Effects of sequence motifs in the yeast 3′ untranslated region determined from massively parallel assays of random sequences

Abstract

Background The 3′ untranslated region (UTR) plays critical roles in determining the level of gene expression through effects on activities such as mRNA stability and translation. Functional elements within this region have largely been identified through analyses of native genes, which contain multiple co-evolved sequence features. Results To explore the effects of 3′ UTR sequence elements outside of native sequence contexts, we analyze hundreds of thousands of random 50-mers inserted into the 3′ UTR of a reporter gene in the yeast Saccharomyces cerevisiae. We determine relative protein expression levels from the fitness of transformants in a growth selection. We find that the consensus 3′ UTR efficiency element significantly boosts expression, independent of sequence context; on the other hand, the consensus positioning element has only a small effect on expression. Some sequence motifs that are binding sites for Puf proteins substantially increase expression in the library, despite these proteins generally being associated with post-transcriptional downregulation of native mRNAs. Our measurements also allow a systematic examination of the effects of point mutations within efficiency element motifs across diverse sequence backgrounds. These mutational scans reveal the relative in vivo importance of individual bases in the efficiency element, which likely reflects their roles in binding the Hrp1 protein involved in cleavage and polyadenylation. Conclusions The regulatory effects of some 3′ UTR sequence features, like the efficiency element, are consistent regardless of sequence context. In contrast, the consequences of other 3′ UTR features appear to be strongly dependent on their evolved context within native genes.

Transcription is ubiquitously terminated in thousands of bidirectional termination zones in yeast

“…Yeast 3'UTRs have been proven to be able to influence gene expression, especially for regions that are UA enriched 34,35 . In this study, we found that there is a transcriptional interference region ranging from 20 to 60 bp upstream of CSs in bidirectional termination zones (Fig.…”

“…Interestingly, this transcriptional interference region coincides with the region where UAUAUA motif is highly enriched. Considering UAUAUA motifs have been shown to influence gene expression in yeast 35 , we speculated that they should play a role in the formation of the transcriptional interference region. Indeed, when AS CSs fall into the transcriptional interference region of sense CSs, and when only one of them harbors UAUAUA motif, there is a great discrepancy between expression levels of sense and AS pAs (Fig.…”

“…With regards to bidirectional termination zones, an interesting question is what type of PAS enables a bidirectional termination pattern. We focused on the UAUAUA motif (and its two important variants: UACAUA and UAUGUA) for three main reasons: 1) these motifs play key roles in cleavage and polyadenylation 32,33 and could affect genes' expression levels 34,35 in yeast; 2) these motifs are highly enriched in bidirectional termination zones (Fig. 1f); 3) these motifs are palindromic, which suggests they could be used for both sense and AS transcription termination.…”

Controlling gene expression with deep generative design of regulatory DNA

“…Similarly, in the 3′ UTR and terminator regions, both the presence and number of poly-A/T, positioning (consensus 5′-AAWAAA-3′) 54,65 and efficiency motifs (5′-TATDTA-3′) 27 were correlated (Spearman's ⍴ between 0.06 and 0.51, p-value < 1e−16, respectively) with the predicted expression levels (Supplementary Fig. 12), with a 9%, 6%, and 45% higher amount of motif-carrying sequences, respectively, found in the high expression sequences compared to low expression ones 26 (Fig. 5g).…”

“…Popular strategies to design synthetic regulatory DNA of varying expression levels include stacking multiple known functional sequence motifs 4,6,18,19 and applying random mutagenesis to a specific region, most commonly the promoter 8,[20][21][22][23] though also UTRs [24][25][26] and terminators 27,28 have been targeted, typically in a form of short sequence segments of <100 bp. Using in silico screening approaches 7,8 , which evaluate the fitness of candidate sequences by predicting their expression levels, more intricate solutions based on evolutionary computation 29 have been implemented, including genetic algorithms 15,24,[29][30][31] .…”