{"doi":"10.7554/elife.104375","title":"Computer prediction and genetic analysis identifies retinoic acid modulation as a driver of conserved longevity pathways in genetically diverse Caenorhabditis nematodes","abstract":"Discovery of new compounds that ameliorate the negative health impacts of aging promises to be of tremendous benefit across a number of age-based comorbidities. One method to prioritize a testable subset of the nearly infinite universe of potential compounds is to use computational prediction of their likely anti-aging capacity. Here, we present a survey of longevity effects for 16 compounds suggested by a previously published computational prediction set, capitalizing upon the comprehensive, multi-species approach utilized by the Caenorhabditis Intervention Testing Program. While 11 compounds (aldosterone, arecoline, bortezomib, dasatinib, decitabine, dexamethasone, erlotinib, everolimus, gefitinib, temsirolimus, and thalidomide) either had no effect on median lifespan or were toxic, 5 compounds (all-trans retinoic acid, berberine, fisetin, propranolol, and ritonavir) extended lifespan in Caenorhabditis elegans . These computer predictions yield a remarkable positive hit rate of 30%. Deeper genetic characterization of the longevity effects of one of the most efficacious compounds, the endogenous signaling ligand all-trans retinoic acid (atRA, designated tretinoin in medical products), demonstrated a requirement for the regulatory kinases AKT-1 and AKT-2. While the canonical Akt-target FOXO/DAF-16 was largely dispensable, other conserved Akt-targets (Nrf2/SKN-1 and HSF1/HSF-1), as well as the conserved catalytic subunit of AMPK AAK-2, were all necessary for longevity extension by atRA. Our results highlight the potential of combining computational prediction of longevity interventions with the power of nematode functional genetics and underscore that the manipulation of a conserved metabolic regulatory circuit by co-opting endogenous signaling molecules is a powerful approach for discovering aging interventions.","journal":"eLife","year":2024,"id":492768,"datarank":0.10397207708399181,"base_score":0.6931471805599453,"endowment":0.6931471805599453,"self_citation_contribution":0.10397207708399181,"citation_network_contribution":0.0,"self_endowment_contribution":0.10397207708399181,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":1,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9535,"is_data_producer":true,"deposit_databanks":{"GEO":["GSE272535"]},"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2024-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":424637,"name":"Christine A. 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Willis","orcid":"0000-0002-4263-7347","position":17,"is_corresponding":false},{"id":485434,"name":"Gavin Woodruff","orcid":"0000-0003-0919-5660","position":18,"is_corresponding":false},{"id":344124,"name":"Monica Driscoll","orcid":"0000-0002-8751-7429","position":19,"is_corresponding":false},{"id":228996,"name":"Gordon J. Lithgow","orcid":"0000-0002-8953-3043","position":20,"is_corresponding":false},{"id":352930,"name":"Patrick C. Phillips","orcid":"0000-0001-7271-342X","position":21,"is_corresponding":false},{"id":424633,"name":"Stephen A. 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