Abstract
Aging strongly influences human morbidity and mortality. Thus, aging-preventive compounds could greatly improve our health and lifespan. Here we screened for such compounds, known as geroprotectors, employing the power of transcriptomics to predict biological age. Using age-stratified human tissue transcriptomes and machine learning, we generated age classifiers and applied these to transcriptomic changes induced by 1,309 different compounds in human cells, ranking these compounds by their ability to induce a "youthful" transcriptional state. Testing the top candidates in C. elegans, we identified two Hsp90 inhibitors, monorden and tanespimycin, which extended the animals' lifespan and improved their health. Hsp90 inhibition induces expression of heat shock proteins known to improve protein homeostasis. Consistently, monorden treatment improved the survival of C. elegans under proteotoxic stress, and its benefits depended on the cytosolic unfolded protein response-inducing transcription factor HSF-1. Taken together, our method represents an innovative geroprotector screening approach and was able to identify a class that acts by improving protein homeostasis.
Original language | English |
---|---|
Pages (from-to) | 467-480.e6 |
Journal | Cell reports |
Volume | 27 |
Issue number | 2 |
DOIs | |
Publication status | Published - 9 Apr 2019 |
Keywords
- Caenorhabditis elegans
- Hsp90
- aging
- drug discovery
- geroprotectors
- healthspan
- lifespan
- machine learning
- monorden
- tanespimycin
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In: Cell reports, Vol. 27, No. 2, 09.04.2019, p. 467-480.e6.
Research output: Contribution to journal › Article › Academic › peer-review
TY - JOUR
T1 - Transcriptomics-Based Screening Identifies Pharmacological Inhibition of Hsp90 as a Means to Defer Aging
AU - Janssens, Georges E.
AU - Lin, Xin Xuan
AU - Millan-Ariño, Lluís
AU - Kavšek, Alan
AU - Sen, Ilke
AU - Seinstra, Renée I.
AU - Stroustrup, Nicholas
AU - Nollen, Ellen A.A.
AU - Riedel, Christian G.
N1 - Funding Information: We thank Peter Swoboda for advice and infrastructure support, João Pedro de Magalhães for providing early access to the DrugAge database, Jeong-Hoon Hahm and Hong Gil Nam for advice regarding the maximum velocity measurements, Xin Zhou for help with the lifespan machine, Rick Morimoto for providing RNAi clones, and Maria Eriksson and Urban Lendahl for comments on the manuscript. N.S. was supported by funding from the Spanish Ministry of Economy, Industry and Competitiveness (MEIC) to the EMBL partnership, the Centro de Excelencia Severo Ochoa , the CERCA Programme/Generalitat de Catalunya , and an award from the Glenn Foundation for Medical Research . E.A.A.N. was supported by the European Research Council (ERC) and the alumni chapter of Gooische Groningers facilitated by Ubbo Emmius Fonds . C.G.R. was supported by the Swedish Research Council (VR) grant 2015-03740 , the COST grant BM1408 ( GENiE ), and an ICMC project grant. Funding Information: We thank Peter Swoboda for advice and infrastructure support, João Pedro de Magalhães for providing early access to the DrugAge database, Jeong-Hoon Hahm and Hong Gil Nam for advice regarding the maximum velocity measurements, Xin Zhou for help with the lifespan machine, Rick Morimoto for providing RNAi clones, and Maria Eriksson and Urban Lendahl for comments on the manuscript. N.S. was supported by funding from the Spanish Ministry of Economy, Industry and Competitiveness (MEIC) to the EMBL partnership, the Centro de Excelencia Severo Ochoa, the CERCA Programme/Generalitat de Catalunya, and an award from the Glenn Foundation for Medical Research. E.A.A.N. was supported by the European Research Council (ERC) and the alumni chapter of Gooische Groningers facilitated by Ubbo Emmius Fonds. C.G.R. was supported by the Swedish Research Council (VR) grant 2015-03740, the COST grant BM1408 (GENiE), and an ICMC project grant. G.E.J. X.-X.L. L.M.-A. E.A.A.N. and C.G.R. conceived and designed the analyses and experiments. G.E.J. conducted the bioinformatic analyses. X.-X.L. G.E.J. R.I.S. A.K. I.S. and L.M.-A. conducted the in vivo experiments and analyzed the resulting data. N.S. helped with the setup and use of the lifespan machine. G.E.J. L.M.-A. X.-X.L. and C.G.R. wrote the manuscript. The authors declare no competing interests. Funding Information: We thank Peter Swoboda for advice and infrastructure support, Jo?o Pedro de Magalh?es for providing early access to the DrugAge database, Jeong-Hoon Hahm and Hong Gil Nam for advice regarding the maximum velocity measurements, Xin Zhou for help with the lifespan machine, Rick Morimoto for providing RNAi clones, and Maria Eriksson and Urban Lendahl for comments on the manuscript. N.S. was supported by funding from the Spanish Ministry of Economy, Industry and Competitiveness (MEIC) to the EMBL partnership, the Centro de Excelencia Severo Ochoa, the CERCA Programme/Generalitat de Catalunya, and an award from the Glenn Foundation for Medical Research. E.A.A.N. was supported by the European Research Council (ERC) and the alumni chapter of Gooische Groningers facilitated by Ubbo Emmius Fonds. C.G.R. was supported by the Swedish Research Council (VR) grant 2015-03740, the COST grant BM1408 (GENiE), and an ICMC project grant. G.E.J. X.-X.L. L.M.-A. E.A.A.N. and C.G.R. conceived and designed the analyses and experiments. G.E.J. conducted the bioinformatic analyses. X.-X.L. G.E.J. R.I.S. A.K. I.S. and L.M.-A. conducted the in vivo experiments and analyzed the resulting data. N.S. helped with the setup and use of the lifespan machine. G.E.J. L.M.-A. X.-X.L. and C.G.R. wrote the manuscript. The authors declare no competing interests. Publisher Copyright: © 2019 The Author(s)
PY - 2019/4/9
Y1 - 2019/4/9
N2 - Aging strongly influences human morbidity and mortality. Thus, aging-preventive compounds could greatly improve our health and lifespan. Here we screened for such compounds, known as geroprotectors, employing the power of transcriptomics to predict biological age. Using age-stratified human tissue transcriptomes and machine learning, we generated age classifiers and applied these to transcriptomic changes induced by 1,309 different compounds in human cells, ranking these compounds by their ability to induce a "youthful" transcriptional state. Testing the top candidates in C. elegans, we identified two Hsp90 inhibitors, monorden and tanespimycin, which extended the animals' lifespan and improved their health. Hsp90 inhibition induces expression of heat shock proteins known to improve protein homeostasis. Consistently, monorden treatment improved the survival of C. elegans under proteotoxic stress, and its benefits depended on the cytosolic unfolded protein response-inducing transcription factor HSF-1. Taken together, our method represents an innovative geroprotector screening approach and was able to identify a class that acts by improving protein homeostasis.
AB - Aging strongly influences human morbidity and mortality. Thus, aging-preventive compounds could greatly improve our health and lifespan. Here we screened for such compounds, known as geroprotectors, employing the power of transcriptomics to predict biological age. Using age-stratified human tissue transcriptomes and machine learning, we generated age classifiers and applied these to transcriptomic changes induced by 1,309 different compounds in human cells, ranking these compounds by their ability to induce a "youthful" transcriptional state. Testing the top candidates in C. elegans, we identified two Hsp90 inhibitors, monorden and tanespimycin, which extended the animals' lifespan and improved their health. Hsp90 inhibition induces expression of heat shock proteins known to improve protein homeostasis. Consistently, monorden treatment improved the survival of C. elegans under proteotoxic stress, and its benefits depended on the cytosolic unfolded protein response-inducing transcription factor HSF-1. Taken together, our method represents an innovative geroprotector screening approach and was able to identify a class that acts by improving protein homeostasis.
KW - Caenorhabditis elegans
KW - Hsp90
KW - aging
KW - drug discovery
KW - geroprotectors
KW - healthspan
KW - lifespan
KW - machine learning
KW - monorden
KW - tanespimycin
UR - http://www.scopus.com/inward/record.url?scp=85063644517&partnerID=8YFLogxK
U2 - https://doi.org/10.1016/j.celrep.2019.03.044
DO - https://doi.org/10.1016/j.celrep.2019.03.044
M3 - Article
C2 - 30970250
SN - 2211-1247
VL - 27
SP - 467-480.e6
JO - Cell reports
JF - Cell reports
IS - 2
ER -