--- _id: '12478' abstract: - lang: eng text: In Gram negative bacteria, the multiple antibiotic resistance or mar operon, is known to control the expression of multi-drug efflux genes that protect bacteria from a wide range of drugs. As many different chemical compounds can induce this operon, identifying the parameters that govern the dynamics of its induction is crucial to better characterize the processes of tolerance and resistance. Most experiments have assumed that the properties of the mar transcriptional network can be inferred from population measurements. However, measurements from an asynchronous population of cells can mask underlying phenotypic variations of single cells. We monitored the activity of the mar promoter in single Escherichia coli cells in linear micro-colonies and established that the response to a steady level of inducer was most heterogeneous within individual colonies for an intermediate value of inducer. Specifically, sub-lineages defined by contiguous daughter-cells exhibited similar promoter activity, whereas activity was greatly variable between different sub-lineages. Specific sub-trees of uniform promoter activity persisted over several generations. Statistical analyses of the lineages suggest that the presence of these sub-trees is the signature of an inducible memory of the promoter state that is transmitted from mother to daughter cells. This single-cell study reveals that the degree of epigenetic inheritance changes as a function of inducer concentration, suggesting that phenotypic inheritance may be an inducible phenotype. acknowledgement: This work was supported by NIH P50 award P50GM081892-02 to the University of Chicago, a catalyst grant from the Chicago Biomedical Consortium with support from The Searle Funds at The Chicago Community Trust to PC, and a Yen Fellowship to CCG. MA was partially supported by PAPIIT-UNAM grant IN-11322. article_number: '1049255' article_processing_charge: Yes article_type: original author: - first_name: Calin C full_name: Guet, Calin C id: 47F8433E-F248-11E8-B48F-1D18A9856A87 last_name: Guet orcid: 0000-0001-6220-2052 - first_name: L full_name: Bruneaux, L last_name: Bruneaux - first_name: P full_name: Oikonomou, P last_name: Oikonomou - first_name: M full_name: Aldana, M last_name: Aldana - first_name: P full_name: Cluzel, P last_name: Cluzel citation: ama: Guet CC, Bruneaux L, Oikonomou P, Aldana M, Cluzel P. Monitoring lineages of growing and dividing bacteria reveals an inducible memory of mar operon expression. Frontiers in Microbiology. 2023;14. doi:10.3389/fmicb.2023.1049255 apa: Guet, C. C., Bruneaux, L., Oikonomou, P., Aldana, M., & Cluzel, P. (2023). Monitoring lineages of growing and dividing bacteria reveals an inducible memory of mar operon expression. Frontiers in Microbiology. Frontiers. https://doi.org/10.3389/fmicb.2023.1049255 chicago: Guet, Calin C, L Bruneaux, P Oikonomou, M Aldana, and P Cluzel. “Monitoring Lineages of Growing and Dividing Bacteria Reveals an Inducible Memory of Mar Operon Expression.” Frontiers in Microbiology. Frontiers, 2023. https://doi.org/10.3389/fmicb.2023.1049255. ieee: C. C. Guet, L. Bruneaux, P. Oikonomou, M. Aldana, and P. Cluzel, “Monitoring lineages of growing and dividing bacteria reveals an inducible memory of mar operon expression,” Frontiers in Microbiology, vol. 14. Frontiers, 2023. ista: Guet CC, Bruneaux L, Oikonomou P, Aldana M, Cluzel P. 2023. Monitoring lineages of growing and dividing bacteria reveals an inducible memory of mar operon expression. Frontiers in Microbiology. 14, 1049255. mla: Guet, Calin C., et al. “Monitoring Lineages of Growing and Dividing Bacteria Reveals an Inducible Memory of Mar Operon Expression.” Frontiers in Microbiology, vol. 14, 1049255, Frontiers, 2023, doi:10.3389/fmicb.2023.1049255. short: C.C. Guet, L. Bruneaux, P. Oikonomou, M. Aldana, P. Cluzel, Frontiers in Microbiology 14 (2023). date_created: 2023-02-02T08:13:28Z date_published: 2023-06-20T00:00:00Z date_updated: 2023-08-02T06:25:04Z day: '20' ddc: - '570' department: - _id: CaGu doi: 10.3389/fmicb.2023.1049255 external_id: isi: - '001030002600001' pmid: - '37485524' file: - access_level: open_access checksum: 7dd322347512afaa5daf72a0154f2f07 content_type: application/pdf creator: dernst date_created: 2023-07-31T07:16:34Z date_updated: 2023-07-31T07:16:34Z file_id: '13322' file_name: 2023_FrontiersMicrobiology_Guet.pdf file_size: 6452841 relation: main_file success: 1 file_date_updated: 2023-07-31T07:16:34Z has_accepted_license: '1' intvolume: ' 14' isi: 1 language: - iso: eng license: https://creativecommons.org/licenses/by/4.0/ month: '06' oa: 1 oa_version: Published Version pmid: 1 publication: Frontiers in Microbiology publication_identifier: eissn: - 1664-302X publication_status: published publisher: Frontiers quality_controlled: '1' scopus_import: '1' status: public title: Monitoring lineages of growing and dividing bacteria reveals an inducible memory of mar operon expression tmp: image: /images/cc_by.png legal_code_url: https://creativecommons.org/licenses/by/4.0/legalcode name: Creative Commons Attribution 4.0 International Public License (CC-BY 4.0) short: CC BY (4.0) type: journal_article user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87 volume: 14 year: '2023' ... --- _id: '10939' abstract: - lang: eng text: Understanding and characterising biochemical processes inside single cells requires experimental platforms that allow one to perturb and observe the dynamics of such processes as well as computational methods to build and parameterise models from the collected data. Recent progress with experimental platforms and optogenetics has made it possible to expose each cell in an experiment to an individualised input and automatically record cellular responses over days with fine time resolution. However, methods to infer parameters of stochastic kinetic models from single-cell longitudinal data have generally been developed under the assumption that experimental data is sparse and that responses of cells to at most a few different input perturbations can be observed. Here, we investigate and compare different approaches for calculating parameter likelihoods of single-cell longitudinal data based on approximations of the chemical master equation (CME) with a particular focus on coupling the linear noise approximation (LNA) or moment closure methods to a Kalman filter. We show that, as long as cells are measured sufficiently frequently, coupling the LNA to a Kalman filter allows one to accurately approximate likelihoods and to infer model parameters from data even in cases where the LNA provides poor approximations of the CME. Furthermore, the computational cost of filtering-based iterative likelihood evaluation scales advantageously in the number of measurement times and different input perturbations and is thus ideally suited for data obtained from modern experimental platforms. To demonstrate the practical usefulness of these results, we perform an experiment in which single cells, equipped with an optogenetic gene expression system, are exposed to various different light-input sequences and measured at several hundred time points and use parameter inference based on iterative likelihood evaluation to parameterise a stochastic model of the system. acknowledgement: We thank Virgile Andreani for useful discussions about the model and parameter inference. We thank Johan Paulsson and Jeffrey J Tabor for kind gifts of plasmids. R was supported by the ANR grant CyberCircuits (ANR-18-CE91-0002). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. article_number: e1009950 article_processing_charge: No article_type: original author: - first_name: Anđela full_name: Davidović, Anđela last_name: Davidović - first_name: Remy P full_name: Chait, Remy P id: 3464AE84-F248-11E8-B48F-1D18A9856A87 last_name: Chait orcid: 0000-0003-0876-3187 - first_name: Gregory full_name: Batt, Gregory last_name: Batt - first_name: Jakob full_name: Ruess, Jakob id: 4A245D00-F248-11E8-B48F-1D18A9856A87 last_name: Ruess orcid: 0000-0003-1615-3282 citation: ama: Davidović A, Chait RP, Batt G, Ruess J. Parameter inference for stochastic biochemical models from perturbation experiments parallelised at the single cell level. PLoS Computational Biology. 2022;18(3). doi:10.1371/journal.pcbi.1009950 apa: Davidović, A., Chait, R. P., Batt, G., & Ruess, J. (2022). Parameter inference for stochastic biochemical models from perturbation experiments parallelised at the single cell level. PLoS Computational Biology. Public Library of Science. https://doi.org/10.1371/journal.pcbi.1009950 chicago: Davidović, Anđela, Remy P Chait, Gregory Batt, and Jakob Ruess. “Parameter Inference for Stochastic Biochemical Models from Perturbation Experiments Parallelised at the Single Cell Level.” PLoS Computational Biology. Public Library of Science, 2022. https://doi.org/10.1371/journal.pcbi.1009950. ieee: A. Davidović, R. P. Chait, G. Batt, and J. Ruess, “Parameter inference for stochastic biochemical models from perturbation experiments parallelised at the single cell level,” PLoS Computational Biology, vol. 18, no. 3. Public Library of Science, 2022. ista: Davidović A, Chait RP, Batt G, Ruess J. 2022. Parameter inference for stochastic biochemical models from perturbation experiments parallelised at the single cell level. PLoS Computational Biology. 18(3), e1009950. mla: Davidović, Anđela, et al. “Parameter Inference for Stochastic Biochemical Models from Perturbation Experiments Parallelised at the Single Cell Level.” PLoS Computational Biology, vol. 18, no. 3, e1009950, Public Library of Science, 2022, doi:10.1371/journal.pcbi.1009950. short: A. Davidović, R.P. Chait, G. Batt, J. Ruess, PLoS Computational Biology 18 (2022). date_created: 2022-04-03T22:01:42Z date_published: 2022-03-18T00:00:00Z date_updated: 2022-04-04T10:21:53Z day: '18' ddc: - '570' - '000' department: - _id: CaGu doi: 10.1371/journal.pcbi.1009950 file: - access_level: open_access checksum: 458ef542761fb714ced214f240daf6b2 content_type: application/pdf creator: dernst date_created: 2022-04-04T10:14:39Z date_updated: 2022-04-04T10:14:39Z file_id: '10947' file_name: 2022_PLoSCompBio_Davidovic.pdf file_size: 2958642 relation: main_file success: 1 file_date_updated: 2022-04-04T10:14:39Z has_accepted_license: '1' intvolume: ' 18' issue: '3' language: - iso: eng month: '03' oa: 1 oa_version: Published Version publication: PLoS Computational Biology publication_identifier: eissn: - 1553-7358 issn: - 1553-734X publication_status: published publisher: Public Library of Science quality_controlled: '1' related_material: link: - relation: software url: https://gitlab.pasteur.fr/adavidov/inferencelnakf scopus_import: '1' status: public title: Parameter inference for stochastic biochemical models from perturbation experiments parallelised at the single cell level tmp: image: /images/cc_by.png legal_code_url: https://creativecommons.org/licenses/by/4.0/legalcode name: Creative Commons Attribution 4.0 International Public License (CC-BY 4.0) short: CC BY (4.0) type: journal_article user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87 volume: 18 year: '2022' ... --- _id: '11713' abstract: - lang: eng text: "Objective: MazF is a sequence-specific endoribonuclease-toxin of the MazEF toxin–antitoxin system. MazF cleaves single-stranded ribonucleic acid (RNA) regions at adenine–cytosine–adenine (ACA) sequences in the bacterium Escherichia coli. The MazEF system has been used in various biotechnology and synthetic biology applications. In this study, we infer how ectopic mazF overexpression affects production of heterologous proteins. To this end, we quantified the levels of fluorescent proteins expressed in E. coli from reporters translated from the ACA-containing or ACA-less messenger RNAs (mRNAs). Additionally, we addressed the impact of the 5′-untranslated region of these reporter mRNAs under the same conditions by comparing expression from mRNAs that comprise (canonical mRNA) or lack this region (leaderless mRNA).\r\nResults: Flow cytometry analysis indicates that during mazF overexpression, fluorescent proteins are translated from the canonical as well as leaderless mRNAs. Our analysis further indicates that longer mazF overexpression generally increases the concentration of fluorescent proteins translated from ACA-less mRNAs, however it also substantially increases bacterial population heterogeneity. Finally, our results suggest that the strength and duration of mazF overexpression should be optimized for each experimental setup, to maximize the heterologous protein production and minimize the amount of phenotypic heterogeneity in bacterial populations, which is unfavorable in biotechnological processes." acknowledgement: "We acknowledge the Max Perutz Labs FACS Facility together with Thomas Sauer. NN is grateful to Călin C. Guet for his support.\r\nThis work was funded by the Elise Richter grant V738 of the Austrian Science Fund (FWF), and the FWF Lise Meitner grant M1697, to NN; and by the FWF grant P22249, FWF Special Research Program RNA-REG F43 (subproject F4316), and FWF doctoral program RNA Biology (W1207), to IM. Open access funding provided by the Austrian Science Fund." article_number: '173' article_processing_charge: No article_type: letter_note author: - first_name: Nela full_name: Nikolic, Nela id: 42D9CABC-F248-11E8-B48F-1D18A9856A87 last_name: Nikolic orcid: 0000-0001-9068-6090 - first_name: Martina full_name: Sauert, Martina last_name: Sauert - first_name: Tanino G. full_name: Albanese, Tanino G. last_name: Albanese - first_name: Isabella full_name: Moll, Isabella last_name: Moll citation: ama: Nikolic N, Sauert M, Albanese TG, Moll I. Quantifying heterologous gene expression during ectopic MazF production in Escherichia coli. BMC Research Notes. 2022;15. doi:10.1186/s13104-022-06061-9 apa: Nikolic, N., Sauert, M., Albanese, T. G., & Moll, I. (2022). Quantifying heterologous gene expression during ectopic MazF production in Escherichia coli. BMC Research Notes. Springer Nature. https://doi.org/10.1186/s13104-022-06061-9 chicago: Nikolic, Nela, Martina Sauert, Tanino G. Albanese, and Isabella Moll. “Quantifying Heterologous Gene Expression during Ectopic MazF Production in Escherichia Coli.” BMC Research Notes. Springer Nature, 2022. https://doi.org/10.1186/s13104-022-06061-9. ieee: N. Nikolic, M. Sauert, T. G. Albanese, and I. Moll, “Quantifying heterologous gene expression during ectopic MazF production in Escherichia coli,” BMC Research Notes, vol. 15. Springer Nature, 2022. ista: Nikolic N, Sauert M, Albanese TG, Moll I. 2022. Quantifying heterologous gene expression during ectopic MazF production in Escherichia coli. BMC Research Notes. 15, 173. mla: Nikolic, Nela, et al. “Quantifying Heterologous Gene Expression during Ectopic MazF Production in Escherichia Coli.” BMC Research Notes, vol. 15, 173, Springer Nature, 2022, doi:10.1186/s13104-022-06061-9. short: N. Nikolic, M. Sauert, T.G. Albanese, I. Moll, BMC Research Notes 15 (2022). date_created: 2022-08-01T09:04:27Z date_published: 2022-05-13T00:00:00Z date_updated: 2022-08-01T09:27:40Z day: '13' ddc: - '570' department: - _id: CaGu doi: 10.1186/s13104-022-06061-9 external_id: pmid: - '35562780' file: - access_level: open_access checksum: 008156e5340e9789f0f6d82bde4d347a content_type: application/pdf creator: dernst date_created: 2022-08-01T09:24:42Z date_updated: 2022-08-01T09:24:42Z file_id: '11714' file_name: 2022_BMCResearchNotes_Nikolic.pdf file_size: 1545310 relation: main_file success: 1 file_date_updated: 2022-08-01T09:24:42Z has_accepted_license: '1' intvolume: ' 15' keyword: - General Biochemistry - Genetics and Molecular Biology - General Medicine language: - iso: eng month: '05' oa: 1 oa_version: Published Version pmid: 1 project: - _id: 26956E74-B435-11E9-9278-68D0E5697425 call_identifier: FWF grant_number: V00738 name: Bacterial toxin-antitoxin systems as antiphage defense mechanisms publication: BMC Research Notes publication_identifier: issn: - 1756-0500 publication_status: published publisher: Springer Nature quality_controlled: '1' related_material: link: - relation: erratum url: https://doi.org/10.1186/s13104-022-06152-7 scopus_import: '1' status: public title: Quantifying heterologous gene expression during ectopic MazF production in Escherichia coli tmp: image: /images/cc_by.png legal_code_url: https://creativecommons.org/licenses/by/4.0/legalcode name: Creative Commons Attribution 4.0 International Public License (CC-BY 4.0) short: CC BY (4.0) type: journal_article user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87 volume: 15 year: '2022' ... --- _id: '10736' abstract: - lang: eng text: Predicting function from sequence is a central problem of biology. Currently, this is possible only locally in a narrow mutational neighborhood around a wildtype sequence rather than globally from any sequence. Using random mutant libraries, we developed a biophysical model that accounts for multiple features of σ70 binding bacterial promoters to predict constitutive gene expression levels from any sequence. We experimentally and theoretically estimated that 10–20% of random sequences lead to expression and ~80% of non-expressing sequences are one mutation away from a functional promoter. The potential for generating expression from random sequences is so pervasive that selection acts against σ70-RNA polymerase binding sites even within inter-genic, promoter-containing regions. This pervasiveness of σ70-binding sites implies that emergence of promoters is not the limiting step in gene regulatory evolution. Ultimately, the inclusion of novel features of promoter function into a mechanistic model enabled not only more accurate predictions of gene expression levels, but also identified that promoters evolve more rapidly than previously thought. acknowledgement: 'We thank Hande Acar, Nicholas H Barton, Rok Grah, Tiago Paixao, Maros Pleska, Anna Staron, and Murat Tugrul for insightful comments and input on the manuscript. This work was supported by: Sir Henry Dale Fellowship jointly funded by the Wellcome Trust and the Royal Society (grant number 216779/Z/19/Z) to ML; IPC Grant from IST Austria to ML and SS; European Research Council Funding Programme 7 (2007–2013, grant agreement number 648440) to JPB.' article_number: e64543 article_processing_charge: No article_type: original author: - first_name: Mato full_name: Lagator, Mato id: 345D25EC-F248-11E8-B48F-1D18A9856A87 last_name: Lagator - first_name: Srdjan full_name: Sarikas, Srdjan id: 35F0286E-F248-11E8-B48F-1D18A9856A87 last_name: Sarikas - first_name: Magdalena full_name: Steinrueck, Magdalena last_name: Steinrueck - first_name: David full_name: Toledo-Aparicio, David last_name: Toledo-Aparicio - first_name: Jonathan P full_name: Bollback, Jonathan P id: 2C6FA9CC-F248-11E8-B48F-1D18A9856A87 last_name: Bollback orcid: 0000-0002-4624-4612 - first_name: Calin C full_name: Guet, Calin C id: 47F8433E-F248-11E8-B48F-1D18A9856A87 last_name: Guet orcid: 0000-0001-6220-2052 - first_name: Gašper full_name: Tkačik, Gašper id: 3D494DCA-F248-11E8-B48F-1D18A9856A87 last_name: Tkačik orcid: 0000-0002-6699-1455 citation: ama: Lagator M, Sarikas S, Steinrueck M, et al. Predicting bacterial promoter function and evolution from random sequences. eLife. 2022;11. doi:10.7554/eLife.64543 apa: Lagator, M., Sarikas, S., Steinrueck, M., Toledo-Aparicio, D., Bollback, J. P., Guet, C. C., & Tkačik, G. (2022). Predicting bacterial promoter function and evolution from random sequences. ELife. eLife Sciences Publications. https://doi.org/10.7554/eLife.64543 chicago: Lagator, Mato, Srdjan Sarikas, Magdalena Steinrueck, David Toledo-Aparicio, Jonathan P Bollback, Calin C Guet, and Gašper Tkačik. “Predicting Bacterial Promoter Function and Evolution from Random Sequences.” ELife. eLife Sciences Publications, 2022. https://doi.org/10.7554/eLife.64543. ieee: M. Lagator et al., “Predicting bacterial promoter function and evolution from random sequences,” eLife, vol. 11. eLife Sciences Publications, 2022. ista: Lagator M, Sarikas S, Steinrueck M, Toledo-Aparicio D, Bollback JP, Guet CC, Tkačik G. 2022. Predicting bacterial promoter function and evolution from random sequences. eLife. 11, e64543. mla: Lagator, Mato, et al. “Predicting Bacterial Promoter Function and Evolution from Random Sequences.” ELife, vol. 11, e64543, eLife Sciences Publications, 2022, doi:10.7554/eLife.64543. short: M. Lagator, S. Sarikas, M. Steinrueck, D. Toledo-Aparicio, J.P. Bollback, C.C. Guet, G. Tkačik, ELife 11 (2022). date_created: 2022-02-06T23:01:32Z date_published: 2022-01-26T00:00:00Z date_updated: 2023-08-02T14:09:02Z day: '26' ddc: - '576' department: - _id: CaGu - _id: GaTk - _id: NiBa doi: 10.7554/eLife.64543 ec_funded: 1 external_id: isi: - '000751104400001' pmid: - '35080492' file: - access_level: open_access checksum: decdcdf600ff51e9a9703b49ca114170 content_type: application/pdf creator: cchlebak date_created: 2022-02-07T07:14:09Z date_updated: 2022-02-07T07:14:09Z file_id: '10739' file_name: 2022_ELife_Lagator.pdf file_size: 5604343 relation: main_file success: 1 file_date_updated: 2022-02-07T07:14:09Z has_accepted_license: '1' intvolume: ' 11' isi: 1 language: - iso: eng month: '01' oa: 1 oa_version: Published Version pmid: 1 project: - _id: 2578D616-B435-11E9-9278-68D0E5697425 call_identifier: H2020 grant_number: '648440' name: Selective Barriers to Horizontal Gene Transfer publication: eLife publication_identifier: eissn: - 2050-084X publication_status: published publisher: eLife Sciences Publications quality_controlled: '1' scopus_import: '1' status: public title: Predicting bacterial promoter function and evolution from random sequences tmp: image: /images/cc_by.png legal_code_url: https://creativecommons.org/licenses/by/4.0/legalcode name: Creative Commons Attribution 4.0 International Public License (CC-BY 4.0) short: CC BY (4.0) type: journal_article user_id: 4359f0d1-fa6c-11eb-b949-802e58b17ae8 volume: 11 year: '2022' ... --- _id: '10812' abstract: - lang: eng text: Several promising strategies based on combining or cycling different antibiotics have been proposed to increase efficacy and counteract resistance evolution, but we still lack a deep understanding of the physiological responses and genetic mechanisms that underlie antibiotic interactions and the clinical applicability of these strategies. In antibiotic-exposed bacteria, the combined effects of physiological stress responses and emerging resistance mutations (occurring at different time scales) generate complex and often unpredictable dynamics. In this Review, we present our current understanding of bacterial cell physiology and genetics of responses to antibiotics. We emphasize recently discovered mechanisms of synergistic and antagonistic drug interactions, hysteresis in temporal interactions between antibiotics that arise from microbial physiology and interactions between antibiotics and resistance mutations that can cause collateral sensitivity or cross-resistance. We discuss possible connections between the different phenomena and indicate relevant research directions. A better and more unified understanding of drug and genetic interactions is likely to advance antibiotic therapy. acknowledgement: The authors thank B. Kavčič and H. Schulenburg for constructive feedback on the manuscript. article_processing_charge: No article_type: review author: - first_name: Roderich full_name: Römhild, Roderich id: 68E56E44-62B0-11EA-B963-444F3DDC885E last_name: Römhild orcid: 0000-0001-9480-5261 - first_name: Mark Tobias full_name: Bollenbach, Mark Tobias id: 3E6DB97A-F248-11E8-B48F-1D18A9856A87 last_name: Bollenbach orcid: 0000-0003-4398-476X - first_name: Dan I. full_name: Andersson, Dan I. last_name: Andersson citation: ama: Römhild R, Bollenbach MT, Andersson DI. The physiology and genetics of bacterial responses to antibiotic combinations. Nature Reviews Microbiology. 2022;20:478-490. doi:10.1038/s41579-022-00700-5 apa: Römhild, R., Bollenbach, M. T., & Andersson, D. I. (2022). The physiology and genetics of bacterial responses to antibiotic combinations. Nature Reviews Microbiology. Springer Nature. https://doi.org/10.1038/s41579-022-00700-5 chicago: Römhild, Roderich, Mark Tobias Bollenbach, and Dan I. Andersson. “The Physiology and Genetics of Bacterial Responses to Antibiotic Combinations.” Nature Reviews Microbiology. Springer Nature, 2022. https://doi.org/10.1038/s41579-022-00700-5. ieee: R. Römhild, M. T. Bollenbach, and D. I. Andersson, “The physiology and genetics of bacterial responses to antibiotic combinations,” Nature Reviews Microbiology, vol. 20. Springer Nature, pp. 478–490, 2022. ista: Römhild R, Bollenbach MT, Andersson DI. 2022. The physiology and genetics of bacterial responses to antibiotic combinations. Nature Reviews Microbiology. 20, 478–490. mla: Römhild, Roderich, et al. “The Physiology and Genetics of Bacterial Responses to Antibiotic Combinations.” Nature Reviews Microbiology, vol. 20, Springer Nature, 2022, pp. 478–90, doi:10.1038/s41579-022-00700-5. short: R. Römhild, M.T. Bollenbach, D.I. Andersson, Nature Reviews Microbiology 20 (2022) 478–490. date_created: 2022-03-04T04:33:49Z date_published: 2022-08-01T00:00:00Z date_updated: 2023-08-02T14:41:44Z day: '01' department: - _id: CaGu doi: 10.1038/s41579-022-00700-5 external_id: isi: - '000763891900001' pmid: - '35241807' intvolume: ' 20' isi: 1 keyword: - General Immunology and Microbiology - Microbiology - Infectious Diseases language: - iso: eng month: '08' oa_version: None page: 478-490 pmid: 1 publication: Nature Reviews Microbiology publication_identifier: eissn: - 1740-1534 issn: - 1740-1526 publication_status: published publisher: Springer Nature quality_controlled: '1' scopus_import: '1' status: public title: The physiology and genetics of bacterial responses to antibiotic combinations type: journal_article user_id: 4359f0d1-fa6c-11eb-b949-802e58b17ae8 volume: 20 year: '2022' ...