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. 2017 Sep;27(9):1525–1535. doi: 10.1101/gr.202150.115

Trans-splicing enhances translational efficiency in C. elegans

Yu-Fei Yang 1,2,3,6, Xiaoqing Zhang 1,2,3,6, Xuehua Ma 2,4,6, Taolan Zhao 1,2,6, Qiushi Sun 1,2,5, Qing Huan 1,2, Shaohuan Wu 1,2,3, Zhuo Du 4, Wenfeng Qian 1,2,3
PMCID: PMC5580712  PMID: 28684554

Abstract

Translational efficiency is subject to extensive regulation. However, the factors influencing such regulation are poorly understood. In Caenorhabditis elegans, 62% of genes are trans-spliced to a specific spliced leader (SL1), which replaces part of the native 5′ untranslated region (5′ UTR). Given the pivotal role the 5′ UTR plays in the regulation of translational efficiency, we hypothesized that SL1 trans-splicing functions to regulate translational efficiency. With genome-wide analysis on Ribo-seq data, polysome profiling experiments, and CRISPR-Cas9–based genetic manipulation of trans-splicing sites, we found four lines of evidence in support of this hypothesis. First, SL1 trans-spliced genes have higher translational efficiencies than non-trans-spliced genes. Second, SL1 trans-spliced genes have higher translational efficiencies than non-trans-spliced orthologous genes in other nematode species. Third, an SL1 trans-spliced isoform has higher translational efficiency than the non-trans-spliced isoform of the same gene. Fourth, deletion of trans-splicing sites of endogenous genes leads to reduced translational efficiency. Importantly, we demonstrated that SL1 trans-splicing plays a key role in enhancing translational efficiencies of essential genes. We further discovered that SL1 trans-splicing likely enhances translational efficiency by shortening the native 5′ UTRs, hence reducing the presence of upstream start codons (uAUG) and weakening mRNA secondary structures. Taken together, our study elucidates the global function of trans-splicing in enhancing translational efficiency in nematodes, paving the way for further understanding the genomic mechanisms of translational regulation.

Although proteins are the major macromolecules performing cellular activities, their relative concentrations are largely unknown (Tyers and Mann 2003; Altelaar et al. 2013). Instead, mRNA concentrations are often used as a proxy for protein concentrations in genome-wide studies. This assumes negligible variation of translational efficiencies among genes. With quantitative proteomic data becoming more widely available (Bantscheff et al. 2012; Bensimon et al. 2012), it was surprisingly found that protein and mRNA concentrations are only moderately correlated. For example, only 32% of the variance in protein concentrations in the budding yeast Saccharomyces cerevisiae could be explained by mRNA concentration (Ghaemmaghami et al. 2003). Similar phenomena were observed in mice (Schwanhausser et al. 2011) and humans (Wilhelm et al. 2014), in which the proportion of explained variance varied from 0.10 to 0.41 across different tissues. In prokaryotes, where operons are pervasive in the genome, the disparity between mRNA and protein concentrations can become extreme. This is because genes in an operon are transcribed together, yet different protein concentrations may be required to maintain the stoichiometric relationship in protein complexes. Indeed, it was discovered that in Escherichia coli, the protein concentration variation in an operon cannot be explained by mRNA concentration (Quax et al. 2013; Li et al. 2014), implying regulation at the level of translation. Furthermore, mRNA concentration differences between haploid and diploid yeast are not an accurate predictor of the differences at the protein level (de Godoy et al. 2008), suggesting the presence of post-transcriptional regulation. Finally, the expression divergence among species is usually smaller at the protein level than that at the mRNA level (Khan et al. 2013), which also suggests the existence of extensive translational regulation (Artieri and Fraser 2014b; McManus et al. 2014).

The process of translation is comprised of three steps: initiation, elongation, and termination (Scheper et al. 2007). Initiation is generally considered the rate-determining step in endogenous genes (Andersson and Kurland 1990; Bulmer 1991; Plotkin and Kudla 2011; Shah et al. 2013; Chu et al. 2014). The 5′ UTR and the 5′ terminus of coding sequences have both been reported to be the main targets for translational regulation (Hall et al. 1982; de Smit and van Duin 1990; Babendure et al. 2006; Kudla et al. 2009; Dvir et al. 2013). Consistently, sequence motifs involved in translational regulation have been identified in the 5′ UTR. Examples include the Kozak sequence (Kozak 1987; Dvir et al. 2013)—the consensus sequence around the start codon AUG—and upstream open reading frame (uORF) (Ingolia et al. 2009; Dvir et al. 2013).

Trans-splicing edits 5′ UTR sequences in nematodes, trypanosomes, dinoflagellates, flatworms, and hydra, among many other species (Blumenthal 2004; Lasda and Blumenthal 2011), and thus may be involved in the regulation of translational efficiency. In Caenorhabditis elegans, the majority of genes are spliced leader (SL) trans-spliced (Blumenthal 2005; Allen et al. 2011). In this process, an SL RNA trims the 5′ UTR of pre-mRNA and then attaches a short (∼22 nucleotide [nt]) sequence to the 5′ terminus (Krause and Hirsh 1987; Hastings 2005). After SL trans-splicing, ∼49% of transcripts retain less than 10 nt of the 5′ UTR sequence of the pre-mRNAs (Lall et al. 2004). SL trans-splicing can be classified into two types based on the sequence of the spliced leader: SL1 and SL2, affecting 62% and 12% of genes, respectively (Allen et al. 2011). SL2 trans-splicing is related to eukaryotic operons. In C. elegans, more than 17% of genes are localized in operons, where they are transcribed into a single nonfunctional polycistronic pre-mRNA (Blumenthal et al. 2002; Blumenthal and Gleason 2003; Blumenthal 2004; Allen et al. 2011; Saito et al. 2013). SL2 trans-splicing is used by downstream genes in operons to generate functional monocistronic mRNAs from the pre-mRNA (Spieth et al. 1993; Zorio et al. 1994; Blumenthal et al. 2002). In contrast, SL1 trans-splicing is used by the first genes in operons and genes that are not in operons. Although SL1 trans-splicing is more prevalent, the function of it is not yet fully understood (Blumenthal 2005). Since SL1 trans-splicing modifies the 5′ UTRs of genes, we hypothesized that it functions to regulate translational efficiency (number of proteins made per mRNA per unit time).

Results

SL1 trans-spliced genes exhibit higher translational efficiencies than non-trans-spliced genes

To test if SL1 trans-splicing regulates translational efficiency, we first compared the translational efficiencies of SL1 trans-spliced genes and non-trans-spliced genes. We calculated ribosome density from previously published ribosome profiling (Ribo-seq) data and the corresponding mRNA-seq data in nematodes (Stadler and Fire 2013) and used this as a measure of translational efficiency (Ingolia et al. 2009). We classified a gene as SL1 trans-spliced if ≥90% of informative mRNA-seq reads supported SL1 trans-splicing, and a gene as non-trans-spliced if no reads supported trans-splicing (Allen et al. 2011).

We found that SL1 trans-spliced genes had significantly higher translational efficiencies than non-trans-spliced genes in C. elegans (P = 1 × 10−214, Mann-Whitney U test) (Fig. 1A). In fact, the median translational efficiency among SL1 trans-spliced genes was approximately twice as much as that of non-trans-spliced genes (Fig. 1A). We further identified SL1 trans-spliced and non-trans-spliced genes in three related nematode species (C. briggsae, C. remanei, and C. brenneri) that diverged from C. elegans tens of millions of years ago (Stein et al. 2003; Cho et al. 2004; Hillier et al. 2007; Cutter 2008). We repeated the analysis in these species and observed similar phenomena (Fig. 1B–D). This indicates that the enhanced translational efficiencies in SL1 trans-spliced genes are evolutionarily conserved. We further demonstrated that the enhanced translational efficiency of SL1 trans-spliced gene is not condition-specific (Supplemental Fig. S1) by comparing translational efficiencies in either L1 diapause or developing worms (Stadler and Fire 2013).

Figure 1.

Figure 1.

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SL1 trans-spliced genes exhibit higher translational efficiencies in nematodes. Comparisons were made in (A) C. elegans, (B) C. briggsae, (C) C. remanei, and (D) C. brenneri. P-values were given by the Mann-Whitney U test.

It is worth noting that the observed higher translational efficiencies in SL1 trans-spliced genes may not be the direct consequence of SL1 trans-splicing. For example, the initiation codon AUG may be in a better context in SL1 trans-spliced genes, which may precipitate higher translational efficiency (Kozak 1987; Dvir et al. 2013). To examine this possibility, we analyzed only those genes with a perfect Kozak consensus sequence (AAAAUG) (Supplemental Fig. S2A) and again found an approximately twofold translational efficiency difference between SL1 trans-spliced and non-trans-spliced genes (P = 1 × 10−41, Mann-Whitney U test) (Supplemental Fig. S2B), indicating that the Kozak sequence was not a confounding factor. Furthermore, we calculated an “initiation score” to quantify the optimality of the initiation context around AUG (similarity of the sequence around AUG to the Kozak consensus sequence) in C. elegans (see Supplemental Methods). As expected, the initiation score was positively correlated with translational efficiency (Supplemental Fig. S2C). Nevertheless, after controlling for the initiation score, we still observed higher translational efficiencies in SL1 trans-spliced genes (P = 6 × 10−10, linear regression model: translational efficiency ∼ trans-splicing + initiation score) (Supplemental Fig. S2C).

In this study, we used ribosome density as a measure of translational efficiency, assuming similar translational elongation rates among genes. Although it has been reported that synonymous codons of the same amino acid are translated at similar speed (Li et al. 2012; Qian et al. 2012; Artieri and Fraser 2014a; Pop et al. 2014), it has also been argued elsewhere that the elongation rate varied among codons in nematodes, partially due to the different decoding rates between wobble pairing and Watson-Crick pairing at the third position of a codon (Stadler and Fire 2011). Nevertheless, after controlling for the codon adaptation index (CAI), which reflects the synonymous codon usage bias of a gene (Sharp and Li 1987), we still observed significantly higher translational efficiencies among SL1 trans-spliced genes (P = 1 × 10−7, linear regression model: translational efficiency ∼ trans-splicing + CAI) (Supplemental Fig. S3).

SL1 trans-spliced genes exhibit higher translational efficiencies than their non-trans-spliced one-to-one orthologs in other nematode species

We further predicted that SL1 trans-spliced genes would exhibit higher translational efficiencies than their non-trans-spliced one-to-one orthologs in other species. To test this prediction, we first retrieved four-way one-to-one orthologous genes among C. elegans, C. briggsae, C. remanei, and C. brenneri (Harris et al. 2014). To avoid false discovery of orthologous genes with trans-splicing turnover (see Supplemental Methods; Supplemental Fig. S4), we focused on the one-to-one orthologous genes with SL1 trans-splicing evidence in C. briggsae, C. remanei, or C. brenneri, but without trans-splicing evidence in C. elegans. We found that SL1 trans-spliced one-to-one orthologous genes indeed had higher translational efficiencies (P = 3 × 10−3, 0.03, and 1 × 10−4, respectively, paired Mann-Whitney U test) (Fig. 2A–C, left panels). As a control, one-to-one orthologous genes with evidence for SL1 trans-splicing in both species did not have a significant difference in translational efficiency (P = 0.1, 0.9, and 0.1, respectively, paired Mann-Whitney U test) (Fig. 2A–C, right panels).

Figure 2.

Figure 2.

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SL1 trans-spliced genes exhibit higher translational efficiencies than their non-trans-spliced one-to-one orthologs. Comparisons were made between C. elegans and (A) C. briggsae, (B) C. remanei, and (C) C. brenneri. P-values were given by the Mann-Whitney U test.

SL1 trans-spliced isoforms exhibit higher translational efficiencies than the non-trans-spliced isoforms of the same genes

It has been reported that the trans-splicing frequencies of some genes are not 100%, i.e., only a proportion of transcripts are trans-spliced (Allen et al. 2011). For these partially trans-spliced genes, we predicted that the SL1 trans-spliced isoform should be translated more efficiently than the non-trans-spliced isoform. To test this prediction, we identified genes with partial SL1 trans-splicing in C. elegans and calculated the SL1 trans-splicing frequency of each gene. Consistent with our prediction, we found that a gene's SL1 trans-splicing frequency was positively correlated to its translational efficiency (ρ = 0.18, P = 2 × 10−51, Spearman's correlation) (Supplemental Fig. S5).

To further test this hypothesis, we cultured wild type C. elegans (N2) in liquid media and performed sucrose gradient centrifugation on the lysate to separate mRNAs into fractions based on the number of ribosomes bound to them (Fig. 3A). Fractions collected later in the experiment (with higher rank) contained more ribosomes per mRNA. Because trans-splicing did not change the length of coding sequence, the isoform with more ribosomes had a higher translational efficiency. Thus, for a partially SL1 trans-spliced gene, our hypothesis predicted an increasing proportion of the SL1 trans-spliced isoform in higher rank fractions. We first randomly chose 12 genes with partial SL1 trans-splicing and designed DNA primers (Supplemental Table S1) to specifically quantify isoforms with or without SL1 trans-splicing in each fraction with quantitative polymerase chain reactions (qPCR) (Fig. 3B). Consistent with our prediction, in 10 out of 12 genes, we observed reproducible significant positive correlations between the SL1/non-SL ratio and the rank of the fractions (Fig. 3C; Supplemental Fig. S6). In contrast, we observed no significant negative correlations in these 12 genes, validating the function of SL1 trans-splicing in promoting translational efficiency (P = 0.002, two-tailed binomial test).

Figure 3.

Figure 3.

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SL1 trans-spliced isoforms exhibit higher translational efficiencies than the non-trans-spliced isoforms of the same genes. (A) mRNAs with different numbers of ribosomes bound to them were fractionated by sucrose gradient centrifugation. (B) Isoform-specific qPCR primers were designed to quantify the concentrations of two isoforms. (C) The concentration ratio of two isoforms (SL1 trans-spliced/non-trans-spliced) was quantified in each fraction of fractions 6–14. The locally weighted scatterplot smoothed (LOWESS) lines are shown in blue. P-values were given by Spearman's rank correlation.

Knocking out SL1 trans-splicing sites reduces translational efficiency

Given that SL1 trans-spliced isoforms exhibited higher translational efficiencies than non-trans-spliced isoforms (Fig. 3), we further predicted that knocking out the SL1 trans-splicing site of a gene should reduce its translational efficiency and thus its protein concentration. To quantify protein concentration, we used three protein::GFP fusion reporters (Supplemental Table S2) in which a gene encoding green fluorescent protein (GFP) had been fused to the 3′ end of an SL1 trans-spliced endogenous gene (Paix et al. 2014). We next attempted to knock out sequences that are important for SL1 trans-splicing frequency. To this end, we retrieved sequences around all the annotated SL1 trans-splicing sites (Allen et al. 2011) and identified their consensus sequence TTNCAG (Fig. 4A) by WebLogo v2.8 (Schneider and Stephens 1990; Crooks et al. 2004), which is in accordance with previous findings (Conrad et al. 1993). We confirmed that the SL1 trans-splicing sites with this consensus sequence exhibited significantly higher trans-splicing frequencies (P = 5 × 10−192, Mann-Whitney U test) (Fig. 4B). We then knocked out the consensus sequence of the trans-splicing sites (TTTCAG) (Fig. 4C) in these GFP fusion strains with the CRISPR-Cas9 approach. After confirming that the SL1 trans-splicing frequency was indeed reduced in SL1 mutants (see Methods; Supplemental Fig. S7A–D), we compared the GFP intensity between the wild type and SL1 mutants at a comparable developmental stage. Consistent with our prediction, GFP intensities were reduced to 50%–75% in all three SL1 mutants (P = 2 × 10−21, 1 × 10−11, and 4 × 10−7, respectively, Mann-Whitney U test) (Fig. 4D–F), while the mRNA levels remained largely unchanged (Supplemental Fig. S7E–G), confirming our prediction that decreased trans-splicing frequency leads to reduced translational efficiency.

Figure 4.

Figure 4.

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Knocking out the consensus sequence of trans-splicing sites leads to reduced translational efficiency. (A) The consensus sequence of SL1 trans-splicing sites in C. elegans (TTNCAG). (B) The consensus sequence significantly increases the efficiency of SL1 trans-splicing (P = 5 × 10−192, Mann-Whitney U test). (C) The sequences of wild type (WT) and SL1 trans-splicing mutants (SL1 MT) around the trans-splicing sites are shown for lin-15B, mes-2, and deps-1. The consensus sequences of trans-splicing sites are marked in red and the start codons are marked in cyan. Silent mutations were made (in purple) to prevent Cas9 from recutting. (D–F) The protein abundances of LIN-15B (D), MES-2 (E), and DEPS-1 (F) were quantified by the fluorescence intensity of GFP. P-values were given by the Mann-Whitney U test. Because knocking out the trans-splicing site of deps-1 frequently resulted in embryonic lethality and a complete loss of GFP signal, only embryos with GFP signal were used for quantification. An image example of each strain is shown. (G–I) Knocking out the trans-splicing site of deps-1 induced partially penetrant embryonic lethality (G), reduced fertility (H), and decreased lifetime fecundity (I). Each dot represents the result of an independent experimental replication. P-values were given by the t-test.

SL1 trans-splicing-mediated translational enhancement is pivotal for fitness. For example, deps-1 encodes a P-granule-associated protein and is required for embryonic viability and fertility (Spike et al. 2008). Although the coding sequence of deps-1 remained unchanged, the deletion of the SL1 trans-splicing site (Fig. 4C,F; Supplemental Fig. S7D,G) was sufficient to induce partially penetrant embryonic lethality, reduced fertility, and decreased lifetime fecundity (P = 0.01, 0.03, and 5 × 10−7, respectively, t-test) (Fig. 4G–I), phenocopying the loss-of-function mutant (Spike et al. 2008) and highlighting the indispensable role of trans-splicing in worm development.

Translational efficiencies of essential genes are enhanced by SL1 trans-splicing

Essential genes are defined as having lethal or sterile phenotypes upon gene perturbation or knockdown (Kamath et al. 2003). Interestingly, we found that essential genes also exhibited higher translational efficiencies than nonessential genes in C. elegans (P = 6 × 10−6, Mann-Whitney U test) (Fig. 5A). To investigate if this difference is attributable to SL1 trans-splicing, we first calculated the proportion of essential genes among SL1 trans-spliced and non-trans-spliced genes, respectively. We found that essential genes were significantly enriched among SL1 trans-spliced genes (P = 1 × 10−82, Fisher's exact test) (Fig. 5B). We further observed that essential and nonessential genes had virtually equal translational efficiencies within both SL1 trans-spliced and non-trans-spliced groups (P = 0.03 and 0.18, respectively, Mann-Whitney U test) (Fig. 5C). More importantly, nonessential genes with SL1 trans-splicing even exhibited higher translational efficiencies than essential genes without trans-splicing (P = 2 × 10−7, Mann-Whitney U test) (Fig. 5C). Taken together, the preference of SL1 trans-splicing among essential genes largely explains the difference in translational efficiency between essential and nonessential genes.

Figure 5.

Figure 5.

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Translational efficiencies of essential genes are enhanced by SL1 trans-splicing. (A) Essential genes exhibit higher translational efficiencies than nonessential genes (P-value was given by the Mann-Whitney U test). (B) The proportion of genes that are essential is higher in SL1 trans-spliced genes (P-values were given by Fisher's exact test). This observation holds when genes were separated into two groups: within operons and out of operons. (C) After controlling for the type of trans-splicing, translational efficiencies are similar between essential and nonessential genes (P-values were given by the Mann-Whitney U test).

It is worth noting that the preference of essential genes to be SL1 trans-spliced might be a byproduct of the enrichment of essential genes in operons (Blumenthal and Gleason 2003; Zaslaver et al. 2011). To examine this possibility, we further divided genes into operon genes and nonoperon genes and found that essential genes were more likely to be SL1 trans-spliced among both operon and nonoperon genes (Fig. 5B), indicating that the enrichment of essential genes in SL1 trans-spliced genes is independent of operon status.

SL1 trans-splicing trims the 5′ UTR to enhance translational efficiency

How is translational efficiency increased through SL1 trans-splicing? It has been reported that a long 5′ UTR reduces translational efficiency (Chappell et al. 2006; Staley et al. 2012; Paek et al. 2015). Therefore, we hypothesized that trans-splicing increases translational efficiency by reducing the 5′ UTR length. To test the hypothesis, we first confirmed that translational efficiency was indeed negatively correlated with 5′ UTR length in C. elegans, among both SL1 trans-spliced (Fig. 6A) and non-trans-spliced genes (Fig. 6B). Importantly, we found that before SL1 trans-splicing, the 5′ UTRs of SL1 trans-spliced genes (pre-SL1 transcripts) were longer than those of non-trans-spliced genes (P = 5 × 10−20, Mann-Whitney U test) (Fig. 6C). SL1 trans-splicing significantly shortens 5′ UTRs (P = 1 × 10−62, Mann-Whitney U test) (Fig. 6C), making them even shorter than those of non-trans-spliced genes (P = 2 × 10−10, Mann-Whitney U test) (Fig. 6C). This may partly explain the higher translational efficiencies of SL1 trans-spliced genes than those of non-trans-spliced genes (Fig. 1).

Figure 6.

Figure 6.

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SL1 trans-splicing removes the uAUG and attenuates secondary structures in the 5′ UTR to enhance translational efficiency. The 5′ UTR length is negatively correlated with translational efficiency in both (A) SL1 trans-spliced genes (N = 3373) and (B) non-trans-spliced genes (N = 1404). Linear regression lines and their 95% confidence intervals are shown. (C) Before trans-splicing, the 5′ UTRs of SL1 trans-spliced genes (pre-SL1) are longer than those of non-trans-spliced genes (Non), whereas after trans-splicing, they (SL1) are shorter than those of non-trans-spliced genes. P-values were given by the Mann-Whitney U test. (D) Genes without an uAUG exhibit higher translational efficiencies among both SL1 trans-spliced and non-trans-spliced genes. P-values were given by the Mann-Whitney U test. (E) The translational efficiencies of genes with at least one in-frame uORF that terminates at an upstream stop codon (USC) are significantly lower than those with in-frame uORFs that share the stop codons (SSC) with the annotated ORFs (P-value was given by the Mann-Whitney U test). In addition, genes with an in-frame uAUG exhibit higher translational efficiencies than those with an out-of-frame uAUG (P = 7 × 10−5, Mann-Whitney U test). (F) The proportion of genes without an uAUG in non-trans-spliced genes is larger than that in pre-SL1 transcripts but is smaller than that in SL1 transcripts (Fisher's exact test). Error bars represent standard errors estimated from a binomial distribution. (G,H) The smallest MFE (sMFE) was used to estimate the free energy (in units of kcal/mol) of the most stable secondary structure in the 5′ UTR. The sMFE is positively correlated with translational efficiency in both (G) SL1 trans-spliced genes (N = 3373) and (H) non-trans-spliced genes (N = 1055). Schematic secondary structures are shown on the x-axis. (I) The sMFE in non-trans-spliced genes is slightly higher than that in pre-SL1 transcripts but is much smaller than that in SL1 transcripts. P-values were given by the Mann-Whitney U test.

SL1 trans-splicing removes uAUGs in 5′ UTRs to enhance translational efficiency

It has been reported that an uAUG may interfere with canonical translational initiation sites and thus reduces translational efficiency (Ingolia et al. 2009; Dvir et al. 2013). Indeed, we found that genes with uAUG had significantly lower translational efficiencies among both SL1 trans-spliced genes (fold change of median = 1.8, P = 4 × 10−62, Mann-Whitney U test) (Fig. 6D) and non-trans-spliced genes (fold change of median = 1.4, P = 4 × 10−7, Mann-Whitney U test) (Fig. 6D). In addition, genes with an out-of-frame uAUG had lower translational efficiencies than those with an in-frame uAUG (P = 7 × 10−5, Mann-Whitney U test). Furthermore, genes with an in-frame uORF that terminates at an upstream stop codon (USC) were translated less efficiently than those with an in-frame uORF that shares the stop codon (SSC) with the annotated ORF (P = 3 × 10−6, Mann-Whitney U test) (Fig. 6E) and were translated with similar efficiencies to genes with an out-of-frame uORF (P = 0.7, Mann-Whitney U test) (Fig. 6E). This observation suggests that an uAUG may decrease translational efficiency by early release of ribosomes. Given that the SL1 sequence does not contain AUG, SL1 trans-splicing can potentially eliminate an uAUG in the 5′ UTR by trimming it. Indeed, we found that SL1 trans-splicing increased the proportion of genes without an uAUG (P = 2 × 10−87, Fisher's exact test) (Fig. 6F), making it even higher than that of non-trans-spliced genes (P = 5 × 10−34, Fisher's exact test) (Fig. 6F).

SL1 trans-splicing attenuates 5′ UTR secondary structure to enhance translational efficiency

Stable secondary structures in the 5′ terminus of an mRNA can slow down ribosome scanning or translocation at the initiation step and thus reduce translational efficiency (Sen et al. 2015). Therefore, we hypothesized that SL1 trans-splicing, which replaces part of the native 5′ UTR, might increase translational efficiency by attenuating secondary structures in the 5′ UTR.

We first confirmed that stable secondary structures in the 5′ UTR reduced translational efficiency in C. elegans. Specifically, we calculated the minimum free energy (MFE) (Lorenz et al. 2011) for all 22-nt windows in the 5′ UTR for each gene and used the smallest MFE (sMFE) to describe the stability of the most stable secondary structure in a given 5′ UTR. The length of 22 nt was used because it is the shortest 5′ UTR length after SL1 trans-splicing. As expected, the sMFE was positively correlated with translational efficiency among both SL1 trans-spliced genes [ρ = 0.18, P = 3 × 10−25, Spearman's correlation, slope b = 0.02, linear regression of log10(TE) ∼ sMFE] (Fig. 6G) and non-trans-spliced genes [ρ = 0.13, P = 3 × 10−5, Spearman's correlation, b = 0.03, linear regression of log10(TE) ∼ sMFE] (Fig. 6H).

We found that SL1 trans-splicing significantly elevated the sMFE (Fig. 6I). Before SL1 trans-splicing, the difference between the sMFE values of pre-SL1 trans-spliced transcripts and non-trans-spliced transcripts was small (P = 0.002, Mann-Whitney U test) (Fig. 6I). After SL1 trans-splicing, however, the values of the sMFE of SL1 trans-spliced transcripts were significantly increased (P = 1 × 10−22, two-tailed Mann-Whitney U test) (Fig. 6I), above those of non-trans-spliced transcripts (P = 7 × 10−7, Mann-Whitney U test) (Fig. 6I).

uAUG and mRNA secondary structure largely explain the regulatory effect of 5′ UTR length on translational efficiency

We next asked if the removal of an uAUG and the attenuation of secondary structures in the 5′ UTR can explain all the contributions of 5′ UTR shortening to the enhancement of translational efficiency. To this end, we built various linear regression models and calculated Akaike information criterion (AIC) to examine the relative contributions of different sequence properties in explaining translational efficiency (Table 1). We found that adding 5′ UTR length to the null model significantly reduced the AIC (model 5 vs. model 1). In contrast, after including both the presence/absence of the uAUG and the stability of secondary structures (model 4), adding 5′ UTR length to the model no longer reduced the AIC (model 6), indicating that shortening of the 5′ UTR increased translational efficiency mainly through regulation of the uAUG and 5′ UTR secondary structures in C. elegans.

Table 1.

Models on sequence properties that regulate translational efficiency (TE)

graphic file with name 1525tb01.jpg

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We noted that adding trans-splicing type (here defined as SL1 trans-splicing or not) to model 4 continued to reduce the AIC (model 8), suggesting additional molecular mechanisms to enhance translational efficiency by SL1 trans-splicing. For example, SL1 trans-splicing replaces the canonical monomethylguanosine (m7GpppN, MMG) cap with a trimethylguanosine (m32,2,7GpppN, TMG) cap, which may also contribute to the regulation of translational efficiency (Maroney et al. 1995). Nevertheless, non-trans-spliced genes without an uAUG exhibited higher translational efficiencies than SL1 trans-spliced genes that contained an uAUG (P = 1 × 10−5, Mann-Whitney U test) (Fig. 6D), suggesting a minor role of the TMG cap compared with the uAUG in the regulation of translation.

In Figure 4, we observed reduced translational efficiency in SL1 mutants. To examine whether SL1 trans-splicing increases translational efficiency by removing the inhibitory uAUG or secondary structure from the pre-mRNA, we performed 5′ RACE (rapid amplification of 5′ complementary DNA ends) in each SL1 mutant to obtain the 5′ UTR sequence of pre-mRNA (Supplemental Table S3) and compared it with the SL1 trans-spliced transcript. Although the SL1 trans-splicing in lin-15B did not significantly change the sMFE in the 5′ UTR, it removed three out-of-frame uAUGs and an in-frame uORF that terminates at a USC (Table 2). The difference in GFP intensity between the wild type and the SL1 mutant of lin-15B (fold change of median = 2.2) (Fig. 4D) is largely consistent with the average effect of uORFs observed in Figure 6D (1.4- to 1.8-fold). Note that each individual uAUG may have its specific features (such as Kozak context of this uAUG) that make its effect different from the average effect estimated in Figure 6D. SL1 trans-splicing also removed an out-of-frame uAUG in mes-2 (Table 2), largely explaining the difference in GFP intensity between the wild type and the SL1 mutant (fold change of median = 1.3) (Fig. 4E). SL1 trans-splicing in this gene also increased the sMFE by 0.6 kcal/mol (Table 2), which may also contribute to the higher translational efficiency in the wild type. In deps-1, there is no uORF in pre-mRNA. Nevertheless, SL1 trans-splicing attenuated secondary structures by 2 kcal/mol in this gene (Table 2). Based on the linear regression models in Figure 6, G and H, this attenuation on average could lead to an ∼1.1-fold increase of translational efficiency, which is smaller than the observed difference in GFP intensity between the wild type and the SL1 mutant of this gene (fold change of median = 1.8) (Fig. 4E). It is likely that other factors such as the TMG cap and the SL1 sequence (Maroney et al. 1995) also contribute to the regulation of translational efficiency in this gene.

Table 2.

SL1 trans-splicing removed uAUGs or weakened mRNA structures in the 5′ UTRs of reporter genes

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Discussion

SL2 trans-spliced genes also exhibit higher translational efficiencies

In addition to the major role SL2 trans-splicing plays in converting a polycistronic pre-mRNA into multiple monocistronic mRNAs, SL2 trans-splicing also attaches a 22-nt sequence to the monocistronic mRNA, which becomes part of the 5′ UTR. We speculated that SL2 might also be involved in the regulation of translational efficiency. To examine this possibility, we compared the translational efficiencies of SL1 trans-spliced genes, SL2 trans-spliced genes, and non-trans-spliced genes (Supplemental Fig. S1). Although less efficiently translated than SL1 trans-spliced genes (P = 1 × 10−11, Mann-Whitney U test) (Supplemental Fig. S1C), SL2 trans-spliced genes exhibited higher translational efficiencies than non-trans-spliced genes (P = 2 × 10−19, Mann-Whitney U test) (Supplemental Fig. S1C). Similar results were obtained in other nematode species (P = 4 × 10−5, 3 × 10−31, 2 × 10−5, in C. briggsae, C. remanei, and C. brenneri, respectively, Mann-Whitney U test) (Supplemental Fig. S1F,I,L). These findings suggest the additional role of SL2 trans-splicing in translational regulation. The difference in translational efficiency between SL2 trans-spliced genes and non-trans-spliced genes was highly significant in developing worms (Supplemental Fig. S1). The significance decreased in L1 diapause, likely due to the reduced transcriptional activities of operon genes in growth-arrested states (Zaslaver et al. 2011), which increased the experimental errors in quantifying translational efficiency of SL2 trans-spliced genes.

SL1 trans-splicing provides a great in vivo system for studying the molecular mechanisms of translational regulation

Regulation at the translation level has attracted increasing interest over the past few years (de Godoy et al. 2008; Ingolia et al. 2009, 2013; Kudla et al. 2009; Tuller et al. 2010; Stadler and Fire 2011, 2013; Bantscheff et al. 2012; Bensimon et al. 2012; Li et al. 2012, 2014; Qian et al. 2012; Khan et al. 2013; Quax et al. 2013; Chu et al. 2014; McManus et al. 2014; Pop et al. 2014). Although it is widely accepted that the 5′ UTR is the major target for translational regulation, the exact regulatory mechanisms remain elusive. The Segal group addressed this issue by systematically generating point mutations in a 10-nt region in a 5′ UTR and identified a number of sequence properties that regulate translational efficiency (Dvir et al. 2013). However, it remains unclear if the length of the 5′ UTR regulates the translational initiation efficiency. To study the regulatory role of 5′ UTR length, one could generate multiple 5′ UTR sequence variants with various lengths while keeping the other relevant factors constant for the same reporter gene. However, such experiments are labor-intensive, and it is unclear whether the conclusion would be applicable to other genes. Fortunately, trans-splicing in nematodes spontaneously “conducted” such experiments at the genomic scale. In this study, we took advantage of this system and identified molecular mechanisms regulating translational efficiency.

In fact, studying the effect of trans-splicing on translation regulation has a longstanding history. The first study attempted to address this question with one reporter gene in a cell-free translation system extracted from Ascaris lumbricoides. They found that a RNA molecule with both SL1 sequence and a TMG cap translated more efficiently than one with neither (Maroney et al. 1995). However, an opposite conclusion was reached in another in vitro translation system extracted from A. suum, which exhibited cap-tail synergism (Lall et al. 2004). The latter group further performed a particle bombardment assay to transiently express reporter RNA in Ascaris embryos (Cheng et al. 2007) and reported similar results as Lall et al. (2004). However, this pattern was no longer observed in a more recent paper from the same group (Wallace et al. 2010). The discrepancy among studies may be attributed to the unstable (in vitro translation and transient expression) system and/or the limited number of artificially designed reporter sequences.

SL1 trans-splicing leads to a number of consequences for endogenous genes, including but not limited to (1) the switch from an MMG cap to a TMG cap, (2) the addition of the SL1 sequence, (3) the removal of uAUGs, (4) the attenuation of mRNA secondary structures, and (5) the proteins binding to mRNA during trans-splicing that may be cotransported to the cytoplasm. Previous studies mainly focused on (1) and (2) for artificially synthesized RNA (Maroney et al. 1995; Lall et al. 2004; Cheng et al. 2007; Wallace et al. 2010). However, the overall effect of SL1 trans-splicing ([1] to [5]) remains unclear for endogenous genes, especially at the genomics scale. The quantification of this effect is of central importance to understand why SL1 trans-splicing is pervasive in nematode genomes. In this study, we provide four lines of evidence supporting that SL1 trans-splicing enhances translational efficiencies of endogenous genes.

SL1 trans-splicing also contributes to the environmental robustness of translational efficiency in essential genes

Essential genes are more likely to participate in basic biological processes and therefore require more robust translational efficiencies across variable conditions. We compared the fold change of translational efficiency during the exit from L1 diapause (Stadler and Fire 2013) and found that the translational efficiencies of essential genes were more stable during this process (P = 2 × 10−21, F-test) (Supplemental Fig. S8A). We speculated that such robustness might be partially attributable to differential SL1 trans-splicing between essential and nonessential genes. To test this hypothesis, we compared the translational robustness between SL1 trans-spliced and non-trans-spliced genes and found that this difference was even larger (P = 6 × 10−137, F-test) (Supplemental Fig. S8B), implicating the role that SL1 trans-splicing plays in maintaining environmental robustness of translational efficiency. Similar phenomena were also observed in other nematode species (Supplemental Fig. S9A–C). Because essential genes are enriched in SL1 trans-spliced genes (Fig. 5B), SL1 trans-splicing should contribute to the elevated environmental robustness of translational efficiency among these essential genes. We further compared the translational robustness among four groups of genes based on gene essentiality and trans-splicing type in C. elegans (Supplemental Fig. S8C) and found that essential genes had stronger robustness among both SL1 trans-spliced and non-trans-spliced genes (P = 8 × 10−6 and 6 × 10−3, respectively, F-test) (Supplemental Fig. S8C), suggesting that additional mechanisms must exist to regulate the translational robustness of essential genes.

Evolutionary model of SL1 trans-splicing

In previous studies, it has been argued that gains of operons are nearly neutral, whereas losses of operons and SL2 trans-splicing are extremely deleterious, because the downstream genes in operons have lost promoters (Lawrence 1999; Nimmo and Woollard 2002; Blumenthal 2004). Based on this idea, we previously proposed a quantitative model called “easy come, slow go” to explain the evolution of operons in nematodes (Qian and Zhang 2008). In the present study, we found genomic evidence supporting the hypothesis that SL1 trans-splicing promotes translational efficiency by removing the uAUG and attenuating secondary structures in the 5′ UTR (Fig. 6; Table 2). This may also be partly explained by the “easy come, slow go” model. That is, the gain of SL1 trans-splicing in a gene is nearly neutral, but once an SL1 trans-splicing site is obtained, the accumulation of deleterious mutations (such as uAUGs and stable secondary structures) in the 5′ UTR of the pre-trans-spliced transcripts, which are no longer visible to natural selection (Blumenthal 2005), is accelerated. Consistently, the proportion of pre-SL1 transcripts that are without an uAUG was lower than that of the transcripts from non-trans-spliced genes (Fig. 6F). In addition, pre-SL1 transcripts contained more stable secondary structures than the transcripts from non-trans-spliced genes (Fig. 6I). These uAUGs and stable secondary structures prevent efficient translation and make SL1 trans-splicing sites under purifying selection.

However, there are a number of phenomena in the evolution of SL1 trans-splicing that cannot be explained by the “easy come, slow go” model. First, the translational efficiencies of SL1 trans-spliced genes are higher than non-trans-spliced genes (Fig. 1; Supplemental Fig. S1). Second, essential genes tend to be SL1 trans-spliced (Fig. 5B). Third, the proportion of SL1 transcripts that are without an uAUG is higher than that of the transcripts from non-trans-spliced genes (Fig. 6F). Fourth, SL1 transcripts contain less stable secondary structures than the transcripts from non-trans-spliced genes (Fig. 6I). All these observations can be explained by positive selection for incorporation of SL1 trans-splicing sites on the genes requiring higher translational efficiencies (such as essential genes). Taken together, our observations imply that SL1 trans-splicing contributed to molecular adaptation in nematode evolution.

Methods

Worm strains

Three C. elegans transgenic strains JH3203, JH3205, and JH3207 were ordered from the Caenorhabditis Genetics Center. In these strains, GFP had been fused to the 3′ terminus of endogenous genes mes-2, lin-15B, and deps-1, respectively. These three genes were chosen because (1) they are SL1 trans-spliced, and (2) their protein::GFP fusion reporters were available. Liquid culture of N2 worms was performed following Koelle and Herman's protocol. Specifically, worms were grown at 20°C for 4 d in liquid S complete media supplemented with E. coli X1666 (Kenyon 1997).

Sucrose gradient centrifugation and fraction collection

The frozen worms were crushed with a mortar and pestle in a liquid nitrogen bath, and the resulting powder was transferred to three volumes of ice-cold polysome lysis buffer (200 mM Tris pH 8.0, 200 mM KCl, 35 mM MgCl2, 1% Triton X-100, 5 mM DTT, 100 μg/mL cycloheximide, and 0.5 mg/mL heparin). Sucrose gradient ultracentrifugation and fraction collection were performed largely as described in a previous study (Ingolia et al. 2009). The detailed protocol is provided in the Supplemental Methods.

Generation and analysis of SL1 trans-splicing mutants

We used the CRISPR-Cas9 system to remove the consensus sequence of trans-splicing sites of the GFP fusion genes in strains JH3203, JH3205, and JH3207, respectively. Two guide sequences were designed for each gene (Supplemental Table S4). To construct the sgRNA expression plasmids, the guide sequences were inserted into the pDD162 plasmid by PCR (Supplemental Table S5) following previous protocols (Paix et al. 2014). Repair template sequences (Supplemental Table S6) were used to remove trans-splicing sites (Fig. 4C). Mutations were confirmed by single worm PCR (Supplemental Table S7) and Sanger sequencing. Transcript abundances and trans-splicing frequencies of reporter genes were quantified by qPCR (Supplemental Table S8). Embryos were mounted following a well-established protocol with minor modifications (Bao and Murray 2011). Fluorescence micrographs were taken using spinning disk confocal microscopy (PerkinElmer) with Hamamatsu Flash4.0 and 9100-23B cameras. A detailed protocol is described in the Supplemental Methods.

Rapid amplification of 5′ complementary DNA ends

The 5′ UTR sequences of pre-mRNAs were identified with a SMARTer RACE 5′/3′ kit (Clontech, 634858) with primers listed in Supplemental Table S9 and Sanger sequencing. The 5′ RACE products were directionally cloned into the pRACE vector, as described in the manufacturer's protocol.

Genome information

The assembly and genome annotation were downloaded from WormBase (www.wormbase.org, version WS230 for C. elegans, WS227 for C. brenneri, WS225 for C. briggsae and C. remanei) (Harris et al. 2014). Orthologous genes were retrieved from WormBase (version WS243, ftp://ftp.wormbase.org/pub/wormbase/species/c_elegans/annotation/orthologs/). Only four-way one-to-one orthologous groups were used in this analysis. A list of essential genes was retrieved through WormMine (WS238 IM v1.2.1, http://www.wormbase.org/tools/wormmine/begin.do).

Classification of trans-splicing

The trans-splicing sites in C. elegans were downloaded from Allen et al. (2011). To guarantee the accurate classification of trans-spliced genes, trans-splicing sites supported by less than 10 RNA-seq reads were discarded in the following analysis. Each trans-splicing site was classified into one of the two categories, “SL1” or “SL2 and SL2 variants” (“SL2” for short in this study) if ≥90% of reads covering this site supported the trans-splicing type. Ambiguous sites with both substantial SL1 trans-splicing and SL2 trans-splicing were not considered further. If multiple trans-splicing sites exist on a single transcript, the trans-splicing type of this transcript was determined by the type of trans-splicing site that was covered by the largest number of reads. If a gene contained multiple mRNA isoforms, the one with the highest expression level was chosen to represent the gene. Genes without trans-splicing sites were defined as non-trans-spliced genes.

The trans-splicing sites of C. brenneri, C. briggsae, and C. remanei were parsed similarly from transcriptomic data generated in the modENCODE project (Celniker et al. 2009; Hillier et al. 2009; Gerstein et al. 2010). Specifically, transcriptomes of C. remanei (projects 4707–4713), C. brenneri (projects 4703–4706), and C. briggsae (projects 4495–4498 and 4693–4697) were downloaded from http://www.modencode.org. Genes annotated as SL1-only were used in the comparison between one-to-one orthologous genes without SL1 turnover.

Calculation of translational efficiency

The mRNA-seq and Ribo-seq data of four nematode species were retrieved from Stadler and Fire (2013). Genes with a normalized read count <1 in mRNA-seq were discarded to minimize the effect of inaccurate measurement of lowly expressed genes. The read count values in mRNA-seq (mRNA) and Ribo-seq (Ribo) were used to calculate ribosome density, which can infer translational initiation rate, also called translational efficiency (TE):

TE=RibomRNA.

To compare translational efficiencies between orthologous genes, quantile normalization was performed on translational efficiencies among the four species.

Codon adaptation index

Synonymous codons are used with bias in the C. elegans genome. Following Sharp and Li (1987), we calculated the CAI, which measures the codon usage similarity of a gene to a set of 1000 most highly expressed genes in C. elegans (Supplemental Table S10). A detailed protocol is described in the Supplemental Methods.

Data access

The 5′ UTR sequences of pre-mRNAs of lin-15B, mes-2, and deps-1 from this study have been submitted to GenBank (http://www.ncbi.nlm.nih.gov/genbank/) under accession numbers MF352827, MF352828, and MF352826, respectively.

Supplementary Material

Supplemental Material
supp_27_9_1525__index.html (726B, html)

Acknowledgments

The authors thank Qiwen Gan, Youli Jian, and Chonglin Yang for their help on worm experiments; Shiqin Jia from the core facility of the State Key Laboratory of Plant Genomics for her help on polysome profiling experiments; Yi Liu and Xiaofeng Cao for valuable discussions; and Bryan Moyers and John R. Speakman for critical reading of the manuscript. This work was supported by grants from the National Natural Science Foundation of China to W.Q. (31571308, 91331112), Y.-F.Y. (31601061), and Z.D. (31571535), and the “Strategic Priority Research Program” of the Chinese Academy of Sciences to Z.D. (XDB19000000). Three worm strains were provided by the Caenorhabditis Genetics Center, which is funded by the NIH Office of Research Infrastructure Programs (P40 OD010440).

Author contributions: Y.-F.Y., Q.S., and W.Q. conceived the research; Y.-F.Y., X.Z., T.Z., Z.D., and W.Q. designed the experiments; X.Z., X.M., T.Z., and Q.H. conducted the experiments; Y.-F.Y., X.Z., T.Z., Q.S., Z.D., and W.Q. analyzed the data; Y.-F.Y., X.Z., T.Z., S.W., Z.D., and W.Q. wrote the manuscript.

Footnotes

[Supplemental material is available for this article.]

Article published online before print. Article, supplemental material, and publication date are at http://www.genome.org/cgi/doi/10.1101/gr.202150.115.

References

  1. Allen MA, Hillier LW, Waterston RH, Blumenthal T. 2011. A global analysis of C. elegans trans-splicing. Genome Res 21: 255–264. [DOI] [PMC free article] [PubMed] [Google Scholar]
  2. Altelaar AF, Munoz J, Heck AJ. 2013. Next-generation proteomics: towards an integrative view of proteome dynamics. Nat Rev Genet 14: 35–48. [DOI] [PubMed] [Google Scholar]
  3. Andersson SG, Kurland CG. 1990. Codon preferences in free-living microorganisms. Microbiol Rev 54: 198–210. [DOI] [PMC free article] [PubMed] [Google Scholar]
  4. Artieri CG, Fraser HB. 2014a. Accounting for biases in riboprofiling data indicates a major role for proline in stalling translation. Genome Res 24: 2011–2021. [DOI] [PMC free article] [PubMed] [Google Scholar]
  5. Artieri CG, Fraser HB. 2014b. Evolution at two levels of gene expression in yeast. Genome Res 24: 411–421. [DOI] [PMC free article] [PubMed] [Google Scholar]
  6. Babendure JR, Babendure JL, Ding JH, Tsien RY. 2006. Control of mammalian translation by mRNA structure near caps. RNA 12: 851–861. [DOI] [PMC free article] [PubMed] [Google Scholar]
  7. Bantscheff M, Lemeer S, Savitski MM, Kuster B. 2012. Quantitative mass spectrometry in proteomics: critical review update from 2007 to the present. Anal Bioanal Chem 404: 939–965. [DOI] [PubMed] [Google Scholar]
  8. Bao Z, Murray JI. 2011. Mounting Caenorhabditis elegans embryos for live imaging of embryogenesis. Cold Spring Harb Protoc 2011: pdb.prot065599. [DOI] [PubMed] [Google Scholar]
  9. Bensimon A, Heck AJ, Aebersold R. 2012. Mass spectrometry-based proteomics and network biology. Annu Rev Biochem 81: 379–405. [DOI] [PubMed] [Google Scholar]
  10. Blumenthal T. 2004. Operons in eukaryotes. Brief Funct Genomic Proteomic 3: 199–211. [DOI] [PubMed] [Google Scholar]
  11. Blumenthal T. 2005. Trans-splicing and operons. In WormBook (ed. The C. elegans Research Community), 10.1895/wormbook.1.5.1 http://www.wormbook.org. [DOI] [Google Scholar]
  12. Blumenthal T, Gleason KS. 2003. Caenorhabditis elegans operons: form and function. Nat Rev Genet 4: 112–120. [DOI] [PubMed] [Google Scholar]
  13. Blumenthal T, Evans D, Link CD, Guffanti A, Lawson D, Thierry-Mieg J, Thierry-Mieg D, Chiu WL, Duke K, Kiraly M, et al. 2002. A global analysis of Caenorhabditis elegans operons. Nature 417: 851–854. [DOI] [PubMed] [Google Scholar]
  14. Bulmer M. 1991. The selection-mutation-drift theory of synonymous codon usage. Genetics 129: 897–907. [DOI] [PMC free article] [PubMed] [Google Scholar]
  15. Celniker SE, Dillon LA, Gerstein MB, Gunsalus KC, Henikoff S, Karpen GH, Kellis M, Lai EC, Lieb JD, MacAlpine DM, et al. 2009. Unlocking the secrets of the genome. Nature 459: 927–930. [DOI] [PMC free article] [PubMed] [Google Scholar]
  16. Chappell SA, Edelman GM, Mauro VP. 2006. Ribosomal tethering and clustering as mechanisms for translation initiation. Proc Natl Acad Sci 103: 18077–18082. [DOI] [PMC free article] [PubMed] [Google Scholar]
  17. Cheng G, Cohen L, Mikhli C, Jankowska-Anyszka M, Stepinski J, Darzynkiewicz E, Davis RE. 2007. In vivo translation and stability of trans-spliced mRNAs in nematode embryos. Mol Biochem Parasitol 153: 95–106. [DOI] [PMC free article] [PubMed] [Google Scholar]
  18. Cho S, Jin SW, Cohen A, Ellis RE. 2004. A phylogeny of Caenorhabditis reveals frequent loss of introns during nematode evolution. Genome Res 14: 1207–1220. [DOI] [PMC free article] [PubMed] [Google Scholar]
  19. Chu D, Kazana E, Bellanger N, Singh T, Tuite MF, von der Haar T. 2014. Translation elongation can control translation initiation on eukaryotic mRNAs. EMBO J 33: 21–34. [DOI] [PMC free article] [PubMed] [Google Scholar]
  20. Conrad R, Liou RF, Blumenthal T. 1993. Functional analysis of a C. elegans trans-splice acceptor. Nucleic Acids Res 21: 913–919. [DOI] [PMC free article] [PubMed] [Google Scholar]
  21. Crooks GE, Hon G, Chandonia JM, Brenner SE. 2004. WebLogo: a sequence logo generator. Genome Res 14: 1188–1190. [DOI] [PMC free article] [PubMed] [Google Scholar]
  22. Cutter AD. 2008. Divergence times in Caenorhabditis and Drosophila inferred from direct estimates of the neutral mutation rate. Mol Biol Evol 25: 778–786. [DOI] [PubMed] [Google Scholar]
  23. de Godoy LM, Olsen JV, Cox J, Nielsen ML, Hubner NC, Frohlich F, Walther TC, Mann M. 2008. Comprehensive mass-spectrometry-based proteome quantification of haploid versus diploid yeast. Nature 455: 1251–1254. [DOI] [PubMed] [Google Scholar]
  24. de Smit MH, van Duin J. 1990. Secondary structure of the ribosome binding site determines translational efficiency: a quantitative analysis. Proc Natl Acad Sci 87: 7668–7672. [DOI] [PMC free article] [PubMed] [Google Scholar]
  25. Dvir S, Velten L, Sharon E, Zeevi D, Carey LB, Weinberger A, Segal E. 2013. Deciphering the rules by which 5′-UTR sequences affect protein expression in yeast. Proc Natl Acad Sci 110: E2792–E2801. [DOI] [PMC free article] [PubMed] [Google Scholar]
  26. Gerstein MB, Lu ZJ, Van Nostrand EL, Cheng C, Arshinoff BI, Liu T, Yip KY, Robilotto R, Rechtsteiner A, Ikegami K, et al. 2010. Integrative analysis of the Caenorhabditis elegans genome by the modENCODE project. Science 330: 1775–1787. [DOI] [PMC free article] [PubMed] [Google Scholar]
  27. Ghaemmaghami S, Huh WK, Bower K, Howson RW, Belle A, Dephoure N, O'Shea EK, Weissman JS. 2003. Global analysis of protein expression in yeast. Nature 425: 737–741. [DOI] [PubMed] [Google Scholar]
  28. Hall MN, Gabay J, Debarbouille M, Schwartz M. 1982. A role for mRNA secondary structure in the control of translation initiation. Nature 295: 616–618. [DOI] [PubMed] [Google Scholar]
  29. Harris TW, Baran J, Bieri T, Cabunoc A, Chan J, Chen WJ, Davis P, Done J, Grove C, Howe K, et al. 2014. WormBase 2014: new views of curated biology. Nucleic Acids Res 42: D789–D793. [DOI] [PMC free article] [PubMed] [Google Scholar]
  30. Hastings KEM. 2005. SL trans-splicing: easy come or easy go? Trends Genet 21: 240–247. [DOI] [PubMed] [Google Scholar]
  31. Hillier LW, Miller RD, Baird SE, Chinwalla A, Fulton LA, Koboldt DC, Waterston RH. 2007. Comparison of C. elegans and C. briggsae genome sequences reveals extensive conservation of chromosome organization and synteny. PLoS Biol 5: e167. [DOI] [PMC free article] [PubMed] [Google Scholar]
  32. Hillier LW, Reinke V, Green P, Hirst M, Marra MA, Waterston RH. 2009. Massively parallel sequencing of the polyadenylated transcriptome of C. elegans. Genome Res 19: 657–666. [DOI] [PMC free article] [PubMed] [Google Scholar]
  33. Ingolia NT, Ghaemmaghami S, Newman JR, Weissman JS. 2009. Genome-wide analysis in vivo of translation with nucleotide resolution using ribosome profiling. Science 324: 218–223. [DOI] [PMC free article] [PubMed] [Google Scholar]
  34. Kamath RS, Fraser AG, Dong Y, Poulin G, Durbin R, Gotta M, Kanapin A, Le Bot N, Moreno S, Sohrmann M, et al. 2003. Systematic functional analysis of the Caenorhabditis elegans genome using RNAi. Nature 421: 231–237. [DOI] [PubMed] [Google Scholar]
  35. Kenyon C. 1997. Environmental factors and gene activities that influence life span. In C. elegans II (ed. Riddle DL, et al. ). Cold Spring Harbor Laboratory Press, Cold Spring Harbor, NY. [PubMed] [Google Scholar]
  36. Khan Z, Ford MJ, Cusanovich DA, Mitrano A, Pritchard JK, Gilad Y. 2013. Primate transcript and protein expression levels evolve under compensatory selection pressures. Science 342: 1100–1104. [DOI] [PMC free article] [PubMed] [Google Scholar]
  37. Kozak M. 1987. An analysis of 5′-noncoding sequences from 699 vertebrate messenger RNAs. Nucleic Acids Res 15: 8125–8148. [DOI] [PMC free article] [PubMed] [Google Scholar]
  38. Krause M, Hirsh D. 1987. A trans-spliced leader sequence on actin mRNA in C. elegans. Cell 49: 753–761. [DOI] [PMC free article] [PubMed] [Google Scholar]
  39. Kudla G, Murray AW, Tollervey D, Plotkin JB. 2009. Coding-sequence determinants of gene expression in Escherichia coli. Science 324: 255–258. [DOI] [PMC free article] [PubMed] [Google Scholar]
  40. Lall S, Friedman CC, Jankowska-Anyszka M, Stepinski J, Darzynkiewicz E, Davis RE. 2004. Contribution of trans-splicing, 5′ -leader length, cap-poly(A) synergism, and initiation factors to nematode translation in an Ascaris suum embryo cell-free system. J Biol Chem 279: 45573–45585. [DOI] [PubMed] [Google Scholar]
  41. Lasda EL, Blumenthal T. 2011. Trans-splicing. Wiley Interdiscip Rev RNA 2: 417–434. [DOI] [PubMed] [Google Scholar]
  42. Lawrence J. 1999. Selfish operons: the evolutionary impact of gene clustering in prokaryotes and eukaryotes. Curr Opin Genet Dev 9: 642–648. [DOI] [PubMed] [Google Scholar]
  43. Li GW, Oh E, Weissman JS. 2012. The anti-Shine-Dalgarno sequence drives translational pausing and codon choice in bacteria. Nature 484: 538–541. [DOI] [PMC free article] [PubMed] [Google Scholar]
  44. Li GW, Burkhardt D, Gross C, Weissman JS. 2014. Quantifying absolute protein synthesis rates reveals principles underlying allocation of cellular resources. Cell 157: 624–635. [DOI] [PMC free article] [PubMed] [Google Scholar]
  45. Lorenz R, Bernhart SH, Honer Zu Siederdissen C, Tafer H, Flamm C, Stadler PF, Hofacker IL. 2011. ViennaRNA Package 2.0. Algorithms Mol Biol 6: 26. [DOI] [PMC free article] [PubMed] [Google Scholar]
  46. Maroney PA, Denker JA, Darzynkiewicz E, Laneve R, Nilsen TW. 1995. Most mRNAs in the nematode Ascaris lumbricoides are trans-spliced: a role for spliced leader addition in translational efficiency. RNA 1: 714–723. [PMC free article] [PubMed] [Google Scholar]
  47. McManus CJ, May GE, Spealman P, Shteyman A. 2014. Ribosome profiling reveals post-transcriptional buffering of divergent gene expression in yeast. Genome Res 24: 422–430. [DOI] [PMC free article] [PubMed] [Google Scholar]
  48. Nimmo R, Woollard A. 2002. Widespread organisation of C. elegans genes into operons: fact or function? Bioessays 24: 983–987. [DOI] [PubMed] [Google Scholar]
  49. Paek KY, Hong KY, Ryu I, Park SM, Keum SJ, Kwon OS, Jang SK. 2015. Translation initiation mediated by RNA looping. Proc Natl Acad Sci 112: 1041–1046. [DOI] [PMC free article] [PubMed] [Google Scholar]
  50. Paix A, Wang Y, Smith HE, Lee CY, Calidas D, Lu T, Smith J, Schmidt H, Krause MW, Seydoux G. 2014. Scalable and versatile genome editing using linear DNAs with microhomology to Cas9 Sites in Caenorhabditis elegans. Genetics 198: 1347–1356. [DOI] [PMC free article] [PubMed] [Google Scholar]
  51. Plotkin JB, Kudla G. 2011. Synonymous but not the same: the causes and consequences of codon bias. Nat Rev Genet 12: 32–42. [DOI] [PMC free article] [PubMed] [Google Scholar]
  52. Pop C, Rouskin S, Ingolia NT, Han L, Phizicky EM, Weissman JS, Koller D. 2014. Causal signals between codon bias, mRNA structure, and the efficiency of translation and elongation. Mol Syst Biol 10: 770. [DOI] [PMC free article] [PubMed] [Google Scholar]
  53. Qian W, Zhang J. 2008. Evolutionary dynamics of nematode operons: easy come, slow go. Genome Res 18: 412–421. [DOI] [PMC free article] [PubMed] [Google Scholar]
  54. Qian W, Yang JR, Pearson NM, Maclean C, Zhang J. 2012. Balanced codon usage optimizes eukaryotic translational efficiency. PLoS Genet 8: e1002603. [DOI] [PMC free article] [PubMed] [Google Scholar]
  55. Quax TE, Wolf YI, Koehorst JJ, Wurtzel O, van der Oost R, Ran W, Blombach F, Makarova KS, Brouns SJ, Forster AC, et al. 2013. Differential translation tunes uneven production of operon-encoded proteins. Cell Rep 4: 938–944. [DOI] [PubMed] [Google Scholar]
  56. Saito TL, Hashimoto S, Gu SG, Morton JJ, Stadler M, Blumenthal T, Fire A, Morishita S. 2013. The transcription start site landscape of C. elegans. Genome Res 23: 1348–1361. [DOI] [PMC free article] [PubMed] [Google Scholar]
  57. Scheper GC, van der Knaap MS, Proud CG. 2007. Translation matters: protein synthesis defects in inherited disease. Nat Rev Genet 8: 711–723. [DOI] [PubMed] [Google Scholar]
  58. Schneider TD, Stephens RM. 1990. Sequence logos: a new way to display consensus sequences. Nucleic Acids Res 18: 6097–6100. [DOI] [PMC free article] [PubMed] [Google Scholar]
  59. Schwanhausser B, Busse D, Li N, Dittmar G, Schuchhardt J, Wolf J, Chen W, Selbach M. 2011. Global quantification of mammalian gene expression control. Nature 473: 337–342. [DOI] [PubMed] [Google Scholar]
  60. Sen ND, Zhou F, Ingolia NT, Hinnebusch AG. 2015. Genome-wide analysis of translational efficiency reveals distinct but overlapping functions of yeast DEAD-box RNA helicases Ded1 and eIF4A. Genome Res 25: 1196–1205. [DOI] [PMC free article] [PubMed] [Google Scholar]
  61. Shah P, Ding Y, Niemczyk M, Kudla G, Plotkin JB. 2013. Rate-limiting steps in yeast protein translation. Cell 153: 1589–1601. [DOI] [PMC free article] [PubMed] [Google Scholar]
  62. Sharp PM, Li WH. 1987. The Codon Adaptation Index—a measure of directional synonymous codon usage bias, and its potential applications. Nucleic Acids Res 15: 1281–1295. [DOI] [PMC free article] [PubMed] [Google Scholar]
  63. Spieth J, Brooke G, Kuersten S, Lea K, Blumenthal T. 1993. Operons in C. elegans: polycistronic mRNA precursors are processed by trans-splicing of SL2 to downstream coding regions. Cell 73: 521–532. [DOI] [PubMed] [Google Scholar]
  64. Spike CA, Bader J, Reinke V, Strome S. 2008. DEPS-1 promotes P-granule assembly and RNA interference in C. elegans germ cells. Development 135: 983–993. [DOI] [PMC free article] [PubMed] [Google Scholar]
  65. Stadler M, Fire A. 2011. Wobble base-pairing slows in vivo translation elongation in metazoans. RNA 17: 2063–2073. [DOI] [PMC free article] [PubMed] [Google Scholar]
  66. Stadler M, Fire A. 2013. Conserved translatome remodeling in nematode species executing a shared developmental transition. PLoS Genet 9: e1003739. [DOI] [PMC free article] [PubMed] [Google Scholar]
  67. Staley CA, Huang A, Nattestad M, Oshiro KT, Ray LE, Mulye T, Li ZH, Le T, Stephens JJ, Gomez SR, et al. 2012. Analysis of the 5′ untranslated region (5′UTR) of the alcohol oxidase 1 (AOX1) gene in recombinant protein expression in Pichia pastoris. Gene 496: 118–127. [DOI] [PMC free article] [PubMed] [Google Scholar]
  68. Stein LD, Bao Z, Blasiar D, Blumenthal T, Brent MR, Chen N, Chinwalla A, Clarke L, Clee C, Coghlan A, et al. 2003. The genome sequence of Caenorhabditis briggsae: a platform for comparative genomics. PLoS Biol 1: e45. [DOI] [PMC free article] [PubMed] [Google Scholar]
  69. Tuller T, Carmi A, Vestsigian K, Navon S, Dorfan Y, Zaborske J, Pan T, Dahan O, Furman I, Pilpel Y. 2010. An evolutionarily conserved mechanism for controlling the efficiency of protein translation. Cell 141: 344–354. [DOI] [PubMed] [Google Scholar]
  70. Tyers M, Mann M. 2003. From genomics to proteomics. Nature 422: 193–197. [DOI] [PubMed] [Google Scholar]
  71. Wallace A, Filbin ME, Veo B, McFarland C, Stepinski J, Jankowska-Anyszka M, Darzynkiewicz E, Davis RE. 2010. The nematode eukaryotic translation initiation factor 4E/G complex works with a trans-spliced leader stem-loop to enable efficient translation of trimethylguanosine-capped RNAs. Mol Cell Biol 30: 1958–1970. [DOI] [PMC free article] [PubMed] [Google Scholar]
  72. Wilhelm M, Schlegl J, Hahne H, Moghaddas Gholami A, Lieberenz M, Savitski MM, Ziegler E, Butzmann L, Gessulat S, Marx H, et al. 2014. Mass-spectrometry-based draft of the human proteome. Nature 509: 582–587. [DOI] [PubMed] [Google Scholar]
  73. Zaslaver A, Baugh LR, Sternberg PW. 2011. Metazoan operons accelerate recovery from growth-arrested states. Cell 145: 981–992. [DOI] [PMC free article] [PubMed] [Google Scholar]
  74. Zorio DA, Cheng NN, Blumenthal T, Spieth J. 1994. Operons as a common form of chromosomal organization in C. elegans. Nature 372: 270–272. [DOI] [PubMed] [Google Scholar]

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