G3 Genes Genomes GeneticsGenomicsPeer-reviewed journal article
Massively Parallel Analysis of Human 3' UTRs Reveals that AU-Rich Element Length and Registration Predict mRNA Destabilization
David A SiegeliD, Olivier Le TonquèzeiD, Anne BitoniD, Noah ZaitleniD, David J. ErleiD
Abstract
AU-rich elements (AREs) are 3' UTR cis-regulatory elements that regulate the stability of mRNAs. Consensus ARE motifs have been determined, but little is known about how differences in 3' UTR sequences that conform to these motifs affect their function. Here we use functional annotation of sequences from 3' UTRs (fast-UTR), a massively parallel reporter assay (MPRA), to investigate the effects of 41,288 3' UTR sequence fragments from 4,653 transcripts on gene expression and mRNA stability. The library included 9,142 AREs, and incorporated a set of fragments bearing mutations in each ARE. Our analyses demonstrate that the length of an ARE and its registration (the first and last nucleotides of the repeating ARE motif) have significant effects on gene expression and stability. Based on this finding, we propose improved ARE classification and concomitant methods to categorize and predict the effect of AREs on gene expression and stability. Our new approach explains 64{+/-}13% of the contribution of AREs to the stability of human 3' UTRs in Jurkat cells and predicts ARE activity in an unrelated cell type. Finally, to investigate the advantages of our general experimental design for annotating 3' UTR elements we examine other motifs including constitutive decay elements (CDEs), where we show that the length of the CDE stem-loop has a significant impact on steady-state expression and mRNA stability. We conclude that fast-UTR, in conjunction with our analytical approach, can produce improved yet simple sequence-based rules for predicting the activity of human 3' UTRs containing functional motifs.
- organism: human
- assay: DNA methylation
- assay: whole-genome sequencing
- research focus: differential expression
- approach: experimental assay
- Citations
- 63
- FWCI
- 2.28
- Field percentile
- top 9%
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