Author response:
The following is the authors’ response to the previous reviews.
Reviewer #1 (Public review):
Summary:
In this descriptive study, Tateishi et al. report a Tn-seq based analysis of genetic requirements for growth and fitness in 8 clinical strains of Mycobacterium intracellulare Mi), and compare the findings with a type strain ATCC13950. The study finds a core set of 131 genes that are essential in all nine strains, and therefore are reasonably argued as potential drug targets. Multiple other genes required for fitness in clinical isolates have been found to be important for hypoxic growth in the type strain.
Strengths:
The study has generated a large volume of Tn-seq datasets of multiple clinical strains of Mi from multiple growth conditions, including from mouse lungs. The dataset can serve as an important resource for future studies on Mi, which despite being clinically significant remains a relatively understudied species of mycobacteria.
Thank you for the comment on the significance of our manuscript on the basic research of non-tuberculous mycobacteria.
Weaknesses:
The primary claim of the study that the clinical strains are better adapted for hypoxic growth is yet to be comprehensively investigated. However, this reviewer thinks such an investigation would require a complex experimental design and perhaps forms an independent study
Thank you for the comment on the issue of the claim of better adaptation for hypoxic growth in the clinical strains being not completely revealed. We agree the reviewer’s comment that comprehensive investigation of adaptation for hypoxic growth in the clinical strains should be a future project in terms of the complexity of an experimental design.
Reviewer #4 (Public review):
Summary:
In this study Tateishi et al. used TnSeq to identify 131 shared essential or growth defect-associated genes in eight clinical MAC-PD isolates and the type strain ATCC13950 of Mycobacterium intracellulare which are proposed as potential drug targets. Genes involved in gluconeogenesis and the type VII secretion system which are required for hypoxic pellicle-type biofilm formation in ATCC13950 also showed increased requirement in clinical strains under standard growth conditions. These findings were further confirmed in a mouse lung infection model.
Strengths:
This study has conducted TnSeq experiments in reference and 8 different clinical isolates of M. intracellulare thus producing large number of datasets which itself is a rare accomplishment and will greatly benefit the research community
Thank you for the comment on the significance of our manuscript on the basic research of non-tuberculous mycobacteria.
Weaknesses:
(1) A comparative growth study of pure and mixed cultures of clinical and reference strains under hypoxia will be helpful in supporting the claim that clinical strains adapt better to such conditions. This should be mentioned as future directions in the discussion section along with testing the phenotype of individual knockout strains.
Thank you for the comment on the idea of a comparative growth assay of pure and mixed cultures of clinical and reference strains under hypoxia. We appreciate the idea that showing the phenomenon of advantage of bacterial growth of the clinical strains under hypoxia in mixed culture with the ATCC strain would be important to strengthen the claim of better adaptation for hypoxic growth in the clinical strains. However, co-culture conditions introduce additional variables, including inter-strain competition or synergy, which can obscure the specific contributions of hypoxic adaptation in each strain. Therefore, we consider that our current approach using monoculture growth curves under defined oxygen conditions offers a clearer interpretation of strain-specific hypoxic responses.
Following the comment, we have added the mention of the mixed culture experiment and the growth assay using individual knockout strains as future directions (page 35 lines 614-632 in the revised manuscript).
“We have provided the data suggesting the preferential hypoxic adaptation in clinical strains compared to the ATCC type strain by the growth assay of individual strains. To strengthen our claim, several experiments are suggested including mixed culture experiments of clinical and reference strains under hypoxia. However, co-culture conditions introduce additional variables, including inter-strain competition or synergy, which can obscure the specific contributions of hypoxic adaptation in each strain. Therefore, we took the current approach using monoculture growth curves under defined oxygen conditions, which offers a clearer interpretation of strainspecific hypoxic responses. Furthermore, one of the limitations of this study is the lack of validation of TnSeq results with individual gene knockouts. Contrary to the case of Mtb, the technique of constructing knockout mutants of slow-growing NTM including M. intracellulare has not been established long time. We have just recently succeeded in constructing the vector plasmids for making knockout mutants of M intracellulare (Tateishi. Microbiol Immunol. 2024). Growth assay of individual knockout strains of genes showing increased genetic requirements such as pckA, glpX, csd, eccC5 and mycP5 in the clinical strains is suggested to provide the direct involvement of these genes on the preferential hypoxic adaptation in clinical strains. We have a future plan to construct knockout mutants of these genes to confirm the involvement of these genes on preferential hypoxic adaptation.”
Reference
Tateishi, Y., Nishiyama, A., Ozeki, Y. & Matsumoto, S. Construction of knockoutmutants in Mycobacterium intracellulare ATCC13950 strain using a thermosensitive plasmid containing negative selection marker rpsL+. Microbiol Immunol 68, 339-347 (2024).
(2) Authors should provide the quantitative value of read counts for classifying a gene as "essential" or "non-essential" or "growth-defect" or "growthadvantage". Merely mentioning "no insertions in all or most of their TA sites" or "unusually low read counts" or "unusually high low read counts" is not clear
Thank you for the comment on the issue of not providing the quantitative value of read counts for classifying the gene essentiality. In this study, we used an Hidden Markov Model (HMM) to predict gene essentiality. The HMM does not classify the 4 gene essentiality uniquely by the quantitative number of read counts but uses a probabilistic model to estimate the state at each TA based on the read counts and consistency with adjacent sites (Ioerger. Methods Mol Biol 2022).
The HMM uses consecutive data of read counts and calculates transition probability for predicting gene essentiality across the genome. The HMM allows for the clustering of insertion sites into distinct regions of essentiality across the entire genome in a statistically rigorous manner, while also allowing for the detection of growth-defect and growth-advantage regions. The HMM can smooth over individual outlier values (such as an isolated insertion in any otherwise empty region, or empty sites scattered among insertion in a non-essential region) and make a call for a region/gene that integrates information over multiple sites. The gene-level calls are made based on the majority call among the TA sites within each gene. The HMM automatically tunes its internal parameters (e.g. transition probabilities) to the characteristics of the input datasets (saturation and mean insertion counts) and can work over a broad range of saturation levels (as low as 20%) (DeJesus. BMC Bioinformatics 2013). Thus, HMM can represent the more nuanced ways the growth of an organism might be affected by the disruption of its genes (https://orca1.tamu.edu/essentiality/Tn-HMM/index.html)
Thus, the prediction of gene essentiality by the HMM does not rely on the quantitative threshold of Tn insertion reads independently at each TA site, but rather it is the most probable states for the whole sequence taken together (computed using Vitebri algorithm). Of the statistical methods, the HMM is a standard method for predicting gene essentiality in TnSeq (Ioerger TR. Methods Mol Biol. 2022) since a substantial number of TnSeq studies adopt this method for predicting gene essentiality (Akusobi. mBio 2025, DeJesus. mBio 2017, Dragset mSystems 2019, Mendum. BCG Genomics 2019). The HMM can be applied in many bioinformatics fields such as profiling functional protein families, identifying functional domains, sequence motif discoveries and gene prediction.
Taken together, we do not have the quantitative value of read counts for classifying gene essentiality by an HMM because the statistical methods for predicting gene essentiality do not uniquely use the quantitative value of read counts but use the transition of the read counts across the genome.
Reference
Ioerger TR. Analysis of Gene Essentiality from TnSeq Data Using Transit. Methods Mol Biol. 2022 ; 2377: 391–421. doi:10.1007/978-1-0716-1720-5_22.
DeJesus MA, Ioerger TR (2013) A Hidden Markov Model for identifying essential and 5 growth-defect regions in bacterial genomes from transposon insertion sequencing data. BMC Bioinformatics 14:303 [PubMed: 24103077]
Website by Ioerger: A Hidden Markov Model for identifying essential and growthdefect regions in bacterial genomes from transposon insertion sequencing data. https://orca1.tamu.edu/essentiality/Tn-HMM/index.html
Akusobi. C. et al. Transposon-sequencing across multiple Mycobacterium abscessus isolates reveals significant functional genomic diversity among strains. mBio 6, e0337624 (2025).
DeJesus, M.A. et al. Comprehensive essentiality analysis of the Mycobacterium tuberculosis genome via saturating transposon mutagenesis. mBio 8, e02133-16 (2017).
Dragset, M.S., et al. Global assessment of Mycobacterium avium subsp. hominissuis genetic requirement for growth and virulence. mSystems 4, e00402-19 (2019). Mendum T.A., et al. Transposon libraries identify novel Mycobacterium bovis BCG genes involved in the dynamic interactions required for BCG to persist during in vivo passage in cattle. BMC Genomics 20, 431 (2019)
(3) One of the major limitations of this study is the lack of validation of TnSeq results with individual gene knockouts. Authors should mention this in the discussion section.
Thank you for the comment on the issue of the lack of validation of TnSeq results by using individual knockout mutants. We agree that the lack of validation of TnSeq results is one of the limitations of this study. We have just recently succeeded in constructing the vector plasmids for making knockout mutants of M intracellulare (Tateishi. Microbiol Immunol. 2024). We will proceed to the validation experiment of TnSeq-hit genes by constructing knockout mutants.
Following the comment, we have added the description in the Discussion (page 35 lines 622-632 in the revised manuscript) as follows: “Furthermore, one of the limitations of this study is the lack of validation of TnSeq results with individual gene knockouts. Contrary to the case of Mtb, the technique of constructing knockout mutants of slow-growing NTM including M. intracellulare has not been established long time. We have just recently succeeded in constructing the vector plasmids for making knockout mutants of M intracellulare (Tateishi. Microbiol Immunol 2024). Growth assay of individual knockout strains of genes showing increased genetic requirements such as pckA, glpX, csd, eccC5 and mycP5 in the clinical strains is suggested to provide the direct involvement of these genes on the 6 preferential hypoxic adaptation in clinical strains. We have a future plan to construct knockout mutants of these genes to confirm the involvement of these genes on preferential hypoxic adaptation.”
Reference
Tateishi, Y., Nishiyama, A., Ozeki, Y. & Matsumoto, S. Construction of knockout mutants in Mycobacterium intracellulare ATCC13950 strain using a thermosensitive plasmid containing negative selection marker rpsL + . Microbiol Immunol 68, 339-347 (2024).
Reviewer #5 (Public review):
Summary:
In the research article, "Functional genomics reveals strain-specific genetic requirements conferring hypoxic growth in Mycobacterium intracellulare" Tateshi et al focussed their research on pulmonary disease caused by Mycobacterium avium-intracellulare complex which has recently become a major health concern. The authors were interested in identifying the genetic requirements necessary for growth/survival within host and used hypoxia and biofilm conditions that partly replicate some of the stress conditions experienced by bacteria in vivo. An important finding of this analysis was the observation that genes involved in gluconeogenesis, type VII secretion system and cysteine desulphurase were crucial for the clinical isolates during standard culture while the same were necessary during hypoxia in the ATCC type strain.
Strength of the study:
Transposon mutagenesis has been a powerful genetic tool to identify essential genes/pathways necessary for bacteria under various in vitro stress conditions and for in vivo survival. The authors extended the TnSeq methodology not only to the ATCC strain but also to the recently clinical isolates to identify the differences between the two categories of bacterial strains. Using this approach they dissected the similarities and differences in the genetic requirement for bacterial survival between ATCC type strains and clinical isolates. They observed that the clinical strains performed much better in terms of growth during hypoxia than the type strain. These in vitro findings were further extended to mouse 7 infection models and similar outcomes were observed in vivo further emphasising the relevance of hypoxic adaptation crucial for the clinical strains which could be explored as potential drug targets.
Thank you for the comment on the significance of our manuscript on the basic research of non-tuberculous mycobacteria.
Weakness:
The authors have performed extensive TnSeq analysis but fail to present the data coherently. The data could have been well presented both in Figures and text. In my view this is one of the major weakness of the study.
Thank you for the comment on the issue of data presentation. Our point-by-point response to the Reviewer’s comments is shown below.
Reviewer #5 (Recommendations for the authors):
Major comments:
(1) The result section could have been better organized by splitting into multiple sections with each section focusing on a particular aspect.
Thank you for the comment on the organization of the section. We have split into multiple sections with each section focusing on a particular aspect as follows:
(1) Common essential and growth-defect-associated genes representing the genomic diversity of M. intracellulare strains (page 6 lines 102-103 in the revised manuscript)
(2) The sharing of strain-dependent and accessory essential and growth-defectassociated genes with genes required for hypoxic pellicle formation in the type strain ATCC13950 (page 8 lines 129-131 in the revised manuscript)
(3) Partial overlap of the genes showing increased genetic requirements in clinical MAC-PD strains with those required for hypoxic pellicle formation in the type strain ATCC13950 (page 9 lines 151-153 in the revised manuscript)
(4) Minor role of gene duplication on reduced genetic requirements in clinical MACPD strains (page 11 lines 184-185 in the revised manuscript)
(5) Identification of genes in the clinical MAC-PD strains required for mouse lung infection (page 12 lines 210-211 in the revised manuscript) 8
(6) Effects of knockdown of universal essential or growth-defect-associated genes in clinical MAC-PD strains (page 17 lines 305-306 in the revised manuscript)
(7) Differential effects of knockdown of accessory/strain-dependent essential or growth-defect-associated genes among clinical MAC-PD strains (page 19 lines 325- 326 in the revised manuscript)
(8) Preferential hypoxic adaptation of clinical MAC-PD strains evaluated with bacterial growth kinetics (page 21 lines 365-366 in the revised manuscript)
(9) The pattern of hypoxic adaptation not simply determined by genotypes (page 22 line 386 in the revised manuscript)
(2) The different strains that were used in the study, how they were isolated and some information on their genotypes could have been mentioned in brief in the main text and a table of different strains included as a supplementary table
Thank you for the comment on the information on the clinically isolated strains used in this study. All clinical strains were isolated from sputum of MAC-PD patients (Tateishi. BMC Microbiol. 2021, BMC Microbiol. 2023). Sputum samples were treated by the standard method for clinical isolation of mycobacteria with 0.5% (w/v) Nacetyl-L-cysteine and 2% (w/v) sodium hydroxide and plated on 7H10/OADC agar plates. Single colonies were picked up for use in experiments as isolated strains.
Following the comment, we have added the description on the information of the strains (page 37 lines 652-660 in the revised manuscript). “All eleven clinical strains from MAC-PD patients in Japan were isolated from sputum (Tateishi. BMC Microbiol 2021, BMC Microbiol 2023). Sputum samples were treated by the standard method for clinical isolation of mycobacteria with 0.5% (w/v) N-acetyl-L-cysteine and 2% (w/v) sodium hydroxide and plated on 7H10/OADC agar. Single colonies were picked up for use in experiments as isolated strains. Of these strains, ATCC13950, M.i.198, M.i.27, M018, M005 and M016 belong to the typical M. intracellulare (TMI) genotype and M001, M003, M019, M021 and MOTT64 belong to the M. paraintracellulare-M. indicus pranii (MP-MIP) genotype (Fig. 1, new Supplementary Table 1)”
Moreover, we have added the Supplementary Table showing the information on genotypes of each strain and the purpose of the use of study strains as new Supplementary Table 1
References
Tateishi, Y. et al. Comparative genomic analysis of Mycobacterium intracellulare: implications for clinical taxonomic classification in pulmonary Mycobacterium aviumintracellulare complex disease. BMC Microbiol 21, 103 (2021). Tateishi, Y. et al. Virulence of Mycobacterium intracellulare clinical strains in a mouse model of lung infection - role of neutrophilic inflammation in disease severity. BMC Microbiol 23, 94 (2023).
(3) As stated by the previous reviews, an explanation for the variation in the Tn insertion across different strains has not been provided and how they derive conclusions when the Tn frequency was not saturating.
Thank you for the comment on how to predict gene essentiality from our TnSeq data under the variation in the Tn insertion reads with suboptimal levels of saturation without reaching full saturation of Tn insertion.
As for the overcome of the Tn insertion variation, we normalized data by using Beta-Geometric correction (BGC), a non-linear normalization method. BGC normalizes the datasets to fit an “ideal” geometric distribution with a variable probability parameter ρ, and BGC improves resampling by reducing the skew. On TRANSIT software, we set the replicate option as Sum to combine read counts. And we normalized the datasets by Beta-Geometric correction (BGC) to reduce variabilities and performed resampling analysis by using normalized datasets to compare the genetic requirements between strains.
Following the comment, we have explained the variation in the Tn insertion across different strains in the manuscript (pages 39-40, lines 700-708 in the revised manuscript). “The number of Tn insertion in our datasets varied between 1.3 to 5.8 million among strains. To reduce the variation in the Tn insertion across strains, we adopt a non-linear normalization method, Beta-Geometric correction (BGC). BGC normalizes the datasets to fit an “ideal” geometric distribution with a variable probability parameter ρ, and BGC improves resampling by reducing the skew. On TRANSIT software, we set the replicate option as Sum to combine read counts. And we normalized the datasets by BGC and performed resampling analysis by using normalized datasets to compare the genetic requirements between strains.”
As for the issue of saturation levels of Tn insertion in our Tn mutant libraries, we made a description in the Discussion in the 1st version of the revised manuscript (pages 33-35 lines 592-613 in the 2nd version of the revised manuscript). The saturation of our Tn mutant libraries became 62-79% as follows: ATCC13950: 67.6%, M001: 72.9%, M003: 63.0%, M018: 62.4%, M019: 74.5%, M.i.27: 76.6%, M.i.198: 68.0%, MOTT64: 77.6%, M021: 79.9% by combining replicates. That is, we calculated gene essentiality from the Tn mutant libraries with 62-79% saturation in each strain. The levels of saturation of transposon libraries in our study are similar to the very recent TnSeq anlaysis by Akusobi where 52-80% saturation libraries (so-called “high-density” transposon libraries) are used for HMM and resampling analyses (Supplemental Methods Table 1[merged saturation] in Akusobi. ｍBio. 2025). The saturation of Tn insertion in individual replicates of our libraries is also comparable to that reported by DeJesus (Table S1 in mBio 2017). Thus, we consider that our TnSeq method of identifying essential genes and detecting the difference of genetic requirements between clinical MAC-PD strains and ATCC13950 is acceptable.
As for the identification of essential or growth-defect-associated genes by an HMM analysis, we do not consider that we made a serious mistake for the classification of essentiality by an HMM method in most of the structural genes that encode proteins. Because, as DeJesus shows, the number essential genes identified by TnSeq are comparable in large genes possessing more than 10 TA sites between 2 and 14 TnSeq datasets, most of which seem to be structural genes (Supplementary Fig 2 in mBio 2017). If the reviewer intends to regard our libraries far less saturated due to the smaller replicates (n = 2 or 3) than the previous DeJesus’ and Rifat’s reports using 10-14 replicates obtained to acquire so-called “high-density” transposon libraries (DeJesus. mBio 2017, Rifat. mBio 2021), there is a possibility that not all genes could be detected as essential due to the incomplete 11 covering of Tn insertion at nonpermissive TA sites, especially the small genes including small regulatory RNAs. Even if this were the case, it would not detract from the findings of our current study
As for the identification of genetic requirements by a resampling analysis, we consider that our data is acceptable because we compared the normalized data between strains whose saturation levels are similar to the previous report by Akusobi with “high-density” transposon libraries as mentioned above.
References
DeJesus, M.A., Ambadipudi, C., Baker, R., Sassetti, C. & Ioerger, T.R. TRANSIT--A software tool for Himar1 TnSeq analysis. PLoS Comput Biol 11, e1004401 (2015). Akusobi. C. et al. Transposon-sequencing across multiple Mycobacterium abscessus isolates reveals significant functional genomic diversity among strains. mBio 6, e0337624 (2025).
DeJesus, M.A. et al. Comprehensive essentiality analysis of the Mycobacterium tuberculosis genome via saturating transposon mutagenesis. mBio 8, e02133-16 (2017).
Rifat, D., Chen L., Kreiswirth, B.N. & Nuermberger, E.L.. Genome-wide essentiality analysis of Mycobacterium abscessus by saturated transposon mutagenesis and deep sequencing. mBio 12, e0104921 (2021).
(4) ATCC strain is missing in the mouse experiment.
Thank you for the comment on the necessity of setting ATCC13950 as a control strain of mouse TnSeq experiment. To set ATCC13950 as a control strain in mouse infection experiments would be ideal. However, we have proved that ATCC13950 is eliminated within 4 weeks of infection in mice (Tateishi. BMC Microbiol 2023). To perform TnSeq, it is necessary to collect colonies at least the number of TA sites mathematically (Realistically, colonies with more than the number of TA sites are needed to produce biologically robust data.). That means, it is impossible to perform in vivo TnSeq study using ATCC13950 due to the inability to harvest sufficient number of colonies.
To make these things understood clearly, we have added the description of being unable to perform in vivo TnSeq in ATCC13950 in the result section (page 13 lines 221-222 in the revised manuscript).
“(It is impossible to perform TnSeq in lungs infected with ATCC13950 because ATCC13950 is eliminated within 4 weeks of infection) (Tateishi. BMC Microbiol 2023)”
Reference
Tateishi, Y. et al. Virulence of Mycobacterium intracellulare clinical strains in a mouse model of lung infection - role of neutrophilic inflammation in disease severity. BMC Microbiol 23, 94 (2023).
(5) The viability assays done in 96 well plate may not be appropriate given that mycobacterial cultures often clump without vigorous shaking. How did they control evaporation for 10 days and above?
Thank you for the comment on the issue of viability assay in terms of bacterial clumping. As described in the Methods (page 44 lines 778-781 in the revised manuscript), we have mixed the culture containing 250 μL by pipetting 40 times to loosen clumping every time before sampling 4 μL for inoculation on agar plates to count CFUs. By this method, we did not observe macroscopic clumping or pellicles like of Mtb or M. bovis BCG as seen in statistic culture.
We used inner wells for culture of bacteria in hypoxic growth assay. To control evaporation of the culture, we filled the distilled water in the outer wells and covered the plates with plastic lids. We cultured the plates with humidification at 37°C in the incubator.
(6) Fig. 7a many time points have only two data points and in few cases. The Y axis could have been kept same for better comparison for all strains and conditions.
Thank you for the comments on the data presentation of hypoxic growth assay in original Fig. 7a (new Fig 8a). The reason of many time points with only two data points is the close values of data in individual replicates. For example, the log10- transformed values of CFUs in ATCC13950 under aerobic culture are 4.716, 4.653, 4.698 at day 5, 4.949, 5.056, 4.954 at day 6, and 5.161, 5.190, 5.204 at day 8. We have added the numerical data of CFUs used for drawing growth curves as new Supplementary Table 19. Therefore, the data itself derives from three independent replicates.
Following the comment, we have revised the data presentation in new Fig 8a (original Fig. 7a) by keeping the same maximal value of Y axis across all graphs. In addition, we have revised the legend to designate clearly how we obtained the data of growth curves as follows (page 63 lines 1107-1108 in the revised manuscript): “Data on the growth curves are the means of three biological replicates from one experiment. Data from one experiment representative of three independent 13 experiments (N = 3) are shown.”
(7) The relevance of 7b is not well discussed and a suitable explanation for the difference in the profiles of M001 and MOTT64 between aerobic and hypoxia is not provided. Data representation should be improved for 7c with appropriate spacing.
Thank you for the comments on the relevance of original Fig. 7b (new Fig. 8b). In order to compare the pattern of logarithmic growth curves between strains quantitatively, we focused on time and slope at midpoint. The time at midpoint is the timing of entry to logarithmic growth phase. The earlier the strain enters logarithmic phase, the smaller the value of the time at midpoint becomes.
The two strains belonging to the MP-MIP subgroup, MOTT64 and M001 showed similar time at midpoint under aerobic conditions. However, the time at midpoint was significantly different between MOTT64 and M001 under hypoxia, the latter showing great delay of timing of entry to logarithmic phase. In contrast to the majority of the clinical strains that showed reduced growth rate at midpoint under hypoxia, neither strain showed such phenomenon under hypoxia. Although the implication in clinical situations has not been proven, strains without slow growth under hypoxia may have different (possibly strain-specific) mechanisms of hypoxic adaptation corresponding to the growth phenotypes under hypoxia.
Following the comment, we have added the explanation on the difference in the profiles of M001 and MOTT64 between aerobic and hypoxia in the Discussion (page 31 lines 552-557, page 32 lines 562-567 in the revised manuscript). “The two strains belonging to the MP-MIP subgroup, MOTT64 and M001 showed similar time at midpoint under aerobic conditions. However, the time at midpoint was significantly different between MOTT64 and M001 under hypoxia, the latter showing great delay of timing of entry to logarithmic phase. In contrast to the majority of the clinical strains that showed slow growth at midpoint under hypoxia, neither strain showed such phenomenon.”.
” Our inability to construct knockdown strains in M001 and MOTT64 prevented us from clarifying the factors that discriminate against the pattern of hypoxic adaptation. Although the implication in clinical situations has not been proven, strains without slow growth under hypoxia may have different (possibly strainspecific) mechanisms of hypoxic adaptation corresponding to the growth phenotypes under hypoxia.”
Following the comment, we have made the space between new Fig. 8b and 14 new Fig. 8c (original Fig. 7b and Fig. 7c).
(8) Fig. 8a, the antibiotic sensitivity at early and later time points do not seem to correlate. Any explanation?
Thank you for the comment on the uncorrelation of data of growth inhibition in knockdown strains of universal essential genes between early and later time points. The diminished effects of growth inhibition observed at Day 7 in knockdown strains may be due to the “escape” clones of knockdown strains under long-term culture by adding anhydrotetracycline (aTc) that induces sgRNA. As described in the Methods (pages 42-43 lines 754-758), we added aTc repeatedly every 48 h to maintain the induction of dCas9 and sgRNAs in experiments that extended beyond 48 h (Singh. Nucl Acid Res 2016). Such phenomenon has been reported by McNeil (Antimicrob Agent Chem. 2019) showing the increase in CFUs by day 9 with 100 ng/mL aTc with bacterial growth being detected between 2 and 3 weeks. These phenotypes of “escape” mutants is considered to be attributed to the promotor responsiveness to aTc.
Nevertheless, except for gyrB in M.i.27, the effect of growth inhibition at Day 7 in knockdown strains of universal essential genes was 10-1 or less of comparative growth rates of knockdown strains to vector control strains (y-axis of original Fig. 8). In this study, we judged the positive level of growth inhibition as 10-1 or less of comparative growth rates of knockdown strains to vector control strains (y-axis of new Fig. 7). Thus, we consider that the CRISPR-i data overall validated the essentiality of these genes.
References
Singh A.K., et al. Investigating essential gene function in Mycobacterium tuberculosis using an efficient CRISPR interference system, Nucl Acid Res 44, e143 (2016) McNeil M.B. &, Cook, G.M. Utilization of CRISPR interference to validate MmpL3 as a drug target in Mycobacterium tuberculosis. Antimicrob Agent Chem 63, e00629-19 (2019)
(9) Fig. 8b and c very data representation could have been improved. Some strains used in 7 are missing. The authors refer to technical challenge with respect to M001. Is it the same for others as well (MOTT64). The interpretation of data in result and discussion section is difficult to follow. Is the data subjected to statistical analysis?
Thank you for the comment on data presentation in original Fig. 8b (new Fig 7b). As 15 mentioned in the Discussion (page 18 lines 316-31 in the revised manuscript), the reason of missing M001 and MOTT64 in CRISPR-i experiment in original Fig. 7 (new Fig. 8) was we were unable to construct the knockdown strains in M001 and MOTT64. We consider these are the same technical challenges between M001 and MOTT64.
Following the comment, we have added the explanation of the technical challenge with respect to M001 and MOTT64 in the Discussion (page 32 lines 561- 566 in the revised manuscript). ”Our inability to construct knockdown strains in M001 and MOTT64 prevented us from clarifying the factors that discriminate against the pattern of hypoxic adaptation. Although the implication in clinical situations has not been proven, strains without slow growth under hypoxia may have different (possibly strain-specific) mechanisms of hypoxic adaptation corresponding to the growth phenotypes under hypoxia.”
As for the interpretation of growth suppression in knockdown experiments described in original Fig. 8 (new Fig. 7), We judged the positive level of growth inhibition as 10-1 or less of comparative growth rates of knockdown strains to vector control strains (y-axis of new Fig. 7). We interpreted the results based on whether the level of growth inhibition was positive or not (i.e. the comparative growth rates of knockdown strains to vector control strains became below 10-1 or not). Since our aim was to investigate whether knockdown of the target genes in each strain leads to growth inhibition, we did not perform statistical analysis between strains or target genes.
The major weakness of the study is the organization and data representation. It became very difficult to connect the role of gluconeogenesis, secretion system and others identified by authors to hypoxia, pellicle formation. The authors may consider rephrasing the results and discussion sections.
Thank you for the comments on the issue of organization and data presentation. Following the comment, we have revised the manuscript to indicate the relevance of the role of gluconeogenesis, secretion system and others defined by us more clearly (page 23 lines 404-408 in the revised manuscript).
“Because the profiles of genetic requirements reflect the adaptation to the environment in which bacteria habits, it is reasonable to assume that the increase of genetic requirements in hypoxia-related genes such as gluconeogenesis (pckA, glpX), type VII secretion system (mycP5, eccC5) and cysteine desulfurase (csd) play an important role on the growth under hypoxia-relevant conditions in vivo.”
Following the comments, we have exchanged the order of data presentation as follows: in vitro TnSeq (pages 6-12 lines 102-208 in the revised manuscript) , Mouse TnSeq (pages 12-17 lines 210-303 in the revised manuscript), Knockdown experiment (pages 17-21 lines 305-363 in the revised manuscript), Hypoxic growth assay (pages 21-23 lines 365-408 in the revised manuscript).
In association with the exchange of the order of data presentation, we have changed the order of the contents of the Discussion as follows: Preferential carbohydrate metabolism under hypoxia such as pckA and glpX (pages 24-26 lines 424-466 in the revised manuscript), Cysteine desulfurase gene (csd) (pages 26-27 lines 467-482 in the revised manuscript), Conditional essential genes in vivo such as type VII secretion system (pages 27-28 lines 483-497 in the revised manuscript), Knockdown experiment (pages 28-30 lines 498-536 in the revised manuscript), Hypoxic growth pattern (pages 30-32 lines 537-571 in the revised manuscript), Failure of assay using PckA inhibitors (pages 32-33 lines 572-578 in the revised manuscript), Transformation efficiencies (page 33 lines 579-591 in the revised manuscript), Saturation of Tn insertion (pages 33-35 lines 592-613 in the revised manuscript), Suggested future experiment plan (pages 35-36 lines 614-632 in the revised manuscript).
