEX21, RTG3, KAP114, MSP1, COG8, PEX15, SCS2, Protein targeting ATG2, VPS9 SIL1, TOM71, PEX14, SEC66, SYS1 VPS8 SKY1, TRS85, VPS73 CYB5, ECM22, BST1, BTS1, ETR1, Lipid metabolic CSH1, HMG2, ARV1, CRD1, FAB1, MDH3, SPO14, IPT1, LAC1, ORM2, process OPI3 HTD2, ORM1, PCT1 YPC1 PLB2, SCS2, SCT1 BGL2, ECM33, Cell wall CHS5, HOC1, GAS1, GSC2, LRG1, GIP1, RCR1, CHS7, ROM2, TUS1 organisation MNN10 MTL1, SLG1, UBC7 SVP26 Mitochondrial ATG32, COX14,
ATG2, EMI1 N/A AEP2 organisation PET191, RSM25 Golgi vesicle APL6, SVP26, APL4, APM3, BUG1, ARL3, BST1, CHS7, CHS5, VPS9 transport TRX2 GCS1, SOP4, TRS85 COG8, SYS1 Endosomal RIC1, SFT2, SYS1,
VPS9 VPS8 YPT6, YPT7 transport VPS35, VPS5 protein
MNN10 SVP26 GTB1 MNN5 glycosylation
4.3.1 Chemical genetic interactions atorvastatin which overlap in all DMA’s The five genes which interact with all DMA’s are as follows: HMG1, encodes HMG-CoA reductase, the major target of atorvastatin; PDR1, a major transcription factor of the pleotropic drug resistance (PDR) network; VRP1, an actin cytoskeletal protein, with an important role in endocytosis; MID1, N-glycosylated plasma membrane protein
53 functioning as a calcium cation channel; MID2, an O-glycosylated plasma membrane protein which acts as a sensor for cell wall integrity signalling and interacts with guanine nucleotide exchange factors.
4.3.2 Chemical genetic interactions with atorvastatin which are unique to ssDMA’s The nine genes unique to the ssDMA’s created in this study are: SLT2 a Ser/Thr MAP kinase, regulating cell wall integrity; SPF1 an ion transporter of the ER membrane;
YOR1, a plasma membrane ATP-binding cassette acting within the PDR network,
RIM15, protein kinase involved in cell proliferation; MCP2, a mitochondrial protein of unknown function involved in lipid homeostasis; YFL032W, a dubious open reading frame (ORF) partially overlapping HAC1 – a transcription factor which regulates the unfolded protein response (UPR); YLR255C, dubious ORF; and YLR257W, protein of unknown function.
4.3.3 Chemical genetic interactions atorvastatin which overlap in two ssDMA’s 17 genes are shared between the Y55 and SK1 ssDMAs: ALG6, involved in N-linked glycosylation; ECM31, mitochondrial protein involved in pantothenic acid biosynthesis; ENT5, involved in Golgi to endosome clatherin mediated vesicle transport; FMS1, polyamine oxidase required for modification of eIF-5A and is also involved in pantothenic acid biosynthesis; GIC1, protein involved in the initiation of budding and cellular polarization; LRS4, nucleolar protein involved in chromosomal segregation; MRH1, plasma membrane protein; NDL1, regulator of dynein targeting to microtubules; NEJ1, involved in DNA repair, via non-homologous end joining;
RPL34A, ribosomal 60s subunit subunit of RNA polymerase II complex; SYM1, mitochondrial protein required for ethanol metabolism; VHS3, regulatory subunit of
54 protein phosphatase 1, PPZ1, involved in co-enzyme A biosynthesis; EMC1, endoplasmic reticulum protein involved in protein folding; RRT13, putative protein of unknown function; IRC20, E3 ubiquitin ligase involved in homologous recombination and DNA repair; YLR252W, dubious ORF partially overlapping SYM1.
A total of 10 genes were common between SK1 and YPS606 ssDMA’s: FYV8, a protein of unknown function; IES1, subunit of the INO80 chromatin remodelling complex;
RCN1, involved in calcium signalling; RPN4, transcription factor of proteasome genes;
SUR2, sphinganine C4-hydrolase involved in sphingolipid biosynthesis; UBX3, subunit of an E3 ubiquitin ligase complex; YBL065W, dubious ORF partially overlaps SEF1 – a putative transcription factor; YBL081W, protein of unknown function; YJR087W, dubious ORF, partially overlapping STE18 – G protein subunit involved in the mating signalling pathway and EMC2 – a member of an ER transmembrane complex involved in protein folding; YNL296W, dubious ORF.
There were 17 interacting genes shared with both Y55 and YPS606: ABP1, acting binding protein involved in cytoskeletal organisation; ACB1, acyl-CoA binding protein, transports acyl CoA esters from fatty acid synthetase to acyl-CoA-consuming processes; APA2, diadenosine phosphorylase involved in nucleoside catabolism; ARP6, actin related protein which binds nucleosomes, involved in chromatin remodelling;
CAP1, alpha subunit of the capping protein complex; DCR2, phosphoesterase, involved in downregulation of the UPR via dephosphorylation of Ire1p; DEP1, component of the
Rpd3L histone deacetylase complex, required for diauxic shift; FUB1, proteasome binding protein, interacts with subunits of the 20s proteasome; IRE1, Ser/Thr kinase, a transmembrane protein that regulates the UPR via regulation of Hac1p; JSN1, RNA
55 binding protein, interacts with mRNA’s encoding membrane proteins; LIF1, component of the DNA ligase IV complex; MPH1, 3’-5’ DNA helicase involved in error free bypass of DNA legions; MPM1, mitochondrial protein of unknown function; PBS2,
MAP kinase of the HOG signalling pathway, activated under osmotic stress; SSK2, MAP kinase kinase of the HOG1 signalling pathway, interacts with Ssk1p causing the activation of Ssk2p which phosphorylates Pbs2p and also mediates recovery from osmotic stress; VPS53, component of the GARP Golgi-associated retrograde protein complex required for recycling proteins from endosomes to the late Golgi; YBL083C, dubious ORF, overlaps ALG3 – involved in dolichol synthesis; YFR018C, putative protein of unknown function.
4.3.4 Gene ontology The genes identified to have a chemical genetic interaction with atorvastatin were subjected to gene ontology analysis using Yeast Mine (Balakrishnan et al., 2012), however, no significant enrichment was seen for the commercial DMA or the ssDMA’s.
The genes which interacted with atorvastatin and were common to two or more
DMA’s were subjected to GO analysis. This resulted in an enrichment for the ER unfolded protein response (p = 0.043) and included the following genes: BCK1, DCR2,
IRE1, MID2 and SLT2.
4.4 Cerivastatin chemical genetic screen results
The chemical genetic screens on cerivastatin were performed as described in Chapter
2.11, and analysed with Screen Mill using a z-score cut-off of 1.0 (Chapter 2.12), the genes that were considered hits are displayed in the tables of Appendix 1 and
56 summarised in Table 9. All hits were independently verified as hits by serial spot dilution assay’s and each gene was scored on a scale of 0-4, 0 being no growth at all and 4 slight growth inhibition compared to the control Genes with a growth score of less than 2 are considered to be strong chemical genetic interactions (Appendix 1).
Table 9 Cerivastatin - summary of chemical genetic interactions
Strain Boone ssDMA Y55 ssDMA SK1 ssDMA YPS606 Boone 309 25 49 36 ssDMA Y55 25 206 33 33 ssDMA SK1 49 33 185 32 ssDMA YPS606 36 33 32 171
The data displayed in Table 9 is a summary of the cerivastatin chemical genetic screens performed with the S288C DMA and ssDMA’s showing the overlaps between the strains. As can be seen from this table there is little overlap between the strains.
Furthermore, there are only six genes which interact with all strains, and eight genes which interact with only the ssDMA’s. Finally, there are 37 genes which share interactions with atorvastatin in two/three of the ssDMA’s (Figure 16).
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Figure 16 Cerivastatin chemical genetic interaction network. Blue indicates a genetic interaction with one strain, green indicates genetic interactions with two strains, red indicates genetic interactions with three strains and pink indicates genetic interactions with four strains.
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Table 10 GO-slim categories of cerivastatin chemical genetic interactions
GO Term S288c SK1 Y55 YPS606 AKL1, ALK2, ATG13, CLB5, DBF2, HOS2, CKA1, PCL7, BUB1, NNK1, Protein
KNS1, PHO80, PPS1, RCK1, SSK1, SSK2 PRK1, PRR1, PSK1, Phosphorylation PSK2, SAT4, SIC1, YCK1 RTF1, SNF1 SIF2, SNT1 BEM2, BIT2, EDE1, CMP2, CSI1, HOS2, MFA1, PHO80, FAR8, MDS3, Signalling PPS1, SAP190, SIF2, MFA2, SSK1 IRS4, ROD1 NPR3, ROM2, SLM4, SNT1, TIP41, SSK2, TUS1 YJR084W ACM1, ATG13, CHD1, Regulation of CLB5, HOS2, MCX1, MAD2, PCL7, protein N/A BUB1, HIR3, RTF1 NEM1, PHO80, PPS1, SSK1 modification SIC1, SIF2, SNT1 ATG13, ATG2, ATG7, APL6, HSE1, NUP133, PEX8, TRE1, BTT1, DID2, Protein targeting LOT6, SEC66, PEX18, SSA1 VPS27, VPS51, VPS8, MMM1 VAC8, VPS73 VPS9 CLD1, EGH1, GUP1, DEP1, ETR1, BST1, DFG10, Lipid metabolic HMG2, INO4, NEM1, FAB1, FIG4, ICL2, MDH3, INP51, NSG1, process OPI3, ORM1, POX1, MDM31, PLB2, MGA2, SKN1 PSD2 SPT23 SAY1, VAC7 ECM33, GAS1, GSC2, DCW1, HPF1, IRS4, MTL1, Cell wall HOC1, MNN10, CRH1, OSW2, KTR6, ROM2, MYO3, SKN1, organisation RCR1,SPO71, YEA4, SMI1 TUS1, UBC7, SNF1, SPO21, ZEO1 ZRG8 SVP26 MDM34, AIM23, ATP20, ATG13, ATG2, ATG7, COX17, MMM1, Mitochondrial MTO1, PCP1, CBP4, CMC1, EMI1, FMC1, PET123, PET494, THI4, organisation PHB2, PPE1, COA4, MDM31, PTC6, SML1 WHI2, YMR31 RRM3 PET191, SSA1 APM1, BRE5, GET2, APL4, ARF1, Golgi vesicle DSS4, SVP26, IMH1, RCY1, RUD3, APL6, BST1 AVL9, SED4, transport VPS53 TRS33, VPS9 TRS65 RCY1, RIC1, TRE1, Endosomal VPS27, VPS51, VPS8, HSE1, VPS38 N/A DID2, VPS53 transport VPS9 ERD1, GNT1, MNN1, Protein
MNN10, MNN2, GDA1 KTR6 OST4, SVP26 glycosylation OST6
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4.4.1 Chemical genetic interactions with cerivastatin which overlap in all DMA’s The six genes which interact with all DMA’s are as follows: HMG1, MID1 and MID2, which also interacted with atorvastatin in all DMA’s described in 4.3.1; SWE1, protein kinase which regulates the G2/M transition; TPO1, polyamine transporter; YFL032W, dubious ORF which partially overlaps HAC1.
4.4.2 Chemical genetic interactions with cerivastatin which are unique to ssDMA’s The eight genes which overlap all ssDMA’s created in this study are: APE2, aminopeptidase yscII; HMS2, protein with similarity to heat shock transcription factors, involved in the suppression of pseudohyphal growth; KGD2, dihydrolipoyl trans-succinylase of the mitochondria involved in oxidative phosphorylation; LHS1, an
ER chaperone regulated by the UPR pathway; STE24, zinc metalloprotease, involved in a-factor maturation STR2, cystathionine gamma-synthase; TDA4, putative protein of unknown function YKL091C, putative phosphatidylinositol/phosphotidylcholine transfer protein probably involved in lipid metabolism.
4.4.3 Chemical genetic interactions with cerivastatin which overlap in two ssDMA’s 20 genes are shared between the Y55 and SK1 ssDMAs: APA2, diadenosine phosphorylase II; ARP6, actin related protein which binds nucleosomes, involved in chromatin remodelling; CAF4, WD40 repeat protein of the mitochondria; CCH1, voltage gated calcium channel involved in calcium influx in response to environmental stress; DCR2, phosphoesterase, involved in downregulation of the UPR via dephosphorylation of Ire1p; EMC6, a member of an ER transmembrane complex involved in protein folding; FUS3, Ser/Thr MAP kinase involved in mating; HXK1, hexokinase 1, catalyses the phosphorylation of glucose during glucose metabolism;
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IRE1, Ser/Thr kinase, a transmembrane protein that regulates the UPR via regulation of Hac1p; LAP4, vacuolar aminopeptidase; YSC1, zinc metalloproteinase; MCP2, a mitochondrial protein of unknown function involved in lipid homeostasis; PTR2, integral membrane peptide transporter; RLM1, transcription factor, component of the protein kinase C mediated MAP kinase pathway; SNF11, subunit of the SWI/SNF chromatin remodelling complex, involved in transcriptional regulation; SPF1 an ion transporter of the ER membrane; SYT1, guanine nucleotide exchange factor, involved in vesicular transport; YBR235W, vacuolar membrane cation chloride cotransporter, possibly mediates potassium and chloride transport into the vacuole; YER188W, dubious ORF.
There are 19 genes shared between Y55 and YPS606: DST1, general elongation transcription factor, involved with RNA polymerase II; FMP48, putative protein of unknown function, has been detected in mitochondria; GDH2, glutamate dehydrogenase, involved with suppression of stress – induced apoptosis; GRR1, F-box protein component of an SCF containing ubiquitin ligase complex; HOS4, subunit of the Set3 complex, involved in repression of sporulation; HTZ1, histone variant H2AZ, involved in transcriptional regulation; YDR193W, dubious ORF; CNL1, subunit of the
BLOC-1 complex involved in endosomal maturation; PHO92, post transcriptional regulator of phosphate metabolism; PFA5, palmitoyltransferase with autoacylation activity, likely to function in pathways outside Ras; YER034W, protein of unknown function; YER066C-A, dubious ORF; YER158C, protein of unknown function; YER158C, putative protein of unknown function, induced in respiratory deficient cells;
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YFL040W, putative transporter protein; ZNF1, zinc cluster transcription factor, regulates respiratory growth.
Finally, there are 18 genes that are common between SK1 and YPS606: ADR1, zinc finger transcription factor, involved in non-fermentable carbon source utilisation;
EMC2, a member of an ER transmembrane complex involved in protein folding; LAC1, ceramide synthase component, involved in the synthesis of ceramide from acyl- coenzyme A and dihydrosphingosine; MPM1, mitochondrial protein of unknown function; MRPL36, mitochondrial ribosomal protein of the large subunit; NUT1, component of the RNA polymerase II mediator complex; OSM1, fumarate reductase, catalyses the reduction of fumarate to succinate, required for anaerobic growth;
PTC7, mitochondrial Ser/Thr protein phosphatase; RMD8, cytosolic protein required for sporulation; RKM2, ribosomal protein lysine methyltransferase; SEC28, epsilon-
COP subunit of the coatomer, regulates Golgi to ER transport; SSD1, translational repressor involved in polar growth and wall integrity; SYM1,mitochondrial protein required for ethanol metabolism, induced by heat shock; VTC4, vacuolar membrane polyphosphate polymerase, involved in membrane trafficking; YDR199W, dubious
ORF, partially overlaps VPS64 – required for targeting of proteins to the vacuole; BRP1, dubious ORF located in the upstream region of PMA1 – plasma membrane ATPase pump; YJL049W, putative protein of unknown function; YPF1, intramembrane aspartyl protease of the ER membrane, involved in the ERAD pathway.
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4.4.4 Gene ontology Similar to the results for atorvastatin, the genes identified to have a chemical genetic interaction with cerivastatin were subjected to gene ontology analysis using Yeast
Mine (Balakrishnan et al., 2012), however no significant enrichment was seen for the commercial S288C DMA or the ssDMA’s. Therefore we subjected the genes which interacted with cerivastatin that were common to two or more DMA’s. There was significant enrichment in the following categories: ER unfolded protein response (p =
0.0073) which includes the following genes, BCK1, DCR2, HAC1, IRE1, MID2 and SLT2.
Also, signalling (p = 0.04) including the genes: BCK1, CNB1, DCR2, FUS3, HAC1, IRE1,
MID2, MKK2, RCN1, RGD1, RIM15, RLM1, RRI1, SLT2, SPO14, SRO7 and SYT1.
4.5 Discussion
4.5.1 Genetic interactions conserved across all DMA’s The atorvastatin chemical genetic screen resulted in five genes (HMG1, PDR1, MID1,
MID2 and VRP1) that showed chemical genetic interactions in all four DMA’s used in this study. The cerivastatin chemical genetic screen resulted in six genes, three of which are consistent with those seen to interact with atorvastatin (HMG1, MID1 and
MID2), and three of which are unique to cerivastatin (SWE1, TPO1 and YFL032W). It was expected that we would observe HMG1 as a hit in all the screens as this gene encodes the major drug target of atorvastatin (Basson et al., 1986), HMG-CoA reductase. Furthermore, if this gene did not show any interaction in any one of the newly created ssDMA’s it would bring the backcross strategy into doubt, however since this is not the case it supports that the DMA’s are all behaving as they should.
PDR1 is one of the transcription factors which regulate the pleotropic drug resistance network (PDR), this gene was also expected to be highly conserved as atorvastatin is
63 a known inducer of the PDR network. Furthermore, TPO1 is a polyamine transporter, and also part of the pleotropic drug resistance network.
MID1 and MID2, are N- and O-glycosylated (respectively) membrane proteins of the
ER and plasma membrane, interacting with both atorvastatin and cerivastatin. The inhibition of HMG-CoA reductase not only causes a reduction in ergosterol, it also causes a decrease in the isoprenoid metabolites, isopentyl pyrophosphate (PP) and farnesyl PP, of which dolichol, is derived from (Vaklavas et al., 2009). Therefore inhibiting these isoprenoids leads to a reduction in dolichol, whose primary role is the mediation of N-linked glycosylation of nascent peptides. This could also lead to misfolded proteins due to the concurrent inhibition of dystroglycan, a glycoprotein existing as α and β subunits which undergo extensive post translational modification in the Golgi and ER. The α subunit is a secreted peripheral membrane protein which undergoes O-linked glycosylation, whereas the β subunit interacts with dystrophin which binds to filamentous actin (Baker, 2005; Siddals et al., 2004). Therefore, these proteins play important roles in the cytoskeleton of the cell. Moreover, VRP1, which is associated with organisation of the cytoskeleton is also likely a result of a decrease in isoprene intermediates, as it has been shown to interact with the Rho GTPase activating proteins Rho3p and Rho4p and they are also dependent on isoprenylation
(Sun et al., 2006). Moreover, several types of muscular dystrophy (one of the side effects of statin use) have been associated with impaired protein glycosylation (Baker,
2005).
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4.5.2 Conservation of unfolded protein response genes It can be seen from the networks resulting from the chemical genetic screens performed on atorvastatin and cerivastatin that there is conservation of a core set of genes involved in the UPR, based on GO enrichment (Chapter 4.3.4 and 4.4.4). This was expected as previous studies in our lab (Yibmantasiri P., 2012, personal communication, unpublished) and others (Mörck et al., 2009; Shechtman et al., 2011) showed atorvastatin and cerivastatin are inducers of the UPR. Furthermore, as mentioned in the previous paragraph, the deficiency in glycosylation (via inhibition of
MID2) can lead to an increase in misfolded proteins, thus leading to activation of the
UPR. It has also been shown that statins induce the UPR in C. elegans through a similar mechanism involving IRE1 (Mörck et al., 2009). It is apparent that statins are inducing the UPR via inhibition of phosphorylation (BCK1, IRE1, and SLT2), and Ire1p phosphorylates Hac1p that in turn regulates the UPR (Chen et al., 2005). Studies conducted in human fibroblasts (Lecca et al., 2005) and in Aspergillus fumigatus (Li et al., 2011) also describe inhibition of glycosylation leading to activation of the UPR.
4.5.3 Conservation of the chemical genetic interaction networks surrounding atorvastatin and cerivastatin It is apparent from data presented in this chapter that one process affected by the statin treatment is the UPR which is conserved across all strains in the chemical genetic interaction networks generated for atorvastatin and cerivastatin. This result is similar to that described in a study using an epistasis map in S. pombe by Roguev et. al., who concluded there was little conservation of the general network, but that there was, conservation of specific modules in a given network (Roguev et al., 2008), in this instance the conserved module being the UPR. However, it is unsurprising that the current observed conservation of similar processes in these strains is because the
65 processes described here are essential. However, what is interesting is the specific genes which show chemical genetic interactions in a specific process in one strain are not necessarily showing the same chemical genetic interactions in another strain. An example is the chemical genetic interactions seen with atorvastatin and genes in the sphingolipid pathway, namely CSH1 (S288C), YPC1 (SK1), ORM1 (Y55), IPT1, LAC1 and
ORM2 (YPS606). These are all genes in the sphingolipid pathway, which is inhibited at a different gene in each of the four strains tested in this study. Therefore, the process of sphingolipid metabolism inhibition is conserved however the individual genes are not.
Furthermore, the chemical genetic interaction data in Table 8 and Table 10 show a general snapshot of the GO slim mapper, biological process (SGD, 2009) terms (but not enrichments) across the strains tested in this study. It is apparent from this table, that similar processes are affected by statin drug treatment in different individual yeast strains, although the processes are similar the genes encoding the proteins in theses complexes are strain specific. Therefore, it is possible to conclude that the biological processes that are essential when different strains of S. cerevisiae are challenged with statins are in fact conserved. However, the genes which encode these processes are not. This lack of conservation could be attributed to re-wiring of the genetic interaction networks encoding a specific cellular process (Dixon et al., 2008;
Roguev et al., 2008). This may be one explanation why statin efficacy and side effects vary among individuals (Liao and Laufs, 2005; Oh et al., 2007; Reiner, 2014), and may be one step closer to understanding the paradigm of relating genotype to phenotype.
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4.5.4 Small GTPases in statin response Small GTPases are second important processes shown here (and elsewhere) to be associated with statin side effects, it is thought these effects are due to inhibition of the isoprenoid intermediates farnesyl–PP and geranylgeranyl-PP where both are responsible for post translational modification and membrane association of small
GTPases (Rauthan et al., 2013; Rikitake and Liao, 2005). These small GTPases have roles in many cellular functions including cytoskeleton organisation, intracellular trafficking, transcriptional regulation and cell development and growth (Cordle et al.,
2005; Parri and Chiarugi, 2010). Furthermore, the Rho family of GTPases are major regulators of the actin cytoskeleton. However, although it has been previously described that statins disrupt the normal function of G-protein functions, the specific mechanism still remains unclear (Laufs et al., 2002; Zhu et al., 2013). Interestingly, in our study each strain shows a genetic interaction with a gene encoding a small GTPase activating protein, including BEM2, YPT6, RGD1 and ROM2 in S288C, Y55, SK1 and
YPS606 respectively. BEM2 encodes a Rho GTPase activating protein, involved in organisation of the cytoskeleton; RGD1 encodes a GTPase activating protein, involved in cytoskeleton organisation; YPT6, a Rab GTPase, involved in the vesicle mediated transport within the secretory pathway; ROM2 is a GDP/GTP exchange factor for
Rho1p and Rho2p. These data indicate that the statins are interacting with the same pathways in the different strains also suggesting the possibility that a major cellular effect caused by statin inhibition leads to instability of the cytoskeleton. This might provide insight into the underlying mechanism for the varying degrees of muscular myopathy experienced by individuals taking statin therapy. In addition, a study conducted by Rauthan et.al., using C. elegans suggests the cholesterol independent
67 effects (inhibition of isoprenylation) of statin inhibition are attributed to regulation of the UPR between individuals, which may account for the varied susceptibility in those experiencing side effects whilst talking statins (Rauthan et al., 2013).
4.5.5 Implications of this study This study screened two statin drugs against four independent DMA’s, resulting in
1424 unique interactions. This list was then further narrowed down via subsequent analysis of serial spot dilution assays to around 300. Thus, we have created a powerful screening tool to investigate the genetic basis of phenotypic variation between individuals which has not been described elsewhere and can be applied to any phenotype.
Furthermore, we have uncovered two major cellular processes which are affected by individual strains with respect to statin inhibition, namely the UPR and small GTPase activity. These processes are seen to confer statin sensitivity across all strains described in this chapter, however it has become apparent that different genes in different strains are responsible for statin susceptibility.
Secondly, the UPR has been previously implicated in statin treatment,a major pathway affected by statin therapy. In many species the UPR is activated by IRE1 – inositol requiring Ser/Thr protein kinase, which is shown to interact with the statins in this study. Furthermore, in previous studies it has been shown that the addition of mevalonate or farnesyl-PP is able to rescue the UPR activation, Morck et al., 2009, showed this mechanism occured via farnysylation of small GTPases rather than via geranylgeranylation and they concluded that inhibition of the mevalonate pathway leads to activation of the UPR by inhibition of protein prenylation (Mörck et al., 2009).
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However, C. elegans lacks part of the mevalonate pathway which leads to synthesis of cholesterol so is only a model for the non-sterol – effects of statin drugs. Therefore, the data in the current study is similar to the C. elegans study, but the yeast model described here also allows investigation of the sterol mediated effects of statin.
Thirdly, the yeast model is amenable to high throughput screening for example to rescue the non-sterol effects of statin inhibition using SMI libraries seeking rescue of the unwanted phenotype. Furthermore, when looking at inhibition of small Rho
GTPases, from another angle, these proteins are heavily implicated in cancer, in almost every stage of tumorigenesis (Sahai and Marshall, 2002). Therefore, understanding the exact mechanism by which statins (and potentially other SMI’s) inhibit Rho proteins could provide beneficial groundwork in the treatment of many cancers, as to date there are no clinically effective drugs which target Rho GTPase’s
(Lin and Zheng, 2015).
Lastly, the genes which show interactions with either atorvastatin or cerivastatin are clearly different, which may be a basis for investigating side-effects. Furthermore, the genes which only interact with the ssDMAs should provide a starting point for future studies surrounding statin resistance.
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Results synthetic genetic array analysis using ssDMA’s
5.1 Introduction
Genetic interaction buffering may explain why the majority of single gene knockouts exhibit little or no effect on phenotype (Hartman et al., 2001) and genes can be assembled in functional genetic interaction networks by SGA technology (Tong et al.,
2001). Naturally occurring biological networks are “small world”, i.e. any gene is functionally connected to others in pathways of about 4-5 genes (Barabasi and Oltvai,
2004; Hartman et al., 2001) a property that makes analysis of such buffering redundancy genetic interaction networks meaningful. Though it is possible to analyse such buffering at the single gene level, it is also possible to avoid the bias inherent in single gene analysis by performing high-throughput screening thus allowing the assembly of genetic interaction networks (SGAs) on a genome-wide basis.
In planning such systemic SGAs it must be noted that the commercially available yeast deletion sets (DMAs) do not provide query strains, therefore they must be constructed de novo with the appropriate antibiotic marker to provide for diploid and haploid selections. Recapitulating the descriptions in Chapter 2, query strains are mass mated to the DMA’s using the SGA procedure (Chapter 2.8) which relies on the ability of yeast to grow as haploids, mate to form diploids, undergo meiosis and be selected for haploid double-mutants. This procedure on a genome-wide basis allows the visualisation of those synthetic lethal/sick double mutants (Figure 5) displaying epistatic genetic interactions (Boone et al., 2007; Tong et al., 2001; Tong et al., 2004) and unless stated otherwise are the only type of genetic interaction discussed in this thesis and visualised as network diagrams.
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This chapter describes the SGA genetic interaction networks elicited using specifically chosen query genes and the ssDMAs previously described with the purpose of comparing such networks between the strains. The query genes chosen are the two statin target genes, HMG1 and HMG2 (HMG-CoA reductase paralogs that catalyse the conversion of HMG-CoA to mevalonate, the rate limiting enzyme in sterol biosynthesis, Chapter 1.2.4) and three genes (ARV1, OPI3 and BTS1) that were previously described as statin interactors (Busby, 2009) and successfully used as query genes in the current study. The current work, therefore, extended these observations to investigate the genetic interaction networks surrounding these query genes in addressing the main question of this dissertation i.e. how conserved are genetic interaction networks for a given phenotype (statin effects) in different individual strains? Extending the reasoning for query gene choice, ARV1 has been shown to be involved with sterol transport and storage within the cell (Shechtman et al., 2011) and has also been implicated in glycosylphosphatidylinositol biosynthesis that is associated with GPI anchored proteins and lipid rafts (Kajiwara et al., 2008). Similarly,
OPI3, catalyses the last two steps of de novo phosphatidylcholine synthesis within the
ER (Daum et al., 1998). Moreover, OPI3 is also known to interact with statins but has wider implications as to membrane lipid composition and BTS1, encoding geranylgeranyl diphosphate synthase, is located at a branch point where the synthesis of isoprenoids branches from the sterol synthesis pathway. Furthermore, BTS1 has been described as a regulator of HMG-CoA reductase (Garza et al., 2009) resulting in increased ubiquination and subsequent degradation of Hmg2p (Federovitch et al.,
2008). On this basis it is concluded that these genes involved in different aspects of lipid biosynthesis also showing inhibition by statins in different strains (Chapter 3) are
71 a reasonable choice to probe genetic interaction network differences or similarities surrounding the effects of statin drugs, in an overall context of lipid biosynthesis.
5.2 Results - Query strain construction
Prior to SGA analysis, query strains were constructed using the PCR mediated gene disruption method (Tong and Boone, 2005). Briefly, two 77 bp gene deletion primers were constructed for each of the five query genes described in this chapter (ARV1,
BTS1, HMG1, HMG2 and OPI3) containing 55 base pairs (bp) of sequence homology to either the flanking upstream or downstream region to each of the five query genes, excluding the start and stop codons (Table 5 Primers used). These primers also included the 22bp of sequence homology at the 3’ end specific for the amplification of the NatR cassette, carried on the plasmid p4339 (Table 3). The resultant NatR
(sequence conferring nourseothricin resistance) PCR product is contained within 55bp target sequences that are homologous for the flanking region of the open reading frame to be disrupted and was then used to individually transform (Chapter 2.7) the four strains utilised for crossing with the ssDMA’s selecting for NAT resistant (NatR) colonies.
Verification of correct integration of the NatR cassette was carried out by PCR on genomic DNA (gDNA) of each of the transformants and analysing product size.
Genomic DNA was extracted using the method described in Chapter 2.4 from Nat- resistant colonies. The gDNA was used as the template in PCR utilising the confirmation forward and reverse PCR primers (Table 5) which flank each integration site with the corresponding internal NatR cassette primer. This resulted in the construction of the 20 query strains (yCG595-yCG614, Table 1), necessary for the
72 construction of genetic interaction networks described in this chapter, i.e. the five chosen query genes in each of the four strains described below.
As is usual with SGAs a chromosomally contiguous set of around 10 genes physically adjacent to the query gene locus are unable to undergo genetic recombination as a result of linkage disequilibrium with the query gene construct and appear as contiguous genetic interactions on the appropriate SGA plate. Genes of this linkage group are not counted as SGA epistatic hits and are removed from the genetic interaction hit list. Such linkage groups are a useful indication that the SGA was working correctly and further confirming that the query strain was properly constructed.
5.3 Results ARV1 SGA’s
The ARV1 query strains generated in the genetic backgrounds of S288C, SK1, Y55 and
YPS606 were mated to each of the ssDMA’s as described in Chapter 2.8. The resulting haploid double mutants were imaged, colony sizes were measured using ‘gitter in r’ and analysed using ‘screen mill’ (Dittmar et al., 2010; Wagih and Parts, 2014) with a Z- score cut off of 1.0 (Chapter 2.12). The 1058 genes considered to be genetic interaction double mutant hits (i.e. having reduced or no growth on the SGA plates) are displayed in Appendix 2 and summarised in Table 11. The genes, that were independently verified as hits via a SGA mini array (Chapter 2.8.6), are designated in bold-type in the tables of Appendix 2.
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Table 11 ARV1 – numerical summary of genetic interactions
Strain S288C DMA ssDMA SK1 ssDMA Y55 ssDMA YPS606 S288C 202 48 48 61 SK1 48 280 48 55 Y55 48 48 302 60 YPS606 61 55 60 273
The data displayed in Table 11 are a numerical summary of the genetic interactions of
ARV1 and show the genes which overlap between the strains, the bold numbers indicate the total number of genetic interactions and the other numbers represent the number of shared genetic interactions between strains i.e. S288C and SK1 share 48 interacting genes. As can be seen from this table there is limited overlap between strains. There are 15 genes common to all the strains which interact with ARV1, 43 genes share interactions with three out of the four DMA’s and 108 genes which share interactions in two of the DMA’s (Figure 17). All descriptions of genes in the following chapter are summarised from the Saccharomyces Genome Database (Cherry et al.,
2012) as currently found on the SGD website.
Table 12 summarises the genetic interactions, catogorised by GO-slim analysis categories, generated with all the DMAs (S288C and three ssDMAs) using ARV1 as a query gene.
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Figure 17 ARV1 genetic interaction network. Blue indicates a genetic interaction with one strain, green indicates genetic interactions with two strains, red indicates genetic interactions with three strains and pink indicates genetic interactions with four strains.
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Table 12 GO slim categories of ARV1 query gene hits
GO term S288C SK1 Y55 YPS606 ARL1, GET3, TCA17, GET2, COG7, GET1, GET3, ARF1, SNC1, ATG8, ERP1, GET3, Golgi GOS1, CHS7, TED1, VPS74, BST1, TRS85, VPS74, ATG20, GET2, BST1, vesicle SEC28, SYS1, RCY1, GET1, CHS7, TCA17, GET2, GET1, TRX2, GOS1, transport LST4, SEC22, CHS5, VPS53 CHS7, TED1, COY1 CHS7, APS3, RCY1 COG8, VPS9, COG5, RUD3 RRI1, YDR018C, OAF1, ETR1, IPT1, UME6, CSG2, FAT1, CSG2, IFA38, SUR2, VPS74, YPC1, FRM2, CSG2, ARE1, IPT1, INO2, MDH3, IPT1, UME6, Lipid CEM1, BST1, MDH3, YDL109C, UME6, CEM1, CHO2, BST1, ATF2, LAS21, metabolic GUP1, ORM1, YDR018C, VPS74, LAS21, OPI3, SAC1, ELO1, ECM22, process SKN1, ECM22, LAS21, ATG26, ECM22, HMG2, INO4 ERG6, HMG1, PLB2, HMG1, PAH1, UPS1, ECM22, HFA1, BTS1 TGL3, CPT1, HMG1, PLB2, BTS1 SCS7, BTS1 GET3, BTN2, FLC2, CNE1, SSE2, CNE1, HSP26, Protein GET3, CHS7, YKE2 CHS7, HSP104, CHS7, HSP104, GET3,BTN2, CHS7, folding YKE2, CPR6 CPR6, YDJ1 EMC3, YKE2 PRR2, CKB1, FUS3, SIF2, STE50, PSK1, SIF2, SLI15, PKP2, RTF1, KIN3, ALK2, FUS3, SNT1, NBP2, CKB1, PCL2, NBP2, SIP1, Protein FMP48, BUB1, CHK1, SIP1, CKB1, HOS2, RTF1, KSS1, SLT2, IRE1, IME2, phosphor- SLT2, IRE1, RCK1, RTF1, SLT2, DBF2, SLT2, IRE1, BCK1, SWE1, PRR1, rylation BCK1, CDC73, IRE1, KSP1, BCK1, SWE1, HSL1, CTK1, CDC73, YPK2, ARK1, PCL1, HSL1, CDC73 CDC73, CKB2, MKK1 FPK1, CLB5 PSK2 VPS8, ATG8, ARL1, GET3, BLM10, VID24, TRS85, GET3, SBH2, AST1, GET3, ATG20, COG7, KAP122, SLT2, AFG1, SLT2, Protein PEX8, SLT2, VAC8, SLT2, APS3, ICE2, SYS1, VPS51, MIC60, SEC72, targeting MIC60, BOR1, SPC1, LHS1, SEC72, VPS63, COG8, VPS9, NUP188, SAM37, MDJ2, PEX15 SHR5, MDY2 COG5, ATG2, BOR1 EAR1, YDJ1, MDY2 FUS3, SIF2, RGD1, PMT1, GET3, SIF2, BIT2, PMT1, STE50, SNT1, VAM6, SNF3,RRI1, PMT2, FUS3, SEA4, ACK1, NBP2, GET3, ACK1, NBP2, YOS9, CSN9, RGD1, GET3, ACK1, VPS74, SAC7, BEM2, HAC1, HOS2, VPS74, ROG3, SAC7, SIP1, RSR1, HKR1, SIP1, HAC1, Signalling KSS1, SLT2, IRE1, BCK1, SLT2, IRE1, GTR2, SLT2, IRE1, YHI9, SLT2, IRE1, FAR1, CNB1, SAP190, BCK1, CNB1, KSP1, BCK1, CNB1, BAR1, SAP190, MID2, ROM2, WSC2, SAP190, MID2, MID2, DCR2, RLM1, MID2, LEM3, LEM3, SLG1, MKK1, PSY2, PEX15, SYT1 TCO89 RLM1, SYT1 RLM1
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5.3.1 Genetic interactions that are common to all the DMA’s The known function of the 15 genes which interact with all DMA’s are as follows: BRE1,
E3 ubiquitin ligase, regulates ubiquitination in response to oxidative stress; CDC73, component of the Paf1p complex which mediates the activity of RNA Pol I and II; CHS3, chitin synthase III, catalyses the transfer of GlcNAc to chitin; CHS7, protein of unknown function, may be involved in the regulation of CHS3 export from the ER; ECM22, sterol regulatory element binding protein, regulates the transcription of sterol biosynthetic genes; EST1, TLC1-RNA associated factor involved in telomere length regulation; GSY2, glycogen synthase, induced by nutrient limitation; IRE1, Ser/Thr kinase, a transmembrane protein that regulates the UPR via regulation of Hac1p; MID2, an O- glycosylated plasma membrane protein which acts as a sensor for cell wall integrity signalling and interacts with guanine nucleotide exchange factors; MUD2, protein involved in early pre-mRNA splicing; SKT5, activator of Chs3p during vegetative growth; SLT2, a Ser/Thr MAP kinase, regulating cell wall integrity; TOP3, DNA
Topoisomerase III; YBL062W, dubious ORF; YLR428C, dubious ORF, partially overlaps
CRN1.
5.3.2 Genetic interactions that are unique to the ssDMAs There are 9 genes which interact with the ARV1 query gene in all the ssDMAs created in this study. Their functions are: ARO80, zinc finger transcriptional activator; BTS1, geranylgeranyl diphosphate synthase; HMG1, HMG-CoA reductase, catalyses the conversion of HMG-CoA to mevalonate; HRD3, ER membrane protein which plays a central role in ER associated protein degradation; LEO1, component of the Paf1p complex, associates with RNA Pol II and is involved in histone modification; LRS4, nucleolar protein which forms a complex at kinetochores during meiosis I; MCS3,
77 protein of unknown function; RPS8A, protein component of the 40s ribosomal subunit.
5.3.3 Gene ontology The genes having genetic interactions with ARV1 were grouped by the GO-slim mapper tool located on the SGD database to group genes into the “cellular process” category (Ashburner et al., 2000; Cherry et al., 2012), as summarised in Table 12.
Furthermore, the interacting genes were also analysed for significant GO enrichment using Yeast Mine (Balakrishnan et al., 2012). The Y55 and YPS606 ssDMA’s had no significant enrichments (p = <0.05, Holm-Bonferroni correction), S288C and SK1 enrichments are described further below.
S288C showed significant GO enrichment in the following specific categories: protein phosphorylation (p = 0.017) with 19 genes (BCK1, CDC73, CKB1, CKB2, CTK1, DBF2,
FAR1, FUS3, HOS2, HSL1, IRE1, KSS1, MKK1, NBP2, RCK2, RTF2,SIF2, SLT2, SNT1, STE50 and SWE1), Map Kinase cascade (p = 0.019) with eight genes (FUS3, HOS2, KSS1, NBP2,
SIF2, SLT2, SNT1 and STE50) and Golgi vesicle transport (p = 0.045) with 19 genes
(ARL1, CHS5, CHS7, COG5, COG7, COG8, GET1, GET2, GET3, GOS1, LST4, RCY1, RUD3,
SEC22, SEC28, SYS1, TCA17, TED1 and VPS9).
There was also significant enrichment in the following broader categories: 31 genes in signal transduction (p = 1.39x10-5); 31 genes in single organism signalling (p = 1.39x10-
5); 96 genes in biological regulation (p = 1.8x10-5); 31 genes in signalling (p = 1.5x10-
5), 35 genes in vesicle mediated transport (p = 3.6x10-5); 62 genes in cellular response to stimulus (p = 2.91x10-4); 36 genes in cell communication (p = 3.36x10-4); 80 genes in the regulation of cellular process (p = 0.001); 154 genes in regulation of cellular
78 process (p = 0.0016); 82 genes in regulation of biological process (p = 0.0021); 65 genes in response to stimulus (p = 0.005); 19 genes in response to organic substance (p =
0.013); and 50 genes in macromolecule localisation (p = 0.034).
SK1 showed significant enrichment in the following specific category: cellular response to topologically incorrect protein (p = 0.045) with nine genes (BCK1, IRE1, MID2, PMT1,
RAD6, SAN1, SLT2, VPS74 and YOS9). There was also significant enrichment in the following broader categories: 22 genes in cellular response to organic substance (p =
0.016); 40 genes in response to chemical stimulus (p = 0.039); and 26 genes in response to organic substance (p = 0.0466).
The genes which share genetic interactions with ARV1 that are common to two or more DMA’s resulted in the following specific GO enrichment: ER UPR/cellular response to unfolded protein (p = 0.035) with seven genes (BCK1, HAC1, IRE1, MID2,
PMT1, SLT2 and VPS74); protein insertion into the ER membrane (p = 0.076) with four genes (GET1, GET2, GET3 and MDY2); response to ER stress (p = 0.001) with 10 genes
(BCK1, BST1, CNE1, HAC1, HRD3, IRE1, MID2, PMT1, SLT2 and VPS74); protein insertion into the membrane (p = 0.041) with five genes (GET1, GET2, GET3, MDM38 and
MDY2). There was also significant enrichment in the following broader categories: 22 genes in signal transduction (p = 0.0084); 22 genes in single organism signalling (p =
0.0084); and 47 genes in cellular response to stimulus (p = 0.0089).
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5.4 Results BTS1 SGA’s
The BTS1 query strains generated in the genetic backgrounds of S288C, SK1, Y55 and
YPS606 were mated to each of the ssDMA’s as described in Chapter 2.8. The resulting haploid double mutants were imaged, colony sizes were measured using ‘gitter in r’ and analysed using ‘screen mill’ (Dittmar et al., 2010; Wagih and Parts, 2014) with a Z- score cut off of 1.0 (Chapter 2.12), the 1019 genes considered to be hits are displayed in Appendix 2 and summarised in Table 13. The genes that have been independently verified as hits via a SGA mini array are shown in bold in the tables of Appendix 2.
Table 13 BTS1 - summary of genetic interactions
Strain S288C DMA ssDMA Y55 ssDMA SK1 ssDMA YPS606 S288C 305 25 42 97 Y55 25 202 43 27 SK1 42 43 395 60 YPS606 97 27 60 368
The data displayed in Table 13 are a numerical summary of the genetic interactions surrounding BTS1, showing the overlaps between the strains, the bold numbers indicate the total number of genetic interactions and the other numbers represent the number of shared genetic interactions between strains i.e. S288C and Y55 share 25 interacting genes. As can be seen from this table there is limited overlap between strains. Furthermore, there are only four genes across all strains which interact with
BTS1, 28 genes that share interactions with three out of the four DMA’s and 185 genes which interact with two DMA’s (Figure 18).
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Figure 18 BTS1- genetic interaction network Blue indicates a genetic interaction with one strain, green indicates genetic interactions with two strains, red indicates genetic interactions with three strains and pink indicates genetic interactions with four strains. 81
Table 14 - GO slim categories of BTS1 query gene hits
GO Term S288C Y55 SK1 YPS606 VPS8, DRS2, SFT2, RRT2, ENT5, TCA17, RRT2, RHB1, ATG20, ATG18, VPS8, DRS2, ENT5, YPT31, VPS29, GGA2, SEC28, SYS1, RCY1, LAA1, VPS60, TCA17, YPT31, Endosomal SEC28, SYS1, VPS53, RCY1, LAA1, RAV1, VPS51, YPT32, GGA2, SEC28, transport PEP8, VPS35, RCY1, RAV1, DID2, DID2, YPT7, PEP8, EMP70, YPT7, ENT3, VPS51, YPT6, VPS38 VPS5, RTT10 VPS9, MVP1, VPS5 VPS38, VPS9, VPS5, VPS21, VPS17, VPS30 DRS2, ARL1, BUG1, GET3, ARF1, GCS1, TRS85, ENT5, GRH1, DRS2, ARL1, GET3, TCA17, YPT31, GET2, ARF1, TRS85, ENT5, UBP3, COG7, GET1, ATG20, ARF1, TCA17, YPT31, GET2, VPS74, BST1, ERV14, TRS65, GOS1, GCS1, UBP3, COG7, GET1, YPT32, Golgi vesicle SEC28, SYS1, GGA2, SEC28, SYS1, BST1, GOS1, TRS65, GOS1, GGA2, transport RCY1, AVL9, VPS53, PHO86, RCY1, RCY1, GEA1, SEC28, COY1, SEC22, CHS5 ENT3, SEC22, IMH1, BRE5, APL4 COG8, VPS9, SSO2, COG8, VPS9, COG5, COG5, BRE5, TRS33, BRE5, TLG2, VPS21, SNC2, DSS4 TRS33, RUD3, SNC2, ARL3, SRO7 SCS22, CRD1, RRI1, YDR018C, ETR1, YPC1, TGL2, IPT1, ETR1, IPT1, CHO2, CSG2, FAT1, IPT1, FRM2, RRI1, ADR1, CEM1, ATF2, HTD2, MCR1, INO2, CBF1, SAC1, YDR018C, BST1, GUP1, OSH6, TGL4, YEH2, Lipid ECM22, HMG2, IPT1, GIS1, SKN1, CHO2, ICT1, UPS1, ECI1, metabolic HMG1, SCS7, INP52, ADR1, VPS74, HTD2, URA8, ORM2, HMG1, SCS7, process ARE2, TLG2, INO4, BST1, MGA2, ICT1, PLB1, CYB5, SPS19, ARE2, SUR1, YDC1, VPS30 OPI3, ICT1, PAH1, TGL3, INP53, SUR1, EEB1, CIT3 CYB5, PSD1, DAP1 ARE2, ALG8, LPX1, EEB1 ATS1, RRP8, MRM2, NCS6, ELP2, UBA4, TRM82, NCS6, ELP2, RNA URM1, KTI12, SAP190, TRM7, PUS2, URM1, RIT1, ELP6, TRM82 modification IKI3, ELP6, PUS4, TAD1, TRM10 RCM1, SWM2, PPM2, SWM2, ELP3, ELP4, ELP3, TGS1 TGS1, PUS1 ATS1, NCS6, ELP2, UBA4, URM1, KTI12, TRM82, NCS6, ELP2, tRNA SAP190, IKI3, TRM7, PUS2, TRM82 URM1, RIT1, ELP6, processing YMR087W, ELP6, TAD1, TRM10 PPM2, ELP3 PUS4, ELP3, ELP4, PUS1 TRS85, GOS1, SEC28, VAC8, BST1, TRS85, VAM7, GOS1, Vesicle BST1, SEC28, SYS1, VPS51, SEC22, GOS1, VPS51, SEC28, SEC22, SSO2, organization SYS1 TLG2, SNC2 VAM3 SNC2
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5.4.1 Genetic interactions common to all DMA’s There are four genes which interact with BTS1 in all DMA’s. Their functions are:
BUD13, subunit of the RES complex, required for nuclear pre mRNA retention and splicing; CPR7, peptidyl-prolyl cis-trans isomerase, involved in the isomerisation of proline residues; IPT1, inositolphosphotransferase; involved in synthesis of mannose- inositol-phosphoceramide; ISY1, a member of the NineTeen complex involved in spliceosome fidelity.
5.4.2 Genes unique to ssDMAs There are five genes which interact with all the ssDMAs created in this study: CLG1, cyclin-like protein that interacts with Pho85p; ICT1, lysophosphatidic acid acyltransferase; MDV1, peripheral protein of cytosolic face of mitochondrial outer membrane; NVJ3, protein with a potential role in tethering ER and vacuoles; TMA22, protein of unknown function.
5.4.3 Gene ontology The genes identified to have a genetic interaction with BTS1 were grouped by gene ontology analysis using the GO-slim mapper tool on SGD for the “cellular process” category, summarised in Table 14. Furthermore, the interacting genes were also analysed for significant GO enrichment using Yeast Mine (Balakrishnan et al., 2012).
The Y55, YPS606 and SK1 ssDMA’s had no significant enrichments (p = <0.05, Holm-
Bonferroni correction).
S288C showed significant GO enrichment in the following specific categories: endosomal transport (p = 4.57x10-9) with 25 genes (DRS2, ENT3, ENT5, GGA2, MUK1,
PEP8, RCY1, RRT2, SEC28, SFT2, SYS1, TCA17, VPS5, VPS8, VPS9, VPS17, VPS21, VPS29,
VPS30, VPS35, VPS38, VPS51, VPS53, YPT6 and YPT31); retrograde transport,
83 endosome to Golgi (p = 7.29x10-8) with 15 genes (ENT3, ENT5, PEP8, RCY1, SFT2,
TCA17, VPS5, VPS17, VPS29, VPS31, VPS35, VPS51, VPS53, YPT6 and YPT31); tRNA wobble uridine/base modification (p = 1.15x10-4) with 11 genes (ATS1, ELP2, ELP3,
ELP4, ELP6, IKI3, KTI12, NCS6, SAP190, UBA4 and URM1); protein urmylation (p =
4.6x10-4) with six genes (ELP2, ELP6, NCS6, UBA4, URE2 and URM1); post –Golgi vesicle mediated transport (p = 2.87 x10-4) with 17 genes (ARF1, ARL1, ARL3, DRS2, ENT3
ENT5, GCS1, GGA2, IMH1, MUK1, SNC2, SRO7, SYS1, TLG2, VPS9, VPS21 and VPS53) ; histone exchange (p = 0.0045) with eight genes (ARP6, ISW1, SET2, SWC3, SEC5, SWR1
VPS71 and VPS72); Golgi to endosome transport (p = 0.042) with 7 genes (ENT3, ENT5,
GGA2, MUK1, SYS1, VPS9 and VPS21).
There was also significant enrichment in the following broader categories: 64 genes in vesicle mediated transport (p = 1.16 x10-15); 39 genes in Golgi vesicle transport (p =
4.57 x10-11); 33 genes in cytoplasmic translation (p = 1.65 x10-7); 81 genes macromolecule localisation (p = 5.09 x10-6); 71 genes in intracellular transport (p = 9.6 x10-6); 85 genes in cellular localisation (p = 1.09 x10-5); 87 genes in cytoplasmic transport (p = 4.39x10-4); 57 genes in protein transport (p = 7.17 x10-4); 62 genes in regulation of RNA metabolic process (p = 0.044).
The genes which share genetic interactions with BTS1 that are common to two or more DMA’s resulted in the following specific GO enrichment: endosomal transport
(p = 1.83x10-7) with 19 genes (DID2, DRS2, ENT5, GGA2, LAA1, PEP8, RAV1, RCY1, RRT2,
SEC28, SYS1, TCA17, VPS5, VPS8, VPS9, VPS31, VPS38, VPS51, YPT7 and YPT31); retrograde transport, endosome to Golgi (p = 0.0049) with nine genes (ENT5, LAA1,
PEP8, RCY1, TCA17, VPS5, VPS31, VPS51, YPT7 and YPT31).
84
There was also significant enrichment in the following broader categories: 44 genes in vesicle mediated transport (p = 2.77x10-10); 27 genes in Golgi vesicle mediated transport (p = 4.17x107); 52 genes intracellular transport (p = 5.83x10-5); 61 genes in cellular localisation (p = 1.24x10-4); 55 genes in macromolecule transport (p = 0.0015);
42 genes in protein transport (p = 0.0022).
5.5 Results HMG1 SGA’s
The HMG1 query strains generated in the genetic backgrounds of S288C, SK1, Y55 and
YPS606 were mated to each of the ssDMA’s as described in Chapter 2.8.
Unfortunately, the YPS606 HMG1 SGA showed no typical SGA linkage group surrounding the HMG1 locus (YML075C) indicating that the SGA was defective, therefore, these results are omitted from further analysis. YPS606 did, however, show the expected linkage group hits in three out of the four query strain SGAs so it is concluded that there is no systematic problem with this ssDMA. The resulting haploid double mutants were imaged, colony sizes were measured using ‘gitter in r’ and analysed using ‘screen mill’ (Dittmar et al., 2010; Wagih and Parts, 2014) with a Z- score cut off of 1.0 (Chapter 2.12), the 758 genes considered to be hits are displayed in Appendix 2 and summarised in Table 13. The genes which have been independently verified as hits via a SGA mini array and are shown in bold in the tables of Appendix 2.
Table 15 HMG1 numerical summary of genetic interactions
Strain S288C DMA SK1 ssDMA Y55 ssDMA S288C 165 38 19 SK1 38 428 46 Y55 19 46 258
85
The data displayed in Table 15 are a summary of the genetic interactions surrounding
HMG1, showing the overlaps between the three strains, the bold numbers indicate the total number of genetic interactions and the other numbers represent the number of shared genetic interactions between strains i.e. S288C and SK1 share 38 interacting genes. As can be seen from this table there is limited overlap between strains.
However, there are eight genes across all strains which interact in common with
HMG1 and 80 genes which interact with two or more DMA’s (Figure 19).
86
Figure 19 HMG1 - genetic interaction network. Blue indicates a genetic interaction with one strain, green indicates genetic interactions with two strains and red indicates genetic interactions with three strains
87
Table 16 GO slim categories of HMG1 query gene hits
GO term S288C Y55 SK1 SRO9, RPP1B, RPL27B, RPL34A, TRM7, FES1, RPS0A, SLH1, RPL9A, RPS25A, SSA1, RPS16B, RPS4B, RPL40A, RPL17B, Cytoplasmic RPS21B, RPS28B, RPS27A, RPS1B, RPS1A, RPL6B, RPS1B, translation RPL6B, RPS18B, RPL20B RPL36A, RPL16B, RPL18B, RPS1B, RPS19B, RPS19B, RPS6A RPS10A, RPL20B SNC1, APM3, VPS74, YPT31, UBP3, VPS53, AGE1, TCA17, YPT31, Golgi vesicle GET1, APM2, GOS1, VPS53, RCY1, LST4, CHS5, TRS65, APM2, AGE2, transport CHS6, RCY1, LST4, BCH2, ERV41 APS1, TRS33, APL4 ERV41, SSO2 FAT1, CSH1, PER1, ALG3, YPC1, SLC1, UPC2, IPT1, HST4, UME6, Lipid CSG2, INO2, ORM1, YFT2, VPS74, CEM1, FAB1, GUP1, ORM1, HTD2, metabolic OPI3, HMG2, NTE1, CHO2, HTD2, LSB6, URA8, LSB6, HMG2, NTE1, process ERG24 HMG2, CYB5, PSD1, ALG6, LPX1, TCB1, CRC1, ALG8, INP53, VPS4 DGK1 Protein BLM10, PEX8, ICE2, HSP26, SSE2, AHA1, CPR7, SSA1, ECM10, TSA1 targeting PEX2, VPS70, BOR1 EMC6, HSP104, YKE2, GSF2 FUS3, STE50, SSA1, MDM10, PEP1, PEX22, PEP1, RTG3, MTC5, NBP2, SEC66, PEX7, ECM10, YBR137W, SEC66, APM3, BMH1, HOS2, Signalling NUP100, DID2, SPC2, ICE2, DJP1, PBS2, PEX2, LHS1, FAR1, CNB1, SST2, NUP188, BOR1, DID2, PAM17,MLP1, IMP2, CMP2, GTR1, RSF1, MDY2, MDH2 VPS68 WSC2
5.5.1 Genes which interact with S288C, SK1 and Y55 There are eight genes showing genetic interactions that are in common in all three
DMA’s. Their functions are: ARP6, actin-related protein that binds nucleosomes, also a component of the SWR1 complex; CYB2, cytochrome b2; HMG2, HMG-CoA reductase, catalyses the conversion of HMG-CoA to mevalonate, a paralog of HMG1;
OGG1, nuclear and mitochondrial glycosylase/lyase; PRM6, potassium transporter that mediates K+ influx; RPS1B, protein component of the small 40S ribosomal subunit; SAC3, mRNA export factor, required for biogenesis of the small ribosomal subunit, involved in transcription elongation; SPF1, P-type ATPase, an ion transporter
88 of the ER membrane, involved in lipid homeostasis and targeting of mitochondrial membrane proteins.
5.5.2 Gene ontology As with previous queries, genes identified to have a genetic interactions with HMG1 were grouped by gene ontology analysis using the GO-slim mapper tool on SGD for the “cellular process” category as summarised in Table 16. The interacting genes were also analysed for significant GO enrichment using Yeast Mine (Balakrishnan et al.,
2012). The Y55 and SK1 ssDMA’s and the genes which share genetic interactions with
HMG1 common to two or more DMA’s had no significant enrichment (p = <0.05,
Holm-Bonferroni correction). The only strain to show any showed significant GO enrichment was S288C but only in broad categories: 80 genes in biological regulation
(p = 0.0092); 68 genes in regulation of cellular process (p = 0.044).
5.6 Results HMG2 SGA’s
The HMG2 query strains generated in the genetic backgrounds of S288C, SK1, Y55 and
YPS606 were mated to each of the ssDMA’s as described in chapter 2.8. However, the
YPS606 HMG2 SGA showed no genetic linkage group surrounding the HMG2
(YLR450W) locus and the SGA was concluded as being defective. Therefore, these results were omitted from further analysis. The resulting haploid double mutants were imaged, colony sizes were measured using ‘gitter in r’ and analysed using ‘screen mill’
(Dittmar et al., 2010; Wagih and Parts, 2014) with a z-score cut off of 1.0 (chapter
2.12), the 485 genes considered to be hits are displayed in Appendix 2 and summarised in Table 17. The genes which have been independently verified as hits via a SGA mini array and are shown in bold in the tables of Appendix 2.
89
Table 17 HMG2 – numerical summary of genetic interactions
Strain S288C DMA ssDMA Y55 ssDMA SK1 S288C 138 19 19 Y55 19 182 22 SK1 19 22 220
The data displayed in Table 17 are a summary of the genetic interactions surrounding
HMG2 showing the overlaps between the three strains, the bold numbers indicate the total number of genetic interactions and the other numbers represent the number of shared genetic interactions between strains i.e. S288C and Y55 share 19 interacting genes. As can be seen from this table there is limited overlap between strains.
Furthermore, there are six genes across all strains which interact with HMG1 and 40 genes which interact with two DMA’s (Figure 20).
90
Figure 20 HMG2 - genetic interaction network. Blue indicates a genetic interaction with one strain, green indicates genetic interactions with two strains and red indicates genetic interactions with three strains
91
Table 18 GO slim categories of HMG2 query gene hits
GO term S288C Y55 SKI RPL19B, RPS8A, RPS14A, RPL13A, RPL29, SRO9, RPL9A, RPL37A, RPS28B, Cytoplasmic RPS16B, RPL38, RPS1A, RPS25A, RPS28B, RPS29A, CLU1, RPL18B, translation RPL20B RPS1A, RPS10A, RPS19B, RPL33B, RPL20B, RPS6A RPL20B, RPS6A DRS2, ERV46, Golgi vesicle BUG1, ATG20, SFH5, LST4, VPS53, RCY1, COG7, RCY1, ERP4 transport COG6, VPS21 LST4, VPS9 ETR1, IFA38, YPC1, PER1, ETR1, CSG2, MDH3, IPT1, UME6, Lipid YPC1, FAB1, CLD1, SKN1, MDH3, INO2, CEM1, ORM1, SFH5, metabolic HMG1, TGL3, VAC7, POX1, OPI3, LAC1, HMG1, PLB1, ERG5, process CPT1, PSD1, LPX1 HMG1, INO4 VAC7, TCB1, CRC1, CIT3, PDH1 Protein YKE2 HSP104, CPR6 SSE2, PHB1, BTN2 folding PEX22, YBR137W, SEC66, ATG20, PEX18, ICE2, Protein BLM10, PEX8, ATG18, COG7, VPS73, NUP100, NUP53, COG6, targeting ICE2, VPS9 KAP114, LHS1, ATG23, URE2, VPS21 PEX15 FUS3, STE50, FUS3, SIF2, SNF3, AFR1, VAM6, MTC5, Signalling KSS1, KSP1, RCN1, DCK1, YGL046W PTC2, HOS2, CNA1 ROM2, GTR1
5.6.1 Genes which interact with S288C, SK1 and Y55 There are six genes that show genetic interaction with HMG2 in the S288C, SK1 and
Y55 DMA’s. Their functions are: HMG1, HMG-CoA reductase, catalyses the conversion of HMG-CoA to mevalonate; PAU1, member of the seripauperin multigene family, encoded mainly in subtelomeric regions; RPL20B ribosomal 60S subunit protein; TSR2, protein with a potential role in pre-rRNA processing; YLR428C, dubious ORF; YLR460C, member of the oxidoreductase family.
5.6.2 Gene ontology The genes having genetic interactions with HMG2 were grouped by gene ontology analysis using the GO-slim mapper tool on SGD, as previously described for other
92 query genes, for “cellular process” as summarised in Table 18. The list of interacting genes was also analysed for significant GO enrichment using Yeast Mine (Balakrishnan et al., 2012). The S288C, Y55, SK1 ssDMA’s and the genes which share genetic interactions with HMG2 common to two or more DMA’s had no significant enrichment
(p = <0.05, Holm-Bonferroni correction).
5.7 Results OPI3 SGA’s
The OPI3 query strains generated in the genetic backgrounds of S288C, SK1, Y55 and
YPS606 were mated to each of the ssDMA’s as described in Chapter 2. However, the
SK1 OPI3 SGA showed no linkage group surrounding the OPI3 (YJR073C) locus indicating a defective SGA, therefore, these results were omitted from further analysis. The resulting haploid double mutants were imaged, colony sizes were measured using ‘gitter in r’ and analysed using ‘screen mill’ (Dittmar et al., 2010;
Wagih and Parts, 2014) with a z-score cut off of 1.0 (Chapter 2), the 611 genes considered to be hits are displayed in Appendix 2 and summarised in Table 19 The genes which have been independently verified as hits via a SGA mini array and are shown in bold in the tables of Appendix 2.
Table 19 OPI3 – numerical summary of genetic interactions
Strain S288C DMA ssDMA Y55 ssDMA YPS606 S288C 245 72 93 Y55 72 242 76 YPS606 93 76 314
The data displayed in Table 19 are a summary of the genetic interactions surrounding
OPI3, showing the overlaps between the three strains the bold numbers indicate the
93 total number of genetic interactions and the other numbers represent the number of shared genetic interactions between strains i.e. S288C and Y55 share 72 interacting genes. As can be seen from this table there is considerable overlap between strains.
Furthermore, there are 49 genes across all strains which interact with OPI3 and 92 genes which interact with two out of three DMA’s (Figure 21).
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Figure 21 OPI3 genetic interaction network. Blue indicates a genetic interaction with one strain, green indicates genetic interactions with two strains and red indicates genetic interactions with three strains
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Table 20 GO slim categories of OPI3 query gene hits
GO term S288C Y55 YPS606 SEC28, VPS53, DRS2, VPS60, PKH1, PEP8, RCY1, VPS24, VPS29, GGA2, SYS1, VPS60,SYS1,VPS53,SNX4, Endosomal VPS51, YPT6, VPS9, VPS53, RCY1, RAV1, RCY1,ENT3,SSH4,VPS51, transport MON2, VPS5, VPS24, VPS51, EMP70, EMP70,VTA1,YPT6,YPT7 VPS17, VPS4 YPT6, YPT7, VPS5 ERV46, ARL1, TRS85, VPS74, DRS2, ARL1, GET3, ARF1, GET2, UBP3, BST1, TRS85, VPS74, GET2, ARL1, GET3, TRS85, COG7, GET1, BST1, VPS74, GET2, BST1, Golgi TRS65, SEC28, COG7,GET1,GGA2,SYS1, COG7, GET1, ERV29, vesicle VPS53, PHO86, VPS53, BCH2, SEC22, SVP26, SYS1, VPS53, transport RCY1, LST4, CHS5, IMH1, COG8, BCH1, SNX4, RCY1, ENT3, COG8, ERV41, COG8, COG6, COG5, BRE5, COG5, BRE5 VPS9, COG5, APM1 MON2, BRE5, TLG2, RUD3 CSG2, FAT1, PER1, TGL2, IPT1, INO2, ETR1, CSG2, FAT1, SLC1, ALG3, FAT1, YPC1, SLC1, UME6, SUR2, TGL2, IPT1, HST4, UME6, IPT1, UME6, VPS74, BST1, VPS74, BST1, Lipid VPS74, BST1, ROG1, FAB1, CHO2, PCT1, OPI1, GUP1, CHO2, PCT1, metabolic CHO2, PCT1, OPI1, YEH2, DFG10, LAS21, URA8, OPI1, MGA2, process CKI1, ORM2, SCS7, VAC7, SPT23, ICT1, PAH1, SCS7, LAS21, CKI1, ERG6, PSD1, LRO1, DGK1, SUR1, APP1, CYB5, LRO1, ARE2, PAH1, ERG2, SCS7, FMP30, DAP1 ALG6, ALG8, DGK1 PSD1, TLG2, INO4, DGK1, VPS4 EMC1, EMC3, EMC1, EMC3, EMC4, EMC4, Protein EMC1, EMC3, EMC4, EMC5, EMC6, CPR7, EMC5, EMC6, folding EMC5, EMC6, GET3 HSP104, GSF2, SCJ1, PHO86, CPR7, GET3, PLP1, XDJ1, YKE2, YME1
5.7.1 Genes which interact in the S288C, SK1 and Y55 DMAs There are 49 genes that show genetic interactions with OPI3 in the S288C, SK1 and
Y55 DMA’s. Their functions are: BRE5, ubiquitin protease, involved in the regulation of anterograde and retrograde transport between the Golgi and ER; BRP1, dubious
ORF, located in the upstream region of PMA1; BST1, GPI inositol deacylase of the ER, negative regulator of COPII vesicle formation; CEX1, component of the nuclear aminoacylation dependent tRNA pathway; CLB3, B-type cyclin involved in cell cycle
96 progression; COG5 and COG8, components of the conserved oligomeric Golgi complex, functioning in protein trafficking; DRS2, trans Golgi aminophospholipid translocase, involved in vesicle trafficking between the Golgi and endosomal system;
CUE3, protein of unknown function; DGK1, diacylglycerol kinase, localised to the ER;
ECM30, putative protein of unknown function; EMC1, EMC3, EMC5 and EMC6, members of the ER transmembrane complex, required for the efficient folding of proteins in the ER; ENV10, protein proposed to be involved in vacuolar function; FAR3, protein of unknown function; GET1 and GET2, subunits of the GET complex, involved in insertion of proteins into the ER membrane; HNM1, plasma membrane transporter of choline, ethanolamine and carnitine; IRE1, Ser/Thr kinase, a transmembrane protein that regulates the UPR via regulation of Hac1p; MRPL36, mitochondrial ribosomal protein of the large subunit; NBP2, protein involved in the HOG pathway;
PTC1, cholinephosphate cytidyltransferase, the rate determining enzyme of the CDP- choline pathway for phosphatidylcholine synthesis; PKR1, V-ATPase assembly factor, functions with other V-ATPase assembly factors in the ER; PRM3, protein required for nuclear envelope fusion; PTC4, cytoplasmic type 2C protein phosphatase, involved in regulation of the HOG pathway; RPN4, transcription factor that stimulates the expression of proteasomal genes; SAM37, component of the sorting and assembly machinery complex, located in the mitochondrial outer membrane; SBH2, member of the Ssh1p-Sss1p-Sbh2p complex involved in protein translocation into the ER; SCS7, spingolipid alpha hydrolase, involved in the synthesis of long chain fatty acids; TRS85, component of the transport protein particle complex III, a multimeric guanine nucleotide exchange factor for the GTPase Ypt1p, involved in the regulation of endosome – Golgi traffic and membrane expansion during autophagy; UBP1, ubiquitin
97 specific protease; UME6, subunit of the Rpd3L histone deacetylase complex, transcriptional regulator of mitotic genes; VPS51 and VPS53, components of the Golgi- associated retrograde protein complex responsible for the recycling of proteins from endosomes to the late Golgi; VPS74, Golgi phosphatidylinositol-4-kinase effector and
PtdInseffector, mediates the targeting of glycosyltransferases to the Golgi; YPT6, Rab family GTPase, involved in the secretory pathway; YSA1, nudix hydrolase member with
ADP ribose pyrophosphatase activity; YCL046W, dubious ORF, partially overlaps
YCL045C; YER084W, protein of unknown function; YDR455C, dubious ORF, partially overlaps YDR456W; YGL081W, putative protein of unknown function; YMR052C-A,
YMR075C-A and YNL296W, dubious ORF’s; YNR021W, putative protein of unknown function.
5.7.2 Gene ontology The genes having a genetic interaction with OPI3 were grouped by gene ontology analysis, using the GO-slim mapper tool on SGD as previously described, in the GO category “cellular process” and are summarised in Table 20. The interacting genes were also analysed for significant GO enrichment using Yeast Mine (Balakrishnan et al., 2012).
S288C showed significant GO enrichment in the following specific categories: Golgi vesicle transport (p = 0.0017) with 24 genes (ARL1, BRE5, BST1, CHS5, COG5, COG7,
COG8, ERV41, ERV46, GET1, GET2, LST4, MON2, PHO86, RCY1, RUD3, SEC28, TLG2,
TRS65, TRS85, UBP3 and VPS53); phospholipid metabolic process (p = 0.0035) with 19 genes, (BST1, CHO2, CKI1, DGK1, GUP1, INO2, INO4, IPT1, OPI1, PAH1, PCT1, PER1 and PSD1); protein folding in the ER (p = 0.023) with five genes (EMC1, EMC3, EMC4,
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EMC5 and EMC6); lipid biosynthetic process (p = 0.0436) with 21 genes (CHO2, CKI1,
CSG2, DGK1, ERG2, ERG6, GUP1, INO2, INO4, IPT1, LAS21, MGA2, OPI1 and PAH1).
S288C also showed significant GO enrichment in the following broad categories: 66 genes in macromolecule localisation (p = 7.35x10-5), 58 genes in protein localisation (p
= 1.71x10-4), 36 genes in protein localisation to an organelle (p = 9.29x10-4), 48 genes in protein transport (p = 0.00130, 55 genes in intracellular transport (p = 0.0019), 36 genes in vesicle mediated transport (p = 0.0027), 54 genes in protein modification process (p = 0.025) and 25 genes in response to organic stimulus (p = 0.044).
Y55 had significant enrichment in the following categories: vesicle mediated transport
(p = 4.06x10-5) with 37 genes (APM1, APS2, ARF1, ART10, BCH1, BCH2, BRE5, BST1,
COG5, COG6, COG8, DGK1, DRS2, GET1, GET2, GET3, GGA2, GYP1, IMH1, MYO5, PKH1,
PRK1, RAV1, SEC22, SWA2, SWH1, SYS1, TRS85, VPS5, VPS24, VPS29, VPS51, VPS53,
VPS74, YPT6 and YPT7) and endosomal transport (p = 0.0091) with 14 genes (DRS2,
EMP70, GGA2, PKH1, RAV1, SYS1, VPS5, VPS24, VPS29, VPS51, VPS53, VPS60, YPT6 and YPT7).
YPS606 showed significant enrichment in the following specific categories: protein folding in the ER (p = 1.49x10-4) with 7 genes (EMC1, EMC3, EMC4, EMC5, EMC6,
HSP104 and SCJ1) and regulation of cell wall organisation or biogenesis (p = 0.0041) with 8 genes (BCK1, NBP2, ROM2, SAC7, SDP1, SLT2, SMI1 and SSD1). Also in the following broad categories: 52 genes in cellular protein localisation (p = 8.72x10-4) and
43 genes in protein localisation to organelle (p = 1.60x10-4).
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It is noted that YPS606 alone among the strains showed significant pathway enrichment with five genes (ALG3, ALG6, ALG9 and DIE2) that were significantly enriched in the lipid-linked oligosaccharide biosynthesis pathway (p = 0.025).
The genes which share genetic interactions with OPI3 that are common to two or more
DMA’s resulted in the following specific GO enrichment: 14 genes (BST1, CHO2, CKI1,
DGK1, IPT1, LAS21, LRO1, PAH1, PCT1, PSD1, SLC1, TGL2, UME6 and VPS74) in glycerolipid metabolic process (p = 9.76x10-4), 15 genes (BST1, CHO2, CK1, DGK1, IPT1,
LAS21, OPI1, PAH1, PCT1, PSD1, SLC1, TGL2 and UME6) in phospholipid metabolic process (p = 0.0019), 10 genes (BCK1, BST1, CUE1, HAC1, HLJ1, IRE1, OPI1, SLT2, UBR1 and VPS74) in response to ER stress (p = 0.0045), 20 genes(APS2, ARL1, BRE5, BST1,
COG5, COG7, COG8, DGK1, GET1, GET2, GET3, GYP1, LTE1 and RCY1) in vesicle mediated transport (p = 0.027), five genes (EMC1, EMC3, EMC4, EMC5 and EMC6) in protein folding in the ER (p = 0.024), six genes (BCK1, HAC1, IRE1, OPI1, SLT2 and
VPS74) in the ER unfolded protein response and 12 genes (ARL1, GET1, GET2, GET3,
GTR1, GTR2, SAM37, SBH2, SEC66, SSH1 and SYS1) in protein localisation to the membrane (p = 0.036).
The genes which share genetic interactions with OPI3 that are common to two or more
DMA’s resulted in the following broad GO enrichment: 26 genes in protein localisation to an organelle (p = 2.19x10-4), 32 genes in cellular macromolecule organisation (p =
3.22x10-4), 31 genes in establishment of protein localisation (p = 4.34x10-4), 31 genes in protein transport (p = 0.0056) and 38 genes in organic substance transport (p =
0.043).
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There was also significant enrichment in the following biological pathways: four genes
(CHO2, CKI1, PCT1 and PSD1) in the super pathway of phospholipid biosynthesis
(defined on SGD as a combination of phospholipid related pathways; p = 0.039) and three genes (LRO1, PAH1 and SLC1) in triglyceride biosynthesis (p = 0.043).
5.8 Discussion
The results described in this chapter show that the specific gene chosen as a query gene can influence whether there appears to be conservation of genetic interactions among the individual strains. For example, a major genetic hub gene has a more diverse role because of a higher amount of interactions and are generally evolutionary conserved (Costanzo et al., 2010a; Mackay, 2014). In this study OPI3 is such a hub gene, in contrast to the other query genes analysed in the current chapter, and the genetic interaction network of OPI3 shows a much higher degree of network conservation. Another point of discussion is the reliability of the SGA’s described in this chapter. In technical terms the SGAs appeared to be working properly because of the appearance of the expected characteristic linkage group surrounding the query genes in each of the strains. This characteristic was so reliable in fact that it was used to eliminate from further consideration those few SGAs that did not work. Another point of reliability is the appearance of a large number of genetic interactions that previously have been described for the S288C strain on SGD. Relating to this point the presence of genetic interactions using HMG1 as a query gene showed interactions with HMG2 in all three strains that gave reliable SGAs. Similarly, HMG2 query gene
SGA showed genetic interactions with HMG1 in all three strains. Furthermore, using
OPI3 as a query gene the SGAs in the three ssDMA strains that worked showed
101 interactions with EMC1, EMC3, EMC4, EMC5 and EMC6 as previously reported on SGD for S288C. These observations taken together with the appearance of a numerous other interactions in all the strains described here that had been previously identified on SGD strongly supports the efficacy of the SGA procedures in the current work. The characteristics of some of these interactions in the strains are further described below.
5.8.1 ARV1 SGA When the genes that interact with ARV1 in two or more DMA’s were combined there is significant GO enrichment in genetic interactions of genes functioning in ‘protein insertion into the ER membrane’ and ‘response to ER stress’. The ‘unfolded protein response’ category though not GO-enriched shows interactions with IRE1 and HAC1 in all the strains (Table 12) supporting an ER stress relationship.Whilst conservation is observed in basic cellular process required for survival, this does not necessarily mean the genetic interactions underpinning the processes are conserved. In illustration the group of interacting genes for ER membrane insertion and response to ER stress only accounts for 20% of the total genetic interactions observed when using ARV1 as a query strain and it is possible there could be alternative genes used that are not yet annotated in an incomplete GO-database.
ARV1 is of unknown molecular function but appears at least to be involved in glycosylphosphatidylinositol’s (GPI) synthesis. These GPI’s are complex lipids present in all eukaryotes that are synthesised on the ER lumen and they are covalently linked to the carboxyl-terminus of some membrane proteins and function as lipid anchors
(Kinoshita et al., 2013). GPI anchored proteins are commonly associated with lipid rafts and are responsible for the binding to membranes of numerous enzymes, receptors, antigens and other active proteins (Bagnat and Simons, 2002; Ikezawa, 102
2002). GPI synthesis is highly conserved between yeast and humans though S. cerevisiae has two forms of GPI anchors containing either diacylglycerol and ceramide.
GPI anchored proteins are transported from the ER to the plasma membrane via the trans Golgi network, mediated by COPII coated vesicles (Kinoshita et al., 2013). Prior to transport through the Golgi, GPI anchored proteins are modified for efficient transport, the acyl chain is removed by deacylases, PGAP1 in mammalian cells and
Bst1p in S. cerevisiae. The GET (Golgi-ER-Trafficking) complex (Schuldiner et al., 2005) is responsible for the correct insertion of secretory tail anchored proteins into the ER membrane (Schuldiner et al., 2008). Interestingly, GET3 (interacting with ARV1 in
S288C, SK1 and YPS606) is able to distinguish between proteins destined for the secretory pathway and those destined for the mitochondria (Schuldiner et al., 2005;
Schuldiner et al., 2008). Thus the GET complex is known to be involved in the basic cell biological process of membrane trafficking and it is therefore unsurprising that members of the GET complex interact with ARV1, interactions that are preserved across all four strains.
Also pertinent to this discussion is that members of the conserved oligomeric (COG) complex (COG5, COG7 and COG8), implicated in vesicle mediated transport of GPI anchored proteins through the Golgi (Kajiwara et al., 2008), only show ARV1 genetic interactions in the S288C background, suggesting there could be two complexes, the
COG complex and another process in other strains for the transport of GPI anchored proteins. This raises the question of how the functions of this complex with respect to
GPI protein transport is achieved in the other three strains. COG5, 7 and 8 are not
103 synthetically lethal with each other and it is possible that a different combination of genes could replace their function.
From the foregoing discussion it appears that functions of ARV1 are related to lipid transport throughout the cell. It has also been found here that UPR genes (IRE1 and
HAC1) are interactors with ARV1. This raises the question of whether there is lipid dependent activation of the UPR related to Ire1p, in its relationship to the activation of INO1p – inositol-3-phosphate synthase, the rate limiting step in phosphatidylinositol synthesis (Volmer and Ron, 2015).
5.8.2 BTS1 SGA BTS1 has a known role in synthesis of geranylgeranyl pyrophosphate (GGPP) in the well-established superpathway of ergosterol biosynthesis (see current SGD). S288C with BTS1 as a query gene resulted in 202 genetic interactions with BTS1, with significant GO enrichment in three categories namely ‘retrograde mediated and Golgi mediated transport’, ‘Golgi mediated vesicle transport’. There was no such significant
GO enrichment observed for the three ssDMA strains SK1, Y55 or YPS606. However, when the combined genetic interactions for two or more strains were considered there was also significant enrichment for ‘retrograde transport’ and ‘Golgi vesicle mediated transport’. The ergosterol superpathway has 30 enzymes involved (including shunts) most of which are essential and would not appear in the SGA analyses reported here. However, non-essential genes modifying the main pathway through interaction with BTS1 (which is non-essential) obviously do appear in the S288C SGA.
That most of the S288C interactions do not appear in the ssDMA based-SGAs shows that the interaction networks in these strains for the essential GO-enriched processes
(that are by definition side-effects processes of statins) mentioned above are
104 different. There are other genes (Table 14) showing numerous other BTS1 interactions that could as feasibly be used in the processes retrograde/Golgi mediated transport’,
‘Golgi mediated vesicle transport’ mentioned above. It is surmised that they do not appear as GO enrichments in the ssDMA SGAs owing to limitations in GO database.
The mevalonate pathway (Figure 2) whose primary function is the production of sterols (Goldstein and Brown, 1990) is also responsible for the production of GGPP, synthesised from farnesyl diphosphate and isopentenyl diphosphate via geranylgeranyl diphosphate synthase (Chang et al., 2013). This enzyme is encoded by
BTS1 in S. cerevisiae and by GGPPS1 in mammalian cells (Ericsson et al., 1998).
Protein prenylation is a post translational modification involving the attachment of isoprenoid groups near the C terminal of proteins, a modification that is believed to increase hydrophobicity allowing hydrophilic proteins to associate with membranes.
Known prenylated proteins include: small GTPases of the Ras superfamily, nuclear lamins, the yeast a factor pheromone and trimeric G-proteins - associated with a wide range of cellular processes (Jiang et al., 1995). Both farnesyl and geranylgeranyl are post translationally attached to proteins, however, the majority are geranylgeranylated. Geranylgeranyl modification is carried out via the addition of geranylgeranyl diphosphate (GGPP) to proteins at the C terminal.
Geranylgeranlyation has been well described (Chang et al., 2013; Goldstein and
Brown, 1990), and is required for the correct targeting and function of Rab proteins to the endosomes and if interrupted proteins are mis-targeted to ER membranes
(Gomes et al., 2003). In the current studies, the Rab GTPases YPT31, YPT6, YPT7, VPS9,
VPS21, show genetic interactions with BTS1 in S288C and YPS606 and the function of
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Rab GTPases is dependent on geranylgeranylation (Calero et al., 2003). The Rab
GTPases regulate a variety of essential processes including the docking and fusion of vesicle carriers between compartments of the exocytic and endocytic pathways (Alory and Balch, 2000) and ER budding of COPII coated transport vesicles (Hutagalung and
Novick, 2011). SEC28, showing an interaction with BTS1 in the current SGD and reported studies, regulates retrograde transport and stabilises Cop1p. These vesicles bind to membranes in association with ARF-GTP binding proteins, such as GCS1, which shows a genetic interaction with BTS1 in S288C and ARF1 in S288C and SK1. ARF GTP binding proteins are regulated by GGA2 (Poon et al., 1999) which is shown here for the first time to have genetic interactions with BTS1 in both S288C and YPS606. Thus
BTS1 is involved in the recruitment of COP proteins for vesicle trafficking.
Furthermore, the v-SNARE protein Gos1p is required for vesicular transport between the ER and Golgi (Poon et al., 1999) and is shown here for the first time to have genetic interaction with BTS1.
Interestingly, member genes of the GET and COG complexes (described in 5.8.1) that are involved in the transport of vesicle related proteins through the secretory pathway and show genetic interactions with BTS1 in S288C and YPS606. However, similar to the involvement of the GET complex described in the previous section using ARV1 as a query gene, these two complexes seem to be somewhat strain specific because they are not found in Y55 and SK1 ssDMA SGAs, which leads to the speculation previously presented (in the discussion of ARV1) that Y55 and SK1 do not require BTS1 interactions with the members of the GET and COG complexes for function.
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Three members (TRS33, TRS65 and TRS85) of the TRAPP complex’s I, II and III were shown to have genetic interactions with BTS1 in S288C and YPS606 but not the other two strains. The TRAPP complexes (I, II, and III) are multimeric guanine exchange factors for GTPases which regulate ER to Golgi traffic (I), intra Golgi traffic (II), endosome to Golgi traffic (II and III), and members of all three complexes are implicated with BTS1 (Sacher et al., 2001; Sacher et al., 1998). These complexes work by providing a loose physical connection between the vesicle and target membrane
“tethering”, similar to the COG complex, another tethering complex in S. cerevisiae.
Once tethering is established, membrane fusion mediated by SNARE proteins begins
(Barrowman et al., 2010).
The BTS1 query strain results described in this chapter elucidate mechanisms that appear conserved to a degree between the strains in the various major cellular processes described. In these processes namely those involving genes of GET, COG and TRAPP complexes the BTS1 interactions were seen in S288C and YPS606 but not the Y55 and SK1 strains. In Y55 and SK1 it appears other modifiers of GET, COG and
TRAPP genes, may be involved in their function instead of BTS1.
5.8.3 HMG1 and HMG2 SGA’s When HMG1 and HMG2, paralogue genes encoding HMG-CoA reductase, the rate limiting step in sterol biosynthesis (Chapter 1.2.4 for review), were used as queries
SGA analysis, HMG1 generated a total of 758 and HMG2 generated 485 genetic interactions across all four DMA’s. Furthermore, 8 and 6 genes were common genetic interactions respectively with HMG1 and HMG2 as queries in all four DMA’s and 86 and 48, HMG1 and HMG2 respectively, were common in more than one strain. The
HMG1 genetic interactions in S288C had GO enrichment for the nonspecific - category
107 regulation of cellular process, there was no other specific GO enrichment for either
HMG1 or HMG2 in any of the DMA genetic backgrounds.
The absence of specific GO enrichment observed for these query genes in the strains is likely due to the fact that they are paralogs, i.e. if one is missing the other can compensate (Basson et al., 1986; Musso et al., 2008). However, HMG1 is responsible for approximately 80% of HMG-CoA reductase activity yeast (Basson et al., 1986) and although these genes share 93% sequence similarity in the catalytic domain the genetic interaction networks surrounding these genes are somewhat different (Figure
19, Figure 20, Table 16 and Table 18) and must have individual cellular roles. For example, it was observed that they have mechanistically distinct roles with respect to
ER remodelling, (Federovitch et al., 2008). Furthermore, particular gene enrichment is not conserved across the three strains, when the hits are grouped into similar GO processes (Table 16 and Table 18). It can be seen that though the specific genes in genetic interactions are different in the processes elucidated by the HMG1 and HMG2 query genes, the processes are mostly conserved.
Most of the phospholipids seen in yeast are present in all subcellular membranes, except for phosphatidylserine and cardiolipin which are major components of the plasma membrane and inner mitochondrial membrane (Zinser et al., 1991).
Cardiolipin and phosphadidylserine result from enzymes encoded by the interacting mitochondrial genes PSD1 and CLD1 respectively. Interestingly, there are number of genes that are involved in phospholipid biosynthesis (CEM1, ETR1, CIT3, PDH1, CLD1
IFA38, PSD1; the first 5 genes in this list are newly reported interactions) that were observed in the current study to interact mainly with HMG2 in the genetic
108 backgrounds of Y55 and SK1. Though cholesterol (egosterol in yeast), is found in the highest concentrations in the plasma membranes and secretory vesicles in most eukaryotes, yeast have a higher concentration of ergosterol in the inner mitochondrial membrane as opposed to the outer (Zinser et al., 1991) and may be one reason why there is an increase in mitochondrial interactions with HMG2 as shown in the current work. Moreover, the synthesis of heme, quinones and dolichols require farnesyl, an intermediate of the sterol biosynthesis pathway, along with geranylgeranyl which is required for post translational modifications such as those previously described
(chapter 5.8.1).
Sphingolipids, characterised by the inclusion of ceramide, which in yeast consist of a
26 carbon long fatty-chain, are generally localised in the plasma membrane.
Ceramides are an abundant lipid at around 30% of total phospholipid within the cell.
Furthermore, ceramides are found in other regions within the cell, especially inositolphosphoceramide, which is concentrated in the Golgi and vacuole.
Sphingolipid biosynthesis begins in the ER with the synthesis of ceramide, they are then transported from the ER to the Golgi where they receive their polar heads.
However, even though their synthesis is well documented, their specific roles within the cell remain unclear (Dickson, 2010; Dickson and Lester, 2002). Notably, specific sphingolipid biosynthetic genes were observed to interact with both HMG1 and HMG2 namely CSG2 in S288C, IPT1 (new reported interaction) in Y55, SKN1 and YPC1 (newly reported interactions) in SK1. Analysis of the sphingolipid pathway with respect to these results shows that inhibition of HMG-CoA reductase in S. cerevisiae coupled with
109 disruption of de novo sphingolipid synthesis at a different point in each strain, results in growth inhibition.
Many of the non-sterol related effects of HMG-CoA reductase inhibition results from the depletion of isoprenoid pools, as described in previous sections of this chapter, including farnesyl PP and geranylgeranyl PP. The Rab GTP gene interactions elucidated by the HMG1 query in Y55 and in SK1 suggests that either HMG1 has a more non- sterol related role in these strains, or the pathway is more reliant on its isoprenoid intermediates. Furthermore, the genetic interactions observed with HMG1 in Y55 with members of the TRAPP complex, namely TRS33, TRS65 and TCA17 (all newly reported interactions) as previously described (Chapter 5.8.2) also suggests the availability of isoprenoids is required more extensively in Y55 than in S288C. Notably, HMG1 showed interactions with genes of the COPII vesicle mediated transport process that are common across all the three strains analysed. Also of note is that vesicular transport genes show interactions across all three strains using HMG2 as a query gene but by involvement of different genes. This is illustrated by different vesicular transport complexes in different strains, the GARP complex (interaction with VPS53) in S288C and the interactions with COG6 and COG7 (newly reported HMG2 interactions) in Y55 and SK1, respectively. The data discussed here support studies conducted in skeletal muscle cells by Sakemoto et al 2007 and 2013, where it was concluded that statin associated myopathy is caused by a reduction of GGPP, thence reduction in Rab proteins causing inhibition of vesicular transport within the cell (Sakamoto et al., 2007;
Sakamoto and Kimura, 2013).
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Dolichol is a polyisoprenol, whose synthesis is carried out in the ER membrane at an intermediate stage of sterol biosynthesis, namely farnesyl-PP. Farnesyl-PP is also where the isoprenoid synthesis pathway branches off from the sterol synthesis pathway (Figure 2). Inhibition of sterol synthesis in yeast has previously been shown to cause a reduction in the production of dolichol’s (Liao and Laufs, 2005). Dolichol pyrophosphate-sugars are key substrates for a number of enzymes in the N- glycosylation pathway (Burda and Aebi, 1999) described in Figure 22. Notably, in the current study the glycosyltransferases ALG3, ALG6 and ALG8 are shown to interact with HMG1 in SK1 and not the other strains. N-linked glycosylation of membrane and secreted proteins is an evolutionarily conserved essential modification, necessary for quality control of protein folding in the ER, modulating protein function during development and many processes involved in intermolecular recognition (Bailey et al.,
2012). Therefore, as the ALG3, ALG6 and ALG8 interactions with HMG1 appear only in
SK1 this is a major difference between strains. Furthermore, these (ALG) interactions are newly reported and do not appear in the SGD database, in its current iteration. In addition, previous SGA studies by the author of this dissertation using HMG1 and
HMG2 queries were conducted in S288C (Busby, 2009) and did not show the ALG gene interactions discussed here, thus emphasising that these SK1 interactions are likely to be unique.
A recent study conducted by Forbes et al., 2015, describes the ability of statins to inhibit N-linked glycosylation of insulin-like growth factor to a similar level of inhibition to the known N-glycosylation inhibitor tunicamycin, a phenotype that was rescued by the addition of exogenous dolichol-PP (Forbes et al., 2015). Although, the SK1 HMG1
111 mediated glycosylation interactions described here have not been previously described in S. cerevisiae, they have been observed in other studies focussing on off target effects caused by statin treatment (Forbes et al., 2015; Winterfeld et al., 2013).
For the current study it is concluded that the unique HMG1 and the glycosylation gene interactions are likely due to an increased reduction or an increased need for dolichol intermediate pools in SK1, compared to that of the other strains. This could be explained by changes in the “wiring” of the genetic interaction networks surrounding glycosylation in SK1; the concept of “wiring” changes of functional modules has previously been published (Roguev et al., 2008).
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Figure 22 N-linked glycosylation of dolichol pyrophosphate. Crosses indicate where inhibition of HMG1 inhibits the addition of glycosyl residues to dolichol. Reprinted from (Burda and Aebi, 1999), copyright 1999, with permission from Elsevier.
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5.8.4 OPI3 SGA OPI3 encodes a phospholipid methytransferase responsible for catalysing the last two steps in phosphatidylcholine biosynthesis in which a null mutation results in the overproduction of inositol. When OPI3 was subjected to SGA analysis as a query gene, a total of 611 genetic interactions across the three analysed DMA’s was observed; 49 genes were common to all three DMA’s and 142 are common in more than one strain.
Phosphatidylcholine (PTC) is a key component of eukaryotic membranes including vesicular membranes and in S. cerevisiae PTC comprises up to 50% of the total lipid content within the cell (Howe and McMaster, 2001). PTC is generated by three sequential methylations of phosphatidylethanolamine by the integral membrane N- methyltransferases Cho2p and Opi3p (encoded by CHO2 and OPI3 respectively), also via the Kennedy pathway by action of CKI1, which shows a genetic interaction with
S288C and Y55 but not YPS606. In contrast to S. cerevisiae, mammalian cells possess a single gene encoding phospholipid methyltransferase (Boumann et al., 2004; Klug and Daum, 2014). In the current studies, CHO2 has a synthetic lethal interaction with
OPI3 in the three DMA strains tested, consistent with previous studies conducted in
S288C (Tanaka et al., 2008). PTC forms a part of sphingolipids (Klug and Daum, 2014), which becomes sparse when their precursor phosphoinositol is sparse owing to disruption of OPI3. Genetic interactions with OPI3 and CSG2, SUR2, ORM2 SCS7, IPT1
YPC1 (the last three are newly reported interactions) that are involved in the de novo synthesis sphingolipids were mainly conserved across all strains (YPC1 in Y55 and
ORM2 in YPS606 being the exceptions).
OPI3 showed genetic interactions of the genes of the EMC complex (EMC1, EMC3,
EMC4, EMC5 and EMC6 in S288C, Y55 and YPS606 (EMC2, is in linkage disequilibrium
114 with OPI3 and is therefore not seen as an interaction). The molecular function of the
EMC complex is still being elucidated. When the complex was first described it was concluded that these genes are involved in protein folding in the ER (Jonikas et al.,
2009). More recently it has also been suggested these proteins are involved with the transfer of lipids from the ER to the mitochondria via tethering between these organelles (Lahiri et al., 2014) and previous work (Bircham et al., 2011) also suggested post-folding events . The current study thus suggests a function of the EMC genes in phospholipid transfer, separate from its role in the UPR, because the deletion of OPI3 apparently did not result in activation of the UPR as previously described.
ALG3, ALG6, and ALG8, similar to their interactions with HMG1 in the SK1 strain, also show ssDMA strain specific interactions with OPI3 in YPS606 only, but concur with interactions previously seen with S288C (Costanzo et al., 2010a). Interactions of OPI3 with ALG6, and ALG8 reported in the literature are conflicting. One study showed
ALG6 and ALG8 as suppressors (Jonikas et al., 2009) whereas another study, shows genetic interactions similar to those presented here (Schuldiner et al., 2005).
5.8.5 Implications of this study The results discussed in this Chapter, taken with those described in Chapter 4, provide persuasive evidence of the involvement of statin effects in several key cellular processes namely protein folding, small GTPase activity, vesicular transport and lipid metabolism/synthesis in the S. cerevisiae strains S288C, SK1, Y55 and YPS606. Though many of the genetic interactions of the genes in these processes have been previously described, a number have not and in cases alternative gene usage in the different strains has been implied in some of the processes. The results also describe strain specific genetic interactions and those elicited with HMG1 and OPI3 query genes
115 involved in N-glycosylation are given as an example. The concept of “functional rewiring” (Koch et al., 2012; Roguev et al., 2008) has been invoked in the current studies as one possible explanation for these strain specific differences. Although it has been argued that major processes associated with specific genetic interactions are conserved, one might expect to see conservation of basic cellular processes across organisms. However, the extent of similarity of the genes acting in them has not been examined in closely related strains, such as those described in the present studies.
There are in fact significant strain specific interactions, making up the genetic interaction networks described here, of particular note in the current study are the conserved essential processes which show strain to strain variation in contributing genes. For example, as discussed, the HMG1 SGA, the genes CSG2, IPT1 and YPC1 in the strains S288C, Y55 and SK1 respectively, are involved at different stages in sphingolipid biosynthesis were elicited.
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Discussion
6.1 Conceptual Limitations of the current study
This thesis describes the construction of genome wide deletion sets (‘ssDMAs’) in three distinct strains of S. cerevisiae that were utilised to further understand the genetic interaction network basis for phenotypic variation between individuals, specifically, response to the statin drugs, atorvastatin and cerivastatin. There are limitations to this approach that are now further discussed. Firstly, a subset of a panel of 26 distinct yeast strains was assessed as a model for human individual phenotypic responses to statin treatment. This is a first approximation to understanding phenotypic variation among individuals (Dowell et al., 2010) and the phenotype investigated, statin resistance, is a known problem in individual human patients.
However, how much an understanding of the individual resistance genetic basis in yeast strains may translate to human therapeutics remains to be seen. There is some expectation that this might be the case because approximately 30% of S. cerevisiae genes are conserved with humans and approximately 1000 yeast genes have a human homologue implicated in disease (Botstein et al., 1997; Heinicke et al., 2007).
Secondly, this dissertation aimed to document genetic interaction networks in the individual strains with respect to the statin phenotypes. The current studies did document individual strain genetic interaction networks but did not allow a definitive answer of whether specific genetic interaction networks underpin specific phenotypes. There is some evidence, however, supporting this supposition, because the chemical genetic interactions of strains with opposite phenotypes were studied i.e. S288C (susceptible) versus Y55, SK1, YPS60 (resistant). Concerning this comparison
117 it is noted that a number of genes showed interaction in atorvastatin chemical genetic profiling in Y55, SK1, YPS60 that were not shared with S288C. These genes were
YLR257W (unknown), YLR253W (MCP2; mitochondrial lipids), YFL032W (overlaps
HAC1), YLR255C (dubious unknown), RIM15 (protein kinase), SLT2 (MAP kinase), SPF1
(lipid homeostasis in membranes of subcellular compartments), CKA2 (protein kinase),
YOR1 (ABC transporter, human homologue), SYT1 (guanine nucleotide exchange factor in vesicular transport). These genes are not represented in cerivastatin chemical genetic interactions possibly because though these statins have the same primary target they may have quite different off-target side effects (Furberg and Pitt, 2001).
Thirdly, this study has also identified that the genetic interaction networks around specific processes related to statin resistance are different in each of the strains tested. Even though the outcome, i.e. statin resistance, of the networks may well be similar the detailed genetic mechanisms underlying this phenotype have not been fully elucidated here. Nonetheless, the current work provides significant ground work for future investigations to assess more exactly the role strain specific interactions are playing in the mechanism of such individual’s response to statins. Fourthly, although this study focuses on genetic analysis of the processes surrounding the statin inhibition phenotype, the newly created ssDMA’s could be applied to investigate the genetic differences between individuals in response to perturbation from treatment with any small molecule inhibitor. The following sections further discuss the implications of this study.
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6.2 Reliability of ssDMA’s
The SGA’s and chemical genetic screens described in this thesis show that the new ssDMA’s performed in basically the same way as the commercial S288C-derived DMA much utilised in the literature. The reasoning for this statement is as follows. Firstly, the linkage group surrounding the query genes in each of the strains appeared as would be expected. So reliable was this characteristic that it was used to eliminate some SGA data from further consideration in three (out of 20) SGAs that did not work.
Secondly, a large number of known, previously described, S288C-DMA genetic interactions were largely recapitulated in both the primary and secondary screens performed with the new ssDMAs in each of the query strains used. Specific telling examples are that when HMG1 was used as a query gene the ssDMAs showed interactions with HMG2 (and vice versa) in S288C, Y55 and SK1 (the YPS606 SGA was defective). Extending this line of argument, when OPI3 was used as a query gene the
SGAs in the S288C, Y55 and YPS606 DMAs (the SK1 SGA was defective for this query) all showed interactions with all members of the EMC complex as previously reported on SGD for S288C. Thirdly, atorvastatin and cerivastatin showed chemical genetic interactions with HMG1 in all the new ssDMAs as it would be expected they should.
Thus, it is fair to assume the newly created ssDMA’s are working correctly and reliably.
Furthermore, the diversity of the genetic interactions described in Chapters 4 and 5, implies the ssDMAs have, as expected, minimal genetic background from the original
S288C DMA, i.e. are made up of ~98% in the new strain 2% from the original DMA after six backcrossings. The screens producing these results are high throughput i.e. expensive and time consuming and were conducted only once. Nonetheless, the
119 majority of the primary screen high throughput interactions were replicated in secondary screen serial spot dilution assays and smaller SGA mini arrays.
6.3 Chemical genetic interactions
Numerous chemical genetic interactions (Chapter 4) of the two statin drugs, atorvastatin and cerivastatin, were elucidated in the ssDMAs, some of which have been previously described for the S288C DMA. Two important cellular processes stand out in these chemical genetic analyses and they are the unfolded protein response
(UPR) and the involvement of GTPases. Though these processes are conserved in the strains the particular genes which show chemical genetic interactions in a specific process in one strain did not necessarily show the same interacting genes in another strain. An example is the chemical genetic interactions seen with atorvastatin and the genes in the sphingolipid pathway (as discussed in Chapter 4) which showed atorvastatin interacting with different genes in the pathway in each of the four strains.
Clearly, different chemical genetic interaction networks are being used to differently buffer the same fundamental process.
Involvement of small GTPases has been suggested as a possible mechanism of statin associated myopathy (Laufs et al., 2002; Zhu et al., 2013) and have been identified as a process of importance in the current study. Such a process is conserved (expectedly) in the strains. Notably, though it has been found here that the overall genetic interaction networks underpinning them are not conserved. There were other network features that were more conserved, for example hub genes in the GTPases- process with numerous connections that appear in all the strains. For example, BCK1
(which shares chemical genetic interactions with S288C, SK1 and YPS606), is a kinase
120 with over 1000 interactions on SGD, including interactions with a number of GTPase
Rho proteins and also with genes involved in activation of the UPR. This suggests the possibility that the use of another SMI, such as a known kinase inhibitor, used in combination with statins might alleviate the statin off-target phenotypes either by suppression or enhancement of gene expression.
6.4 SGA analyses of ARV1, BTS1, HMG1, HMG2 and OPI3
The performance of different query genes (in the four DMAs) each involved in different parts of lipid biosynthesis also deserves comment. It has become apparent throughout this dissertation that the basic cellular processes elucidated by most of the query strains are represented across all strains. This is unsurprising because all cells require the same basic cellular processes to operate, but the interacting genes implicated in these processes were in general different from one strain to another. In contrast, using OPI3 as a query resulted in a much higher degree of conservation of specific genes in the genetic interaction networks, when compared to the other query genes used in this study. This may be a property of OPI3 as a highly connected hub gene compared to other query strains. That said, there is a major difference in the SK1 strain in the genetic interactions observed with HMG1 as a query gene as seen in interactions with the N-glycosylation genes ALG3, ALG6 and ALG8. This result pertains to the major question of this thesis, namely are the genetic interaction networks surrounding specific processes different between the individual strains? In the case of
SK1, the answer is clearly, yes.
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6.5 Genetic interaction networks
As just discussed, genetic interaction networks show various levels of conservation across the strains of genes that are involved in the same fundamental processes.
However, the genes that are conserved in these essential cellular processes may not be of high interest when drug side-effects are sought. It is argued that such conserved genes would not be the first place to look for off-target therapeutic perturbations. It follows that in investigating side-effects, the genes to focus on would be those involved in conserved essential processes which show strain to strain variation, such as that seen in the HMG1 SGA. Of particular interest are genes like CSG2, IPT1 and
YPC1 in the strains S288C, Y55 and SK1 respectively that show up differently in the strains and are known to be involved at different stages in sphingolipid biosynthesis.
A previous study (Dixon et al, 2009) of negative genetic interaction network conservation between S. cerevisiae and S. pombe in a mini-array concluded that there was substantial (~29 %) conservation of genetically interacting genes between these yeast species. The current thesis, comparing closely related strains, did not find such conservation for example when the genetic interactions observed in S288C and Y55 were combined, there was only 8.9% overlap of negative genetic interactions between these strains (Table 12, Table 14, Table 16 and Table 18). An explanation may be that a mini array consists of 222 x 222 knockout combinations of genes selected to be orthologous and representing major cellular processes (Dixon et al., 2009). This mini array methodology may show bias towards genes that are more likely to have conserved interactions and conceivably would give different results than the current thesis where the entire non-essential genome of ~4300 genes was probed and where
122 query genes were not selected to be orthologous rather selected based on their broad involvement in lipid biosynthesis. It follows that the appearance of network conservation could depend significantly on the strain and the query genes, as discussed above, chosen for such analysis.
6.6 Future directions
6.6.1 Chemical genetic and genetic interaction data The genetic interactions surrounding HMG1 and HMG2 do not show much overlap in the chemical interaction profiles elucidated with statins (atorvastatin and cerivastatin). This may in part be because of duplication of these gene functions in encoding the primary target of the statins, HMG-CoA reductase. Therefore, it would be useful to repeat the atorvastatin and cerivastatin chemical genetic screens with the ssDMA’s created here, but harbouring a null mutation for either HMG1 or HMG2. This would further uncover the potential chemical genetic interactions for the off-target effects of statins in S. cerevisiae, which in turn could then be tested in mammalian cells.
6.6.2 Reproducibility of genetic interactions High throughput screening methods involving genetic interactions show a lot of variability, in particular SGAs, not only between research laboratories published in the literature but even within the same laboratory in our local experience. Technical factors in something as complex as an SGA clearly are a consideration but it is suggested here that “genetic repeatability” (Boake, 1989) must also be taken into account especially in consideration of epistatic gene-gene interaction data. Genetic
123 repeatability, for which a well-developed statistical methodology has been described
(Lessells and Boag, 1987), holds that there will be a genetic variance measured within a species population owing to individual differences within the population that could be inhomogeneously sampled in repeated sampling. It follows that lack of close replication should not be expected in genetics experiments involving populations, to the point that its existence (some lack of replication) may be revealing of underlying genetic architecture (Greene et al., 2009). Therefore, although the majority of genetic interactions discussed throughout this thesis were able to be reproduced, a secondary screen might comprise the equivalent of a repeat sampling and not show 100% replication, as was the case found in the current dissertation studies.
6.6.3 SK1 strain specific glycosylation interactions An intriguing outcome of this dissertation is the newly described interaction of HMG1 with several ALG-genes involved in N-linked glycosylation. N-glycosylation is an essential and conserved process in all eukaryotes including mammals. Even small alterations in interactions involving N-glycosylation are likely to cause profound phenotypic effects. If the statin effects just described can be shown in the mammalian model systems, it might suggest a possible mechanism for the birth defects known to be caused by statin treatment (Forbes et al., 2015; Winterfeld et al., 2013). These presumably would be subtle effects because major disruption in N-glycosylation is known to result in lethal phenotypes similar to those seen in congenital disorders of glycosylation (Grunewald et al., 2002). It’s hard to see how such subtleties of genetic interactions might be uncovered without the genetic interaction network analyses afforded by the yeast model as described here and in the previous work of many other authors on yeast genetic systems. As mammalian gene knockout mutation cell lines
124 increase in genome coverage some yeast subtleties will presumably be translatable, but mating and meiotic segregation on which revealing genetic interactions are based are not in the mammalian cell culturing armamentarium.
6.6.4 Lipid profiling of strains There’s a need to further investigate other phenotypes surrounding lipid/sterol biosynthesis of these strains. Ancillary quantitative GC-MS data collected in the current study (not presented in this thesis) shows that total cellular ergosterol levels are higher in the statin resistant strains SK1, Y55 and YPS606 – compared to S288C.
This alternative type of phenotype resulting from effects of statins could be correlated to the different genes making up the genetic interaction networks described here perhaps further explaining differences in in statin susceptibility. This is a line of future investigation that is likely to be useful.
6.6.5 Inclusion of essential genes A limitation of this study was the lack of interaction data with essential genes, especially given that the majority of genes in the ergosterol biosynthesis pathway are essential and also that essential genes account for around 20% of the genome. ssDMA’s were also constructed for the DAmP collection of essential genes but the essential gene SGAs were not included in this dissertation owing to time restraints.
Understanding essential gene genetic interaction involvement in statin responses would be a useful further investigation of the phenotypes discussed in this thesis.
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6.7 Conclusion
By use of three newly created deletion mutant arrays (ssDMAs) of three individual statin resistant yeast strains it is concluded that the genetic interaction networks concerning statins are different between individuals. There is also some, though not definitive, evidence that differences in genetic interaction networks might explain a statin resistant phenotype in Saccharomyces cerevisiae. It is noted that the use of these ssDMA’s is not limited to the off-target phenotypes described in this study, they are a powerful screening tool which can be utilised to study any environmental change that causes a phenotype in yeast.
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Appendix 1 Atorvastatin and Cerivastatin chemical genetic interactions
Appendix 2 Genetic interactions surrounding ARV1,
BTS1, HMG1, HMG2 and OPI3
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