[{"ddc":["571","530","570"],"page":"271","publisher":"Institute of Science and Technology Austria","doi":"10.15479/AT:ISTA:8657","alternative_title":["ISTA Thesis"],"article_processing_charge":"No","type":"dissertation","date_updated":"2023-09-07T13:20:48Z","_id":"8657","status":"public","degree_awarded":"PhD","acknowledgement":"I thank Life Science Facilities for their continuous support with providing top-notch laboratory materials, keeping the devices humming, and coordinating the repairs and building of custom-designed laboratory equipment with the MIBA Machine shop.","date_published":"2020-10-14T00:00:00Z","related_material":{"record":[{"status":"public","relation":"part_of_dissertation","id":"7673"},{"relation":"part_of_dissertation","status":"public","id":"8250"}]},"year":"2020","abstract":[{"lang":"eng","text":"Synthesis of proteins – translation – is a fundamental process of life. Quantitative studies anchor translation into the context of bacterial physiology and reveal several mathematical relationships, called “growth laws,” which capture physiological feedbacks between protein synthesis and cell growth. Growth laws describe the dependency of the ribosome abundance as a function of growth rate, which can change depending on the growth conditions. Perturbations of translation reveal that bacteria employ a compensatory strategy in which the reduced translation capability results in increased expression of the translation machinery.\r\nPerturbations of translation are achieved in various ways; clinically interesting is the application of translation-targeting antibiotics – translation inhibitors. The antibiotic effects on bacterial physiology are often poorly understood. Bacterial responses to two or more simultaneously applied antibiotics are even more puzzling. The combined antibiotic effect determines the type of drug interaction, which ranges from synergy (the effect is stronger than expected) to antagonism (the effect is weaker) and suppression (one of the drugs loses its potency).\r\nIn the first part of this work, we systematically measure the pairwise interaction network for translation inhibitors that interfere with different steps in translation. We find that the interactions are surprisingly diverse and tend to be more antagonistic. To explore the underlying mechanisms, we begin with a minimal biophysical model of combined antibiotic action. We base this model on the kinetics of antibiotic uptake and binding together with the physiological response described by the growth laws. The biophysical model explains some drug interactions, but not all; it specifically fails to predict suppression.\r\nIn the second part of this work, we hypothesize that elusive suppressive drug interactions result from the interplay between ribosomes halted in different stages of translation. To elucidate this putative mechanism of drug interactions between translation inhibitors, we generate translation bottlenecks genetically using in- ducible control of translation factors that regulate well-defined translation cycle steps. These perturbations accurately mimic antibiotic action and drug interactions, supporting that the interplay of different translation bottlenecks partially causes these interactions.\r\nWe extend this approach by varying two translation bottlenecks simultaneously. This approach reveals the suppression of translocation inhibition by inhibited translation. We rationalize this effect by modeling dense traffic of ribosomes that move on transcripts in a translation factor-mediated manner. This model predicts a dissolution of traffic jams caused by inhibited translocation when the density of ribosome traffic is reduced by lowered initiation. We base this model on the growth laws and quantitative relationships between different translation and growth parameters.\r\nIn the final part of this work, we describe a set of tools aimed at quantification of physiological and translation parameters. We further develop a simple model that directly connects the abundance of a translation factor with the growth rate, which allows us to extract physiological parameters describing initiation. We demonstrate the development of tools for measuring translation rate.\r\nThis thesis showcases how a combination of high-throughput growth rate mea- surements, genetics, and modeling can reveal mechanisms of drug interactions. Furthermore, by a gradual transition from combinations of antibiotics to precise genetic interventions, we demonstrated the equivalency between genetic and chemi- cal perturbations of translation. These findings tile the path for quantitative studies of antibiotic combinations and illustrate future approaches towards the quantitative description of translation."}],"acknowledged_ssus":[{"_id":"LifeSc"},{"_id":"M-Shop"}],"has_accepted_license":"1","publication_identifier":{"isbn":["978-3-99078-011-4"],"issn":["2663-337X"]},"publication_status":"published","file_date_updated":"2021-10-07T22:30:03Z","title":"Perturbations of protein synthesis: from antibiotics to genetics and physiology","oa_version":"Published Version","author":[{"first_name":"Bor","orcid":"0000-0001-6041-254X","last_name":"Kavcic","full_name":"Kavcic, Bor","id":"350F91D2-F248-11E8-B48F-1D18A9856A87"}],"day":"14","date_created":"2020-10-13T16:46:14Z","language":[{"iso":"eng"}],"oa":1,"user_id":"c635000d-4b10-11ee-a964-aac5a93f6ac1","citation":{"short":"B. Kavcic, Perturbations of Protein Synthesis: From Antibiotics to Genetics and Physiology, Institute of Science and Technology Austria, 2020.","ieee":"B. Kavcic, “Perturbations of protein synthesis: from antibiotics to genetics and physiology,” Institute of Science and Technology Austria, 2020.","ama":"Kavcic B. Perturbations of protein synthesis: from antibiotics to genetics and physiology. 2020. doi:<a href=\"https://doi.org/10.15479/AT:ISTA:8657\">10.15479/AT:ISTA:8657</a>","mla":"Kavcic, Bor. <i>Perturbations of Protein Synthesis: From Antibiotics to Genetics and Physiology</i>. Institute of Science and Technology Austria, 2020, doi:<a href=\"https://doi.org/10.15479/AT:ISTA:8657\">10.15479/AT:ISTA:8657</a>.","apa":"Kavcic, B. (2020). <i>Perturbations of protein synthesis: from antibiotics to genetics and physiology</i>. Institute of Science and Technology Austria. <a href=\"https://doi.org/10.15479/AT:ISTA:8657\">https://doi.org/10.15479/AT:ISTA:8657</a>","ista":"Kavcic B. 2020. Perturbations of protein synthesis: from antibiotics to genetics and physiology. Institute of Science and Technology Austria.","chicago":"Kavcic, Bor. “Perturbations of Protein Synthesis: From Antibiotics to Genetics and Physiology.” Institute of Science and Technology Austria, 2020. <a href=\"https://doi.org/10.15479/AT:ISTA:8657\">https://doi.org/10.15479/AT:ISTA:8657</a>."},"month":"10","supervisor":[{"id":"3D494DCA-F248-11E8-B48F-1D18A9856A87","full_name":"Tkačik, Gašper","last_name":"Tkačik","orcid":"0000-0002-6699-1455","first_name":"Gašper"},{"orcid":"0000-0003-4398-476X","first_name":"Mark Tobias","last_name":"Bollenbach","full_name":"Bollenbach, Mark Tobias","id":"3E6DB97A-F248-11E8-B48F-1D18A9856A87"}],"file":[{"creator":"bkavcic","embargo":"2021-10-06","date_updated":"2021-10-07T22:30:03Z","file_size":52636162,"date_created":"2020-10-15T06:41:20Z","file_id":"8663","content_type":"application/pdf","access_level":"open_access","file_name":"kavcicB_thesis202009.pdf","checksum":"d708ecd62b6fcc3bc1feb483b8dbe9eb","relation":"main_file"},{"embargo_to":"open_access","file_id":"8664","date_updated":"2021-10-07T22:30:03Z","creator":"bkavcic","date_created":"2020-10-15T06:41:53Z","file_size":321681247,"checksum":"bb35f2352a04db19164da609f00501f3","relation":"source_file","content_type":"application/zip","access_level":"closed","file_name":"2020b.zip"}],"department":[{"_id":"GaTk"}]},{"status":"public","extern":"1","date_published":"2019-05-09T00:00:00Z","year":"2019","related_material":{"record":[{"relation":"part_of_dissertation","status":"public","id":"1029"}]},"page":"103","ddc":["570"],"doi":"10.15479/AT:ISTA:6392","alternative_title":["IST Austria Thesis"],"publisher":"IST Austria","date_updated":"2023-09-22T09:19:41Z","_id":"6392","type":"dissertation","oa":1,"language":[{"iso":"eng"}],"citation":{"ama":"Lukacisin M. Quantitative investigation of gene expression principles through combinatorial drug perturbation and theory. 2019. doi:<a href=\"https://doi.org/10.15479/AT:ISTA:6392\">10.15479/AT:ISTA:6392</a>","ieee":"M. Lukacisin, “Quantitative investigation of gene expression principles through combinatorial drug perturbation and theory,” IST Austria, 2019.","short":"M. Lukacisin, Quantitative Investigation of Gene Expression Principles through Combinatorial Drug Perturbation and Theory, IST Austria, 2019.","ista":"Lukacisin M. 2019. Quantitative investigation of gene expression principles through combinatorial drug perturbation and theory. IST Austria.","chicago":"Lukacisin, Martin. “Quantitative Investigation of Gene Expression Principles through Combinatorial Drug Perturbation and Theory.” IST Austria, 2019. <a href=\"https://doi.org/10.15479/AT:ISTA:6392\">https://doi.org/10.15479/AT:ISTA:6392</a>.","apa":"Lukacisin, M. (2019). <i>Quantitative investigation of gene expression principles through combinatorial drug perturbation and theory</i>. IST Austria. <a href=\"https://doi.org/10.15479/AT:ISTA:6392\">https://doi.org/10.15479/AT:ISTA:6392</a>","mla":"Lukacisin, Martin. <i>Quantitative Investigation of Gene Expression Principles through Combinatorial Drug Perturbation and Theory</i>. IST Austria, 2019, doi:<a href=\"https://doi.org/10.15479/AT:ISTA:6392\">10.15479/AT:ISTA:6392</a>."},"user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","supervisor":[{"last_name":"Bollenbach","full_name":"Bollenbach, Mark Tobias","id":"3E6DB97A-F248-11E8-B48F-1D18A9856A87","orcid":"0000-0003-4398-476X","first_name":"Mark Tobias"}],"month":"05","department":[{"_id":"ToBo"}],"file":[{"relation":"hidden","checksum":"829bda074444857c7935171237bb7c0c","file_name":"Thesis_Draft_v3.4Final.docx","access_level":"closed","content_type":"application/vnd.openxmlformats-officedocument.wordprocessingml.document","file_id":"6409","embargo_to":"open_access","file_size":43740796,"date_created":"2019-05-10T13:51:49Z","creator":"mlukacisin","date_updated":"2020-07-14T12:47:29Z"},{"file_id":"6410","date_created":"2019-05-10T14:13:42Z","file_size":35228388,"embargo":"2020-04-17","creator":"mlukacisin","date_updated":"2021-02-11T11:17:16Z","relation":"main_file","checksum":"56cb5e97f5f8fc41692401b53832d8e0","file_name":"Thesis_Draft_v3.4FinalA.pdf","access_level":"open_access","content_type":"application/pdf"}],"has_accepted_license":"1","abstract":[{"lang":"eng","text":"The regulation of gene expression is one of the most fundamental processes in living systems. In recent years, thanks to advances in sequencing technology and automation, it has become possible to study gene expression quantitatively, genome-wide and in high-throughput. This leads to the possibility of exploring changes in gene expression in the context of many external perturbations and their combinations, and thus of characterising the basic principles governing gene regulation. In this thesis, I present quantitative experimental approaches to studying transcriptional and protein level changes in response to combinatorial drug treatment, as well as a theoretical data-driven approach to analysing thermodynamic principles guiding transcription of protein coding genes.  \r\nIn the first part of this work, I present a novel methodological framework for quantifying gene expression changes in drug combinations, termed isogrowth profiling. External perturbations through small molecule drugs influence the growth rate of the cell, leading to wide-ranging changes in cellular physiology and gene expression. This confounds the gene expression changes specifically elicited by the particular drug. Combinatorial perturbations, owing to the increased stress they exert, influence the growth rate even more strongly and hence suffer the convolution problem to a greater extent when measuring gene expression changes. Isogrowth profiling is a way to experimentally abstract non-specific, growth rate related changes, by performing the measurement using varying ratios of two drugs at such concentrations that the overall inhibition rate is constant. Using a robotic setup for automated high-throughput re-dilution culture of Saccharomyces cerevisiae, the budding yeast, I investigate all pairwise interactions of four small molecule drugs through sequencing RNA along a growth isobole. Through principal component analysis, I demonstrate here that isogrowth profiling can uncover drug-specific as well as drug-interaction-specific gene expression changes. I show that drug-interaction-specific gene expression changes can be used for prediction of higher-order drug interactions. I propose a simplified generalised framework of isogrowth profiling, with few measurements needed for each drug pair, enabling the broad application of isogrowth profiling to high-throughput screening of inhibitors of cellular growth and beyond. Such high-throughput screenings of gene expression changes specific to pairwise drug interactions will be instrumental for predicting the higher-order interactions of the drugs.\r\n\r\nIn the second part of this work, I extend isogrowth profiling to single-cell measurements of gene expression, characterising population heterogeneity in the budding yeast in response to combinatorial drug perturbation while controlling for non-specific growth rate effects. Through flow cytometry of strains with protein products fused to green fluorescent protein, I discover multiple proteins with bi-modally distributed expression levels in the population in response to drug treatment. I characterize more closely the effect of an ionic stressor, lithium chloride, and find that it inhibits the splicing of mRNA, most strongly affecting ribosomal protein transcripts and leading to a bi-stable behaviour of a small ribosomal subunit protein Rps22B. Time-lapse microscopy of a microfluidic culture system revealed that the induced Rps22B heterogeneity leads to preferential survival of Rps22B-low cells after long starvation, but to preferential proliferation of Rps22B-high cells after short starvation. Overall, this suggests that yeast cells might use splicing of ribosomal genes for bet-hedging in fluctuating environments. I give specific examples of how further exploration of cellular heterogeneity in yeast in response to external perturbation has the potential to reveal yet-undiscovered gene regulation circuitry.\r\n\r\nIn the last part of this thesis, a re-analysis of a published sequencing dataset of nascent elongating transcripts is used to characterise the thermodynamic constraints for RNA polymerase II (RNAP) elongation. Population-level data on RNAP position throughout the transcribed genome with single nucleotide resolution are used to infer the sequence specific thermodynamic determinants of RNAP pausing and backtracking. This analysis reveals that the basepairing strength of the eight nucleotide-long RNA:DNA duplex relative to the basepairing strength of the same sequence when in DNA:DNA duplex, and the change in this quantity during RNA polymerase movement, is the key determinant of RNAP pausing. This is true for RNAP pausing while elongating, but also of RNAP pausing while backtracking and of the backtracking length. The quantitative dependence of RNAP pausing on basepairing energetics is used to infer the increase in pausing due to transcriptional mismatches, leading to a hypothesis that pervasive RNA polymerase II pausing is due to basepairing energetics, as an evolutionary cost for increased RNA polymerase II fidelity.\r\n\r\nThis work advances our understanding of the general principles governing gene expression, with the goal of making computational predictions of single-cell gene expression responses to combinatorial perturbations based on the individual perturbations possible. This ability would substantially facilitate the design of drug combination treatments and, in the long term, lead to our increased ability to more generally design targeted manipulations to any biological system. "}],"acknowledged_ssus":[{"_id":"LifeSc"},{"_id":"M-Shop"},{"_id":"Bio"}],"publication_status":"published","publication_identifier":{"isbn":["978-3-99078-001-5"],"issn":["2663-337X"]},"file_date_updated":"2021-02-11T11:17:16Z","author":[{"id":"298FFE8C-F248-11E8-B48F-1D18A9856A87","full_name":"Lukacisin, Martin","last_name":"Lukacisin","first_name":"Martin","orcid":"0000-0001-6549-4177"}],"day":"09","oa_version":"Published Version","title":"Quantitative investigation of gene expression principles through combinatorial drug perturbation and theory","date_created":"2019-05-09T19:53:00Z"},{"supervisor":[{"last_name":"Bollenbach","id":"3E6DB97A-F248-11E8-B48F-1D18A9856A87","full_name":"Bollenbach, Tobias","orcid":"0000-0003-4398-476X","first_name":"Tobias"}],"month":"12","department":[{"_id":"ToBo"}],"file":[{"file_id":"6264","embargo":"2020-01-25","date_updated":"2021-02-11T11:17:17Z","creator":"dernst","file_size":5656866,"date_created":"2019-04-09T13:49:24Z","checksum":"fc60585c9eaad868ac007004ef130908","relation":"main_file","access_level":"open_access","content_type":"application/pdf","file_name":"2018_Thesis_Lukacisinova.pdf"},{"file_id":"6265","embargo_to":"open_access","file_size":5168054,"date_created":"2019-04-09T13:49:23Z","date_updated":"2020-07-14T12:47:25Z","creator":"dernst","relation":"source_file","checksum":"264057ec0a92ab348cc83b41f021ba92","file_name":"2018_Thesis_Lukacisinova_source.docx","access_level":"closed","content_type":"application/vnd.openxmlformats-officedocument.wordprocessingml.document"}],"oa":1,"language":[{"iso":"eng"}],"citation":{"ista":"Lukacisinova M. 2018. Genetic determinants of antibiotic resistance evolution. Institute of Science and Technology Austria.","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>.","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>","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>.","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>","ieee":"M. Lukacisinova, “Genetic determinants of antibiotic resistance evolution,” Institute of Science and Technology Austria, 2018.","short":"M. Lukacisinova, Genetic Determinants of Antibiotic Resistance Evolution, Institute of Science and Technology Austria, 2018."},"user_id":"c635000d-4b10-11ee-a964-aac5a93f6ac1","day":"28","author":[{"orcid":"0000-0002-2519-8004","first_name":"Marta","last_name":"Lukacisinova","id":"4342E402-F248-11E8-B48F-1D18A9856A87","full_name":"Lukacisinova, Marta"}],"oa_version":"Published Version","title":"Genetic determinants of antibiotic resistance evolution","date_created":"2019-04-09T13:57:15Z","has_accepted_license":"1","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"}],"file_date_updated":"2021-02-11T11:17:17Z","publication_status":"published","publication_identifier":{"issn":["2663-337X"]},"year":"2018","related_material":{"record":[{"status":"public","relation":"part_of_dissertation","id":"1619"},{"relation":"part_of_dissertation","status":"public","id":"696"},{"relation":"part_of_dissertation","status":"public","id":"1027"}]},"degree_awarded":"PhD","status":"public","date_published":"2018-12-28T00:00:00Z","article_processing_charge":"No","alternative_title":["ISTA Thesis"],"doi":"10.15479/AT:ISTA:th1072","publisher":"Institute of Science and Technology Austria","_id":"6263","date_updated":"2023-09-22T09:20:37Z","type":"dissertation","page":"91","ddc":["570","576","579"]},{"status":"public","degree_awarded":"PhD","date_published":"2017-09-27T00:00:00Z","acknowledgement":"First of all, I would like to express great gratitude to my PhD supervisor Tobias Bollenbach. Through his open and trusting attitude I had the freedom to explore different scientific directions during this project, and follow the research lines of my interest. I am thankful for constructive and often extensive discussions and his support and commitment during the different stages of my PhD. I want to thank my committee members, Călin Guet, Terry Hwa and Nassos Typas for their interest and their valuable input to this project. Special thanks to Nassos for career guidance, and for accepting me in his lab. A big thank you goes to the past, present and affiliated members of the Bollenbach group: Guillaume Chevereau, Marjon de Vos, Marta Lukačišinová, Veronika Bierbaum, Qi Qin, Marcin Zagórski, Martin Lukačišin, Andreas Angermayr, Bor Kavčič, Julia Tischler, Dilay Ayhan, Jaroslav Ferenc, and Georg Rieckh. I enjoyed working and discussing with you very much and I will miss our lengthy group meetings, our inspiring journal clubs, and our common lunches. Special thanks to Bor for great mental and professional support during the hard months of thesis writing, and to Marta for very creative times during the beginning of our PhDs. May the ‘Bacterial Survival Guide’ decorate the walls of IST forever! A great thanks to my friend and collaborator Georg Rieckh for his enthusiasm and for getting so involved in these projects, for his endurance and for his company throughout the years. Thanks to the FriSBi crowd at IST Austria for interesting meetings and discussions. In particular I want to thank Magdalena Steinrück, and Anna Andersson for inspiring exchange, and enjoyable time together. Thanks to everybody who contributed to the cover for Cell Systems: The constructive input from Tobias Bollenbach, Bor Kavčič, Georg Rieckh, Marta Lukačišinová, and Sebastian Nozzi, and the professional implementation by the graphic designer Martina Markus from the University of Cologne. Thanks to all my office mates in the first floor Bertalanffy building throughout the years: for ensuring a pleasant working atmosphere, and for your company! In general, I want to thank all the people that make IST such a great environment, with the many possibilities to shape our own social and research environment. I want to thank my family for all kind of practical support during the years, and my second family in Argentina for their enthusiasm. Thanks to my brother Bernhard and my sister Martina for being great siblings, and to Helena and Valentin for the joy you brought to my life. My deep gratitude goes to Sebastian Nozzi, for constant support, patience, love and for believing in me. ","related_material":{"record":[{"status":"public","relation":"part_of_dissertation","id":"2001"},{"status":"public","relation":"part_of_dissertation","id":"666"}]},"year":"2017","publist_id":"6831","ddc":["571","579"],"page":"113","publisher":"Institute of Science and Technology Austria","doi":"10.15479/AT:ISTA:th_862","article_processing_charge":"No","alternative_title":["ISTA Thesis"],"type":"dissertation","date_updated":"2023-09-07T12:00:26Z","_id":"818","language":[{"iso":"eng"}],"pubrep_id":"862","oa":1,"user_id":"c635000d-4b10-11ee-a964-aac5a93f6ac1","citation":{"ista":"Mitosch K. 2017. Timing, variability and cross-protection in bacteria – insights from dynamic gene expression responses to antibiotics. Institute of Science and Technology Austria.","chicago":"Mitosch, Karin. “Timing, Variability and Cross-Protection in Bacteria – Insights from Dynamic Gene Expression Responses to Antibiotics.” Institute of Science and Technology Austria, 2017. <a href=\"https://doi.org/10.15479/AT:ISTA:th_862\">https://doi.org/10.15479/AT:ISTA:th_862</a>.","mla":"Mitosch, Karin. <i>Timing, Variability and Cross-Protection in Bacteria – Insights from Dynamic Gene Expression Responses to Antibiotics</i>. Institute of Science and Technology Austria, 2017, doi:<a href=\"https://doi.org/10.15479/AT:ISTA:th_862\">10.15479/AT:ISTA:th_862</a>.","apa":"Mitosch, K. (2017). <i>Timing, variability and cross-protection in bacteria – insights from dynamic gene expression responses to antibiotics</i>. Institute of Science and Technology Austria. <a href=\"https://doi.org/10.15479/AT:ISTA:th_862\">https://doi.org/10.15479/AT:ISTA:th_862</a>","ama":"Mitosch K. Timing, variability and cross-protection in bacteria – insights from dynamic gene expression responses to antibiotics. 2017. doi:<a href=\"https://doi.org/10.15479/AT:ISTA:th_862\">10.15479/AT:ISTA:th_862</a>","short":"K. Mitosch, Timing, Variability and Cross-Protection in Bacteria – Insights from Dynamic Gene Expression Responses to Antibiotics, Institute of Science and Technology Austria, 2017.","ieee":"K. Mitosch, “Timing, variability and cross-protection in bacteria – insights from dynamic gene expression responses to antibiotics,” Institute of Science and Technology Austria, 2017."},"month":"09","supervisor":[{"full_name":"Bollenbach, Mark Tobias","id":"3E6DB97A-F248-11E8-B48F-1D18A9856A87","last_name":"Bollenbach","orcid":"0000-0003-4398-476X","first_name":"Mark Tobias"}],"file":[{"checksum":"da3993c5f90f59a8e8623cc31ad501dd","relation":"source_file","content_type":"application/vnd.openxmlformats-officedocument.wordprocessingml.document","access_level":"closed","file_name":"Thesis_KarinMitosch.docx","file_id":"6210","creator":"dernst","date_updated":"2020-07-14T12:48:09Z","date_created":"2019-04-05T08:48:51Z","file_size":6331071},{"date_created":"2019-04-05T08:48:51Z","file_size":9289852,"creator":"dernst","date_updated":"2020-07-14T12:48:09Z","file_id":"6211","file_name":"Thesis_KarinMitosch.pdf","content_type":"application/pdf","access_level":"open_access","relation":"main_file","checksum":"24c3d9e51992f1b721f3df55aa13fcb8"}],"department":[{"_id":"ToBo"}],"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","image":"/images/cc_by.png","short":"CC BY (4.0)"},"license":"https://creativecommons.org/licenses/by/4.0/","abstract":[{"lang":"eng","text":"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. "}],"has_accepted_license":"1","publication_status":"published","publication_identifier":{"issn":["2663-337X"]},"file_date_updated":"2020-07-14T12:48:09Z","title":"Timing, variability and cross-protection in bacteria – insights from dynamic gene expression responses to antibiotics","oa_version":"Published Version","author":[{"first_name":"Karin","last_name":"Mitosch","id":"39B66846-F248-11E8-B48F-1D18A9856A87","full_name":"Mitosch, Karin"}],"day":"27","date_created":"2018-12-11T11:48:40Z"}]