Pterocarpus marsupium extract extends replicative lifespan in budding yeast

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 the 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.

Supplementary Information

The online version contains supplementary material available at 10.1007/s11357-021-00418-x.

Introduction

The field of geroscience has yielded important mechanistic insights into biological aging processes and their connection to disease [1], with invertebrate model organisms at the forefront of these discoveries [2]. 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 [3, 4]. As our understanding of aging biology evolves, a growing emphasis is now being placed on identifying interventions that target these hallmarks of aging [5]. 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 [6, 7]. Replicative lifespan (RLS) in yeast is defined as the number of daughter cells produced by a mother cell prior to irreversible cell cycle arrest [8]. 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 [9, 10], sirtuins [11, 12], and mechanistic target of rapamycin (mTOR) [13, 14] signaling impact aging, and how conserved molecular processes including mitochondrial function [15–20], pH homeostasis [21–25], proteasome activity [26, 27], autophagy [28, 29], and genome instability [30, 31] 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 [32]. 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 [33, 34]. 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 the 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 [35]. 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 [35], we identified caffeine as an mTOR inhibitor. This may explain its lifespan-extending properties in yeast [36] and worms [37, 38]. 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 μg/mL in a 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 Fig. 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 tor1Δ strain relative to wild type and fpr1Δ [35]. 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 (Fig. 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 μg/mL. All of the cultures treated with ellipticine failed to achieve an OD of 0.3 after 20 h 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 threshold for quantitative doubling time calculation and those that did grow to OD > 0.3 barely achieving this threshold, indicating severe inhibition (Fig. 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 Fig. 1).

Table 1.

Natural product compounds and extracts used in this study along with reported lifespan extension. For compound

source information, see the “Methods” section

Compound/extract	Lifespan extension
Allantoin	Worms [39]
Alpha-lipoic acid	Worms [40, 41], flies [42]
Ashwagandha root	Worms [43]
Berberine	Mice [44], flies [45]
Broccoli concentrate	-
Cardamonin	-
Celastrol	Worms [46]
Choline bitartrate	-
Coumaric Acid	-
Curcumin phytosome complex	-
Dong Quai	-
Ellipticine	-
Ginseng	-
Ginsenoside Rc	Worms [47]
Glucosamine	Mice and worms [48]
Grape seed #1	-
Grape seed #2	-
Green tea	Worms [49], flies [50, 51], mice – midlife effect in females [52]
Hesperidin	Yeast [53]
Hydroxytyrosol	-
Isoliquiritigenin	-
Licorice root	Worms [54]
Lutein	Flies [55]
Lycopene	-
Milk thistle	Worms [56]
N-acetyl L-cysteine	Worms [57], flies [58, 59], mice [60]
Olive fruit	-
Polygonum cuspidatum root	-
Pterocarpus marsupium	-
Quercetin	Worms [61, 62], flies [63]
Rutin	Flies [64], mice [65]
Turmeric	Flies [66]
Tyrosol	Worms [67]
Umbelliferone	-
Verbascoside	-
Withaferin A	Mice [68], flies [69]

Table 2.

Natural product mixtures used in this study. Composition of mixtures is 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% hesperidin, 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% biotin, 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

Treatment	WT			tor1Δ			fpr1Δ
	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

^#0.27 N NaOH vehicle

^†2% DMSO vehicle

^††Doubling time for four of six tested WT cultures could not be calculated

Fig. 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 (Trt) to vehicle (Veh) (*p < 0.05, **p < 0.01, ***p < 0.001). NG denotes no growth (see Methods for more details)

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

Treatment	WT			tor1Δ			fpr1Δ
	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

Twenty-four compounds and mixtures were selected from our initial screen and a prior study [35] for RLS analysis in wild-type cells (Supplemental Fig. 1 and Table 5). Across all of the compounds and mixtures tested, no significant correlation was observed between doubling time and lifespan (Fig. 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 Fig. 1). Significant shortening of lifespan was observed for green tea extract (p-value < 0.001, Wilcoxon rank-sum test, Bonferroni corrected) (Fig. 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 Fig. 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).

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-acetyl 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)

Fig. 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

Berberine, in particular, has been described as a potential geroprotective compound with mTOR inhibitory properties [70]. In a previous growth screen, we failed to detect an mTOR inhibitory effect in cells treated with 100 μg/mL berberine [35]. However, the severe growth inhibition at 100 μg/mL 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 μg/mL (Fig. 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 [71–73]. 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 Figs. 1 and 2). The significant growth inhibitory effect of berberine seen at 100 μg/mL was also observed in all strains at 50 μg/mL (p-value ≤ 0.01, Welch’s t-test, Bonferroni corrected) with less pronounced effects on growth at lower concentrations (Fig. 2C and Table 4). RLS was significantly shortened at 50 μg/mL berberine (p-value < 0.001, Wilcoxon rank-sum test), but no difference was detected at either 10 μg/mL or 1 μg/mL compared to YPD control (berberine suspended in sterile H₂O instead of DMSO for RLS) (Fig. 2D and Table 6).

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

Only one treatment, Pterocarpus marsupium extract (PME), significantly extended lifespan (p-value < 0.001, Wilcoxon rank-sum test, Bonferroni corrected) (Table 5 and Fig. 3). Mixture 1 significantly 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 Fig. 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.

Fig. 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 [74–76]. 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 [77–79].

Two of the most abundant PME constituents, pterostilbene and epicatechin, are suggested to promote longevity [80, 81]. These major components were quantified in our extract using HPLC, and our extract contained 5.00–5.50% pterostilbene w/w and 0.01–1.00% epicatechin w/w. 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 [82]; however, other studies failed to detect significant lifespan extension from epicatechin in worms or flies [63, 83, 84]. 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 [85, 86] and berberine [87–89]. 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 [35, 90]. 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 [91–93]. 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 [94]. 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 pure resveratrol preparation [95]. 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 [96]. 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 [35], 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 [44]. 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 the RLS assay method; manual dissection of yeast on solid media was used in our study as opposed to a microfluidic-based approach where yeast is 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 [97–99]. 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 [100, 101], and S. cerevisiae and related fungal species are present in the human microbiome [102]. 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 an 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 [35] except for the celastrol RLS dose response which used the BY4741 genetic background (MATa his3Δ1 leu2Δ0 met15Δ0 ura3Δ0) [103]. 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

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 [35, 104–106]. 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) [107]. 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). In one case (isoliquiritigenin), 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 [35]. Welch’s (unequal variance) t-test with Bonferroni multiple testing corrections 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 [108, 109]. 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 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. A 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.

Supplementary Information

Below is the link to the electronic supplementary material.

Funding

This work was supported by the University of Washington Nathan Shock Center of Excellence in the Basic Biology of Aging Invertebrate Longevity and Healthspan Core (NIH P30AG013280) and a grant to MK from USANA Health Sciences. M.B.L. was supported by the National Institutes of Health (NIH) Alzheimer’s Disease Training Program (NIH T32 AG052354), the Howard Hughes Medical Institute (HHMI) Gilliam Fellowship for Advanced Study, the NIH Cellular and Molecular Biology training grant (NIH T32 GM727039), and the University of Washington Graduate Opportunities and Minority Achievement Program (UW GO-MAP) Bank of America Fellowship. M.G.K. was supported by NIH award R01AG056359 and the Biological Mechanisms of Healthy Aging Training Program (NIH T32 AG066574). D.P. was supported in part by NIH award R01AG049494.