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Metabolomic and proteomic approaches to understanding senescence and aging in mammary epithelial cells
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Metabolomic and proteomic approaches to understanding senescence and aging in mammary epithelial cells
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Content
METABOLOMIC AND PROTEOMIC APPROACHES TO
UNDERSTANDING SENESCENCE AND AGING
IN MAMMARY EPITHELIAL CELLS
by
Alireza Delfarah
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
(CHEMICAL ENGINEERING)
December 2020
Copyright 2020 Alireza Delfarah
i
Acknowledgments
I would like to express my appreciation and gratitude for the people that have supported me
throughout my PhD program. I would like to thank my advisor and mentor, Dr. Nicholas Graham, who has
always supported me in any possible way. I have been blessed by Nick’s guidance, patience and knowledge,
and am thankful for the opportunity that I had to do research in his lab. I thank my committee, Dr. Stacey
Finley, Dr. Noah Malmstadt, Dr. Muhammad Sahimi and Dr. Pin Wang, for providing constructive input
to my research, Qualifying Exam and Dissertation.
I appreciate having the opportunity to collaborate with Dr. Amir Goldkorn, Dr. Arunika Ekanayake,
Dr. Emma Fong, Dr. John Mac, Dr. Jason Junge, Dr. Jun Zhao, Dr. Junmin Wang, Dr. Pinghui Feng, Dr.
Si Li, Dr. Scot Fraser, Dr. Shannon Mumenthaler, Dr. Stacey Finley, Dr. Tong Xu and The Translational
Imaging Center of USC on several research projects. I would like to thank my lab-mates in the Graham
Lab, Belinda Garana, DongQing Zheng, James Joly, Jesse Yang, Jessica Zhou, Melanie MacMullan,
Nicholas Hartel, and Sydney Parrish, as well as my PhD cohort and friends Andre Kovach, Christine Cheng,
Gunce Cinay, Mahsa Rahro, Nami Mogharabin, Nariman Pirouzan, Prathamesh Karandikar, Sarah Katz,
and Zumra Peksaglam. I am grateful to have met and worked with a group of extremely talented and well-
rounded peers. I appreciate the support of staff members in the MORK Family Department and Viterbi
School of Engineering. I would like to specially thank Andy Chen and Tracy Charles for their kind support
of me and other Viterbi students to have a pleasant experience at USC. I thank members of USC Triathlon
Club for their friendship and for creating a unique and fun athletic experience for me.
I would like to thank people of Amyris Inc. for providing an opportunity for me to learn about
research and development in the Biotech. I would particularly like to acknowledge my supervisor Dr. Mona
Elbadawi-Sidhu for her guidance and support of me during and after my internship.
Finally, I would like to express my gratitude to my beloved mother, father, sister and brother. Even
though several thousands of miles away from me now, I have always felt and been blessed by their love
and support of me, and I cannot wait to see them again soon.
ii
Table of Contents
Acknowledgments ........................................................................................................................................ i
List of Figures ............................................................................................................................................. iv
List of Tables ............................................................................................................................................... v
Abstract ....................................................................................................................................................... vi
1. Chapter 1. Introduction ...................................................................................................................... 1
1.1 Cellular senescence, aging and cancer .......................................................................................... 1
1.2 LC-MS based metabolomics ......................................................................................................... 3
1.3 LC-MS based proteomics.............................................................................................................. 7
2. Chapter 2. Investigating metabolomic regulation of senescence .................................................. 10
2.1 Objective ..................................................................................................................................... 10
2.2 Materials and Methods ................................................................................................................ 10
2.3 Results ......................................................................................................................................... 14
2.3.1 Development and optimization of LC-MS method for metabolomics ................................ 14
2.3.2 Establishment of a human mammary epithelial cell model of senescence and
immortalization ................................................................................................................... 15
2.3.3 Senescent HMEC do not exhibit a glycolytic shift and energy crisis ................................. 17
2.3.4 Replicative senescence in HMEC is accompanied by significant alterations in nucleotide
and nucleoside pool sizes .................................................................................................... 20
2.3.5 Glucose-derived carbon fuels nucleotide synthesis in proliferating but not senescent
HMEC ................................................................................................................................. 21
2.3.6 Glutamine-derived carbon fuels pyrimidines synthesis in proliferating but not senescent
HMEC ................................................................................................................................. 24
2.3.7 HMEC immortalization with telomerase restores nucleotide pools and fluxes .................. 25
2.3.8 Inhibiting ribonucleotide reductase induces senescence ..................................................... 28
2.3.9 Integrative analysis of glucose contribution to metabolites in proliferating and senescent
HMEC ................................................................................................................................. 31
2.4 Discussion and Conclusion ......................................................................................................... 32
3. Chapter 3. Proteomic profiling of senescent HMECs and identification of senescence
biomarkers ......................................................................................................................................... 35
3.1 Objective ..................................................................................................................................... 35
3.2 Materials and Methods ................................................................................................................ 35
3.3 Results ......................................................................................................................................... 39
3.3.1 Replicative senescence alters the HMECs proteome .......................................................... 39
3.3.2 The proteome of hTERT-immortalized HMECs resembles that of proliferating HMECs . 42
iii
3.3.3 The proteome of RRM2 inhibition-induced senescence resembles that of replicative
senescence ........................................................................................................................... 44
3.3.4 Data integration identifies a proteomic signature of HMEC senescence ............................ 46
3.3.5 Defining a senescence score that predicts HMECs senescence .......................................... 48
3.3.6 Large-scale drug screening databases predict that EGFR and MEK inhibitors are senolytic
compounds in HMECs ........................................................................................................ 49
3.4 Discussion and conclusion .......................................................................................................... 56
Tables ......................................................................................................................................................... 60
References .................................................................................................................................................. 95
iv
List of Figures
Figure 1.1 Workflow of LC-MS based metabolomics ........................................................................... 5
Figure 1.2 Integrative metabolomics analysis ....................................................................................... 7
Figure 1.3 Workflow of LC-MS based proteomics ................................................................................ 9
Figure 2.1 Development of LC-MS-based metabolomics method ....................................................... 15
Figure 2.2 Establishment of an HMEC model of senescence. ............................................................. 16
Figure 2.3 Replicative senescence in HMEC is accompanied by significant alterations in metabolite
pool sizes.. ........................................................................................................................... 19
Figure 2.4 Glucose-derived carbon fuels nucleotide synthesis in young but not senescent HMEC. ... 22
Figure 2.5 RNA expression analysis confirms reduced nucleotide synthesis in senescent cells.. ........ 24
Figure 2.6 Glutamine-derived carbon fuels pyrimidines synthesis in proliferating but not senescent
HMEC.. ............................................................................................................................... 25
Figure 2.7 hTERT immortalization restores nucleotide pools and fluxes ............................................ 26
Figure 2.8 Inhibiting ribonucleotide reductase induces senescence. .................................................... 29
Figure 2.9 Integrative analysis of glucose contribution to metabolites in proliferating and senescent
HMEC. ................................................................................................................................ 31
Figure 3.1 Replicative senescence alters the HMECs proteome.. ........................................................ 41
Figure 3.2 The proteome of hTERT-immortalized HMECs resembles that of proliferating HMECs.. 43
Figure 3.3 The proteome of RRM2 inhibition-induced senescence resembles that of replicative
senescence. .......................................................................................................................... 45
Figure 3.4 Data integration identifies a proteomic signature of HMEC senescence. ........................... 47
Figure 3.5 Defining a senescence score that predicts HMEC senescence. ........................................... 49
Figure 3.6 Large-scale drug screening databases correctly identify that dasatinib but not navitoclax is
senolytic in adipocytes.. ...................................................................................................... 51
Figure 3.7 Gene expression of HMECs senescence signature hits are co-regulated in cancer lines.. .. 52
Figure 3.8 Protein expression of HMECs senescence signature hits are co-regulated in cancer lines. 53
Figure 3.9 Large-scale drug screening databases predict that EGFR and MEK inhibitors are senolytic
compounds in HMECs. ....................................................................................................... 55
v
List of Tables
Table 1: Replicative senescence metabolite consumption/secretion .......................................................... 60
Table 2: Replicative senescence metabolite pool sizes .............................................................................. 61
Table 3: Replicative senescence [U-
13
C]-glucose labeling ........................................................................ 64
Table 4: Replicative senescence [1,2-
13
C]-glucose labeling ...................................................................... 67
Table 5: Metabolic pathway analysis of senescent HMEC and IMR90 gene expression data .................. 70
Table 6: Replicative senescence [U-
13
C]-glutamine labeling .................................................................... 71
Table 7: hTERT-immortalization metabolite consumption/secretion ........................................................ 74
Table 8: hTERTimmortalization metabolite pool sizes ............................................................................. 75
Table 9: hTERT immortalization [U-
13
C]-glucose labeling ....................................................................... 77
Table 10: Triapine-induced senescence metabolite pool sizes ................................................................... 80
Table 11: Triapine-induced senescence [U-
13
C]-glucose labeling ............................................................. 82
Table 12: Proteomics analysis of replicative senescence ........................................................................... 85
Table 13: Proteomics analysis of hTERT-immortalization ........................................................................ 88
Table 14: Proteomics analysis of triapine-induced senescence ................................................................. 90
Table 15: Integrated proteomics analysis of HMECs senescence proteomics analysis ............................. 92
vi
Abstract
Cellular senescence is a mechanism by which cells permanently withdraw from the cell cycle in
response to stresses including telomere shortening, DNA damage, or oncogenic signaling. Underlying
mechanisms that initiate and force the senescence program produce dual effects on the nearby tissue and
consequently on the organism. The irreversible mechanism of senescence against proliferation constitutes
a barrier against cancer cell development. In addition, senescent cells interact with their microenvironment
through secretion of numerous inflammatory signals which modulate immune response and wound healing,
however, as senescent cells accumulate in the aging tissue they contribute to chronic inflammation and age-
related diseases. In culture, normal human epithelial cells enter senescence after a limited number of cell
divisions, known as replicative senescence. To investigate how metabolic pathways regulate replicative
senescence, we used LC-MS–based metabolomics to analyze senescent primary human mammary epithelial
cells (HMECs). Analysis of intracellular metabolite pool sizes indicated that senescent cells exhibit
depletion of metabolites from nucleotide synthesis pathways. Furthermore, stable isotope tracing with
13
C-
labeled glucose or glutamine revealed a dramatic blockage of flux of these two metabolites into nucleotide
synthesis pathways in senescent HMECs. Because of the importance of senescent cells in determining fate
of human tissues, organs and lifespan, it is crucial to effectively identify senescent cells with specific
biomarkers in order to a) measure degree of aging in each tissue and b) be able to therapeutically target
senescent cells for removal. Therefore, we utilized mass spectrometry-based proteomics method to identify
biomarkers of aging mammary tissue using HMECs model of senescence. By applying statistical and
computational tools we identified the most consistently upregulated and downregulated proteins in
senescent cells some of which could serve as novel biomarkers of senescence. Additionally, we leveraged
HMECs senescence signature with large-scale drug screening to infer novel therapeutics to specifically kill
senescent cells. Our computational analysis suggests that inhibitors of epidermal growth factor receptor
(EGFR) and mitogen-activated protein kinase (MEK) are promising drugs for removing senescent cells.
Taken together, our LC-MS-based metabolomics and proteomics approach significantly extends the
vii
molecular knowledge of epithelial senescence. We demonstrate that inhibition of nucleotide synthesis plays
a causative role in the establishment of replicative senescence. Additionally, we identified several novel
biomarkers of senescence and potential therapeutic targets for selective removal of senescent cells.
1
1. Chapter 1. Introduction
1.1 Cellular senescence, aging and cancer
Senescence is known as the irreversible cell cycle arrest that is induced by different stimuli such as
shortening of telomeres, oncogenes and DNA damaging agents stress (1). In culture, replicative senescence
occurs in normal cells after a limited number of population doublings, the so-called Hayflick limit (2).
Following activation of oncogenes including KRAS and BRAF, senescence acts to prevent proliferation,
thus preventing against cancer (3–6). In fact, senescent cells have been identified in the benign and
premalignant lesions (7). Moreover, senescence cells have been found to play constructive role in wound
healing (8) through their secretory phenotype. In addition to its role in aging and tumor suppression,
senescent cells can contribute to hyperplastic pathologies including cancer through secretion of numerous
pro-inflammatory signals (ie, senescence-associated secretory phenotype, or SASP) (9). Senescence also
contributes to genomic alterations in cancer. For example, the degree of senescence correlates with the
overall level of genomic instability in immortalized mouse fibroblasts (10).
Senescent cells accumulate with age as a result of compromised immune system or inability of
senescent cells to attract immune cells for surveillance (11, 12). The accumulation of senescent cells
promotes a decline in the regenerative capability of tissue due to lack of enough cells with normal function.
Moreover, the secretory phenotype of the lingering senescent cells exerts disease-causing stresses,
manipulates the microenvironment and disturbs the stem cell function which can result in tissue
inflammation and tumorigenesis (9, 13–17). These findings may support the hypothesis that senescence
promotes aging in an antagonistic pleiotropy manner (18). However, it is not yet clear whether positive and
negative effects of senescence prevail at younger and older age, respectively (19). Accumulating evidence
from literature also demonstrates that induction of senescence in cancer cells can be applied as a novel
therapeutic method in cancer treatment (20, 21).
2
Although senescent cells are permanently arrested, they are highly metabolically active and
demonstrate significant metabolic differences compared to proliferating, non-senescent cells (22).
Senescence metabolism has primarily been studied in the context of oncogene-induced senescence. Studies
of human fibroblasts in culture have shown that replicative senescence is accompanied by increased
glycolysis (23–25). In oncogene-induced senescence, increased glucose consumption is shunted away from
the pentose phosphate pathway, leading to decreased nucleotide synthesis (3, 26, 27). Alterations in
mitochondrial function can also induce senescence through AMPK and p53-dependent pathways (28, 29).
Importantly, it has also been demonstrated that metabolic genes including phosphoglycerate mutase,
pyruvate dehydrogenase and malic enzymes can regulate entry into and escape from senescence (5, 30, 31).
Notably, in a study of IMR90 lung fibroblasts, oncogenic RAS induced senescence by suppressing
nucleotide metabolism, leading to the depletion of deoxynucleoside triphosphates (dNTPs), the building
blocks of DNA and accumulation of γ-H2AX foci (3). This may couple senescence to the genomic
instability observed in cancer, whereby early stages of oncogene-induced tumorigenesis is coupled to
replication stress caused by insufficient availability of nucleotides (32). Several other studies showed that
maintenance of sufficient and balanced dNTPs pools is essential for maintaining genomic integrity (33, 34),
and unbalanced dNTPs pools causes replication stress, genomic instability, and mutagenesis (35, 36).
Therefore, identifying the mechanisms by which metabolism regulates senescence is essential to
understanding the senescence program during aging and tumor suppression.
Induction of senescence with drugs has been used as a therapeutic strategy for treatment of cancer.
Therapy-induced senescence is beneficial in the sense that it causes cell cycle arrest of cancer cells,
however, therapy-induced senescent cells, similar to the lingering senescent cells in aged tissue, continue
to secrete inflammatory factors and alter the microenvironment which may partially explain the long-term
side effects of chemotherapy. Supporting this hypothesis is the occurrence of premature aging (37) and
increased p16 expression in the tissue of some of the cancer survivors (38). Side effects of persisting
senescent cells both in age-related conditions and upon cancer treatment necessitates therapeutic
3
interventions such as use of senolytic compounds for clearance of the senescent cells. Interestingly, several
recent studies have demonstrated that removal of senescent cells improves healthy lifespan of humans (15,
39–41). Design of senolytics requires molecular targets in order to specifically kill senescent cells and
minimize off-target effects of the therapy.
Here, we used normal diploid human mammary epithelial cells (HMEC) to investigate how
metabolic pathways regulate replicative senescence in a primary epithelial cell type. Previously, these cells
have been shown to accurately represent the molecular changes that occur during replicative senescence in
vivo (42). Additionally, we studied the proteome of senescent human mammary epithelial cells, identified
novel markers of senescence and explored potential targets for design of senolytic compounds.
1.2 LC-MS based metabolomics
Metabolites are the reactants and products of enzymatic reactions that regulate usage of available
nutrients for biomass production and energy balance in biological units such as human cells (43). In recent
years, metabolism has regained attention in biomedical research being recognized as a driver of human
disease such as cancer and diabetes (44). Because metabolites serve as direct signatures of biochemical
activity, it can be thought of as the “omic” network most closely related to phenotype (45). Metabolic
dysregulation is a hallmark of cancer (46) and drives chronic inflammation in aging (47), therefore,
accumulating evidence underlines regulatory role of metabolism in health and disease. Investigating the
regulations and dysregulations of the complex metabolic network requires quantitative methods with a
systems-level approach.
Metabolomics is the large-scale analysis of global metabolites data from complex biological
samples. The diversity of compounds as well as the extreme sensitivity of mass spectrometry (MS) have
made it the tool of choice for metabolomics. It is possible to measure thousands of metabolites per sample
using MS, although careful curation is required (48). Because of the complexity of the metabolome, MS is
4
usually coupled with a separation technique such as gas chromatography (GC-MS) or liquid
chromatography (LC-MS) (Fig. 1.1.). Since many metabolites are non-volatile, GC-MS analysis often
requires metabolite derivatization with chemicals including MTBSTFA (hydroxy, acid, amino-groups) or
methoxyamine (ketones, aldehydes). For LC-MS, derivatization is not required. In addition, the type of
chromatography used in LC can be adjusted depending on the type of metabolite analysis desired (eg,
reversed-phase C18 for hydrophobic analytes, hydrophilic-interaction chromatography (HILIC) for
hydrophilic analytes) (49, 50). In LC-MS metabolomics, metabolites are usually identified by a
combination of known retention time and accurate mass. This means that at present most metabolomics
groups build their own in-house databases of detected metabolites. To determine the identity of unidentified
features (m/z peaks), the accurate mass of the compound is used to i) predict atomic composition; and ii)
searched in metabolite databases such as the Human Metabolome Database or METLIN. However, any
database match must be confirmed by comparing the retention time and fragmentation spectra against a
known standard. Fortunately, most biologically-relevant metabolites are commercially available at
reasonable cost. However, databases of empirically-observed fragmentation patterns for metabolites are
improving (eg, METLIN, mzCloud) and can sometimes be used to identify metabolites in complex samples
without standards.
5
Figure 1.1 Workflow of LC-MS based metabolomics
Metabolite extraction is often achieved by adding a cold organic solvent directly to the samples to
lyse cell membranes, slow down metabolism, denature enzymes and isolate metabolites. Separating
compounds with chromatography facilitates detection of low abundance metabolites and separation of
isomers. Next, the elution from the chromatography column is introduced into the mass spectrometer by
ionization and sent to the mass analyzer to measure mass-to-charge ratio based on their velocity or
frequency of oscillation in an electric field. Subsequently, raw data from LC-MS is processed to identify
and assign peaks to metabolites by matching LC-MS retention times and exact mass to charge ratios to a
library of metabolites. Integrating the area under the peak for identified metabolites provides relative
abundance of the compounds. These abundances can be converted to metabolite concentrations by fitting a
standard curve for each metabolite with a range of low to high concentrations analyzed by LC-MS. LC-
MS-based metabolomic profiling provides multi-dimensional data including rates of nutrient consumption
6
and secretion, intracellular metabolite concentrations, and reaction fluxes in the metabolic network.
Metabolic fluxes are correlated with the enzymatic functions within the network and are frequently
dysregulated in human disease (51). Although metabolite concentrations can be an important metric of
metabolic activity in biological samples, the measurement of metabolite concentrations cannot reveal the
ultimate function of metabolic pathways, namely the flow of biomass through a reaction per unit time, i.e.,
metabolic flux (43). In biological systems, metabolite build-up can occur either because of increased
production (activation of an upstream reaction) or decreased consumption (inhibition of a downstream
reaction). For example, the removal of glucose from yeast causes a build-up of lower glycolytic
intermediates even though total glycolytic flux is decreased (52). As such, a holistic understanding of
metabolic state is often best achieved by measuring both metabolite levels and fluxes. Because fluxes are
not physical entities, they must be inferred through use of isotope tracers such as
13
C. The choice of isotope
tracer and the kinetics of the targeted pathway will influence the information about metabolic flux gained
from an experiment. For example, [U-
13
C]-glucose can be used to determine the flux through glycolysis,
whereas [U-
13
C]-glutamine will reveal the ratio of ‘forward’ to ‘backward’ (eg, reductive carboxyation)
flux in the TCA cycle. Often, manual inspection of isotopomer distributions (eg, the number of stable
isotopes incorporated per molecule) will reveal differences in metabolic flux. However, for more accurate
measurements of metabolic flux, it is necessary to use mathematical modeling techniques that derive fluxes
from empirically-observed isotopomer distributions.
7
Figure 1.2 Integrative metabolomics analysis
Integration of large data sets of extracellular media footprint, intracellular pool sizes, and
13
C
isotope flux analysis creates a holistic picture of cell metabolism and can differentiate between distinct
cellular conditions, symptoms and diseases (Fig. 1.2.).
1.3 LC-MS based proteomics
Proteomics is the study of the "proteome" or the entire protein complement of a genome (53) that
concerns large scale quantitative investigation of cell, tissue, and whole organism functions at protein level
(54, 55). By global measurements of proteins and peptides, proteomics has significantly extended our
understanding of biological functions and regulations (53, 56). The proteome comprises all proteins present
in a system (e.g. cell, organ or organism) at a given time. This includes not only those proteins translated
directly from genetic material but also the variety of modified proteins arising from alternative splicing of
transcripts and/or extensive post-translational processing. Since proteins are involved in virtually every
cellular function, control every regulatory mechanism, and are modified in disease (as the cause or effect),
the proteome dictates the phenotype of the cell and, collectively, the tissue or organ that the cells comprise
(57). This phenotype varies under normal conditions, such as cell cycle stage, differentiation, function, and
8
age, or as a result of the onset of or interventions in response to acute insults or chronic diseases. Acute
responses to a stimulus lead to rapid post-translational modification (PTM) of proteins, whereas in chronic
disease states, co-translational and post-translational protein modifications occur in concert with altered
gene expression, leading to varied protein levels. For specific proteins, disease-induced modification will
substantially affect function, which in turn has the potential to affect other proteins. The result is a dynamic,
ongoing process of protein expression and modification. The complex nature of cellular proteome and low
abundancy of many functional proteins requires high throughput and extremely sensitive technologies for
quantitative analysis of global protein expressions, post-translational modification and protein-protein
interactions.
Over the past decade, improvements in MS have made it an unrivaled technique, by virtue of its
accuracy of mass detection, its detection sensitivity, its ability to deal simultaneously with mixtures of
multiple proteins, and its increasing amenity to automation and therefore, high throughput. Liquid
chromatography coupled to mass spectrometry due to its power to analyze thousands of sequences per day
with femtomole sensitivity has become the most popular technique for identification and quantification of
proteins. LC-MS-based proteomics relies on separation of proteins or peptides on a chromatography column
and subsequent identification by a mass analyzer. Prior to proteomics analysis by LC-MS, proteins need to
be extracted by cell lysis and fractionated to isolate from the rest of unwanted components. Global protein
measurements can be done with a technique termed whole cell lysate proteomics in which all proteins from
a given biological sample are extracted, purified, and analyzed with mass spectrometry (Fig. 1.3.). In
peptide mass fingerprinting, extracted proteins are digested with a site-specific protease such as trypsin.
The peptide fragment mixture is then desalted before MS analysis. The fragments are analyzed on the basis
of their m/z ratios. By comparing the experimental mass values obtained from MS to a set generated by in
silico digestion of all possible peptides derived from protein and DNA sequence databases, the exact match
or similar matches can be determined. For unambiguous protein identification, additional protein sequence
information is often required, which can be achieved by tandem MS. In MS/MS, an ionized peptide of
9
interest is selected by the first MS and fragmented by collision with inert gas, then a second MS
measurement analyzes the fragment ions. To identify proteins, peptide fragmentation sequences obtained
from LC-MS/MS analysis should be matched to the available sequence libraries by computational
algorithms to reconstruct the sequence of proteins and abundances.
Figure 1.3 Workflow of LC-MS based proteomics
Proteomics, like metabolomics analysis, has great potential to differentiate between biological
conditions and symptoms by analysis of protein expression levels, modifications, and interactions. In
addition, integrating large scale data from several molecular levels, i.e. metabolomics, proteomics,
transcriptomics, and genomics provides deeper insight into the molecular basis of human disease (58, 59).
10
2. Chapter 2. Investigating metabolomic regulation of senescence
2.1 Objective
Primary human mammary epithelial cells (HMEC) have been shown to exhibit two mechanistically
distinct senescence barriers to immortalization: stasis and agonescence (42). Stasis is a retinoblastoma-
mediated growth arrest that occurs in the absence of DNA damage and is independent of p53 (60).
Agonescence, or telomere dysfunction-associated senescence, is driven by critically shortened telomeres
that trigger both a p53-dependent cell cycle arrest and a DNA damage response (61, 62). Because properties
associated with senescence in mesenchymal cell types such as fibroblasts may not accurately reflect
senescence in epithelial cells (60) and given the fact that most breast carcinomas arise from epithelial tissue,
the study of primary HMEC in vitro is required to understand how these senescence barriers are involved
in normal HMEC cell biology, including aging and oncogenesis. How these senescence barriers in HMEC
are regulated by metabolomic changes in this primary cell type has not been previously investigated and is
the focus of this study. A detailed understanding of the metabolomic alterations that occur during
senescence will provide insight into the molecular mechanisms underlying senescence, which will have
implications for both aging and cancer.
2.2 Materials and Methods
Cell culture - HMEC cells were purchased from Thermo Scientific and cultured in M87A medium (50%
MM4 medium and 50% MCDB170 supplemented with 5 ng/ml EGF, 300 ng/ml hydrocortisone, 7.5 ug/ml
insulin, 35 ug/ml BPE, 2.5 ug/ml transferrin, 5 µM isoproterenol, 50 µM ethanolamine, 50 µM o-
phosphoethanolamine, 0.25 % FBS, 5 nM triiodothyronine, 0.5 nM estradiol, 0.5 ng/ml cholera toxin, 0.1
nM oxytocin, 1% anti-anti, no AlbuMax) in atmospheric oxygen. Glucose and glutamine-free DMEM was
purchased from Corning (90-113-PB), Ham’s F12 was purchased from US Biological (N8542-12), and
11
MCD170 medium was purchased from Caisson Labs (MBL04). Glucose and glutamine were added to the
media at the appropriate concentration for each media type. Cells were lifted with TrypLE at 80-90%
confluency and seeded at a density of 2.3× 103/cm2. Cell viability and diameter was measured with trypan
blue assay using TC20 automated cell counter (Bio-Rad).
Genetic modification - Proliferating HMEC were infected at PD 14 with pLenti-PGK-hygro (Addgene
19066) encoding either hTERT or firefly luciferase. Following infection, cells were selected with 5 µg/ml
hygromycin for 7 days. Following selection, cells were maintained in culture with 2 µg/ml hygromycin.
Immunoblot analysis - Cells were lysed in modified RIPA buffer (50 mM Tris–HCl (pH 7.5), 150 NaCl,
50 mM β-glycerophosphate, 0.5 mM NP-40, 0.25% sodium deoxycholate, 10 mM sodium pyrophosphate,
30 mM sodium fluoride, 2 mM EDTA, 1 mM activated sodium vanadate, 20 µg/ml aprotinin, 10 µg/ml
leupeptin, 1 mM DTT, and 1 mM phenylmethylsulfonyl fluoride). Whole-cell lysates were resolved by
SDS–PAGE on 4–15% gradient gels and blotted onto nitrocellulose membranes (Bio-Rad). Membranes
were blocked for 1 h, and then incubated with primary overnight and secondary antibodies for 2 h. Blots
were imaged using the Odyssey Infrared Imaging System (Li-Cor). Primary antibodies used for Western
blot analysis were: γ-H2AX (9718S, Cell Signaling), RRM2 (103193, GeneTex, and HPAA056994,
Millipore Sigma), luciferase (L0159, Sigma-Aldrich), hTERT (600-401-252S, Rockland).
Senescence-associated β-galactosidase measurements─HMEC were incubated with 100 nM bafilomycin
A1 (Sigma-Aldrich) for 1 h, followed by incubation with 33 µM C12FDG (Setareh Biotech) for 1 h, lifted
with TrypLE, resuspended in ice-cold PBS, and then analyzed on a Miltenyi Biotec MACSQuant flow
cytometer to measure fluorescence (26). Data was processed and analyzed with FlowJo 7.6.1 software and
the mean fluorescent signal for each sample was exported. Values for SA-β-gal activity were calculated as
(mean of samples labelled with C12FDG - mean of samples without C12FDG). Data was normalized to the
SA-β-gal activity of non-senescent samples.
12
EdU incorporation - EdU staining was performed using Click-iT Plus EdU Alexa Fluor 594 Imaging kit
(Thermo Fisher, C10639). Briefly, cells were seeded in glass bottom dishes (MatTek Part No: P35G-1.5-
10-C) with appropriate density and then incubated with 10 µM EdU for 6 h at 37 °C. If membrane staining
desired, cells were incubated with 1X Membrite Fix 640/660 (Biotium 30097-T) for 5 mins at 37 °C. Next,
cells were fixed with 3.7% formaldehyde followed by 0.5% Triton X-100 permeabilization at room
temperature. Cells were incubated with Click-iT Plus reaction cocktail for 30 min at room temperature
protected from light. Finally, for nuclear staining, cells were incubated with 1X Hoechst 33342 for 30 mins
at room temperature protected from light and stored in PBS until imaging.
Confocal microscopy and cell counting - All microscopy was performed on Zeiss 780 or 880 confocal
systems. The excitation wavelengths for the stained cells were: 405 nm for Hoechst, 594 nm for EdU, and
633 nm for MemBrite membrane dye (Biotium). All high magnification images (40x/1.1NA and
63x/1.4NA, Zeiss) were sampled at Nyquist with 1 Airy Unit pinhole diameters and all low magnification
images (10x/0.45NA, Zeiss) were collected with pinhole diameters such that 40 µm optical sections were
obtained for cell counting. Tiled images were stitched together and analyzed using Imaris (Bitplane) image
processing and analysis tools. Images were interrogated for nuclei and presence of EdU through the Spots
module in Imaris.
LC-MS-based metabolomics analysis - HMEC were plated onto 6-well plates at density of 1-3 × 104
cells/cm2 depending on the experiment. For flux analysis, after 24 h media was replaced by [U-13C]-
labeled glucose, [1,2-13C]-labeled glucose, or [U-13C]-labeled glutamine (Cambridge Isotope
Laboratories). Metabolite extraction was performed 24 h after adding labeled media. For extraction of
intracellular metabolites, cells were washed on ice with 1 ml ice-cold 150 mM ammonium acetate
(NH4AcO, pH 7.3). 1 ml of -80 °C cold 80% MeOH was added to the wells, samples were incubated at -
80 °C for 20 mins, then cells were scraped off and supernatants were transferred into microfuge tubes.
Samples were pelleted at 4°C for 5 min at 15k rpm. The supernatants were transferred into LoBind
Eppendorf microfuge tube, the cell pellets were re-extracted with 200 µl ice-cold 80% MeOH, spun down
13
and the supernatants were combined. Metabolites were dried at room temperature under vacuum and re-
suspended in water for LC-MS run. For extraction of extracellular metabolites, 20 µl of cell-free blank and
conditioned media samples were collected from wells. Metabolites were extracted by adding 500 µl -80 °C
cold 80% MeOH, dried at room temperature under vacuum and re-suspended in water for LC-MS analysis.
Samples were randomized and analyzed on a Q Exactive Plus hybrid quadrupole-Orbitrap mass
spectrometer coupled to an UltiMate 3000 UHPLC system (Thermo Scientific). The mass spectrometer was
run in polarity switching mode (+3.00 kV/-2.25 kV) with an m/z window ranging from 65 to 975. Mobile
phase A was 5 mM NH4AcO, pH 9.9, and mobile phase B was acetonitrile. Metabolites were separated on
a Luna 3 µm NH2 100 Å (150 × 2.0 mm) column (Phenomenex). The flowrate was 300 µl/min, and the
gradient was from 15% A to 95% A in 18 min, followed by an isocratic step for 9 min and re-equilibration
for 7 min. All samples were run in biological triplicate.
Metabolites were detected and quantified as area under the curve based on retention time and accurate mass
(≤ 5 ppm) using the TraceFinder 3.3 (Thermo Scientific) software. Raw data was corrected for naturally
occurring 13C abundance (48). Extracellular data was normalized to integrated cell number, which was
calculated based on cell counts at the start and end of the time course and an exponential growth equation.
Intracellular data was normalized to the cell number and cell volume at the time of extraction. Pathway
maps were made with Cytoscape software (49).
Hierarchical clustering - Clustering was performed using Cluster 3.0 software.
Gene set and metabolite set enrichment analysis - For gene set expression analysis of RNA data,
microarray data from HMEC (22) was compared across pairwise comparisons for stasis and pre-stasis cell
cultures. Genes were then ranked by their average rank from individual experiments. RNAseq data from
IMR90 cells (2) was ranked based on signal to noise ratio of senescent/non-senescent cells. GSEA was run
with the unweighted statistic using the GSEA java applet (33). For metabolite set enrichment analysis of
metabolomic data, HMEC intracellular pool sizes or 13C-glucose fractional contribution data was ranked
14
based on log 2 fold change of senescent/proliferating, luciferase/hTERT or triapine/DMSO. Enrichment
analysis was run with unweighted statistic using the GSEA java applet.
2.3 Results
2.3.1 Development and optimization of LC-MS method for metabolomics
To perform high-throughput analysis of metabolites from biological samples, we first established
a LC-MS metabolomics method in the Graham Lab (Fig. 2.1). We selected hydrophilic interaction liquid
chromatography (HILIC) column coupled to a hybrid quadrupole-Orbitrap mass spectrometer with a
negative/positive polarity switching method in order to enable simultaneous measurement of hundreds of
metabolites (63). Next, using our established LC-MS method we ran standards from a comprehensive
library of compounds to identify and detect metabolites by retention time, exact mass to charge ratio, and
optimal polarity mode on our high-resolution LC-MS system. These standard runs were used to construct
our metabolite library that was used for quantification of all subsequent metabolomics analyses. Currently,
our method features over 150 metabolites from key metabolic pathways including glycolysis, TCA cycle,
pentose phosphate pathway, and nucleotide. Having established the LC-MS method and metabolite library,
next we developed methods on the TraceFinder software to facilitate quantification of intracellular
metabolites pool sizes, extracellular media footprint, and isotopomer distributions with
13
C stable isotope
tracing.
15
Figure 2.1 Development of LC-MS-based metabolomics method
2.3.2 Establishment of a human mammary epithelial cell model of senescence and
immortalization
To study the metabolic alterations that accompany replicative senescence, we used normal diploid
human mammary epithelial cells (HMEC). These cells have been previously shown to accurately represent
the molecular changes that occur during replicative senescence in vivo (42). We observed linear growth for
approximately 15 population doublings (PD) after which cell growth slowed until cells ceased proliferation
at approximately 40 PD (Fig. 2.2.A). HMEC at ~40 PD were viable and showed little to no cell death (Fig.
2.2.B). However, these cells showed an enlarged, flattened and irregular morphology that is typical of
senescent cells (64) (Fig. 2.2.C).
16
Figure 2.2 Establishment of an HMEC model of senescence. A) HMEC ceased proliferation at 35-40
population doublings. HMEC were cultured in M87A media with media replacement every 2 days (every day after
50% confluency) and passaged at 80-90% confluency (1). Four independent cultures are shown. B) HMEC viability
was unaffected by senescence arrest. Viability of proliferating (<15 population doublings) and senescent (>35
population doublings). HMEC viability was measured by trypan blue staining. C) HMEC acquired enlarged, flattened
and irregular morphology at PD 40. Phase contrast images and cell sizes represented at PD 10 and 40. Scale bar is 100
µm. ** denotes p-values less than 2x10-16 by Mann-Whitney U-test. D) HMEC showed increased activity of
senescence associated beta galactosidase at PD 35 measured by fluorescence signal of C12FDG. SA-β-gal
measurements are shown at PD 10 and 35. SA-β-gal activity was calculated as (mean of samples labelled with
C12FDG - mean of samples without C12FDG). Data was normalized to the SA-β-gal activity of proliferating sample.
* denotes p-value less than 0.0001 by Student’s t-test. E) Immunoblot for γ-H2AX with lysates from proliferating and
senescent HMEC. A lysate from 293T cell line treated with the DNA damaging agent doxorubicin (1 µM for 24 h) or
control was used as a positive control. Actin was used as an equal loading control.
To confirm that HMEC at ~40 PD were senescent, we first measured senescence-associated beta-
galactosidase (SA-β-gal) activity using C 12FDG, a fluorogenic substrate for SA-β-gal activity in live cells
(65) and observed increased SA-β-gal activity at PD 40 (Fig. 2.2.D). Next, we confirmed that senescent
17
HMEC exhibited a lack of DNA synthesis by measuring incorporation of the thymidine analog 5-ethynyl-
2´-deoxyuridine (EdU) (Fig. 2.2.E). Notably, Hoechst staining revealed that senescent HMEC did not
exhibit senescence-associated heterochromatic foci (SAHF), a frequent but not obligatory marker of
senescence (66). However, we observed that a significant fraction of senescent HMEC were multi-nuclear
(Fig. 2.2.F), which has been observed in senescent human melanocytes systems (67) and can result from
aberrant mitotic progression in oncogene-induced senescence (68). Next, we tested for expression of the
senescence markers p16 and p21. Western blotting revealed that senescent HMEC exhibited increased
expression of p21 but not p16 (Fig. 2.2.G). Because SAHF formation is driven by p16 but not p21, this
observation is consistent with our finding that senescent HMEC do not exhibit SAHF (66, 69). We also
tested for expression of the senescence marker PAI-1 (plasminogen activator inhibitor-1) (70–72) by
Western blot and observed increased expression in senescent HMEC (Fig. 2.2.G). Finally, to investigate if
senescent HMEC experienced DNA damage, we tested for expression of the DNA damage marker γ-H2AX
by Western blotting. Interestingly, expression of γ-H2AX was slightly decreased as cells entered senescence
(Fig. 2.2.H) suggesting that senescent HMEC cells do not exhibit double-stranded DNA breaks (DSBs)
(60). Taken together, these data demonstrate that HMEC enter senescence around 35-40 PD.
2.3.3 Senescent HMEC do not exhibit a glycolytic shift and energy crisis
Having established a model system for replicative senescence, we analyzed proliferating and
senescent HMEC using LC-MS-based metabolomics (10, 63). First, we measured the consumption and
secretion rates of glucose and lactate, respectively (Fig. 2.3.A, Table 1). Neither glucose consumption,
lactate secretion, or the ratio of glucose consumption to lactate secretion was significantly altered,
suggesting that senescent HMEC do not exhibit an overall glycolytic shift. Because senescent fibroblasts
increase secretion of citrate (24), we next examined the secretion of TCA cycle metabolites. Senescent
HMEC exhibited decreased secretion of most TCA cycle metabolites, though only aconitate and malate
demonstrated significant reductions (Fig. 2.3.B). Finally, we examined the consumption and secretion of
18
amino acids in proliferating and senescent cells. Although consumption and secretion of many amino acids
was reduced in senescent HMEC, only aspartate secretion was significantly reduced compared to
proliferating HMEC (Table 1). Notably, glutamine consumption, a significant carbon source for the TCA
cycle, was not significantly reduced. Taken together, this data demonstrates that senescent HMEC are
highly metabolically active, without significant changes in glycolytic ratio or in exchange of TCA cycle
intermediates and amino acids with the extracellular medium.
19
Figure 2.3 Replicative senescence in HMEC is accompanied by significant alterations in metabolite pool sizes.
A) Glucose consumption, lactate secretion and ratio of glucose consumption to lactate secretion are unchanged in
senescent HMEC. Metabolite extracts from blank and conditioned media were analyzed by LC-MS. Secretion or
uptake is calculated as (conditioned media – blank media) normalized to integrated cell number. Secreted metabolites
have positive values, and consumed metabolites have negative values. B) Extracellular metabolite secretion data for
TCA cycle metabolites in proliferating and senescent HMEC. Metabolite extracts from blank and conditioned media
were analyzed by LC-MS. Secretion or uptake values were normalized to integrated cell number. Secreted metabolites
20
have positive values, and consumed metabolites have negative values. * denotes p-value less than 0.05 by FDR-
corrected Student’s t-test. C) Intracellular pool sizes of AMP and ATP of proliferating and senescent HMEC. * denotes
p-value less than 0.05 by FDR-corrected Student’s t-test. D) Volcano plot of intracellular pool sizes for all measured
metabolites. Data represents average weighted log2 fold change (senescent/proliferating) and FDR-corrected Fisher’s
combined p-value from five independent experiments. Depicted metabolites were measured in at least two
independent experiments. E) Depletion of dNDPs and dNTPs and accumulation of nucleobases and nucleosides in
senescent HMEC. Same data as in B for selected dNDPs, dNTPs, guanine and uridine. * and ** denote FDR-corrected
Student’s t-test p-value less than 0.02 and 0.002, respectively. F) Extracellular secretion and consumption measured
by LC-MS as in A for the nucleobases adenine, guanine, uracil and the nucleoside uridine. Secreted metabolites have
positive values, and consumed metabolites have negative values. * denotes FDR-corrected Student’s t-test p-value
less than 0.001. G) Metabolic pathway map depicting the log2 fold change (senescent/proliferating) intracellular pool
sizes of metabolites in glycolysis, pentose phosphate pathway, nucleotide synthesis, and TCA cycle using the indicated
color scale. Metabolites that were not measured are shown as small circles with grey color. Isomers that were not
resolved with LC-MS are shown as diamonds.
2.3.4 Replicative senescence in HMEC is accompanied by significant alterations in
nucleotide and nucleoside pool sizes
Next, we quantified the intracellular metabolite pool sizes in both proliferating and senescent
HMEC. In five independent experiments, we measured 111 metabolites in at least two independent
experiments (Table 2, only most significantly altered metabolites are shown). Of the measured metabolites,
11 were significantly increased and 31 were significantly decreased in senescent cells (false discovery rate-
corrected p-value less than 0.01). Notably, we did not observe significant change in the AMP to ATP ratio
of senescent HMEC (Fig. 2.3.C) as previously reported in human senescent fibroblasts (25, 73).
Visualization of this data on a volcano plot revealed that several deoxyribonucleoside di- and tri- phosphates
(dNDPs and dNTPs) including dCDP, dTDP, dADP, dATP, and dUTP were significantly downregulated
in senescent cells (Fig. 2.3.D,E). Conversely, intracellular levels of guanine and uridine were significantly
upregulated. Increased intracellular levels of uridine were accompanied by increased secretion of uridine
and the corresponding nucleobase uracil (Fig. 2.3.F). Visualization of the log 2 fold changes in intracellular
pool size on a metabolic pathway map also revealed decreased metabolite pool sizes for most of glycolysis,
the TCA cycle, pentose phosphate pathway and nucleotide synthesis metabolites (Fig. 2.3.G). Taken
together, this data suggests that replicative senescence in HMEC induces dramatic changes in global
metabolism with a particularly strong decrease in nucleotide metabolic pool sizes.
21
2.3.5 Glucose-derived carbon fuels nucleotide synthesis in proliferating but not senescent
HMEC
Because changes in metabolite poo size does not always accurately reflect changes in metabolic
flux (43), we next analyzed metabolic flux in proliferating and senescent HMEC by stable isotope labeling.
We cultured proliferating and senescent HMEC with [U-
13
C]-glucose followed by LC-MS metabolomics.
To gain a global picture of the contribution of glucose-derived carbon to intermediary metabolites, we
calculated glucose fractional contribution for each metabolite (74) (Table 3). Plotting the glucose fractional
contribution on a volcano plot revealed that metabolites from the pyrimidine synthesis including UMP,
UDP, UTP, dCDP, dCTP, dTDP, and dTTP showed significantly decreased incorporation of glucose-
derived carbon (Fig. 2.4.A ,B). Notably, while phosphoribosyl pyrophosphate (PRPP), one of the precursors
for pyrimidine synthesis, demonstrated only a small change in glucose-derived carbon incorporation, UMP
and the downstream products of pyrimidine synthesis (eg, dCDP) exhibited almost complete loss of
glucose-derived carbon incorporation (Fig. 2.4.B ,C). Visualization of the [U-
13
C]-glucose fractional
incorporation on a metabolic pathway map confirmed that the pyrimidine synthesis pathway demonstrated
the largest decrease in glucose fractional incorporation (Fig 5D). In contrast, metabolites from the TCA
cycle showed little to no change in [U-
13
C]-glucose fractional incorporation or isotopomer distributions
(Fig. 2.4.C).
22
Figure 2.4 Glucose-derived carbon fuels nucleotide synthesis in young but not senescent HMEC. A) Purine
and pyrimidine metabolites show significantly reduced [U-
13
C]-glucose carbon labeling in senescent HMEC. The
volcano plot represents the average log 2 fold change (senescent/proliferating) for fractional contribution (74) of [U-
13
C]-labeled glucose and FDR-corrected Fisher’s combined p-value from two independent experiments. B) Glucose
flux into nucleotide synthesis pathways is globally reduced in senescent HMEC. Fractional contribution of [U-13C]-
labeled glucose for selected pyrimidines. * and ** denote FDR-corrected Student’s t-test p-values less than 0.01 and
0.0001, respectively. C) [U-13C]-glucose isotopomer distributions for dCDP but not for PRPP, citrate, and fumarate
are significantly different in senescent HMEC. D) Metabolic pathway map depicting the average log2 fold change of
23
fractional contribution from [U-13C]-glucose in senescent/proliferating HMEC on the indicated color scale for
metabolites in glycolysis, pentose phosphate pathway, nucleotide synthesis, and the TCA cycle. Metabolites that were
not measured are shown as small circles with grey color. Metabolites with isomers that were not resolved on LC-MS
are shown as diamonds. E) [1,2-13C]-labeled glucose isotopomer distribution. The ratio of M1 to M2 lactate does not
show a significant change between proliferating and senescent HMEC. F) Immunoblot for RRM2 with lysates from
proliferating and senescent HMEC. A protein lysate from 293T cell line was used as a positive control, and actin was
used as an equal loading control. The RRM2 antibody was obtained from GeneTex.
We next used [1,2-
13
C]-glucose to investigate possible alterations in the ratio of glucose that enters
the pentose phosphate pathway versus glycolysis (75). However, we observed no significant difference in
the percentages of M1 and M2 lactate when comparing proliferating and senescent cells (Fig. 2.4.E). There
was, however, a significant decrease of
13
C incorporation into UMP and downstream pyrimidines,
consistent with our observation using [U-
13
C]-labeled glucose (Table 4).
Next, we queried published gene expression data to ask whether inhibition of nucleotide synthesis
is broadly reflected at the transcriptional level in senescent cells. We analyzed i) microarray data from
HMEC in replicative senescence (ie, stasis) (60); and ii) RNASeq data from IMR90 fibroblasts induced to
senesce by ionizing radiation (76). Gene set expression analysis (GSEA) (77) across all KEGG metabolic
pathways (n=78) (78) demonstrated a robust suppression of both pyrimidine metabolism (hsa00240) and
purine (hsa00230) pathways in senescent cells (Fig. 2.5. and Table 5). In both data sets, expression of
ribonucleotide-diphosphate reductase subunit M2 (RRM2), which catalyzes the biosynthesis of
deoxyribonucleotides from ribonucleotides, was strongly decreased in senescent cells (not shown).
Consistent with this data, Western blotting in our HMEC demonstrated downregulation of RRM2 in
senescent cells (Fig. 2.4.F). Taken together, this data suggests that senescent HMEC exhibit reduced
nucleotide synthesis.
24
2.3.6 Glutamine-derived carbon fuels
pyrimidines synthesis in proliferating but
not senescent HMEC
Next, we analyzed proliferating and
senescent HMEC labeled with [U-
13
C]-glutamine
to test for changes in TCA cycle flux. We
observed decreased ratio of reductive to oxidative
flux in the TCA cycle for senescent HMEC,
however the fold change was small (Fig. 2.6.A).
Visualization of the [U-
13
C]-glutamine fractional
contribution to metabolites revealed significant
changes in contribution of glutamine-derived
carbon into pyrimidine synthesis (Fig. 2.6.B,C and
Table 6). Taken together, this data suggests that
senescent cells strongly downregulate the flux of
carbon into pyrimidine synthesis while leaving
glycolysis and the TCA cycle unaffected.
Figure 2.5 RNA expression analysis
confirms reduced nucleotide synthesis in senescent
cells. A-B) GSEA analysis of A) microarray data
from senescent HMEC (58) and B) RNAseq data
from senescent IMR90 cells (14). For HMEC
microarray data, genes were compared across
pairwise comparisons for stasis and pre-stasis cell
cultures. Genes were then ranked by their average
rank from individual experiments. For IMR90
RNAseq data, genes were ranked based on signal to
noise ratio of senescent/non-senescent. Results
show significant suppression of genes in the purine
(hsa00230) and pyrimidine (hsa00240) pathways in
senescent cells.
25
Figure 2.6 Glutamine-derived carbon fuels pyrimidines synthesis in proliferating but not senescent HMEC.
A) [U-13C]-labeled glutamine isotopomer distributions for TCA cycle metabolites. The ratio of reductive to
oxidative TCA cycle is slightly decreased in senescent HMEC. B) Fractional contribution of [U-13C]-glutamine to
pyrimidines is downregulated in senescent HMEC. Volcano plot represents average log2 fold change
(senescent/proliferating) for fractional contribution of [U-13C]-labeled glutamine and FDR-corrected combined
Fisher’s combined p-value from two independent experiments. C) Metabolic pathway map depicting the average
log2 fold change (senescent/proliferating) of fractional contribution of [U-13C]-glutamine using the indicated color
scale. Metabolites that were not measured or had less than 3% fractional contribution are shown as small grey
colored shapes.
2.3.7 HMEC immortalization with telomerase restores nucleotide pools and fluxes
Because transduction of HMEC with exogenous telomerase (hTERT) can immortalize pre-stasis
HMEC (79), we next tested the effects of hTERT-mediated immortalization on the metabolic profiles of
HMEC. We expressed either hTERT or a firefly luciferase control in proliferating HMEC (PD 13). hTERT-
expressing HMEC continued to grow logarithmically in culture while HMEC expressing luciferase ceased
to proliferate at PD ~33 (Fig. 2.7.A). 60 days after transduction with luciferase and hTERT (PD 35 and 85
for luciferase and hTERT, respectively), we observed cuboidal cell morphology in hTERT-expressing cells,
similar to proliferating, non-immortalized HMEC (Fig. 2.7.B). In contrast, luciferase-expressing cells
acquired a senescent morphology and exhibited increased SA-β-gal activity (Fig. 2.7.B,C). Next, we
26
confirmed that senescent luciferase-expressing HMEC exhibited a lack of DNA synthesis (Fig. 2.7.D) and
increased expression of the senescence markers p21 and PAI-1 (Fig. 2.7.E). Taken together, this data
demonstrates that hTERT expression efficiently immortalized HMEC.
Figure 2.7 hTERT immortalization restores nucleotide pools and fluxes. A) Expression of hTERT in
proliferating HMEC prevented occurrence of senescence growth arrest. A representative growth curve demonstrating
that hTERT-expressing HMEC continued to grow logarithmically in culture while HMEC expressing luciferase
27
ceased to proliferate at PD ~33. Filled triangles and squares indicate the PD at which cells were extracted for
metabolomics. The x-axis represents days since completion of drug selection. hTERT and luciferase expression were
confirmed by Western blotting. Actin was used as an equal loading control. B) Phase contrast images of luciferase-
and hTERT-expressing HMEC at PD 35 and 85, respectively. Luciferase-expressing HMEC acquired enlarged,
flattened and irregular morphology typical of senescent cells. hTERT- expressing HMEC maintained their epithelial
cell morphology. Scale bar is 100 µm. C) Luciferase-expressing HMEC showed increased activity of SA-β-gal
measured by fluorescence signal of C12FDG at SA-β-gal measurements are shown at Day ~60 following drug
selection . SA-β-gal activity was calculated as (mean of samples labelled with C12FDG - mean of samples without
C12FDG). ** denotes p-value less than 0.0001 by Student’s t-test. D) Measurement of DNA synthesis by EdU
incorporation showed decreased DNA synthesis in senescent, luciferase-expressing cells at Day ~45 following drug
selection. ** denotes p-value less than 0.00004 by Student’s t-test. E) Immunoblot for p21, PAI-1, and actin with
lysates from luciferase- and hTERT-expressing HMEC at Day ~45 following drug selection. F) hTERT-expressing
HMEC maintain purine and pyrimidine pools. Luciferase- and hTERT-expressing cells were profiled by LC-MS
metabolomics at Days ~15 and ~45 following drug selection. Luciferase-expressing cells were senescent at Day ~45,
but all other samples were still proliferating. Data from proliferating (PD 9) and senescent primary HMEC (PD 37) is
shown for comparison. * and ** denote p-value less than 0.04 and 0.008, respectively. G) hTERT-expressing HMEC
maintain glucose flux to purines and pyrimidines as measured by fractional contribution of [U-13C]-labeled glucose
for selected purines and pyrimidines, as well as the nucleotide synthesis precursor PRPP. The number of days
following drug selection for hTERT- and luciferase-expressing HMEC was the same as in F). Data from proliferating
and senescent primary HMEC is shown for comparison. * and ** denote p-value less than 0.04 and 0.002, respectively.
H) Metabolic pathway map depicting the average log2 fold change of [U-13C]-glucose fractional contribution for
senescent, luciferase- (Day ~45) compared to proliferating, hTERT-expressing cells (Day ~45) on the indicated color
scale for metabolites in glycolysis, pentose phosphate pathway, nucleotide synthesis, and the TCA cycle. Metabolites
that were not measured are shown as small circles with grey color. Isomers that were not resolved with LC-MS are
shown as diamonds. I) Immunoblot for RRM2 and actin with lysates from senescent, luciferase- and proliferating,
hTERT-expressing HMEC.
We then profiled luciferase- and hTERT-expressing HMEC using LC-MS metabolomics.
Consistent with unmodified HMEC, media footprint analysis revealed that glucose consumption and lactate
secretion rates were not significantly different in proliferating, hTERT-expressing and senescent,
luciferase-expressing cells (Table 7). Secretion of guanine and uridine was increased in senescent,
luciferase-expressing HMEC. We next examined intracellular metabolite pool sizes in luciferase- and
hTERT-expressing cells at Day ~15 and ~60 following viral transduction and drug selection. At Day ~15,
both luciferase- and hTERT-expressing cells were proliferating, whereas at Day ~60, luciferase- but not
hTERT-expressing cells were senescent. Metabolomic profiling demonstrated that hTERT-expressing cells
experienced no significant changes in dNDP and dNTP pool sizes between Days ~15 and ~60 (Fig. 2.7.F
and Table 8). In contrast, luciferase-expressing cells exhibited significantly decreased dNDP and dNTP
metabolite pool sizes, closely mirroring observations in untransduced proliferating and senescent HMEC.
28
Next, we used [U-
13
C]-labeled glucose to assess the fractional contribution of glucose to the
metabolism of HMEC expressing hTERT and luciferase. This analysis revealed that [U-
13
C]-glucose
fractional incorporation in hTERT-expressing cells was unchanged between Days ~15 and ~60 for
pyrimidines and purines including UMP, UDP, UTP, dADP, dATP, dTDP, and dCDP (Fig. 2.7.G and Table
9). In contrast, luciferase-expressing HMEC exhibited significantly reduced fractional incorporation of [U-
13
C]-glucose into these pyrimidines and purines, similar to observations in untransduced proliferating and
senescent HMEC.
Visualization of the glucose fractional contribution on a metabolic pathway map confirmed a global
inhibition of carbon flux to nucleotides downstream of PRPP (Fig. 2.7.H). Notably, hTERT also induced
moderate downregulation of glucose-derived carbon to TCA cycle metabolites, a difference that was not
observed comparing unmodified proliferating and senescent HMEC. Finally, we measured protein
expression of RRM2 using western blot and observed significant decrease in high PD senescent luciferase-
expressing HMEC (Fig. 2.7.I). Taken together, these results demonstrate that hTERT-immortalization
maintains nucleotides pools and fluxes in HMEC.
2.3.8 Inhibiting ribonucleotide reductase induces senescence
Based on the metabolomic profile of senescent and proliferating HMEC, we hypothesized that
reduced nucleotide synthesis plays a causative role in induction of senescence. To test this hypothesis, we
first treated proliferating HMEC with triapine, an inhibitor of ribonucleotide reductase regulatory subunit
(RRM2) (80). Indeed, treatment of proliferating HMEC (PD 10) with triapine effectively blocked cell
proliferation (Fig. 2.8.A). Following triapine treatment, cells acquired a typical senescent morphology and
were significantly increased in size (Fig. 2.8.B). Additionally, triapine-treated HMEC exhibited increased
SA-β-gal activity (Fig. 2.8.C), reduced DNA synthesis (Fig. 2.8.D), and increased expression of p21 and
PAI-1 (Fig. 2.8.E). To confirm the role of RRM2 in senescence, we next used CRISPRi-mediated
knockdown of RRM2 expression (CRISPR-dCas9-KRAB) (81). Expression of a sgRNA against RRM2 but
29
not a non-targeting sgRNA control efficiently knocked down RRM2 expression and inhibited cell growth
in proliferating HMEC (Fig. 2.8.F). We then confirmed that RRM2 knockdown increased SA-β-gal activity
(Fig. 2.8.G), reduced DNA synthesis (Fig. 2.8.H), and increased expression of p21 and PAI-1 (Fig. 2.8.I).
Taken together, these data demonstrate that inhibition of RRM2 is sufficient to induce senescence in
primary HMEC.
Figure 2.8 Inhibiting ribonucleotide reductase induces senescence. A) Treating proliferating HMEC with the
RRM2 inhibitor triapine inhibits cell growth. Cells at PD ~8 were treated with 2 µM of triapine or DMSO for 72 h.
30
B) Phase contrast images and cell sizes of HMEC treated with triapine or DMSO. HMEC acquire enlarged, flattened
and irregular morphology typical of senescent cells within 3 days of treatment with triapine. Scale bar is 100 µm. **
denotes p-value less than 2x10-16 by Mann-Whitney U-test. C) Triapine-treated HMEC showed increased activity of
SA-β-gal measured by fluorescence signal of C12FDG. SA-β-gal activity was calculated as (mean of samples labelled
with C12FDG - mean of samples without C12FDG). ** denotes p-values less than 0.0001 by Student’s t-test. D)
Measurement of DNA synthesis by EdU incorporation showed decreased DNA synthesis in triapine-treated cells. *
denotes p-value less than 0.003 by Student’s t-test. E) Western blot of p21, PAI-1, and actin for triapine-treated cells.
F) Genetic knockdown of RRM2 expression inhibits HMEC growth. Cells at PD~10 were infected with sgRNA
against RRM2 or non-targeting control. Western blot of RRM2 and actin for HMEC infected with sgRNA against
RRM2 or non-targeting control. G) Knockdown of RRM2 expression induces SA-β-gal. SA-β-gal measured by
fluorescence signal of C12FDG. SA-β-gal activity was calculated as (mean of samples labelled with C12FDG - mean
of samples without C12FDG). ** denotes p-values less than 0.0001 by Student’s t-test. H) Measurement of DNA
synthesis by EdU incorporation showed decreased DNA synthesis in RRM2 knockdown cells. * denotes p-value less
than 0.02 by Student’s t-test. I) Western blot of p21, PAI-1, and actin for HMEC infected with sgRNA against RRM2
or non-targeting control. J) Triapine-treatment results in depletion of dNDPs and dNTPs and induces uridine
accumulation. * denotes p-values less than 0.003 by Fisher’s combined t-test. K) Fractional contribution of [U-13C]-
labeled glucose for selected pyrimidines. * and ** denote p-values less than 0.03 and 0.0001 by Fisher’s combined t-
test, respectively. L) Metabolic pathway map depicting average log2 fold change of [U-13C]-glucose fractional
incorporation for triapine/DMSO on the indicated color scale for metabolites in glycolysis, pentose phosphate
pathway, nucleotide synthesis, and the TCA cycle. Metabolites that were not measured are shown as small circles with
grey color. Isomers that were not resolved with LC-MS are shown as diamonds.
To investigate metabolomic alterations upon triapine-induced senescence, we again performed
untargeted LC-MS metabolomics. HMEC treated with triapine exhibited depleted dNDP and dNTP pool
sizes and increased intracellular levels of the nucleoside uridine (Fig. 2.8.J). In contrast, we observed no
significant change in the intracellular pool sizes of glucose, ribose-5-phosphate and PRPP (Fig 5J and Table
10). Tracing [U-
13
C]-labeled glucose in triapine-treated HMEC highlighted the downregulation of glucose
flux into nucleotide biosynthesis (Fig. 2.8.K). Examination of the [U-
13
C]-labeled glucose incorporation
levels in triapine-treated cells showed that fractional contribution of glucose to PRPP was not significantly
changed, similar to replicative senescence (Fig. 2.8.K,L and Table 11). However, there was a significant
inhibition of glucose flux into pyrimidines in triapine-induced senescent HMEC (Fig. 2.8.K,L). In contrast,
glucose flux into TCA cycle did not significantly change upon triapine treatment (Fig. 2.8.L). Taken
together, these results indicate that inhibition of nucleotide synthesis is sufficient to trigger senescence in
non-immortalized HMEC.
31
2.3.9 Integrative analysis of glucose contribution to metabolites in proliferating and
senescent HMEC
To identify the most consistent alterations in senescent cells across our metabolomic experiments,
we conducted unsupervised hierarchical clustering of the glucose fractional contribution to each metabolite
across experiments. Proliferating HMEC cultures (low PD, hTERT expression, and DMSO treatment) and
senescent HMEC cultures (high PD, luciferase expression, and triapine treatment) were separated into two
well defined clusters (Fig. 2.9.).
Figure 2.9 Integrative analysis of glucose contribution to metabolites in proliferating and senescent HMEC.
Heatmap comparing fractional contribution of glucose to commonly measured metabolites. Columns represent
individual samples. Red indicates upregulation and blue indicates downregulation. Nucleotides cluster together and
are upregulated in proliferating samples compared to senescent samples.
32
In addition, nucleotides were tightly clustered together to reveal decreased levels of glucose-
derived carbon incorporation in senescent cells (red cluster). This heatmap also highlighted the moderate
downregulation of glucose contribution to TCA cycle metabolites in hTERT-expressing HMEC (blue
cluster). Taken together, this confirms that inhibited nucleotide synthesis is a hallmark of senescent HMEC.
2.4 Discussion and Conclusion
Cellular senescence is a state of irreversible cell cycle arrest that contributes to degenerative and
hyperplastic phenotypes in aging and cancer. Although senescent cells are withdrawn from the cell cycle,
they remain highly metabolically active (22). Here, we show that inhibition of nucleotide synthesis
regulates replicative senescence in primary HMEC. The inhibition of nucleotide synthesis in senescent
HMEC was reflected both in reduced pool sizes (Fig. 2.3.) and in reduced metabolic flux into nucleotide
synthesis (Figs. 5, 6). In contrast, fluxes from glucose and glutamine into glycolysis, the pentose phosphate
pathway, and the TCA cycle were not significantly changed. Importantly, treatment of proliferating HMEC
with an inhibitor of RRM2, a key enzyme in dNTP synthesis, demonstrated that inhibition of nucleotide
synthesis was sufficient to induce senescence and recapitulate the metabolomic signature of HMEC
replicative senescence. Taken together, these findings demonstrate the crucial role of nucleotide synthesis
in cellular senescence of a primary epithelial cell type, a finding with implications for both aging and tumor
development.
The study of metabolism during replicative senescence has relied heavily on fibroblasts, a
mesenchymal cell type. These studies have demonstrated that human fibroblasts gradually exhibit a more
glycolytic metabolism as they become senescent (23–25). Similarly, senescence induced by oncogene
activation also leads to increased glycolysis (5, 27). Here, using a primary epithelial cell type (HMEC), we
did not observe significant alterations in either glucose uptake or lactate secretion in senescent HMEC (Fig.
2.3.A). Additionally, the fractional contribution of glucose and glutamine to glycolysis and the TCA cycle
was unchanged in senescent HMEC. In contrast to senescent fibroblasts (25), HMEC senescence was also
33
not accompanied by an energy crisis (Fig. 2.3.C). Taken together, these results suggest that epithelial and
fibroblast cells exhibit distinct metabolic alterations during senescence. These metabolic differences may
reflect the fact that senescent epithelial and fibroblast cells exhibit distinct transcriptional profiles (60).
Although replicative senescence in our HMEC system does not involve oncogene activation, the
inhibition of nucleotide synthesis we observed resembles the metabolic alterations found in oncogene-
induced senescence. Notably, in fibroblasts, oncogenic RAS induces senescence by suppressing nucleotide
metabolism, leading to the depletion of dNTP pools (3, 82). This depletion of dNTP pools is mediated by
RAS-induced repression of RRM2 mRNA and protein levels. Similarly, senescent HMEC showed a
significant downregulation of RRM2 protein expression (Fig. 2.4.F). Notably, in HMEC, chemical
inhibition of RRM2 was sufficient to induce senescence and recapitulated the metabolomic signature of
senescent HMEC (Figs. 9 and 10). Together, our findings extend the relevance of nucleotide metabolism
in senescence to a non-transformed, primary cell type in the absence of oncogene activation.
In the current study, we observed depletion of dNTPs in the absence of γ-H2AX induction,
suggesting that senescent HMEC do not experience DNA double strand breaks during the time course of
our experiments (Fig. 1E). However, because the maintenance of sufficient and balanced dNTP pools is
essential for maintaining genomic integrity (33, 34), the inability to synthesize dNTPs may eventually lead
to replication stress, genomic instability, and mutagenesis (35, 36). Indeed, genomic instability in the early
stages of tumorigenesis induced by cyclin E oncogenes is coupled to insufficient nucleotide availability
(32). Additionally, the extent of senescence experienced by mouse embryonic fibroblasts during
immortalization correlates with the amount of genomic instability in immortalized cell lines (10).
Interestingly, in RAS-expressing fibroblasts, senescence-associated nucleotide deficiencies can be
bypassed by inactivation of the serine/threonine kinase ATM, which is activated by DNA damage (83).
However, because senescent HMEC in this study are not experiencing DNA double strand breaks, they
may more closely resemble hypoxic cells which experience replication stress but not DNA damage (84).
Similar to hypoxic cells, senescent cells upregulate RRM2B (85), a subunit of the ribonucleotide reductase
34
complex which may preserve the capacity for sufficient nucleotide synthesis during hypoxia to avoid DNA
damage.
In senescent HMEC, we observed that the flux of glucose-derived carbon was more severely
downregulated in pyrimidine synthesis than in purine synthesis (Figs. 5D, 8H, and 9G). This difference
between pyrimidine and purine synthesis was not reflected in metabolite pool sizes, highlighting the power
of stable isotope tracing to reveal metabolic changes not apparent in metabolite pool sizes (43, 86).
Pyrimidine synthesis can be specifically controlled through mTOR/S6K1-mediated phosphorylation of
CAD (carbamoyl-phosphate synthetase 2, aspartate transcarbamylase, and dihydroorotase), the enzyme that
catalyzes the first three steps of de novo pyrimidine synthesis (87). Additionally, loss of urea cycle enzymes
including carbamoyl phosphate synthetase-1 (CPS1) can lead to pyrimidine but not purine depletion (88).
Although it is not yet clear why pyrimidines are more affected than purines in senescent HMEC, this
observation remains a subject of active investigation.
Taken together, our results indicate that suppression of nucleotide synthesis is a critical metabolomic
alteration regulating senescence in primary HMEC. A more detailed understanding of altered metabolism
as both a cause and a consequence of senescence will have important implications. In both aging (76) and
cancer (89), therapeutic targeting of senescence has shown great promise. Similarly, therapeutics targeting
the emerging metabolic differences between senescent and non-senescent cells may prove useful tools for
improving human health.
35
3. Chapter 3. Proteomic profiling of senescent HMECs and
identification of senescence biomarkers
3.1 Objective
The identification of senescent cells in vivo and in vitro has important diagnostic and therapeutic
potential. Proteomics is a useful approach for identification of proteins that demark and drive cellular
phenotypes. Although proteomic approaches have been used to identify novel cell surface biomarkers of
senescence in a bladder cancer cell line model (90), the global changes in protein expression associated
with senescence of human mammary epithelial cells have not been previously investigated. To characterize
novel biomarkers of HMECs senescence and identify proteins that might regulate senescence we performed
global protein expression profiling by LC-MS proteomics. Additionally, to identify potential novel targets
for selective elimination of senescent cells we aim to exploit proteomic signature of senescence. In the
recent years, targeting senescence with therapeutic interventions in order to remove senescent cells or
alleviate their negative impacts on the tissue has emerged as an existing area of research with promising
preliminary results (91, 92).
3.2 Materials and Methods
Cell culture - HMECs were cultured as described in chapter 2.
LC-MS-based whole cell lysate proteomics - Cell culture dishes were placed on ice and washed with
PBS. Cells were then scraped and pelleted by centrifugation. The cell pellets were lysed by probe sonication
in 8 M urea (pH 7.5), 50 mM Tris, 1 mM activated sodium vanadate, 2.5 mM sodium pyrophosphate, 1
mM β-glycerophosphate, and 100 mM sodium phosphate. The above procedures were performed in 0-4˚C.
Insoluble cell debris were filtered by 0.22 um syringe filter. Protein concentration was measured by BCA
assay (Pierce, PI23227). Lysates were reduced with 5 mM DTT, alkylated with 25 mM iodoacetamide,
quenched with 10 mM DTT, and acidified to pH 2 with 5% trifluoracetic acid. Proteins were then digested
36
to peptides using a 1:100 trypsin to lysate ratio by weight. Tryptic peptides were desalted by reverse phase
C18 StageTips and eluted with 30% acetonitrile. The eluents were vacuumed dried, and 250 ng/injection
was submitted to LC-MS. Samples were randomized and injected into an Easy 1200 nanoLC ultra high-
performance liquid chromatography coupled with a Q Exactive quadruple orbitrap mass spectrometry
(Thermo Fisher). Peptides were separated by a reverse-phase analytical column (PepMap RSLC C18, 2
µm, 100Å, 75 µm X 25 cm). Flow rate was set to 300 nL/min at a gradient from 3% buffer B (0.1% formic
acid, 80% acetonitrile) to 38% B in 110 min, followed by a 10-min washing step to 85% B. The maximum
pressure was set to 1,180 bar and column temperature was maintained at 50˚C. Peptides separated by the
column were ionized at 2.4 kV in the positive ion mode. MS1 survey scans were acquired at the resolution
of 70k from 350 to 1800 m/z, with maximum injection time of 100 ms and AGC target of 1e6. MS/MS
fragmentation of the 14 most abundant ions were analyzed at a resolution of 17.5k, AGC target 5e4,
maximum injection time 65 ms, and normalized collision energy 26. Dynamic exclusion was set to 30 s and
ions with charge +1, +7, and >+7 were excluded. MS/MS fragmentation spectra were searched with
Proteome Discoverer SEQUEST (version 2.2, Thermo Scientific) against in-silico tryptic digested Uniprot
all-reviewed Homo sapiens database (release Jun 2017, 42,140 entries) plus all recombinant protein
sequences used in this study. The maximum missed cleavages was set to 2. Dynamic modifications were
set to oxidation on methionine (M, +15.995 Da) and acetylation on protein N-terminus (+42.011 Da).
Carbamidomethylation on cysteine residues (C, +57.021 Da) was set as a fixed modification. The maximum
parental mass error was set to 10 ppm, and the MS/MS mass tolerance was set to 0.02 Da. The false
discovery threshold was set strictly to 0.01 using the Percolator Node validated by q-value. The relative
abundance of parental peptides was calculated by integration of the area under the curve of the MS1 peaks
using the Minora LFQ node.
LC-MS-based proteomics sequence database searching and data analysis - MS/MS fragmentation
spectra were searched with Proteome Discoverer SEQUEST (version 2.2, Thermo Scientific) against in-
silico tryptic digested Uniprot all-reviewed Homo sapiens database (release Jun 2017, 42,140 entries) plus
37
all recombinant protein sequences used in this study. The maximum missed cleavages was set to 2. Dynamic
modifications were set to oxidation on methionine (M, +15.995 Da) and acetylation on protein N-terminus
(+42.011 Da). Carbamidomethylation on cysteine residues (C, +57.021 Da) was set as a fixed modification.
The maximum parental mass error was set to 10 ppm, and the MS/MS mass tolerance was set to 0.02 Da.
The false discovery threshold was set strictly to 0.01 using the Percolator Node validated by q-value.
Relative abundances of parental peptides were calculated by integration of area-under-the-curve of the MS1
peaks. Minora LFQ node was enabled to identified and quantified peptides between injections. Peptide
groups abundance values from Proteome Discoverer were normalized to the corresponding samples’
median values. After normalization, the missing values were imputed using the K-nearest neighbor
algorithm (48). The optimized number of neighbors was determined to be n = 10. The protein copy numbers
were assessed using intensity-based absolute quantification (iBAQ) (49).
Statistical analysis - For metabolomics data (n=3 in each experiment) p-value was calculated with a two-
tailed Student’s t-test. To evaluate combined significance from independent experiments, p-values were
combined with Fisher’s method and corrected for false discovery rate using the Benjamini-Hochberg
method. For SA- β-gal activity, p-values were calculated with a Student’s t-test. For cell size analysis, p-
values were calculated with a Wilcox-Mann-Whitney test because the data were not normally distributed.
DEqMS - DEqMS statistical analysis was performed as described in a previous publication (93) .Briefly,
PSMs were aggregated into protein log 2 ratio by median sweeping method: raw intensity values were log 2
transformed, median of log 2 intensity was subtracted for each PSM, for each protein, relative log 2 ratio was
calculated as the median of log 2 ratio of the PSMs assigned to that protein. To normalize, for all samples,
the median of protein log 2 ratios were subtracted, assuming that total protein content does not change in
different samples. Log 2 proteins ratios without missing values were used for t-test, Limma, and DEqMS
analysis. Analysis was performed in R (version 1.3.1056).
38
Hierarchical clustering - Clustering was performed using Morpheus web tool by Broad Institute. One
minus Pearson correlation coefficient metric was used for clustering. Data was transformed following the
clustering by subtracting row mean and dividing by row standard deviation.
Gene set and metabolite set enrichment analysis - For Gene Ontology enrichment analysis, proteins
were ranked by their log 2 (senescent / proliferating) values. Gene Ontology 1D-Enrichment analysis was
performed in Perseus (version 1.6.2.2). For transcription factor targets enrichment analysis, proteins were
ranked by their log 2 (senescent / proliferating) values. Gene Set Enrichment Analysis (GSEA) was run
with the unweighted statistic using the GSEA java applet with Broad Institute C3 TFT:GTRD (GTRD
transcription factor targets, 526 gene sets) (77).
Drug Set Enrichment Analysis (DrugSEA) - Drugs were mapped to their metabolic pathway using the
annotated target(s) and genes from KEGG metabolic pathways. Since the PRISM database contains both
activators and inhibitors, we annotated all activators by mechanism of action and multiplied their
correlation coefficients by -1. Therefore, a pathway activator would be counted similarly to a pathway
inhibitor. Pathways with 4 or more drugs were kept. Then, GSEA was run on the rank lists of 1448
correlation coefficients.
Senescence score - Log 2-transformed, RMA-normalized Entrez Gene expression values for adipocyte
senescence (GSE66236) were averaged for senescent and proliferating conditions, for each gene, average
value for proliferating samples was subtracted from the average of senescent samples to obtain log 2
(senescent / proliferating) values. Data was filtered for genes with absolute log 2 (senescent / proliferating)
> 2.5 to create the adipocyte signature matrix. HMEC senescence signature matrix was created using
proteins with absolute average log 2 (senescent / proliferating) > 1 and FDR-corrected p-value < 0.01 of our
combined HMEC proteomics analysis (Fig. 3.4.B). CCLE Log2 transformed RNAseq TPM gene
expression data for just protein coding genes using RSEM “CCLE_expression_v2.csv” was downloaded
from DepMap portal (https://depmap.org/portal/download/), same signature genes (from adipocyte or
HMEC senescence) were selected to create the CCLE gene expression matrix. CCLE senescence scores
39
vector (of adipocyte or HMEC) was calculated by multiplying senescence signature matrix (of adipocyte
or HMEC) with CCLE gene expression matrix.
PRISM analysis - CCLE PRISM Repurposing 19Q4 data “secondary-screen-dose-response-curve-
parameters.csv” was downloaded from DepMap portal (https://depmap.org/portal/download/). For each
drug, Person correlation coefficient and t-test p-value was calculated between CCLE senescence scores
and PRISM area under the curve (auc) values (only cell lines present in both CCLE gene expression and
PRISM Repurposing were used). p-values were corrected for false-discovery rate. Drugs with most
negative correlation coefficient were selected as potential senolytics. Similar analysis was performed
using HMEC or adipocyte senescence signature matrix to calculate CCLE senescence scores and correlate
them with PRISM auc values.
3.3 Results
3.3.1 Replicative senescence alters the HMECs proteome
To identify protein biomarkers of replicative senescence, we analyzed primary human mammary
epithelial cells (HMECs) using quantitative, label-free LC-MS-based proteomics. We have previously
found that HMECs enter senescence at ~40 population doublings (PD) (Fig. 3.1.A) and exhibit molecular
markers of senescence including upregulation of senescence-associated β-galactosidase (SA-β-gal),
upregulation of the cell cycle inhibitor p21, and cessation of DNA synthesis (94). Comparing proliferating
and senescent HMECs with LC-MS, we measured 1,234 proteins in two independent biological replicates
(Fig. 3.1.B and Table 12). Of these proteins, 55 were significantly upregulated and 34 were significantly
downregulated in senescent HMECs (FDR-corrected p-value < 0.01 and average absolute log 2 fold change
> 1) (Fig. 3.1.C). Among the most upregulated proteins in senescent cells was annexin A1 (ANXA1), which
is associated with aging in the rat prostate (95). Additionally, the β-galactosidase GLB1, which is associated
with SA-β-gal activity (96), was significantly upregulated in senescent HMECs. Significantly
downregulated proteins in senescent HMECs included histone H4 (HIST1H4A) and SLC3A2 (also known
40
as 4F2), a component of several heterodimeric amino acid transporter complexes including the cystine-
glutamate antiporter xCT. Hierarchical clustering of the individual sample values for significantly changing
proteins demonstrated high reproducibility across biological and technical replicates (Fig. 3.1.D). Next, to
understand the functional classes of proteins altered upon replicative senescence, we performed gene
ontology (GO) enrichment analysis. The most significantly upregulated GO terms in senescent HMECs
included vesicle, extracellular organelle, lysosome, and vacuole, consistent with the known upregulation of
secretory pathways and lysosomes in senescence (Fig. 3.1.E) (9). The most significantly downregulated
GO terms in senescent cells were ribosomal, translational, and RNA-related terms, consistent with reports
that reduced RNA turnover and alterations in translation drive cellular senescence (97, 98). Taken together,
our proteomic profiling reveals significant changes in the proteome of senescent HMECs including
upregulation of secretory pathways and downregulation of protein translation.
41
Figure 3.1 Replicative senescence alters the HMECs proteome. A) Primary HMECs proliferate for ~40
population doublings. Proteomics samples were collected at PD ~10 and PD ~40 to compare proliferating and
senescent HMECs. At each time, two biologically independent replicates were collected. The growth curve represents
the approximate collection times of the proliferating and senescent samples. B) Number of proteins quantified in two
independent biological replicates as described in A. Proteomics sample prep was performed as two independent
experiments with one proliferating and one senescent sample in each experiment. For each experiment, duplicate
injections (technical replicates) were run from each proliferating and senescent sample. C) Volcano plot representing
average of log2 fold change of protein levels comparing senescent versus proliferating HMECs plotted against the -
log10 p-value. Red and blue denote the 55 significantly upregulated and 34 significantly down-regulated proteins,
respectively (FDR-corrected p-value < 0.01 and average absolute log 2 fold change > 1). D) Hierarchical clustering of
protein expression levels for differentially expressed proteins in senescent (Sen.) and proliferating (Prol.) HMECs
across biological (Expt) and technical replicates (Rep). Proteins with FDR-corrected p-value < 0.0001 and average
absolute log2 fold change > 1 are shown. E) Gene Ontology enrichment analysis performed in Perseus software (1D-
enrichment analysis). The color of the circle denotes the enrichment score, and the size of the circle denotes the
statistical significance of the enrichment, as shown in the legend.
42
3.3.2 The proteome of hTERT-immortalized HMECs resembles that of proliferating
HMECs
We have previously shown that expression of human telomerase reverse transcriptase (hTERT)
immortalizes HMECs and enables bypass of replicative senescence (94). We thus used LC-MS proteomics
to compare hTERT-immortalized HMECs to senescent HMECs expressing the negative control protein
luciferase. At ~60 days in culture, corresponding to 35 and 85 PD for luciferase and hTERT, respectively,
hTERT-expressing HMECs continue to proliferate but luciferase-expressing HMECs are senescent (Fig.
3.2.A). By performing LC-MS proteomic analysis on hTERT-expressing and luciferase-expressing HMECs
in technical duplicate, we quantified 1,436 proteins (Table 13, of which 142 and 126 were significantly
upregulated and downregulated, respectively, in senescent luciferase-expressing HMECs (Fig. 3.2.B).
Interestingly, we found that five members of the lipid regulatory protein family of annexins were
significantly upregulated in senescent luciferase-expressing HMECs: ANXA1, ANXA2, ANXA3,
ANXA4, and ANXA5. Among the most downregulated proteins were the nucleolar RNA helicase DDX21
and the nuclear lamina component lamin-B1 (LMNB1), both of which have known roles in senescence (99,
100). Consistent with results from replicative senescence (Fig. 3.1.), GO enrichment analysis revealed that
vesicle, extracellular organelle, lysosome, and vacuole were significantly enriched in senescent luciferase-
expressing HMECs, whereas mRNA metabolic processes, ribonucleoprotein complex, RNA binding, and
RNA splicing were among the most significantly downregulated GO terms in senescent luciferase-
expressing HMECs (Fig. 3.2.C). Lastly, we compared the proteomic signature of proliferating hTERT-
expressing versus senescent luciferase-expressing HMECs to replicative senescence and found that the
signatures were broadly correlated (Pearson’s r = 0.71) (Fig. 3.2.D). Notably, two members of the cathepsin
family of proteases, CTSA and CTSD, were significantly upregulated in both data sets. Several proteins
were significantly downregulated in both data sets including SLC3A2, the serine protease HTRA1, lamina-
associated polypeptide 2 (TMPO, also known as thymopoietin or LAP2), and histone H1.5 (HIST1H1B).
Taken together, proteomic analysis of hTERT-immortalized HMECs compared to senescent luciferase-
43
expressing HMECs revealed broad similarity to replicative senescence both at the individual protein level
and in GO enrichment.
Figure 3.2 The proteome of hTERT-immortalized HMECs resembles that of proliferating HMECs. A)
Representative growth curve for HMECs infected with either luciferase (negative control, senescent) or hTERT
(immortalized). Circles represent the approximate time of sample collection. One biological replicate was collected
and analyzed in technical duplicate by LC-MS proteomics. B) Volcano plot representing log2 fold change of protein
levels for luciferase versus hTERT plotted against the -log 10 p-value. Red and blue denote significantly up- and down-
regulated proteins, respectively (FDR-corrected p-value < 0.01 and absolute log2 fold change > 1). C) Gene Ontology
enrichment analysis performed in Perseus software (1D-enrichment analysis). The color of the circle denotes the
enrichment score, and the size of the circle denotes the statistical significance of the enrichment, as shown in the
legend. D) Comparison of protein expression changes in luciferase versus hTERT HMECs against senescent versus
proliferating HMECs (i.e., replicative senescence). Red and blue circles denote proteins that were significantly
upregulated and downregulated, respectively, in both data sets (FDR corrected p-value < 0.01, and absolute log2 fold
change > 1). Green and orange circles represent proteins that were significantly changed in senescent versus
proliferating cells but not in luciferase versus hTERT cells or in luciferase versus hTERT cells but not senescent versus
proliferating cells, respectively. The Pearson correlation coefficient is shown.
44
3.3.3 The proteome of RRM2 inhibition-induced senescence resembles that of replicative
senescence
We have previously shown that inhibition of the nucleotide synthesis enzyme RRM2 induces
senescence in proliferating HMECs (94). Thus, we next sought to investigate the proteomic signature of
RRM2 inhibition-induced senescence. After 3 days of treatment with either DMSO (control) or the RRM2
inhibitor triapine, HMECs were analyzed in biological triplicate using LC-MS proteomics (Fig. 3.3.A).
Here, we identified 1,840 proteins (Table 14), of which 32 and 15 were significantly upregulated and
downregulated, respectively, in triapine-treated senescent HMECs (Fig. 3.3.B). Galectin-7 (LGALS7), a β-
galactosidase-binding protein that can regulate cell-cell and cell-matrix interactions, was the most
significantly upregulated protein in triapine-treated senescent HMECs. The tumor suppressor protein p63
(TP63) and the nuclear lamina component lamin-B1 (LMNB1) were among the significantly downregulated
proteins in triapine-treated senescent HMECs. GO enrichment analysis revealed that extracellular
organelle, vesicle, cytosol, and cytoskeleton were enriched in triapine-treated senescent HMECs (Fig.
3.3.C). Conversely, RNA processing, RNA splicing, and nucleoplasm were downregulated GO terms in
triapine-treated HMECs. We next compared the proteomic signatures of replicative senescence (Fig. 1)
with that of triapine-induced senescence and found that the two signatures were broadly correlated
(Pearson’s r = 0.65) (Fig. 3.3.D). Several proteins were significantly upregulated in both signatures
including ANXA1, LGALS7, and heat shock protein beta-1 (HSPB1). One protein, MCM3, a member of
the minichromosome maintenance protein complex (MCM) that is essential for genomic DNA replication,
was significantly downregulated in both replicative senescence and triapine-induced senescence. Taken
together, senescence induced by the inhibition of nucleotide synthesis comprises proteomic changes that
broadly resemble replicative senescence.
45
Figure 3.3 The proteome of RRM2 inhibition-induced senescence resembles that of replicative senescence. A)
Representative growth curve for HMECs treated with either DMSO (negative control, proliferating) or triapine
(senescent). Circles represent the approximate time of sample collection. Three biological replicate was collected and
analyzed in technical singlicate by LC-MS proteomics. B) Volcano plot representing log2 fold change of protein levels
for triapine versus DMSO plotted against the -log 10 p-value. Red and blue denote significantly up- and down-regulated
proteins, respectively (FDR-corrected p-value < 0.01 and absolute log2 fold change > 1). C) Gene Ontology
enrichment analysis performed in Perseus software (1D-enrichment analysis). The color of the circle denotes the
enrichment score, and the size of the circle denotes the statistical significance of the enrichment, as shown in the
legend. D) Comparison of protein expression changes in triapine versus DMSO HMECs against senescent versus
proliferating HMECs (i.e., replicative senescence). Red and blue circles denote proteins that were significantly
upregulated and downregulated, respectively, in both data sets (FDR corrected p-value < 0.01, and absolute log2 fold
change > 1). Green and orange circles represent proteins that were significantly changed in senescent versus
proliferating cells but not in triapine versus DMSO cells or in triapine versus DMSO cells but not senescent versus
proliferating cells, respectively. The Pearson correlation coefficient is shown.
46
3.3.4 Data integration identifies a proteomic signature of HMEC senescence
Recent studies have demonstrated the benefit of using computational methods in biomarker
discovery and identification of gene signatures associated with certain biological phenotypes (101–107).
To identify a core signature of HMEC senescence, we integrated the proteomic data from replicative
senescence (Fig. 3.1.), hTERT immortalization (Fig. 3.2.), and RRM2 inhibition-induced senescence (Fig.
3.3.). In total, 958 proteins were quantified in all three data sets (Fig. 3.4.A and Table 15). Overall, 59 and
30 proteins were significantly upregulated and downregulated across all three senescence signatures,
respectively (FDR-corrected p-value < 0.01 and average absolute log 2 fold change > 1). Among the most
significantly upregulated proteins were annexin 1 (ANXA1), the tumor suppressor serpin B5 (SERPINB5),
and four members of the cathepsin family of proteases: CTSA, CTSB, CTSD, and CTSZ (Fig. 3.4.B).
Among the most downregulated proteins were SLC3A2 (4F2), lamina-associated polypeptide 2 (TMPO),
and six individual histones: H1.3 (HIST1H1D), H1.5 (HIST1H1B), H2A.Z (H2AFZ), H2B type 1-J
(HIST1H2BJ), H2B type 2-F (HIST2H2BF), and H4 (HIST1H4A). Hierarchical clustering of the individual
biological and technical replicates demonstrated consistent upregulation or downregulation for the most
significantly changing proteins across the three individual proteomic signatures (Fig. 3.4.C). Next, to
identify transcription factors that might regulate senescence, we performed enrichment analysis on the
combined proteomics data using transcription factor target (TFT) gene lists (108). This analysis identified
3 TFTs that were significantly upregulated in senescent HMECs (TFEB, MAFG, PCGF1), and 40 TFTs
that were significantly downregulated including SUPT20H, SETD1A, and ZFKX3 (Fig. 3.4.D, p-value <
0.05 and FDR q-value < 0.1). To our knowledge, several of these transcription factors including MAFG,
PCGF1, SUPT20H, and ZFHX3 have not been previously linked to senescence. Taken together, our
combined analysis identified a core proteomic signature of HMEC senescence including potential
senescence biomarkers and transcription factor regulators of senescence.
47
Figure 3.4 Data integration identifies a proteomic signature of HMEC senescence. A) Venn diagram showing
the overlap in the number of proteins identified in each dataset: senescent versus proliferating HMECs (Fig. 3.1.),
luciferase- versus hTERT-expressing HMECs (Fig. 3.2.), and triapine- versus DMSO-treated HMECs (Fig. 3.3.). B)
Volcano plot representing average of log 2 (senescent / proliferating) vs. -log 10 p-value combined statistical significance
of data from 3 datasets shown on Figures 1-3. Red and blue circles show proteins that were consistently up- or down-
regulated upon integration of the 3 datasets, respectively (FDR corrected p-value < 0.01, average absolute log 2 fold
change > 1). C) Hierarchical clustering of significantly altered proteins across all 3 datasets. Proteins with FDR
corrected p-value < 1e
-6
and average absolute log 2 fold change > 1 are shown. All biological and technical replicates
are shown. D) Gene Set Enrichment Analysis (GSEA) to identify enrichment of transcription factor targets gene lists.
The color of the circle denotes the enrichment score, and the size of the circle denotes the statistical significance of
the enrichment, as shown in the legend.
48
3.3.5 Defining a senescence score that predicts HMECs senescence
Having identified an HMEC proteomic signature of senescence, we next asked whether our
signature could predict senescence in an independent data set. Because we are unaware of other HMEC
proteomic data sets, we turned to transcriptomic profiling data from pre-stasis HMECs (i.e., proliferating),
intermediate HMECs, or HMECs at stasis (i.e., a stress-associated senescence barrier associated with
elevated levels of p16 and/or p21, G1 arrest, and the absence of genomic instability) (109). We then defined
a weighted voting scheme (110) where the matrix of log 2 fold change of the 86 core senescence proteins
(Fig. 3.4.) was multiplied by the matrix of gene expression data from the same 86 genes. The result is a
“senescence score” for each individual sample where increasing scores predict senescence (Fig. 3.5.A).
Testing this approach, the senescence score was significantly increased for five independent HMEC cell
lines as they entered stasis (Fig. 3.5.B). The average increase in senescence score from pre-stasis to stasis
was 3.6 ± 1.4 (standard deviation, p = 0.0014). These results indicate that the senescence score can predict
whether HMEC cultures are proliferating or senescent.
49
Figure 3.5 Defining a senescence score that predicts HMEC senescence. A) Schematic representing calculation
of HMEC senescence score using weighted voting (110) The proteomic signature of HMEC senescence (Fig. 4C) was
used as voting weights (log 2 fold change of protein expression comparing senescent and proliferating HMEC, 86
proteins total). Weights were multiplied by gene expression data to calculate a HMEC senescence score for each
sample. B) Gene expression data from five independent HMEC cell lines (109) was analyzed using weighted voting
as in panel A. Samples for each cell line are arranged in increasing passage number and colored according to pre-
stasis (i.e., proliferating), intermediate, or stasis (i.e., senescent) as in the original publication. M85, M85X, and
MCDB represent different media formulations. Samples profiled at the same passage are connected by a thin dark
gray line. p = 0.0014 comparing the senescence scores from pre-stasis and stasis using a paired (by cell line) t-test.
3.3.6 Large-scale drug screening databases predict that EGFR and MEK inhibitors are
senolytic compounds in HMECs
We next sought to leverage our HMEC proteomic signature of senescence to identify novel
senolytic compounds in HMEC. We hypothesized that large panels of molecularly characterized human
cancer cell lines (e.g., the Cancer Cell Line Encyclopedia (CCLE)) (111) paired with large-scale drug
50
screening databases (e.g., PRISM Repurposing Screen from the Cancer Dependency Map (DepMap)) (112,
113) would enable us to identify drugs that are selectively toxic to senescent cells (i.e., senolytic
compounds). To test this hypothesis, we first asked whether we could use gene expression data from
senescent adipocytes to recapitulate the discovery of dasatinib as a senolytic compound in adipocytes (114).
Using the 104 most differentially expressed genes between proliferating and senescent adipocytes (log 2 fold
change > 2.5), we first calculated an “adipocyte senescence score” for ~500 cell lines using weighted voting.
This approach is analogous to calculation of the HMEC senescence score (Fig. 3.5.A) except that log 2 fold
change values were derived from transcriptomic analysis of proliferating and senescent adipocytes instead
of HMEC proteomic profiling. We then correlated the adipocyte senescence score with sensitivity to 1,448
drugs in the DepMap drug sensitivity database. Here, because a smaller dose-response area under the curve
(AUC) indicates higher sensitivity to the small molecule, compounds with negative correlations are more
toxic to senescent cells. Confirming the validity of this approach, the drug whose sensitivity was most
negatively correlated with the adipocyte senescence score was dasatinib (FDR-corrected p-value 9x10
-4
)
(Fig. 3.6.A,B). In contrast, the drug navitoclax, which is senolytic in human umbilical vein epithelial cells
(HUVECs) but not in adipocytes, was not significantly correlated with the adipocyte senescence score
(FDR-corrected p-value 0.6) (Fig. 3.6.C). Taken together, this confirms that combining senescence
signatures with large-scale databases of transcriptomic profiling and drug sensitivity data can be used to
identify senolytic compounds.
51
Figure 3.6 Large-scale drug screening databases correctly identify that dasatinib but not navitoclax is senolytic
in adipocytes. A) Pearson correlation coefficients for 1,448 drug sensitivities with the adipocyte sensitivity score in
~500 cancer cell lines. Using the 104 most differentially expressed genes comparing proliferating and senescent
adipocytes (Zhu et al., 2015), we calculated the adipocyte senescence score for ~500 cancer cell lines using gene
expression data from the Cancer Cell Line Encyclopedia (CCLE) (Ghandi et al., 2019). This approach is analogous to
the HMEC senescence score (Fig. 3.5.A) except that log2 fold change values are derived from transcriptomic analysis
of proliferating and senescent adipocytes instead of HMEC proteomic profiling. The adipocyte senescence score was
then correlated with the sensitivity (i.e., dose-response area under the curve (AUC)) to 1,448 drugs in the DepMap
PRISM Repurposing Screen (Corsello et al., 2020). B) Among 1,448 drugs tested, the adipocyte senescence score was
most correlated with sensitivity to the tyrosine kinase inhibitor dasatinib, which has been shown to be senolytic in
adipocytes (Zhu et al., 2015). In contract, the adipocyte senescence score was not significantly correlated with
sensitivity to navitoclax, a BCL inhibitor which is senolytic in human umbilical vein epithelial cells (HUVECs) but
not in adipocytes (Zhu et al., 2016). The p-values shown have been subjected to a Benjamini-Hochberg FDR
correction.
52
Next, we applied this approach to discovery of senolytic compounds in HMECs. We first asked
whether the 86 proteins in our HMEC proteomic signature of senescence were directionally correlated in
the CCLE gene expression data. Strikingly, most of the upregulated HMEC senescence proteins were
positively correlated with one another and negatively correlated with the downregulated HMEC senescence
proteins (Fig. 3.7.).
Figure 3.7 Gene expression of HMECs senescence signature hits are co-regulated in cancer lines. Hierarchical
clustering of Pearson correlation coefficients between gene expression values of Broad Institute Cancer Cell Line
Encyclopedia (CCLE) database (Ghandi et al., 2019). Only hits which had abs(average(log 2(senescent/proliferating)))
> 1 and FDR < 0.01 were selected for the correlation analysis. Blue to red color map represents negative to positive
Pearson correlation coefficient between genes in cancer cell lines. Orange and green represent upregulated and
downregulated HMEC senescence hits, respectively.
53
Analysis of proteomic profiling data from CCLE (115) revealed similar trends (Fig. 3.8.). These
results indicate that the HMEC senescence proteins are co-regulated and could be used to predict an HMEC
senescence-like signature in cancer cell lines.
Figure 3.8 Protein expression of HMECs senescence signature hits are co-regulated in cancer lines.
Hierarchical clustering of Pearson correlation coefficients between protein expression values of Broad Institute Cancer
Cell Line Encyclopedia (CCLE) database (Nusinow et al., 2020). Only hits which had
abs(average(log 2(senescent/proliferating))) > 1 and FDR < 0.01 were selected for the correlation analysis. Blue to red
color map represents negative to positive Pearson correlation coefficient between proteins in cancer cell lines. Orange
and green represent upregulated and downregulated HMEC senescence hits, respectively.
Therefore, we calculated the HMEC senescence score for ~500 cancer cell lines present in both the
CCLE and the DepMap drug screening databases. Although no voting proteins overlap between HMEC
54
and adipocyte senescence scores, the two senescence scores were significantly correlated (data not shown).
Next, we correlated the HMEC senescence scores with drug sensitivity (AUC) (Fig. 3.9.A). Interestingly,
the two drugs whose sensitivity was most negatively correlated with the HMEC senescence score were the
EGFR inhibitors dacomitinib and AZD8931 (Fig. 3.9.B,C). Dasatinib but not navitoclax was also
significantly negatively correlated with the HMEC senescence score (Fig. 3.9.B). Conversely, the two drugs
whose sensitivity was most positively correlated were anguidine and indisulum, inhibitors of protein
synthesis and CDK, respectively, suggesting that these drugs are more toxic to proliferating HMEC than
senescent HMEC (data not shown). Lastly, we tested for global enrichment of drug targets in the rank list
of HMEC senescence score and drug sensitivity correlation coefficients using Drug Set Enrichment
Analysis (DrugSEA), a variant of GSEA. Overall, both EGFR and MEK inhibitors were significantly
negatively enriched, indicating selective toxicity against cell lines with HMEC senescence-like signatures
(Fig. 3.9.D). Taken together, these results suggest that EGFR and MEK inhibitors including dacomitinib
and AZD8931 are senolytics in HMEC.
55
Figure 3.9 Large-scale drug screening databases predict that EGFR and MEK inhibitors are senolytic
compounds in HMECs. A) Schematic of analysis workflow. Senescence score was calculated for each of the CCLE
cell lines using HMEC senescence signature matrix and CCLE gene expression data. For each drug, we calculated a
Pearson correlation coefficient between PRISM area under the curves (auc) and senescence scores. Smaller PRISM
auc indicates higher sensitivity of a cell line to the drug treatment, thus, more negative correlation coefficient between
PRISM auc and senescence score indicates higher sensitivity of senescence-like cancer cell lines to the drug treatment.
B) Pearson correlation coefficients for all drug treatments of CCLE PRISM. C) Top two drugs with the most negative
correlation coefficients both are EGFR inhibitors. D) Drug Set Enrichment Analysis (DrugSEA) indicates enrichment
of EGFR and MEK inhibitors as potential senolytic targets.
56
3.4 Discussion and conclusion
Cellular senescence is a state of irreversible cell cycle arrest that contributes to degenerative and
hyperplastic phenotypes in aging, cancer, and many other diseases. The targeted elimination of senescent
cells with senolytic compounds has emerged as a promising therapeutic approach for both disease and
healthy aging. Here, we were motivated by the paucity of senescence biomarkers and the need to identify
cell type-specific senolytic compounds. First, we used LC-MS proteomics to characterize the proteome of
senescent primary HMECs and identified a robust signature of 86 HMEC senescence biomarkers (Fig. 3.4).
Then, we integrated our proteomic signature of HMEC senescence with large-scale drug screening
databases to predict that EGFR inhibitors, MEK inhibitors, and dasatinib are novel senolytic drugs for
HMEC. Taken together, our study adds to the growing literature on senescence biomarkers, senolytic
agents, and computational approaches to identify novel therapeutics from large-scale public databases.
Proteomics has emerged as a powerful tool for the identification of novel senescence biomarkers
(116), proteomic alterations in the aging lung (117), the therapy-induced senescence (118), SASP (119),
and signatures of aging in biofluids like plasma (120, 121). Here, in our HMEC model system of aging,
several proteins identified as members of the HMEC senescence proteomic signature are previously known
senescence biomarkers. For example, in HMECs, expression of lamin-B1 (LMNB1), a component of the
nuclear lamina, was significantly decreased (average log2 fold change in senescent cells -1.09, FDR-
corrected p-value 2.26 x 10
-8
). Loss of lamin-B1 expression in senescent cells has been extensively
documented, including in replicative senescence, oncogene-induced senescence, and UV-induced
senescence (99, 122–125). Notably, the lamin-B1-binding partner TMPO (LAP2) was also part of our
senescence signature, although decreases in TMPO expression are not unique to senescent cells, as
downregulation also occurs in quiescent cells (122). Regardless, the concordance of LMNB1 expression in
our HMEC system and other studies provides adds additional support that LMNB1 in a bona fide
senescence biomarker.
57
The most upregulated protein in our HMEC senescence signature was the calcium-dependent
phospholipid-binding protein annexin 1 (ANXA1) with an average log 2 fold change in senescent cells of
2.35 (Fig. 3.4.B). We also observed significant upregulation of two other annexins, ANXA3 and ANXA5,
in senescent HMEC, although these proteins were less upregulated than ANXA1 (average log 2 fold change
1.08 for both proteins). Interestingly, the upregulation of annexins has been previously linked to increased
lipid metabolism in a model of therapy-induced senescence (126). Moreover, ANXA1 is upregulated in
aged rat prostate (95), accumulation of nuclear ANXA5 is a biomarker of replicative and therapy-induced
fibroblast senescence (127), and secretion of ANXA1, ANXA3, and ANXA5 is upregulated in senescent
fibroblasts (128). In addition, we observed upregulation of several lysosomal proteins in senescent HMEC
including GLB1 (β-galactosidase), four cathepsins (CTSA, CTSD, CTSD, and CTSZ), and the glycosylase
MAN2B1. These results are consistent with previous reports of increased lysosomal activity in senescence
(129, 130). Additionally, cathepsins are known to regulate senescence (131) and pathogenesis of age-related
disease (129) and are also secreted by senescent cells (128). Moreover, the upregulation of ANXA1 and
CTSD has been reported as candidate biomarkers of spinal cord injury (132) which involves the appearance
of senescent cells (133–135). Finally, both the β-galactoside-binding proteins galectin-3 (LGALS3) and
galectin-7 (LGALS7) were significantly upregulated in senescent HMECs. Galectin-3 can coordinate
repair, removal, and replacement of lysosomes (136), and its upregulation may reflect attempts by senescent
cells to repair deteriorating lysosomes (137). To our knowledge, galectin-7 has not been reported to be
involved in senescence, but we speculate that it may also play a role in lysosomal repair and homeostasis
in senescent HMEC. Taken together, these results suggest that annexins, cathepsins, and galectins are
potential senescence biomarkers across many cell types.
The most downregulated protein in our proteomic signature of HMEC senescence was the histone
H1.5 (HIST1H1B) with an average log 2 fold change in senescent cells of -2.13 (Fig. 3.4.B). We additionally
observed downregulation of five additional histone proteins in our combined proteomics analysis (Fig. 3.4)
including H1.3 (HIST1H1D), H2A.Z (H2AFZ), H2B type 1-J (HIST1H2BJ), H2B type 2-F (HIST2H2BF),
58
and H4 (HIST1H4A). Consistent with our findings, several studies have reported loss of histone H1 and
DNA methylation in senescence and aging (138–140). In addition, increased lysosomal activity has been
linked to proteolysis of histones in senescent cells (141). These results support the regulatory role of
chromatin remodeling and reduced DNA methylation in senescence of HMECs.
Our analysis of transcription factors targets (Fig. 3.4.D) revealed significant upregulation or
downregulation of several transcription factors that have been previously linked to senescence and aging
including downregulation of SETD1A (142), KAT5 (143), and DOT1L (144) as well as upregulation of
TFEB (145). Interestingly, we also identified significant upregulation of MAFG and PCG1 targets and
significant downregulation of NKX2-2, ZFHX3 and SUPT20H targets. To our knowledge, these
transcription factors do not have reported roles in aging or senescence. Future studies are necessary to
investigate whether these transcription factors are regulators of cellular senescence in HMECs and other
cell types.
Senolytics have emerged as an exciting area with great therapeutic promise in aging (91, 114),
cancer (146, 147), and other diseases. In mice, clearance of senescent cells restores tissue homeostasis and
delays age-related dysfunction (15, 39, 76, 148). Furthermore, clinical trials of the senolytic combination
dasatinib and quercetin have shown encouraging results (149). Our study is the first, to our knowledge, to
leverage proteomic or transcriptomic signatures of senescence with large-scale drug screening (e.g., the
PRISM drug repurposing resource from DepMap) to infer novel senolytic agents. Although these drug
screening is conducted using non-senescent cancer cell lines, the fact that the top hit in our computational
analysis of senescent adipocytes was the senolytic drug dasatinib proved the validity of our approach (114)
(Fig. 3.6). Here, based on our proteomic signature of senescence, we predicted that EGFR inhibitors (e.g.,
dacomitinib, AZD8931), MEK inhibitors, and dasatinib are senolytic agents for HMECs. Interestingly,
MEK inhibitors have been shown to eliminate senescent Ras-expressing cells (150). Moreover, in IMR90
and HUVECs, the cytokine-mediated induction of senescence can be blocked by pharmacological inhibition
59
or genetic knockdown of EGFR (151). Taken together, our results suggest that large-scale drug screening
databases are a powerful resource for senolytic discovery in HMECs and other senescence models.
In conclusion, our results support that the combination of quantitative proteomics and public drug
screening databases is a powerful approach to identify senescence biomarkers and novel senolytic
compounds. Future research into the mechanisms affecting the efficacy and cell-type specificity of senolytic
drugs will have important implications for the usage of senolytics in clinical trials. Furthermore, unlocking
the transformative power of senolytics will require minimizing off-target effects and an improved
understanding of the impact of eliminating senescent cells on health and age-related disease.
60
Tables
Table 1: Replicative senescence metabolite consumption/secretion
Metabolite
Proliferating
HMEC weighted
average
Senescent
HMEC
weighted
average
Proliferating
HMEC weighted
standard deviation
Senescent
HMEC
weighted
standard
deviation
FDR corrected
Fisher’s
combined p-
value
ACONITATE 1.000 0.504 0.007 0.016 0.000
ADENINE -1.000 -1.290 0.000 0.002 0.000
ALANINE 1.000 0.709 0.041 0.193 0.945
ALPHA-KETOGLUTARATE 1.000 0.501 0.065 0.087 0.446
ARGININE -1.000 -1.032 0.132 0.301 0.945
ASPARTATE 1.000 0.212 0.076 0.122 0.000
CITRATE/ISOCITRATE 1.000 0.489 0.030 0.025 0.247
CYSTEINE -1.000 -0.456 0.035 0.091 0.104
CYSTINE -1.000 -0.486 0.038 0.089 0.084
FUMARATE 1.000 0.240 0.102 0.207 0.247
GLUCOSE -1.000 -1.002 0.025 0.055 0.518
GLUTAMATE 1.000 0.677 0.086 0.042 0.430
GLUTAMINE -1.000 -0.818 0.013 0.109 0.928
GLYCINE -1.000 0.841 0.284 2.304 0.190
GUANINE 1.000 2.155 0.043 0.486 0.054
GUANOSINE 1.000 0.886 0.400 0.212 0.787
HISTIDINE -1.000 -0.818 0.184 0.706 0.970
HYPOXANTHINE -1.000 -1.159 0.000 0.018 0.084
LACTATE 1.000 1.298 0.007 0.128 0.247
LACTATE/GLUCOSE 1.000 1.153 0.021 0.160 0.873
LEUCINE/ISOLEUCINE -1.000 -0.841 0.039 0.057 0.175
LYSINE -1.000 -0.439 0.224 0.499 0.518
MALATE 1.000 0.351 0.039 0.042 0.000
METHIONINE -1.000 -0.111 0.021 0.207 0.190
ORNITHINE -1.000 -0.081 0.060 0.046 0.928
PHENYLALANINE -1.000 -0.514 0.034 0.134 0.190
PROLINE 1.000 2.235 0.158 0.904 0.535
SERINE -1.000 -0.783 0.012 0.041 0.247
SUCCINATE 1.000 1.077 0.075 0.182 0.928
THREONINE -1.000 -0.816 0.283 0.662 0.945
TRYPTOPHAN -1.000 -0.210 0.064 0.301 0.362
TYROSINE -1.000 -0.212 0.610 1.856 0.928
61
URACIL -1.000 20.380 0.108 0.573 0.000
URATE 1.000 2.324 0.016 0.738 0.175
URIDINE -1.000 14.500 0.029 0.662 0.000
VALINE -1.000 -0.609 0.073 0.080 0.104
XANTHINE -1.000 0.754 0.001 0.175 0.459
Table 2: Replicative senescence metabolite pool sizes
Metabolite
Proliferating
HMEC
weighted
average
Senescent
HMEC
weighted
average
Proliferating
HMEC
weighted
standard
deviation
Senescent
HMEC
weighted
standard
deviation
FDR corrected
Fisher's
combined p-
value
2,3-cCMP 1.000 0.506 0.055 0.085 0.730
2,3-cUMP 1.000 0.044 0.109 0.009 0.004
2-AMINOADIPATE 1.000 1.048 0.118 0.043 1.000
3,5-cGMP 1.000 0.705 0.038 0.100 0.883
3PG 1.000 0.157 0.048 0.008 0.000
5,6-DIHYDROURACIL 1.000 0.656 0.037 0.012 0.014
5PRA 1.000 0.735 0.057 0.012 0.220
6PG 1.000 0.551 0.033 0.007 0.000
ACETYLCARNITINE 1.000 0.983 0.025 0.027 1.000
Acetyl-CoA 1.000 0.500 0.047 0.018 0.036
ACONITATE 1.000 0.550 0.028 0.012 0.000
ADENINE 1.000 0.525 0.070 0.028 0.598
ADP 1.000 0.288 0.015 0.005 0.000
ALANINE 1.000 0.014 0.030 0.003 0.000
ALLANTOIN 1.000 1.658 0.045 0.064 0.048
ALPHA-KETOGLUTARATE 1.000 0.414 0.027 0.017 0.000
AMP 1.000 0.098 0.037 0.000 0.034
ARGININE 1.000 0.936 0.028 0.014 0.980
ASPARAGINE 1.000 0.499 0.069 0.007 0.883
ASPARTATE 1.000 0.322 0.019 0.013 0.000
ATP 1.000 0.716 0.033 0.018 0.730
BETAINE 1.000 0.836 0.035 0.040 0.018
cAMP 1.000 0.012 0.023 0.001 0.000
cGMP 1.000 1.990 0.104 0.139 0.141
CITRATE/ISOCITRATE 1.000 0.494 0.043 0.011 0.003
CMP 1.000 0.122 0.032 0.004 0.701
COENZYME-A 1.000 0.522 0.097 0.053 0.022
CREATINE 1.000 1.745 0.016 0.055 0.000
CTP 1.000 0.352 0.103 0.044 0.453
CYSTATHIONINE 1.000 0.141 0.018 0.001 0.000
62
CYSTINE 1.000 0.969 0.016 0.044 0.359
CYTOSINE 1.000 0.233 0.042 0.009 0.828
dADP 1.000 0.018 0.098 0.004 0.013
dATP 1.000 0.212 0.052 0.009 0.000
dC 1.000 0.722 0.091 0.057 1.000
dCDP 1.000 0.095 0.034 0.006 0.000
dCMP 1.000 0.704 0.053 0.101 1.000
dCTP 1.000 0.466 0.098 0.013 0.141
DEOXYRIBOSE 1.000 0.180 0.160 0.004 1.000
dI 1.000 0.050 0.032 0.009 0.000
DIHYDROOROTATE 1.000 0.982 0.365 0.181 1.000
dIMP 1.000 0.792 0.051 0.031 0.522
dTDP 1.000 0.144 0.038 0.008 0.000
dTTP 1.000 0.244 0.074 0.027 0.670
dUDP 1.000 1.842 0.133 0.093 1.000
dUMP 1.000 0.755 0.116 0.168 1.000
dUTP 1.000 0.005 0.048 0.001 0.001
F-1,6-BP 1.000 0.138 0.025 0.002 0.000
FUMARATE 1.000 0.469 0.012 0.002 0.000
G3P 1.000 1.166 0.067 0.023 0.002
G6P/F6P 1.000 0.838 0.008 0.016 0.334
GDP 1.000 0.269 0.015 0.006 0.000
GLUCOSAMINATE 1.000 0.285 0.051 0.028 0.004
GLUCOSE 1.000 0.489 0.064 0.026 0.539
GLUTAMATE 1.000 0.516 0.013 0.005 0.000
GLUTAMINE 1.000 0.760 0.025 0.037 0.565
GLYCINE 1.000 1.085 0.060 0.080 0.322
GMP 1.000 0.097 0.050 0.005 1.000
GSH 1.000 0.699 0.004 0.016 0.004
GSSG 1.000 0.484 0.026 0.023 0.936
GTP 1.000 0.714 0.036 0.025 1.000
GUANIDINOACETATE 1.000 2.563 0.230 0.080 0.178
GUANINE 1.000 1.561 0.026 0.080 0.000
GUANOSINE 1.000 0.362 0.016 0.017 1.000
HISTIDINE 1.000 0.715 0.052 0.025 0.006
HYDROXYPYRUVATE 1.000 0.339 0.085 0.095 0.037
HYPOTAURINE 1.000 0.856 0.135 0.059 0.730
HYPOXANTHINE 1.000 1.384 0.042 0.066 0.027
IDP 1.000 0.006 0.042 0.001 0.001
IMP 1.000 0.256 0.097 0.018 0.536
INOSINE 1.000 1.050 0.024 0.031 0.139
ITP 1.000 0.139 0.026 0.008 0.000
LACTATE 1.000 0.874 0.065 0.039 1.000
LEUCINE/ISOLEUCINE 1.000 1.447 0.030 0.018 0.000
63
LYSINE 1.000 1.225 0.036 0.012 0.883
MALATE 1.000 0.437 0.006 0.006 0.000
MALONATE 1.000 0.339 0.085 0.095 0.037
METHIONINE 1.000 1.327 0.026 0.013 0.013
N-ACETYL-ALANINE 1.000 0.858 0.062 0.080 0.670
N-ACETYL-GLUTAMATE 1.000 0.499 0.040 0.006 0.000
N-ACETYL-L-CYSTEINE 1.000 0.616 0.069 0.059 0.034
N-ACETYL-METHIONINE 1.000 0.511 0.028 0.010 0.013
N-ACETYL-SERINE 1.000 1.023 0.042 0.016 0.670
NAD 1.000 1.103 0.010 0.015 1.000
ORNITHINE 1.000 1.015 0.029 0.042 1.000
OROTATE 1.000 0.143 0.733 0.154 1.000
PEP 1.000 0.319 0.112 0.049 0.084
PHENYLALANINE 1.000 1.074 0.028 0.016 0.001
PHOSPHOETHANOLAMINE 1.000 1.411 0.009 0.011 0.000
PROLINE 1.000 0.877 0.014 0.007 1.000
PRPP 1.000 0.058 0.076 0.005 1.000
PURINE 1.000 0.637 0.071 0.022 0.161
R5P/Ru5P 1.000 1.083 0.066 0.019 1.000
RIBOSE 1.000 0.351 0.054 0.022 0.044
RIBULOSE-1,5-BISPHOSPHATE 1.000 0.067 0.012 0.003 0.001
SERINE 1.000 1.658 0.027 0.077 0.000
SUCCINATE 1.000 0.582 0.018 0.006 0.000
TAURINE 1.000 0.867 0.054 0.023 1.000
THREONINE 1.000 0.644 0.085 0.015 0.883
TRYPTOPHAN 1.000 1.210 0.020 0.030 0.013
TYROSINE 1.000 0.800 0.060 0.049 1.000
UDP 1.000 0.219 0.026 0.007 0.001
UDP-N-ACETYL-GLUCOSAMINE 1.000 1.091 0.027 0.017 0.319
UMP 1.000 0.092 0.020 0.005 0.883
URACIL 1.000 0.164 0.057 0.031 0.084
URATE 1.000 2.110 0.062 0.070 0.000
URIDINE 1.000 2.322 0.019 0.031 0.000
UTP 1.000 1.504 0.036 0.026 1.000
VALINE 1.000 1.482 0.024 0.023 0.002
XANTHINE 1.000 18.591 0.369 1.249 0.000
XMP 1.000 0.396 0.037 0.017 0.000
64
Table 3: Replicative senescence [U-
13
C]-glucose labeling
Metabolite
Proliferating
HMEC
weighted
average
Senescent
HMEC
weighted
average
Proliferating
HMEC weighted
standard
deviation
Senescent
HMEC
weighted
standard
deviation
FDR corrected
Fisher's
combined p-
value
2,3-cCMP 0.3433 0.3438 0.0005 0.0009 0.5366
2,3-cUMP 0.1322 0.1138 0.0020 0.0143 0.4315
2-AMINOADIPATE 0.0432 0.0123 0.0132 0.0053 0
3,5-cGMP 0.0000 0.0000 0.0000 0.0000 0
3PG 0.9172 0.9239 0.0130 0.0172 0.9427
5,6-DIHYDROURACIL 0.0006 0.0010 0.0006 0.0003 0.4708
5PRA 0.3365 0.3361 0.0141 0.0204 0
5PRA 0.2498 0.2527 0.0253 0.0038 0.9390
6PG 0.7599 0.7056 0.0110 0.0080 0.0609
ACETYLCARNITINE 0.1707 0.1628 0.0008 0.0012 0
ACETYL-COA 0.7501 0.4342 0.0106 0.0102 0.0000
ACONITATE 0.4985 0.4645 0.0023 0.0035 0.0855
ADENINE 0.0361 0.0002 0.0007 0.0003 0.0000
ADP 0.4895 0.1878 0.0003 0.0008 0.0000
ADP-RIBOSE 0 0 0 0 0
AICAR 0.111111111 0.286591251 0.15713484 0.066103286 0
ALANINE 0.851482675 0.706635892 0.027000717 0.003104901 0
ALANINE 0.725757497 0.280717286 0.001174574 0.007994876 0.000997145
ALLANTOIN 0 0.006141942 0 0.008686018 0
ALPHA-KETOGLUTARATE 0.324399582 0.350378645 0.003636963 0.009686011 0.139136279
AMP 0.452473459 0.202576172 0.00070292 0.001831513 1.74821E-08
ARGININE 0.010878069 2.58944E-06 0.003340054 3.66202E-06 0
ASPARAGINE 0.007429863 0.006522502 0.002602388 0.001095053 0.942699349
ASPARTATE 0.352167615 0.23373322 0.007545419 0.008836672 0
ATP 0.480311828 0.19059351 0.0004258 0.000502668 4.88777E-10
BETAINE 1.75667E-05 7.32923E-05 2.4843E-05 0.000103651 0
cAMP 0.115733855 0.081799238 0.001111997 0.001035271 0.441689283
cGMP 0.068919532 0.004553854 0.004919894 0.002532296 0.001288615
CITRATE/ISOCITRATE 0.498460569 0.479735738 0.002340949 0.004658909 0.041255333
CMP 0.199587822 0.005571477 0.004524719 0.000545596 1.47249E-05
COENZYME-A 0.496166441 0.144159216 0.016901828 0.023120408 1.16173E-05
CREATINE 0.0000038 0 5.37401E-06 0 0
CYSTATHIONINE 0.017600068 0.015497512 0.002069688 0.011440053 0
CYSTINE 0.769439893 0.77421044 0.004391855 0.004873418 0.619881628
CYTOSINE 0.001395343 0.000267292 5.80477E-05 0.000177371 3.83227E-05
dADP 0.197515508 0.088495551 0.001575718 0.000489672 0.248255907
65
dAMP 0 0.2 0 2.77556E-17 0
dATP 0.253479456 0.179532288 0.003459879 0.002104563 3.7876E-06
dC 0.106390787 0.105176103 0.000593265 0.000239261 0.942699349
dCDP 0.363670752 1.86168E-21 0.006192884 2.62439E-21 1.07075E-05
dCMP 0.000109314 0.005642164 0.000154594 0.001354111 0
dCTP 0.24600312 4.6984E-25 0.021435275 6.64454E-25 0.001373227
DEOXYRIBOSE 2.33599E-24 6.5003E-27 3.30359E-24 9.19281E-27 0.581792876
DEOXYURIDINE 0.133942161 0.275108811 0.147129936 0.182770825 0.942699349
dI 2.58E-21 0 3.64867E-21 0 0
dIMP 0.40351879 0.431026283 0.003134644 0.004348387 0.010742976
dITP 0.020860131 0.003581258 0.001699445 0.002662194 0.004569203
dTDP 0.193011395 0.001 0.001649042 0 4.8136E-06
dTTP 0.245657483 9.03333E-19 0.014381699 1.27751E-18 0.00049434
dUDP 0 0 0 0 0
dUMP 0 0 0 0 0
dUTP 0.085761368 0 0.003039669 0 0
F-1,6-BP 0.930680297 0.875345472 0.00093491 0.003129208 0.000304762
FUMARATE 0.367823468 0.330255772 0.00305163 0.005319996 0.21283918
G3P 0.982932188 0.867526015 0.001188306 0.00097767 0.037430928
G6P/F6P 0.727946793 0.634550165 0.003027509 0.002471506 1.38577E-05
GDP 0.427363634 0.21807661 0.001173359 0.001755488 4.88777E-10
GLUCOSAMINATE 0.250551611 0.161321587 0.080082515 0.079738301 0
GLUCOSE 0.972749397 0.951794655 0.001316724 0.004340467 0.028184128
GLUTAMIC-ACID 0.339841597 0.35552932 0.001567765 0.003329684 0.001136686
GLUTAMINE 0.064333613 0.067284556 0.000786426 0.001045619 0.078643734
GLYCINE 0.131996832 0.011207139 0.002273402 0.000751568 0.000001708
GMP 0.419931437 0.192332971 0.001747557 0.006040886 5.06835E-05
GSH 0.166575354 0.155281074 0.000736163 0.001996898 0.028529459
GSSG 0.176022755 0.351917672 0.011933513 0.021434249 0.005855345
GTP 0.430081395 0.224396869 0.001559892 0.001462605 3.59354E-10
GUANIDINOACETATE 0.099002477 4.83333E-19 0.140010645 6.83537E-19 0
GUANINE 0.001 0.001 9.66758E-19 0 0.004254183
GUANOSINE 0.11704745 0.048266068 0.002714589 0.001011933 2.23152E-06
HISTIDINE 0.002351691 0.003611044 0.001832965 0.004716655 0
HYDROXYPYRUVATE 0 5.13333E-23 0 7.25963E-23 0
HYPOTAURINE 6.46667E-19 0 9.14525E-19 0 0
HYPOXANTHINE 0.001666813 0 0.000245755 0 0.000332275
IDP 0.376464585 0.323537923 0.001756449 0.012843815 0.000332275
IMP 0.505169467 0.218912289 0.006737056 0.00906132 7.95876E-06
INOSINE 0.454113347 0.087830469 0.002029974 0.006120342 0.000101352
ISOLEUCINE 9.9609E-05 0.000132119 2.55848E-05 2.45945E-05 0.292985116
ITP 0.055970668 0.174472853 0.005215968 0.008310834 0.002978822
LACTATE 0.850206575 0.827574556 0.005979868 0.006044297 0.061487458
LYSINE 0.000290975 0 0.000120225 0 0
66
MALATE 0.383708157 0.285369515 0.000539476 0.001696435 0.372348006
MALONATE 0 0 0 0 0
METHIONINE 0.034960054 0.004460598 0.000378468 0.00026111 2.45476E-05
N-ACETYL-ALANINE 0.135501398 0.023307045 0.056531241 0.008801997 0
N-ACETYL-GLUTAMATE 0.253488139 0.281931238 0.004982171 0.004587233 0
N-ACETYL-L-CYSTEINE 0.10726144 0.071200456 0.026896517 0.00978641 0
N-ACETYL-METHIONINE 0.203522312 0.213074514 0.010509673 0.078653023 0
N-ACETYL-SERINE 0.337493535 0.363067055 0.001757634 0.004380439 0
NAD 0.389089306 0.474022851 0.001552434 0.001080677 4.8136E-06
ORNITHINE 0 0 0 0 0
OROTATE 0.298844348 0.066666667 0.292822158 0.094280904 0
PEP 0.973032671 0.960104267 0.019261125 0.030234618 0.619881628
PHENYLALANINE 0.003005431 0.000551502 0.000241134 2.39774E-05 0.003499999
PHOSPHOETHANOLAMINE 4.63333E-19 0 6.55252E-19 0 0
PROLINE 0.160991603 0.140154926 0.000514498 0.004918403 0.001915406
PRPP 0.932778979 0.906352666 0.009429732 0.00256769 0.00719824
PURINE 0.745475972 0.740518214 0.008217222 0.005320691 0
R5P/Ru5P 0.934384504 0.508676712 0.013456137 0.032763813 0.000937822
RIBOSE 0.989652131 0.983545164 0.00098269 0.002221981 0
RIBULOSE-1,5-BP 0.945030259 0.964418177 0.005041767 0.00223646 0.030146007
S-DIHYDROOROTATE 0.155221925 0 0.219516951 0 0
SERINE 0.080913403 0.001879297 0.003782095 0.000556082 7.51502E-05
SUCCINATE 0.325558584 0.321660221 0.004880573 0.003841158 0.08768967
TAURINE 0.001323953 0.000205535 0.000328382 0.00021676 0.038018063
THREONINE 0.000827555 1.25007E-05 0.000114919 1.67758E-05 0.337621609
TRYPTOPHAN 0.000417875 0.000106519 0.000372472 9.55722E-05 0.092207715
TYROSINE 0.00872723 0.000309686 0.000825856 0.000126001 0.004218902
UDP 0.47663288 0.062223557 0.00111016 0.002231061 3.89456E-10
UDP-N-ACETYL-GLUCOSAMINE 0.46519993 0.588521742 0.000711547 0.001294145 6.55251E-06
UMP 0.357061825 0.00125316 0.002705678 0.000267672 4.64573E-09
URACIL 0.219123414 0.005092981 0.008720785 0.00093772 0.000332275
URATE 0.000164571 0.000240817 0.00015502 0.000174727 0.036903696
URIDINE 0.126435697 0.017030183 0.001492394 0.000814367 2.09541E-08
UTP 0.482386842 0.090507309 0.000722215 0.000200272 2.09541E-08
VALINE 0 0 0 0 0
XANTHINE 6.46723E-27 2.55752E-24 9.14605E-27 3.60277E-24 0.921814398
XMP 0.172073121 0.173013458 0.001234007 0.001041561 0.266271026
67
Table 4: Replicative senescence [1,2-
13
C]-glucose labeling
Metabolite
Proliferating
HMEC average
Senescent
HMEC average
Proliferating
HMEC
standard
deviation
Senescent
HMEC
standard
deviation
FDR corrected
p-value
2,3-cCMP 0.2265 0.5751 0.1603 0.1854 0.2484
2,3-cUMP 0.1364 0.0000 0.1176 0.0000 0.4230
2-AMINOADIPATE 0.3070 0.2668 0.0139 0.0335 0.4031
3,5-cGMP 0.2747 0.2182 0.3685 0.2712 0.9155
3PG 0.2631 0.2400 0.0108 0.1292 0.8745
5,6-DIHYDROURACIL 0.4840 0.3989 0.0094 0.0623 0.3692
5PRA 0.3815 0.3413 0.0239 0.0470 0.5428
6PG 0.3012 0.2611 0.0107 0.0032 0.0717
ACETYLCARNITINE 0.0951 0.0796 0.0020 0.0009 0.0163
ACETYL-COA 0.4303 0.0000 0.0926 0.0000 0.0666
ACONITATE 0.2136 0.1783 0.0002 0.0016 0.0057
ADENINE 0.0092 0.0013 0.0016 0.0009 0.0313
ADP 0.1904 0.0420 0.0046 0.0010 0.0027
ADP-RIBOSE 0.2000 0.4845 0.2828 0.3478 0.5428
AICAR 0.0000 0.2963 0.0000 0.4190 0.5428
ALANINE 0.2585 0.2040 0.0007 0.0093 0.0475
ALLANTOIN 0.0000 0.0000 0.0000 0.0000 0.5428
ALPHA-KETOGLUTARATE 0.1461 0.1205 0.0029 0.0061 0.0475
AMP 0.1880 0.0374 0.0044 0.0026 0.0003
ARGININE 0.0000 0.0000 0.0000 0.0000 0.6471
ASPARAGINE 0.0003 0.0008 0.0001 0.0002 0.1690
ASPARTATE 0.1368 0.0559 0.0021 0.0029 0.0003
ATP 0.1922 0.0454 0.0050 0.0011 0.0027
BETAINE 0.0000 0.0000 0.0000 0.0000 0.5428
cAMP 0.0586 0.0000 0.0113 0.0000 0.0563
cGMP 0.4667 0.2333 0.3300 0.3300 0.6455
CITRATE/ISOCITRATE 0.2188 0.1762 0.0016 0.0014 0.0003
CMP 0.0975 0.1209 0.0054 0.0768 0.7941
CREATINE 0.0000 0.0000 0.0000 0.0000 0.5428
CREATININE 0.5938 0.2558 0.4293 0.3617 0.5638
CYSTATHIONINE 0.0281 0.7676 0.0292 0.2452 0.1245
CYSTEINE 0.0000 0.0003 0.0000 0.0004 0.5428
CYSTINE 0.7549 0.8101 0.0352 0.0118 0.3080
CYTOSINE 0.0000 0.0078 0.0000 0.0072 0.4380
dADP 0.0859 0.0000 0.0640 0.0000 0.3714
dAMP 0.0366 0.0000 0.0264 0.0000 0.3692
dATP 0.0000 0.0000 0.0000 0.0000 0.0000
dC 0.1710 0.0069 0.1178 0.0060 0.3692
dCDP 0.0885 0.0455 0.0250 0.0376 0.4340
68
dCMP 0.3244 0.2310 0.1050 0.1612 0.6471
dCTP 0.1108 0.0615 0.0631 0.0139 0.5428
DEOXYRIBOSE 0.0000 0.0000 0.0000 0.0000 0.0000
dI 0.2853 0.1402 0.1735 0.1860 0.5855
DIHYDROOROTATE 0.2317 0.2399 0.1429 0.0464 0.9846
dIMP 0.2009 0.1607 0.0008 0.0104 0.0826
dTDP 0.0165 0.0000 0.0023 0.0000 0.0353
dTTP 0.1667 0.1667 0.2357 0.2357 1.0000
dUDP 0.0000 0.0000 0.0000 0.0000 0.5428
dUMP 0.0542 0.0000 0.0766 0.0000 0.5428
dUTP 0.1481 0.0000 0.2095 0.0000 0.5428
F-1,6-BP 0.3187 0.2764 0.0071 0.0057 0.0162
FUMARATE 0.1817 0.1779 0.0136 0.0022 0.7954
G3P 0.2762 0.2712 0.0061 0.0013 0.5428
G6P/F6P 0.3019 0.2568 0.0063 0.0097 0.0313
GDP 0.0881 0.0270 0.0039 0.0157 0.0717
GLUCOSAMINATE 0.3190 0.3144 0.0062 0.0128 0.7715
GLUCOSE 0.3291 0.3149 0.0008 0.0031 0.0563
GLUTAMATE 0.1524 0.1241 0.0016 0.0015 0.0007
GLUTAMINE 0.0240 0.0233 0.0040 0.0003 0.8742
GLYCINE 0.0165 0.0005 0.0010 0.0002 0.0086
GMP 0.1730 0.0126 0.0076 0.0073 0.0004
GSH 0.0830 0.0592 0.0007 0.0004 0.0003
GSSG 0.7038 0.7690 0.0088 0.0018 0.0281
GTP 0.1682 0.0148 0.0049 0.0010 0.0027
GUANIDINOACETATE 0.3318 0.1537 0.1392 0.0306 0.3788
GUANINE 0.0000 0.0000 0.0000 0.0000 0.5428
GUANOSINE 0.0364 0.0000 0.0015 0.0000 0.0057
HISTIDINE 0.0000 0.0000 0.0000 0.0000 0.0000
HOMOCYSTEINE 0.2479 0.2551 0.0025 0.0002 0.1296
HYDROXYPYRUVATE 0.0028 0.0002 0.0004 0.0002 0.0118
HYPOTAURINE 0.0000 0.0000 0.0000 0.0000 0.0000
HYPOXANTHINE 0.0030 0.0000 0.0011 0.0000 0.1541
IDP 0.3908 0.4638 0.1127 0.1092 0.6538
IMP 0.0000 0.5928 0.0000 0.1515 0.0826
INOSINE 0.0924 0.0355 0.0082 0.0032 0.0205
ITP 0.0000 0.0000 0.0000 0.0000 0.0000
LACTATE 0.2887 0.2840 0.0024 0.0008 0.2033
LEUCINE/ISOLEUCINE 0.0000 0.0001 0.0000 0.0000 0.4340
LYSINE 0.0000 0.0000 0.0000 0.0000 0.0000
MALATE 0.1603 0.1272 0.0012 0.0024 0.0037
MALONATE 0.0028 0.0002 0.0004 0.0002 0.0118
METHIONINE 0.0054 0.0000 0.0012 0.0000 0.0689
N-ACETYL-ALANINE 0.0754 0.0894 0.0116 0.0315 0.7009
69
N-ACETYL-GLUTAMATE 0.2126 0.2334 0.0208 0.0416 0.6720
N-ACETYL-L-CYSTEINE 0.0275 0.0552 0.0223 0.0331 0.5428
N-ACETYL-METHIONINE 0.0404 0.0393 0.0038 0.0256 0.9881
N-ACETYL-SERINE 0.0834 0.2223 0.0141 0.1709 0.5428
NAD 0.4741 0.2243 0.0018 0.0015 0.0000
ORNITHINE 0.0066 0.0000 0.0047 0.0000 0.3692
OROTATE 0.0000 0.0092 0.0000 0.0130 0.5428
PEP 0.3925 0.3929 0.0721 0.0764 1.0000
PHENYLALANINE 0.0000 0.0000 0.0000 0.0000 0.7947
PHOSPHOETHANOLAMINE 0.0000 0.0000 0.0000 0.0000 0.3692
PROLINE 0.0586 0.0228 0.0043 0.0002 0.0281
PRPP 0.3574 0.1025 0.0095 0.0485 0.0491
PURINE 0.0716 0.1276 0.0802 0.1804 0.7947
R5P/Ru5P 0.2368 0.2008 0.0123 0.0138 0.1296
RIBOSE 0.3022 0.3073 0.0199 0.0140 0.8489
RIBULOSE-1,5-BP 0.3794 0.1406 0.0009 0.0209 0.0170
SERINE 0.0143 0.0000 0.0042 0.0000 0.1066
SUCCINATE 0.1315 0.0993 0.0137 0.0029 0.1690
TAURINE 0.0000 0.0000 0.0000 0.0000 0.0000
THREONINE 0.0003 0.0002 0.0002 0.0002 0.7834
TRYPTOPHAN 0.0000 0.0000 0.0000 0.0000 0.0000
TYROSINE 0.0004 0.0001 0.0002 0.0000 0.4031
UDP 0.1601 0.0085 0.0094 0.0005 0.0108
UDP-N-ACETYL-GLUCOSAMINE 0.6663 0.2221 0.0050 0.0026 0.0001
UMP 0.1574 0.0048 0.0094 0.0014 0.0095
URACIL 0.0148 0.0000 0.0110 0.0000 0.3714
URATE 0.0004 0.0000 0.0005 0.0000 0.5428
URIDINE 0.0607 0.0007 0.0066 0.0001 0.0256
UTP 0.1612 0.0093 0.0099 0.0002 0.0116
VALINE 0.0000 0.0000 0.0000 0.0000 0.0000
XANTHINE 0.0000 0.0000 0.0000 0.0000 0.5428
XMP 0.0503 0.0000 0.0452 0.0000 0.4340
70
Table 5: Metabolic pathway analysis of senescent HMEC and IMR90 gene expression data
Pathway SIZE NES Rank
PYRIMIDINE_METABOLISM_HSA00240 89 -2.90 1
CITRATE_CYCLE_TCA_CYCLE_HSA00020 29 -2.51 3
PURINE_METABOLISM_HSA00230 147 -2.40 2
LYSINE_DEGRADATION_HSA00310 43 -1.97 4
ONE_CARBON_POOL_BY_FOLATE_HSA00670 17 -1.84 5
TERPENOID_BACKBONE_BIOSYNTHESIS_HSA00900 10 -1.80 6
PENTOSE_PHOSPHATE_PATHWAY_HSA00030 27 -1.62 7
SYNTHESIS_AND_DEGRADATION_OF_KETONE_BODIES_HSA00072 8 -1.58 8
BUTANOATE_METABOLISM_HSA00650 23 -1.57 9
GLYOXYLATE_AND_DICARBOXYLATE_METABOLISM_HSA00630 17 -1.43 10
PROPANOATE_METABOLISM_HSA00640 31 -1.42 11
PYRUVATE_METABOLISM_HSA00620 39 -1.41 13
VALINE_LEUCINE_AND_ISOLEUCINE_BIOSYNTHESIS_HSA00290 11 -1.38 12
CORE_GLYCOLYSIS-GLUCONEOGENESIS_HSA_M00001 25 -1.32 14
ALL_METABOLIC_GENES 1371 -1.25 42
AMINO_SUGAR_AND_NUCLEOTIDE_SUGAR_METABOLISM_HSA00520 44 -1.18 16
GLYCOLYSIS-GLUCONEOGENESIS_HSA00010 61 -1.17 15
SELENOCOMPOUND_METABOLISM_HSA00450 17 -1.17 18
BIOSYNTHESIS_OF_UNSATURATED_FATTY_ACIDS_HSA01040 20 -1.10 17
GLYCOSPHINGOLIPID_BIOSYNTHESIS-GLOBO_SERIES_HSA00603 14 -1.09 20
OXIDATIVE_PHOSPHORYLATION_HSA00190 111 -1.03 45
GLYCOSAMINOGLYCAN_BIOSYNTHESIS-CHONDROITIN_SULFATE_HSA00532 22 1.00 59
OTHER_TYPES_OF_O-GLYCAN_BIOSYNTHESIS_HSA00514 33 1.02 63
NITROGEN_METABOLISM_HSA00910 21 1.03 69
PHENYLALANINE_METABOLISM_HSA00360 13 1.07 70
CAFFEINE_METABOLISM_HSA00232 6 1.11 65
OTHER_GLYCAN_DEGRADATION_HSA00511 16 1.13 64
SPHINGOLIPID_METABOLISM_HSA00600 36 1.33 68
LINOLEIC_ACID_METABOLISM_HSA00591 22 1.41 72
NICOTINATE_AND_NICOTINAMIDE_METABOLISM_HSA00760 22 1.47 71
STEROID_HORMONE_BIOSYNTHESIS_HSA00140 36 1.55 73
METABOLISM_OF_XENOBIOTICS_BY_CYTOCHROME_P450_HSA00980 53 1.58 74
RETINOL_METABOLISM_HSA00830 42 1.60 75
MUCIN_TYPE_O-GLYCAN_BIOSYNTHESIS_HSA00512 26 1.64 77
ARACHIDONIC_ACID_METABOLISM_HSA00590 44 1.71 76
DRUG_METABOLISM-CYTOCHROME_P450_HSA00982 55 1.84 78
71
Table 6: Replicative senescence [U-
13
C]-glutamine labeling
Metabolite
Proliferating
HMEC weighted
average
Senescent
HMEC weighted
average
Proliferating
HMEC weighted
standard deviation
Senescent
HMEC weighted
standard
deviation
FDR corrected
Fisher's
combined p-
value
2,3-cCMP 0.34460 0.34292 0.00024 0.00097 0.87444
2,3-cUMP 0.03107 0.00000 0.02540 0.00000 0.40453
2-AMINOADIPATE 0.12892 0.00189 0.18232 0.00151 0.53322
3,5-cGMP 0.00000 0.00011 0.00000 0.00015 0.07206
3PG 0.00449 0.00035 0.00048 0.00050 0.00298
5,6-DIHYDROURACIL 0.00061 0.00046 0.00020 0.00007 0.52452
5PRA 0.39863 0.39546 0.00104 0.00558 0.59532
5PRA 0.28650 0.26767 0.01373 0.00917 0.30491
6PG 0.04233 0.03978 0.00440 0.00032 0.16584
ACETYLCARNITINE 0.00928 0.00556 0.00025 0.00052 0.01145
ACETYL-COA 0.00753 0.02843 0.00100 0.00481 0.40453
ACONITATE 0.32070 0.22677 0.00187 0.00144 0.00000
ADENINE 0.00192 0.00001 0.00048 0.00001 0.07206
ADP 0.00610 0.00504 0.00007 0.00001 0.08613
ADP-RIBOSE 0.00000 0.00000 0.00000 0.00000 0.00000
AICAR 0.18929 0.00000 0.14302 0.00000 0.31216
ALANINE 0.05411 0.03040 0.00087 0.00079 0.00802
ALLANTOIN 0.00000 0.00000 0.00000 0.00000 0.00000
ALPHA-
KETOGLUTARATE
0.53347 0.43320 0.00434 0.00735 0.00147
AMP 0.01459 0.00430 0.00111 0.00388 0.51627
ARGININE 0.00936 0.00000 0.00368 0.00000 0.13854
ASPARAGINE 0.31232 0.30431 0.02967 0.01146 0.85965
ASPARTATE 0.37629 0.20985 0.00618 0.00525 0.00012
ATP 0.01157 0.00730 0.00017 0.00004 0.00019
BETAINE 0.00000 0.00000 0.00000 0.00000 0.30499
cAMP 0.09078 0.01733 0.01683 0.01111 0.02618
cGMP 0.00000 0.00000 0.00000 0.00000 0.00000
CITRATE/ISOCITRATE 0.28534 0.39822 0.00250 0.00103 0.00298
CMP 0.00763 0.00770 0.00057 0.00198 0.11540
COENZYME-A 0.00467 0.00084 0.00660 0.00106 0.79745
CREATINE 0.07178 0.05437 0.01078 0.00230 0.24815
CYSTATHIONINE 0.00000 0.00000 0.00000 0.00000 0.00000
CYSTINE 0.74088 0.74832 0.00787 0.00854 0.38692
CYTOSINE 0.12530 0.00054 0.00188 0.00031 0.00019
dADP 0.00100 0.00100 0.00000 0.00000 0.00000
dAMP 0.00100 0.00100 0.00000 0.00000 0.53158
dATP 0.00000 0.00000 0.00000 0.00000 0.57602
72
dC 0.28931 0.23227 0.07292 0.08646 0.79745
dCDP 0.08576 0.00000 0.00357 0.00000 0.00039
dCMP 0.00005 0.00000 0.00007 0.00000 0.31343
dCTP 0.06011 0.01424 0.00569 0.02014 0.01523
DEOXYRIBOSE 0.00000 0.13333 0.00000 0.09428 0.29644
DEOXYURIDINE 0.59259 0.47036 0.22831 0.33922 0.13595
dI 0.00000 0.00032 0.00000 0.00025 0.00597
dIMP 0.47133 0.42645 0.00119 0.00200 0.01380
dITP 0.53955 0.36106 0.05551 0.24834 0.79745
dTDP 0.01858 0.00000 0.00110 0.00000 0.00039
dTTP 0.00000 0.00000 0.00000 0.00000 0.15815
dUDP 0.00100 0.00147 0.00000 0.00066 0.00000
dUMP 0.00100 0.00100 0.00000 0.00000 0.30036
dUTP 0.10683 0.00100 0.00747 0.00000 0.00890
F-1,6-BP 0.01442 0.00592 0.00015 0.00005 0.00067
FUMARATE 0.37726 0.11253 0.00424 0.00081 0.00024
G3P 0.08650 0.00831 0.00255 0.00083 0.14344
G6P/F6P 0.01258 0.00230 0.00131 0.00044 0.02567
GDP 0.00625 0.00492 0.00016 0.00021 0.01759
GLUCOSAMINATE 0.15525 0.15074 0.05494 0.01222 0.92698
GLUCOSE 0.00002 0.00031 0.00002 0.00043 0.83915
GLUTAMATE 0.58092 0.42271 0.00293 0.00081 0.00000
GLUTAMINE 0.92982 0.86246 0.00179 0.00166 0.00115
GLYCINE 0.00007 0.00027 0.00005 0.00036 0.47003
GMP 0.00286 0.00376 0.00004 0.00003 0.57602
GSH 0.33460 0.24215 0.00179 0.00144 0.00000
GSSG 0.66272 0.71685 0.00261 0.00679 0.00356
GTP 0.01171 0.00706 0.00008 0.00030 0.03708
GUANIDINOACETATE 0.00000 0.00000 0.00000 0.00000 0.53158
GUANINE 0.00100 0.00100 0.00100 0.00100 0.10238
GUANOSINE 0.00288 0.00257 0.00327 0.00081 0.92393
HISTIDINE 0.00303 0.00745 0.00048 0.00187 0.14039
HYDROXYPYRUVATE 0.00112 0.00071 0.00158 0.00100 0.83666
HYPOTAURINE 0.00000 0.00000 0.00000 0.00000 0.53158
HYPOXANTHINE 0.00316 0.00100 0.00052 0.00000 0.06561
IDP 0.56543 0.32332 0.07855 0.14241 0.21996
IMP 0.01379 0.00174 0.01099 0.00246 0.38433
INOSINE 0.00496 0.00093 0.00062 0.00030 0.02162
ITP 0.00000 0.00000 0.00000 0.00000 0.00000
LACTATE 0.03559 0.01388 0.00020 0.00080 0.00033
LEUCINE/ISOLEUCINE 0.00017 0.00019 0.00003 0.00001 0.24210
LYSINE 0.00002 0.00000 0.00003 0.00000 0.53158
MALATE 0.24097 0.12844 0.00114 0.00015 0.00041
MALONATE 0.00110 0.00087 0.00155 0.00090 0.88798
73
METHIONINE 0.00098 0.00143 0.00066 0.00157 0.87444
N-ACETYL-ALANINE 0.26785 0.11954 0.04076 0.06526 0.13203
N-ACETYL-GLUTAMATE 0.37545 0.28210 0.00025 0.00513 0.00598
N-ACETYL-L-CYSTEINE 0.00000 0.00000 0.00000 0.00000 0.00000
N-ACETYL-METHIONINE 0.11812 0.27002 0.00670 0.01518 0.00599
N-ACETYL-SERINE 0.52402 0.42514 0.00341 0.00143 0.00057
NAD 0.01814 0.01705 0.00081 0.00033 0.29644
ORNITHINE 0.00000 0.00000 0.00000 0.00000 0.00000
OROTATE 0.06667 0.26667 0.09428 0.37712 0.53158
PEP 0.00100 0.00100 0.00000 0.00000 0.00000
PHENYLALANINE 0.00288 0.00064 0.00029 0.00013 0.02522
PHOSPHOETHANOLAMI
NE
0.00000 0.00000 0.00000 0.00000 0.53158
PROLINE 0.40033 0.21003 0.00350 0.00203 0.00000
PRPP 0.00442 0.00765 0.00112 0.00030 0.79745
PURINE 0.00743 0.10150 0.00338 0.01073 0.02162
R5P/Ru5P 0.00217 0.00000 0.00184 0.00000 0.69422
RIBOSE 0.00100 0.00100 0.00000 0.00000 0.00000
RIBULOSE-1,5-BP 0.00519 0.00000 0.00079 0.00000 0.03524
S-DIHYDROOROTATE 0.18268 0.13347 0.25835 0.18876 0.87444
SERINE 0.00134 0.00022 0.00056 0.00013 0.79745
SUCCINATE 0.52181 0.32618 0.00734 0.00705 0.00001
TAURINE 0.00088 0.00045 0.00024 0.00012 0.28691
THREONINE 0.00003 0.00003 0.00004 0.00004 0.69819
TRYPTOPHAN 0.00000 0.00003 0.00000 0.00001 0.51426
TYROSINE 0.00184 0.00001 0.00202 0.00001 0.16758
UDP 0.11276 0.01587 0.00048 0.00006 0.00000
UDP-N-ACETYL-
GLUCOSAMINE
0.31328 0.06684 0.00239 0.00244 0.00000
UMP 0.11331 0.01343 0.00083 0.00003 0.00001
URACIL 0.29166 0.00899 0.00746 0.00104 0.03955
URATE 0.06917 0.00100 0.09632 0.00000 0.53158
URIDINE 0.05606 0.00611 0.00260 0.00039 0.00024
UTP 0.11347 0.01970 0.00072 0.00026 0.00000
VALINE 0.00000 0.00000 0.00000 0.00000 0.00000
XANTHINE 0.00100 0.00100 0.00000 0.00000 1.00000
XMP 0.22285 0.17297 0.00059 0.00064 0.00000
74
Table 7: hTERT-immortalization metabolite consumption/secretion
Compound
hTERT weighted
average
Luciferase
weighted average
hTERT weighted
standard deviation
Luciferase
weighted standard
deviation
FDR corrected
Fisher's combined
p-value
ADENINE -1.000 -0.483 0.001 0.002 0.008
ALANINE 1.000 0.957 0.007 0.035 0.469
ASPARAGINE -1.000 -0.061 1.071 0.072 0.534
CYSTEINE -1.000 -0.441 0.006 0.327 0.469
CYSTINE -1.000 -0.437 0.183 0.043 0.431
GLUCOSE -1.000 -0.904 0.738 0.025 0.935
GLUTAMATE 1.000 1.199 0.010 0.200 0.534
GLUTAMINE -1.000 -0.367 0.443 0.182 0.469
GLYCINE -1.000 -0.277 1.054 0.249 0.549
GUANINE 1.000 89.725 0.000 6.455 0.329
HISTIDINE -1.000 0.000 0.064 0.309 0.431
HYPOXANTHINE -1.000 -0.194 0.021 0.040 0.074
ISOLEUCINE -1.000 -0.299 0.469 0.014 0.469
LACTATE 1.000 0.807 0.092 0.006 0.469
LACTATE/GLUCOSE 1.000 0.520 0.408 0.018
LYSINE -1.000 0.013 0.731 0.835 0.436
METHIONINE -1.000 -0.020 0.843 0.128 0.469
ORNITHINE -1.000 0.834 0.354 0.678 0.431
PHENYLALANINE -1.000 0.068 0.877 0.219 0.469
SERINE -1.000 -0.130 0.347 0.518 0.469
THREONINE -1.000 -0.887 1.817 0.158 0.549
TRYPTOPHAN -1.000 -0.211 0.556 0.169 0.469
TYROSINE -1.000 0.225 0.156 0.481 0.436
URATE 1.000 6.498 0.392 0.918 0.329
URIDINE -1.000 3.121 0.044 0.124 0.104
VALINE -1.000 -0.279 1.111 0.031 0.549
XANTHINE -1.000 23.407 1.907 7.936 0.431
75
Table 8: hTERTimmortalization metabolite pool sizes
Metabolite
Average Standard deviation
FDR-corrected
p-value
hT-
mid
hT-
high
Luc-
mid
Luc-
high
hT-
mid
hT-
high
Luc-
mid
Luc-
high
hT-
mid v.
hT-
high
Luc-
mid
v.
Luc-
high
2-AMINOADIPATE 1.000 1.439 0.718 0.214 0.214 0.812 0.413 0.310 0.582 0.360
3,5-cGMP 1.000 2.842 1.802 1.020 1.020 0.537 0.671 0.440 0.191 0.837
3PG 1.000 0.806 0.991 0.111 0.111 0.123 0.051 0.015 0.241 0.119
5,6-DIHYDROURACIL 1.000 0.632 0.782 0.259 0.259 0.189 0.020 0.051 0.258 0.169
6PG 1.000 1.290 0.522 0.178 0.178 0.186 0.030 0.026 0.252 0.710
ACETYLCARNITINE 1.000 0.554 0.934 0.616 0.616 0.140 0.104 0.184 0.487 0.014
ACONITATE 1.000 0.859 1.735 0.099 0.099 0.025 0.040 0.063 0.278 0.000
ADENINE 1.000 2.138 1.341 0.224 0.224 1.462 0.148 0.380 0.467 0.679
ADP 1.000 0.682 1.147 0.098 0.098 0.091 0.105 0.058 0.079 0.003
ALANINE 1.000 1.355 1.793 0.027 0.027 0.011 0.048 0.445 0.016 0.573
ALLANTOIN 1.000 25.099 0.879 0.671 0.671 17.231 0.438 0.489 0.298 0.144
ALPHA-KETOGLUTARATE 1.000 1.734 0.897 0.073 0.073 0.106 0.019 0.041 0.021 0.403
AMP 1.000 1.094 1.067 0.151 0.151 0.420 0.110 0.334 0.808 0.950
ARGININE 1.000 0.849 0.878 0.425 0.425 0.038 0.214 0.025 0.704 0.226
ASPARAGINE 1.000 0.530 1.232 0.108 0.108 0.102 0.057 0.088 0.047 0.000
ASPARTATE 1.000 0.601 0.913 0.211 0.211 0.104 0.045 0.038 0.183 0.041
ATP 1.000 1.259 1.439 0.321 0.321 0.157 0.042 0.149 0.446 0.031
BETAINE 1.000 1.529 0.754 0.121 0.121 0.412 0.027 0.073 0.304 0.520
cAMP 1.000 0.749 0.734 0.228 0.228 0.083 0.053 0.011 0.344 0.275
76
CITRATE/ISOCITRATE 1.000 0.593 1.553 0.142 0.142 0.147 0.013 0.110 0.107 0.003
CMP 1.000 0.864 1.263 0.155 0.155 0.242 0.093 0.037 0.581 0.026
CREATINE 1.000 1.295 1.718 0.030 0.030 0.066 0.056 0.385 0.061 0.596
CYSTINE 1.000 0.000 0.000 1.414 1.414 0.000 0.000 0.174 0.500 0.423
dADP 1.000 0.341 0.321 0.265 0.265 0.035 0.027 0.024 0.171 0.002
dCDP 1.000 0.097 0.475 0.547 0.547 0.011 0.130 0.020 0.258 0.036
dCTP 1.000 0.609 0.865 0.206 0.206 0.074 0.052 0.021 0.197 0.043
DEOXYRIBOSE 1.000 0.516 1.059 0.110 0.110 0.105 0.180 0.107 0.046 0.011
dIMP 1.000 0.023 0.024 1.414 1.414 0.033 0.015 0.039 0.507 0.474
dTDP 1.000 0.455 0.553 0.191 0.191 0.027 0.063 0.027 0.149 0.003
dTTP 1.000 0.959 0.505 0.193 0.193 0.263 0.029 0.143 0.875 0.257
F-1,6,BP 1.000 0.901 0.517 0.234 0.234 0.081 0.036 0.027 0.657 0.978
FUMARATE 1.000 0.673 0.606 0.195 0.195 0.060 0.022 0.022 0.232 0.222
G3P 1.000 0.639 1.980 0.155 0.155 0.079 0.020 0.255 0.139 0.754
G6P/F6P 1.000 1.016 1.176 0.048 0.048 0.058 0.048 0.079 0.799 0.047
GDP 1.000 0.562 1.461 0.112 0.112 0.133 0.083 0.073 0.073 0.000
GLUCOSE 1.000 4.129 0.637 0.650 0.650 1.557 0.079 0.455 0.178 0.226
GLUTAMATE 1.000 0.733 1.316 0.187 0.187 0.028 0.075 0.030 0.287 0.001
GLUTAMINE 1.000 0.961 1.086 0.200 0.200 0.266 0.026 0.063 0.884 0.005
GLYCINE 1.000 0.792 1.435 0.106 0.106 0.080 0.140 0.226 0.168 0.012
GMP 1.000 0.790 1.304 0.161 0.161 0.205 0.073 0.346 0.377 0.091
GSH 1.000 1.267 1.141 0.228 0.228 0.120 0.043 0.226 0.315 0.515
GSSG 1.000 1.341 0.952 0.126 0.126 0.077 0.037 0.127 0.106 0.630
GTP 1.000 1.093 1.970 0.437 0.437 0.264 0.048 0.254 0.826 0.096
GUANIDINOACETATE 1.000 1.608 2.454 0.371 0.371 1.094 0.299 0.049 0.573 0.163
GUANOSINE 1.000 6.920 13.224 0.893 0.893 1.579 0.083 0.314 0.067 0.003
HISTIDINE 1.000 0.602 0.848 0.123 0.123 0.050 0.135 0.006 0.100 0.026
HYPOXANTHINE 1.000 0.041 1.218 0.318 0.318 0.004 2.003 0.022 0.147 0.417
IDP 1.000 10.733 2.467 0.815 0.815 0.539 1.294 2.647 0.009 0.331
LACTATE 1.000 1.037 0.663 0.245 0.245 0.032 0.214 0.016 0.866 0.639
LEUCINE/ISOLEUCINE 1.000 1.671 0.773 0.078 0.078 0.651 0.026 0.169 0.380 0.385
LYSINE 1.000 1.519 0.901 0.431 0.431 0.200 0.102 0.047 0.310 0.791
MALATE 1.000 0.667 0.662 0.222 0.222 0.069 0.029 0.020 0.259 0.129
MALONATE 1.000 0.881 1.065 0.199 0.199 0.090 0.296 0.112 0.552 0.156
METHIONINE 1.000 1.795 0.896 0.244 0.244 0.423 0.042 0.116 0.179 0.522
N-ACETYL-ALANINE 1.000 0.678 1.681 0.202 0.202 0.251 1.806 1.498 0.299 0.693
N-ACETYL-GLUTAMATE 1.000 2.563 1.509 0.043 0.043 1.158 0.323 0.132 0.307 0.766
N-ACETYL-L-CYSTEINE 1.000 0.580 1.159 0.379 0.379 0.377 0.339 0.568 0.382 0.110
N-ACETYL-METHIONINE 1.000 0.340 0.277 0.530 0.530 0.311 0.037 0.023 0.296 0.129
N-ACETYL-SERINE 1.000 0.746 1.297 0.011 0.011 0.058 0.053 0.047 0.093 0.001
NAD 1.000 0.831 1.312 0.160 0.160 0.121 0.060 0.124 0.363 0.061
ORNITHINE 1.000 0.952 0.769 0.588 0.588 0.186 0.136 0.068 0.929 0.445
PEP 1.000 1.841 0.759 0.131 0.131 0.159 0.108 0.244 0.031 0.167
PHENYLALANINE 1.000 1.629 0.877 0.207 0.207 0.390 0.027 0.114 0.220 0.603
77
PHOSPHOETHANOLAMINE 1.000 1.036 1.602 0.173 0.173 0.056 0.072 0.176 0.821 0.468
PROLINE 1.000 1.424 1.019 0.161 0.161 0.298 0.050 0.013 0.254 0.925
PRPP 1.000 0.131 0.565 0.294 0.294 0.021 0.069 0.001 0.148 0.008
R5P/Ru5P 1.000 2.054 0.317 0.017 0.017 0.040 0.023 0.037 0.006 0.577
RIBOSE 1.000 0.710 0.793 0.018 0.018 0.094 0.111 0.061 0.133 0.043
RIBULOSE-1,5-BISPHOSPHATE 1.000 0.497 0.536 0.243 0.243 0.015 0.048 0.005 0.209 0.154
SERINE 1.000 0.496 0.756 0.336 0.336 0.104 0.146 0.150 0.260 0.068
SUCCINATE 1.000 0.363 2.013 0.078 0.078 0.023 0.310 0.136 0.040 0.005
TAURINE 1.000 2.155 2.203 0.146 0.146 0.427 0.207 0.647 0.134 0.419
THREONINE 1.000 1.061 0.911 0.149 0.149 0.051 0.031 0.085 0.665 0.492
TRYPTOPHAN 1.000 1.711 0.800 0.150 0.150 0.376 0.037 0.126 0.194 0.537
TYROSINE 1.000 1.465 0.862 0.128 0.128 0.237 0.016 0.101 0.172 0.689
UDP 1.000 0.349 1.035 0.070 0.070 0.008 0.070 0.005 0.046 0.003
UDP-N-ACETYL-GLUCOSAMINE 1.000 0.667 1.377 0.120 0.120 0.043 0.012 0.175 0.128 0.142
URACIL 1.000 1.591 0.848 0.187 0.187 0.476 0.047 0.096 0.305 0.370
URATE 1.000 2.840 3.028 0.514 0.514 0.615 0.503 0.330 0.087 0.276
URIDINE 1.000 2.066 4.928 0.042 0.042 0.687 0.279 1.881 0.271 0.369
UTP 1.000 0.548 1.051 0.400 0.400 0.051 0.112 0.075 0.353 0.017
VALINE 1.000 1.529 0.754 0.121 0.121 0.412 0.027 0.073 0.304 0.520
XANTHINE 1.000 2.065 0.215 0.211 0.211 0.224 0.021 0.413 0.040 0.008
XMP 1.000 6.191 0.444 0.719 0.719 5.304 0.224 1.035 0.395 0.275
Table 9: hTERT immortalization [U-
13
C]-glucose labeling
Compound
Average Standard deviation
FDR-corrected p-
value
hT-mid hT-high
Luc-
mid
Luc-
high
hT-mid hT-high
Luc-
mid
Luc-
high
hT-mid
v. hT-
high
Luc-
mid v.
Luc-
high
3,5-cGMP 0.06631
5763
0.10114
869
0.04483
9409
0.20598
87
0.08062
422
0.07663
1811
0.00608
5622
0.05162
7711
0.72566
8325
0.02882
0146
3PG 0.98137
7846
0.85032
8627
0.93096
8473
0.97171
7761
0.01203
6455
0.15756
9963
0.01285
2219
0.00394
6067
0.75627
5982
0.09608
5324
5,6-
DIHYDROURACI
L
0.00933
1319
0.44974
2546
0.00329
5353
0.47536
79
0.01009
1273
0.08172
8558
0.00082
1412
0.07940
765
0.53519
3733
0.53557
4968
5PRA 0.95559
804
0 0.94922
697
0 0.06050
5723
0 0.06345
3916
0 0.72566
8325
0.67643
9486
6PG 0.92412
1477
0.92355
368
0.90363
0462
0.87526
9409
0.00340
5797
0.00795
8552
0.00090
1293
0.02363
2273
0.32624
9138
0.16554
6311
ACETYLCARNITI
NE
0.63484
7211
0.42892
0329
0.63460
1801
0.29571
2527
0.00650
744
0.01130
3297
0.02077
4551
0.01078
0479
0.94502
4333
0.69797
8453
ACETYL-COA 0.26841
3745
0.16117
6022
0.14503
9908
0.12500
056
0.02214
1211
0.00038
0989
0.06005
1001
0.14351
8048
0.67770
4109
0.12415
8891
ACONITATE 0.37660
0629
0.26558
4055
0.45712
8159
0.34188
2881
0.00967
7474
0.00314
9432
0.00152
1072
0.00294
6733
0.25317
6977
0.04261
5783
ADENINE 0.01431
5271
0.00066
6667
0.00215
139
0.00079
9766
0.00132
3683
0 0.00026
2216
0.00018
8231
0.25317
6977
0.73726
6139
78
ADP 0.44326
8421
0.45106
7536
0.27088
3484
0.13965
8837
0.00138
8518
0.00130
1139
0.00116
1545
0.00193
3391
0.18542
4382
0.02270
6301
ALANINE 0.00454
812
0.15182
7121
0.00286
7258
0.04132
1009
0.00048
7378
0.03977
6069
0.00058
0338
0.01506
1596
0.94502
4333
0.61154
6522
ALPHA-
KETOGLUTARAT
E
0.16117
8332
0.09725
5155
0.33563
4536
0.23072
7032
0.01640
0657
0.00406
484
0.00972
1072
0.00243
8234
0.30383
1357
0.01117
1395
AMP 0.45154
5779
0.47628
2741
0.28546
4708
0.13643
1227
8.70484
E-05
0.00940
9502
0.00312
1243
0.00408
9262
0.67770
4109
0.08182
4411
ASPARAGINE 0.00303
0842
0.00123
7715
0.02217
8908
0.00688
945
0.00019
4082
0.00058
6082
0.03358
216
0.00843
7495
0.85409
9397
0.95752
7446
ASPARTATE 0.21440
6689
0.13161
8019
0.32538
4054
0.17378
2319
0.02877
8873
0.00379
0127
0.00973
77
0.00717
5323
0.31395
7443
0.02882
0146
ATP 0.44742
8626
0.45308
7105
0.27196
4132
0.14203
1844
0.00132
5076
0.00199
7439
0.00120
8987
0.00166
4401
0.18542
4382
0.04229
0792
BETAINE 0.00202
1155
0.00086
6907
0.00219
0041
0.00078
3614
2.77822
E-05
0.00014
1693
7.12849
E-05
4.96753
E-05
0.72566
8325
0.10521
4646
cAMP 0.10791
4373
0.09581
5256
0.13650
2818
0.07967
246
0.01320
4419
0.00713
1384
0.01481
92
0.00772
5695
0.24703
0618
0.02840
4451
cGMP 0.702 0 0.22774
2247
0 0.14142
1356
0 0.39099
7041
0 0.72566
8325
0.73550
3458
CITRATE/ISOCIT
RATE
0.37114
9865
0.26865
9887
0.45320
5555
0.33479
872
0.01116
8511
0 0.00478
9051
0 0.24703
0618
0.00579
599
CMP 0.63682
8533
0.67123
8058
0.13410
7311
0.12155
6399
0.09016
7769
0.01612
9218
0.05960
0293
0.05161
9647
0.33489
8754
0.61154
6522
CREATINE 0.00200
505
0.00066
8082
0.00201
7167
0.00067
4704
7.14178
E-06
2.00111
E-06
2.97335
E-05
5.55081
E-06
0.94502
4333
0.60184
4616
CREATININE 0.21899
2516
0.26917
5118
0.51923
1759
0.16733
3333
0.17937
2267
0.15713
9726
0.25008
9168
0.11785
113
0.54437
4454
0.15135
3058
CYSTATHIONINE 0.57137
9816
0.19049
6794
0.46317
3767
0.25596
9625
0.06623
5807
0.15828
3947
0.25857
3872
0.18718
0254
0.24703
0618
0.87378
3686
CYSTEINE 0.002 0.11215
5841
0.00656
5849
0.00111
2122
0 0.09072
3418
0.00507
1361
0.00062
9969
0.72566
8325
0.60184
4616
CYSTINE 0.36130
3081
0.12482
7333
0.002 0.40054
1483
0.50813
1289
0.04241
0888
0 0.16272
0863
0.72566
8325
0.65222
0507
CYTIDINE 0.09413
3268
0.14023
8096
0.10101
1852
0 0.05280
8584
0.01589
4043
0.01093
107
0 0.94502
4333
0.04261
5783
CYTOSINE 0.31176
2947
0 0.29198
4481
0 0.10470
8081
0 0.02888
6092
0 0.31395
7443
0.19168
4297
dADP 0.49909
0967
0.26091
1553
0.46960
4051
0.00066
6667
0.00520
8718
0.09428
1196
0.02026
1834
0 0.32624
9138
0.00750
3324
dAMP 0.11521
7413
0 0.21108
3933
0 0.02324
597
0 0.10198
7752
0 0.41890
792
0.69357
666
dATP 0.49812
1785
0.24662
1418
0.39017
2437
0.001 0.00548
4624
0.00619
2251
0.04925
1691
1.0842E
-19
0.81602
73
0.02613
3285
dC 0.55473
9225
0.22941
7601
0.50333
5427
0.21089
882
0.13462
5893
0.06050
4082
0.03549
4645
0.07329
3588
0.72737
8219
0.83214
2957
dCDP 0.55091
1962
0.53692
5282
0.13377
2217
0.00155
8892
0.02435
6873
0.03046
0152
0.02043
1023
0.00126
1798
0.85409
9397
0.03289
118
dCMP 0.002 0.00164
6962
0.08940
1728
0.00383
3416
0 0.00011
0812
0.14918
5323
0.00259
9875
0.25317
6977
0.60184
4616
dCTP 0.33888
8672
0.34244
8891
0.32760
2548
0.11557
6616
0.00126
0935
0.08736
3697
0.00521
0926
0.00644
4065
0.31395
7443
0.87378
3686
DEOXYRIBOSE 0.07060
6717
0.00720
9509
0.03187
9133
0.01910
6025
0.01315
9673
0.00057
7035
0.01404
0832
0.01859
7633
0.31395
7443
0.73686
1847
DEOXYURIDINE 0.54505
7798
0 0.48395
6831
0 0.00387
9555
0 0.02167
1065
0 0.31395
7443
0.06674
0001
dI 0.30568
1994
0 0.21999
6628
0 0.05832
0346
0 0.15354
2008
0 0.36874
4215
0.66866
0123
dIMP 0.05560
5944
0.11418
1725
0.17865
1603
0.20753
1692
0.07581
0252
0.10066
5189
0.04044
061
0.02594
4961
0.75627
5982
0.83087
2605
dTDP 0.48728
3308
0.36135
5623
0.14874
1133
0.00468
2001
0.00420
4256
0.01913
3256
0.07674
9051
0.00567
854
0.26266
9953
0.16974
8232
dTTP 0.50868
0985
0.38676
365
0.00950
2196
0.00066
6667
0.00119
8187
0.03369
7521
0.01299
4185
4.86483
E-34
0.35398
3307
0.60184
4616
F1,6-BP 0.93779
9276
0.93506
5475
0.93140
5796
0.89035
8326
0.00509
1027
0.00267
172
0.00250
9883
0.00626
2678
0.54437
4454
0.42323
0397
FUMARATE 0.19817
7423
0.19497
5281
0.27203
7928
0.25078
1717
0.00309
2959
0.00252
1349
0.00268
206
0.01060
7058
0.29142
9745
0.09608
5324
G3P 0.86660
8413
0.86736
3142
0.86586
1054
0.84400
6952
0.01617
3812
0.01132
3768
0.01010
4817
0.00300
0509
0.71264
7288
0.07866
9325
G6P/F6P 0.73563
7808
0.81565
5407
0.66847
572
0.64441
0324
0.01957
6727
0.00677
5314
0.00648
6638
0.01450
7777
0.29142
9745
0.80199
1106
GDP 0.44655
0456
0.37891
2064
0.26383
5993
0.06809
9231
0.00017
859
0.01600
8106
0.00297
2296
0.00268
2792
0.24703
0618
0.00161
5093
GLUCOSAMINAT
E
0.38291
7274
0 0.40464
1394
0 0.11151
3205
0 0.07114
447
0 0.36874
4215
0.15135
3058
GLUCOSE 0.95512
5112
0.95318
9922
0.95504
6408
0.93525
5625
0.00202
2617
0.00279
4407
0.01059
2127
0.01031
2512
0.31395
7443
0.55387
5022
GLUTAMATE 0.16571
7625
0.10598
6994
0.32896
6971
0.22584
4167
0.00793
417
0.00183
1656
0.00623
7567
0.00443
825
0.25317
6977
0.00909
469
GLUTAMINE 0.00608
0854
0.00153
7085
0.03513
0151
0.01970
0326
0.00064
7865
0.00018
2295
0.00171
4192
0.00130
9488
0.26177
0576
0.07069
3865
GLYCINE 0.03296
2637
0.04588
8146
0.03042
2736
0.01273
6911
0.00807
6837
0.01847
2335
0.00154
9587
0.00533
9268
0.54437
4454
0.05283
4117
GMP 0.43634
4935
0.43500
8803
0.25542
9772
0.08500
5082
0.00755
3935
0.00456
6017
0.00206
6792
0.00083
816
0.19346
1399
0.00161
5093
GSH 0.07825
0463
0.11094
4508
0.12604
6505
0.14291
1184
0.00258
4101
0.00255
9674
0.00175
2721
0.00067
3589
0.18542
4382
0.00211
257
79
GSSG 0.07477
4513
0.44967
2075
0.12427
8391
0.39831
8662
0.00190
683
0.03553
8277
0.00159
0933
0.01865
4427
0.18542
4382
0.01477
7633
GTP 0.45960
9588
0.46229
1898
0.25726
185
0.08865
4513
0.00551
0799
0.00410
6267
0.00412
2442
0.00146
9372
0.18542
4382
0.00189
7584
GUANIDINOACE
TATE
0.66878
2178
0.23726
2956
0.21221
3193
0.19656
8023
0.34941
691
0.16677
4366
0.12769
9813
0.06293
6212
0.80736
3593
0.37142
9024
GUANINE 0.002 0 0.002 0 0 0 1.25505
E-18
0 0.72566
8325
0.65222
0507
GUANOSINE 0.14817
3639
0.15864
6505
0.05153
9342
0.01563
4449
0.17366
4918
0.02697
3165
0.00236
7148
0.00185
5584
0.88283
3128
0.05050
7132
HISTIDINE 0.00206
93
0.00068
1283
0.00209
2387
0.00066
6667
7.35391
E-06
2.06711
E-05
8.24084
E-05
0 0.81322
2138
0.33933
7815
HYDROXYPYRUV
ATE
0.00393
6781
0 0.00333
2181
0 0.00273
9021
0 0.00130
9442
0 0.88283
3128
0.37142
9024
HYPOXANTHINE 0.02025
9693
0.07006
0614
0.18487
7626
0.14277
8454
0.00139
1513
0.04992
9781
0.17297
5845
0.09612
7485
0.57064
909
0.60184
4616
IDP 0.33548
0112
0.44141
4074
0.45028
069
0.35169
8789
0.33019
074
0.01625
9049
0.00958
4931
0.03860
6387
0.88282
7688
0.02270
6301
IMP 0.48371
1323
0.17368
478
0.64020
9641
0.09558
8526
0.00762
2411
0.00898
2302
0.07459
4528
0.00016
8957
0.54437
4454
0.05058
5061
INOSINE 0.36506
3423
0 0.36681
3902
0 0.00091
2528
0 0.00179
8478
0 0.85409
9397
0.60184
4616
LACTATE 0.83452
1337
0.80573
159
0.81037
736
0.82194
2931
0.00528
9423
0.00563
0206
0.05976
4855
0.00838
3801
0.45680
3214
0.95752
7446
LEUCINE/ISOLEU
CINE
0.00201
565
0.00076
3561
0.00201
52
0.00076
2951
5.86899
E-06
3.10356
E-05
6.27455
E-06
6.83873
E-05
0.54437
4454
0.65461
5827
MALATE 0.24940
8072
0.19590
7532
0.36097
2273
0.27284
4362
0.00540
4981
0.00484
3928
0.00430
6986
0.00139
4325
0.31395
7443
0.01117
1395
MALONATE 0.00393
6781
0.00367
0386
0.00333
2181
0.00089
6233
0.00273
9021
0.00158
3313
0.00130
9442
0.00012
5832
0.88283
3128
0.37142
9024
METHIONINE 0.01660
9903
0.00675
6066
0.00745
9196
0.00093
7513
0.00083
1584
0.00026
3769
0.00028
094
0.00027
0795
0.24703
0618
0.00909
469
N-ACETYL-
ALANINE
0.01216
2739
0.10252
9694
0.05673
2952
0.03974
5278
0.00948
3562
0.02700
52
0.05708
6779
0.01534
9846
0.67770
4109
0.72505
5605
N-ACETYL-
GLUTAMATE
0.22775
2503
0.19909
7824
0.24929
7069
0.19570
8005
0.02682
7182
0.03990
1011
0.06687
9962
0.01789
0676
0.57064
909
0.15826
1696
N-ACETYL-L-
CYSTEINE
0.28711
1796
0.13428
9214
0.18924
3559
0.16610
5694
0.24321
0024
0.06831
5391
0.03838
4752
0.07972
6636
0.75627
5982
0.88380
5853
N-ACETYL-
METHIONINE
0.12800
0771
0.19040
7178
0.11590
6399
0.13690
0377
0.04303
6465
0.10461
1312
0.01065
3902
0.05026
8902
0.76154
6555
0.06674
0001
N-ACETYL-
SERINE
0.16876
3086
0.25800
789
0.32990
924
0.41572
6992
0.00656
6857
0.06908
0682
0.00634
7737
0.07944
6549
0.24703
0618
0.01643
5726
NAD 0.34650
0496
0.33207
2954
0.24679
692
0.25226
3803
0.00041
8517
0.00258
9209
0.00058
8218
0.00128
4684
0.25964
3725
6.6781E
-05
PEP 0.67951
0137
0.81778
7223
0.66998
6501
0.84444
27
0.00340
8989
0.01678
3601
0.00831
6344
0.00352
0093
0.31395
7443
0.73726
6139
PHENYLALANIN
E
0.00207
3906
0.00068
8236
0.00200
9833
0.00067
4812
0.00010
4518
1.08645
E-05
1.70318
E-05
6.56816
E-06
0.75627
5982
0.60184
4616
PROLINE 0.06309
8879
0.02981
0131
0.12556
1757
0.06371
771
0.00924
158
0.00196
0052
0.00276
3057
0.00385
9987
0.30383
1357
0.11707
52
PRPP 0.96546
4918
0.99108
6582
0.95936
3196
0.97317
0678
0.01253
313
0.00413
2655
0.00412
7598
0.01108
7714
0.73536
5245
0.88380
5853
PURINE 0.002 0.40186
3202
0.402 0.28865
6182
0 0.08452
6067
0.34641
0162
0.09482
2191
0.26895
8908
0.51148
25
R5P/Ru5P 0.96045
8729
0.98951
8135
0.85672
2619
0.67272
7883
0.02804
175
0.00126
9864
0.02526
0114
0.04280
9497
0.59030
2966
0.69749
6462
RIBOSE 1.00195
9181
0.98826
2687
1.00195
0757
0.97336
3079
5.77275
E-05
0.00228
3274
6.02836
E-06
0.00269
5099
0.94502
4333
0.53557
4968
SERINE 0.08624
6808
0.02983
0903
0.02587
1049
0.00184
7045
0.02060
6956
0.00496
5528
0.00307
9925
0.00035
4581
0.30383
1357
0.02136
8289
SUCCINATE 0.17121
6396
0.06166
5864
0.19530
5374
0.20894
6844
0.02217
2846
0.00476
6498
0.03021
5768
0.00659
8927
0.25317
6977
0.85076
2238
TAURINE 0.00269
0079
0.00172
4562
0.00358
2798
0.00126
1539
0.00047
4459
8.54058
E-05
0.00017
4776
5.04639
E-05
0.93990
7356
0.06674
0001
THREONINE 0.00387
4227
0.00072
9875
0.00391
0051
0.00126
9835
0.00179
5834
1.76644
E-05
0.00081
2943
0.00031
4868
0.64281
9805
0.88380
5853
TRYPTOPHAN 0.00228
7747
0.00070
4308
0.00235
9111
0.00087
3685
0.00034
5275
1.06776
E-05
0.00018
6358
4.67803
E-05
0.75627
5982
0.33933
7815
TYROSINE 0.00202
675
0.00067
12
0.00202
71
0.00068
9801
4.73762
E-06
6.59966
E-07
1.49703
E-05
2.43733
E-05
0.31395
7443
0.69357
666
UDP 0.58070
4895
0.54691
6763
0.36227
3128
0.02785
7734
0.00174
8889
0.01384
6855
0.00173
1033
0.00115
9815
0.18542
4382
0.00750
3324
UDP-GlcNAc 0.67122
1518
0.60585
8299
0.51952
764
0.34227
8557
0.01435
5072
0.02083
9193
0.00257
0973
0.01264
9255
0.94502
4333
0.07069
3865
UMP 0.59544
0107
0.60623
7107
0.36477
5688
0.08621
6687
0.00100
9425
0.01048
3738
0.00206
8635
0.02137
7016
0.52911
8144
0.16974
8232
URACIL 0.02319
2582
0.01067
6065
0.01546
7129
0.00189
3527
0.00470
9985
0.00144
7837
0.00614
9453
0.00133
9561
0.26963
4759
0.14279
189
URIDINE 0.32045
8765
0.29713
6647
0.10717
0603
0.01455
5557
0.00703
69
0.03310
392
0.00400
6084
0.01227
6281
0.57025
9643
0.00189
7584
UTP 0.58365
6217
0.54649
9028
0.35312
5733
0.02739
0515
0.00199
9554
0.00406
2884
0.02322
0899
0.00057
7201
0.18542
4382
0.01239
9349
VALINE 0.00202
1155
0.00083
7067
0.00219
0041
0.00074
3365
2.77822
E-05
0.00013
5263
7.12849
E-05
1.65398
E-05
0.72566
8325
0.10521
4646
80
Table 10: Triapine-induced senescence metabolite pool sizes
Metabolite
DMSO
weighted
average
Triapine
weighted
average
DMSO
weighted
standard
deviation
Triapine
weighted
standard
deviation
FDR corrected
Fisher's
combined p-
value
2,3-cCMP 1.000 0.712 0.164 0.054 0.463
2,3-cUMP 1.000 0.021 0.001 0.000 0.000
2-AMINOADIPATE 1.000 1.129 0.020 0.035 0.957
3,5-cGMP 1.000 1.783 0.064 0.001 0.980
3PG 1.000 2.296 0.012 0.000 0.086
5,6-DIHYDROURACIL 1.000 1.456 0.010 0.013 0.470
5PRA 1.000 2.885 0.030 0.168 0.056
6PG 1.000 0.864 0.002 0.001 0.137
ACETYLCARNITINE 1.000 2.522 0.004 0.008 0.000
ACONITATE 1.000 1.629 0.002 0.000 0.000
ADENINE 1.000 0.933 0.006 0.001 0.786
ADP 1.000 0.627 0.001 0.005 0.401
ALANINE 1.000 0.991 0.002 0.001 0.980
ALLANTOIN 1.000 2.199 0.024 0.084 0.077
ALPHA-KETOGLUTARATE 1.000 1.421 0.001 0.006 0.036
AMP 1.000 0.621 0.021 0.000 0.813
ARGININE 1.000 1.099 0.001 0.001 0.413
ASPARAGINE 1.000 1.139 0.001 0.001 0.153
ASPARTATE 1.000 1.660 0.002 0.001 0.000
ATP 1.000 1.074 0.003 0.000 0.874
BETAINE 1.000 1.089 0.002 0.001 0.540
cAMP 1.000 0.165 0.030 0.000 0.037
CITRATE/ISOCITRATE 1.000 1.778 0.001 0.001 0.000
CMP 1.000 1.526 0.028 0.003 0.280
CREATINE 1.000 1.218 0.002 0.002 0.606
CREATININE 1.000 3.203 0.500 10.259 0.980
CYSTATHIONINE 1.000 0.148 0.017 0.001 0.036
CYSTEINE 1.000 0.813 0.005 0.196 0.980
CYSTINE 1.000 2.726 0.216 0.004 0.100
CYTOSINE 1.000 0.982 0.003 0.017 0.980
dADP 1.000 0.318 0.208 0.101 0.442
dATP 1.000 0.402 0.094 0.012 0.389
dC 1.000 0.775 0.132 0.069 0.980
dCDP 1.000 #DIV/0! 0.012 #DIV/0! 0.003
dCMP 1.000 0.048 0.233 0.001 0.532
dCTP 1.000 #DIV/0! 0.059 #DIV/0! 0.056
DEOXYRIBOSE 1.000 0.790 0.044 0.005 0.874
dGDP 1.000 0.504 0.001 0.000 0.442
81
dGMP 1.000 0.635 0.021 0.000 0.813
dGTP 1.000 1.074 0.003 0.000 0.874
dI 1.000 0.134 0.005 0.010 0.088
DIHYDROOROTATE 1.000 1.364 0.002 0.007 0.140
dIMP 1.000 1.054 0.007 0.000 0.445
dTDP 1.000 0.202 0.020 0.000 0.074
dTTP 1.000 0.648 0.031 0.001 0.335
dUMP 1.000 0.027 0.023 0.001 0.077
F-1,6-BP 1.000 0.754 0.001 0.003 0.797
FUMARATE 1.000 1.089 0.000 0.003 0.141
G3P 1.000 0.853 0.001 0.002 0.299
G6P/F6P 1.000 1.205 0.003 0.000 0.100
GDP 1.000 0.729 0.000 0.002 0.937
GLUCOSAMINATE 1.000 1.145 0.007 0.026 0.532
GLUCOSE 1.000 1.014 0.008 0.016 0.997
GLUTAMATE 1.000 1.182 0.001 0.001 0.074
GLUTAMINE 1.000 1.302 0.001 0.004 0.056
GLYCINE 1.000 1.745 0.007 0.043 0.099
GMP 1.000 1.321 0.019 0.000 0.980
GSH 1.000 0.805 0.000 0.002 0.074
GSSG 1.000 2.273 0.001 0.020 0.002
GTP 1.000 1.104 0.009 0.002 0.400
GUANIDINOACETATE 1.000 0.826 0.013 0.059 1.000
GUANINE 1.000 8.279 0.025 0.432 0.000
GUANOSINE 1.000 2.563 0.004 0.023 0.000
HISTIDINE 1.000 0.982 0.000 0.001 0.980
HOMOCYSTEINE 1.000 1.235 0.006 0.011 0.383
HYDROXYPYRUVATE 1.000 0.505 0.003 0.002 0.007
HYPOTAURINE 1.000 0.863 0.007 0.007 0.797
HYPOXANTHINE 1.000 1.150 0.006 0.013 0.369
IDP 1.000 0.224 0.112 0.012 0.894
INOSINE 1.000 2.661 0.006 0.034 0.002
ITP 1.000 0.873 0.012 0.005 0.188
LACTATE 1.000 0.694 0.003 0.002 0.065
LEUCINE/ISOLEUCINE 1.000 1.035 0.001 0.000 0.815
LYSINE 1.000 1.435 0.004 0.010 0.077
MALATE 1.000 1.165 0.001 0.003 0.074
MALONATE 1.000 0.504 0.003 0.002 0.007
METHIONINE 1.000 1.087 0.003 0.001 0.860
N-ACETYL-ALANINE 1.000 1.239 0.001 0.003 0.034
N-ACETYL-GLUTAMATE 1.000 1.304 0.004 0.002 0.937
N-ACETYL-L-CYSTEINE 1.000 1.405 0.002 0.007 0.111
N-ACETYL-METHIONINE 1.000 1.434 0.002 0.025 0.210
N-ACETYL-SERINE 1.000 2.044 0.010 0.026 0.028
82
NAD 1.000 1.376 0.001 0.002 0.007
ORNITHINE 1.000 1.104 0.000 0.001 0.648
PEP 1.000 0.988 0.022 0.008 0.551
PHENYLALANINE 1.000 1.008 0.002 0.001 1.000
PHOSPHOETHANOLAMINE 1.000 1.779 0.001 0.004 0.000
PROLINE 1.000 0.815 0.002 0.000 0.140
PRPP 1.000 0.825 0.000 0.004 0.400
PURINE 1.000 0.400 0.077 0.001 0.235
R5P/Ru5P 1.000 0.941 0.003 0.002 0.900
RIBOSE 1.000 1.052 0.009 0.015 0.980
RIBULOSE-1,5-BP 1.000 0.508 0.011 0.000 0.074
SERINE 1.000 1.140 0.006 0.006 0.335
SUCCINATE 1.000 1.837 0.002 0.003 0.001
TAURINE 1.000 0.494 0.001 0.000 0.002
THREONINE 1.000 1.214 0.002 0.001 0.077
TRYPTOPHAN 1.000 0.945 0.004 0.001 0.883
TYROSINE 1.000 1.266 0.000 0.002 0.141
UDP 1.000 1.178 0.009 0.002 0.172
UDP-N-ACETYL-GLUCOSAMINE 1.000 2.031 0.000 0.009 0.001
UMP 1.000 0.717 0.010 0.002 0.663
URACIL 1.000 2.050 0.001 0.041 0.028
URATE 1.000 3.021 0.009 0.130 0.023
URIDINE 1.000 6.248 0.001 0.368 0.002
UTP 1.000 2.156 0.001 0.003 0.002
VALINE 1.000 0.989 0.001 0.000 0.980
XANTHINE 1.000 1.566 0.004 0.045 0.074
XMP 1.000 0.603 0.019 0.001 0.101
Table 11: Triapine-induced senescence [U-
13
C]-glucose labeling
Metabolite
DMSO HMEC
weighted
average
Triapine
HMEC
weighted
average
DMSO HMEC
weighted
standard
deviation
Triapine
HMEC
weighted
standard
deviation
FDR corrected
Fisher's
combined p-
value
2,3-cCMP 0.3567 0.3806 0.0827 0.0234 0.7003
2,3-cUMP 0.1603 0.1826 0.0043 0.1394 0.6864
2-AMINOADIPATE 0.8274 0.8322 0.0336 0.0458 0.8724
3,5-cGMP 0.2395 0.1918 0.0621 0.0644 0.2744
3PG 0.9821 0.9927 0.0241 0.0036 0.6626
5,6-DIHYDROURACIL 0.7320 0.6520 0.0545 0.0081 0.7382
5PRA 0.2877 0.2747 0.0558 0.0145 0.6616
6PG 0.7548 0.4757 0.0351 0.0138 0.0076
ACETYLCARNITINE 0.1980 0.1608 0.0087 0.0029 0.0906
83
ACETYL-COA 0.8841 0.2029 0.0410 0.2869 0.1720
ACONITATE 0.4198 0.5209 0.0043 0.0021 0.0000
ADENINE 0.0110 0.0000 0.0042 0.0000 0.1529
ADP 0.4513 0.2040 0.0066 0.0647 0.0096
ADP-RIBOSE 0.0000 0.3111 0 0
AICAR 0.0000 0.0000 0 0
ALANINE 0.6837 0.5919 0.0027 0.0082 0.0000
ALLANTOIN 0.0000 0.0000 0 0
ALPHA-KETOGLUTARATE 0.2473 0.3911 0.0070 0.0053 0.0000
AMP 0.4467 0.2081 0.0064 0.0078 0.0001
ARGININE 0.0000 0.0000 0.0000 0.0000 0.6183
ASPARAGINE 0.0003 0.0003 0.0001 0.0001 0.8724
ASPARTATE 0.2692 0.4405 0.0057 0.0129 0.0009
ATP 0.4607 0.2641 0.0057 0.0033 0.0000
BETAINE 0.0001 0.0000 0.0001 0.0000 0.6748
cAMP 0.1268 0.1436 0.0180 0.0020 0.4382
cGMP 0.0000 0.3333 0 0
CITRATE/ISOCITRATE 0.4187 0.5025 0.0032 0.0025 0.0000
CMP 0.2182 0.0320 0.0546 0.0530 0.2324
CREATINE 0.0000 0.0000 0 0
CREATININE 0.2000 0.2500 0.2635 0.1768 0.8705
CYSTATHIONINE 0.0044 0.0102 0.0183 0.0106 1.0000
CYSTEINE 0.0000 0.0000 0.1571 0.0009 0.7678
CYSTINE 0.7591 0.7966 0.1985 0.1978 0.8195
CYTIDINE 0.4384 0.9588 0.0787 0.1043 0.6312
CYTOSINE 0.0029 0.0011 0.0039 0.0226 0.9699
dADP 0.4000 0.0000 0.2000 0.0000 0.2957
dAMP 0.4281 0.0000 0.1220 0.0000 0.0957
dATP 0.2000 0.2000 0.0471 0.0000 0.8859
dC 0.2825 0.0213 0.1012 0.0786 0.4260
dCDP 0.1662 0.0000 0.0956 0.0000 0.0248
dCMP 0.0000 0.2692 0.0627 0.1448 0.5045
dCTP 0.0607 0.0000 0.0396 0.0000 0.2114
DEOXYRIBOSE 0.0000 0.0000 0.0000 0.0000 0.3803
dI 0.5500 0.5607 0.1690 0.1583 1.0000
DIHYDROOROTATE 0.2267 0.2028 0.0218 0.0254 0.6183
dIMP 0.3091 0.4395 0.0179 0.0117 0.0016
dTDP 0.1656 0.0000 0.0175 0.0000 0.0022
dTTP 137.1399 0.0000 0.0302 0.0000 0.1557
dUDP 0.0000 0.0000 0 0
dUMP 0.1111 0.1111 0.0786 0.1111 0.6626
dUTP 0.0000 0.0000 0 0
F-1,6-BP 0.8739 0.8536 0.0064 0.0048 0.4846
FUMARATE 0.3293 0.4125 0.0048 0.0046 0.0000
84
G3P 0.8449 0.8108 0.0097 0.0024 0.1143
G6P/F6P 0.8202 0.7249 0.0111 0.0204 0.0124
GDP 0.1563 0.2088 0.0276 0.0052 0.0018
GLUCOSAMINATE 0.3279 0.3357 0.0060 0.0065 0.3799
GLUCOSE 0.9729 0.9689 0.0037 0.0041 1.0000
GLUTAMATE 0.2501 0.3783 0.0060 0.0063 0.0000
GLUTAMINE 0.0083 0.0234 0.0024 0.0010 0.0019
GLUTATHIONE 0.1358 0.1948 0.0021 0.0011 0.0000
GLYCINE 0.0526 0.0482 0.0036 0.0013 0.0042
GMP 0.3750 0.2613 0.0220 0.0209 0.0004
GSSG 0.4616 0.5748 0.0185 0.0083 0.0187
GTP 0.4350 0.1894 0.0502 0.0040 0.0000
GUANIDINOACETATE 0.3157 0.2441 0.1272 0.0781 0.6183
GUANINE 0.0000 0.0000 0.0000 0.0000 0.6183
GUANOSINE 0.0340 0.0293 0.0067 0.0028 0.7382
HISTIDINE 0.0000 0.0000 0 0
HOMOCYSTEINE 0.2542 0.2509 0.0026 0.0002 0.4547
HYDROXYPYRUVATE 0.0031 0.0069 0.0013 0.0018 0.0017
HYPOTAURINE 0.0000 0.0000 0.0000 0.0000 0.6183
HYPOXANTHINE 0.0000 0.0000 0 0
IDP 0.0991 0.4141 0.0594 0.2793 0.8724
IMP 0.2667 0.0000 0.3771 0.3300 0.6183
INOSINE 0.0008 0.0012 0.0060 0.0030 0.6362
ITP 0.0000 0.0000 0 0
LACTATE 0.8365 0.7983 0.0031 0.0078 0.0171
LEUCINE/ISOLEUCINE 0.0000 0.0000 0.0000 0.0000 1.0000
LYSINE 0.0000 0.0000 0.0001 0.0000 0.8724
MALATE 0.3092 0.4439 0.0043 0.0036 0.0000
MALONATE 0.0031 0.0069 0.0013 0.0018 0.0017
METHIONINE 0.0122 0.0057 0.0009 0.0003 0.0004
N-ACETYL-ALANINE 0.2077 0.1392 0.0134 0.0332 0.0676
N-ACETYL-GLUTAMATE 0.3355 0.4234 0.0161 0.0280 0.2114
N-ACETYL-L-CYSTEINE 0.1715 0.2066 0.0223 0.0049 0.0825
N-ACETYL-METHIONINE 0.0352 0.0205 0.0033 0.0213 1.0000
N-ACETYL-SERINE 0.5903 0.2377 0.0883 0.0670 0.3799
NAD 0.4098 0.5678 0.0018 0.0025 0.0000
ORNITHINE 0.0006 0.0000 0.0008 0.0000 0.6183
OROTATE 0.0000 0.0000 0.0000 0.0000 0.6183
PEP 0.9559 0.9067 0.0078 0.0133 0.2493
PHENYLALANINE 0.0000 0.0000 0.0000 0.0000 1.0000
PHOSPHOETHANOLAMINE 0.0000 0.0000 0.0000 0.0001 0.6183
PROLINE 0.0738 0.0387 0.0061 0.0023 0.0028
PRPP 0.9655 0.9443 0.0106 0.0132 0.0124
PURINE 0.0995 0.1619 0.1974 0.1145 0.9874
85
R5P/Ru5P 0.9905 0.7223 0.0240 0.0224 0.0604
RIBOSE 0.9759 0.9834 0.0026 0.0014 0.8291
RIBULOSE-1,5-BP 0.9646 0.9193 0.0140 0.0085 0.0676
SERINE 0.0588 0.0627 0.0016 0.0036 0.8808
SUCCINATE 0.2651 0.3707 0.0106 0.0114 0.0015
TAURINE 0.0007 0.0001 0.0001 0.0001 0.0004
THREONINE 0.0007 0.0002 0.0004 0.0001 0.6183
TRYPTOPHAN 0.0000 0.0000 0 0
TYROSINE 0.0000 0.0000 0.0000 0.0000 0.6183
UDP 0.4720 0.0500 0.0151 0.0015 0.0001
UDP-N-ACETYL-GLUCOSAMINE 0.4472 0.6196 0.0068 0.0034 0.0000
UMP 0.4782 0.0468 0.0144 0.0029 0.0000
URACIL 0.0205 0.0023 0.0055 0.0004 0.0957
URATE 0.0008 0.0000 0.0012 0.0001 0.6312
URIDINE 0.1855 0.0172 0.0241 0.0012 0.0042
UTP 0.4903 0.0525 0.0141 0.0017 0.0000
VALINE 0.0000 0.0000 0.0000 0.0001 0.6183
XANTHINE 0.0001 0.0003 0.0045 0.0014 0.7949
XMP 0.0684 0.1239 0.0121 0.0116 0.3124
Table 12: Proteomics analysis of replicative senescence (only proteins with abs (log2 FC) > 1 are
shown)
Protein
symbol
Average
log2
(senescent
/
proliferati
ng)
FDR-corrected
Fisher's
combined p-
value
Experiment 1 log2
(senescent /
proliferating)
Experiment 1
FDR-corrected
p-value
Experiment 2 log2
(senescent /
proliferating)
Experiment 2
FDR-corrected
p-value
ALDOC 1.95 9.02E-07 2.24 2.90E-04 1.65 9.55E-04
CTSA 1.95 5.52E-06 1.96 1.81E-03 1.95 2.65E-03
UPF1 -1.87 5.52E-06 -1.27 4.33E-03 -2.47 9.55E-04
SH3BGRL3 1.49 2.25E-05 1.76 1.25E-03 1.23 5.51E-03
YWHAZ 1.44 4.68E-05 2.13 6.26E-04 0.75 2.02E-02
NDUFA8 -1.35 8.81E-05 -1.44 3.08E-03 -1.25 7.21E-03
STOM -1.83 1.30E-04 -1.43 1.78E-02 -2.23 5.98E-03
UBE2I -1.27 2.04E-04 -1.55 3.04E-03 -1.00 1.83E-02
MCM5 -1.08 2.12E-04 -1.52 8.05E-04 -0.64 1.87E-02
PALLD -1.28 4.42E-04 -1.92 1.88E-03 -0.64 7.50E-02
STAU1 -1.04 4.53E-04 -1.28 3.09E-03 -0.80 2.10E-02
GORASP2 -1.69 5.81E-04 -2.14 7.04E-03 -1.24 5.28E-02
TACSTD2 1.56 6.21E-04 2.35 3.09E-03 0.78 1.16E-01
H2AX -1.49 7.73E-04 -1.46 1.97E-02 -1.52 2.32E-02
SEC61B -1.78 1.32E-03 -1.46 4.54E-02 -2.10 2.11E-02
PI4K2A 1.14 1.43E-03 1.01 2.80E-02 1.27 1.95E-02
86
EXOSC9 -1.26 1.45E-03 -1.56 1.02E-02 -0.96 6.10E-02
NOP16 -1.09 1.45E-03 -1.45 5.95E-03 -0.72 6.98E-02
KPNA2 -1.23 1.45E-03 -1.02 3.65E-02 -1.45 1.83E-02
POP1 -2.72 1.75E-03 -2.58 3.89E-02 -2.86 3.82E-02
NDUFA4 1.41 2.11E-03 0.13 7.53E-01 2.69 2.48E-03
ANP32E 1.32 2.66E-03 1.42 2.63E-02 1.23 5.01E-02
RAB8A -1.01 3.08E-03 -0.98 2.83E-02 -1.03 3.24E-02
EMG1 -1.00 3.45E-03 -1.50 5.64E-03 -0.51 1.66E-01
TST 1.31 3.78E-03 0.30 4.84E-01 2.32 5.51E-03
ANGPTL4 -1.43 9.71E-03 -1.54 5.05E-02 -1.32 9.86E-02
COMT 1.19 1.15E-02 1.52 2.96E-02 0.86 1.68E-01
PRXL2A 1.18 1.22E-02 1.15 7.37E-02 1.20 7.69E-02
SOD1 1.28 2.52E-02 2.35 1.45E-02 0.21 7.94E-01
DPY30 -1.06 2.82E-02 -1.71 2.30E-02 -0.40 5.16E-01
UGP2 1.93 2.85E-02 2.17 8.61E-02 1.68 1.84E-01
DDX18 -1.00 2.94E-02 -1.04 9.36E-02 -0.97 1.32E-01
PRDX2 1.12 4.36E-02 2.01 2.50E-02 0.23 7.75E-01
RANBP2 -1.21 4.84E-02 -1.54 7.88E-02 -0.88 2.95E-01
H2AC4 -1.27 1.14E-01 -1.30 2.23E-01 -1.24 2.59E-01
CLTA 1.28 4.34E-06 1.22 1.81E-03 1.35 2.22E-03
PI3 -2.53 6.38E-06 0.00 9.98E-01 -5.05 2.22E-05
YWHAB 1.99 9.71E-06 1.86 4.51E-03 2.12 5.00E-03
BTF3 -1.56 8.13E-05 -3.04 1.86E-04 -0.09 7.00E-01
CYB5R1 1.83 8.68E-05 2.65 1.89E-03 1.01 5.40E-02
MAN2B1 1.97 1.91E-04 2.47 5.41E-03 1.47 3.82E-02
S100A13 1.03 2.17E-04 0.78 2.12E-02 1.27 6.08E-03
RPS15 -1.09 6.21E-04 -1.25 1.12E-02 -0.93 3.79E-02
SCARB2 1.08 9.45E-04 1.50 6.18E-03 0.67 9.37E-02
TXN 1.32 1.47E-03 1.84 9.28E-03 0.81 1.26E-01
TXNDC17 1.68 2.02E-03 1.55 4.45E-02 1.81 3.79E-02
PRDX6 1.04 6.16E-03 2.03 3.59E-03 0.04 9.57E-01
PPIF -1.02 7.29E-03 -0.94 7.37E-02 -1.10 5.90E-02
SRP9 -1.00 3.89E-02 -1.73 2.75E-02 -0.27 6.87E-01
VASP 1.31 4.41E-02 1.57 9.39E-02 1.05 2.52E-01
COPG1 -1.04 7.88E-03 -2.08 4.33E-03 0.00 1.00E+00
EIF4A1 -1.08 1.91E-02 -2.17 9.51E-03 0.00 1.00E+00
VAT1 1.45 2.13E-05 1.92 1.81E-03 0.98 2.11E-02
LGALS3BP 1.03 1.93E-04 1.02 1.12E-02 1.04 1.58E-02
TUBB 1.22 2.34E-04 1.44 7.29E-03 1.00 3.24E-02
S100A6 1.48 5.59E-03 1.83 2.86E-02 1.13 1.34E-01
MRPL12 -1.13 3.09E-02 -1.87 2.78E-02 -0.39 5.96E-01
S100A7 -1.33 4.43E-04 0.18 4.67E-01 -2.84 8.56E-04
ASAH1 1.36 8.71E-07 1.52 7.63E-04 1.20 2.48E-03
S100A2 1.40 8.71E-07 1.13 1.90E-03 1.68 9.55E-04
87
HIST1H1B -1.31 3.49E-06 -0.92 6.04E-03 -1.70 1.39E-03
LGALS3 1.34 4.09E-06 1.91 6.26E-04 0.76 1.72E-02
TMPO -1.50 6.93E-06 -1.51 3.34E-03 -1.49 5.51E-03
TUBA4A 1.66 1.26E-05 2.74 6.26E-04 0.57 7.50E-02
PSMA5 1.15 1.98E-05 0.71 1.78E-02 1.60 2.17E-03
TUBA1A 1.34 1.13E-04 1.92 2.30E-03 0.75 6.10E-02
TUBB6 1.03 1.64E-04 1.11 8.97E-03 0.96 2.00E-02
MMP14 -1.44 2.23E-04 -1.53 1.19E-02 -1.36 2.32E-02
SUCLG2 -1.04 9.31E-04 -1.97 1.34E-03 -0.10 7.40E-01
YWHAG 1.24 1.34E-03 1.48 1.70E-02 0.99 7.11E-02
CAPN1 1.22 4.26E-02 1.46 9.57E-02 0.99 2.47E-01
HBA1 -2.57 1.03E-06 -2.38 2.16E-03 -2.75 2.41E-03
GLB1 1.10 1.92E-05 1.38 2.08E-03 0.82 1.75E-02
TPD52L2 1.10 1.26E-04 1.35 4.90E-03 0.84 3.17E-02
MCM3 -1.23 7.64E-04 -1.20 2.68E-02 -1.25 3.16E-02
CA2 -1.08 2.03E-01 0.08 9.51E-01 -2.24 1.04E-01
TALDO1 1.63 8.91E-07 1.40 2.46E-03 1.87 1.64E-03
HTRA1 -1.74 2.53E-06 -1.58 3.34E-03 -1.90 3.01E-03
TUBA1A 1.41 5.52E-06 1.85 1.41E-03 0.96 1.62E-02
YWHAQ 1.38 6.93E-06 1.16 6.67E-03 1.59 4.09E-03
H3C1 -1.38 3.77E-05 -0.30 2.18E-01 -2.46 9.55E-04
CTSC 1.37 6.99E-07 1.23 1.89E-03 1.50 1.67E-03
PFN1 1.22 4.09E-06 0.93 7.33E-03 1.51 2.48E-03
DSG3 1.29 1.99E-04 1.49 8.97E-03 1.09 3.24E-02
CES2 1.50 6.57E-02 1.90 1.08E-01 1.09 3.48E-01
RPL26 -1.16 1.35E-06 -1.68 6.26E-04 -0.64 1.77E-02
CAST 1.79 3.49E-06 2.13 1.81E-03 1.45 8.83E-03
AHCY 1.10 4.09E-06 1.40 1.55E-03 0.81 1.29E-02
SSBP1 1.09 8.68E-05 0.71 3.50E-02 1.48 5.32E-03
KRT6A 1.20 6.22E-06 1.94 6.26E-04 0.45 6.87E-02
LGALS7 2.34 1.17E-04 2.83 5.95E-03 1.85 3.24E-02
HSPB1 2.13 2.72E-04 3.29 2.93E-03 0.96 1.46E-01
GSTP1 1.35 2.91E-04 1.57 1.11E-02 1.13 3.98E-02
H4-16 -1.56 1.81E-07 -1.14 3.10E-03 -1.98 9.55E-04
LDHA 1.09 5.96E-05 0.87 2.19E-02 1.31 7.91E-03
KRT10 -1.13 2.12E-04 0.04 8.82E-01 -2.30 9.55E-04
THBS1 -1.03 1.67E-06 -0.90 5.66E-03 -1.17 4.15E-03
SERPINB5 1.88 4.09E-06 2.61 1.25E-03 1.14 2.23E-02
SLC3A2 -1.09 1.14E-06 -1.02 4.10E-03 -1.16 4.41E-03
ANXA1 2.38 5.17E-08 3.26 2.99E-04 1.50 4.41E-03
CTSD 1.63 1.14E-06 1.87 1.81E-03 1.40 6.34E-03
KRT16 1.20 3.28E-05 1.90 1.45E-03 0.49 1.09E-01
ENO1 1.33 1.17E-04 1.43 1.11E-02 1.24 2.36E-02
KRT15 1.38 6.35E-06 0.95 1.68E-02 1.81 3.07E-03
88
GAPDH 1.30 1.61E-07 1.01 3.59E-03 1.60 1.39E-03
PKM 1.07 5.35E-07 1.36 1.34E-03 0.77 1.19E-02
LAMB3 -1.03 1.26E-06 -0.96 5.41E-03 -1.09 5.51E-03
DSP 1.18 5.17E-08 1.78 6.26E-04 0.57 2.13E-02
Table 13: Proteomics analysis of hTERT-immortalization (only proteins with abs (log2 FC) > 2
are shown)
Protein symbol log2 (luciferase / hTERT) t-test p-value FDR-corrected p-value
CTSD 4.95 1.40E-04 4.88E-03
PYGB 4.05 1.30E-03 8.58E-03
AP3B1 3.97 1.43E-02 3.61E-02
PLBD2 3.87 4.04E-05 4.88E-03
TALDO1 3.67 1.79E-05 4.88E-03
RSU1 3.65 2.92E-04 5.41E-03
CTSA 3.62 1.25E-05 4.88E-03
ANP32A 3.59 1.07E-03 7.78E-03
CTSB 3.28 4.55E-05 4.88E-03
PSAP 3.05 4.78E-05 4.88E-03
TPI1 2.97 6.20E-05 4.88E-03
SFXN3 2.93 5.43E-05 4.88E-03
PGK1 2.91 1.58E-04 4.92E-03
PRCP 2.90 2.70E-03 1.19E-02
S100A6 2.87 1.39E-04 4.88E-03
NAGA 2.87 5.14E-05 4.88E-03
TUBB3 2.84 3.57E-05 4.88E-03
PTGES 2.84 2.58E-02 5.71E-02
SCARB2 2.82 6.16E-05 4.88E-03
TMED4 2.78 2.17E-02 5.00E-02
ANXA4 2.73 4.45E-05 4.88E-03
HEXA 2.70 1.43E-02 3.61E-02
GYS1 2.68 8.37E-02 1.43E-01
ANXA1 2.64 5.80E-04 6.24E-03
HSPB1 2.62 1.93E-03 1.01E-02
GSN 2.61 9.63E-05 4.88E-03
CKAP4 2.60 9.12E-05 4.88E-03
ARL8B 2.57 4.65E-03 1.65E-02
ERAP2 2.57 1.31E-04 4.88E-03
MAN2B1 2.56 1.06E-03 7.78E-03
ITPR3 2.56 5.62E-04 6.24E-03
SERPINB5 2.53 4.53E-05 4.88E-03
GPRC5A 2.52 1.57E-04 4.92E-03
MFGE8 2.51 6.48E-05 4.88E-03
ANXA5 2.45 5.32E-05 4.88E-03
89
CLN5 2.45 7.78E-03 2.34E-02
MLEC 2.40 2.23E-04 5.14E-03
CTSZ 2.37 4.86E-04 6.24E-03
ARF6 2.37 8.04E-04 6.62E-03
KRT15 2.35 6.25E-05 4.88E-03
PGRMC2 2.33 3.81E-03 1.47E-02
LPCAT2 2.31 1.11E-04 4.88E-03
STX8 2.30 6.28E-03 2.02E-02
VPS26A 2.29 3.61E-03 1.41E-02
IMPAD1 2.29 6.41E-04 6.28E-03
PRDX2 2.26 7.24E-05 4.88E-03
TRMT10C 2.26 2.15E-04 5.14E-03
KRT15 2.25 1.86E-04 5.04E-03
PPIA 2.25 2.01E-04 5.08E-03
LGALS3 2.25 1.25E-04 4.88E-03
GSTP1 2.25 7.47E-05 4.88E-03
ALG5 2.18 3.09E-02 6.46E-02
JUP 2.17 1.44E-04 4.88E-03
GLB1 2.16 1.43E-02 3.61E-02
RALA 2.16 5.62E-03 1.89E-02
LAMP1 2.15 1.04E-04 4.88E-03
ACSL1 2.12 3.81E-04 5.72E-03
GSTO1 2.11 7.16E-04 6.28E-03
MGST1 2.11 1.16E-04 4.88E-03
SUB1 2.10 1.52E-04 4.92E-03
ARMC10 2.08 2.85E-04 5.41E-03
NLN 2.07 2.39E-04 5.16E-03
ANP32B 2.06 6.39E-02 1.15E-01
GBA 2.04 1.71E-03 9.61E-03
PCYT1A 2.03 4.95E-04 6.24E-03
CYB5R3 2.03 8.17E-05 4.88E-03
DDOST 2.03 7.30E-04 6.28E-03
HDGF 2.02 7.80E-04 6.46E-03
PHLDB2 2.02 8.45E-05 4.88E-03
RALB 2.01 2.40E-03 1.13E-02
SRSF5 -2.04 1.88E-03 1.01E-02
UTP18 -2.05 4.42E-02 8.63E-02
CYP51A1 -2.06 2.04E-04 5.08E-03
SRRM1 -2.15 4.75E-03 1.67E-02
SLC3A2 -2.16 2.48E-04 5.17E-03
ARF3 -2.21 3.93E-02 7.87E-02
MRPS27 -2.27 3.49E-03 1.38E-02
HTRA1 -2.30 2.16E-03 1.08E-02
SHMT2 -2.34 7.02E-04 6.28E-03
90
SLC7A5 -2.46 2.69E-04 5.41E-03
DDX21 -2.51 1.14E-04 4.88E-03
HMGA1 -2.53 2.34E-04 5.14E-03
PCNA -2.56 3.91E-04 5.76E-03
TMPO -2.59 1.25E-04 4.88E-03
MTREX -2.67 1.72E-03 9.61E-03
H2BC11 -2.70 3.11E-05 4.88E-03
POLR2H -2.77 9.79E-04 7.47E-03
FDFT1 -2.81 8.50E-05 4.88E-03
YLPM1 -2.92 5.19E-04 6.24E-03
PPHLN1 -3.00 2.34E-03 1.12E-02
HIST1H1B -3.37 4.64E-05 4.88E-03
Table 14: Proteomics analysis of triapine-induced senescence (only proteins with abs (log2 FC)
> 1.5 are shown)
Protein symbol log2 (Tripaine / DMSO) t-test p-value FDR-corrected p-value
LGALS7 2.88 6.53E-07 1.44E-03
LIMA1 2.74 7.45E-05 6.33E-03
SPRR1B 2.40 2.00E-04 9.22E-03
CES2 2.22 8.06E-05 6.59E-03
TPM4 2.14 8.13E-06 2.24E-03
TPM3 2.10 2.92E-06 2.15E-03
ANXA1 2.04 5.74E-05 6.33E-03
TPM1 2.04 7.51E-04 1.74E-02
TKT 1.94 1.46E-04 8.08E-03
INTS3 1.92 5.47E-02 1.51E-01
TPM4 1.91 1.36E-02 6.75E-02
TMSB10 1.81 1.78E-05 3.93E-03
CALD1 1.77 1.06E-04 7.01E-03
TMSB4X 1.74 2.42E-03 2.83E-02
FASTKD2 1.72 5.68E-02 1.55E-01
PTMA 1.71 5.78E-04 1.57E-02
TPM4 1.71 3.97E-03 3.68E-02
TPM1 1.71 4.46E-02 1.35E-01
SUB1 1.70 9.39E-03 5.44E-02
EEF1G 1.69 7.28E-06 2.24E-03
EEF1D 1.64 1.63E-05 3.93E-03
KRT6B 1.64 7.88E-02 1.87E-01
TOP2B 1.62 6.06E-02 1.59E-01
MARCKSL1 1.62 2.17E-02 8.90E-02
TPM2 1.60 4.04E-02 1.27E-01
HSPB1 1.58 2.08E-04 9.35E-03
91
S100A6 1.58 1.14E-03 2.01E-02
EIF4A2 1.56 1.46E-03 2.21E-02
CSRP1 1.53 3.09E-05 4.70E-03
YWHAB 1.53 4.90E-03 3.95E-02
TUBB3 1.52 4.42E-05 5.41E-03
H2BC11 -1.51 5.46E-05 6.33E-03
H2BC18 -1.52 4.15E-05 5.38E-03
EXOSC4 -1.52 1.06E-03 1.95E-02
H2BC18 -1.52 9.61E-04 1.89E-02
ITGA6 -1.53 8.23E-03 5.20E-02
UBE2N -1.56 1.09E-04 7.01E-03
DHX30 -1.60 1.87E-02 8.16E-02
EXOSC2 -1.64 4.68E-02 1.38E-01
H1-3 -1.68 2.10E-02 8.74E-02
HIST1H1B -1.72 4.37E-02 1.33E-01
LLPH -1.73 2.01E-04 9.22E-03
SLC20A1 -1.74 3.75E-02 1.22E-01
TP63 -1.74 7.54E-06 2.24E-03
NOL11 -1.84 5.47E-03 4.08E-02
MRPS17 -1.85 2.80E-02 1.02E-01
MCM3 -1.86 5.41E-04 1.56E-02
DDX47 -1.93 2.33E-03 2.76E-02
NSA2 -2.06 3.53E-02 1.18E-01
NUP85 -2.12 2.64E-02 9.90E-02
TPBG -2.12 9.58E-03 5.50E-02
NOP14 -2.30 8.70E-04 1.83E-02
SLC2A1 -2.33 2.59E-03 2.98E-02
H2AZ1 -2.62 7.01E-06 2.24E-03
92
Table 15: Integrated proteomics analysis of HMECs senescence proteomics analysis (only
proteins with consistent direction of change, log2 FC >1 and FDR < 0.01 are shown)
Protein name Average log2 FC Fisher's combined p-value FDR-corrected Fisher's combined p-value
ANXA1 2.35 1.78E-16 1.80E-13
SERPINB5 1.93 1.92E-13 9.71E-11
YWHAQ 1.36 6.49E-13 1.32E-10
KRT16 1.41 5.34E-13 1.32E-10
LGALS7 2.23 4.93E-13 1.32E-10
CTSD 2.35 1.97E-12 3.32E-10
TALDO1 2.06 2.55E-12 3.33E-10
CTSA 1.99 4.18E-12 3.53E-10
TMPO -1.25 6.47E-06 5.43E-10
EEF1G 1.01 1.16E-11 8.42E-10
SLC3A2 -1.45 1.37E-11 9.26E-10
LGALS3 1.42 2.03E-11 1.28E-09
YWHAB 1.05 2.33E-04 2.65E-09
HTRA1 -1.77 6.48E-11 3.28E-09
KRT15 1.27 7.08E-10 3.28E-09
HIST1H1B -2.13 7.34E-11 3.54E-09
CKAP4 1.37 9.62E-11 4.43E-09
H4-16 -1.12 1.19E-10 5.25E-09
TPM3 1.26 1.28E-10 5.39E-09
GAPDH 1.00 1.39E-10 5.62E-09
DDX21 -1.51 2.07E-10 6.61E-09
NUMA1 -1.12 2.53E-10 8.88E-09
NUMA1 -1.12 2.53E-10 8.88E-09
AHCY 1.18 2.54E-10 8.88E-09
GM2A 1.23 5.77E-10 1.95E-08
MAN2B1 1.82 6.53E-10 2.13E-08
HSPB1 2.11 7.00E-10 2.22E-08
UBA1 1.11 7.24E-10 2.22E-08
KRT6A 1.04 7.26E-09 2.63E-08
TKT 1.46 1.02E-09 2.63E-08
PCNA -1.42 1.08E-09 2.67E-08
GSTP1 1.60 1.23E-09 2.97E-08
SHMT2 -1.21 1.61E-09 3.78E-08
LDHA 1.23 1.69E-09 3.88E-08
PGK1 1.44 2.50E-09 5.62E-08
PKM 1.09 6.23E-09 1.32E-07
FDFT1 -1.32 7.59E-09 1.54E-07
CTSB 1.49 7.49E-09 1.54E-07
S100A6 1.98 2.42E-08 4.71E-07
ALDOA 1.23 3.12E-08 5.91E-07
GLB1 1.22 3.79E-08 6.72E-07
93
PRDX6 1.26 5.73E-08 9.21E-07
YWHAG 1.52 5.88E-08 9.31E-07
SUPT16H -1.11 7.16E-08 1.10E-06
SSRP1 -1.10 8.64E-08 1.29E-06
JUP 1.16 1.55E-05 1.39E-06
EBNA1BP2 -1.04 1.06E-07 1.52E-06
UPF1 -1.01 1.13E-07 1.57E-06
DDX18 -1.39 1.46E-07 2.00E-06
TPD52L2 1.11 1.70E-07 2.26E-06
CTSZ 1.35 1.82E-07 2.40E-06
PRDX2 1.54 2.42E-07 3.03E-06
GSN 1.32 2.52E-07 3.07E-06
DSG3 1.00 3.39E-07 4.04E-06
TUBA4A 1.02 3.99E-07 4.65E-06
CHERP -1.16 4.05E-07 4.66E-06
ANXA3 1.08 5.60E-07 5.97E-06
GRSF1 -1.19 9.92E-07 9.95E-06
H2BC11 -1.45 1.12E-06 1.11E-05
TPI1 1.45 1.30E-06 1.22E-05
CES2 1.86 1.26E-06 1.22E-05
MCM3 -1.14 1.32E-06 1.22E-05
ITGA6 -1.04 1.59E-06 1.47E-05
H2BC18 -1.40 1.67E-06 1.51E-05
TACSTD2 1.41 1.70E-06 1.51E-05
MCM5 -1.12 1.91E-06 1.66E-05
ERAP2 1.20 2.07E-06 1.75E-05
ARL8B 1.39 2.22E-06 1.86E-05
CYB5R1 1.03 2.61E-06 2.13E-05
ANXA5 1.08 3.08E-06 2.46E-05
HDGF 1.20 5.23E-06 3.78E-05
TPM1 1.25 2.95E-05 8.02E-05
TPM1 1.25 2.95E-05 8.02E-05
CAPG 1.13 1.34E-05 8.32E-05
JUP 1.16 1.55E-05 9.45E-05
POP1 -1.35 4.15E-05 2.13E-04
H2AZ1 -1.27 7.21E-05 3.41E-04
RALA 1.14 1.42E-05 4.82E-04
H1-3 -1.18 1.41E-04 5.93E-04
HLA-A 1.04 3.44E-05 7.57E-04
CAPN1 1.22 3.68E-04 1.36E-03
ARSA 1.15 3.77E-04 1.38E-03
SLC7A5 -1.33 6.37E-04 2.10E-03
SFXN3 1.07 8.42E-04 2.65E-03
ALDH1A3 1.05 2.69E-04 3.82E-03
94
CSTF2 -1.05 7.89E-04 6.65E-03
SLC4A7 -1.03 2.67E-03 7.16E-03
H2BC18 -1.40 1.67E-06 9.02E-03
ITPR3 1.00 2.37E-05 9.28E-03
95
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Abstract (if available)
Abstract
Cellular senescence is a mechanism by which cells permanently withdraw from the cell cycle in response to stresses including telomere shortening, DNA damage, or oncogenic signaling. Underlying mechanisms that initiate and force the senescence program produce dual effects on the nearby tissue and consequently on the organism. The irreversible mechanism of senescence against proliferation constitutes a barrier against cancer cell development. In addition, senescent cells interact with their microenvironment through secretion of numerous inflammatory signals which modulate immune response and wound healing, however, as senescent cells accumulate in the aging tissue they contribute to chronic inflammation and age-related diseases. In culture, normal human epithelial cells enter senescence after a limited number of cell divisions, known as replicative senescence. To investigate how metabolic pathways regulate replicative senescence, we used LC-MS–based metabolomics to analyze senescent primary human mammary epithelial cells (HMECs). Analysis of intracellular metabolite pool sizes indicated that senescent cells exhibit depletion of metabolites from nucleotide synthesis pathways. Furthermore, stable isotope tracing with ¹³C-labeled glucose or glutamine revealed a dramatic blockage of flux of these two metabolites into nucleotide synthesis pathways in senescent HMECs. Because of the importance of senescent cells in determining fate of human tissues, organs and lifespan, it is crucial to effectively identify senescent cells with specific biomarkers in order to a) measure degree of aging in each tissue and b) be able to therapeutically target senescent cells for removal. Therefore, we utilized mass spectrometry-based proteomics method to identify biomarkers of aging mammary tissue using HMECs model of senescence. By applying statistical and computational tools we identified the most consistently upregulated and downregulated proteins in senescent cells some of which could serve as novel biomarkers of senescence. Additionally, we leveraged HMECs senescence signature with large-scale drug screening to infer novel therapeutics to specifically kill senescent cells. Our computational analysis suggests that inhibitors of epidermal growth factor receptor (EGFR) and mitogen-activated protein kinase (MEK) are promising drugs for removing senescent cells. Taken together, our LC-MS-based metabolomics and proteomics approach significantly extends the molecular knowledge of epithelial senescence. We demonstrate that inhibition of nucleotide synthesis plays a causative role in the establishment of replicative senescence. Additionally, we identified several novel biomarkers of senescence and potential therapeutic targets for selective removal of senescent cells.
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Asset Metadata
Creator
Delfarah, Alireza
(author)
Core Title
Metabolomic and proteomic approaches to understanding senescence and aging in mammary epithelial cells
School
Viterbi School of Engineering
Degree
Doctor of Philosophy
Degree Program
Chemical Engineering
Publication Date
10/23/2020
Defense Date
09/22/2020
Publisher
University of Southern California
(original),
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Tag
aging,bioinformatics,epithelial cells,metabolomics,OAI-PMH Harvest,proteomics,senescence,systems biology
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English
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Graham, Nicholas Alexander (
committee chair
), Finley, Stacey (
committee member
), Malmstadt, Noah (
committee member
)
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delfarah.alireza@gmail.com,delfarah@usc.edu
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Tags
bioinformatics
epithelial cells
metabolomics
proteomics
senescence
systems biology