Analysis of lifespan extending compounds using growth kinetics and replicative lifespan in 
Saccharomyces cerevisiae 
Michael G. Kiflezghi 
 
A dissertation 
Submitted in partial fulfillment of the 
requirements for the degree of  
Doctor of Philosophy 
 
University of Washington 
2023 
 
Reading Committee:  
Matt Kaeberlein, Chair 
Peter Rabinovitch 
William Banks 
 
Program Authorized to Offer Degree: 
Molecular Medicine and Mechanisms of Disease 
 
 
  
 
 
 
 
 
©Copyright 2023 
Michael G. Kiflezghi 
  
 University of Washington 
Abstract 
Analysis of lifespan extending compounds using growth kinetics and replicative lifespan in 
Saccharomyces cerevisae 
Michael G. Kiflezghi 
Chair of the Supervisory Committee: 
Matt Kaeberlein 
Department of Laboratory Medicine and Pathology 
Advances in modern medicine have facilitated stunning rises in life expectancy over the last two 
centuries. This longer life, however, is concomitant with a gradual decline in health and the onset of 
multiple age-associated pathologies including heart disease, neurodegeneration and cancer. Risk of 
developing and dying from these age-associated diseases increases dramatically with age suggesting a 
common underlying cause. Targeting the aging process itself with clinical intervention may allow for 
amelioration of the onset and progression of multiple age-associated pathologies simultaneously. The 
mechanistic Target of Rapamycin (mTOR) signaling pathway has emerged as an important clinical target 
in this regard with its dysregulation being observed in multiple pathologies including many cancers, 
autoimmune disorders, heart failure, neurodegeneration and type 2 diabetes. Importantly, genetic or 
pharmacological inhibition of mTOR signaling results in increased life-span across evolutionary distant 
organisms suggesting a conserved role for this signaling pathway in longevity regulation. A novel yeast-
based system to identify putative mTOR inhibitors is described. Utilizing differential growth kinetics of WT 
and mutant strains sensitized to mTOR perturbations, this system successfully discriminates between 
allosteric (i.e. rapamycin) and ATP competitive mTOR inhibitors. A number of nutraceutical compounds 
were screened and of these, caffeine was confirmed to be an mTOR inhibitor (Chapter two). Next a 
screen of natural products and natural product mixtures were screened for effects on growth rate, mTOR-
mediated growth inhibition, and replicative lifespan (RLS). No mTOR inhibitors were identified but two 
treatments, berberine and green tea extract significantly reduced RLS. Pterocarpus marsupium extract 
(PME), a plant extract with a long history of use in Ayurvedic medicine, extended cellular lifespan 
(Chapter three). Constituents of this extract include several compounds with reported health span and life 
span extending properties: epicatechin, quercetin, berberine, and pterostilbene. Of these PME 
constituents, quercetin and epicatechin had no effect on cellular life span, while berberine decreased 
cellular life span. We describe the lifespan and outgrowth kinetics of pterostilbene-treated cells and report 
on dose-dependent effects on longevity, potent cytotoxicity, and a possible role for mitochondrial function 
in mediating these phenotypes (Chapter Four).  
 
  
Table of Contents 
Abstract ......................................................................................................................................................... 3 
Chapter 1: Introduction .................................................................................................................................. 7 
An aging world .......................................................................................................................................................... 7 
The Biology of Aging .................................................................................................................................................. 8 
mTOR signaling in aging .......................................................................................................................................... 10 
Chapter 2: A System to Identify Inhibitors of mTOR Signaling Using High-resolution Growth Analysis in 
Saccharomyces cerevisiae ......................................................................................................................... 13 
Abstract ................................................................................................................................................................... 14 
Introduction ............................................................................................................................................................ 15 
Results ..................................................................................................................................................................... 19 
TOR1 mutants are hypersensitive and FPR1 mutants are resistant to rapamycin and rapalogs ........................ 19 
ATP competitive inhibitors of mTORC1 produce a distinct growth inhibitory profile compared to rapamycin 
treatment ............................................................................................................................................................ 21 
Among several putative mTORC1 inhibitory nutraceuticals, only caffeine shows specificity for mTORC1 in yeast
 ............................................................................................................................................................................ 23 
Discussion ................................................................................................................................................................ 25 
Materials and Methods ........................................................................................................................................... 27 
Chapter 3: Pterocarpus marsupium extract extends replicative lifespan in budding yeast ........................ 34 
Abstract ................................................................................................................................................................... 35 
Introduction ............................................................................................................................................................ 36 
Results ..................................................................................................................................................................... 37 
Discussion ................................................................................................................................................................ 42 
Materials and Methods ........................................................................................................................................... 45 
Outgrowth analysis ............................................................................................................................................. 45 
Replicative lifespan analysis................................................................................................................................ 46 
Intervention preparation and suppliers .............................................................................................................. 46 
Chapter 4: Dose-dependent effects of pterostilbene on cellular lifespan and proliferation ........................ 97 
Abstract ................................................................................................................................................................... 97 
Introduction ............................................................................................................................................................ 98 
Results ................................................................................................................................................................... 101 
Pterostilbene inhibits yeast growth independently of mTOR inhibition and can increase or decrease yeast RLS.
 .......................................................................................................................................................................... 101 
High dose pterostilbene inhibits post-diauxic shift growth ............................................................................... 103 
High dose pterostilbene induces mitochondrial instability ............................................................................... 103 
Pterostilbene treatment results in dose dependent cytotoxicity ...................................................................... 107 
Discussion .............................................................................................................................................................. 110 
Materials and Methods ......................................................................................................................................... 113 
Yeast strains and culture conditions ................................................................................................................. 113 
Outgrowth Analysis ........................................................................................................................................... 113 
Replicative lifespan analysis.............................................................................................................................. 113 
Flow Cytometry ................................................................................................................................................. 114 
Supplemental Figures ....................................................................................................................................... 116 
Bibliography .............................................................................................................................................. 118 
 
  
 Chapter 1: Introduction 
An aging world 
 
 In 1800, estimated global life expectancy was about 30 years [1]. By the early 1900s, the global 
figure rose modestly to about 34 years. Over the last century, advances in medicine and public health 
have facilitated stunning increases in life expectancy, bringing the global average to 72 years in 2020 [2]. 
While the advent of modern medicine has enabled this increase in life expectancy, aging populations 
have a range of age-associated diseases to contend with that negatively impact the quality of life. Age is 
the major risk factor for multiple diseases affecting mortality, including cardiovascular disease, 
neurodegenerative diseases, and cancer [3]. Among other factors, increases in life expectancy and 
population size have altered the population structure of the United States. In 1900, 4.1% of the population 
was 65 and older. Nearly 120 years later in 2019, 65 and older individuals comprised 16% of the 
population. This figure is projected to rise to over 20% by 2050 [4, 5]. This growing population of aged 
individuals will require more significant investments in healthcare to protect health and quality of life. 
While modern medicine has facilitated increases in average lifespan, aging itself has not been a target for 
therapeutic intervention. 
This increase in average lifespan is not necessarily concomitant with a prolongation of the quality 
of life, thus leaving many to suffer periods of illness and reduced quality of life as they age. There is a 
distinction between the measured lifespan of an individual and the amount of time they remain free of 
diseases or in good health. This period of time in good health is sometimes called health span, but is not 
rigorously defined [6]. Regardless, this concept remains useful in contextualizing an individual’s lifespan. 
Aging has primarily been viewed as an intractable and inevitable end state. However, nearly a 
century of research has shown that restricting the energy intake of an organism, also referred to as 
caloric restriction (CR) or dietary restriction (DR), can extend lifespan and delay the onset of age-
associated pathologies in multiple animal models, including vertebrate and invertebrate species. 
Research into the molecular mechanisms of the CR response and the underlying biology of aging has 
yielded several potential therapeutic targets, the controlled disruption of which extends lifespan in 
multiple, evolutionary distant model organisms.  
The Biology of Aging  
 
From the ambrosia of the Greek Gods to the ancient Egyptian Cup of Ankh and the age-old tale 
of the fountain of youth, antiquity is replete with references to extreme longevity conferred through food 
and drink, supernatural or otherwise. Humanity’s fascination with extreme longevity has remained largely 
philosophical until relatively recently. In the early 20th century, nutrition experiments conducted in rats 
uncovered an unexpected phenomenon: restricting the food of laboratory animals would sometimes 
paradoxically extend their lifespan [7-9]. These works were the first published evidence that diet could 
influence longevity. These initial reports, however, had two common themes that complicated the 
interpretation of their results: to achieve growth inhibition, food restriction without regard for malnutrition 
was utilized, and animals were segregated on the basis of their growth rates. In their seminal 1935 
publication McKay et al. addressed these issues, suggesting that prior work had not truly tested the 
hypothesis that inhibited growth results in extended lifespan because animals were segregated on the 
basis of their growth rates and, therefore, the slower-growing group would be biased towards individuals 
that die prematurely. McKay believed that food restriction in the context of malnutrition likely resulted in 
metabolic issues that extended beyond inhibiting growth [10]. They showed, for the first time, that CR 
extended the mean and maximal lifespan of rats compared to ad libitum fed animals [11]. Over the next 
several decades, the CR effect on longevity would be validated in multiple model organisms spanning a 
large evolutionary distance suggesting a conserved role for this effect.  
Paralleling these experimental observations was the development of various theories of aging. In 
general, theories of aging can be segregated into two categories: programmed or damaged. Programmed 
theories of aging suggest that individuals age due to a deliberate or programmed deterioration. The 
disposable soma theory, which posits that an evolutionary trade-off between growth and reproduction 
drives organismal aging [12], is one such example. Theories of aging that fall into the damaged category 
generally suggest that aging results from the stochastic accumulation of damage intersecting with or 
causing the failure of repair mechanisms to repair this damage and that this inevitable damage 
accumulation contributes to age-related declines in function. The free radical theory of aging [13] is a 
successful example with widespread experimental support and has even entered the general lexicon.  
The first direct evidence that organismal longevity can be influenced genetically came from a 
screen for long-lived mutants in the nematode Caenorhabditis elegans. In 1983 Michael Klass published 
a method for isolating long-lived C. elegans mutants [14]. Klass had previously shown that CR could 
increase C. elegans lifespan [15] and was interested in identifying individual genes that influence aging. 
He was able to isolate eight long-lived mutants, none of which, he concluded, showed increased life 
spans as a primary effect: two of the mutants showed increased life span due to dauer formation, and the 
other six showed increased lifespan presumably due to CR caused by a pharyngeal pump defect. A few 
years later, Friedman and Johnson, working with Klass’ mutants, were able to isolate one (hx564) that 
lacked the pharyngeal pump defect but retained a long lifespan finding that a mutation in the age-1 gene 
increased mean lifespan by over 50% [16]. In another screen for long-lived mutants by Cynthia Kenyon, it 
was found that a mutation in daf-2 could double C. elegans lifespan and that daf-16 was required for this 
extension [17]. daf-2 encodes the C. elegans homolog of human insulin and IGF-1 receptors [18], 
implicating the insulin/IGF-1 signaling (IIS) pathway in longevity regulation.  
The unicellular eukaryote Saccharomyces cerevisiae has been successfully exploited to identify 
and characterize signaling pathways that may modulate biological aging. The mechanistic Target of 
Rapamycin (mTOR; TOR in budding yeast) signaling pathway is one such example [19]. The mTOR 
protein is a serine/threonine kinase and an atypical member of the phosphatidylinositol 3 kinase family 
that serves as a master regulator of cell growth and proliferation, allowing the cell to tie environmental 
nutrient status to cell fate. Budding yeasts are among the only eukaryotes to possess two mTOR genes: 
TOR1 and TOR2, which arose from a genome duplication event. The yeast mTOR kinases comprise the 
catalytic subunit of two structurally and functionally distinct protein complexes: mTOR complex 1 
(mTORC1; may contain TOR1 or TOR2) and mTOR complex 2 (mTORC2; TOR2 exclusive) [20]. Unlike 
yeast, multicellular eukaryotes possess a single mTOR-encoding gene, with its gene product comprising 
the catalytic subunit of both mTORC1 and mTORC2.  
The macrolide antibiotic rapamycin is a specific and potent inhibitor of the mTOR kinase. 
Rapamycin inhibits mTORC1 activity by first complexing with the peptidyl-prolyl cis-trans isomerase 
FKBP12 (Fpr1 in yeast). The rapamycin-FKBP12 complex then forms a ternary complex with the mTOR 
kinase via the FKBP-rapamycin-binding (FRB) domain, likely resulting in both steric-occlusion of the 
kinase active site and inhibition of substrate recruitment [21-23]. Among its many functions, mTORC1 
regulates protein synthesis and mRNA translation globally through two distinct and highly conserved 
substrates: ribosomal S6 kinase (S6K1) and eukaryotic translation initiation factor 4E-binding protein (4E-
BP1). 
mTORC2 is said to be “rapamycin insensitive” because it does not respond to direct inhibition by 
rapamycin or its analogs (rapalogs). This is perhaps due to steric occlusion of the FRB domain by 
mTORC2 component Rictor as was shown to be the case in budding yeast with the Rictor homolog Avo3 
[24]. However, prolonged treatment with rapamycin has been shown to inhibit mTORC2 activity. This is 
thought to occur through sequestration of free mTOR kinases by the rapamycin-FKBP12 complex 
inhibiting the assembly of mTORC2 [25]. mTORC1 signaling has been characterized to a greater extent 
than mTORC2 signaling owing largely to the ability to interrogate mTORC1 function through genetic or 
pharmacological inhibition. While dual mTORC1/mTORC2 catalytic inhibitors have been developed, until 
recently, direct and specific pharmacological inhibition of mTORC2 had not yet been demonstrated. JR-
AB2-011, a derivative of a compound (CID613034) from the NCI/DTP (National Cancer Institute 
Developmental Therapeutics Program) small molecule library was identified as a specific, sub-micromolar 
mTORC2 inhibitor. It was shown to be capable of binding to mTORC2 obligate regulatory subunit Rictor 
thereby preventing the association of mTOR with Rictor precluding the formation of functional mTORC2 
[26].  
mTOR signaling in aging  
As a master regulator of cell growth and proliferation, the mTOR kinase plays a role in a diverse set of 
cellular functions. While mTOR inhibition has been shown to increase lifespan in multiple model 
organisms, the specific mechanism by which this occurs is not well understood. The role of the mTOR 
pathway in regulating life span has been attributed, in part, to its function as a nutrient sensor. Nutrient 
sensing pathways are thought to act as major determinants of longevity. This is consistent with evidence 
that inhibition of both the mTOR signaling pathway and IIS pathway increase longevity in multiple model 
organisms. Suppression of these signaling pathways is also thought to be one of the mechanisms by 
which CR mediates its beneficial effects. Consistent with this notion are the observations that CR does 
not further increase life span in: genetically modified S. cerevisiae lacking TOR1 [27]; RNAi of TOR in C. 
elegans [28]; or overexpression of the negative upstream mTOR regulator dTsc2 in D. melanogaster [29].  
 mTOR was first implicated in the regulation of longevity when it was found that a mutation or 
deletion of SCH9, a yeast functional homolog of mammalian S6K1 and mTORC1 substrate, increased the 
chronological lifespan of yeast cells [30]. This was soon followed with studies in multiple models 
establishing a direct role for mTOR in aging: RNA interference (RNAi) knockdown of mTOR (let-363) [31] 
or mutation of mTORC1 component Raptor (daf-15) [32] extended lifespan in Caenorhabditis elegans; 
overexpression of upstream negative mTORC1 regulators tuberous sclerosis complex genes 1 and 2 
(TSC1, TSC2) as well as dominant negative forms of mTOR or S6K1 increased lifespan in Drosophila 
melanogaster [29]; deletion of either SCH9 or TOR1 increased lifespan in Saccharomyces cerevisiae 
[27]. Building on this paradigm, rapamycin has been used to effect pharmacological inhibition of mTOR 
resulting in lifespan extension in yeast [33, 34], nematodes [35], fruit flies [36], and mice [37-39]. These 
data demonstrate a clear and important role for mTOR in the regulation of longevity across evolutionarily 
distant species.  
In 2013 the “Hallmarks of Aging” was published [40]. In an attempt to consolidate knowledge in 
the field à la the “Hallmarks of Cancer,” this seminal work proposed 9 “hallmarks” as common 
denominators of aging: genomic instability, telomere attrition, epigenetic alterations, loss of proteostasis, 
deregulated nutrient-sensing, mitochondrial dysfunction, cellular senescence, stem cell exhaustion, and 
intracellular communication [40]. In keeping with the observations implicating mTOR in longevity 
regulation, a number of these “hallmarks” are known to be affected by mTOR.  
 Alterations in protein homeostasis or proteostasis are linked to aging [41]. The maintenance of 
proteostasis occurs via a complex interplay of protein synthesis, protein clearance, and quality control 
mechanisms such as the unfolded protein response (UPR). Deletion of different components of the 
translational machinery resulting in a decrease in protein biosynthesis has been shown to increase 
lifespan in S. cerevisiae, C. elegans, and D. melanogaster [42]. mTORC1 stimulates protein synthesis 
through its evolutionary conserved substrates S6K and 4E-BPs [43] and suppresses autophagy. 
mTORC1 may also suppress protein degradation by controlling proteasomal activity: mTORC1 inhibition 
enhances ubiquitination, thereby increasing protein degradation by the proteasome [44].  
 Mitochondrial dysfunction is a distinct characteristic of aging cells characterized by: elevated ROS 
production and mitochondrial DNA mutations; decreased electron transport function, membrane potential, 
and ATP production; altered mitochondrial dynamics or dysregulated mitophagy [40, 45]. mTORC1 
stimulates transcription and subsequent translation of nuclear-encoded mitochondrial genes such as 
components of the electron transport chain and mitochondrial ribosomal proteins [46]. In addition to its 
established role as an inhibitor of autophagy [47], mTORC1 has been shown to be involved in the 
regulation of mitophagy [48].  
 Cellular senescence is characterized by a permanent exit from the cell cycle [49]. Senescent cells 
display an increase in cell size and mitochondrial mass as well as mitochondrial dysfunction and the 
development of the senescence-associated secretory phenotype (SASP) [50]. mTOR plays a role in the 
promotion of the secretory phenotype of senescent cells, while its inhibition has been shown to prevent 
stem cell senescence [51-53].  
 mTOR is truly a master regulator of cell fate. Its impact on many functions speaks to an incredibly 
complex cross-talk with multiple signaling pathways influencing diverse cellular processes. Its 
pharmacological or genetic inhibition can extend lifespan in multiple model organisms. It has been 
implicated in the CR response and multiple age-associated pathologies. As such, there is growing interest 
in identifying novel mTOR inhibitors. 
  
Chapter 2: A System to Identify Inhibitors of mTOR Signaling Using High-resolution Growth Analysis in 
Saccharomyces cerevisiae 
 
This chapter is lightly modified from Lee, et al. Published in Geroscience 39(4):419-428. 2017. doi: 
10.1007/s11357-017-9988-4. 
 
  
Abstract 
 
The mechanistic target of rapamycin (mTOR) is a central regulator of growth and proliferation. 
mTOR inhibition is a promising therapy for a variety of diseases and disorders. Inhibition of mTOR 
complex I (mTORC1) with rapamycin delays aging, increases healthy longevity in laboratory animals, is 
used clinically at high doses to prevent organ transplant rejection and as a treatment of some cancers. 
Clinical use of rapamycin is associated with several unwanted side effects, however, and several 
strategies are being taken to identify mTORC1 inhibitors with fewer side effects. We describe here a 
yeast-based growth assay that can be used to screen for novel inhibitors of mTORC1. By testing 
compounds using a wild-type strain and isogenic cells lacking either TOR1 or FPR1, we can resolve not 
only whether a compound is an inhibitor of mTORC1 but also whether the inhibitor acts through a 
mechanism similar to rapamycin by binding Fpr1. Using this assay, we show that rapamycin derivatives 
behave similarly to rapamycin, while caffeine and the ATP competitive inhibitors Torin 1 and 
GSK2126458 are mTORC1 inhibitors in yeast that act independently of Fpr1. Some mTOR inhibitors in 
mammalian cells do not inhibit mTORC1 in yeast, and several nutraceutical compounds were not found to 
specifically inhibit mTOR but resulted in a general inhibition of yeast growth. Our screening method holds 
promise as a means of effectively assaying drug libraries for mTOR-inhibitory molecules in vivo that may 
be adapted as novel treatments to fight diseases and extend healthy longevity.   
Introduction 
 
The budding yeast, Saccharomyces cerevisiae, is a premier model system for identifying conserved 
genetic and pharmacological interventions that extend lifespan [54, 55].  A particularly good example of this 
is the mechanistic target of rapamycin (mTOR) pathway that was first genetically implicated in aging in 
yeast [56] and has since emerged as an important target for delaying aging in multicellular invertebrates 
and mice [57, 58].  Likewise, the small molecule mTOR inhibitor, rapamycin, was first shown to extend 
lifespan in yeast [59] and has since been shown to have similar pro-longevity effects in nematodes [60], 
fruit flies [61], and mice [37].   
In addition to increased lifespan, rapamycin maintains organismal health during aging in mice, 
evidenced by decreased occurrence of multiple age-related diseases [62].  Rapamycin treatment in aging 
mice reduces cancer [63, 64], prevents cognitive dysfunction [65, 66], attenuates declining renal and 
hepatic function [67], improves muscle and visual performance [67], and reverses cardiac [68, 69] and 
immune decline [70].  Improved cardiac function from rapamycin treatment has recently been similarly 
observed in middle-aged companion dogs [71], while improved immune function has been observed in 
healthy elderly people treated with the rapamycin derivative everolimus (RAD001) [72].  Transient 
rapamycin treatment regimens lasting as few as twelve weeks and initiated late in life are also effective at 
increasing lifespan and healthspan in mice [73].  These findings place rapamycin and other mTOR inhibitors 
among the leading candidates for translational interventions to promote healthy aging in people and 
companion animals [74-76].   
The mechanistic target of rapamycin (mTOR) is a nutrient and growth factor responsive kinase that 
functions in two distinct protein complexes: mTOR complex 1 (mTORC1) and mTOR complex 2 (mTORC2) 
[77, 78].  Rapamycin binds to the FK506 binding protein FKPB12 (Fpr1 in yeast), and the FKBP12-
rapamycin complex inhibits the activity of mTORC1 by disrupting the physical interaction between the 
mTOR protein and a second mTORC1 component, raptor (Kog1 in yeast) [79, 80]. Deletion of FPR1 confers 
resistance to rapamycin in yeast [79, 81].  The mTORC1 complex regulates a variety of downstream cellular 
processes including mRNA translation, autophagy, and mitochondrial metabolism, all of which are affected 
by rapamycin treatment [82].  Unlike mTORC1, mTORC2 activity is not directly inhibited by rapamycin [83].  
The mTORC2 complex is less well characterized than mTORC1, but is similarly involved in regulating a 
variety of cellular processes including cytoskeleton organization and regulation of metabolism.  Long-term 
treatment with rapamycin in mammals is reported to cause indirect inhibition of mTORC2, which is 
implicated in metabolic defects including insulin resistance and glucose intolerance [84].  Both mTOR 
complexes are essential for viability in yeast and multicellular eukaryotes. 
Budding yeast contain two genes that encode the mTOR kinase: TOR1 and TOR2.  Tor1 functions 
exclusively in mTORC1, while Tor2 functions in both complexes [85].  Consistent with this, deletion of TOR2 
leads to inviability due to lack of functional mTORC2 activity [86], while deletion of TOR1 results in viable 
cells that are long-lived and sensitive to rapamycin, due to reduced mTORC1 activity [19, 79].  Despite their 
sensitivity to rapamycin, tor1Δ yeast cells do not show a substantial reduction in mRNA translation or 
doubling time [87, 88], indicating that Tor2 provides sufficient mTORC1 activity for normal growth in rich 
medium.      
Multiple pharmaceutical mTORC1 and general mTOR inhibitors are used as chemotherapeutic 
agents [89-93].  Additionally, several natural products and natural product mixtures are reported to inhibit 
mTOR signaling in cell culture and rodent models, including: curcumin [94, 95], green tea extract [96], 
epigallocatechin-3-gallate [96, 97], caffeine [98, 99], genistein [100], lycopene and eicosapentaenoic acid 
(EPA) [93], sulforaphane [101], alpha-lipoic acid [102, 103], glucosamine [104], quercetin [105, 106], 
berberine [107] and resveratrol [108].  In most cases, the mechanistic basis for inhibition of mTOR via these 
natural products is unknown, and it remains unclear whether these compounds act via direct inhibition of 
mTOR, mTORC1, or through indirect effects on components of the mTOR/nutrient response network. 
  To facilitate identification of new small molecule mTOR inhibitors in vivo, we have developed a 
simple yeast-based assay that quantifies differential growth inhibition in rapamycin-sensitized and 
rapamycin-resistant genetic backgrounds using a Bioscreen C MBR plate reader/shaker/incubator.  We 
have previously optimized the Bioscreen C MBR machine to obtain high-resolution growth curves of 
budding yeast cells for chronological lifespan analysis [109, 110] and assessing differential sensitivities of 
yeast strains to different chemical and environmental stressors [111].   Here we extend this method by 
assessing the impact of rapamycin and several other small molecules on doubling time and outgrowth in 
rich media for wild type BY4742 cells and isogenic tor1Δ and fpr1Δ cells.  Our method relies on the fact 
that deletion of TOR1 confers sensitivity to rapamycin due to diminished mTORC1 activity, while deletion 
of FPR1 confers resistance to rapamycin due to the necessary role of Fpr1 in rapamycin-mediated 
mTORC1 inhibition.  Given this, we predict that mTORC1 inhibitors with mechanisms similar to rapamycin 
will display similar differential growth inhibition across the three genotypes, while compounds that inhibit 
mTORC1 by a mechanism distinct from rapamycin will have a greater inhibitory effect on growth of tor1Δ 
cells relative to WT or fpr1Δ cells, but that WT and fpr1Δ cells will show similar inhibition of growth at a 
given drug concentration.  Drugs that do not inhibit mTORC1 will either have no effect on growth in any of 
the genotypes or will similarly inhibit growth across all three genotypes.  We report the validation of this 
assay using rapamycin, rapamycin derivatives, and mTOR catalytic inhibitors.  We also report the effect of 
several natural product compounds on mTORC1 inhibition and outgrowth, finding that among the natural 
products tested, only caffeine displays the outgrowth profile expected for an in vivo inhibitor of mTORC1 in 
yeast.  
  
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Figure 1.  tor1Δ mutants are hypersensitive and fpr1Δ mutants are resistant to rapamycin and rapalogs.  
Representative growth curves of WT (black), tor1∆ (red), and fpr1∆ (gray) yeast grown in YPD with A) 
1.5% DMSO (vehicle) or 2.5-40 ng/mL (left to right) B) rapamycin (R), C)  everolimus (E), or D) 
temsirolimus (T). 
 
Results 
 
TOR1 mutants are hypersensitive and FPR1 mutants are resistant to rapamycin and rapalogs 
 
We tested the effect of known mTORC1 inhibitors on growth kinetics in three haploid yeast strains: 
wild type (WT) BY4742 and isogenic tor1Δ, and fpr1Δ single-gene deletion mutants.  We began by 
analyzing dose responses for rapamycin and two rapalogs, everolimus and temsirolimus. As expected, all 
concentrations of rapamycin tested reduced tor1Δ outgrowth to a greater extent than WT cells, while fpr1Δ 
cells were resistant to growth inhibition (Figures 1 and 2).  Everolimus and rapamycin produced remarkably 
similar growth inhibitory responses, while higher concentrations of temsirolimus were necessary to achieve 
growth inhibition comparable to the other two drugs.     
 Sensitivity of the tor1Δ strain to mTORC1 inhibitors can been seen at 2.5 ng/mL rapamycin or 
everolimus and at 20 ng/mL temsirolimus, where deletion of TOR1 results in a significantly greater increase 
 
Figure 2.  Peak doubling times (DT) (minutes/cell division) of WT (black), tor1∆ (red), and fpr1∆ (gray) in 
vehicle and 2.5-40 ng/mL A) rapamycin (R), B) everolimus (E), or C) temsirolimus (T) (n=3-5 for each 
treatment).  Error bars = SEM.  * = significantly different from 1.5% DMSO control (p < 0.05, Welch’s t-
test).  x = doubling time could not be calculated due to no growth, indicated by OD ≤ 0.3 after 20 hours. 
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in doubling time relative to WT (Figure 2 and Supplemental table 1).   Resistance of the fpr1Δ strain is 
most evident at the highest concentration of each drug tested, where both WT and tor1Δ strains have 
doubling times exceeding 200 minutes, while the fpr1Δ cells are still growing as rapidly as in the vehicle 
control (doubling time 80-90 minutes). 
One consequence of diminished mTORC1 signaling is diminished protein translation [112, 113].  
To determine if diminished protein translation is sufficient to recapitulate rapamycin-like patterns of growth 
inhibition, we tested concentrations of the general protein translation inhibitor cycloheximide (CHX) ranging 
from 1-500 ng/mL. Cycloheximide treatment potently and similarly inhibits growth of all three strains at 
concentrations of 50 ng/mL and greater (Supplemental figure 1). 
ATP competitive inhibitors of mTORC1 produce a distinct growth inhibitory profile compared to rapamycin 
treatment 
 
In addition to rapamycin-like compounds that inhibit mTORC1 signaling via interaction with Fpr1, 
several drugs have been developed that act as ATP-competitive inhibitors of mTOR in mammalian cells.  
To understand how these compounds differentially impact growth in our yeast strains, we tested dose 
responses for Torin 1, GSK2126458, GDC-0941, and AZD8055 (Figure 3, Supplemental figure 2, and 
Supplemental table 2).  All of the ATP-competitive inhibitors required micromolar concentrations to inhibit 
growth, compared to nanomolar concentrations when using rapamycin and rapalogs.  At 10 µM Torin 1, 
tor1Δ cells were strongly growth inhibited (87% increase in doubling time relative to vehicle, p < 0.01, 
Welch’s t-test), while WT (42% increase, p < 0.01, Welch’s t-test) or fpr1Δ (30% increase, p < 0.05, Welch’s 
t-test) were less effected (Figure 3 and Supplemental table 2).  At 25 µM Torin 1, growth of all three 
strains was severely impacted, with WT and fpr1Δ cells showing similar reductions in growth rate, as 
expected for a catalytic inhibitor of mTOR that does not act through Fpr1.  
 Figure 3.  Torin 1 (a catalytic inhibitor of mTOR) produces a distinct growth inhibitory profile relative to 
rapamycin.  tor1Δ mutants are hypersensitive to Torin 1 and fpr1Δ mutants are not resistant.  A) 
Representative growth curves of WT, tor1Δ, and fpr1Δ outgrown in the presence of 2.5% DMSO (vehicle 
control) (top left), 1µM Torin 1 (top right), 10µM Torin 1 (bottom left), or 25µM Torin 1 (bottom right).  B)  
Doubling time (minutes/cell division) for WT, tor1Δ, and fpr1Δ strains grown in either 2.5% DMSO (vehicle 
control) (n=9 for WT and fpr1Δ, and n=10 for tor1Δ) or 1-25µM Torin 1 (n=4 for each strain in 1µM Torin 
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1 and n=5 for each strain in 10 and 25µM Torin 1). * = significantly different from 1.5% DMSO control (p 
< 0.05, Welch’s t-test).  
 
GSK2126458, another catalytic inhibitor of mTOR, only weakly impacted yeast growth 
(Supplemental figure 2 and Supplemental table 2).  Doubling time was modestly increased by 100 µM 
GSK2126458 in all three strains, while growth of only the tor1Δ cells was impacted by lower concentrations. 
Interestingly, neither GDC-0941 nor AZD8055 produced a measurable change in growth in any yeast 
strains (Supplemental figure 2 and Supplemental table 2). 
 
Among several putative mTORC1 inhibitory nutraceuticals, only caffeine shows specificity for mTORC1 in 
yeast 
 
Many nutraceutical compounds are described as having an mTORC1 modulatory effect, 
particularly in the context of human cancer cell culture models.  To identify and validate mTORC1-
modulating nutraceuticals in yeast, we tested a subset of these compounds in our Bioscreen C MBR assay 
(Table 1).  Most of the tested nutraceuticals produced no effect on growth at concentrations up to 100 
µg/mL (Table 1).  Quercetin significantly inhibited WT and tor1Δ doubling time by 23% and 25%, 
respectively, at the highest concentration tested (p < 0.01, Welch’s t-test), but did not produce a significant 
difference in fpr1Δ doubling time (p = 0.24, Welch’s t-test) even though a trend toward increased doubling 
time is seen in the strain. Two compounds tested, berberine and lycopene, inhibited growth in a genotype-
independent manner.  Lycopene only modestly impacted growth, while berberine strongly inhibited growth 
at 100 µg/mL.   
Among the nutraceuticals tested, caffeine was the only compound that showed growth inhibition 
consistent with an mTOR inhibitory effect, increasing doubling time specifically in the tor1Δ mutant cells at 
100 µg/mL (Figure 4).  Caffeine also increased WT and fpr1Δ doubling times at concentrations greater 
than 750 µg/mL.  To assess epistatic relationships between Fpr1 and mTORC1, we constructed a tor1Δ 
fpr1Δ double mutant.  Growth inhibitory profiles in tor1Δ fpr1Δ were identical to those of tor1Δ in our caffeine 
dose response (Figure 4C), confirming that Fpr1 is not required for caffeine-mediated growth inhibition in 
yeast. 
 
Figure 4.  Caffeine inhibits yeast growth in tor1Δ cells independently of Fpr1. 
 A) Representative growth curves of 6.67% H2O (vehicle)  (top left), 500µg/mL (middle), and 
1000µg/mL (right).  B) Peak doubling time (minutes/cell division) for WT, tor1Δ, and fpr1Δ yeast grown 
in H2O or 10-1000µg/mL caffeine (n=3-5 independent cultures for each strain*treatment tested). * = 
significantly different from 1.5% DMSO control (p < 0.05, Welch’s t-test).  C) tor1Δ fpr1Δ are sensitive 
to caffeine, indicating that caffeine-mediated growth inhibition in tor1Δ cells is independent of Fpr1.  D)  
Peak doubling times for tor1Δ fpr1Δ grown in H2O or 250-1000µg/mL caffeine (n=6 independent 
cultures for each strain*treatment tested). 
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Discussion 
 
 Rapamycin and other mTOR inhibitors have emerged as one of the most promising classes of 
molecules for treating a variety of diseases and promoting healthy longevity [114].  The largest barrier to 
clinical utilization of these compounds for such purposes is the risk of adverse side effects.  While it remains 
unclear how significant an issue this is in relatively healthy people at lower doses, the perception that mTOR 
inhibitors are risky drugs remains a significant challenge.  Thus, there is a need to develop novel mTOR 
inhibitors that are effective in vivo and which may have fewer side effects.  Of particular interest are natural 
product “nutraceutical” mTOR inhibitors. 
By utilizing yeast strains with differential sensitivity to mTORC1 inhibition, we developed an in vivo 
screening platform that allows us to preliminarily categorize compounds as growth inhibitory, mTOR 
inhibitory, and/or rapamycin mimetics.  Compounds that inhibit WT, tor1Δ, and fpr1Δ cells similarly are 
general growth inhibitors but are unlikely to be either direct or indirect mTOR inhibitors, at least in yeast.   
Compounds that induce growth inhibition preferentially or specifically in tor1Δ cells are candidate mTOR 
inhibitors, and if fpr1Δ cells are resistant to these compounds, then they are likely inhibiting mTOR by a 
mechanism similar to rapamycin.  The assay system performed as expected here for rapamycin, the 
rapalogs everolimus and temsirolimus, the catalytic mTOR inhibitors Torin 1 and GSK2126458, and the 
natural product mTOR inhibitor caffeine.  Therefore, we conclude that this assay system is likely to be 
suitable for identification of novel, unknown pharmaceutical and natural product mTOR inhibitors.    
Our analyses also confirmed previously observed cases of differential sensitivity between yeast 
and mammals with regard to the catalytic mTOR inhibitors AZD8055 and GDC-0941, which may be due to 
sequence differences between the mammalian and yeast proteins [115].   It is also possible that some 
compounds will be more or less able to get into yeast cells relative to mammalian cells and that yeast may 
be able to clear or otherwise detoxify certain compounds more or less effectively.  Thus, it is important to 
recognize that any hits identified in the yeast-based screening system described here will need to be 
validated in mammalian cells to confirm similar mTOR-inhibitory effects, and that failure to detect mTOR 
inhibition in this system for a given compound does not rule out the possibility that the compound could 
inhibit mTOR in mammalian cells.  Nonetheless, given the biochemical and mechanistic similarities between 
yeast and mammalian mTORC1 and mTORC2, we anticipate that many compounds will behave similarly 
in both systems, as is the case for several examples reported here. 
It is of particular interest that, among the nutraceutical compounds tested, only caffeine 
demonstrated growth kinetics consistent with mTORC1 inhibition in our yeast assay.  This supports prior 
studies of caffeine on mTOR activity in budding yeast [99, 116] and fission yeast [117], and is of interest in 
light of numerous reports that caffeine can extend lifespan in invertebrate models [118, 119] and that coffee 
consumption is associated with reduced mortality in people [120, 121].  It is intriguing to speculate that 
these effects could be related to caffeine’s mTOR inhibitory activities. 
The absence of effects from the other nutraceutical compounds tested suggests that they may not 
be true mTOR inhibitors.  One potential explanation is that some of these compounds, which were generally 
reported to inhibit mTOR in cancer cell culture models, act via indirect mechanisms through targets that are 
not present or not similarly affected in yeast.  Another possibility is that some of these compounds inhibit 
mammalian cell growth or nutrient uptake, perhaps specifically in the context of cancer cell culture models, 
which could have indirect effects on mTOR signaling in response.  One interesting example is resveratrol, 
which is reported to have numerous targets in mammalian cells and to inhibit mTOR through both direct 
and indirect mechanisms.  In one study, resveratrol was found to enhance the physical interaction between 
mTOR and its inhibitor DEPTOR [122], which has no obvious yeast ortholog.  Resveratrol has also been 
suggested to inhibit mTOR by indirect mechanisms including regulation of phosphoinositide 3-kinase (PI3K), 
Akt [123], and AMP-activated protein kinase (AMPK) [124].   We found no evidence here to support an 
mTOR-inhibitory role for resveratrol in yeast, nor any effect on growth at all, consistent with prior work 
showing that resveratrol does not impact yeast replicative lifespan [125]. 
Overall, the system described here represents a sensitive, high-throughput, and inexpensive 
approach to identify growth inhibitory molecules with specificity for mTOR in vivo.   It may be possible to 
further optimize this system, for example by screening compounds in a drug sensitized background or by 
humanizing the system through expression of human proteins in the mTOR signaling pathway in yeast. 
Additionally, this method is easily adapted for testing drug interactions by adding a mixture of two or more 
compounds, as well as for investigating the impact of genetic diversity on mTORC1 inhibition through use 
of large-scale genetic libraries or wild isolate yeast strains [126, 127].   The identification of new mTOR 
inhibitors and genetic variants that impact sensitivity to mTOR inhibition will facilitate the development of 
new therapies and personalized approaches for a breadth of conditions where mTOR signaling is perturbed.     
 
Materials and Methods 
 
Yeast strains and culture conditions. 
 
All yeast strains used were in the BY4742 genetic background (MATα his3Δ1 leu2Δ0 lys2Δ0 
ura3Δ0).  BW1121 (BY4742) was purchased from Thermo Fisher Scientific (Waltham, MA).  GS668 
(fpr1Δ::kanMX4) and GS983 (tor1Δ::kanMX4) were obtained from the haploid MATα yeast deletion 
collection [128].  PCR was used to confirm knockout identity.  For GS983, 5’-
TTGAATCCTAATTTCTTGCTCAATC-3’ and 5’-AAGGCATATATTGATGCTCAAAAAG-3’ primers were 
used to confirm knockout.  For GS668, 5’-GTTACTTGATGATATTAAGCACGGG-3’ and 5’-
ACAAAAATGAACCATTAGCAAAGAG-3’ primers were used to confirm knockout.  The tor1Δ fpr1Δ double 
deletion strain was constructed by mating tor1Δ::URA3 (gift of Lindsay Fox) and fpr1Δ::kanMX4 and 
selecting for Ura+ G418r haploid spores after sporulation and tetrad dissection.  PCR was used to verify 
presence of gene deletions.  For overnight culture and growth analysis, YPD (1% w/v BactoTM yeast extract 
(BD), 2% w/v BactoTM peptone (BD), 2% w/v dextrose) media was used.  Yeast were cultured at 30oC for 
all experiments. 
Growth analysis using Bioscreen C MBR 
 
Growth analysis of yeast strains was performed using a Bioscreen C CMB (Growth curves USA, 
Piscataway NJ, USA) as previously described [109]. Briefly, colonies outgrown from frozen stocks were 
inoculated into 5 mL YPD and incubated at 30oC in a roller drum for 12-16 hours.   Two µL of outgrown 
culture was used to inoculate 148 µL YPD into 100-well honeycomb plates for growth analysis.  At least 
three colonies per strain were analyzed in triplicate for each drug treatment.   Doubling times were 
calculated identifying the slope of the inflection point along growth curves using the online web tool Yeast 
Outgrowth Data Analyzer (YODA) [129].  Welch’s (unequal variance) t-test was used to assess statistical 
significance.   
Drug preparation and suppliers  
 
All drugs except caffeine and cycloheximide were suspended in DMSO (these drugs suspended in 
H2O).  Rapamycin, everolimus, and temsirolimus, were purchased from LC Laboratories (Woburn MA, 
USA).  Cycloheximide and berberine were purchased from Sigma-Aldrich (St. Louis MO, USA).  Torin 1 
was purchased from Cayman Chemical (Ann Arbor MI, USA).  AZD8055, GDC-0941, and GSK2126458 
were kind gifts from Jason Pitt.  Caffeine was purchased from MP Biomedicals (Santa Ana CA, USA).  
Resveratrol was purchased from AstaTech Inc. (Bristol PA, USA).  Alpha-lipoic acid, broccoli concentrate, 
glucosamine, lycopene, and quercetin were provided by USANA Health Sciences, Inc. (Salt Lake City UT, 
USA). 
 
 
 
 
 
 
 
 
 
 
Supplemental Figures 
 
 
 
Supplemental figure 1.  Cycloheximide (CHX) is a general growth inhibitor in yeast.  A) WT (black), 
tor1Δ (red), and fpr1Δ (gray) grown in YPD with vehicle (top left), 50ng/mL (top right), 100ng/mL (bottom 
left), and 500ng/mL (bottom right) CHX.  B) Peak doubling time (DT) (minutes/cell division) of WT, tor1Δ, 
fpr1Δ cells outgrown in the presence of 1-100ng/mL CHX (n=3-5 biological replicate cultures tested for 
each strain and treatment). *significantly different from 1.5% DMSO control (p < 0.05, Welch’s t-test).  
Error bars = SEM.  500ng/mL CHX treatment could not be calculated because outgrowing cultures failed 
to reach OD = 0.3 after 20 hours. 
 
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 Supplemental figure 2.  Many mTOR catalytic inhibitors have limited or no effect on yeast growth.  
GSK2126458, but not AZD8055 or GDC-0941, impacts growth of tor1Δ mutant yeast.  Outgrowth of WT 
(A), tor1Δ (B), and fpr1Δ (C) in 2.5% DMSO (vehicle control) or 10-100µM GSK2126458 (left), AZD8055 
(middle), or GDC-0941 (right). n=3-5 biological replicate cultures tested for each strain and treatment.  
 
 
 
 
 
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AZD8055 GDC-0941
Table 1.  Doubling time (DT) (minutes/cell division), standard error of the mean (SEM), percent change, and number of cultures tested (n) for 
putative mTOR-inhibitory neutraceutical compounds. 
 
WT tor1Δ fpr1Δ 
Treatment DT (SEM) % change n DT (SEM) % change n DT (SEM) % change n 
1% DMSO 83.3 (0.9) - 13 82.8 (0.9) - 13 90.8 (1.0) - 13 
100µg/mL alpha lipoic acid 85.4 (2.3) 2.6 3 85.2 (1.2) 2.9 3 91.7 (2.1) -6.2 3 
100µg/mL Broccoli concentrate 83.3 (1.1) 0.0 3 82.7 (2.8) -0.2 3 89.2 (1.9) -1.8 3 
100µg/mL Glucosamine 81.0 (2.5) -2.8 4 81.2 (2.1) -2.0 4 87.5 (2.6) -3.6 4 
100µg/mL Lycopene 95.8 (0.3) 15.0* 3 101.1 (2.8) 22.2* 3 107.7 (0.7) 18.6* 3 
100µg/mL Quercetin 104.1 (3.1) 25.0* 4 102.0 (6.0) 23.2* 4 105.4 (10.2) 16.1 4 
100µg/mL Resveratrol 82.7 (0.8) -0.7 3 82.4 (0.5) -0.5 3 87.0 (1.5) -4.2 3 
1µg/mL Berberine 80.2 (2.4) -3.7 3 79.7 (2.6) -3.7 3 87.0 (4.2) -4.2 3 
10µg/mL Berberine 102.2 (1.9) 22.7* 3 95.2 (1.8) 14.9* 3 101.8 (2.3) 12.1* 3 
100µg/mL Berberine 227.1 (6.7) 172.6* 3 228.6 (2.9) 176.1* 3 238.1 (2.5) 162.3* 3 
*= p < 0.05, compared to 1% DMSO, Welch's (unequal variance) t-test. 
  
Supplemental Table 1.  Doubling time (DT) (minutes/cell division), standard error of the mean (SEM), percent 
change, and number of cultures tested (n) for rapamycin and rapalogs. 
  WT tor1Δ fpr1Δ 
Treatment DT (SEM) 
% 
change n DT (SEM) 
% 
change n 
DT 
(SEM) 
% 
change n 
1.5% DMSO 82.8 (2.2) - 5 80.9 (1.2) - 5 84.3 (6.4) - 5 
2.5ng/mL Rapamycin 104.6 (5.6) 26.3* 4 211.4 (28.3) 161.3* 4 90.6 (1.0) 7.5 4 
5ng/mL Rapamycin 221.3 (19.5) 167.2* 4 NG - 4 91.5 (0.8) 8.5 4 
10ng/mL Rapamycin 245.1 (11.0) 196.1* 4 NG - 4 91.0 (0.9) 7.9 4 
20ng/mL Rapamycin 292.0 (22.2) 252.7* 4 NG - 4 91.1 (0.6) 8.1 4 
40ng/mL Rapamycin 278.9 (25.5) 236.9* 4 NG - 4 90.9 (2.2) 7.8 4 
2.5ng/mL Everolimus 120.5 (2.2) 45.5* 4 241.0 (46.3) 198.0* 4 89.7 (2.2) 6.4 4 
5ng/mL Everolimus 203.4 (19.1) 145.7* 3 NG - 3 91.2 (2.7) 8.2 3 
10ng/mL Everolimus 239.1 (18.4) 188.7* 3 NG - 3 90.0 (1.7) 6.7 3 
20ng/mL Everolimus 244.8 (29.2) 195.7* 3 NG - 3 90.3 (2.4) 7.1 3 
40ng/mL Everolimus NG - 3 NG - 3 92.2 (2.4) 9.4 3 
2.5ng/mL Temsirolimus 85.1 (2.2) 2.8 3 84.4 (1.5) 4.3 3 91.3 (1.8) 8.3 3 
5ng/mL Temsirolimus 84.2 (1.4) 1.7 3 85.2 (1.5) 5.3 3 91.2 (1.4) 8.2 3 
10ng/mL Temsirolimus 84.9 (1.3) 2.5 3 103.4 (3.2) 27.8* 3 92.1 (1.5) 9.2 3 
20ng/mL Temsirolimus 103.0 (2.5) 24.4* 3 233.6 (13.1) 188.8* 3 91.2 (2.6) 8.2 3 
40ng/mL Temsirolimus 241.3 (14.7) 191.4* 3 NG - 3 92.4 (1.9) 9.6 3 
* = p < 0.05, compared to 2.5% DMSO, Welch's (unequal variance) t-test. 
NG = DT could not be calculated due to no growth, indicated by OD ≤ 0.3 after 20 hours. 
 
 
 
 
 
 
 Supplemental Table 2.  Doubling time (DT) (minutes/cell division), standard error of the mean (SEM), percent change, and 
number of cultures tested (n) for catalytic mTOR inhibitors. 
  WT tor1Δ fpr1Δ 
Treatment DT (SEM) % change n DT (SEM) % change n DT (SEM) % change n 
2.5% DMSO 85.9 (0.9) - 12 84.6 (1.0) - 13 96.9 (1.1) - 12 
10µM AZD8055 92.4 (8.4) 7.5 4 93.0 (7.5) 9.9 4 104.4 (10.8) 7.7 4 
25µM AZD8055 86.2 (1.4) 0.3 3 87.3 (2.2) 3.1 3 95.7 (1.1) -1.2 3 
50µM AZD8055 87.6 (2.6) 1.9 3 89.5 (2.6) 5.8 3 96.9 (3.2) 0.0 3 
100µM AZD8055 87.7 (3.3) 2.1 3 89.6 (2.3) 5.9 3 98.6 (2.8) 1.8 3 
10µM GDC-0941 85.7 (2.0) -0.3 3 84.2 (2.1) -0.5 3 95.9 (2.4) -1.0 3 
25µM GDC-0941 84.6 (1.6) -1.6 3 82.5 (1.0) -2.6 3 95.2 (1.2) -1.7 3 
50µM GDC-0941 86.8 (2.8) 1.0 3 85.8 (2.1) 1.4 3 96.8 (2.9) -0.1 3 
100µM GDC-0941 94.3 (3.3) 9.7 3 93.1 (2.6) 10.0 3 102.5 (4.4) 5.8 3 
10µM GSK2126458 85.4 (1.5) -0.6 6 92.5 (1.6) 9.3* 6 97.8 (2.9) 1.0 6 
25µM GSK2126458 89.9 (2.5) 4.6 7 105.7 (2.6) 24.9* 7 96.7 (2.3) -0.1 7 
50µM GSK2126458 80.6 (6.3) -6.2 6 93.2 (4.1) 10.1 6 95.6 (1.6) -1.3 6 
100µM GSK2126458 103.8 (5.4) 20.8* 6 117.3 (6.8) 38.6* 6 112.4 (5.3) 16.0* 6 
1µM Torin 1 88.1 (2.0) 2.6 4 88.7 (2.1) 4.9 4 96.2 (2.0) -0.7 4 
10µM Torin 1 122.7 (5.0) 42.8* 5 157.5 (8.6) 86.2* 5 125.5 (8.3) 29.6* 5 
25µM Torin 1 179.9 (11.2) 109.4* 5 165.1 (19.1) 95.1* 5 158.4 (5.6) 63.5* 5 
*= p < 0.05, compared to 2.5% DMSO, Welch's (unequal variance) t-test.           
Chapter 3: Pterocarpus marsupium extract extends replicative lifespan in budding yeast 
 
This chapter is lightly modified from Lee, et al. Published in Geroscience 2021 Oct; 43(5):2595-2609. doi: 
10.1007/s11357-021-00418-x   
 Abstract 
 
As the molecular mechanisms of biological aging become better understood, there is growing 
interest in identifying interventions that target those mechanisms to promote extended health and longevity.  
The budding yeast Saccharomyces cerevisiae has served as a premier model organism for identifying 
genetic and molecular factors that modulate cellular aging and is a powerful system in which to evaluate 
candidate longevity interventions.  Here we screened a collection of natural products and natural product 
mixtures for effects on growth rate, mTOR-mediated growth inhibition, and replicative lifespan.  No mTOR 
inhibitory activity was detected, but several of the treatments affected growth rate and lifespan.  The 
strongest lifespan shortening effects were observed for green tea extract and berberine.  The most robust 
lifespan extension was detected from an extract of Pterocarpus marsupium and another mixture containing 
Pterocarpus marsupium extract.  These findings illustrate the utility of the yeast system for longevity 
intervention discovery and identify Pterocarpus marsupium extract as a potentially fruitful longevity 
intervention for testing in higher eukaryotes.        
  
Introduction 
 
The field of geroscience has yielded important mechanistic insights into biological aging processes 
and their connection to disease [130], with invertebrate model organisms at the forefront of these 
discoveries [131].  Several evolutionarily conserved molecular “hallmarks” or “pillars” of aging appear to 
play a causal role in the cellular, tissue, and functional declines that accompany old age [132, 133].  As our 
understanding of aging biology evolves, a growing emphasis is now being placed on identifying 
interventions that target these hallmarks of aging.  Such interventions have the potential to delay the onset 
and progression of multiple age-related diseases simultaneously, with the goal of significantly extending 
both lifespan and healthspan.   
 
The budding yeast Saccharomyces cerevisiae has been used extensively to characterize genetic 
and molecular mechanisms of cellular aging [54, 55].  Replicative lifespan (RLS) in yeast is defined as the 
number of daughter cells produced by a mother cell prior to irreversible cell cycle arrest [134].  Numerous 
studies over the past two decades indicate that many of the genetic and environmental determinants of 
aging in multicellular eukaryotes are shared with yeast during replicative aging.  For example, studies in 
budding yeast contributed substantially to our understanding of the mechanisms by which caloric restriction 
[135, 136], sirtuins [137, 138], and mechanistic target of rapamycin (mTOR) [19, 56] signaling impact aging, 
and how conserved molecular processes including mitochondrial function [111, 139-143], pH homeostasis 
[144-148], proteasome activity [149, 150], autophagy [151, 152], and genome instability [153, 154] all play 
important roles during yeast replicative aging.   
 
Identifying pharmacological interventions that directly target the molecular mechanisms of 
biological aging and thereby increase healthspan and lifespan is a rapidly growing area of research [155].  
The nematode Caenorhabditis elegans is used effectively in this regard, as evidenced by the fact that more 
than 50% of the experiments contained in the DrugAge database are performed in this organism [156, 157].  
Budding yeast is the fourth most utilized model system (3.5%) in DrugAge, behind both fruit flies (26.2%) 
and mice (6%).  While this frequency does not specify magnitude or direction of effect on lifespan, it does 
provide an indication of the relative use of each model system for intervention studies within the field.  
Despite the fact that lifespan studies in yeast are less expensive and less time consuming than studies in 
mice, or even fruit flies, the utility of this system as a discovery platform for pro-longevity interventions has 
lagged. 
 
We recently described a system for identifying candidate pro-longevity compounds in yeast through 
analysis of high-resolution growth kinetics [158].  This system specifically detects compounds with either 
direct or indirect mTOR-inhibitory effects by quantifying differential effects on growth rate in different genetic 
backgrounds.  This system also successfully discriminates between allosteric rapamycin-like inhibitors and 
ATP-competitive catalytic inhibitors.  From a small unbiased natural product screen [158], we identified 
caffeine as an mTOR inhibitor.  This may explain its lifespan-extending properties in yeast [116] and worms 
[118, 119].  Here we describe lifespan and outgrowth analyses of additional natural products from a 
collection of natural product compounds, extracts, and mixtures.  While no new mTOR-inhibitory 
compounds were identified, we did observe significant lifespan extension from an extract of Pterocarpus 
marsupium, a tree found in southeast Asia with a long history of use in Ayurvedic medicine. 
 
Results 
 
A screen of 42 natural product compounds, extracts, (Table 1) and mixtures (Table 2) was 
conducted to understand their impact on growth, mTOR inhibition, and lifespan in yeast.  Several of the 
tested compounds or mixture components were previously reported to impact lifespan in different 
organisms (Table 1).  All compounds and mixtures were initially tested at a concentration of 100 ug/mL in 
rich growth medium (YPD) (see Materials and Methods).  The results of the growth analyses are 
summarized in Table 3 with outgrowth curves and doubling time analysis provided in Supplemental Figure 
1.  None of the compounds or mixtures tested showed evidence for particular mTOR inhibitory activity in 
the initial screen, which is determined by greater growth inhibition in a tor1D strain relative to wild type and 
fpr1D [158].  Several compounds, extracts, and mixtures significantly inhibited growth in at least one strain, 
including grape seed (Indena), lutein, mixture 2, mixture 6, Polygonum cuspidatum root, Pterocarpus 
marsupium, turmeric, and withaferin A (Figure 1).  While not every strain reached statistically significant 
growth inhibition with these treatments, all strains showed a trend toward increased doubling time. Two 
compounds, ellipticine and isoliquiritigenin, had particularly pronounced effects on growth at 100 ug/mL.  
All of the cultures treated with ellipticine failed to achieve OD of 0.3 after 20 hours of growth and therefore 
fell below our threshold for doubling time quantitation (see Methods).  For isoliquiritigenin, all strains were 
also severely growth inhibited at this concentration, with most cultures falling below our  
 
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threshold for quantitative doubling time calculation and those that did grow to OD > 0.3 barely achieving 
this threshold, indicating severe inhibition (Figure 1).  To better understand the growth inhibitory effect of 
isoliquiritigenin, we performed a follow-up dose response (Table 4).  Gradual growth inhibition was 
observed with increasing concentration of isoliquiritigenin from 1 to 50 µg/mL without evidence for mTOR-
inhibitory effects (Supplemental Figure 1).  
 
Twenty-four compounds and mixtures were selected from our initial screen and a prior study [158] 
for RLS analysis in wild type cells (Supplemental Figure 1 and Table 5).  Across all of the compounds 
and mixtures tested, no significant correlation was observed between doubling time and lifespan (Figure 
2A, Standard major axis regression; adjusted R-squared = 0.06, p-value = 0.24).  Most substances had no 
significant effect on lifespan, including alpha-lipoic acid, glucosamine, quercetin, and Polygonum 
cuspidatum root extract, which is 50% resveratrol (w/w) (Supplemental Figure 1).  Significant shortening 
of lifespan was observed for green tea extract (p-value < 0.001, Wilcoxon rank sum test, Bonferroni-
corrected) (Figure 2B).  Turmeric extract (raw p-value = 0.009, Bonferroni-corrected p-value = 0.208, 
Wilcoxon rank sum test) and mixture 5 (raw p-value = 0.042, Bonferroni-corrected p-value = 0.997, 
Wilcoxon rank sum test) significantly shortened lifespan when p-values were uncorrected, but these did not 
reach significance after Bonferroni-correction (Supplemental Figure 1).  Mixture 5 is 13.9% w/w turmeric 
and one of only two mixtures tested that contains turmeric (the other, mixture 4, contains only 3.4% turmeric 
w/w) (Table 2). 
 
Berberine, in particular, has been described as a potential geroprotective compound with mTOR 
inhibitory properties [159].  In a previous growth screen, we failed to detect an mTOR inhibitory effect in 
cells treated with 100 ug/mL berberine [158].  However, the severe growth inhibition at 100 ug/mL 
Figure 1. Doubling times for compounds, extracts, and mixtures that significantly inhibit growth. 
Inflection doubling time (DT) of WT, tor1Δ and fpr1Δ microcultures. Number of replicates n = 3 for panels 
A, B, D-F, H and I. Number of replicates n = 6 for panels C, G, and J. Welch’s T-test with Bonferroni-
correction used to compare treatment (Tx) to vehicle (Ctrl) (* p<0.05, ** p<0.01, *** p<0.001). NG denotes 
no growth (see methods for more details). 
suggested that this concentration is above the toxic threshold for this compound.  We therefore performed 
a dose-response RLS experiment and follow-up mTOR activity screen for concentrations of berberine 
ranging from 1-50 ug/mL (Figure 2C-D and Tables 4 and 6).  We also performed dose response mTOR 
activity and RLS experiments for 5-100 µM celastrol, an activator of the heat shock response and putative 
healthspan intervention [160-162].  We resolved no significant RLS effects with any celastrol dose, but 
concentrations ≥10 µM weakly inhibited growth in all strains (Tables 4 and 6 and Supplemental Figures 
1 and 2).  The significant growth inhibitory effect of berberine seen at 100 ug/mL was also observed in all 
strains at 50 ug/mL (p-value ≤ 0.01, Welch’s t-test, Bonferroni-corrected) with less pronounced effects on 
growth at lower concentrations (Figure 2C and Table 4).  RLS was significantly shortened at 50 ug/mL 
berberine (p-value < 0.001, Wilcoxon rank sum test), but no difference was detected at either 10 ug/mL or 
1 ug/mL compared to YPD control (berberine suspended in sterile H2O instead of DMSO for RLS) (Figure 
2D and Table 6).   
 Figure 2.  Doubling time and lifespan of natural product treated yeast with outgrowth kinetics and 
Kaplan-Meier curves for lifespan decreasing treatments.  A) Relationship between doubling time 
(minutes/cell division) and median RLS for natural product-treated yeast (Standard major axis 
regression, adjusted R-squared = 0.06, p-value = 0.24).  B) Green tea extract (GTE) decreases RLS 
(Wilcoxon rank sum test, Bonferroni-corrected (* p<0.05, ** p<0.01, *** p<0.001)).  C) Berberine is a 
general inhibitor of yeast growth (Welch’s T-test, Bonferroni-corrected).  D) Berberine RLS dose 
response (Wilcoxon rank sum test, Bonferroni-corrected). For number of cells or cultures tested and p-
values, see Tables 5 and 6. 
 
Only one treatment, Pterocarpus marsupium extract (PME), significantly extended lifespan (p-value 
< 0.001, Wilcoxon rank sum test, Bonferroni-corrected) (Table 5 and Figure 3).  Mixture 1 significantly 
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extended lifespan when p-values were uncorrected but did not reach significance after Bonferroni-
correction (raw p-value = 0.009, Bonferroni-corrected p-value = 0.219, Wilcoxon rank sum test) (Table 5 
and Supplemental Figure 1).  Interestingly, mixture 1 is the only mixture to contain PME as its primary 
constituent, along with lesser amounts of alpha-lipoic acid, quercetin, and olive fruit extract (Table 2). This 
suggests that the lifespan extension from mixture 1 may be related to the presence of PME in the mixture.   
 
Figure 3.  Pterocarpus marsupium extract (PME) extends replicative lifespan.  100 µg/mL PME (n 
= 104) extends lifespan compared to 1% DMSO vehicle control (n = 198) (p < 0.001, Wilcoxon rank sum 
test, Bonferroni-corrected). 
 
Discussion 
 
From a screen of 42 natural products and natural product mixtures, we identified PME as a novel 
pro-longevity intervention in yeast.  PME is an extract from the plant Pterocarpus marsupium with well-
known antidiabetic properties [163-165].  PME contains several components, presumably one or more of 
which contributes to its positive effects on RLS.  Common components of PME include pterostilbene, 
epicatechin, pterosupin, marsupin, liquiritigenin, along with a variety of other molecules present in lower 
abundance [166-168].   
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 Two of the most abundant PME constituents, pterostilbene and epicatechin, are suggested to 
promote longevity [169, 170], although we could find no published data supporting lifespan extension from 
pterostilbene.  Epicatechin, on the other hand, has been reported to increase lifespan in fruit flies [171]; 
however, other studies failed to detect significant lifespan extension from epicatechin in worms or flies [172-
174].  It will be of interest in future studies to determine which of the various components of PME contribute 
to the RLS extension observed here, as well as their mechanisms of action.  
 
It is unexpected that none of the compounds or mixtures tested showed consistent evidence for 
mTOR inhibitory activity, as several have previously been reported to inhibit mTOR signaling.  These 
include both resveratrol [122, 175] and berberine [176-178].  While the Bioscreen C MBR assay is extremely 
sensitive, there are several potential explanations for the lack of mTOR inhibition observed from the 
compounds and mixtures in this study.  For example, some pharmaceutical compounds that act as ATP-
competitive mTOR inhibitors are less effective in yeast relative to mammalian cells [158, 179].  Likewise, 
compounds that affect mTOR signaling indirectly in mammalian cells, via upstream regulators of mTOR or 
through other components of the mTOR signaling network such as AMP kinase, may not have identical 
effects in yeast.  For example, the tuberous sclerosis complex proteins TSC1 and TSC2 act as key 
upstream regulators of mTOR in mammals but do not have clear orthologs in either S. cerevisiae or C. 
elegans [180-182]. Thus, compounds affecting mTOR activity via interactions with TSC1/2 in mammalian 
cells would not be predicted to have these effects in budding yeast.   
 
Some compounds previously reported to extend lifespan in various model systems did not show 
lifespan extending effects in our screen and, in some cases, shortened lifespan.  There are many potential 
explanations for this, including the possibility that lifespan extension would have been observed at 
concentrations other than those tested here.  Resveratrol was initially reported to extend RLS in yeast by 
more than 70% in another strain background [183].  However, we failed to observe any effect on lifespan 
in the BY4742 strain background with a Polygonum cuspidatum root extract containing 50% w/w resveratrol, 
which is consistent with prior data using a >95% pure resveratrol preparation [125].  Importantly, while 
resveratrol has continued to be widely studied for a variety of health effects in both mice and people, it does 
not appear to extend mouse lifespan [184].  Interestingly, in our mTOR inhibitor screen, Polygonum 
cuspidatum root extract behaved as a general growth inhibitor.  A previous study by our group showed no 
effect of pure resveratrol on yeast growth [158], suggesting that the growth inhibitory effects of Polygonum 
cuspidatum root extract are independent of, or at least not solely mediated by, resveratrol.  
 
Berberine was recently reported to attenuate senescence in fibroblasts, as well as extend lifespan 
in middle-aged mice and in yeast [185].  However, using similar doses of berberine, we failed to resolve a 
lifespan extending effect and instead observed significant growth inhibition and shortening of RLS.  A key 
difference between the two studies is RLS assay method; manual dissection of yeast on solid media was 
used in our study as opposed to a microfluidics-based approach where yeast are submerged in liquid media.  
While it could be argued that effects in human cells and mice are likely to be more translationally relevant, 
the strong dose-dependent toxicity of berberine seen in our study suggests that caution is warranted when 
considering its untested and unregulated use by people, especially over months or years.   
 
Green tea has long been touted as a health and longevity-promoting beverage [186-188].  In yeast, 
however, it shortens lifespan, at least at the concentrations tested here.  One reported mechanism for green 
tea’s health benefits is by altering the gut and oral microbiomes [189, 190], and S. cerevisiae and related 
fungal species are present in the human microbiome [191].  For interventions that improve human health 
by modifying the microbiome, it is not obvious what effects to expect on individual microbes from among 
the impacted community.  It could be that the set of interventions that negatively impact yeast lifespan are 
enriched for interventions that could improve human health by remodeling the microbiome.  More broadly, 
novel antifungal compounds identified as part of screening for lifespan-extending interventions may have 
clinical utility outside of geroscience. 
 
Using combined growth and lifespan analysis in yeast, we identified a lifespan-extending natural 
product, Pterocarpus marsupium extract. Our approach sets the foundation for future genetic and 
biochemical studies to characterize the mechanistic basis for lifespan extension from PME and provides 
incentive to test PME in invertebrate and mammalian model systems.  Overall, our study demonstrates the 
utility of using yeast as a rapid and inexpensive screening tool to identify interventions of interest for 
geroscience and medicine. 
 
Materials and Methods 
Yeast strains and culture conditions 
All yeast strains used were in the BY4742 genetic background (MATα his3Δ1 leu2Δ0 lys2Δ0 
ura3Δ0) and have been previously described [158] except for the celastrol RLS dose response which used 
the BY4741 genetic background (MATa his3Δ1 leu2Δ0 met15Δ0 ura3Δ0) [192].  For overnight culture and 
growth analysis, YPD (1% w/v BactoTM yeast extract (BD), 2% w/v BactoTM peptone (BD), 2% w/v dextrose) 
media was used.  Yeast were cultured at 30oC for all experiments.   
Outgrowth analysis 
 
All compounds and mixtures were initially screened at a concentration of 100 µg/mL in DMSO, 
except for hesperidin which was suspended in 0.027 N NaOH.  Analysis of maximal growth rate in yeast 
strains was performed to identify growth inhibiting interventions using a Bioscreen C MBR (Growth curves 
USA, Piscataway NJ, USA) as previously described [109, 158, 193, 194].  Raw optical density data were 
smoothed using the R package “smooth.spline”.  Doubling times were calculated using the inflection method 
- identifying the maximum semi-log slope along growth curves within the optical density range linearly 
correlated with number of yeast cells - with the online web tool Yeast Outgrowth Data Analyzer (YODA) 
[129].  All experiments were repeated with at least three biological replicates.  The number of biological 
replicates is indicated in each figure caption.  Doubling time was calculated for all cultures where OD ≥ 0.3 
after 20 hours growth.  Otherwise, culture was listed as no growth (NG).  In one case (isoliquirtiginin), 
growth was below this threshold in most cultures and near the threshold in the remaining cultures and is 
also reported as NG.  Interventions were classified as mTOR-inhibitors based on preferential growth 
inhibition in the tor1Δ strain, relative to WT and fpr1Δ strains, as previously described [195].  Welch’s 
(unequal variance) t-test with Bonferroni multiple testing correction was used to assess differences relative 
to experiment-matched vehicle control cultures using R.  We applied multiple testing correction using the 
p.adjust function.   
Replicative lifespan analysis 
 
A modified RLS protocol for treatments was developed based on previously described methods 
[196, 197].  Briefly, cells were grown on freshly prepared YPD plates at 30°C until single colonies were 
visible.  Cells were selected from a single colony and lightly patched onto YPD plates supplemented with 
the designated treatment or vehicle overnight.  In the morning, founder cells were aligned and selected as 
newborn daughter cells using a micromanipulator.  Cells were monitored for cell divisions every 90-120 
minutes, and subsequent budded daughter cells were separated and removed as they formed.  The process 
continued until cells stopped dividing.  Replicative lifespan was calculated as the number of times each 
mother cell divided before it underwent permanent cell cycle arrest.  Plates were kept in the refrigerator at 
4°C overnight.  Plates were kept wrapped in tinfoil except while being dissected to avoid potential light 
sensitivity of any compounds.  All experimenters were blinded to the identity of any of the treatments at the 
time of dissection.  Cells were dissected in groups of 20 and 39-198 cells dissected for each intervention 
tested.  Linear model comparing doubling time and median RLS of treated yeast constructed using the 
standard major axis regression (SMA) option in the lmodel2 package in R. 
Intervention preparation and suppliers  
 
All compounds and mixtures were suspended in DMSO, except hesperidin which was suspended 
in 0.027 N NaOH.  Berberine was purchased from Sigma-Aldrich (St. Louis MO, USA).  Celastrol was 
purchased from Cayman Chemical (Ann Arbor MI, USA).  All other compounds and mixtures were provided 
by USANA Health Sciences, Inc. (Salt Lake City UT, USA).  The composition of the mixtures tested is 
provided in Table 2. 
  
Tables 
Table 1.  Natural product compounds and extracts used in this study along with reported lifespan 
extension.  For compound source information, see methods. 
Compound/extract Lifespan extension 
Allantoin Worms [198] 
Alpha-lipoic acid Worms [199, 200], flies [201] 
Ashwagandha root Worms [202] 
Berberine Mice [185], flies [203] 
Broccoli concentrate - 
Cardamonin - 
Celastrol Worms [204] 
Choline bitartrate - 
Coumaric Acid - 
Curcumin phytosome complex - 
Dong Quai - 
Ellipticine  
Ginseng - 
Ginsenoside Rc Worms [205] 
Glucosamine Mice and worms [206] 
Grape seed #1 - 
Grape seed #2 - 
Green tea Worms [207], flies [208, 209], mice – midlife effect in females [210] 
Hesperidin Yeast [211] 
Hydroxytyrosol - 
Isoliquiritigenin - 
Licorice root Worms [212] 
Lutein Flies [213] 
Lycopene - 
Milk thistle Worms [214] 
N-acetyl L-cysteine Worms [215], flies [216, 217], mice [218] 
Olive fruit - 
Polygonum cuspidatum root - 
Pterocarpus marsupium - 
Quercetin Worms [219, 220], flies [172] 
Rutin Flies [221], mice [222] 
Turmeric Flies [223] 
Tyrosol Worms [224] 
Umbelliferone - 
Verbascoside - 
Withaferin A Mice [225], flies [226] 
 
  
Table 2.  Natural product mixtures used in this study.  Composition of mixtures are shown by weight. 
All mixtures provided by USANA Health Science Inc. 
Mixture Description 
1 40.4% Pterocarpus marsupium extract, 34.6% alpha lipoic acid, 23.1% quercetin, 1.9% 
olive fruit extract 
2 22.7% alpha lipoic acid, 19.2% hesperdin, 14.1% resveratrol, 12.1% curcumin phytosome 
complex, 11.7% green tea extract, 10.6% quercetin, 7.1% rutin, 2.5% olive fruit extract    
3 87.8% choline bitartrate, 4.5% coenzyme Q10, 4.2% lutein, 3.5% lycopene 
4 30.1 % choline bitartrate, 19.0% milk thistle extract, 17.8% N-acetyl L-cysteine, 16.8% 
alpha lipoic acid, 5.7% broccoli concentrate, 3.6% green tea extract, 3.4% turmeric extract, 
1.9% biotine, 1.7% olive fruit extract 
5 86.1% glucosamine, 13.9% turmeric extract 
6 60% grape seed extract #1, 40% grape seed extract #2 
 
 Table 3.  Growth and mTOR inhibitor screen of natural product compounds, extracts, and mixtures.  Doubling time (DT) 
(minutes/cell division), standard error of the mean (SEM), percent change compared to experiment-matched vehicle control, and 
number of biological replicate cultures tested (n) for wild type (WT), tor1Δ, and fpr1Δ cells.  Interventions tested at 100 µg/mL. 
 
WT tor1Δ fpr1Δ 
Treatment DT (SEM) 
% 
change n DT (SEM) 
% 
change n DT (SEM) 
% 
change n 
1% DMSO 79.5 (1.0) - 29 79.6 (0.8) - 29 89.8 (0.7) - 29 
2% DMSO 85.4 (1.6) - 6 84.2 (2.2) - 6 92.3 (3.1) - 6 
0.27 N NaOH 74.5 (0.04) - 3 77.4 (0.2) - 3 85.8 (1.7) - 3 
Compounds and extracts          
Allantoin 81.7 (1.2) -2.0 6 80.0 (1.0) -1.1 6 87.7 (2.2) -3.1 6 
Ashwagandha root 82.5 (1.1) -0.9 3 80.4 (1.1) 0.6 3 87.9 (1.6) -2.4 3 
Cardamonin† 78.7 (2.0) -7.9 6 84.4 (1.1) 0.3 6 96.4 (1.1) 4.4 6 
Choline bitartrate 82.0 (2.5) 14.8 3 81.2 (1.8) 1.6 3 89.4 (1.6) -1.9 3 
Coumaric acid 82.2 (1.3) -1.4 6 81.0 (1.3) 0.1 6 89.3 (2.5) -1.4 6 
Curcumin phytosome complex 75.8 (0.9) 2.0 3 71.0 (1.5) -2.6 3 89.8 (0.7) 6.0 3 
Dong Quai 79.9 (1.6) -4.2 6 81.0 (1.2) 0.1 6 87.7 (2.3) -3.2 6 
Ellipticine NG** - 3 NG** - 3 NG** - 3 
Ginseng 83.4 (2.6) 0.04 6 81.9 (1.7) 1.2 6 89.3 (3.0) -1.4 6 
Ginsenoside Rc 83.4 (3.7) 0.2 3 80.1 (2.3) 0.2 3 90.5 (2.4) 0.5 3 
Grape seed #1 88.4 (1.8) 18.9* 3 91.6 (2.8) 25.6* 3 102.4 (2.1) 20.8* 3 
Grape seed #2 99.0 (5.6) 38.5 3 77.3 (2.6) -3.3 3 88.2 (2.0) -3.2 3 
Green tea 91.6 (0.2) 11.9 3 92.9 (1.8) 13.5 3 103.3 (1.6) 13.2 3 
Hesperidin# 76.5 (0.9) 2.7 3 76.6 (0.2) -1.0 3 80.7 (1.2) -6.0 3 
Hydroxytyrosol 83.3 (2.3) -0.1 6 82.5 (1.8) 2.0 6 90.9 (2.7) 0.4 6 
Isoliquiritigenin†† 169.9 (26.2) 103.7 2 NG** - 6 NG** - 6 
Licorice root 83.8 (2.2) 1.4 6 83.6 (2.9) 2.2 6 89.9 (3.0) -1.4 6 
Lutein 90.2 (0.6) 21.3* 3 90.4 (1.0) 23.9* 3 97.1 (1.8) 14.5 3 
Milk thistle 85.4 (1.5) 3.3 6 85.8 (2.9) 4.8 6 94.7 (3.0) 3.9 6 
N-acetyl L-cysteine 82.0 (1.2) -0.9 6 81.0 (1.3) -1.0 6 89.0 (2.3) -2.3 6 
Olive fruit 84.6 (1.4) 2.3 6 84.4 (2.0) 3.1 6 92.1 (3.1) 1.1 6 
Polygonum cuspidatum root 102.6 (2.8) 24.0*  6 99.6 (1.4) 21.7* 6 106.6 (4.1) 17.0 6 
Pterocarpus marsupium 89.1 (4.6) 14.5 3 93.9 (3.2) 22.6* 3 103.3 (1.9) 11.6* 3 
Rutin 85.6 (1.2) 3.5 6 83.7 (1.5) 2.3 6 92.8 (2.8) 1.8 6 
Turmeric 111.5 (4.6) 36.1* 3 110 (13.2) 34.9 3 107.7 (1.2) 18.0* 3 
Tyrosol 82.0 (2.0) -1.5 3 79.5 (0.3) -0.5 3 88.3 (0.8) -1.9 3 
Umbelliferone 86.5 (1.3) 3.7 6 83.7 (0.5) 3.5 6 91.9 (2.8) 1.5 6 
Verbascoside 84.2 (2.2) 1.2 3 82.6 (1.1) 3.3 3 91.2 (3.7) 1.3 3 
Withaferin A 106.6 (4.4) 27.9* 6 110.9 (4.3) 37.0* 6 102.6 (3.4) 13.3 6 
Mixtures          
1 95.1 (4.8) 22.2 3 103.9 (8.7) 35.6 3 110.2 (13.4) 19.0 3 
2 93.3 (1.7) 25.4* 3 94.3 (1.9) 29.3* 3 104 (5.5) 22.7 3 
3 88.4 (0.7) 23.8 3 88.2 (1.2) 10.4 3 94.2 (0.8) 3.3 3 
4 77.9 (7.5) 9.0 3 91.2 (2.9) 14.1 3 83.1 (4.1) -8.9 3 
5 81.8 (1.7) 10.0 3 76.5 (1.7) 4.9 3 81.3 (1.0) -4.1 3 
6 94.6 (1.9) 32.4* 3 93.5 (5.4) 17.0 3 105.8 (8.5) 16.0 3 
* = p < 0.05, compared to vehicle, Welch's (unequal variance) t-test 
** = Doubling time not calculated due to minimal growth over the time-course of the experiment.  See Methods. 
# = 0.27N NaOH vehicle 
† = 2% DMSO vehicle 
†† = Doubling time for four of six tested WT cultures could not be calculated. 
  
       
 
 
 
 
 
 
 
 
Table 4.  Dose response for selected natural product compounds.  Doubling time (DT) (minutes/cell division), standard 
error of the mean (SEM), percent change, and number of biological replicate cultures tested (n) for wild type (WT), tor1Δ, and 
fpr1Δ cells. 
 
WT tor1Δ fpr1Δ 
Treatment DT (SEM) 
% 
change n DT (SEM) 
% 
change n DT (SEM) 
% 
change n 
1% DMSO 80.0 (2.8) - 3 81.1 (1.8) - 3 85.9 (1.3) - 3 
Berberine 1 µg/mL 81.2 (1.2) 1.5 3 85.2 (0.4) 5.1 3 89.5 (0.3) 4.2 3 
Berberine 10 µg/mL 93.7 (1.4) 17.0 3 88.3 (3.2) 9.0 3 98.1 (4.8) 14.2 3 
Berberine 50 µg/mL 146.3 (4.2) 82.8* 3 135.7 (1.0) 67.4* 3 143.1 (0.9) 66.7* 3 
1% DMSO 83.1 (1.6) - 3 82.3 (0.7) - 3 92.0 (1.4) - 3 
Celastrol 5 µM 86.0 (0.8) 3.4 3 85.8 (1.1) 4.3 3 92.4 (0.7) 0.4 3 
Celastrol 10 µM 92.7 (2.1) 11.5 3 92.4 (1.2) 12.3* 3 102.0 (0.8) 10.9* 3 
Celastrol 100 µM 94.2 (2.8) 13.4 3 92.5 (4.2) 12.4 3 100.8 (2.4) 9.5 3 
1% DMSO 81.6 (0.8) - 3 79.1 (1.4) - 3 90.7 (1.6) - 3 
Isoliquiritigenin 1 µg/mL 79.3 (1.2) -2.8 3 77.7 (1.2) -1.8 3 87.1 (1.1) -4.0 3 
Isoliquiritigenin 10 µg/mL 82.4 (2.1) 1.0 3 81.3 (1.1) 2.8 3 92.4 (2.5) 2.0 3 
Isoliquiritigenin 50 µg/mL 98.7 (2.9) 21.0 3 99.0 (1.7) 25.2* 3 113.6 (2.5) 25.3* 3 
*= p < 0.05, compared to 1% DMSO, Welch's (unequal variance) t-test 
 Table 5.  Median RLS (95% confidence intervals), number of cells dissected (n), percent 
change, and p-value (Wilcoxon rank sum test, Bonferroni-corrected p-value for multiple 
testing and uncorrected p-value in parentheses) of natural product compounds, extracts, 
and mixtures compared to vehicle (1% DMSO) control. All interventions tested at 100 µg/mL. 
Treatment n Median RLS 
(95% CI) 
% change Bonferroni-corrected 
p-value (uncorrected) 
1% DMSO (vehicle control) 198 21 (20-24) 0.0 - 
Compounds and extracts     
Alpha-lipoic acid 40 21 (19-26) 0.0 1 (0.859) 
Broccoli concentrate 80 19.5 (16-24) -7.2 1 (0.342) 
Choline bitartrate 160 21 (19-23) 0.0 1 (0.820) 
Curcumin phytosome complex 40 19 (17-24) -9.5 1 (0.480) 
Glucosamine HCl 116 21 (19-25) 0.0 1 (0.815) 
Grape seed #1 118 19.5 (18-24) -7.2 1 (0.913) 
Grape seed #2 80 20.5 (19-24) -2.4 1 (0.945) 
Green tea 160 12 (11-14) -42.9 < 0.001 (< 0.001) 
Lutein 39 18 (16-28) -14.3 1 (0.984) 
Lycopene 118 19 (17-22) -9.5 1 (0.096) 
Milk thistle 40 19 (15-26) -9.5 1 (0.540) 
N-actetyl L-cysteine 39 20 (17-27) -4.8 1 (0.664) 
Olive fruit 80 20.5 (19-26) -2.4 1 (0.722) 
Polygonum cuspidatum root 40 16 (13-22) -23.8 1 (0.073) 
Pterocarpus marsupium 104 28 (25-31) 33.3 0.001 (< 0.001) 
Quercetin 40 18.5 (16-27) -11.9 1 (0.836) 
Rutin 40 18.5 (15-26) -11.9 1 (0.309) 
Turmeric 40 16.5 (14-22) -21.4 0.208 (0.009) 
Mixtures     
1 80 25 (22-27) 19.0 0.219 (0.009) 
2 40 18 (14-21) -14.3 1 (0.056) 
3 80 21 (18-23) 0.0 1 (0.609) 
4 40 22.5 (20-26) 7.1 1 (0.883) 
5 40 15.5 (13-21) -26.2 0.997 (0.042) 
6 40 20 (16-24) -4.8 1 (0.377) 
 
 
 
 
 
 
 
 
 
 
Table 6.  Median RLS (95% confidence intervals), number of cells dissected (n), percent 
change, and p-value (Wilcoxon rank sum test, Bonferroni-corrected for multiple testing 
within each dose response) of natural products compared to vehicle control. Note: separate 
control used for each compound. Berberine suspended in H2O and thus compared to YPD. 
Treatment Concentration n Median RLS 
(95% CI) 
% change Bonferroni-corrected p-
value 
YPD NA 160 21.5 (20-23) - - 
Berberine  1 µg/mL 155 22 (21-25) 2.3 0.173 
Berberine 10 µg/mL 160 21 (20-23) -2.3 1 
Berberine 50 µg/mL 157 10 (9-11) -53.5 < 0.001 
      
DMSO 1% 60 23.5 (21-30) - - 
Celastrol 5 µM 60 24.5 (22-29) 4.3 1 
Celastrol 10 µM 60 25 (23-29) 6.4 1 
Celastrol 100 µM 60 23 (18-28) -2.1 0.841 
 
 
  
 Supplemental Figures 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
WT 
tor1Δ 
fpr1Δ 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Figure S1-1. Representative growth curves, doubling times, and replicative lifespan for 
mixture 1. Representative growth kinetics of WT, tor1Δ, and fpr1Δ microcultures at A) 1% 
DMSO and B) 100 µg/mL mixture 1. C) Inflection doubling time (DT) of WT, tor1Δ, and fpr1Δ 
microcultures. Mixture 1 does not significantly increase DT (control n = 3, treated n = 3). 
Treatment compared to vehicle control using Welch’s T-test; * p<0.05, ** p<0.01, *** p<0.001. 
D) Mixture 1 replicative lifespan (RLS) survival curve. For number of cells tested in RLS and 
Wilcoxon rank sum p-values, see Table 4. 
 
 
 
 
 
 
 
 
 
 
 
 
 
WT 
tor1Δ 
fpr1Δ 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
** ** 
 
 
 
 
 
 
 
 
 
 
Figure S1-2. Representative growth curves, doubling times, and replicative lifespan for 
mixture 2. Representative growth kinetics of WT, tor1Δ, and fpr1Δ microcultures at A) 1% 
DMSO and B) 100 µg/mL mixture 2. C) Inflection doubling time (DT) of WT, tor1Δ, and fpr1Δ 
microcultures. Mixture 2 significantly increases WT and tor1Δ DT (control n = 3, treated n = 3). 
Treatment compared to vehicle control using Welch’s T-test; * p<0.05, ** p<0.01, *** p<0.001. 
D) Mixture 2 replicative lifespan (RLS) survival curve. For number of cells tested in RLS and 
Wilcoxon rank sum p-values, see Table 4. 
 
 
 
 
 
 
 
 
 
 
 
 
 
WT 
tor1Δ 
fpr1Δ 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Figure S1-3. Representative growth curves, doubling times, and replicative lifespan for 
mixture 3. Representative growth kinetics of WT, tor1Δ, and fpr1Δ microcultures at A) 1% 
DMSO and B) 100 µg/mL mixture 3. C) Inflection doubling time (DT) of WT, tor1Δ, and fpr1Δ 
microcultures. Mixture 3 does not significantly increase DT (control n = 3, treated n = 3). 
Treatment compared to vehicle control using Welch’s T-test; * p<0.05, ** p<0.01, *** p<0.001. 
D) Mixture 3 replicative lifespan (RLS) survival curve. For number of cells tested in RLS and 
Wilcoxon rank sum p-values, see Table 4. 
 
 
 
 
 
 
 
 
 
 
 
 
 
WT 
tor1Δ 
fpr1Δ 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Figure S1-4. Representative growth curves, doubling times, and replicative lifespan for 
choline bitartrate. Representative growth kinetics of WT, tor1Δ, and fpr1Δ microcultures at A) 
1% DMSO and B) 100 µg/mL choline bitartrate. C) Inflection doubling time (DT) of WT, tor1Δ, 
and fpr1Δ microcultures. Choline bitartrate does not significantly increase DT (control n = 3, 
treated n = 3). Treatment compared to vehicle control using Welch’s T-test; * p<0.05, ** 
p<0.01, *** p<0.001. D) Choline bitartrate replicative lifespan (RLS) survival curve. For 
number of cells tested in RLS and Wilcoxon rank sum p- values, see Table 4. 
 
 
 
 
 
 
 
 
 
 
 
 
 
WT 
tor1Δ 
fpr1Δ 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Figure S1-5. Representative growth curves, doubling times, and replicative lifespan for 
curcumin phytosome complex (CPC). 
Representative growth kinetics of WT, tor1Δ, and fpr1Δ microcultures at A) 1% DMSO and B) 
100 µg/mL curcumin phytosome complex. C) Inflection doubling time (DT) of WT, tor1Δ, and 
fpr1Δ microcultures. Curcumin phytosome complex does not significantly increase DT (control 
n = 3, treated n = 3).Treatment compared to vehicle control using Welch’s T-test; * p<0.05, ** 
p<0.01, *** p<0.001. D) Curcumin phytosome complex replicative lifespan (RLS) survival 
curve. For number of cells tested in RLS and Wilcoxon rank sum p-values, see Table 4. 
 
 
 
 
 
 
 
 
 
 
 
 
 
WT 
tor1Δ 
fpr1Δ 
 
 
 
 
 
 
 
 
 
 
 
 
 
* 
 
* * 
 
 
 
 
 
 
 
 
 
Figure S1-6. Representative growth curves, doubling times, and replicative lifespan for 
grape seed extract #1. Representative growth kinetics of WT, tor1Δ, and fpr1Δ microcultures 
at A) 1% DMSO and B) 100 µg/mL grape seed extract #1. C) Inflection doubling time (DT) of 
WT, tor1Δ, and fpr1Δ microcultures. Grape seed extract #1 significantly increases WT, tor1Δ 
and fpr1Δ DT (control n = 3, treated n = 3). Treatment compared to vehicle control using 
Welch’s T-test; * p<0.05, ** p<0.01, *** p<0.001. D) Grape seed extract #1 replicative lifespan 
(RLS) survival curve. For number of cells tested in RLS and Wilcoxon rank sum p-values, see 
Table 4. 
 
 
 
 
 
 
 
 
 
 
 
 
 
WT 
tor1Δ 
fpr1Δ 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Figure S1-7. Representative growth curves, doubling times, and replicative lifespan for 
grape seed extract #2. Representative growth kinetics of WT, tor1Δ, and fpr1Δ microcultures 
at A) 1% DMSO and B) 100 µg/mL grape seed extract #2. C) Inflection doubling time (DT) of 
WT, tor1Δ, and fpr1Δ microcultures. Grape seed extract #2 does not significantly increase DT 
(control n = 3, treated n = 3). Treatment compared to vehicle control using Welch’s T-test; * 
p<0.05, ** p<0.01, *** p<0.001. D) Grape seed extract #2 replicative lifespan (RLS) survival 
curve. For number of cells tested in RLS and Wilcoxon rank sum p-values, see Table 4. 
 
 
 
 
 
 
 
 
 
 
 
 
 
WT 
tor1Δ 
fpr1Δ 
 
 
 
 
 
 
 
 
 
 
 
 
 
*** 
 
 
 
 
 
 
 
 
 
 
 
Figure S1-8. Representative growth curves, doubling times, and replicative lifespan for 
green tea extract. Representative growth kinetics of WT, tor1Δ, and fpr1Δ microcultures at A) 
1% DMSO and B) 100 µg/mL green tea extract. C) Inflection doubling time (DT) of WT, tor1Δ, 
and fpr1Δ microcultures. Green tea extract does not significantly increase DT (control n = 3, 
treated n = 3). Treatment compared to vehicle control using Welch’s T-test; * p<0.05, ** 
p<0.01, *** p<0.001. D) Green tea extract replicative lifespan (RLS) survival curve. For 
number of cells tested in RLS and Wilcoxon rank sum p- values, see Table 4. 
 
 
 
 
 
 
 
 
 
 
 
 
 
WT 
tor1Δ 
fpr1Δ 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Figure S1-9. Representative growth curves, doubling times, and replicative lifespan for 
mixture 4. Representative growth kinetics of WT, tor1Δ, and fpr1Δ microcultures at A) 1% 
DMSO and B) 100 µg/mL mixture 4. C) Inflection doubling time (DT) of WT, tor1Δ, and fpr1Δ 
microcultures. Mixture 4 does not significantly increase DT (control n = 3, treated n = 3). 
Treatment compared to vehicle control using Welch’s T-test; * p<0.05, ** p<0.01, *** p<0.001. 
D) Mixture 4 replicative lifespan (RLS) survival curve. For number of cells tested in RLS and 
Wilcoxon rank sum p-values, see Table 4. 
 
 
 
 
 
 
 
 
 
 
 
 
 
WT 
tor1Δ 
fpr1Δ 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
* ** 
 
 
 
 
 
 
 
 
Figure S1-10. Representative growth curves, doubling times, and replicative lifespan for 
lutein. Representative growth kinetics of WT, tor1Δ, and fpr1Δ microcultures at A) 1% DMSO 
and B) 100 µg/mL lutein. C) Inflection doubling time (DT) of WT, tor1Δ, and fpr1Δ 
microcultures. Lutein significantly increases WT and tor1Δ DT (control n = 3, treated n = 3). 
Treatment compared to vehicle control using Welch’s T-test; * p<0.05, ** p<0.01, *** p<0.001. 
D) Lutein replicative lifespan (RLS) survival curve. For number of cells tested in RLS and 
Wilcoxon rank sum p-values, see Table 4. 
 
 
 
 
 
 
 
 
 
 
 
 
 
WT 
tor1Δ 
fpr1Δ 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Figure S1-11. Representative growth curves, doubling times, and replicative lifespan for 
milk thistle extract. Representative growth kinetics of WT, tor1Δ, and fpr1Δ microcultures at 
A) 1% DMSO and B) 100 µg/mL milk thistle extract. C) Inflection doubling time (DT) of WT, 
tor1Δ, and fpr1Δ microcultures. Milk thistle extract does not significantly increase DT (control 
n = 6, treated n = 6). Treatment compared to vehicle control using Welch’s T- test; * p<0.05, ** 
p<0.01, *** p<0.001. D) Milk thistle extract replicative lifespan (RLS) survival curve. For 
number of cells tested in RLS and Wilcoxon rank sum p-values, see Table 4. 
 
 
 
 
 
 
 
 
 
 
 
 
 
WT 
tor1Δ 
fpr1Δ 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Figure S1-12. Representative growth curves, doubling times, and replicative lifespan for 
N-acetyl L-cysteine. Representative growth kinetics of WT, tor1Δ, and fpr1Δ microcultures at 
A) 1% DMSO and B) 100 µg/mL N- acetyl L-cysteine. C) Inflection doubling time (DT) of WT, 
tor1Δ, and fpr1Δ microcultures. N-acetyl L-cysteine does not significantly increase DT (control 
n = 6, treated n = 6). Treatment compared to vehicle control using Welch’s T- test; * p<0.05, ** 
p<0.01, *** p<0.001. D) N-acetyl L-cysteine replicative lifespan (RLS) survival curve. For 
number of cells tested in RLS and Wilcoxon rank sum p-values, see Table 4. 
 
 
 
 
 
 
 
 
 
 
 
 
 
WT 
tor1Δ 
fpr1Δ 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Figure S1-13. Representative growth curves, doubling times, and replicative lifespan for 
olive fruit extract. Representative growth kinetics of WT, tor1Δ, and fpr1Δ microcultures at A) 
1% DMSO and B) 100 µg/mL olive fruit extract. C) Inflection doubling time (DT) of WT, tor1Δ, 
and fpr1Δ microcultures. Olive fruit extract does not significantly increase DT (control n = 6, 
treated n = 6). Treatment compared to vehicle control using Welch’s T-test; * p<0.05, ** 
p<0.01, *** p<0.001. D) Olive fruit extract RLS survival curve. For number of cells tested in 
RLS and Wilcoxon rank sum p-values, see Table 4. 
 
 
 
 
 
 
 
 
 
 
 
 
 
WT 
tor1Δ 
fpr1Δ 
 
 
 
 
 
 
 
 
 
 
 
 
* ** 
* 
 
 
 
 
 
 
 
 
 
 
Figure S1-14. Representative growth curves, doubling times, and replicative lifespan for 
Pterocarpus marsupium extract (PME). Representative growth kinetics of WT, tor1Δ, and 
fpr1Δ microcultures at A) 1% DMSO and B) 100 µg/mL Pterocarpus marsupium extract. C) 
Inflection doubling time (DT) of WT, tor1Δ, and fpr1Δ microcultures. Pterocarpus marsupium 
extract significantly increases tor1Δ and fpr1Δ DT (control n = 3, treated n = 3). Treatment 
compared to vehicle control using Welch’s T-test; * p<0.05, ** p<0.01, *** p<0.001. D) 
Pterocarpus marsupium extract replicative lifespan (RLS) survival curve. For number of cells 
tested in RLS and Wilcoxon rank sum p-values, see Table 4. 
 
 
 
 
 
 
 
 
 
 
 
 
 
WT 
tor1Δ 
fpr1Δ 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Figure S1-15. Representative growth curves, doubling times, and replicative lifespan for 
mixture 5. Representative growth kinetics of WT, tor1Δ, and fpr1Δ microcultures at A) 1% 
DMSO and B) 100 µg/mL mixture 5. C) Inflection doubling time (DT) of WT, tor1Δ, and fpr1Δ 
microcultures. Mixture 5 does not significantly increase DT (control n = 3, treated n = 3). 
Treatment compared to vehicle control using Welch’s T-test; * p<0.05, ** p<0.01, *** p<0.001. 
D) Mixture 5 replicative lifespan (RLS) survival curve. For number of cells tested in RLS and 
Wilcoxon rank sum p-values, see Table 4. 
 
 
 
 
 
 
 
 
 
 
 
 
 
WT 
tor1Δ 
fpr1Δ 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
** 
 
 
 
 
 
 
 
 
 
Figure S1-16. Representative growth curves, doubling times, and replicative lifespan for 
mixture 6. Representative growth kinetics of WT, tor1Δ, and fpr1Δ microcultures at A) 1% 
DMSO and B) 100 µg/mL mixture 6.C) Inflection doubling time (DT) of WT, tor1Δ, and fpr1Δ 
microcultures. Mixture 6 significantly increases WT DT (control n = 3, treated n = 3). Treatment 
compared to vehicle control using Welch’s T-test; * p<0.05, ** p<0.01, *** p<0.001. D) Mixture 6 
RLS survival curve. For number of cells tested in RLS and Wilcoxon rank sum p-values, see 
Table 4. 
 
 
 
 
 
 
 
 
 
 
 
 
 
WT 
tor1Δ 
fpr1Δ 
 
 
 
 
 
 
 
 
 
 
 
 
** 
*** 
 
 
 
 
 
 
 
 
 
 
 
Figure S1-17. Representative growth curves, doubling times, and replicative lifespan for 
Polygonum cuspidatum root extract (PCE). Representative growth kinetics of WT, tor1Δ, 
and fpr1Δ microcultures at A) 1% DMSO and B) 100 µg/mL Polygonum cuspidatum root. C) 
Inflection DT of WT, tor1Δ, and fpr1Δ microcultures. Polygonum cuspidatum root significantly 
increases WT and tor1Δ DT (control n = 6, treated n = 6). Treatment compared to vehicle 
control using Welch’s T-test; * p<0.05, ** p<0.01, *** p<0.001. D) Polygonum cuspidatum root 
replicative lifespan (RLS) survival curve. For number of cells tested in RLS and Wilcoxon rank 
sum p-values, see Table 4. 
 
 
 
 
 
 
 
 
 
 
 
 
 
WT 
tor1Δ 
fpr1Δ 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Figure S1-18. Representative growth curves, doubling times, and replicative lifespan for 
rutin. Representative growth kinetics of WT, tor1Δ, and fpr1Δ microcultures at A) 1% DMSO 
and B) 100 µg/mL rutin. C) Inflection doubling time (DT) of WT, tor1Δ, and fpr1Δ 
microcultures. Rutin does not significantly increase DT (control n = 6, treated n = 6). 
Treatment compared to vehicle control using Welch’s T-test; * p<0.05, ** p<0.01, *** p<0.001. 
D) Rutin replicative lifespan (RLS) survival curve. For number of cells tested in RLS and 
Wilcoxon rank sum p-values, see Table 4. 
 
 
 
 
 
 
 
 
 
 
 
 
 
WT 
tor1Δ 
fpr1Δ 
 
 
 
 
 
 
 
 
 
 
 
* 
* 
 
 
 
 
 
 
 
 
 
 
 
Figure S1-19. Representative growth curves, doubling times, and replicative lifespan for 
turmeric extract. Representative growth kinetics of WT, tor1Δ, and fpr1Δ microcultures at A) 
1% DMSO and B) 100 µg/mL turmeric extract. C) Inflection DT of WT, tor1Δ, and fpr1Δ 
microcultures. Turmeric extract significantly increases WT and fpr1Δ DT (control n = 3, treated 
n = 3). Treatment compared to vehicle control using Welch’s T-test; * p<0.05, ** p<0.01, *** 
p<0.001. D) Turmeric extract replicative lifespan (RLS) survival curve. For number of cells 
tested in RLS and Wilcoxon rank sum p- values, see Table 4. 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Figure S1-20. Representative growth curves and doubling times for allantoin. 
Representative growth kinetics of WT, tor1Δ, and fpr1Δ microcultures at A) 1% DMSO and B) 
100 µg/mL allantoin. C) Inflection doubling time (DT) of WT, tor1Δ, and fpr1Δ microcultures. 
Allantoin does not significantly increase DT (control n = 6, treated n = 6). Treatment compared 
to vehicle control using Welch’s T-test; * p<0.05, ** p<0.01, *** p<0.001. 
WT 
tor1Δ 
fpr1Δ 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Figure S1-21. Representative growth curves and doubling times for ashwagandha root. 
Representative growth kinetics of WT, tor1Δ, and fpr1Δ microcultures at A) 1% DMSO and B) 
100 µg/mL ashwagandha root. C) Inflection doubling time (DT) of WT, tor1Δ, and fpr1Δ 
microcultures. Ashwagandha root does not significantly increase DT (control n =3, treated n = 
3). Treatment compared to vehicle control using Welch’s T-test; * p<0.05, ** p<0.01, *** 
p<0.001. 
WT 
tor1Δ 
fpr1Δ 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Figure S1-22. Representative growth curves and doubling times for cardamonin. 
Representative growth kinetics of WT, tor1Δ, and fpr1Δ microcultures at A) 2% DMSO and 
B) 100 µg/mL cardamonin. C) Inflection doubling time (DT) of WT, tor1Δ, and fpr1Δ 
microcultures. Cardamonin does not significantly increase DT (control n = 6 , treated n = 6). 
Treatment compared to vehicle control using Welch’s T-test; * p<0.05, ** p<0.01, *** p<0.001. 
WT 
tor1Δ 
fpr1Δ 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Figure S1-23. Representative growth curves and doubling times for coumaric acid. 
Representative growth kinetics of WT, tor1Δ, and fpr1Δ microcultures at A) 1% DMSO and 
B) 100 µg/mL coumaric acid. C) Inflection doubling time (DT) of WT, tor1Δ, and fpr1Δ 
microcultures. Coumaric acid does not significantly increase DT (control n = 6, treated n = 6). 
Treatment compared to vehicle control using Welch’s T-test; * p<0.05, ** p<0.01, *** p<0.001. 
WT 
tor1Δ 
fpr1Δ 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Figure S1-24. Representative growth curves and doubling times for Dong Quai. 
Representative growth kinetics of WT, tor1Δ, and fpr1Δ microcultures at A) 1% DMSO and B) 
100 µg/mL Dong Quai. C) Inflection doubling time (DT) of WT, tor1Δ, and fpr1Δ microcultures. 
Dong Quai does not significantly increase DT (control n = 6, treated n = 6). Treatment 
compared to vehicle control using Welch’s T-test; * p<0.05, ** p<0.01, *** p<0.001. 
WT 
tor1Δ 
fpr1Δ 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Figure S1-25. Representative growth curves and doubling times for ellipticine. 
Representative growth kinetics of WT, tor1Δ, and fpr1Δ microcultures at A) 1% DMSO and B) 
100 µg/mL ellipticine. C) Inflection doubling time (DT) of WT, tor1Δ, and fpr1Δ microcultures. 
Ellipticine suppresses growth such that DT cannot be calculated (control n = 3, treated n = 3). 
Treatment compared to vehicle control using Welch’s T-test; * p<0.05, ** p<0.01, *** p<0.001. 
NG = No growth, indicated by culture failing to reach OD ≥ 0.3 after 20 hours outgrowth. 
WT 
tor1Δ 
fpr1Δ 
NG NG NG 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Figure S1-26. Representative growth curves and doubling times for ginseng extract. 
Representative growth kinetics of WT, tor1Δ, and fpr1Δ microcultures at A) 1% DMSO and B) 
100 µg/mL ginseng extract. C) Inflection doubling time (DT) of WT, tor1Δ, and fpr1Δ 
microcultures. Ginseng extract does not significantly increase DT (control n = 6, treated n = 6). 
Treatment compared to vehicle control using Welch’s T-test; * p<0.05, ** p<0.01, *** p<0.001. 
WT 
tor1Δ 
fpr1Δ 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Figure S1-27. Representative growth curves and doubling times for ginsenoside Rc. 
Representative growth kinetics of WT, tor1Δ, and fpr1Δ microcultures at A) 1% DMSO and B) 
100 µg/mL ginsenoside Rc. C) Inflection doubling time (DT) of WT, tor1Δ, and fpr1Δ 
microcultures. Ginsenoside Rc does not significantly increase DT (control n = 3, treated n = 
3). Treatment compared to vehicle control using Welch’s T-test; * p<0.05, ** p<0.01, *** 
p<0.001. 
WT 
tor1Δ 
fpr1Δ 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Figure S1-28. Representative growth curves and doubling times for hesperidin. 
Representative growth kinetics of WT, tor1Δ, and fpr1Δ microcultures at A) 1% 0.027N NaOH 
and B) 100 µg/mL hesperidin. C) Inflection doubling time (DT) of WT, tor1Δ, and fpr1Δ 
microcultures. Hesperidin does not significantly increase DT (control n = 3 , treated n = 3). 
Treatment compared to vehicle control using Welch’s T-test; * p<0.05, ** p<0.01, *** p<0.001. 
WT 
tor1Δ 
fpr1Δ 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Figure S1-29. Representative growth curves and doubling times for hydroxytyrosol. 
Representative growth kinetics of WT, tor1Δ, and fpr1Δ microcultures at A) 1% DMSO and B) 
100 µg/mL hydroxytyrosol. C) Inflection doubling time (DT) of WT, tor1Δ, and fpr1Δ 
microcultures. Hydroxytyrosol does not significantly increase DT (control n = 6, treated n = 6). 
Treatment compared to vehicle control using Welch’s T-test; * p<0.05, ** p<0.01, *** p<0.001. 
WT 
tor1Δ 
fpr1Δ 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Figure S1-30. Representative growth curves and doubling times for isoliquiritigenin. 
Representative growth kinetics of WT, tor1Δ, and fpr1Δ microcultures at A) 1% DMSO and B) 
100 µg/mL isoliquiritigenin. C) Inflection doubling time (DT) of WT, tor1Δ, and fpr1Δ 
microcultures. Isoliquiritigenin suppresses growth such that DT cannot be calculated. Treatment 
compared to vehicle control using Welch’s T-test; * p<0.05, ** p<0.01, *** p<0.001. NG = No 
growth, indicated by culture failing to reach OD ≥ 0.3 after 20 hours outgrowth.(control n = 6, 
treated n = 6, of which 2 WT and no tor1Δ or fpr1Δ cultures grew). 
WT 
tor1Δ 
fpr1Δ 
NG NG 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Figure S1-31. Representative growth curves and doubling times for licorice root. 
Representative growth kinetics of WT, tor1Δ, and fpr1Δ microcultures at A) 1% DMSO and 
B) 100 µg/mL licorice root. C) Inflection doubling time (DT) of WT, tor1Δ, and fpr1Δ 
microcultures. Licorice root does not significantly increase DT (control n = 6, treated n = 6). 
WT 
tor1Δ 
fpr1Δ 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Figure S1-32. Representative growth curves and doubling times for tyrosol. 
Representative growth kinetics of WT, tor1Δ, and fpr1Δ microcultures at A) 1% DMSO and B) 
100 µg/mL tyrosol. C) Inflection doubling time (DT) of WT, tor1Δ, and fpr1Δ microcultures. 
Tyrosol does not significantly increase DT (control n = 3, treated n = 3). Treatment compared 
to vehicle control using Welch’s T-test; * p<0.05, ** p<0.01, *** p<0.001. 
WT 
tor1Δ 
fpr1Δ 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Figure S1-33. Representative growth curves and doubling times for umbelliferone. 
Representative growth kinetics of WT, tor1Δ, and fpr1Δ microcultures at A) 1% DMSO and B) 
100 µg/mL umbelliferone. C) Inflection doubling time (DT) of WT, tor1Δ, and fpr1Δ microcultures. 
Umbelliferone does not increase DT (control n = 6, treated n = 6). Treatment compared to 
vehicle control using Welch’s T-test; * p<0.05, ** p<0.01, *** p<0.001. 
WT 
tor1Δ 
fpr1Δ 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Figure S1-34. Representative growth curves and doubling times for verbascoside. 
Representative growth kinetics of WT, tor1Δ, and fpr1Δ microcultures at A) 1% DMSO and 
B) 100 µg/mL verbascoside. C) Inflection doubling time (DT) of WT, tor1Δ, and fpr1Δ 
microcultures. Verbascoside does not significantly increase DT (control n = 3, treated n = 3). 
Treatment compared to vehicle control using Welch’s T-test; * p<0.05, ** p<0.01, *** p<0.001. 
WT 
tor1Δ 
fpr1Δ 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Figure S1-35. Representative growth curves and doubling times for withaferin A. 
Representative growth kinetics of WT, tor1Δ, and fpr1Δ microcultures at A) 1% DMSO and B) 
100 µg/mL withaferin A. C) Inflection doubling time (DT) of WT, tor1Δ, and fpr1Δ microcultures. 
Withaferin A significantly increases WT and tor1Δ DT (control n = 6, treated n = 6). Treatment 
compared to vehicle control using Welch’s T-test; * p<0.05, ** p<0.01, *** p<0.001. 
WT 
tor1Δ 
fpr1Δ 
* ** 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Figure S1-36. Representative growth curves and doubling times for 1 µg/mL 
isoliquiritigenin. Representative growth kinetics of WT, tor1Δ, and fpr1Δ microcultures at A) 
1% DMSO and B) 1 µg/mL isoliquiritigenin. C) Inflection doubling time (DT) of WT, tor1Δ, and 
fpr1Δ microcultures. 1 µg/mL Isoliquiritigenin does not significantly increase DT (control n = 3, 
treated n = 3). Treatment compared to vehicle control using Welch’s T-test; * p<0.05, ** 
p<0.01, *** p<0.001. 
WT 
tor1Δ 
fpr1Δ 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Figure S1-37. Representative growth curves and doubling times for 10 µg/mL 
isoliquiritigenin. Representative growth kinetics of WT, tor1Δ, and fpr1Δ microcultures at A) 
1% DMSO and B) 10 µg/mL isoliquiritigenin. C) Inflection doubling time (DT) of WT, tor1Δ, and 
fpr1Δ microcultures. 10 µg/mL isoliquiritigenin does not significantly DT (control n = 3, treated n 
= 3). Treatment compared to vehicle control using Welch’s T-test; * p<0.05, ** p<0.01, *** 
p<0.001. 
WT 
tor1Δ 
fpr1Δ 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Figure S1-38. Representative growth curves and doubling times for 50 µg/mL 
isoliquiritigenin. Representative growth kinetics of WT, tor1Δ, and fpr1Δ microcultures at A) 
1% DMSO and B) 50 µg/mL isoliquiritigenin. C) Inflection doubling time (DT) of WT, tor1Δ, and 
fpr1Δ microcultures. 50 µg/mL isoliquiritigenin significantly increases tor1Δ and fpr1Δ DT 
(control n = 3, treated n = 3). Treatment compared to vehicle control using Welch’s T-test; * 
p<0.05, ** p<0.01, *** p<0.001. 
WT 
tor1Δ 
fpr1Δ 
** 
** 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Figure S1-40. Representative growth curves and doubling times for 5 µM celastrol. 
Representative growth kinetics of WT, tor1Δ, and fpr1Δ microcultures at A) 1% DMSO and B) 
5 µM celastrol. C) Inflection doubling time (DT) of WT, tor1Δ, and fpr1Δ microcultures. 5 µM 
celastrol does not significantly increase DT (control n = 3, treated n = 3). Treatment compared 
to vehicle control using Welch’s T-test; * p<0.05, ** p<0.01, *** p<0.001. 
WT 
tor1Δ 
fpr1Δ 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Figure S1-41. Representative growth curves and doubling times for 10 µM celastrol. 
Representative growth kinetics of WT, tor1Δ, and fpr1Δ microcultures at A) 1% DMSO and B) 
10 µM celastrol. C) Inflection doubling time (DT) of WT, tor1Δ, and fpr1Δ microcultures. 10 µM 
celastrol significantly increases tor1Δ and fpr1Δ DT (control n = 3, treated n = 3). Treatment 
compared to vehicle control using Welch’s T-test; * p<0.05, ** p<0.01, *** p<0.001. 
WT 
tor1Δ 
fpr1Δ 
* 
* 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Figure S1-42. Representative growth curves and doubling times for 100 µM celastrol. 
Representative growth kinetics of WT, tor1Δ, and fpr1Δ microcultures at A) 1% DMSO and B) 100 
µM celastrol. C) Inflection doubling time (DT) of WT, tor1Δ, and fpr1Δ microcultures. 100 µM 
celastrol does not significantly increase DT (control n = 3, treated n = 3). Treatment compared to 
vehicle control using Welch’s T-test; * p<0.05, ** p<0.01, *** p<0.001. 
 
 
 
 
WT 
tor1Δ 
fpr1Δ 
  
Supplemental Figure 2.  RLS dose response for celastrol (BY4741 genetic background). N=60 
for each treatment. For number of cells tested and Wilcoxon rank sum p-value, see Table 6. 
 Chapter 4: Dose-dependent effects of pterostilbene on cellular lifespan and proliferation 
 
Abstract 
The advent of the field of geroscience has brought with it significant insights into the connection 
between aging and disease allowing for the possibility that multiple age-related pathologies could be 
attenuated by directly modulating biological aging. Invertebrate model organisms have served as a first 
line of discovery in this area owing to their relatively short lifespans, genetic tractability, and their role in 
the identification of evolutionary conserved signaling pathways shown to be modulators of aging. The 
mechanistic Target of Rapamycin (mTOR) signaling pathway has emerged as an important clinical target 
in this regard with its dysregulation being observed in several pathological conditions, including many 
cancers, autoimmune disorders, heart failure, neurodegeneration, epilepsy, type 2 diabetes, and autism. 
Importantly, inhibition of this signaling pathway has been shown to extend lifespan in evolutionary distant 
organisms suggesting conserved role in longevity regulation. We recently developed a high throughput 
system to identify candidate TOR inhibitory and pro-longevity compounds in yeast. Leveraging this TOR 
inhibitory assay and replicative lifespan (RLS) analysis in yeast we have tested a variety of compounds. 
We recently reported that Pterocarpus marsupium extract (PME) extends yeast RLS. Here we describe 
the lifespan and outgrowth kinetics of pterostilbene treated cells, report on dose-dependent longevity 
effects, potent cytotoxicity, and a possible role for mitochondrial function in mediating these phenotypes. 
  
Introduction 
 
The advent of the field of geroscience has brought with it significant insights into the connection 
between aging and disease allowing for the possibility that multiple age-related pathologies could be 
attenuated by directly modulating biological aging [132, 227]. Invertebrate model organisms have served 
as a first line of discovery in this area owing to their relatively short lifespans, genetic tractability, and the 
identification of evolutionary conserved signaling pathways shown to be modulators of aging [19, 228]. As 
such, interest in identifying interventions that can target these pathways to ameliorate the onset of 
disease and, potentially, modulate biological aging has increased [229].  
mTOR (mechanistic Target of Rapamycin) is a serine/threonine protein kinase and an atypical 
member of the phosphoinositide 3-kinase (PI3K)-related kinase family. It comprises the catalytic subunit 
of two functionally distinct protein complexes: mTORC1 (mTOR complex 1) and mTORC2 (mTOR 
complex 2). mTORC1 functions in numerous cellular processes to promote cell growth and proliferation 
by mediating the phosphorylation of several downstream effectors enabling the cell to tie environmental 
nutrient status to cell fate. mTORC2 functions in multiple cellular processes by mediating phosphorylation 
of several AGC kinase targets: potentiation of mTORC1 signaling via phosphorylation of Akt1; actin 
cytoskeleton organization via phosphorylation of PKCα/βII; promotion of Na+ transport via phosphorylation 
of SGK1. mTORC1 signaling has been well characterized relative to mTORC2 signaling. This is due in 
large part to the ability to interrogate mTORC1 function via genetic or pharmacological inhibition. 
Rapamycin is a potent and specific inhibitor of mTORC1 that works by binding to the protein FKBP12 
(FPR1 in budding yeast). The FKBP12-rapamycin complex then forms a ternary complex with the 
mTORC1 catalytic subunit (mTOR) likely resulting in both steric-occlusion of the kinase active site and 
inhibition of substrate recruitment [21-23] 
The mTOR signaling pathway has emerged as an important clinical target with its dysregulation 
being observed in several pathological conditions, including many cancers [230-235], autoimmune 
disorders [236, 237], heart failure [238-240], neurodegeneration [241-243], epilepsy [244], type 2 diabetes 
[245], and autism [246].  
mTOR was initially implicated in longevity regulation in the budding yeast Saccharomyces 
cerevisiae when it was found that mutation of mTORC1 substrate S6K1 (SCH9 in yeast) increased yeast 
chronological lifespan [30]. Inhibition of mTORC1 has been shown to increase lifespan in yeast [33, 34], 
nematodes [35], fruit flies [36], and mice [37-39] clearly demonstrating a conserved and central role for 
mTOR in the regulation of longevity.   
One method of lifespan analysis in yeast is the replicative lifespan (RLS) defined as the number 
of times a daughter cell can bud off from a single mother. Using light microscopy and manually controlled 
fiber optic needles, individual daughter cells are removed from mother cells on agar plates to determine 
the replicative lifespan of yeast cells in a variety of environmental conditions. mTOR was discovered as a 
key regulator of yeast aging in a large-scale RLS analysis of 564 single-gene-deletion strains with the 
tor1Δ extending mean and maximum lifespan by about 20% [27]. Extending this work, the RLS of 4,698 
single-gene deletion strains comprising the haploid yeast deletion collection was determined yielding the 
most comprehensive data set on aging in yeast [87]. Single gene deletions extending lifespan were found 
to cluster in several functional pathways including the cytosolic and mitochondrial ribosomes and the TCA 
cycle.  
We recently developed a high throughput system to identify candidate TOR inhibitory and pro-
longevity compounds in yeast [195]. Utilizing differential growth kinetics of a WT strain, tor1Δ mutant and 
an fpr1Δ mutant, the TOR inhibitory status of a compound can be inferred. This system successfully 
discriminates between first generation allosteric TOR inhibitors like rapamycin and second generation 
ATP-competitive inhibitors. Leveraging the RLS and TOR inhibitory assays, we have tested a variety of 
compounds. We recently reported that Pterocarpus marsupium extract (PME) extends yeast replicative 
lifespan [247].  
PME is an extract of the heartwood from the Indian kino tree with a long history of use in 
Ayurvedic medicine. In typical usage a tumbler is made from the heartwood, filled with water, and left to 
sit overnight. The aqueous preparation is then consumed the following morning. A number of individual 
compounds have been identified from both aqueous and organic extracts of the heartwood utilizing 
various techniques [248]. Commonly identified bioactive compounds include the hydroxychalcone 
isoliquiritigenin [249-253], the proanthocyanidin epicatechin [249, 250, 254, 255], the flavonol quercetin 
[249, 250, 256], the alkaloid berberine [249, 250], and the stilbene pterostilbene [249, 250, 252, 253, 255, 
257].  
Pterostilbene is a natural stilbenoid compound and a dimethylated analog of resveratrol. It is 
present in foods like blueberries and almonds and is widely available as an unregulated supplement. It 
has been reported to be more potent and bioavailable than resveratrol and other stilbenes. This is thought 
to be due to methylation of the phenolic hydroxyls increasing lipophilicity and therefore membrane 
permeability as well as increasing metabolic stability [258]. Several of these PME constituents have been 
previously reported to extend lifespan in other model organisms: berberine in mice [185] and flies [259]; 
quercetin in flies [260] and worms [261, 262]; epicatechin in flies [263]. Here we describe the lifespan and 
outgrowth kinetics of pterostilbene treated cells, report on dose-dependent longevity effects, potent 
cytotoxicity, and a possible role for mitochondrial function in mediating these phenotypes. 
  
Results 
Pterostilbene inhibits yeast growth independently of mTOR inhibition and can increase or decrease yeast 
RLS. 
 
We previously reported that PME extends yeast replicative lifespan [264]. Because PME is a 
plant extract, we wished to resolve which constituents may be conferring the lifespan extending benefit. 
Epicatechin and quercetin (Figure 1) had no significant effect on lifespan relative to vehicle control when 
tested at the same dose that PME significantly extends lifespan (100 µg/mL).  Both berberine and 
pterostilbene were toxic at this concentration, and we were unable to perform RLS analysis due to severe 
growth inhibition.  We therefore performed dose response experiments for these two constituents to 
determine whether lower doses might have positive effects on lifespan.  Berberine shortened lifespan at 
50 ug/mL and had no effect on lifespan at any of the lower doses tested (Figure 1).   
 
Figure 1. Replicative lifespan (RLS) survival curves for bioactive constituents of Pterocarpus 
marsupium extract (PME). A)100 µg/mL epicatechin (n = 120) does not significantly alter RLS 
compared to vehicle control (n = 119) (p > 0.05, Wilcoxon rank sum test). B) 100 µg/mL quercetin (n = 
40) does not significantly alter RLS compared to vehicle control (n = 198) (p > 0.05, Wilcoxon rank sum 
test). C) 1 µg/mL (n = 155) and 10 µg/mL (n = 160) Berberine do not significantly alter RLS; 50 µg/mL 
Berberine (n = 157) significantly reduces RLS compared to YPD control (n = 160) (p <0.001, Wilcoxon 
rank sum test). 
 
Pterostilbene, in contrast, showed a remarkable dose response profile with respect to lifespan. At 
doses above 70 µM (~18 µg/mL), pterostilbene significantly reduced lifespan. Interestingly, this effect was 
reversed at lower concentration, significantly increasing lifespan at both 20 µM (~5 µg/mL) and 40 µM 
(~10 µg/mL) (Figure 2). 
While performing the RLS analysis, we noted that cell division times appeared to be significantly 
extended by pterostilbene in a dose-dependent manner. To quantify this, we assessed the effects of 
higher dose pterostilbene on yeast growth kinetics using our mTOR inhibitor assay [265]. Doubling time of 
WT, tor1Δ, and fpr1Δ microcultures were similarly and significantly increased relative to strain matched 
vehicle controls (Figure 3C). This is inconsistent with the outgrowth kinetic “signature” and doubling times 
of rapamycin treated cultures (Figure 3B,D) suggesting a growth inhibitory mechanism distinct from 
mTOR inhibition. A comparison of doubling time to lifespan dose-response for pterostilbene indicates a 
clear relationship between growth inhibition and reduced RLS (Figure 3E).  
 
Figure 2. Pterostilbene (PTE) Replicative lifespan (RLS) dose response. 20 and 40 µm PTE 
significantly extend RLS compared to vehicle control. 60 µM PTE did not significantly alter RLS. 70 µM 
PTE and 80 µM PTE significantly decreased RLS compared to vehicle control. (Wilcoxon rank sum test, 
Bonferroni-corrected). For number of cells tested, median lifespans, and p-values, see Table 1. *p<0.05, 
** p<0.01, *** p<0.001. 
0 20 40 60 80
0.00
0.25
0.50
0.75
1.00
Age (cell divisions)
Su
rv
iv
or
sh
ip
1% DMSO
20 µM ***
40 µM *
60 µM
70 µM ***
80 µM ***
 High dose pterostilbene inhibits post-diauxic shift growth 
 
While we were unable to obtain RLS data at doses of pterostilbene higher than 80 µM (~21 
µg/mL) due to apparent cell cycle arrest in many cells, we observed that some liquid cultures eventually 
outgrew after a prolonged “lag” period (Figure 4A). This extended lag period with undetectable outgrowth 
exceeded 24 hours at doses above 100 µM (~26 µg/mL) and reached nearly 96 hours at 160 µM (~41 
µg/mL).  
In addition to the pronounced lag in outgrowth, we also noted a striking “flat lining” phenotype 
after the diauxic shift, where the maximum optical density of the culture is much lower (~1.0) compared to 
untreated cells (~1.5) at 160 µM (~41 µg/mL) pterostilbene (Figure 4A).  We speculated that this may 
represent a defect in respiratory metabolism in cells treated with high doses of pterostilbene, as the 
accumulation of biomass after the diauxic shift is primarily a result of a switch from fermentative 
metabolism to mitochondrial respiration. Consistent with this, an untreated rho0 strain which has had its 
mtDNA depleted via exposure to ethidium bromide also displayed the post-diauxic shift “flat line” 
phenotype (Figure 4B). We noted a sudden increase in well-to-well variability in outgrowth kinetics 
beyond 100 µM PTE (~26 µg/mL). Interestingly, rho0 cultures only display this well to well variability at 
160 µM PTE (~41 µg/mL). 
 
High dose pterostilbene induces mitochondrial instability 
 
To further assess the interaction between mitochondrial function and pterostilbene, we performed 
a dose response outgrowth experiment in both WT and rho0 microcultures.  As expected, untreated rho0 
cells grow significantly slower than untreated WT cells in YPD.  Pterostilbene inhibited growth in a dose-
dependent manner in both WT and rho0 cells.  Interestingly, however, the growth inhibition of rho0 cells 
was attenuated relative to WT cells such that at doses exceeding ~100 µM (~26 µg/mL) pterostilbene, 
rho0 cells actually grew faster than WT cells (Figure 5).  
 
Figure 3. Representative growth kinetics, doubling times and median lifespans for Pterostilbene 
(PTE) treated cells. A) Representative growth kinetics of WT, tor1Δ, and fpr1Δ microcultures at 1% 
DMSO (dashed lines) and 80 µM PTE (solid lines). B) Representative growth kinetics of WT, tor1Δ, and 
fpr1Δ microcultures at 1% DMSO (dashed lines) and 5 ng/mL Rapamycin (solid lines). C) Inflection 
doubling time (DT) of PTE treated WT, tor1Δ, and fpr1Δ microcultures. PTE significantly increases WT, 
tor1Δ, and fpr1Δ DT (control n = 3, treated n = 3, error bars = SEM). D) Inflection doubling time (DT) of 
Rapamycin treated WT, tor1Δ, and fpr1Δ microcultures. Rapamycin significantly increases WT and tor1Δ 
but not fpr1Δ DT (control n = 3, treated n = 3, error bars = SEM). Treatment (Tx) compared to strain 
matched vehicle control (Ctrl) using Welch’s T-test; X = doubling time could not be calculated due to 
limited growth, indicated by OD ≤ 0.3 after 20 h. * p<0.05, ** p<0.01, *** p<0.001. E) PTE RLS dose 
response median lifespans (left axis, circles) and DT (right axis, squares). 
 
To determine whether the post-diauxic shift flat lining was an inherited or a transient effect of 
pterostilbene exposure, we conducted an experiment exposing cells to high dose pterostilbene. Once 
these cultures outgrew and displayed the post-diauxic shift “flat lining” they were used as inoculum in a 
subsequent experiment lacking pterostilbene. There was no lag phase extension observed however, the 
post-diauxic shift “flat lining” persisted. Cultures that display the post-diauxic shift flat-lining are also 
unable to grow on plates containing glycerol as the primary carbon source indicating an inability to 
proliferate when forced to respire.  
 
 Figure 4. Representative growth kinetics for Pterostilbene (PTE) treated microcultures. A) 
Representative growth kinetics of BY4741 (orange, n = 3), BY4742 (black, n = 8) and BY4743 (blue, n = 
3) WT cultures treated with 60 µM, 120 µM, 160 µM PTE or 1% DMSO. B) Representative growth kinetics 
of rho0 cultures treated with 60 µM, 100 µM, 120 µM, 160 µM PTE or 1% DMSO (n = 4 for all doses). 
 
 
 
 
 
 
 
 
24 48 72 96 12
0
14
4
16
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0.0
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2.0
Time (Hours)
0
24 48 72 96 12
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0.0
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2.0
160 µM PTE
024 48 72 96 12
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120 µM PTE
0
24 48 72 96 12
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Time (Hours)
O
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24 48 72 96 12
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0 24 48 72 96 12
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0
24 48 72 96 12
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A
24 48 72 96 12
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1.0
1.5
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60 µM PTE
0
WT WT WT WT
rho0 rho0 rho0 rho0
BY4741
BY4742
BY4743
 Figure 5. Doubling times and representative growth kinetics for Pterostilbene (PTE) treated 
microcultures. Representative growth kinetics of A) WT and B) rho0 cultures treated with 60, 70, 80, 
90, 100, 120, 140, 150, and 160 µM PTE or 1% DMSO. C) Inflection DT for PTE treated WT (n = 5) and 
rho0 (n = 4) cultures. Error bars indicate SD. X denotes doubling time not reliably calculated due to lack 
of growth. 
 
Pterostilbene treatment results in dose dependent cytotoxicity  
 
 We conducted time-course flow cytometric analysis of yeast cultures exposed to 80 (~21 µg/mL) 
or 160 µM (~41 µg/mL) pterostilbene to investigate effects on cell cycle dynamics (Sytox green staining) 
and cell viability (Propidium iodide and Syto9 staining). These experiments revealed unexpected and 
rapid cytotoxicity in both WT and rho0 cells occurring within 1 hour of treatment (Figure 6). Consistent 
with the observation that growth inhibition of pterostilbene treated rho0 cells is attenuated relative to WT 
cells (Figure 5), cytotoxicity of pterostilbene treated rho0 cells is attenuated relative to dose matched WT 
cells (Figure 6).  
 Cell cycle dynamics of untreated WT and rho0 cells revealed an expansion of the G1 cell 
population between 4 and 24 hours. This G1 expansion is pronounced in rho0 cells relative to WT control. 
The timing of this expansion in the vehicle samples is consistent with typical exit from log phase growth 
and induction of fermentative growth. On treatment with 80 µM (~21 µg/mL) pterostilbene rho0 cell cycle 
dynamics remain largely unaffected relative to DMSO control. This is in contrast to WT cell cycle 
dynamics at the same dose where G1 expansion is ostensibly delayed until the cytotoxic effect begins to 
wane (Figure 6). Both WT and rho0 cells treated with 160 µM (~41 µg/mL) pterostilbene display 
pronounced and rapid cytotoxicity however this effect is attenuated in rho0 cells. At this dose cell cycle 
dynamics remain similar to early control conditions in both strains for the duration of the experiment 
suggesting a pterostilbene mediated delay in cell cycle transit. Subsequent experiments in WT cells 
support this notion with clear indications that cell cycle dynamics remain static on pterostilbene treatment 
until cytotoxicity begins to wane after which G1 expansion is observed (Supplemental Figure 1).  
 To validate the pronounced cell toxicity observed in the flow cytometry experiments we conducted 
colony forming unit assays on WT and rho0 cells. Cultures were exposed to 10, 40, 80 or 160 µM 
pterostilbene then plated at 15 min, 1 hour and 24 hour timepoints. WT and rho0 cultures displayed no 
growth on YPD plates after 15 minutes of exposure to 160 µM pterostilbene confirming the rapidity of the 
cytotoxic effect (Supplemental Figure 2).  
 Figure 6. Cell viability and cell cycle dynamics of Pterostilbene treated cells. A) Live/dead 
frequencies and B) cell cycle dynamics of WT cells treated with 1% DMSO, 80 µM or 160 µM PTE. C) 
Live/dead frequencies and D) Cell cycle dynamics of rho0 cells treated with 1% DMSO, 80 µM or 160 
µM PTE. PTE treatment results in acute, dose dependent cytotoxicity concomitant with cell cycle transit 
delay relative to vehicle control. These effects are somewhat attenuated in rho0 cells. 
 
Discussion 
 
We previously identified PME as a longevity extending intervention in yeast [264]. PME is a plant 
extract with a long history of use in Ayurvedic medicine. As a plant extract PME contains a number of 
phytochemicals one or more of which ostensibly contributes to its lifespan extending benefit in yeast. 
Berberine was recently identified as a constituent of PME [249, 250]. Other commonly identified 
components of PME include epicatechin [249, 250, 254, 255], isoliquiritigenin [249-253], quercetin [249, 
250, 256], and pterostilbene [249, 250, 252, 253, 255, 257]. We found that pterostilbene can increase or 
decrease yeast replicative lifespan in a dose dependent fashion.  
Pterostilbene has been previously reported as an inhibitor or attenuator of mTOR signaling in 
various human cell line models including human umbilical vein endothelial cells (HUVECs) [266], human 
bladder cancer cells [267], and breast cancer cells [268]. Pterostilbene did not suppress yeast growth in a 
manner consistent with known mTOR inhibitors in our growth kinetic assay. This may reflect differences in 
pterostilbene activity between human cell lines and yeast, as we previously reported that certain ATP 
competitive mTOR inhibitors that robustly inhibit mammalian cell proliferation do not show similar growth 
inhibition in yeast using our system [265]. It is also possible that pterostilbene acts to indirectly inhibit 
mTOR by acting on upstream components of the pathway that do not have yeast equivalents.  
 The acute cell toxicity observed at higher doses of pterostilbene may serve to explain the 
dramatic reduction in lifespan at 80 µM and above. It is worth noting however, that cells that die 
immediately in response to a drug treatment are not included in the RLS assay, so the reduction in RLS 
from 80 µM pterostilbene cannot be solely explained by acute lethality of the treatment.  
Unexpectedly, we observed that cells grown in higher doses of pterostilbene display a “flat-line” 
phenotype after the diauxic shift in the outgrowth experiments, which we interpreted as inhibition of 
mitochondrial respiration. This was confirmed genetically using rho0 cells lacking mitochondrial DNA, 
which were unexpectedly somewhat resistant to the growth inhibitory effects of pterostilbene. 
Pterostilbene exposure only consistently resulted in this flat-line phenotype at doses exceeding 120 µM. 
This led us to consider two possibilities: that pterostilbene was directly inhibiting mitochondrial respiratory 
capacity or that pterostilbene treatment was selecting for respiratory-deficient cells, or both. Growth 
kinetic analysis revealed that the cells persisting in the pterostilbene culture after outgrowth are 
permanently respiratory deficient, consistent with pterostilbene selecting for survival of respiratory-
deficient cells. Interestingly, rho0 cells are slightly resistant to pterostilbene mediated growth inhibition as 
well. One plausible explanation for these observations is that pterostilbene is causing cells to lose 
mitochondrial DNA during the prolonged “lag phase”, which results in a rho0 state that is then able to 
outgrow.  
 Flow cytometry revealed a rapid and pronounced dose dependent cell toxicity in pterostilbene 
treated cells. While this toxicity was observed in both WT and rho0 cells, the effect was attenuated in the 
rho0 cells and was concomitant with an observable delay in cell cycle transit. Untreated cells displayed an 
expansion of the G1 population the timing of which is consistent with induction of normal fermentative 
growth. Treated cells displayed a dose dependent delay in cell cycle transit that abated only when the 
toxicity began to wane.  
Natural stilbenes like pterostilbene are secondary metabolites generated by plants to protect 
against stressors such as phytopathogenic fungal/bacterial infection, insect attacks and UV irradiation 
among others [269]. This could explain the toxicity observed in our yeast-based system. However, a 
number of in vitro studies have reported cytotoxic [270-277] and genotoxic [270-272, 278-280] effects of 
pterostilbene in both cancer and non-cancerous human cell lines. Generally, pterostilbene was reported 
to induce dose-dependent cytotoxic effects in various cancer cell lines in a range of 25-100 µM. Data in 
non-cancerous cell lines is sparse, however reductions in cell viability have been observed [281]. 
Contrasting with these data are reports that pterostilbene use in murine models is not toxic as well as a 
clinical trial in humans [282, 283].  
As a polyphenol, pterostilbene is already a part of the human diet. It is also on the market as a 
widely available, unregulated supplement and has been investigated as a chemosensitizer in cancer cell 
models indicating research interest in its use as a cancer adjuvant. Additionally, the use of stilbenes as 
natural preservatives is a growing area of interest as synthetic preservatives are increasingly rejected by 
consumers potentially increasing general consumption of this class of polyphenols [281]. Available data 
suggests that pterostilbene is well tolerated in-vivo with little effect on measured biochemical or 
physiological markers [282, 283]. However, there are virtually no studies investigating the cytotoxic and 
genotoxic effects that have been observed in cell culture models in-vivo. This disconnect between in vitro 
and in vivo studies is particularly relevant as pterostilbene has been shown to decrease human 
melanoma growth (murine model) in vivo while the same dose detected in the tumor had no effect on 
growth in vitro [284].  
There have been multiple reports of stilbenes like resveratrol inhibiting the activity of various 
cytochrome p450 isoenzymes (CYPs). The CYP superfamily plays a key role in drug metabolism [285]. 
Inhibition of these enzymes can alter the pharmacokinetics of administered drugs resulting in strong 
effects on drug plasma or tissue concentrations potentially resulting in serious toxic side effects [286]. 
The CYP3A subfamily, especially the CYP3A4, is responsible for metabolizing a broad range of drugs 
including a large number of anti-cancer medicines [287]. Pterostilbene has been shown to dose 
dependently attenuate CYP3A4 activity in vitro however strong inhibition as was seen with other stilbenes 
including resveratrol was not observed [288, 289]. Pterostilbene has also been shown to strongly inhibit 
enzymatic activity of some members of the CYP2C subfamily [288, 289]. While toxicity studies have 
demonstrated that pterostilbene is tolerated in vivo, in vitro evidence suggests that unregulated 
pterostilbene supplementation may increase the risk for unwanted toxic side effects in people ingesting 
certain medications.  
   
 Materials and Methods 
 
Yeast strains and culture conditions 
 
Yeast strains used were in the BY4741 (MATa his3Δ1 leu2Δ0 met15Δ0 ura3Δ0), BY4742 (MATα his3Δ1 
leu2Δ0 lys2Δ0 ura3Δ0), and BY4743 (MATa/α his3Δ1/his3Δ1 leu2Δ0/leu2Δ0 LYS2/lys2Δ0 
met15Δ0/MET15 ura3Δ0/ura3Δ0) genetic backgrounds  and have been previously described [195]. For 
overnight culture and growth analysis, YPD (1% w/v Bacto™ yeast extract (BD), 2% w/v Bacto™ peptone 
(BD), 2% w/v dextrose) media was used. Yeast was cultured at 30 °C for all experiments. 
 
Outgrowth Analysis 
 
Analysis of maximal growth rate in yeast strains was performed to characterize growth-inhibition of 
pterostilbene using a Bioscreen C MBR (Growth Curves USA, Piscataway NJ, USA) as previously 
described [195]. Raw optical density data were smoothed using the R package “smooth.spline.” Doubling 
times were calculated using the inflection method—identifying the maximum semi-log slope along growth 
curves within the optical density range linearly correlated with a number of yeast cells—with the online 
web tool Yeast Outgrowth Data Analyzer (YODA)[129]. All experiments were repeated with at least three 
biological replicates. The number of biological replicates is indicated in each figure caption. Doubling time 
was calculated for all cultures where OD ≥ 0.3 after 20-h growth. Otherwise, culture was listed as no 
growth (NG). Welch’s (unequal variance) t-test with Bonferroni multiple testing correction was used to 
assess differences relative to experiment-matched vehicle control cultures using R. We applied multiple 
testing correction using the p.adjust function. 
Replicative lifespan analysis 
 
A modified RLS protocol for treatments was developed based on previously described methods [196, 
197]. Briefly, cells were grown on freshly prepared YPD plates at 30 °C until single colonies were visible. 
Cells were selected from a single colony and lightly patched onto YPD plates supplemented with the 
designated treatment or vehicle overnight. In the morning, founder cells were aligned and selected as 
newborn daughter cells using a micromanipulator. Cells were monitored for cell divisions every 90–120 
min, and subsequent budded daughter cells were separated and removed as they formed. The process 
continued until cells stopped dividing. Replicative lifespan was calculated as the number of times each 
mother cell divided before it underwent permanent cell cycle arrest. Plates were kept in the refrigerator at 
4 °C overnight. Plates were kept wrapped in tinfoil except while being dissected to avoid potential light 
sensitivity of any pterostilbene. All experimenters were blinded to the identity of any of the treatments at 
the time of dissection. Cells were dissected in groups of 20 and 20-60 cells dissected for each condition 
tested. 
 
Flow Cytometry 
 
All flow cytometry experiments were conducted in the University of Washington Pathology Flow 
Cytometry Core Facility using a BD FACSCanto™ II. Forward angle, 90°-side scatter (FSC and SSC) and 
fluorescence signals were accumulated. The fluorescence signals (pulse area measurements) were 
screened by a 530/30 nm band-pass filter for SYTOX Green and SYTO 9 and a 670 nM long-pass filter 
for Propidium Iodide. Instrument calibration and gating were performed as previously described [290].  
 
 
Table 1.  Median RLS (95% confidence intervals), number of cells dissected (n), percent change, and p-value (Wilcoxon rank sum test, 
Bonferroni-corrected p-value for multiple testing and uncorrected p-value in parentheses) of Pterostilbene compared to vehicle (1% 
DMSO) control. 
Treatment Concentration n Median RLS 
(95% CI) 
% change Bonferroni-corrected p-value 
(uncorrected) 
DMSO 1% 120 22 (20-25) - - 
Pterostilbene  20 µM (~5 µg/mL) 120 33 (33-36) 50.0 < 0.001 (< 0.001) 
Pterostilbene 40 µM (~10 µg/mL) 120 29 (27-35) 31.8 0.025 (0.005) 
Pterostilbene 60 µM (~15 µg/mL) 120 24 (19-28) 9.1 1 (0.763) 
Pterostilbene 70 µM (~18 µg/mL) 120 16 (12-23) -27.3 < 0.001 (< 0.001) 
Pterostilbene 80 µM (~21 µg/mL) 120 1 (1-1) -95.5 < 0.001 (< 0.001) 
Supplemental Figures 
 
Supplemental Figure 1. Cell viability and cell cycle dynamics of Pterostilbene (PTE) treated cells. A) Live/dead 
frequencies and B) Cell cycle dynamics of WT cells treated with 1% DMSO, 80 µM or 160 µM PTE in a 72 hour 
experiment. C) Live/dead frequencies and D) Cell cycle dynamics of WT cells treated with 1% DMSO, 80 µM or 160 
µM PTE in a 120 hour experiment. PTE treatment results in acute, dose dependent cytotoxicity concomitant with cell 
cycle transit delay relative to vehicle control. 
 
Supplemental Figure 2. Cell viability of pterostilbene (PTE) treated cells. Colony forming unit assay 
reveals PTE mediated cytotoxicity occurs within 15 minutes of exposure in both WT and rho0 cells. Rho0 
cells are somewhat resistant to this effect. X denotes no growth; L denotes a lawn. Error bars indicate SD. 
Chapter 5: Conclusion 
 
Over the last century advances in modern medicine have facilitated stunning increases in life 
expectancy with the global average rising to 72 years in 2020. Longer life is concomitant with a period of 
decline in the later years characterized by the onset and progression of multiple age associated diseases 
resulting in reduced quality of life. As a field, geroscience is focused on elucidating the molecular 
mechanisms of biological aging. Identifying pharmacological interventions that directly target these 
molecular mechanisms thereby increasing healthspan and lifespan is a growing area of research [229]. 
The mTOR signaling pathway has emerged as an important clinical target in this regard.  
Rodent models are a widely used standard in terms of translatability to human aging. For 
example, the Interventions Testing Program (ITP) is a multi-site program designed to identify agents that 
extend lifespan and healthspan in mice. Programs like this are crucial in establishing the translatability of 
putative longevity promoting compounds. However, rodent lifespan averages ~3 years making this model 
less than ideal for compound discovery. In this context, leveraging invertebrate model systems for 
compound discovery makes sense given the conserved nature of nutrient sensing pathways involved in 
longevity regulation, their genetic tractability, and relatively short lifespans. Indeed, much of our 
understanding of aging biology comes from the use of invertebrate model systems. Utilization of 
invertebrate systems for compound discovery enables less time consuming, higher throughput 
experiments. This, in turn helps to advance the field as it opens up the possibility for screening large 
numbers of compounds for longevity effects at relatively low cost with the added benefit of serving 
programs like the ITP with evidence-based candidate pro-longevity compounds. Despite the fact that 
lifespan and screening studies in yeast are less expensive and less time consuming than studies in 
rodents, roundworms or fruitflies, utilization of this system has lagged. This is evidenced by the fact that 
48.5% of experiments contained in the DrugAge database are performed in C. elegans and 30% of 
experiments in the database are performed in D. melanogaster while yeast based studies make up only 
2.4% of studies.  
This thesis presents a novel, high-throughput method of identifying candidate pro-longevity 
compounds that will advance the use of S. cerevisae in the context of aging biology research. We first 
present the development of a yeast-based growth assay to identify putative mTOR inhibitors that is 
sensitive enough to discern between allosteric and ATP-competitive modes of inhibition. We then 
leverage this system along with replicative life-span analysis to identify putative longevity compounds in a 
screen of natural products and natural product mixtures identifying Pterocarpus marsupium extract (PME) 
as a pro-longevity compound mixture. Finally, we investigate the effects on yeast growth and lifespan of a 
group of compounds found to be constituents of PME. 
 While the material presented herein demonstrates the potential of leveraging a yeast based 
system for drug discovery, there are notable drawbacks in the utilization of this system stemming from 
biological constraints imposed by the use of S. cerevisiae as a model organism. We have observed two 
instances where our yeast based system does not properly resolve known ATP-competitive mTOR 
inhibitors identified as such in mammals. This is consistent with a previous report observing differential 
sensitivity between and yeast and mammals [291]. This discrepancy is likely due to sequence differences 
between mammalian and yeast proteins. ATP-competitive mTOR inhibitors bind the mTOR kinase ATP-
binding site. In mTOR this structure is characterized by a hydrophobic cleft in which kinase inhibitors form 
hydrogen bonds with various residues. Previous studies have established that the differential sensitivity of 
ATP-competitive mTOR inhibitors within a single model system arise from differential binding affinity to 
the ATP-binding site [23]. This limitation does not preclude the use of this system for drug discovery but 
does indicate that further testing, in mammalian cell culture for example, may be warranted in some cases 
prior to making a determination of mTOR inhibitory status.  
  Sirtuins are a family of protein deacetylases homologous to the yeast silent information regulator 
(SIR) 2. Increased expression of Sir2 orthologs is sufficient to extend lifespan in yeast, worms, and flies 
[292]. Interest in sirtuins as anti-aging drug targets stems partly from the observation that the stilbene 
resveratrol activates Sir2 in-vitro and that resveratrol treatment has been reported to increase lifespan in 
yeast, worms and flies [293]. These claims were widely spread in popular media where red wine was 
touted as healthy because it contained resveratrol, glazing over the fact that sirtuin biology is complex 
and the effects of sirtuin activation may be tissue or context dependent. In yeast, Sir2 overexpression 
increases replicative life span but shortens chronological lifespan [294]. Additionally, the initial reports of 
Sir2-dependent life-span extension from resveratrol in yeast, worms and flies have been difficult to 
replicate [295]. In-vivo, different labs have reported that resveratrol can activate, inhibit or have no effect 
on sirtuins. Resveratrol is also reported to have numerous molecular targets making it difficult to identify a 
dominant mechanism of action. 
 Pterostilbene is an analog of resveratrol that has been touted as more effective than resveratrol 
due to its increased bioavailability. In keeping with the conflicting observations seen on resveratrol 
treatment, we find that pterostilbene can increase or decrease yeast replicative lifespan. The mechanism 
by which pterostilbene effects lifespan is not known. A next step in this regard would be to test the effects 
of pterostilbene on sir2delta cells. Additionally, pterostilbene rapidly induces potent cytotoxicity at higher 
doses and inhibits yeast growth at doses that also extend cellular life span. 
The story of resveratrol is a cautionary tale that should inform how we move forward with 
pterostilbene. Pterostilbene is an example of an attractive putative anti-aging drug that could possibly do 
harm in the wrong context (i.e. patients taking certain medications).  
The most exciting next step is the continued search for novel mTOR inhibitors. Rapamycin was 
first isolated from a soil sample on Rapa Nui (Easter Island). Our group has developed a yeast based 
assay to identify novel mTOR inhibitors. Screening libraries of already approved compounds is the lowest 
hanging fruit in this regard. However, the more exciting alternative in my opinion is to screen libraries of 
compounds isolated from plant and animal sources in a sort of bench chemist to bench scientist pipeline. 
  
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