[{"department":[{"_id":"CaGu"},{"_id":"GaTk"},{"_id":"NiBa"}],"file":[{"success":1,"file_name":"2022_ELife_Lagator.pdf","access_level":"open_access","content_type":"application/pdf","relation":"main_file","checksum":"decdcdf600ff51e9a9703b49ca114170","file_size":5604343,"date_created":"2022-02-07T07:14:09Z","creator":"cchlebak","date_updated":"2022-02-07T07:14:09Z","file_id":"10739"}],"article_number":"e64543","month":"01","citation":{"ista":"Lagator M, Sarikas S, Steinrueck M, Toledo-Aparicio D, Bollback JP, Guet CC, Tkačik G. 2022. Predicting bacterial promoter function and evolution from random sequences. eLife. 11, e64543.","chicago":"Lagator, Mato, Srdjan Sarikas, Magdalena Steinrueck, David Toledo-Aparicio, Jonathan P Bollback, Calin C Guet, and Gašper Tkačik. “Predicting Bacterial Promoter Function and Evolution from Random Sequences.” <i>ELife</i>. eLife Sciences Publications, 2022. <a href=\"https://doi.org/10.7554/eLife.64543\">https://doi.org/10.7554/eLife.64543</a>.","apa":"Lagator, M., Sarikas, S., Steinrueck, M., Toledo-Aparicio, D., Bollback, J. P., Guet, C. C., &#38; Tkačik, G. (2022). Predicting bacterial promoter function and evolution from random sequences. <i>ELife</i>. eLife Sciences Publications. <a href=\"https://doi.org/10.7554/eLife.64543\">https://doi.org/10.7554/eLife.64543</a>","mla":"Lagator, Mato, et al. “Predicting Bacterial Promoter Function and Evolution from Random Sequences.” <i>ELife</i>, vol. 11, e64543, eLife Sciences Publications, 2022, doi:<a href=\"https://doi.org/10.7554/eLife.64543\">10.7554/eLife.64543</a>.","ama":"Lagator M, Sarikas S, Steinrueck M, et al. Predicting bacterial promoter function and evolution from random sequences. <i>eLife</i>. 2022;11. doi:<a href=\"https://doi.org/10.7554/eLife.64543\">10.7554/eLife.64543</a>","ieee":"M. Lagator <i>et al.</i>, “Predicting bacterial promoter function and evolution from random sequences,” <i>eLife</i>, vol. 11. eLife Sciences Publications, 2022.","short":"M. Lagator, S. Sarikas, M. Steinrueck, D. Toledo-Aparicio, J.P. Bollback, C.C. Guet, G. Tkačik, ELife 11 (2022)."},"user_id":"4359f0d1-fa6c-11eb-b949-802e58b17ae8","oa":1,"language":[{"iso":"eng"}],"volume":11,"article_type":"original","date_created":"2022-02-06T23:01:32Z","author":[{"full_name":"Lagator, Mato","id":"345D25EC-F248-11E8-B48F-1D18A9856A87","last_name":"Lagator","first_name":"Mato"},{"first_name":"Srdjan","last_name":"Sarikas","full_name":"Sarikas, Srdjan","id":"35F0286E-F248-11E8-B48F-1D18A9856A87"},{"full_name":"Steinrueck, Magdalena","last_name":"Steinrueck","first_name":"Magdalena"},{"full_name":"Toledo-Aparicio, David","last_name":"Toledo-Aparicio","first_name":"David"},{"last_name":"Bollback","full_name":"Bollback, Jonathan P","id":"2C6FA9CC-F248-11E8-B48F-1D18A9856A87","first_name":"Jonathan P","orcid":"0000-0002-4624-4612"},{"orcid":"0000-0001-6220-2052","first_name":"Calin C","last_name":"Guet","id":"47F8433E-F248-11E8-B48F-1D18A9856A87","full_name":"Guet, Calin C"},{"last_name":"Tkačik","id":"3D494DCA-F248-11E8-B48F-1D18A9856A87","full_name":"Tkačik, Gašper","first_name":"Gašper","orcid":"0000-0002-6699-1455"}],"day":"26","scopus_import":"1","oa_version":"Published Version","title":"Predicting bacterial promoter function and evolution from random sequences","publication_status":"published","publication_identifier":{"eissn":["2050-084X"]},"file_date_updated":"2022-02-07T07:14:09Z","has_accepted_license":"1","intvolume":"        11","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)"},"abstract":[{"text":"Predicting function from sequence is a central problem of biology. Currently, this is possible only locally in a narrow mutational neighborhood around a wildtype sequence rather than globally from any sequence. Using random mutant libraries, we developed a biophysical model that accounts for multiple features of σ70 binding bacterial promoters to predict constitutive gene expression levels from any sequence. We experimentally and theoretically estimated that 10–20% of random sequences lead to expression and ~80% of non-expressing sequences are one mutation away from a functional promoter. The potential for generating expression from random sequences is so pervasive that selection acts against σ70-RNA polymerase binding sites even within inter-genic, promoter-containing regions. This pervasiveness of σ70-binding sites implies that emergence of promoters is not the limiting step in gene regulatory evolution. Ultimately, the inclusion of novel features of promoter function into a mechanistic model enabled not only more accurate predictions of gene expression levels, but also identified that promoters evolve more rapidly than previously thought.","lang":"eng"}],"isi":1,"year":"2022","external_id":{"pmid":["35080492"],"isi":["000751104400001"]},"pmid":1,"ec_funded":1,"date_published":"2022-01-26T00:00:00Z","acknowledgement":"We thank Hande Acar, Nicholas H Barton, Rok Grah, Tiago Paixao, Maros Pleska, Anna Staron, and Murat Tugrul for insightful comments and input on the manuscript. This work was supported by: Sir Henry Dale Fellowship jointly funded by the Wellcome Trust and the Royal Society (grant number 216779/Z/19/Z) to ML; IPC Grant from IST Austria to ML and SS; European Research Council Funding Programme 7 (2007–2013, grant agreement number 648440) to JPB.","project":[{"_id":"2578D616-B435-11E9-9278-68D0E5697425","name":"Selective Barriers to Horizontal Gene Transfer","grant_number":"648440","call_identifier":"H2020"}],"publication":"eLife","status":"public","date_updated":"2023-08-02T14:09:02Z","_id":"10736","type":"journal_article","doi":"10.7554/eLife.64543","article_processing_charge":"No","publisher":"eLife Sciences Publications","quality_controlled":"1","ddc":["576"]},{"month":"11","article_number":"e28921","file":[{"relation":"main_file","checksum":"273ab17f33305e4eaafd911ff88e7c5b","file_name":"IST-2017-918-v1+1_elife-28921-figures-v3.pdf","content_type":"application/pdf","access_level":"open_access","file_id":"5096","file_size":8453470,"date_created":"2018-12-12T10:14:42Z","creator":"system","date_updated":"2020-07-14T12:47:10Z"},{"relation":"main_file","checksum":"b433f90576c7be597cd43367946f8e7f","file_name":"IST-2017-918-v1+2_elife-28921-v3.pdf","access_level":"open_access","content_type":"application/pdf","file_id":"5097","date_created":"2018-12-12T10:14:43Z","file_size":1953221,"date_updated":"2020-07-14T12:47:10Z","creator":"system"}],"department":[{"_id":"CaGu"},{"_id":"JoBo"},{"_id":"NiBa"}],"pubrep_id":"918","language":[{"iso":"eng"}],"oa":1,"user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","citation":{"mla":"Lagator, Mato, et al. “Regulatory Network Structure Determines Patterns of Intermolecular Epistasis.” <i>ELife</i>, vol. 6, e28921, eLife Sciences Publications, 2017, doi:<a href=\"https://doi.org/10.7554/eLife.28921\">10.7554/eLife.28921</a>.","apa":"Lagator, M., Sarikas, S., Acar, H., Bollback, J. P., &#38; Guet, C. C. (2017). Regulatory network structure determines patterns of intermolecular epistasis. <i>ELife</i>. eLife Sciences Publications. <a href=\"https://doi.org/10.7554/eLife.28921\">https://doi.org/10.7554/eLife.28921</a>","ista":"Lagator M, Sarikas S, Acar H, Bollback JP, Guet CC. 2017. Regulatory network structure determines patterns of intermolecular epistasis. eLife. 6, e28921.","chicago":"Lagator, Mato, Srdjan Sarikas, Hande Acar, Jonathan P Bollback, and Calin C Guet. “Regulatory Network Structure Determines Patterns of Intermolecular Epistasis.” <i>ELife</i>. eLife Sciences Publications, 2017. <a href=\"https://doi.org/10.7554/eLife.28921\">https://doi.org/10.7554/eLife.28921</a>.","short":"M. Lagator, S. Sarikas, H. Acar, J.P. Bollback, C.C. Guet, ELife 6 (2017).","ieee":"M. Lagator, S. Sarikas, H. Acar, J. P. Bollback, and C. C. Guet, “Regulatory network structure determines patterns of intermolecular epistasis,” <i>eLife</i>, vol. 6. eLife Sciences Publications, 2017.","ama":"Lagator M, Sarikas S, Acar H, Bollback JP, Guet CC. Regulatory network structure determines patterns of intermolecular epistasis. <i>eLife</i>. 2017;6. doi:<a href=\"https://doi.org/10.7554/eLife.28921\">10.7554/eLife.28921</a>"},"oa_version":"Published Version","title":"Regulatory network structure determines patterns of intermolecular epistasis","scopus_import":1,"day":"13","author":[{"last_name":"Lagator","id":"345D25EC-F248-11E8-B48F-1D18A9856A87","full_name":"Lagator, Mato","first_name":"Mato"},{"first_name":"Srdjan","id":"35F0286E-F248-11E8-B48F-1D18A9856A87","full_name":"Sarikas, Srdjan","last_name":"Sarikas"},{"last_name":"Acar","id":"2DDF136A-F248-11E8-B48F-1D18A9856A87","full_name":"Acar, Hande","orcid":"0000-0003-1986-9753","first_name":"Hande"},{"id":"2C6FA9CC-F248-11E8-B48F-1D18A9856A87","full_name":"Bollback, Jonathan P","last_name":"Bollback","orcid":"0000-0002-4624-4612","first_name":"Jonathan P"},{"first_name":"Calin C","orcid":"0000-0001-6220-2052","last_name":"Guet","id":"47F8433E-F248-11E8-B48F-1D18A9856A87","full_name":"Guet, Calin C"}],"date_created":"2018-12-11T11:47:14Z","volume":6,"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)"},"abstract":[{"text":"Most phenotypes are determined by molecular systems composed of specifically interacting molecules. However, unlike for individual components, little is known about the distributions of mutational effects of molecular systems as a whole. We ask how the distribution of mutational effects of a transcriptional regulatory system differs from the distributions of its components, by first independently, and then simultaneously, mutating a transcription factor and the associated promoter it represses. We find that the system distribution exhibits increased phenotypic variation compared to individual component distributions - an effect arising from intermolecular epistasis between the transcription factor and its DNA-binding site. In large part, this epistasis can be qualitatively attributed to the structure of the transcriptional regulatory system and could therefore be a common feature in prokaryotes. Counter-intuitively, intermolecular epistasis can alleviate the constraints of individual components, thereby increasing phenotypic variation that selection could act on and facilitating adaptive evolution. ","lang":"eng"}],"intvolume":"         6","has_accepted_license":"1","file_date_updated":"2020-07-14T12:47:10Z","publication_status":"published","publication_identifier":{"issn":["2050084X"]},"year":"2017","publist_id":"7244","publication":"eLife","status":"public","project":[{"_id":"25681D80-B435-11E9-9278-68D0E5697425","call_identifier":"FP7","name":"International IST Postdoc Fellowship Programme","grant_number":"291734"},{"_id":"2578D616-B435-11E9-9278-68D0E5697425","name":"Selective Barriers to Horizontal Gene Transfer","grant_number":"648440","call_identifier":"H2020"}],"date_published":"2017-11-13T00:00:00Z","ec_funded":1,"publisher":"eLife Sciences Publications","doi":"10.7554/eLife.28921","type":"journal_article","_id":"570","date_updated":"2021-01-12T08:03:15Z","ddc":["576"],"quality_controlled":"1"}]