Plasmodium vivax (Pv) infections were 68% of the total malaria burden in Laos in 2019. The parasite causes frequent relapses, which can be prevented by primaquine (PMQ). Testing for glucose-6-phosphate-dehydrogenase (G6PD) deficiency is recommended before giving PMQ to avoid haemolysis. Because of the risk of haemolysis in G6PD intermediate deficiencies among females, Laos uses the PMQ 14-days regimen only in G6PD normal females. Among G6PD point-of-care tests, qualitative tests cannot differentiate between G6PD normal and intermediate females. Quantitative tests are required to differentiate between G6PD normal and intermediate deficiencies. However, the quantitative test lacks the cost-effectiveness evidence necessary for decision-making for large-scale adoption. This study examined the cost-effectiveness of quantitative G6PD test, with either supervised PMQ treatment or unsupervised PMQ treatment, against the usual unsupervised PMQ 8-weeks strategy. Supervised PMQ 8-weeks strategy without G6PD testing was also compared against the unsupervised PMQ 8-weeks strategy since the former had recently been adopted in malaria high burden villages that had village malaria volunteers. A budget impact analysis was conducted to understand the incremental cost and effect needed for a nationwide scale-up of the chosen strategy.A decision tree model compared the cost-effectiveness of implementing four strategies at one health facility with an average of 14 Pv cases in one year. The strategies were unsupervised PMQ strategy, supervised PMQ strategy, G6PD test with unsupervised PMQ strategy, and G6PD test with supervised PMQ strategy. Disability Adjusted Life Years (DALYs) was the effect measure. Costs were calculated from a payer perspective, and sensitivity analyses were conducted. One Gross Domestic Product (GDP) per capita of Laos was set as the cost-effectiveness threshold. Budget impact analysis was conducted using the health facility wise Pv data in Laos in 2020.Supervised PMQ strategy was extendedly dominated by G6PD test strategies. When compared against the unsupervised PMQ strategy, both G6PD test strategies were more costly but more effective. Their Incremental Cost-Effectiveness Ratios (ICER) were 96.72US$ for the G6PD test with unsupervised PMQ strategy and 184.86US$ for the G6PD test with supervised PMQ strategy. Both ICERs were lower than one GDP per capita in Laos. Following the sensitivity analysis, low adherence for PMQ 14 days made both G6PD test strategies less cost-effective. The lower the Pv case number reported in a health facility, the higher the ICER was. In the budget impact analysis, the expected budget need was only half a million US$ when the G6PD test rollout was discriminately done depending on the Pv case number reported at the health facilities. Indiscriminate roll out of G6PD test to all health facilities was most expensive with least effect impact. Plasmodium vivax (Pv) malaria infections globally in 2019 [The World Health Organization (WHO) estimated a total of 6.4 million  in 2019 . A revie in 2019  and prev in 2019 . This is in 2019 . G6PD de in 2019 . In Laos in 2019 . To pres in 2019 . When th in 2019 . In Laos in 2019 . Since G in 2019 . Because in 2019 , two dif in 2019 .™ G6PD quantitative test has become the only point-of-care G6PD test that is currently eligible for procurement using major donors’ funding such as the Global Fund and Unitaid [For G6PD testing, the gold standard is spectrophotometry. However, it requires intensive resources to operate  and henc Unitaid . The tes Unitaid , 15. It  Unitaid .Only three cost-effectiveness studies have been done elsewhere and published for G6PD point of care tests, and none have been done for the G6PD quantitative test. At the Thai-Myanmar border, Devine et al.  studied Health care in Laos is delivered mainly through the public sector . The hig™ G6PD qualitative tests at the hospital level since 2017 [™ G6PD tests, the national malaria program in Laos is considering scaling up the rollout of these tests up to the health center level in addition to the hospital level. However, in the absence of cost-effectiveness data on the G6PD quantitative test, the rollout may likely face policy, implementation, and funding challenges in the medium and long term, especially when the donor funding phases out. To help the policymakers, program managers, and funding donors with the informed investment choices, this study examined the cost-effectiveness of the quantitative G6PD test, with either supervised PMQ treatment or unsupervised PMQ treatment, against the usual unsupervised primaquine 8 weeks strategy. Supervised PMQ 8-weeks strategy without G6PD testing was also compared against the unsupervised PMQ 8-weeks strategy since the former had recently been adopted in malaria high burden villages that had village malaria volunteers. Further analysis was made to understand the budget impact from a nationwide scale up with the chosen strategy or strategies informed by the cost-effectiveness results.Laos aims to eliminate all forms of malaria infection by the end of 2030. The country has been using CareStartnce 2017 . Howevernce 2017 , and Laonce 2017 . In areance 2017 . With thThe study examined the cost and effectiveness of implementing different Pv radical cure strategies at the health center level over a 1-year time horizon in Laos. The health center level is the focus of the study because the malaria program in Laos is planning to roll out the G6PD quantitative tests to health centers in high and moderate malaria burden areas . A decisA decision tree model was developed in R version 3.6.3. The base case model was built to calculate the cost and effect of each of the four Pv radical cure strategies in one health center in Laos, which has a total of 14 reported Pv cases in a year. A year’s data was used because malaria stratification in Laos is planned to be revised every year, depending on which malaria control interventions need to be adjusted. The number of Pv cases was taken from the mean number of Pv cases detected among health facilities in Laos in the year 2020 . Among aThe four strategies compared were: (i) unsupervised PMQ strategy where PMQ 8 weeks regimen was provided to all Pv cases without G6PD testing and supervised treatment for PMQ compliance, (ii) supervised PMQ strategy where PMQ treatment compliance was supervised by village malaria volunteers, (iii) G6PD test with unsupervised PMQ strategy where G6PD quantitative test was performed to all Pv cases, and PMQ was provided without supervision for treatment compliance and (iv) G6PD test with supervised PMQ strategy where G6PD quantitative test was done for all Pv cases followed by PMQ compliance which was supervised by village malaria volunteers. These four strategies were chosen because the first strategy has traditionally been used while the second strategy has been recently adopted in Laos. The third and fourth strategies are planned for roll out in the coming years.In the first and second strategies, a probability for Pv recurrence among patients who receive 8 weeks PMQ regimen led a patient to have either recurrence or no recurrence. In the absence of studies specific to Laos, recurrence probabilities were taken from the best available literatures . Since tFor recurrent cases in first and second strategies, end nodes’ effect was scaled by DALYs for PMQ 8 weeks recurrence multiplied with the number of Pv cases expected at one health center in a year and PMQ adherence rate with or without supervision. The aim of multiplying DALYs at the end nodes with the number of Pv cases was to understand the total DALYs averted by implementing a selected Pv radical cure strategy at one health facility in one year. Further scaling of that result with the adherence rate varied the recurrence probabilities—high adherence rate lowered the recurrence probabilities and increased the total DALYs averted, while the reverse was true for low adherence rate. The same scaling methodology was applied to the effect at the end nodes for non-recurrent cases. For these, DALYs for no recurrence were multiplied with the Pv case number in a year and PMQ adherence rate.In the absence of empirical data, the adherence rate for PMQ 8 weeks regimen was assumed at 50% of the 14 days PMQ adherence rate based on a study conducted on the Thai-Myanmar border . The refFor the third and fourth strategies of the decision tree model, a Pv patient was led to be male or female because G6PD deficiency prevalence is different between the male and female population. According to Laos’s national malaria program data in 2020, 67% of Pv cases were males . G6PD leWhen the G6PD test result was false normal, G6PD deficient patients were provided with PMQ 14 days regimen, which could lead to haemolysis. The proportion that had haemolysis among PMQ 14 days’ patients in the presence of G6PD deficiency was taken from a study conducted on the Thai-Myanmar border . The basThe decision tree model is described in Figs The primary effect measure was Disability Adjusted Life Year (DALY) averted by each strategy, calculated using Laos’ life expectancy . DisabilDALY was calculated for five categories: (i) DALY for no Pv recurrence in patients treated with either 14 days or 8 weeks PMQ regimen, (ii) DALY for Pv recurrence after 8 weeks of PMQ, (iii) DALY for Pv recurrence after 14 days of PMQ, (iv) DALY for no Pv recurrence in haemolytic patients, and (v) DALY for Pv recurrence in haemolytic patients. For haemolytic patients, any ongoing PMQ treatment was assumed to be withheld. There were five calculations for DALYs because the decision tree model assumed that effect and cost were measured for implementation at the health facility level. Outcomes beyond it were not considered as recurrent Pv patients may or may not come back to the same health facility for future treatment. For that reason, life expectancies for recurrent cases used the median time taken to have the first recurrent Pv episode after PMQ 8 weeks, 14 days treatment, and no PMQ. A study on the Thai-Myanmar border showed that it took 49 days to have first Pv recurrence after no PMQ and 195 days to have first Pv recurrence after 14 days PMQ regimen . For medCosts were calculated from the payer perspective, either the Ministry of Health, Laos, or the donors. There was no difference between these two since the Laos malaria program is funded primarily by donors except for government staff salaries and a portion of the operation cost. Once the donor funding phases out, all the costs related to the malaria program will need to be absorbed by the government. The one-year cost was calculated for one health facility with 14 Pv cases reported in 12 months. For the PMQ strategies, four cost types were included: training of health facility staff, supervision to the health facility, health facility’s human resource and operation, and PMQ cost. For the G6PD test strategies, four additional cost types were added: cost of G6PD testing, cost of G6PD biosensor, cost for G6PD quality control, and cost for treatment of haemolytic episode. For strategies that include PMQ supervision, costs related to village volunteers were added, which were training for volunteers, supervision to volunteers, incentives, and reporting support for volunteers.All the cost incurred in Lao KIP was converted to US$ by using the Bank of Laos reference exchange rate for the year 2020 . BecauseCost data are described in Training cost for health facility staff was taken from the Global Fund’s Regional Artemisinin-resistance Initiative 2 Elimination (RAI2E) grant, implemented in Laos from 2018 to 2020 . AlthougSupervision cost for a health facility was calculated for one year, using RAI2E’s 2020 cost data. Supervision cost was not linked to the number of Pv cases since supervision visits were supposed to be carried out for all health facilities regardless of the malaria burden. For human resource and operational costs for a health facility, the government’s monthly cost to support a health facility  was propG6PD biosensor was assumed to last for three years; hence, its three-year capital cost was annuitized to obtain a year’s cost using the following formula: K was the capital cost, E was the annual cost and r was the discount rate of 3% .For total G6PD test cost required for a year, the model was programmed to take the cost based on either of the two calculations, whichever was higher: (a) total test consumption based on reported Pv numbers in a year for a health facility or (b) minimum stock quantity required for a year based on packaging size (25 tests per box) and tests’ shelf life that remains upon arrival at health facilities (8 months). This was done because the cost of G6PD tests could not simply be attained by multiplication of G6PD test unit cost and the number of Pv cases. Other factors such as minimum stock quantity based on packaging size  and the shelf life had to be considered. For this reason, the G6PD test minimum stock for a year was calculated by summation of 25 tests which was the number of tests that came in a box that would last for eight months  and 12 tests which were half of the number of tests in a box that can be used for the remaining four months of a year. Cost of blood draw for G6PD testing was considered to be included under the G6PD test unit cost since the test box contains all the items necessary for a finger prick test.G6PD test quality control was assumed to be done once every three months by district or province-level staff during their supervision visits to a health facility. For the unit cost, the cost of the G6PD test quality control kits containing ten units of test was divided by 10. To get the G6PD test quality control cost needed for a health facility for a year, the unit cost was multiplied with four, which was the number of quality control tests for a health facility in a year. The cost of the G6PD test quality control kit was referenced from RAI3E grant data . The freCost for PMQ 8 weeks and 14 days courses were calculated by multiplying the unit cost for PMQ one tablet with the total number of tablets required for each course .All the unit cost for PMQ, G6PD test, and device, including the international procurement and in-country supply chain cost, were taken from RAI2E  and RAI3The cost for treatment of one haemolytic episode was calculated by summation of cost for seven days’ hospital stay in Laos and cost for one blood bag. Each cost referenced was from past years; hence, the Bank of Lao PDR’s annual inflation rate  was applFor strategies involving PMQ supervision, village volunteers’ costs were included because village volunteers supervised PMQ compliance. The cost for six village volunteers for a year was calculated since there is an average of six volunteers under one health facility in Laos . As PMQ Incremental Cost-Effectiveness Ratio (ICER) for a base case was calculated by dividing the net cost  with net effect . The unsupervised PMQ strategy was considered the conventional strategy against which the comparison was made with other strategies. Strategies with strong dominance and extended dominance were identified. Strong dominant strategies cost more and bring less effect than all the other strategies in comparison . To deteSensitivity analysis was done by both one-way deterministic analysis and probabilistic sensitivity analysis (PSA). ICERs for all interventions regardless of extended dominance were included in one-way sensitivity analysis. For one-way sensitivity analysis, variation was made for one parameter at one time to observe how ICER was changed. Lower and upper values for one-way analysis were taken from the range determined for probabilistic sensitivity analysis. For PSA, the Monte Carlo simulation was run 10,000 times  by progrMalaria funding committed by donors and the government for Lao PDR for 2021 is approximately 8 million US$: 2 million US$ from the domestic financing , 5 milliIn Laos, hospitals exist at the provincial and district level, whereas health centers are at the sub-district/village level. Based on these levels of health facilities and Laos’ national strategic plan , scenariEthical approval was not relevant for this modeling study. Lao PDR’s malaria program data were utilized with permission from the Center for Malaria, Parasitology, and Entomology, Ministry of Health of Lao PDR.None of the interventions included in the analysis had strong dominance, i.e., higher cost and lower effect. However, the supervised PMQ strategy was extendedly dominated by the G6PD test with unsupervised PMQ strategy and the G6PD test with supervised PMQ strategy. The ICER for the G6PD test with unsupervised PMQ strategy compared against the conventional unsupervised PMQ strategy was 96.72. This means that an extra 96.72 US$ will need to be spent for the G6PD test with unsupervised PMQ strategy to avert one additional DALY compared to the unsupervised PMQ strategy. The ICER for the G6PD test with supervised PMQ strategy against the conventional strategy was 184.86. Details of the base-case analysis are shown in One-way sensitivity analysis was done for G6PD test with unsupervised PMQ strategy versus unsupervised PMQ strategy, G6PD test with supervised PMQ strategy versus unsupervised PMQ strategy, and supervised PMQ strategy versus unsupervised PMQ strategy using the parameter values listed in Tables The top 10 parameters with the biggest influence on the ICERs are shown in Figs As presented in 18% of health facilities  were eligible for G6PD test strategies in scenarios 1 and 3. As presented in This study is the first of its kind in Laos and one of the few studies ever done to assess the cost-effectiveness of P. vivax radical cure strategies using the point-of-care G6PD quantitative test. The analysis is timely as Laos aims to eliminate malaria by 2030, and there is a critical need for evidence to support cost-effective Pv radical cure approaches. The results from this study will help policymakers and program managers plan and allocate resources more efficiently and strategically in eliminating a dominant form of malaria species in the country.The results from this study point to the cost-effectiveness of both G6PD test strategies compared to the conventional approach of unsupervised PMQ strategy. However, the cost-effectiveness was sensitive to PMQ adherence and the Pv recurrence rate. In one-way sensitivity analysis, the ICER value for G6PD test strategies became larger, i.e., G6PD test strategies became less cost-effective, when the PMQ adherence rate for 14 days regimen and adherence rate for PMQ supervision was reduced from 85% to 50% and 100% to 86%, respectively. ICER values for G6PD test strategies were also increased when the Pv recurrence rate among 14 days PMQ patients was increased from 17% to 25%. The PMQ adherence rate and the Pv recurrence rate have a one-directional relationship. A low adherence rate will reduce the PMQ completion rate, hence, increase the likelihood of experiencing Pv recurrence, thus lowering the cost-effectiveness. Low adherence is expected of a drug regimen that needs to be continued even after symptoms improvement, such as PMQ for Pv radical cure. Research has shown that Pv recurrence risk could be increased by 3 to 4 folds when PMQ intake is unsupervised . HoweverThe cost-effectiveness of the G6PD test was also largely influenced by the number of Pv cases found in a health facility over a year, which was not a surprising finding. Reduced Pv case number resulted in effect size reduction, i.e., fewer DALYs averted, and hence the intervention became less cost-effective. The reverse was true for scenarios with an increased number of Pv cases. Considering a similar level of investment made to every health facility while holding other variables constant, the only differentiating point for cost-effectiveness would be the number of Pv cases diagnosed and treated in a health facility. Therefore, a more relevant question was how the investment should be strategized, whether to roll out the G6PD test to all health facilities or only focus on a selective number of health facilities as guided by the Pv burden. The budget impact analysis of this research provided useful insights.From the budget impact analysis, a nationwide rollout of G6PD tests to all health centers would be more costly and less effective; hence, the approach should be avoided. The choice for policymakers and program managers would be to selectively roll out the G6PD tests based on the expected burden of Pv for a health facility. Using health facility-wise Pv data in Laos from 2020, just over half a million US$ a year would be needed to have a maximum DALYs aversion from rolling out G6PD test and supervised PMQ strategy to all hospitals and health centers with at least 5 Pv cases in a year while having conventional unsupervised PMQ only strategy in the rest of the health centers. In relative terms, the amount required would be only about 10% of the Global Fund’s RAI3E grant that Laos receives from 2021–2023 . For polThis study has some limitations. As is the nature of the modelling studies, uncertainties with the parameter values, especially because many of them are taken from studies in other countries, can lead to variable cost-effectiveness results. Although base case analysis showed cost-effectiveness results for both G6PD test with unsupervised PMQ strategy and G6PD test with supervised PMQ strategy, the probability of each strategy being cost-effective, at probabilistic sensitivity analysis, was only 81% and 76% respectively at a cost-effectiveness threshold of 1 GDP per capita for Laos which was 2,534 US$ as at the time of this analysis. Even at a 10,000 US$ willingness-to-pay amount (close to four times GDP per capita in Laos), both G6PD strategies were cost-effective only 87–90% of the time. This was due to the wide range of parameter values in the model. When robust country-specific data is scarce, especially for PMQ adherence rate and Pv recurrence data, such a level of uncertainties will not be avoidable. On the other hand, modeling results give the policymakers an idea of how the cost-effectiveness results could turn out by different parameter values, and hence, how to improve the program implementation to achieve the favorable cost-effectiveness results. The study did not consider the societal costs, which, if included, are likely to produce smaller cost-effectiveness results . BecauseG6PD test with unsupervised PMQ strategy and G6PD test with supervised PMQ strategy are found to be cost-effective at a willingness to pay threshold of 2,534 US$ when compared to unsupervised PMQ strategy. However, uncertainties in input parameters would largely influence cost-effectiveness, particularly the level of PMQ adherence and the total number of Pv cases reported by a health facility in a year. At budget impact analysis, rolling out the G6PD test to all health facilities in Laos will be the most expensive approach with fewer increments in effect. However, a more strategic rollout of G6PD tests based on the expected Pv burden will cost less with only half a million US$ in 2020, with maximum effect result. The findings from this study suggest two key policy and implementation recommendations:indiscriminate roll out of G6PD test strategy in every health facility should be avoided, and instead, program managers should prioritize the roll out to a certain number of health facilities with moderate to high Pv burden.once the G6PD test strategy has been rolled out, implementation challenges that could lower the PMQ adherence rate and hence the cost-effectiveness of the G6PD test should be anticipated and managed appropriately so that the strategy remains cost-effective. The choice for adding supervised PMQ treatment to improve the PMQ adherence rate would depend on the policy makers’ acceptability to incremental budget implication from such an approach. In the current context of Lao PDR, adding supervised PMQ treatment to the G6PD test strategy should be a natural thing since there are pre-existing village malaria volunteers who provide routine malaria testing and treatment services in moderate and high malaria burden villages.S1 Appendix(PDF)Click here for additional data file.S2 Appendix(DOCX)Click here for additional data file.


Molecular similarity is an impressively broad topic with many implications in several areas of chemistry. Its roots lie in the paradigm that ‘similar molecules have similar properties’. For this reason, methods for determining molecular similarity find wide application in pharmaceutical companies, e.g., in the context of structure-activity relationships. The similarity evaluation is also used in the field of chemical legislation, specifically in the procedure to judge if a new molecule can obtain the status of orphan drug with the consequent financial benefits. For this procedure, the European Medicines Agency uses experts’ judgments. It is clear that the perception of the similarity depends on the observer, so the development of models to reproduce the human perception is useful. In this paper, we built models using both 2D fingerprints and 3D descriptors, i.e., molecular shape and pharmacophore descriptors. The proposed models were also evaluated by constructing a dataset of pairs of molecules which was submitted to a group of experts for the similarity judgment. The proposed machine-learning models can be useful to reduce or assist human efforts in future evaluations. For this reason, the new molecules dataset and an online tool for molecular similarity estimation have been made freely available. An orphan drug is a medicinal product used to treat a rare disease that affects only a small number of patients  . Given tTo encourage pharmaceutical companies to develop orphan drugs, regulatory agencies have brought forward legislation that provides a range of incentives. Such incentives include grants, financial incentives, the possibility of an accelerated review, and market exclusivity. Market exclusivity is arguably the most important incentive: under the EU legislation, a pharmaceutical company that develops an orphan drug for a specific rare disease is given a 10-year period of market exclusivity. During this period, no products that are considered similar to that orphan drug can be accepted or authorized by any European regulatory competent authority. Orphan drugs have less competition than conventional drugs, which encourages pharmaceutical companies to invest in researching novel medicines for rare diseases.The assessment of similarity between two drugs takes into account three criteria: molecular structure, mechanism of action, and therapeutic indication. Two drugs will be considered diverse if there are significant differences in one or more of the three aforementioned criteria. Thus far, the European Medicines Agency (EMA) has used majority voting on discretional judgments of similarity when assessing new drugs for rare diseases. Similarity is an inherently subjective concept, which depends on individual factors such as gender, age, state of mind, and previous experiences ,5. In geAutomated procedures that quantitatively evaluate molecular similarity are desirable, and the use of quantitative estimations of molecular similarity is well established in cheminformatics for virtual screening purposes ,9,10. SuFranco et al. developed Logistic Regression (LogReg) models that calculate the probability that a pair of molecules will be considered similar by a crowd of experts ,12. LogRFranco et al.  also repWhen gathering their dataset, Franco et al.  presentehttps://chematlas.chimie.unistra.fr/ReadySim/ (accessed on 26 May 2022)). We suggest using these publicly available models in agencies, such as EMA, in order to focus attention on those cases where the similarity is predicted to be debatable in a panel of experts. Such tools will also be useful for pharmaceutical companies and drug designers to take objective decisions regarding the development of a lead suitable for receiving the orphan drug status. Our WEB-based tool for similarity prediction takes as input a pair of molecules, which can be either drawn by the user into a graphical interface or submitted in SMILE format, and gives as output the probability that the pair will be considered similar by a panel of experts.We used the survey results to produce new LogReg models predicting for a pair of compounds the likeliness to be considered as similar or dissimilar by a panel of experts. These models are publicly available on our WEB site  that are rather difficult to subject to human analysis. Selection of molecular pairs was performed on the basis of both 2D and 3D similarity measures. The former was approximated by Tanimoto coefficient calculated with CDK Extended fingerprints  or dissimilarity  of the MP, and two subsets in which the calculated similarity measures were diametrically opposite for a given MP: sim2D,dis3D and sim3D,dis2D . On the other hand, users considered molecular pairs belonging to the dis2D,dis3D subset to be dissimilar (92.0% on average). As we expected, users did not agree very strongly on the similarity of molecules in the sim2D,dis3D and dis2D,sim3D subsets (55.5 and 50.7% respectively). The selected subsets are in excellent agreement with the similarity assessments by survey users . Moleculsim2D,sim3D and dis2D,dis3D subsets only. The presence in our data set of more problematic sim2D,dis3D and dis2D,sim3D subsets makes the similarity prediction task more challenging.It should be noted that the data set , bottom Similar to Franco et al., we developed machine-learning models aiming to predict human assessment of similarity for molecular pairs. One- and two-feature Logistic Regression models were built on the collected data using Equations (2) and (4), respectively see . The Fracorrect) is 81 and 70 for correct = 84), but still not perfectly. On the other hand, at the validation stage, all models demonstrated very good performance on the Franco set; the number of correctly predicted molecular pairs (out of 100) was 92 for both single-feature models and 95 for the double-feature models , whereas the As illustrated in In contrast to the models built on the collected set, those trained on the Franco set demonstrated a reasonable performance at the training stage, but performed poorly at the validation stage see . Indeed,Using Equation (4) and the single-feature model for the collected set, we identified the thresholds We created a new dataset of human assessments of molecular similarity to replace the missing test set from the original Franco et al. work. We changed the design of this dataset in order to investigate new situations. This new dataset had to include pairs of compounds that could display a significant 2D dissimilarity but be able to participate in equivalent 3D interactions. For this reason, we decided to select compounds binding to proteins with well-defined binding sites. Since the new dataset needed also pairs of compounds sharing a certain degree of 2D similarity but admittedly different 3D pattern of interaction, we investigated ligands of cytochromes. Finally, we queried the ChEMBL 27 database ,26,27 fosim2D,sim3D), pairs that are similar in 2D and dissimilar in 3D , pairs that are dissimilar in 2D and similar in 3D , and pairs that are dissimilar in 2D and dissimilar in 3D . We finally selected 25 pairs from each set. We thus obtained a data set with 100 MP, containing 25 pairs from each similarity subset. We applied the 2D protocol to all compounds, calculating Tanimoto CDK Extended between each unique MP. We then applied the OpenEye 3D protocol. Several molecules did not pass the Omega conformer generation step, and were discarded. We randomly selected 3000 molecular pairs for each target, and performed ROCS alignment and scoring between all conformers of each of the 9000 total MP. This dataset has been divided in 4 subsets: pairs that are similar in 2D and in 3D . Users were also shown 3D interactive molecular representations: the best ROCS alignments of  NGLview ,36. UserUsers had to express a similarity assessment for each of the 5 MP that were presented to them. The application did not allow users to proceed in the survey without answering. Users could not go back and change similarity assessments of previous molecules. After the 5 similarity assessments were expressed, users were asked about their academic qualification. The application stored only the answers of users who completed the survey. Yes) or dissimilar (No) molecules. The authors then calculated the percentage . We asked CHMP to provide us confidential access to the original test set. Our request was kindly approved, but at the time of writing we did not receive the dataset.The protocol for building 2D similarity prediction models involved preprocessing of original SMILES using RDKit  and MolVFilter command  model is built according to Equation (1) for a logistic transform of the percentage of expert opinions The predicted percentage of experts with the opinion that a pair is similar, The LogReg models were built using scikit-learn  using L1correct) and the Area Under the Receiver Operating Characteristic curve (ROC AUC).We evaluated the similarity prediction models using a variety of performance metrics for classification problems . We focuhttps://chematlas.chimie.unistra.fr/ReadySim/ (accessed on 26 May 2022) (The single-feature ay 2022) . This seIn this paper, we re-investigated the work of Franco et al., aiming to model the decision of a panel of experts concerning the similarity of pairs of organic drug-like molecules. In this context, we have gathered a new dataset comprising pairs of compounds that are likely to be perceived as similar in 2D while being dissimilar in 3D, and vice versa. The datasets are publicly available (Zenodo: 10.5281/zenodo.6472293).Building and testing models, we observed that predicted 2D similarity is the dominant factor corelating with the perception of similarity by the experts. However, the addition of 3D similarity concepts improves the model. This can originate from a bias of the experts to favor 2D information over 3D one when in doubt.https://chematlas.chimie.unistra.fr/ReadySim (accessed on 26 May 2022) to compute the predicted similarity. The models can also be used as a tool by pharmaceutical companies to perform a preliminary screening of molecules that may be suitable to receive the orphan drug status.Models built on the new dataset can be used to calculate the expected outcome of majority votes on molecular similarity. The models can be used as an additional tool by agencies tasked with evaluating molecules for the status of orphan drug: since such agencies rely on majority voting as their main decision-making tool, comparing their judgement with the output of a computational model trained to reproduce expert assessments on such a complex data set can be useful. According to the developed models, similarity thresholds


Escherichia coli by deleting 28 tRNA genes, 3 tRNA modifying systems, and 4 rRNA operons in various combinations. We find that redundancy in tRNA pools is beneficial when nutrients are plentiful and costly under nutrient limitation. This nutrient-dependent cost of redundant tRNA genes stems from upper limits to translation capacity and growth rate, and therefore varies as a function of the maximum growth rate attainable in a given nutrient niche. The loss of redundancy in rRNA genes and tRNA modifying enzymes had similar nutrient-dependent fitness consequences. Importantly, these effects are also contingent upon interactions across translation components, indicating a layered hierarchy from copy number of tRNA and rRNA genes to their expression and downstream processing. Overall, our results indicate both positive and negative selection on redundancy in translation components, depending on a species’ evolutionary history with feasts and famines.The rate and accuracy of translation hinges upon multiple components – including transfer RNA (tRNA) pools, tRNA modifying enzymes, and rRNA molecules – many of which are redundant in terms of gene copy number or function. It has been hypothesized that the redundancy evolves under selection, driven by its impacts on growth rate. However, we lack empirical measurements of the fitness costs and benefits of redundancy, and we have poor a understanding of how this redundancy is organized across components. We manipulated redundancy in multiple translation components of  Translation is the process by which cellular machines called ribosomes use the information encoded in genes to make proteins . Every organism requires two types of RNA molecules to make new proteins: ribosomal RNAs  and transfer RNAs . These RNA molecules are coded in the genome, but different organisms have different ‘copy numbers’: some genomes contain just a few copies of each of these genes, while others have thousands.This apparent redundancy – the presence of several copies of the same gene – is puzzling because it is costly to make and maintain DNA and RNA. This leads to an important question: how does redundancy in these important genes (coding for tRNAs and rRNAs) evolve? The answer is key to understanding how one of the most fundamental cellular processes, the making of proteins from DNA, has evolved.A possible reason for organisms to have many copies of the genes required to make proteins is to allow rapid translation, which allows cells to divide faster, and populations of cells to grow more quickly. However, this would likely mean that, when nutrients are scarce, carrying and translating many copies of the same gene would become a burden on the cell. Raval et al. set out to test this idea by measuring the costs and benefits of seemingly redundant translation components.Escherichia coli and asked if that changed bacterial growth. The experiments showed that when nutrients were plentiful, cells with more copies of the genes (high redundancy) were better able to use the nutrients and divide rapidly. However, when nutrients were limited, bacteria with extra gene copies divided more slowly, showing that the extra genes are indeed a big burden on the cell.To do this, Raval et al. deleted some of the redundant gene copies in the bacterium Raval et al. propose that nutrients available in the environment ultimately determine whether redundancy of the translation machinery is a blessing or a curse. This suggests that the redundancy and underlying growth strategies of different organisms are forged by their experiences of feast and famine during their evolutionary past.Importantly, by testing the joint effect of many different molecules involved in translation, Raval et al. uncovered several strategies that may maximize bacterial growth and protein production. Their results could thus be useful for optimizing the synthesis of important products that use growing cells as factories – from beer to insulin – where the rate of growth is critical. Escherichia coli the codon CCG can be decoded by the non-target tRNACGG (encoded by the gene proK), but also by the target tRNAUGG (encoded by proM) after the U at position 34 is modified to cmo5U by the cmo modification pathway (in principle rendering the gene proK redundant). MEs are thus critical for maximizing cellular decoding capacity  and are thus functionally degenerate. In addition, tRNA modifying enzymes (MEs) post-transcriptionally alter specific tRNAs , allowing them to read codons that are otherwise decoded by ‘non-target’ tRNAs that are not modified by MEs . Hence, capacity , and maicapacity . In addicapacity . Similarcapacity , which gcapacity . Thus, tE. coli  is thought to be an important force that shapes the evolution of many translation components. The maximum possible growth rate is determined by translation efficiency, which in turn depends on the concentrations of ribosomes and tRNAs . Across E. coli  and theiE. coli . During E. coli . ConsequE. coli . Indeed,or media .E. coli. As predicted, in this case, the loss of some gene copies is deleterious in rich media and advantageous in poor media . However, as discussed above we have very limited empirical evidence for the fitness consequences of redundancy in different translation components, particularly in the case of tRNA pools. We addressed these gaps by analyzing the nutrient-dependent impact of changing redundancy in multiple translational components, alone as well as in combination. Specifically, we tested the following predictions: (1) redundancy in tRNA and rRNA GCN, on the whole, should be important to maintain rapid growth, and the benefits of increased redundancy should be proportional to the achievable growth rate (2) broadly, a reduction in rRNA GCN should have stronger fitness impacts than tRNA GCN (3) across tRNAs, the loss of redundancy should be most impactful for major tRNA isotypes, and for non-target tRNAs when combined with the loss of a relevant ME (4) the fitness impact of reduced redundancy should increase with the severity of the loss, e.g., due to the deletion of multiple gene copies or multiple translational components. We worked with achinery  that allE. coli MG1655  encodes 42 tRNA isotypes with varying copy numbers   , five tRA genes)  that mod operons . We redu operons . (1) We  operons . (2) Pos operons , as well operons  and a ta operons   were predicted to be redundant because the genome has other gene copies encoding the same tRNA isotype (set I in phe U/V and ser W/X). In five other strains, we deleted single tRNA genes that appear redundant because MEs should allow other (target) tRNAs to perform their function . Only 10 of 20 strains showed a significant difference in at least one of these complex rich media, six with faster growth and four with slower growth than WT (sets I and II in proL had negligible effects on growth (GUU-Asn) did not have identical growth impacts, corroborating previous work suggesting that copies of the same tRNA contribute differently to translation   . Over ha change) . Consist change) .rel = 0.84 and 0.88 respectively; rel = 0.98 and 1.02 respectively; Next, we tested the fitness of tRNA deletion strains in more permissive rich media with easy-to-use sources of carbon (glucose) and amino acids (casamino acids) (‘GA’), where the WT growth rate is similar to that in complex rich media. Here, tRNA gene loss was uniformly deleterious, with 18 of 20 strains showing significantly slower growth , the loss of redundancy had a weak and variable impact, with only 5 of 23 strains (across sets I–III) showing a significant difference from WT . HoweverFinally, we found that the low growth rate of two of the tRNA deletion strains (ΔglyU and ΔthrW) was rescued by plasmid-borne tRNA copies, confirming that the observed fitness effects were indeed explained by tRNA loss . Overallrel) and the maximum attainable WT growth rate (Rmax), across all growth media tested . Of 28 strains (including five rRNA operon deletions described later in the results section), 26 showed a negative correlation between Rmax and Rrel, with a significant relationship in 11 cases  and a poor medium . In the WT, 26 of 42 (~62%) tRNA isotypes did not show a significant difference in relative levels across media . This isch media , media-dNext, we compared the composition of tRNA pools between WT and tRNA deletion strains. We expected that tRNA gene deletion would reduce the proportional contribution of the respective tRNA isotype to the total pool unless the deletion was compensated by expression from the remaining gene copies. In all four gene deletion mutants, the contribution of the focal (deleted) tRNA isotype to the total tRNA pool was lower than in the WT tRNA pool, in both rich and poor medium . Thus, ethrW or glyU caused near-elimination of these tRNA isotypes from the tRNA pool  showed stronger fitness effects than strains in set I and II. Furthermore, strains in set II had similar fitness as those in set I, potentially because the fitness impact of the tRNA deletion  was masked by the presence of MEs. Thus, the fitness impact of redundancy generally increases with the magnitude of the loss, as predicted by comparative evidence across genomes; but this has not been explicitly demonstrated. To do so, we further lowered translational redundancy by simultaneously deleting multiple translation components.cmoA, cmoB, mnmG, and tgt  had a significantly different impact from the deletion of the target tRNA alone in all media. This suggested that the effect of co-deletion is largely driven by ME loss, provided that the co-deleted tRNA is not a target tRNA. As observed with tRNA gene deletions alone  and extra-operonic tRNA genes set VII,  – had miTogether, our results lead to the following conclusions. First, we find that the simultaneous deletion of multiple copies of tRNA genes or rRNA genes has more severe fitness consequences than the loss of single gene copies. Second, increasing the severity of reduction in translational redundancy via co-deletion of MEs and tRNA genes amplifies the fitness consequences of losing redundancy. Lastly, when nutrients are in plenty, rRNA becomes limiting and the loss of tRNA genes has little additional impact on fitness; but when nutrients are limiting, shedding different translation components (rRNA and tRNA) additively increases fitness.The process of protein synthesis is central to life and is especially important to understand bacterial evolution given the direct link between translation, growth rate, and fitness. Translation rate is affected by several genomic components  and environmental factors (e.g. nutrient availability). Comparative analyses show that the genomic components have different degrees of redundancy across taxa, and suggest that this redundancy (in the fitness context) should be shaped by the strength of ecological selection for rapid growth . While lE. coli translation apparatus are indeed functionally redundant and that the costs and benefits of this redundancy vary with nutrient availability. When nutrients permit rapid growth, the loss of redundancy in both tRNAs and rRNAs is detrimental, as these components become limiting for translation. This is especially true for multiple deletions of abundantly used components such as rRNAs, MEs that modify many different tRNAs, and frequently used tRNAs (such as glycine and leucine). These results support prior predictions of larger fitness consequences following the loss of major tRNAs that read abundant codons and respond strongly to fast growth  amongst different components suggest multiple routes of optimization . We obsey number , and liny number .E. coli have evolved to rely strongly on gene copy numbers to maximize translation rates. Presumably, such species are either able to bear the costs of maintenance of surplus tRNA genes during periods of slow growth, or across longer evolutionary timescales the extra tRNA copies provide a net benefit. We therefore suggest a model whereby bacterial growth rate is primarily limited by external nutrient availability, then by rRNA molecules, and finally by tRNA pools . Thus, the layered costs and benefits of high translational redundancy – both within and across distinct components – are ultimately determined by the environmental context. Importantly, this model predicts that prolonged selection for rapid growth should cause successive evolutionary changes in the redundancy of different translation components, with the general order of events determined  by the hierarchical layering of components . tRNA deletions were made using Red recombinase, slightly modifying the Datsenko-Wanner method  and added 50 μl of phage lysate from the donor strain. After incubating at 37 °C (20 min), we washed the cells twice with 0.1 M citrate buffer (0.06 M citric acid C6H8O7 and 0.04 M sodium citrate dihydrate C6H9Na3O9). After the second wash, we resuspended the cells in 1 mL LB with 20 mM sodium citrate and incubated at 37 °C (60–90 min). Finally, we pelleted the cells, resuspended in 100 μl of citrate buffer and 5 mM sodium citrate, plated them on LB agar with Kanamycin (30 μg/ml), and incubated overnight at 37 °C.To combine ME and rRNA deletions with tRNA deletions, we used P1 transduction . We grewWe removed the kanamycin cassette that replaced the deleted gene using plasmid pCP20 . BrieflyFor all experiments, we used independently grown colonies or cultures to serve as biological replicates. Hence, unless mentioned otherwise, reported sample sizes refer to biological replicates.r) as the change in optical density (OD) read at 600 nm every 20 min or 45 min (for rapid and slow growth respectively), using an automated system . We estimated r by fitting exponential equations to the first few data points representing the exponential growth phase from OD vs. time curves, using Curvefitter software  from individual colonies grown from freezer stocks, and incubated cultures at 37 °C with shaking at 180 rpm for 14–16 hr (preculture). For growth rate measurement, we sub-cultured 1% v/v in 48 well microplates (Corning) in the appropriate growth medium: LB, TB  or M9 minimal medium  supplemented with specific carbon and nitrogen sources . We measured growth rate . From late log phase culture in the rich medium (after 6 hr of growth), we again sub-cultured as above into poor medium . When these cultures reached late log phase in the poor medium (12–16 hr), we again sub-cultured them back into the rich medium . After each transfer (downshift or upshift), we measured the growth rate as described above.For a subset of our tRNA gene deletion strains and WT, we measured the relative abundance of each tRNA isotype and compared this proportion across strains as described previously , or acroE. coli MG1655  . We assei MG1655 , allowini MG1655  in R  with 0.5 mM isopropyl-β-D-thiogalactoside (IPTG). Every 15 s, we pipetted out 500 µl culture and immediately mixed it with 100 µl of chloramphenicol (3 mg/ml) to block translation. After 10 mins of incubation on ice, we added 350 µl of Z buffer (reaction buffer) and continued incubation on ice for 1 hr. Next, we added 200 µl of 12 mg/ml ONPG . After 1–1.5 hr of incubation at 30 °C to allow the full development of colored product (o-nitrophenol) due to enzyme activity, we stopped the reaction by adding 500 µl of 1 M Na2CO3. After a brief centrifugation step to collect debris , we transferred the supernatant to a 96-well microplate to assay the formation of o-nitrophenol by measuring OD420. We converted OD values to Miller Units (MU) as per the original protocol, and from a plot of Miller Units (MU) of β-galactosidase vs. time, we estimated the first time point showing an increase in MU (after induction) as the time is taken to synthesize one molecule of β-galactosidase. The elongation rate (in amino acids per second) was inferred by dividing the length of the β-galactosidase protein (1019 amino acids) by this time.We measured the translation elongation rate for a subset of our strains, using the native β-galactosidase protein as a reporter as described earlier , with sorph frameshift mutation that leads to pyrimidine starvation . We first normalized GFP readout with OD at the same time point and then took the difference in GFP reading between time zero (immediately after induction) and after 1 hr to estimate the amount of GFP produced in an hour. We then normalized this readout for each mutant with the WT, to get the relative translation capacity of mutants.We grew WT and mutants transformed with plasmid pKEN::GFP2 overnight in LB medium with ampicillin for 14–16 hr. We subcultured 1% v/v into M9 with 0.8% glucose and amino acids (with 100 μg/ml ampicillin) and incubated for around 4–5 hr (mid-log phase) before inducing with 0.5 mM IPTG. After induction, we measured GFP and ODWe used GraphPad Prism (9.0.0) for all statistical analysis (except for YAMAT-seq) and to generate heatmaps, and DEseq2  and R ve The authors investigate the cost and benefits of maintaining seemingly redundant multiple copies of the translation machinery components. The authors demonstrate that while redundant multiple copies are beneficial in a nutrient-rich environment, maintaining these extra copies is costly and deleterious in a nutrient-poor environment. This explains why copy numbers of translation machinery genes are under selection according to the environmental niche an organism occupies. The work is very important and the findings exciting and supported by compelling evidence, in particular, the fitness gain upon deletion of translation genes in poor conditions is an insightful observation. public reviews designed to be posted alongside the preprint for the benefit of readers; (ii) feedback on the manuscript for the authors, including requests for revisions, shown below. We also include an acceptance summary that explains what the editors found interesting or important about the work.Our editorial process produces two outputs: (i) Decision letter after peer review:eLife. Your article has been reviewed by 3 peer reviewers, and the evaluation has been overseen by Yitzhak Pilpel as Reviewing Editor and Naama Barkai as the Senior Editor. The following individual involved in the review of your submission has agreed to reveal their identity: Dvir Schirman (Reviewer #3).Thank you for submitting your article "The layered costs and benefits of translational redundancy" for consideration by The reviewers have discussed their reviews with one another, and the Reviewing Editor has drafted this to help you prepare a revised submission. Each of the reviewers has comments for you to address (see below).Reviewer #1 (Recommendations for the authors):1) The induction lag method used to measure the "elongation rate" in Figure 5 quantifies the sum of the time to transcribe lacZ and the time to translate lacZ mRNA. To call it "elongation rate", the authors should stress the underlying assumption that all steps in b-galactosidase synthesis except elongation are equal in WT and mutant. Of note, MG16555 has an rph frameshift mutation that leads to pyrimidine starvation due to low pyrE expression levels (DOI: 10.1128/jb.175.11.3401-3407.1993). Thus, transcription is likely substantially faster in LB (contains pyrimidines) than in the M9 media.2) The concentration of galactose used for poor quality growth medium is not mentioned in the methods.3) Line 82: the reason rRNA concentration is more limiting for translation than tRNA is probably not that it is more abundant, but that rRNA concentration affects the concentration of ribozymes/catalysts for translation, while tRNA concentration affects the concentration of tertiary complex (the "substrate").4) Line 317: "so that both rRNA and tRNA would be limiting" is a problematic statement. The levels of both would presumably be reduced, but not likely to both be limiting at the same time.5) Line 336: presumably, the proM deletion mutant is growing OK because proL can read the same codon, not because the codon is rare.6) Line 339: perhaps more likely than assigning different functionality to identical RNAs, the different deletions result in different concentrations of the tRNA due to e.g. polarity effects in transcription.7) Line 464: what is meant by "reduced expression of the deleted tRNA"?E. coli and other bacteria . Gene copy numbers of genes close to the origin are more sensitive to growth rate than genes located close to the terminus. This work would benefit from including the distance to origin in the analysis of the impact of various gene deletions.8) A compelling case that differences in tRNA levels and tRNA/rRNA ratios at different growth rates are largely determined by the location of the respective genes relative to the origin of replication was made recently for Regarding tRNA measurements by YAMAT-seq:The authors should express more clearly in which sense the measurements are "relative", as it is not the relative expression level but the relative fractional contribution of each tRNA to the respective tRNA pool that is reported .A final note is that the reason YAMAT-seq does not report on levels of tRNAs is because a fixed amount of total RNA was used for the procedure (10 ug from each sample). Fast-growing cells have more RNA than slow-growing cells , so 10 ug represents RNA from more cells in the slow-growing cultures than it does in the fast-growing cultures.Line 42: it is not completely clear to this reviewer what is meant by "target" and "non-target" tRNAs. Further explanation in the Introduction would be beneficial for some readers (like myself).Line 282: was a pBAD plasmid really induced with IPTG, or is it a typo? I am only familiar with the arabinose-induction of pBAD plasmids.Figure 2 - Figure Supplement 1 1: Growth curves on a linear scale cannot be used to evaluate growth rates by eye. Showing the curves on semi-log graphs would be much more informative to the reader.Reviewer #2 (Recommendations for the authors):1. Please be clear and consistent in referencing the figure.2. Please address or better explain your methodology, especially how Rrel can be negative.3. Please change figure 3 to mark significant and non-significant data points and discuss them in the text.4. Please tone down your arguments in places where they sound general when, in effect, they are specific to a condition .5. I am missing controls. Please express deleted tRNAs from a plasmid in at least some deletion strains. If the author's hypothesis is true, it will rescue the negative fitness observed in deletion strains in rich media. These controls are needed at least in Figures 2 and 3. Notably, I would like to stress again that it should be done on a subset of the strains, probably the ones that display the strongest effect.Reviewer #3 (Recommendations for the authors):E. coli, and since EF-Tu is known as the most abundant cytoplasmic protein in E. coli I think it is an interesting case of translation machinery redundancy, and addressing it should benefit the manuscript.1. I think that in the discussion part the authors can discuss the possible cost/benefit effects of deleting one of the two copies of the translation elongation factor EF-Tu . As this is another example of seeming redundancy of the translation machinery in 2. In the discussion the authors suggest a model that explains their results (line 636). I think this suggested model will be clearer and easier to understand if it is described also through a graphical illustration.3. In Figure 2D, I think the figure will be easier to understand with a clearer example of the relevant correlation using real data points. I would replace the bottom right example with one of the panels from Figure 2 —figure supplement 5.4. In Figure 3 both panels have the same axes labels (Glycerol to TB / TB to glycerol), according to the figure legend one of them should be switched to . Reviewer #1 (Recommendations for the authors):1) The induction lag method used to measure the "elongation rate" in Figure 5 quantifies the sum of the time to transcribe lacZ and the time to translate lacZ mRNA. To call it "elongation rate", the authors should stress the underlying assumption that all steps in b-galactosidase synthesis except elongation are equal in WT and mutant. Of note, MG16555 has an rph frameshift mutation that leads to pyrimidine starvation due to low pyrE expression levels (DOI: 10.1128/jb.175.11.3401-3407.1993). Thus, transcription is likely substantially faster in LB (contains pyrimidines) than in the M9 media.Thank you for pointing this out. We have now mentioned both points in the methods section “Measuring translation elongation”2) The concentration of galactose used for poor quality growth medium is not mentioned in the methods.We have now added this in the methods section "Measuring growth parameters”.3) Line 82: the reason rRNA concentration is more limiting for translation than tRNA is probably not that it is more abundant, but that rRNA concentration affects the concentration of ribozymes/catalysts for translation, while tRNA concentration affects the concentration of tertiary complex (the "substrate").Agreed, we have now incorporated this (line 83-84).4) Line 317: "so that both rRNA and tRNA would be limiting" is a problematic statement. The levels of both would presumably be reduced, but not likely to both be limiting at the same time.We have modified the sentence to just say that they will both be reduced (line 338-39).5) Line 336: presumably, the proM deletion mutant is growing OK because proL can read the same codon, not because the codon is rare.The deletion mutant mentioned here is proL (not proM). The proL tRNA is a single-copy tRNA with no tRNA (or ME) backup. Hence, we speculate that its loss has negligible effects because the cognate codon is only used rarely.6) Line 339: perhaps more likely than assigning different functionality to identical RNAs, the different deletions result in different concentrations of the tRNA due to e.g. polarity effects in transcription.By functional differences we meant that at the level of contribution to translation the two tRNAs are different, e.g. due to polarity effects as mentioned in the comment. We did not mean to say the tRNAs have completely distinct functions (outside the context of translation). We have now edited the sentence to avoid confusion (lines 361-362).7) Line 464: what is meant by "reduced expression of the deleted tRNA"?Here, we refer to the deletion of single-copy tRNA genes, which should lead to a complete loss of the relevant tRNA isotypes. In YAMAT-seq data, this will show up as a large drop in the relative contribution of these tRNA isotypes to the tRNA pool (compared to WT). We have now rephrased the text here to avoid confusion (lines 488-489). We have also ensured that throughout the manuscript we have clarified whether we are referring to tRNA genes or tRNA isotypes.E. coli and other bacteria . Gene copy numbers of genes close to the origin are more sensitive to growth rate than genes located close to the terminus. This work would benefit from including the distance to origin in the analysis of the impact of various gene deletions.8) A compelling case that differences in tRNA levels and tRNA/rRNA ratios at different growth rates are largely determined by the location of the respective genes relative to the origin of replication was made recently for Thank you for this suggestion. The paper mentioned above also led us to other useful references, which we now cite in the manuscript.Distance from the replication origin does not explain differences in the fitness impacts of copies of the same tRNA isotype (e.g. Asn-GUU). We have now included this in the text (lines 362-367) and in a new supplemental figure .Regarding tRNA measurements by YAMAT-seq:The authors should express more clearly in which sense the measurements are "relative", as it is not the relative expression level but the relative fractional contribution of each tRNA to the respective tRNA pool that is reported .A final note is that the reason YAMAT-seq does not report on levels of tRNAs is because a fixed amount of total RNA was used for the procedure (10 ug from each sample). Fast-growing cells have more RNA than slow-growing cells , so 10 ug represents RNA from more cells in the slow-growing cultures than it does in the fast-growing cultures.Thank you very much for these thoughtful comments. We agree that the phrasing was incorrect and confusing. We have now clarified in the methods section that YAMAT-seq is like RNA-seq, and only measures the contribution of a given tRNA isotype to the total tRNA pool (lines 221-225). We have also rephrased the text in the entire results section about YAMAT-seq as well as figure legends, to clearly convey the comparisons being made.Line 42: it is not completely clear to this reviewer what is meant by "target" and "non-target" tRNAs. Further explanation in the Introduction would be beneficial for some readers (like myself).This was described with an example in the Introduction (lines 45-52). We have now edited the text there to explain why we call them “target” tRNAs, which is hopefully helpful; and we have now added more explanation in the legend for figure 1.Line 282: was a pBAD plasmid really induced with IPTG, or is it a typo? I am only familiar with the arabinose-induction of pBAD plasmids.This was a mistake, thank you for pointing it out. It was a pKEN plasmid, not pBAD; we have now corrected this.Figure 2 - Figure Supplement 1 1: Growth curves on a linear scale cannot be used to evaluate growth rates by eye. Showing the curves on semi-log graphs would be much more informative to the reader.We have now added growth curves on a log scale to this supplementary figure.Reviewer #2 (Recommendations for the authors):1. Please be clear and consistent in referencing the figure.2. Please address or better explain your methodology, especially how Rrel can be negative.3. Please change figure 3 to mark significant and non-significant data points and discuss them in the text.4. Please tone down your arguments in places where they sound general when, in effect, they are specific to a condition .5. I am missing controls. Please express deleted tRNAs from a plasmid in at least some deletion strains. If the author's hypothesis is true, it will rescue the negative fitness observed in deletion strains in rich media. These controls are needed at least in Figures 2 and 3. Notably, I would like to stress again that it should be done on a subset of the strains, probably the ones that display the strongest effect.Thank you for these points, which we have addressed as detailed above.Reviewer #3 (Recommendations for the authors):E. coli, and since EF-Tu is known as the most abundant cytoplasmic protein in E. coli I think it is an interesting case of translation machinery redundancy, and addressing it should benefit the manuscript.1. I think that in the discussion part the authors can discuss the possible cost/benefit effects of deleting one of the two copies of the translation elongation factor EF-Tu . As this is another example of seeming redundancy of the translation machinery in Thank you for this suggestion, we have now discussed this work (lines 693-696).2. In the discussion the authors suggest a model that explains their results (line 636). I think this suggested model will be clearer and easier to understand if it is described also through a graphical illustration.Thank you for this suggestion. We have now added a graphical representation as (new) Figure 7.3. In Figure 2D, I think the figure will be easier to understand with a clearer example of the relevant correlation using real data points. I would replace the bottom right example with one of the panels from Figure 2 —figure supplement 5.Here, we wanted to show the possible relationships and various implications, which would be difficult if we used actual data. Also, given the large variation across panels in the supplemental figure, we worry that choosing one would be misleading. Hence, we have retained the schematic.4. In Figure 3 both panels have the same axes labels (Glycerol to TB / TB to glycerol), according to the figure legend one of them should be switched to .Corrected, thank you.