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The paper would be strengthened if the authors engaged with this contradiction head-on by more thoroughly discussing exactly which ideas in this field are and are not consistent with their new data. This manuscript addresses the relationship between steps in translation and mRNA degradation in yeast. The manuscript addresses an important and timely issue in the field: Is decay rate coupled to stalled ribosomes, or to poor translation initiation? This has become an issue because of two competing lines of work.

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First, previous work using reporter mRNAs showed that translation initiation was in competition with decay both deadenylation and decapping. However, more recent work, using both genome wide analyses and reporter mRNAs has argued that inefficient translation elongation as defined by non-optimal codons is the predominant driver of mRNA decay rates. Thus, resolving this issue will be important for understanding mRNA decay control.

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The strength of this manuscript is that is uses genome wide analyses of mRNA decay rates both under normal conditions, and with perturbations that affect ribosome elongation rates or translation initiation rates. The main conclusion from the work is that slow ribosome movement actually stabilizes mRNAs, while decreasing translation initiation increases mRNA degradation.

This is a useful contribution and worthy of consideration for publication in eLife , although there are substantive issues that should be addressed before publication. Thus, the approach is really only measuring the deadenylation rate of mRNAs and not overall decay. The best approach for the authors here would be to repeat the experiments using ribo0 RNA, which would allow them to access the full mRNA decay pathway.

Alternatively, they could re-write the manuscript as simply studying deadenylation, but this would diminish the impact of the work. In fact, the data might be more convincing if decay rate was measured with ribo0 RNA since previous work Beelman and Parker, and others had shown cycloheximide primarily inhibits decapping with little effect on deadenylation rate. Thank you for resubmitting your work entitled "Non-invasive measurement of mRNA decay reveals translation initiation as the major determinant of mRNA stability" for further consideration at eLife. All three reviewers agreed that the paper has been significantly improved by the new experiments that were carried out, as well as the revisions of text; and your efforts to address all of the major criticisms of the first version were greatly appreciated.

In particular, the new genome-wide data obtained from analyzing total RNA generally supports the conclusions reached from poly A selection, which resolves the most important criticism of the original version of the paper. There are, however, some remaining issues that require your attention, raised by reviewer 1, including the lack of statistical significance of small differences in mRNA half-lives for certain mRNAs, and the apparent lack of any replicates for the new half-life measurements conducted with total RNA. It is felt that these shortcomings detract from the overall scientific quality of the work and do not satisfy the journal's rigorous standards for replicate measurements and statistical analyses of data.

Finally, you have been asked to consider whether the magnitude of the observed effects of reducing initiation or elongation in decreasing or increasing mRNA stabilities, respectively, are substantial enough to account for the order-of-magnitude-range of mRNA stabilities observed in wild-type cells. It's unfortunate that they chose not to use ribo0 to deplete rRNA, as only a few percent of the total mRNA reads they obtained in the new experiments come from mRNA, and they claim that the relatively weak correlation between mRNA half-lives for total vs polyA selected mRNA is attributable in part to the low read depth for total RNA.

There are remaining issues however concerning the changes in half-lives of specific mRNAs in response to different drugs or genetic manipulations. Two additional replicates are needed for the total RNA measurements, allowing them to provide mean and SD values. In addition, they need to indicate which differences in Figure 3 and Figure 3—figure supplement. It appears that the experiments were done only once, as there is no mention of data from replicates.

m-RNA stability

Also, the data in panel D of Figure 3—figure supplement. By comparison, the HIP treatment in panel B shows no substantial change in overall mRNA level despite similar decreases in half-life for these mRNAs in the two different treatments and does exhibit the expected shift of all 3 mRNAs to smaller polysomes. It seems possible therefore that the effects of reduced initiation or elongation in decreasing or increasing mRNA stability, respectively, are not really strong enough to explain the order of magnitude range of mRNA stabilities observed in wild-type cells.

Perhaps the authors should consider softening statements about initiation being the primary determinant of mRNA stability and instead identify it as one important determinant. Is it possible that on some mRNAs, the presence of suboptimal codons is equally important, with ribosomes containing empty A sites being recognized by Dhh1 in the manner suggested by Coller and Green?

Relevant panels should be cited individually in the text-otherwise the reader has to inspect the entire figure without the guidance of text to find the supporting results being cited. This should be fixed throughout the manuscript. P-values for these differences need to provided in the figures or legends.

This underscores the need to cite specific panels in the Figure supplements. As noted above, it appears that the experiments on total mRNA in Figure 3—figure supplement 1A-C were done on only one replicate. Were these cells also treated with hippuristanol? If not, then presumably the reductions in polysomes conferred by HIP is even larger than indicated. This needs to be explained better in the legend. As noted above, the experiments in panels A-D appear to have been done only once, with no biological replicates. In addition, the overall levels of the transcripts are greatly reduced here, but not in the HIP treated cells shown in panel B, despite similar effects of the two treatments on mRNA half-life shown in Figure 3I-J.

Given that mRNA half-lives vary over more than an order of magnitude, it's unclear that the changes in half-lives of min conferred by substantially inhibiting initiation or elongation rates are adequate to explain the natural range in half-lives. Explain better the dhh1 mutants analyzed in panel C. In Pgal shut-off experiments, cells are shifted to glucose to shut off new synthesis and the amounts of the remaining mRNAs are followed by Northerns. Growth in glucose is not stressful, but it could be that the carbon source shift per se alters the level or function of the decay machinery.

I don't see how effects of GC-content on transcription would affect the measurements of half-lives by this approach. I agree with reviewer 1 that in an ideal world the authors would had employed an rRNA subtraction procedure such as ribo0, in addition to the total RNA analysis, as this would have allowed them to recover far more mRNA reads. Nonetheless, the data do argue strongly against the possibility that the discrepancies between the authors' mRNA half life data and previous studies was simply an artifact of looking at polyA selected mRNAs.

This experiment and the expanded discussion thus address the major concern I had with the original submission. I find the manuscript to be significantly improved, and the authors have done an excellent job of addressing the first round of comments. I am now supportive of publication. To address this important issue, we have now performed decay experiments in the complete absence of any mRNA enrichment by analyzing total RNA samples. We opted for this approach —instead of ribo0- to avoid any potential bias that could be introduced by a selection step.

Overall, these new measurements confirm our prior conclusions. First, using transcriptome-wide decay profiling we find that the transcriptome has almost identical average and median half-lives with very similar shaped distributions in the unselected and poly A -selected datasets supporting our finding that the transcriptome is shorter lived than most previous measurements have found. On an individual transcript level, we do observe differences between the two datasets. We show that this is in part due to lower sequencing coverage in the unselected samples and but also partly reveals true biological differences likely in steps downstream of deadenylation.

Second, we have gone back and reanalyzed all of the translational perturbation experiments in the absence of any mRNA enrichment. Again, these new results are entirely consistent with the polyA selected data, and if anything, we find more exaggerated effects, especially in the presence of the elongation inhibitors cycloheximide and sordarin as was pointed out by reviewer 3. We have included all biological replicates and computed average half-lives with standard deviations and indicated changes in half-life that were statistically significant.

We have also included P-values for all comparative cumulative histograms in the Results section to quantify the significance of the differences observed. As requested we performed a transcriptome-wide profile of mRNA half-lives in the presence and absence of 3AT. We find that there is significant stabilization of mRNAs across the transcriptome in 3AT treated cells, strengthening our prior conclusion.

This analysis also suggests there is a thresholding effect where a minimal number of histidine and glycine codons must be present for a strong stabilization effect to be observed. To our knowledge translation initiation rates were never been directly measured in vivo. We find that in the case of hippuristanol treatment, ribosomes are redistributed from heavy polysome fractions to the lighter fractions.

This redistribution is also seen for specific mRNAs that were examined.


We have included a broader discussion of the possible sources of discrepancy between our observations and previous observations regarding codon optimality. We have expanded our discussion of the site of mRNA decay in the cell and have tried to provide are more multifaceted and nuanced interpretation of these experiments. We try to make explicit that we view mRNA decay occurring not just in P-bodies and the polysome but we reason —also based on other recent publications on this topic- that neither of these sites are likely the primary sites of mRNA decay and that it is more likely that most mRNA turnover occurs in sub-microscopic decay mRNPs.

As discussed in our response to point 2 above, we have now computed average half-lives with standard deviations for these experiments.


This analysis revealed that CIS3 is not significantly stabilized in the presence of 3AT, and we have modified the results to reflect this. We hesitate to propose some sort of ribosomal protein gene feedback mechanism as it is well outside the scope of this manuscript and have thus opted to not make such an addition.

We have extended our discussion of the justification of an efficiency parameter in the main text but reserve the full discussion to the extended technical supplement as this important but technical point would distract from the biological questions the manuscript is attempting to address. We would also point out that the gold standard for metabolic labeling to date which is 14C-adenosine incorporation agrees very well with our observations.

This is a useful contribution and worthy of consideration for publication in eLife, although there are substantive issues that should be addressed before publication. Thank you and we agree that the new genome-wide data have strengthened our manuscript. In response to this, we have subjected our individual transcript measurements to a more rigorous statistical analysis and we have found that the majority of our measurements support a model where competition with translation initiation determines the stability of an mRNA.

None of our results refute this model. Furthermore, we have found no evidence for the stalled ribosome leading to mRNA decay model. In light of this analysis and the lack of ambiguity in our findings, we would argue that further biological replicates for the same experiments using total RNA rather than polyA selected mRNA would only serve to delay publication of this manuscript.

Furthermore, we have removed the polysome analysis from the main paper and included it only as a figure in response to reviewers as this was an experiment that was originally requested by comments of reviewer 1. Moreover, repeating the hippuristanol experiment is not a simple matter as this drug is not commercially available. These experiments require large amount of drug and the small amount that we have was a generous gift of our collaborator, Junichi Tanaka, who unfortunately could not provide us with more hippuristanol at this point.

Lastly, given the results of our statistical analysis, we decided against softening our conclusions. However, we have included an extensive discussion where we attempt to account for the differences between the Coller group conclusions and our findings.

Control of Messenger RNA Stability

We opted to analyze total RNA to avoid any biases that are introduced by RNA selection methods, and we concur that our new analysis fully supports the validity of our initial measurements. We thank the reviewer for encouraging us to consider our data in a more rigorous statistical framework. Since all drug treatment experiments were performed by collecting a treated and mock treated sample at the same time, we have used a paired t-test and to allow for hypothesis testing of the two models of decay determination ribosome stalling vs translation factor protection , we have sampled from a one-tailed distribution.

We have included all P-values in a revised Figure 3. In contrast, 0 of the 21 measurements testing the stalled ribosome model have p-values less than 0. Thus, we conclude that the majority of our data support the translation-factor protection model and none of the data we have collected support the stalled ribosome-triggered decay model. We have included the additional statistical analyses in the text and Figure of a revised manuscript. Furthermore, we would like to point out that these small-scale experiments are significantly bolstered by the transcriptome-wide analyses that are presented in the manuscript and the global trends are completely in line with our small-scale experiments.

Moreover, the effect we have observed with cycloheximide has been previously reported Beelman and Parker, The effect of attenuated eIF4A is likely not a complete block in initiation as cap recognition is still possible and the importance of eIF4A in efficient translation is likely transcript specific as suggested by papers of the Ingolia, Fanidi and Hinnebusch groups. In limiting hippuristanol where eIF4A is partially inhibited, one would expect that without full helicase activity, ribosomes would likely kinetically pile up during the scanning phase prior to AUG recognition and a shift from heavier polysomes to lighter polysomes would be expected.

This is indeed what is observed.

Control of translation and mRNA degradation by miRNAs and siRNAs

Given the critical role of the pioneer translation initiation round for sustained translation cycles reviewed by Maquat, Tarn and Isken, , general mRNA attenuation in the polysome is a logical outcome. The data presented have clearly demonstrated that translation is indeed defective. With respect to biological replication, this is currently not feasible as this experiment requires a large amount of hippuristanol, which had received only a limited amount from a generous collaborator. We feel this is appropriate as the in depth discussion of the results of this experiment are not at all the focus of this paper and would only serve to distract and dilute the main points of the manuscript.

As was previously requested by a reviewer, we have presented the significance values for the transcriptome-wide data and the p-values indicate significance. Moreover, in light of the statistical analysis of the single decay plots which revealed that the large majority of our measurements are in significant support of the translation-factor protection model, none of our unbiased experiments argue against the translation-factor protection model and importantly, none of our measurements came out in support of the ribosome-triggered decay model, we feel that it would be inappropriate to soften our claims or suggest that ribosome-stalling plays a role in general mRNA decay rate determination.

We have suggested in the discussion that this model may make more sense in the stress conditions that were used to measure mRNA stability of engineered transcripts or for select individual transcripts. We would also like to reiterate that we are not proposing a new model in this manuscript. Rather, we are testing in an unbiased manner two prevailing theories of how mRNA stability is determined. We have found that our data support the model proposed by Parker and others in the s which is based on data gathered from multiple types of experiments where translation initiation is perturbed either in cis or in trans and mRNA stability was found to be decreased see Beelman and Parker, , Schwartz and Parker, and LeGrandeur and Parker, Furthermore, our data are the first to contradict a stalled ribosome-triggered decay model.

With respect to magnitude, we intentionally chose doses of drug treatments that had modest effects on cell physiology. Indeed, when strong doses are employed as in the case of high doses of cycloheximide, we and others previously have reported dramatic effects on mRNA stabilization. However, we wished to measure half-lives in cells that were not completely shut down and therefore chose drug doses that were well below lethal levels. We provide a complete formulation of the media used in the Materials and methods section. To do so in the body of the text would be disruptive. The value is presented as Pearson correlation, which is by definition r.

We have now indicated that the value is r in the figure. We agree that all of these parameters correlate with one another. We present one possible explanation of one of these correlations, but we do not draw any conclusions as this is merely correlation and causation cannot be implied. It is only cited in the Discussion section. We did not include this in the Results section as this translation initiation rate dataset was derived using a kinetic model and is not a result of direct initiation rate measurements.

Thus we did not deem it fair to include in the analysis in the main Figure 2. P-values for these differences need to be provided in the figures or legends. The citation refers to the titration of cycloheximide to a sub-lethal dose, which is Figure 3—figure supplement 6H. The new in text citation of subpanels in the supplemental figures hopefully clarifies this. We have included glycine and histidine amino acid contents for all transcripts in Figure 3E-G.

Role of mRNA Stability during Bacterial Adaptation

The marker for SGs is indicated in the figure. We have extended the explanation of the dhh1 mutants in the text. Growth is glucose is not stressful but an acute shift from any carbon source to another will lead to remodeling of the gene expression profile brought upon by growth regulatory and nutrient sensing pathways. The GC-content effect on transcription is only relevant when stability is inferred from steady state abundance measurements, not direct kinetic measurements.

This is more directly examined in Zhou et al. We have clarified this point in the text. Nonetheless, the data do argue strongly against the possibility that the discrepancies between the authors' mRNA half-life data and previous studies was simply an artifact of looking at polyA selected mRNAs. Thank you and we concur that the analysis of total RNA further bolsters our original conclusions. The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

We are grateful to Dr. Junichi Tanaka for the generous gift of hippuristanol and to Dr. We would like to thank Joshua Bloom for helpful discussions and advice regarding statistical frameworks. We also thank Dr. 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.

Article citation count generated by polling the highest count across the following sources: Crossref , PubMed Central , Scopus. Cited 13 Views 5, Annotations Open annotations.

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The current annotation count on this page is being calculated. Cite this article as: eLife ;7:e doi: Figure 1 with 2 supplements see all. Download asset Open asset. Figure 2 with 1 supplement see all. Figure 3 with 5 supplements see all. Figure 4 with 1 supplement see all. Codon influence on protein expression in E. Synergism of aminotriazole and phosphate on the inhibition of yeast imidazole glycerol phosphate dehydratase T Klopotowski A Wiater Archives of Biochemistry and Biophysics — Poly A -binding proteins regulate both mRNA deadenylation and decapping in yeast cytoplasmic extracts.

Alan G Hinnebusch. James L Manley. Roy Parker. Summary: Chan et al. Essential revisions: The most important issue to be addressed is whether your use of poly A selection has led you to measure the half-lives of only a fraction of mRNAs with poly A tails long enough for oligo-dT selection or, in the limit case, to measure rates of deadenylation only.

Reviewer 1: This paper describes an improved, non-invasive method for genome-wide measurements of mRNA half-lives in growing yeast cells. Please see our response to point 3 above. Please see our response to point 4 above. Please see our response to point 6 above. Reviewer 2: Chan et al.

Please see our response to point 1 above. Please see our response to point 5 above. Reviewer 3: This manuscript addresses the relationship between steps in translation and mRNA degradation in yeast. Details of the pathways vary between different eukaryotes. PAB: poly-A binding protein.

Figure 2: Examples of protein decapping enzymes that can adept different conformations. Left: the Dcp1:Dcp2 complex She et. Mol Cell , She et. Nat Struct Mol Biol. Skip to main content Skip to table of contents. Contents Search. Messenger RNA Stability. How to cite.