Machine learning-based phenotypic screening for postmitotic growth inducers uncover vitamin D3 metabolites as small molecule ribosome agonists
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. Ribo-MEGA size exclusion chromatography (SEC) analysis was used to verify whether the ribosome proteins bound to calcitriol.
Results
We discovered both the inactive cholecalciferol and the bioactive calcitriol are amongst the top hits that boost post-mitotic growth. A large number of ribosomal proteins' melting curves were affected by calcitriol treatment, suggesting that calcitriol binds to the ribosome complex directly. Purified ribosomes directly bound to pure calcitriol. Moreover, we found that calcitriol could increase myosin heavy chain (MHC) protein translation and overall nascent protein synthesis in a cycloheximide-sensitive manner, indicating that calcitriol can directly bind and enhance ribosomal activity to boost muscle growth.
Conclusion
Through the combined strategy of ML-based phenotypic screening and MS-based omics, we have fortuitously discovered a new class of metabolite small molecules that can directly activate ribosomes to promote post-mitotic growth.