Natural divergence of traits in species of mice reveals novel molecular mechanisms of cellular senescence

PDF

Download

Open document

Flip pages

Download a page range

Download transcript

Copy asset link

Request this asset

Request accessible transcript

Transcript (if available)

Content
Natural divergence of traits in species of mice reveals novel molecular mechanisms of cellular
senescence

by

Taekyu Kang

A Dissertation Presented To The
FACULTY OF THE USC GRADUATE SCHOOL
UNIVERSITY OF SOUTHERN CALIFORNIA
In Partial Fulfillment of the
Requirements for the Degree
DOCTOR OF PHILOSOPHY
(BIOLOGY OF AGING)
December 2022

Copyright 2022 Taekyu Kang

ii

Dedicated to:
My family
Old, new, and soon to be

iii

Acknowledgements

The work presented in this thesis would not have been possible without the aid of so many
people across numerous institutions. I would like to thank the members of my Dissertation Com-
mittee, Drs. Rachel Brem, Eric Verdin, Judy Campisi, Bérénice Benayoun, and Matt Dean for
always being so supportive and encouraging of my work. I would not be where I am today without
all the guidance and instruction you have given me over the years. Judy and Eric, I’d like to thank
you both and the members of your labs for opening up your space and facilities to me, and for
taking the time out of your busy schedules for all those one-on-one meetings. Bérénice, thank
you for making my first rotation such a fun and rewarding experience, and for giving me a strong
foundation to build on throughout the program. Matt, thank you for your insightful suggestions that
pushed us in the direction of our soon-to-be publication. You and your family will always be the
only party I ever gave a tour of the Buck to.
I especially owe all of my accomplishments to my mentor Rachel, whose endless patience
and wisdom has kept me afloat all these years. Rachel, thank you for always taking my shortcom-
ings in stride and helping me build up my skills and confidence as a scientist. Although public
speaking still makes me extremely nervous, I know that I’ve improved so much thanks to your
consistent guidance. I’d also like to thank Dr. Herb Kasler of the Verdin Lab, who taught me so
much in what now seems like such a short time. Herb, nearly all the skills I have now can be
traced back to something you taught me, and I truly appreciate all the time and knowledge that
you’ve given me over the years.
I would like to thank everyone from the Buck and USC communities who have been with
me throughout my journey. To Dr. Chris Benz and the Benz Lab, thank you for allowing me to
rotate in your lab and the continued collaboration that led to my first first-author paper. To all my
friends at the Buck, thank you for making my time here as adventurous and exciting as it was.
Meeting you all has enriched my life in so many ways and I’m excited to see what the future holds
iv

for all of us. Thank you to everyone in the Brem Lab at UCB, especially Melissa Sui, who contrib-
uted to much of the data presented in this thesis. Thanks to you, I know that the project is in good
hands and I look forward to seeing all that you will accomplish.
To my family, thank you so much for providing me with the opportunity to pursue this
degree at all, and for all the sacrifices you’ve made along the way. Mom and dad, I know that my
broken mix of Konglish when trying to explain my work makes no sense at all, but thank you for
always asking about it and supporting my decisions. To my older brother Nam, who beat me to a
doctoral degree, thank you for inspiring me and always showing me new ways to succeed and
improve myself.
Last but definitely not least, to my fiancé Elaine, thank you for putting up with the distance
for all these years and supporting me every step of the way. The goal of going home to you has
kept me sane and focused, and I can’t wait to start our lives together when I finally do. We’ve got
a lot of time to make up for, but I know that we’ll make every second count.

v

Table of Contents
Dedication ................................................................................................................................... ii
Acknowledgements .................................................................................................................... iii
List of Tables ............................................................................................................................ vii
List of Figures .......................................................................................................................... viii
Abbreviations .............................................................................................................................. x
Abstract..................................................................................................................................... xii
Chapter 1: Introduction ................................................................................................................ 1
Chapter 2: Divergence in cellular senescence phenotypes between species of mice ................ 12
Abstract ......................................................................................................................... 12
Introduction ................................................................................................................... 12
Results .......................................................................................................................... 13
Discussion ..................................................................................................................... 20
Methods ........................................................................................................................ 22
Chapter 3: A natural-variation based screen in mouse cells reveals USF2 as a ....................... 41
novel regulator of the DNA damage response and cellular senescence
Abstract ......................................................................................................................... 41
Introduction ................................................................................................................... 41
Results .......................................................................................................................... 43
Discussion ..................................................................................................................... 53
Methods ........................................................................................................................ 55
Chapter 4: Toward a genome-wide reciprocal hemizygosity test in mammalian ..................... 106
cell culture models
Abstract ....................................................................................................................... 106
Introduction ................................................................................................................. 106
Results ........................................................................................................................ 108
vi

Discussion ................................................................................................................... 111
Methods ...................................................................................................................... 113
Conclusions ............................................................................................................................ 120
References ............................................................................................................................. 121

vii

List of Tables

Supplemental Table 2.1: Functional-genomic enrichment analysis of expression .................... 34
profiles of unirradiated purebred M. musculus and M. spretus primary cells.
Supplemental Table 2.2: Functional-genomic enrichment analysis of expression .................... 37
profiles of senescent purebred M. musculus and M. spretus primary cells.
Supplemental Table 2.3: Mouse metadata ............................................................................... 40
Supplemental Table 3.1: A screen correlating divergence in transcription factor ...................... 72
binding sites and cis-regulatory expression during senescence.
Supplemental Table 3.2: Functional-genomic enrichment analysis of purebred ....................... 76
M. musculus primary cell transcriptomes upon Usf2 knockdown and irradiation.
Supplemental Table 3.3: Functional-genomic enrichment analysis of purebred ....................... 80
M. musculus primary cell transcriptomes upon Usf2 knockdown, irradiation,
and senescence establishment.
Supplemental Table 3.4: Functional-genomic enrichment analysis of purebred ....................... 86
M. musculus primary cell transcriptomes upon irradiation, senescence establishment,
Usf2 knockdown, and long-term senescence.
Supplemental Table 3.5: Expression analysis of purebred M. musculus primary ...................... 91
cells across a timecourse of irradiation and senescence.
Supplemental Table 3.6: In silico regulatory network reconstruction from irradiation .............. 105
and senescence transcriptomes.

viii

List of Figures

Figure 2.1: Cells from both M. musculus and M. spretus mice display expected ...................... 15
morphologies of senescence.
Figure 2.2: Senescent M. musculus cells exhibit higher -galactosidase activity and ............... 16
lysosomal mass relative to M. spretus.
Figure 2.3: SASP is detected at higher levels in M. musculus cells. .......................................... 17
Supplemental Figure 2.1: M. musculus and M. spretus cells represent the extremes ............... 30
in the natural variation of senescent phenotypes across Mus.
Supplemental Figure 2.2: Senescent M. musculus primary fibroblasts display enhanced ........ 31
mRNA induction of genes of the senescence-associated secretory phenotype.
Supplemental Figure 2.3: Senescent M. musculus primary fibroblasts display enhanced ........ 32
secretion of proteins of the senescence-associated secretory phenotype.
Supplemental Figure 2.4: Irradiated M. spretus cells release less apoptotic and ...................... 33
necroptotic factors relative to M. musculus cells.
Figure 3.1: USF2 emerges as a senescence regulator candidate from a natural ...................... 44
variation-based transcription factor screen.
Figure 3.2: Usf2 depletion results in more DNA damage but a muted DNA damage ................. 48
response following irradiation.
Figure 3.3: Usf2 knockdown results in an enhanced senescence profile. ................................ 51
Supplemental Figure 3.1: Senescent M. musculus x M. spretus F1 primary fibroblasts ............ 61
display intermediate activity of senescence-associated 𝛃 -galactosidase (SABG).
Supplemental Figure 3.2: Variation in gene expression during senescence between ............... 62
M. musculus and M. spretus is controlled by both cis- and trans-acting elements.
Supplemental Figure 3.3: Senescent M. musculus x M. spretus F1 primary cells ..................... 63
display intermediate induction in mRNA expression of genes of the
ix

senescence-associated secretory phenotype.
Supplemental Figure 3.4: Usf2-targeting shRNAs knock down Usf2 expression. ...................... 64
Supplemental Figure 3.5: Usf2 depletion slightly slows growth of primary M. musculus ........... 65
cells independent of irradiation.
Supplemental Figure 3.6: Usf2 knockdown results in less transcriptional repression ................ 66
following acute DNA damage.
Supplemental Figure 3.7: Expression of core DNA damage response genes are ..................... 67
largely not affected by Usf2 knockdown.
Supplemental Figure 3.8: Usf2 knockdown prior to DNA damage also results in ...................... 68
an enhanced senescence profile.
Supplemental Figure 3.9: Expression profiling of control cells during a senescence ................ 70
timecourse reveals a dynamic expression program with senescence progression.
Supplemental Figure 3.10: Usf2 knockdown results in an enhanced senescent gene .............. 71
expression profile.
Figure 4.1: Reciprocal hemizygosity test. ................................................................................ 107
Figure 4.2: Primary M. spretus cells exhibit slower growth rate relative to .............................. 109
M. musculus cells.
Figure 4.3: RH-seq lentivirus integrate site library prep outline. ............................................... 110
Figure 4.4: Lentiviral insertions are evenly distributed across the hybrid genome. .................. 112
Supplemental Figure 4.1: M. musculus and M. spretus cells represent the extremes in ......... 119
the natural variation of senescent phenotypes across Mus.

x

Abbreviations

USF2: Upstream stimulatory factor 2
SASP: Senescence associated secretory phenotype
ASE: Allele specific expression
SABG: Senescence associated -galactosidase
KD: Knock down
shRNA: Short hairpin RNA
SCR: Scrambled shRNA
SH: shRNA treated
IR: Ionizing radiation
SEN: Senescent
EdU: 5-ethynyl-2´-deoxyuridine
γH2AX: Phosphorylated H2A histone family member X
TPM: Transcript per million
ANOVA: Analysis of variance
TF: Transcription factor
MS: Mass spectrometry
GTRD: Gene Transcription Regulation Database
GFF: General feature format
DMEM: Dulbecco’s Modified Eagle Medium
RPMI: Roswell Park Memorial Institute
PBS: Phosphate buffered saline
FBS: Fetal bovine serum
BSA: Bovine serum albumin
DAPI: 4′,6-diamidino-2-phenylindole
xi

RH-seq: reciprocal hemizygosity analysis via sequencing

xii

Abstract

Aging is a risk factor for chronic human diseases, and age-associated phenotypes consti-
tute a major economic health-care burden as well as a threat to quality of life for elderly popula-
tions. Against the backdrop of landmark successes in the molecular and genetic study of aging
biology, many of the mechanisms of aging remain incompletely understood. To fill this knowledge
gap, classic research paradigms in the field have emphasized a few animal models. But the di-
versity of aging phenotypes across the natural world can also provide a rich resource for the
discovery of aging determinants—including pro-lifespan, pro-healthspan factors that have
evolved in non-model systems. This thesis describes my work to characterize and screen natural
genetic variation between mouse species in a cell-autonomous program called cellular senes-
cence, a major driver of aging phenotypes in animal models.
In our first approach, we surveyed natural variation of senescence phenotypes between
species of the Mus genus. We applied genotoxic stress to primary tail fibroblasts extracted from
purebred mice of various Mus species and observed that cells from M. musculus, the classic
laboratory model, were unique across the genus in their avid senescence program. In our second
approach, we developed a natural variation-based screen which takes as input transcription factor
binding sites and gene expression as they differ between species, and finds signatures of regu-
latory function during cellular senescence. We applied our technique in M. musculus x M. spretus
interspecific hybrid cells, identified the transcription factor USF2 as a top candidate, and validated
novel roles for this factor in the DNA damage response and cellular senescence. Finally, in our
third approach we developed foundational methods for a tool to genetically dissect natural trait
variation across species in mammalian cell culture. Together, our findings highlight the power of
natural variation-based studies to uncover molecular mechanisms of aging-relevant traits.

1

Chapter 1: Introduction

Aging overview

Aging can be defined as the systemic loss of function and homeostasis in tissues over
time, leading to increased susceptibility to morbidity and mortality (López-Otín et al. 2013). As
human lifespans have been drastically increasing for the past several hundred years (Finch 2010),
so have the prevalence and recognition of age-related pathologies. Aging is now recognized as
one of the leading risk factors for a number of debilitating diseases (Niccoli and Partridge 2012),
driving the need to better understand the underlying molecular mechanisms of this process to
improve quality of life for a growing subset of our population. One of the most compelling models
driving aging to emerge in recent years is cellular senescence, first described by Leonard Hayflick
(Hayflick 1965) as a state of cell cycle arrest after cells reach their natural replicative limit. Cellular
senescence has been proposed as a cellular model of the aging of whole organisms (Campisi
and Fagagna 2007), and landmark studies have shown that the accumulation of senescent cells
with age may be drivers of many aging-related phenotypes (Baker et al. 2011; Demaria et al.
2014; Olivieri et al. 2018; Davalos et al. 2010; Krtolica et al. 2001; Parrinello et al. 2005; Bitto et
al. 2010; Salminen et al. 2011b; Noureddine et al. 2011; Coppé et al. 2006).

Cellular senescence overview

Cellular senescence represents one of several possible responses to stress available to
a mammalian cell. These have been best studied for the example of cytotoxic stress. Upon expo-
sure to a DNA damaging agent, cells will enter a transient state of cell cycle arrest and, if possible,
carry out DNA repair, resolve errors, stabilize the genome, and resume normal cell cycling. By
contrast, more severe stress levels trigger either senescence or apoptosis (Harper and Elledge
2

2007; Abraham 2001). The latter are considered polar opposites, in that senescence leads to
resistance to apoptosis. Senescence is also defined by its program of physiologically irreversible
growth arrest and the secretion of soluble factors involved in inflammation, tissue remodeling, and
cell cycle collectively known as the senescence associated secretory phenotype (SASP) (Coppé
et al. 2008). These characteristics set senescent cells apart from other non-dividing populations
in multicellular organisms such as post-mitotic and quiescent cells. Post-mitotic cells are those
that have lost their ability to divide during differentiation (Sapieha and Mallette 2018), and quies-
cent cells are those undergoing a temporary arrest in growth with the potential to resume the
normal cell cycle given the appropriate stimuli (Cheung and Rando 2013). Although cellular se-
nescence was initially characterized in proliferating cell types, it has also been found to occur in
a variety of post-mitotic cells, and has been established as a significant driver of aging-related
phenotypes in vivo (Sapieha and Mallette 2018; von Zglinicki et al. 2021). The growth arrest in
senescent cells has also been found to be reversible in certain cell types through non-physiolog-
ical stimuli, such as p53 inactivation (Beauséjour et al. 2003), inhibition of PDK1 (An et al. 2020),
and hTERT activation (Patel et al. 2016). Senescent cells are also set apart from other non-diving
cells through their apoptosis resistance, which is mediated by inactivation of the pro-apoptotic
functions of p53 (Chaturvedi et al. 2004), and persistent upregulation of Bcl-2 (Ryu et al. 2007).
NF-kB, which is activated in both cellular senescence and the aging process overall (Salminen
and Kaarniranta 2010), is also known to inhibit apoptosis via control of the reactive oxygen spe-
cies (ROS)/JNK pathway (Papa et al. 2006), and is maintained throughout the senescent state
through a positive feedback loop of increased inflammatory and cytokine signaling (Salminen et
al. 2011a).
Cells can become senescent upon administration of a number of stimuli, including telo-
mere shortening at replicative limits (Martens et al. 2000), strong mitogenic signals (Chen et al.
2005), treatment with reactive oxygen species (Chen and Ames 1994), and excessive DNA dam-
age repair signaling (Di Leonardo et al. 1994). Growth arrest in senescent cells is maintained by
3

two tumor suppressor pathways, p53 and p16
INK4a
-pRB. Over-activation of either of the latter can
also lead to senescence, with altered characteristics relative to the more physiologically normal
inducers (Coppé et al. 2011; Helman et al. 2016). p53 is an important tumor-suppressive tran-
scription factor that regulates the cell cycle and responds to DNA damage response (DDR) sig-
naling by activating p21, a cyclin dependent kinase (CDK) inhibitor that can induce both transient
and permanent cell cycle arrest. p16
INK4a
is also a CDK inhibitor that regulates pRB, which in turn
is a tumor-suppressor protein that, among its many activities, suppresses the transcription factor
E2F1 by direct binding and remodeling chromatin to produce senescence associated heterochro-
matin foci (SAHF) (Campisi 2013). E2F1 regulates cell cycle progression by activating transcrip-
tion of DNA replication genes, and overexpression of E2F1 is also sufficient to induce cell cycle
arrest (Dimri et al. 2000). Other transcription factors that are involved in cell proliferation and
suppressed during senescence include AP-1, a dimer consisting of a combination of c-fos and c-
jun proto-oncogene family members, and Id-1 and Id-2, members of a family of helix-loop-helix
(HLH) proteins that negatively regulate basic HLH transcription factors (Dimri et al. 1996).

Senescence and DDR

Cellular senescence is marked by persistent DDR signaling that remains active long past
the initial genotoxic stressor. The two systems are deeply intertwined, as senescence can be
viewed as part of the response to DNA damage in physiological conditions. Many of the conditions
that activate the DDR are likewise known to induce senescence, including telomere shortening
(Chen et al. 2007), oncogene activation (Seoane et al. 2017), and DNA damage via reactive ox-
ygen species (ROS) or ionizing radiation treatment (Campisi and Fagagna 2007; Casella et al.
2019; Zhao and Darzynkiewicz 2013). Single-stranded DNA and double-stranded breaks recruit
the protein kinases ATM and ATR respectively, which results in the phosphorylation of the histone
variant H2AX (γH2AX). γH2AX then serves as a scaffold to recruit further components of the DDR
4

to facilitate repair (d’Adda di Fagagna 2008). DDR signals are transient in cases of manageable
levels of DNA damage; in more dramatic treatments that induce senescence, persistent γH2AX
foci can be detected well after the initial damaging event (Fumagalli et al. 2014). Some compo-
nents of the SASP are also positively regulated by DDR proteins, including ATM, NBS1, and
CHK2

(Rodier et al. 2009), further highlighting the importance of the DDR in establishing and
maintaining the senescent state.

Commitment to senescence

In any stress response scenario, the fate the cell commits to—senescence, apoptosis, or
repair and proliferation—can depend on the cell type and the level of the stressor (Campisi and
Fagagna 2007; Campisi 2013). The exact mechanisms are not yet fully understood; the central
p53/p21 and p16
INK4a
-pRB pathways appear to play a partial role in this decision. The initial spike
in p21 levels after damage can predict whether a cell will enter senescence or continue to prolif-
erate, with high levels leading to the former and low levels to the latter (Hsu et al. 2019). In ex-
perimental models in which the p53/p21 pathway was disabled, stressed cells were more likely to
enter apoptosis, and disrupting the p53 antagonist MDM-2 enhanced entry into senescence
(Childs et al. 2014). The interaction of p16
INK4a
and caspase-3 has also been shown to control this
axis following telomeric stress (Panneer Selvam et al. 2018). Persistent upregulation of p16
INK4a

is necessary to maintain senescence in the presence of oncogenic stimuli (Beauséjour et al.
2003). Additional factors such as ING (Ludwig et al. 2011) and Bcl-2 (Vicencio et al. 2008) are
also thought to be involved in this process. Against the backdrop of this rich molecular literature,
many gaps remain in our understanding of the molecular mechanisms underlying cell commitment
to long-term fates after damage. In this thesis, I describe efforts to identify molecular players in
damage response and establishment of cellular fate.

5

Markers of senescence

SASP, SAHF, and overexpression of p21 and p16
INK4a
are well-established markers of
senescence, along with loss of lamin B1 (Freund et al. 2012), nuclear loss of HMGB1 (Davalos
et al. 2013), flattened and enlarged morphology (Zhao and Darzynkiewicz 2013), persistent
γH2AX foci, and senescence-associated β-galactosidase (SABG) activity (Dimri et al. 1995). Of
these, SABG is one of the most commonly used markers for detecting senescence in both cell
culture and tissue sections. Although several studies have shown that this is not a unique marker
of senescent cells (Hall et al. 2017; de Mera-Rodríguez et al. 2021), it remains widely used in
combination with other characters to identify and quantify senescence. SABG stems from an
overexpression of the endogenous β-galactosidase in senescent cells (Lee et al. 2006), leading
to activity at sub-optimal pH levels. This overexpression can be attributed to a rise in insoluble
aggregates in the lysosomes during senescence (Salmonowicz and Passos 2017), which further
leads to an increase in lysosomal mass and number (Kurz et al. 2000). Studies have linked this
marker to greater protein misfolding during senescence due to the increased secretory burden in
cells expressing the SASP (Dörr et al. 2013; Pluquet et al. 2015).

Role and function of the SASP

Pro-aging effects of senescent cells are mediated largely by the SASP (Bitto et al. 2010;
Salminen et al. 2011b; Noureddine et al. 2011; Coppé et al. 2006). Secreted factors include in-
flammatory and immune-regulating chemokines and cytokines (e.g. IL-6, IL-7, IL-8, MCP-2, MIP-
3a, MMPs, MCPs), growth factors (e.g. GRO, HGF, IGFBPs), shed cell surface molecules (e.g.
ICAMs, uPAR, TNFRs), and survival factors (e.g. OPG and sTNF RI) (Coppé et al. 2008). The
SASP primarily manifests in cells that senesce due to genetic or epigenetic damage; cells in which
senescence has been induced by p21 or p16
INK4a
overexpression do not express a SASP
6

(Campisi and Fagagna 2007). Most SASP components are upregulated at the mRNA level, and
are positively regulated by transcription factors NF-kB (Salminen et al. 2012) and C/EBP-β (Salotti
and Johnson 2019), both of which are active in pathways that regulate inflammatory cytokine
gene expression. In contrast, the SASP is negatively regulated by p53, as inactivation of the latter
leads to higher levels of SASP expression (Coppé et al. 2008). Aside from these major regulators,
landmark studies have identified additional transcription factors regulating the SASP (Chan et al.
2022; Wang et al. 2016; Xie et al. 2014; Han et al. 2018; Martínez-Zamudio et al. 2020; Brück-
mann et al. 2019; Tyler et al. 2021; Zhang et al. 2021), some of which are active in different cell
types and senescence inducers. In fact, the profile of the SASP itself can vary across these lines
(Casella et al. 2019; Basisty et al. 2020), highlighting the complexity and variability of the senes-
cence response.
Normal physiological roles of the SASP include signaling from damaged cells to stimulate
repair and immune clearance, and to reinforce senescence growth arrest (Campisi 2013). When
senescent cells are transient the SASP is beneficial, aiding in wound repair (Demaria et al. 2014),
regeneration (Antelo-Iglesias et al. 2021), development, and tumor suppression (Campisi and
Fagagna 2007; Campisi 2013). However, as the senescent cell burden increases with age due to
a decline in the immune system’s ability to clear them, the constant release of proinflammatory
factors can contribute to the increased chronic sterile inflammation with age, termed “inflammag-
ing” (Olivieri et al. 2018; Freund et al. 2010). Ironically, this tumor suppression system can actually
lead to cancer progression, as some SASP components, such as MMP3, IL-6, and IL-8, can pro-
mote tumor growth, epithelial-mesenchymal transition, and invasion, contributing to metastasis of
malignant tumors (Krtolica et al. 2001; Parrinello et al. 2005; Laberge et al. 2012). The chronic
inflammatory state induced by senescent cells has also been linked to other age-related patholo-
gies such as neurodegeneration (Ungerleider et al. 2021), sarcopenia (He et al. 2022), stem cell
depletion (Sun et al. 2022), lower back pain (Patil et al. 2018), and cataracts (Yan et al. 2019).

7

Senotherapeutics

Several strategies have been proposed to counteract the pro-aging effects of senescent
cells. Senolytic therapies are aimed at the outright removal of senescent cells (Kang 2019). As a
genetic model of the senolytic paradigm, the 3MR mouse harbors a drug-inducible pro-apoptosis
construct under the control of the p16
INK4a
promoter (Demaria et al. 2014). Meanwhile, pharma-
ceutical senolysis has been achieved by compounds such as dasatinib and quercetin, which im-
prove health and even lifespan in mouse models (Xu et al. 2018). However, the removal of se-
nescent cells has proven detrimental in some cases (Demaria et al. 2014), impairing the normal
physiological roles that they play in a healthy organism. As such, there has been a rising interest
in alternative senomorphic strategies aimed at manipulating specific pathways or characteristics
of senescent cells. The latter have been pioneered with compounds affecting processes central
to the senescence response, such as the IKK/NF-kB or ATM pathways (Kim and Kim 2019), with
some success in alleviating pro-aging effects of senescent cells.

Comparative biology of aging and longevity

The diversity of form and function in the natural world represents a snapshot of the four-
billion-year old process of evolution. Nature has invented unique traits across the tree of life, from
the bright colors of a peacock’s feathers to the twisting tusk of a narwhal to thermotolerance in
deep vent microbes (Ranawat and Rawat 2017) or the life cycle of the immortal jellyfish (Matsu-
moto et al. 2019). In many such cases, organisms from the wild have achieved genetic solutions
to stress- and disease-resistance problems that we face in the clinic. The underlying molecular
mechanisms thus hold promise for the development of mimetics that would help advance human
health.
8

Studies into the comparative biology of lifespan have long informed our understanding of
the effect of environment and evolutionary pressures on aging (Healy et al. 2014; Williams 1957;
Promislow 1993). Of particular interest are species that display exceptional longevity beyond ex-
pected patterns of size (Austad and Fischer 1991). Environmental and physiological factors are
known to contribute to these lifespans, such as flight (Holmes and Austad 1994), low offspring
numbers (Garbino et al. 2021), and subterranean habitation (Healy et al. 2014). Molecular insights
into species-specific longevity traits have also emerged.
For example, species of bats reaching maximum lifespans of 30-40 years display unique
inflammatory and immune responses, resistance to viruses (Gorbunova et al. 2020), and evi-
dence of positive selection in DNA repair pathways (Zhang et al. 2013), which are all believed to
contribute to longevity.
Burrowing naked mole rats (Schulze-Makuch 2019), which have lifespans exceeding 30
years in captivity (Edrey et al. 2011), are resistant to a variety of stressors compared to other
rodent species (Lewis et al. 2012) and exhibit very low levels of cancer. They express a unique
INK4 isoform that is more effective in inducing growth arrest than p16
INK4a
(Takasugi et al. 2020).
Naked mole rats also produce high molecular weight hyaluronan (HMMHA), which increases con-
tact inhibition, slows cellular growth rate, and increases stress resistance by acting on p53 (Tian
et al. 2013; Seluanov et al. 2009). Applications of HMMHA have proven protective for cancer
(Tian et al. 2013), oxidative stress (Takasugi et al. 2020), inflammation (Chistyakov et al. 2019),
and cellular senescence (Alessio et al. 2018) in cells of other species. As another facet of naked
mole rat aging biology, senescent cells from these animals exhibit lower levels of senescence
markers and extended survival under stress relative to mouse cells (Zhao et al. 2018).
Furthermore, a recent analysis across rodents has focused on SIRT6 as a modulator of
DNA repair and longevity (Tian et al. 2019). Here the authors identified five amino acid changes
that resulted in higher SIRT6 activity in longer-lived species, including bats and beavers, relative
to other lineages in the rodent clade.
9

In this thesis I use long-diverged mouse species in comparative-biological studies of ag-
ing-relevant traits.

Genotype-to-phenotype mapping within species in aging biology

Any phenotype of interest in aging biology, when it manifests differently in different genetic
backgrounds, raises the possibility of molecular insights from unbiased, genome-wide mapping
of genotype to phenotype. Existing methods for this purpose, which have been developed far
outside the aging field but are readily applied to aging phenotypes, mainly take advantage of
natural genetic variation within populations of the same species (Visscher et al. 2017; Slatkin
2008). For example, the first implication of the APOE locus to Alzheimer’s disease (AD) was found
in a genetic linkage mapping study performed on familial late-onset AD (Roses 2006). Linkage
mapping traces recombination events across generations and identifies DNA sequence variants
on recombinant chromosomes that co-inherit with the trait of interest (Morgan 1910). More re-
cently, numerous genome-wide association studies (GWAS) have been performed on AD, leading
to the discovery of additional loci that have furthered our understanding of the risk factors and
progression of the disease (Bertram and Tanzi 2009; Elsheikh et al. 2020; Bellenguez et al. 2022).
GWAS maps genotype to phenotype across unrelated individuals, allowing for larger sample sizes
relative to the family-based linkage mapping method (Ozaki et al. 2002). Among the thousands
of GWAS reports in the current literature, some of the most exciting for aging biologists focus on
lifespan, a trait that has been shown to be partially controlled by heritable genetic variation (Pas-
sarino et al. 2016). Such studies performed on centenarians have identified loci associated with
healthy aging and longevity (Garagnani et al. 2021; Zeng et al. 2016; Pilling et al. 2017). In this
thesis, I describe a new approach to map genotype to phenotype that complements linkage and
GWAS strategies in that it is not limited to studies of individuals of a given species.

10

Mus spretus as a model of natural variation

The Algerian mouse Mus spretus has served as a genetic model for 30 years (Dejager et
al. 2009). This species originates from the Mediterranean region and is ~1.5 million years diverged
from the M. musculus lineage (Bonhomme et al. 1978), with one polymorphism every 50 to 100
bases on average between the two (Mahler et al. 2008; Harr et al. 2016). As F1 hybrids from M.
musculus x M. spretus crosses are viable and partly fertile, interspecies linkage mapping has
achieved some success in dissecting trait divergence between the species (Nagase et al. 2001;
de Gouyon et al. 1993; Lazzarano et al. 2018). However, many unique traits in M. spretus mice
remain incompletely understood.
For example, M. spretus resist infection from a variety of sources, including bacteria, vi-
ruses, and parasites, at doses lethal in laboratory M. musculus. The inbred SPRET/EiJ strain of
M. spretus mice is extremely resistant to influenza, a trait that was mapped to the gene Mx1. Mx1,
which is usually defective in other species of mice, was found to be functional in M. spretus, and
proved to be protective from influenza infection in congenic strains containing the SPRET/EiJ
allele (Vanlaere et al. 2008). SPRET/EiJ is also resistant to septic shock from bacterial infection
and lipopolysaccharide (LPS) treatment, with a mechanism that may rely on a dampened inflam-
matory response: SPRET/EiJ mice exhibit high levels of anti-inflammatory proteins such as the
glucocorticoid receptor (Dejager et al. 2010b) and Gilz (Pinheiro et al. 2013). By the same token,
during infection by Salmonella enterica, SPRET/EiJ exhibit faster recruitment of neutrophils to the
site of infection than do laboratory M. musculus mice, resulting in lower bacterial loads and en-
hanced survival in the former (Dejager et al. 2010a). A similar program of early immune cell re-
cruitment was found in cases of Schistomsoma mansoni infections, in which M. spretus mice were
found to tolerate higher parasitic loads compared to M. musculus (Pérez del Villar et al. 2013).
In addition to their resistance to inflammation and infection, M. spretus mice also exhibit
resistance to several types of cancer. Genetic insights have been suggested from genome-wide
11

mapping, though most of the emergent loci have yet to be validated molecularly (Nagase et al.
2001, 1995; Mahler et al. 2008; Manenti et al. 1996; Santos et al. 2002).
Furthermore, M. spretus mice are resistant to warfarin (Goulois et al. 2017; Müller et al.
2014), a blood-thinning and anticoagulant drug mixed into bait to control mouse populations. War-
farin acts by inhibiting vitamin K epoxide reductase (VKOR) complexes that are essential for vit-
amin K recycling and blood coagulation. Vkorc1 is the specific subunit of these complexes tar-
geted by warfarin, and molecular studies have shown that the M. spretus allele at this gene is
sufficient for resistance to the drug (Song et al. 2011). Populations of M. musculus mice resistant
to this rodenticide in Europe were found to harbor an introgression of the M. spretus allele for
Vkorc1, likely mediating their resistance. In this thesis, I use cell culture models to study diver-
gence between M. spretus and M. musculus in aging-relevant traits.

12

Chapter 2: Divergence in cellular senescence phenotypes between species of mice

Abstract

Cellular senescence is a program of irreversible growth arrest and pro-immune protein
secretion by mammalian cells of many types upon stress exposure. Senescent cells play a causal
role as determinants of lifespan and the etiology of aging phenotypes. Strategies to manipulate
senescence phenotypes are of abiding interest in the field for biomedical and basic-biological
applications. We used a fibroblast cell culture system to characterize the natural variation of se-
nescence phenotypes between multiple species across the Mus genus. Primary cells extracted
from all Mus species tested exhibited growth arrest and senescent morphologies following geno-
toxic stress. However, only cells from M. musculus displayed high levels of senescence markers,
such as senescence-associated β-galactosidase, pro-inflammatory protein secretion, and hyper-
active lysosomes. Wild-derived mice from other species exhibited a dampened-senescence phe-
notype in cell culture models, with muted protein secretion and little or no overload to the proteo-
stasis system. These data attest to a tuning by evolution of senescence phenotypes across
mouse species, and they point to the diversity of the natural world as an inspiration for future
senescence interventions.

Introduction

Mammalian cells exposed to stress have a choice between multiple response pathways.
In a scenario of manageable stress levels, a cell can mount damage repair processes and ulti-
mately, if it is in a mitotic tissue, return to cycling. In more severe stress conditions, a cell has two
primary fates: apoptosis or immune clearance stimulated by cellular senescence (Vicencio et al.
2008; Campisi and Fagagna 2007; Childs et al. 2014). The senescence program contributes to
13

wound healing and cancer prevention in young animals. However, when senescent cells persist
in the body due to faulty clearance late in life, they can damage tissues via sterile inflammation,
largely owing to secretion of pro-inflammatory factors called the senescence-associated secretory
phenotype (SASP) (Bitto et al. 2010; Salminen et al. 2011b; Noureddine et al. 2011; Coppé et al.
2006). The decision to commit to senescence over apoptosis after stress exposure is thus con-
sidered a double-edged sword with respect to the fitness of the organism—beneficial in some
contexts and deleterious in others.
The evolutionary history of the cellular senescence program has to date been unknown.
Classic studies of senescence have primarily focused on a given genotype of human cells or Mus
musculus mice (Itahana et al. 2004; Coppé et al. 2010b), with one recent study in naked mole
rats (Zhao et al. 2018). In principle, senescence characters could vary widely across genotypes.
Such differences would be particularly likely if the cost-benefit balance of the senescence program
has changed through evolution as organisms adopted distinct forms and functions. We set out to
use the Mus genus to investigate species-level differences in senescence. We surveyed differ-
ences between mouse species in primary fibroblast culture models of senescence, and we used
the results to trace changes across Mus in how cells commit to long-term fates after stress expo-
sure.

Results

Uniquely high levels of senescence markers in M. musculus fibroblasts relative to other
mice

To study natural variation in senescence phenotypes, we made use of a classic in vitro
cell model of senescence, namely primary fibroblasts from tail skin treated with 15 Gy of ionizing
radiation (IR) (Casella et al. 2019; Campisi and Fagagna 2007). We isolated primary tail
14

fibroblasts from the PWK and STF wild-derived purebred lines of M. musculus musculus and M.
spretus respectively. Seven days after IR treatment, cells from both species had arrested growth
and exhibited the expected flattened and enlarged morphologies of senescent cells (Figure 2.1A-
B). Assaying these cultures for γH2AX foci, a marker of the DNA-damage response (DDR) and a
hallmark of long-term senescence (Rodier et al. 2009; Siddiqui et al. 2015), we found that counts
were indistinguishable in primary M. musculus and M. spretus fibroblasts after irradiation, though
higher in the latter in the absence of treatment (Figure 2.1C). These data indicated that cells of
both species had sustained DNA damage and entered the senescent state in our treatment and
incubation regime.
To begin to compare the senescence program between cells of M. musculus and M. spre-
tus, we assayed primary fibroblasts from each species for senescence-associated β-galacto-
sidase (SABG), which reports lysosomal hyperactivity during senescence and has served as a
classic marker of senescence (Dimri et al. 1995). After irradiation we detected robust signal in
this assay from cells of both species, as expected; however, the proportion of SABG-positive cells
in M. spretus fibroblast cultures was two to eight-fold lower than that of M. musculus cells (Figure
2.2A-B). Primary fibroblasts from M. domesticus, a close relative of M. musculus, largely pheno-
copied M. spretus cells in SABG assays after irradiation (Supplementary Figure 2.1A). These data
suggested a history of lineage-specific changes in M. musculus driving marked differences in
fibroblast lysosomal activity.
SABG signal can be attributed in part to an increase in lysosomal mass and number during
senescence (Kurz et al. 2000). Given the differences we had seen between fibroblasts from our
focal species in the SABG assay, we hypothesized that they would also manifest differences in
lysosomal content after irradiation. Staining lysosomes with the Lysotracker dye, which fluoresces
in acidic cellular compartments (Tai et al. 2016), bore out this prediction: irradiated M. musculus
primary fibroblasts exhibited a thirty-fold increase in Lysotracker staining relative to irradiated M.
15

spretus cells (Figure 2.2C), and the latter closely resembled the results of assays of fibroblasts
from M. domesticus and M. castaneus (Supplementary Figure 2.1B). Such a trend echoed the
phenotypes we had seen in SABG staining experiments (Figure 2.2A-B), and provided further
support of our inference of marked divergence in lysosomal activity after irradiation, in M. muscu-
lus fibroblasts relative to cells from other species in its clade.

The high-amplitude SASP of M. musculus fibroblasts is unique relative to M. spretus

In M. musculus cells, the massive lysosomal changes seen after irradiation likely result
from overload of the proteostasis system during SASP production (Park et al. 2018; Brunk and
Terman 2002; Pluquet et al. 2015). Given that we had observed much weaker effects of irradiation
Figure 2.1: Cells from both M. musculus and M. spretus mice display expected mor-
phologies of senescence. (A) Representative images of senescent primary fibroblasts from
M. musculus (above) and M. spretus (below) seven days after irradiation exhibiting a flattened
and enlarged morphology. (B) Each column reports the average percentage of cells with EdU
incorporation seven days after IR treatment (SEN) set relative to the same in unirradiated
controls (CONT) for each species reported in (A). For a given column, points report technical
replicates (M. musculus n = 3, M. spretus n = 3). (C) Each columns reports the average number
of γH2AX foci per cell for each species as reported in (B). For a given column, points represent
biological and technical replicates (M. musculus n = 9, M. spretus n = 9). **, p < 0.01, one-
tailed Wilcoxon test comparing species.

16

on lysosomal content and activity in fibroblasts from non-M. musculus species, we hypothesized
that the latter would likewise exhibit a dampened-SASP phenotype. To test this, we focused on
M. musculus and M. spretus as representatives of the extremes of the phylogeny. We profiled
bulk RNA levels in irradiated and control primary fibroblast cultures from each species. In the
resulting profiles, we inspected genes of the SASP immune-stimulatory program (Coppé et al.
2008), and found that this gene cohort was induced more highly in M. musculus cells than in those
of M. spretus after irradiation (Figure 2.3A). Likewise, in an unbiased search of Gene Ontology
terms, we identified several suites of immune response and NF-κB signaling genes that were
enriched for senescence-specific differential expression between the cultures. In each case, the
Figure 2.2: Senescent M. musculus cells exhibit higher -galactosidase activity and ly-
sosomal mass relative to M. spretus. (A) Representative images of senescent primary fi-
broblasts from M. musculus (above) and M. spretus (below) stained with the -galactosidase
indicator X-Gal seven days after irradiation. (B) Each trace reports results from a timecourse
of X-Gal staining assays of primary senescent cells of the indicated species as in (A). The y-
axis reports the proportion of cells stained positive for senescence-associated -galactosidase
(SABG) activity. In a given column, small points report biological and technical replicates and
large points report their average (M. musculus n = 9, M. spretus n = 5). ***, p < 0.001, ****, p
< 0.0001, one-tailed Wilcoxon test comparing species. (C) Each column reports the average
Lysotracker® fluorescent signal set relative to the average value in senescent (SEN) M. mus-
culus cells, for both senescent and unirradiated controls (CONT) of each species as in (B). For
a given column, points represent technical replicates (M. musculus n = 4, M. spretus n = 2). *,
p < 0.05, ****, p < 0.0001, one-tailed t-test comparing species.
17

gene groups were more strongly induced during senescence in M. musculus cells than in M.
spretus cultures (Figure 2.3A and Supplemental Tables 2.1 and 2.2); among members of the latter
we noted Cxcl1 (Kim et al. 2018), Il6 (Coppé et al. 2010b), Ccls 2,7 and 8 (Coppé et al. 2010a),
Mmp13 (Levi et al. 2020), and other reported SASP genes (Supplemental Figure 2.2). These data
Figure 2.3: SASP is detected at higher levels in M. musculus cells. (A) Each trace reports
a cumulative distribution of the change, in senescent primary fibroblasts of the indicated spe-
cies, in mRNA levels of genes of the senescence associated secretory phenotype with senes-
cence (Coppé et al. 2008). The y-axis reports the proportion of genes with the expression
change on the x-axis, with the latter taken as an average across replicates. (B) Each row
shows results from a test of the genes of the indicated Gene Ontology term for enrichment of
expression change between the species during senescence, with p-values from a resampling-
based test, corrected for multiple testing. (C) Annotations are as in (A) except that measure-
ments were of secreted peptides, and a curated list of known SASP factors are shown (Coppé
et al. 2008; Basisty et al. 2020).

18

make clear that, at the level of mRNA, the senescence regulatory program differs markedly be-
tween fibroblasts of our focal species.
We hypothesized that much of the mRNA expression divergence between M. musculus
and M. spretus cells during senescence would result in differential protein abundance. In pursuing
this notion, we focused on proteins secreted into the medium by senescent cells, owing to the
physiological importance of the SASP (Coppé et al. 2010a). We collected conditioned media from
senescent and control cultures of primary fibroblasts of each genotype, and we used it as input
into unbiased mass spectrometry to quantify protein abundance. Focusing on SASP proteins in
this data source, we found higher levels overall in the medium of irradiated M. musculus cells
relative to that of M. spretus (Figure 2.3C). This trend was borne out for a broad representation
of SASP factors, including CCL chemokines, matrix metalloproteases, and serpins (Supplemental
Figure 2.3). Together, our omics profiles reveal striking quantitative differences in SASP levels
between M. spretus fibroblasts and those of M. musculus, with higher mRNA expression and
protein secretion in the latter.

Differential entry into apoptosis does not explain senescence variation among mouse fi-
broblasts

In light of our findings of a low-abundance SASP profile in M. spretus cells relative to those
of M. musculus (Figure 2.3), we considered the possibility that a lower proportion of the former
cultures were committing to senescence upon genotoxic stress, and more to other fates. Avid
repair and return to proliferation is one such long-term response to DNA damage, but our analysis
of EdU incorporation had largely ruled out a difference in this behavior in cells of the two species
(Figure 2.1B). We thus turned to the potential for differential apoptosis between M. musculus and
M. spretus fibroblasts. As a test of this notion, we used a panel of proteins known to be secreted
into the extracellular space during apoptosis, and we also evaluated a similar panel characteristic
19

of necroptosis (Tanzer et al. 2020). In each case, we quantified the abundance of proteins de-
tected in conditioned medium from our two focal species. In each case, results revealed higher
levels in irradiated M. musculus fibroblast cultures relative to those of M. spretus (Supplemental
Figure 2.4). These data strongly suggest that M. spretus cells do not adopt the apoptotic fate
more than M. musculus cells do, which, combined with our observations of senescence-like mor-
phology and growth arrest, supports the model of a dampened immune-stimulatory secretion pro-
file in senescent M. spretus cells.

Discussion

In this work, we have found that fibroblasts from Mus species exhibit similar DNA damage,
growth arrest, and damage response signaling immediately after genotoxic stress, but different
phenotypes on a longer timescale. We have shown that the massive boost in lysosomal content
and activity in fibroblasts several days after irradiation seen classically in M. musculus models
(Qin et al. 2018; Tai et al. 2016; Coppé et al. 2010b) is unique to this species, and does not
manifest in any other tested Mus genotypes. And in a focus on transcriptional and proteomic
profiles of irradiated fibroblasts from M. spretus, we have traced a quantitatively more muted
SASP relative to M. musculus cells. We infer that these cellular and molecular phenotypes are
closely linked, since with low SASP translation and secretion in the cells of non-M. musculus
species after irradiation, there would be no need for extraordinary lysosomal capacity. Thus, a
given DNA damage input yields very different outputs in terms of senescence characters across
Mus in fibroblast cell culture—with the avid senescence response by M. musculus cells repre-
senting an outlier in the genus.
Furthermore, our analysis of apoptotic factors in our two focal species argues against a
model in which M. musculus cells favor senescence over apoptosis after damage to an unusual
extent. Rather, we favor the interpretation that the decision set point for commitment to
20

senescence by irradiated cells is similar across species, and that, considered at any given time
after damage exposure, it is the amplitude of the SASP that has been tuned by evolution. This
idea would have precedent in the gradual ramp-up of senescence expression in M. musculus
cells (Chan et al. 2022): plausibly, M. spretus could be hard-wired for slower kinetics of this pro-
gression, in the fibroblasts we study here. M. spretus cells could also simply cap the amplitude of
their SASP, limiting the immune recruitment function of senescent cells at any timepoint.
We envision that, like the low lysosome activity and content characters we have studied
here, the muted SASP phenotype in M. spretus will prove to represent the rule rather than the
exception in fibroblast irradiation response across the Mus genus. More broadly, it is tempting to
speculate that dampened senescence, especially if it extended beyond fibroblasts, could result in
anti-aging effects in vivo in non-M. musculus species. The muted SASP we have observed in cell
culture could also be linked to other stress- and pathogen-resistance phenotypes characterized
at the organismal level in M. spretus (Pinheiro et al. 2013; Vanlaere et al. 2008; Blanchet et al.
2011; Pérez del Villar et al. 2013; Dejager et al. 2009).
The dampened cellular senescence we have seen in non-M. musculus mice provides an
intriguing contrast to the trend for fibroblasts from naked mole rat in culture to avoid both senes-
cence and apoptosis altogether, after irradiation (Zhao et al. 2018). Instead, a given naked mole
rat cell can often resolve DNA damage sufficiently to re-enter the cell cycle, to a degree several-
fold beyond that seen in M. musculus. Likewise, the beaver allele of the DNA damage factor
SIRT6 confers a similar effect in a heterologous system (Tian et al. 2019). These represent evo-
lutionary innovations in other rodents distinct from the dampened senescence we infer to be the
ancestral program in Mus. The emerging picture is one in which no single irradiation response
mechanism manifests in all species, even in the simplest cell culture systems. Indeed, against
the backdrop of the classic literature on M. musculus senescence (Itahana et al. 2004; Coppé et
al. 2010b), many other irradiation response behaviors may remain to be discovered in additional
non-model species. Human cells exhibit an avid senescence response, on par with that of M.
21

musculus (Coppé et al. 2010b). As such, the programs nature has invented in other lineages may
hold promise in the search for therapeutics that would tamp down the pro-aging effects of senes-
cence in a clinical context.

Methods

Primary cell extraction and culture

Wild-derived, inbred lines of Mus musculus (PWK/PhJ) and Mus spretus (STF/Pas), gen-
erously provided by Dr. Jeff Good’s lab at the University of Montana, were maintained in standard
conditions under Montana Institutional Animal Care and Use Committee protocol number 062-
1JGDBS-120418. Animals were included based on age (3 to 5 months) and availability at the time
of request, and were humanely euthanized prior to tissue collection via CO 2 treatment and cervical
dislocation. Five tails from each genotype were collected into chilled Dulbecco’s Modified Eagle
Medium (DMEM) (Gibco cat. #21-063-029) and shipped to the Buck Institute for further pro-
cessing. No blinding was required for tail collection. Metadata for the mice used in this experiment
is provided in (Supplemental Table 2.3). Primary tail fibroblasts were extracted from the cuttings
essentially as described (Khan and Gasser 2016). Briefly, tail cuttings first were soaked in 70%
ethanol for 5 minutes and then laid out in sterile 10 cm tissue culture dishes in a biosafety cabinet
for 5 minutes to dry. The tails were then transferred to a 10-cm dish containing 10 mL of complete
medium composed of RPMI 1640 (Corning cat. # 17105CV), 10% fetal bovine serum (FBS) (Gen-
esee Scientific cat. #25-514H), 50µM 2-mercaptoethanol (MP Biomedicals cat. #194705), 100µM
asparagine (Sigma-Aldrich cat. #C980A53), 2mM glutamine (Sigma-Aldrich cat. #G8540), and
1% penicillin-streptomycin (pen-strep) (Corning cat. #30002CI). Hair was removed from the tails
using sterile forceps and razor blade, and the tails were cut into 2-3 mm pieces using sterile
surgical scissors. These pieces were then transferred into a 2 mL cryotube containing a
22

collagenase D–pronase (Thomas Scientific cat. #C756Y81, VWR cat. #97062-916) solution and
left in a shaking incubator at 37˚C for 90 minutes. Following incubation, the contents of the cryo-
tubes were placed into a 70 µm cell strainer in a new 10-cm dish containing 10 mL of compete
medium. Using the back end of a sterile 10 mL syringe plunger, the tissue was ground into the
strainer for 5-10 minutes to release the cells into the media. The cell suspension was then col-
lected into 15 mL conical tubes and centrifuged at 580 x g and 4˚C for 5 minutes. The supernatant
was removed and replaced with fresh complete medium, and the centrifugation was repeated for
two additional rounds. After the final spin, the media was replaced with complete medium supple-
mented with 250 ng/mL of amphotericin B (Thomas Scientific cat. #CHM00A358). The cell sus-
pension was placed in a 10-cm dish and incubated in a 37˚C humidified incubator at 3% O 2 and
10% CO2 for two days before passage and continued culture in complete medium (DMEM, 10%
FBS, 1% pen-strep). This complete medium was used for the remainder of our experiments. For
long term storage, cells were suspended in 5% DMSO (Sigma-Aldrich cat. #D2438) in FBS, ali-
quoted into cryotubes, and placed in a slow cooling container in a -80˚C freezer overnight. The
following day, the cryotubes were moved out of the slow cooling container and placed into long
term containers in the freezer. To thaw cells for continued culture, cryotubes containing frozen
aliquots were swirled in a 37˚C water bath until only a thin layer of ice remained in the vial. The
contents of the cryotube were then immediate transferred into a flask containing pre-warmed
complete medium and incubated overnight. The following day the cells were washed twice with
PBS to remove any trace of DMSO and cultured in complete medium. For experiments in wild-
type M. musculus and M. spretus cells we considered the culture from each individual animal to
represent one biological replicate of the respective genotype. In either setup, we refer to the split
cultures from a given biological replicate as technical replicates in a given assay. Biological repli-
cates from each corresponding species were chosen at random for each experiment.

Irradiation treatment
23

To treat a given cell culture replicate with ionizing radiation (Campisi and Fagagna 2007;
Casella et al. 2019; Zhao and Darzynkiewicz 2013) for a cell-biological assay or omics profiling,
we proceeded as follows. The day before irradiation, cells were seeded at 60-70% confluency
and incubated in a 37˚C humidified incubator at 3% O 2 and 10% CO2 overnight in complete me-
dium. The next day, a subset of cells was collected and used as input into the respective experi-
ment as the unirradiated control. The remainder of the culture was transferred into an X-RAD 320
X-Ray Biological Irradiator and treated with 15 Gy of X-ray irradiation. Cultures were then placed
back into the 37˚C humidified incubator at 3% O 2 and 10% CO2 until sampling at a the indicated
timepoints for marker assays and RNA-seq and proteomics focused on senescence (in which
case the medium was replaced 6-8 hours after irradiation, then every 48 hours for the remainder
of the experiment), as detailed below.

Senescence marker assays

For a given replicate culture after irradiation and incubation (see above), senescence-
associated -galactosidase activity was measured using the BioVision Inc. Senescence Detection
Kit (BioVision cat. #K320): cells were fixed, permeabilized, and incubated with the staining solu-
tion containing X-gal overnight. Multiple images were taken the following day using a brightfield
microscope, and the image names were randomized before the proportion of -galactosidase-
positive cells was counted manually to remove potential sources of bias. The Click-iT™ EdU
Alexa Fluor™ 488 Flow Cytometry Assay Kit (Thermo Fisher cat. #C10420) was used for as-
sessing DNA synthesis in senescent cells. The day of testing, half the cell culture medium was
replaced with medium containing 10 µM EdU and left in a the 37˚C humidified incubator at 3% O 2
and 10% CO2 overnight. The following day, each culture was lifted off the plates using 0.25%
24

Trypsin, 0.1% EDTA (Corning cat. #25053Cl), washed once with phosphor-buffered saline (PBS)
(Corning cat. #21040CV), fixed, permeabilized, and treated with Alexa Fluor™ 488 as instructed.
Samples were analyzed on a BD LSR Fortessa
TM
for fluorescence along the FITC channel to
determine EdU incorporation. Lysosomal mass was measured using the Lysotracker® Green
DND-26 kit (Cell Signaling cat. #8783) as instructed. Working solution of Lysotracker® was added
to the cell culture medium to a final concentration of 50 nM and incubated for 30 minutes. Follow-
ing incubation, the cells were washed once with PBS, lifted off the plates using 0.25% Trypsin,
0.1% EDTA, and analyzed on BD LSR Fortessa
TM
for fluorescence along the FITC channel to
determine staining intensity.

DNA damage assays

For H2AX assays, for a given replicate, cells were cultured in 8-chamber Nunc
TM
Lab-
Tek
TM
II Chamber Slides
TM
(Thermo Fisher cat. # 154453). The day before staining, all cells were
seeded at 60-70% confluence in each chamber. Cells were fixed by incubating with 4% paraform-
aldehyde (Electron Microscopy cat. # 15681) for 5 minutes at 4˚C and permeabilized using 0.1%
Triton X-100 (Fisher Scientific cat. #BP151-100) for 15 minutes at room temperature. The cells
were then blocked using 3% bovine serum albumin (BSA) (Sigma-Aldrich cat. #A3803) in PBS
for 45 minutes at room temperature, then incubated with 1 µg/mL of primary antibodies specific
to phosphorylated (Ser 139) H2AX (Santa Cruz Biotechnology cat. #sc-517348) in 3% BSA over-
night at 4˚C. The following day the cells were washed in PBS three times before incubating with
2 µg/mL of Alexa 488 secondary antibodies (Invitrogen cat. #A11001) for two hours at room tem-
perature. Cells were washed three times with PBS then incubated with 0.5 µg/mL DAPI (Sigma-
Aldrich cat. #5087410001) for 5 minutes at room temperature. The cells were washed once more
with PBS before mounting for imaging. Multiple representative confocal images of each sample
were taken using a Zeiss LSM 710 AxioObserver. Images were processed using ImageJ, taking
25

the average background fluorescence across several nuclei in each field of vision and only count-
ing foci with fluorescence intensities above the background threshold.

RNA collection and sequencing

For a given replicate culture of a given genotype, cells were treated with TRIzol
TM
(Ambion
cat. #15596018) and RNA was extracted using chloroform (Ricca cat. #RSOC00020-1C) and
ethanol (Sigmal-Aldrich cat. #E7023) precipitation. The RNA was then further purified using the
Qiagen RNeasy Kit (Qiagen cat. #74004) for DNase treatment and column cleanup. The purity of
the extracted RNA was verified using a NanoDrop ND-1000 Spectrophotometer (Marshall Scien-
tific cat. #ND-1000); all samples had 260/280 and 260/230 ratios greater than 2.0. Purified RNA
in distilled RNase/DNase free water was snap frozen using dry ice and stored at -80˚C. Samples
were then either transferred to the QB3 Genomics core at University of California, Berkeley for
library prep and sequencing on 150PE NovaSeq S4 or shipped to Novogene Co. (Sacramento,
CA) for the same. Both facilities provided 25 million paired end reads per sample. For expression
profiles of purebred cells, we subjected three biological replicates of a given genotype to irradia-
tion (see above) followed by RNA-seq at the indicated timepoints.

Pseudogenome and VCF generation

As publicly available annotations for M. musculus and M. spretus are in the context of their
reference genomes (GRCm38.96 and SPRET_EiJ_v1.96 respectively), custom pseudogenomes
for strains PWK and STF were generated and used for this study. For the PWK pseudogenome,
variant calls between PWK and the reference genome in the form of a variant call file (VCF) were
downloaded from the Sanger Mouse Genomes Project database
(https://www.sanger.ac.uk/data/mouse-genomes-project/). A pseudogenome using the VCF and
26

the GRCm38.96 reference genome was created using bcftools v1.9 (Danecek et al. 2021). To
generate shotgun sequence data for the STF pseudogenome, DNA was extracted from M. spretus
liver tissue using Qiagen DNeasy spin columns (Qiagen cat. #69506). The sample was sheared
via sonication (Covaris E220), and prepared using the New England Biolabs NEBNext Ultra DNA
Library kit (NEB cat. #E7370L). The final library was sequenced on a single lane of 150bp PE
Illumina HiSeq X at Novogene, Inc. The latter reads were aligned to the SPRET_EiJ_v1.96 refer-
ence genome using bowtie v2.2.3 (Langmead and Salzberg 2012), and a VCF was generated
using bfctools mpileup and filtered for quality, depth, and SNPs using vcfutils (Li et al. 2009; Li
2011). The VCF was then used with the reference genome to create a STF pseudogenome using
bcftools. The pseudogenomes were verified by identifying a number of called variants by hand. A
VCF of variants between the STF and PWK pseudogenomes was generated as above aligning
the STF whole genome sequencing reads to the PWK pseudogenome.

RNA-seq processing

In transcriptional profiles of purebred M. musculus and M. spretus cells, sequencing reads
from a given replicate were aligned to the PWK or STF pseudogenome respectivlty using tophat
v2.1.1 (Kim et al. 2013a). Alignments were then filtered for uniquely mapped reads using samtools
v1.3.1, and gene counts were generated using HTSeq v0.11.2 (Putri et al. 2022) and genome
annotations (GRCm38.96, SPRET_EiJ_v1.96) for both species from Ensembl. Counts were then
converted to transcripts per million (TPM) using custom R scripts, and genes were filtered for
those showing counts in more than half the samples sequenced.

Gene Ontology enrichment analysis of RNA-seq data

27

For the comparison of transcriptional profiles of M. musculus and M. spretus purebred
cells, for each gene in turn we tabulated the average TPM count from each species across repli-
cates, and then took the log2 of the ratio of these averages, rsen,true. We downloaded Gene Ontol-
ogy annotations from the AmiGO 2 (Ashburner et al. 2000; Gene Ontology Consortium 2021)
database and filtered for those with supporting biological data. For each term, we summed the
rsen,true values across all genes of the term, yielding s sen,true. To assess the enrichment for high or
low values of this sum, we first took the absolute value, |ssen,true|. We then sampled, from the total
set of genes with expression data, a random set of the same number as that in the true data for
the term; we calculated the species difference rsen,rand for each such gene and the absolute value
of the sum over them all, |ssen,rand|. We used as a p-value the proportion of 10,000 resampled data
sets in which |ssen,true| > |ssen,rand|.

Proteomic analysis of secreted proteins

Conditioned medium preparation. For a given replicate culture, either before irradiation or
10 days after irradiation (see above), cells were washed three times with PBS and incubated with
serum and phenol red free DMEM containing 1% pen-strep for 24 hours. The following day the
conditioned medium was collected and passed through a 0.45 µm filter to remove cellular debris.
The conditioned medium was placed in a -80˚C freezer for storage before use as input into pro-
teomic profiling (see below). For proteomic profiles of purebred cells, we carried out this proce-
dure for three technical replicate cultures of one biological replicate per species.
Sample concentration. 30 mL of conditioned media for each replicate were concentrated
to 400 µL with 15 mL 3 kDa filters (Millipore Sigma, Burlington, MA). Protein concentration was
determined using the Bicinchoninic Acid (BCA) assay (Thermo Fisher Scientific, Waltham, MA).
Protein digestion and desalting. Aliquots of 200 µg protein lysates for each sample were
brought to the same overall volume of 52 µL with water, reduced using 20 mM dithiothreitol in 50
28

mM triethylammonium bicarbonate buffer (TEAB) at 50˚C for 10 min, cooled to room temperature
(RT) and held at RT for 10 min, and alkylated using 40 mM iodoacetamide in 50 mM TEAB at RT
in the dark for 30 min. Samples were acidified with 12% phosphoric acid to obtain a final concen-
tration of 1.2% phosphoric acid. S-Trap buffer consisting of 90% methanol in 100 mM TEAB at
pH ~7.1, was added and samples were loaded onto the S-Trap mini spin columns. The entire
sample volume was spun through the S-Trap mini spin columns at 4,000 x g and RT, binding the
proteins to the mini spin columns. Subsequently, S-Trap mini spin columns were washed twice
with S-Trap buffer at 4,000 x g at RT and placed into clean elution tubes. Samples were incubated
for one hour at 47
o
C with sequencing grade trypsin (Promega, San Luis Obispo, CA) dissolved in
50 mM TEAB at a 1:25 (w/w) enzyme:protein ratio. Afterwards, trypsin solution was added again
at the same ratio, and proteins were digested overnight at 37˚C.
Peptides were sequentially eluted from mini S-Trap spin columns with 50 mM TEAB, 0.5%
formic acid (FA) in water, and 50% acetonitrile (ACN) in 0.5% FA. After centrifugal evaporation,
samples were resuspended in 0.2% FA in water and desalted with Oasis 10 mg Sorbent Car-
tridges (Waters, Milford, MA). The desalted elutions were then subjected to an additional round
of centrifugal evaporation and re-suspended in 0.2% FA in water at a final concentration of 1
µg/µL. Subsequently, 8 µL of each sample were diluted with 2% ACN in 0.1% FA to obtain a
concentration of 400 ng/µL. Finally, 1 µL of indexed Retention Time Standard (iRT, Biognosys,
Schlieren, Switzerland) was added to each sample, thus bringing up the total volume to 20 µL
(Escher et al. 2012).
Mass spectrometric analysis. Reverse-phase HPLC-MS/MS analyses were performed on
a Dionex UltiMate 3000 system coupled online to an Orbitrap Exploris 480 mass spectrometer
(Thermo Fisher Scientific, Bremen, Germany). The solvent system consisted of 2% ACN, 0.1%
FA in water (solvent A) and 80% ACN, 0.1% FA in ACN (solvent B). Digested peptides (400 ng)
were loaded onto an Acclaim PepMap 100 C 18 trap column (0.1 x 20 mm, 5 µm particle size;
Thermo Fisher Scientific) over 5 min at 5 µL/min with 100% solvent A. Peptides (400 ng) were
29

eluted on an Acclaim PepMap 100 C 18 analytical column (75 µm x 50 cm, 3 µm particle size;
Thermo Fisher Scientific) at 300 nL/min using the following gradient: linear from 2.5% to 24.5%
of solvent B in 125 min, linear from 24.5% to 39.2% of solvent B in 40 min, up to 98% of solvent
B in 1 min, and back to 2.5% of solvent B in 1 min. The column was re-equilibrated for 30 min
with 2.5% of solvent B, and the total gradient length was 210 min. Each sample was acquired in
data-independent acquisition (DIA) mode (Gillet et al. 2012; Bruderer et al. 2017; Collins et al.
2017), in triplicates. Full MS spectra were collected at 120,000 resolution (Automatic Gain Control
(AGC) target: 3e6 ions, maximum injection time: 60 ms, 350-1,650 m/z), and MS2 spectra at
30,000 resolution (AGC target: 3e6 ions, maximum injection time: Auto, Normalized Collision En-
ergy (NCE): 30, fixed first mass 200 m/z). The isolation scheme consisted of 26 variable windows
covering the 350-1,650 m/z range with an overlap of 1 m/z (Bruderer et al. 2017).
DIA data processing and statistical analysis. DIA data was processed in Spectronaut (ver-
sion 15.6.211220.50606) using directDIA. Data extraction parameters were set as dynamic and
non-linear iRT calibration with precision iRT was selected. Data was searched against the Mus
musculus reference proteome with 58,430 entries (UniProtKB-TrEMBL), accessed on
01/31/2018. Trypsin/P was set as the digestion enzyme and two missed cleavages were allowed.
Cysteine carbamidomethylation was set as a fixed modification; methionine oxidation and protein
N-terminus acetylation were set as dynamic modifications. Identification was performed using 1%
precursor and protein q-value. Quantification was based on the peak areas of extracted ion chro-
matograms (XICs) of 3 – 6 MS2 fragment ions, specifically b- and y-ions, with local normalization
and q-value sparse data filtering applied. In addition, iRT profiling was selected. Senescence
specific differential protein secretion analysis comparing samples derived from unirradiated and
senescent M. musculus and M. spretus cells was performed using the proteomics data as input
for a two-factor ANOVA with Benjamini-Hochberg multiple testing correction (Benjamini and
Hochberg 1995). Only those proteins with a corrected p-value < 0.05 were considered significantly
differentially secreted between the species during senescence.
30

Supplemental Figures

Supplemental Figure 2.1: M. musculus and M. spretus cells represent the extremes in the
natural variation of senescent phenotypes across Mus. (A) Each bar reports the average
proportion of senescence-associated β-galactosidase (SABG) positive cells set relative to the
value in senescent (SEN) M. musculus cells, for both senescent and unirradiated controls (CONT)
of each species as described on the x-axis. For a given column, points represent technical repli-
cates (M. musculus n = 11, M. domesticus n = 4, M. spretus n = 5). ***, p < 0.001, one-tailed
Wilcoxon comparing species in senescence. (B) Each bar reports the average Lysotracker® flu-
orescent signal set relative to the average value in SEN M. musculus cells for each species as in
(A). For a given column, points represent technical replicates (M. musculus n = 7, M. domesticus
n = 7, M. castaneus n = 3, M. spretus n = 5). *, p < 0.05, **, p < 0.01, ***, p < 0.001, one-tailed
Wilcoxon test comparing species in senescence.
31

Supplemental Figure 2.2: Senescent M. musculus primary fibroblasts display enhanced
mRNA induction of genes of the senescence-associated secretory phenotype. Each column
reports expression (TPM, transcripts per million) from RNA-seq profiling of primary fibroblasts
from the indicated species for the indicated genes, in control or irradiation-induced senescent
(SEN) cells. In a given column, points report biological replicates and the bar height reports their
average (n = 3).

32

Supplemental Figure 2.3: Senescent M. musculus primary fibroblasts display enhanced
secretion of proteins of the senescence-associated secretory phenotype. Data are as in
Supplemental Figure 2.2 except that protein abundance from conditioned medium is shown.

33

Supplemental Figure 2.4: Irradiated M. spretus cells release less apoptotic and necroptotic
factors relative to M. musculus cells. (A) Each trace reports a cumulative distribution of the
change, in senescent primary fibroblasts of the indicated species, in levels of protein secretion for
factors released during apoptosis (Tanzer et al. 2020). The y-axis reports the proportion of genes
with the expression change on the x-axis, with the latter taken as an average across replicates.
(B) Annotations are as in (A) except that proteins released during necroptosis is shown.

34

Supplemental Tables

GO_term real_val number_of_genes prop_over_real BH
intermediate filament cytoskeleton organization 45.1077950296 18 0 0
synaptic transmission, cholinergic 44.899017587 11 0 0
cellular copper ion homeostasis 58.9940746498 28 0 0
oligodendrocyte differentiation 48.1486465249 23 0 0
cell-matrix adhesion 121.36974315 87 0 0
post-embryonic development 115.032945545 82 0 0
extrinsic apoptotic signaling pathway via death domain receptors 72.2422248633 44 0 0
response to hypoxia 147.300461162 125 0 0
positive regulation of phosphatidylinositol 3-kinase signaling 104.515596693 65 0 0
surfactant homeostasis 49.8809446741 19 0 0
calcium-dependent activation of synaptic vesicle fusion 38.1350764957 12 0 0
B cell homeostasis 58.0249676532 28 0 0
positive regulation of ossification 59.7839278308 23 0 0
cell-cell adhesion 203.167730688 150 0 0
endoderm development 57.4293703324 28 0 0
cardiac muscle cell differentiation 91.2419084968 33 0 0
heterophilic cell-cell adhesion via plasma membrane cell adhesion molecules 65.0918750397 37 0
0
semaphorin-plexin signaling pathway involved in axon guidance 47.7688117911 21 0 0
homocysteine metabolic process 50.1800878338 18 0 0
embryonic heart tube development 84.6927363106 46 0 0
antigen processing and presentation of exogenous peptide antigen via MHC class II 43.2530323568 20
0 0
postsynapse to nucleus signaling pathway 62.3454013982 25 0 0
synapse assembly 78.8323499373 52 0 0
cell communication 46.6571800481 17 0 0
blood vessel remodeling 73.4558976462 39 0 0
sympathetic neuron axon guidance 60.3687322442 14 0 0
heart development 259.642576371 252 0 0
biomineralization 114.355883728 64 0 0
kidney development 161.057154335 145 0 0
response to muscle activity 31.6248553216 12 0 0
synaptic vesicle uncoating 51.7962087425 17 0 0
removal of superoxide radicals 62.4889713676 23 0 0
positive regulation of transcription by RNA polymerase II 1369.91177532 1772 0 0
peptide hormone processing 38.0566409436 16 0 0
post-Golgi vesicle-mediated transport 33.7671150357 10 0 0
BMP signaling pathway 190.550625125 148 0 0
vesicle organization 35.1335434242 13 0 0
leukocyte cell-cell adhesion 80.6979821212 30 0 0
peptidyl-L-cysteine S-palmitoylation 78.0673585035 38 0 0
regulation of cell-cell adhesion 57.37811756 17 0 0
outflow tract septum morphogenesis 64.0860133145 26 0 0
sphingosine-1-phosphate receptor signaling pathway 40.918587463 16 1e-04 0.00488846153846154
copper ion transport 31.7454236394 12 1e-04 0.00488846153846154
pancreas development 41.3844065977 18 1e-04 0.00488846153846154
complement activation 34.7827688507 13 1e-04 0.00488846153846154
chemokine-mediated signaling pathway 46.9870663758 22 1e-04 0.00488846153846154
cell adhesion mediated by integrin 69.1770480942 37 1e-04 0.00488846153846154
regulation of alternative mRNA splicing, via spliceosome 90.2861025848 66 1e-04
0.00488846153846154
neurofilament cytoskeleton organization 30.9711822833 11 1e-04 0.00488846153846154
membranous septum morphogenesis 30.4079204522 10 1e-04 0.00488846153846154
negative regulation of gene expression 266.601327028 294 1e-04 0.00488846153846154
hindbrain development 37.2877765034 15 1e-04 0.00488846153846154
cell maturation 51.2725826168 29 2e-04 0.0083344262295082
regulation of postsynapse organization 80.6516435381 57 2e-04 0.0083344262295082
blood vessel morphogenesis 56.7908827992 35 2e-04 0.0083344262295082
pharyngeal arch artery morphogenesis 37.7853208531 16 2e-04 0.0083344262295082
35

branching morphogenesis of an epithelial tube 52.64321388 31 2e-04 0.0083344262295082
chloride transmembrane transport 56.9962230249 33 2e-04 0.0083344262295082
positive regulation of apoptotic process 361.51739228 415 2e-04 0.0083344262295082
SMAD protein signal transduction 92.0304781518 57 2e-04 0.0083344262295082
cell-cell junction organization 46.3252363745 23 2e-04 0.0083344262295082
N-acylethanolamine metabolic process 30.9414502084 13 3e-04 0.0115545454545455
phosphate ion homeostasis 43.6492123919 25 3e-04 0.0115545454545455
phosphatidylinositol 3-kinase signaling 62.4215726644 36 3e-04 0.0115545454545455
neuromuscular synaptic transmission 42.4754575363 20 3e-04 0.0115545454545455
odontogenesis of dentin-containing tooth 73.0655092558 50 3e-04 0.0115545454545455
choline transport 34.307441704 12 4e-04 0.0143211267605634
neuron apoptotic process 77.8435205384 60 4e-04 0.0143211267605634
activation of NF-kappaB-inducing kinase activity 32.8660782629 14 4e-04 0.0143211267605634
positive regulation of heart rate 35.177596003 16 4e-04 0.0143211267605634
positive regulation of BMP signaling pathway 52.4390177701 32 4e-04 0.0143211267605634
negative regulation of actin filament polymerization 43.6715688344 25 5e-04 0.0160886075949367
cardiac muscle contraction 53.6287260773 31 5e-04 0.0160886075949367
mRNA modification 31.8764671091 12 5e-04 0.0160886075949367
ventricular cardiac muscle cell action potential 28.0477730136 10 5e-04 0.0160886075949367
muscle organ development 65.6655211419 42 5e-04 0.0160886075949367
metanephros development 61.6328792467 41 5e-04 0.0160886075949367
regulation of GTPase activity 95.7878650166 78 5e-04 0.0160886075949367
negative regulation of Rho protein signal transduction 50.6432924079 30 5e-04 0.0160886075949367
positive regulation of DNA damage response, signal transduction by p53 class mediator 27.3586010851 11
6e-04 0.0181571428571429
cartilage condensation 29.5394045552 13 6e-04 0.0181571428571429
retinoic acid receptor signaling pathway 46.6409516016 27 6e-04 0.0181571428571429
anatomical structure formation involved in morphogenesis 28.1006834284 11 6e-04
0.0181571428571429
neuron projection development 143.820818737 141 6e-04 0.0181571428571429
regulation of actin cytoskeleton organization 91.7931598056 73 7e-04 0.0199932584269663
positive regulation of pathway-restricted SMAD protein phosphorylation 63.2307272039 46 7e-04
0.0199932584269663
lymph vessel development 30.3882408554 14 7e-04 0.0199932584269663
negative regulation of cell cycle 61.2803748146 43 7e-04 0.0199932584269663
eye development 46.9490631078 27 7e-04 0.0199932584269663
positive regulation of chondrocyte differentiation 44.2033714959 26 8e-04 0.0216340425531915
action potential 31.9794172389 15 8e-04 0.0216340425531915
branching involved in blood vessel morphogenesis 63.9943792098 43 8e-04 0.0216340425531915
osteoblast differentiation 98.7872313442 86 8e-04 0.0216340425531915
actin cytoskeleton organization 172.051118377 182 8e-04 0.0216340425531915
regulation of neurotransmitter secretion 27.9980860283 11 9e-04 0.022878
positive regulation of gene expression 359.75468003 456 9e-04 0.022878
positive regulation of transcription, DNA-templated 573.939407954 793 9e-04 0.022878
regulation of lipid metabolic process 60.5973028802 40 9e-04 0.022878
positive regulation of actin filament bundle assembly 33.1251386894 15 9e-04 0.022878
axonogenesis 110.477927436 95 9e-04 0.022878
establishment of endothelial intestinal barrier 29.9035498238 12 0.001 0.024921568627451
cell migration 232.580290078 266 0.001 0.024921568627451
leukocyte migration 36.8922504647 19 0.0011 0.0261327102803738
protein localization to plasma membrane 154.385459417 157 0.0011 0.0261327102803738
negative regulation of neuron differentiation 74.8017354626 61 0.0011 0.0261327102803738
T cell homeostasis 45.4183516976 27 0.0011 0.0261327102803738
negative regulation of cell motility 27.636572958 12 0.0011 0.0261327102803738
negative regulation of apoptotic process 420.668714232 555 0.0012 0.0272357142857143
brain development 148.119159408 155 0.0012 0.0272357142857143
elastic fiber assembly 41.0088593165 24 0.0012 0.0272357142857143
store-operated calcium entry 36.8768597195 19 0.0012 0.0272357142857143
anion transmembrane transport 26.796802443 11 0.0012 0.0272357142857143
neuron development 67.0781808754 50 0.0013 0.0284879310344828
pulmonary valve morphogenesis 28.884311447 14 0.0013 0.0284879310344828
artery smooth muscle contraction 24.44398737 10 0.0013 0.0284879310344828
36

positive regulation of natural killer cell differentiation 27.3537520604 12 0.0013 0.0284879310344828
receptor guanylyl cyclase signaling pathway 24.7573637325 10 0.0014 0.030417094017094
neuron fate commitment 30.1439960271 15 0.0015 0.0323135593220339
cellular response to osmotic stress 26.0896248539 11 0.0016 0.0341781512605042
peptidyl-tyrosine dephosphorylation 55.0483990335 38 0.0017 0.0357140495867769
negative regulation of stress fiber assembly 46.7170772157 30 0.0017 0.0357140495867769
mesenchymal cell differentiation 23.9170599765 10 0.0018 0.0363142857142857
negative regulation of endothelial cell apoptotic process 38.2638427271 22 0.0018
0.0363142857142857
sarcomere organization 45.5531486536 30 0.0018 0.0363142857142857
negative regulation of cell-substrate adhesion 28.7647492118 14 0.0018 0.0363142857142857
filopodium assembly 35.6944105374 20 0.0018 0.0363142857142857
tetrahydrobiopterin biosynthetic process 27.3033544666 13 0.0019 0.0371523076923077
positive regulation of neuron apoptotic process 102.953308896 96 0.0019 0.0371523076923077
embryonic organ development 53.8787182357 38 0.0019 0.0371523076923077
positive regulation of long-term synaptic potentiation 34.4222224917 19 0.0019 0.0371523076923077
Tie signaling pathway 24.6486381098 11 0.002 0.0376592592592593
negative regulation of T cell activation 36.048345347 19 0.002 0.0376592592592593
retinoic acid biosynthetic process 24.6927371766 11 0.002 0.0376592592592593
cell fate commitment 65.1884808486 50 0.002 0.0376592592592593
negative regulation of systemic arterial blood pressure 27.7767895696 14 0.002 0.0376592592592593
receptor clustering 50.27364149 34 0.0021 0.0389649635036496
cellular response to nutrient levels 26.8838350507 12 0.0021 0.0389649635036496
embryonic hemopoiesis 34.8667050963 21 0.0022 0.0405246376811594
protein targeting 56.1901329313 40 0.0023 0.0420618705035971
heart morphogenesis 61.3654456387 47 0.0024 0.0435771428571429
positive regulation of neuron differentiation 81.5812081772 72 0.0025 0.0441319444444444
intestinal absorption 24.0791625499 10 0.0025 0.0441319444444444
cell-substrate adhesion 34.2439950544 20 0.0025 0.0441319444444444
clathrin-dependent endocytosis 50.8325848411 37 0.0025 0.0441319444444444
muscle contraction 46.7926117038 31 0.0027 0.0466897959183674
reactive oxygen species metabolic process 47.7163752254 33 0.0027 0.0466897959183674
ear development 23.556127875 11 0.0027 0.0466897959183674
copper ion import 26.8647642335 13 0.0029 0.0491453333333333
platelet-derived growth factor receptor signaling pathway 53.2763283235 40 0.0029
0.0491453333333333
humoral immune response 25.6530610539 12 0.0029 0.0491453333333333

Supplemental Table 2.1: Functional-genomic enrichment analysis of expression profiles
of unirradiated purebred M. musculus and M. spretus primary cells. Each row reports the
results of a resampling test for enrichment of expression change between unirradiated primary
fibroblasts from purebred M. musculus and M. spretus for the indicated Gene Ontology biologi-
cal process term. The second column represents the absolute value of the sum of the ratios of
expression in M. musculus versus M. spretus for all the genes in the indicated term. The third
column reports the number of genes with expression data in the term. The fourth column re-
ports the raw p-value from resampling analysis, and the final column reports Benjamini-
Hochberg corrected p-values. Only terms with significant enrichment (corrected p < 0.05) are
shown.
37

GO_term real_val number_of_genes prop_over_real BH
antimicrobial humoral immune response mediated by antimicrobial peptide 52.6811524306 25 0
0
phosphate ion homeostasis 71.9212238747 25 0 0
cellular response to estradiol stimulus 46.9504532548 26 0 0
synaptic transmission, cholinergic 39.4570152166 11 0 0
positive regulation of necroptotic process 97.8479502216 24 0 0
collagen fibril organization 105.254720571 85 0 0
growth plate cartilage development 39.7633166798 15 0 0
sarcomere organization 62.6536723317 30 0 0
response to sodium phosphate 46.7599292657 24 0 0
defense response to bacterium 84.5402178898 63 0 0
sphingosine-1-phosphate receptor signaling pathway 49.4415787823 16 0 0
negative regulation of transcription by RNA polymerase II 727.913899407 1062 0 0
positive regulation of neuron apoptotic process 111.5939346 96 0 0
regulation of NIK/NF-kappaB signaling 33.9985386489 13 0 0
calcium-dependent activation of synaptic vesicle fusion 53.3860814041 12 0 0
elastic fiber assembly 45.7861243658 24 0 0
mRNA modification 37.8368479176 12 0 0
cell-cell adhesion 163.772777825 150 0 0
complement activation 65.8521589732 13 0 0
endoderm development 50.9927975151 28 0 0
bone mineralization 112.938288859 67 0 0
cardiac muscle cell differentiation 74.4674994195 33 0 0
homocysteine metabolic process 51.197539435 18 0 0
embryonic heart tube development 67.1984790797 46 0 0
chemokine-mediated signaling pathway 68.2527787297 22 0 0
postsynapse to nucleus signaling pathway 51.4056137809 25 0 0
synapse assembly 84.0696623477 52 0 0
SMAD protein signal transduction 79.7812486579 57 0 0
regulation of alternative mRNA splicing, via spliceosome 81.1414550221 66 0 0
protein localization to plasma membrane 155.397684659 157 0 0
sympathetic neuron axon guidance 48.6982252279 14 0 0
biomineralization 168.555670736 64 0 0
negative regulation of glial cell proliferation 26.3979985653 11 0 0
removal of superoxide radicals 55.948333707 23 0 0
negative regulation of transcription regulatory region DNA binding 33.4828673372 12 0 0
immune response 108.830807733 87 0 0
modification of postsynaptic actin cytoskeleton 60.5204593934 33 0 0
peptidyl-L-cysteine S-palmitoylation 83.4511951608 38 0 0
axonogenesis 111.285864655 95 0 0
regulation of cell-cell adhesion 55.5694750648 17 0 0
outflow tract septum morphogenesis 52.3516637651 26 0 0
behavioral response to pain 38.3777870828 18 1e-04 0.00462181818181818
intermediate filament cytoskeleton organization 40.241438559 18 1e-04 0.00462181818181818
regulation of Rac protein signal transduction 28.864432687 12 1e-04 0.00462181818181818
Tie signaling pathway 28.1184504458 11 1e-04 0.00462181818181818
post-embryonic development 93.9903796446 82 1e-04 0.00462181818181818
defense response to Gram-positive bacterium 73.6864190572 58 1e-04 0.00462181818181818
chondrocyte proliferation 27.9709178119 12 1e-04 0.00462181818181818
copper ion import 37.934047102 13 1e-04 0.00462181818181818
surfactant homeostasis 38.295005289 19 1e-04 0.00462181818181818
pancreas development 34.7707860646 18 1e-04 0.00462181818181818
positive regulation of ossification 48.3545269585 23 1e-04 0.00462181818181818
chloride transmembrane transport 53.4782743345 33 1e-04 0.00462181818181818
defense response 54.6924594827 35 1e-04 0.00462181818181818
blood vessel remodeling 59.454297104 39 1e-04 0.00462181818181818
killing of cells of other organism 28.457459479 12 2e-04 0.00847333333333333
semaphorin-plexin signaling pathway involved in axon guidance 40.9650238747 21 2e-04
0.00847333333333333
calcium ion homeostasis 86.3254005321 67 2e-04 0.00847333333333333
cellular homeostasis 29.3452067582 14 2e-04 0.00847333333333333
38

BMP signaling pathway 145.019155115 148 2e-04 0.00847333333333333
oligodendrocyte differentiation 39.9052622201 23 3e-04 0.0113820895522388
microtubule cytoskeleton organization 128.88165741 126 3e-04 0.0113820895522388
retinoic acid biosynthetic process 26.5258433899 11 3e-04 0.0113820895522388
cell communication 36.2046018211 17 3e-04 0.0113820895522388
response to ATP 57.9359000673 40 3e-04 0.0113820895522388
positive regulation of BMP signaling pathway 48.6183029571 32 3e-04 0.0113820895522388
neural crest cell development 42.8537235135 26 3e-04 0.0113820895522388
negative regulation of cytokine production involved in inflammatory response 32.4775507883 16 4e-04
0.0133789473684211
piRNA metabolic process 36.3915076824 17 4e-04 0.0133789473684211
positive regulation of membrane protein ectodomain proteolysis 33.0096076983 17 4e-04
0.0133789473684211
brown fat cell differentiation 48.7072263809 31 4e-04 0.0133789473684211
protein dephosphorylation 154.809236404 173 4e-04 0.0133789473684211
neuromuscular synaptic transmission 36.3562834837 20 4e-04 0.0133789473684211
positive regulation of transcription by RNA polymerase II 1079.8353905 1772 4e-04
0.0133789473684211
negative regulation of cell cycle 62.4063897875 43 4e-04 0.0133789473684211
post-Golgi vesicle-mediated transport 25.8178154324 10 4e-04 0.0133789473684211
negative regulation of vascular endothelial growth factor receptor signaling pathway 25.2188476062 11
5e-04 0.0162948717948718
negative regulation of neuron differentiation 73.9369675728 61 5e-04 0.0162948717948718
neutrophil chemotaxis 54.9437734152 39 6e-04 0.0186
inosine biosynthetic process 31.0876374916 15 6e-04 0.0186
muscle organ development 57.8553850816 42 6e-04 0.0186
filopodium assembly 37.0864289595 20 6e-04 0.0186
phosphatidylinositol 3-kinase signaling 48.7511039725 36 7e-04 0.0206906976744186
regulation of translation at postsynapse, modulating synaptic transmission 40.0956366568 24 7e-04
0.0206906976744186
lymphocyte chemotaxis 28.5998951384 14 7e-04 0.0206906976744186
actin filament organization 112.199067469 116 7e-04 0.0206906976744186
negative regulation of actin filament polymerization 40.0641590281 25 8e-04 0.0225955555555556
regulation of membrane depolarization 26.5071348858 11 8e-04 0.0225955555555556
T cell homeostasis 44.1000641636 27 8e-04 0.0225955555555556
actin cytoskeleton organization 162.620993247 182 8e-04 0.0225955555555556
positive regulation of dendrite development 30.1774353401 16 9e-04 0.0246
blood vessel morphogenesis 49.5087988543 35 9e-04 0.0246
leukocyte cell-cell adhesion 45.7030370682 30 9e-04 0.0246
protein homotrimerization 23.3414953059 10 0.001 0.0264791666666667
positive regulation of interleukin-12 production 42.3415768641 28 0.001 0.0264791666666667
mammary gland alveolus development 29.4311525154 15 0.001 0.0264791666666667
store-operated calcium entry 33.898859141 19 0.0011 0.0282444444444444
positive regulation of neuron differentiation 79.5727001895 72 0.0011 0.0282444444444444
anatomical structure formation involved in morphogenesis 24.5121218788 11 0.0011
0.0282444444444444
positive regulation of phosphatidylinositol 3-kinase signaling 74.9717718987 65 0.0012
0.0293307692307692
muscle contraction 45.2531052151 31 0.0012 0.0293307692307692
negative regulation of neural precursor cell proliferation 24.405799154 12 0.0012
0.0293307692307692
anion transmembrane transport 25.4588935596 11 0.0012 0.0293307692307692
vesicle organization 25.4589968885 13 0.0012 0.0293307692307692
dendritic spine maintenance 24.4934703125 12 0.0013 0.0300418181818182
peptidyl-tyrosine autophosphorylation 29.6333925701 17 0.0013 0.0300418181818182
chloride transport 66.4024409986 56 0.0013 0.0300418181818182
heart development 203.908381585 252 0.0013 0.0300418181818182
positive regulation of B cell differentiation 28.191401576 13 0.0013 0.0300418181818182
negative regulation of cell motility 25.2397233802 12 0.0013 0.0300418181818182
establishment or maintenance of cell polarity 53.7197040872 41 0.0014 0.0320612612612613
negative regulation of organ growth 25.4769411502 12 0.0015 0.0337433628318584
eye development 40.6937003465 27 0.0015 0.0337433628318584
39

positive regulation of epidermal growth factor receptor signaling pathway 35.9244574326 23 0.0017
0.0379070175438596
B cell homeostasis 41.2381604993 28 0.0018 0.0391076923076923
embryonic organ development 49.3463192282 38 0.0018 0.0391076923076923
metanephros development 52.7261309715 41 0.0018 0.0391076923076923
release of cytochrome c from mitochondria 52.989444913 41 0.0019 0.0405865546218487
negative regulation of Rho protein signal transduction 41.3771739005 30 0.0019 0.0405865546218487
activation of cysteine-type endopeptidase activity involved in apoptotic process 88.8844241195 85 0.002
0.0423666666666667
ear development 23.0142530126 11 0.0021 0.0441173553719008
response to muramyl dipeptide 29.0258376732 15 0.0022 0.0454666666666667
cell chemotaxis 54.5979982237 44 0.0022 0.0454666666666667
complement activation, classical pathway 22.2693686715 10 0.0023 0.0467728
synaptic vesicle uncoating 28.8557628117 17 0.0023 0.0467728
retina homeostasis 23.6262387084 12 0.0024 0.0484190476190476

Supplemental Table 2.2: Functional-genomic enrichment analysis of expression profiles
of senescent purebred M. musculus and M. spretus primary cells. Data are as in Supple-
mental Table 2.1 except that M. musculus and M. spretus cells were analyzed 10 days after irra-
diation.

40

Species Strain mouse birthdate age (days) Date collected Weight for Females, pre-preg
weight length, tail inclusive (mm) just tail (mm) left foot (mm) right ear (mm) TK (samples)
M. m. musculus PWK PPPP142.6M 22-Apr-19 105 5-Aug-19 21.104 149.45
62.62 14.38 9.29 1
M. m. musculus PWK PPPP140.7M 9-Mar-19 156 12-Aug-19 17.7936 143.12
62.92 16.78 9.11 2
M. m. musculus PWK PPPP146.4f 14-Jul-19 85 7-Oct-19 15.6176 15.6176 145.13 66.57
14.45 9.57 3
M. m. musculus PWK PPPP146.5f 14-Jul-19 85 7-Oct-19 13.9293 13.9293 144.25 65.82
14.79 8.67 4
M. m. musculus PWK PPPP146.6f 14-Jul-19 85 7-Oct-19 14.8564 14.8564 146.96 68.34
15.78 8.99 5

M. spretus STF STF104.3M 27-Mar-19 131 5-Aug-19 16.3872 128.32
51.6 12.85 8.4 1

M. spretus STF STF103.2M 11-Mar-19 154 12-Aug-19 15.3316 126.54
50.17 13.19 6.99 2

M. spretus STF STF109.1F 12-Jun-19 123 13-Oct-19 17.8539 11 125.18
48.98 14.51 8.79 3

M. spretus STF STF111.1F 24-Jul-19 92 24-Oct-19 14.7549 11 123.23
45.32 14.46 10.22 4

M. spretus STF STF111.2F 24-Jul-19 92 24-Oct-19 13.6456 11 123.19
47.81 13.95 7.33 5

mus x spret F1 hybrid PWK x STF F1 PP.TF10.1M 27-Mar-19 131 5-Aug-19 16.8554
145.36 61.11 15.02 9.78 1
mus x spret F1 hybrid PWK x STF F1 PP.TF11.6M 16-Apr-19 118 12-Aug-19 13.2545
139.06 58.7 15.47 9.22 2
mus x spret F1 hybrid PWK X STF F1 PP.TF35.1f 13-Jul-19 86 7-Oct-19 33.2177 28
158.63 66.22 15.74 10.52 3
mus x spret F1 hybrid PWK X STF F1 PP.TF37.4f 18-Jul-19 81 7-Oct-19 22.5612 12
146.22 60.47 13.07 8.4 4
mus x spret F1 hybrid PWK X STF F1 PP.TF37.2f 18-Jul-19 81 7-Oct-19 30.2886 14
159.7 67.62 15.66 8.96 5

Supplemental Table 2.3: Mouse metadata. Shown are characteristics of mice used for tail
cuttings and fibroblast isolation in this study.

41

Chapter 3: A natural-variation based screen in mouse cells reveals USF2 as a novel regu-
lator of the DNA damage response and cellular senescence

Abstract

Cellular senescence is a program of cell cycle arrest, apoptosis resistance, and cytokine
release induced by stress exposure in mammalian cells. Landmark studies in laboratory mice
have characterized a number of master senescence regulators, including p16
INK4a
, p21, NF-kB,
p53, and C/EBPβ. To discover other molecular players in senescence, we developed a screening
approach to harness the evolutionary divergence between mouse species. We used allele-spe-
cific expression profiling to catalog senescence-dependent cis-regulatory variation between two
species of mice, Mus musculus and M. spretus, at thousands of genes. We then tested for corre-
lation between these expression changes and interspecies sequence variants in the binding sites
of transcription factors. Among the emergent candidate senescence regulators, we chose a little-
studied cell cycle factor, USF2, for molecular validation. In acute irradiation experiments, cells
lacking USF2 exhibited compromised DNA damage repair and response. Longer-term senescent
cultures without USF2 mounted an exaggerated senescence regulatory program—shutting down
cell cycle and DNA repair pathways, and turning up cytokine expression, more avidly than wild-
type. We interpret these findings under a model of pro-repair, anti-senescence regulatory function
by USF2. Our study affords new insights into the mechanisms by which cells commit to senes-
cence, and serves as a validated proof of concept for natural variation-based regulator screens.

Introduction

Mammalian cells of many types, upon exposure to stress, can enter a senescence pro-
gram, in which they stop dividing, become refractory to apoptosis, and release soluble
42

inflammation and tissue remodeling factors termed the senescence-associated secretory pheno-
type (SASP) (Hayflick 1965; Campisi 2005; Campisi and Fagagna 2007; Coppé et al. 2008). The
resulting acute immune response can clear debris, promote wound healing, and/or suppress tu-
morigenesis (Wan et al. 2021; Paramos-de-Carvalho et al. 2021; Baker et al. 2011; Demaria et
al. 2014). However, during aging, senescent cells can remain long past any initial triggering event,
resulting in chronic inflammation that damages the surrounding tissue (Wan et al. 2021; Olivieri
et al. 2018; Davalos et al. 2010; Krtolica et al. 2001; Parrinello et al. 2005). Landmark work has
revealed the benefits of eliminating senescent cells to treat age-related pathologies and boost
median lifespan (Baker et al. 2011; Demaria et al. 2014; Kang 2019; Kim and Kim 2019).
Establishment of the senescent state and the activity of senescent cells hinge in large part
on gene regulatory events. Finding molecular players that control this process is an active area
of research. Now-classic work has implicated p16
INK4a
and p21 in the repression of pro-cell cycle
genes and promotion of growth arrest (Campisi 2013) after DNA damage, and NF-kB, p53, and
C/EBPβ (Salotti and Johnson 2019) as regulators of the SASP. Despite this rich molecular litera-
ture, many of the complexities of senescent cells, from dose-response and kinetics to tissue spec-
ificity (Campisi 2013; Chan et al. 2022; Casella et al. 2019; Basisty et al. 2020; Purcell et al. 2014),
remain incompletely understood at a mechanistic level. Indeed, bioinformatic approaches have
identified dozens of other transcription factor candidates in senescence (Chan et al. 2022; Wang
et al. 2016; Xie et al. 2014; Han et al. 2018; Martínez-Zamudio et al. 2020; Brückmann et al. 2019;
Tyler et al. 2021; Zhang et al. 2021), many of which remain unvalidated (but see (Wang et al.
2016; Xie et al. 2014; Han et al. 2018; Martínez-Zamudio et al. 2020) for recent discoveries of the
roles of DLX2, FOXO3 and AP-1 in senescence).
We recently discovered variation between Mus species in irradiation response and senes-
cence characters, including mRNA expression profiles (Chapter 2). We set out to harness these
differences in a screening approach to survey transcription factors that play a role in cellular se-
nescence. The Algerian mouse Mus spretus, an emergent model organism in its own right,
43

(Dejager et al. 2009), is ~1.5 million years diverged from the M. musculus lineage (Bonhomme et
al. 1978). The two exhibit extensive variation in transcription factor binding (Villar et al. 2014).
Focusing on cis-regulatory changes between the species in sequence and senescence gene ex-
pression, we searched for signatures of transcription factor function in senescence programs.
Among the top hits from our analysis, we chose the under-studied factor USF2 for validation,
testing its role in gene regulation and cellular phenotypes during senescence induction and
maintenance.

Results

A genomic screen for senescence transcription factors using cis-regulatory sequence var-
iations

Having established divergence between M. musculus and M. spretus primary fibroblasts
in multiple senescence phenotypes (Chapter 2), we reasoned that such differences could be har-
nessed in an in silico screen for senescence regulators. We designed an analysis focused on
gene regulation—in particular, on variation between the species at the binding sites of transcrip-
tion factors (Figure 3.1A). We expected that, at some genes, cis-regulatory elements encoded in
the M. musculus genome would drive expression during senescence differently than those in the
M. spretus genome. We reasoned that if cis-acting variation effects were enriched among the loci
bound by a given transcription factor across the genome, the signal could be interpreted as a
signpost for the factor’s activity during senescence (Figure 3.1A). In this way, all cis-regulatory
variants between the species that manifested in cultured primary fibroblasts, whether of large or
small effect size, and regardless of their potential for phenotypic impact, could contribute to the
search for transcription factors relevant for senescence.
44

Figure 3.1: USF2 emerges as a senescence regulator candidate from a natural variation-
based transcription factor screen. (A) M. musculus (blue) and M. spretus (red) alleles of a
gene are expressed differently in interspecific F1 hybrid cells in a senescence-dependent
manner, as a product of a sequence variant (green striped) in the binding site for a transcrip-
tion factor (yellow). (B) Each row reports the multiple testing-corrected p-value from a Fisher’s
Exact Test of target genes of the indicated transcription factor, quantifying association be-
tween species differences in experimentally determined binding sites (Yevshin et al. 2019)
and allele-specific expression in primary cells of the M. musculus x M. spretus F1 hybrid back-
ground before and after senescence induction. Results for all tested factors are listed in Sup-
plemental Table 3.1. (C) Shown are the input data for the Fisher’s Exact Test in (B) for USF2.
Each bar reports analysis of genes with USF2 binding sites in their upstream regions that do
(right) or do not (left) harbor sequence variants between M. musculus and M. spretus. For a
given bar, genes are categorized by the presence or absence of senescence-dependent dif-
ferential allele-specific expression (  ASE). (D) For each trace, the x-axis reports the number
of sequence variants between M. musculus and M. spretus in a given USF2 binding site, and
the y-axis reports the proportion of all USF2 target genes bearing the number of variants on
the x, as a kernel density estimate. Colors denote the presence or absence of senescence-
dependent differential allele-specific expression at USF2 target genes as in (C). (E) Data are
as in (D) except that the x-axis reports the distance of the variant from the center of the USF2
binding site.

45

As a resource for this approach, we mated the PWK strain of M. musculus and the STF
strain of M. spretus to yield hybrid animals from which we derived primary tail fibroblasts for culture
and irradiation. These M. musculus x M. spretus F1 hybrid fibroblasts, when irradiated, exhibited
a flattened morphology reflecting entry into senescence; senescence-associated -galactosidase
activity was of a magnitude between those of purebred M. musculus and M. spretus fibroblasts
upon irradiation (Supplemental Figure 3.1). We subjected senescent and control F1 hybrid fibro-
blasts to RNA-seq profiling, and we used the results to quantify levels of transcripts derived from
the M. musculus and M. spretus alleles of each gene in each condition. At a given gene, any
difference between allele-specific expression in an F1 hybrid can be attributed to variants inher-
ited from the parent species that perturb gene regulation in cis at the locus, because trans-acting
factors impinge to the same extent on both alleles (Sun and Hu 2013). Analyzing the response to
senescence induction for each gene in turn, we found that the allele-specific expression difference
between the alleles in the F1 hybrid was a partial predictor of the expression divergence between
the M. musculus and M. spretus purebreds, in our primary cell system (Supplemental Figure 3.2).
The latter trend reflects the joint contributions of cis- and trans-acting variants to total expression
divergence between the species, as expected (Wittkopp et al. 2004). Separately, to survey overall
regulatory programs in F1 hybrid primary fibroblasts, we formulated the expression level of a given
gene in a given condition as the sum of the measured levels of the M. musculus and M. spretus
alleles. In this analysis, focusing on SASP genes as we had done for the purebreds (Figure 2.3),
we found that the expression program of senescent F1 hybrid cells was, for most genes, interme-
diate between the low levels seen in M. spretus cells and the high levels in M. musculus (Supple-
mental Figure 3.3). These data indicate that M. musculus x M. spretus F1 hybrid fibroblasts do
not exhibit heterosis with respect to senescence-associated genes, and do manifest extensive,
senescence-dependent cis-regulatory variation.
We next used the expression measurements from F1 hybrid cells as input into our in silico
screen to identify senescence-dependent transcription factor activity. For a given transcription
46

factor, we collated binding sites detected by chromatin immunoprecipitation upstream of genes
across a panel of tissues (Yevshin et al. 2019). At each site, we tabulated the presence or ab-
sence of DNA sequence variants in the respective genomes of M. musculus and M. spretus. We
then tested whether, across the genome, genes with these binding site variants were enriched for
senescence-associated expression differences between the two alleles in the F1 hybrid. This test
had the capacity for high power to detect even subtle contributions from transcription factors if
they had deep binding-site coverage in the input data; five factors attained genome-wide signifi-
cance (Figure 3.1B and Supplemental Table 3.1).
Among our screen hits, PBX1 (Wang et al. 2021) and CREBBP (Bandyopadhyay et al.
2002; Yang et al. 2021) had been previously implicated in cellular senescence, providing a first
line of evidence for the strength of our approach to identify signatures of condition-dependent
transcription factor function. The top-scoring transcription factor in our screen results, a basic-
helix-loop-helix leucine-zipper protein called upstream stimulatory factor 2 (USF2; Figure 3.1B-
C), had not been experimentally characterized in stress response or senescence. However, clas-
sic studies had established USF2 as a regulator of the cell cycle and tumor suppression (Qyang
et al. 1999; Pawar et al. 2004; Chen et al. 2006; Aperlo et al. 1996). USF2 has also been tied to
TGF-β-induced apoptosis (Sato et al. 2011), and more recently was shown to control cytokine
release in immune cells (Hu et al. 2020). Considering these known functions, and bioinformatic
analyses suggesting a link between USF2 and senescence programs (Martínez-Zamudio et al.
2020), we chose USF2 for in-depth validation. In detailed genomic tests, single variants between
M. musculus and M. spretus at USF2 binding sites drove most of the relationship with allele-
specific expression in hybrid senescent cells (Figure 3.1D). These variants were over-represented
at positions central to, and slightly downstream of, experimentally determined peaks for USF2
(Figure 3.1E), highlighting the likely importance of this region in USF2’s mechanisms of binding
and regulation.

47

USF2 modulates cell proliferation and the acute DNA damage response

Our question at this point was whether and how USF2 regulated senescence programs.
As such, we shifted our focus from natural genetic variation to controlled, laboratory-induced ge-
netic perturbations in a single genetic background. We designed two short hairpin RNAs
(shRNAs) targeting Usf2, each in a lentiviral vector under the U6 promoter. Expression measure-
ments upon transformation of PWK M. musculus primary tail fibroblasts confirmed 2.5 and 3-fold
knockdown of Usf2 expression, respectively, from these shRNAs (Supplemental Figure 3.4).
To use the knockdown paradigm to study USF2 function, we began with experiments on
resting (unirradiated) cells. Uptake of the nucleotide analog EdU, a marker of DNA synthesis, was
reduced by 40% in resting primary fibroblasts expressing the Usf2 shRNA relative to scrambled
shRNA controls (Supplemental Figure 3.5), consistent with studies of USF2 in cell growth in other
tissues and contexts (Zhao and Darzynkiewicz 2013; Qyang et al. 1999; Pawar et al. 2004; Chen
et al. 2006; Aperlo et al. 1996). Transcriptional profiling, followed by functional-genomic (Gene
Ontology) analyses, detected no enrichment for pathways altered upon Usf2 knockdown relative
to controls, reflecting modest and/or heterogeneous regulatory effects of Usf2 depletion in these
resting cultures.
We next directly pursued the importance of USF2 during the acute response to irradiation
(Figure 3.2A). In wild-type cells, DNA damage signaling, an inducer of cellular senescence
(Campisi 2005; Campisi and Fagagna 2007), drops sharply in intensity within eight hours and
then more gradually over several day after irradiation (Redon et al. 2009), culminating in a lower
persistent signal (Fumagalli et al. 2014; Chen and Ozanne 2006; Bakkenist et al. 2004; Siddiqui
et al. 2015). We focused on the early phase of this process (six hours after irradiation) in cultures
of primary fibroblasts expressing Usf2 shRNAs or scrambled shRNA controls. Transcriptional pro-
filing and Gene Ontology analyses identified gene groups enriched for expression changes de-
pendent on condition and USF2 (Figure 3.2B and Supplemental Table 3.2). Most salient was a
48

trend of pervasive repression transcriptome-wide six hours after irradiation, which was detected
in control cells as expected (Venkata Narayanan et al. 2017; Silva and Ideker 2019), and was
blunted in cells with Usf2 knocked down. The latter effect was particularly enriched in the tran-
scriptional machinery, repressors of apoptosis, and several cell proliferation regulators (Figure
3.2B-C and Supplemental Figure 3.6A). Notable among the significantly affected pathways was
the ERK1/2 cascade (Figure 3.2B and 3.2D and Supplemental Figure 3.6B), a determinant of
entry into senescence in some contexts (Zou et al. 2019; Lu and Xu 2006). DNA repair genes, a
likely target for changes upon irradiation, are regulated primarily at the post-transcriptional level
(Huang and Zhou 2020; Nickoloff et al. 2017), and we did not detect effects of Usf2 knockdown
on their transcripts (Supplemental Figure 3.7). From these data we conclude that USF2
Figure 3.2: Caption on next page
49

contributes to the expression response after irradiation, acting largely to repress housekeeping
transcripts.
We hypothesized that Usf2 knockdown during the acute DNA damage response would
also have cell-physiological effects. Assays of EdU incorporation to report on DNA synthesis
showed effects of Usf2 knockdown after irradiation to the same degree as in resting cultures
(Supplemental Figure 3.5). To focus on phenotypes more proximal to DNA damage, we used the
neutral comet assay (Olive and Banáth 2006) to measure DNA double-stranded breaks on a per-
cell basis. In this setup, Usf2 depletion increased comet tail moments by 50% six hours after
irradiation, with an effect that was similar, though of smaller magnitude, in resting cell controls
(Figure 3.2E). Next, we tracked foci of phosphorylated histone H2AX ( H2AX) in fibroblasts as a
marker of chromatin decondensation, preceding the repair of DNA double-stranded breaks (Pod-
horecka et al. 2010). Cells harboring Usf2 shRNAs exhibited 30% fewer H2AX foci than cells of
the control genotype, six hours after irradiation (Figure 3.2F). These data establish a role for USF2
Figure 3.2: Usf2 depletion results in more DNA damage but a muted DNA damage re-
sponse following irradiation. (A) M. musculus primary fibroblasts were infected with a lenti-
virus encoding a short hairpin RNA (shRNA, SH) targeting Usf2 or a scrambled control (SCR),
and analyzed before (SH unirradiated) or six hours after (SH→IR) treatment with ionizing ra-
diation. (B) In a given row, the second column reports the average, across genes of the indi-
cated Gene Ontology term, of the log 2 of the ratio of expression between Usf2 knockdown
(KD) and SCR-treated cells, six hours after irradiation. The third column reports significance
in a resampling-based test for enrichment of directional differential expression between Usf2
KD and SCR-treated cells in the respective term, corrected for multiple testing. The fourth
column reports the direction of the change in expression six hours after irradiation in SCR-
treated cells. (C) Each trace reports a cumulative distribution of the log2 of the ratio of expres-
sion between Usf2 KD or SCR-treated cells in genes annotated in apoptosis, before or six
hours after irradiation treatment as indicated. The y-axis reports the proportion of genes with
the expression change on the x-axis. (D) Data are as in (C), except that genes involved in the
positive regulation of transcription were analyzed. (E) Each column reports tail moments de-
tected in a comet assay on primary fibroblasts harboring the indicated shRNAs, before or six
hours after irradiation. In a given column, points report biological and technical replicates and
the bar height reports their average (SCR n = 5, Usf2 KD n = 20). ****, p < 0.0001, one-tailed
Wilcoxon test. (F) Left, each column reports number of γH2AX foci per cell detected in primary
fibroblasts harboring the indicated shRNAs six hours after irradiation. Data are displayed as
in (E) (SCR n = 12, Usf2 KD n = 28). ***, p < 0.001, one-tailed Wilcoxon test. Right, repre-
sentative images of the indicated cultures.

50

in the response to irradiation, with knockdown of this factor compromising cells’ ability to mount
the classical transcriptional program under this stress, and to carry out DNA damage repair.

USF2 tunes the commitment to senescence

We now sought to understand the role of USF2 in long-term establishment of the senes-
cence fate. We reasoned that when we irradiated cells harboring the Usf2 knockdown construct,
their increased DNA damage (Figure 3.2) could lead to more pronounced senescence pheno-
types. Indeed, we observed marked regulatory changes and enhanced SABG staining in cells
that had been first irradiated in the absence of USF2, and then incubated for senescence estab-
lishment (Supplemental Figure 3.8 and Supplemental Table 3.3). To focus on the function of
USF2 in long-term senescence response, we designed a complementary experimental design in
which we first irradiated wild-type cells to induce DNA damage and allow the initial stages of
commitment to senescence; we then administered Usf2 shRNAs or scrambled controls by lenti-
viral infection and incubated for another ten days, expecting Usf2 knockdown effects to manifest
in the later stages of senescence development (Figure 3.3A).
To investigate quantitative characteristics of these senescent cultures, we subjected them
to expression profiling and Gene Ontology enrichment analyses (Supplemental Tables 3.3 and
3.4). We first examined control cells (harboring a scrambled shRNA) and observed expression
changes for many genes from the resting state through irradiation and early and late senescence
51

(Supplemental Table 3.5, and Supplemental Figure 3.9). Such trends attest to the dynamics of
Figure 3.3: Usf2 knockdown results in an enhanced senescence profile. (A) M. musculus
primary fibroblasts were irradiated, incubated for 10 days to senesce, then infected with
shRNAs (SH) targeting Usf2 or a scrambled control, and analyzed after 10 additional days
(SEN→SH). Cells were also treated with shRNAs and analyzed without irradiation (SH unir-
radiated). (B) Data are as in Figure 3.2B except that cells were analyzed 20 days after irradi-
ation. (C) Each trace reports a cumulative distribution of the log 2 of the ratio of expression in
Usf2 knockdown (KD) and scrambled control (SCR)-treated cells, in genes annotated in the
G2/M transition of mitotic cell cycle, when shRNAs were administered to a resting culture (SH
unirradiated), or after irradiation and senescence establishment as in (A) (SEN→SH). The y-
axis reports the proportion of genes with the expression change on the x-axis, with the latter
taken as an average across replicates. (D) Data are as in (C), except that genes involved in
DNA repair were analyzed. (E) Data are as in (C), except that genes involved in inflammatory
response were analyzed.

52

wild-type senescence across a timecourse as expected (Kim et al. 2013b; Chan et al. 2022).
We now turned our attention to the expression effects of Usf2 knockdown in long-term
senescent cultures. Among top-scoring gene groups, the most dramatic effects were in those that
had dropped in expression in senescent cultures of the control genotype. The latter, as expected
(Kim et al. 2013b; Chan et al. 2022), included cell cycle and DNA repair pathways (Figure 3.3B
and Supplemental Table 3.4). mRNA levels of these cohorts were even lower in senescent cells
that had been irradiated after Usf2 knockdown, showing a reduction of ~20% on average (Figure
3.3B). Among these cell cycle factors, some of which declined in expression by >5-fold with Usf2
knockdown in senescence (Figure 3.3C), we noted cell cycle regulators (Ccna2, Cdc20, Cdk1),
kinesin components (Kif2c, Knl1), the DNA polymerase Pole, and the DNA damage checkpoint
ubiquitin ligase Uhrf1 (Supplemental Figure 3.10A). The expression pattern of the latter and other
genes involved in DNA repair (Figure 3.3D), combined with our previous findings (Figure 3.2)
make clear that USF2 is not only involved in acute DNA damage but the persistent regulation of
this pathway during senescence as well. We conclude that genes of the cell cycle and DNA repair
machinery are detectable at a low but non-zero expression level in wild-type senescent cells, and
that these pathways are subject to further reduction when Usf2 expression is limiting.
Next, we inspected the impact of Usf2 knockdown on inflammation and immune recruiting
factors, another set of processes that had emerged from our Gene Ontology analyses of expres-
sion (Supplemental Tables 3.3 and 3.4). Cells of the control genotype induced these pathways
during senescence, as expected (Campisi 2005; Campisi and Fagagna 2007; Olivieri et al. 2018;
Coppé et al. 2010b; Santoro et al. 2018; Kale et al. 2020); in cells subject to irradiation, senes-
cence initiation, and then Usf2 knockdown, induction of inflammatory factors was amplified by
~10% on average (Figures 3.3B and 3.3E, and Supplemental Figure 3.10B). Together, our profil-
ing data establish that senescence development of primary fibroblasts with reduced Usf2 expres-
sion leads to a quantitatively perturbed, exaggerated senescent state, with reduced expression
of proliferation and DNA repair pathways, and elevated pro-inflammatory gene expression. Thus,
53

USF2 acts a senescence regulator at least in part independent of its role in the acute DNA dam-
age response, such that in its absence, cells commit even more strongly to senescence.

Discussion

Complex regulatory networks likely underlie many of the quantitative behaviors of senes-
cent cells, including kinetics and dependence on cell type and inducer (Campisi 2013; Chan et al.
2022; Casella et al. 2019; Basisty et al. 2020; Purcell et al. 2014). Exactly how these nuances are
encoded remains poorly understood. In this study, we pioneered the use of interspecies genetic
divergence to screen for components of the senescence regulatory machinery. This strategy com-
plements previous studies of transcription factor binding sites in genes of the senescence pro-
gram in a single genetic background (Chan et al. 2022; Martínez-Zamudio et al. 2020). Our ap-
proach harnesses the correlation between interspecies variation in sequence and expression lev-
els, as an additional line of evidence for senescence-specific regulatory functions by a given fac-
tor. Such a paradigm parallels similar tools previously used to dissect genetic divergence in ex-
pression (Villarroel et al. 2021; Veyrieras et al. 2008) and transcription factor binding (Vierbuchen
et al. 2017; Heinz et al. 2013) in other contexts. Broadly speaking, these methods are not highly
powered for pathways under strong evolutionary constraint, which, by definition, will not vary
enough among species to yield the raw observations that would go into a screening pipeline.
Rather, we expect the natural variation-based approach to work best for discovering less-con-
strained modifiers, many of which may confer layers of quantitative regulation onto a master reg-
ulatory pathway.
We focused our experimental validation on one such modifier, the transcription factor
USF2. By tracing USF2’s function in proliferation and genome-wide expression in untreated cells,
we extended conclusions from studies of USF2 in tumor suppression and cell cycle regulation
(Qyang et al. 1999; Pawar et al. 2004; Chen et al. 2006; Hu et al. 2020), apoptosis (Sato et al.
54

2011) and ERK1/2 signaling (Zou et al. 2019; Lu and Xu 2006). In an acute DNA damage setting,
we discovered that USF2 is required for cells to mount DNA repair and downstream DNA damage
responses. And in senescence proper, we showed that USF2 acts as a repressor, such that in its
absence, the senescence program—shutoff of cell proliferation and DNA repair, and induction of
cytokines—is amplified. A compelling model is thus that even long after damage exposure, cells
have access to expression states along a continuum of commitment to senescence (Chan et al.
2022), and that USF2 acts to help determine which state they occupy. If so, USF2 would take a
place among a network of factors, including p53, ING, Rb (Vicencio et al. 2008; Childs et al. 2014),
FOXO4 (Baar et al. 2017), p21 (Zhang et al. 2005; Hsu et al. 2019), and p16 (Panneer Selvam et
al. 2018), that govern the choice between senescence, apoptosis, and repair and proliferation,
depending on cell type (Vicencio et al. 2008) and the amount of damage or stress incurred (Childs
et al. 2014).
As a corollary of these conclusions from expression profiling, we note that cell cycle and
DNA repair genes, classically known to be repressed during senescence (Kim et al. 2013b; Chan
et al. 2022), did not hit a floor of expression in senescent cultures: we could detect them at even
lower expression levels upon Usf2 knockdown. Since our cultures comprise >99% arrested cells
within several days of irradiation, the emerging picture is that the proliferation machinery is main-
tained at non-zero levels even in such a population. Any ability of these gene products to reattain
activity could be of particular interest as a potential mediator of the return to proliferation seen
among senescent cells in certain scenarios (Lee and Schmitt 2019; Beauséjour et al. 2003).
Our work leaves open the mechanisms by which USF2 exerts its effects in the DNA dam-
age response and cellular senescence. It is tempting to speculate that USF2 ultimately works in
these processes in concert with its better-studied family member, USF1. Indeed, USF1 has been
implicated in DNA repair (Baron et al. 2012), inflammation (Song et al. 2018; Ruuth et al. 2018),
immune responses (Corre and Galibert 2005), and p53-mediated cell cycle arrest (Bouafia et al.
2014) in contexts other than senescence. In addition, given that USF2 has been implicated in the
55

TGFβ-p53 axis in apoptosis (Sato et al. 2011) and fibrosis (Samarakoon et al. 2012), the latter
pathway could mediate some part of the USF2 effects we have seen. Furthermore, regulatory
network reconstruction (Supplemental Table 3.6) suggests that USF2 acts upstream of several
other transcription factors (KLF3, GLI3, NFIL3) with direct targets in DNA repair, DNA damage
response and senescence pathways.

Methods

Primary cell extraction and culture

M. musculus x M. spretus F1 interspecific hybrid mice were maintained as described in
Chapter 2. Primary cell extraction and culture was performed as described in Chapter 2.

Irradiation treatment

Performed as described in Chapter 2.

Senescence marker assays

Performed as described in Chapter 2.

RNA collection and sequencing

Performed as described in Chapter 2.

Pseudogenome and VCF generation
56

Performed as described in Chapter 2.

RNA-seq procesing

Performed as described in Chapter 2.

Gene Ontology enrichment analysis of RNA-seq data

Performed as described in Chapter 2. For analysis of the impact of Usf2 knockdown on
expression before or 6 hours, 10 days, or 20 days after irradiation (see below), Gene Ontology
enrichment tests were as above except that we took the ratio, for a given gene, between the
average expression in purebred M. musculus (PWK) cells infected with lentivirus harboring scram-
bled shRNA and the analogous quantity across both Usf2-targeting shRNA treatments.

Transcriptomic screen for senescence regulators

To associate expression variation in genes with sequence variation in their upstream bind-
ing sites for a given transcription factor, we proceeded as follows. From RNA-seq profiling of M.
musculus x M. spretus F1 hybrid cells (see above), we used the TPM counts for each parent
species’ allele from each replicate profile from control and senescent conditions as input into a
two-factor ANOVA. A given gene was categorized as exhibiting senescence-associated differen-
tial allele specific expression if the interaction F statistic value from this ANOVA was among the
top 25% of all genes tested. Separately, we used compiled data from chromatin immunoprecipi-
tation via high-throughput sequencing from the Gene Transcription Regulation Database (GTRD)
(Yevshin et al. 2019) to identify all experimentally determined transcription factor (TF) binding
57

sites located within a 5kb window upstream of the transcriptional start site for each gene in turn
in the M. musculus genome; we refer to the downstream gene of each such binding location as
the target of the TF. This calculation used M. musculus gene start sites from the Ensembl
GRCm38.96 GFF. Next, for each binding site, we used the VCF between PWK and STF
pseudogenomes (see above) to identify single nucleotide variants between PWK and STF in the
binding site locus. Now, for all the target genes of a given TF, we categorized them as having
sequence variants or not in the respective binding site, and exhibiting senescence-associated
differential allele-specific expression. We eliminated from further consideration any TF with fewer
than 250 target genes in each of the four categories. For all remaining TFs, the 2 x 2 contingency
table was used as input into a Fisher’s exact test with Benjamini-Hochberg multiple testing cor-
rection.

Usf2 shRNA vector design, construction and application

Usf2 knockdown shRNA sequences were obtained from the Broad Institute Genetic Per-
turbation Portal (https://portals.broadinstitute.org/gpp/public/). Two shRNA sequences for Usf2
(CCGGGCAAGACAGGAGCAAGTAAAGCTCGAGCTTTACTTGCTCCTGTCTTGCTTTTTT-
GAAT; CCGGACAAGGAGACATAATGCATTTCTCGAG-AAATGCATTATGTCTCCTT-
GTTTTTTTGAAT), and, separately, a scrambled control sequence
(CCTAAGGTTAAGTCGCCCTCGCTCGAGCGAGGGCGACTTAACCTTAGG, Addgene cat.
#1864), were each cloned into pLKO.1 puro lentiviral vectors (Addgene cat. #8453). Lentiviral
particles containing each of the shRNA constructs were generated by calcium phosphate co-
transfection of HEK 293T cells with the shRNA pLKO.1 puro vectors and separate pMDLg/pRRE
packaging and pCMV-VSV-G envelope plasmids generously provided by Dr. Marius Walter of the
Verdin Lab at the Buck Institute. The number of viral particles generated was determined using
the Origene One-Wash
TM
Lentivirus Titer Kit, p24 ELISA (Origene cat. #TR30038). These
58

particles were used to infect two biological replicates of purebred M. musculus (PWK) primary tail
fibroblasts at a multiplicity of infection of 5 with 4 µg/mL of polybrene (Thomas Scientific cat.
#C788D57), and infected cells were selected by incubating with 2 µg/mL puromycin (Neta Scien-
tific cat. #58582) for 10 days, changing media and antibiotic every other day. Knockdown of Usf2
was determined by qPCR, using Usf2 qPCR primer sequences chosen through NCBI Primer
Blast, filtering for those spanning an exon-exon junction. The primer pair with the same efficiency
(calculated as 10
(-1/slope)
when plotting log concentration of template cDNA versus Ct) as the inter-
nal control Actb qPCR primers was chosen: Usf2 forward 5’ TTCGGCGACCACAATATCCAG 3’,
Usf2 reverse 5’ TTCGGCGACCACAATATCCAG 3’, Actb forward 5’ CAACCGTGAAAA-
GATGACCC 3’, Actb reverse 5’ GTAGATGGGCACAGTGTGGG 3’. Usf2 expression was calcu-
lated using the Delta-Delta Ct method (Livak and Schmittgen 2001).

Cell proliferation and DNA damage assays

For each of two biological replicates of purebred M. musculus (PWK) cells infected with
lentivirus harboring the scrambled control and two of each Usf2 knockdown, either before irradi-
ation or 6 hours after irradiation (see above), we measured cell proliferation and DNA damage
response as follows.
For a given replicate, DNA synthesis was measured via 5-ethynyl-2´-deoxyuridine (EdU)
incorporation assays using the Invitrogen
TM
Click-iT
TM
Edu Alexa Fluor
TM
488 Flow Cytometry
Assay Kit (Thermo Fisher cat. #C10420). Cells were treated with 5 µM EdU in complete medium
and left in the incubator overnight. The following day cells were fixed, permeabilized, and treated
with Alexa Fluor
TM
488 azide before running through a BD LSRFortessa
TM
Cell Analyzer to identify
the percentage of EdU positive cells.
For a given replicate, we carried out a comet assay to measure levels of DNA double
stranded breaks for a given replicate culture as described (Olive and Banáth 2006). Briefly, slides
59

scored with a diamond tipped scribe were dipped in 1% low melting point agarose (Sigma-Aldrich
cat. #A4018) and left at 4˚C overnight. Cells of all genotypes tested were mixed into 1% low
melting point agarose at 2 × 10
4
cells/ml, spread over the prepared slides and left to solidify for 1
hour. Slides were then incubated at 37˚C in 2% sarkosyl, 0.5 M NA 2EDTA (Thermo Fisher cat.
#15576028) and 0.5 mg/mL proteinase K pH 8 lysis buffer overnight. The following day the slides
were washed three times in 90 mM Tris, 90 mM boric acid (Neta Scientific cat. #31146), and 2
mM NA2EDTA pH 8.5 wash buffer, then subject to electrophoresis at 0.6 V/cm for 25 minutes in
wash buffer. The slides were then washed three times in distilled water and placed in a staining
solution containing 5 µg/mL of propidium iodide (Sigma-Aldrich cat. #P4170) for 20 minutes. The
slides were washed again once with distilled water and multiple representative images were taken
of each sample using a Zeiss AxioObserver epi-fluorescent microscope. Images were processed
via ZEN Digital Imaging for Light Microscopy (RRID:SCR_013672) and comet tail moments were
analyzed via OpenComet (Gyori et al. 2014) in ImageJ (Schneider et al. 2012).
For H2AX assays, for a given replicate, cells were cultured in 8-chamber Nunc
TM
Lab-
Tek
TM
II Chamber Slides
TM
(Thermo Fisher cat. # 154453). The day before staining, all cells were
seeded at 60-70% confluence in each chamber. Cells were fixed by incubating with 4% paraform-
aldehyde (Electron Microscopy cat. # 15681) for 5 minutes at 4˚C and permeabilized using 0.1%
Triton X-100 (Fisher Scientific cat. #BP151-100) for 15 minutes at room temperature. The cells
were then blocked using 3% bovine serum albumin (BSA) (Sigma-Aldrich cat. #A3803) in PBS
for 45 minutes at room temperature, then incubated with 1 µg/mL of primary antibodies specific
to phosphorylated (Ser 139) H2AX (Santa Cruz Biotechnology cat. #sc-517348) in 3% BSA over-
night at 4˚C. The following day the cells were washed in PBS three times before incubating with
2 µg/mL of Alexa 488 secondary antibodies (Invitrogen cat. #A11001) for two hours at room tem-
perature. Cells were washed three times with PBS then incubated with 0.5 µg/mL DAPI (Sigma-
Aldrich cat. #5087410001) for 5 minutes at room temperature. The cells were washed once more
with PBS before mounting for imaging. Multiple representative confocal images of each sample
60

were taken using a Zeiss LSM 710 AxioObserver. Images were processed using ImageJ, taking
the average background fluorescence across several nuclei in each field of vision and only count-
ing foci with fluorescence intensities above the background threshold.

Multivariate ANOVA of irradiation and senescence timecourse

To identify genes whose expression changed in wild-type cells through irradiation and
senescence, we used RNA-seq profiling data from purebred M. musculus (PWK) cells harboring
a scrambled shRNA before and 6 hours, 10 days, and 20 days after irradiation (see Supplemental
Table 3.5) as input into a multivariate ANOVA test.

MERLIN regulatory network reconstruction

To reconstruct a regulatory network we used RNA-seq profiling data for purebred M. mus-
culus (PWK) cells harboring a scrambled shRNA or a Usf2-targeting RNA, before and 6 hours,
10 days and 20 days after irradiation, as input into MERLIN (Roy et al. 2013) with default settings.
Analysis used a catalog of murine transcription factors from the Gene Transcription Regulation
Database (Yevshin et al. 2019).

61

Supplemental Figures

Supplemental Figure 3.1: Senescent M. musculus x M. spretus F1 primary fibroblasts dis-
play intermediate activity of senescence-associated 𝛃 -galactosidase (SABG). Each column
reports the proportion of SABG positive cells for the indicated genotype in control cells or in se-
nescent cells seven days after irradiation (SEN). In a given column, points report biological and
technical replicates and the bar height reports their average (M. musculus n = 9, M. spretus n =
5, F1 hybrid n = 2).

62

Supplemental Figure 3.2: Variation in gene expression during senescence between M.
musculus and M. spretus is controlled by both cis- and trans-acting elements. Each point
represents expression of one gene in primary fibroblasts induced to senesce. The x-axis reports
the ratio of expression, as an average across biological replicates, measured in purebred M. mus-
culus (Mm) and M. spretus (Ms) cells; the y-axis reports the ratio of allele-specific expression from
the M. musculus and M. spretus alleles in cells from the interspecific F1 hybrid.

63

Supplemental Figure 3.3: Senescent M. musculus x M. spretus F1 primary cells display
intermediate induction in mRNA expression of genes of the senescence-associated secre-
tory phenotype. Each trace reports a cumulative distribution of the change, in senescent primary
fibroblasts of the indicated genotype, in mRNA levels of genes of the senescence associated
secretory phenotype with senescence (Coppé et al. 2008). The y-axis reports the proportion of
genes with the expression change on the x-axis, with the latter taken as an average across repli-
cates.

64

Supplemental Figure 3.4: Usf2-targeting shRNAs knock down Usf2 expression. Each set of
bars reports Usf2 expression measured via qPCR in M. musculus primary cells, in one biological
replicate. In a given replicate, each bar reports the fold change (FC) in Usf2 expression between
cells harboring Usf2-targeting shRNA (Usf2 KD) and those with a scrambled shRNA (SCR), nor-
malized with respect to the value of the latter.

65

Supplemental Figure 3.5: Usf2 depletion slightly slows growth of primary M. musculus
cells independent of irradiation. Each column reports the percentage of EdU incorporation in
primary fibroblasts harboring Usf2 or scrambled shRNAs, before or six hours after irradiation as
indicated. *, p < 0.05, one-tailed Wilcoxon test. In a given column, points report biological and
technical replicates and the bar height reports their average (SCR n = 11, Usf2 KD n = 22).

66

Supplemental Figure 3.6: Usf2 knockdown results in less transcriptional repression fol-
lowing acute DNA damage. In a given panel, each column reports mRNA expression of the
indicated gene in transcripts per million (TPM) in primary fibroblasts harboring Usf2 or scrambled
shRNAs, before or six hours after irradiation as indicated. (A) Shown are a representative subset
of the genes in the Gene Ontology (GO) term “negative regulation of apoptotic process” that were
repressed in cells harboring the scrambled shRNA control following acute DNA damage. (B)
Shown are genes in the GO term “positive regulation of transcription, DNA-templated”. In a given
column, points report biological replicates and the bar height reports their average (n = 3).

67

Supplemental Figure 3.7: Expression of core DNA damage response genes are largely not
affected by Usf2 knockdown. Data are as in Supplemental Figure 3.6 except that DNA damage
response genes are shown.

68

Supplemental Figure 3.8: Usf2 knockdown prior to DNA damage also results in an en-
hanced senescence profile. (A) M. musculus primary fibroblasts were infected with a lentivirus
encoding an shRNA (SH) targeting Usf2 or a scrambled control, and analyzed before (SH unirra-
diated) or after (SH→SEN) treatment with ionizing radiation (IR) to induce senescence (SEN). (B)
Data are as in Figure 3.2B except that cells were analyzed 10 days after irradiation. (C) Each
trace reports a cumulative distribution of the log 2 of the ratio of expression in Usf2 knockdown
(KD) and scrambled control (SCR)-treated cells, in genes annotated in the immune response,
when shRNAs were administered to a resting culture (SH unirradiated), or to resting cells followed
by irradiation as in (A) (SH→SEN). The y-axis reports the proportion of genes with the expression
69

change on the x-axis, with the latter taken as an average across replicates. (D) Data are as in
(C), except that genes involved in G2/M transition of mitotic cell cycle were analyzed. (E) Left,
each column reports the proportion of senescence associated β-galactosidase (SABG)-positive
cells treated with the indicated shRNAs in resting culture (unirradiated) or 7 days after irradiation
(SEN) as in (A). In a given column, points report biological and technical replicates and the bar
height reports their average (unirradiated n = 4, SEN n = 24). **, p < 0.01, one-tailed Wilcoxon
test. Right, representative images of the indicated cultures.

70

Supplemental Figure 3.9: Expression profiling of control cells during a senescence
timecourse reveals a dynamic expression program with senescence progression. Each
trace reports a cumulative distribution of gene expression in primary M. musculus fibroblasts har-
boring a scrambled shRNA, at the indicated timepoint after irradiation treatment relative to unir-
radiated controls. Included in the distribution are all genes significant in a test for significant ex-
pression change across the timepoints by multivariate ANOVA (Supplemental Table 3.5). The y-
axis reports the proportion of genes with the expression change on the x-axis.

71

Supplemental Figure 3.10: Usf2 knockdown results in an enhanced senescent gene ex-
pression profile. In a given panel, each column reports mRNA expression in transcripts per mil-
lion (TPM) in primary fibroblasts harboring Usf2 or scrambled shRNAs, when shRNAs were ad-
ministered to a resting culture (unirradiated), to resting cells followed by irradiation (→SEN), or
after irradiation and senescence establishment (→KD). (A) Genes annotated in inflammation and
immune response that were upregulated during senescence in scrambled shRNA controls. (B)
Genes annotated in cell cycle that were repressed with senescence in scrambled shRNA controls.
In a given column, points report biological replicates and the bar height reports their average
(SCR n = 2, Usf2 KD n = 4).

72

Supplemental Tables

Transcription_factor fisher_pval BH
Usf2 0.00028956504080984 0.0490259968424357
Crebbp 0.00080290811056706 0.0490259968424357
Gata2 0.000937869371035779 0.0490259968424357
Prdm1 0.00103944175322789 0.0490259968424357
Pbx1 0.00123803022329383 0.0490259968424357
Arntl 0.00241172504512983 0.0795869264892844
Brd9 0.00298812228116643 0.0845211730958504
Erg 0.0035151440438834 0.0869998150861142
Foxp3 0.00498938381033073 0.0952621511090631
Kdm4c 0.00508874616108775 0.0952621511090631
Nkx3-1 0.00564638216714043 0.0952621511090631
Hnf4a 0.00577346370357958 0.0952621511090631
Smc1a 0.00712211752098151 0.0995558504940929
Nelfe 0.00801968554073265 0.0995558504940929
Shox2 0.00803844942996988 0.0995558504940929
Elf1 0.00804491721164387 0.0995558504940929
Cdk6 0.00932452346409116 0.10860327328765
Irf4 0.0101496735499437 0.111646409049381
Ncor1 0.0112993171571524 0.111948992081559
Etv6 0.0116941838368604 0.111948992081559
Men1 0.0126210702845091 0.111948992081559
Klf5 0.0133506299392459 0.111948992081559
Kdm2b 0.0135673766547983 0.111948992081559
Jund 0.0139797655476854 0.111948992081559
Atrx 0.0141349737476716 0.111948992081559
Gata4 0.0166424577729955 0.123485364915939
Taf12 0.016877401515439 0.123485364915939
Ar 0.0174625768567994 0.123485364915939
Tcf7 0.0188457621880118 0.128671065973322
Chd4 0.0196973160570457 0.130002285976502
Ppara 0.0208406739529233 0.133111401376736
Esr1 0.0225482137704391 0.137984226158635
Nr1h3 0.0235642171342186 0.137984226158635
Rag1 0.0236942610575433 0.137984226158635
Yy1 0.0245816522510319 0.139061918448695
Dnmt3a 0.0289193941761083 0.159056667968596
Foxa1 0.0298774960469095 0.159884978845624
Pparg 0.0338391656987376 0.174635583233459
Dnmt3b 0.0343979179096208 0.174635583233459
Bhlhe40 0.037196105083822 0.182369161705566
Cbfb 0.0384550490899297 0.182369161705566
Srf 0.0389695061267498 0.182369161705566
Gtf2b 0.0396054240067644 0.182369161705566
Hdac3 0.0410726496823848 0.184826923570732
Elf5 0.0440137196043266 0.189985246592483
Hdac1 0.0442123545106348 0.189985246592483
Creb1 0.0450975080295288 0.189985246592483
Ncapd3 0.0484811284576443 0.196587126319795
Nr1d2 0.0486503494427775 0.196587126319795
Hand2 0.0583993184345815 0.229672355082705
Prdm13 0.0591580308546361 0.229672355082705
Kmt2b 0.0613679986045836 0.233670456225145
Brd2 0.0673024687148561 0.246934568873065
Tet2 0.0673457915108358 0.246934568873065
Zic2 0.0701961400651171 0.250086422326138
Hcfc1 0.0707315133851703 0.250086422326138
73

Mef2c 0.0734302944429943 0.252031138454312
Batf 0.0738273031835864 0.252031138454312
Meis1 0.0760031299382753 0.25506135131828
Med12 0.0796544851353758 0.26285980094674
Vdr 0.0938770112085311 0.304715544578511
Esrrb 0.095526096585173 0.30506721167523
Rxra 0.0992029815853713 0.31178079926831
Dmc1 0.106946673551502 0.325830920707542
Kmt2d 0.10696469619187 0.325830920707542
Ldb1 0.117006102737258 0.351018308211774
Ncor2 0.121273030529115 0.358388955892012
Mta2 0.126640631453691 0.365097799542543
Smad4 0.12965550328399 0.365097799542543
Pou5f1 0.131877982261145 0.365097799542543
Isx 0.132738169619602 0.365097799542543
Rest 0.133163437846749 0.365097799542543
Atf3 0.135800265388176 0.365097799542543
Stat4 0.138071558725555 0.365097799542543
Fli1 0.138294621038842 0.365097799542543
Gata3 0.140355860972809 0.365663953587055
Cdx2 0.15326326389932 0.388450029099174
Tal1 0.155864381417675 0.388450029099174
Trim28 0.157939440164418 0.388450029099174
Rara 0.159498148373662 0.388450029099174
Stat3 0.160472490622653 0.388450029099174
Gps2 0.160888220786537 0.388450029099174
Kmt2a 0.1637942092484 0.388450029099174
Rag2 0.165925027773917 0.388450029099174
Stat5a 0.166758850875908 0.388450029099174
Rela 0.17328529852027 0.393407197850747
Zbtb17 0.173296389717775 0.393407197850747
Suz12 0.175652371450699 0.393407197850747
Notch1 0.176834548528871 0.393407197850747
Hey2 0.180550437562927 0.397210962638439
Irf8 0.182888336242194 0.397932863472026
Onecut2 0.187880966815592 0.40010187442028
Stat1 0.187926637985283 0.40010187442028
Kmt2c 0.192971608542596 0.405414563695418
Twist2 0.194517088641741 0.405414563695418
Ets1 0.202125111459185 0.411795684611047
Sox2 0.202160330210951 0.411795684611047
Hdac2 0.203818066120619 0.411795684611047
Foxa2 0.225308454614617 0.442003817429288
Tead4 0.230123128136549 0.442003817429288
Nr1d1 0.231420442979301 0.442003817429288
Rnf2 0.232052174119357 0.442003817429288
Pgr 0.234627097993877 0.442003817429288
Nfe2l2 0.238047401715528 0.442003817429288
Gfi1 0.238474698679504 0.442003817429288
Nr6a1 0.241686269528526 0.442003817429288
Pbrm1 0.244557478748019 0.442003817429288
Brca1 0.247136650688248 0.442003817429288
Gata1 0.24998021428909 0.442003817429288
Setd1a 0.251918008557956 0.442003817429288
Srebf1 0.253397020006971 0.442003817429288
Tbp 0.253568268003684 0.442003817429288
Junb 0.25438228299549 0.442003817429288
Satb1 0.254487046398681 0.442003817429288
Kdm6a 0.269752372858519 0.463013569612807
Irf3 0.271260475126695 0.463013569612807
Irf1 0.27533305337749 0.46405830356642
Pcgf6 0.276559999095139 0.46405830356642
74

Trim33 0.280506400833985 0.46672493584142
Nipbl 0.286045318237737 0.471895834186132
Nelfa 0.288380787558192 0.471895834186132
Anpep 0.297465696546843 0.476651561247787
Smc3 0.299561363886103 0.476651561247787
Tbx3 0.301402516837361 0.476651561247787
Sox9 0.302496592646634 0.476651561247787
Smad3 0.303323720794046 0.476651561247787
Purb 0.308504737018794 0.480975889210403
Stat6 0.318090506483905 0.492046252217291
Cebpa 0.323562800367677 0.496102346681725
Atf2 0.32572376297285 0.496102346681725
Med1 0.34343621476687 0.51908679789191
Ncoa3 0.354514381456196 0.526929377764371
Zeb1 0.354854575770825 0.526929377764371
Sp1 0.356608770810231 0.526929377764371
Fos 0.365721587634934 0.536053046140984
Ezh2 0.368198051894817 0.536053046140984
Sox17 0.371046993852537 0.536257699144542
Jun 0.377157250003479 0.537302622124905
Pax5 0.37719729533011 0.537302622124905
Stag2 0.390627788993112 0.544949519700652
Olig2 0.392330907753207 0.544949519700652
Rfx1 0.394904543670747 0.544949519700652
Nr3c1 0.396247142881659 0.544949519700652
Arnt2 0.396326923418656 0.544949519700652
Sin3a 0.402703511461272 0.549898588064358
Ptf1a 0.406346337623426 0.551072430475605
Tp53 0.409405062397552 0.551443553433437
Ikzf1 0.412383702392672 0.551702520768575
Kdm1a 0.418412378067722 0.556011079579926
Nanog 0.423351102291487 0.558823455024763
Tcf3 0.444550072335212 0.582431757770103
Zfp57 0.447119329197251 0.582431757770103
Bcl11b 0.455205382027717 0.589089317918222
Hnf1a 0.484393768943811 0.622791988642043
Esr2 0.504955160863596 0.642221718811426
Ebf1 0.505992869366578 0.642221718811426
Rfx3 0.519341529024149 0.651641365737723
Nr4a1 0.519996645386668 0.651641365737723
Six4 0.528859449799275 0.654182892107617
Klf4 0.533187766292217 0.654182892107617
Ctcf 0.534844148377613 0.654182892107617
Baz1b 0.53524054808805 0.654182892107617
Mecp2 0.542463950275718 0.658165870056245
Rad51 0.545147488329415 0.658165870056245
Rad21 0.55078526628513 0.658808456637976
Myod1 0.552334362635879 0.658808456637976
Runx1 0.561826286906917 0.666117394057303
Tbx21 0.565327099689837 0.666278367491594
Smarca4 0.582496884452715 0.68245197113395
Brd4 0.607439549813636 0.706818703855381
Tp63 0.616012368899885 0.706818703855381
Nfia 0.616901093877206 0.706818703855381
Hey1 0.6197793985907 0.706818703855381
Tcf12 0.621143709448668 0.706818703855381
Smad2 0.638255447723197 0.72214044942396
Myc 0.646151864772197 0.72292369812802
Neurog2 0.646249972568988 0.72292369812802
Ep300 0.6502296745203 0.723289188511345
Cebpb 0.65476094902932 0.724260714568745
E2f1 0.660418085736108 0.726459894309719
75

Tbx5 0.668701048092965 0.731507223880702
Yap1 0.678641751512782 0.738302564832587
Ebf2 0.705118929171118 0.762915562709734
Tfap2c 0.723327581564614 0.778363375814095
Spi1 0.755363417888911 0.80844300941624
Smarcad1 0.787773596254529 0.835610130629332
Tet1 0.789187345594369 0.835610130629332
Nrf1 0.804763376821939 0.846964068911311
Rbpj 0.808465702142615 0.846964068911311
Ascl1 0.81319136909359 0.847431005687004
Utf1 0.838272832220692 0.868994873192131
Mfsd11 0.847852965633685 0.874348370809738
Runx2 0.858924656562236 0.881176590670066
Arid3a 0.895767066246754 0.913978410277332
Otx2 0.900130252545857 0.913978410277332
Stat5b 0.956393526622067 0.964396517400851
Cbx7 0.959525827919029 0.964396517400851
Epop 0.983177145966311 0.983177145966311

Supplemental Table 3.1: A screen correlating divergence in transcription factor binding
sites and cis-regulatory expression during senescence. Each row reports the results of a
Fisher’s exact test relating, for the indicated transcription factor, two measures of variation be-
tween M. musculus and M. spretus: sequence variants at the factor’s experimentally determined
binding sites (Yevshin et al. 2019) upstream of genes, and, at these genes, senescence-specific
expression differences between the two species’ alleles in fibroblasts of the F1 hybrid back-
ground. The second and third columns report nominal and Benjamini-Hochberg corrected p-
values respectively.

76

GO_term real_val number_of_genes prop_over_real BH
positive regulation of peptidyl-tyrosine phosphorylation 24.9656587443 102 0 0
negative regulation of apoptotic process 83.7706409264 569 0 0
regulation of cell population proliferation 32.967314443 173 0 0
regulation of transcription, DNA-templated 109.974287067 753 0 0
N-acylethanolamine metabolic process 10.0316196895 13 0 0
negative regulation of cell population proliferation 112.198289474 483 0 0
positive regulation of bone resorption 9.72879686804 22 0 0
positive regulation of osteoclast differentiation 12.4550147797 25 0 0
regulation of RNA splicing 18.4204298424 53 0 0
cellular copper ion homeostasis 16.8382919388 54 0 0
positive regulation of B cell activation 8.84493845585 11 0 0
regulation of gene expression 47.9096043801 276 0 0
cell adhesion 57.2372876681 377 0 0
positive regulation of gene expression 83.6710896921 464 0 0
regulation of AMPA receptor activity 9.91597594434 17 0 0
positive regulation of cell population proliferation 88.1633018272 508 0 0
positive regulation of epidermal growth factor-activated receptor activity 8.87406508502 12 0 0
positive regulation of synapse assembly 14.450265775 43 0 0
social behavior 16.3583179705 50 0 0
amyloid-beta clearance 8.84382783044 17 0 0
positive regulation of transcription by RNA polymerase II 308.481174793 1739 0 0
cell-cell adhesion 27.502959496 134 0 0
positive regulation of multicellular organism growth 15.6004743488 32 0 0
negative regulation of transcription by RNA polymerase II 148.210716738 1095 0 0
regulation of release of sequestered calcium ion into cytosol by sarcoplasmic reticulum 8.84087104464 14
0 0
heart morphogenesis 18.8758049996 49 0 0
response to muramyl dipeptide 7.79613966341 15 0 0
positive regulation of phosphorylation 12.1996582916 34 0 0
negative regulation of T cell activation 12.8174141305 15 0 0
negative regulation of signal transduction 17.8018381551 42 0 0
DNA demethylation 13.1316521165 30 0 0
myelination 31.6171922665 54 0 0
calcium ion homeostasis 19.567955367 66 0 0
pigmentation 24.0221613919 83 0 0
circadian regulation of gene expression 30.7892881636 120 0 0
zinc ion import into synaptic vesicle 19.405440168 17 0 0
negative regulation of angiogenesis 21.4075136408 83 0 0
negative regulation of transcription, DNA-templated 91.3966499709 643 0 0
synaptic membrane adhesion 19.7051588274 40 0 0
positive regulation of transcription, DNA-templated 146.847719907 793 0 0
retrograde trans-synaptic signaling by endocannabinoid 11.2253697525 14 0 0
G protein-coupled receptor signaling pathway 59.2497831519 276 0 0
regulation of transcription by RNA polymerase II 193.711913537 1110 0 0
epidermal growth factor receptor signaling pathway 38.1460804576 73 0 0
smooth muscle contraction 6.94495461902 14 1e-04 0.00539583333333333
lymphocyte homeostasis 7.75987305044 13 1e-04 0.00539583333333333
T cell differentiation 12.1814442165 32 1e-04 0.00539583333333333
positive regulation of DNA replication 12.8883171261 41 1e-04 0.00539583333333333
phagocytosis, engulfment 6.94532752423 14 2e-04 0.00877966101694915
biomineralization 17.1435860498 64 2e-04 0.00877966101694915
positive regulation of ERK1 and ERK2 cascade 32.6832351191 181 2e-04 0.00877966101694915
angiogenesis 42.5025400275 255 2e-04 0.00877966101694915
animal organ morphogenesis 23.4635122258 107 2e-04 0.00877966101694915
negative regulation of p38MAPK cascade 6.64071202494 12 2e-04 0.00877966101694915
inner ear morphogenesis 14.0999978574 47 2e-04 0.00877966101694915
positive regulation of protein phosphorylation 35.2014268319 206 2e-04 0.00877966101694915
positive regulation of epidermal growth factor receptor signaling pathway 9.05280521084 19 2e-04
0.00877966101694915
gene expression 28.661409406 142 2e-04 0.00877966101694915
positive regulation of mitotic nuclear division 9.4266670924 23 2e-04 0.00877966101694915
77

epithelial tube branching involved in lung morphogenesis 9.77086276517 24 3e-04
0.0115970149253731
regulation of ventricular cardiac muscle cell membrane repolarization 7.54802664643 15 3e-04
0.0115970149253731
central nervous system myelination 7.19992516398 14 3e-04 0.0115970149253731
negative regulation of tumor necrosis factor production 14.901329006 54 3e-04
0.0115970149253731
positive regulation of G protein-coupled receptor signaling pathway 5.89876941756 10 3e-04
0.0115970149253731
negative regulation of fat cell differentiation 15.0204302821 57 3e-04 0.0115970149253731
learning 12.7885793106 45 3e-04 0.0115970149253731
positive regulation of cell migration 36.5997176796 221 3e-04 0.0115970149253731
protein geranylgeranylation 6.84915793708 15 4e-04 0.0138133333333333
signal transduction 78.0324216935 615 4e-04 0.0138133333333333
nervous system development 43.6486554213 294 4e-04 0.0138133333333333
negative regulation of ERK1 and ERK2 cascade 16.7368693997 72 4e-04 0.0138133333333333
positive regulation of angiogenesis 26.1414412808 137 4e-04 0.0138133333333333
homophilic cell adhesion via plasma membrane adhesion molecules 14.3409044332 57 4e-04
0.0138133333333333
microglial cell activation 7.40690247119 15 4e-04 0.0138133333333333
heart development 39.3281287648 246 4e-04 0.0138133333333333
positive regulation of branching involved in ureteric bud morphogenesis 9.10625132053 23 5e-04
0.0170394736842105
post-embryonic development 18.3289007361 84 6e-04 0.0199230769230769
cell differentiation 72.498902273 587 6e-04 0.0199230769230769
positive regulation of macroautophagy 9.82325351873 31 7e-04 0.0213294117647059
skeletal muscle tissue development 12.5840280594 42 7e-04 0.0213294117647059
positive regulation of B cell proliferation 10.4334492604 33 7e-04 0.0213294117647059
fibroblast growth factor receptor signaling pathway 17.7711714938 78 7e-04 0.0213294117647059
regulation of peptidyl-tyrosine phosphorylation 8.3009491909 22 7e-04 0.0213294117647059
negative regulation of extrinsic apoptotic signaling pathway 12.220858221 42 7e-04
0.0213294117647059
response to X-ray 8.88463491777 22 7e-04 0.0213294117647059
excitatory postsynaptic potential 9.40809535286 29 8e-04 0.0232808988764045
ionotropic glutamate receptor signaling pathway 6.3168305148 13 8e-04 0.0232808988764045
blood vessel remodeling 11.4296659319 38 8e-04 0.0232808988764045
positive regulation of interleukin-10 production 8.13247784122 24 8e-04 0.0232808988764045
recognition of apoptotic cell 5.6402728178 10 9e-04 0.0240309278350515
response to toxic substance 9.99077681133 31 9e-04 0.0240309278350515
glial cell differentiation 6.20292841159 14 9e-04 0.0240309278350515
muscle cell cellular homeostasis 8.3570709419 25 9e-04 0.0240309278350515
posttranscriptional regulation of gene expression 10.9993071713 40 9e-04 0.0240309278350515
positive regulation of cell migration involved in sprouting angiogenesis 7.46414346897 20 9e-04
0.0240309278350515
cell migration involved in sprouting angiogenesis 7.83137109466 20 9e-04 0.0240309278350515
collagen metabolic process 7.69309639957 20 9e-04 0.0240309278350515
positive regulation of mast cell degranulation 6.46791248458 14 0.001 0.0249038461538462
engulfment of apoptotic cell 7.49859016381 18 0.001 0.0249038461538462
cellular response to estradiol stimulus 8.71249131534 25 0.001 0.0249038461538462
leukocyte migration involved in inflammatory response 5.86283827921 12 0.001
0.0249038461538462
regulation of osteoclast differentiation 6.84815873514 16 0.001 0.0249038461538462
positive regulation of interferon-alpha production 7.5849204227 21 0.001 0.0249038461538462
synapse pruning 5.48107326331 11 0.001 0.0249038461538462
calcium-dependent activation of synaptic vesicle fusion 5.9486526308 12 0.0011
0.026877358490566
mammary gland development 10.2514850539 35 0.0011 0.026877358490566
negative regulation of cell-cell adhesion 6.22681198618 15 0.0012 0.0285137614678899
positive regulation of toll-like receptor 4 signaling pathway 5.84650696511 12 0.0012
0.0285137614678899
regulation of heart rate 8.13965770194 22 0.0012 0.0285137614678899
ribosome biogenesis 20.7894370803 108 0.0013 0.0300625
78

negative regulation of smoothened signaling pathway 10.7625042931 37 0.0013 0.0300625
regulation of cell motility 5.70395251895 12 0.0013 0.0300625
insulin secretion 9.45345999432 28 0.0014 0.0315304347826087
locomotory behavior 16.9039183932 80 0.0014 0.0315304347826087
negative regulation of G protein-coupled receptor signaling pathway 5.83664629516 11 0.0014
0.0315304347826087
semaphorin-plexin signaling pathway 11.9126689085 46 0.0015 0.0329237288135593
antigen processing and presentation of exogenous peptide antigen via MHC class II 5.81240686893 12
0.0015 0.0329237288135593
positive regulation of defense response to virus by host 8.52226049592 25 0.0015
0.0329237288135593
negative regulation of blood pressure 6.66381462106 16 0.0016 0.0336910569105691
positive regulation of protein kinase B signaling 19.0623488771 98 0.0016 0.0336910569105691
myoblast migration 5.23594409197 11 0.0016 0.0336910569105691
decidualization 6.25664827273 15 0.0016 0.0336910569105691
regulation of heart rate by cardiac conduction 8.06584181885 23 0.0016 0.0336910569105691
muscle organ development 11.2615084288 44 0.0017 0.0349444444444444
positive regulation of smooth muscle cell proliferation 15.1650124738 69 0.0017 0.0349444444444444
outflow tract morphogenesis 11.8259327993 47 0.0017 0.0349444444444444
cementum mineralization 7.94897687131 23 0.0018 0.036421875
keratinocyte differentiation 10.0013600872 36 0.0018 0.036421875
positive regulation of systemic arterial blood pressure 5.24257184119 10 0.0019 0.0378538461538462
positive regulation of phosphatidylinositol 3-kinase signaling 13.5771267094 57 0.0019
0.0378538461538462
L-cystine transport 4.9975034188 10 0.002 0.0392424242424242
regulation of alternative mRNA splicing, via spliceosome 16.592139617 78 0.002
0.0392424242424242
oligodendrocyte differentiation 7.72988880839 22 0.0021 0.0402888888888889
negative regulation of wound healing 5.33958911642 12 0.0021 0.0402888888888889
positive regulation of tyrosine phosphorylation of STAT protein 10.3655258717 40 0.0021
0.0402888888888889
leukocyte tethering or rolling 5.24122599759 12 0.0022 0.0415912408759124
negative regulation of endothelial cell apoptotic process 7.29938369196 22 0.0022
0.0415912408759124
phagocytosis 13.535297696 59 0.0023 0.0431666666666667
positive regulation of receptor-mediated endocytosis 7.747969406 23 0.0024 0.0444
negative regulation of neuron apoptotic process 26.715227339 161 0.0024 0.0444
hyaluronan biosynthetic process 4.7406190575 10 0.0025 0.0455985915492958
vocalization behavior 4.97883578014 10 0.0025 0.0455985915492958
microtubule anchoring at centrosome 5.79912460444 14 0.0026 0.0461232876712329
protein dephosphorylation 28.4098890783 178 0.0026 0.0461232876712329
Notch signaling pathway 22.0645103617 130 0.0026 0.0461232876712329
skeletal muscle cell differentiation 10.3180606341 40 0.0026 0.0461232876712329
negative regulation of gene expression 40.0816113256 288 0.0027 0.0475714285714286
phosphate ion homeostasis 7.39613999521 24 0.0028 0.0483466666666667
regulation of synaptic vesicle priming 11.2124728428 47 0.0028 0.0483466666666667
embryonic morphogenesis 6.71596049048 19 0.0028 0.0483466666666667
B cell homeostasis 8.00255983036 27 0.0029 0.0497417218543046
Supplemental Table 3.2: Functional-genomic enrichment analysis of purebred M. muscu-
lus primary cell transcriptomes upon Usf2 knockdown and irradiation. Data are as in Sup-
plemental Table 2.1 except that M. musculus (PWK) primary cells harboring short hairpin RNAs
targeting Usf2 or a scrambled control were treated with ionizing radiation, and profiled 6 hours
afterward. For each gene we formulated the average expression measurement across
79

replicates from each shRNA category in turn, and we then took the ratio between these aver-
ages for input into Gene Ontology enrichment tests.
80

GO_term real_val number_of_genes prop_over_real BH
retinoic acid biosynthetic process 8.19803630426 12 0 0
negative regulation of apoptotic process 42.9682370072 567 0 0
chromosome segregation 49.2814849513 95 0 0
biomineralization 14.838985476 64 0 0
cellular response to DNA damage stimulus 88.9616889242 523 0 0
centrosome duplication 8.69700933501 18 0 0
neuron migration 19.4772092428 124 0 0
negative regulation of blood coagulation 5.99639473302 10 0 0
negative regulation of cell population proliferation 41.0785008069 480 0 0
bone mineralization 15.3402513481 61 0 0
attachment of spindle microtubules to kinetochore 17.9167999285 17 0 0
activation of protein kinase activity 12.3883519167 39 0 0
sensory perception of sound 23.2755534372 137 0 0
double-strand break repair via break-induced replication 7.70744478768 11 0 0
oocyte development 7.10204645713 10 0 0
DNA replication initiation 17.6597832546 29 0 0
homocysteine metabolic process 12.5273552262 19 0 0
chromosome condensation 9.32549721858 16 0 0
mitotic sister chromatid cohesion 8.49266154968 17 0 0
positive regulation of gene expression 50.7902213826 457 0 0
DNA replication 58.0520993806 145 0 0
auditory receptor cell stereocilium organization 10.4442949138 27 0 0
telomere maintenance 16.0660672292 68 0 0
postsynapse organization 34.6650441735 22 0 0
positive regulation of transcription by RNA polymerase II 79.0361283657 1717 0 0
actin cytoskeleton organization 28.9556609802 187 0 0
negative regulation of double-strand break repair via homologous recombination 11.502210437 20 0
0
xenobiotic metabolic process 11.0226423552 25 0 0
mitotic cell cycle 41.2713762069 111 0 0
maintenance of DNA methylation 11.7671685972 15 0 0
defense response to Gram-negative bacterium 11.1517441042 40 0 0
negative regulation of transcription by RNA polymerase II 73.9651766366 1071 0 0
somatic hypermutation of immunoglobulin genes 13.3429389251 25 0 0
mitotic cytokinesis 22.983181231 56 0 0
protein phosphorylation 57.6337859496 733 0 0
ribosome biogenesis 19.105041781 108 0 0
mismatch repair 27.8965796214 87 0 0
female gonad development 8.88292839577 21 0 0
glutathione metabolic process 19.4284012883 66 0 0
peptidyl-serine phosphorylation 30.5788798641 236 0 0
positive regulation of cytokinesis 14.5554547662 29 0 0
skeletal system development 18.3696533879 100 0 0
cellular response to ionizing radiation 11.2358740954 39 0 0
inner ear morphogenesis 14.4454388458 43 0 0
phosphorylation 45.3276948326 468 0 0
DNA topological change 11.1018855076 15 0 0
positive regulation of cell-cell adhesion 8.57117042791 23 0 0
water transport 17.2734145204 22 0 0
fibroblast growth factor receptor signaling pathway 14.2663482733 77 0 0
positive regulation of chromosome segregation 11.3138593882 14 0 0
calcium ion homeostasis 18.8252431713 66 0 0
pulmonary valve morphogenesis 7.47363194495 13 0 0
regulation of G2/M transition of mitotic cell cycle 7.62243272882 10 0 0
DNA repair 83.8255703408 418 0 0
interstrand cross-link repair 9.28356291942 30 0 0
response to ATP 17.7212784083 38 0 0
cell cycle 142.652891213 538 0 0
reciprocal meiotic recombination 7.99561973798 21 0 0
DNA recombination 19.3161277712 81 0 0
DNA unwinding involved in DNA replication 17.6640470304 30 0 0
81

Tie signaling pathway 7.42745425046 11 0 0
mitotic spindle assembly 18.8393144036 48 0 0
mitotic spindle assembly checkpoint signaling 23.1241565126 36 0 0
cell division 112.760777212 337 0 0
spindle organization 11.9224082492 17 0 0
mitotic spindle organization 24.1962357254 55 0 0
exocytic insertion of neurotransmitter receptor to postsynaptic membrane 9.6365894 29 0
0
DNA duplex unwinding 12.9117615796 34 0 0
fertilization 8.4103598235 27 0 0
negative regulation of axon extension involved in axon guidance 10.2958602959 23 0 0
positive regulation of mitotic cell cycle spindle assembly checkpoint 6.97859057824 10 0 0
microtubule-based movement 20.6215542689 62 0 0
double-strand break repair via homologous recombination 25.4204494651 116 0 0
synaptic vesicle fusion to presynaptic active zone membrane 12.27609827 18 0 0
mitotic sister chromatid segregation 23.4987974416 32 0 0
immune response 13.4683033634 82 0 0
establishment of protein localization 11.0350523088 38 0 0
positive regulation of transcription, DNA-templated 73.2578168576 770 0 0
collagen catabolic process 14.2368182278 32 0 0
negative regulation of peptidase activity 15.4588414706 44 0 0
mitotic metaphase plate congression 16.5155361521 41 0 0
attachment of mitotic spindle microtubules to kinetochore 10.7576118717 12 0 0
metaphase plate congression 15.9944973625 14 0 0
phosphatidylcholine catabolic process 8.91578064284 13 0 0
protein localization to kinetochore 14.2541095323 18 0 0
negative regulation of endopeptidase activity 13.6989445288 39 0 0
mitotic chromosome condensation 12.896666569 20 0 0
regulation of mitotic cell cycle 12.2168097156 47 1e-04 0.00225221238938053
neuronal stem cell population maintenance 7.73473813358 20 1e-04 0.00225221238938053
double-strand break repair 16.2790243225 100 1e-04 0.00225221238938053
cementum mineralization 7.51950093112 17 1e-04 0.00225221238938053
replication fork processing 11.9130713856 37 1e-04 0.00225221238938053
mitotic DNA replication checkpoint signaling 7.73985071572 11 1e-04 0.00225221238938053
monounsaturated fatty acid biosynthetic process 6.7696288 10 1e-04 0.00225221238938053
chromatin organization 29.3488622968 281 1e-04 0.00225221238938053
regulation of AMPA receptor activity 8.76498893015 13 1e-04 0.00225221238938053
positive regulation of neuroblast proliferation 7.81443573166 19 1e-04 0.00225221238938053
regulation of cell migration 16.344372414 86 1e-04 0.00225221238938053
dTMP biosynthetic process 6.35768109411 10 1e-04 0.00225221238938053
DNA replication-dependent nucleosome assembly 6.59291544313 12 1e-04 0.00225221238938053
positive regulation of myoblast differentiation 6.43295808624 17 1e-04 0.00225221238938053
defense response to bacterium 11.7726264732 59 1e-04 0.00225221238938053
response to antibiotic 7.26954943018 17 1e-04 0.00225221238938053
microtubule cytoskeleton organization 20.672902692 134 1e-04 0.00225221238938053
female meiotic nuclear division 5.90636022853 11 1e-04 0.00225221238938053
G2/M transition of mitotic cell cycle 10.611604708 42 1e-04 0.00225221238938053
glucose transmembrane transport 6.86329831 16 1e-04 0.00225221238938053
T cell homeostasis 8.9127623347 25 1e-04 0.00225221238938053
ceramide biosynthetic process 14.8382337341 66 1e-04 0.00225221238938053
G-quadruplex DNA unwinding 6.1940843309 12 1e-04 0.00225221238938053
negative regulation of gene expression 28.0032379839 295 1e-04 0.00225221238938053
melanocyte differentiation 9.24137038889 25 1e-04 0.00225221238938053
ubiquitin-dependent protein catabolic process 25.0647224708 286 1e-04 0.00225221238938053
DNA-dependent DNA replication 8.39001948842 32 2e-04 0.00388549618320611
anatomical structure morphogenesis 8.95274494533 31 2e-04 0.00388549618320611
regulation of cytokinesis 11.3234232284 44 2e-04 0.00388549618320611
regulation of mitotic nuclear division 7.55401713153 20 2e-04 0.00388549618320611
learning 9.9058318641 44 2e-04 0.00388549618320611
post-embryonic development 12.9178011138 85 2e-04 0.00388549618320611
protein autophosphorylation 22.8542654155 222 2e-04 0.00388549618320611
positive regulation of microglial cell activation 5.55404614459 10 2e-04 0.00388549618320611
82

regulation of synaptic vesicle priming 10.737238122 47 2e-04 0.00388549618320611
negative regulation of transcription, DNA-templated 39.8910456911 630 2e-04 0.00388549618320611
cholesterol homeostasis 13.6187906869 85 2e-04 0.00388549618320611
DNA biosynthetic process 7.58857955043 24 2e-04 0.00388549618320611
microtubule depolymerization 7.50455499096 16 2e-04 0.00388549618320611
peptidyl-tyrosine phosphorylation 14.6234172697 106 2e-04 0.00388549618320611
meiotic cell cycle 12.6610033901 65 2e-04 0.00388549618320611
steroid metabolic process 10.4895180322 58 2e-04 0.00388549618320611
proteolysis 33.8046575621 392 2e-04 0.00388549618320611
DNA metabolic process 5.43253261857 10 2e-04 0.00388549618320611
metanephros development 8.72897735743 32 3e-04 0.00549280575539568
cholesterol biosynthetic process 9.91266761143 46 3e-04 0.00549280575539568
G1/S transition of mitotic cell cycle 12.2477406592 60 3e-04 0.00549280575539568
cellular sodium ion homeostasis 6.55426381398 16 3e-04 0.00549280575539568
positive regulation of myoblast fusion 5.96330175603 13 3e-04 0.00549280575539568
embryonic digestive tract morphogenesis 6.32273895372 15 3e-04 0.00549280575539568
establishment of mitotic spindle orientation 8.64563295651 34 3e-04 0.00549280575539568
negative regulation of viral genome replication 9.08972774155 32 3e-04 0.00549280575539568
microtubule bundle formation 9.26025647536 40 4e-04 0.00687837837837838
negative regulation of neuron differentiation 11.69857883 57 4e-04 0.00687837837837838
heart morphogenesis 10.1891483484 46 4e-04 0.00687837837837838
chemotaxis 11.9109449151 57 4e-04 0.00687837837837838
response to acidic pH 5.09753883765 10 4e-04 0.00687837837837838
peptide catabolic process 6.41303574313 18 4e-04 0.00687837837837838
DNA replication, synthesis of RNA primer 5.24102799152 11 4e-04 0.00687837837837838
mitotic G2 DNA damage checkpoint signaling 10.9798307113 45 4e-04 0.00687837837837838
spindle assembly 9.03816351152 35 4e-04 0.00687837837837838
collagen-activated tyrosine kinase receptor signaling pathway 4.96108995384 10 5e-04
0.00757440476190476
protein localization to centrosome 8.78321969025 32 5e-04 0.00757440476190476
regulation of cardiac muscle contraction 5.38580009483 13 5e-04 0.00757440476190476
positive regulation of interferon-gamma production 11.1486562595 53 5e-04 0.00757440476190476
positive regulation of double-strand break repair via homologous recombination 9.02021159187 35 5e-04
0.00757440476190476
resolution of meiotic recombination intermediates 5.61925312479 11 5e-04 0.00757440476190476
secondary palate development 6.00725515417 14 5e-04 0.00757440476190476
locomotory behavior 12.1293909446 79 5e-04 0.00757440476190476
hard palate development 5.69814897568 12 5e-04 0.00757440476190476
cell population proliferation 17.7029486856 152 5e-04 0.00757440476190476
gamete generation 6.5439425919 18 5e-04 0.00757440476190476
pigmentation 13.152222819 83 5e-04 0.00757440476190476
glycoprotein catabolic process 6.24183139215 16 5e-04 0.00757440476190476
rhythmic process 15.8937468377 130 5e-04 0.00757440476190476
synapse pruning 5.24175794899 11 5e-04 0.00757440476190476
regulation of cell cycle 21.9968477255 241 5e-04 0.00757440476190476
kinetochore assembly 5.6055655383 15 5e-04 0.00757440476190476
synaptic vesicle exocytosis 7.38186864042 25 5e-04 0.00757440476190476
metaphase/anaphase transition of mitotic cell cycle 5.55544309319 12 5e-04 0.00757440476190476
skeletal muscle cell differentiation 9.02264099491 38 5e-04 0.00757440476190476
semaphorin-plexin signaling pathway involved in axon guidance 6.69273873715 18 6e-04
0.00877586206896552
innate immune response 22.3342136912 286 6e-04 0.00877586206896552
positive regulation of pathway-restricted SMAD protein phosphorylation 9.94200373751 49 6e-04
0.00877586206896552
brown fat cell differentiation 7.60093685766 23 6e-04 0.00877586206896552
digestive tract development 6.05824804214 16 6e-04 0.00877586206896552
intracellular signal transduction 23.7893509078 368 6e-04 0.00877586206896552
steroid biosynthetic process 9.17985989633 41 7e-04 0.00995251396648045
nucleobase-containing compound metabolic process 7.39840729818 26 7e-04 0.00995251396648045
isotype switching 6.09049555101 17 7e-04 0.00995251396648045
muscle cell development 7.74246276359 27 7e-04 0.00995251396648045
response to ionizing radiation 9.99939465589 54 7e-04 0.00995251396648045
83

branching involved in ureteric bud morphogenesis 8.64492477616 35 8e-04 0.0107724867724868
toxin metabolic process 4.92089204623 11 8e-04 0.0107724867724868
DNA damage checkpoint signaling 8.68980005695 39 8e-04 0.0107724867724868
activation of GTPase activity 13.7466953496 96 8e-04 0.0107724867724868
lipoprotein metabolic process 6.07275956484 15 8e-04 0.0107724867724868
negative regulation of peptidyl-tyrosine phosphorylation 4.95540193795 12 8e-04
0.0107724867724868
regulation of mitotic spindle organization 5.51746171091 15 8e-04 0.0107724867724868
regulation of epithelial cell proliferation 5.67952381609 14 8e-04 0.0107724867724868
positive regulation of necroptotic process 5.39645849451 12 8e-04 0.0107724867724868
positive regulation of DNA-directed DNA polymerase activity 5.22020464779 12 8e-04
0.0107724867724868
regulation of double-strand break repair via homologous recombination 5.94987245096 18 9e-04
0.0115100502512563
ribosomal large subunit biogenesis 7.29669220424 30 9e-04 0.0115100502512563
histone monoubiquitination 5.56689090168 15 9e-04 0.0115100502512563
bone mineralization involved in bone maturation 4.79575249216 10 9e-04 0.0115100502512563
base-excision repair, gap-filling 5.31397246228 12 9e-04 0.0115100502512563
antigen processing and presentation of exogenous peptide antigen via MHC class II 5.1140957828 12
9e-04 0.0115100502512563
astral microtubule organization 5.20446726478 12 9e-04 0.0115100502512563
DNA methylation involved in embryo development 4.64520629686 10 9e-04 0.0115100502512563
regulation of calcium-mediated signaling 5.00243927045 11 9e-04 0.0115100502512563
olfactory bulb development 5.75979090092 14 9e-04 0.0115100502512563
negative regulation of astrocyte differentiation 5.27570327635 13 0.001 0.0125369458128079
mitotic cell cycle checkpoint signaling 4.9071522506 12 0.001 0.0125369458128079
sphingosine-1-phosphate receptor signaling pathway 5.50163810574 14 0.001 0.0125369458128079
homologous chromosome pairing at meiosis 6.43032653511 20 0.001 0.0125369458128079
positive regulation of protein processing 4.78883425456 12 0.0011 0.0132677725118483
rRNA processing 15.9753557758 152 0.0011 0.0132677725118483
positive chemotaxis 6.45420128584 18 0.0011 0.0132677725118483
central nervous system myelination 5.44240281464 14 0.0011 0.0132677725118483
protein localization to synapse 4.75787430854 12 0.0011 0.0132677725118483
peptidyl-threonine phosphorylation 12.5026663845 84 0.0011 0.0132677725118483
neural tube closure 14.0959358238 114 0.0011 0.0132677725118483
positive regulation of axonogenesis 8.80498967105 42 0.0011 0.0132677725118483
positive regulation of T cell mediated cytotoxicity 5.67780350037 16 0.0012 0.014073732718894
excitatory postsynaptic potential 6.97129531559 27 0.0012 0.014073732718894
protein import into nucleus 14.1304647927 116 0.0012 0.014073732718894
chromosome organization 8.92893658933 44 0.0012 0.014073732718894
actin filament network formation 4.81745430313 10 0.0012 0.014073732718894
inner ear auditory receptor cell differentiation 4.99688789459 11 0.0012 0.014073732718894
positive regulation of epithelial to mesenchymal transition 9.49825228934 49 0.0013
0.0149031531531532
induction of positive chemotaxis 5.39110927879 16 0.0013 0.0149031531531532
blood vessel development 10.8019881067 62 0.0013 0.0149031531531532
defense response to Gram-positive bacterium 11.1091987188 64 0.0013 0.0149031531531532
male gonad development 10.3335391783 69 0.0013 0.0149031531531532
response to virus 10.2388815625 58 0.0014 0.0158355555555556
anaphase-promoting complex-dependent catabolic process 5.50563233962 18 0.0014
0.0158355555555556
cardiac septum morphogenesis 4.58278678533 10 0.0014 0.0158355555555556
positive regulation of pri-miRNA transcription by RNA polymerase II 9.6925051202 48 0.0015
0.0166703056768559
oocyte maturation 5.61356362058 17 0.0015 0.0166703056768559
lipid metabolic process 26.9477756359 501 0.0015 0.0166703056768559
cellular homeostasis 5.47825428051 15 0.0015 0.0166703056768559
negative regulation of Notch signaling pathway 8.09367441225 40 0.0016 0.0174763948497854
lymphocyte homeostasis 5.32645363834 14 0.0016 0.0174763948497854
mitotic cell cycle phase transition 5.5869296924 17 0.0016 0.0174763948497854
aortic valve morphogenesis 5.99233712985 20 0.0016 0.0174763948497854
regulation of exocytosis 7.00804478304 29 0.0018 0.0193291139240506
84

positive regulation of double-strand break repair 5.1970026286 15 0.0018 0.0193291139240506
negative regulation of smooth muscle cell proliferation 8.07677693124 37 0.0018 0.0193291139240506
sterol biosynthetic process 6.9734607197 28 0.0018 0.0193291139240506
N-acylethanolamine metabolic process 4.8667203879 13 0.0019 0.0200643153526971
embryonic heart tube development 7.45066937553 33 0.0019 0.0200643153526971
cerebral cortex development 9.44338215543 56 0.0019 0.0200643153526971
ossification involved in bone maturation 5.3332681525 16 0.0019 0.0200643153526971
osteoblast differentiation 11.0654009085 83 0.002 0.0207755102040816
GMP biosynthetic process 4.92646330002 14 0.002 0.0207755102040816
positive regulation of protein phosphorylation 17.1206674368 204 0.002 0.0207755102040816
positive regulation of smoothened signaling pathway 7.34302587056 34 0.002 0.0207755102040816
kidney development 14.0778090191 140 0.0021 0.0216376518218623
positive regulation of G2/M transition of mitotic cell cycle 6.53105865151 27 0.0021
0.0216376518218623
centriole-centriole cohesion 4.96725476014 15 0.0022 0.0220433070866142
male meiotic nuclear division 5.66839833768 20 0.0022 0.0220433070866142
extracellular matrix disassembly 4.58878350467 12 0.0022 0.0220433070866142
cellular response to UV 9.60013300715 57 0.0022 0.0220433070866142
type I interferon signaling pathway 6.03704361407 24 0.0022 0.0220433070866142
skin development 8.34594933101 42 0.0022 0.0220433070866142
cellular aldehyde metabolic process 4.18698814261 10 0.0022 0.0220433070866142
regulation of postsynaptic membrane neurotransmitter receptor levels 8.25485729112 39 0.0023
0.022687984496124
intermediate filament organization 6.15787313215 23 0.0023 0.022687984496124
regulation of osteoclast differentiation 5.13887991593 16 0.0023 0.022687984496124
regulation of cell shape 14.6735948979 138 0.0023 0.022687984496124
modification of postsynaptic actin cytoskeleton 6.75368092738 28 0.0024 0.0234923076923077
T-tubule organization 4.41233578571 11 0.0024 0.0234923076923077
centriole replication 7.43925489983 34 0.0025 0.0243773946360153
positive regulation of mast cell degranulation 4.66006871242 14 0.0027 0.0260284090909091
limb development 8.3385731938 47 0.0027 0.0260284090909091
smoothened signaling pathway 11.9177228229 113 0.0027 0.0260284090909091
cellular zinc ion homeostasis 6.85192353046 29 0.0028 0.0267894736842105
vocalization behavior 4.69967111536 13 0.0028 0.0267894736842105
membranous septum morphogenesis 4.32845535414 11 0.003 0.0285955056179775
synaptic vesicle docking 4.57676048696 15 0.0031 0.0292203703703704
NADP metabolic process 4.04835691457 10 0.0031 0.0292203703703704
in utero embryonic development 18.88814848 278 0.0031 0.0292203703703704
actomyosin structure organization 7.35872949456 36 0.0032 0.0300516605166052
bleb assembly 4.26155903676 12 0.0033 0.03054
cell-cell adhesion 12.9060833403 135 0.0033 0.03054
sarcomere organization 5.77790029951 21 0.0033 0.03054
negative regulation of microtubule depolymerization 7.29546157951 35 0.0033 0.03054
negative regulation of mitotic nuclear division 4.11387915936 10 0.0034 0.0313514492753623
RNA processing 10.0632214923 73 0.0035 0.0321570397111913
heart development 16.7988860154 244 0.0036 0.0329568345323741
DNA ligation 3.99022196028 10 0.0037 0.0333918439716312
cerebral cortex neuron differentiation 4.87597583913 15 0.0037 0.0333918439716312
thymus development 8.13667543231 48 0.0037 0.0333918439716312
receptor clustering 6.8887994517 34 0.0037 0.0333918439716312
copper ion import 5.16219506716 18 0.0038 0.0339333333333333
muscle contraction 6.36119895773 26 0.0038 0.0339333333333333
transforming growth factor beta receptor signaling pathway 12.749947789 129 0.0038
0.0339333333333333
lysosomal lumen acidification 4.39817451972 14 0.0039 0.0347045454545455
diacylglycerol metabolic process 5.73838585949 23 0.004 0.0354703832752613
regulation of BMP signaling pathway 4.15804646816 12 0.0041 0.0358573883161512
negative regulation of I-kappaB kinase/NF-kappaB signaling 8.17171339547 51 0.0041
0.0358573883161512
myelin assembly 4.62548634452 15 0.0041 0.0358573883161512
peptidyl-tyrosine autophosphorylation 4.98627451969 16 0.0041 0.0358573883161512
translational initiation 9.61981131203 72 0.0042 0.036481228668942
85

neutrophil chemotaxis 7.41782239445 39 0.0042 0.036481228668942
lysosomal protein catabolic process 4.05132193389 11 0.0043 0.0369712837837838
positive regulation of phosphatidylinositol 3-kinase signaling 8.69519441997 56 0.0043
0.0369712837837838
skeletal muscle fiber development 5.12197691738 19 0.0043 0.0369712837837838
positive regulation of osteoclast differentiation 5.87261595082 26 0.0044 0.0373266666666667
multicellular organism aging 9.05429713691 67 0.0044 0.0373266666666667
centrosome localization 6.13268858456 28 0.0044 0.0373266666666667
response to glucocorticoid 5.01032156941 18 0.0044 0.0373266666666667
osteoblast development 5.14037991876 20 0.0045 0.0379221854304636
regulation of transcription by RNA polymerase II 31.9492866581 1087 0.0045 0.0379221854304636
regulation of mitochondrial mRNA stability 3.71007839361 10 0.0046 0.0386369636963696
positive regulation of alpha-beta T cell proliferation 4.22935246745 13 0.0047 0.0393470394736842
B cell homeostasis 5.88072025427 27 0.0048 0.0400524590163934
cellular potassium ion homeostasis 4.09151640315 11 0.0049 0.0406205211726384
killing of cells of other organism 4.19124591591 12 0.0049 0.0406205211726384
ATP metabolic process 7.36472539122 42 0.0051 0.0421412337662338
regulation of heart contraction 4.76397895665 18 0.0052 0.0426903225806452
tetrahydrobiopterin biosynthetic process 3.70093174217 10 0.0052 0.0426903225806452
smooth muscle contraction 4.19774487291 13 0.0053 0.043094249201278
iron ion homeostasis 7.70161309846 45 0.0053 0.043094249201278
hematopoietic progenitor cell differentiation 9.13247567442 67 0.0053 0.043094249201278
positive regulation of kinase activity 7.70966143417 46 0.0055 0.044578025477707
ganglioside catabolic process 5.35169039002 24 0.0056 0.0451012658227848
negative regulation of cell migration 10.2240916465 101 0.0056 0.0451012658227848
chromatin assembly 3.59937683286 10 0.0058 0.0462727272727273
positive regulation of defense response to virus by host 5.56106415688 24 0.0058
0.0462727272727273
cell-cell adhesion via plasma-membrane adhesion molecules 4.72248729757 18 0.0058
0.0462727272727273
embryonic eye morphogenesis 3.52858955032 10 0.0059 0.0467772585669782
lymph node development 4.49214393683 17 0.0059 0.0467772585669782
SNARE complex assembly 4.49896432 16 0.006 0.0471296296296296
cilium assembly 16.9997697817 293 0.006 0.0471296296296296
sphingosine metabolic process 4.01015695747 12 0.006 0.0471296296296296
translesion synthesis 4.28284545724 15 0.0061 0.0477676923076923
microtubule polymerization 4.15684135387 13 0.0063 0.0490321100917431
oogenesis 4.87345759609 20 0.0063 0.0490321100917431
inner ear development 6.4461352651 37 0.0064 0.0495075987841945
cochlea development 4.5401419731 17 0.0064 0.0495075987841945
response to interferon-gamma 3.75891351359 11 0.0065 0.0499773413897281
negative regulation of autophagosome assembly 4.71118188139 18 0.0065 0.0499773413897281

Supplemental Table 3.3: Functional-genomic enrichment analysis of purebred M. muscu-
lus primary cell transcriptomes upon Usf2 knockdown, irradiation, and senescence es-
tablishment. Data are as in Supplemental Table 3.2 except that cells were infected with lentivi-
rus expressing the indicated short hairpin RNA (shRNA), treated with ionizing radiation, and pro-
filed 10 days afterward.

86

GO_term real_val number_of_genes prop_over_real BH
positive regulation of MAP kinase activity 14.0881716183 68 0 0
axon guidance 32.7205215351 180 0 0
chromosome segregation 34.7555556912 95 0 0
biomineralization 34.5537000781 64 0 0
cellular response to DNA damage stimulus 69.7363137985 523 0 0
regulation of transcription, DNA-templated 42.3405103823 748 0 0
bone mineralization 15.2558418594 61 0 0
attachment of spindle microtubules to kinetochore 10.4997222197 17 0 0
inflammatory response 27.5075551128 219 0 0
phosphate ion homeostasis 16.9443012399 24 0 0
neuron differentiation 14.730317376 100 0 0
regulation of postsynapse organization 18.0551669064 54 0 0
cilium assembly 26.5419136442 291 0 0
homocysteine metabolic process 12.3329281513 19 0 0
cell adhesion 72.244064465 381 0 0
smooth muscle contraction 15.6078983064 13 0 0
angiogenesis 41.6902758199 250 0 0
chromatin organization 28.1161315076 281 0 0
DNA replication 22.0680840907 145 0 0
memory 14.3805810041 77 0 0
regulation of blood pressure 15.2331403231 54 0 0
positive regulation of transcription by RNA polymerase II 66.4684114646 1717 0 0
extracellular matrix organization 18.8031014257 133 0 0
mitotic cell cycle 24.8896994824 111 0 0
neurotransmitter receptor localization to postsynaptic specialization membrane 16.1601020728 39 0
0
negative regulation of transcription by RNA polymerase II 55.6017041237 1071 0 0
ion transport 30.2438595027 310 0 0
mitotic cytokinesis 14.0991177444 56 0 0
regulation of heart rate 9.58829640794 20 0 0
calcium ion transport 18.6136528436 110 0 0
water transport 61.3188028289 22 0 0
positive regulation of phosphatidylinositol 3-kinase signaling 11.6986386378 56 0 0
calcium ion homeostasis 25.6601859802 66 0 0
DNA repair 58.2441628155 418 0 0
positive regulation of blood pressure 11.4572517586 18 0 0
response to ATP 15.6669449031 38 0 0
cell cycle 96.2790163415 538 0 0
response to hypoxia 16.5552722856 118 0 0
mRNA processing 28.9966478298 331 0 0
peptide hormone processing 13.3578231763 23 0 0
mitotic spindle assembly checkpoint signaling 16.2133933242 36 0 0
collagen fibril organization 23.9434055578 87 0 0
cell division 77.322878175 337 0 0
spindle organization 8.13240259267 17 0 0
mitotic spindle organization 14.2551933848 55 0 0
mitotic sister chromatid segregation 14.1492058037 32 0 0
G protein-coupled receptor signaling pathway 37.9352646735 236 0 0
renal water absorption 31.0813516581 12 0 0
cell-matrix adhesion 18.8941735736 82 0 0
regulation of transcription by RNA polymerase II 75.2883463123 1087 0 0
positive regulation of neuron projection development 19.3043960758 140 0 0
protein localization to kinetochore 8.09912231752 18 0 0
receptor internalization 14.8952313696 32 0 0
visual perception 16.7543686236 59 0 0
positive regulation of cell migration 27.9384157945 213 0 0
defense response to virus 23.447223384 152 0 0
leukocyte cell-cell adhesion 8.75614246695 24 1e-04 0.00363571428571429
negative chemotaxis 11.0712322931 34 1e-04 0.00363571428571429
mismatch repair 14.1335301516 87 1e-04 0.00363571428571429
mRNA splicing, via spliceosome 16.6881481537 186 1e-04 0.00363571428571429
87

hard palate development 7.97443723949 12 1e-04 0.00363571428571429
positive regulation of angiogenesis 21.0611944998 134 1e-04 0.00363571428571429
negative regulation of transcription, DNA-templated 34.9504239899 630 1e-04 0.00363571428571429
homophilic cell adhesion via plasma membrane adhesion molecules 12.5826525142 58 1e-04
0.00363571428571429
double-strand break repair via homologous recombination 14.1492919204 116 1e-04
0.00363571428571429
mitotic metaphase plate congression 11.8453784077 41 1e-04 0.00363571428571429
negative regulation of BMP signaling pathway 13.0816579015 62 1e-04 0.00363571428571429
response to sodium phosphate 10.2981238372 24 1e-04 0.00363571428571429
sarcomere organization 8.86801033338 21 1e-04 0.00363571428571429
vascular endothelial growth factor receptor signaling pathway 11.9385955806 37 1e-04
0.00363571428571429
regulation of postsynaptic neurotransmitter receptor activity 8.86165428956 26 2e-04
0.00628395061728395
calcium ion import 9.4479188804 24 2e-04 0.00628395061728395
female pregnancy 8.03065300853 16 2e-04 0.00628395061728395
adenylate cyclase-activating G protein-coupled receptor signaling pathway 14.0943629913 82 2e-04
0.00628395061728395
response to virus 12.9437199877 58 2e-04 0.00628395061728395
integrin-mediated signaling pathway 12.6189307911 75 2e-04 0.00628395061728395
mitotic spindle assembly 13.9262245617 48 2e-04 0.00628395061728395
regulation of calcium-mediated signaling 6.80916442418 11 2e-04 0.00628395061728395
response to amphetamine 7.49497050296 18 2e-04 0.00628395061728395
metaphase plate congression 8.12028183249 14 2e-04 0.00628395061728395
blood vessel morphogenesis 10.4305222923 35 2e-04 0.00628395061728395
substrate adhesion-dependent cell spreading 10.9713003861 53 3e-04 0.00887790697674419
sensory perception of sound 16.8033622667 137 3e-04 0.00887790697674419
innate immune response 22.2815282074 284 3e-04 0.00887790697674419
DNA demethylation 9.36299195978 30 3e-04 0.00887790697674419
response to endoplasmic reticulum stress 12.9297037958 98 3e-04 0.00887790697674419
face morphogenesis 9.87057885732 39 4e-04 0.0111868131868132
RNA splicing 19.6653165432 251 4e-04 0.0111868131868132
G2/M transition of mitotic cell cycle 10.8048093958 42 4e-04 0.0111868131868132
kinetochore assembly 7.23203817001 15 4e-04 0.0111868131868132
proteolysis 23.5232723677 392 4e-04 0.0111868131868132
mitotic chromosome condensation 7.56512140462 20 5e-04 0.0138315217391304
negative regulation of smooth muscle cell proliferation 9.11115260597 37 6e-04 0.0155816326530612
positive regulation of cell population proliferation 23.2832113019 490 6e-04 0.0155816326530612
negative regulation of Notch signaling pathway 8.78906929065 40 6e-04 0.0155816326530612
centriole replication 10.0536035963 34 6e-04 0.0155816326530612
cell-cell adhesion via plasma-membrane adhesion molecules 7.17403480445 18 6e-04
0.0155816326530612
phototransduction 6.62522505423 12 6e-04 0.0155816326530612
determination of adult lifespan 9.02013786055 37 7e-04 0.0166495327102804
positive regulation of phagocytosis, engulfment 6.55887919029 14 7e-04 0.0166495327102804
cellular response to interferon-beta 7.96919024692 21 7e-04 0.0166495327102804
cell-cell adhesion 15.3541259959 135 7e-04 0.0166495327102804
myelination 10.5633890905 55 7e-04 0.0166495327102804
positive regulation of cytosolic calcium ion concentration 12.2094690127 77 7e-04
0.0166495327102804
negative regulation of neural precursor cell proliferation 7.47772778808 12 7e-04
0.0166495327102804
regulation of cell cycle 19.7799879155 241 7e-04 0.0166495327102804
epidermal growth factor receptor signaling pathway 11.0240898212 66 7e-04 0.0166495327102804
negative regulation of cholesterol storage 5.43472990132 11 8e-04 0.0180176991150443
methylation 14.9713140425 152 8e-04 0.0180176991150443
apoptotic process 22.4469888498 507 8e-04 0.0180176991150443
TORC2 signaling 5.57371370174 10 8e-04 0.0180176991150443
camera-type eye morphogenesis 7.52498907557 22 8e-04 0.0180176991150443
gene expression 13.9517445267 135 8e-04 0.0180176991150443
positive regulation of cell-matrix adhesion 7.28709736307 23 9e-04 0.0194110169491525
88

DNA damage checkpoint signaling 8.53381322265 39 9e-04 0.0194110169491525
excitatory postsynaptic potential 8.0705760719 27 9e-04 0.0194110169491525
regulation of cytokine production 7.39598181398 23 9e-04 0.0194110169491525
positive regulation of chromosome segregation 5.67407909286 14 9e-04 0.0194110169491525
neuronal action potential 5.86759002629 12 0.001 0.0210330578512397
bicarbonate transport 7.11925550066 21 0.001 0.0210330578512397
microtubule-based movement 10.5591605995 62 0.001 0.0210330578512397
protein ubiquitination 19.5610760625 346 0.0011 0.022396
signal transduction 23.9750658612 591 0.0011 0.022396
ionotropic glutamate receptor signaling pathway 6.54781898592 15 0.0011 0.022396
attachment of mitotic spindle microtubules to kinetochore 5.90840074037 12 0.0011 0.022396
bone mineralization involved in bone maturation 5.45301835741 10 0.0012 0.023859375
regulation of synapse assembly 6.28075294956 17 0.0012 0.023859375
response to stimulus 7.81585673733 31 0.0012 0.023859375
double-strand break repair 12.4053222117 100 0.0013 0.0239746376811594
nerve development 5.54693262684 11 0.0013 0.0239746376811594
SCF-dependent proteasomal ubiquitin-dependent protein catabolic process 10.5565484671 68 0.0013
0.0239746376811594
protein polyubiquitination 15.217016737 186 0.0013 0.0239746376811594
response to bacterium 11.1187055118 75 0.0013 0.0239746376811594
cellular response to hormone stimulus 5.67889352292 11 0.0013 0.0239746376811594
secondary palate development 6.65884936608 14 0.0013 0.0239746376811594
regulation of neuronal synaptic plasticity 6.79741962975 24 0.0013 0.0239746376811594
positive regulation of protein phosphorylation 17.3204002627 204 0.0013 0.0239746376811594
protein side chain deglutamylation 6.88916427723 20 0.0013 0.0239746376811594
positive regulation of calcium ion-dependent exocytosis 5.888562807 10 0.0014
0.0250915492957746
cell migration 18.1017131142 261 0.0014 0.0250915492957746
actin filament network formation 5.38689614911 10 0.0014 0.0250915492957746
centrosome cycle 9.15053857503 46 0.0014 0.0250915492957746
regulation of synaptic plasticity 8.96910015589 49 0.0015 0.0265104166666667
positive regulation of cell adhesion mediated by integrin 5.82811450802 13 0.0015
0.0265104166666667
regulation of potassium ion transmembrane transport 6.43398827082 18 0.0016 0.0280827586206897
cellular response to mechanical stimulus 7.10432073066 27 0.0017 0.0290369127516779
positive regulation of cell-substrate adhesion 8.02179184005 37 0.0017 0.0290369127516779
elastic fiber assembly 7.75034212213 27 0.0017 0.0290369127516779
smoothened signaling pathway 11.8058606656 113 0.0017 0.0290369127516779
chemical synaptic transmission 10.6252272486 81 0.0018 0.0301381578947368
reciprocal meiotic recombination 6.23260677015 21 0.0018 0.0301381578947368
negative regulation of axon extension involved in axon guidance 6.8785017005 23 0.0018
0.0301381578947368
behavioral response to pain 6.54991344025 18 0.0019 0.0311967741935484
chloride transmembrane transport 7.2099002596 24 0.0019 0.0311967741935484
brown fat cell differentiation 6.68978429573 23 0.0019 0.0311967741935484
establishment or maintenance of epithelial cell apical/basal polarity 6.50781813991 22 0.002
0.0324203821656051
heterophilic cell-cell adhesion via plasma membrane cell adhesion molecules 7.81784760991 30 0.002
0.0324203821656051
positive regulation of ERK1 and ERK2 cascade 13.9973005514 179 0.0021 0.0336132075471698
lipoprotein metabolic process 5.66096604726 15 0.0021 0.0336132075471698
inner ear development 7.65204324135 37 0.0022 0.0343496932515337
transmembrane transport 15.1199220456 216 0.0022 0.0343496932515337
sphingosine-1-phosphate receptor signaling pathway 5.41066115409 14 0.0022 0.0343496932515337
positive regulation of microglial cell activation 5.21768889221 10 0.0022 0.0343496932515337
positive regulation of T cell mediated cytotoxicity 5.80089397572 16 0.0023 0.0350508982035928
positive regulation of axon extension 8.68128376979 43 0.0023 0.0350508982035928
transmembrane receptor protein tyrosine kinase signaling pathway 10.844468506 86 0.0023
0.0350508982035928
anterior/posterior pattern specification 13.9663911245 161 0.0023 0.0350508982035928
auditory receptor cell stereocilium organization 6.57181198067 27 0.0024 0.035719298245614
sensory perception of pain 7.93792410486 42 0.0024 0.035719298245614
89

negative regulation of vascular associated smooth muscle cell proliferation 5.92347694895 18 0.0024
0.035719298245614
positive regulation of macrophage chemotaxis 6.21859465445 20 0.0024 0.035719298245614
positive regulation of phosphatidylinositol 3-kinase activity 6.40118213827 24 0.0025
0.0367774566473988
negative regulation of smooth muscle cell migration 5.94723711178 15 0.0025 0.0367774566473988
mitotic cell cycle phase transition 5.69144725689 17 0.0026 0.0375965909090909
acyl-CoA metabolic process 6.21392411661 25 0.0026 0.0375965909090909
cerebrospinal fluid circulation 5.0587528101 11 0.0026 0.0375965909090909
endoplasmic reticulum unfolded protein response 9.71748475372 72 0.0027 0.038175
spermatogenesis 16.6173019928 274 0.0027 0.038175
lipoprotein transport 5.84097530267 17 0.0027 0.038175
regulation of membrane potential 9.84035422473 72 0.0027 0.038175
execution phase of apoptosis 6.50656856381 21 0.0028 0.0393701657458564
cellular potassium ion homeostasis 5.00545138467 11 0.003 0.039765625
supramolecular fiber organization 4.84114281 13 0.0029 0.039765625
peptide cross-linking 5.40265463585 14 0.0029 0.039765625
positive regulation of endothelial cell migration 9.23943598492 64 0.003 0.039765625
negative regulation of osteoblast differentiation 8.07277695595 52 0.0029 0.039765625
negative regulation of type I interferon-mediated signaling pathway 5.85603190632 19 0.0029
0.039765625
positive regulation of smooth muscle cell proliferation 9.17896939284 67 0.003 0.039765625
positive regulation of G2/M transition of mitotic cell cycle 6.89020978207 27 0.003 0.039765625
positive regulation of interferon-beta production 7.85376921073 43 0.003 0.039765625
glucose homeostasis 11.1714163213 112 0.003 0.039765625
positive regulation of kinase activity 8.00296768931 46 0.003 0.039765625
associative learning 6.46455138967 28 0.0031 0.0406675257731959
meiotic cell cycle 9.46309789859 65 0.0031 0.0406675257731959
long-chain fatty acid transport 4.85484635272 11 0.0032 0.0417641025641026
centriole-centriole cohesion 5.43782475394 15 0.0033 0.0422035175879397
cellular response to hypoxia 9.73005746019 76 0.0033 0.0422035175879397
positive regulation of phosphorylation 7.19862224879 33 0.0033 0.0422035175879397
male gonad development 9.27687310611 69 0.0033 0.0422035175879397
negative regulation of cold-induced thermogenesis 7.66576838015 43 0.0034 0.043265
maintenance of DNA methylation 5.32930679658 15 0.0036 0.0455820895522388
positive regulation of gene expression 18.3897384579 457 0.0038 0.0478762376237624
myeloid cell differentiation 6.1046581076 24 0.0039 0.0486544117647059
induction of positive chemotaxis 5.32482705728 16 0.0039 0.0486544117647059
centrosome duplication 5.69538453045 18 0.004 0.0489423076923077
response to hormone 6.45016215454 28 0.004 0.0489423076923077
positive regulation of synaptic transmission, glutamatergic 5.61011276951 20 0.004
0.0489423076923077
mitotic G2 DNA damage checkpoint signaling 7.7005179221 45 0.004 0.0489423076923077
microtubule depolymerization 5.30912353856 16 0.0041 0.0499258373205742

Supplemental Table 3.4: Functional-genomic enrichment analysis of purebred M. muscu-
lus primary cell transcriptomes upon irradiation, senescence establishment, Usf2 knock-
down, and long-term senescence. Data are as in Supplemental Table 3.2 except that M.
musculus (PWK) primary cells were irradiated, incubated for 10 days, infected with lentivirus
harboring short hairpin RNAs targeting Usf2 or a scrambled control, and allowed to maintain se-
nescence for another 10 days; for each gene we formulated the average expression
90

measurement across replicates from each shRNA category in turn, and we then took the ratio
between these averages for input into Gene Ontology enrichment tests.

91

Gene Condition_pval cond_BH
Lif 3.61644625004792e-07 0.00423232704643108
Lzic 2.41824442872615e-06 0.00930081825986356
Pip4k2a 3.17895181060021e-06 0.00930081825986356
Tmem120a 2.84611399899598e-06 0.00930081825986356
Apex1 5.53642305562824e-06 0.0107987931700029
Snx32 4.74470593135326e-06 0.0107987931700029
Echdc1 8.41302181637706e-06 0.0123071992896326
Rps4x 7.84408009160588e-06 0.0123071992896326
Ocel1 1.03099624649556e-05 0.0134063878585973
Bend6 1.59886611686543e-05 0.0162887819598925
Ilk 1.67021604305486e-05 0.0162887819598925
Kmt5a 1.43123413792014e-05 0.0162887819598925
Rad54l 1.8416471501095e-05 0.0163987087370793
Sdhaf1 1.96173564316081e-05 0.0163987087370793
Ppib 2.1870606781624e-05 0.0170204375198212
Tlk1 2.3269845365901e-05 0.0170204375198212
Ier5 2.79468491705027e-05 0.0192389397554349
Zmym2 3.38681671667118e-05 0.0220199533528905
Ythdf3 3.72481974503809e-05 0.0229429291979899
Dnajc19 4.10272169321868e-05 0.0235707849575081
Gns 4.77258315871379e-05 0.0235707849575081
Kcp 4.55723802855188e-05 0.0235707849575081
Lysmd3 4.83379337759715e-05 0.0235707849575081
Snx22 4.28906397826831e-05 0.0235707849575081
4930404N11Rik 0.000205490376079042 0.0237343042843888
Adarb1 0.000203646624525066 0.0237343042843888
Alg11 0.000125270283934494 0.0237343042843888
Arl4d 0.000192363055591571 0.0237343042843888
Ate1 0.000142228223341625 0.0237343042843888
BC024978 0.000153762030242197 0.0237343042843888
Capn7 6.30339637682424e-05 0.0237343042843888
Ccdc167 0.00020368537007683 0.0237343042843888
Ccpg1 9.49369181109508e-05 0.0237343042843888
Ccser2 0.000100930520678743 0.0237343042843888
Cdr2 0.000158333134263841 0.0237343042843888
Cfl1 6.74533127121465e-05 0.0237343042843888
Chil1 0.000212300941157152 0.0237343042843888
Clip1 0.00015666562634638 0.0237343042843888
Cln5 0.000160389045372694 0.0237343042843888
Col3a1 0.000122811598867907 0.0237343042843888
Col4a3bp 0.000208055237403101 0.0237343042843888
Comp 0.000223085830238637 0.0237343042843888
Cul1 0.000193167048362133 0.0237343042843888
Cxcl5 0.000109692186691848 0.0237343042843888
Ddx24 7.54204629761118e-05 0.0237343042843888
Dnajc13 0.000183513365006903 0.0237343042843888
Dvl2 0.000158150857417714 0.0237343042843888
Dynlt3 0.000107774645895903 0.0237343042843888
Epc2 7.02700427313264e-05 0.0237343042843888
Eps15 0.000220377027753086 0.0237343042843888
Ercc6l2 0.000169432011417328 0.0237343042843888
Esyt2 5.8615911080004e-05 0.0237343042843888
Galnt1 0.000123446069972208 0.0237343042843888
Gbe1 0.000120430992919868 0.0237343042843888
Gjc1 0.000217017126596731 0.0237343042843888
Gm9913 0.000125435907140153 0.0237343042843888
Gmfb 0.000213004619274976 0.0237343042843888
Gnas 0.000161645418116773 0.0237343042843888
Gorab 8.32081003358008e-05 0.0237343042843888
Grina 0.000157929354803488 0.0237343042843888
Gucd1 0.000133021738087258 0.0237343042843888
92

Has3 0.000145196607616002 0.0237343042843888
Hist1h1e 6.89156056910016e-05 0.0237343042843888
Inpp5e 8.07521398705305e-05 0.0237343042843888
Iqcg 0.000168639908124576 0.0237343042843888
Jmjd1c 0.00020482908092898 0.0237343042843888
Kif13a 0.00015752031905212 0.0237343042843888
Kif3b 0.000215468397883382 0.0237343042843888
Kirrel 0.000174530908698184 0.0237343042843888
Ldha 7.07632264329139e-05 0.0237343042843888
Lgr6 0.000193786203150029 0.0237343042843888
Mterf4 0.000201185669793629 0.0237343042843888
Mtmr14 6.70868510233822e-05 0.0237343042843888
Nbea 0.000140777920066671 0.0237343042843888
Nek9 0.000186837439296461 0.0237343042843888
Nr2c2 0.000184040978952916 0.0237343042843888
Nrg4 0.000207274497654366 0.0237343042843888
Pcnx 0.000183792586632569 0.0237343042843888
Pfkl 9.0121497606838e-05 0.0237343042843888
Pkd1l3 0.000168933708652786 0.0237343042843888
Pnpla8 7.02180045564084e-05 0.0237343042843888
Ppp3r1 0.000192990031199859 0.0237343042843888
Rnf146 0.000216010029957714 0.0237343042843888
Rragd 0.000221090661465992 0.0237343042843888
Sap30bp 0.000221537561528785 0.0237343042843888
Scarb1 0.000131127151174434 0.0237343042843888
Sgpl1 0.000106000904867477 0.0237343042843888
Sh3gl3 0.000126874037207552 0.0237343042843888
Slc11a2 0.000111085053232165 0.0237343042843888
Slk 0.000102202813763739 0.0237343042843888
Smpd1 0.000146593212208929 0.0237343042843888
Socs6 7.84159269644894e-05 0.0237343042843888
Sphkap 7.12125638893605e-05 0.0237343042843888
Sptbn1 0.000127542457009458 0.0237343042843888
Srpk2 0.000162624230141281 0.0237343042843888
Stag2 8.87604597798807e-05 0.0237343042843888
Suv39h1 0.000112992720255582 0.0237343042843888
Syt5 6.64979569988774e-05 0.0237343042843888
Taf10 7.5325005061812e-05 0.0237343042843888
Tgfbrap1 0.0001398641901213 0.0237343042843888
Trappc8 7.20629752252324e-05 0.0237343042843888
Ttc26 8.89781491844184e-05 0.0237343042843888
Ttll5 0.000123407262620982 0.0237343042843888
Tvp23a 0.000206966086909906 0.0237343042843888
Ubxn2a 0.000125774254803126 0.0237343042843888
Usp8 0.000173146395125449 0.0237343042843888
Vmac 0.000204856530969607 0.0237343042843888
Wasf2 9.23011559327617e-05 0.0237343042843888
Zfp292 0.000101486107922638 0.0237343042843888
Zfp647 0.000204169925610575 0.0237343042843888
Mapk9 0.000232717489683121 0.0241017060332882
Mettl23 0.000231785618445734 0.0241017060332882
Phkb 0.000229314312702643 0.0241017060332882
Prickle3 0.000237604958753645 0.0243920248446834
Acadvl 0.000242202550344787 0.0245300049265343
Rnf11 0.000243141123769801 0.0245300049265343
Nrd1 0.000246127193083893 0.0246190302620581
Pdcd6ip 0.000249327193531112 0.0247277639482594
Cwf19l2 0.000252351008602811 0.0247825179792845
Tet1 0.000254114514014709 0.0247825179792845
Cdk14 0.000268732580097373 0.0257785031547505
Rala 0.000267724557097957 0.0257785031547505
Acbd5 0.000317165189780085 0.0258053154831371
93

Arhgap18 0.000275485524730042 0.0258053154831371
Bcl2l12 0.000317522466852238 0.0258053154831371
Ccl8 0.000303752971912602 0.0258053154831371
Ccno 0.000314269701229169 0.0258053154831371
Gcc1 0.000312779161449466 0.0258053154831371
Ifitm1 0.000285165844487416 0.0258053154831371
Il18bp 0.00031286397474875 0.0258053154831371
Kdm5a 0.000288502201212464 0.0258053154831371
Matr3 0.000289544468895852 0.0258053154831371
Met 0.000310853931924195 0.0258053154831371
Myo19 0.000307022437718017 0.0258053154831371
Ociad2 0.000306437384140232 0.0258053154831371
Ostm1 0.000275527201073867 0.0258053154831371
Pi15 0.000299496815203848 0.0258053154831371
Ptpn21 0.000280252173341751 0.0258053154831371
Rab2a 0.000304085493441333 0.0258053154831371
Rbm14 0.000310259667413954 0.0258053154831371
Sord 0.000297449999082851 0.0258053154831371
Tspan12 0.000307091575983927 0.0258053154831371
Vps37a 0.000290763994054322 0.0258053154831371
Zfp655 0.000314077960473671 0.0258053154831371
Ubxn7 0.000321034149274768 0.0259107768893973
Tab3 0.000326575597444945 0.026177494636289
Bhlhe40 0.000341430751437375 0.0271820685991265
Polr3h 0.000344700331300006 0.0272569457919187
Cavin3 0.000362376637290918 0.0273363070979098
Epb41l1 0.000364617078784648 0.0273363070979098
Mindy2 0.000352194579499147 0.0273363070979098
Qtrt1 0.000364584464527408 0.0273363070979098
Spsb3 0.000355631714006538 0.0273363070979098
St6galnac4 0.000356587637225087 0.0273363070979098
Tusc5 0.000366726498707326 0.0273363070979098
Ubxn6 0.000361312959965329 0.0273363070979098
Wtip 0.00035696794973825 0.0273363070979098
F7 0.00037166374942655 0.0275289927818919
Entpd5 0.00038689337751268 0.0282988324814431
Hydin 0.00038543186876302 0.0282988324814431
1110008F13Rik 0.000395243600961777 0.0285526905065165
1810013L24Rik 0.000394585273610238 0.0285526905065165
C1qtnf6 0.000405256116367143 0.0287212239070307
Fmr1 0.000406985933371392 0.0287212239070307
Mphosph6 0.00040691474612003 0.0287212239070307
Timm50 0.000409847423094431 0.0287212239070307
Tm9sf3 0.000409749584869802 0.0287212239070307
Micu3 0.000413891063626625 0.0288319471287047
Gm10762 0.000416634225851916 0.0288395352347099
Pmpca 0.000421392850135469 0.0288395352347099
Pstk 0.000421127981692626 0.0288395352347099
Tmem238 0.000426956364798596 0.0290504089374301
Dusp3 0.000440271740383976 0.0297832380214663
Hsdl2 0.000451555156189991 0.030290409987312
Vcpkmt 0.000452945547960318 0.030290409987312
Chd7 0.000462152919284686 0.0305569243750773
Fgf10 0.000461570943263641 0.0305569243750773
Nr1d2 0.000469523965525191 0.0308698818457377
Actr1b 0.000499012645091728 0.031186323988337
Gja1 0.000479765705040521 0.031186323988337
Ift22 0.000503649938801649 0.031186323988337
Nrp2 0.000488000608265491 0.031186323988337
Rictor 0.000492614529214026 0.031186323988337
Rpl29 0.000503104800745097 0.031186323988337
Sec62 0.000487683314517895 0.031186323988337
94

Smarcd3 0.000500619123063188 0.031186323988337
Vcpip1 0.000499280527628832 0.031186323988337
Zc3hav1 0.000500408998836549 0.031186323988337
Zfp65 0.00048184892797359 0.031186323988337
Dis3l 0.000514861006734965 0.0317127282201016
1700037C18Rik 0.000547826576770937 0.0318003625050984
Agpat3 0.000519112729874722 0.0318003625050984
Arl4a 0.000559859189857065 0.0318003625050984
Cog5 0.000590568916348447 0.0318003625050984
Cyp1b1 0.000610802501480443 0.0318003625050984
Deptor 0.000567034029053541 0.0318003625050984
Dhx40 0.000553866472399835 0.0318003625050984
Eif3d 0.000567589454399737 0.0318003625050984
Fcho2 0.000590153294054751 0.0318003625050984
Fgf7 0.000591937902337043 0.0318003625050984
G3bp2 0.000603104429805812 0.0318003625050984
Gm16181 0.000618640120810171 0.0318003625050984
Irak4 0.000569035673599739 0.0318003625050984
Irx2 0.000585643164554512 0.0318003625050984
Kat6a 0.000553998166598531 0.0318003625050984
Kdm7a 0.000610132123500267 0.0318003625050984
Maip1 0.000533281800595499 0.0318003625050984
Mbtd1 0.000532690882274503 0.0318003625050984
Mob4 0.000607418237253136 0.0318003625050984
Nceh1 0.000598310591391829 0.0318003625050984
Ndufa11 0.000619540515351827 0.0318003625050984
Pik3r2 0.000580501937307787 0.0318003625050984
Podxl2 0.000553417551663521 0.0318003625050984
Ppp1r12b 0.000606537867397833 0.0318003625050984
Ptpn5 0.00055199263494371 0.0318003625050984
Pusl1 0.000563066687421729 0.0318003625050984
Pxk 0.000539815894325499 0.0318003625050984
Rpl10a 0.000526281438492455 0.0318003625050984
Rps15 0.000616042081000134 0.0318003625050984
Slc43a1 0.000543594653583016 0.0318003625050984
Snai2 0.000595199724846086 0.0318003625050984
Susd2 0.00059857295757643 0.0318003625050984
Synj2bp 0.000571418030197189 0.0318003625050984
Tlr4 0.00056023842080836 0.0318003625050984
Tmed7 0.000566840698991004 0.0318003625050984
Xrcc6 0.000588558389633443 0.0318003625050984
Zbtb33 0.000567426967219963 0.0318003625050984
Zfp597 0.000611653619945349 0.0318003625050984
Prrt4 0.000629340535466406 0.0320224882024493
Sec31b 0.000627715399795107 0.0320224882024493
Fignl2 0.000647113872346888 0.0322054198119653
Msantd4 0.000645767704562207 0.0322054198119653
Ndrg4 0.000649356778237447 0.0322054198119653
Sned1 0.000642728853584917 0.0322054198119653
Teddm2 0.000645375085066375 0.0322054198119653
Vat1 0.000649447071317082 0.0322054198119653
Rtn3 0.000652929496454507 0.0322414932363169
Ap4m1 0.000671262918273182 0.0324880300422149
Grem1 0.000671802381459114 0.0324880300422149
Ppip5k1 0.000663224086161366 0.0324880300422149
Trim44 0.000664288886399502 0.0324880300422149
Zfp282 0.000667207236836045 0.0324880300422149
Cul5 0.000675421479291333 0.0325064158152153
Klhdc3 0.000677737798762072 0.0325064158152153
Cpa6 0.000707595748816185 0.0325098062195461
Fmnl2 0.000701355279097593 0.0325098062195461
Katnb1 0.000706506181773597 0.0325098062195461
95

Mbp 0.00070887172381816 0.0325098062195461
Nedd4 0.000682344154235777 0.0325098062195461
Papolb 0.000711143330103718 0.0325098062195461
Sec61a1 0.000691106426507114 0.0325098062195461
Selenot 0.00069800255870053 0.0325098062195461
Thrb 0.000687071404410939 0.0325098062195461
Trpm7 0.000689589267318203 0.0325098062195461
Uaca 0.000703751564358329 0.0325098062195461
Ugdh 0.000699002536077324 0.0325098062195461
2300009A05Rik 0.000723309901782248 0.0325107967101552
Efr3a 0.000721626411107527 0.0325107967101552
Epn1 0.000726844461611037 0.0325107967101552
Gsto2 0.000727821902358777 0.0325107967101552
Heatr5b 0.000727832926434304 0.0325107967101552
Mdm2 0.000716774973035197 0.0325107967101552
Tti2 0.00073369999072515 0.0326482547203667
Sppl2a 0.000751039413388954 0.0331676009618526
Tank 0.000749134823784255 0.0331676009618526
Dennd1b 0.000754125959449935 0.0331787071557992
Jade1 0.000764444169669839 0.0333473939216484
Tgfbi 0.000769357973070586 0.0333473939216484
Tmem237 0.000764769579279156 0.0333473939216484
Znfx1 0.000769112946686036 0.0333473939216484
Pomt2 0.000775802943690267 0.0335026636531631
Armc1 0.000789146420603557 0.033759829658575
Dennd4c 0.000793897815145429 0.033759829658575
Lrrc58 0.000786729853924823 0.033759829658575
Nfkbia 0.00079555723361931 0.033759829658575
Pcif1 0.000800512210627739 0.033759829658575
Ube2r2 0.000796315306406442 0.033759829658575
Zfp386 0.000801951007868397 0.033759829658575
Plekhb2 0.000809581258305856 0.033915319106532
Ubc 0.000811440600686061 0.033915319106532
Gpatch2l 0.000827024330164366 0.0339903893720656
Pus3 0.000818972292135175 0.0339903893720656
Smad2 0.00082775877732536 0.0339903893720656
Spag9 0.000821968007932333 0.0339903893720656
Suds3 0.000827435224407061 0.0339903893720656
Bid 0.000843098655303249 0.0341277838845648
Mrpl48 0.000845523066189117 0.0341277838845648
Shc4 0.000834454970576644 0.0341277838845648
Tmc6 0.000845685493166179 0.0341277838845648
Tstd2 0.000837461085335208 0.0341277838845648
Aida 0.000866351462895154 0.0341472400967427
Arid4b 0.000867911375242343 0.0341472400967427
Asap1 0.000901606185584336 0.0341472400967427
Gabra3 0.000870826689162591 0.0341472400967427
Gpr85 0.000884615821015456 0.0341472400967427
Mob1b 0.000876627429453567 0.0341472400967427
Nav1 0.000852877676821482 0.0341472400967427
Ncdn 0.00085405327871349 0.0341472400967427
Nol12 0.000895417722388813 0.0341472400967427
Nucb2 0.000879839066267738 0.0341472400967427
Pde3a 0.000891811855671612 0.0341472400967427
Plekha3 0.000885609824727753 0.0341472400967427
Prkx 0.000900661220787117 0.0341472400967427
Retreg1 0.000856116604742848 0.0341472400967427
Scd2 0.000900537125965176 0.0341472400967427
Sec11a 0.000892222184224413 0.0341472400967427
Snrpa 0.000891110624369779 0.0341472400967427
St3gal2 0.000899082239681804 0.0341472400967427
Tnfrsf10b 0.000864052118611636 0.0341472400967427
96

Nell1 0.000905634288705293 0.0341891550990905
Ankrd28 0.000920768444808636 0.0342200969921346
Ccdc82 0.000918458580367015 0.0342200969921346
Cnbd2 0.000967226348454564 0.0342200969921346
Cox10 0.000931156753361003 0.0342200969921346
Crabp1 0.000974939618196381 0.0342200969921346
Dgcr8 0.000957515725702299 0.0342200969921346
Dlk2 0.000939402357012318 0.0342200969921346
Dpy19l4 0.000968653815553504 0.0342200969921346
Evi5 0.000938303140036396 0.0342200969921346
Fam124a 0.00094020762158267 0.0342200969921346
Hnrnpul2 0.000970319324174483 0.0342200969921346
Itgb3bp 0.000938535103762631 0.0342200969921346
Nqo1 0.000952286237001506 0.0342200969921346
Olfml2a 0.000931901164676328 0.0342200969921346
Pdzd7 0.000936570799587983 0.0342200969921346
Phb2 0.000951777036925245 0.0342200969921346
Pigx 0.000979004145387516 0.0342200969921346
Raf1 0.000978527896063124 0.0342200969921346
Recql 0.00097955502797275 0.0342200969921346
Serinc1 0.000963353923613856 0.0342200969921346
Slc25a30 0.000969136041858422 0.0342200969921346
Spred1 0.000926124539847563 0.0342200969921346
Sptlc2 0.000915525645458462 0.0342200969921346
Thy1 0.000976530054209653 0.0342200969921346
Zc3h12b 0.000948520974201042 0.0342200969921346
Ptgfrn 0.000984418919232756 0.0342876625350623
Eef1b2 0.00099222494986945 0.034355054995036
Fbxl12 0.000990535089028786 0.034355054995036
Tax1bp1 0.000995971916190802 0.0343830658854895
Cdc40 0.00101354211023979 0.0346826997547844
Eif3k 0.00101316071382566 0.0346826997547844
Ubac1 0.00101034719262788 0.0346826997547844
G430095P16Rik 0.00102297691065281 0.0346975263044656
Mkrn2 0.00102725887450369 0.0346975263044656
Mrps27 0.00102879959221136 0.0346975263044656
Prelid2 0.0010184976785632 0.0346975263044656
Rasa1 0.00102322560487182 0.0346975263044656
AI314180 0.00104091005229796 0.034705898410379
Hid1 0.0010356023205982 0.034705898410379
Ice1 0.00103304588704577 0.034705898410379
Rab5b 0.00103880888979129 0.034705898410379
Ino80c 0.00105036325332648 0.0349215941865903
Bin3 0.00105634966437482 0.0350211334905907
Gmppb 0.00107154371014096 0.0350294674288082
Hectd1 0.00106780634200779 0.0350294674288082
Map3k4 0.00107303671619922 0.0350294674288082
Ncor1 0.00106661778898497 0.0350294674288082
Rsph3b 0.00106161242407777 0.0350294674288082
Znrf1 0.0010745602671915 0.0350294674288082
Chac2 0.00111226818460662 0.0355712171466599
Dcaf5 0.00113309824485081 0.0355712171466599
Egln3 0.00112619088303907 0.0355712171466599
Fam8a1 0.00110123746958955 0.0355712171466599
Fbxo6 0.00110490656880457 0.0355712171466599
G6pdx 0.00112046310416436 0.0355712171466599
Impdh2 0.00113677135886959 0.0355712171466599
Irak1 0.00110954219252881 0.0355712171466599
Itsn2 0.00112234010032948 0.0355712171466599
Lats1 0.00112220310919035 0.0355712171466599
Neurl2 0.00113323762199388 0.0355712171466599
Phykpl 0.00109623059494257 0.0355712171466599
97

Rab2b 0.00113663057037995 0.0355712171466599
Setd2 0.00112296334117491 0.0355712171466599
Tgm4 0.00111874735104856 0.0355712171466599
Zc3h14 0.00114149726334652 0.0356238465945182
Akap11 0.00115325223581498 0.0357744224235567
Gzf1 0.00115555213232253 0.0357744224235567
Ints14 0.00115797484973954 0.0357744224235567
Pi4k2a 0.00115854961108502 0.0357744224235567
Aes 0.0011719761255 0.0359048078448338
Pomgnt2 0.00117165327545411 0.0359048078448338
Slc39a6 0.00116777778133625 0.0359048078448338
Map3k12 0.00117604054938356 0.0359352546982658
Dnal1 0.00119844116275947 0.0364052992917641
Fars2 0.0012007558341127 0.0364052992917641
Pfn1 0.00119615833539253 0.0364052992917641
Adamts10 0.001221454141798 0.0368138800399713
Dync1li2 0.00122366908788762 0.0368138800399713
Ppp1ca 0.00121924518662897 0.0368138800399713
Blmh 0.00123765964847241 0.0369498236379403
Rexo4 0.00123322877472064 0.0369498236379403
Slit3 0.00123626917306752 0.0369498236379403
Tpbg 0.00125181292278877 0.0372772687923587
Sbk1 0.00126056087078571 0.0374424971340233
Acsl3 0.00126790024607129 0.0374702943933644
Pafah1b1 0.00126605321619075 0.0374702943933644
Tcf3 0.00127120122070321 0.0374732188561453
Fez1 0.00129300986667033 0.0375726591632964
Kctd20 0.00128497408373237 0.0375726591632964
Mex3a 0.00129383761794484 0.0375726591632964
Rsf1 0.00129070116954785 0.0375726591632964
Rspo3 0.0012869720646825 0.0375726591632964
Slc2a6 0.00129347560525862 0.0375726591632964
Krit1 0.00130457443154096 0.0377459789124662
Ppic 0.00130625664013918 0.0377459789124662
Orc4 0.00131273563937398 0.0378397664719056
Runx1 0.00131710181478162 0.037865878122979
Zdhhc16 0.00132011264412334 0.037865878122979
Gss 0.00132551267999612 0.037892445005575
Pth1r 0.00132751452211277 0.037892445005575
Senp6 0.00133653374838655 0.0380570668062477
Slc2a12 0.00134250644579423 0.0381343517842958
Gm26558 0.00134630649833462 0.0381496972155207
Mrps25 0.00135009753289367 0.0381647135928855
Jkamp 0.00135652172650984 0.0382020429138256
Magi1 0.00136773955232786 0.0382020429138256
Prkab1 0.00136524911502932 0.0382020429138256
Rpgrip1 0.00136587965796747 0.0382020429138256
Syt17 0.00135986737396869 0.0382020429138256
Eif3f 0.00137412700157467 0.0382890673795913
Frmd8 0.00137816261056406 0.0383103017373663
Ptpn9 0.00139983940774138 0.0388206649023634
Anapc15 0.0014142240325981 0.039034584560131
Aste1 0.00141228018661365 0.039034584560131
Abhd5 0.00144237107806398 0.0396290286169354
Ddr1 0.001442533212921 0.0396290286169354
Rarres1 0.00145031850416598 0.039656723023959
Xrcc4 0.00144710979242684 0.039656723023959
Abca3 0.00146535471839255 0.0396968663642315
Pdk1 0.00146182684026589 0.0396968663642315
Prkar1a 0.00145552797613169 0.0396968663642315
Pyroxd1 0.00146217672245385 0.0396968663642315
Galnt9 0.00147119863957737 0.0397467069027891
98

Mroh8 0.00147398707987785 0.0397467069027891
Rsbn1l 0.00147969514467571 0.0398089017888272
Pou2f1 0.00150316386120077 0.0403475382285152
Tmem161b 0.00150914838746724 0.0404154772964053
Zfp446 0.00151596513680439 0.0405053424566707
Brd1 0.00153013119339701 0.0405854067809323
Ctu2 0.00153283344417432 0.0405854067809323
Gsdmd 0.0015277365743003 0.0405854067809323
Hddc3 0.00152714541803102 0.0405854067809323
Rnf14 0.00153741251542481 0.0406147599729493
Secisbp2l 0.00154377913673373 0.0406910973810695
Golga1 0.00154806038782406 0.0407122488060786
Jmjd8 0.00155932916089971 0.0409166573318594
Fgf11 0.00157120281573365 0.0409576361672333
Sash1 0.00156596615536515 0.0409576361672333
Zfc3h1 0.00157139012553087 0.0409576361672333
Med12l 0.00158189393245537 0.0411397882033893
Ei24 0.00159435921703955 0.0412181245594342
Nubp1 0.00159547213752232 0.0412181245594342
Rufy1 0.00159217392844616 0.0412181245594342
Akt3 0.00164164701247428 0.0412278862381684
Atg12 0.00161194287119419 0.0412278862381684
Atl1 0.00163282309150613 0.0412278862381684
Dnah17 0.00163382869628697 0.0412278862381684
Mpzl1 0.00161122537688127 0.0412278862381684
Rab1a 0.00163098884366677 0.0412278862381684
Sec11c 0.00161653734991519 0.0412278862381684
Snx29 0.00162786128799171 0.0412278862381684
Tmx3 0.0016410525593463 0.0412278862381684
Uap1l1 0.00160094011580325 0.0412278862381684
Wdr38 0.00164055009574984 0.0412278862381684
Ybey 0.00163978403838318 0.0412278862381684
Znhit1 0.00160683754070374 0.0412278862381684
B930094E09Rik 0.00164959516324776 0.0412493886915141
Gnal 0.00165307727047083 0.0412493886915141
Trove2 0.00165069286914041 0.0412493886915141
Taok1 0.0016604602499458 0.0413454602236504
Arfgap1 0.00167481006354655 0.0415340012057976
Cdc42ep1 0.00170876863898278 0.0415340012057976
Cfap69 0.00170708142978913 0.0415340012057976
Cyb561a3 0.00168996489640469 0.0415340012057976
Ebf1 0.00170768473006885 0.0415340012057976
Eda2r 0.0017106202325211 0.0415340012057976
Reep3 0.00170220822628482 0.0415340012057976
Sgta 0.00170687010947131 0.0415340012057976
Sh3bp5l 0.00169884569276802 0.0415340012057976
Snx13 0.00168482130598728 0.0415340012057976
Tomm40 0.00168184079078761 0.0415340012057976
Usp32 0.00167767285742739 0.0415340012057976
Atad2b 0.00172118423031159 0.0415962589852628
Hprt 0.00172384735604994 0.0415962589852628
Rpl8 0.00171736537031335 0.0415962589852628
Slc39a8 0.00174185142607646 0.041944212426693
Glo1 0.00175808960594749 0.0420283094031726
Gprc5a 0.0017514788973983 0.0420283094031726
Ormdl2 0.00175970875908353 0.0420283094031726
Smarcad1 0.00175583160538894 0.0420283094031726
Ggt5 0.00176453100699418 0.0420576504579489
Foxo4 0.00177528902506692 0.0422280639438174
Cpeb3 0.00178390268217263 0.0422650625680148
Gpnmb 0.00178956239480028 0.0422650625680148
Rps15a 0.0017862787160508 0.0422650625680148
99

Rps6kb1 0.00179129035578359 0.0422650625680148
Zfp955a 0.00179678372469529 0.0423093761169195
Aen 0.00183446169674164 0.0423237457682073
Ankrd13c 0.00182414615871809 0.0423237457682073
Ccl2 0.00184649403576233 0.0423237457682073
Gmpr2 0.00185525776118007 0.0423237457682073
Gprasp1 0.00182492770264837 0.0423237457682073
Homer3 0.00183431850729095 0.0423237457682073
Kcnj15 0.00182264710239648 0.0423237457682073
Lyrm9 0.00180933343661369 0.0423237457682073
Naa40 0.00182372509933662 0.0423237457682073
Nanos1 0.00183836329961786 0.0423237457682073
Rrp9 0.00185255138251814 0.0423237457682073
Sp7 0.00181392752669002 0.0423237457682073
Thsd4 0.00185398600072924 0.0423237457682073
Tm4sf1 0.00184105771822013 0.0423237457682073
Tmem37 0.00184856937025224 0.0423237457682073
Ufm1 0.00180731469718586 0.0423237457682073
Cntd1 0.00186259077341167 0.0423802269327466
Puf60 0.00186854595761504 0.0423802269327466
Tgoln1 0.00187144686428219 0.0423802269327466
Tmem198b 0.00187221886048278 0.0423802269327466
Brpf3 0.00189306419970268 0.0424432923388907
Ddx49 0.00189005374884888 0.0424432923388907
Jam3 0.00189313839194232 0.0424432923388907
Sh3bgrl 0.00188443345636806 0.0424432923388907
Vapb 0.00188265579957504 0.0424432923388907
Mrrf 0.00189765675052289 0.0424632446488898
Ntrk3 0.00191257517292761 0.0426340328548035
Plcd4 0.00191106917833239 0.0426340328548035
Abracl 0.00191672269871943 0.0426452580667557
Plxdc2 0.00192548659878299 0.0426779728514343
Slc30a4 0.00192439433306526 0.0426779728514343
Mtpn 0.00194151880811393 0.0427097643070626
Sos2 0.00193542155278211 0.0427097643070626
Tmem30a 0.0019389253384296 0.0427097643070626
Yipf2 0.00193523029793108 0.0427097643070626
Emg1 0.00194995274250151 0.0427421547200638
Myo1d 0.0019576001820007 0.0427421547200638
Slc46a1 0.00195738468022717 0.0427421547200638
Ssbp3 0.00195513919060173 0.0427421547200638
Rin1 0.00197107649660571 0.0429562537053568
Arfgef1 0.00198570023199804 0.0430340815951542
Atp11b 0.00199638260644561 0.0430340815951542
Dna2 0.00199671078408688 0.0430340815951542
Plxnd1 0.00199561987804796 0.0430340815951542
Usp33 0.00199282698139588 0.0430340815951542
Zfp532 0.0019952908795784 0.0430340815951542
Adamts2 0.00200299378855986 0.0430792506641733
Cyhr1 0.00200616864154272 0.0430792506641733
Herc3 0.00201011360776819 0.0430849076038665
Bzw2 0.00202460759362907 0.0430973397603998
Ehmt2 0.0020149695213198 0.0430973397603998
Sfr1 0.00202089134163369 0.0430973397603998
Tspyl5 0.00202542398258736 0.0430973397603998
Tnrc6b 0.00202919048660575 0.0430991220775809
Smpd2 0.00203612819932913 0.0431681310086029
Ubqln2 0.00204145030159732 0.0432026996014348
Adamts6 0.00204845827981418 0.0432101886300777
Kmt2c 0.00205092598547067 0.0432101886300777
Lrif1 0.00205288087484604 0.0432101886300777
Hapln4 0.00207721118983056 0.0433522685069849
100

Mapkapk2 0.00206660429658469 0.0433522685069849
Psd4 0.00206961009683339 0.0433522685069849
Tdrd3 0.00207815283537713 0.0433522685069849
Trim33 0.00207766561690848 0.0433522685069849
Cdkn1a 0.00208475431845788 0.0434125974891683
Jund 0.00209041252199774 0.0434142508824348
Zdhhc3 0.00209225305457517 0.0434142508824348
Cct8 0.00209729048448689 0.0434417531680532
Esf1 0.00211183607434682 0.0436608363223195
Rsbn1 0.00211532890666967 0.0436608363223195
Ankrd12 0.00212319264707388 0.0436691099274264
Gadd45gip1 0.0021205512168994 0.0436691099274264
Acap2 0.00213734906473588 0.0437477757564065
Gdf11 0.00213369479659183 0.0437477757564065
Zfp955b 0.00213823188350547 0.0437477757564065
Eef1g 0.0021454019389068 0.0437571771536175
Peak1 0.00215189579046459 0.0437571771536175
Pum2 0.00215738624435079 0.0437571771536175
Sel1l 0.00215203180715107 0.0437571771536175
Slain2 0.00215469804282225 0.0437571771536175
Agap3 0.0022068696495297 0.0438644218354268
Atxn2 0.00219539263628074 0.0438644218354268
Cav2 0.00219740607146843 0.0438644218354268
Cp 0.0021936121374566 0.0438644218354268
Daglb 0.0022076514108405 0.0438644218354268
Donson 0.00220177826562028 0.0438644218354268
Gga3 0.00216919169540876 0.0438644218354268
Ggt7 0.00217019008186953 0.0438644218354268
Nphp4 0.00219680012243086 0.0438644218354268
Scyl2 0.0021936903566935 0.0438644218354268
Vps35 0.00220458748160184 0.0438644218354268
Zfp512b 0.00217920246318337 0.0438644218354268
D5Ertd579e 0.00222739273083406 0.0439581401837285
Hyls1 0.00221804010152817 0.0439581401837285
Pop4 0.00222155947644084 0.0439581401837285
Sparc 0.00222518690168794 0.0439581401837285
Agbl3 0.00226401022589146 0.0439754216897786
Cyp1a1 0.00227335983271948 0.0439754216897786
Gtf3a 0.00225272452210319 0.0439754216897786
Kdm4b 0.00226990605023055 0.0439754216897786
Kri1 0.00226393761182008 0.0439754216897786
Lurap1l 0.00224532425261073 0.0439754216897786
Phldb1 0.00224830580121512 0.0439754216897786
Psmb5 0.0022669327497765 0.0439754216897786
Serpinh1 0.00224516151796201 0.0439754216897786
Tbcel 0.00225891293436036 0.0439754216897786
Tmco3 0.00225884564797201 0.0439754216897786
Vcp 0.00223719301102067 0.0439754216897786
Cdkl2 0.00229841039282672 0.0441500324182948
Mex3b 0.00230124923311628 0.0441500324182948
Piwil4 0.00230064050303919 0.0441500324182948
Zbtb37 0.0023003070383047 0.0441500324182948
Zfp943 0.00229581144648497 0.0441500324182948
Cxcl1 0.00231734194659935 0.0442259682876857
Focad 0.00231330451740883 0.0442259682876857
Ptpra 0.0023157974931768 0.0442259682876857
Timm10b 0.00232032338106802 0.0442259682876857
Arhgap5 0.0023298814504391 0.0442344200166977
Dcun1d3 0.00233428381163721 0.0442344200166977
Fhdc1 0.0023396655550146 0.0442344200166977
Igf2bp2 0.0023338799994756 0.0442344200166977
Zfp37 0.00233643290572032 0.0442344200166977
101

Serhl 0.00234561029038385 0.0442752858521971
Zfp715 0.00235386886347242 0.0443596252966469
Slc48a1 0.00235988960876683 0.0444015885713798
Dnase1l1 0.00237797500288125 0.0445271063339508
Fam166a 0.00237482805111812 0.0445271063339508
Snai1 0.0023771409664913 0.0445271063339508
Lyst 0.00238730600181235 0.0445521382138544
Thap2 0.00239073252997527 0.0445521382138544
Tsg101 0.00238834941415186 0.0445521382138544
Ap3b1 0.00241438788587913 0.0445796981820622
Gpbp1 0.00241506696124305 0.0445796981820622
Krcc1 0.00241131537102314 0.0445796981820622
Rnd2 0.00240375974547163 0.0445796981820622
Trmt13 0.00241166427676657 0.0445796981820622
Tysnd1 0.00240750113427477 0.0445796981820622
1700022I11Rik 0.00243635148377759 0.0446206907897483
Plppr3 0.00242275061138224 0.0446206907897483
Scfd1 0.00243296381916005 0.0446206907897483
Tmem55a 0.00243295174691333 0.0446206907897483
Trim35 0.00243337322787499 0.0446206907897483
Dcaf4 0.00245418957554554 0.0447402339742705
Dennd5a 0.00246985014439604 0.0447402339742705
H2-Eb1 0.0024698339215856 0.0447402339742705
Ier5l 0.00247132209385498 0.0447402339742705
Ppil3 0.00246888150550354 0.0447402339742705
Ptcd1 0.00247254693337038 0.0447402339742705
Rcor1 0.00247471766818657 0.0447402339742705
Sec61a2 0.00245355204226752 0.0447402339742705
Srsf2 0.00247728544948537 0.0447402339742705
Ckap2 0.00248164981899272 0.0447499966589704
Adamts16 0.00250789822438098 0.0449023647708695
Amer1 0.00252561677122434 0.0449023647708695
Atp5b 0.00251070000855503 0.0449023647708695
Eif5 0.00252911020661156 0.0449023647708695
Faap100 0.00253230460127949 0.0449023647708695
Gsto1 0.00252402712966371 0.0449023647708695
Ptprk 0.00249530905537965 0.0449023647708695
Spryd4 0.00251182552261541 0.0449023647708695
Strn3 0.00250458239968161 0.0449023647708695
Timm22 0.00252336141490154 0.0449023647708695
Zfp689 0.00251435199383584 0.0449023647708695
Sesn2 0.00253963827686235 0.0449642764812709
Atp6v1h 0.00258644395378666 0.0449943109579204
Calhm2 0.00259516020649374 0.0449943109579204
Eid1 0.00255620080095216 0.0449943109579204
Irf5 0.00256855051198341 0.0449943109579204
Nelfcd 0.00256908911062898 0.0449943109579204
Pidd1 0.00259216298000798 0.0449943109579204
Prrg2 0.00257340176217969 0.0449943109579204
Rrp36 0.0025875433366765 0.0449943109579204
Sacm1l 0.00256172508976782 0.0449943109579204
Smarcc2 0.00258299417965601 0.0449943109579204
Tfg 0.00258088332647149 0.0449943109579204
Ttc9 0.00256956242017354 0.0449943109579204
Zfp668 0.00256620701197397 0.0449943109579204
Zfp719 0.00256447495179649 0.0449943109579204
Grb10 0.00261567587276254 0.0452682448895403
Ulk4 0.00261869621381003 0.0452682448895403
Ak5 0.0026247964762796 0.045306774575074
Azi2 0.00263611025475796 0.0453152516150112
Cfap97 0.00264464811185616 0.0453152516150112
Ddx17 0.0026431395802532 0.0453152516150112
102

Deaf1 0.00262996177851527 0.0453152516150112
Kifap3 0.00263839547093255 0.0453152516150112
Arap3 0.00269189372879344 0.0454625435188677
Btg2 0.00271200848538838 0.0454625435188677
Dcaf6 0.00268116234028576 0.0454625435188677
Dgat2 0.00270505613384846 0.0454625435188677
Fam168a 0.00267759528780747 0.0454625435188677
Kidins220 0.00270837430307915 0.0454625435188677
Limd1 0.00271539929246249 0.0454625435188677
Micu1 0.00270790887844251 0.0454625435188677
Mri1 0.00269580587229086 0.0454625435188677
Nectin2 0.00267890599875453 0.0454625435188677
Pfn2 0.00270562572151829 0.0454625435188677
Rdx 0.00270954230782148 0.0454625435188677
Rhbdf2 0.00269420724720976 0.0454625435188677
Tkfc 0.00269517570942778 0.0454625435188677
Zdhhc20 0.00266814561604829 0.0454625435188677
Zfp503 0.00269717599572244 0.0454625435188677
6430548M08Rik 0.00274913137032035 0.0457190589918092
Atp9b 0.00274994314511989 0.0457190589918092
Gsk3b 0.00274402407689961 0.0457190589918092
Pde7b 0.00274623654213857 0.0457190589918092
Prkag1 0.00275025357004475 0.0457190589918092
Cenpb 0.00275981531291714 0.0457285137270405
Kif3a 0.00276994911841744 0.0457285137270405
Mcrs1 0.00275580161947591 0.0457285137270405
Sh3d19 0.00276606521397261 0.0457285137270405
Toe1 0.0027703594148912 0.0457285137270405
Osbpl8 0.00277510700205022 0.0457423623168926
Hist1h1c 0.00277950087023166 0.0457503497669777
Nbr1 0.00280594752474514 0.0460349147395325
Nup188 0.00280649374262384 0.0460349147395325
Ptdss1 0.0028094053742908 0.0460349147395325
Snrpn 0.00281252362973304 0.0460349147395325
Ccdc181 0.00282852294493966 0.0462321285260179
Mia3 0.0028383693276064 0.0463283629581279
Nr4a1 0.0028494288269445 0.0463795070399603
Rad17 0.00284664621876424 0.0463795070399603
Larp6 0.00287475793058237 0.0465973574260464
Med22 0.00287301446528594 0.0465973574260464
Yipf5 0.00287253547118843 0.0465973574260464
Tmem151a 0.00288088507924787 0.0466320858678255
Nek1 0.00289500816051162 0.0467314213827138
Rpl18 0.00289226787380503 0.0467314213827138
Gemin7 0.00291148399985159 0.0467395024009097
Nr2c2ap 0.00290545382651056 0.0467395024009097
Prpf19 0.00290994853304372 0.0467395024009097
Trp53inp2 0.00291007086504017 0.0467395024009097
Cds2 0.00291584382775103 0.0467453702961237
Spout1 0.00292408100591829 0.0468132968703991
Sart1 0.00293324742969076 0.046895894357474
Rabgap1l 0.00293893686520099 0.0469227532516333
Gaa 0.00296781572914456 0.0470205687924436
Ift80 0.00296188157522503 0.0470205687924436
Katnal1 0.00296349562714427 0.0470205687924436
Larp4 0.00295992366812987 0.0470205687924436
Pxn 0.00295660420641053 0.0470205687924436
Slc39a14 0.00296917032706279 0.0470205687924436
Rtn4rl2 0.00297741658971535 0.0470874410127551
Stk3 0.00298164649489977 0.0470907003101377
Pi4kb 0.00299850460362181 0.0472931258439165
Mmp17 0.00301075274674171 0.0474223948790286
103

1810043G02Rik 0.0030364351213963 0.0475849738379005
Gab2 0.00302857707258339 0.0475849738379005
Lonp2 0.00304058041934244 0.0475849738379005
Ufl1 0.00303234815073539 0.0475849738379005
Zfp933 0.00304140480481497 0.0475849738379005
Msto1 0.00305080253891176 0.0476449288949789
Slc25a24 0.00305745036316578 0.0476449288949789
Srr 0.00305543533092519 0.0476449288949789
Tbc1d2b 0.00307186524516989 0.0477424156231384
Tor2a 0.00306916620985068 0.0477424156231384
Fam3c 0.00308038489729304 0.0478113321658096
Arl14ep 0.00310278913011641 0.0479921000208064
Sdk1 0.00310105606830974 0.0479921000208064
Slpi 0.00310592178465418 0.0479921000208064
Zfp953 0.00310843474457586 0.0479921000208064
Rbm3 0.00311823738814704 0.0480800160124965
4933428G20Rik 0.00313916070826723 0.0483389444326992
Tmem191c 0.00314763705298451 0.0483555762441172
Zfp455 0.0031485045798528 0.0483555762441172
Capn5 0.00315273613837363 0.0483571048851725
Atp7a 0.00316825935121883 0.0484481567751072
Sos1 0.00316677444943876 0.0484481567751072
Zfp518a 0.0031710918644563 0.0484481567751072
Fuz 0.00317863649745478 0.0485001081221816
Pdpk1 0.00318526609293268 0.0485379805801968
Fchsd2 0.00319786859916121 0.0485512891653182
Rpl22l1 0.00320020626370427 0.0485512891653182
Ssr4 0.00319414977180663 0.0485512891653182
St13 0.00320273393451471 0.0485512891653182
Mlst8 0.00321074864255014 0.0486098206517002
Chst12 0.00321976491534443 0.0486317482762234
Snx27 0.00322050798206213 0.0486317482762234
Acy3 0.00323047380454695 0.0487193749157383
Col1a1 0.00325353138145361 0.048871766869847
Dync2h1 0.00325194934822195 0.048871766869847
Fam184a 0.00326145859398022 0.048871766869847
Mast1 0.00325994204844695 0.048871766869847
Trappc10 0.00324548807989832 0.048871766869847
Nemf 0.00327339260069479 0.048987869061293
Mpi 0.0032994100562724 0.0493141709943243
Mospd2 0.00330909530230999 0.0493958447996605
Ascc3 0.00332626129606021 0.04948933861678
Bace1 0.00333227367598245 0.04948933861678
Rragc 0.003329443932147 0.04948933861678
Tada1 0.00332745166753126 0.04948933861678
Abhd15 0.00336518134426996 0.0495380091471589
Ctc1 0.00336423549251342 0.0495380091471589
Cul3 0.0033579588893774 0.0495380091471589
Dnajc5 0.0033531700585276 0.0495380091471589
Fam199x 0.00335504023431886 0.0495380091471589
Oxsm 0.00334669024239005 0.0495380091471589
Rock2 0.00336296749587083 0.0495380091471589
Ephx1 0.00338501100126122 0.0497310448400086
Gopc 0.00338679336387993 0.0497310448400086
Cenpt 0.00339272517168217 0.049755717649369
Efcab2 0.00341126659339317 0.0498443543090954
Oxr1 0.00341154642412932 0.0498443543090954
Sgsh 0.00340536641267281 0.0498443543090954
Pdpn 0.00341621924803686 0.0498503913463533
Ctnna1 0.00342868333822219 0.0499438687538738
Pawr 0.00343542803955126 0.0499438687538738
Ube2l3 0.00343509101093967 0.0499438687538738
104

Supplemental Table 3.5: Expression analysis of purebred M. musculus primary cells
across a timecourse of irradiation and senescence. Each row reports results from analysis
of expression of one gene in M. musculus (PWK) primary cells harboring a scrambled short
hairpin RNA, before and 6 hours, 10 days, and 20 days after irradiation. Second and third col-
umns report the raw and Benjamini-Hochberg corrected p-values respectively from a multivari-
ate ANOVA test for variation in expression across the timecourse. Only genes with significant
differential expression across the timecourse (corrected p < 0.05) are shown.

105

TF Parameter Innate_immune_response Negative_regulation_of_apoptotic_process
Inflammatory_response Epidermal_growth_factor_receptor_signaling_pathway
Gli3 0.0791425 Yes Yes Yes Yes
Klf3 -0.0688293 Yes Yes No No
Nfil3 0.178251 Yes Yes Yes No
Ogt -0.232745 Yes Yes No No
Phf19 -0.0357631 Yes Yes No No
Satb1 -0.00670806 Yes Yes Yes Yes
Shox2 -0.0252908 Yes Yes Yes No
Sp9 0.0335431 Yes Yes No No

Supplemental Table 3.6: In silico regulatory network reconstruction from irradiation and
senescence transcriptomes. Shown are results from regulatory network inference using all
shRNA treated transcriptomes in this study as input into the MERLIN package (Roy et al. 2013).
Each row reports results from a transcription factor inferred to be a downstream target of USF2.
The second column reports the strength of the inferred regulation by USF2; negative values in-
dicate repression. In the remaining columns, “Yes” indicates that one or more genes of the indi-
cated GO term were inferred to be targets of the indicated factor.

106

Chapter 4: Toward a genome-wide reciprocal hemizygosity test in mammalian cell culture
models

Abstract

The search for the molecular basis of naturally occurring phenotypic variation is a central
focus of modern genetics. Powerful unbiased approaches toward this end have been used for
decades in the field, but they can only be applied to cases of population variation within a species.
To overcome this limitation, we have developed methods for a genome-wide version of the recip-
rocal hemizygosity test, which maps genotype to phenotype in an F1 hybrid between divergent
parents, with no requirement for meiotic progeny from the respective parental cross. As a proof
of concept we used fibroblasts from two mouse species, Mus musculus and M. spretus. We dis-
covered a cell-autonomous phenotype of differential growth rate in fibroblasts from purebreds of
the two species. We then established a pipeline for lentiviral insertional mutagenesis in fibroblasts
from Mus musculus x M. spretus F1 animals. And we developed a short-read sequencing proce-
dure to map and quantify insertional mutant clones in the F1 background after irradiation. Our
results lay the groundwork for genomic screens to identify loci at which variants between the two
species contribute to the cell growth trait.

Introduction

As evidenced by the work of Darwin and his successors, biologists have long since been
fascinated by the diversity in phenotypes in the natural world. Understanding the molecular basis
of this variation remains a major focus of modern genetics. Powerful statistical-genetic tools for
this purpose have been established and used in eukaryotic systems from microbes to mammals
(Visscher et al. 2017; Slatkin 2008). Essentially all such strategies trace the co-inheritance of DNA
107

sequence variants with phenotypic variance across panels of recombinant offspring from genet-
ically distinct parents. As such, they cannot be brought to bear in the study of trait variation be-
tween reproductively isolated species, where even if mating is possible, chromosomal crossover
events do not occur (Hale et al. 1993) and/or hybrid offspring are sterile (Orr et al. 2004). Many
striking traits have arisen and fixed in reproductively isolated species adapted to a unique niche;
unbiased genetic dissection in any such case requires statistical-genetic mapping methods that
can overcome the species barrier.
One such method is the reciprocal hemizy-
gosity test (Steinmetz et al. 2002; Stern 1998). Clas-
sically, in this paradigm, each allele for a candidate
gene is inactivated in turn in interspecific hybrid
progeny, to create a pair of clones with the allele
from just one of the parent species uncovered and
functional in a hemizygous state (Figure 4.1). These
hemizygotes are isogenic except at the manipulated
locus, and thus for any phenotypic difference ob-
served between the two, allelic variation at this locus
is implicated in the trait of interest. A genome-wide version of this approach, RH-seq (Weiss et al.
2018), was recently pioneered in yeast: here random insertional mutagenesis creates a pool of
reciprocal hemizygotes, which can then be screened and sequenced to identify those genes at
which allelic variation resulted in differential susceptibility of the hemizygotes to the selection.
We set out to adapt RH-seq to a mammalian cell culture system. In this chapter, we de-
scribe the workflow we have developed for genome-scale insertional mutagenesis and sequenc-
ing. And we report the discovery of a cell-autonomous phenotype well-suited for proof of principle
application of RH-seq: natural variation in growth rate of primary cells from purebred M. musculus
and M. spretus mice.
Figure 4.1: Reciprocal hemizygosity
test. In the traditional test, each allele
from two parent species (blue from one
parent, red from the other) for a candi-
date gene is knocked out in turn in an
interspecific hybrid to create a pair of re-
ciprocal hemizygotes. These hemizy-
gotes are isogenic except for the dis-
rupted allele; therefore any phenotypic
difference that can be observed be-
tween the two can be attributed to vari-
ation at the remaining allele.

108

Results

Variation in growth rate of fibroblasts from between mouse species

To serve as a proof of principle for the application of RH-seq to a mammalian cell culture
system, we first sought to identify an easily quantifiable, robust phenotype varying between M.
musculus and M. spretus cells. During the regular culturing of the two cell populations, we noticed
that cells from M. spretus grew more slowly. To validate this observation, we first seeded cells
from each purebred species at the same densities and calculated the number of population dou-
blings each culture underwent in a 48-hour period (Figure 4.2A). M. musculus cultures exhibited
a ~4-fold increase in population doublings relative to M. spretus cells. Fibroblasts from M. mus-
culus domesticus exhibited an intermediate growth phenotype (Supplemental Figure 4.1), and we
elected to focus on M. musculus and M. spretus as representatives of the extremes of this spec-
trum.
We next sought to validate our observation of variation between species in the growth rate
of primary fibroblasts using CellTrace Violet, a dye that binds to free amine groups in the cell; its
fluorescent signal dilutes over cell divisions, reporting the extent of doubling over time in a given
culture (Filby et al. 2015). At all timepoints tested, M. musculus cells displayed a faster growth
rate than M. spretus fibroblasts in this assay (Figure 4.2B), and the former was largely recapitu-
lated by fibroblasts from M. musculus x M. spretus F1 hybrid animals (Figure 4.2B). We conclude
that natural variation in cellular growth rate under standard culture conditions between our two
species of mice represents a robust phenotype well suited for potential genetic dissection by the
RH-seq technique.

Unbiased lentiviral mutagenesis of interspecific hybrid primary fibroblasts
109

Reciprocal hemizygosity mapping requires large pools of insertional mutant clones in a
diploid hybrid background, in each of which the allele of one gene from one of the two divergent
parents is uncovered and in a hemizygous state. We elected to use lentiviral insertional mutagen-
esis for this purpose in primary cells from M. musculus x M. spretus F1 hybrid animals. Using a
virus derived from HIV harboring a blue fluorescent protein (BFP) marker, we first developed a
workflow for low-titer infection of hybrid fibroblasts in bulk and BFP selection followed by out-
growth (see Methods). We then established a bulk sequencing protocol (Serrao et al. 2016), to
sequence each viral junction with the genome in turn among the insertional mutants in the pool,
and to quantify their abundance. Briefly, after DNA isolation from the culture of the mutant pool,
Figure 4.2: Primary M. spretus cells exhibit slower growth rate relative to M. musculus
cells. (A) Each bar reports the number of population doublings for primary cell cultures of each
species depicted on the x-axis. For a given column, points represent independent biological
and technical replicates (M. musculus n = 5, M. spretus n = 5). **, p < 0.01, one-tailed Wilcoxon
test. (B) Each trace reports the results of a timecourse of CTV fluorescent signal dilution for
M. musculus and M. spretus primary cells along with the interspecific F1 hybrid. The y-axis
reports the average CTV signal set relative to the value on the day of exposure (Day0). The
x-axis reports the days of testing, with each point in a given day reporting independent tech-
nical replicates (M. musculus n = 2, M. spretus n = 2, F1 n = 2). *, p < 0.05, one-tailed t-test.
110

we fragmented by restriction digest, ligated custom adapters to fragment ends, and sequenced
using a forward primer recognizing the 3’ end of the lentivirus long term repeat, and a reverse
primer specific to the adapter (Figure 4.3). The final PCR products were then subjected to high-
throughput sequencing, from which we took the number of counts of reads mapping to each in-
ferred insertion location as an estimate of the prevalence of the respective mutant in the culture.
As a test of this strategy, we carried out a pilot sequencing experiment on a moderate
scale as follows. We infected a culture of primary fibroblasts from M. musculus x M. spretus F1
animals and isolated 1.2 million mutants. From this culture we isolated DNA and sequenced 400
million 150-bp paired-end reads, which we mapped to a concatenation of the M. musculus and
Figure 4.3: RH-seq lentivirus integrate site library prep outline. (A) Extracted gDNA from
infected cells are digested with restriction enzymes and ligated to custom Y-adapters of known
sequence. Ligated fragments are then PCR amplified using a forward primer specific to the end
of the LTR sequence, with an Illumina P5 adapter sequence for high-throughput sequencing. (B)
The forward primer must prime first and extend to the end of the fragment to create a recognition
site for the reverse adapter-specific primer with an Illumina P7 sequence attached. This reduces
background amplification off the adapter sequences alone and ensures that only fragments of
gDNA containing the LTR are amplified. (C) The final PCR product contains both Illumina adapter
sequences and a small portion of the 3’ end of the LTR to filter for insertion sites in downstream
sequencing data analysis.
111

M. spretus genomes. We identified those reads that mapped uniquely and allele-specifically, and
we inferred each such read to derive from the genome of a mutant with the viral insertion at the
respective location. We collated 32 million such reads in total, affording a total of 92,521 inferred
insertion mutants with representation (at two or more normalized read counts) in the sequenced
set.
To evaluate this pool of hemizygote fibroblast clones in the M. musculus x M. spretus F1
background, we assessed its depth and coverage. In a survey of the lentiviral insertion locations
in the clones of the pool as inferred from sequencing data, we found a uniform distribution of these
insertions across chromosomes and across both parent species’ alleles (Figure 4.4). We then
evaluated how many genes were represented by clones corresponding to insertional mutants in
both the M. musculus and M. spretus alleles, which would both be required for a reciprocal hem-
izygosity test on growth or any other trait of interest. From the genomic data we tabulated 13,845
such genes (with representation of the respective hemizygote clones at two or more normalized
read counts). Together, these data attest to the performance of our infection and sequencing
workflows, and to the coverage of the mutant pool in our pilot. We conclude that this approach is
well-positioned for appropriately scaled future applications of RH-seq to dissect the genetics of
cell-autonomous traits as they vary between our focal species.

Discussion

Currently available strategies for the unbiased mapping of natural trait variation are limited
in their applicability across reproductive barriers (Morgan 1910; Orr et al. 2004; Bertram and Tanzi
2009; Hale et al. 1993). Here we present a mammalian cell culture adaptation of a novel screening
tool for interspecies genetics, RH-seq, complementing previous work with this approach in Sac-
charomyces yeasts (Weiss et al. 2018).
112

We have developed a straightforward lentiviral insertional mutagenesis and sequencing
platform in primary cells from an interspecific mouse hybrid. With a pilot application of this work-
flow, we have shown that a pool of ~1 million viral insertional mutant fibroblasts of the M. musculus
x M. spretus background can yield unbiased mutant coverage across the genome, and sufficient
depth to serve as a resource for reciprocal hemizygosity tests for roughly half of the annotated
coding genes of the hybrid. Doubling this scope would be easily within the capacity of most
Figure 4.4: Lentiviral insertions are evenly distributed across the hybrid genome. Each
point on the plot represents in independent lentivirus insertion in the indicated species’ allele.
The y-axis reports the proportion of total sequencing reads that mapped to each insert, while
the x-axis reports the genomic location of each insert, with additive positions on chromosomes
1 through 19 presented in series. Gray dotted lines indicate the start of the next chromosome.
113

practitioners of mammalian cell culture. We thus have every expectation that the tools we have
generated here will be well-suited for highly powered genomic screens—that is, they will put sta-
tistical mapping of cell-autonomous phenotypes via RH-seq within reach.
We have also established a robust difference in growth rate between M. musculus and M.
spretus fibroblasts in standard culturing procedures. Since the latter were established for M. mus-
culus models, it is tempting to speculate that the variation we have observed in growth rate will
prove to be mediated by differences between the species in their cells’ requirements for nutrients,
oxygenation, or some other attribute of culture conditions. Future application of RH-seq will enable
tests of this notion, as well as genetic dissection of differences in senescence between M. mus-
culus and M. spretus fibroblasts (Chapter 2). And in the long term, RH-seq will likely be applicable
to a wealth of additional naturally varying cell-autonomous phenotypes, even between deeply
diverged, reproductively isolated species.

Methods

Primary cell extraction and culture

Performed as described in Chapter 2.

Cell trace violet and population doubling

To assess variation in cellular growth rates between M. musculus and M. spretus, primary
cells from both species were seeded at appropriate densities in a 37˚C humidified incubator at
3% O2 and 10% CO2 to reach 30% confluence by the following day. One sample population for
each species were counted using a hemacytometer the next day to serve as the count at day0,
while the remainder of the cells were left undisturbed and allowed to grow for two days. Following
114

the outgrowth, all replicates were lifted off the plates and counted to serve as the cell count at
day2. The number of population doublings each culture went through were calculated as follows:
PD new = PDold + ( 3.32*( log(day2count) – log(day0count))).
The rate of cell division was further measured using the CellTrace Violet Cell Proliferation
Kit (Thermo Fisher cat. #C34557). The day before the experiment, cells from both purebred spe-
cies were seeded at appropriate densities in a 37˚C humidified incubator at 3% O 2 and 10% CO2
to reach 30% confluence by the following day. Replicates for each day of testing were plated into
separate dishes. The next day, all replicates were washed with once with PBS and incubated with
PBS containing 5 μM of CellTrace Violet for 20 minutes. Fluorescence readings for the day0
replicates were then measured on a BD LSR II Flow Cytometer. The remaining samples were
washed three times with complete medium (DMEM, 10% FBS, 1% pen-strep), then placed back
in the incubator for the indicated timepoints and were split appropriately during this time to allow
for continuous growth. Fluorescence readings for each timepoint were also taken on the BD LSR
II.

Lentiviral particle generation

Lentiviral particles were generated via calcium phosphate transfection in HEK 293T cells
cultured in DMEM (Gibco cat. #21-063-029) supplemented with 10% FBS (Genesee Scientific
cat. #25-514H) and 1% pen-strep (Corning cat. #30002CI). The lentiviral backbone, packaging,
and envelope vectors were generously provided by Dr. Marius Walter of the Verdin Lab at the
Buck Institute. The lentiviral backbone employed was a third-generation modified HIV vector with
BFP and puromycin resistance markers driven by a Ef1α promoter and containing standard cen-
tral polypurine tract-central termination sequences (cPPT-CTS). The pMDLg/pRRE packaging
and pCMV-VSV-G envelope plasmids used were as described in Chapter 3. The day before trans-
fection, HEK 293T cells were seeded in 15cm cell culture dishes at an appropriate density to
115

reach 50-70% confluence by the following day. A 2x HeBS solution was prepared by dissolving
NaCl (Sigma-Aldrich cat. #S7653), HEPES (Sigma-Aldrich cat. #H7523), and Na2HPO4 (Sigma-
Aldrich cat. #S7907) into water, and adjusting the pH to 7.0 with NaOH (Thomas Scientific cat.
#C753N15) solution. The day of transfection, the medium for the HEK 293T cells was changed
and supplemented with chloroquine (Thermo Scientific cat. #C868U90) to a final concentration of
25 μM. 40, 15, and 20 μg of the lentiviral backbone, envelope, and packaging vectors respectively
were mixed into 500 μL of water and added to 500 μL of 0.5 M CaCl 2 (Sigma-Aldrich cat. #C5080).
This solution was then mixed dropwise into 1 mL of 2x HeBS while vortexing, and left for 20-30
minutes at room temperature. An hour after chloroquine supplementation, the resulting precipitate
solution was added, dropwise, onto the cell monolayer. The cells were then placed in a 37˚C
humidified incubator with 10% CO 2 for 8-10 hours before changing to fresh complete medium.
Two days after transfection, the viral supernatant was collected and passed through a 0.45 μm
filter before being snap-frozen in dry ice. The frozen supernatant was then transferred to a -80˚C
freezer for long term storage.

Lentiviral random insertional mutagenesis

The day before infection, F1 hybrid primary cells were plated at the appropriate density to
reach 60-80% confluence by the following day and left in a 37˚C humidified incubator at 10% CO 2
and 3% O2. The next day, the cells were treated with complete medium supplemented with 4
μg/mL of polybrene (Thomas Scientific cat. #C788D57) and lentiviral supernatent. The treated
cultures were incubated overnight in the infection solution and the medium was replaced the fol-
lowing day.

FACS for hemizygotes

116

Two days after infection, cells were lifted off the plate using 0.25% Trypsin, 0.1% EDTA
(Corning cat. #25053Cl), washed once with PBS, resuspended in 2% FBS in PBS, passed
through a 70μm strainer, and transported in ice to the Flow Cytometry facility at UCB. The sam-
ples were then sorted on an Aria Fusion for the expression of a BFP marker to select for infected
cells. Viral dose was tuned to achieve a collection rate of 10-15% BFP-positive cells, correspond-
ing to multiplicity of infection of ~1. Immediately after sorting, the cells were spun down and re-
suspended in complete medium before placing in a 37˚C humidified incubator at 10% CO 2 and
3% O2.

Growth RH-seq

Three days after sorting, the purified hemizygote cell culture was lifted of the plate using
0.25% Trypsin, 0.1% EDTA and counted using a hemacytometer. Half the cells were collected for
genomic DNA (gDNA) extraction as the day0 sample, while the other half were placed back into
culture and allowed to grow out for seven additional days. The cells were split into appropriate
containers during this outgrowth phase to allow for uninterrupted growth. After the seven days,
the final samples were collected for gDNA extraction as the day7 sample.

Genomic DNA extraction and library prep

gDNA and lentiviral integration library prep was mostly performed as described in (Serrao
et al. 2016). Cells collected on each day spun down, washed twice with PBS, and resuspended
in 1 mL of digest buffer (100mM NaCl (Sigma-Aldrich cat. #S7653), 100 mM Tris (Corning cat.
#45031CM), 25 mM EDTA (Corning cat. #43034Cl), 0.5% SDS (Bio-Rad cat. #1610416) and 0.1
mg/mL proteinase K (NEB cat. #P8107S)) per one million cells. Cells were incubated in the digest
buffer overnight in a 50˚C water bath. The following day, an equal volume of phenol-chloroform-
117

isoamyl alcohol (Sigma-Aldrich cat. #P2069) was used to extract the gDNA, which was then pu-
rified using ethanol precipitation. All DNA described from this point on was quantified at every
step using the Qubit
TM
4 fluorometer (Thermo Fisher cat. #Q33238) and the Qubit
TM
1x dsDNA
High Sensitivity kit (Thermo Fisher cat. #Q33231). The gDNA was then digested using 10 units
of MseI (NEB cat. #R3589S) and BanII (NEB cat. #R0119S) per 1 μg of gDNA at 37˚C overnight.
Digested gDNA was then column purified using the Qiagen PCR Purification Kit (Qiagen cat.
#28006). Custom adapters were then ligated to the digested gDNA overnight at 12˚C using T4
ligase (NEB cat. #M0202S). Ligated DNA fragments were again column purified and then subject
to PCR using a custom forward primer specific to the end of the lentiviral long terminal repeat
(LTR) sequence and a reverse primer recognizing the customer ligated adapter sequence, each
containing standard Illumina P5 and P7 priming sites and chip adapter sequences. PCR products
were then column purified and further cleaned using AMPure XP beads (Beckman-Coulter cat.
#A63881). The final products were then submitted to the UCB QB3 Genomics core for sequencing
on the NovSeq SP 150PE for 400M reads.

Data analysis

Sequencing reads returned from QB3 were first filtered for those containing the last 20
bases of the LTR sequence, and then trimmed using custom Python scripts. Trimmed reads were
then mapped to a concatenated PWK/STF pseudogenome generated as described in Chapters
1 and 2 using pblat v2.5 (Wang and Kong 2019) with minimum identity of 95%. Mapped reads
were then filtered for those that only mapped to one unique site, to ensure allele specific mapping.
The number of mapped reads were then counted for each detected insert location, serving as a
proxy for the abundance of that hemizygote in the respective sample. Fitness ratios were then
calculated by taking the log2 fold change in abundance for each hemizygote in the day7 sample
relative to day0. All the fitness ratios for the two allele for a given gene were then put into a Mann-
118

Whitney U test (Mann and Whitney 1947) with Benjimini-Hochberg multiple testing correction to
identify candidate genes.

119

Supplemental Figures

Supplemental Figure 4.1: M. musculus and M. spretus cells represent the extremes in the
natural variation of senescent phenotypes across Mus. Each bar reports the average number
of population doublings that cultures from each species depicted on the x-axis went through in a
48-hour period. For a given column, points represent independent biological and technical repli-
cates (M. musculus n = 5, M. domesticus n = 4, M. spretus n = 4). **, p < 0.01, one-tailed Wilcoxon
test.

120

Conclusions

Senescent phenotypes are divergent across the Mus genus

Here we present the divergence in cellular senescence phenotypes across several mem-
bers of the Mus genus. When treated to the same doses of ionizing radiation, cells from M. mus-
culus exhibited abnormally high lysosomal mass and senescence-associated β-galactosidase ac-
tivity relative to other Mus species. We further detected higher SASP mRNA and secretion in M.
musculus cells relative to that of M. spretus, suggesting a relatively heightened senescent phe-
notype to be the exception rather than the rule. Overall, our findings highlight the potential of
natural variation-based systems in providing novel insights that may otherwise be overlooked in
studies of a single background.

Natural variation-based screens can reveal novel molecular mechanisms of relevant phe-
notypes

In addition to characterizing the variation in senescent phenotypes between M. musculus
and M. spretus cells, we employed a natural variation-based transcription factor screen in the
cells of the interspecific F1 hybrid to identify USF2 as a regulator of DNA damage response and
cellular senescence. We found that disruption of USF2 activity resulted in a muted DNA damage
response and an exaggerated senescent phenotype in mouse primary cells. We further speculate
that USF2 may play a role in the cell’s commitment and decision to enter senescence following
genotoxic stress, with wild type USF2 acting to repress senescence and promote DNA repair and
resuming of normal cell cycle. These findings highlight the complexity of senescence-related reg-
ulatory programs, and they serve as a precedent for the use of our screening technique to identify
novel regulators in other conditions.
121

References

Abraham RT. 2001. Cell cycle checkpoint signaling through the ATM and ATR kinases. Genes
Dev 15: 2177–2196.
Alessio N, Stellavato A, Squillaro T, Del Gaudio S, Di Bernardo G, Peluso G, De Rosa M, Schi-
raldi C, Galderisi U. 2018. Hybrid complexes of high and low molecular weight hyalu-
ronan delay in vitro replicative senescence of mesenchymal stromal cells: a pilot study
for future therapeutic application. Aging 10: 1575–1585.
An S, Cho S-Y, Kang J, Lee S, Kim H-S, Min D-J, Son E, Cho K-H. 2020. Inhibition of 3-phos-
phoinositide–dependent protein kinase 1 (PDK1) can revert cellular senescence in hu-
man dermal fibroblasts. Proc Natl Acad Sci 117: 31535–31546.
Antelo-Iglesias L, Picallos-Rabina P, Estévez-Souto V, Da Silva-Álvarez S, Collado M. 2021.
The role of cellular senescence in tissue repair and regeneration. Mech Ageing Dev 198:
111528.
Aperlo C, Boulukos KE, Pognonec P. 1996. The basic region/helix-loop-helix/leucine repeat
transcription factor USF interferes with Ras transformation. Eur J Biochem 241: 249–
253.
Ashburner M, Ball CA, Blake JA, Botstein D, Butler H, Cherry JM, Davis AP, Dolinski K, Dwight
SS, Eppig JT, et al. 2000. Gene Ontology: tool for the unification of biology. Nat Genet
25: 25–29.
Austad SN, Fischer KE. 1991. Mammalian aging, metabolism, and ecology: evidence from the
bats and marsupials. J Gerontol 46: B47-53.
Baar MP, Brandt RMC, Putavet DA, Klein JDD, Derks KWJ, Bourgeois BRM, Stryeck S, Rijksen
Y, van Willigenburg H, Feijtel DA, et al. 2017. Targeted Apoptosis of Senescent Cells
Restores Tissue Homeostasis in Response to Chemotoxicity and Aging. Cell 169: 132-
147.e16.
Baker DJ, Wijshake T, Tchkonia T, LeBrasseur NK, Childs BG, van de Sluis B, Kirkland JL, van
Deursen JM. 2011. Clearance of p16Ink4a-positive senescent cells delays ageing-asso-
ciated disorders. Nature 479: 232–236.
Bakkenist CJ, Drissi R, Wu J, Kastan MB, Dome JS. 2004. Disappearance of the telomere dys-
function-induced stress response in fully senescent cells. Cancer Res 64: 3748–3752.
Bandyopadhyay D, Okan NA, Bales E, Nascimento L, Cole PA, Medrano EE. 2002. Down-regu-
lation of p300/CBP histone acetyltransferase activates a senescence checkpoint in hu-
man melanocytes. Cancer Res 62: 6231–6239.
Baron Y, Corre S, Mouchet N, Vaulont S, Prince S, Galibert M-D. 2012. USF-1 Is Critical for
Maintaining Genome Integrity in Response to UV-Induced DNA Photolesions. PLoS
Genet 8: e1002470.
122

Basisty N, Kale A, Jeon OH, Kuehnemann C, Payne T, Rao C, Holtz A, Shah S, Sharma V, Fer-
rucci L, et al. 2020. A proteomic atlas of senescence-associated secretomes for aging
biomarker development. PLOS Biol 18: e3000599.
Beauséjour CM, Krtolica A, Galimi F, Narita M, Lowe SW, Yaswen P, Campisi J. 2003. Reversal
of human cellular senescence: roles of the p53 and p16 pathways. EMBO J 22: 4212–
4222.
Bellenguez C, Küçükali F, Jansen IE, Kleineidam L, Moreno-Grau S, Amin N, Naj AC, Campos-
Martin R, Grenier-Boley B, Andrade V, et al. 2022. New insights into the genetic etiology
of Alzheimer’s disease and related dementias. Nat Genet 54: 412–436.
Benjamini Y, Hochberg Y. 1995. Controlling the False Discovery Rate: A Practical and Powerful
Approach to Multiple Testing. J R Stat Soc Ser B Methodol 57: 289–300.
Bertram L, Tanzi RE. 2009. Genome-wide association studies in Alzheimer’s disease. Hum Mol
Genet 18: R137–R145.
Bitto A, Sell C, Crowe E, Lorenzini A, Malaguti M, Hrelia S, Torres C. 2010. Stress-induced se-
nescence in human and rodent astrocytes. Exp Cell Res 316: 2961–2968.
Blanchet C, Jaubert J, Carniel E, Fayolle C, Milon G, Szatanik M, Panthier J-J, Montagutelli X.
2011. Mus spretus SEG/Pas mice resist virulent Yersinia pestis, under multigenic con-
trol. Genes Immun 12: 23–30.
Bonhomme F, Martin S, Thaler L. 1978. [Hybridization between Mus musculus L. and Mus spre-
tus Lataste under laboratory conditions (author’s transl)]. Experientia 34: 1140–1141.
Bouafia A, Corre S, Gilot D, Mouchet N, Prince S, Galibert M-D. 2014. p53 Requires the Stress
Sensor USF1 to Direct Appropriate Cell Fate Decision. PLoS Genet 10: e1004309.
Brückmann NH, Bennedsen SN, Duijf PHG, Terp MG, Thomassen M, Larsen M, Pedersen CB,
Kruse T, Alcaraz N, Ditzel HJ, et al. 2019. A functional genetic screen identifies the Me-
diator complex as essential for SSX2-induced senescence. Cell Death Dis 10: 1–12.
Bruderer R, Bernhardt OM, Gandhi T, Xuan Y, Sondermann J, Schmidt M, Gomez-Varela D,
Reiter L. 2017. Optimization of Experimental Parameters in Data-Independent Mass
Spectrometry Significantly Increases Depth and Reproducibility of Results. Mol Cell Pro-
teomics MCP 16: 2296–2309.
Brunk UT, Terman A. 2002. Lipofuscin: mechanisms of age-related accumulation and influence
on cell function. Free Radic Biol Med 33: 611–619.
Campisi J. 2013. Aging, cellular senescence, and cancer. Annu Rev Physiol 75: 685–705.
Campisi J. 2005. Senescent cells, tumor suppression, and organismal aging: good citizens, bad
neighbors. Cell 120: 513–522.
Campisi J, Fagagna FDD. 2007. Cellular senescence: when bad things happen to good cells.
Nat Rev Mol Cell Biol.
123

Casella G, Munk R, Kim KM, Piao Y, De S, Abdelmohsen K, Gorospe M. 2019. Transcriptome
signature of cellular senescence. Nucleic Acids Res 47: 7294–7305.
Chan M, Yuan H, Soifer I, Maile TM, Wang RY, Ireland A, O’Brien JJ, Goudeau J, Chan LJ, Vi-
jay T, et al. 2022. Novel insights from a multiomics dissection of the Hayflick limit. eLife
11. https://doi.org/10.7554/eLife.70283 (Accessed March 10, 2022).
Chaturvedi V, Qin J-Z, Stennett L, Choubey D, Nickoloff BJ. 2004. Resistance to UV-induced
apoptosis in human keratinocytes during accelerated senescence is associated with
functional inactivation of p53. J Cell Physiol 198: 100–109.
Chen J-H, Hales CN, Ozanne SE. 2007. DNA damage, cellular senescence and organismal
ageing: causal or correlative? Nucleic Acids Res 35: 7417–7428.
Chen J-H, Ozanne SE. 2006. Deep senescent human fibroblasts show diminished DNA dam-
age foci but retain checkpoint capacity to oxidative stress. FEBS Lett 580: 6669–6673.
Chen N, Szentirmay MN, Pawar SA, Sirito M, Wang J, Wang Z, Zhai Q, Yang H-X, Peehl DM,
Ware JL, et al. 2006. Tumor-suppression function of transcription factor USF2 in pros-
tate carcinogenesis. Oncogene 25: 579–587.
Chen Q, Ames BN. 1994. Senescence-like growth arrest induced by hydrogen peroxide in hu-
man diploid fibroblast F65 cells. Proc Natl Acad Sci U S A 91: 4130–4134.
Chen Z, Trotman LC, Shaffer D, Lin H-K, Dotan ZA, Niki M, Koutcher JA, Scher HI, Ludwig T,
Gerald W, et al. 2005. Crucial role of p53-dependent cellular senescence in suppression
of Pten-deficient tumorigenesis. Nature 436: 725–730.
Cheung TH, Rando TA. 2013. Molecular regulation of stem cell quiescence. Nat Rev Mol Cell
Biol 14: 329–340.
Childs BG, Baker DJ, Kirkland JL, Campisi J, van Deursen JM. 2014. Senescence and apopto-
sis: dueling or complementary cell fates? EMBO Rep 15: 1139–1153.
Chistyakov DV, Astakhova AA, Azbukina NV, Goriainov SV, Chistyakov VV, Sergeeva MG.
2019. High and Low Molecular Weight Hyaluronic Acid Differentially Influences Oxylipins
Synthesis in Course of Neuroinflammation. Int J Mol Sci 20: 3894.
Collins BC, Hunter CL, Liu Y, Schilling B, Rosenberger G, Bader SL, Chan DW, Gibson BW,
Gingras A-C, Held JM, et al. 2017. Multi-laboratory assessment of reproducibility, quali-
tative and quantitative performance of SWATH-mass spectrometry. Nat Commun 8: 291.
Coppé J-P, Desprez P-Y, Krtolica A, Campisi J. 2010a. The senescence-associated secretory
phenotype: the dark side of tumor suppression. Annu Rev Pathol 5: 99–118.
Coppé J-P, Kauser K, Campisi J, Beauséjour CM. 2006. Secretion of vascular endothelial
growth factor by primary human fibroblasts at senescence. J Biol Chem 281: 29568–
29574.
Coppé J-P, Patil CK, Rodier F, Krtolica A, Beauséjour CM, Parrinello S, Hodgson JG, Chin K,
Desprez P-Y, Campisi J. 2010b. A Human-Like Senescence-Associated Secretory
124

Phenotype Is Conserved in Mouse Cells Dependent on Physiological Oxygen. PLOS
ONE 5: e9188.
Coppé J-P, Patil CK, Rodier F, Sun Y, Muñoz DP, Goldstein J, Nelson PS, Desprez P-Y,
Campisi J. 2008. Senescence-Associated Secretory Phenotypes Reveal Cell-Nonauton-
omous Functions of Oncogenic RAS and the p53 Tumor Suppressor. PLOS Biol 6:
e301.
Coppé J-P, Rodier F, Patil CK, Freund A, Desprez P-Y, Campisi J. 2011. Tumor suppressor and
aging biomarker p16(INK4a) induces cellular senescence without the associated inflam-
matory secretory phenotype. J Biol Chem 286: 36396–36403.
Corre S, Galibert M-D. 2005. Upstream stimulating factors: highly versatile stress-responsive
transcription factors. Pigment Cell Res 18: 337–348.
d’Adda di Fagagna F. 2008. Living on a break: cellular senescence as a DNA-damage re-
sponse. Nat Rev Cancer 8: 512–522.
Danecek P, Bonfield JK, Liddle J, Marshall J, Ohan V, Pollard MO, Whitwham A, Keane T,
McCarthy SA, Davies RM, et al. 2021. Twelve years of SAMtools and BCFtools. Giga-
Science 10: giab008.
Davalos AR, Coppe J-P, Campisi J, Desprez P-Y. 2010. Senescent cells as a source of inflam-
matory factors for tumor progression. Cancer Metastasis Rev 29: 273–283.
Davalos AR, Kawahara M, Malhotra GK, Schaum N, Huang J, Ved U, Beausejour CM, Coppe J-
P, Rodier F, Campisi J. 2013. p53-dependent release of Alarmin HMGB1 is a central
mediator of senescent phenotypes. J Cell Biol 201: 613–629.
de Gouyon B, Melanitou E, Richard MF, Requarth M, Hahn IH, Guenet JL, Demenais F, Julier
C, Lathrop GM, Boitard C. 1993. Genetic analysis of diabetes and insulitis in an interspe-
cific cross of the nonobese diabetic mouse with Mus spretus. Proc Natl Acad Sci U S A
90: 1877–1881.
de Mera-Rodríguez JA, Álvarez-Hernán G, Gañán Y, Martín-Partido G, Rodríguez-León J, Fran-
cisco-Morcillo J. 2021. Is Senescence-Associated β-Galactosidase a Reliable in vivo
Marker of Cellular Senescence During Embryonic Development? Front Cell Dev Biol 9.
https://www.frontiersin.org/articles/10.3389/fcell.2021.623175 (Accessed July 5, 2022).
Dejager L, Libert C, Montagutelli X. 2009. Thirty years of Mus spretus: a promising future.
Trends Genet 25: 234–241.
Dejager L, Pinheiro I, Bogaert P, Huys L, Libert C. 2010a. Role for neutrophils in host immune
responses and genetic factors that modulate resistance to Salmonella enterica serovar
typhimurium in the inbred mouse strain SPRET/Ei. Infect Immun 78: 3848–3860.
Dejager L, Pinheiro I, Puimège L, Fan Y-D, Gremeaux L, Vankelecom H, Libert C. 2010b. In-
creased glucocorticoid receptor expression and activity mediate the LPS resistance of
SPRET/EI mice. J Biol Chem 285: 31073–31086.
125

Demaria M, Ohtani N, Youssef SA, Rodier F, Toussaint W, Mitchell JR, Laberge R-M, Vijg J,
Van Steeg H, Dollé MET, et al. 2014. An essential role for senescent cells in optimal
wound healing through secretion of PDGF-AA. Dev Cell 31: 722–733.
Di Leonardo A, Linke SP, Clarkin K, Wahl GM. 1994. DNA damage triggers a prolonged p53-
dependent G1 arrest and long-term induction of Cip1 in normal human fibroblasts.
Genes Dev 8: 2540–2551.
Dimri GP, Itahana K, Acosta M, Campisi J. 2000. Regulation of a senescence checkpoint re-
sponse by the E2F1 transcription factor and p14(ARF) tumor suppressor. Mol Cell Biol
20: 273–285.
Dimri GP, Lee X, Basile G, Acosta M, Scott G, Roskelley C, Medrano EE, Linskens M, Rubelj I,
Pereira-Smith O. 1995. A biomarker that identifies senescent human cells in culture and
in aging skin in vivo. Proc Natl Acad Sci U S A 92: 9363–9367.
Dimri GP, Testori A, Acosta M, Campisi J. 1996. Replicative senescence, aging and growth-reg-
ulatory transcription factors. Biol Signals 5: 154–162.
Dörr JR, Yu Y, Milanovic M, Beuster G, Zasada C, Däbritz JHM, Lisec J, Lenze D, Gerhardt A,
Schleicher K, et al. 2013. Synthetic lethal metabolic targeting of cellular senescence in
cancer therapy. Nature 501: 421–425.
Edrey YH, Hanes M, Pinto M, Mele J, Buffenstein R. 2011. Successful aging and sustained
good health in the naked mole rat: a long-lived mammalian model for biogerontology and
biomedical research. ILAR J 52: 41–53.
Elsheikh SSM, Chimusa ER, Mulder NJ, Crimi A. 2020. Genome-Wide Association Study of
Brain Connectivity Changes for Alzheimer’s Disease. Sci Rep 10: 1433.
Escher C, Reiter L, MacLean B, Ossola R, Herzog F, Chilton J, MacCoss MJ, Rinner O. 2012.
Using iRT, a normalized retention time for more targeted measurement of peptides. Pro-
teomics 12: 1111–1121.
Filby A, Begum J, Jalal M, Day W. 2015. Appraising the suitability of succinimidyl and lipophilic
fluorescent dyes to track proliferation in non-quiescent cells by dye dilution. Methods
San Diego Calif 82: 29–37.
Finch CE. 2010. Evolution of the human lifespan and diseases of aging: Roles of infection, in-
flammation, and nutrition. Proc Natl Acad Sci 107: 1718–1724.
Freund A, Laberge R-M, Demaria M, Campisi J. 2012. Lamin B1 loss is a senescence-associ-
ated biomarker. Mol Biol Cell 23: 2066–2075.
Freund A, Orjalo AV, Desprez P-Y, Campisi J. 2010. Inflammatory networks during cellular se-
nescence: causes and consequences. Trends Mol Med 16: 238–246.
Fumagalli M, Rossiello F, Mondello C, d’Adda di Fagagna F. 2014. Stable Cellular Senescence
Is Associated with Persistent DDR Activation. PLoS ONE 9: e110969.
126

Garagnani P, Marquis J, Delledonne M, Pirazzini C, Marasco E, Kwiatkowska KM, Iannuzzi V,
Bacalini MG, Valsesia A, Carayol J, et al. 2021. Whole-genome sequencing analysis of
semi-supercentenarians eds. J.K. Tyler, Y. Suh, and A.R. Shuldiner. eLife 10: e57849.
Garbino GST, Feijó A, Beltrão-Mendes R, Da Rocha PA. 2021. Evolution of litter size in bats
and its influence on longevity and roosting ecology. Biol J Linn Soc 132: 676–684.
Gene Ontology Consortium. 2021. The Gene Ontology resource: enriching a GOld mine. Nu-
cleic Acids Res 49: D325–D334.
Gillet LC, Navarro P, Tate S, Röst H, Selevsek N, Reiter L, Bonner R, Aebersold R. 2012. Tar-
geted Data Extraction of the MS/MS Spectra Generated by Data-independent Acquisi-
tion: A New Concept for Consistent and Accurate Proteome Analysis*. Mol Cell Prote-
omics 11: O111.016717.
Gorbunova V, Seluanov A, Kennedy BK. 2020. The World Goes Bats: Living Longer and Toler-
ating Viruses. Cell Metab 32: 31–43.
Goulois J, Hascoët C, Dorani K, Besse S, Legros L, Benoit E, Lattard V. 2017. Study of the effi-
ciency of anticoagulant rodenticides to control Mus musculus domesticus introgressed
with Mus spretus Vkorc1. Pest Manag Sci 73: 325–331.
Gyori BM, Venkatachalam G, Thiagarajan PS, Hsu D, Clement M-V. 2014. OpenComet: An au-
tomated tool for comet assay image analysis. Redox Biol 2: 457–465.
Hale DW, Washburn LL, Eicher EM. 1993. Meiotic abnormalities in hybrid mice of the C57BL/6J
x Mus spretus cross suggest a cytogenetic basis for Haldane’s rule of hybrid sterility. Cy-
togenet Cell Genet 63: 221–234.
Hall BM, Balan V, Gleiberman AS, Strom E, Krasnov P, Virtuoso LP, Rydkina E, Vujcic S, Balan
K, Gitlin II, et al. 2017. p16(Ink4a) and senescence-associated β-galactosidase can be
induced in macrophages as part of a reversible response to physiological stimuli. Aging
9: 1867–1884.
Han R, Li L, Ugalde AP, Tal A, Manber Z, Barbera EP, Chiara VD, Elkon R, Agami R. 2018.
Functional CRISPR screen identifies AP1-associated enhancer regulating FOXF1 to
modulate oncogene-induced senescence. Genome Biol 19: 118.
Harper JW, Elledge SJ. 2007. The DNA damage response: ten years after. Mol Cell 28: 739–
745.
Harr B, Karakoc E, Neme R, Teschke M, Pfeifle C, Pezer Ž, Babiker H, Linnenbrink M, Montero
I, Scavetta R, et al. 2016. Genomic resources for wild populations of the house mouse,
Mus musculus and its close relative Mus spretus. Sci Data 3: 160075.
Hayflick L. 1965. The limited in vitro lifetime of human diploid cell strains. Exp Cell Res 37: 614–
636.
He Y, Xie W, Li H, Jin H, Zhang Y, Li Y. 2022. Cellular Senescence in Sarcopenia: Possible
Mechanisms and Therapeutic Potential. Front Cell Dev Biol 9. https://www.fron-
tiersin.org/articles/10.3389/fcell.2021.793088 (Accessed July 5, 2022).
127

Healy K, Guillerme T, Finlay S, Kane A, Kelly SBA, McClean D, Kelly DJ, Donohue I, Jackson
AL, Cooper N. 2014. Ecology and mode-of-life explain lifespan variation in birds and
mammals. Proc R Soc B Biol Sci 281: 20140298.
Heinz S, Romanoski CE, Benner C, Allison KA, Kaikkonen MU, Orozco LD, Glass CK. 2013. Ef-
fect of natural genetic variation on enhancer selection and function. Nature 503: 487–
492.
Helman A, Klochendler A, Azazmeh N, Gabai Y, Horwitz E, Anzi S, Swisa A, Condiotti R, Granit
RZ, Nevo Y, et al. 2016. p16Ink4a-induced senescence of pancreatic beta cells en-
hances insulin secretion. Nat Med 22: 412–420.
Holmes DJ, Austad SN. 1994. Fly Now, Die Later: Life-History Correlates of Gliding and Flying
in Mammals. J Mammal 75: 224–226.
Hsu C-H, Altschuler SJ, Wu LF. 2019. Patterns of Early p21 Dynamics Determine Proliferation-
Senescence Cell Fate after Chemotherapy. Cell 178: 361-373.e12.
Hu D, Tjon EC, Andersson KM, Molica GM, Pham MC, Healy B, Murugaiyan G, Pochet N,
Kuchroo VK, Bokarewa MI, et al. 2020. Aberrant expression of USF2 in refractory rheu-
matoid arthritis and its regulation of proinflammatory cytokines in Th17 cells. Proc Natl
Acad Sci 117: 30639–30648.
Huang R-X, Zhou P-K. 2020. DNA damage response signaling pathways and targets for radio-
therapy sensitization in cancer. Signal Transduct Target Ther 5: 1–27.
Itahana K, Campisi J, Dimri GP. 2004. Mechanisms of cellular senescence in human and
mouse cells. Biogerontology 5: 1–10.
Kale A, Sharma A, Stolzing A, Desprez P-Y, Campisi J. 2020. Role of immune cells in the re-
moval of deleterious senescent cells. Immun Ageing 17: 16.
Kang C. 2019. Senolytics and Senostatics: A Two-Pronged Approach to Target Cellular Senes-
cence for Delaying Aging and Age-Related Diseases. Mol Cells 42: 821–827.
Khan M, Gasser S. 2016. Generating Primary Fibroblast Cultures from Mouse Ear and Tail Tis-
sues. J Vis Exp JoVE. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4781275/ (Ac-
cessed March 23, 2022).
Kim D, Pertea G, Trapnell C, Pimentel H, Kelley R, Salzberg SL. 2013a. TopHat2: accurate
alignment of transcriptomes in the presence of insertions, deletions and gene fusions.
Genome Biol 14: R36.
Kim E-C, Kim J-R. 2019. Senotherapeutics: emerging strategy for healthy aging and age-related
disease. BMB Rep 52: 47–55.
Kim EK, Moon S, Kim DK, Zhang X, Kim J. 2018. CXCL1 induces senescence of cancer-associ-
ated fibroblasts via autocrine loops in oral squamous cell carcinoma. PLOS ONE 13:
e0188847.
128

Kim Y-M, Byun H-O, Jee BA, Cho H, Seo Y-H, Kim Y-S, Park MH, Chung H-Y, Woo HG, Yoon
G. 2013b. Implications of time-series gene expression profiles of replicative senescence.
Aging Cell 12: 622–634.
Krtolica A, Parrinello S, Lockett S, Desprez PY, Campisi J. 2001. Senescent fibroblasts promote
epithelial cell growth and tumorigenesis: a link between cancer and aging. Proc Natl
Acad Sci U S A 98: 12072–12077.
Kurz DJ, Decary S, Hong Y, Erusalimsky JD. 2000. Senescence-associated (beta)-galacto-
sidase reflects an increase in lysosomal mass during replicative ageing of human endo-
thelial cells. J Cell Sci 113 ( Pt 20): 3613–3622.
Laberge R-M, Awad P, Campisi J, Desprez P-Y. 2012. Epithelial-mesenchymal transition in-
duced by senescent fibroblasts. Cancer Microenviron Off J Int Cancer Microenviron Soc
5: 39–44.
Langmead B, Salzberg SL. 2012. Fast gapped-read alignment with Bowtie 2. Nat Methods 9:
357–359.
Lazzarano S, Kučka M, Castro JPL, Naumann R, Medina P, Fletcher MNC, Wombacher R,
Gribnau J, Hochepied T, Van Montagu M, et al. 2018. Genetic mapping of species differ-
ences via in vitro crosses in mouse embryonic stem cells. Proc Natl Acad Sci U S A 115:
3680–3685.
Lee BY, Han JA, Im JS, Morrone A, Johung K, Goodwin EC, Kleijer WJ, DiMaio D, Hwang ES.
2006. Senescence-associated beta-galactosidase is lysosomal beta-galactosidase. Ag-
ing Cell 5: 187–195.
Lee S, Schmitt CA. 2019. The dynamic nature of senescence in cancer. Nat Cell Biol 21: 94–
101.
Levi N, Papismadov N, Solomonov I, Sagi I, Krizhanovsky V. 2020. The ECM path of senes-
cence in aging: components and modifiers. FEBS J 287: 2636–2646.
Lewis KN, Mele J, Hornsby PJ, Buffenstein R. 2012. Stress Resistance in the Naked Mole-Rat:
The Bare Essentials – A Mini-Review. Gerontology 58: 453–462.
Li H. 2011. A statistical framework for SNP calling, mutation discovery, association mapping and
population genetical parameter estimation from sequencing data. Bioinforma Oxf Engl
27: 2987–2993.
Li H, Handsaker B, Wysoker A, Fennell T, Ruan J, Homer N, Marth G, Abecasis G, Durbin R,
1000 Genome Project Data Processing Subgroup. 2009. The Sequence Alignment/Map
format and SAMtools. Bioinforma Oxf Engl 25: 2078–2079.
Livak KJ, Schmittgen TD. 2001. Analysis of Relative Gene Expression Data Using Real-Time
Quantitative PCR and the 2−ΔΔCT Method. Methods 25: 402–408.
López-Otín C, Blasco MA, Partridge L, Serrano M, Kroemer G. 2013. The Hallmarks of Aging.
Cell 153: 1194–1217.
129

Lu Z, Xu S. 2006. ERK1/2 MAP kinases in cell survival and apoptosis. IUBMB Life 58: 621–631.
Ludwig S, Klitzsch A, Baniahmad A. 2011. The ING tumor suppressors in cellular senescence
and chromatin. Cell Biosci 1: 25.
Mahler KL, Fleming JL, Dworkin AM, Gladman N, Cho H-Y, Mao J-H, Balmain A, Toland AE.
2008. Sequence divergence of Mus spretus and Mus musculus across a skin cancer
susceptibility locus. BMC Genomics 9: 626.
Manenti G, Gariboldi M, Elango R, Fiorino A, De Gregorio L, Falvella FS, Hunter K, Housman D,
Pierotti MA, Dragani TA. 1996. Genetic mapping of a pulmonary adenoma resistance
(Par1) in mouse. Nat Genet 12: 455–457.
Mann HB, Whitney DR. 1947. On a Test of Whether one of Two Random Variables is Stochasti-
cally Larger than the Other. Ann Math Stat 18: 50–60.
Martens UM, Chavez EA, Poon SSS, Schmoor C, Lansdorp PM. 2000. Accumulation of Short
Telomeres in Human Fibroblasts Prior to Replicative Senescence. Exp Cell Res 256:
291–299.
Martínez-Zamudio RI, Roux P-F, de Freitas JANLF, Robinson L, Doré G, Sun B, Belenki D, Mi-
lanovic M, Herbig U, Schmitt CA, et al. 2020. AP-1 imprints a reversible transcriptional
programme of senescent cells. Nat Cell Biol 22: 842–855.
Matsumoto Y, Piraino S, Miglietta MP. 2019. Transcriptome Characterization of Reverse Devel-
opment in Turritopsis dohrnii (Hydrozoa, Cnidaria). G3 GenesGenomesGenetics 9:
4127–4138.
Morgan TH. 1910. SEX LIMITED INHERITANCE IN DROSOPHILA. Science 32: 120–122.
Müller E, Keller A, Fregin A, Müller CR, Rost S. 2014. Confirmation of warfarin resistance of nat-
urally occurring VKORC1 variants by coexpression with coagulation factor IX and in sil-
ico protein modelling. BMC Genet 15: 17.
Nagase H, Bryson S, Cordell H, Kemp CJ, Fee F, Balmain A. 1995. Distinct genetic loci control
development of benign and malignant skin tumours in mice. Nat Genet 10: 424–429.
Nagase H, Mao J-H, de Koning JP, Minami T, Balmain A. 2001. Epistatic Interactions between
Skin Tumor Modifier Loci in Interspecific (spretus/musculus) Backcross Mice1. Cancer
Res 61: 1305–1308.
Niccoli T, Partridge L. 2012. Ageing as a risk factor for disease. Curr Biol CB 22: R741-752.
Nickoloff JA, Boss M-K, Allen CP, LaRue SM. 2017. Translational research in radiation-induced
DNA damage signaling and repair. Transl Cancer Res 6: S875–S891.
Noureddine H, Gary-Bobo G, Alifano M, Marcos E, Saker M, Vienney N, Amsellem V, Maitre B,
Chaouat A, Chouaid C, et al. 2011. Pulmonary artery smooth muscle cell senescence is
a pathogenic mechanism for pulmonary hypertension in chronic lung disease. Circ Res
109: 543–553.
130

Olive PL, Banáth JP. 2006. The comet assay: a method to measure DNA damage in individual
cells. Nat Protoc 1: 23–29.
Olivieri F, Prattichizzo F, Grillari J, Balistreri CR. 2018. Cellular Senescence and Inflammaging
in Age-Related Diseases. Mediators Inflamm 2018: e9076485.
Orr HA, Masly JP, Presgraves DC. 2004. Speciation genes. Curr Opin Genet Dev 14: 675–679.
Ozaki K, Ohnishi Y, Iida A, Sekine A, Yamada R, Tsunoda T, Sato H, Sato H, Hori M, Naka-
mura Y, et al. 2002. Functional SNPs in the lymphotoxin-alpha gene that are associated
with susceptibility to myocardial infarction. Nat Genet 32: 650–654.
Panneer Selvam S, Roth BM, Nganga R, Kim J, Cooley MA, Helke K, Smith CD, Ogretmen B.
2018. Balance between senescence and apoptosis is regulated by telomere damage-
induced association between p16 and caspase-3. J Biol Chem 293: 9784–9800.
Papa S, Bubici C, Zazzeroni F, Pham CG, Kuntzen C, Knabb JR, Dean K, Franzoso G. 2006.
The NF-kappaB-mediated control of the JNK cascade in the antagonism of programmed
cell death in health and disease. Cell Death Differ 13: 712–729.
Paramos-de-Carvalho D, Jacinto A, Saúde L. 2021. The right time for senescence ed. M.E.
Bronner. eLife 10: e72449.
Park JT, Lee Y-S, Cho KA, Park SC. 2018. Adjustment of the lysosomal-mitochondrial axis for
control of cellular senescence. Ageing Res Rev 47: 176–182.
Parrinello S, Coppe J-P, Krtolica A, Campisi J. 2005. Stromal-epithelial interactions in aging and
cancer: senescent fibroblasts alter epithelial cell differentiation. J Cell Sci 118: 485–496.
Passarino G, De Rango F, Montesanto A. 2016. Human longevity: Genetics or Lifestyle? It
takes two to tango. Immun Ageing A 13: 12.
Patel PL, Suram A, Mirani N, Bischof O, Herbig U. 2016. Derepression of hTERT gene expres-
sion promotes escape from oncogene-induced cellular senescence. Proc Natl Acad Sci
113: E5024–E5033.
Patil P, Niedernhofer LJ, Robbins PD, Lee J, Sowa G, Vo N. 2018. Cellular senescence in inter-
vertebral disc aging and degeneration. Curr Mol Biol Rep 4: 180–190.
Pawar SA, Szentirmay MN, Hermeking H, Sawadogo M. 2004. Evidence for a cancer-specific
switch at the CDK4 promoter with loss of control by both USF and c-Myc. Oncogene 23:
6125–6135.
Pérez del Villar L, Vicente B, Galindo-Villardón P, Castellanos A, Pérez-Losada J, Muro A.
2013. Schistosoma mansoni experimental infection in Mus spretus (SPRET/EiJ strain)
mice. Parasite 20: 27.
Pilling LC, Kuo C-L, Sicinski K, Tamosauskaite J, Kuchel GA, Harries LW, Herd P, Wallace R,
Ferrucci L, Melzer D. 2017. Human longevity: 25 genetic loci associated in 389,166 UK
biobank participants. Aging 9: 2504–2520.
131

Pinheiro I, Dejager L, Petta I, Vandevyver S, Puimège L, Mahieu T, Ballegeer M, Van Hau-
wermeiren F, Riccardi C, Vuylsteke M, et al. 2013. LPS resistance of SPRET/Ei mice is
mediated by Gilz, encoded by the Tsc22d3 gene on the X chromosome. EMBO Mol Med
5: 456–470.
Pluquet O, Pourtier A, Abbadie C. 2015. The unfolded protein response and cellular senes-
cence. A review in the theme: cellular mechanisms of endoplasmic reticulum stress sig-
naling in health and disease. Am J Physiol Cell Physiol 308: C415-425.
Podhorecka M, Skladanowski A, Bozko P. 2010. H2AX Phosphorylation: Its Role in DNA Dam-
age Response and Cancer Therapy. J Nucleic Acids 2010: 920161.
Promislow DE. 1993. On size and survival: progress and pitfalls in the allometry of life span. J
Gerontol 48: B115-123.
Purcell M, Kruger A, Tainsky MA. 2014. Gene expression profiling of replicative and induced se-
nescence. Cell Cycle Georget Tex 13: 3927–3937.
Putri GH, Anders S, Pyl PT, Pimanda JE, Zanini F. 2022. Analysing high-throughput sequencing
data in Python with HTSeq 2.0. Bioinformatics btac166.
Qin D, Ren R, Jia C, Lu Y, Yang Q, Chen L, Wu X, Zhu J, Guo Y, Yang P, et al. 2018. Rapamy-
cin Protects Skin Fibroblasts from Ultraviolet B-Induced Photoaging by Suppressing the
Production of Reactive Oxygen Species. Cell Physiol Biochem 46: 1849–1860.
Qyang Y, Luo X, Lu T, Ismail PM, Krylov D, Vinson C, Sawadogo M. 1999. Cell-type-dependent
activity of the ubiquitous transcription factor USF in cellular proliferation and transcrip-
tional activation. Mol Cell Biol 19: 1508–1517.
Ranawat P, Rawat S. 2017. Radiation resistance in thermophiles: mechanisms and applica-
tions. World J Microbiol Biotechnol 33: 112.
Redon CE, Dickey JS, Bonner WM, Sedelnikova OA. 2009. γ-H2AX as a biomarker of DNA
damage induced by ionizing radiation in human peripheral blood lymphocytes and artifi-
cial skin. Adv Space Res Off J Comm Space Res COSPAR 43: 1171–1178.
Rodier F, Coppé J-P, Patil CK, Hoeijmakers WAM, Muñoz DP, Raza SR, Freund A, Campeau
E, Davalos AR, Campisi J. 2009. Persistent DNA damage signalling triggers senes-
cence-associated inflammatory cytokine secretion. Nat Cell Biol 11: 973–979.
Roses AD. 2006. On the discovery of the genetic association of Apolipoprotein E genotypes and
common late-onset Alzheimer disease. J Alzheimers Dis JAD 9: 361–366.
Roy S, Lagree S, Hou Z, Thomson JA, Stewart R, Gasch AP. 2013. Integrated Module and
Gene-Specific Regulatory Inference Implicates Upstream Signaling Networks. PLOS
Comput Biol 9: e1003252.
Ruuth M, Soronen J, Kaiharju E, Merikanto K, Perttilä J, Metso J, Lee-Rueckert M, Taskinen M-
R, Kovanen PT, Öörni K, et al. 2018. USF1 deficiency alleviates inflammation, enhances
cholesterol efflux and prevents cholesterol accumulation in macrophages. Lipids Health
Dis 17: 285.
132

Ryu SJ, Oh YS, Park SC. 2007. Failure of stress-induced downregulation of Bcl-2 contributes to
apoptosis resistance in senescent human diploid fibroblasts. Cell Death Differ 14: 1020–
1028.
Salminen A, Kaarniranta K. 2010. Genetics vs. entropy: longevity factors suppress the NF-kap-
paB-driven entropic aging process. Ageing Res Rev 9: 298–314.
Salminen A, Kauppinen A, Kaarniranta K. 2012. Emerging role of NF-κB signaling in the induc-
tion of senescence-associated secretory phenotype (SASP). Cell Signal 24: 835–845.
Salminen A, Ojala J, Kaarniranta K. 2011a. Apoptosis and aging: increased resistance to apop-
tosis enhances the aging process. Cell Mol Life Sci 68: 1021–1031.
Salminen A, Ojala J, Kaarniranta K, Haapasalo A, Hiltunen M, Soininen H. 2011b. Astrocytes in
the aging brain express characteristics of senescence-associated secretory phenotype.
Eur J Neurosci 34: 3–11.
Salmonowicz H, Passos JF. 2017. Detecting senescence: a new method for an old pigment. Ag-
ing Cell 16: 432–434.
Salotti J, Johnson PF. 2019. Regulation of senescence and the SASP by the transcription factor
C/EBPβ. Exp Gerontol 128: 110752.
Samarakoon R, Overstreet JM, Higgins SP, Higgins PJ. 2012. TGF-β1 → SMAD/p53/USF2 →
PAI-1 transcriptional axis in ureteral obstruction-induced renal fibrosis. Cell Tissue Res
347: 117–128.
Santoro A, Spinelli CC, Martucciello S, Nori SL, Capunzo M, Puca AA, Ciaglia E. 2018. Innate
immunity and cellular senescence: The good and the bad in the developmental and
aged brain. J Leukoc Biol 103: 509–524.
Santos J, Montagutelli X, Acevedo A, López P, Vaquero C, Fernández M, Arnau M-R, Szatanik
M, Salido E, Guénet J-L, et al. 2002. A new locus for resistance to γ-radiation-induced
thymic lymphoma identified using inter-specific consomic and inter-specific recombinant
congenic strains of mice. Oncogene 21: 6680–6683.
Sapieha P, Mallette FA. 2018. Cellular Senescence in Postmitotic Cells: Beyond Growth Arrest.
Trends Cell Biol 28: 595–607.
Sato AYS, Antonioli E, Tambellini R, Campos AH. 2011. ID1 inhibits USF2 and blocks TGF-β-
induced apoptosis in mesangial cells. Am J Physiol Renal Physiol 301: F1260-1269.
Schneider CA, Rasband WS, Eliceiri KW. 2012. NIH Image to ImageJ: 25 years of image analy-
sis. Nat Methods 9: 671–675.
Schulze-Makuch D. 2019. The Naked Mole-Rat: An Unusual Organism with an Unexpected La-
tent Potential for Increased Intelligence? Life 9: 76.
Seluanov A, Hine C, Azpurua J, Feigenson M, Bozzella M, Mao Z, Catania KC, Gorbunova V.
2009. Hypersensitivity to contact inhibition provides a clue to cancer resistance of naked
mole-rat. Proc Natl Acad Sci 106: 19352–19357.
133

Seoane M, Costoya JA, Arce VM. 2017. Uncoupling Oncogene-Induced Senescence (OIS) and
DNA Damage Response (DDR) triggered by DNA hyper-replication: lessons from pri-
mary mouse embryo astrocytes (MEA). Sci Rep 7: 12991.
Serrao E, Cherepanov P, Engelman AN. 2016. Amplification, Next-generation Sequencing, and
Genomic DNA Mapping of Retroviral Integration Sites. J Vis Exp JoVE.
Siddiqui MS, François M, Fenech MF, Leifert WR. 2015. Persistent γH2AX: A promising molec-
ular marker of DNA damage and aging. Mutat Res Rev Mutat Res 766: 1–19.
Silva E, Ideker T. 2019. Transcriptional Responses to DNA Damage. DNA Repair 79: 40–49.
Slatkin M. 2008. Linkage disequilibrium — understanding the evolutionary past and mapping the
medical future. Nat Rev Genet 9: 477–485.
Song X, Zhu M, Li H, Liu B, Yan Z, Wang W, Li H, Sun J, Li S. 2018. USF1 promotes the devel-
opment of knee osteoarthritis by activating the NF ‑κB signaling pathway. Exp Ther Med
16: 3518–3524.
Song Y, Endepols S, Klemann N, Richter D, Matuschka F-R, Shih C-H, Nachman MW, Kohn
MH. 2011. Adaptive Introgression of Anticoagulant Rodent Poison Resistance by Hybrid-
ization between Old World Mice. Curr Biol 21: 1296–1301.
Steinmetz LM, Sinha H, Richards DR, Spiegelman JI, Oefner PJ, McCusker JH, Davis RW.
2002. Dissecting the architecture of a quantitative trait locus in yeast. Nature 416: 326–
330.
Stern DL. 1998. A role of Ultrabithorax in morphological differences between Drosophila spe-
cies. Nature 396: 463–466.
Sun W, Hu Y. 2013. eQTL Mapping Using RNA-seq Data. Stat Biosci 5: 198–219.
Sun Y, Wang X, Liu T, Zhu X, Pan X. 2022. The multifaceted role of the SASP in atherosclero-
sis: from mechanisms to therapeutic opportunities. Cell Biosci 12: 74.
Tai H, Wang Z, Gong H, Han X, Zhou J, Wang X, Wei X, Ding Y, Huang N, Qin J, et al. 2016.
Autophagy impairment with lysosomal and mitochondrial dysfunction is an important
characteristic of oxidative stress-induced senescence. Autophagy 13: 99–113.
Takasugi M, Firsanov D, Tombline G, Ning H, Ablaeva J, Seluanov A, Gorbunova V. 2020. Na-
ked mole-rat very-high-molecular-mass hyaluronan exhibits superior cytoprotective prop-
erties. Nat Commun 11: 2376.
Tanzer MC, Frauenstein A, Stafford CA, Phulphagar K, Mann M, Meissner F. 2020. Quantitative
and Dynamic Catalogs of Proteins Released during Apoptotic and Necroptotic Cell
Death. Cell Rep 30: 1260-1270.e5.
Tian X, Azpurua J, Hine C, Vaidya A, Myakishev-Rempel M, Ablaeva J, Mao Z, Nevo E, Gor-
bunova V, Seluanov A. 2013. High molecular weight hyaluronan mediates the cancer re-
sistance of the naked mole-rat. Nature 499: 346–349.
134

Tian X, Firsanov D, Zhang Z, Cheng Y, Luo L, Tombline G, Tan R, Simon M, Henderson S,
Steffan J, et al. 2019. SIRT6 Is Responsible for More Efficient DNA Double-Strand Break
Repair in Long-Lived Species. Cell 177: 622-638.e22.
Tyler EJ, Gutierrez Del Arroyo A, Hughes BK, Wallis R, Garbe JC, Stampfer MR, Koh J, Lowe
R, Philpott MP, Bishop CL. 2021. Early growth response 2 (EGR2) is a novel regulator of
the senescence programme. Aging Cell 20: e13318.
Ungerleider K, Beck J, Lissa D, Turnquist C, Horikawa I, Harris BT, Harris CC. 2021. Astrocyte
senescence and SASP in neurodegeneration: tau joins the loop. Cell Cycle Georget Tex
20: 752–764.
Vanlaere I, Vanderrijst A, Guénet J-L, De Filette M, Libert C. 2008. Mx1 causes resistance
against influenza A viruses in the Mus spretus-derived inbred mouse strain SPRET/Ei.
Cytokine 42: 62–70.
Venkata Narayanan I, Paulsen MT, Bedi K, Berg N, Ljungman EA, Francia S, Veloso A, Mag-
nuson B, di Fagagna F d’Adda, Wilson TE, et al. 2017. Transcriptional and post-tran-
scriptional regulation of the ionizing radiation response by ATM and p53. Sci Rep 7:
43598.
Veyrieras J-B, Kudaravalli S, Kim SY, Dermitzakis ET, Gilad Y, Stephens M, Pritchard JK. 2008.
High-Resolution Mapping of Expression-QTLs Yields Insight into Human Gene Regula-
tion. PLOS Genet 4: e1000214.
Vicencio JM, Galluzzi L, Tajeddine N, Ortiz C, Criollo A, Tasdemir E, Morselli E, Ben Younes A,
Maiuri MC, Lavandero S, et al. 2008. Senescence, apoptosis or autophagy? When a
damaged cell must decide its path--a mini-review. Gerontology 54: 92–99.
Vierbuchen T, Ling E, Cowley CJ, Couch CH, Wang X, Harmin DA, Roberts CWM, Greenberg
ME. 2017. AP-1 Transcription Factors and the BAF Complex Mediate Signal-Dependent
Enhancer Selection. Mol Cell 68: 1067-1082.e12.
Villar D, Flicek P, Odom DT. 2014. Dynamics, mechanisms, and functional implications of tran-
scription factor binding evolution in metazoans. Nat Rev Genet 15: 221–233.
Villarroel CA, Bastías M, Canessa P, Cubillos FA. 2021. Uncovering Divergence in Gene Ex-
pression Regulation in the Adaptation of Yeast to Nitrogen Scarcity. mSystems 6:
e0046621.
Visscher PM, Wray NR, Zhang Q, Sklar P, McCarthy MI, Brown MA, Yang J. 2017. 10 Years of
GWAS Discovery: Biology, Function, and Translation. Am J Hum Genet 101: 5–22.
von Zglinicki T, Wan T, Miwa S. 2021. Senescence in Post-Mitotic Cells: A Driver of Aging? An-
tioxid Redox Signal 34: 308–323.
Wan M, Gray-Gaillard EF, Elisseeff JH. 2021. Cellular senescence in musculoskeletal homeo-
stasis, diseases, and regeneration. Bone Res 9: 1–12.
Wang M, Kong L. 2019. pblat: a multithread blat algorithm speeding up aligning sequences to
genomes. BMC Bioinformatics 20: 28.
135

Wang Y, Sui Y, Lian A, Han X, Liu F, Zuo K, Liu M, Sun W, Wang Z, Liu Z, et al. 2021. PBX1
Attenuates Hair Follicle-Derived Mesenchymal Stem Cell Senescence and Apoptosis by
Alleviating Reactive Oxygen Species-Mediated DNA Damage Instead of Enhancing DNA
Damage Repair. Front Cell Dev Biol 9: 739868.
Wang Y, Xu Q, Sack L, Kang C, Elledge SJ. 2016. A gain-of-function senescence bypass
screen identifies the homeobox transcription factor DLX2 as a regulator of ATM–p53 sig-
naling. Genes Dev 30: 293–306.
Weiss CV, Roop JI, Hackley RK, Chuong JN, Grigoriev IV, Arkin AP, Skerker JM, Brem RB.
2018. Genetic dissection of interspecific differences in yeast thermotolerance. Nat Genet
50: 1501–1504.
Williams GC. 1957. Pleiotropy, Natural Selection, and the Evolution of Senescence. Evolution
11: 398–411.
Wittkopp PJ, Haerum BK, Clark AG. 2004. Evolutionary changes in cis and trans gene regula-
tion. Nature 430: 85–88.
Xie Q, Peng S, Tao L, Ruan H, Yang Y, Li T-M, Adams U, Meng S, Bi X, Dong M-Q, et al. 2014.
E2F Transcription Factor 1 Regulates Cellular and Organismal Senescence by Inhibiting
Forkhead Box O Transcription Factors. J Biol Chem 289: 34205–34213.
Xu M, Pirtskhalava T, Farr JN, Weigand BM, Palmer AK, Weivoda MM, Inman CL, Ogrodnik
MB, Hachfeld CM, Fraser DG, et al. 2018. Senolytics improve physical function and in-
crease lifespan in old age. Nat Med 24: 1246–1256.
Yan Y, Yu H, Sun L, Liu H, Wang C, Wei X, Song F, Li H, Ge H, Qian H, et al. 2019. Laminin α4
overexpression in the anterior lens capsule may contribute to the senescence of human
lens epithelial cells in age-related cataract. Aging 11: 2699–2723.
Yang K, Wang F, Zhang H, Wang X, Chen L, Su X, Wu X, Han Q, Chen Z, Chen Z-S, et al.
2021. Target Inhibition of CBP Induced Cell Senescence in BCR-ABL- T315I Mutant
Chronic Myeloid Leukemia. Front Oncol 10. https://www.frontiersin.org/arti-
cle/10.3389/fonc.2020.588641 (Accessed March 10, 2022).
Yevshin I, Sharipov R, Kolmykov S, Kondrakhin Y, Kolpakov F. 2019. GTRD: a database on
gene transcription regulation-2019 update. Nucleic Acids Res 47: D100–D105.
Zeng Y, Nie C, Min J, Liu X, Li M, Chen H, Xu H, Wang M, Ni T, Li Y, et al. 2016. Novel loci and
pathways significantly associated with longevity. Sci Rep 6: 21243.
Zhang C, Zhang X, Huang L, Guan Y, Huang X, Tian X-L, Zhang L, Tao W. 2021. ATF3 drives
senescence by reconstructing accessible chromatin profiles. Aging Cell 20: e13315.
Zhang G, Cowled C, Shi Z, Huang Z, Bishop-Lilly KA, Fang X, Wynne JW, Xiong Z, Baker ML,
Zhao W, et al. 2013. Comparative analysis of bat genomes provides insight into the evo-
lution of flight and immunity. Science 339: 456–460.
136

Zhang X, Li J, Sejas DP, Pang Q. 2005. The ATM/p53/p21 pathway influences cell fate decision
between apoptosis and senescence in reoxygenated hematopoietic progenitor cells. J
Biol Chem 280: 19635–19640.
Zhao H, Darzynkiewicz Z. 2013. Biomarkers of Cell Senescence Assessed by Imaging Cytome-
try. Methods Mol Biol Clifton NJ 965: 83–92.
Zhao Y, Tyshkovskiy A, Muñoz-Espín D, Tian X, Serrano M, de Magalhaes JP, Nevo E, Glady-
shev VN, Seluanov A, Gorbunova V. 2018. Naked mole rats can undergo developmen-
tal, oncogene-induced and DNA damage-induced cellular senescence. Proc Natl Acad
Sci 115: 1801–1806.
Zou J, Lei T, Guo P, Yu J, Xu Q, Luo Y, Ke R, Huang D. 2019. Mechanisms shaping the role of
ERK1/2 in cellular senescence. Mol Med Rep 19: 759–770.

Asset Metadata

Creator Kang, Taekyu (author)

Core Title Natural divergence of traits in species of mice reveals novel molecular mechanisms of cellular senescence

Contributor Electronically uploaded by the author (provenance)

School Leonard Davis School of Gerontology

Degree Doctor of Philosophy

Degree Program Biology of Aging

Degree Conferral Date 2022-12

Publication Date 09/12/2022

Defense Date 08/26/2022

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

Tag cellular senescence,Mus spretus,natural variation,OAI-PMH Harvest,RH-seq,USF2

Format application/pdf (imt)

Language English

Advisor Brem, Rachel (committee chair), Benayoun, Berenice (committee member), Campisi, Judith (committee member), Dean, Matthew (committee member), Verdin, Eric (committee member)

Creator Email kang.taekyu.94@gmail.com,taekyuka@usc.edu

Permanent Link (DOI) https://doi.org/10.25549/usctheses-oUC111992644

Unique identifier UC111992644

Legacy Identifier etd-KangTaekyu-11200

Document Type Thesis

Format application/pdf (imt)

Rights Kang, Taekyu

Type texts

Source 20220917-usctheses-batch-980 (batch), University of Southern California (contributing entity), University of Southern California Dissertations and Theses (collection)

Access Conditions The author retains rights to his/her dissertation, thesis or other graduate work according to U.S. copyright law. Electronic access is being provided by the USC Libraries in agreement with the author, as the original true and official version of the work, but does not grant the reader permission to use the work if the desired use is covered by copyright. It is the author, as rights holder, who must provide use permission if such use is covered by copyright. The original signature page accompanying the original submission of the work to the USC Libraries is retained by the USC Libraries and a copy of it may be obtained by authorized requesters contacting the repository e-mail address given.

Repository Name University of Southern California Digital Library

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

Repository Email uscdl@usc.edu

Abstract (if available)

Abstract Aging is a risk factor for chronic human diseases, and age-associated phenotypes consti-tute a major economic health-care burden as well as a threat to quality of life for elderly popula-tions. Against the backdrop of landmark successes in the molecular and genetic study of aging biology, many of the mechanisms of aging remain incompletely understood. To fill this knowledge gap, classic research paradigms in the field have emphasized a few animal models. But the diver-sity of aging phenotypes across the natural world can also provide a rich resource for the discov-ery of aging determinants—including pro-lifespan, pro-healthspan factors that have evolved in non-model systems. This thesis describes my work to characterize and screen natural genetic variation between mouse species in a cell-autonomous program called cellular senescence, a ma-jor driver of aging phenotypes in animal models.
In our first approach, we surveyed natural variation of senescence phenotypes between species of the Mus genus. We applied genotoxic stress to primary tail fibroblasts extracted from purebred mice of various Mus species and observed that cells from M. musculus, the classic la-boratory model, were unique across the genus in their avid senescence program. In our second approach, we developed a natural variation-based screen which takes as input transcription factor binding sites and gene expression as they differ between species, and finds signatures of regula-tory function during cellular senescence. We applied our technique in M. musculus x M. spretus interspecific hybrid cells, identified the transcription factor USF2 as a top candidate, and validated novel roles for this factor in the DNA damage response and cellular senescence. Finally, in our third approach we developed foundational methods for a tool to genetically dissect natural trait variation across species in mammalian cell culture. Together, our findings highlight the power of natural variation-based studies to uncover molecular mechanisms of aging-relevant traits.