CoChat
Sign Up
Cited by 45

The DOI matches its Crossref record; 16 of the 16 references it cites were found in a registry or an index.

Youthful and age-related matreotypes predict drugs promoting longevity

Statzer, Cyril, Jongsma, Elisabeth, Liu, Sean X., Dakhovnik, Alexander, Wandrey, Franziska, Mozharovskyi, Pavlo, Zülli, Fred, Ewald, Collin Y.·2021·Aging Cell
DOI 10.1111/acel.13441PMC PMC8441316

Published version of a preprint

Open this paper in CoChat — free account

Abstract

The identification and validation of drugs that promote health during aging (“geroprotectors”) are 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 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 Caenorhabditis 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.

Overview

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1111/acel.13441

Cites

16 of 16 references verified0 citation errors · 0 not checkable

Statistics

Figures

FIGURE 1 Geroprotective compounds shown to affect the ECM. (a) Schematic depiction of the literature and database mining approach used. (b) Compounds which contained ECM keywords in their abstract were ranked by their highest mean lifespan increase in any species. Open circle = not assessed, black closed circle = lifespan increased, and yellow circle = compound assessed in clinical trials (CT). The number of ECM keywords quantified in the abstract is displayed below. *Resveratrol only on a high‐fat diet increased lifespan in fly and mouse. For details and references, see Table S1
FIGURE 1 Geroprotective compounds shown to affect the ECM. (a) Schematic depiction of the literature and database mining approach used. (b) Compounds which contained ECM keywords in their abstract were ranked by their highest mean lifespan increase in any species. Open circle = not assessed, black closed circle = lifespan increased, and yellow circle = compound assessed in clinical trials (CT). The number of ECM keywords quantified in the abstract is displayed below. *Resveratrol only on a high‐fat diet increased lifespan in fly and mouse. For details and references, see Table S1CC BY · PubMed Central
FIGURE 2 Drugs affecting matrisome gene expression. (a) Z‐score of altering matrisome gene expression across all 1309 CMap drugs. Vertical lines indicate compounds shown to increase lifespan in any organism. (b–c) Top 12 compounds that increase (b) or decrease (c) the overall matrisome gene expression. For details, see Table S2
FIGURE 2 Drugs affecting matrisome gene expression. (a) Z‐score of altering matrisome gene expression across all 1309 CMap drugs. Vertical lines indicate compounds shown to increase lifespan in any organism. (b–c) Top 12 compounds that increase (b) or decrease (c) the overall matrisome gene expression. For details, see Table S2CC BY · PubMed Central
FIGURE 3 Matrisome genes affected by aging across human tissues. (a–b) Absolute (a) and relative (b) expression changes of all detected genes during aging are shown. Matrisome genes are displayed in red and non‐matrisome genes in black. (c–d) Absolute (c) and relative (d) expression changes of only detected matrisome genes during aging are shown and are colored by tissue. For details, see Table S3
FIGURE 3 Matrisome genes affected by aging across human tissues. (a–b) Absolute (a) and relative (b) expression changes of all detected genes during aging are shown. Matrisome genes are displayed in red and non‐matrisome genes in black. (c–d) Absolute (c) and relative (d) expression changes of only detected matrisome genes during aging are shown and are colored by tissue. For details, see Table S3CC BY · PubMed Central
FIGURE 4 Matreotype signature gene expression correlating with CMap expression pattern. (a–b) Matreotype genes that are strongly upregulated (a) or downregulated (b) during aging are hierarchically clustered with gene expression patterns of small molecules on human cell lines from CMap library (Table S4). Black rectangles indicate that the respective compound increases lifespan in any organism (Table S5)
FIGURE 4 Matreotype signature gene expression correlating with CMap expression pattern. (a–b) Matreotype genes that are strongly upregulated (a) or downregulated (b) during aging are hierarchically clustered with gene expression patterns of small molecules on human cell lines from CMap library (Table S4). Black rectangles indicate that the respective compound increases lifespan in any organism (Table S5)CC BY · PubMed Central
FIGURE 5 Validating compounds that were predicted to modulate the age‐related matreotype for in‐vivo collagen expression and lifespan. (a) Heterogeneity in decline of collagen col‐144 promoter‐driven GFP (LSD2002 [Pcol‐144::GFP]) at day 3 of adulthood. GFP levels were categorized in 4 groups (none/very low, low, medium, and high). (b) Approximately linear decline of Pcol‐144::GFP expression during the first 5 days of adulthood. (c) A single concentration per matreotype‐predicted compound tested for their efficacy on prolonging Pcol‐144::GFP expression during aging. (d) Lifespan corresponding to the matreotype‐predicted compounds with prolonged Pcol‐144::GFP expression during aging. (e–f) Metformin (e) and tretinoin (f) were observed to partially prolong Pcol‐144::GFP expression during aging in certain dosage regimes. (g–h) Representative image (g) and quantification (h) of Pcol‐144::GFP expression at day 4 of adulthood upon tretinoin treatment from day 1 of adulthood. (i) Adulthood treatment (start at day 2 of adulthood) of tretinoin extended lifespan. (j) Adulthood treatment of genistein increased Pcol‐144::GFP expression at day 4 of adulthood. (k) Liposomal‐encapsulated genistein showed higher collagen over total protein levels at day 8 of adulthood. (l) Adulthood treatment (start at day 2 of adulthood) of liposomal‐encapsulated genistein extended lifespan. (m) Adulthood treatment of chondroitin increased Pcol‐144::GFP expression at day 4 of adulthood. (n) Adulthood treatment of hyaluronic acid increased Pcol‐144::GFP expression at day 5 of adulthood. (o) Adulthood treatment (start at day 2 of adulthood) of chondroitin or hyaluronic acid extended lifespan. For raw data and statistics for (b, c, e, f, h, j, k, m, n), see Table S6. For raw data, statistics, and additional trials (d, i, l, o), see Table S7
FIGURE 5 Validating compounds that were predicted to modulate the age‐related matreotype for in‐vivo collagen expression and lifespan. (a) Heterogeneity in decline of collagen col‐144 promoter‐driven GFP (LSD2002 [Pcol‐144::GFP]) at day 3 of adulthood. GFP levels were categorized in 4 groups (none/very low, low, medium, and high). (b) Approximately linear decline of Pcol‐144::GFP expression during the first 5 days of adulthood. (c) A single concentration per matreotype‐predicted compound tested for their efficacy on prolonging Pcol‐144::GFP expression during aging. (d) Lifespan corresponding to the matreotype‐predicted compounds with prolonged Pcol‐144::GFP expression during aging. (e–f) Metformin (e) and tretinoin (f) were observed to partially prolong Pcol‐144::GFP expression during aging in certain dosage regimes. (g–h) Representative image (g) and quantification (h) of Pcol‐144::GFP expression at day 4 of adulthood upon tretinoin treatment from day 1 of adulthood. (i) Adulthood treatment (start at day 2 of adulthood) of tretinoin extended lifespan. (j) Adulthood treatment of genistein increased Pcol‐144::GFP expression at day 4 of adulthood. (k) Liposomal‐encapsulated genistein showed higher collagen over total protein levels at day 8 of adulthood. (l) Adulthood treatment (start at day 2 of adulthood) of liposomal‐encapsulated genistein extended lifespan. (m) Adulthood treatment of chondroitin increased Pcol‐144::GFP expression at day 4 of adulthood. (n) Adulthood treatment of hyaluronic acid increased Pcol‐144::GFP expression at day 5 of adulthood. (o) Adulthood treatment (start at day 2 of adulthood) of chondroitin or hyaluronic acid extended lifespan. For raw data and statistics for (b, c, e, f, h, j, k, m, n), see Table S6. For raw data, statistics, and additional trials (d, i, l, o), see Table S7CC BY · PubMed Central

Verification

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Citations

Cites

Show all 16 references

Research works printed but not linked: 45

  1. 10.18632/aging.101047 · DOI
  2. 10.1111/dom.12354 · DOI · Paper page
  3. 10.1111/acel.12585 · DOI · Paper page
  4. 10.1007/s10654-012-9714-6 · DOI
  5. 10.1111/acel.12432 · DOI
  6. 10.2174/157340611796799212 · DOI
  7. 10.1371/journal.pone.0033626 · DOI
  8. 10.1007/s11357-019-00113-y · DOI · Paper page
  9. 10.1093/bioinformatics/btp073 · DOI · Paper page
  10. 10.1016/j.tem.2018.11.005 · DOI
  11. 10.1111/acel.12819 · DOI
  12. 10.1038/nature14021 · DOI
  13. 10.1111/acel.12509 · DOI
  14. 10.1159/000504295 · DOI
  15. 10.1016/j.bjps.2019.03.032 · DOI
  16. 10.1371/journal.pcbi.1006639 · DOI
  17. 10.1056/NEJM199308193290803 · DOI
  18. 10.1159/000450832 · DOI
  19. 10.1371/journal.pone.0086842 · DOI
  20. 10.1016/j.celrep.2019.03.044 · DOI
  21. 10.1371/journal.pcbi.1007162 · DOI
  22. 10.1126/science.1132939 · DOI · Paper page
  23. 10.4062/biomolther.2014.075 · DOI
  24. 10.1177/1535370217723628 · DOI
  25. 10.3748/wjg.v20.i16.4648 · DOI
  26. Li Z.‐H., 2020, Associations of regular glucosamine use with all‐cause and cause‐specific mortality: A large prospective cohort study, Annals of the Rheumatic Diseases, 79, annrheumdis‐202
  27. 10.1093/ecam/nel081 · DOI
  28. Liu H., 2016, Screening lifespan‐extending drugs in Caenorhabditis elegans via label propagation on drug‐protein networks, BMC Systems Biology, 10
  29. 10.1038/ng.2653 · DOI · Paper page
  30. 10.1161/HYPERTENSIONAHA.112.191445 · DOI
  31. 10.18632/aging.100799 · DOI
  32. 10.2147/ciia.2006.1.4.327 · DOI · Paper page
  33. 10.1038/s41586-018-0457-8 · DOI · Paper page
  34. 10.1111/j.1476-5381.2011.01619.x · DOI
  35. 10.1038/nm.4005 · DOI
  36. 10.1016/j.semcdb.2018.06.005 · DOI
  37. 10.1016/j.mbplus.2020.100039 · DOI · Paper page
  38. 10.1038/nature16550 · DOI · Paper page
  39. 10.1042/EBC20190001 · DOI
  40. 10.1038/s41598-019-43075-z · DOI
  41. 10.21769/BioProtoc.2940 · DOI
  42. 10.1016/j.mbplus.2018.11.001 · DOI
  43. 10.1001/archderm.143.2.155 · DOI
  44. 10.1038/ncomms4563 · DOI
  45. 10.1111/acel.13121 · DOI

Cited by

Sorted by citations

See all 45

Metadata

Authors
Statzer, C., Jongsma, E., Liu, S. X., Dakhovnik, A., Wandrey, F., Mozharovskyi, P., Zülli, F., & Ewald, C. Y.
Affiliations
  • Department of Health Sciences and Technology Institute of Translational Medicine Eidgenössische Technische Hochschule Zürich Schwerzenbach‐Zürich Switzerland
  • Mibelle Biochemistry, Mibelle AG Buchs Switzerland
  • LTCI Telecom Paris Institut Polytechnique de Paris Palaiseau France
ORCID
Ewald, Collin Y.
Venue
Aging Cell
Publisher
Wiley
Published
2021-08-04
Volume
20
Issue
9
ISSN
  • 1474-9718
  • 1474-9726
DOI
10.1111/acel.13441
PMC
PMC8441316
Cited by
45
References
16
Language
en
Licence
creativecommons.org/licenses/by/4.0/ (vor)
Funders
Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung (PP00P3_163898)

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review

How to cite

Cite CoChat for the checks on this page, and the paper for its own findings.

This page
CoChat. (2026). Citation check: Youthful and age-related matreotypes predict drugs promoting longevity. https://app.cochat.ai/papers/10.1111/acel.13441
The paper
Statzer, C., Jongsma, E., Liu, S. X., Dakhovnik, A., Wandrey, F., Mozharovskyi, P., Zülli, F., & Ewald, C. Y. (2021). Youthful and age-related matreotypes predict drugs promoting longevity. Aging Cell. https://doi.org/10.1111/acel.13441