<HashMap><database>biostudies-literature</database><scores/><additional><submitter>Jiang Z</submitter><funding>Whole People Nutrition Research Fund</funding><funding>National Key R&amp;D Program of China</funding><funding>Howard Hughes Medical Institute</funding><funding>Strategic Priority Research Program of the CAS</funding><funding>State Key Laboratory of Stem Cell and Reproductive Biology</funding><funding>National Natural Science Foundation of China</funding><funding>Key Research Program of the CAS</funding><funding>CAS Project for Young Scientists in Basic Research</funding><funding>Bill and Melinda Gates Foundation</funding><pagination>e13214</pagination><full_dataset_link>https://www.ebi.ac.uk/biostudies/studies/S-EPMC9136510</full_dataset_link><repository>biostudies-literature</repository><omics_type>Unknown</omics_type><volume>55(5)</volume><pubmed_abstract>&lt;h4>Objectives&lt;/h4>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.&lt;h4>Materials and methods&lt;/h4>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. </pubmed_abstract><journal>Cell proliferation</journal><pubmed_title>Machine learning-based phenotypic screening for postmitotic growth inducers uncover vitamin D3 metabolites as small molecule ribosome agonists.</pubmed_title><pmcid>PMC9136510</pmcid><funding_grant_id>2021YFE0111800</funding_grant_id><funding_grant_id>2018YFC1004102</funding_grant_id><funding_grant_id>2019YFA0801701</funding_grant_id><funding_grant_id>81900782</funding_grant_id><funding_grant_id>YSBR-012</funding_grant_id><funding_grant_id>2018YFE0201100</funding_grant_id><funding_grant_id>CNSNNSRG2021-129</funding_grant_id><funding_grant_id>91957202</funding_grant_id><funding_grant_id>XDA16010109</funding_grant_id><funding_grant_id>KJZD-SW-L04</funding_grant_id><pubmed_authors>Jiang Z</pubmed_authors><pubmed_authors>Zheng Z</pubmed_authors><pubmed_authors>Yao Z</pubmed_authors><pubmed_authors>Ma S</pubmed_authors><pubmed_authors>Guang L</pubmed_authors><pubmed_authors>Li C</pubmed_authors><pubmed_authors>Cao W</pubmed_authors><pubmed_authors>Shyh-Chang N</pubmed_authors><pubmed_authors>Chen Y</pubmed_authors><pubmed_authors>Zhang L</pubmed_authors><pubmed_authors>Yu K</pubmed_authors></additional><is_claimable>false</is_claimable><name>Machine learning-based phenotypic screening for postmitotic growth inducers uncover vitamin D3 metabolites as small molecule ribosome agonists.</name><description>&lt;h4>Objectives&lt;/h4>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.&lt;h4>Materials and methods&lt;/h4>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. </description><dates><release>2022-01-01T00:00:00Z</release><publication>2022 May</publication><modification>2025-06-01T00:13:02.544Z</modification><creation>2025-02-19T04:57:07.066Z</creation></dates><accession>S-EPMC9136510</accession><cross_references><pubmed>35411556</pubmed><doi>10.1111/cpr.13214</doi></cross_references></HashMap>