[{"page":"113-118","type":"journal_article","month":"05","oa_version":"Published Version","date_updated":"2023-08-03T06:44:50Z","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"}],"volume":605,"file_date_updated":"2022-08-05T06:08:24Z","date_created":"2022-05-01T22:01:42Z","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.","year":"2022","_id":"11341","publication_status":"published","oa":1,"has_accepted_license":"1","date_published":"2022-05-05T00:00:00Z","ddc":["570"],"acknowledged_ssus":[{"_id":"LifeSc"},{"_id":"M-Shop"},{"_id":"Bio"}],"status":"public","external_id":{"isi":["000784934100003"],"pmid":["35444278"]},"intvolume":"       605","citation":{"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>","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.” <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>.","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>","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.","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>.","short":"M. Lukacisin, A. Espinosa-Cantú, M.T. Bollenbach, Nature 605 (2022) 113–118."},"author":[{"id":"298FFE8C-F248-11E8-B48F-1D18A9856A87","orcid":"0000-0001-6549-4177","full_name":"Lukacisin, Martin","first_name":"Martin","last_name":"Lukacisin"},{"last_name":"Espinosa-Cantú","first_name":"Adriana","full_name":"Espinosa-Cantú, Adriana"},{"last_name":"Bollenbach","first_name":"Mark Tobias","orcid":"0000-0003-4398-476X","id":"3E6DB97A-F248-11E8-B48F-1D18A9856A87","full_name":"Bollenbach, Mark Tobias"}],"file":[{"success":1,"file_name":"2022_Nature_Lukacisin.pdf","content_type":"application/pdf","relation":"main_file","file_size":25360311,"creator":"dernst","date_updated":"2022-08-05T06:08:24Z","file_id":"11727","checksum":"d68cd1596bb9fd819b750fe47c8a138a","date_created":"2022-08-05T06:08:24Z","access_level":"open_access"}],"license":"https://creativecommons.org/licenses/by/4.0/","day":"05","title":"Intron-mediated induction of phenotypic heterogeneity","user_id":"4359f0d1-fa6c-11eb-b949-802e58b17ae8","publisher":"Springer Nature","pmid":1,"publication":"Nature","tmp":{"legal_code_url":"https://creativecommons.org/licenses/by/4.0/legalcode","image":"/images/cc_by.png","name":"Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)","short":"CC BY (4.0)"},"article_type":"original","ec_funded":1,"article_processing_charge":"No","scopus_import":"1","publication_identifier":{"issn":["0028-0836"],"eissn":["1476-4687"]},"doi":"10.1038/s41586-022-04633-0","quality_controlled":"1","project":[{"grant_number":"303507","_id":"25E83C2C-B435-11E9-9278-68D0E5697425","call_identifier":"FP7","name":"Optimality principles in responses to antibiotics"},{"name":"Revealing the mechanisms underlying drug interactions","_id":"25E9AF9E-B435-11E9-9278-68D0E5697425","call_identifier":"FWF","grant_number":"P27201-B22"}],"isi":1,"language":[{"iso":"eng"}]},{"publication_status":"published","oa":1,"has_accepted_license":"1","date_published":"2021-01-07T00:00:00Z","ddc":["570"],"external_id":{"isi":["000608045000010"]},"status":"public","intvolume":"        17","related_material":{"record":[{"id":"7673","status":"public","relation":"earlier_version"},{"status":"public","id":"8930","relation":"research_data"}]},"citation":{"short":"B. Kavcic, G. Tkačik, M.T. Bollenbach, PLOS Computational Biology 17 (2021).","chicago":"Kavcic, Bor, Gašper Tkačik, and Mark Tobias Bollenbach. “Minimal Biophysical Model of Combined Antibiotic Action.” <i>PLOS Computational Biology</i>. Public Library of Science, 2021. <a href=\"https://doi.org/10.1371/journal.pcbi.1008529\">https://doi.org/10.1371/journal.pcbi.1008529</a>.","ieee":"B. Kavcic, G. Tkačik, and M. T. Bollenbach, “Minimal biophysical model of combined antibiotic action,” <i>PLOS Computational Biology</i>, vol. 17. Public Library of Science, 2021.","apa":"Kavcic, B., Tkačik, G., &#38; Bollenbach, M. T. (2021). Minimal biophysical model of combined antibiotic action. <i>PLOS Computational Biology</i>. Public Library of Science. <a href=\"https://doi.org/10.1371/journal.pcbi.1008529\">https://doi.org/10.1371/journal.pcbi.1008529</a>","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.” <i>PLOS Computational Biology</i>, vol. 17, e1008529, Public Library of Science, 2021, doi:<a href=\"https://doi.org/10.1371/journal.pcbi.1008529\">10.1371/journal.pcbi.1008529</a>.","ama":"Kavcic B, Tkačik G, Bollenbach MT. Minimal biophysical model of combined antibiotic action. <i>PLOS Computational Biology</i>. 2021;17. doi:<a href=\"https://doi.org/10.1371/journal.pcbi.1008529\">10.1371/journal.pcbi.1008529</a>"},"date_updated":"2024-02-21T12:41:41Z","abstract":[{"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.","lang":"eng"}],"month":"01","type":"journal_article","oa_version":"Published Version","volume":17,"file_date_updated":"2021-02-04T12:30:48Z","date_created":"2021-01-08T07:16:18Z","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.). ","year":"2021","_id":"8997","publication_identifier":{"issn":["1553-7358"]},"doi":"10.1371/journal.pcbi.1008529","quality_controlled":"1","project":[{"grant_number":"P27201-B22","name":"Revealing the mechanisms underlying drug interactions","_id":"25E9AF9E-B435-11E9-9278-68D0E5697425","call_identifier":"FWF"},{"grant_number":"P28844-B27","name":"Biophysics of information processing in gene regulation","_id":"254E9036-B435-11E9-9278-68D0E5697425","call_identifier":"FWF"}],"isi":1,"keyword":["Modelling and Simulation","Genetics","Molecular Biology","Antibiotics","Drug interactions"],"language":[{"iso":"eng"}],"author":[{"last_name":"Kavcic","first_name":"Bor","full_name":"Kavcic, Bor","orcid":"0000-0001-6041-254X","id":"350F91D2-F248-11E8-B48F-1D18A9856A87"},{"first_name":"Gašper","last_name":"Tkačik","orcid":"0000-0002-6699-1455","id":"3D494DCA-F248-11E8-B48F-1D18A9856A87","full_name":"Tkačik, Gašper"},{"orcid":"0000-0003-4398-476X","id":"3E6DB97A-F248-11E8-B48F-1D18A9856A87","full_name":"Bollenbach, Tobias","first_name":"Tobias","last_name":"Bollenbach"}],"day":"07","file":[{"content_type":"application/pdf","relation":"main_file","file_size":3690053,"creator":"dernst","success":1,"file_name":"2021_PlosComBio_Kavcic.pdf","date_created":"2021-02-04T12:30:48Z","access_level":"open_access","date_updated":"2021-02-04T12:30:48Z","file_id":"9092","checksum":"e29f2b42651bef8e034781de8781ffac"}],"title":"Minimal biophysical model of combined antibiotic action","article_number":"e1008529","user_id":"4359f0d1-fa6c-11eb-b949-802e58b17ae8","publisher":"Public Library of Science","department":[{"_id":"GaTk"}],"publication":"PLOS Computational Biology","article_processing_charge":"Yes","article_type":"original","tmp":{"legal_code_url":"https://creativecommons.org/licenses/by/4.0/legalcode","image":"/images/cc_by.png","name":"Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)","short":"CC BY (4.0)"}},{"date_created":"2021-11-11T10:39:37Z","file_date_updated":"2021-11-11T10:54:40Z","volume":12,"oa_version":"Published Version","type":"journal_article","month":"10","date_updated":"2023-08-14T11:43:23Z","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."}],"_id":"10271","year":"2021","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.","ddc":["610"],"date_published":"2021-10-20T00:00:00Z","has_accepted_license":"1","oa":1,"publication_status":"published","citation":{"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>","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>.","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.","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>","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>.","short":"Q. Qi, S.A. Angermayr, M.T. Bollenbach, Frontiers in Microbiology 12 (2021)."},"intvolume":"        12","external_id":{"isi":["000715997300001"],"pmid":["34745067"]},"status":"public","article_number":"760017","title":"Uncovering Key Metabolic Determinants of the Drug Interactions Between Trimethoprim and Erythromycin in Escherichia coli","file":[{"access_level":"open_access","date_created":"2021-11-11T10:54:40Z","checksum":"d41321748e9588dd3cf03e9a7222127f","file_id":"10272","date_updated":"2021-11-11T10:54:40Z","creator":"cchlebak","file_size":2397203,"content_type":"application/pdf","relation":"main_file","file_name":"2021_FrontiersMicrob_Qi.pdf","success":1}],"day":"20","author":[{"full_name":"Qi, Qin","id":"3B22D412-F248-11E8-B48F-1D18A9856A87","orcid":"0000-0002-6148-2416","first_name":"Qin","last_name":"Qi"},{"full_name":"Angermayr, S. Andreas","first_name":"S. Andreas","last_name":"Angermayr"},{"first_name":"Mark Tobias","last_name":"Bollenbach","full_name":"Bollenbach, Mark Tobias","id":"3E6DB97A-F248-11E8-B48F-1D18A9856A87","orcid":"0000-0003-4398-476X"}],"article_type":"original","tmp":{"legal_code_url":"https://creativecommons.org/licenses/by/4.0/legalcode","image":"/images/cc_by.png","name":"Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)","short":"CC BY (4.0)"},"scopus_import":"1","article_processing_charge":"No","ec_funded":1,"publication":"Frontiers in Microbiology","pmid":1,"user_id":"4359f0d1-fa6c-11eb-b949-802e58b17ae8","publisher":"Frontiers","quality_controlled":"1","doi":"10.3389/fmicb.2021.760017","publication_identifier":{"eissn":["1664-302X"]},"language":[{"iso":"eng"}],"keyword":["microbiology"],"isi":1,"project":[{"name":"Revealing the mechanisms underlying drug interactions","_id":"25E9AF9E-B435-11E9-9278-68D0E5697425","call_identifier":"FWF","grant_number":"P27201-B22"},{"name":"Optimality principles in responses to antibiotics","_id":"25E83C2C-B435-11E9-9278-68D0E5697425","call_identifier":"FP7","grant_number":"303507"}]},{"status":"public","external_id":{"pmid":["32561723"],"isi":["000545685100002"]},"citation":{"short":"M. Lukacisinova, B. Fernando, M.T. Bollenbach, Nature Communications 11 (2020).","chicago":"Lukacisinova, Marta, Booshini Fernando, and Mark Tobias Bollenbach. “Highly Parallel Lab Evolution Reveals That Epistasis Can Curb the Evolution of Antibiotic Resistance.” <i>Nature Communications</i>. Springer Nature, 2020. <a href=\"https://doi.org/10.1038/s41467-020-16932-z\">https://doi.org/10.1038/s41467-020-16932-z</a>.","ieee":"M. Lukacisinova, B. Fernando, and M. T. Bollenbach, “Highly parallel lab evolution reveals that epistasis can curb the evolution of antibiotic resistance,” <i>Nature Communications</i>, vol. 11. Springer Nature, 2020.","apa":"Lukacisinova, M., Fernando, B., &#38; Bollenbach, M. T. (2020). Highly parallel lab evolution reveals that epistasis can curb the evolution of antibiotic resistance. <i>Nature Communications</i>. Springer Nature. <a href=\"https://doi.org/10.1038/s41467-020-16932-z\">https://doi.org/10.1038/s41467-020-16932-z</a>","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.” <i>Nature Communications</i>, vol. 11, 3105, Springer Nature, 2020, doi:<a href=\"https://doi.org/10.1038/s41467-020-16932-z\">10.1038/s41467-020-16932-z</a>.","ama":"Lukacisinova M, Fernando B, Bollenbach MT. Highly parallel lab evolution reveals that epistasis can curb the evolution of antibiotic resistance. <i>Nature Communications</i>. 2020;11. doi:<a href=\"https://doi.org/10.1038/s41467-020-16932-z\">10.1038/s41467-020-16932-z</a>"},"intvolume":"        11","extern":"1","has_accepted_license":"1","publication_status":"published","oa":1,"ddc":["570"],"date_published":"2020-06-19T00:00:00Z","year":"2020","_id":"8037","date_updated":"2023-08-22T07:48:30Z","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."}],"oa_version":"Published Version","type":"journal_article","month":"06","date_created":"2020-06-29T07:59:35Z","file_date_updated":"2020-07-14T12:48:08Z","volume":11,"project":[{"call_identifier":"FWF","_id":"25E9AF9E-B435-11E9-9278-68D0E5697425","name":"Revealing the mechanisms underlying drug interactions","grant_number":"P27201-B22"},{"grant_number":"RGP0042/2013","name":"Revealing the fundamental limits of cell growth","_id":"25EB3A80-B435-11E9-9278-68D0E5697425"}],"language":[{"iso":"eng"}],"isi":1,"publication_identifier":{"eissn":["20411723"]},"quality_controlled":"1","doi":"10.1038/s41467-020-16932-z","pmid":1,"user_id":"4359f0d1-fa6c-11eb-b949-802e58b17ae8","publisher":"Springer Nature","article_processing_charge":"No","scopus_import":"1","article_type":"original","tmp":{"legal_code_url":"https://creativecommons.org/licenses/by/4.0/legalcode","image":"/images/cc_by.png","name":"Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)","short":"CC BY (4.0)"},"publication":"Nature Communications","day":"19","file":[{"file_size":1546491,"relation":"main_file","content_type":"application/pdf","creator":"cziletti","file_name":"2020_NatureComm_Lukacisinova.pdf","date_created":"2020-06-30T09:58:50Z","access_level":"open_access","file_id":"8071","date_updated":"2020-07-14T12:48:08Z","checksum":"4f5f49d63add331d5eb8a2bae477b396"}],"author":[{"last_name":"Lukacisinova","first_name":"Marta","orcid":"0000-0002-2519-8004","id":"4342E402-F248-11E8-B48F-1D18A9856A87","full_name":"Lukacisinova, Marta"},{"full_name":"Fernando, Booshini","first_name":"Booshini","last_name":"Fernando"},{"last_name":"Bollenbach","first_name":"Mark Tobias","full_name":"Bollenbach, Mark Tobias","orcid":"0000-0003-4398-476X","id":"3E6DB97A-F248-11E8-B48F-1D18A9856A87"}],"title":"Highly parallel lab evolution reveals that epistasis can curb the evolution of antibiotic resistance","article_number":"3105"},{"article_number":"4013","title":"Mechanisms of drug interactions between translation-inhibiting antibiotics","author":[{"last_name":"Kavcic","first_name":"Bor","id":"350F91D2-F248-11E8-B48F-1D18A9856A87","orcid":"0000-0001-6041-254X","full_name":"Kavcic, Bor"},{"last_name":"Tkačik","first_name":"Gašper","full_name":"Tkačik, Gašper","orcid":"0000-0002-6699-1455","id":"3D494DCA-F248-11E8-B48F-1D18A9856A87"},{"first_name":"Tobias","last_name":"Bollenbach","orcid":"0000-0003-4398-476X","id":"3E6DB97A-F248-11E8-B48F-1D18A9856A87","full_name":"Bollenbach, Tobias"}],"file":[{"creator":"dernst","relation":"main_file","content_type":"application/pdf","file_size":1965672,"success":1,"file_name":"2020_NatureComm_Kavcic.pdf","access_level":"open_access","date_created":"2020-08-17T07:36:57Z","checksum":"986bebb308850a55850028d3d2b5b664","date_updated":"2020-08-17T07:36:57Z","file_id":"8275"}],"day":"11","publication":"Nature Communications","tmp":{"legal_code_url":"https://creativecommons.org/licenses/by/4.0/legalcode","image":"/images/cc_by.png","name":"Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)","short":"CC BY (4.0)"},"article_type":"original","article_processing_charge":"No","user_id":"4359f0d1-fa6c-11eb-b949-802e58b17ae8","publisher":"Springer Nature","department":[{"_id":"GaTk"}],"doi":"10.1038/s41467-020-17734-z","quality_controlled":"1","publication_identifier":{"issn":["2041-1723"]},"isi":1,"language":[{"iso":"eng"}],"project":[{"name":"Revealing the mechanisms underlying drug interactions","_id":"25E9AF9E-B435-11E9-9278-68D0E5697425","call_identifier":"FWF","grant_number":"P27201-B22"},{"grant_number":"P28844-B27","name":"Biophysics of information processing in gene regulation","_id":"254E9036-B435-11E9-9278-68D0E5697425","call_identifier":"FWF"}],"volume":11,"date_created":"2020-08-12T09:13:50Z","file_date_updated":"2020-08-17T07:36:57Z","oa_version":"Published Version","type":"journal_article","month":"08","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."}],"date_updated":"2024-03-25T23:30:05Z","_id":"8250","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.","year":"2020","date_published":"2020-08-11T00:00:00Z","ddc":["570"],"oa":1,"publication_status":"published","has_accepted_license":"1","related_material":{"record":[{"relation":"dissertation_contains","id":"8657","status":"public"}]},"intvolume":"        11","citation":{"ieee":"B. Kavcic, G. Tkačik, and M. T. Bollenbach, “Mechanisms of drug interactions between translation-inhibiting antibiotics,” <i>Nature Communications</i>, vol. 11. Springer Nature, 2020.","chicago":"Kavcic, Bor, Gašper Tkačik, and Mark Tobias Bollenbach. “Mechanisms of Drug Interactions between Translation-Inhibiting Antibiotics.” <i>Nature Communications</i>. Springer Nature, 2020. <a href=\"https://doi.org/10.1038/s41467-020-17734-z\">https://doi.org/10.1038/s41467-020-17734-z</a>.","short":"B. Kavcic, G. Tkačik, M.T. Bollenbach, Nature Communications 11 (2020).","ama":"Kavcic B, Tkačik G, Bollenbach MT. Mechanisms of drug interactions between translation-inhibiting antibiotics. <i>Nature Communications</i>. 2020;11. doi:<a href=\"https://doi.org/10.1038/s41467-020-17734-z\">10.1038/s41467-020-17734-z</a>","mla":"Kavcic, Bor, et al. “Mechanisms of Drug Interactions between Translation-Inhibiting Antibiotics.” <i>Nature Communications</i>, vol. 11, 4013, Springer Nature, 2020, doi:<a href=\"https://doi.org/10.1038/s41467-020-17734-z\">10.1038/s41467-020-17734-z</a>.","ista":"Kavcic B, Tkačik G, Bollenbach MT. 2020. Mechanisms of drug interactions between translation-inhibiting antibiotics. Nature Communications. 11, 4013.","apa":"Kavcic, B., Tkačik, G., &#38; Bollenbach, M. T. (2020). Mechanisms of drug interactions between translation-inhibiting antibiotics. <i>Nature Communications</i>. Springer Nature. <a href=\"https://doi.org/10.1038/s41467-020-17734-z\">https://doi.org/10.1038/s41467-020-17734-z</a>"},"external_id":{"isi":["000562769300008"]},"status":"public"},{"year":"2020","department":[{"_id":"GaTk"}],"publisher":"Cold Spring Harbor Laboratory","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","article_processing_charge":"No","publication":"bioRxiv","_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."}],"date_updated":"2024-03-25T23:30:05Z","day":"18","oa_version":"Preprint","month":"04","type":"preprint","author":[{"orcid":"0000-0001-6041-254X","id":"350F91D2-F248-11E8-B48F-1D18A9856A87","full_name":"Kavcic, Bor","last_name":"Kavcic","first_name":"Bor"},{"last_name":"Tkačik","first_name":"Gašper","orcid":"0000-0002-6699-1455","id":"3D494DCA-F248-11E8-B48F-1D18A9856A87","full_name":"Tkačik, Gašper"},{"id":"3E6DB97A-F248-11E8-B48F-1D18A9856A87","orcid":"0000-0003-4398-476X","full_name":"Bollenbach, Tobias","first_name":"Tobias","last_name":"Bollenbach"}],"date_created":"2020-04-22T08:27:56Z","title":"A minimal biophysical model of combined antibiotic action","status":"public","project":[{"grant_number":"P27201-B22","_id":"25E9AF9E-B435-11E9-9278-68D0E5697425","call_identifier":"FWF","name":"Revealing the mechanisms underlying drug interactions"},{"call_identifier":"FWF","_id":"254E9036-B435-11E9-9278-68D0E5697425","name":"Biophysics of information processing in gene regulation","grant_number":"P28844-B27"}],"citation":{"ieee":"B. Kavcic, G. Tkačik, and M. T. Bollenbach, “A minimal biophysical model of combined antibiotic action,” <i>bioRxiv</i>. Cold Spring Harbor Laboratory, 2020.","chicago":"Kavcic, Bor, Gašper Tkačik, and Mark Tobias Bollenbach. “A Minimal Biophysical Model of Combined Antibiotic Action.” <i>BioRxiv</i>. Cold Spring Harbor Laboratory, 2020. <a href=\"https://doi.org/10.1101/2020.04.18.047886\">https://doi.org/10.1101/2020.04.18.047886</a>.","short":"B. Kavcic, G. Tkačik, M.T. Bollenbach, BioRxiv (2020).","ama":"Kavcic B, Tkačik G, Bollenbach MT. A minimal biophysical model of combined antibiotic action. <i>bioRxiv</i>. 2020. doi:<a href=\"https://doi.org/10.1101/2020.04.18.047886\">10.1101/2020.04.18.047886</a>","mla":"Kavcic, Bor, et al. “A Minimal Biophysical Model of Combined Antibiotic Action.” <i>BioRxiv</i>, Cold Spring Harbor Laboratory, 2020, doi:<a href=\"https://doi.org/10.1101/2020.04.18.047886\">10.1101/2020.04.18.047886</a>.","ista":"Kavcic B, Tkačik G, Bollenbach MT. 2020. A minimal biophysical model of combined antibiotic action. bioRxiv, <a href=\"https://doi.org/10.1101/2020.04.18.047886\">10.1101/2020.04.18.047886</a>.","apa":"Kavcic, B., Tkačik, G., &#38; Bollenbach, M. T. (2020). A minimal biophysical model of combined antibiotic action. <i>bioRxiv</i>. Cold Spring Harbor Laboratory. <a href=\"https://doi.org/10.1101/2020.04.18.047886\">https://doi.org/10.1101/2020.04.18.047886</a>"},"language":[{"iso":"eng"}],"related_material":{"record":[{"relation":"later_version","status":"public","id":"8997"},{"relation":"dissertation_contains","status":"public","id":"8657"}]},"oa":1,"publication_status":"published","main_file_link":[{"url":"https://doi.org/10.1101/2020.04.18.047886 ","open_access":"1"}],"doi":"10.1101/2020.04.18.047886","date_published":"2020-04-18T00:00:00Z"},{"external_id":{"isi":["000499495400003"]},"status":"public","intvolume":"         9","citation":{"ieee":"M. Lukacisin and M. T. Bollenbach, “Emergent gene expression responses to drug combinations predict higher-order drug interactions,” <i>Cell Systems</i>, vol. 9, no. 5. Cell Press, pp. 423-433.e1-e3, 2019.","chicago":"Lukacisin, Martin, and Mark Tobias Bollenbach. “Emergent Gene Expression Responses to Drug Combinations Predict Higher-Order Drug Interactions.” <i>Cell Systems</i>. Cell Press, 2019. <a href=\"https://doi.org/10.1016/j.cels.2019.10.004\">https://doi.org/10.1016/j.cels.2019.10.004</a>.","short":"M. Lukacisin, M.T. Bollenbach, Cell Systems 9 (2019) 423-433.e1-e3.","ama":"Lukacisin M, Bollenbach MT. Emergent gene expression responses to drug combinations predict higher-order drug interactions. <i>Cell Systems</i>. 2019;9(5):423-433.e1-e3. doi:<a href=\"https://doi.org/10.1016/j.cels.2019.10.004\">10.1016/j.cels.2019.10.004</a>","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.” <i>Cell Systems</i>, vol. 9, no. 5, Cell Press, 2019, pp. 423-433.e1-e3, doi:<a href=\"https://doi.org/10.1016/j.cels.2019.10.004\">10.1016/j.cels.2019.10.004</a>.","apa":"Lukacisin, M., &#38; Bollenbach, M. T. (2019). Emergent gene expression responses to drug combinations predict higher-order drug interactions. <i>Cell Systems</i>. Cell Press. <a href=\"https://doi.org/10.1016/j.cels.2019.10.004\">https://doi.org/10.1016/j.cels.2019.10.004</a>"},"publication_status":"published","oa":1,"has_accepted_license":"1","date_published":"2019-11-27T00:00:00Z","ddc":["570"],"acknowledged_ssus":[{"_id":"LifeSc"}],"year":"2019","_id":"7026","page":"423-433.e1-e3","oa_version":"Published Version","type":"journal_article","month":"11","date_updated":"2023-08-30T07:24:58Z","abstract":[{"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.","lang":"eng"}],"volume":9,"date_created":"2019-11-15T10:51:42Z","file_date_updated":"2020-07-14T12:47:48Z","project":[{"name":"Revealing the mechanisms underlying drug interactions","_id":"25E9AF9E-B435-11E9-9278-68D0E5697425","call_identifier":"FWF","grant_number":"P27201-B22"},{"grant_number":"RGP0042/2013","_id":"25EB3A80-B435-11E9-9278-68D0E5697425","name":"Revealing the fundamental limits of cell growth"}],"isi":1,"issue":"5","language":[{"iso":"eng"}],"publication_identifier":{"issn":["2405-4712"]},"doi":"10.1016/j.cels.2019.10.004","quality_controlled":"1","publisher":"Cell Press","user_id":"4359f0d1-fa6c-11eb-b949-802e58b17ae8","department":[{"_id":"ToBo"}],"publication":"Cell Systems","article_type":"original","tmp":{"legal_code_url":"https://creativecommons.org/licenses/by/4.0/legalcode","image":"/images/cc_by.png","name":"Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)","short":"CC BY (4.0)"},"scopus_import":"1","article_processing_charge":"No","author":[{"first_name":"Martin","last_name":"Lukacisin","full_name":"Lukacisin, Martin","id":"298FFE8C-F248-11E8-B48F-1D18A9856A87","orcid":"0000-0001-6549-4177"},{"full_name":"Bollenbach, Tobias","orcid":"0000-0003-4398-476X","id":"3E6DB97A-F248-11E8-B48F-1D18A9856A87","last_name":"Bollenbach","first_name":"Tobias"}],"day":"27","file":[{"content_type":"application/pdf","relation":"main_file","file_size":4238460,"creator":"dernst","file_name":"2019_CellSystems_Lukacisin.pdf","date_created":"2019-11-15T10:57:42Z","access_level":"open_access","date_updated":"2020-07-14T12:47:48Z","file_id":"7027","checksum":"7a11d6c2f9523d65b049512d61733178"}],"title":"Emergent gene expression responses to drug combinations predict higher-order drug interactions"},{"publication":"Molecular systems biology","scopus_import":"1","article_processing_charge":"No","publisher":"Embo Press","user_id":"4359f0d1-fa6c-11eb-b949-802e58b17ae8","pmid":1,"department":[{"_id":"GaTk"}],"title":"Temporal order and precision of complex stress responses in individual bacteria","article_number":"e8470","author":[{"first_name":"Karin","last_name":"Mitosch","id":"39B66846-F248-11E8-B48F-1D18A9856A87","full_name":"Mitosch, Karin"},{"id":"34DA8BD6-F248-11E8-B48F-1D18A9856A87","full_name":"Rieckh, Georg","last_name":"Rieckh","first_name":"Georg"},{"full_name":"Bollenbach, Mark Tobias","id":"3E6DB97A-F248-11E8-B48F-1D18A9856A87","orcid":"0000-0003-4398-476X","first_name":"Mark Tobias","last_name":"Bollenbach"}],"day":"14","isi":1,"language":[{"iso":"eng"}],"issue":"2","project":[{"_id":"25E9AF9E-B435-11E9-9278-68D0E5697425","call_identifier":"FWF","name":"Revealing the mechanisms underlying drug interactions","grant_number":"P27201-B22"},{"name":"Revealing the fundamental limits of cell growth","_id":"25EB3A80-B435-11E9-9278-68D0E5697425","grant_number":"RGP0042/2013"}],"doi":"10.15252/msb.20188470","quality_controlled":"1","_id":"6046","year":"2019","volume":15,"date_created":"2019-02-24T22:59:18Z","abstract":[{"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.","lang":"eng"}],"date_updated":"2023-08-24T14:49:53Z","month":"02","oa_version":"Submitted Version","type":"journal_article","intvolume":"        15","citation":{"ama":"Mitosch K, Rieckh G, Bollenbach MT. Temporal order and precision of complex stress responses in individual bacteria. <i>Molecular systems biology</i>. 2019;15(2). doi:<a href=\"https://doi.org/10.15252/msb.20188470\">10.15252/msb.20188470</a>","apa":"Mitosch, K., Rieckh, G., &#38; Bollenbach, M. T. (2019). Temporal order and precision of complex stress responses in individual bacteria. <i>Molecular Systems Biology</i>. Embo Press. <a href=\"https://doi.org/10.15252/msb.20188470\">https://doi.org/10.15252/msb.20188470</a>","mla":"Mitosch, Karin, et al. “Temporal Order and Precision of Complex Stress Responses in Individual Bacteria.” <i>Molecular Systems Biology</i>, vol. 15, no. 2, e8470, Embo Press, 2019, doi:<a href=\"https://doi.org/10.15252/msb.20188470\">10.15252/msb.20188470</a>.","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.","chicago":"Mitosch, Karin, Georg Rieckh, and Mark Tobias Bollenbach. “Temporal Order and Precision of Complex Stress Responses in Individual Bacteria.” <i>Molecular Systems Biology</i>. Embo Press, 2019. <a href=\"https://doi.org/10.15252/msb.20188470\">https://doi.org/10.15252/msb.20188470</a>.","ieee":"K. Mitosch, G. Rieckh, and M. T. Bollenbach, “Temporal order and precision of complex stress responses in individual bacteria,” <i>Molecular systems biology</i>, vol. 15, no. 2. Embo Press, 2019.","short":"K. Mitosch, G. Rieckh, M.T. Bollenbach, Molecular Systems Biology 15 (2019)."},"external_id":{"isi":["000459628300003"],"pmid":["30765425"]},"status":"public","date_published":"2019-02-14T00:00:00Z","main_file_link":[{"url":"https://www.ncbi.nlm.nih.gov/pubmed/30765425","open_access":"1"}],"acknowledged_ssus":[{"_id":"Bio"}],"oa":1,"publication_status":"published"},{"year":"2017","_id":"822","oa_version":"Submitted Version","month":"10","type":"journal_article","date_updated":"2023-09-26T16:18:48Z","abstract":[{"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. ","lang":"eng"}],"page":"10666 - 10671","date_created":"2018-12-11T11:48:41Z","volume":114,"external_id":{"isi":["000412130500061"],"pmid":["28923953"]},"status":"public","citation":{"short":"M. de Vos, M.P. Zagórski, A. Mcnally, M.T. Bollenbach, PNAS 114 (2017) 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.","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>.","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>.","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.","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>","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>"},"intvolume":"       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","department":[{"_id":"ToBo"}],"pmid":1,"user_id":"c635000d-4b10-11ee-a964-aac5a93f6ac1","publisher":"National Academy of Sciences","scopus_import":"1","article_processing_charge":"No","ec_funded":1,"publication":"PNAS","day":"03","author":[{"last_name":"De Vos","first_name":"Marjon","id":"3111FFAC-F248-11E8-B48F-1D18A9856A87","full_name":"De Vos, Marjon"},{"full_name":"Zagórski, Marcin P","id":"343DA0DC-F248-11E8-B48F-1D18A9856A87","orcid":"0000-0001-7896-7762","first_name":"Marcin P","last_name":"Zagórski"},{"first_name":"Alan","last_name":"Mcnally","full_name":"Mcnally, Alan"},{"orcid":"0000-0003-4398-476X","id":"3E6DB97A-F248-11E8-B48F-1D18A9856A87","full_name":"Bollenbach, Mark Tobias","first_name":"Mark Tobias","last_name":"Bollenbach"}],"publist_id":"6827","title":"Interaction networks, ecological stability, and collective antibiotic tolerance in polymicrobial infections","project":[{"grant_number":"303507","name":"Optimality principles in responses to antibiotics","call_identifier":"FP7","_id":"25E83C2C-B435-11E9-9278-68D0E5697425"},{"grant_number":"P27201-B22","name":"Revealing the mechanisms underlying drug interactions","call_identifier":"FWF","_id":"25E9AF9E-B435-11E9-9278-68D0E5697425"}],"issue":"40","language":[{"iso":"eng"}],"isi":1,"publication_identifier":{"issn":["00278424"]},"quality_controlled":"1","doi":"10.1073/pnas.1713372114"},{"project":[{"grant_number":"303507","_id":"25E83C2C-B435-11E9-9278-68D0E5697425","call_identifier":"FP7","name":"Optimality principles in responses to antibiotics"},{"grant_number":"P27201-B22","name":"Revealing the mechanisms underlying drug interactions","_id":"25E9AF9E-B435-11E9-9278-68D0E5697425","call_identifier":"FWF"},{"name":"Revealing the fundamental limits of cell growth","_id":"25EB3A80-B435-11E9-9278-68D0E5697425","grant_number":"RGP0042/2013"}],"language":[{"iso":"eng"}],"issue":"4","pubrep_id":"901","publication_identifier":{"issn":["24054712"]},"quality_controlled":"1","doi":"10.1016/j.cels.2017.03.001","department":[{"_id":"ToBo"},{"_id":"GaTk"}],"publisher":"Cell Press","user_id":"3E5EF7F0-F248-11E8-B48F-1D18A9856A87","article_processing_charge":"Yes (in subscription journal)","ec_funded":1,"scopus_import":1,"tmp":{"name":"Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0)","short":"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"},"publication":"Cell Systems","license":"https://creativecommons.org/licenses/by-nc-nd/4.0/","file":[{"checksum":"04ff20011c3d9a601c514aa999a5fe1a","date_updated":"2020-07-14T12:47:35Z","file_id":"5041","access_level":"open_access","date_created":"2018-12-12T10:13:54Z","file_name":"IST-2017-901-v1+1_1-s2.0-S2405471217300868-main.pdf","creator":"system","relation":"main_file","content_type":"application/pdf","file_size":2438660}],"day":"26","author":[{"first_name":"Karin","last_name":"Mitosch","id":"39B66846-F248-11E8-B48F-1D18A9856A87","full_name":"Mitosch, Karin"},{"full_name":"Rieckh, Georg","id":"34DA8BD6-F248-11E8-B48F-1D18A9856A87","last_name":"Rieckh","first_name":"Georg"},{"orcid":"0000-0003-4398-476X","id":"3E6DB97A-F248-11E8-B48F-1D18A9856A87","full_name":"Bollenbach, Tobias","last_name":"Bollenbach","first_name":"Tobias"}],"publist_id":"7061","title":"Noisy response to antibiotic stress predicts subsequent single cell survival in an acidic environment","status":"public","citation":{"short":"K. Mitosch, G. Rieckh, M.T. Bollenbach, Cell Systems 4 (2017) 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>.","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.","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>","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.” <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>.","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>"},"related_material":{"record":[{"status":"public","id":"818","relation":"dissertation_contains"}]},"intvolume":"         4","has_accepted_license":"1","publication_status":"published","oa":1,"date_published":"2017-04-26T00:00:00Z","ddc":["576","610"],"year":"2017","_id":"666","abstract":[{"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.","lang":"eng"}],"date_updated":"2023-09-07T12:00:25Z","type":"journal_article","month":"04","oa_version":"Published Version","page":"393 - 403","date_created":"2018-12-11T11:47:48Z","file_date_updated":"2020-07-14T12:47:35Z","volume":4},{"volume":127,"date_created":"2018-12-11T11:47:53Z","page":"2051 - 2065","abstract":[{"lang":"eng","text":"Protective responses against pathogens require a rapid mobilization of resting neutrophils and the timely removal of activated ones. Neutrophils are exceptionally short-lived leukocytes, yet it remains unclear whether the lifespan of pathogen-engaged neutrophils is regulated differently from that in the circulating steady-state pool. Here, we have found that under homeostatic conditions, the mRNA-destabilizing protein tristetraprolin (TTP) regulates apoptosis and the numbers of activated infiltrating murine neutrophils but not neutrophil cellularity. Activated TTP-deficient neutrophils exhibited decreased apoptosis and enhanced accumulation at the infection site. In the context of myeloid-specific deletion of Ttp, the potentiation of neutrophil deployment protected mice against lethal soft tissue infection with Streptococcus pyogenes and prevented bacterial dissemination. Neutrophil transcriptome analysis revealed that decreased apoptosis of TTP-deficient neutrophils was specifically associated with elevated expression of myeloid cell leukemia 1 (Mcl1) but not other antiapoptotic B cell leukemia/ lymphoma 2 (Bcl2) family members. Higher Mcl1 expression resulted from stabilization of Mcl1 mRNA in the absence of TTP. The low apoptosis rate of infiltrating TTP-deficient neutrophils was comparable to that of transgenic Mcl1-overexpressing neutrophils. Our study demonstrates that posttranscriptional gene regulation by TTP schedules the termination of the antimicrobial engagement of neutrophils. The balancing role of TTP comes at the cost of an increased risk of bacterial infections."}],"date_updated":"2024-03-25T23:30:12Z","month":"06","type":"journal_article","oa_version":"Submitted Version","_id":"679","acknowledgement":"This work was supported by grants from the Austrian Science Fund (FWF) (P27538-B21, I1621-B22, and SFB 43, to PK); by funding from the European Union Seventh Framework Programme Marie Curie Initial Training Networks (FP7-PEOPLE-2012-ITN) for the project INBIONET (INfection BIOlogy Training NETwork under grant agreement PITN-GA-2012-316682; and by a joint research cluster initiative of the University of Vienna and the Medical University of Vienna.","year":"2017","date_published":"2017-06-01T00:00:00Z","main_file_link":[{"open_access":"1","url":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5451238/"}],"oa":1,"publication_status":"published","related_material":{"record":[{"status":"public","id":"12401","relation":"dissertation_contains"}]},"intvolume":"       127","citation":{"short":"F. Ebner, V. Sedlyarov, S. Tasciyan, M. Ivin, F. Kratochvill, N. Gratz, L. Kenner, A. Villunger, M.K. Sixt, P. Kovarik, The Journal of Clinical Investigation 127 (2017) 2051–2065.","ieee":"F. Ebner <i>et al.</i>, “The RNA-binding protein tristetraprolin schedules apoptosis of pathogen-engaged neutrophils during bacterial infection,” <i>The Journal of Clinical Investigation</i>, vol. 127, no. 6. American Society for Clinical Investigation, pp. 2051–2065, 2017.","chicago":"Ebner, Florian, Vitaly Sedlyarov, Saren Tasciyan, Masa Ivin, Franz Kratochvill, Nina Gratz, Lukas Kenner, Andreas Villunger, Michael K Sixt, and Pavel Kovarik. “The RNA-Binding Protein Tristetraprolin Schedules Apoptosis of Pathogen-Engaged Neutrophils during Bacterial Infection.” <i>The Journal of Clinical Investigation</i>. American Society for Clinical Investigation, 2017. <a href=\"https://doi.org/10.1172/JCI80631\">https://doi.org/10.1172/JCI80631</a>.","mla":"Ebner, Florian, et al. “The RNA-Binding Protein Tristetraprolin Schedules Apoptosis of Pathogen-Engaged Neutrophils during Bacterial Infection.” <i>The Journal of Clinical Investigation</i>, vol. 127, no. 6, American Society for Clinical Investigation, 2017, pp. 2051–65, doi:<a href=\"https://doi.org/10.1172/JCI80631\">10.1172/JCI80631</a>.","ista":"Ebner F, Sedlyarov V, Tasciyan S, Ivin M, Kratochvill F, Gratz N, Kenner L, Villunger A, Sixt MK, Kovarik P. 2017. The RNA-binding protein tristetraprolin schedules apoptosis of pathogen-engaged neutrophils during bacterial infection. The Journal of Clinical Investigation. 127(6), 2051–2065.","apa":"Ebner, F., Sedlyarov, V., Tasciyan, S., Ivin, M., Kratochvill, F., Gratz, N., … Kovarik, P. (2017). The RNA-binding protein tristetraprolin schedules apoptosis of pathogen-engaged neutrophils during bacterial infection. <i>The Journal of Clinical Investigation</i>. American Society for Clinical Investigation. <a href=\"https://doi.org/10.1172/JCI80631\">https://doi.org/10.1172/JCI80631</a>","ama":"Ebner F, Sedlyarov V, Tasciyan S, et al. The RNA-binding protein tristetraprolin schedules apoptosis of pathogen-engaged neutrophils during bacterial infection. <i>The Journal of Clinical Investigation</i>. 2017;127(6):2051-2065. doi:<a href=\"https://doi.org/10.1172/JCI80631\">10.1172/JCI80631</a>"},"status":"public","external_id":{"pmid":["28504646"]},"title":"The RNA-binding protein tristetraprolin schedules apoptosis of pathogen-engaged neutrophils during bacterial infection","publist_id":"7038","author":[{"last_name":"Ebner","first_name":"Florian","full_name":"Ebner, Florian"},{"last_name":"Sedlyarov","first_name":"Vitaly","full_name":"Sedlyarov, Vitaly"},{"first_name":"Saren","last_name":"Tasciyan","full_name":"Tasciyan, Saren","id":"4323B49C-F248-11E8-B48F-1D18A9856A87","orcid":"0000-0003-1671-393X"},{"first_name":"Masa","last_name":"Ivin","full_name":"Ivin, Masa"},{"full_name":"Kratochvill, Franz","last_name":"Kratochvill","first_name":"Franz"},{"full_name":"Gratz, Nina","last_name":"Gratz","first_name":"Nina"},{"first_name":"Lukas","last_name":"Kenner","full_name":"Kenner, Lukas"},{"full_name":"Villunger, Andreas","last_name":"Villunger","first_name":"Andreas"},{"id":"41E9FBEA-F248-11E8-B48F-1D18A9856A87","orcid":"0000-0002-6620-9179","full_name":"Sixt, Michael K","last_name":"Sixt","first_name":"Michael K"},{"full_name":"Kovarik, Pavel","last_name":"Kovarik","first_name":"Pavel"}],"day":"01","publication":"The Journal of Clinical Investigation","scopus_import":1,"publisher":"American Society for Clinical Investigation","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","pmid":1,"department":[{"_id":"MiSi"}],"doi":"10.1172/JCI80631","quality_controlled":"1","publication_identifier":{"issn":["00219738"]},"language":[{"iso":"eng"}],"issue":"6","project":[{"grant_number":"T00817-B21","name":"The biochemical basis of PAR polarization","_id":"25985A36-B435-11E9-9278-68D0E5697425","call_identifier":"FWF"},{"_id":"25E9AF9E-B435-11E9-9278-68D0E5697425","call_identifier":"FWF","name":"Revealing the mechanisms underlying drug interactions","grant_number":"P27201-B22"}]},{"title":"Mapping the mouse Allelome reveals tissue specific regulation of allelic expression","article_number":"e25125","publist_id":"6971","author":[{"last_name":"Andergassen","first_name":"Daniel","full_name":"Andergassen, Daniel"},{"first_name":"Christoph","last_name":"Dotter","full_name":"Dotter, Christoph","id":"4C66542E-F248-11E8-B48F-1D18A9856A87"},{"last_name":"Wenzel","first_name":"Dyniel","full_name":"Wenzel, Dyniel"},{"full_name":"Sigl, Verena","last_name":"Sigl","first_name":"Verena"},{"full_name":"Bammer, Philipp","last_name":"Bammer","first_name":"Philipp"},{"full_name":"Muckenhuber, Markus","first_name":"Markus","last_name":"Muckenhuber"},{"last_name":"Mayer","first_name":"Daniela","full_name":"Mayer, Daniela"},{"full_name":"Kulinski, Tomasz","first_name":"Tomasz","last_name":"Kulinski"},{"last_name":"Theussl","first_name":"Hans","full_name":"Theussl, Hans"},{"full_name":"Penninger, Josef","last_name":"Penninger","first_name":"Josef"},{"last_name":"Bock","first_name":"Christoph","full_name":"Bock, Christoph"},{"first_name":"Denise","last_name":"Barlow","full_name":"Barlow, Denise"},{"first_name":"Florian","last_name":"Pauler","full_name":"Pauler, Florian","id":"48EA0138-F248-11E8-B48F-1D18A9856A87"},{"full_name":"Hudson, Quanah","last_name":"Hudson","first_name":"Quanah"}],"day":"14","file":[{"file_name":"IST-2017-885-v1+1_elife-25125-figures-v2.pdf","creator":"system","file_size":6399510,"relation":"main_file","content_type":"application/pdf","checksum":"1ace3462e64a971b9ead896091829549","file_id":"5020","date_updated":"2020-07-14T12:47:50Z","access_level":"open_access","date_created":"2018-12-12T10:13:36Z"},{"file_name":"IST-2017-885-v1+2_elife-25125-v2.pdf","file_size":4264398,"content_type":"application/pdf","relation":"main_file","creator":"system","file_id":"5021","date_updated":"2020-07-14T12:47:50Z","checksum":"6241dc31eeb87b03facadec3a53a6827","date_created":"2018-12-12T10:13:36Z","access_level":"open_access"}],"publication":"eLife","scopus_import":1,"tmp":{"legal_code_url":"https://creativecommons.org/licenses/by/4.0/legalcode","image":"/images/cc_by.png","name":"Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)","short":"CC BY (4.0)"},"user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","publisher":"eLife Sciences Publications","department":[{"_id":"GaNo"},{"_id":"SiHi"}],"doi":"10.7554/eLife.25125","quality_controlled":"1","publication_identifier":{"issn":["2050084X"]},"pubrep_id":"885","language":[{"iso":"eng"}],"project":[{"grant_number":"P27201-B22","name":"Revealing the mechanisms underlying drug interactions","call_identifier":"FWF","_id":"25E9AF9E-B435-11E9-9278-68D0E5697425"}],"volume":6,"file_date_updated":"2020-07-14T12:47:50Z","date_created":"2018-12-11T11:48:05Z","abstract":[{"lang":"eng","text":"To determine the dynamics of allelic-specific expression during mouse development, we analyzed RNA-seq data from 23 F1 tissues from different developmental stages, including 19 female tissues allowing X chromosome inactivation (XCI) escapers to also be detected. We demonstrate that allelic expression arising from genetic or epigenetic differences is highly tissue-specific. We find that tissue-specific strain-biased gene expression may be regulated by tissue-specific enhancers or by post-transcriptional differences in stability between the alleles. We also find that escape from X-inactivation is tissue-specific, with leg muscle showing an unexpectedly high rate of XCI escapers. By surveying a range of tissues during development, and performing extensive validation, we are able to provide a high confidence list of mouse imprinted genes including 18 novel genes. This shows that cluster size varies dynamically during development and can be substantially larger than previously thought, with the Igf2r cluster extending over 10 Mb in placenta."}],"date_updated":"2021-01-12T08:11:57Z","type":"journal_article","month":"08","oa_version":"Published Version","_id":"713","year":"2017","date_published":"2017-08-14T00:00:00Z","ddc":["576"],"publication_status":"published","oa":1,"has_accepted_license":"1","intvolume":"         6","citation":{"ieee":"D. Andergassen <i>et al.</i>, “Mapping the mouse Allelome reveals tissue specific regulation of allelic expression,” <i>eLife</i>, vol. 6. eLife Sciences Publications, 2017.","chicago":"Andergassen, Daniel, Christoph Dotter, Dyniel Wenzel, Verena Sigl, Philipp Bammer, Markus Muckenhuber, Daniela Mayer, et al. “Mapping the Mouse Allelome Reveals Tissue Specific Regulation of Allelic Expression.” <i>ELife</i>. eLife Sciences Publications, 2017. <a href=\"https://doi.org/10.7554/eLife.25125\">https://doi.org/10.7554/eLife.25125</a>.","short":"D. Andergassen, C. Dotter, D. Wenzel, V. Sigl, P. Bammer, M. Muckenhuber, D. Mayer, T. Kulinski, H. Theussl, J. Penninger, C. Bock, D. Barlow, F. Pauler, Q. Hudson, ELife 6 (2017).","ama":"Andergassen D, Dotter C, Wenzel D, et al. Mapping the mouse Allelome reveals tissue specific regulation of allelic expression. <i>eLife</i>. 2017;6. doi:<a href=\"https://doi.org/10.7554/eLife.25125\">10.7554/eLife.25125</a>","ista":"Andergassen D, Dotter C, Wenzel D, Sigl V, Bammer P, Muckenhuber M, Mayer D, Kulinski T, Theussl H, Penninger J, Bock C, Barlow D, Pauler F, Hudson Q. 2017. Mapping the mouse Allelome reveals tissue specific regulation of allelic expression. eLife. 6, e25125.","mla":"Andergassen, Daniel, et al. “Mapping the Mouse Allelome Reveals Tissue Specific Regulation of Allelic Expression.” <i>ELife</i>, vol. 6, e25125, eLife Sciences Publications, 2017, doi:<a href=\"https://doi.org/10.7554/eLife.25125\">10.7554/eLife.25125</a>.","apa":"Andergassen, D., Dotter, C., Wenzel, D., Sigl, V., Bammer, P., Muckenhuber, M., … Hudson, Q. (2017). Mapping the mouse Allelome reveals tissue specific regulation of allelic expression. <i>ELife</i>. eLife Sciences Publications. <a href=\"https://doi.org/10.7554/eLife.25125\">https://doi.org/10.7554/eLife.25125</a>"},"status":"public"},{"language":[{"iso":"eng"}],"isi":1,"project":[{"grant_number":"P27201-B22","call_identifier":"FWF","_id":"25E9AF9E-B435-11E9-9278-68D0E5697425","name":"Revealing the mechanisms underlying drug interactions"},{"name":"Optimality principles in responses to antibiotics","_id":"25E83C2C-B435-11E9-9278-68D0E5697425","call_identifier":"FP7","grant_number":"303507"},{"grant_number":"RGP0042/2013","name":"Revealing the fundamental limits of cell growth","_id":"25EB3A80-B435-11E9-9278-68D0E5697425"}],"quality_controlled":"1","doi":"10.1016/j.copbio.2017.02.013","pubrep_id":"801","scopus_import":"1","ec_funded":1,"article_processing_charge":"Yes (in subscription journal)","tmp":{"name":"Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0)","short":"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"},"article_type":"original","publication":"Current Opinion in Biotechnology","department":[{"_id":"ToBo"}],"publisher":"Elsevier","user_id":"c635000d-4b10-11ee-a964-aac5a93f6ac1","publist_id":"6364","title":"Toward a quantitative understanding of antibiotic resistance evolution","file":[{"date_created":"2019-01-18T09:57:57Z","access_level":"open_access","file_id":"5846","date_updated":"2019-01-18T09:57:57Z","file_size":858338,"relation":"main_file","content_type":"application/pdf","creator":"dernst","file_name":"2017_CurrentOpinion_Lukaciinova.pdf","success":1}],"day":"01","author":[{"first_name":"Marta","last_name":"Lukacisinova","id":"4342E402-F248-11E8-B48F-1D18A9856A87","orcid":"0000-0002-2519-8004","full_name":"Lukacisinova, Marta"},{"last_name":"Bollenbach","first_name":"Mark Tobias","full_name":"Bollenbach, Mark Tobias","id":"3E6DB97A-F248-11E8-B48F-1D18A9856A87","orcid":"0000-0003-4398-476X"}],"citation":{"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>.","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.","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>","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.” <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>."},"intvolume":"        46","related_material":{"record":[{"id":"6263","status":"public","relation":"dissertation_contains"}]},"status":"public","external_id":{"isi":["000408077400015"]},"ddc":["570"],"date_published":"2017-08-01T00:00:00Z","has_accepted_license":"1","oa":1,"publication_status":"published","_id":"1027","year":"2017","file_date_updated":"2019-01-18T09:57:57Z","date_created":"2018-12-11T11:49:45Z","volume":46,"date_updated":"2024-03-25T23:30:15Z","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"}],"month":"08","oa_version":"Published Version","type":"journal_article","page":"90 - 97"},{"issue":"11","language":[{"iso":"eng"}],"project":[{"_id":"25EB3A80-B435-11E9-9278-68D0E5697425","name":"Revealing the fundamental limits of cell growth","grant_number":"RGP0042/2013"},{"grant_number":"P27201-B22","name":"Revealing the mechanisms underlying drug interactions","call_identifier":"FWF","_id":"25E9AF9E-B435-11E9-9278-68D0E5697425"},{"grant_number":"303507","name":"Optimality principles in responses to antibiotics","call_identifier":"FP7","_id":"25E83C2C-B435-11E9-9278-68D0E5697425"}],"quality_controlled":"1","doi":"10.1371/journal.pbio.1002299","pubrep_id":"468","tmp":{"legal_code_url":"https://creativecommons.org/licenses/by/4.0/legalcode","image":"/images/cc_by.png","name":"Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)","short":"CC BY (4.0)"},"ec_funded":1,"scopus_import":1,"publication":"PLoS Biology","department":[{"_id":"ToBo"}],"publisher":"Public Library of Science","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","publist_id":"5547","article_number":"e1002299","title":"Quantifying the determinants of evolutionary dynamics leading to drug resistance","file":[{"checksum":"0e82e3279f50b15c6c170c042627802b","file_id":"4723","date_updated":"2020-07-14T12:45:07Z","access_level":"open_access","date_created":"2018-12-12T10:09:00Z","file_name":"IST-2016-468-v1+1_journal.pbio.1002299.pdf","creator":"system","file_size":1387760,"relation":"main_file","content_type":"application/pdf"}],"day":"18","author":[{"first_name":"Guillaume","last_name":"Chevereau","id":"424D78A0-F248-11E8-B48F-1D18A9856A87","full_name":"Chevereau, Guillaume"},{"first_name":"Marta","last_name":"Dravecka","full_name":"Dravecka, Marta","orcid":"0000-0002-2519-8004","id":"4342E402-F248-11E8-B48F-1D18A9856A87"},{"full_name":"Batur, Tugce","first_name":"Tugce","last_name":"Batur"},{"first_name":"Aysegul","last_name":"Guvenek","full_name":"Guvenek, Aysegul"},{"last_name":"Ayhan","first_name":"Dilay","full_name":"Ayhan, Dilay"},{"full_name":"Toprak, Erdal","first_name":"Erdal","last_name":"Toprak"},{"first_name":"Mark Tobias","last_name":"Bollenbach","full_name":"Bollenbach, Mark Tobias","id":"3E6DB97A-F248-11E8-B48F-1D18A9856A87","orcid":"0000-0003-4398-476X"}],"citation":{"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.","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>.","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>","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>.","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.","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>"},"related_material":{"record":[{"relation":"research_data","id":"9711","status":"public"},{"relation":"research_data","status":"public","id":"9765"},{"relation":"dissertation_contains","status":"public","id":"6263"}]},"intvolume":"        13","status":"public","date_published":"2015-11-18T00:00:00Z","ddc":["570"],"has_accepted_license":"1","oa":1,"publication_status":"published","_id":"1619","year":"2015","file_date_updated":"2020-07-14T12:45:07Z","date_created":"2018-12-11T11:53:04Z","volume":13,"month":"11","type":"journal_article","oa_version":"Published Version","date_updated":"2024-03-25T23:30:14Z","abstract":[{"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.","lang":"eng"}]},{"pubrep_id":"493","quality_controlled":"1","doi":"10.1016/j.mib.2015.05.008","project":[{"_id":"25E9AF9E-B435-11E9-9278-68D0E5697425","call_identifier":"FWF","name":"Revealing the mechanisms underlying drug interactions","grant_number":"P27201-B22"},{"name":"Optimality principles in responses to antibiotics","_id":"25E83C2C-B435-11E9-9278-68D0E5697425","call_identifier":"FP7","grant_number":"303507"},{"grant_number":"RGP0042/2013","name":"Revealing the fundamental limits of cell growth","_id":"25EB3A80-B435-11E9-9278-68D0E5697425"}],"language":[{"iso":"eng"}],"file":[{"file_id":"5277","date_updated":"2020-07-14T12:45:17Z","checksum":"1683bb0f42ef892a5b3b71a050d65d25","date_created":"2018-12-12T10:17:23Z","access_level":"open_access","file_name":"IST-2016-493-v1+1_1-s2.0-S1369527415000594-main.pdf","file_size":1047255,"relation":"main_file","content_type":"application/pdf","creator":"system"}],"day":"01","author":[{"last_name":"Bollenbach","first_name":"Mark Tobias","id":"3E6DB97A-F248-11E8-B48F-1D18A9856A87","orcid":"0000-0003-4398-476X","full_name":"Bollenbach, Mark Tobias"}],"publist_id":"5298","title":"Antimicrobial interactions: Mechanisms and implications for drug discovery and resistance evolution","department":[{"_id":"ToBo"}],"user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","publisher":"Elsevier","tmp":{"legal_code_url":"https://creativecommons.org/licenses/by/4.0/legalcode","image":"/images/cc_by.png","name":"Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)","short":"CC BY (4.0)"},"ec_funded":1,"scopus_import":1,"publication":"Current Opinion in Microbiology","has_accepted_license":"1","oa":1,"publication_status":"published","ddc":["570"],"date_published":"2015-06-01T00:00:00Z","status":"public","citation":{"short":"M.T. Bollenbach, Current Opinion in Microbiology 27 (2015) 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>.","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.” <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>.","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>"},"intvolume":"        27","oa_version":"Published Version","month":"06","type":"journal_article","abstract":[{"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.","lang":"eng"}],"date_updated":"2021-01-12T06:53:21Z","page":"1 - 9","file_date_updated":"2020-07-14T12:45:17Z","date_created":"2018-12-11T11:54:08Z","volume":27,"year":"2015","_id":"1810"},{"intvolume":"        11","citation":{"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>","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>.","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>","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.","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>.","short":"G. Chevereau, M.T. Bollenbach, Molecular Systems Biology 11 (2015)."},"status":"public","ddc":["570"],"date_published":"2015-04-01T00:00:00Z","publication_status":"published","oa":1,"has_accepted_license":"1","_id":"1823","year":"2015","volume":11,"file_date_updated":"2020-07-14T12:45:17Z","date_created":"2018-12-11T11:54:12Z","month":"04","type":"journal_article","oa_version":"Published Version","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"}],"date_updated":"2021-01-12T06:53:26Z","issue":"4","language":[{"iso":"eng"}],"project":[{"grant_number":"P27201-B22","call_identifier":"FWF","_id":"25E9AF9E-B435-11E9-9278-68D0E5697425","name":"Revealing the mechanisms underlying drug interactions"},{"grant_number":"RGP0042/2013","name":"Revealing the fundamental limits of cell growth","_id":"25EB3A80-B435-11E9-9278-68D0E5697425"},{"grant_number":"303507","_id":"25E83C2C-B435-11E9-9278-68D0E5697425","call_identifier":"FP7","name":"Optimality principles in responses to antibiotics"}],"doi":"10.15252/msb.20156098","quality_controlled":"1","pubrep_id":"395","publication":"Molecular Systems Biology","tmp":{"legal_code_url":"https://creativecommons.org/licenses/by/4.0/legalcode","image":"/images/cc_by.png","name":"Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)","short":"CC BY (4.0)"},"scopus_import":1,"ec_funded":1,"user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","publisher":"Nature Publishing Group","department":[{"_id":"ToBo"}],"article_number":"807","title":"Systematic discovery of drug interaction mechanisms","publist_id":"5283","author":[{"id":"424D78A0-F248-11E8-B48F-1D18A9856A87","full_name":"Chevereau, Guillaume","last_name":"Chevereau","first_name":"Guillaume"},{"first_name":"Mark Tobias","last_name":"Bollenbach","orcid":"0000-0003-4398-476X","id":"3E6DB97A-F248-11E8-B48F-1D18A9856A87","full_name":"Bollenbach, Mark Tobias"}],"file":[{"date_created":"2018-12-12T10:14:34Z","access_level":"open_access","file_id":"5087","date_updated":"2020-07-14T12:45:17Z","checksum":"4289b518fbe2166682fb1a1ef9b405f3","file_size":1273573,"content_type":"application/pdf","relation":"main_file","creator":"system","file_name":"IST-2015-395-v1+1_807.full.pdf"}],"day":"01"}]