bioRxiv preprint first posted online Nov. 3, 2016; doi: http://dx.doi.org/10.1101/085522. The copyright holder for this preprint (which was
not peer-reviewed) is the author/funder. It is made available under a CC-BY-NC-ND 4.0 International license.
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Sequence features explain most of the mRNA
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stability variation across genes
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Jun cheng1,2, Žiga Avsec1,2, Julien Gagneur1,2,*
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1. Department of Informatics, Technical University of Munich, Boltzmannstraße 3,
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85748 Garching, Germany
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2. Graduate School of Quantitative Biosciences (QBM), Ludwig-Maximilians-
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Universität München, Germany
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* Correspondence: [email protected]
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Abstract
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The stability of messenger RNA (mRNA) is one of the major determinants of gene
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expression. Although a wealth of sequence elements and mechanisms regulating
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mRNA stability has been described, their quantitative contributions in determining
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mRNA half-life is unknown. Here, we built quantitative models for two eukaryotic
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genomes Saccharomyces cerevisiae and Schizosaccharomyces pombe that, for the
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first time, explain most of the half-life variation between genes based on mRNA
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sequence alone. The models integrate known functional cis-regulatory elements,
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identify novel ones, and quantify their contributions at single-nucleotide resolution.
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We show quantitatively that codon usage is the major determinant of mRNA stability,
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and that this effect depends on canonical mRNA degradation pathways. Altogether,
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our results integrate and quantitatively delineate mRNA stability cis-regulatory
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elements and provide a methodology that can serve as a scaffold to study the
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function of cis-regulatory elements and to discover novel ones.
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Introduction
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The stability of messenger RNAs is an important aspect of gene regulation. It
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influences the overall cellular mRNA concentration, as mRNA steady-state levels are
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the ratio of synthesis and degradation rate. Moreover, low stability confers high
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turnover to mRNA and therefore the capacity to rapidly reach a new steady-state
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level in response to a transcriptional trigger. Hence, stress genes, which must rapidly
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bioRxiv preprint first posted online Nov. 3, 2016; doi: http://dx.doi.org/10.1101/085522. The copyright holder for this preprint (which was
not peer-reviewed) is the author/funder. It is made available under a CC-BY-NC-ND 4.0 International license.
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respond to environmental signals, show low stability (Zeisel et al, 2011; Rabani et al,
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2014). In contrast, high stability provides robustness to variations in transcription.
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Accordingly, a wide range of mRNA-half-lives is observed in eukaryotes, with typical
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variations in a given genome spanning one to two orders of magnitude
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(Schwanhäusser et al, 2011; Schwalb et al, 2016; Eser et al, 2016).
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How mRNA stability is encoded in a gene sequence has long been a subject of
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study. Cis-regulatory elements (CREs) affecting mRNA stability are mainly encoded
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in the mRNA itself. They include but are not limited to secondary structure, sequence
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motifs present in the 3’UTR including binding sites of RNA-binding proteins (Olivas &
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Parker, 2000; Vasudevan & Peltz, 2001), and, in higher eukaryotes, microRNAs (Lee,
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1993). Moreover, codon usage, which affects translation elongation rate, also
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regulates mRNA stability (Hoekema et al, 1987; Presnyak et al, 2015; Boël et al,
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2016; Mishima & Tomari, 2016). For instance, inserting strong secondary structure
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elements in the 5’UTR or modifying the translation start codon context strongly
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destabilizes the long-lived PGK1 mRNA in S. cerevisiae (Muhlrad et al, 1995;
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LaGrandeur & Parker, 1999).
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Since the RNA degradation machineries are well conserved among eukaryotes, the
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pathways have been extensively studied using Saccharomyces cerevisiae as a
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model organism (Caponigro & Parker, 1996; Parker, 2012). The general mRNA
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degradation pathway starts with the removal of the poly(A) tail by the Pan2/Pan3
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(Boeck et al, 1996; Brown et al, 1996) and Ccr4/Not complexes (Tucker et al, 2001;
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Collart, 2003; Yamashita et al, 2005). Subsequently, mRNA is subjected to
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decapping carried out by Dcp2 and promoted by several factors including Dhh1 and
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Pat1 (Pilkington & Parker, 2008; She et al, 2008). The decapped and deadenylated
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mRNA can be degraded rapidly in the 3’ to 5’ direction by the exosome (Anderson &
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Parker, 1998) or in the 5’ to 3’ direction by Xrn1 (Muhlrad et al, 1994; Hsu & Stevens,
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1993). Further mRNA degradation pathways are triggered when aberrant
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translational status is detected, including Nonsense-mediated decay (NMD), No-go
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decay (NGD) and Non-stop decay (NSD) (Garneau et al, 2007; Parker, 2012).
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Despite all this knowledge, prediction of mRNA half-life from a gene sequence is still
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not established. Most of the mechanistic studies could only be performed on
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individual genes and it is unclear how the effects generalize genome-wide. In
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addition, neither the contribution of different CREs to the overall stability and to the
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variation between genes has been systematically studied, nor have the dependences
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of CREs to distinct mRNA degradation pathways.
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Here, we used an integrative approach where we mathematically modelled mRNA
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half-life as a function of its sequence and applied it to S .cerevisiae. For the first time,
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our model can explain most of the between-gene half-life variance from sequence
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alone. We used a semi-mechanistic model which allowed us to interpret it in terms of
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individual sequence features in the 5’UTR, coding region, and 3’UTR. Our approach
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de novo recovered known cis-regulatory elements and identified novel ones.
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Quantification of the respective contributions revealed that codon usage is the major
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contributor to mRNA stability. Interpretability of the model also allowed studying how
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the function of CREs depends on different mRNA degradation pathways. The
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application of the modeling approach to S. pombe supports the generality of these
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findings.
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RESULTS
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Regression reveals novel mRNA sequence features associated with
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mRNA stability
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To study cis-regulatory determinants of mRNA stability in S. cerevisiae, we chose the
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dataset by Sun and colleagues (Sun et al, 2013), which provides genome-wide half-
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life measurements for 4,388 expressed genes of a wild type lab strain (Fig 1). The
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same study also measured genome-wide mRNA half-lives for 34 strains knocked out
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for RNA degradation pathway genes (Table EV1), allowing for investigating how the
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association of candidate CREs with half-life depends on RNA degradation pathways.
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When applicable, associations were also investigated in the S. pombe genome, using
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half-life values for 3,614 expressed mRNAs in a wild-type lab strain from Eser and
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colleagues (Eser et al, 2016). We considered sequence features within 5 overlapping
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regions: the 5’UTR, the start codon context, the coding sequence, the stop codon
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context and the 3’UTR. Half-life associated negatively with 5’UTR length (Spearman
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ρ = -0.17, P = 3 × 10-12 for S. cerevisiae and Spearman ρ = -0.27, P = 2 × 10-11 for S.
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pombe), with the coding sequence (Spearman ρ = -0.23, P < 2 × 10-16 for S.
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cerevisiae and Spearman ρ = -0.32, P = 4 × 10-15 for S. pombe), and with 3’UTR
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length (Spearman ρ = -0.06, P = 1 × 10-8 for S. cerevisiae and Spearman ρ = -0.23, P
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= 6 × 10-12 for S. pombe) (Fig EV1A, B, C, D, E, F). Moreover, S .cerevisiae half-life
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was found to be negatively associated with the GC-content of the 5’UTR (Spearman
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ρ = -0.11, P = 3 × 10-14) and of the 3’UTR (Spearman ρ = -0.17, P < 2 × 10-16),
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whereas the GC-content of the CDS region correlated positively with half-life
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(Spearman ρ = 0.27, P < 2 × 10-16) (Fig EV2A, C, E). In contrast, S. pombe showed
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consistent positive association between GC-content and half-life (Spearman ρ = 0.16,
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0.24, 0.05 and P < 2 × 10-16, P < 2 × 10-16, P = 0.01 respectively for 5’UTR, CDS and
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3’UTR, Fig EV2B, D, F). Also, secondary structure in 5’UTR (Materials and Methods)
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associated with RNA instability (Spearman ρ = -0.22, P = 8 × 10-12 for S. cerevisiae
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and Spearman ρ = -0.24, P < 2 × 10-16 for S. pombe, Fig EV1G, H).
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Novel candidate cis-regulatory elements were searched for using a robust k-mer
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based regression, investigating all 3- to 8-mers, and followed by k-mer assembly
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(Materials and Methods). This recovered de novo the Puf3 binding motif TGTAAATA
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in 3’UTR (Gerber et al, 2004; Gupta et al, 2014), a well-studied CRE that confers
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RNA instability, as well as the Whi3 binding motif TGCAT (Colomina et al, 2008; Cai
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& Futcher, 2013). Two new motifs were found: AAACAAA in 5’UTR, and ATATTC in
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3’UTR (Fig 1A). Notably, the motif TGCAT, was found in 21% of the genes (938 out
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of 4,388) and significantly co-occurred with the other two destabilizing motifs found in
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3’UTR: Puf3 and the Whi3 binding site motifs (Fig 1B). Except for AAACAAA and
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TTTTTTA, all motifs associated with shorter half-lives (Fig 1A).
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In the following subsections, we describe first the findings for each of the 5 gene
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regions and then a model that integrates all these sequence features.
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Upstream AUGs destabilize mRNAs by triggering nonsense-
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mediated decay
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Genome-wide, occurrence of an upstream AUG (uAUG) associated with 1.37 fold
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shorter half-life (fold change between medians, P < 2.2 × 10-16), and the effect
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strengthened for genes with two or more AUGs (Fig 2A, B). Among the 34 knock-out
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strains, the association between uAUG and shorter half-life was almost lost only for
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mutants of the two essential components of the nonsense-mediated mRNA decay
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(NMD) UPF2 and UPF3 (Leeds et al, 1992; Cui et al, 1995), and for the general 5’-3’
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exonuclease Xrn1 (Fig 2A). The dependence on NMD suggested that the association
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between uAUG and shorter half-life might be due to the occurrence of a premature
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stop codon. Indeed, the association of uAUG with decreased half-lives was only
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found for genes with a premature stop codon cognate with the uAUG (Fig 2C). This
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held not only for cognate premature stop codons within the 5’UTR, leading to a
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potential upstream ORF, but also for cognate premature stop codons within the ORF,
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which occurred almost always for uAUG out-of-frame with the main ORF (Fig 2C).
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This finding likely holds for many other eukaryotes as we found the same trends in S.
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pombe (Fig 2D). This observations are consistent with single-gene studies
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demonstrating that translation of upstream ORFs can lead to RNA degradation by
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nonsense-mediated decay (Gaba et al, 2005; Barbosa et al, 2013). Altogether, these
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results show that uAUGs are mRNA destabilizing elements as they almost surely
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match with a cognate premature stop codon, which, whether in frame or not with the
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gene, and within the UTR or in the coding region, trigger NMD.
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Translation initiation predicts mRNA stability
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Several sequence features in the 5’UTR associated significantly with mRNA half-life.
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First, the folding energy of the 5’ initiation sequence, defined as the 5’UTR and the
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first 10 codons (Zur & Tuller, 2013), positively correlated with mRNA stability in S.
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cerevisiae (Spearman ρ = -0.22, P = 8 × 10-12 for S. cerevisiae and Spearman ρ = -
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0.24, P < 2 × 10-16 for S. pombe, Fig EV1G, H). The mRNA destabilizing function of
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strong secondary structure elements had been demonstrated for the gene PGK1
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(LaGrandeur & Parker, 1999; Muhlrad et al, 1995). Our genome-wide analysis
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indicates that this is a general phenomenon. Possibly, strong secondary structure in
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the 5’ UTR interferes with ribosome scanning, thus slows down translation initiation,
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and therefore decreases the level of mRNA protection by the translation machinery
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(Roy & Jacobson, 2013; Huch & Nissan, 2014).
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Second, longer 5’UTRs associated with less stable mRNAs (Spearman ρ = -0.17, P =
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3 × 10-12 for S. cerevisiae and Spearman ρ = -0.27, P = 2 × 10-11 for S. pombe, Fig
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EV1A, B). In mouse cells, mRNA isoforms with longer 5’UTR are translated with
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lower efficiency (Wang et al, 2016), possibly because longer 5’UTR generally harbor
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more translation repression elements. Hence, longer 5’UTR may confer mRNA
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instability by decreasing translation initiation and therefore decreasing the protection
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by the translation machinery.
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Third, a significant association between the third nucleotide before the start codon
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and mRNA half-life was observed (Fig 3A). The median half-life correlated with the
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nucleotide frequency (Fig EV3A), leading to 1.28 fold difference between the 2,736
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genes with an adenosine, the most frequent nucleotide at this position, and the 360
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genes with a cytosine, the least frequent nucleotide at this position (P = 1.7x10-11).
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The same correlation was also found to be significant for S. pombe (Fig EV3B, C).
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Functional effect of the start codon context on mRNA stability had been established
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as the long-lived PGK1 mRNA was significantly destabilized when substituting the
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sequence context around its start codon with the one from the short-lived MFA2
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mRNA (LaGrandeur & Parker, 1999). Our genome-wide analysis indicates that this
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effect generalizes to other genes. Possibly the start codon context, which controls
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translation initiation efficiency (Kozak, 1987; Dvir et al, 2013), affects thereby mRNA
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stability.
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Altogether, these findings indicate that 5’UTR elements, including the start codon
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context, may affect mRNA stability by altering translation initiation. De novo search
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for regulatory motifs identified AAACAAA to be significantly associated with longer
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mRNA half-lives. However, further analysis did not provide strong support for this
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motif, indicating it might be merely correlative (Fig EV5).
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Codon usage regulates mRNA stability through common mRNA
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decay pathways
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Codon usage has been reported to affect mRNA stability through a translation-
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dependent manner (Presnyak et al, 2015; Mishima & Tomari, 2016; Bazzini et al,
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2016). However, it is unclear on which mRNA degradation pathway it depends.
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The species-specific tRNA adaptation index (sTAI) was used to quantify the level of
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codon usage bias (Sabi & Tuller, 2014). First, we confirmed on this independent half-
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life dataset the strong correlation between codon optimality and mRNA stability in S.
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cerevisiae (Fig 3C, Spearman ρ = 0.55, P < 2.2x10-16). Using the out-of-folds
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explained variance as a summary statistics, we assessed its variation across different
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gene knockouts (Materials and Methods). The effect of codon usage exclusively
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depended on the genes from the common deadenylation- and decapping-dependent
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5’ to 3’ mRNA decay pathway and the NMD pathway (Fig 3D). In particular, all
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assayed genes of the Ccr4-Not complex, including CCR4, NOT3, CAF40 and POP2,
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are required for wild-type level effects of codon usage on mRNA decay. Among them,
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CCR4 has the largest effect. This confirmed recent studies showing that accelerated
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decay of non-optimal codon genes requires deadenylation activities of Ccr4-Not
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(Presnyak et al, 2015; Mishima & Tomari, 2016). Moreover, our results confirmed the
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dependence on Dhh1 (Radhakrishnan et al, 2016), but also on its interacting partner
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Pat1. In contrast to genes of the Ccr4-Not complex, PAN2/3 genes which encode
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also deadenylation enzymes, were not found to be essential for the coupling between
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codon usage and mRNA decay (Fig 3D). Notably, we did not observe any change of
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effect upon knockout of DOM34 and HBS1 (Figure EV5), which are essential genes
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for the No-Go decay pathway. This implies that the effect of codon usage is unlikely
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due to stalled ribosomes at non-optimal codons.
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Our systematic analysis revealed two additional novel dependencies: First, on the
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common 5’ to 3’ exonuclease Xrn1, and second, on UPF genes, which are essential
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players of NMD (Fig 3D). Previous studies have shown that NMD is more than just a
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RNA surveillance pathway, but rather one of the general mRNA decay mechanisms
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that target a wide range of mRNAs, including aberrant and normal ones (He et al,
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2003; Hug et al, 2015). Altogether, our analysis strongly indicates that, the so-called
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“codon-mediated decay” is not an mRNA decay pathway itself, but a regulatory
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mechanism of the common mRNA decay pathways.
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Stop codon context associates with mRNA stability
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Linear regression against the 6 bases 5’ and 3’ of the stop codon revealed the first
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nucleotide 3’ of the stop codon to most strongly associate with mRNA stability. This
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association was observed for each of the three possible stop codons, and for each
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codon a cytosine significantly associated with lower half-life (Fig. 3B). This also held
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for S. pombe (Fig EV3D). A cytosine following the stop codon structurally interferes
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with stop codon recognition (Brown et al, 2015), thereby leading to stop codon read-
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through events (Bonetti et al, 1995; McCaughan et al, 1995). Of all combinations,
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TGA-C is known to be the leakiest stop codon context (Jungreis et al, 2011) and also
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associated with shortest mRNA half-life (Fig. 3B). These results are consistent with
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non-stop decay, a mechanism that triggers exosome-dependent RNA degradation
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when the ribosome reaches the poly(A) tail. Consistent with this interpretation,
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mRNAs with additional in-frame stop codons in the 3’UTR, which are over-
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represented in yeast (Williams et al, 2004), exhibited significantly higher half-life (Fig
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EV3E, F). However, the association between the stop codon context and half-life was
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not weakened in mutants of the Ski complex, which is required for the cytoplasmic
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functions of the exosome (Fig EV5). These results strongly indicate that the fourth
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nucleotide after the stop codon is an important determinant of mRNA stability, likely
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because of translational read-through.
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Sequence motifs in 3’UTR
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Four motifs in the 3’UTR were found to be significantly associated with mRNA
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stability (Fig 5A, FDR < 0.1, Materials and Methods). This analysis recovered three
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described motifs: the Puf3 binding motif TGTAAATA (Gerber et al, 2004), the Whi3
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binding motif TGCAT (Colomina et al, 2008; Cai & Futcher, 2013), and a poly(U)
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motif TTTTTTA, which is likely bound by Pub1 (Duttagupta et al, 2005), or is part of
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the long poly(U) stretch that form a looping structure with poly(A) tail (Geisberg et al,
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2014). We also identified a novel motif, ATATTC, that associated with lower mRNA
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half-life.
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Four lines of evidence supported the potential functionality of the new motif. First, it
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preferentially localizes in the vicinity of the poly(A) site (Fig 5B), and functionally
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depends on Ccr4 (Fig EV5), suggesting a potential interaction with deadenylation
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factors. Second, single nucleotide deviations from the consensus sequence of the
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motif associated with decreased effects on half-life (Fig 5C, linear regression allowing
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for one mismatch, Materials and Methods). Moreover, the flanking nucleotides did not
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show further associations indicating that the whole lengths of the motifs were
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recovered (Fig 5C). Third, when allowing for one mismatch, the motif still showed
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strong preferences (Fig 5D). Fourth, the motif instances were more conserved than
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their flanking bases (Fig 5E).
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Consistent with the role of Puf3 in recruiting deadenylation factors, Puf3 binding motif
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localized preferentially close to the poly(A) site (Fig 5B). The effect of the Puf3 motifs
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was significantly lower in the knockout of PUF3 (Fig EV5). We also found a
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significant dependence on the deadenylation (CCR4, POP2) and decapping (DHH1,
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PAT1) pathways (Fig EV5), consistent with previous single gene experiment showing
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that Puf3 binding promotes both deadenylation and decapping (Olivas & Parker,
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2000; Goldstrohm et al, 2007). Strikingly, Puf3 binding motif switched to a
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stabilization motif in the absence of Puf3 and Ccr4, suggesting that deadenylation of
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Puf3 motif containing mRNAs is not only facilitated by Puf3 binding, but also depends
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on it.
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Whi3 plays an important role in cell cycle control (Garí et al, 2001). Binding of Whi3
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leads to destabilization of the CLN3 mRNA (Cai & Futcher, 2013). A subset of yeast
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genes are up-regulated in the Whi3 knockout strain (Cai & Futcher, 2013; Holmes et
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al, 2013). However, it was so far unclear whether Whi3 generally destabilizes mRNAs
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upon its binding. Our analysis showed that mRNAs containing the Whi3 binding motif
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(TGCAT) have significantly shorter half-life (FDR = 6.9x10-04). Surprisingly, this
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binding motif is extremely widespread, with 896 out of 4,388 (20%) genes that we
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examined containing the motif on the 3’UTR region, suggesting possible broader and
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genome-wide role of Whi3 and its binding motif. However, no significant genetic
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dependence of the effect of the Whi3 binding motif was found (Fig EV5).
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The mRNAs harboring the TTTTTTA motif tended to be more stable (Fig 5A). No
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positional preferences was observed for this motif (Fig 5B), so as the poly(U) stretch
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reported before (Geisberg et al, 2014). Effects of this motif depends on genes from
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Ccr4-Not complex and Xrn1 (Fig EV5).
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60% between-gene half-life variance can be explained by sequence
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features
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We next asked how well one could predict mRNA half-life from these mRNA
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sequence features, and what their respective contributions were when considered
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jointly. To this end, we performed a multivariate linear regression of the logarithm of
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the half-life against the identified sequence features. The predictive power of the
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model on unseen data was assessed using 10-fold cross validation (Material and
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Methods, Table EV2). Altogether, 60% of S. cerevisiae half-life variance in the
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logarithmic scale can be explained by simple linear combinations of the above
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sequence features (Fig 6A). The median out-of-folds error across genes is 30%.
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Prediction accuracy of half-life of 30% is remarkably high because it is in the order of
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magnitude of the expression variation that is typically physiologically tolerated, and it
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is also about the amount of variation observed between replicate experiments (Eser
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et al, 2016). The same model applied to S. pombe explained 42% of the total half-life
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variance. Because the measures also entail measurement noise, these numbers are
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conservative underestimations of the total biological variance explained by our
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model.
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The uAUG, 5’UTR length, 5’UTR GC content, 61 coding codons, CDS length, all four
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3’UTR motifs, and 3’UTR length remained significantly associated with half-life in the
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joint model indicating that they contributed independently to half-life (the complete list
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of features and their p-values are given in Table EV3). In contrast, start codon
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context, stop codon context, 5’ folding energy, the 5’UTR motif AAACAAA, and
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3’UTR GC content dropped below the significance when considered in the joint
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model (Materials and Methods). This loss of statistical significance may be due to
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lack of statistical power or because the marginal association of these sequence
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features with half-life is a consequence of a correlation with other sequence features.
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Among all sequence features, codon usage as a group is the best predictor both in a
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univariate model (55.90%) and in the joint model (44.23%) (Figure 6B). This shows
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that, quantitatively, codon usage is the major determinant of mRNA stability in yeast.
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The variance analysis quantifies the contribution of each sequence feature to the
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variation across genes. Features that vary a lot between genes, such as UTR length
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and codon usage, favorably contribute to the variation. This does not reflect,
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however, the effect on a given gene of elementary sequence variations in these
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features. For instance, a single-nucleotide variant can lead to the creation of an
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uAUG with a strong effect on half-life, but a single nucleotide variant in the coding
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sequence may have little impact on overall codon usage. We used the joint model to
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assess the sensitivity of each feature as median fold-change across genes upon
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single-nucleotide variants, simulating single-nucleotide deletions for the length
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features and single nucleotide substitutions for the remaining ones (Materials and
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Methods). Single-nucleotide variations typically altered half-life by less than 10%. The
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largest effects were observed in the 3’UTR motifs and uAUG (Fig 6D). Notably,
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although codon usage was the major contributor to the variance, synonymous
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variation on codons typically affected half-life by less than 2% (Fig 6D; Fig EV6). For
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those synonymous variations that changed half-life by more than 2%, most of them
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were variations that involved the most non-optimized codons CGA or ATA (Fig EV6,
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Presnyak et al. 2015).
345
Altogether, our results show that most of yeast mRNA half-life variation can be
346
predicted from mRNA sequence alone, with codon usage being the major contributor.
347
However, single-nucleotide variation at 3’UTR motif or uAUG had the largest
348
expected effect on mRNA stability.
349
350
351
DISCUSSION
352
We systematically searched for mRNA sequence features associating with mRNA
353
stability and estimated their effects at single-nucleotide resolution in a joint model.
354
Overall, the joint model showed that 60% of the variance could be predicted from
355
mRNA sequence alone in S. cerevisiae. This analysis showed that translation-related
356
features, in particular codon usage, contributed most to the explained variance. This
357
findings strengthens further the importance of the coupling between translation and
358
mRNA degradation (Roy & Jacobson, 2013; Huch & Nissan, 2014). Moreover, we
359
assessed the RNA degradation pathway dependencies of each sequence feature.
360
Remarkably, we identified that codon-mediated decay is a regulatory mechanism of
361
the canonical decay pathways, including deadenylation- and decapping-dependent 5’
362
to 3’ decay and NMD (Figure 6E).
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not peer-reviewed) is the author/funder. It is made available under a CC-BY-NC-ND 4.0 International license.
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364
Integrative analyses of cis-regulatory elements as we used here complement
365
mechanistic single-gene studies for important aspects. It allows assessing genome-
366
wide the importance of CREs that have been reported previously with single-gene
367
experiments, as Vogel and colleagues (Vogel et al, 2010) had shown when modeling
368
protein abundance from mRNA levels and sequence. Also, single-nucleotide effect
369
prediction can more precisely supports the interpretation of genetic variants, including
370
mutations in non-coding region as well as synonymous transitions in coding region.
371
Furthermore, such integrative analyses can be combined with a search for novel
372
sequence features, as we did here with k-mers, allowing the identification of novel
373
candidate cis-regulatory elements. An alternative approach to the modeling of
374
endogenous sequence is to use large-scale perturbation screens as developed by
375
the group of Segal (Dvir et al, 2013; Shalem et al, 2015). Although very powerful to
376
dissect known cis-regulatory elements or to investigate small variations around select
377
genes, the sequence space is so large that these large-scale perturbation screens
378
cannot uncover all regulatory motifs. It would be interesting to combine both
379
approaches and design large-scale validation experiments guided by insights coming
380
from modeling of endogenous sequences as we developed here.
381
382
Causality cannot be proven through a regression analysis approach. However, we
383
provided several complementary analyses to further assess the potential functionality
384
of candidate CREs. These include conservation, positional preferences, and epistasis
385
analyses to assess the dependencies on RNA degradation pathways. One of the
386
motif we found by regression, AAACAAA in 5’UTR, was neither supported by these
387
complementary analyses, nor it remained significant in the joint model. We think this
388
motifs is most likely correlative. In contrast, the ATATTC motif in 3’UTR is strongly
389
supported by these complementary analyses and is also significant in the joint model.
390
Two of the most interesting sequence features that we identified but still need to be
391
functionally assayed are the start codon context and 5’ sequence free energy. Given
392
their established effect on translation initiation (Kozak, 1986; Dvir et al, 2013; Tuller &
393
Zur, 2014), the general coupling between translation and mRNA degradation (Huch &
394
Nissan, 2014; Roy & Jacobson, 2013), as well as several observations directly on
395
mRNA stability for single genes (Muhlrad et al, 1995; Barnes, 1998; LaGrandeur &
396
Parker, 1999; Schwartz & Parker, 1999), they are very likely to be functional on most
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not peer-reviewed) is the author/funder. It is made available under a CC-BY-NC-ND 4.0 International license.
397
genes. Consistent with this hypothesis, large scale experiments that perturb 5’
398
sequence secondary structure and start codon context indeed showed a wide range
399
of mRNA level changes in the direction that we would predict (Dvir et al, 2013).
400
Altogether, such integrative approaches allow the identification of candidate
401
regulatory elements that could be functionally tested later on.
402
403
We are not aware of previous studies that systematically assessed the effects of cis-
404
regulatory elements in the context of knockout backgrounds, as we did here. This
405
part of our analysis turned out to be very insightful. We recovered results from recent
406
studies about the dependencies of the effect of codon usage on RNA stability on the
407
Ccr4-Not complex and Dhh1, but also identified important novel ones including NMD
408
factors, Pat1 and Xrn1. With the growing availability of knockout or mutant
409
background in model organisms and human cell lines, we anticipate this approach to
410
become a fruitful methodology to unravel regulatory mechanisms.
411
412
Materials and Methods
413
414
Data and Genomes
415
Wild-type and knockout genome-wide S. cerevisiae half-life data were obtained from
416
Sun et al. 2013. S. cerevisiae gene boundaries were taken from the boundaries of
417
the most abundant isoform quantified by Pelechano et al. 2013. Reference genome
418
fasta file and genome annotation were obtained from the Ensembl database (release
419
79). UTR regions were defined by subtracting out gene body (exon and introns from
420
the Ensembl annotation) from the gene boundaries.
421
Genome-wide half-life data of S. pombe as well as refined transcription unit
422
annotation were obtained from Eser et al. 2016. Reference genome version
423
ASM294v2.26 was used to obtain sequence information.
424
For both half-life datasets, only mRNAs with mapped 5’UTR and 3’UTR were
425
considered. mRNAs that have 5’UTR length shorter than 6nt were further filtered out,
426
so that start codon context could always be defined. Half-life outliers of S. pombe
427
(half-life less than 1 or larger than 250 mins) were removed.
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428
Codon-wise species-specific tRNA adaptation index (sTAI) of yeasts were obtained
429
from Sabi & Tuller 2014. Gene-wise sTAIs were calculated as the geometric mean of
430
sTAIs of all its codons (stop codon excluded).
431
432
Analysis of knockout strains
433
The effect level of an individual sequence feature was compared against the wild-
434
type with Wilcoxon rank-sum test followed by multiple hypothesis testing p-value
435
correction (FDR < 0.1). The sequence feature effect levels were defined as follows
436
for different classes of sequence features:
437
uAUG:
438
Motifs:
439
half-life of mRNAs that has zero and one instance of the motif respectively.
440
Codon usage: For each knockout or wild-type, a linear model was fitted with all
441
coding codons as covariates. The effect size of codon usage (joint effect of all
442
codons) on half-life was defined by the explained variance of out-of-sample
443
predictions.
444
Start codon -3 position:
445
mRNAs with base adenine and cytosine at start codon -3 position respectively.
446
Stop codon +1 position:
447
half-life of mRNAs that has stop codon TGA followed by guanine and cytosine
448
respectively.
449
5’ folding energy: genome-wide Spearman rank correlation between 5’ folding energy
450
and half-life.
(𝑚𝑒𝑑𝑖𝑎𝑛(𝐻𝑤𝑖𝑡ℎ𝑢𝐴𝑈𝐺 )− 𝑚𝑒𝑑𝑖𝑎𝑛(𝐻𝑤𝑖𝑡ℎ𝑜𝑢𝑡𝑢𝐴𝑈𝐺 ))
𝑚𝑒𝑑𝑖𝑎𝑛(𝐻𝑤𝑖𝑡ℎ𝑜𝑢𝑡𝑢𝐴𝑈𝐺 )
(𝑚𝑒𝑑𝑖𝑎𝑛(𝐻𝑐𝑜𝑢𝑛𝑡=1 )− 𝑚𝑒𝑑𝑖𝑎𝑛(𝐻𝑐𝑜𝑢𝑛𝑡=0 ))
𝑚𝑒𝑑𝑖𝑎𝑛(𝐻𝑐𝑜𝑢𝑛𝑡=0 )
where 𝐻 stands for half-life.
where 𝐻𝑐𝑜𝑢𝑛𝑡=0 and 𝐻𝑐𝑜𝑢𝑛𝑡=1 stands for the
(𝑚𝑒𝑑𝑎𝑖𝑛(𝐻𝐴 )−𝑚𝑒𝑑𝑖𝑎𝑛(𝐻𝑐 ))
𝑚𝑒𝑑𝑖𝑎𝑛(𝐻𝐴 )
where 𝐻𝐴 and 𝐻𝐶 is the half-life of
(𝑚𝑒𝑑𝑖𝑎𝑛(𝐻𝑇𝐺𝐴𝐺 )−𝑚𝑒𝑑𝑖𝑎𝑛(𝐻𝑇𝐺𝐴𝐶 ))
𝑚𝑒𝑑𝑖𝑎𝑛(𝐻𝑇𝐺𝐴𝐺 )
where 𝐻𝑇𝐺𝐴𝐺 and 𝐻𝑇𝐺𝐴𝐶 is the
451
452
Motif discovery
453
Motif discovery was conducted similarly to Eser et al. 2016 for the 5’UTR, the CDS
454
and the 3’UTR regions. A linear mixed effect model was used to assess the effect of
455
each individual k-mer (fixed effect) while controlling the effects of the others (random
456
effect) and for the region length as a covariate. For CDS we also used codons as
457
further covariates. We systematically tested the effects of all possible k-mers with
458
length from 3 to 8. The linear mixed model for motif discovery was fitted with GEMMA
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459
software (Zhou et al, 2013). The p-values were corrected for multiple testing using
460
Benjamini-Hochberg’s FDR. Motifs were subsequently manually assembled based on
461
overlapping significant k-mers.
462
463
Folding energy calculation
464
5’ ensemble folding energy was calculated with RNAfold from ViennaRNA version
465
2.1.9 (Lorenz et al, 2011), with default parameters. 5’ sequence was defined as
466
5’UTR sequence plus the first 10 codons within the coding region. Genes with
467
5’UTRs that are shorter than 30 bps were excluded from the analysis.
468
469
S. cerevisiae conservation analysis
470
The phastCons (Siepel et al, 2005) conservation track for S. cerevisiae was
471
downloaded from the UCSC Genome browser
472
(http://hgdownload.cse.ucsc.edu/goldenPath/sacCer3/phastCons7way/). Motif single-
473
nucleotide level conservation scores were computed as the mean conservation score
474
of each nucleotide (including 2 extended nucleotide at each side of the motif) across
475
all motif instances genome-wide (removing NA values).
476
477
Linear model for genome-wide half-life prediction (joint model)
478
Multivariate linear regression models were used to predict genome-wide mRNA half-
479
life on the logarithmic scale from sequence features. Only mRNAs that contain all
480
features were used to fit the models, resulting with 3,862 mRNAs for S. cerevisiae
481
and 3,130 mRNAs for S. pombe. Linear models were first fitted with all sequence
482
features as covariates, non-significant sequence features were then removed from
483
the final prediction models, ending up with 70 features for S. cerevisiae model and 73
484
features for S. pombe (each single coding codon was fitted as a single covariate). L1
485
or L2 regularization were not necessary, as they did not improve the out-of-fold
486
prediction accuracy (tested with glmnet R package (Friedman et al, 2010)). Out-of-
487
fold prediction was applied with 10-fold cross validation for any prediction task in this
488
study. A complete list of model features and their p-values for both yeast species are
489
provided in Table EV2.
490
491
Variance explained for linear model
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492
The percentage of explained variance for a linear model was calculated by 100 ∗
493
(1 − 𝑣𝑎𝑟𝑟𝑒𝑠𝑖𝑑𝑢𝑎𝑙 / 𝑣𝑎𝑟𝑡𝑜𝑡𝑎𝑙 ), where 𝑣𝑎𝑟𝑟𝑒𝑠𝑖𝑑𝑢𝑎𝑙 and 𝑣𝑎𝑟𝑡𝑜𝑡𝑎𝑙 are the variance of
494
regression residual and the total half-life variance respectively.
495
496
Analysis of sequence feature contribution
497
The contribution of each sequence feature was analyzed individually as a univariate
498
regression and also jointly in a multivariate regression model. We quantified the
499
feature contributions in 10-fold cross-validation. The contribution of each feature
500
individually was calculated as the variance explained by a univariate model. We then
501
added the features in a descending order of their individual explained variance to a
502
joint model, and recorded the cumulative variance explained as adding more
503
features. As compensation to the issue that the additional variance explained by a
504
single feature to a joint model depends on the order of adding it, the drop of variance
505
explained upon leaving out one feature separately from the full model was also
506
assessed.
507
508
Single-nucleotide variant effect prediction
509
The effects of single-nucleotide variation (except for motifs) were predicted by
510
introducing a single nucleotide perturbation into the full prediction model for each
511
gene, and summarizing the effect with the median half-life change across all genes.
512
For example, the effect of a single CGT to CGA transition was assessed by
513
decreasing the count of CGT for each gene by one while increasing the count of CGA
514
for each gene (only perturbing genes that had at least one CGT). In the case of
515
length and GC content, we decreased the length or number of GC count by one for
516
each gene. Note that the side effects of varying the length, such as disrupting
517
reading-frame, were not considered. Only synonymous transitions were considered
518
for codons.
519
520
Motif single-nucleotide variant analysis
521
Effects of motif single-nucleotide variants were predicted with linear model modified
522
from (Eser et al, 2016). When assessing the effect of a given motif variation, we
523
controlled for the effect of all other sequence features using a linear model with the
524
other features as covariates.
525
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526
Code availability
527
Analysis scripts are available at: https://github.com/s6juncheng/Cheng_et_al_2016
528
529
Acknowledgements
530
We are thankful to Fabien Bonneau (Max Planck Institute of Biochemistry) for helpful
531
discussions on motifs and RNA degradation pathways, as well as useful feedback on
532
the manuscript. We thank Björn Schwalb for communication on analyzing the
533
knockout data. We thank Vicente Yépez for useful feedback on the manuscript. JC
534
and ŽA is supported by a DFG fellowship through QBM. JG was supported by the
535
Bundesministerium für Bildung und Forschung, Juniorverbund in der Systemmedizin
536
“mitOmics” (grant FKZ 01ZX1405A).
537
538
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not peer-reviewed) is the author/funder. It is made available under a CC-BY-NC-ND 4.0 International license.
Sequence features explain most of the mRNA stability variation across genes
Jun Cheng, Žiga Avsec, Julien Gagneur
November 2016
1
Main Figures
Data
Genome-wide mRNA half-lives in S. cerevisiae: wild-type + 34 strains knocked out for RNA degradation pathway genes
(Sun et al. Mol. Cell. 2013)
Analysis
Dependence on degradation pathways
Cis-regulatory elements
AUG
AAA AUG
…CGA…GGU…
UGAC
AUAUUC AAAAAA…
• Novel elements discovery
• Integrate novel and known elements
• Single-nucleotide effect estimation
Integrative model
Out-of-sample predictions
Relative contributions
CRE1
Single-nucleotide effects
...CATACGATA...
...CATAGGATA...
CRE2
CRE3
Fold-change
Percentage of variance explained by (%)
Figure 1: Study overview. The goal of this study is to discover and integrate cis-regulatory mRNA elements affecting mRNA
stability and assess their dependence on mRNA degradation pathways. Data) We obtained S. cerevisiae genome-wide half-life data
from wild-type (WT) as well as from 34 knockout strains from Sun et al. 2013. Each of the knockout strains has one gene closely
related to mRNA degradation pathways knocked out. Analysis) We systematically searched for novel sequence features associating
with half-life from 5’UTR, start codon context, CDS, stop codon context, and 3’UTR. Effects of previously reported cis-regulatory
elements were also assessed. Moreover, we assessed the dependencies of different sequence features on degradation pathways by
analyzing their effects in the knockout strains. Integrative model) We build a statistical model to predict genome-wide half-life
solely from mRNA sequence. This allowed the quantification of the relative contributions of the sequence features to the overall
variation across genes and assessing the sensitivity of mRNA stability with respect to single-nucleotide variants.
bioRxiv preprint first posted online Nov. 3, 2016; doi: http://dx.doi.org/10.1101/085522. The copyright holder for this preprint (which was
not peer-reviewed) is the author/funder. It is made available under a CC-BY-NC-ND 4.0 International license.
A
Stabilizing elements
Destabilizing elements
Elements effect sign context dependent
TSS
CDS
5' UTR
Stop codon context
Start codon context
ATG
AAACAAA
Secondary structure
GC-content
Length
B
3' UTR
Codon usage
GC-content
Length
5' UTR
TGTAAATA (Puf3)
TGCAT (Whi3)
TTTTTTA
ATATTC
GC-content
Length
3' UTR
ATG (uAUG)
AAACAAA
log10(FDR)
Enriched
ATATTC
4
2
TGCAT
0
-2
-4
TGTAAATA
Depleted
TTTTTTA
TGTAAATA
TGCAT
ATATTC
AAACAAA
ATG (uAUG)
TTTTTTA
0
200
400
600
800
1000
Number of mRNAs
Figure 2: Overview of identified or collected sequence features. (A) Sequence features that were identified or collected
from different sequence regions in this study. When applicable, stabilizing elements are shown in blue, destabilizing in red. (B)
Co-occurrence significance (FDR, Fisher test p-value corrected with Benjamini-Hochberg) between different motifs (left). Number
of occurrences among the 4,388 mRNAs (right).
bioRxiv preprint first posted online Nov. 3, 2016; doi: http://dx.doi.org/10.1101/085522. The copyright holder for this preprint (which was
not peer-reviewed) is the author/funder. It is made available under a CC-BY-NC-ND 4.0 International license.
A
WT
xrn1Δ
upf2Δ
upf3Δ
40
3631
20
3648
3647
10
739
3648
738
739
5
P < 2e-16
Median FC = 1.37
1
no uAUG
P = 0.022
Median FC = 1.07
with uAUG
no uAUG
S. cerevisiae
B
501
238
5
20
1
1
Number of uAUGs
>=2
3649
203
P = 0.75
519
5
no PTC
no uAUG
in-frame
no PTC
with PTC
out-frame
PTC in CDS
P = 1.7e-13
150
P = 0.098
17
10
with uAUG
S. pombe
D
40
1
0
no uAUG
P < 2e-16
P = 0.53
Half-life [min]
10
with uAUG
P = 8e-13
P = 0.013
3648
P = 8e-04
Median FC = 1.05
P < 2e-16
40
20
no uAUG
S. cerevisiae
150
P < 2e-16
150
P = 1e-04
Median FC = 1.09
with uAUG
C
P < 2e-16
half-life [min]
738
uORF
Half-life [min]
Half-life [min]
150
P = 0.99
40
20
2051
11
10
1377
196
5
1
no PTC
no uAUG
in-frame
no PTC
with PTC
out-frame
PTC in CDS
uORF
Figure 3: Upstream AUG codon (uAUG) destabilize mRNA. (A) Distribution of mRNA half-life for mRNAs without
uAUG (left) and with at least one uAUG (right) in, from left to right: wild type, XRN1, UPF2 and UPF3 knockout S. cerevisiae
strains. Median fold-change (Median FC) calculated by dividing the median of the group without uAUG with the group with
uAUG. (B) Distribution of mRNA half-lives for mRNAs with zero (left), one (middle), or more (right) uAUGs in S. cerevisiae.
(C) Distribution of mRNA half-lives for S. cerevisiae mRNAs with, from left to right: no uAUG, with one in-frame uAUG but no
cognate premature termination codon, with one out-of-frame uAUG and one cognate premature termination codon in the CDS,
and with one uAUG and one cognate stop codon in the 5’UTR (uORF). (D) Same as in (C) for S. pombe mRNAs. All p-values
were calculated with Wilcoxon rank-sum test. Numbers in the boxes indicate number of members in the corresponding group.
Boxes represent quartiles, whiskers extend to the highest or lowest value within 1.5 times the interquartile range and horizontal
bars in the boxes represent medians. Data points falling further than 1.5-fold the interquartile distance are considered outliers and
are shown as dots.
bioRxiv preprint first posted online Nov. 3, 2016; doi: http://dx.doi.org/10.1101/085522. The copyright holder for this preprint (which was
not peer-reviewed) is the author/funder. It is made available under a CC-BY-NC-ND 4.0 International license.
S. cerevisiae
P = 9.2e-08
40
2736
872
419
360
5
1
T
C
Half-life [min]
5 20 100
Deadenylation
Decapping
Exosome
RBP
Surveillance
WT
XRN1
Knockouts
S. cerevisiae
D
P = 1.8e-09
P = 0.0049
P = 7.5e-07
150
Half-life [min]
Spearman rho=0.55
R-squared=0.3
*
Stop_codon
40
20
486
10
798
624
151
227
378
TAA
294
141
248
491
442
108
TAG
TGA
5
TGAC
TGAT
TGAA
stop codon\+1
TGAG
TAGC
TAGT
TAGA
0.35
0.45 0.55
0.70
S. cerevisiae-specific tAI
TAGG
1
1
TAAC
B
G
start codon\-3
Group
0
TAAT
A
**
**
20
TAAA
10
*
40
***
TAAG
20
60
WT
ccr4Δ
caf40Δ
not3Δ
pop2Δ
pan2Δ
pan3Δ
dhh1Δ
pat1Δ
lsm1Δ
dcs1Δ
lsm7Δ
lsm6Δ
scd6Δ
edc3Δ
dcs2Δ
edc2Δ
rrp6Δ
rrp47Δ
ski3Δ
ski2Δ
ski7Δ
ski8Δ
pub1Δ
puf4Δ
puf6Δ
puf3Δ
puf5Δ
puf1Δ
puf2Δ
upf3Δ
upf2Δ
hbs1Δ
dom34Δ
xrn1Δ
P = 6.9e-12
150
half-life [min]
C
P = 1.7e-11
Codon-explained-variance (%)
A
Figure 4: Translation initiation, elongation and termination features associate with mRNA half-life. (A) Distribution
of half-life for mRNAs grouped by the third nucleotide before the start codon. Group sizes (numbers in boxes) show that nucleotide
frequency at this position positively associates with half-life. (B) mRNA half-life (y-axis) versus species-specific tRNA adaptation
index (sTAI) (x-axis). (C) mRNA half-life explained variance (y-axis, Materials and Methods) in wild-type (WT) and across all 34
knockout strains (grouped according to their functions). Each blue dot represents one replicate, bar heights indicate means across
replicates. Bars with a red star are significantly different from the wild type level (FDR <0.1, Wilcoxon rank-sum test, followed by
Benjamini-Hochberg correction). (D) Distribution of half-life for mRNAs grouped by the stop codon and the following nucleotide.
Colors represent three different stop codons (TAA, TAG and TGA), within each stop codon group, boxes are shown in G, A, T,
C order of their following base. Only the P-values for the most drastic pairwise comparisons (A versus C within each stop codon
group) are shown. All p-values in boxplots were calculated with Wilcoxon rank-sum test. Boxplots computed as in Fig 3.
bioRxiv preprint first posted online Nov. 3, 2016; doi: http://dx.doi.org/10.1101/085522. The copyright holder for this preprint (which was
not peer-reviewed) is the author/funder. It is made available under a CC-BY-NC-ND 4.0 International license.
A
ATATTC
FDR=1.96e−05
TGCAT
FDR=6.94e−04
TGTAAATA
FDR=3.2e−05
TTTTTTA
FDR=0.086
150
Half-life [min]
40
20
3797
10
529
62
1
>=2
3492
4179
769
100
27
1
2
>=3
94
610
3684
209
5
1
0
Motif density
B
0.4
0.3
0.2
0.1
0.0
0
ATATTC
150
100
50
TGCAT
0
150
100
50
Effects on half-life
base
ATATTC
>=1
0
TGTAAATA
0
150
100
Distance from poly(A) site
C
1.3
1.2
1.1
1.0
0.9
0
Occurence per transcript
A
C
G
TGCAT
50
1
>=2
TTTTTTA
0
150
100
50
0
T
TGTAAATA
TTTTTTA
-2 -1 1 2 3 4 5 6 +1 +2
-2 -1 1
2
3
4
5 +1 +2
-2 -1 1 2 3 4 5 6 7 8 +1 +2
-2 -1 1 2 3 4 5 6 7 +1 +2
-2 -1 1 2 3 4 5 6 +1 +2
-2 -1 1
2
3
4
5 +1 +2
-2 -1 1 2 3 4 5 6 7 8 +1+2
-2 -1 1 2 3 4 5 6 7 +1 +2
Frequency
D
1.00
0.75
0.50
0.25
0.00
conservation
E
ATATTC
0.70
0.65
0.60
0.55
-2 -1 1 2 3 4 5 6 +1 +2
0.325
0.300
0.275
0.250
0.225
Position
TGCAT
TGTAAATA
0.9
0.22
0.7
2
3
4
5 +1 +2
TTTTTTA
0.24
0.8
-2 -1 1
0.26
-2 -1 1 2 3 4 5 6 7 8 +1+2
Position
0.20
-2 -1 1 2 3 4 5 6 7 +1 +2
Figure 5: 3’UTR half-life determinant motifs in S. cerevisiae. (A) Distribution of half-lives for mRNAs grouped by the
number of occurrence(s) of the motif ATATTC, TGCAT (Whi3), TGTAAATA (Puf3) and TTTTTTA respectively in their 3’UTR
sequence. Numbers in the boxes represent the number of members in each box. FDR were reported from the linear mixed effect
model (Materials and Methods). (B) Fraction of transcripts containing the motif (y-axis) within a 20-bp window centered at a
position (x-axis) with respect to poly(A) site for different motifs (facet titles). Positional bias was not observed when aligning 3’UTR
motifs with respect to the stop codon. (C) Prediction of the relative effect on half-life (y-axis) for single-nucleotide substitution
in the motif with respect to the consensus motif (y=1, horizontal line). The motifs were extended 2 bases at each flanking site
(positions +1, +2, -1, -2). (D) Nucleotide frequency within motif instances, when allowing for one mismatch compared to the
consensus motif. (E) Mean conservation score (phastCons, Materials and Methods) of each base in the consensus motif with 2
flanking nucleotides (y-axis).
bioRxiv preprint first posted online Nov. 3, 2016; doi: http://dx.doi.org/10.1101/085522. The copyright holder for this preprint (which was
not peer-reviewed) is the author/funder. It is made available under a CC-BY-NC-ND 4.0 International license.
S. cerevisiae
S. pombe
R-squared=0.60
R-squared=0.42
50
20
1
5
20
50
Measured half-life [min]
100
B
5
Measured half-life [min]
A
50
100
1
Predicted half-life [min]
C
Individual
Cumulative
Drop
Codon usage
CREs
55.90
55.92
44.23
uAUG
4.46
56.20
0.05
3' UTR motifs
5.53
57.59
1.29
UTR5_length
4.24
58.41
0.56
CDS_length
3.70
59.82
1.58
GC_content_UTR5 1.27
60.09
0.22
0.28
60.17
0.10
50
Effect of single-nucleotide variations
1.3
1.2
1.1
1.0
U
C
D
S_
UTR3_length
20
Predicted half-life [min]
D
Contribution of each sequence features (%)
5
le
ng
TR
th
3_
le
ng
U
TR
th
5_
le
ng
th
G
C
_c
co
on
do
te
n
nt
_U
TR
5
uA
U
G
TT
TT
TT
A
TG
CA
T
AT
AT
TC
TG
TA
AA
TA
20
Expected half-life fold-change
5
E
Figure 6: Genome-wide prediction of mRNA half-lives from sequence features and analysis of the contributions.
(A-B) mRNA half-lives predicted (x-axis) versus measured (y-axis) for S. cerevisiae (A) and S. pombe (B) respectively. (C)
Contribution of each sequence feature individually (Individual), cumulatively when sequentially added into a combined model
(Cumulative) and explained variance drop when each single feature is removed from the full model separately (Drop). Values
reported are the mean of 100 times cross-validated evaluation (Materials and Methods). (D) Expected half-life fold-change of
single-nucleotide variations on sequence features. For length and GC, dot represent median half-life fold change of one nucleotide
shorter or one G/C to A/T transition respectively. For codon usage, each dot represents median half-life fold-change of one type
of synonymous mutation, all kinds of synonymous mutations are considered. For uAUG, each dot represents median half-life foldchange of mutating out one uAUG. For motifs, each dot represents median half-life fold-change of one type of nucleotide transition
at one position on the motif (Materials and Methods). Medians are calculated across all mRNAs. (E) Overview of conclusions.
2
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peer-reviewed) is the author/funder. It is made available under a CC-BY-NC-ND 4.0 International license.
ExpandednotView
bioRxiv preprint first posted online Nov. 3, 2016; doi: http://dx.doi.org/10.1101/085522. The copyright holder for this preprint (which was
not peer-reviewed) is the author/funder. It is made available under a CC-BY-NC-ND 4.0 International license.
S. cerevisiae
S. pombe
Spearman rho=-0.17
Spearman rho=-0.27
B
50
10
half-life [min]
200
50
5 10
1
1
2
2
half-life [min]
P = 2.1e-11
200
P = 3.4e-12
5
A
500
2000
10 20
S. cerevisiae
Spearman rho=-0.23
C
500
2000
P = 4.2e-15
200
half-life [min]
200
50
5 10
half-life [min]
1
1
2
2
200
S. pombe
Spearman rho=-0.32
D
P < 2e-16
50
5'UTR length
50
50 100
5'UTR length
10
20
5
10
500 1000
5000
CDS length
100
S. cerevisiae
Spearman rho=-0.06
P = 5.7e-12
200
half-life [min]
200
50
5 10
1
1
2
2
half-life [min]
S. pombe
Spearman rho=-0.23
F
P = 1.4e-08
2000 5000
50
E
500
CDS length
10
200
5
100
500
10
50
1000
S. pombe
Spearman rho=-0.24
H
P < 2e-16
-50
half-life [min]
-100
-1
-2
-10
200
3'UTR length
-200
P = 7.8e-12
5
-20
50
3'UTR length
S. cerevisiae
Spearman rho=-0.21
G
half-life [min]
5
-5
1
1e-02
1e-01
1e+00
1e+01
5' Free Energy
1e+02
2
5
10 20
50
200 500
5' Free Energy
Figure EV1: Length of 5’UTR, CDS and 3’UTR as well as 5’ folding energy correlate with mRNA half-life. (A-B)
5’UTR length (x-axis) versus half-life (y-axis) for S. cerevisiae (A) and S. pombe (B). (C-D) CDS length (x-axis) versus half-life
(y-axis) for S. cerevisiae (C) and S. pombe (D). (E-F) 3’UTR length (x-axis) versus half-life (y-axis) for S. cerevisiae (E) and S.
pombe (F). (G-H) 5’ free energy (x-axis) versus half-life (y-axis) for S. cerevisiae (G) and S. pombe (H).
bioRxiv preprint first posted online Nov. 3, 2016; doi: http://dx.doi.org/10.1101/085522. The copyright holder for this preprint (which was
not peer-reviewed) is the author/funder. It is made available under a CC-BY-NC-ND 4.0 International license.
S. cerevisiae
Spearman rho=-0.11
A
S. pombe
Spearman rho=0.16
P < 2e-16
1
1
2
2
5
5
10
10
50
50
half-life [min]
half-life [min]
200
200
P = 2.8e-14
0.1
0.2
0.3
0.4
0.6
GC content 5'UTR
0.2
0.3
0.4
0.5
0.7
GC content 5'UTR
S. cerevisiae
Spearman rho=0.27
S. pombe
Spearman rho=0.24
P < 2e-16
1
1
2
2
5
5
10
10
50
50
half-life [min]
half-life [min]
200
200
P < 2e-16
0.40 0.45
0.55
0.30
S. cerevisiae
Spearman rho=-0.17
0.35
0.40
0.45
0.50
GC content CDS
S. pombe
Spearman rho=0.05
P < 2e-16
P = 0.012
1
1
2
2
5
5
10
10
50
50
half-life [min]
half-life [min]
200
E
0.35
GC content CDS
200
0.30
0.2
0.3
0.4
GC content 3'UTR
0.5
0.15
0.20
0.25
0.30
GC content 3'UTR
0.40
Figure EV2: GC content of 5’UTR, CDS and 3’UTR correlate with mRNA half-life (A-B) 5’UTR GC content (x-axis)
versus half-life (y-axis) for S. cerevisiae (A) and S. pombe. (C-D) CDS GC content (x-axis) versus half-life (y-axis) for S. cerevisiae
(C) and S. pombe (D). (E-F) 3’UTR GC content (x-axis) versus half-life (y-axis) for S. cerevisiae (E) and S. pombe (F).
Information content
bioRxiv preprint first posted online Nov. 3, 2016; doi: http://dx.doi.org/10.1101/085522. The copyright holder for this preprint (which was
not peer-reviewed) is the author/funder. It is made available
under a CC-BY-NC-ND 4.0 International license.
A
S. cerevisiae
0.6
0.5
0.4
0.3
0.2
0.1
0
−6
−5
−4
−3
−2
−1
A
Information content
B
T
G
1
2
3
4
5
6
1
2
3
4
5
6
S. pombe
0.6
0.5
0.4
0.3
0.2
0.1
0
−6
−5
−4
−3
−2
−1
A
T
G
Position
C
F
S. cerevisiae
150
P = 7.5e−05
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
10
2241
S. pombe
P = 0.013
●
150
●
●
●
●
●
●
●
●
●
●
●
●
40
20
stop codon\+1
2146
5
TGAC
T
●
TGAA
G
start codon\+3
E
half−life [min]
●
46
●
●
●
●
TGAG
A
●
●
●
●
●
●
TAAG
●
●
●
●
●
●
148 284 241
102
●
1
1
P = 0.0076
TAGC
5
232
TAGT
505
316
118
166
TAGA
10
294
692
TAAC
578
321 948
TAAT
2237
40
20
10
5
TAAA
half−life [min]
40
P = 0.0091
TGAT
150
●
●
20
P = 8.2e−08
half−life [min]
●
P = 8e−06
P < 2e−16
P = 7.6e−05
half−life [min]
150
S. Pombe
D
TAGG
S. Pombe
C
●
●
●
40
20
10
1840
1774
5
●
●
●
●
●
●
1
FALSE
TRUE
3'UTR with in−frame stop codon
●
●
●
●
●
●
1
FALSE
TRUE
3'UTR with in−frame stop codon
Figure EV3: Start codon context (Kozak sequence) and S. pombe half-life versus start and stop codon context.
(A-B) Start codon context (Kozak sequence) generated from 4388 S. cerevisiae genes (A) and 3713 S. pombe genes (B). (C)
Distribution of half-life for mRNAs grouped by the third nucleotide before the start codon for S. pombe. Group sizes (numbers in
boxes) show that nucleotide frequency at this position positively associates with half-life. (D) Distribution of half-life for mRNAs
grouped by the stop codon and the following nucleotide for S. pombe. Colors represent three different stop codons (TAA, TAG
and TGA), within each stop codon group, boxes are shown in G, A, T, C order of their following base. Only the P-values for the
most drastic pairwise comparisons (A versus C within each stop codon group) are shown. (E) Distribution of half-life for mRNAs
grouped by with or without additional 3’UTR in-frame stop codon for S. cerevisiae. 30 bases window after the main stop codon
was considered. (F) Same as (E) for S. pombe. All p-values in boxplot were calculated with Wilcoxon rank-sum test. Boxplots
computed as in Fig 3.
bioRxiv preprint first posted online Nov. 3, 2016; doi: http://dx.doi.org/10.1101/085522. The copyright holder for this preprint (which was
not peer-reviewed) is the author/funder. It is made available under a CC-BY-NC-ND 4.0 International license.
AAACAAA
FDR=0.049
A
half-life (mins)
150
40
20
228
4160
10
5
1
0
effects on half-life
B
Frequency
C
AAAAACAAAAA
1.3
1.2
1.1
1.0
0.9
0.8
1.00
0.75
0.50
0.25
0.00
base
A
C
G
T
1
2
3
4
5
6
7
positions
8
9 10 11
-2 -1 1 2 3 4 5 6 7 +1 +2
D
conservation
>= 1
AAACAAA
0.16
0.14
0.12
0.10
0.08
-2
-1
1
2
3
4
5
Position
6
7
+1
+2
Figure EV4: S. cerevisiae 5’UTR mRNA half-life associated motif. (A) Distribution of half-lives for mRNAs grouped by
the number of occurrence(s) of the motif AAACAAA in their 5’UTR sequence. Numbers in the boxes represent the number of
members in each box. FDR were reported from the linear mixed effect model (Materials and Methods). (B) Prediction of the
relative effect on half-life (y-axis) for single-nucleotide substitution in the motif with respect to the consensus motif (y=1, horizontal
line). The motifs were extended 2 bases at each flanking site (positions +1, +2, -1, -2). (C) Nucleotide frequency within motif
instances, when allowing for one mismatch compared to the consensus motif. (D) Mean conservation score (phastCons, Materials
and Methods) of each base in the consensus motif with 2 flanking nucleotides (y-axis).
bioRxiv preprint first posted online Nov. 3, 2016; doi: http://dx.doi.org/10.1101/085522. The copyright holder for this preprint (which was
not peer-reviewed) is the author/funder. It is made available under a CC-BY-NC-ND 4.0 International license.
xrn1Δ
dom34Δ
hbs1Δ
upf2Δ
upf3Δ
puf2Δ
puf1Δ
puf5Δ
puf3Δ
puf6Δ
puf4Δ
pub1Δ
ski8Δ
ski7Δ
ski2Δ
ski3Δ
rrp47Δ
rrp6Δ
edc2Δ
dcs2Δ
edc3Δ
scd6Δ
lsm6Δ
lsm7Δ
dcs1Δ
lsm1Δ
pat1Δ
dhh1Δ
pan3Δ
pan2Δ
pop2Δ
not3Δ
caf40Δ
ccr4Δ
WT
*
*
*
*
*
*
*
*
*
*
*
*
*
*
*
*
*
*
*
*
*
*
*
Effects
*
*
*
0.2
0.0
-0.2
*
*
*
*
*
*
0.4
*
*
*
*
*
*
*
*
*
*
*
*
*
*
*
*
*
*
*
*
*
*
*
*
*
*
*
*
A
TT
TT
TT AT
C
TG TC A
AT AT
AT A AG
TA TG e
TG - g
AC usa
TG on
d A
co C-3 gy
St ner A
e A
5' CA
A
AA G
U
uA
Figure EV5: Summary of CREs effect changes across all 34 knockouts comparing with WT. Colour represent the
relative effect size (motifs, St-3 C-A, TGAG-TGAC, uAUG), correlation (5’ folding energy) or explained variance (codon usage)
upon knockout of different genes (y-axis) (Materials and Methods for detailed description). Wild-type label is shown in the bottom
(WT) P-values calculated with Wilcoxon rank-sum test by comparing each mutant to wild-type level, multiple testing p-values
corrected with Bonferroni & Hochberg (FDR). Stars indicating significance of statistical testing (FDR <0.1). 5’ energy: correlation
of 5’end (5’UTR plus first 10 codons) folding energy with mRNA half-lives; St-3 C-A: relative median half-life difference between
genes with cytosine and adenine at start codon -3 position; TGAG-TGAC: relative median half-life difference between genes with
stop codon +1 TAAG and TGAC. codon usage: codon usage explained mRNA half-lives variance. uAUG: relative median half-life
difference between genes without and with upstream AUG in the 5’UTR (Materials and Methods)
codons
bioRxiv preprint first posted online Nov. 3, 2016; doi: http://dx.doi.org/10.1101/085522. The copyright holder for this preprint (which was
not peer-reviewed) is the author/funder. It is made available under a CC-BY-NC-ND 4.0 International license.
AAT
AAC
TAT
GGC
GGA
GAT
GGG
TAC
GAC
TTT
TGT
TTC
TGC
CTC
CTA
AGC
AGT
GCG
GCT
GAG
GGT
CAT
CTT
TCG
CAC
GAA
TCT
TCA
GCC
CTG
GCA
CAG
ACG
CAA
ACT
ACC
TTA
CCT
CCC
CGG
ACA
AGG
CGC
TCC
ATC
ATT
CCA
CCG
AAA
AAG
AGA
TTG
CGA
CGT
ATA
AAC
AAT
TAC
GGT
GGA
GGG
GGT
GGC
GGG
GAC
GGT
GGA
GGC
TAT
GAT
TTC
TGC
TTT
TGT
CTT
CTA
CTG
CTGCTC
CTT TTA
AGT
AGC
GCTGCC
GCA
GCC
GCG
GCA
GAA
GGC
GGA
GGG
CAC
CTG
CTACTC
TCC
TCT
TCA
CAT
GAG
TCC
TCG
TCA
TCC
TCT
TCG
GCT
GCG
GCA
TTG
CTA CTC CTT
GCC
GCT
GCG
CAA
ACT
ACC
ACA
CAG
ACC
ACG
ACA
ACT
ACG
ACA
TTG
CTA
CCA
CCC
CCG
CCA
CCT
CCG
CGACGT
CGC
AGG
ACC
ACT
ACG
CGG
AGA
CGA
CGT
CGG
TCT
TCG
TCA
ATA
ATT
ATC
ATA
CCT
CCC
CCG
CCA
CCT
CCC
AAG
AAA
CGA
AGG
CTG
TTA
CGG CGC
AGA
CGC CGG
ATC
ATT
1.00
1.01
1.02
Expected half-life fold-change
CGT
CGA
1.03
Figure EV6: Predicted effects of synonymous codon transitions on half-life. Expected half-life fold-change (x-axis) at
each synonymous codon transitions. Each row represent transition from one codon (y-axis) to its synonymous partners. Only
synonymous codons that differ by one base were considered.