Translation of messenger RNA (mRNA) is a central regulatory layer determining which genomic information is expressed as functional protein. In this process, cis-regulatory sequence elements (CREs) interact with a wide range of regulatory factors to control RNA processing and protein output depending on contexts, such as cellular state, cell type and species. The 5' untranslated region (5UTR) of mRNA, with a median length of around 200 nucleotides in vertebrates, has been identified as a major determinant of ribosome recruitment, accounting for much of the cis-regulatory information determining translation initiation efficiency. Recent studies applied massively parallel reporter assays (MPRAs) of short in-vitro transcribed reporter constructs to quantify the effect of 5UTR sequence variation on translation initiation for a large number of arbitrary sequences and design synthetic sequences for tailored protein output using deep neural networks (DNNs). However, limitations in 5UTR sequence composition, length and experimental scale have limited efforts to systematically interpret learned representations of regulatory phenomena.
We developed a stochastic multi-species 5UTR MPRA platform, which connects (A) ~112.000 tiles of 200 nucleotides from all human and zebrafish 5UTRs and (B) massive randomised libraries of >10^7 million expected high-confidence reporters to a polysome profiling readout of translation initiation in human cell lines. By interpreting supervised sequence-to-translation models trained on a combination of native and randomised MPRA reporters, we aim to uncover cis-regulatory interactions, reveal mechanisms behind disease-causing 5UTR mutations and engineer 5UTRs for tailored translation initiation.

Co-authors: Antje Hirsekorn, Zhanel Ibrayeva, Sameer Singh, Uwe Ohler