bioRxivBiochemistryPreprint
Toward a model of uORF-mediated translational control: An integrated bioinformatic and experimental approach
Britt HansoniD, Nenad SvrzikapaiD, Ning FengiD, Hans J. Friedrichsen, Angus J. Lennaárd, Rushdie Abuhamdah, Nicole Hemmer, Katarzyna Chwalenia, Matthew Drake, Yahya E. JadiD, Samir EL AndaloussiiD, Matthew J. A. WoodiD, Thomas C. RobertsiD
Abstract
Upstream open reading frames (uORFs) are short translated regions that occur in the 5{square} untranslated regions (5{square} UTRs) of mRNA transcripts where they primarily serve to repress expression translation of the downstream primary open reading frame (pORF). Their widespread presence across mammalian transcriptomes suggests an important role in shaping the proteome, although the mechanistic basis of their regulatory effects remain incompletely understood. Here we present an integrated experimental and computational investigation into the features that govern uORF-mediated translation control. Using high-resolution proteomics data from 29 healthy human tissues and machine learning-based simulations, we have systematically dissected how features including uORF length, amino acid composition, start codon position, stop codon position, and Kozak context influence repressive activity, and performed experimental validation using reporter gene constructs. We also investigated how multiple uORFs within a single 5{square} UTR can interact in synergistic or antagonistic ways, with the potential to produce counterintuitive effects on pORF translation. From these studies, we present a model of uORF function, suggesting a hierarchy of uORF feature importance, and proposing that a combination of uORF translation initiation probability, ribosome recycling rate, intercistronic ternary complex recharging requirements, and ribosome stalling mechanisms underlie uORF repressive activity. Together, these studies provide a comprehensive view of the molecular logic underlying uORF activity, offering new insights into their endogenous and highlighting their potential as targets for drug development.
- organism: human
- assay: proteomics
- research focus: cell signaling & trafficking
- approach: machine learning (classical)
- Citations
- 2
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