Identification of Genetic Regulators of Mitochondrial DNAContent during Respiratory Growth

by

Yutong Ma

A thesis submitted in conformity with the requirementsfor the degree of Master of Science

Graduate Department of Molecular GeneticsUniversity of Toronto

© Copyright by Yutong Ma 2020

ii

Identification of Genetic Regulators of Mitochondrial DNA Content

during Respiratory Growth

Yutong Ma

Master of Science

Molecular GeneticsUniversity of Toronto

2020

Abstract

In order to discover novel regulatory pathways of mitochondrial DNA (mtDNA) content, I

developed and carried out a high-throughput screen to quantitatively measure mtDNA level

across two yeast whole-genome deletion collections with different HAP1 genotype. I inoculated

and extracted genomic DNA from over eleven-thousand cultures and quantified mtDNA content

by quantitative PCR using Taqman probes. The screen was able to identify the known genes

required for respiratory growth and mtDNA maintenance, indicating the accuracy of the screen

results. As expected, mutants that increase cell size also showed increased mtDNA. I showed

that the mtDNA level but not the mitochondrial volume was affected by the HAP1 genotype in

wild-type strains. According to the gene ontology enrichment analysis, mutants lacking ESCRT-I

and ESCRT-II components were enriched in the high mtDNA content. Follow-up experiments

showed that these mutants also had increased mitochondrial volume. I hypothesized that the

ESCRT machinery may play a role in mitochondrial turnover process under active respiratory

growth. This work can lead to future studies on mtDNA regulation under diverse conditions.

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AcknowledgmentsProduction of this thesis would not have been possible without my supervisor Dr. Amy A. Caudy.

I owe many thanks to her for giving me the opportunity to work and study in her lab as an

international graduate student. At the beginning of my graduate studies, I faced a lot of

difficulties not only in science but also in daily life when moving abroad to an unfamiliar country.

Her supports and encouragements helped me to adapt myself to the new life quickly and

smoothly. I am very grateful to her guidance on my project and always motivated by her

enthusiasm for science.

I would like to thank Dr. Adam Rosebrock for providing the excellent idea about the aptamer I

used in my screen which accelerated the experimental process and improved the screen quality.

Additionally, I would not be able to start this project without his previous work on replacing the

hap1 mutation in the S288c deletion collection.

Special thanks to my supervisory committee members Dr. Leah Cowen and Dr. Angus

McQuibban. They have been so generous for their advice and guidance on research and

presentation skills at all committee meetings. They did everything they could to keep me on track

and provide valuable inspirations during the ongoing meetings and discussions.

I feel grateful to have these friendly and brilliant fellow colleagues, Dr. Julia Hanchard, Dr. Olga

Zaslaver, Dr. Soumaya Zlitni, Kathleen Dolan and Yoomi Oh. They made me feel comfortable

and productive throughout the time at the lab. I would also like to thank my undergraduate

students, Elissa Khalife-Sayah, Charlie (Seung Ho) Choi, and Xueting Xiong. Their hard work

and passion for learning made me become a better mentor and collaborator. In addition, I greatly

appreciate the staffs from Microscopy Imaging Laboratory (MIL) and Advanced Optical Microscopy

Facility (AOMF) for their help with imaging capture and analysis.

Last but not the least, I owe many thanks to my parents, Shoumin Ma and Hong Fu. Even though

they are resident far away in China, we communicate through Wechat Video every week which

is a constant source of emotional support. Their selfless love is the greatest motivation I have for

my whole life.

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Table of ContentsAcknowledgments.......................................................................................................................... iii

Table of Contents............................................................................................................................iv

List of Tables................................................................................................................................. vii

List of Figures...............................................................................................................................viii

List of Abbreviations...................................................................................................................... ix

List of Appendices........................................................................................................................... x

Chapter 1 General Introduction....................................................................................................... 1

1 Current knowledge on mitochondrial DNA regulation...............................................................1

1.1 The origin and function of mitochondria............................................................................. 1

1.2 Comparison of yeast and human mitochondrial genome.....................................................2

1.3 The mechanisms of mtDNA replication.............................................................................. 3

2 Mitochondrial diseases................................................................................................................6

2.1 Abnormal mtDNA in disease...............................................................................................6

2.2 mtDNA changes along with aging.......................................................................................7

3 ESCRT complex and the interconnectivity among cellular membranes and organelles............ 8

3.1 ESCRT subunits...................................................................................................................8

3.2 Functions of the ESCRT machinery.................................................................................... 9

3.3 ESCRTs are involved in autophagy...................................................................................10

3.4 Connections between mitochondria an other membrane compartments........................... 10

Chapter 2 Experimental Methods.................................................................................................. 12

4 Yeast strains and husbandry......................................................................................................12

4.1 List of strains......................................................................................................................12

4.2 Growth conditions..............................................................................................................14

4.3 Generation of mpapaya-labeled-mitochondria haploid strains.......................................... 14

5 High-throughput mitochondrial DNA measurement................................................................ 14

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5.1 High-throughput gDNA extraction.................................................................................... 14

5.2 Quantitative PCR cycling conditions.................................................................................15

6 Spinning disk confocal microscopy.......................................................................................... 16

6.1 Fixation and staining..........................................................................................................16

6.2 Microscope slides preparation........................................................................................... 16

6.3 Imaging conditions and analysis........................................................................................17

Chapter 3 Results........................................................................................................................... 18

7 A screen for quantifying mtDNA content across two yeast whole-genome deletioncollections................................................................................................................................. 18

7.1 Optimization of qPCR assay to quantify mtDNA..............................................................20

7.2 Optimization of aptamer treatment for qPCR reactions storage........................................23

7.3 High-throughput gDNA extraction assay using glass fiber plates is able to capturenuclear and mitochondrial DNA with similar efficiency...................................................29

7.4 Inoculation condition affects the mtDNA level................................................................. 31

7.5 The pipeline of the screen.................................................................................................. 33

8 Screen results overview.............................................................................................................35

8.1 mtDNA level but not mitochondrial volume is affected by the HAP1 genotype.............. 35

8.2 Comparison with a published respiratory-defecient strains dataset...................................40

8.3 Gene ontology enrichment analysis reveals potential regulators of mtDNA.................... 42

9 ESCRT complex has the potential of being involved in mitochondrial turnover process........45

9.1 ESCRT I/II subunit deletion mutants have increased mtDNA copy number.................... 45

9.2 ESCRT I/II subunit deletion mutants have increased mitochondrial volume....................47

Chapter 4 Conclusions................................................................................................................... 49

10 Summary of work......................................................................................................................49

11 Future studies on ESCRT complex and mitochondrial turnover.............................................. 50

12 Impact statement....................................................................................................................... 52

References......................................................................................................................................53

vi

Appendices.....................................................................................................................................61

vii

List of TablesTable 1. Strains used in this study ………………………………………………………………12

Table 2. Gene list of Class I and IV identified by Merz et al. …………………………………..13

Table 3. Primer and probe sequences …………………………………………………………...15

Table 4. Representative examples of GO enrichment analysis results ………………………….44

viii

List of FiguresFigure 1. The level of mtDNA is regulated by mitochondrial biogenesis and turnover ….…..…5

Figure 2. S288c strains carry a Ty1 insertion in HAP1 gene ……………………………………19

Figure 3. The result of the primers and probes concentration optimization …………………….21

Figure 4. 3’-ddC-terminated aptamer had superior performance to the aptamer without ddC …24

Figure 5. Optimization of anti-Taq aptamer ...……………………………………………….….27

Figure 6. The optimized mtDNA extraction and qPCR accurately determines nuclear/mtDNA

ratio ……………………………………………………………………………………………...30

Figure 7. Growth plate format affects the mtDNA level of S288c wild-type strain …………….32

Figure 8. Workflow of quantifying mtDNA level across yeast whole-genome deletion

collections………………………………………………………………………………………..34

Figure 9. The wild-type strains with the ancestral HAP1 gene show lower mtDNA content

compared to the S288c wild-type …………………………………………………………….....37

Figure 10. mPapaya fluorescence signal remains after fixation ………………………………...38

Figure 11. The wild-type strains with the ancestral HAP1 gene show similar mitochondrial

volume compared to the S288c wild type …………………………………………………….....39

Figure 12. Comparison of mtDNA levels with existing data on mitochondrial genome

maintenance ……………………………………………………………………………………..41

Figure 13. Overview of the mtDNA level of all deletion strains ………………………………..43

Figure 14. ESCRT I/II subunit gene deletion mutants have higher mtDNA level comparing to

wild-type strain ………………………………………………………………………………….46

Figure 15. ESCRT I/II subunit gene deletion mutants have larger mitochondria volume ….......48

ix

List of Abbreviations

ALP alkaline phosphatase

ATP adenosine triphosphate

BHQ black hole quencher dye

ConA Concanavalin A

Ct threshold cycle

DAPI 4′,6-diamidino-2-phenylindole

D-loop displacement-loop

ER endoplasmic reticulum

ERMES ER-mitochondria encounterstructure

ESCRT endosomal sorting complexrequired for transport

FADH2 Flavin adenine dinucleotide

FAM 6-carboxyfluorescein

gDNA genomic DNA

GO gene ontology

HEX hexachloro-fluorescein

HSP H-strand promoters

LSP L-strand promoters

MDV mitochondrial-derived vesicle

MERRF myoclonic epilepsy with ragged-red fibers

mtDNA mitochondrial DNA

mt-nucleoid mitochondrial nucleoid

MVB multivesicular body

NADH nicotinamide adenine dinucleotide

OXPHOS oxidative phosphorylation

PMD primary mitochondrial disease

pNP para-Nitrophenol

pNPP para-Nitrophenylphosphate

qPCR quantitative PCR

RDR recombination-dependent replication

ROS reactive oxygen species

SCMD S. cerevisiaeMorphologicalDatabase

SGA Synthetic Genetic Array

SGD Saccharomyces genome database

SMD secondary mitochondrial disease

TCA tricarboxylic acid cycle

TFAM mitochondrial transcription factor A

vCLAMP vacuole and mitochondria patch

YPD yeast peptone dextrose

YPG yeast peptone glycerol

x

List of AppendicesAppendix 1: The full list of GO enrichment analysis results…………………………………….61

1

Chapter 1General Introduction

1 Current knowledge on mitochondrial DNA regulation

1.1 The origin and function of mitochondriaMitochondria, as one of the “hallmark” organelles in eukaryotes, have attracted broad attention

for many decades. Particularly, the origin of mitochondria has been a controversial topic with

many hotly debated theories. Along with more and more species’ mitochondrial DNA (mtDNA),

ribosomal, and now genomic DNA (gDNA) being sequenced and analyzed through phylogenetic

and phylogenomic studies, at present the community widely agrees with the endosymbiotic

model which suggested that mitochondria evolved from a bacterial progenitor that entered into a

host cell via endocytosis at a specific time point during evolution (Margulis and Bermudes 1985).

However, there are still various points of view about when the mitochondrial symbiosis

happened. The two fundamental theories are “archezoan scenario” and “symbiogenesis scenario”

(Koonin 2010). The former theory believes that the host cell was already essentially eukaryotic

while the later one posits it to be a prokaryote. In other words, they argued whether the

complexity and the defined features of the eukaryotic cells arose before or after mitochondrial

symbiosis (Dover and Doolittle 1980; Martin and Muller 1998).

Even though there isn't a complete answer for the origin of mitochondria, it is clear that the

majority of the ancestral mitochondrial genome has been either lost or transferred to the nuclear

genome throughout evolution, resulting in diversity of mitochondrial genome size and encoded

genes among eukaryotes (Gray, Burger et al. 1999). It's still debated why mitochondria have

retained some genes in their own genome. One theory suggests that it is important to regulate the

expression of energy-transducing enzyme genes directly responding to physical environmental

changes which requires the colocation of gene and gene product (Allen 2015). Also, encoding

mitochondrial proteins in its own genome reduces the mislocalization rate and transportation

time (Bjorkholm, Harish et al. 2015).

Mitochondria are known to be the "powerhouse" of eukaryotes that supply energy in the form of

ATP to meet their cellular needs. The main energy production processes within the mitochondria

are the TCA cycle and oxidative phosphorylation (OXPHOS), which produce the energy-rich

2

molecules, NADH and FADH2. The electron transport chain in the mitochondrial intermembrane

space then transfer the redox energy from NADH and FADH2 to oxygen in several steps which

also pump protons into the intermembrane space. This resulting electrochemical gradient across

the inner membrane is then used to synthesize ATP when the protons return to the mitochondrial

matrix through the ATP synthase complex (Schultz and Chan 2001; Rich 2003). Besides the

dominant role of energy production, mitochondria also have many other functions. For example,

in human brown adipose tissue, protons can re-enter the mitochondrial matrix without

contributing to ATP synthesis but causing the heat release (Nicholls and Lindberg 1973). In

addition, mitochondria play multiple roles in metabolic pathways including reactive oxygen

species (ROS)-mediated regulation (Jastroch, Divakaruni et al. 2010; Liu 2010), heme synthesis

(Hoffman, Gora et al. 2003) and branched-chain amino acids biosynthesis (Falco, Dumas et al.

1985; Kohlhaw 2003).

1.2 Comparison of yeast and human mitochondrial genomeDue to the long evolutionary process, the mitochondrial genome among eukaryotes has

diversified in genome size and gene components. The human mitochondrial genome is about 16

kb with heavy (H)- and light (L)- strands. It encodes 13 proteins from the respiratory complexes

I, III, IV, and V, as well as 22 tRNAs and 2 rRNAs with no introns (Anderson, Bankier et al.

1981). Saccharomyces cerevisiae has a larger mitochondrial genome, about 85 kb, but only

encodes 7 key subunits of respiratory complexes and one ribosomal protein (Foury, Roganti et al.

1998). The genome size variability is mainly driven by the differences in non-coding regions

such as the intron numbers and the length of intergenic regions. Instead of the circular structure

of human mtDNA molecules (Anderson, Bankier et al. 1981), the majority of mtDNA in yeast

resides head-to-tail linear multimers of several genome units and a minor proportion of circular

form exist as well (Williamson 2002; Westermann 2014). Given the limited number of proteins

encoded by the mitochondrial genome, most of genes required for mitochondrial structure and

function (>99%) are encoded in the nuclear genome (Reinders, Zahedi et al. 2006).

The fermentative ability of yeast makes it a powerful model to study dysfunctional mitochondria

without being lethal. Yeast are able to survive even with complete loss of mtDNA as long as a

fermentable sugar such as glucose is supplemented (Chen and Clark-Walker 2000). Respiration-

deficient mutants of yeast are called "petite" due to the slow growth and small colony size

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(Goldring, Grossman et al. 1971). Petite yeast can arise from either mtDNA or nuclear DNA

mutations that compromise the oxidative phosphorylation or mtDNA replication process

(Slonimski, Cooper et al. 2016). Given these features of yeast, it now became an excellent model

for studying the mechanisms of mtDNA regulation and gaining insights into the human

mitochondrial disorders.

1.3 The mechanisms of mtDNA replicationThe replication of human mtDNA is discovered as a transcription-primed mechanism which is

conserved in all vertebrates (Shadel and Clayton 1993). Human mtDNA contains two origins of

replication (OH and OL) which are unidirectional and physically separated on the mtDNA

molecule (Kasamatsu and Vinograd 1973). In the displacement-loop (D-loop) region (Arnberg,

van Bruggen et al. 1971), the mitochondrial H-strand and L-strand promoters (HSP and LSP) are

located immediately adjacent to OH. The H-strand DNA synthesis requires the RNA transcripts

initiated at the LSP which are matured to RNA primers and then recognized by DNA polymerase

gamma to initiate the replication (Clayton 1982; Brown, Cecconi et al. 2005).

In lower eukaryotes such as S. cerevisiae, the mitochondrial genome contains three active

bidirectional origins of replication (ori/rep) and multiple ori-like elements (Foury, Roganti et al.

1998). Similarly to human mtDNA replication, RNA transcripts initiated at the ori/rep promoters

are used as primers for the leading-strand mtDNA synthesis (Baldacci, Cherif-Zahar et al. 1984).

Besides this transcription-primed replication, yeast has developed an alternative replication

mechanism which is recombination-dependent replication (RDR). In this model, the initiation of

replication is mediated by double-stranded breaks at sites of homologous recombination and then

a 3' single-stranded tail is generated and homologously paired with template circular mtDNA

followed by rolling circle replication (Maleszka, Skelly et al. 1991). This type of replication

may be the reason that the yeast mtDNA is organized into linear multimers (Maleszka, Skelly et

al. 1991). Despite the different mechanisms of initiating mtDNA replication, the elongation of

mtDNA in yeast requires the mtDNA polymerase which is encoded by MIP1 in yeast (Foury

1989). Disruption of Mip1 causes reduction or even complete depletion of mtDNA (Baruffini,

Ferrero et al. 2010; Stumpf, Bailey et al. 2010). In addition, 162 genes have been identified to be

essential for mtDNA maintenance through genome-wide screens (Merz and Westermann 2009).

4

However, there are still many unknowns about the mtDNA regulation, especially in disease

circumstances.

The quantity of mitochondria is regulated by the balance of mitochondrial biogenesis and

turnover (Figure 1). In terms of mtDNA level, it can be regulated together with mitochondrial

proteins or through the control of mtDNA replication. When mitochondrial biogenesis is induced,

both the replication of mtDNA and synthesis of mitochondrial proteins are promoted. However,

mtDNA replication could also happen without the biogenesis of the rest of mitochondrial

proteins which leads to the accumulation of mtDNA within the same amount of mitochondrial

volume (Hori, Yoshida et al. 2011). In contrast, the turnover of mitochondria usually leads to the

degradation of both mitochondrial protein and mtDNA. This can happen via canonical

mitophagy process, which occurs under stress conditions such as starvation and electron

transport disruption. However, the elimination of mtDNA can happen separately from mitophagy,

which is responsible for the clearance of mutant mtDNA (Bacman, Williams et al. 2013;

Gammage, Rorbach et al. 2014; Reddy, Ocampo et al. 2015) and paternal mitochondrial

genomes during maternal inheritance (Yu, O'Farrell et al. 2017). Therefore, the mtDNA level is

not always proportional to mitochondrial volume. Altered mtDNA levels may be caused by

changes in either or both biogenesis and turnover.

5

Figure 1. The level of mtDNA is regulated by mitochondrial biogenesis and turnover

The biogenesis of mitochondria requires the replication of mtDNA and synthesis of

mitochondrial proteins encoded in nuclear and mitochondrial genome. MtDNA synthesis and

mitochondrial biogenesis are coordinated. When mitochondrial biogenesis is inhibited, the

mtDNA level is decreased. When mitochondrial biogenesis is activated, the mtDNA level is

increased. Mitochondrial turnover leads to the degradation of mitochondria and mtDNA.

Blocking of turnover causes an increase in mtDNA content.

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2 Mitochondrial diseasesMitochondrial diseases are defined as disorders caused by dysfunctional mitochondria. As the

dominant function of mitochondria is ATP production, mitochondrial diseases can affect any

energy-intensive systems in the human body such as muscles, brain, liver and so on which could

cause diverse symptoms including but not limited to delay of development, increased infection

risk and muscle weakness (Turnbull and Rustin 2016). Mitochondrial diseases can be divided

into two subgroups based on pathogenesis: primary mitochondrial disease (PMD) and secondary

mitochondrial dysfunction (SMD) (Niyazov, Kahler et al. 2016). PMD is a group of

mitochondrial diseases result from heritable mutations on mtDNA and/or nuclear DNA which

directly encode electron transport chain proteins. Hundreds of mutations have been identified

that are associated with mitochondrial dysfunction both within the gene coding region and the

regulatory regions such as the D-loop of mtDNA (Ryzhkova, Sazonova et al. 2018). In contrast,

SMD doesn't include the OXPHOS-related mutations but usually are accompanied by other

diseases or result from environmental factors. SMD may also be a result of drug toxicity

(Shirasaka, Chokekijchai et al. 1995). However, distinguishing PMD and SMD in the clinic is

still a big challenge due to the incomplete understanding of gene functions and mitochondrial

regulations.

2.1 Abnormal mtDNA in diseaseIn pathology, mtDNA has gotten considerable attention by researchers, not only because many

mutations in mtDNA are known as fundamental to mitochondrial disorders but also due to the

widely-observed mtDNA depletion associated with various diseases. A number of studies have

compared the mtDNA level in normal and tumor tissues in paired samples and found decreased

mtDNA level in lung (Hosgood, Liu et al. 2010), renal (Selvanayagam and Rajaraman 1996),

liver (Yin, Lee et al. 2004), gastric (Wu, Yin et al. 2005) and prostate (de Bari, Moro et al. 2013)

cancer. Furthermore, reduced mtDNA content was reported to be correlated with tumor

progression and prognosis in breast cancer (Yu, Zhou et al. 2007).

There are many fewer kinds of disease caused by mitochondrial over-proliferation. One example

is myoclonic epilepsy with ragged-red fibers (MERRF). Patients with MERRF usually show

ragged appearance in muscle cells caused by the accumulation of abnormal mitochondria which

can also be seen in biopsy specimens from older people (Rifai, Welle et al. 1995). The most

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common point mutation causing MERRF was in the tRNALys gene on mtDNA (Shoffner, Lott

et al. 1990). Even though the ragged-red fibers is an important feature to diagnose MERRF, there

is a reported case where no ragged-red fiber phenotype was found but the patient suffered from

myoclonic epilepsy with the common MERRF mutation in tRNALys gene (Mancuso, Petrozzi et

al. 2007). On the other hand, the over-replication of mtDNA could also be a secondary

phenotype because of primary defects in nuclear genes (Bai and Wong 2005) or a compensatory

result in patients with high proportions of deletion mutants (Wong, Perng et al. 2003). In addition,

increasing mtDNA copy number may be a potential therapy for rescuing infertility. The mtDNA

mutator mouse expressed a proof-reading-deficient mtDNA polymerase with one amino acid

alteration in catalytic domain causing reduced male fertility (Trifunovic, Wredenberg et al. 2004).

In a mouse model, researchers were able to rescue the male infertility by increasing total mtDNA

copy number (Jiang, Kauppila et al. 2017). When the mitochondrial transcription factor A

(TFAM) was overexpressed in the infertile mouse, increased mtDNA level was detected

(Ekstrand, Falkenberg et al. 2004) and the male infertility was cured even with unaltered mtDNA

mutation load (Jiang, Kauppila et al. 2017).

2.2 mtDNA changes along with agingAging is an inescapable and complicated process in all organisms. There are many studies

focusing on the aging mechanisms in humans and one of the most fields with the greatest

momentum is investigating the changes of mtDNA features associated with aging including

mtDNA polymorphism, mutations and copy number (Bratic and Larsson 2013; Srisubat, Potisat

et al. 2014). As mitochondria lack an efficient DNA repair system, it is not surprising that

mtDNA accumulates more aging-associated mutations. For example, by quantifying the oxidized

bases of mtDNA and nuclear DNA in Alzheimer's patients and age-matched control subjects,

higher levels of mtDNA damage was reported in Alzheimer's disease (Wang, Xiong et al. 2005).

Similarly, another study has found increased mtDNA deletions in neurons in Parkinson disease

patients (Bender, Krishnan et al. 2006).

Despite the higher frequency of mtDNA mutations, mtDNA copy number is another target of

interest when studying aging and aging-related diseases. Analyzing the whole-genome

sequencing data of peripheral blood mononuclear cells from more than 1500 17-85 years old

individuals (Consortium, Walter et al. 2015), people have found that mtDNA copy number

8

decrease is significantly associated with age accompanied with mtDNA heteroplasmy (Zhang,

Wang et al. 2017). Another group reported a decline in mtDNA copy number in aged human

pancreatic islets which contributed to the diabetes pathology (Cree, Patel et al. 2008). In contrast,

an increased mtDNA content was found in human brain tissues with aging which was considered

as an inefficient manner to compensate the reduced steady-state level of mitochondrial

transcripts (Barrientos, Casademont et al. 1997).

In addition, researchers have also found that increasing mtDNA copy number can delay the

aging process. A study examined the arterial features in mice at different time points and

established a timeline of vascular aging (Foote, Reinhold et al. 2018). They found that

overexpression of the mitochondrial helicase Twinkle was able to increase mtDNA copy number

which enhanced mitochondrial respiration ability and delayed the vascular aging process (Foote,

Reinhold et al. 2018). Similarly, another group reported a successful rescue of oocyte quality and

infertility in aged mice by transferring autologous mitochondria from adipose tissue-derived

stem cells with increased mtDNA copy number into oocytes (Wang, Hao et al. 2017).

Taking all of the findings together, we can conclude that studying mtDNA copy number is

extremely important for understanding disease mechanisms and the aging process. However, the

regulation of mtDNA is still not completely uncovered, providing an opportunity for additional

work to assist in understanding diseases including cancer, aging, and infertility.

3 ESCRT complex and the interconnectivity amongcellular membranes and organelles

3.1 ESCRT subunitsThe endosomal sorting complex required for transport (ESCRT) machinery is composed of four

subunits ESCRT-0, -I, -II, and -III. The ESCRT complex was first identified to mediate the

ubiquitin-modified cargo via multivesicular bodies (MVBs). The components of the ESCRT

complex are quite conserved in eukaryotes and the subunits are sequentially recruited during

protein sorting. In S. cerevisiae, ESCRT-0 is made up of Vps27 and Hse1, forming a complex

that is responsible for the initial selection of ubiquitylated cargo at the endosomal membrane

(Bilodeau, Urbanowski et al. 2002). Then ESCRT-I is recruited through the direct binding

between ESCRT-0 component Vps27 and Vps23, one component of ESCRT-I (Katzmann,

9

Stefan et al. 2003). ESCRT-I is a heterotetramer including Vps23, Vps28, Vps37, and Mvb12

(Katzmann, Babst et al. 2001; Oestreich, Davies et al. 2007). The ESCRT-I also binds ubiquitin,

which increases the ubiquitylated cargo sorting efficiency (Bilodeau, Winistorfer et al. 2003).

ESCRT-II consists of Vps22, Vps25, and Vps36, which facilitates the recruitment of ESCRT-III

to endosomes (Babst, Katzmann et al. 2002). ESCRT-III is the only subunit without ubiquitin-

recognizing module but mediates the recruitment of deubiquitylating enzymes to remove the

ubiquitin from the cargo and the disassemble of ESCRTs from endosomal membrane (Amerik

and Hochstrasser 2004).

3.2 Functions of the ESCRT machineryAs indicated by its name, the ESCRT machinery was first discovered to be an important player

during endosomal sorting and ubiquitylated cargo transporting. Later on, additional cellular

processes were found to involve ESCRT components.

Firstly, components of both ESCRT-I and ESCRT-II were reported to regulate transcriptional

expression. Tsg101, an ortholog of Vps23 in mammals, represses transcription by either directly

binding to the promoter region (Sunohara, Kriz et al. 2019) or by recruiting other histone

deacetylation and/or DNA methylation enzymes (Rountree, Bachman et al. 2000; Muromoto,

Sugiyama et al. 2004). Vps22 of ESCRT-II interacts with a helicase enzyme involved in DNA

replication (Mathews, Holland et al. 2009). Secondly, the ESCRT machinery is also involved in

virus budding since the release of viruses from the plasma membrane is topologically similar to

the formation of multivesicular endosomes (Garrus, von Schwedler et al. 2001). Additionally, the

role of ESCRTs during cytokinesis was established based on the facts that ESCRT-I and -II were

both found to be located on centrosomes (Xie, Li et al. 1998; Jin, Mancuso et al. 2005) and

recruited ESCRT-III to mediate the membrane fission (Saksena, Wahlman et al. 2009).

Futhermore, the multiple nuclei phenotype observed in a tsg101 mutant has been observed in

various cell types including Arabidopsis (Spitzer, Schellmann et al. 2006) and human tumor cell

lines (Xie, Li et al. 1998).

Therefore, the ESCRT machinery does participate in a variety of biological processes which

makes it a valuable target to study in order to better understand multiple cellular processes and

functional connections.

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3.3 ESCRTs are involved in autophagyThe ESCRT machinery has been proposed to be involved in autophagy in different organisms

because disruption of ESCRTs in nematodes, flies, and mammals result in the accumulation of

autophagosomes (Roudier, Lefebvre et al. 2005; Lee, Beigneux et al. 2007; Rusten, Vaccari et al.

2007). In terms of mitochondrial clearance, there are two studies show the potential of ESCRTs

to be involved in mouse and Drosophila (Hammerling, Najor et al. 2017; Anding, Wang et al.

2018). In 2016, researchers reported a novel mitochondrial clearance pathway in autophagy-

deficient mouse embryonic fibroblasts where the ESCRT machinery recognized the ubiquitin-

labeled mitochondria by Parkin, a E3 ubiquitin ligase in mammals (Hammerling, Najor et al.

2017). In parallel, a screen for genes encoding putative ubiquitin-binding-domain proteins in

Drosophila identified that TSG101, VPS36, and VPS13D are essential for cell size reduction and

autophagy during intestine development. Particularly, they have shown that the Vps13D is

required for mitochondrial clearance and size control via ubiquitin binding (Anding, Wang et al.

2018). Recently, ESCRT-III subunits were shown to be recruited to the mitophagosome and

complete the phagophore sealing during mitophagy (Zhen, Spangenberg et al. 2019). These

studies strongly supported the existence of other mitochondrial turnover pathways which may be

mediated by the ESCRT machinery other than the canonical mitophagy process.

3.4 Connections between mitochondria and other membranecompartments

The eukaryotic cell is a complicated system containing multiple membrane-bounded organelles.

The communication between organelles by direct contacts, inter-organelle trafficking, small

molecules and ions transportation is essential for functioning and adapting to environmental

changes. Endosomes and MVBs are well-known examples of membrane and material exchange

among different organelles and the plasma membrane. In addition, mitochondrial-derived

vesicles (MDVs) were observed by electron microscopy in mouse heart and were shown to be

relevant to mitochondrial quality control process (Cadete, Deschenes et al. 2016). Besides this,

direct contacts were also discovered to be essential for the endomembrane system. The

endoplasmic reticulum (ER) mitochondria encounter structure (ERMES) is a complex that

directly connects ER and mitochondria membrane. It was found to contribute to mitophagy in

yeast which presented at the mitophagosome biogenesis sites (Bockler and Westermann 2014).

11

ERMES and vCLAMP (vacuole and mitochondria patch) are the two important contact sites for

transporting lipids and small molecules between mitochondria and the endomembrane system.

Disruption of either ERMES or vCLAMP causes the overexpression of the other contact site and

depletion of both is lethal (Elbaz-Alon, Rosenfeld-Gur et al. 2014). The crosstalk between

mitochondria and other membrane compartments is critical for mitochondrial function and

quality control. However, there are still many functional connections that remain unknown.

In summary, the balance of mitochondrial biogenesis and turnover is important for homeostasis

of the compartment. The mtDNA content is also relevant to many disease conditions and aging

processes. To better understand the regulation of mtDNA, I performed a high-throughput screen

which quantified the mtDNA level across two yeast whole-genome deletion collections and I

will now describe the results of my screen, which implicated ESCRT components.

12

Chapter 2Experimental Methods

4 Yeast strains and husbandry

4.1 List of strainsThe yeast strains used throughout this study are listed in Table 1.

Table 1. Strains used in this study

Strain MatingType

Genotype Reference

RCY861 a lyp1Δ0, his3Δ1, can1Δ::STE2pr-Sphis5,YDL242WΔ::TPIpr-mtGFP-ClonNATMX

taken fromprototrophicyeast mtGFPcollection

YMY57 a lyp1Δ0, his3Δ1, can1Δ::STE2pr-Sphis5,YDL242WΔ::TPIpr-mtmPapaya-HYGMX

This study

YMY68 α his3Δ1, YDL242WΔ::TPIpr-mtmPapaya-HYGMX

This study

YMY147 a his3Δ1, can1Δ::STE2pr-Sphis5,YDL227CΔ::TPIpr-kanMX

taken fromprototrophicyeast deletioncollection

Deletionstrains

a his3Δ1, can1Δ::STE2pr-Sphis5,yfgΔ::TPIpr-kanMX

taken fromprototrophicyeast deletioncollection

mPapaya-labeled-

mitochondriastrains

a his3Δ1, can1Δ::STE2pr-Sphis5,yfgΔ::TPIpr-kanMX,YDL242WΔ::TPIpr-mtmPapaya-HYGMX

This study

13

Table 2. Gene list of Class I and IV identified by Merz et al. (Merz and Westermann 2009)

Class I Gene List

YAL039C YML061C YMR267W YOR375C YPL173W YBR268W YDR350C YHR011W

YLL033W YMR015C YMR282C YOL009C YPL104W YDR065W YDR377W YHR038W

YLR067C YMR188C YMR287C YOL033W YPL097W YDR079W YER050C YHR120W

YLR069C YMR193W YMR293C YOL083W YBR179C YDR115W YGR215W YCR003W

YLR070C YMR228W YOR330C YPL271W YBR251W YDR347W YHL038C YLR139C

YKL003C YKL170W YPR116W YNL252C YNL081C YLR439W YER154W YLR295C

YKL114C YGR076C YNL213C YJL063C YNL073W YBR146W YHR091C YDR114C

YKL134C YGR102C YNL177C YJR144W YKL194C YDL044C YJL096W YEL050C

YKL138C YOR211C YMR089C YDL129W YLR382C YGL129C YBL021C YGR171C

YKL169C YPL013C YHR168W YDL133W YPL078C YGL143C YBL090W YOL095C

YNR037C YOR158W YLL027W YNL184C YOR150W YFL016C YOR187W YBR282W

YLR304C YDR296W YKL155C YPL148C YMR098C YBL080C YGL240W YMR064W

Class IV Gene List

YAL026C YOR127W YBL046W YMR077C YDL012C YER114C YLR125W YBL062W

YLL042C YOR155C YBL053W YHR006W YDL056W YER131W YBL031W YMR072W

YML087C YLR260W YGL017W YHR009C YDL091C YDR523C YBL032W YGL218W

YCL010C YLR270W YNL159C YDL192W YDR491C YER155C YBL036C YER087W

YLR144C YGR243W YMR070W YDL157C YGL165C YGR180C YBL057C YCR028C

14

4.2 Growth conditionsFor the yeast growth preceding gDNA extraction, strains from the deletion collections stored as

glycerol stocks in 96-well plates were first pinned onto rectangular yeast peptone dextrose (YPD)

agar plates and incubated at 30 ˚C overnight. Then, the yeast colonies were transferred onto

rectangular yeast peptone glycerol (YPG) agar plates by pinning. The yeast culture was

inoculated in YPG media in the 96-well plates. With the assistance of a liquid handling robot

(Beckman Coulter Biomek NX), the yeast culture was diluted back to OD600=0.2 twice with

approximately 10 doubling times before gDNA extraction.

4.3 Generation of mPapaya-labeled-mitochondria haploid strainsCultures of haploid candidate deletion strains and control strains were pinned onto YPD plates

with a lawn of YMY68 and incubated at 30 ˚C overnight. Diploids were selected by streaking

cells on YPD+G418+Hygromycin plates and sporulated on 1% potassium acetate+0.5%

dextrose+G418+Hygromycin. Sporulated cultures were spread first on SD-

Lys/His/Arg+canavanine+thialysine for Mata haploid selection and then on (SD/MSG)-

His/Arg/Lys+canavanine/thialysine/G418/Hygromycin for deletion mutant and mPapaya-

labeled-mitochondria selection.

5 High-throughput mitochondrial DNA measurement

5.1 High-throughput gDNA extraction100 µL culture was mixed with 455 µL EtOH/NaOH solution (240 mM NaOH, 2.7 mM EDTA,

74% ethanol) and heated at 80 ˚C for 10 min. The lysate was then transferred to a Corning glass

fiber plate (REF3511, 66 mm glass fiber with 1.2 µm PES overlay). The lysate was spun through

the glass fiber plate into the receiver plate at 1000 rpm for 1 min. Each well was washed with

250 µL wash buffer (0.1 mM Tris-HCl pH=7.5, 0.01 mM EDTA, 80% ethanol) for three times at

1000 rpm for 1 min. The glass fiber plate was placed onto a new empty receiver plate and spun at

1000 rpm for another minute to remove excess wash buffer. The glass fiber plate was allowed to

air dry for 1-2 hours. 100 µL elution buffer (10 mM Tris-HCl pH=8.0) was added to each well to

elute gDNA into a new storage plate by spinning at 3000 rpm for 5 min. Sealed the plate and

kept frozen before PCR amplification.

15

5.2 Quantitative PCR cycling conditionsThe total volume of the qPCR mixtures was 15 µL. The mixture contained 2 µL 10x PCR buffer

(200 mM Tris-HCl pH=8.4, 500 mM KCl), 2 mM MgCl2, 4 nmol dNTPs, forward and reverse

primers (TUB1-F and TUB1-R,12 pmol; COX1-F and COX1-R, 2.4 pmol), probes (TUB1-P, 2.4

pmol; COX1-P, 2 pmol), and 5 µL yeast gDNA from the glass fiber extraction. 1 unit Taq was

pre-treated with 10 pmol 3' ddC aptamer at 70 ˚C for 2 min and then added to the qPCR reaction

mastermix. PCR cycling conditions consisted of an initial 2 min of preheating at 95 ˚C followed

by 35 amplification cycles of 95 ˚C for 15 s and 60 ˚C for 1 min. The fluorescent signal was read

at 60 ˚C for each cycle.

Table 3. Primer and probe sequences

Name Sequence

TUB1-F 5’ - AAA GCC GAA GGG AGG AGA AG - 3'

TUB1-R 5' - AGC CCT TGG AAC GAA CTT ACC - 3'

TUB1-P 5' - /5HEX/TCC ACG TTT TTC CAT GAA ACC GGC T/3BHQ_2/ - 3'

COX1-F 5' - AAC ATT GCT TTT TGA GTA TTA CCT ATG GG - 3'

COX1-R 5' - GGT CCT GAA TGT GCC TGA ATA GA - 3'

COX1-P 5' - /56-FAM/CAG TTC ACC CTG TAC CAG CAC CTG ATT CTA

CTA AA/3BHQ_1/ - 3'

* FAM: 6-carboxyfluorescein; HEX: hexachloro-fluorescein; BHQ: black hole quencher dye

16

6 Spinning disk confocal microscopy

6.1 Fixation and staining250 µL fixation solution (1:1 mixture of 32% paraformaldehyde from Electron Microscopy

Sciences, Inc. and 1 M potassium phosphate buffer pH=6.5) was added to 1 mL culture. The

mixture was agitated for 30 min at room temperature. Centrifuged at 3000 rpm for 5 min to

discard the supernatant and resuspended the cell pellet in 200 µL MilliQ water. 50 µL fixation

solution was added and the sample was agitated at room temperature for 45 min. Cells were spun

down at 3000 rpm for 5 min in an Eppendorf microfuge and the supernatant was discarded. The

cell pellet was resuspended in 250 µL PBS buffer with 0.1 M glycine to quench remaining

paraformaldehyde. The cells were incubated for 5 min at room temperature and then centrifuged

at 10,000 rpm for 30 s to pellet cells. The cell pellet was resuspended with 200 µL P buffer (10

mM sodium phosphate pH=7.2, 150 mM NaCl) and then 200 µL ConA Alexa 594 dye was

added (ConA stock was diluted with P buffer to 7.5 µg/mL). The mixture was incubated for 10

min in the dark at 4 ˚C. The cells were centrifuged at 10,000 rpm for 30 s and the cell pellet was

resuspended with 500 µL P buffer to wash. The cells were spun down again and resuspended in

50 µL DAPI dye (DAPI stock was diluted with glycerol solution to 0.5 µg/mL with 0.1% Triton

X100, glycerol solution is made from 9:1 of glycerol and 10X PBS buffer). A 5 µL sample was

loaded onto a ConA-coated slide (described below) to immobilize the yeast cells and the edge of

the cover-slip was sealed with transparent nail polish for long-term storage.

6.2 Microscope slide preparationMicroscope slides and cover-slips were heated in a loosely covered 750ml glass beaker in 1M

HCl at 50-60 ˚C for 4-16 hours. Slides and coverslips were allowed to cool to room temperature

and rinsed out 1M HCl with multiple changes of MilliQ water. Slides and coverslips were

washed by filling the container with MilliQ water and sonicating in a water bath for 30 minutes.

The wash step was repeated twice more. The slides and cover-slips were then covered with a

mixture of 50% EtOH and 50% MilliQ water and sonicated in water bath for 30 min. These

washes were repeated with 70%, 95% and 100% EtOH, respectively, for a total of three times,

each with 30 minutes sonication in a water bath. Slides and coverslips were allowed to dry by

resting on one end in an open container.

17

The washed and dried slides and coverslips were coated with Concanavalin A (ConA) by

spreading 30 µL ConA solution on the surface (2 mg/mL ConA, 5 mM CaCl2, 5 mM MnCl2).

The slides and cover-slips were air-dried and then washed by immersing into three changes of

MilliQ water. The slides and cover-slips were dried overnight and stored for future use.

6.3 Imaging conditions and analysisThe 3D confocal images were captured by Zeiss spinning disk confocal microscope,

AxioObserverZ1, equipped with a Yokogawa CSU-X1 SDC and an Axiocam 506 high-

resolution camera. The objective lens was Plan-Apochromat 100x/1.40 Oil DIC M27. The z-

stack images spacings were 0.0454x0.0454x0.4 (µm). The three channels (excitation wavelength,

emission wavelength, light source intensity, exposure time) were: Alexa Fluor 594 (280 nm, 618

nm, 50%, 8 s), DAPI (353 nm, 465 nm, 50%, 4 s), and ZsYellow1 (529 nm, 548 nm, 50%, 10 s).

Acquisition software was ZEN.

To analyze the 3D confocal images, deconvolution was performed using AutoQuant X3 from

Media Cybernetics Inc using the adaptive point spread function (PSF) and default settings.

Imaris 8 from Bitplane was used to reconstruct mitochondrial structure based on mPapaya signal

using "surface" function and quantify the volume of the whole cell and mitochondria.

18

Chapter 3Results

7 A screen for quantifying mtDNA content across twoyeast whole-genome deletion collections

The budding yeast, S. cerevisiae, is a genetically tractable organism that has been widely used

for many fields of biological research. The laboratory strains of S. cerevisiae used for the

original genome sequencing program and many subsequent large-scale functional analysis

projects are derivatives of S288c which carry a Ty1 insertion in the HAP1 gene (Figure 2). The

HAP1 gene encodes a transcriptional regulator which is important for functions such as electron-

transfer reactions, heme-depleted growth and anaerobic growth (Hon, Lee et al. 2005; Hickman

and Winston 2007). In the original yeast prototrophic whole-genome deletion collection

(hereinafter referred to as "S288c background") (VanderSluis, Hess et al. 2014), this Ty1

insertion in the HAP1 gene causes a partially deficient mutation and modifies the interpretation

of many results obtained with these strains (Gaisne, Becam et al. 1999). Using the MagicEraser

method developed in the Rosebrock lab (personal communication), another prototrophic deletion

collection was generated where the hap1 mutant is replaced with the wild-type HAP1 gene

(hereinafter referred to as "HAP1 background"). The two yeast whole-genome deletion

collections each contain 4,772 single non-essential gene deletion strains with a number of wild-

type control strains with different genomic backgrounds (S288c and HAP1). Therefore, I'm able

to not only measure the mtDNA content in the set of genome-wide deletion strains but also

investigate the effect on mtDNA level caused by the HAP1 genotype.

19

Figure 2. S288c strains carry a Ty1 insertion in HAP1 gene

The top panel shows the site of insertion of the Ty1 element in the S288c hap1 sequence. The

HAP1 ORF is a solid bar and the Ty1 element is shown as a hatched bar. The bottom is a

comparison of the peptide sequence of the C-terminus of Hap1p with and without the insertion.

Figure adapted from Gaisne et al. (Gaisne, Becam et al. 1999).

20

7.1 Optimization of qPCR assay to quantify mtDNATaqman quantitative PCR (qPCR) is a powerful method to accurately quantify the nucleic acid

amount by measuring the accumulation of the product during PCR amplification. To set up a

qPCR assay, the first thing is to optimize and quantify the amplification efficiency which

determines the accuracy of template quantification. Ideally, the number of molecules of the

target sequence should double during each replication cycle, corresponding to a 100%

amplification efficiency. To determine the amplification efficiency, a serial dilution of template

was used to create a standard curve by plotting the template concentration and Ct (threshold

cycle). The Ct is determined by the chosen threshold which must be set in the linear phase of the

amplification plot. Then the amplification efficiency is calculated from the slope of the Ct as

compared to the log2 standard curve. As TUB1 and COX1 were detected in the same reaction

using probes with different fluorophores to represent the nuclear DNA and mtDNA, if the

amplification efficiency of the two probes are similar, the mtDNA signal can be normalized with

the nuclear DNA content. The amplification efficiency is largely affected by primer and probe

concentrations which can be optimized as described below. Since there are multiple copies of

COX1 in a cell verse only one copy of the nuclear gene TUB1, I used a much higher

concentration of TUB1 primers comparing to COX1 in order to get similar Ct values. If the Ct

values are extremely different, the PCR reaction components can be consumed by the earlier

amplifying reaction, inhibiting efficient amplification of the later amplifying component. Figure

3 shows the optimization results where I tested all 16 combinations of two concentrations for two

pairs of primers and two probes. The primer and probe concentration range for this experiment

was chosen based on preliminary optimizations (not shown). For each condition, I did the qPCR

with six templates which were serially diluted 1:2 to create a standard curve and calculate the

efficiency based on the slope of trendline. Comparing the efficiency of TUB1 and COX1

amplification from each condition, Condition No.4 was chosen which has the highest and most

similar efficiency of TUB1 and COX1, 96.3% and 99.7%, respectively. With several rounds of

optimization, the qPCR assay was able to measure both nuclear DNA and mtDNA with nearly

100% efficiency, setting up the fundamental conditions necessary to accurately quantify mtDNA

level by normalizing to nuclear DNA level.

21

22

Figure 3. The result of the primers and probes concentration optimization

(A) Y-axis shows the Ct of COX1 and TUB1 in an experiment using the concentrations of

primers and probes from condition 4 as shown in 3C. The template on the X-axis is a series of

two-fold dilutions of genomic DNA starting from 2 ng/µL. The efficiency and R2 are shown as

calculated by Microsoft Excel using a log-linear trendline. The efficiency is corresponding to the

slope of the log-linear trendline.

(B) Y-axis shows the values of efficiency and R2 from each condition.

(C) Detailed primer and probe final concentrations in reactions of all tested conditions.

23

7.2 Optimization of aptamer treatment for qPCR reactionsstorage

My screen required qPCR analysis of over 120 plates of reactions. In order to use our

laboratory's plate handling robot system to automatically transfer the qPCR plates to the qPCR

system for analysis, I wanted to develop a qPCR assay which can be stored at room temperature

for up to 40 hours without significant changes on target detection. To achieve this goal, I tested

an aptamer which had been shown to inhibit Taq polymerase activity in a thermally reversible

manner: at temperatures below 40 ˚C, the aptamer inhibits the enzyme, whereas at high

temperatures (> 40 ˚C) the aptamer is no longer inhibitory (Lin and Jayasena 1997).

At the suggestion of a collaborator that the aptamer designed in the literature might be extended

by polymerase, I tested a 3' dideoxy-modified version of this aptamer (3’-ddC-apt) and found

that it had superior performance to the aptamer described in the literature (apt w/o ddC). As

shown in Figure 4, the 3’-ddC-aptamer was able to stabilize the qPCR reaction at room

temperature up to 46 hour supported by the qPCR amplification curve (top), the Ct value

(middle), and the visualization of product by electrophoresis (bottom). However, the aptamer w/o

ddC only maintained the qPCR performance with high concentration template (left). What's

more, the aptamer without ddC inhibited or competed with the amplification of target product.

When 1 unit of Taq was treated with 2 pmol aptamer without ddC, the Ct value was increased.

Therefore, the ddC-apt was chosen to be used in the screen after the following optimization.

24

25

Figure 4. 3’-ddC-terminated aptamer had superior performance to the aptamer without ddC

qPCR of the COX1 and TUB1 genes using gDNA (A) 0.625 ng, (B) 0.02 ng, and (C) 0.0006 ng, in 15-µL qPCR mixture. The top row is

the amplification curve of COX1 gene; the middle row is the Ct value of COX1 detection with manually set threshold; the bottom is the

qPCR mixture analyzed on non-denaturing polyacrylamide gels (10%). The light colors represent the reactions performed immediately

after preparation and the dark colors are the data for reactions which were stored at room temperature for 46 hours. The reactions were

carried out with 1 unit Taq polymerase with no aptamer treatment (blue), 10 pmol 3’-ddC-aptamer (orange), 1 pmol aptamer w/o ddC

(green), and 2 pmol aptamer w/o ddC (yellow)

26

My collaborator also suggested that the aptamer might function better if it was pre-incubated

with the polymerase. I tested this by pre-incubating the aptamer with the Taq polymerase at 70

˚C for 2 min before adding to the qPCR reaction which improved the sensitivity of qPCR. As

shown in Figure 5F, when the Taq polymerase and the aptamer were co-incubated, the Ct of that

condition remained identical after 4 hours storage and was the lowest compared to no aptamer

qPCR and adding the heated or unheated aptamer directly to the mastermix without the co-

incubation procedure.

The ratio of aptamer to polymerase was optimized by comparing the qPCR results before and

after storage. Two sets of qPCR reactions were prepared in parallel and one was run immediately

after preparation and the other was stored at room temperature for 40 h. As shown in Figure 5A-

D, when 1 unit Taq polymerase was treated with 10 pmol aptamer, the Ct of qPCR reactions

remained identical after storage which was the best performance among the tested amounts. With

the optimized aptamer treatment, not only the qPCR stability but also the sensitivity of detecting

target was improved (Figure 5E). At 0 h time point, the relative fluorescent signal of the aptamer

treatment reaction was higher than no aptamer control which suggested that the aptamer was able

to improve the ability of detecting target genes. The slightly decrease of relative fluorescent

signal after 40-hour storage with the aptamer treatment may due to the bleach of the dye.

27

28

Figure 5. Optimization of anti-Taq aptamer

(A-D) Y-axis shows the Ct of COX1 with different amount of aptamer pre-heated with Taq

polymerase at 70 ˚C for 2 min ahead of qPCR mixture preparation. The template on the X-axis is

a series of two-fold dilutions of gDNA starting from 2 ng/µL. The squares represent the qPCR

result running at 0 h time point after preparation and the diamonds represent the result after 40-

hour storage at room temperature. (E) The best aptamer treatment condition (B) was chosen to

compare to a qPCR reaction without aptamer. The qPCR amplification curve is created by

plotting the ΔRn values from each qPCR cycle which show the normalized fluorescent signal.

(F) Y-axis shows the Ct of COX1 with different aptamer treatment methods from qPCR reactions

run 0 hours after reaction setup or after 4-hour room temperature storage in blue and red,

respectively. Heat treatment used for aptamer only or co-incubation with Taq polymerase was 70

˚C for 2 min. Error bar shows the standard deviation of four replicates.

29

7.3 High-throughput gDNA extraction assay using glass fiberplates is able to capture nuclear and mitochondrial DNA withsimilar efficiency

A high-throughput gDNA extraction assay was developed using a 96-well glass fiber plate to

bind DNA (personal communication, A. Caudy). It was necessary to verify that the assay

extracted nuclear DNA and mtDNA in equal proportion in order to be confident that the assay

correctly reported the quantity of mtDNA relative to nuclear DNA. Firstly, I tried two kinds of

lysis methods, zymolyase and ethanol/sodium hydroxide (see recipe in Methods) to extract

gDNA. To test the recovery of nuclear and mtDNA, I prepared mixtures of DNA from rho0

petite cells and wild-type cells. The rho0 petite cells contain only nuclear DNA (verified by

competitive genomic array hybridization, A. Caudy), while wild-type cells contain both mtDNA

and nuclear DNA. I prepared these mixtures by separately growing wild-type and petite strains in

YPD and then mixed them at different ratios according to the optical density. Then the gDNA of

each mixture was extracted by lysing the cells, binding the lysate to the glass fiber plate, washing

the DNA, eluting it, and analyzing with the optimized qPCR assay to measure the nuclear and

mtDNA. The Ct of TUB1 is expected to be identical in all mixtures because the number of

nuclear genomes is constant, while the Ct of COX1 increases as the proportion of rho0 DNA

increases because the mixture has less mtDNA and thus more PCR cycles are required to detect

it. Therefore, ΔCt = (Ct TUB1 - Ct COX1) can be calculated to represent the ability of capturing

nuclear DNA and mtDNA.

As shown in Figure 6A & B, the performance of EtOH/NaOH buffer was much better than the

zymolyase buffer with more consistent Ct of TUB1 and gradual decrease of COX1 Ct. Then I

optimized the procedure of EtOH/NaOH extraction by including a incubation step and found that

incubating the culture and EtOH/NaOH buffer at 80 ˚C for 10 min resulted in the best

performance (Figure 6C), where the ΔCt representing the ratio of mtDNA to nuclear DNA

decreased by 1 unit when the quantity of wild-type genomic DNA was decreased by half.

Therefore, after these optimizations, this high-throughput gDNA extraction assay could extract

nuclear DNA and mtDNA with identical efficiency.

30

Figure 6. The optimized mtDNA extraction and qPCR accurately determines

nuclear/mtDNA ratio

The left Y-axis shows the Ct of COX1 and TUB1 (blue diamond and red square); and the right Y-

axis show the ΔCt = (Ct COX1- Ct TUB1) with green triangles. Wild-type and petite strains were

mixed at different ratios prior to extracting gDNA. Methods for extracting gDNA were as

indicated: (A) Zymolyase digestion, (B) EtOH/NaOH digestion at room temperature, (C)

EtOH/NaOH with 80 ˚C incubation for 10 min. The percentage of wild-type is shown on X-axis.

The trendline of ΔCt is shown, as calculated by Microsoft Excel using a log-linear trendline.

31

7.4 Inoculation condition affects the mtDNA levelThe mtDNA content is known to be affected by nutritional conditions (Miyakawa 2017). When

growing in YPD, the cell primarily uses glucose for energy production through glycolysis and

represses the mitochondrial transcription, enzyme activity, and biogenesis (Perlman and Mahler

1974; Ulery, Jang et al. 1994). Whereas, when glycerol is the primary carbon source available in

YPG media, mitochondria are required for the respiration process which then relieves glucose

repression, promoting mitochondrial biogenesis and a concomitant increase in mtDNA. However,

whether other growth conditions besides media type would affect the mtDNA level remained to

be tested. Therefore, I compared the mtDNA level of wild-type strains growing in 24-well and

96-well plates with YPG. The surface area of 24-well plate is about 4-fold larger than the 96-

well plate. The inoculation culture volume in 24-well is 10-times more than the 96-well plate.

Therefore, the ratio of surface area to volume is smaller in 24-well plate. Oxygen is another

important factor during respiration which affects the mitochondrial biogenesis through the

hypoxia signaling pathway (Schonenberger and Kovacs 2015). As the strains growing in 24-well

plate have more oxygen exposure due to wider well dimension and more sufficient shaking, I

suspect that the different mtDNA levels may result from the oxygen concentration available in

the different growth formats (Figure 7). Further experiments would be required to fully explore

the effect of growth format. However, since mtDNA levels are strongly affected by growth

format, the same growth conditions in 96 well plates were used for all of the experiments in

order to control this factor.

32

Figure 7. Growth plate format affects the mtDNA level of S288c wild-type strain

Five biological replicates of S288c wild-type strain (YMY147) were inoculated in YPG media

either in 24-well deep well or 96-well deep well plate and incubated under identical conditions.

ΔΔCt = mean of 96-well (Ct COX1 - Ct TUB1) - sample (Ct COX1 - Ct TUB1). ΔΔCt and mtDNA are

positively related; each ΔΔCT unit corresponds to a 2-fold increase in mtDNA. Each marker

represents one biological replicate and the box plot shows the first quartile, median, and third

quartile of the data; the mean is shown by the red diamond.

33

7.5 The pipeline of the screenAfter the optimization of inoculation procedure, gDNA extraction assay and the qPCR

conditions, the complete screen workflow was established as below (Figure 8). The two deletion

collections are comprised of 118 96-well plates in total which were first pinned onto YPD agar

plates, then repinned onto YPG agar plates to select for respiratory growth, followed by

inoculation into YPG media in 96-well round-bottom deep plates. Each plate of the deletion

collection contains a total of 12 wild-type strains (one in each row and column) that provide a

basis for comparison among plates. As described below, these also allow us to rigorously

compare the effect of the S288C allele of HAP1 to the wild-type allele. It is important to select

for respiratory growth because of the natural mtDNA loss and petite formation in S288c strains.

Growth of the deletion collections on YPG allowed me to select against any potential petite cells

formed during storage. The inoculated plates were placed in a Hi-Gro shaker (400 rpm) at 30 ˚C

with continual supply of water-saturated air. After two cycles of growth and back dilution using

the liquid handling robot, which allowed approximately 10 doubling times to maintain the stable

level of mtDNA without saturation, the strains were harvested and gDNA was extracted in a

high-throughput manner, providing the template for qPCR. Each gDNA sample was measured by

qPCR with four technical replicates.

The TUB1 and COX1 genes were detected in the same qPCR reaction with two designed Taqman

probes which represent the nuclear DNA and mtDNA level, respectively. The Ct values of TUB1

and COX1 for each reaction were determined and ΔCt = (Ct COX1 - Ct TUB1) was calculated to

represent the normalized mtDNA level in each strain. Then I used the average ΔCt of all wild-

type controls to further analyze the effect on mtDNA level by each gene deletion which is

represented by ΔΔCt (ΔΔCt = [mean of ΔCtWT] - ΔCt DEL). Therefore, the deletion strains with

high mtDNA level correspond to positive ΔΔCt value. With the established pipeline, I measured

the mtDNA content across the two yeast whole-genome deletion collections.

34

Figure 8. Workflow of quantifying mtDNA level across yeast whole-genome deletion collections

Two yeast whole-genome prototrophic deletion collections (each contains 59 96-well plates), one in the S288c background and the other

with a repaired HAP1 allele, were first pinned onto rectangular YPD plates and re-pinned onto rectangular YPG plates. With the

assistance of a liquid handling robot, strains are grown in YPG media and diluted back to OD600=0.2 twice and allowed to grow

exponentially before gDNA extraction. MilliQ glass fiber plates are used to extract gDNA in a high-throughput manner. The qPCR then is

performed using the extracted gDNA as the template. See details in methods section.

35

8 Screen results overviewAfter removing data from strains that had not passed quality controls, I calculated ΔΔCt for the

remaining 4565 mutants shared across both screens and generated a 2-dimensional dot plot with

the ΔΔCt of S288c and HAP1 background. To analyze the screen data, I defined the mutants

with ΔΔCt > 1 as high mtDNA content, ΔΔCt < -1 as low mtDNA content, and -1 < ΔΔCt < 1 as

medium mtDNA content. The strains with optical density less than 0.2 when the gDNA was

extracted were identified as respiratory deficient whose ΔΔCt values were manually set to -10 in

order to visualize the data.

8.1 mtDNA level but not mitochondrial volume is affected by theHAP1 genotype

In order to investigate whether the wild-type strains in S288c and HAP1 background have

similar or different mtDNA content, I calculated the ΔΔCt normalized to the average ΔCt of

S288c wild-type strains. As shown in Figure 9, the mtDNA of HAP1 wild-type strains were

significantly lower by 2-fold than the S288c wild-type strains. The S288c strains with the hap1

mutation are known to have incomplete mitochondrial function (Gaisne, Becam et al. 1999).

Therefore, the increased mtDNA content in the S288c strains may be the consequence of a

compensatory mechanism. This hypothesis is supported by the observation of the over-

replication of mtDNA in patients with a mitochondrial respiratory chain disorder (Bai and Wong

2005).

My observation of increased mtDNA in the S288C wild-type controls relative to the HAP1 wild-

type controls raised the question of whether this increase in mtDNA reflected an increase in the

mitochondrial content of the cell. Therefore, I constructed a strain with mPapaya-labeled

mitochondria (YMY68) to visualize and quantify the mitochondrial volume in S288c and HAP1

background. I developed and optimized the method of fixing the mPapaya fluorescent signal for

microscopy. I first tried three fixatives, paraformaldehyde, formaldehyde, and glutaraldehyde,

and compared the signal-to-noise ratio of mPapaya after fixation. Since paraformaldehyde

fixation had the best performance, then I optimized the fixation time to further improve the

signal to noise ratio. (See Methods for the detailed fixation procedure.) A spinning disk confocal

microscope was used to take z-stack 3D images which then were deconvolved and merged into a

36

3D volume for analysis of mitochondrial structure. The mean of mPapaya signal intensity from

the mitochondrial region after fixation was about 15 % lower than before (p = 0.0027) but still

remained at a detectable level (Figure 10).

To quantify mitochondrial volume, the mPapaya-labeled mitochondria strains in S288c and

HAP1 background were grown exactly as during the screen. The strains were fixed and stained

by DAPI and ConA Alexa 594 dye which respectively labeled the DNA and cell wall. The cell

wall was determined in order to quantify the whole cellular volume. The mPapaya signal was

used to reconstruct the 3D structure of mitochondria and measure the volume. The ratio of

mitochondrial and cellular volume was calculated. Surprisingly, the mitochondrial volume ratio

of S288c and HAP1 were almost the same though the S288c strains contained significantly more

mtDNA (Figure 11). Also, I didn't observe significant change in cellular volume of S288c and

HAP1 strains. Therefore, with the same mitochondrial volume, the S288c strains may form more

mitochondrial nucleoids (mt-nucleoids) or more copies of mtDNA within one mt-nucleoid which

remains to be investigated by accurately counting the mt-nucleoid number.

37

Figure 9. The wild-type strains with the ancestral HAP1 gene show lower mtDNA content

compared to the S288c wild-type

ΔΔCt = mean of S288c (Ct COX1 - Ct TUB1) - sample (Ct COX1 - Ct TUB1). ΔΔCt and mtDNA have

positive correlation, each ΔΔCT unit corresponds to a 2-fold increase in mtDNA. Each marker

represents one biological replicate and the box plot shows the first quartile, median, and third

quartile of the data; the mean is shown by the red diamond.

38

Figure 10. mPapaya fluorescence signal remains after fixation

Cultures were grown and imaged immediately or after fixation with paraformaldehyde. (A) The

mitochondrial compartment of each cell was identified by the "surface" function in Imaris 8

software and the mean fluorescent signal intensity is plotted as a single dot for each cell. The box

plot shows the first quartile, median, and third quartile of the data; the mean is shown by the red

diamond. N=50 cells. (B) Representative images of microscopy with bright-field (top) and

mPapaya channel (bottom).

39

Figure 11. The wild-type strains with the ancestral HAP1 gene show similar mitochondrial

volume compared to the S288c wild type

The Y-axis represents the ratio of mitochondrial volume to the cellular volume measured by

microscopy. Each marker represents one biological replicate and the box plot shows the first

quartile, median, and third quartile of the data; the mean is shown by the red diamond. N=34,31.

40

8.2 Comparison with a published respiratory-deficient strainsdataset

To assess the quality of my screen, I compared my observations to those of a number of related

studies. Merz et al. (Merz and Westermann 2009) did a genome-wide deletion mutant analysis

based on the auxotrophic deletion collection which is in a similar S288c background as my

screen, identifying genes required for respiratory growth and mitochondrial genome maintenance.

From that analysis, five of the newly named genes were added to the mitochondrial genome

maintenance GO term in the Saccharomyces genome database (SGD). I compared my screen

data with the genes they identified in their study (Figure 12). They categorized genes into four

classes, of which Class I and IV are relevant to my work (the other classes assessed mutants in

mtDNA). Class I contains genes essential for respiration and maintenance of mtDNA; the

deletion of these genes results in mitochondrial genome loss. Class IV genes are dispensable for

respiration and the deletion of these genes causes a gradual loss of mtDNA. Consistent with the

results of the Merz’s paper (Merz and Westermann 2009), I observed that more than 60% of the

Class I genes were respiratory deficient in the S288c background. Notably, only half of the Class

I genes showed a respiration-deficient phenotype in the HAP1 background in my screen. This is

consistent with Hap1p being involved in the maintenance of normal mitochondrial function in

combination with other factors. My screen data also partially supported their conclusion about

Class IV which is dispensable for respiration with gradual loss of mtDNA. Only a minimal

number of deletion strains in Class IV show a respiratory deficient phenotype. The distribution

of Class IV is similar to the overall screen results in both S288c and HAP1 background mtDNA.

My explanation about this observation is that the deletion collections I used for the screen were

prototrophic deletion sets which were created by backcrossing the auxotrophic deletion set to a

wild-type strain containing mtDNA. This process would have restored the mtDNA since the

strains had been through relatively few generations before the screen. Also, I grew the strains on

YPG for the screen which could select against the mtDNA loss caused by the Class IV gene

deletions. Therefore, these population still have similar mtDNA level to wild-type.

41

Figure 12. Comparison of mtDNA levels with existing data on mitochondrial genome

maintenance

(A) An overall analysis of my screen data. (B) and (C) are based on the Merz et al. (Merz and

Westermann 2009) research where they identified Class I genes as essential for maintenance of

mtDNA or genes essential for respiration with gradual loss of mtDNA, and Class IV genes as

dispensable for respiration with gradual loss of mtDNA. The number of the genes that are not

included in my screen for technical reasons are indicated in the middle orange circle. The strains

showing a respiratory defect with no analyzed data for mtDNA in my screen are those with

OD600 less than 0.2 when harvested. The low mtDNA group is ΔΔCT < -1 and the high mtDNA

group is ΔΔCT > 1 and the rest are grouped as medium.

42

8.3 Gene ontology enrichment analysis reveals potentialregulators of mtDNA

To present an overview of mtDNA levels of deletion strains, I plotted the ΔΔCt of the deletion

strains in the S288c and the HAP1 background (Figure 13). The cluster of genes in the top-right

corner have high mtDNA in both backgrounds while the genes in the bottom left have low

mtDNA in both backgrounds. Mutants that were measured in only one background are not

presented in this plot. The strains with ΔΔCt > 1, corresponding to a 2-fold increase in mtDNA

level, were grouped as high mtDNA level whereas the low mtDNA group were the ΔΔCt < -1. In

order to get an overall understanding of the data, I did gene ontology (GO) analysis based on the

mtDNA content in S288c and HAP1 background (Table 4). However, I was only able to identify

significant candidate pathways from two categories: respiratory deficient group and high mtDNA

in both backgrounds. The other regions contained several genes affecting mtDNA level but not

being enriched in established pathways. As expected, in both backgrounds, the respiratory

deficient strains were enriched in mitochondrial components and the mitochondrial translation

process which is in agreement with previously published work (Dimmer, Fritz et al. 2002; Merz

and Westermann 2009). Mitochondrial dysfunction resulting in loss of mtDNA and the

formation of petite colonies has been well studied (Dimmer, Fritz et al. 2002). My screen was

able to identify these known regulators suggesting that the screen accurately measured mtDNA.

With this verification of the screen quality, I then examined the other significantly enriched

functional groups of genes that have not been previously explored as regulators of mtDNA.

I was particularly interested in the strains with increased mtDNA level, as this phenotype has not

been well-studied, unlike the gene deletions that lead to the loss of mtDNA. Meanwhile, the loss

of mtDNA is a widely observed phenotype in many diseases and the aging process

(Selvanayagam and Rajaraman 1996; Hosgood, Liu et al. 2010; Bratic and Larsson 2013;

Shokolenko, Wilson et al. 2014). I aimed to discover novel pathways of mtDNA regulation

which in the long term might lead to new disease therapies to overcome the symptoms caused by

decreased mtDNA level. Therefore, I focused on the mutants with increased mtDNA levels.

However, one trivial reason for observing a change in mtDNA could result from a change in cell

size. For example, mnn10Δ, anp1Δ and swi4Δ strains all show increased cell size according to

the S. cerevisiaeMorphological Database (SCMD) which measured cell size growing in YPD. In

the screen, mnn10Δ and anp1Δ were grouped into the high mtDNA category in both S288c and

43

HAP1 background with ΔΔCt > 2.3. While the swi4Δ strain in S288c background had low

mtDNA content and medium mtDNA content in HAP1 background. But this exception might

result from the different nutritional condition in the screen comparing to SCMD. Therefore, these

facts suggested that the larger cells may have more mtDNA compared to wild-type sized cells

but size does not necessarily explain mtDNA content. To identify candidate genes with high

mtDNA, I did the GO analysis for the group with high mtDNA level (ΔΔCt > 1, corresponding

to at least a two-fold increase relative to wild-type) in both S288c and HAP1 background (Table

4) and the ESCRT complex was the top hit and was chosen for follow up.

Figure 13. Overview of the mtDNA level of all deletion strains

X and Y axis show the ΔΔCT value of each gene deletion with S288c and HAP1 background,

respectively. The ΔΔCT of respiratory deficient strains (whose OD600<0.2 when the gDNA is

extracted) is set to -10 for purposes of plotting. The threshold values of ΔΔCT for high, medium

and low mtDNA levels are -1 and 1 indicating by the orange dash lines. Class I (same as Figure

12) is labeled with green and Class IV (same as Figure 12) is labeled with blue. ESCRT complex

components and mitophagy-related genes are labeled with red and purple along with gene name.

44

Table 4. Representative examples of GO enrichment analysis results

Criteria GO term Clusterfrequency

Backgroundfrequency P-value

S288c&

HAP1

high mtDNA

ESCRT complex 6 / 175 10 / 3868 0.00029Ski complex 4 / 175 4 / 3868 0.00082nuclear-transcribed mRNAcatabolic process,deadenylation-dependent decay

9 / 175 28 / 3868 0.00154

tRNA wobble uridinemodification 7 / 175 16 / 3868 0.00203

ATP export 6 / 175 11 / 3868 0.00219

S288c defectiverespiration

mitochondrial part 140 / 286 504 / 4526 5.24E-61

mitochondrial matrix 76 / 286 182 / 4526 8.89E-44

mitochondrion 160 / 286 883 / 4526 1.71E-43

mitochondrial ribosome 45 / 286 72 / 4526 7.97E-35

HAP1 defectiverespiration

mitochondrial part 104 / 286 504 / 4526 4.78E-41

mitochondrion 125 / 286 883 / 4526 5.02E-33

mitochondrial matrix 56 / 286 182 / 4526 3.99E-29

mitochondrial inner membrane 52 / 286 204 / 4526 1.94E-22

mitochondrial ribosome 32 / 286 72 / 4526 1.99E-21

45

9 ESCRT complex has the potential of being involved inmitochondrial turnover process

Transportation of cellular cargo between different organelles relies on endosomal trafficking

machinery, notably the ESCRT complex. ESCRT is well conserved between humans and S.

cerevisiae and is composed of four complexes: ESCRT-0/I/II/III (Williams and Urbe 2007). As

the ESCRT complex was captured by the GO analysis under the high mtDNA criteria as the top

hit and its known function showed the potential of being involved in mitochondrial turnover, I

decided to investigate the mechanism of increased mtDNA caused by ESCRT deletions.

9.1 ESCRT I/II subunit deletion mutants have increased mtDNAcopy number

Before moving onto other experiments, I repeated the measurement of mtDNA in strains

carrying deletions of the ESCRT complex. Mutants of each subunit of the ESCRT complex and

the wild-type strain in S288c background were inoculated as per the screen procedure with four

biological replicates. In agreement with the original screen results, the deletion of genes

encoding ESCRT I/II subunits caused a significant increase in mtDNA in comparison to wild-

type (Figure 14). Therefore, ESCRT complex is a valuable candidate for further investigation.

Simply, the copy number of mtDNA can be regulated through mitochondrial biogenesis and

turnover. Based on the known functions of the ESCRT complex, which is largely involved in

cargo transporting and fission or fusion of membranes, I hypothesized that the ESCRT complex,

especially ESCRT I/II subunits may participate in the mitochondrial turnover process under

respiratory growth. Thus, I decided to measure the mitochondrial volume in ESCRT deletion

strains which could answer whether the increase in mtDNA content is associated with increased

mitochondrial volume.

46

Figure 14. ESCRT I/II subunit gene deletion mutants have higher mtDNA level compared

to wild-type strains

Four biological replicates of each strain were measured by qPCR to determine the content of

mtDNA normalized to nuclear DNA. ΔΔCT = mean of wild-type (Ct COX1 - Ct TUB1) - deletion (Ct

COX1 - Ct TUB1). Each marker represents one biological replicate and the box plot shows the first

quartile, median, and third quartile of the data; the mean is shown by the red diamond. P-value is

calculated by t-test. * p<0.05, ** p<0.01, *** p<0.001

47

9.2 ESCRT I/II subunit deletion mutants have increasedmitochondrial volume

To test whether the ESCRT complex is involved in the mitochondrial turnover process, I first

measured the mitochondrial volume of the ESCRT deletion strains. In order to visualize

mitochondria and measure the mitochondrial volume, I used Synthetic Genetic Array (SGA) to

construct ESCRT deletions that carried the mPapaya mitochondrial reporter (see methods for

details). As described in 8.1, these newly constructed fluorescent strains were grown, fixed,

imaged and analyzed. As expected for my model that ESCRT is involved in bulk turnover of

mitochondria, the deletions of ESCRT I/II subunit showed an increased amount of mitochondria

in the cell compared to wild-type (Figure 15). Meanwhile, I confirmed that there was no increase

in cell size of ESCRT deletion strains based on both the SCMD and my own imaging data.

However, the magnitude of increase in mitochondrial volume did not match the increase in

relative mtDNA copy number. As discussed below, there may be an upper limit on the fraction

of the cytosol that can be occupied by mitochondria. The wild-type strain growing in YPG

contains about 17% cellular volume for mitochondria. This number determined by fluorescence

measurement is slightly larger than the value established by electron microscopy where a

consecutive series of thin image sections was reconstructed from yeast cells grown on different

carbon sources (Visser, van Spronsen et al. 1995). In that electron microscopy dataset, wild-type

cells grown in YPD only use about 3% cellular volume for mitochondria and the value for YPG

was 10%-12% (Visser, van Spronsen et al. 1995). The difference between my observations and

the reconstructions from electron microscopy may due to sample preparation, imaging

technology, and the analysis method. The ESCRT I/II subunit deletions had a significant increase,

although less than two-fold, even though stp22Δ and vps28Δ had increases of 8-fold in mtDNA.

In other words, it is very possible that there is a maximum limit for mitochondrial volume in

order to maintain all of the other organelles. In addition, I didn't observe any growth defect in the

ESCRT deletion strains by comparing the optical density to wild-type strains when they were

harvested for extracting gDNA. However, to further confirm this observation, more accurate

competitive experiments need to be done.

48

Figure 15. ESCRT I/II subunit gene deletions have larger mitochondria volume

Y-axis represents the ratio of mitochondrial volume to the cellular volume of wild-type and

ESCRT deletion strains. Each marker represents one biological replicate and the box plot shows

the first quartile, median, and third quartile of the data; the mean is shown by the red diamond.

N>15. P-value is calculated by t-test. * p<0.05, ** p<0.01, *** p<0.001

49

Chapter 4Conclusions

With the goal of identifying biological pathways that regulate mtDNA content, I have developed

and carried out a genome-wide screen measuring mtDNA copy number in different genomic

backgrounds that has discovered valuable candidates for future investigation. This project shows

the utility of a high-throughput qPCR screen for studying mtDNA regulation.

10 Summary of workThis thesis describes the process of establishing a high-throughput screen using qPCR to

quantify the mtDNA content across two yeast whole-genome deletion collections grown in

respiratory conditions. The culture inoculation procedure, gDNA extraction method, and the

qPCR assay have been carefully optimized to be able to accurately report the mtDNA level

relative to nuclear DNA in all deletion strains. Notably, an aptamer was successfully used in the

qPCR assay which allowed the storage of pre-prepared qPCR reactions at room temperature,

making it possible to automate the qPCR assay.

The screen was able to identify genes that have been previously shown to be required for

mtDNA maintenance (Merz and Westermann 2009), demonstrating the accuracy of the assay. As

expected, mutants that cause large cells showed increased mtDNA, further evidence of the

accuracy of my screen. Besides identifying potential regulators of mtDNA level, my screen also

uncovered the increase in mtDNA level caused by hypomorphic HAP1 function. All large-scale

collections derived from S288c strains contain a hypomorphic mutation in HAP1 resulting from

a transposon insertion. However, the mitochondrial volume is not affected by the HAP1

genotype even though the mtDNA levels in S288c and HAP1 wild-type strains are different,

demonstrating the potential for uncoupling between mtDNA levels and mitochondrial quantity.

The relationship between mtDNA content and mitochondrial volume remains to be investigated

by counting the number of mt-nucleoids and quantifying the copy number of mtDNA in the mt-

nucleoids under these different conditions (Miyakawa 2017). The increased mtDNA level in

S288c wild-type strain may result from the increased number of mt-nucleoids or increased copy

of mtDNA in each mt-nucleoids.

50

The GO enrichment analysis of the screen results provided several candidate pathways for

mtDNA regulation. Among them, the ESCRT complex was one of the top hits and showed high

potential of being involved in mitochondrial turnover which was supported by the observation of

increased mitochondrial volume in ESCRT I/II deletion strains.

11 Future studies on ESCRT complex and mitochondrialturnover

The original screen and the validation experiments showed that the ESCRT I/II deletion strains

contained a higher amount of mtDNA and an increased volume of mitochondria which led me to

the hypothesis that the ESCRT complex may participate in the mitochondrial turnover process.

However, whether the ESCRT 0 and III subunits are not required in this process or whether there

are redundant proteins that compensate their functions still need to be answered with further

study.

It is notable that the mitochondrial turnover has been mainly studied in the context of

mitochondrial destruction as occurs with nitrogen starvation or some drug treatments (Jelew,

Stawrew et al. 1986; Erenberg 1988; Girdhar, Mishra et al. 1989). The canonical pathway to

eliminate unnecessary or damaged mitochondria is mitophagy. However, during the screen, the

strains were maintained under active respiratory growth, differing from studies of mitophagy. In

addition, the published studies were not able to measure mitophagy level using western bolt to

detect GFP protein genetically tagged to the mitochondrial outer membrane protein, Om45

(Kanki, Wang et al. 2009; Mao, Wang et al. 2011). Other studies showed less than 10% of

mitochondrial turnover level during respiratory growth comparing to nitrogen-starvation-induced

mitophagy using an enzyme activity assay (Campbell and Thorsness 1998; Mao, Wang et al.

2011). In the screen, the deletions of known mitophagy-related genes didn't cause the

accumulation of mtDNA which suggested the mitophagy pathway was not active under the

screen growth condition (Figure 13). Therefore, the screen results may lead to the identification

of other parallel turnover pathways to mitophagy which are more dominant when the cells are

actively growing.

To test the hypothesis that the ESCRT complex plays a role in the mitochondrial turnover

process, other experiments need to be performed to directly measure the mitochondrial turnover

level. One of the methods that can be used is based on alkaline phosphatase (ALP) activity

51

measurement which has been used to measure the basal level of mitochondrial turnover in wild-

type strains (Campbell and Thorsness 1998). In yeast, there are two alkaline phosphatases

encoded by PHO8 and PHO13. To reduce the background signal caused by Pho13 enzyme

activity, the PHO13 gene will be deleted. The Pho8 enzyme is localized in the vacuole and only

becomes active when the C-terminal propeptide is proteolytically removed in the vacuole lumen.

To generate a mitochondrial turnover reporter assay, the first 60 amino acids of Pho8 that encode

the vacuole-localized domain will be replaced with the mitochondrial localization sequence.

When mitochondria are turned over, they fuse with the vacuole, allowing activation of the mt-

Pho8Δ60 enzyme by the process of propeptide cleavage. Then the alkaline phosphatase activity

can be measured after protein extraction by adding the substrate para-Nitrophenylphosphate

(pNPP) which can be cleaved, generating para-Nitrophenol (pNP) which can be quantitatively

measured by absorbance.

By combining the modified PHO8 gene and pho13 deletion with the ESCRT deletions of interest,

the ALP assay will be able to quantify the mitochondrial turnover level of ESCRT deletion and

wild-type strains to either support or disprove the hypothesis about ESCRT complex

involvement in mitochondrial turnover process. In preliminary experiments with this system, I

observed increased ALP activity when the cells were nitrogen starved, but future work will need

to address whether the ESCRT deletions affect ALP activity during respiratory growth which

reflects the mitochondrial turnover level.

The detailed mechanism by which ESCRT may contribute to mitochondrial turnover needs to be

studied as well. One of the important aspects is to identify other players in this pathway such as

the signal transductors that recruit the ESCRT components to the recycled mitochondria. During

the canonical cargo transport process, the ESCRT subunits recognize a ubiquitin modification

and recruit other components to assist the transport. Therefore, it is worthwhile to investigate

whether ubiquitination is also required for the mitochondrial turnover driven by ESCRT complex.

In preliminary experiments, I observed that mutants lacking the ubiquitin ligase system

components Rps5 and Dia2 had increased levels of mtDNA, and these may be of interest in the

system.

52

12 Impact statementMy screen provides the first measurement of mtDNA content in a functional HAP1 background,

allowing us to assess gene function without the compromised respiratory function in the S288c

strains. Comparison of the wild-type strains between the two screens demonstrated an effect of

HAP1 on mtDNA and mitochondrial volume regulation. My comprehensive measurement of the

mtDNA content of each deletion strain can stimulate further work that could lead to the

identification of novel mtDNA regulators which could ultimately benefit the mitochondrial

diseases treatment and diagnosis. The optimization procedure for developing a high-throughput

screen can also guide future work, and my characterization of the aptamer may assist others in

similar qPCR stabilization.

In terms of the ESCRT complex, my work shed light on a novel role of ESCRT function in yeast.

These observations are supported by the studies in mouse and Drosophila (Hammerling, Najor et

al. 2017; Anding, Wang et al. 2018) that showed the role of ESCRT subunits in clearance of

mitochondria under stress (mouse) and developmental (Drosophila) conditions. In addition, the

accumulation of autophagosomes was observed in ESCRT-depleted cells which suggested its

function in autophagy process (Filimonenko, Stuffers et al. 2007; Lee, Beigneux et al. 2007;

Rusten, Vaccari et al. 2007). Since the canonical mitophagy pathways are responsible for

recycling unneeded mitochondria during stress-induced condition such as starvation and

disruption, the ESCRT complex-driven turnover is more likely playing a role in the ongoing

maintenance of mitochondria which differs from the canonical mitophagy pathways.

This work can lead to the future studies on mtDNA regulation under diverse conditions.

53

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AppendicesAppendix 1: The full list of GO enrichment analysis results

Criteria GO term Clusterfrequency

Backgroundfrequency P-value

S288c & HAP1high mtDNA

ESCRT complex 6 / 175 10 / 3868 0.00029Ski complex 4 / 175 4 / 3868 0.00082

nuclear-transcribed mRNAcatabolic process, deadenylation-

dependent decay9 / 175 28 / 3868 0.00154

tRNA wobble uridine modification 7 / 175 16 / 3868 0.00203ATP export 6 / 175 11 / 3868 0.00219

tRNA wobble base modification 7 / 175 17 / 3868 0.00332ATP transport 6 / 175 12 / 3868 0.00422

purine nucleotide transport 6 / 175 13 / 3868 0.00754purine ribonucleotide transport 6 / 175 13 / 3868 0.00754adenine nucleotide transport 6 / 175 13 / 3868 0.00754RNA metabolic process 47 / 175 567 / 3868 0.00896

S288cdefectiverespiration

mitochondrial part 140 / 286 504 / 4526 5.24E-61mitochondrial matrix 76 / 286 182 / 4526 8.89E-44

mitochondrion 160 / 286 883 / 4526 1.71E-43organellar ribosome 45 / 286 72 / 4526 7.97E-35

mitochondrial ribosome 45 / 286 72 / 4526 7.97E-35mitochondrial inner membrane 69 / 286 204 / 4526 2.79E-32organelle inner membrane 69 / 286 211 / 4526 3.29E-31

mitochondrial membrane part 50 / 286 145 / 4526 4.99E-23mitochondrial envelope 77 / 286 351 / 4526 2.77E-22mitochondrial membrane 72 / 286 320 / 4526 2.62E-21

organellar large ribosomal subunit 26 / 286 39 / 4526 1.79E-20mitochondrial large ribosomal

subunit 26 / 286 39 / 4526 1.79E-20

membrane-enclosed lumen 103 / 286 678 / 4526 1.11E-17organelle lumen 103 / 286 678 / 4526 1.11E-17

intracellular organelle lumen 103 / 286 678 / 4526 1.11E-17intracellular organelle part 194 / 286 1910 / 4526 3.22E-17

organelle part 194 / 286 1918 / 4526 5.71E-17ribosome 50 / 286 195 / 4526 1.36E-16

organelle envelope 77 / 286 434 / 4526 3.19E-16envelope 77 / 286 434 / 4526 3.19E-16

ribosomal subunit 46 / 286 178 / 4526 2.97E-15cytoplasmic part 210 / 286 2369 / 4526 8.57E-12

extrinsic component ofmitochondrial inner membrane 16 / 286 26 / 4526 2.74E-11

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extrinsic component of organellemembrane 19 / 286 41 / 4526 1.33E-10

organellar small ribosomal subunit 16 / 286 29 / 4526 2.94E-10mitochondrial small ribosomal

subunit 16 / 286 29 / 4526 2.94E-10

inner mitochondrial membraneprotein complex 21 / 286 56 / 4526 1.21E-09

large ribosomal subunit 28 / 286 100 / 4526 1.25E-09intracellular membrane-bounded

organelle 234 / 286 2921 / 4526 5.80E-09

mitochondrial protein complex 23 / 286 73 / 4526 7.47E-09intracellular ribonucleoprotein

complex 55 / 286 348 / 4526 9.72E-09

ribonucleoprotein complex 55 / 286 354 / 4526 1.93E-08membrane-bounded organelle 235 / 286 2970 / 4526 2.65E-08macromolecular complex 124 / 286 1213 / 4526 6.67E-08

extrinsic component of membrane 23 / 286 84 of / 4526 1.75E-07organelle membrane 88 / 286 766 / 4526 3.52E-07cytochrome complex 10 / 286 18 / 4526 5.16E-06intracellular organelle 240 / 286 3178 / 4526 5.51E-06

organelle 240 / 286 3180 / 4526 6.02E-06membrane protein complex 30 / 286 160 / 4526 8.20E-06

cytoplasm 238 / 286 3172 / 4526 2.21E-05respiratory chain complex 10 / 286 23 / 4526 0.0001small ribosomal subunit 18 / 286 78 / 4526 0.00021

mitochondrial respiratory chain 10 / 286 25 / 4526 0.00025intracellular part 257 / 286 3623 / 4526 0.00068intracellular 257 / 286 3625 / 4526 0.00074

respiratory chain 10 / 286 28 / 4526 0.00087mitochondrial intermembrane space 13 / 286 50 / 4526 0.00176

mitochondrial inner boundarymembrane 4 / 286 4 / 4526 0.00332

lumenal side of membrane 4 / 286 4 / 4526 0.00332matrix side of mitochondrial inner

membrane 4 / 286 4 / 4526 0.00332

intrinsic component ofmitochondrial membrane 14 / 286 61 / 4526 0.00379

organelle envelope lumen 13 / 286 54 / 4526 0.00438intrinsic component of

mitochondrial inner membrane 10 / 286 34 / 4526 0.00622

integral component ofmitochondrial membrane 13 / 286 58 / 4526 0.00992

HAP1 defectiverespiration

mitochondrial part 104 / 286 504 / 4526 4.78E-41mitochondrion 125 / 286 883 / 4526 5.02E-33

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mitochondrial matrix 56 / 286 182 / 4526 3.99E-29mitochondrial inner membrane 52 / 286 204 / 4526 1.94E-22organelle inner membrane 52 / 286 211 / 4526 1.10E-21

organellar ribosome 32 / 286 72 / 4526 1.99E-21mitochondrial ribosome 32 / 286 72 / 4526 1.99E-21

mitochondrial membrane part 39 / 286 145 / 4526 3.80E-17mitochondrial envelope 58 / 286 351 / 4526 2.78E-15mitochondrial membrane 54 / 286 320 / 4526 1.86E-14intracellular organelle part 154 / 286 1910 / 4526 3.73E-14

organelle part 154 / 286 1918 / 4526 5.90E-14organelle envelope 62 / 286 434 / 4526 2.65E-13

envelope 62 / 286 434 / 4526 2.65E-13organellar large ribosomal subunit 19 / 286 39 / 4526 4.42E-13mitochondrial large ribosomal

subunit 19 / 286 39 / 4526 4.42E-13

membrane-enclosed lumen 79 / 286 678 / 4526 1.56E-12organelle lumen 79 / 286 678 / 4526 1.56E-12

intracellular organelle lumen 79 / 286 678 / 4526 1.56E-12ribosome 37 / 286 195 / 4526 6.87E-11

ribosomal subunit 34 / 286 178 / 4526 5.80E-10cytoplasmic part 166 / 286 2369 / 4526 1.33E-09

extrinsic component ofmitochondrial inner membrane 13 / 286 26 / 4526 9.31E-09

macromolecular complex 104 / 286 1213 / 4526 1.69E-08intracellular membrane-bounded

organelle 187 / 286 2921 / 4526 2.19E-08

organelle membrane 76 / 286 766 / 4526 3.32E-08proton-transporting two-sector

ATPase complex 12 / 286 24 / 4526 5.39E-08

membrane protein complex 29 / 286 160 / 4526 1.03E-07membrane-bounded organelle 187 / 286 2970 / 4526 1.75E-07

intracellular organelle 194 / 286 3178 / 4526 6.52E-07organelle 194 / 286 3180 / 4526 7.08E-07

extrinsic component of organellemembrane 14 / 286 41 / 4526 7.91E-07

organellar small ribosomal subunit 12 / 286 29 / 4526 8.29E-07mitochondrial small ribosomal

subunit 12 / 286 29 / 4526 8.29E-07

intracellular ribonucleoproteincomplex 43 / 286 348 / 4526 1.71E-06

large ribosomal subunit 21 / 286 100 / 4526 1.99E-06ribonucleoprotein complex 43 / 286 354 / 4526 2.89E-06

inner mitochondrial membraneprotein complex 15 / 286 56 / 4526 9.12E-06

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mitochondrial protein complex 17 / 286 73 / 4526 1.09E-05proton-transporting V-type ATPase

complex 7 / 286 12 / 4526 8.87E-05

cytoplasm 185 / 286 3172 / 4526 0.00171extrinsic component of membrane 15 / 286 84 / 4526 0.00239

cytochrome complex 7 / 286 18 / 4526 0.00276proton-transporting two-sectorATPase complex, proton-transporting domain

6 / 286 14 / 4526 0.00617

vacuolar proton-transporting V-typeATPase complex 5 / 286 9 / 4526 0.00635

proton-transporting two-sectorATPase complex, catalytic domain 5 / 286 9 / 4526 0.00635

cell 206 / 286 3745 / 4526 0.00798