Decoding post-transcriptional regulatory programs in RNA is a critical step towards the larger goal of developing predictive dynamical models of cellular behaviour. Despite recent efforts1,2,3, the vast landscape of RNA regulatory elements remains largely uncharacterized. A long-standing obstacle is the contribution of local RNA secondary structure to the definition of interaction partners in a variety of regulatory contexts, including—but not limited to—transcript stability3, alternative splicing4 and localization3. There are many documented instances where the presence of a structural regulatory element dictates alternative splicing patterns (for example, human cardiac troponin T) or affects other aspects of RNA biology5. Thus, a full characterization of post-transcriptional regulatory programs requires capturing information provided by both local secondary structures and the underlying sequence3,6. Here we present a computational framework based on context-free grammars3,7 and mutual information2 that systematically explores the immense space of small structural elements and reveals motifs that are significantly informative of genome-wide measurements of RNA behaviour. By applying this framework to genome-wide human mRNA stability data, we reveal eight highly significant elements with substantial structural information, for the strongest of which we show a major role in global mRNA regulation. Through biochemistry, mass spectrometry and in vivo binding studies, we identified human HNRPA2B1 (heterogeneous nuclear ribonucleoprotein A2/B1, also known as HNRNPA2B1) as the key regulator that binds this element and stabilizes a large number of its target genes. We created a global post-transcriptional regulatory map based on the identity of the discovered linear and structural cis-regulatory elements, their regulatory interactions and their target pathways. This approach could also be used to reveal the structural elements that modulate other aspects of RNA behaviour.
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Gene Expression Omnibus
The microarray and high-throughput sequencing data are deposited at GEO under the umbrella accession number GSE35800.
We thank the members of the Tavazoie laboratory for comments on the project and manuscript. We are also grateful to N. Pencheva, B. Tsui, S. Tavazoie and L. Dölken for their intellectual and technical contributions. L.F. was supported by a Ruth L. Kirschstein National Research Service Award (T32-GM066699). S.T. was supported by grants from NHGRI (2R01HG003219) and the NIH Director's Pioneer Award.
This file contains Supplementary Figures 1-15, Supplementary Tables 1-2 and additional references.