The DOI matches its Crossref record; 5 of the 5 references it cites were found in a registry or an index.
Comprehensive Essentiality Analysis of the Mycobacterium tuberculosis Genome via Saturating Transposon Mutagenesis
Open accessAbstract
For decades, identifying the regions of a bacterial chromosome that are necessary for viability has relied on mapping integration sites in libraries of random transposon mutants to find loci that are unable to sustain insertion. To date, these studies have analyzed subsaturated libraries, necessitating the application of statistical methods to estimate the likelihood that a gap in transposon coverage is the result of biological selection and not the stochasticity of insertion. As a result, the essentiality of many genomic features, particularly small ones, could not be reliably assessed. We sought to overcome this limitation by creating a completely saturated transposon library in Mycobacterium tuberculosis . In assessing the composition of this highly saturated library by deep sequencing, we discovered that a previously unknown sequence bias of the Himar1 element rendered approximately 9% of potential TA dinucleotide insertion sites less permissible for insertion. We used a hidden Markov model of essentiality that accounted for this unanticipated bias, allowing us to confidently evaluate the essentiality of features that contained as few as 2 TA sites, including open reading frames (ORF), experimentally identified noncoding RNAs, methylation sites, and promoters. In addition, several essential regions that did not correspond to known features were identified, suggesting uncharacterized functions that are necessary for growth. This work provides an authoritative catalog of essential regions of the M. tuberculosis genome and a statistical framework for applying saturating mutagenesis to other bacteria. IMPORTANCE Sequencing of transposon-insertion mutant libraries has become a widely used tool for probing the functions of genes under various conditions. The Himar1 transposon is generally believed to insert with equal probabilities at all TA dinucleotides, and therefore its absence in a mutant library is taken to indicate biological selection against the corresponding mutant. Through sequencing of a saturated Himar1 library, we found evidence that TA dinucleotides are not equally permissive for insertion. The insertion bias was observed in multiple prokaryotes and influences the statistical interpretation of transposon insertion (TnSeq) data and characterization of essential genomic regions. Using these insights, we analyzed a fully saturated TnSeq library for M. tuberculosis , enabling us to generate a comprehensive catalog of in vitro essentiality, including ORFs smaller than those found in any previous study, small (noncoding) RNAs (sRNAs), promoters, and other genomic features.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Fast and accurate short read alignment with Burrows-Wheeler transform
Li, H., & Durbin, R. (2009) · Bioinformatics · doi:10.1093/bioinformatics/btp324 · Cited by 58,607
Genes required for mycobacterial growth defined by high density mutagenesis
Sassetti, C. M., Boyd, D. H., & Rubin, E. J. (2003) · Molecular Microbiology · doi:10.1046/j.1365-2958.2003.03425.x · Cited by 2,544
Self-citationGriffin, J. E., Gawronski, J. D., DeJesus, M. A., Ioerger, T. R., Akerley, B. J., & Sassetti, C. M. (2011) · PLoS Pathogens · doi:10.1371/journal.ppat.1002251 · Cited by 1,048
Self-citationShell, S. S., Wang, J., Lapierre, P., Mir, M., Chase, M. R., Pyle, M. M., Gawande, R., Ahmad, R., Sarracino, D. A., Ioerger, T. R., et al. (2015) · PLOS Genetics · doi:10.1371/journal.pgen.1005641 · Cited by 238
Self-citationA bend, flip and trap mechanism for transposon integration
Morris, E. R., Grey, H., McKenzie, G., Jones, A. C., & Richardson, J. M. (2016) · eLife · doi:10.7554/elife.15537 · Cited by 26
Cited by
Genome-wide gene expression tuning reveals diverse vulnerabilities of M. tuberculosis
Bosch, B., DeJesus, M. A., Poulton, N. C., Zhang, W., Engelhart, C. A., Zaveri, A., Lavalette, S., Ruecker, N., Trujillo, C., Wallach, J. B., et al. (2021) · Cell · doi:10.1016/j.cell.2021.06.033 · Cited by 316
Self-citationBacPROTACs mediate targeted protein degradation in bacteria
Morreale, F. E., Kleine, S., Leodolter, J., Junker, S., Hoi, D. M., Ovchinnikov, S., Okun, A., Kley, J., Kurzbauer, R., Junk, L., et al. (2022) · Cell · doi:10.1016/j.cell.2022.05.009 · Cited by 196
Structures of cell wall arabinosyltransferases with the anti-tuberculosis drug ethambutol
Zhang, L., Zhao, Y., Gao, Y., Wu, L., Gao, R., Zhang, Q., Wang, Y., Wu, C., Wu, F., Gurcha, S. S., et al. (2020) · Science · doi:10.1126/science.aba9102 · Cited by 173
Self-citationMycobacterium smegmatis: The Vanguard of Mycobacterial Research
Sparks, I. L., Derbyshire, K. M., Jacobs, W. R., & Morita, Y. S. (2023) · Journal of Bacteriology · doi:10.1128/jb.00337-22 · Cited by 149
Xu, W., DeJesus, M. A., Rücker, N., Engelhart, C. A., Wright, M. G., Healy, C., Lin, K., Wang, R., Park, S. W., Ioerger, T. R., et al. (2017) · Antimicrobial Agents and Chemotherapy · doi:10.1128/aac.01334-17 · Cited by 115
Self-citationde Wet, T. J., Winkler, K. R., Mhlanga, M., Mizrahi, V., & Warner, D. F. (2020) · eLife · doi:10.7554/elife.60083 · Cited by 79
Rifat, D., Chen, L., Kreiswirth, B. N., & Nuermberger, E. L. (2021) · mBio · doi:10.1128/mbio.01049-21 · Cited by 76
Cryo-EM structure and resistance landscape of M. tuberculosis MmpL3: An emergent therapeutic target
Adams, O., Deme, J. C., Parker, J. L., Fowler, P. W., Lea, S. M., & Newstead, S. (2021) · Structure · doi:10.1016/j.str.2021.06.013 · Cited by 63
Akusobi, C., Benghomari, B. S., Zhu, J., Wolf, I. D., Singhvi, S., Dulberger, C. L., Ioerger, T. R., & Rubin, E. J. (2022) · eLife · doi:10.7554/elife.71947 · Cited by 45
“We used Mtb essentiality data generated from a comprehensive analysis using 14 highquality TnSeq datasets (DeJesus et al., 2017).”
Self-citationDragset, M. S., Ioerger, T. R., Zhang, Y. J., Mærk, M., Ginbot, Z., Sacchettini, J. C., Flo, T. H., Rubin, E. J., & Steigedal, M. (2019) · Scientific Reports · doi:10.1038/s41598-019-47905-y · Cited by 49
Self-citationA Mycobacterial Systems Resource for the Research Community
Judd, J. A., Canestrari, J., Clark, R., Joseph, A., Lapierre, P., Lasek-Nesselquist, E., Mir, M., Palumbo, M., Smith, C., Stone, M., et al. (2021) · mBio · doi:10.1128/mbio.02401-20 · Cited by 45
Self-citationTargeting de novo purine biosynthesis for tuberculosis treatment
Lamprecht, D. A., Wall, R. J., Leemans, A., Truebody, B., Sprangers, J., Fiogbe, P., Davies, C., Wetzel, J., Daems, S., Pearson, W., et al. (2025) · Nature · doi:10.1038/s41586-025-09177-7 · Cited by 24
Interactome Analysis Identifies MSMEI_3879 as a Substrate of Mycolicibacterium smegmatis ClpC1
Ogbonna, E. C., Anderson, H. R., Beardslee, P. C., Bheemreddy, P., & Schmitz, K. R. (2023) · Microbiology Spectrum · doi:10.1128/spectrum.04548-22 · Cited by 15
Gray, J., Torres, V. V. L., Goodall, E., McKeand, S. A., Scales, D., Collins, C., Wetherall, L., Lian, Z. J., Bryant, J. A., Milner, M. T., et al. (2024) · eLife · doi:10.7554/elife.88971.3 · Cited by 12
The cell wall polymer initiates plasma membrane partitioning in mycobacteria
Kado, T., Akbary, Z., Motooka, D., Sparks, I. L., Melzer, E. S., Nakamura, S., Rojas, E. R., Morita, Y. S., & Siegrist, M. S. (2022) · doi:10.1101/2022.06.12.495848 · Cited by 16
de Munnik, M., Calvopiña, K., Rabe, P., & Schofield, C. J. (2025) · Antimicrobial Agents and Chemotherapy · doi:10.1128/aac.00126-25 · Cited by 1
Gupta, K. R., Gwin, C. M., Rahlwes, K. C., Biegas, K. J., Wang, C., Park, J. H., Liu, J., Swarts, B. M., Morita, Y. S., & Rego, E. H. (2019) · doi:10.1101/633768 · Cited by 1
Metadata
- Authors
- DeJesus, M. A., Gerrick, E. R., Xu, W., Park, S. W., Long, J. E., Boutte, C. C., Rubin, E. J., Schnappinger, D., Ehrt, S., Fortune, S. M., Sassetti, C. M., & Ioerger, T. R.
- Venue
- mBio
- Publisher
- American Society for Microbiology
- Published
- 2017-03-08
- Volume
- 8
- Issue
- 1
- ISSN
- 2150-7511 (ISSN-L)
- 2161-2129
- OpenAlex
- W2574984917
- Cited by
- 668
- References
- 5
- FWCI
- 33.71
- Type
- journal article
- Language
- en
- Open access
- gold
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
