{"ddc":["570"],"title":"Flow cytometry YFP and CFP data and deep sequencing data of populations evolving in galactose","related_material":{"record":[{"id":"12333","relation":"used_in_publication","status":"public"}]},"status":"public","publisher":"Dryad","year":"2022","oa":1,"oa_version":"Published Version","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","date_created":"2023-01-23T09:00:37Z","department":[{"_id":"CaGu"}],"_id":"12339","main_file_link":[{"open_access":"1","url":"https://doi.org/10.5061/dryad.rfj6q57ds"}],"abstract":[{"lang":"eng","text":"Copy-number and point mutations form the basis for most evolutionary novelty through the process of gene duplication and divergence. While a plethora of genomic sequence data reveals the long-term fate of diverging coding sequences and their cis-regulatory elements, little is known about the early dynamics around the duplication event itself. In microorganisms, selection for increased gene expression often drives the expansion of gene copy-number mutations, which serves as a crude adaptation, prior to divergence through refining point mutations. Using a simple synthetic genetic system that allows us to distinguish copy-number and point mutations, we study their early and transient adaptive dynamics in real-time in Escherichia coli. We find two qualitatively different routes of adaptation depending on the level of functional improvement selected for: In conditions of high gene expression demand, the two types of mutations occur as a combination. Under low gene expression demand, negative epistasis between the two types of mutations renders them mutually exclusive. Thus, owing to their higher frequency, adaptation is dominated by copy-number mutations. Ultimately, due to high rates of reversal and pleiotropic cost, copy-number mutations may not only serve as a crude and transient adaptation but also constrain sequence divergence over evolutionary time scales."}],"citation":{"mla":"Tomanek, Isabella, and Calin C. Guet. <i>Flow Cytometry YFP and CFP Data and Deep Sequencing Data of Populations Evolving in Galactose</i>. Dryad, 2022, doi:<a href=\"https://doi.org/10.5061/dryad.rfj6q57ds\">10.5061/dryad.rfj6q57ds</a>.","ieee":"I. Tomanek and C. C. Guet, “Flow cytometry YFP and CFP data and deep sequencing data of populations evolving in galactose.” Dryad, 2022.","apa":"Tomanek, I., &#38; Guet, C. C. (2022). Flow cytometry YFP and CFP data and deep sequencing data of populations evolving in galactose. Dryad. <a href=\"https://doi.org/10.5061/dryad.rfj6q57ds\">https://doi.org/10.5061/dryad.rfj6q57ds</a>","ista":"Tomanek I, Guet CC. 2022. Flow cytometry YFP and CFP data and deep sequencing data of populations evolving in galactose, Dryad, <a href=\"https://doi.org/10.5061/dryad.rfj6q57ds\">10.5061/dryad.rfj6q57ds</a>.","chicago":"Tomanek, Isabella, and Calin C Guet. “Flow Cytometry YFP and CFP Data and Deep Sequencing Data of Populations Evolving in Galactose.” Dryad, 2022. <a href=\"https://doi.org/10.5061/dryad.rfj6q57ds\">https://doi.org/10.5061/dryad.rfj6q57ds</a>.","ama":"Tomanek I, Guet CC. Flow cytometry YFP and CFP data and deep sequencing data of populations evolving in galactose. 2022. doi:<a href=\"https://doi.org/10.5061/dryad.rfj6q57ds\">10.5061/dryad.rfj6q57ds</a>","short":"I. Tomanek, C.C. Guet, (2022)."},"date_published":"2022-12-23T00:00:00Z","month":"12","article_processing_charge":"No","date_updated":"2023-08-03T14:23:06Z","type":"research_data_reference","doi":"10.5061/dryad.rfj6q57ds","day":"23","author":[{"orcid":"0000-0001-6197-363X","full_name":"Tomanek, Isabella","id":"3981F020-F248-11E8-B48F-1D18A9856A87","first_name":"Isabella","last_name":"Tomanek"},{"id":"47F8433E-F248-11E8-B48F-1D18A9856A87","full_name":"Guet, Calin C","first_name":"Calin C","last_name":"Guet","orcid":"0000-0001-6220-2052"}]}