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    Below is our point-by-point response:


    Reviewer #2 (Evidence, reproducibility and clarity (Required)):

    The manuscript of Lalanne and coworkers address the cellular responses to varied translation termination factor expression in Bacillus subtilis. The authors set-up a system to fine-tune the expression of release factor RF1, RF2 as well as PrmC that post-translationally modifies RF1/RF2 to maximize their catalytic hydrolysis activity. They then monitor the fitness costs associated with overexpression or depletion of the factor by following the changes in growth rate. The set-up is nicely illustrated in Figure 1. The results in Figure 2 show that overexpression of RF1 and RF2 has relatively modest effect on the growth rate compared to overexpression of PrmC that leads to dramatic growth rate reduction. By contrast, depletion of RF1 has a strong negative influence on fitness, whereas a similar level of depletion of RF2 had little influence on fitness. PrmC overexpression appears to be correlated with the induction of the sigmaB regulon, however, the authors do not manage to ascertain why this is. By contrast, RF2 depletion also results in the induction of the sigmaB regulon and the authors demonstrate convincingly that this is due to a termination defect within the rsbQ-rsbV operon that contains an overlapping start-stop AUGA

    A few points that the authors might consider discussing

    The natural abundance of each RF in bacteria in relation to the usage of different stop codons in different organisms.

    __Response: __We thank the reviewer for their suggestion. A correlation between RF abundance and stop codon usage across bacterial species has been previously reported (Korkmaz et al., 2014; Wei et al., 2016), which is corroborated by our quantification (see below). This correlation provides further evidence that the RF expression may be optimized to meet their demands in translation termination. We now include a new discussion in the main text (p. 9, lines 410-415): "Our data thus corroborate several previous lines of evidence suggesting that RF expression might be precisely tuned. First, it was found that the relative expression between RF1 and RF2 correlates with stop codon usage between different species (Korkmaz et al., 2014; Wei et al., 2016). For instance, B. subtilis has a higher abundance of RF1 and more frequent UAG usage compared to E. coli, suggesting that RF1’s expression setpoint meets translational demand (Methods).”

    Below we include additional analyses that may be of interests to the Reviewer.

    From our ribosome profiling quantification in E. coli, B. subtilis, C. crescentus, and V. natriegens (Lalanne et al., 2018), we can compare the relative usage of the three stop codons (frequency of stop codons weighted by expression) with abundances of RF1 and RF2:

    Despite the limited sample size, we find reasonable agreement with the expected correlation between codon usage and cognate RF abundance. In species with substantial differences between RF1 and RF2 abundances (E. coli and B. subtilis), the most heavily used non-UAA stop corresponds to the most highly expressed RF. This argues in favor of expression tuning of these important enzymes and is consistent with the growth optimization we directly observe.

    As a word of caution, although the low usage of UAG in *E. coli *and low expression of RF1 (reported long ago, e.g., (Adamski et al., 1994)) is well established, it should be noted that strain MG1655’s RF2 factor harbors a debilitating missense A246T mutation near its active site (Dinçbas-Renqvist et al., 2000), which potentially complicates interpretation of the expression of E. coli’s release factors [interestingly, we do not see any difference in RF1 and RF2 expression from ribosome profiling data in strain NCM3722, which contains the RF2 variant without the A246T mutation (JBL, unpublished data)].

    The role of the frameshifting mechanism in RF2 and how then RF1 levels are regulated.

    Response: We thank the reviewer to raising the interesting topic of release factor expression regulation. We have added a section in our discussion to comment on RF2 regulation (p. 9, lines 415-420).

    “Second, the gene encoding RF2 has a broadly conserved UGA-based frameshift event that autoregulates the expression based on its own activity (Baranov et al., 2002; Craigen and Caskey, 1986; Craigen et al., 1985). Interestingly, there are no reports of RF1 autoregulation to our knowledge, and we found that ectopic over- or under-expression does not affect its own promoter activity (Fig. S7). Therefore, a lack of autoregulation does not necessarily indicate that cells are less sensitive to small perturbations on its expression.”

    The statement above includes an additional analysis on RF1 regulation that was motivated by the Reviewer’s comment. In contrast to RF2, no definitive evidence exists on autoregulatory mechanisms for RF1. Following the Reviewer’s comment, we realized that our dataset allowed us to search for evidence of endogenous regulation in B. subtilis: our RF1 expression strain has a markerless deletion of prfA and prmC genes, leaving the surrounding regions, and notably the promoter, intact. As such, possible unbeknownst regulatory mechanisms at the promoter level could be identified in our RNA-seq data under steady-state perturbation of RF1 levels. Quantifying the expression of the 5’ untranslated region and operonic gene *ywkF *at the ablated prfAlocus (presented in__ Fig. S7__, reproduced below), we find no significant changes in expression across over 30-fold range in RF1 expression, arguing against such transcriptional regulatory mechanisms. Although this does not rule out other regulatory mechanisms at the post-transcriptional level, no such mechanisms have been documented for RF1 to our knowledge.

    The authors observe queuing in front of the relevant stop codons upon RF depletion, however, do not discuss about readthrough events, which are usually competing with termination. Surprisingly, in this context the authors don't discuss the work from Mankin and coworkers showing sequestration of RFs from termination by peptides such as apideacin leads to translational readthrough.

    __Response: __We concur with the Reviewer about the importance of the recent work from Mankin et al. This paper was referenced in our original submission, but our literature management software improperly formatted its citation. The corrected reference to (Mangano et al., 2020) is now included in the revised manuscript.

    Translational readthrough is indeed clearly visible in our ribosome profiling data from acute CRISPRi knockdown of RF1/PrmC and RF2. Using an approach analogous to Mangano and Florin et al, we quantified readthrough as the ribosome footprint density downstream of the stop codon (+5 to +45 bp) to the density in the gene body for isolated genes (no codirectional genes within 55 bp). We find five-fold increase in the median readthrough for genes that are terminated by the RF under perturbation (shown in a new panel in the main text, Fig. 4b, reproduced below). This new analysis is included in the section regarding translational phenotypes identified from ribosome profiling under RF depletion, p. 7, lines 309-312.

    “The stop-codon-specific queuing is associated with translational readthrough downstream (Fig. 4b), consistent with a recent observation based on inhibition of peptide release by the antimicrobial apidaecin in E. coli (Mangano et al., 2020).”

    This additional analysis, in conjunction with (Mangano et al., 2020), also allows us to calibrate the depletion of RFs in our non steady-state CRISPRi perturbation. Given that apidaecin treatment (shown to lead to a nearly complete depletion of free RF in the cell) causes a >100-fold increase in readthrough, this suggests that our CRISPRi perturbation experiments only led to partial RF depletion at the moment of cell harvesting.

    The efficiency of translation termination is well-known to be dependent on the context of the stop codon. Do the authors also observe such a trend. Especially, UGAC for RF2, one would expect to observe high levels of readthrough upon RF2 depletion.

    __Response: __Further assessment of the sequence determinants that dictate susceptibility of certain genes and regulatory elements to RF perturbation is of great interest. We now include additional analyses for the effect of stop codon context on readthrough.

    In our RF2 CRISPRi knockdown data, stratifying the translational readthrough (data from Fig. 4b) by stop codon and its next nucleotide, we observe only a modest (≈2×, p“We also observed a trend of tetranucleotide-dependent (UGAN) readthrough for RF2 knockdowns (Methods, Appendix Fig. 2) consistent with previous characterizations (Poole et al., 1995).”

    As an additional point of interest, the importance of the 4th nucleotide in termination has not been studied outside of E. coli. Although indirect, one way to assess the influence of the 4th nucleotide is to determine the aggregated usage of each tetranucleotide stop signal by ribosome profiling. Interestingly, and as pointed out by the Reviewer, whereas E. coli (MG1655) displays a 16× increase in usage between the maximum UGAU (tetranucleotide usage 0.064) and minimum UGAC (tetranucleotide usage 0.004), no such difference is observed in B. subtilis (usage for UGAU and UGAC both at 0.015), suggesting that the immediate sequence context surrounding stop codons could have different consequences in different species.

    Reviewer #2 (Significance (Required)):

    Overall, the experiments are clearly performed and beautifully illustrated. Clearly, a lot of work has gone into this study but the end message that the cell regulates carefully RF concentrations is not surprising. Especially given that RF2 carefully regulates its own levels using an autoregulatory frameshifting mechanism. The major finding that the rsbQ-rsbV operon with the RF2 dependence leading to induction of the sigmaB regulon is in the end rather trivial since these regulators depend on RF2 for termination. Therefore, this manuscript is unlikely to have general interest to people in the translation field (such as myself) but rather those working in the field of synthetic biology.

    __Response: __We thank the Reviewer for their positive assessment of our presentation and experimental methods, and for their judgment that our work will be of interest to synthetic biologists.

    In our study, we used translation as a well-characterized system to interrogate the cellular response when enzyme concentrations are perturbed. Because the system is so well characterized, it allowed to ask whether the fitness effects are due to perturbations to the translation flux itself, or rather driven by spurious distal connections in the regulatory network. The end message we wish to convey is that enzyme expression is entrenched by spurious regulatory connections, suggesting that predictive bottom-up models of expression-fitness landscapes will require near-exhaustive characterization of parts.

    Although our focus is on the cellular response, there are several interesting findings related to translation. First, we show that even though RF1 and PrmC are not subject to the strict autoregulation as RF2 is, cell growth is similarly or even more sensitive to RF1 and PrmC abundance. Second, among the numerous regulators that depend on RF2 for termination, RbsV/RbsW is exceptionally sensitive to RF2 depletion (Fig. 4e). This result not only points to our incomplete understanding of translation regarding what makes this pair particularly susceptible, and further underscores the spurious nature of the cellular response to perturbations. We have expanded the discussions on the implication of these findings in the revised manuscript.


    Reviewer #3 (Evidence, reproducibility and clarity (Required)):

    In this paper, the authors use a combination of RNA sequencing, ribosome profiling and measurements of cellular composition and growth rate to gain insight into the multi-scale affects that perturbations to translation termination factors have on general physiological states and reproductive fitness using Bacillus subtilis as their model organism. Specifically, they find that perturbing the expression levels of peptide chain release factors in any direction has a negative effect on growth-rate. This negative effect was not due to a direct impact of the gene on the cell, but instead due to a chain of regulatory interactions that cause the activation of the general stress regulon. This leads to upregulation of a large chunk of the genome and an indirect impact on the expression of all other genes. Critically, the knock-on effects observed for the specific perturbations studied suggest that it may be difficult to predict expression-fitness landscapes of a cell, without carrying out a detailed mapping of all genes and the cell's physiological state.

    Overall, the core findings in the paper are well justified by the data presented and the experiments appear to have been rigorously carried out.

    Response: We thank the reviewer for their positive assessment.

    My only concern is that it is unclear if biological replicates of the ribosome profiling were performed. Also, biological replicates are mentioned for the RNA-seq data, but no data is shown. Even a simple graph demonstrating the expression levels across these would be useful to be assured of no issues in reproducibility given the complex processing of the data involved.

    __Response: __We now include additional analyses for biological replicates of RNA-seq and ribosome profiling experiments, which show the same high degree of reproducibility as we have demonstrated in previous studies (Johnson et al., 2020; Lalanne et al., 2018; Li et al., 2014).

    With respect to RNA-seq quantification, we compared our 6 wild-type datasets (biological replicates except for different inert inducer concentrations, using the same batch of conditioned MCC medium) against each other in all possible pairs. The data is now included as __Appendix Fig. 1a __(referred to in the main text, p. 4, line 138), and is reproduced below. Across pairs, the mRNA level quantification shows a median FC1090 (10th and 90th percentile in fold-change) between 0.86 to 1.16, and median R2 of log-transformed data at 0.99. These statistics showcasing reproducibility of our RNA-seq methodology are now included in our description of our RNA-seq approach in the Methods, p. S8, lines 313-320.

    Regarding ribosome profiling quantification, we now include comparisons between pairs of two replicates for wild type cells, and pairs of replicates wild-type with inert fluorescent protein expression, each pair of samples with their own batch of conditioned MCC medium. These samples were taken under different inducer concentrations, which are expected to affect the expression of two genes and not others. As indicated in Appendix Fig. 1b and reproduced below, the Pearson correlation of log-transformed footprint density is respectively of R2=0.98 and 0.99 (genes with >100 reads mapped), with a 10th to 90th percentile of fold-changes between 0.83 to 1.17, and 0.91 to 1.12. These results are described in the Methods, p. S9, lines 339-345.

    Related to this, I see no mention of data availability in the paper. For this study to be useful to others, providing the raw data (unprocessed) would be essential (ideally in a public repository).

    __Response: __We are sorry that the statement on data availability was buried in the original Methods section that was not a part of the merged PDF file. The raw sequencing data were submitted to Gene Expression Omnibus under the accession number GSE162169. The processed data, including fitness scores, mRNA levels, protein synthesis rates, were included as Supplementary Data Tables 1-9. We now moved the data availability statement to the main document at p. 12, lines 512-516.

    The presentation of the work is excellent, with very clear figures and text that helped guide the reader through the results. There were a few minor comments:

    1. Abstract: "in bacterium Bacillus subtilis" should read "in the bacterium Bacillus subtilis".

    __Response: __This typo is now corrected.

    Page 4: "found that under numerous ways" should read "found that under the numerous ways".

    __Response: __This typo is now corrected.

    The authors mention that changes in the expression level of RF1 impacted motility and biofilm genes, but not how this impacts fitness. Would they be able to experimentally identify origin of RF1 growth defects in the same way they did for PrmC? This is not essential for the main findings but would help strengthen the work.

    __Response: __The cause of the growth defect under RF1 knockdown is indeed interesting. We now present evidence ruling out the hypothesis that the growth defect is caused by the expression decrease for motility and biofilm genes.

    This hypothesis is driven by our result that ablation of SigB regulon rescues the fitness defect during PrmC overexpression (Fig. 3g) and by the observed downregulation of motility and lyt operons and upregulation of the eps operon during RF1 knockdown. To test this hypothesis, we used a strain without sigD (the motility sigma factor), which displays similar expression changes to what we observed in RF1 knockdown (Chai et al., 2009). Comparing the growth rates of wild-type to DsigD, we found only a slight difference (30% growth defect measured upon RF1 knockdown, it appears that transcriptional changes to the motility regulon can only partially explain of the RF1 growth defect. These results are discussed on p. 10, lines 459-463. Further assessment will constitute interesting future research avenues.

    It is difficult to know how generalisable the findings of this work are due to the very limited scope. It could be helpful for the authors in the discussion to consider and comment on how such approaches might be scaled-up to enable broader and more general studies of expression-fitness landscapes and where they will find most use.

    Response: Indeed, the spurious nature of the expression-fitness landscape makes it difficult to generalize the exact mechanisms that we described here to other proteins. However, what is generalizable is our conclusion that such spurious connections limit the feasibility of bottom-up models for predicting fitness landscapes unless one has near-exhaustive characterization of all parts.

    Our approach of mechanistic profiling of cell states under perturbations therefore provides a path forward that can be scaled up by recent developments in multiscale measurements. We now include a discussion for broader and more general studies on p. 11, lines 473-480.

    “Various strategies can now generate expression-fitness landscapes for a large number of genes in parallel, for example using suites of promoters (Keren et al., 2016), genome-scale library of inducible gene expression (Arita et al., 2021), or tunable CRISPR perturbations (Hawkins et al., 2020; Jost et al., 2020; Mathis et al., 2021). Together with the advent of single-cell transcriptomics in bacteria (Blattman et al., 2020; Imdahl et al., 2020; Kuchina et al., 2020), these methods open the possibility of dissecting the molecular underpinnings of expression-fitness landscapes genome-wide, and to comprehensively identify instances of regulatory entrenchment.”

    Reviewer #3 (Significance (Required)):

    This work has a number of contributions. Firstly, it demonstrates how to combine several complementary sequencing approaches to characterize in detail the transcriptional and translational state of a cell, as well as its overall growth rate to generate comprehensive expression-fitness maps. Secondly, it shows how the interwoven nature of cellular regulatory networks and the molecular interactions encoded within the genome can lead to cryptic responses in cellular behavior and fitness at a system-level that can only be understood by taking a detailed "bottom-up" approach. Finally, it suggests that some of these regulatory interactions may in fact "entrench" an organism's evolutionary path, by causing small genetic perturbations to propagate and potentially amplify their negative effect. While the results are compelling and well supported by experiments, the limited scope of the work makes it difficult to know whether this is in fact a rare or common occurrence. However, I do believe there is significance to these findings and that it will likely spur further studies to assess the generality of these findings.

    Overall, I believe the work will have a wide appeal covering areas such as Systems Biology, Gene Regulation, Evolution, Quantitative Biology, Sequencing, High-throughput Technologies.

    __Response: __We thank the reviewer for their assessment that our work will be of appeal to a broad audience.

    My field of expertise is in the quantitative measurement of core cellular processes (e.g. transcription and translation) using novel sequencing techniques and the application of this knowledge to biological design. As such, I believe I have sufficient expertise to review this paper in detail.

    Response references

    Adamski, F.M., McCaughan, K.K., Jørgensen, F., Kurland, C.G., and Tate, W.P. (1994). The concentration of polypeptide chain release factors 1 and 2 at different growth rates of Escherichia coli. J. Mol. Biol. 238, 302–308.

    Arita, Y., Kim, G., Li, Z., Friesen, H., Turco, G., Wang, R.Y., Climie, D., Usaj, M., Hotz, M., Stoops, E., et al. (2021). A genome-scale yeast library with inducible expression of individual genes. BioRxiv 2020.12.30.424776.

    Baranov, P. V, Gesteland, R.F., and Atkins, J.F. (2002). Release factor 2 frameshifting sites in different bacteria. 3, 373–377.

    Blattman, S.B., Jiang, W., Oikonomou, P., and Tavazoie, S. (2020). Prokaryotic single-cell RNA sequencing by in situ combinatorial indexing. Nat. Microbiol. 5, 1192–1201.

    Chai, Y., Normam, T., Kolter, R., and Losick, R. (2009). An epigenetic switch governing daughter cell separation in Bacillus subtilis. Genes Dev. 7824, 754–765.

    Craigen, W.J., and Caskey, C.T. (1986). Expression of peptide chain release factor 2 requires high-efficiency frameshift. Nature 322, 273–275.

    Craigen, W.J., Cook, R.G., Tate, W.P., and Caskey, C.T. (1985). Bacterial peptide chain release factors: Conserved primary structure and possible frameshift regulation of release factor 2. Proc. Natl. Acad. Sci. U. S. A. 82, 3616–3620.

    Dinçbas-Renqvist, V., Engström, Å., Mora, L., Heurgué-Hamard, V., Buckingham, R., and Ehrenberg, M. (2000). A post-translational modification in the GGQ motif of RF2 from Escherichia coli stimulates termination of translation. EMBO J. 19, 6900–6907.

    Hawkins, J.S., Silvis, M.R., Koo, B.M., Peters, J.M., Osadnik, H., Jost, M., Hearne, C.C., Weissman, J.S., Todor, H., and Gross, C.A. (2020). Mismatch-CRISPRi Reveals the Co-varying Expression-Fitness Relationships of Essential Genes in Escherichia coli and Bacillus subtilis. Cell Syst. 11, 523-535.e9.

    Imdahl, F., Vafadarnejad, E., Homberger, C., Saliba, A.E., and Vogel, J. (2020). Single-cell RNA-sequencing reports growth-condition-specific global transcriptomes of individual bacteria. Nat. Microbiol. 5, 1202–1206.

    Johnson, G.E., Lalanne, J.B., Peters, M.L., and Li, G.W. (2020). Functionally uncoupled transcription–translation in Bacillus subtilis. Nature 585, 124–128.

    Jost, M., Santos, D.A., Saunders, R.A., Horlbeck, M.A., Hawkins, J.S., Scaria, S.M., Norman, T.M., Hussmann, J.A., Liem, C.R., Gross, C.A., et al. (2020). Titrating gene expression using libraries of systematically attenuated CRISPR guide RNAs. Nat. Biotechnol. 38, 355–364.

    Keren, L., Hausser, J., Lotan-Pompan, M., Vainberg Slutskin, I., Alisar, H., Kaminski, S., Weinberger, A., Alon, U., Milo, R., and Segal, E. (2016). Massively Parallel Interrogation of the Effects of Gene Expression Levels on Fitness. Cell 166, 1282-1294.e18.

    Korkmaz, G., Holm, M., Wiens, T., and Sanyal, S. (2014). Comprehensive analysis of stop codon usage in bacteria and its correlation with release factor abundance. J. Biol. Chem. 289, 30334–30342.

    Kuchina, A., Brettner, L.M., Paleologu, L., Roco, C.M., Rosenberg, A.B., Carignano, A., Kibler, R., Hirano, M., William DePaolo, R., and Seelig, G. (2020). Microbial single-cell RNA sequencing by split-pool barcoding. Science (80-. ).

    Lalanne, J.B., Taggart, J.C., Guo, M.S., Herzel, L., Schieler, A., and Li, G.W. (2018). Evolutionary Convergence of Pathway-Specific Enzyme Expression Stoichiometry. Cell 749–761.

    Li, G.W., Burkhardt, D., Gross, C., and Weissman, J.S. (2014). Quantifying absolute protein synthesis rates reveals principles underlying allocation of cellular resources. Cell 157, 624–635.

    Mangano, K., Florin, T., Shao, X., Klepacki, D., Chelysheva, I., Ignatova, Z., Gao, Y., Mankin, A.S., and Vázquez-Laslop, N. (2020). Genome-wide effects of the antimicrobial peptide apidaecin on translation termination in bacteria. Elife 9, 1–24.

    Mathis, A.D., Otto, R.M., and Reynolds, K.A. (2021). A simplified strategy for titrating gene expression reveals new relationships between genotype, environment, and bacterial growth. Nucleic Acids Res. 49, e6.

    Poole, E.S., Brown, C.M., and Tate, W.P. (1995). The identity of the base following the stop codon determines the efficiency of in vivo translational termination in Escherichia coli. EMBO J. 14, 151–158.

    Wei, Y., Wang, J., and Xia, X. (2016). Coevolution between Stop Codon Usage and Release Factors in Bacterial Species. Mol. Biol. Evol. 33, 2357–2367.

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    Referee #3

    Evidence, reproducibility and clarity

    In this paper, the authors use a combination of RNA sequencing, ribosome profiling and measurements of cellular composition and growth rate to gain insight into the multi-scale affects that perturbations to translation termination factors have on general physiological states and reproductive fitness using Bacillus subtilis as their model organism. Specifically, they find that perturbing the expression levels of peptide chain release factors in any direction has a negative effect on growth-rate. This negative effect was not due to a direct impact of the gene on the cell, but instead due to a chain of regulatory interactions that cause the activation of the general stress regulon. This leads to upregulation of a large chunk of the genome and an indirect impact on the expression of all other genes. Critically, the knock-on effects observed for the specific perturbations studied suggest that it may be difficult to predict expression-fitness landscapes of a cell, without carrying out a detailed mapping of all genes and the cell's physiological state.

    Overall, the core findings in the paper are well justified by the data presented and the experiments appear to have been rigorously carried out. My only concern is that it is unclear if biological replicates of the ribosome profiling were performed. Also, biological replicates are mentioned for the RNA-seq data, but no data is shown. Even a simple graph demonstrating the expression levels across these would be useful to be assured of no issues in reproducibility given the complex processing of the data involved. Related to this, I see no mention of data availability in the paper. For this study to be useful to others, providing the raw data (unprocessed) would be essential (ideally in a public repository).

    The presentation of the work is excellent, with very clear figures and text that helped guide the reader through the results. There were a few minor comments:

    1. Abstract: "in bacterium Bacillus subtilis" should read "in the bacterium Bacillus subtilis".
    2. Page 4: "found that under numerous ways" should read "found that under the numerous ways".
    3. The authors mention that changes in the expression level of RF1 impacted motility and biofilm genes, but not how this impacts fitness. Would they be able to experimentally identify origin of RF1 growth defects in the same way they did for PrmC? This is not essential for the main findings but would help strengthen the work.
    4. It is difficult to know how generalisable the findings of this work are due to the very limited scope. It could be helpful for the authors in the discussion to consider and comment on how such approaches might be scaled-up to enable broader and more general studies of expression-fitness landscapes and where they will find most use.

    Significance

    This work has a number of contributions. Firstly, it demonstrates how to combine several complementary sequencing approaches to characterize in detail the transcriptional and translational state of a cell, as well as its overall growth rate to generate comprehensive expression-fitness maps. Secondly, it shows how the interwoven nature of cellular regulatory networks and the molecular interactions encoded within the genome can lead to cryptic responses in cellular behavior and fitness at a system-level that can only be understood by taking a detailed "bottom-up" approach. Finally, it suggests that some of these regulatory interactions may in fact "entrench" an organism's evolutionary path, by causing small genetic perturbations to propagate and potentially amplify their negative effect. While the results are compelling and well supported by experiments, the limited scope of the work makes it difficult to know whether this is in fact a rare or common occurrence. However, I do believe there is significance to these findings and that it will likely spur further studies to assess the generality of these findings.

    Overall, I believe the work will have a wide appeal covering areas such as Systems Biology, Gene Regulation, Evolution, Quantitative Biology, Sequencing, High-throughput Technologies.

    My field of expertise is in the quantitative measurement of core cellular processes (e.g. transcription and translation) using novel sequencing techniques and the application of this knowledge to biological design. As such, I believe I have sufficient expertise to review this paper in detail.

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    Referee #2

    Evidence, reproducibility and clarity

    The manuscript of Lalanne and coworkers address the cellular responses to varied translation termination factor expression in Bacillus subtilis. The authors set-up a system to fine-tune the expression of release factor RF1, RF2 as well as PrmC that post-translationally modifies RF1/RF2 to maximize their catalytic hydrolysis activity. They then monitor the fitness costs associated with overexpression or depletion of the factor by following the changes in growth rate. The set-up is nicely illustrated in Figure 1. The results in Figure 2 show that overexpression of RF1 and RF2 has relatively modest effect on the growth rate compared to overexpression of PrmC that leads to dramatic growth rate reduction. By contrast, depletion of RF1 has a strong negative influence on fitness, whereas a similar level of depletion of RF2 had little influence on fitness. PrmC overexpression appears to be correlated with the induction of the sigmaB regulon, however, the authors do not manage to ascertain why this is. By contrast, RF2 depletion also results in the induction of the sigmaB regulon and the authors demonstrate convincingly that this is due to a termination defect within the rsbQ-rsbV operon that contains an overlapping start-stop AUGA

    A few points that the authors might consider discussing

    1. The natural abundance of each RF in bacteria in relation to the usage of different stop codons in different organisms.
    2. The role of the frameshifting mechanism in RF2 and how then RF1 levels are regulated.
    3. The authors observe queuing in front of the relevant stop codons upon RF depletion, however, do not discuss about readthrough events, which are usually competing with termination. Surprisingly, in this context the authors don't discuss the work from Mankin and coworkers showing sequestration of RFs from termination by peptides such as apideacin leads to translational readthrough.
    4. The efficiency of translation termination is well-known to be dependent on the context of the stop codon. Do the authors also observe such a trend. Especially, UGAC for RF2, one would expect to observe high levels of readthrough upon RF2 depletion.

    Significance

    Overall, the experiments are clearly performed and beautifully illustrated. Clearly, a lot of work has gone into this study but the end message that the cell regulates carefully RF concentrations is not surprising. Especially given that RF2 carefully regulates its own levels using an autoregulatory frameshifting mechanism. The major finding that the rsbQ-rsbV operon with the RF2 dependence leading to induction of the sigmaB regulon is in the end rather trivial since these regulators depend on RF2 for termination. Therefore, this manuscript is unlikely to have general interest to people in the translation field (such as myself) but rather those working in the field of synthetic biology.

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