--- _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' ... --- _id: '11341' abstract: - lang: eng text: Intragenic regions that are removed during maturation of the RNA transcript—introns—are universally present in the nuclear genomes of eukaryotes1. The budding yeast, an otherwise intron-poor species, preserves two sets of ribosomal protein genes that differ primarily in their introns2,3. Although studies have shed light on the role of ribosomal protein introns under stress and starvation4,5,6, understanding the contribution of introns to ribosome regulation remains challenging. Here, by combining isogrowth profiling7 with single-cell protein measurements8, we show that introns can mediate inducible phenotypic heterogeneity that confers a clear fitness advantage. Osmotic stress leads to bimodal expression of the small ribosomal subunit protein Rps22B, which is mediated by an intron in the 5′ untranslated region of its transcript. The two resulting yeast subpopulations differ in their ability to cope with starvation. Low levels of Rps22B protein result in prolonged survival under sustained starvation, whereas high levels of Rps22B enable cells to grow faster after transient starvation. Furthermore, yeasts growing at high concentrations of sugar, similar to those in ripe grapes, exhibit bimodal expression of Rps22B when approaching the stationary phase. Differential intron-mediated regulation of ribosomal protein genes thus provides a way to diversify the population when starvation threatens in natural environments. Our findings reveal a role for introns in inducing phenotypic heterogeneity in changing environments, and suggest that duplicated ribosomal protein genes in yeast contribute to resolving the evolutionary conflict between precise expression control and environmental responsiveness9. acknowledged_ssus: - _id: LifeSc - _id: M-Shop - _id: Bio acknowledgement: We thank the IST Austria Life Science Facility, the Miba Machine Shop and M. Lukačišinová for support with the liquid handling robot; the Bioimaging Facility at IST Austria, J. Power and B. Meier at the University of Cologne, and C. Göttlinger at the FACS Analysis Facility at the Institute for Genetics, University of Cologne, for support with flow cytometry experiments; L. Horst for the development of the automated experimental methods in Cologne; J. Parenteau, S. Abou Elela, G. Stormo, M. Springer and M. Schuldiner for providing us with yeast strains; B. Fernando, T. Fink, G. Ansmann and G. Chevreau for technical support; H. Köver, G. Tkačik, N. Barton, A. Angermayr and B. Kavčič for support during laboratory relocation; D. Siekhaus, M. Springer and all the members of the Bollenbach group for support and discussions; and K. Mitosch, M. Lukačišinová, G. Liti and A. de Luna for critical reading of our manuscript. This work was supported in part by an Austrian Science Fund (FWF) standalone grant P 27201-B22 (to T.B.), HFSP program Grant RGP0042/2013 (to T.B.), EU Marie Curie Career Integration Grant No. 303507, and German Research Foundation (DFG) Collaborative Research Centre (SFB) 1310 (to T.B.). A.E.-C. was supported by a Georg Forster fellowship from the Alexander von Humboldt Foundation. article_processing_charge: No article_type: original author: - first_name: Martin full_name: Lukacisin, Martin id: 298FFE8C-F248-11E8-B48F-1D18A9856A87 last_name: Lukacisin orcid: 0000-0001-6549-4177 - first_name: Adriana full_name: Espinosa-Cantú, Adriana last_name: Espinosa-Cantú - first_name: Mark Tobias full_name: Bollenbach, Mark Tobias id: 3E6DB97A-F248-11E8-B48F-1D18A9856A87 last_name: Bollenbach orcid: 0000-0003-4398-476X citation: ama: Lukacisin M, Espinosa-Cantú A, Bollenbach MT. Intron-mediated induction of phenotypic heterogeneity. Nature. 2022;605:113-118. doi:10.1038/s41586-022-04633-0 apa: Lukacisin, M., Espinosa-Cantú, A., & Bollenbach, M. T. (2022). Intron-mediated induction of phenotypic heterogeneity. Nature. Springer Nature. https://doi.org/10.1038/s41586-022-04633-0 chicago: Lukacisin, Martin, Adriana Espinosa-Cantú, and Mark Tobias Bollenbach. “Intron-Mediated Induction of Phenotypic Heterogeneity.” Nature. Springer Nature, 2022. https://doi.org/10.1038/s41586-022-04633-0. ieee: M. Lukacisin, A. Espinosa-Cantú, and M. T. Bollenbach, “Intron-mediated induction of phenotypic heterogeneity,” Nature, vol. 605. Springer Nature, pp. 113–118, 2022. ista: Lukacisin M, Espinosa-Cantú A, Bollenbach MT. 2022. Intron-mediated induction of phenotypic heterogeneity. Nature. 605, 113–118. mla: Lukacisin, Martin, et al. “Intron-Mediated Induction of Phenotypic Heterogeneity.” Nature, vol. 605, Springer Nature, 2022, pp. 113–18, doi:10.1038/s41586-022-04633-0. short: M. Lukacisin, A. Espinosa-Cantú, M.T. Bollenbach, Nature 605 (2022) 113–118. date_created: 2022-05-01T22:01:42Z date_published: 2022-05-05T00:00:00Z date_updated: 2023-08-03T06:44:50Z day: '05' ddc: - '570' doi: 10.1038/s41586-022-04633-0 ec_funded: 1 external_id: isi: - '000784934100003' pmid: - '35444278' file: - access_level: open_access checksum: d68cd1596bb9fd819b750fe47c8a138a content_type: application/pdf creator: dernst date_created: 2022-08-05T06:08:24Z date_updated: 2022-08-05T06:08:24Z file_id: '11727' file_name: 2022_Nature_Lukacisin.pdf file_size: 25360311 relation: main_file success: 1 file_date_updated: 2022-08-05T06:08:24Z has_accepted_license: '1' intvolume: ' 605' isi: 1 language: - iso: eng license: https://creativecommons.org/licenses/by/4.0/ month: '05' oa: 1 oa_version: Published Version page: 113-118 pmid: 1 project: - _id: 25E83C2C-B435-11E9-9278-68D0E5697425 call_identifier: FP7 grant_number: '303507' name: Optimality principles in responses to antibiotics - _id: 25E9AF9E-B435-11E9-9278-68D0E5697425 call_identifier: FWF grant_number: P27201-B22 name: Revealing the mechanisms underlying drug interactions publication: Nature publication_identifier: eissn: - 1476-4687 issn: - 0028-0836 publication_status: published publisher: Springer Nature quality_controlled: '1' scopus_import: '1' status: public title: Intron-mediated induction of phenotypic heterogeneity 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: 605 year: '2022' ... --- _id: '12261' abstract: - lang: eng text: 'Dose–response relationships are a general concept for quantitatively describing biological systems across multiple scales, from the molecular to the whole-cell level. A clinically relevant example is the bacterial growth response to antibiotics, which is routinely characterized by dose–response curves. The shape of the dose–response curve varies drastically between antibiotics and plays a key role in treatment, drug interactions, and resistance evolution. However, the mechanisms shaping the dose–response curve remain largely unclear. Here, we show in Escherichia coli that the distinctively shallow dose–response curve of the antibiotic trimethoprim is caused by a negative growth-mediated feedback loop: Trimethoprim slows growth, which in turn weakens the effect of this antibiotic. At the molecular level, this feedback is caused by the upregulation of the drug target dihydrofolate reductase (FolA/DHFR). We show that this upregulation is not a specific response to trimethoprim but follows a universal trend line that depends primarily on the growth rate, irrespective of its cause. Rewiring the feedback loop alters the dose–response curve in a predictable manner, which we corroborate using a mathematical model of cellular resource allocation and growth. Our results indicate that growth-mediated feedback loops may shape drug responses more generally and could be exploited to design evolutionary traps that enable selection against drug resistance.' acknowledged_ssus: - _id: M-Shop acknowledgement: This work was in part supported by Human Frontier Science Program GrantRGP0042/2013, Marie Curie Career Integration Grant303507, AustrianScience Fund (FWF) Grant P27201-B22, and German Research Foundation(DFG) Collaborative Research Center (SFB)1310to TB. SAA was supportedby the European Union’s Horizon2020Research and Innovation Programunder the Marie Skłodowska-Curie Grant agreement No707352. We wouldlike to thank the Bollenbach group for regular fruitful discussions. We areparticularly thankful for the technical assistance of Booshini Fernando andfor discussions of the theoretical aspects with Gerrit Ansmann. We areindebted to Bor Kavˇciˇc for invaluable advice, help with setting up theluciferase-based growth monitoring system, and for sharing plasmids. Weacknowledge the IST Austria Miba Machine Shop for their support inbuilding a housing for the stacker of the plate reader, which enabled thehigh-throughput luciferase-based experiments. We are grateful to RosalindAllen, Bor Kavˇciˇc and Dor Russ for feedback on the manuscript. Open Accessfunding enabled and organized by Projekt DEAL. article_number: e10490 article_processing_charge: No article_type: original author: - first_name: Andreas full_name: Angermayr, Andreas id: 4677C796-F248-11E8-B48F-1D18A9856A87 last_name: Angermayr orcid: 0000-0001-8619-2223 - first_name: Tin Yau full_name: Pang, Tin Yau last_name: Pang - first_name: Guillaume full_name: Chevereau, Guillaume last_name: Chevereau - first_name: Karin full_name: Mitosch, Karin id: 39B66846-F248-11E8-B48F-1D18A9856A87 last_name: Mitosch - first_name: Martin J full_name: Lercher, Martin J last_name: Lercher - first_name: Mark Tobias full_name: Bollenbach, Mark Tobias id: 3E6DB97A-F248-11E8-B48F-1D18A9856A87 last_name: Bollenbach orcid: 0000-0003-4398-476X citation: ama: Angermayr A, Pang TY, Chevereau G, Mitosch K, Lercher MJ, Bollenbach MT. Growth‐mediated negative feedback shapes quantitative antibiotic response. Molecular Systems Biology. 2022;18(9). doi:10.15252/msb.202110490 apa: Angermayr, A., Pang, T. Y., Chevereau, G., Mitosch, K., Lercher, M. J., & Bollenbach, M. T. (2022). Growth‐mediated negative feedback shapes quantitative antibiotic response. Molecular Systems Biology. Embo Press. https://doi.org/10.15252/msb.202110490 chicago: Angermayr, Andreas, Tin Yau Pang, Guillaume Chevereau, Karin Mitosch, Martin J Lercher, and Mark Tobias Bollenbach. “Growth‐mediated Negative Feedback Shapes Quantitative Antibiotic Response.” Molecular Systems Biology. Embo Press, 2022. https://doi.org/10.15252/msb.202110490. ieee: A. Angermayr, T. Y. Pang, G. Chevereau, K. Mitosch, M. J. Lercher, and M. T. Bollenbach, “Growth‐mediated negative feedback shapes quantitative antibiotic response,” Molecular Systems Biology, vol. 18, no. 9. Embo Press, 2022. ista: Angermayr A, Pang TY, Chevereau G, Mitosch K, Lercher MJ, Bollenbach MT. 2022. Growth‐mediated negative feedback shapes quantitative antibiotic response. Molecular Systems Biology. 18(9), e10490. mla: Angermayr, Andreas, et al. “Growth‐mediated Negative Feedback Shapes Quantitative Antibiotic Response.” Molecular Systems Biology, vol. 18, no. 9, e10490, Embo Press, 2022, doi:10.15252/msb.202110490. short: A. Angermayr, T.Y. Pang, G. Chevereau, K. Mitosch, M.J. Lercher, M.T. Bollenbach, Molecular Systems Biology 18 (2022). date_created: 2023-01-16T09:58:34Z date_published: 2022-09-01T00:00:00Z date_updated: 2023-08-04T09:51:49Z day: '01' ddc: - '570' department: - _id: ToBo doi: 10.15252/msb.202110490 external_id: isi: - '000856482800001' file: - access_level: open_access checksum: 8b1d8f5ea20c8408acf466435fb6ae01 content_type: application/pdf creator: dernst date_created: 2023-01-30T09:49:55Z date_updated: 2023-01-30T09:49:55Z file_id: '12446' file_name: 2022_MolecularSystemsBio_Angermayr.pdf file_size: 1098812 relation: main_file success: 1 file_date_updated: 2023-01-30T09:49:55Z has_accepted_license: '1' intvolume: ' 18' isi: 1 issue: '9' keyword: - Applied Mathematics - Computational Theory and Mathematics - General Agricultural and Biological Sciences - General Immunology and Microbiology - General Biochemistry - Genetics and Molecular Biology - Information Systems language: - iso: eng month: '09' oa: 1 oa_version: Published Version publication: Molecular Systems Biology publication_identifier: eissn: - 1744-4292 publication_status: published publisher: Embo Press quality_controlled: '1' scopus_import: '1' status: public title: Growth‐mediated negative feedback shapes quantitative antibiotic response 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: 18 year: '2022' ... --- _id: '10271' abstract: - lang: eng text: Understanding interactions between antibiotics used in combination is an important theme in microbiology. Using the interactions between the antifolate drug trimethoprim and the ribosome-targeting antibiotic erythromycin in Escherichia coli as a model, we applied a transcriptomic approach for dissecting interactions between two antibiotics with different modes of action. When trimethoprim and erythromycin were combined, the transcriptional response of genes from the sulfate reduction pathway deviated from the dominant effect of trimethoprim on the transcriptome. We successfully altered the drug interaction from additivity to suppression by increasing the sulfate level in the growth environment and identified sulfate reduction as an important metabolic determinant that shapes the interaction between the two drugs. Our work highlights the potential of using prioritization of gene expression patterns as a tool for identifying key metabolic determinants that shape drug-drug interactions. We further demonstrated that the sigma factor-binding protein gene crl shapes the interactions between the two antibiotics, which provides a rare example of how naturally occurring variations between strains of the same bacterial species can sometimes generate very different drug interactions. acknowledgement: High-throughput sequencing data were generated by the Vienna BioCenter Core Facilities. The authors would like to thank Karin Mitosch, Bor Kavcic, and Nadine Kraupner for their constructive feedback. The authors would also like to thank Gertraud Stift, Julia Flor, Renate Srsek, Agnieszka Wiktor, and Booshini Fernando for technical support. article_number: '760017' article_processing_charge: No article_type: original author: - first_name: Qin full_name: Qi, Qin id: 3B22D412-F248-11E8-B48F-1D18A9856A87 last_name: Qi orcid: 0000-0002-6148-2416 - first_name: S. Andreas full_name: Angermayr, S. Andreas last_name: Angermayr - first_name: Mark Tobias full_name: Bollenbach, Mark Tobias id: 3E6DB97A-F248-11E8-B48F-1D18A9856A87 last_name: Bollenbach orcid: 0000-0003-4398-476X citation: ama: Qi Q, Angermayr SA, Bollenbach MT. Uncovering Key Metabolic Determinants of the Drug Interactions Between Trimethoprim and Erythromycin in Escherichia coli. Frontiers in Microbiology. 2021;12. doi:10.3389/fmicb.2021.760017 apa: Qi, Q., Angermayr, S. A., & Bollenbach, M. T. (2021). Uncovering Key Metabolic Determinants of the Drug Interactions Between Trimethoprim and Erythromycin in Escherichia coli. Frontiers in Microbiology. Frontiers. https://doi.org/10.3389/fmicb.2021.760017 chicago: Qi, Qin, S. Andreas Angermayr, and Mark Tobias Bollenbach. “Uncovering Key Metabolic Determinants of the Drug Interactions Between Trimethoprim and Erythromycin in Escherichia Coli.” Frontiers in Microbiology. Frontiers, 2021. https://doi.org/10.3389/fmicb.2021.760017. ieee: Q. Qi, S. A. Angermayr, and M. T. Bollenbach, “Uncovering Key Metabolic Determinants of the Drug Interactions Between Trimethoprim and Erythromycin in Escherichia coli,” Frontiers in Microbiology, vol. 12. Frontiers, 2021. ista: Qi Q, Angermayr SA, Bollenbach MT. 2021. Uncovering Key Metabolic Determinants of the Drug Interactions Between Trimethoprim and Erythromycin in Escherichia coli. Frontiers in Microbiology. 12, 760017. mla: Qi, Qin, et al. “Uncovering Key Metabolic Determinants of the Drug Interactions Between Trimethoprim and Erythromycin in Escherichia Coli.” Frontiers in Microbiology, vol. 12, 760017, Frontiers, 2021, doi:10.3389/fmicb.2021.760017. short: Q. Qi, S.A. Angermayr, M.T. Bollenbach, Frontiers in Microbiology 12 (2021). date_created: 2021-11-11T10:39:37Z date_published: 2021-10-20T00:00:00Z date_updated: 2023-08-14T11:43:23Z day: '20' ddc: - '610' doi: 10.3389/fmicb.2021.760017 ec_funded: 1 external_id: isi: - '000715997300001' pmid: - '34745067' file: - access_level: open_access checksum: d41321748e9588dd3cf03e9a7222127f content_type: application/pdf creator: cchlebak date_created: 2021-11-11T10:54:40Z date_updated: 2021-11-11T10:54:40Z file_id: '10272' file_name: 2021_FrontiersMicrob_Qi.pdf file_size: 2397203 relation: main_file success: 1 file_date_updated: 2021-11-11T10:54:40Z has_accepted_license: '1' intvolume: ' 12' isi: 1 keyword: - microbiology language: - iso: eng month: '10' oa: 1 oa_version: Published Version pmid: 1 project: - _id: 25E9AF9E-B435-11E9-9278-68D0E5697425 call_identifier: FWF grant_number: P27201-B22 name: Revealing the mechanisms underlying drug interactions - _id: 25E83C2C-B435-11E9-9278-68D0E5697425 call_identifier: FP7 grant_number: '303507' name: Optimality principles in responses to antibiotics publication: Frontiers in Microbiology publication_identifier: eissn: - 1664-302X publication_status: published publisher: Frontiers quality_controlled: '1' scopus_import: '1' status: public title: Uncovering Key Metabolic Determinants of the Drug Interactions Between Trimethoprim and Erythromycin 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: 4359f0d1-fa6c-11eb-b949-802e58b17ae8 volume: 12 year: '2021' ... --- _id: '8997' abstract: - lang: eng text: Phenomenological relations such as Ohm’s or Fourier’s law have a venerable history in physics but are still scarce in biology. This situation restrains predictive theory. Here, we build on bacterial “growth laws,” which capture physiological feedback between translation and cell growth, to construct a minimal biophysical model for the combined action of ribosome-targeting antibiotics. Our model predicts drug interactions like antagonism or synergy solely from responses to individual drugs. We provide analytical results for limiting cases, which agree well with numerical results. We systematically refine the model by including direct physical interactions of different antibiotics on the ribosome. In a limiting case, our model provides a mechanistic underpinning for recent predictions of higher-order interactions that were derived using entropy maximization. We further refine the model to include the effects of antibiotics that mimic starvation and the presence of resistance genes. We describe the impact of a starvation-mimicking antibiotic on drug interactions analytically and verify it experimentally. Our extended model suggests a change in the type of drug interaction that depends on the strength of resistance, which challenges established rescaling paradigms. We experimentally show that the presence of unregulated resistance genes can lead to altered drug interaction, which agrees with the prediction of the model. While minimal, the model is readily adaptable and opens the door to predicting interactions of second and higher-order in a broad range of biological systems. acknowledgement: 'This work was supported in part by Tum stipend of Knafelj foundation (to B.K.), Austrian Science Fund (FWF) standalone grants P 27201-B22 (to T.B.) and P 28844(to G.T.), HFSP program Grant RGP0042/2013 (to T.B.), German Research Foundation (DFG) individual grant BO 3502/2-1 (to T.B.), and German Research Foundation (DFG) Collaborative Research Centre (SFB) 1310 (to T.B.). ' article_number: e1008529 article_processing_charge: Yes article_type: original author: - first_name: Bor full_name: Kavcic, Bor id: 350F91D2-F248-11E8-B48F-1D18A9856A87 last_name: Kavcic orcid: 0000-0001-6041-254X - first_name: Gašper full_name: Tkačik, Gašper id: 3D494DCA-F248-11E8-B48F-1D18A9856A87 last_name: Tkačik orcid: 0000-0002-6699-1455 - first_name: Tobias full_name: Bollenbach, Tobias id: 3E6DB97A-F248-11E8-B48F-1D18A9856A87 last_name: Bollenbach orcid: 0000-0003-4398-476X citation: ama: Kavcic B, Tkačik G, Bollenbach MT. Minimal biophysical model of combined antibiotic action. PLOS Computational Biology. 2021;17. doi:10.1371/journal.pcbi.1008529 apa: Kavcic, B., Tkačik, G., & Bollenbach, M. T. (2021). Minimal biophysical model of combined antibiotic action. PLOS Computational Biology. Public Library of Science. https://doi.org/10.1371/journal.pcbi.1008529 chicago: Kavcic, Bor, Gašper Tkačik, and Mark Tobias Bollenbach. “Minimal Biophysical Model of Combined Antibiotic Action.” PLOS Computational Biology. Public Library of Science, 2021. https://doi.org/10.1371/journal.pcbi.1008529. ieee: B. Kavcic, G. Tkačik, and M. T. Bollenbach, “Minimal biophysical model of combined antibiotic action,” PLOS Computational Biology, vol. 17. Public Library of Science, 2021. ista: Kavcic B, Tkačik G, Bollenbach MT. 2021. Minimal biophysical model of combined antibiotic action. PLOS Computational Biology. 17, e1008529. mla: Kavcic, Bor, et al. “Minimal Biophysical Model of Combined Antibiotic Action.” PLOS Computational Biology, vol. 17, e1008529, Public Library of Science, 2021, doi:10.1371/journal.pcbi.1008529. short: B. Kavcic, G. Tkačik, M.T. Bollenbach, PLOS Computational Biology 17 (2021). date_created: 2021-01-08T07:16:18Z date_published: 2021-01-07T00:00:00Z date_updated: 2024-02-21T12:41:41Z day: '07' ddc: - '570' department: - _id: GaTk doi: 10.1371/journal.pcbi.1008529 external_id: isi: - '000608045000010' file: - access_level: open_access checksum: e29f2b42651bef8e034781de8781ffac content_type: application/pdf creator: dernst date_created: 2021-02-04T12:30:48Z date_updated: 2021-02-04T12:30:48Z file_id: '9092' file_name: 2021_PlosComBio_Kavcic.pdf file_size: 3690053 relation: main_file success: 1 file_date_updated: 2021-02-04T12:30:48Z has_accepted_license: '1' intvolume: ' 17' isi: 1 keyword: - Modelling and Simulation - Genetics - Molecular Biology - Antibiotics - Drug interactions language: - iso: eng month: '01' oa: 1 oa_version: Published Version project: - _id: 25E9AF9E-B435-11E9-9278-68D0E5697425 call_identifier: FWF grant_number: P27201-B22 name: Revealing the mechanisms underlying drug interactions - _id: 254E9036-B435-11E9-9278-68D0E5697425 call_identifier: FWF grant_number: P28844-B27 name: Biophysics of information processing in gene regulation publication: PLOS Computational Biology publication_identifier: issn: - 1553-7358 publication_status: published publisher: Public Library of Science quality_controlled: '1' related_material: record: - id: '7673' relation: earlier_version status: public - id: '8930' relation: research_data status: public status: public title: Minimal biophysical model of combined antibiotic action 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: 17 year: '2021' ... --- _id: '8037' abstract: - lang: eng text: 'Genetic perturbations that affect bacterial resistance to antibiotics have been characterized genome-wide, but how do such perturbations interact with subsequent evolutionary adaptation to the drug? Here, we show that strong epistasis between resistance mutations and systematically identified genes can be exploited to control spontaneous resistance evolution. We evolved hundreds of Escherichia coli K-12 mutant populations in parallel, using a robotic platform that tightly controls population size and selection pressure. We find a global diminishing-returns epistasis pattern: strains that are initially more sensitive generally undergo larger resistance gains. However, some gene deletion strains deviate from this general trend and curtail the evolvability of resistance, including deletions of genes for membrane transport, LPS biosynthesis, and chaperones. Deletions of efflux pump genes force evolution on inferior mutational paths, not explored in the wild type, and some of these essentially block resistance evolution. This effect is due to strong negative epistasis with resistance mutations. The identified genes and cellular functions provide potential targets for development of adjuvants that may block spontaneous resistance evolution when combined with antibiotics.' article_number: '3105' article_processing_charge: No article_type: original author: - first_name: Marta full_name: Lukacisinova, Marta id: 4342E402-F248-11E8-B48F-1D18A9856A87 last_name: Lukacisinova orcid: 0000-0002-2519-8004 - first_name: Booshini full_name: Fernando, Booshini last_name: Fernando - first_name: Mark Tobias full_name: Bollenbach, Mark Tobias id: 3E6DB97A-F248-11E8-B48F-1D18A9856A87 last_name: Bollenbach orcid: 0000-0003-4398-476X citation: ama: Lukacisinova M, Fernando B, Bollenbach MT. Highly parallel lab evolution reveals that epistasis can curb the evolution of antibiotic resistance. Nature Communications. 2020;11. doi:10.1038/s41467-020-16932-z apa: Lukacisinova, M., Fernando, B., & Bollenbach, M. T. (2020). Highly parallel lab evolution reveals that epistasis can curb the evolution of antibiotic resistance. Nature Communications. Springer Nature. https://doi.org/10.1038/s41467-020-16932-z chicago: Lukacisinova, Marta, Booshini Fernando, and Mark Tobias Bollenbach. “Highly Parallel Lab Evolution Reveals That Epistasis Can Curb the Evolution of Antibiotic Resistance.” Nature Communications. Springer Nature, 2020. https://doi.org/10.1038/s41467-020-16932-z. ieee: M. Lukacisinova, B. Fernando, and M. T. Bollenbach, “Highly parallel lab evolution reveals that epistasis can curb the evolution of antibiotic resistance,” Nature Communications, vol. 11. Springer Nature, 2020. ista: Lukacisinova M, Fernando B, Bollenbach MT. 2020. Highly parallel lab evolution reveals that epistasis can curb the evolution of antibiotic resistance. Nature Communications. 11, 3105. mla: Lukacisinova, Marta, et al. “Highly Parallel Lab Evolution Reveals That Epistasis Can Curb the Evolution of Antibiotic Resistance.” Nature Communications, vol. 11, 3105, Springer Nature, 2020, doi:10.1038/s41467-020-16932-z. short: M. Lukacisinova, B. Fernando, M.T. Bollenbach, Nature Communications 11 (2020). date_created: 2020-06-29T07:59:35Z date_published: 2020-06-19T00:00:00Z date_updated: 2023-08-22T07:48:30Z day: '19' ddc: - '570' doi: 10.1038/s41467-020-16932-z extern: '1' external_id: isi: - '000545685100002' pmid: - '32561723' file: - access_level: open_access checksum: 4f5f49d63add331d5eb8a2bae477b396 content_type: application/pdf creator: cziletti date_created: 2020-06-30T09:58:50Z date_updated: 2020-07-14T12:48:08Z file_id: '8071' file_name: 2020_NatureComm_Lukacisinova.pdf file_size: 1546491 relation: main_file file_date_updated: 2020-07-14T12:48:08Z has_accepted_license: '1' intvolume: ' 11' isi: 1 language: - iso: eng month: '06' oa: 1 oa_version: Published Version pmid: 1 project: - _id: 25E9AF9E-B435-11E9-9278-68D0E5697425 call_identifier: FWF grant_number: P27201-B22 name: Revealing the mechanisms underlying drug interactions - _id: 25EB3A80-B435-11E9-9278-68D0E5697425 grant_number: RGP0042/2013 name: Revealing the fundamental limits of cell growth publication: Nature Communications publication_identifier: eissn: - '20411723' publication_status: published publisher: Springer Nature quality_controlled: '1' scopus_import: '1' status: public title: Highly parallel lab evolution reveals that epistasis can curb the evolution of antibiotic resistance 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: '2020' ... --- _id: '8250' abstract: - lang: eng text: 'Antibiotics that interfere with translation, when combined, interact in diverse and difficult-to-predict ways. Here, we explain these interactions by “translation bottlenecks”: points in the translation cycle where antibiotics block ribosomal progression. To elucidate the underlying mechanisms of drug interactions between translation inhibitors, we generate translation bottlenecks genetically using inducible control of translation factors that regulate well-defined translation cycle steps. These perturbations accurately mimic antibiotic action and drug interactions, supporting that the interplay of different translation bottlenecks causes these interactions. We further show that growth laws, combined with drug uptake and binding kinetics, enable the direct prediction of a large fraction of observed interactions, yet fail to predict suppression. However, varying two translation bottlenecks simultaneously supports that dense traffic of ribosomes and competition for translation factors account for the previously unexplained suppression. These results highlight the importance of “continuous epistasis” in bacterial physiology.' acknowledgement: "We thank M. Hennessey-Wesen, I. Tomanek, K. Jain, A. Staron, K. Tomasek, M. Scott,\r\nK.C. Huang, and Z. Gitai for reading the manuscript and constructive comments. B.K. is\r\nindebted to C. Guet for additional guidance and generous support, which rendered this\r\nwork possible. B.K. thanks all members of Guet group for many helpful discussions and\r\nsharing of resources. B.K. additionally acknowledges the tremendous support from A.\r\nAngermayr and K. Mitosch with experimental work. We further thank E. Brown for\r\nhelpful comments regarding lamotrigine, and A. Buskirk for valuable suggestions\r\nregarding the ribosome footprint size. This work was supported in part by Austrian\r\nScience Fund (FWF) standalone grants P 27201-B22 (to T.B.) and P 28844 (to G.T.),\r\nHFSP program Grant RGP0042/2013 (to T.B.), German Research Foundation (DFG)\r\nstandalone grant BO 3502/2-1 (to T.B.), and German Research Foundation (DFG)\r\nCollaborative Research Centre (SFB) 1310 (to T.B.). Open access funding provided by\r\nProjekt DEAL." article_number: '4013' article_processing_charge: No article_type: original author: - first_name: Bor full_name: Kavcic, Bor id: 350F91D2-F248-11E8-B48F-1D18A9856A87 last_name: Kavcic orcid: 0000-0001-6041-254X - first_name: Gašper full_name: Tkačik, Gašper id: 3D494DCA-F248-11E8-B48F-1D18A9856A87 last_name: Tkačik orcid: 0000-0002-6699-1455 - first_name: Tobias full_name: Bollenbach, Tobias id: 3E6DB97A-F248-11E8-B48F-1D18A9856A87 last_name: Bollenbach orcid: 0000-0003-4398-476X citation: ama: Kavcic B, Tkačik G, Bollenbach MT. Mechanisms of drug interactions between translation-inhibiting antibiotics. Nature Communications. 2020;11. doi:10.1038/s41467-020-17734-z apa: Kavcic, B., Tkačik, G., & Bollenbach, M. T. (2020). Mechanisms of drug interactions between translation-inhibiting antibiotics. Nature Communications. Springer Nature. https://doi.org/10.1038/s41467-020-17734-z chicago: Kavcic, Bor, Gašper Tkačik, and Mark Tobias Bollenbach. “Mechanisms of Drug Interactions between Translation-Inhibiting Antibiotics.” Nature Communications. Springer Nature, 2020. https://doi.org/10.1038/s41467-020-17734-z. ieee: B. Kavcic, G. Tkačik, and M. T. Bollenbach, “Mechanisms of drug interactions between translation-inhibiting antibiotics,” Nature Communications, vol. 11. Springer Nature, 2020. ista: Kavcic B, Tkačik G, Bollenbach MT. 2020. Mechanisms of drug interactions between translation-inhibiting antibiotics. Nature Communications. 11, 4013. mla: Kavcic, Bor, et al. “Mechanisms of Drug Interactions between Translation-Inhibiting Antibiotics.” Nature Communications, vol. 11, 4013, Springer Nature, 2020, doi:10.1038/s41467-020-17734-z. short: B. Kavcic, G. Tkačik, M.T. Bollenbach, Nature Communications 11 (2020). date_created: 2020-08-12T09:13:50Z date_published: 2020-08-11T00:00:00Z date_updated: 2024-03-28T23:30:08Z day: '11' ddc: - '570' department: - _id: GaTk doi: 10.1038/s41467-020-17734-z external_id: isi: - '000562769300008' file: - access_level: open_access checksum: 986bebb308850a55850028d3d2b5b664 content_type: application/pdf creator: dernst date_created: 2020-08-17T07:36:57Z date_updated: 2020-08-17T07:36:57Z file_id: '8275' file_name: 2020_NatureComm_Kavcic.pdf file_size: 1965672 relation: main_file success: 1 file_date_updated: 2020-08-17T07:36:57Z has_accepted_license: '1' intvolume: ' 11' isi: 1 language: - iso: eng month: '08' oa: 1 oa_version: Published Version project: - _id: 25E9AF9E-B435-11E9-9278-68D0E5697425 call_identifier: FWF grant_number: P27201-B22 name: Revealing the mechanisms underlying drug interactions - _id: 254E9036-B435-11E9-9278-68D0E5697425 call_identifier: FWF grant_number: P28844-B27 name: Biophysics of information processing in gene regulation publication: Nature Communications publication_identifier: issn: - 2041-1723 publication_status: published publisher: Springer Nature quality_controlled: '1' related_material: record: - id: '8657' relation: dissertation_contains status: public status: public title: Mechanisms of drug interactions between translation-inhibiting antibiotics 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: '2020' ... --- _id: '7673' abstract: - lang: eng text: Combining drugs can improve the efficacy of treatments. However, predicting the effect of drug combinations is still challenging. The combined potency of drugs determines the drug interaction, which is classified as synergistic, additive, antagonistic, or suppressive. While probabilistic, non-mechanistic models exist, there is currently no biophysical model that can predict antibiotic interactions. Here, we present a physiologically relevant model of the combined action of antibiotics that inhibit protein synthesis by targeting the ribosome. This model captures the kinetics of antibiotic binding and transport, and uses bacterial growth laws to predict growth in the presence of antibiotic combinations. We find that this biophysical model can produce all drug interaction types except suppression. We show analytically that antibiotics which cannot bind to the ribosome simultaneously generally act as substitutes for one another, leading to additive drug interactions. Previously proposed null expectations for higher-order drug interactions follow as a limiting case of our model. We further extend the model to include the effects of direct physical or allosteric interactions between individual drugs on the ribosome. Notably, such direct interactions profoundly change the combined drug effect, depending on the kinetic parameters of the drugs used. The model makes additional predictions for the effects of resistance genes on drug interactions and for interactions between ribosome-targeting antibiotics and antibiotics with other targets. These findings enhance our understanding of the interplay between drug action and cell physiology and are a key step toward a general framework for predicting drug interactions. article_processing_charge: No author: - first_name: Bor full_name: Kavcic, Bor id: 350F91D2-F248-11E8-B48F-1D18A9856A87 last_name: Kavcic orcid: 0000-0001-6041-254X - first_name: Gašper full_name: Tkačik, Gašper id: 3D494DCA-F248-11E8-B48F-1D18A9856A87 last_name: Tkačik orcid: 0000-0002-6699-1455 - first_name: Tobias full_name: Bollenbach, Tobias id: 3E6DB97A-F248-11E8-B48F-1D18A9856A87 last_name: Bollenbach orcid: 0000-0003-4398-476X citation: ama: Kavcic B, Tkačik G, Bollenbach MT. A minimal biophysical model of combined antibiotic action. bioRxiv. 2020. doi:10.1101/2020.04.18.047886 apa: Kavcic, B., Tkačik, G., & Bollenbach, M. T. (2020). A minimal biophysical model of combined antibiotic action. bioRxiv. Cold Spring Harbor Laboratory. https://doi.org/10.1101/2020.04.18.047886 chicago: Kavcic, Bor, Gašper Tkačik, and Mark Tobias Bollenbach. “A Minimal Biophysical Model of Combined Antibiotic Action.” BioRxiv. Cold Spring Harbor Laboratory, 2020. https://doi.org/10.1101/2020.04.18.047886. ieee: B. Kavcic, G. Tkačik, and M. T. Bollenbach, “A minimal biophysical model of combined antibiotic action,” bioRxiv. Cold Spring Harbor Laboratory, 2020. ista: Kavcic B, Tkačik G, Bollenbach MT. 2020. A minimal biophysical model of combined antibiotic action. bioRxiv, 10.1101/2020.04.18.047886. mla: Kavcic, Bor, et al. “A Minimal Biophysical Model of Combined Antibiotic Action.” BioRxiv, Cold Spring Harbor Laboratory, 2020, doi:10.1101/2020.04.18.047886. short: B. Kavcic, G. Tkačik, M.T. Bollenbach, BioRxiv (2020). date_created: 2020-04-22T08:27:56Z date_published: 2020-04-18T00:00:00Z date_updated: 2024-03-28T23:30:08Z day: '18' department: - _id: GaTk doi: 10.1101/2020.04.18.047886 language: - iso: eng main_file_link: - open_access: '1' url: 'https://doi.org/10.1101/2020.04.18.047886 ' month: '04' oa: 1 oa_version: Preprint project: - _id: 25E9AF9E-B435-11E9-9278-68D0E5697425 call_identifier: FWF grant_number: P27201-B22 name: Revealing the mechanisms underlying drug interactions - _id: 254E9036-B435-11E9-9278-68D0E5697425 call_identifier: FWF grant_number: P28844-B27 name: Biophysics of information processing in gene regulation publication: bioRxiv publication_status: published publisher: Cold Spring Harbor Laboratory related_material: record: - id: '8997' relation: later_version status: public - id: '8657' relation: dissertation_contains status: public status: public title: A minimal biophysical model of combined antibiotic action type: preprint user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87 year: '2020' ... --- _id: '6046' abstract: - lang: eng text: Sudden stress often triggers diverse, temporally structured gene expression responses in microbes, but it is largely unknown how variable in time such responses are and if genes respond in the same temporal order in every single cell. Here, we quantified timing variability of individual promoters responding to sublethal antibiotic stress using fluorescent reporters, microfluidics, and time‐lapse microscopy. We identified lower and upper bounds that put definite constraints on timing variability, which varies strongly among promoters and conditions. Timing variability can be interpreted using results from statistical kinetics, which enable us to estimate the number of rate‐limiting molecular steps underlying different responses. We found that just a few critical steps control some responses while others rely on dozens of steps. To probe connections between different stress responses, we then tracked the temporal order and response time correlations of promoter pairs in individual cells. Our results support that, when bacteria are exposed to the antibiotic nitrofurantoin, the ensuing oxidative stress and SOS responses are part of the same causal chain of molecular events. In contrast, under trimethoprim, the acid stress response and the SOS response are part of different chains of events running in parallel. Our approach reveals fundamental constraints on gene expression timing and provides new insights into the molecular events that underlie the timing of stress responses. acknowledged_ssus: - _id: Bio article_number: e8470 article_processing_charge: No author: - first_name: Karin full_name: Mitosch, Karin id: 39B66846-F248-11E8-B48F-1D18A9856A87 last_name: Mitosch - first_name: Georg full_name: Rieckh, Georg id: 34DA8BD6-F248-11E8-B48F-1D18A9856A87 last_name: Rieckh - first_name: Mark Tobias full_name: Bollenbach, Mark Tobias id: 3E6DB97A-F248-11E8-B48F-1D18A9856A87 last_name: Bollenbach orcid: 0000-0003-4398-476X citation: ama: Mitosch K, Rieckh G, Bollenbach MT. Temporal order and precision of complex stress responses in individual bacteria. Molecular systems biology. 2019;15(2). doi:10.15252/msb.20188470 apa: Mitosch, K., Rieckh, G., & Bollenbach, M. T. (2019). Temporal order and precision of complex stress responses in individual bacteria. Molecular Systems Biology. Embo Press. https://doi.org/10.15252/msb.20188470 chicago: Mitosch, Karin, Georg Rieckh, and Mark Tobias Bollenbach. “Temporal Order and Precision of Complex Stress Responses in Individual Bacteria.” Molecular Systems Biology. Embo Press, 2019. https://doi.org/10.15252/msb.20188470. ieee: K. Mitosch, G. Rieckh, and M. T. Bollenbach, “Temporal order and precision of complex stress responses in individual bacteria,” Molecular systems biology, vol. 15, no. 2. Embo Press, 2019. ista: Mitosch K, Rieckh G, Bollenbach MT. 2019. Temporal order and precision of complex stress responses in individual bacteria. Molecular systems biology. 15(2), e8470. mla: Mitosch, Karin, et al. “Temporal Order and Precision of Complex Stress Responses in Individual Bacteria.” Molecular Systems Biology, vol. 15, no. 2, e8470, Embo Press, 2019, doi:10.15252/msb.20188470. short: K. Mitosch, G. Rieckh, M.T. Bollenbach, Molecular Systems Biology 15 (2019). date_created: 2019-02-24T22:59:18Z date_published: 2019-02-14T00:00:00Z date_updated: 2023-08-24T14:49:53Z day: '14' department: - _id: GaTk doi: 10.15252/msb.20188470 external_id: isi: - '000459628300003' pmid: - '30765425' intvolume: ' 15' isi: 1 issue: '2' language: - iso: eng main_file_link: - open_access: '1' url: https://www.ncbi.nlm.nih.gov/pubmed/30765425 month: '02' oa: 1 oa_version: Submitted Version pmid: 1 project: - _id: 25E9AF9E-B435-11E9-9278-68D0E5697425 call_identifier: FWF grant_number: P27201-B22 name: Revealing the mechanisms underlying drug interactions - _id: 25EB3A80-B435-11E9-9278-68D0E5697425 grant_number: RGP0042/2013 name: Revealing the fundamental limits of cell growth publication: Molecular systems biology publication_status: published publisher: Embo Press quality_controlled: '1' scopus_import: '1' status: public title: Temporal order and precision of complex stress responses in individual bacteria type: journal_article user_id: 4359f0d1-fa6c-11eb-b949-802e58b17ae8 volume: 15 year: '2019' ... --- _id: '7026' abstract: - lang: eng text: Effective design of combination therapies requires understanding the changes in cell physiology that result from drug interactions. Here, we show that the genome-wide transcriptional response to combinations of two drugs, measured at a rigorously controlled growth rate, can predict higher-order antagonism with a third drug in Saccharomyces cerevisiae. Using isogrowth profiling, over 90% of the variation in cellular response can be decomposed into three principal components (PCs) that have clear biological interpretations. We demonstrate that the third PC captures emergent transcriptional programs that are dependent on both drugs and can predict antagonism with a third drug targeting the emergent pathway. We further show that emergent gene expression patterns are most pronounced at a drug ratio where the drug interaction is strongest, providing a guideline for future measurements. Our results provide a readily applicable recipe for uncovering emergent responses in other systems and for higher-order drug combinations. A record of this paper’s transparent peer review process is included in the Supplemental Information. acknowledged_ssus: - _id: LifeSc article_processing_charge: No article_type: original author: - first_name: Martin full_name: Lukacisin, Martin id: 298FFE8C-F248-11E8-B48F-1D18A9856A87 last_name: Lukacisin orcid: 0000-0001-6549-4177 - first_name: Tobias full_name: Bollenbach, Tobias id: 3E6DB97A-F248-11E8-B48F-1D18A9856A87 last_name: Bollenbach orcid: 0000-0003-4398-476X citation: ama: Lukacisin M, Bollenbach MT. Emergent gene expression responses to drug combinations predict higher-order drug interactions. Cell Systems. 2019;9(5):423-433.e1-e3. doi:10.1016/j.cels.2019.10.004 apa: Lukacisin, M., & Bollenbach, M. T. (2019). Emergent gene expression responses to drug combinations predict higher-order drug interactions. Cell Systems. Cell Press. https://doi.org/10.1016/j.cels.2019.10.004 chicago: Lukacisin, Martin, and Mark Tobias Bollenbach. “Emergent Gene Expression Responses to Drug Combinations Predict Higher-Order Drug Interactions.” Cell Systems. Cell Press, 2019. https://doi.org/10.1016/j.cels.2019.10.004. ieee: M. Lukacisin and M. T. Bollenbach, “Emergent gene expression responses to drug combinations predict higher-order drug interactions,” Cell Systems, vol. 9, no. 5. Cell Press, pp. 423-433.e1-e3, 2019. ista: Lukacisin M, Bollenbach MT. 2019. Emergent gene expression responses to drug combinations predict higher-order drug interactions. Cell Systems. 9(5), 423-433.e1-e3. mla: Lukacisin, Martin, and Mark Tobias Bollenbach. “Emergent Gene Expression Responses to Drug Combinations Predict Higher-Order Drug Interactions.” Cell Systems, vol. 9, no. 5, Cell Press, 2019, pp. 423-433.e1-e3, doi:10.1016/j.cels.2019.10.004. short: M. Lukacisin, M.T. Bollenbach, Cell Systems 9 (2019) 423-433.e1-e3. date_created: 2019-11-15T10:51:42Z date_published: 2019-11-27T00:00:00Z date_updated: 2023-08-30T07:24:58Z day: '27' ddc: - '570' department: - _id: ToBo doi: 10.1016/j.cels.2019.10.004 external_id: isi: - '000499495400003' file: - access_level: open_access checksum: 7a11d6c2f9523d65b049512d61733178 content_type: application/pdf creator: dernst date_created: 2019-11-15T10:57:42Z date_updated: 2020-07-14T12:47:48Z file_id: '7027' file_name: 2019_CellSystems_Lukacisin.pdf file_size: 4238460 relation: main_file file_date_updated: 2020-07-14T12:47:48Z has_accepted_license: '1' intvolume: ' 9' isi: 1 issue: '5' language: - iso: eng month: '11' oa: 1 oa_version: Published Version page: 423-433.e1-e3 project: - _id: 25E9AF9E-B435-11E9-9278-68D0E5697425 call_identifier: FWF grant_number: P27201-B22 name: Revealing the mechanisms underlying drug interactions - _id: 25EB3A80-B435-11E9-9278-68D0E5697425 grant_number: RGP0042/2013 name: Revealing the fundamental limits of cell growth publication: Cell Systems publication_identifier: issn: - 2405-4712 publication_status: published publisher: Cell Press quality_controlled: '1' scopus_import: '1' status: public title: Emergent gene expression responses to drug combinations predict higher-order drug interactions 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: 9 year: '2019' ... --- _id: '674' abstract: - lang: eng text: Navigation of cells along gradients of guidance cues is a determining step in many developmental and immunological processes. Gradients can either be soluble or immobilized to tissues as demonstrated for the haptotactic migration of dendritic cells (DCs) toward higher concentrations of immobilized chemokine CCL21. To elucidate how gradient characteristics govern cellular response patterns, we here introduce an in vitro system allowing to track migratory responses of DCs to precisely controlled immobilized gradients of CCL21. We find that haptotactic sensing depends on the absolute CCL21 concentration and local steepness of the gradient, consistent with a scenario where DC directionality is governed by the signal-to-noise ratio of CCL21 binding to the receptor CCR7. We find that the conditions for optimal DC guidance are perfectly provided by the CCL21 gradients we measure in vivo. Furthermore, we find that CCR7 signal termination by the G-protein-coupled receptor kinase 6 (GRK6) is crucial for haptotactic but dispensable for chemotactic CCL21 gradient sensing in vitro and confirm those observations in vivo. These findings suggest that stable, tissue-bound CCL21 gradients as sustainable “roads” ensure optimal guidance in vivo. author: - first_name: Jan full_name: Schwarz, Jan id: 346C1EC6-F248-11E8-B48F-1D18A9856A87 last_name: Schwarz - first_name: Veronika full_name: Bierbaum, Veronika id: 3FD04378-F248-11E8-B48F-1D18A9856A87 last_name: Bierbaum - first_name: Kari full_name: Vaahtomeri, Kari id: 368EE576-F248-11E8-B48F-1D18A9856A87 last_name: Vaahtomeri orcid: 0000-0001-7829-3518 - first_name: Robert full_name: Hauschild, Robert id: 4E01D6B4-F248-11E8-B48F-1D18A9856A87 last_name: Hauschild orcid: 0000-0001-9843-3522 - first_name: Markus full_name: Brown, Markus id: 3DAB9AFC-F248-11E8-B48F-1D18A9856A87 last_name: Brown - first_name: Ingrid full_name: De Vries, Ingrid id: 4C7D837E-F248-11E8-B48F-1D18A9856A87 last_name: De Vries - first_name: Alexander F full_name: Leithner, Alexander F id: 3B1B77E4-F248-11E8-B48F-1D18A9856A87 last_name: Leithner - first_name: Anne full_name: Reversat, Anne id: 35B76592-F248-11E8-B48F-1D18A9856A87 last_name: Reversat orcid: 0000-0003-0666-8928 - first_name: Jack full_name: Merrin, Jack id: 4515C308-F248-11E8-B48F-1D18A9856A87 last_name: Merrin orcid: 0000-0001-5145-4609 - first_name: Teresa full_name: Tarrant, Teresa last_name: Tarrant - first_name: Tobias full_name: Bollenbach, Tobias id: 3E6DB97A-F248-11E8-B48F-1D18A9856A87 last_name: Bollenbach orcid: 0000-0003-4398-476X - first_name: Michael K full_name: Sixt, Michael K id: 41E9FBEA-F248-11E8-B48F-1D18A9856A87 last_name: Sixt orcid: 0000-0002-6620-9179 citation: ama: Schwarz J, Bierbaum V, Vaahtomeri K, et al. Dendritic cells interpret haptotactic chemokine gradients in a manner governed by signal to noise ratio and dependent on GRK6. Current Biology. 2017;27(9):1314-1325. doi:10.1016/j.cub.2017.04.004 apa: Schwarz, J., Bierbaum, V., Vaahtomeri, K., Hauschild, R., Brown, M., de Vries, I., … Sixt, M. K. (2017). Dendritic cells interpret haptotactic chemokine gradients in a manner governed by signal to noise ratio and dependent on GRK6. Current Biology. Cell Press. https://doi.org/10.1016/j.cub.2017.04.004 chicago: Schwarz, Jan, Veronika Bierbaum, Kari Vaahtomeri, Robert Hauschild, Markus Brown, Ingrid de Vries, Alexander F Leithner, et al. “Dendritic Cells Interpret Haptotactic Chemokine Gradients in a Manner Governed by Signal to Noise Ratio and Dependent on GRK6.” Current Biology. Cell Press, 2017. https://doi.org/10.1016/j.cub.2017.04.004. ieee: J. Schwarz et al., “Dendritic cells interpret haptotactic chemokine gradients in a manner governed by signal to noise ratio and dependent on GRK6,” Current Biology, vol. 27, no. 9. Cell Press, pp. 1314–1325, 2017. ista: Schwarz J, Bierbaum V, Vaahtomeri K, Hauschild R, Brown M, de Vries I, Leithner AF, Reversat A, Merrin J, Tarrant T, Bollenbach MT, Sixt MK. 2017. Dendritic cells interpret haptotactic chemokine gradients in a manner governed by signal to noise ratio and dependent on GRK6. Current Biology. 27(9), 1314–1325. mla: Schwarz, Jan, et al. “Dendritic Cells Interpret Haptotactic Chemokine Gradients in a Manner Governed by Signal to Noise Ratio and Dependent on GRK6.” Current Biology, vol. 27, no. 9, Cell Press, 2017, pp. 1314–25, doi:10.1016/j.cub.2017.04.004. short: J. Schwarz, V. Bierbaum, K. Vaahtomeri, R. Hauschild, M. Brown, I. de Vries, A.F. Leithner, A. Reversat, J. Merrin, T. Tarrant, M.T. Bollenbach, M.K. Sixt, Current Biology 27 (2017) 1314–1325. date_created: 2018-12-11T11:47:51Z date_published: 2017-05-09T00:00:00Z date_updated: 2023-02-23T12:50:44Z day: '09' department: - _id: MiSi - _id: Bio - _id: NanoFab doi: 10.1016/j.cub.2017.04.004 ec_funded: 1 intvolume: ' 27' issue: '9' language: - iso: eng month: '05' oa_version: None page: 1314 - 1325 project: - _id: 25681D80-B435-11E9-9278-68D0E5697425 call_identifier: FP7 grant_number: '291734' name: International IST Postdoc Fellowship Programme - _id: 25A8E5EA-B435-11E9-9278-68D0E5697425 call_identifier: FWF grant_number: Y 564-B12 name: Cytoskeletal force generation and transduction of leukocytes (FWF) publication: Current Biology publication_identifier: issn: - '09609822' publication_status: published publisher: Cell Press publist_id: '7050' quality_controlled: '1' scopus_import: 1 status: public title: Dendritic cells interpret haptotactic chemokine gradients in a manner governed by signal to noise ratio and dependent on GRK6 type: journal_article user_id: 4435EBFC-F248-11E8-B48F-1D18A9856A87 volume: 27 year: '2017' ... --- _id: '666' abstract: - lang: eng text: Antibiotics elicit drastic changes in microbial gene expression, including the induction of stress response genes. While certain stress responses are known to “cross-protect” bacteria from other stressors, it is unclear whether cellular responses to antibiotics have a similar protective role. By measuring the genome-wide transcriptional response dynamics of Escherichia coli to four antibiotics, we found that trimethoprim induces a rapid acid stress response that protects bacteria from subsequent exposure to acid. Combining microfluidics with time-lapse imaging to monitor survival and acid stress response in single cells revealed that the noisy expression of the acid resistance operon gadBC correlates with single-cell survival. Cells with higher gadBC expression following trimethoprim maintain higher intracellular pH and survive the acid stress longer. The seemingly random single-cell survival under acid stress can therefore be predicted from gadBC expression and rationalized in terms of GadB/C molecular function. Overall, we provide a roadmap for identifying the molecular mechanisms of single-cell cross-protection between antibiotics and other stressors. article_processing_charge: Yes (in subscription journal) author: - first_name: Karin full_name: Mitosch, Karin id: 39B66846-F248-11E8-B48F-1D18A9856A87 last_name: Mitosch - first_name: Georg full_name: Rieckh, Georg id: 34DA8BD6-F248-11E8-B48F-1D18A9856A87 last_name: Rieckh - first_name: Tobias full_name: Bollenbach, Tobias id: 3E6DB97A-F248-11E8-B48F-1D18A9856A87 last_name: Bollenbach orcid: 0000-0003-4398-476X citation: ama: Mitosch K, Rieckh G, Bollenbach MT. Noisy response to antibiotic stress predicts subsequent single cell survival in an acidic environment. Cell Systems. 2017;4(4):393-403. doi:10.1016/j.cels.2017.03.001 apa: Mitosch, K., Rieckh, G., & Bollenbach, M. T. (2017). Noisy response to antibiotic stress predicts subsequent single cell survival in an acidic environment. Cell Systems. Cell Press. https://doi.org/10.1016/j.cels.2017.03.001 chicago: Mitosch, Karin, Georg Rieckh, and Mark Tobias Bollenbach. “Noisy Response to Antibiotic Stress Predicts Subsequent Single Cell Survival in an Acidic Environment.” Cell Systems. Cell Press, 2017. https://doi.org/10.1016/j.cels.2017.03.001. ieee: K. Mitosch, G. Rieckh, and M. T. Bollenbach, “Noisy response to antibiotic stress predicts subsequent single cell survival in an acidic environment,” Cell Systems, vol. 4, no. 4. Cell Press, pp. 393–403, 2017. ista: Mitosch K, Rieckh G, Bollenbach MT. 2017. Noisy response to antibiotic stress predicts subsequent single cell survival in an acidic environment. Cell Systems. 4(4), 393–403. mla: Mitosch, Karin, et al. “Noisy Response to Antibiotic Stress Predicts Subsequent Single Cell Survival in an Acidic Environment.” Cell Systems, vol. 4, no. 4, Cell Press, 2017, pp. 393–403, doi:10.1016/j.cels.2017.03.001. short: K. Mitosch, G. Rieckh, M.T. Bollenbach, Cell Systems 4 (2017) 393–403. date_created: 2018-12-11T11:47:48Z date_published: 2017-04-26T00:00:00Z date_updated: 2023-09-07T12:00:25Z day: '26' ddc: - '576' - '610' department: - _id: ToBo - _id: GaTk doi: 10.1016/j.cels.2017.03.001 ec_funded: 1 file: - access_level: open_access checksum: 04ff20011c3d9a601c514aa999a5fe1a content_type: application/pdf creator: system date_created: 2018-12-12T10:13:54Z date_updated: 2020-07-14T12:47:35Z file_id: '5041' file_name: IST-2017-901-v1+1_1-s2.0-S2405471217300868-main.pdf file_size: 2438660 relation: main_file file_date_updated: 2020-07-14T12:47:35Z has_accepted_license: '1' intvolume: ' 4' issue: '4' language: - iso: eng license: https://creativecommons.org/licenses/by-nc-nd/4.0/ month: '04' oa: 1 oa_version: Published Version page: 393 - 403 project: - _id: 25E83C2C-B435-11E9-9278-68D0E5697425 call_identifier: FP7 grant_number: '303507' name: Optimality principles in responses to antibiotics - _id: 25E9AF9E-B435-11E9-9278-68D0E5697425 call_identifier: FWF grant_number: P27201-B22 name: Revealing the mechanisms underlying drug interactions - _id: 25EB3A80-B435-11E9-9278-68D0E5697425 grant_number: RGP0042/2013 name: Revealing the fundamental limits of cell growth publication: Cell Systems publication_identifier: issn: - '24054712' publication_status: published publisher: Cell Press publist_id: '7061' pubrep_id: '901' quality_controlled: '1' related_material: record: - id: '818' relation: dissertation_contains status: public scopus_import: 1 status: public title: Noisy response to antibiotic stress predicts subsequent single cell survival in an acidic environment tmp: image: /images/cc_by_nc_nd.png legal_code_url: https://creativecommons.org/licenses/by-nc-nd/4.0/legalcode name: Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0) short: CC BY-NC-ND (4.0) type: journal_article user_id: 3E5EF7F0-F248-11E8-B48F-1D18A9856A87 volume: 4 year: '2017' ... --- _id: '822' abstract: - lang: eng text: 'Polymicrobial infections constitute small ecosystems that accommodate several bacterial species. Commonly, these bacteria are investigated in isolation. However, it is unknown to what extent the isolates interact and whether their interactions alter bacterial growth and ecosystem resilience in the presence and absence of antibiotics. We quantified the complete ecological interaction network for 72 bacterial isolates collected from 23 individuals diagnosed with polymicrobial urinary tract infections and found that most interactions cluster based on evolutionary relatedness. Statistical network analysis revealed that competitive and cooperative reciprocal interactions are enriched in the global network, while cooperative interactions are depleted in the individual host community networks. A population dynamics model parameterized by our measurements suggests that interactions restrict community stability, explaining the observed species diversity of these communities. We further show that the clinical isolates frequently protect each other from clinically relevant antibiotics. Together, these results highlight that ecological interactions are crucial for the growth and survival of bacteria in polymicrobial infection communities and affect their assembly and resilience. ' article_processing_charge: No author: - first_name: Marjon full_name: De Vos, Marjon id: 3111FFAC-F248-11E8-B48F-1D18A9856A87 last_name: De Vos - first_name: Marcin P full_name: Zagórski, Marcin P id: 343DA0DC-F248-11E8-B48F-1D18A9856A87 last_name: Zagórski orcid: 0000-0001-7896-7762 - first_name: Alan full_name: Mcnally, Alan last_name: Mcnally - first_name: Mark Tobias full_name: Bollenbach, Mark Tobias id: 3E6DB97A-F248-11E8-B48F-1D18A9856A87 last_name: Bollenbach orcid: 0000-0003-4398-476X citation: ama: de Vos M, Zagórski MP, Mcnally A, Bollenbach MT. Interaction networks, ecological stability, and collective antibiotic tolerance in polymicrobial infections. PNAS. 2017;114(40):10666-10671. doi:10.1073/pnas.1713372114 apa: de Vos, M., Zagórski, M. P., Mcnally, A., & Bollenbach, M. T. (2017). Interaction networks, ecological stability, and collective antibiotic tolerance in polymicrobial infections. PNAS. National Academy of Sciences. https://doi.org/10.1073/pnas.1713372114 chicago: Vos, Marjon de, Marcin P Zagórski, Alan Mcnally, and Mark Tobias Bollenbach. “Interaction Networks, Ecological Stability, and Collective Antibiotic Tolerance in Polymicrobial Infections.” PNAS. National Academy of Sciences, 2017. https://doi.org/10.1073/pnas.1713372114. ieee: M. de Vos, M. P. Zagórski, A. Mcnally, and M. T. Bollenbach, “Interaction networks, ecological stability, and collective antibiotic tolerance in polymicrobial infections,” PNAS, vol. 114, no. 40. National Academy of Sciences, pp. 10666–10671, 2017. ista: de Vos M, Zagórski MP, Mcnally A, Bollenbach MT. 2017. Interaction networks, ecological stability, and collective antibiotic tolerance in polymicrobial infections. PNAS. 114(40), 10666–10671. mla: de Vos, Marjon, et al. “Interaction Networks, Ecological Stability, and Collective Antibiotic Tolerance in Polymicrobial Infections.” PNAS, vol. 114, no. 40, National Academy of Sciences, 2017, pp. 10666–71, doi:10.1073/pnas.1713372114. short: M. de Vos, M.P. Zagórski, A. Mcnally, M.T. Bollenbach, PNAS 114 (2017) 10666–10671. date_created: 2018-12-11T11:48:41Z date_published: 2017-10-03T00:00:00Z date_updated: 2023-09-26T16:18:48Z day: '03' department: - _id: ToBo doi: 10.1073/pnas.1713372114 ec_funded: 1 external_id: isi: - '000412130500061' pmid: - '28923953' intvolume: ' 114' isi: 1 issue: '40' language: - iso: eng main_file_link: - open_access: '1' url: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5635929/ month: '10' oa: 1 oa_version: Submitted Version page: 10666 - 10671 pmid: 1 project: - _id: 25E83C2C-B435-11E9-9278-68D0E5697425 call_identifier: FP7 grant_number: '303507' name: Optimality principles in responses to antibiotics - _id: 25E9AF9E-B435-11E9-9278-68D0E5697425 call_identifier: FWF grant_number: P27201-B22 name: Revealing the mechanisms underlying drug interactions publication: PNAS publication_identifier: issn: - '00278424' publication_status: published publisher: National Academy of Sciences publist_id: '6827' quality_controlled: '1' scopus_import: '1' status: public title: Interaction networks, ecological stability, and collective antibiotic tolerance in polymicrobial infections type: journal_article user_id: c635000d-4b10-11ee-a964-aac5a93f6ac1 volume: 114 year: '2017' ... --- _id: '1027' abstract: - lang: eng text: The rising prevalence of antibiotic resistant bacteria is an increasingly serious public health challenge. To address this problem, recent work ranging from clinical studies to theoretical modeling has provided valuable insights into the mechanisms of resistance, its emergence and spread, and ways to counteract it. A deeper understanding of the underlying dynamics of resistance evolution will require a combination of experimental and theoretical expertise from different disciplines and new technology for studying evolution in the laboratory. Here, we review recent advances in the quantitative understanding of the mechanisms and evolution of antibiotic resistance. We focus on key theoretical concepts and new technology that enables well-controlled experiments. We further highlight key challenges that can be met in the near future to ultimately develop effective strategies for combating resistance. article_processing_charge: Yes (in subscription journal) article_type: original author: - first_name: Marta full_name: Lukacisinova, Marta id: 4342E402-F248-11E8-B48F-1D18A9856A87 last_name: Lukacisinova orcid: 0000-0002-2519-8004 - first_name: Mark Tobias full_name: Bollenbach, Mark Tobias id: 3E6DB97A-F248-11E8-B48F-1D18A9856A87 last_name: Bollenbach orcid: 0000-0003-4398-476X citation: ama: Lukacisinova M, Bollenbach MT. Toward a quantitative understanding of antibiotic resistance evolution. Current Opinion in Biotechnology. 2017;46:90-97. doi:10.1016/j.copbio.2017.02.013 apa: Lukacisinova, M., & Bollenbach, M. T. (2017). Toward a quantitative understanding of antibiotic resistance evolution. Current Opinion in Biotechnology. Elsevier. https://doi.org/10.1016/j.copbio.2017.02.013 chicago: Lukacisinova, Marta, and Mark Tobias Bollenbach. “Toward a Quantitative Understanding of Antibiotic Resistance Evolution.” Current Opinion in Biotechnology. Elsevier, 2017. https://doi.org/10.1016/j.copbio.2017.02.013. ieee: M. Lukacisinova and M. T. Bollenbach, “Toward a quantitative understanding of antibiotic resistance evolution,” Current Opinion in Biotechnology, vol. 46. Elsevier, pp. 90–97, 2017. ista: Lukacisinova M, Bollenbach MT. 2017. Toward a quantitative understanding of antibiotic resistance evolution. Current Opinion in Biotechnology. 46, 90–97. mla: Lukacisinova, Marta, and Mark Tobias Bollenbach. “Toward a Quantitative Understanding of Antibiotic Resistance Evolution.” Current Opinion in Biotechnology, vol. 46, Elsevier, 2017, pp. 90–97, doi:10.1016/j.copbio.2017.02.013. short: M. Lukacisinova, M.T. Bollenbach, Current Opinion in Biotechnology 46 (2017) 90–97. date_created: 2018-12-11T11:49:45Z date_published: 2017-08-01T00:00:00Z date_updated: 2024-03-28T23:30:29Z day: '01' ddc: - '570' department: - _id: ToBo doi: 10.1016/j.copbio.2017.02.013 ec_funded: 1 external_id: isi: - '000408077400015' file: - access_level: open_access content_type: application/pdf creator: dernst date_created: 2019-01-18T09:57:57Z date_updated: 2019-01-18T09:57:57Z file_id: '5846' file_name: 2017_CurrentOpinion_Lukaciinova.pdf file_size: 858338 relation: main_file success: 1 file_date_updated: 2019-01-18T09:57:57Z has_accepted_license: '1' intvolume: ' 46' isi: 1 language: - iso: eng month: '08' oa: 1 oa_version: Published Version page: 90 - 97 project: - _id: 25E9AF9E-B435-11E9-9278-68D0E5697425 call_identifier: FWF grant_number: P27201-B22 name: Revealing the mechanisms underlying drug interactions - _id: 25E83C2C-B435-11E9-9278-68D0E5697425 call_identifier: FP7 grant_number: '303507' name: Optimality principles in responses to antibiotics - _id: 25EB3A80-B435-11E9-9278-68D0E5697425 grant_number: RGP0042/2013 name: Revealing the fundamental limits of cell growth publication: Current Opinion in Biotechnology publication_status: published publisher: Elsevier publist_id: '6364' pubrep_id: '801' quality_controlled: '1' related_material: record: - id: '6263' relation: dissertation_contains status: public scopus_import: '1' status: public title: Toward a quantitative understanding of antibiotic resistance evolution tmp: image: /images/cc_by_nc_nd.png legal_code_url: https://creativecommons.org/licenses/by-nc-nd/4.0/legalcode name: Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0) short: CC BY-NC-ND (4.0) type: journal_article user_id: c635000d-4b10-11ee-a964-aac5a93f6ac1 volume: 46 year: '2017' ... --- _id: '1154' abstract: - lang: eng text: "Cellular locomotion is a central hallmark of eukaryotic life. It is governed by cell-extrinsic molecular factors, which can either emerge in the soluble phase or as immobilized, often adhesive ligands. To encode for direction, every cue must be present as a spatial or temporal gradient. Here, we developed a microfluidic chamber that allows measurement of cell migration in combined response to surface immobilized and soluble molecular gradients. As a proof of principle we study the response of dendritic cells to their major guidance cues, chemokines. The majority of data on chemokine gradient sensing is based on in vitro studies employing soluble gradients. Despite evidence suggesting that in vivo chemokines are often immobilized to sugar residues, limited information is available how cells respond to immobilized chemokines. We tracked migration of dendritic cells towards immobilized gradients of the chemokine CCL21 and varying superimposed soluble gradients of CCL19. Differential migratory patterns illustrate the potential of our setup to quantitatively study the competitive response to both types of gradients. Beyond chemokines our approach is broadly applicable to alternative systems of chemo- and haptotaxis such as cells migrating along gradients of adhesion receptor ligands vs. any soluble cue. \r\n" acknowledgement: 'This work was supported by the Swiss National Science Foundation (Ambizione fellowship; PZ00P3-154733 to M.M.), the Swiss Multiple Sclerosis Society (research support to M.M.), a fellowship from the Boehringer Ingelheim Fonds (BIF) to J.S., the European Research Council (grant ERC GA 281556) and a START award from the Austrian Science Foundation (FWF) to M.S. #BioimagingFacility' article_number: '36440' author: - first_name: Jan full_name: Schwarz, Jan id: 346C1EC6-F248-11E8-B48F-1D18A9856A87 last_name: Schwarz - first_name: Veronika full_name: Bierbaum, Veronika id: 3FD04378-F248-11E8-B48F-1D18A9856A87 last_name: Bierbaum - first_name: Jack full_name: Merrin, Jack id: 4515C308-F248-11E8-B48F-1D18A9856A87 last_name: Merrin orcid: 0000-0001-5145-4609 - first_name: Tino full_name: Frank, Tino last_name: Frank - first_name: Robert full_name: Hauschild, Robert id: 4E01D6B4-F248-11E8-B48F-1D18A9856A87 last_name: Hauschild orcid: 0000-0001-9843-3522 - 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: Savaş full_name: Tay, Savaş last_name: Tay - first_name: Michael K full_name: Sixt, Michael K id: 41E9FBEA-F248-11E8-B48F-1D18A9856A87 last_name: Sixt orcid: 0000-0002-6620-9179 - first_name: Matthias full_name: Mehling, Matthias id: 3C23B994-F248-11E8-B48F-1D18A9856A87 last_name: Mehling orcid: 0000-0001-8599-1226 citation: ama: Schwarz J, Bierbaum V, Merrin J, et al. A microfluidic device for measuring cell migration towards substrate bound and soluble chemokine gradients. Scientific Reports. 2016;6. doi:10.1038/srep36440 apa: Schwarz, J., Bierbaum, V., Merrin, J., Frank, T., Hauschild, R., Bollenbach, M. T., … Mehling, M. (2016). A microfluidic device for measuring cell migration towards substrate bound and soluble chemokine gradients. Scientific Reports. Nature Publishing Group. https://doi.org/10.1038/srep36440 chicago: Schwarz, Jan, Veronika Bierbaum, Jack Merrin, Tino Frank, Robert Hauschild, Mark Tobias Bollenbach, Savaş Tay, Michael K Sixt, and Matthias Mehling. “A Microfluidic Device for Measuring Cell Migration towards Substrate Bound and Soluble Chemokine Gradients.” Scientific Reports. Nature Publishing Group, 2016. https://doi.org/10.1038/srep36440. ieee: J. Schwarz et al., “A microfluidic device for measuring cell migration towards substrate bound and soluble chemokine gradients,” Scientific Reports, vol. 6. Nature Publishing Group, 2016. ista: Schwarz J, Bierbaum V, Merrin J, Frank T, Hauschild R, Bollenbach MT, Tay S, Sixt MK, Mehling M. 2016. A microfluidic device for measuring cell migration towards substrate bound and soluble chemokine gradients. Scientific Reports. 6, 36440. mla: Schwarz, Jan, et al. “A Microfluidic Device for Measuring Cell Migration towards Substrate Bound and Soluble Chemokine Gradients.” Scientific Reports, vol. 6, 36440, Nature Publishing Group, 2016, doi:10.1038/srep36440. short: J. Schwarz, V. Bierbaum, J. Merrin, T. Frank, R. Hauschild, M.T. Bollenbach, S. Tay, M.K. Sixt, M. Mehling, Scientific Reports 6 (2016). date_created: 2018-12-11T11:50:27Z date_published: 2016-11-07T00:00:00Z date_updated: 2021-01-12T06:48:41Z day: '07' ddc: - '579' department: - _id: MiSi - _id: NanoFab - _id: Bio - _id: ToBo doi: 10.1038/srep36440 ec_funded: 1 file: - access_level: open_access content_type: application/pdf creator: system date_created: 2018-12-12T10:09:32Z date_updated: 2018-12-12T10:09:32Z file_id: '4756' file_name: IST-2017-744-v1+1_srep36440.pdf file_size: 2353456 relation: main_file file_date_updated: 2018-12-12T10:09:32Z has_accepted_license: '1' intvolume: ' 6' language: - iso: eng month: '11' oa: 1 oa_version: Published Version project: - _id: 25A603A2-B435-11E9-9278-68D0E5697425 call_identifier: FP7 grant_number: '281556' name: Cytoskeletal force generation and force transduction of migrating leukocytes (EU) - _id: 25A8E5EA-B435-11E9-9278-68D0E5697425 call_identifier: FWF grant_number: Y 564-B12 name: Cytoskeletal force generation and transduction of leukocytes (FWF) publication: Scientific Reports publication_status: published publisher: Nature Publishing Group publist_id: '6204' pubrep_id: '744' quality_controlled: '1' scopus_import: 1 status: public title: A microfluidic device for measuring cell migration towards substrate bound and soluble chemokine gradients 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: 3E5EF7F0-F248-11E8-B48F-1D18A9856A87 volume: 6 year: '2016' ... --- _id: '1581' abstract: - lang: eng text: In animal embryos, morphogen gradients determine tissue patterning and morphogenesis. Shyer et al. provide evidence that, during vertebrate gut formation, tissue folding generates graded activity of signals required for subsequent steps of gut growth and differentiation, thereby revealing an intriguing link between tissue morphogenesis and morphogen gradient formation. article_processing_charge: No author: - 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: Carl-Philipp J full_name: Heisenberg, Carl-Philipp J id: 39427864-F248-11E8-B48F-1D18A9856A87 last_name: Heisenberg orcid: 0000-0002-0912-4566 citation: ama: Bollenbach MT, Heisenberg C-PJ. Gradients are shaping up. Cell. 2015;161(3):431-432. doi:10.1016/j.cell.2015.04.009 apa: Bollenbach, M. T., & Heisenberg, C.-P. J. (2015). Gradients are shaping up. Cell. Cell Press. https://doi.org/10.1016/j.cell.2015.04.009 chicago: Bollenbach, Mark Tobias, and Carl-Philipp J Heisenberg. “Gradients Are Shaping Up.” Cell. Cell Press, 2015. https://doi.org/10.1016/j.cell.2015.04.009. ieee: M. T. Bollenbach and C.-P. J. Heisenberg, “Gradients are shaping up,” Cell, vol. 161, no. 3. Cell Press, pp. 431–432, 2015. ista: Bollenbach MT, Heisenberg C-PJ. 2015. Gradients are shaping up. Cell. 161(3), 431–432. mla: Bollenbach, Mark Tobias, and Carl-Philipp J. Heisenberg. “Gradients Are Shaping Up.” Cell, vol. 161, no. 3, Cell Press, 2015, pp. 431–32, doi:10.1016/j.cell.2015.04.009. short: M.T. Bollenbach, C.-P.J. Heisenberg, Cell 161 (2015) 431–432. date_created: 2018-12-11T11:52:50Z date_published: 2015-04-23T00:00:00Z date_updated: 2022-08-25T13:56:10Z day: '23' department: - _id: ToBo - _id: CaHe doi: 10.1016/j.cell.2015.04.009 intvolume: ' 161' issue: '3' language: - iso: eng month: '04' oa_version: None page: 431 - 432 publication: Cell publication_status: published publisher: Cell Press publist_id: '5590' quality_controlled: '1' scopus_import: '1' status: public title: Gradients are shaping up type: journal_article user_id: 3E5EF7F0-F248-11E8-B48F-1D18A9856A87 volume: 161 year: '2015' ... --- _id: '1810' abstract: - lang: eng text: Combining antibiotics is a promising strategy for increasing treatment efficacy and for controlling resistance evolution. When drugs are combined, their effects on cells may be amplified or weakened, that is the drugs may show synergistic or antagonistic interactions. Recent work revealed the underlying mechanisms of such drug interactions by elucidating the drugs'; joint effects on cell physiology. Moreover, new treatment strategies that use drug combinations to exploit evolutionary tradeoffs were shown to affect the rate of resistance evolution in predictable ways. High throughput studies have further identified drug candidates based on their interactions with established antibiotics and general principles that enable the prediction of drug interactions were suggested. Overall, the conceptual and technical foundation for the rational design of potent drug combinations is rapidly developing. author: - first_name: Mark Tobias full_name: Bollenbach, Mark Tobias id: 3E6DB97A-F248-11E8-B48F-1D18A9856A87 last_name: Bollenbach orcid: 0000-0003-4398-476X citation: ama: 'Bollenbach MT. Antimicrobial interactions: Mechanisms and implications for drug discovery and resistance evolution. Current Opinion in Microbiology. 2015;27:1-9. doi:10.1016/j.mib.2015.05.008' apa: 'Bollenbach, M. T. (2015). Antimicrobial interactions: Mechanisms and implications for drug discovery and resistance evolution. Current Opinion in Microbiology. Elsevier. https://doi.org/10.1016/j.mib.2015.05.008' chicago: 'Bollenbach, Mark Tobias. “Antimicrobial Interactions: Mechanisms and Implications for Drug Discovery and Resistance Evolution.” Current Opinion in Microbiology. Elsevier, 2015. https://doi.org/10.1016/j.mib.2015.05.008.' ieee: 'M. T. Bollenbach, “Antimicrobial interactions: Mechanisms and implications for drug discovery and resistance evolution,” Current Opinion in Microbiology, vol. 27. Elsevier, pp. 1–9, 2015.' ista: 'Bollenbach MT. 2015. Antimicrobial interactions: Mechanisms and implications for drug discovery and resistance evolution. Current Opinion in Microbiology. 27, 1–9.' mla: 'Bollenbach, Mark Tobias. “Antimicrobial Interactions: Mechanisms and Implications for Drug Discovery and Resistance Evolution.” Current Opinion in Microbiology, vol. 27, Elsevier, 2015, pp. 1–9, doi:10.1016/j.mib.2015.05.008.' short: M.T. Bollenbach, Current Opinion in Microbiology 27 (2015) 1–9. date_created: 2018-12-11T11:54:08Z date_published: 2015-06-01T00:00:00Z date_updated: 2021-01-12T06:53:21Z day: '01' ddc: - '570' department: - _id: ToBo doi: 10.1016/j.mib.2015.05.008 ec_funded: 1 file: - access_level: open_access checksum: 1683bb0f42ef892a5b3b71a050d65d25 content_type: application/pdf creator: system date_created: 2018-12-12T10:17:23Z date_updated: 2020-07-14T12:45:17Z file_id: '5277' file_name: IST-2016-493-v1+1_1-s2.0-S1369527415000594-main.pdf file_size: 1047255 relation: main_file file_date_updated: 2020-07-14T12:45:17Z has_accepted_license: '1' intvolume: ' 27' language: - iso: eng month: '06' oa: 1 oa_version: Published Version page: 1 - 9 project: - _id: 25E9AF9E-B435-11E9-9278-68D0E5697425 call_identifier: FWF grant_number: P27201-B22 name: Revealing the mechanisms underlying drug interactions - _id: 25E83C2C-B435-11E9-9278-68D0E5697425 call_identifier: FP7 grant_number: '303507' name: Optimality principles in responses to antibiotics - _id: 25EB3A80-B435-11E9-9278-68D0E5697425 grant_number: RGP0042/2013 name: Revealing the fundamental limits of cell growth publication: Current Opinion in Microbiology publication_status: published publisher: Elsevier publist_id: '5298' pubrep_id: '493' quality_controlled: '1' scopus_import: 1 status: public title: 'Antimicrobial interactions: Mechanisms and implications for drug discovery and resistance evolution' 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: 27 year: '2015' ... --- _id: '1823' abstract: - lang: eng text: Abstract Drug combinations are increasingly important in disease treatments, for combating drug resistance, and for elucidating fundamental relationships in cell physiology. When drugs are combined, their individual effects on cells may be amplified or weakened. Such drug interactions are crucial for treatment efficacy, but their underlying mechanisms remain largely unknown. To uncover the causes of drug interactions, we developed a systematic approach based on precise quantification of the individual and joint effects of antibiotics on growth of genome-wide Escherichia coli gene deletion strains. We found that drug interactions between antibiotics representing the main modes of action are highly robust to genetic perturbation. This robustness is encapsulated in a general principle of bacterial growth, which enables the quantitative prediction of mutant growth rates under drug combinations. Rare violations of this principle exposed recurring cellular functions controlling drug interactions. In particular, we found that polysaccharide and ATP synthesis control multiple drug interactions with previously unexplained mechanisms, and small molecule adjuvants targeting these functions synthetically reshape drug interactions in predictable ways. These results provide a new conceptual framework for the design of multidrug combinations and suggest that there are universal mechanisms at the heart of most drug interactions. Synopsis A general principle of bacterial growth enables the prediction of mutant growth rates under drug combinations. Rare violations of this principle expose cellular functions that control drug interactions and can be targeted by small molecules to alter drug interactions in predictable ways. Drug interactions between antibiotics are highly robust to genetic perturbations. A general principle of bacterial growth enables the prediction of mutant growth rates under drug combinations. Rare violations of this principle expose cellular functions that control drug interactions. Diverse drug interactions are controlled by recurring cellular functions, including LPS synthesis and ATP synthesis. A general principle of bacterial growth enables the prediction of mutant growth rates under drug combinations. Rare violations of this principle expose cellular functions that control drug interactions and can be targeted by small molecules to alter drug interactions in predictable ways. article_number: '807' author: - first_name: Guillaume full_name: Chevereau, Guillaume id: 424D78A0-F248-11E8-B48F-1D18A9856A87 last_name: Chevereau - first_name: Mark Tobias full_name: Bollenbach, Mark Tobias id: 3E6DB97A-F248-11E8-B48F-1D18A9856A87 last_name: Bollenbach orcid: 0000-0003-4398-476X citation: ama: Chevereau G, Bollenbach MT. Systematic discovery of drug interaction mechanisms. Molecular Systems Biology. 2015;11(4). doi:10.15252/msb.20156098 apa: Chevereau, G., & Bollenbach, M. T. (2015). Systematic discovery of drug interaction mechanisms. Molecular Systems Biology. Nature Publishing Group. https://doi.org/10.15252/msb.20156098 chicago: Chevereau, Guillaume, and Mark Tobias Bollenbach. “Systematic Discovery of Drug Interaction Mechanisms.” Molecular Systems Biology. Nature Publishing Group, 2015. https://doi.org/10.15252/msb.20156098. ieee: G. Chevereau and M. T. Bollenbach, “Systematic discovery of drug interaction mechanisms,” Molecular Systems Biology, vol. 11, no. 4. Nature Publishing Group, 2015. ista: Chevereau G, Bollenbach MT. 2015. Systematic discovery of drug interaction mechanisms. Molecular Systems Biology. 11(4), 807. mla: Chevereau, Guillaume, and Mark Tobias Bollenbach. “Systematic Discovery of Drug Interaction Mechanisms.” Molecular Systems Biology, vol. 11, no. 4, 807, Nature Publishing Group, 2015, doi:10.15252/msb.20156098. short: G. Chevereau, M.T. Bollenbach, Molecular Systems Biology 11 (2015). date_created: 2018-12-11T11:54:12Z date_published: 2015-04-01T00:00:00Z date_updated: 2021-01-12T06:53:26Z day: '01' ddc: - '570' department: - _id: ToBo doi: 10.15252/msb.20156098 ec_funded: 1 file: - access_level: open_access checksum: 4289b518fbe2166682fb1a1ef9b405f3 content_type: application/pdf creator: system date_created: 2018-12-12T10:14:34Z date_updated: 2020-07-14T12:45:17Z file_id: '5087' file_name: IST-2015-395-v1+1_807.full.pdf file_size: 1273573 relation: main_file file_date_updated: 2020-07-14T12:45:17Z has_accepted_license: '1' intvolume: ' 11' issue: '4' language: - iso: eng month: '04' oa: 1 oa_version: Published Version project: - _id: 25E9AF9E-B435-11E9-9278-68D0E5697425 call_identifier: FWF grant_number: P27201-B22 name: Revealing the mechanisms underlying drug interactions - _id: 25EB3A80-B435-11E9-9278-68D0E5697425 grant_number: RGP0042/2013 name: Revealing the fundamental limits of cell growth - _id: 25E83C2C-B435-11E9-9278-68D0E5697425 call_identifier: FP7 grant_number: '303507' name: Optimality principles in responses to antibiotics publication: Molecular Systems Biology publication_status: published publisher: Nature Publishing Group publist_id: '5283' pubrep_id: '395' quality_controlled: '1' scopus_import: 1 status: public title: Systematic discovery of drug interaction mechanisms 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: 11 year: '2015' ... --- _id: '9711' article_processing_charge: No author: - first_name: Guillaume full_name: Chevereau, Guillaume id: 424D78A0-F248-11E8-B48F-1D18A9856A87 last_name: Chevereau - first_name: Marta full_name: Lukacisinova, Marta id: 4342E402-F248-11E8-B48F-1D18A9856A87 last_name: Lukacisinova orcid: 0000-0002-2519-8004 - first_name: Tugce full_name: Batur, Tugce last_name: Batur - first_name: Aysegul full_name: Guvenek, Aysegul last_name: Guvenek - first_name: Dilay Hazal full_name: Ayhan, Dilay Hazal last_name: Ayhan - first_name: Erdal full_name: Toprak, Erdal last_name: Toprak - first_name: Mark Tobias full_name: Bollenbach, Mark Tobias id: 3E6DB97A-F248-11E8-B48F-1D18A9856A87 last_name: Bollenbach orcid: 0000-0003-4398-476X citation: ama: Chevereau G, Lukacisinova M, Batur T, et al. Excel file containing the raw data for all figures. 2015. doi:10.1371/journal.pbio.1002299.s001 apa: Chevereau, G., Lukacisinova, M., Batur, T., Guvenek, A., Ayhan, D. H., Toprak, E., & Bollenbach, M. T. (2015). Excel file containing the raw data for all figures. Public Library of Science. https://doi.org/10.1371/journal.pbio.1002299.s001 chicago: Chevereau, Guillaume, Marta Lukacisinova, Tugce Batur, Aysegul Guvenek, Dilay Hazal Ayhan, Erdal Toprak, and Mark Tobias Bollenbach. “Excel File Containing the Raw Data for All Figures.” Public Library of Science, 2015. https://doi.org/10.1371/journal.pbio.1002299.s001. ieee: G. Chevereau et al., “Excel file containing the raw data for all figures.” Public Library of Science, 2015. ista: Chevereau G, Lukacisinova M, Batur T, Guvenek A, Ayhan DH, Toprak E, Bollenbach MT. 2015. Excel file containing the raw data for all figures, Public Library of Science, 10.1371/journal.pbio.1002299.s001. mla: Chevereau, Guillaume, et al. Excel File Containing the Raw Data for All Figures. Public Library of Science, 2015, doi:10.1371/journal.pbio.1002299.s001. short: G. Chevereau, M. Lukacisinova, T. Batur, A. Guvenek, D.H. Ayhan, E. Toprak, M.T. Bollenbach, (2015). date_created: 2021-07-23T11:53:50Z date_published: 2015-11-18T00:00:00Z date_updated: 2023-02-23T10:07:02Z day: '18' department: - _id: ToBo doi: 10.1371/journal.pbio.1002299.s001 month: '11' oa_version: Published Version publisher: Public Library of Science related_material: record: - id: '1619' relation: used_in_publication status: public status: public title: Excel file containing the raw data for all figures type: research_data_reference user_id: 6785fbc1-c503-11eb-8a32-93094b40e1cf year: '2015' ... --- _id: '9765' article_processing_charge: No author: - first_name: Guillaume full_name: Chevereau, Guillaume id: 424D78A0-F248-11E8-B48F-1D18A9856A87 last_name: Chevereau - first_name: Marta full_name: Lukacisinova, Marta id: 4342E402-F248-11E8-B48F-1D18A9856A87 last_name: Lukacisinova orcid: 0000-0002-2519-8004 - first_name: Tugce full_name: Batur, Tugce last_name: Batur - first_name: Aysegul full_name: Guvenek, Aysegul last_name: Guvenek - first_name: Dilay Hazal full_name: Ayhan, Dilay Hazal last_name: Ayhan - first_name: Erdal full_name: Toprak, Erdal last_name: Toprak - first_name: Mark Tobias full_name: Bollenbach, Mark Tobias id: 3E6DB97A-F248-11E8-B48F-1D18A9856A87 last_name: Bollenbach orcid: 0000-0003-4398-476X citation: ama: Chevereau G, Lukacisinova M, Batur T, et al. Gene ontology enrichment analysis for the most sensitive gene deletion strains for all drugs. 2015. doi:10.1371/journal.pbio.1002299.s008 apa: Chevereau, G., Lukacisinova, M., Batur, T., Guvenek, A., Ayhan, D. H., Toprak, E., & Bollenbach, M. T. (2015). Gene ontology enrichment analysis for the most sensitive gene deletion strains for all drugs. Public Library of Science. https://doi.org/10.1371/journal.pbio.1002299.s008 chicago: Chevereau, Guillaume, Marta Lukacisinova, Tugce Batur, Aysegul Guvenek, Dilay Hazal Ayhan, Erdal Toprak, and Mark Tobias Bollenbach. “Gene Ontology Enrichment Analysis for the Most Sensitive Gene Deletion Strains for All Drugs.” Public Library of Science, 2015. https://doi.org/10.1371/journal.pbio.1002299.s008. ieee: G. Chevereau et al., “Gene ontology enrichment analysis for the most sensitive gene deletion strains for all drugs.” Public Library of Science, 2015. ista: Chevereau G, Lukacisinova M, Batur T, Guvenek A, Ayhan DH, Toprak E, Bollenbach MT. 2015. Gene ontology enrichment analysis for the most sensitive gene deletion strains for all drugs, Public Library of Science, 10.1371/journal.pbio.1002299.s008. mla: Chevereau, Guillaume, et al. Gene Ontology Enrichment Analysis for the Most Sensitive Gene Deletion Strains for All Drugs. Public Library of Science, 2015, doi:10.1371/journal.pbio.1002299.s008. short: G. Chevereau, M. Lukacisinova, T. Batur, A. Guvenek, D.H. Ayhan, E. Toprak, M.T. Bollenbach, (2015). date_created: 2021-08-03T07:05:16Z date_published: 2015-11-18T00:00:00Z date_updated: 2023-02-23T10:07:02Z day: '18' department: - _id: ToBo doi: 10.1371/journal.pbio.1002299.s008 month: '11' oa_version: Published Version publisher: Public Library of Science related_material: record: - id: '1619' relation: used_in_publication status: public status: public title: Gene ontology enrichment analysis for the most sensitive gene deletion strains for all drugs type: research_data_reference user_id: 6785fbc1-c503-11eb-8a32-93094b40e1cf year: '2015' ...