@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}, } @phdthesis{818, abstract = {Antibiotics have diverse effects on bacteria, including massive changes in bacterial gene expression. Whereas the gene expression changes under many antibiotics have been measured, the temporal organization of these responses and their dependence on the bacterial growth rate are unclear. As described in Chapter 1, we quantified the temporal gene expression changes in the bacterium Escherichia coli in response to the sudden exposure to antibiotics using a fluorescent reporter library and a robotic system. Our data show temporally structured gene expression responses, with response times for individual genes ranging from tens of minutes to several hours. We observed that many stress response genes were activated in response to antibiotics. As certain stress responses cross-protect bacteria from other stressors, we then asked whether cellular responses to antibiotics have a similar protective role in Chapter 2. Indeed, we found that the trimethoprim-induced acid stress response protects bacteria from subsequent acid stress. We combined microfluidics with time-lapse imaging to monitor survival, intracellular pH, and acid stress response in single cells. This approach revealed that the variable expression of the acid resistance operon gadBC strongly correlates with single-cell survival time. Cells with higher gadBC expression following trimethoprim maintain higher intracellular pH and survive the acid stress longer. Overall, we provide a way to identify single-cell cross-protection between antibiotics and environmental stressors from temporal gene expression data, and show how antibiotics can increase bacterial fitness in changing environments. While gene expression changes to antibiotics show a clear temporal structure at the population-level, it is unclear whether this clear temporal order is followed by every single cell. Using dual-reporter strains described in Chapter 3, we measured gene expression dynamics of promoter pairs in the same cells using microfluidics and microscopy. Chapter 4 shows that the oxidative stress response and the DNA stress response showed little timing variability and a clear temporal order under the antibiotic nitrofurantoin. In contrast, the acid stress response under trimethoprim ran independently from all other activated response programs including the DNA stress response, which showed particularly high timing variability in this stress condition. In summary, this approach provides insight into the temporal organization of gene expression programs at the single-cell level and suggests dependencies between response programs and the underlying variability-introducing mechanisms. Altogether, this work advances our understanding of the diverse effects that antibiotics have on bacteria. These results were obtained by taking into account gene expression dynamics, which allowed us to identify general principles, molecular mechanisms, and dependencies between genes. Our findings may have implications for infectious disease treatments, and microbial communities in the human body and in nature. }, author = {Mitosch, Karin}, issn = {2663-337X}, pages = {113}, publisher = {Institute of Science and Technology Austria}, title = {{Timing, variability and cross-protection in bacteria – insights from dynamic gene expression responses to antibiotics}}, doi = {10.15479/AT:ISTA:th_862}, year = {2017}, } @article{666, abstract = {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.}, author = {Mitosch, Karin and Rieckh, Georg and Bollenbach, Tobias}, issn = {24054712}, journal = {Cell Systems}, number = {4}, pages = {393 -- 403}, publisher = {Cell Press}, title = {{Noisy response to antibiotic stress predicts subsequent single cell survival in an acidic environment}}, doi = {10.1016/j.cels.2017.03.001}, volume = {4}, year = {2017}, } @article{822, abstract = {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. }, author = {De Vos, Marjon and Zagórski, Marcin P and Mcnally, Alan and Bollenbach, Mark Tobias}, issn = {00278424}, journal = {PNAS}, number = {40}, pages = {10666 -- 10671}, publisher = {National Academy of Sciences}, title = {{Interaction networks, ecological stability, and collective antibiotic tolerance in polymicrobial infections}}, doi = {10.1073/pnas.1713372114}, volume = {114}, year = {2017}, } @misc{5563, abstract = {MATLAB code and processed datasets available for reproducing the results in: Lukačišin, M.*, Landon, M.*, Jajoo, R*. (2016) Sequence-Specific Thermodynamic Properties of Nucleic Acids Influence Both Transcriptional Pausing and Backtracking in Yeast. *equal contributions}, author = {Lukacisin, Martin}, publisher = {Institute of Science and Technology Austria}, title = {{MATLAB analysis code for 'Sequence-Specific Thermodynamic Properties of Nucleic Acids Influence Both Transcriptional Pausing and Backtracking in Yeast'}}, doi = {10.15479/AT:ISTA:64}, year = {2017}, } @article{1029, abstract = {RNA Polymerase II pauses and backtracks during transcription, with many consequences for gene expression and cellular physiology. Here, we show that the energy required to melt double-stranded nucleic acids in the transcription bubble predicts pausing in Saccharomyces cerevisiae far more accurately than nucleosome roadblocks do. In addition, the same energy difference also determines when the RNA polymerase backtracks instead of continuing to move forward. This data-driven model corroborates—in a genome wide and quantitative manner—previous evidence that sequence-dependent thermodynamic features of nucleic acids influence both transcriptional pausing and backtracking.}, author = {Lukacisin, Martin and Landon, Matthieu and Jajoo, Rishi}, issn = {19326203}, journal = {PLoS One}, number = {3}, publisher = {Public Library of Science}, title = {{Sequence-specific thermodynamic properties of nucleic acids influence both transcriptional pausing and backtracking in yeast}}, doi = {10.1371/journal.pone.0174066}, volume = {12}, year = {2017}, } @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}, }