Author response: Tunable protein synthesis by transcript isoforms in human cells (2015)

Article Figures and data Abstract eLife digest Introduction Results Discussion Materials and methods Data availability References Decision letter Author response Article and author information Metrics Abstract Eukaryotic genes generate multiple RNA transcript isoforms though alternative transcription, splicing, and polyadenylation. However, the relationship between human transcript diversity and protein production is complex as each isoform can be translated differently. We fractionated a polysome profile and reconstructed transcript isoforms from each fraction, which we term Transcript Isoforms in Polysomes sequencing (TrIP-seq). Analysis of these data revealed regulatory features that control ribosome occupancy and translational output of each transcript isoform. We extracted a panel of 5′ and 3′ untranslated regions that control protein production from an unrelated gene in cells over a 100-fold range. Select 5′ untranslated regions exert robust translational control between cell lines, while 3′ untranslated regions can confer cell type-specific expression. These results expose the large dynamic range of transcript-isoform-specific translational control, identify isoform-specific sequences that control protein output in human cells, and demonstrate that transcript isoform diversity must be considered when relating RNA and protein levels. https://doi.org/10.7554/eLife.10921.001 eLife digest To produce a protein, a gene's DNA is first copied to make molecules of messenger RNA (mRNA). The mRNAs pass through a molecular machine known as the ribosome, which translates the genetic code to make a protein. Not all of an mRNA is translated to make a protein; the "untranslated" regions play crucial roles in regulating how much of the protein is produced. In animals, plants and other eukaryotes, many mRNAs are made up of small pieces that are "spliced" together. During this process, proteins are deposited on the mRNA to mark the splice junctions, which are then cleared when the mRNA is translated. Many different mRNAs can be produced from the same gene by splicing different combinations of RNA pieces. Each of these mRNA "isoforms" can, in principle, contain a unique set of features that control its translation. Hence each mRNA isoform can be translated differently so that different amounts of the corresponding protein product are produced. However, the relationship between the variety of isoforms and the control of translation is complex and not well understood. To address these questions, Floor and Doudna measured the translation of over 60,000 mRNA isoforms made from almost 14,000 human genes. The experiments show that untranslated regions at the end of the mRNA (known as the 3′ end) strongly influence translation, even if the protein coding regions remain the same. Furthermore, the data showed that mRNAs with more splice junctions are translated better, implying an mRNA has some sort of memory of how many junctions it had even after the protein markers have been cleared. Next, Floor and Doudna inserted regulatory sequences from differently translated isoforms into an unrelated "reporter" gene. This dramatically changed the amount of protein produced from the reporter gene, in a manner predicted by the earlier experiments. Untranslated regions at the beginning of the mRNAs (known as the 5′ end) controlled the amount of protein produced from the reporter consistently across different types of cells from the body. On the other hand, the 3′ regions can tune the level of protein production in particular types of cells. Floor and Doudna's findings demonstrate that differences between mRNA isoforms of a gene can have a big effect on the level of protein production. Changes in the types of mRNA made from a gene are often associated with human diseases, and these findings suggest one reason why. Additionally, the ability to engineer translation of an mRNA using the data is likely to aid the development of mRNA-based therapies. https://doi.org/10.7554/eLife.10921.002 Introduction Eukaryotic genes can produce a staggering diversity of messenger RNA products. In humans, there is a median of five annotated transcript isoforms per gene, with more than 80 annotated isoforms in some cases (Ensembl release 75). These transcript isoforms arise from the combined action of alternative transcription initiation, splicing and cleavage, and polyadenylation, which are often cell type specific (Barbosa-Morais et al., 2012; Mele et al., 2015; Merkin et al., 2012; Wang et al., 2008; Xiong et al., 2015; Zheng and Black, 2013). As each of these transcript isoforms may in principle harbor a unique set of translational control elements, gene-level expression, which averages out the translational potential of each individual transcript isoform, may not be an accurate measure of protein levels. Since changes in both translation and splicing are linked to numerous human disorders, it is critical to understand the relationship between the two (Maslon et al., 2014; Piccirillo et al., 2014; Ruggero, 2013; Xiong et al., 2015). Eukaryotic mRNAs are decorated with diverse sequence features that can control translation, which can vary between transcript isoforms (de Klerk and 'tHoen, 2015; Ingolia et al., 2011; Sterne-Weiler et al., 2013). Classic examples of translational control include upstream ORFs (uORF)-mediated translational control of yeast GCN4 (Hinnebusch, 2005), protein binding such as the iron regulatory protein (Gray and Hentze, 1994), and the action of micro-RNAs (Nottrott et al., 2006; Wilczynska and Bushell, 2015) or DEAD-box proteins such as eIF4A and Ded1 (Chuang et al., 1997; Hinnebusch and Lorsch, 2012; Sen et al., 2015). Alternative 5′ leader sequences, uORFs, and select tandem 3′ untranslated region (UTR) isoforms have been demonstrated to influence protein production (Brar et al., 2012; Hinnebusch, 2005; Ingolia et al., 2011; Mayr and Bartel, 2009; Sandberg et al., 2008; Zhang et al., 2012). Any of these features may in principle be different between transcript isoforms, but the prevalence and dynamic range of isoform-specific translational control across the human genome is currently unknown. Previous work measuring genome-wide translation in human cells has focused largely on the relationship between gene-level mRNA abundance and protein levels, which is blind to the contribution of transcript isoforms. Ribosome profiling is not well-suited for measuring transcript isoform-specific translation, primarily due to the short ~30 bp length of ribosome-protected fragments (Ingolia, 2014). Prior attempts to characterize isoform-specific translation have measured the effects of 5′ end diversity in yeast (Arribere and Gilbert, 2013) and 3′ end diversity in mouse cells (Spies et al., 2013), or splicing differences between cytoplasmic and aggregate polysomal mRNAs (Maslon et al., 2014; Sterne-Weiler et al., 2013). However, sequencing just the ends of transcripts cannot distinguish between transcript isoforms of the same gene harboring degenerate termini. In addition, aggregating polysome fractions averages lowly- and highly-ribosome-associated messages. Therefore, a different strategy is required to understand how the diversity of the human transcriptome impacts translational output. Here, we adapt a classic approach of polysome profiling coupled with global gene expression analysis (Arava et al., 2003) to measure transcript-isoform specific translation using deep sequencing, which we term Transcript Isoforms in Polysomes sequencing (TrIP-seq). By using high gradient resolution and sequencing depth, this approach yields polysome profiles for over 60,000 individual transcript isoforms representing almost 14,000 protein coding genes. We observe frequent intron retention on ribosome-associated transcripts, even in high-polysome fractions, identifying a population of retained but not nuclear-detained introns (Boutz et al., 2015). Properties of 3′ untranslated regions predominate over the 5′ leader sequence as the driving force behind differential polysome association for transcript isoforms of the same gene among the transcript features tested. We show that regulatory sequences differentially included in transcript isoforms of the same gene are modular and can trigger differences in the translation of reporters spanning two orders of magnitude. These findings provide a lens through which to ascribe functional consequences to RNA-seq-generated transcriptomes. Moreover, TrIP-seq analysis uncovers regulatory elements that can be utilized to tune translation of synthetic messages robustly in cells. Results TrIP-seq measures transcript isoform-specific translation in human cells We determined the ribosomal association of transcript isoforms by sequencing transcripts cofractionating with different numbers of ribosomes with sufficient depth to determine isoform abundances, as was performed at the gene level in yeast (Arava et al., 2003). We treated HEK 293T cells with cycloheximide to stall translation and fractionated the cytoplasm into ribosome-containing samples including one to eight or more ribosomes (Figures 1A and Figure 1—figure supplement 1A; see Materials and methods for details). We made RNA sequencing libraries from each fraction in biological duplicate and obtained transcript-level abundances using the Cufflinks suite (Figure 1—source data 1 and 2; [Trapnell et al., 2010]). Clustering of the samples recapitulates the gradient order (Figure 1B), indicating the polysome profile was accurately fractionated. Four subgroups emerge from this clustering: the 80S (monosome), low polysomes (two-four ribosomes), high polysomes (five-eight+ ribosomes), and total cytoplasmic RNA. We tested the robustness of the clustering of samples by computing the average Jaccard distance between clusters from data subjected to three different resampling methods, which was ≥0.75, suggesting stable clusters (Figure 1—figure supplement 1G; Materials and methods). This suggests that in cells, transcript isoforms are predominantly poorly- or highly-ribosome associated, causing low polysomes to cluster away from high polysomes, which could be have numerous biological origins including highly abundant short ORFs. Figure 1 with 3 supplements see all Download asset Open asset Transcript Isoforms in Polysomes sequencing (TrIP-seq) measures transcript isoform specific translation. (A) HEK 293T cells were treated with cycloheximide and the cytoplasmic fraction was extracted and applied to a sucrose gradient, which was further fractionated into individual polysomes that were converted into sequencing libraries. (B) Intersample clustering recapitulates the gradient order of polysomes, indicating the sequenced fractions are faithful to the gradient profile. (C,D) RT-PCR analysis and transcript-level quantification for ACTB and ATF4 (C) and three transcripts of EEF1B2 (D) demonstrating concordance of sequencing and transcript-specific RT-PCR. TPM – transcripts per million. *nonspecific amplicon. (E) Members of transcript classes with more than 100 reads in indicated fractions show that coding genes are represented across the polysome while noncoding genes are preferentially in low polysome fractions. (F) Spearman's correlation (RS) between gene (left) or isoform (right) read counts from ribosome profiling or TrIP-seq. See also Figure 1—figure supplement 1 and Materials and methods for calculation of TrIP-seq polysome counts. https://doi.org/10.7554/eLife.10921.003 Figure 1—source data 1 Gene-level abundances for all Ensembl 75 annotated human genes across all sequenced polysome fractions. https://doi.org/10.7554/eLife.10921.004 Download elife-10921-fig1-data1-v2.zip Figure 1—source data 2 Transcript isoform abundances for all Ensembl 75 annotated human transcripts across all sequenced polysome fractions. https://doi.org/10.7554/eLife.10921.005 Download elife-10921-fig1-data2-v2.zip To confirm the sequencing data, we first analyzed beta-actin (ACTB), which is known to be heavily translated with many ribosomes on each message, and migrates with high polysomes in a gradient (Figure 1C; (Sterne-Weiler et al., 2013; Zhang et al., 2015). However, ACTB only has one transcript isoform, so we examined ATF4, which has one dominant isoform exhibiting low-polysome association in TrIP-seq data and by RT-PCR (Figure 1C), consistent with it being translationally repressed in the absence of cellular stress (Harding et al., 2000). We then analyzed eukaryotic elongation factor 1 beta 2 (EEF1B2), which has three isoforms with the same coding sequence but alternative 5′ leaders as a representative gene that exhibits isoform-specific ribosome association (Figure 1—figure supplement 1B). The isoform-specific polysome profiles and RT-PCR conducted from polysome fractions agree qualitatively, validating the accuracy of the isoform-level quantifications (Figure 1D). Weaker amplification of the longer isoform (EEF1B2-003) in high polysomes may be due to PCR bias towards smaller amplicons (Walsh et al., 1992), and replicate experiments show EEF1B2-003 present in high polysomes (Figure 1—figure supplement 1C). We additionally validated the TrIP-seq data using qRT-PCR from polysome fractions and find that the two measurements agree for both EEF1B2 and SRSF5 (Figure 1—figure supplement 2). Both coding mRNAs and long noncoding RNAs (lncRNAs) associate with ribosomes (Ingolia et al., 2014; van Heesch et al., 2014), so we measured their polysomal abundance. Protein-coding genes and isoforms are found across the polysome, but noncoding and lncRNA genes are predominantly in the low polysome fractions indicating that they are generally weakly ribosome associated or in other large macromolecular complexes (Figure 1E and Figure 1—figure supplement 1D). To determine whether TrIP-seq is a measure of translation as opposed to cryptic association with large macromolecular complexes or stalled ribosomes (Darnell et al., 2011; Ishimura et al., 2014), we compared TrIP-seq data to ribosome profiling and proteomics datasets. First, we reanalyzed a ribosome profiling dataset from HEK 293T cells and compared the number of ribosome-protected fragments to a weighted sum of the TrIP-seq polysome reads (Materials and methods; nribo ≥2) (Sidrauski et al., 2015). We observe a strong correlation between ribosome profiling and TrIP-seq at the gene level (RS = 0.88; Figure 1F). However, at the isoform level, the correlation decreases (RS = 0.47), which is worse than the correlation between TrIP-seq replicates (RS = 0.90; Figure 1—figure supplement 1E), suggesting the discrepancy is not due to variability in TrIP-seq but instead is likely due to known issues quantifying transcript isoforms in ribosome profiling (Ingolia, 2014). We next computed the translation efficiency (TE) of ribosome profiling and TrIP-seq data by dividing by cytoplasmic RNA levels. TE measured by the two methods correlated with RS = 0.24, perhaps due to variability in culture conditions or in either technique. We compared the data of Sidrauski et al to an unpublished ribosome profiling dataset in 293T cells from the Yoon-Jae Cho lab. Ribosome-protected fragments between experiments correlate with RS = 0.78, while TE has a substantially lower correlation of RS = 0.41, suggesting biological or technical variability disproportionately affects TE. We then compared TrIP-seq data to protein abundances from HEK 293T cells, and find that the two datasets correlate (RS = 0.57, Figure 1—figure supplement 1F, (Geiger et al., 2012), which is better than RNA-seq (RS = 0.45; not shown). The observations that most translational stalling in mammalian cells is transient and translation elongation rates are homogeneous (Ingolia et al., 2011), TrIP-seq appears to underestimate rather than overestimate protein abundance (Figure 1—figure supplement 1F), and TrIP-seq correlates well at the gene-level with ribosome profiling (Figure 1F) suggest that TrIP-seq primarily measures translating ribosome association as opposed RNAs bound to other large complexes. Diverse human isoform-specific polysome association patterns To extract global trends in isoform-specific translation, we hierarchically clustered transcript isoform polysome profiles and selected eight clusters that are representative of general trends in the data (Figure 2; Materials and methods). The depth of sequencing (Figure 1—figure supplement 3), augmented by the fractionation strategy, enables detection of 62,703 transcript isoforms in the polysome profile (Figure 2—source data 1). Isoforms in the observed clusters exhibit diverse average patterns across polysomes (Figure 2A,B), from clusters 1 and 2, which contain isoforms primarily in high polysomes, to cluster 3 with isoforms in the middle, to clusters 6 and 7 where isoforms are in low polysomes. Independent clustering of the two biological replicate TrIP-seq datasets shows that the high- and low-polysome clusters (1, 2 and 6) appear more robust when comparing between averaged and individual replicate clusterings (Figure 2—figure supplement 1A). Many clusters have similar total polysome abundance but different distributions, indicating that to obtain accurate measurements of isoform-specific translatability it is crucial to fractionate the polysome profile. Figure 2 with 1 supplement see all Download asset Open asset Clustering of transcript distributions yields eight major clusters with diverse behavior across the polysome profile. (A) The average relative abundance of all isoforms in each cluster across polysomes is shown. Error is s.d. (B) Hierarchical clustering of 62,703 transcript isoform distributions across the polysome profile and cytoplasmic fraction. Yellow: above isoform average, cyan: below isoform average. (C) Transcript type distribution per cluster from Ensembl-annotated biotypes. Dotted lines mark the abundance of each transcript type in all isoforms that went in to the clustering. See also Figure 1—source data 1 and 2 and Figure 2—figure supplement 1. https://doi.org/10.7554/eLife.10921.009 Figure 2—source data 1 Variance stabilized transcript isoform abundances (see Materials and methods) and cluster number for all Ensembl 75 annotated human transcripts across all sequenced polysome fractions. https://doi.org/10.7554/eLife.10921.010 Download elife-10921-fig2-data1-v2.zip Surprisingly, the poorly translated cluster 7, which has ~9,000 transcripts, contains a similar number of annotated retained intron or protein-coding isoforms (Figure 2C). We queried the gene ontology (GO) terms associated with each cluster and found that the poorly translated clusters 6 and 7 are enriched for translation and splicing genes (Figure 2—figure supplement 1B), implicating the alternative splicing-nonsense-mediated decay (AS-NMD) pathway (Jangi et al., 2014; Lareau et al., 2007). However, not all retained intron transcripts are subject to NMD (Boutz et al., 2015; Gohring et al., 2014), and we also observe retained intron transcripts in the best-translated clusters (1 and 2), indicating at least some of the introns may be translated. In sum, clustering of isoform abundance distributions across polysomes immediately provides insight into the diverse patterns of ribosome association by individual isoforms and transcript types in cells, and reveals intron-rich clusters of transcripts associated with polysomes that escape nuclear detention. 3′ UTRs and introns drive isoform-specific polysome association We reasoned we could extract features regulating the translation of a gene by comparing transcript isoforms of the same gene that are well- or poorly-translated. We selected 24 features to explore that are involved in translational control, such as the length of the coding sequence and untranslated regions, predicted secondary structure, and microRNA binding sites. As their average polysome profiles and composition are broadly similar (Figure 2), we merged clusters 1 and 2 to generate a larger pool of high-polysome isoforms (Figure 3A), and compared these to cluster 6, representing poorly translated isoforms in low polysomes. We then compared transcript isoforms of the same gene that are in high- and low-polysome clusters, or gene-linked isoforms, to extract transcript features that influence translation. The number of gene-linked isoforms per feature per set varies between 569 and 6491. We then measured the effect size and calculated statistical significance for all features between gene-linked isoforms (Figures 3B and Figure 3—figure supplement 1A; Materials and methods; [Cliff, 1993]). Figure 3 with 1 supplement see all Download asset Open asset Effect of transcript features on polysome association. (A) Meta-transcript distributions for two high polysome clusters (1 and 2) and one low polysome cluster (6). Clusters 1 and 2 were pooled for the analysis in (B). (B) The distance between distributions for 24 different transcript features evaluated for transcripts strongly or weakly associated with polysomes. Distance is the nonparametric effect size, measured as the dimensionless quantity Cliff's d (see Materials and methods) and error bars are bootstrapped 95% confidence intervals. All differences except 5′ UTR and transcript length are significant at the p = 0.001 level based on two-tailed Mann-Whitney U-tests (Figure 3—figure supplement 1A). See Materials and methods for a description of all features and how they were tabulated. UTR – untranslated region; CDS – coding sequence. (C) Enrichment of either cognate (ATG start codon) or non-cognate (non-ATG) uORFs in high polysome versus low polysome clusters. Density is uORFs per 100 isoforms. See also Figure 3—figure supplement 1. https://doi.org/10.7554/eLife.10921.012 We find that longer coding sequences and highly abundant versions of gene-linked isoforms are biased towards high polysomes, likely because shorter coding sequences cannot accommodate as many ribosomes (Figure 3B and Figure 3—figure supplement 1A). However, the length of the coding sequence is not the sole determinant of polysome association, since the ribosome density (measured by dividing the weighted sum of TrIP-seq polysome reads by the number of cytoplasmic reads) is also higher in gene-linked isoforms found in high polysomes (Figure 3B). We also find that transcripts from gene-linked isoforms with more exons also tend to be better translated, as was observed using reporter genes (Nott et al., 2003; 2004). This is not due to overall transcript length, as there is no significant between transcript length in gene-linked isoforms, a influence of splicing on translation. translated isoforms contain on average, but contain more of perhaps indicating a for translational (Figure 3B). The that exons translatability has been for select and we show that this across the human of the effects on polysome association of gene-linked isoforms from the length and of the 3′ UTR (Figure 3B and Figure 3—figure supplement 1A). we find that gene-linked 3′ UTRs in low polysomes are longer than on high polysomes length versus are numerous regulatory elements 3′ including microRNA binding elements, and 2009; and 2013). The fraction of the 3′ UTR elements is in low polysome gene-linked isoforms, due to translational and 2013; et al., 2014). predicted binding for et al., are also more abundant in poorly translated gene-linked isoforms, but this could be due to a correlation between 3′ UTR length and the number of binding sites. the to bound to or in HEK 293T cells et al., In this HEK the for both the number of binding and their between gene-linked isoforms, suggesting that binding may be in this (Figure 3B and Figure 3—figure supplement 1A). elements and binding to for low polysome isoforms, which is out in (Figure 3B and Figure 3—figure supplement 1A; et al., Surprisingly, gene-linked isoforms in high polysomes have predicted both at the and in in the 5′ are at least three that could this First, RNA can both or translation in a manner et al., so it is that some isoforms are being to high polysomes by of of global RNA secondary have consistently observed different in cells than in et al., 2014; et al., 2015). HEK 293T cells may messages with 5′ leaders more than other cell In we find no significant for the length of the 5′ leader region on polysome association based on this (Figures 3B and Figure 3—figure supplement 1A). However, isoforms 5′ leaders over long are poorly ribosome-associated relative to shorter 5′ leaders (Figure 3—figure supplement and cluster 7, which is associated with contains longer 5′ leaders (Figure 3—figure supplement 1C). is likely that more features of the 5′ leader also influence as in the examples in Figure can or influence translation (Brar et al., 2012; et al., 2009; et al., 2013; Hinnebusch, the number of determined uORFs and in each cluster and observe complex uORFs with or translation, while uORFs generally translation (Arribere and Gilbert, 2013; et al., 2012). these lines, we find that cluster 1 is enriched for cognate and uORFs while cluster 6 is enriched for cognate However, cluster 2 is enriched for only uORFs, of the complex and behavior of we find for the 3′ UTR and the number of introns in polysome occupancy of gene-linked transcript isoforms in human cells, while a diversity of other features can influence translatability to a translation changes human development RNA-seq the isoform diversity of a but not the functional consequences of this We reasoned that TrIP-seq data could be to which isoform changes are likely to to translation changes in other We a human dataset individual cells from and these data to to TrIP-seq data (Materials and methods; et al., We transcripts each the TrIP-seq clusters, to to insight into global translational in human (Figure In analysis a towards the of translation, with an in the of transcript isoforms that are both highly clusters 1 and 2) and cluster 6) translated isoforms in HEK 293T cells (Figure translation is in and it is that the observed in poorly translated isoforms a for translational control and 2008; et al., 2014). is also that translation in is differentially than in HEK 293T cells, which is by TrIP-seq to other cell cell type-specific translational control Figure with 2 supplements see all Download asset Open asset isoform-specific translational control changes human (A) each transcripts are all eight TrIP-seq clusters, as in Figure The of transcripts to each cluster per is then and compared to the for TrIP-seq – human – or Error is s.d. between cells at each or between TrIP-seq biological (B) for two transcripts of across development and polysome fractions demonstrating a in translational Error is TPM – transcripts per million. (C) of two transcript isoforms of different between the two isoforms are in and select regions have been for See also Figure supplement 1. We then genes with isoforms that between and