--- _id: '12261' abstract: - lang: eng text: 'Dose–response relationships are a general concept for quantitatively describing biological systems across multiple scales, from the molecular to the whole-cell level. A clinically relevant example is the bacterial growth response to antibiotics, which is routinely characterized by dose–response curves. The shape of the dose–response curve varies drastically between antibiotics and plays a key role in treatment, drug interactions, and resistance evolution. However, the mechanisms shaping the dose–response curve remain largely unclear. Here, we show in Escherichia coli that the distinctively shallow dose–response curve of the antibiotic trimethoprim is caused by a negative growth-mediated feedback loop: Trimethoprim slows growth, which in turn weakens the effect of this antibiotic. At the molecular level, this feedback is caused by the upregulation of the drug target dihydrofolate reductase (FolA/DHFR). We show that this upregulation is not a specific response to trimethoprim but follows a universal trend line that depends primarily on the growth rate, irrespective of its cause. Rewiring the feedback loop alters the dose–response curve in a predictable manner, which we corroborate using a mathematical model of cellular resource allocation and growth. Our results indicate that growth-mediated feedback loops may shape drug responses more generally and could be exploited to design evolutionary traps that enable selection against drug resistance.' acknowledged_ssus: - _id: M-Shop acknowledgement: This work was in part supported by Human Frontier Science Program GrantRGP0042/2013, Marie Curie Career Integration Grant303507, AustrianScience Fund (FWF) Grant P27201-B22, and German Research Foundation(DFG) Collaborative Research Center (SFB)1310to TB. SAA was supportedby the European Union’s Horizon2020Research and Innovation Programunder the Marie Skłodowska-Curie Grant agreement No707352. We wouldlike to thank the Bollenbach group for regular fruitful discussions. We areparticularly thankful for the technical assistance of Booshini Fernando andfor discussions of the theoretical aspects with Gerrit Ansmann. We areindebted to Bor Kavˇciˇc for invaluable advice, help with setting up theluciferase-based growth monitoring system, and for sharing plasmids. Weacknowledge the IST Austria Miba Machine Shop for their support inbuilding a housing for the stacker of the plate reader, which enabled thehigh-throughput luciferase-based experiments. We are grateful to RosalindAllen, Bor Kavˇciˇc and Dor Russ for feedback on the manuscript. Open Accessfunding enabled and organized by Projekt DEAL. article_number: e10490 article_processing_charge: No article_type: original author: - first_name: Andreas full_name: Angermayr, Andreas id: 4677C796-F248-11E8-B48F-1D18A9856A87 last_name: Angermayr orcid: 0000-0001-8619-2223 - first_name: Tin Yau full_name: Pang, Tin Yau last_name: Pang - first_name: Guillaume full_name: Chevereau, Guillaume last_name: Chevereau - first_name: Karin full_name: Mitosch, Karin id: 39B66846-F248-11E8-B48F-1D18A9856A87 last_name: Mitosch - first_name: Martin J full_name: Lercher, Martin J last_name: Lercher - first_name: Mark Tobias full_name: Bollenbach, Mark Tobias id: 3E6DB97A-F248-11E8-B48F-1D18A9856A87 last_name: Bollenbach orcid: 0000-0003-4398-476X citation: ama: Angermayr A, Pang TY, Chevereau G, Mitosch K, Lercher MJ, Bollenbach MT. Growth‐mediated negative feedback shapes quantitative antibiotic response. Molecular Systems Biology. 2022;18(9). doi:10.15252/msb.202110490 apa: Angermayr, A., Pang, T. Y., Chevereau, G., Mitosch, K., Lercher, M. J., & Bollenbach, M. T. (2022). Growth‐mediated negative feedback shapes quantitative antibiotic response. Molecular Systems Biology. Embo Press. https://doi.org/10.15252/msb.202110490 chicago: Angermayr, Andreas, Tin Yau Pang, Guillaume Chevereau, Karin Mitosch, Martin J Lercher, and Mark Tobias Bollenbach. “Growth‐mediated Negative Feedback Shapes Quantitative Antibiotic Response.” Molecular Systems Biology. Embo Press, 2022. https://doi.org/10.15252/msb.202110490. ieee: A. Angermayr, T. Y. Pang, G. Chevereau, K. Mitosch, M. J. Lercher, and M. T. Bollenbach, “Growth‐mediated negative feedback shapes quantitative antibiotic response,” Molecular Systems Biology, vol. 18, no. 9. Embo Press, 2022. ista: Angermayr A, Pang TY, Chevereau G, Mitosch K, Lercher MJ, Bollenbach MT. 2022. Growth‐mediated negative feedback shapes quantitative antibiotic response. Molecular Systems Biology. 18(9), e10490. mla: Angermayr, Andreas, et al. “Growth‐mediated Negative Feedback Shapes Quantitative Antibiotic Response.” Molecular Systems Biology, vol. 18, no. 9, e10490, Embo Press, 2022, doi:10.15252/msb.202110490. short: A. Angermayr, T.Y. Pang, G. Chevereau, K. Mitosch, M.J. Lercher, M.T. Bollenbach, Molecular Systems Biology 18 (2022). date_created: 2023-01-16T09:58:34Z date_published: 2022-09-01T00:00:00Z date_updated: 2023-08-04T09:51:49Z day: '01' ddc: - '570' department: - _id: ToBo doi: 10.15252/msb.202110490 external_id: isi: - '000856482800001' file: - access_level: open_access checksum: 8b1d8f5ea20c8408acf466435fb6ae01 content_type: application/pdf creator: dernst date_created: 2023-01-30T09:49:55Z date_updated: 2023-01-30T09:49:55Z file_id: '12446' file_name: 2022_MolecularSystemsBio_Angermayr.pdf file_size: 1098812 relation: main_file success: 1 file_date_updated: 2023-01-30T09:49:55Z has_accepted_license: '1' intvolume: ' 18' isi: 1 issue: '9' keyword: - Applied Mathematics - Computational Theory and Mathematics - General Agricultural and Biological Sciences - General Immunology and Microbiology - General Biochemistry - Genetics and Molecular Biology - Information Systems language: - iso: eng month: '09' oa: 1 oa_version: Published Version publication: Molecular Systems Biology publication_identifier: eissn: - 1744-4292 publication_status: published publisher: Embo Press quality_controlled: '1' scopus_import: '1' status: public title: Growth‐mediated negative feedback shapes quantitative antibiotic response tmp: image: /images/cc_by.png legal_code_url: https://creativecommons.org/licenses/by/4.0/legalcode name: Creative Commons Attribution 4.0 International Public License (CC-BY 4.0) short: CC BY (4.0) type: journal_article user_id: 4359f0d1-fa6c-11eb-b949-802e58b17ae8 volume: 18 year: '2022' ... --- _id: '7026' abstract: - lang: eng text: Effective design of combination therapies requires understanding the changes in cell physiology that result from drug interactions. Here, we show that the genome-wide transcriptional response to combinations of two drugs, measured at a rigorously controlled growth rate, can predict higher-order antagonism with a third drug in Saccharomyces cerevisiae. Using isogrowth profiling, over 90% of the variation in cellular response can be decomposed into three principal components (PCs) that have clear biological interpretations. We demonstrate that the third PC captures emergent transcriptional programs that are dependent on both drugs and can predict antagonism with a third drug targeting the emergent pathway. We further show that emergent gene expression patterns are most pronounced at a drug ratio where the drug interaction is strongest, providing a guideline for future measurements. Our results provide a readily applicable recipe for uncovering emergent responses in other systems and for higher-order drug combinations. A record of this paper’s transparent peer review process is included in the Supplemental Information. acknowledged_ssus: - _id: LifeSc article_processing_charge: No article_type: original author: - first_name: Martin full_name: Lukacisin, Martin id: 298FFE8C-F248-11E8-B48F-1D18A9856A87 last_name: Lukacisin orcid: 0000-0001-6549-4177 - first_name: Tobias full_name: Bollenbach, Tobias id: 3E6DB97A-F248-11E8-B48F-1D18A9856A87 last_name: Bollenbach orcid: 0000-0003-4398-476X citation: ama: Lukacisin M, Bollenbach MT. Emergent gene expression responses to drug combinations predict higher-order drug interactions. Cell Systems. 2019;9(5):423-433.e1-e3. doi:10.1016/j.cels.2019.10.004 apa: Lukacisin, M., & Bollenbach, M. T. (2019). Emergent gene expression responses to drug combinations predict higher-order drug interactions. Cell Systems. Cell Press. https://doi.org/10.1016/j.cels.2019.10.004 chicago: Lukacisin, Martin, and Mark Tobias Bollenbach. “Emergent Gene Expression Responses to Drug Combinations Predict Higher-Order Drug Interactions.” Cell Systems. Cell Press, 2019. https://doi.org/10.1016/j.cels.2019.10.004. ieee: M. Lukacisin and M. T. Bollenbach, “Emergent gene expression responses to drug combinations predict higher-order drug interactions,” Cell Systems, vol. 9, no. 5. Cell Press, pp. 423-433.e1-e3, 2019. ista: Lukacisin M, Bollenbach MT. 2019. Emergent gene expression responses to drug combinations predict higher-order drug interactions. Cell Systems. 9(5), 423-433.e1-e3. mla: Lukacisin, Martin, and Mark Tobias Bollenbach. “Emergent Gene Expression Responses to Drug Combinations Predict Higher-Order Drug Interactions.” Cell Systems, vol. 9, no. 5, Cell Press, 2019, pp. 423-433.e1-e3, doi:10.1016/j.cels.2019.10.004. short: M. Lukacisin, M.T. Bollenbach, Cell Systems 9 (2019) 423-433.e1-e3. date_created: 2019-11-15T10:51:42Z date_published: 2019-11-27T00:00:00Z date_updated: 2023-08-30T07:24:58Z day: '27' ddc: - '570' department: - _id: ToBo doi: 10.1016/j.cels.2019.10.004 external_id: isi: - '000499495400003' file: - access_level: open_access checksum: 7a11d6c2f9523d65b049512d61733178 content_type: application/pdf creator: dernst date_created: 2019-11-15T10:57:42Z date_updated: 2020-07-14T12:47:48Z file_id: '7027' file_name: 2019_CellSystems_Lukacisin.pdf file_size: 4238460 relation: main_file file_date_updated: 2020-07-14T12:47:48Z has_accepted_license: '1' intvolume: ' 9' isi: 1 issue: '5' language: - iso: eng month: '11' oa: 1 oa_version: Published Version page: 423-433.e1-e3 project: - _id: 25E9AF9E-B435-11E9-9278-68D0E5697425 call_identifier: FWF grant_number: P27201-B22 name: Revealing the mechanisms underlying drug interactions - _id: 25EB3A80-B435-11E9-9278-68D0E5697425 grant_number: RGP0042/2013 name: Revealing the fundamental limits of cell growth publication: Cell Systems publication_identifier: issn: - 2405-4712 publication_status: published publisher: Cell Press quality_controlled: '1' scopus_import: '1' status: public title: Emergent gene expression responses to drug combinations predict higher-order drug interactions tmp: image: /images/cc_by.png legal_code_url: https://creativecommons.org/licenses/by/4.0/legalcode name: Creative Commons Attribution 4.0 International Public License (CC-BY 4.0) short: CC BY (4.0) type: journal_article user_id: 4359f0d1-fa6c-11eb-b949-802e58b17ae8 volume: 9 year: '2019' ... --- _id: '6392' 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 alternative_title: - IST Austria Thesis author: - first_name: Martin full_name: Lukacisin, Martin id: 298FFE8C-F248-11E8-B48F-1D18A9856A87 last_name: Lukacisin orcid: 0000-0001-6549-4177 citation: ama: Lukacisin M. Quantitative investigation of gene expression principles through combinatorial drug perturbation and theory. 2019. doi:10.15479/AT:ISTA:6392 apa: Lukacisin, M. (2019). Quantitative investigation of gene expression principles through combinatorial drug perturbation and theory. IST Austria. https://doi.org/10.15479/AT:ISTA:6392 chicago: Lukacisin, Martin. “Quantitative Investigation of Gene Expression Principles through Combinatorial Drug Perturbation and Theory.” IST Austria, 2019. https://doi.org/10.15479/AT:ISTA:6392. ieee: 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. mla: Lukacisin, Martin. Quantitative Investigation of Gene Expression Principles through Combinatorial Drug Perturbation and Theory. IST Austria, 2019, doi:10.15479/AT:ISTA:6392. short: M. Lukacisin, Quantitative Investigation of Gene Expression Principles through Combinatorial Drug Perturbation and Theory, IST Austria, 2019. date_created: 2019-05-09T19:53:00Z date_published: 2019-05-09T00:00:00Z date_updated: 2023-09-22T09:19:41Z day: '09' ddc: - '570' department: - _id: ToBo doi: 10.15479/AT:ISTA:6392 extern: '1' file: - access_level: closed checksum: 829bda074444857c7935171237bb7c0c content_type: application/vnd.openxmlformats-officedocument.wordprocessingml.document creator: mlukacisin date_created: 2019-05-10T13:51:49Z date_updated: 2020-07-14T12:47:29Z embargo_to: open_access file_id: '6409' file_name: Thesis_Draft_v3.4Final.docx file_size: 43740796 relation: hidden - access_level: open_access checksum: 56cb5e97f5f8fc41692401b53832d8e0 content_type: application/pdf creator: mlukacisin date_created: 2019-05-10T14:13:42Z date_updated: 2021-02-11T11:17:16Z embargo: 2020-04-17 file_id: '6410' file_name: Thesis_Draft_v3.4FinalA.pdf file_size: 35228388 relation: main_file file_date_updated: 2021-02-11T11:17:16Z has_accepted_license: '1' language: - iso: eng month: '05' oa: 1 oa_version: Published Version page: '103' publication_identifier: isbn: - 978-3-99078-001-5 issn: - 2663-337X publication_status: published publisher: IST Austria related_material: record: - id: '1029' relation: part_of_dissertation status: public status: public supervisor: - first_name: Mark Tobias full_name: Bollenbach, Mark Tobias id: 3E6DB97A-F248-11E8-B48F-1D18A9856A87 last_name: Bollenbach orcid: 0000-0003-4398-476X title: Quantitative investigation of gene expression principles through combinatorial drug perturbation and theory type: dissertation user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87 year: '2019' ... --- _id: '6263' abstract: - lang: eng 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. ' acknowledged_ssus: - _id: M-Shop - _id: LifeSc alternative_title: - ISTA Thesis article_processing_charge: No author: - first_name: Marta full_name: Lukacisinova, Marta id: 4342E402-F248-11E8-B48F-1D18A9856A87 last_name: Lukacisinova orcid: 0000-0002-2519-8004 citation: ama: Lukacisinova M. Genetic determinants of antibiotic resistance evolution. 2018. doi:10.15479/AT:ISTA:th1072 apa: Lukacisinova, M. (2018). Genetic determinants of antibiotic resistance evolution. Institute of Science and Technology Austria. https://doi.org/10.15479/AT:ISTA:th1072 chicago: Lukacisinova, Marta. “Genetic Determinants of Antibiotic Resistance Evolution.” Institute of Science and Technology Austria, 2018. https://doi.org/10.15479/AT:ISTA:th1072. ieee: M. Lukacisinova, “Genetic determinants of antibiotic resistance evolution,” Institute of Science and Technology Austria, 2018. ista: Lukacisinova M. 2018. Genetic determinants of antibiotic resistance evolution. Institute of Science and Technology Austria. mla: Lukacisinova, Marta. Genetic Determinants of Antibiotic Resistance Evolution. Institute of Science and Technology Austria, 2018, doi:10.15479/AT:ISTA:th1072. short: M. Lukacisinova, Genetic Determinants of Antibiotic Resistance Evolution, Institute of Science and Technology Austria, 2018. date_created: 2019-04-09T13:57:15Z date_published: 2018-12-28T00:00:00Z date_updated: 2023-09-22T09:20:37Z day: '28' ddc: - '570' - '576' - '579' degree_awarded: PhD department: - _id: ToBo doi: 10.15479/AT:ISTA:th1072 file: - access_level: open_access checksum: fc60585c9eaad868ac007004ef130908 content_type: application/pdf creator: dernst date_created: 2019-04-09T13:49:24Z date_updated: 2021-02-11T11:17:17Z embargo: 2020-01-25 file_id: '6264' file_name: 2018_Thesis_Lukacisinova.pdf file_size: 5656866 relation: main_file - access_level: closed checksum: 264057ec0a92ab348cc83b41f021ba92 content_type: application/vnd.openxmlformats-officedocument.wordprocessingml.document creator: dernst date_created: 2019-04-09T13:49:23Z date_updated: 2020-07-14T12:47:25Z embargo_to: open_access file_id: '6265' file_name: 2018_Thesis_Lukacisinova_source.docx file_size: 5168054 relation: source_file file_date_updated: 2021-02-11T11:17:17Z has_accepted_license: '1' language: - iso: eng month: '12' oa: 1 oa_version: Published Version page: '91' publication_identifier: issn: - 2663-337X publication_status: published publisher: Institute of Science and Technology Austria related_material: record: - id: '1619' relation: part_of_dissertation status: public - id: '696' relation: part_of_dissertation status: public - id: '1027' relation: part_of_dissertation status: public status: public supervisor: - first_name: Tobias full_name: Bollenbach, Tobias id: 3E6DB97A-F248-11E8-B48F-1D18A9856A87 last_name: Bollenbach orcid: 0000-0003-4398-476X title: Genetic determinants of antibiotic resistance evolution type: dissertation user_id: c635000d-4b10-11ee-a964-aac5a93f6ac1 year: '2018' ... --- _id: '520' abstract: - lang: eng text: Cyanobacteria are mostly engineered to be sustainable cell-factories by genetic manipulations alone. Here, by modulating the concentration of allosteric effectors, we focus on increasing product formation without further burdening the cells with increased expression of enzymes. Resorting to a novel 96-well microplate cultivation system for cyanobacteria, and using lactate-producing strains of Synechocystis PCC6803 expressing different l-lactate dehydrogenases (LDH), we titrated the effect of 2,5-anhydro-mannitol supplementation. The latter acts in cells as a nonmetabolizable analogue of fructose 1,6-bisphosphate, a known allosteric regulator of one of the tested LDHs. In this strain (SAA023), we achieved over 2-fold increase of lactate productivity. Furthermore, we observed that as carbon is increasingly deviated during growth toward product formation, there is an increased fixation rate in the population of spontaneous mutants harboring an impaired production pathway. This is a challenge in the development of green cell factories, which may be countered by the incorporation in biotechnological processes of strategies such as the one pioneered here. article_type: letter_note author: - first_name: Wei full_name: Du, Wei last_name: Du - first_name: Andreas full_name: Angermayr, Andreas id: 4677C796-F248-11E8-B48F-1D18A9856A87 last_name: Angermayr orcid: 0000-0001-8619-2223 - first_name: Joeri full_name: Jongbloets, Joeri last_name: Jongbloets - first_name: Douwe full_name: Molenaar, Douwe last_name: Molenaar - first_name: Herwig full_name: Bachmann, Herwig last_name: Bachmann - first_name: Klaas full_name: Hellingwerf, Klaas last_name: Hellingwerf - first_name: Filipe full_name: Branco Dos Santos, Filipe last_name: Branco Dos Santos citation: ama: Du W, Angermayr A, Jongbloets J, et al. Nonhierarchical flux regulation exposes the fitness burden associated with lactate production in Synechocystis sp. PCC6803. ACS Synthetic Biology. 2017;6(3):395-401. doi:10.1021/acssynbio.6b00235 apa: Du, W., Angermayr, A., Jongbloets, J., Molenaar, D., Bachmann, H., Hellingwerf, K., & Branco Dos Santos, F. (2017). Nonhierarchical flux regulation exposes the fitness burden associated with lactate production in Synechocystis sp. PCC6803. ACS Synthetic Biology. American Chemical Society. https://doi.org/10.1021/acssynbio.6b00235 chicago: Du, Wei, Andreas Angermayr, Joeri Jongbloets, Douwe Molenaar, Herwig Bachmann, Klaas Hellingwerf, and Filipe Branco Dos Santos. “Nonhierarchical Flux Regulation Exposes the Fitness Burden Associated with Lactate Production in Synechocystis Sp. PCC6803.” ACS Synthetic Biology. American Chemical Society, 2017. https://doi.org/10.1021/acssynbio.6b00235. ieee: W. Du et al., “Nonhierarchical flux regulation exposes the fitness burden associated with lactate production in Synechocystis sp. PCC6803,” ACS Synthetic Biology, vol. 6, no. 3. American Chemical Society, pp. 395–401, 2017. ista: Du W, Angermayr A, Jongbloets J, Molenaar D, Bachmann H, Hellingwerf K, Branco Dos Santos F. 2017. Nonhierarchical flux regulation exposes the fitness burden associated with lactate production in Synechocystis sp. PCC6803. ACS Synthetic Biology. 6(3), 395–401. mla: Du, Wei, et al. “Nonhierarchical Flux Regulation Exposes the Fitness Burden Associated with Lactate Production in Synechocystis Sp. PCC6803.” ACS Synthetic Biology, vol. 6, no. 3, American Chemical Society, 2017, pp. 395–401, doi:10.1021/acssynbio.6b00235. short: W. Du, A. Angermayr, J. Jongbloets, D. Molenaar, H. Bachmann, K. Hellingwerf, F. Branco Dos Santos, ACS Synthetic Biology 6 (2017) 395–401. date_created: 2018-12-11T11:46:56Z date_published: 2017-03-17T00:00:00Z date_updated: 2021-01-12T08:01:21Z day: '17' department: - _id: ToBo doi: 10.1021/acssynbio.6b00235 external_id: pmid: - '27936615' intvolume: ' 6' issue: '3' language: - iso: eng month: '03' oa_version: None page: 395 - 401 pmid: 1 publication: ACS Synthetic Biology publication_identifier: issn: - '21615063' publication_status: published publisher: American Chemical Society publist_id: '7298' quality_controlled: '1' scopus_import: 1 status: public title: Nonhierarchical flux regulation exposes the fitness burden associated with lactate production in Synechocystis sp. PCC6803 type: journal_article user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87 volume: 6 year: '2017' ...