1. Biochemistry
  2. Genes and Chromosomes
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Distinct stages of the translation elongation cycle revealed by sequencing ribosome-protected mRNA fragments

  1. Liana F Lareau Is a corresponding author
  2. Dustin H Hite
  3. Gregory J Hogan
  4. Patrick O Brown
  1. Howard Hughes Medical Institute, Stanford University School of Medicine, United States
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Cite as: eLife 2014;3:e01257 doi: 10.7554/eLife.01257

Abstract

During translation elongation, the ribosome ratchets along its mRNA template, incorporating each new amino acid and translocating from one codon to the next. The elongation cycle requires dramatic structural rearrangements of the ribosome. We show here that deep sequencing of ribosome-protected mRNA fragments reveals not only the position of each ribosome but also, unexpectedly, its particular stage of the elongation cycle. Sequencing reveals two distinct populations of ribosome footprints, 28–30 nucleotides and 20–22 nucleotides long, representing translating ribosomes in distinct states, differentially stabilized by specific elongation inhibitors. We find that the balance of small and large footprints varies by codon and is correlated with translation speed. The ability to visualize conformational changes in the ribosome during elongation, at single-codon resolution, provides a new way to study the detailed kinetics of translation and a new probe with which to identify the factors that affect each step in the elongation cycle.

https://doi.org/10.7554/eLife.01257.001

eLife digest

To make a protein from a gene, the gene is first transcribed to produce a molecule of messenger RNA (mRNA), which then passes through a molecular machine called a ribosome. The ribosome reads the genetic code in the mRNA in groups of three letters at a time, and each triplet of letters (or codon) represents an amino acid. The ribosome then joins the relevant amino acids together to build a protein.

The ribosome processes about six amino acids per second, on average, but the mRNA is not fed through at a constant rate. Instead, the ribosome changes its shape to ratchet along the mRNA from one codon to the next: it then reads the new codon and adds another amino acid to the protein. However, many of the details of this ratcheting process are not fully understood.

In this study, Lareau, Hite et al. have used a technique called ‘ribosome profiling’ to explore the movement of ribosomes along mRNA molecules. First, all of the pieces of mRNA molecules that are not protected inside a ribosome were chemically destroyed. The sequences of the protected fragments were then read and matched to the full-length gene sequences. The protected fragments came in two different sizes: some were about 28–30 letters long, and others were about 20–22 letters long. Lareau, Hite et al. suggest that these different fragment sizes occur because the ribosome switches between two shapes at each codon as it ratchets along the mRNA, and so it protects different lengths of mRNA.

In previous ribosome-profiling experiments, the fragments had all been about 28 letters long; but these experiments had used a chemical to halt the progress of the ribosomes along the mRNAs before measuring the length of the fragments. Lareau, Hite et al. argue that this chemical locks the ribosome in the same shape when it brings the ribosome to a halt, and so the protected fragments always have the same length. Further, other chemicals that halt ribosomes appear to lock this molecular machine in the other shape, and so it can only protect the shorter fragments.

The findings of Lareau, Hite et al. show that ribosomal profiling experiments can reveal much more than simply where a ribosome is on an mRNA molecule. Further study into the different stages of the ribosome ratcheting process will help uncover how the speed that a ribosome translates an mRNA into a protein can be encoded in the mRNA sequence itself.

https://doi.org/10.7554/eLife.01257.002

Introduction

To accomplish the huge task of translation elongation—in each cycle, accurately incorporating a new amino acid into a nascent peptide every 1/6th of a second, then moving precisely three nucleotides along the mRNA template—the ribosome undergoes a series of major structural rearrangements (Figure 1) (reviewed in Chen et al., 2012 and Noeske and Cate, 2012). During the initial decoding step of elongation, aminoacylated tRNAs are delivered to the decoding site (A site) as part of a ternary complex with EF-Tu (in prokaryotes) or the orthologous eEF1A (in eukaryotes). When the anticodon of one of these aminoacylated tRNAs is able to base-pair stably with the specific mRNA codon in the decoding site (A site), a new peptide bond is formed between the nascent polypeptide and the specified amino acid. The ribosome then undergoes a massive rearrangement in which the ribosomal subunits rotate relative to each other (Frank and Agrawal, 2000; Zhang et al., 2009). Along with this rotation, the A and P site tRNAs move from ‘classic’ to ‘hybrid’ states: the anticodon ends stay in their original A and P sites and the acceptor ends move to the P and E sites (Moazed and Noller, 1989; Munro et al., 2007). This rotated state of the ribosome undergoes additional conformational changes in preparation for translocation (Zhang et al., 2009; Fu et al., 2011). The ribosome can fluctuate between rotated and non-rotated states until EF-G (eEF2 in eukaryotes) binds and stabilizes the rotated ribosome (Agirrezabala et al., 2008). GTP hydrolysis by EF-G then promotes translocation of the mRNA along the ribosome, coupled to a large intra-subunit rotation of the 30S head (Ratje et al., 2010), after which the ribosome subunits rotate back to a closed formation for the next cycle (Gao et al., 2009). Structural and biochemical studies have revealed many of the atomic-level changes that allow this complicated process to occur (Pulk and Cate, 2013; Tourigny et al., 2013; Zhou et al., 2013), and new details continue to emerge, reshaping models, raising new questions, and leaving other questions still unanswered.

Schematic representation of the eukaryotic elongation cycle.

Blue overlay denotes stages at which the ribosome has undergone a large inter-subunit rotation. Ribosome shapes are for illustration only, not a literal representation of the structure or degree of rotation.

https://doi.org/10.7554/eLife.01257.003

Recently, ‘ribosome profiling’ by high-throughput sequencing of ribosome-protected fragments has provided a powerful tool for identifying the position of ribosomes on mRNAs across the entire transcriptome (Ingolia et al., 2009). Cell lysates are treated with nuclease to degrade all mRNA not physically protected by ribosomes, and the ribosome-protected fragments are extracted, sequenced, and mapped back to the genome to show ribosome positions, revealing the overall translation level of each gene as well as the distribution of ribosomes along the mRNA. Nucleotide-level precision of ribosome positions is possible because of the very consistent size of ribosome footprints in the conditions assayed. The authors of the method used a nuclease protection assay to establish that, in yeast treated with the elongation inhibitor cycloheximide, each ribosome protects a footprint of 28 nucleotides (nt), confirming earlier reports (Steitz, 1969; Wolin and Walter, 1988).

While performing ribosome-profiling experiments in Saccharomyces cerevisiae, we serendipitously noticed a population of smaller ribosome-protected fragments. To better capture these fragments and to investigate their origins, we revised the ribosome-profiling protocol originally established by Ingolia et al. Our experiments revealed that, in the absence of cycloheximide, the small ribosome-protected fragments were abundant, consistent with an early observation of short ribosome footprints in the absence of cycloheximide (Wolin and Walter, 1988). We show here that the small fragments originate from ribosomes in a conformation distinct from that previously observed in the presence of cycloheximide. The ability to discern distinct ribosomal structural states by ribosome profiling has given us insight into how codon, tRNA, and amino acid identity and translational speed relate to ribosome structure. This additional dimension of ribosome-profiling data will provide a valuable new layer of molecular and mechanistic information, at codon resolution, for future studies of translation.

Results

Ribosomes can protect two distinct mRNA fragment sizes

We began our investigation of ribosome footprint size by isolating ribosome-protected mRNA fragments from yeast using a modified ribosome-profiling procedure. The standard ribosome-profiling protocol includes a size selection for RNA fragments of around 28 nt. To eliminate the bias against smaller fragments, we broadened the initial size range and selected RNA fragments between 18 and 32 nt after RNase I digestion. By selecting fragments in this broader size range, and, importantly, by carrying out the entire procedure in the absence of cycloheximide or other inhibitors, we observed two clearly distinct, abundant populations of ribosome-protected mRNA fragments (‘footprints’), 28–30 nt and 20–22 nt long. We visualized fragment lengths and positions with a three-dimensional ‘metagene’ representation: sequence reads representing the ribosome-protected fragments from all expressed genes were aligned relative to the start codon of the corresponding gene and tallied by fragment length and position to show the average pattern of translation along all annotated coding regions (Figure 2A–C, Figure 2—figure supplement 1).

Figure 2 with 1 supplement see all
Ribosome-protected fragment positions and size distributions from yeast not treated with elongation inhibitors.

(A) The position of each fragment was calculated relative to the start codon of its gene. The 5′ end positions (x axis) and lengths of all fragments (y axis) were tallied across all genes with a coding region of at least 300 nt. Higher color intensity reflects more fragments. RNA fragments between 18 and 32 nucleotides were selected after gel electrophoresis; shorter and longer fragments are not entirely excluded but their read counts are presumed to be unrepresentative of their true abundance. (B) Profiles of the 5′ end positions of all 20 nt and 28 nt fragments relative to the start codon of their genes, as in (A). (C) Total counts of mapped fragment lengths. (D) Distribution of 21 nt and 28 nt fragments in coding regions and untranslated regions of mRNAs. (E) Positions of 21 nt and 28 nt fragments relative to the reading frame. (F) Interpretation of fragment positions on an arbitrary gene fragment. Arrowheads show hypothetical nuclease cleavage sites relative to a ribosome in a non-rotated or rotated conformation (shape is for illustration only). The resulting fragments are shown with the inferred decoding site (A site), and their positions in a grid as in Figure 2A are shown with corresponding colors.

https://doi.org/10.7554/eLife.01257.004

We found overwhelming evidence that both populations of fragments came from translating ribosomes. The 21 and 28 nt fragments were both found almost entirely within annotated coding regions (CDS) and not in 5′ or 3′ UTRs; 98.3–99.7% of mappable 21 nt fragments, and 96.5–99.6% of mappable 28 nt fragments, mapped within the annotated CDS in three replicates (Figure 2D). Both populations also showed the 3-nucleotide periodicity expected of fragments originating from elongating ribosomes (Figure 2E). We conclude that fragments of both sizes are footprints of translating ribosomes.

The 5′-most peaks in the metagene represent ribosomes with the start codon in the P site and the second codon in the A site (Kapp and Lorsch, 2004; Ingolia et al., 2009). Using this as a reference for phasing all the footprints, we inferred that for ribosomes with a given codon in the A site, small and large footprints generally had the same 5′ ends positioned 15–16 nt upstream of the A-site codon, and differed at their 3′ ends: extending 2–3 nt beyond the A-site codon in the small footprints and 10 nt beyond the A-site codon in the large footprints, respectively (Figure 2F).

Different elongation inhibitors stabilize distinct conformations and bias the footprint size distribution

During elongation, at each codon, the ribosome cycles through a stereotyped sequence of steps as it incorporates the specified amino acid and translocates to the next codon. These steps are accompanied by major rearrangements of the ribosome structure, including a rotation of the large subunit relative to the small subunit upon peptide bond formation. We hypothesized that the non-rotated, pre-peptide-bond ribosomes and rotated, post-peptide-bond ribosomes might protect different lengths of mRNA, and that the two resulting footprint sizes might, therefore, represent these two conformations.

To determine what footprint sizes were protected by ribosomes in distinct stages of elongation, we performed ribosome profiling on yeast treated with inhibitors that block different steps of the cycle. Cycloheximide is an elongation inhibitor that binds to the E site of ribosomes, preventing the E site tRNA from leaving the ribosome. When cycloheximide was added to the yeast immediately before harvest and was present throughout lysis and RNase I treatment, the most prevalent footprints were 28–30 nt long and were distributed along the coding sequence with a 3-nt periodicity (Figure 3A–C, Figure 3—figure supplement 1). Apart from a distinct peak at the start codon, there were very few 20–22 nt footprints.

Figure 3 with 1 supplement see all
Ribosome-protected fragment positions and size distributions from yeast treated with elongation inhibitors.

(A and B) As in Figure 2A,B, fragment position and size distribution for yeast treated with cycloheximide. (C) Distribution of mapped fragment lengths for yeast treated with cycloheximide. (D and E) Fragment position and size distribution for yeast treated with anisomycin. (F) Distribution of mapped fragment lengths for yeast treated with anisomycin.

https://doi.org/10.7554/eLife.01257.006

Our data confirmed previous evidence that the ribosome predominantly protects a 28 nt footprint in the presence of cycloheximide, and suggest that cycloheximide stabilizes one stage of the elongation cycle. Previous work shows that cycloheximide bound alongside a tRNA in the E site prevents either the incorporation of the next aminoacylated tRNA in the A site or peptide bond formation (Schneider-Poetsch et al., 2010). In either case, it is expected to trap the ribosome in a non-rotated conformation, suggesting that the non-rotated conformation protects 28–30 nt of mRNA.

We next conducted ribosome-profiling experiments using yeast treated with anisomycin, an elongation inhibitor that binds to the peptidyl transferase center (Grollman, 1967; Hansen et al., 2003). We observed almost exclusively small footprints in yeast treated with anisomycin (Figure 3D–F, Figure 3—figure supplement 1). By comparison to the effects of cycloheximide treatment, we inferred that anisomycin stabilizes a distinct conformation of the ribosome that protects 20–22 nt of mRNA. Although anisomycin's precise mechanism is not characterized, it has higher affinity for post-translocation ribosomes than for pre-translocation, cycloheximide-treated ribosomes, suggesting that it preferentially binds a ribosome conformation distinct from that stabilized by cycloheximide (Barbacid and Vazquez, 1974, 1975). Lincomycin and other antibiotics that bind the peptidyl transferase center induce translocation, and lincomycin-treated ribosomes prefer a rotated conformation in in vitro FRET experiments (Fredrick and Noller, 2003; Ermolenko et al., 2013). It is possible that anisomycin acts similarly to stabilize a rotated conformation.

We have thus demonstrated that two distinct ribosome conformations can be stabilized using elongation inhibitors. Stabilization of distinct conformations by two drugs resulted in a nearly complete reciprocal bias in the size of ribosome footprints, providing evidence that large and small footprints originate from distinct ribosomal conformations. We hypothesize that each ribosome cycles through both conformations, protecting first a large footprint and then a small footprint at each codon. The footprints identified by high-throughput sequencing in a ribosome-profiling experiment represent a deep sampling of ribosomes in different states, and thus the ratio of large to small footprints in untreated cells could show, at single-codon resolution, how many ribosomes are in each stage of elongation.

Increased decoding time produces more large footprints

To enrich for ribosomes in a single, defined stage of the elongation cycle, we induced conditions expected to result in the depletion of a specific aminoacyl-tRNA and thus to increase the decoding time when the cognate codon is in the A site. We treated yeast with 3-amino-1,2,4-triazole (3-AT), an inhibitor of histidine biosynthesis, to create a specific shortage of His-acylated tRNA and cause ribosomes to pause on histidine codons (Figure 4A). We would therefore expect ribosomes to accumulate at histidine codons in a pre-peptide-bond conformation. Estimating codon-specific occupancy as described in more detail below, we found that the shortage of His-tRNA dramatically increased the relative abundance of large footprints from ribosomes with His codons in the A site, with minimal effect on the abundance of small footprints (Figure 4B,C, Figure 4—figure supplement 1). During the decoding phase of elongation, before peptide bond formation, the ribosome is in a non-rotated conformation (Frank and Agrawal, 2000; Gao et al., 2009); these results therefore strongly suggest that the decoding phase of elongation (the non-rotated conformation) is represented by large footprints.

Figure 4 with 1 supplement see all
Effect of 3-amino 1,4 triazole on translation of histidine codons.

(A) Schematic representation of the hypothesized effect of 3-AT. 3-AT reduces intracellular concentrations of histidyl-tRNA and thus is expected to increase time spent decoding histidine codons (i.e., in the decoding phase of the cycle, with a His codon in the A-site). (B) All 61 sense codons are plotted by the log2 of the relative abundance of large footprints with the specified codon in the A-site for untreated cells (x axis) against the log2 relative abundance of large footprints for yeast treated with 3-AT (y axis). Values shown are the average of three untreated replicates and two 3-AT treatments (10 min and 60 min). Histidine codons are denoted in red (CAT) and cyan (CAC). (C) As in (B), showing the relative abundance of small footprints.

https://doi.org/10.7554/eLife.01257.008

The footprint size distribution varies by codon

Recently, ribosome profiling has revealed that translation speed varies systematically by codon (Tuller et al., 2010; Stadler and Fire, 2011; Dana and Tuller, 2012); we hypothesized that there might be distinct codon-specific effects on the rate of the two distinct phases of elongation represented by small and large footprints.

Using data from untreated cells, we calculated the number of large and small footprints corresponding to ribosomes with a given codon in the A site, for each codon position in the yeast transcriptome. Large footprints were defined as 28 or 29 nt and small footprints were defined as 20, 21, or 22 nt with 5′ ends positioned relative to the inferred A site as depicted in Figure 2F. We found substantial variation in the characteristic length distribution between codons: small footprints ranged from 38 ± 12% (UUU) to 87 ± 9% (CGG) of the total footprints for a given codon identity, averaged across three replicates.

To explore this codon effect, we computed the relative occupancy of each of the 61 sense codons in the A site. We started by considering an individual gene and calculated the over- or underrepresentation of footprints at each codon position compared to the average for all codon positions in that gene, including both small and large footprints (an example from a highly expressed gene is shown in Figure 5A). After performing this computation for every gene, we averaged these multipliers across all occurrences of a given codon in the genome to provide the ‘relative occupancy’ for that codon, representing, on a relative scale, how frequently we observed ribosomes with that codon positioned at the A site. The relative occupancies varied over a fivefold range, from 0.48 ± 0.04 (GGU) to 2.6 ± 0.67 (CCG) (unitless, average of three replicates) and were highly correlated between independent replicates (Figure 5B). As a control, we also analyzed the occupancy based on the codon one position 3′ of the A site, which has not yet entered the decoding site. We found that the range of occupancies relative to the codon in the A site was much broader than the range of occupancies relative to the next codon, suggesting that the A-site occupancies reflect an aspect of translation, not merely confounding factors such as biases in fragment capture (Figure 5—figure supplement 1).

Figure 5 with 1 supplement see all
Codon-specific variation in large and small footprint abundance.

(A) Distribution of ribosome footprint counts on the highly expressed gene FBA1, highlighting an arbitrary window, codons 250–279. Ribosome footprint counts per position were consistent between replicates and varied between instances of the same codon in this window. Relative occupancy was estimated based on the codon in the inferred A site. Total (large + small) footprint coverage at each codon of a gene was computed relative to the average coverage for that gene, then averaged by codon across all genes to provide per-codon relative occupancies. Relative abundance of small or large footprints was computed similarly, comparing the count of small or large footprints at each codon of a gene against the average coverage (large + small) for that gene, then averaged by codon across all genes. Examples of small and large footprint abundance values at two specific TTC codons in FBA1 are shown. (B) Relative occupancies of all 61 codons compared between two replicates, with Spearman correlation of 0.81. Stop codons and the first 50 codons of each gene were excluded from analysis. Similarly, small footprint abundance (C) and large footprint abundance (D) compared between replicates.

https://doi.org/10.7554/eLife.01257.010

Codon-specific differences in ribosome occupancy could have been driven by variation in small footprint counts, variation in large footprint counts, or both, potentially revealing the variability of each stage of elongation. We inferred the relative abundance of ribosomes in each state at each codon using a model similar to the one we used to estimate overall relative occupancy, but considering counts of either small or large footprints separately (Figure 5A). As with overall occupancy, the relative abundances of small footprints and the relative abundance of large footprints were both highly correlated between replicates (Figure 5C,D). This suggests that codon identity affected both the pre-peptide-bond and post-peptide-bond stages of elongation. However, the effect of codon identity on the inferred duration of these two phases of the elongation cycle was distinct: the codon-specific relative abundances of small and large footprints were almost uncorrelated (Spearman's r = 0.11, average of three replicates). This led us to search for physical correlates of the codon-specific differences.

Relative occupancy is related to amino acid polarity and codon:tRNA interactions

We found that a major and unexpected determinant of the abundance of footprints from each conformation was the identity of the amino acid encoded by the A-site codon. We found a much greater density of small footprints at codons encoding smaller, polar amino acids than at codons encoding large, aromatic amino acids. The relative abundance of small footprints at codons encoding a given amino acid was correlated with measures of polarity of the cognate amino acid, such as the Kd of transfer of side chains from vapor to water (Spearman's r = −0.75 when grouped by amino acid, r = −0.58 by codon, Figure 6A), while the relative abundance of large footprints showed no correlation to amino acid polarity (Spearman's r = 0.11 by amino acid, r = 0.02 by codon) (Wolfenden, 2007). These data strongly suggest that the chemical properties of the amino acid specified by the codon in the A site affect the stability of the rotated, post-peptide-bond conformation of the ribosome. We hypothesize that interactions between the ribosome and polar amino acids acylated to the A-site tRNA can slow translocation substantially.

Correlates of footprint abundance.

(A) Small footprint abundance, averaged for all codons encoding the same amino acid plotted against Kd of transfer of side chain from vapor to water as a measure of polarity (Wolfenden, 2007), with Spearman correlation from the average of three samples. (B) Relative occupancy of directly paired codons vs relative occupancy of codons that recognize the same tRNA with wobble pairing. Values are the average of three replicates. Dashed line shows y = x, the expected relationship if occupancy were determined solely by tRNA identity. (C and D) As in (B), showing small and large footprint abundance.

https://doi.org/10.7554/eLife.01257.012

Many factors have been proposed to affect translation speed at a given codon, particularly tRNA abundance. In yeast, the number of genes encoding a specific tRNA has been shown to be highly correlated with both codon usage and cellular tRNA concentrations (Percudani et al., 1997). A related measure of codon optimality is the tRNA adaptation index (tAI), which attempts to rank codons in translational efficiency by accounting for tRNA copy number, wobble pairing constraints, and codon usage (dos Reis et al., 2004). We found that the relative occupancy per codon was only weakly correlated with tAI and with tRNA genomic copy number (Spearman's r = −0.39 and −0.28, respectively; average of three replicates) and that the tAI was not particularly correlated with the relative abundance of either small footprints or large footprints (r = −0.34 and r = −0.20, respectively; average of three replicates). Thus, unexpectedly, codon ‘optimality’, as represented by the tAI, does not appear to be a major determinant of relative ribosome occupancy under the conditions tested here. The 3-AT data show that in an extreme case, a limited supply of the tRNA cognate to the A-site codon slows translation during the large-footprint stage. In contrast, our overall results in untreated yeast suggest that the differences in abundance among tRNAs in wild-type cells have only a minor effect on relative ribosome occupancy of the cognate codons under optimum growth conditions.

We also investigated the relationship between wobble base pairing, relative occupancy, and the density of large and small footprints. Wobble base pairing at the A site has recently been linked with slowed elongation in humans and worms (Stadler and Fire, 2011). We compared codons with perfect Watson-Crick complementarity vs the synonymous codons that pair imperfectly with the same tRNA (Johansson et al., 2008). While we found no consistent trend toward increased occupancy at wobble-paired codons, we observed notably higher occupancy on a subset of wobble-paired codons comprising proline CCG (G-U base pairing), leucine CUG (G-U), and arginine CGA (A-I) (Figure 6B). For these three wobble codon outliers, we see a dramatic increase in short footprints, representing post-decoding stages of translation (Figure 6C,D). The arginine CGA codon is known to be a strong inhibitor of translation in yeast, and its inhibitory effect is due more to wobble decoding than tRNA abundance and may include interactions after the initial decoding (Letzring et al., 2010). Our data confirm that CGA is indeed one of the most slowly translated codons, and its high relative occupancy is due to increased abundance of small footprints, suggesting that its slow elongation is primarily due to a prolonged post-decoding stage. Overall, the abundance of footprints from each step of elongation was clearly affected by several distinct codon-specific features with sometimes synergistic and sometimes opposing effects.

Discussion

A ribosome must cycle through a series of consecutive associations with mRNA to decode the message one codon at a time. The stability of the ribosome-mRNA association allows one to observe precisely where ribosomes reside on transcripts—down to the codon being decoded—by isolating and sequencing ribosome-protected mRNA fragments. We were quite surprised to discover that the ribosome protects two different footprint sizes (28–30 nt and 20–22 nt), as the original ribosome-profiling experiments and nuclease protection assays only captured the longer footprints (Ingolia et al., 2009). The difference is explained by the experimental conditions: the small footprints were revealed only after we left out cycloheximide, a translation inhibitor commonly used to stabilize ribosomes on mRNA for ribosome profiling. Indeed, early study of ribosome pausing found that when cycloheximide was omitted, 20–24 nt footprints accumulated in addition to the larger footprints they saw from cycloheximide-treated ribosomes (Wolin and Walter, 1988). As in our own experiments, the small and large footprints they observed had the same 5′ terminus and differed at the 3′ end.

We propose that the two footprints sizes originate from two ribosome conformations corresponding to different stages of elongation: large footprints from non-rotated ribosomes during the decoding stage before peptide bond formation, and small footprints from rotated ribosomes during the translocation stage after peptide bond formation. Additional biochemical and structural studies will be required to pinpoint the exact stages of elongation and ribosome conformations responsible for the two footprint sizes. It is not clear which of the known conformational changes during the elongation cycle are most relevant: the inter-subunit rotation after peptide bond formation, the intra-subunit swivel of the 30S head during translocation, or smaller rearrangements such as movement of the L1 stalk.

As for the physical origin of the small and large mRNA fragments, crystal structures of rotated and non-rotated ribosomes show that mRNA accessibility is not likely to be dramatically different between the two conformations (Ben-Shem et al., 2010, 2011). RNAse I may be small enough to penetrate into the mRNA entrance channel and cleave the mRNA just two nucleotides from the A site. Alternately, the ribosome itself may be more susceptible to RNAse degradation in the rotated conformation, allowing ribosomal RNA cleavage that in turn enables RNAse I to access the mRNA entrance channel, yielding a smaller mRNA footprint. Importantly, however, both small and large footprints have also been observed in wheat germ extract treated with micrococcal nuclease, indicating that the two footprint sizes are neither species- nor nuclease-specific (Wolin and Walter, 1988).

We hypothesize that the relative abundance of large and small footprints reflects the relative duration of different stages of elongation at each codon. (We use the A site codon by default in this discussion, though in principle we could compile results based on the codon in the P site or any other frame of reference.) Comparing our relative occupancy values to an estimated bulk elongation rate of 5.6 amino acids per second (Ingolia et al., 2011), our model would predict variation in average codon elongation time from as little as 0.08 s (GGU) to as much as 0.5 s (CCG). A number of caveats apply to this interpretation, and any hypotheses must be pursued with complementary approaches. Ribosome footprint data have inherent biases from ligation and other steps of the library preparation. Further, the overall balance of small and large footprints varied between replicates, leaving open the question of which conformation is more populated in vivo. Some variability arises from the mRNA fragment isolation. In this work, we chose size markers of 18 and 32 nt, but size selection from polyacrylamide gel is imprecise. (This choice also limits what we can observe: recent work found distinct 16 nt fragments from ribosomes stalled on truncated mRNAs, Guydosh and Green, 2014.) The size distribution may also reflect differential efficiency of library preparation from smaller or larger fragments. Nonetheless, although the overall ratio of small to large footprints varied, the codon-specific variation in this ratio was robust.

Our results also highlight the effects of harvest methods and inhibitors such as cycloheximide on footprint distribution. Ribosomes are depleted from the first 50 codons when yeast are harvested by the procedure we used without inhibitors. We interpret this as evidence that elongation continues for around 10 s after initiation ceases during the harvest process. Because the selective depletion of ribosomes from this part of the mRNA could enrich for special cases, we excluded the first 50 codons from our analysis of per-codon footprint distributions. Different harvest methods had large effects on the precise footprint locations even when the overall translation per gene was highly reproducible (data not shown). Similarly, the average occupancies per codon with and without cycloheximide were surprisingly uncorrelated (Spearman's r = 0.02, comparing the average of three untreated samples and the average of two cycloheximide-treated samples), though the total footprints per gene correlated quite well (Spearman's r = 0.97 between the average fpkm in three untreated samples and the average fpkm in two cycloheximide-treated samples). Ribosomes in different positions may be differentially affected either by the drug treatment or by runoff elongation during harvest without inhibitors. In either case, some ribosomes may halt while others undergo several more rounds of elongation.

There are many potentially rate-controlling steps of elongation and many factors necessary for each cycle, including aminoacylated tRNA and elongation factors eEF1, eEF2, and the yeast-specific eEF3 (Kapp and Lorsch, 2004). For example, interactions between the tRNA anticodon and the mRNA codon, the tRNAs and the ribosome, the amino acids and the peptidyl transferase center, and the nascent peptide and the tunnel, as the tRNAs move through the A, P, and E sites, can all presumably affect the speed of each step. Thus, the speed of each elongation cycle is expected to be influenced by codon, tRNA, and amino acid identity.

One of the surprising aspects of this study is that tRNA abundance or codon optimality failed to predict variation in observed ribosome occupancy and, further, that much of the variation in codon-specific occupancy was in the steps following decoding and peptide bond formation. Biochemical evidence suggests that evolution has tuned tRNA sequence and modifications to balance the contributions of amino acid identity, codon pairing strength, and tRNA structure to binding affinity of a given tRNA, such that most aminoacylated tRNAs have similar affinity to ribosomal A sites (Olejniczak et al., 2005; Dale et al., 2009; Shepotinovskaya and Uhlenbeck, 2013). While this affinity tuning is a plausible result of selection for fidelity in decoding, ribosome profiling has revealed a lack of uniformity both in decoding and post-decoding steps. Once the interactions that determine the codon-specific rate of decoding are decoupled and replaced by a new set of codon-specific interactions in the subsequent steps of elongation, the great diversity in physical properties of amino acids and in the intrinsic stability of the codon–anticodon interaction may lead to wide variation in the kinetics of post-decoding steps.

New methods for high-throughput measurement of translation have led to renewed interest in modeling the constraints on coding sequence and the effects of codon choice on translation efficiency (Tuller et al., 2010; Plotkin and Kudla, 2011; Dana and Tuller, 2012; Charneski and Hurst, 2013; Shah et al., 2013). The ability to distinguish ribosome conformations at codon resolution now allows us to map these effects to specific phases of the elongation cycle, initiation, or termination. Future in vivo and in vitro experiments using this approach to monitor the decoding and translocation steps at each codon should provide new precision in dissecting the mechanisms by which mRNA sequence, core translation factors and regulatory factors control initiation, elongation, and termination of translation.

Materials and methods

Yeast strains and growth conditions

For all experiments, excluding 3-AT drug treatment experiments, BY4741 was grown overnight in YPD at 30°C; two 500 ml cultures of YPD were inoculated from the overnight culture to an OD600 of ∼0.2. For experiments involving 3-AT, S288C was grown as above in SC-His media at 30°C. Cells were then grown to mid-log phase, OD600 ∼0.6, prior to harvest. (Strain information: BY4741 derived from S288C: MATa his3Δ1/his3Δ1 leu2Δ0/leu2Δ0 lys2Δ0/LYS2 MET15/met15Δ0 ura3Δ0/ura3Δ0. S288C: MATa SUC2 gal2 mal mel flo1 flo8-1 hap1.)

Cells were harvested by filtration at room temperature and then quickly frozen in liquid N2. Resulting cell pellets were then pulverized using a MM301 Retsch mixer mill at 30 Hz for 3 min. All chambers and tubes were pre-frozen in liquid N2 or dry ice. Approximately 400–500 µl of cold lysis buffer (20 mM Tris pH 8.0, 140 mM KCl, 1.5 mM MgCl2, 1% Triton) was added to cell powder. Resulting lysates were pre-cleared by centrifugation at 2000 rpm for 5–10 min at 4°C. Lysate was transferred to a clean pre-chilled tube and further clarified by centrifugation at 20,000×g for 10 min at 4°C. Lysate was then stored at −80°C until RNase digestion.

For the cycloheximide experiments, cycloheximide was added to cells prior to harvest at 100 µg/ml and was also present at 100 µg/ml in the lysis buffer. For the anisomycin experiment, anisomycin was added to mid-log cells at 100 µg/ml and cells were allowed to grow for an additional 30 min prior to harvest. Anisomycin was also present at 100 µg/ml in the lysis buffer. For the 3-AT experiments, 3-amino-1,2,4-triazole was added to mid-log cells to reach a final concentration of 100 mM, then cells were grown with shaking for 10 and 60 min prior to harvest.

RNase digestion and monosome isolation

RNase digestion and monosome isolation were performed similar to Ingolia et al. (2009, 2012). Cell lysate (∼800 µg total RNA measured by Nanodrop) was allowed to thaw on ice. 600 U of RNase I (AM2294; Life Technologies, Carlsbad, CA) was added to cell lysate and placed on a nutator at room temperature for 1 hr. A second cell lysate served as an undigested control; 120 U of SUPERase-In was added and placed on a nutator as above. Linear 10–50% sucrose gradients were prepared using a BioComp Gradient Master (Biocomp Instruments, Fredericton, Canada) according to manufacturer's instructions. Sucrose was dissolved in 20 mM Tris pH 8.0, 140 mM KCl, 5 mM MgCl2, 0.5 mM DTT, 20 U/ml SUPERase-In; 100 µg/ml cycloheximide or 100 μg/ml anisomycin were added to buffer for corresponding experiments. After RNase digestion, lysate was added to the top of gradients and sedimented at 35,000 rpm in a SW41 rotor for 3 hr.

Gradients were fractionated at 0.17 mm per second using the BioComp Gradient Master while the A260 was continuously monitored. Fractions corresponding to the monosome peak were collected and pooled. RNA was then purified using a miRNeasy Mini kit from Qiagen (cat# 217004; Qiagen, Venlo, Netherlands) as per manufacturer's instructions.

Library preparation and high-throughput sequencing

Ribosome footprint libraries were prepared similar to Ingolia et al. (2012). Purified RNA was separated on a 15% TBE-Urea gel. RNA oligonucleotides of 18 and 34 nucleotides were run side by side with isolated RNA and used as size markers to cut RNA of desired size for gel extraction. Size-selected RNA fragments were then treated with polynucleotide kinase to remove the 3′ phosphate. After isopropanol precipitation, dephosphorylated fragments were ligated to Universal miRNA cloning linker from New England Biolabs, Ipswich, MA (cat# S1315S). Ligated fragments were separated from excess linker by gel electrophoresis on a 15% TBE-Urea gel. After gel extraction, ligated fragments were then reverse transcribed using SuperScript III from Life Technologies (cat# 18080-085) according to manufacturer's instructions. Reverse transcriptase reactions were primed with 1 µl of 1.25 µM NI-NI-9 primer (Supplementary file 1). Additionally 20 U of SUPERase-In was added to each RT reaction. Reactions were incubated at 48°C for 30 min.

After reverse transcription, RNA template was removed by the addition of 2.2 µl of 1 N NaOH and incubation at 98°C for 20 min. After precipitation, cDNA was separated from excess primer by gel electrophoresis on a 5% TBE-Urea gel. cDNA was then circularized using CircLigase ssDNA ligase from Epicentre, Madison, WI (cat# CL4115K) according to manufacturer's instructions. After circularization, 5 µl of the circularization reaction was added to 1 µl of pooled ribosomal subtraction oligos (Supplementary file 1), 1 µl of 20x SSC, and 3 µl of water. Each sample was then denatured for 90 s at 100°C and then annealed to 37°C. MyOne Streptavidin C1 DynaBeads (25 µl per reaction) were washed three times in 1x Bind/Wash buffer (1 M NaCl, 1 mM EDTA, 10 mM Tris, pH 8.0). Beads were then resuspended in 2x Bind/Wash buffer (10 µl per reaction). Beads were added to each cDNA/oligo mixture and incubated for 15 min at 37°C in an Eppendorf ThermoMixer at 1000 rpm. Beads were collected on a magnetic stand and ∼17.5 µl of eluate was recovered for each reaction. Resulting eluate was then used as a template for PCR amplification.

Pilot PCR reactions were prepared in order to determine the number of cycles necessary for adequate amplification. PCR reactions consisted of 20 µl of 5x HF buffer, 2 µl of 10 mM dNTPs, 0.5 µl of 100 µM NI-NI-2 primer, 0.5 µl of 100 µM indexing primer (Supplementary file 1), 5 µl of eluate template, 71 µl of water and 1 µl of Phusion polymerase (cat# M0530L; NEB). Each 100 µl reaction was separated into five 16.7 µl aliquots. PCR conditions were as follows: initial denaturation for 30 s at 98°C, followed by cycles of 10 s at 98°C, 10 s of annealing at 65°C, and 5 s of extension at 72°C. One aliquot was removed after 8, 10, 12, and 14 cycles. Amplification was examined by gel electrophoresis on an 8% TBE polyacrylamide gel. Once optimal cycle was determined, an additional 100 µl PCR was performed and run on an 8% TBE polyacrylamide gel. The product band was then cut out and DNA extracted from the gel slice. Libraries were quantified by Bioanalyzer using a DNA High Sensitivity kit (cat# 5067-4626; Agilent, Santa Clara, CA). Libraries were then sequenced on an Illumina Genome Analyzer 2 according to manufacturer's instructions by the Stanford Functional Genomics Facility. Raw sequence data are available in the Gene Expression Omnibus under accession GSE58321.

Sequence alignment and analysis

Cloning linker sequences were trimmed from Illumina reads and the trimmed fasta sequences were aligned to S. cerevisiae ribosomal and noncoding RNA sequences using bowtie v. 0.12.7 or v. 1.0.0 to remove rRNA reads (Langmead et al., 2009). The non-rRNA reads were aligned to the S. cerevisiae genome as a first pass to remove any reads that mapped to multiple locations. Reads that passed this filter (those that mapped uniquely to the genome, or those that did not map at all, such as splice junction reads) were then aligned to the S. cerevisiae transcriptome with bowtie, allowing two mismatches and only reporting alignments of reads that mapped uniquely in the transcriptome (bowtie -v 2 -m 1 -a --norc --best –strata).

The S. cerevisae transcriptome sequences were based on CDS sequences downloaded from the UCSC genome browser, sacCer2 assembly, in August 2011. Untranslated region coordinates were taken from supplemental table S4 of Nagalakshmi et al. (2008). When no UTR was annotated, 50 nt upstream and/or downstream of the CDS was included by default.

A list of read counts and read lengths per nucleotide position in the transcriptome, based on the 5′ end of the mapped read, was generated. From that list, metagene grids as in Figure 2A were made by tabulating all footprints 11–36 nt long within the following regions: last 25 nt of 5' UTR, first 200 nt of CDS, last 100 nt of CDS, and first 50 nt of 3' UTR, for all genes with a CDS of at least 300 nt.

Per-codon analysis

Non-unique positions in the transcriptome were filtered by splitting the yeast transcriptome into all overlapping 20mers, mapping this set of all 20mers back to the transcriptome with bowtie, and collecting the mapped locations of any 20mers with more than one perfect match in the transcriptome.

The counts of small and large footprints from ribosomes with each codon positioned in the inferred A site were generated from the list of reads at each nucleotide position as depicted in Figure 2F. The large footprints were defined as 28 nt reads with the 5′ end 15 nt upstream of the codon at position i, and 29 nt reads with the 5′ end 16 nt upstream of i. Small footprints included 20 nt and 21 nt reads with the 5′ end 15 nt upstream of i and 21 nt and 22 nt reads with the 5′ end 16 nt upstream of i. For each gene, the analysis included codons 51 through the second codon before the stop codon, to avoid the region at the beginning of genes from which ribosomes have been depleted by runoff elongation during harvest. Genes with fewer than 10 footprints in total were excluded, as were any non-unique positions within genes. Processed data are available in the Gene Expression Omnibus under accession GSE58321.

The ‘relative occupancy’ per codon was generated by first computing the average number of footprints (large + small) across the gene. Then, at each position i in gene g, compute (large + small at position i)/(average large + small in gene g). These ratios were then averaged across all instances of a given codon (e.g., CGA) in the transcriptome to give the relative occupancy.

The densities of small and large footprints were computed as above: (small at i)/(average large + small in gene g) and similarly (large at i)/(average large + small in gene g).

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Decision letter

  1. Roy Parker
    Reviewing Editor; University of Colorado, United States

eLife posts the editorial decision letter and author response on a selection of the published articles (subject to the approval of the authors). An edited version of the letter sent to the authors after peer review is shown, indicating the substantive concerns or comments; minor concerns are not usually shown. Reviewers have the opportunity to discuss the decision before the letter is sent (see review process). Similarly, the author response typically shows only responses to the major concerns raised by the reviewers.

Thank you for sending your work entitled “Distinct stages of the translation elongation cycle revealed by sequencing ribosome-protected mRNA fragments” for consideration at eLife. Your article has been favorably evaluated by a Senior editor and 3 reviewers, one of whom, Roy Parker, is a member of our Board of Reviewing Editors.

The Reviewing editor and the other reviewers discussed their comments before we reached this decision, and the Reviewing editor has assembled the following comments to help you prepare a revised submission.

Major issues:

1) A critical aspect of the work is that the 28 and 20-21 nucleotide fragments have the same 5' end and therefore can be mapped to similar codons. The data presented in Figure 2 are consistent with an interpretation that the short fragments are truncated at their 3' (rather than their 5') ends. That, taken together with the antibiotic and 3-AT data, makes for a strong overall conclusion. However, the figures are extremely confusing and fail to clearly convey this interpretation. While the 3D presentation of data in 2A is helpful, the authors failed to include a z-axis scale to calibrate the varied levels of shading. Also, they should have “projected” the data from short and long fragment distributions onto two more standard “metagene” style plots above of graph (much like 2C serves as a horizontal projection) since that is what most people have become familiar with in understanding this sort of data.

While the data are convincing, certain aspects of it do raise questions about the potential for random RNase degradation. For example, why do all reads in between the 28- and 20-nt peaks seem to derive from a region around the start codon? In the plot, this appears as a diagonal line extending from (-12,28) on the plot to (0,15). Perhaps this could be addressed? Ideally, the authors would have done some kind of an RNase protection assay in vitro, possibly with multiple nucleases, to establish the certainty of their results. In the end, the authors are arguing that the alternative conformation of the ribosome is hyper-susceptible to RNase cleavage, thus revealing the mRNA to the nucleases in a way that is surprising given what we know about ribosome structure. So the clearer this section is the better. In addition, perhaps it would help to emphasize earlier and more emphatically that Walter et al. saw these fragments long ago in his in vitro experiments.

In a related issue, the overall pattern of 20 nt reads in Figure 2–figure supplement 1 had huge variation at each position and did not match the 28 nt reads pattern from the same sample. These results do not support that both populations of RPF reads have the same 5' end. This needs to be clarified.

2) A second key issue is the reproducibility of the data presented. It is unclear from the text whether these experiments have been done more than once in each case. This is an issue since the authors’ present data that differences in harvesting protocols can affect the observed data. Given this, it should either a) be clarified that different experiments have been done more than once with good reproducibility, or b) the experiments repeated to demonstrate reproducibility.

3) The discussion/presentation of data on start and stop codon should be improved.

In general, wide variation in relative ribosome occupancies have been reported at start and stop codons. For example, it has been suggested that CHX treatment leads to high occupancy at start codons as initiation of ribosomes continues despite the inability of the ribosome to move away from the start codon (Ingolia et al., Nature Protocols 2012). Therefore, the authors should be more cautious in interpreting a selective enrichment of small fragments at start codons in CHX-treated yeast. At the least, the authors should have performed replicate experiments to verify that slight variation in sample preparation isn't leading to this effect. Similarly, the effects at stop codons, while intriguing, ought to be taken with a grain of salt because of widespread variation in the level of previously reported stop codon occupancy. Unlike the data reported here, higher than average occupancy at stop codons was shown in wild-type cells in Ingolia et al. Cell 2011, Liu et al. Cell 2013, and Lee et al. PNAS 2012. The authors' own experiments seem to suggest that this variability may be sometimes derived from differences in preparation (they show that variation in cell freezing rate can dramatically affect occupancy at stop codons). For these reasons, additional replicates are needed (and should be explicitly documented) to make these interpretations believable.

The results specific to the dom34 knockout yeast, while again intriguing, should not be overinterpreted. How many times were these effects observed?

Other factors, such as shifts in the placement of the footprint are not considered in this section. Such shifts have been reported by Tatyana Pestova and should be addressed and cited (Alkalaeva et al., Cell 2006) as they may explain why occupancy isn't always seen exactly 15 nt upstream at the termination codon.

Finally, some of the conclusions from these sections are not particularly insightful and often overinterpreted. For example, “dom34 has an important general role in ribosome recycling” is overly generic. Similarly, “following formation of this 'nonsense-suppressor' peptide bond, the ensuing translocation step appears to be dramatically slowed” is purely speculative. How do we know that the high-occupancy footprints correspond to ribosomes that are decoding the stop codon vs. stalled with eRF1 bound?

In general, this section should be trimmed back as it seems superficial to the main claims presented earlier. These results (specifically all the data in Figure 6) together distract from the already very interesting story presented earlier in the paper and should be left for further development in a subsequent study. If the authors insist on inclusion, then replicates on each sample must be performed and documented.

4) The authors should avoid improper terms about time-dependent effects. The authors have made generous use of terms like “speed,” “rate,” and “dwell time” throughout the manuscript when referring to their data. Such language is not justified because no time course experiments were performed here. While it is conceivable (and even likely) that ribosome occupancy levels may correspond to actual ribosome dwell times, this has never been proven. We do not know, for example, that either of the footprint sizes studied here are derived from a ribosome in a rate-limiting waiting state during peptide elongation. Comparisons between rate and occupancy should be left to the Discussion section, where such speculative analysis is more appropriate. Moreover, it is unclear to discuss data with these terms or show figures with axes labeled with “dwell time” (Figures 4 and 5) or “speed” (Figure 5A) because these quantities were not measured (the units given are not seconds or inverse seconds) and they burden the reader to understand intricate models for how these quantities were derived. The variable of interest here is “ribosome occupancy” or “fraction of reads” and must be used when describing the data for the sake of both clarity and honesty.

5) Several areas of the manuscript employ language that is unclear or suggests ideas that are not necessarily true and should be fixed.

i) In the abstract, the ribosome is said to “ratchet” along the mRNA. “Ratchet” is a fairly loaded word that signifies certain types of biophysical model and should be replaced by something less specific.

ii) It was unclear in the main text whether CHX was added to cell culture or simply used in the lysis buffer, since both approaches have been reported previously. This should be explicit here.

iii) The language about antibiotics is also unclear. For example, the phrase “anisomycin may act like lincomycin” is confusing because nowhere is it explained how lincomycin works. Overall, it would be helpful if the authors review more of the literature on the mechanism of anisomycin and other antibiotics. Alternatively, given the often pleiotropic or poorly defined effects of antibiotics on the translation cycle (e.g., paromomycin on decoding and recycling), some caution in interpretation would be warranted.

iv) It is not clear what a “translocation conformation” signifies?; The state just before translocation? Similarly, “translocation of the second codon” is an awkward and nonstandard expression.

v) The authors state that, “the extent of ribosome pausing after 3-AT treatment may have been attenuated by the general amino acid control mechanism...” This statement makes no sense: how does alteration of the initiation rate (via the GCN2 pathway) affect the time the ribosome spends at a particular downstream codon? These would seem to be physically separate events.

vi) The authors state, “Dom34 is implicated in triggering cleavage of mRNAs.” Recent work from both Toshi Inada and Roy Parker has shown this to be untrue. Rather, Dom34 is known to rescue ribosomes on cleaved mRNAs. In general, the discussion of termination and recycling is rather wanting. Statements such as “the identity of general recycling factors in eukaryotes has been unclear” are misleading because Tatyana Pestova and Rachel Green have both implicated eRF1 and Rli1 as having specific roles in recycling at stop codons.

vii) T phrase, “evidence that ribosome profiling without inhibitors provides an accurate in vivo snapshot of translation” is unclear and likely overstated. Each profiling procedure is attempting to trap an “accurate in vivo snapshot” and I don't think we yet know which does the best job.

6) The manuscript would be improved by error analysis. The average occupancy values in Figures 4 and 5 do not contain any assessment of uncertainty for each measurement. While the data are convincing without such a statistical workup, it would be reassuring to see an example where the variation in occupancy at a particular codon is computed. In other words, what is the inherent variability in such measurements that derive from all sources of variability (variation between cultures, sequence context effects on translation rate, library preparation, etc).

7) A number of problems arise in the analysis of wobble-paired codons. Figure 5E shows the majority of codons fall on the y=x line. Thus, the authors' interpretation of these data as showing that “elongation was dramatically slower at codons with wobble pairing” seems vastly overstated. Instead, it appears that a handful of codons seem to have increased ribosome occupancy, and may therefore indicate slower translation. The authors should indicate that it may not be wobble pairing per se that is the cause here or that it may only be one particular type of wobble pairing that is problematic.

In addition, the authors appear to include more than simple wobble pairings in Figure 5E. They show 3 (or possibly 4 as the labels are very difficult to read and ought to be larger and show the full codon sequence) proline codons on the plot. There are at most 2 wobble-paired proline codons given that there are 2 proline tRNAs (UGG and IGG anticodons). The UGG anticodon decodes CCA with Watson-Crick pairing and CCG with U-G wobble pairing. The IGG anticodon decodes CCU with I-U wobble pairing and CCC with an I-C, essentially Watson-Crick pairing (not the same as a wobble pairing). Overall, it would be useful if the authors distinguished between these various non-canonical pairings by giving the occupancies for each codon separately, as it may explain why only a small fraction of the codons show any adverse effects on the inferred translation rate.

8) Biases introduced during library creation.

One of the unsaid truths about high-throughput methods is that the level of amplification of a given nucleic acid sequence during library preparation is highly dependent on the content of the sequence due to bias at each of the enzymatic steps in the process (ligations, reverse transcription, PCR, etc.). The authors did not address how these biases apply to the two different sizes of ribosome-protected fragments. Are the 20mers more abundant than 28mers because they are more efficiently turned into cDNA? Does codon identity introduce different bias to 20mer preparation vs 28mer preparation? While the systematic effect of amino acid polarity for various codons would suggest biases are not important here, it would be ideal if the authors had prepared mRNA-Seq libraries that consisted of fragments of 20- and 28-nt in size to get a handle on the bias. If the authors have such data, it ought to be included in the manuscript to put this concern to rest.

https://doi.org/10.7554/eLife.01257.014

Author response

1) A critical aspect of the work is that the 28 and 20-21 nucleotide fragments have the same 5' end and therefore can be mapped to similar codons. […]

While the data are convincing, certain aspects of it do raise questions about the potential for random RNase degradation. For example, why do all reads in between the 28- and 20-nt peaks seem to derive from a region around the start codon? In the plot, this appears as a diagonal line extending from (-12,28) on the plot to (0,15). Perhaps this could be addressed? Ideally, the authors would have done some kind of an RNase protection assay in vitro, possibly with multiple nucleases, to establish the certainty of their results. In the end, the authors are arguing that the alternative conformation of the ribosome is hyper-susceptible to RNase cleavage, thus revealing the mRNA to the nucleases in a way that is surprising given what we know about ribosome structure. So the clearer this section is the better. In addition, perhaps it would help to emphasize earlier and more emphatically that Walter et al. saw these fragments long ago in his in vitro experiments.

In a related issue, the overall pattern of 20 nt reads in Figure 2–figure supplement 1 had huge variation at each position and did not match the 28 nt reads pattern from the same sample. These results do not support that both populations of RPF reads have the same 5' end. This needs to be clarified.

We’ve added the z-axis scale and projected metagenes to Figure 2 and 3. We hope this makes our fairly dense figures easier to interpret. We performed replicate experiments as detailed below in point 2, and these replicates further support our conclusion that the short and long fragments have the same 5' end (see metagenes of the new replicates, Figure 2 and Figure 2–figure supplement 1). The diagonal line extending from (-12,28) on the plot to (0,15) is not present in all replicates. We think it is 5’ degradation of extremely abundant start codon footprints (perhaps the ribosome at the start codon is somehow more susceptible to degradation). However, we’ve removed any discussion of start and stop codons from our paper, so we haven’t investigated this further though we agree it is curious. All of our codon analyses start at codon 50 to avoid runoff (see below) and start codon artifacts at the beginning of the gene.

The supplement to Figure 2 has been removed because it was confusing and tangential. It showed the count per codon (summed across 3 nt using the same A-site inference rules we use later) rather than the count per nucleotide. Relative to the total metagene plots, this plot displayed higher variability since it was limited to long genes and thus included less data. Our intention was to show that one artifact of inhibitor-free profiling is runoff of ribosomes near the beginning of the gene. These data convinced us to exclude the first 50 codons from the codon-specific analysis. The metagenes, on the other hand, are a zoomed-in view of the per-nucleotide counts in the first 36 codons, a region that is relatively depleted of ribosomes – but it is essential to include the beginning of the metagene to show that the 5’ ends of the first peaks of 21 and 28 nt fragments are in the same place.

2) A second key issue is the reproducibility of the data presented. It is unclear from the text whether these experiments have been done more than once in each case. This is an issue since the authors’ present data that differences in harvesting protocols can affect the observed data. Given this, it should either a) be clarified that different experiments have been done more than once with good reproducibility, or b) the experiments repeated to demonstrate reproducibility.

In our original paper, we included results from one experiment under each condition. While we had previously done each of these experiments a few times with the same overall results, the data were not directly comparable as we were optimizing drug treatment and nuclease conditions.

We have now performed two more biological replicates on untreated yeast, one cycloheximide replicate, and one anisomycin replicate (which were then split into two technical replicates for all stages after we collected the yeast lysate). Along with the new replicates, we’re including one of these older samples (treatment with 3-AT for 10 minutes, along with the 60 minute treatment included in our previous manuscript) to strengthen the 3-AT results.

In our manuscript, we have clarified that the experiments have been performed multiple times. We now use the highest coverage samples for our main metagene figures and include the other replicates as supplements to Figures 2 and 3. We use the average of three replicates for codon-specific values.

We note here, since it’s a change from the original paper, that the overall ratio of small and large footprints is somewhat variable between our untreated replicates. All of the untreated samples have roughly a 50-50 mix of small & large footprints, and one state is not consistently preferred over the other. We can no longer conclude that the variation in small footprints (post-decoding) drives total occupancy, because those correlations are necessarily dependent on the overall ratio of small to large footprints. Instead, we now include much stronger evidence that both small and large footprints show variation between codons that is very consistent between replicates (Figure 5B-D), and that these two values (abundance of large and small footprints per codon) are independent of each other. Also, we can say with more certainty that features such as amino acid polarity is correlated with the small footprint abundance (post-decoding).

Another minor change throughout the paper: because of slight differences in digested footprint size, we’ve realized that 21 nt footprints are a better representative of the small (20-22nt) population. In the few cases where we need to look at a single protected-fragment size (as in the projected standard metagenes) we have now used the 21 nt fragments. For all codon analyses we use the same 20-22 and 28-29 range as in the first version.

3) The discussion/presentation of data on start and stop codon should be improved.

[…] Unlike the data reported here, higher than average occupancy at stop codons was shown in wild-type cells in Ingolia et al. Cell 2011, Liu et al. Cell 2013, and Lee et al. PNAS 2012. The authors' own experiments seem to suggest that this variability may be sometimes derived from differences in preparation (they show that variation in cell freezing rate can dramatically affect occupancy at stop codons). For these reasons, additional replicates are needed (and should be explicitly documented) to make these interpretations believable.

The results specific to the dom34 knockout yeast, while again intriguing, should not be overinterpreted. How many times were these effects observed?

Other factors, such as shifts in the placement of the footprint are not considered in this section. Such shifts have been reported by Tatyana Pestova and should be addressed and cited (Alkalaeva et al., Cell 2006) as they may explain why occupancy isn't always seen exactly 15 nt upstream at the termination codon.

[…] In general, this section should be trimmed back as it seems superficial to the main claims presented earlier. These results (specifically all the data in Figure 6) together distract from the already very interesting story presented earlier in the paper and should be left for further development in a subsequent study. If the authors insist on inclusion, then replicates on each sample must be performed and documented.

We’ve entirely removed the start and stop codon and dom34 sections from this manuscript (and hope to expand them with more evidence in a future paper). We agree with the reviewers that there is a lot of variation in stop codon occupancy between experiments; this is something we intend to explore further.

4) The authors should avoid improper terms about time-dependent effects. The authors have made generous use of terms like “speed,” “rate,” and “dwell time” throughout the manuscript when referring to their data. Such language is not justified because no time course experiments were performed here. […] The variable of interest here is “ribosome occupancy” or “fraction of reads” and must be used when describing the data for the sake of both clarity and honesty.

We replaced these terms with “relative occupancy” for the overall density of ribosomes on a codon and “relative abundance” for the density of small and large footprints. We think this makes our results clearer and more direct.

5) Several areas of the manuscript employ language that is unclear or suggests ideas that are not necessarily true and should be fixed.

i) In the abstract, the ribosome is said to “ratchet” along the mRNA. “Ratchet” is a fairly loaded word that signifies certain types of biophysical model and should be replaced by something less specific.

We would very much like to keep this metaphor because it evokes the motion we are studying. A number of papers describe the ribosome as ratcheting, for example, “Structures of the ribosome in intermediate states of ratcheting,” Zhang et al, Science 2009. (Some also propose the idea of the ribosome as a Brownian ratchet – a much more specific claim that we are not trying to make.) We realize that the exact nature of the pawl of the ribosome’s ratcheting motion is not known conclusively.

We know that this is an active topic of research, and if absolutely necessary we could replace “ratchets along” with “steps along.”

ii) It was unclear in the main text whether CHX was added to cell culture or simply used in the lysis buffer, since both approaches have been reported previously. This should be explicit here.

CHX was added to the yeast media ∼30-60 seconds before harvesting and was also present in the lysis buffer. We’ve clarified this in the main text.

iii) The language about antibiotics is also unclear. For example, the phrase “anisomycin may act like lincomycin” is confusing because nowhere is it explained how lincomycin works. Overall, it would be helpful if the authors review more of the literature on the mechanism of anisomycin and other antibiotics. Alternatively, given the often pleiotropic or poorly defined effects of antibiotics on the translation cycle (e.g., paromomycin on decoding and recycling), some caution in interpretation would be warranted.

Unfortunately, anisomycin is much less studied than other peptidyl transferase inhibitors. We’ve revised this section to be more careful and complete about what is known about this drug. (In retrospect we could have tried other antibiotics instead.)

iv) It is not clear what a “translocation conformation” signifies?; The state just before translocation? Similarly, “translocation of the second codon” is an awkward and nonstandard expression.

We rewrote the first of these (in the paragraph about anisomycin’s mechanism) and the second was in a section that we have removed entirely.

v) The authors state that, “the extent of ribosome pausing after 3-AT treatment may have been attenuated by the general amino acid control mechanism...” This statement makes no sense: how does alteration of the initiation rate (via the GCN2 pathway) affect the time the ribosome spends at a particular downstream codon? These would seem to be physically separate events.

We have removed the statement, but if the cells respond to insufficient histidine by reducing initiation, this mitigates the histidine deficiency, by allowing the impaired histidine biosynthetic pathway to better keep up with the (reduced) demands of protein synthesis (and thus reduced histidyl-tRNA turnover).

vi) The authors state, “Dom34 is implicated in triggering cleavage of mRNAs.” Recent work from both Toshi Inada and Roy Parker has shown this to be untrue. Rather, Dom34 is known to rescue ribosomes on cleaved mRNAs. In general, the discussion of termination and recycling is rather wanting. Statements such as “the identity of general recycling factors in eukaryotes has been unclear” are misleading because Tatyana Pestova and Rachel Green have both implicated eRF1 and Rli1 as having specific roles in recycling at stop codons.

We removed this whole set of results.

vii) T phrase, “evidence that ribosome profiling without inhibitors provides an accurate in vivo snapshot of translation” is unclear and likely overstated. Each profiling procedure is attempting to trap an “accurate in vivo snapshot” and I don't think we yet know which does the best job.

We have removed the statement.

6) The manuscript would be improved by error analysis. The average occupancy values in Figures 4 and 5 do not contain any assessment of uncertainty for each measurement. While the data are convincing without such a statistical workup, it would be reassuring to see an example where the variation in occupancy at a particular codon is computed. In other words, what is the inherent variability in such measurements that derive from all sources of variability (variation between cultures, sequence context effects on translation rate, library preparation, etc).

We’ve replaced the individual values in most figures with the average of three replicates, which reduces noise and spurious associations. We also compare the values between replicates in the new Figure 5B-D. We’ve included an arbitrary example of footprint distribution along FBA1 (one of the few genes with high enough coverage) to give a qualitative picture of the variability from position to position and the surprising consistency between replicates.

We’ve chosen not to include explicit error measurements for a few reasons. First, count-based RNA sequencing is not modeled properly by conventional error models (read counts are overdispersed and not normally distributed). Second, the coverage in our samples (even the new replicates) is low enough that many genes only have a small handful of footprints, so we’d be comparing a lot of sites with few or no reads (i.e., the variability is not informative). In the future we hope to scale up to much higher coverage to allow position-by-position analysis instead of averages.

7) A number of problems arise in the analysis of wobble-paired codons. Figure 5E shows the majority of codons fall on the y=x line. Thus, the authors' interpretation of these data as showing that “elongation was dramatically slower at codons with wobble pairing” seems vastly overstated. Instead, it appears that a handful of codons seem to have increased ribosome occupancy, and may therefore indicate slower translation. The authors should indicate that it may not be wobble pairing per se that is the cause here or that it may only be one particular type of wobble pairing that is problematic.

In addition, the authors appear to include more than simple wobble pairings in Figure 5E. They show 3 (or possibly 4 as the labels are very difficult to read and ought to be larger and show the full codon sequence) proline codons on the plot. […] Overall, it would be useful if the authors distinguished between these various non-canonical pairings by giving the occupancies for each codon separately, as it may explain why only a small fraction of the codons show any adverse effects on the inferred translation rate.

We’ve reworded this section to make it clearer (and remade the associated figure incorporating data from replicates). For our pairing rules we had used data from Johansson et al that found that certain proline and leucine tRNAs could bind three or four codons, not two. We’ve updated our pairing rules to remove those nonstandard pairings and we’ve labeled the outlying points with the codon sequences.

8) Biases introduced during library creation.

One of the unsaid truths about high-throughput methods is that the level of amplification of a given nucleic acid sequence during library preparation is highly dependent on the content of the sequence due to bias at each of the enzymatic steps in the process (ligations, reverse transcription, PCR, etc.). The authors did not address how these biases apply to the two different sizes of ribosome-protected fragments. Are the 20mers more abundant than 28mers because they are more efficiently turned into cDNA? Does codon identity introduce different bias to 20mer preparation vs 28mer preparation? While the systematic effect of amino acid polarity for various codons would suggest biases are not important here, it would be ideal if the authors had prepared mRNA-Seq libraries that consisted of fragments of 20- and 28-nt in size to get a handle on the bias. If the authors have such data, it ought to be included in the manuscript to put this concern to rest.

This is an interesting question and we’ve added Figure 5–figure supplement 1 and a few lines to the Results and Discussion to address it. The supplementary figure compares occupancies relative to the A site codon and, as a control, occupancies relative to the next codon past the A site. We find that the not-yet-translated position has much less variance in occupancy, which is what we’d expect as this position should not be affecting translation substantially. If library creation bias had a big effect on which sequences (of either size) were captured, we would see similar ranges of variability in both.

We’ve also done a preliminary analysis of biases in the ends of footprint sequences and found no strong bias affecting which footprints sequences are captured by the method (not included in the manuscript). We don’t know if 20 or 28 nt fragments overall are captured more efficiently. In light of the variability in overall small footprint abundance between replicates, we now look at variation between different codons within each experiment separately.

https://doi.org/10.7554/eLife.01257.015

Article and author information

Author details

  1. Liana F Lareau

    Department of Biochemistry, Howard Hughes Medical Institute, Stanford University School of Medicine, Stanford, United States
    Present address
    California Institute for Quantitative Biosciences, University of California, Berkeley, United States
    Contribution
    LFL, Conception and design, Acquisition of data, Analysis and interpretation of data, Drafting or revising the article
    Contributed equally with
    Dustin H Hite
    For correspondence
    lareau@berkeley.edu
    Competing interests
    The authors declare that no competing interests exist.
  2. Dustin H Hite

    Department of Biochemistry, Howard Hughes Medical Institute, Stanford University School of Medicine, Stanford, United States
    Contribution
    DHH, Conception and design, Acquisition of data, Analysis and interpretation of data, Drafting or revising the article
    Contributed equally with
    Liana F Lareau
    Competing interests
    The authors declare that no competing interests exist.
  3. Gregory J Hogan

    Department of Biochemistry, Howard Hughes Medical Institute, Stanford University School of Medicine, Stanford, United States
    Contribution
    GJH, Acquisition of data, Drafting or revising the article
    Competing interests
    The authors declare that no competing interests exist.
  4. Patrick O Brown

    Department of Biochemistry, Howard Hughes Medical Institute, Stanford University School of Medicine, Stanford, United States
    Contribution
    POB, Conception and design, Analysis and interpretation of data, Drafting or revising the article
    Competing interests
    The authors declare that no competing interests exist.

Funding

Howard Hughes Medical Institute

  • Liana F Lareau
  • Dustin H Hite
  • Gregory J Hogan
  • Patrick O Brown

Damon Runyon Cancer Research Foundation (DRG-2033-09)

  • Liana F Lareau

National Science Foundation

  • Dustin H Hite

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

Acknowledgements

We thank Nicholas Ingolia, Harry Noller, and Jody Puglisi for helpful discussion.

Reviewing Editor

  1. Roy Parker, Reviewing Editor, University of Colorado, United States

Publication history

  1. Received: July 19, 2013
  2. Accepted: May 8, 2014
  3. Accepted Manuscript published: May 9, 2014 (version 1)
  4. Version of Record published: June 11, 2014 (version 2)

Copyright

© 2014, Lareau et al.

This article is distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use and redistribution provided that the original author and source are credited.

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