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The complex genetic and molecular basis of oxidative stress tolerance

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The complex genetic and molecular basis of oxidative stress tolerance

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THE COMPLEX GENETIC AND MOLECULAR BASIS OF OXIDATIVE
STRESS TOLERANCE

by

Robert A Linder

A Dissertation Presented to the
FACULTY OF THE GRADUATE SCHOOL
UNIVERSITY OF SOUTHERN CALIFORNIA
In Partial Fulfillment of the
Requirements for the Degree
DOCTOR OF PHILOSOPHY
(MOLECULAR BIOLOGY)

December 2016

Copyright 2016 Robert A Linder

Dedication

To the incredible people who have believed in me and cared for me in the times when I
needed it most.

ii

Acknowledgements
There was a time in my life when there did not seem to be a way forward. I think
that everyone at some point encounters this wall. I do not know how others find a path
forward, but I know that, for me, without my parents being there for me every step of
that difficult time, I could not possibly have made it to where I am today. I love you both,
mom and dad, and I want you to know that you are the best parents I could have ever
asked for.
To my brother Jared, aka J-man: I love you bro. I think I got incredibly lucky having you
as my brother. You can do anything you put your mind to- never forget that.
To my fiancé, Joann: having you by my side is more of a reward than anything I could
possibly have asked for. I love you, more than I can say. I could not have done this
without you.
I have also been incredibly lucky to have stumbled upon a fantastic mentor and friend
whose kindness I will never forget. Thank you Ian, for everything.
I also don’t think I could have asked for better lab-mates, past and present. I consider
every one of you to be a good friend. There will definitely be an Ehrenreich lab reunion,
and it will be the most epic lab reunion ever. Count on it.
To the rest of my family and all of my friends: I love you all, and I know that I have been
incredibly lucky to have you in my life.
I would also like to thank the members of my committee, Steve Finkel, Matt Dean, and
James Boedicker, with special thanks to Steve Finkel for helping me make it through a
difficult time.
iii

Table of Contents
Dedication ii
Acknowledgements iii
List of Tables ix
List of Figures x
Abstract 1
Chapter 1: Introduction 2
1.1 Oxidative stress: causes and consequences 2
1.2 Cellular tolerance to oxidative stress 4
1.3 Oxidative stress tolerance as a model quantitative trait 6
1.4 Aneuploidies can be conditionally beneficial 8
1.5 Goals of this dissertation 10
1.6 Summary of chapters 11
Chapter 2: Regulatory rewiring in a cross causes extensive genetic heterogeneity 13
2.1 Overview 13
2.2 Introduction 14
2.3 Many BYxYJM segregants show invasion that is independent of FLO8 16
2.4 Initial effort to identify loci underlying FLO8-independent invasion 17
2.5 FLO8-independent invasion in glucose-only individuals depends on the MAPK cascade 17
2.6 Multiple architectures of FLO8-independent invasion in ethanol-only individuals 18
2.7 Testing for effects of mating type and non-genetic factors on FLO8-independent invasion 21
2.8 Segregants that invade in a FLO8-independent manner require different transcription factors and
cell surface proteins 22
2.9 Conclusion 23
2.10 Material and Methods 25
2.10.1 Generation of initial mapping population 25
2.10.2 Phenotyping for invasive growth 26
2.10.3 Genotyping by sequencing 26
2.10.4 Detection of loci influencing ability to invade 27
2.10.5 Genetic engineering 27
iv

2.10.6 Generation of backcross segregants 28
2.10.7 Screening for mating type and non-genetic effects 28
2.10.8 Amplification of the FLO11 coding region 29
2.11 Acknowledgements 29
2.12 Figures 30
2.13 Supporting Information 34
Chapter 3: The complex genetic and molecular basis of a model quantitative trait 48
3.1 Overview 48
3.2 Introduction 49
3.3 Generation of mapping population using recurrent backcrossing with phenotypic selection 53
4.4 Identification of loci that contribute to hydrogen peroxide resistance 54
4.5 Resolution of loci within individual families 54
3.6 Validation and deeper genetic analysis of a subset of loci 56
3.7 Using detection of loci in multiple families to improve mapping resolution 57
3.8 Linkage among genetic variants strongly influences how loci are detected 62
3.9 Discussion 64
3.10 Materials and Methods 68
3.10.1 Screening for hydrogen peroxide resistance 68
3.10.2 Generation of resistant advanced backcross populations 68
3.10.3 Generation of parental reference genomes 69
3.10.4 Low coverage whole genome sequencing of resistant backcross segregants 69
3.10.5 Examination of RMxYPS loci 70
3.10.6 Genetic mapping 71
3.10.7 Reciprocal hemizygosity analysis 72
3.10.8 Allele replacements 72
3.10.9 Quantitative PCR 73
3.11 Acknowledgements 74
3.12 Supporting Information 75
Chapter 4: A yeast chromosome duplication confers a conditional growth benefit by
buffering the expression of oxidative stress-responsive genes 124
4.1 Overview 124
4.2 Introduction 125
v

4.3 Screen for spontaneous mutants with exceptional tolerance to hydrogen peroxide 127
4.4 Sequencing of mutants reveals a conditionally beneficial Chromosome IV aneuploidy 129
4.5 Genetic dissection of a conditionally beneficial chromosome-scale duplication 130
4.6 Effect of the Chromosome IV duplication on transcription 133
4.7 Ectopic overexpression of TSA2 has a limited effect on tolerance 134
4.8 Discussion 135
4.8.1 Chromosome IV disomy provides high oxidative stress tolerance 135
4.8.2 TSA2 is mainly responsible for the effect of the Chromosome IV disomy 136
4.8.3 Chromosome IV disomy buffers expression of TSA1 and TSA2 during prolonged hydrogen
peroxide exposure 137
4.8.4 Transcriptional buffering of TSA2 and TSA1 may involve YAP1 137
4.9 Materials and Methods 138
4.9.1 Screening for extremely high tolerance to hydrogen peroxide 138
4.9.2 Strategy for improved detection of causal mutations via whole-genome sequencing 140
4.9.3 Whole genome sequencing of highly tolerant mutants from the BYxRM and RMxYPS crosses
as well as the original F
2
progenitors 141
4.9.4 GO enrichment analysis 143
4.9.5 Using PCD to fine map the causal locus on Chromosome IV 143
4.9.6 Individual gene and intergenic deletions to find the causal factor 145
4.9.7 Plasmid-based overexpression of TSA2 146
4.9.8 qPCR analysis of TSA1 and TSA2 expression levels 146
4.10 Figures 148
4.11 Supplementary Information 154
Chapter 5: Concluding remarks 164
5.1 Multiple regulatory architectures can underlie invasive growth in budding yeast 164
5.2 Oxidative stress tolerance is predominately influenced by additive effect loci that can be closely
linked 165
5.3 Many different cellular processes contribute to oxidative stress tolerance 166
5.4 A single gene underlies a conditionally beneficial aneuploidy 167
5.5 Impact of my work 167
5.6 Future directions 168
References 170
Appendix A: Quantitative Trait Variation, Molecular Basis of 193
vi

A.1 Glossary 193
A.2 Introduction 195
A.3 Characterizing the Molecular Basis of a Quantitative Trait 198
A.3.1 Identifying QTGs 198
A.3.2 Characterizing QTNs 199
A3.3 Studying the Mechanisms by which Multiple QTNs Influence a Trait 201
A.4 The Molecular Underpinnings of Quantitative Genetic Effects 202
A.4.1 Additivity 202
A.4.2 Dominance 203
A.4.3 Pleiotropy 206
A4.4 Genetic Interactions 207
A.4.5 Gene–Environment and Genotype–Environment Interactions 208
A.5 Conclusion 210
Appendix B: Nrf2-dependent Induction of Proteasome and Pa28αβ Regulator are
Required for Adaptation to Oxidative Stress 212
B.1 Overview 212
B.2 Introduction 213
B.3 H
2
O
2
, Peroxynitrite, Paraquat, and Menadione Pretreatment All Increase Proteolytic Capacity 216
B.4 H
2
O
2
Adaptation Increases Nrf2 Protein Levels and Nrf2 Nuclear Translocation 218
B.5 Nrf2 Is an Important Regulator for H
2
O
2
-induced Increase in Proteolytic Capacity 220
B.6 Nrf2 Is an Important Regulator of the H2O2-induced Increase in Proteolytic Capacity to Degrade
Oxidized Proteins 221
B.7 Nrf2 Regulates H
2
O
2
-induced Expression of 20S Proteasome and Pa28αβ but Not
Immunoproteasome 221
B.8 Pretreatment with Nrf2 “Inducers” Causes Increased Tolerance to Oxidative Stress 224
B.9 Nrf2, 20S Proteasome, Pa28αβ, and Immunoproteasome Play Important Roles in H
2
O
2
-induced
Adaptive Increase in Oxidative Stress Tolerance 226
B.10 Nrf2 and Proteasome Are Key Factors in Adaptive Increase in Tolerance to Oxidative Stress
Produced by Nrf2 Inducers 227
B.11 Discussion 228
B.12 Materials and Methods 234
B.12.1 Materials 234
B.12.2 Adaptation to Oxidants 234
vii

B.12.3 Induction or Inhibition of Nrf2 235
B.12.4 Western Blot Analysis 236
B.12.5 siRNA “Knockdown” of Nrf2 or Proteasome 236
B.12.6 Fluoropeptide Proteolytic Assays 237
B.12.7 Proteolytic Assay of Radiolabeled Proteins 238
B.12.8 Cell Counting Assay 238
B.12.9 Chromatin Immunoprecipitation (ChIP) Assay 239
B.12.10 Real Time PCR Assay of mRNA Levels 240

viii

List of Tables

Table S2.1. Phenotype data and Short Read Archive identifiers for the segregants examined in the paper.
38
Tables S2.2-s2.6 45
Table S2.3. Genotype data for the additional 55 BYxYJM segregants that show FLO8-independent
invasion. 45
Table S2.4. Genotype data for the backcross of Segregant 2 to BY. 45
Table S2.5. Genotype data for the backcross of Segregant 2 to YJM. 46
Table S2.6. Genotype data for the backcross of Segregant 3 to BY. 46
Table S2.7. Analysis of dissected tetrads from homozygous diploid derivatives of specific segregants. 47
Table S3.1. Phenotype data for all F
2
progenitor strains as well as all F
2
B
3
segregants used in this study. 83
Table S3.2. All putative QTL and associated genes. 101
Table S3.3. Phenotype data for reciprocal hemizygosity and marked allele replacement assays. 115
Table S3.4. Phenotype and normalized OD600 data for a representative panel of 48 F
2
B
3
segregants
screened for high resistance to hydrogen peroxide exposure. 120
Table S4.1. All non-structural mutations detected in mutants derived from the BYxRM, RMxYPS, and
BYxYPS crosses. 162

ix

List of Figures

Fig. 2.1. Effects of FLO8 on ability to invade. 30
Fig. 2.2. Genetic dissection of FLO8-independent glucose-only invasion. 31
Fig. 2.3. Genetic dissection of ethanol-only invasion by backcrossing Segregant 2 to BY and YJM. 31
Fig. 2.4. Genetic dissection of FLO11-independent, ethanol-only invasion by backcrossing of Segregant 3
to BY. 32
Fig. 2.5. Deletion screen of known FLO11 activators. 33
Fig. S2.1. Construction of allele replacements. 34
Fig. S2.2. Initial results from selective genotyping of segregants that show FLO8-independent invasion. 35
Fig. S2.3. Differences FLO11 coding region length between BY and YJM. 36
Fig. S2.4. Replacement of the FLO11 coding region in segregant 2 with the BY allele causes loss of
invasion. 37
Fig. S2.5. Alignment of the BNI1 gene. 38
Fig. 3.1. Backcrossing strategy. 51
Fig. 3.3. Validation of loci detected in families derived from the RMxYPS cross. 58
Fig. 3.4. Localization and cloning of loci detected in multiple BYxYPS advanced backcross families. 62
Fig 3.5. A cis regulatory polymorphism in the BYxYPS cross causes differential expression of SDP1 in
response to hydrogen peroxide. 62
Fig. 3.6. Two different quantitative trait genes segregate in the same genomic region. 66
Fig. S3.1. Distribution of hydrogen peroxide resistance among parents and cross progeny. 75
Fig. S3.2. Genome-wide allele frequency plots of all families for each cross, with families from a cross
ordered by their founding segregant. 77
Fig. S3.3. Detection of nominally significant loci (p ≤ 0.05) in multiple advanced backcross families and
crosses. 78
Fig. S3.4. L7-II and L16-I act additively. 79
Fig. S3.5. RH and allele replacement results for the five BYxYPS loci described in Figure 3.4. 79
Figure S3.6. Raw data plotted for the RH results depicted in the previous figure. 80
Fig. S3.7. Cloning of AQY1 in the RMxYPS cross. 81
Fig. S3.8. The correlation between MIC and OD600 values for 48 haploid strains. 82
Fig. 4.1. Detection of Chromosome IV disomy. 148
Fig. 4.2. Fine-mapping the causal locus of the Chromosome IV disomy. 149
Fig.4.3. TSA2 deletion is mainly responsible for the benefit conferred by the Chromosome IV disomy. 150
Fig. 4.4. TSA2 expression. 151
Fig. 4.5. TSA1 expression. 152
Fig. 4.6. Plasmid-based overexpression of TSA2. 153
Fig. S4.1. Screen to verify higher tolerance of mutants than the original F2 progenitor. 154
Fig. S4.2. Fitness of aneuploidy versus haploid strains on rich media. 155
Fig. S543. The Chromosome IV disomy was found to confer a conditional benefit only when cells are
exposed to hydrogen peroxide on agar plates. 156
Fig. S4.4. Verification of chromosome-scale deletions. 157
Fig. S4.5. Phenotypes of PCD strains. 158
Fig. S4.6. Phenotypes of individual gene and intergenic deletion strains. 160
x

Fig. S4.7. Construction of low and high copy plasmid. 161
Fig A.1. Determining the molecular factors underlying a quantitative trait locus 197
Fig A.2. Molecular effects of quantitative trait nucleotides (QTNs). 201
Fig A.3. Mechanisms of allelic dominance. 205
Fig A.4 Mechanisms of genetic interactions. 208
Fig A.5. Mechanisms of pleiotropy. 209
Fig. B.1. Oxidant pretreatment increases proteolytic capacity in a proteasome-dependent manner. 217
Fig. B.2. Nrf2 protein levels and nuclear translocation during oxidative stress adaptation. 219
Fig. B.3. Increased proteolytic capacity is blocked by inhibition of Nrf2. 223
Fig. B.4. H2O2-induced expression of proteasome and proteasome regulators is Nrf2 dependent. 226
Fig. B.5. 20 S proteasome is required for H2O2 adaptation, and contains active EpRE elements. 229
Fig. B.6. Pretreatment with Nrf2 inducers causes increased tolerance to oxidative stress. 232
Fig. B.7. Lipoic acid, DL-sulforaphane, and curcumin promote adaptation in an Nrf2 and proteasome-
dependent manner. 233

xi

Abstract

Oxidative stress is a condition that all aerobic organisms must face due to the
production of potentially harmful reactive oxygen species (ROS) as a by-product of
oxygen utilization. ROS are capable of reacting with and damaging all types of
macromolecules. Due to the many disorders associated with the accumulation of
damage caused by oxidative stress, a large body of research has been devoted to
understanding the mechanisms through which cells are able to prevent and/or repair
damage caused by ROS. Although large strides have been made in understanding the
molecular factors that affect the ability to tolerate oxidative stress, much still remains
unknown about this extremely complex trait.
In order to further understand the molecular and genetic complexity of oxidative stress
tolerance, I undertook several different projects during the course of my graduate
studies. The majority of these studies make use of the powerful model organism,
Saccharomyces cerevisiae. By using this organism, I was able to conduct a large-scale
genetic mapping study aimed at dissecting the genetic and molecular basis of natural
variation in oxidative stress tolerance in budding yeast. The approach used in this study
facilitated the cloning of several genes that influence this trait, including some that had
not, to the best of my knowledge, been previously shown to have a role in oxidative
stress tolerance. Furthermore, by using highly tolerant strains generated from this study,
I was able to find mechanisms that take individuals to a level of tolerance beyond what
can be achieved through segregating variation alone.

1

Chapter 1: Introduction

1.1 Oxidative stress: causes and consequences
All forms of aerobic life utilize molecular oxygen in the process of obtaining energy. The
cost of using oxygen in this manner involves the production of reactive oxygen species
(ROS) and reactive nitrogen species (RNS) that can be highly deleterious and cause
damage to almost all types of macromolecules (Amir Aslani & Ghobadi, 2016; Avery,
2011; Cabiscol, Tamarit, & Ros, 2000; Cooke, Evans, Dizdaroglu, & Lunec, 2003;
Herrero, Ros, Belli, & Cabiscol, 2008; Knoefler et al., 2014; Stadtman, 2006; Valko,
Rhodes, Moncol, Izakovic, & Mazur, 2006; X. Wei & Yin, 2015; Wurtmann & Wolin,
2009). Although these potentially toxic compounds have been shown to be an integral
part of cell signaling and the immune response, exposure to high levels of ROS or RNS
(collectively known as reactive oxygen and nitrogen species, or RONS) can lead to an
often detrimental condition known as oxidative or nitrosative stress, respectively. For the
sake of brevity, both conditions will be referred to collectively as oxidative stress. This
condition can occur either as a short, transient spike in levels of RONS or a prolonged,
chronic exposure to high levels of RONS (Lushchak, 2014). The most highly damaging
forms of RONS include the hydroxyl radical (
•
OH), peroxynitrite (ONOO
-
), and
hypochlorous acid (HOCL
-
) (Weidinger & Kozlov, 2015). These compounds are
themselves secondary by-products of the reactions of less directly harmful RONS such
as the superoxide anion (O2
•-
), nitric oxide (NO
•
), and hydrogen peroxide (H2O2), either
2

with one another, certain transition metal ions (predominantly iron and copper ions), or,
in the case of HOCL
-
, with Cl
-
(Weidinger & Kozlov, 2015).
The primary endogenous source of RONS is the mitochondria due to leakage of
electrons from the electron transport chain (Amir Aslani & Ghobadi, 2016; Fridovich,
1978). This can cause the partial reduction of molecular oxygen to O2
•-
which can lead
to the formation of the highly damaging
•
OH radical. Other prominent enzymatic sources
of endogenous RONS include NAD(P)H oxidases, which can lead to the formation of
O2
•-
, xanthine oxidase, which can produce O2
•-
, H2O2, and NO
•
, as well as nitric oxide
synthases, which are capable of producing both NO
•
and O2
•-
(Dhawan, 2014). Less
prominent enzymatic sources of RONS include myeloperoxidase, lipoxygenase,
aldehyde oxidase, cyclooxygenase, dehydrogenase, tryptophan dioxygenase, and
flavoprotein dehydrogenase (Dhawan, 2014; Gielis et al., 2011).
The specific types of molecular damage caused by RONS have been studied
extensively and are now relatively well understood. Oxidative modifications to nucleic
acids can cause, “… base and sugar modifications, covalent crosslinks, and single- and
double-stranded breaks.” (Sung, Hsu, Chen, Lin, & Wu, 2013), which can lead to
mutations and genome instability. One of the most common DNA modifications, caused
by the reaction between
•
OH and guanine, is the formation of 8-oxo-7,8,dihydro-2’-
deoxyguanosine (8-oxodG), which can lead to a transversion mutation (G-C --> T-A).
This potentially mutagenic event has also been shown to occur to RNA (Wurtmann &
Wolin, 2009). Proteins can be oxidatively damaged through a variety of mechanisms,
including direct reactions of
•
OH with the polypeptide backbone as well as oxidation of
the side chains of amino acid residues, which can lead to protein cross-linking, the
3

generation of advanced glycation end products (Dhar, Sägesser, Weikert, & Wagner),
and the formation of toxic protein aggregates (Trnkova, Drsata, & Bousova, 2015).
Modifications of different types of lipids, including poly-unsaturated fatty acids (PUFA)
and cholesterol, can also be mediated by RONS (Yoshida et al., 2015).
As can be imagined from the large number of cellular factors that can be modified by
RONS, there are many types of disorders that are associated with damage caused by
oxidative stress. Cancer, neurodegenerative disorders such as Alzherimer’s Disease
and Parkinson’s Disease, traumatic brain injury, cardiovascular disorders, as well as the
aging process itself have all been shown to be affected by oxidative stress
(Anthonymuthu, Kenny, & Bayir, 2016; Harman, 1956; C. K. Roberts & Sindhu, 2009; R.
A. Roberts et al., 2009; Sanz & Stefanatos, 2008; Sung et al., 2013; Yoshida et al.,
2015).

1.2 Cellular tolerance to oxidative stress
Due to the wide spectrum of damage RONS can cause, there are a number of different
defense mechanisms present within cells to prevent and/or ameliorate this damage. The
primary first line of defense against RONS is the action of antioxidants, which are
substances capable of neutralizing free radicals (Dhawan, 2014; Ji, 1999) . The most
prominent cellular antioxidants include superoxide dismutase (Silventoinen et al.),
catalase, glutathione peroxidase, and thioredoxin peroxidases (Dhawan, 2014) (Amir
Aslani & Ghobadi, 2016; Chaudiere & Ferrari-Iliou, 1999; Dickinson & Forman, 2002).
These enzymes are capable of directly reacting with and neutralizing free radicals. SOD
4

reacts with the superoxide anion in a dismutation reaction to produce molecular oxygen
and H2O2. H2O2 itself can be detoxified through the actions of either catalase, which
converts H2O2 to water and molecular oxygen, or glutathione peroxidase, which is
capable of catalyzing the conversion of H2O2 or organic peroxide (ROOH) to water or
alcohol, respectively (Amir Aslani & Ghobadi, 2016; Chaudiere & Ferrari-Iliou, 1999;
Dickinson & Forman, 2002; Rahman, 2007; Valko et al., 2006). Thioredoxin peroxidases
are also capable of directly reducing H2O2 as well as alkyl hydroperoxides, primarily
thorough the action of a conserved cysteine residue. These enzymes exist in all
kingdoms and are present in multiple isoforms that have different subcellular
distributions (Amir Aslani & Ghobadi, 2016; Baek et al., 2012; Nordberg & Arner, 2001).
Non-enzymatic antioxidants include GSH, the thioredoxin system, the glutaredoxin
system, lipoic acid, melatonin, coenzyme Q10, vitamin C, vitamin E, carotenoids, and
polyphenols (Amir Aslani & Ghobadi, 2016). Cellular systems that act as damage
control mechanisms for the effects of RONS if the first line of defense fails mainly
include DNA repair mechanisms, proteasomal degradation of oxidized proteins, as well
as autophagy, which is responsible for the clearance of large protein aggregates as well
as the turnover of damaged organelles such as mitochondria (known as mitophagy) (N.
Chondrogianni et al., 2015; Sureshbabu, Ryter, & Choi, 2015; H. Wei, Liu, & Chen,
2015; L. J. Yan, 2014).

5

1.3 Oxidative stress tolerance as a model quantitative trait
Although much is known about the specific mechanisms cells employ to deal with
RONS, a complete understanding of the genetic complexity underlying tolerance to
oxidative stress has yet to be fully achieved. Oxidative stress tolerance is viewed as a
model quantitative trait. In general, quantitative traits are defined by the expression of a
continuous range of phenotypic values across individuals in a population. These
complex traits are influenced by genetic, environmental, and stochastic factors (Barton
& Turelli, 1987, 1989). The genetic component of a quantitative trait can be divided into
narrow sense and broad sense heritability (reviewed in (Visscher, Hill, & Wray, 2008)).
These categories refer to the amount of variation in a trait that can be explained by
additive effects alone versus the effects of additivity plus all other genetic factors,
including epistasis, dominance, etc…, respectively. Some well-studied quantitative traits
include morphology (Bradshaw, Otto, Frewen, McKay, & Schemske, 1998; Juenger,
Purugganan, & Mackay, 2000), disease susceptibility (Richard et al., 2016) (Yu, Pal, &
Moult, 2016), and tolerance to external stressors. An advantage to studying quantitative
traits is that natural populations can have a large amount of variation in the expression
of these traits that can be leveraged to identify causal genes. Genes identified in this
way may be different from what can be found by querying gene deletions, as gene
knock outs can have large, unanticipated effects that differ depending on the genetic
background as reviewed in (Gasch, Payseur, & Pool, 2016). However, despite
advances in detecting and identifying causal sites within the genome (referred to as loci)
that contribute to such traits, often only a relatively modest proportion of the total
genetic component is found. This ‘missing heritability’ issue has been reviewed
6

extensively in the literature and several solutions have been proposed, including the
potential contribution of rare variants that have gone undetected in genome-wide
association studies (GWAS), the possibility that many common small-effect variants
influence the phenotypic outcome of such traits, linkage between closely associated
variants, interactions between genes (referred to as epistasis), pleiotropy, as well as
inflations in the estimates of heritability (C. H. Chen et al., 2015; Edwards, Symbor-
Nagrabska, Dollard, Gifford, & Fink, 2014; Golan, Lander, & Rosset, 2014; Gusev et al.,
2013; Manolio et al., 2009; Marian, 2012; J. Yang et al., 2015; Yu et al., 2016; Z. Zhu et
al., 2015; Zuk, Hechter, Sunyaev, & Lander, 2012; Zuk et al., 2014). Recent studies
have begun to tease apart the extent to which these factors contribute to specific
quantitative traits (Agarwala, Flannick, Sunyaev, Go, & Altshuler, 2013; Ehrenreich &
Pfennig, 2016; J. T. Lee, Taylor, Shen, & Ehrenreich, 2016; Linder, Seidl, Ha, &
Ehrenreich, 2016; Y. H. Liu et al., 2016; T. Matsui & Ehrenreich, 2016; Taylor &
Ehrenreich, 2014; Taylor & Ehrenreich, 2015b; Taylor, Phan, Lee, McCadden, &
Ehrenreich, 2016).
One extensively studied area of quantitative genetics research is the response that
individuals display to different types of stress, such as extreme temperatures or pH
(Morgan & Mackay, 2006; Sinha et al., 2008; Sinha, Nicholson, Steinmetz, & McCusker,
2006). Often, chemical treatment is used to induce the specific type of stress and
individuals displaying the phenotype of interest, most frequently low or high sensitivity,
are selected for downstream analyses in order to dissect the genetic basis of this
response. In this fashion, quantitative trait loci can be identified that potentially
contribute to the trait of interest (Steinmetz et al., 2002). Several studies have
7

comprehensively examined the genetic basis of tolerance to a variety of chemicals
using the budding yeast, Saccharomyces cerevisiae, as a model system to dissect
these traits (Ehrenreich et al., 2012; Ehrenreich et al., 2010; H. S. Kim & Fay, 2007;
Meijnen et al., 2016; Swinnen, Thevelein, & Nevoigt, 2012). This organism offers
several advantages for studying complex traits, including a short generation time, the
ability to undergo sexual reproduction, as well as a large tool-box of genetic engineering
techniques. Some studies have found that, at least for the traits examined, additive
effect loci predominantly explain the variation displayed by genetically distinct
individuals (Bloom et al., 2015; Linder et al., 2016). Instances of genetic linkage,
pleiotropy, and epistasis have also been found to influence quantitative traits, albeit to a
lesser degree than additivity (Bloom et al., 2015; Linder et al., 2016; Sinha et al., 2006).
Of the studies examining tolerance to chemically-induced stress in budding yeast,
several have used substances known to induce oxidative stress within cells, including
diamide, paraquat, ethanol, and hydrogen peroxide (Bloom et al., 2015; Ehrenreich et
al., 2012; Ehrenreich et al., 2010; Linder et al., 2016). Although a large number of loci
have been found that influence tolerance to these oxidants, only in a relatively small
number of instances has the causal factor underlying these regions been cloned. More
research is needed to further our understanding of the precise mechanisms that
influence this important trait.

1.4 Aneuploidies can be conditionally beneficial
Although having an abnormal number of chromosomes, a condition known as
aneuploidy, is implicated in a number of developmental disorders and many, if not all,
8

types of cancer (Akasaka et al., 2013; Bose et al., 2015; Davoli et al., 2013; Durrbaum
& Storchova, 2015, 2016; Gannon, Martinez, Anderson, & Swingle, 2011; Laubert et al.,
2015; Mohr, Zaenker, & Dittmar, 2015; Nicholson & Cimini, 2015; Pinto, Pereira, Silva,
& Andre, 2015; Potapova, Zhu, & Li, 2013; Santaguida & Amon, 2015b; Schatten &
Sun, 2015; Sheltzer, 2013), several studies have shown that certain aneuploidies can
have a beneficial effect on fitness in a very context-dependent manner(G. Chen,
Rubinstein, & Li, 2012; Pavelka et al., 2010; Sunshine et al., 2015; Yona et al., 2012)}.
In budding yeast, several stressors have been shown to lead to the selection of
individuals that have gained an extra copy of a particular chromosome, including
nutrient deficiency (Sunshine et al., 2015), heat shock (Yona et al., 2012), translational
stress (G. Chen et al., 2012), loss-of-function mutations, as well as oxidative stress (G.
Chen et al., 2012). Several of these studies have found that a small number of genes
present on a duplicated chromosome can mediate the effects of an adaptive aneuploidy
(Kaya et al., 2015). Further, a handful of studies have found that, in certain cases, a
single gene can explain most of the benefit conferred by a specific aneuploidy (G. Liu et
al., 2015; Pavelka et al., 2010; Tan et al., 2013). In the majority of these studies, the
beneficial effect is often caused by the increased dosage of a particular gene or set of
genes present on the aneuploid chromosome. One example of this is a study conducted
by Pavelka et al, in which an extra copy of the plasma membrane-localized multidrug
efflux pump, ATR1, was found to be entirely responsible for the effect of a whole-
chromosome aneuploidy that conferred resistance to the DNA-damaging agent 4-NQO
(Pavelka et al., 2010).
9

Although these findings have advanced our understanding of how aneuploidies can be
conditionally beneficial, more examples are needed to further explore the relationship
between specific aneuploidies, the genetic and environmental contexts in which they
are adaptive, and the factors that mediate this benefit. Furthermore, in most studies that
have found the causal gene(s) responsible for the effect of an adaptive aneuploidy, a
relatively small subset of candidate genes are queried based on known function. A more
unbiased approach used RNA-seq to identify a small number of candidate genes (Kaya
et al., 2015). However, a more powerful approach is needed to enable the systematic
query of every gene within a chromosomal duplication.

1.5 Goals of this dissertation
To achieve both a broader and deeper understanding of the molecular mechanisms that
allow cells to tolerate oxidative stress, I decided to begin my PhD by studying the
proteasome, a key component of the cells’ defense against oxidized proteins.
Specifically, I was involved in research that examined proteasomal regulation during
adaptation to oxidative stress induced by treatment with H2O2 in murine embryonic
fibroblast cells (MEFs). The primary goal of this study was to show that a known
regulator of the cellular response to oxidative stress, Nrf2, is necessary for the adaptive
upregulation of different proteasomal subunits that allow these cells to tolerate relatively
high doses of known inducers of oxidative stress.
Although this approach helped to further understanding of a specific mechanism that
regulates oxidative stress tolerance, I wished to transition to a broader understanding of
10

different molecular mechanisms that contribute to this trait. To achieve this, I moved to a
lab that studies the genetic basis of complex traits. As a primer for working within the
field of complex trait genetics, I joined an ongoing project in the lab examining the
molecular basis of an invasive growth phenotype in a cross of two genetically distinct
Saccharomyces cerevisiae strains. This study deepened my understanding of complex
trait genetics and helped to advance understanding of how multiple genetic
architectures can lead to a similar phenotypic outcome.
After I had achieved a firmer grip on complex traits, I undertook a project designed to
advance our knowledge of a key quantitative trait, the ability to tolerate oxidative stress.
Deeping our understanding of the genetic and molecular basis of this trait has important
implications for human health as detailed above.
A final question I wished to address during the course of my studies involved finding
mechanisms that would allow individuals to achieve a level of tolerance to oxidative
stress beyond what would normally be seen in natural populations. Answering this
question will help clarify how organisms can reach an extreme phenotypic state and
whether or not there is a strict upper limit to the ability to tolerate oxidative stress.
1.6 Summary of chapters
In Chapter 2, I help characterize the basis of a genetically heterogeneous trait, the
ability to grow invasively, in Saccharomyces cerevisiae. This work provides further
evidence that regulatory rewiring can provide multiple paths to a similar phenotypic
outcome.

11

In Chapter 3, I examine the complex genetic and molecular basis of tolerance to
oxidative stress in pairwise crosses of budding yeast using a reciprocal backcrossing
strategy. This strategy enabled me to find a large number of different loci that contribute
to oxidative stress tolerance within each cross. The increased resolution of this
approach allowed me to clone genes underlying several of the detected loci, furthering
our understanding of this model quantitative trait.
In Chapter 4, I outgrow multiple clones of the most highly tolerant F2 individuals
generated from my previous study in order to find potential mechanisms that can take
individuals to a level of oxidative stress tolerance beyond what would normally be found
in natural populations. This study reveals a common mechanism to achieving this: a
single aneuploidy in which strains carrying an extra copy of Chromosome IV are able to
reach extremely high levels of oxidative stress tolerance. Furthermore, I adapt a
chromosomal-scale deletion technique in order to fine map the causal region and
subsequently clone the causal factor, a single gene that has an important role in
protecting cells from damage caused by oxidative stress.
12

Chapter 2: Regulatory rewiring in a cross causes extensive genetic
heterogeneity
Takeshi Matsui, Robert Linder, Joann Phan, Fabian Seidl, and Ian M. Ehrenreich

This work appears essentially as published in 2015 in
Genetics, DOI: 10.1534/genetics.115.180661

2.1 Overview
Genetic heterogeneity occurs when individuals express similar phenotypes due to
different underlying mechanisms. Although such heterogeneity is known to be a potential
source of unexplained heritability in genetic mapping studies, its prevalence and
molecular basis are not fully understood. Here, we show that substantial genetic
heterogeneity underlies a model phenotype—the ability to grow invasively—in a cross of
two Saccharomyces cerevisiae strains. The heterogeneous basis of this trait across
genotypes and environments makes it difficult to detect causal loci with standard genetic
mapping techniques. However, using selective genotyping in the original cross, as well
as in targeted backcrosses, we detect four loci that contribute to differences in the ability
to grow invasively. Identification of causal genes at these loci suggests they act by
changing the underlying regulatory architecture of invasion. We verify this point by
deleting many of the known transcriptional activators of invasion, as well as the cell
surface protein FLO11, from five relevant segregants and showing that these individuals
differ in the genes that they require for invasion. Our work illustrates the extensive
genetic heterogeneity that can underlie a trait and suggests that regulatory rewiring is a
basic mechanism that gives rise to this heterogeneity.

13

2.2 Introduction
Genetic studies in humans and model organisms have reported unexplained
heritability for many traits (Manolio et al., 2009). A possible contributor to this ‘missing’
heritability is genetic heterogeneity—individuals exhibiting similar phenotypes due to
different genetic and molecular mechanisms (McClellan & King; Risch; Wray). Genetic
heterogeneity can reduce the statistical power of mapping studies (Manchia et al.;
Wray), and may involve multiple variants segregating in the same gene (‘allelic’
heterogeneity) or different genes (‘non-allelic’ heterogeneity) (Risch, 2000). Work to
date has shown that allelic heterogeneity is widespread (Ehrenreich et al., 2012; Long,
Macdonald, & King, 2014, and often involves two or more null or partial loss-of-function
variants segregating in a single phenotypically important gene (Nogee, 2000 #345;
McClellan & King, 2010; Sutcliffe et al., 2005; Will et al., 2010). However, the prominence
and underlying mechanisms of non-allelic heterogeneity are less understood.

In this paper, we describe an example of non-allelic heterogeneity, using heritable
variation in the ability of Saccharomyces cerevisiae strains to undergo haploid invasive
growth as our model. Invasive growth is a phenotype that is triggered by low carbon or
nitrogen availability, and is thought to be an adaptive response that allows yeast cells
to adhere to and penetrate surfaces (Cullen & Sprague, 2000). Invasion typically
requires expression of FLO11, which encodes a cell surface glycoprotein that facilitates
cell-cell and cell-surface adhesion (Lo & Dranginis, 1998; Rupp, Summers, Lo,
Madhani, & Fink, 1999). In addition to FLO11, S. cerevisiae possesses other cell
surface proteins that can contribute to adhesion-related traits (Guo, Styles, Feng, &
14

Fink, 2000; Halme, Bumgarner, Styles, & Fink, 2004). In some cases, these cell
surface proteins are regulated by multiple signaling cascades (Bruckner & Fems),
potentially providing an opportunity for genetic variants in different pathways to have
similar effects on invasion.

Here, we examine the genetic basis of variation in the ability to invade on two carbon
sources—glucose and ethanol—in a cross of the lab strain BY4716 and the clinical
isolate YJM789 (‘BY’ and ‘YJM’, respectively) (Gianni Liti et al., 2009). YJM is highly
invasive on both carbon sources (Figure 2.1A). In contrast, BY cannot grow invasively
on either carbon source (Figure 2.1A). This is because BY carries a nonsense allele
of FLO8 (Figure 2.1B; Methods), which encodes a transcriptional activator that is
regulated by the Ras-cAMP-PKA pathway. Flo8 is typically required for invasive
growth in both S. cerevisiae (H. Liu, Styles, & Fink, 1996) and Candida albicans (Cao
et al., 2006). Consistent with the importance of FLO8 for invasion, deletion of this
gene from YJM significantly reduces its invasive growth on both carbon sources
(Figure 2.1B; Methods).

While screening BYxYJM segregants for invasion on the two carbon sources, we
found that many individuals exhibit invasion even though they possess the FLO8
BY
nonsense allele, a result that was also recently reported in (Song et al., 2014). We
show that this FLO8-independent growth has a heterogeneous genetic basis that
reflects the presence of multiple distinct regulatory architectures that enable FLO8-
independent invasion. Most of these regulatory architectures are FLO11-dependent
15

but require different transcriptional activators; however, we also provide evidence for
an architecture that is FLO11-independent. Our results suggest that regulatory
rewiring is an important source of non-allelic genetic heterogeneity and illustrate how
studying the causes of phenotypic similarities among genetically distinct individuals
can advance our understanding of complex traits.

2.3 Many BYxYJM segregants show invasion that is independent of FLO8
We examined a population of 127 genotyped BYxYJM MATa segregants
for ability to invade on two carbon sources—glucose and ethanol (Methods).
Despite the major role of FLO8 in the invasion phenotypes of BY and YJM
(Figures 2.1A and 2.1B), we unexpectedly found that a large fraction (52%) of
segregants with the FLO8BY nonsense allele were capable of invading in at least
one condition (Figure 2.1C). A possible explanation for these individuals’
phenotypes is that FLO8BY is partially functional in some genetic backgrounds.
Flo8 is comprised of a LisH domain (amino acids 72-105) that is involved in
physical interactions with the transcription factor Mss11 and a transcriptional
activation domain (amino acids 701-799) that is necessary for DNA binding (H. Y. Kim,
Lee, Kang, Oh, & Kim, 2014). The nonsense polymorphism in FLO8BY occurs after the
LisH domain at amino acid 142, suggesting that the truncated Flo8 may retain some
functionality. We tested for partial functionality of FLO8BY by deleting the entire
coding portion of FLO8 from multiple invasive FLO8BY segregants, and
phenotyping them for invasive growth on glucose and ethanol (Methods).
Complete deletion of FLO8 had no effect on invasion, suggesting that other
16

mechanisms enable these individuals to grow invasively.

2.4 Initial effort to identify loci underlying FLO8-independent invasion
As a first step in identifying the genetic basis of FLO8-independent invasion, we screened 384
additional F2 segregants for invasion on glucose and ethanol. We obtained 55 invasive
FLO8
BY
individuals from this experiment, bringing the total number of invasive FLO8
BY
individuals to 97. Among these 97 individuals, 50% were invasive on both glucose and ethanol,
37% were invasive only on glucose, and 12% were invasive only on ethanol (Figure 2.1D). We
genotyped the 55 new individuals using low-coverage genome sequencing and attempted to
detect enriched alleles among the larger set of 97 genotyped FLO8
BY
strains that were capable
of invasion (Methods). Although our past work suggests that such selective genotyping should
have high statistical power (Ehrenreich et al., 2010), even in the presence of complex
non-additive genetic effects (Taylor & Ehrenreich), we failed to detect any loci using this strategy
(Figure S2.2A).

2.5 FLO8-independent invasion in glucose-only individuals depends on the
MAPK cascade

We hypothesized that FLO8-independent invasion is genetically heterogeneous in the
BYxYJM cross, reducing the statistical power of our genetic mapping effort. To mitigate
this potential problem, we attempted to identify causal loci by focusing on different
classes of FLO8
BY
segregants. We first looked at FLO8
BY
individuals that show
invasion on both glucose an ethanol, but this analysis did not identify any loci (Figure
S2.2B). We next examined individuals that invade in only one condition, under the
assumption that different mechanisms might underlie condition-specific invasion.
17

Among the segregants showing FLO8- independent invasion only on glucose (n = 36),
nearly all of these individuals carried the BY allele of a locus on Chromosome VIII,
which we were able to delimit to 10 genes (Figure 2.2A; Methods). To determine the
causal gene(s) at the Chromosome VIII locus, we replaced the BY allele of each gene
in this interval with the YJM allele in a FLO8
BY
segregant that was invasive only on
glucose (‘Segregant 1’; Methods). Each replacement spanned the promoter, coding
region, and part of the downstream region of the tested gene (Figure S2.1). The only
replacement that had an effect was GPA1, a subunit of the G-protein coupled receptor
involved in the Mitogen-Activated Protein Kinase (MAPK) cascade pheromone
response (Fujimura). Converting Segregant 1’s GPA1 allele to the YJM version
rendered the strain nearly incapable of invading on glucose and had no effect on
ethanol (Figure 2.2B). BY is known to possess a lab-derived amino acid variant
(S469I) in GPA1 that causes a large number of gene expression changes specifically in
glucose (E. N. Smith & S., 2008; Yvert et al., 2003). This amino acid substitution may
also be the causal variant in our study.

2.6 Multiple architectures of FLO8-independent invasion in ethanol-only
individuals

We next studied FLO8
BY
individuals that were invasive only on ethanol. Because our
sample size for this group was small (n = 12), we generated backcross populations in a
manner similar to (Taylor & Ehrenreich) and used these populations to identify loci that
influence invasive growth in a single segregant (‘Segregant 2’; Methods). In the
backcross to BY, we screened 192 segregants and found that 16% were invasive only
18

on ethanol. Among these individuals (n = 30), we identified a single locus that was
nearly fixed for the YJM allele (Figure 2.3A top), which was located on Chromosome
IX and overlapped FLO11. FLO11 is known to harbor extensive functional variation
across yeast isolates in both its coding and noncoding regions (Fidalgo, Barrales,
Ibeas, & Jimenez, 2006; Fidalgo, Barrales, & Jimenez, 2008). To test for functional
variation at FLO11 in the BYxYJM cross, we separately replaced the coding and
noncoding regions of FLO11 in Segregant 2 with the BY alleles (Figure S2.1;
Methods). We found that replacement of the FLO11 coding region caused a loss of
invasion on ethanol (Figure 2.3B), while replacement of the noncoding region had no
effect. A number of amino acid differences, as well as ~700 base pair length
difference, distinguish the BY and YJM alleles of FLO11 (Figure S2.3 and S2.4),
making it difficult to determine the causal variant.

In the backcross of Segregant 2 to YJM, we also screened 192 segregants and found
that 11% were invasive only on ethanol. Among these individuals (n = 22), we
identified a single locus on Chromosome XIV that was fixed for the BY allele. Based on
the genotype data, we delimited this interval to 16 candidate genes (Figure 2.3A
bottom; Methods). We tested every gene in this interval for an effect on Segregant 2’s
ability to invade using gene knockouts and found that only deletion of BNI1 resulted in
a loss of invasion (Figure 2.3B; Methods). The BY and YJM alleles of BNI1 possess
31 coding SNPs, 7 of which are nonsynonymous, as well as 3 SNPs upstream of the
gene (Figure S2.5). Bni1, which has previously been shown to affect invasive growth
(Kang & Jiang; Mosch), is involved in the assembly of actin cables (Sagot, Klee, &
19

Pellman, 2002 and physically interacts with multiple components of the MAPK cascade
involved in pheromone response (Chen, #371).

Although the FLO11
YJM
coding region contributes to invasion on ethanol, not all of
the ethanol-only segregants possessed this allele. Among the 12 individuals that were
invasive only on ethanol in our genotyped F2 population, two carried FLO11
BY
. To
determine the mechanism that allows these individuals to invade only on ethanol, we
backcrossed one relevant segregant (‘Segregant 3’) to BY and YJM. The YJM
backcross exhibited very low sporulation; for this reason, we were only able to perform
genetic mapping in the BY backcross. We screened 192 segregants and found 32
individuals (17%) that grew invasively only on ethanol. We performed genetic mapping
to look for enriched alleles and identified a single locus on Chromosome II, at which
individuals were fixed for the YJM allele (Figure 2.4A). This locus was detected at a
resolution of four genes, of which only AMN1 had an effect when deleted. To verify
that the BY and YJM alleles functionally differ, we replaced Segregant 3’s AMN1
YJM
with AMN1
BY
and found that this resulted in a loss of invasion (Figure 2.4B and S2.1;
Methods). An amino acid variant (D368V) in AMN1, which plays a role in daughter cell
separation and exit from mitosis (WANG et al. 2003), has been implicated as amajor
determinant of FLO11-independent cell clumping in multiple studies (J. Li et al.,
2013; Yvert et al., 2003), and may also be the causal variant in our study.

20

2.7 Testing for effects of mating type and non-genetic factors on FLO8-
independent invasion

Non-genetic factors are known to influence the expression of traits in yeast crosses
(Sirr et al., 2015) and might also contribute to FLO8- independent invasion.
Additionally, because our experiments were conducted exclusively in MATa haploids,
some of the FLO8-independent invasion might be mating type-dependent. To test both
of these possibilities, we generated and sporulated homozygous diploid versions of
Segregants 1, 2, and 3 (Methods). From each individual, we obtained 7 to 10 four-
spore tetrads. Only mating type and non-genetic factors should segregate among these
spores (Methods). If we have identified loci that depend on mating type, then invasion
should co- segregate 2:2 with mating type. Alternatively, if non-genetic factors
contribute to FLO8-independent invasion, then less than 100% of the examined spores
should show the same phenotype as their progenitor.

The effects of mating type and non-genetic factors varied among the tested
segregants. For Segregants 2 and 3, which only invade on ethanol, all of the haploid
spores also showed ethanol-only invasion (Table S2.7). This indicates that mating type
and non-genetic factors likely do not influence the phenotypes of these individuals. In
contrast, Segregant 1, which only invades on glucose, provided evidence for both
mating type- and non-genetic effects. Among the 40 tested spores from this individual,
16 out of 20 MATa spores showed glucose- only invasion, while none of the 20 MATα
spores exhibited invasion (Table S2.7). This suggests that Segregant 1’s phenotype is
mating type-dependent and may also have a non-genetic component.
21

2.8 Segregants that invade in a FLO8-independent manner require different
transcription factors and cell surface proteins

Our results to this point indicate that FLO8-independent invasion has a heterogeneous
basis that is largely genetic. This genetic heterogeneity might arise if distinct regulatory
factors and/or cell surface proteins facilitate invasion in different segregants and
environments. The possibility of such rewiring of invasive growth is supported by
recent work showing that the Σ1278b strain requires the transcription factor Tec1 to
express FLO11, while BY does not (Chin, Ryan, Lewitter, Boone, & Fink, 2012), as well
as by experiments demonstrating extensive variability in transcription factor binding
among progeny from the BYxYJM cross (Zheng, Zhao, Mancera, Steinmetz, &
Snyder). Further supporting such a scenario, some of the genes that we cloned have
regulatory functions. For example, GPA1 influences signaling through the MAPK
cascade and the MAPK cascade is known to regulate Ste12, which is a transcriptional
activator required for invasion in many pathogenic fungi (Felden, Weisser, & Bruckner,
2014; Lo & Dranginis, 1998).

To explore whether regulatory rewiring might contribute to the genetic heterogeneity in
our study, we deleted 11 transcription factors that are known to regulate invasion, as
well as FLO11, from Segregants 1, 2, and 3 (Methods). We also performed these
deletions in two additional individuals that showed FLO8- independent invasion on
22

both glucose and ethanol (hereafter referred to as ‘Segregant 4’ and ‘Segregant 5’).
Although some deletions had quantitative effects on invasion (Figure 2.5), we focused
on cases where deletion of one of the examined genes caused inability to invade.
Such complete losses of the phenotype indicate genes that are required for a particular
segregant to express FLO8-independent invasion.

The examined segregants differed in their requirements of FLO11 and four transcription
factors— MGA1, MSN1, RME1, and STE12 (Figure 2.5). None of the deletions caused
Segregant 3 to lose its ability to invade, implying that this individual invades in a
FLO11-independent manner that may not require the examined transcription factors. In
contrast, Segregants 1, 2, 4, and 5 showed FLO11-dependent invasion, but differed in
the transcription factors that they require. Segregants 1 and 4 lost the ability to invade
when STE12 was deleted, suggesting that their ability to invade is MAPK-dependent.
Segregants 2 and 5 required MSN1, a transcriptional activator that influences many
traits in yeast. While MSN1 was the only transcription factor that caused loss of
invasion in Segregant 2, Segregant 5 also lost its ability to invade when MGA1 and
RME1 were deleted. The finding that individuals differ in the transcription factors and
cell surface proteins that they require for invasion supports regulatory rewiring as a
cause of genetic heterogeneity in our study.

2.9 Conclusion
We have shown that a model phenotype in yeast—haploid invasive growth—exhibits
23

extensive non-allelic genetic heterogeneity. This heterogeneity is caused by genetic
variants that change the regulation of invasive growth and enable FLO8-independent
invasion in specific cross progeny. Our results from genetic mapping and genetic
engineering experiments suggest that multiple distinct regulatory architectures of
FLO8-independent invasion segregate in the BYxYJM cross. Although these
regulatory architectures require different transcription factors and/or cell surface
proteins, they lead to similar abilities to invade.

The present data do not shed light on the specific details of these different regulatory
architectures. However, the finding that most BYxYJM segregants that show FLO8-
independent invasion require FLO11 suggests that FLO11 expression is an important
component of most of the regulatory architectures. This is of note because FLO11 has
one of the largest promoters in the yeast genome, and is thought to be influenced by at
least 8 pathways and 15 transcription factors, as well as linked noncoding RNAs and
chromatin remodeling complexes (Bruckner & Fems). The potential of FLO11 to be
regulated by a number of different pathways may facilitate some of the variability in
wiring that we have described.

Our finding that different transcription factors and cell surface proteins are required for
different genetic backgrounds to invade is similar to the recent discovery of ‘conditional
essential’ genes in yeast (Dowell et al., 2010). These conditional essential genes are
necessary for viability in some isolates, but dispensable in others. Our work suggests
that conditional essentiality may arise because genetically distinct individuals express
24

similar phenotypes due to different underlying regulatory mechanisms. If this is true,
then the essentiality of a gene for a trait will depend on which signaling cascade(s) or
pathway(s) an individual employs to express a given phenotype in a particular
environment.

Given that we have examined a single phenotype in only one pairwise cross and two
conditions, we cannot comment on the broader extent of this heterogeneity across
species, traits, and environments. However, we note that our results are comparable to
recent studies in humans (as summarized in (McClellan, 2010 #347)) and mice (Shao
et al., 2008; Spiezio, Takada, Shiroishi, & Nadeau, 2012),which have shown that many
genetic perturbations can produce comparable phenotypic outcomes. To some
degree, our effort also represents an integration of previous work describing genetic
variation in regulatory pathways (Yvert et al., 2003) and transcription factor activity
(Chin et al., 2012; Zheng et al., 2010) across yeast isolates. Importantly, we have
extended these past studies by connecting changes in signaling and transcription
factor activity, as identified through genetic techniques, to phenotypic outcomes.

2.10 Material and Methods
2.10.1 Generation of initial mapping population
We used the synthetic genetic array marker system (A. H. Tong et al., 2001) to
generate recombinant BYxYJM MATa segregants. The BY parent of our cross was
MATα can1∆::STE2pr-SpHIS5 lyp1∆ his3∆, while the YJM parent was MATa
his3∆::natMX ho::kanMX. We mated these BY and YJM haploids to produce the
25

diploid progenitor of our cross, which was sporulated using standard techniques
(Sherman & San). MATa segregants were obtained using random spore plating on
minimal media containing canavanine, as previously described (Ehrenreich et al.,
2010; Taylor & Ehrenreich, 2014).

2.10.2 Phenotyping for invasive growth
Strains were phenotyped for invasive growth on 2% agar plates containing yeast
extract and peptone (YP) with either 2% glucose (dextrose) or 2% ethanol as the
carbon source (YPD and YPE, respectively). Prior to pinning onto the agar plates,
strains were grown overnight to stationary phase in liquid YPD. After this culturing step,
strains were then pinned onto agar plates and allowed to grow for 5 days. Following
this incubation period, we screened for invasive growth by applying water to the agar
plates, manually scrubbing colonies, and decanting the mixture of water and cells.
Presence or absence of invasion was scored by eye under a light microscope. Each
segregant was phenotyped three independent times and the median phenotype was
used in analyses (Table S2.1).
2.10.3 Genotyping by sequencing
Segregants were genotyped by Illumina sequencing. Whole genome libraries were
constructed using the Illumina Nextera kit. These libraries were then sequenced in
multiplex to at least 5X genomic coverage on either a HiSeq or a NextSeq with 100
base pair (bp) x 100 bp reads. We also sequenced BY and YJM to ~100X genomic
coverage, and used the data to identify 57,402 high confidence SNPs. Reads for
segregants were mapped to the BY genome using Burrows-Wheeler Aligner (BWA) (H.
26

Li & Durbin) and SAMTOOLS (H. Li & Durbin). We called genotypes for each individual
by taking the base calls at the SNPs and employing a Hidden Markov Model by
chromosome, using the HMM(H. Li & Durbin, 2009) package in R, as described in
(Taylor & Ehrenreich). The sequence data from our experiments is available from the
NCBI Short Read Archive under accession numbers SRR2039809- SRR2039935,
SRR2039936 to SRR2039992, SRR2040045 to SRR2040076, SRR2040023 to
SRR2040044, and SRR2039993 to SRR2040022 (Tables S2.1 through S2.6).

2.10.4 Detection of loci influencing ability to invade
Allele frequency analyses were computed using the genotype data of all individuals
from a particular mapping population that exhibited the same phenotype. To determine
the intervals of the identified causal loci, we identified regions where the alleles were
either fixed or at a frequency of 95% or higher.

2.10.5 Genetic engineering
Knockouts were generated by PCR amplifying the CORE cassette with homology-tailed
primers and then selecting for transformants on G418 (Storici, Lewis, & Resnick, 2001).
Phusion high-fidelity DNA polymerase was used for PCR under the recommended
reaction conditions with 35 cycles and an extension time of 30 seconds per kilobase.
The entire coding region of target genes was deleted in these strains. Correct
integration of the CORE cassette was checked for each deletion strain using PCR.
Allele replacement strains were constructed using the co-transformation of two partially
overlapping PCR products (Figure S2.1), similar to (Erdeniz, Mortensen, & Rothstein,
27

1997). One product contained the promoter and coding region of the gene to be
replaced, while the other included (in order) 60 bp of overlap with the 3’ end of the gene
PCR product, kanMX or natMX, and 30 to 50 bp of the genomic region immediately
downstream of the transcribed portion of the gene. Replacement of a gene was verified
using Sanger sequencing.

2.10.6 Generation of backcross segregants
Backcrosses were conducted by mating a BYxYJM segregant to a MATα his3∆ version
of BY or YJM. Sporulation and selection for MATa backcross segregants was
performed as described for the initial mapping population.

2.10.7 Screening for mating type and non-genetic effects
To induce mating type switching in our MATa segregants, we first deleted URA3 from
these individuals using the hphMX cassette with homology-tailed primers, as described
above. Correct integration of the cassette was verified using PCR and further checked
by plating the ura3∆ strains onto 5-FOA plates. Next, mating type switching was
performed using the pGAL-HO plasmid, as previously described (Herskowitz & Jensen,
1991. Otherwise isogenic MATa and MATα individuals were mated to produce
homozygous diploids. These individuals were sporulated as described above and
standard microdissection techniques were used to obtain spores from the homozygous
diploids. Tetrads from which all four spores were recovered were then grown on glucose
and ethanol, and checked for ability to invade (Table S2.7).

28

2.10.8 Amplification of the FLO11 coding region
The entire FLO11 coding region was PCR amplified using 5’-
GGAAGAGCGAGTAGCAACCA as the forward primer and 5’-
TTGTAGGCCTCAAAAATCCA as the reverse primer. The size of the BY and YJM
alleles were compared on a 2% agarose gel.

2.11 Acknowledgements
We thank Jonathan Lee, Martin Mullis, Matthew Taylor, as well as Lars Steinmetz and
two anonymous reviewers, for critically reviewing a draft of this manuscript. We also
thank Sammi Ali for technical assistance with this project, Oscar Aparicio for the pGAL-
HO plasmid, Charles Nicolet and the USC Epigenome Center staff and Jinliang Li and
the staff at Laragen for their help with Illumina sequencing, and Peter Calabrese for
comments on this project during its implementation. Our work was supported in part by
grants from the National Institutes of Health (R01GM110255 and R21AI108939),
National Science Foundation (MCB1330874), Army Research Office (W911NF-14-1-
0318), Alfred P. Sloan Foundation, and Rose Hills Foundation to I.M.E.

29

2.12 Figures

Fig. 2.1. Effects of FLO8 on ability to invade. (A) BY and YJM were grown for five
days on YPD or YPE plates at 30°C. Colonies were then washed off the plates
using water and examined for invasion. (B) Comparison of BY with a functional
allele of FLO8 and YJM f l o8∆ . (C) Fraction of the initial mapping population of
127 F2 BYxYJM segregants that show invasion on glucose (‘glu’) or ethanol
(‘eth’) in each FLO8 genotype class. (D) Fraction of the 97 invasive FLO8
BY
segregants that show invasion on glucose, ethanol, or both carbon sources
(‘both’).

30

Fig. 2.2. Genetic dissection of FLO8-independent glucose-only invasion. (A)
Genome-wide relative allele frequency plot of glucose-only FLO8
BY
BYxYJM
segregants. FLO8 and the markers used to generate haploid progeny are
highlighted by red vertical bars, while the strongly enriched locus on
Chromosome VIII, which was nearly fixed for the BY allele, is highlighted by a
green vertical bar. The genomic interval underlying the Chromosome VIII peak is
also provided. (B) Comparison of Segregant 1, a glucose-only FLO8
BY
individual, and the GPA1
YJM
Segregant 1 supports GPA1 as the causal gene
underlying the Chromosome VIII locus.

Fig. 2.3. Genetic dissection of ethanol-only invasion by backcrossing Segregant 2 to
BY and YJM. (A) Genome-wide relative allele frequency plots for the BY and YJM
backcrosses are shown on the top and bottom, respectively. FLO8 and the
markers used to generate haploid progeny are highlighted with red vertical bars,
while the strongly enriched intervals on Chromosome IX and XIV are highlighted
with green vertical bars. The genomic intervals underlying the Chromosome IX
and XIV loci are also provided. (B) Comparison of Segregant 2, an ethanol-only
FLO8
BY
individual, to FLO11
YJM
replacement and BNI1 deletion strains in the
Segregant 2 background supports FLO11 and BNI1 as the causal genes
underlying the Chromosome IX and XIV loci, respectively.

31

Fig. 2.4. Genetic dissection of FLO11-independent, ethanol-only invasion by
backcrossing of Segregant 3 to BY. (A) Genome-wide relative allele frequency plot
of ethanol-only invasion in the backcross of Segregant 3 to BY. The marker
used to generate haploid progeny is highlighted with a red vertical bar, while the
enriched locus on Chromosome II is highlighted with a green vertical bar. The
genomic interval underlying the Chromosome II locus is also provided. (B)
Comparison of Segregant 3, a FLO11-independent ethanol-only FLO8
BY
individual, to AMN1
BY
replacement strains in the Segregant 3 background
supports AMN1 as the causal gene underlying the Chromosome II locus.
32

Fig. 2.5. Deletion screen of known FLO11 activators. FLO11 and a number of
transcription factors that are known to regulate invasive growth were knocked
out in Segregants 1 through 5. These deletion strains were then phenotyped for
their ability to invade.

33

2.13 Supporting Information

Fig. S2.1. Construction of allele replacements. In the first step, one pair of primers
(F1 and R1) was used to amplify the promoter and the coding sequence of the
gene to be replaced with 60 bp overlapping the 5’ end of the resistance marker
attached at the 3’ end of the PCR product (shown in orange). Another pair of
primers (F2 and R2) was used to amplify the resistance marker with 60 bp
overlapping the genomic region immediately downstream of the transcribed
potion of the gene using the first primer pair attached at the 3’ end of the PCR
product. In the second step, the two overlapping PCR products were transformed
into the strains. Integration into the genome requires recombination between the
PCR products and the target locus.

34

Fig. S2.2. Initial results from selective genotyping of segregants that show FLO8-
independent invasion. (A) Comparison of genome-wide relative allele frequency
plot among FLO8
BY
invasive progeny to a non-invasive FLO8
BY
control
population. (B) Genome-wide relative allele frequency plot among FLO8
BY
segregants that invade on both glucose and ethanol.

35

Fig. S2.3. Differences FLO11 coding region length between BY and YJM. PCR was
used to amplify the FLO11 coding region from the BY and YJM strains. The size of
FLO11
BY
was ~4.1kb, while FLO11
YJM
was ~3.4kb.
36

Fig. S2.4. Replacement of the FLO11 coding region in segregant 2 with the BY allele
causes loss of invasion. To verify that FLO11
BY
was correctly integrated and
replaced using our one-step allele replacement, we PCR amplified the 5’ end of
the gene, and Sanger sequenced multiple invasive and non- invasive
transformants. Only the transformants carrying the BY SNPs (marked in black)
toward the 5’ end showed loss of invasion, implying that only individuals with
most of the FLO11 gene replaced exhibited loss of invasion. Flo11 protein is
comprised of three domains, which are reflected in the sequence of the FLO11
gene. The N-terminal portion of the protein encodes a hydrophobic signal
sequence, is exposed at the cell surface, and binds to ligands. The middle domain
largely contains variable length tandem repeats that are enriched for serines and
threonines, and is the part of the protein where heavy glycosylation occurs. The
C-terminal portion of the protein is a GPI anchor that localizes Flo11 to the cell
wall. The highly repetitive nature of the middle portion of FLO11 makes it difficult
to accurately determine the length and sequence of the gene using short Illumina
reads. In the regions that we were able to confidently align, we identified 69 SNPs
between the BY and the YJM allele, of which 31 were non- synonymous. In
addition, we identified that the YJM allele of FLO11 has a 45bp insertion in the N-
terminal region between amino acid position 123 and 124. We also found that no
sequencing reads from the YJM mapped to 635 base positions in comparison to
BY, which is most likely due to deletions given that the YJM allele of FLO11 was
~700 bases smaller in comparison to the BY allele (Figure S2.4). In particular,
large stretches of the middle domains were missing from amino acid positions 207
to 315, 359 to 372, 409 to 449, 795 to 808, 824 to 845, and 881 to 899 in the YJM
allele. We have not yet determined how these changes alter the functionality of
Flo11. We note that this portion of the gene is known to be highly variable across
yeast strains, affecting many FLO11-dependent traits, such as biofilm formation,
flocculation, and invasion.

37

Fig. S2.5. Alignment of the BNI1 gene. (A) Alignment of the nucleotide sequences
identified 31 SNPs between the BY and YJM allele of BNI1. (B) Alignment of the
translated amino acid sequence revealed that 7 SNPs were nonsynonymous.

Table S2.1. Phenotype data and Short Read Archive identifiers for the
segregants examined in the paper.

Study # SRP058517

1= invasive
0 = non-
invasive

Sample # SRS941591 Initial 127 BYxYJM segregants

ID

Strain
median
glucose
median
ethanol

SRR#

F2_1 1_A02.bam 1 1 SRR2039809

F2_2 1_A03.bam 1 0 SRR2039810

F2_3 1_A05.bam 0 0 SRR2039811

F2_4 1_A06.bam 0 0 SRR2039812

F2_5 1_A07.bam 1 1 SRR2039813

F2_6 1_A08.bam 1 1 SRR2039814

F2_7 1_A09.bam 1 1 SRR2039815

F2_8 1_A10.bam 1 0 SRR2039816

F2_9 1_A11.bam 1 0 SRR2039817

F2_10 1_A12.bam 1 0 SRR2039818

F2_11 1_B01.bam 1 1 SRR2039819

F2_12 1_B02.bam 0 0 SRR2039820

F2_13 1_B03.bam 0 0 SRR2039821

F2_14 1_B04.bam 0 0 SRR2039822

F2_15 1_B05.bam 0 0 SRR2039823

F2_16 1_B06.bam 1 0 SRR2039824

F2_17 1_B07.bam 0 0 SRR2039825

F2_18 1_B09.bam 1 1 SRR2039826

F2_19 1_B10.bam 0 0 SRR2039827

F2_20 1_B11.bam 1 1 SRR2039828

F2_21 1_B12.bam 1 1 SRR2039829

38

F2_22 1_C01.bam 0 0 SRR2039830

F2_23 1_C02.bam 1 1 SRR2039831

F2_24 1_C03.bam 1 1 SRR2039832

F2_25 1_C05.bam 1 1 SRR2039833

F2_26 1_C06.bam 0 0 SRR2039834

F2_27 1_C07.bam 1 0 SRR2039835

F2_28 1_C08.bam 0 0 SRR2039836

F2_29 1_C09.bam 1 1 SRR2039837

F2_30 1_C10.bam 1 1 SRR2039838

F2_31 1_C11.bam 1 0 SRR2039839

F2_32 1_C12.bam 0 0 SRR2039840

F2_33 1_D02.bam 0 0 SRR2039841

F2_34 1_D03.bam 0 0 SRR2039842

F2_35 1_D04.bam 0 0 SRR2039843

F2_36 1_D05.bam 0 0 SRR2039844

F2_37 1_D06.bam 1 0 SRR2039845

F2_38 1_D07.bam 0 1 SRR2039846

F2_39 1_D08.bam 0 0 SRR2039847

F2_40 1_D09.bam 1 1 SRR2039848

F2_41 1_D10.bam 1 0 SRR2039849

F2_42 1_D11.bam 0 0 SRR2039850

F2_43 1_D12.bam 1 1 SRR2039851

F2_44 1_E01.bam 0 0 SRR2039852

F2_45 1_E02.bam 0 0 SRR2039853

F2_46 1_E03.bam 1 1 SRR2039854

F2_47 1_E04.bam 1 0 SRR2039855

F2_48 1_E05.bam 0 0 SRR2039856

F2_49 1_E06.bam 1 1 SRR2039857

F2_50 1_E07.bam 1 1 SRR2039858

F2_51 1_E10.bam 1 1 SRR2039859

F2_52 1_E11.bam 1 0 SRR2039860

F2_53 1_E12.bam 1 1 SRR2039861

F2_54 1_F01.bam 0 0 SRR2039862

F2_55 1_F03.bam 1 1 SRR2039863

F2_56 1_F04.bam 0 0 SRR2039864

F2_57 1_F05.bam 0 0 SRR2039865

F2_58 1_F06.bam 0 0 SRR2039866

F2_59 1_F07.bam 0 0 SRR2039867

F2_60 1_F08.bam 1 1 SRR2039868

F2_61 1_F09.bam 0 0 SRR2039869

F2_62 1_F10.bam 1 1 SRR2039870

F2_63 1_F11.bam 0 1 SRR2039871

39

F2_64 1_F12.bam 0 0 SRR2039872

F2_65 1_G01.bam 1 1 SRR2039873

F2_66 1_G02.bam 0 0 SRR2039874

F2_67 1_G03.bam 0 0 SRR2039875

F2_68 1_G04.bam 1 1 SRR2039876

F2_69 1_G05.bam 1 1 SRR2039877

F2_70 1_G06.bam 0 0 SRR2039878

F2_71 1_G07.bam 1 0 SRR2039879

F2_72 1_G08.bam 1 1 SRR2039880

F2_73 1_G09.bam 1 1 SRR2039881

F2_74 1_G10.bam 0 0 SRR2039882

F2_75 1_G11.bam 0 0 SRR2039883

F2_76 1_H02.bam 1 0 SRR2039884

F2_77 1_H03.bam 1 1 SRR2039885

F2_78 1_H05.bam 1 0 SRR2039886

F2_79 1_H06.bam 0 0 SRR2039887

F2_80 1_H07.bam 1 1 SRR2039888

F2_81 1_H08.bam 1 1 SRR2039889

F2_82 1_H09.bam 1 1 SRR2039890

F2_83 1_H10.bam 0 0 SRR2039891

F2_84 1_H11.bam 0 0 SRR2039892

F2_85 2_A01.bam 0 0 SRR2039893

F2_86 2_A08.bam 1 0 SRR2039894

F2_87 2_A10.bam 1 1 SRR2039895

F2_88 2_A11.bam 0 0 SRR2039896

F2_89 2_A12.bam 0 0 SRR2039897

F2_90 2_B01.bam 1 1 SRR2039898

F2_91 2_B08.bam 0 0 SRR2039899

F2_92 2_B11.bam 0 0 SRR2039900

F2_93 2_B12.bam 1 1 SRR2039901

F2_94 2_C03.bam 1 1 SRR2039902

F2_95 2_C06.bam 1 0 SRR2039903

F2_96 2_C11.bam 1 1 SRR2039904

F2_97 2_C12.bam 1 1 SRR2039905

F2_98 2_D01.bam 0 0 SRR2039906

F2_99 2_D02.bam 1 1 SRR2039907

F2_100 2_D03.bam 0 0 SRR2039908

F2_101 2_D09.bam 1 1 SRR2039909

F2_102 2_D11.bam 0 0 SRR2039910

F2_103 2_D12.bam 1 0 SRR2039911

F2_104 2_E01.bam 0 0 SRR2039912

F2_105 2_E02.bam 0 0 SRR2039913

40

F2_106 2_E04.bam 0 0 SRR2039914

F2_107 2_E06.bam 1 1 SRR2039915

F2_108 2_E07.bam 0 0 SRR2039916

F2_109 2_E08.bam 1 1 SRR2039917

F2_110 2_E09.bam 0 0 SRR2039918

F2_111 2_E10.bam 1 1 SRR2039919

F2_112 2_E11.bam 1 1 SRR2039920

F2_113 2_E12.bam 1 1 SRR2039921

F2_114 2_F01.bam 1 0 SRR2039922

F2_115 2_F02.bam 1 1 SRR2039923

F2_116 2_F04.bam 1 1 SRR2039924

F2_117 2_F08.bam 0 0 SRR2039925

F2_118 2_F10.bam 0 0 SRR2039926

F2_119 2_F11.bam 1 0 SRR2039927

F2_120 2_F12.bam 1 1 SRR2039928

F2_121 2_G01.bam 1 0 SRR2039929

F2_122 2_G10.bam 1 1 SRR2039930

F2_123 2_G11.bam 1 1 SRR2039931

F2_124 2_G12.bam 1 1 SRR2039932

F2_125 2_H04.bam 1 0 SRR2039933

F2_126 2_H08.bam 1 1 SRR2039934

F2_127 2_H10.bam 1 1 SRR2039935

Sample # SRS945356 Additional invasive FLO8BY segregants

ID

Strain
median
glucose
median
ethanol

SRR#

F2_128 1_B01.bam 1 1 SRR2039936

F2_129 1_B02.bam 1 0 SRR2039937

F2_130 1_B03.bam 1 1 SRR2039938

F2_131 1_B04.bam 1 0 SRR2039939

F2_132 1_B05.bam 1 1 SRR2039940

F2_133 1_B06.bam 1 0 SRR2039941

F2_134 1_B07.bam 1 0 SRR2039942

F2_135 1_B08.bam 0 1 SRR2039943

F2_136 1_B09.bam 1 1 SRR2039944

F2_137 1_B10.bam 1 1 SRR2039945

F2_138 1_B11.bam 1 0 SRR2039946

F2_139 1_F01.bam 1 1 SRR2039947

F2_140 1_F02.bam 1 0 SRR2039948

F2_141 1_F03.bam 1 1 SRR2039949

F2_142 1_F04.bam 1 1 SRR2039950

F2_143 1_F05.bam 1 1 SRR2039951

41

F2_144 1_F06.bam 1 0 SRR2039952

F2_146 1_F08.bam 1 1 SRR2039954

F2_147 1_F09.bam 1 0 SRR2039955

F2_148 1_F10.bam 1 0 SRR2039956

F2_149 1_F11.bam 1 0 SRR2039957

F2_150 1_F12.bam 1 1 SRR2039958

F2_151 1_H01.bam 1 0 SRR2039959

F2_152 1_H02.bam 1 0 SRR2039960

F2_153 1_H03.bam 1 0 SRR2039961

F2_154 1_H04.bam 1 0 SRR2039962

F2_155 1_H05.bam 1 1 SRR2039963

F2_156 1_H06.bam 0 0 SRR2039964

F2_157 1_H07.bam 1 1 SRR2039965

F2_158 1_H08.bam 1 0 SRR2039966

F2_159 1_H09.bam 0 1 SRR2039967

F2_160 1_H10.bam 1 0 SRR2039968

F2_161 1_H11.bam 0 0 SRR2039969

F2_162 1_H12.bam 1 1 SRR2039970

F2_163 2_F01.bam 1 0 SRR2039971

F2_164 2_F02.bam 0 1 SRR2039972

F2_165 2_F03.bam 0 0 SRR2039973

F2_167 2_F07.bam 1 0 SRR2039975

F2_168 2_F08.bam 0 1 SRR2039976

F2_169 2_F09.bam 0 1 SRR2039977

F2_170 2_F10.bam 1 1 SRR2039978

F2_171 2_F11.bam 0 1 SRR2039979

F2_172 2_F12.bam 1 0 SRR2039980

F2_173 2_G03.bam 0 1 SRR2039981

F2_174 2_G04.bam 1 0 SRR2039982

F2_175 2_G06.bam 0 1 SRR2039983

F2_176 2_G07.bam 1 0 SRR2039984

F2_177 2_G08.bam 0 1 SRR2039985

F2_178 2_G09.bam 1 0 SRR2039986

F2_179 2_G10.bam 1 1 SRR2039987

F2_180 2_G11.bam 1 1 SRR2039988

F2_181 2_G12.bam 1 0 SRR2039989

F2_182 2_H01.bam 0 1 SRR2039990

F2_183 2_H02.bam 1 1 SRR2039991

F2_184 2_H04.bam 1 1 SRR2039992

Sample # SRS945354 Segregant 2 backcrossed to BY
42

ID

Strain
median
glucose
median
ethanol

SRR#

BY_F2B_1 3-A01.bam 0 1 SRR2039993

BY_F2B_2 3-A03.bam 0 1 SRR2039994

BY_F2B_3 3-A06.bam 0 1 SRR2039995

BY_F2B_4 3-A07.bam 0 1 SRR2039996

BY_F2B_5 3-A08.bam 0 1 SRR2039997

BY_F2B_6 3-A09.bam 0 1 SRR2039998

BY_F2B_7 3-A10.bam 0 1 SRR2039999

BY_F2B_8 3-A11.bam 0 1 SRR2040000

BY_F2B_9 3-A12.bam 0 1 SRR2040001

BY_F2B_10 3-B01.bam 0 1 SRR2040002

BY_F2B_11 3-B02.bam 0 1 SRR2040003

BY_F2B_12 3-B03.bam 0 1 SRR2040004

BY_F2B_13 3-B04.bam 0 1 SRR2040005

BY_F2B_14 3-B06.bam 0 1 SRR2040006

BY_F2B_15 3-B07.bam 0 1 SRR2040007

BY_F2B_16 3-B08.bam 0 1 SRR2040008

BY_F2B_17 3-B09.bam 0 1 SRR2040009

BY_F2B_18 3-B10.bam 0 1 SRR2040010

BY_F2B_19 3-B12.bam 0 1 SRR2040011

BY_F2B_20 3-C01.bam 0 1 SRR2040012

BY_F2B_21 3-C04.bam 0 1 SRR2040013

BY_F2B_22 3-C06.bam 0 1 SRR2040014

BY_F2B_23 3-C07.bam 0 1 SRR2040015

BY_F2B_24 3-C08.bam 0 1 SRR2040016

BY_F2B_25 3-C09.bam 0 1 SRR2040017

BY_F2B_26 3-C10.bam 0 1 SRR2040018

BY_F2B_27 3-C12.bam 0 1 SRR2040019

BY_F2B_28 3-D01.bam 0 1 SRR2040020

BY_F2B_29 3-D02.bam 0 1 SRR2040021

BY_F2B_30 3-D03.bam 0 1 SRR2040022

Sample # SRS945353 Segregant 2 backcrossed to YJM

ID

Strain
median
glucose
median
ethanol

SRR#

YJM_F2B_1 1-G01.bam 0 1 SRR2040023

YJM_F2B_2 1-G02.bam 0 1 SRR2040024

YJM_F2B_3 1-G03.bam 0 1 SRR2040025

YJM_F2B_4 1-G04.bam 0 1 SRR2040026

YJM_F2B_5 1-G07.bam 0 1 SRR2040027

YJM_F2B_6 1-G08.bam 0 1 SRR2040028

43

YJM_F2B_7 1-G09.bam 0 1 SRR2040029

YJM_F2B_8 1-G10.bam 0 1 SRR2040030

YJM_F2B_9 1-G11.bam 0 1 SRR2040031

YJM_F2B_10 1-G12.bam 0 1 SRR2040032

YJM_F2B_11 1-H01.bam 0 1 SRR2040033

YJM_F2B_12 1-H02.bam 0 1 SRR2040034

YJM_F2B_13 1-H03.bam 0 1 SRR2040035

YJM_F2B_14 1-H04.bam 0 1 SRR2040036

YJM_F2B_15 1-H05.bam 0 1 SRR2040037

YJM_F2B_16 1-H06.bam 0 1 SRR2040038

YJM_F2B_17 1-H07.bam 0 1 SRR2040039

YJM_F2B_18 1-H08.bam 0 1 SRR2040040

YJM_F2B_19 1-H09.bam 0 1 SRR2040041

YJM_F2B_20 1-H10.bam 0 1 SRR2040042

YJM_F2B_21 1-H11.bam 0 1 SRR2040043

YJM_F2B_22 1-H12.bam 0 1 SRR2040044

Sample # SRS945351 Segregant 3 backcrossed to BY

ID

Strain
median
glucose
median
ethanol

SRR#

BY_F2B_2_1 3_A01.bam 0 1 SRR2040045

BY_F2B_2_2 3_A02.bam 0 1 SRR2040046

BY_F2B_2_3 3_A03.bam 0 1 SRR2040047

BY_F2B_2_4 3_A05.bam 0 1 SRR2040048

BY_F2B_2_5 3_A07.bam 0 1 SRR2040049

BY_F2B_2_6 3_A08.bam 0 1 SRR2040050

BY_F2B_2_7 3_A09.bam 0 1 SRR2040051

BY_F2B_2_8 3_A10.bam 0 1 SRR2040052

BY_F2B_2_9 3_A11.bam 0 1 SRR2040053

BY_F2B_2_10 3_A12.bam 0 1 SRR2040054

BY_F2B_2_11 3_B01.bam 0 1 SRR2040055

BY_F2B_2_12 3_B02.bam 0 1 SRR2040056

BY_F2B_2_13 3_B04.bam 0 1 SRR2040057

BY_F2B_2_14 3_B06.bam 0 1 SRR2040058

BY_F2B_2_15 3_B07.bam 0 1 SRR2040059

BY_F2B_2_16 3_B08.bam 0 1 SRR2040060

BY_F2B_2_17 3_B09.bam 0 1 SRR2040061

BY_F2B_2_18 3_B10.bam 0 1 SRR2040062

BY_F2B_2_19 3_B11.bam 0 1 SRR2040063

BY_F2B_2_20 3_B12.bam 0 1 SRR2040064

BY_F2B_2_21 3_C01.bam 0 1 SRR2040065

BY_F2B_2_22 3_C03.bam 0 1 SRR2040066

44

BY_F2B_2_23 3_C06.bam 0 1 SRR2040067

BY_F2B_2_24 3_C07.bam 0 1 SRR2040068

BY_F2B_2_25 3_C08.bam 0 1 SRR2040069

BY_F2B_2_26 3_C09.bam 0 1 SRR2040070

BY_F2B_2_27 3_C10.bam 0 1 SRR2040071

BY_F2B_2_28 3_C11.bam 0 1 SRR2040072

BY_F2B_2_29 3_C12.bam 0 1 SRR2040073

BY_F2B_2_30 3_D01.bam 0 1 SRR2040074

BY_F2B_2_31 3_D02.bam 0 1 SRR2040075

BY_F2B_2_32 3_D04.bam 0 1 SRR2040076

Tables S2.2-s2.6
Click on the paperclip icons below the titles of these tables to open the tables in
excel.

Table S2.2. Genotype data for the initial 127 BYxYJM segregants. Genotypes in
this table, as well as the following tables, are encoded as 0 for BY and 1 for
YJM.

Table S2.3. Genotype data for the additional 55 BYxYJM segregants that show
FLO8-independent invasion.

Table S2.4. Genotype data for the backcross of Segregant 2 to BY.

45

Table S2.5. Genotype data for the backcross of Segregant 2 to YJM.

Table S2.6. Genotype data for the backcross of Segregant 3 to BY.

46

Table S2.7. Analysis of dissected tetrads from homozygous diploid derivatives of
specific segregants.

47

Chapter 3: The complex genetic and molecular basis of a model
quantitative trait
Robert A. Linder, Fabian Seidl, Kimberly Ha, and Ian M. Ehrenreich

This work appears essentially as published in 2016 in
Molecular Biology of the Cell, 27(1): 209-18. doi: 10.1091/mbc.E15-06-0408

3.1 Overview
Quantitative traits are often influenced by many loci with small effects. Identifying most of these loci
and resolving them to specific genes or genetic variants is challenging. Yet, achieving such a detailed
understanding of quantitative traits is important, as it can improve our knowledge of the genetic and
molecular basis of heritable phenotypic variation. In this study, we use a genetic mapping strategy
that involves recurrent backcrossing with phenotypic selection to obtain new insights into an
ecologically, industrially, and medically relevant quantitative trait- tolerance of oxidative stress, as
measured based on resistance to hydrogen peroxide. We examine the genetic basis of hydrogen
peroxide resistance in three related yeast crosses and detect 64 distinct genomic loci that likely
influence the trait. By precisely resolving or cloning a number of these loci, we demonstrate that a
broad spectrum of cellular processes contribute to hydrogen peroxide resistance, including DNA
repair, scavenging of reactive oxygen species, stress-induced MAPK signaling, translation, and
water transport. Consistent with the complex genetic and molecular basis of hydrogen peroxide
resistance, we show two examples where multiple distinct causal genetic variants underlie what
appears to be a single locus. Our results improve understanding of the genetic and molecular
basis of a highly complex, model quantitative trait.

48

3.2 Introduction

Mapping experiments in model organisms have served and continue to play a crucial
role in advancing our understanding of the genetic and molecular basis of
quantitative traits (Bloom et al., 2015; Mackay, Stone, & Ayroles, 2009).
However, as discussed elsewhere (Cubillos et al., 2013; Parts et al., 2011), these
studies typically in- volve genetic mapping strategies that are capable of identifying loci
at the resolution of broad genomic regions. For quantitative traits that are influenced
by a large number of loci, this mapping resolution can make it challenging to detect
and localize causal genetic variants, and can also make it difficult to measure
accurately the effects of individual loci.

Recent work in Saccharomyces cerevisiae highlights this problem. Studies using large,
statistically powerful mapping populations in crosses involving two strains have
detected dozens of loci per phenotype (Bloom, Ehrenreich, Loo, Lite, & Kruglyak,
2013; Ehrenreich et al., 2012; Ehrenreich et al., 2010; Parts et al., 2011; Taylor &
Ehrenreich, 2014; Treusch, Albert, Bloom, Kotenko, & Kruglyak, 2015). However, as
more strains are considered, the number of loci identified for a given trait typically
increases substantially, with loci often showing complicated patterns of detection across
genetic backgrounds (Cubillos et al., 2011; Ehrenreich et al.,
2012; Treusch et al., 2015) . A variety of biological phenomena, including
genetic interactions among loci or close linkage of different genetic variants with
phenotypic effects, might explain these observations. Differentiating among these
possibilities requires identifying combinations of interacting alleles and precisely
49

delimiting loci to very small genomic intervals, ideally to specific genes and
nucleotides.
In this study, we dissect at high resolution the genetic basis of heritable variation in
hydrogen peroxide resistance among three budding yeast isolates-the lab strain
BY4716, the wine strain RM11-1a, and the oak strain YPS163 (hereafter BY, RM, and
YPS, respectively). We chose these strains because they are known to possess
genetically complex differences in their tolerances of hydrogen peroxide (Ehrenreich et al.,
2012; Kvitek, Will, & Gasch, 2008). To determine the genetic basis of this heritable
phenotypic variation, we generated multiple backcross populations from each cross of
the three strains and implemented additional rounds of backcrossing coupled to
stringent phenotypic selections (Fig. 3.1). The rationale behind our approach was to
delimit causal loci to discrete genomic intervals that had been introgressed into the
backcross parent’s genome. By conducting this process multiple times in parallel, we
could then combine information from related backcrosses and map loci even more
precisely while retaining high statistical power (Supplemental Note S3.1). Furthermore,
by using such a mapping strategy, we hoped to introgress combinations of alleles that
collectively confer resistance, as these alleles could then be tested for additive effects
and genetic interactions in a manner similar to ( Tay l or & E hr enr ei c h , 2 014; Tay l or & E hr enr ei c h,
2015b).
50

Fig. 3.1. Backcrossing strategy. F
2
B
3
segregants with high hydrogen peroxide
resistance were generated through multiple rounds of backcrossing with
phenotypic selection. The filled rectangles at each stage represent chromosomes,
with gray and black depicting chromosomal regions inherited from Parent 1 and
Parent 2, respectively. The brackets shown beneath the chromosomes from
resistant F
2
B
3
segregants indicate causal loci. Numbers of individuals screened at
each stage of crossing are reported in Materials and Methods.

We focused on hydrogen peroxide resistance because it is commonly used as a proxy for
oxidative stress tolerance, a trait that has potential relevance for both human health and
yeast biology. Susceptibility to oxidative stress has been linked to aging (Braun &
Westermann, 2011; Cui, Wang, & Qin, 2012; Fabrizio et al.,
2003; Longo, Shadel, Kaeberlein, & Kennedy, 2012; Petti,
Crutchfield, Rabinowitz, & Botstein, 2011) , as well as to Alzheimer’s
51

disease (Greenough, Camakaris, & Bush, 2013; Jomova, Vondrakova,
Lawson, & Valko, 2010; Koppenhofer et al., 2015), diabetes (Aouacheri,
Saka, Krim, Messaadia, & Maidi, 2015; Varvarovska et al., 2004), and other
disorders. Furthermore, tolerance of oxidative stress has ecological and economic
ramifications for yeast, in particular for strains that mainly inhabit aerobic environments
or are used in fermentations or industrial applications (Brown et al., 2014; Dhar et
al., 2013; Fierro-Risco, Rincón, Benítez, & Codón, 2013; Higgins, Beckhouse,
Oliver, Rogers, & Dawes, 2003; Kitagaki & Takagi, 2014; Sasano et al.,
2012).

By applying our backcrossing strategy to variation in hydrogen peroxide resistance in the
BYxRM, BYxYPS, and RMxYPS crosses, we identified 64 distinct genomic loci that likely
contribute to the trait. Analysis of allele combinations at certain subsets of these loci
suggests that variability in hydrogen peroxide resistance has a genetic basis that is
largely additive. Furthermore, identification of a number of these loci at the resolution of
small genomic windows, as well as cloning of certain quantitative trait genes and
nucleotides, indicates that many distinct molecular processes contribute to hydrogen
peroxide resistance. Consistent with the high genetic and molecular complexity of this
trait, we show that multiple causal genetic variants can underlie what appears to be a
single locus based on genetic mapping data. Our results advance understanding of the
genetic and molecular basis of highly complex quantitative traits in yeast and
potentially other organisms as well.

52

3.3 Generation of mapping population using recurrent backcrossing with
phenotypic selection

We first determined the minimum inhibitory concentrations (MICs) for hydrogen
peroxide of BYxRM, BYxYPS, and RMxYPS segregants (Fig. S3.1; Methods). For each
cross, 864 haploid recombinants were screened. After this initial phenotyping, each of
the five most resistant F2 segregants from the three crosses were individually subjected
to several rounds of selective backcrossing to both of their parents (Fig. 3.1; Methods).
This resulted in the generation of two F2B3 families per hydrogen peroxide-resistant F2
segregant. During the iterations of backcrossing, haploid recombinants were frozen to
create immortalized stocks and then phenotyped using colony growth assays conducted
across a range of hydrogen peroxide doses (Methods). The single most resistant
haploid segregant in a given backcross was then used as the founder for the next round
of backcrossing (Methods). To prevent hydrogen peroxide-induced mutations from
accumulating during the backcrossing, each round of crossing was performed using
fresh cultures from the frozen stocks that had never been exposed to hydrogen
peroxide.

From each of the 30 backcross families, 12 to 15 highly resistant F2B3 progeny were
genotyped by low coverage whole genome sequencing and used to detect loci
(Methods). 417 F2B3 segregants were genotyped in total. However, we detected
karyotype instability in families derived from one of the BYxYPS F2 segregants and
therefore excluded individuals in these families from the study (Supplemental Note
S3.2). Thus, the analyses described in this paper are based on the 392 F2B3 segregants
53

that were generated from the remaining 28 backcross families that did not show
aneuploidies.

4.4 Identification of loci that contribute to hydrogen peroxide resistance
Loci were identified within individual families based upon allele frequency skew among
the 12 to 15 resistant backcross segregants that had been genotyped (Methods). Given
that resistant individuals from the same family were generated from a common diploid
progenitor and identified by screening of individual strains (as opposed to pools of
segregants), we do not expect bias in our results due to inadvertent selection on other
traits, such as sporulation or mating efficiency. Excluding the MAT locus on
Chromosome III, which is a control marker that we used to generate haploid
segregants, 60 loci were identified at a q-value of less than or equal to 0.05: 10 in
BYxRM, 28 in BYxYPS, and 22 in RMxYPS (Fig. 3.2 and Fig. S3.2; Methods). Lowering
the threshold to 0.1 or 0.2 resulted in the detection of 79 and 104 total loci, respectively,
with crosses involving YPS showing the highest number of detected loci (Fig. 3.2).

4.5 Resolution of loci within individual families
Because we identified loci using segregants that had been individually genotyped, we
were able to delimit loci detected in each family based on recombination breakpoints
that were observed in the data (Methods). Among the 60 loci detected at a q-value ≤
0.05, this resulted in an average resolution of 54.1 kb (Methods; Table S3.1). However,
11 of these loci were delimited to genomic intervals of under 10 kb (Methods). Two loci
were detected at a resolution of a single gene: YIL177C, a putative Y’ element helicase
54

that was detected on Chromosome IX in two RMxYPS families, and PFK2, a subunit of
phosphofructokinase involved in glycolysis that was detected on Chromosome XIII in

Fig. 3.2. Genetic mapping results. Regions of the genome that showed nominal
significance (p ≤ 0.05) are plotted with their associated false discovery rates.
Results from advanced backcross families derived from different BYxRM,
BYxYPS, and RMxYPS F2 segregants are shown in A, B, and C, respectively. Each
row represents an F2 segregant from which two advanced backcross families
were derived by recurrent backcrossing to each of the strain’s parents.
one BYxYPS family. An additional three loci were detected at a resolution of two
genes (Table S1). These occurred on Chromosomes I, IX, and XIV in the BYxYPS
cross and corresponded to the lectin-like cell wall protein FLO1 and the
oxysterol-binding protein SWH1, the vitamin-related transcriptional activator
VHR1 and the respiratory induced gene RGI2, and the BLOC1 component SNN1
and the highly pleiotropic gene MKT1, respectively. We note that MKT1 and SWH1
are known quantitative trait genes in the BYxRM cross that were identified
through mapping studies focused on other phenotypes, as reviewed in
(Ehrenreich, Gerke, & Kruglyak, 2009) and recently shown in (Wang & Kruglyak,
2014), respectively.

55

3.6 Validation and deeper genetic analysis of a subset of loci
When we compared data from families and crosses, we found that detected loci (q-
value ≤ 0.2) could be collapsed into 64 distinct genomic regions (Fig. 3.2; Methods). For
the remainder of the paper, we refer to these distinct regions as ‘loci’. Although 16 of
these loci were identified in at least two different families from the same cross, most
were not replicated among families from the same cross (Figs. 3.2 and Fig. S3.3). Some
of the loci that were not replicated may be false positives. However, we expect that a
large fraction of the detected loci, even those only seen a single time, have biological
effects (Supplemental Note S3.1).

To check whether the identified loci have biological effects, we examined two
(RMxYPS)xYPS families in more detail. None of the loci detected in either of these
families were the same (Fig. 3.3A) and some of the loci identified in these families were
not seen in mapping data for other RMxYPS families (Fig. 3.2). We generated 96
random F2B3s from each of these families, screened these segregants for their MICs,
and genotyped these segregants at the relevant loci (Methods). We then used the
genotype and phenotype data to test whether the loci individually exhibited effects
(Methods). When we did this, six of the eight (75%) tested loci showed significant
additive effects (t-test, p ≤ 0.045; Fig. 3.3, B and C), a result that is consistent with our
FDR threshold.

We also examined whether genetic interactions influenced the effects of any of the eight
queried loci. Specifically, we used full factorial ANOVA models to assess the
56

relationship between genotype at the loci with significant additive effects and MIC in the
two (RMxYPS)xYPS families (Methods). These models included not only the additive
terms corresponding to each individual locus, but also all possible pairwise and higher-
order interaction terms among the loci. Although all of the loci continued to show
significant additive effects in these models, only one significant interaction term (p <
0.05) was observed, which was between L7-I and L15 (Fig. 3.3A). We also tested for
statistically significant genetic interactions between alleles detected in the two families
using F2 segregants, but did not identify any such interactions (Fig. S3.4, A and B).
These results suggest that genetic interactions contribute little to heritable variation in
hydrogen peroxide resistance (Fig. 3.3, D and E). Thus, even though our approach
should be capable of revealing pairwise and higher-order genetic interactions (Taylor,
2014 #106;Taylor, 2015 #33), our results agree with recent work demonstrating that
quantitative traits in yeast have a genetic basis that is largely additive (Bloom et al.,
2013).

3.7 Using detection of loci in multiple families to improve mapping resolution
We attempted to improve our resolution of loci that were detected in multiple families.
There were 5, 5, and 6 such loci in the BYxRM, BYxYPS, and RMxYPS crosses,
respectively (Fig. 3.2). By aggregating data from the families in which these loci were
detected at an FDR of 0.2 or lower (Fig. 3.2; Methods), we achieved an average
resolution for these loci of 14, 6, and 13 genes in the BYxRM, BYxYPS, and RMxYPS
crosses, respectively.

57

Fig. 3.3. Validation of loci detected in families derived from the RMxYPS cross. (A)
different combinations of alleles that were detected in RMxYPS backcrosses to
YPS are shown in A. 95% confidence intervals of MIC for alleles tested in larger
F2B3 populations are shown in B and C. In B, 96 YPS-backcrossed RMxYPS F2B3
segregants from a family in which a combination of three loci were detected were
phenotyped and genotyped at these loci. In C, 96 YPS-backcrossed RMxYPS F2B3
segregants from a family in which a combination of four alternate loci were
detected as well as a locus shared with only one other YPS-backcrossed RMxYPS
family were similarly phenotyped and genotyped at these loci. The additive
effects of these alleles across genotypes are provided in D and E, with black lines
illustrating regression models that only include additive effects.

We concentrated on cloning quantitative genes underlying loci detected in multiple
families in the BYxYPS cross, as these were identified at the most precise resolution
58

(Fig. 3.2). We first used reciprocal hemizygosity analysis (RH) (Steinmetz et al., 2002)
to examine nearly all of the non-essential candidate genes underlying the five loci,
which were located on Chromosomes V, VII, IX, XIV, and XVI (25 total genes examined;
Fig. S3.5A; Methods). This successfully identified two quantitative trait genes: MKT1
(Chromosome XIV; Fig. 3.4A) and the aquaporin AQY1 (Chromosome XVI; Fig. 3.4B).
We validated AQY1 and MKT1 by performing allele replacements spanning the entire
coding and noncoding regions of these genes in BYxYPS F2B3 segregants that carried
the resistance allele at the locus being tested (Fig. S3.5B; Methods).

We also used allele replacements that spanned the entire coding and noncoding
regions of candidate genes to test every gene underlying the Chromosomes V, VII, and
IX loci in resistant BYxYPS F2B3 segregants (Fig. S3.5B; Methods). By doing this, we
identified a single quantitative trait gene at two of the loci: MMS21, an essential SUMO
ligase that is involved in DNA repair (Chromosome V; Fig. 3.4C), and MRP13, a
nuclear-encoded component of the mitochondrial ribosome (Chromosome VII; Fig.
3.4D). Additionally, we found that the causal variant underlying the Chromosome IX
locus is a cis regulatory polymorphism in the intergenic region between the
mitochondrial porin POR2 and the stress-inducible MAP kinase (MAPK) phosphatase
SDP1, which are transcribed in opposite directions. This was found because replacing
the entire coding and noncoding regions of either POR2 or SDP1 decreased hydrogen
peroxide resistance (Fig. 3.4E), while replacing only the coding region of either POR2 or
SDP1 had no effect.

59

A single SNP differentiates the BY and YPS alleles of the POR2-SDP1 intergenic
region. To determine whether this SNP affects the expression of POR2 or SDP1, we
used qPCR to measure the transcription of these genes in the presence and absence of
hydrogen peroxide (Methods). QPCR was performed in two genetic backgrounds—a
BYxYPS F2B3 segregant that carried the BY allele of the POR2-SDP1 region and a
genetically engineered version of the same strain that carried the YPS allele of the
region. No expression differences were observed in the absence of hydrogen peroxide
(Fig. 3.5A). However, the qPCR experiments revealed that the intergenic SNP affects
SDP1 expression specifically in the presence of hydrogen peroxide (Fig. 3.5B).

The functionally diverse quantitative trait genes (AQY1, MKT1, MMS21, MRP13, and
SDP1) that were cloned as a part of this section, as well as the loci that were detected
at a resolution of one or two genes (Table S3.1), indicate that variation in hydrogen
peroxide resistance is shaped by a broad space of molecular mechanisms and cellular
processes.
60

61

Fig. 3.4. Localization and cloning of loci detected in multiple BYxYPS advanced
backcross families. By combining data from advanced backcross families, we
improved our mapping resolution. Grey vertical bars indicate the bounds
delimited by aggregated data. Horizontal dashed lines represent the nominal
significance cut-off (p ≤ 0.05) for a one-tailed binomial test. The causal gene or
variant at each locus is illustrated with a red box or asterisk, respectively. Genes
represented with white boxes had no effect on hydrogen peroxide resistance.

Fig 3.5. A cis regulatory polymorphism in the BYxYPS cross causes differential
expression of SDP1 in response to hydrogen peroxide. QPCR analyses of POR2 and
SDP1 are shown in A and B, respectively. Isogenic strains that only differed in
their genotype at the POR2-SDP1 locus were employed in these experiments.

3.8 Linkage among genetic variants strongly influences how loci are detected
Recent studies applying statistically powerful genetic mapping techniques to multiple
crosses have shown that loci can exhibit complicated patterns of detection among
interrelated mapping populations (e.g., (Ehrenreich, 2012 #102;Treusch, 2015 #124)).
Arguably, the simplest explanation for this phenomenon is that multiple genetic variants
segregate on each chromosome and interference among these genetic variants affects
how loci are detected in any given cross.

62

Our study strongly supports linked genetic polymorphisms as the major cause of loci
being detected in complicated patterns in studies involving multiple crosses. For
example, in addition to identifying AQY1 as a quantitative trait gene in the BYxYPS
cross, we also found that AQY1 is a quantitative trait gene in the RMxYPS cross (Fig.
S3.6A). This was determined through a combination of mapping the Chromosome XVI
locus spanning AQY1 to two genes using data from four RMxYPS families (Fig. 3.2 and
Fig. S3.6A), as well as by replacing AQY1
RM
with AQY1
YPS
in a relevant RMxYPS F2B3.
(Fig. S3.6B; Methods). These results might suggest that BY and RM share an allele of
AQY1 that confers hydrogen peroxide resistance and differentiates them from YPS.
However, this is not the case; there are no polymorphisms shared between BY and RM
in the promoter, coding region, or 3’ UTR of AQY1. Instead, AQY1
BY
and AQY1
RM
are
known to harbor distinct loss-of-function variants (a missense polymorphism in BY and
a frameshift in RM), which were originally identified due to their similar effects on freeze-
thaw tolerance (Will et al., 2010).

We also found evidence for the presence of closely linked causal genetic variants in
different quantitative trait genes. In this case, a locus overlapping the BYxYPS
quantitative trait gene MRP13 was also identified in the RMxYPS cross (Fig. 3.6A).
However, replacement of MRP13
RM
with MRP13
YPS
in a resistant RMxYPS F2B3 did not
have an effect on hydrogen peroxide resistance (Fig. 3.6C). To determine the causal
gene underlying this locus in the RMxYPS cross, we replaced the RM allele for every
gene in this interval with the YPS allele in a relevant RMxYPS F2B3 (Methods). The only
gene in this interval that had an effect in the RMxYPS cross was CTT1, which is located
63

three genes away from MRP13 (Fig. 3.6, B and D). These findings are important
because they show how individual genomic loci detected in genetic mapping
experiments in yeast can in fact correspond to multiple quantitative trait genes and
nucleotides.

3.9 Discussion
Recent studies have revealed the very high genetic complexity that can underlie
heritable phenotypes in budding yeast (e.g, (Ehrenreich, 2010 #97;Bloom, 2013
#143;Ehrenreich, 2012 #102;Cubillos, 2011 #37;Granek, 2013 #43;Wilkening, 2014
#116;Lorenz, 2012 #183)). Much of this work has focused on chemical resistance traits,
which can easily be screened in large populations of segregants and in multiple
crosses. Despite the valuable insights gained from efforts to map the genetic basis of
these phenotypes, questions about the molecular mechanisms and statistical genetic
architecture underlying these traits remain unanswered, largely due to the limited
resolution of current genetic mapping approaches.

In this paper, we focused on a single chemical resistance trait—hydrogen peroxide
resistance—and employed a genetic mapping strategy that resulted in the precise
detection of combinations of loci. Our strategy was in part motivated by recent
discoveries of higher-order genetic interactions in yeast (Taylor, 2014 #106;Taylor, 2015
#33;Dowell, 2010 #51), which were found for binary traits, and was aimed at
determining whether such complex genetic interactions also contribute to quantitative
traits. Our work suggests that higher-order genetic interactions are unlikely to make a
64

significant contribution to quantitative traits in yeast and instead supports a largely
additive genetic basis for such phenotypes in this organism, as was recently argued
(Bloom et al., 2013).

In relation to our past work (Bloom et al., 2013; Ehrenreich et al., 2012), we used a
different mapping technique to characterize the genetic basis of hydrogen peroxide
resistance in the present study. Comparison of results from this paper to our other
publications, or of our past manuscripts to each other, suggests that roughly half of the
loci detected in one study replicate in another. For example, analysis of pools of
hydrogen peroxide-resistant BYxRM, BYxYPS, and RMxYPS segregants identified 28
loci (Ehrenreich et al., 2012). Of these, 16 (57%) were also detected in the current
study. Disparities in the loci identified by these studies may be due to either biological or
technical factors, and are consistent with the high genetic complexity of hydrogen
peroxide resistance.

Of note, our current study is distinguished from our past efforts by the concerted effort
we have made to clone multiple quantitative trait genes that contribute to hydrogen
peroxide resistance. This resulted in the identification of CTT1, MKT1, MMS21, MRP13,
SDP1, and two different alleles of AQY1. Our results indicate that many molecular and
cellular processes influence hydrogen peroxide resistance. It is possible that this
breadth of mechanisms that contribute to the trait may have provided a large mutational
target space for the accumulation of functional genetic variation. However, proving such
a point is difficult without knowing more of the genes that contribute to hydrogen
65

peroxide resistance in BY, RM, YPS, and other strains, and also determining the causal
variants in these genes.

Fig. 3.6. Two different quantitative trait genes segregate in the same genomic region.
Overlapping peaks on Chromosome VII were detected in BYxYPS and RMxYPS
backcrosses to YPS (A). These peaks are shown here, with ‘B’ and ‘R’
representing the BY and RM allele frequencies in these crosses, respectively. In
B, MRP13 and CTT1 are depicted in bold to emphasize their close physical
proximity within this overlapping interval. As we showed earlier, MRP13
BY
confers
resistance in the BYxYPS cross. However, in the RMxYPS cross, the causal allele
in this interval is instead CTT1
RM
(C and D). In C and D, 95% confidence intervals
of MIC are shown for allele replacement strains, which were generated in a YPS-
backcrossed RMxYPS F2B3 segregant with the causal allele at the overlapping
locus.
66

Our findings also provide insights into why certain loci show complicated patterns of
detection in studies involving more than two strains. We find this arises both due to the
presence of multiple causal variants in the same gene, as well as due to closely linked
functional variants in different genes. Which of these scenarios is more prevalent is
unclear from the current work due to the limited number of examples provided by this
study, as well as the fact that AQY1 is a common target for environment-specific
adaptive mutations and thus may be unusual (Will et al., 2010).

In conclusion, although we have focused on hydrogen peroxide resistance, our findings
may have general relevance for other chemical resistance phenotypes and highly
complex quantitative traits. Given that we have examined only three related crosses, we
expect that the effects of high genetic complexity and linkage that we have described
will be more severe in studies that involve a larger number of strains. If this is true, then
precisely resolving loci to specific genes and causal variants will be crucial for studies of
highly complex, quantitative traits in yeast and other model organisms moving forward.
Such resolution will be necessary to maximize the insights that genetic mapping can
provide into the genetic and molecular basis of quantitative traits.

67

3.10 Materials and Methods
3.10.1 Screening for hydrogen peroxide resistance
Strains were first inoculated into 96 deep-well plates with liquid yeast-peptone-dextrose
(YPD) media. These cultures were then incubated for a 24 hour period at 30°C with
shaking. After incubation, cells were manually pinned onto YPD agar plates containing
different concentrations of hydrogen peroxide. Pinned colonies were incubated at 30°C
for a 48 hr period, after which colonies were imaged using a standard digital camera.
MIC was calculated as the lowest concentration of hydrogen peroxide at which a
segregant was incapable of growing. Representative images of individuals pinned
across a range of doses are shown in Fig. S4.8A. Fig. S4.8B shows that, in our
experimental setup, there is a low correlation between OD600 readings and MICs. This
finding implies that the preculturing steps in our experiments are unlikely to have an
effect on our genetic mapping results.

3.10.2 Generation of resistant advanced backcross populations
Standard yeast techniques were used for mating and sporulation. At each stage of
crossing, the Synthetic Genetic Array marker system (A. H. Y. Tong & Boone, 2006)
was used to generate large numbers of MATa recombinant segregants through random
spore analysis (Ehrenreich et al., 2010). We started by screening 864 F2 segregants
each from the BYxRM, BYxYPS, and RMxYPS crosses. The five most hydrogen
peroxide-resistant F2 segregants from each cross were then backcrossed to both of
their parents. At the F2B and F2B2 steps, 96 recombinants were screened, with the most
resistant individual used for the subsequent backcrossing. Freezer stocks were
68

generated for every segregant prior to phenotyping for hydrogen peroxide resistance
and all crosses were performed using these freezer stocks. At the F2B3 stage, 672
segregants were phenotyped per backcross family. Of these, 12 to 15 of the most
resistant segregants were genotyped using low coverage whole genome sequencing.

3.10.3 Generation of parental reference genomes
We sequenced whole genome libraries from each of our parent strains to ~50X
coverage on an Illumina HiSeq, and used these data to identify genetic differences
between our cross parents and the S288c reference genome. Sequencing reads were
mapped to the S288c reference genome using Burrows-Wheeler Aligner (BWA-MEM)
(H. Li & Durbin, 2009) using the commands ‘bwa –mem –t 6 ref.fsa read1.fq read2.fq >
output.sam’. Duplicate reads were removed using the SAMtools rmdup command.
Mpileup files were generated in SAMtools (H. Li & Durbin, 2009) using the commands
‘samtools mpileup –f ref.fsa read.rmdp.srt.bam > output.mp and SNPs were identified
using custom Python scripts. 111, 46,900, and 65,193 SNPs and small indels were
identified between our BY, RM, and YPS cross parents and the S288c reference
genome, respectively. We constructed strain-specific reference genomes for each strain
by integrating these variants into the S288c reference genome. These strain-specific
reference genomes were used in subsequent analyses.

3.10.4 Low coverage whole genome sequencing of resistant backcross
segregants
Whole genome libraries were generated for resistant F2B3 segregants using the Illumina
Nextera kit. Each backcross segregant was tagged with a unique barcode identifier.
69

Equimolar fractions of the libraries were mixed together and sequenced at low coverage
on an Illumina HiSeq. The average coverage per sequenced segregant was 4.31X.
After demultiplexing the sequencing reads, reads were aligned to the reference genome
of the parent strain used for backcrossing with BWA-MEM using the same parameters
described above (H. Li & Durbin, 2009). This was followed by the generation of mpileup
files with SAMtools (H. Li & Durbin, 2009). Haplotypes were determined by using the
fraction of reads from each parental strain as input for Hidden Markov Models that were
executed in R chromosome-by-chromosome (Taylor & Ehrenreich, 2014). Aneuploidies
were identified by examining coverage throughout the genome, as determined from the
mpileup files. All sequencing data are available from the Sequencing Read Archive
under the bioproject accession identifier PRJNA291876, study accession identifier
SRP063000, and the biosample accession identifiers SAMN03998889 through
SAMN03999280.

3.10.5 Examination of RMxYPS loci
To determine the effects of sets of RM alleles detected in the RMxYPS cross, we
genotyped F2B3 segregants at the relevant loci and combined these data with our
knowledge of MICs for each F2B3. Genotyping at these loci was conducted using PCR
and restriction enzyme digests. T-tests and ANOVAs were conducted using the t.test()
and lm() functions in R.

70

3.10.6 Genetic mapping
Loci were identified by one-tailed binomial tests conducted on the F2B3 genotype data
for single families, with statistical tests implemented in R. We conducted one-tailed tests
because we expected causal alleles from a given parent to largely be fixed and
undetectable in repeated backcrosses to that parent. In contrast, causal alleles from the
non-backcross parent should show significant enrichment among F2B3s from the same
family, while non-causal alleles should segregate at roughly 50% frequency. The
binomial tests were conducted for all genomic positions at which the non-backcross
parental genotype was detected in a given family. The FDR associated with each p-
value was determined using the QVALUE package in R (Storey & Tibshirani, 2003). To
estimate q-values for each test, p-values from all families were combined into a single
vector and inputted into QVALUE at the same time. This was done under the
assumption that data from different families share a common null distribution, which can
be more accurately estimated using the whole dataset generated in this paper. Detected
intervals were determined as the regions of maximal significance at a given locus. The
size of an identified locus was determined as the region of a chromosome at which a
locus showed maximal significance. To more finely map loci that were identified in
multiple families from the same cross, we pooled data from each of the families from the
same cross in which a locus was nominally significant. We then delimited the locus as
the region of the chromosome showing maximal significance in the pooled data. The 64
unique genomic loci described in the paper were identified by combining detected loci
from all crosses. In cases where overlapping loci were detected in multiple families from
71

the same cross or from different crosses, the region of overlap was defined as the
unique genomic locus.

3.10.7 Reciprocal hemizygosity analysis
For a given gene, haploid deletion strains were generated in both of the haploid parents
of a cross using the CORE cassette (Storici et al., 2001). To facilitate these knockouts,
60 bp tails were added to the CORE cassette by PCR. The tails were designed so that
integration of the cassette into the genome resulted in loss of the entire coding region of
that gene in the recipient strain. To generate hemizygotes, haploid deletion strains were
mated to a wild type version of the other cross parent. These hemizygotes were then
screened for hydrogen peroxide resistance as described above. We considered a gene
cloned by RH if a significant effect was detected based on the examination of at least
three independently generated hemizygotes per allele (i.e., six total hemizygotes per
gene). Significance was assessed using t-tests implemented in R.

3.10.8 Allele replacements
Marker-assisted allele replacement was conducted as described in (Takeshi Matsui,
Linder, Phan, Seidl, & Ehrenreich, 2015). Unless stated otherwise, for a given allele
replacement, the full donor allele (200-300 bp of upstream sequence, the entire coding
region, and around 50 bp of downstream sequence) was amplified from the relevant
haploid cross parent. At the same time, the hphMX cassette (Goldstein & McCusker)
was amplified with 60 bp homology tails. One of the hphMX tails was designed to be
identical to the 3’ end of the donor allele PCR product. The other hphMX tail was
72

designed to be identical to the region just downstream of the donor sequence in the
recipient strain. Selection for hygromycin resistance was used to obtain at least 20
transformants per attempted replacement. Because recombination between the donor
allele and the recipient genome can occur anywhere along the length of the donor
allele, marked replacements often result in only partial replacement of a gene. Thus, we
used Sanger sequencing or restriction typing to identify transformants that possessed
complete replacement of a gene or that only integrated the hphMX cassette. These two
groups of strains served as the allele replacement and control strains described in the
paper.

3.10.9 Quantitative PCR
A BYxYPS F2B3 segregant with the BY version of the POR2-SDP1 region, as well as a
version of this strain that was genetically engineered to carry the YPS allele of this
region, were cultured overnight in YPD. These cultures were then transferred to fresh
YPD the next day and incubated for three hours at 30˚C with shaking (200 RPM). After
this setback step, six replicate YPD cultures were generated from each strain, each of
which contained approximately 2 × 10
7
cells. For both genotypes, half of the cultures
were kept as controls that were never exposed to hydrogen peroxide, while the other
half of the cultures were supplemented with hydrogen peroxide to a concentration of 3
mM. After 30 minutes, the cultures were pelleted by centrifugation. The supernatant was
decanted from these pellets and each pellet was washed with sterile water. Following
the wash step, the cultures were pelleted again by centrifugation, snap frozen using
liquid nitrogen, and stored at -80˚C for subsequent total RNA extraction. The Qiagen
73

RNeasy Plant Mini Kit was used for RNA extraction and cDNA was obtained using the
SuperScript VILO cDNA Synthesis Kit. QPCRs were then performed on each sample
using the KAPA SYBR Fast qPCR kit and the DNA Engine Opticon 2. The relative
abundances of POR2 and SDP1 in each sample were determined through comparison
to the reference gene ACT1.
3.11 Acknowledgements
We thank Jonathan Lee, Takeshi Matsui, Joann Phan, and Matthew Taylor for
critically reviewing a draft of this manuscript. We also thank Charles Nicolet and the
USC Epigenome Center staff for their help with Illumina sequencing, and Norman
Arnheim and Jordan Eboreime for assisting us with qPCR experiments. This work was
supported by grants from the National Institutes of Health (R01GM110255 and
R21AI108939), National Science Foundation (MCB1330874), Alfred P. Sloan
Foundation, and Rose Hills Foundation to I.M.E.

74

3.12 Supporting Information

Fig. S3.1. Distribution of hydrogen peroxide resistance among parents and cross
progeny. YPS was most sensitive to hydrogen peroxide: the MIC of YPS was 3.5
mM, while BY and RM showed MICs of 4.5 and 5 mM, respectively (depicted as
dashed vertical lines above) . For each of the three pairwise crosses of the three
strains, we generated 864 recombinants using random spore techniques
(Methods). We screened these recombinants across six concentrations of
hydrogen peroxide and found that the trait was transgressive in every cross.
Furthermore, minimal and maximal trait values that were approximately the same
were observed in each of the crosses (minima ≈ 2 mM and maxima ≈ 7 mM),
although the proportion of progeny with intermediate phenotypes varied. These
results suggest that BY, RM, and YPS each carry a mixture of resistance and
sensitivity alleles.
75

76

Fig. S3.2. Genome-wide allele frequency plots of all families for each cross, with
families from a cross ordered by their founding segregant. The BYxRM, BYxYPS,
and RMxYPS crosses are shown in A, B, and C, respectively. Dashed horizontal
lines represent nominal significance (p ≤ 0.05) in one-tailed binomial tests. The
number of F2B3 segregants that carry the non-backcross parental allele is
depicted on the y-axis, with ‘B’, ‘R’, and ‘Y’ indicating BY, RM, and YPS,
respectively. Regions supporting transient aneuploidy during early rounds of
backcrossing are indicated with asterisks. These sites were identified because
the sites segregate in both backcrosses derived from their founding segregant.
Under a scenario in which aneuploidy does not occur, regions that segregate in
one family derived from a given segregant should not segregate with the other
family derived from that segregant.

77

Fig. S3.3. Detection of nominally significant loci (p ≤ 0.05) in multiple advanced
backcross families and crosses. The BYxRM, BYxYPS, and RMxYPS crosses are
shown in A, B, and C, respectively. The y-axis depicts the number of advanced
backcross families that were significant at a given genomic position.

78

Fig. S3.4. L7-II and L16-I act additively. 470 RMxYPS F2 segregants were
phenotyped and genotyped at L7-II and L16-I. The 95% confidence intervals of
MIC for each genotype are shown in A, while the additive regression model for
MIC as a function of haploid combinations of the two loci is provided in B.

Fig. S3.5. RH and allele replacement results for the five BYxYPS loci described in
Figure 3.4. In A, RH successfully identified AQY1 (Chromosome XVI) and MKT1
(Chromosome XIV) as quantitative trait genes. In B, allele replacements confirmed
79

that AQY1 and MKT1 are quantitative genes, and showed that MMS21
(Chromosome V), MRP13 (Chromosome VII), and SDP1 (Chromosome IX) are
causal. As we discuss in the main text, the causal variant at SDP1 is located in
the intergenic region between this gene and POR2. All plots show 95%
confidence intervals for MIC, with ‘B’ and ‘Y’, indicating BY and YPS, respectively.

Figure S3.6. Raw data plotted for the RH results depicted in the previous figure.

80

Fig. S3.7. Cloning of AQY1 in the RMxYPS cross. In A and B, allele replacements in
a relevant RMxYPS F2B3 segregant were used to show that AQY1 is the causal
gene underlying the locus at the distal end of Chromosome XVI. ‘R’ and ‘Y’ refer
to RM and YPS, respectively. 95% confidence intervals for MIC are provided in B.

81

Fig. S3.8. The correlation between MIC and OD600 values for 48 haploid strains. In A,
a representative set of images depicting 48 F2B3 segregants that were pinned
across a range of doses of hydrogen peroxide is shown. The OD600 values of the
cultures these strains were pinned from were measured just prior to pinning. As
can be seen in B, for this experimental setup, there is virtually no correlation
between the MIC of these strains and the OD600 values of the cultures they were
pinned from.

82

Table S3.1. Phenotype data for all F2 progenitor strains as well as all F2B3
segregants used in this study.

Strain Name Description MIC (mM)
2A4
BYxRM F
2

Families 1 and 6
progenitor
7.5
2C12
BYxRM F
2

Families 2 and 7
progenitor
7.5
4E12
BYxRM F
2

Families 3 and 8
progenitor
6.5
3C9
BYxRM F
2

Families 4 and 9
progenitor
6.5
3C11
BYxRM F
2

Families 5 and
10 progenitor
6.5
5H4
RMxYPS F
2

Families 11 and
16 progenitor
>8
1E11
RMxYPS F
2

Families 12 and
17 progenitor
7.5
5E4
RMxYPS F
2

Families 13 and
18 progenitor
7
4E12
RMxYPS F
2

Families 14 and
19 progenitor
7
5C12
RMxYPS F
2

Families 15 and
20 progenitor
7
8A5
BYxYPS F
2

Families 21 and
26 progenitor
7
7H4
BYxYPS F
2

Families 22 and
28 progenitor
7
83

8H3
BYxYPS F
2

Families 23 and
29 progenitor
7
8A6
BYxYPS F
2

Families 24 and
27 progenitor
8
7H12
BYxYPS F
2

Families 25 and
30 progenitor
8
1bC2 BYxRMxBY F
2
B
3
7
1bD10 BYxRMxBY F
2
B
3
7
1bG5 BYxRMxBY F
2
B
3
7
1dC3 BYxRMxBY F
2
B
3
7
1dD9 BYxRMxBY F
2
B
3
7
1dE10 BYxRMxBY F
2
B
3
7
1dE12 BYxRMxBY F
2
B
3
7
1dF6 BYxRMxBY F
2
B
3
8
1gB11 BYxRMxBY F
2
B
3
6
1gB12 BYxRMxBY F
2
B
3
6
1gC11 BYxRMxBY F
2
B
3
6
1gC12 BYxRMxBY F
2
B
3
7
2aC8 BYxRMxBY F
2
B
3
6
2aC9 BYxRMxBY F
2
B
3
7
2aD11 BYxRMxBY F
2
B
3
7
2aE9 BYxRMxBY F
2
B
3
7
2aF9 BYxRMxBY F
2
B
3
7
2bD9 BYxRMxBY F
2
B
3
7
2bE12 BYxRMxBY F
2
B
3
7
2cD10 BYxRMxBY F
2
B
3
7
2cD12 BYxRMxBY F
2
B
3
7
2cF11 BYxRMxBY F
2
B
3
7
2gB11 BYxRMxBY F
2
B
3
7
2gE7 BYxRMxBY F
2
B
3
7
2gE8 BYxRMxBY F
2
B
3
7
2gE9 BYxRMxBY F
2
B
3
7
2gG11 BYxRMxBY F
2
B
3
<8
3cC10 BYxRMxBY F
2
B
3
6
84

3cC11 BYxRMxBY F
2
B
3
6
3cC9 BYxRMxBY F
2
B
3
6
3cD10 BYxRMxBY F
2
B
3
6
3cD11 BYxRMxBY F
2
B
3
6
3cD12 BYxRMxBY F
2
B
3
6
3cD7 BYxRMxBY F
2
B
3
5
3cD8 BYxRMxBY F
2
B
3
5
3cD9 BYxRMxBY F
2
B
3
7
3dD9 BYxRMxBY F
2
B
3
6
3eA4 BYxRMxBY F
2
B
3
5
3eB7 BYxRMxBY F
2
B
3
6
3eC7 BYxRMxBY F
2
B
3
5
4aB5 BYxRMxBY F
2
B
3
6
4aC9 BYxRMxBY F
2
B
3
6
4aD9 BYxRMxBY F
2
B
3
6
4aE5 BYxRMxBY F
2
B
3
5
4aE9 BYxRMxBY F
2
B
3
6
4fA11 BYxRMxBY F
2
B
3
6
4fB10 BYxRMxBY F
2
B
3
7
4fB11 BYxRMxBY F
2
B
3
6
4fB12 BYxRMxBY F
2
B
3
5
4fC10 BYxRMxBY F
2
B
3
6
4gB11 BYxRMxBY F
2
B
3
6
4gE10 BYxRMxBY F
2
B
3
5
4gF10 BYxRMxBY F
2
B
3
5
4gF11 BYxRMxBY F
2
B
3
5
4gG5 BYxRMxBY F
2
B
3
6
5bB11 BYxRMxBY F
2
B
3
<5
5bE4 BYxRMxBY F
2
B
3
6
5gA2 BYxRMxBY F
2
B
3
6
5gA6 BYxRMxBY F
2
B
3
5
5gB2 BYxRMxBY F
2
B
3
5
5gB5 BYxRMxBY F
2
B
3
5
5gD2 BYxRMxBY F
2
B
3
5
5gE2 BYxRMxBY F
2
B
3
6
5gE4 BYxRMxBY F
2
B
3
6
5gF1 BYxRMxBY F
2
B
3
5
85

5gF2 BYxRMxBY F
2
B
3
6
5gG3 BYxRMxBY F
2
B
3
5
5gG6 BYxRMxBY F
2
B
3
5
6bC2
BYxRMxRM
F
2
B
3

8
6bD10
BYxRMxRM
F
2
B
3

8
6bD2
BYxRMxRM
F
2
B
3

10
6bD4
BYxRMxRM
F
2
B
3

9
6bD8
BYxRMxRM
F
2
B
3

8
6bE8
BYxRMxRM
F
2
B
3

10
6cB8
BYxRMxRM
F
2
B
3

8
6dD11
BYxRMxRM
F
2
B
3

10
6dE11
BYxRMxRM
F
2
B
3

7
6dF10
BYxRMxRM
F
2
B
3

9
6gF1
BYxRMxRM
F
2
B
3

9
6gG11
BYxRMxRM
F
2
B
3

9
7aE3
BYxRMxRM
F
2
B
3

8
7aE8
BYxRMxRM
F
2
B
3

8
7aG12
BYxRMxRM
F
2
B
3

9
7aG9
BYxRMxRM
F
2
B
3

8
7bE10
BYxRMxRM
F
2
B
3

9
7bE11
BYxRMxRM
F
2
B
3

8
7bE3
BYxRMxRM
F
2
B
3

10
7bE4 BYxRMxRM 8
86

F
2
B
3

7bE9
BYxRMxRM
F
2
B
3

8
7bF11
BYxRMxRM
F
2
B
3

7
7bF12
BYxRMxRM
F
2
B
3

7
7bF9
BYxRMxRM
F
2
B
3

8
8aD9
BYxRMxRM
F
2
B
3

8
8aE9
BYxRMxRM
F
2
B
3

8
8bB11
BYxRMxRM
F
2
B
3

8
8bC11
BYxRMxRM
F
2
B
3

8
8cB5
BYxRMxRM
F
2
B
3

8
8cD5
BYxRMxRM
F
2
B
3

8
8cE2
BYxRMxRM
F
2
B
3

7
8cE3
BYxRMxRM
F
2
B
3

7
8cE5
BYxRMxRM
F
2
B
3

8
8eC10
BYxRMxRM
F
2
B
3

8
8eD10
BYxRMxRM
F
2
B
3

8
8eD3
BYxRMxRM
F
2
B
3

8
8eE1
BYxRMxRM
F
2
B
3

7
8eE2
BYxRMxRM
F
2
B
3

7
8eE3
BYxRMxRM
F
2
B
3

7
9aC11
BYxRMxRM
F
2
B
3

9
9aC4
BYxRMxRM
F
2
B
3

8
87

9aC8
BYxRMxRM
F
2
B
3

10
9aD10
BYxRMxRM
F
2
B
3

9
9aD2
BYxRMxRM
F
2
B
3

8
9aD9
BYxRMxRM
F
2
B
3

9
9bC9
BYxRMxRM
F
2
B
3

9
9bD8
BYxRMxRM
F
2
B
3

9
9bE11
BYxRMxRM
F
2
B
3

8
9bG8
BYxRMxRM
F
2
B
3

9
9cE10
BYxRMxRM
F
2
B
3

9
9cF11
BYxRMxRM
F
2
B
3

9
9cF9
BYxRMxRM
F
2
B
3

9
9cG8
BYxRMxRM
F
2
B
3

9
10bH7
BYxRMxRM
F
2
B
3

7
10cD9
BYxRMxRM
F
2
B
3

7
10cE8
BYxRMxRM
F
2
B
3

7
10cE9
BYxRMxRM
F
2
B
3

8
10cF9
BYxRMxRM
F
2
B
3

8
10eE2
BYxRMxRM
F
2
B
3

8
10eE4
BYxRMxRM
F
2
B
3

8.5
10eF1
BYxRMxRM
F
2
B
3

7
10eF4
BYxRMxRM
F
2
B
3

8
10eG3 BYxRMxRM 8
88

F
2
B
3

10gC8
BYxRMxRM
F
2
B
3

7
10gD7
BYxRMxRM
F
2
B
3

7
10gF7
BYxRMxRM
F
2
B
3

7
10gG7
BYxRMxRM
F
2
B
3

7
10gG9
BYxRMxRM
F
2
B
3

8
11aC12
RMxYPSxRM
F
2
B
3

9
11aE11
RMxYPSxRM
F
2
B
3

9
11bC2
RMxYPSxRM
F
2
B
3

8
11bC5
RMxYPSxRM
F
2
B
3

8
11bD4
RMxYPSxRM
F
2
B
3

8
11dA4
RMxYPSxRM
F
2
B
3

8
11dB2
RMxYPSxRM
F
2
B
3

8
11dB3
RMxYPSxRM
F
2
B
3

7
11dB4
RMxYPSxRM
F
2
B
3

8
11dB5
RMxYPSxRM
F
2
B
3

7
11dC2
RMxYPSxRM
F
2
B
3

7
11dC3
RMxYPSxRM
F
2
B
3

6
11dC4
RMxYPSxRM
F
2
B
3

7
11dC5
RMxYPSxRM
F
2
B
3

8
11fC3
RMxYPSxRM
F
2
B
3

7
12aD8
RMxYPSxRM
F
2
B
3

7
89

12aE8
RMxYPSxRM
F
2
B
3

7
12aG7
RMxYPSxRM
F
2
B
3

8
12bB5
RMxYPSxRM
F
2
B
3

8
12bB6
RMxYPSxRM
F
2
B
3

7
12bC5
RMxYPSxRM
F
2
B
3

7
12bG7
RMxYPSxRM
F
2
B
3

7
12cC10
RMxYPSxRM
F
2
B
3

7
12cD10
RMxYPSxRM
F
2
B
3

7
12cD11
RMxYPSxRM
F
2
B
3

7
12eD9
RMxYPSxRM
F
2
B
3

7
12eE9
RMxYPSxRM
F
2
B
3

7
12fF2
RMxYPSxRM
F
2
B
3

7
12fG2
RMxYPSxRM
F
2
B
3

7
13aB12
RMxYPSxRM
F
2
B
3

7
13aC12
RMxYPSxRM
F
2
B
3

8
13aD11
RMxYPSxRM
F
2
B
3

7
13gD11
RMxYPSxRM
F
2
B
3

8
13gD3
RMxYPSxRM
F
2
B
3

7
13gD4
RMxYPSxRM
F
2
B
3

8
13gD5
RMxYPSxRM
F
2
B
3

7
13gE3
RMxYPSxRM
F
2
B
3

7
13gF2 RMxYPSxRM 8
90

F
2
B
3

13gF3
RMxYPSxRM
F
2
B
3

7
13gG2
RMxYPSxRM
F
2
B
3

8
13gG3
RMxYPSxRM
F
2
B
3

7
13gG4
RMxYPSxRM
F
2
B
3

7
14aD2
RMxYPSxRM
F
2
B
3

7
14aD3
RMxYPSxRM
F
2
B
3

6
14aD4
RMxYPSxRM
F
2
B
3

7
14bD2
RMxYPSxRM
F
2
B
3

7
14bE3
RMxYPSxRM
F
2
B
3

7
14cC4
RMxYPSxRM
F
2
B
3

7
14cD2
RMxYPSxRM
F
2
B
3

6
14cD5
RMxYPSxRM
F
2
B
3

7
14cE2
RMxYPSxRM
F
2
B
3

7
14cF1
RMxYPSxRM
F
2
B
3

7
14cF2
RMxYPSxRM
F
2
B
3

7
14eB11
RMxYPSxRM
F
2
B
3

7
14fE10
RMxYPSxRM
F
2
B
3

7
14fF3
RMxYPSxRM
F
2
B
3

8
15aC3
RMxYPSxRM
F
2
B
3

7
15aD1
RMxYPSxRM
F
2
B
3

7
15aD2
RMxYPSxRM
F
2
B
3

7
91

15eB2
RMxYPSxRM
F
2
B
3

7
15eB3
RMxYPSxRM
F
2
B
3

7
15eB4
RMxYPSxRM
F
2
B
3

6
15eC3
RMxYPSxRM
F
2
B
3

6
15eC5
RMxYPSxRM
F
2
B
3

7
15eD12
RMxYPSxRM
F
2
B
3

6
15eD5
RMxYPSxRM
F
2
B
3

6
15eE10
RMxYPSxRM
F
2
B
3

7
15eE3
RMxYPSxRM
F
2
B
3

6
15eF2
RMxYPSxRM
F
2
B
3

7
15eF3
RMxYPSxRM
F
2
B
3

7
15eF9
RMxYPSxRM
F
2
B
3

6
16aF3
RMxYPSxYPS
F
2
B
3

5
16bD7
RMxYPSxYPS
F
2
B
3

<5
16bE7
RMxYPSxYPS
F
2
B
3

<5
16bF8
RMxYPSxYPS
F
2
B
3

<5
16bF9
RMxYPSxYPS
F
2
B
3

<5
16dB12
RMxYPSxYPS
F
2
B
3

<5
16dC11
RMxYPSxYPS
F
2
B
3

<5
16eB7
RMxYPSxYPS
F
2
B
3

5
16eD7
RMxYPSxYPS
F
2
B
3

5
16fB5 RMxYPSxYPS 5
92

F
2
B
3

16fH6
RMxYPSxYPS
F
2
B
3

5
16gD12
RMxYPSxYPS
F
2
B
3

4
16gE11
RMxYPSxYPS
F
2
B
3

<4
17aA4
RMxYPSxYPS
F
2
B
3

6
17aA5
RMxYPSxYPS
F
2
B
3

7
17aB4
RMxYPSxYPS
F
2
B
3

4
17aC11
RMxYPSxYPS
F
2
B
3

5
17aC5
RMxYPSxYPS
F
2
B
3

5
17aC7
RMxYPSxYPS
F
2
B
3

5
17aD3
RMxYPSxYPS
F
2
B
3

6
17aD9
RMxYPSxYPS
F
2
B
3

5
17aE6
RMxYPSxYPS
F
2
B
3

5
17aF4
RMxYPSxYPS
F
2
B
3

5
17bB11
RMxYPSxYPS
F
2
B
3

5
17bD1
RMxYPSxYPS
F
2
B
3

5
17bD2
RMxYPSxYPS
F
2
B
3

5
17cA10
RMxYPSxYPS
F
2
B
3

5
17cB8
RMxYPSxYPS
F
2
B
3

<4
18cA4
RMxYPSxYPS
F
2
B
3

5
18cA5
RMxYPSxYPS
F
2
B
3

6
18cB11
RMxYPSxYPS
F
2
B
3

6
93

18cB8
RMxYPSxYPS
F
2
B
3

6
18cC8
RMxYPSxYPS
F
2
B
3

6
18cE12
RMxYPSxYPS
F
2
B
3

5
18cF2
RMxYPSxYPS
F
2
B
3

6
18fC12
RMxYPSxYPS
F
2
B
3

5
18fD4
RMxYPSxYPS
F
2
B
3

5
18gB11
RMxYPSxYPS
F
2
B
3

5
18gB4
RMxYPSxYPS
F
2
B
3

5
18gC8
RMxYPSxYPS
F
2
B
3

6
18gD8
RMxYPSxYPS
F
2
B
3

5
18gF2
RMxYPSxYPS
F
2
B
3

5
18gF7
RMxYPSxYPS
F
2
B
3

6
19eB9
RMxYPSxYPS
F
2
B
3

6
19eC10
RMxYPSxYPS
F
2
B
3

6
19eF8
RMxYPSxYPS
F
2
B
3

6
19eF9
RMxYPSxYPS
F
2
B
3

6
19fA1
RMxYPSxYPS
F
2
B
3

<5
19fB1
RMxYPSxYPS
F
2
B
3

6
19fC1
RMxYPSxYPS
F
2
B
3

5
19fC4
RMxYPSxYPS
F
2
B
3

5
19fD9
RMxYPSxYPS
F
2
B
3

6
19fG2 RMxYPSxYPS 5
94

F
2
B
3

19fG4
RMxYPSxYPS
F
2
B
3

5
19fG6
RMxYPSxYPS
F
2
B
3

5
19gC11
RMxYPSxYPS
F
2
B
3

8
19gD1
RMxYPSxYPS
F
2
B
3

6
19gE11
RMxYPSxYPS
F
2
B
3

5
20cC2
RMxYPSxYPS
F
2
B
3

5
20cC8
RMxYPSxYPS
F
2
B
3

5
20cD12
RMxYPSxYPS
F
2
B
3

5
20cD7
RMxYPSxYPS
F
2
B
3

5
20cF3
RMxYPSxYPS
F
2
B
3

4
20dB11
RMxYPSxYPS
F
2
B
3

6
20dC11
RMxYPSxYPS
F
2
B
3

5
20dD4
RMxYPSxYPS
F
2
B
3

6
20dF10
RMxYPSxYPS
F
2
B
3

5
20eA5
RMxYPSxYPS
F
2
B
3

6
20eC2
RMxYPSxYPS
F
2
B
3

6
20eD1
RMxYPSxYPS
F
2
B
3

5
20eD2
RMxYPSxYPS
F
2
B
3

5
21aA10 BYxYPSxBY F
2
B
3
6
21aB5 BYxYPSxBY F
2
B
3
5
21aC5 BYxYPSxBY F
2
B
3
6
21aE2 BYxYPSxBY F
2
B
3
6
21aF7 BYxYPSxBY F
2
B
3
6
95

21aF8 BYxYPSxBY F
2
B
3
6
21bC11 BYxYPSxBY F
2
B
3
6
21bC3 BYxYPSxBY F
2
B
3
6
21cB10 BYxYPSxBY F
2
B
3
5
21cB5 BYxYPSxBY F
2
B
3
6
21cE2 BYxYPSxBY F
2
B
3
5
21cE8 BYxYPSxBY F
2
B
3
6
21dC5 BYxYPSxBY F
2
B
3
6
21dF3 BYxYPSxBY F
2
B
3
6
22cC2 BYxYPSxBY F
2
B
3
8
22cC3 BYxYPSxBY F
2
B
3
8
22cD3 BYxYPSxBY F
2
B
3
8
22cD4 BYxYPSxBY F
2
B
3
8
22cE3 BYxYPSxBY F
2
B
3
7
22cE4 BYxYPSxBY F
2
B
3
7
22cF4 BYxYPSxBY F
2
B
3
7
22cF5 BYxYPSxBY F
2
B
3
7
22cF8 BYxYPSxBY F
2
B
3
7
22dE12 BYxYPSxBY F
2
B
3
8
22dE3 BYxYPSxBY F
2
B
3
7
22dF5 BYxYPSxBY F
2
B
3
8
22dG4 BYxYPSxBY F
2
B
3
7
23dC11 BYxYPSxBY F
2
B
3
7
23dF3 BYxYPSxBY F
2
B
3
7
23fB2 BYxYPSxBY F
2
B
3
7
23fB3 BYxYPSxBY F
2
B
3
7
23fC11 BYxYPSxBY F
2
B
3
7
23fE1 BYxYPSxBY F
2
B
3
7
23fE7 BYxYPSxBY F
2
B
3
7
23fF7 BYxYPSxBY F
2
B
3
7
23fF8 BYxYPSxBY F
2
B
3
6
23fF9 BYxYPSxBY F
2
B
3
7
23gD11 BYxYPSxBY F
2
B
3
7
23gD9 BYxYPSxBY F
2
B
3
7
23gE8 BYxYPSxBY F
2
B
3
6
23gF10 BYxYPSxBY F
2
B
3
7
23gG3 BYxYPSxBY F
2
B
3
7
96

25cA2
BYxYPSxYPS
F
2
B
3

6
25cA5
BYxYPSxYPS
F
2
B
3

6
25cB4
BYxYPSxYPS
F
2
B
3

6
25cC4
BYxYPSxYPS
F
2
B
3

6
25cC8
BYxYPSxYPS
F
2
B
3

6
25cE2
BYxYPSxYPS
F
2
B
3

6
25cF2
BYxYPSxYPS
F
2
B
3

6
25eA10
BYxYPSxYPS
F
2
B
3

6
25eA11
BYxYPSxYPS
F
2
B
3

6
25eA12
BYxYPSxYPS
F
2
B
3

6
25eB10
BYxYPSxYPS
F
2
B
3

6
25eB9
BYxYPSxYPS
F
2
B
3

6
25eD10
BYxYPSxYPS
F
2
B
3

6
25eE2
BYxYPSxYPS
F
2
B
3

6
25eF2
BYxYPSxYPS
F
2
B
3

6
26aC10
BYxYPSxYPS
F
2
B
3

6
26aE1
BYxYPSxYPS
F
2
B
3

6
26aF7
BYxYPSxYPS
F
2
B
3

6
26bC9
BYxYPSxYPS
F
2
B
3

5
26bD11
BYxYPSxYPS
F
2
B
3

6
26eA1
BYxYPSxYPS
F
2
B
3

6
26eC7 BYxYPSxYPS 7
97

F
2
B
3

26eE10
BYxYPSxYPS
F
2
B
3

7
26eG11
BYxYPSxYPS
F
2
B
3

7
26gA7
BYxYPSxYPS
F
2
B
3

8
26gB2
BYxYPSxYPS
F
2
B
3

6
26gD10
BYxYPSxYPS
F
2
B
3

6
26gD7
BYxYPSxYPS
F
2
B
3

6
26gG11
BYxYPSxYPS
F
2
B
3

6
28aC2
BYxYPSxYPS
F
2
B
3

6
28bB4
BYxYPSxYPS
F
2
B
3

6
28bC5
BYxYPSxYPS
F
2
B
3

6
28bH6
BYxYPSxYPS
F
2
B
3

6
28cD5
BYxYPSxYPS
F
2
B
3

7
28cD7
BYxYPSxYPS
F
2
B
3

6
28cE1
BYxYPSxYPS
F
2
B
3

6
28cH8
BYxYPSxYPS
F
2
B
3

6
28dA4
BYxYPSxYPS
F
2
B
3

6
28dC9
BYxYPSxYPS
F
2
B
3

6
28dD7
BYxYPSxYPS
F
2
B
3

6
28fB2
BYxYPSxYPS
F
2
B
3

6
28fB6
BYxYPSxYPS
F
2
B
3

6
28fC2
BYxYPSxYPS
F
2
B
3

6
98

28fD9
BYxYPSxYPS
F
2
B
3

6
29eB3
BYxYPSxYPS
F
2
B
3

6
29eC6
BYxYPSxYPS
F
2
B
3

7
29eD2
BYxYPSxYPS
F
2
B
3

5
29eD4
BYxYPSxYPS
F
2
B
3

6
29eE7
BYxYPSxYPS
F
2
B
3

6
29fA3
BYxYPSxYPS
F
2
B
3

6
29fB3
BYxYPSxYPS
F
2
B
3

6
29fD12
BYxYPSxYPS
F
2
B
3

6
29fG12
BYxYPSxYPS
F
2
B
3

6
29gB3
BYxYPSxYPS
F
2
B
3

6
29gC1
BYxYPSxYPS
F
2
B
3

7
29gD2
BYxYPSxYPS
F
2
B
3

4
29gD9
BYxYPSxYPS
F
2
B
3

6
29gF7
BYxYPSxYPS
F
2
B
3

5
29gG7
BYxYPSxYPS
F
2
B
3

6
30aD4
BYxYPSxYPS
F
2
B
3

6
30bB12
BYxYPSxYPS
F
2
B
3

6
30bB4
BYxYPSxYPS
F
2
B
3

6
30cB4
BYxYPSxYPS
F
2
B
3

6
30cD8
BYxYPSxYPS
F
2
B
3

6
30eB2 BYxYPSxYPS 5
99

F
2
B
3

30eC12
BYxYPSxYPS
F
2
B
3

5
30eE4
BYxYPSxYPS
F
2
B
3

6
30eE9
BYxYPSxYPS
F
2
B
3

5
30fE2
BYxYPSxYPS
F
2
B
3

5
30fE4
BYxYPSxYPS
F
2
B
3

5
30gC1
BYxYPSxYPS
F
2
B
3

5
30gF1
BYxYPSxYPS
F
2
B
3

5

100

Table S3.2. All putative QTL and associated genes.

Cross Family Backcross_Parent Chromosome Pos_Start Pos_End Q-value Gene(s)
BYxRM 5 BY 1 203222 229104 0.491374926
YAR053W, IMD1, FLO1, YAR060C,
YAR069C, YAR068W, YAR075W,
YAR066W, YAR070C, PHO11,
YAR064W
BYxYPS 21 BY 1 195677 206489 0.010700326 FLO1, SWH1, YAR047C
RMxYPS 15 RM 2 711791 713780 0.063778622 ENP1, RRT2
RMxYPS 18 YPS 2 741096 801523 0.018056481
MAL33, CHK1, SAF1, VBA2,
YBR284W, BIT2, MRPL37, SUL1,
YBR287W, SDH8, UBX7, BSD2,
SSH1, RIF1, DPB3, EFM2, SNF5,
PCA1, APE3, MRPL27, PPS1,
PHO89, CTP1, PAF1, APM3,
YBR292C, YBR296C-A, HSM3,
DUG2, YBR285W, YBR277C
RMxYPS 19 YPS 2 751853 799717 0.007614068
SAF1, VBA2, YBR284W, SUL1,
YBR287W, BSD2, SSH1, RIF1, DPB3,
SNF5, PCA1, APE3, MRPL27, PPS1,
PHO89, CTP1, PAF1, APM3,
YBR292C, DUG2, YBR285W,
YBR277C
BYxYPS 22 BY 2 183084 204924 0.125418612
YBL012C, FUS3, POP8, FMT1, PEP1,
ACH1, APN2, RRN6, RFT1, SCT1
BYxYPS 26 YPS 2 489432 496917 0.055277584
ATG14, TPS1, OPY1, SHE3, VMA2,
YBR126W-B, YBR126W-A
BYxYPS 26 YPS 2 683414 699559 0.155461499
YBR232C, ABD1, YBR238C, PBP2,
ERT1, PRP5, DAD3, ARC40, VHC1
BYxRM 3 BY 3 46486 51491 0.253092233
MGR1, YCL046W, GLK1, EMC1,
YCL042W, YCL041C, PDI1
BYxRM 10 RM 3 105047 145017 0.002878526
ADY2, CIT2, ADP1, LDB16, MRPL32,
STP22, PGK1, YCR013C, CDC10,
YCL007C, YCL001W-B, YCL001W-A,
RVS161, POL4, YCR001W, YCL002C,
PGS1, YCR015C, ILV6, SAT4,
YCR007C, RER1, YCR016W, YCP4,
YCR006C, VMA9, CWH43
BYxRM 2 BY 3 157998 201190 0.003769973
TAF2, RRP43, BPH1, RBK1,
MATALPHA1, RIM1, YCR023C,
SNT1, ELO2, RPS14A, YCR025C,
BUD5, SLM5, YCR041W,
MATALPHA2, YCR038W-A, PHO87,
RHB1, FEN2, SYP1, NPP1, PMP1,
YCR024C-B
BYxRM 1 BY 3 167725 201259 0.008827553
TAF2, RRP43, BPH1, RBK1,
MATALPHA1, RIM1, SNT1, ELO2,
RPS14A, BUD5, YCR041W,
MATALPHA2, YCR038W-A, PHO87,
RHB1, FEN2, SYP1
BYxRM 4 BY 3 167725 204898 0.001477316
TAF2, RRP43, BPH1, RBK1,
MATALPHA1, RIM1, SNT1, ELO2,
RPS14A, BUD5, YCR041W,
MATALPHA2, YCR038W-A, PHO87,
RHB1, FEN2, SYP1
BYxRM 3 BY 3 188419 204751 0.007469736
TAF2, RRP43, RBK1, MATALPHA1,
SNT1, ELO2, BUD5, YCR041W,
MATALPHA2, YCR038W-A, PHO87
BYxRM 5 BY 3 193829 201190 0.049211775
TAF2, RBK1, MATALPHA1, BUD5,
YCR041W, MATALPHA2, YCR038W-
A, PHO87
RMxYPS 20 YPS 3 57843 95149 0.081297613 LEU2, FRM2, HBN1, AGP1,
101

YCL019W, GFD2, KCC4, RRP7,
YCL023C, DCC1, MXR2, YCL022C,
HIS4, NFS1, GRX1, YCL021W-A,
BIK1, YCL020W, STE50, RNQ1,
FUS1, SRO9, LSB5
RMxYPS 12 RM 3 81816 233246 0.000807773
LEU2, ADY2, HSP30, CIT2, TAF2,
RRP43, SRD1, BPH1, YCR018C-A,
SGF29, PWP2, YCR045W-A,
YCL019W, ADP1, RBK1, LDB16,
PET18, YCR061W, RRT12, GBP2,
RSC6, KCC4, YCR051W, YCR064C,
RAD18, MRPL32, STP22,
MATALPHA1, RIM1, YCR023C,
ARE1, SNT1, PGK1, HTL1, DCC1,
YIH1, YCR013C, BUD31, YCR022C,
ELO2, SED4, CDC10, YCR050C,
YCL022C, NFS1, YCR043C, YCL007C,
THR4, YCL001W-B, YCL001W-A,
RVS161, RPS14A, POL4, YCR001W,
YCR025C, YCL021W-A, CTR86,
IMG1, BUD3, YCL002C, PGS1,
BUD5, YCL020W, YCR015C, ILV6,
YCR049C, SLM5, SAT4, HCM1,
YCR007C, YCR041W, MATALPHA2,
RER1, YCR038W-A, YCR047W-A,
YCR016W, PHO87, RHB1, TAH1,
YCL012C, MAK32, FEN2, PER1,
YCP4, YCR006C, VMA9, SYP1,
CWH43, NPP1, BUD23, MAK31,
PMP1, YCR024C-B
RMxYPS 14 RM 3 93902 209649 0.000775724
ADY2, HSP30, CIT2, TAF2, RRP43,
SRD1, BPH1, YCR018C-A, SGF29,
YCR045W-A, ADP1, RBK1, LDB16,
PET18, RRT12, GBP2, MRPL32,
STP22, MATALPHA1, RIM1,
YCR023C, SNT1, PGK1, HTL1, DCC1,
YCR013C, YCR022C, ELO2, CDC10,
NFS1, YCR043C, YCL007C,
YCL001W-B, YCL001W-A, RVS161,
RPS14A, POL4, YCR001W, YCR025C,
BUD3, YCL002C, PGS1, BUD5,
YCR015C, ILV6, SLM5, SAT4,
YCR007C, YCR041W, MATALPHA2,
RER1, YCR038W-A, YCR016W,
PHO87, RHB1, YCL012C, MAK32,
FEN2, PER1, YCP4, YCR006C, VMA9,
SYP1, CWH43, NPP1, MAK31,
PMP1, YCR024C-B
RMxYPS 11 RM 3 152950 201983 0.001049241
HSP30, TAF2, RRP43, BPH1, RBK1,
PET18, MATALPHA1, RIM1,
YCR023C, SNT1, HTL1, YCR022C,
ELO2, RPS14A, YCR025C, BUD5,
SLM5, YCR041W, MATALPHA2,
YCR038W-A, PHO87, RHB1,
MAK32, FEN2, SYP1, NPP1, MAK31,
PMP1, YCR024C-B
RMxYPS 13 RM 3 165408 206234 0.003780424
TAF2, RRP43, BPH1, RBK1,
MATALPHA1, RIM1, SNT1, ELO2,
RPS14A, BUD5, YCR041W,
MATALPHA2, YCR038W-A, PHO87,
RHB1, FEN2, SYP1, NPP1
RMxYPS 15 RM 3 201027 207706 0.001132588 TAF2, YCR043C, YCR041W, PER1
RMxYPS 16 YPS 3 208955 209911 0.178486112 YCR045W-A, RRT12
BYxYPS 21 BY 3 77849 201190 0.00096418
LEU2, ADY2, HSP30, CIT2, TAF2,
RRP43, SRD1, AGP1, BPH1,
102

YCR018C-A, SGF29, YCL019W,
ADP1, RBK1, LDB16, PET18, GBP2,
KCC4, MRPL32, STP22,
MATALPHA1, RIM1, YCR023C,
YCL023C, SNT1, PGK1, HTL1, DCC1,
YCR013C, YCR022C, ELO2, CDC10,
YCL022C, NFS1, YCL007C,
YCL001W-B, YCL001W-A, RVS161,
RPS14A, POL4, YCR001W, YCR025C,
YCL021W-A, BUD3, YCL002C, PGS1,
BUD5, YCL020W, YCR015C, ILV6,
SLM5, SAT4, YCR007C, YCR041W,
MATALPHA2, RER1, YCR038W-A,
YCR016W, PHO87, RHB1, YCL012C,
MAK32, FEN2, YCP4, YCR006C,
VMA9, SYP1, CWH43, NPP1,
MAK31, PMP1, YCR024C-B
BYxYPS 22 BY 3 95658 205887 0.001740149
ADY2, HSP30, CIT2, TAF2, RRP43,
SRD1, BPH1, YCR018C-A, SGF29,
ADP1, RBK1, LDB16, PET18, GBP2,
MRPL32, STP22, MATALPHA1,
RIM1, YCR023C, SNT1, PGK1, HTL1,
DCC1, YCR013C, YCR022C, ELO2,
CDC10, YCL007C, YCL001W-B,
YCL001W-A, RVS161, RPS14A,
POL4, YCR001W, YCR025C, BUD3,
YCL002C, PGS1, BUD5, YCR015C,
ILV6, SLM5, SAT4, YCR007C,
YCR041W, MATALPHA2, RER1,
YCR038W-A, YCR016W, PHO87,
RHB1, YCL012C, MAK32, FEN2,
YCP4, YCR006C, VMA9, SYP1,
CWH43, NPP1, MAK31, PMP1,
YCR024C-B
BYxYPS 25 BY 3 187854 211349 0.002028799
TAF2, RRP43, YCR045W-A, RBK1,
RRT12, MATALPHA1, SNT1, ELO2,
YCR043C, IMG1, BUD5, YCR041W,
MATALPHA2, YCR038W-A,
YCR047W-A, PHO87, PER1, BUD23
BYxYPS 23 BY 3 197218 201628 0.003984867
TAF2, MATALPHA1, BUD5,
YCR041W, MATALPHA2, YCR038W-
A
BYxYPS 28 YPS 3 252270 303525 0.008591062
HMRA2, HMRA1, KIN82, TUP1,
YCR081C-A, CDC39, TRX3,
YCR099C, GIT1, MSH3, ABP1,
YCR101C, SRB8, YCR085W,
YCR095W-A, PTC6, CDC50, CSM1,
YCR097W-A, YCR087W, OCA4,
PAT1, YCR100C, FIG2, AHC2,
YCR087C-A, YCR090C
BYxRM 2 BY 4 1394088 1417873 0.107647236
YDR476C, TRS31, SDC1, SNF1,
YDR467C, DIG2, PRP3, PKH3,
UGO1, TLG1, JIP4, RPL27B, RMT2,
PEX29, SNM1
RMxYPS 20 YPS 4 625474 714612 0.022125027
YDR114C, YDR102C, BMH2, TRM1,
YDR090C, YDR094W, FOB1,
YDR124W, INO2, TRS85, YDR109C,
TMA64, TMN2, DPB4, COX26,
ARP10, MTC5, SPO71, RLI1, ECM18,
PDS1, STE5, MRPL1, TVP15, UBC13,
SAC6, GIS1, ALT2, KIN1, IRC2,
SWF1, VBA4, TMS1, YDR118W-A,
DNF2, YDR115W, GRX3, ARX1,
ARO1, YDR095C, APC4, YDR098C-B,
YDR098C-A, MSH6
103

BYxYPS 28 YPS 4 115568 130869 0.031997294
RPL41A, YDL185C-A, RPL35A,
PPH22, ARF1, VMA1, RBS1,
YDL186W, UFD2, YDL187C, NUS1
BYxYPS 22 BY 4 637466 751285 0.125418612
YDR114C, YDR102C, BMH2, DOP1,
TRM1, FOB1, VPS61, TAF12,
YDR124W, INO2, TRS85, YDR109C,
TMA64, TMN2, PEX7, DPB4, YCF1,
COX26, ARP10, MTC5, SPO71,
ECM18, MTQ2, PDS1, STE5, SWI5,
YDR131C, MRPL1, TVP15, SAC6,
GIS1, ALT2, KIN1, IRC2, SWF1,
SAN1, YDR134C, RUB1, VBA4,
TMS1, YDR118W-A, FIN1, YDR132C,
YDR115W, GRX3, ARX1, RGP1,
MKC7, ARO1, APC4, YDR098C-B,
YDR098C-A, MSH6, YDR133C, HPR1
BYxRM 2 BY 5 190283 206003 0.107647236
SRB4, YER023C-A, GPA2, ISC1,
PRO3, GCD11, RPN3, SPC25, YAT2,
SBH2, AFG3
BYxYPS 22 BY 5 28649 29909 0.125418612 AVT2, SIT1
BYxYPS 29 YPS 5 116577 334274 0.087365008
ZRG8, EAF5, PIC2, THO1, HEM14,
SAH1, URA3, SRB4, NOP16, UTP7,
GIM4, MEI4, YER006C-A, YEL020C,
VAB2, MXR1, ICP55, GIP2, MMS21,
CHZ1, MNN1, GPP2, KRE29,
YEL020C-B, YEL010W, SER3, WBP1,
RNR1, YER023C-A, YER039C-A,
JHD1, FMP52, MIG3, ACA1,
YER079C-A, GPA2, UBC8, YEL008C-
A, ARG5,6, PRE1, YER077C,
YEL008W, RPS24A, EDC3, CEM1,
ISC1, PCL6, YER087C-A, YER084W,
IRC22, ERG28, PRO3, HOM3, ILV1,
FCY21, VAC8, YER068C-A, PMI40,
GCD11, PRP22, FIR1, RPL34A,
YER078W-A, YER076W-A, PET117,
VTC1, PHM8, HMF1, MOT2,
YER053C-A, NUG1, DOT6, CHO1,
SAP1, YER010C, PMP2, GTT3,
YER084W-A, TMA20, MIT1, RGI1,
YEL018C-A, SMB1, YND1, YER067C-
A, RPN3, ISD11, GCN4, SPO73,
ALD5, TIR1, YER034W, YER079W,
YER085C, YEA6, YER046W-A,
RSM18, RRT13, YEL014C, SPC25,
HIS1, YPT31, BIM1, GET2, YEA4,
YEL009C-A, HVG1, YAT2, YEN1,
TDA2, NTF2, EDC2, GLC3, AIM9,
PTP3, SBH2, TPA1, SEC3, FAA2,
YER066C-A, FCY2, AIM10, PAC2,
GAL83, AFG3, ARB1, YER038W-A,
GLN3, CAJ1, YER076C, SBH1, FCY22,
BUD25, VHR2, YOS1, TIM9, ICL1,
NPP2
BYxYPS 30 YPS 5 117889 193525 0.001861178
EAF5, HEM14, NOP16, GIM4,
YER006C-A, YEL020C, VAB2,
MMS21, MNN1, YEL010W, WBP1,
FMP52, UBC8, YEL008C-A, PRE1,
YEL008W, EDC3, ISC1, IRC22, VAC8,
PMI40, PRP22, NUG1, YER010C,
PMP2, GTT3, TMA20, MIT1,
YEL018C-A, YND1, GCN4, TIR1,
YEA6, YEL014C, SPC25, BIM1, YEA4,
YEL009C-A, NTF2, GLC3, SEC3,
FAA2, PAC2, AFG3, BUD25, NPP2
104

BYxYPS 21 BY 5 379332 402268 0.033741162
KAP123, SPR6, TMN3, SWI4,
YER119C-A, AVT6, BOI2, SHO1,
RPL23B, SCS2, SLX8, LSM4
BYxYPS 23 BY 5 547317 564921 0.245655034
YER186C, PDA1, FAU1, ISC10,
FMP10, YER181C, SLO1, TOG1,
DMC1, PUG1
RMxYPS 16 YPS 6 30312 74155 0.063848262
OTU1, RPL22B, MOB2, SEC53,
YFL051C, TUB2, FMP32, RPO41,
RGD2, ACT1, YFL040W, EMP47,
YPT1, FET5, SWP82, ALR2, YFL042C,
YFL034W, YFL041W-A, RIM15
RMxYPS 19 YPS 6 43492 58329 0.028072187
OTU1, SEC53, TUB2, ACT1,
YFL040W, YPT1, FET5, YFL042C,
YFL041W-A
RMxYPS 15 RM 7 190640 197152 0.020837377 YGL165C, YRB30, RAD54, CUP2
RMxYPS 16 YPS 7 363327 678868 0.038924507
TAM41, ALG13, GEP7, PNC1, SEC9,
MSB2, TYW3, PRP18, YGR079W,
TRP5, RSC1, CAX4, VMA7, MPO1,
ALG2, PMC1, TPC1, MRP13,
YGR066C, YGR054W, ALK1, PMA1,
TFC4, YGR042W, PEX8, CUL3, DST1,
RRP46, UGA1, YGR051C, YGL063C-
A, PYC1, NMA2, SLX9, GSC2, STT3,
PUF4, RPL7A, DBF2, RNA15, SPT4,
LST7, MST27, PAC10, YGR017W,
ECT1, PDR1, MUP1, CDH1, RAD6,
YGL074C, YGR039W, YGR025W,
IMO32, ERG25, PRP31, RPL24A,
RPL11B, BUD9, SDS23, HEM2,
YGR067C, YGL036W, YGR073C,
YGR018C, YGR038C-A, YGR038C-B,
SNU71, YGR050C, PUS2, TOM20,
CWH41, YGR021W, YGR035C,
RPB9, ATE1, AGA2, UPF3, BRP1,
SPR3, RIM8, YGR064W, MIG1,
MRPL25, MNP1, LEU1, YGL052W,
AFT1, NQM1, ERG4, SCW11,
RPN14, NPY1, HNM1, YGR045C,
ORM1, YGR026W, PIL1, YGR027W-
A, YGR027W-B, VAS1, PIB2, PRP38,
PDC6, OLE1, RME1, YGL041C, ART5,
YGL039W, YGL006W-A, CKB1,
PEX31, RPT6, YGR016W, YGL024W,
RPL26B, CTT1, HSF1, KAP122,
YGL007C-A, YGR022C, RPL30,
UTP22, ADE6, YGR035W-A,
YGL069C, YGR015C, EFM5, PKP2,
KSS1, ERG26, YGR012W, YGR053C,
STF2, YGL014C-A, YBP2, PEF1,
YGL041W-A, ASK10, VHT1, GET1,
POP6, PRM8, ENV11, SGF73, NAG1,
YGL034C, JAC1, SCM4, TFG2,
TIF4632, YGR011W, CGR1, SCL1,
COG7, MPS2, ACB1, RPS25A,
MRH4, HOP2, DRN1, COX18, TWF1,
YGR068W-A, SMD1, MSP1, ROM1,
ERV14, YGL041C-B, SWC4,
YGL042C, OCH1, GCD2, YGR069W,
ERP6, FMP48, UFD1, THG1, DUO1,
ERV1, PGD1, NNF2, MTL1,
YGL015C, TIM21, YGL072C
RMxYPS 16 YPS 7 761881 768254 0.178486112
LSB1, YGR137W, TPO2, CBF2,
YGR139W, PRE9
BYxYPS 29 YPS 7 42652 52589 0.035015252
YGL239C, KAP114, YGL242C, CSE1,
TAD1, RTF1, DOC1
105

BYxYPS 26 YPS 7 627366 667124 0.113087961
YGR079W, MRP13, PEX8, SLX9,
PAC10, PRP31, RPL11B, YGR073C,
TOM20, UPF3, MRPL25, PIL1,
PRP38, PDC6, CTT1, UTP22, ENV11,
TWF1, SMD1, ROM1, GCD2,
YGR069W, NNF2
BYxYPS 30 YPS 7 645458 739655 0.000322546
MEP1, TPC1, SPT6, MRP13, TEL2,
RRP46, DAM1, SRB5, DBF2, NOP7,
PRP31, YGR117C, RPL11B, CLD1,
ESP1, VOA1, CLB1, COG2, GTF1,
RPS23A, YGR114C, PIL1, VAS1,
CLB6, NUP57, PDC6, MDR1, CTT1,
PPT1, UTP22, YGR111W, YGR122C-
A, YGR115C, ASK10, PCP1,
YGR109W-B, YGR109W-A, DRN1,
VMA21, SHY1, GCD2, YGR122W,
YGR107W, YGR121W-A, NNF2
BYxYPS 28 YPS 7 645497 750922 0.002065011
MEP1, TPC1, SPT6, MRP13, TEL2,
UTP8, RRP46, DAM1, YGR125W,
SRB5, DBF2, NOP7, PRP31,
YGR117C, RPL11B, CLD1, ESP1,
VOA1, CLB1, COG2, YGR126W,
GTF1, SYF2, RPS23A, YGR114C,
PIL1, VAS1, CLB6, NUP57, ASN2,
PDC6, MDR1, CTT1, PPT1, UTP22,
YGR111W, YGR122C-A, YGR115C,
ASK10, PCP1, YGR127W,
YGR109W-B, YGR109W-A, DRN1,
VMA21, SHY1, GCD2, YGR122W,
YGR107W, YGR121W-A, NNF2
BYxYPS 21 BY 7 718809 801320 0.033741162
RPL24B, FHN1, LSB1, YGR151C,
MEP1, SPT6, UTP8, ECL1, CCM1,
DAM1, CYS4, YGR125W, PHB1,
BTN2, YGR117C, ENP2, YGR137W,
COG2, YGR126W, PEX4, TPO2,
SYF2, RPS23A, YGR114C, VPS62,
PTI1, NUP57, ASN2, CAF130, PPT1,
THI4, YGR122C-A, YGR115C, CBF2,
RSR1, NAT2, YGR149W, YGR130C,
YGR139W, YGR146C-A, YGR127W,
PRE9, SKN1, YGR122W, YGR121W-
A, YGR153W, GTO1
BYxYPS 26 YPS 7 833108 843710 0.155461499
CLC1, LSO2, PSD2, PUS6, YGR168C,
MSM1, YIP1
BYxRM 2 BY 8 144394 192125 0.107647236
ERC1, RPN1, BCD1, PUT2, BRL1,
ECM12, SRB2, YHR020W,
YHR028W-A, DAP2, THR1, RRM3,
YHR033W, MSC7, VMA10, NCP1,
SLT2, RRF1, YHR022C-A, VMA16,
MYO1, MAS2, YHR032W-A,
RPS27B, YHI9, NEL1, YHR032C-A,
YHR022C, PIH1
BYxRM 9 RM 8 457581 483296 0.104940603
GND1, STB5, YHR182W, PFS1,
SVP26, SSP1, YHR180W-A, GPI16,
YHR177W, IKI1, YHR180C-B,
YHR182C-A, KOG1, OYE2,
YHR180W
BYxYPS 25 BY 8 37676 66281 0.073334861
SPO11, ECM29, NPR3, RIM4, GOS1,
SNF6, YHL026C, OCA5, YHL030W-A,
RIM101, GUT1, OPI1, AIM17, WSC4
BYxRM 10 RM 9 102974 126569 0.042293767
RRT14, AYR1, TAO3, STH1, KGD1,
MET18, ASG1
RMxYPS 11 RM 9 83 996 0.025494016 YIL177C, YIL177W-A
RMxYPS 13 RM 9 83 996 0.31547553 YIL177C, YIL177W-A
RMxYPS 16 YPS 9 70698 90546 0.063848262 AXL2, TMA108, YIL141W, ATG32,
106

SLN1, NDC80, YIL142C-A, PAN6,
REV7, CCT2, TPM2, SSL2
BYxYPS 29 YPS 9 108378 150602 0.001715324
PRM5, RRT14, AYR1, HIS5, TAO3,
QDR2, YIL115W-A, STH1, RHO3,
SDP1, KGD1, SIM1, MET18, QDR1,
NUP159, POR2, RPI1, POG1
BYxYPS 28 YPS 9 145698 170435 0.008591062
PFK26, YIL108W, SLM1, COX5B,
MOB1, HPM1, SDP1, HOS4, SHQ1,
YIL105W-A, NUP159, SEC24, POR2
BYxYPS 28 YPS 9 247035 250908 0.031997294 VHR1, RGI2, YIL058W
RMxYPS 13 RM 10 61893 67457 0.31547553 UBP12, YJL197C-A, PHO90
RMxYPS 12 RM 10 373527 388629 0.007353678
BET4, SNX4, YJL032W, IRC18, LOH1,
KAR2, HCA4, MAD2, NUP192,
VPS53, TAD2
RMxYPS 18 YPS 10 639618 692808 0.000385758
SGM1, IBA57, TIM8, YJR115W,
HIR3, STR2, MET5, RSF2, XPT1,
RPS5, YJR124C, JHD2, YJR120W,
EFM3, YJR128W, TTI2, NMD5,
HOM6, ENT3, STE24, YJR140W-A,
MCM22, ILM1, ATP2, TDA4, MNS1,
IML1, VPS70
BYxYPS 22 BY 10 422597 461507 0.043052729
YJR011C, COX16, AVT1, MHO1,
YJR012C, APL1, CTK2, CYR1, OST1,
LSO1, SAG1, PRE3, GPI14, YJR003C,
MET3, SUI2, SYS1, MPP10, SPC1,
TDH2, YJL007C, POL31
BYxRM 10 RM 11 212878 221687 0.096694763
PRR1, YKL118W, SRP21, SBA1,
VPH2, DGR2, OAC1
RMxYPS 15 RM 11 6098 43246 0.063778622
YRA2, MCH2, DOA1, JEN1, COS9,
SAC1, SRY1, URA1, TRP3, OXP1,
STE6, FRE2, UBA1
BYxYPS 21 BY 11 306549 361056 0.109238733
YKL044W, MPE1, NUP100, ASK1,
MDM35, TMA19, YKL065W-A,
YKL068W-A, VPS24, MNR2,
YKL063C, TOA2, FBA1, SFK1, PRI2,
YET1, OAR1, SPC42, YKL050C,
DCW1, BLI1, YKL069W, YKL066W,
ELM1, DEF1, MSN4, YKL053W,
PHD1, YNK1, YKL070W, CSE4,
NUP120, ANR2
BYxRM 10 RM 12 362425 388904 0.042293767
YLR120W-A, YLR112W, YLR108C,
CFT2, YLR111W, YPS1, MDN1,
AHP1, AVL9, CLF1, REX3, YPS3,
SRN2, CCW12, HOG1, YLR118C,
MSL5
BYxRM 1 BY 12 627007 645539 0.143349655
MCP2, SSP120, IRC20, NDL1, ERF2,
SYM1, YLR252W, RCK2, YEF3
RMxYPS 18 YPS 12 111423 207072 0.000385758
RAD5, UBR2, SSK1, PPR1, RLP24,
DNM1, BPT1, RPL15A, LOT6, NOC3,
SSL1, YEH1, AAT2, SPO75, YLR001C,
BRE2, PAM18, YLR012C, IZH3,
COX17, THI73, ORC3, YLR030W,
PML1, ADE16, POM34, SFI1, PUF3,
YLL006W-A, DRS1, SDO1, GAT3,
PSR1, YLL007C, MEU1, SED5, NSE1,
RTT109, EMC6, SNF7, CMS1, TEN1,
YLR031W, MMM1, YEH2, DPS1,
IRC25, PSR2, SOF1
RMxYPS 19 YPS 12 520774 538387 0.028072187
SKG3, MMR1, MDL1, TOS4,
YLR184W, ATG26, RPL37A, PEX13,
EMG1
RMxYPS 19 YPS 12 722344 729605 0.007614068
YLR299C-A, EXG1, YLR297W,
ATP14, YHC1, YLR296W, ECM38
BYxYPS 26 YPS 12 13986 79672 0.155461499
MHT1, VPS13, YLL058W, MMP1,
JLP1, YLL047W, GRC3, YLL054C,
107

FPS1, RIX7, YLL053C, PRP19, GPI13,
RNP1, COF1, YLL032C, YLL059C,
GTT2, ATG10, LDB18, UBI4, YCT1,
YLL037W, ENT4, AYT1, RPL8B,
FRE6, IRC19, YBT1, YLL056C, SDH2,
YLL044W, AQY2
BYxYPS 29 YPS 12 35854 85632 0.035015252
RRT7, VPS13, YLL047W, FRA1,
GRC3, FPS1, RIX7, YLL053C, PRP19,
GPI13, RNP1, COF1, TPO1, YLL032C,
ATG10, LDB18, UBI4, YLL037W,
ENT4, RPL8B, FRE6, IRC19, YBT1,
SDH2, YLL044W, AQY2
BYxYPS 26 YPS 12 893373 904916 0.113087961
REH1, ECM19, RPS29A, SWC7,
CCW14, STE23, ART10, VAC14
BYxRM 5 BY 13 37888 46262 0.172241211 CTK3, ATR1, DAT1, TAF8, VAN1
BYxRM 2 BY 13 70163 73368 0.083462688 TSL1, YML100W-A
BYxRM 8 RM 13 225572 310534 0.00485162
YMR013W-A, YAP1, PSP2, ERG5,
ADI1, MIX17, YMR001C-A, UNG1,
PLB2, YMR013C-A, YML012C-A,
YMR010W, MRPL39, BUD22,
AIM34, SEC59, PPZ1, GLO1, OST6,
YMR018W, SPO20, TRM12, APT1,
YPT7, TAF11, YMR007W, RAD33,
YML018C, CLU1, MVP1, NSE5,
RPS17A, YML007C-A, YML002W,
PLB1, CDC5, SOK2, YML020W,
UBX2, GIS4, TRM9, YML009C-A,
HXT2, TAF4, ERV25, YML003W,
ERG6, SPT5, YML009W-B
BYxRM 6 RM 13 232662 291877 0.056693699
YAP1, PSP2, ADI1, MIX17,
YMR001C-A, PLB2, YML012C-A,
YMR010W, MRPL39, AIM34, PPZ1,
GLO1, OST6, TRM12, YPT7, TAF11,
YMR007W, RAD33, YML018C,
CLU1, MVP1, YML007C-A,
YML002W, PLB1, CDC5, YML020W,
UBX2, GIS4, TRM9, YML009C-A,
HXT2, TAF4, ERV25, YML003W,
ERG6, SPT5, YML009W-B
BYxRM 7 RM 13 243473 298845 0.069492625
YMR013W-A, YAP1, ADI1, MIX17,
YMR001C-A, PLB2, YMR013C-A,
YML012C-A, YMR010W, MRPL39,
AIM34, SEC59, GLO1, TRM12, YPT7,
YMR007W, RAD33, CLU1, MVP1,
YML007C-A, YML002W, PLB1,
CDC5, UBX2, GIS4, TRM9,
YML009C-A, HXT2, TAF4, ERV25,
YML003W, ERG6, SPT5, YML009W-
B
BYxRM 9 RM 13 244141 298845 0.001481259
YMR013W-A, YAP1, ADI1, MIX17,
YMR001C-A, PLB2, YMR013C-A,
YML012C-A, YMR010W, MRPL39,
AIM34, SEC59, GLO1, TRM12, YPT7,
YMR007W, RAD33, CLU1, MVP1,
YML007C-A, YML002W, PLB1,
CDC5, UBX2, GIS4, YML009C-A,
HXT2, TAF4, ERV25, YML003W,
ERG6, SPT5, YML009W-B
BYxRM 5 BY 13 478297 516134 0.491374926
SHH3, YMR122C, FOL3, PKR1,
SPG4, YMR114C, YMR122W-A,
HFD1, RPL15B, MYO5, ILV2, ASC1,
ADE17, YMR119W-A, MGR3,
YMR111C, MED11, YKU80, EPO1,
ASI1, SPC24
RMxYPS 11 RM 13 215391 274252 0.004766449 YAP1, PSP2, MIX17, YMR001C-A,
108

TSA1, UNG1, YML012C-A, NDC1,
MRPL39, AIM34, RPS18B, USA1,
PPZ1, GLO1, OST6, TRM12, APT1,
YPT7, TAF11, RCF1, YOX1, RAD33,
YML018C, MVP1, NSE5, RPS17A,
YML007C-A, YML002W, YML6,
CDC5, YML020W, UBX2, GIS4,
TRM9, YML009C-A, ERV25,
YML003W, ERG6, SPT5, YML009W-
B
RMxYPS 12 RM 13 247126 283371 0.007353678
YAP1, MIX17, YMR001C-A, PLB2,
MRPL39, AIM34, GLO1, TRM12,
YPT7, YMR007W, RAD33, MVP1,
YML007C-A, YML002W, PLB1,
CDC5, GIS4, YML009C-A, TAF4,
YML003W, ERG6, SPT5, YML009W-
B
RMxYPS 14 RM 13 247466 305906 0.007061165
YMR013W-A, YAP1, ERG5, ADI1,
MIX17, YMR001C-A, PLB2,
YMR013C-A, YMR010W, MRPL39,
BUD22, AIM34, SEC59, GLO1,
TRM12, YPT7, YMR007W, CLU1,
MVP1, YML007C-A, YML002W,
PLB1, CDC5, SOK2, GIS4, YML009C-
A, HXT2, TAF4, YML003W, ERG6,
SPT5, YML009W-B
RMxYPS 13 RM 13 253616 305303 0.003780424
YMR013W-A, YAP1, ERG5, ADI1,
MIX17, YMR001C-A, PLB2,
YMR013C-A, YMR010W, BUD22,
AIM34, SEC59, GLO1, TRM12, YPT7,
YMR007W, CLU1, MVP1,
YML002W, PLB1, CDC5, SOK2, GIS4,
HXT2, TAF4, YML003W
BYxYPS 21 BY 13 24474 93961 0.033741162
RPM2, YML119W, SEC65, CTK3,
YML122C, ZDS2, YML094C-A,
YML089C, ATR1, GIM5, TSL1, GTR1,
YML101C-A, YML099W-A,
YML116W-A, NUP188, DAT1,
ARG81, MDM1, YML090W,
YML100W-A, YML108W, VPS9,
NAB6, PML39, COQ5, TAF13,
UTP14, NDI1, TAF8, CAC2, BUL2,
NGL3, YML096W, URA5, PHO84,
CUE4, RAD10, VAN1, UFO1, PRE8
BYxYPS 23 BY 13 51084 102554 0.245655034
RPM2, SEC65, YML082W, ZDS2,
YML094C-A, YML089C, ALO1,
GIM5, TSL1, YML101C-A,
YML099W-A, NUP188, YML083C,
ARG81, MDM1, YML090W,
YML100W-A, YML108W, VPS9,
TUB1, PML39, AIM33, TAF13,
UTP14, CAC2, YML084W,
YML096W, URA5, CUE4, RAD10,
UFO1, PRE8
BYxYPS 23 BY 13 512216 551554 0.245655034
STO1, ERG29, PKR1, CIN4, DLT1,
RRB1, RPL13B, YMR135W-A, GID8,
RIM11, JLP2, YMR141C, YMR141W-
A, POM152, GAT2, SIP5, EPO1,
REC114, PSO2, YMR130W, ECM16,
SAS2
BYxYPS 22 BY 13 672760 674283 0.01357787 PFK2
BYxRM 1 BY 14 231035 282033 0.008827553
YNL203C, RRG9, SPS18, YNL195C,
RIO2, YNL217W, YNL208W, GCR2,
ADE12, MER1, YNL194C, SPS19,
PEX17, YNL193W, YNL211C,
109

YNL198C, RTT106, MGS1, WHI3,
IES2, PSY2, CHS1, YNL205C, ALG9,
SLZ1, VID27, SSB2, YNL200C, RAP1,
POP1, DUG3
BYxRM 3 BY 14 244309 298111 0.120168868
YNL203C, RRG9, SPS18, IPI3, SRP1,
YNL195C, RIO2, YNL208W, UBP10,
YNL190W, SWT21, GCR2, MER1,
YNL194C, SPS19, PEX17, NPR1,
YNL193W, YNL211C, YNL198C,
RTT106, WHI3, IES2, PSY2,
YNL184C, CHS1, YNL205C, KAR1,
SLZ1, VID27, SSB2, YNL200C,
MRPL19, DUG3
BYxRM 1 BY 14 441346 467221 0.008827553
PHO23, YPT53, APP1, RHO2, TCB2,
YNL089C, YNL095C, YNL097W-A,
NST1, RPS7B, SNN1, TOP2,
YNL092W, MKT1
BYxRM 2 BY 14 445768 467221 0.003769973
YPT53, APP1, RHO2, TCB2,
YNL089C, YNL095C, NST1, SNN1,
TOP2, YNL092W, MKT1
RMxYPS 15 RM 14 116882 145579 0.001132588
TOF1, PIK1, MET2, ALP1, LYP1,
GOR1, YNL266W, IST1, BNI1, SEC2,
BOR1, PDR17, YNL276C, BSC4
RMxYPS 13 RM 14 119277 265691 0.138936379
YNL203C, RRG9, YNL228W, CSL4,
SPS18, TOF1, MPA43, LAP3, GIS2,
CNM67, NAR1, ATG2, YTP1, RIO2,
YNL217W, YNL208W, PIK1, CWC25,
YNL235C, NRD1, GCR2, BNI4, ALP1,
LYP1, ADE12, MER1, ZWF1, SPS19,
PEX17, GOR1, YNL266W, RPA49,
SQS1, FOL1, YNL211C, YNL234W,
RTT106, MGS1, POL2, ATG4, SUI1,
MRPL17, IES2, PSY2, IST1, BNI1,
SIP3, YNL247W, JJJ1, ELA1, URE2,
SEC2, YNL205C, ALG9, DSL1, BOR1,
ORC5, SLA2, TEX1, VID27, PDR16,
PDR17, SSB2, YIF1, SSU72, KEX2,
RTC4, SIN4, YNL200C, RAP1, BSC4,
ATX1, LTO1, POP1, RAD50,
YNL226W, VPS75
RMxYPS 12 RM 14 149055 256858 0.088672194
RRG9, YNL228W, CSL4, MPA43,
LAP3, GIS2, CNM67, NAR1, ATG2,
YTP1, RIO2, YNL217W, YNL208W,
CWC25, YNL235C, NRD1, BNI4,
ADE12, MER1, ZWF1, PEX17,
RPA49, SQS1, FOL1, YNL211C,
YNL234W, RTT106, MGS1, POL2,
ATG4, SUI1, MRPL17, IES2, SIP3,
YNL247W, JJJ1, ELA1, URE2, ALG9,
DSL1, ORC5, SLA2, TEX1, VID27,
PDR16, SSB2, SSU72, KEX2, RTC4,
SIN4, RAP1, ATX1, LTO1, POP1,
RAD50, YNL226W, VPS75
RMxYPS 15 RM 14 481385 579135 0.004028506
RNH201, YDJ1, HHT2, BOP3, LAP2,
YNL028W, RPL9B, APJ1, RPL16B,
COG5, YNL035C, MSK1, MTQ1,
SIW14, POR1, COX5A, MLF3, FKH2,
OCA2, NCE103, YNL054W-B,
YNL054W-A, YIP3, GPI15,
YNL042W-B, SUN4, TOM7,
YNL040W, MSG5, IMP4, YNL033W,
YNL058C, GCD10, SLM2, COG6,
AQR1, HHF2, LAT1, YNL057W,
IDH1, BDP1, NOP2, YNL034W,
ARP5, YNL043C, YNL050C, ALG11,
110

KTR5, MKS1, YNL067W-A,
YNL046W, YNL067W-B, VAC7, SFB2
RMxYPS 14 RM 14 739359 751723 0.106963807
YNR062C, YNR061C, FRE4,
YNR065C, YNR064C, YNR063W
BYxYPS 22 BY 14 70598 90032 0.125418612
YNL295W, PUS4, CLA4, YNL296W,
MSB3, MON2, RFC3, RIM21, PCL1,
MID1
BYxYPS 22 BY 14 318592 488761 0.01357787
PHO23, MET4, FMP41, RPC19,
INN1, YNL122C, YNL114C, RIA1,
YNL162W-A, MFA2, YPT53, MLS1,
YAF9, APJ1, FAR11, EOS1, NIS1,
YNL109W, TPM1, CPT1, ALF1,
CUZ1, MIC27, THO2, AAH1, MSK1,
NMA111, YGP1, END3, NCS2,
YNL134C, YNL146W, APP1, MLF3,
LEU4, TEP1, RPC31, DBP2, SAL1,
BNI5, PGA2, SRV2, YCK2, AVT4,
RHO2, IBD2, DMA2, DGR1, NAF1,
PGA1, TCB2, FYV6, YNL089C,
YNL095C, ASI2, YNL144W-A, CBK1,
YNL150W, RRT16, CYB5, IMP4,
NRK1, YNL144C, YNL120C,
YNL097W-A, DCP2, IGO1, FPR1,
NAM9, LSM7, NST1, YNL097C-B,
YNL165W, RPL42A, ESBP6,
YNL115C, MEP2, YNL146C-A, RAS2,
PMS1, SKO1, KRE33, RPS7B, GIM3,
YNL143C, POL1, SWS2, TOM70,
YSF3, SNN1, YNL103W-A, EAF7,
NSG2, YNL140C, TOP2, YNL092W,
NOP15, MKS1, SPC98, INP52,
OCA1, MKT1, YNL108C, TOM22
BYxYPS 23 BY 14 442650 532222 0.003984867
RNH201, YDJ1, YPT53, RPL9B, APJ1,
RPL16B, EOS1, NIS1, TPM1, MSK1,
MTQ1, POR1, END3, COX5A, APP1,
MLF3, FKH2, SAL1, OCA2,
YNL054W-B, YNL054W-A, RHO2,
SUN4, TOM7, MSG5, TCB2,
YNL089C, YNL095C, IMP4,
YNL058C, GCD10, NST1, AQR1,
LAT1, YNL057W, PMS1, RPS7B,
SWS2, NOP2, ARP5, SNN1, TOP2,
YNL092W, MKS1, YNL067W-A,
YNL067W-B, VAC7, MKT1
BYxYPS 25 BY 14 466586 468490 0.002028799 SNN1, MKT1
BYxYPS 23 BY 14 660540 689793 0.245655034
ATP23, TIM23, RCF2, YNR021W,
MPP6, YNR029C, ARE2, HUB1,
PPG1, BUD17, SEC12, ALG12, CPR8,
SNF12, ABZ1, ACC1, MRPL50,
YNR025C, SSK2
BYxRM 1 BY 15 125569 184291 0.032620126
YOL099C, TPT1, ADH1, MSH2,
MPD2, TRM10, IZH4, ATG34, RFC4,
HAL9, WRS1, DUF1, REX4, YPQ1,
PKH2, PHM7, YOL085C, YOL083C-A,
IRA2, YOL079W, YOL085W-A,
COQ3, YOL097W-A, SPO21, ITR2,
MHF1, YOL098C, HMI1, ATG19,
AVO1
BYxRM 2 BY 15 172956 195072 0.013560214
ATP19, MDM20, YOL075C, THP1,
REX4, IRA2, YOL079W, BRX1, DSC2,
AVO1
BYxRM 4 BY 15 202012 212987 0.339183462
CRT10, MET22, PRS5, INP54, APM4,
RIB2, RTG1
BYxRM 3 BY 15 202087 240094 0.253092233
MAM3, ARG1, GAL11, GPD2,
CRT10, MET22, PSH1, PRS5, INP54,
111

DDR2, YOL057W, APM4, AIM39,
RIB2, GPM3, GSH2, SPE2, RTG1,
YOL050C, THI20
RMxYPS 15 RM 15 232036 274962 0.063778622
SMC5, YOL046C, GAL11, YOL029C,
NGL1, MSE1, RPP2A, LAG2,
YOL037C, RRT8, YOL038C-A, LDS2,
OPI10, PEX15, YAP7, NTG2, MIM1,
YOL036W, RPS15, GSH2, SIL1,
PRE6, SPE2, MDM38, YOL035C,
PSK2, YOL050C, GAS5, NOP12
RMxYPS 16 YPS 15 496695 521081 0.006706095
NUP1, YOR093C, YOR102W, RPS7A,
VAM3, RKI1, RAS1, ECM3,
YOR105W, CRC1, ARF3, OST2,
KTR1, PIN2, YOR097C
RMxYPS 18 YPS 15 943688 969591 0.065974632
UBC11, MRS2, RPA190, ALA1,
VMA4, RPA43, YOR342C,
YOR338W, KRE5, YOR333C,
YOR343C, YOR335W-A, YOR331C,
TEA1
BYxYPS 22 BY 15 170 10912 0.125418612
YOL163W, YOL166C, YOL162W,
AAD15, YOL166W-A, BDS1,
YOL164W-A
BYxYPS 22 BY 15 88581 216399 0.043052729
YOL099C, MAM3, MDY2, TPT1,
YOL118C, ADH1, MSH2, CRT10,
MPD2, ATP19, MET22, MDM20,
TRM10, IZH4, MSB4, ATG34, INO4,
YOL106W, RFC4, YOL075C, PRS5,
HAL9, THP1, HST1, HRP1, WSC3,
MCH4, WRS1, DUF1, ZEO1, INP54,
SDH5, REX4, RRI2, MSN1, APM4,
NUF2, SMF1, YPQ1, PKH2, SKM1,
PHM7, RIB2, YOL085C, YOL083C-A,
IRA2, YOL114C, YOL079W,
YOL103W-B, YOL103W-A,
YOL085W-A, NDJ1, COQ3,
YOL097W-A, RPL18A, NBA1,
SPO21, ITR2, BRX1, MHF1, RTG1,
YOL098C, SHR5, RPS19A,
YOL107W, HMI1, PAP2, DSC2,
ATG19, AVO1
BYxYPS 26 YPS 15 215865 244803 0.155461499
MAM3, YOL046C, ARG1, GAL11,
GPD2, PSH1, DDR2, YOL057W,
RRT8, LDS2, AIM39, GPM3, GSH2,
SPE2, PSK2, YOL050C, THI20
BYxYPS 26 YPS 15 380731 397645 0.155461499
EXO1, HMS1, CIN5, YOR032W-A,
CRS5, BUB3, YOR034C-A,
YOR029W, AKR2, DFG16, STI1
BYxYPS 26 YPS 15 764185 768822 0.113087961 HER1, MCP1, WTM2
RMxYPS 18 YPS 16 465889 562779 0.065974632
ISM1, YPL039W, RMI1, PMA2,
RET3, PDH1, NCR1, SGF11,
YPL044C, SRL4, SUV3, VTC3, ULP1,
SNF8, NOP4, EGD1, SMA1, RQC2,
HST2, CIT3, ERG10, MET12,
YPL038W-A, TAF3, AEP3, SSN3,
ELC1, YPR002C-A, TRM44, CHL1,
SWI1, HAT1, YPL034W, RRP12,
LSP1, SKS1, YPL025C, PHO85,
RAD1, MET31, YPL041C, CTF19,
YPL035C, ECM23, YPR003C, TFC8,
IRC15, SVL3, VPS16, YPL014W,
ULA1, MRPS16
RMxYPS 19 YPS 16 501341 611686 0.002425991
YPR015C, RMI1, RET3, PDH1, NCR1,
YPR011C, YPR010C-A, VTC3, ULP1,
ICL2, SNF8, ATP20, TIF6, RQC2,
HST2, CIT3, EAF3, YPR012W, YME1,
112

MET12, CMR3, HAL1, TAF3, AEP3,
DSS4, YPR002C-A, CHL1, SWI1,
SUT2, HAT1, RRP12, AIM45, LSP1,
YPR022C, RLF2, SKS1, YPL025C,
RAD1, HAA1, YPR014C, CTF19,
ECM23, YPR003C, TFC8, IRC15,
YPR016W-A, AGC1, MCM4,
RPA135, YPL014W, ULA1, REC8,
MRPS16
RMxYPS 20 YPS 16 504837 747971 0.000456378
TIF5, YPR015C, YPR084W, NHP6A,
CCL1, TAH18, YPR109W, ARP7,
RDS3, RET3, SEC8, FHL1, PDH1,
NCR1, YPR063C, LTP1, SRO7,
YPR011C, YPR010C-A, VTC3, ULP1,
ICL2, SNF8, MED1, HOS1, MDM36,
TFB4, ATP20, VMA13, TIF6, UBA3,
IRC16, RQC2, HST2, CIT3, SNT309,
EAF3, HTS1, YMC1, FCY1, TIP41,
COG4, YPR012W, YME1, VPS69,
YPR053C, YPR074W-A, MET12,
SYT1, YPR089W, CMR3, OPY2,
HAL1, TAF3, AEP3, DIB1, YPR098C,
DSS4, YPR027C, ERV2, TKL1,
MRPL51, MSF1, YPR002C-A,
YPR039W, YPR096C, RPL11A, CHL1,
SWI1, YPR078C, NVJ2, SUT2, HAT1,
ROX1, CSR2, YPR071W, RPL43A,
YPR050C, SUA7, THP3, DBF20,
SRP54, APL4, YPR092W, RRP12,
AIM45, LSP1, YPR076W, GRS2,
YPR059C, YPR022C, RLF2, JID1,
RPN7, BRR1, RAD1, MRL1, NOT5,
HAA1, YPR064W, NTO1, MCM16,
SMK1, ASA1, ATH1, ATG11,
YPR099C, YPR014C, CTF19, ECM23,
YPR097W, PRE2, YPR003C, TFC8,
IRC15, YPR016W-A, ISR1, TEF1,
SPE3, MAK3, YPR108W-A, GLN1,
AGC1, ARO7, MCM4, RPC40, PUF2,
OPI11, RPA135, SPO24, YPL014W,
YOP1, ASR1, YTH1, ULA1, YPR077C,
ISA2, REC8, MRPS16
RMxYPS 17 YPS 16 526392 677025 0.003060262
TIF5, YPR015C, NHP6A, CCL1,
TAH18, ARP7, RET3, SEC8, PDH1,
NCR1, SRO7, YPR011C, YPR010C-A,
ICL2, SNF8, TFB4, ATP20, VMA13,
TIF6, IRC16, RQC2, HST2, CIT3,
EAF3, HTS1, YMC1, TIP41,
YPR012W, YME1, YPR053C, CMR3,
HAL1, TAF3, AEP3, DSS4, YPR027C,
ERV2, MSF1, YPR002C-A, YPR039W,
CHL1, SUT2, HAT1, CSR2, RPL43A,
YPR050C, THP3, APL4, RRP12,
AIM45, LSP1, YPR059C, YPR022C,
RLF2, JID1, BRR1, HAA1, NTO1,
MCM16, SMK1, ATH1, ATG11,
YPR014C, YPR003C, TFC8,
YPR016W-A, MAK3, GLN1, AGC1,
ARO7, MCM4, PUF2, OPI11,
RPA135, SPO24, YPL014W, YOP1,
ULA1, REC8, MRPS16
RMxYPS 19 YPS 16 787032 792213 0.028072187
ANT1, CTR1, YLH47, YPR126C,
YPR127W
RMxYPS 20 YPS 16 832297 837009 0.081297613 PIN3, YPR153W, URN1, NCA2
RMxYPS 19 YPS 16 900008 947540 0.002425991 RPC82, PZF1, YPR203W, YPR202W,
113

HPA2, DPM1, OPT2, SKI3, RPO26,
YPR197C, ARR2, SGE1, ARR3, AQY1,
MLC2, YPR196W, YPR204C-A,
QCR2, ARR1, ATG13, YPR204W,
YPR195C, GDB1, SMX3
RMxYPS 20 YPS 16 901087 923983 0.000456378
RPC82, PZF1, HPA2, DPM1, SKI3,
RPO26, AQY1, MLC2, QCR2, ATG13,
GDB1
RMxYPS 17 YPS 16 919896 924612 0.003060262 HPA2, OPT2, AQY1, QCR2
RMxYPS 18 YPS 16 922561 927175 0.000385758 HPA2, OPT2, AQY1
BYxYPS 28 YPS 16 898150 942359 0.002065011
RPC82, PZF1, HPA2, DPM1, OPT2,
SKI3, RPO26, YPR197C, ARR2, SGE1,
ARR3, AQY1, SEC23, MLC2,
YPR196W, QCR2, ARR1, ATG13,
YPR195C, GDB1, SMX3
BYxYPS 30 YPS 16 914217 918565 0.001861178 RPC82, SKI3
BYxYPS 29 YPS 16 914775 946418 0.00922265
RPC82, YPR203W, YPR202W, HPA2,
OPT2, SKI3, YPR197C, ARR2, SGE1,
ARR3, AQY1, YPR196W, QCR2,
ARR1, YPR204W, YPR195C
BYxYPS 26 YPS 16 922477 946418 0.055277584
YPR203W, YPR202W, HPA2, OPT2,
YPR197C, ARR2, SGE1, ARR3, AQY1,
YPR196W, ARR1, YPR204W,
YPR195C
This table shows all loci genome-wide that reached at least nominal significance within
all three crosses using a one-tailed exact binomial test. The bounds of each interval
were determined by the nominal significance cut-off for each family. All open reading
frames, confirmed and dubious, are provided.

114

Table S3.3. Phenotype data for reciprocal hemizygosity and marked allele
replacement assays.

Technique Date Strain * MIC(mM) Chr Cross
M.A.R. 1/14/15 AEP3(Y-R)15a 5 16 RxY
M.A.R. 1/14/15 AEP3(Y-R)15b 5 16 RxY
M.A.R. 1/14/15 AEP3(Y-R)19a 4.5 16 RxY
M.A.R. 1/14/15 AEP3(Y-R)19b 4.5 16 RxY
M.A.R. 1/14/15 AEP3(Y-R)19c 4.5 16 RxY
M.A.R. 1/14/15 20dD4aa 5 NA RxY
M.A.R. 1/14/15 20dD4ab 5.5 NA RxY
M.A.R. 1/14/15 20dD4ac 5.5 NA RxY
M.A.R. 1/14/15 20dD4ba 5.5 NA RxY
M.A.R. 1/14/15 20dD4bb 6 NA RxY
M.A.R. 2/4/2015 SDP1(Y-B)5a 3.5 9 BxY
M.A.R. 2/4/2015 SDP1(Y-B)5b 3.5 9 BxY
M.A.R. 2/4/2015 SDP1(Y-B)5c 3.5 9 BxY
M.A.R. 2/4/2015 SDP1(Y-B)12a 5.5 9 BxY
M.A.R. 2/4/2015 SDP1(Y-B)12b 5.5 9 BxY
M.A.R. 2/4/2015 SDP1(Y-B)12c 5 9 BxY
M.A.R. 2/4/2015 29gC1aa 6 NA BxY
M.A.R. 2/4/2015 29gC1ab 6 NA BxY
M.A.R. 2/4/2015 29gC1ac 6 NA BxY
M.A.R. 2/4/2015 29gC1ba 5.5 NA BxY
M.A.R. 2/4/2015 29gC1bb 5.5 NA BxY
M.A.R. 2/4/2015 29gC1bc 6 NA BxY
M.A.R. 2/4/2015 29gC1ca 6.5 NA BxY
M.A.R. 2/4/2015 29gC1cb 6.5 NA BxY
M.A.R. 2/4/2015 29gC1cc 7 NA BxY
M.A.R. 1/19/15 CTT1(Y-R)14a 4.5 7 RxY
M.A.R. 1/19/15 CTT1(Y-R)14b 4.5 7 RxY
M.A.R. 1/19/15 CTT1(Y-R)14c 5 7 RxY
M.A.R. 1/19/15 CTT1(Y-R)16a 4.5 7 RxY
M.A.R. 1/19/15 CTT1(Y-R)16b 4.5 7 RxY
M.A.R. 1/19/15 CTT1(Y-R)16c 4.5 7 RxY
M.A.R. 1/19/15 CTT1(Y-R)6a 4.5 7 RxY
M.A.R. 1/19/15 CTT1(Y-R)6b 4.5 7 RxY
M.A.R. 1/19/15 CTT1(Y-R)6c 4.5 7 RxY
M.A.R. 1/19/15 16eD7aa 5.5 NA RxY
M.A.R. 1/19/15 16eD7ab 5 NA RxY
M.A.R. 1/19/15 16eD7ac 5.5 NA RxY
M.A.R. 1/19/15 16eD7ba 4.5 NA RxY
115

M.A.R. 1/19/15 16eD7bb 5 NA RxY
M.A.R. 1/19/15 16eD7bc 5 NA RxY
M.A.R. 1/19/15 MMS21(Y-B)11a 5 5 BxY
M.A.R. 1/19/15 MMS21(Y-B)11b 5.5 5 BxY
M.A.R. 1/19/15 MMS21(Y-B)11c 5.5 5 BxY
M.A.R. 1/19/15 MMS21(Y-B)13a 5 5 BxY
M.A.R. 1/19/15 MMS21(Y-B)13b 5.5 5 BxY
M.A.R. 1/19/15 MMS21(Y-B)13c 5.5 5 BxY
M.A.R. 1/19/15 MMS21(Y-B)6a 4 5 BxY
M.A.R. 1/19/15 MMS21(Y-B)6b 4 5 BxY
M.A.R. 1/19/15 MMS21(Y-B)6c 3.5 5 BxY
M.A.R. 1/19/15 29gC1aa 5 NA BxY
M.A.R. 1/19/15 29gC1ab 6.5 NA BxY
M.A.R. 1/19/15 29gC1ac 5.5 NA BxY
M.A.R. 1/19/15 29gC1ba 5 NA BxY
M.A.R. 1/19/15 29gC1bb 6 NA BxY
M.A.R. 1/19/15 29gC1bc 6 NA BxY
M.A.R. 1/26/15 MRP13(Y-B)15a 5.5 7 BxY
M.A.R. 1/26/15 MRP13(Y-B)15b 5.5 7 BxY
M.A.R. 1/26/15 MRP13(Y-B)15c 5.5 7 BxY
M.A.R. 1/26/15 MRP13(Y-B)3a 5 7 BxY
M.A.R. 1/26/15 MRP13(Y-B)3b 5.5 7 BxY
M.A.R. 1/26/15 MRP13(Y-B)3c 5.5 7 BxY
M.A.R. 1/26/15 29gC1aa 5.5 NA BxY
M.A.R. 1/26/15 29gC1ab 6 NA BxY
M.A.R. 1/26/15 29gC1ac 5.5 NA BxY
M.A.R. 1/26/15 29gC1ba 6 NA BxY
M.A.R. 1/26/15 29gC1bb 6 NA BxY
M.A.R. 1/26/15 29gC1bc 6 NA BxY
M.A.R. 1/26/15 29gC1ca 5.5 NA BxY
M.A.R. 1/26/15 29gC1cb 5.5 NA BxY
M.A.R. 1/26/15 29gC1cc 5.5 NA BxY
M.A.R. 3/8/15 AQY1(Y-B)10a 6 16 BxY
M.A.R. 3/8/15 AQY1(Y-B)10b 6.5 16 BxY
M.A.R. 3/8/15 AQY1(Y-B)10c 6.5 16 BxY
M.A.R. 3/8/15 AQY1(Y-B)12a 6 16 BxY
M.A.R. 3/8/15 AQY1(Y-B)12b 6 16 BxY
M.A.R. 3/8/15 AQY1(Y-B)12c 6.5 16 BxY
M.A.R. 3/8/15 AQY1(Y-B)15a 6.5 16 BxY
M.A.R. 3/8/15 AQY1(Y-B)15b 6.5 16 BxY
M.A.R. 3/8/15 AQY1(Y-B)15c 6 16 BxY
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M.A.R. 3/8/15 AQY1(Y-B)17a 6 16 BxY
M.A.R. 3/8/15 AQY1(Y-B)17b 6.5 16 BxY
M.A.R. 3/8/15 AQY1(Y-B)17c 6 16 BxY
M.A.R. 3/8/15 AQY1(Y-B)20a 6.5 16 BxY
M.A.R. 3/8/15 AQY1(Y-B)20b 6 16 BxY
M.A.R. 3/8/15 AQY1(Y-B)20c 6.5 16 BxY
M.A.R. 3/8/15 29gC1_a1 6 NA BxY
M.A.R. 3/8/15 29gC1_a2 6.5 NA BxY
M.A.R. 3/8/15 29gC1_a3 6.5 NA BxY
M.A.R. 3/8/15 29gC1_b1 6.5 NA BxY
M.A.R. 3/8/15 29gC1_b2 6.5 NA BxY
M.A.R. 3/8/15 29gC1_b3 6.5 NA BxY
M.A.R. 3/8/15 29gC1_c1 6.5 NA BxY
M.A.R. 3/8/15 29gC1_c2 6.5 NA BxY
M.A.R. 3/8/15 29gC1_c3 7 NA BxY
M.A.R. 3/8/15 AQY1(Y-R)12a 5.5 16 RxY
M.A.R. 3/8/15 AQY1(Y-R)12b 5.5 16 RxY
M.A.R. 3/8/15 AQY1(Y-R)12c 5.5 16 RxY
M.A.R. 3/8/15 AQY1(Y-R)14a 6 16 RxY
M.A.R. 3/8/15 AQY1(Y-R)14b 5 16 RxY
M.A.R. 3/8/15 AQY1(Y-R)14c 6 16 RxY
M.A.R. 3/8/15 AQY1(Y-R)15a 5.5 16 RxY
M.A.R. 3/8/15 AQY1(Y-R)15b 5 16 RxY
M.A.R. 3/8/15 AQY1(Y-R)15c 5.5 16 RxY
M.A.R. 3/8/15 AQY1(Y-R)16a 6 16 RxY
M.A.R. 3/8/15 AQY1(Y-R)16b 5.5 16 RxY
M.A.R. 3/8/15 AQY1(Y-R)16c 5.5 16 RxY
M.A.R. 3/8/15 AQY1(Y-R)18a 5.5 16 RxY
M.A.R. 3/8/15 AQY1(Y-R)18b 5.5 16 RxY
M.A.R. 3/8/15 AQY1(Y-R)18c 5.5 16 RxY
M.A.R. 3/8/15 AQY1(Y-R)19a 5.5 16 RxY
M.A.R. 3/8/15 AQY1(Y-R)19b 6 16 RxY
M.A.R. 3/8/15 AQY1(Y-R)19c 5.5 16 RxY
M.A.R. 3/8/15 20dD4a_1 5.5 NA RxY
M.A.R. 3/8/15 20dD4a_2 6 NA RxY
M.A.R. 3/8/15 20dD4a_3 6 NA RxY
M.A.R. 3/8/15 20dD4b_1 6 NA RxY
M.A.R. 3/8/15 20dD4b_2 5.5 NA RxY
M.A.R. 3/8/15 20dD4b_3 6 NA RxY
M.A.R. 3/8/15 20dD4c_1 6 NA RxY
M.A.R. 3/8/15 20dD4c_2 6.5 NA RxY
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M.A.R. 3/8/15 20dD4c_3 6 NA RxY
M.A.R. 3/8/15 MKT1(B-Y)15a 7 14 BxY
M.A.R. 3/8/15 MKT1(B-Y)15b 6.5 14 BxY
M.A.R. 3/8/15 MKT1(B-Y)17a 6.5 14 BxY
M.A.R. 3/8/15 MKT1(B-Y)17b 6.5 14 BxY
M.A.R. 3/8/15 22F2B2s_a1 7.5 NA BxY
M.A.R. 3/8/15 22F2B2s_a2 7.5 NA BxY
M.A.R. 3/8/15 22F2B2s_a3 7 NA BxY
M.A.R. 3/8/15 22F2B2s_b1 7.5 NA BxY
M.A.R. 3/8/15 22F2B2s_b2 7.5 NA BxY
M.A.R. 3/8/15 22F2B2s_b3 7.5 NA BxY
RH 7/2/14 MKT1(B)a 6 14 BxY
RH 7/2/14 MKT1(B)b 6 14 BxY
RH 7/2/14 MKT1(B)c 6 14 BxY
RH 7/2/14 MKT1(Y)a 7 14 BxY
RH 7/2/14 MKT1(Y)b 7 14 BxY
RH 7/2/14 MKT1(Y)c 7 14 BxY
RH 7/26/14 AQY1(B)1 6.3 16 BxY
RH 7/26/14 AQY1(B)2 7 16 BxY
RH 7/26/14 AQY1(B)3 7 16 BxY
RH 7/26/14 AQY1(Y)1 5.9 16 BxY
RH 7/26/14 AQY1(Y)2 6 16 BxY
RH 7/26/14 AQY1(Y)3 5.7 16 BxY
RH 11/19/14 AQY1(R)a 5.5 16 RxY
RH 11/19/14 AQY1(R)b 6 16 RxY
RH 11/19/14 AQY1(R)c 6.5 16 RxY
RH 11/19/14 AQY1(Y)a 5 16 RxY
RH 11/19/14 AQY1(Y)b 5.5 16 RxY
RH 11/19/14 AQY1(Y)c 5.5 16 RxY
RH 11/19/14 MRP13(B)a 5 7 BxY
RH 11/19/14 MRP13(B)b 4.5 7 BxY
RH 11/19/14 MRP13(B)c 5 7 BxY
RH 11/19/14 MRP13(Y)a 4.5 7 BxY
RH 11/19/14 MRP13(Y)b 4.5 7 BxY
RH 11/19/14 MRP13(Y)c 5 7 BxY
RH 6/14/14 SDP1(B)aa 6 9 BxY
RH 6/14/14 SDP1(B)ab 6 9 BxY
RH 6/14/14 SDP1(B)ac 6 9 BxY
RH 6/14/14 SDP1(B)ba 6 9 BxY
RH 6/14/14 SDP1(B)bb 6 9 BxY
RH 6/14/14 SDP1(B)bc 6 9 BxY
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RH 41804 SDP1(B)ca 6 9 BxY
RH 41804 SDP1(B)cb 6 9 BxY
RH 41804 SDP1(B)cc 6 9 BxY
RH 41804 SDP1(Y)aa 6 9 BxY
RH 41804 SDP1(Y)ab 6 9 BxY
RH 41804 SDP1(Y)ac 6 9 BxY
RH 41804 SDP1(Y)ba 6 9 BxY
RH 41804 SDP1(Y)bb 6 9 BxY
RH 41804 SDP1(Y)bc 6 9 BxY
RH 41804 SDP1(Y)ca 6 9 BxY
RH 41804 SDP1(Y)cb 6 9 BxY
RH 41804 SDP1(Y)cc 6 9 BxY
*20dD4 is the F2B3 RMxYPSxYPS strain in which M.A.R. was performed on AEP3 and AQY1, 29gC1 is
the F2B3 BYxYPSxYPS strain in which M.A.R. was performed on SDP1, MMS21, MRP13 and AQY1,
16eD7 is the F2B3 RMxYPSxRM strain in which M.A.R. was performed on CTT1, while 22F2B2s is the
F2B2 BYxYPSxBY strain in which M.A.R. was performed on MKT1; these 4 strains served as controls for
the allele swaps that were performed in them.

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Table S3.4. Phenotype and normalized OD600 data for a representative panel of
48 F2B3 segregants screened for high resistance to hydrogen peroxide
exposure.
Well * MIC (mM) OD600 **
A1 8.5 0.253
A2 8.5 0.218
A3 8 0.233
A4 8.5 0.222
A5 8.5 0.237
A6 6 0.221
B1 8 0.235
B2 7 0.211
B3 6.5 0.234
B4 8.5 0.232
B5 7 0.21
B6 6 0.244
C1 7.5 0.228
C2 6.5 0.204
C3 8.5 0.229
C4 7 0.231
C5 8.5 0.212
C6 7 0.242
D1 6.5 0.244
D2 8.5 0.218
D3 6.5 0.252
D4 7 0.209
D5 5.5 0.213
D6 8 0.249
E1 6 0.253
E2 7 0.216
E3 7 0.26
E4 7 0.283
E5 7 0.255
E6 6 0.238
F1 6 0.204
F2 7.5 0.233
F3 6 0.218
F4 8.5 0.214
F5 7 0.219
F6 6.5 0.277
G1 7 0.305
G2 7.5 0.238
120

G3 6 0.219
G4 6 0.22
G5 7 0.196
G6 6 0.195
H1 6 0.262
H2 6 0.23
H3 7 0.226
H4 7.5 0.198
H5 7 0.198
H6 *** 8 0.274
* The F2B3 segregants screened in this panel are from the (RxY Seg3)xY family.
** O.D. 600 data was generated using a BioTek ELx808 plate reader. Wells with only
media were used as blanks.
*** This position contains the F2 from which this family was derived.

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Note S3.1. Statistical power of the genetic mapping strategy in this paper. We
simulated the genetic mapping strategy described in this paper under a
commonly used genetic architecture model: 10 additive loci with equal effects
that collectively explain half of the phenotypic variance. The other half of the
phenotypic variance was attributed to random environmental effects. In these
simulations, we generated 900 F2 progeny from a cross. We then picked the most
resistant F2 and backcrossed it, examining 100 offspring from the first backcross.
The second backcross was repeated in the same manner as the first backcrosss.
Finally, in the third backcross, we examined 700 total individual and tested
whether we detected segregating loci using a binomial test applied to allele
frequencies in the 15 most resistant F2B3s. 5,000 individual F2B3 families and
1,000 sets of five F2B3 families were simulated. We describe these results in two
ways: the number of the 10 loci that were detected in single F2B3 families (A) and
the total number of the 10 loci that were detected at least once in five replicate
F2B3 families descended from the same F2 cross (B). As shown in A, 4 or 5 loci
are typically identified in individual F2B3 families, though a range between 1 and 9
was seen. In contrast, when five F2B3 families were generated from the same F2
cross, more than half of the time, all of the detected loci were identified at least
once. These results imply that although individual F2B3 families provide lower
statistical power than other mapping strategies (see (Ehrenreich, 2010 #97) and
(Bloom et al., 2013), statistical power can be recovered by conducting the
procedure multiple times in parallel, as we have done in the current paper. When
multiple F2B3 families are generated, the statistical power of our method can
approach 100% under the model we have employed.

Note S3.2. We observed karyotype instability in F2B3 families derived from one of
the BYxYPS F2 segregants. Each segregant in this family possessed different
combinations of aneuploidies, which precluded efforts to perform genetic
122

mapping. We also found that aneuploidy had occurred at some stage of advanced
backcrossing for F2B3 segregants derived from one BYxRM and two other
BYxYPS F2s. This was identified because both alleles at certain loci were
detectable in the backcross families, as highlighted by the asterisks in Figure S2.
However, sequencing data showed that these F2B3s were entirely haploid; thus,
these individuals were included in our analyses.

123

Chapter 4: A yeast chromosome duplication confers a conditional
growth benefit by buffering the expression of oxidative stress-
responsive genes

This work is presented as a manuscript in preparation for publication in 2016

The author list is as follows:
Robert A. Linder, John P. Greco, Fabian Seidl, and Ian M. Ehrenreich

4.1 Overview

Increases in chromosome copy number are common in cancers, experimental
evolutions, and natural populations. In some cases, these aneuploidies confer
condition-specific growth advantages. The mechanisms underlying these conditionally
beneficial effects of aneuploidization are not fully understood, but should be possible
to determine by identifying the specific genes that cause particular aneuploidies to be
advantageous and determining how copy number changes at these genes affect
phenotype. Here, we show that Chromosome IV duplication is the main genetic
change that causes exceptional tolerance to hydrogen peroxide in certain
Saccharomyces cerevisiae backgrounds. Comprehensive genetic mapping
demonstrates that most of the aneuploidy’s effect arises due to a single duplicated
cytoplasmic thioredoxin peroxidase (TSA2), which is induced in response to oxidative
stress. Transcriptional analysis in a haploid disomic for Chromosome IV, as well as in
its monosomic progenitor, reveals that, although the aneuploid shows initially higher
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expression of TSA2 in response to hydrogen peroxide, it fails to overexpress not only
TSA2, but also a paralog on a different chromosome (TSA1) during prolonged
hydrogen peroxide exposure. Additionally, duplication of TSA2 on a plasmid does not
reproduce the effect of the Chromosome IV disomy on hydrogen peroxide tolerance.
These results suggest that aneuploidies can facilitate conditionally beneficial
transcriptional buffering that may not be achievable through simple gene duplications.

4.2 Introduction
Aneuploidy—i.e., possessing an abnormal number of chromosomes—often has
negative consequences, such as accelerated aging (Andriani et al., 2016; Sunshine et
al., 2016), cancer (Davoli et al., 2013; Durrbaum & Storchova, 2015, 2016; Laubert et
al., 2015; Mohr et al., 2015; Nicholson & Cimini, 2015; Pinto et al., 2015; Potapova et
al., 2013; Santaguida & Amon, 2015a; Sheltzer, 2013), diminished growth (Ottesen et
al., 2010), or impaired development (Akasaka et al., 2013; Bose et al., 2015; Gannon et
al., 2011; Schatten & Sun, 2015; Siegel & Amon, 2012). However, multiple aneuploidies
have been reported to confer fitness benefits to cells (G. Chen et al., 2012; Pavelka et
al., 2010; Sunshine et al., 2015; Yona et al., 2012). Many of these cases involve
increases in chromosome copy number that provide growth advantages when cells are
challenged with particular loss-of-function mutations or environmental stresses (G.
Chen et al., 2012; Kaya et al., 2015; G. Liu et al., 2015; Yona et al., 2012).

125

Budding yeast has emerged as a powerful model system for studying the mechanisms
by which aneuploidies affect phenotype (Sunshine et al., 2015) (G. Chen et al., 2012;
Kaya et al., 2015; G. Liu et al., 2015; Pavelka et al., 2010; Yona et al., 2012). Specific
gene(s) have been identified that contribute to the beneficial effects of certain
chromosomal duplications in particular conditions (Kaya et al., 2015; Pavelka et al.,
2010) (G. Liu et al., 2015; Tan et al., 2013). However, a systematic strategy for
resolving the conditionally beneficial effects of aneuploidies to individual genes has yet
to be reported. Such an approach is essential to fully understanding the mechanisms
that render certain chromosome duplications conditionally beneficial.

In this paper, we perform a screen for spontaneous mutations that result in exceptional
tolerance to hydrogen peroxide among Saccharomyces cerevisiae haploids.
Chromosome IV disomy is the main genetic change, and the only aneuploidy, recovered
from our screen. To systematically map the gene(s) that confer the conditional benefit of
Chromosome IV duplication, we perform genetic mapping using PCR-mediated
chromosomal deletion (PCD) (Kaboli et al., 2016; Sugiyama, Ikushima, Nakazawa,
Kaneko, & Harashima, 2005; Sugiyama et al., 2008). By conducting PCD-based genetic
mapping, we show that the effect of the Chromosome IV disomy is mostly due to a
single duplicated gene, TSA2, which encodes a stress-inducible, cytoplasmic
thioredoxin peroxidase (Munhoz & Netto, 2004; Park, Cha, Jeong, & Kim, 2000; Wong,
Zhou, Ng, Kung Hf, & Jin, 2002)[classic Gasch MBoC paper](Nielsen, Kidmose, &
Jenner, 2016; Ogusucu, Rettori, Munhoz, Netto, & Augusto, 2007; Park et al., 2000).

126

Identification of TSA2 as the primary causative factor underlying the Chromosome IV
duplication’s effect suggests that the disomy might enable strains to express TSA2
more highly than usual in response to hydrogen peroxide and that such
overexpression might be advantageous. Although this expectation proves correct
during initial hydrogen peroxide exposure, we also show that duplication of
Chromosome IV results in a failure to overexpress TSA2 under prolonged exposure to
hydrogen peroxide. This effect extends to TSA1, a paralog of TSA2, that is located on
a different chromosome (Munhoz & Netto, 2004) (Park et al., 2000) (Wong et al.,
2002), implying that Chromosome IV duplication provides transcriptional buffering of
genes that are induced by hydrogen peroxide exposure. Extrachromosomal
duplication of TSA2 does not reproduce the aneuploidy’s effect on hydrogen peroxide
tolerance, which suggests that duplication of TSA2 at its endogenous locus may be
necessary to see its full phenotypic effect. Our study not only provides an example of
how the effects of aneuploidies can be systematically dissected to their underlying
causative gene(s), but also sheds light on mechanisms that cause certain
aneuploidies to be conditionally beneficial

4.3 Screen for spontaneous mutants with exceptional tolerance to hydrogen
peroxide
To identify de novo mutations that increase tolerance to hydrogen peroxide, we
screened three haploid yeast strains for spontaneous resistance mutations using
phenotypic selection on agar plates. These strains were F2 segregants that were
derived from crosses of the lab reference strain BY4742 (BY), the vineyard isolate
RM11-1a (RM), and the oak isolate YPS163 (YPS) (Ehrenreich et al., 2012; Ehrenreich
127

et al., 2010; Linder et al., 2016). In our past work (Linder et al., 2016), we found that the
BYxRM, BYxYPS, and RMxYPS crosses exhibit similar minimal (2 mM) and maximal
(6.5 mM) tolerances to hydrogen peroxide, suggesting that the levels of tolerance
achievable through natural genetic variation in S. cerevisiae may be bounded. We
sought to exceed these bounds by screening maximally tolerant F2s from each cross for
de novo mutations that confer exceptionally high tolerance to hydrogen peroxide.

Each F2 was grown in 24 independent cultures for two days (approximately 20
generations), after which cultures were transferred to agar plates supplemented with a
range of hydrogen peroxide doses (Methods). All mutants that grew on doses that were
at least 1 mM (14%) higher than the minimum inhibitory concentration (MIC) of their
corresponding F2 progenitor were sampled for verification of increased tolerance
(Methods). Confirmed mutants were included in subsequent analyses (Methods). In
total, 37 hydrogen peroxide-tolerant mutants from 28 independent cultures were
obtained, including 14 from the BYxRM cross (from 12 independent cultures), 9 from the
RMxYPS cross (from 5 independent cultures), and 14 from the BYxYPS cross (from 11
independent cultures) (Fig. S4.1A-C). Mutants derived from the, BYxRM, RMxYPS, and
BYxYPS crosses were, on average, 2.3 mM (32%), 2.1 mM (25%), and 0.6 mM (7%)
more tolerant than their corresponding F2 progenitors, respectively (Figure S4.1A-C). Of
note, mutants derived from the BYxYPS cross were significantly less tolerant than
mutants derived from the other two crosses, suggesting that this cross background may
be constrained in the tolerance it can achieve via mutational changes.

128

4.4 Sequencing of mutants reveals a conditionally beneficial Chromosome IV
aneuploidy
Analysis of genome-wide sequencing coverage revealed that, while all three F2
progenitors were haploid, 43% of the mutants shared the same aneuploidy: a
duplication of Chromosome IV (Fig. 4.1A and B). No other aneuploidies were seen in
our experiment (Fig. 4.1A), indicating that Chromosome IV duplication specifically is
advantageous under the conditions of our screen. We note, different proportions of
Chromosome IV duplication were seen among mutants derived from the three crosses.
79%, 45%, and none of the BYxRM-, RMxYPS-, and BYxYPS-derived mutants were
disomic for Chromosome IV, respectively (Fig. 4.1B).

We also observed a single translocation mutation. One of the BYxRM-derived mutants
possessed a translocation of the distal portion of Chromosome IV, which rendered this
strain partially disomic (58%) for this chromosome (Fig. 4.1A). The translocated portion
of Chromosome IV is approximately 890 kb in length, spanning from position 644,030
bp to the end of the chromosome. Including this translocation mutant, 86% of the
BYxRM-derived mutants were disomic for the right arm of Chromosome IV. These
results suggest that disomy for the right arm of Chromosome IV is one of the main
genetic changes that confers increased oxidative stress tolerance in this genetic
background.

In addition to the changes in genome structure, we identified 39 unique point mutations
and no small indels (Table S4.1). Across the data, we found 22 total nonsyonymous
129

mutations, with 10, 7, and 5 detected in the BYxRM, BYxYPS, and RMxYPS mutants,
respectively. In addition, 8 synonymous mutations and 5 mutations in upstream
intergenic regions close to the translation start site of 5 different genes were detected.
Several of these mutations occurred in or near genes that are known to influence
oxidative stress tolerance, such as a SUMO E3 ligase involved in DNA repair (MMS21),
the translation activator of cytochrome oxidase 1 (PET309), a subunit of cytochrome c
oxidase (COX1), a negative regulator of Ras-cAMP-PKA signalling (GPB1), and a zinc
cluster protein (YLR278C). A GO term enrichment analysis was carried out using the full
list of genes in (Table S4.1; Methods). Only a single functional category was found to be
significantly enriched: protein serine/threonine kinase inhibitor activity, which reached a
p-value of 0.0492 with an FDR of 0.18. Genes in this category included GPB2 and
MMS21. This result, as well as the fact that no genes were mutated in multiple crosses
(Table S4.1), is consistent with our previous work, which suggested that many genes
and cellular processes contribute to oxidative stress tolerance (Linder et al., 2016).

4.5 Genetic dissection of a conditionally beneficial chromosome-scale
duplication
Phenotyping of the Chromosome IV aneuploids revealed that they grow worse than
their progenitors in the absence of hydrogen peroxide and also that the aneuploidy only
increases tolerance to hydrogen peroxide when cells are grown on agar plates (Fig.
S4.2A and B and Fig. S4.3A-C ). This is consistent with the notion that changes in
chromosome number are usually deleterious, but can be beneficial under some
130

circumstances. We sought to identify the specific gene(s) that cause the Chromosome
IV disomy to be conditionally beneficial.

At roughly 1.5 Mb in length, Chromosome IV is the largest chromosome and
encompasses nearly 13% of the entire nuclear genome. This poses a challenge for
determining the specific portion(s) of the chromosome that confer the disomy’s effect.
The single translocation event that we recovered aided us in this regard, as it delimited
the effect of the disomy to the distal arm of the chromosome. To further map the effect
of Chromosome IV duplication, we adapted a technique known as PCR-mediated
chromosome deletions (PCD) (Fig. 4.2A). PCD involves deleting segments of a
chromosome that are distal to a centromere by inserting synthetic telomere seed
sequences (Kaboli et al., 2016; Sugiyama et al., 2005; Sugiyama et al., 2008)
(Methods). A targeting sequence with 300-600 bases of homology to the genomic
region to be deleted is fused to the telomere seed sequence via fusion PCR as
described in (Sugiyama et al., 2005). This technique has been successfully used to
delete single copies of large chromosomal segments in diploid yeast (Kaboli et al.,
2016).

To first test the efficacy of PCD, we deleted the right half of the duplicated portion of the
Chromosome IV disomy from an aneuploid BYxRM mutant (Methods). Whole genome
sequencing of the resulting genetically modified strain confirmed that approximately 430
kb of the disomic chromosome had been deleted (Fig. S4.4). This initial mutant
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exhibited the same tolerance as the original BYxRM haploid progenitor (Fig. S4.5A),
implying that the segment of the disomic Chromosome IV that had been deleted was
necessary for the aneuploidy to show its phenotypic effect. After this initial result, we
generated a panel of PCD strains to further map the conditionally beneficial effect of the
Chromosome IV disomy. These large-scale deletions were staggered approximately
every 50 kb. This led to the identification of a single 40 kb region towards the end of
Chromosome IV that explained 67% of the Chromosome IV disomy’s effect (Fig. S4.5B-
E).

To further resolve this locus, we performed two additional PCD transformations, which
fine-mapped the causal interval to 7 kb, spanning positions 1,362,862 bp to 1,369,812
bp (Fig. 4.2B and Fig. S4.5F and G). This interval contains five genes as well as a
dubious ORF (YDR455C). Genes present within this interval include PPN1, a dual
endo- and exopolyphosphatase; TSA2, a thioredoxin peroxidase; GUK1, a guanylate
kinase; NHX1, a sodium:proton and potassium:proton antiporter; and TOM1, an E3
ubiquitin ligase. We then used standard gene deletion techniques to delete each of
these genes, as well as the intergenic regions between them, from an aneuploid
BYxRM mutant. Also, because genes more than 20 kb away from a telomere can be
silenced due to telomeric position effects (Gottschling, Aparicio, Billington, & Zakian,
1990) (Aparicio & Gottschling, 1994), we also deleted the six genes upstream of PPN1
(Fig. 4.3 and Fig. S4.6A-G). The only gene-scale deletion that showed a phenotypic
effect was TSA2, which encodes a thioredoxin peroxidase (Fig. 4.2B and Fig. 4.3). Loss
of the coding region was sufficient to eliminate most of the beneficial effect of the
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aneuploidy (Fig. 4.3), implying that expression of TSA2 transcript or Tsa2 protein is
required for the locus’ effect.

4.6 Effect of the Chromosome IV duplication on transcription
Previous work has shown that Tsa1 and Tsa2 cooperate to protect cells from oxidative
stress (Wong et al., 2002) (Munhoz & Netto, 2004) (Park et al., 2000). Thus, we
checked the expression of TSA2 and TSA1 in the haploid progenitor and an aneuploid
strain fully disomic for Chromosome IV after hydrogen peroxide exposure using qPCR
(Methods). Strains were grown on plates supplemented with a range of doses of
hydrogen peroxide. Plates were harvested multiple times over a three-day period
(Methods). qPCR analysis revealed that, although initial levels of TSA2 after twelve
hours of exposure to hydrogen peroxide were two-fold higher in the fully disomic strain
than the haploid progenitor, expression of TSA2 increased in the haploid progenitor, but
not in the fully disomic strain, after thirty-six hours of exposure to the same dose (Fig.
4.4A). Levels of TSA2 were four-fold higher in the haploid progenitor than the fully
disomic strain at this later time (Fig. 4.4A). This suggests that the Chromosome IV
disomy buffers TSA2 expression during chronic exposure to hydrogen peroxide.

qPCR analysis also confirmed that TSA2 is highly induced in both strains upon
exposure to hydrogen peroxide (Fig. 4.4A and B). This induction was found to vary in a
dose-dependent manner in the aneuploid strain (Fig. 4.4B). Furthermore, in contrast to
previous studies, we found evidence that TSA1 is also highly induced upon exposure to
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hydrogen peroxide in a manner similar to TSA2 (Fig. 4.5A and B). The dynamics of
TSA1 expression at different times and doses followed the same pattern of TSA2
expression, with levels of TSA1 increasing only in the haploid progenitor from twelve to
thirty-six hours of exposure (Fig. 4.5A). TSA1 levels also increased in a dose-dependent
manner in the aneuploid strain (Fig. 4.5B). This suggests that the Chromosome IV
disomy has an effect on transcriptional buffering that extends to genes on other
chromosomes.

4.7 Ectopic overexpression of TSA2 has a limited effect on tolerance
We examined whether most of the effect of the Chromosome IV disomy could be
recapitulated using plasmid-based, ectopic overexpression of TSA2. The haploid
BYxRM strain used in the initial screen, as well as an aneuploid mutant derived from it,
were transformed with plasmids that carried the full length TSA2 ORF, along with its
promoter and 3’-UTR (Methods). Two types of plasmids were employed: low copy
(using a centromere and ARS sequence) and high copy (using a 2 µ plasmid origin; Fig.
S4.7). Although introduction of extra copies of TSA2 into cells on a plasmid did confer,
on average, a benefit in both the haploid and aneuploid backgrounds, only
transformants with the high copy plasmid showed a significant increase in tolerance
(Fig. 4.6). However, this increase recapitulated only 38% of the benefit of having an
extra copy of Chromosome IV in the haploid progenitor, suggesting that the
chromosomal context of TSA2 is important in enabling its duplication to exert its full
effect.

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4.8 Discussion
4.8.1 Chromosome IV disomy provides high oxidative stress tolerance
Previous work in budding yeast has shown that aneuploidies occur at a rate of about 1
in 100,000 cell divisions (Esposito, Maleas, Bjornstad, & Bruschi, 1982), can
accumulate when grown under standard lab conditions (Lynch et al., 2008), and can be
conditionally beneficial (G. Chen et al., 2012; Kaya et al., 2015; G. Liu et al., 2015;
Pavelka et al., 2010; Sunshine et al., 2015; Yona et al., 2012). Our work adds to the
growing body of literature that supports the conditionally beneficial nature of certain
aneuploidies by showing that strains disomic for Chromosome IV have a fitness
advantage when exposed to high levels of oxidative stress. This specific disomy
occurred in multiple independently cultured strains, suggesting that it may be a relatively
common means of quickly acquiring high oxidative stress tolerance.

The phenotypic effect of the Chromosome IV disomy appears to depend not only on
environmental context but also on genetic background. Mutants derived from the
BYxRM and RMxYPS crosses achieved high levels of oxidative stress tolerance
through this aneuploidy, while mutants derived from the BYxYPS cross did not have any
aneuploidies and were, on average, much less tolerant than mutants from the other
crosses. It is unclear based on the current data whether this reflects a difference
between strains in terms of the capacity to acquire and tolerate aneuploidies in general
or in terms of the capacity of these strains to utilize the Chromosome IV aneuploidy in a
conditionally beneficial manner. Supporting the former possibility, we previously showed
that MMS21
RM
confers increased hydrogen peroxide tolerance (Linder et al., 2016), and
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the only F2 progenitor lacking this allele in the current study was the one derived from
the BYxYPS cross. Sister chromatid cohesion, which is influenced by Mms21 and might
be affected by MMS21
RM
, can lead to an increased frequency of whole chromosome
gain in budding yeast (Covo, Puccia, Argueso, Gordenin, & Resnick, 2014). Future work
will be needed to assess whether this allele, or others that segregate in these crosses,
influence the ability to produce aneuploidies.

4.8.2 TSA2 is mainly responsible for the effect of the Chromosome IV disomy
Most of the effect of the Chromosome IV disomy is explained by duplication of TSA2.
This raises questions about why duplication of TSA2 specifically is beneficial. Of the
three known cytoplasmic thioredoxin peroxidases, Tsa1 and Tsa2 respond more
potently to hydrogen peroxide than to alkyl hydroperoxides, while Ahp1 shows the
converse relationship (Wong et al., 2002). Furthermore, both Tsa1 and Tsa2 have been
shown to cooperate in protecting cells from nitrosative stress in addition to oxidative
stress (Wong et al., 2002). TSA1 and TSA2 are closely related paralogs, sharing 86%
identity in their amino acid sequences. Both proteins react rapidly with hydrogen
peroxide, with reaction efficiencies as high as other antioxidant enzymes such as
catalase (Ogusucu et al., 2007). Furthermore, previous work has shown that TSA2 is
usually much more strongly induced by exposure to oxidants than TSA1 (Wong et al.,
2002). Although our results suggest that, in the genetic backgrounds used in this study,
TSA1 expression is potently upregulated in response to hydrogen peroxide, TSA2
expression was found to be significantly more strongly induced.

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4.8.3 Chromosome IV disomy buffers expression of TSA1 and TSA2 during
prolonged hydrogen peroxide exposure
Although both TSA1 and TSA2 are known to protect cells from oxidative stress, we
observed that having an extra copy of TSA2 actually led to a decrease in levels of both
transcripts compared to a haploid progenitor when chronically exposed to hydrogen
peroxide. One hypothesis to explain this apparently contradictory result is that having an
additional copy of TSA2 present on the duplicated chromosome may increase the
buffering capacity of this gene. This may enable TSA2 to more tightly regulate
expression of both itself and, indirectly, TSA1, in a conditionally beneficial manner.
Previous work in Drosophila (Stenberg et al., 2009; Y. Zhang et al., 2010) and, more
recently, in budding yeast (Hose et al., 2015), has shown that aneuploidies can,
counterintuitively, have a buffering effect on the expression of genes in aneuploid
regions. This effect can be mediated at the level of transcription (Stenberg et al., 2009;
Y. Zhang et al., 2010). These findings may explain why ectopic overexpression of TSA2
did not fully recapitulate the aneuploid phenotype, as the chromosomal context of TSA2
may have significantly increased the buffering capacity of this gene.

4.8.4 Transcriptional buffering of TSA2 and TSA1 may involve YAP1
Transcriptional upregulation of TSA2 as well as TSA1 is mediated in part by YAP1, a
redox-sensitive transcription factor that is normally maintained within the cytoplasm
(Isoyama, Murayama, Nomoto, & Kuge, 2001; J. Lee et al., 1999; Wong, Ching, Zhou,
Kung, & Jin, 2003; C. Yan, Lee, & Davis, 1998). Increased levels of hydrogen peroxide
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cause Yap1 to re-localize to the nucleus where it upregulates the expression of many
stress-responsive genes (Delaunay, Isnard, & Toledano, 2000; Gulshan, Thommandru,
& Moye-Rowley, 2012; Jun, Kieselbach, & Jonsson, 2012; Kuge, Jones, & Nomoto,
1997; Veal, Ross, Malakasi, Peacock, & Morgan, 2003; Wood, Storz, & Tjandra, 2004).
This establishes a negative feedback loop, in which Yap1 activity decreases with the
concomitant increase in expression of anitoxidant enzymes (Delaunay et al., 2000).
Thus, a possible explanation for the increased buffering conferred by the Chromosome
IV disomy is that higher initial levels of Tsa2 in aneuploid strains decrease the pool of
cytoplasmic hydrogen peroxide available to activate Yap1. This, in turn, keeps levels of
TSA2 and TSA1 stable during continued exposure to hydrogen peroxide. Conversely, if
not enough Tsa2 and Tsa1 are present initially in the haploid strain, levels of
cytoplasmic hydrogen peroxide rise sharply which would then lead to the accumulation
of large amounts of Yap1 in the nucleus. TSA2 and TSA1 levels would then be
expected to increase dramatically. In this scenario, it may be that the increase in
expression comes too late or that overcompensation of TSA2 and/or TSA1 expression
is detrimental under these conditions. If this is the case, it is possible that there is a
‘sweet spot’ of expression for both TSA2 and TSA1 that varies in a time- and dose-
dependent manner during chronic exposure to high levels of oxidative stress.

4.9 Materials and Methods
4.9.1 Screening for extremely high tolerance to hydrogen peroxide
Three highly resistant F2 segregants, generated from each pairwise cross of BY, RM,
and YPS, were used to find mutants that expressed extremely high levels of oxidative
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stress tolerance. Each F2 was streaked out onto Yeast Peptone Dextrose Agar (YPDA)
plates and incubated for two days at 30°C. Twenty-four colonies of each F2 were
transferred to separate wells of a 96 deep-well plate with 800uL of YPD in order to
establish twenty-four independent cultures per F2. These clones were outgrown for two
days at 30°C with shaking at 200RPM. This corresponds to roughly 20 generations,
enough time to allow all possible mutations to accumulate in each independent culture.
Independent cultures were used to maximize the potential to find different mutations
that confer very high tolerance to hydrogen peroxide.

After two days of outgrowth, 20uL from each culture were combined with 80uL of
molecular biology grade sigma water and transferred to separate plates supplemented
with a range of doses of hydrogen peroxide, including doses up to 5mM higher than the
maximal resistance of each cross. Plates were incubated at 30°C for four to six days in
order to give slow growing mutants enough time to form visible colonies. Mutants that
formed visible colonies at a dose at least 1mM higher than the F2 progenitor were then
transferred to a 96 deep-well plate with 800uL of YPD and incubated for 2 days at 30°C
along with the original F2 progenitor. Extremely high resistance was confirmed by
pinning these strains onto plates supplemented with a range of doses of hydrogen
peroxide. All strains were at the same time archived at -80°C. Mutants that grew at a
dose at least 1mM higher than the F2 progenitor were selected for downstream
analyses. These included fourteen mutants from the BYxRM cross (from twelve
independent cultures), nine mutants from the RMxYPS cross (from five independent
cultures), and fourteen mutants from the BYxYPS cross (from eleven independent
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cultures). Images of plates were taken using a Biorad Gel Doc XR+ with an exposure
time of 0.5 seconds.

4.9.2 Strategy for improved detection of causal mutations via whole-genome
sequencing
In order to increase our ability to find causal mutations that were acquired during
outgrowth before exposure to hydrogen peroxide, mutants were inoculated directly from
frozen stock into liquid culture containing 5mL of YPD for two days in order to extract
genomic DNA for sequencing. This was done under the assumption that mutations that
enable individuals to survive on very high doses of hydrogen peroxide would have
occurred during outgrowth. However, it is formally possible that these mutations may
only have facilitated survival, but not growth, once individuals were exposed to
hydrogen peroxide. Additional mutations may have occurred at this stage to enable
these individuals to re-enter the cell cycle. However, for the purposes of this study, both
kinds of mutations- those that enabled survival per se as well as those that conferred
the ability to grow and divide, were of interest. Furthermore, as oxidative stress is
known to cause mutations, we wanted to increase our ability to weed out random
mutations that occurred during growth on hydrogen peroxide-supplemented plates. To
accomplish this, in the initial screen for mutants, most of the colony that formed from
each potential mutant was transferred to liquid culture for verification. This ensured that
any random mutations that had accumulated during exposure to hydrogen peroxide
would occur in a very small proportion of the population of cells transferred from a
colony, while the causal mutation(s) should be either fixed or near fixation. This
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heterogenous population was archived after two days of growth in liquid culture.
Streaking out for single colonies at this stage would have meant that individuals with a
significant number of random mutations would be sequenced, with no way of
distinguishing between potentially causal mutations and those that had accumulated
randomly. To avoid this, a relatively large fraction of the archived population was
inoculated directly into liquid culture for DNA extraction and subsequent whole-genome
sequencing, under the assumption that causal mutations should be nearly fixed within
the sequenced population, while random mutations should represent a very small
fraction of the total reads at a particular genomic site.

4.9.3 Whole genome sequencing of highly tolerant mutants from the BYxRM
and RMxYPS crosses as well as the original F2 progenitors
As mutants from the BYxRM and RMxYPS derived F2 progenitors displayed higher
levels of tolerance than mutants from the BYxYPS derived F2, we decided to sequence
these individuals at high coverage first to find potentially causal mutations. In order to
account for any mutations that had accumulated in the original F2 progenitors prior to
outgrowth, these strains were sequenced at high coverage as well. Whole genome
libraries from each strain were generated using the Illumina Nextera kit. Each strain was
tagged with a unique barcode identifier. Sequencing was done on an on an Illumina
NextSeq500 insturment. The BYxRM and RMxYPS F2 progenitors each were
sequenced at approximately 150X coverage, while the mutant strains received between
100-300X coverage each, with an average of approximately 200X coverage each.
While analysing genome-wide coverage, we discovered multiple mutants from both
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crosses that were disomic for Chromosome IV (with a BYxRM mutant having a large
duplicated portion of Chromosome IV translocated into the genome).

After demultiplexing sequencing reads, the Burrows-Wheeler Aligner (BWA-MEM)
program (H. Li & Durbin, 2009) was used to align reads from the two F2 progenitors to
the BY reference genome (for the BYxRM progenitor) and the YPS reference genome
(for the RMxYPS progenitor), both of which were generated in our previous study
(Linder et al., 2016). The parameters used for alignment were: ‘bwa –mem –t 6 ref.fsa
read1.f1 read2.fq > output.sam’. To remove duplicate reads, the rmdup command was
used in SAMtools. In order to generate Mpileup files, SAMtools (H. Li & Durbin, 2009)
was used with the commands ‘samtools mpileup –f ref.fsa read.rmdp.srt.bam >
ouput.mp. SNPs were identified using custom Python scripts. 1651 SNPs and small
indels were identified between the BYxRM F2 and the BY reference genome, while 2407
SNPs and small indels were identified betweent the RMxYPS F2 and the YPS reference
genome. To create reference genomes for the F2 progenitors to be used in subsequent
analyses, identified variants were integrated into the BY or YPS reference genomes.

Mutant strains were then aligned to the newly created F2 reference genomes with BWA-
MEM using the same parameters as described above, followed by the generation of
mpileup files with SAMtools. Potentially causal mutations were called using custom
Python scripts which only pulled mutations that had occurred in at least 90% of the
reads at a particular genomic site.
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4.9.4 GO enrichment analysis
GO analysis was carried out on the Saccharomyces Genome Database website using
GO Term Finder version 0.83 with the molecular function category selected. All genes
listed in Table S4.1 were included in the analysis. A p.value cutoff of < 0.05 was used
as the cut-off for significance.

4.9.5 Using PCD to fine map the causal locus on Chromosome IV
In order to fine map the causal factor present on the translocated portion of
Chromosome IV, multiple PCDs were carried out in an aneuploid BYxRM mutant. PCD
was carried out as described in (Sugiyama et al., 2008) with a slight modification.
Targeted deletion constructs consisted of a 300-600bp sequence homologous to the
targeted region which was fused via overlap extension PCR (as described in (Sugiyama
et al., 2005)) to a sequence that consisted of the KanMX cassette fused to a telomeric
seed. The telomeric seed consists of six repeats of a 5’-CCCCAA-3’ sequence that
serves as a substrate for the in vivo amplification of a full-length telomere. The targeting
sequence was amplified using primer pairs in which both primers contained ~20 bases
of sequence homologous to the ends of the targeted region while the reverse primer
contained a 30bp 5’-overhang to facilitate PCR fusion. The sequence of this 30bp
overlap is
5’-GGCCGCCAGCTGAAGCTTCGTACGCTGCAG-3’ (reverse complemented in the
reverse primer for the targeting construct), as described in (Sugiyama et al., 2005). The
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KanMX/telomeric seed was made by amplifying the KanMX cassette using tailed
primers. The forward primer consisted of 20 bases of homology to pTEF with the same
30bp 5’-overhang while the reverse primer consisted of 20 bases of homology to the
reverse complement of tTEF with a 5’-overhang that consisted of the telomeric seed
sequence. As this product was used to make each PCD construct, a large amount was
amplified at once and purified via gel extraction using the Qiagen gel extraction kit.
Separate PCR reactions were carried out to make the targeting and KanMX/telomeric
seed sequences, after which the two sequences were fused together in a PCR reaction
that contained the forward primer used to make the targeting sequence, the reverse
primer used to make the KanMX/telomeric seed sequence, as well as equimolar
amounts of the two sequences. All PCR reactions used the high-fidelity Phusion
polymerase with buffer HF. PCR products were verified by running a small amount on
an agarose gel, then purified using either gel extraction for the KanMX/telomeric seed
sequence or the Qiagen PCR Purification kit for the targeting seqeunce and the final
deletion construct.

Complete deletion constructs were transformed into aneuploid yeast cells using the
lithium acetate method (Gietz, Schiestl, Willems, & Woods, 1995). Approximately 5ug of
the deletion construct was used for a single transformation. Transformants were spread
onto YPDA plates supplemented with Geneticin to select for successfully transformed
individuals. Transformants were then transferred to liquid cultures containing 800uL of
YPD along with the original BYxRM F2 progenitor and the aneuploid BYxRM mutant
used for transformations. After two days of incubation at 30°C with shaking, strains were
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pinned onto YPDA plates supplemented with a range of doses of hydrogen peroxide,
which were incubated for three days at 30°C. Phenotyping was carried out by imaging
plates in a GelDoc imaging device using a 0.5 second exposure time. Correct
placement of the deletion constructs was confirmed using colony PCR with primers
internal to the KanMX cassette and just external to the targeting sequence. To
demonstrate that this correlated with deletion of the targeted portion of Chromosome IV,
representative transformants were sequenced on an Illumina NextSeq500 instrument as
above, which revealed that the targeted portion of the duplicated chromosome had been
reduced down to a single copy.

4.9.6 Individual gene and intergenic deletions to find the causal factor
Once the causal locus had been fine-mapped to a 16kb region spanning from positions
1,353,686 to 1,369,812 of Chromosome IV, individual gene and intergenic deletions
were carried out. In a manner analogous to making the PCD constructs described
above, gene deletion products were made by fusing a 300-600bp targeting sequence to
the KanMX cassette, with the caveat that the telomeric seed sequence was replaced by
a sequence with 30-60 bases of homology to a region just downstream of the gene or
intergenic region being deleted. Approximately 5ug of the final deletion product was
used for lithium acetate-mediated transformation.

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4.9.7 Plasmid-based overexpression of TSA2
Low and high copy plasmids were constructed using a modified version of the protocol
described in (van Leeuwen, Andrews, Boone, & Tan, 2015). Each plasmid consisted of
three pieces: the TSA2 CDS with the full 3’-UTR and the 5’-upstream sequence
spanning from the translation start site to a region either 287 bases or 506 bases
upstream, the KanMX cassette, as well as either the CEN6/ARSH4 sequence or the 2µ
origin of replication for the low copy or high copy plasmids, respectively. The KanMX
sequence was fused to either the CEN6/ARSH4 or 2µ origin of replication using overlap
extension PCR as described above. This product was then PCR purified and co-
transformed into yeast strains with equimolar amounts of the TSA2 sequence using the
lithium acetate method.

4.9.8 qPCR analysis of TSA1 and TSA2 expression levels
For qPCR of TSA1 and TSA2 expression levels, the original BYxRM F2 progenitor, a
BYxRM mutant disomic for Chromosome IV, and the same mutant with TSA2 deleted
were streaked onto YPDA plates. After two days at 30°C, sixteen colonies from each
strain were transferred to 96 deep-well plates with 800uL of YPD and incubated at 30°C
for two days with shaking, after which strains were pinned onto YPDA plates
supplemented with a range of doses of hydrogen peroxide. Individuals were placed into
wells in such a way so that only the sixteen colonies from a single strain would be
pinned onto an agar plate so as to prevent cross-contamination when harvesting cells
for qPCR. Six technical replicates were pinned per dose as cells were harvested every
twelve hours after pinning over a three-day period. Cells were harvested off plates in
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2mL of 1X PBS using a cell scraper to detach cells from the plate. Cells were
transferred to eppindorf tubes, spun down at >13,000 RPM for 10 seconds, after which
the supernatant was removed and cells were resuspended in 500ul- 1mL of RNAlater
(Ambion). Samples were stored at 4°C for 12 hours, spun down at max speed for 5
minutes, and snap frozen in liquid nitrogen for archiving at -80°C. The Qiagen RNeasy
Plant Mini Kit was used to extract total RNA, after which samples were treated with
DNase I, RNase-free for 1 hour at 30°C. After DNase I treatment, RNA was purified
using Qiagen RNeasy Mini Spin Columns. cDNA libraries were prepped using the
SuperScript VILO cDNA Synthesis Kit. The KAPA SYBR Fast qPCR kit was used for
qPCR analysis on the DNA Engine Opticon 2. ACT1 was used as a reference gene in
order to determine the relative abundances of TSA1 and TSA2 for each sample.

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4.10 Figures

Fig. 4.1. Detection of Chromosome IV disomy. Genome-wide coverage plots of five
representative strains are depicted in (A). The top plot represents coverage of the
RMxYPS F2 progenitor genome, with the next plot down depicting a RMxYPS-
derived mutant with a Chromosome IV disomy. The next plot down depicts the
BYxRM F2 progenitor genome, with the following plot showing a BYxRM-derived
mutant with a Chromosome IV disomy. The bottom plot depicts the BYxRM-
derived mutant in which the distal part of Chromosome IV was translocated into
the genome. Bar plots depicting the number of sequenced mutants derived from
each cross that were disomic for the right arm of Chromosome IV are shown in
(B), along with the number of mutants that were fully haploid.
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Fig. 4.2. Fine-mapping the causal locus of the Chromosome IV disomy. PCR-
mediated chromosome deletions (PCD) were carried out to fine map the causal
locus. As shown in (A), by staggering these deletions along the distal part of the
chromosome and phenotyping strains with each deletion, the causal locus can be
bracketed to a much narrower region. The final chromosome-scale deletion
delimited the causal region to about 7kb (B). At this point we switched to
knocking out individual genes and intergenic regions, including genes up to 10kb
upstream of the causal region in case the proximity to a telomere led to gene
silencing. This resulted in unambiguously identifying TSA2 as the causal factor.

149

Fig.4.3. TSA2 deletion is mainly responsible for the benefit conferred by the
Chromosome IV disomy. Deletion of TSA2 in a strain disomic for Chromosome IV
revealed that the coding region of TSA2 has a significant effect on the phenotype.
This effect is nearly identical to the effect of deleting the CDS of TSA2 along with
its’ promoter and 3’-UTR.

.
150

Fig. 4.4. TSA2 expression. Expression of TSA2 in response to treatment with
hydrogen peroxide. Strains exposed to a range of doses of hydrogen peroxide on
agar plates were harvested at different time-points over a three-day period in
order to examine the temporal and dose-dependent expression dynamics of
TSA2. The BYxRM-derived haploid progenitor and a BYxRM-derived mutant fully
disomic for Chromosome IV were used. As shown in (A), TSA2 was highly
induced after twelve hours of exposure to hydrogen peroxide in the haploid and
disomic strains. In the haploid strain, TSA2 levels increased over five-fold while
in the disomic strain TSA2 levels increased over seven-fold. However, TSA2
expression in the disomic strain remained relatively unchanged after thirty-six
hours of exposure compared to an eight-fold increase in expression of TSA2 in
the haploid strain after the same time of exposure. Over a range of increasing
doses, levels of TSA2 increased continuously after about three days of exposure,
as shown in (B).

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Fig. 4.5. TSA1 expression. Expression of TSA1 in response to treatment with
hydrogen peroxide. The same samples were used to determine levels of TSA1
expression as in (Figure 4.4). Similarly to TSA2, levels of TSA1 were highly
induced after twelve hours of exposure to hydrogen peroxide in the haploid and
disomic strains (A). In the haploid strain, TSA1 levels increased six-fold while
TSA1 levels rose almost five-fold in the disomic strain. TSA1 expression in the
disomic strain increased by about 13% after thirty-six hours compared to an
80% increase in expression of TSA1 in the haploid strain after the same time of
exposure. In a manner similar to TSA2, TSA1 levels increased continuously over
a range of increasing doses after about three days of exposure, as shown in (B).

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Fig. 4.6. Plasmid-based overexpression of TSA2. In order to provide further
evidence that the increased copy number TSA2 is responsible for most of the
benefit conferred by the Chromosome IV disomy, two types of plasmids were
constructed. Both plasmids had a genomic region including the 3’-UTR, CDS,
and about 300 bases of the 5’-upstream sequence of TSA2 cloned into them.
The low copy plasmid had a centromere and ARS to promote stable propagation
at low copy number, while the high copy plasmid contained instead a fragment
of the yeast 2μ plasmid in order to enable maintenance at high copy numbers. In
(A), the BYxRM-derived haploid F2 progenitor was transformed with both
plasmids. This led to almost no increase in tolerance in strains transformed with
the low copy plasmid, while strains with the high copy plasmid showed a more
significant gain in tolerance. Similarly, in (B), a BYxRM-derived disomic strain
was transformed with both types of plasmids. Again, only an incremental gain in
tolerance was seen with transformation of the low copy plasmid, while a more
significant gain was seen accompanying transformation of the high copy
plasmid. The magnitude of the changes in tolerance with both types of plasmid
was similar in the haploid progenitor and fully disomic strains.

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4.11 Supplementary Information

Fig. S4.1. Screen to verify higher tolerance of mutants than the original F2 progenitor.
Panels (A-C) depict phenotyping results of mutants generated from the BYxRM,
RMxYPS, and BYxYPS crosses, respectively. The first plot in each panel depicts
154

the tolerance of the original F2 progenitor. Mutants highlighted in blue were found
to be disomic for Chromosome IV, while the mutant highlighted in red was found
to be partially disomic for the distal part of Chromosome IV. Shown are 95%
confidence intervals for MIC.

Fig. S4.2. Fitness of aneuploidy versus haploid strains on rich media. A panel of
sixteen BYxRM-derived haploid progenitor strains and sixteen BYxRM-derived
disomic strains were pinned onto separate YPDA plates. End-point growth of
colonies from both plates was quantified by imaging the plates and using ImageJ
to analyze the average pixel intensity of each colony (A) as well as the average
colony area, which was measured using pixels as units (B). Both plots show 95%
confidence intervals.
155

Fig. S543. The Chromosome IV disomy was found to confer a conditional benefit only
when cells are exposed to hydrogen peroxide on agar plates. The haploid progenitor
and a strain disomic for Chromosome IV were exposed to hydrogen peroxide in
three different conditions. In (A), replicates of both strains were exposed to 40mM
of hydrogen peroxide for three days in liquid culture, after which they were
pinned onto YPDA plates. In this condition, the haploid strain was significantly
more tolerant than the disomic strain. In contrast, after three days of exposure to
hydrogen peroxide on agar plates (B), the disomic strain was more tolerant than
the haploid strain. In (C), both strains were exposed to hydrogen peroxide for
three days in liquid culture followed by three days of exposure on agar plates
supplemented with hydrogen peroxide. This resulted in the disomic strain once
again showing significantly increased tolerance compared with the haploid strain.

156

Fig. S4.4. Verification of chromosome-scale deletions. In order to empirically show
that the chromosomal-scale deletions were in fact removing a single copy of the
targeted regions, four deletion strains were sequenced on an Illumina
NextSeq500. The top plot depicts a strain completely disomic for Chromosome IV
as a comparison, while the two plots just below depict two independent PCD1
strains. The two bottom plots show two independent PCD11 strains. Blue boxes
highlight the targeted region of Chromosome IV, with the remaining downstream
sequence showing coverage half that of the remaining disomic part of the
Chromosome. These results convinced us that the chromosome-scale deletion
technique worked as anticipated.

157

Fig. S4.5. Phenotypes of PCD strains. Panels A-G show the phenotypes of all
chromosomal-scale deletion strains used to fine map the causal locus. Each
panel represents a separate experiment in which replicates of the haploid
progenitor as well as a fully disomic strain were plated along with the deletion
strains to serve as controls. The number after PCD (PCR-mediated chromosomal
158

deletion strains) represents the order in which these strains were used to map the
causal locus. PCD1-6 all led to a significant decrease in tolerance, while PCD7-9
did not show any significant loss in tolerance, signifying that the causal locus
lies between PCD6 and PCD7. PCD10 and PCD11 were constructed later to fine
map the causal locus from a ~39kb region to a ~7kb interval. All plots show 95%
confidence intervals for MIC.

159

Fig. S4.6. Phenotypes of individual gene and intergenic deletion strains. All
remaining gene and intergenic deletions that were phenotyped after fine-mapping
the causal locus are shown (A-G). Each panel again represents an individual
experiment with the haploid progenitor and disomic strain present as controls.
None of the individual deletions shown here had a significant effect on tolerance.
Shown are 95% confidence intervals for MIC.
160

Fig. S4.7. Construction of low and high copy plasmid. Depicted above is the method
used to construct both a low copy and a high copy plasmid containing TSA2. The
process used to make each was the same, with the only difference being that the
CEN6/ARS4 was used in the low copy plasmid, while the 2μ fragment was used to
construct the high copy plasmid.

161

Table S4.1. All non-structural mutations detected in mutants derived from the
BYxRM, RMxYPS, and BYxYPS crosses.
Chr Pos Refpos* Gene Lesion** Strain*** Location**** Effect*****
1 40914 40916 GPB2 G
Br3C11d3,
Br3C11l2,
Br3C11u3
CDS NS
1 57765 57765
YAL044
W-A
T Br3C11a3 CDS NS
2 144094 144084 URA7 G By8H3h5 CDS NS
2 483354 483244 GRS1 T Br3C11o5 CDS NS
2 593896 593740 SMP1 A Br3C11l2 CDS S
4 16532 16522 THI13 T By8H3i3 CDS S
4 305868 305840 SUB2 T By8H3o5 CDS NS
4 461128 460794 SOK1 A Ry5H4x3 CDS NS
4 822859 822773 SCC2 T Br3C11w5 CDS NS
5 38482 38484 CIN8 C By8H3m3 CDS NS
5 91037 90931 SPF1 G Ry5H4d1 CDS NS
5 118146 118032 RPR1 A Ry5H4d1 ncRNA_gene U
5 120972 120856 MMS21 A Ry5H4d1 CDS NS
6 83148 83128 STE2 G Ry5H4d1 CDS NS
7 93906 93900 YPT32 T By8H3h5 CDS NS
7 354692 354636 SCY1 T By8Ho5 CDS NS
7 857992 857570 TIM13 T Ry5H4h1 I U
8 528379 528255 FLO5 G Br3C11x1 CDS NS
9 348524 348522 BET1 A By8H3i3 I, promoter U
10 65759 65755 UBP12 C Br3C11a3 CDS NS
11 208307 208097 RRN3 C
Br3C11a3,
Br3C11h4,
Br3C11q2,
Br3C11w5
I, promoter U
11 521871 521501 SHB17 A Br3C11f2 CDS S
11 625954 625916 PCC1 T By8H3i4 Intronic U
12 185005 185003 SDO1 C Br3C11u3 I, promoter Unknown
12 268070 267996 PET309 T Ry5H4v2 CDS NS
12 701887 701823 YLR278C A Br3C11q2 CDS NS
13 141798 141788 SMA2 T By8H3m3 I, promoter U
13 531606 531434 POM152 T Br3C11u3 CDS NS
13 695503 695287 SCJ1 A Ry5H4x3 I, promoter U
14 40328 40298 HXT14 G Br3C11q2 CDS NS
14 153094 153064 POL2 G Br3C11w5 CDS S
162

14 467526 467492 MKT1 A Br3C11e3 CDS NS
15 214314 214288 MAM3 A
By8H3mutc
1
CDS S
15 391070 391020 HMS1 T By8H3h5 CDS NS
15 819835 819779 DSE3 T By8H3b1 CDS NS
16 400233 400227 MOT1 A By8H3m3 CDS S
16 776403 776315
YPRW
delta14
C
Br3C11a3,
Br3C11c2,
Br3C11d3,
Br3C11e3,
Br3C11h4,
Br3C11l1,
Br3C11l2,
Br3C11q2,
Br3C11u3,
Br3C11w5
Ty1 LTR U
16 835165 835077 PIN3 C Br3C11e3 CDS S
17 20568 20570 COX1 A By8H3i4 CDS S

* This refers to the corresponding genomic position in the lab reference BY strain.
** The specific base alteration that occurred
*** Strains in which the mutation was detected in. Strains that begin with ‘Br’ are derived from
the BYxRM cross, strains that begin with ‘Ry’ are derived from the RMxYPS cross, while strains
that begin with ‘By’ are derived from the BYxYPS cross.
**** The genomic context of each mutation. ‘CDS’, coding sequence of a gene; ‘I’, intergenic
region; ‘promoter’, 5’-upstream region of a gene; ‘Intronic’, within an intron; ‘nc_RNA_gene’,
non-coding RNA; ‘Ty1 LTR’, Ty1 retrotransposon.
***** The effect of each mutation. ‘NS’, non-synonymous change; ‘S’, synonymous change; ‘U’,
unknown

.
163

Chapter 5: Concluding remarks

In this thesis, I have helped to both broaden and deepen our understanding of the
genetic and molecular mechanisms that influence the cellular response to oxidative
stress. This was accomplished by helping to show how proteasome-mediated
adaptation to oxidative stress is regulated, by cloning several genes that were not, to
the best of my knowledge, previously known to have a functional role in oxidative stress
tolerance, as well as by characterizing the phenotypic effects of individual loci that
contribute to oxidative stress tolerance. Additionally, I showed that a common path to
very high oxidative stress tolerance is mediated by a single aneuploidy that exerts a
beneficial effect in a very context-specific manner. Further, I was able to clone the
causal factor, a single gene present on the duplicated chromosome. This adds to an
emerging body of work revealing that aneuploidies can be conditionally beneficial and
that a single gene can be responsible for most of this effect. In this chapter I will tie
together the work presented above and explain how this work has helped to advance
our understanding of a model quantitative trait. I will also talk about future directions this
work can be taken in.
5.1 Multiple regulatory architectures can underlie invasive growth in budding
yeast
In Chapter 3, I helped characterize how a model complex trait, invasive growth, can be
regulated by multiple different pathways within a cross of two genetically distinct strains
of Saccharomyces cerevisiae. This study provided additional evidence that invasion can
occur in a Flo8-independent manner as well as showing that invasion can occur in a
164

Flo11-indepent manner. Four genes were cloned in individuals that were able to invade
when grown on either glucose or ethanol in a Flo8-independent manner, including
GPA1, FLO11, BNI1, and AMN1. This study also established the important role of non-
allelic heterogeneity in the contexts examined as functional variants in multiple genes
were found to segregate within the cross. Furthermore, different transcription factors
were shown to regulate invasive growth in multiple segregants generated from this
cross, which demonstrated that regulatory rewiring can be a cause of genetic
heterogeneity.
5.2 Oxidative stress tolerance is predominately influenced by additive effect
loci that can be closely linked
One of the most significant findings to emerge from the work described in Chapter 4 is
that the genetic basis of natural variation in oxidative stress tolerance in budding yeast
is primarily additive. This is in agreement with other genetic mapping studies that have
shown the additive nature of this trait. The extent to which epistasis, pleiotropy, and
other genetic phenomena influence quantitative traits such as oxidative stress tolerance
is an important question in the field of quantitative trait genetics that has yet to be fully
resolved. However, from this study and others, it would appear that phenotypic variation
in oxidative stress tolerance is a manifestation of the large number of cellular processes
that can influence this trait. This may have provided a large mutational target space
within which many small-effect functional variants have accumulated.
This study also highlighted the potential contribution of genetic linkage to quantitative
traits, as multiple instances of linkage were observed. In one case, different causal
alleles of a single gene, the aquaporin AQY1, were detected in two different crosses.
165

Both alleles are nonfunctional, but differ in the precise lesion that causes the loss of
function. Additionally, two different causal genes were shown to underlie a single
detected QTL shared between two crosses. These genes, MRP13 and CTT1, are in
close physical proximity to one another, being just three genes apart. Together, these
results indicate that both closely linked causal variants within the same gene and
variation within different genes in close physical proximity can impact quantitative traits
such as oxidative stress tolerance and may be a cause of the underlying genetic
complexity of such traits. These results highlight the importance of resolving detected
loci to the underlying causal gene and variant as multiple causal variants can underlie
what appears to be a single locus.
5.3 Many different cellular processes contribute to oxidative stress tolerance
In Chapter 4, I find that multiple processes can influence oxidative stress tolerance. This
result was most likely driven by the fact that oxidative stress impacts multiple aspects of
cellular physiology and can cause damage to all types of macromolecules. Specifically,
genes involved in stress-induced MAPK signaling, (SDP1), water transport (AQY1),
hydrogen peroxide detoxification (CTT1), DNA repair (MMS21), and mitochondrial
translation (MRP13) were identified as contributing to this trait. Furthermore, a total of
64 distinct genomic loci, most of which likely influence the trait, were detected in this
study. These results together highlight the complexity of oxidative stress tolerance and
the need to resolve detected loci to causal genes and variants in order to further
increase our understanding of the diverse mechanisms that can enable certain
individuals to tolerate high levels of oxidative stress.
166

5.4 A single gene underlies a conditionally beneficial aneuploidy
In Chapter 5, I find that a single gene, the cytoplasmically localized hydrogen peroxide-
responsive thioredoxin peroxidase TSA2, is the causal factor driving selection of a
Chromosome IV duplication in multiple strains from two crosses. As detailed above,
adaptive aneuploidies that confer a conditional fitness benefit have become a subject of
increasing attention, as aneuploidies are known to be involved in many different types of
cancers as well as several developmental disorders. Studying how particular
aneuploidies can be adaptive under certain conditions is key to understanding why cells
with an abnormal chromosome content can, paradoxically, gain a context-specific
growth advantage that can contribute to tumorigenesis.
5.5 Impact of my work
In the research that I detailed above, I have helped to further our understanding of
complex traits in general and oxidative stress tolerance in particular. My research has
added to the body of literature supporting a predominantly additive basis for highly
complex quantitative traits such as oxidative stress tolerance. Moreover, I have shown
that a duplication of a single chromosome provides a common path to very high
tolerance in individuals from multiple crosses. This specific mechanism may not have
been observed in my original genetic mapping study due to the fact that any individuals
that developed this duplication in culture would have represented a very small fraction
of the total population that was ultimately sequenced. However, by specifically
screening for individuals that could grow at extremely high doses of hydrogen peroxide,
I was able to isolate populations in which the majority of individuals contained the
conditionally beneficial duplication, as evidenced by the ability to detect this event in
167

multiple sequenced populations. This finding adds to the growing body of literature
showing that specific aneuploidies, normally associated with a loss of fitness, can be
beneficial depending on both the genetic-background and environmental context of the
individuals that harbor them.
In conclusion, the plethora of factors that have been found to influence this key
quantitative trait, including those that my research has uncovered, make it clear that
more research is needed to gain a more complete understanding of the mechanisms
that cause this trait to vary between individuals.

5.6 Future directions
The data that I have generated during the course of my graduate studies can be used to
gain further mechanistic insights into the basis of oxidative stress tolerance in budding
yeast. Although I was able to clone genes underlying a number of loci in the genetic
mapping study described in Chapter 3, the majority of detected QTL remain unresolved.
Potentially more causal factors will emerge from further investigation of these loci.
Genetic mapping studies conducted in a similar manner using different crosses will
likely shed light on additional mechanisms that can influence this trait. These findings
can, in turn, be used to investigate conserved mechanisms present in higher
eukaryotes, including humans.
The strains that I generated in Chapters 3 and 4 can be used to further understand the
precise mechanisms that enable the Chromosome IV duplication to exert a beneficial
effect. Teasing apart the genetic factor(s) that cause this duplication to be beneficial in
two of the three crosses may lead to important insights as to how the genetic
168

background can influence the outcome of specific aneuploidies. Furthermore, examining
how regulation of gene expression can, in general, influence phenotypic outcomes of
changes in gene copy number will significantly advance our understanding of the
biology of aneuploidies.

169

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Appendix A: Quantitative Trait Variation, Molecular Basis of
This work appears essentially as published in 2016 in
The Encyclopedia of Evolutionary Biology, vol. 3 pp. 388-394.
The author list is as follows:
Seidl, F., Linder, R., and Ehrenreich, I.M.

A.1 Glossary
Additive genetic effect The effect of substituting one allele at a locus with
another allele. For a given bi-allelic locus, this value is typically measured as
half of the difference between the mean phenotypic values of the two
homozygotes.
Cloning The process of refining a quantitative trait locus to specific genes and
genetic variants that have phenotypic effects. Cloning typically requires a
combination of genetic mapping and genetic engineering techniques.
Co-immunoprecipitation A biochemical approach for studying physical
interactions among proteins and other molecules.
Cryptic genetic variation Polymorphisms that only show phenotypic effects when
a genetic or environmental perturbation occurs.
Dominance When a heterozygote exhibits a phenotype that is associated with
one allele of a quantitative trait locus.
Dominance genetic effect For a given bi-allelic locus, the difference between
the phenotype of heterozygotes and the average phenotype of the two
homozygotes.
Dominant negative When one allele of a gene also prevents another allele from
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functioning when they co- occur in a heterozygote.
Fluorescence resonance energy transfer A microscopy approach for studying
physical interactions between proteins.
Genetic background effect When the effect of an allele is influenced by the
genome in which the allele occurs.
Genetic buffering When inherited mechanisms prevent mutations and genetic
variants from having phenotypic effects.
Genetic interactions (or epistatic interactions) When sets of two or more genetic
variants show a combined phenotypic effect that is nonadditive.
Genome An organism’s complete set of genetic material.
Genotype–environment interaction When the effect of one or more genetic
variants change across environmental conditions. Alternatively, when the
effect of the environment changes across genotypes.
Haploinsufficiency When a diploid organism possesses a single functional
copy of a gene, resulting in insufficient gene product and an abnormal
phenotypic state.
Heritability The proportion of variance in a trait that is explained by genetic
factors, as opposed to the environment.
Linkage The tendency of genes or alleles that are located on the same
chromosome to be inherited together.
Linkage disequilibrium The nonrandom association of genetic polymorphisms
at different loci. Linkage disequilibrium is expected among sites that are
closely linked, but may also arise for other reasons, such as selection on loci
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that both influence a beneficial phenotype.
Orthologs Genes that occur in different species, but have the same
evolutionary origin and molecular function.
Pleiotropy When a genetic variant influences multiple traits.
Quantitative trait gene A gene that harbors a genetic variant that has a
phenotypic effect.
Quantitative trait locus A region of the genome that harbors one or more
genetic variants with phenotypic effects.
Quantitative trait nucleotide A genetic variant that contributes to heritable
variation in a phenotype.
Statistical genetic architecture A statistical characterization of a phenotype’s
genetic basis. This may include information on the number of loci that
contribute to a phenotype, as well as their effects and interactions.
Yeast two-hybrid A molecular approach for studying physical interactions
between proteins.

A.2 Introduction
Genetic mapping is commonly used to dissect quantitative traits that are
important to agriculture, evolutionary biology, and medicine (Georges, 2007;
Mackay et al., 2009). Most mapping efforts detect quantitative trait loci
(QTLs) that span tens to hundreds of genes (J. Flint & Mott, 2001;
Remington, Ungerer, & Purugganan). These QTLs are useful for studying the
statistical genetic architecture of a phenotype. However, they are of limited
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value in determining how a trait is specified at the molecular level
(Drinkwater & Gould, 2012; Jonathan Flint, Valdar,
Shifman, & Mott, 2005) .

Knowing how genetic variants alter cellular and develop- mental processes to
produce phenotypic changes is important for many reasons. Such knowledge
can be used to improve our ability to predict individuals’ phenotypes based on
their genotypes and to treat heritable diseases with targeted therapies (Albert
& Kruglyak, 2015). Moreover, this information can reveal the types of
functional genetic variants that segregate in populations and provide a
potential substrate for evolution (Seidel et al., 2011).

To determine the functional mechanisms that give rise to heritable phenotypic
variation, specific quantitative trait genes (QTGs) and quantitative trait
nucleotides (QTNs) for a trait must be identified (Aitman, Petretto, &
Behmoaras, 2010; Drinkwater & Gould, 2012; Jonathan Flint et al.,
2005; Georges, 2007; G. Liti & Louis, 2012; Mackay et al., 2009;
Remington et al., 2001; Weigel & Nordborg, 2005) (Fig. A1). After these
QTGs and QTNs have been determined, bioinformatic and experimental
approaches can be used to assess how alleles of these factors functionally
differ (Balasubramanian et al., 2006; Deutschbauer & Davis, 2005; Filiault et al.,
2008; Grisart et al., 2004; Rosas et al., 2014; Smemo et al., 2014; Erin N. Smith &
Kruglyak, 2008; Yvert et al., 2003)(Fridman, 2004 #483)(Johanson, 2000 #470).
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Furthermore, combinations of QTNs can be examined at the molecular and
systems levels to assess how these variants collectively influence a trait {e.g.,
(Sudarsanam & Cohen, 2014; J. Zhu et al., 2008)}.

Fig A.1. Determining the molecular factors underlying a quantitative trait locus (QTL).
An abstract example of how quantitative trait genes (QTGs) and quantitative trait
nucleotides (QTNs) are typically identified is shown. In (a), a QTL is detected at the
resolution of five genes. To identify the QTG, different strategies may be employed,
such as gene deletion and plasmid complementation (b). Gene replacement, in
which genetic engineering is used to switch the allele of a gene that an individual
carries, and complementation by crossing are other approaches that might be used
to go from QTL to QTG. Further resolving a QTG to a specific QTN may be achieved
by replacing individual nucleotides (c). We use haploid examples for simplicity.
Furthermore, we note that these approaches may be more difficult for genes that are
involved in genetic interactions (Mackay, 2014; Taylor & Ehrenreich, 2015a or
show different effects across genetic backgrounds {Chandler, 2013 #44)). In
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such cases, it might be necessary to conduct the experiments shown in this figure
in multiple genetic backgrounds.
In this article, we describe how research on the molecular basis of heritable
phenotypes enhances our understanding of quantitative genetics. We discuss
the distinct insights into phenotypic variation that arise from moving beyond
QTLs, and identifying QTGs and QTNs. We then highlight how work on the
molecular basis of quantitative trait variation has begun to shed light on
functional mechanisms that give rise to commonly observed quantitative
genetic effects.

A.3 Characterizing the Molecular Basis of a Quantitative Trait
Determining the molecular basis of a quantitative trait requires pinpointing
QTGs and QTNs, and assessing how changes in molecular function caused
by these variants result in phenotypic effects. Here, we describe specific
insights gained at each of these steps.

A.3.1 Identifying QTGs
Relative to mapping QTLs at the resolution of many genes, determining
specific QTGs represents a major step forward in our understanding of a
trait’s molecular basis (Drinkwater & Gould, 2012; Jonathan
Flint et al., 2005; Weigel & Nordborg, 2005) . This is because a
QTG connects diversity in a pheno- type to variation in specific biochemical,
metabolic, and regulatory processes. Making these links to molecular
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mechanisms is the easiest when the genes that contribute to trait variation
were previously identified through mutagenesis screens, and characterized
using biochemical and molecular approaches (Balasubramanian et al.,
2006; Filiault et al., 2008; Rosas et al., 2014). In such instances, this
prior knowledge can be used to formulate hypotheses about the
mechanisms that enable alleles of a QTG to differ in their phenotypic
effects. This is true even in non-model species, where valuable information
about a QTG’s function can be obtained from its orthologs in model
organisms. However, efforts to identify QTGs can also discover new genes and
novel functions for known genes (Seidel et al., 2011), thereby advancing
our core knowledge of the genetics and development of the species and trait
of interest.

A.3.2 Characterizing QTNs
While QTGs reveal specific genes and pathways involved in heritable trait
variation, QTNs provide valuable insights into how these QTGs contribute to
phenotypic diversity. Once a QTN has been identified, primary sequence
analysis can be used to predict the polymorphism’s effect on its cognate gene
and protein. QTNs can affect transcription and translation, alter protein
structure and activity, or have other effects at the molecular level (see
for some examples). After QTNs have been found, molecular, cellular,
developmental, and physiological assays can be conducted to study how
these polymorphisms result in phenotypic changes (Balasubramanian et
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al., 2006; Deutschbauer & Davis, 2005; Filiault et al., 2008;
Fridman, Carrari, Liu, Fernie, & Zamir, 2004; Grisart et al.,
2004; Johanson et al., 2000; Rosas et al., 2014; Smemo et al.,
2014; Erin N. Smith & Kruglyak, 2008; Yvert et al., 2003). For a
polymorphism located in a cis-regulatory element, one might examine whether
the SNP causes a change in transcription factor binding or gene expression.
Likewise, for a QTN that changes the amino acid sequence of a protein, one
might look at its effects on protein activity, stability, or physical interactions
with other proteins. After examining how a QTN alters the activity of a gene, it
is important to assess how this functional perturbation impacts downstream
cellular, physiological, and developmental processes. In some organisms,
these studies can be performed in individuals that are genetically identical
other than at a QTL. Such individuals can be produced using either crossing
(Kooke, Wijnker, & Keurentjes, 2012) or genetic engineering (Storici
et al., 2001) and serve as a powerful resource for determining how a
QTN’s effect is transduced from the molecular level to the phenotypic level.

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Fig A.2. Molecular effects of quantitative trait nucleotides (QTNs). Here, we show a
number of ways that polymorphisms can lead to functional changes in
quantitative trait genes (QTGs) at the levels of gene regulation and protein
function. Effects are separated roughly into the levels of DNA, RNA, and protein.

A3.3 Studying the Mechanisms by which Multiple QTNs Influence a Trait
Given that quantitative traits arise due to the effects of multiple QTNs,
determining how these factors collectively influence a phenotype is important.
This is especially true when sets of QTNs involved in genetic–environment and
genotype–environment interactions influence a phenotype (Gerke, Lorenz,
Ramnarine, & Cohen, 2010; Taylor & Ehrenreich, 2015b). A
variety of techniques, such as yeast two-hybrid (Fields & Song, 1989),
fluorescence resonance energy transfer (Ma, Yang, & Zheng, 2014), and
co-immunoprecipitation (Sambrook & Russell, 2006) , can be used to
study how pairs of QTNs affect direct functional interactions between genes.
However, a large fraction of non- additive genetic variation may arise due to
statistical interactions that occur between QTNs that are involved in different
pathways and cellular processes (Gjuvsland, Hayes, Omholt, & Carlborg,
2007; Omholt, Plahte, Oyehaug, & Xiang, 2000; Taylor & Ehrenreich,
2015a). This suggests that examining the effects of multiple QTNs at the
systems level is important. An increasingly common strategy for such work is
to measure global gene expression, as well as the levels of metabolites,
proteins, and other molecular intermediates, across genetically distinct
individuals (Ayroles et al., 2009; Civelek & Lusis, 2014). By integrating these
data with in- formation on the identities and functions of QTNs, researchers can
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develop network models that explain how gene regulation, metabolism, and
physiology are influenced by multiple polymorphisms (Rockman, 2008; J. Zhu
et al., 2008). These models can then be linked to phenotypic outcomes,
thereby providing insights into the systems level mechanisms that specify
variation in a trait (Rockman, 2008; J. Zhu et al., 2008).

A.4 The Molecular Underpinnings of Quantitative Genetic Effects

Heritable variation in quantitative traits arises due to QTNs that influence the
activities and functions of genes, proteins, pathways, and networks. Given
that all organisms are comprised of these same building blocks, shared
functional mechanisms may underlie commonly observed quantitative genetic
effects, such as additivity, dominance, pleiotropy, genetic interaction, and
genotype–environment interaction (Omholt et al., 2000). Here, we summarize
how these different classes of statistical effects may arise at the molecular
level.

A.4.1 Additivity
A large fraction of quantitative trait variation is caused by additive QTNs that
act independently of the genetic background in which they occur (Hill et al.,
2008). Although many QTNs are likely additive because they have subtle loss-
or gain-of-function effects on their cognate genes, even seemingly large
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molecular perturbations, such as deletions of entire genes, can have additive
effects (Baryshnikova et al., 2010; DeLuna et al., 2008). This suggests
that part of the reason that some QTNs appear to be additive is that their
phenotypic effects are constrained by genetic buffering. A number of
mechanisms are known to buffer biological systems against the effects of gen-
etic variants and the environment, including redundancy among genes and
pathways, genetic network architecture, and specific proteins and protein
complexes (e.g., heat shock proteins) {as discussed in (Boone, Bussey, &
Andrews, 2007; Gu et al., 2003; Hartman, Garvik, & Hartwell, 2001;
Jarosz, Taipale, & Lindquist, 2010; Jeong, Mason, Barabási, &
Oltvai, 2001; Kitano, 2004; Rutherford, 2000) and elsewhere}.

A.4.2 Dominance
The molecular basis of dominance has long been a subject of interest among
both empiricists and theorists {e.g., (Kacser & Burns, 1981; Keightley, 1996;
Orr, 1991)}. Extensive research on mutations and QTNs has identified a
number of general molecular mechanisms that result in dominance (Wilkie,
1994). One of the most straightforward causes of dominance is when a QTN
has a complete or partial loss-of-function effect on its cognate gene, which is
complemented in a heterozygote (Fig. A.3a). However, loss-of-function alleles
can also be dominant if they result in ‘haploinsufficiency,’ which occurs when
heterozygotes possess a level of an important transcript or protein that falls
below a critical threshold (Fig. A.3b). ‘Dominant negatives’ are an additional
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class of dominant QTN; such variants prevent the other allele of a gene from
functioning properly (Fig. A.3c). Gain-of-function polymorphisms can exhibit
dominance if they increase the abundance of a transcript or protein above a
tolerable threshold, or lead to a new spatiotemporal expression pattern (Fig.
A.3d). Gain-of-function dominant alleles can also arise at the level of protein
structure–function relationships: a dominant polymorphism may change a
protein’s activity or structure, or may even render a protein toxic to cells
(Wilkie, 1994). Overdominance may occur if being heterozygous at a locus
confers higher fitness than being homozygous. A classic example of such
overdominance is the genetic polymorphism that causes sickle cell anemia.
This same variant provides protection against malaria and is therefore beneficial
in parts of the world where malaria is prominent. Thus, individuals that are
heterozygous for the sickle cell allele receive some protection against both
malaria and sickle cell disease (Serjeant, 2013).
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Fig A.3. Mechanisms of allelic dominance. As described in the text, dominance can
arise due to a number of reasons at the molecular level. (a) Shows an example
where a single copy of a gene is sufficient to maintain gene dosage above a
required threshold. In contrast, (b) shows an example where a single loss-of-
function allele at the level of gene expression is sufficient to reduce gene dosage
below a required threshold. (c) Illustrates an example of a dominant negative, where
the dominant allele prevents the other allele from functioning. This component of
Figure 3 is based on a figure shown in (Alberts, Johnson, Lewis, & York, 2002).
In (d), a quantitative trait nucleotide (QTN) causes a gene to be expressed in a new
tissue (commonly referred to as ‘ectopic expression’), resulting in an increase in
flower size that segregates in a dominant manner.

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A.4.3 Pleiotropy
When a QTN either directly or indirectly has an effect on multiple traits, it is
described as ‘pleiotropic’ (Paaby & Rockman, 2013) (Fig. A.4a). As shown in
Fig. A.4b, a pleiotropic QTN may affect a trait that plays a role in determining
an individual’s outcome for other phenotypes. Alternatively, a QTN may
directly impact multiple downstream pathways that each influences a distinct
phenotype (Fig. A.4c). Distinguishing between direct and indirect pleiotropy
requires knowing the pathways and networks that underlie a trait, as these
forms of pleiotropy may be indistinguishable at the phenotypic level. Research
attempting to find common features of pleiotropic genes suggests that genes
with higher levels of pleiotropy tend to encode proteins that are distributed
throughout more cellular compartments or tissues, and are involved in more
protein–protein interactions (Dudley, Janse, Tanay, Shamir, &
Church, 2005; He & Zhang, 2006) . Genetic mapping studies
focused on quantitative traits have also provided an additional important
insight into pleiotropy: pleiotropic QTLs often harbor multiple QTNs that
influence different traits, rather than a single QTN that affects multiple
traits (Y. Chen & Lübberstedt, 2010). Because closely linked QTNs that
underlie distinct phenotypes will segregate together in a population if they are
in linkage disequilibrium, the traits they affect may be correlated. Thus, cloning
the QTGs and QTNs underlying pleiotropic QTLs is essential for determining
the molecular basis of correlations among traits.

206

A4.4 Genetic Interactions
Genetic interactions occur when the effects of QTNs depend on an individual’s
genotype at other positions in the genome. These interactions can involve two
QTNs (referred to as ‘gene– gene interactions’) or more than two QTNs
(referred to as ‘higher-order genetic interactions’). The mechanisms under-
lying gene–gene interactions have largely been determined through work on
large collections of mutants in model organisms (Boone et al., 2007;
Lehner, Duringer, Estill, Tobin, & Craig, 2011). Typically these
interactions arise when genetic variants compromise two pathways or protein
complexes that act in parallel, or perturb proteins that function in the same
complex (Boone et al., 2007) (Fig. A.5a and b). However, other
sources of gene–gene interactions have been proposed; these include the
disruption of robustness and the uncovering of otherwise cryptic genetic
variants, combinations of QTNs collectively altering the dosage of critical
biomolecules either above or below a threshold level (Fig. A.5c), and
variation in gene regulatory networks (Gjuvsland et al., 2007; Lehner
et al., 2011; Omholt et al., 2000). Less is known about the
mechanisms underlying higher-order genetic interactions because these
interactions have proven to be more difficult to detect and dissect than
gene–gene interactions (Pettersson, Besnier, Siegel, & Carlborg,
2011; Taylor & Ehrenreich, 2014). The most likely source of higher-
order genetic interactions is thought to be combinations of three or more
genetic variants in gene regulatory networks (Gjuvsland et al., 2007; Taylor &
207

Ehrenreich, 2015a). However, there has yet to be empirical proof that this or
any other mechanism underlies higher-order genetic interactions.

Fig A.4 Mechanisms of genetic interactions. We show some mechanisms that can
cause genetic interactions, using haploid examples for simplicity. In (a), loss-of-
function quantitative trait nucleotides (QTNs) in parallel pathways that converge on
the same metabolite or downstream gene result in a genetic interaction when they
occur in a particular combination. In (b), two QTNs that disrupt protein structure
show an interaction by preventing protein complex formation. Lastly, in (c), we
illustrate how combinations of QTNs can impact the level of an important metabolite,
protein, or transcript, resulting in a phenotypic effect when the level falls below a
threshold.

A.4.5 Gene–Environment and Genotype–Environment Interactions
Many QTNs show effects that depend on the environment. Interactions with
the environment can involve single genetic variants (referred to as ‘gene–
environment interaction’) or multiple genetic variants (referred to as ‘genotype–
208

Fig A.5. Mechanisms of pleiotropy. Quantitative trait nucleotides (QTNs) that occur
in genes that regulate multiple phenotypes can cause correlated trait changes (a).
In this figure, we show how this can arise due to polymorphisms that alter the
function of a single protein. In (b), QTNs directly affect Trait 1 and only influence
Trait 2 through Trait 1. In (c), QTNs directly affect Traits 1 and 2 by influencing
how the protein interacts with different partners involved in the respective
phenotypes.

Environment interaction’) (Des Marias, 2013). The molecular basis of gene–
environment and genotype–environment interactions may depend on the
conditions being considered and the mechanisms an organism uses to
perceive these conditions. Gene–environment and genotype–environment
interactions can arise from QTNs in sensors that respond to specific
environmental cues (Filiault et al., 2008) (Balasubramanian et al.,
2006). Furthermore, transcription factors, signaling cascades, or other
components of the cell that act downstream of these sensors and mediate
perception of cues may harbor QTNs that interact with the environment
(Erin N. Smith & Kruglyak, 2008) . Biological systems can also show
more general responses to the environment (Des Marias, 2013). For
209

example, temperature can generally change the kinetic rates and patterns of
physical interactions among proteins. Environmental change may also disrupt
an organism’s ability to buffer itself against genetic variants, thereby
uncovering genetic variants that typically do not have phenotypic effects
(Gibson & Dworkin, 2004) (Paaby & Rockman, 2014).

A.5 Conclusion
Quantitative genetics has historically been a field rooted in statistics.
However, statistical patterns of heritable phenotypic variation result from the
individual and collective effects of functional genetic variants that segregate in
populations. Thus, work aimed at identifying these factors and examining how
they exert their effects is crucial for bridging the gap between our statistical
and molecular understanding of heritable trait variation. Such information will
be crucial in improving our ability to predict and treat heritable diseases, and
will also likely improve our understanding of how traits evolve in natural
populations. Moving forward, technological advances in genetic mapping and
genome editing (Ryan et al., 2014) (Cong et al., 2013) are likely to
accelerate progress in this area. Such research will undoubtedly continue to
improve our understanding of the molecular mechanisms that determine the
relationship between genotype and phenotype.
A.6 Acknowledgments
We thank Jonathan Lee, Takeshi Matsui, Joann Phan, Matthew Taylor, and
Jason Wolf for critically reviewing drafts of this article. This work was supported
210

by grants from the National Institutes of Health (R01GM110255 and
R21AI108939), National Science Foundation (MCB1330874), Rose Hills
Foundation, and Alfred P. Sloan Foundation to I.M.E.

211

Appendix B: Nrf2-dependent Induction of Proteasome and Pa28αβ
Regulator are Required for Adaptation to Oxidative Stress
Andrew M. Pickering, Robert A. Linder, Hongqiao Zhang, Henry J. Forman, and Kelvin
J.A. Davies

This work appears essentially as published in 2012 in
Journal of Biological Chemistry, 287(13): 10021-10031

B.1 Overview
The ability to adapt to acute oxidative stress (e.g. H
2
O
2
, peroxynitrite, menadione,
and paraquat) through transient alterations in gene expression is an important
component of cellular defense mechanisms. We show that such adaptation
includes Nrf2-dependent increases in cellular capacity to degrade oxidized
proteins that are attributable to increased expression of the 20 S proteasome and
the Pa28a13 (11 S) proteasome regulator. Increased cellular levels of Nrf2,
translocation of Nrf2 from the cytoplasm to the nucleus, and increased binding of
Nrf2 to antioxidant response elements (AREs) or electrophile response elements
(EpREs) in the 5'-untranslated region of the proteasome 135 subunit gene
(demonstrated by chromatin immuno- precipitation (or ChIP) assay) are shown to
be necessary requirements for increased proteasome/Pa28a13 levels, and for
maximal increases in proteolytic capacity and stress resistance; Nrf2 siRNA and the
Nrf2 inhibitor retinoic acid both block these adaptive changes and the Nrf2 inducers
DL-sulforaphane, lipoic acid, and curcumin all replicate them without oxidant
exposure. The immunoproteasome is also induced during oxidative stress
adaptation, contributing to overall capacity to degrade oxidized proteins and stress
212

resistance. Two of the three immunoproteasome subunit genes, however, contain
no ARE/EpRE elements, and Nrf2 inducers, inhibitors, and siRNA all have
minimal effects on immunoproteasome expression during adaptation to oxidative
stress. Thus, immunoproteasome appears to be (at most) minimally regulated by
the Nrf2 signal transduction pathway.

B.2 Introduction
Despite antioxidant defenses, such as superoxide dismutases and glutathione
peroxidases, oxidative stress represents a constant danger to cell and organismal
viability. Reactive oxygen and nitrogen species can cause protein, lipid, sugar,
DNA, and RNA modification. Oxidative modifications to proteins are common,
and a major cellular defense strategy is to rapidly degrade mildly oxidized
proteins before they can aggregate and cross-link to form insoluble cell inclusion
bodies (Bota & Davies, 2002; Bota, Ngo, & Davies, 2005; Bulteau, Lundberg,
Ikeda-Saito, Isaya, & Szweda, 2005; Bulteau, Szweda, & Friguet, 2006; Niki
Chondrogianni et al., 2003; Davies, 1986, 2001; Davies & Goldberg, 1987;
Fucci, Oliver, Coon, & Stadtman, 1983; Grune, Reinheckel, & Davies, 1996;
Keller et al., 2005; Pacifici, Kono, & Davies, 1993; Shang & Taylor, 1995;
Shringarpure, Grune, Mehlhase, & Davies, 2003; Ullrich et al., 1999; Whittier,
Xiong, Rechsteiner, & Squier, 2004). Insufficient proteolytic capacity or
increased oxidant generation, or both, can result in compromised cell function or
even cell death (Conconi, Szweda, Levine, Stadtman, & Friguet, 1996; Davies,
1986, 1993; Ding, Dimayuga, Markesbery, & Keller, 2004; Fucci et al., 1983;
213

Pacifici & Davies, 1991; Stadtman, 1986; Starke-Reed & Oliver, 1989; Wolff &
Dean, 1986). Over a period of many years, we (Davies, 1986, 2001; Davies &
Goldberg, 1987; Grune et al., 1996; Pacifici et al., 1993; Shringarpure et al.,
2003; Ullrich et al., 1999) and others (11–14, 16, 25) (Ahn et al., 1996; Niki
Chondrogianni et al., 2003; Fucci et al., 1983; Keller et al., 2005; Shang &
Taylor, 1995; Whittier et al., 2004) have demonstrated that the bulk of
oxidatively damaged proteins in the cytoplasm, endoplasmic reticulum, and
nucleus are degraded by the proteasome. More recently, we have also shown
that the immunoproteasome plays a significant role (26, 27) (Pickering et al.,
2010; Teoh & Davies, 2004). In mitochondria, oxidized proteins are
preferentially degraded by the Lon protease (Bota & Davies, 2002; Bota et al.,
2005; Bulteau et al., 2005; Bulteau et al., 2006; Ngo & Davies, 2009).

In previous studies, we have demonstrated that mammalian cells, as well as
bacteria and yeast, can transiently adapt to oxidative stress (Davies, 1993, 2000;
Ermak, Harris, & Davies, 2002; Pickering et al., 2010; Wiese, Pacifici, &
Davies, 1995). This is an adaptive process (sometimes called hormesis) in which
cells treated with a mild dose of an oxidant will, for a period of time (=24 – 48 h),
become more resistant to a higher dose of the same (or related) oxidant that
would normally be toxic. Recently, we have demonstrated that this adaptive
response includes an increased abundance of 20 S proteasomes,
immunoproteasomes, and Pa28αβ (or 11 S) proteasome regulators (Pickering et
al., 2010); all these proteins were shown to play key roles in the oxidative stress
214

response, and each was required for full adaptation. Other groups have also
reported induction of various forms of the proteasome, and proteasome
regulators, by oxidative stress (Ferrington, Husom, & Thompson, 2005; Ferrington
et al., 2008; Husom et al., 2004; Kotamraju et al., 2006; Yamano et al., 2002).

The nuclear factor (erythroid-derived 2)-like 2 (Nrf2)
1
transcription factor is an
important component of responses to oxidative stress (K. Itoh et al., 1997; Ken Itoh
et al., 2003; Kwak, Wakabayashi, Greenlaw, Yamamoto, & Kensler, 2003; M.
McMahon et al., 2001; Moi, Chan, Asunis, Cao, & Kan, 1994; Nguyen, Sherratt, &
Pickett, 2003; Rushmore, Morton, & Pickett, 1991; Venugopal & Jaiswal, 1996).
Under non-stressful conditions, Nrf2 is maintained at low levels through rapid
degradation via Keap1- dependent ubiquitin conjugation (K. Itoh et al., 1999; Kwak
et al., 2003; Michael McMahon, Itoh, Yamamoto, & Hayes, 2003), followed by
targeted degradation by the 26 S proteasome. As a product of this rapid turnover,
newly translated Nrf2 is found predominantly in the cytoplasm. With Keap1
inactivation, as a product of factors such as oxidative stress, Nrf2 levels increase
due to diminished proteasomal degradation, and Nrf2 is phosphorylated and
translocated to the nucleus in mechanisms mediated by PKC8 and Akt (H. Zhang
& Forman, 2008). Once in the nucleus, Nrf2 binds to a cis-acting enhancer
sequence, upstream of numerous antioxidant genes, known as the antioxidant
response element (Barrett & Teare) or electrophile responsive element (EpRE),
and promotes the synthesis of several antioxidants, and enzymes responsible for
repairing/removing oxidative damage and restoring cell viability (Rushmore et al.,
215

1991). It has been shown that Nrf2 knock-out in mice results in decreased
tolerance to oxidative stress (Enomoto et al., 2001; Ramos-Gomez et al., 2001).
Additionally, results by Kwak et al. (Kwak et al., 2003) showed that the phenolic
antioxidant [
3
H]1,2-dithiole-3-thione, which induces many cellular antioxidants and
phase 2 enzymes, can also enhance mammalian proteasome expression through
the Keap1-Nrf2 signaling path- way. These results led us to hypothesize that the
transient stress adaptation, involving proteasome and proteasome regulators,
which we described previously (Pickering et al., 2010), might be primarily under
the control of the Nrf2 transcription factor. In the present study we have, therefore,
tested whether oxidative stress-induced increases in 20 S proteasome,
immunoproteasome, and the Pa28αβ regulator, as well as increased stress
resistance, are actually under the control of Nrf2, and whether Nrf2 is necessary
and/or sufficient for their induction and for adaptation to various forms of oxidative
stress.

B.3 H2O2, Peroxynitrite, Paraquat, and Menadione Pretreatment All Increase
Proteolytic Capacity
We have previously reported that adaptation to H
2
O
2
includes large increases in
proteasomal proteolytic capacity (Pickering et al., 2010). We now needed to
determine whether the increase in proteasome is specific to H
2
O
2
, or if it is a
more general response to oxidants. We first pretreated MEF cells with various
concentrations of H
2
O
2
, peroxynitrite, or the redox cycling agents paraquat and
menadione for 1 h. Then, 24 h later, we harvested and lysed the cells and
216

measured the proteolytic capacity by degradation of the fluorogenic peptide, Suc-
LLVY-AMC, which is widely used to estimate the chymotrypsin-like activity of the
proteasome (Reinheckel, Grune, & Davies, 2000; Seifert et al., 2010; Ullrich et al.,
1999). We saw a 2-fold increase in proteolytic capacity with H
2
O
2
or paraquat
pretreatment, a 2.5-fold increase with peroxynitrite pretreatment, and a 2–3-fold
increase with menadione pre-treatment (Fig. B.1, A–D). In lysates of untreated
cells, the selective proteasome inhibitor lactacystin caused an 80 –90% inhibition of
proteolysis. In lysates of oxidant-pretreated cells, lactacystin inhibited degradation
by 90 –95%, indicating that proteasome is largely responsible for most of the
oxidant-induced adaptive

Fig. B.1. Oxidant pretreatment increases proteolytic capacity in a proteasome-
dependent manner. Cells treated with a mild dose of a range of oxidants exhibit
increased proteolytic capacity, the majority of which (80 –95%) are blocked by the
proteasome selective inhibitor lactacystin. MEF cells were grown to 10% confluence
(=250,000 cells per ml) and treated with: A, 0 –100 µM H
2
O
2
; B, 0–1 µM peroxynitrite;
C, 0 –100 nM paraquat; or D, 0 –100 nM menadione. All treatments were for 1 h in
217

complete medium, following which the medium was removed and replaced with
fresh complete medium (see ”Experimental Procedures“). After 24 h, cells were
lysed and diluted to a protein concentration of 50 µg/ml. Proteolytic capacity was
determined by cleavage of the proteasome chymotrypsin-like substrate Suc-LLVY-
AMC (see ”Experimental Procedures“). Where used, 5 µM lactacystin was added to
samples 30 min prior to incubation with Suc-LLVY-AMC. Values are mean ± S.E., n = 3.
E, MEF cells were prepared as described in A and pretreated with 100 nM peroxynitrite, 1
µM H
2
O
2
, 1 nM menadione, or 1 nM paraquat for1h in complete medium; following
this, the medium was removed and replaced with fresh complete medium. In some
samples, 1 µM MG132 was added 30 min prior to incubation with Suc-LLVY-AMC. Cells
were incubated, harvested, lysed, diluted, and analyzed for proteolytic capacity by lysis
of the fluorogenic peptide Suc-LLVY-AMC, as in panels A–D, and under ”Experimental
Procedures.“ Values are mean ± S.E., n = 3. F, results from E were re-plotted with the
decrease in activity resulting from addition of MG132 plotted as a percent of the
proteolytic capacity of cells not treated with the inhibitor.

increase in proteolytic capacity (Fig. B.1, A–D). This experiment was repeated
using another proteasome-selective inhibitor, MG132, which blocked 50% of
activity in untreated cells, and 60% of activity following oxidative stress adaptation
(Fig. B.1, E and F).

B.4 H2O2 Adaptation Increases Nrf2 Protein Levels and Nrf2 Nuclear
Translocation
ARE/EpRE sequences are present in the upstream untranslated region of all 20
S proteasome sub-unit genes examined. If Nrf2 is involved in our model of
adaptation to oxidative stress, we would expect to see an increase in total Nrf2
protein levels as a product of enhanced stability following detachment from the
Keap1 complex, as well as translocation of Nrf2 from the cytosol to the
nucleus: indicative of Nrf2 functioning as a nuclear transcription factor (K. Itoh
et al., 1997; K. Itoh et al., 1999; Ken Itoh et al., 2003). For initial experiments
218

we used H
2
O
2
as our adaptive oxidant and found that a mild dose of H
2
O
2
caused a 2-fold increase in cellular Nrf2 levels (Fig. 2.2A); this is consistent with
previous reports of stress-related induction of Nrf2 (K. Itoh et al., 1999; Ken Itoh
et al., 2003; Kwak et al., 2003; M. McMahon et al., 2001; Venugopal & Jaiswal,
1996). When we blocked Nrf2 synthesis, using Nrf2 siRNA, we lost the increase
in Nrf2 protein (Fig. B.2B). We next examined Nrf2 localization using
immunocytochemistry, and saw a notably stronger nuclear-localized staining of
Nrf2 in H
2
O
2
-treated cells compared with a more widespread staining of all cell
compartments in untreated cells (Fig. B.2C).

Fig. B.2. Nrf2 protein levels and nuclear translocation during oxidative stress
adaptation. A, H
2
O
2
treatment causes an increase in whole cell levels of Nrf2. MEF
cells were grown to 10% confluence, treated for 1 h with 0 –100 µM H
2
O
2
, and then
washed and resuspended (see ”Experimental Procedures“). Cells were harvested and
lysed 24 h following oxidant pretreatment as described for the Western blot under
”Experimental Procedures.“ Cell lysates (20 µg) were run on SDS-PAGE and transferred
to PVDF membranes. The membranes were screened with antibodies directed against
Nrf2 and β-tubulin and results were quantified by densitometry. All experiments were
repeated in triplicate and densitometric band intensities for Nrf2 were normalized to
those of β-tubulin. Values are mean ± S.E., n = 3. B, the increase in Nrf2 band intensity
is lost with Nrf2 siRNA pretreatment. Samples were prepared as in panel A except that 4
219

h prior to H
2
O
2
treatment, cells were pretreated with siRNA against Nrf2, or with a
scrambled vector, and gels were then run as in panel A. C, treatment of cells with H
2
O
2
causes Nrf2 to shift from a broad cytoplasmic distribution to a nuclear localization. MEF
cells were grown to 50% confluence and treated with 100 µM H
2
O
2
for 1 h, then fixed
and stained with an antibody directed against Nrf2, as described under ”Experimental
Procedures.“ Representative photographs are shown, but the experiment was
repeated several times with similar results.

B.5 Nrf2 Is an Important Regulator for H2O2-induced Increase in Proteolytic
Capacity

Having determined that Nrf2 levels were increased, and that Nrf2 was translocated
to the nucleus under the conditions of our cellular H
2
O
2
adaptation model, we
next wanted to determine whether Nrf2 is actually required for the increased
proteolytic capacity reported in Fig. B.1. To examine this we blocked Nrf2
expression by two distinct methods: siRNA and retinoic acid. First, we explored
an Nrf2 siRNA treatment level and time period that would not diminish basal Nrf2
levels, as Nrf2 is maintained at extremely low levels in unstressed cells, but
would block adaptive increases in Nrf2. As shown in both Fig. B.2B and the inset to
Fig. B.3A, we were successful in blocking the oxidative stress-induced increase in
Nrf2 levels, without reducing the basal levels of Nrf2. Cells pretreated with Nrf2
siRNA and then exposed to an adaptive dose of H
2
O
2
did not exhibit an H
2
O
2
-
induced increase in proteolytic capacity, but cells treated with a scrambled siRNA
vector showed normal induction of proteolytic capacity (Fig. B.3A). As a further test
of Nrf2 involvement, we repeated the experiment of Fig. B.3A using retinoic acid
treatment, which has been shown to prevent Nrf2 expression in cells (52), as a
different means of blocking Nrf2. When we pretreated cells with retinoic acid and
220

then attempted to adapt the cells to H
2
O
2
as in Fig. B.3A, we saw no significant
increase in Nrf2 levels, and no increase in proteolytic capacity (Fig. 2B3B).

B.6 Nrf2 Is an Important Regulator of the H2O2-induced Increase in
Proteolytic Capacity to Degrade Oxidized Proteins

Although the degradation of Suc-LLVY-AMC provides a good approximation of the
chymotrypsin-like activity of the proteasome,what really counts is the proteasomal
capacity to degrade oxidized proteins. To examine this question, we incubated cell
lysates with tritium-labeled hemoglobin ([3 H]Hb) and oxidized [3 H]hemoglobin ([3
H]Hbox ). Adaptation to H2O2 pretreatment caused a 2-fold increase in capacity to
degrade [3 H]Hb, but an almost 4-fold increase in selectivity for [3 H]Hbox (Fig. B.3C ).
In contrast, cells pretreated with siRNA against Nrf2, prior to H2O2 treatment, exhibited
no increase in [3 H]Hb degradation and less than a 25% increase in capacity to degrade
[3 H]Hbox (Fig. B.3C ). The results of Fig. B.3 provide strong evidence that Nrf2 has an
important role in the increase in proteolytic capacity induced during adaptation to
oxidative stress.

B.7 Nrf2 Regulates H2O2-induced Expression of 20S Proteasome and Pa28αβ
but Not Immunoproteasome

Because oxidative stress can increase the levels of 20 S proteasome,
immunoproteasome, and the proteasome regulator Pa28αβ (Ferrington et al., 2005;
Ferrington et al., 2008; Kotamraju et al., 2006; Kotamraju et al., 2003; Pickering et
al., 2010; Thomas, Kotamraju, Zielonka, Harder, & Kalyanaraman, 2007), all of
221

which have been shown to have critical roles in adaptation to oxidative stress (26,
32, 33, 35, 53, 54) (Pickering et al., 2010) (Ferrington et al., 2005; Ferrington et al.,
2008; Kotamraju et al., 2006; Kotamraju et al., 2003; Thomas et al., 2007), we
wanted to determine whether the increases in proteolytic capacity reported in Fig.
B.3 are explained by changes in proteasome, and to determine whether Nrf2 plays
a critical upstream role. For these studies, we used Western blot analyses of
control and H
2
O
2
-adapted cells, pretreated with either Nrf2 siRNA or a
scrambled siRNA vector. With scrambled (Holliday et al.) siRNA treatment we saw
a 2–3-fold H
2
O
2
-induced increase in 20 S proteasome, immunoproteasome, and
Pa28αβ protein levels (Fig. B.4, A–C). With Nrf2 siRNA pretreatment, however,
the H
2
O
2
-induced increase in 20 S proteasome (Fig. B.4A) and Pa28αβ (Fig. B.4B)
was lost, indicating that 20 S proteasome and Pa28αβ are regulated by Nrf2 during
adaptation to stress. In contrast to 20 S proteasome and Pa28αβ, Nrf2 siRNA treat-
ment had only a weak effect on the H
2
O
2
-induced increase in immunoproteasome
levels (Fig. B.4C). Nevertheless, H2O2 –mediated increases in 20 S proteasome,
immunoproteasome, and Pa28αβ were clearly all important for adaptive increases in
cell tolerance (survival) of H2O2 challenge treatments (Fig. B.5A ). Thus, we must
conclude that immunoproteasome regulation during oxidative stress is either wholly or
partially independent of Nrf2, and other factor(s) must be involved. In support of this
idea, we find that, although 20 S proteasome subunits contain a few ARE/EpRE
sequences in their promoter regions, ARE/ EpRE sequences are completely absent in
two of the three immunoproteasome-specific subunits (Fig. B.5). Although such
analyses are not conclusive, the results are
222

Fig. B.3. Increased proteolytic capacity is blocked by inhibition of Nrf2. A, the increase
in proteolytic capacity caused by H
2
O
2
treatment was blocked by inhibition of Nrf2
expression through pretreatment with Nrf2 siRNA. MEF cells were grown to 10%
confluence and treated with either control or Nrf2 siRNA for 24 h as described under
“Experimental Procedures.” Then, 4 h after initiation of siRNA treatment, half the cells
were exposed to 1 µM H
2
O
2
for 1 h, washed, and resuspended (see ”Experimental
Procedures“). The capacity to degrade the fluorogenic peptide Suc-LLVY-AMC was
determined 24 h after initiation of siRNA treatment (see ”Experimental Procedures“).
Values are mean ± S.E., n = 3. The inset to panel A shows a representative Western
blot. B, treatment of cells with the Nrf2 inhibitor retinoic acid also blocked the H
2
O
2
-
induced increase in proteolytic capacity. MEF cells were seeded at 5% confluence and
treated with 3 µM retinoic acid. When cells reached 10% confluence one-half were
exposed to 1 µM H
2
O
2
and proteolytic capacity (Suc-LLVY-AMC lysis) was determined
24 h after treatment as in panel A. Values are mean ± S.E., n = 3. The inset to panel B
shows a representative Western blot. C, the H
2
O
2
-induced increase in selective
capacity to degrade oxidized proteins is also blocked by inhibition of Nrf2 expression.
MEF cells were prepared and lysed as described in panels A and B. Lysates were
incubated for 4 h with [
3
H]Hb or [
3
H]Hb
ox
. Percent protein degraded was calculated
after addition of 20% TCA and 3% BSA, and centrifugation to precipitate the
remaining intact proteins (5, 12, 15, 26). Percent protein degradation was determined by
release of acid soluble counts in TCA supernatants, by liquid scintillation as follows: %
degradation = (acid soluble counts - background counts)/total counts X 100. Results
are mean ± S.E., n = 3.

certainly suggestive. We confirmed that at least some of the EpRE elements upstream
223

of 20 S proteasome subunits are not only present, but H2O2 induces binding of Nrf2 to
these sequences. To test this we performed a ChIP assay on an EpRE element in the 5’
-untranslated region (5’ -UTR) of the proteasome β5 subunit gene, which has previously
been shown to have functional EpRE elements (44). This EpRE element showed a
strong increase in Nrf2 binding under H2O2 exposure (Fig. B.5C), thus demonstrating
that 20 S proteasome induction under our H
2
O
2
adaptation conditions is mediated
by the Nrf2 signal transduction pathway. Using RT-PCR, we were also able to
demonstrate a corresponding, hydrogen peroxide-induced, 2-fold increase in
cellular mRNA levels of the 20 S proteasome β5 subunit during the same time period
(Fig. B.5D).

B.8 Pretreatment with Nrf2 “Inducers” Causes Increased Tolerance to
Oxidative Stress

We have developed a transient oxidative stress-adaptive model in which
pretreatment of cells with a low concentration of H
2
O
2
causes changes in gene
expression that permit survival of a much higher, normally toxic, challenge dose
of H
2
O
2
delivered 24 h later (Pickering et al., 2010; Wiese et al., 1995). Without
pretreatment with a mild dose of H
2
O
2
, the challenge dose causes protein
oxidation, growth arrest, diminished DNA and protein synthesis, and some degree
of apoptosis; all these measures of toxicity are avoided or minimized if cells are
adapted by pretreatment with a mild dose of H
2
O
2
before being exposed to the
challenge dose (Davies, 1999, 2000; Ermak et al., 2002; Pickering et al., 2010;
224

Wiese et al., 1995). We now wanted to test if adaptive resistance to H
2
O
2
toxicity
could be achieved by pretreatment with a wide range of Nrf2 inducers (both
oxidative and non-oxidative). In other words, we wanted to test whether adaptive
increases in oxidative stress resistance, via increased proteasomal capacity, is a
general feature of the Nrf2 signal transduction pathway. As shown in Fig. B.6A, 1.0
mM H
2
O
2
challenge caused a 65% decrease in cell counts in non-adapted, naive
cells; this was mostly due to prolonged growth arrest, as previously shown (Davies,
1999, 2000; Ermak et al., 2002; Pickering et al., 2010; Wiese et al., 1995). In
contrast, cells that had been pretreated with (low concentrations of) a range of
oxidants exhibited substantially less toxicity: only a 29% growth arrest with H
2
O
2
pretreatment, 37% with paraquat, 42% with menadione, and 50% with
peroxynitrite (Fig. B. 6A). We also tested other inducers of Nrf2, including DL-
sulforaphane (Kamanna, Saied, Evans-Hexdall, & Kirschenbaum, 1991; Mandal et
al., 2009; C. Yang, Zhang, Fan, Liu, & Nrf, 2009), curcumin (Gan et al., 2010; Lii et
al., 2010; Nair et al., 2010; Zhao et al., 2010), and lipoic acid (Shay, Moreau, Smith,
Smith, & Hagen, 2009; Shenvi, Smith, & Hagen, 2009; Suh et al., 2004). Growth
arrest induced by H
2
O
2
challenge was decreased (from 65%) to 39% with DL-
sulforaphane pretreatment, to 31% with curcumin pre-treatment, and to only 35%
with lipoic acid pretreatment (Fig. B.6A). Although it is important to note that these
agents are not exclusive inducers Nrf2, the fact that all produced protective
effects provides additional support for an important role for Nrf2 in oxidative
stress adaptation.

225

B.9 Nrf2, 20S Proteasome, Pa28αβ, and Immunoproteasome Play Important
Roles in H2O2-induced Adaptive Increase in Oxidative Stress Tolerance
The 20 S proteasome, immunoproteasome, and the Pa28αβ regulator all seem to
play important roles in adaptation (Pickering et al., 2010). We now confirmed this
conclusion, using siRNA directed against the 20 S proteasome,
immunoproteasome, and Pa28αβ regulator. With H
2
O
2
challenge there was a 55%
decrease in cell growth; this was reduced to only a 30% decrease with H2O2
pretreatment and adaptation (Fig. B.5A).

Fig. B.4. H2O2-induced expression of proteasome and proteasome regulators is Nrf2
dependent. H
2
O
2
treatment causes an increase in 20 S proteasome (A) and the
proteasome regulator Pa28αβ(B), both of which appear to depend upon Nrf2
expression. The increase in immunoproteasome (C) with H
2
O
2
treatment may be only
partly Nrf2 dependent, at best. MEF cells were prepared, treated, and harvested as
described in the legend to Fig. 3. The cells were then lysed, and samples were run on
SDS-PAGE gels and transferred to PVDF membranes as described in the legend to Fig
2.2. Membranes were treated with antibodies directed against 20 S proteasome subunit
β1, immunoproteasome subunit β1i (LMP2), proteasome regulator subunit PA28α, Nrf2,
and β-tubulin. Graphs in A–C show the levels of 20 S proteasome β1 subunit (panel A),
226

Pa28α regulator (panel B), and immunoproteasome β1i or LMP2 (panel C) each divided
by β-tubulin levels for each well and then plotted as a percent of control. Values are
mean ± S.E., n = 3. D, representative Western blots for β1, β-tubulin, β1i (LMP2), and
Pa28α (all ± Nrf2 siRNA) for graphs in A-C.
However, if cells were first pretreated with siRNA against 20 S proteasome,
immunoproteasome, or Pa28αβ, the adaptive response was severely blunted and
the H
2
O
2
challenge-induced growth arrest returned to 50 – 60% (Fig. B . 5A).
Having shown that Nrf2 plays a key regulatory role in H
2
O
2
- induced increases in
20 S proteasome and Pa28αβ (Fig. B.4) we were interested in testing if the adaptive
role of these proteins in increasing tolerance to H
2
O
2
challenge is also Nrf2
dependent. Using the “pretreatment and challenge model” there was a shift from
65% growth arrest to only 35% growth arrest with H
2
O
2
pretreatment; this
returned to 67% growth arrest if cells were pretreated with Nrf2 siRNA (Fig. B.6B),
indicating a significant role for Nrf2 in H
2
O
2
-induced tolerance to oxidative stress.

B.10 Nrf2 and Proteasome Are Key Factors in Adaptive Increase in Tolerance to
Oxidative Stress Produced by Nrf2 Inducers

Having observed an adaptive response with the use of multiple Nrf2 inducers we
wanted to determine whether proteasome and the Pa28αβ regulator are always
involved in Nrf2-dependent adaptation. To test this we performed Western blots on
cells 24 h after pretreatment with a range of concentrations of various Nrf2
inducers. We observed modest increases (=40%) in 20 S proteasome with lipoic
acid and curcumin treatment, and more than a 2-fold increase with DL-
sulforaphane (Fig. B.7A). To test the role of both Nrf2 and proteasome in the
227

adaptive response to Nrf2 inducers we used the pretreatment and challenge model
of Fig. B.6, with a background of scrambled siRNA, Nrf2 siRNA, or 20 S proteasome
siRNA (Fig. B.7B). With H
2
O
2
challenge of non-adapted cells there was a 68%
growth arrest. Lipoic acid pretreatment reduced growth arrest to =50%; however,
growth arrest returned to =85% with either Nrf2 or 20 S proteasome siRNA
treatment. Similarly, DL-sulforaphane treatment reduced growth arrest to =40%,
which was returned to =85% with either Nrf2 or 20 S proteasome siRNA treatment.
Curcumin treatment reduced growth arrest to ≈40%, which was restored to ≈85%
with 20 S proteasome siRNA and 70% with Nrf2 siRNA (Fig. B.7B).

B.11 Discussion
Our studies reveal a mechanistic link between Nrf2, the 20 S proteasome, the
Pa28αβ (11 S) proteasome regulator, and transient adaptation to oxidative stress.
It now appears clear that the Nrf2 signal transduction pathway plays a major role
in both the increased proteasomal capacity to degrade oxidized proteins, and
the increased cellular tolerance to oxidative stress that are induced by
pretreatment with a mild dose of oxidant.

We find that the cellular capacity to degrade oxidized proteins, and intracellular
levels of the 20 S proteasome, immunoproteasome, and the Pa28αβ (11 S)
regulator are all increased 2–3-fold during adaptation to oxidative stress. Similar
228

results were obtained with the H
2
O
2
and peroxynitrite oxidants, and the redox-
cycling agents menadione and paraquat. Proteasome inhibitors, and siRNA directed

Fig. B.5. 20 S proteasome is required for H2O2 adaptation, and contains active EpRE
elements. A, MEF cells were grown, and incubated with siRNA directed against, 20 S
proteasome subunit β1, immunoproteasome subunit β1i (LMP2), proteasome regulator
subunit Pa28cx, or a scrambled vector, then pretreated with 1 µM H
2
O
2
as described in
the legend to Fig. 3. After 24 h, cells were challenged with a dose of 1 mM H
2
O
2
for 1 h,
washed, and resuspended in fresh complete medium. After another 24 h, cells were
harvested and cell counts were taken. Values are plotted as a percent of unchallenged
samples treated with scrambled siRNA, which had an average cell density of 110,000
cells/ml at the point of counting. Values are mean ± S.E., n = 4. B, ARE/EpRE consensus
sequences (TGANNNNGC/GCNNNNTCA) are present upstream of all 20 S proteasome and
immunoproteasome subunit genes. Data represent sequences 5 kb upstream of
promoters, based on the NCBI database of Mus musculus. ARE/EpRE sequences are
highlighted. C, H
2
O
2
treatment causes increased binding of Nrf2 to one of the EpRE
elements upstream of the promoter of the proteasome β5 subunit gene. Cells were grown
to 10% confluence then exposed to 1 µM H
2
O
2
for 1 h. ChIP analysis was then performed
as described under “Experimental Procedures.” Nonspecific binding was measured
through performing a ChIP assay in the absence of the Nrf2 antibody and input was as an
internal control by representing 1% of the sample prior immunoprecipitation. D, H
2
O
2
treatment causes increased mRNA expression of the 20 S proteasome β5 subunit. Cells
were grown to 10% confluence, then exposed to 1 µM H
2
O
2
for 1 h. After this cells were
harvested, the mRNA levels of the 20 S proteasome subunit β5 and the loading control
229

GAPDH were then determined through reverse transcriptase PCR followed by
quantitative PCR. Values are plotted, in arbitrary units, adjusted by levels of GAPDH.
Values are mean ± S.E., where n = 3.
against the 20 S proteasome β1 subunit, the immunoproteasome β1i (LMP2)
subunit, or the Pa28α (11 S) regulator subunit, all significantly limit the increase in
cellular proteolytic capacity and partially prevented the increased resistance to
oxidative stress (cell growth).

Cellular levels of Nrf2 were significantly increased by adaptation to oxidative stress,
and Nrf2 was seen to translocate to the nucleus, and to bind to ARE/EpRE
sequence(s) upstream of the proteasome β5 subunit gene. Blocking the induction of
Nrf2, with siRNA or retinoic acid, significantly limited the adaptive increases in cellular
proteolytic capacity, 20 S proteasome, and the Pa28αβ regulator. Increases in the
immunoproteasome, however, were only partially blocked by Nrf2 siRNA. Blocking
Nrf2 induction also limited the increase in oxidative stress resistance (cell growth).
When, instead of using oxidant exposure, we pretreated cells with Nrf2 inducers lipoic
acid, curcumin, or sulforaphane, we observed an increased cellular proteolytic capacity,
increased 20 S proteasome, and increased cellular resistance to oxidative stress
(cell growth); both Nrf2 siRNA and 20 S proteasome β1 subunit siRNA effectively
blocked these increases.

These results suggest that oxidants, redox cycling agents, and other Nrf2 inducers
cause adaptation through the up-regulation of Nrf2 and its translocation to the
nucleus. This, in turn, induces expression of the 20 S proteasome and the Pa28αβ
230

regulator. In contrast, the immunoproteasome, whose levels were also increased
by adaptation to oxidative stress, appears to be only partially regulated by Nrf2, if at
all.

The Nrf2 signal transduction pathway is known to respond to stressful
conditions (K. Itoh et al., 1997; K. Itoh et al., 1999; Ken Itoh et al., 2003; Kraft,
Deocaris, Wadhwa, & Rattan, 2006; Kwak et al., 2003; M. McMahon et al.,
2001; Michael McMahon et al., 2003; Moi et al., 1994; Nguyen et al., 2003;
Rushmore et al., 1991; Venugopal & Jaiswal, 1996). Under non-stress
conditions Nrf2 is retained in the cytoplasm through the formation of a complex
with several proteins, including Keap1. In this state it is constantly turned over
through ubiquitin-dependent 26 S proteasome degradation. This permits a high
expression rate, enabling rapid accumulation of Nrf2 when degradation is
blocked, whereas ensuring low Nrf2 steady-state levels under normal
conditions. Pretreatment with an oxidant, or other Nrf2 inducer, liberates Nrf2
from the Keap1 complex. This also prevents further Nrf2 degradation resulting in
a dramatic rise in Nrf2 cellular levels as well as its translocation to the nucleus.
Once there, it can bind to AREs, which have also been called EpREs, in a range
of genes.

We find that genes encoding many 20 S proteasome subunits contain at least
one if not multiple ARE/EpRE sequences in their upstream, untranslated
regions (Fig. 2.5B) and have shown that at least some of these ARE/EpRE
231

sequences have a strong increase in Nrf2 binding under H
2
O
2
exposure. In
contrast, we find that only a single subunit of the three immunoproteasome
subunits contains the ARE/EpRE sequence. It is tempting to suggest that this
difference in density of ARE/EpRE sequences may explain the differential

Fig. B.6. Pretreatment with Nrf2 inducers causes increased tolerance to oxidative
stress. A, pretreatment of cells with a mild, nontoxic, dose of a range of oxidants, and
other inducers of Nrf2, causes increased tolerance to a subsequent toxic H
2
O
2
challenge. MEF cells were grown to 10% confluence and treated with 1 nM peroxynitrite,
1 nM menadione, 10 pM DL-sulforaphane, 1 pM paraquat, 500 pM curcumin, 100 nM
H
2
O
2
, or 500 pM lipoic acid for 1 h, then washed and resuspended (see ”Experimental
Procedures“). After 24 h, cells were challenged with 1 mM H
2
O
2
for 1 h, then washed and
resuspended. After another 24 h, cells were harvested and counted. Values are mean ±
S.E., n = 3. B, the increase in tolerance to H
2
O
2
challenge induced by mild oxidant
pretreatment is lost by blocking Nrf2 expression. MEF cells were grown, and treated
with siRNA directed against Nrf2 or a scrambled vector, then pretreated (or not) with 1
µM H
2
O
2
as described in the legend to Fig. 3. After 24 h, cells were challenged with 1
mM H
2
O
2
for 1 h, then washed and resuspended in fresh complete medium. After
another 24 h, cells were harvested and cell counts were taken. Values are plotted as a
percent of unchallenged samples treated with scrambled siRNA, and values are mean ±
S.E., n = 4

sensitivity of the 20 S proteasome and the immunoproteasome to Nrf2 siRNA
and retinoic acid, and to propose that immunoproteasome may be regulated by
232

another mechanism.

Nrf2 is not the only protein that can bind to ARE/EpRE sequences, and it is

Fig. B.7. Lipoic acid, DL-sulforaphane, and curcumin promote adaptation in an Nrf2
and proteasome-dependent manner. A, MEF cells were grown to 10% confluence and
treated with 1–100 nM lipoic acid, 1–100 nM DL-sulforaphane, or 1–100 nM curcumin.
After 24 h, cells were harvested, lysed, run on SDS-PAGE gels, and transferred to
PVDF membranes as described in the legend to Fig. 2. Membranes were treated with
antibodies directed against 20 S proteasome subunit β1, immunoproteasome subunit
β1i (LMP2), proteasome regulator subunit PA28α, and β-tubulin. B, the adaptive response
produced by Nrf2 inducers is lost or blunted by blocking either Nrf2 or proteasome
expression. MEF cells were grown and pretreated with siRNA directed against Nrf2, 20
S proteasome subunit β1, or a scrambled vector then, 4 h later, treated with 500 pM
lipoic acid, 10 pM DL-sulforaphane, or 500 pM curcumin, as described in the legend to
Fig. B.6. After 24 h, cells were challenged with 1 mM H
2
O
2
for 1 h, then washed and
resuspended in fresh complete medium. After another 24 h, cells were harvested and
cell counts were taken. Values are mean ± S.E., n = 4, plotted as a percent of
unchallenged samples treated with scrambled siRNA.
certainly possible that other signal transduction proteins may bind to proteasomal
and Pa28αβ (11 S) regulator ARE/EpRE elements, and/or to
immunoproteasome. We are also searching for other potential pathways for
immunoproteasome induction, of which the interferon regulatory factor 1 (Foss
& Prydz, 1999; Namiki et al., 2005; Seifert et al., 2010) appears to be a good
candidate. Finally, there may well be overlapping pathways of signal
233

transduction that act synergistically, or antagonistically, to dynamically adjust
proteasome/immunoproteasome levels during adaptation to oxidative stress.
In conclusion, we find that increases in 20 S proteasome and Pa28αβ (11 S)
regulator expression are largely mediated by the Nrf2 signal transduction pathway
during adaptation to oxidative stress. These Nrf2-dependent increases in 20 S
proteasome and Pa28αβ (11 S) are shown to be important for fully effective adaptive
increases in cellular stress resistance. In contrast, the immunoproteasome, which
also contributes to oxidative stress adaptation, is shown to be minimally
responsive to Nrf2 control.

B.12 Materials and Methods
B.12.1 Materials
All materials were purchased from VWR unless otherwise stated. Murine embryonic
fibroblasts (MEF), catalog number CRL-2214, were purchased from ATCC
(Manassas, VA). Cells were grown in Dulbecco’s modified Eagle’s medium (DMEM),
catalog number 10-013-CV, from Mediatech (Manassas, VA) and supplemented
with 10% fetal bovine serum (catalog number SH30070.03) from Hyclone (Logan,
UT): henceforth referred to as “complete medium.” Cells were typi- cally incubated
at 37 °C under 5% CO
2
and ambient oxygen.

B.12.2 Adaptation to Oxidants
MEF cells were grown to 10% confluence (=250,000 cells/ml) then pretreated with
100 nM to 100µM H
2
O
2
(catalog number H1009-100 ml) from Sigma, 1 nM to 1 µM
234

peroxynitrite (catalog number 516620) from Merck (Darmstadt, Germany), 0.2–100
nM menadione (catalog number ME105) from Spectrum Chemicals (Gardena, CA),
or 10 pM to 100 nM paraquat (catalog number PST-740AS) from Ultra Scientific
(Kingstown, RI), for 1 h at 37 °C under 5% CO
2
to induce adaptation to oxidative
stress. Cells were then washed once with phosphate-buffered saline (PBS), which
was finally replaced with fresh complete medium.

B.12.3 Induction or Inhibition of Nrf2
MEF cells were grown to 5% confluence and treated with varying concentrations of
Nrf2 inducers. DL-Sulforaphane (catalog number S2441-5 mg) or curcumin
(catalog number C1386-5G) from Sigma. Lipoic acid (catalog number L1089) was
purchased from Spectrum Chemicals, dissolved in N,N-dimethylformaldehyde, and
combined with complete medium at a final concentration of 0.1%; and a
comparable concentration of N,N-dimethylformaldehyde was added to control
cells. Curcumin was dissolved in ethanol and combined with complete medium at
a final concentration of 0.1%, and a comparable concentration of ethanol was
added to control cells. In some assays cells were treated with the Nrf2 inhibitor all-
trans-retinoic acid (catalog number R2625- 100MG) purchased from Sigma. trans-
Retinoic acid was dissolved in ethanol and combined with complete medium at a
final concentration of 0.1%; a comparable concentration of ethanol was added to
control cells.

235

B.12.4 Western Blot Analysis
MEF cells were harvested from 25–75-cm
2
flasks by trypsinization. Cells were
washed twice with PBS to remove trypsin and then lysed in RIPA buffer, (catalog
number 89901) from Thermo Fisher (Waltham, MA), sup- plemented with protease
inhibitor mixture (catalog number 11836170001) from Roche Applied Science.
Protein content was quantified with the BCA Protein Assay Kit (Pierce) according to
the manufacturer’s instructions. For Western analysis, 5–20 µg of protein was run
on SDS-PAGE and transferred to PVDF membranes. Using standard Western
blot techniques, membranes were treated with proteasome regulator subunit
Pa28cx antibodies (catalog number PW8185-0100) from Enzo Life Sciences
(Plymouth Meeting, PA), immunoproteasome subunit anti-LMP2/β1i antibody
(catalog number ab3328) was purchased from Abcam (Cambridge, MA), 20 S
proteasome anti-β1 antibody (catalog number sc-67345) or anti-Nrf2 anti- body
(catalog number sc-722) were both from Santa Cruz Bio- technology (Santa Cruz,
CA). The blocking buffer employed for Western blotting was Startingblock
TM
buffer
(catalog number 37539) from Thermo Fisher and the wash buffer was 1X PBS
containing 0.1% Tween 20. An enhanced chemiluminescence kit (Pierce) was
used for chemiluminescent detection and membranes were analyzed using the
biospectrum imaging system (UVP, Upland, CA).

B.12.5 siRNA “Knockdown” of Nrf2 or Proteasome
Nrf2 (catalog number sc-37049), β1 (catalog number sc-62865), β1i (Lmp2) (catalog
number sc-35821), Pa28α (catalog number sc-151977), and Scrambled Control
236

(catalog number sc-37007) siRNA were purchased from Santa Cruz
Biotechnology. For experiments with these siRNAs, MEF were seeded at a density
of 100,000 cells/well in 6- or 48-well plates and grown to 10% confluence. siRNA
treatment was then performed as described in the Santa Cruz Biotechnology
product manual.

B.12.6 Fluoropeptide Proteolytic Assays
MEF were harvested by cell scraping in phosphate buffer. Cells were then re-
suspended in 50 mM Tris, 25 mM KCl, 10 mM NaCl, 1 mM MgCl
2
, 1 mM DTT (pH
7.5) and lysed by 3 freeze-thaw cycles. Protein was quantified using a BCA Protein
Assay Kit. Then 5.0 µg of cell lysate per sample was transferred in triplicate to 96-
well plates, and 2 µM of N-succinyl-Leu-Leu-Val-Tyr-AMC (catalog number 80053-
860) purchased from VWR was then added to the plates. Plates were incubated at
37 °C and mixed at 300 rpm for 4 h. Fluorescence readings were taken at 10-min
intervals using an excitation wavelength of 355 nm and an emission of 444 nm.
Following subtraction of background fluorescence, fluorescence units were
converted to moles of free AMC, with reference to an AMC standard curve of
known amounts of AMC (catalog number 164545) purchased from Merck
(Whitehouse Station, NJ). In some experiments, cells were treated with 1 µM of the
proteasome inhibitor MG132 (catalog number 474790) from Merck (Whitehouse
Station, NJ) or 5 µM of the proteasome inhibitor lactacystin (catalog number
80052-806) from VWR, 30 min prior to incubation and addition of substrates.
MG132 was dissolved in DMSO at a X100 concentration and combined with
237

samples at a concentration of 1%. In these experiments, control cells were
treated with an equivalent concentration of plain DMSO.

B.12.7 Proteolytic Assay of Radiolabeled Proteins
Tritium-labeled hemoglobin ([
3
H]Hb) was generated in vitro as described previously
(Davies, 2001; Grune et al., 1996; Pickering et al., 2010; Shringarpure et al., 2003;
Ullrich et al., 1999) using the [
3
H]formaldehyde and sodium cyanoborohydride
method of Jentoft and Dearborn (Jentoft & Dearborn, 1979), and then extensively
dialyzed. Before dialysis, some purified radio- labeled proteins were oxidatively
modified by exposure to 1.0 mM H
2
O
2
for1 h to generate oxidized substrates. All
substrates were then incubated with cell lysates to measure proteolysis.
Percentage of protein degraded for both Hb and oxidized Hb was calculated by
release of acid-soluble (supernatant) counts, by liquid scintillation after addition of
20% TCA (trichloroacetic acid) and 3% BSA (as carrier) to precipitate remaining
intact proteins (5, 12, 15, 26), in which % degradation = 100 X (acid-soluble counts
- background counts)/total counts.

B.12.8 Cell Counting Assay
Cells were seeded in 100-µl samples at a density of 100,000 cells/ml in 48-well
plates. Twenty-four hours after seeding, some cells were pretreated with an oxidant
or an Nrf2 inducer. At 48 h after seeding, cells were challenged with a toxic dose of
100 µM to 1 mM hydrogen peroxide for 1 h followed by addition of fresh complete
238

medium. Cells were harvested 24 h after challenge, using trypsinization. The cell
density of 100-µl samples of cell suspensions was then obtained using a Cell
Counter purchased from Beckman Coulter (Fullerton, CA).

B.12.9 Chromatin Immunoprecipitation (ChIP) Assay
Four million cells were prepared at 10% confluence, the cells were exposed to
either 0 or 1 µM H
2
O
2
for 1 h. ChIP analysis was performed using the reagents
and methods provided in a Chromatin Immunoprecipitation Assay Kit (catalog
number 17-295) purchased from Millipore (Temecula, CA). Briefly cells were cross-
linked with 1% formaldehyde for 10 min, washed twice with PBS, dislodged
through scraping, and re-suspended in 1 ml of 1% SDS lysis buffer containing
protease inhibitor. Samples were sonicated using 10 bursts of 5 s, output of 50
watts (Branson Sonifier 140, Branson Ultrasonic, Danbury, CT), and then
centrifuged at 13,000 X g for 10 min. The super- natant was removed and diluted
in a 10-fold excess of ChIP dilution buffer (1% of samples were removed at this
point to later form the input samples). Samples were pre-cleared using a 30-min
incubation with 30 µl/ml of salmon sperm DNA/Protein A-agarose slurry. Samples
were then incubated for 1 h with 8 µg/ml of Nrf2 antibody (catalog number sc-13032)
purchased from Santa Cruz Biotechnology, then 30 µl/ml of salmon sperm
DNA/Protein A-agarose slurry was added and samples were incubated overnight
at 4 °C under gentle agitation. After this, the bead slurry was subjected to
sequential 10-min washes with low salt immune complex, high salt immune
complex, LiCl immune complex, and TE buffer. Samples were detached from the
239

bead slurry with two washes of 250 µl of 1% SDS, 0.1 M NaHCO
3
, then reverse
cross-linked by incubation with 200 µM NaCl for4 h at 65 °C. 10 µM EDTA, 40 µM
Tris-HCl, and 20 µg of proteinase K were then added to the samples, and the
samples were incubated for 1 h at 45 °C. DNA was isolated and purified from the
samples using phenol/chloroform/isoamyl alcohol. PCR was then performed on
samples as described below. 5 µl of DNA from each sample was combined with 15
µl of the PCR SYBR Green Master Mix purchased from Applied Biosystems, 1.5 µl
each of 5 µM working solutions of forward and reverse PSMB5 primers designed
by Kwak et al. (44) (catalog number 2110654) and purchased from Invitrogen, and 7
µl of DNase/RNase-free ddH
2
O. The forward primer sequence used was
CAGACCGGCGCTGGTATTTAGAGG and the reverse primer sequence was
TAGCCAGCGCCATGTTTAG- CAAGG. PCR was carried out in a 7500 Real Time
PCR System device from Applied Biosystems, using an annealing temperature of
61 °C and an extension temperature of 72 °C, for a total of 55 cycles. PCR products
were then examined on a 1% agarose gel containing 0.001% ethidium bromide.

B.12.10 Real Time PCR Assay of mRNA Levels
Total RNA was extracted using TRIzol reagent and treated with DNA-free reagent
according to the manufacturer’s (Invitrogen, catalog number 1908) protocol to
remove DNA. RNA samples were then reverse transcribed using the TaqMan
random hexamers (catalog number N808-0234) purchased from Applied
Biosystems and the mRNA levels were measured by real time PCR (RT- PCR)
using a 7500 real time PCR system purchased from Applied Biosystems. In
240

brief, 5 µl of reverse transcription reaction product was added to a reaction tube
containing 12.5 µl of SYBR Green PCR Master Mix, 5.5 µl of sterile water, and 1 µl of
a 5 µM working solution of each primer (forward and reverse) for the proteasome
β5 subunit or GAPDH mRNA. The total PCR sample was 25 µl. The primer
sequences used were as follows: 20 S proteasome subunit β5, 5'-
GCTGGCTAACATGGTGTATCAT-3 and 5'-AAGTCAGCTCATTGTCACTGG-3 as
used previously (Kwak et al., 2003) and GAPDH, 5'-GATGCAGGGAT-
GATGTTC-3' and 5'-TGCACCACCAACTGCTTAG-3'.

241

Abstract (if available)

Abstract Oxidative stress is a condition that all aerobic organisms must face due to the production of potentially harmful reactive oxygen species (ROS) as a by-product of oxygen utilization. ROS are capable of reacting with and damaging all types of macromolecules. Due to the many disorders associated with the accumulation of damage caused by oxidative stress, a large body of research has been devoted to understanding the mechanisms through which cells are able to prevent and/or repair damage caused by ROS. Although large strides have been made in understanding the molecular factors that affect the ability to tolerate oxidative stress, much still remains unknown about this extremely complex trait. In order to further understand the molecular and genetic complexity of oxidative stress tolerance, I undertook several different projects during the course of my graduate studies. The majority of these studies make use of the powerful model organism, Saccharomyces cerevisiae. By using this organism, I was able to conduct a large-scale genetic mapping study aimed at dissecting the genetic and molecular basis of natural variation in oxidative stress tolerance in budding yeast. The approach used in this study facilitated the cloning of several genes that influence this trait, including some that had not, to the best of my knowledge, been previously shown to have a role in oxidative stress tolerance. Furthermore, by using highly tolerant strains generated from this study, I was able to find mechanisms that take individuals to a level of tolerance beyond what can be achieved through segregating variation alone.

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Asset Metadata

Creator Linder, Robert A. (author)

Core Title The complex genetic and molecular basis of oxidative stress tolerance

School College of Letters, Arts and Sciences

Degree Doctor of Philosophy

Degree Program Molecular Biology

Publication Date 09/29/2016

Defense Date 08/01/2016

Publisher University of Southern California (original), University of Southern California. Libraries (digital)

Tag aneuploidy,budding yeast,complex traits,OAI-PMH Harvest,oxidative stress,quantitative traits

Format application/pdf (imt)

Language English

Contributor Electronically uploaded by the author (provenance)

Advisor Ehrenreich, Ian M. (committee chair), Boedicker, James (committee member), Dean, Matt (committee member), Finkel, Steven (committee member)

Creator Email rlinder@usc.edu,rlinder02@gmail.com

Permanent Link (DOI) https://doi.org/10.25549/usctheses-c40-309602

Unique identifier UC11213623

Identifier etd-LinderRobe-4840.pdf (filename),usctheses-c40-309602 (legacy record id)

Legacy Identifier etd-LinderRobe-4840.pdf

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Document Type Dissertation

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Rights Linder, Robert A.

Type texts

Source University of Southern California (contributing entity), University of Southern California Dissertations and Theses (collection)

Access Conditions The author retains rights to his/her dissertation, thesis or other graduate work according to U.S. copyright law. Electronic access is being provided by the USC Libraries in agreement with the a...

Repository Name University of Southern California Digital Library

Repository Location USC Digital Library, University of Southern California, University Park Campus MC 2810, 3434 South Grand Avenue, 2nd Floor, Los Angeles, California 90089-2810, USA