Youthful and age-related matreotypes predict drugs promoting longevity

The identification and validation of drugs that promote health during aging (‘geroprotectors’) is key to the retardation or prevention of chronic age-related diseases. Here we found that most of the established pro-longevity compounds shown to extend lifespan in model organisms also alter extracellular matrix gene expression (i.e., matrisome) in human cell lines. To harness this novel observation, we used age-stratified human transcriptomes to define the age-related matreotype, which represents the matrisome gene expression pattern associated with age. Using a ‘youthful’ matreotype, we screened in silico for geroprotective drug candidates. To validate drug candidates, we developed a novel tool using prolonged collagen expression as a non-invasive and in-vivo surrogate marker for C. elegans longevity. With this reporter, we were able to eliminate false positive drug candidates and determine the appropriate dose for extending the lifespan of C. elegans. We improved drug uptake for one of our predicted compounds, genistein, and reconciled previous contradictory reports of its effects on longevity. We identified and validated new compounds, tretinoin, chondroitin sulfate, and hyaluronic acid, for their ability to restore age-related decline of collagen homeostasis and increase lifespan. Thus, our innovative drug screening approach - employing extracellular matrix homeostasis - facilitates the discovery of pharmacological interventions promoting healthy aging. Highlights Many geroprotective drugs alter extracellular matrix gene expression Defined young and old human matreotype signatures can identify novel potential geroprotective compounds Prolonged collagen homeostasis as a surrogate marker for longevity


Introduction
The demographic shift in the human population reflects an ageing society -over 20% of Europeans are predicted to be 65 or over by the year 2025 (Riera and Dillin, 2015).
Aging is the major risk factor for developing chronic diseases, such as cancer, Alzheimer's disease, and cardiovascular complications (Niccoli and Partridge, 2012).
Unfortunately, humans spend on average one-fifth of their lifetime in poor health suffering from one or multiple age-related chronic diseases . However, the onset of age-related pathologies is not fixed, and the rate of aging was shown to be malleable. The goal of biomedical research on aging or geroscience is to identify interventions that compress late-life morbidity to increase the period spent healthy and free from disease (Barzilai et al., 2018;Campisi et al., 2019;Kennedy et al., 2014;Olshansky, 2018;Partridge et al., 2018;Riera and Dillin, 2015).
A key signature of aging is the continuous decline of collagen and cell adhesion gene expression (Ewald, 2019;Magalhães et al., 2009;Zhavoronkov et al., 2014) accompanied with an increase in matrix metalloproteinase expression (Ewald, 2019;Fisher et al., 2009). Gene expression ontologies of extracellular matrix (ECM) genes have been associated with healthy aging in humans . The ECM not only embeds cells and tissues but also provides instructive cues that change cellular function and identity. For instance, placing old cells into a 'young' ECM rejuvenates senescent cells or stem cells and even reprograms tumor cells (reviewed in (Ewald, 2019)). Moreover, collagen homeostasis is required and sufficient for longevity in C.
elegans (Ewald et al., 2015). Heparan/chondroitin biosynthesis and TGFβ pathway are frequently enriched in C. elegans longevity drug screens (Liu et al., 2016). Collectively taken, these functionally implicated genes are all members of the matrisome.
The human matrisome encompasses 1027 protein-encoding genes that either form the ECM, such as collagens, glycoproteins, and proteoglycans; associate with ECM (e.g., TGFβ, Wnts, cytokines); or remodel the ECM (e.g., matrix metalloproteinases (MMPs)) (Naba et al., 2016). The matrisome represents about 4% of the human genome and is functionally implicated in about 8% of the total 7037 unique human phenotypes (Statzer and Ewald, 2020). Age-related phenotypes rank among the top matrisome-phenotypic categories across species (Ewald, 2019;Taha and Naba, 2019). Proteomics approaches have revealed unique ECM compositions associated with health and disease status (Socovich and Naba, 2019) -ECM compositions can even be used to identify distinct cancer-cell types (Ewald, 2019). Therefore, organismal phenotypes, physiological states, and cellular identity are characterized by distinct sets of expressed ECM proteins. Since these unique ECM compositions are an expression profile on a temporary, sometimes local basis and do not involve the entire matrisome, we coined the term matreotype (Ewald, 2019). The matreotype is the acute state of an ECM composition associated with-or causal fora given physiological condition or phenotype (Ewald, 2019).
Given the functional implication of ECM in healthy aging, we hypothesized that a youthful matreotype might predict drugs promoting healthy aging. Here we define a youthful human matreotype using data from the Genotype-Tissue Expression (GTEx) project (Consortium, 2013). We query this young matreotype signature with the drug resource Connectivity Map (CMap) (Lamb et al., 2006) data to identify longevitypromoting compounds. We then developed a novel in-vivo tool as a surrogate marker for longevity to find appropriate drug doses to be used for C. elegans' lifespan assays.
Our results implicate previously known longevity drugs as well as novel drugs, providing a proof-of-concept for our approach.

Geroprotective compounds associated with altering ECM
We first applied literature and database mining to search for compounds that have been shown to increase lifespan and are known to alter ECM in any organism. We acquired lifespan data from databases DrugAge and GeroProtectors ( Figure 1A) (Barardo et al., 2017a;Moskalev et al., 2015). We filtered for reported mean lifespan extensions that were above 5% for compounds compared to control ( Figure 1A; Supplementary Table 1)-then queried all PubMed abstracts for the given agent and our ECM key terms, including collagen, MMP, proteoglycan, integrin, TGFβ ( Figure   1A; Supplementary Table 1). After manual curation, we identified approximately 3% (16 out of 567) of the examined known longevity-promoting compounds that slow aging and also had been reported to affect proteins outside of cells, such as collagens and other matrisome proteins ( Figure 1B; Supplementary Table 1).

Longevity compounds affect matrisome gene expression
Next, we investigated whether compound treatments, in general, would alter matrisome expression. Connectivity Map (CMap) is a library of 1.5 million gene expression profiles comparing 1309 different compound treatments on human cell cultures (Lamb et al., 2006). We queried the CMap library for compound treatments that either increase or decrease the expression levels of matrisome genes. Using a zscore threshold of + 1.5, we identified 167 compounds that strongly regulate the 594 out of the 1027 matrisome genes compared to the background (13752 total quantified genes; Figure Table 2). Out of the 12 most up and 12 most down-regulated matrisome expression profiles upon a compound treatment, we identified ten agents linked to longevity or impairment of age-related pathologies ( Figure 2B, 2C, Supplementary

Defining young and old human matreotypes
For a more targeted approach, we reasoned that using a young age-associated matrisome gene expression signature (i.e., youthful matreotype) to query CMap data should reveal more compound treatments that might promote longevity. To define a young and old human matreotype, we built upon the previous analysis of the GTEx dataset (Consortium, 2013) comparing a young versus an old gene expression pattern of more than 50 tissues and 8000 transcriptomes by Janssens and colleagues (Janssens et al., 2019). From these 50 tissues, we only identified 15 tissues that had on average 138 age-related transcripts per tissue, which we then filtered for matrisome genes (Supplementary Figures 5,6,Supplementary Table 3). In our analysis, we used two different approaches to quantify age-related transcript changes: the difference in expression ('absolute') and the fraction of change ('relative'). The absolute expression change preferentially captures genes exhibiting high baseline expression levels since any change in their abundance translates to a large absolute change. By contrast, the relative expression difference quantifies the change to the previously measured value and favors lower expressed genes.
Among these age-related transcripts, matrisome genes were well represented with collagen genes and matrix proteases (MMPs, ADAMs) being decreased and increased, respectively, with age ( Figure 3A-D, Supplementary Table 3). These findings were consistent with previous reports (Ewald, 2019;Fisher et al., 2009;Magalhães et al., 2009). However, we noted a number of novel observations. Firstly, each tissue has a unique age-related tissue-specific matreotype gene expression signature (Supplementary Figures 5,6,Supplementary Table 3). In the remaining tissues, the number of ageassociated transcripts was too low to quantify the contribution of the matrisome conclusively. Thirdly, certain matrisome genes, such as GDF15, experienced both increasing and decreasing expression levels during aging, depending on the tissue ( Figure 3B). With these observations in mind, our aim was to construct a multi-tissue compendium of matrisome members, which were most affected by aging. To build upon our five tissues, we combined the findings of eight studies that also included transcriptomes across ages from different tissues in order to validate and identify additional multi-tissue and age-related matrisome genes (Supplementary Table 3). By considering both absolute and relative aging-gene expression changes, we determined the age-related matreotype of brain, fat, skin, and other tissues to   Table 4). Using this approach, we defined here, for the first time, a multi-tissue compendium of approximately 100 genes across 15 human tissues, which we define as the young and aged matreotype.

Use of the young and aged matreotype to identify new pro-longevity compounds
The ultimate goal of our matreotype signature is for it to be used to identify new geroprotective compounds that modulate the matrisome, and thus, longevity. To first validate this approach, we used known pro-longevity compounds and identified those causing a youthful matreotype (i.e., similar gene expression; Figure Table 5). To our surprise, these 24 compounds with reported lifespan increase did not cluster preferentially with the 'reversed aging signature' compounds as predicted but rather were almost equally distributed among all categories ( Figure 4). This suggests that at least for matrisome genes, longevity might not be a simple reversion of gene expression associated with aging. Or it might be more complex given that each tissue has a unique matreotype ( Supplementary   Figures 5-7), such as that seen with GDF15 ( Figure 3). Despite this unexpected finding, we note that our matreotype 100 gene compendium was able to identify 185 unique compounds, of which 13% have previously been reported to extend lifespan ( Figure 4). This is an enrichment compared to the 5% of the 67 longevity-promoting compounds found in all 1309 CMap assessed small molecules ( Figure 2A). Thus, independent of directionality, both youthful and age-related matreotype, i.e., the matreotype signature itself predicts longevity-promoting drugs.

Validating matreotype-predicted compounds with lifespan assays using C. elegans
To translate the in-silico analysis to an in-vivo functional relevance for healthy aging, lifespan assays in model organisms, such as C. elegans or mice are commonly used.
The limitation of these lifelong assays, especially in mice, is that often one does not know if the optimal dose is applied until the end of the study three years later. To overcome this limitation, we developed an in-vivo screening assay measuring collagen biosynthesis. Similar to humans, collagen biosynthesis in C. elegans declines with age, and we recently discovered that many, if not all, longevity interventions prolong the expression of collagen genes in C. elegans (Ewald et al., 2015). This prolonged-expression of key collagen genes is required and sufficient for longevity (Ewald et al., 2015). Thus, we hypothesized that prolonged collagen expression, quantified on the transcriptional level by a collagen promoter-driven GFP, would constitute a useful surrogate marker to predict longevity ( Figure 5A). Indeed, we found that the GFP intensity driven by collagen col-144 promoter declined almost linearly within the first five days of adulthood ( Figure 5B, Supplementary Table 6).
In our youthful matreotype-associated drugs, we have identified phenformin ( Figure Table 6). This is consistent with a study showing metformin slows extracellular matrix morphological decline of the cuticle (Haes et al., 2014). This suggests that one unexplored aspect of the metformin's mechanism-of-action might be via improved collagen homeostasis. Given this exciting finding, we decided to prioritize our investigations into drugs that will enhance collagen homeostasis in mammals but have not shown any pro-longevity phenotypes in any organisms. We, therefore, chose to test the retinoic acid receptor agonist tretinoin since tretinoin treatment prevents MMP upregulation and stimulates collagen synthesis in photo-aged skin (Griffiths et al., 1993). Our analysis showed that tretinoin had an enriched differential expression of matrisome genes (Supplementary Figure 9). Furthermore, tretinoin has been predicted to associate with a youthful expression pattern by the in-silico analysis of Janssens and colleagues but did not increase C. Following the same rationale, in our drug hits that change matrisome expression, we identified genistein, an isoflavone (phytoestrogen) derived from soybeans to have a youthful matreotype profile (Supplementary Figure 9). Genistein has been predicted to associate with a youthful expression pattern by a previous in-silico analysis but failed to increase C. elegans lifespan at 50 μM (Janssens et al., 2019). By contrast, other groups reported a lifespan increase using 50 and 100 μM genistein (Lee et al., 2015). To reconcile this, we generated new 98% pure genistein and found prolonged collagen expression in aged C. elegans and a mild increase in lifespan ( Figure  To expand our findings, we searched for compounds that could alter ECM composition and were not included in the CMap library. Besides collagens, glycoprotein and proteoglycans are the other two major components of ECMs across species (Naba et al., 2016;Teuscher et al., 2019a). We decided to investigate ECM precursors, such as glucosamine, chondroitin sulfate, and hyaluronic acid, which are part of the sugars added to these ECM proteins as potential mediators of the youthful matreotype. In humans, cohort studies of over 70-500 thousand participants who took glucosamine or chondroitin supplements showed a 15-18% and 22% reduction in total mortality, respectively (Bell et al., 2012a;Li et al., 2020). Here, we found that glucosamine treatment increased collagen expression during aging (Supplementary reporter system is a powerful tool to identify drug-response doses that promote healthy aging.

Discussion
Recent artificial intelligence, in-silico, and other computational approaches have been harnessed to predict beneficial and longevity-promoting effects of compounds, which were previously not considered to mediate effects on ECM gene expression (Bakula et al., 2018;Barardo et al., 2017b;Janssens et al., 2019;Moskalev et al., 2015;Vanhaelen et al., 2017). A major challenge lies in the validation of the health-promoting results of a novel compound. Here we demonstrated that a concise list of about 1000 matrisome or about 100 matreotype genes facilitates the identification of lifespan enhancing drugs. To establish a proof-of-concept for our matreotype approach, we developed a new non-invasive and in-vivo reporter system, which we used to validate known and novel geroprotective drugs. We used our system to determine the appropriate dose to unravel the compounds' longevity potential indicated with our, and previous, in-silico approaches.
Established geroprotective drugs, such as metformin, rapamycin, resveratrol, and others, with known lifespan-extending effects, have previously been reported to alter the expression of ECM components ( Figure 1). When we compared gene expression signatures, we found that almost ninety percent of the known longevity-promoting compounds in the CMap library showed changes in matrisome gene expression. To identify novel geroprotective drugs, we refined our approach by parsing ECM gene signatures resembling a young or aged matreotype to correlate with a given drug's gene expression pattern. We found 185 candidate drugs, of which 24 showed lifespan increase in model organisms and 42 unique compounds had previously been predicted as potential geroprotectors (Supplementary Table 5).
The generally accepted assumption is that there is a drift in gene expression during aging, and that restoration of a younger gene expression pattern indicates rejuvenation of cells or tissues. This premise has been extensively used as a biomarker for the restoration of health in clinical trials (NCT02432287, NCT02953093), parsing healthy versus common aging cohorts , reprogramming cells into a younger state (Lu et al., 2020), or in previous in-silico approaches (Tyshkovskiy et al., 2019). This premise also requires that longevity or rejuvenating interventions work through temporal scaling, a process that has been shown to be the case for lifespan extension and aging-associated gene expression in C. elegans (Stroustrup et al., 2016;Tarkhov et al., 2019) but not yet for mammals. We found that longevity-promoting drugs either increase or decrease matrisome gene expression ( Figure 2). With a more refined approach using the youthful matreotype, we observed that both reversing or propagating the aging gene signatures could predict geroprotective drugs (Figure 4). One explanation could be that there is overlap in the gene expression signatures during aging and chronic diseases are similar (Fernandes et al., 2016;Wang et al., 2009a;Yang et al., 2015;Zeng et al., 2020).
Clearly, independent of directionality, the current defined age-related matreotype holds predictive power to identify new lifespan enhancing drugs. A shortcoming of our definition of the aging-and youth-associated expression signatures is that we do not take the context of the individual tissues into consideration. Unfortunately, insufficient studies are available to compile high-quality subsets for each tissue, which should be addressed in further experimental investigations. This is especially important in the case of collagen expression at an advanced age that can be both associated with improved tissue maintenance as observed in the skin and joints, while at the same time be implicated in fibrotic changes in the liver and kidney. For instance, downregulation of ECM in fat tissue but not in blood vessels is a key gene signature for healthy elderly individuals . During aging, inflammation increases, which proceeds fibrosis (Wick et al., 2013) associated with 45% of deaths in the US alone (Wynn, 2008). On the other hand, collagen synthesis declines during aging and degradation or fragmentation by increased MMP activities, evident in aging skin (Fisher et al., 2009). Several drugs, including rapamycin , tretinoin (Griffiths et al., 1993), genistein (Polito et al., 2012), resveratrol (Lephart and Andrus, 2017), increase collagen synthesis in the skin. By contrast, resveratrol, rapamycin, and genistein suppress fibrotic collagen deposition in intestinal fibroblasts, kidney, or pulmonary fibrosis, respectively (Chen et al., 2012;Li et al., 2014;Matori et al., 2012). Thus, drugs might act as a geroprotector and increase lifespan by either inhibiting or enhancing the matrisome expression depending on preexisting tissue damage or disease.
The model organism C. elegans does not show inflammation nor fibrosis during aging.
Thus, collagen expression in C. elegans might reflect restoration or repair of the progressive decline of ECM homeostasis during aging analogous to human skin. This makes C. elegans the ideal readout for any age-dependent changes of matrisome genes observed from human or mammalian omics approaches. Based on this, we established an age-dependent collagen homeostasis read-out as a predictive marker for longevity.
In previous work, more than 100'000 compounds have been screened, and about 100 compounds have been identified to increase C. elegans lifespan (Supplementary Table 5) Lucanic et al., 2013;Petrascheck et al., 2007;Ye et al., 2014). A practical limitation in verifying drug candidates is the unknown dosage to be used. Usually, one dose is chosen for all compound treatments for C. elegans lifespan screening assays, potentially leading to many false-negative results and limiting its interpretation. Dose-response curves are not linear, but often J-or U-shaped, whereby in general, high doses are toxic and low doses lead to hormetic responses increasing lifespan (Ristow and Schmeisser, 2014). We showed that two previously predicted but regarded as false-positive compounds -tretinoin and genisteinrobustly increase lifespan when assayed at the appropriate dosage. Furthermore, optimization for the route of uptake improves robustness, promoting healthy aging.
Thus, our novel reporter system serves as a predictive tool to identify the appropriate dosage for lifespan assays.
It is striking that longevity-promoting compounds identified in model organisms showed enriched matrisome gene expression signature in human cells treated with these compounds (Supplementary Figures 2-4). The proposed mechanisms for longevity-compounds discovered with C. elegans, mostly work through intercellular communication (Petrascheck et al., 2007;Ye et al., 2014). Pathway analysis showed an enrichment for chondroitin and heparan sulfate biogenesis and TGFβ pathway as predicted drug-protein targets (Liu et al., 2016). On the other hand, the outcome of  (Chaturvedi et al., 2011). Proper ECM protein homeostasis is essential to ensure intercellular communication, a hallmark lost during aging (Ewald, 2019;López-Otín et al., 2013). This raises the question whether geroprotective drugs improve ECM homeostasis. There is tantalizing evidence with established longevitypromoting medications, such as rapamycin, resveratrol, metformin, and others ( Figure   1B), and we provided experimental evidence for this with tretinoin, genistein, glucosamine, chondroitin, and hyaluronic acid ( Figure 5). Consistent with this is that glucosamine and chondroitin stimulate collagen synthesis in vitro and ex vivo of elderly human skin in a clinical trial (Gueniche and Castiel-Higounenc, 2017;Lippiello, 2007), extracellular matrix component hyaluronic acid treatments stimulate collagen synthesis in human photo-aged skin (Wang et al., 2007), and in mice (Fan et al., 2019), and topical application of rapamycin restores collagen VII levels in a clinical trial (NCT03103893) . ECM homeostasis might remodel or prevent glycation and crosslinking of collagens (Ewald, 2019). There are currently 27 clinical trials addressing ECM stiffness and its role in diseases by investigating eleven different molecular targets (Lampi and Reinhart-King, 2018). Furthermore, different matreotypes might be valuable prognostic factors or biomarkers (Ewald, 2019). For instance, ECM is one of the strongest associated aging-protein signatures in blood plasma or urine proteomics of healthy older adults (Nkuipou-Kenfack et al., 2015;Sathyan et al., 2020). Thus, defining matreotypes related to healthy aging or agerelated chronic diseases might be a strategy for personalized medicine approaches.
In summary, we demonstrated that prolonged ECM homeostasis is a biomarker for C. elegans longevity and harnessed this to establish a novel in-vivo assay. We provided evidence that gene expression patterns of human cells treated with known geroprotective drugs alter ECM genes, and developed an age-stratified matreotype.
We then used this matreotype to identify novel geroprotective compounds based upon their transcriptomes. Our method highlights a previous unused potential of ECM reprogramming as a means to identify and validate novel compounds, licensed drugs, natural compounds, and supplements that potentially retard or prevent age-related pathologies. Understanding pharmacological reprogramming of extracellular environments may provide a new platform to discover previously unidentified therapeutic avenues and holds significant translational value for disease diagnostics.

Literature search
Compounds that extended the mean lifespan of the organism by more than 5%, according to the DrugAge (Barardo et al., 2017a) and Geroprotector (Moskalev et al., 2015) databases, were selected for further investigation. The abstracts of studies associated with lifespan extension were filtered for the occurrence of at least one of the following matrisome keywords: collagen, ECM, extracellular, matrix, proteoglycan, hyaluronic, hyaluronan, TGF, integrin, TGFbeta.

Aging matreotype definition
To define the human aging matreotype, we performed a literature search and extracted the age-association of all genes involved in forming the human matrisome.
A large part of aging datasets were obtained from a large-scale meta-analysis conducted by (Blankenburg et al., 2018). If the datasets have not yet been subjected to a significance cutoff, we applied multiple testing corrected (Benjamini-Hochberg) threshold of 0.05 to each dataset if applicable. Studies analyzing individual tissues were treated as separate sources. To define the aging matrisome, we acquired data from at least three sources implicating the gene in the aging process. Studies that offer directionality were further utilized to determine matrisome genes that were upregulated or downregulated with age using the same thresholds.
For some experiments, animals were mounted onto 2% agar pads and pictures were taken with an upright fluorescent microscope (Tritech Research, model: BX-51-F). To separate the GFP signal from the autofluorescence of the gut, we used the microscope, settings and triple-band filterset as described by Teuscher (Teuscher and Ewald, 2018). The total intensity per animal, intensity [a.u.], is calculated from fluorescence images using FIJI.

Manual lifespan measurements
Manual scoring of lifespan as previously described by Ewald et. al. 2016(Ewald et al., 2016. In brief, about 100 day-2 adult C. elegans were picked to NGM plates containing the solvent either water or 0.1% dimethyl sulfoxide (DMSO) alone as control or tretinoin (Sigma PHR1187), hyaluronic acid (Sigma H5388), chondroitin sulfate (Sigma 27042). Animals were classified as dead if they failed to respond to prodding. Exploded, bagged, burrowed, or animals that left the agar were excluded from the statistics. The estimates of survival functions were calculated using the product-limit (Kaplan-Meier) method. The log-rank (Mantel-Cox) method was used to test the null hypothesis and calculate P values (JMP software v.14.1.0.).

Automated survival assays using the lifespan machine
Automated survival analysis was conducted using the lifespan machine described by Stroustrup and colleagues (Stroustrup et al., 2013). Approximately 500 L4 animals were resuspended in M9 and transferred to NGM plates containing 50 µM 5-Fluoro-2'deoxyuridine (FUdR) seeded either with OP50 bacteria, or with RNAi bacteria supplemented with 100 μg/ml carbenicillin, or with heat-killed OP50 bacteria, or with UV-inactivated E. coli strain NEC937 B (OP50 uvrA; KanR) containing 100 μg/ml carbenicillin. Animals were kept at 20°C until measurement. Tight-fitting Petri dishes (BD Falcon Petri Dishes, 50 x 9 mm) were used for lifespan experiments. Tight-fitting plates were dried without lids in a laminar flow hood for 40 minutes before starting the experiment. Air-cooled Epson V800 scanners were utilized for all experiments operating at a scanning frequency of one scan per 10 -30 minutes. Temperature probes (Thermoworks, Utah, U.S.) were used to monitor the temperature on the scanner flatbed and maintain 20°C. Animals that left the imaging area during the experiment were censored. Population survival was determined using the statistical software R (Ihaka and Gentleman, 2012) with the survival (Therneau and Grambsch, 2000) and survminer (https://rpkgs.datanovia.com/survminer/) packages. Lifespans were calculated from the L4 stage (= day 0).

Compound preparation for lifespan and oxidative stress assays
Compounds are received freeze-dried, except for the liposomes, which were acquired in 100% saturated suspension. All compounds were blinded with a serial number.
Compounds and control solvents are administered to C. elegans by mixing it in the Nematode Growth Medium (NGM) immediately before pouring the plates. Compound stock solutions were made by dissolving 100 mg/mL in their solvent, 100% DMSO for genistein, water for the royal jelly oil in lecithin-based nanoemulsion. The royal jelly oil was prepared from dispersing royal jelly powder in oil (mygliol) but it was not soluble in the NGN agar C. elegans culturing plates. In a second step, we encapsulated the royal jelly oil by homogenizing the oil with lecithin. The liposomal genistein and empty liposomes were suspended in water. These stocks were consequently used to make dilution series. The final concentration of DMSO on the lifespan plates did not exceed 0.2%. The C. elegans strain TJ1060 was age-synchronized by extracting the eggs with bleach and were made infertile by culturing at 25°C from egg to day-1 of adulthood. On day-2-of adulthood, 30-40 animals were placed per 6 cm plates, four plates for each compound. Subsequently, the plates are loaded onto the scanners, kept in a controlled environment at 20°C. Every scanner includes at least four control plates. For the manual lifespan at 25°C, three plates were used per compound, and death events were counted once per day.

Oxidative stress assays
Oxidative stress assay was modified from Ewald et al., 2017(Ewald et al., 2017. C. elegans of the L1 or day-1-adult stage were shifted on compound-containing or control plates washed off at the indicated time point, incubated with 14 mM sodium arsenite (Honeywell International 35000) in U-Shaped 96 well plates, and put into the wMicroTracker (MTK100) for movement scoring. For statistical analysis, the area under the curve was measured, and the mean for each run was calculated. Statistical analysis was performed by using a paired sample t-test.

Quantifying total collagen over protein content.
Collagen levels were determined by hydroxyproline content as described in Teuscher et al., 2019(Teuscher et al., 2019b. In brief, about 10 000 TJ1060 C. elegans eggs were placed at 25°C until day-1 of adulthood and then transferred on plates containing the compounds at 20°C. Day-8-adult animals were harvested for the collagen and protein assays.

Author contributions
All authors participated in analyzing and interpreting the data. CYE and CS designed the experiments. CS established and performed in-silico analysis. AD and PM translated compound names for list comparison and automated analysis. EJ, CS, and CYE performed lifespan assays. EJ and CYE performed oxidative stress assays. EJ and SXL performed collagen expression assays. FW and FZ prepared and encapsulated compounds. CYE wrote the manuscript in consultation with the other authors.

Author Information
The authors have no competing interests to declare. Correspondence should be addressed to C. Y. E.

Acknowledgement
We thank Anne Häfke for help with analysing GFP expression, Katharina Tarnutzer Table 4). Black rectangles indicate that the respective compound increases lifespan in any organism (Supplementary Table 5). The relative change in expression with age is displayed for all matrisome genes.
Each gene is colored by its matrisome category.

Supplementary Figure 8. Genetic fingerprint of the aging human matreotype
Meta analysis of 48 human aging datasets to characterize matrisome aging on a single-gene level. In (A), studies were gathered which implicated matrisome genes in human aging without determining the regulation of the specific genes. A different perspective is provided by studies which assess the age-specific regulation change for each gene that are shown in (B) as up-regulation) and (C) asdown-regulation. The rows of the heatmap refer to the most age-regulated matrisome genes with the tissues shown as columns in which a significant age-associated link has been observed for each gene. The color of each heatmap cell corresponds to the number of studies in support of this age-association. Both genes and tissues are clustered hierarchically to identify shared aging patterns. (D) Liposomal genistein 1 mg/mL 20.9% mean lifespan extension compared to H2O
For raw data, detailed statistics and additional trials for (A-E), see Supplementary   Table 7.

Supplementary Figure 12. Genistein increases oxidative stress resistance
(A) Temperature-sensitive sterile wild-type background C. elegans (TJ1060) were fed S-822a genistein 0.05 mg/mL from day-1 of adulthood and at day-5 of adulthood tested for oxidative stress resistance. Genistein treated C. elegans survived longer in 14 mM arsenite.
(B) Wild type (N2) animals were fed S-822c liposomal encapsulated genistein 0,5 mg/mL from L1 stage and tested for oxidative stress resistance at day-1 of adulthood.
Genistein treated C. elegans survived longer in 14 mM arsenite.
(C) Wild type (N2) animals were fed either S-867d liposomal vehicle or S-867c liposomal encapsulated genistein 0,5 mg/mL from L1 stage and tested for oxidative stress resistance at day-1 of adulthood. Genistein treated C. elegans survived longer in 14 mM arsenite.
(D) Temperature-sensitive steril wild-type background C. elegans (TJ1060) were fed either S-867d liposomal vehicle or S-867c liposomal encapsulated genistein 0,5 mg/mL from day-1 of adulthood and at day-5 of adulthood tested for oxidative stress resistance. Genistein treated C. elegans survived longer in 14 mM arsenite.
For raw data and statistics, see Supplementary Table 8.