{"day":"18","language":[{"iso":"eng"}],"author":[{"full_name":"Lukacisinova, Marta","id":"4342E402-F248-11E8-B48F-1D18A9856A87","first_name":"Marta","orcid":"0000-0002-2519-8004","last_name":"Lukacisinova"},{"orcid":"0000-0002-2519-824X","last_name":"Novak","first_name":"Sebastian","id":"461468AE-F248-11E8-B48F-1D18A9856A87","full_name":"Novak, Sebastian"},{"full_name":"Paixao, Tiago","id":"2C5658E6-F248-11E8-B48F-1D18A9856A87","first_name":"Tiago","orcid":"0000-0003-2361-3953","last_name":"Paixao"}],"oa":1,"abstract":[{"lang":"eng","text":"Mutator strains are expected to evolve when the availability and effect of beneficial mutations are high enough to counteract the disadvantage from deleterious mutations that will inevitably accumulate. As the population becomes more adapted to its environment, both availability and effect of beneficial mutations necessarily decrease and mutation rates are predicted to decrease. It has been shown that certain molecular mechanisms can lead to increased mutation rates when the organism finds itself in a stressful environment. While this may be a correlated response to other functions, it could also be an adaptive mechanism, raising mutation rates only when it is most advantageous. Here, we use a mathematical model to investigate the plausibility of the adaptive hypothesis. We show that such a mechanism can be mantained if the population is subjected to diverse stresses. By simulating various antibiotic treatment schemes, we find that combination treatments can reduce the effectiveness of second-order selection on stress-induced mutagenesis. We discuss the implications of our results to strategies of antibiotic therapy."}],"year":"2017","scopus_import":1,"quality_controlled":"1","date_published":"2017-07-18T00:00:00Z","volume":13,"has_accepted_license":"1","tmp":{"name":"Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)","legal_code_url":"https://creativecommons.org/licenses/by/4.0/legalcode","short":"CC BY (4.0)","image":"/images/cc_by.png"},"article_number":"e1005609","citation":{"ista":"Lukacisinova M, Novak S, Paixao T. 2017. Stress induced mutagenesis: Stress diversity facilitates the persistence of mutator genes. PLoS Computational Biology. 13(7), e1005609.","mla":"Lukacisinova, Marta, et al. “Stress Induced Mutagenesis: Stress Diversity Facilitates the Persistence of Mutator Genes.” PLoS Computational Biology, vol. 13, no. 7, e1005609, Public Library of Science, 2017, doi:10.1371/journal.pcbi.1005609.","apa":"Lukacisinova, M., Novak, S., & Paixao, T. (2017). Stress induced mutagenesis: Stress diversity facilitates the persistence of mutator genes. PLoS Computational Biology. Public Library of Science. https://doi.org/10.1371/journal.pcbi.1005609","ama":"Lukacisinova M, Novak S, Paixao T. Stress induced mutagenesis: Stress diversity facilitates the persistence of mutator genes. PLoS Computational Biology. 2017;13(7). doi:10.1371/journal.pcbi.1005609","chicago":"Lukacisinova, Marta, Sebastian Novak, and Tiago Paixao. “Stress Induced Mutagenesis: Stress Diversity Facilitates the Persistence of Mutator Genes.” PLoS Computational Biology. Public Library of Science, 2017. https://doi.org/10.1371/journal.pcbi.1005609.","short":"M. Lukacisinova, S. Novak, T. Paixao, PLoS Computational Biology 13 (2017).","ieee":"M. Lukacisinova, S. Novak, and T. Paixao, “Stress induced mutagenesis: Stress diversity facilitates the persistence of mutator genes,” PLoS Computational Biology, vol. 13, no. 7. Public Library of Science, 2017."},"related_material":{"record":[{"id":"9849","relation":"research_data","status":"public"},{"id":"9850","status":"public","relation":"research_data"},{"id":"9851","status":"public","relation":"research_data"},{"status":"public","relation":"research_data","id":"9852"},{"relation":"dissertation_contains","status":"public","id":"6263"}]},"title":"Stress induced mutagenesis: Stress diversity facilitates the persistence of mutator genes","intvolume":" 13","ddc":["576"],"publication_identifier":{"issn":["1553734X"]},"issue":"7","publication":"PLoS Computational Biology","publisher":"Public Library of Science","date_created":"2018-12-11T11:47:58Z","month":"07","publication_status":"published","pubrep_id":"894","_id":"696","file":[{"file_size":3775716,"date_updated":"2020-07-14T12:47:46Z","creator":"system","access_level":"open_access","date_created":"2018-12-12T10:15:01Z","file_name":"IST-2017-894-v1+1_journal.pcbi.1005609.pdf","checksum":"9143c290fa6458ed2563bff4b295554a","file_id":"5117","relation":"main_file","content_type":"application/pdf"}],"status":"public","article_type":"original","doi":"10.1371/journal.pcbi.1005609","oa_version":"Published Version","department":[{"_id":"ToBo"},{"_id":"NiBa"},{"_id":"CaGu"}],"publist_id":"7004","project":[{"name":"Speed of Adaptation in Population Genetics and Evolutionary Computation","_id":"25B1EC9E-B435-11E9-9278-68D0E5697425","grant_number":"618091","call_identifier":"FP7"}],"user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","date_updated":"2024-05-08T22:30:28Z","type":"journal_article","file_date_updated":"2020-07-14T12:47:46Z","ec_funded":1}