A-T.25: Deep Learning-Informed Interpretation of 3'UTR Variants Controlling Inflammation
Pavel Kovarik
Max Perutz Labs, University of Vienna
Keywords
Gene Regulation, Deep Learning, RNA Biology
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The mammalian immune system relies on precise mRNA stability control to balance pathogen defence and prevent damaging hyperinflammation. AU-rich elements (AREs) in 3' UTRs are the key regulatory elements of mRNA decay in the immune system. However, ARE positions and, importantly, their functional variants in the human genome remain largely unknown. This limited data availability precludes the diagnostic and therapeutic exploitation of AREs in precision medicine. To fill this knowledge gap, we aim to annotate AREs functionally and their genetic variants using deep learning models in combination with experimental systems.
AREs drive degradation of mRNAs via ARE-mediated mRNA decay (AMD). AMD employs RNA-binding proteins (RBPs) that bind AREs and directly or indirectly promote RNA degradation. Dysfunctional AMD leads to immune disorders and failure of immune homeostasis in mice. We integrate our resources on AMD in mice, including transcriptome-wide mRNA stability data and binding sites of AMD-active RBPs, such as Zfp36, in immune cells, with human CLIP-Seq datasets and RNA structure modelling to train deep learning models. As a result, we present a comprehensive deep learning framework that will eventually allow us to predict how genetic variation alters AMD in immune genes.
Our prototype achieves an AUPRC of 0.9571 and an AUROC of 0.9987 on held-out test data. Clinical relevance spans rheumatoid arthritis, lupus, IBD, and hepatitis C clearance conditions, where disrupted AMD is directly implicated.
This framework establishes new standards for interpreting non-coding regulatory variants and delivers actionable tools for personalised therapies in inflammatory and autoimmune disease.
Co-authors: Pavel Kovarik
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