{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Jiang Z"],"funding":["Whole People Nutrition Research Fund","National Key R&D Program of China","Howard Hughes Medical Institute","Strategic Priority Research Program of the CAS","State Key Laboratory of Stem Cell and Reproductive Biology","National Natural Science Foundation of China","Key Research Program of the CAS","CAS Project for Young Scientists in Basic Research","Bill and Melinda Gates Foundation"],"pagination":["e13214"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC9136510"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["55(5)"],"pubmed_abstract":["Objectives
To restore tissue growth without increasing the risk for cancer during aging, there is a need to identify small molecule drugs that can increase cell growth without increasing cell proliferation. While there have been numerous high-throughput drug screens for cell proliferation, there have been few screens for post-mitotic anabolic growth.Materials and methods
A machine learning (ML)-based phenotypic screening strategy was used to discover metabolites that boost muscle growth. Western blot, qRT-PCR and immunofluorescence staining were used to evaluate myotube hypertrophy/maturation or protein synthesis. Mass spectrometry (MS)-based thermal proteome profiling-temperature range (TPP-TR) technology was used to identify the protein targets that bind the metabolites. "],"journal":["Cell proliferation"],"pubmed_title":["Machine learning-based phenotypic screening for postmitotic growth inducers uncover vitamin D3 metabolites as small molecule ribosome agonists."],"pmcid":["PMC9136510"],"funding_grant_id":["2021YFE0111800","2018YFC1004102","2019YFA0801701","81900782","YSBR-012","2018YFE0201100","CNSNNSRG2021-129","91957202","XDA16010109","KJZD-SW-L04"],"pubmed_authors":["Jiang Z","Zheng Z","Yao Z","Ma S","Guang L","Li C","Cao W","Shyh-Chang N","Chen Y","Zhang L","Yu K"],"additional_accession":[]},"is_claimable":false,"name":"Machine learning-based phenotypic screening for postmitotic growth inducers uncover vitamin D3 metabolites as small molecule ribosome agonists.","description":"Objectives
To restore tissue growth without increasing the risk for cancer during aging, there is a need to identify small molecule drugs that can increase cell growth without increasing cell proliferation. While there have been numerous high-throughput drug screens for cell proliferation, there have been few screens for post-mitotic anabolic growth.Materials and methods
A machine learning (ML)-based phenotypic screening strategy was used to discover metabolites that boost muscle growth. Western blot, qRT-PCR and immunofluorescence staining were used to evaluate myotube hypertrophy/maturation or protein synthesis. Mass spectrometry (MS)-based thermal proteome profiling-temperature range (TPP-TR) technology was used to identify the protein targets that bind the metabolites. ","dates":{"release":"2022-01-01T00:00:00Z","publication":"2022 May","modification":"2025-06-01T00:13:02.544Z","creation":"2025-02-19T04:57:07.066Z"},"accession":"S-EPMC9136510","cross_references":{"pubmed":["35411556"],"doi":["10.1111/cpr.13214"]}}