@article{8037, abstract = {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.}, author = {Lukacisinova, Marta and Fernando, Booshini and Bollenbach, Mark Tobias}, issn = {20411723}, journal = {Nature Communications}, publisher = {Springer Nature}, title = {{Highly parallel lab evolution reveals that epistasis can curb the evolution of antibiotic resistance}}, doi = {10.1038/s41467-020-16932-z}, volume = {11}, year = {2020}, } @phdthesis{6263, 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. }, author = {Lukacisinova, Marta}, issn = {2663-337X}, pages = {91}, publisher = {Institute of Science and Technology Austria}, title = {{Genetic determinants of antibiotic resistance evolution}}, doi = {10.15479/AT:ISTA:th1072}, year = {2018}, } @article{696, abstract = {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.}, author = {Lukacisinova, Marta and Novak, Sebastian and Paixao, Tiago}, issn = {1553734X}, journal = {PLoS Computational Biology}, number = {7}, publisher = {Public Library of Science}, title = {{Stress induced mutagenesis: Stress diversity facilitates the persistence of mutator genes}}, doi = {10.1371/journal.pcbi.1005609}, volume = {13}, year = {2017}, } @article{1027, abstract = {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.}, author = {Lukacisinova, Marta and Bollenbach, Mark Tobias}, journal = {Current Opinion in Biotechnology}, pages = {90 -- 97}, publisher = {Elsevier}, title = {{Toward a quantitative understanding of antibiotic resistance evolution}}, doi = {10.1016/j.copbio.2017.02.013}, volume = {46}, year = {2017}, } @misc{9849, abstract = {This text provides additional information about the model, a derivation of the analytic results in Eq (4), and details about simulations of an additional parameter set.}, author = {Lukacisinova, Marta and Novak, Sebastian and Paixao, Tiago}, publisher = {Public Library of Science}, title = {{Modelling and simulation details}}, doi = {10.1371/journal.pcbi.1005609.s001}, year = {2017}, } @misc{9850, abstract = {In this text, we discuss how a cost of resistance and the possibility of lethal mutations impact our model.}, author = {Lukacisinova, Marta and Novak, Sebastian and Paixao, Tiago}, publisher = {Public Library of Science}, title = {{Extensions of the model}}, doi = {10.1371/journal.pcbi.1005609.s002}, year = {2017}, } @misc{9851, abstract = {Based on the intuitive derivation of the dynamics of SIM allele frequency pM in the main text, we present a heuristic prediction for the long-term SIM allele frequencies with χ > 1 stresses and compare it to numerical simulations.}, author = {Lukacisinova, Marta and Novak, Sebastian and Paixao, Tiago}, publisher = {Public Library of Science}, title = {{Heuristic prediction for multiple stresses}}, doi = {10.1371/journal.pcbi.1005609.s003}, year = {2017}, } @misc{9852, abstract = {We show how different combination strategies affect the fraction of individuals that are multi-resistant.}, author = {Lukacisinova, Marta and Novak, Sebastian and Paixao, Tiago}, publisher = {Public Library of Science}, title = {{Resistance frequencies for different combination strategies}}, doi = {10.1371/journal.pcbi.1005609.s004}, year = {2017}, } @article{1619, abstract = {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.}, author = {Chevereau, Guillaume and Dravecka, Marta and Batur, Tugce and Guvenek, Aysegul and Ayhan, Dilay and Toprak, Erdal and Bollenbach, Mark Tobias}, journal = {PLoS Biology}, number = {11}, publisher = {Public Library of Science}, title = {{Quantifying the determinants of evolutionary dynamics leading to drug resistance}}, doi = {10.1371/journal.pbio.1002299}, volume = {13}, year = {2015}, } @article{1509, abstract = {The Auxin Binding Protein1 (ABP1) has been identified based on its ability to bind auxin with high affinity and studied for a long time as a prime candidate for the extracellular auxin receptor responsible for mediating in particular the fast non-transcriptional auxin responses. However, the contradiction between the embryo-lethal phenotypes of the originally described Arabidopsis T-DNA insertional knock-out alleles (abp1-1 and abp1-1s) and the wild type-like phenotypes of other recently described loss-of-function alleles (abp1-c1 and abp1-TD1) questions the biological importance of ABP1 and relevance of the previous genetic studies. Here we show that there is no hidden copy of the ABP1 gene in the Arabidopsis genome but the embryo-lethal phenotypes of abp1-1 and abp1-1s alleles are very similar to the knock-out phenotypes of the neighboring gene, BELAYA SMERT (BSM). Furthermore, the allelic complementation test between bsm and abp1 alleles shows that the embryo-lethality in the abp1-1 and abp1-1s alleles is caused by the off-target disruption of the BSM locus by the T-DNA insertions. This clarifies the controversy of different phenotypes among published abp1 knock-out alleles and asks for reflections on the developmental role of ABP1.}, author = {Michalko, Jaroslav and Dravecka, Marta and Bollenbach, Tobias and Friml, Jirí}, journal = {F1000 Research }, publisher = {F1000 Research}, title = {{Embryo-lethal phenotypes in early abp1 mutants are due to disruption of the neighboring BSM gene}}, doi = {10.12688/f1000research.7143.1}, volume = {4}, year = {2015}, } @misc{9711, author = {Chevereau, Guillaume and Lukacisinova, Marta and Batur, Tugce and Guvenek, Aysegul and Ayhan, Dilay Hazal and Toprak, Erdal and Bollenbach, Mark Tobias}, publisher = {Public Library of Science}, title = {{Excel file containing the raw data for all figures}}, doi = {10.1371/journal.pbio.1002299.s001}, year = {2015}, } @misc{9765, author = {Chevereau, Guillaume and Lukacisinova, Marta and Batur, Tugce and Guvenek, Aysegul and Ayhan, Dilay Hazal and Toprak, Erdal and Bollenbach, Mark Tobias}, publisher = {Public Library of Science}, title = {{Gene ontology enrichment analysis for the most sensitive gene deletion strains for all drugs}}, doi = {10.1371/journal.pbio.1002299.s008}, year = {2015}, }