Predicting the translation efficiency of messenger RNA in mammalian cells.

Autor:	Zheng D; mRNA Center of Excellence, Sanofi, Waltham, MA 02451, USA., Wang J; mRNA Center of Excellence, Sanofi, Waltham, MA 02451, USA., Persyn L; Department of Molecular Biosciences, University of Texas at Austin, Austin, TX 78712, USA., Liu Y; Department of Molecular Biosciences, University of Texas at Austin, Austin, TX 78712, USA., Montoya FU; mRNA Center of Excellence, Sanofi, Waltham, MA 02451, USA., Cenik C; Department of Molecular Biosciences, University of Texas at Austin, Austin, TX 78712, USA., Agarwal V; mRNA Center of Excellence, Sanofi, Waltham, MA 02451, USA.
Jazyk:	angličtina
Zdroj:	BioRxiv : the preprint server for biology [bioRxiv] 2024 Aug 11. Date of Electronic Publication: 2024 Aug 11.
DOI:	10.1101/2024.08.11.607362
Abstrakt:	The degree to which translational control is specified by mRNA sequence is poorly understood in mammalian cells. Here, we constructed and leveraged a compendium of 3,819 ribosomal profiling datasets, distilling them into a transcriptome-wide atlas of translation efficiency (TE) measurements encompassing >140 human and mouse cell types. We subsequently developed RiboNN, a multitask deep convolutional neural network, and classic machine learning models to predict TEs in hundreds of cell types from sequence-encoded mRNA features, achieving state-of-the-art performance (r=0.79 in human and r=0.78 in mouse for mean TE across cell types). While the majority of earlier models solely considered 5' UTR sequence, RiboNN integrates contributions from the full-length mRNA sequence, learning that the 5' UTR, CDS, and 3' UTR respectively possess ~67%, 31%, and 2% per-nucleotide information density in the specification of mammalian TEs. Interpretation of RiboNN revealed that the spatial positioning of low-level di- and tri-nucleotide features ( i.e. , including codons) largely explain model performance, capturing mechanistic principles such as how ribosomal processivity and tRNA abundance control translational output. RiboNN is predictive of the translational behavior of base-modified therapeutic RNA, and can explain evolutionary selection pressures in human 5' UTRs. Finally, it detects a common language governing mRNA regulatory control and highlights the interconnectedness of mRNA translation, stability, and localization in mammalian organisms. Competing Interests: DECLARATION OF INTERESTS D.Z., J.W., F.M., and V.A. are employees of Sanofi and may hold shares and/or stock options in the company.
Databáze:	MEDLINE
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