{"department":[{"_id":"ToBo"}],"user_id":"c635000d-4b10-11ee-a964-aac5a93f6ac1","title":"Genetic determinants of antibiotic resistance evolution","month":"12","_id":"6263","citation":{"short":"M. Lukacisinova, Genetic Determinants of Antibiotic Resistance Evolution, Institute of Science and Technology Austria, 2018.","chicago":"Lukacisinova, Marta. “Genetic Determinants of Antibiotic Resistance Evolution.” Institute of Science and Technology Austria, 2018. <a href=\"https://doi.org/10.15479/AT:ISTA:th1072\">https://doi.org/10.15479/AT:ISTA:th1072</a>.","ista":"Lukacisinova M. 2018. Genetic determinants of antibiotic resistance evolution. Institute of Science and Technology Austria.","ieee":"M. Lukacisinova, “Genetic determinants of antibiotic resistance evolution,” Institute of Science and Technology Austria, 2018.","ama":"Lukacisinova M. Genetic determinants of antibiotic resistance evolution. 2018. doi:<a href=\"https://doi.org/10.15479/AT:ISTA:th1072\">10.15479/AT:ISTA:th1072</a>","mla":"Lukacisinova, Marta. <i>Genetic Determinants of Antibiotic Resistance Evolution</i>. Institute of Science and Technology Austria, 2018, doi:<a href=\"https://doi.org/10.15479/AT:ISTA:th1072\">10.15479/AT:ISTA:th1072</a>.","apa":"Lukacisinova, M. (2018). <i>Genetic determinants of antibiotic resistance evolution</i>. Institute of Science and Technology Austria. <a href=\"https://doi.org/10.15479/AT:ISTA:th1072\">https://doi.org/10.15479/AT:ISTA:th1072</a>"},"page":"91","ddc":["570","576","579"],"year":"2018","status":"public","oa_version":"Published Version","related_material":{"record":[{"status":"public","relation":"part_of_dissertation","id":"1619"},{"relation":"part_of_dissertation","id":"696","status":"public"},{"status":"public","relation":"part_of_dissertation","id":"1027"}]},"publication_identifier":{"issn":["2663-337X"]},"type":"dissertation","degree_awarded":"PhD","author":[{"full_name":"Lukacisinova, Marta","orcid":"0000-0002-2519-8004","first_name":"Marta","id":"4342E402-F248-11E8-B48F-1D18A9856A87","last_name":"Lukacisinova"}],"supervisor":[{"orcid":"0000-0003-4398-476X","full_name":"Bollenbach, Tobias","last_name":"Bollenbach","id":"3E6DB97A-F248-11E8-B48F-1D18A9856A87","first_name":"Tobias"}],"publication_status":"published","date_published":"2018-12-28T00:00:00Z","alternative_title":["ISTA Thesis"],"date_updated":"2023-09-22T09:20:37Z","article_processing_charge":"No","has_accepted_license":"1","day":"28","file":[{"embargo":"2020-01-25","creator":"dernst","date_created":"2019-04-09T13:49:24Z","file_size":5656866,"date_updated":"2021-02-11T11:17:17Z","file_name":"2018_Thesis_Lukacisinova.pdf","content_type":"application/pdf","file_id":"6264","checksum":"fc60585c9eaad868ac007004ef130908","relation":"main_file","access_level":"open_access"},{"embargo_to":"open_access","date_updated":"2020-07-14T12:47:25Z","date_created":"2019-04-09T13:49:23Z","file_size":5168054,"creator":"dernst","access_level":"closed","relation":"source_file","checksum":"264057ec0a92ab348cc83b41f021ba92","file_id":"6265","content_type":"application/vnd.openxmlformats-officedocument.wordprocessingml.document","file_name":"2018_Thesis_Lukacisinova_source.docx"}],"doi":"10.15479/AT:ISTA:th1072","file_date_updated":"2021-02-11T11:17:17Z","oa":1,"publisher":"Institute of Science and Technology Austria","date_created":"2019-04-09T13:57:15Z","language":[{"iso":"eng"}],"acknowledged_ssus":[{"_id":"M-Shop"},{"_id":"LifeSc"}],"abstract":[{"text":"Antibiotic  resistance  can  emerge  spontaneously  through  genomic  mutation  and  render treatment   ineffective.   To   counteract   this process, in   addition   to   the   discovery   and description of resistance mechanisms,a deeper understanding of resistanceevolvabilityand its  determinantsis  needed. To address  this challenge,  this  thesisuncoversnew  genetic determinants   of   resistance   evolvability   using   a   customized   robotic   setup, exploressystematic   ways   in   which   resistance   evolution   is   perturbed   due   to dose-responsecharacteristics  of  drugs and  mutation  rate  differences,and  mathematically  investigates the evolutionary fate of one specific type of evolvability modifier -a stress-induced mutagenesis allele.We  find  severalgenes  which  strongly  inhibit  or  potentiate  resistance  evolution.  In  order to identify   them,   we   first developedan   automated   high-throughput   feedback-controlled protocol whichkeeps the population size and selection pressure approximately constant for hundreds  of  cultures  by  dynamically  re-diluting  the  cultures  and  adjusting  the  antibiotic concentration.  We  implementedthis  protocol  on  a  customized  liquid  handling  robot  and propagated  100  different  gene  deletion  strains  of Escherichia  coliin  triplicate  for  over  100 generations  in  tetracycline  and  in  chloramphenicol,  and  comparedtheir  adaptation  rates.We  find  a  diminishing  returns  pattern,  where  initially  sensitive  strains  adapted  more compared to less sensitive ones.  Our data uncover that deletions of certain genes which do not  affect  mutation  rate,including  efflux  pump  components,  a  chaperone  and severalstructural  and regulatory  genes  can strongly  and  reproducibly  alterresistance  evolution. Sequencing   analysis of   evolved   populations   indicates   that   epistasis   with   resistance mutations  is  the  most  likelyexplanation. This  work  could  inspire  treatment  strategies  in which  targeted  inhibitors  of  evolvability  mechanisms  will  be  given  alongside  antibiotics  to slow down resistance evolution and extend theefficacy of antibiotics.We implemented  astochasticpopulation  genetics  model, toverifyways  in  which  general properties,  namely,  dose-response  characteristics  of  drugs  and  mutation  rates,  influence evolutionary  dynamics.  In  particular,  under  the  exposure  to  antibiotics  with  shallow  dose-response  curves,bacteria  have  narrower  distributions  of  fitness  effects  of  new  mutations. We  show  that in  silicothis  also  leads  to  slower  resistance  evolution.  We see and  confirm with experiments that increased mutation rates, apart from speeding up evolution, also leadto high reproducibility of phenotypic adaptation in a context of continually strong selection pressure.Knowledge  of  these  patterns  can  aid  in  predicting  the  dynamics  of  antibiotic resistance evolutionand adapting treatment schemes accordingly.Focusing on   a   previously   described   type   of   evolvability   modifier –a   stress-induced mutagenesis  allele –we  find  conditions  under  which  it  can  persist  in  a  population  under periodic  selectionakin  to  clinical  treatment. We  set  up  a  deterministic infinite  populationcontinuous  time  model  tracking  the  frequencies  of  a  mutator  and  resistance  allele  and evaluate  various  treatment  schemes  in  how  well  they  maintain  a stress-induced mutator allele. In particular,a high diversity  of stresses  is  crucial  for  the  persistence of the  mutator allele. This leads to a general trade-off where exactly those diversifying treatment schemes which  are  likely  to  decrease  levels  of  resistance  could  lead  to  stronger  selection  of  highly evolvable genotypes.In  the  long  run,  this  work  will  lead  to  a  deeper  understanding  of  the  genetic  and  cellular mechanisms involved in antibiotic resistance evolution and could inspire new strategies for slowing down its rate. ","lang":"eng"}]}