[{"article_type":"original","has_accepted_license":"1","oa_version":"Published Version","year":"2022","external_id":{"isi":["000784934100003"],"pmid":["35444278"]},"scopus_import":"1","user_id":"4359f0d1-fa6c-11eb-b949-802e58b17ae8","date_updated":"2023-08-03T06:44:50Z","acknowledged_ssus":[{"_id":"LifeSc"},{"_id":"M-Shop"},{"_id":"Bio"}],"publication_identifier":{"issn":["0028-0836"],"eissn":["1476-4687"]},"file":[{"success":1,"date_created":"2022-08-05T06:08:24Z","content_type":"application/pdf","file_id":"11727","relation":"main_file","date_updated":"2022-08-05T06:08:24Z","access_level":"open_access","checksum":"d68cd1596bb9fd819b750fe47c8a138a","file_name":"2022_Nature_Lukacisin.pdf","file_size":25360311,"creator":"dernst"}],"date_published":"2022-05-05T00:00:00Z","_id":"11341","abstract":[{"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.","lang":"eng"}],"article_processing_charge":"No","file_date_updated":"2022-08-05T06:08:24Z","tmp":{"legal_code_url":"https://creativecommons.org/licenses/by/4.0/legalcode","name":"Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)","image":"/images/cc_by.png","short":"CC BY (4.0)"},"volume":605,"publication_status":"published","oa":1,"day":"05","author":[{"full_name":"Lukacisin, Martin","first_name":"Martin","last_name":"Lukacisin","orcid":"0000-0001-6549-4177","id":"298FFE8C-F248-11E8-B48F-1D18A9856A87"},{"first_name":"Adriana","full_name":"Espinosa-Cantú, Adriana","last_name":"Espinosa-Cantú"},{"id":"3E6DB97A-F248-11E8-B48F-1D18A9856A87","orcid":"0000-0003-4398-476X","last_name":"Bollenbach","full_name":"Bollenbach, Mark Tobias","first_name":"Mark Tobias"}],"type":"journal_article","citation":{"apa":"Lukacisin, M., Espinosa-Cantú, A., &#38; Bollenbach, M. T. (2022). Intron-mediated induction of phenotypic heterogeneity. <i>Nature</i>. Springer Nature. <a href=\"https://doi.org/10.1038/s41586-022-04633-0\">https://doi.org/10.1038/s41586-022-04633-0</a>","ama":"Lukacisin M, Espinosa-Cantú A, Bollenbach MT. Intron-mediated induction of phenotypic heterogeneity. <i>Nature</i>. 2022;605:113-118. doi:<a href=\"https://doi.org/10.1038/s41586-022-04633-0\">10.1038/s41586-022-04633-0</a>","short":"M. Lukacisin, A. Espinosa-Cantú, M.T. Bollenbach, Nature 605 (2022) 113–118.","mla":"Lukacisin, Martin, et al. “Intron-Mediated Induction of Phenotypic Heterogeneity.” <i>Nature</i>, vol. 605, Springer Nature, 2022, pp. 113–18, doi:<a href=\"https://doi.org/10.1038/s41586-022-04633-0\">10.1038/s41586-022-04633-0</a>.","ieee":"M. Lukacisin, A. Espinosa-Cantú, and M. T. Bollenbach, “Intron-mediated induction of phenotypic heterogeneity,” <i>Nature</i>, 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.","chicago":"Lukacisin, Martin, Adriana Espinosa-Cantú, and Mark Tobias Bollenbach. “Intron-Mediated Induction of Phenotypic Heterogeneity.” <i>Nature</i>. Springer Nature, 2022. <a href=\"https://doi.org/10.1038/s41586-022-04633-0\">https://doi.org/10.1038/s41586-022-04633-0</a>."},"ec_funded":1,"title":"Intron-mediated induction of phenotypic heterogeneity","language":[{"iso":"eng"}],"doi":"10.1038/s41586-022-04633-0","ddc":["570"],"project":[{"_id":"25E83C2C-B435-11E9-9278-68D0E5697425","call_identifier":"FP7","name":"Optimality principles in responses to antibiotics","grant_number":"303507"},{"grant_number":"P27201-B22","name":"Revealing the mechanisms underlying drug interactions","call_identifier":"FWF","_id":"25E9AF9E-B435-11E9-9278-68D0E5697425"}],"pmid":1,"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.","month":"05","date_created":"2022-05-01T22:01:42Z","page":"113-118","publication":"Nature","quality_controlled":"1","intvolume":"       605","status":"public","publisher":"Springer Nature","isi":1},{"file_date_updated":"2021-11-11T10:54:40Z","volume":12,"tmp":{"legal_code_url":"https://creativecommons.org/licenses/by/4.0/legalcode","name":"Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)","image":"/images/cc_by.png","short":"CC BY (4.0)"},"publication_status":"published","oa":1,"article_number":"760017","file":[{"content_type":"application/pdf","relation":"main_file","file_id":"10272","success":1,"date_created":"2021-11-11T10:54:40Z","file_name":"2021_FrontiersMicrob_Qi.pdf","file_size":2397203,"creator":"cchlebak","date_updated":"2021-11-11T10:54:40Z","access_level":"open_access","checksum":"d41321748e9588dd3cf03e9a7222127f"}],"date_published":"2021-10-20T00:00:00Z","_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."}],"article_processing_charge":"No","date_updated":"2023-08-14T11:43:23Z","user_id":"4359f0d1-fa6c-11eb-b949-802e58b17ae8","external_id":{"pmid":["34745067"],"isi":["000715997300001"]},"scopus_import":"1","publication_identifier":{"eissn":["1664-302X"]},"article_type":"original","has_accepted_license":"1","oa_version":"Published Version","year":"2021","quality_controlled":"1","publication":"Frontiers in Microbiology","intvolume":"        12","status":"public","publisher":"Frontiers","isi":1,"month":"10","date_created":"2021-11-11T10:39:37Z","language":[{"iso":"eng"}],"keyword":["microbiology"],"ddc":["610"],"doi":"10.3389/fmicb.2021.760017","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.","project":[{"call_identifier":"FWF","_id":"25E9AF9E-B435-11E9-9278-68D0E5697425","name":"Revealing the mechanisms underlying drug interactions","grant_number":"P27201-B22"},{"name":"Optimality principles in responses to antibiotics","grant_number":"303507","_id":"25E83C2C-B435-11E9-9278-68D0E5697425","call_identifier":"FP7"}],"pmid":1,"day":"20","type":"journal_article","author":[{"full_name":"Qi, Qin","first_name":"Qin","last_name":"Qi","orcid":"0000-0002-6148-2416","id":"3B22D412-F248-11E8-B48F-1D18A9856A87"},{"first_name":"S. Andreas","full_name":"Angermayr, S. Andreas","last_name":"Angermayr"},{"orcid":"0000-0003-4398-476X","id":"3E6DB97A-F248-11E8-B48F-1D18A9856A87","last_name":"Bollenbach","first_name":"Mark Tobias","full_name":"Bollenbach, Mark Tobias"}],"ec_funded":1,"citation":{"short":"Q. Qi, S.A. Angermayr, M.T. Bollenbach, Frontiers in Microbiology 12 (2021).","apa":"Qi, Q., Angermayr, S. A., &#38; Bollenbach, M. T. (2021). Uncovering Key Metabolic Determinants of the Drug Interactions Between Trimethoprim and Erythromycin in Escherichia coli. <i>Frontiers in Microbiology</i>. Frontiers. <a href=\"https://doi.org/10.3389/fmicb.2021.760017\">https://doi.org/10.3389/fmicb.2021.760017</a>","ama":"Qi Q, Angermayr SA, Bollenbach MT. Uncovering Key Metabolic Determinants of the Drug Interactions Between Trimethoprim and Erythromycin in Escherichia coli. <i>Frontiers in Microbiology</i>. 2021;12. doi:<a href=\"https://doi.org/10.3389/fmicb.2021.760017\">10.3389/fmicb.2021.760017</a>","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.","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,” <i>Frontiers in Microbiology</i>, vol. 12. Frontiers, 2021.","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.” <i>Frontiers in Microbiology</i>. Frontiers, 2021. <a href=\"https://doi.org/10.3389/fmicb.2021.760017\">https://doi.org/10.3389/fmicb.2021.760017</a>.","mla":"Qi, Qin, et al. “Uncovering Key Metabolic Determinants of the Drug Interactions Between Trimethoprim and Erythromycin in Escherichia Coli.” <i>Frontiers in Microbiology</i>, vol. 12, 760017, Frontiers, 2021, doi:<a href=\"https://doi.org/10.3389/fmicb.2021.760017\">10.3389/fmicb.2021.760017</a>."},"title":"Uncovering Key Metabolic Determinants of the Drug Interactions Between Trimethoprim and Erythromycin in Escherichia coli"},{"department":[{"_id":"ToBo"}],"quality_controlled":"1","publication":"PNAS","intvolume":"       114","status":"public","publisher":"National Academy of Sciences","isi":1,"month":"10","date_created":"2018-12-11T11:48:41Z","page":"10666 - 10671","language":[{"iso":"eng"}],"doi":"10.1073/pnas.1713372114","pmid":1,"project":[{"grant_number":"303507","name":"Optimality principles in responses to antibiotics","call_identifier":"FP7","_id":"25E83C2C-B435-11E9-9278-68D0E5697425"},{"_id":"25E9AF9E-B435-11E9-9278-68D0E5697425","call_identifier":"FWF","grant_number":"P27201-B22","name":"Revealing the mechanisms underlying drug interactions"}],"day":"03","author":[{"id":"3111FFAC-F248-11E8-B48F-1D18A9856A87","last_name":"De Vos","first_name":"Marjon","full_name":"De Vos, Marjon"},{"orcid":"0000-0001-7896-7762","id":"343DA0DC-F248-11E8-B48F-1D18A9856A87","first_name":"Marcin P","full_name":"Zagórski, Marcin P","last_name":"Zagórski"},{"last_name":"Mcnally","first_name":"Alan","full_name":"Mcnally, Alan"},{"id":"3E6DB97A-F248-11E8-B48F-1D18A9856A87","orcid":"0000-0003-4398-476X","last_name":"Bollenbach","full_name":"Bollenbach, Mark Tobias","first_name":"Mark Tobias"}],"type":"journal_article","ec_funded":1,"citation":{"ama":"de Vos M, Zagórski MP, Mcnally A, Bollenbach MT. Interaction networks, ecological stability, and collective antibiotic tolerance in polymicrobial infections. <i>PNAS</i>. 2017;114(40):10666-10671. doi:<a href=\"https://doi.org/10.1073/pnas.1713372114\">10.1073/pnas.1713372114</a>","apa":"de Vos, M., Zagórski, M. P., Mcnally, A., &#38; Bollenbach, M. T. (2017). Interaction networks, ecological stability, and collective antibiotic tolerance in polymicrobial infections. <i>PNAS</i>. National Academy of Sciences. <a href=\"https://doi.org/10.1073/pnas.1713372114\">https://doi.org/10.1073/pnas.1713372114</a>","short":"M. de Vos, M.P. Zagórski, A. Mcnally, M.T. Bollenbach, PNAS 114 (2017) 10666–10671.","mla":"de Vos, Marjon, et al. “Interaction Networks, Ecological Stability, and Collective Antibiotic Tolerance in Polymicrobial Infections.” <i>PNAS</i>, vol. 114, no. 40, National Academy of Sciences, 2017, pp. 10666–71, doi:<a href=\"https://doi.org/10.1073/pnas.1713372114\">10.1073/pnas.1713372114</a>.","chicago":"Vos, Marjon de, Marcin P Zagórski, Alan Mcnally, and Mark Tobias Bollenbach. “Interaction Networks, Ecological Stability, and Collective Antibiotic Tolerance in Polymicrobial Infections.” <i>PNAS</i>. National Academy of Sciences, 2017. <a href=\"https://doi.org/10.1073/pnas.1713372114\">https://doi.org/10.1073/pnas.1713372114</a>.","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.","ieee":"M. de Vos, M. P. Zagórski, A. Mcnally, and M. T. Bollenbach, “Interaction networks, ecological stability, and collective antibiotic tolerance in polymicrobial infections,” <i>PNAS</i>, vol. 114, no. 40. National Academy of Sciences, pp. 10666–10671, 2017."},"title":"Interaction networks, ecological stability, and collective antibiotic tolerance in polymicrobial infections","volume":114,"publication_status":"published","oa":1,"main_file_link":[{"open_access":"1","url":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5635929/"}],"date_published":"2017-10-03T00:00:00Z","_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. "}],"issue":"40","article_processing_charge":"No","date_updated":"2023-09-26T16:18:48Z","user_id":"c635000d-4b10-11ee-a964-aac5a93f6ac1","scopus_import":"1","external_id":{"isi":["000412130500061"],"pmid":["28923953"]},"publication_identifier":{"issn":["00278424"]},"publist_id":"6827","year":"2017","oa_version":"Submitted Version"},{"related_material":{"record":[{"status":"public","id":"818","relation":"dissertation_contains"}]},"project":[{"_id":"25E83C2C-B435-11E9-9278-68D0E5697425","call_identifier":"FP7","name":"Optimality principles in responses to antibiotics","grant_number":"303507"},{"_id":"25E9AF9E-B435-11E9-9278-68D0E5697425","call_identifier":"FWF","grant_number":"P27201-B22","name":"Revealing the mechanisms underlying drug interactions"},{"name":"Revealing the fundamental limits of cell growth","grant_number":"RGP0042/2013","_id":"25EB3A80-B435-11E9-9278-68D0E5697425"}],"ddc":["576","610"],"doi":"10.1016/j.cels.2017.03.001","language":[{"iso":"eng"}],"title":"Noisy response to antibiotic stress predicts subsequent single cell survival in an acidic environment","citation":{"mla":"Mitosch, Karin, et al. “Noisy Response to Antibiotic Stress Predicts Subsequent Single Cell Survival in an Acidic Environment.” <i>Cell Systems</i>, vol. 4, no. 4, Cell Press, 2017, pp. 393–403, doi:<a href=\"https://doi.org/10.1016/j.cels.2017.03.001\">10.1016/j.cels.2017.03.001</a>.","ieee":"K. Mitosch, G. Rieckh, and M. T. Bollenbach, “Noisy response to antibiotic stress predicts subsequent single cell survival in an acidic environment,” <i>Cell Systems</i>, 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.","chicago":"Mitosch, Karin, Georg Rieckh, and Mark Tobias Bollenbach. “Noisy Response to Antibiotic Stress Predicts Subsequent Single Cell Survival in an Acidic Environment.” <i>Cell Systems</i>. Cell Press, 2017. <a href=\"https://doi.org/10.1016/j.cels.2017.03.001\">https://doi.org/10.1016/j.cels.2017.03.001</a>.","apa":"Mitosch, K., Rieckh, G., &#38; Bollenbach, M. T. (2017). Noisy response to antibiotic stress predicts subsequent single cell survival in an acidic environment. <i>Cell Systems</i>. Cell Press. <a href=\"https://doi.org/10.1016/j.cels.2017.03.001\">https://doi.org/10.1016/j.cels.2017.03.001</a>","ama":"Mitosch K, Rieckh G, Bollenbach MT. Noisy response to antibiotic stress predicts subsequent single cell survival in an acidic environment. <i>Cell Systems</i>. 2017;4(4):393-403. doi:<a href=\"https://doi.org/10.1016/j.cels.2017.03.001\">10.1016/j.cels.2017.03.001</a>","short":"K. Mitosch, G. Rieckh, M.T. Bollenbach, Cell Systems 4 (2017) 393–403."},"ec_funded":1,"type":"journal_article","author":[{"id":"39B66846-F248-11E8-B48F-1D18A9856A87","full_name":"Mitosch, Karin","first_name":"Karin","last_name":"Mitosch"},{"first_name":"Georg","full_name":"Rieckh, Georg","last_name":"Rieckh","id":"34DA8BD6-F248-11E8-B48F-1D18A9856A87"},{"first_name":"Tobias","full_name":"Bollenbach, Tobias","last_name":"Bollenbach","orcid":"0000-0003-4398-476X","id":"3E6DB97A-F248-11E8-B48F-1D18A9856A87"}],"day":"26","publisher":"Cell Press","intvolume":"         4","status":"public","publication":"Cell Systems","department":[{"_id":"ToBo"},{"_id":"GaTk"}],"quality_controlled":"1","page":"393 - 403","date_created":"2018-12-11T11:47:48Z","month":"04","publication_identifier":{"issn":["24054712"]},"scopus_import":1,"user_id":"3E5EF7F0-F248-11E8-B48F-1D18A9856A87","date_updated":"2023-09-07T12:00:25Z","has_accepted_license":"1","year":"2017","oa_version":"Published Version","publist_id":"7061","oa":1,"publication_status":"published","pubrep_id":"901","tmp":{"name":"Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0)","image":"/images/cc_by_nc_nd.png","legal_code_url":"https://creativecommons.org/licenses/by-nc-nd/4.0/legalcode","short":"CC BY-NC-ND (4.0)"},"volume":4,"file_date_updated":"2020-07-14T12:47:35Z","article_processing_charge":"Yes (in subscription journal)","issue":"4","date_published":"2017-04-26T00:00:00Z","_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."}],"file":[{"content_type":"application/pdf","relation":"main_file","file_id":"5041","date_created":"2018-12-12T10:13:54Z","file_name":"IST-2017-901-v1+1_1-s2.0-S2405471217300868-main.pdf","creator":"system","file_size":2438660,"date_updated":"2020-07-14T12:47:35Z","access_level":"open_access","checksum":"04ff20011c3d9a601c514aa999a5fe1a"}]},{"language":[{"iso":"eng"}],"ddc":["570"],"doi":"10.1016/j.copbio.2017.02.013","project":[{"name":"Revealing the mechanisms underlying drug interactions","grant_number":"P27201-B22","_id":"25E9AF9E-B435-11E9-9278-68D0E5697425","call_identifier":"FWF"},{"call_identifier":"FP7","_id":"25E83C2C-B435-11E9-9278-68D0E5697425","name":"Optimality principles in responses to antibiotics","grant_number":"303507"},{"grant_number":"RGP0042/2013","name":"Revealing the fundamental limits of cell growth","_id":"25EB3A80-B435-11E9-9278-68D0E5697425"}],"related_material":{"record":[{"relation":"dissertation_contains","id":"6263","status":"public"}]},"day":"01","type":"journal_article","author":[{"last_name":"Lukacisinova","first_name":"Marta","full_name":"Lukacisinova, Marta","orcid":"0000-0002-2519-8004","id":"4342E402-F248-11E8-B48F-1D18A9856A87"},{"last_name":"Bollenbach","full_name":"Bollenbach, Mark Tobias","first_name":"Mark Tobias","orcid":"0000-0003-4398-476X","id":"3E6DB97A-F248-11E8-B48F-1D18A9856A87"}],"citation":{"short":"M. Lukacisinova, M.T. Bollenbach, Current Opinion in Biotechnology 46 (2017) 90–97.","ama":"Lukacisinova M, Bollenbach MT. Toward a quantitative understanding of antibiotic resistance evolution. <i>Current Opinion in Biotechnology</i>. 2017;46:90-97. doi:<a href=\"https://doi.org/10.1016/j.copbio.2017.02.013\">10.1016/j.copbio.2017.02.013</a>","apa":"Lukacisinova, M., &#38; Bollenbach, M. T. (2017). Toward a quantitative understanding of antibiotic resistance evolution. <i>Current Opinion in Biotechnology</i>. Elsevier. <a href=\"https://doi.org/10.1016/j.copbio.2017.02.013\">https://doi.org/10.1016/j.copbio.2017.02.013</a>","chicago":"Lukacisinova, Marta, and Mark Tobias Bollenbach. “Toward a Quantitative Understanding of Antibiotic Resistance Evolution.” <i>Current Opinion in Biotechnology</i>. Elsevier, 2017. <a href=\"https://doi.org/10.1016/j.copbio.2017.02.013\">https://doi.org/10.1016/j.copbio.2017.02.013</a>.","ista":"Lukacisinova M, Bollenbach MT. 2017. Toward a quantitative understanding of antibiotic resistance evolution. Current Opinion in Biotechnology. 46, 90–97.","ieee":"M. Lukacisinova and M. T. Bollenbach, “Toward a quantitative understanding of antibiotic resistance evolution,” <i>Current Opinion in Biotechnology</i>, vol. 46. Elsevier, pp. 90–97, 2017.","mla":"Lukacisinova, Marta, and Mark Tobias Bollenbach. “Toward a Quantitative Understanding of Antibiotic Resistance Evolution.” <i>Current Opinion in Biotechnology</i>, vol. 46, Elsevier, 2017, pp. 90–97, doi:<a href=\"https://doi.org/10.1016/j.copbio.2017.02.013\">10.1016/j.copbio.2017.02.013</a>."},"ec_funded":1,"title":"Toward a quantitative understanding of antibiotic resistance evolution","publication":"Current Opinion in Biotechnology","quality_controlled":"1","department":[{"_id":"ToBo"}],"intvolume":"        46","status":"public","publisher":"Elsevier","isi":1,"month":"08","date_created":"2018-12-11T11:49:45Z","page":"90 - 97","scopus_import":"1","external_id":{"isi":["000408077400015"]},"date_updated":"2024-03-25T23:30:15Z","user_id":"c635000d-4b10-11ee-a964-aac5a93f6ac1","article_type":"original","publist_id":"6364","year":"2017","oa_version":"Published Version","has_accepted_license":"1","file_date_updated":"2019-01-18T09:57:57Z","pubrep_id":"801","tmp":{"name":"Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0)","image":"/images/cc_by_nc_nd.png","legal_code_url":"https://creativecommons.org/licenses/by-nc-nd/4.0/legalcode","short":"CC BY-NC-ND (4.0)"},"volume":46,"publication_status":"published","oa":1,"file":[{"date_created":"2019-01-18T09:57:57Z","success":1,"file_id":"5846","relation":"main_file","content_type":"application/pdf","access_level":"open_access","date_updated":"2019-01-18T09:57:57Z","file_size":858338,"creator":"dernst","file_name":"2017_CurrentOpinion_Lukaciinova.pdf"}],"abstract":[{"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.","lang":"eng"}],"_id":"1027","date_published":"2017-08-01T00:00:00Z","article_processing_charge":"Yes (in subscription journal)"},{"date_created":"2018-12-11T11:53:04Z","month":"11","publisher":"Public Library of Science","intvolume":"        13","status":"public","department":[{"_id":"ToBo"}],"quality_controlled":"1","publication":"PLoS Biology","title":"Quantifying the determinants of evolutionary dynamics leading to drug resistance","ec_funded":1,"citation":{"chicago":"Chevereau, Guillaume, Marta Lukacisinova, Tugce Batur, Aysegul Guvenek, Dilay Ayhan, Erdal Toprak, and Mark Tobias Bollenbach. “Quantifying the Determinants of Evolutionary Dynamics Leading to Drug Resistance.” <i>PLoS Biology</i>. Public Library of Science, 2015. <a href=\"https://doi.org/10.1371/journal.pbio.1002299\">https://doi.org/10.1371/journal.pbio.1002299</a>.","ieee":"G. Chevereau <i>et al.</i>, “Quantifying the determinants of evolutionary dynamics leading to drug resistance,” <i>PLoS Biology</i>, vol. 13, no. 11. Public Library of Science, 2015.","ista":"Chevereau G, Lukacisinova M, Batur T, Guvenek A, Ayhan D, Toprak E, Bollenbach MT. 2015. Quantifying the determinants of evolutionary dynamics leading to drug resistance. PLoS Biology. 13(11), e1002299.","mla":"Chevereau, Guillaume, et al. “Quantifying the Determinants of Evolutionary Dynamics Leading to Drug Resistance.” <i>PLoS Biology</i>, vol. 13, no. 11, e1002299, Public Library of Science, 2015, doi:<a href=\"https://doi.org/10.1371/journal.pbio.1002299\">10.1371/journal.pbio.1002299</a>.","short":"G. Chevereau, M. Lukacisinova, T. Batur, A. Guvenek, D. Ayhan, E. Toprak, M.T. Bollenbach, PLoS Biology 13 (2015).","ama":"Chevereau G, Lukacisinova M, Batur T, et al. Quantifying the determinants of evolutionary dynamics leading to drug resistance. <i>PLoS Biology</i>. 2015;13(11). doi:<a href=\"https://doi.org/10.1371/journal.pbio.1002299\">10.1371/journal.pbio.1002299</a>","apa":"Chevereau, G., Lukacisinova, M., Batur, T., Guvenek, A., Ayhan, D., Toprak, E., &#38; Bollenbach, M. T. (2015). Quantifying the determinants of evolutionary dynamics leading to drug resistance. <i>PLoS Biology</i>. Public Library of Science. <a href=\"https://doi.org/10.1371/journal.pbio.1002299\">https://doi.org/10.1371/journal.pbio.1002299</a>"},"author":[{"full_name":"Chevereau, Guillaume","first_name":"Guillaume","last_name":"Chevereau","id":"424D78A0-F248-11E8-B48F-1D18A9856A87"},{"orcid":"0000-0002-2519-8004","id":"4342E402-F248-11E8-B48F-1D18A9856A87","last_name":"Dravecka","full_name":"Dravecka, Marta","first_name":"Marta"},{"last_name":"Batur","full_name":"Batur, Tugce","first_name":"Tugce"},{"first_name":"Aysegul","full_name":"Guvenek, Aysegul","last_name":"Guvenek"},{"full_name":"Ayhan, Dilay","first_name":"Dilay","last_name":"Ayhan"},{"last_name":"Toprak","full_name":"Toprak, Erdal","first_name":"Erdal"},{"first_name":"Mark Tobias","full_name":"Bollenbach, Mark Tobias","last_name":"Bollenbach","orcid":"0000-0003-4398-476X","id":"3E6DB97A-F248-11E8-B48F-1D18A9856A87"}],"type":"journal_article","day":"18","project":[{"_id":"25EB3A80-B435-11E9-9278-68D0E5697425","grant_number":"RGP0042/2013","name":"Revealing the fundamental limits of cell growth"},{"call_identifier":"FWF","_id":"25E9AF9E-B435-11E9-9278-68D0E5697425","name":"Revealing the mechanisms underlying drug interactions","grant_number":"P27201-B22"},{"call_identifier":"FP7","_id":"25E83C2C-B435-11E9-9278-68D0E5697425","grant_number":"303507","name":"Optimality principles in responses to antibiotics"}],"related_material":{"record":[{"status":"public","id":"9711","relation":"research_data"},{"status":"public","id":"9765","relation":"research_data"},{"status":"public","relation":"dissertation_contains","id":"6263"}]},"doi":"10.1371/journal.pbio.1002299","ddc":["570"],"language":[{"iso":"eng"}],"issue":"11","date_published":"2015-11-18T00:00:00Z","_id":"1619","abstract":[{"lang":"eng","text":"The emergence of drug resistant pathogens is a serious public health problem. It is a long-standing goal to predict rates of resistance evolution and design optimal treatment strategies accordingly. To this end, it is crucial to reveal the underlying causes of drug-specific differences in the evolutionary dynamics leading to resistance. However, it remains largely unknown why the rates of resistance evolution via spontaneous mutations and the diversity of mutational paths vary substantially between drugs. Here we comprehensively quantify the distribution of fitness effects (DFE) of mutations, a key determinant of evolutionary dynamics, in the presence of eight antibiotics representing the main modes of action. Using precise high-throughput fitness measurements for genome-wide Escherichia coli gene deletion strains, we find that the width of the DFE varies dramatically between antibiotics and, contrary to conventional wisdom, for some drugs the DFE width is lower than in the absence of stress. We show that this previously underappreciated divergence in DFE width among antibiotics is largely caused by their distinct drug-specific dose-response characteristics. Unlike the DFE, the magnitude of the changes in tolerated drug concentration resulting from genome-wide mutations is similar for most drugs but exceptionally small for the antibiotic nitrofurantoin, i.e., mutations generally have considerably smaller resistance effects for nitrofurantoin than for other drugs. A population genetics model predicts that resistance evolution for drugs with this property is severely limited and confined to reproducible mutational paths. We tested this prediction in laboratory evolution experiments using the “morbidostat”, a device for evolving bacteria in well-controlled drug environments. Nitrofurantoin resistance indeed evolved extremely slowly via reproducible mutations—an almost paradoxical behavior since this drug causes DNA damage and increases the mutation rate. Overall, we identified novel quantitative characteristics of the evolutionary landscape that provide the conceptual foundation for predicting the dynamics of drug resistance evolution."}],"article_number":"e1002299","file":[{"date_created":"2018-12-12T10:09:00Z","content_type":"application/pdf","file_id":"4723","relation":"main_file","checksum":"0e82e3279f50b15c6c170c042627802b","date_updated":"2020-07-14T12:45:07Z","access_level":"open_access","file_name":"IST-2016-468-v1+1_journal.pbio.1002299.pdf","file_size":1387760,"creator":"system"}],"oa":1,"publication_status":"published","volume":13,"tmp":{"legal_code_url":"https://creativecommons.org/licenses/by/4.0/legalcode","name":"Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)","image":"/images/cc_by.png","short":"CC BY (4.0)"},"pubrep_id":"468","file_date_updated":"2020-07-14T12:45:07Z","oa_version":"Published Version","year":"2015","has_accepted_license":"1","publist_id":"5547","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","date_updated":"2024-03-25T23:30:14Z","scopus_import":1},{"language":[{"iso":"eng"}],"ddc":["570"],"doi":"10.1016/j.mib.2015.05.008","project":[{"name":"Revealing the mechanisms underlying drug interactions","grant_number":"P27201-B22","call_identifier":"FWF","_id":"25E9AF9E-B435-11E9-9278-68D0E5697425"},{"call_identifier":"FP7","_id":"25E83C2C-B435-11E9-9278-68D0E5697425","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"}],"day":"01","author":[{"id":"3E6DB97A-F248-11E8-B48F-1D18A9856A87","orcid":"0000-0003-4398-476X","last_name":"Bollenbach","first_name":"Mark Tobias","full_name":"Bollenbach, Mark Tobias"}],"type":"journal_article","ec_funded":1,"citation":{"ista":"Bollenbach MT. 2015. Antimicrobial interactions: Mechanisms and implications for drug discovery and resistance evolution. Current Opinion in Microbiology. 27, 1–9.","ieee":"M. T. Bollenbach, “Antimicrobial interactions: Mechanisms and implications for drug discovery and resistance evolution,” <i>Current Opinion in Microbiology</i>, vol. 27. Elsevier, pp. 1–9, 2015.","chicago":"Bollenbach, Mark Tobias. “Antimicrobial Interactions: Mechanisms and Implications for Drug Discovery and Resistance Evolution.” <i>Current Opinion in Microbiology</i>. Elsevier, 2015. <a href=\"https://doi.org/10.1016/j.mib.2015.05.008\">https://doi.org/10.1016/j.mib.2015.05.008</a>.","mla":"Bollenbach, Mark Tobias. “Antimicrobial Interactions: Mechanisms and Implications for Drug Discovery and Resistance Evolution.” <i>Current Opinion in Microbiology</i>, vol. 27, Elsevier, 2015, pp. 1–9, doi:<a href=\"https://doi.org/10.1016/j.mib.2015.05.008\">10.1016/j.mib.2015.05.008</a>.","short":"M.T. Bollenbach, Current Opinion in Microbiology 27 (2015) 1–9.","apa":"Bollenbach, M. T. (2015). Antimicrobial interactions: Mechanisms and implications for drug discovery and resistance evolution. <i>Current Opinion in Microbiology</i>. Elsevier. <a href=\"https://doi.org/10.1016/j.mib.2015.05.008\">https://doi.org/10.1016/j.mib.2015.05.008</a>","ama":"Bollenbach MT. Antimicrobial interactions: Mechanisms and implications for drug discovery and resistance evolution. <i>Current Opinion in Microbiology</i>. 2015;27:1-9. doi:<a href=\"https://doi.org/10.1016/j.mib.2015.05.008\">10.1016/j.mib.2015.05.008</a>"},"title":"Antimicrobial interactions: Mechanisms and implications for drug discovery and resistance evolution","quality_controlled":"1","department":[{"_id":"ToBo"}],"publication":"Current Opinion in Microbiology","status":"public","intvolume":"        27","publisher":"Elsevier","month":"06","date_created":"2018-12-11T11:54:08Z","page":"1 - 9","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","date_updated":"2021-01-12T06:53:21Z","scopus_import":1,"publist_id":"5298","year":"2015","has_accepted_license":"1","oa_version":"Published Version","file_date_updated":"2020-07-14T12:45:17Z","volume":27,"tmp":{"legal_code_url":"https://creativecommons.org/licenses/by/4.0/legalcode","name":"Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)","image":"/images/cc_by.png","short":"CC BY (4.0)"},"pubrep_id":"493","publication_status":"published","oa":1,"file":[{"file_name":"IST-2016-493-v1+1_1-s2.0-S1369527415000594-main.pdf","creator":"system","file_size":1047255,"date_updated":"2020-07-14T12:45:17Z","access_level":"open_access","checksum":"1683bb0f42ef892a5b3b71a050d65d25","content_type":"application/pdf","file_id":"5277","relation":"main_file","date_created":"2018-12-12T10:17:23Z"}],"_id":"1810","date_published":"2015-06-01T00:00:00Z","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."}]},{"citation":{"short":"G. Chevereau, M.T. Bollenbach, Molecular Systems Biology 11 (2015).","ama":"Chevereau G, Bollenbach MT. Systematic discovery of drug interaction mechanisms. <i>Molecular Systems Biology</i>. 2015;11(4). doi:<a href=\"https://doi.org/10.15252/msb.20156098\">10.15252/msb.20156098</a>","apa":"Chevereau, G., &#38; Bollenbach, M. T. (2015). Systematic discovery of drug interaction mechanisms. <i>Molecular Systems Biology</i>. Nature Publishing Group. <a href=\"https://doi.org/10.15252/msb.20156098\">https://doi.org/10.15252/msb.20156098</a>","chicago":"Chevereau, Guillaume, and Mark Tobias Bollenbach. “Systematic Discovery of Drug Interaction Mechanisms.” <i>Molecular Systems Biology</i>. Nature Publishing Group, 2015. <a href=\"https://doi.org/10.15252/msb.20156098\">https://doi.org/10.15252/msb.20156098</a>.","ieee":"G. Chevereau and M. T. Bollenbach, “Systematic discovery of drug interaction mechanisms,” <i>Molecular Systems Biology</i>, 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.” <i>Molecular Systems Biology</i>, vol. 11, no. 4, 807, Nature Publishing Group, 2015, doi:<a href=\"https://doi.org/10.15252/msb.20156098\">10.15252/msb.20156098</a>."},"ec_funded":1,"title":"Systematic discovery of drug interaction mechanisms","day":"01","author":[{"id":"424D78A0-F248-11E8-B48F-1D18A9856A87","full_name":"Chevereau, Guillaume","first_name":"Guillaume","last_name":"Chevereau"},{"last_name":"Bollenbach","first_name":"Mark Tobias","full_name":"Bollenbach, Mark Tobias","orcid":"0000-0003-4398-476X","id":"3E6DB97A-F248-11E8-B48F-1D18A9856A87"}],"type":"journal_article","project":[{"grant_number":"P27201-B22","name":"Revealing the mechanisms underlying drug interactions","call_identifier":"FWF","_id":"25E9AF9E-B435-11E9-9278-68D0E5697425"},{"_id":"25EB3A80-B435-11E9-9278-68D0E5697425","grant_number":"RGP0042/2013","name":"Revealing the fundamental limits of cell growth"},{"name":"Optimality principles in responses to antibiotics","grant_number":"303507","_id":"25E83C2C-B435-11E9-9278-68D0E5697425","call_identifier":"FP7"}],"language":[{"iso":"eng"}],"doi":"10.15252/msb.20156098","ddc":["570"],"month":"04","date_created":"2018-12-11T11:54:12Z","publisher":"Nature Publishing Group","publication":"Molecular Systems Biology","department":[{"_id":"ToBo"}],"quality_controlled":"1","intvolume":"        11","status":"public","publist_id":"5283","has_accepted_license":"1","oa_version":"Published Version","year":"2015","scopus_import":1,"user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","date_updated":"2021-01-12T06:53:26Z","issue":"4","file":[{"file_id":"5087","relation":"main_file","content_type":"application/pdf","date_created":"2018-12-12T10:14:34Z","file_size":1273573,"creator":"system","file_name":"IST-2015-395-v1+1_807.full.pdf","access_level":"open_access","date_updated":"2020-07-14T12:45:17Z","checksum":"4289b518fbe2166682fb1a1ef9b405f3"}],"article_number":"807","_id":"1823","date_published":"2015-04-01T00:00:00Z","abstract":[{"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.","lang":"eng"}],"publication_status":"published","oa":1,"file_date_updated":"2020-07-14T12:45:17Z","pubrep_id":"395","tmp":{"legal_code_url":"https://creativecommons.org/licenses/by/4.0/legalcode","name":"Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)","image":"/images/cc_by.png","short":"CC BY (4.0)"},"volume":11},{"abstract":[{"lang":"eng","text":"Antibiotics affect bacterial cell physiology at many levels. Rather than just compensating for the direct cellular defects caused by the drug, bacteria respond to antibiotics by changing their morphology, macromolecular composition, metabolism, gene expression and possibly even their mutation rate. Inevitably, these processes affect each other, resulting in a complex response with changes in the expression of numerous genes. Genome‐wide approaches can thus help in gaining a comprehensive understanding of bacterial responses to antibiotics. In addition, a combination of experimental and theoretical approaches is needed for identifying general principles that underlie these responses. Here, we review recent progress in our understanding of bacterial responses to antibiotics and their combinations, focusing on effects at the levels of growth rate and gene expression. We concentrate on studies performed in controlled laboratory conditions, which combine promising experimental techniques with quantitative data analysis and mathematical modeling. While these basic research approaches are not immediately applicable in the clinic, uncovering the principles and mechanisms underlying bacterial responses to antibiotics may, in the long term, contribute to the development of new treatment strategies to cope with and prevent the rise of resistant pathogenic bacteria."}],"_id":"2001","date_published":"2014-06-22T00:00:00Z","issue":"6","volume":6,"publication_status":"published","publist_id":"5076","year":"2014","oa_version":"None","date_updated":"2023-09-07T12:00:25Z","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","scopus_import":1,"month":"06","date_created":"2018-12-11T11:55:08Z","page":"545 - 557","department":[{"_id":"ToBo"}],"quality_controlled":"1","publication":"Environmental Microbiology Reports","intvolume":"         6","status":"public","publisher":"Wiley","day":"22","author":[{"id":"39B66846-F248-11E8-B48F-1D18A9856A87","full_name":"Mitosch, Karin","first_name":"Karin","last_name":"Mitosch"},{"id":"3E6DB97A-F248-11E8-B48F-1D18A9856A87","orcid":"0000-0003-4398-476X","full_name":"Bollenbach, Tobias","first_name":"Tobias","last_name":"Bollenbach"}],"type":"journal_article","ec_funded":1,"citation":{"chicago":"Mitosch, Karin, and Mark Tobias Bollenbach. “Bacterial Responses to Antibiotics and Their Combinations.” <i>Environmental Microbiology Reports</i>. Wiley, 2014. <a href=\"https://doi.org/10.1111/1758-2229.12190\">https://doi.org/10.1111/1758-2229.12190</a>.","ista":"Mitosch K, Bollenbach MT. 2014. Bacterial responses to antibiotics and their combinations. Environmental Microbiology Reports. 6(6), 545–557.","ieee":"K. Mitosch and M. T. Bollenbach, “Bacterial responses to antibiotics and their combinations,” <i>Environmental Microbiology Reports</i>, vol. 6, no. 6. Wiley, pp. 545–557, 2014.","mla":"Mitosch, Karin, and Mark Tobias Bollenbach. “Bacterial Responses to Antibiotics and Their Combinations.” <i>Environmental Microbiology Reports</i>, vol. 6, no. 6, Wiley, 2014, pp. 545–57, doi:<a href=\"https://doi.org/10.1111/1758-2229.12190\">10.1111/1758-2229.12190</a>.","short":"K. Mitosch, M.T. Bollenbach, Environmental Microbiology Reports 6 (2014) 545–557.","ama":"Mitosch K, Bollenbach MT. Bacterial responses to antibiotics and their combinations. <i>Environmental Microbiology Reports</i>. 2014;6(6):545-557. doi:<a href=\"https://doi.org/10.1111/1758-2229.12190\">10.1111/1758-2229.12190</a>","apa":"Mitosch, K., &#38; Bollenbach, M. T. (2014). Bacterial responses to antibiotics and their combinations. <i>Environmental Microbiology Reports</i>. Wiley. <a href=\"https://doi.org/10.1111/1758-2229.12190\">https://doi.org/10.1111/1758-2229.12190</a>"},"title":"Bacterial responses to antibiotics and their combinations","language":[{"iso":"eng"}],"doi":"10.1111/1758-2229.12190","related_material":{"record":[{"id":"818","relation":"dissertation_contains","status":"public"}]},"project":[{"grant_number":"RGP0042/2013","name":"Revealing the fundamental limits of cell growth","_id":"25EB3A80-B435-11E9-9278-68D0E5697425"},{"grant_number":"303507","name":"Optimality principles in responses to antibiotics","_id":"25E83C2C-B435-11E9-9278-68D0E5697425","call_identifier":"FP7"}]}]