Evidence map›Paper›PMID 25754370›Full record

ArticleAging cell2015

The Achilles' heel of senescent cells: from transcriptome to senolytic drugs.

Yi Zhu, Tamara Tchkonia, Tamar Pirtskhalava, Adam C Gower, Husheng Ding, Nino Giorgadze, Allyson K Palmer, Yuji Ikeno, Gene B Hubbard, Marc Lenburg and 23 more

Open access · goldAbstract read
In one paragraph

Article in Aging cell, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1,478 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
1,478citing papers in PubMed, 2 pooled it
62.0field-weighted citation impact, top 1% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1,478 citing papers in PubMed, 2 syntheses or guidelines pooled it, 2,329 citations in OpenAlex.

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1,418 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

33 authors at 6 institutions in 1 country.

Yi ZhuRobert and Arlene Kogod Center on Aging, Mayo Clinic, Rochester, MN, USA.
Tamara TchkoniaRobert and Arlene Kogod Center on Aging, Mayo Clinic, Rochester, MN, USA.
Tamar PirtskhalavaRobert and Arlene Kogod Center on Aging, Mayo Clinic, Rochester, MN, USA.
Adam C GowerSection of Computational Biomedicine, Boston University School of Medicine, Boston, MA, USA.
Husheng DingRobert and Arlene Kogod Center on Aging, Mayo Clinic, Rochester, MN, USA.
Nino GiorgadzeRobert and Arlene Kogod Center on Aging, Mayo Clinic, Rochester, MN, USA.
Allyson K PalmerRobert and Arlene Kogod Center on Aging, Mayo Clinic, Rochester, MN, USA.
Yuji IkenoDepartments of Pathology, Barshop Institute for Longevity and Aging Studies, The University of Texas Health Science Center, San Antonio, TX, USA.
Gene B HubbardDepartments of Pathology, Barshop Institute for Longevity and Aging Studies, The University of Texas Health Science Center, San Antonio, TX, USA.
Marc LenburgSection of Computational Biomedicine, Boston University School of Medicine, Boston, MA, USA.
Steven P O'HaraRobert and Arlene Kogod Center on Aging, Mayo Clinic, Rochester, MN, USA.
Nicholas F LaRussoRobert and Arlene Kogod Center on Aging, Mayo Clinic, Rochester, MN, USA.
Jordan D MillerRobert and Arlene Kogod Center on Aging, Mayo Clinic, Rochester, MN, USA.
Carolyn M RoosRobert and Arlene Kogod Center on Aging, Mayo Clinic, Rochester, MN, USA.
Grace C VerzosaRobert and Arlene Kogod Center on Aging, Mayo Clinic, Rochester, MN, USA.
Nathan K LeBrasseurRobert and Arlene Kogod Center on Aging, Mayo Clinic, Rochester, MN, USA.
Jonathan D WrenDepartment of Biochemistry and Molecular Biology, Oklahoma Medical Research Foundation, Oklahoma City, OK, USA.
Joshua N FarrRobert and Arlene Kogod Center on Aging, Mayo Clinic, Rochester, MN, USA.
Sundeep KhoslaRobert and Arlene Kogod Center on Aging, Mayo Clinic, Rochester, MN, USA.
Michael B StoutRobert and Arlene Kogod Center on Aging, Mayo Clinic, Rochester, MN, USA.
Sara J McGowanDepartment of Metabolism and Aging, The Scripps Research Institute, Jupiter, FL, USA.
Heike Fuhrmann-StroissniggDepartment of Metabolism and Aging, The Scripps Research Institute, Jupiter, FL, USA.
Aditi U GurkarDepartment of Metabolism and Aging, The Scripps Research Institute, Jupiter, FL, USA.
Jing ZhaoDepartment of Metabolism and Aging, The Scripps Research Institute, Jupiter, FL, USA.
Debora ColangeloDepartment of Metabolism and Aging, The Scripps Research Institute, Jupiter, FL, USA.
Akaitz DorronsoroDepartment of Metabolism and Aging, The Scripps Research Institute, Jupiter, FL, USA.
Yuan Yuan LingDepartment of Metabolism and Aging, The Scripps Research Institute, Jupiter, FL, USA.
Amira S BarghouthyDepartment of Metabolism and Aging, The Scripps Research Institute, Jupiter, FL, USA.
Diana C NavarroDepartment of Metabolism and Aging, The Scripps Research Institute, Jupiter, FL, USA.
Tokio SanoDepartment of Metabolism and Aging, The Scripps Research Institute, Jupiter, FL, USA.
Paul D RobbinsDepartment of Metabolism and Aging, The Scripps Research Institute, Jupiter, FL, USA.
Laura J NiedernhoferDepartment of Metabolism and Aging, The Scripps Research Institute, Jupiter, FL, USA.
James L KirklandRobert and Arlene Kogod Center on Aging, Mayo Clinic, Rochester, MN, USA.
Mayo Clinic in Florida · USScripps Research Institute · USBoston University · USMurphy Oil Corporation (United States) · USOklahoma Medical Research Foundation · USSouth Texas Veterans Health Care System · US

Funding

TRANSGENIC COREP30AG013319 · NIA · UNIVERSITY OF TEXAS HLTH SCIENCE CENTER · PI PETER J HORNSBY · 1995 to 2026
$30.6M
PILOT AND FEASIBILTY PROGRAMP30DK084567 · NIDDK · MAYO CLINIC ROCHESTER · PI GREGORY J. GORES · 2009 to 2026
$22.2M
Obesity and Energy Metabolism CoreP30DK050456 · NIDDK · UNIVERSITY OF MINNESOTA TWIN CITIES · PI BERNLOHR, DAVID A · 1995 to 2015
$17.0M
PROTEOMICSP01AG043376 · NIA · UNIVERSITY OF MINNESOTA · PI ROBBINS, PAUL D. · 2013 to 2019
$11.2M
Systems Biology and Bioinformatics CoreP01AG041122 · NIA · MAYO CLINIC ROCHESTER · PI CAMPISI, JUDITH · 2012 to 2016
$9.8M
The Somatotropic Axis and Health Aging: A Search for MechanismsP01AG031736 · NIA · SOUTHERN ILLINOIS UNIVERSITY SCH OF MED · PI BARTKE, ANDRZEJ · 2009 to 2013
$8.4M
Boston University Clinical and Translational Science Award (CTSA) Program UL1UL1TR000157 · NCATS · BOSTON UNIVERSITY MEDICAL CAMPUS · PI CENTER, DAVID M. · 2012 to 2013
$7.9M
Pathophysiology of Biliary DiseaseR01DK057993 · NIDDK · MAYO CLINIC ROCHESTER · PI LARUSSO, NICHOLAS F., O'HARA, STEVEN P · 2001 to 2025
$7.1M
Effect of Aging on Preadipocyte DifferentiationR37AG013925 · NIA · MAYO CLINIC ROCHESTER · PI KIRKLAND, JAMES L. · 2016 to 2025
$6.6M
Effect of Aging on Preadipocyte DifferentiationR01AG013925 · NIA · MAYO CLINIC ROCHESTER · PI KIRKLAND, JAMES L. · 1997 to 2012
$3.7M
Role of SIRT6 in calcific aortic valve diseaseR01HL111121 · NHLBI · MAYO CLINIC ROCHESTER · PI MILLER, JORDAN D · 2013 to 2017
$2.0M
Geroscience NetworkR24AG044396 · NIA · MAYO CLINIC ROCHESTER · PI KIRKLAND, JAMES L. · 2013 to 2015
$684k
NCATS NIH HHS UL1-TR000157NHLBI NIH HHS HL111121NHLBI NIH HHS R01 HL111121NIA NIH HHS AG013925NIA NIH HHS AG031736NIA NIH HHS AG041122NIA NIH HHS AG043376NIA NIH HHS AG044396NIA NIH HHS F30 AG046061NIA NIH HHS P01 AG041122NIA NIH HHS P01 AG043376NIA NIH HHS P30 AG013319NIDDK NIH HHS DK050456NIDDK NIH HHS P30 DK084567NIDDK NIH HHS R01 DK057993
6 · The paper itself

Abstract

The healthspan of mice is enhanced by killing senescent cells using a transgenic suicide gene. Achieving the same using small molecules would have a tremendous impact on quality of life and the burden of age-related chronic diseases. Here, we describe the rationale for identification and validation of a new class of drugs termed senolytics, which selectively kill senescent cells. By transcript analysis, we discovered increased expression of pro-survival networks in senescent cells, consistent with their established resistance to apoptosis. Using siRNA to silence expression of key nodes of this network, including ephrins (EFNB1 or 3), PI3Kδ, p21, BCL-xL, or plasminogen-activated inhibitor-2, killed senescent cells, but not proliferating or quiescent, differentiated cells. Drugs targeting these same factors selectively killed senescent cells. Dasatinib eliminated senescent human fat cell progenitors, while quercetin was more effective against senescent human endothelial cells and mouse BM-MSCs. The combination of dasatinib and quercetin was effective in eliminating senescent MEFs. In vivo, this combination reduced senescent cell burden in chronologically aged, radiation-exposed, and progeroid Ercc1(-/Δ) mice. In old mice, cardiac function and carotid vascular reactivity were improved 5 days after a single dose. Following irradiation of one limb in mice, a single dose led to improved exercise capacity for at least 7 months following drug treatment. Periodic drug administration extended healthspan in Ercc1(-/∆) mice, delaying age-related symptoms and pathology, osteoporosis, and loss of intervertebral disk proteoglycans. These results demonstrate the feasibility of selectively ablating senescent cells and the efficacy of senolytics for alleviating symptoms of frailty and extending healthspan.

Indexed as

TranscriptomeAdipocytesAgingAnimalsbcl-X ProteinCarotid ArteriesCellular SenescenceClass I Phosphatidylinositol 3-KinasesCyclin-Dependent Kinase Inhibitor p21DasatinibDNA-Binding ProteinsDrug CombinationsEndonucleasesEndothelial CellsEphrinsFibroblastsBcl2l1 protein, mousebcl-X ProteinClass I Phosphatidylinositol 3-KinasesCyclin-Dependent Kinase Inhibitor p21DasatinibDNA-Binding ProteinsDrug CombinationsEndonucleasesEphrinsErcc1 protein, mousePhosphatidylinositol 3-KinasesPik3cd protein, mousePlasminogen Activator Inhibitor 2Quercetindasatinibdependence receptorsephrinsp21PI3K deltaplasminogen-activated inhibitorquercetin

Identifiers

PMID25754370
PMCPMC4531078
OpenAlexW2128807350

What OpenQuestion holds

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Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.