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Combining stem cell rejuvenation and senescence targeting to synergistically extend lifespan

Why biological age is a major risk factor for many of the most important human diseases remains mysterious. We know that as organisms age, stem cell pools are exhausted while senescent cells progressively accumulate. Independently, induction of pluripotency via expression of Yamanaka factors (Oct4, Klf4, Sox2, c-Myc; OKSM) and clearance of senescent cells have each been shown to ameliorate cellular and physiological aspects of aging, suggesting that both processes are drivers of organismal aging. But stem cell exhaustion and cellular senescence likely interact in the etiology and progression of age-dependent diseases because both undermine tissue and organ homeostasis in different if not complementary ways. Here, we combine transient cellular reprogramming (stem cell rejuvenation) with targeted removal of senescent cells to test the hypothesis that simultaneously targeting both cell-fate based aging mechanisms will maximize life and health span benefits. We find that OKSM extends lifespan and show that both interventions protect the intestinal stem cell pool, lower inflammation, activate pro-stem cell signaling pathways, and synergistically improve health and lifespan. Our findings suggest that a combination therapy, simultaneously replacing lost stem cells and removing senescent cells, shows synergistic potential for anti-aging treatments. Our finding that transient expression of both is the most effective suggests that drug-based treatments in non-genetically tractable organisms will likely be the most translatable.


INTRODUCTION
Life is a constant struggle. This is true at cellular and molecular levels where tissue homeostasis requires constant surveillance, repair and replacement of cells damaged or lost due to intrinsic and extrinsic insults (1). Stem cells play a pivotal role in this tissue homeostasis by providing a reservoir of pluripotent precursor cells, needed to replace fully differentiated cells that are lost or damaged (2). At the opposite end of the cell-fate spectrum are senescent cells, or cells that have permanently withdrawn from the cell cycle (3). Cellular senescence can be replicative, where it is triggered by telomere shortening or mediated by stochastic damage, such as oxidative damage to DNA. Senescent cells can also arise as a response to oncogene activation to oppose transformation and cancerous growth (4). By entering permanent replicative arrest, senescent cells prevent mutations from expanding, thereby providing a sink for genotoxic damage. However, the senescent state does not simply result in passive replicative arrest but instead leads to transcriptional changes causing resistance to apoptosis and increased secretion of pro-inflammatory signaling molecules, a process known as Senescence Associated Secretory Phenotype (SASP). Senescent cell induced SASP in turn promotes inflammation and contributes to age-dependent dysfunction and to the development of age-related diseases (5).
While the number of stem cells decreases in aging animals, senescent cells accumulate with age (6). Manipulating cell fates by cellular reprogramming (to rejuvenate somatic cells) and by senolytic interventions (to remove senescent cells) are two promising approaches to restore homeostasis in aged individuals and to prevent age-dependent diseases. Cellular reprogramming allows differentiated cells to regain plasticity and to take on more stem cell-like qualities. A major step towards this goal was the demonstration of cellular reprogramming of terminally differentiated cells into pluripotent embryonic-like stem cell states (7). Importantly, such reprogramming reverses epigenetic aging marks, demonstrating that even mature, terminally differentiated cells can be returned to a younger state (8). While continuous expression of the Yamanaka factors (Oct4, Klf4, Sox2, c-Myc; OKSM) in mice led to the formation of teratomas and decreased lifespan (9,10), repeated short term expression in adult mice succeeded in ameliorating cellular and physiological signs of aging (11)(12)(13). Subsequently, several studies have suggested that this approach can be applied to human aging and age-related disease (14)(15)(16)(17)(18), and cycling expression can rejuvenate stem cells in vitro (19).
Ablation of senescent cells has been shown to reverse tissue dysfunction and extend healthspan in mice (20,21). A recent study using a senolytic construct (FOXO4-DRI peptide) that induced apoptosis in senescent cells, by interfering with the binding of p53 to FOXO4 thereby freeing p53 to activate apoptosis, showed that the clearing of senescent cells both counteracted senescent cell induced chemotoxicity and restored age-dependent declines in physical performance, fur density, and renal function in aging mice (22). Several studies have further explored applications of different senolytic strategies to ameliorate age-related decline and disease (6,(23)(24)(25)(26).
Accumulation of senescent cells and loss of stem cells are not independent processes. Through SASP, senescent cells release large amounts of pro-inflammatory cytokines which contribute to chronic inflammation and mTOR activation, ultimately leading to stem cell exhaustion (27). This interaction suggests that senolytic therapies might interact with cellular reprogramming strategies in delaying age-dependent decline and disease. We have previously explored drug-drug interactions as synergistic aging interventions (28), and here we ask whether a combinatorial treatment of OKSM and senolytic (Sen) expression could mitigate or reverse the effects of aging more efficiently than either intervention alone. To test this hypothesis, we induced expression of OKSM, Sen and an OKSM-Sen combination in adult flies and compared their effects on health and lifespan. We find that each treatment alone had limited benefits, with OKSM alone benefiting maximum lifespan at the expense of healthspan while Sen expression alone increased mean lifespan but had no effect on maximum lifespan. In contrast, animals subjected to the combined intervention experienced substantially longer mean and maximum lifespan. Our data is consistent with a synergistic interaction between the two interventions, simultaneously rejuvenating stem cells and removing senescent cells.

RESULTS
To test the interaction between senolytic removal of senescent cells and cellular reprograming, we designed a model combining these two interventions in an inducible overexpression system in Drosophila. First, we used the four Yamanaka factor based OKSM approach as this had been previously shown to induce pluripotent stem cells in mice (7), humans (29)(30)(31) and nonmammalian vertebrate and invertebrate species (32). To make a senolytic factor for Drosophila, we took advantage of the mouse sequence (FOXO4-DRI (22)) to design an orthologous peptide optimized to disrupt the Drosophila p53-dFoxO interaction. We then characterized effects of these two interventions independently as well as in combination.
We began by looking at the effect of OKSM and Sen on stem cells in an intestinal stem cell (ISC) model (33,34). We chose to investigate phenotypic effects specifically in the digestive system of Drosophila (S. Fig. 1). As in mammals, the Drosophila gastric lining has a high turnover of cells which is enabled by stem cell pools that replenish the epithelia (33). Age-dependent loss of stem cells and degradation of barrier function has been shown to contribute to age-dependent functional decline and mortality in Drosophila (35). The Drosophila gut is composed of four cell types: enterocytes (ECs or absorptive cells), enteroendocrine (EEs or secretory cells), enteroblasts (EBs or transit amplifying cells) and intestinal stem cells (ISCs). ISCs rest on the external surface of the gut epithelium away from the gut lumen, and divide symmetrically to generate more ISCs, or asymmetrically to form EBs (36). The small, bright green cells or ISCs, can be observed either by expression of the stem cell determinant escargot (esgGal4>UAS-GFP), or by using a marker of Wnt activation, b-catenin (armadillo or arm in Drosophila), observable by GFP construct inserted into the endogenous locus (37).
We looked at the effect of expression of the two factors separately or together over a time course of 28 days. We observed a marked increase in ISC numbers starting at day 7 and continuing into day 28 in all three experimental conditions (Fig. 1). We observed an increase in ISCs and transit amplifying cells in OKSM expressing epithelia (Fig. 1B, F, J, N), an effect likely explained by the presence of Myc and suggesting that stem cell exhaustion may occur (38). We saw a similar increase in ISCs and transit amplifying cells in Sen expressing epithelia possibly due to the effect of p53 on stem cells (39). The increase in ISC numbers in animals expressing OKSM was expected, but surprisingly we observed a large increase in ISCs when Sen was expressed (Fig. 1C).
Overall, the treatments showed higher numbers of stem cells over time as compared to wildtype flies. We next looked at lifespan effects. Continuous expression of OKSM is detrimental in mice while repeated short-term expression was beneficial . We expressed OKSM in ISCs which led to a significant detriment in lifespan (S. Fig. 2A).
To look more closely at the effect of Sen and OKSM, we determined the transcriptional profiles of the three conditions as compared to wildtype. We dissected the midguts of flies expressing In the ubiquitous expression model, 1282 genes (FDR < 10%) were identified as significantly differentially expressed (Table S1). Clustering of these genes identified seven distinct clusters, each representing groups of genes with similar expression profiles across the four conditions (  Table 1). Cluster I (N = 300) contained genes that were downregulated by Sen but were unaffected in the other conditions. Genes in this cluster were associated with cytokinesis, cell cycle and DNA replication. These would be expected results as p53 release from FoxO should lead to apoptosis rather than cell proliferation. Genes in Cluster II (N = 201) were upregulated in all conditions compared to WT. This cluster was enriched for processes associated with Glutathione and sugar metabolism and lysosomal activity, which are related to tissue building and repair.
Cluster III (N = 157) consisted of genes that were upregulated only in the OKSM condition and was enriched for lipid and amino acid metabolism. Cluster IV (N = 173) consisted of genes that were downregulated by all conditions relative to WT and was enriched for genes associated with secretion and phagocytosis. Cluster V (N = 147) represented genes that were downregulated in both OKSM and in the combined OKSM-Sen conditions and contained genes related to translation.
Cluster VI (N = 204) contained genes that were upregulated specifically by Sen alone and consisted mainly of genes involved in homeostasis. Cluster VII (N = 100) contained genes upregulated by the OKSM-Sen combination which were involved in Arginine and Proline amino acid metabolism.
Overall, our results suggested that genes affected by expression of Sen correlated with lower cell division and FoxO signaling, and a higher level of amino acid metabolism. Expression of OKSM upregulated amino acid and lipid metabolism and proteolysis while downregulating translation and sugar metabolism.
In the ISC-restricted expression model, we identified 2967 genes (FDR < 10%) as significantly differentially expressed across conditions (Supplementary Table 2 Toll and mTOR pathways. Taken together, this indicates that the observed changes in mean and maximum lifespan are associated with two distinct transcriptional programs and is not the result of Sen-OKSM specifically activating or repressing a shared transcriptional pathway. Next, we compared the sets of differentially expressed genes identified in both models to determine if there was also a shared core transcriptional program was altered in both systems. Overall, 686 genes were differentially expressed in both models (Fig 2E, p<1x10 -6 ), with these genes being enriched for DNA replication, regulation of epithelial cell migration, mitosis, inflammation, various metabolic processes and specific developmental signaling pathways (Fig. 2F). The majority of these genes have similar responses to transgene expression in either model, while some exhibit differences in their response between models.
To evaluate lifespan effects under optimized conditions, we designed two approaches for cycling expression to overcome the continuous expression detriment. We first used a drug induced expression model where the polycistronic OKSM transgene, under the transcriptional control of UAS regulatory sequences, was driven by the Actin-Switch-GAL4 driver activated by RU486 (40). Flies were placed on fresh food supplemented with the drug weekly leading to periodic, ubiquitous expression. We found that OKSM expression alone resulted in mean lifespan extension in both male and female flies, with female flies showing an increase maximum lifespan as well (S. Fig. 2B). The advantage of this system was that flies could be cultured at higher temperatures reducing the overall length of lifespan studies and allowing rapid confirmation of lifespan benefits. However, this system does not allow for more precise control of expression due to drug half-life, consumption, and distribution to all tissues. For this we turned to a temperature sensitive expression system where a ubiquitous GAL4 driver was combined with a ubiquitous, temperature sensitive GAL80 inhibitor. This system allowed us to generate adults with no embryonic expression, and by modulating the temperature of culture, we were able to precisely induce expression in all tissues for defined periods ranging from constant to once per week. We found that continuous expression of OKSM was detrimental (Fig. 3C), expression for 24 hours twice per week was mildly beneficial (Fig. 3B), and for 12 hours twice per week showed lifespan extension (Fig. 3A).
We carried out similar optimization experiments for the senolytic peptide (Sen) alone and found that, in terms of median lifespan, continuous expression was also detrimental while expression for either 24 or 12 hours twice per week resulted in significant lifespan extension (Fig. 3 Where A is the initial mortality rate in young animals, B is the age-independent mortality and MRDT is a characteristic time interval over which age-dependent mortality doubles. To quantify the impact of single interventions and the combined intervention on age-dependent mortality, we followed an approach recently described by Axel Kowald and Tom Kirkwood, fitting Gompertz-Makeham survival functions to our experimental survival data (42). This fit yielded estimates for the initial mortality A and the MRDT parameters of flies (Supplementary Table 3). All fits resulted in a good agreement between experimental data and the Gompertz curve, with a mean residual standard error of 0.03 (3% survival) across all conditions (Fig. 5 B, D, F, Supplementary Table   3). Mortality trajectories were then visualized by plotting the logarithm of mortality against age ( Fig. 5 A, C, E). In this graph the initial mortality A is the intercept of the mortality trajectory at time zero while the slope of the line is proportional to the inverse of the MRDT. Sen-driven lifespan extension showed a substantial decrease in early mortality A, relative to control. However, this decrease was associated with a significant penalty in the form of age-acceleration (decreased MRDT). For the 12h induction, early mortality decreases almost 100-fold while MRDT decreases from 22.7 days to 7.9 days (p<0.05). In other words, while initial mortality is substantially lower following Sen treatment, the treated flies age approximately 290% faster than WT. This pattern was consistent with previously described mouse data and explains why Sen treatment results in mortality compression with increased mean but not maximum lifespan. By contrast, the impact of OKSM induction on initial mortality and MRDT was much smaller. OKSM induction for 24h and 12h significantly reduced initial mortality A by 46% and 30.1%, (p<0.05), respectively (Supplementary Table 3). While OKSM was also associated with a slight age acceleration penalty in terms of MRTD, this effect was much smaller than for Sen. For 12h OKSM induction, MRDT only decreased from 22.7 days to 19.6 days (p<0.05); a 15.8% increase in aging rate. The result of this change can be seen when plotting log mortality as a function of age (Fig. 5 C, E). OKSM mortality was shifted downwards relative to control but ran largely parallel to control mortality, meaning that age-dependent mortality for 12h OKSM animals remained lower than for WT at later ages. This pattern explains why OKSM impacted maximum lifespan and was not associated with mortality compression.
OKSM-Sen flies with 24h of induction experienced a significant reduction in the age-acceleration penalty (p<0.05) relative to 24h Sen-only flies (Fig. 5 C, Supplementary Table 3). OKSM-Sen flies with 12h induction also showed this trend, but the difference was not statistically significant ( Fig. 5 E, Supplementary Table 3). In contrast, the initial mortality A was decreased by over 330fold in 12h OKSM-Sen compared to WT. This means that initial mortality rate was significantly lower in OKSM-Sen than even in Sen only (p < 0.05), suggesting that adding OKSM to Sen When investigating the interaction between Sen and OKSM treatments, it is useful to compare observed effects to a hypothetical survival and mortality trajectory constructed by assuming that the two interventions act independently (see materials and methods for details). Comparing the OKSM-Sen group to this hypothetical cohort (purple dashed lines in Fig. 5) revealed that these effects cannot be explained without a direct interaction between Sen and OKSM in terms of aging rate. On their own, both interventions accelerate aging rate (decrease MRDT) but improve early mortality. When combined, the intervention results in a reduction, rather than further increase, in the age-acceleration penalty while further augmenting early mortality benefits. This synergistic interaction between OKSM and Sen is the reason why the combined treatment improves both maximum and median lifespan more significantly than either of the two interventions alone.
Mechanistically, these data imply a direct interaction between partial reprogramming via OKSM and the Sen-driven senescent cell apoptosis. Indeed, this is what we observed on the cellular level, as expression of Sen impacted the number of stem cells directly (Fig. 1) even without OKSM expression.

DISCUSSION
Here we show that it is possible to extend both the mean and maximum lifespans by combining anti-aging strategies targeting two different ageing mechanisms related to cell fate. Pulsed expression of the four Yamanaka transcription factors to rejuvenate cells combined with a Senolytic factor kept flies healthier and extended their lives. Although not tested in our study, reprogramming leads to a change in DNA methylation and other epigenetic markers leading to a more youthful gene expression signature (16). The periodic removal of senescent cells leads to fewer chemotoxic molecules being produced and in rejuvenation of organs (22). Both interventions are rejuvenating in the sense that they reverse cellular tissue composition towards a more youthful state. The substantial reduction in initial mortality following both interventions is consistent with this mechanism. Here we report that these two interventions are more closely related than previously appreciated.
Senescent cells show persistent activation of the mTOR pathway, a state that promotes secretion of a wide range of signaling molecules, including proinflammatory cytokines (43)(44)(45). As these molecules are secreted, they have the potential to impact neighboring or distant cells increasing the number of senescent cells, impairing tissue homeostasis (46,47). In the ubiquitous expression model, we found that expression of the Senolytic peptide led to a decrease in Tgfb (-0.51 log2 Fold decrease, FDR=7.51x10 -2 ) and the cytokine Upd3 (-1.0 log2 Fold decrease, FDR=2.91x10 -4 ) compared to Control. Upd3 activates Jak/Stat signaling often related to stem cell activation upon injury leading to asymmetric divisions and lower numbers of stem cells (48,49). Tgfb is upstream of Upd3 and involved in promoting senescence (50)(51)(52). SASP-mediated activation of cytokines and mTOR therefore directly link age-dependent accumulation of senescent cells to accelerated loss of stem cells and declining capacity for repair and tissue regeneration. This mechanism suggests a model by which removal of senescent cells would promote increased resilience and improved maintenance of stem cell pools a phenotype we observe in the fly.
Although OKSM must function through partial reprogramming of cells, the exact mechanism of how this works in adult tissues is not entirely clear (13,(16)(17)(18). We observed changes in Hedgehog signaling recently proposed as a neuroprotective and life extending pathway (53) along with genes associated with cytokinesis and DNA replication. Importantly, we observe that OKSM has limited effect on maximum lifespan unless senescent cells are removed suggesting that SASP counteracts the benefits of rejuvenation. Previous studies have shown either OKSM or Sen to be anti-aging, but in both cases the effects did not affect maximum lifespan. In our combinatorial approach, we can now extend both mean and maximum. We have further established that both approaches can be studied in the much shorter-lived Drosophila model. We suggest that reprogramming accomplished through gene therapy, or another method combined with senolytic peptides or drugs could promote both tissue repair and reverse age-related decline.

Molecular Cloning of Transgenes
The Oct4

Fly lines used in this study:
Actin5C (

Gut preparations
Adult fly midguts were dissected in 200 µl of 1x PBS in a PYREX™ Spot Plates concave glass dish (FisherScientific). The midguts were rinsed with PBS and stained with 10mg/ml Hoechst 33342 diluted 200 times in 1X PBS for 1min. Subsequently, the guts were carefully transferred onto a small droplet of 1x PBS on a 35mm glass bottom dish. Using fine forceps, the gut was repositioned to resemble its natural orientation. PBS was then removed from the area surrounding the gut, leaving a small amount of excess PBS to hold the gut in place and prevent desiccation.
The 3mm glass bottom dish was then mounted onto the Zeiss LSM800 (Carl Zeiss AG, Germany) for imaging. For each construct, midguts from at least 3 flies were dissected and imaged at the 25% percentile from the anterior midgut (63).

Fluorescence microscopy
Images were acquired on the Zeiss LSM 800 (Carl Zeiss, Germany) using the Plan-Apochromat

Lifespan studies
For each experiment, more than 50 F1 flies were cultured at 25° or 18°C. Flies were counted daily noting the number of dead and censored subjects. Lifespans were scored every day. Flies that failed to respond to taps were scored as dead, and those that were stuck to the food were censored.
Lifespan curves and statistical analysis of lifespan studies were performed using OASIS 2 (Online Application for Survival Analysis 2 (64,65)). For studies using Actin-Switch, flies were moved to fresh vials with food supplemented by 200µM RU486 (mifepristone) weekly. For studies using the temperature sensitive expression inhibitor tubGal80 ts flies were raised in a Torrey Pines IN35 programable incubator where the temperature was automatically cycled from 18° to 25° twice per week for either 12 or 24 hours.

RNA-Seq Analysis
RNA-seq was aligned against BDGP6.22 (Ensembl version 97) using STAR v2.7.1a (66), and quantified using RSEM v1.3.1 (67). Reads mapping to genes annotated as rRNA, snoRNA, or snRNA were removed. Genes which had less than 10 reads mapping on average across all samples were also removed. A differential expression analysis was performed using DESeq2 (68). The likelihood ratio test (LRT) was used to identify any genes that show change in expression across the different conditions. Pairwise comparisons were performed using a Wald test, with independent filtering. To control for false positives due to multiple comparisons in the genomewide differential expression analysis, the false discovery rate (FDR) was computed using the Benjamini-Hochberg procedure. The gene level counts were transformed using a regularized log transformation, converted to z-scores, and clustered using partitioning around medoids (PAM), using correlation distance as the distance metric. Gene ontology (GO) and KEGG pathway enrichments for each cluster were performed using EnrichR (69)(70)(71). Terms with an FDR < 10% were defined as significantly enriched.

Mortality Analysis
All analysis of lifespan data and curve fitting was performed using the nls non-linear least square tools in the R programming language. Survival data was imported into R and a survival curve derived from Gompertz-Makeham mortality law was fitted according to (41,42). Briefly, survival curves are the integral of (Eq. 1), that is: Survival at a given age can be expressed as     OKSM+Sen for 12h every three days and matched controls. Note that flies subject to continuous induction (Panels A and B) are permanently kept at 25° and are therefore aging more rapidly than flies cultured at 18° and induced for only for short periods. The slope of the mortality trajectory of controls in A is therefore approximately two times as larger, compared to that of controls in panels C and E. For exact MRTD and A parameter values and associated confidence intervals see: (Supplementary Table 2).

Supplementary Figure 1: (A)
Schematic of the Drosophila digestive system comprising enterocytes (ECs), enteroendocrine (EEs), enteroblasts (EBs) and intestinal stem cells (ISCs). ISCs are located externally, away from the lumen. They divide symmetrically to make more ISCs or asymmetrically to form EBs or transit amplifying cells that further differentiate into ECs and EEs. (B) Model for the the two treatments, rejuvenating differentiated cells to more stem cell like cells through Yamanaka factor expression, and activating apoptosis of senescent cells through peptidebased interference in FoxO-p53 binding.   Table 3: Fitting parameters and statistics from Gompertz-Makeham mortality and survival analysis. Best fit value and 95% confidence interval are listed for each parameter. Mortality rate doubling time is given in days and rounded to one decimal place. Initial mortality parameter A is normalized to that of the relevant control cohort (WT flies subjected to the same induction condition). All A parameter values are expressed in percent of this control.

Data availability:
RNA-seq data from this study has been deposited to GEO (X).

Code availability
All code necessary to recreate the results from the analysis presented is available from: https://github.com/harmstonlab/OKSM_Senolytic