---
res:
  bibo_abstract:
  - 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. @eng
  bibo_authorlist:
  - foaf_Person:
      foaf_givenName: Marta
      foaf_name: Lukacisinova, Marta
      foaf_surname: Lukacisinova
      foaf_workInfoHomepage: http://www.librecat.org/personId=4342E402-F248-11E8-B48F-1D18A9856A87
    orcid: 0000-0002-2519-8004
  bibo_doi: 10.15479/AT:ISTA:th1072
  dct_date: 2018^xs_gYear
  dct_isPartOf:
  - http://id.crossref.org/issn/2663-337X
  dct_language: eng
  dct_publisher: Institute of Science and Technology Austria@
  dct_title: Genetic determinants of antibiotic resistance evolution@
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